esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Dual-Hop Hybrid RF-UOWC NOMA Systems
## 5 Proposed Power Allocation Algorithm  
In this section, we propose a power allocation algorithm for optimizing the system OP under the condition \(a_{i}=b_{i}\), where \(i\in\{1,2\}\) or equivalently \(\tau_{i}=\beta_{i}\) and \(\tau=\beta\). The proposed optimization problem is expressed as  
\[\min_{a_{1}}\ OP_{sys} \tag{29a}\] \[\text{s.t.}\ \ \frac{\gamma_{1}}{1+\gamma_{1}}<a_{1}<\frac{1}{1+ \eta\gamma_{2}}\] (29b) \[\ \ \ a_{1}+a_{2}=1. \tag{29c}\]  
We provide the following Theorem to solve Problem (29).  
Theorem 5.1: _Problem (29) is a convex problem, and the optimal power allocation factor value is \(a_{1}^{*}=\frac{\gamma_{1}(1+\gamma_{2})}{\gamma_{1}+\gamma_{2}+\gamma_{1} \gamma_{2}(1+\eta)}\)._  
Proof: See Appendix A.  
Figure 6 graphically verifies that the obtained result in Theorem 5.1 is correct. We set \(R_{1}=0.5\) and \(R_{2}=0.75\) as a test values, which implies that \(a_{1}^{*}\approx 0.58\) mathematically, which is consistent with the optimal value in Figure 6.
## 2 Materials and Methods
### Determination of Bioactive Compounds
#### 2.4.2 Total Flavonoid Quantification  
The total flavonoid content was determined according to the Dowd method adapted by [PERSON] et al. [21] with some modifications: a dilution of propolis extract (1:1000 \(w\)/\(v\)) was made using pure methanol, it was homogenized until complete solubility using a Vortex, then the extracts were centrifuged at 18,510\(\times\)\(g\) for 15 min at 4 \({}^{\circ}\)C (Hermle, Z 36 HK, Gosheim, Germany). A solution of 2% aluminum chloride (AlCl\({}_{3}\)) in methanol was prepared. For this assay, 2 mL of the supernatant with 2 mL of AlCl\({}_{3}\) were placed in a test tube, homogenized and left to stand at room temperature for 20 min in the absence of light. The absorbance was measured at 415 nm in a spectrophotometer (Jenway 6715, Ware, UK) using methanol as a blank. Quercetin was used for the elaboration of the standard curve (0 to 100 mg/L). The total content of flavonoids was expressed in equivalent mg of quercetin/100 g of propolisis extract (mg QE/100 g of propolisis extract), according to the following equation. All samples were analyzed in triplicate.  
Total flavonoids (mg QE/100 g) = (QC \(\times\) V \(\times\) DF \(\times\) 100)/(weight of the sample (g))  
where:  
QC = Quercetin concentration from the standard curve (mg/mL);  
V = Volume of sample (mL);  
DF = Sample dilution factor.
# Empowering Healthcare: TinyML for Precise Lung Disease Classification
## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Work** & **Lung Diseases** & **Dataset** & **Feature(s)** & **Model** & **Results** & **Deployment** \\ \hline \multirow{4}{*}{[24]} & Asthma, COPD, & \multirow{4}{*}{Respiratory Sound} & \multirow{4}{*}{MFCC} & \multirow{4}{*}{CNN} & \multirow{4}{*}{95.7\%} & \multirow{4}{*}{Cloud} \\  & URTI, LRTI, and & & & & & \\  & bronchiectasis and & & & & & \\  & normal & & & & & \\ \hline \multirow{4}{*}{[25]} & Normal and & ICBHI 2017 and & \multirow{4}{*}{STFT and MFCC} & ANN, SVM, KNN, & ANN: 98.61\%; & \multirow{4}{*}{Cloud} \\  & abnormal & KAUH & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{4}{*}{[5]} & Asthma, BRON, & \multirow{4}{*}{-} & CNN and LSTM & CNN: 99.81\%; & \multirow{4}{*}{Cloud} \\  & COPD, heart & \multirow{4}{*}{-} & CAUH & & & \\  & failure, lung & \multirow{4}{*}{-} & CAUH & & & \\  & fibrosis, normal, and pleural & & & & & \\  & effusion & & & & & \\  & pneumonia & & & & & \\ \hline \multirow{4}{*}{[29]} & Asthma, COPD, & \multirow{4}{*}{-} & CNN & \multirow{4}{*}{LSTM} & CNN: 99.81\%; & \multirow{4}{*}{Cloud} \\  & ILD, bronchitis, and pneumonia & & & & & \\ \cline{1-1}  & Healthy, chronic, & \multirow{4}{*}{ICBHI 2017} & MFCC & CNN & Accuracy: 99\% & Cloud \\  & and non-chronic & & & & & \\ \hline \multirow{4}{*}{[31]} & Healthy and & \multirow{4}{*}{Self-collected data} & MFCC and & SVM-MFCC, & MFCC-SVM: 86\%; & \multirow{4}{*}{Cloud} \\  & pathological & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{[31]} & Rale, rhonchus and & \multirow{4}{*}{Self-collected data} & MFCC and & SVM-MFCC, & MFCC-SVM: 76\%; & \multirow{4}{*}{Cloud} \\  & normal & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{[32]} & Asthma and & \multirow{4}{*}{Self-collected data} & Chroma, RMS, and & \multirow{4}{*}{ConvNet} & Accuracy: 99.8\% & Mobile \\  & non-asthma & & MFCC & & & \\ \hline \multirow{4}{*}{[33]} & Healthy and & \multirow{4}{*}{KAUH} & \multirow{4}{*}{Mel-spectrogram} & RDsLINet & Accuracy: 94\% & Edge \\  & asthma, BRON, & & & & & \\ \cline{1-1}  & COPD, pneumonia, & \multirow{4}{*}{KAUH} & Mel-spectrogram & RDsLINet & Accuracy: 94\% & Edge \\ \cline{1-1}  & heart failure, and & \multirow{4}{*}{DEHI 2017} & \multirow{4}{*}{MFCC} & LSTM & Accuracy: 81.4\% & Edge \\ \cline{1-1}  & unnormal & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work summary.
