esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Review of Studies Related to Integration in the Context of Multi-Modal Mobility
### Summary of Studies  
As MaaS relies on integration to provide seamless, high-quality door-to-door transport services for users, \"integration\" can be selected as a candidate keyword to search for related papers. We used the query strings AB = ((\"MaaS\" AND \"integration\")) and AB = ((\"MaaS\" AND \"pricing\")) to search for the papers for the fifth category related to integration issue and pricing issue in the context of multi-modal mobility. The sources of the literature were obtained based on the Web of Science database. The search resulted in 98 papers. Although query strings were used to search for papers potentially relevant to the combination of keywords for the review, many papers containing the keywords in the abstract did not focus on research issues related to the keywords. If a paper was not directly related to the research issues associated with the keywords after content analysis, it was excluded from this review. After screening the papers found by searching the Web of Science database, 21 papers were obtained. Figure 8 shows the distribution of these papers published over the years. Although the number of publications did not increase monotonically, publications continued to grow in 2024 to fill the gap in the existing literature.  
The research questions addressed in each paper found by the refined search of this section are summarized in Table 8.  
\begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline
**Paper** & **Research Questions** \\ \hline
[105] & Are consumers willing to pay for collaboration activities arising from institutionally facilitated interaction between operators? \\ \hline
[80] & How to model demand and plan for ride-shared mobility services running in integration with mass transit? \\ \hline
[81] & How to evaluate the integrated behavior of MaaS? \\ \hline
[115] & Can consumers benefit from bundling transportation services? \\ \hline
[109] & How to integrate freight transport into a Mobility-as-a-Service (MaaS) environment? \\ \hline
[110] & How to achieve operational, informational and transactional integration required for MaaS to deliver an exceptional user experience to rival the private cars? What factors affect how people make travel choices for adoption of any MaaS offer? \\ \hline
[111] & How to extend the concept of Mobility as a Service (MaaS) to fully integrate public transportation to make public transport an attractive alternative to private transport? \\ \hline \hline \end{tabular}
\end{table}
Table 8: Summary of research questions addressed in the papers published between 2017 and 2024 in the Web of Science Core Collection with keywords “MaaS” and “integration/pricing” in the abstract.
# DFDT: An End-to-End DeepFake Detection Framework Using Vision Transformer
## 3 Methodology
### Attention-Based Patch Selection  
Other researchers have demonstrated that transformer models cannot accurately represent the importance level of input tokens, especially in higher layers, due to a lack of token identifiability of the embeddings [52]. One solution to improve the transformer model's capability in capturing sensitive information is to pay more attention to discriminative patches within the training and inference phases, as depicted in Figure 2. In addition, attention-based mechanisms have demonstrated their strong capability in improving the performance of traditional CNN-based deepfake detection models [18]. Therefore, inspired by [53], a patch selection mechanism based on attention weights is 1 in this study. Generally, for a transformer model with \(K\) attention heads and \(L\) layers, the input feature to the last layer and attention weights of \(l\)th layer can be represented as (3) and (4), respectively.  
\[\mathbf{Z}_{L-1}=\left[Z_{L-1}^{0};Z_{L-1}^{1},Z_{L-1}^{2},\dots,Z_{L-1}^{N}\right] \tag{3}\]  
\[\mathbf{a}_{l} =\left[a_{l}^{0},a_{l}^{1},a_{l}^{2},\cdots,a_{l}^{K}\right]\quad l \in 1,2,\cdots,L-1\] \[\mathbf{a}_{l}^{i} =\left[a_{l}^{i_{0}};a_{l}^{i_{1}},a_{l}^{i_{2}},\cdots,a_{l}^{i_{ N}}\right]\quad\quad i\in 0,1,\cdots,K \tag{4}\]  
It is critically important to learn how information propagates through different layers and ensure the correspondence of attention weights with input tokens. Therefore, the raw attention weights are fused throughout the transformer model using matrix multiplication as shown in (5). Note that \(\mathbf{a}_{\text{final}}\) provides better discriminative information on selection of top patches compared to single-layer raw attention weights, i.e., \(\mathbf{a}_{l}\) as in (4) [53]. Therefore, positional index of maximum attention weights corresponding to \(K\) attention heads in \(\mathbf{a}_{\text{final}}\) are calculated, i.e., \(A_{1}\), \(A_{2}\), \(\cdots\), \(A_{K}\) and utilized to extract corresponding key tokens in \(\mathbf{z}_{L-1}\). Ultimately, the original input sequence in (3) is substituted with an updated sequence consisting of the concatenation of key tokens corresponding to more informative regions along with the classification token as (6). The new input sequence maintains the global information of the input image. This process makes sure that the model pays specific attention to the subtle traces of deepfakes while dismissing less discriminative regions like the background area.  
