esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results
### Model Performance
#### 4.4.1 Baseline Model Performance  
The baseline model's performance using different features and models was evaluated in terms of WA and UA for both individual and joint models. The results are summarized in Table 3. The joint model consistently outperforms the baseline model, demonstrating higher WA, UA, and CCC values, indicating its effectiveness in capturing emotional information from speech signals.
# Role-Based Access Control Model for Inter-System Cross-Domain in Multi-Domain Environment
## 3 Proposed Model
### Formal Definition of Model Relationships  
\(\textbf{US}R_{d}\subseteq\textbf{U}_{d}\times\textbf{SR}_{d}\).denote a set of many-to-many relationships from users to specific roles. Formally:  
\[\forall(\textit{user}_{i},\textit{specific\_role}_{j})\in\textit{US}R_{d}\ ( \textit{user}_{i}\in\textit{U}_{d},\textit{specific\_role}_{j}\in SR_{d})\ (d=1,2,\ldots,l;i=1,2,\ldots,m;j=1,2,\ldots,n) \tag{7}\]  
\(\textbf{SR}P_{d}\subseteq\textbf{SR}_{d}\times\textbf{P}_{d}\).denote a set of many-to-many relationships from specific roles to permissions. Domain administrators need to assign permissions to specific roles according to the principle of least privilege [30] and the associated abstract roles. The user needs to access the object through specific roles that conforms to the principle of minimum authority, and cannot directly access the object by bypassing the specific role, nor can they directly assign the permission to the user. Formally:  
\[\forall(\textit{specific\_role},\textit{permission}_{j})\in\textit{SR}P_{d}\ ( \textit{specific\_role}_{i}\in SR_{d},\textit{permission}_{j}\in P_{d})\ (d=1,2,\ldots,l;i=1,2,\ldots,m;j=1,2,\ldots,n) \tag{8}\]  
\(\textbf{US}_{d}\subseteq\textbf{U}_{d}\times\textbf{S}_{d}\).denote a set of one-to-many relationships from a user to sessions. Formally:  
\[\forall(\textit{user},\textit{session}_{i})\in\textit{US}_{d}\ (\textit{user}\in\textit{U}_{d},\textit{session}_{i}\in S_{d})\ (d=1,2,\ldots,m;i=1,2,\ldots,n) \tag{9}\]  
\(\textbf{SR}R_{d}\subseteq\textbf{SR}_{d}\times\textbf{AR}\).denote a many-to-one relationship set from specific roles to an abstract role. A specific role can only be associated with one abstract role, and an abstract role can be associated with multiple specific roles. Formally:  
\[\forall(\textit{specific\_role}_{i},\textit{abstract\_role})\in\textit{SR}R_{d} \ (\textit{specific\_role}_{i}\in SR_{d},\textit{abstract\_role}\in AR)\ (d=1,2,\ldots,m;i=1,2,\ldots,n) \tag{10}\]  
\(\textbf{sr\_association}(\textit{specific\_role}\in\textbf{SR}_{d})\to AR\).denote a mapping from a specific role to an abstract role. Formally:  
\[\textit{sr\_association}(\textit{specific\_role}\in SR_{d})=\{\textit{abstract\_role}\in AR\ |\ (\textit{specific\_role},\textit{abstract\_role})\in SRR_{d}\} \tag{11}\]  
**Property 1.**_A specific role has one and only one associated abstract role._  
**Proof of Property 1.** The formal proof is as follows:  
Suppose,  
\[\forall\textit{specific\_role}_{1}\in SR_{1};\]  
\[\forall\textit{abstract\_role}_{1},\textit{abstract\_role}_{2}\in AR;\]  
If,  
\[\textit{sr\_association}(\textit{specific\_role}_{1})=\textit{abstract\_role}_{1};\]  
\[\textit{sr\_association}(\textit{specific\_role}_{1})=\textit{abstract\_role}_{2};\]  
Because,  
\[\textit{sr\_association}(\textit{specific\_role}\in SR_{d})\to AR;\]  
So,  
\[\textit{abstract\_role}_{1}=\textit{abstract\_role}_{2}.\]
# Robust Near-Optimal Aerocapture Guidance Method Based on Saturation Function
## 1 Introduction
To solve the above problems of parameter sensitivity and poor robustness by utilizing the saturation function's characteristics, this paper presents a numerical predictor-corrector guidance method based on the saturation function profile, which can basically simulate the bang-bang control structure while reducing the dependence on an algorithm on the relevant parameters, so as to enhance the algorithm's robustness. Through Monte Carlo simulation and comparison with existing advanced algorithms, the performance of the proposed algorithm is verified. The following paper is organized as follows. In Section 2, the 3 degree-of-freedom (DOF) dynamic model during atmospheric flight is presented, and the impulsive Orbital Maneuvers, including single-impulse aerocapture maneuver and the two-impulse aerocapture maneuvers, are described briefly. In Section 3, based on the decoupling idea of longitudinal and lateral channels, the longitudinal and lateral guidance laws are introduced respectively, and the complete numerical predictor-corrector guidance's architecture is given. In Section 4, Monte Carlo simulation and parameter influence analysis are carried out in both the Earth and Mars aerocapture scenarios to prove the robustness and efficiency of the proposed method. Finally, conclusions are drawn in Section 5.
