esa-sceva/satcom-chunk-collection huggingface dataset:
# Utilization of Machine Learning Algorithms
## 3 Results
### Successes of Machine Learning Interventions in HIV Testing
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Predictive Model** & **Studies** & **Mean** & **Std. Err** & **Std. Dev** & **95\% CI** \\ \hline Machine Learning & 35 & 86.66 & 1.79 & 10.61 & 82.01–89.03 \\ Traditional Method & 8 & 73.13 & 2.29 & 6.47 & 67.72–78.54 \\ Combined Values & 43 & 83.33 & 1.69 & 11.07 & 79.92–87.73 \\ Difference in Values & 12.53 & 2.91 & & 6.39–18.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: \(t\)-test results of the comparison between traditional and machine learning. Another advantage of ML interventions highlighted in this study is their ability to forecast future trends and patterns of HIV/AIDS, which enables the early detection of HIV, allowing for timely intervention and improved management of the infection [13; 38; 59]. Moreover, ML algorithms can adapt and customize testing approaches based on individual risk factors, optimizing the testing process for different populations (including KPs) and improving the overall effectiveness of HIV testing programs [13; 14; 15; 19; 20; 21; 24; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 56; 59; 68]. Fascinatingly, ML algorithms can be integrated into mobile-based applications, providing opportunities for widespread and accessible HIV testing. This is very beneficial in remote or underserved areas where access to traditional healthcare facilities may be limited. It also improves HIVST automation to boost privacy, confidentiality, accessibility, and acceptance of HIV testing. This is particularly important in the context of HIV testing, where stigma and discrimination pose significant obstacles to the KPs [17; 48; 50; 55]. This study highlights that ML has the potential to be integrated into a variety of health technologies, such as telemedicine and electronic health records, resulting in a comprehensive and interconnected healthcare system. This integration has the potential to improve healthcare professionals' collaboration and communication, resulting in a complete approach to HIV testing and care [23; 38; 59; 68].
## 2 Tower Capacity  
The steel lattice tower considered in the paper comprises a series of steel members which can be classified into main members, diagonal members, and auxiliary members(Figure 1). The main members and diagonal members are the primary force-bearing members. The main members determine the global stability of the tower and the diagonal members provide the main members with constraints. There are two types of auxiliary members: the diaphragm and secondary bracing, which improve the overall stability of a tower by offering redundant constraints.  
The design function of the steel members is given by DL/T 5154-2012 [17]:  
\[\lambda R\geq\gamma_{0}\left(\sum\gamma_{Gi}\cdot S_{Gik}+\varepsilon{\sum \gamma_{Qj}\cdot S_{Qik}}\right) \tag{1}\]  
where \(\lambda\) is the strength factor and \(R\) is the nominal strength, \(\gamma_{0}\) is an importance factor taken to be 1.0, \(\gamma_{Gi}\) is the dead load factor taken to be 1.0 for tower gravity and 1.2 for the equivalent gravity transferred from transmission wires (including the conductors and ground wires), \(S_{GR}\) is the effect of nominal dead loads on tower members, \(\varepsilon\) is the weight coefficient of variable loads taken to be 1.0, \(\gamma_{Qj}\) is the variable load factor taken to be 1.4, and \(S_{Qik}\) is the effect of nominal variable loads on tower members.  
As the supporting structure, the total design load of transmission towers can be classified into two parts: one is the vertical load, which contains the tower gravity and the equivalent gravity transferred from transmission wires; the other is the horizontal load, which contains the wind load acting on towers directly and the equivalent wind load transferred from transmission wires. Wind loads are the only variable load concerned in this paper and all the tower members are in the elastic state under the action of maximum design wind speed.
# Probabilistically-Shaped DMT for IM-DD Systems with Low-Complexity Fast WHT-Based PDSP
## 2 Operation Principle
### Complexity Comparison  
The computational complexities of FDSP/PSDP schemes are analyzed and listed in Table 1. The number of data-carrying SCs \(F\) is not an integer power of two in the WHT-based FDSP scheme, and the required real-valued addition operations may be resource-intensive. However, the fast WHT can be implemented when the number of precoded data-carrying SCs \(P\) is an integer power of two. Therefore, the computational complexity of the fast WHT-based PDSP scheme can be significantly reduced.
