esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Elemental Geochemical Analysis
#### 4.4.2 Trace Elements  
The trace element concentration coefficient (CC) is the ratio of the element concentration in the investigated coals to that in world hard coals [53]. The result (Figure 9) shows that Li, Mo, In, Pb, U, Ta, Zr, and Hf are slightly enriched in the coal (2 \(<\) CC \(<\) 5); Be, Sc, Cr, Cu, Zn, Ga, W, Th, Nb, and REY are normally enriched in the coal (1 \(<\) CC \(<\) 2); V, Co, Sr, and Cd are slightly depleted in the coal (0.5 \(<\) CC \(<\) 1); the remaining elements of Ni, Rb, Sb, Cs, Ba, Tl, and Bi are significantly depleted in the coal (CC \(<\) 0.5). The average abundance of Li in the coal was 66.59 \(\upmu\)g/g, higher than in world hard coals (14.00 \(\upmu\)g/g) and Chinese coals (31.8 \(\upmu\)g/g).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Samples** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **CaO** & **MgO** & **K\({}_{2}\)O** & **MnO** & **Na\({}_{2}\)O** & **TiO\({}_{2}\)** & **TFe\({}_{2}\)O\({}_{3}\)** & **P\({}_{2}\)O\({}_{5}\)** \\ \hline
15R & 43.20 & 16.15 & 0.39 & 0.50 & 0.82 & 0.05 & 0.07 & 0.47 & 19.96 & 0.02 \\ C1 & 3.05 & 0.63 & 0.45 & 0.12 & 0.06 & 0.00 & 0.02 & 0.07 & 3.02 & 0.01 \\ C2 & 1.69 & 1.00 & 4.58 & 0.14 & 0.07 & 0.00 & 0.04 & 0.14 & 1.06 & 0.01 \\ C3 & 14.72 & 10.22 & 1.16 & 0.19 & 0.17 & 0.00 & 0.04 & 0.43 & 1.37 & 0.01 \\ C4 & 7.70 & 5.02 & 0.54 & 0.17 & 0.13 & 0.00 & 0.04 & 0.20 & 1.49 & 0.01 \\ C5 & 8.21 & 5.63 & 0.51 & 0.16 & 0.11 & 0.00 & 0.03 & 0.46 & 0.24 & 0.01 \\ C6 & 13.68 & 9.72 & 0.38 & 0.23 & 0.28 & 0.00 & 0.07 & 0.85 & 0.39 & 0.02 \\ C7 & 3.74 & 2.70 & 0.57 & 0.13 & 0.09 & 0.00 & 0.05 & 0.10 & 1.36 & 0.01 \\ C8 & 2.24 & 1.65 & 0.50 & 0.12 & 0.07 & 0.00 & 0.03 & 0.04 & 2.63 & 0.01 \\
15P & 39.85 & 33.84 & 0.42 & 0.17 & 0.16 & 0.02 & 0.05 & 1.59 & 1.02 & 0.02 \\ C9 & 7.14 & 5.20 & 0.34 & 0.13 & 0.07 & 0.00 & 0.03 & 0.15 & 10.09 & 0.01 \\
15F & 27.63 & 21.75 & 0.30 & 0.16 & 0.12 & 0.01 & 0.10 & 0.68 & 4.74 & 0.03 \\ WA–C & 6.91 & 4.64 & 1.00 & 0.15 & 0.12 & 0.00 & 0.04 & 0.27 & 2.40 & 0.01 \\ Late-Paleozoic coals of & 8.14 & 6.78 & 1.2 & 0.28 & 0.17 & 0.01 & 0.15 & 0.38 & 1.31 & 0.13 \\ North China & & & & & & & & & & \\ Chinese coals [52] & 8.47 & 5.98 & 1.23 & 0.22 & 0.19 & 0.02 & 0.16 & 0.33 & 4.85 & 0.09 \\ \hline \hline \end{tabular} Note: WA–C, weighted average for coal samples.  
