esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup and Materials  
The experimental setup can be found in [PERSON] et al. [25]. A single-mode Quasi-Continuous Ytterbium Fiber laser (IPG YLM-200/2000-QCW) was used in a continuous mode with a wavelength of 1070 nm. Its M\({}^{2}\) value was 1.05. The maximum average laser power was 250 W. The total thickness of the workpiece was 50 \(\upmu\)m and an active electrode material was coated on a current collector with a thickness of 30 \(\upmu\)m. The material of the active electrode was 90% graphite and 10% Polyvinylidene fluoride, which was used as a binder. The current collector material was a copper foil. The workpiece was placed on the flat plate and fixed at the end of the workpiece. The distance between the workpiece and the F-theta lens was 100 mm. The laser spot size was 23 \(\upmu\)m. The laser parameters chosen were laser power and laser scanning speed since these play a major role during laser cutting. The laser power was varied from 50 W to 250 W with in steps of 50 W. The laser scanning speed was varied from 500 mm/s to 5000 mm/s in steps of 500 mm/s. The laser parameters used for the experiments are tabulated in Table 1 with a line energy.  
To observe the physical phenomena during laser cutting of the electrodes, Scanning Electron Microscopy (SEM) was used. The specific parameters for the analysis of physical phenomena are defined in Figure 1 for the anode. The top width (\(W_{top}\)) is defined by the width of the active electrode  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**\#** & **Power (W)** & **Speed (mm/s)** & **Line Energy (U/m\({}^{3}\))** \\ \hline
50, 100, 150, 200, 250 & 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000 & 4.348 \(\times\) 10\({}^{5}\)-2.174 \(\times\) 10\({}^{7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Laser parameters.  
material removed by the laser, the melting width (\(W_{melt}\)) is defined by the width of the current collector with melting marks, and the kerf width (\(W_{krf}\)) is defined as the fully removed width.
## 3 The Proposed Method
### The Proposed Framework Structure  
This study proposes an XCLHG framework, which consists of four key components: node embedding, heterogeneous graph construction, information propagation, and multi-view contrastive learning, as illustrated in Figure 1. Specifically, the input texts are preprocessed using a machine translation system and language-specific taggers. Subsequently, node embeddings are generated by encoding source and target language words and documents using a multilingual pre-trained language model. To capture both syntactic and semantic information from the source and target languages, this study constructs a heterogeneous text graph, where documents and words are the nodes, connected by multiple types of edges. A GAT is employed to aggregate information from neighboring nodes. After the GAT encoders integrate information from each type of edge to produce comprehensive node embeddings, a multi-view contrastive learning module, which consists of translation-level and label-level views, is applied to align these embeddings. In this step, distances between corresponding feature representations from the two views (positive samples) are minimized, while distances between non-corresponding feature representations (negative samples) are maximized. This collaborative integration mechanism ensures that the complementary features from both views are mutually refined,resulting in robust and discriminative representations for CLTC. The detailed algorithm of XCLHG is presented in Algorithm 1. Based on this framework, three loss functions are formulated as follows:  
* \(L_{CE}\) encourages the GAT to output predictions similar to the ground truth, which encodes structured semantic and syntactic information of the source and target language document nodes.
* \(L_{TL-CL}\) encourages the model to minimize the distance between the original documents and their translated counterparts via contrastive learning, thereby reducing errors introduced during translation.
* \(L_{LL-CL}\) reduces the distance between samples with the same label, while increasing the distance between those with different labels.  
In the following sections, the construction of a heterogeneous graph is first presented (Section 3.3), followed by an introduction to the application of a GAT for cross-lingual text classification (Section 3.4). Additionally, a detailed description of the employed multi-view contrastive learning strategies, including the formulation of the loss functions, is provided (Section 3.5).
# Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches
## 4 Results
### Results Obtained without Feature Selection: Data Set 1
#### 4.3.1 1 DCNN  
The confusion matrixes for fivefold cross-validations of 1 DCNN are shown in Table 11. The average accuracy rate is 93.40% using optimizer Rmsprop; epoch is set at 100; batch_size is 30. Take the confusion matrix with CV = 1 as an example; among the outcomes of the 972 matches, 483 matches were correctly predicted wins, 29 matches were incorrectly predicted losses, 427 matches were correctly predicted losses, and 33 matches were incorrectly predicted wins. A total of 910 matches were predicted correctly and 62 matches were predicted incorrectly, yielding an accuracy rate of 93.62%.