## 4 SMS Configurations Setup
### Fuzzy Logic Configurations
#### 4.2.1 The Setting of Membership Functions (MF)  
The data used in the fuzzification process are predicted or unsupervised data, which will be validated by engineers three later on using the developed model. Membership Functions are graphical representations used to define the values for each variable that will be later used to define the prediction model's rules. The setup of the Membership functions for each input is shown below in Figure 5, Figure 6, Figure 7, and Figure 8, respectively. Lead time, Quality, and Cost are used as Inputs to determine the output which is the Cost Justification. For example, in Figures 5 and 6 below, the range of values specified is from 0 to 90. This range was divided into categories to further classify the values for Low, Medium, and High, respectively. These range of values were chosen randomly to provide equal division for the three different value categories for Low, Medium, and High throughout the configurations, further validated by actual field engineers upon obtaining the final results corresponding to these specified values. Tables 2 and 3 additionally display the categorization of these ranges of values used for the configurations.  
Table 2 further shows the values configured for the input variables: Lead time, Quality, and Cost. In contrast, Table 3 shows values used for the Cost Justification, which plays an output variable in the evaluation model.
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 1 Introduction
### Tower Companies  
TowerCos are independent companies that own the passive infrastructure, such as towers, cooling, and power, and provide space at the sites on lease to the mobile network operators (MNO). They provide the RAN infrastructure which is shared by multiple MNOs, thereby reducing the operator's CAPEX and OPEX without compromising on the coverage they could provide. It is a growing trend internationally, specifically in the European wireless network industry.  
The independent TowerCos in Europe have expanded significantly in the last few years. For example, Cellnex, a Spanish TowerCo originally providing broadcasting equipment to television towers, has now expanded into wireless networks by acquiring sites from operators in various European countries. Moreover, operators create separate TowerCos where they usually are the majority shareholder, for example, Vantage from Vodafone. Another emerging trend is to share the sites and the infrastructure between operators, especially in less profitable areas, such as rural or suburban areas.  
Figure 1: Share of ICT energy consumption by different categories.
###### Abstract  
A workshop measuring and positioning system (wMPS) is a large three-dimensional (3D) coordinate-measurement system based on optoelectronic scanning. It is capable of large-range coverage, high measurement accuracy and frequency, and multi-task synchronous measurement. Existing geodetic instruments cannot measure the intrinsic parameters of strong rigid structures. Thus, this study conducted experiments to explore the feasibility of the wMPS to measure the intrinsic parameters of rigid structures. A test bed was established using a reverse-engineering method to simulate the oscillation frequency of the structure. Displacement data, which changed with the time series through the fast Fourier transformation method, were analyzed to determine the feasibility and range of the wMPS in measuring intrinsic parameters of the structure. The experimental results demonstrated that the wMPS can measure the vibrational frequency up to 9 Hz with a 3-mm amplitude and up to 4 Hz with a 30-mm amplitude.  
