esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions  
In this section, the results are discussed for the proposed FFB algorithm. We have considered the nonlinearity distortion due to clipping and the TFNN method; keeping information of the acoustic channel is known at the receiver. We trained the data over high SNRs (20 dB- 25 dB); so to have a less noisy training data. MATLAB version 2017a was used for simulation purposes. The first important block at the receiver side is to estimate the proposed PA model. Additionally, the algorithm is evaluated in the least estimation time, and the BER should show improvement in the nonalgorithm case. There is a different machine learning algorithm that can be used to design PAs if the data is available. The key parameters that should be considered while implementing the machine learning algorithm are training time, amount of data available, acceptable training errors, and validation errors. The simulation parameters are given in Table 1.  
The parameters given in Table 2 are the bellhop channel configuration for simulation, adopting QAM modulation. A shallow water bellhop channel is generated by MATLAB, assuming the total depth of water is 100 m, with a horizontal range of 2 km. The depth of the transducer is 30 m simultaneously; the depth of the hydrophone is kept at 50 m. Figure 6 shows the ideal SSPA model  
\begin{table}
\begin{tabular}{c c c} \hline
**NO.** & **Parameter** & **Data** \\ \hline
1 & Sampling frequency & 100 kHz \\
2 & Bandwidth & 6.25 kHz \\
3 & FFT points & 8192 \\
4 & OFDM symbol number & 23 \\
5 & Number of subcarriers & 512 \\
6 & Cyclic prefix time & 25 ms \\
7 & Modulation & 64-QAM \\
8 & Sound speed & 1500 m/s \\
9 & PA Saturation Level & 7.0 \\
10 & Signal Clipping & \(\pm\) 7 \\ \hline \end{tabular}
\end{table}
Table 1: Simulation system parameters.  
used in transmitting transducer; it is compared with the exact linear PA model. As mentioned in our proposed (FFB) model, the choice of SSPA is explained from the real scenario, namely, the fact that SSPA nonlinearity can be approximated from Equation (16), which is the summation of linear and nonlinear values. The saturation level is kept around 7 dB in simulation, which is higher than the transmit out voltage. The distortion, which is created by the PAPR, helps us to analyze the effectiveness of the algorithm being researched. The machine learning algorithm is taken to learn the model. We mention the algorithm above as Algorithm 1. It is essential to compare the results between the learned model and the ideal one in terms of learning time and accuracy at the transmitter for the FFB algorithm to achieve constructive results.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 3 Network and Propagation Model
### HetNet Deployment with Uniformly Distributed Small Cells
#### 3.2.3 Received SIR  
The received SIR for user \(u_{i}\) can be obtained by dividing Equation (6) for small cells by Equation (19) as:  
\[SIR_{\rm rx,\mu_{i}}=\frac{p_{\rm rx,small,\mu_{i}}}{I_{\rm HetNet}} \tag{20}\]
## 5 Results  
This section presents the system and optimization settings and the results of simulating the proposed method for different cases to test its effectiveness. The developed control system is compared with the H\({}_{2}\) control system, whose \(3\times 3\) performance weighting matrix is also optimized by PSO. This weighting matrix consists of three second-order transfer functions (each with three coefficients in its numerator and the same in its denominator, and a gain) on its diagonal, and is used for weighting the system's tracking accuracy and robustness to uncertainty. The optimization problem of the two control systems is set as given in Table 2, with the cost function given by Equation (49).  
The hybrid H\({}_{\infty}\)/SMC system (built in MATLAB/Simulink R2023b) used in optimization is given in Figure 6, where, in each iteration, the candidate solutions (\(\eta_{11}\) and \(\eta_{22}\)) are passed from the optimization code (written in MATLAB script) to the controller block (written in MATLAB S-function); the control law is calculated and applied by the actuator to the system block (written in MATLAB S-function), and the states \(x_{1}(t)\), \(x_{2}(t)\), \(x_{3}(t)\) and the actuator signals \(t_{m1}(t)\) and \(t_{m2}(t)\) are passed back to the optimization code to calculate the cost function of that candidate solution and to update the solutions accordingly. The convergence progress of the optimization algorithm is depicted in Figure 7. The PSO algorithm converges in the 59 th iteration to the cost value of 1488.76. The best solution returned by PSO is [\(\eta_{11}=168.4755\), \(\eta_{22}=1.0173\times 10^{3}\)].  
