esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Experiment I  
In the context of EIT, the experimental results depicted in Figures 5 and 6 of Experiment I are employed to comprehend the dependent conductivity imaging, specifically for the copper material. Figure 5 illustrates the reconstructed conductivity cross-section of a phantom, acquired at frequencies spanning from 1 kHz to 220 kHz. The high-resolution \(256~{}\times~{}256\) pixel image distinctly depicts variations in conductivity across different frequencies, emphasizing the crucial impact of the frequency selection on improving the image clarity and detail in EIT.  
The further analysis in Figure 6A shows the profile line of normalized conductivity intensities along the 128 th pixel row for Experiment I.1. The placement of the object spans pixels 1 to 32, with the profile line reaching its peak value at the 16 th pixel. This aligns with the object's center, showcasing the effective signal reception of the system. An essential observation involves the identification of the highest contrast values within the profile line, which occur in the frequency range that exceeds 2 kHz and extends to 40 kHz.  
In Figure 6B, the normalized contrast values of the image are illustrated, highlighting the higher contrast in the frequency range of 10 kHz to 60 kHz with a threshold of 0.95. This finding is supported by the analysis of two crucial normalized parameters: sharpness (Figure 6C) and CNR (Figure 6D). The peak values in both graphs consistently fall within the 15-38 kHz frequency window with a threshold of 0.95, indicating that this range is most advantageous to achieve a higher contrast in EIT imaging for copper material.  
Figure 5: Analysis of Experiment I.1 with normalized conductivity reconstruction image with a resolution of \(256~{}\times~{}256\) pixels and a scale of 10 mm/16 pixels.  
Figure 6: Graph of analytical indices in Experiment I.1: (**A**) profile graph at the 128 th pixel row. The red-dashed square represents the position of the object; (**B**) graph of contrast values; (**C**) graph of normalized sharpness values; (**D**) graph of normalized CNR values corresponding to the surveyed frequency range.
## 3 Results and Discussion
### Chemical Component Prediction
\begin{table}
\begin{tabular}{c c|c|c c|c c c} \hline  & & & \multicolumn{3}{c|}{**Calibration (n = 35)**} & \multicolumn{3}{c}{**Prediction (n = 15)**} \\ \hline
**Sample** & **Parameter** & **Analysis** & **R\({}^{2}\)c** & **RMSEC** & **R\({}^{2}\)p** & **RMSEP** & **LV** \\ \hline \multirow{3}{*}{Sirloin} & Fat & & 0.89/0.97 & 1.25/0.57 & 0.87/0.96 & 1.74/1.09 & 4 \\  & Moisture & & 0.92/0.96 & 1.75/1.11 & 0.74/0.88 & 2.52/1.61 & 6 \\  & Protein & & 0.90/0.91 & 0.69/0.68 & 0.87/0.89 & 0.94/0.89 & 3 \\ \hline \multirow{3}{*}{Turkey} & Fat & & 0.92/0.96 & 4.40/2.88 & 0.84/0.91 & 5.82/4.18 & 4 \\  & Moisture & & 0.89/0.97 & 3.57/1.79 & 0.88/0.94 & 4.77/3.24 & 5 \\  & Protein & & 0.74/0.95 & 2.52/1.12 & 0.60/0.92 & 3.51/1.58 & 4 \\ \hline \multirow{3}{*}{Tenderloin} & Fat & & 0.94/0.99 & 0.64/0.30 & 0.92/0.96 & 1.11/0.71 & 3 \\  & Moisture & & 0.92/0.99 & 0.77/0.29 & 0.87/0.88 & 1.04/1.17 & 7 \\ \cline{1-1}  & Protein & & 0.88/0.96 & 1.38/0.76 & 0.76/0.91 & 2.34/1.47 & 7 \\ \hline \end{tabular}
\end{table} 
Table 3: Chemical component prediction in meat samples based on hyperspectral imaging (his) with SNV preprocessing method.
## 3 Data and Methods
### Methods  
The methodology comprises four parts. First, the optimal experimental bands are determined. Second, potential errors in the offset field are analyzed. Third, corresponding correction methods for each error component are proposed. Finally, based on the content of the first three parts, a comprehensive workflow for surface deformation monitoring using RapidEye is developed.