\end{table}
Table 4: Test results for major elements (wt.%) in coal and non-coal samples.  
Figure 8: Vertical distribution of major element content of No. 15 coal.  
The trace elements in the roof, parting, and floor of the coal seam are more concentrated than those in the coal samples. The CC values of Li in the parting and floor samples are 40.40 and 26.40 (Figure 9), which is highly enriched (10 \(<\) CC \(<\) 100), and the Li contents in the parting and floor samples are 566.00 \(\upmu\)g/g and 396.00 \(\upmu\)g/g, respectively (Table 5). The parting is highly enriched in Th,
## 3 Joint Cooperative Spectrum-Sharing Scheme
### Analysis of the Proposed Two-Stage Stackelberg Game  
According to the considered system model, the interaction between the \(E\!U_{m}\) and the \(DT_{n}\) may be modeled as a two-stage Stackelberg game where one is the leader and the other is the follower. \(E\!U_{m}\) owns the authorised spectrum and shares the the authorised spectrum with \(DT_{n}\) in the propose RSWW-OSSS. Moreover, in economic matters, the leader of Stackelberg game is the one who decides the strategy firstly and the follower decides the strategy based on the leader's strategy. Hence, \(E\!U_{m}\) is the leader, and the \(DT_{n}\) is the follower [42; 43]. More explicitly, during Stage \(I\), the \(E\!U_{m}\) first calculates the optimal time allocation coefficient \(\tau_{mn}^{*}\) for the sake of maximizing its utility. During stage \(II\), as the follower of Stage \(I\), \(DT_{n}\) calculates the optimal power allocation coefficient \(\alpha_{mn}^{*}\) for the sake of maximizing its utility. According to the proposed two-stage Stackelberg game, the strategies in different stages have sequential dependence. Hence, the backward induction is introduced to analyse the game and to derive the solution, namely Stackelberg equilibrium. Based on the backward induction [34], we commence from the \(DT_{n}\)'s strategies in Stage \(II\), given the \(E\!U_{m}\)'s time allocation strategy. Then, based on the analysis of \(DT_{n}\)'s optimal power allocation coefficient, namely \(\alpha_{mn}^{*}\), the optimal time allocation of \(E\!U_{m}\) in Stage \(I\), namely \(\tau_{mn}^{*}\) is derived.
# Back to the Metrics: Exploration of Distance Metrics in Anomaly Detection
## 3 Experiments
### Performance of kNN Squared Distance Mean Metrics on 2D Datasets with the PatchCore Method  
In this experiment, we used the official code and only modified the \(k\) value, keeping the evaluation metrics consistent with those in the original paper.  
As shown in Figure 10, this work tests the impact of different \(k\) values on the anomaly detection and segmentation performance of PatchCore on the MVTec AD dataset (using the mean of squared distances to calculate anomaly scores).  
As shown in Figure 10, for the anomaly detection task, the algorithm performs best when \(k=1\); as \(k\) increases, the detection performance declines. As shown in Figure 10,c, the algorithm's segmentation performance is poor when \(k=1\); as \(k\) increases, the segmentation performance first improves and then declines, achieving the best segmentation performance at \(k=4\).  
It is noteworthy that compared to the segmentation results in Figure 10, which exclude normal samples (anomalous samples also have many normal pixels), the performance decline after reaching the peak value in Figure 10 is noticeably more gradual. This further supports the view that anomaly detection metrics require smaller \(k\) values.
