esa-sceva/satcom-chunk-collection huggingface dataset:
# Learning Part-Based Features for Vehicle Re-Identification with Global Context
## 3 Proposed Unified Framework for Global and Part-Based Local Features
### Global-Local Similarity Score Generator  
A copy of the convolutional tensor \(T^{b}\) is fed into the score generator network. Similar to the first cycle, the partitioned convolutional tensor set \(T^{p}\) is generated. Subsequently, the scores are generated as described below.  
Let \(T^{b}\) be the convolutional backbone tensor of size \(BxCxHxV\), which is then passed through an average pooling layer to obtain the tensor \(T^{bn}\) of size \(Bxx1x1\) (global feature vector). Let \(T^{p}=\{t^{1},t^{2},\ldots t^{n}\}\) denote the partitioned convolutional tensor set comprising \(n\) parts, each of size \(BxCx1x1\) (part-based local feature vector).  
Then, we have  
\[D^{p}=\{d^{1},d^{2},\ldots,d^{n}\}=\mathit{distance}\,\mathit{set} \tag{6}\]  
where \(d^{n}\) is the distance between each \(T^{n}\) and \(T^{b}\). The distance may be any type of vector distance.  
The similarity score between global and local features is then defined as  
\[S=f(D^{p}) \tag{7}\]  
where \(f\) is any function such that the score is the inverse of the distance.  
\[S=\{s^{1},s^{2},\ldots,s^{n}\} \tag{8}\]  
The above score set is returned by the network. We then modify our training scheme during the calculation of the total loss in order to incorporate the effect of the weighted scores \(S\).  
During the training of a convolutional neural network, the weights are updated according to a loss function. For classification problems, the cross-entropy loss is the widely used loss function. For an \(N\)-category classification problem, with the probabilities of each class denoted by \(p_{i}\) the cross-entropy loss is given by  
\[L_{CE}=-\sum_{i=1}^{N}q_{i}*\log p_{i} \tag{9}\]  
where \(p_{i}\) is the probability of each class and \(q_{i}\) is the ground-truth label for that class. Let the cross-entropy loss for each part \(i\) be \(L_{i}\). The total cross-entropy loss becomes  
\[L_{tot}=\sum_{i=1}^{n}s^{i}*L_{i} \tag{10}\]  
where \(s^{i}e\)\(S\) is the similarity score for a part. Here, the set \(S\) can be considered the unifying factor.  
From Equation (9), we find that the loss function is the negative logarithm of the probabilities of the classes; the loss value will be lower for higher-ranked classes. Also, from (4), we find that the total loss is updated in such a way that parts with higher similarity with the global feature will have a lower absolute value of the loss. The framework penalizes parts that are farther from the global feature and reinforces parts that are more similar to it. This creates a scenario in which fine-grained local features are learned by training the network to focus more on parts that are relevant to the global context.  
The rest of the training process, including weight updates after backpropagation, follows the standard process for training CNNs.
# Evaluation of the Urban Weather Generator on the City of Toulouse (France)
## 2 Materials and Methods
### Study Site and Associated Data
#### 2.1.1 City of Toulouse, France  
Toulouse is located in southwest France, on the Garonne plain between the Pyrenees and the Massif Central mountains. It is halfway between the Atlantic Ocean and the Mediterranean Sea generating relatively mild winters and warm, sunny summers. It covers 118.3 km\({}^{2}\). Toulouse is the fourth-largest city in France with a population approaching half a million. With Rennes [36] and Dijon [37; 38], Toulouse [39] has one of the most advanced urban meteorological networks in France, enabling acquiring a set of local-scale air temperature data. The Toulouse meteorological network has been deployed since 2017 and new stations are continuously added to enrich the network and enable long-term surveys. The data are freely available (See [[https://data.toulouse-metropole.fr/](https://data.toulouse-metropole.fr/)]([https://data.toulouse-metropole.fr/](https://data.toulouse-metropole.fr/)) (accessed on 2 December 2023)) [40]. Our choice of the city of Toulouse has been motivated by, among other reasons, its hot summers and favourable meteorological conditions for UHI, as can be seen in Table 1.