esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Conventional Language Identification Approaches
### Feature Extraction Approaches for LID  
The conventional feature extraction approaches have concentrated on the search for useful features in LID. Various types of features from one-dimensional and simple ones (including pitch, speed, and energy) to acoustic features such as MFCC and shifted delta cepstral coefficients have been applied for LID [3, 4, 15]. Among them, the MFCC has been used as a fundamental feature for LID as in speech recognition. However, the MFCC may insufficiently convey language-specific information as they aim to discriminate the general tendency of phonemic information.  
To derive further language-specific information from the acoustic features, additional approaches were introduced [7, 8]. The representative approach is to apply the i-vector for LID [7]. The i-vector was firstly introduced for speaker recognition [16]. The i-vector-based LID approach mainly employs Joint Factor Analysis (JFA), in which respective feature vectors are regarded as a linear combination with language-independent and language-dependent features [17]. The language-independent featuresare modeled in the form of a Gaussian Mixture Model (GMM)-based Universal Background Model (UBM), and a GMM supervector is obtained. Next, language-dependent features are configured as two measurements (an eigenmatrix and an eigenvector). The eigenmatrix is generally regarded as a Total-Variability Matrix (TV-Matrix). The i-vector-based approach is summarized as:  
\[M=m+Tw \tag{1}\]  
where \(M\) is regarded as a combination of language-independent features described as a GMM-supervector \(m\) and language-dependent features deriving a TV-matrix \(T\) and i-vector \(w\). The main procedure is to estimate i-vector using both a GMM-supervector and a TV-matrix.  
Another attempt for deriving more reliable LID features is based on DNN. A representative feature is the DNN-Bottle Neck (BN), which is an intermediate result of feedforward neural network used in conventional DNN-based speech recognition approaches [18; 19]. The DNN-BN feature was applied to the LID task in the expectation that the feature is pertinent to language-discriminative information. Another DNN-based approach employs the Convolutional Neural Network (CNN) [2; 20]. The CNN-based speech recognition generates outputs called'senone'. Some studies have reported that the senone represents language-based information and therefore can be used as a LID feature.
# A Fault Detection Framework for Rotating Machinery with a Spectrogram and Convolutional Autoencoder
## 3 Proposed Framework  
The proposed fault detection framework postulates that the fault signals are not available in the training phase. Thus, both the feature extraction and fault detection models are trained using only the signals from the normal operating state. The overall process of the proposed fault detection framework is presented in Figure 1. As shown in Figure 1, the training phase of the proposed framework comprises three subsequent procedures: (1) Firstly, raw vibration signals are obtained from the accelerometer sensors installed in the rotating machinery, and these signals are transformed into spectrograms. To this end, we applied the STFT technique to the raw time-domain vibration signals. (2) Then, a two-dimensional CAE (2D-CAE) structure is trained to reconstruct the input spectrogram obtained only from the normal operating state. In the 2D-CAE neural network, the latent feature vector is derived to summarize information on the input spectrogram. Thus, the latent feature vectors are used as extracted features of the input vibration signal in the proposed fault detection framework. (3) Finally, a fault detection model is trained with the latent feature vector of the training input spectrograms. In the fault detection model, anomaly scores of newly collected signals are computed, and those having large anomaly scores are finally rejected as fault states.  
Figure 1: The overall process of the proposed fault detection framework.
## 5 Performance Evaluation
### Experiments  
#### 5.2.1 RaDROO Algorithm  
First, we observe the practical results of our proposed RaDROO algorithm in the context of addressing the optimization of offloading costs for edge network tasks. The optimal values of lambda in the range of \((0,1)\) are given as \(\alpha\) of 0.5, 0.7, and 0.9, respectively. As illustrated in Figure 2, when lambda is 0, it means that CVaR is not considered, is weakened to a classical distribution robust problem, and the obtained results converge to a point. However, as \(\lambda\) increases, the risk consideration is reinforced step by step, further constraining the range of values provided within the uncertainty set. This results in a progressive increase in the amount to be spent, but at the same time, a more superior and stable optimal choice is obtained. At the same time, the larger the lambda, i.e., the larger the weight considering the CVaR value, the worse the optimal value of the required spend.  
