esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Procedures  
Kinematic activity data were collected during each World Cup, encompassing a total of 7 matches in the senior category, 4 matches in the U-20 category, and 6 matches in the U-17 category. Only players who completed 80 min or more during a match were included in the analysis. As a result, 52 observations were recorded for the senior category (13 CB, 12 FB, 17 CM, 4 WG, and 6 ST), 27 for the U-20 category (6 CB, 7 FB, 6 CM, 6 WG, and 2 ST), and 45 for the U-17 category (11 CB, 10 FB, 12 CM, 8 WG, and 4 ST). All match activity was recorded using the WIMU PRO GPS device (RealtrackSystems S.L., Almeria, Spain). Intra- and inter-unit reliability was considered acceptable, with an intraclass correlation coefficient of 0.65 for the x-coordinate and 0.85 for the y-coordinate in the systems analyzed [10]. GPS  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & \multicolumn{2}{c}{**Senior**} & \multicolumn{2}{c}{**U20**} & \multicolumn{2}{c}{**U17**} \\ \hline  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline Age (years) & 24.9 & 3.5 & 19.2 & 0.8 & 16.8 & 0.5 \\ Body mass (Kg) & 62.1 & 6.4 & 61.8 & 6.1 & 60.6 & 4.4 \\ Height (cm) & 167.5 & 6.4 & 164.4 & 5.8 & 161.7 & 4.8 \\ \hline \hline \end{tabular}  
* \(\mathrm{Kg=kilograms;cm=cemitters}\).  
\end{table}
Table 1: Descriptive Characteristics of Players by Age Category.  
devices were activated 15 min before players put them on to ensure adequate satellite signal acquisition. Prior to the start of the pre-match warm-up, players placed the device into a vast worn on their upper back at scapular level. Each player consistently used the same GPS unit to minimize inter-unit measurement error. Upon match completion, GPS data were extracted and analyzed using SPRO Software (version 958; RealtrackSystems, Almeria, Spain). These procedures were part of the standard pre-match protocol, ensuring that players did not deviate from their usual routines.  
The variables analyzed for each match included TD, HSR distance (HSR; \(\geq\)18 km\(\cdot\)h\({}^{-1}\)), and sprint distance (\(\geq\)21 km\(\cdot\)h\({}^{-1}\)) [11]. Additionally, the number of high-intensity accelerations (\(>\)3 m\(\cdot\)s\({}^{-2}\)) and decelerations (\(<\)-3 m\(\cdot\)s\({}^{-2}\)) were recorded [12], as well as HMLD, defined as the distance covered while expending more than 25.5 W\(\cdot\)kg\({}^{-1}\). HMLD provides a comprehensive measure of high-intensity activity, as it incorporates not only HSR (\(\geq\)19.8 km\(\cdot\)h\({}^{-1}\)), but also accelerations and decelerations within a range of 2 m\(\cdot\)s\({}^{-2}\) to 4 m\(\cdot\)s\({}^{-2}\)[13,14].
## 4 Methodology And Implementation
### Metrics  
Evaluating the performance of UDC recommender systems requires metrics that account for both textual similarity and the hierarchical nature of library classification schemes. String similarity measures such as the Levenshtein and Jaro-Winkler similarity are commonly used to assess how closely a predicted string matches the correct one in terms of character-level edits or shared prefixes. While effective for detecting surface-level differences, these metrics may overlook the semantic or structural proximity of UDC codes. To complement them, we develop the adjusted hierarchical similarity metric which extends component-wise string matching by incorporating both positional alignment and a penalty for excessive prediction length. This approach maps more naturally onto the faceted, hierarchical structure of UDC codes. Together, these three metrics form a basis for the robust evaluation of our proposed UDC recommendation approach.
# Performance Assessment and Comparison of Deployment Options for 5G Millimeter Wave Systems
## 4 Numerical Results
### Spectral Efficiency  
We start our analysis in Figure 2 illustrating the LoS probability (Figure 1(a)) and the spectral efficiency (Figure 1(b)) as a function of the distance between the UE and BS for three considered deployment options: (i) BS-UE, (ii) BS-UAV-UE, and (iii) BS-Terr-UE, where _Terr_ implies terrestrial IAB node deployment. For this and further analyses, we consider different antenna arrays, but, since the plot behaviors for different arrays were similar, we present those that give better insight into the impact the number of elements has on the discussed metrics.  