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Climate Var**} & \multicolumn{1}{c}{**Month**} & \multicolumn{1}{c}{**Jan**} & \multicolumn{1}{c}{**Feb**} & \multicolumn{1}{c}{**Mar**} & \multicolumn{1}{c}{**Apr**} & \multicolumn{1}{c}{**May**} & \multicolumn{1}{c}{**Jun**} & \multicolumn{1}{c}{**Jul**} & \multicolumn{1}{c}{**Aug**} & \multicolumn{1}{c}{**Sep**} & \multicolumn{1}{c}{**Oct**} & \multicolumn{1}{c}{**Nov**} & \multicolumn{1}{c}{**Dec**} \\ \hline
**Mean Temp. (Mean in \({}^{\circ}\)C)** & 6.3 & 7.1 & 10.3 & 12.7 & 16.4 & 20.3 & 22.6 & 22.8 & 19.3 & 15.3 & 9.9 & 7 \\
**Min Temp. (Mean in \({}^{\circ}\)C)** & 2.9 & 3.1 & 5.5 & 7.9 & 11.4 & 15 & 17 & 17.1 & 13.9 & 10.9 & 6.3 & 3.6 \\
**Max Temp. (Mean in \({}^{\circ}\)C)** & 9.7 & 11.2 & 15 & 17.6 & 21.4 & 25.7 & 28.2 & 28.5 & 24.8 & 19.7 & 13.5 & 10.4 \\
**Min Temp. (Extreme in \({}^{\circ}\)C)** & \(-\)18.6 & \(-\)19.2 & \(-\)8.4 & \(-\)3 & \(-\)0.8 & 4 & 7.6 & 5.5 & 1.9 & \(-\)3 & \(-\)7.5 & \(-\)12 \\
**Max Temp. (Extreme in \({}^{\circ}\)C)** & 21.2 & 24.1 & 27.1 & 30 & 33.4 & 40.2 & 40.2 & 42.4 & 35.3 & 33 & 24.3 & 21.1 \\
**Precipitation (Mean in mm)** & 52.5 & 37.2 & 45.3 & 65.2 & 73.6 & 64.2 & 40.1 & 44.6 & 45.7 & 54.3 & 55 & 49.3 \\
**Wind Speed (Mean over \(10\) min** & 3.7 & 4 & 4.3 & 4.3 & 4.1 & 3.8 & 3.7 & 3.5 & 3.5 & 3.7 & 3.6 & 3.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average and Extreme values of some climate variables at Toulouse-Balgnac between 1991 and 2020—Code: 31069001, alt: 151 m, lat: 43\({}^{\circ}\)3\({}^{\circ}\)3\({}^{\prime}\)7\({}^{\circ}\)7\({}^{\circ}\) N, long: 1\({}^{\circ}\)22\({}^{\prime}\)43\({}^{\prime}\) E [41].
# Torque Characteristic Analysis of a Transverse Flux Motor Using a Combined-Type Stator Core
## 5 Influence of Structure Parameters on Torque Density
### Air-Gap Length  
From Equation (25), the torque density will be improved when the air-gap length is decreased. The air-gap length is analyzed as the unique variable by 3D FEM, and the results are shown in Figure 13. From Figure 13, when the air-gap length is decreased from 1.4 mm to 0.4 mm, the average torque is improved significantly. However, the torque ripple is increased at the same time. The average torque under 0.4 mm gap is approximately 1.56 times the one under 1 mm.  
In addition, the air-gap length is always limited to the mechanical structure and manufacturing technique, and it should be chosen reasonably under the overall considerations of both working characteristics and machining technique.  
Figure 12: The influence of permanent magnet thickness \(h_{\mathrm{m}}\) on average torque and torque ripple.
## 2 Requirements and Challenges of the Smart Grid
### Scalability  
The sensing and control system must work in optimal conditions even when the Smart Grid grows significantly. A typical utility of 25,000 km of high voltage power lines and thousands of capacitors and transformers could require the monitoring of over 100,000 distinct elements and distributed sensors or sources of data that may be spread over a 20-80,000 sq. km area [16].  
From the technologies' perspective, there are a couple of challenges to be faced in order to provide high scalability to a Smart Grid. First of all, the information network must be able to grow inexpensively and on demand, to enable this, monitoring systems composed of WSN devices must be of very low cost and be available to designers and end users. Secondly, when the network increases in terms of WSN devices it does also in size. It is therefore of paramount importance the implementation of multi-hop algorithms allowing the network to operate for long distances and providing communication from any arbitrary element of the Smart Grid to the central node [17, 18].
