esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Convergence and Complexity Analysis  
### Convergence  
The effectiveness of the algorithm depends on its convergence. For the convergence of the CCCP algorithm, the convergence has been proven by [37]. To prove the convergence of the penalty iteration algorithm, let the variable solution and objective function value of the optimization problem \(Q_{10}\) be \(\left(\boldsymbol{\eta},\boldsymbol{W},\text{SINR}_{l}^{\min},\zeta\right)^{( k+1)}\) and \(F_{EE}\bigg{(}\left(\boldsymbol{\eta},\boldsymbol{W},\text{SINR}_{l}^{\min}, \zeta\right)^{(k+1)}\bigg{)}\) at the \(k\)th iteration. Therefore, the convergence can be proved as follows:\[F_{EE}\bigg{(}\Big{(}\mathbf{\eta},\mathbf{W},\text{SINR}_{l}^{\text{min}}, \zeta\Big{)}^{(k+1)}\bigg{)}=F\bigg{(}\Big{(}\mathbf{\eta},\mathbf{W},\text{SINR}_{l}^{ \text{min}},\zeta\Big{)}^{(k+1)}\bigg{)}-\lambda_{3}\frac{\sum\limits_{l}^{L} \left(\text{Tr}\Big{(}\mathbf{W}_{l}^{(k+1)}\Big{)}-\lambda_{\text{max}}\Big{(}\bm {W}_{l}^{(k+1)}\Big{)}\right) \tag{79}\] \[\geq F\bigg{(}\Big{(}\mathbf{\eta},\mathbf{W},\text{SINR}_{l}^{\text{min}},\zeta\Big{)}^{(k+1)}\bigg{)}-\lambda_{3}\frac{\sum\limits_{l}^{L}\left(\text {Tr}\Big{(}\mathbf{W}_{l}^{(k+1)}\Big{)}-\lambda_{\text{max}}\Big{(}\mathbf{W}_{l}^{(k )}\Big{)}-\Big{\langle}\mathbf{w}_{l,\text{max}}\mathbf{w}_{l,\text{max}}^{H},\mathbf{W}_{ l}^{(k+1)}-\mathbf{W}_{l}^{(k)}\Big{\rangle}\right)\] \[\overset{by(\ref{eq:FEE})}{\geq}F\bigg{(}\Big{(}\mathbf{\eta},\mathbf{W}, \text{SINR}_{l}^{\text{min}},\zeta\Big{)}^{(k)}\bigg{)}-\lambda_{3}\frac{\sum \limits_{l}^{L}\left(\text{Tr}\Big{(}\mathbf{W}_{l}^{(k)}\Big{)}-\lambda_{\text{ max}}\Big{(}\mathbf{W}_{l}^{(k)}\Big{)}\right)\] \[=F_{EE}\bigg{(}\Big{(}\mathbf{\eta},\mathbf{W},\text{SINR}_{l}^{\text{min} },\zeta\Big{)}^{(k)}\bigg{)}\]  
The convergence can be proved according to (79). Therefore, after initializing the values of \(\Big{(}\mathbf{\eta},\mathbf{W},\text{SINR}_{l}^{\text{min}},\zeta\Big{)}^{(k=0)}\), \(\lambda_{1}^{(k=0)}\), \(\lambda_{2}^{(k=0)}\) and \(\lambda_{3}^{(k=0)}\), the proposed algorithm can iteratively converge to an optimal solution by setting a reasonable convergence threshold.
# Bidirectional Sliding Window for Boundary Recognition of Pavement Construction Area Using GPS-RTK
## 3 Experiments
### Single-Point Positioning Accuracy and the Measurement Error Distribution Model  
In order to recognize the quadrilateral PCA boundary with the proposed BSW algorithm in simulation, we needed to evaluate single-point positioning accuracy and determine the measurement error distribution model in the field experiments.
