esa-sceva/satcom-chunk-collection huggingface dataset:
# Pyramid Spatial-Temporal Graph Transformer for Skeleton-Based Action Recognition
## 3 Method
### Pyramid Architecture  
In our work, a pyramid architecture is introduced inspired by PVT [37]. In the previous the PVT for image-based vision tasks, and multi-scale feature maps are generated for more robust representations. Therefore, our PGT adopts a similar architecture consisting of four stages, and all of them share a similar structure. In the first stage, the input skeleton tensor of a size \(\mathbb{R}^{T\times V\times 3}\) is calculated with a spatial-temporal graph convolution module named graph embedding. Then, the tensor \(\mathbf{x}_{1}\in\mathbb{R}^{T/4\times V\times C_{1}}\) is divided into \(V\) tokens in the spatial domain and \(T/4\) in the temporal domain for the cascaded spatial attention (SSA) and temporal self-attention (TSA). After that, the local feature of each node of the graph is reweighted with the global nodes according to the transformer block. By this operation, the following feature maps are obtained: \(\mathbf{D}_{1}\in\mathbb{R}^{T/4\times V\times C_{1}},\mathbf{D}_{2}\in \mathbb{R}^{T/8\times V\times C_{2}},\mathbf{D}_{3}\in\mathbb{R}^{T/16\times V \times C_{3}}\) and \(\mathbf{D}_{4}\in\mathbb{R}^{T/32\times V\times C_{4}}\). The pooling operation reduces the redundancy in the temporal domain.
## 3 The Critical Importance of Product Identity in Agricultural Supply Chains
### Current Landscape: Traceability Systems and Limitations  
Traditional agricultural traceability systems rely on centralised databases and paper records, which suffer from several limitations. Data about farm inputs, processing, transport, among others is often siloed within each company's system, leading to fragmented records that are hard to integrate [38]. As a result, supply chain information becomes incomplete and disjointed, making end-to-end tracking slow and error prone. For example, one study notes that conventional supply chains create \"fragmented data silos\" that are vulnerable to single points of failure [38]. If a central server is hacked or goes down, critical traceability information could be lost or compromised.  
Key concerns in the current landscape include the following:Data vulnerability: Centralised or paper-based records can be tampered with or lost, and data manipulation or inaccuracies may go undetected. This undermines trust in the authenticity of products (e.g., labels can be falsified) and opens the door to fraud. Companies find it challenging to verify claims such as organic origin or fair trade status with high confidence under current systems [38].
* Lack of real-time visibility: Because each stakeholder (farmer, distributor, retailer) maintains their own logs, obtaining a real-time view of a product's status is difficult. There is often a time lag in information sharing. As reported in European supply chains, stakeholders struggle to obtain timely, accurate data on the movement of goods and inventory levels [39]. This lack of visibility can delay responses to issues (such as contamination or delays) and leads to inefficiencies such as overstock or stockouts.
* Supply chain fragmentation: Agricultural supply chains typically involve many independent actors (producers, processors, transporters, wholesalers, retailers) often across regions or countries. Without a unifying platform, information becomes fragmented across disparate systems [32]. This fragmentation makes it hard to trace a product's journey end-to-end. For instance, if a food safety recall is needed, tracing the origin through a fragmented chain can take days or weeks, as data must be manually collected from each link.  
These limitations have tangible consequences. One notable example was a trace-back exercise where a retailer's team took nearly a week to trace the source farm of a mango shipment using traditional methods [40]. Such delays in identifying contamination sources or verifying authenticity can lead to larger public health risks, higher waste, and loss of consumer confidence. Overall, the current traceability landscape is opaque, slow, and prone to errors, highlighting the need for more robust solutions to secure product identity and improve transparency.