## 1 Introduction
Unlike traditional matrix decomposition methods such as SVD [26] and principal component analysis (PCA) [27], Tucker decomposition is well-suited for 3D data analysis. It reduces data dimensionality and extracts key features of the physical field, making the data analysis process more efficient [28]. [PERSON] et al. applied Tucker decomposition to reconstruct 3D wind velocity fields using a limited number of measurement points, demonstrating its effectiveness in reducing data dimensionality and improving reconstruction accuracy [29; 30]. Similarly, [PERSON] et al. optimized the selection of measurement points and quantifies, further enhancing the noise resistance and accuracy of Tucker decomposition-based algorithms [31; 32]. It is noteworthy that all the above-mentioned studies utilized contact-based point measurements as the data source, involving a limited amount of data. In contrast, by utilizing acoustic temperature measurement as an alternative, it is possible to address the limitations of contact-based measurements, especially in terms of interference from flow fields. Therefore, in order to solve the limitations of existing methods, such as sparse measurement points, sensitivity to noise, and high computational complexity, this study proposes a novel multi-layer temperature field reconstruction algorithm, which combines the Tucker decomposition with the acoustic temperature measurement technique, and on the one hand, it can solve the shortcomings of fewer measurement points, poorer accuracy, and weaker noise immunity in acoustic laminar temperature measurement, and on the other hand, it can solve the intrusive Tucker decomposition algorithm of the previous limitations of measuring data. A tangentially fired furnace model is developed using computational fluid dynamics (CFD) to generate a set of temperature fields under various operating conditions. The time-of-flight (TOF) data obtained from a substantial number of acoustic measurement points form the prior dataset. Tucker decomposition is then applied to extract the core tensor and factor matrices, enabling efficient reconstruction of 3D TOF data from sparse measurements. By replacing contact-based sensors with acoustic TOF measurements, the method eliminates flow field interference and improves spatial resolution. The results show that the proposed algorithm improves the reconstruction accuracy of acoustic temperature measurements in complex temperature fields, achieves good results in 3D reconstruction, and has a fast reconstruction speed.
# Evolution of Core Stability, Athletic Performance, and ACL Injury Risk across a Soccer Season
## 4 Discussion
### Effects of Soccer Training Program on Core Stability  
The improvement of the CKCLEST score was in line with the training program that players carried out over the season. Indeed, CKCLEST performance depends on lower limb explosivity and core stability, which were part of the training, especially during the PRE to START phase. Then, during the first half of the season, due to the first matches and the prevention program, players strengthened their core and improved lower limb explosivity. This is likely to explain why core strength, expressed by CKCLEST score, improved until mid-season with the potential help of the prevention program. Afterwards, soccer players undertook less explosive training and more aerobic training, explaining why CKCLEST levelled off but potentially in favor of the endurance index improvement between MID and END. Regarding LQ-YBT, pre-season training did not impact the score, but the prevention program, established between START and MID, led to significantly better LQ-YBT performance. So, prevention training, mostly based on trunk and lower limb proprioception and on lower limb strength, influenced core stability. These results are in line with previous studies reporting greater performance in the LQ-YBT after 8 weeks of proprioceptive training [41; 42] and core training [43]. However, unlike core strength and endurance, core motor control performance decreased between MID and END, which shows the importance of specific and regular training to maintain this capacity.  
Contrary to [PERSON] et al. [43], no significant evolution of the LSD score was observed. This difference might come from the specific core training protocol and/or from the constraints induced by the box height. Indeed, in their study, the box was 15 cm high while, in the present study, it was 31 cm high (based on [PERSON] et al. [37]), probably leading to higher constraints that could not be overpassed with classic in-season training. Overall, to evaluate core stability in football, CKCLEST and LQ-YBT seem to be the most suitable tests as they reflect the physical training throughout the full season. Lateral step down could be used as pre-season screening test to examine pelvic and knee control, and multiple testing throughout the season would therefore not be needed.  
Overall, the core stability test performances, in particular for the CKCLEST and LQ-YBT, were improved through the soccer training because they were chosen to be as functional as possible for football. As recommended by [PERSON] et al. [44], the test battery had to be the most ecological in regard to the sport assessed.
# ICN-Based Enhanced Content Delivery for CDN
## 5 Simulation Study
### Result Analysis
#### 5.2.3 Resilience to Traffic Surge  
At last, we attempt to dig deeper into our design's resilience to traffic surge compared with conventional CDN. By increasing the content demand frequency, more content is demanded to be routed in the network simultaneously, thus increasing the traffic. We also set the demand rate at two different values, 0.5 and 1.0 Gbps. Both Abilene and Geant2 topologies are used for analysis. Figure 9 depicts the trend of total bandwidth consumption in the Abilene and Geant2 network topologies as the content demand frequency increases.  
