esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review on IoT and Lightweight Security Protocols
### Building Blocks  
Figure 3 shows an IoT system's building blocks: end nodes such as sensors/actuators; base stations or gateways; central servers, also known as network servers; and cloud applications. It shows how an IoT system sends its small data packets from the IoT device to the user application in the cloud.  
Base stations and network servers are located in an IP-based core network and are connected to each other through back-haul links [39], such as point-to-point Ethernet or multi-protocol layer switching (MPLS) links or wireless links such as cellular, microwave, or satellite. When receiving data packets from the end nodes, the network server's responsibility is to check the packets' integrity. If the end node is not an intruder, it also removes duplicate packets. This server then sends the packet to an application server that users access through the internet. For download packets, the network server stores data from the user application to later send them to the end devices.  
In most IoT systems, downlink transmissions are delay-tolerant and do not represent the typical data flow; therefore, security on the uplink is emphasized, i.e., from device to cloud. In the example network shown in Figure 3, the communication networks that provide connectivity to the sensors and actuators are located in outdoor fields or large industrial areas, such as low-power wide-area (LPWA) networks. Reviewing their physical, data link, and network layer protocols will allow us to understand their security protocols.  
Cloud services consist of software applications that analyze the data collected by an IoT system, provide visual user-friendly information on the real-time status of the system, and, in some cases, send control commands back to the actuator end nodes [6]. They provide data storage and computational resources, thus reducing the need for computation at the end nodes [40]. This is also true for the base station and network server nodes, as they have more resources than the end nodes and can be used to provide security at different segments of the network. Many papers refer to the base station/gateway and network server as devices located in edge and fog computing [19; 21] nodes, respectively.  
Although cloud services, which are located in remote networks, provide opportunities for security threats, this topic is not addressed in detail in this review. More details on cloud services can be found in a collection of studies on the issue of big data and privacy [37]. These studies cover the problem of the large volume of devices, which generate lots of data, and how to protect the privacy of these data. They also address how to ensure the authenticity of the data and the device and who can access the data.
## 2 Materials and Methods
The electrical resistivity was determined by the following Equation (1):  
\[\rho=\frac{R\cdot A}{L} \tag{1}\]  
where \(R\)--electrical resistance; \(A\)--cross-sectional area; \(L\)--length. Contact pads were formed at both ends of the lines using silver paste \(\text{PELCO}^{\textregistered}\) (product No. 16031, Ted Pella, Inc., Redding, CA, USA) to measure their resistance after the sintering process. Silver paste solidified at room temperature, which excluded further heating of the sintered lines. The electrical resistance of the line \(R\) was measured using a multimeter (U1272A, Agilent Technologies Inc., Santa Clara, CA, USA). The cross-sectional area \(A\) and the length \(L\) of each of the five lines formed on the same type of substrate and sintered at the same temperature were independently measured by an optical 3D profilometer (S neox, Sensofar, Terrassa, Spain). Wherein, the initial unsintered lines of nanoparticles were investigated as gas-sensitive microstructures. These lines were formed on a special alumina substrate containing a platinum heater and built-in electrodes for measuring the resistance of the gas-sensitive layer. The resistance of the gas-sensitive layer to the action of the target gases CO and NH\({}_{3}\) mixed with air was measured using a controlled gas mixer \"Mikrogaz-F\" [24]. Synthetic air con  
Figure 1: Scheme of the formation of printed lines from nanoparticles on various substrates, including (**a**) generation, (**b**) focused deposition, and (**c**) thermal sintering processes. sisting of oxygen and nitrogen of high purity (99.999%) was used in the experiments. The \"Mikrogaz-F\" unit fed into the gas chamber the content of target gases in the concentration range from 1 to 50 ppm with controlled humidity in the air of 50% RH. The investigated range captured the threshold limits of values according to Russian Hygiene Norm [29]. For example, the threshold limit value of CO and NH\({}_{3}\) is 17 ppm and 28 ppm, respectively. The mode of measuring the gas-sensitive characteristics of the original unsintered lines is presented in Table 1.  
The microstructure and elemental composition of the printed lines were investigated using a scanning electron microscope SEM (JSM-7001F, JEOL Ltd., Tokyo, Japan) with an attachment for energy-dispersive X-ray (EDX) spectroscopy. The EDX analysis was performed by XFlash detector (6-30, Bruker Corporation, Billerica, MA, USA).
