esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Review of Previous Works
### Throughput Estimation Model
#### 3.1.2 Throughput Estimation from Received Signal Strength  
From _received signal strength_\(P_{d}\), the throughput/link speed is calculated by using the _sigmoid function_[24]:  
\[tp=\frac{a}{1+e^{-(\frac{(120+p_{d})-b}{c})}} \tag{3}\]  
where \(tp\) represents the estimated throughput (Mbps); \(P_{d}\) does \(RSS\) (\(-\)dBm) at the host; and \(a\), \(b\), and \(c\) are constant parameters obtained using measurements. These values are optimized by the _parameter optimization tool_[23] in Table 2.
# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
## 3 RFF Identification Scheme
### Existing DCTF-Based CNN Structures  
Drawing from existing research on DCTFs in fields such as LTE, and considering the characteristics of the 5G PRACH preamble, two CNN structures were used to classify the DCTF of a 5G PRACH signal. The single-channel CNN structure synthesized the overall features, and the multi-channel CNN structure emphasized the transient part of the signal.
# Biocompatibility and Osteo/Odontogenic Potential of Various Bioactive Root-End Filling Materials
## 2 Materials and Methods
### Cell Viability Assay  
Cells were seeded at 10,000 cells/well for 96-well plates in 200 \(\mu\)L of growth medium. Plates were incubated in an incubator at 37 \({}^{\circ}\)C with 5% CO\({}_{2}\) overnight for cell adherence. The next day, MSCs were exposed to 200 \(\mu\)L of extract solutions diluted in growth medium, and a control group was included with cells incubated only in growth medium. An osteogenic group was determined for the positive control of osteeo/odontogenic differentiation in which the cells were cultured in osteogenic medium (DMEM with low glucose containing 10% FBS, 1% PSA, 50 \(\mu\)M ascorbic acid, 10 nM dexamethasone, and 10 nM \(\delta\)-glycerophosphate). CellTiter 96\({}^{\circledR}\) Aqueous One Solution Cell Proliferation Assay (MTS, Promega, Madison, WI, USA) was used to measure cell viability according to the manufacturer's instructions on days 1, 3, and 7 of cell culture. Absorbance was determined with an ELISA Plate Reader (Variouskan LUX, Thermo Fisher Scientific, Waltham, MA, USA) at 490 nm. The reading of the control group was adjusted as 100% to calculate the viability in test groups accordingly. Each group was worked in triplicates.
## 3 Results and Discussion
### Change of Viability of Lactobacillus after Tabletting and Tablet Disintegration
\begin{table}
\begin{tabular}{c c c} \hline
**Samples** & **Number of Viable Cells** & **The Survival Rate after Tabletting** \\ \hline Mixed lactobacilli powder & \((7.14\pm 0.52)\times 10^{10}\) CFU/g powder & \\ \hline Control tablet & \({}^{\#}\)\((1.55\pm 0.80)\times 10^{9}\) CFU/Tablet a & 14.50\% \\ L-group tablet & \((5.18\pm 3.71)\times 10^{9}\) CFU/Tablet b & 48.37\% \\ M-group tablet & \((7.00\pm 1.14)\times 10^{9}\) CFU/Tablet b,c & 65.36\% \\ H-group tablet & \((9.03\pm 1.77)\times 10^{9}\) CFU/Tablet c & 84.31\% \\ \hline \end{tabular} All data show the means \(\pm\) SD. \({}^{\#}\) ANOVA was performed among different kinds of effervescent tablets. Different letters in the same column indicate a significant difference.  
\end{table} 
Table 3: Viability of lactobacilli cells in the mixed lactobacilli powder before tabletting and in different kinds of lactobacilli effervescent tablets. and ascribed this lack of antimicrobial activity of the whole plant to possible antagonist effects between ginsenosides and other ginseng components. Considering the protective effect of the herbs on the viability of lactobacilli after tabletting, the changes of the viability of lactobacilli after tabletting and tablet disintegration for tablets containing the herbs may be due to possible antagonistic effect between saponins and other components in the herbs.