# Adaptive Damping Log-Domain Message-Passing Algorithm for FTN-OTFS in V2X Communications
###### Abstract  
To enable highly reliable and spectrum-efficient vehicle-to-everything (V2X) communications under conditions with severe Doppler effects and rapidly time-varying channels, we propose a novel faster-than-Nyquist orthogonal time frequency space (FTN-OTFS) modulation scheme. In this scheme, FTN signaling is integrated with spectrally efficient frequency division multiplexing (SEFDM) within the OTFS framework, enabling a higher symbol-transmission density within a fixed time-frequency resource block and thus enhancing spectral efficiency without increasing the occupied bandwidth. An analytical input-output model is derived in both the delay-Doppler and time-frequency domains. To further enhance numerical stability, an improved detection algorithm called adaptive damping log-domain message-passing (ADL-MP) is developed for the proposed scheme. Simulation results demonstrate that the proposed scheme achieves robust and reliable performance in high-mobility scenarios and that the proposed algorithm consistently outperforms conventional methods in terms of bit error rate (BER) under both the extended vehicular A (EVA) model and the high-speed train (HST) scenario, confirming its effectiveness and superiority for V2X communications.  
orthogonal time frequency space (OTFS); vehicle-to-everything (V2X); faster-than-Nyquist (FTN) signaling; spectrally efficient frequency division multiplexing (SEFDM) +
Footnote †: journal:  
Article
# Distributed Information Compression for Target Tracking in Cluster-Based Wireless Sensor Networks
## 5 Simulation
### The Parameters
#### 5.1.1 Mobility Model  
Suppose that the target movement within the \(x-y\) sensing field is based on the standard second-order model [14]:  
\[X_{k}=\Phi X_{k-1}+\Gamma\mathbf{w}_{k} \tag{10}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline The sensing field & 100 \(\times\) 100 m\({}^{2}\) \\ Base station location & (50,50) \\ Number of sensors & 200 \\ Transmission range & 15 m \\ Number of samples & 15 \\ Sub-cluster size \(n\) & 2 \\ TOA measurement deviation \(\sigma_{w}\) & 2 \\ AOA measurement deviation \(\sigma_{\theta}\) & 2 \\ Compressing bound parameters & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The values of the simulation parameters.  
Figure 13: The impacts of system parameters on the tracking performance from two perspectives: (1) distance estimation and (2) energy conservation.  
over a four-dimensional state space, where \(X_{k}=(x,\dot{x},y,\dot{y})_{k}^{T}\), \(\mathbf{w}_{k}=(w_{x},w_{y})_{k}^{T}\). Note that \(x\) and \(y\) denote the Cartesian coordinate of the target, and the uncertainties are described by uncorrelated Gaussian diffusion terms,  
\[\Phi=\left(\begin{array}{cccc}1&1&0&0\\ 0&1&0&0\\ 0&0&1&1\\ 0&0&0&1\end{array}\right),\text{and}\,\Gamma=\left(\begin{array}{ccc}0.5&0 \\ 1&0\\ 0&0.5\\ 0&1\end{array}\right)\]  
Assuming that the measurement is contaminated by noise, the noisy measurement is represented by:  
\[z_{k}=\tan^{-1}(y_{k}/x_{k})+v_{k} \tag{11}\]  
where the measurement noise, \(v_{k}\), is a zero mean Gaussian white noise process with a finite variance \(\sigma_{\theta}^{2}\). Moreover, the system initial state is given by a Gaussian distribution with known mean \(\bar{x}_{1}\) and covariance:  
\[M_{1}=\left(\begin{array}{cccc}\sigma_{1}^{2}&0&0&0\\ 0&\sigma_{2}^{2}&0&0\\ 0&0&\sigma_{3}^{2}&0\\ 0&0&0&\sigma_{4}^{2}\end{array}\right)\]  
The target trajectory monitoring and measurements are generated based on Equations (10) and (11). The values of the parameters are the following: the covariance of the system noise, \(Q=qI_{2}\), where \(I_{2}\) is the 2 \(\times\) 2 identity matrix, \(\sqrt{q}=0.001\). The target initial state is \(x_{1}=(0.0,0.6,0.0,2.5)^{T}\). Referring to the settings in [14], the prior parameter values are set to \(\bar{x}_{1}=(0.0,0.0,0.4,0.05)^{T}\), \(\sigma_{1}=0.5\), \(\sigma_{2}=0.001\), \(\sigma_{3}=0.05\) and \(\sigma_{4}=0.01\).
## 3 Medium Access Control (MAC) Layered UWAC Protocols
### Contention-Based  
In contention based FD-UWAC protocols, users compete with one another for channel access to send their data. The user who wins the contention takes over the medium for a period of time, while the others remain silent and watch for a free channel. Contention based FD-MAC protocols are attractive due to their enhance throughput, energy efficiency, and robustness capabilities but these benefits are decline when network traffic load increases and results in huge data collision and energy consumptions [35; 36]. To prevent contention-based FD-MAC protocols from these issues, advanced techniques, such as future scheduling (FS), interference cancellation, temporal and special reuse methods, IBFD properties, and FD-fixed time stamp handshakes, were introduced. As discussed below, contention based FD-MAC-UWAC protocols are divided into hand-shaking and random access protocols.
