esa-sceva/satcom-chunk-collection huggingface dataset:
# Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture
## 4 Current Applications and Future Perspectives
### Elastic Spectroscopy
[26] presented a comprehensive review on some potential solutions that would significantly benefit and upgrade the analytic process, such as to build a library of simulated SRS profiles or the use of databased models, such as metamodels. For airborne methods, the real breakthrough came with the advent of UAVs capable of carrying spectral cameras, which has dramatically reduced the cost and complexity of data collection [28]. Airborne multispectral surveillance has become affordable for organizations of any profile and size; the turn-key integrated systems with automation of flight control and georeferencing eliminate the need to hire expensive specialists [49]. As to the satellite-borne surveillance, since the early Landsat missions, the set of satellites has grown into an impressive constellation, with the tendency of harnessing each time more precise, sensitive instruments with an increasing number of spectral channels [50; 51]. Overall, for all sensors systems described above, the ultimate breakthrough is linked with today's explosive development of advanced and powerful machine learning methods of data processing, harnessing big data to infer critical information, such as, the classic partial least squares (PLS), support vector machines, artificial neural networks, classification techniques, deep learning, and other artificial intelligence (AI) approaches [60; 65; 68; 70; 81; 86; 87; 88; 89]. This opens a number of novel perspectives in the assessment and classification, beyond the state-of-the-art, whose current landmarks can be represented by the following examples: the automated identification and classification of Chinese medicinal plants with different sensing techniques, including Vis-NIRS [90]; the prediction of quality attributes and internal browning disorder in \"Rocha\" pear by Vis-NIRS reflectance and semi-transmittance spectra taken under real-life conditions met in an automated inline grading system [79; 80; 91]; the assessment of citrus ripening on-tree [83]; the in situ grapevine identification (down to the level of varieties) via leaf reflectance spectra [92]; the anthocyanins fingerprinting in intact grape berries [93]; the detection of mercury induced stress in tobacco plants [94]. Additionally, it is worth mentioning the use of specific algorithms, as the hyperspectral insect damage detection algorithm (HIDDA), which allowed automatic detection of insect-damaged coffee beans using only a few bands and one hyperspectral signature [70]; or the RELIEF-F algorithm used to select the most discriminative features (wavelengths) and two band normalized differences for developing spectral disease indices for SCR detection and severity classification [65].
## 4 Security Analysis
Other types of attacks that are addressed by the general SRTLD encryption mechanism and thus are also efficiently addressed by E-BIOSARP are listed below. * _Altered, spoofed, or replayed routing information attack or wormhole attack_: In this type of attack, the adversary or malicious node targets the routing information exchanged between nodes. The rapid authentication process in E-BIOSARP protects the network from these types of attacks by protecting the packet header. Therefore, when a sensor node transmits a packet, it encrypts the header, and upon receipt, the proposed mechanism confirms that certain fields are encrypted with the correct key. If not, the packet is dropped without being opened, as shown in Figure 6.
* _Acknowledgment spoofing and selective forwarding_: A malicious node executes this type of attack by receiving and dropping packets as they travel through the network; as a result, the nodes at one hop conclude that there is a problem with the forwarding of data packets through the nodes in the routing table. Alternatively, a malicious node may use feedback to alter a node's decision regarding which strong link to use to send packets. The fast authentication process in E-BIOSARP protects the WSN from acknowledgment spoofing and selective forwarding attacks by rapidly identifying and dropping non-self packets, as shown in Figure 6.
* _Sybil attack_: In a Sybil attack, a malicious node begins advertising itself with multiple IDs. When a self-node receives these RTR packets, they cannot be authenticated because of their different header structure and/or field values, and they are consequently dropped, as presented in Figure 6.
* _HELLO flood attack_: An invader deploys a strong device that generates and broadcasts RTR/HELLO packets with a high signal strength to convince the WSN nodes that the adversary node is a neighboring self-node. Under E-BIOSARP, the possibility of forwarding data to such non-self or HELLO-flood-generated nodes is avoided through the dropping of these false packets.