The H\({}_{2}\) control system optimization results in the following performance weighting matrix \(W(s)\):  
\[W(s)=\begin{bmatrix}10\frac{51.123s^{2}+94.763s+10}{10s^{2}+100s+10}&0&0\\ 0&39.14\frac{17.412s^{2}+10s+10}{53.782s^{2}+55.429s+14.368}&0\\ 0&0&10\frac{53.8e^{2}+100s+100}{100s^{2}+100s+73.998}.\end{bmatrix}\]  
The performance index of the controller is 6.102.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Values for** & **Values for** \\
**Parameter** & **Hybrid H\({}_{\infty}\)/SMC** & **H\({}_{2}\)** \\  & **Control System** & **Control System** \\  & **Optimization** & **Optimization** \\ \hline dimension & 2 & 21 \\ lower bound & 100 & 10 \\ upper bound & 2000 & 100 \\ number of search agents & 10 & 10 \\ maximum number of iterations & 100 & 100 \\ \(\overline{w}_{PSO}\) & 0.7 & 0.7 \\ \(\overline{w}_{PSO}\) & 0.2 & 0.2 \\ \(c_{PSO1}\) & 2 & 2 \\ \(c_{PSO2}\) & 2 & 2 \\ \(r_{PSO1}\) & random number & random number \\  & in the interval (0, 1) & in the interval (0, 1) \\  & random number & random number \\ \(r_{PSO2}\) & in the interval (0, 1) & in the interval (0, 1) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Setting of the PSO algorithm.
# A Resilient Large-Scale Trajectory Index for Cloud-Based Moving Object Applications
## 2 Related Work
### Parallel Databases  
On the other hand, the authors of [14] implement a parallel spatial-temporal database to manage both network transportation and trajectory and to support spatio-temporal SQL queries. They use a space-based index that partitions the data based on a space-splitting technique. Any trajectory that crosses a partition boundary is split into sub-trajectories, whereas any sector of the transportation network that crosses a partition boundary is replicated in all of the crossed partitions. Grid indexes [15; 16] that function as in-memory grid indexes are designed for thread-level parallelism. TwinGrid [15] maintains two grids for moving object updates and queries. However, PGrid [16] depends on a single grid structure for running queries and data updating, where it is optimized for up-to-date query results and relies on an atomic concurrency mechanism.
## 3 The PFAC Measurements and the FRM Uncertainty Analysis
### Uncertainty Budget for Transponder Calibration  
Following a similar analysis, the FRM uncertainty for the transponder calibration has also been determined. Here, the atmospheric propagation delays of satellite signals and the transponder's internal delay dominate the overall uncertainty [9].
## 5 Simulation Framework and Experimental Scheme
### Simulation Locations  
Figure 4 displays the ten selected locations within the city of Inoglstadt, chosen for their high traffic density and congestion patterns. These sites represent critical traffic hotspots, enabling the simulation to replicate real-world vehicular patterns essential for accurately assessing 5G network performance under congested conditions.  
The selected locations were as follows:  
* **Cells 1 and 2**: Cover entrances and parking areas of the Audi factory, experiencing heavy traffic during shift changes.
* **Cell 3**: Located at a major intersection leading to the Audi production plant.
* **Cells 4 and 5**: Intersections managing traffic flow to and from the city center, consistently showing congestion according to real-time data.
* **Cells 6, 7, 9, and 10**: Encompass commercial areas, schools, kindergartens, and restaurants, representing diverse urban activities.
* **Cell 8**: Positioned at the Technische Hochschule Inoglstadt, an educational hub with approximately 6500 students and staff.  
These locations were specifically chosen to ensure the simulation captures high-density vehicular traffic and its effects on 5G network performance, particularly during hours of congestion.