# Hierarchical and Unsupervised Graph Representation Learning with Loukas's Coarsening
## 5 Evaluation
### Latent Space  
Figure 6 illustrates some graphs (leftmost column) with their six closest neighbors in the learned latent space (right columns 1 to 6), from the closest to the farthest, taken from MNIST and IMDB. We observe that isomorphic graphs share similar embeddings, and even when the graphs are not quite isomorphic they exhibit similar structures.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**HG2V**} & \multicolumn{3}{c}{**Graph2 Vec + GNN**} & \multicolumn{3}{c}{**Graph2 Vec + Loukas**} & \multicolumn{1}{c}{**Graph2 Vec + Loukas**} \\
**DATASET** & **\#nodes** & **Accuracy** & **Delta** & **Accuracy** & **Delta** & **Accuracy** & **Delta** & \\ \hline
**IMDB-b** & 20 & 70.85 & +7.75 & 70.70 & +7.60 & 57.5 & \(-\)5.60 & 63.10 \\ \hline
**NCI1** & 30 & 77.97 & +4.75 & 75.40 & +2.18 & 65.45 & \(-\)7.77 & 73.22 \\ \hline
**MNIST** & 151 & 95.83 & +39.56 & 91.05 & +34.78 & 72.5 & +16.23 & 56.27 \\ \hline
**D\&D** & 284 & 78.01 & +19.37 & 79.26 & +13.16 & 66.10 & +7.45 & 58.64 \\ \hline
**REDDIT-B** & 430 & 91.95 & +16.23 & **OOM** & ✗ & 82.50 & +6.78 & 75.72 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablative studies. The accuracy on test set is in the column “Accuracy”. The column “Delta” corresponds to the difference in average accuracy with Graph2 Vec. **OOM—**Out of Memory Error.
## 5 Experiments and Results  
After generating the dataset following the steps detailed in Section 3, with 10,200 rows and three classes: Birth Certificate (BC), National Identity Card (CIN), Passport (PASS),we trained and tested the algorithms listed in Section 4 for text classification. This section outlines the classification performance of the different algorithms that were benchmarked.
# Effect of Zr Modification on NH\({}_{3}\)-SCR Reaction Performance of Cu-Ce/SAPO-34 Catalysts
## 3 Results and Discussion
### In Situ DRIFTS Measurements
#### 3.5.4 Transient Reaction of NH\({}_{3}\) after NO+O\({}_{2}\) Adsorption Saturation  
NO+O\({}_{2}\) was adsorbed on the surface of the 4 Cu-4 Ce-4 Zr/SAPO-34 catalyst for 40 min. After the adsorption was saturated, a transient reaction experiment of NH\({}_{3}\) was carried out. The sample was scanned at 3, 6, 10, 20, and 30 min to analyze its changes. Figure 9 shows the characteristic reaction curve of the above experiment. It can be seen that after NH\({}_{3}\) was introduced, the bridging nitrate characteristic peak at 1244 cm\({}^{-1}\) and the monodentate nitrate characteristic peak at 1384 cm\({}^{-1}\) did not change significantly. However, new characteristic peaks were formed at 1624 and 3334 cm\({}^{-1}\), and the characteristic peak at 1624 cm\({}^{-1}\) was attributed to weakly adsorbed NO\({}_{2}\)[35]. It was speculated that the introduction of NH\({}_{3}\) might enhance the conversion and adsorption of NO. The characteristic peak at 3334 cm\({}^{-1}\) was considered to be due to the NH\({}_{3}\) species adsorbed on the Lewis acid site [36; 37]. During the reaction process, the main characteristic peaks of the nitrate species formed on the catalyst surface did not change significantly, and new NH\({}_{3}\) adsorption species were formed,  
Figure 8: In situ DRIFTS of NO+O\({}_{2}\) adsorbed over the 4 Cu-4 Ce-4 Zr/SAPO-34 and 4 Cu-4 Ce/SAPO-34 catalyst.  
Figure 7: In situ DRIFTS of NO+O\({}_{2}\) adsorbed over the 4 Cu-4 Ce-4 Zr/SAPO-34 catalyst.  
but both of them did not change significantly after they became stable, indicating that no obvious reaction occurred between the adsorbed NO and NH\({}_{3}\), that is, the process did not follow the L-H mechanism.
## 3 Results and Discussion
### Dominant Loading Analysis for Future Application of the SER Approach  
To investigate dominant loading in the SER approach, we separated the total loading into the aerodynamic part (in the air) and the hydrodynamic part (underwater). The aerodynamic part was governed by the wind-loading-induced-bending moment to the tower bottom, where the moment was then transmitted to the substructure. By contrast, the hydrodynamic loading indicated that the wave loading directly acted on the structural member of the substructure (which was mainly out of  
Figure 5: Same damage in multiple locations for (**a**) Case 1 (damage in the same pipe) and (**b**) Case 2 (damage in different pipes).  
Figure 6: Damage of differing severity in multiple locations.  
plane). In Case 3-1, the _DI_ values at Location 1 under wind loading and wave loading were 9.89 and 1.69, respectively.  
