esa-sceva/satcom-chunk-collection huggingface dataset:
# Conceptual and Technical Design Aspects of Accelerators for External Injection in LWFA
## 2 Requirements for the Externally-Injected-Electron Bunch  
### Requirements on e-Bunch Length and Arrival Time Jitter  
Plasma-based accelerators are especially interesting because of their ability to sustain extremely high gradients. The expression for the cold non-relativistic wave-breaking field gives us an estimation of the lower boundary of the field in the plasma bubble. This boundary is a function of the plasma density \(n_{0}\) according to:  
\[E_{0}\left[V/m\right]\simeq 96\sqrt{n_{0}\left[cm^{-3}\right]}. \tag{1}\]  
The length of the accelerating wave in a plasma-based accelerator is approximately the plasma wavelength \(\lambda_{p}\):  
\[\lambda_{p}\left[\mu m\right]\simeq 3.3\cdot 10^{10}/\sqrt{n_{0}\left[cm^{-3} \right]}. \tag{2}\]  
Table 2 shows some examples of peak field and wavelength of the accelerating gradient calculated using the formulas above for typical plasma densities values. By increasing the plasma density the accelerating gradient increases but the accelerating period becomes shorter.  
Working with shorter periods of the accelerating field makes it more difficult to preserve the beam quality, especially the beam energy spread, during acceleration. A correlated energy spread is induced by a sinusoidal field if the bunch length is not short enough with respect to the wavelength of the field. This energy spread is due to the fact that the head and the tail of the bunch experience a different acceleration because of the slightly different injection phase. For a fixed amplitude of the field, the difference of the accelerating field seen by the head and the tail of the beam increases for shorter wavelengths. This induced energy spread can be minimized by ensuring that the bunch is much shorter than the period of the accelerating field (e.g., shorter than 1 degree of the corresponding RF phase).  
It has been observed also in simulations that the total energy spread of an e-bunch at the exit of a plasma accelerator is in general proportional to its bunch length at the injection [19]. This observation justifies the request of fs or few fs long e-bunches at the injection point with an arrival time jitter below a few fs.
## 4 Experiments
### Speed  
The inference speed is equally essential for NMS. In order to obtain a speed comparison, we apply the well-known NMS algorithms [16, 17] to Faster R-CNN separately and conduct the experiments in the same environment. Table 4 shows the results of the accuracy and speed comparison. Despite exhibiting a slight decrease in speed, MA-NMS demonstrates significant improvements in other metrics, particularly a 3.8% _Recall_ improvement and a 2.5% \(MR^{-2}\) improvement compared to Soft NMS [16]. The improvement in _Recall_ implies that MA-NMS retains more pedestrians than other NMS algorithms, especially occluded pedestrians that are prone to being mistakenly removed, as no changes have been made to the generic pedestrian detection branch [8]. Additionally, the improved \(MR^{-2}\) value indicates that MA-NMS performs more potent suppression on duplicate proposals, resulting in fewer false positives. In summary, while the FPS of MA-NMS is slightly lower than that of the previously superior Greedy NMS, the decrease is only 3.9%. On the other hand, the accuracy is significantly increased. These findings imply that MA-NMS achieves a balance between accurate detection results and comparable speed for pedestrian detection in crowded scenes.
## 6 Conclusions  
In this study, we propose DRDA-Net, a novel end-to-end network for the automated segmentation of liver and tumor lesions in CT images, designed to enhance clinical diagnostics and support computer-aided systems. The framework integrates a Residual UNet architecture with dual-attention mechanisms, significantly improving segmentation accuracy. By incorporating multi-scale tile and patch extraction, the model effectively identifies and focuses on critical regions, enabling the precise delineation of liver structures and tumor boundaries. The methodology addresses key challenges, such as intensity variations and spatial relationships, which are critical for robust segmentation in medical imaging. Additionally, we introduce a unique pre-processing pipeline that employs a two-channel denoising technique using convolutional neural networks (CNNs) and stationary wavelet transforms (SWTs) to reduce noise while preserving structural details. The Ensemble approach further enhances the model's robustness, ensuring consistent performance across diverse scenarios. Evaluations on the 3 DIRCADb dataset demonstrate the framework's effectiveness, achieving Dice scores of 97.03% for liver segmentation and 75.4% for tumor segmentation, indicating strong alignment with ground truth annotations.  
