esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Evaluation of GPP Estimated by the TL-CLUE Model
#### 4.2.2 Accuracy of GPP Estimation under Three and Two Estimations of CI  
The TL-CLUE model can decrease the relative bias of GPP estimation. When the TL-CLUE model used three \(\Omega\)s, the relative bias of GPP estimation was approximately 1.71 for SAV, 7.09 for EBF, 7.70 for MF, 9.47 for ENF, 10.50 for WSA, 12.66 for GRA, 13.77 for CRO, and 15.18% for OSH (Figure 7). Throughout all vegetation types, three and two \(\Omega\)s reduced the relative bias of GPP estimation by approximately 9.76 and 8.97%, respectively (Figure 7), indicating that the improvement in GPP estimation in the TL-CLUE model relying on  
Figure 6: Comparison of the consistency of the observed GPP with GPP estimated by the TL-CLUE (**a1–a5**) and TL-LUE (**b1–b5**) models in the LOS (**a1,b1**), LGS (**a2,b2**), LSS (**a3,b3**), LFS (**a4,b4**), and OYC (**a5,b5**), respectively. The 1:1 theory line is shown by the black dashed line, and the regression line is displayed by the blue dashed line.  
two \(\Omega\)s was nearly as good as the improvement depending on three \(\Omega\)s. Therefore, we recommend using two \(\Omega\)s from the leaf-on and leaf-off seasons to optimize the constant \(\Omega\) that is currently used in the TL-LUE model.
## 2 Materials and Methods
### Methodology
#### 2.3.4 LULC Classification  
The LULC classification is based on a typical supervised strategy that requires collecting the relevant information from the training points to train the classifier [66]. In GEE, a \"sampleRegion\" operation transfers the features' information to the training and validation sample points. The \"scale\" parameter (set at 20 m for the S2 and S1 outputs, and 10 m for the PL-based outputs) averages the features locally by a downscaling step. The classification is achieved using the RF machine learning classifier, which uses bootstrap aggregation (bagging) to create multiple decision trees (DTs). The algorithm then combines the outputs using a majority voting approach to obtain a more accurate prediction. The classifiers were trained using a dataset obtained by randomly picking 70% of sample points for training, with the remaining 30% being utilized for validation. The training and validation subsets were the same for all the studied approaches to effectively compare the various approaches' performance. In RF applications, this 70/30 ratio is quite prevalent [67; 68; 69]. A graph was plotted using the variable importance included in the output from the \"explain\" command to visualize features' relevance in each classification. This graph was used for each approach to empirically analyze and iteratively select the more crucial bands for the LULC classification. This step increased the OB-RF classification speed and overcame some of GEE's image size limitations.
## 7 Conclusions  
Based on the research on vehicle lane changing, this paper decouples the longitudinal and lateral trajectories of intelligent vehicle lane changing, adds constraints, and performs polynomial planning. From the above research, we can draw the following conclusions:  
(1) The polynomial coefficients are determined by the lane-changing boundary conditions. The economy is introduced into a multi-objective function that considers comfort, safety, and lane-changing efficiency. This can more perfectly describe the lane-changing behavior and obtain a safe, comfortable, and energy-saving lane-changing trajectory. In addition, considering economic indicators while meeting lane change constraints can reduce vehicle energy consumption, which is beneficial for energy conservation.  
(2) The weight coefficients of each indicator in the multi-objective function are adjusted using the Analytic Hierarchy Process for different lane-changing scenarios and driving needs. The particle swarm optimization algorithm is then introduced to optimize the solution. This makes the lane-changing model simple, practical, and advantageous. At the same time, the planned trajectory is the optimal trajectory in the current environment, which is adaptive.  
(3) The simulation is carried out in Carsim, and the results show that the planned trajectory curve is gentle, satisfies the constraints, and has no collision risk, whether in the obstacle-free lane-change scene or in the obstacle-changed lane-change scene. In addition, this paper fully considers the safety, comfort, economy, and efficiency of lane changing, which reflects the rationality of lane-changing trajectory planning.  