# Multi-Point Displacement Synchronous Monitoring Method for Bridges Based on Computer Vision
## 4 A Method for Multi-Point Displacement Mismatch Elimination
### Mismatch Elimination for Multi-Point Displacements
The maximum rotation angle of the structure is at the ends of the beam, and the rotation angle values at the ends often differ. We selected the larger of these two rotation angle values as the threshold. Table 4 lists the extracted rotation angle thresholds under load conditions of 100-400 kN. Figure 18 shows the rotation angle threshold mismatch elimination process. \begin{table}
\begin{tabular}{c c} \hline
**Load Level** & **Rotation Angle Threshold (Rad)** \\ \hline
100 kN & 0.055 \\
200 kN & 0.059 \\
300 kN & 0.098 \\
\begin{tabular}{c} \hline \end{tabular}
\end{table} 
Table 4: Rotation angle thresholds. Figure 17: Displacement mismatch elimination method. Figure 18: Rotation angle threshold mismatch elimination process. Special attention should be paid to checking whether the calculated multi-point displacement meets the deformation coordination of the structure after the feature point displacement mismatch is eliminated. If the multi-point displacement monitoring results of the structure show regularity, the monitoring results can be considered accurate. Suppose the monitoring result shows a sudden change in the displacement's rotation angle in some areas and suddenly disappears. In that case, it is necessary to relax the rotation angle threshold to avoid the displacement rotation angle being greater than the rotation angle threshold due to some special conditions, such as structural damage leading to sudden changes in the rotation angle of displacement.
# 5G Price Competition with Social Equilibrium Optimality for Social Networks
## 4 Game Analysis
### Suppliers' Equilibrium Strategy  
The user's utility functions determine the suppliers' optimal choice of prices. A utility function \(u_{n}\) can be characterized by its coefficient of relative risk aversion [37], i.e., \(k^{n}_{RRA}=-\frac{yu^{\sigma^{\prime\prime}}_{n}(y)}{u^{\prime}_{n}(y)}\). This quantity characterizes the relationship between price and user demand.  
**Assumption 3**.: _Relative risk-aversion coefficient. \(k^{n}_{RRA}<1,\forall n\in N\)._  
Some utility functions satisfy Assumption 3--for example, \(\log(1+y)\) and the \(\varphi-fair\) utility function s \(\frac{v^{1-\varphi}}{T-\varphi}\), for \(\varphi\in(0,1)\)[38]. To maximize the return, a monopoly will sell all the resources \(R_{m}\). Once a supplier decreases the price, the users' demand substantially increases, resulting in the supplier earning more than before. Thus, the supplier will lower the price until total supply and total demand are equal.  
**Theorem 2**.: _In keeping with Assumption 3, SPE as a price vector tuple meeting KKT conditions. We constitute the supplier competition game's SPE using the Lagrange multiplier vector \(b^{*}\) and SWO's unique socially optimal demand vector \(r^{*}\)._  
Proof.: Suppose \(b=[b_{1},b_{2},\ldots,b_{M}]\) is the price that suppliers charge. As defined in Equation (3), every user faces a local maximization problem \(\text{UPM}_{n}(b)\). Given Assumption 3, we further remark that \(r\) is an SPE of the supplier competition game only if each supplier's supply equals demand, n.e., \(\sum_{n=1}^{N}r_{nm}=R_{m}\) for every \(m\in\mathcal{M}\). So, we consider the SPE as a price vector tuple meeting KKT conditions. Moreover, these meet the KKT conditions for any vector tuple \(b\), \(r\) to be the SWO's maximizing solution. We, therefore, designed a formal equivalence between the maximizing demand vector and SPE of the supplier competition game and the SWO problem \((r^{*},b^{*})\)'s Lagrangian multipliers. From this, we deduce that \((r^{*},b^{*})\) form the supplier competition game's unique SPE.  
That social efficiency is not reduced by suppliers' competition results in users' utility functions being strictly concave and the users' demand is relatively elastic. Therefore, if the price decreases a bit; demand will increase so much that the return is more than before. If suppliers set the price different from the optimal price \(b^{*}\), the supply and demand are unequal. According to Theorem 2, we define the supplier competition game's unique SPE \((b^{*},r^{*})\) as the equilibrium.
