esa-sceva/satcom-chunk-collection huggingface dataset:
# A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning
## 4 FL Node Selection Algorithm Based on DQN  
In complex and variable edge networks, node selection policies need to adapt to changes in environmental state information. The DRL-based node selection framework introduced in this paper consists of three components: the environment, the agent, and the reward. It enables the agent to interact with the environment to learn an effective node selection policy that maximizes the reward. Figure 2 illustrates the architecture of our proposed DRL-based node selection framework.  
The environment comprises network states, devices, and target model information. The agent interacts with the environment, selects actions based on its policy distribution from a given state, and receives rewards [34; 35]. The actions, rewards, and environment states obtained by the agent are used to update the Q-network and the Target Q-network.  
We propose a DQN-based approach for client selection in FL. Initially, clients send information about their resources to the server. The server then selects a specific \(N^{{}^{\prime}}=\{1,2,\cdots d\}\) to estimate the transmission delay in the next global model training process. The FL client selection problem based on DRL can be formulated as an MDP model [36]. Subsequently, a DQN-based node selection algorithm is designed to solve this problem as follows.  
Figure 2: DRL-based node selection framework.
## 10 Conclusions and Future Works  
In this paper, we presented an efficient authentication solution based on the HIP DEX protocol as a keying material, CoAP for the application layer, and RPL in the routing process. It is an End-to-End establishment security solution between HIP-IoT devices feasible for T2T and M2M architecture. The proposed solution is based on an optimal compression HIP header with the support of an extension of 6 LoWPAN compression. HIP DEX uses ECDH to minimize the cryptographic computation overhead during the authentication mechanism. Hence, we contribute to minimize the overall handshake transmission time with HIP DEX protocol, and we demonstrate that we improved the detailed delays of the total transmission delay comparing with previous and recent works based on HIP solutions and other existing solutions. The obtained results prove an excellent minimization regarding the energy communication and computational costs. Our novel distribution model based on the HIP DEX opportunistic mode and the delegation of the heavy scalar multiplication to compute the session keys are the strength of LC-DEX that contribute to secure the communication over the IoT based-WSN architectures with a minimal energy consumption. For future work, we assume that the integration of virtual abstraction of the Software Defined Networking in the definition of the WSN connected to IoT is an excellent future research field that deal with the improvement of the energy consumption in the constrained environments. Our solution can be expanded in the Industrial IoT solution like Deep-Green [28] to deal with the security requirements of the networking access of IoT devices for architectures based on dispersed computing that extends the existing computing model joint the optimization of computing resources in correlation with network resources.  
Conceptualization, B.B. and R.B.; methodology, B.B.; software, B.B; validation, B.B., R.B.; formal analysis, B.B.; investigation, B.B.; resources, R.B.; data curation, B.B.; writing--original draft preparation, B.B.; writing--review and editing, B.B.; visualization, B.B.; supervision, R.B.; project administration, R.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work does not involve humans nor animals.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available because it is the subject of a work in progress.  
This research work is developed within the framework of the European project H2020 SECREDAS (PRODUCT SECURITY FOR CROSS DOMAIN RELIABLE DEPENDABLE AUTOMATED SYSTEMS), 2018-2021.  
The authors declare no conflict of interest.
# Harnessing Odorant Receptor Activation to Suppress Real Malodor
## 2 Results and Discussions
### Amine Compounds as Key Contributors in Cat Urine Odor  
The intensity of the pungent odor of cat urine varied depending on the collection date. Among the six urine samples, those from Day 2 and Day 6, which exhibited particularly strong odor and high TAAR5 response, were selected for analysis (Figure 1B,G). In contrast, urine samples from Day 3 and Day 5, which had weak odor and no TAAR5 response, were chosen as controls. To investigate the chemical basis of this variation, we analyzed multiple urine samples with different odor intensities by gas chromatography-mass spectrometry (GC-MS) after collecting volatile compounds from the headspace of cat urine using a solid-phase microextraction (SPME) fiber. Trimethylamine (TMA) was prominently detected in samples with a strong pungent odor (Peak a on Days 2 and 6) (Figure 2 and Table 1). TAAR5 responds to amines, including TMA, and it is predicted that amines are consistently present in pungent cat urine odors [26].  
