esa-sceva/satcom-chunk-collection huggingface dataset:
# Variability of Diffuse Daylight Due to the Diversity of Cloud Arrays
## 3 Results
### Different Cloud Distributions  
Now, let us take one specific type of clouds and examine how their different distribution affects illuminance values. We specifically simulated 10 various random distributions of clouds with the radius of 0.25 km and the base altitude of 0.5 km. The computed diffuse illuminances for two cloud fractions of 0.1 and 0.5 are shown in Figures 2 and 3. The plots contain also the illuminances computed under the clear sky conditions (\(C_{F}=0\)), which could serve as reference values for comparison.  
Figure 2: Diffuse illuminance on the horizontal plane (**a**) and vertical planes oriented to the south (**b**), east (**c**), and north (**d**) as a function of the solar zenith angle calculated for ten random distributions of identical clouds with the cloud fraction equal to 0.1.  
It is evident that a variation of cloud layout in the sky (while preserving their fraction) can significantly alter the illuminance magnitude. Individual values can differ by more than 10 klx at low cloud fractions; but also at higher fractions the illuminance variation is not negligible. To demonstrate quantitatively this variability, we calculated relative standard deviations of the illuminances within the examined sample. The results for the horizontal illuminances are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cloud Fraction**} & \multicolumn{5}{c}{**Solar Zenith Angle (\({}^{\circ}\))**} \\ \cline{2-5}  & **0** & **20** & **40** & **60** & **80** \\ \cline{2-5}  & \multicolumn{5}{c}{**Standard Deviation of Diffuse Horizontal Illuminance (\%)**} \\ \hline
0.1 & 8.0 & 13.3 & 18.0 & 21.0 & 20.1 \\
0.3 & 8.4 & 8.8 & 13.0 & 17.8 & 16.7 \\
0.5 & 17.7 & 17.6 & 15.6 & 12.6 & 9.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relative standard deviation of diffuse horizontal illuminance for the sample of 10 random cloud distributions.  
Figure 3: Diffuse illuminance on the horizontal plane (**a**) and vertical planes oriented to the south (**b**), east (**c**), and north (**d**) as a function of the solar zenith angle calculated for ten random distributions of identical clouds with the cloud fraction equal to 0.5.
# EC-Kad: An Efficient Data Redundancy Scheme for Cloud Storage
## 5 Conclusions  
This paper proposed an efficient cloud storage data redundancy scheme based on erasure codes and studied the data redundancy optimization of a cloud storage system composed of a cloud storage server, computing server and terminal node. The original data file was erased at the server node, and the encoded data block was distributed to each storage node and finally retrieved and decoded at the terminal node and restored to the data file required by the user. The distributed storage Kademlia algorithm was used to improve the efficiency of uploading and storage retrieval between the terminal nodes and cloud storage platform. This scheme ensures the reliability of users' cloud data while reducing the redundant computation overhead of cloud storage data and improves the success rate of data recovery to a certain extent when the storage node fails and becomes unavailable. In future work, we will focus on the following aspects to improve our cloud storage solution. (1) Cross-data-center synchronization and load balancing in multi-tenant environments: We plan to introduce a dynamic load-balancing mechanism in the future to dynamically adjust data storage and access policies. We will also investigate optimization strategies for cross-data center data synchronization. (2) Applicability in edge computing scenarios: We plan to study the integration of our storage solution with edge computing architectures to achieve local data caching and preliminary processing. We will also explore how to leverage the computational capabilities of edge nodes to optimize the data encoding and decoding processes. (3) Adaptive redundancy factor adjustment strategy: Finally, we plan to design an adaptive redundancy factor adjustment algorithm based on data access frequency in the future to ensure the effectiveness of the solution in different scenarios.  
Methodology, M.C. and Y.W.; software, M.C.; validation and experiment, M.C. and Y.W.; writing, M.C.; writing--review and editing, M.C. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data used to support the research are available from the corresponding author upon request reasonably.  
The authors declare no conflicts of interest.
## 2 Materials and Methods  
We searched for published articles through four databases in April 2021, ProQuest (1983 to 2021), PubMed (1976 to 2021), ScienceDirect (1998 to 2021), and Web of Science (1977 to 2021). Search protocols were made based on search guidelines using the Patient, Intervention, Comparison and Outcome model (PICO) with the search terms: diabetes AND foot AND pressure AND inside AND walking. We performed inclusion criteria and exclusion criteria to answer the research objective. We identified additional studies based on Google Scholar conducted by previous studies [45; 46].  
