esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Multi-Criteria Decision Making Model Based on AHP  
The suggested sourcing framework as shown in Figure 4, and the project assessment model presented in our previous article [1] are both used to evaluate a project based on the critical success factors that contribute to an effective sourcing decision. However, applying the suggested sourcing framework and project assessment model to evaluate a project prior to making a sourcing decision requires sufficient domain expertise, and the decision making is time consuming as well.  
Second, all the sourcing decisions of application maintenance cannot be made on the basis of \"Yes\" or \"No\" as shown in Figure 4, and \"Low\", \"Medium\" or \"High\" as published and reported in [1]. Therefore, an effective MCDM approach is needed to assist the practitioners in prioritizing the sourcing alternatives and making the best possible sourcing decision. To achieve this goal, the current study presents a decision support system based on AHP that incorporates the input of experts and ranks the sourcing alternatives.  
The main steps of the AHP approach for developing decision support system are defining objectives, identifying criteria and alternatives, a pairwise comparison of criteria, assigning a score to alternatives with respect to each criteria, calculating the weights of criteria and alternatives and identifying the final ranking of alternatives by combining the weights of criteria and alternatives [79; 80; 81]. The following subsections discuss each of these steps in detail.
## 1 Introduction
The motivation to solve the physical and structural issues related to ultrathin metal layers originates from our very recently published article [21], where the possible effects of metal dilution importing a dielectric substance on their physical and apparent visual aesthetics were explained. However, in this work, we propose to develop a new nanocomposite-type thin-film material (Ag+ SiC) that can be found suitable for broadening the application range of thin-film materials in ultra-stable coating design options, with better control over their properties. SiC is a well-known semiconductor material and many reports have been made about the properties of silicon carbide (SiC) materials of various compositions. Wide bandgap, high breakdown voltage, high thermal conductivity, high electron drift velocity, high surface hardness, high bulk plasticity, and low density are all characteristic of SiC. Silicon carbide is chemically inert at high temperatures and has good abrasion and radiation resistance. Silicon carbide's superior physical and chemical properties make it ideal for a wide range of applications, including optoelectronics, high-temperature, high-power, and high-frequency electronic devices, X-ray mask materials, solar selective coatings, solar cells, high-temperature gas sensors, phototransistors, blue-light diodes, and wear- and corrosion-resistant applications [22; 23; 24; 25; 26; 27; 28; 29]. The properties of SiC films can be easily tailored in the coating composition for any film thicknesses with the optimal microstructural, optical, and electrical properties for different reflective or antireflective optical and optoelectronic applications (in terms of composition and deposition process as well). The intrinsic properties of SiC thin films are highly affected by the incorporation of dopant elements during the growth processes. The inclusion of dopants enables the regulation of SiC-based thin-film characteristics, particularly the optical bandgap and electrical conductivity, which are appealing for a variety of applications. The literature concerns SiC thin-film growth and customizing characteristics based on application [22; 30; 31]. To the best of our knowledge, no other research has been conducted to develop a robust and highly stable metallic composite material of this type and investigate its physical and optical properties. In this work, we adopted the basic concepts of the RF magnetron co-sputtering technique to synthesize the Ag+ SiC composite layer. This study was conducted systematically, including the basic properties of thin layers of silver and SiC, and presents a comparison of the physical and optical properties of newly developed composite material with those of the pure metal (Ag) layer. However, our new nanocomposite (Ag+ SiC) material can enable cost-effective mass production of smart coatings of multiple types, useful for energy efficiency applications, particularly in building-integrated products for smart city infrastructure.