Identification occurred of sample-specific compounds in cat urine exhibiting strong malodor (Days 6 and 2) and weak malodor (Days 3 and 5) (refer to Figure 1B). Sample-specific peaks are labeled with letters. Refer to Table 1 for the corresponding compounds for each labeled peak.  
In addition to TMA, we exclusively detected other compounds (Peaks d, e, j, o, and p on Days 2 and 6) in the pungent samples but none in the non-pungent samples (Days 3 and 5). Although these compounds lacked known associations with malodor, some were described as having a \"nutty\" aroma (referred to VCF online website, Version VCF16.10, released on 23 December 2022: [[https://www.vcf-online.nl/VcfHome.cfm](https://www.vcf-online.nl/VcfHome.cfm)]([https://www.vcf-online.nl/VcfHome.cfm](https://www.vcf-online.nl/VcfHome.cfm)), accessed on 8 October 2024), suggesting that they can influence the overall characteristic of cat urine odor, distinct from TMA. Remarkably, we did not detect previously reported odor compounds in cat urine, such as 3-mercapto-3-methylbutyl formate and 3-mercapto-3-methyl-1-butanol [24], in any of the samples. It is plausible that the levels of these compounds are less than the detection limit in neutered cats, where biosynthesis may be reduced.  
Considering that TAAR5 demonstrated a significant response correlating with the intensity of the pungent odor, TMA may be a key fluctuating component responsible for producing this odor.  
Figure 2: Identification of malodor-causing volatile compounds in cat urine.
## 2 Related Work
### CNN-Based Road Extraction Methods  
CNN-based methods have been widely adopted in road extraction tasks because of their ability to learn hierarchical features from input images. These methods typically utilize convolutional neural networks to extract the discriminative features, followed by classification or segmentation modules for road identification. CNNs inherently possess translation invariance, meaning they can recognize road patterns regardless of their location in the image. This property is particularly advantageous for road extraction, where road layouts may vary in different parts of the image. Several classic CNN architectures, such as U-Net [4] SegNet [5], and DeepLab [6], have been successfully applied to road extraction tasks, and these methods have proven their effectiveness in accurately capturing road patterns and structures. In recent years, CNN models such as DDU-Net [7], SDUNet [8], and L-DeepLabv3+ [9] have also shown very good results in road extraction. And some networks like MECA-Net [10], ASPP-U-Net [11], and MSM-RE [12] with multi-scale features also have good performance in road extraction. However, most of these CNN-based methods only focus on the multi-scale encoder structure or multiple branches of the neural network, while ignoring some inherent characteristics of the road surface. And compared with a transformer, CNN (convolution neural network) has some unique advantages: local perception, parameter sharing, and translation invariance. Some methods include DeepWindow [13], FuNet [14], and HsgNet [15]. And some networks like CADUNet [16], VNet [17], DA-RoadNet [18], and the literature [19] have proved that the integration of an attention mechanism in CNN architecture also works in road extraction tasks.