Despite these advancements, the proposed method has certain limitations. Due to the high computational demands and memory requirements of 3D networks, the model was trained and tested on 2D images, which excluded inter-slice contextual information. Additionally, the limited size of the dataset may restrict the model's generalizability. To address these limitations, future work will focus on developing hybrid networks that combine shallow 3D architectures with 2D components to efficiently capture both intra- and inter-slice features from 3D CT scans. Lightweight network designs will also be explored to handle high-resolution images effectively. Expanding the dataset to include multi-center collections with diverse imaging protocols and patient populations will further enhance the model's applicability and clinical relevance. Beyond liver cancer, the framework will be extended to other medical imaging tasks, such as segmenting lesions in the lungs and kidneys, highlighting its versatility and broad clinical utility. These advancements aim to refine clinical decision-making processes and improve the efficiency of computer-aided diagnostic systems across various medical domains.  
Conceptualization, W.M.I.; methodology, W.M.I.; software, W.M.I. and K.A.A.; formal analysis, W.M.I. and S.Y.; investigation, Y.Z. and S.Y.; writing--original draft preparation, W.M.I. and S.Y.; writing--review and editing, S.Y., K.A.A., W.M.I. and Y.Z.; supervision, Y.Z. and S.Y.; project administration, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Joint Funds of the National Natural Science Foundation of China (Grant No. U23B2063) and the National Natural Science Foundation of China (Grant No. 62076256).
# A Poisson Process-Based Random Access Channel for 5G and Beyond Networks +
#### Contributions of the Paper  
The motive to introduce a Poisson process-based RA framework is to spread the UEs in a parametric set of a probability distribution. The parametric distribution approach allows the system to disperse RVs exponentially. Therefore, in this paper, we suggest using RVs with Poisson distribution, such as a continuous exponential distribution (ED). The proposed mechanism is named as Poisson process-based RACH (2 PRACH). The contributions of this paper are twofold:  
* This paper assesses the strengths of Poisson distribution RVs as compared to the uniform distribution RVs.
* We propose a 2 PRACH mechanism, which suggests replacing UD with ED in random access mechanism for 5G/B5G networks.  
The rest of the paper is organized as follows. In Section 2, we present related work on enhancing the existing RACH procedure in 5G cellular networks. Section 3 describes the existing RACH mechanism in 5G networks. In Section 4, we present our proposed 2 PRACH mechanism for random access in 5G networks. Simulation results and performance evaluation is discussed in Section 5. Finally, in Section 6, a conclusion is given, along with future work considerations.
# Combining InSAR and GNSS to Track Magma Transport at Bastic Volcano
## 6 Discussion
### Discrepancies between Independent Datasets Reveal Hidden Processes
#### 6.1.2. Discrepancies in Amplitude Along the Different Los Reveal Flank Displacement  
We noticed systematic discrepancies between the fit of data acquired along ascending and descending LOS, with data acquired in the descending LOS being systematically better fitted than data acquired along the ascending LOS. Increasing the weighting of the ascending data did not produce different models or ones with a significantly better fit on the ascending LOS. We propose that this might be due to the ascending interferograms being more sensitive to a different source than the descending ones. For instance, a sill injection leads to ground inflation and produces a signal with similar amplitudes on the ascending and the descending LOS (Figure 8). However, an eastward movement of the eastern flank creates an eastward and downward directed displacement, which tends to cancel out on the descending LOS as opposed to cumulating on the ascending LOS. Such flank displacement could result from slip on a detachment fault [30] or a more complex host rock behavior for example due to plasticity [25].
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Reinforcement Learning
#### 4.3.10 Fault/Anomaly Management  
In [126], a deep Q-learning approach is proposed for fault detection and diagnosis in 6G networks. Simulation results show that the algorithm can use less features and achieve higher accuracy, up to 96.7%  
Table 9 holds a brief summary of the RL models used in various 6G problems.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Paper** & **ML Approach** & **Application Problem** & **Description** \\ \hline \multirow{2}{*}{[110]} & RL-based on auction model & \multirow{2}{*}{Channel allocation} & Based on a carrier sensing multiple access (CSMA) implementation, performs well for LTE scenarios \\ \cline{3-4}  & & & \\ \hline \multirow{2}{*}{[111]} & MDP & Channel allocation & Allocates channels in densely deployed WLANs, leading to throughput enhancement \\ \cline{3-4}  & & & \\ \hline \multirow{2}{*}{[112]} & Q-learning, Deep Q-learning & \multirow{2}{*}{Energy consumption} & Used in cooperative networks on user devices and SBS, respectively, achieving great energy saving results \\ \cline{3-4}  & & & \\ \hline \multirow{2}{*}{[113]} & DRL & Energy consumption, security & Accelerates block verification, where the reward function considers energy for trasnmission and caching, while providing privacy protection \\ \cline{3-4}  & Hybrid-AC, MD-Hybrid-AC & \multirow{2}{*}{Dynamic computation offloading} & Theactor outputs offloading ratio and local computation capacity and the critic evaluates these continuous outputs with discrete server selection \\ \cline{3-4}  & DQN, two-layered Rl algorithm & \multirow{2}{*}{Energy consumption, joint access control} & Minimizes prediction error and predict a battery’s energy consumption, while producing access policy \\ \cline{3-4}  & Multi-agent RL, DNN & Power control & Maximizes throughput in energy-harvesting super IoT systems, while studying PC policies \\ \hline \end{tabular}
\end{table}
Table 9: RL models in 6G various problems.