Both the LoS probability and spectral efficiency drop with distance. The vertical lines represent the maximum distance at which corresponding devices (i.e., UE, terrestrial and aerial re-transmitters), equipped with antenna arrays mentioned in the plot legend element numbers, can be placed, while maintaining an outage probability of 0.1. It is evident that, while the terrestrial re-transmitter can be placed further than the UE by several tens of meters, the aerial UAV gives a much higher advantage in terms of a distance of 1-2 km.
## 3 Proposed Method
### Channel Attention Mechanism (CAM)  
The channel attention mechanism (CAM) [27] is a channel feature enhancement module for the feature tensor and it can help the feature map obtain more channel-focused features from it. Our modified CAM architecture is shown in Figure 5, which is different from the general channel attention mechanism.  
This modified CAM uses reshape to reorder the feature maps on three branches. The third branch uses transpose to perform matrix multiplication with the second branch and the softmax function to obtain the feature tensor of \(C\times C\). Then, the feature tensor of \(C\times C\) is multiplied with the reshaped feature from the first branch to obtain the enhanced channel feature tensor. Finally, the enhanced channel feature tensor is multiplied by the input feature for the output.
## 2 Materials and Methods
### Ndvi  
Observed 16-day composite NDVI values from the Advanced Very High Resolution Radiometer (AVHRR) [34] for the period 1982-2009 were spatially averaged over each hydrological unit, then summed to obtain total annual NDVI (by adding all 23 NDVI composites for each year), which isproportional to annual Gross Primary Production (GPP). In general, annual NDVI is an aggregate indicator of vegetation greening and the larger the NDVI, the greener the vegetation [35]. Furthermore, the total annual NDVI masks the seasonal variation, making it suitable to explore the existence of a potential relationship between long-term annual (P-Ru) and annual vegetation greening trends. The term vegetation greening is used instead of change in vegetation density, since NDVI trends can be driven by factors that are potentially unrelated to density, such as alterations to vegetation composition [36] or lengthening growing seasons [37]. Similarly, the NDVI time series were passed through a 3-year moving average to isolate long-term trends.
## 6 Experimental Validation
### Experimental Procedure  
Based on the dynamic VAD platform, a virtual source localization experiment was conducted to validate the algorithm. The experiment was designed to compare the directional localization and distance perception performance of the proposed and conventional algorithms. Therefore, there were two algorithms to be evaluated:  
1. Binaural signals synthesized by the proposed method;
2. Binaural signals synthesized by the conventional method.  
For each algorithm, there were five target virtual source distances at \(r=0.2\), 0.3, 0.5, 0.75, and 1.0 m; and at each virtual source distance, there were 11 target source directions distributed in the right-hemispherical space, including \(\theta=0^{\circ},90^{\circ},180^{\circ}\) on the \(\phi=\pm 45^{\circ}\) elevation planes and \(\theta=0^{\circ},45^{\circ},90^{\circ},135^{\circ},180^{\circ}\) on the \(\phi=0^{\circ}\) (horizontal) plane. The input signal was pink noise with a length of 15 s.  
The experiment was conducted in a listening room with a background-noise level less than 30 dB. The subjects sat in the center of the room. Eight subjects (ages 23-27, six male and two female) with normal hearing participated in the experiment. Subjects judged the perceived virtual source direction and distance and reported the results by pointing to an electromagnetic receiver at one end of a wooden rod to the perceived position. If inside-the-head localization occurs, the subject reported the results orally. During the experiment, the subjects were asked to close their eyes and encouraged to turn their heads during the perception process.  
If no inside-the-head localization occurs, the perceived virtual source position is further analyzed (inside-the-head localization means the algorithm or experiment is invalid). Directional localization and distance perception are analyzed separately. In the analysis of the directional localization, the reversal errors (front-back or up-down confusion) are first corrected in the raw localization results by reflecting the results against the symmetric plane, and the percentage of confusion is calculated. The accuracy of directional localization is evaluated by the following overall mean angular error between the perceived and target angles in each case of the algorithm, direction, and distance [28].  
\[\Delta_{\Omega}=\frac{1}{L}\sum_{l=1}^{L}\mid\arccos\left[\frac{\mathbf{r}_{l}(l) \bullet\mathbf{r}}{|\mathbf{r}_{l}(l)||\mathbf{r}|}\right], \tag{16}\]where \(r\) is the vector from the origin (head center) to the target position. \(r_{I}(l)\) is the vector from the origin to the \(l\) th judged position, and \(L\) is the total number of judgments. The dot denotes the scalar multiplication of two vectors.  
