esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Forest Fire Detection by the Satellite data in China
### The ESA ENVISAT Satellite  
The ESA satellite, ENVISAT, was launched in 2002 with an expected lifespan of five years [54]. The ENVISAT satellite provides both optical and radar measurements of the Earth's surface. Three ENVISAT instruments can be used to investigate the extent and impact of the forest and peatland fire. Reduced spatial resolution Medium Resolution Imaging Spectrometer (MERIS) imagery was used to identify simple land cover features and smoke plumes. The fire hot spots were detected by band 3.7 \(\upmu\)m of Advanced Along Track Scanning Radiometer (AATSR) nighttime acquisitions, and burned areas were detected by Advanced Synthetic Aperture Radar (ASAR) wide swath radar imagery acquired before and after the fire events. The performance and the capability of the ENVISAT to acquire data from different sensors simultaneously or within a short period of time greatly enhance the possibilities to monitor forest fires occurrence [55,56].  
Under the Dragon Program sponsored by the ESA and the Ministry of Science and Technology of China, the studies have been carried out in detecting forest fires using AATSR/ENVISAT data [16,57,58], and extracting edges of burned region using MERIS/ENVISAT data. Validation of the algorithms has been made by using ground fire information and detected forest fire hot spot obtained from the MODIS data. It is found that the edges of burned region can be efficiently extracted from the MERIS images through image enhancement technique and by human-machine interactive interpretation [16,58].
# Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling
## 3 Cimkg
### Co-Interactive Module
#### 3.2.1 Intent and Slot Label Attention Layer  
The intent and slot label attention layer uses a label attention mechanism to compute the label attention for the hidden state of each token in the input sequence, thereby obtaining explicit representations of the intent and slots.  
Specifically, unlike the hidden states \(H\) of the input sequence used in [12], the hidden states \(H\) that are obtained after the knowledge graph-based shared encoding layer not only include the token ids and seg-ids for each token in the input sequence but also include visible mask information. The hidden states \(H\in\mathbb{R}^{n\times d}\) (\(n\) represents the number of tokens in the input sequence, \(d\) represents the hidden layer dimension) are used as queries, whereas the label embedding matrix \(W^{V}\in\mathbb{R}^{d\times|val|label|}(v\in\{\text{lorS}\},\) where \(I\) represents intent, \(S\) represents slot, and \(\left|v^{label}\right|\) represents the number of labels for either intent or slot), serves as a key and value. The hidden state \(H\) is dotted with the label embedding matrix \(W^{V}\), and the result is normalized using the softmax function to obtain the attention weight matrix \(A\) as shown in (7):  
\[A=softmax\left(HW^{V}\right) \tag{7}\]  
By performing a linear combination of the attention weight matrix \(A\) and label embedding matrix \(W^{V}\), the original hidden state \(H\) is updated to obtain the final intent or slot representation \(H_{V}\) as shown in (8):  
\[H_{V}=H+AW^{V} \tag{8}\]  
In particular, the label embedding matrix \(W^{V}\) is represented by the parameters of the fully connected slot filling decoder layer and intent detection decoder layer. This means that the intent detection embedding matrix \(W^{I}\in\mathbb{R}^{d\times|val|}\) and the slot filling embedding matrix \(W^{S}\in\mathbb{R}^{d\times|val|}\) are actually the weight matrices of the decoder layers \(\left(\left|S^{label}\right|\right|\)_and_\(\left|I^{label}\right|\) represent the number of slots and intent labels, respectively). Through the above calculation, the intent representation \(H_{I}\in\mathbb{R}^{n\times d}\) and slot representation \(H_{S}\in\mathbb{R}^{n\times d}\), which capture semantic information, are obtained.
## 3 V2X System Architecture When Integrating Blockchain Technology and MEC
### V2X  
In recent decades, the dedicated vehicle network has become a major network technology for the comfort and safety of drivers in vehicle environments. However, new applications and services require major changes in the underlying network models and calculations, which requires new road network planning [22].  