# Graph-Based Modeling in Shop Scheduling Problems: Review and Extensions
## 5 Theory and the Application of Different Types of Graphs
### Markov Random Fields  
_Markov Random Fields_ (_MRFs_) are probabilistic graphical models and categorized as undirected graphs [119]. MRFs are used to model the existing correlation between the decision variables. Simply, MRF describes the dependency relationship of the variables. Nodes represent variables and edges represent the dependency between variables. That dependency can now be conditional according to the probabilities associated with each node.  
In the shop scheduling context, the corresponding MRF model is defined as follows: Let \((G,\Phi)\) be a MRF where \(G=(N,A)\) is the graph structure (\(N\) is set of nodes and \(A\) is set of arcs) and \(\Phi\) is parameters of the MRF. Nodes denote operations and an undirected arc between two nodes represent the correlation relationship between the two nodes.  
[PERSON], [PERSON] [119] used MRF to solve a real-world stochastic flexible job shop scheduling problem with the objective to minimize the expectation and variance of makespan. Unlike the traditional problem, in this stochastic scheduling problem the processing time of each operation is a stochastic value and not predefined. The concept of maximal clique is utilized to solve the stochastic problem which is modeled by MRF [119].  
Despite the importance of stochastic nature of real-world manufacturing systems/ processes, only one reference has been found (in our dataset) using MRF. We believe that shop scheduling field can clearly benefit from this graph-based approach in future. In addition to MRF, Bayesian network is another great probabilistic graph-based model that can potentially be utilized in shop scheduling problems.
## 4 Discussion  
The purpose of this study was to evaluate the variations of internal and external load between different forms of SSGs in professional and sub-elite U-23 soccer players. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Absolute** & **Absolute** & **Absolute** & **Absolute** & **Absolute** & **Absolute** \\
**SSGs** & **Speed 0–6** & **Speed 6–12** & **Speed 12–18** & **Speed 18–21** & **Speed 21–24** & **Speed 24–50** \\  & **(m/min)** & **(m/min)** & **(m/min)** & **(m/min)** & **(m/min)** & **(m/min)** \\ \hline SSG1 & 104.7 \(\pm\) 14.2 & 196.9 \(\pm\) 29.2 & 65.4 \(\pm\) 15.6 & 2.4 \(\pm\) 2.5 & 0 & 0 \\ SSG2 & 81.9 \(\pm\) 29.2 & 205.9 \(\pm\) 21.8 & 85.8 \(\pm\) 28.9 & 2.3 \(\pm\) 3.4 & 0 & 0 \\ SSG3 & 114.3 \(\pm\) 13.2 & 151.0 \(\pm\) 32.8 & 40.7 \(\pm\) 27.2 & 3.7 \(\pm\) 2.5 & 1.2 \(\pm\) 1.9 & 0 \\ SSG4 & 82.40 \(\pm\) 36.0 & 199.5 \(\pm\) 31.5 & 96.3 \(\pm\) 26.7 & 11.7 \(\pm\) 11.2 & 6.4 \(\pm\) 7.7 & 0.9 \(\pm\) 1.6 \\ SSG5 & 109.7 \(\pm\) 14.7 & 91.7 \(\pm\) 40.2 & 14.1 \(\pm\) 8.0 & 4.0 \(\pm\) 5.5 & 0.2 \(\pm\) 0.5 & 0 \\ \(F\) & 2.734 & 14.861 & 13.926 & 2.639 & 3.676 & 2.236 \\ \(P\) & 0.047 \({}^{*}\) & 0.000 \({}^{*}\) & 0.000 \({}^{*}\) & 0.053 & 0.015 \({}^{*}\) & 0.089 \\ \(\eta_{P}^{2}\) & 0.267 & 0.665 & 0.650 & 0.260 & 0.329 & 0.230 \\  & & 1 \({}^{**}\) 5 (0.00); & 1 \({}^{*}\) 5 (0.03); & & \\  & & 2 \({}^{*}\) 3 (0.03); & 2 \({}^{**}\) 1 (0.01); & & \\ Tukey HSD & 2 \({}^{**}\) 5 (0.00); & 2 \({}^{**}\) 5 (0.00); & 1 \({}^{**}\) 4 (0.03); & \\  & & 3 \({}^{*}\) 5 (0.02); & 3 \({}^{**}\) 4 (0.00); & & \\  & & 4 \({}^{**}\) 5 (0.00) & & & \\ \hline SSG1: 4 vs. 4, 16 \(\times\) 24 m; SSG2: 4 vs. 4, 20 \(\times\) 30 m; SSG3: GK + 4 vs. 4 + GK, 20 \(\times\) 30 m; SSG4: 4 vs. 4, 24 \(\times\) 36 m; SSG5: GK + 4 vs. 4 + GK, 24 \(\times\) 36 m. All SSGs with 3 min duration and 3 min rest (ratio 1:). \({}^{**}\) Significant differences between SSGs (\(p\) < 0.01); \({}^{**}\) Significant differences between SSGs (\(p\) < 0.05). & \({}^{**}\) Significant differences between SSGs (\(p\) < 0.01). \({}^{**}\) Significant differences between SSGs (\(p\) < 0.05). & \({}^{**}\) Significant differences \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Soccer players distance covered at different interval speeds in all the performed small-sided games. This is one of the few studies conducted with international level players since these are exposed to intense training and weighty competitive density.