# A Review of Photothermal Detection Techniques for Gas Sensing Applications
###### Abstract  
Photothermal spectroscopy (PTS) is a technique used for determining the composition of liquids, solids and gases. In PTS, the sample is illuminated with a radiation source, and the thermal response of the analyte (e.g., refractive index) is analyzed to gain information about its content. Recent advances in this unique method of detecting gaseous samples show that photothermal gas spectroscopy can be an interesting alternative to commonly used absorption techniques. Moreover, if designed properly, sensors using PTS detection technique can not only reach sensitivities comparable with other, more complex techniques, but can significantly simplify the design of the sensor. In this review, recent developments in photothermal spectroscopy of gases will be summarized and discussed.  
photothermal spectroscopy; gas sensing; detection limit; laser spectroscopy; practical applications; intracavity gas detection; interferometric gas detection +
Footnote †: journal: Applied Physics Letters
# Geo-DMP: A DTN-Based Mobile Prototype for Geospatial Data Retrieval
## 4 Experimental Validation and Evaluation
### Validation in Simple Scenarios
#### 4.2.1 Validation of Delay-Tolerant Multi-Hop Content Retrieval  
Figure 15 illustrates the scenario for experiments in this part. We took a portion of a bus route at the northwest of the third ring road of Beijing from the OSM dataset (osmid: 2094880) as the track. Four nodes participate in these experiments: two mobile nodes plus two stationary nodes. The mobility pattern of these nodes is listed in Table 2. Thirty seconds at the very beginning are reserved for the initialization of nodes and the creation of query bundles. Node 1 acts as the requester and Node 4 is the RSU. They stay motionlessly at point A and E, respectively. Point C is the rendezvous point of Nodes 2 and 3. Node 2 moves between points B and C, while Node 3 moves between points C and D. The communication range is set to 300 m. Next, we conduct experiments in three situations with different distributions of the requested contents.
# Assessment of the Impact of Electric Vehicle Batteries in the Non-Linear Control of DC Microgrids
## 3 Methodology
### Discussion  
The need for more sustainable transportation and electricity generation will introduce significant challenges by implementing DC microgrids and the massive introduction of EVs.  
This work has assessed the impact of charging EVs in DC microgrids by using nonlinear control. The modeling of the batteries is more accurate than in [25], where a simplified model for batteries was considered. For both of the models, the non-linear control results in the adequate stability of the systems, with less than 1% of voltage deviations. In this work, it was observed that the output current suffer some distortion, but is minimal when compared to the reference value.  
Some considerations need to be accounted for in implementing this model. Appropriate DC-DC converters should be used to implement this non-linear control. Moreover,in real implementation, some variation from the presented results could be observed, since the modeling is not 100% accurate to the reality.
## 6 Results and Discussion
### Results on Scenario 1: Workstation Number \(I=3\) and Forklift Number \(K=2\)
It is clear that the agent visits workstations \(o_{1}\to o_{2}\to o_{3}\) first, then passes through doors #3 and #6 before proceeding to the other workshop for delivery tasks, in the order \(g_{2}\to g_{3}\to g_{1}\). Unfortunately, when the agent requests to pass through door #3 at time step \(t=36\), the door system tends to refuse due to the conflict risk from forklifts in the corridor. The risk values of door #3 at time steps 37 and 38 are \(r(B_{3},37)=0.5\), \(r(B_{3},38)=0.15\), respectively (Table 5). Apparently, it is not a safe po  
Figure 10: Convergence curves of the learning process. The proposed FSA-RL method generates a different policy for moving through the corridor, as shown in Figure 12b. The pickup policy is same as with the RL method, but the policy is not a good choice. Figure 11: Simulation results of scenario 1: (**a**) refusals, (**b**) average task success rate, (**c**) average reward of generated paths, and (**d**) average length of generated paths. Figure 12: Examples of generated paths in scenario 1: (**a**) classic RL method, path length 77; (**b**) proposed FSA-RL method, path length 79. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Time-Step \(t\)** & **37** & **38** & **39** & **40** & **41** & **42** & **43** & **44** & **45** & **46** \\ \hline door \#1 & 0.010 & 0.150 & 0.480 & 0.650 & 0.460 & 0.190 & 0.050 & 0.010 & 0.000 & 0.000 \\ door \#2 & 0.500 & 0.150 & 0.110 & 0.050 & 0.010 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ door \#3 & 0.500 & 0.150 & 0.060 & 0.020 & 0.010 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Scenario 1: Risk level of each door when the agent passes through. visiting \(o_{1}\to o_{2}\to o_{3}\) sequentially. The agent then chooses to pass through doors #1 and #4. Moreover, the delivery policy is also different. In the path generated with FSA-RL, the agent performs delivery tasks following the order \(g_{1}\to g_{2}\to g_{3}\). The agent requests to open door #1 at time step \(t=44\) with \(r(B_{1},45)=0.0\), \(r(B_{1},46)=0.0\). Clearly, door #1 is safe for the agent to pass through.
