esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of Transcranial Magnetic Stimulation and Its Application in Multiple Sclerosis
###### Abstract  
Transcranial magnetic stimulation (TMS) is a flexible, non-invasive technique that involves the production of a brief magnetic field to stimulate the conductive structures of the brain. When TMS is specifically employed as a single- or paired-pulse over the motor cortex, the function and integrity of the descending motor pathways can be assessed through the generation of a motor evoked potential (MEP). Important MEP-derived measures include the motor threshold, MEP amplitude and latency, central motor conduction time, silent period, intracortical inhibition, and intracortical facilitation. These functional measures may have use in individuals with multiple sclerosis (MS), a common chronic autoimmune disorder of the central nervous system, due to their useful diagnostic and prognostic implications. As a representation of excitability and conductivity, TMS measures may have the potential to serve as objective markers of corticospinal tract integrity, which is a major aspect of clinical disability in MS. Additionally, TMS may be employed to help monitor and provide insight on the effects of therapies for patients with MS over a longitudinal timeframe. In this review, we focus on the application of TMS in the context of MS, with an emphasis on the relationship between TMS measures and widely used clinical assessment measures used for patients with MS.  
transcranial magnetic stimulation; TMS; multiple sclerosis; MS; motor evoked potentials; central motor conduction time
## 4 Results
### Land Surface Classification Results
#### 4.2.3 Results with the Multi-Spectral UAV Moonlight Image  
Finally, land surface classification with the UAV moonlight image well reflected the spatial distribution characteristics of each land type (Figure 12). The overall accuracy and kappa coefficient are 82.33% and 0.77, respectively. While some parts of the results are misclassified because of similar spectral characteristics, such as ponds and farmlands, the results prove that UAV data taken under faint lunar illumination are useful for characterizing land surfaces, particularly those that cannot emit light.  
The above results have shown that these moonlight remote-sensing data could be applied well to the classification of non-self-luminous land surfaces at night.
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Alloys**} & \multicolumn{8}{c}{**Chemical Composition (wt \%)**} \\ \cline{2-10}  & **Cu** & **Mg** & **Mn** & **Ti** & **Zr** & **V** & **Zn** & **Si** & **Fe** \\ \hline ER2319 & 5.8–6.8 & \(\leq\)0.02 & 0.2–0.4 & 0.1–0.2 & 0.1–0.25 & 0.05–0.15 & \(\leq\)0.1 & \(\leq\)0.2 & \(\leq\)0.3 \\
2219-T87 & 5.8–6.8 & \(\leq\)0.02 & 0.2–0.4 & 0.02–0.1 & 0.1–0.25 & 0.05–0.15 & \(\leq\)0.1 & \(\leq\)0.2 & \(\leq\)0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Nominal compositions of ER2319 wire and 2219-T87 substrate. Figure 3: The depositing paths for (**a**) thin wall samples and (**b**) block samples. Figure 2: Actual arc current and voltage waveforms of (**a**) CMT-P and (**b**) CMT-ADV. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Process**} & \multirow{2}{*}{**Deposition**} & **Wire Feed** & **Travel Speed** & **Deposition** & **Heet** & **Flow Rate of** & **Layer** & **Pass** & **Offset** & **Dimensions of Component** \\  & & **Path** & **Speed (WWS)** & **(TS)** & **Rate** & **Input (H)** & **99.99\% Ar** & **Number** & **Distance** & **(mm)** & **(mm)** \\  & & & **(mm/min)** & **(mm/min)** & **(kg/n)** & **(f/mm)** & **(f/mm)** & **(f/mm)** & **(mm)** & **Length** & **Height** & **Width** \\ \hline P0 & & Thin wall & 6 & 0.8 & 1.1 & 189.1 & 25 & 14 & 1 & - & 150 & 16.5 & 7.3 \\ P1 & CMT-P & Block & 7.5 & 0.6 & 1.38 & 305.7 & 25 & 4 & 10 & 4.2 & 150 & 14.8 & 42.2 \\ P2 & Block & 7.5 & 0.5 & 1.38 & 366.8 & 25 & 4 & 10 & 4.5 & 150 & 15.2 & 41.9 \\ A0 & & Thin wall & 6 & 0.8 & 1.1 & 137.5 & 25 & 10 & 1 & - & 150 & 15.0 & 7.2 \\ A1 & CMT-ADV & Block & 7.5 & 0.6 & 1.38 & 227.8 & 25 & 4 & 10 & 3.8 & 150 & 14.9 & 41.7 \\ A2 & & Block & 7.5 & 0.5 & 1.38 & 273.4 & 25 & 4 & 10 & 4.2 & 150 & 15.7 & 41.