Thus, the emergence of critical messages and applications related to the safety of road users, which leads to high performance requirements and strict reliability of data transmission, should be noted. The transmitted messages can be divided into two categories, safety messages and general-purpose (non-safety-related) messages. For information about any emergency, vehicles can transmit or broadcast messages with high priority and high requirements. For information that is not an emergency, the requirements can be lowered. Based on the different requirements, V2X services can be divided into several basic types [23; 24].  
Depending on the severity of the emergency, event messages are divided into different levels according to priority, where level 1 defines critical event messages with the highest priority, etc.  
The primary goal of the vehicle network is to accurately disseminate information in a short time, with the required reliability and safety. There is a high risk that modern vehicles are subjected to cyber-attacks targeting vehicular communications [25]. Due to inaccurate information sent by malicious vehicles, some important messages cannot be accurately disseminated in real time, resulting in damage to other traffic participants. Another problem could be the theft of important and sensitive information from traffic participants.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & **V2X** & **Edge** & **Blockchain** & **EE** & **KPIs** & **Applications** \\ \hline [PERSON] et al. [16] & No & \(\times\) & \(\surd\) & \(\times\) & No eval. & Traffic modeling \\ [PERSON] et al. [17] & Yes & \(\times\) & \(\surd\) & \(\times\) & Resources utilization & System analysis \\ [PERSON] et al. [18] & No & \(\times\) & \(\times\) & \(\times\) & Security & Safety applications \\ [PERSON] et al. [19] & No & \(\times\) & \(\surd\) & \(\times\) & No eval. & Review \\ [PERSON] et al. [20] & Yes & \(\times\) & \(\surd\) & \(\times\) & No eval. & Real-time message \\ Proposed work & Yes & \(\surd\) & \(\surd\) & \(\surd\) & Reliability, availability, & \\  & & & & & latency, energy & uRLL V2X \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._
## 4 Motion Planning and Control in ADAS for City Buses
### Path Planning in Restricted Areas
```  
**Algorithm 1** Generating bounding rectangles. This algorithm takes in at input the current and final configuration of the bus \(P_{0}(x_{0},y_{0},\theta_{1})\) and \(P_{F}(x_{F},y_{F},\theta_{F})\), and then, after computing slopes of lines along with these two configurations, calculates the intersecting point \(P_{c}\). Two line segments, \((P_{0},P_{c})\) and \((P_{c},P_{F})\), are generated. By expanding the intersection configuration, an additional line segment is generated toward the final and initial configuration, which guarantees the coverage between two other rectangles. While obstacle free, it then iterates over all cells of the grid map perpendicularly to these three lines. Given a set of control points \(P=p_{0},p_{1},\ldots,p_{m}\), where \(p_{0}\) is the current configuration of the bus and \(p_{m}\) is the final configuration, the objective is to find a polynomial function \(f(x)=c_{0}+c_{1}x+c_{2}x^{2}+...+c_{n}x^{n}\) that passes through all the control points. The optimisation problem is subject to a number of constraints, including the initial and final configuration of the path, as well as constraints on the control points, the curvature and rate of curvature of the path, and the position of the path at each control point. The optimisation problem is presented by  
\[\underset{c_{0},c_{1},\ldots,c_{n}}{\text{minimize}} \sum_{i=1}^{m}f_{i}^{2}\quad\text{subject to}\] \[f(x_{j})=y_{j}\quad\forall j\in 1,2,\ldots,m,\] \[f(x_{0})=p_{0},\quad f(x_{m})=p_{m},\] \[\left|\kappa(f(x_{i}))\right|\stackrel{{(2)}}{{ \leq}}x_{\max}\quad\forall i\in 1,2,\ldots,m,\] \[\left|\hat{\kappa}(f(x_{i}))\right|\stackrel{{(2)}}{{ \leq}}\hat{\kappa}_{\max}\quad\forall i\in 1,2,\ldots,m,\] \[y_{i}\quad\text{bounded inside rectangles from Algorithm 1},\quad\forall i\in 1,2,\ldots,m,\]  
where \(c_{0},c_{1},\ldots,c_{n}\) are the coefficients of the polynomial, \(f(x)\) is the polynomial function, \(x_{i}\) and \(y_{i}\) are the \(i\)-th control point coordinates, \(m\) is the total number of control points, and \(f_{i}\) is the distance between the \(i\)-th control point and the polynomial evaluated at \(x_{i}\). The objective function minimises the sum of the squares of the distances between each control point and the polynomial, which is subject to the constraints that the polynomial passes through each control point and satisfies the specified constraints at each control point. The first constraint ensures that the polynomial function passes through the starting points and endpoints. The second constraint limits the maximum curvature of the path to \(\kappa_{max}\) to ensure that the path is feasible for the kinematics of the bus. The optimisation problem can be solved using the Interior Point Optimiser [54], which is a type of non-linear programming algorithm that can handle inequality constraints. The algorithm iteratively updates the solution until a convergence criterion is met.
