esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Reinforcement Learning for Energy-Efficient 6G V2X Networks
## 3 System Model and Problem Formulation
### Optimization Model  
Considering \(N\) RSUs with a number of available uplink RBs (namely, \(M_{n}\)) and \(V\) vehicles with a number of required RBs per time slot for sending the sensing data from vehicle \(v\) to the RSU \(n\) (namely, \(R_{n,v}\)), we need to determine \(y_{n,v}\), which denotes whether the vehicle \(v\) is associated with RSU \(n\) or not; \(x_{n}\), which indicates whether to turn the RSU \(n\) on or off; and \(z_{v}\), which determines whether a vehicle is not allocated. The whole optimization problem minimizes the number of active RSUs and the energy consumption while satisfying the constraints on maximum allowed latency and the available resources. We formulate it as follows:  
\[\min\quad\omega_{1}\sum_{n\in N}x_{n}\,+\,\omega_{2}\sum_{n\in N }\sum_{v\in V}E_{n,v}\,y_{n,v}\,+\,\omega_{3}\sum_{v\in V}z_{v}\] (12) s.t. \[\sum_{v\in V}R_{n,v}\,y_{n,v}\leq M_{n},\quad n\in N, \tag{13}\] \[\sum_{v\in V}\Gamma_{n,v}\,y_{n,v}\geq H_{n,v},\quad n\in N,\] (14) \[\sum_{v\in V}f_{n,v}\,y_{n,v}\leq F_{n},\quad n\in N,\] (15) \[\sum_{n\in N}L_{n,v}\,y_{n,v}\leq\mathcal{L}_{v},\quad v\in V,\] (16) \[\sum_{n\in N}y_{n,v}\,+\,z_{v}=1,\quad v\in V,\] (17) \[x_{n}\geq y_{n,v},\quad n\in N,v\in V,\] (18) \[x_{n},z_{v},\ y_{n,v}\in\{0,1\},\quad n\in N,v\in V. \tag{19}\]  
Our objective (12) is to jointly minimize the weighted sum of active RSUs and overall energy consumption. The above-mentioned objective function is subject to the following requirements: constraint (13) ensures that the number of required RBs per time slot to uplink data from vehicle \(v\) to the RSU \(n\) should not exceed the number of available uplink RBs at the RSU \(n\), constraint (14) indicates that vehicle \(v\) can be assigned to RSU \(n\) if the predicted SINR is larger than the assumed threshold \(H\), constraint (15) guarantees that the computational capacity assigned from RSU \(n\) to vehicle \(v\) should not exceed the maximum available computational capacity of RSU \(n\), constraint (16) ensures that the task offloading time from vehicle \(v\) to RSU \(n\) should not exceed the maximum allowed latency for vehicle \(v\), constraint (17) states that each allocated vehicle \(v\) should be associated with one and only one RSU, constraint (18) ensures that vehicle \(v\) can be connected to RSU \(n\) only if RSU \(n\) is turned on, and constraint (19) indicates the lower and the upper bounds of the binary decision variables.  
In complex real-world V2X environments, rapid solutions are required for the combinatorial optimization model presented in (12)-(19). Therefore, approximate solutions are used for real-time allocation. We implement a DRL environment to provide online allocation, where we consider a snapshot of vehicle locations instead of mobility to reduce the complexity of the model. As we focus on uplink in 6G V2X Open RAN, the effect of mobility can be neglected.
# Data Augmentation for Audio-Visual Emotion Recognition with an Efficient Multimodal Conditional GAN
## 4 Experiments
### Networks  
In Section 4.1, we show that the audio modality with the size of \(94\times 94\times 3\) and the visual modality with the size of \(160\times 160\times 3\) are extracted from original videos. If we directly feed them into our multimodal condition GAN, it will be difficult for our model to learn useful information from audio and visual modalities due to the high dimensionality of the multimodal data. Inspired by [17; 44], we utilize the fully connected layer before the softmax layer of ResNet-34 [93] to extract 512-dimensional features from the raw audio and visual modalities as the input of our framework. In other words, the dimensionality of both \(\mathbf{x}_{A}\) and \(\mathbf{x}_{V}\) is 512. In this way, audio and visual modalities are appropriately represented in our system. The dimensions of Gaussian noise vectors \(\mathbf{z}_{A}\) and \(\mathbf{z}_{V}\) are set to 50. In addition, the label \(y\) corresponding to \(\mathbf{x}_{A}\) and \(\mathbf{x}_{V}\) are converted into the label embedding, and then sent to the generators of our multimodal conditional GAN along with \(\mathbf{z}_{A}\) and \(\mathbf{z}_{V}\).  
