esa-sceva/satcom-chunk-collection huggingface dataset:
# Coupled Mode Sound Propagation in Inhomogeneous Stratified Waveguides
## 2 Method
### Optimal Incident Waves for Sound Focusing  
As a byproduct of our coupled mode algorithms, the optimal incident wave that leads to sound focusing at a certain position in a multilayered waveguide is investigated.  
From Equation (27), the acoustic pressure at any position \((x_{0},z_{0})\) can be rewritten as  
\[p(x_{0},z_{0})=\begin{cases}\left\langle\left(\mathbf{\mathrm{\Psi}}^{\mathrm{T}}( x_{0},z_{0})M(x_{0})(1+R)\right)^{+},\mathbf{\mathrm{P}}_{i}(0)\right\rangle,&0\leq x _{0}\leq x_{R},\\ \left\langle\left(\mathbf{\mathrm{\Psi}}^{\mathrm{T}}(x_{0},z_{0})\mathrm{exp}\left( A^{-1}(x_{R})Y(x_{R})(x_{0}-x_{R})\right)T\right)^{+},\mathbf{\mathrm{P}}_{i}(0) \right\rangle,&x_{0}>x_{R},\end{cases} \tag{31}\]  
where \(<\mathbf{\mathrm{a}}_{1},\mathbf{\mathrm{a}}_{2}>=\mathbf{\mathrm{a}}_{1}^{\dagger}\mathbf{ \mathrm{a}}_{2}\) denotes the scalar product of vectors in Hilbert space, and \({}^{+}\) denotes the conjugate transpose. The energy flux of the incident wave is assumed to be unity. Then, acoustic pressure modulus at \((x_{0},z_{0})\) can reach its maximum value, \(\max\limits_{p_{i}}|p(x_{0},z_{0})|\), when the modal amplitudes of the incident wave take the following form:\[\mathbf{P}_{i}(0;x_{0},z_{0})=\begin{cases}\dfrac{1}{\sqrt{\Lambda}}\Big{(} \mathbf{\Psi}(x_{0},z_{0})^{\mathrm{T}}\mathrm{M}(x_{0})(1+R)\Big{)}^{\mathrm{t}},&0\leq x_{0}\leq x_{R},\\ \dfrac{1}{\sqrt{\Lambda}}\Big{(}\mathbf{\Psi}(x_{0},z_{0})^{\mathrm{T}} \mathrm{exp}\Big{(}\mathrm{A}^{-1}(x_{R})\mathrm{Y}(x_{R})(x_{0}-x_{R})\Big{)} \mathrm{T}\Big{)}^{\mathrm{t}},&x_{0}>x_{R},\end{cases} \tag{32}\]  
where \(\Lambda\) is fixed by the normalization of the sound energy flux of incident wave \(E_{pi}=\mathrm{Im}\big{(}\int_{0}^{\eta_{\mathrm{B}}(0)}\rho^{-1}p_{i}^{z} \partial_{x}p_{i}\mathrm{d}y\big{)}/2\omega\).
## 3 Technical and Operational Challenges
### Interoperability and Vendor Lock-In  
The proprietary nature of many low-code platforms creates interoperability barriers, particularly in industries reliant on legacy systems. For instance, energy grid operators using SCADA systems face challenges integrating low-code dashboards with decades-old control protocols, as platforms like Mendix prioritize modern REST APIs over legacy communication standards. A case study by [PERSON] et al. [63] reveals that aerospace firms spend most of their low-code project budgets on custom connectors to bridge avionics data silos.  
Moreover, vendor lock-in risks emerge when organizations depend on platform-specific features. For example, Siemens' MindSphere offers robust IoT integration for automotive production lines but lacks compatibility with competing cloud ecosystems like AWS IoT Core, limiting scalability. Open-source initiative [54] knowledge graph frameworks aim to mitigate this by enabling cross-platform data harmonization, yet adoption remains limited due to immature tooling.  