\[\mathbf{a}_{\text{final}}=\prod_{l=0}^{L-1}\text{Softmax}\left(\mathbf{a}_{l}\right) \tag{5}\]  
\[\mathbf{Z}_{\text{L-1}}=\left[Z_{L-1}^{0};Z_{L-1}^{A_{1}},Z_{L-1}^{A_{2}}, \cdots,Z_{L-1}^{A_{K}}\right] \tag{6}\]  
Figure 2: The attention-based patch selection module forces the transformer model to put more weight on key patches, while dismissing less informative regions.
## 3 Results
At 50 ng/mL, the mean concentrations were 55.45 ng/mL and 56.94 ng/mL, with SD values of 3.67 and 2.20, RSDwR values of 6.63% and 3.86%, and mean recoveries of 110.9% and 113.9%. For isotonitazene at 10 ng/mL, the mean concentrations were 7.48 ng/mL and 7.44 ng/mL, with SD values of 0.29 and 0.41, RSDwR values of 3.91% and 5.54%, and mean recoveries of 74.8% and 74.4%. At 50 ng/mL, the mean concentrations were 36.19 ng/mL and 36.78 ng/mL, with SD values of 0.65 and 1.17, RSDwR values of 1.81% and 3.19%, and mean recoveries of 72.4% and 73.6%. The results indicate that flualprazolam exhibits higher reproducibility with acceptable recovery rates, while isotonitazene shows lower recoveries, particularly at the higher fortification level. This discrepancy suggests challenges in consistent quantification \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline \multirow{3}{*}{\begin{tabular}{} \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{} \end{tabular} } & \multicolumn{6}{c}{**Fortification, ng/mL**} \\ \cline{2-10}  & & & **10** & & & & **50** & \\ \cline{2-10}  & & & **10** & & & & **\%** & **\%** \\ \cline{2-10}  & & & **Mean** & **SD** & **\%RSD** & **\%RSD** & **\%RSD** & **\%RSD** & **
\begin{tabular}{} \end{tabular}** \\ \cline{2-10}  & & & & **(Mean)** & & & & **(Mean)** \\ \hline Flualprazolam & 1 & 11.49 & 0.44 & 3.80 & 114.9 & 54.89 & 3.88 & 7.07 & 109.8 \\  & 2 & 11.50 & 0.42 & 3.61 & 115.0 & 56.27 & 1.38 & 2.45 & 112.5 \\ \hline Isotonitazene & 1 & 7.55 & 0.37 & 4.94 & 75.5 & 35.96 & 2.25 & 6.24 & 71.9 \\  & 2 & 7.56 & 0.28 & 3.64 & 75.6 & 35.18 & 1.85 & 5.26 & 70.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: For repeatability (RSDr), % mean recovery, SD, and RSD% data. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multicolumn{2}{c}{**Theoretical**} & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } \\ \cline{2-2} \cline{4-7}  & & & **Concentration (ng/mL)** & & & & **(70–120\%)** \\ \hline Flualprazolam & 9.8 & 10.0 & 98.0 & Yes \\  & 48.5 & 50.0 & 97.0 & Yes \\ \hline Isotonitazene & 10.5 & 10.0 & 105.0 & Yes \\  & 49.2 & 50.0 & 98.4 & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Recovery test results. tion for isotonitazene, which may require further optimization of the analytical method (Table 5).