# Multi-Scale Aggregation Stereo Matching Network Based on Dense Grouping Atrous Convolution
## 1 Introduction
The network is composed of a dense grouping atrous spatial pyramid pooling (DenseGASPP) module and multi-scale cost aggregation module. Our contributions are as follows:  
1. We propose a dense grouping atrous convolution pyramid pooling (DenseGASPP) module to reduce the hole part of the network to obtain a large dense receptive field that improves the accuracy of segmentation. 2. We introduce multi-scale cost aggregation to replace the 3D convolution method commonly used in traditional stereo matching to reduce the mismatching problems in the ill-posed region. DenseGASPP densely connects feature maps of different scales and balances the influence of network holes on the segmentation task while meeting the size change of moving objects, improving the accuracy of stereo matching. The multi-scale cost aggregation method we introduced can effectively utilize the details of the image and reduce the mismatching problem in the ill-posed region. Therefore, the network is suitable for the vision-based environment perception task used in autonomous driving.
# The Importance of Context Awareness in Acoustics-Based Automated Beehive Monitoring
## 5 Conclusions  
In this study, we collected audio, internal temperature, and internal humidity from 10 beehives and 1 empty hive in an urban apiary in Montreal, Canada. We showed the impact that three prominent noise sources (urban sounds, background speech, and heavy rain) had on three widely used features (spectrogram, MFCCs, and DWT). These impacts include significant changes in important bands of frequency in spectrogram, changes in the MFCCs distribution, especially in the first two coefficients, and lastly, distortion in the details and approximation DWT coefficients. These features were then used to train three separate hive-strength classifiers. The results indicated the potential of audio used in a deep learning model for predicting beehive population and health state, as the system based on MFCCs achieved an MAE of 0.86 for the clean samples. Tests using clean audio segments and noisy segments showed the sensitivity of the systems to environmental noise. We finalized the paper with recommendations on potential future works that could rely on new modulation spectral features for background beekeeper speech detection.  
Conceptualization, T.H.F. and P.G.; data collection, E.H.; methodology, T.H.F., P.G. and M.A.; writing--original draft preparation, M.A.; writing--review and editing, P.G., T.H.F. and E.H.; visualization, M.A.; supervision, T.H.F. and P.G.; funding acquisition, T.H.F. and P.G. All authors have read and agreed to the published version of the manuscript.  
This work was made available through funding from NSERC Canada via its Alliance Grants program (ALLRP 548872-19) in partnership with Nectar Technologies Inc. and the Deschambault Animal Science Research Centre (CRSAD).  
Not applicable.  
Due to the sensitive nature of the data, information created during and/or analysed during the current study is available from the corresponding author on reasonable request to bona fide researchers.  
Figure 16: Modulation spectrogram of a segment, including (**a**) no noise and (**b**) speech artifacts.
# Proximate Composition and Antioxidant Activity of Selected Morphological Parts of Herbs
## 4 Discussion  
### Proximate Composition  
The oldest method of herb preservation is drying, which is associated with the loss of water and inactivation of enzymes when begun immediately after harvest. Properly dried herbs do not ferment and do not turn moldy. Moreover, the levels of their active substances do not change over a long time.  
The available literature contains little information about the proximate composition of many of the tested plants presented in this study. What is more, there is little information concerning proximate composition of morphological parts of herbs and medical plants.  
It should be emphasized that some of the plants we analyzed are a rich source of CP (fenugreek), CF (stinging nettle), EE (black cumin) and NFE (psyllium). However, taking into account their consumption in the average daily diet, which is small and results from the use of these plants as an addition to dishes, their nutritional significance is minimal. Albeit they can be considered for use as raw materials for the production of food for special nutritional purposes.  
Concerning protein content, [PERSON] et al. [18] reported that whole plant of dried thyme contained 6.48 g of protein per DM; these results are lower than ours.  
Some authors reported in their researches that black cumin contains about 32-40% of oil, 16-19.9% of protein, 1.79-3.74% of minerals, 5.5% of fiber [28]. These results are lower than we presented in our study (about 42% of fat, 21% of protein, almost 4% of ash, and 8% of fiber). On the other hand, [PERSON] and [PERSON] [29] showed that this plant contains 26.7% of protein, 28.5% of fat, 8.4% of crude fiber, and 4.8% of total ash.  
