esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient MAC Protocol for Blockchain-Enabled Patient Monitoring in a Vehicular Network
## 3 Proposed Superframe Structures
### Proposed Algorithm
#### 3.2.2 Case 2  
If accumulating data slot requests are less than the initially available slots, then the slot size needs to be shrunk by a factor of \(X\) to allow maximum slot utilization with reduced network delay. The reducing slot factor (\(X\)) is calculated as:  
\[X=\frac{S_{Avail}-S_{total}}{S_{Avail}} \tag{1}\]  
The new slot size (\(Z_{new}\)) is calculated as:  
\[Z_{new}=Z\times(1-X) \tag{2}\]  
Now, \(MN\) has to compute the new slots requirements (\(S_{n}^{\prime}\)) according to the \(Z_{new}\) as:  
\[S_{n}^{\prime}=\frac{(Z\times S_{n})}{Z_{new}} \tag{3}\]  
\(MN\) computes the divider (\(D\)) to divide each slot by that divider to efficiently assign the slots to vehicles to achieve high channel utilization. This \(D\) factor is achieved by dividing four bits maximum number 15 with the highest data-requesting vehicle as:  
\[D=\frac{15}{S_{HJ}} \tag{4}\]  
After the division of slots, the slot requirements will be changed, and \(MN\) updates that as per the new slot size after division.:  
\[R_{n}=S_{n}^{\prime}\times D \tag{5}\]
## Materials and Methods
### Dam Operation Module
To compute the dam outflow, the initial storage in each hour was computed using the relationship between water level and storage (\(H\)-\(S\) curve) for each hour based on Equation (4) as follows:  
\[S_{i}(t_{0})=\frac{S_{i+1}-S_{i-1}}{H_{i+1}-H_{i-1  
in 2 cases: Dam water level below threshold water level (\(H_{i}\) \(\leq\) h\({}_{\text{threshold}}\)) and dam water level over threshold water level (\(H_{i}\) \(>\) h\({}_{\text{threshold}}\)).  
Moreover, in the case of water level being below threshold water level, the purpose of a dam should be considered. For the single-purpose dam, in the model, hydroelectric discharge is set up as 0 m\({}^{3}\)/s. If the dam is a multi-purpose dam, the hydroelectric discharge is considered in the simulation. Thus, dam outflow is considered as follows:  
* If water level is below spillway water level and dam inflow is greater or equal to hydroelectric discharge, then outflow is equal to hydroelectric discharge;
* If water level is below spillway water level and dam inflow is less than hydroelectric discharge, then outflow is equal to 0 m\({}^{3}\)/s;
* If water level is over spillway water level but below threshold water level, then outflow is considered as minimum discharge.  
In the case of water level over threshold water level, hourly average dam outflow Q\({}_{h}\) was computed by averaging the summation of dam outflow Q\({}_{s}\) in 1 h, in which dam outflow Q\({}_{s}\) was computed based on the free-orifice equation (Equation (7)) as follows:  
\[Q_{h}=\frac{1}{3600}\sum_{n=1}^{3600}Q_{s}=\frac{1}{3600}\sum_{n=1}^{3600}\frac {2}{3}\Big{(}C\times L\times\sqrt{2 gH^{3}}\Big{)} \tag{7}\]  
where \(Q_{h}\) is hourly average dam outflow (m\({}^{3}\)/s); \(Q_{s}\) is dam outflow (m\({}^{3}\)/s); \(H\) is height of the water level above the crest of spillway (m); \(L\) is crest width (m); and \(C\) is free-orifice discharge coefficient, approximated from [PERSON]'s experimental formula [30].