# XOR Multiplexing Technique for Nanocomputers
## 3 Bifurcation Analysis of the XOR Multiplexing System
When \(0\leq\varepsilon<\varepsilon_{*}\), the system exhibits periodic motion with period 2, namely \(z_{0}\), loses stability. Figure 6: Schematic of a full binary tree whose nodes are XOR and NAND gates with \(\varepsilon\). We denote those two points by \(Z_{+}\) and \(Z_{-}\). At \(n\)th stage, when \(Z_{+}\) is input, then \(Z_{-}\) would be output and vice versa [14; 23]. That means  
\[Z_{+}=1-\varepsilon+(2\varepsilon-1){Z_{-}}^{2} \tag{9}\]  
\[Z_{-}=1-\varepsilon+(2\varepsilon-1){Z_{+}}^{2} \tag{10}\]  
From Equations (9) and (10), one obtains  
\[Z_{\pm}=\frac{1\pm\sqrt{4(1-\varepsilon)(1-2\varepsilon)-3}}{2(1-2\varepsilon)} \tag{11}\]  
Clearly, when \(\varepsilon=\varepsilon_{*}\), we have \(Z_{+}=Z_{-}\) and it can be derived that the bifurcation point \(\varepsilon_{*}=\left(3-\sqrt{\varepsilon}\right)/4\ =0.08856\cdots\). Now, it is easy to see that the error probability interval where the system functions is \(0\leq\varepsilon<\varepsilon_{*}\). When \(\varepsilon>\varepsilon_{*}\), the outputs converge to the stable fixed point \(z_{0}\) regardless of what the initial inputs are. Hence, the gate error threshold is the bifurcation point \(\varepsilon_{*}=\left(3-\sqrt{\varepsilon}\right)/4\ =0.08856\cdots\). Using fixed error probability \(\varepsilon\) from 0 to 0.1, and plotting the 3-D diagrams of \(X\), \(Y\) and \(Z\) for the XOR multiplexing system, leads to Figure 5:0 From Figure 8, we can clearly observe the transformation of output from two distinct states to a fixed point when we fixed error probability \(\varepsilon\) from 0 to 0.1, with \(\varepsilon_{*}\) as the bifurcation point. Figure 5:1 3-D diagrams of XOR multiplexing for \(X\), \(Y\) and \(Z\). Figure 5:2 Bifurcation diagram for cascaded XOR gates and NAND gates.
# Enhanced YOLOv8 with BiFPN-SimAM for Precise Defect Detection in Miniature Capacitors
## 3 Materials and Methods  
### YOLOv8 Improved Model  
In the dynamic landscape of target detection algorithms, the YOLO (you only look once, only vision) series stands as a paragon of speed and accuracy. Among its iterations, the latest, YOLOv8, has rapidly gained prominence within the series. The model is offered in various configurations, such as YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x [24], each tailored to specific performance requirements. In our work, YOLOv8n was selected as the foundational model. The structure of YOLOv8n is shown in Figure 1.  
While the original YOLOv8n model boasts commendable overall performance, it exhibits certain limitations in identifying small dense defects. To surmount these challenges and enhance the model's efficacy in pinpointing diminutive flaws in micro-capacitors, this paper introduces three significant enhancements to the YOLOv8 architecture. Figure 2 displays the enhanced network architecture. First, we integrated an attention mechanism known as the simplified attention module (SimAM) into the YOLOv8 framework. The purpose of this integration was to increase the model's attention to smaller targets to improve its ability to identify and categorize critical minor flaws during micro-capacitor inspections. Second, the model's multi-scale information fusion was improved by BiFPN combined with the p2 layer, which also increases the accuracy of small target detection. Lastly, the WISE-IOU [12; 25] loss function replaced the traditional CIoU loss function. The purpose of this modification was to focus the model's attention on higher-quality, representative examples. WISE-IOU adoption greatly strengthened the model's capacity for generalization, which in turn improved the model's robustness and performance in a variety of detection scenarios.  
Figure 1: YOLOv8n network structure.
## 3 Definitions and Basic Concepts
### Occlusion  
In this article, we take as reference the modeling of obstacles and the computation of occlusions as proposed in Reference [11]. The obstacles are considered being 2D rectangles, which is a reasonable simplification of real objects, providing a practical and yet effective mathematical model. An obstacle can be defined by its origins (\(x1_{obs}\), \(y1_{obs}\)), a width \(w_{obs}\), a height \(h_{obs}\) and an orientation angle \(\beta_{obs}\). The remaining vertices can be computed similarly to Equation (3).  
In order to compute the visual occlusion caused by an obstacle, the field of view of each visual sensor is processed against a set of obstacles, computing _occlusion vertices_, which are mostly resulted from the intersection of the obstacles' edges with the visual sensors' edges, resulting in convex or concave polygons for each visual sensor. This visual coverage area derived from an original FoV is defined as the _Occluded FoV_ (OFoV). Hence, the computed area of each OFoV jointly with their associated QoM will be exploited to identify if a certain visual sensor is under a coverage failure. More details about how to compute the occlusion vertices and the OFoV can be found in Reference [11].  