Generators \(G_{A}\) and \(G_{V}\) have the same architecture with four fully connected layers. The first layer is the input layer with the noise vector and the label embedding, the second layer has 128 nodes, the third layer has 256 nodes, and the last layer is the output layer with 512 nodes to generate fake audio modality or fake visual modality. The second and third layers are followed by the LeakyReLU function. Similarly, discriminators \(D_{A}\) and \(D_{V}\) have the same architecture. We first concatenate the label embedding with the input modality, which can be either a real modality or a fake one generated by generators. Then, we send them to the discriminator with four fully connected layers. The first layer is the input layer. Each of the following two layers has 256 LeakyReLU nodes, and the last layer has only one node to determine whether the input data is real or fake.  
After generating data with our multimodal conditional GAN, we use classifiers to evaluate the generated data and perform the final emotion classification task. In [94; 95; 96], DNN is shown to be effective in learning feature representations from audio and visual modalities. Inspired by this, we adopt DNN to design our emotion classifier. To be specific, we first concatenate the audio modality and the visual modality into a feature vector, and then send it into the DNN classifier with four fully connected layers. The first layer is the input layer with 1024 nodes. Each of the following two layers has 128 LeakyReLU nodes. The number of nodes in the last layer is the number of emotion categories (six for eNTERFACE'05 and RAVDESS, and four for CMEW).
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 4 FOXS-GSC Performance Evaluation
### Traffic Efficiency Evaluation  
In this evaluation, we compared FOX-GSC with existing solutions from the literature, namely, DESTINy [16], CHIMERA [17], and PAN [18], the latter utilizing two different variations. The first variation, PAN1, involves computing a single route and assigning it to the vehicle. The second variation, PAN3, computes three routes and randomly selects one. Additionally, our solution was compared to a baseline scenario (base) without any vehicle routing solution and with Dynamic Shortest Path (DSP), which simply reroutes vehicles along the shortest path.
# Empirical Evaluation of Privacy Efficiency in Blockchain Networks: Review and Open Challenges
### Gap Analysis and Contribution  
We surveyed numerous articles relating to blockchain privacy, classified privacy protecting solutions based on the fundamental blockchain component targeted. During the survey, we found that most of these solutions are evaluated based on the computational performance and proof-of-concept, which is not acceptable. Therefore, research on privacy solutions for blockchain is not progressing significantly. To bridge this gap, this research study was carried out. The originality and contribution of this article is multifold:  
1. Novel Classification of Privacy Solutions with respect to Blockchain Components  
We present a novel classification of existing privacy preserving solutions in blockchain networks based on the component involved in privacy protection. We classify the existing solutions into the categories of hashing, cryptographic primitives and smart contracts, all of which are significant components of blockchain functionality. To the best of our knowledge, such a classification has yet not been presented anywhere in the literature at the time of writing this manuscript. The purpose of this classification is to highlight the state-of-the-art methods preserving privacy in correspondence to the fundamentals to be tuned. This will be beneficial for the concerned individuals to make an informed decision about building a better privacy protecting blockchain for their applications.  
2. Emphasizing on Insufficiency of State-of-the-Art Privacy Evaluation Criteria for Estimating the Potential of a Solution  
We extensively studied evaluation criteria adopted in various blockchain based privacy solutions for analysis. Using the literary evidence, we show that the evaluation is done mainly based on performance and proof of concept. However, we argue that such analysis is not sufficient to evaluate the privacy provided by a technology merely based on system performance, computational cost, and time and hence a proper framework with different criteria and parameters must be introduced for the evaluation. Therefore, we come to our third major contribution which is mentioned next.  
3. Proposing Novel Framework to Empirically Evaluate Privacy Solutions (Beyond Performance)To support the argument, we further present a framework with around 10 different criteria and sub-criteria, divided as privacy attributes and risks, that can effectively evaluate and quantify any blockchain based privacy solution irrespective of its category. With this, we also introduce the concept of privacy precision that is the empirical value calculated based on the efficiency of chosen parameters. This empirical value, ranging from 0 to 1, quantifies the degree of privacy provided by a solution.  