Keywords:natural frequency; health monitoring; rigid structure; wMPS +
Footnote †: journal: Applied Sciences
# Study of the Process of Packet Arrival at a Multiservice Node
## 2 Mathematical Model of a Multiservice Node
### Areas for Further Research  
According to the authors of this paper, further research should be carried out in four main fields. These fields are indirectly related to each other since they pursue one goal. It consists in optimizing a number of basic processes related to servicing multiservice traffic.  
The first field of further research is related to the analysis of the stability of histograms, which are obtained following the result of measuring traffic parameters. Packet traffic is subject to dramatic fluctuations. For this reason, it is necessary to assess the results obtained in terms of their stability.  
The second field of further research is aimed at solving forecasting problems. Changes in the form of the histograms and their parameters may serve as a good base for predicting changes in the CDF \(A(t)\). The results of such a forecast are important, in turn, for the design of telecommunication networks, as well as for their management in the event of congestion.  
The third field is tied to the relevance of the analysis of the functions \(A(t)\), in which the value of the intensity of the arriving request flow is not a constant value. In other words, it is advisable to obtain results that will be similar to those obtained in this paper but after the \(\lambda(t)\) function has been introduced instead of a point assessment for the intensity of the request flow.  
The fourth field is related to the study of continuous functions that are represented by histograms. This approach is relevant for the investigation of symmetric beta distribution [22]. It is recommended for the analysis of traffic related to the Internet of Things [13].
## 2 Materials and Methods
### Proposed Data Augmentation Method
#### 2.2.1 One-Channel Augmentation  
This section discusses the first of the two steps of data augmentation. We performed one-channel augmentation via four approaches: random pixel noise, bright adjustment, blur, and edge extraction. Edge extraction is conducted to extract structural information of the inspection target. The other approaches imitate image changes that can occur when acquiring images from the CMOS camera.  
Pixel Noise  
As shown in Figure 6, there are two types of image sensors used in machine vision, namely charge-coupled device (CCD) and CMOS. A CCD is a sensor that accumulates and transmits charges generated by using light energy and eventually converts them into electrical signals. CMOS sensors immediately amplify and transmit the charges generated by using light energy into electrical signals. CMOS sensors outperform CCD sensors regarding the number of frames per second, resolution, and power consumption. As a result, the CMOS sensor is used for high-resolution machine vision cameras; however, it has the disadvantage of pixel-level noise, as shown in Figure 7. We performed data augmentation by imitating such pixel noise; the pseudo-code is shown in Algorithm 1.  
Input: Original grayscale image  
Output: Grayscale image with pixel noise.  
for x in range of 0 to width of image  
for y in range of 0 to height of image  
value = image[x, y] + random number in range of \(-\)10 to 10  
if value > 255  
value = 255  
else if value < 0  
value = 0  
image[x, y] = value  
end for  
Figure 6: Difference between CCD and CMOS sensors: (**a**) CCD sensor array and (**b**) CMOS sensor array.  
Contrast Limited Adaptive Histogram Equalization (CLAHE)  
Even if the optical system that inspects the product is configured to minimize the effect of external light sources, the brightness of the captured image is sometimes different because of the external reflective light. Neural networks can get robust against brightness changes by adjusting the brightness distribution of the dataset. To equalize the brightness distribution, we used the CLAHE algorithm published by [PERSON] et al. [25]. Figure 8a,b shows the difference in brightness before and after the application of CLAHE, respectively.
# Object/Scene Recognition Based on a Directional Pixel Voting Descriptor
## 3 The Proposed Algorithm
### Feature Extraction and Description
#### 3.2.1. Feature Extraction  
As mentioned before, our approach is based on edge primitives. Although there are existing algorithms, such as Sobel and Canny, for that purpose, they are sensitive to noise or difficult to segment properly. To address those issues, we propose a new extraction method as follows, where the edge map \(e(i,j)\) (see Figure 4) is defined as follows:  
\[e(i,j)=\left\{\begin{array}{ll}1&\text{if }((\text{dif}_{1}\geq\text{th \& dif}_{2}\geq\text{th}))\text{ \& }d(i,j)==1\\ 1&\text{if }((\text{dif}_{3}\geq\text{th \& dif}_{4}\geq\text{th}))\text{ \& }d(i,j)==2\\ 0&\text{otherwise}\end{array}\right. \tag{3}\]  
where \(\text{dif}_{1}\), \(\text{dif}_{2}\), \(\text{dif}_{3}\), \(\text{dif}_{4}\), and \(g_{xy}(i,j)\) are defined as follows: \(g_{xy}(i,j)=g_{x}(i,j)+g_{y}(i,j)\)  
\(\text{dif}_{1}=g_{xy}(i,j)-g_{xy}(i,j-1)\),  
\(\text{dif}_{2}=g_{xy}(i,j)-g_{xy}(i,j+1)\),  
\(\text{dif}_{3}=g_{xy}(i,j)-g_{xy}(i-1,j)\),  
\(\text{dif}_{4}=g_{xy}(i,j)-g_{xy}(i+1,j)\).  