In the future, this article will continue to study how to complete various driving demand planning in the entire driving task scenario using V2X technology, consider incorporating uncertainty factors to analyze the impact on the algorithm in this article, and improve the algorithm's anti-interference ability in real environments. In addition, better planning for speed during lane changing is also being considered in future research.  
Conceptualization, R.S. and X.Z.; funding acquisition, X.Z.; investigation, H.Z. and S.T.; resources, R.S.; supervision, X.Z.; validation, R.S. and Y.D.; writing--original draft, X.Z.; writing--review and editing, X.Z., Y.D. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Key Research and Development Program of Shaanxi (No. 2022 GY-303) and by Xi'an Science and Technology Planning Project (Grant No. 2022 GXFW0152, and No. 2022 GXFW0116), and Key-Area Research and Development Program of Guangdong Province under Grant 2020B0909050003.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
The authors declare no conflict of interest.
## 3 Selected Research Results
### Wavelet Analysis  
This study looked for suitable signal analysis methods, before using wavelet analysis. The analysis began with the Haar function. The basic Haar wavelet is defined as follows:  
\[\Psi(\tau)=\left\{\begin{array}{c}1\text{ for }0\leq\tau\leq 0.5\\ -1\text{ for }0.5\leq\tau\leq 1\\ 0\text{ for }\tau<0\text{ and }\tau\geq 1\end{array}\right. \tag{10}\]  
The statistical measures of the output signal at different levels of approximation were determined. When using Haar wavelets at level 5 for the original signals (Figure 8a), for the mean and mode values, the highest sensitivity coefficient values were obtained for individual cylinders No. 1 to 6 from the available wavelet measures (Figure 8b). The maximum of the Haar wavelet (Figure 8c) was a more useful indicator for boundary values.  
Figure 8 shows that the Harr wavelets resulted in the effective detection of the injector nozzle's propagating crack.  
\begin{table}
\begin{tabular}{l l} \hline
**Phase Number** & **Description of the Crack Development Phase** \\ \hline
1 & Cracking \\
2 & Crack propagation \\
3 & A fragment tearing off \\
4 & A fragment breaking off \\
5 & Secondary damage to other objects \\ \hline \end{tabular}
\end{table}
Table 2: Phases of damage development leading to breakage of the injector nozzle body.  
Figure 7: Injector nozzle of the diagnosed 6 AL20/24D engine with a broken part of the body (**a**) and mass flux \(q_{m}\) of the flowing fuel at the full needle lift for a nozzle in a suitable state and another nozzle with a cracked nozzle body (**b**).
## 2 Materials and Methods
### Background
#### 2.1.1 Capacity and Coverage Challenges Due to Radio Spectrum Issues and Collisions
In detail, interference exists when multiple transmissions simultaneously collide in time, frequency, and spreading factor [1]. One case exists when the increase in the number of long messages could cause higher probability of collision between packets due to the increase in the longer interval of high spreading factor messages, causing severe interferences on same or different spreading factors [6]. In addition, if there is a cluster of EDs far away from the GW, their transmissions will experience higher SNR coming from neighboring EDs [3]. The way to overcome this capacity issue on a LoRaWAN is to use multiple GWs deployment and broadcast transmissions of EDs. While a packet might not be successfully decoded by one GW, there is still a chance that it might be decoded by another [9]. In addition, an increased number of GWs increases the amount of time the network server can send downlink frames, a fact that enables ADR commands to be sent faster, which drops the average convergence time, while also offering availability for smaller spreading factors [14]. Conclusively, in order for an ED to have a successful transmission to a GW, it should be within its coverage, and there must not be any colliding packets at that GW during its transmission. It is noted that while successful coverage of LoRa signals could be mainly attributed to noise-limited and interference-limited considerations, the reality is that the SNR threshold depicts a decreasing function of the signal-to-interference ratio (SIR) experienced by the ED of interest [11]. LoRa uses forward error correction (FEC), which allows recovery and increases reliability and range, but slightly increases the time on air because it adds some encoding overhead [3; 5]. In this way there is avoidance in transmission errors due to bursts of interference. Nevertheless, the authors of [4] have provided some insights for interference within the license-free ISM bands and how it is problematic to LoRaWAN. The level of interference is caused by both radio access technologies (RFID, alarms etc. [4]) and other LPWAN deployments (LoRa and SigFox [4]). It has been measured that car keys are generating noise in the 868 MHz band. When a car is passing 5 m from the receiver it may generate noise levels up to \(-95\) dBm. It is quoted that RFID deployment at 865-868 MHz may affect even military telecommunication devices [4]. The level of external interference such as this, in the 868 MHz EU ISM band, is anticipated to grow with the deployment of several wireless IoT solutions which are not limited to Sigfox and LoRaWAN. For that reason, it will be difficult to deploy predictable and reliable communication, with capacity and wide area coverage, due to the increased BER caused by the frequent and significant level of external interference [25].