# Phase Imbalance Analysis of GF-3 Along-Track InSAR Data for Ocean Current Measurement
## 1 Introduction
However, the interferometric phases are not simply proportional to the velocities of the ocean currents because the average surface current does not completely determine the average Doppler of the radar backscatter on the sea surface, but also includes the large-scale wave orbit velocity and the phase velocities of Bragg-resonant waves [3]. The empirical models can be used to remove each component to extract the ocean currents. Of course, only the line-of-sight (LOS) velocities of ocean currents can be obtained, but the direction of ocean currents cannot be determined. On 10 August 2016, Gaofen-3 (GF-3) was successfully launched into space at the Taiyuan Satellite Launch Center. GF-3 is China's first C-band full-polarization synthetic aperture radar (SAR) imaging satellite with the highest resolution of 1 m. GF-3 has 12 imaging modes, covering traditional strip imaging mode, scanning imaging mode, wave imaging mode, and global observation imaging mode for marine applications [12]. It is the SAR satellite with the most imaging modes in the world. Besides, GF-3 also carried out multiple ATI test missions in 2018 and 2019, imaging by dual-receiving, and obtained two fine strip-map (FSI) mode images of the same scene with a time lag of milliseconds, which used an effective along-track antenna separation of 3.75 m. We analyzed the dual-channel space-time variability of phase imbalance in GF-3 ultra-fine strip-map (UFS) mode through simulation and a large amount of actual data [13], considering that the dual-channel space-time variability of phase imbalance is attributed to the antenna phase position error and phase mismatch of the antenna pattern. This paper starts with Section 2, which introduces the current measurements method based on GF-3 ATI data. Through the GF-3 imaging geometric model with errors, all the error sources are analyzed, and a calibration method of phase imbalance is introduced. After describing and analyzing the possible causes of phase imbalance in GF-3 ATI mode by real data processing, Section 3 presents the ocean current obtained by the GF-3 ATI data. Section 4 summarizes the results of experiments with some discussion and research perspectives for the future, and Section 5 gives the conclusions of this paper.
# Automatic Needle Route Proposal in Preoperative Neck CT for Injection Laryngoplasty
## 2 Methodology
### Search Space  
To define the search space for the needle route, we first identify the center of the vocal fold line (the segment that connects two vocal folds) as: \(\mathbf{p}_{c}=(\mathbf{p}_{r}+\mathbf{p}_{l})/2\). Then, we find the midline that passes through the center and is axially perpendicular to the vocal fold line (Figure 3). Thus, the equation of the mid-line is as follows:  
\[L_{m}:\mathbf{p}=(p^{x},p^{y},p^{z})=(p_{c}^{x},p_{c}^{y},p_{c}^{z})+t(p_{r}^{y}-p_ {l}^{y},p_{l}^{x}-p_{r}^{x},0) \tag{3}\]  
We also find the midpoint \(\mathbf{p}_{m}\) where the midline meets the skin. Computationally, we find this as the anterior-most point of the midline before the outer air region, which can be expressed as follows:  
\[\mathbf{p}_{m}=\arg\min_{\mathbf{p}\in L_{m}}\frac{p^{y}}{\gamma_{\hat{\mathcal{P}}( \mathbf{p})>0}} \tag{4}\]  
where  
\[\gamma_{\hat{\mathcal{D}}(\mathbf{p})>0}=\begin{cases}1&\text{if }\hat{D}(\mathbf{p})>0 \,(\text{not outer-air})\\ 0&\text{if }\hat{D}(\mathbf{p})=0\,(\text{outer-air})\end{cases} \tag{5}\]  
Figure 3: The search space for needle route. The initial search space is obtained by spanning 10 mm to the right (\(\mathbf{e}_{r}\)) and left (\(\mathbf{e}_{l}\)) from the midpoint \(\mathbf{p}_{m}\) on the skin w.r.t. and the vocal fold line. The final search space is obtained excluding the trajectories that pass through the airway.  
where \(p^{y}\) is the component of position vector \(\mathbf{p}\) along anterior-to-posterior axis.  