Figure 8: Comparison of total bandwidth consumption (**a**) and average delay (**b**) under different alpha parameter between IECDN and conventional CDN.  
It is observed that at the demand rate of 0.5 Gbps, the total bandwidth consumption is trended as near linear in all scenarios. This meets the expectation because even with the largest demand frequency of 20, the network links are not subject to congestion. Apart from that, there is a subtle feature that the line of conventional CDN rises more steeply than that of our proposed design. Specifically, we calculate the slope of each fitted line (as indicated by the dotted lines) to provide a clearer indication of its rate of growth, and it shows that the growth rate of the total bandwidth consumption in conventional CDN outweighs that of our proposed design. At the demand rate of 1.0 Gbps, congestion is more possibly to occur in the network links as demand frequency increases. More and more content has to make a longer detour to avoid congestion instead of a Dijkstra shortest path as imposed by link constraints. Under these circumstances, a linear trend of bandwidth consumption is unsustainable in conventional CDN. However, in our proposed design, the bandwidth consumption still maintains closely linear, which benefits from the presence of in-network caching that alleviates network congestion. This demonstrates a better resilience to traffic surge by integrating ICN and is of great importance to traffic engineering.
# Global Buckling Resistance of Cold-Formed Steel Beams with Omega-Shaped Sections
## 3 Experimental Campaign
### Comparison with Design Codes  
The failure modes observed during the tests conducted on the 70 \(\times\) 1.5, 170 \(\times\) 2.0, and 300 \(\times\) 2.5 profiles under downward loads are local failures, occurring near or in zones where the loads are applied, as illustrated in Figures 6a, 7a and 8. However, when upward loads are applied, the 70 \(\times\) 1.5 and 170 \(\times\) 2.0 profiles primarily exhibit distortional failure modes.  
The maximum loads obtained during the experimental tests are compared to the anticipated loads when applying both the Effective Width Method (EWM) and the Direct Strength Method (DSM), as indicated, respectively, by the European [2], Brazilian [6] and North American [4] standards.  
The Effective Width Method (EWM) involves calculating the effective properties of the cross-section, considering the reduction of resistance due to local and distortional buckling modes. The global resistant moment is computed using effective properties influenced by a buckling resistance factor, which, in turn, depends on the critical lateral- torsional moment value. On the other hand, the application of the Direct Strength Method (DSM) involves calculating the critical elastic local, distortional, and global buckling moments through an elastic buckling analysis, conducted using CUFSM. The characteristic resistant bending moment is determined as the smallest value calculated for global, local, and distortional buckling.  
In the case of downward load application, the observed failure modes are mainly local, occurring near or in zones where the loads are applied. Therefore, for the purpose of sectional resistance evaluation and prediction of the maximum loads, the global buckling phenomena can be ignored. The comparison of the maximum recorded loads and expected loads, as illustrated in Table 3, confirms that both the EWM and DSM provide consistent results since the analytical expected loads closely align with the recorded experimental loads.  
Additionally, it was observed that the expected load values calculated with the EWM for the profiles 70 \(\times\) 1.5 and 170 \(\times\) 2.0 are slightly lower than the recorded load values, indicating that the European standard provides conservative estimates of bending resistance. DSM yields slightly higher expected loads compared to the EWM, although the maximum observed difference between these methods is 10%, and this pertains to the 300 \(\times\) 2.5 profiles.