## 2 State of the Art
### Quality Assurance on Data
#### 2.3.1 Types of Data  
All data circulating in a production company can first be divided into two basic types: master data (context data) and transaction data.  
Master data includes information that usually does not change at all or rarely, such as:* material numbers, order data, or inventory levels,
* product prices,
* tool types,
* manufacturing equipment types,
* technology data (\"recipes\") and lot numbers,
* names, addresses, and telephone numbers of customers.  
Transaction data, on the other hand, is data that changes continuously. In a production example, this is mainly:  
1. machine and plant data (status data and signal data),
2. sensor values from external measurement technology (signal data),
3. control data (states and signal data from PLC and CNC),
4. data from quality control (metrology data).  
Among motion data, signal data have a special meaning. Signal data are mostly high-frequency measurement data or time series that characterize the technical process running in a CPPS, corresponding technical system, or control system and map its properties and processes. Signal data are the data that are continuously generated in a CPPS and characterize it at any time. The signal data change constantly, usually at a high frequency--depending on the type of process and the type of plant. The signal data form the basis of all data-driven applications and strategies in modern CPPS.  
In addition to the signal data, state data provides further information about current machine states such as utilization, operating modes, machine alarms, etc. The change frequency in this case is rather low. Additionally, there are quality data, which describe the final states (or intermediate states) of a product, are relevant. The change frequency of the quality data is usually low.  
As can be seen, these subsets of transaction data differ according to where the data originates, but also in the frequency of their possible changes.  
All processes that take place in a company or a CPPS use both master and transaction data. In Industrie 4.0, access is largely automated, making high quality of all master and transaction data increasingly important. The networking of different subsystems to form the CPPS also means that different types of data must be correctly linked to be able to represent the complex system behavior for the first time and also to analyze it. For example, to be able to carry out failure cause analyses, the master data of the ERP system describing the processed raw materials are combined with the signal data from the sensors of machines and tools and the measurement data of the quality assurance.
# Antenna Designs for 5G/IoT and Space Applications, 2 nd Edition
## 2 Short Presentation of the Papers
The proposed design is placed on a low-cost FR-4 substrate and provides high performance and low loss due to the air-filled element configuration. It features a broad impedance bandwidth, an end-fire radiation mode, and wide beam steering, making it ideal for 5G applications. The array is insensitive to various substrates and performs well in terms of gain, efficiency, and radiation properties, even in the presence of the user's hand. These characteristics make it a suitable design for modern 5G smartphones, offering high efficiency and stable performance across varying conditions. [PERSON] et al. [12] designed a flat-panel, metasurface, reflectarray antenna for satellite applications, particularly in the C-band (5.8 GHz). The reflectarray antenna is designed using a dual-ring resonator unit cell, which allows for steering the reflection angle of incident waves without relying on Snell's law. The antenna array consists of a 16 \(\times\) 16 unit-cell array, which was validated through measurement. The proposed antenna is fed by a low-cost circular horn antenna, and the design procedure is scalable to any electromagnetic (EM) solvers for analysis. The authors reported a measured gain of 22.4 dBi, with a cross-polarization suppression of 36 dB and a reflection angle of 15\({}^{\circ}\) at normal incidence. The design procedure is fully automated and demonstrates good agreement between the simulated and measured results, with a slight error of 1-2\({}^{\circ}\) in the beam steering direction. Conceptualization, F.T., L.M. and R.R. ; methodology, F.T. ; validation, L.M. and R.R. ; investigation, R.R., F.T. and R.R. ; resources, F.T., L.M. and R.R. ; writing--original draft preparation, F.T. ; writing--review and editing, L.M. and R.R. ; visualization, F.T., L.M. and R.R. ; supervision, F.T. ; project administration, F.T., L.M. and R.R. All authors have read and agreed to the published version of the manuscript. The authors declare no conflicts of interest.
## Theoretical Discussion
### 3D Space Partitioning Method  
Studies on the conversion of 3D spaces into data are being conducted in other areas as well as in construction. Studies based on tomography, which is mainly used in medical care, include brain volume analysis using contrast values [22], automatic extraction, and volume calculation of nodular lung cancer from CT images [23]. Analysis of the volume of other objects through tomography was discussed by [PERSON] [24].  