In contrast, open-core platforms like AppMaster and vf-OS demonstrate how architectural flexibility can mitigate interoperability hurdles. AppMaster, a low-code solution designed for enterprise backend development, emphasizes compatibility with heterogeneous systems by supporting both modern APIs and legacy protocols like FTP and SOAP. This enables manufacturers to integrate newly developed low-code applications with decades-old ERP systems without extensive custom coding. Meanwhile, the vf-OS project, an open-source low-code ecosystem focused on industrial automation, provides a standardized interface layer that connects proprietary PLCs and IoT devices through a unified data model.
# UAV-BS Site Planning Based on Circular Coverage Strategy
## 3 Theory  
Convex optimization theory has a wide range of applications in the field of communications and can be used to improve the reliability, performance, and efficiency of communication systems.
# A Novel Efficient Borehole Cleaning Model for Optimizing Drilling Performance in Real Time
###### Abstract
The flow rate determines the maximum speed at which the conveyor belt may travel. Mud characteristics assist its flow in the removal of a bed of cuttings by adjusting the PV and YP ratio and removing the beds of cuttings more quickly. Also, the presence of a strong gel to suspend the cuttings if the pump is turned off is essential [16; 25; 26]. Based on the aforementioned parameters and problems, several models, tools, chemicals, charts, methodologies and correlations have been used for borehole cleaning. However, in certain cases, they are not applicable, inefficient, high in cost, and not feasible with drilling operations [10]. For instance, the borehole cleaning ratio (HCR) indicates the amount of cuttings beds removed by determining their critical height [27]. The cutting concentration in the annulus (CCA) is an effective tool that can indicate the amount of cuttings generated while drilling are loaded in the annulus. Moreover, the cutting carrying index (CCI) provides knowledge of the size of cuttings, size of annulus, flow pattern, and down-borehole fluid properties that cannot be determined with a high degree of accuracy [13]. Considering the aforementioned details, the objective of this study is to present various indicators of borehole cleaning that can help determine the extent of borehole cleaning during the drilling of vertical, deviated, and horizontal wells. Additionally, the study aims to demonstrate how existing parameters can be used to develop a new transport index indicator (TIm) for improving drilling performance and continuously monitoring and assessing borehole cleaning in real time during drilling. Figure 1 depicts the flowchart of the work. Figure 1 demonstrates a comprehensive overview of the factors that can impact borehole cleaning, which are thoroughly examined in this study. The paper outlines various real-time models that serve as indicators for evaluating these factors as well as their respective limitations. Additionally, the mathematical development of the TIm model is presented, which takes into account all relevant parameters necessary for evaluating the extent of hole cleaning in real time. The performance of the TIm model is validated through field applications, highlighting its importance as a novel indicator model. Finally, the study concludes with insightful recommendations for future research in this field.
# Voice Communication Systems Impact on Navigating Officers
## 4 Call Management
The term precedence indicates the priority of the outgoing call, while the term preemption indicates the current call termination when a higher priority call is received. In the standard MLPP service, such as in the one used in Cisco Unified Communications Manager [22] there are five levels of priorities: Highest Level--Flash Override (level 0), Second-Highest Level--Flash (level 1), Third-Highest Level--Immediate (level 2), Fourth-Highest Level--Priority (level 3) and Routine (level 4). For such a system to work, the system administrator needs to define an access digit for priority calls and assign a maximum precedence level (0-4) to individual phone(s), i.e., for satellite, mobile and/or internal phone systems. Therefore, when making a priority call in a certain communication system, the caller needs to dial the access digit and the required precedence level (0-4) before dialing the needed phone number. All of the above-mentioned features can be easily implemented as an on-board call management system [22]. For example, the indication status of the MLPP service can be set to determine ringing tones and indications for a priority call. This indication can be a special tone (alert) or a display notification for both the calling and called side. In addition, the alternate party timeout parameter could be set to determine the time before the priority call is forwarded to the alternative route. These settings provide the configuration of an alternative destination for priority calls that could not be answered. Moreover, there is a setting that determines the duration of ringing (in seconds) before forwarding a priority call. The main advantage of such a call management system would be that the OOW would know, by the sound of phone ringing itself, if the call is of high or low priority. In the case of a low-priority call, the OOW may choose whether to answer it or not, depending on the prevailing situation on the bridge and his/her workload level. On the other hand, the main drawback would be the familiarization of the usual callers (crew and employees ashore) with new calling procedure and, consequently, learning it. The calling procedure should be stated officially in the Safety Management System. Another drawback is the fact that the OOW on the bridge should learn more ringing patterns (sounds) in order to recognize different call priorities.