# Wintertime Local Wind Dynamics from Scanning Doppler Lidar and Air Quality in the Arve River Valley
## 5 Discussion
### Cause of the Observed Wind Patterns
#### 5.1.2 Day-to-Day Evolution  
The day-to-day comparison in Figure 10 highlights the weakness of the down-valley flow during the night of 10 to 11 February, with \(V_{los}\) intensities that do not exceed 1.25 m\(\cdot\)s\({}^{-1}\), whereas they can reach 2 m\(\cdot\)s\({}^{-1}\) during the other nights of the episode. The \([PM10]\) maxima for Passy and Sallanches were reached this same night as noted in Table 1.  
Temperature profiles from radiosoundings showed that this night was one of the coldest, with a minimum temperature of \(-10\)\({}^{\circ}\)C at 5 m and a thermal stability reaching its maximum over the episode with +13 \({}^{\circ}\)C in the first 600 m AVL (the temperature gradient being positive up to 800 m AVL at 6:00 a.m. UTC). This strong thermal inversion may have inhibited the formation of the down-valley flow or at least prevented it from penetrating into the lowest-level layer of the Passy basin since it was only detectable above 250 m AVL as seen in Figure 10.  
Figure 14: Conceptual scheme of the main night-time wind structures observed from WLS200S measurements within the Passy basin. Blue (red) arrows represent down-valley (up-valley) flows. Wind oscillations are represented by the hatched red and blue wave. Dotted arrows represent the flows observed intermittently during the night. The tributary valley locations are indicated in capital letters and the main towns within the Passy basin in lower-case letters.
## 2 Materials and Methods
### Analysis of ECG Signals  
The ECG signals were recorded at a rate of 1 kHz. The elapsed time periods between the two successive R-peaks of the ECGs (RR signals) were extracted from LabChart7, ADInstruments, and analyzed by the software Kubios HRV Standard [37]. [PERSON]'s Stress Index (SI) [18] was evaluated for each subject in 30 s consecutive windows. The SI from Kubios is the square root (to make the index normally distributed) of the Baevsky's Stress Index proposed in Reference [18].  
[PERSON]'s SI is calculated based on the distribution of the RR intervals as reported in Equation (1):  
\[SI=\frac{AMo\times 100\%}{2 Mo\times MxDMn} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technical Data** & **Intel RealSense D415** & **FLIR Boson 320 LWIR** \\ \hline Weight & 4.54 g & 7.5 g without lens \\ Dimensions & \(99\times 20\times 23\) mm & \(21\times 21\times 11\) mm without lens \\ Spatial resolution & Full HD 1080p (\(1920\times 1080\)) & \(320\times 256\) \\ Acquisition rate & 30 fps @ 1080p & 30 fps \\ Field of view (FOV) & \(69.4^{\circ}\times 42.5^{\circ}\times 77^{\circ}\) (\(\pm 3^{\circ}\)) & \(92^{\circ}\) HFCoV 1 \\ Sensors technology & Rolling Shutter, 1.4 μm \(\times\) 1.4 μm pixel size & Uncooled VOx microbolometer \\ Thermal Sensitivity & - & \(<\)50 mK (Professional) \\ \hline \hline \end{tabular} \({}^{1}\) Horizontal field of view.  
\end{table}
Table 2: Technical characteristics of the depth camera Intel RealSense D415 and FLIR Boson 320 LW IR thermal camera.  
Figure 2: Imaging acquisition system: (**a**) depth visible camera and (**b**) thermal camera.  
where _Mo_ is the mode (the most frequent RR interval), _AMo_ is the mode amplitude expressed in percent, and _MxDMn_ is the variation scope reflecting the degree of RR interval variability.  
Values of Baevsky's stress index between 80 and 150 are considered normal [18].
## 4 Experimental Results and Analysis
### Discussion
#### 4.5.4 Comparison with Other Methods  
To verify the effectiveness of the proposed method, the support vector machine (SVM), LeNet-5, Resnet [41], and multigrained cascade forest (gcForest) [42] methods were selected to conduct comparison group tests for verification. SVM adopts one-to-many classification, the Gaussian kernel function is selected as the kernel function, the penalty parameter is 30, and gamma is 0.01. LeNet-5 adopts a classical structure. The two-dimensional convolution kernel was improved to one-dimensional convolution to directly process one-dimensional vibration data, two-layer convolution, and three-layer full connection, and the full connection parameter is 120. Resnet was modified to directly process the input 1D vibration signal. The network structure of Resnet consists of five residual blocks, two pooling layers, and a fully connected layer. For gcForest, the number of estimators in each cascade layer n_estimators is set to 20, the number of trees in each cascade layer n_trees is set to 100, and the maximum cascade layer max_layers is set to 20.  