[PERSON] et al. [30] examined basic composition of chokeberry pomace fractions achieved as a result of industrial-scale processing of fruit into juice. They showed that this valuable raw material is rich in fat (13.9%), and proteins (24%). Chokeberry fruit are good sources of dietary fiber, i.e., at the level of 5.6% of fresh mass [31]. The lower content was determined in the present study (6.6%).  
[PERSON] et al. [32] showed that the contents of fiber, ash, protein, fat in fenugreek seed were 6.50%, 3.20%, 28.55% and 4%, respectively. Other authors analyzed chemical composition of different varieties of fenugreek and the ash content was in the range 3-7%, protein content 23.1-26.8%, fat content 8.8-21.0% and fiber content 5.1-7.1% [33]. These results are similar to our results, except fat content. In our study, the content of fat is much lower (4.5%).  
[PERSON] et al. [34] determined proximate composition of common dandelion. The amount of protein was 15.25%, the crude fiber 13.09%, fat 6.81% and ash 9.11%. These results are similar to ours, except ash content. The content of ash was established as 24.5%.
# AOA-Based Three-Dimensional Multi-Target Localization in Industrial WSNs for LOS Conditions
## 6 Testbed Development and Field Experiments
### Experiment Results  
To evaluate the AOA estimation performance in the experiment, we used the relative error. For azimuth estimation, it is defined as  
\[e_{r,a}=\frac{\left|\tilde{\phi}_{i,k,1}-\phi_{i,k,1}\right|}{\phi_{i,k,1}} \times 100\%. \tag{20}\]  
The results and relative errors of the azimuth AOA estimation are plotted in Figure 12. We can see that the estimation errors are mostly within 2%. Therefore, the relative positions of the MSNs can be determined accurately in the experiment. The estimation errors may be introduced when the positions of the Tx's were determined manually on the square. Adjusting the horizontal direction of the Rx antenna panel manually may also affect the accuracy of the AOA estimation.  
As indicated by the simulation results, AOA estimation error mainly depends on the antenna array dimension and the number of MSNs. The antenna array used in the field experiment had the horizontal dimension of four antennas. In the simulation results, when the number of MSNs equals 10 and the antenna array dimensions is \(4\times 4\), the angle estimation error is about \(0.5^{\circ}\). The angle estimation error in the measurement results is mostly below \(2^{\circ}\). The simulation and experimental results are in the same order, and the latter is slightly larger than the former. This may be caused by, as mentioned earlier, the manual setup of the MSN positions and the attitude of the antenna panel. Therefore, the field experimental results agree with the simulation results reasonably.  
Figure 11: The pattern diagrams of the two cross-polarized dipoles on the \((1,1)\) patch in the antenna array.  
The computational complex and energy consumption of the ATML scheme are analyzed as following.  
1. The joint estimation of azimuth and elevation AOAs (\(\tilde{\phi}_{i,k,1}\) and \(\tilde{\theta}_{i,k,1}\)) brings great difficulty to ML estimation. In ATML, the two AOAs are estimated separately and iteratively. Thus, the multi-parameter ML estimation is divided into single-parameter ML estimation, which greatly reduces the algorithm complexity.
2. The simulation and experimental results show that the iterative ML algorithm can achieve convergence quickly (e.g., within three steps) and achieve high estimation accuracy. Therefore, the computational load and resources requirements are low.
3. An MSN can broadcast a spreading code with very low transmission power (e.g., 10 mW in the field experiment). By taking advantage of spread spectrum gain and antenna array gain, ATML can ensure a high SINR to achieve high accuracy in AOA estimation and 3D multi-target localization. Hence, ATML has high energy efficiency for MSNs.