## 6 Stage 1: TS Modeling
### Model Parameter Determination
#### 6.2.2 BIC-Based Candidate Model Selection  
BIC values were explored for sub-models of ARIMA(\(p_{max},1,q_{max}\)) with different combinations of \(p=0\) to 7 and \(q=0\) to 6. In Figure 10, each row represents one specific sub-model with different combinations of the model parameters for the AR(\(p\)) and MA(\(q\)) terms, represented by the gray color depth in each column. The horizontal axis labels the TS \(x\)-lag \(i\), \(x(t-i)\), and error-lag \(j\), \(e(t-j)\), as expressed in Equation (13), contributing to order \(i\) AR(\(\alpha_{i}\)) and order \(j\) MA(\(\beta_{j}\)) terms, respectively. The intercept is the bias term in the regression.  
The vertical axis is the BIC computed for each sub-model. The darker the color and the lower the BIC value, the better the model. For example, in Figure 10a, the first row represents the sub-model of ARIMA(7,1,6) with the BIC value \(-\)590, and its parameters are related to TS \(x\)-lag (AR terms) 1, 2, 3, and 7 and error-lag (MA terms) 2, 4, and 6. Better models are in the top rows with lower BIC values. There were multiple sub-models sharing the same BIC values, i.e., the top four rows in Figure 10a.  
Although they show contributions from different combinations of the lower order lags, the higher orders TS \(x\)-lag 7 and, to a lesser extent, the error-lag 6 are present in most cases. Based on the results of other segments, Figure 10b,c, the influence of TS \(x\)-lag 7 and error-lags 2 and 6 in dominating the BIC performance was detected. This is evidence to incorporate higher orders in the ARIMA models. Thus, combining the observations that PACF cuts off from lag 6 or 7 and ACF tails off simultaneously, ARI(7,1), ARIMA(6,1,6), and ARIMA(7,1,6) became candidate models.  
Figure 9: The label distribution among all segments.
## 4 Experiments and Analysis
### Pruning Performance Analysis on Taylor-Expansion-Based Filter Sorting Criterion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**CIFAR10**} \\ \hline \hline
**Network** & & **Acc** & \(\Delta\)**Acc** & **Params.(\(\times\))** & **FLOPs.(\(\times\))** \\ \hline \multirow{4}{*}{VGG-16} & Baseline & 93.67\% & - & - & - \\ \cline{2-6}  & Minimum weight & 93.24\% & \(-\)0.43\% & 11.42\(\times\) & 3.53\(\times\) \\ \cline{2-6}  & Taylor expansion & 93.29\% & \(-\)0.38\% & 12.11\(\times\) & 3.60\(\times\) \\ \hline \multirow{4}{*}{VGG-19} & Baseline & 92.58\% & - & - & - \\ \cline{2-6}  & Minimum weight & 92.26\% & \(-\)0.32\% & 21.32\(\times\) & 5.28\(\times\) \\ \cline{2-6}  & Taylor expansion & 92.31\% & \(-\)0.27\% & 22.94\(\times\) & 5.76\(\times\) \\ \hline \multirow{4}{*}{ResNet-18} & Baseline & 95.28\% & - & - & - \\ \cline{2-6}  & Minimum weight & 94.51\% & \(-\)0.77\% & 11.67\(\times\) & 3.27\(\times\) \\ \cline{2-6}  & Taylor expansion & 94.76\% & \(-\)0.52\% & 11.43\(\times\) & 3.43\(\times\) \\ \hline \multirow{4}{*}{ResNet-34} & Baseline & 94.46\% & - & - & - \\ \cline{2-6}  & Minimum weight & 93.92\% & \(-\)0.54\% & 13.30\(\times\) & 3.67\(\times\) \\ \cline{2-6}  & Taylor expansion & 94.07\% & \(-\)0.39\% & 14.10\(\times\) & 3.69\(\times\) \\ \hline \multicolumn{6}{c}{CIFAR100} \\ \hline \multirow{4}{*}{VGG-16} & Baseline & 71.10\% & - & - & - \\ \cline{2-6}  & Minimum weight & 69.12\% & \(-\)1.98\% & 11.35\(\times\) & 3.59\(\times\) \\ \cline{2-6}  & Taylor expansion & 69.15\% & \(-\)1.95\% & 12.10\(\times\) & 3.78\(\times\) \\ \hline \multirow{4}{*}{VGG-19} & Base & 69.71\% & - & - & - \\ \cline{2-6}  & Minimum weight & 67.76\% & \(-\)1.95\% & 17.27\(\times\) & 4.10\(\times\) \\ \cline{2-6}  & Taylor expansion & 