The performance of distance perception is simply evaluated by the mean perceived distance  
\[\bar{d}_{I}=\frac{1}{L}\sum_{l=1}^{L}|d_{I}(l)|, \tag{17}\]  
where \(d_{I}(l)\) is the perceived distance of the \(l\) th judgment. Corresponding standard deviations are also calculated.  
Moreover, multi-way ANOVA is also applied to the experimental results.
## 3 The Optimization and Reconfiguration of UDNs Incorporating VPPs
### Constraints
#### 3.2.3 Power Flow Constraints  
The power flow constraint is as shown in (29) and (30).  
\[\left\{\begin{array}{l}\sum\limits_{i\in u(j)}\left(P_{ij}^{t}-I_{ij}^{t}R_ {ij}\right)+P_{j}^{t}=\sum\limits_{i\in(j)}P_{jk}^{t}\\ \sum\limits_{i\in u(j)}\left(Q_{ij}^{t}-I_{ij}^{t}X_{ij}\right)+Q_{j}^{t}= \sum\limits_{i\in(j)}Q_{jk}^{t}\end{array}\right. \tag{29}\]  
\[U_{j}^{t2}=U_{i}^{t2}-2\left(R_{ij}P_{ij}^{t}+X_{ij}Q_{ij}^{t}\right)+\left(R_ {ij}^{2}+X_{ij}^{2}\right)I_{ij}^{t} \tag{30}\]  
where \(X_{ij}\) is the reactance of line \(ij\) in the UDN; \(Q_{j}^{t}\) is the reactive power injected at bus \(j\); \(u(j)\) is the set of buses from which power flows to bus \(j\); and \(v(j)\) is the set of buses to which power flows from bus \(j\).  
It has been derived and proven in the relevant literature that when the bus load is unbounded and the objective function is a strictly increasing function of branch current,the above relaxation is exact and can be equivalently transformed into the standard second-order cone form as follows:  
\[\left\{\begin{array}{l}\left|\left|\begin{array}{l}2P_{ij}^{t}\\ 2Q_{ij}^{t}\\ I_{ij}^{t}-U_{i}^{\text{sqr},t}\\ U_{i}^{\text{sqr},t}=U_{i}^{2,t}\end{array}\right|\right|_{2}\leq I_{ij}^{t}+U _{i}^{\text{sqr},t}\\ \\ \left|\begin{array}{l}U_{i}^{\text{sqr},t}\\ \end{array}\right|=U_{i}^{2,t}\end{array}\right. \tag{31}\]
# Educational 5G Edge Computing: Framework and Experimental Study
###### Abstract  
Benefiting from the large-scale commercial use of 5G, smart campuses have attracted increasing research attention in recent years and are expected to revolutionize traditional campus activities. However, there are some obstacles that hinder the practical deployment of MEC (multi-access edge computing). First, traditional information infrastructures on campus cannot support latency-sensitive and computing-intensive smart applications, such as AR/VR, live interactive lectures and digital twin experiments. In addition, the mixture of old and new applications, isolated data islands and heterogeneous equipment management introduce more challenges. Moreover, the existing MEC framework proposed by ETSI and 3 GPP cannot meet the specific deployment requirements of smart campuses, e.g., educational data security, real-time interactive applications, heterogeneous connections, and others. In this paper, we propose a 5G-based architecture for smart education information infrastructure; a new dedicated cloud architecture eMEC (educational multi-access edge computing) is defined. It consists of a UGW (universal access gateway) and an eMEP (educational multi-access edge computing platform), making it possible to satisfy education-specific requirements and long-term evolution. Furthermore, we implement the framework and conduct real-world field tests for eMEC in a university campus. Based on the framework and practical field tests, we also conduct a measurement study to unveil the spatial-temporal characteristics of mobile users in the smart campus and discuss exploiting them for better network performance. The experimental results show that the system achieves satisfactory performance in terms of both throughput and latency.  