Figure 4 shows an example of OFoV from sensors \(vs_{2}\) ans \(vs_{3}\). Obviously, an occlusion reduces the covered area and the quality of monitoring, which can configure a visual coverage failure, directly affecting a system's dependability.
## 7 Discussions and Clarification of Comparison Results
### Primary Configurations (i.e., LTP LTP PRIM and UDP LTP PRIM)
#### 7.2.1 LTP on the Proximity Segment (i.e., LTP LTP PRIM)  
In this configuration, there may be congestion loss due to the use of UDP/IP/PPP under LTP on the proximity segment. The rate of data arrival at the orbiter--and hence the rate of initiation of traffic on the cislunar segment--is still high, though: when there is congestion loss on the proximity segment, the signal propagation delay on this segment is so low that LTP retransmission and successful delivery are very rapid.  
However, the operation of LTP on the proximity segment has the effect of \"screening\" much of the potential congestion loss out of the cislunar segment. The effective rate of arrival of data at the orbiter is by definition reduced to a level that introduces no further congestion loss. Since this data arrival rate is the upper limit of the rate at which traffic on the cislunar segment can be initiated, the opportunity for further congestion loss on the cislunar link is minimized.  
Since (a) there is no uncorrected data loss on either segment, (b) congestion loss on the proximity segment introduces little goodput degradation due to the low signal propagation delay, and (c) congestion loss on the cislunar segment is minimized, there is little net goodput degradation on the end-to-end path. This configuration may be expected to exhibit the best performance among the four.
## 2 Statistical Models
### Bag of Words  
Bag of words (BoW) [8] is the simplest form of statistical representation of text. In this method, a document is represented by a set of words with associated occurrence frequency. An important drawback of this representation is an absence of word order and a lack of context. To reduce the size of the resulting vectors, generally the following preprocessing steps are applied: stop words removal and lemmatization or stemming. To mitigate the lack of context, one can use n-grams instead of word frequencies, for example, bigrams or trigrams.  
Because of its simplicity and speed, it is often used as a preprocessing stage in more advanced statistical methods. However, it is limited in processing complex relations between documents and exploiting semantic information.  
BoW can be extended by using weighting schemes that relate documents with words. One of the most popular extensions is Term Frequency-Inverse Document Frequency (TFIDF) [9; 10], a statistical method for computing the weights of words based on the frequency of their occurrence among documents in a dataset. For all words in the corpus, the ratio of inverse frequency to a number of all documents containing this word is calculated. Terms with the highest value of TFIDF weight usually characterize the document well.
# Effects of Driving Frequency and Voltage on the Performances of Vibrating Mesh Nebulizers
## 3 Results
### Resonance Frequency Changes by Impedance Matching  
Resonance frequencies were determined before and after impedance matching (Table 3). Three PZT samples with the same nominal resonance frequency (135 \(\pm\) 8 kHz) had different measured resonance frequencies. Resonance frequency differences were 2.4-2.9% after IM because of the inductance change caused by the transformer.
# A Study of the Bond Strength and Mechanism between Basalt Fibers and Asphalt Binders
## 2 Materials and Methods
### Pull-Out Test
#### 2.2.2 Testing Procedure  
The asphalt binders were prepared and heated at 150 \({}^{\circ}\)C (neat asphalt), 170 \({}^{\circ}\)C (SBS-modified asphalt), and 180 \({}^{\circ}\)C (rubber asphalt) to a fluid state. Then, half of the asphalt binders were poured into the trial mold carefully, and a bundle of BFs was placed into the gap. After that, the other half of the asphalt binders were poured into the trial mold, as shown in Figure 3. The specimens were placed in the POT at 60 \({}^{\circ}\)C for 1 h, and by this time, the loading device had been activated, operating at a speed of 10 mm/min.  
The bond strength \(\tau_{m}\) between BFs and asphalt binders was calculated according to Equation (1) using the maximum tensile force \(F_{max}\) measured using the POT (as shown in Figure 2) and the contact area \(S\).  
\[\tau_{m}=\frac{F_{max}}{S} \tag{1}\]  
\[\text{S}=2(\text{w}+\text{h})\text{L} \tag{2}\]  
\[\text{h}=100\ \pi d^{2}/\text{w} \tag{3}\]  
where \(F_{max}\) is the maximum tensile force measured using the POT (as shown in Figure 2); S is the contact area; w is the width of fiber cross section; h is the embedment depth (the length of fiber in the asphalt binder) of the fibers; and d is the single-fiber diameter.
