esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The morphological environmental scanning electron microscope and energy-dispersive X-ray spectroscopy (ESEM-EDX) study revealed a homogeneous micro-threaded neck surface with a 60 \(\upmu\)m roughness (Ultrathin Threaded Microsurface, UTM) [8]. The UTM surface enabled the excellent adhesion of the collagen fibers and consequently a healthy supracrestal peri-implant mucosa, characterized by a barrier epithelium of variable length and connective tissue in tight apposition with the transmucosal implant components with no signs of inflammation [6]. As well as other surfaces, the endosseous implant surface (ZirTi--Zirconium Sand-Blasted Acid-Etched Titanium) is moderately rough (S\({}_{\text{a}}\) = 1.4 \(\upmu\)m) according to a previous widely accepted classification [10]. Histological studies and animal models demonstrated thigh bone tissue formed on this surface within 6-8 weeks following the implant insertion [11; 12; 13]. The ZirTi surface enabled early cell adhesion, widespread cell-to-cell contact, stimulated an increase in the primary osteoblast cell differentiation, high cytocompatibility, and can precociously activate the cell growth and adhesion [14]. The use of such a two-piece implant with a convergent neck configuration and the application of the BOPT were expanded during the last 10 years, as confirmed by several clinical studies [15; 16; 17]. Different techniques and modifications have been proposed and introduced with the consequent modification of the clinical protocols. In particular, the use of this implant has been implemented for both non-submerged and submerged approaches. Finally, the implementation of digital scanner technologies and new materials to accomplish prosthetic rehabilitation workflows also led to some variations in the original prosthetic rehabilitation protocol [18], with the need to re-think some procedures and re-evaluate the great possibilities proposed by the neck configurations and the BOPT [16]. The present study aimed to systematically review the use of the Prama implant in clinical practice to establish some concepts for using this implant in several clinical applications.
# Online Sparse DOA Estimation Based on Sub-Aperture Recursive LASSO for TDM-MIMO Radar
## 4 Discussion
### Extension to Optical Communication  
In free-space optical communication, high-speed transfer of effective information can be achieved by quantum cascade laser (QCL) [50; 51]. The article [52], discusses the possibility of realizing high-power broadband QCL. The article [53], demonstrates that  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **IE** \\ \hline DAS & 4.0273 \\ IAA & 3.7029 \\ LASSO & 1.1087 \\ Proposed method & 1.1122 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Image entropy of 2D measured data.  
optical radiation with a wavelength of about 10 \(\upmu\)m in limited visibility is characterized by better transmission properties than near-infrared waves. The article [54] analyzes the performance of optical communication channels in the marine environment. The article [55] analyzes the performance of free space optics communication under atmospheric turbulence. This paper mainly discusses the TDM-MIMO radar in the microwave band. Moreover, in the field of FSOC, MIMO technology can be used to solve the problem in attenuation and atmospheric turbulence, which contributes to the atmospheric channel gain and the reliability of the FSOC system. In the article [8; 56], a maximum likelihood parameter estimation method is used to improve the bit error rate (BER) performance of the MIMO FSOC system. Due to the large number of massive FSOC-MIMO antennas and the complex atmospheric turbulent propagation environment [3; 57], the computational complexity of matrix inversion in the channel estimation is quite high. Therefore, traditional channel estimation methods cannot be directly applied in massive MIMO. A low-complexity massive MIMO channel estimation method must be employed. The proposed online sub-aperture recursive LASSO estimation method may also be employed to lower the estimation error and improve the running speed. This paper mainly discusses radar applications and the above articles belong to the field of communications. We should note that although the mathematical principle is the same, when this method is applied to MIMO optical communication, minor changes are required, and we will not go into much detail here.
## 5 Performance Evaluation
### Performance Results and Analysis  
Figures 15-17 show both mathematical analysis and simulation results. In these figures, a continuous line indicates the simulation results, and a dotted line indicates the mathematical analysis results. We analyze the handover latency, packet loss, signaling cost, and power consumption.
