esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Design and Procedure  
Players were equipped with individual 10 Hz GPS units (PlayerTek Pod, Catapult Sports, Melbourne, VI, Australia) containing triaxial accelerometers. These devices, weighing 42 g and measuring 84 mm \(\times\) 42 mm \(\times\) 21 mm, were securely positioned between the shoulder blades in bespoke rests or custom-sewn pockets on the players' jerseys [3; 10; 11; 12; 13; 14]. Prior to the competitive season, participants underwent a maximal velocity assessment as part of their pre-season evaluation. This involved a 40 m linear sprint while wearing their assigned GPS units [3], establishing each player's peak velocity (m\(\cdot\)s\({}^{-1}\)) for subsequent match-play comparisons. On match days, GPS devices were activated approximately 75 \(\pm\) 10 min before throw-in to ensure satellite lock without disrupting pre-game routines [3; 14]. The validity and reliability of these units had been previously established in locomotion studies [3; 19]. Post-match data quality was verified by analysing the satellite distribution and horizontal dilution of precision (HDOP). The mean values were 13.1 \(\pm\) 2.8 satellites and 0.9 \(\pm\) 0.3 HDOP, respectively. GPS data were then downloaded, segmented into quarters of play, and processed using proprietary software (PlayerTek Team Application, V2.5.5). Only the active playing time was included in the final analysis, resulting in 145 individual data sets [full-back (\(n\) = 31), half-back (\(n\) = 35), midfield (\(n\) = 24), half-forward (\(n\) = 24), and full-forward (\(n\) = 31)] being collected across 18 competitive games during the 2021-2022 LGF seasons. The data were then extracted from the proprietary software via comma-separated value files with the data placed into a bespoke Microsoft Excel Database (Version 2407; build 17830.20138; Microsoft, Redmond, WA, USA) with these data visualised within Microsoft Power BI (Microsoft, Redmond, WA, USA) for retrospective analysis and communication to coaching stakeholders. The exported CSV file provided analysis for various locomotor variables: total distance (TD; m); relative distance (m\(\cdot\)min\({}^{-1}\)); high-speed running (HSR; m\(\cdot\) \(\geq\) 4.4 m\(\cdot\)s\({}^{-1}\)); relative HSR (m\(\cdot\)min\({}^{-1}\)); very high-speed running (VHSR; m\(\cdot\) \(\geq\) 5.5 m\(\cdot\)s\({}^{-1}\)); peak velocity (m\(\cdot\)s\({}^{-1}\)); percentage of peak velocity (% Peak V); accelerations (\(n\); \(\geq\) 3 m\(\cdot\)s\({}^{-2}\)); decelerations (\(n\); \(\leq\) \(-\)3 m\(\cdot\)s\({}^{-2}\)). These thresholds align with previous research in female team sports and LGF specifically [2; 3; 8; 14].
# Safety Control for Satellite Formation Flying via High-Order Control Barrier Functions
## 2 Problem Formulation and Preliminaries  
This section mainly discusses how to establish the model of the satellite formation flying and introduces some preliminaries of safety control based on the control barrier function. Following this description, the purpose of this paper is presented. The details are given in the rest of the section.
## 2 Results and Discussion
### Gel Percentage  
Figure 5 depicts how the gel percentage of polymer in the nanoparticles evolves with the polymerization, showing a continued increase for both cases. Gel percentage is defined as the polymer fraction which turns non-solvent soluble as a result of the cross-linking between polymer chains.  
Here, the larger values correspond to the run at higher dosing rate, with 80%-85% at the initial part of the polymerization and around 98% at the end of it. In contrast, the other polymerization shows values ranging 70% to 84% in the same interval. The continued increase in gel percentage could be explained as a result of an increase in the chain number inside the particles as the polymerization progresses, which would increase the number of cross-linking points. Furthermore, the direct dependence of the gel  
Figure 4: Representative micrographs from samples of final latexes in polymerizations at 0.05 g/min (**a**); 0.15 g/min (**b**) in organic phase dosing rate.  
Figure 5: Variation of gel percentage in polymer in nanoparticles with global conversion from polymerizations at 0.05 (**a**) and 0.15 (**b**) g/min in organic phase dosing rate. Runs M (\(\blacktriangle\)); runs MR (\(\blacktriangle\)).  
percentage on the dosing rate would also arise from a possible increase in the polymer concentration in the particles.