## 2 Materials and Methods
### WRF Dataset  
The WRF model version 4.3 [34], available at [[https://github.com/wrf-model/WRF](https://github.com/wrf-model/WRF)]([https://github.com/wrf-model/WRF](https://github.com/wrf-model/WRF)) (accessed on 24 January 2024), was used to improve the grid resolution of ERA5. It was set up with two domains. The parent domain, centered on Houston, with a horizontal grid spacing of 12 \(\times\) 12 km, covers 2,872,800 km\({}^{2}\) (1800 km in the east-west direction and 1596 km in the north-south direction). It is large enough to include the southeast part of the U.S., making it possible to simulate large hurricanes (like Hurricane Harvey) without creating discontinuities in the boundaries. The inner domain has a grid spacing of 4 \(\times\) 4 km covering 1,044,975 km\({}^{2}\) (1084 km in the east-west direction and 964 km in the north-south direction). The model top reaches 10 hPa (about 30 km) and has 100 vertical levels. At 4 km horizontal resolution, the convection is explicitly solved. The remaining setup and the main physics options selected in the WRF model can be seen in [36]. As previously stated, the initial and lateral boundary conditions used in the parent domain are derived from the ERA5 data. Using the WRF output, \(\rho_{v}\) is given by  
\[\rho_{v}=\frac{e}{461.51\cdot T} \tag{2}\]where \(T\) is the air temperature (K), and \(e\) is the water vapor pressure (Pa), given by  
\[e=\frac{r\cdot P}{0.622+r} \tag{3}\]  
where \(r\) is the water vapor mixing ratio (kg/kg) [39].
# A Lightweight Reconstruction Model via a Neural Network for a Video Super-Resolution Model
## 5 Conclusions  
The field of video super-resolution has gained substantial attention from researchers and industrial professionals due to its significant impact on various applications. To ensure fair evaluation, we utilized the Vimeo-90k dataset to train all models in this study and employed consistent downsampling filters and loss functions.  
In this research, we systematically explored different temporal modeling techniques for video super-resolution tasks while maintaining the L1 loss function and consistent training data. Our primary focus was on incorporating 3D residual connections into hidden states and employing deeply separable convolution in recursive networks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Car** & **hdctub** & **hitachi\_isee** & **hk** & **jvc** \\ \hline SSIM & 0.80 & 0.58 & 0.69 & 0.80 & 0.82 \\ PSNR & 28.05 & 21.12 & 22.19 & 28.32 & 27.02 \\ Time & 0.0475 & 0.0467 & 0.048 & 0.0475 & 0.053 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of DBRRN on SPMCS and UMD10 dataset.  
Figure 4: The PSNR and Loss of the three models on Vid4. (**a**) PSNR, (**b**) loss.  
Our notable contribution introduces a Deeply Residual Recursive Network (DRRN) optimized for video super-resolution. The network's design, particularly the distinct structure of the hidden state, facilitates the transfer of detailed image information between layers through an identity branch. This approach enhances the representation of detailed image features and addresses the common challenge of vanishing gradients during training of Recurrent Neural Networks.  
To enhance the performance of DRRN, we proposed the Dual-Branch Residual Recursive Network. Experimental results demonstrated that the DBRRN model outperformed the DRRN model, showcasing a significant improvement in video super-resolution performance.  
This study paves the way for more advanced video super-resolution models that strike a balance between computational efficiency and performance. Future research could concentrate on further optimizing and evaluating these models across diverse settings and applications.  
Conceptualization, X.T. and F.O.; Methodology, Y.X.; Validation, Y.X.; Formal analysis, X.T.; Writing--original draft, Y.X.; Writing--review & editing, X.T. and F.O.; Project administration, L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Academy of Broadcasting Science (No. JBKY20230160).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# R-PreNet: Deraining Network Based on Image Background Prior
## 3 Proposed Work
### Progressive Recurrent Network  
The progressive recurrent network consists of the following four parts: (i) a convolutional layer \(f_{in}\) receives network inputs, (ii) a recurrent layer \(f_{recurrent}\) propagates cross-stage feature dependencies, (iii) several residual blocks \(f_{res}\) extract the deep representation, and (ii) a convolutional layer \(f_{out}\) outputs deconvolutional results. Where \(f_{in}\) takes as input the current estimation \(\text{x}^{t-1}\), the rainy image y, and the concatenation of the background fusion prior features G. A convolutional long short-term memory (LSTM) is employed for the recurrent layers, given its empirical advantage in image deraining, through which cross-stage feature dependencies can be propagated to facilitate rain streaks removal:  
\[\text{x}^{t-0.5}=f_{in}\left(\text{x}^{t-1},\text{y},\text{G}\right) \tag{9}\]  
\[\text{s}^{t}=f_{recurrent}\left(\text{s}^{t-1},\text{x}^{t-0.5}\right) \tag{10}\]  
Figure 5: Interactive fusion feature module.  
\[x^{t}=f_{out}\big{(}f_{res}(s^{t})\big{)} \tag{11}\]  
where \(f_{in},f_{res}\), and \(f_{out}\) are stage-invariant, the network parameters are reused across different stages. The recurrent layer \(f_{recurrent}\) takes \(x^{t-0.5}\) and the recurrent state \(s^{t-1}\) as inputs to stage \(t-1\). By unfolding PreNet [7] with T recurrent stages, the deep representation of rain streak removal is favored by recurrent state propagation. The deraining results from the intermediate stages of the network structure indicate that the accumulation of storm streaks can be gradually eliminated.
# Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments
## 3 The Proposed Method
### Confidence Function and Update Strategy  
The tracking confidence parameter is an index for evaluating if the target has been lost. Most DCF-based trackers use the maximum response \(R_{\text{max}}\) to locate the target in the next frame. However, in a complex scene, it is not ideal to rely only on this parameter. [PERSON] et al. proposed large margin object tracking method with circulant feature maps (LMCF) [41] with average peak-to-correlation energy (APCE) (Equation (10)), which can effectively deal with the target occlusion and loss. [PERSON] et al. proposed a motion-aware correlation filter (MACF) [42] based on the confidence of squared response map (CSRM) (Equation (11)), which compensated for the lack of APCE discrimination during long-term occlusion:  
\[APCE=\frac{|R_{\text{max}}-R_{\text{min}}|^{2}}{mean\left(\sum_{w,h}\left(R_{ w,h}-R_{\text{min}}\right)^{2}\right)}, \tag{10}\]\[CSRM=\frac{\left|R_{\max}^{2}-R_{\min}^{2}\right|^{2}}{mean\bigg{(}\sum_{w,h} \left(R_{w,h}^{2}-R_{\min}^{2}\right)^{2}\bigg{)}}, \tag{11}\]  
where \(R_{max}\), \(R_{min}\), and \(R_{w,h}\), respectively, denote the maximum, minimum, and the \(w\)-th row \(h\)-th column elements of the peak value of the response. A comparison was conducted to determine whether the combination of multiple confidence parameters improved the tracker's performance. The results are presented in Section 4.  
In the traditional DCF-based tracker [11; 12; 13], it is common to train a sample and the filter in each frame and update the filter. Although an iterative search can be conducted effectively, updating the filter in each frame increases the computational complexity because the optimization of the filter is the core calculation step in the algorithm. We adopt the ECO [14] method to reduce computational complexity by updating the filter template in every \(N_{\text{S}}\)-th frame. This strategy improves the running speed of the filter and prevents overfitting. However, the target sample is updated in each frame.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 1 Introduction
V2I communications span a wide range of applications, including safety and non-safety related. V2I safety applications include collision warning, emergency vehicle priority, driver assistance and road hazards warming, and speed and intersection warning messages, all aimed at preventing road crashes and enhancing mobility [16]. Meanwhile, non-safety applications focus on traffic efficiency and optimisation, remote vehicle diagnostics, air pollution monitoring and onboard infotainment [17]. These diverse arrays of applications stem from various components, technologies and data types involved in the interconnected V2I ecosystems. These applications are facilitated using Dedicated Short Range Communication (DSRC) [18] and Cellular-Vehicle-to-Everything C-V2X technologies [19]. The DSRC and C-V2X technologies establish a communication link between Onboard Units (OBU) and Roadside Units (RSU) [20]. An OBU, which is mounted on vehicles, allows communication with other OBUs and RUSs [21]. RSUs are part of the infrastructure which are strategically positioned along road networks to serve as communication nodes or access points for exchanging vital information with trusted authorities for traffic management [22]. Collectively, this ecosystem exchanges data for time-critical, urban planning, and infotainment use cases with the aim of revolutionising and redefining road transportation to improve road safety, comfort, and efficiency. Figure 1 shows the V2I communications landscape. The success of the V2I communications ecosystem hinges heavily on the quality of data being exchanged and, hence, the importance of assuring DQ in V2I interactions. Vehicular communication has a plethora of use cases, and some of these use cases have a unique and customised protocol stack. Some use cases in V2I communications are time-sensitive or time-critical. Delayed or incomplete data can result in vehicles receiving inaccurate information, potentially leading to catastrophic outcomes. Therefore, ensuring DQ in V2I communication scenarios is of paramount importance. This research aims to investigate
## 3 Results and Discussions
### Density and Porosity Variations  
Some views under SEM of a selected sample (Sample 36) are presented in Figure 4. In the sample, it was observed that artificial pores were generated, especially in the cement part of the samples (pores are formed with the swelling of resin in water and removal of the absorbed water during the drying process), in addition to the pores existing in the exclay aggregate. As a result, the pores decreased the density.  