To the best of our knowledge, none of the contributions have been published in any study so far.
# Point-Kernel Code Development for Gamma-Ray Shielding Applications
###### Abstract  
The point-kernel (PK) technique has a long history in applied radiation shielding, originating from the early days of digital computers. The PK technique applied to gamma-ray attenuation is one of many successful applications, based on the linear superposition principle applied to distributed radiation sources. Mathematically speaking, the distributed source will produce a detector response equivalent to the numerical integration of the radiation received from an equivalent number of point sources. In this treatment, there is no interference between individual point sources, while inherent limitations of the PK method are its inability to simulate gamma scattering in shields and the usage of simple boundary conditions. The PK method generally works for gamma-ray shielding with corrective \(B\)-factor for scattering and only specifically for fast neutron attenuation in a hydrogenous medium with the definition of cross section removal. This paper presents theoretical and programming aspects of the PK program developed for a distributed source of photons (line, disc, plane, sphere, slab volume, etc.) and slab shields. The derived flux solutions go beyond classical textbooks as they include the analytical integration of Taylor \(B\)-factor, obtaining a closed form readily suitable for programming. The specific computational modules are unified with a graphical user interface (GUI), assisting users with input/output data and visualization, developed for the fast radiological characterization of simple shielding problems. Numerical results of the selected PK test cases are presented and verified with the CADIS hybrid shielding methodology of the MAVRIC/SCALE6.1.3 code package from the ORNL.  
point-kernel; radiation shielding; gamma-ray; buildup factor; slab shield; Monte Carlo; variance reduction; CADIS; SCALE6.1.3
## 2 Materials and Methods
### Bridge Assessments Using Health Index
#### 2.3.2 Component-Level HI (CHI)  
As mentioned previously, different types of deteriorations have different effects on the bridge-performance level. Therefore, weights must be assigned to each \(DHI_{(t)}^{(i)}\) in Figure 6 to derive the CHI. Because the weights for different deterioration types have not been established in maintenance standards or previous research, this study arbitrarily determined them based on the deterioration process. For a more detailed categorization, the deterioration factors affecting the structural safety of RC slab bridges were classified into four modules: cracking, material, other, and corrosion defects (Table 3). According to previous studies on the deterioration process of RC slab bridges [29], cracking defects, which include microcracks, macroracks, and crazing, significantly affect bridge safety. If these defects continue to develop, they can lead to concrete peeling, punching shear failure, and rebar exposure. Therefore, this study considered cracking and corrosion defects (exposed rebar) as accounting for 70% of the overall bridge deterioration, and exposed rebar is given a weight of 0.4 because it is the most significant deterioration factor in the deterioration process. Material defects and other types of defects do not directly affect the safety of the bridge but can contribute to performance degradation over time. As there is no clear evidence on which of these two factors has a greater impact on bridge aging, each was assigned a weight of 0.15.  
Based on the weights listed in Table 3, the CHI for the \(j\)-th deterioration factor, \(CHI_{(t)}^{(j)}\), was calculated as follows:  
\[\begin{split} 0.3\Big{(}0.25\cdot DHI_{(t)}^{(Micro\ crack)}+0.375 \cdot DHI_{(t)}^{(Macro\ crack)}+0.375 DHI_{(t)}^{(Crazing)}\Big{)}\\ CHI_{(t)}^{(j)}=\qquad+0.15\begin{pmatrix}0.167\cdot DHI_{(t)}^{( Separation)}+0.167\cdot DHI_{(t)}^{(Spalling)}\\ +0.167\cdot DHI_{(t)}^{(Concrete\ failure)}+0.167\cdot DHI_{(t)}^{(Exfoliation)}\\ +0.167\cdot DHI_{(t)}^{(Segregation)}+0.167\cdot DHI_{(t)}^{(D delamination)}\end{pmatrix} \\ +0.15\Big{(}0.5\cdot DHI_{(t)}^{(Efflorescence)}+0.5\cdot DHI_{(t)}^{( Leakage)}\Big{)}\\ +0.4\cdot DHI_{(t)}^{(Exposed\ rebar)}\end{split} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Deterioration Type** & **Deterioration Weight** & **Module** & **Module Weight** \\ \hline Microcrack & 0.25 & & \\ Macrocrack & 0.375 & Cracking defects & 0.3 \\ Crazing & 0.375 & & \\ \hline Separation & 0.167 & & \\ Spalling & 0.167 & & \\ Concrete failure & 0.167 & & \\ Exfoliation & 0.167 & & \\ Segregation & 0.167 & & \\ Delamination & 0.167 & & \\ \hline Efflorescence & 0.5 & Other types of defects & 0.15 \\ Leakage & 0.5 & & \\ \hline Exposed rebar & 1 & Corrosion defect & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Deterioration weight for DHI.  