## 3 Results  
### Evaluation of HM Simulations  
We first evaluate the performance of the modeled HM LAI and \(\text{LUE_{\text{Canopy}}}\) at the Mead, Nebraska sites. In Figure 1a,b, we show scatterplots between the modeled HM LAI and \(\text{LUE_{\text{Canopy}}}\) values and the actual values on the ground. As discussed in Section 2.3.1, only dates that have both LAI and \(\text{LUE_{\text{Canopy}}}\) measurements are included in Figure 1a,b for consistent comparison of the modeling performance of these two variables. The figures show strong performance for modeled LAI and \(\text{LUE_{\text{Canopy}}}\) with \(\text{R}^{2}\) values of 0.91 and 0.77 and RMSE values of 0.62 and 0.30, respectively; although, the bias for \(\text{LUE_{\text{Canopy}}}\) is relatively high.  
Figure 1: Comparison of actual versus Hybrid-Maize modeled (**a**) LAI and (**b**) \(\text{LUE_{\text{Canopy}}}\). The color bars represent the number of points at each marker on the scatter plot.  
In Figure 2, the performance of modeled \(\mathrm{LUE_{Canopy}}\) and \(\Delta\mathrm{LUE_{Canopy}}\) are shown for all ground measurements of \(\mathrm{LUE_{Canopy}}\), not only those that also have a LAI measurement on the same date. Figure 2a shows the scatterplot of modeled \(\mathrm{LUE_{Canopy}}\) versus actual \(\mathrm{LUE_{Canopy}}\) with no smoothing, while Figure 2b shows the \(\mathrm{R^{2}}\) value between modeled and actual \(\mathrm{LUE_{Canopy}}\) and \(\Delta\mathrm{LUE_{Canopy}}\) at different levels of smoothing and values of \(\Delta\). As seen in Equation (3), a smoothing window of 1 represents no smoothing. Only days where modeled \(\mathrm{LUE_{Canopy}}\) is greater than zero are included in Figure 2. In addition, a small number of days which have less than 95% of the underlying GPP time series available are not included in Figure 2.  
The results in Figure 2 show that the performance of modeled \(\mathrm{LUE_{Canopy}}\) is strong with an \(\mathrm{R^{2}}\) of 0.76 in the absence of smoothing and slightly higher with smoothing. In contrast, as seen in Figure 2b, the performance of \(\Delta\mathrm{LUE_{Canopy}}\) is dependent on the level of smoothing and value of \(\Delta\), with stronger performance with longer \(\Delta\) windows and more smoothing.
# Measuring Efficiency and Effectiveness of Highway Management in Sustainability
## 3 Data, Methods, and Results
### Modeling Relationship between Efficiency and Effectiveness
#### 3.3.2 Dependent and Explanatory Variables  
As seen in Figure 4, to investigate the relationship between efficiency and effectiveness, we set the two-stage regression model. At the first stage, we examine the determinants of highway expenditure, in which the model considers political factors, fiscal capacity, median voter, and economic condition as explanatory variables, according to prior literature [5; 41; 42; 43; 44]. At the second stage, we explain the dependent variable, effectiveness, as the function of the predicted value of expenditure by the first regression, efficiency scores by DEA, urbanization, the number of cars, the number of drivers, and spatial factor (rho) conducted by a spatial regression model.  
The study operationalizes raw data in order to articulate variables and adjust for regression analysis. The effectiveness indicator, which is estimated using a combination of road condition, mobility, and safety, was modified by adding the constant 1 to take the log in the regression model (this is because the indicator's original value has negative values that become imaginary numbers when taking the log). For road conditions, a higher value means better conditions. Mobility and safety are coded as the reciprocal of the annual average daily traffic (AADT) and the number of people fatally injured, respectively. Higher values of the reciprocals of these two variables indicate greater effectiveness.  
The study suggests that effectiveness is determined by expenditures and other conditions such as the level of urbanization, the number of automobiles and drivers, and spatial factors while controlling other variables that affect expenditures at the first stage. In addition, efficiency scores are added as the independent variables so as to test for any meaningful relationship between efficiency and effectiveness.  
Table 6 shows detailed explanations of each variable, and Table 7 reports descriptive statistics of each variable.  
Figure 4: Research Framework.