5G; multi-access edge computing; educational system; eMEC; field test Article
## 4 Discussion  
Figure 7 shows the mean deviations of ERA5 and remote-sensing observations for different meteorological variables. In Figure 7a, the horizontal wind speed of the ERA5 reanalysis data is positively deviated at a maximum of 9% when presented below 400 m. Above 400 m, there is a tendency of an increasing negative deviation, and a large deviation of the horizontal wind speed at upper levels will generate larger errors in the operation of the climate models. The ERA5 vertical wind speed shows a more obvious negative deviation below 1800 m, which indicates that the ERA5 reanalysis data are unable to  
Figure 6: Same as Figure 2, but for the relative humidity (RH) at different heights. Time series (**a1–i1**) and scatter plots (**a2–i2**) of the observed relative humidity at various heights compared with the ERA5 reanalysis data values. The zoomed-in time series of 13 and 15 July 2023 were selected for comparison, with the red and blue lines representing the relative humidity from ERA5 and MWR, respectively. The areas circled by ellipses are the locations of relative humidity changes during extreme weather.  
accurately represent the state of the local vertical motions (Figure 7b). Some of the poor agreement between the model and observations for the vertical velocities can be attributed to differences in the averaging timescales used for the wind lidar and the ERA5. Wind radar observations have a second resolution. We compared their averaged hourly data to the ERA5 data at an hourly resolution, and a large error was found. Figure 7c shows that the deviation of the ERA5 wind direction varies within 10%. Affected by the local circulations and the terrain forcings, it is normal to fluctuate the small deviations. In Figure 7d, although the deviation shown is only within 2%, we observed higher temperature deviations at higher altitudes occurs above 2000 m. The humidity data from the ERA5 reanalysis datasets show more pronounced positive deviations from 500 to 2500 m, as shown in Figure 7e, with that of the ERA5 data, which is twice as high as the observed values.  
The ERA5 reanalysis datasets were shown to poorly capture the vertical wind speeds in the context of extreme weather events. The temperature data from ERA5 agree well with the measurements at low and medium levels, with deviations of less than 0.2%, but systematic errors of high deviations appear at higher than 2000 m, illustrating the importance of the optimization and improvement of ERA5 upper-level temperature data. When unusual weather occurred, the meteorological variables changed rapidly within a small scale, and the ERA5 was unable to accurately represent these conditions, leading to biases. Please refer to the Supplementary Materials for the vertical profile of each meteorological element under extreme weather conditions. Aiming to compensate for the disadvantages of the ERA5, several methodologies have potential applications in improving the accuracy its accuracy. For example, mechanical learning methods have been evidenced to be an useful tool for reducing the errors of the boundary layer height of the ERA5 data [10].
# Parallel Ghost Imaging with Extra Large Field of View and High Pixel Resolution
## 4 Conclusions  
In conclusion, after achieving high spatial resolution and high imaging efficiency in parallel ghost imaging, we further dramatically expanded the imaging result's field of view by constructing a vast speckle space. This major advancement came at no unexpected cost, meaning that the experiment can still collect data at a rate of 6 min per sample, without any increase in radiation dose. Using the complex fish brain as a sample, we achieved ghost imaging with an image size of 14,000 \(\times\) 10,000 pixels (4.55 mm \(\times\) 3.25 mm, millimeter-scale field of view) and a pixel resolution of 0.325 um (sub-micron pixel resolution). We present this framework that enables ghost imaging with an extra-large field of view, ultra-high efficiency, and ultra-high pixel resolution, with the aim of providing a solution for the practical application of ghost imaging. This method can be implemented with simple experimental equipment and is feasible for X-rays, neutrons, and visible light, offering broad prospects for future applications.  
**Author Contributions:** Conceptualization, C.Z., J.T., D.L. and T.X.; Methodology, N.Z., J.T., D.L., H.G., H.Z. and T.X.; Software, N.Z., H.G. and H.Z.; Validation, N.Z.; Formal analysis, N.Z.; Investigation, N.Z. and J.W.; Resources, N.Z., C.Z. and T.X.; Writing--original draft, N.Z.; Writing--review & editing, N.Z.; Visualization, N.Z. and T.X.; Supervision, H.G., H.Z. and T.X.; Project administration, T.X.; Funding acquisition, T.X. All authors have read and agreed to the published version of the manuscript.  
The National Key Research and Development Program of China (Grant Nos. 2022 YFA1603601, 2021 YFE0601203, 2021 YFA1600703), the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 12205361).  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for their kind help and fruitful discussion on experiments and data processing.  
