esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks
## 4 Multi-Agent System for Train Rescheduling Problems
### Problem Decomposition  
Typically, the MILP model for train rescheduling can be addressed using global search techniques in a centralized manner. However, as the size of the network increases, the search space expands significantly, potentially leading to degraded solutions and extensive computational times--both of which are unsuitable for real-time decision-making. To overcome this challenge, this paper proposes a method to divide large-scale networks into manageable segments. By decoupling the train rescheduling problem into localized decision-making areas, we can solve the problem more efficiently within each area.  
In the proposed MAS, the railway infrastructure is decomposed down to the single-junction level. Figure 1 illustrates an example of this decomposition. A single junction is defined as the area starting from the signal before the junction and includes the entire control area around the junction, encompassing all associated signals. Additionally, platform areas can also be segmented at the junction level. The junctions surrounding the station areas are capable of adjusting train arrival and departure times at the station by determining the train's passing time through the junction. To balance computational complexity and minimize the geographical coverage of each agent, the control boundary of an agent is defined by all preceding signals before entering the junction, ensuring that it encompasses the associated routing and interlocking decisions.  
In each junction area, the local decision-making process is solely responsible for generating dispatching solutions within that specific junction area. Therefore, the variables under optimization for a single junction area \(J_{m}\) include only the train passing time \(t_{\tau_{m},\tau_{n}}^{J_{m}}\) and the indicator \(i_{\tau_{m},\tau_{n}}^{J_{m}}\). However, these variables are strongly coupled with the dispatching decisions of neighboring junctions, influenced by the value of \(i_{\tau_{m},\tau_{n}}^{J_{m},\tau_{n}}\). Consequently, the decision-making process for a local area must also consider a global view of the entire network. To address this, a collaborative approach has been designed, which involves post-correcting the values of \(t_{\tau_{m},\tau_{n}}^{J_{m}}\) and \(i_{\tau_{m},\tau_{n}}^{J_{m}}\), as detailed in Section 4.4. The objective function (Equation (1)) can then be allocated to each junction area as follows:  
\[TD^{J_{m}}=\sum_{\tau_{m}\in\mathbb{T}}\left|t_{\tau_{m}}^{J_{m}}-t_{\tau_{m}} ^{J_{m}}\right| \tag{13}\]  
The optimization within a local area takes into account all the constraints outlined in Section 3, with the exception of Equation (6). This selective consideration reduces the computational complexity of each local area, ensuring that the decision-making process can meet the time constraints required for real-time traffic control.
## 2 Results and Discussion
### Characterization  
#### 2.3.1 \({}^{1}\)H NMR Analysis  
The \({}^{1}\)H NMR spectra of PDMAEMA-CPTCD and SPDMAEMA-8 was shown in Figure 5A,B. As shown in Figure 5A, the chemical shifts at 2.58 ppm (a) and 4.06 ppm (b) are assigned, respectively, to the methylene protons (-NCH\({}_{2}\)-) and (-OCH\({}_{2}\)-) of the PDMAEMA side chain. Besides the characteristic signals of PDMAEMA, the signal at 3.21\(\sim\)3.26 ppm (j) ascribes to the methylene protons next to the trithiocarbonate group and other signals arise from the methyl protons and the methylene protons of CPTCD. All these results indicate the linear macro RAFT agent, PDMAEMA-CPTCD, has been successfully synthesized. The signal at 7.28 ppm is attributed to solvent peak. \({}^{1}\)H NMR spectrum of SPDMAEMA-8 was shown in Figure 5B. In comparison with Figure 5A, we could not see the proton signal at 3.21\(\sim\)3.26 ppm (j) ascribed to the methylene protons next to the trithiocarbonate group of CPTCD. Meanwhile, almost no characteristic signals ascribed to aromatic protons of DVB could be observed. The reason for this phenomenon may be that the PDMAEMA stars have tightly cross-linked cores, which is why we call these polymers star polymers [37,49].  
Figure 5: \({}^{1}\)H NMR spectra of PDMAEMA-CPTCD (**A**) and SPDMAEMA-8 (**B**).
## 1 Introduction
### Mission Links  
To create a design space for MOCT, three optical link scenarios were investigated: Moon to Earth, LEO crosslink, and LEO to Earth. These links could be incorporated into a nanosatellite optical communication network that could relay information around Earth and as far as the Moon. Furthermore, these links could be adapted for other astronomical bodies (i.e., Mars) so lasercom could be utilized throughout the solar system, where power efficiency is desirable and communication windows are short.  
