esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed DRFENet Construction
### Residual Dilated Block  
The RDB consists of nine convolution layers and four dilation convolution layers. Dilation convolution layers are arranged in the 8 th, 11 th, 15 th, and 18 th layers of the overall network in DRFENet. As shown in Figure 1, a CCD block consists of two common convolutions and dilated convolution. The position of the dilated convolution and skip connections is the essence of this module. Due to the characteristics of the CNN, the convolutional layer in the first few layers of the network often represents low-level features, such as pixel-level features. In contrast, the convolutional layer in the deep layer usually represents high-level features, such as semantic features. As the network layer deepens, the extracted features become more accurate, but part of the contextual information will also disappear. Therefore, we designed a network structure combining skip connections and dilated convolution and use a progressive reuse strategy to build a network. Dilated convolution is used to capture more context information, and skip connections are used to transfer image characteristics and captured contextual information to deeper layers of the network. The skip connection in the RDB ranges from five convolution layers in the middle to four convolution layers and then to three convolution layers. It is more convenient for high-level information transmission to form an arithmetic sequence descending step-by-step. This helps for learning features of different depths, making more effective use of the feature, and strengthening the feature's transmission of information.  
Dilated convolution can obtain a larger receptive field and thus obtain more intensive data and map more context. Therefore, it is very cost-effective to take the output of dilation convolution as the input of the skip connection, since there is more information though the network structure, and parameters are unchanged. See Equation (3) for the formula of the RDB:  
\[O_{RDB}=F_{RDB}(O_{DEB},O_{DEB}), \tag{3}\]  
where \(F_{RDB}\), \(O_{RDB}\) express the function and the output of the RDB, respectively. As the input of the RDB, \(O_{DEB}\) is passed to the next layer connected with the second dilation convolution feature map, so \(O_{DEB}\) is input twice. The \(O_{RDB}\) will be the input to the FEB.
# Novel Q-Carbon Anodes for Sodium-Ion Batteries
## 1 Introduction
Q-carbon has not previously been applied as an anode for sodium-ion batteries and its unique synthesis method and high-sp\({}^{3}\) carbon content make it an interesting candidate. The diamond tetrahedra structure of the carbon may have a different storage mechanism for sodium. Various carbons with high sp\({}^{3}\) content have been theoretically proposed as anode materials in the literature [26; 27; 28]; herein, we for the first time we test the hypothesis experimentally and report the findings. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Carbon Type**} & **Discharge Capacity** & **Current Density** & \multirow{2}{*}{**Ref.**} \\  & **[mAh\(\cdot\)g\({}^{-1}\)]** & **[mA\(\cdot\)g\({}^{-1}\)]** & \\ \hline Commercial hard carbon & 270 & 20 & [16] \\ \hline Cellulose & 300 & 37.2 & [17] \\ \hline Coal tar & 270 & 100 & [18] \\ \hline Peanut shells & 193 & 250 & [19] \\ \hline Hard–soft composite derived from biomass and oil waste & 282 & 30 & [20] \\ \hline Sucrose & 286 & 30 & [21] \\ \hline Coconut oil & 277 & 100 & [22] \\ \hline Rice husk & 372 & 25 & [23] \\ \hline Epoxy phenol novolac resin & 480.3 & 50 & [24] \\ \hline Cotton & 315 & 30 & [25] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of discharge capacities of different carbon from the literature.
## 2 Framework Design of Gateway
### System Workflow  
After completing the system framework and functional design, we next defined the workflow of each function. The operational flow of the gateway is shown in Figure 4 and further explained below.  
Figure 4: Operational cycle of the embedded communication gateway with automatic back-up capabilities.
# Depletion-of-Battery Attack: Specificity, Modelling and Analysis
## 4 DoB Analysis  
In this section, we present a theoretical framework for modeling and analyzing DoB attacks. The proposed models are based on continuous time Markov chains (CTMC). This technique is generally used when the various performance metrics of WSNs are calculated. For example, these metrics include the effectiveness of battery recovery [39], characteristics of Rayleigh fading channels [40], the average number of successful transmissions completed by WSN nodes [41], and the effectiveness of protection against timing attacks [42]. Here, we consider the impact of a DoB attack, as well as the sensor battery capacity, on the system lifetime. Approaches to WSN lifetime estimation are discussed, as well.