Point cloud data acquired through a laser scanner from construction sites have a hardware problem (processing time, speed) when converting them to data because the data consists of many points. Thus, many studies proposed methods to control them. [PERSON] (2013) reduced point cloud information by setting the priorities of all points by considering their relationships with neighboring points [25]. [PERSON] (2016) proposed a three-step octree-based segmentation technique [26]. [PERSON] (2004) investigated the appropriate resolution and use of 3D laser scanners in a research of the discontinuous surface of rocks [27]. Alternative algorithms and data structures were also proposed by [PERSON] (2013) for handling of large amount of data [28]. These studies used the 3D space segmentation method and cloud point data restriction method for data control [27].  
3D space partitioning methods can be largely divided into regular partitioning and irregular partitioning. Regularly segmented space can be represented by voxel. Space partitioning methods such as voxel model objects in the 3D space as groups of nodes having the same size. If the object has a curved boundary, the smaller the cell size is, the higher the accuracy that can be obtained because the accuracy is determined by the cell size. However, reducing the cell size to increase accuracy also has the disadvantage of increased calculation time and memory consumption [29]. A 3D grid structure is a representative space partitioning method that uses multi-resolution cube grid structures. It distinguishes spaces by creating cube-shaped grids with free size change even though they have the same length, width, and height. Multi-resolution cubes can be changed to create multiple grids as needed [30].  
The difference with the Octree algorithm, which is a similar space partitioning method, can be explained by a hierarchical structure. The Octree algorithm has superior and subordinate relationships between nodes which has an advantage in indexing or search tasks. However, this study is about overlapping and comparing the cloud point data of two objects, assuming them as forms of space occupation. Therefore, such a hierarchical structure is not relevant to this study. Hence, the space was divided using the multi-resolution cube grid method among the 3D space partitioning methods.  
For this study, the most appropriate method from the two indicated is the 3D space partitioning method due to its superior performance for data comparison in voxel units through the regular partitioning method.
## 2 Digital Twin-Assisted Gearbox Monitoring
### Gear Faults
#### 2.2.2 Experimental Procedure  
For gear faults, the maximum fault frequency occurs when the fault is distributed across all the teeth, resulting in a vibration at the same frequency as the meshing frequency. In contrast, a localized fault on the gear will have the same frequency as the rotational frequency. This allows us to define the maximum acquisition frequency as at least twice the meshing frequency. This information is crucial for selecting the acquisition board and the sensor. Regarding sensor placement, it should be fixed on the bearing, as it is the closest location to the rotor [69].  
To evaluate digital twin-based approaches for gear condition monitoring, two experimental studies were conducted under different setups and objectives. In the first study [64], an endurance testing campaign was carried out to assess gear surface degradation monitoring methodology. Two tests were performed: one under lubricated conditions and the other under dry conditions. The lubricated test was conducted at an applied torque of 20 Nm and an operational speed of 20 Hz, whereas the dry test used a reduced torque of 5 Nm and a speed of 10 Hz. High-resolution vibration signals were collected using Bruel&Kjaer (B&K) 4396 accelerometers, sampled at 100 kHz over 10-s intervals. This setup ensured detailed data acquisition under controlled laboratory conditions, allowing the researchers to evaluate the severity and progression of gear surface wear.  
In the second study [70], a digital twin framework was tested for fault diagnosis in a wind turbine planetary gearbox. The test bench included a motor, a spur gear stage, a planetary gear system, a magnetic powder brake, a data acquisition board (STM32), and several strain gauge sensors. The system was tested under realistic conditions mimicking a wind turbine environment. The motor was operated at 750 rpm (12.5 Hz), resulting in a spindle speed of around 18 rpm and a torque of 33 Nm. Fault scenarios included healthy and broken tooth conditions on the planetary gear. Strain gauge sensors (type 350-3 AA,350 \(\Omega\) resistance) were strategically installed on critical regions such as the ring gear and tooth root. Vibration and strain data were collected at a sampling frequency of 500 Hz.  
While both studies focus on gear health evaluation using digital twin principles, they differ in their experimental contexts and signal acquisition setups. The first experiment benefits from highly controlled lab conditions and high-frequency sampling (100 kHz), ideal for tracking fine surface degradation. In contrast, the second study replicates real wind turbine operating conditions with lower sampling frequency (500 Hz) but includes realistic mechanical configurations and physical constraints, which makes it highly relevant for practical fault diagnosis applications.