## 4 Simulation and Water Tank Experiment Results
### Results of Water Tank Experiment  
The water tank experiment system is shown in Figure 11. At the sending end, we use a Field Programmable Gate Array (FPGA) (ALINX, AX516) to perform OOK modulation on the received data, and then drive the LED to turn on and off. A diaphragm is placed behind the LED, which is mainly used to adjust the optical power of the LED. We use a tank full of water to simulate the underwater channel; the length of the tank is about 1.5 m. Because the length of the water tank is not sufficient, in order to simulate deep-sea communication, an attenuator is placed behind the diaphragm to attenuate the light signal emitted by the LED and the tank is opaque. At the receiving end, we use another FPGA (ALTERA, DE2-115) to sample the single photon pulse signal output by the SPAD (THORLABS, SPCM 20A), and send the sampled data to the computer via Ethernet for time slot synchronous clock and data recovery.
# Seamless 5G Multi-Hop Connectivity Architecture and Trials for Maritime Applications
## 3 Maritime Network Architecture and Solutions for Seamless Connectivity
### Cross-Border/-MNO Service Continuity for Ship-Shore Connection  
Cross-border/-MNO service continuity for 5G networks has been studied in the EU-funded projects 5 GcroCo, 5G-MOBIX and 5 GACMREN. While nuances and naming might be different across the projects, three solutions have been proposed:  
* Inter-PLMN handover;
* Release with redirect (RwR);
* With S10/N14 interface;
* Without S10/N14 interface;
* Modem-based solutions;
* With one modem;
* With two modems.  
The first two, including the two options for RwR, are described in [20], and their performance, regarding their service interruption duration, has been evaluated in [32]. Inter-PLMN handover results in a service interruption time of around 100 ms, which is typically not noticed by application services. RwR with an S10 interface (5G non-standalone (NSA)) or an N14 interface (5G standalone (SA)) results in an interruption time of less than one second, which can be noticeable but likely with no serious impact for most of the use cases evaluated within the 5 GROUTES project. Without an S10/N14 interface, it depends on the time required to notice a loss of 5G network connectivity and reconnecting. This is typically in the range of one to a few seconds.  
Regarding the third option, cross-border/-MNO service continuity by actively adjusting the parameters of one or two 5G modems is discussed in [33]. With only one modem available, the current network connection must first be terminated, and then a manual network scan must be performed. Limiting the scan to certain known networks and/or frequency bands can speed up the process, but it usually results in multiple seconds of service interruption. In the case of having two 5G modems, such an interruption would be prevented, as one modem would remain connected to the current network while the other one would be set to search for the new one and connect to it. This solution is not very attractive for automotive scenarios, which were the focus of the three mentioned projects, as each modem significantly contributes to the cost of the overall solution, and it would be unclear which operators and corresponding SIMs to choose. In case of sea ferries, this option could make more sense, as the cost of another 5G modem is only a small portion of the overall cost. ABC routers often include more two or more 5G modems. Furthermore,it would be clear which operators and SIMs to choose, as these would correspond to the countries connected by the ferry line.  
Note that this only affects the IP connectivity discussed in previous sections. This is used to transmit data according to the selected split between the onboard and onshore networks. The actual network on the ship could, but does not have to, be completely different than the ones providing ship-to-shore IP connectivity.
