esa-sceva/satcom-chunk-collection huggingface dataset:
# MSPNet: Multi-Scale Strip Pooling Network for Road Extraction from Remote Sensing Images
## 3 Materials and Methods  
### Dataset  
A public road extraction dataset DeepGlobe [27] is applied for evaluating the performance of the proposed method. The dataset provides images with a pixel size of \(1024\times 1024\) and a pixel resolution of 50 cm/pixel which includes multiple scenes such as cities, villages, wild suburbs, seashores, tropical rainforests, etc. The dataset contains 6226 images and corresponding annotated ground truth labels. In this paper, we divide it into 4696 images for training and 1530 for testing following [4]. Some samples are shown in Figure 1.  
Data enhancement is a common and useful strategy, which can enhance the generalizability of deep learning models. In this paper, data enhancement is applied by flipping and shifting the images randomly with a probability of 0.5. The visualization of data enhancement is shown in Figure 2.
# Harnessing the Potential of Emerging Technologies to Break Down Barriers in Tactical Communications
## 5 Discussion and Future Trends  
Today, the modern battlefield has undergone a radical change, where multiple assets of different types come into play, carrying out new military missions with more complex tactical scenarios. In this evolving landscape, conventional tactical radios and waveforms fall short in addressing critical requirements like security, flexibility, speed, and cost effectiveness. These constraints need to be met in order to effectively navigate the demanding conditions of the modern battlefield. Therefore, potential lines of action to renew tactical communications must be considered. On the one hand, from a technological point of view, and on the other hand, from a signal processing point of view, to define new waveforms which enhance and overcome the current network performance.
## 3 Control Structure
### Harmonic Instruction Generation Module  
The harmonic instruction generation module detects and compensates for voltage distortions at the PCC caused by nonlinear loads. It begins by transforming the PCC voltages \(v_{\text{gabc}}\) into the synchronous \(dq\) reference frame, converting time-varying AC signals into DC-like quantities for easier harmonic analysis. A low-pass filter isolates the fundamental voltage component \(v_{\text{g\_fund}}\), leaving behind harmonic distortions. The harmonic voltages \(v_{\text{g\_phabc}}\) are derived by subtracting the filtered fundamental component from the original PCC voltage, capturing harmonics such as the 3 rd, 5 th, and 7 th orders produced by nonlinear loads.  
The harmonic voltages \(v_{\text{ghabc}}\) are then transformed into the \(dq\) frame as \(v_{\text{ghdq}}\) generating reference signals for compensation. A key parameter, the short-circuit coefficient \(K_{\text{sc}}\), scales these harmonic references to balance compensation strength and system stability. A higher \(K_{\text{sc}}\) increases the compensating voltage magnitude, enhancing harmonic suppression but risking instability due to grid impedance interactions. A lower \(K_{\text{sc}}\) prioritizes stability but may allow residual harmonics to persist. The scaled references \(v_{\text{ghdq}-\text{ref}}\) are processed by a repetitive controller, which leverages its periodic error-tracking capability to inject counteracting harmonic voltages into the grid dynamically. The repetitive controller's internal model, tuned to the grid's fundamental frequency, enables precise cancellation of repetitive harmonic distortions, even under varying load conditions. This approach ensures compliance with power quality standards like IEEE 519-2014 [22] by actively suppressing harmonics at the PCC.
## 3 Results and Discussion  
### Fatigue Life  
A stress-controlled fatigue test was conducted according to DIN 50100 [85] and based on a logarithmic normal distribution. Table 1 displays the fatigue life of Ti-6 Al-4V specimens before and after UIT application. The specimens were tested in a servo-hydraulic 810 Material Test system (MTS) at a peak stress level of 400 MPa and minimum stress of 0 MPa. The average number of cycles for the treated and untreated specimens is compared in Figure 8.  