# An Improved Ciphertext-Policy Attribute-Based Encryption Scheme in Power Cloud Access Control
## Preliminaries and Definitions
### Attributes and Access Structures  
Our access structure is similar to Waters [12]. Let \(P=\{P_{1},P_{2},\cdots,P_{n}\}\) be a set of parties. A collection \(\mathbb{A}\subseteq 2^{\{P_{1},P_{2},\cdots,P_{n}\}}\) is monotone if \(\forall\)B,C: if \(\text{B}\in\mathbb{A}\) and \(\text{B}\subseteq\mathbb{C}\) then \(\mathbb{C}\in\mathbb{A}\). An access structure (monotone access structure) is a collection (monotone collection) \(\mathbb{A}\) of non-empty subsets of \(\{P_{1},P_{2},\cdots,P_{n}\}\), i.e., \(\mathbb{A}\subseteq 2^{\{P_{1},P_{2},\cdots,P_{n}\}}\backslash\{\phi\}\). The sets in \(\mathbb{A}\) are called the authorized sets, and the sets not in \(\mathbb{A}\) are called the unauthorized sets.  
In our PCAC scheme, the role of the parties is taken by the attributes. Different from most access control scheme, the access structure adopted in this paper is composed of two parts, one is the static general access structure, denoted as \(T_{1}\), and the other is the dynamic restrictive access structure, denoted as \(T_{2}\), while the final access structure is \(T=T_{1}\)_and_\(T_{2}\). A detailed description will be given in Section 4.
## 3 Results
### ADME-Tox and Bioavailability Prediction  
Membrane permeability and distribution are essential pharmacokinetic parameters that influence the optimal bioactivity of compounds. SwissADME analysis revealed that the synthesized molecule **4d** molecule has a consensus LogPo/w value of 2.36, indicating a higher lipophilicity compared to the reference compound, Aspirin, which has a consensus LogPo/w value of 1.28. This suggests that the **4d** molecule may have improved membrane permeability and distribution characteristics relative to Aspirin, potentially enhancing its pharmacokinetic profile. Consequently, the synthesized molecule exhibits optimal aqueous solubility alongside efficient permeability through biological membranes, which suggests a strong potential for bioavailability. It is also estimated to have high gastrointestinal (GI) absorption and a robust binding affinity to plasma proteins, factors that together enhance its distribution profile and overall pharmacokinetic properties. Furthermore, the synthesized molecule does not inhibit the essential functions of liver enzymes, specifically the cytochrome P450 (CYP450) family. This characteristic suggests a reduced likelihood of metabolic interference and fewer adverse drug interactions, enhancing the molecule's  
Figure 5: Molecular interactions of the two best synthetized molecules and Aspirin within the active site of the BSA protein. (**A**) Aspirin, (**B**) **4d** and (**C**) **3c**.  
safety profile. In conclusion, the synthesized molecule is in accordance with [PERSON]'s rules, indicating its potential as a viable drug candidate. The findings from the ADME study are detailed in Table 4, providing insights into the absorption, distribution, metabolism and excretion characteristics of the synthesized molecule and Aspirin.  
Table 5 details the toxicity predictions for the molecule **4d** as evaluated by ProTox-II. The analysis encompasses various toxicity endpoints, including hepatotoxicity, neurotoxicity, cardiotoxicity, carcinogenicity and cytotoxicity. Additionally, it provides estimates for the LD\({}_{50}\) values and categorizes the overall class of toxicity, offering a comprehensive assessment of the molecule's safety profile. The findings reveal that the molecule **4d** does not demonstrate any of the toxicities mentioned previously. Additionally, it is categorized with a toxicity level of class 4, indicating a relatively low toxicity risk, and has an estimated lethal dose (LD\({}_{50}\)) of 690 mg/kg. This suggests a favorable safety profile for **4d**.