###### Abstract
Additionally, UDEEP (underwater deep learning edge computing platform) plays a crucial role in environmental monitoring and combating invasive species by detecting the presence and spread of signal crayfish and plastic waste using artificial intelligence, Internet of Things devices, and edge computing (2018). The main disease symptoms observed in crayfish include melanization, color lesions, and exoskeleton erosion (2018). These symptoms allow diseased individuals to quickly transmit the disease to healthy ones within the population, leading to significant declines in crayfish populations. The spread of disease in this way can result in rapid population decreases and disrupt the ecosystem balance (2018). Advances in image processing technology are expected to play a crucial role in detecting these diseases and preventing their spread in crayfish populations. These technologies can enable early diagnosis and isolation of diseased individuals, helping to control the disease and protect crayfish populations. Detecting dark color changes on crayfish shells using artificial intelligence techniques and image processing methods is considered important for preventing the spread of disease in the ecosystem and identifying affected individuals. This study aims to detect changes in images of affected crayfish using canonical machine learning, deep learning, and vision transformer models. This approach can enable early intervention and control of the disease. The contribution of our study can be presented in two main points. First, to the best of our knowledge, this is the first time that deep learning and vision transformer algorithms were utilized for disease detection in crayfish. Second, by combining the mechanisms of canonical machine learning and deep learning algorithms, disease detection with higher performance in terms of sensitivity than the models used in both learning categories was achieved. The other sections of the study are as follows. Section 2 details the dataset used, the overarching framework, and the algorithms related to traditional machine learning,deep learning, and vision transformers. Section 3 discusses the metrics for evaluating the experiments, the statistical analyses performed, the configuration of the experiments, and the findings obtained. The paper culminates with Section 4, where the final assessments are articulated.
# Nondestructive Monitoring Techniques for Crack Detection and Localization in RC Elements
## 8 FOS Strain Measurements
### Cracks Pattern  
The results of FOS and AE monitoring are compared with the actual pattern of cracks directly observable in the laboratory. Figure 14 shows the cracks at loading step #7 where the crack development at the bottom side of the beam can be noticed. Furthermore, the transition of crack development from the vertical shape (due to pure bending moment at the center of the beam) to the inclined shape at the extremity (due to shear effects at the support) is highlighted.  
Figure 14: Crack pattern at loading step #7 and numbered AE sensors.  
Figure 13: Cumulative AE activity and FOS strain time histories for: (**a**) fiber optic n.7 at x = \(-\)1.6 m and AE sensor n.0; (**b**) fiber optic n.7 at x = \(-\)1.2 m and AE sensor n.1. The dashed lines identify the different loading stages.
## 2 Materials and Methods
### Motif Analysis of Mutations in Z-DNA-Associated Enhancer Regions  
Single-nucleotide polymorphisms (SNPs) and insertion-deletion variants (indels) were annotated using snpEff [45], referencing dbSNP [46] (build version hg38) and ClinVar [47] databases to retrieve known clinical associations and variant metadata. Clinically significant mutations were filtered based on specific clinical significance categories, including 'Likely risk allele', 'Pathogenic', 'Pathogenic'/Likely pathogenic', 'Pathogenic/Likely risk allele risk factor', 'risk factor', and 'drug response', as annotated in the clinical significance field. Both clinically significant and novel mutations were retained only if their genomic coordinates overlapped predicted Z-DNA-forming sequences.  