Medically, the needle is inserted 5-10 mm lateral to the midpoint. Therefore, we also include all possible lines to the vocal folds from the skin points up to 10 mm (\(\approx\)20 degrees) lateral to the midpoint \(\mathbf{p}_{m}\). Thus, the search space \(\Omega_{r}\) for the right vocal fold spans 20 degrees to the right and the one for the left fold (\(\Omega_{l}\)) spans 20 degrees to the left from the midpoint. Here, the center \(\mathbf{p}_{c}\) of the vocal fold line is the center of rotation. In Figure 3, \(\mathbf{e}_{r}\) and \(\mathbf{e}_{l}\) are the endpoints of the span of \(\Omega_{r}\) and \(\Omega_{l}\). Considering the relative position of the cricthyroid membrane (i.e., the usual needle insertion region), each search space also spans 60 degrees inferior from the axial slice where the vocal folds are situated.  
Finally, we refine the search space by excluding the lines that pass through the segmented airway. Thus, the refined search space holds all possible line segments from the skin to the target vocal fold, which do not enter the airway.
# Vision-Based System for Black Rubber Roller Surface Inspection
## 2 Visual Inspection System
### Defect Detection Based on the Deep Learning Method
#### 2.4.4 Evaluation Metrics  
The evaluation indices used to evaluate the performance of the algorithm in this paper are precision (\(P\)), recall (\(R\)), mean precision (\(mAP\)), and \(F1\) score.  
Precision expresses the proportion of positive samples in the samples with positive prediction results. It is calculated as follows [13]:  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Learning rate & 0.01 & Weight decay & 0.005 \\ \hline Batch size & 12 & Momentum & 0.937 \\ \hline Image size & 640 \(\times\) 640 \(\times\) 3 & Epochs & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training parameters.  
Figure 15: Defect detection results: (**a**) damage; (**b**) torn areas; (**c**) porosity. The purple box is associated with damage, the green box is associated with tears, and the blue box is associated with porosity.  
Recall expresses the prediction result as the proportion of the actual positive samples in the positive samples to the positive samples in the whole sample, calculated as follows [13]:  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
The \(F1\) score is the weighted average of precision and recall, calculated as follows [13]:  
\[F1=\left(\frac{2}{Recall^{-1}+Precision^{-1}}\right)=2.\frac{Precision\cdot Recall} {Precision+Recall} \tag{6}\]  
The model's ability to distinguish between positive and negative samples is shown by its accuracy. The model's ability to distinguish between negative samples increases with accuracy. The model's capacity to identify positive samples is reflected in recall. The model's capacity to identify positive samples increases with recall. The two are combined in the \(F1\) score. A stronger model is indicated by a higher F1 score.  
Average accuracy (\(AP\)) is the average value of the highest precision under different recall conditions. (Usually, \(AP\) is calculated for each category separately.)  
The mean average precision (\(mAP\)) is the mean value of the average precision and the mean \(AP\) value of each category. The calculation formula is as follows [13]:  
\[mAP=\sum_{j=1}^{s}\frac{AP(j)}{S} \tag{7}\]  
where \(S\) is the number of all categories, and the denominator is the sum of the APs of all categories. The object detection object in this study comprises three defects of the rubber roller; therefore, \(S=3\).
## 3 Adaptive In-Transit Co-Processing
### Data Distribution  
Load balancing blocks of 3D data sets has been the focus of extensive research [35; 36; 37; 38; 39; 40]. A load balancing technique can either create a static distribution of the data or dynamically change the distribution based on some variables. Typically, dynamic load balancing techniques strive to minimize either the difference in computation time or the difference in memory usage on each process.  
Scientific simulations often generate large quantities of data, meaning that memory usage is of primary concern. Therefore, we currently consider a static and a dynamic different load balancing technique. The static technique is based on the initial distribution in a k-d tree [41], which distributes blocks to all processes on the transit nodes. However, some blocks may have been removed using the Skip action. As such, using this technique could result in a memory imbalance on the transit nodes. The dynamic technique partly resolves this issue by rebalancing the k-d tree. Let \(\mathcal{B}_{t}\) be the list of blocks on transit process \(t\), \(\mu\) the average number of blocks per process, and \(n\) the number of transit processes. The goal is then to minimize  
\[\sum_{t=1}^{n}\sqrt{\frac{|\mathcal{B}_{t}|-\mu}{n}}, \tag{3}\]  
i.e., the difference in allocated memory on each process on the transit nodes.  
