esa-sceva/satcom-chunk-collection huggingface dataset:
# 3 GPP 5G V2X Scenarios: Performance of QoS Parameters Using Turbo Codes
## 3 QoS Analysis
### Data Rate per Vehicle and Mobile Terminal Speed QoS Parameters  
The BER performance of the simulated system can be seen at Figure 2 for scenarios 1, 2, 3 and 4 of Table 2 considering SOVA, log-MAP and max-log-MAP algorithms. Here, we investigate the effect of data rate per vehicle and mobile terminal speed. The short frame length is 256 bits and the number of turbo decoding iterations is considered to be 3, because in [22] it is shown that is adequate to achieve the lowest BER.  
For an increase of data rate from 6 to 12 Mbps (comparison of scenarios 1 and 2 for terminal speed of 60 km/h), a loss of 1.8 dB at a BER of \(10^{-3}\) is seen for SOVA. At the same BER, a gain of 0.2 dB is observed using log-MAP or max-log-MAP compared to SOVA for 6 Mbps. For an increased data rate of 12 Mbps this gain remains the same. Similarly, for data rate increase from 6 to 12 Mbps (comparison of scenarios 3 and 4 for terminal speed of 100 km/h), a loss of 1.6 dB at a BER of \(10^{-3}\) is seen for SOVA. At the same BER, a gain of 0.45 dB is observed using log-MAP or max-log-MAP compared to SOVA for 6 Mbps (scenario 3). For an increased data rate of 12 Mbps (scenario 4) this gain decreases to 0.16 dB.  
Figure 1: Proposed dynamic selection channel coding system model.  
For vehicle speed increase from 60 to 100 km/h (comparison of scenarios 1 and 3 for data rate of 6 Mbps), a loss of 0.4 dB at a BER of \(10^{-3}\) is seen for SOVA. Similarly, for speed increase from 60 to 100 km/h (comparison of scenarios 2 and 4 for data rate of 12 Mbps), a loss of 0.2 dB at a BER of \(10^{-3}\) is seen for SOVA.  
We follow the same approach at Figure 3, but here the frame length increases to 512 bits (scenarios 5, 6, 7, 8 and 9 of Table 2). As expected, the BER performance for the different SNR values is better, compared to Figure 2. For a data rate increase from 6 to 12 Mbps (comparison of scenarios 5 and 6 for terminal speed of 60 km/h), a loss of 1.85 dB at a BER of \(10^{-3}\) is seen for SOVA. At the same BER and for the same decoding algorithm, for a data rate increase from 12 to 18 Mbps (comparison of scenarios 6 and 9 for terminal speed of 60 km/h), a loss of 1 dB is observed.  
Similarly, for data rate increase from 6 to 12 Mbps (comparison of scenarios 7 and 8 for terminal speed of 100 km/h), a loss of 1.6 dB at a BER of \(10^{-3}\) is seen for SOVA. For vehicle speed increase from 60 to 100 km/h (comparison of scenarios 5 and 7 for data rate of 6 Mbps), a loss of 0.4 dB at a BER of \(10^{-3}\) is seen for SOVA. Using the same approach for terminal speed increase from 60 to 100 km/h (comparison of scenarios 6 and 8 for data rate of 12 Mbps), a loss of 0.15 dB at a BER of \(10^{-3}\) is seen for SOVA.  
Figure 2: BER vs. SNR for GEMV channel, 4 different Table 2 scenarios, different decoding algorithms, 3 iterations, frame length of 256 bits.
# Spatially-Precise Video Editing with Reference Imitation: A Region-Aware Cross-Modal Framework
## 3 Materials and Methods
### Generate the Canonical Image
#### 3.3.2 The Definition and Processing of the Canonical Field  
In the CoDeF method, the Canonical Content Field is utilized to unify the dynamic content of a video into a static, time-independent coordinate system. The introduction of the Canonical Content Field imparts consistency to the content representation across video frames. This, in turn, enables efficient video editing with temporal consistency.  
The Canonical Content Field \(C\) is a continuous representation of all the flattened textures in a video, specified by the function:  
\[F:x\to c \tag{1}\]  
This function maps a two-dimensional position \(X=(x,y)\) to a color vector \(\mathbf{c}=(r,g,b)\). Put simply, for each position coordinate within the video frame, the corresponding color value can be retrieved via the Canonical Content Field.  