## 4 Dataset Generation and Simulation Environment
### Robustness to Noisy and Missing Sensor Data  
The simulation study evaluates the MHMM's performance under realistic data challenges, including noisy and missing sensor data, as outlined in the stress-testing scenarios. In real-world industrial settings, such as smart factories or surgical training, sensor data like HRV, TCT, and NASA-TLX are often subject to noise from environmental interference or missing due to sensor failures, such as wearable device dropouts. The MHMM is designed to handle these challenges robustly, ensuring reliable proficiency assessment in Industry 5.0's human-centric, technology-augmented environments. This subsection outlines the MHMM's mechanisms for managing noisy and missing data, as tested in the simulation, enabling continuous operator support without workflow disruption.  
For missing data, the MHMM employs a native approach in its _emission_log_probability function, which conditionally includes only non-missing modalities when computing the log probability of an observation vector for a given proficiency state (Novice, Intermediate, Expert). For instance, if HRV data is unavailable (NaN), the calculation omits HRV's contribution while incorporating TCT and NASA-TLX, ensuring partial observations remain usable without requiring imputation. This approach was tested in the Missing Data scenario, simulating intermittent sensor failures common in industrial training systems.  
For noisy data, the MHMM leverages continuous probability distributions parameterized by empirically grounded means and standard deviations from [PERSON] et al. (2025) [24]. These distributions, detailed above, capture the expected variability in each modality, allowing the model to absorb moderate noise perturbations as part of the natural spread of the data rather than interpreting them as significant changes. Numerical stability is ensured through log-space computations and probability clipping in the Baum-Welch and Viterbi algorithms, preventing underflow or overflow issues when processing noisy data. The Trained MHMM's parameter initialization, using K-Means clustering, promotes robust convergence on noisy training data by identifying stable initial emission parameters, as evaluated in the Noise Robustness scenario.
# Dynamic Downsampling Algorithm for 3D Point Cloud Map Based on Voxel Filtering
## 2 Related Work
### Deep Learning-Based Downsampling  
The application of downsampling methods based on deep learning in point cloud processing is relatively scarce. Many combine deep learning technology with traditional machine learning methods. [PERSON] [18] proposed SIEV-Net, using a height information supplementary module to minimize the height information loss during the aggregation of point features in the voxel network. [PERSON] [19] proposed a sparse voxel map attention network, SVGA-Net, using a voxel map module and a sparse-to-density regression module to achieve comparable 3D detection tasks from original lidar data with a good simplification rate. [PERSON] [20] proposed VoxelContext-Net for static and dynamic point cloud compression. [PERSON] [25] proposed a learning-based static point cloud geometric downsampling method that exploited a deep convolutional neural network with a mask to learn the probability distribution of voxels. [PERSON] [26] applied deep learning methods to point cloud downsampling and proposed a Gaussian model voxel network--GVnet. It introduces a lightweight convolutional neural network to learn point cloud representation and semantic information while using part of the point cloud information to improve the efficiency and accuracy of point cloud sampling. FoldingNet, proposed by [PERSON] [27], is a point cloud downsampling method based on autoencoders. [PERSON] [28] used a laser point cloud downsampling method based on deep convolutional autoencoders to build a sampling module based on a combined hybrid model. Point cloud and voxel data are used to determine the relationship between each pair of point voxels. In this model, each voxel is embedded using the magnitude view, which is the Euclidean distance between the view and the center of the object, as well as the angle between each pair of views.
# A 3D Object Detection Based on Multi-Modality Sensors of USV
## 3 Method
### Deep Fusion Detection Network
#### 3.3.2 Generating Detection Network  
* **3D Bounding Box Regression:** As shown in Figure 4, traditional axis aligned coding uses centers of mass and axes. [PERSON] et al. [6] used eight-corner box coding. We used a more simplified four-corner coding. Considering that the bottom four corners must be aligned with the top, the physical constraint of the 3D bounding box is added. We encoded the border as four corners and two heights. Thus, the original regression target of the 24-dimensional vector of the eight-corner box is changed to (x1.\(\dots\)x2. y1.\(\dots\) y2. h1, h2). To reduce the amount of calculation and to improve the speed of calculation, we selected the four-corner encoding and introduced orientation estimation. From the orientation estimation, we extracted four possible directions of the border box and explicitly computed the box regression loss.
* **Generating Detection:** The fused features are input to the full connection layer. Box regression and classification operation are carried out after the three fully-connected layers of size 2048.  
Figure 4: Modified Resnet-50 feature extractor.  
Similarly, Smooth L1 loss is still used for box regression, and cross-entropy loss is used for classification. To eliminate overlap, we used IoU threshold of 0.01 for NMS.