Blocks sent to a specific process on a transit node make up a contiguous and convex subset of the original 3D data set. They can, as such, be decompressed in parallel and combined to reconstruct a single region in 3D space. This step is essential to perform certain types of analyses and visualizations efficiently. For example, many volume rendering engines utilize internal block structures to perform empty space skipping [42] and optimization techniques such as early ray termination [43; 44]. prematurely partitioning the 3D data set into multiple blocks can negatively affect the effectiveness of such techniques.  
At the start of the distribution stage, we utilize distributed filters to apply filters that require information about blocks from multiple processes. For evaluation, only one distributed filter is used, Filter Borders. The Filter Borders distributed filter attempts to identify blocks of a specific action on the border of the 3D data set. If a slice of border blocks can be identified, their action is changed to the target action specified by the filter. For example, this distributed filter can be used to identify slices of homogeneous blocks along the border of the 3D volume, and then exclude them from the in-transit rendering process by changing the action to Skip.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 4 HSTN Applications
### Emergency Communications  
In emergency situations, communication support is essential for both public safety and disaster relief. Terrestrial communication may be paralyzed or destroyed in emergencies. In such desperate situations, terrestrial connectivity may be rendered useless or may even be obliterated. The 2011 Japan earthquake resulted in almost complete infrastructure disaster. VSATs were used to established communication links. A VSAT is a small earth station that receives and transmits real-time data via satellite [148]. Portable VSATs for disaster-resilient wireless networks [149] and software-defined radio-based multi-mode low-power and emergency-mode-adaptive bandwidth control VSATs [150] were developed to establish communication links in disaster-hit areas.  
Although there are several ways to improve the stability and reliability of terrestrial networks, doing so requires a significant amount of time and resources. HSTNs are a promising solution to guarantee communication and can be utilized to establish emergency communication links and to construct a temporary WLAN to provide services to users through temporary BSs established through aerial platforms such as UAVs, balloons, or emergency ground vehicles.
# Analysis of Mobile Edge Computing for Vehicular Networks +
## 3 Comparison of Task Delegation Scheme with Experimental Results  
To demonstrate the trade-offs of executing computational tasks in different geographical regions, we developed two test cases that performed computations on data sets of varying sizes. The data we focus on for these tasks is GPS trace data that was collected by an Android smart phone while in a vehicle under normal driving conditions. The data was retrieved from a Google Maps Timeline and was filtered to include only records collected when the device was predicted to be in a moving vehicle. The first of the two tasks was to parse the raw GPS trace data, which is in the JavaScript object notation (JSON) format, to a more condensed and readable comma separated values (CSV) format. This task involves a large amount of text parsing, whereas the second task involves a large number of geographical distance calculations using a Haversine-based formula [20]. The second task seeks to discover which intersections were encountered by the driver over a period of time. This task requires a database of intersection coordinates, which is obtained through OpenStreetMap and the Overpass-Turbo API. Our data was collected in the Washington, D.C. metro area and spans the period of June 6 2018 to August 6 2018. We break this data set into chunks representing one week of GPS data each. It should be noted that the size of each chunk can vary slightly depending on the amount of driving. On average each week of raw data consisted of 2639 GPS coordinate pairs.  
For our remote servers, we utilized the Google Cloud Infrastructure, particularly Google Cloud Compute Engine. This service allowed us to easily replicate the server in different geographical regions and adjust the hardware of each resource. In each case our servers were configured identically with Ubuntu 14.04 and all processing is handled by PHP, Java, and Apache web server. For portability, each task was implemented in Java and exported to a Jar file. Requests to the server were handled by a simple PHP script that makes calls to execute the correct Jar file with the corresponding data.