# A Novel Edge Platform Streamlining Connectivity between Modern Edge Devices and the Cloud
## 5 The \"PAIR Mechanism\"--Ensuring Data Integrity in Edge-Cloud Synchronization
#### How It Improves Synchronization  
The PAIR Mechanism improves synchronization through its robust, uncomplicated, and direct approach. Traditional synchronization methods often rely on incremental updates and intricate checks that can become complex and error-prone, especially in distributed systems with a high volume of data transactions. In contrast, PAIR simplifies the process by operating under a bulk transfer strategy that can be more reliable in certain contexts. One of the most significant improvements PAIR offers is its ability to ensure complete data consistency. By resending the entire dataset when discrepancies are detected, PAIR guarantees that both the cloud and edge databases are identical down to the last byte. This is particularly useful in environments where even minor data inconsistencies can lead to significant problems, such as in financial systems or healthcare records management.  
Moreover, the PAIR Mechanism's scheduled synchronization checks serve as a proactive measure to maintain data integrity. Instead of waiting for a user or system to flag a data mismatch, which could potentially lead to further complications, PAIR routinely verifies data integrity. This scheduled approach means that issues can be identified and resolved swiftly, minimizing the window in which data inconsistencies can affect system operations. Another advantage of PAIR is its resilience to catastrophic events. In scenarios where an edge device is destroyed or a cloud database is compromised, the bulk transfer method allows for a rapid restoration of the lost or corrupted data. Unlike incremental sync methods that may struggle to reconcile complex historical changes, PAIR's strategy of re-establishing the last known good state is straightforward and effective. The PAIR Mechanism also improves synchronization by reducing the complexity of error handling. In traditional systems, a significant amount of logic is dedicated to managing the various states and scenarios of data mismatches. PAIR's approach eliminates many of these complexities by reducing the number of states the system must handle. This simplification can lead to increased reliability and easier troubleshooting when issues arise.  
Finally, PAIR's methodology aligns with the current trends towards more data-driven decision-making processes. As the volume and velocity of data grows, the ability to synchronize large datasets quickly and reliably becomes increasingly important. PAIR's bulk transfer method, despite potentially requiring more bandwidth, ensures that data-driven systems have the up-to-date information they need to function correctly.
# Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data
## 4 Results
### Recognition of Meteor Data Samples
The architecture is shown in Figure 4.  
Figure 3: Mean values of different performance metrics as a function of the data chunk size \(P\) for models trained on all possible combinations of seven sessions of observations and tested on the remaining session. See the text for details. The way we employed to prepare input data allowed us to obtain \(\approx\)18,000-24,000 thousand meteor chunks of the size \(8\times 8\times 48\) depending on the set of sessions used for training, see details in Table 1. These chunks were augmented then by the standard procedure of image rotation thus providing four times more samples. Non-meteor data chunks were selected in a random fashion, with their number being six times larger than the total number of meteor chunks (after augmentation). Twenty percent of the training dataset were used for validation during training. PR AUC was utilized as a performance metric during the training process. The loss function was defined as binary crossentropy. Validation loss was employed to adjust the learning rate and to avoid overfitting. Testing datasets included 100,000 non-meteor samples and all meteor data chunks available for the particular session varying from 474 chunks for session 7 up to 6446 chunks for session 6, see Table 1.  