## 4 Dataset
We believe that preserving the full spectrum of topics, even those poorly covered, reflects a more realistic and nuanced representation of real-world scenarios. Alongside the MM18x58 dataset, we created its English version by machine-translating all non-English texts in the training and validation splits into English using the Googletrans Python library ([[https://pypi.org/project/googletrans/](https://pypi.org/project/googletrans/)]([https://pypi.org/project/googletrans/](https://pypi.org/project/googletrans/)) (accessed on 2 May 2022)). Approximately 10% of texts in both the training and testing splits were already in English and required no translation. The testing split remained original (i.e., untranslated). We intentionally translated the training and validation splits into English, as this version will be utilized in crosslingual experiments, where training is recommended to be conducted consistently in a resource-rich language. This modified version of the dataset is named MM18x58_En. Both MM18x58 and MM18x58_En datasets can be used for the closed-set text classification problems (see Section 3). For the binary classification problem, we created an additional dataset MM18x58_binary (refer to Table 2). This dataset includes texts where the cosine similarities between the text vectors and the vectors of all 18 topics (as listed in Table 1) were lower than 0, assigned to the _new_ (positive) class. The _known_ (negative) class comprises all instances from MM18x58 belonging to those 18 categories. The MM18x58_binary dataset can be used for the open-set text classification problems (see Section 3). \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Topic** & **Training** & **Validation** & **Testing** \\ \hline
1. AlternativeEnergy & 10 & 19 & 14 \\
2. ClimateAction & 10 & 14 & 23 \\
3. ClimateChange & 475 & 484 & 358 \\
4. CoronavirusInfrection & 71 & 108 & 95 \\
5. CybersecurityAntifraud & 62 & 118 & 122 \\
6. Drugs & 18 & 39 & 39 \\
7. EUEconomy & 28 & 28 & 23 \\
8. EUInternet & 13 & 25 & 16 \\
9. EnergyMarketsandStrategies & 6 & 10 & 1 \\
10. EuropeanCouncil & 107 & 174 & 111 \\
11. Europol & 169 & 432 & 233 \\
12. FightagainstFraud & 21 & 24 & 38 \\
13. FinancialEconomicCrime & 6 & 9 & 15 \\
14. ForgeryMoney & 2 & 3 & 4 \\
15. InformationSecurity & 26 & 31 & 56 \\
16. Migration & 49 & 65 & 67 \\
17. NATO & 3516 & 3865 & 3415 \\
18. TerroristAttack & 92 & 117 & 125 \\ \hline In total: & 4681 & 5565 & 4755 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The distribution of instances over different topics (categories) and training/validation/testing subsets in the MM18x58 dataset.
## 4 Experiments and Results
### Experiments and Analysis  
During the experiments, it was found that for the data sets with large data volume, time cost increases dramatically when the number of nodes is too small. The reason is that the computation of the tree structure needs to constantly operate the stack. If the JVM stack is not enough, it can only keep replacing the stack. In this paper, experiments have been done on a small number of nodes for small data sets and on a large number of nodes for large data sets.
# Simultaneous Coded Plane-Wave Imaging Using an Advanced Ultrasound Forward Model
## 2 Materials and Methods
### Temporal Encoding for Simultaneous Emission of Plane Waves
 \tag{18}\]  
Using Equations (16)-(18), we can deduce the expression of the crosstalk noise \(\zeta^{(k)}(t)\):  
\[\zeta^{(k)}(t)=\hat{g}_{i}^{(k)}(t)-\overline{\hat{g}}_{i}^{(k)}(t)=\sum_{l=1,l\
eq k}^{N_{\text{pw}}}f^{(k)}(t)*e^{l}(t)*g_{i}^{l}(t). \tag{19}\]  
To minimize \(\zeta^{(k)}(t)\), there is the need to find the optimal filters \(f^{(k)}(t)\) given the excitation signals \(a^{l}(t)\). Directly minimizing Equation (19) is a complex problem, and most importantly, its solution depends directly on the imaged medium (through \(g_{i}^{l}(t)\)). Instead, we propose to minimize the following expression that is TRF invariant:  
\[\zeta_{r}^{(k)}(t)=\sum_{l=1,l\
eq k}^{N_{\text{pw}}}f^{(k)}(t)*e^{l}(t). \tag{20}\]  
The optimal filters \(f^{(k)}(t)\) that minimize Equation (20) are Wiener filters empirically adapted to the waveforms \(e^{(k)}(t)\) as follows:  
\[F^{(k)}(\
u)=\frac{\left(E^{(k)}(\
u)\right)^{\star}}{\left|E^{(k)}(\
u) \right|^{2}+S(\
u)}, \tag{21}\]  
where \(F^{(k)}(\
u)\) and \(E^{(k)}(\
u)\) are the Fourier transforms of the corresponding temporal signals, \(()^{\star}\) is the complex conjugate, and \(S(\
u)\) is the spectral density of the crosstalk noise. From Equation (21), it can be seen that the signals \(e^{(k)}(t)\) (and the corresponding compression filters \(f^{(k)}(t)\)) that yield the lowest level of crosstalk need to be orthogonal or quasi-orthogonal (i.e., high, pronounced main lobe for the convolution \(f^{(k)}(t)*e^{(k)}(t)\), and low values of the product \(f^{l}(t)*e^{(k)}(t),l\
eq k\)). The choice of the specific excitation signals used in this study is detailed in Section 2.10.