# Vehicle Control Strategy Evaluation Based on the Driving Stability Region
## 2 The Vehicle Dynamics Model with the Control Strategy
### Verification of the Model  
#### 2.3.1 The Dynamic Characteristics Analysis of the Vehicle Model with DYC  
In order to analyze the dynamic characteristics of the 5 DOF vehicle system model with DYC, specific driving conditions are selected for the simulation. The initial conditions of the simulation are longitudinal velocity \(v_{x}\) = 25 m/s, lateral velocity \(v_{y}\) = 10 m/s, and yaw rate \(\omega\) = 1 rad/s. The simulations with and without DYC control are carried out, respectively.  
Figure 1 shows the comparison diagrams of the phase space trajectory, state variable time series, and body attitude of the vehicle system with or without DYC control under the above simulation conditions, respectively. It can be seen that without DYC control, the longitudinal velocity, lateral velocity, and yaw rate of the vehicle have larger fluctuations, and the vehicle body rotates, resulting in vehicle instability. On the contrary, in a vehicle with DYC control, the range of phase trajectory changes is small, and the lateral velocity and yaw rate are infinitely close to a non-zero constant, allowing the vehicle to drive stably. Figure 1d shows the driving status of the vehicle. It should be noted that due to the control effect of DYC, the longitudinal velocity is reduced by braking. However, there is no significant decrease in the longitudinal velocity. This is because a large portion of lateral kinetic energy and yaw kinetic energy converted into longitudinal kinetic energy in a short period of time. Therefore, even with braking interference, changes in the longitudinal velocity are insignificant.
# A GNSS/5G Integrated Three-Dimensional Positioning Scheme Based on D2D Communication
## 1 Introduction
Moreover, the TOA/RSS measurements in the 5G network are combined to estimate the distances between the requesting nodes and the reference nodes. 2. The three-dimensional distances between the requesting nodes and the reference nodes are considered to obtain their positions, and accordingly, a three-dimensional signal propagation model is exploited to characterize the path loss of the positioning signals in the RSS method. 3. The combination algorithm of DBSCAN and PF is employed in the GNSS/5G integrated three-dimensional positioning scheme. The inaccurate position information of the cooperative nodes is considered, and the DBSCAN algorithm, based on the GNSS and TOA/RSS measurements, is developed, which reduce the position deviation of the cooperative nodes and obtain relatively accurate positions of the requesting nodes. Furthermore, a PF algorithm integrating the TOA/RSS measurements with the above results as initial values is presented. The remainder of the paper is organized as follows. The system model and positioning model are described in Section 2. Section 2 also introduces DBSCAN and PF, and it further illustrates the GNSS/5G integrated three-dimensional positioning scheme with inaccurate cooperative nodes. Section 3 presents numerical results to demonstrate the performance of the proposed algorithm. We discuss the simulation results and elaborate on the future research direction in Section 4. Finally, Section 5 concludes the paper.
# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
## 4 Experiments and Results
### Experiments on Recorded Data in an Anechoic Chamber
#### 4.2.2 Experiment 5: Performance versus DOA Mismatch with Recorded Data  
This experiment is carried out to evaluate the performance of speech enhancement algorithms when there are DOA mismatches. The experimental settings are as follows. (1) PESQ and LSD are taken as the performance measurement metric. (2) The parameters of algorithms are set the same as those of _Experiment 1_. (3) The target speaker is at \((\theta_{s}\phi_{s})=(45^{\circ}A5^{\circ})\), and the interference speaker is at \((\theta_{1}\phi_{1})=(90^{\circ}\),\(135^{\circ})\). The azimuth angle \(\phi_{s}\) is assumed to be mismatched. We consider the mismatch of \(\phi_{s}\) (denoted as \(\phi_{s}\)\") varying from \(0^{\circ}\) to \(30^{\circ}\) with \(5^{\circ}\) increments. The experimental results are shown in Figure 11, where the x-axis is the mismatch of the azimuth angle \(\phi_{s}\) (\(\phi_{s}\)\"). It is noted that our proposed AVS-SMASK is superior to the compared algorithm under all conditions. It is clear to see that our proposed algorithm is not sensitive to DOA mismatch when the DOA mismatch is smaller than \(23^{\circ}\).  