Figure 7 presents a comparison of expanded strain and measured strain. The two strains in Figure 7a exhibited the same trend under wind loading; this was because the whole member was subjected to the same compression and tensile forces at the ends, which were caused by the bending moment from the tower. Due to reduced thickness at the damaged element, the measured strain was approximately twice the expanded strain, resulting in a higher _DI_ value. However, the expanded strain under wave loading (Figure 7b) did not accord with the measurements. This was attributable to out-of-plane forces, such as wave slamming forces, that were applied inconsistently on the different locations of a member. The governing loading in this proposed method is, in short, the aerodynamic loading on the wind turbine. This finding suggests that the SER approach can be employed not only under normal operating conditions but also when the wind speed is sufficiently high. Moreover, the axial strain of a member under wind loading is greater than that under wave loading. Damage detection can be construed as being independent from wave loading. Therefore, for future applications, the measured _DI_ can be set as a criterion to determine whether the member is damaged.
## 2 Materials and Methods
### Membrane Filtration Experiments  
Membrane filtration experiments were conducted using a dead-end filtration module (Sterlitech HP4750 Stirred Cell) with magnetic stirring at 500 rpm, using an effective membrane area of 14.6 cm\({}^{2}\). Initially, the membrane was compacted at 30 psi for 1 h; after this time, 100 mL of deionized pure water was permeated through the membrane to determine an initial flux \(J_{\text{w}1}\), followed by three 100 mL filtration cycles of a 1.0 mg/mL BSA solution to obtain \(J_{\text{BSA}}\). Finally, the membrane was cleaned with distilled water for 30 min, and the first permeate after cleaning was taken as water flux \(J_{\text{w}2}\). This procedure allowedfor the assessment of the flux recuperation ratio (FRR) and BSA flux recovery ratio (Rr), which were calculated using Equations (5) and (6) [27].  
\[\mathrm{FRR}(\%)=\frac{\mathrm{J_{w2}}}{\mathrm{J_{w1}}}\times\;100 \tag{5}\]  
\[\mathrm{Rr}(\%)\;=\left(\frac{\mathrm{J_{w2}}\;-\;\mathrm{J_{BSA}}}{\mathrm{J _{w1}}}\right)\;\times\;100 \tag{6}\]  
Moreover, total flux loss caused by the total fouling (Rt) and percentage of irreversible flux loss caused by irreversible fouling (Rir) were calculated using Equations (7) and (8), respectively.  
\[\mathrm{Rt}(\%)\;=\left(1\;-\;\frac{\mathrm{J_{BSA}}}{\mathrm{J_{w1}}} \right)\;\times\;100 \tag{7}\]  
\[\mathrm{Rir}(\%)\;=\left(\frac{\mathrm{J_{w1}}\;-\;\mathrm{J_{w2}}}{\mathrm{ J_{w1}}}\right)\;\times 100 \tag{8}\]
## 4 Real-Time Tissue Recognition and Margin Assessment
### Ex Vivo Imaging Technologies  
To overcome the limitations of a conventional FS analysis during radical prostatectomy, several ex vivo imaging technologies have emerged, offering rapid tissue assessment combined with high-resolution digital imaging capabilities. Among the emerging intraoperative technologies, fluorescence confocal microscopy (FCM) has shown promise as a rapid, high-resolution imaging method for evaluating prostate margins during robot-assisted radical prostatectomy. Unlike the conventional FS analysis, FCM does not require tissue fixation or sectioning. Instead, freshly excised tissue is stained (typically with acridine orange), digitally scanned, and immediately analyzed. Early studies have demonstrated strong concordance with final histopathology, with reported diagnostic accuracy exceeding 90% and short turnaround times [23]. Recent developments have introduced standardized protocols for applying FCM to assess the neurovascular bundles and posterolateral margins intraoperatively. In comparative analyses, FCM has performed similarly to the NeuroSAFE technique in detecting clinically significant positive margins, while offering the advantage of significantly shorter processing times [24]. Moreover, the digital nature of FCM allows for a remote pathological review, which may be particularly valuable for centers without onsite pathology support. As the technology evolves, FCM may become an important adjunct to real-time surgical decision-making in robotic prostatectomy, enhancing oncologic safety while preserving functional outcomes. Another promising approach is specimen PET-CT imaging, exemplified by platforms, such as Aura-10 TM. This method involves the preoperative administration of PSMA-targeted radiotracers, followed by PET-CT imaging of the excised prostate specimen. The technique provides three-dimensional visualization of radiotracer uptake at the tumor margins with submillimetric resolution. Preliminary findings indicate a strong concordance with final histopathology, and the potential to detect focal positive surgical margins and lymph node metastases with high precision [25]. However, concerns regarding radiation exposure, cost, and clinical workflow integration remain challenges to its widespread adoption. Together, these ex vivo imaging modalities represent a shift toward faster, more precise intraoperative margin assessments, and may complement or eventually reduce the reliance on the conventional FS analysis in prostate cancer surgery.