## 2 Sensing Field
Other useful algorithms are Discrete Fourier Transform (DFT), and K-Means clustering algorithm (machine learning unsupervised algorithm). Two possible upgraded systems are in the management of energy used in production, and in energy digital twins using quantum computing. A possible future professional skill would be an energy manager that optimizes electrical consumption in industrial processes. Machine learning algorithms are also suitable for defect classification and defect prediction. Table 3  
\ \begin{table}
\\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \\hline \\hline
**Electronic and** **Mechatronic Technologies** & **Topic** & **Description** & **Reference** & **Possible Implementation in Industry 5.0 Proposed in This Work** \\\\ \\hline \\multirow{4}{*}{Roasting process} & \\multirow{2}{*}{Roasting process} & Temperature [61] and Near-Infrared Spectroscopy (NIRS) [62] to check food quality in roasting processes & [61, 62] & Auto-calibration of temperatures ensuring a uniform roasting process (production lines made up of different ovens). \\\\ \\cline{3-4}  & & Precision agriculture combining different technologies such as multispectral imaging, infrared thermography, and Unmanned Aerial Vehicles (UAV) & [63, 64, 65] & Implementation of Decision Support Systems (DSS) based on data fusion and local management of agriculture. \\\\ \\cline{3-4}  & & Switch-activated and sensor-triggered proximity sensors detecting the arrival of the workpiece [66], and image processing [67] & [66, 67] & Improvement of a reconfigurable manufacturing process by means of AI algorithms \\\\ \\cline{2-4}  & & X-rays for the detection of unknown elements in food products, thermal imaging (temperature and humidity check), volumetric sensors (check of raw materials in silos), NIR spectrometry (controlling moisture), image processing & [68, 67] & Feedback control applied to an adaptive framework based on an AI engine optimizing the whole supply chain \\\\ \\hline \\multirow{4}{*}{Hole machining process} & \\multirow{2}{*}{Profile machines} & Drilling, broaching, countersinking, and homing processes controlled by sensors detecting cutting forces, vibration, current/power, acoustic emission and temperature; and other tools such as microscopes, laser scanners, surface roughness testers, galvanometers, scanning galvanometers, profilometers, and internal micrometers & [69] & AI prediction of hole defects adjusting in real time machine parameters; predictive maintenance of machines \\\\ \\cline{1-1} \\cline{2-4}  & & Strain gauge measuring cutting forces, cutting power & [70, 71] & Auto-adaptive control predicting tool wear and tool breakage \\\\ \\hline \\hline \\end{tabular}
\\end{table} 
Table 2: _Cont._discusses some topics about detection algorithms and their possible implementation in an Industry 5.0 framework. \
## 3 Results and Discussion
### The Extraction Yield
pinaster_ bark. Monoterpenes are common components of pine wood turpentine [46], and, in particular, \(\alpha\)-terpineol (**18**) has been identified in _P. pinea_ needles [20], whereas longifolene (**20**) has been identified in _P. pinea_ wood [23]. As in the case of DHAA and its oxidized derivatives, terpin is a dehydrated monoter that is not naturally found in wood. Therefore, its abundance in bark extracts should be associated with exposure to an external environment. Finally, the low abundance of monoterpenes that was observed in both studied _Pinus_ spp. can be justified by their possible loss during the drying and grinding processes. The methodology  
