esa-sceva/satcom-chunk-collection huggingface dataset:
# Studying Semi-TCP and Its Application in Marine Internet
###### Abstract  
TCP protocol has good performance on the Internet, but its performance is significantly reduced when it is applied to Marine Internet (MI). How to improve the performance of TCP protocol in Marine Internet has become an important research topic. In this paper, an improved Semi-TCP is adopted for Marine Internet, and the implementation scheme of Semi-TCP congestion control is introduced. The exposed terminal problem and congestion control problem of high load networks are analyzed in detail. By using a timer, the congestion control algorithm is improved. Performance analysis and comparison of TCP-Lite, Semi-TCP-RTS, and improved Semi-TCP (Semi-TCP-RTS-V2) are carried out on Exata simulation platform, and the experimental results show that Semi-TCP-RTS-V2 has better transmission performance in ship ad hoc networks.  
Marine Internet; ship ad hoc network; TCP protocol; congestion control; network performance
# A Registration Method for Historical Maps Based on Self-Supervised Feature Matching
## 4 Results
### Metrics  
Because there has been no relevant research on historical map image registration thus far, we adopt evaluation metrics from SAR image registration, which is similar to historical map registration, to assess the registration framework proposed in our study:  
* \(N_{all}\). This is the total number of keypoint matching pairs obtained in the feature matching module [32]. A higher \(N_{all}\) means that there are more correspondences between keypoints in different images.
* NOCC (Number of Correct Correspondences [32]). This metric measures the number of correct correspondences between keypoints. A higher number of correct matches leads to a more accurate estimation of the geometric transformation model.
* ROCC (Ratio of Correct Correspondences [33]). This metric evaluates the proportion of accurate matches by assessing the presence of outlier matches among key points during the registration process. A higher ROCC indicates that a greater number of key points have been successfully matched, suggesting that the registration is more resilient to incorrect matches and outliers. The ROCC is calculated as follows: \[ROCC=\frac{NOCC}{N_{all}}\] (15)
* RMSE (Root Mean Squared Error [33]). RMSE is a metric used to gauge the reliability and precision of the registration procedure. It quantifies how accurately two images are aligned by inspecting both the forward and inverse geometric transformations. A lower RMSE score indicates greater registration accuracy, reflecting fewer alignment errors. \[RMSE=\sqrt{\frac{1}{N}\sum_{i=0}^{N}\left\|P_{i}-g(f(P_{i}))\right\|^{2}}\] (16)  
\(P_{i}\) denotes a key point within the sensed image, and \(N\) signifies the total number of key points present in the sensed image. The functions \(f\) and \(g\) correspond to the forward and inverse transformation models. Here, \(P_{i}\) represents a key point in the sensed image, and \(N\) indicates the total number of key points within that image. The functions \(f\) and \(g\) refer to the forward and backward transformation models, respectively.
* RT (Runtime [32]). This means the time complexity of the algorithm. Registration efficiency is also a key focus of our study. By comparing the runtime differences of various methods across different modules, we can clearly illustrate the time complexity of each approach.
# Edge-Driven Multiple Trajectory Attention Model for Vehicle Routing Problems
## 2 Related Work
### Deep Reinforcement Learning Methods  
The application of deep reinforcement learning (DRL) methods has seen a significant rise in recent years for solving complex optimization problems. In response to the growing demand for optimal solutions to VRPs, considerable research has been dedicated to developing end-to-end deep learning models. Key architectures in this area include recurrent neural networks (RNNs), graph neural networks (GNNs), and transformer-based models. A notable advantage of these DRL-based methods is their capacity to handle problems of varying sizes with identical combinatorial structures once trained, thus eliminating the need for retraining when scaling up the problem.
## 4 Results and Discussion
### Contribution of Different Parameters to SAR SSW Speed Uncertainty
The contribution derived from \(\Delta\theta\) appears to be negligible with respect to those from both \(\Delta\sigma_{0}\) and \(\Delta\varphi\). Figure 4 displays, for a fixed true state (W,\(\theta\),\(\varphi\)) = (15 m/s,37.5\({}^{\circ}\),175\({}^{\circ}\)), the CMOD5.N-derived SAR SSW speed uncertainty \(\Delta\text{W}_{\text{max}}\) (m/s) plotted as a function of the varying triplet of uncertainties (\(\Delta\sigma_{0}\),\(\Delta\theta\),\(\Delta\varphi\)), respectively in the range [0, 0.005] (\(y\) axis), [0\({}^{\circ}\), 0.2\({}^{\circ}\)] (with a step of 0.1\({}^{\circ}\)) (\(z\) axis) and [0\({}^{\circ}\), 90\({}^{\circ}\)] (\(x\) axis). In the shown example, \(\Delta\text{W}_{\text{max}}\) significantly increases with an increasing SSW direction uncertainty \(\Delta\varphi\), and it also slightly increases with both increasing incidence angle and sigma nought uncertainties, \(\Delta\theta\) and \(\Delta\sigma_{0}\), respectively.