A link equation is used to predict how much of the transmitted signal is captured by the receiver. Equation (1) is the optical link equation that was used to determine the power at a distant receiver from the MOCT transmitter. Equation (1) shares many similarities to link Equation (2.2-1) in [5] and was derived into this form in [6]. The terms in Equation (1) are all in decibels (dB), which is logarithmically scaled. In Equation (1), \(P_{rx}\) is the received peak power, \(P_{tx}\) is the transmitted peak power, \(G_{rx}\) is the transmitter gain, \(G_{rx}\) is the receiver gain, and \(L_{range}\) is the range loss. Additionally, \(\eta_{tx}\) is the transmitter optical efficiency, \(\eta_{atm}\) is the atmospheric transmission efficiency, \(\eta_{rx}\) is the receiver optical efficiency, \(T_{point}\) is the transmitter pointing gain, and \(R_{point}\) is the receiver pointing loss:  
\[P_{rx}=P_{tx}+G_{tx}+G_{rx}+L_{range}+\eta_{tx}+\eta_{atm}+\eta_{rx}+T_{point }+R_{point} \tag{1}\]By using Equation (1) and constraining the receiver peak power to be 200 nW (100 nW with 3 dB margin), the required transmitter peak power was calculated for each link scenario. The required transmitter peak power can be seen in Table 1 with the selected transmitter and receiver sizes used in the link scenario. The maximum required transmitter peak power and design goal for MOCT is 1 kW. Additional detail on the optical link parameters, terms, and equations used for the MOCT link senarios can be found in Appendix A.1.
# AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
## 4 Experiment and Analysis
### Comparison with State-of-the-Art Methods  
To evaluate the effectiveness of AuxDepthNet, we compare its performance with state-of-the-art monocular 3D object detection methods on the KITTI 3D Object Detection Benchmark, as shown in Table 2. The benchmark measures Average Precision (AP) under IoU 0.7 for both 3D detection \(AP_{3D}\) and bird's-eye view detection \(AP_{BEV}\) across three levels: Easy, Moderate, and Hard.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**AP3D@IoU = 0.7**} & \multicolumn{3}{c}{**APBEV@IoU = 0.7**} \\ \cline{2-7}
**Method** & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **Hard** \\ \hline DDMP-3D [18] & 19.71 & 12.78 & 9.80 & 28.08 & 17.89 & 13.44 \\ CaDDN [21] & 19.17 & 13.41 & 11.46 & 27.94 & 18.91 & 17.19 \\ DFRNet [35] & 19.40 & 13.63 & 10.35 & 28.17 & 19.17 & 14.84 \\ MonoEF [36] & 21.29 & 13.87 & 11.71 & 29.03 & 19.70 & 17.26 \\ MonoFlex [37] & 19.94 & 13.89 & 12.07 & 28.23 & 19.75 & 16.89 \\ GUPNet [38] & 20.11 & 14.20 & 11.77 & - & - & - \\ MonoDTR [39] & 21.99 & 15.39 & 12.73 & 28.59 & 20.38 & 17.14 \\ MonoJSG [40] & 24.69 & 16.14 & 13.64 & 32.59 & 21.26 & 18.18 \\ DEVIANT [41] & 21.88 & 14.46 & 11.89 & 29.65 & 20.44 & 17.43 \\ DCD [42] & 23.94 & 17.38 & 15.32 & 32.55 & 21.50 & 18.25 \\ DID-M3D [43] & 24.40 & 16.29 & 13.75 & 32.95 & 22.76 & 19.83 \\ Cube R-CNN [44] & 23.59 & 15.01 & 12.56 & 31.70 & 21.20 & 18.43 \\ MonoUNI [45] & 24.75 & 16.73 & 13.49 & 33.28 & 23.05 & 19.39 \\ MonoATT [46] & 24.72 & 17.37 & 15.00 & 36.87 & 24.42 & 21.88 \\ DPL [47] & 24.19 & 16.67 & 13.83 & 33.16 & 22.12 & 18.74 \\ DPM3D [48] & 23.41 & 13.65 & 12.91 & 32.23 & 20.13 & 17.14 \\ AuxDepthNet & 24.72 & 18.63 & 15.31 & 34.11 & 25.18 & 21.90 \\ (Ours) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detection performance of Car category on the KITTI validation set; the best and second-best results are highlighted in red and blue, respectively.  
Figure 6: Training loss curve during optimization. The figure shows that the loss steadily decreases and converges, confirming the stability of the training process.  