# Two-Stage Stochastic Programming for Precast Module Water Transportation: A Case Study in Hong Kong
###### Abstract  
Transporting precast modules via water is a vital component of multimodal transportation systems, increasingly utilized in large-scale Modular integrated Construction (MiC) projects where modules are prefabricated in remote factories. The effectiveness of module transportation planning significantly impacts the overall costs and productivity of MiC projects. However, existing studies on module transportation planning neglect the uncertainty inherent in MiC projects, thereby resulting in increased costs. This study proposes a two-stage stochastic programming model to optimize transportation planning through water, addressing this uncertainty. A real Hong Kong case study with 418 modules is employed to assess the effectiveness of the proposed model in comparison with three deterministic models. The optimal transportation plan of modules solved by the proposed model costs HKD 148,951, comprising 21% from temporary rentals and 79% from advance bookings. The results show that the three deterministic models, without considering the uncertainty in module demand, will incur additional transportation costs that are 25% higher on average than the results of the developed two-stage stochastic model. Additionally, this paper conducts a sensitivity analysis on the price ratio of pre-booked barges to on-demand barges to evaluate its impact on total transportation costs. The two-stage programming model developed in this paper can effectively help transport planners reduce the costs associated with module water transportation.  
modular integrated construction; precast modules; water transportation planning; stochastic programming; demand uncertainty
## 3 Methodology  
This section provides a detailed explanation of the proposed method for addressing the gaps identified in Section 1.  
The first step toward achieving autonomous excavation is to accurately identify the surrounding environment. In this study, a depth camera is strategically positioned to monitor the excavation area. The camera captures and reports depth information. However, as with any sensor, the cameras are prone to errors, occasionally producing invalid data. Consequently, it is necessary to filter out such data to generate precise and reliable information.  
Raw data from the camera often contain invalid pixels and outliers, necessitating refinement. To address this, the pixelated data are converted into analytical representations. This approach provides two key advantages: enabling data interpolation and reconstruction of soil surface. Detailed discussions of these processes are provided in Sections 3.3 and 3.4, respectively. Section 3.5 includes the marching cubes approach used for soil volume estimation.  
The proposed methodology introduces several enhancements aimed at increasing the accuracy of construction site modeling, particularly by refining raw sensor data and enabling detailed surface reconstruction. A key improvement involves filtering and transforming depth camera outputs, which are often noisy and contain invalid or missing pixels. By converting these raw, pixelated measurements into analytical representations using curve approximation techniques, the method allows for continuous interpolation across the excavation area. This process ensures that even unsampled points between pixels canbe accurately estimated, significantly enhancing the resolution and completeness of the surface model.  
To further improve modeling fidelity, the methodology emphasizes continuity in the interpolated data. Soil surfaces are naturally smooth and lack abrupt geometric changes; therefore, the algorithm imposes \(C^{0}\) and \(c^{1}\) continuity constraints on the fitted splines. Each image row is treated as a signal and approximated using third-degree polynomial splines, with coefficients optimized through the Levenberg-Marquardt algorithm. This eliminates abrupt transitions and outliers, producing a coherent and realistic representation of the terrain that is better suited for autonomous excavation tasks.  
Additionally, the approach extends beyond interpolation to construct a complete and hole-free 3D surface. Using the analytical curves, the surface is densely reconstructed by connecting interpolated points into quads, preserving the spatial relationships captured in the depth image. The marching cubes algorithm is then applied to this surface for accurate volume estimation, leveraging precomputed templates to ensure both efficiency and consistency. This deterministic pipeline, free from random optimization processes, ensures repeatable and high-fidelity modeling results, effectively addressing the challenges of data noise, continuity, and volumetric precision in excavation environments.
# A Review on Upper Limb Rehabilitation Robots
## EMG-Driven Exoskeleton Robots
### NEUROExos Based on the Proportional Control Method  
NEUROBOTICS Elbow Exoskeleton (NEUROExos) is also an EMG-driven rehabilitation robot that uses the principal of proportional control method as a mechanism of control [14], where the linear envelope (LE) is extracted from the processed EMG by means of a full wave rectifier and low pass filtering, specifically the Butterworth filter [66]. The LE represents the waveforms of the muscle tension during the force dynamic change [72].  
NEUROExos uses two EMG signals extracted from the biceps and triceps; both signals are amplified gradually until the user feels comfortable with the provided support [66]. NEUROExos employs the neuro-fuzzy modifier to help the user obtain the required motion in the minimum time [65].