## 2 Spatial Variability of Hydrothermal Parameter
### Variability Distribution Characteristics
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Thermal Conductivity**} & \multicolumn{4}{c}{**Heat Capacity**} & \multicolumn{4}{c}{**Hydraulic Conductivity**} \\ \cline{2-13}
**NO.** & \multicolumn{2}{c}{**Frozen State**} & \multicolumn{2}{c}{**Unfrozen State**} & \multicolumn{2}{c}{**Frozen State**} & \multicolumn{2}{c}{**Unfrozen State**} & \multicolumn{2}{c}{**Horizontal**} & \multicolumn{2}{c}{**Vertical**} \\  & & & & & & & & & & & & \\ \cline{2-13}
**1\#** & 0.19 & 1.84 & 0.12 & 2.63 & 0.24 & 3.34 & 0.14 & 1.65 & 0.12 & 3.17 & 0.14 & 2.32 \\
2\#** & 0.23 & 2.38 & 0.18 & 2.32 & 0.13 & 2.75 & 0.14 & 1.99 & 0.16 & 3.48 & 0.17 & 2.84 \\
3\#** & 0.20 & 2.09 & 0.15 & 3.02 & 0.12 & 2.52 & 0.12 & 1.91 & 0.17 & 2.21 & 0.14 & 1.89 \\
4\#** & 0.09 & 2.56 & 0.18 & 1.81 & 0.18 & 3.60 & 0.12 & 1.73 & 0.15 & 3.05 & 0.15 & 3.53 \\
5\#** & 0.16 & 1.61 & 0.21 & 1.94 & 0.22 & 2.39 & 0.13 & 2.16 & 0.17 & 2.81 & 0.13 & 2.47 \\
6\#** & 0.20 & 2.32 & 0.18 & 3.60 & 0.19 & 3.27 & 0.10 & 1.49 & 0.08 & 2.10 & 0.13 & 1.66 \\
7\#** & 0.10 & 1.48 & 0.14 & 2.87 & 0.18 & 2.55 & 0.15 & 2.25 & 0.15 & 2.73 & 0.18 & 2.90 \\
8\#** & 0.16 & 1.48 & 0.19 & 3.50 & 0.15 & 3.26 & 0.14 & 1.27 & 0.16 & 2.75 & 0.18 & 3.70 \\
9\#** & 0.19 & 2.24 & 0.14 & 2.70 & 0.13 & 3.31 & 0.14 & 2.36 & 0.17 & 2.25 & 0.11 & 2.91 \\
10\#** & 0.07 & 2.03 & 0.13 & 2.91 & 0.17 & 2.38 & 0.05 & 2.32 & 0.16 & 1.70 & 0.12 & 2.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Variability of the hydrothermal parameters for the soft clay rock. processes, environmental influences, and heterogeneity in soil composition and structure. By representing soft clay rock parameters as random fields, stochastic field theory captures the complex and nonlinear spatial patterns of variability observed in soft clay rock properties. Statistical descriptors such as mean, variance, and spatial correlation length are used to quantify the average behavior, degree of variability, and spatial dependence of soft clay rock parameters, respectively. Variogram analysis is often employed to characterize the spatial correlation structure, providing insights into the range over which parameter values exhibit spatial correlation. Through stochastic field theory, spatial realizations of soil parameter fields can be generated, simulating multiple plausible spatial distributions of parameters. Based on the variability results of the hydrothermal parameters and random field, the COV, SOF, and ACD can be obtained. The variability distribution characteristics of the hydrothermal parameters for the soft clay rock are shown in Figure 3. The observation reveals that the COV varies between 0.05 and 0.25, the SOF varies between 1.2 m and 4.0 m, and the ACD varies between 0.4 m and 1.4 m, respectively.
# LoRA-Tuned Multimodal RAG System for Technical Manual QA: A Case Study on Hyundai Staria
## 5 Experimental Results and Analysis
### Limitations and Future Directions
#### 5.4.2 Comparative Evaluation and Benchmarking  
The absence of standardized evaluation benchmarks for domain-specific multimodal RAG systems presents a fundamental challenge for comparative analysis. Unlike general-purpose question-answering tasks that benefit from established datasets such as SQuAD or Natural Questions, technical manual question answering lacks comprehensive benchmarks that capture the procedural complexity and multimodal nature of maintenance queries. Consequently, direct quantitative comparison with existing frameworks including LangChain, GPT-4 RAG, and MuRAG remains challenging. Future work will prioritize the development of standardized evaluation protocols and benchmark datasets specifically designed for technical documentation question answering, enabling systematic comparison across different architectural approaches.