We are encouraged to conclude that our proposed algorithm will offer a good speech enhancement performance in practical applications when the DOA may not be accurately estimated.
## 4 Case Study
### Deployment of the INPPCS  
For the deployment of the INPPCS in the case company, the implementation is composed of three phases: (i) data collection, (ii) case retrieval, and (iii) case adaptation.  
Specifically, the web scraper, CBR engine, and transfer learning to fine-tune the BERT model for text classification are integrated into the aforementioned solution.
## 2 Structure, Arrangement, and Mechanisms
The vision unit is connected to the interface program and provides a live video stream of the animal on the experimenter's computer. Additionally, as is required in many biomedical research studies, the vision unit is able to detect the animal and its location  
Figure 1: Schematic representation of CageView, including the system, tested in the laboratory of the University of Manitoba Department of Animal Science (**right box**), schematic of the communication system (**middle box**), and the experimenter’s desktop-based site in the remote location (**left box**). Figure 2: (**a**) Overall scheme of the apparatus that consists of the plastic frame, feeding mechanism, vision unit, food container, and power system. (**b**) Feeding mechanism and (**c**) vision unit mounted on and placed next to the mouse cage, respectively. within the cage and provide a history of the animal's activity, its location, and the required statistics and analytics for the experimenter. CageView also includes a software interface installed on a desktop computer through which the feeding and fasting schedule is set up, the status of access to food can be changed, the history of food access may be viewed, the animal may be monitored in real time, and the record of an animal's activity and its metrics or parameters may be viewed. Figure 3 shows a sample screenshot of the interface software. It is worth mentioning that the software can be simultaneously connected to any arrangement of multiple feeding mechanisms and vision units without limitations. The software allows the experimenter to set the feeding and fasting schedule for 7 consecutive days for time windows of 30 min. However, if required, the experimenter can change the animal's access to food regardless of the preset schedule. The interface software records the animal's activity for the entire duration of the experiment with no time limit.
## 4 Proposed Model
### Data Collection Phase  
Any model requires data to work on and test its validity. For OM, this phase is essential to identify most of the opinion elements (i.e., \(h_{k}\), \(e_{i}\), \(a_{ij}\) and \(t_{i}\)). In this proposed model, Twitter is the chosen OSN, and thus, the Twitter API was used to collect the necessary data. For the proposed model and adding to the traditional data required in any OM process, data about opinion holders and their tweets' reaction-based features are to be essentially required for collection. The previously collected data in [10] were used for this work due to the lack of availability of data that include both aspects of opinion holders, and their tweets' features, along with the main opinion texts. Accordingly, for this model, the data required to represent opinion elements were as follows:  
* Opinion text in the English language, per user;
* \(e_{i}\), \(a_{ij}\) : the opinion entity is the World Football Cup 2018 and all its possible aspects (e.g., video assistant referee (VAR));
* \(h_{k}\) : indicates username of the opinion holder;
* \(t_{l}\) : opinions that were collected during the period from 14 June 2018 till 15 July 2018, and;
* The contribution support data that include: follower/following list of \(h_{k}\) and the likes, retweets, and replies counts for their collected opinion tweet.