# Perception of Risks and Usefulness of Smart Video Surveillance Systems
## 1 Introduction
### Perceived Usefulness  
The perceived usefulness reflects the extent to which an increase in security through smart video surveillance is considered to be purposeful [6]. Video surveillance is usually an invasion of privacy. However, this invasion of privacy becomes acceptable for the affected people if the importance of the objective outweighs the invasion. There is almost no information on existing systems that work in the presented way despite a German project [10; 40], although there are many cities all over the world using video surveillance and even smart systems, as presented in [1].  
The authorities report that the use of smart video surveillance at different public areas in Mannheim produces different effects on crime frequencies [3]. Earlier, [PERSON] [41] investigated various public areas, concerning their perception of usefulness using video surveillance. Therefore, the author chose areas such as train stations, playgrounds, parks, workplaces and residential areas for his study. In these public areas, the rated usefulness of traditional video surveillance differs descriptively. [PERSON] [42] asked participants whether they felt disturbed by conventional video surveillance or not. Related to this, he examined, e.g., residential areas, department stores and parks as well as the entrances of shops. Additionally, there was already an investigation by [PERSON] [43] regarding the acceptance of smart video surveillance in airports.  
Furthermore, a study by [PERSON] et al. [44] report an increase of the perception of security through video surveillance at large events as well as within crowds. Moreover, a descriptive difference between daytime hours in public areas such as train stations was also discovered [5].  
In addition, a longitudinal study found a difference in approval of conventional video surveillance for different genders [45; 46]. They indicate that men are generally more skeptical about video surveillance than women. In addition, [PERSON] [42] reports that  
Figure 2: Exemplary skeleton sequence visualizing the movement of a person in five timesteps. The length of the sequence depends on the chosen algorithm. Each timestep consists of 15 keypoints, which again is just exemplary and depends on the chosen pose model. However, the basic shape of a person is recognizable.  
women show a stronger acceptance of video surveillance compared to men. However, previous studies regarding attitudes toward video surveillance systems have not dealt with interactions of different variables such as gender, public areas or the time of day when the different places or public areas are visited. Therefore, there is still uncertainly regarding whether such effects of interactions do exist and to what extend they are able to influence the perceived usefulness of video surveillance systems. Consequently, this will be explored in the presented study.
# Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis
## 5 Results & Analysis
### Aspect Sentiment Classification Result
#### 5.2.2 Analyze the Advantages of ASRD  
Since aspect sentiment is determined by sentiment words, which exist in context, determining the syntactically local context of an aspect can accurately capture aspect sentiment information. [PERSON] et al. [5] pointed out that determining the context based on positional relative distance does not include the affective words in the local context, so a dependent syntactic tree is used to determine the syntactic relative distance. For different texts, the semantic relative distance thresholds are different, so it is necessary to give the adapted semantic relative distance thresholds according to the characteristics of each text. According to this situation, we propose an adaptive semantic relative distance, which is determined by the text length. If there is only one aspect in the text and there are no other superfluous emotional words, the local context is the whole text. If there are irrelevant negative words in the text, the threshold of semantic relative distance will be adjusted to exclude them. If the text contains multiple data items, different semantic relative distance thresholds are determined for each data item.  
After using adaptive semantic relative distance, accuracy and F1 improved by 1.11% and 1.06% on the Laptop dataset, and accuracy and F1 improved by 0.73% and 1.11% on the Restaurant dataset, respectively.
## 5 Conclusions  
The miniaturization of scientific payloads for space deployment onboard CubeSat platforms remains a challenging task, especially in the field of high energy astrophysics where most of traditional techniques (such as coded masks) are large and heavy. At the same time, there is a very large competition in proposing and accepting new space missions by major space agencies, so the role of minisatellites is essential and growing with the progress in CubeSat technologies and its overall reliability. The developed highly miniaturized device provides valuable and unique capabilities for high-resolution spectral-imaging in wide FoV in-orbit observations. The novel design is made possible by the segmented optics-collimator element coupled to the high-sensitivity pixel detector Timepix equipped with a high-Z semiconductor sensor. The payload described in this paper will be tested in real space fight conditions onboard the VZLUSAT-2 3U cubesatellite presently launched in LEO. This payload type offers numerous potential future applications in various fields of X-ray astronomy and astrophysics as well as in the detection of high-energy events in the Earth atmosphere such as TGF and auroral emissions.  