# Convolved Feature Vector Based Adaptive Fuzzy Filter for Image De-Noising
## 3 Basic Reviews
### Deviation from a Reference Point  
Simple median filter and its variations are quite effective for the images having smooth regions/variations or when the noise ratio in an image is very low. For all other cases, the performance is badly affected due to non-utilization of neighboring pixels. To address this problem, the second most important component used in filter design is absolute deviation of each element/pixel from a certain reference point. This reference point can be any constant number, median type variable or a statistical parameter which can be modeled as,  
\[AD(p_{i,j})=\abs{p_{i,j}-r},p_{i,j}\in\Omega \tag{3}\]  
where \(r\) is a reference point, \(p_{i,j}\) represents each element of \(\Omega\) which is a kernel in this case. The subsequent values are then further checked with a certain value called 'threshold' to construct a noise map (NM) as,  
\[NM=\left\{\begin{array}{ll}1,\ AD(p_{i,j})\ \geq\ threshold\\ 0,\ Otherwise\end{array}\right. \tag{4}\]  
where '_threshold_' can be fixed or adaptive.  
The techniques explained above are essential part of most of the impulse noise removal filters. Fuzzy logic based pixel density based models [19; 20], reasoning model [22], switching [23], directional [24], clustering model [25], computation models [27] and hybrid filter [28] use generic linear and non-linear statistical methods which may produce good results under certain conditions due to non-linear nature of the median filter but generality is not true. Cluster based median filter (CMF) [25], region adaptive filter (RAF) [26], new weighted mean (NWM) filter [29] and others [5-20; 30-34] mainly use above said criteria's in noise detection phases. CMF works well against salt & peppers noise but gives unsatisfactory results against random noise. CMF, RAF and NWM use fixed threshold values (_threshold_ = 12 to _threshold_ = 22) during noise detection process which may give satisfactory performance for certain type of images but may lead to wrong classification of noisy and noise free pixels for large datasets against random noise.
# 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
#### 3.2.1 Single-Point Positioning Accuracy  
We collected 20-40 data at each vertex of pavement construction area 1 in Figure 1b. To enable the intuitive understanding of positioning accuracy, coordinate error of four vertices, shown in Figure 6a(38 data), 6b(30 data), 6c(29 data), 6d(23 data) respectively, were plotted at the meter scale in Gaussian plane format, where the average value of each vertex coordinate was taken as the true value around which the error distribution was calculated. In this way, we could determine whether the non-vertex coordinates were artificially collected by mistake. Table 1 shows the covariance of the four vertices, and their values tend to zero, which means that the latitude and longitude of the four vertex coordinates are independent, and that the manually collected data could be further analyzed.  
Figure 5: (**a**) Base station and transmitting station; (**b**) Mobile station.  
To further calculate the true values and confidence intervals for vertex coordinates, four vertices of the coordinates were plotted according to the double vertical axis system, as shown in Figure 7a-d, respectively. The horizontal axis represents the number of GPS data collected, the left vertical axis represents the x-axis of the Gaussian plane coordinate system in meters and the right vertical axis represents the y-axis of the Gaussian plane coordinate system in meters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Cov(X1,Y1)** & **X1** & **Y1** & **Cov(X2,Y2)** & **X2** & **Y2** \\ \hline X1 & \(2.93\times 10^{-6}\) & \(1.71\times 10^{-7}\) & X2 & \(9.65\times 10^{-7}\) & \(2.81\times 10^{-7}\) \\ Y1 & \(1.71\times 10^{-7}\) & \(1.04\times 10^{-6}\) & Y2 & \(2.81\times 10^{-7}\) & \(4.97\times 10^{-7}\) \\ Cov(X3,Y3) & X3 & Y3 & Cov(X4,Y4) & X4 & Y4 \\ X3 & \(1.86\times 10^{-6}\) & \(8.94\times 10^{-7}\) & X4 & \(9.18\times 10^{-7}\) & \(5.59\times 10^{-7}\) \\ Y3 & \(8.94\times 10^{-7}\) & \(1.30\times 10^{-6}\) & Y4 & \(5.59\times 10^{-7}\) & \(1.72\times 10^{-6}\) \\ \hline \end{tabular}
\end{table}
Table 1: Covariance between abscissa and ordinates of the Gaussian plane coordinate system.  
Figure 6: Coordinate error of four vertices (38 data(a), 30 data(b), 29 data(c) and 23 data(d)).  