Evaluation of the fatigue performance of DMLS Ti-6 Al-4V shows that the fatigue life of untreated specimens is 77% lower than handbook values [37]. The results in Table 1 and Figure 8 show that the fatigue life of treated specimens at 400 MPa corresponds to a 200% increase compared with that of untreated specimens. In other words, the lifetime of the treated samples is three times as long as untreated samples. [PERSON] et al. [47] only achieved a 4% fatigue improvement by blasting Ti-6 Al-7 Nb, whereas [PERSON] et al. [51] prolonged the lifetime of Ti-6 Al-4V, manufactured using the cost-effective blended elemental powder metallurgy technique, by two orders of magnitude after applying ultrasonic impact treatment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen Number** & **1** & **2** & **3** & **Average** \\ \hline Untreated & \(2.39\times 10^{4}\) & \(2.77\times 10^{4}\) & \(4.02\times 10^{4}\) & \(2.90\times 10^{4}\) \\ Treated & \(8.47\times 10^{4}\) & \(8.84\times 10^{4}\) & \(9.63\times 10^{4}\) & \(8.97\times 10^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fatigue life of treated and untreated specimens at 400 MPa.
# Enhanced Performance of nZVI/MXene@CNTs for Rapid As(III) Removal from Aqueous Solutions
## 3 Results and Discussion
### Synthesis and Characterization of nZVI/MXene@CNTs
To better understand the change of the surface functional groups on nzVI/MXene@CNTs, the X-ray photoelectron spectroscopy (XPS) analysis was used to discriminate between the types of the functional groups and distinct forms of the elements. As shown in Figure 3b, the peaks in the survey spectrum of nZVI/MXene@CNTs at 285.20, 459.11, 531.24 and 711.52 eV were attributed to C 1s, O 1s and Fe 2p, respectively. The C 1s, Fe 2p and O 1s XPS fine spectra of nZVI/MXene@CNTs are shown in Figure 3c-e. The peaks at 282.15, 284.80, 286.07 and 288.90 eV corresponded to Ti-C, C-C, C-O-C and O-C=O in Figure 3c, respectively [50,51]. The C-C peak was obviously strong, which corresponded to the interactions between the carbon atoms in the carbon nanotubes. In Figure 3d, the peak at 707.16 eV indicated the presence of Fe\({}^{0}\) contained within the nanomaterial. The peaks at 711.07 eV and 724.43 eV suggested that the nZVI nanoparticles on the surfaces were partially oxidized [52,53]. Figure 3e shows that the peaks at 530.23, 530.57, 531.80 and 533.56 eV were attributed to Fe-O, Ti-O, C-OH and C=O, respectively [54], revealing that the functionalized CNTs with abundant oxygen-containing groups successfully loaded onto the materials. Therefore, the XPS results suggested that nZVI/MXene@CNTs were composites composed of Ti\({}_{3}\)C\({}_{2}\), nZVI and CNTs cross-linked together. The hysteresis line diagram of nZVI/MXene@CNTs samples recorded at room temperature with a vibrating sample magnetometer (VSM) is shown in Figure 3f, which shows a ferromagnetic property of 40.93 emu/g. The magnetization strength of the material rapidly fell to zero when the external magnetic field was removed, showing the superparamagnetic [55]. The outstanding magnetic characteristic ensured that the nZVI/MXene@CNTs could be retrieved by an external magnetic field drive after purification of the water body, which significantly helped to overcome the problem of poor recovery and secondary contamination of the nanomaterials in the real treatment of water pollution.
## 4 Discussion
### Implications on Practical Welding  
The results of this work have some implications on practical welding. Until now, the welding guidelines [9; 10; 11] were clear in limiting the maximum arc energy used to 1.5 kJ/mm. However, in the case of large thickness weldments such as the 20 mm studied here, the use of 2.0-2.2 kJ/mm was not found to be detrimental. The number of weld passes was reduced from 10 to 7, which implies a lower number of reheating cycles for the deposited passes and therefore less secondary austenite formation. In addition, the weld with higher-than-recommended arc energy (GH) showed no nitrides in the as-deposited weld and fewer nitrides in the overall weld. The CPT values for both GMAW welds (GR and GH) were found to be the same, and the results of the toughness test exceeded the acceptance criteria. These findings promote the use of a higher-than-recommended arc energy in large thickness weldments of super-duplex stainless steels.  
Based on the results from this work, the welding sequence layout should be considered when welding superduplex stainless steels. To have weld passes surrounded by three or more subsequent weld passes should be avoided, as this would result in larger amounts of secondary austenite and \(\sigma\)-phase.