SVM is a traditional machine learning method, LeNet-5 is a classical convolutional neural network model, Resnet is a recent CNN architecture, and gcForest is a new tree-based model. Under the same input signal setting, this paper compares these methods to ensure the reliability of the proposed method. To test the generalization ability of the model, the training data are divided into training sets and validation sets by quadrifold cross-validation for model evaluation. The cross-validation results of different models through multiple experiments are shown in Figure 22. To eliminate random errors, repeated validation and testing are carried out. The experimental results of different types of fault data under each model adopted the average results of 10 experiments, and the model performance evaluation results were finally obtained, as shown in Table 8. The table shows that the proposed method has the highest classification accuracy and the best stability.  
Figure 21: Results of t-SNE visualization analysis.  
The experimental results show that the accuracy of our method reached 99.98% on the test set, which is higher than the existing SVM, LeNet-5, gcForest, and Resnet methods, and the loss function value is 0.00003, which is very small. This indicates that the actual output of the proposed method is closest to the predicted value and has the best recognition effect compared with the other methods.
# Discretly Exploiting Inter-Session Information for Session-Based Recommendation
## 5 Experiments
### Impact of Hyperparameters(RQ2)  
In the experimental settings, the number of factors \(\mathcal{K}\) is the most significant hyperparameter of the DEISI model, which is used to fine-tune the degree of refinement of the intra and inter-session dependencies. A smaller value of \(\mathcal{K}\) may hinder the complete separation of dependencies, while a larger value of \(\mathcal{K}\) may lead to excessive refinement of the dependency. Furthermore, increasing \(\mathcal{K}\) may result in a small factor-level embedding dimension, which may not fully represent the semantics of the factor-level. For datasets exhibiting higher complexity, exemplified by but not limited to those with dense interaction patterns, a larger value of k is essential to enable the model to capture intricate inter-session dependencies. Conversely, for simpler datasets, a smaller k value suffices. To investigate the impact of different \(\mathcal{K}\) values on the performance of DEISI, we set \(\mathcal{K}\) to 2, 5, 8, and 10, respectively, and evaluate the corresponding model performance on the three datasets, as shown in Figure 3a,b. Ultimately, \(\mathcal{K}=5\) for the Diginetica and Yoochoose1/64 datasets, and \(\mathcal{K}=10\) for the Nowplaying dataset.
# A Routing Optimization Method for LEO Satellite Networks with Stochastic Link Failure
## 3 Optimization Method  
The complexity of the network topology increases dramatically with the number of satellites. On the one hand, it is hard to solve the multi-constraint problem by various shortest-route algorithms; on the other hand, the initial solution of intelligent optimization algorithms is generally generated by a stochastic approach, so there is a large uncertainty in the optimization direction. Based on the above considerations, we propose the improved GA algorithm based on A* (GA-AS), which introduces the roulette wheel selection into the A* algorithm to generate a series of solutions, and then the solutions are used as the initial population of the Genetic Algorithm (GA).
# Enhancing Periodontial Bone Loss Diagnosis Through Advanced AI Techniques
## 4 Discussion
### Evaluation of Developed Models
#### 4.2.2 The Secondary Model (YOLOv8)  
A comparison with related models that exist in the literature was summarized in Table 3, which gives insight into the effectiveness of different models in dental image analysis, hence providing a comprehensive view of their performance across varied datasets. Our developed YOLOv8 model, trained on a set of 817 panoramic images, yielded a commendable precision of 0.74 and a recall of 0.70, thus showing its capability of correct detection of PBL while outperforming other existing models [18]. Moreover, slightly increased sensitivity and precision values were noted with the YOLOv5 model when it was tested on a larger dataset involving 1543 images, suggesting the efficacy of this model in similar tasks [26].  