Subsequent analysis prioritized unknown mutations due to the absence of known pathogenic variants within Z-DNA-forming regions. These mutations were categorizedinto SNPs, long insertions (>10 bp), short insertions (\(\leq\)10 bp), long deletions (>10 bp), and short deletions (\(\leq\)10 bp). Inherited mutations were prioritized by selecting sequences where mutations were detected in at least 10% of the analyzed genomes to reduce the likelihood of including somatic mutations or sequencing artifacts.  
For each selected mutation, nucleotide sequences were generated from the human reference genome (hg38 assembly), encompassing the mutation site and extending 100 nucleotides upstream and downstream. These sequences served as input for motif prediction analyses using HOMER2 (findMotifs.pl) [48]. Motif presence was evaluated separately for mutation-containing sequences and reference sequences from the corresponding genomic regions in thehg38 assembly. For the analysis, only known motifs with a \(p\)-value < 0,01 were considered.  
To identify transcription factor (TF) interactions for the detected motifs, a motif similarity search was conducted using MEME Tomtom [49] with an e-value cutoff of 0.01. Subsequently, motifs identified in mutated sequences were mapped onto the reference genome using MEME Fimo [50] to determine whether mutations led to motif gain or loss. Fimo was executed with a \(p\)-value threshold of 0.0001. Conversely, reference motifs were also mapped to the mutated sequences to identify motifs disrupted by mutations.
# The Latest Advances in Wireless Communication in Aviation, Wind Turbines and Bridges
## 4 WSN Systems in Different Applications: Aerospace, Bridge Structures, and Wind Turbines
### WSN in Aerospace
#### 4.1.1 Wireless Signal Propagation  
However, its non-deterministic behavior and its sensitivity to interference and jamming can solve many problems. However, these features may not be sufficient to provide the real hard telecommunication required by aircraft applications.  
Research in [32] has indicated the use of an access point with 802.11a technology in the aircraft. Omnidirectional antennas should be used. Thanks to that, the access point can evenly send waves in cabin space with equal probability. This ensures that no area in the cabin will contain more waves than another. It is worth emphasizing that, the higher power coming out of the access point, the more interference can be caused by aircraft electronics. Therefore, it is essential to select such power and the number of access points to eliminate this problem [32].  
The authors in [32] state that the four access points with 802.11a technology cover almost all planes. These four access points must have reasonable transmit power levels that ensure minimal interferences with plane electronics.
## 4 Results and Discussion  
The proposed ensemble RFMLP is tested on three datasets namely, KDD-CUP 99, NSL-KDD, and STIN to provide high security to both satellite and terrestrial networks.
## 4 Experimentation and Evaluation
### Multiclass Classification
#### 4.2.4 KNN  
For the KNN model, the number of neighbors ranges between 5, 15, 35, and 75.  
Binary Classification  
The results for the different combinations and binary classifications were analyzed. In this case, optimal results for F1, Recall, and Precision were obtained for all models, ultimately choosing the model with KNN equal to 5, which displays the highest metrics. Once stored, we used the developed explainer generator to generate its SHAP values. The influence of each feature for this model on classifying a trace as an anomaly was generated, revealing that most features for this model do not have a significant influence, except for Sttl, Dload, and Dbytes (Figure 8).  
Figure 6: Influence of each feature by average SHAP values—Logistic Regression Binary Classifier.  
Figure 7: Influence of each feature by average SHAP values—Logistic Regression Multiclass Classifier.  
The TPR and FPR values measured for multiclass classification and the balanced and unbalanced datasets for each class in this type of model were also analyzed. As in binary training, the best model is one with K equal to 5, but this time with a balanced dataset, significantly outperforming the other options with the highest TPR and the lowest FPR. Continuing with the same process as in binary classification, the model was saved and the explainer generated. We observed the importance of each feature for classifying each type of anomaly in this model. In this case, Dmean, Dloss, and Sttl do not have great relevance for classification despite the relevance these features provide in the Kendall linear relationship (Figure 9).