# Progress in Transparent Nano-Ceramics and Their Potential Applications
## 3 Magneto-Optical Transparent Nano-Ceramics
### Tag
sintering process parameters and optical properties of Y\({}^{3+}\)-doped TAG transparent nano-ceramics, and they found that the samples sintered at 1680 \({}^{\circ}\)C showed the best optical properties, obtaining transmittance of 75% in the range of 900 to 1600 nm. Additionally, X-ray diffraction (XRD) results showed that samples had pure garnet crystal structure without secondary phases.
## 5 Measurement Campaign
### Description of the Flight Chain
The pre-positioning (Figure 9d) determined previously by the flight simulation software allows us to recover the AMULSE (Figure 9e). Figure 8: **(a)** A schematic diagram of the four steps showing the principle of the dual-balloon sounding ([PERSON] _et al._, 2015); **(b)** Transmission/Reception of the information during the flight. A Vaisala radiosonde RS92 is connected in order to send instantaneous P, T, relative humidity and GPS data to a ground mobile station to inject those data in the trajectory model and hence ensure the tracking/recovery of the instrument. Note that the trigger release system is controlled by a home-made smartphone application via satellite communication “Iridium”. The latter transmits the GPS position, pressure and temperature data every 5 min that ensure redundant retrieval before the probe lands.
## 3 System Model and Problem Description
### Problem Description  
Based on the above model, the multi-satellite multi-task aggregation problem in this paper can be described as follows: the meta task set \(GT\), the satellite resource set \(GS\), and the observation time window information \(GTW_{i,j}\) of any meta task \(T_{i}\in GT\) under the satellite resource \(S_{j}\in GS\) are known, all the meta tasks in \(GT\) that satisfy the aggregation constraints with each other are aggregated together to form a new task-aggregation task, and the satellite resources corresponding to the aggregated task are determined as well as the start execution time and the end execution time. The final result of task aggregation is the aggregated task set \(GC=\left\{C_{1},C_{2},\cdots,C_{L}\right\}\), where \(L\) is the number of aggregated  
Figure 1: Satellite Earth observation schematic. missions. Any of the aggregated tasks \(C_{k}=(M_{k},l_{k},st_{k},et_{k},\delta_{k})\) in the set, where \(M_{k}\), \(l_{k}\), \(st_{k}\), \(et_{k}\), \(\delta_{k}\) denote the set of meta-tasks corresponding to the aggregated task \(C_{k}\), the satellite resources, the start execution time, the end execution time, and the side-swing angle of the remote sensor during execution, respectively. In order to clarify the methodology and the satellite Earth observing model, Table 1 summarizes the essential symbols and their corresponding descriptions. The purpose of mission aggregation in this paper is to reduce the energy consumption on the satellite star. Therefore, the aggregation process of the mission mainly considers the energy consumption required by the satellite to perform the task before and after the aggregation, and the final aggregated mission requires the minor energy consumption as a whole. In the actual process of satellite execution of observation tasks, satellite energy consumption mainly comes from the remote sensor to perform observation activities and side pendulum activities, each observation requires two side pendulums (open and close), then the objective function of mission aggregation as shown in Equation (1). \[\min E=\sum_{k=1}^{|F|}{(et_{k}-st_{k})\times oe_{j}+2\times\frac{|\delta_{k}|} {\omega_{j}}\times se_{j}} \tag{1}\]  
The constraints mainly include satellite resource constraints and observation time window constraints. Assuming that \(k\) meta-tasks \(GT_{p}=\left\{T_{p1},T_{p2},\cdots,T_{pk}\right\}\) can be aggregated to obtain aggregated task \(C_{p}\), the satellite resource constraint between the meta-tasks is shown in Equation (2). \[\exists S_{j}\in GS:\forall T_{pi}\in GT_{p},GTW_{pi,j}\
eq\emptyset \tag{2}\]  
The satellite resource constraint indicates the existence of at least one satellite resource \(S_{j}\) capable of performing all the meta-tasks in the aggregated mission. The observation time window between meta-missions is shown in Equation (3). \[\forall T_{pq}\in GT_{p},\exists S_{j}\in GS,tw_{pq,j,k}\in GTW_{pq,j} \tag{3}\]
## 8 Archiving of Surveyed Data and Management of Survey Results  
For a project of this importance, a crucial aspect is the organization of the flow of the surveyed data, the manner in which the results are delivered, and the archiving of the results. Such data flow must facilitate:  
* The quality control actions of the survey operations and the delivered deliverables.
* The data navigation and asset recognition operations by MM technicians.
* The archiving of the survey products.  