From Table 2, AuxDepthNet achieves competitive results. For 3D detection, it obtains an AP of 24.72, 18.63, and 15.31 on Easy, Moderate, and Hard levels, respectively. Compared to MonoUNI with 24.75, 16.73, and 13.43, our method improves the Moderate and Hard scores by 1.90 and 1.88 points. Similarly, AuxDepthNet outperforms Cube R-CNN, which achieves 23.59, 15.01, and 12.65, across all three categories.  
For BEV detection, AuxDepthNet achieves an AP of 34.11, 25.18, and 21.90. Compared to MonoATT, which achieves 36.87, 24.42, and 21.88, our method improves the Moderate score by 0.76 points while maintaining competitive performance on other levels.  
Overall, AuxDepthNet surpasses MonoUNI, Cube R-CNN, and MonoATT, especially on Moderate and Hard levels. These results demonstrate the effectiveness of our Depth Position Mapping (DPM) module in capturing depth-sensitive features and enhancing 3D detection accuracy on the KITTI benchmark.
# Person Search via Deep Integrated Networks
## 5 Conclusions  
Person search is a practical application which includes two kernel parts, person detection and re-identification, and it is more challenging than person ReID because most existing ReID studies have only processed and classified input pedestrian images enclosed within a bounding box, but have ignored the fact that an imperfect bounding box or background clutter is inevitable. For practical surveillance scenarios, where scene images are captured by two or multiple cameras, this study proposed an integrated network consisting of a detection and identification module to implicitly consider the effect of the detection results. Two images captured by non-overlapped cameras were input to the Faster R-CNN detection network. A multi-class CNN network was trained with the auto-detected bounding boxes from the detection module instead of manually cropped data to extract discriminative features. Hence, the FC layers of the multi-class CNN can be utilized as the discriminative features, and the similarity of the two feature vectors are calculated via a learnable distance metric. In addition, the proposed framework is more flexible; when one new person is detected, the feature vector is then extracted by the multi-class CNN and compared with other images in the dataset without retraining the network. The experimental result show that the multi-class CNN network for the identification module can provide a 62.7% accuracy, which is significantly higher than that in the two-class CNN network. Moreover, more experiments have been designed to apply sharing features to improve the computational efficiency. However, the results of the identification are not satisfactory because the feature maps of the detection network contained less and unclear information about subjects. In the future, we would like to design a search strategy for real-world applications and re-design the network configuration such that the detection and identification module can utilize shared convolution feature maps for computational efficiency.  
Figure 8: Examples of the identification results in Experiment 2 (identification network): (**a**) successful and (**b**) failure cases.  
**Author Contributions:** Conceptualization, C.-F.W., J.-C.C. and C.-H.C.; Methodology, C.-F.W. and J.-C.C.; Software, C.-H.C. and C.-R.L.; Writing--original draft, C.-F.W.; Writing--review & editing. J.-C.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Ministry of Science and Technology of Taiwan, R.O.C., under grant No. 108-2221-E-992-032-.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Plum Tree Algorithm and Weighted Aggregated Ensembles for Energy Efficiency Estimation
## 6 Discussions
### Data Standardization Results  
The application of data standardization led to much better results in terms of RMSE. Figure 13 presents the detailed results summary for the runs which led to the best RMSE values. The results were almost identical when other indicators were considered as selection criteria for the most representative runs.  
The prediction results were much better for the Standardized Heating Dataset than for the Standardized Cooling Dataset for each fold, as shown in the Heating Dataset and Cooling Dataset energy prediction results. The values of \(R^{2}\) were close to 1, which means that predictability performance was similar for both standardized datasets. Regarding running time, the average running times for the best runs for the Standardized Cooling Dataset and Standardized Heating Dataset were 29.35312 s and 24.94228 s, respectively. These values are slightly higher than when no standardization was applied, which can be justified by the time required to standardize the data and the way in which the algorithms behave when the data is standardized.  
Figure 14 presents the weights of each algorithm of the ensemble for each fold when data standardization is applied. These weights are the ones which correspond to the best runs in terms of RMSE.  
The weights of the ensemble for the Standardized Cooling Dataset are almost identical to the weights obtained for the Cooling Dataset. Even though the weights were similar for fold 1, fold 3, and fold 5 for the Standardized Heating Dataset and the Heating Dataset,  
Figure 14: PTA Ensemble Weight Summary When Standardization is Applied.  