# Comparative Study of Blockchain Hashing Algorithms with a Proposal for HashLEA
## 3 Background
### Hash Functions
#### 3.3.4 X11/X13/X15/X17  
The hashing algorithms belonging to the \"X\" family include X11, X13, X15, and X17. The \"X\" algorithms family is known for their high degree of defense against hacker attacks. The number of hash functions used in the \"X\" family algorithms is determining their names. The X11 algorithm uses 11 cryptographic hash functions: Keccak, Blake, BMW, Groestl, Skein, JH, CubeHash, Luffa, SHAvite, SIMD, and Echo. This multi-hashing structure adds complexity, initially providing significant resistance to ASIC mining. The X13 algorithm builds upon the X11 algorithm by incorporating two additional functions, namely HAMSI and FUGUE, thereby increasing the total number of cryptographic hash functions to 13. The X17 algorithm represents a further expansion of X13, incorporating four additional functions: Shabal, Whirlpool, LOSELOSE, and DJB2. This brings the total number of cryptographic functions to 17. The X-family algorithms have been widely adopted in the field of cryptocurrencies. The first implementation of X11 was in DarkCoin, now Dash, which was the first to utilize this algorithm in blockchain and cryptocurrency mining. X13 has been employed in cryptocurrencies such as DANK, AERO, and NAV. X15 has been used in coins like KOBO, HAL, SOLE, and MARYJ. X17 has been applied in other cryptocurrencies due to its broader range of cryptographic algorithms [60; 61; 62; 63].
## 7 Simulation and Evaluation
### Performance Metrics
#### 7.1.2 Route Discovery Time  
The time required to discover a route reflects the protocol's efficiency in establishing connectivity, especially in dynamic networks where rapid changes are common.  
As evidenced by Figures 11 and 12 MMS-DSR demonstrates superior performance in this aspect, with route discovery times increasing from only 15 ms at a density of 10 vehicles/km\({}^{2}\) to 55 ms at 100 vehicles/km\({}^{2}\). This progressive increase is significantly slower compared to other protocols like SOL-DSR and Enhanced OLSR, where times escalate more steeply from 20 ms to 70 ms and from 25 ms to 80 ms, respectively, under similar conditions. This highlights the effectiveness of the integrated machine learning model in MMS-DSR, which uses Long Short-Term Memory networks to predict and adjust routes dynamically, facilitating quick and efficient pathfinding even as network complexity increases. As vehicle density improves along with network congestion, MMS-DSR efficiently predicts potential bottlenecks and reroutes data packets through less congested paths. This ability is reflected in the moderate increase in discovery times from 30 ms at 50 vehicles/km\({}^{2}\) to only 55 ms at 100 vehicles/km\({}^{2}\), demonstrating an exceptional capacity to handle increased traffic and complexity without a corresponding steep rise in discovery times.  
Figure 10: Latency comparison in function on vehicle speed.
## 3 Results and Discussion
### Correlation Analysis  
The heatmap in Figure 4a, derived from the 90-sample dataset, reveals the relationships among the features: energy, interval time, pulse shot, pad position, and laser ablation quality \(r_{eb}\). Pearson correlation coefficients were employed to quantify the associations between these pairs of variables [22].  
A strong positive association was observed between energy and interval time with respect to \(r_{eb}\), as evidenced by their respective correlation coefficients of 0.82 and 0.55. This suggests that higher energy levels and longer interval times are significantly linked to increased \(r_{eb}\). Pulse shot and pad position, however, exhibited relatively modest correlations with \(r_{eb}\), at 0.17 and 0.05, respectively. Furthermore, pulse shot and pad position displayed minimal linear relationships with other features, as indicated by their near-zero correlations.  
The heatmap of the 48-sample dataset, Figure 4b, provides a nuanced perspective on the interplay between the features and the laser ablation quality \(r_{eb}\). Pearson correlation coefficients revealed moderate positive associations, with energy and interval time exhibiting correlations of 0.62 and 0.53, respectively, with laser ablation quality \(r_{eb}\).  