## 5 Experiments and Results
### Comparison with Related Works  
The comparison between the proposed method in this research paper and other competitive methods reported in the literature is given in Table 5. The classification performance for the six natural scenes, as well as the mean average performance, are presented. The comparison also includes the best competitive method using Convolutional Neural Networks [12; 28; 29; 30]. It can be seen that our proposal accurately recognizes (100%) each of the natural images under consideration. As shown in Table 5, using the same database found in other research works, our proposed method shows 100% recognition.  
Figure 17: Retrieval results related to the concept of foliage.  
Figure 18: Retrieval results related to the concept of rock.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Coast** & **Forest** & **Mountain** & **Follage** & **Rever/Lake** & **Sky/Cloud** & **Mean Average** \\ \hline
**Our Proposal** & **100\%** & **100\%** & **100\%** & **100\%** & **100\%** & **100\%** & **100\%** \\ \hline [PERSON] [28] & 86\% & 92\% & 94\% & 86\% & 86\% & 86\% & 88.33\% \\ \hline [PERSON] [29] & 84.64\% & 84.64\% & 84.64\% & 84.64\% & 84.64\% & 84.64\% \\ \hline [PERSON] [30] & 93\% & 91\% & 98\% & 88\% & 88\% & 88\% & 91\% \\ \hline [PERSON] [12] & 96\% & 100\% & 88\% & 88\% & 96\% & 86\% & 92.33\% \\ \hline \end{tabular}
\end{table}
Table 5: Detection performance of the most competitive methods in natural image recognition using the same databases Vogel-Schiele.
# WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks
## 5 Simulation Results
### Results and Analysis
#### 5.3.2 Hop Count  
The presented graph illustrates the variation in hop counts that data packets undergo, from their generation to eventual reception at the sink node, as the number of nodes increases. It is evident from the graph that, with an escalation in the node count, the hop count in the WLQRP protocol demonstrates a steady decrease, in contrast to the VBF and HH-VBF protocols, where the hop count experiences an upward trend. This contrast emerges due to the employed VBF and HH-VBF network structure, which is akin to the multi-source node network configuration employed by WLQRP. In other words, the network includes multiple pipelines. The relay selection mechanism of VBF and HH-VBF involves selecting the neighbor node within the pipeline with the minimal anticipated factor as the optimal relay. This selection strategy results in an inability to effectively choose appropriately distanced next-hop nodes as the node density increases, leading to an increase in the hop count for packet forwarding to the sink node. In contrast, WLQRP's relay selection mechanism comprehensively accounts for distance factors of the next-hop node (i.e., the hop count to the sink node), enabling it to choose relay nodes as proximate to the sink node as feasible. Consequently, WLQRP excels in densely populated networks, effectively minimizing hop counts for packet forwarding (Figure 8).  
Figure 8: Hop count.  
Figure 7: Packet loss ratio.
## 2 Materials and Methods
### Research Variables and Tools
#### 2.5.2 Research Tools  
Kinetic data during exercise were captured using the PASCO PS-3230, a wireless two-axis force platform developed by PASCO (Roseville, CA, USA). This platform measures forces at each of its four corners with a range of \(\pm\)1100 N and a total vertical force capacity of 4400 N. The system is equipped with overload protection of up to 1700 N per element and 6600 N for the total vertical load. The force plate's sampling rate was configured to 1000 Hz, yielding 1000 data points per second. This instrument is extensively employed for the mechanical analysis of various human movement behaviors [34].  
Hand grip strength was quantified using a hand grip dynamometer (Camry (Shenzhen, China), model EH101), which operates within a range of 0-90 kg and possesses an accuracy of 0.1 kg.