# Incorporating Grey Total Influence into Tolerance Rough Sets for Classification Problems
## 2 Tolerance Rough Sets
### Rough Set Theory  
Uncertainty and vagueness can be handled by rough set theory. Let \(S=(U,A\bigcup\{d\})\) be a decision table, where \(U\), \(A\), and \(D\) are nonempty finite sets. \(U\) is the universe of objects, \(A\) is a set of conditional attributes, and \(d\
otin A\) is a decision attribute. An information function \(a\): \(U\to V_{a}\) can be defined for \(a\in A\), where \(V_{a}\) is the set of values of \(a\) called the domain of \(a\). An indiscernibility relation \(\mathrm{Ind}(P)\) is defined for any \(P\subseteq A\):  
\[\mathrm{Ind}(P)=\left\{(\mathbf{x}_{i},\mathbf{x}_{j})\in U^{2}\Big{|}a( \mathbf{x}_{i})=a(\mathbf{x}_{j}),\forall a\in P\right\} \tag{1}\]  
where \(\mathbf{x}_{i}\) and \(\mathbf{x}_{j}\) are indiscernible if \((\mathbf{x}_{i},\,\mathbf{x}_{j})\) belongs to \(\mathrm{Ind}(P)\). \(\mathrm{Ind}(P)\) is called the \(P\)-indiscernibility relation, and its equivalence classes are denoted by \([\mathbf{x}]_{P}=\{\mathbf{y}\in U|a(\mathbf{x})=a(\mathbf{y}),\,\forall a\in P\}\). A \(P\)-definable set denotes any finite union of elementary sets [26]. In a classification problem, a concept \(X\) is composed of elements with the same class label such that \(X\in U/[d]\).  
Sometimes, \(X\subseteq U\) is not \(P\)-definable. If \(X\) is a vague concept, a pair of precise concepts, \(P\)-upper (\(\overline{P}X\)) and \(P\)-lower approximations (\(\underline{P}X\)), can be used to approximate \(X\)[1, 8]:  
\[\overline{P}X=\{\mathbf{x}|\mathbf{x}\in U,\,\,[\mathbf{x}]_{P}\cap X\
eq\phi\} \tag{2}\]  
\[\underline{P}X=\{\mathbf{x}|\mathbf{x}\in U,\,[\mathbf{x}]_{P}\subseteq X\} \tag{3}\]  
It is clear that \(\underline{P}X\subseteq\overline{P}X\). Elements that certainly belong to \(X\) constitute \(\underline{P}X\), whereas those possibly belonging to \(X\) constitute \(\overline{P}X\). The tuple \(\langle\underline{P}X,\,\overline{P}X\rangle\) is called a rough set, and \(\underline{P}X\) and \(\overline{P}X\) are singleton approximations. When \(\overline{P}X=\underline{P}X\), \(X\) is definable because \(X\) is precise with respect to \(P\). In contrast, \(\overline{P}X\
eq\underline{P}X\) means that \(X\) is undefinable.  
A boundary region \(\mathrm{BND}_{P}(X)\) defined for a vague concept \(X\) is as follows:  
\[BND_{P}(X)=\overline{P}X-\underline{P}X \tag{4}\]  
A rough membership function defines the degree of inclusion of \(\mathbf{x}\) within \(X\) with respect to \(P\):  
\[\mu_{X}^{P}(\mathbf{x})\,\,=\,\frac{|[\mathbf{x}]_{P}\cap X|}{|[\mathbf{x}]_{P }|} \tag{5}\]  
where \(\mu_{X}^{P}(\mathbf{x})\in[0,1]\), and \(\,|\,[\mathbf{x}]_{P}|\,\) denotes the cardinality of \([\mathbf{x}]_{P}\).