During the model-training process, to accelerate the training and enable the network to capture high-frequency details, the multi-resolution hash encoding is introduced:  
\[\gamma_{2D}:\mathbb{R}^{2}\rightarrow\mathbb{R}^{2+F\times L} \tag{2}\]  
It is introduced to convert the coordinate \(X\) into a feature vector. Here, \(L\) is the number of layers of multiresolution, and \(F\) is the feature dimension of each layer.  
Specifically, the coordinates \(X\) of the canonical field are fed into the \(\gamma_{2D}\) multi-resolution hash encoder to generate high-dimensional features:  
\[\gamma_{2D}(x)=(x,F_{1}(x),...,F_{L}(x)) \tag{3}\]  
Such an operation is beneficial for the model in capturing high-frequency details. \(F_{i}(x)\) are the features obtained by linearly interpolating \(X\) at the \(i\)-th resolution.  
By performing linear interpolation on the coordinates at multiple resolutions, different features are obtained. This enables the network to capture the high-frequency detailsin the data more comprehensively and meticulously. Consequently, it can enhance the model's ability to represent and process the data. After obtaining the high-dimensional features, a Multi-Layer Perceptron (MLP) is used to generate RGB values from the high-dimensional features:  
\[C(x)=MLP(\gamma_{2D}(x)) \tag{4}\]  
By doing so, the model can comprehend and handle various detailed information in the video more comprehensively and meticulously. As a result, it provides more accurate data support for subsequent video reconstruction and processing tasks. The ultimate goal is to achieve better results. For instance, when processing videos with complex textures or fast-moving objects, this multi-resolution hash encoding technology can enable the model to better capture subtle changes and features, thereby enhancing the quality and accuracy of video processing.
# Solar Concentration for Wastewaters Remediation: A Review of Materials and Technologies
## 3 Advanced Oxidation Processes and Photocatalysis
### Homogeneous Versus Heterogeneous Photocatalysis  
Among the major solar photoreactor system design issues is whether to use a homogeneous route with a dissolved catalyst or a heterogeneous one with a suspended or supported catalyst. This decision is greatly affected by the availability of facile and cost-effective fabrication methods. A large part of the investigations performed so far have used suspended particles in the contaminated water, which, as with the case of a dissolved catalyst, demands a final extra step to separate and recover the catalyst. A solution to this problem is the use of supported catalyst configurations, but this has, to date, been hindered by higher costs due to more complex synthesis procedures. Thus, research efforts on improved manufacturing techniques, such as the use of PLD, will likely offer possible solutions to this issue [85; 115; 116; 117; 118; 119].  
A supported-catalyst design also needs to take into account several additional problems. As the catalyst dispersion is not optimal, the greatest possible surface area is needed to ensure a good contact between active sites and target substrates, and an effective irradiation geometry must be identified. A good adhesion with the supporting material is also critical, as it dictates the capability of withstanding the mechanical wear from the water flow, and thus, the durability and operational lifetime of the catalyst coating. All these issues can, however, be addressed by careful design and improvements in coating technology.  
Overall, the heterogeneous route to photocatalytic water treatments with supported semiconductors is a more promising technology when compared to its suspended catalyst counterpart or to the homogeneous route, mainly due to the necessity of easy industrial handling and replacement of the materials.
## 7 Experiments
### Experimental Test: Tracking Performance of VTL with Marginal Number of Visible Satellites  
In this test, VTL tracking performance is studied when the number of visible satellites is marginal, three for 2-dimensional positioning. The transmission power of PRN 4 decreases gradually. It is an  
Figure 21: Test 2: Test setup and user trajectory.  
Figure 22: Navigation results: ITL (left) and VTL (right).  
experimental test that the steadily increasing noise was applied to the tracking channel of PRN 4 with stored IF signal to make the situation of gradual transmission power decrease in a specified PL. A code discriminator output and carrier to noise ratio (C/No) are shown in Figure 23 as tracking results of ITL and VTL, respectively. After 12 s, the code discriminator output increases rapidly and then ITL comes to lose tracking lock. It shows that ITL could keep track the signal until C/No is about 35 dB-Hz. On the other hand, VTL could maintain tracking lock until 21 seconds when C/No is about 25 dB-Hz. It shows the robust signal tracking performance of the implemented VTL.
# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
## 3 Methodology
### Task-Weighting
#### 3.6.2 MTL Training Objective  
As Figure 3 illustrates, the global loss is computed by combining multiple loss functions corresponding to the various tasks involved in the model training. It is essential to carefully balance all tasks' joint learning to prevent a situation in which one or more tasks have a significant influence on network weights, a phenomenon known as task imbalance. Specifically, if one loss function is significantly larger than the other, the corresponding task may dominate the training process, exacerbating the task imbalance problem. Furthermore, some loss functions may converge more quickly or become more crucial to the overall system objective.  
Additionally, the optimization approach does not explicitly account for the losses associated with each task. Consequently, the relative weights assigned to each task heavily influence the MTL model's performance. For example, when the weights for all tasks, except one, are set to zero, the optimization concentrates solely on the task with a nonzero weight, effectively disregarding the others. In the developed MTL model, our focus was on the main task of propaganda detection. Thus, the propaganda detection task must be prioritized during training while considering sentiment task and emotion detection as auxiliary tasks. To achieve this goal, the MTL model's objective function, as presented inTable 2, was modified by introducing a task importance coefficient (i.e., weight \(w_{t}\)) for task \(t\), as seen in Equation (7):  
\[\text{Obj}(\text{MTL})=\text{min}_{\theta_{\text{sh}},\theta_{1},\ldots,\theta_ {t}\sum}_{t=1}^{t}\text{w}_{t}\text{L}_{t}(\{\,\theta_{\text{sh}},\theta_{t}\}, \text{D}_{t}) \tag{7}\]  
It is a widely adopted practice to treat weights as hyperparameters, which are often determined using methods such as grid search or expert judgment. Furthermore, some weight adaptation techniques modify the MTL optimization framework by adjusting the task weights during training, as Section 3.6.3 explains. In the next subsection, various weighting schemes proposed for the MTL models are explained.
## 3 Results and Discussions
### Soil Reinforcement Tests  
#### 3.2.1 Optimum Concentration of Organic Materials  
Figure 5 shows the distribution of the UCS and BTS for soil treated by the improved EICP with organic materials added. With an increase in the concentration of each admixture, the UCS and BTS of the samples increased continuously, which was positively correlated with the added concentration, but the growth was different. The UCS and BTS with skim milk powder were generally the largest herein, followed by glutinous rice powder and brown sugar. When the concentration of skim milk powder increased from 2 g/L to 8 g/L, the UCS and BTS increased by 25.32% and 30.18%, respectively. Beyond this range, the increase rate decreased, indicating that the strength improvement limit was being approached. When the concentration of glutinous rice powder increased from 2 g/L to 8 g/L, the UCS increased by 19.53%, and the increase in the UCS was minimal as it continued to increase. The BTS stopped increasing when the concentration exceeded 4 g/L. When 2 g/L brown sugar was added, both BTS and UCS were low, and the strength increment was larger if the content exceeded 8 g/L. In short, when the concentration of glutinous rice powder and skim milk powder was higher than 16 g/L, the increase in the strength was small and not economical. If brown sugar was added, the effect of brown sugar at a low concentration was not obvious, which may be because brown sugar provided a decreased amount of base mass for templating and nucleation sites. The optimum concentrations for skim milk powder, glutinous rice powder, and brown sugar (\(\alpha_{1}\), \(\alpha_{2}\), and \(\alpha_{3}\)) were 8 g/L, 8 g/L, and 16 g/L, corresponding to treatment numbers A4, B4, and C5, respectively.
## 4 The Proposed IDS Framework at Flow-Level  
In flow-level intrusion detection, the traffic flow characteristics, which usually contain numerous packets, are extracted for detecting attacks [4; 5; 6; 7]. Flow information includes the statistics of a flow, such as the number of packets, the duration, the average packet size, etc. A 5-tuple knowledge defines a flow that includes source IP, source port, transportation protocol, destination IP and destination port [41]. The number of packets in the flow is a valuable feature for flow-level IDS. The work in [4] contains evidence that Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks tend to transmit a large number of packets in a short time. The flow duration, the average packet size in the flow, and the transportation protocol can also be considered important features. Recently, many datasets have been collected and published for flow-level intrusion detection research.  
The whole IDS framework at the flow level is shown in Table 5. The overall architecture is quite similar to the one proposed in Table 1 with some minor modifications in two modules, i.e., preprocessing and RL module.