Figure 13: Summed spectra of three glows observed by NaI (solid circles) and CsI (open circles) detectors. For comparison, the spectra of many summed TGFs from RHESSI and AGILE are shown. None of the spectra are corrected for the various responses of the instruments, but the similarity is obvious. Adapted from [7].  
**Author Contributions:** Conceptualization, A.I., L.P., P.O., V.D. and V.M.; methodology, A.I., R.H., V.M., D.D. and C.G.; software, D.D. and Z.M.; validation, A.I., D.D., V.M. and C.G.; formal analysis, D.D.; C.G.; A.I.; investigation, R.H.; resources, V.M.and R.H.; data curation, A.I.; writing--original draft preparation, R.H. and C.G.; writing--review and editing, R.H. and C.G.; visualization, V.M.; supervision, V.M.; project administration, V.M.; funding acquisition, P.O., V.D. and L.P.; astrophysical goals and issues, R.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Ministry of Education, Youth and Sports of the Czech Republic grant LTAUSA18094, European Space Agency Contract 40001250020/18/NL/GLC/hh, EU AHEAD2020 project grant agreement ID 871158 and by the Grant Project of the Czech Technical University in Prague, No. SGS21/120/OHK3/2T/13.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We acknowledge the cooperation and support provided by the Czech Aerospace Center (VZLU) Prague. We also acknowledge the valuable comments by the referees and editors which helped to improve the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Emerging Lightweight Security Approaches  
This section presents emerging techniques for lightweight AES and ECC. They are categorized in terms of the following security primitives [23]:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Communications** & **LoRaWAN** & **NB-IoT** & **C-UNB/Sigfox** \\ \hline Uplink Rate & 0.3-50 kbps & 159 kbps (Rel14) & 0.6 kbps \\ Spectrum & ISM band & licensed & ISM band \\ Channel Bandwidth & 125, 250, 500 kHz & 180, 200 kHz & 19 kHz \\ One Micro-Channel & 125 kHz & 15 or 3.75 kHz & 0.6 kHz \\ Number of Uplink Channels & 72 & 12, 48 & 320 \\ Uplink Access & random (ClassA) & scheduled & random \\ Uplink Packet Size & 51 bytes & 128 bytes & 12 bytes \\ \hline
**Security** & **LoRaWAN** & **NB-IoT** & **C-UNB/Sigfox** \\ \hline Strategy & Encrypt-then-MAC & MAC-then-Encrypt & Encrypt-then-MAC \\ Confidentiality & AES-CTR & AES-CTR, SNOW, ZUC & AES-CTR \\ Authentic & AES-CMAC & AES-CMAC, SNOW, ZUC & AES-CMAC \\ Integrity\(-\)MIC Size & 32 bits & 32 bits & 16 bits \\ Replay Protection & 32-bit counter & 32-bit counter & 12-bit counter \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of LPWA networks’ communication and security properties.  
* Lightweight block cipher, which includes smaller block and key sizes, simpler rounds, and key schedules.
* Lightweight hash function, with enhancements such as smaller inputs and smaller outputs.
* High-performance system and whether it has a custom CPU or crypto-arrays to perform encryption tasks in parallel.
* Low-resource device in terms of software or hardware implementations.  
Using these security primitives, Table 7 categorizes a few emerging lightweight schemes, and which areas they propose improvements for. Each of these schemes is explained next.
# A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas
## 4 Applications in Energy Field
### Electric Power System
To improve the efficiency of the energy system, [PERSON] et al. [269] proposed a model based on cloud computing (CC) framework that modifies the classic energy hub (EH) to support managing communication between utility companies and smart energy hubs (S.E. hub). The approach is applied for users to obtain demand side management (DSM) while the Nash equilibrium is achieved by a subgradient optimization algorithm. [PERSON] et al. [270] drew their interest in energy hub and studied the multi-neighbor cooperative economic scheduling problem, where EHs compose a community to exchange energy with each other to minimize operational cost. The authors built a model of a bargaining, cooperative game for this management problem and found the Nash equilibrium to ensure optimal operations. [PERSON] et al. [271] built a bottom-up inter-regional transaction model for electricity pricing mechanism. The results quantitatively showed the relationship between the retailers' behavior and benefit and cost of electricity under the certain economic assumption. [PERSON] et al. [272] proposed a mechanism based on the Stachelberg game for demand response (DR) scheduling optimization problems with load uncertainty. The authors took into account the users' optimal consumption as well as the price of electricity to model the interaction between the service provider and users for the smart grid. The results reached a balanced demand versus supply. For the same interest, [PERSON] et al. [273] also reported an optimization framework based on the Stachelberg game to deal with scheduling of the DR energy system, setting profits of the integrated energy operator (IEO) as the objective. The game was built into a MINLP formulation and solved with an introduced sequence operation theory. The results were verified to be applicable by real-world instances. More widely used ML approaches are shown in this section on the aspect of management, where each energy hub is considered an agent bargaining with each other. Unlike the petroleum supply chain, the electric power system has a shorter supply chain, and the problem is closer to the market. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Problem** & **Reference** & **Year** & **Approach** \\ \hline energy-consuming device optimization & [118] & 2000 & ANN-GA \\ optimal reactive power dispatch & [117] & 2012 & DE + ant system \\ DR scheduling optimization & [272] & 2012 & Stachelberg-ML \\ energy system efficiency & [269] & 2015 & CC \\ generation expansion planning & [119] & 2016 & Q-learning + GA \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Application in electric power system.