## 2 Setting the Stage: Fundamental Mathematics and Knowledge
### Computation of the PM Process with the NAF Algorithm  
The computations for the PM process can be formulated as [24]  
\[Q=k.p=k(p+p+,\ldots,+p). \tag{3}\]  
Variable Q in Equation (3) depicts the endpoint. Similarly, the value of k represents a scalar multiplier while p is the initial point. Algorithm 1, known as the point multiplication with NAF, is employed for the execution of the PM process. The core of the algorithm lies in the efficient execution of point multiplication on a BEC within field \(GF(2^{m})\). The input parameters are expressed as \(E_{B,d_{1},d_{2}}/GF(2^{m})\), where \(B\), \(d_{1}\), and \(d_{2}\) characterize the curve's attributes. Additionally, the algorithm requires a foundational base point \(P\) and a scalar \(k\) expressed as a binary sequence, \((k_{m-1},\ldots,k_{1},k_{0})\). The algorithm's primary aim revolves around the computation of the resultant point \(Q\). Using NAF methodology, it is obtained by multiplying the base point \(P\) with the scalar \(k\). The algorithm employs the following key steps:
# Robust and Accurate Recognition of Carriage Linear Array Images for Train Fault Detection
## 1 Introduction
To ensure that the coordinate transformation in the template matching process can accommodate the nonlinear scale distortions present in line-scan images of carriages, this study employs a weighted radial basis function to describe the nonlinear transformation relationship between the horizontal coordinates of the two point sets. Furthermore, to address the issues of unknown point correspondences and mismatched point quantities during point set registration, we designed an objective function that maximizes the similarity of the mixed Gaussian distribution between the point sets, thereby solving for the weights of the radial basis function. 3. Extensive experiments demonstrate that our method exhibits excellent performance in terms of recognition accuracy, processing speed, and robustness. In the task of recognizing high-resolution line-scan images of 76 carriage categories, the algorithm achieves 100% recognition accuracy when the local nonlinear scale distortion is less than 15%. Furthermore, it can accurately identify image categories even when the number of false detections increases or decreases by up to 10%. The entire image identification process takes an average of only 24 ms on an i7 CPU. The rest of this paper is organized as follows: Section 2 provides a review of related work. Section 3 introduces the proposed methodology framework in detail. Section 4 discusses experimental results. Finally, Section 5 concludes this article.
# Trusted Data Access Control Based on Logistics Business Collaboration Semantics
## 3 Collaborative Logistics Business Processes
Industry-specific demands for localized data and privacy concerns often lead to the disconnected systems, forming \"information islands\" and hampering effective information sharing and resource integration. Such traditional situations prevail due to mutual distrust among logistics entities, resulting in low efficiency and unclear responsibilities. In addition, differences in views among different logistics business entities regarding the purpose, scope, and authority of data usage, further complicate data sharing. Therefore, we propose a unified access control and authorization model, grounded in the collaborative semantics of logistics business processes, aims to overcome the challenges posed by semantic heterogeneity in information sources by aligning logistics business requirements with data characteristics.
## 5 Results and Discussions
### Bit Error Rate  
The modulation used in our work is the simplest modulation technique used in this type of VLC systems, which is called on-off keying (OOK), complying with the paradigm that VLC schemes must use IM in \(T_{x}\) and DD in \(R_{x}\). Additionally, and since it is not the focus of our research to analyze the modulation techniques that can be used, it is valid to use OOK to verify the BER parameter, which is expressed in terms of the distance between two bits. In the case of OOK modulation, as the \(R_{x}\) observes which of the possible signals they handle is closer to the received signal, it is logical to think that there will be a lower error probability caused by noise and interference. Consequently, it is more efficient in terms of BER than other more complex modulations in indoor VLC environments.  
The BER was estimated via Monte Carlo simulations with the direct error counting method, i.e., 21 runs of \(10^{5}\) bits were performed to have a confidence interval of 95% with an uncertainty factor of two on the error rate scale [11; 50]. The simulated BER performance is shown in Figure 13 for a single PD, and the hemi-dodecahedron and pyramidal ADRs for different analyzed scenarios. This is for the corresponding signal combining scheme located in all possible positions of the mining roadway and mining working face.  