## 6 Scaling Phase  
Once the technical and economic feasibility of the TUCAN3G proposal was verified, it was still necessary to verify its technical and economic viability in the long term. Large operators such as Telefonica are aware that they need to find solutions for rural environments due to pressure from governments and the need to attract new customers. However, rural communities have a high opportunity cost because they obtain more benefits in urban or peri-urban areas where competition is stronger. For this reason, Telefonica was reluctant to assume the responsibility of scaling up TUCAN3G and assuming the maintenance and management of this type of network. That is where the new role defined by the Peruvian regulation, the SRO, becomes important.  
The SRO is an operator with license to deploy access and transport networks in communities without service, but does not have its own core network and therefore has to direct its traffic to the central network of a large operator in order to manage customers and interconnect with other networks. The MNO pays the SRO a rate for each minute of voice or per Mbyte of data transferred, and in return the SRO is authorized to use the frequencies of the MNO. Moreover, the SRO may require the regulator to mediate in the negotiation process if the MNO is not willing to reach an agreement. This opens the door to small operators interested in entering the market niche of rural communications.  
From the MNO's point of view, the role of the SRO is interesting, since it allows them to attract new clients and meet the demands of governments while limiting the resources allocated to this goal. For this reason, the EHAS Foundation and some of its partners decided to start a new stage in which they included an SRO in the consortium. This SRO was Mayu Telecomunicaciones, which was the first SRO registered in Peru. The consortium was also expanded with PANGO, a Peruvian NGO that manages the Napo River healthcare system, and with Hispasat, a satellite operator that should connect isolated regions. In this way, the consortium was formed by EHAS, PUCP, Telefonica, Hispasat, and PANGO. The GOREL is also connected to this consortium since it is the owner of the communications towers and has the ultimate responsibility for healthcare in the region. The NAPO initiative is explained in the video \"From TUCAN3G the NAPO-CAF project\", whose link is available at supplementary materials.
# Cooperative Jamming-Based Physical-Layer Group Secret and Private Key Generation
## 4 Performance Analysis
### SK/PK Lower Bound
#### 4.1.2 Derivation of \(R_{p}\)  
According to (14) and (15), one can observe that \(R_{s}\) and \(R_{p}\) only differ by one term. Therefore, only the expression of \(I\big{(}\tilde{Y}_{A};\tilde{Y}_{R}^{(1)},\tilde{Y}_{R}^{(2)}\big{)}\) needs to be derived, which is calculated as  
\[\begin{split} I\Big{(}\tilde{Y}_{A};\tilde{Y}_{R}^{(1)},\tilde{Y }_{R}^{(2)}\Big{)}&=h\big{(}\tilde{Y}_{A}\big{)}+h\Big{(}\tilde{ Y}_{R}^{(1)},\tilde{Y}_{R}^{(2)}\Big{)}-h\Big{(}\tilde{Y}_{A},\tilde{Y}_{R}^{(1)}, \tilde{Y}_{R}^{(2)}\Big{)}\\ &=\log_{2}\Bigg{(}\frac{\big{(}p_{A}+\sigma_{1}^{2}\big{)}\Big{|} \mathbf{C}_{\tilde{Y}_{R}^{(1)}\cap\tilde{Y}_{R}^{(2)}}\Big{|}}{\bigg{|} \mathbf{C}_{\tilde{Y}_{A}\tilde{Y}_{R}^{(1)}\cap\tilde{Y}_{R}^{(2)}}\Big{|}} \Bigg{)},\end{split} \tag{21}\]  
where the covariance matrices \(\mathbf{C}_{\tilde{Y}_{R}^{(1)}\cap\tilde{Y}_{R}^{(2)}}\) and \(\mathbf{C}_{\tilde{Y}_{A}\tilde{Y}_{R}^{(1)}\cap\tilde{Y}_{R}^{(2)}}\) can be given by  
\[\mathbf{C}_{\tilde{Y}_{R}^{(1)}\cap\tilde{Y}_{R}^{(2)}}=\left[\begin{array}[] {cc}p_{A}+p_{B}+\sigma_{2}^{2}&p_{A}\\ p_{A}&p_{A}+p_{C}+\sigma_{3}^{2}\end{array}\right], \tag{22}\]  
\[\mathbf{C}_{\tilde{Y}_{A}\tilde{Y}_{R}^{(1)}\cap\tilde{Y}_{R}^{(2)}}=\left[ \begin{array}{cc}p_{A}+\sigma_{1}^{2}&p_{A}\\ p_{A}&p_{A}+p_{B}+\sigma_{2}^{2}&p_{A}\\ p_{A}&p_{A}&p_{A}+p_{C}+\sigma_{3}^{2}\end{array}\right], \tag{23}\]  
respectively. Hence, the expression of \(R_{p}\) can be obtained by substituting (19), (20), and (21) into (15).