## 2 Study Area and Data
### Study Datasets  
#### 2.2.1 SRTM DEM  
Shuttle Radar Topography Mission (SRTM) is a project jointly carried out by the National Aeronautics and Space Administration (NASA) and the National Imagery and Mapping Agency (NIMA) of the U.S. Department of Defense. Its main objective was to produce a DEM of the Earth's land surface between 60 deg N and 56 deg S [31]. The data were acquired from the 11 th to the 22 nd of February 2000, by two InSAR sensors, the C-band radar system (SIR-C) and the X-band system (X-SAR). The SRTM X-band has a small swath width that leads to small coverage [3]. This study assesses the C-band SRTM DEM, which is better known and more widely available.  
The first version (1\" resolution and 3\" resolution) of the C-band SRTM data was provided in 2003. The 1\" resolution data (approximately 30 m at the equator) were available only for the United States, and the 3\" resolution data (approximately 90 m at the equator) data were sampled for the rest of the world. On 23 September 2014, the 1\" global SRTM-1 V3 DEM product that is known as \"SRTM plus\" or \"SRTM NASA Version 3\" became freely available for the regions outside the United States. It provides the worldwide coverage of void-filled elevation data using extra auxiliary DEM datasets. Version 4.1 is the highest accuracy dataset of the SRTM, at 3\" resolution, which was released by the Consultative Group for International Agriculture Research Consortium for Spatial Information (CGIAR-CSI). Different from Version 3, Version 4.1 uses several interpolation techniques [32] and extra auxiliary DEMs to fill the voids, and uses SRTM-30 for large voids [33]. Considering the consistency of geolocation [34], the SRTM-1 and SRTM-3 of Version 3 were selected and downloaded from the United States Geological Survey (USGS) Earth Explorer site ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/))) for this study.
## 6 Results and Discussion
### PQD Classification Accuracy Using the Adaptive SSA  
To look closer into the mechanism of the adaptive SSA in PQD classification, Table 6 demonstrates the classification accuracy using the adaptive SSA and without feature optimal selection. The difference in performing different statistical parameters in classification was also taken into account. It is seen that using a single statistical parameter has poor capability in classification. Meanwhile, the classification accuracy improves when using a greater number of statistical parameters. This finding indicates that multiple statistical parameters are typically required to achieve high accuracy in PQD classification. The 94.38% accuracy was obtained for a case with all statistical parameters included excluding the optimal feature selection process. The highest accuracy was demonstrated for a case using 13 statistical parameters with the proposed adaptive SSA al optimal feature selection algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & & & & & **Classification Accuracy (\%)** & & & & \\ \cline{3-11}
**Type of PQD** & **1 Type** & **1 Type** & **1 Type** & **(Energy,** & **Energy,** & **Energy,** & **Energy,** & **S.D,** & **All** & **Adaptive** \\  & **(Energy)** & **(Entropy)** & **(Kurtosis** & **Entropy,** & **S.D.)** & **Entropy,** & **S.D,** & **Mean, Kurtosis** & & **Types** & **SSA** \\ \hline Pure sine & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ Sag & 73 & 76 & 77 & 86 & 96 & 98 & 96 \\ Swell & 82 & 89 & 56 & 95 & 98 & 99 & 98 \\ Interruption & 81 & 96 & 60 & 96 & 100 & 99 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification accuracy using different statistical parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Feature Data** & **Feature Selected** & **Accuracy (\%)** \\ \hline
1 & 37 & 65.62 \\
2 & 72 21 & 7
# Random Segmentation: New Traffic Obfuscation against Packet-Size-Based Side-Channel Attacks
## 4 Materials and Methods
### Multi-Level Segmentation  
Due to the significant differences in the packet size range of many IoT devices operating in different modes, choosing the appropriate range for length randomization (i.e., \(Min\) and \(Max\)) is challenging. For example, our preliminary analysis of our camera traffic shows that 93% of the packets are below 150 bytes when the camera is idle. On the other hand, when the camera becomes active, 58% of packets are above 1000 bytes. Hence, utilizing one range to mask all functional scenarios, such as splitting all segments into chunks between 100 and 150 bytes, will obfuscate the whole traffic but lead to excessive segmentation and increase the overhead.  
Given the limitation of the one-level segmentation, it is necessary to make our defense adapt to the change in traffic volume. Thus, we adopt a multi-level segmentation to enable our algorithm to use a suitable range based on the traffic intensity. For example, we use three levels for the high-bandwidth devices. Level 1 splits messages \(\leq\) 200 bytes into random chunks between 20 and 40. Other messages above 200 and \(\leq\)500 are randomized using random lengths between 100 and 300 in level 2. Larger streams in level 3 that are above 500 are obfuscated using a random size between 500 and 1000. Obviously, that creates non-overlapping bands where the observer can recognize the corresponding band, but it is still insufficient to perform the attack due to the randomness within each band.