# Rainfall Similarity Search Based on Deep Learning by Using Precipitation Images
## 2 Related Studies
### Similarity Search  
Similarity search (also known as the nearest neighbor search) is the problem of searching the data items that are nearest to a query item under some distance measure from a search (reference) dataset, which is the foundation for data mining tasks such as clustering and classification and has been applied to time series prediction in image retrieval. Generally, a similarity search relies on a distance measurement that quantifies how close two elements are in the feature space. The closer the elements are, the higher the similarity between them. Currently, there are many distance measurements, such as Minkowski distance [17], Dynamic Time Warping Distance (DTW) [18], and editing distance [19].  
Minkowski distance is also known as \(lp\) distance. If two time series \(Q=\{q_{1},q_{2},\,\ldots,q_{n}\}\) and \(C=\{c_{1},c_{2},\,\ldots,\,c_{n}\}\), then the Minkowski distance \(D(Q,\,C)\) between \(Q\) and \(C\) is calculated as the following equation.  
\[Lp=D(Q,C)=\left(\sum_{i=1}^{n}|q_{i}-c_{i}|^{p}\right)^{\frac{1}{p}} \tag{1}\]  
The value of \(p\) is a positive integer. Minkowski distance is defined differently according to different values of \(p\). If \(p=1\), \(L_{1}\) is called the Manhattan distance; if \(p=2\), \(L_{2}\) is called the Euclidean distance, which is the most widely used measure in time series similarity research; If \(p=\infty\), \(L_{\infty}\) is the Chebyshev distance.
## 3 Results
### Three-Satellite Constellation
It can be seen how the most frequent distance separation between the first and the third RO lies around 100 km for the consecutive satellites, and around 200 km for the furthest ones, although these separations are distributed over a wide range of distances. Figure 6: The location of the RO tangent points for the events where the intersected precipitation induced a propagation cumulative differential phase shift (\(\Delta\Phi\)) \(>\)6 mm. The different colors indicate each low earth-orbiting (LEO) satellite, where LEO = 1 (blue) and LEO = 3 (green) are the leading and trailing satellite in the constellation, respectively. A similar situation happens with the time separation. Notice that although the time separation between the satellites is two minutes, the time difference between the corresponding acquired RO is not necessarily two (or four for the ones in the edges) minutes, due to the relative motion between the GNSS and the receiving satellites. The most frequent time difference between the first and the second is around 140 s, while between the first and the third RO is around 280 s.
# Low Tensor Rank Constrained Image Inpainting Using a Novel Arrangement Scheme
## 4 Experimental Results and Analyses
### Analyses of the Three Flexible QA Schemes  
Next, we used the first, second, and third flexible QA schemes. The PSNRs (dB)/SSIMs of the UnfoldingLR, tSVDLR, and TTLR methods with and without the QA scheme are shown in Table 3. The red numerical values correspond to the worst results. It can be seen that, without the QA scheme, Lena and Airplane could not be recovered. The UnfoldingLR, tSVDLR, and TTLR methods with the QA scheme had better numerical results than those without the QA scheme. In the low matrix rank completion method (i.e., MatrixLR), no QA scheme was applied, i.e., the color image was dealt with as three-channel matrices directly.  
Due to the support of the QA scheme, the low tensor rank-based methods (TTLR, tSVDLR, and UnfoldingLR) with the QA scheme provided better results than the traditional low matrix rank completion method (i.e., MatrixLR method). Therefore, the QA scheme can be successfully used as the first step to explore the low tensor rank prior to an image.  