Second, observing the value of V gives a more intuitive view of the degree of consideration of risk in CVaR. As shown in Figures 3 and 4, it is obvious that the value of V is larger when a higher alpha value is taken, i.e., when the worse part of the fuzzy set is used as a criterion; at the same time, this value is almost unaffected by the value of \(\lambda\) taken. However, with a fixed value of \(\alpha\), as the simultaneous uploading of tasks increases, the value of V for each experiment also increases gradually and steadily, which means that the value of CVaR also increases; that is, the risk is further considered as a way to ensure the authenticity and practicality of the whole system.
# A Review of Air Pollution Monitoring Technology for Ports
## 2 Detection Methods for Air Pollutants in Ports
### Detection Methods for Gaseous Pollutants
#### 2.2.1 SO\({}_{2}\) Detection Methods  
SO\({}_{2}\) is one of the major air pollutants as a Class 3 carcinogen and is a necessary item for environmental monitoring. For this purpose, [PERSON] [29] and [PERSON] [30] described the detection method of SO\({}_{2}\) concerning the technical standards and norms of environmental monitoring. The detection methods of SO\({}_{2}\) gas can be roughly divided into manual analysis methods and automatic analysis methods according to their detection methods. The commonly used methods of manual analysis methods include spectrophotometry and iodometry. These methods generally take the pollutant samples collected through the sampler to the laboratory to determine the concentration of pollutants by chemical and other methods. The automatic analysis methods do not require specific laboratory conditions, and the monitoring equipment using this method can automatically obtain the concentration of the detected pollutants without manual comparison and calculation of the experimental detection results. The commonly used methods of automatic analysis include the ultraviolet fluorescence method, DOAS (Differential Optical Absorption Spectroscopy),  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Pollutants** & **Manual Analysis Methods** & **Automatic Analysis Methods** \\ \hline \multirow{3}{*}{SO\({}_{2}\)} & Spectrophotometry, & Ultraviolet fluorescence method, \\  & iodometry & DOAS, \\  & & Constant potential electrolysis \\ \hline \multirow{3}{*}{NO\({}_{\chi}\)} & Ethylene diamine & \\  & dihydrochloride & Chemiluminescence, \\  & spectrophotometric method & DOAS \\ \hline \multirow{3}{*}{CO} & NDIR spectrometry method & Gas filter correlation IR absorption, \\  & & Non-dispersive IR absorption method \\ \hline TSP & Gravimetric methods & — \\ \hline \multirow{3}{*}{PM2.5} & Gravimetric methods & Micro-oscillation balance method, Light \\  & & scattering method, The beta-ray method \\ \hline \multirow{3}{*}{PM10} & Gravimetric methods & Micro-oscillation balance method, Light \\  & & scattering method, The beta-ray method \\ \hline \hline \end{tabular}
\end{table}
Table 4: Analytical methods for each type of pollutant.  
and constant potential electrolysis. These methods are often used in various detection equipment and sensors to achieve automatic continuous monitoring. Now automatic online monitoring is also the mainstream of development.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 6 Mathematical Models
### Fresnel Zones  
Path loss is crucial for assessing link feasibility and is easily calculated in free space, but environmental factors, like urban obstacles such as buildings, vehicles, and trees, can disrupt the path. Maintaining a Line-of-Sight (LoS) between the sender and receiver is vital as obstacles significantly increase path loss.  
Another critical factor is the Fresnel zones, elliptical areas around LoS between transmitter and receiver. As shown in Figure 7, Fresnel zones help identify where obstacles might cause signal phase issues. For optimal signal strength, at least 60% of the first Fresnel  
Figure 6: Wireless link budget components, showing transmitter power, antenna gain, and propagation loss [31].  
zone should be clear of obstacles, though more clearance is ideal to minimize interference and signal degradation [30; 31]. It can be calculated as follows:  
\[Fn=\sqrt{\frac{n\lambda d_{1}d_{2}}{D}} \tag{3}\]
## 3 Compression Algorithm Design
### Performance Analysis of the Proposed Algorithm
#### 3.3.2 University of Patras, EEG and ECG Signals  
The dataset is provided by the Neurophysiology Unit, Laboratory of Physiology School of Medicine, University of Patras (UoP) [23]. EEG was recorded using 58 EEG Ag-AgCl electrodes according to the extended international 10-20 system.