# Experimental Study on the Soil-Structure Responses Induced by Tunnelling in Limited Space
## 4 Results of the Model Test
### Ground Settlement Induced by Tunnelling  
The ground settlements induced by tunneling in the limited space and \"greenfield\" site are depicted in Figure 11. In Figure 11, \(\eta\) represents the volume loss and the variations of \(\eta\) represent the different stages of the model test. From Figure 11, it can be seen that there are some differences between the soil settlement pattern under these two circumstances. Similar to the findings reported by many scholars [10; 11; 14], when the new tunnel was constructed at the \"greenfield\" site, the ground settlement experienced a Gaussian distribution curve. However, when the tunnel was excavated in the limited space formed by an underpass and diaphragm wall (see Figure 1), a distinctive analogous flat-shaped ground settlement pattern was exhibited. When investigating the range of the ground settlement, as shown in Figure 11b, the analogous flat part of the settlement was \(-12\) m to \(12\) m, which corresponded to the length of the underpass. Hence, it can be deduced that the underground structure experienced an overall subsidence. Moreover, the maximum ground settlement caused by the tunnelling in the limited space was \(4.3\) mm, which was \(60\%\) smaller than the result induced by tunneling at the \"greenfield\" site. In addition, the range of the ground settlement caused by the construction of the tunnel in the limited space was also much smaller (about \(40\%\)) than that obtained from tunnelling at the \"greenfield\" site. The results shown in Figure 11 indicated that the existing underground structure could restrain the ground settlement induced by the excavation of the new tunnel to some extent.  
Figure 10: Layout of the measuring sections.
## 3 System Model and Problem Description
### Energy Consumption Model  
System energy consumption includes user device energy consumption and UAV energy consumption, among which UAV energy consumption consists of four parts: flight energy consumption, task receiving energy consumption, computing and processing energy consumption, and communication energy consumption. Due to the magnitude difference between user device energy consumption and UAV energy consumption, this paper only considers the top three types of energy consumption components in UAV energy consumption that are most affected by flight trajectory and offloading data.  
The amount of data that the user device needs to process in time slot \(t\) is defined as \(l_{u}(t)\), where the number of CPU cycles required for processing each bit of data is \(C_{u}\). Due to local computing limitations, user devices need to offload some tasks to the UAV for processing. \(f\) represents the computing capability of the UAV.  
The energy consumption of UAVs receiving pending tasks sent by user devices is  
\[E_{u}^{rec}(t)=\frac{p_{u}(t)l_{u}(t)\eta_{u}(t)}{R_{u}(t)} \tag{15}\]  
UAVs calculate the energy consumption of the tasks they receive:  
\[E_{u}^{comp}(t)=l_{u}(t)\eta_{u}(t)C_{u}f^{2}\psi \tag{16}\]  
where \(\psi\) depends on the capacitance coefficient of the UAV chip.  
The energy consumption of UAV flight is mainly related to its flight trajectory and quality:  
\[E^{fly}(t)=\xi(\frac{\mid\mid c_{(t+1)}-c_{(t)}\mid\mid}{\delta_{t}})^{2} \tag{17}\]  
where \(\xi=0.5M\), with \(M\) the payload of the UAV.  
Finally, we get the total energy consumption \(E_{total}(t)\), which is  
\[E_{total}(t)=E_{u}^{rec}(t)+E_{u}^{comp}(t)+E^{fly}(t). \tag{18}\]
# High-Speed Visible Light Communications: Enabling Technologies and State of the Art
## 3 Enabling Technologies for a High-Speed VLC System
### Advanced Modulation
#### Cap  
CAP is a kind of high-order multi-dimensional modulation with low complexity and high spectral efficiency, which was proposed by Bell Labs in the 1970s. In a CAP scheme, as shown in Figure 7, the input bit stream is first mapped with Quadrature Amplitude Modulation (QAM), then up-sampled by a factor of \(M\), the number of samples per symbol, by inserting \(M-1\) zeros between two symbols. The in-phase (\(I(t)\)) and quadrature (\(Q(t)\)) sequences are separated and the shaping filters are applied. The outputs of the filers \(s(t)\) can be expressed as  
\[s(t)=I(t)\otimes f_{I}(t)-Q(t)\otimes f_{Q}(t)\]  
where \(f_{I}(t)\) and \(f_{Q}(t)\) are the filter functions of in-phase and quadrature shaping filter. The two orthogonal signals are separated by two matched filters (matched to in-phase and quadrature shaping filters) the. Then down-sampling and signal recovery (including equalization and de-mapping) are carried out to extract the original bit stream. As CAP can be applied without FFT and IFFT blocks, thus, commonly, CAP offers a simpler implementation than OFDM. However, CAP requires a flat-band response for optimum performance, and complex equalizers are necessary for a non-flat spectral system [41], which is less flexible than OFDM [42; 43].