We can see in Figure 13a the BER curves for the mining roadway scenario. The \(R_{x}\) with a single PD has a poorer BER performance compared to the \(R_{x}\)s that have the hemi-dodecahedron and pyramidal ADRs. In contrast, the hemi-dodecahedron ADR with the MRC scheme has the best BER performance. For example, for a BER of \(10^{-4}\), the \(E_{b}/N_{0}\) improvement of this ADR are 3 dB and 19 dB, compared with the pyramidal ADR with the MRC scheme and the single PD, respectively. The results for the mine working face scenario are shown in Figure 13b. Similar to the previous scenario the hemi-dodecahedron ADR with the MRC scheme has the best BER performance. Specifically for a BER of \(10^{-4}\), the \(E_{b}/N_{0}\) improvement of this ADR are 2 dB and 11 dB, compared with the pyramidal ADR with the MRC scheme and the single PD, respectively. It can be seen that for both ADRs and for the only PD, the BER curves of this scenario have lower performance compared to the mining roadway scenario; this is due to the physical characteristics of \(T_{x}\)s location and scenario dimensions, which causes the VLC channel to be degraded. Finally, the best BER performance in both scenarios is the ADR hemi-dodecahedron with the MRC scheme; this is due to its optimal physical construction and PD location characteristics, which allows for lower errors in data reception.  
Figure 12: UDR distribution of the pyramidal ADR in the mine working face scenario: (**a**) MRC, (**b**) EGC, and (**c**) SBC schemes.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Iceberg  
Icebergs are freely floating chunks of freshwater ice carved from marine glaciers, ice shelves, or ice tongues, interacting with the ocean, atmosphere, and cryosphere [5]. With continuous and accelerating global climate change, research on the cryosphere has emerged as a pivotal discipline in climate change studies [6]. Moreover, the cryosphere elements (e.g., icebergs, glaciers, and ice sheets) are recognized as natural climate change indicators due to their sensitivity to small-scale meteorological changes [7]. Recent calving icebergs in both the Arctic and Antarctic have created enormous tabular icebergs, drifting in the open ocean [8; 9]. Icebergs, ranging from a few square kilometers up to hundreds of square kilometers, can freely drift in the ocean based on several environmental features, such as ocean currents, waves, wind, and seafloor topography [10]. Iceberg deterioration produces meltwater into the ocean, disrupting and influencing SI formation [11], ocean circulation [12], marine ecosystems [13], SST [14], OSS [14], as well as biological activities [15]. Finally, icebergs can threaten vessel navigation [16] and offshore structures, such as oil and gas platforms [17].  
As mentioned, icebergs have many environmental, ecological, and socio-economic impacts. Thus, developing efficient workflows to monitor and track icebergs has been considered a high priority. For instance, the database of icebergs positions was generated to facilitate the navigation of vessels and to conduct research on icebergs and their surroundings [18]. Conventionally, in situ measurements and marine vessels have been employed to obtain accurate information about icebergs [19]. For example, an Aircraft Deployable Ice Observation System (ADIOS) was developed to deploy tracking devices on icebergs from fixed-wing aircraft, enabling the tracking of icebergs through Global Positioning System (GPS) observations [20; 21].  
Furthermore, other types of sensors, such as terrestrial laser scanners, SONAR, and Autonomous Underwater Vehicle (AUV), have been utilized to collect data about the position, geometry, and morphology of icebergs [22, 23]. Although the above-mentioned approaches provide accurate information, they are resource-intensive and logistically arduous in the oceans, especially in remote locations of polar regions [24]. Consequently, it is efficient to employ other RS systems, such as satellites, which can provide broad observations about icebergs through space and time.  
Various RS systems have been so far applied to identify and track icebergs [10, 25, 26, 27, 28, 29, 30, 31, 32, 33]. Optical, SAR, scatterometer, altimeter, and HF radar systems have been widely used for iceberg studies. Table 1 summarizes the advantages and disadvantages of each of these systems for iceberg mapping and monitoring. Figure 2 also demonstrates an example of iceberg detection using optical and SAR imagery.  
Figure 1: Overview of the met-ocean applications of RS, which are discussed in this review paper.