The data were processed using the SPSS 22 program. [PERSON]'s Kappa statistics and percentage agreement were calculated to determine whether the two reviewers agreed with the selected papers [47].  
The inclusion criteria for this study were as follows: (1) participants had diabetes aged \(\geq\)18 years old or healthy people; (2) studies written in English; (3) type of studies: an experimental study, non-experimental study; (4) types of exposure: had activities wearing insoles; (5) types of outcomes: at least one of walking speed, plantar pressures, plantar pressures reductions, and insole materials. The study's exclusion criterion was regarding the type of studies--namely, abstract-only publications (symposium, conference, proceeding book), protocols, review papers, systematic reviews, meta-analysis studies, and literature reviews were excluded.  
The selection process based on the titles and abstracts of the studies identified in the literature against the inclusion and exclusion criteria was conducted independently by two researchers ([PERSON] and [PERSON]). Further, they ([PERSON] and [PERSON]) independently analyzed the selected studies. The agreement between two independent reviewers for screening titles and abstracts (Kappa 0.814, agreement percentage 99.2%), full-text screening (Kappa 0.773, agreement percentage 91.9%). A third researcher ([PERSON]) consulted disagreement between two researchers, who provided the final decision. There was no disagreement on the articles selected for this review.  
A matrix table was developed to extract the data from the selected studies. The leading information to be collected was related to the two PPP aspects: (1) plantar pressure distribution at different walking speed selection and (2) insole mechanical properties.
# User-Cell Association for Security and Energy Efficiency in Ultra-Dense Heterogeneous Networks
## 2 System Model
The serving BS is selected as the one that provides the maximum secrecy rate in [27; 28] while in [29] the maximum secure area. In this paper, we focus on an new association policy that aims at optimizing security jointly with power consumption. Indeed, we can observe that being cells active or in idle state depending if they have or not users to serve, different association choices determine different patterns of active cells, that lead to different SINR/interference distributions in the considered area. This impacts both the security and the power consumption of the system. The association policy we propose here is executed centrally, following the cloud/centralized radio access (C-RAN) concept: a large number of access points are distributed over the coverage area and are connected to a central processing unit (CPU) that allows cooperation among cells. Hence, the CPU decides the pattern of activation of the cells, and then each user associates with the (active) cell that provides the highest utility. The CPU needs to know the users' SINR that each user can derive from the synchronization signals periodically broadcasted by each BS. Then, the SINR measures are fed back to the BSs and forwarded to the CPU. Power Consumption Model  
To evaluate the energy efficiency of the system, it is necessary to consider the total power consumption of each BS, namely \(P\). The total power consumption of a generic _active BS_ is composed by the radiated power, \(P^{[TX]}\), and by the power consumed for signal processing, power amplifiers, cooling systems, etc., namely \(P_{0}\). Furthermore, even if on idle mode_, the BS has a constant power consumption, \(P_{idle}<P_{0}\). Referring to [39], the total power consumption of each BS can be expressed as  
\[P=\left\{ \begin{array}{ccc}P_{0}+\Omega_{p}p^{\text{[TX]}}&\text{if}&0<p^{ \text{[TX]}}\leq P_{max}\\ P_{idle}&\text{if}&p^{\text{[TX]}}=0\end{array} \right. \tag{5}\]  
where \(\Omega_{p}\) is a scaling factor. Different BS types have different parameters that depend on the specific implementation as detailed in Table 1.
## 2 Materials and Methods
### Sorption Experiments  
The sorption experiments were performed in model solutions freshly prepared and containing a mixture of three elements, namely Cu(II), Zn(II) and Pb(II), with equal concentrations of the elements: 100 \(\upmu\)g/L (level close to those found in rainwater), 250 \(\upmu\)g/L (high level), and 500 \(\upmu\)g/L (very high level). These solutions were prepared on a daily basis by dilution in ultrapure water of stock solutions of Cu(NO\({}_{3}\))\({}_{2}\).6H\({}_{2}\)O, Zn(NO\({}_{3}\))\({}_{2}\).6H\({}_{2}\)O, and Pb(NO\({}_{3}\))\({}_{2}\), containing each one 1000 mg/L (in 1% of HNO\({}_{3}\) (65% suprapure)). Since the pH value of water directly affects metal solubility and the functional groups located on the surface of the biosorbent, the effect of pH on the removal of PTEs from water by the bean peels was evaluated for three initial pH values: 4.0 (acid pH value for atmospheric waters), 5.6 (neutral pH for atmospheric waters), and 7.0 (basic pH value for atmospheric waters) [32]. The pH of solutions was adjusted with nitric acid (0.1 M) and sodium hydrogen carbonate (0.1 M) solutions.  