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The parameters of the depositing process and dimensions of the components. Figure 4: Wire + arc additive manufactured aluminum alloy structures. Samples of cross sections for microstructure analysis and samples of longitudinal sections for porosity analysis were both taken from the middle parts of these components. An optical microscope (OM) (Nikon OPTIPHOT) (Nikon, Tokyo, Japan) was employed for taking images of porosity and microstructure. The resin mounted specimens were ground with 400, 800, 1200 and 2000 meshes waterproof abrasive paper in order, and then polished with 3 \(\upmu\)m diamond paste and SiO\({}_{2}\) suspension, respectively. Porosity distribution was observed firstly. Etching was performed with [PERSON]'s reagent solution (6% HNO\({}_{3}\); 2% HF; 92% H\({}_{2}\)O) before microstructure observation. Micro hardness testing was performed using Zwick/Roell ZHV30 (Zwick/Roell, Ulm, Germany) with 200 g load for 10 s duration, from the bottom of each sample along the vertical direction with 0.5 mm intervals, as shown in Figure 5.
## 3 Results
### Co-Occurrence Network Analysis  
To investigate the changes in interconnections among soil fungal community members and their relationships with different agricultural practices, this study performed a microbial co-occurrence network analysis of soil fungal communities and calculated the network's topological characteristics. The analysis revealed that winter soil fungi exhibited higher network complexity than summer soil fungi. Additionally, the complexity under no-tillage and stubble retention conditions was higher than under conventional tillage and stubble retention in both summer and winter (Figure 6). The topological analysis results further highlight the differences among microbial networks. Compared to conventional tillage and stubble retention, no-tillage and stubble retention had more nodes and edges, a higher average degree, and a higher average weighted degree, with the NT treatment showing the highest numbers in both summer and winter. The clustering coefficient and modularity also varied (Table 3).  
Figure 4: Principal co-ordinates analysis (PCoA) of fungal diversity in all soil samples. S = summer, W = winter, CT = conventional tillage, CTR = conventional tillage plus previous crop residue, NT = o-tillage, NTR = no-tillage plus previous crop residue.  
Figure 5: Heatmap analysis of the highly represented fungal taxa at the genus level. S = summer, W = winter, CT = conventional tillage, CTR = conventional tillage plus previous crop residue, NT = no-tillage, NTR = no-tillage plus previous crop residue.  
Figure 6: Co-occurrence networks of soil fungi between different agricultural practices. S = summer, W = winter, NT = no-tillage, CT = conventional tillage, NR = stubble retention, R = crop residue. Different colors in the figure represent the phylum to which different bacteria belong.
# A Context-Aware Edge-Based VANET Communication Scheme for ITS +
## 2 Related Work
### Vehicular Edge Computing  
[PERSON] et al. [1] have formulated the resource allocation strategy in vehicular networks as a joint optimization problem where the networking, caching and computing are taken into consideration. However, how to achieve the optimization in a decentralized network is not seriously addressed. [PERSON] et al. [2] have demonstrated that content caching at base stations and autonomous vehicles could improve the content delivery performance. [PERSON] [3] has proposed an admission control approach and a flow scheduling mechanism that work on network edges to optimize and control traffic flows and network resources. [PERSON] et al. [4] have proposed a collaborative vehicular edge computing framework where a software defined network-based approach is employed to program, manipulate and configure network in a logically centralized way. While existing studies mainly focus on the use of caching and computation technologies to improve the networking performance, the joint route selection and edge selection problem in a decentralized network is not discussed adequately.