# Smart Ocean: A New Fast Deconvolved Beamforming Algorithm for Multibeam Sonar
## 3 Experimental Results
### Real Data Experiment
#### 3.3.2 Experimental Data Results of Kongsberg M3 Multibeam Sonar
Figure 12: The 2D acoustic image of two close small balls by Kongsberg M3 multibeam sonar, (**a**) the display control software output, (**b**) CBF beam power (solid line) and deconvolved beam power (dashed line) at the distance where the targets exist, (**c**) 2D acoustic image obtained utilizing CBF, (**d**) the enlarged area where the two targets exist in (c), (**e**) 2D acoustic image obtained utilizing deconvolved beamforming, (**f**) the enlarged area where the two targets exist in (e). Figure 13: The 2D acoustic image of the pool wall by Kongsberg M3 multibeam sonar, (**a**) the display control software output, (**b**) the CBF beam power (solid line) and deconvolved beam power (dashed line) from the radial distance 10 m of the sonar, (**c**) 2D acoustic image obtained utilizing CBF, (**d**) 2D acoustic image obtained utilizing deconvolved beamforming. Figure 13d is the 2D acoustic image obtained from deconvolved beamforming utilizing raw data, and obviously, the profile formed by the pool wall was sharper and clearer, and the bright arc caused by the side lobe leakage vanished. As shown in Figure 13b, which shows the CBF beam power (solid line) and deconvolved beam power (dashed line) from the radial distance 10 m of the sonar, the CBF beam power had two fat main lobes, and this corresponds to the wall of the two sides. The deconvolved beam power had two narrow main lobes and had about 15 dB lower of a side lobe level (the corresponding bright arc side lobe disappeared in the 2D acoustic image).
## 2 Methodology
### Simulation Steps and Related Software
#### 2.4.1 Model Building and Grid Setting  
In the geometry module of CFD, the reconstructed building model was built according to the actual scale, and the grid was divided according to the method provided by [PERSON] et al. [50], and the finite element simulation method was used to analyze the distribution of indoor temperature and airflow. The second-order upwind advection scheme was used in the model, which is conducive to the discretization of variables. In order to increase the reliability of the simulated data, it was necessary to establish a convergence criterion for the case study. The convergence standard for energy and temperature is \(10^{-6}\) and the convergence standard for other parameters is \(10^{-4}\). Reasonable setting of the mesh grid plays a vital role in the accuracy of the solution and will have a huge impact on the simulation time and the degree of convergence. [PERSON] et al. [50] used 0.34, 0.55, 0.98, 1.54, 1.92, and 2.09 million hexagonal grids in the simulation during the simulation, and performed local encryption processing in the details of the model. In this study, the division method of the overall division and partial encryption was also adopted. Meshing uses the proximity and curvature of the surface grid to divide the model into 5.409 million units. The number of grids is controlled by changing the maximum and minimum values of the grid and the growth rate of the grid size change. This will significantly speed up the calculation time and can more finely process the details of the interior of the building, thereby making the simulated results more accurate. For the setting of wall layers, it was necessary to ensure that all wall layers were evenly distributed in the model. The uniform wall layer distribution will make the radiation heat transfer more accurate. In this study, five wall layers were set up; the layer coefficient was 0.65, the level was 1.3, and the difference between two adjacent grids was kept at about 5%. According to the verification of [PERSON] et al. [50], after considering the configuration of the computer and calculating the deviation of each parameter, it was found that the deviation of the average wind speed was within 2.8%, and the deviation of the temperature was only 0.8%.
## 4 Experiments and Analysis
### Results Analysis and Discussion
#### 4.4.3 Analysis of Results on the Sentiment140-MV Dataset  
Table 5 illustrates the comparative analysis of the proposed model's performance using two models. One uses the TF-IDF feature method with four different machine learning classifiers, and the second employs the TF-IDF-Glove method with five model variation neural networks. The Sentiment140-MV dataset is a modified form of the Sentiment140 dataset with 18,000 tweets. We applied these previous machine learning and deep learning methods on this dataset to compare our model's performance with others. In comparison, our proposed model outperformed the other models. Meanwhile, deep learning models acquired 91.94% average accuracy; however, the machine learning model achieved 81.25% average accuracy with the Sentiment140-MV dataset. It enables us to conclude that our proposed model attains adequate accuracy with this modified dataset.