## 4 Results and Discussion
#### 4.1.1 Subjective Evaluation Value  
The results of the subjective evaluation value corresponding to the drowsiness levels and the thermal comfort sensation vote of subjects under conditions A, B, and C are shown in Figures 4 and 5. We used multiple comparison based on the Bonferroni method to investigate significant differences of the subjective evaluation value due to changes in the indoor ambient temperature. There was a small change in the comfort sensation vote, but the drowsiness level increased under condition A, where the indoor temperature was maintained at 27 \({}^{\circ}\)C. Under condition B, the comfort sensation vote decreased to an uncomfortable state when the indoor ambient temperature dropped, and increased to a comfortable state when the indoor ambient temperature increased. Under condition C, the comfort sensation vote decreased to an uncomfortable state when the indoor temperature decreased and was then maintained in this uncomfortable state. Under conditions B and C, the drowsiness levels decreased and were maintained at an alert state, corresponding to the drop in the indoor temperature. After the completion of set 4, the comfort sensation vote increased, corresponding to the increase in the indoor temperature under condition B, but was maintained at an uncomfortable state under condition C. The drowsiness level was maintained at a low state under both conditions. According to this result, the arousal level was maintained at a high state even when the temperature increased, and subjects felt comfortable even when the temperature dropped, leading to an increased arousal level. This suggests that there is not always a dependence relationship between the arousal levels and feelings of thermal comfort. Figures 4 and 5 show that when condition B is applied in sets 6 and 7, it becomes possible to improve both the arousal level and thermal comfort of occupants by changing the indoor ambient temperature. These results show that the hypothesis that thermal stimulation due to cooling can improve the arousal level of occupants, after which thermal comfort can also be improved while maintaining a high arousal levels due to the removal of thermal stimulation was verified for condition B. Figure 4 shows the result of the thermal comfort sensation vote. Figure 5 shows the result of the drowsiness level sensation vote.  
Figure 3: Field photos of the experimental scene.
# Semantics-Preserving RDB2 RDF Data Transformation Using Hierarchical Direct Mapping
## 6 Conclusions  
The paper focuses on the problem of the existing direct-mapping methods that they do not fully support mapping-integrity constraints. The problems are observed in specific cases in which semantic information loss or incorrect RDF data generation occurred. In this paper, the improved definition of semantics preservation is provided to solve the problems and augment the RDB2 RDF mapping methods.  
Three lemmas are defined to describe the features of semantic resources during the mapping process. Lemma 1 stated that each semantic resource could be inferred from an OWL class (i.e., semantic resource). Lemma 2 stated that each semantic resource referencing other resources could be typed by an OWL object property (i.e., referential relationship). Lemma 3 stated that each subgraph having the same semantic resource as a root node could be merged into a single graph (i.e., union of semantic resources). A hierarchical structured semantic vocabulary was also defined for use in direct-mapping rules.  
Figure 21: Completeness of RDB2 RDF data transformation methods.  
Figure 20: Soundness of RDB2 RDF data transformation methods.  
Rule sets are defined based on the lemmas to transform relational tables and attributes. The mapping rules comprised general- and constraint-mapping rules. The general-mapping rules are used for mapping relations, attributes, and other general relational objects and are defined to avoid semantic information loss during the transformation of general relational objects. Constraint mapping rules are used for mapping the integrity constraints and are defined to reduce the volume of incorrect RDF data generated.  
Finally, the semantics-preserving direct-mapping method was implemented, and a comparative experimental study was performed with both synthetic and real datasets. The experiments demonstrated that the proposed mapping method performs semantics-preserving RDB2 RDF transformation and generates semantically accurate results. In the future, we will study the methods of synchronization to achieve RDF data consistency when the original relational data are modified [38]. We will also build a cost-benefit model that reduces the number of repetitive processes.  
[PERSON] conceive the problem and implemented the algorithm and performed the experiments; [PERSON] supervised the whole research work and revised the algorithm and the theorems; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) support program (IITP-2020-2018-08-01417) supervised by the Institute for Information & communications Technology Promotion (IITP).  
The authors declare no conflict of interest.
## 4 Experiments
### Ablation Study  
We conducted ablation experiments on various components of the CARLA32-128 dataset. Since TSE-UNet is based on UNet, we gradually added the key components used in this method to the original UNet network, ultimately forming the TSE-UNet model. The experimental results are shown in Table 4. The original UNet achieved a PSNR of 25.11 and an SSIM of \(30.99\times 10^{-5}\); when combined with temporal information (Variant 1 model in the table), PSNR improved to 26.97, while SSIM decreased to \(11.89\times 10^{-5}\). Furthermore, after applying the temporal feature attention module (Variant 2 model in the table), PSNR improved to 27.38, while SSIM decreased to \(9.16\times 10^{-5}\); when applying the spatial feature attention module, the model expanded to TSE-UNet, with a PSNR of 27.52 and an SSIM of \(8.38\times 10^{-5}\), which are improved by 2.41 and reduced by \(22.61\times 10^{-5}\), respectively, compared to the original UNet.