where \(j\) denotes the bridge component (slab, pier, or bridge).
# Antioxidant Activity According to the Planting Interval and Collecting Time of _Rosa daavrica_
###### Abstract  
This study aimed to assess functional substances and antioxidant effects with respect to planting intervals and collection timing, with the goal of developing efficient planting methods for the mass harvesting of _Rosa daavrica_ and enhancing the availability of the plant's edible parts in Korea. High-Performance Liquid Chromatography (HPLC) was employed to analyze the ethyl gallate content, a recognized functional component present in _R. daavrica_, while the antioxidant effect was verified using DPPH, ABTS, and SOD assays. The findings revealed an increased yield of buds and leaves in _R. daavrica_ when the planting interval was reduced to 120 \(\times\) 50 cm compared to the conventional 120 \(\times\) 100 cm spacing. Specifically, the content of ethyl gallate remained consistent across different collection periods, and this result was associated with the varying planting intervals. While there were no significant changes in total polyphenol and flavonoid content, the collection from September demonstrated higher levels. Furthermore, the study established that leaf antioxidant activity, determined through IC\({}_{50}\) values of DPPH and ABTS, surpassed that of the buds, with no significant difference in SOD activity being observed. Overall, no substantial differences were observed in the content of functional components between the buds and leaves of _R. daavrica_, regardless of planting interval and collection timing. These results contribute valuable insights for optimizing planting methods for the large-scale collection of _R. daavrica_ leaves.  
Rosa daavrica; antioxidant; planting interval; yield; collecting Article
## 4 Results and Discussions
### Effects of Fire on Mechanical Properties
#### 4.2.2 Effects of Fire on the Microstructure  
In fiber-reinforced concrete, the microstructure of the hydrated product, as well as around the fibers, influences various mechanical and durability properties of the matrix. Therefore, in the study, microstructural analysis of the samples taken from the top of the surface of the slab (the surface directly exposed to fire) was conducted using Hitachi Mode; S-2500 Scanning Electron Microscope (SEM) and Polaron Model SC7620 Sputter Coater. SEM micrographs were obtained using 1000 times magnification of the samples before exposure to fire and after exposure to fire for 120 min in the furnace. The main focus of the interpretation of SEM micrographs was to determine the effects of fire on the microstructure of the matrix around the fibers as well as any changes that happened to fibers after 120 min of exposure to fire.  
Figures 14(a1,a2) and 15(a1,a2) showed the effects of fire on FRC containing 1% and 1.5% PF. It can be observed that after 120 min of exposure to fire, a large void area formed (dark black spots  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mix Type**} & \multicolumn{2}{c}{**Split Cylindrical Tensile Strength (Mpa) at 90 Days Curing**} & \multicolumn{2}{c}{**Reduction in Strength (\%)**} \\  & \multicolumn{2}{c}{**(Average of 3 Samples)**} & \multicolumn{2}{c}{**(After 120 min of Fire**} \\ \cline{2-4}  & **Unfired Samples** & **Fired Samples** & **Exposure)** \\ \hline M1-0F & 3.0 & 2.4 & 20.00 \\ M2-1.0 BF & 3.5 & 2.7 & 22.86 \\ M3-1.5 BF & 3.7 & 2.8 & 24.32 \\ M4-2 BF & 2.9 & 2.4 & 17.24 \\ M5-1 PF & 3.5 & 2.6 & 25.71 \\ M6-1.5 PF & 4.1 & 3.2 & 21.95 \\ M7-2.0 PF & 4.0 & 2.9 & 27.50 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Reduction in split cylindrical tensile strength.  