Table 3 shows that there is an inverse relationship between density and porosity. When porosity increases, the density decreases. In Figure 5, the density values are plotted with bar graphs for better evaluation. According to Figure 5, the density decreases with an increasing exclay ratio in the concrete. Although the effect of exclay diameter on density is not very apparent, the density of samples with 4-6 mm diameter exclay aggregates is slightly lower than the samples with 2-4 mm diameter aggregates. Finally, regardless of the exclay particle diameter, increasing the resin ratio causes a decrease in density, as expected.  
As is known, lightweight concrete has a lower density than normal-weight concrete. This reduction in density is achievable by different methods, such as using lightweight aggregate in concrete, foamed concrete or autoclaved aerated concrete or by any other techniques that reduce the final specific weight of the product, thus achieving a lower weight than normal weight concrete mixtures. Whereas the density of normal-weight concrete varies from 2240 to 2450 kg/m\({}^{3}\), the density of lightweight concrete is \(\sim\)300-2000 kg/m\({}^{3}\), but the practical range of density for lightweight concrete is 500-1850 kg/m\({}^{3}\)[19]. In the present study, the lowest and highest densities were measured as 532 kg/m\({}^{3}\) and 1608 kg/m\({}^{3}\), which shows that all produced samples can be classified as lightweight concrete.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Code** & **d (mm)** & **x (\%)** & **\(\rho\) (g/cm\({}^{3}\))** & **\(\Phi\) (\%)** & **\(k\) (W/mK)** & **\(f_{\rm cs}\) (MPa)** & **WAR (\%)** \\ \hline  
\hline \end{tabular}
\end{table}
Table 3: _Cont._
# Thermogenic Supplementation and Fat Loss in Resistance-Trained Males: A Randomized Controlled Trial
###### Abstract  
As interest in weight management and metabolic health grows, thermogenic supplements have gained attention for their potential to enhance caloric expenditure and fat loss. This study aimed to investigate the effects of chronic thermogenic supplement ingestion on body composition and strength outcomes during an 8-week periodized resistance training program. Thirty-four resistance-trained males were randomized into thermogenic supplementation (TS) (n = 18) or placebo (n = 16) groups in a double-blind, placebo-controlled design. Participants consumed thermogenic or placebo capsules daily, with dosage adjusted midway through the intervention. Body composition was assessed using ultrasound-based body fat analysis, and strength was measured via bench press and back squat one-repetition maximum (1-RM) tests. Results indicated that TS experienced significantly greater reductions in fat mass (\(-\)0.65 kg vs. 0.12 kg, \(p\) = 0.029) and body fat percentage (\(-\)0.89% vs. \(-\)0.08%, \(p\) = 0.039) compared to the placebo. Both groups demonstrated significant and comparable improvements in squat (TS: 16.87 kg, placebo: 18.47 kg) and bench press strength (TS: 7.80 kg, placebo: 7.13 kg). These findings suggest that thermogenic supplement ingestion can enhance fat loss without impeding strength adaptations. Future research should explore the dose-response effects and applicability of thermogenics in different populations and over extended durations.  