## 3 Results and Discussion
### Delamination Factors and Optimization Process
#### 3.2.2 Optimization of SF  
The ANOVA of the experimental data (Table 6) emphasized that the quadratic model was significant (Model Prob > F less than 0.05) for the SF response. The main effects of the feed rate and the interaction effect of all parameters were substantial. The fitted equation in terms of actual factors for predicting shape factor was obtained as Equation (7) for each angle of the layers' types. For investigating the effect of independent variables on the shape factor, the interaction, surface, and contour plots relating to the significant items were depicted. In these figures, the angle of layers was considered the average of its two levels. As shown in Figure 8a,b, the maximum SF value was obtained at lower and higher levels of feed rate and rotational speed, respectively. In these levels of feed rate and rotational speed, the higher value of SF was obtained on the angle of the layers equal to 15 degrees. Softening of the composite at a higher rotational speed due to increasing the temperature, reducing the violent fraction of composite at a lower feed rate and a smoother distribution of the thrust force at lower angles between the sequence of the layers, leads to a higher shape factor response (Figure 8c,d).  
Figure 8: (**a**) The 3D surface plot including the effect of rotational speed and feed rate on the shape factor. (**b**) The contour plot including the effect of rotational speed and feed rate on the shape factor. (**c**) The interaction plot includes the effect of feed rate and angle of layers on the shape factor. (**d**) The interaction plot consists of the effect of rotational speed and angle of layers on the shape factor.  
\[SF=\left\{\begin{array}{l l l l l}\text{{For type }}&45:167.89+0.18A-0.03B-0.00001A \times B-4.19\times 10^{-6}A^{2}+2\times 10^{-6}B^{2}\\ \text{{For type }}&15:155.25+0.19A-0.03B-0.00001A\times B-4.19\times 10^{-6}A^{2}+2 \times 10^{-6}B^{2}\\ \end{array}\right. \tag{7}\]
## 4 Results
### Evaluation Metrics
#### 4.2.1 Dice Score (DICE)  
The dice similarity coefficient (DSC), or the F1 score, is a statistical metric that measures the similarity between two datasets. Medical image segmentation quantifies the overlap between the predicted segmentation (SR) and the ground truth (GT). It is calculated as follows:  
\[DICE(GT,SR)=\frac{2\times|GT|\cap SR|}{|GT|+|SR|}=\frac{2\times TP}{2\times TP+ FP+FN} \tag{6}\]  
The DICE ranges from 0 to 1, where 1 indicates perfect segmentation, and 0 signifies no overlap between the ground truth and the segmented result.
# A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion
## 2 Background
### Task Definition  
A temporal knowledge graph (TKG) is a multi-relational directed graph with labeled timestamped edges between entities. It is usually expressed as \(\mathcal{G}=(\mathcal{F}|\mathcal{E},\mathcal{R},\mathcal{T})\), where \(\mathcal{E}\), \(\mathcal{R}\), and \(\mathcal{T}\) are the sets of entities, relations, and timestamps, respectively, and \(\mathcal{F}\subset\mathcal{E}\times\mathcal{R}\times\mathcal{E}\times\mathcal{T}\) is the set of facts that represent real events in the world. A fact \(f\) is denoted as \((s,r,o,t)\), where \(s\in\mathcal{E}\), \(r\in\mathcal{R}\), \(o\in\mathcal{E}\), and \(t\in\mathcal{T}\) are the subject (head) entity, relation, object (tail) entity, and timestamp, respectively.  
It is worth mentioning that, in TKGs, the timestamp is attached to each fact indicating when it is valid. According to the form of the timestamp, facts in TKGs can be classified into two categories [13]: (a) discrete fact, in which the valid time of the fact is a time point, denoted as \((s,r,o,t_{p})\), and (b) continuous fact, in which the valid time of the fact is a time  
Figure 1: A knowledge graph example that contains temporal information [10].  
period, denoted as \((s,r,o,[t_{b},t_{e}])\), with \(t_{b}\) and \(t_{e}\) representing the beginning and the end of the valid time.  