## 5 Applications
### Land Cover Classification  
Satellite images have been widely used for land cover classification. However, errors in land cover classification are unavoidable if different land covers have similar spectral characteristics in satellite images. For example, most land cover types of vegetation, such as grassland, farming land and forest,have very similar spectral signals in the summer season; it is impossible to accurately differentiate these land cover types using only summer satellite images [97]. Data generated by spatiotemporal data fusion can improve the accuracy from two aspects: high frequency and high spatial resolution. High frequency can obtain temporal information of different land cover types which is a valuable supplement to spectral information, and high spatial resolution can reduce the number of mixed pixels.  
A study applied and compared two data fusion models, STARFM and ESTARFM, in generating synthetic images from Landsat and MODIS for mapping flooded areas. Results show that both fusion methods can get flood maps with much higher accuracy than those only using MODIS images [98]. Another study classified the land cover using seven phenological parameters derived from NDVI with high spatial and temporal resolutions. The NDVI data was generated by STARFM fusing GF-1 and MODIS NDVI dataset in Hebei Province of China. The overall classification accuracy of using fused data is 88.8%, which represents an increase of 9.3 percentage compared to GF-1 spectral data alone [99]. A recent study fusing Landsat with MODIS, China Environment 1A series (HJ-1A), and Advanced Spaceborne Thermal Emission and Reflection (ASTER) digital elevation model (DEM) data, showed that the fused data, integrating temporal, spectral, angular, and topographic features can get better land cover classification accuracy than the original data from an individual source [100].
# Communication Technologies in Emergency Situations
## 5 Discussion
### Future Work Directions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Works Ref.** & **Technologies** & **Communication System** & **Communication Layer** & **Requirements** \\ \hline
[24] & AWRN with multicopter UAVs & SR & Access & Rapid deployment, Robustness and reliability, Scalability \\ \hline
[33] & AWRN with tethered UAVs, optical fibre & Rapid deployment, Interoperability, Robustness and (done-chip) mesh equipment, 4-G-LTE, & SR, CIRS & Access + Middleware & Reliability, Scalability, Mobility, Scalability, Voice and Data support \\  & Automatic Identification System receiver & & Data support & \\ \hline
[34] & SDN-based UAV network & SR & Access + Middleware & Rapid deployment, Robustness and reliability, \\ \hline
[35] & Hybrid wireless mesh networks & SR, CIRS & Access & Rapid deployment, Robustness and reliability, \\  & & & & Scalability, Voice and data support \\ \hline
[36] & UAV-supported wireless networks (D2D communications), SWIP technology & SR & Sensors + Access & Rapid deployment, Robustness and reliability \\ \hline
[37] & UAV-enabled SWIP technology, IoT & SR & Sensors + Access & Rapid deployment, Robustness and reliability, Scalability \\ \hline
[38] & NOMA-based UAV-aided wireless network, IoT & SR & Sensors + Access & Rapid deployment, Robustness and reliability, Voice and Data support, Scalability, Interoperability, Cost \\ \hline
[39] & Tethered balloon, WiMax & SR & Access & Rapid deployment, Robustness and reliability, Voice and Data support, Scalability \\ \hline
[40] & Aerial wireless mesh networks & SR & Access & Rapid deployment, Robustness and reliability, Voice and Data support, Scalability \\ \hline
[41] & UAV transporting data from and to & & & \\  & truck-mounted BSs, Delay Tolerant Networks & CIRS, SR & Sensor + Access & Rapid deployment, Scalability, Cost \\ \hline
[42] & IEEE 80211 compliant antenna with WLAN & & Sensor & Rapid deployment \\  & mobile multimedia application & & & \\ \hline
[43] & Delay Tolerant Networks + UAVs & SR, CIRS & Sensor + Access & Rapid deployment, Robustness and reliability, Scalability \\ \hline
[44] & UAVs + D2D communications & SR & Sensor + Access & Robustness and reliability \\ \hline
[45] & Ad-hoc networks, Wi-Fi tethering & SR, CIRS, WS & Sensor & Rapid deployment, Robustness and reliability, Scalability \\ \hline
[46] & 5C UAVs-assisted mobile edge communications, networking privacy-preserving FML & SR, CIRS & Access & Rapid deployment, Robustness and reliability, \\  & & & & Scalability, Security \\ \hline
[47] & Wi-Fi network on Drones & SR & Sensor + Access & Rapid deployment, Robustness and reliability, \\  & & & & Scalability, Cost \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Data analysis of the references included in the systematic review.