# A Tour Recommendation System Considering Implicit and Dynamic Information
## 3 Research Methodology  
The framework of the proposed tour recommendation system is visually illustrated in Figure 1. First, for each photo record in the social media dataset, we need to find the POI closest to the photo's GPS coordinate and assign the POI as the attraction the user is visiting. Second, this research uses the implicit feature extracted from the textual description of the POI as a POI representation through appropriate text-preprocessing and feature-reduction techniques. Third, to make the recommendation result more accurate for each tourist, tourists are further clustered into several disjointed sub-groups based on their similarity to their visiting sequence. Fourth, before planning an itinerary, user preference for each POI must be inferred according to historical records in the social media dataset. Fifth, since staying time might vary for different users, time slots, and POIs, dynamic staying time prediction is proposed using the Non-negative Matrix Factorization (NMF) algorithm. Finally, a personalized itinerary algorithm is proposed to consider user preference, dynamic staying time, and time limitation. The input to the proposed algorithm includes the user's previous visiting sequence, the request, and the time limitation, while the output is the personalized itinerary with the highest preference score and the longest remaining time. Each critical component will be introduced in detail in the following sub-sections.
# A Study on the Optimisation of Tennis Players' Match Strategies from the Perspective of Momentum
## 4 Construction of a Momentum-Based Player Match Strategy Optimisation Model
### Prediction of Match Trends
#### 4.2.3 Multicollinearity Test  
In order to assess and quantify the degree of multicollinearity, researchers commonly utilise the variance inflation factor (VIF) as a statistical tool [21]. The formula for calculating VIF is shown in Equation (15):  
\[VIF_{m}=\frac{1}{1-R_{1-k\backslash m}^{2}}, \tag{15}\]  
where \(R_{1-k\backslash m}^{2}\) represents the goodness of fit obtained by regressing the m-th independent variable against the remaining k \(-\) 1 independent variables. It is important to note that an increase in the VIF value signifies a stronger correlation between the m-th variable and the other variables, which may result in greater variance inflation.  
In practical applications, the VIF of a regression model is defined as the maximum VIF value among all independent variables. The calculation results reveal that four indicators have VIF values greater than 10, indicating a relatively severe multicollinearity problem in the regression equation. To address the issues arising from multicollinearity, this study employs the backward stepwise regression method. Initially, all variables are included in the model. Then, one independent variable is removed at a time, and the model is observed to determine whether there is a significant change in the dependent variable. Finally, the variable with the least explanatory power is eliminated through comparison. This process is repeated iteratively until all variables that meet the predetermined elimination criteria have been removed. During the regression process, robust standard errors and standardised regression coefficients are employed to enhance the precision with which key factors influencing match trends can be identified. The final selection of 13 indicators from the original 17 is determined by their correlation strength, as outlined below: delta_point, break_pt_won, net_pt_won, ace, break_pt, net_pt, double_fault, serve_width, winner_shot_type, unforced_error, distance, winner, and break_pt_missed. Among these, delta_point has the highest correlation, reaching 0.872.