Temporal knowledge graph completion (TKGC) is the task of inferring missing facts based on the set of observed facts, which usually includes three subtasks: subject entity prediction, object entity prediction, and relation prediction.  
1. Subject entity prediction: for an incomplete temporal fact \((?,r,o,t)\), predict the subject entity \(s\) from the entity set \(\mathcal{E}\) at timestamp \(t\in\mathcal{T}\).
2. Object entity prediction: for an incomplete temporal fact \((s,r,?,t)\), predict the object entity \(o\) from the entity set \(\mathcal{E}\) at timestamp \(t\in\mathcal{T}\).
3. Relation prediction: for an incomplete temporal fact \((s,?,o,t)\), predict the relation \(r\) from the relation set \(\mathcal{R}\) at timestamp \(t\in\mathcal{T}\).  
In addition to the above three subtasks, several studies [14; 15] have attempted to predict temporal information, namely, time prediction, which aims to predict the timestamp for which the given triple is consider valid, i.e., \((s,r,o,?)\). Depending on the timestamp range, TKGC has two settings: interpolation and extrapolation. Specifically, for a TKG from timestamp \(t_{0}\) to timestamp \(t_{T}\), the interpolation setting aims to infer the facts with timestamp \(t_{0}<t<t_{T}\), and the extrapolation setting aims to predict future facts that occur at timestamp \(t\) with \(t>t_{T}\). This paper focuses on the DL-based TKGC methods that adopt the interpolation setting.
## 3 Results and Discussion
### Degradation  
The rate at which the hydrogels degraded (in terms of weight change) in the mimic in vivo environment, is shown in Figure 3A,B. The degradation of the SCL hydrogel groups varied (Figure 3A). Specifically, in the first 96 h, the 0.8% SCL hydrogel degraded the fastest, the 2% SCL hydrogel degraded the slowest, and the 1.1% and 1.5% SCL hydrogels had similar degradation rates, that were slightly slower than the 2% SCL hydrogel. Thereafter, the hydrogels in all four groups disappeared, indicating complete degradation. In contrast, all four Ca\({}^{2+}\) hydrogels demonstrated similar trends and degradation rates in the presence of the enzyme (collagenase type II), until they disappeared completely. This indicates that their degradation is solely a function of time and is not affected by the concentration of alginate.  
Figure 3: ConfirmedDegradation profiles of SCL and Ca\({}^{2+}\) hydrogels. 0.8%, 1.1%, 1.5%, and 2% are the concentrations of alginate in both types of hydrogels, and the varying concentrations of alginate present for the different groups of hydrogels. (**A**) The mass change of SCL hydrogels with hours; (**B**) The mass change of Ca\({}^{2+}\) hydrogels with hours.  
Figure 2: Rheometry data with respect to the mechanical strength of the hydrogels. 0.8%, 1.1%, 1.5%, and 2% are the concentrations of alginate in both types of hydrogels, and the varying concentrations of alginate present for the different groups of hydrogels. (**A**) The mechanical strength of SCL hydrogels with changing frequencies; (**B**) The mechanical strength of Ca\({}^{2+}\) hydrogels with changing frequenciesThe varying degradation rates across the SCL hydrogel groups is likely due to their different mechanical strengths. The hydrogels with the lowest mechanical strength degraded the fastest, whereas the hydrogels with a higher mechanical strength degraded more slowly. In contrast, all Ca\({}^{2+}\) hydrogels degraded more slowly than the SCL hydrogels, due to their collectively higher mechanical strengths. This suggests that all of the Ca\({}^{2+}\) hydrogels were strong enough to resist the action of the enzyme, as reflected in the rather slow rate of degradation; thus, the only factor affecting the degradation rate was time. The 2% SCL hydrogels, the strongest of the SCL hydrogels, demonstrated the slowest degradation rate, and only a slight weight loss in the first 48 h after exposure to the enzyme.
# Design and Analysis of Systematic Batched Network Codes
## 5 Inner Code for Systematic Batches  
In this section, we study the design of the inner code for systematic batches. Based on the discussion in Section 3.3, the decoding complexity at the destination node depends on the number of message packets received. However, using the existing inner coding schemes, the number of message packets in a systematic batch reduces significantly during communication. In the worst case, when no message packets are received at the destination node, the decoding computation cost at the destination node is doubled when compared with the ordinary BATS outer code. To resolve this issue, we discuss how to design the inner code to preserve the message packets in systematic batches.