###### Abstract  
Efficient and low-cost modes for detecting metro track geometry defects (TGDs) are essential for condition-prediction-based preventive maintenance, which can help improve the safety of metro operations and reduce the maintenance cost of metro tracks. Compared with the traditional TGD detection method that utilizes the track geometry car, the method that uses a portable detector to acquire the car-body vibration data (CVD) can be used on an ordinary in-service train without occupying the metro schedule line, thereby improving efficiency and reducing the cost. A convolutional neural network-based identification model for TGD, built on a differentiable architecture search, is proposed in this study to employ only the CVD acquired by a portable detector for integrated identification of the type and severity level of TGDs. Second, the random oversampling method is introduced, and a strategy for applying this method is proposed to improve the poor training effect of the model caused by the natural class-imbalance problem arising from the TGD dataset. Subsequently, a comprehensive performance-evaluation metric (track geometry defect F-score) is designed by considering the actual management needs of the metro infrastructure. Finally, a case study is conducted using actual field data collected from Beijing Subway to validate the proposed model.  
metro; track geometry defect; car-body vibration data; convolution neural network; network architecture search; differentiable architecture search +
Footnote †: journal: Applied Sciences
# Efficient Traffic Flow Guidance Feedback Strategy Considering Drivers' Disobedience in ITS
## 3 Proposed Approach
### Classification Setup  
In this work, the driver's classification mainly focuses on drivers' driving habits and styles. To pre-filter the drivers regarding whether they will obey an instruction or not, an obedience feature vector is proposed. Many factors can be taken into account:  
* The distance from home: Usually, drivers tend to follow the instructions of the vehicular system when they are not familiar with the routes. However, when they know the routes very well, there is a higher possibility for them to disobey the instructions. Thus, the distance between the vehicle's current location and the driver's home (or daily activity radius) is one of the factors that determines the driver's next action.
* The age of the driver: Generally, a driver who is older tends to know more routes than young drivers (i.e., the drivers usually become more stubborn when they are young). We may conclude that the drivers who are older may have a higher possibility of disobeying the driving instructions.  
* The gender of the driver: Male drivers are usually more confident about themselves; sometimes this kind of confidence is closer to arrogance, which leads to the disregard of the driving instructions.
* The ratio of current velocity/max velocity of the route: Generally, a driver drives faster when he is in a hurry, which leads to a higher ratio. The ratio thus mainly represents the mood of the driver. If the driver is in a hurry, we believe he will tend to follow the instructions.
* The rate of obedience at past times: Although the four factors above have certain influences when the driver makes a decision, the driver's obedience rate is always important. This rate reflects the driver's habits to some extent. If the driver has tended to follow the driving instructions in the past, he will tend to follow the instructions in the near future. Thus, the input vector is shown as follows: ObedienceFeatureVect: {HistoricalObedienceRate, Distance, Age, Gender, VelocityRatio} After describing the driver's classification, we discuss the proposed approach.
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 1 Introduction
### Deep Learning in Remote Sensing
#### 1.2.1 Convolutional Neural Networks  
CNNs have been widely applied to various remote sensing tasks including land cover classification of very high spatial resolution images [25; 26], semantic segmentation [27], object detection [28], reconstruction of missing data [29], or pansharpening [30]. In these works, CNN models make the most of the spatial structure of the data by applying convolutions in both \(x\) and \(y\) dimensions. The main successful application of CNNs in remote sensing remains the classification of hyperspectral images, where 2D-CNNs across the spatial dimension have also been tested [31], as well as 1D-CNNs across the spectral dimension [32], and even 3D-CNNs across spectral and spatial dimensions [33; 34].  
Regarding the classification of multi-source and multi-temporal data, 1D-CNN and 2D-CNN have been used without taking advantage of the temporal dimension [35; 36]: convolutions are applied either in the spectral domain or in the spatial domain, excluding the temporal one. In other words, the order of the images still has no influence on the model and results.  
Meanwhile, temporal 1D-CNNs (TempCNNs) where convolutions are applied in the temporal domain have proven to be effective for handling the temporal dimension for (general) time series classification [37], and 3D-CNN for both the spatial and temporal dimension in video classification [38]. Consequently, these TempCNN architectures, that could make the most of the temporal structure of SITS have started to be explored in remote sensing where convolutions are applied across the temporal dimension alone [39; 40], and also 3D-CNNs where convolutions are applied in both temporal and spatial dimensions [41]. In particular, these preliminary works highlight the potential of TempCNNs with higher accuracy than traditional algorithms such as RF. Although similar in nature with the work proposed in [40], this paper proposes an exhaustive study of TempCNNs (presented in Section 2) where we show how to handle multi-spectral images and use a broader nomenclature (not specific to summer crops).