The KA scheme and the third flexible QA scheme can both rearrange an image into a high-order tensor. However, our QA scheme is different from the KA scheme used in [23],  
Figure 5: Original color images and missing patterns.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Methods**} & \multicolumn{3}{c}{**PSNR (dB)/SSIM of Different Color Images Under Different Missing Patterns**} \\ \cline{3-6}  & & **House** & **Lena** & **Airplane** & **Boats** \\  & & **Random 50\%** & **Lines** & **Random line** & **Random 80\%** \\ \hline \multirow{3}{*}{**Without rearrangement**} & MatrixLR & 9.38/0.8970 & 13.34/0.5850 & 7.118/0.1308 & 19.18/0.5680 \\  & TTLR & 28.61/0.871 & 13.34/0.585 & 7.11/0.130 & 19.25/0.519 \\  & tSVDLR & 32.30/0.932 & 13.34/0.585 & 7.11/0.130 & 21.60/0.707 \\  & UnfoldingLR & 7.83/0.093 & 13.34/0.585 & 7.11/0.130 & 6.32/0.102 \\ \hline \multirow{3}{*}{**With rearrangement**} & TTLR & 30.21/0.9251 & 31.79/0.9559 & 25.77/0.8796 & 21.44/0.7144 \\  & tSVDLR & 29.79/0.8989 & 31.20/0.9561 & 18.91/0.8386 & 21.34/0.6879 \\ \cline{1-1}  & UnfoldingLR & 32.58/0.9416 & 33.45/0.9771 & 28.75/0.9464 & 23.46/0.8139 \\ \hline \hline \end{tabular}
\end{table}
Table 3: PSNR (dB)/SSIM of Different Color Images Under Different Missing PatternsThe KA scheme can maintain the local block similarity of the image, while the third flexible QA scheme can use adjacent pixels to maintain the global similarity of the image. We conducted a comparison of KA and the third flexible QA scheme under the corresponding TMac-TTKA [23] and TTLR methods. As shown in Figure 6, the small blocks are obvious in the recovered images obtained by the TMac-TTKA method. The images recovered by the TTLR method preserve more details without the obvious blocks.
## 4 Accuracy Analysis of LSTM Network Models for Predicting Buildings' Settlement
### Analysis of the Effect of Engineering Geological Parameters on the Accuracy of Model  
The buildings' settlement and deformation in the process of pit construction are closely related to the stratum changes (the stratum parameters in this paper include soil gravity, friction angle, cohesion, Poisson's ratio, and pore ratio); in order to further analyze the influence of the stratum-related parameters on the predicted value of the building settlement, keep the other parameters unchanged, and take the building settlement monitoring values and engineering geological parameters under the three conditions of the total reversal method as input, and the predicted value of the building settlement as output. The prediction results are shown in Figure 8.  
As can be seen from Figure 8, the prediction methods with or without considering the effect of stratigraphic changes reflect the same pattern on the predicted values of single-point settlement of buildings. The average relative error of the predictions for the three conditions considering the effect of ground changes is around 8.23%, and the accuracy  
Figure 7: Comparison of settlement monitoring values and predicted values for various conditions of the total reverse method.  
of the model prediction is improved by 6% compared with the one without considering ground changes.
# Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
## 3 The Proposed Mechanism
### Preliminary Formation for the Proposed Work
#### 3.1.5 Energies of Neighbors  
The main concern is energy optimization in the IoT sensors. Therefore, each node keeps its own as well as its neighbors' energy information. The energy of node \(i\) can be denoted as \(E_{i}\). The values of \(E_{i}\) can be determined as follows:  
\[0\leq E_{i}\leq E^{max} \tag{4}\]  
The nodes with energy levels equal to 0 are considered dead nodes. Such nodes are automatically omitted from the network. The features of the dead nodes cannot be used in the formation of scores for routing. Each node keeps track of its neighbors and deletes the dead nodes from the neighbors' lists.