## 3 Experimental Results
### Spectroscopic Ellipsometer
#### 3.2.1 Precursors, Solvents and MAPbI\({}_{3}\)  
According to Equation (1), the decomposition of perovskite results in the appearance of PbI\({}_{2}\) and CH\({}_{3}\)NH\({}_{3}\)I. Therefore, absorbance curves were performed to fresh deposited layers of MAPbI\({}_{3}\), PbI\({}_{2}\) and CH\({}_{3}\)NH\({}_{3}\)I in order to obtain their characteristic peaks (Figure 4). Thus, the PbI\({}_{2}\) absorbance spectrum presents three characteristic peaks, centered in 330 nm, 420 nm and close to 500 nm, while the CH\({}_{3}\)NH\({}_{3}\)I only presents absorbance in the UV range of the spectrum, with a peak at 300 nm. The MAPbI\({}_{3}\) sample shows broader absorbance bands, in comparison with PbI\({}_{2}\), although its characteristic peaks are shifted 50 nm to longer wavelengths. Furthermore, a band gap of 1.6 eV was calculated from the absorbance spectrum of the MAPbI\({}_{3}\) sample [62; 63].  
Figure 4: Absorbance curve of the MAPbI\({}_{3}\), PbI\({}_{2}\) and CH\({}_{3}\)NH\({}_{3}\)I pristine samples.  
Figure 3: Microscope images (500\(\times\)) of the samples during the degradation process.
# Geotechnical Property Modeling and Construction Safety Zoning Based on GIS and BIM Integration
## 1 Introduction
### Previous BIM-GIS Integration
This paper presents the integration of BIM and GIS for 3D modeling and visualization of geotechnical information and segmentation into zones considering planning for safe construction. The key novelty of this research is the integration of GIS to BIM for the three-dimensional geotechnical parameters modeling and division into safe construction zones. The study provides a detailed novel methodology to combine GIS with BIM, map regional geotechnical parameters, and use this for safe construction zones. Furthermore, the study provides a new concept of utilizing the OSHA soil classification for making safe construction zones that can be used by construction planners and managers to plan the right protective system for worker's safety in the excavation construction. The use of GIS is obvious for soil mapping, however, its integration with BIM provides the detailed insight to calculate the volume of each sublayer available underground, because it is very important to know the depth of each layer as the safety plan can be changed according to the soil type and condition because an excavation can have a cut through more than one soil type [3; 5]. Also, this study attempts to use the entity IfcGeotechnicalAssembly, and their subtypes, IfcBorehole, IfcGeomodel, and IfcGeoslice for the representation of the geotechnical information in BIM. The appropriateness of the proposed method of integrating BIM and GIS for the geotechnical property and safety zoning has been verified using actual geotechnical data of the Peshawar region.
## 3 Results
### Thumb Deflection Operation Area  
All 15 sets of experimental results are presented in Figure 8a (P10*5, P50*5, P90*5). There are thresholds in both vertical and depth directions for the thumb operation in this area. However, noteworthy, the minimum depth of every dye result is adjacent to the panel (see Figure 8b). Therefore, we exclude it in our following analysis. The Shapiro-Wilk normality test results were consistent with normality. As indicated in Table 2, statistical significances are found in the upper limit and maximum depth across different percentiles, while the lower limit does not show statistical significance.  
Post hoc Tukey HSD results are shown in Figure 8c. Regarding the upper limit, there is a statistical significance between P10 and P50 (\(p<0.05\)) and a higher level of significance between P10 and P90 (\(p<0.01\)), whereas there is only a low assumption for the difference between P50 and P90 percentile (\(p<0.1\)). And for the maximum depth, there is a statistical significance between P10 and P50 (\(p<0.05\)), a higher level of significance between P10 and P90 (\(p<0.01\)), and a slight significance between P50 and P90 (\(p<0.1\)). With increasing hand length, the upper limit and the maximum depth increase, but its rate decreases.  