67.95\% & \(-\)1.76\% & 17.67\(\times\) & 4.15\(\times\) \\ \hline \multirow{4}{*}{ResNet-18} & Baseline & 74.71\% & - & - & - \\ \cline{2-6}  & Minimum weight & 72.44\% & \(-\)2.27\% & 9.20\(\times\) & 3.37\(\times\) \\ \cline{2-6}  & Taylor expansion & 72.67\% & \(-\)2.04\% & 10.93\(\times\) & 3.55\(\times\) \\ \hline \multirow{4}{*}{ResNet-34} & Baseline & 74.51\% & - & - & - \\ \cline{2-6}  & Minimum weight & 72.11\% & \(-\)2.30\% & 15.34\(\times\) & 3.95\(\times\) \\ \cline{2-6}  & Taylor expansion & 72.24\% & \(-\)2.27\% & 16.18\(\times\) & 4.02\(\times\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pruning performance with different sorting criteria. Figure 3: The pruning rate of each convolution layer in VGG-16.
# Methodology of Testing the Security of Cryptographic Protocols Using the CMMTree Framework
## 4 Description of the Methodology
### Primary Structures and Generalization of the Problem
The content of each vector may be complex, and everything depends on the protocol structure. Tables 2 and 3 present two fragments of the SoC for protocol execution involving an Intruder. Both tables have the same structure. Column **Send.**\(\rightarrow\)**Rec. ** contains information about the sender and receiver in the current protocol step. Column **Execution & step** includes execution and step number information. The rest of the columns (**Needs, Products**, and **Knowledge**) contain information about the content of corresponding vectors. Table 2 contains the chains for execution no. 1 generated for the NSPK protocol in Alice-Bob notation. Each chain from this execution consists of each part described earlier in this section, separated by semicolons. The Intruder is a sender in the first and third steps, so he can execute each of these steps in two ways (sending the entire ciphertext or the ciphertext composed using his knowledge). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Send.**\(\rightarrow\)**Rec.;** & **Execution \& Step;** & **Needs;** & **Products;** & **Knowledge;** \\ \hline \(I\to A\); & (1, 1); & \(\langle i(I),N_{I}\rangle_{K^{+}_{A}}\); & ; & N\({}_{I}\); \\ \(I\to A\); & (1, 1); & ; & ; & N\({}_{I}\); \\ \(A\to I\); & (1, 2); & \(N_{I}\); & \(N_{A}\); & N\({}_{A}\); \\ \(I\to A\); & (1, 3); & \(\langle N_{A}\rangle_{K^{+}_{A}}\); & ; & N\({}_{A}\); \\ \(I\to A\); & (1, 3); & N\({}_{A}\); & ; & N\({}_{A}\); \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The fragment of SoC for NSPK protocol (execution no. 1). Table 3 contains the chains for execution no. 9, generated for the KaoChow protocol in Alice-Bob notation. The KaoChow protocol has a different and more complex structure than the NSPK protocol, so this chain contains more entries and objects in individual vectors. The Intruder is a sender in the third step only, but he can execute this step in four ways when his knowledge contains the following objects:  
* two entire ciphertexts;
* the entire first ciphertext and objects from the second ciphertext;
* the objects from both ciphertexts. In our opinion, the above-described method of combining many different data types into a hierarchy of classes that inherit from a very general base class is a very flexible solution. If a new data type appears, it is enough to \"put\" it in the right place in such a hierarchy to be able to study the impact of the new data on the overall structure of the relationship between all data. However, in each case of the use of any particular non-abstract type of data, we assume that it is provided with the right set of functionalities--that is, functions which, if necessary, can \"cooperate\" with other types of that hierarchy.