Figure 4: Architecture of the CNN used for binary classification in meteor and non-meteor data chunks. The total number of trainable parameters equals 125,465. Table 2 contains values of five performance metrics described above for different combinations of testing and training datasets in the task of classification of \(8\times 8\times 48\) chunks into meteor and non-meteor groups. For example, the column labeled as \"5\" presents results obtained with sessions 6-14 employed for training, and session 5 used for testing the CNN. It can be seen that zero out of 1068 meteors were lost in all sessions. Notice however that values of the PR AUC, the MCC, and the \(F_{1}\) score vary considerably from one session to another.
# A Constrained Coding-Aware Routing Scheme in Wireless Ad-Hoc Networks
## 3 Routing Implementation
### Routing Coding Benefit
#### 3.4.1 Coding Benefit Function with Queue Length
Moreover, in the coding node \(C_{m}\), we define that \(L(O_{e})\) is the length of the output queue, and \(L(O_{n})\) is the length of encoded packets in the output queue. The encoded packets' occupancy proportion \(\frac{L(O_{n})}{L(O_{e})}\) shows the coding ability and state. A larger value of occupancy proportion shows more encoded packets in the output queue and better coding effects. Moreover, in a perfect situation, the output queue may be all encoded packets, for which the length of encoded packets will be approximately \(\left\lfloor\frac{\sum_{j=1}^{t}L(f_{j})}{t}\right\rfloor\). To this end, we use a weight \(\alpha\) ( \(0<\alpha\leq 1\)) to react to the state of coding node \(C_{m}\) as follows:  
\[\alpha(C_{m})= \begin{cases}1&\lfloor\frac{\sum_{j=1}^{t}L(f_{j})}{t}\rfloor \geq L(O_{e})\\ \frac{L(O_{n})}{L(O_{e})}&\lfloor\frac{\sum_{j=1}^{t}L(f_{j})}{t}\rfloor<L(O_ {e})\end{cases} . \tag{6}\]  
The coding benefit in a multi-rate scenario can be recalculated as \(\beta_{r}(C_{m})=\alpha(C_{m})\beta(C_{m})=\alpha(C_{m})(n_{m}-1)\) by combining Equations (3) and (6). \(D_{r}(P_{l})\) represents the benefit of candidate path \(P_{l}\), which can be recalculated as \(D_{r}(P_{l})=\sum_{m=1}^{z}\beta_{r}(C_{m})\). Then, we can define the EXQL metric as follows:  
\[EXQL(P_{l}) =D_{r}(P_{l})-M(P_{l})\] \[=\sum_{m=1}^{v}\beta_{r}(C_{m})-M(P_{l})\] \[=\sum_{m=1}^{v}[\alpha(C_{m})\beta(C_{m})]-M(P_{l}) \tag{7}\]  
In the scheme, considering the impact of the state of coding nodes, we used the EXQL metric to calculate the coding benefit of each candidate route. The sources can transmit the packets through a better route path to reduce the negative impact of multi-rate and -flow in the coding nodes.
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
## 4 Results  
We apply three distinct phases of implementation. In the first phase, simulation was conducted in a virtual environment between two host computers to provide benchmark statistics and the assurances of an error-free MLS implementation over an 802.11 WiFi network. The second phase builds upon the first phase by replacing a single host computer with the ScanEagle platform virtual environment. The 802.11 WiFi network was replaced by using military-grade mesh network radios. This testing phase was used to assess performance in a semi-realistic communications environment and provide assurances of secure and efficient data exchange between the two devices. The final phase builds upon the prior two phases to provide command and control functionality between the ScanEagle and CASSMIR platforms. Live platform testing was also used in this phase to meet the overall research objective of providing command and control between two disparate UxS operating in the sea and air domain.