Comparing the heatmap of Figure 4b to Figure 4a reveals a substantial decrease in the correlation between energy and \(r_{eb}\), dropping from 0.82 to 0.62. Additionally, the association between interval time and \(r_{eb}\) exhibited a significant reduction, from 0.55 to 0.53. Furthermore, \(r_{eb}\) displays a correlation of 0.032 with pulse shot and 0.036 with pad position, both of which are relatively weak associations. The correlation of \(r_{eb}\) with pulse shot experienced a noticeable difference, from 0.17 to 0.032. The correlation of interval time with energy also experienced a noticeable difference, from 0.69 to 0.4. It is worth noting that pulse shot and pad position continue to display correlations close to zero with other features, indicating little to no linear relationship with them.  
levels in the 90-sample dataset contributes to a more accurate correlation coefficient of 0.82 between energy and \(r_{eb}\). Likewise, the broader range of interval times in the 48-sample data set fosters a more informative correlation coefficient of 0.53 between interval time and \(r_{eb}\).
# Angle-Based Parametrization with Evolutionary Optimization for OESCL-Band Y-Junction Splitters
## 2 Angle-Based Parametrization  
Our goal is to propose a parametrization that achieves functional devices with a high tolerance to fabrication errors (i) using a lower quantity (\(\sim\)15) of parameters compared to the conventional methods, and (ii) by avoiding sharp angles.
## 5 Performance Study
### Simulation Setup  
In order to evaluate the performance of RoCoMAR, we consider a mobile sensor network where multiple regular nodes (e.g., sensors that are carried by unmanned vehicles or humans) are deployed. Those nodes sense environment to collect task-related data and send the data to the sink at a given position for further analysis. Since those nodes move cooperatively with other nodes to perform the duty (e.g., target tracking and search&rescue operation), they move in a group. In this paper, reference point group mobility model (RPGM) [38] is used to emulate movements of nodes, since RPGM can provide both the group mobility and a random mobility of each member in a group, which is well appropriate for tracking and monitoring operations in mobile sensor networks.  
In RPGM, a group has a logical group center that moves according to group movement vector, which determines the group-wise movement. Each member node in a group is assigned with a reference point which determines the relative mobility of the node. Reference points move using the group movement vector. In addition, each member node has a random movement vector that allows the node to move randomly in the group. Note that movements of member nodes in the group are also limited by the group boundary. The random waypoint model (RWP) [6] is used to simulate both group-wise movements and member movements inside the group. In RWP, a node moves towards a randomly selected destination with a speed that is also randomly chosen in the range of minimum and maximum speeds. The movement is repeated after a randomly chosen pause time period.  
The terrain dimension is 1000 meters \(\times\) 1000 meters, and nodes are randomly placed in a circular group area with the radius of 400 meters. Network size in terms of the number of nodes is set to 25 for  
Figure 5: The structure of ConMoSim.  
AODV, DSR, OLSR and GFR. On the other hand, for a fair comparison, the network contains 20 nodes and 5 RNs for RoCoMAR.  
A network simulator QualNet 5.1, integrated with ConMosim, is used for simulation. In the scenario under consideration, mobile sensors transmit video data to the sink to share their findings and report the status. MPEG-4 is used for video data encoding, and in this paper, from 300 Kbps to 1.2 Mbps video streaming rate is used. In order to emulate the MPEG-4 data transmission, constant bit rate traffics with 1500 bytes data packets on top of UDP is used.  
RoCoMAR is compared with AODV, DSR, OLSR and GFR routing protocols. IEEE 802.11 DCF is used as a MAC layer protocol. For the Physical layer, IEEE 802.11g radio is used with the maximum data rate of 54 Mbps. Also, the Rician model is used as a fading model to make the simulation environment more realistic. The average transmission radio range is set to approximately 225 meters. The average throughput and average end-to-end delay are chosen as performance metrics.  
Some important simulation parameters are shown in the Table 1.