## 2 Related Work
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Reference** & **Focus** & **Details** \\ \hline
[15] & Use cases & Use cases for the utilization of the SMGW infrastructure. \\ \hline
[16] & Data acquisition & Concept and simulative evaluation using data acquired from smart meters for more exact calculations of power flows in electrical grids. \\ \hline
[17] & Framework & Framework jOSEF for communication experiments, including a simulator for smart meters, a minimal software implementation of an SMGW and an implementation of an external market participant. \\ \hline
[18] & Control box & Test of communication between SMGW, DSMgr and control box translating signals from the DSMgr to the DER specific protocols. \\ \hline
[19] & Control box & Analysis of response times, testing different communication technologies, such as 2G and 4G. \\ \hline
[20] & Control box & Demonstration of the utilization of a control box implementing IEC 61850 to operate DERs, but without SMGWs. \\ \hline
[21] & Control box & DSMgr directly controls DERs based on the BDEW smart grid traffic light concept [22]. \\ \hline
[23] & Control box & Integration of a commercial platform for the development of energy management solutions into the SMGW infrastructure; the functionality of a control box was implemented and tested, i.e., no automated or optimized control of the DERs. \\ \hline
[24] & Market & Platform for trading flexibility considering iMSys. \\ \hline
[25] & EMS & Extension of the home automation system OpenHAB to interact with SMGWs, and pose as one or multiple endpoints for control signals from a DSMgr; no further details with respect to DSM are given. \\ \hline
[26] & EMS & Options for integrating automated EMS into the SMGW infrastructure based on the various official technical specifications and requirements. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related scientific work based on Smart Meter Gateways (SMGWs). control boxes and therefore the direct control of DERs by a DSMgr. When an EMS is used instead, the flexibility of the DERs can be abstracted and direct access avoided. This is viable, since, in general, the DSMgr needs a certain aggregated response in load rather than access to a specific DER (cf. the implementation of the red traffic light phase in [27], where even in a red phase the EMS keeps control of the DERs). Only in [23; 25; 26] are EMSs considered. However, even though the authors of [23] speak of an EMS, only a control box is implemented and tested. Furthermore, in [25] no DSM specific details are provided, and in [26] only the results of a theoretical analysis are presented.
# Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries
## 3 Methodology  
This research employed a quantitative approach to evaluate the accuracy of the Euclidean distance method. Initially, a dataset was compiled, including hospital and ambulance addresses as points of interest (POIs) and residential addresses as query points. Afterdata collection, the dataset was divided into hospital and ambulance addresses. Subsequently, a data exploration process was undertaken, thereby revealing that only five cities had sufficient points of interest to experiment. Therefore, 30 query points were randomly selected from these five cities for the research. This careful process ensured a thorough evaluation of the accuracy of the Euclidean distance method in the selected urban areas is studied. The query points were further categorized based on quantitative criteria, primarily the density of points of interest (POIs), which was determined using attribute weights for precise calculations. This classification facilitated a systematic analysis of the data.  
In the subsequent step, both Euclidean distance and the road network distance method were employed for POI retrieval, thereby utilizing kNN and range search techniques. The accuracy of the Euclidean distance method was then assessed by comparing its outcomes with those derived from the road network distance method, which served as a reliable quantitative benchmark. The road network distance method provided accurate distance calculations based on actual road networks.  
This study followed a systematic approach as depicted in Figure 7, where the methodology flow chart illustrates the step-by-step process. By conducting thorough quantitative analyses and carefully comparing the results, this research aimed to evaluate the accuracy of the Euclidean distance method in retrieving spatial data, thereby ensuring a comprehensive and reliable assessment.
## 3 Results
### Semantic Analysis Results  
STT might need to be more robust when dealing with small children, as small mistakes or using different words while retaining the meaning can occur. This could confuse users since the expectation of a voice recognition system in robots, such as the one proposed in this article, is to enable natural interaction. This implies multiple ways of asking the same thing without a fixed set of words. For these reasons, LLMs are used to grasp the intention behind the actions. Due to limited GPU resources for in-line LLMs, we opted to test online services, namely ChatGPT and Gemini from Google. To evaluate the suitability of this tool, we performed an intention analysis on each instruction in the dataset to see if the use of LLMs provides not only actionable results but also serves as a helper to improve understanding of common mistakes made by kids or STT systems.  