We set the minimum depth as 0 mm for all groups based on our measurement principle and marked it with an endnote. Measurement data in IFOA and TDOA are stated in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Dependent Variable** & **Independent Variables** & **F Value** & \(p\) Value** \\ \hline \multirow{3}{*}{Index finger} & \multirow{3}{*}{Body size percentiles (P10/P50/P90)} & Upper limit & 3.960149 & 0.048618 \\  & & Lower limit & 1.061501 & 0.376292 \\ \cline{1-1}  & & Minimum depth & 1.149948 & 0.349213 \\ \cline{1-1}  & & Maximum depth & 4.874215 & 0.028218 \\ \hline \multirow{3}{*}{Thumb finger} & \multirow{3}{*}{Body size percentiles (P10/P50/P90)} & Upper limit & 4.452485 & 0.039381 \\ \cline{1-1}  & & Bottom limit & 0.371513 & 0.697352 \\ \cline{1-1}  & & Maximum depth & 4.051831 & 0.0467882 \\ \hline \hline \end{tabular}
\end{table}
Table 2: ANOVA results for different finger dimensions (upper limit, lower limit, minimum depth, maximum depth) with respect to different human body size percentiles.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Upper Limit (mm)**} & **Lower Limit (mm)** & **Minimum Depth (mm)** & **Maximum Depth (mm)** \\ \hline \multirow{3}{*}{Index finger operation area} & P10 & 15.67 & & 61.74 \\  & P50 & 23.89 & & 71.85 \\ \cline{1-1}  & P90 & 29.7 & & 75.55 \\ \cline{1-1}  & Average & & \(-\)22.57 & 29.35 \\ \hline \multirow{3}{*}{Thumb deflection operation area} & P10 & 6.44 & & 15.37 \\ \cline{1-1}  & P50 & 10.97 & & 21.87 \\ \cline{1-1}  & P90 & 14.4 & & 26.10 \\ \cline{1-1}  & Average & & \(-\)20.78 & 0(set) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Thresholds with significant differences are indicated as the mean value for each percentile, respectively, while thresholds with none are indicated as the average value of the total independently.
# Research on Performance Metrics and Environmental Conditions for 5G MIMO OTA
In this paper, MIMO OTA measurement results of different FoMs under both UE noise-limited and interference-limited environmental conditions have been compared and discussed, respectively. At first, two new FoMs, i.e., Peak-Null and Variance, are proposed and compared to characterize the variance of performance per spatial orientation. Secondly, the impact of different channel models on UE MIMO OTA throughput performance is analyzed. Based on the analysis, it can be concluded that TRMS under UE noise-limited environmental condition has a stronger ability to distinguish between good and bad performing UE devices than that of MARSS under CTIA interference-limited environmental condition. At last, the mechanism of this phenomenon \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **3 GPP LTE** & **CTIA LTE** \\ \hline Test Methodology & MPAC (reference) & MPAC (reference) \\ \hline MIMO & 2 \(\times\) 2 & 2 \(\times\) 2 \\ \hline Environmental Condition & UE Noise-Limited & Interference-Limited \\ \hline DUT (devices under test) & sub 1 GHz: black box & sub 1 GHz: black box \\ Placement & 1 GHz above: white box & 1 GHz above: white box \\ \hline FoM & TRMS & MARSS \\ \hline Average Method & Inverse Linear Average & Linear Average \\ \hline Outage Levels & 95\%/90\%/70\% & 95\%/90\%/70\% \\ \hline Channel Models & SCME UMa \& UMi & SCME UMa \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison of MIMO OTA methods in 3 GPP and CTIA. is explored based on an average manner analysis. This discrepancy in capability does not come from the average manner adopted by MARSS and TRMS, but originates from the environmental condition itself, i.e., the difference in signal level between UE noise-limited and interference-limited environmental conditions. In summary, it is proposed to adopt TRMS under UE noise-limited environmental condition as the unique or baseline test condition in 5G MIMO OTA. Peak-Null can be considered as a secondary FoM to characterize the variance per azimuth.