Figure 13: PTA Ensemble Detailed Evaluation Metrics Results Summary for the Best RMSE Values when Data Standardization is Applied.  
there were significant differences between fold 2 and fold 4. However, the GBR and the ETR obtained the highest weights of the ensemble for both fold 2 and fold 4.
## 3 Materials and Methods
### External Validation Datasets  
To evaluate the generalizability of our model, we employed two publicly available external datasets: TCGA-GBM [41] and UPENN-GBM [42]. These datasets were both collected by The Cancer Imaging Archive (TCIA) and annotated using the same labeling protocol adopted in the BraTS-2023 challenge. The TCGA-GBM dataset involves pre-operative multi-institutional MRI scans from The Cancer Genome Atlas (TCGA) Glioblastoma Multiforme (GBM) collection. Each case includes co-registered and skull-stripped multi-parametric MRI (T1, T1 CE, T2, T2-FLAIR) volumes. Segmentation labels were initially generated using the top-performing method from BraTS 2015 and subsequently re viewed and corrected by expert neuroradiologists, resulting in high-quality gold-standard annotations consistent with BraTS framework.  
The UPENN-GBM dataset includes the same mpMRI scans of patients affected by multiform glioblastoma collected at the University of Pennsylvania Health System. Tumor subregion labels were created using a semi-automated pipeline and manually reviewed or approved by neuroradiologists, and are provided in co-registered NIfTI format.  
To ensure an objective assessment of model performance, we selected all cases for which segmentation labels were available and used the official mapping file released by the BraTS 2021 organizers [40] to identify and exclude all subjects that overlapped with the BraTS 2023 training set. After filtering, we finally obtained a total of 92 TCGA-GBM cases and 144 UPENN-GBM cases for external validation. This ensured that the evaluation was performed on fully independent data that were unseen during model training or internal testing.
# Fusing Local and Global Information for One-Step Multi-View Subspace Clustering
## 4 Optimization
### Update \(\mathbf{U}\)  
When \(\mathbf{S}^{v}\) and \(\mathbf{F}\) are fixed, the objective function about \(\mathbf{U}\) is represented as:  
\[\begin{split}\min_{\mathbf{U}}\sum\limits_{v=1}^{V}\sum\limits_{i=1}^{ n}\sum\limits_{j=1}^{n}\left(s_{ij}^{v}-u_{ij}\right)^{2}+2\lambda\mathbf{Tr}(\mathbf{F}^{ \mathrm{T}}\mathbf{L_{U}}\mathbf{F})\\ s.t.u_{ij}\geq 0,\mathbf{1}^{\mathrm{T}}\mathbf{u}_{i}=1\end{split} \tag{10}\]  
where \(\mathbf{u}_{i}\in\mathbb{R}^{n\times 1}\) is a column vector, \(u_{ij}\) is the \(j\)-th element of \(\mathbf{u}_{i}\).  
Noting that \(\mathbf{Tr}(\mathbf{F}^{\mathrm{T}}\mathbf{L_{U}}\mathbf{F})\!=\!1/2\!\sum_{i=1}^{n}\sum_{j=1} ^{n}\left\|\mathbf{f}_{i,\cdot}-\mathbf{f}_{j,\cdot}\right\|_{2}^{2}\!u_{ij}\), we denote \(\mathbf{h}_{i}\) be a vector with the \(j\)-th element \(h_{ij}=\left\|\mathbf{f}_{i,\cdot}-\mathbf{f}_{j,\cdot}\right\|_{2}^{2}\). Through simple mathematical derivation, problem (10) can be rewritten as follows:  
\[\begin{split}\min_{\mathbf{u}_{i}}\sum\limits_{v=1}^{V}\left\|\mathbf{u}_ {i}-\mathbf{s}_{i}^{v}+\frac{\lambda}{2V}\mathbf{h}_{i}\right\|_{2}^{2}\\ s.t.u_{ij}\geq 0,\mathbf{1}^
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques
### Range-Free Localization Techniques
#### 2.3.3 Distance Vector (DV)-Hop  
The distance vector (DV)-hop localization technique is a distributed range-free algorithm based on the distance vector routing protocol. Similar to the other range-free localization techniques, all the network nodes are assumed to broadcast their localization information and estimate their distance to the other nodes, so that all nodes acquire the last hop count required to reach each other [28]. In more detail, the DV-hop algorithm consists of three steps. Firstly, the network minimum hop count is estimated. Secondly, the average hop distance and the distance between the known transmitting and unknown receiving nodes are estimated. Finally, the unknown receiving nodes' coordinates are estimated. The broadcast information includes the identifier \(id\), coordinates \((x_{i},y_{i})\), and hop count for each transmitting node \(i\). Initially, the \(hop_{i}\) parameter is set to zero, and for each broadcast packet, it is compared with the previous recorded value and updated accordingly. Therefore, each receiving node \(R_{i}\) receives the location coordinates, together with the hop count values, from all other transmitting nodes, so it can compute the average distance per hop by using the following formula [28]:  