## 3 Materials and Methods
### Attention Modules
#### 3.4.2 Spatial Attention Module  
[PERSON] et al. [47] present the Spatial Attention U-Net (SA-UNet) model that incorporates SA modules. These SA modules, first proposed by [PERSON] et al. [76], are capable of inferring an attention map along the spatial dimension, then multiplying said attention map by the input feature map to perform adaptive feature refinement [47].  
[PERSON] et al. position the SA module between the encoder and the decoder. The SA module makes use of the spatial relationship between features to create a spatial attention map. First, the SA module applies both max-pooling and average-pooling along the channel axis of the input features. Then, it concatenates them to create a feature descriptor. Next, the feature descriptor goes through a convolutional layer and a sigmoid activation to generate a spatial attention map. Finally, this spatial attention map is multiplied element-wise with the input features. Figure 13 shows the structure of the SA module.  
Figure 12: Structure of the AG proposed by [PERSON] et al. [46].  
Figure 13: Structure of the SA module employed by [PERSON] et al. [47].  
Finally, [PERSON] et al. employ the SA-UNet model to perform semantic segmentation of medical imagery with SOTA performance on relatively small datasets.
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 3 Proposed Hybrid Graph Embedding and Adaptive Convolution Networks
### Spatiotemporal Graph Convolutional Networks (ST-GCN)  
The Graph Construction Module is essential for tasks such as sign language identification because it rapidly extracts spatial-temporal patterns and arranges them into a structured Spatial-Temporal Graph (STG). This graph efficiently captures intra-frame and inter-frame interactions. The graph definition is defined as follows:  
\[\mathrm{G}=(\mathrm{V},\mathrm{E}) \tag{6}\]  
In Equation (6), \(\mathrm{V}\) represents a set of vertices where each vertex corresponds to a detected keypoint. \(\mathrm{E}\) represents a set of edges that capture spatial connections (keypoint relationships within a frame) and temporal connections (keypoint relationships across frames). Dimension: \(G\in\mathbb{R}^{N\times N}\) where \(N\) is the number of keypoints.  
Edge weights are calculated using a Gaussian similarity function to quantify node relationships, ensuring stronger connections are represented with higher weights.  
\[\mathrm{w}_{\mathrm{ij}}=\exp\left(-\frac{\left\|\mathrm{x}_{\mathrm{i}}- \mathrm{x}_{\mathrm{j}}\right\|^{2}}{2\sigma^{2}}\right) \tag{7}\]  
In Equation (7), \(\mathrm{w}_{\mathrm{ij}}\) represented as the edge weight between nodes \(\mathrm{i}\) and \(\mathrm{j}\). \(\mathrm{x}_{\mathrm{i}}\) and \(\mathrm{x}_{\mathrm{j}}\) represented as the nodes of the feature vector \(\mathrm{i}\) and \(\mathrm{j}\). The scale parameter controlling the sensitivity of weight computation is \(\sigma\). Dimension: \(w_{ij}\)\(\in\)\(\mathbb{R}\).  
In Figure 3, the Graph Construction Module organizes collected keypoints into a structured Spatial-Temporal Graph (STG) that efficiently captures intra-frame dependencies and inter-frame interactions. This structure is essential for extracting spatial-temporal patterns for sign language recognition. A graph has vertices representing keypoints and edges representing spatial and temporal connections. A Gaussian similarity function quantifies node relationships to calculate edge weights.  
Figure 3: Graph Construction Module.  
The module starts with each sensor data frame's hand and body landmarks. It then uses anatomical or geographic relationships to create spatial and temporal links between keypoints in the same frame and consecutive frames to record motion dynamics. The Gaussian similarity formula assigns larger weights to closer or more similar keypoints in feature space for each edge. The final graph blends spatial and temporal connections to depict keypoint data spatially and temporally.