## 3 Results
### Temperature
#### 3.1.3 Historic and Future Distribution of Temperature  
The historic and future temperature distributions, as shown in Table 4 and visualized in Figure 6, point towards a warming trend that has significant implications for the region's agricultural and socioeconomic stability. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Raster** & **Mean** & **Min** & **Max** & **SD** & **Range** \\ \hline Max Temp Future & 19.3 & 5.8 & 32.7 & 7.1 & 26.8 \\ \hline Max Temp Historical & 16.4 & 2.9 & 29.8 & 7.1 & 26.9 \\ \hline Min Temp Future & 8.1 & \(-\)4.5 & 19.0 & 6.4 & 23.6 \\ \hline Min Temp Historical & 5.2 & \(-\)7.5 & 16.3 & 6.5 & 23.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The statistics of min and max temperatures both for the future and historically. Figure 5: The raster map of (**a**) minimum and (**b**) maximum temperature change both historically and in the future. Historically, regions such as Chamba and Lahul and Spiti recorded mean temperatures of 12.90 \({}^{\circ}\)C and 5.47 \({}^{\circ}\)C, respectively. These are expected to rise to 15.80 \({}^{\circ}\)C and 8.38 \({}^{\circ}\)C in the future, denoting a substantial increase. The mean maximum temperature, indicative of peak seasonal warmth, shows an ascending pattern, with Chamba's future projection at 25.52 \({}^{\circ}\)C, which is a notable rise from its historical average of 22.64 \({}^{\circ}\)C. Lahul and Spiti's mean maximum temperature is forecasted to climb from a historical 6.20 \({}^{\circ}\)C to 9.11 \({}^{\circ}\)C. Similarly, minimum temperatures are set to increase across all zones, highlighting an overall shift toward higher temperature ranges. Higher temperatures can accelerate glacial melt, impact water supply, alter crop growth cycles, and potentially reduce the yields of temperature-sensitive crops. Such changes necessitate urgent adaptation measures in agricultural practices, water management, and infrastructural development to mitigate adverse impacts. The zonal distribution analysis underscores the urgency to enhance climate resilience in agricultural planning and policymaking. As temperatures rise, understanding localized impacts enables targeted interventions to maintain food security and protect livelihoods in the face of climate change.