# Artificial Intelligence to Reshape the Healthcare Ecosystem
## 2 AI Techniques for Healthcare
### Generative AI
#### 2.4.1 Generative Adversarial Networks  
GANs consist of the combined usage of two neural networks, namely, a generator \(G\) and a discriminator \(D\). They are trained simultaneously by executing an adversarial competition between them [77].  
The generator input is a random noise process \(z\), with distribution \(p_{z}(z)\). Typically, a Gaussian distribution is used. Noise samples are transformed for obtaining synthetic data \(G(z)\) that mimic the distribution of the emulated process \(p_{\text{data}}\). In turn, the discriminator receives both real data \(x\), sampled from the real data distribution \(p_{\text{data}}(x)\), and the synthetic data \(G(z)\). It evaluates the probability \(D(G(z))\) that represents the likelihood of the input being real data. The objective of the discriminator is to correctly classify the input data as real or fake. The objective of the generator is to fool the discriminator into classifying the synthetic data as real. The training process can be modeled as a minimax game, where \(D\) and \(G\) are optimized simultaneously. The objective function \(V(G,D)\) can be written by using the entropy expression as follows:  
\[\min_{G}\max_{D}V(G,D)=\mathbb{E}_{x\sim p_{\text{data}}(x)}[\log D(x)]+ \mathbb{E}_{z\sim p_{z}(z)}[\log(1-D(G(z)))] \tag{31}\]  
Since \(D\) must be trained to maximize the probability of correctly classifying data, we can write  
\[\max_{D}\mathbb{E}_{x\sim p_{\text{data}}(x)}[\log D(x)]+\mathbb{E}_{z\sim p_{ z}(z)}[\log(1-D(G(z)))] \tag{32}\]  
Therefore, training can take advantage of the gradient ascent on the discriminator parameters \(\theta_{D}\):  
\[\theta_{D}\leftarrow\theta_{D}+\
abla_{\theta_{D}}\Big{(}\mathbb{E}_{x\sim p_ {\text{data}}(x)}[\log D(x)]+\mathbb{E}_{z\sim p_{z}(z)}[\log(1-D(G(z)))]\Big{)} \tag{33}\]  
Since \(G\) must be trained in order to compromise the classification performance of D, that is, to maximize the probability of the discriminator being fooled, we can write  
\[\min_{G}\mathbb{E}_{z\sim p_{z}(z)}[\log(1-D(G(z)))] \tag{34}\]  
or  
\[\max_{G}\mathbb{E}_{z\sim p_{z}(z)}[\log D(G(z))] \tag{35}\]  
Also, in this case, the gradient descent algorithm can be applied to the generator parameters \(\theta_{G}\):  
\[\theta_{G}\leftarrow\theta_{G}+\
abla_{\theta_{G}}\mathbb{E}_{z\sim p_{z}(z)} [\log D(G(z))] \tag{36}\]  
By iteratively updating the discriminator and the generator, the generated data tend to be indistinguishable from real data to the discriminator.  
As mentioned above, this approach proves advantageous in different situations. For example, GANs can generate synthetic patient data for privacy protection or to augment training datasets in order to improve the robustness and accuracy of predictive models [78; 79; 80]. A notable example of GAN technologies is StyleGAN, developed by NVIDIA researchers [81].
# LESS: Link Estimation with Sparse Sampling in Intertidal WSNs
## 5 Design of \(Less\) Protocol
### Scheduling Algorithms
#### 5.2.3 **K**-Temporal Sampling (K-T)  
K-Temporal sampling assumes information is distributed with temporal pattern. For example, in a WSN deployed in the intertidal zone, link quality changes slightly when nodes are under water (i.e., link quality is always 0) and changes severely when above water. For this case, we can set smaller sampling rate during under-water period and vice versa. For this type of systems, we divide the time period into static phase (\(T_{1}\)) and dynamic phase (\(T_{2}\)). We define the sampling rate in these two phases as \(s_{3}\) and \(s_{4}\), and the predefined system sampling rate \(s\), \(s_{3}\) and \(s_{4}\) should satisfy Equation (9).  