Experiments started when the peel was placed in 100 mL Erlenmeyer flasks filled with 50 mL of a mixed solution containing Cu(II), Zn(II) and Pb(II) at 100 \(\upmu\)g/L, 250 \(\upmu\)g/L, or 500 \(\upmu\)g/L, and when the flasks were inserted in an incubator at 22 \({}^{\circ}\)C with agitation at 150 rpm, in the dark, for 2 h, 4 h, and 6 h. After the completion of the contact time of each assay, aliquots of 20 mL were collected and acidified to pH \(<\) 2 with HNO\({}_{3}\) (65% suprapure), to posterior analysis of the PTEs by ICP-MS. Controls of the procedure were carried out for the contact times of 2 h, 4 h, and 6 h, where the biomaterial under study was not added to the mixed solution containing the PTEs. The initial concentration of PTEs in solution at 0 h was also determined. The bean peels used in each trial were placed in a desiccator to facilitate drying and to maintain their composition for chemical and morphological analyses.  
All glass material was washed before use by immersion for 30 min in a solution of NaOH 0.1 M, followed by rinsing with distilled water, then immersion for 24 h in an aqueous solution of HNO\({}_{3}\) 4 M, finishing with rinsing first with distilled water and then with ultrapure water.
# A Haar Wavelet Decision Feedback Channel Estimation Method in OFDM Systems
## 3 Conventional Channel Estimation Methods
### DFT Channel Estimation  
Although LS channel estimation method is relatively simple and easy to implement, the significant disadvantage of the method is that it is susceptible to the AWGN of the channel. The DFT channel estimation method is carried out in time-domain to suppress the effect of noise. The channel response values beyond the length of guard interval are set to zero to remove the impact of AWGN in this method. The procedure of the DFT channel estimation method is shown in Figure 2[17; 31].  
As shown in Figure 2, the CFR \(\hat{\mathbf{H}}_{\text{LS}}\) obtained by the LS channel estimation method is converted into time-domain CIR \(\hat{\mathbf{h}}_{\text{LS}}(n)\) with \(n=0,1,2,\cdots,N-1\) by the IFFT operation. Assuming that \(L\) is the number of points of guard interval, the length of CIR should be no longer than that of the guard interval. To suppress the AWGN effectively, \(\hat{\mathbf{h}}_{\text{LS}}(n)\) should be considered to be AWGN when \(n\geq L\). After noise suppression, the estimated CIR \(\hat{\mathbf{h}}_{\text{DFT}}(n)\) can be expressed as [17]  
Figure 2: Discrete Fourier transform (DFT) channel estimation method in cyclic prefix (CP)-OFDM system.  
\[\hat{h}_{\text{DFT}}(n)=\left\{\begin{array}{cc}\hat{h}_{\text{LS}}(n),&0\leq n <L,\\ 0,&n\geq L.\end{array}\right. \tag{2}\]  
At last, the new noise removal CFR \(\hat{H}_{\text{DFT}}\) can be obtained by the FFT operation of \(\hat{h}_{\text{DFT}}(n)\).
## 4 Results and Analyses
### Quantitative Analysis of OD Connectivity  
To quantify the connectivity of Jurong with adjacent cities, we calculated three metrics based on OD analyses: total flow volume, relative connectivity intensity, and flow balance (Table 1, Figure 8).  
Figure 6: Sankey chart of the population flow of Jurong City from other provinces in China in November 2019.  
Figure 7: Sankey chart showing the population of Jurong City visited by residents of cities in Jiangsu Province, November 2019.  
1. Total flow volume: The Jurong-Nanjing connection dominates regional interactions, constituting 45.2% of all OD trips, with approximately 78,900 weekly intercity trips recorded. In contrast, Jurong-Zhenjiang flows total approximately 54,900 trips (31.5% of total flows), while direct flows between Nanjing and Zhenjiang only total about 40,800 trips (23.3%). This pattern reveals the critical intermediary role in regional connectivity in Jurong, capturing interactions that may otherwise bypass its territory.