Figure 3: The chemical structures of the diterpene resin acids found in the Portuguese _P. pinaster_ and _P. pinea_ barks. employed in the present work is not the most suitable for the characterization of their volatile fraction, which is beyond the scope of this work. Betulin (**23**) was the single pentacyclic triterpene present in the _P. pinaster_ bark lipophilic fraction, representing 15% \(w/w\) of the total terpene content (Table 1). Its identification was achieved through the detection of the molecular ion at \(m/z\) 586 from the corresponding TMS derivative, and the product ions common to the mass fragmentation of trimethylsilyl derivatives of lupane-type pentacyclic triterpenes, namely at \(m/z\) 571 ([M-CH\({}_{3}\)]\({}^{+}\)), 496 ([M-TMSOH]\({}^{+}\)), 279, 190, and 189 as the base peak [47]. To the best of our knowledge, betulin (**23**) has been described herein for the first time in the bark of _P. pinaster_. Nevertheless, this compound has recently been detected in both the outer and inner bark of _Pinus merkusii_ and the outer bark of _Pinus montezumae_[48]. The presence of this compound in _P. pinaster_ bark can be an additional factor for exploiting this forest by-product, as it can be converted into betulinic acid, which exhibits several interesting biological properties, including Human Immunodeficiency Virus (HIV) inhibition, antimicrobial, and anti-inflammatory activity [49].
## 2 Materials and Methods
### Satellite Data Pre-Processing
#### 2.3.2 MOD09Q1 Processing  
For MOD09Q1, the quality band was used to filter out the disturbance that could affect the Vegetation Index (VI) data quality. To do this, a mask was created using the State band and filtering the bits 0 and 1 (Cloud), 2 (Cloud shadow), and 8 and 9 (Cirrus cloud).
## 3 Results and Discussion  
### HPLC Detection of HNS  
#### 3.1.1 Determination of the Mobile Phase  
In exploring the detection methods for HNS, this study utilized the ultraviolet channels of high-performance liquid chromatography at 226 nm and 271 nm. Mixed mobile phases of methanol (MeOH) and water, as well as acetonitrile (ACN) and water, were employed to separately identify and analyze simulated wastewater containing HNS explosives. It was assumed that the flow rate of the mobile phase was 1 mL/min, the column temperature was maintained at room temperature of 25 \({}^{\circ}\)C, the injection volume was 20 \(\mu\)L, and the duration of sample testing was set at 10 min. For both mixed mobile phases (MeOH and water, ACN and water), the volume fractions of MeOH and ACN were adjusted to 50%, 60%, 70%, 80%, and 90%. Selected optimal chromatograms are displayed in Figure 2 (chromatograms of other mixed mobile phases were similar). In the ACN and water mixed mobile phases, the best chromatographic results were obtained with an ACN volume fraction of 60%. Under the dual detection wavelengths, the retention times for DMF and HNS were relatively close. The retention time for HNS was notably longer, as shown in Figure 2a,b. This delay prolonged the detection time for HNS and increased the cost of detection. In the MeOH and water mixed mobile phases, the comprehensive performance was best at a MeOH volume fraction of 70%, with the next best at 80%. To compare with the chromatogram at an ACN volume fraction of 60%, the chromatogram at a MeOH volume fraction of 60% is presented in Figure 2c,d. Under the dual detection wavelengths, it was observed that at the main wavelength of 271 nm, the peak shape deteriorated, and the HNS retention time was extended, leading to increased detection costs. The detection was most effective at a MeOH volume fraction of 70%, with the chromatogram shown in Figure 2e,f exhibiting the best peak shape and moderate retention times for HNS, with minimal interference between the peaks. The performance at a MeOH volume fraction of 80% was inferior. Despite shorter HNS retention times, the retention times for DMF and HNS were close. Consequently, a MeOH volume fraction of 70% and an ultrapure water volume of 30% were chosen for subsequent experiments.  
Figure 2: _Cont._