\[s*|T|=s_{3}*|T_{1}|+s_{4}*|T  
```
0: System running period \(|T|\), \(T_{1}\), \(T_{2}\), new sampling rate \(s_{3},s_{4}\)
0: The decision set \(D\) for the \(N\) nodes in time \(T\)
1:for\(i\in N\) and \(t\in T\)do
2:if\(t\in T_{1}\)then
3:\(D(i,t)=Rand()<s_{3}\) // the stationary phase.
4:elseif\(t\in T_{2}\)then
5:\(D(i,t)=Rand()<s_{4}\) // the dynamic case.
6:endif
7:endfor
8:return\(D\)
```  
**Algorithm 3** The **K**-Temporal sampling (K-T) Algorithm.  
**Comparison of the three algorithms.** The three scheduling algorithms are designed for different systems. The random sampling algorithm is of less overhead and suits for general systems, while K-S and K-T are specially designed for systems with spatial and/or temporal characteristics. K-S and K-T require system knowledge in advance, which can be obtained during deployment of systems or from empirical analysis. With the system information, LESS provides better services in both accuracy and efficiency. In Section 7, we give a detailed performance comparison between of three scheduling algorithms.
## 4 Fault Monitoring Using KPCA-KDE-SSGLR
### Offline Model Development  
1. Collect the results during normal operating conditions (NOC) and level the dataset utilizing the mean and standard deviation of the sample collection sections describing different variables.  
Figure 1: Flowchart of KPCA-KDE-SSGLR monitoring.  
Pick the type of kernel structure to be used and specify the kernel parameter.
* Develop and cluster the kernel module of the NOC dataset utilizing Equation (5).
* Attain eigenvalues and their consistent eigenvectors then reorganize in a descendent direction.
* Orthonormalize the eigenvectors employing Equation (6).
* Achieve nonlinear modules by applying Equation (7).
* Calculate the optimum number of principal components to be utilized by CPV.
* Calculate monitoring indices \(\hat{R}_{k,m,l}\), T\({}^{2}\) and Q grounded on the kernelized NOC data through Equations (9), (12) and (16).
* Accomplish control limits of \(\hat{R}_{k,m}\), T\({}^{2}\) and Q by Equations (10), (14) and (17).
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 4 Comparison of the Prestress Loss with Steel Strands
### Comparison of BFRP Tendons and Steel Strands  
#### 4.3.1 Prestress Loss in the Prestressing Stage  
The prestress loss of BFRP tendons and steel strands in the prestressing stage at various distribution angles and with different slab widths is presented in Figure 1. Whether it was BFRP tendon or steel strand, the prestress loss arising from anchorage deformation and prestress retraction (\(\sigma_{11}\)) decreased as the slab width increased, and increased as the distribution angle increased, as shown in Figure 1a, signifying that the type of the prestressed tendon had limited impact on the relationship between the prestress loss \(\sigma_{11}\) and the slab width and distribution angle. For the prestress loss resulting from the friction between the prestressed tendon and the surrounding casing pipe (\(\sigma_{12}\)), its values for BFRP tendons and steel strands were the same, given the same slab width and distribution angle, as shown in Figure 1b, demonstrating that the properties of the prestressed reinforcement had no impact on the prestress loss \(\sigma_{12}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Distribution Angle (\(\%\))**} & \multicolumn{4}{c}{**Total Prestress Loss (\%)**} \\ \cline{2-5}  & **Slab Width = 3.0 m** & **Slab Width = 4.5 m** & **Slab Width = 9.0 m** & **Slab Width = 12.75 m** \\ \hline
20 & 30.8 & 21.9 & 23.6 & 26.5 \\
25 & 25.0 & 22.4 & 22.5 & 24.5 \\
30 & 26.7 & 23.2 & 22.0 & 23.4 \\
35 & 28.5 & 24.2 & 21.8 & 22.7 \\
40 & 30.2 & 25.1 & 21.8 & 22.2 \\
45 & 31.8 & 26.1 & 21.9 & 22.0 \\ \hline \hline \end{tabular}
\end{table}
Table 13: The total prestress loss of steel strands (%).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\sigma_{14}\) (\%) & \(\sigma_{15}\) (\%) & \(\sigma_{14}\) + \(\sigma_{15}\) (\%) \\ \hline
3.5 & 6.9 & 10.4 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Theoretical prestress loss of steel strands during the in-service stage.  