## 4 Experimental Results and Analysis  
### Evaluation Indicators  
To evaluate the SOH prediction accuracy and performance of the proposed method, the model's performance is measured using the (mean absolute error, MAE), (mean absolute percentage error, MAPE), (root mean squared error, RMSE), and (coefficient of determination, R2) [40]. The mathematical expressions for these metrics are as follows:  
\[MAE=\frac{1}{n}{\sum\limits_{i=1}^{n}}\big{|}SOH_{i}-SOH_{i}^{\prime}\big{|} \times 100\% \tag{24}\]  
\[\text{MAPE }=\frac{1}{n}{\sum\limits_{i=1}^{n}}\bigg{|}\frac{SOH_{i}-SOH_{i}^{ \prime}}{SOH_{i}^{\prime}}\bigg{|}\times 100\% \tag{25}\]  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}}\big{(}SOH_{i}-SOH_{i}^{\prime} \big{)}^{2}}\times 100\% \tag{26}\]  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}}{\frac{\sum\limits_{i=1}^{n}}{\frac{\sum \limits_{i=1}^{n}}{\big{(}SOH_{i}-SOH_{i}^{\prime}\big{)}^{2}}}} \tag{27}\]  
where n represents the number of samples, while \(SOH_{i}\), \(SOH_{i}^{\prime}\) and \(\overline{SOH}_{i}\) correspond to the estimated value, actual value, and average value of the SOH for the i-th group of batteries, respectively.  
Figure 8: Framework of the SOH estimation based on PSO–GWO–LSSVM prediction model with multi-dimensional HFs extraction.
## 4 Validation of the Calculation Method
### Analysis of an Active Pile Based on a Typical \(p\)-\(y\) Curve  
A case of active pile in Zhenjiang [24] was selected in order to evaluate the correctness of the methods and procedures in this paper for active pile analysis. The site test pile in this case is a steel pipe with a buried depth of 45 m and a diameter of 1.2 m. The pile stiffness is \(EI=2.88\times 10^{6}\) kN\(\cdot\)m\({}^{2}\), and the average undrained shear strength of the soil measured by static triaxial test is 54 kPa. The horizontal load acts on the pile top, and the action position of the horizontal load is 7.9 m above the ground. Finally, the horizontal displacement of the pile on the ground and the maximum bending moment of the pile were obtained when the horizontal load was 20 kN, 50 kN, 100 kN, 150 kN, 200 kN, 250 kN, and 300 kN. In this paper, the pile top in the calculation model is assumed to be flush with the ground, so according to the principle of force equivalence, the test load is equivalent to the horizontal load and bending moment at the ground.  
Three typical \(p\)-\(y\) curves (Zhang [25], Wang [26], and Matlock [27]) applicable to soft clay in Table 1 were selected to describe the relationship between the lateral soil resistance and the horizontal displacement of the pile. The relevant parameters of the \(p\)-\(y\) curve are taken from the literature. DIAPP was used to calculate the horizontal displacement of the pile top and the maximum bending moment of the pile body under different loads, and the calculated results were then used to compare to the field measured data, as shown in Figure 6a,b.  
In the table, \(y_{50}\) is the corresponding lateral deformation of the pile body when the soil around the pile reached half of the ultimate soil resistance (mm), \(p_{u}\) is the ultimate soil resistance at the pile side (kPa), \(F_{s}\) is the reduction coefficient closely related to soil properties and load forms, and \(a\) and \(b\) were determined by triaxial test.  
Figure 6 shows that under the same load, the ranking of the horizontal displacement of the pile top and the maximum bending moment of the pile body calculated using three \(p\)-\(y\) curve models is [PERSON] model \(>\) Wang model \(>\) Matlock model. The pile-top displacement calculated using the Zhang model is larger than the test results, while the results obtained using the Wang model or Matlock model are smaller than the test results. The maximum bending moment of the pile calculated using the three models is slightly larger than the test values. The variation trend of the above calculated values with the load based on the three models is consistent with the field measured results, and they all increase nonlinearly with the horizontal load, which supports the correctness of the method in this paper.  
Since the horizontal displacements of the pile top calculated by the \(p\)-\(y\) curve model of [PERSON] and Matlock are slightly less than the measured value, we chose the Zhang model for subsequent analysis.