# Robot Delay-Tolerant Sensor Network for Overhead Transmission Line Monitoring
## 3 RDTSN Model and Data Delivery Strategy
### Discrete-Time Multi-Path Routing Algorithm
In the process of message delivery, the transmission paths of different endpoints need to be compared, and the best destination is determined. Unlike common routing algorithms, DTMA is allowed to change the message destination. The determination of the message's ultimate destination usually occurs at \(t=3\) in Figure 8. At this point, the message transmission paths of different endpoints are compared based on the method described above. The message is sent to the best path to satisfy the objective function and constraint conditions. Finally, the node independently completes the optimization of the message destination. Figure 8 gives an example of the DTMA at work. The robot forwards the message to the destination (WCN) via 3 steps, which are listed as follows. Step 1: The robot (node 1) transmits the message to the relay node 2 based on the obtained best transmission path (P\({}_{12}\)). Figure 8: Network routing solution. Step 2: The relay node 4 forwards the message to another relay point by P\({}_{24}\). Step 3: The relay node calculations the best path (P\({}_{45}\)) based on objective function and sends the message to the best destination (WCN, node 5). In addition, this also means eliminating the possibility of node 3 as the destination. Finally, the message is sent via WCN (node 5) to the CMP connected to the OPGW. The DTMA solves the problem of determining the best delivery path by matching the objective function. Particularly, the choice of delivery paths comes from one or more sources to a variable destination. The Discrete Time Multi-Path Routing Algorithm (DTMA) is shown as pseudo code in Algorithm 1. ```
1:node start (initialization)
2:broadcast ID
3:listen for neighbors' IDs
4:wait for messages ()
5:if message receives in queue then
6: update mean time between contacts
7: update availability probability
8: update survivability and delivery probabilities
9: update availability weight
10:if neighbor requests delivery probability then
11:if message destination = my ID then
12: save message in memory and delete it from queue
13:elseif message destination = others' ID then
14: check neighbors' availability weight to destination
15: send a copy of the message to the best availability weight neighbor
16:if is no availability weight then
17: store message in buffer until a path to destination appears
```  
**Algorithm 1** DTMA for every node in the network.
# Numerical Analysis of Human Cancer Therapy Using Microwave Ablation
## 3 Results
### Specific Absorption Rate Profiles  
Through the microwave thermal ablation process the electromagnetic wave propagates into the biological tissue and the energy of this wave is absorbed through the materials. Another interesting parameter is the specific absorption rate, which is defined as the absorbed power density normalized by the tissue density. In order to estimate the ability of heating tissue for different input microwave powers, SAR profiles, at a heating time \(t=300\) s and at a distance of \(r=2.5\) mm from the antenna axis, were generated, and are illustrated in Figure 8.  
In this figure, in all cases of microwave power, the SAR profiles show a similar trend with a difference in magnitude. In addition, the SAR increases slowly along the axis parallel to the antenna, reaches a peak around the slot of the antenna, and after this decreases. It is clear that the SAR contour lines have roughly oval and asymmetrical patterns around the slot. The numerical results are in good agreement with those found in [37]. In particular, the SAR distribution for the highest microwave power causes the greatest value of SAR. Moreover, as expected, the maximal SAR value is obtained within the tumor region. The radial profiles at \(z=20\) mm reveal that the SAR increases sharply from the axis. Then, the curves exhibit a hyperbolic decrease. The spatial distribution of SAR can also be presented. The maximal value of SAR for each power is \(SAR=32.7\) kW/kg for \(P_{in}=10\) W, \(SAR=147\) kW/kg for \(P_{in}=45\) W, and \(SAR=245\) kW/kg for \(P_{in}=75\) W.  
Figure 7: Microwave power density absorbed in liver tissue for various input powers \(P_{in}\): (**a**) \(P_{in}=10\) W, (**b**) \(P_{in}=45\), and (**c**) \(P_{in}=75\) W.