2. Relative connectivity intensity: The relative intensity index normalizes flows according to population size, enabling an unbiased comparison across city pairs. Jurong-Nanjing has an exceptionally high connectivity intensity of 2.18, significantly exceeding the baseline (1.0) and indicating highly intensive functional integration with Nanjing. The Jurong-Zhenjiang interaction is lower (1.15), reflecting modest integration relative to administrative ties, and the direct linkage between Nanjing and Zhenjiang remains notably weaker (0.59).
3. Flow balance: The flow balance metric quantifies the directionality and symmetry of flows. For Jurong-Nanjing, the balance is \(-0.082\), signifying a moderate net outflow of commuters from Jurong toward Nanjing. Similarly, Jurong-Zhenjiang has a slight net outward flow (\(-0.048\)), indicative of the function of Jurong's dependence on its neighbors for employment and specialized services. Conversely, direct flows between Nanjing and Zhenjiang are nearly symmetrical (+0.009), confirming the centrality of Jurong as an intermediary node in regional interactions.
## 5 Validation and Results
### Method Validation
#### 5.3.1 Verify Data Collection  
We generated a series of urban intersection scenarios of varying complexity using the built-in scenario models of the PanoSim platform. Based on these scenarios, we tested the Apollo autonomous driving system for unprotected left turns. Each set of experimental data collected detailed records of three key variables: scenario complexity, autonomous driving risk, and driving efficiency.  
The specific simulation settings are illustrated in Figure 10. In this scenario, the Apollo vehicle executing a left turn encounters traffic from the north, east, and west, which covers six lanes. The planned path of the Apollo vehicle is marked by a red curve. The maximum number of vehicles allowed in each lane is set to two. During the initialization phase of the simulation, these vehicles are randomly positioned within their respective lanes, with the distances between vehicles and their initial speeds randomly determined within the ranges of [3; 10] meters and [0, 5] meters per second, respectively. The simulation continues until all vehicles have safely exited the intersection or until the maximum simulation time of 60 s is reached.  
The performance metrics for autonomous driving include the following:  
(1) Risk metric: The average risk of conflict for self-driving vehicles is calculated using the formula below:  
\[R=\sum_{t=1}^{T}\left(\sum_{j=1}^{M}cr_{ego,j}(t)\right), \tag{14}\]  
where \(cr_{ego,j}(t)\) represents the conflict risk between the ego vehicle and the conflicting vehicle \(j\) at time \(t\). Here, \(M\) denotes the total number of conflicting vehicles for the ego vehicle and \(T\) represents the total number of simulation steps.  
(2) Efficiency metric: The average speed of an autonomous vehicle passing through an intersection is calculated using the formula below:  
\[E=\frac{1}{T}\sum_{t=1}^{T}v_{\text{ego}}\left(t\right), \tag{15}\]  
where \(v_{\text{ego}}\left(t\right)\) denotes the speed of the ego vehicle at time \(t\), and the unit is m/s.  
Figure 10: Setting up of the unprotected left turn test experiment.  
In this study, a total of 6000 simulation experiments were conducted. During the data processing phase, we eliminated abnormal data resulting from timeouts and unreasonable collisions. After this screening process, the total number of valid experimental datasets was 4365. Table 1 shows the statistical results of the dataset.
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 3 Proposed Hybrid Graph Embedding and Adaptive Convolution Networks
### Hybrid Graph Embedding Module
#### 3.4.2 Temporal Attention Mechanism  
The method simulates feature temporal connections throughout time. The computation of attention weights (\(\alpha_{ij}\)) in Equation (9) is as follows:  
\[\alpha_{ij}=\exp\left(\mathrm{LeakyReLU}\left(\mathrm{a}^{\top}\left[Wh_{i} \mid\mid Wh_{j}\right]\right)\right) \tag{9}\]  
In Equation (9), \(\left[Wh_{i}\mid\mid Wh_{j}\right]\) is denoted as a concatenation of features from nodes i and j, transformed by weight matrix W. LeakyReLU is denoted as an Activation function adding non-linearity, and a is denoted as a learnable attention vector. This process allocates a greater position to temporally relevant connections, dynamically weighting node interactions. Dimension: \(\alpha_{ij}\)\(\in\)\(\mathbb{R}\), where attention weights are scalars.  
Figure 4: Hybrid Graph Embedding Module.