## 3 Results and Discussion  
### Regional Geological Background  
The Ordos Basin is situated between the northern Yinshan Mountains, the southern Qinling Mountains, the eastern Liangshan Mountains and the western Tengger Desert. It is the second largest sedimentary basin in China, with an area of approximately 37 \(\times\) 104 km2. Surrounded by Meso-Cenozoic fault basins such as the Hetao, Yinchuan, Bayanhaote, Liupanshan and Weihe, this part is approximately 700 km long from north to south and about 400 km wide from east to west, and the total area is approximately 25,104 square kilometers. The basin is surrounded by mountains, and the basin is bounded by the Great Wall, arid desert grassland area in the north and the semi-arid Loess Plateau area in the south. There are vertical and horizontal valleys and complex terrain. The study area is located south of Wushenqi, east of the western margin of the subduction zone, west of the Mizhi-Qingrun-Yanchang-Yichuan line and north of Binxian. It is characterized by vertical and horizontal valleys and complex terrain. The location of the study area is shown in Figure 1.  
A 5 m thick black shale outcrop is visible in the eastern region of the study area, where the source rock has been successfully drilled. The organic matter content is high, and the maturity level is moderate. The reservoir condition in the Chang 9 formation is favorable. The delta front is characterized by sheet sand deposits, which are primarily dominated by underwater distributary channels and mouth dam sand bodies. The reservoir space is primarily composed of residual primary intergranular pores, exhibiting excellent reservoir performance. The Chang 9 oil layer formation comprises three distinct reservoir combinations: self-generation and self-storage, lower generation and upper storage and upper generation and lower storage. Of these, the self-generation and self-storage combination is particularly noteworthy, as it utilizes the dark mudstone or oil shale of the Chang 9 source rock in conjunction with the oil layer sandstone as the reservoir. This combination is regarded as exhibiting the greatest exploration potential. In light of the aforementioned circumstances, the lithology and physical properties of the Yanchang Formation in the Ordos Basin are being subjected to investigation with a view to providing technical support for oil and gas development in this area.
# Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming
## 3 Hyper-Heuristic Approach for VRP  
### Capacitated Vehicle Touting Problem  
The capacitated VRP (CVRP) is described as follows: a set of n customers must be serviced from a central depot using vehicles of equal given capacity, denoted with \(Q\). Each customer must be served by exactly one vehicle. A customer is defined with the following parameters:  
* demand \(d_{i}\);
* geographical data.  
The scheduling task includes constructing a (minimal) number of routes, specifying the order in which vehicles are servicing disjunct sets of customers. The goal is usually to minimise the number of vehicles and the total distance, while the total demand for each route may not exceed the capacity of the vehicle which serves that route. In other words, for each route \(r\) the following condition must hold: \(\sum_{i=1}^{m}d_{i}<Q\), where \(m\) is the number of customers on route \(r\).
# KGGCN: Knowledge-Guided Graph Convolutional Networks for Distantly Supervised Relation Extraction
## 3 Methodology
### Knowledge Supervised Sentences Selection  
As mentioned in above sections, the latent semantic information contained in KGs plays a vital role in distantly supervised relation extraction, since the training data are obtained by aligning the textual corpus with the existing KGs. These semantic information can provide additional supervision for selecting multiple valid sentences. Previous works use only the textual information to train the relation extractor. Additionally, the distant supervision simply incorporates the KG information as meaningless one-hot labels instead of treating it as a graph, which ignores the rich structure and semantic information present in KGs. In this paper, we extract the interactions between the entity pair and relations in KGs as prior knowledge to guide the selection of valid sentence.
# Goal Oriented Time Adaptivity Using Local Error Estimates
## 5 Numerical Results
### Convection-Diffusion Equation
#### 5.2.1 Discretization  
For our convection-diffusion problem we have a weak solution in the space  
\[V:=H^{1}(t_{0},t_{e};L^{2}(\Omega))\cap L^{2}(t_{0},t_{e};H^{1}(\Omega)),\]  
see [28]. We get the variational formulation  
\[\underbrace{\int_{\Omega}\int_{t_{0}}^{t_{e}}u_{t}\phi+a(\mathbf{v}\cdot\
abla u )\phi-\gamma\Delta u\phi\mathrm{d}t+u(t_{0})\phi(t_{0})\mathrm{d}\mathbf{x}}_{A(u,\phi)}=\underbrace{\int_{\Omega}\int_{t_{0}}^{t_{e}}f\phi\mathrm{d}t+u_{0} \phi(t_{0})\mathrm{d}\mathbf{x}}_{F(\phi)},\quad\forall\ \phi\in V.\]  
By some standard reformulation we get the weak form  
\[\int_{t_{0}}^{t_{e}}\int_{\Omega}(u_{t}+a(\mathbf{v}\cdot\
abla u)-f)\phi-\gamma \
abla u\cdot\
abla\phi\mathrm{d}\mathbf{x}+\gamma c\int_{\partial\Omega}u\phi \mathrm{d}\mathbf{s}\mathrm{d}t=\int_{\Omega}u_{0}\phi(t_{0})-u(t_{0})\phi(t_{0}) \mathrm{d}\mathbf{x},\quad\forall\ \phi\in V.\]  
Figure 3: Geometry of \(\Omega\) for the convection–diffusion Problem (34).  