## 3 Results
### Mathematical Model Development
In fact, after this same time period, STPH was already reduced by 40% at 0.4 m depth when biodegradation was also taken into account (Figure 4A). Since oxygen, nitrate, iron or sulfate are not available below 0.4 m depth at 3-4 years (Figure S3), TPH biodegradation is possibly related with the activity of the methanogens. Methane concentration presents a long lag phase and starts to increase in the third year of simulation (Figure S4). This is in agreement with the literature, since it has been previously reported that the onset of methanogenic hydrocarbon degradation is often preceded by extended lag phases [55]. The observed lag phase was related with (i) the small initial concentration of methanogenic microorganisms and (ii) substrate utilization by other microbial groups. Only when denitrification, iron reduction and sulfate reduction become limited by the lack of electron acceptors, does methanogenesis start (Figures S3 and S4). This tendency was previously reported in contaminated aquifers [56] and as such it can be expected to happen also in soils. Theoretically, the redox cascade will flow from aerobic degradation, to denitrification, iron(III) reduction, sulfate reduction and finally methanogenesis, determined by the availability of electron acceptors [56]. In this work, the decreased time of hydrocarbon concentration in depth (Figure 4) is closely associated with the methane concentrations in the soil column, which increases exponentially at \(\sim\)3.5 years and reaches the highest values around 4.4 years for depths higher than 1 m (Figure S4). These results show that methanogenesis has a high impact on TPH concentration and, therefore, TPH biodegradation under methanogenic conditions appears to be the main process contributing for TPH removal at deeper layers of the soil. Methanogenesis was previously shown to be important in the natural attenuation of hydrocarbon contaminated soils [57; 58].
# GAT-ABiGRU Based Prediction Model for AUV Trajectory
## 4 Experiments and Results
### Experimental Dataset  
In our experimental process, a comprehensive dataset was used, which includes historical AUV kinematic features and marine environmental characteristics. This dataset consists of seven dimensions, covering crucial information, such as time, latitude, longitude, zonal ocean current speed, meridional ocean current speed, temperature, and salinity. These  
Figure 5: ABiGRU structure.  
valuable AUV data resources, including latitude, longitude, seawater temperature, and seawater salinity, are sourced from the BCO-DMO website [38]. The regional ocean's current environmental data were meticulously compiled and provided by the Marine Science Big Data Center. Through in-depth analysis of these multidimensional data, a more comprehensive understanding of the AUV's motion characteristics and the impact of the marine environment on its trajectory can be gained.  
For this experiment, 2000 pieces of data were selected as the dataset for trajectory prediction. First, we conducted a series of preprocessing operations on the dataset, including data cleaning, outlier removal, data interpolation, and data normalization, to ensure the quality and accuracy of the data and to construct a clean and complete dataset for the experiment. Next, the processed AUV trajectory prediction dataset was logically divided, with the first 80% of the data used as the training set to train various prediction models and the remaining 20% used as the test set to evaluate the predictive performance of the models.
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 5 Implementation Setup and Results Discussion
### Scenario 3: Sender View of Idle Time under Normal Circumstances  
Idle time is calculated by the receivers. This is the time that a packet remained in the buffer while waiting for its playout time to arrive. Receivers send this value in RTCP-APP packets to the sender. The sender uses idle time value to estimate the local buffer condition of the receivers. Idle time value also plays important role in the calculation/adjustment of playout time. The sender has a pool of feedback values sent by 100 receivers. We did not change any of the settings during the execution to analyze the idle time under ideal conditions.  
Figure 12: One-way delay (OWD) calculated at sender side using feedback from the receivers.  
Figure 13, represents idle time against the one-way delay. One-way delay reflects that how early or late a packet was received. If the packet is received earlier, it will have a less one-way delay and vice versa. In case of idle time, an early receipt of the packet means longer idle time. Hence lesser the OWD, larger would be the idle time and similarly the other way round. To understand the idle time, we have encircled the area where a packet was received well earlier than rest of the packets. This packet had to stay for longer time in the playout buffer. Longer time in the playout buffer is represented by a larger value of idle time. If a packet is to be played out at the same time at all receivers, the earlier or late arrival of the packet is compensated by varying the idle time.