## 2 Materials and Methods
### Eco-Restoration Vegetation Change Dataset  
Due to the lack of public datasets for monitoring vegetation changes in grassland desertification ecological restoration, and we have a large number of self-built databases, including more than 5.2 million pairs of remote sensing image data, forming a dataset that integrates spatial and statistical information. The dataset can not only reveal the effect of ecological restoration, but also provide decision support for policy planning and long-term monitoring, provide important scientific support for desertification control and ecological restoration, and promote the sustainable management of land resources.  
The self-built dataset consists of 5.2 million pairs of remote sensing image chips (size: 256 \(\times\) 256 pixels), covering grassland scenes with varying levels of degradation (mild, moderate, severe) during the summer season. Due to geographic and climatic factors, grassland vegetation in northern China (Inner Mongolia Autonomous Region) reaches its peak growth only during summer, particularly in August each year. Vegetation conditions in other seasons are relatively poor and therefore excluded from the study. The annotation process was conducted by three remote sensing experts through visual interpretation, followed by double-blind cross-validation to ensure consistency. Discrepancies were resolved through consensus, and inter-annotator agreement was maintained at \(\geq\)95%. The dataset includes three channels: bi-temporal RGB images and a _kNDVI_ difference map. The labels indicate vegetation change (1) or no change (0). Data variety encompasses the following: (1) Spatial diversity: images from different zones of Wuzhumuqin Sandy Land (northwest/south/central regions). (2) Temporal diversity: images acquired from 2019 to 2024, including growing season (August) observations. (3) Feature diversity: integration of spectral (_kNDVI_), spatial (texture), and temporal (multi-phase) features. The dataset is available upon request from the corresponding author, subject to a data use agreement that complies with local environmental protection regulations. Detailed metadata and labeling protocols can be provided to ensure reproducibility.
# Scheduling a Single Machine with Primary and Secondary Objectives
## 4 Partitioning the Scheduling Horizon
If it succeeds (fails, respectively) to create a feasible schedule with the maximal job lateness no more than threshold \(L(\delta)\), the next smaller (larger, respectively) value of \(\delta\) is taken in the binary partition mode. According to our partition, the lateness of no kernel job may surpass the threshold \(L(\delta)\). Hence, it basically remains to schedule the bin intervals (see the next section). As already noted, if while scheduling some bin, the next incoming job results in a lateness greater than \(L(\delta)\), then a new kernel \(K\) including this job is determined (see the next section for the details). Then, the current set of the kernels is updated as \(\mathcal{K}:=\mathcal{K}\cup\{K\}\), and the scheme proceeds with the updated partition. A positive delay for a kernel might be unavoidable in an optimal job arrangement. For instance, the time interval of kernel \(K_{1}\) in schedules \(S\) and \(S_{1}\) (Figures 1 and 2) is \([35,40)\) and \([30,35)\), respectively. The time interval of kernel \(K_{1}\) remains to be \([30,35)\) in the optimal solution. Indeed, let us form our next set \(E\) again with the same emerging Job 1 constructing complementary schedule \((S_{1})_{1}=(2,0)(3,30)(4,36)(5,46)(6,56)(7,66)(1,76)\); see Figure 3 (this complementary schedule has a gap \([35,36)\) marked as a dark region in the figure). It is easily verified that schedule \((S_{1})_{1}\) with kernels \(K_{1}\) and \(K_{2}\) and the corresponding three arranged bins is optimal with \(L_{\max}((S_{1})_{1})=L_{3,(S_{1})_{1}}=35-11=24=L_{7,(S_{1})_{1}}=76-52=24\). In that schedule, we have achieved an optimal balance between the delays of the two kernels by redistributing the available jobs within the bins. As a result, the makespan is reduced without affecting the maximum job lateness. Note that the jobs of kernel \(K_{1}\) and also those of kernel \(K_{2}\) are delayed by an appropriate amount of time units without affecting the maximum job lateness. Our framework determines such an appropriate delay for every detected kernel, resulting in an optimal balance between kernel and bin intervals. Observe that, although the partition of the scheduling horizon in schedules \(S_{1}\) and \((S_{1})_{1}\) is the same, the bins are packed in different ways in these two complementary schedules. Instead of forming the complementary schedules, we shall construct a direct algorithm for scheduling the bins in the following section.