To ensure that the experimental equipment was kept in good condition during data acquisition, the T-test was adopted to determine whether the collected data met the single-point positioning accuracy requirements. As shown in Table 2, the error of coordinate parameters was no more than 0.01 m at the 95% confidence interval. Therefore, the vertex coordinates in the Gaussian plane coordinate system can be finally determined, which are (0.57,0.97), (2.09,-15.93), (6.34,-16.92) and (4.40,1.53) respectively.
# User-BS Selection Strategy Optimization with RSSI-Based Reliability in 5G Wireless Networks
## 5 Wireless BS Selection-Strategy Optimization Based on User Trajectory
### Density Estimation
```
0:\(x\): user's location; BS: BS information, including locations, reliability, and connection limit numbers; \(D\): maximal signal connection distance;
0: s: BS-user connection strategy. 1: initialize: Set \(s=0\);
2: Step 1: Calculate distance \(d_{i}\) from \(x\) to each BS \(i\) and find BS set \(K_{C}\) within the signal effective range of the user SEN on the basis of (17), let \(k=N(K_{C})\). 3: Step 2: Calculate the density at the position of \(x\) according to the given user's trajectory data using the time-space density estimation algorithm. 4: Step 3: Substitute density into the the DP Formula (30) to calculate the optimal user selection strategy \(s\) under the condition of meeting the connection restriction. 5: return s
```  
**Algorithm 3**Wireless BS selection-strategy optimization based on trajectory data. First, the distance from \(x\) to each BS is calculated, and nodes within the effective range of the signal are selected according to Formula (16). Then, the user density obtained in Algorithm 2 is brought into the DP recursive formula of (30), and the user-BS selection strategy is optimized.
## 2 Materials and Methods
### Data Conditioning  
Once all the experimental tests were performed, the next step was the data conditioning. Figure 4 shows the resulting signals of voltage, current, and power before and after filtering during the lifting of a load weight of 500.5 kg. Figure 4a shows that voltage signal is modulated by pulses resulting in a sawtooth current waveform (Figure 4b) with continuous operation (current does not drop to zero). Figure 4a also shows that the DC-DC converter modulation has a period of 2.5 ms and for this example the duty cycle is around 80%. It should be noted that similar tests were performed (not shown) for the traction and power steering system showing the same modulation period for their corresponding DC-DC converters.  
The power signal shown in the Figure 4c was obtained by direct multiplication of voltage and current. As expected, the resulting power signal exhibits an oscillatory waveform, with peak values reaching approximately 8.2 kW. Due to the oscillations observed across all signals, it was necessary to filter them all to get a representative value for easier interpretation. Specifically, a fifth-order Butterworth low-pass filter with a cutoff frequency of 125 Hz was implemented.  
To better illustrate the effect of the selected low-pass filter, Figure 5 shows the voltage, current, and power signals over a full 20 s lifting cycle. This extended view complements the previous zoomed-in version (Figure 4) to observe that the filtered signals maintain a smooth and consistent profile, capturing the essential dynamics of the lifting process while eliminating high-frequency noise caused by high-frequency PWM switching. This figure confirms that the selected cutoff frequency preserves the slow-varying components  
Figure 4: Zoom of original and filtered signals during 500.5 kg lifting operation: (**a**) voltage, (**b**) current, (**c**) and power.  
relevant to mechanical motion of the lifting system. This filtering method ensures a robust and interpretable signal, suitable for further load estimation analysis.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 5 Handover Challenges in Drone Networks
### Interference Probability  
At ground level, handover is generally performed on the distance basis in some cases, indicating that terrestrial UEs receive service from the closest BS. Drones have fewer barriers that may block the signal due to their elevation as compared to ground UEs. Thus, the number of LoS links is greater than that available to terrestrial UEs, as illustrated in Figure 6. The drones may have direct links to non-serving BSs, raising the interference probability. This will create interference issues in the drone's network. This may also lead to the increasing probability of handover execution, especially if the handover decision algorithm is taken based on the RSRP or SINR level.