## 5 Simulations and Performance Evaluations
### Different Numbers of MEC-NBS  
As shown in Figure 6, we measured the total time consumption by the system in completing all tasks under the 3 strategies with different numbers of MEC-NBS. In simulations, we increase \(K\) from 2 to 9, while other settings are listed in Table 1, except fixed values \(M=60\), \(R=6\), \(\phi_{k}=2.4\times 10^{6}\) MBPS, \(s_{k}^{\text{C2 NBS}}=80\) MB/s, \(s_{k}^{\text{NBS2C}}=30\) MB/s, \(s_{k}^{\text{B82 NBS}}=s_{k}^{\text{NBS2 BS}}=30\) MB/s, \(\forall k\in\mathcal{K}\).  
Figure 5: \(f\) gained by the 3 schemes with the increase of \(R\).  
When \(K\) is increased, the total time to complete tasks is decreased correspondingly under OMEC and OCMEC strategies. The curve of NMEC is flat since the further offloading and data caching are disabled, so \(K\) has no effect on NMEC. At the same time, the total time consumed by OMEC and OCMEC is always lower than NMEC, and OCMEC is always lower than OMEC. So, computing offloading and data caching can reduce the system delay.  
With the increase of \(K\), the system can accept more tasks and provide better performance through computing offloading and data caching, the total time-consuming gap between OCMEC and NMEC is increasing, and the gain effect of reducing the delay is getting better and better. But we still observe that, when the number of \(K\) increases to a certain threshold, the optimization effect for time consumption slows down gradually.
# Study of Self-Heating and High-Power Microwave Effects for Enhancement-Mode p-Gate GaN HEMT
## 4 Conclusions  
In this paper, an enhancement-mode p-gate AlGaN/GaN HEMT consistent with the experimental results is established, and the self-heating and high-power microwave effects are investigated from electric and thermal aspects.  
The research results of the self-heating effect show that there is an obvious variation in energy band and carrier distribution when the channel is turned on. Coupled with the influence of various scatterings and temperature rise, the electron mobility decreases which results in the attenuation of output current. In addition, the area where the hot spot under the self-heating effect is located, the area with the largest electric potential gradient, and the area with the largest electric field strength are highly coincident.  
In the simulation of the HPM effect, the thermal behavior of the device was discussed and the mechanism of the hysteresis phenomenon was explained from the perspective of dynamic heat dissipation power. It can be concluded that the self-heating and HPM effects are different in the following two aspects: one is that the tendency of temperature variation caused by the two are different. The temperature variation under the influence of the self-heating effect shows a linear growth trend, while that under the influence of the HPM effect shows a stepwise rising sinusoidal signal-shaped trend. Due to the different operation modes of the two, the temperature rise rate and mechanism will be different. The gate voltage bias between the self-heating study (2 V, DC voltage) and the HPM study (300 V, 5 GHz sine wave) is disparate, resulting in a \(10^{8}\) times difference in temperature rise rate. The HPM attack focuses on releasing high-power electromagnetic radiation within a nanosecond time range, causing the rapid heating and burning down of the equipment. The number of hot spots and distribution is the other difference. In analyzing the damage mechanism it was concluded that the left and right edge of the p-GaN region under the gate of the device is broken down under the action of HPM, and a large amount of breakdown current is generated, which causes the temperature to increase sharply and even burn out. The simulation results show that the two ends of the gate close to the source and drain with the action of HPM are potential damage areas, which are in good agreement with the experimental results. This is an important reference for research on HEMT damage location prediction technology and reliability enhancement.  