Figure 13: Effects of 120 min of fire exposure on the split cylindrical tensile strength.  
around fiber). However, some crystals are formed at the fiber surface. Figures 14(b1,b2) and 15(b1,b2) showed the effects of fire on FRC with 1 and 1.5% BF. It is observed that the outer layer of basalt fiber is restructured, and mineral crystals are formed. In the unfired FRC-BF, the poor interface of basalt fiber in the cement matrix is formed, which may be due to the weak or unbonded nature of the connection between basaltic glass and the cement matrix. It is because a large surface area of basalt fibers was present in the cement paste. This lack of adhesion consequently resulted in the reduction of the compressive strength, as observed in this study. After exposure to 120 min to fire, some distortion (bulging and contraction) in PF can be seen. Whereas, basalt fiber condition after 120 min of fire exposure looked good and undistorted, because the BF melting point is quite high.
# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 3 Methodology
### LightGBM  
LightGBM (Light Gradient Boosting Machine), developed by [PERSON] et al. (2017) [41], is an efficient gradient boosting framework, designed to handle large-scale data. Known for its exceptional processing speed and low memory usage, LightGBM is widely used in data science competitions and commercial applications. Building on the decision tree algorithm, LightGBM enhances efficiency and reduces memory consumption compared to frameworks like XGBoost through two key innovations. First, LightGBM employs a histogram-based decision tree algorithm, which converts continuous feature values into discrete bins, assuming that discretization preserves sufficient predictive information. Second, it utilizes a leaf-wise growth strategy with depth constraints, prioritizing the splitting of leaves with the highest gain to accelerate error reduction. This strategy assumes that local greedy splits accumulate to approximate global optima. For classification tasks, LightGBM efficiently handles both binary and multi-class problems by iteratively optimizing decision trees to minimize prediction errors. Particularly in big data environments, its rapid training capability and low memory requirements make it ideal for resource-constrained settings, while maintaining high-performance processing of large datasets.
## 5 Epigenetic Modulation of Vitamin D Effects
### Epigenetic Modulation of VDR Cistrome and the Epigenetic Control Exerted by Vitamin D/VDR  
VDR cistrome is modulated by ligand binding but not only. A complex system of epigenetic modulators is differentially expressed during embryogenesis and tissue differentiation, and they can change ligand-induced VDR/RXR docking at most sites across the genome responding to the requirements of the tissue. A comparison of the VDR cistromes in osteoblast lineage cells prior to and following differentiation [69; 70] revealed striking changes in the genome after differentiating transition, which demonstrate that the cistrome for VDR can undergo significant remodeling affecting both the expression of the receptor and genomic distribution of its binding.  
The studies of genome-wide analysis measured the consequences of VDR/RXR binding at target genes on the whole chromatin landscape. ChIP-chip and subsequent ChIP-seq analysis revealed that VDR/RXR binding across the genome results in the recruitment of coactivators such as SRC1, a histone acetyltransferase that increases histone acetylation and chromatin decondensation [71]. As consequence, the appearance of the VDR/RXR heterodimer evokes a striking increase in the levels of histone H3 and H4 lysine acetylation and changes in transcription machinery occupancy not only at promoters that are regulated by vitamin D but at other regions as well [72]. The ligand-dependent interference of VDR with chromatin modifiers such as lysine demethylylase 6B explains how vitamin D can affect histone markers for active transcription start sites [73].  
The epigenome-wide effects of vitamin D are also exerted on subsets of the cistromes of several transcription factors, such as the pioneer transcription factors PU.1 (purine-rich box 1), CEBPA (CCAAT/enhancer binding protein alpha), and the transcriptional repressor CTCF (CCCTC-binding factor) [74].  
Based on these observations, it would be extremely useful to search for molecules, either synthetic or natural compounds, able to potentiate the activity of vitamin D/VDR through epigenetic mechanisms, which could influence both the expression of VDR and the actions of vitamin D on a genome-wide scale. Up to date there are no reports of natural compounds able to modulate VDR cistrome or VDR-dependent epigenetic landscape; further genome-wide studies are warranted. Instead, the existence of natural epigenetic modulators that act on VDR expression has been proven and some molecules have been investigated.  
In this review we will deal with three natural modulators of VDR: Curcumin, sulforaphane, and butyrate, the latter as the product of the fermentative intestinal microbiome.