# Low Temperature Microcalorimeters for Decay Energy Spectroscopy
###### Abstract  
Low Temperature Detectors have been used to measure embedded radioisotopes in a measurement mode known as Decay Energy Spectroscopy (DES) since 1992. DES microcalorimeter measurements have been used for applications ranging from neutrino mass measurements to metrology to measurements for safeguards and medical nuclides. While the low temperature detectors have extremely high intrinsic energy resolution (several times better than semiconductor detectors), the energy resolution achieved in practice is strongly dependent on factors such as sample preparation method. This review seeks to present the literature consensus on what has been learned by looking at the energy resolution as a function of various choices of detector, absorber, and sample preparation methods.  
decay energy spectroscopy; Q spectroscopy; microcalorimeter; transition edge sensor; metallic magnetic calorimeter; thermistor; embedded; absorber +
Footnote †: journal: Applied Sciences
# Error Source Analysis and Correction of GF-3 Polarimetric Data
## 6 Conclusions  
In this study, the error sources affecting GF-3 polarization observation are analyzed comprehensively, including: (1) the channel imbalance of the transmit/receive channel; (2) sampling delay error introduced by the transmit/receive channel; (3) the isolation and channel imbalance of the antenna array; (4) the POA caused by satellite attitude; (5) the FR introduced by the ionosphere. And, based on the specific influence of each error source on the GF-3 polarization data and using three groups of data with PARCs, the polarization correction of GF-3 PolSAR system is efficiently achieved during the in-orbit testing phase.  
Based on common distributed targets and corner reflectors, assessment results of the corrected PolSAR data of GF-3 suggest that the amplitude and phase of channel imbalances are basically maintained within 0.5 dB and 10\({}^{\circ}\), and the isolation is higher than 35 dB, which achieve the designed requirement of GF-3 polarimetric performance. Currently, the proposed correction process has been applied to the product generation software of the GF-3 ground segment. In the future, variation in the channel imbalance and delay error will be monitored by internal calibration. And the quality of GF-3 polarization data should be normally evaluated to ensure the provision of good-quality data for applications.  
[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] initiated the research. [PERSON] established the full-link error model in Section 2. Under supervision of [PERSON], [PERSON] performed the experiments and analysis. [PERSON] wrote the manuscript, and [PERSON], [PERSON], [PERSON] and [PERSON] revised the manuscript. All authors read and approved the final version of the manuscript.  
Funding:This research was supported by the National Science Foundation of China under Grant No.61331017 and the GF-3 High-Resolution Earth Observation System under Project (30-Y20A12-9004-15/16, 03-Y20A11-9001-15/16).  
**Acknowledgments:** The authors thank the support from The 14 th Research Institute of China Electronics Technology Group Corporation, and China Center for Resources Satellite Data and Application.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Fisher Discriminative Sparse Representation Based on DBN for Fault Diagnosis of Complex System
## 3 Fisher Discriminative Sparse Representation (FDSR) of the Features Extracted from DBN
### Fault Diagnosis Solution  
Since the dictionary and sparse coefficient are solved above, the diagnosis of the fault type is based on minimizing the reconstruction error. We performed classification using \(identity(y)=\text{argmin}\left\{e_{i}\right\}\), where \(e_{i}=\left\|y-D_{i}X_{i}\right\|^{2}+\lambda\left\|X\right\|_{1},1\leq i\leq c\), and \(X_{i}\) is the coefficient corresponding to subdictionary  
\begin{table}
\begin{tabular}{l} \hline \hline
**Input:** signal \(y\) \\
**Initialization:** Initialize \(D\) \\ We initialized \(D\) using a simple principle component \\ analysis of \(A\) and ensuring every column of \(D\) is a unit vector. \\
**Step 1:** Update the sparse coefficients \(X\). \\ Fix \(D\) and solve \(X_{i},i=1,2,\cdots,c\) one at a time by solving \\ Equation (14). \\
**Step 2:** Update the dictionary \(D\). \\ Fix \(X\) and solve \(D_{i},i=1,2,\cdots,c\) class by class by solving \\ Equation (15). \\
**Step 3:** Repeat Steps 1 and 2 until the maximum number of iterations is reached, or the values of \(J_{(D,X)}\) adjacent \\ iterations are sufficiently close. \\
**Output:** Optimal dictionary \(D=\left[D_{1},D_{2},\cdots,D_{c}\right]\) and \\ sparse coefficient \(X=\left[X_{1},X_{2},\cdots,X_{c}\right]\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fisher discrimination sparse representation algorithm.  
\(D_{i}\). Here, we introduced the \(l_{1}\) norm of \(X\) to ensure the sparsity of the coefficient, if the number of non-zero atoms not small enough, the dictionary cannot be the optimal candidate.