Article
## 4 Discussion  
The theoretical simulations and experimental results are shown in Figure 4. With our current LSTM model-based method, we gain a good and similar result which agrees well with the theoretical predictions, due to the stability of the system and the updating and feedback data. With the data given in Table 1, Table 2, and some other experimental results, we can evaluate the phase error rates and the final secure key rates. At 100 km and 250 km, we obtain the key rates of \(4.24\times 10^{-5}\) and \(2.41\times 10^{-9}\) per pulse, individually. We also run the system with traditional scanning-and-transmitting method at the same environment for comparison. Note that we can get the similar level of key rate per pulse by using our present machine learning-assisted MDI-QKD system compared with using traditional scanning-and-transmitting mode. However, the duty cycle of our present method has been increased by more than 10 percent, giving a significant improved transmission efficiency. On the other hand, in order to verify the long time stability, we run the MDI-QKD system at the transmission distance of 100 km over 48 h and record corresponding QBER, using either machine learning-assisted mode or traditional scanning-and-transmitting mode, see Figure 5. Obviously, these two methods exhibit similar level of QBER and stability. In short, the new method not only improves systematic transmission efficiency, but also avoids potential vulnerabilities as no additional hardware is introduced. Furthermore, the duty cycle and stability time can be further improved by program optimization to match the requirements of various systems.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & \(\mu\) & \(\upsilon\) & \(\omega\) & \(P_{\mu}\) & \(P_{\
u}\) & \(P_{X|\mu}\) \\ \hline
100 km & 0.6353 & 0.0476 & 0.3171 & 0.885 & 0.110 & 0.002 \\
250 km & 0.4492 & 0.0691 & 0.4817 & 0.495 & 0.436 & 0.027 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimized parameters of sources with 100 km and 250 km fibers.  
Figure 4: Theoretical and experimental results of the key rate’s dependence on the transmission distance. system parameters are as follows: dark counting rate per pulse and overall efficiency of detection side are \(4\times 10^{-8}\) and 60%; misalignment errors in Z and X bases are 0.15% and 1.5%, respectively.
# Simulation of Test Arch Based on Concrete Damage Plasticity Model and Damage Evolution Analysis
## 5 Analysis of Results
### Analysis of Displacement Results
#### 5.1.1 Damage Conditions at Arch Foot Base Plate  
Figure 12 illustrates the displacement alterations following damage to the bottom slab of the springing. The data curve trends were largely consistent. In the initial 30 load steps, the impact of displacement damage was marginal, suggesting the stability of the arch structure. Post the 30 th step, a slight elevation in displacement disparity occurred, culminating in a pronounced increase that signified structural collapse. This suggested that the damage rate of the bottom slab does not directly influence the onset of structural failure. The marked surge indicated that damage expedited the structure's brittle breakdown. Intriguingly, under a 5% damage rate, the displacement variance was more pronounced than the 10% and 15% rates and structural failure manifested sooner. This challenged the prevailing belief that \"greater damage equates to a higher likelihood of failure\".
# IoT and Blockchain Integration: Applications, Opportunities, and Challenges
## 6 Security Assurances and IoT Threat Model
### Definitions of Security Assurances
#### 6.1.5 Physical Security  
It is related to the security assurance of availability. Physical security [18] maintains the physical integrity of computing devices, networking devices, and communication channels such as cable, twisted pair cable, fiber optics, satellite, and radio (frequency) spectrum, among others. Activities such as theft of devices and relocation of the device hamper the physical security of the device. Physical security covers hardware and software security. In the context of IoT, physical security should protect every node in each of the components of the IoT ecosystem.
## 6 Assessment of Integration Within Digital Twin
### Non-DT-Testable Requirements and Subjectivity  
Digital twin technology does not yet fully address all categories of requirements. Certain specifications remain difficult or impossible to validate in an entirely virtual space due to their reliance on subjective interpretation, physical interaction, or unpredictable context sensitivity.  
For instance, luminance-related requirements--such as achieving a minimum 1000 lx at a specified distance--can be evaluated through photometric simulation. However, real-world perception of glare, beam uniformity, and visual comfort depends heavily on individual user feedback and environmental influences, which are beyond the scope of current digital models.  
Other examples of non-DT-testable requirements include cybersecurity robustness, where network-based threats and penetration vulnerabilities require real-world testing environments; aerodynamic behavior, which, while partially simulated, still relies on wind tunnel validation for certification; and mechanical tolerances in mounting structures, where fundamental assembly dynamics can introduce deviations difficult to replicate virtually. Additionally, subjective evaluations--such as visual quality, light tone appeal, or perceived system responsiveness--are inherently subjective and cannot be fully validated through DT simulation; therefore, they must be assessed through physical trials or user-based testing protocols.  
These limitations highlight the need for hybrid validation strategies. Digital twins should be complemented with physical prototypes and field trials to fully capture user perception, aesthetic integration, and compliance with regulatory nuances. In such cases, the DT remains an indispensable tool, but one that must be integrated wisely within a broader test ecosystem.