# Device-Free Indoor Location Estimation System Using Commodity Wireless LANs
## 3 DF Indoor Location Estimation
### Signal Processing
#### 3.2.1 Existing Method  
This subsection describes an existing method that uses spatial features [17]. We assumed that \(L\) waves were arriving from angles \(\theta_{1},\cdots,\theta_{L}\). According to Equation (14), the array signal can be expressed as:  
\[\mathbf{x}(t)=\mathbf{A}\mathbf{s}(t)+\mathbf{n}(t), \tag{21}\]  
\[\mathbf{A}=[\mathbf{a}(\theta_{1}),\cdots,\mathbf{a}(\theta_{L})], \tag{22}\]  
where \(\mathbf{A}\) denotes the direction matrix, \(\mathbf{a}(\phi)\) denotes the mode vector defined by Equation (19), and \(s(t)\) and \(n(t)\) denote the signal vector and internal noise, respectively. We assumed that the internal noise component was independent in each antenna with \(mean=0\) and \(variance=\sigma^{2}\). Then, the correlation matrix of the input signal is expressed as:  
\[\mathbf{R}_{xx}=E\Big{[}\mathbf{x}(t)\mathbf{x}^{H}(t)\Big{]}=\mathbf{A}\mathbf{R}_{ss}\mathbf{A}^{H}+ \sigma^{2}\mathbf{I}. \tag{23}\]  
The eigenvalue \(\lambda_{k}\) and eigenvector \(\mathbf{e}_{k}\)\((k=1,2,\cdots,K)\) can be obtained via the eigenvalue decomposition (EVD) of the correlation matrix \(\mathbf{R}_{xx}\), which satisfies the following equation:  
\[\mathbf{R}_{xx}\mathbf{e}_{k}=\lambda_{k}\mathbf{e}_{k}\qquad(k=1,2,\cdots,K). \tag{24}\]  
Then, Equation (23) can be expressed as:  
\[\mathbf{R}_{xx}=\mathbf{E}\mathbf{\Lambda}\mathbf{E}^{H}=\sum_{k=1}^{K}\lambda_{k}\mathbf{e}_{k} \mathbf{e}_{k}^{H}, \tag{25}\]  
where:  
\[\mathbf{E}:=[\mathbf{e}_{1},\mathbf{e}_{2},\cdots,\mathbf{e}_{K}], \tag{26}\]  
\[\mathbf{\Lambda}:=\text{diag}\{\lambda_{1},\lambda_{2},\cdots,\lambda_{K}\}. \tag{27}\]  
Since \(\mathbf{R}_{xx}\) is a Hermitian matrix, the eigenvalues can be sorted as:  
\[\lambda_{1}\geq\lambda_{2}\geq\cdots\geq\lambda_{L}\geq\lambda_{L+1}=\cdots= \lambda_{K}=\sigma^{2}. \tag{28}\]  
Because \(\text{rank}\Big{[}\mathbf{A}\mathbf{R}_{ss}\mathbf{A}^{\text{H}}\Big{]}=1\), the eigenvalue distribution is:  
\[\lambda_{1}>\lambda_{2}=\cdots=\lambda_{L}>\lambda_{L+1}=\cdots=\lambda_{K}= \sigma^{2}. \tag{29}\]  
Thus, the space spanning by the eigenvector matrix \(\mathbf{E}\) can be separated into a signal subspace and a noise subspace.  
The first eigenvalue separated by the process above and the corresponding vector (first eigenvector) were used in machine learning. The feature value for machine learning [17] is expressed as:  
\[P=|\mathbf{v}_{0}^{H}\cdot\mathbf{v}|, \tag{30}\]  
\[Q=1-\frac{\lambda^{\prime}-\lambda_{0}^
# On the Virtual Cell Transmission in Ultra Dense Networks
## 1 Introduction
### Related Works
#### 1.1.2 User-Centric Transmission  
The user-centric transmission scheme is the key concept of virtual cells [10]. The goal of user-centric virtual cells is to eliminate cell edges. It breaks the traditional concept of \"cells\". Instead of the conventional cellular network, those RBAs around the users constitute the virtual cells and jointly serve the users at the \"center\". As the users move on, new RBAs join in the virtual cells, and old ones are removed rapidly. In order to reduce the signaling overhead and signal processing complexity, the transmission scheme of virtual cells should be well designed, including transmitting power control, beamforming, virtual cell updating, as well as RBA selection. The work in [11] investigates the effect of virtual cell size on the average user rate when the maximum ratio transmission (MRT) is adopted in each user's virtual cell. A virtual cell grouping transmission scheme is also proposed to eliminate the intra-group interference caused by no cooperation. What is more, interference alignment (IA) [12; 13; 14; 15] has been considered as a new paradigm of intelligent interference management in wireless networks. The principle of IA is to align the interference signals into a subspace with minimum dimensions at the receiver, and consequently, the degrees-of freedom (DoFs) of the desired signals can be maximized. A limited feedback-based IA scheme has been proposed for the interfering multi-access channel (IMAC) in [12]. The optimized transceivers have been designed with the performance-oriented quantization strategy to minimize the residual ICI, and the proposed scheme achieves a significant gain of system throughput, which can be implemented with flexible antenna configurations. Two new uplink opportunistic interference alignment schemes have been proposed with the active alignment transmit beamforming designs in [13], where both cooperative and distributed solutions have been considered with the angle-based and the strength-based selection criteria.