# Visualization of Distributed Plasticity in Concrete Piles Using OpenSeesPy
## 2 Modeling Methodology  
### Fiber-Based Model of an RC Pile in OpenSeesPy  
This study focuses on the nonlinear modeling of a single RC pile representative of those used in pile-supported wharf structures. Following established nonlinear modeling strategies for reinforced concrete [47], the methodology includes the definition of inelastic properties for concrete and steel using uniaxial OpenSeesPy materials Concrete02 and Steel02, respectively. The pile was modeled using a distributed plasticity formulation. The structural member was discretized into multiple forceBeamColumn elements with five Gauss-Lobatto integration points to capture curvature distribution and inelastic behavior. The discretization of fibers in the cross-section, including both longitudinal steel reinforcement and concrete, plays a significant role on the accurate representation of the load-bearing capacity of pile-to-deck connections. A finer and well-distributed fiber discretization improves the representation of stress and strain gradients across the section, especially under inelastic behavior. Inadequate discretization may lead to inaccurate estimation of sectional stiffness, strength, and energy dissipation. Appropriate fiber resolution is essential to capture the interaction between concrete and reinforcement, particularly in regions where inelastic behavior is expected. In this study, cross-sections are discretized using 160 Concrete02 fibers for confined and cover concrete, and 8 Steel02 fibers for longitudinal reinforcement. This level of discretization enables accurate representation of both the elastic response--governed by the uniaxial material models' initial stiffness--and the progressive stiffness degradation resulting from large load excursions. Material parameter are defined using recommendations by [PERSON] et al. [48] when experimental data are unavailable. Geometric nonlinearity and P-Delta effects are considered in the model by employing forceBeamColumn elements, which inherently capture local second-order effects (P-\(\delta\)) through the nonlinear force-deformation behavior at the fiber-section level. Moreover, the geometric transformation P-\(\Delta\) is assigned to these elements through the Opensees command PDelta, to capture global P-Delta effects.
## 3 Results and Discussion
### Aroma Compounds
#### 3.3.2 Norisoprenoids  
Regarding norisoprenoids, (_E_)-\(\beta\)-damasenone had the highest OAVs compared to other volatiles, ranging from 189.4 (in XJ) to 306.2 (in JDP), which could be attributed to its relatively low odor threshold (0.05 \(\upmu\)g/L in 10% \(w/w\) water/ethanol solution [22]) (Table 2). This result was similar to that of a previous study [10], which indicated this compound could be considered the key odor-active compound in Marselan wines. (_E_)-\(\beta\)-damasenone in JDP and BHB regions presented higher concentrations compared to those in the HZB, LP, and XJ regions (Table 2). [PERSON] et al. (1992) reported that Riesling wines from warm or hot climates (e.g., South Africa) displayed higher concentrations of norisoprenoids (e.g., TDN and vitispirane) compared to those from cool climates (e.g., Germany and Northern Italy) [45]. Previous research in our lab was carried out on Cabernet Sauvignon grapes to investigate the differentiation in the biosynthesis of norisoprenoids between the CL and GT regions by application of the metabolomic and transcriptomics techniques [46]. However, higher concentrations of (_E_)-\(\beta\)-damascenone, as well as total norisoprenoids in mature grapes, were shown in GT (cool climate) than in CL (warm climate) [46]. According to the meteorological data, the XJ region shows similarities to the GT region in terms of the high sunshine duration, PAR, and diurnal temperature range; however, the XJ region has a significantly warmer climate during the growing season than the GT region. It seemed that a complex mechanism was involved in the effect of the environmental factors on the metabolism of norisoprenoids in grapes. Additionally, numerous researchers have investigated the influences of microclimatic conditions on the concentrations of volatile aroma compounds and their precursors. In an investigation on the effects of vine microclimate on norisoprenoid concentrations, Cabernet Sauvignon grapes without leaf removal, as well as their corresponding wines, were found to have the highest concentration of (_E_)-\(\beta\)-damascenone [47]. However, the concentrations of (_E_)-\(\beta\)-damascenone in grapes were linearly and positively correlated with increasing sunlight exposure when leaves were removed. Similar results were obtained by a leaf removal treatment performed on Pinot Noir [44]. Meanwhile, [PERSON] et al. (2015) only found higher concentrations of (_E_)-\(\beta\)-damascenone in grapes with 100% leaf removal. Water deficiency also favors the production of bound (_E_)-\(\beta\)-damascenone in grape berries, such as in Cabernet Sauvignon [48] and Merlot [49]. It seemed that the findings from the viticultural trials related to the modulation of vineyard microclimate could not be directly applied to interpret the differences in the aroma characteristics of wines from different regions with various climatic conditions. Therefore, intensive studies should be carried out to investigate the metabolic mechanism of aroma compounds in grapes in response to either a different climate style or changes to vine microclimate by viticultural practice.