## 2 Materials and Methods  
### Angular Domain Transform  
Signals collected by the accelerometer and the tachometer are employed as the input of the angular domain transform. Numerical integration is then performed to convert the rotational speed pulse signal into a turning angle signal, which is then converted by equal-angle resampling [28]. A non-stationary signal in the time domain can be converted into a stationary one in the angular domain. Then the characteristic indexes (kurtosis, root mean square, etc.) are extracted from the angle domain signal. The time interval of equal angle sampling is determined by the rotation angle of the reference axis. Therefore, the time domain non-stationary signal is converted into the angular domain stationary signal by using the sampling method of computed order tracking and equal angle incremental resampling [29]. In a short time, the rotation axis accelerates uniformly at an angle, and the relationship between the rotation angle and time is given by [30].  
\[\theta(t)=a_{0}+a_{1}t+a_{2}t^{2} \tag{1}\]  
where \(a_{0}\), \(a_{1}\) and \(a_{2}\) are polynomial coefficients.  
\[\left[\begin{array}{c}\theta(t_{1})\\ \theta(t_{2})\\ \theta(t_{3})\end{array}\right]=\left[\begin{array}{ccc}1&t_{1}&{t_{1}}^{2} \\ 1&t_{2}&{t_{2}}^{2}\\ 1&t_{3}&{t_{3}}^{2}\end{array}\right]\left[\begin{array}{c}a_{0}\\ a_{1}\\ a_{2}\end{array}\right] \tag{2}\]  
Supposing the angular interval between two adjacent key phase pulses be a fixed value \(2\pi\), according to Equation (2), the undetermined coefficients can be obtained. By substituting Equation (2) into Equation (1), the time corresponding to the rotation angle change can be obtained as  
\[t(\theta)=\frac{1}{2a_{2}}\bigg{[}\sqrt{4a_{2}(\theta-a_{0})+{a_{1}}^{2}}-a_{ 1}\bigg{]} \tag{3}\]  
The rotation angle is discretized according to the equal angle sampling interval. The above equation is therefore recast as  
\[T_{n}=\frac{1}{2a_{2}}\bigg{[}\sqrt{4a_{2}(n\Delta\theta-a_{0})+{a_{1}}^{2}}- a_{1}\bigg{]} \tag{4}\]  
where the time of equiangular sampling point is \(T_{n}\) and \(\Delta\theta\) is the sampling interval.  
According to the equal angular sampling time, the Lagrange interpolation operation is performed on the signal \(y(t)\), and then the resampled angular domain signal is obtained.
## 1 Introduction
Additionally, the static and dynamic responses of structures subjected to various vehicle types, including heavy trucks and emergency vehicles, have been extensively investigated, demonstrating that both vehicle classification and loading configurations lead to distinct stress patterns within the structure [35; 36; 37; 38]. [PERSON] et al. [39] utilized FE modeling to simulate vehicle and lane loading conditions, concluding that FE-based approaches offer superior accuracy and cost efficiency compared to conventional static load testing. These studies provide crucial insights for refining vehicle load simulation methodologies. Since PCS represents an innovative type of cantilever structure, its distinctive connection mechanisms will inevitably influence internal stress distributions under traffic-induced forces. However, comprehensive research on the overall stress behavior of PCS under traffic loads remains lacking. In conclusion, while considerable advancements have been achieved in the analysis of cantilever structures, there is still a notable gap in understanding the performance of PCSs under extreme traffic loading conditions. Thus, this research systematically fills the existing gap by integrating experimental studies with advanced simulations, focusing on the innovative design of PCSs. This study  
Figure 1: Prefabricated cantilever structure (PCS). (**a**) Schematic of the PCS roadbed; (**b**) details of the beam–column joint; and (**c**) PCS roadbed in a real construction case. develops and validates a novel scaled-down finite element (FE) model for PCSs, established through rigorous experimental testing. Furthermore, a pioneering full-scale PCS FE model is created and validated using real paving test data, enabling a comprehensive analysis of structural responses under diverse traffic-induced effects. Three critical loading scenarios--external eccentric load, internal eccentric load, and full load--are meticulously evaluated. Additionally, the research introduces a groundbreaking approach to structural optimization by proposing the incorporation of prestressed rebars to mitigate stress concentrations in cantilever beams. These findings not only enhance our understanding of PCS behavior but also provide valuable insights for optimizing and practically applying PCS roadbeds in the challenging context of mountainous road construction, setting a new benchmark for future research in this domain.