The project includes an intensive and accurate in-progress review phase of the surveying activities. In particular, an accurate Gantt chart has been drawn up, in which the survey activities have been scheduled. During the survey activities, the surveyors adopted procedures of continuous communication with MM to manage and plan the field survey activities, coordinate access at the building complexes, and manage the survey timelines, processing, and delivery of all deliverables in an organized way.
# Evaluation Method for Rock Burst Hazards in Strip Filling of Working Faces in Deep Coal Mines
## 3 Impact Hazard Evaluation Method
### Indicators and Membership Functions Determination
#### 3.1.2 Elastic Energy Index of Coal Seam  
The elastic energy index \(W_{\text{ET}}\) is an important index to measure the rock burst tendency of the coal seam, and it is also the main parameter to evaluate the impact risk of the coal seam. The engineering practice shows that when \(W_{\text{ET}}<2\), the possibility of spontaneous impact of the coal seam is low; when \(2\leq W_{\text{ET}}<3.5\), the coal seam has a weak impact risk; when \(3.5\leq W_{\text{ET}}<5\), the coal seam has a medium impact risk; and when \(W_{\text{ET}}\geq 5\), the coal seam has a strong impact risk. The greater the \(W_{\text{ET}}\) value of the elastic energy index, the  
Figure 3: Simplified mechanical model of the coal stress.  
Figure 2: Schematic diagram of the evolution of abutment pressure in strip working face. (**a**) Formation of goal on both sides; (**b**) excavation of roadways; and (**c**) balanced stress state.  
higher the possibility of coal seam impact instability. The membership degree of elastic energy index \(W_{\text{ET}}\) and coal seam impact risk is \(\varphi_{W_{\text{ET}}}\)[31]:  
\[\varphi_{W_{\text{ET}}}=\left\{\begin{aligned} 0.125W_{\text{ET}} & W_{\text{ET}}<2\\ 0.1667W_{\text{ET}}-0.0834& 2\leq W_{\text{ET}}<5\\ 1& W_{\text{ET}}\geq 5\end{aligned}\right. \tag{11}\]  
It should be noted that the propensity for coal impact is a property of coal accumulation deformation energy and impact damage generation, which is an inherent attribute of coal and is independent of the stress state of the coal seam. However, when the occurrence state and physical and mechanical properties of the coal seam change, the propensity for coal impact will also change.
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 1 Introduction
We also provide a considerable amount of research related to the use of MoM and the methods employed to accelerate the modeling and calculating process. In addition, we mention some of the hybridization approaches that used MoM in combination with other methods. In addition, we review some research that contains remarkable experience on using MoM for modeling antennas. In the third section, and as one of the most important aspects when modeling antennas, our review focuses on optimization methods, especially genetic algorithms (GA), and the works that combined MoM and GA when modeling antennas. Inthe fourth section, we provide a review of reconfigurable and smart antennas, followed by a review in the fifth section of wire-grid sparse antennas, which are important to us. We also present a new method that can be used both for creating and modeling antennas and scatterers and demonstrate its capabilities. This method can be used for manufacturing a sparse wire-grid antenna. The main idea is to avoid using wires with low currents in the antenna grid, since their contribution to radiation is small.
## 2 Materials
### Expert Delineations  
Depending on the purpose and subset, we prepared ground-truth manual delineations for training and testing. In object detection, that referred to bounding-box-type objects for each of the three classes: lips, teeth, and tongue (Figure 3a) in each subset A-C. The lips/mouth were present in almost all frames (except for some cases where they were covered with a hand), whereas the teeth and tongue appeared in a limited number of images (Table 1). For image segmentation, each object was manually contoured (Figure 3b), but only in subsets B and C. Delineations of both types were prepared using Matlab, including the Image Labeller App. The expert manually drew either bounding boxes or contours of objects of corresponding classes appearing in the frame.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Total Frames** & **Children** & **Frames per Child Median (Min–Max)** & **Lips** & **Teeth** & **Tongue** \\ \hline Subset A & 16,156 & 35 & 407 (290–880) & 16,079 & 10,738 & 6766 \\ Subset B & 1092 & 25 & 45 (29–47) & 1089 & 525 & 289 \\ Subset C & 665 & 16 & 45 (16–54) & 664 & 421 & 212 \\ \hline Total & 17,913 & 76 & & 17,832 & 11,684 & 7267 \\  & & & (99.5\%) & (65.2\%) & (40.6\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Class distribution in the datasets.  
Figure 3: Sample frames with manual delineation for object detection (**a**) and segmentation (**b**). The lips are marked in blue, the teeth in green, and the tongue in red.