# Fourier Ptychographic Reconstruction Method of Self-Training Physical Model
## 4 Experimental Results and Analysis
### Comparison and Analysis of Reconstruction Methods under Different Noise
#### 4.3.2 Comparison and Analysis of Reconstruction Methods under Different Noises  
In the process of real reconstruction, the interference encountered in the experiment is different, so we simulate the reconstruction results and reconstruction indexes of the same set of images under different noise conditions. Among them, the noise used is \(1\times 10^{-4}\), \(2\times 10^{-4}\), and \(3\times 10^{-4}\) respectively. Figure 18 shows the reconstruction results of the same set of images under different noise conditions. Table 3 is the evaluation index value of the same set of images under varied noise conditions.  
Figure 18: Reconstruction results of the same group of images under different noise conditions.  
According to Figure 18, STPM-FPM is visually superior to the other three reconstruction methods in this paper. Moreover, the STPM-FPM method has strong robustness to different noise data sets, which proves the generalization of the network and improves the quality of reconstruction.  
Table 3 uses the method of the deep learning network to analyze the objective evaluation results and study the noise resistance of various methods. From the quantitative analysis results in Table 3, it can be clearly seen that the anti-noise performance of STPM-FPM is obviously due to other methods. The result is consistent with that in Figure 18.
## 4 Experiments and Analysis
### Visual Comparisons
As seen in Figures 8 and 9, Shallownet was incapable of sufficiently restoring underwater images due to its straightforward network structure. On the other hand, NU2 Net lacked precise color control, hence leading to visible chromatic deviations that can be noticed by the human eye. In contrast, our method was better at removing color casts and handling details. Figure 4: Visual comparison of enhancement results on the T90 testing set. Figure 5: Visual comparison of enhancement results on the U45 testing set for generalization ability evaluation. Figure 6: Visual comparison of enhancement results on the U45 testing set for generalization ability evaluation. Figure 7: Visual comparison of enhancement results on the C60 testing set for generalization ability evaluation.
## 2 System Frequency Constraint Model
### Frequency Security Constraints  
#### 2.2.1 System Rate of Change of Frequency (RoCoF) Constraints  
An excessively large RoCoF will impact the lifespan of the units and trigger overspeed protection. The system frequency response model is shown in Equation (6) and RoCoF must satisfy Equation (7):  
\[\frac{M_{sys}}{f_{0}}\frac{d\Delta f(t)}{dt}=\Delta P_{L}, \tag{6}\]  
\[R_{COOF}=\frac{d\Delta f(t)}{dt}=\Delta P_{L}\frac{f_{0}}{M_{sys}}\leq R_{f \max} \tag{7}\]  
In Equations (6) and (7), \(M_{sys}\) represents the total inertia of the system; \(f_{0}\) represents the initial frequency of the system; \(\Delta f(t)\) denotes the frequency variation in the system; \(\Delta P_{L}\) indicates the power change in the system; \(R_{COOF}\) represents the rate of frequency change in the system; \(R_{f\max}\) denotes the upper limit of the Rate of Change of Frequency.  
The system inertia level constraint in Equation (8) can be derived from Equation (7), and the system inertia model considering the retirement of coal-fired power units is obtained from Equation (9):  
\[M_{sys}\geq\frac{\Delta P_{L}f_{0}}{R_{f\max}}, \tag{8}\]  
\[M_{sys,t}=\sum\limits_{i=1}^{N_{G}}H_{Gi}P_{mGi}v_{i,t}+\sum\limits_{j=1}^{N_ {W}}H_{Wj}P_{mWj}v_{j,t}-\sum\limits_{l=1}^{N_{R}}H_{Gl}P_{mGl}u_{l,t} \tag{9}\]  
In Equations (8) and (9), \(N_{R}\) represents the number of retired units; \(H_{Gi}\) denotes the inertia constant of coal-fired power unit \(i\); \(H_{Wj}\) represents the inertia constant of wind power unit \(j\); \(P_{mGi}\) indicates the capacity of coal-fired power unit \(i\); \(P_{mWj}\) denotes the capacity of wind power unit \(j\); and \(u_{l,t}\) is the retirement decision variable for coal-fired power unit \(l\) at time t.