Table 4 presents the semantic analysis results of the VID audios. From these results, it is evident that Gemini delivered the best performance. Notably, Gemini could comprehend instructions that the STT did not transcribe accurately but still preserved the  
Figure 6: Runtime and WER comparison for various STT models, averaged over 311 instances from the VID1S dataset using an RTX 3080 TI. Model initials near each point are as follows: WB = Whisper Base, WT = Whisper Tiny, WS = Whisper Small, WM = Whisper Medium, WL = Whisper Large, VS = Vosk Small, VN = Vosk Normal, G = Google STT, GC = Google STT using context, L = Leopard.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Dataset** & **WB [6]** & **WT [6]** & **WS [6]** & **WM [6]** & **WL [6]** & **VS [17]** & **VN [17]** & **G [18]** & **GC [18]** & **L [16]** \\ \hline VID & 1.20 & 1.47 & 0.67 & 0.90 & 0.37 & 0.64 & 0.42 & 0.45 & 0.33 & 0.73 \\ VID1S & 0.87 & 0.97 & 0.82 & 0.64 & 0.39 & 0.68 & 0.44 & 0.44 & 0.26 & 0.66 \\ \hline \hline \end{tabular}
\end{table}
Table 3: WER comparison across various STT models. Here, WB = Whisper Base, WT = Whisper Tiny, WS = Whisper Small, WM = Whisper Medium, WL = Whisper Large, VS = Vosk Small, VN = Vosk Normal, G = Google STT using context, L = Leopard.  
meaning, providing a secondary verification extension that enhanced the system's robustness. ChatGPT also demonstrated the ability to interpret instructions correctly, albeit with less accuracy than Gemini. Thus, Gemini was chosen for its superior performance in accurately determining actions and faster runtime.
# Geometric Accuracy Evaluation Method for Subway Stations Based on 3D Laser Scanning
## 2 Related Work
### Registration Issues
#### 2.1.1 Registration between Point Clouds  
Coarse registration between point clouds can be divided into target-based registration and feature-based registration. Target-based registration requires placing targets in the scene that are easy to identify and match. The most common target is a sphere because the angle of the sphere remains constant in all directions and the center of the sphere can be used as a common point in multiple datasets. [PERSON] et al. [29] proposed a method to rapidly and automatically identify 3D spherical targets in point clouds. The target-based matching method is often widely used in the architecture, engineering, and construction (AEC) fields due to its accuracy [30]; however, the method requires advance deployment of the scanning site and high-precision scanning of each sphere separately, and these limitations limit its application.  
A feature-based registration method identifies significant geometric features of two datasets for registration by extracting feature points or primitives from the point cloud surface, including point features [31], line features [32], planes [33], local normal vectors [34], and combinations of geometric primitives. Compared to point features, the use of higher latitude geometric primitives can improve the robustness of identifying correspondences [35]. [PERSON] et al. [36] proposed an online pose voting algorithm based on the extended reality technique, which integrates two-dimensional (2D) and 3D data, and first uses the Global Navigation Satellite System (GNSS) to estimate the poses for global registration, followed by registering selected image data and point cloud data separately, achieving reliable registration accuracy by using multiple data sources and registration methods. Compared with target-based registration methods, feature-based registration methods do not rely on target points, and the research focuses on the automated extraction and automated registration of features [27].  
The iterative closest point (ICP) algorithm [37] is the most classical fine registration method, which derives several variants by iterating several times to achieve the best local registration results [38; 39; 40]. Nevertheless, these variant algorithms still need to ensure that the two datasets are close enough to each other to minimize the overall difference.
## 3 Experimental Results and Discussion
### Experimental Result
#### 3.2.2 The Relation between the Debris Velocity and Induced Voltage  
For the debris sensor based on the static magnetic field, the debris velocity is also a key factor of the sensor output. With a z-axis height of 1 mm and a drive current of 0.5 A, we let an iron particle pass the sensor at different speeds. The relationship between the debris speed and peak-to-peak values of the debris waveform are shown in Figure 11. It is observed that the induced voltage increased with the debris velocity. The standard deviation of the experimental data also increased with the velocity from 3.5 to 10.8. Furthermore, there was a linear relationship between the peak-to-peak value and exciting current, which correlates well with Equation (3).  
Figure 10: Variation in the induced voltage noise with the current.  
Figure 9: Variation in the output voltage with the current.