We discretize time along the points \(t_{n}\) with \(I_{n}:=[t_{n},t_{n+1}]\) and space by \(2\left(3\cdot 32\right)\cdot 32=6144\) regular triangular cells \(K\) defining the finite element mesh. We define the global finite element space by  
\[V_{h,k}=\{v\in L^{\infty}(t_{0},t_{e};H^{1}(\Omega)):v(t,\cdot)|_{Q^{n}_{K}}\in Q ^{1}(K),v(\cdot,x)|_{Q^{n}_{K}}\in P^{q}(I_{n}),\forall\ Q^{n}_{K}\},\]  
where \(P^{q}(I_{n})\) is the space of polynomials on \(I_{n}\) of degree up to \(q\) and \(Q^{1}(K)\) the space of polynomials on \(K\) with partial degrees up to \(1\). The adjoint problem to Equations (34) and (35) is  
\[-z_{t}(t,\mathbf{x})-a\,\mathbf{v}\cdot\
abla z(t,\mathbf{x})-\gamma\Delta z(t,\mathbf{x})=\frac{1}{t_{e}-t_{0}}\Big{|}_{\Omega_{*}}, (t,\mathbf{x})\in[t_{0},t_{e}]\times\Omega,\] \[z(t_{e},\mathbf{x})=0, \mathbf{x}\in\Omega,\] \[\
abla z(t,\mathbf{x})\cdot\mathbf{n}=-\frac{a}{\gamma}\,z(t,\mathbf{x})\, \mathbf{v}\cdot\mathbf{n}-c\,z(t,\mathbf{x}), (t,\mathbf{x})\in[t_{0},t_{e}]\times\partial\Omega.\]
## 7 Limitations and Future Work  
While the hybrid LSTM-GA approach for GPS spoofing detection demonstrates significant potential, several limitations must be addressed to advance its practical application. These limitations also point to valuable directions for future research.  
A major challenge lies in the computational complexity and resource demands of the proposed system, which may hinder its implementation on UAV platforms. Given the inherent constraints of UAVs, including limited processing power and battery life, the impact on real-time performance remains unexplored. Future work should focus on optimizing the algorithm to operate it efficiently in resource-constrained environments, such as through integration with AI edge computing platforms or the use of lightweight models tailored for UAV hardware.  
Another significant limitation is the lack of standardized benchmarking and real-world validation frameworks. While this study employed authentic GPS spoofing signal data to ensure the preservation of critical features, comprehensive field testing has yet to be conducted. Establishing standardized testing frameworks and conducting real-world experiments would enable robust validation and facilitate meaningful comparisons across different GPS spoofing detection methodologies.  
Practical implementation in real-world scenarios presents additional challenges. Real-time processing requirements, integration with existing UAV navigation systems, and scalability across diverse UAV platforms remain areas requiring further exploration. Addressing these factors is critical for transitioning from experimental models to deployable systems.  
Currently, this research emphasizes detection over mitigation, leaving open the question of how to counter GPS spoofing attacks once detected. Developing effective countermeasures, such as the real-time suppression of spoofing signals or fail-safe navigation protocols, is a crucial next step. These countermeasures would not only enhance UAV resilience but also ensure reliable navigation even under active spoofing conditions.  
Future research could also investigate hybrid detection strategies that combine multiple techniques to improve robustness. The development of long-range communication protocols and optimized algorithms for constrained environments could enhance the system's scalability and adaptability. Furthermore, integrating these systems with IoT platforms and exploring the use of AI edge computing could provide avenues for overcoming computational constraints.