Project administration, C.C.; writing--original draft preparation, Y.Q.; writing--review and editing, F.L. and Y.Y.; data curation, Q.L.; formal analysis, H.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61974116.  
The authors declare no conflict of interest.
## 4 Discussion
### Movement-Based Classification System for LBP  
The use of movement preference to classify case complexity is a novel idea that could facilitate case assignment to novice physiotherapists. Exercise therapy is recognized by newly minted physiotherapists as a key aspect of physiotherapy practice in managing musculoskeletal pain [24]. For instance, cases B7 and E7 have similar movement preferences, but case E7 is of higher complexity than case B7 because the movement preference side (problem side) is opposite to the side with pain. Based on the subjective history of E7, symptoms did not improve with routine physiotherapy management, primarily because exercises were prescribed on the wrong side (side with pain) and in the flexion direction. Exercising on the wrong side is adverse [16], so the careful identification of movement preference is as important. In this study, we showed the possibility of identifying many different movement preference couplings, which can exceed the 20 permutations found among the cases, exceeding what was previously studied [21]. Furthermore, 80% of the cases studied were classified as non-basic. The high variations in movement preference coupling and the high proportion of complex cases highlight the challenges that novice physiotherapists need to overcome in managing non-specific chronic LBP patients. Thus, it is unsurprising that novice physiotherapists lack confidence in prescribing exercises [25].  
Commonly known practice classifies movement preference into flexion or extension [11; 12], but a recent study suggested that the hip flexion angle matters in identifying mid-range and lateral flexion preferences [10; 22]. Findings from the present study showed that nine participants did not fall within the classification of full flexion or extension preference. For cases I11 and I12, it is likely that their maximum force exertion will be poorer in 60\({}^{\circ}\) and 90\({}^{\circ}\) hip flexion angles when performing side-lying clamshell exercises on the problem side [22]. For better motor relearning, exercises should therefore be performed in the easiest position [16]. The current literature suggests that the understanding of directional preference is still limited to guide translation into efficacious treatment [12]. The DMA Clinical Pilates method provides a clear method to comprehensively assess directional preferences [10]. Thus, the DMA Clinical Pilates method can help facilitate structured learning and build the confidence of novice physiotherapists in the management of LBP cases.  
It should be noted that the current study only described the theoretical considerations and assessment procedures of the DMA Clinical Pilates classification system. Based on the presented information, we cannot say whether patients undergoing this classification system will have better therapeutic outcomes. To address this research question, a randomized controlled trial is necessary in the future to compare patients whose clinical interventions have been informed by the proposed classification system versus those who have not.
# Physicochemical and Microstructural Properties of Red Muds under Acidic and Alkaline Conditions
## 4 Variation in The Main Metal Elements Under the Acidic and Alkaline Conditions
It is important to note that the Al content is still as high as 10.04% and 9.63% for the Shanxi and Shandong red mud after the extraction of aluminum oxide, respectively. Approximately 7.83% and 8.87% of Na was detected in Shanxi red mud and Shandong red mud, respectively, whereas no Na was detected in the Shanxi silty soil (see the untreated column in Table 4), indicating that the presence of Na in the red mud can be mainly attributed to the addition of alkaline substance NaOH during the production of aluminum oxide. Table 4 indicates that the Shanxi red mud immersed in the NaOH solution shows an increase in Na content from 7.83% to 14.23% of the untreated sample, whereas that immersed in the HCl solution shows an increase in Cl content to 25.39%, and 75.7% was leached out in the Na\({}^{+}\) content of the untreated sample (i.e., from 7.83% to 1.9%) due to the release of Na\({}^{+}\) that formerly existed in the solid matrix of the red mud into the filtrate. In addition, the Fe content is reduced to 75.5% of the untreated sample (i.e., from 3.59% to 2.71%) and that of other contents (e.g., Al and Si) is also significantly reduced. Similarly, the Shandong red mud immersed in the HCl solution also shows a 10.4% decrease in the Na content (i.e., from 8.87% to 0.92%; Table 4). Thus, it can be concluded that Na\({}^{+}\) is the predominant exchangeable cation in the fresh red mud residue. [PERSON] et al. [20] investigated the ability of an activated CO\({}_{2}\)-neutralized red mud to remove arsenate (As(V)) from aqueous solutions and showed that the sorption capacity of red mud increases after neutralization. However, it has been reported that red mud treated with an HCl solution has a 30% lower capacity to adsorb metals compared with an untreated red mud [9; 18; 21]. [PERSON] et al. [18] observed a similar phenomenon by many neutralization methods (e.g., acid neutralization, CO\({}_{2}\) neutralization, sintering, seawater neutralization, and bioremediation), especially for the Na\({}^{+}\) content. Partial dissolution of carnitine in an acid-treated red mud causes an increase in other phases, e.g., hematite. In particular, there is a higher fraction of water-soluble and exchangeable metals in an acid-treated red mud than in an untreated red mud. Hence, neutralization is a promising method to reduce the adverse environmental impacts resulting from an uncontrolled release from storage, which also has a favorable economy. Obviously, the immersion of red mud in an HCl solution can also have an effect on its physicochemical, microstructural, and mechanical properties.