# Coupled Mode Sound Propagation in Inhomogeneous Stratified Waveguides
## Appendix B Energy Conservation  
Let us start with the energy conservation property of the boundary value problem of Helmholtz equation Equation (1). Supposing that \(p\) and \(p^{*}\) are two solutions, we have  
\[\frac{\partial}{\partial x}\left[\frac{1}{\rho}\frac{\partial p}{\partial x} \right]+\frac{\partial}{\partial z}\left[\frac{1}{\rho}\frac{\partial p}{ \partial z}\right]+\frac{\omega^{2}}{\rho c}p=0, \tag{10}\]  
\[\frac{\partial}{\partial x}\left[\frac{1}{\rho}\frac{\partial p^{*}}{\partial x }\right]+\frac{\partial}{\partial z}\left[\frac{1}{\rho}\frac{\partial p^{*}}{ \partial z}\right]+\frac{\omega^{2}}{\rho c}p^{*}=0, \tag{11}\]  
By applying the operator \(\int_{0}^{h_{j}(x)}(\)Equation (10)\(\times p^{*}-\) Equation (11)\(\times p)dz\), one can obtain  
\[\int_{0}^{h_{j}(x)}\left[\frac{\partial}{\partial x}\left(p^{*}\frac{1}{\rho} \frac{\partial p}{\partial x}-p\frac{1}{\rho}\frac{\partial p^{*}}{\partial x }\right)+\frac{\partial}{\partial z}\left(p^{*}\frac{1}{\rho}\frac{\partial p }{\partial z}-p\frac{1}{\rho}\frac{\partial p^{*}}{\partial z}\right)\right] dz=0. \tag{12}\]  
Equation (12) can be reduced to  
\[\frac{\partial}{\partial x}\int_{0}^{h_{j}(x)}\left(p^{*}\frac{1}{\rho}\frac{ \partial p}{\partial x}-p\frac{1}{\rho}\frac{\partial p^{*}}{\partial x} \right)dz+\sum_{j=0}^{J-1}\left[p^{*}\frac{1}{\rho_{j+1}}\frac{\partial p}{ \partial n}-p\frac{1}{\rho_{j+1}}\frac{\partial p^{*}}{\partial n}\right]_{h_ {j}^{+}(x)}^{h_{j+}^{-}(x)}=0, \tag{13}\]  
where the second term is equal to zero accounting for the interface and boundary conditions, and the first term corresponds to the conservation of energy flux \(E(x)\),  
\[\frac{\partial}{\partial x}\int_{0}^{h_{j}(x)}\left(p^{*}\frac{1}{\rho}\frac{ \partial p}{\partial x}-p\frac{1}{\rho}\frac{\partial p^{*}}{\partial x} \right)dz\propto E^{\prime}(x)=\frac{d}{dx}\left(\frac{1}{2\omega}\text{Im} \int_{0}^{h_{j}(x)}\frac{1}{\rho}p^{*}\frac{\partial p}{\partial x}dz\right)=0. \tag{14}\]  
Then, translating Equation (14) on our modal amplitudes, we have  
\[\frac{\partial}{\partial x}\left[\mathbf{p}^{\dagger}\mathbf{S}-\mathbf{S}^{ \dagger}\mathbf{P}\right]\propto\frac{d}{dx}(\mathbf{P}^{\dagger}\mathbf{S})=0. \tag{15}\]  
The modal amplitudes satisfy the coupled mode equations Equation (22) with \((-\mathbf{A}^{-1}\mathbf{C})^{\mathrm{T}}+\mathbf{C}^{\mathrm{T}}\mathbf{A}^{- 1}=0\), \(\mathbf{A}^{-1}=(\mathbf{A}^{-1})^{\mathrm{T}}\), and \(\mathbf{L}-\mathbf{K}=(\mathbf{L}-\mathbf{K})^{\mathrm{T}}\), meaning that our numerical scheme preserves the energy conservation property. It follows directly that without the introduction of interface matrices \(\mathbf{D}\) and \(\mathbf{E}\), the coupled mode equations will not conserve the energy among modes.