## 3 Method
### Construction of the State Transition Probability Graph  
#### 3.2.1 Calculation of the transition probability  
The transition probability is calculated according to the Jaccard coefficient, which is used to compare the similarity and differences between the two effective sample sets \(A\) and \(B\), and describe the dissimilarity between the sets. The larger the Jaccard coefficient indicates the higher the similarity, and the smaller the Jaccard coefficient, the lower the similarity. The specific formula for the Jaccard coefficient \(J(A,B)\) between sample set \(A\) and \(B\) is as follows:  
\[J(A,B)=\frac{|A\cap B|}{|A\cup B|}=\frac{|A\cap B|}{|A|+|B|-|A\cup B|} \tag{1}\]  
For disease states, the transition probability was calculated as the proportion of people moving from one state to the next after the specified interval year.
# Review of Magnetoelectric Effects on Coaxial Fibers of Ferrites and Ferroelectrics
## 4 Spinel Ferrite-Ferroelectric Coaxial Fibers
### CoFe\({}_{2}\)O\({}_{4}\)-BaTiO\({}_{3}\) Core-Shell Nanofibers  
Apart from PZT, another well-studied ferroelectric phase in the composites with CFO is BTO with T\({}_{\rm C}\) = 120 \({}^{\circ}\)C and d\({}_{33}\) = 260 pC/N [64,66,67]. [PERSON] et al. reported on the preparation of CFO-BTO fibers by coating the ferrite particles with a sol of BTO. Upon high temperature annealing, XRD measurements revealed the fiber formation free of any impurity phases [66]. An ME susceptibility of \(\alpha_{\rm C}\approx\) (2.2 \(\pm\) 0.1)10\({}^{-11}\) s m\({}^{-1}\) was determined for the ceramic samples of the fibers by the converse ME effect by PFM under an applied H [66].  
[PERSON] et al. prepared coaxial fibers of BTO-CFO by electrospinninging [67]. Upon annealing at high temperature, the fibers had a room temperature magnetization of 50 emu/g. They utilized PFM measurements on fibers on a substrate and on free-standing fibers to measure the ME coupling. They determined the piezoelectric coupling coefficient and its variation due to an applied H from the slope of PFM displacement vs. V profiles for clamped and free-standing fibers. The MFCs estimated from the PFM data were 12 V/cm Oe and 35 V/cm Oe, respectively, for fibers under clamping due to a substrate and free-standing fibers.
## 2 Literature Review
### Research Gap and Novelties of the Proposed Approach  
According to the literature, DLMs have only recently started to be investigated more intensively. Indeed, compared to deterministic and stochastic programming models, DLMs have received less attention, and only a few papers related to the topic can be found [12; 13; 14; 15; 16; 17].  
Moreover, the state of the art of the current proposed DLMs always assumes static values for the forecast and thus does not consider the uncertainty related to changes that newly acquired data can have on this forecast and subsequently on final decisions. As stochastic programming models have been introduced to deal with uncertainty that is not considered in deterministic ones, parametrized DLMs might be an under-investigated potential alternative to cope with the limitations of simple DLMs. Indeed, parametrized DLMs represent an extension of DLMs incorporating tunable parameters that can deal with the uncertainty related to the variability and sensitivity of the system to different inputs or conditions.  
Considering the current literature and its gaps, the main novelty of this study is represented by the introduction of a new approach to solve the multiperiod SSOA when forecasts related to the risk of supplier non-punctual delivery are considered subject to update. Indeed, according to Table 1, approaches based on a parametrized DLM have never been adopted to solve this problem. In addition, the proposed approach, which considers the uncertain benefits of newly acquired data on forecast accuracy and thus on final decisions, optimizes the trade-off between data acquisition benefits and order emission costs for the first time. Purchasing costs, non-punctual delivery risk-related costs, and data acquisition benefits are thus balanced for the first time. Moreover, an empirical investigation of the benefit of considering this new trade-off is reported.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & **Model** & **Ordering** & **Purchasing** & **Shortage** & **Holding** & **Data** \\  & **Type** & **Cost** & **Cost** & **Cost** & **Cost** & **Cost** \\ \hline [21] & DP & ✔ & ✔ & & ✔ & \\ [22] & DP & ✔ & ✔ & ✔ & ✔ & \\ [23] & DP & ✔ & ✔ & ✔ & ✔ & \\ [24] & DP & ✔ & ✔ & & ✔ & \\ [25] & DP & ✔ & ✔ & & & \\ [26] & DP & ✔ & ✔ & & ✔ & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Literature summary.