## 6 Investigating Several RL-Based Routing Methods
### [PERSON] et al. in [55] have suggested the Q-learning-based adaptive geographic routing (QAGR) for VANETs. It uses an aerial network for helping the routing operation in VANET. This aerial network consists of several unmanned aerial vehicles (UAVs) to prevent the selection of wrong routes by vehicles due to their limited communication radius. UAVs calculate a global path for data transfer and send this path to the desired vehicles. As a result, vehicles can filter some of their neighbors when choosing the next-hop node. This increases the convergence speed and improves network performance. QAGR includes two main components: aerial component and ground component. In the aerial network, UAVs employ the depth-first-search algorithm (DFS) and fuzzy logic to obtain the global route. They use the fuzzy technique to calculate a fitness value for each road. This fuzzy system is dependent on the number distribution factor (NDF), the velocity distribution factor (VDF), and the total number proportion (TNP). In the ground component, when creating a new path, the source vehicle first transfers a route request to the corresponding UAV to calculate the global route to destination based on DFS algorithm and fitness value of road segments. Then, the UAV responds to this route request message. After receiving the response message, the vehicle inserts this route into the header of its data packets and employs Q-learning and the global path to choose the next-hop node. In Q-learning algorithm, each vehicle plays the role of an agent and tries to choose the most appropriate node among its neighbors. In this problem, the state space includes a two-dimensional array including the distance between each vehicle and its neighbors and the neighboring degree. In this algorithm, the reward function is obtained from received signal strength indication (RSSI), transmission distance, and collision between vehicles. The learning structure of QAGR is shown in Figure 10. After the algorithm has converged and finished, the node with the highest Q-value obtains a more chance to be selected as the next-hop node. Moreover, if multiple nodes have the same Q-value, QAGR selects a next-hop node among them. In QGAR, if the vehicle cannot find the next-hop node, it sends its packet to the corresponding UAV to perform the data transmission process via the aerial network.  
Figure 9: Learning structure in IV2 XQ.
## 2 Requirements Specifications and Measurements Gaps
### Wind Speed  
This variable presents gaps in the revisit time (\(<\)3 h) and latency time (\(<\)1 h). The required revisit time is 3 h at 10-km spatial resolution, with an accuracy of 0.5 m/s. Typical resolutions for microwave radiometer winds are about 25 km [15] or 12.5 km [16] over oceans; the measurement range is 0-50 m/s [17], and the accuracy is from about 2-10 m/s (depending on the rain flag) [18]. Microwave radiometers infer wind speed from frequencies near 6.8, 10.7, 19 and 37 GHz. Another technology that can be used to measure wind speed is SAR. The current Copernicus infrastructure has a constellation of two SARs, but the revisit time is between one and two days for high latitudes and the high resolution dataset (1 km) [19]. Another solution is to infer the wind vector using radar scatterometers, with a 10-km spatial resolution over the oceans.
# A Sentence-Matching Model Based on Multi-Granularity Contextual Key Semantic Interaction
## 4 Experimental Results and Analysis
### Performance Comparison
The proposed method in this study demonstrated significant performance improvements compared to both neural network-based models and attention-based models, highlighting the effectiveness of building models upon pre-trained language models. Specifically, when compared to pre-trained language models, the method in this study achieved improvements of 1.15% and 0.61% on top of BERT, 1.53% and 0.67% on top of Ernie 3.0-nano, and 1.44% and 0.25% on top of Ernie 3.0-base, respectively, surpassing all baseline models. However, the improvement observed for the model based on Ernie 3.0-base in the BQ dataset is relatively smaller. This can be attributed to two factors. Firstly, as mentioned earlier, the BQ dataset poses greater difficulty in terms of matching compared to open-domain datasets. Secondly, Ernie 3.0, being a more advanced pre-trained language model, has undergone extensive knowledge-enhanced pre-training, equipping it with certain capabilities for modeling semantic matching relationships in sentence pairs. Consequently, the potential for improvement relative to the BERT-based models is relatively smaller. Additionally, this study achieved performance that is comparable to BERT-base and even MacBERT-large by leveraging the capabilities of the lightweight Ernie 3.0-nano model on the LCQMC dataset. This observation suggests that, instead of relying on larger pre-trained language models, the approach presented in this study can attain similar performance levels by utilizing a more lightweight pre-trained language model. This strategy proves effective, particularly when resources are limited. In comparison to the MAGE model, the proposed method achieved improvements of 0.32% and 0.44% on the two datasets. The approach presented in this study utilizes a fusion of multi-scale and multi-level CNNs to extract comprehensive contextual features, enabling the model to capture a wider and deeper range of information. By combining multi-head self-attention and cross-attention mechanisms, the model not only emphasizes key semantic information but also aligns significant details across different levels of semantics. As a result, significant improvements are observed compared to the MAGE model.