# GeoLocator: A Location-Integrated Large Multimodal Model (LMM) for Inferring Geo-Privacy
## 3 Workflow to Develop a New Tool--GeoLocator
It provides a detailed explanation of its reasoning, outlining how it arrives at specific conclusions, including identifying key visual cues, analyzing EXIF data, and cross-referencing contextual information. Second, GeoLocator delivers clear and explicit intermediate outputs. During the inference process, it produces intermediate results, such as identified landmarks, road signs, and coordinates, allowing users to track the progress of its analysis. Additionally, GeoLocator uses visual annotations to highlight specific elements in images used for its reasoning. For example, it may circle landmarks or annotate road signs, clearly indicating the factors influencing its conclusions. External validation is another critical feature, as GeoLocator integrates steps to cross-reference its findings with search engines and external APIs, such as the Google Earth API, helping to verify the accuracy of its inferences and adding a layer of reliability. Moreover, GeoLocator includes a user feedback loop, enabling users to provide feedback on its inferences, which the model can use to improve its accuracy over time. By incorporating the transparency and explainable features, we aim to demystify GeoLocator's operations and ensure that users have a clear understanding of its reasoning process, thereby addressing concerns about it functioning as a black-box algorithm.
## 3 Experimental Results of Static Collapse Tests
### Relationship between Load and Vertical Displacement at the Failed Middle Column  
Figure 7 illustrates the relationship between load and vertical displacement at the failed middle column. The characteristic load values of the sub-assembages are listed in Table 4. The comparison showed that: (1) The group of S1, S2, and S3 was used to investigate the influence of prestress on the collapse responses of the sub-assembages reinforced with BFRP bars. Based on Figure 7, it is easy to find that S3, which was prestressed with bonded prestressing strand, possessed the highest initial stiffness, followed by S2 and S1 in turn. In the initial loading process, the vertical displacement at the middle column of the sub-assembages almost increased linearly with the applied load. At the final failure stages, the maximum load-bearing capacity ranked in descending order was S3, S2, and S1, which indicated that the prestressing strand benefited to improve the ultimate resistance of the structures. In addition, integral stiffness of S2 was obviously weaker than S3, it should relate to the prestress loss after the failure of the middle column. (2) The group of S1, S4, and S6 was designed to investigate the influence of span to depth ratio of frame beams on the collapse resistance. Figure 7 indicates that the smaller the span to depth ratio, the greater the integral stiffness of the sub-assembages. In terms of ultimate load bearing capacity, S1 and S4 had a similar load bearing capacity, which was apparently higher than that of S6. (3) Comparing with S1, S5 possessed similar  
Figure 6: Typical failure modes of the specimens: (**a**) S1, (**b**) S3, (**c**) S5.  
deformability and higher load-bearing capacity before final failure occurred. Therefore, it is feasible to replace steel stirrups of the frame beams by BFRP bars as an alternative without affecting the structural collapse resistance.
## 4 Results
### Features Extraction Results Using CNN-2  
In this section, we will discuss the findings of feature extraction using the Convolutional Neural Network (CNN-2). We discovered that CNN-2 achieves 77.27 percent, 98.91 percent, and 92.22 percent accuracy for individual classes ALL, AML, and MM, respectively. The statistics of CNN-2 upon ALL, AML, and MM, are mentioned below in Table 8.  
Overall accuracy achieved by CNN-2 on ALL, AML and MM was 87.41%. The overall statistics of CNN-2 are given below in Table 9.  
The confusion matrix of CNN-2 is presented in Figure 6.  
Figure 5: Confusion Matrix of CNN-1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **Accuracy\%** & **Sensitivity\%** & **Specificity\%** & **Precision\%** & **False Positive\%** & **F1-Score\%** \\ \hline ALL & 70.455 & 70.455 & 95.604 & 88.571 & 4.3956 & 78.481 \\ \hline AML & 98.913 & 98.913 & 100 & 100 & 0 & 99.454 \\ \hline MM & 92.222 & 92.222 & 85.556 & 76.147 & 14.444 & 83.417 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Statistics of CNN-2 Model of Individual Classes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Statistics** & **Results \%** \\ \hline Accuracy & 87.41 \\ \hline Sensitivity & 87.20 \\ \hline Specificity & 93.72 \\ \hline Precision & 88.24 \\ \hline False Positive Rate & 6.28 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Overall Statistics of CNN-2 Model of All Classes.