# Ionic Liquids Hybridization for Carbon Dioxide Capture: A Review
## 5 Challenges in IL-Hybrid Materials  
The hybridization of ILs has enabled the customization of the properties of pristine material. One key advantage of ILs hybridization is the improvement in CO\({}_{2}\) selectivity of the material during gas capture process. Apart from enhancing the thermal stability of the material, the incorporation of ILs has effectively reduced the heat capacity of the sorbent. However, the benefits of ILs hybridization are accompanied by challenges that are crucial and have to be addressed.  
Compatibility has become a hurdle in hybridization as ILs and other materials may pose differences in chemical structure, solubility behavior and physical properties. Therefore, the search for a suitable combination which will not compromise the original structure and composition of base material has become an utmost important aspect in ILs hybridization. For example, in the case of ILs/MOF hybrid material, the hydrophobicity or hydrophilicity nature of ILs plays a crucial role. Hydrophilic ILs can lead to the collapse of the MOF structure due to their sensitivity to moisture. Therefore, selecting hydrophobic ILs has become essential to prevent any adverse effects on the stability of the MOF structure. Other than that, the separation and regeneration process of captured CO\({}_{2}\) from hybrid materials can be challenging due to the presence of various interactions such as intermolecular forces, ionic interactions and covalent bonds. In addition, scaling up the ILs hybridization process to the industrial level could be intricate as various factors such as ILs availability, material purity, durability of materials and process efficiency must be taken into consideration to ensure economic feasibility in gas capture process.
## 5 Discussions
### Outlook on the Potential for LEO-LEO Occultation Demonstration and Operational Constellations  
The development and establishment of spacecraft such as space stations and meteorological satellites provide experimental platforms for potential LEO-LEO occultation demonstration opportunities. With such an orbit-constrained spacecraft, only (at least) one additional satellite needs to be launched to construct a pair of Rx and Tx LEO-LEO demonstration satellites. Taking a space station as an example, typically its orbit inclination is around 45\({}^{\circ}\) and its height is about 400 km, which means that it can be used as an occultation signal transmitting (Tx) platform. Thus, only a small receiving (Rx) satellite running in a retrograde orbit, with an inclination of about 140\({}^{\circ}\) and a height of about 600 km, is needed to construct a LEO-LEO constellation similar to the Rxi140-Txi40 one shown in Figures 3 and 4. The distribution of its LEO-LEO occultation events is in this case similar to that shown in Figure 3h, while the occultation number and GCF accumulated time dependence are similar to the triangle lines shown in Figure 4, which show about 3000 occultation events in 90 days and a 63% Earth coverage fraction.  
On the other hand, with the development of LEO satellite constellations with large numbers of satellites, such as the Iridium, Spire, and Starlink constellations, the next-generation space-based LEO constellations will tend to integrate satellite communication, satellite navigation, and satellite remote sensing functions in one system. Such a LEO constellation concept has also been proposed in China. If this kind of LEO constellation can be well designed as described in Section 4.3, it will yield a large amount of LEO-LEO occultation data, which can be used for global numerical weather prediction and climate monitoring.
## 3 System Model
### Mobile Devices
#### 3.2.1 Task Offloading  
After a task arrives at a mobile device, a decision is immediately made to either run the task locally or transmit it to an edge node for remote execution. Different algorithms or models can be used to make this offloading decision. Later in this work, we discuss some possible algorithms and their corresponding performance. If a task \(t_{i}\) is to be executed locally, it is then added to a local execution queue \(q(m_{i})\), that is, \(q(m_{i})\gets q(m_{i})\cup\{t_{i}\}\). Otherwise, if a task is to be transmitted to the connected edge, it is added to a transmission queue \(t(m_{i})\), which can be represented as \(t(m_{i})\gets t(m_{i})\cup\{t_{i}\}\).
# Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain
## 3 Proposed Method  
### Watermark Frame  
The two-stage robust reversible watermarking framework based on deep learning is illustrated in Figure 3. This framework consists of two stages. The first stage is the pre-training of the robust watermarking network, which focuses on pre-training the robust watermark embedding and extraction networks. First, the cover image \(I_{o}\) and original watermark \(W_{o}\) are fed into the watermark embedding network \(N_{enc}\) to reconstruct the  
Figure 2: Two-level Haar wavelet decomposition and inverse transform.  
watermarked image \(I_{e}\). Then, the attack layer network (randomly selecting an attack type and its corresponding parameters) containing the watermarked image and geometric attack methods, used to attack the watermarked image \(I_{e}\), generate the distorted image \(I_{a}\). Finally, the watermark extraction network \(N_{dec}\) extracts the watermark information \(W_{d}\) from the distorted image \(I_{a}\).  
The second stage involves adding reversible information embedding on top of the first stage's robust watermarking network. In this stage, the watermark embedding network \(N_{enc}\) and extraction network \(N_{dec}\) load the pre-trained network parameters from the first stage and fix the watermark embedding network \(N_{enc}\) without gradient backpropagation. Reversible information embedding involves using traditional algorithms to reversibly embed the different images between the cover image \(I_{a}\) and the reconstructed watermarked image \(I_{e}\) as compensation information into \(I_{e}\), generating the watermarked image \(I_{m}\). The attack layer network is directly migrated to attack the watermarked image \(I_{m}\), generating the distorted image \(I_{a}^{\prime}\). Finally, the watermark extraction network \(N_{dec}\) extracts the watermark information \(W_{d}\) from the distorted image \(I_{a}^{\prime}\).
# An Efficient Precoding Algorithm for mmWave Massive MIMO Systems
## 3 Proposed Algorithm
### Algorithm Flow  
The proposed Algorithm 1 uses Equation (1) as the fitness function and uses the BSA algorithm to find the global optimal value of Equation (1) in order to transform the high-dimensional matrix into a low-dimensional matrix by using the Banachiewicz-Schur block matrix generalized inverse to avoid matrix inversion.  
The specific steps of the proposed bird swarm algorithm (BSA) based matrix-inversion bypass (MIB) orthogonal matching pursuit (BSAMIBOMP) algorithm flow is as follows in Algorithm 1.  
```
0: Optimal unconstrained precoder \(F_{\mathbf{opt}}\).
0: RF precoder \(F_{\mathbf{RF}}\); baseband precoder \(F_{\mathbf{BB}}\).
1: Initialization: RF precoder matrix \(F_{\mathbf{RF}}=[]\), Residual matrix \(F_{\mathbf{res}}=F_{\mathbf{opt}}\).
2: Let \(i=\mathbf{1}\).
3: According to Equation (1), obtain the optimal array response vector \(w\) using the BSA algorithm.
4: Combine the RF precoding matrix \(F_{\mathbf{RF}}\) with the array response vector \(w\), i.e., \(F_{\mathbf{RF}}=[F_{\mathbf{RF}}|w]\).
5: Calculate \(A,V\), and \(M\) according to Equations (4), (5) and (7), and then use Equation (3) to obtain \(G_{I_{i},I_{i}}^{-1}\).
6: Update the baseband precoding matrix \(F_{\mathbf{BB}}\) according to Equation (8).
7: Update residual matrix \(F_{\mathbf{res}}=\frac{F_{\mathbf{opt}}-F_{\mathbf{RF}}F_{\mathbf{min}}}{ \left\|F_{\mathbf{opt}}-F_{\mathbf{RF}}F_{\mathbf{BB}}\right\|_{\mathbf{r}}}\).
8: Let \(i=i+1\); if \(i\leq N_{t}^{\mathbf{RF}}\), return to step 3, otherwise perform step 9.
9: Normalize the baseband precoding matrix \(F_{\mathbf{BB}}=\frac{F_{\mathbf{BB}}}{\left\|F_{\mathbf{RF}}F_{\mathbf{BB}} \right\|_{\mathbf{r}}}\).
```  
**Algorithm 1** BSAMIBOMP Algorithm