# Deriving Controllable Local Optimal Solutions through an Environment Parameter Fixed Algorithm
## 4 Experiments
### The Robustness of the Trained DNN  
Local optimization derives the CLS using the EPFA-CLS under the experimental conditions described above. In this experiment, we compared the predicted result (MD) of the trained DNN with the output (MD) of the real simulator in the derived CLS.  
Tables 4 and 5 show the results for scenarios 1 and 2, respectively. Local optimization was performed with the initial value (\(E_{initial}\)), and the result derived. Four local optimizers were used; the input to the simulator was the mode of the derived CLS. Using the results of the simulator as ground truth, we proved the robustness of the trained DNN. Table 4 shows the CLS derived using the local optimizer. Among the four optimizers, the most frequently derived CLS was from 182.67 deg for SD and 15 m/s for SV. So, each value was the simulator's input. Naturally, the environment parameter was the same value as in scenario 1. The predicted MD through the trained DNN was 268 m. In addition, MD (the output of the simulator) was 271.3 m. We can see that the value of \(R^{2}\) is predicting the correct value, as demonstrated by 0.97. Table 5 shows the most frequently obtained results from the CLS were SD at 169.7 deg and SV at 15 m/s. So, each value was the simulator's input. The predicted MD through the trained DNN was 379.9 m, and the MD through simulator was 381.4 m. The trained DNN predicted exact values.  
The results from the simulator were more accurate than the values predicted by the trained DNN. However, these results take quite a long time, as shown in Tables 4 and 5. Additionally, it is not possible to find SD and SV in large MD values in one simulation. This is because the simulator outputs only the MD. In this experiment, we measured the accuracy and the reason for requiring the trained DNN objective optimization.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Parameters**} & \multicolumn{5}{c}{**EPEA-CLS**} & \multirow{2}{*}{**Simulator (GT)**} \\ \cline{3-3} \cline{5-6}  & & **Adagrad** & & **GA** & **Momentum** & **RMSProp** \\ \hline \multirow{2}{*}{CLS} & SD [deg] & 182.67 & 182.57 & 182.67 & 182.68 & 182.67 \\  & SV [m/s] & 15 & 15 & 15 & 15 & 15 \\ Output & MD [m] & 268 & 267.98 & 268 & 267.9 & 271.3 \\  & Time [s] & 8.69 & 9.47 & 10.106 & 10.42 & 19 [min] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Derived CLS results in Scenario 1: among the four results, the mode value is input to the simulator. The predicted MD and the MD of the simulator are compared with time and output values. Ground Truth (GT).  
Figure 8: Training accuracy of the optimal course dataset.  
The following is a comparison between local optimizers in Tables 4 and 5. In Table 4, the CLS is the same, and the time is also similar. Table 5 gives a similar CLS and time, but since the concept of the velocity of the gradient is reflected in Momentum, we can see that the result is a little bit more.
## 4 Discussion
### Practical Implications  
The results of this study offer important practical implications for football coaches and performance analysts aiming to optimize match outcomes in elite competitions such as the FIFA Club World Cup. The significant advantage of scoring the first goal highlighted the need for tactical emphasis on rapid offensive transitions to secure early leads, while the temporal distribution of goals near the end of each half suggests conditioning programs should prepare players to maintain high performance during these critical periods. Winning teams exhibited superior possession metrics, including higher ball possession, total passes, pass completion, and completed passes, indicating that training should focus on technical skills and coordinated ball circulation under pressure. Furthermore, attempts at goal on target emerged as a strong predictor of victory, underscoring the importance of shooting accuracy and decision-making in the final third. The inverse relationship between defensive pressures and winning suggests that teams subjected to intense pressing require enhanced resilience and strategies to counteract opposition pressure, such as effective build-up play. Additionally, greater total distance covered by players in winning matches emphasizes the importance of physical conditioning and sustained work rate. The finding that increased receptions in the final third negatively associated with winning indicates that quality of possession and effective penetration are more decisive than quantity of receptions alone, guiding coaches to prioritize efficient final-third actions. Defensive and transition phases, notably high pressing and counter-pressing, were more prominent in winning teams, suggesting training should incorporate aggressive defensive organization and rapid ball recovery. Finally, the diagnostic thresholds derived from ROC analyses, such as a minimum of 3.5 attempts on target and 478.5 completed passes per match, provide measurable performance benchmarks for practitioners to set objectives and monitor team effectiveness. Collectively, these insights advocate for an integrated match strategy combining offensive precision, possession control, physical endurance, and dynamic defensive pressing to enhance competitive success through informed training and tactical decision-making.