As described in Figure 1, given the same slab width and distribution angle of the prestressed reinforcement, the prestress loss \(\sigma_{\text{11}}\) of the BFRP tendon was far lower than that of the steel strand, and the difference showed a tremendous increase with the decrease of the slab width and the increase of the distribution angle. When the slab width was 3.0 m and the distribution angle was 45\({}^{\circ}\), the difference in \(\sigma_{\text{11}}\) between the BFRP tendon and steel strand reached the maximum value of 10.9%, which illustrated that the use of BFRP tendon can significantly reduce the prestress loss during the prestressing stage, especially with a smaller slab width and a greater distribution angle.
## 2 Materials and Methods
### Methods
#### 2.4.3 Model Performance Evaluation Methods  
This research employs six distinct machine learning models for landslide susceptibility assessments: the RF [62], SVM [63] MLP [64], XGB [65], gradient boosting [66], and KNN [67].  
The RF, XGB, and GBM are tree-based ensemble methods that improve model robustness by combining multiple decision trees. The SVM is a kernel-based classifier designed to find the optimal separating hyperplane. The KNN is a distance-based non-parametric method that assigns class labels based on majority voting among the nearest neighbors. MLP belongs to neural network models and is a feedforward network with multiple hidden layers; it is capable of modeling nonlinear relationships.  
To ensure stable performance and fair comparison among different machine learning models, hyperparameter tuning was conducted separately for each model using the GridSearchCV method. All models were trained and validated using 5-fold cross-validation to mitigate the risk of overfitting. During training, feature data were standardized using StandardScaler, and sample imbalance was addressed using the synthetic minority oversampling technique (SMOTE). All computations and experiments were performed in the Python 3.11.7 environment.  
To evaluate model performance comprehensively, several metrics are utilized, including accuracy, recall, the area under the curve (AUC) value, and receiver operating characteristic (ROC) curve analysis. Ideally, the ROC curve should closely approach the y-axis; if the ROC curve falls below the y = x line, it indicates poor model performance. The AUC value reflects the model's discriminative ability, with an ideal value of 1.  
This study also uses five metrics--accuracy, recall, the F1 score, precision, and logarithmic loss--to evaluate model performance. Their respective formulas are as follows:  
\[\text{Accuracy}= \frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}} \tag{8}\]\[\text{Recall}= \frac{\mathit{TP}}{\mathit{TP}+\mathit{FN}} \tag{9}\] \[\text{F1}= 2\ \times\ \frac{\text{Precision}\ \times\ \text{Recall}}{\text{ Precision}+\text{Recall}}\] (10) \[\text{Precision}=\frac{\mathit{TP}}{\mathit{TP}+\mathit{FP}}\] (11) \[\text{Log Loss}=-\frac{1}{N}\sum_{i=1}^{N}\left[y_{i}\times\text{ log}\left(\hat{y_{i}}\right)+\left(1-y_{i}\right)\times\text{log}\left(1-\hat{y_{i}} \right)\right] \tag{12}\]  
where \(\mathit{TP}\) is the number of true positive cases, \(\mathit{TN}\) is the number of true negative cases, \(\mathit{FP}\) is the number of false positive cases, \(\mathit{FN}\) is the number of false negative cases, \(\mathit{N}\) represents the total number of samples, \(y_{i}\) is the true label of the \(i\)th sample (either 0 or 1), and \(\hat{y_{i}}\) is the model's predicted probability that the \(i\)th sample belongs to class 1, which also ranges from 0 to 1.