Other advanced models, such as ViT-base/large, BEiT-base/large, and DeiTbase, trained on a large dataset of 21,819 images, demonstrated a high accuracy of 0.84 [27; 28; 29; 30], further demonstrating the flexibility and adaptability of different approaches in dental image analysis. However, the performance of such advanced AI and transformer models is highly constrained, with very large dataset sizes essential for training [27; 28; 29; 30]. Notably, while all of these models aim to detect PBL, our YOLOv8 model can  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Dataset** & **Results** \\ \hline Our proposed MobileNetV2 & 526 panoramic & Accuracy: 0.8846, recall: 1.00, \\   VGG-19 [25] ([PERSON], 2018) & 1044 periapical & Accuracy: 0.788 \\   U-Net [1] ([PERSON], 2023) & 640 panoramic & Accuracy: 0.73, \\   VGG-16 [13] ([PERSON], 2022) & 1724 intraoral & Precision: 0.73, \\  & periapical & F1-score: 0.75 \\ \hline \hline \end{tabular}  
* Accuracy: The proportion of correctly classified instances out of all instances in the dataset. F1-score: The weighted average of precision and recall, used to evaluate model performance. Loss: A measure of how well a model fits the training data, with lower values indicating better fit. Precision: The proportion of true positives among all predicted positive instances. Recall: The proportion of true positives among all actual positive instances.  
\end{table}
Table 2: Comparison of the proposed MobileNetV2 with other existing classification models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Dataset** & **Results** \\ \hline Our proposed YOLOv8 & 817 panoramic & Precision: 0.74, recall: 0.70 \\ YOLOv5 [26] & 1543 panoramic & Sensitivity: 0.75, precision: 0.76 \\ U-Net [18] & 640 panoramic & Accuracy: 0.73, recall: 0.57 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the proposed YOLOv8 with other existing classification models.  
by classifying the severity level. In addition to localizing areas of bone loss, it also evaluates the severity of the same conditions and thus presents valuable information for treatment planning. All these findings emphasized nuanced analysis for better diagnostic accuracy and effective treatment strategies in dentistry.
## Appendix A.2.1 Architecture
### 4G Long-Term Evolution (LTE)
#### Cell Global Identifiers  
Given the reorganization in the RAN level of LTE architecture, differently from GSM and UMTS, all functions are demanded of eNBs. Following the architectural organization of the network, similarly to UMTS, each LTE cell has a global identifier called a EUTRAN cell global identifier (ECGI) that combines the PLMN identifier (MCC + MNC) with:  
* eNB-ID, which identifies the eNB responsible for managing the cell;
* cell_ID, which identifies a cell/sector within a specific eNB.  
Following the 3 GPP standard, the combination of eNB-ID and cell_ID, also known as eCI, is composed of 28 bits: the most significant 20 bits should be the eNB-ID, and the rest should represent the cell_ID. Unfortunately, the network operators are free to partition the 28 bits in any way they wish [29].  
Administrative areas in EUTRAN are called tracking areas (TA), and they can include single cells or cover an entire set of cells (as a small town) [29]. The tracking area code (TAC) is a 16-bit code broadcast for each eNB by the System Information Block Type 1 (SIB1) about every 80 milliseconds [44]. In the case when a mobile device UE is in idle mode, a page request is sent all over the eNB belonging to the TA where the UE was last registered. TAC is unique within a PLMN, and therefore, each TA is globally identified by the tracking areas identifier (TAI), defined as MCC + MNC + TAC. It is used to indicate to eNB to which TA the eNB belongs (which cell). When a UE connects to a LTE network, it receives a TAI list indicating areas where it can move without sending a tracking area update request. Finally, note that, from the administrative point of view, eCI, combined with TAI, uniquely identifies a cell.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 5 Illustrative Example
### Phase 3: Long-Term Drift Detection  
While immediate alerts address abrupt perturbations, the system also assesses whether machines undergo incremental shifts over extended periods. To accomplish this, data from several days or weeks are aggregated and then compared to the baseline distribution using KL divergence or Wasserstein distance. Furthermore, as shown in Figure 4, every 30 days the system checks for sustained drift, and if it exceeds the designated threshold, the long-term label is set to 1. If this measured gap remains elevated beyond a certain threshold, the model designates a long-term drift. Figure 2 uses intervals shaded orange (long-term label) and red (short-term label) to indicate drift events, such as those observed in Machine 2 from late spring to early summer 2024. At times, maintenance logs made a note stating \"maintenance schedule postponed\", implying that slight, cumulative deviations can escalate without proper calibration or part replacements. By distinguishing chronic drift from short-lived anomalies, the system facilitates well-timed preventative actions.