## 4 Discussion
### EVO Test Cell (HSI-19/HSE-14)  
Total cVOC levels peaked at approximately six weeks following the initial addition of EVO, likely again due to its desorption induced by the EVO. Following the peak, total cVOCs levels steadily dropped over the next month of the test at which point it remained relatively unchanged. M-K trend analyses of these portions of the dataset showed that during the month following the peak in PCE concentration there was evidence of a significant decline, followed by no significant changes in rate. VC was detected following the initial dose of EVO, indicating that complete ERD was occurring. TOC breakthrough and peak detection times did not match well with the conservative tracer test run in this test cell suggesting that EVO has high retention in the aquifer matrix and lower mobility in the groundwater.  
Following the second dose of EVO, total dissolved chlorinated ethene concentrations again increased, including an initial peak in PCE and TCE. However, vinyl chloride also increased in the test cell extraction well, peaking about six weeks following the second EVO dose. Within approximately five weeks following EVO addition, PCE and TCE were below detection limits and total chlorinated ethene concentrations dropped from their peak. In addition, DCE and vinyl chloride concentrations dropped along with TOC levels. The results and findings stemming from the EVO test cell are similar to the successful approaches and studies contained in the protocol for in situ bioremediation of chlorinated solvents using edible oil [26].  
Using the TOC levels detected at HSE-14 following its peak detections, substrate utilization rates were calculated throughout the test. Employing the Lineweaver-Burke Double Reciprocal Method, the inverses of these rates were plotted against the corresponding inverse of the midpoint substate concentrations to determine the half-saturation constant (K\({}_{\rm m}\)) and the maximum utilization rate (V\({}_{\rm max}\)) for this test cell. The Monod kinetics constants estimated were 153 mg/L and 8.7 mg/L per day for the K\({}_{\rm m}\) and V\({}_{\rm max}\), respectively. These results were used to refine the electron donor dosing amounts and system recirculation periods as well as to define TOC monitoring targets for full scale operations.
# A Lightweight Fire Detection Algorithm Based on the Improved YOLOv8 Model
## 2 Model and Methods
### The Content-Aware ReAssembly of Features Module  
The structural diagram of the CARAFE (Content-Aware ReAssembly of Features, CARAFE) module, as depicted in Figure 4, addresses the deficiencies of traditional upsampling methods in preserving fine details and reconstructing semantic information.  
Figure 3: Structure of Ghost Bottleneck.  
Figure 2: Structure of GhostConv.  
By reassembling low-resolution feature maps to generate high-resolution equivalents, the CARAFE model effectively mitigates these shortcomings. Its adaptive convolutional kernel mechanism enables flexible adaptation to various scenes and scales for feature reconstruction, thereby maintaining model lightweightness while enhancing its generalization capabilities.  
The CARAFE upsampling process is primarily divided into two modules: the upsampling kernel prediction module and the content-aware feature reassembly module. In the kernel prediction module, the model's parameter count is reduced by compression based on the number of channels in the input low-resolution feature map. Specifically, if the feature map has the dimensions \(H\times W\times C_{m}\), a \(k_{encoder}\times k_{encoder}\), then the convolutional kernel prediction module processes the compressed input feature map, where the input channel number is \(C_{m}\) and the output channel number is \(\sigma^{2}k_{up}^{2}\). As a result, the upsampling kernel size becomes \(\sigma H\times\sigma W\times k_{up}^{2}\). To ensure feature balance and stability during feature reassembly, the softmax function is used to normalize the weights of each upsampling kernel at each position. This transformation converts the original scores of the upsampling kernel to non-negative values, ensuring that the sum of weights is 1. In the feature reassembly module, regions centered around target positions in the output feature map with the dimensions \(k_{up}\times k_{up}\) are extracted. Then, the predicted upsampling kernel at that point is used for dot product operation with the extracted region. The resulting feature is mapped back to the input feature map, resulting in a feature map of size \(\sigma H\times\sigma W\times C\).  
To address challenges such as insufficiently capturing fire and smoke details, narrow perception range, and underutilization of fire and smoke information in complex backgrounds during fire and smoke detection, CARAFE upsampling is introduced into the YOLOv8 model. This enhancement aims to improve the upsampling module in the original feature pyramid structure, thereby enhancing the capability of the model to extract fire and smoke features.