Then, the set of anchor points \(f(k)\) is computed via Algorithm 1.  
Figure 4: Let (**a**) be the input image template an edge map is computed via Equation (3) given the resulting image presented in (**b**).
# High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5
## 2 Materials and Methods  
### Original YOLOv5 Algorithm  
YOLO series is widely used in many fields with its fast speed and strong portability. The YOLOv5 network model [28] is mainly composed of five parts: Input, Backbone, Neck, Prediction, and Output. As shown in Figure 1, the image entered into the network is sliced first, and the downsampling effect is achieved when the complete image information is saved. The backbone network mainly completes the feature extraction of the image through the convolution module and the cross-stage partial module with residual structure. The neck of the network mainly fuses the image feature information extracted from the backbone network through the spatial pyramid pooling module, retaining rich image feature information for subsequent target object detection. The detection part of the network obtains the category of the target object, the category confidence, and the coordinate information of the object position, and the target area is marked by the anchor frame.  
As shown in Figure 1, in the original YOLOv5 network, for the image input network containing insulators to be detected, the image information is obtained through a series of operations such as slicing, convolution, and sampling, and finally, the detection result map with a detection frame is output to realize end-to-end insulator region recognition.
## 4 Results and Discussion
#### Result Analysis on KITTI MOD Dataset  
Finally, the result analysis of the CIWHO-ORC technique takes place on the KITTI MOD dataset [24; 25; 26; 27; 28], which includes 5997 static vehicles and 2383 dynamic ones labeled. Table 7 and Figure 6 provide brief classification results of the CIWHO-ORC technique under distinct classes. The results stated that the CIWHO-ORC technique has accomplished enhanced classifier results under all classes. For example, under the static-van class, the CIWHO-ORC technique resulted in a precision of 0.895, a recall of 0.795, and an F-score of 0.835. At the same time, under the static-car class, the CIWHO-ORC technique attained a precision of 0.961, a recall of 0.714, and an F-score of  
In order to report the improved outcomes of the CIWHO-ORC technique, a comparative analysis with existing techniques takes place in terms of the mAP in Table 8 and Figure 7[29]. The experimental results indicated that the DS-ODFR technique achieved the least outcome with an mAP of 0.550.  
The MODNET technique obtained a slightly enhanced performance with an mAP of 0.630. However, the CIWHO-ORC technique resulted in effective detection results with a maximum mAP of 0.692. From the result analysis, it can be verified that the CIWHO-ORC technique has the ability to detect and classify objects under several conditions.
## 5 Phase Field Methods
### PFM Variational Formulation
#### 5.2.3 Linear Approximation  
In the quadratic approximations shown in Sections 5.2.1 and 5.2.2, the phase field variable and therefore the degradation function evolve as soon as the structure is loaded. This is clearly not the case in purely elastic brittle materials that demonstrate a linear elastic behavior until a crack initiates.  
Pham et al. [262] addressed this issue by employing a linear approximation of the surface energy integral to achieve a diffused localization band and a purely elastic global response until the onset of damage. The 1-D expression for \(c(x)\) in this case can be given as in Equation (63), which is also illustrated in Figure 7d (See also Figure 10).  
Figure 9: Fourth-order quadratic approximation: Effect of the length-scale parameter \(l_{0}\) on the width of diffusion.  