# Review of Renewable Energy-Based Charging Infrastructure for Electric Vehicles
## 2 Technological Infrastructure
### Energy Storage and Fast Charging Systems  
It was reported in [33] that unregulated charging would contribute to the overloading allocation of transformers and feeders and, eventually, the power supply. Hence, most of the literature has suggested stationary energy storage and fast charging systems to overcome this challenging problem [33]. Energy storage limits the charging infrastructure and runs costs by serving electric vehicles during the system's uttermost load intervals [34; 35]. Energy storage can also improve electric vehicles' stability by supplying necessary and sufficient energy to reach charging stations in the case of emergencies. Many studies were carried out on the benefits of stationary energy storage with fast charging systems [34; 36; 37]. However, to obtain such benefits, an optimum size of the energy storage system is required, taking into account the energy tariffs, expected degree of penetration, and load profiles of EVs [33].
## 2 Materials and Methods
### The Proposed System
#### 2.2.1 Global Network  
Global network focuses on global information in both temporal and spatial dimension. The network directly takes preprocessed facial expression video frames as network inputs.  
The network extracts temporal and spatial features by layers. [PERSON] et al. [24] proposed a medium-sized network could achieve higher accuracy than what a large size network can achieve under the circumstances of a medium-sized dataset. The Oulu-CASIA NIR facial expression database used in this research contains 480 videos of 80 persons, which is similar in size to the database used in [PERSON]'s work [24]. Therefore, we also used a well-designed medium-size network that is VGG-M-2048 [30] with five convolutional layers, three pooling layers, and one fully connected layer that has 2048 neurons. The detailed configurations of global network are given in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layers** & **Patch Size** & **Output Size** & **Output** \\ \hline Data & - & - & \(108\times 88\times 32\) \\ Conv1 & \(5\times 5\times 3\) & 64 & \(54\times 44\times 32\) \\ Pool1 & \(2\times 2\times 2\) & 64 & \(27\times 22\times 16\) \\ Conv2 & \(3\times 3\times 3\) & 128 & \(27\times 22\times 16\) \\ Pool2 & \(2\times 2\times 2\) & 128 & \(14\times 11\times 8\) \\ Conv3 & \(3\times 3\times 3\) & 256 & \(14\times 11\times 8\) \\ Conv4 & \(3\times 3\times 3\) & 256 & \(14\times 11\times 8\) \\ Conv5 & \(3\times 3\times 3\) & 256 & \(14\times 11\times 8\) \\ Pool5 & \(2\times 2\times 2\) & 256 & \(7\times 6\times 1\) \\ FC & & 2048 neurons & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configurations of global network.  
Figure 1: The proposed NIRExpNet for near-infrared facial expression recognition (NIR FER). Global network: the preprocessed whole face acts as input flow of network. The network employed medium-size VGG-M-2048 models. Local network: the two streams using same network that has two convolutional layers and two pool layers and are fused by Fusion 1. The global and local network are fused in Fusion 2.
# SCM-IoT: An Approach for Internet of Things Services Integration and Coordination
## 3 Methodology
### Conceptual Model
#### 3.1.2 Subscription for Notification (Notify)  
A subscription for notification is a page-oriented service whose purpose is to notify the subscribers that a page content has reached a state where the notification conditions are met. After an update, the editor checks if the notebook content satisfies some notification conditions. Once all the changes to the storage medium are completed, the editor looks for the table relating fields modified by topic with the corresponding subscriber. Under this approach, the update operations are the activity triggers looking for applying pertinent application rules.