The authors declare no conflicts of interest.
# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 3 Privacy-Protecting Attributes Classification for the Storage of Cloud Data
### Management of Performance
#### 3.5.3 Privacy Achievements  
Privacy achievement refers to the outcomes or consequences of specific functions within the technique that contribute to its overall effectiveness. Privacy assessment encompasses various crucial aspects. It involves identifying the types of attacks that are addressed by the techniques, assessing the privacy achievement level after implementing a particular algorithm [62; 63]. These techniques prevent unauthorized parties from manipulating data and restrict the external authorities from disclosing high-value information without proper authorization. Furthermore, the use of digital signatures and certifications plays a vital role in ensuring the integrity and trustworthiness of data. By employing these mechanisms, data integrity is protected, and the authenticity of data can be verified.  
Employing a data hiding approach and implementing a multi-cloud storage structure addresses two essential privacy objectives: ensuring data availability and preserving data integrity. By concealing the data and distributing it across multiple cloud providers, the risk of unauthorized access or data leakage is significantly reduced. Finally, employing the multi-layer encoding method enhances privacy by deterring various attempts to expose susceptible data from the users of individual clouds [64]. With multiple layers of encryption, the security of the data is strengthened, making it significantly more challenging for unauthorized parties to decipher. Therefore, it can be said that the application of these privacy-preserving techniques leads to significant privacy achievements, including data confidentiality, integrity, availability, and trustworthiness. By implementing these measures, organizations can better protect sensitive data in cloud storage environments, and these privacy achievements contribute to the overall success and effectiveness of privacy-preserving techniques.
## 2 Materials and Methods
### Kernel Search
This method is based on so-called \(k\)-opt moves, where \(k\) edges in the current solution are replaced by a set of \(k\) different feasible edges in such a way that a better solution is achieved. Given a feasible solution, EIM-CVRP repeatedly performs \(k\)-opt moves that reduce the cost of the current solution, until a solution is reached for which no exchange yields an improvement. The value of \(k\) is changed adaptively throughout the search with step \(\delta\) as the procedure parameter. The Algorithm 2 summarizes the mentioned procedure adapted to the KS-CVRP in pseudocode. ```
0: Kernel \(K\), bucket \(B_{i}\), current best solution \(s^{*}\), cut-off value \(z^{*}\), and parameter \(\delta\). 0: Optimal solution \(z_{i}\) and its value \(s_{i}\). 1:\(s_{i}\gets s^{*}\)
2:\(z_{i}\gets z^{*}\)
3:\(E\gets B_{i}\)
4:\(m_{1}\gets 1\)
5:\(m_{2}\gets m_{1}+\delta\)
6:while\(m_{1}\leq|B_{i}|\)do
7: Solve MILP(\(K\cup B_{i}\)) with additional constraints:  Set an objective function cut-off value to \(z_{i}\)  Use \(k\) variables from \(E\), where \(m_{1}\leq k\leq m_{2}\)
8:if modified MILP(\(K\cup B_{i}\)) is feasible with solution \(s\) and value \(z\)then
9:\(s_{i}\gets s\)
10:\(z_{i}\gets z\)
11: Update \(E\) by removing all variables with positive value in \(s_{i}\)
12:else
13:\(m_{1}\gets m_{2}+1\)
14:\(m_{2}\gets m_{1}+\delta\)
```  
**Algorithm 2** The iterative improvement procedure  
Of course, the procedure can be terminated after a predetermined computational time, or a certain number of iterations without any improvement. If \(z_{i}<z^{*}\), it means  
Figure 3: Evolution of solution improvement by different methods. that the found solution \(s_{i}\) improves the current best one and so \(z^{*}\) and \(s^{*}\) are updated. Additionally, the kernel content is extended by adding the newly discovered variables, which have a non-zero value in the solution \(s_{i}\). When all buckets have been explored, the recent best-found solution \(s^{*}\) and its value \(z^{*}\) are returned as the final output.
# An Efficient Probe-Based Routing for Content-Centric Networking
## 3 Design and Description of Our Proposal
### Probe-Based Routing Algorithms  
The routing procedure is described as follows. A router's routing is triggered by packets. The _interest sending phase_ and the _data packet returning phase_ are the two transmission phases.