More recently, Geelen et al. [250] provided an analogous linear approximation based on the following expressions for \(c_{w}\) and \(\omega(c)\)  
\[c_{w} =\frac{16}{3} \tag{64}\] \[\omega(c) =4(1-c)\]  
which result in the following definition of the crack functional  
\[F_{\Gamma}=\frac{3}{8l_{0}}\bigg{[}1-c+l_{0}^{2}|\
abla c|^{2}\bigg{]}. \tag{65}\]  
In view of Equation (66), the approximation of the surface energy integral in Equation (54) assumes the following form  
\[\int_{\Gamma}\mathcal{G}_{c}d\Gamma\approx\int_{\Omega}\frac{3\mathcal{G}_{c }}{8l_{0}}\bigg{[}1-c+l_{0}^{2}|\
abla c|^{2}\bigg{]}d\Omega \tag{66}\]  
The linear approximation in Equation (66) differs from the corresponding formulation in [250] in the sense that a fully cracked-state in the current study is represented by \(c=0\) in the current study, as opposed to \(c=1\) in [250]. In addition, the total diffusion width in the current model (Equation (66) and Figure 7d) is twice the diffusion width in [250] to maintain consistency with other models.  
It is of interest to note that the quadratic form (Equations (60) and (61)) implicitly guarantees the boundedness of the phase field variable \(c\) within the limits \([0,1]\). However, the solution obtained by Equation (65) is not intrinsically bounded within this interval, and additional constraints must be imposed to ensure boundedness.  
This is achieved by employing a Penalty (see, e.g., [263]) or a Lagrange multiplier method (see, e.g., [250]). In both methods, a staggered iterative scheme is required for the solution of the resulting constrained system of governing phase-field equation. To guarantee both the boundedness and irreversibility of the phase field variable, Gerasimov and De Lorenzis [263] proposed a method to choose the value of an optimal or lower bound of the penalty parameter beyond which adequate constraint enforcement can be ensured.  
Figure 10: Second-order linear approximation: Effect of the length-scale parameter \(l_{0}\) on the width of diffusion.
## 2 Background and Theory
### Classification Algorithms
#### 2.3.1 LinearSVM  
The linear support vector machine (SVM) [56] serves as the first supervised learning model in this study. In the context of EEG analysis, the linear SVM aims to determine an optimal hyperplane that maximizes the margin between two different classes. This margin represents the smallest distance separating the hyperplane from the nearest data points, commonly known as support vectors. The linear SVM's decision boundary is formally defined by Equation (24):  
\[f(x)=w^{\top}x+b \tag{24}\]  
In this context, \(x\) denotes the feature vector derived from EEG signals, \(w\) is the weight vector perpendicular to the hyperplane, and \(b\) indicates the bias term, which adjusts the hyperplane's position.
## 3 Materials and Methods
### Keyword Overlap Analysis  
To systematically evaluate the lexical grounding of model predictions in explicit bias, we constructed a comprehensive Spanish slur dictionary by combining multiple high-coverage resources. The primary source was the Spanish lexicon from HurtLex [80], a multilingual lexicon of offensive and hateful words annotated by semantic category. To further increase coverage of colloquial and internet-specific slurs, we integrated terms from the BigScience ROATS Corpus's [60] offensive language list.  
After merging these resources, we manually curated the resulting list to remove duplicates and harmonize token forms (e.g., stripping diactitics, converting to lowercase, and standardizing variants). In order to reduce false positives arising from homonyms or benign polysemic words, we developed a custom whitelist of Spanish terms that are misidentified as slurs but are not actually offensive in context (for example, animal names or technical terms appearing in HurtLex). All whitelist entries were explicitly excluded from the final dictionary used for model evaluation.  
The resulting slur lexicon was stored in a plain text file, with one term per line, and served as the reference for keyword overlap analysis.  
To detect the presence of slurs in model-predicted positive and negative examples, we implemented a substring-matching approach in Python 3.14. For each annotated dialogue, the script lowercases the input text and checks whether any slur in the lexicon appears as a substring. Each example is thus flagged as containing or not containing a known slur, enabling both aggregate and case-by-case analyses of lexical overlap. This method, while simple, provides high recall for explicit toxic language and offers a transparent, reproducible basis for quantifying the reliance of models on explicit lexical cues.  
This procedure ensures that our keyword overlap analysis meaningfully distinguishes between lexical and context-dependent toxicity, providing insight into both the strengths and weaknesses of the evaluated classifiers when exposed to diverse forms of bias in Spanish-language online dialogues.
# Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence
## 3 Bounded-Error Sensor Data Compression (BESDC)
### Basic Processing Flow
Finally, these compressed data after bounded-error-pruning in BESDC, without jeopardized the required QoS2 and QoD (as indicated in Equations (7) and (8)), will be forwarded to the server via the sink. Finally, the fog-tier server will decompress the compressed data and report the results for IEI applications.