\[HopDistance_{R_{i}}=\frac{\sum_{i\
eq j}\sqrt{(x_{i}-x_{j})^{2}+(y_{i}-y_{j}) ^{2}}}{\sum_{i\
eq j}hop_{ij}}, \tag{20}\]  
where \((x_{i},y_{i})\) are the coordinates of node \(R_{i}\), and \(hop_{ij}\) is the number of hops from \(R_{i}\) to \(R_{j}\). Finally, the average distance per hop \(HopDistance_{R_{i}}\) is broadcast from the unknown receiving node \(R_{i}\), and each sensor node \(N\) computes its estimated distance from the receiving node \(R_{i}\) using the following equation [28]:  
\[distance_{N,R_{i}}=HopDistance_{R_{i}}*hop_{i}, \tag{21}\]  
where \(hop_{i}\) is the number of hops between \(N\) and \(R_{i}\). When at least three receiving nodes have been identified, then node \(N\) can use triangulation and estimate its position. The main drawback of the DV-hop localization technique are the assumptions made, i.e., that the paths are in a straight line or that the nodes are distributed evenly, which can result in errors when estimating the actual distances. Moreover, a combination of DV-hop with range-based measurements can be utilized for improving the localization accuracy, for example, in combination with RSS measurements [51]. Similarly, other range-free position estimation techniques, such as the centroid [52], can also be used.
## 3 Results and Discussion
### Prediction of Ultimate Damage Load  
In the previous section, the damage patterns of crumb rubber concrete were successfully identified using the AE technique combined with the selected optimal clustering method. At the same time, it is important to find a simple and effective method for predicting the ultimate damage load for crumb rubber concrete in order to ensure the safety of this material for a wide range of applications in practical engineering. On the other hand, the combination of damage pattern recognition and ultimate load prediction is more beneficial to characterize the damage mechanism of crumb rubber concrete. Therefore, in this section, a prediction model of ultimate load was proposed from the function of the AE technique with prediction of crack propagation combined with the artificial neural network.
# Shedding of GPP130 by PC7 and Furin: Potential Implication in Lung Cancer Progression
## 2 Results  
### GPP130 Is Cleaved and Shed by PC7 and Furin  
Using a mass spectrometry approach consisting of selective enrichment of N-glycosylated secreted polypeptides, performed previously to define novel PC7 substrates [4]. Our group recently identified a type-II transmembrane protein, GPP130, as a potential PC7 candidate substrate (Figure 1A). In the current study, we investigated the functional relevance of GPP130 shedding by PC7. To do so, we first validated that GPP130 is processed by PC7 in HEK293 cells co-expressing cDNAs encoding GPP130 with a V5-tag at its C-terminus (Figure 1C), PC7, or an empty vector control plasmid (Figure 1D). Western blot (WB) analyses revealed that GPP130 is cleaved and shed by PC7 since we observed the release of a ~100 kDa fragment (S1) in the media in the presence of PC7 when using an antibody targeting the V5 tag on its luminal domain, but not in control cells expressing a cDNA encoding an empty vector (EV) (Figure 1D). In addition, co-expression in HEK293 cells of cDNAs encoding GPP130 with each of the basic aa-specific PCs (Furin, PC5A, PC5B, PACE4, and PC7), or an EV control, revealed that only PC7 and Furin cleave and shed GPP130 into the media (Figure 1E). Interestingly, Furin cleaved GPP130 at two sites within its luminal domain, releasing distinct ~100 kDa (S1) and ~75 kDa (S2) fragments into the media (Figure 1E).  
To better understand the trafficking of GPP130, immunofluorescence analysis demonstrated that when overexpressed in HeLa cells, GPP130 colocalizes mainly with the TGN marker Golgi-97, early endosome antigen 1 marker (EEA1), the ER marker calnexin and to some extent with the plasma membrane marker low-density lipoprotein receptor (LDLR) (Figure 2). These results are coherent with previous publications demonstrating that PC7 and Furin activities mainly occur in the TGN, cell surface, and/or endosomal-like structures [29; 30].