## 1 Introduction
Many universities implemented these technologies and tools, e.g., different massive open online courses (MOOCs) or learning management systems (LMSs), to support different learning forms. These systems serve as the repositories of the curated educational content at least. Moreover, in case of more advanced IT courses allow submitting the programming assignments and their automated evaluation, writing programming code directly in the embedded editor with syntax highlighting, code peer-reviewing, advanced testing, etc. The integrated functions of LMS and their extensions usually support not only progress monitoring but also many activities, quizzes and content sources integration. Students' activities and achievements are monitored, analysed with appropriate statistical methods, and suitably visualised on personalised dashboards (Figure 1). It can be assumed that the activities, which require active student's involvement in learning, can bring more relevant results in comparison with the observation of the passive student's presence in the course. Current technological advancements that allow automated evaluation of the source code written by the student and provide immediate feedback, integrated with other approaches utilising e-learning and microlearning advantages were selected for these study, while they have a potential to engage the students to be more active during their study in introductory programming courses. Therefore, the introductory programming course used in this study has been extended with the automated evaluation of the programming assignments followed by the implementation of a set of suitably designed microlearning units with the following aims:  
* To analyse the relationship between the input student's characteristics and his/her final achievements. * To evaluate the overall contribution of automated evaluation of the programming assignments closely interrelated to the set of suitably designed microlearning units to the students' final grades. * To research the role of these two elements as possible predictors of identification of at-risk students who fail the introductory programming course. While the automated programming code evaluation and microlearning units were implemented subsequently during several years, it was possible to collect four different datasets of students' achievements and their activities, which characterise the students' behaviour in the introductory programming course and can be compared with each other. The structure of the article is as follows. The related work section summarises the current state of the introductory programming education research and emphasises that the understanding the learning process, identifying of the factors, which influence the students' outcomes and the ability to predict the at-risk students are still actual. The third section describes the background of the research, defines the research questions and used methods. The next section is devoted to the obtained results, which are consequently thoroughly discussed in the next section. The conclusion section summarises the main findings and suggests the direction of future research.
## 2 The Model and Architecture of the Remote Prototyping System
In the course of its execution, the employee was able to contact the customer in order to clarify the production parameters, testing process, etc. 3. Sharing production resources through programming interfaces (e.g., RestAPI) or direct access to the device was made available via TCP/IP protocol. In the proposed cloud model, the central cloud is responsible for connecting resources within a given project group, to which it provides rules of access to individual resources, manages directory services, provides access to data repository services, creates a communication platform for the team, and conducts the acquisition of data downloaded from machines. Within the resources of the central cloud, it is possible to implement a given number of virtual machines on which specialized design software or analytical systems (artificial intelligence, Big Data, etc.) can be installed. One of the  
Figure 4: Remote prototyping system architecture. most important functions of the system is a system of harmonizing tasks and managing the flow of semi-finished products and results of engineers' work. This approach allows to coordinate the work of all resources in the asynchronous mode, which results directly in the acceleration of prototyping work. This ensures high flexibility and heterogeneity of the architecture, in contrast to the existing solutions [54, 55, 56, 57]. Further complication of the model and, consequently, the architecture decreases its usefulness and hinders implementation in the real environment. What is essential is that the entities can make their resources available by moving from a pseudo-batch processing architecture to more complex architectures as their machines are more widely used. Based on the proposed model, the architecture presented in Figure 4 was implemented by InfoSoftware Polska Ltd. With the support of the scientific staff of the Rzeszow University of Technology. As a result of the research, engineering and implementation work, the iS Rapid system, which enables remote prototyping and sharing of design and production resources, was created. The system is based on a cloud computing, which facilitates access to its resources and ensures its scalability with the growing demand for larger resources from users. Due to the involvement of specialists from various fields, functionality from all layers has been integrated: physical, communication, application, and analytical services. iS Rapid enables remote prototyping and production of both individual tasks as well as teamwork nature. The available machine park has been integrated with the database, which supports the users in the process of selecting appropriate design and manufacturing parameters. The whole system provides the user with control and monitoring of individual activities both at design and manufacturing stages. Additionally, resources of cooperation between dispersed team members have been made accessible. Thus, an actual distributed environment for rapid prototyping based on remote access was implemented.