## 2 Fundamental Principles of Quasi-Zero Stiffness
### Basic Types of QZS Vibration Isolators
#### 2.2.4 Cam-Roller as Negative Stiffness Mechanism  
The vibration isolation system, employing either a pair of oblique springs or a spring-rod mechanism as a negative stiffness component, demonstrates high-static but low-dynamic-stiffness characteristics, often exhibiting a nonlinear jump phenomenon under light system damping and large excitation amplitudes. The jump phenomenon can be mitigated by adjusting the end trajectories of these springs or rods. To address this issue, [PERSON] [46] introduced a vibration isolation system featuring a cam-roller-spring-rod mechanism, along with detailed numerical and experimental studies examining the mechanism's impact on vibration isolation performance. [PERSON] [47] introduced a QZS isolator featuring a cam-based negative-stiffness mechanism. The cam utilizes a user-defined noncircular profile to generate negative stiffness, offsetting the positive stiffness of the vertical spring and achieving QZS behavior near the equilibrium position. In contrast to earlier studies, the proposed QZS isolator offers a significant advantage: the desired cubic restoring force can be directly achieved through a meticulously crafted cam profile within the negative-stiffness mechanism, accounting for friction during the model design. This approach sidesteps the approximation errors inherent in theoretical design based solely on Taylor expansions and assumptions neglecting friction. [PERSON] [48] introduced a novel design for a high-static, low-dynamic-stiffness (HSLDS) isolator featuring an adjustable cam profile. This isolator utilizes the interaction force between the cam and roller to provide negative stiffness, while a linear spring contributes positive stiffness within the HSLDS system. The various structures of the cam-roller as negative stiffness mechanisms proposed by these studies are illustrated in Figure 8.  
Figure 7: Schematic diagram of [PERSON]’s [45] proposed high-static low-dynamic-stiffness vibration isolator.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 9 Discussion
### Performance Comparison
#### 9.3.4 Economic Considerations and Practical Implications  
Both wireless networks explored in this study demonstrate distinct advantages depending on the specific operational context of the water station. To facilitate a clear understanding of these differences, Table 16 qualitatively summarizes key practical factors such as initial investment, scalability, complexity of installation, maintenance requirements, and long-term reliability for the (IEEE 802.11) and Trusted Wireless configurations. This comparison highlights essential considerations for effectively selecting the most suitable technology for robust, long-range water distribution network automation.
## 3 Materials and Methods
### SPME-GC/MS of Volatile Compounds  
SPME-GC/MS was used for sampling and analysis of the volatile compounds in powdered green and red perilla leaves, according to [PERSON] et al. [69] with slight modifications. A SPME holder containing a fused-silica fibre coated with a 50/30 \(\upmu\)m layer of divinyl-benzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS; Supelco, Bellefonte, PA, USA) was used for the absorption of the volatile compounds. Two grams of freeze-dried powdered leaves were weighed and immediately introduced into a 20-mL headspace vial. The SPME fibre was exposed in the head space of the vial for 20 min at 45 \({}^{\circ}\)C and then introduced directly into the GC injector where the thermal desorption of the analytes was performed at 250 \({}^{\circ}\)C for 5 min. An Agilent 6890N GC system equipped with a 5973 mass detector (Agilent Technologies, Santa Clara, CA, USA) were used.  
The analytes were separated on a 30 m \(\times\) 0.250 mm capillary column coated with a 0.25 \(\upmu\)m film of 5% diphenyl195% dimethylpolysiloxane (HP5 MS J&W Scientific, Folsom, CA, USA) and were inserted directly into the ion source of the mass detector. Splitless injection was used for the samples. The column oven temperature was ramped at 10 \({}^{\circ}\)C min\({}^{-1}\) from an initial temperature of 50 (held for 2 min) to 150 \({}^{\circ}\)C, then at 15 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) to 300 \({}^{\circ}\)C, which was held for 10 min. The injection and ion source temperatures were 250 and 230 \({}^{\circ}\)C, respectively. Helium (99.999%) was used as carrier gas at a flow rate of 1 mL min\({}^{-1}\). The ionizing electron energy was 70 eV and the mass range scanned was 40-450 amu in full-scan acquisition mode. The volatile compounds were identified by comparing their retention indices and mass spectra with those found in the libraries NIST Atomic Spectra Database version 1.6 using a range of 85-100% similarity values. The relative amounts were calculated on the basis of peak-area ratios. Analyses of volatile compounds were also performed in triplicate. Representative SPME-GC/MS chromatogram of aroma volatile compounds of perilla leaves is reported in Figure S2.
# Scalable and Reliable Data Center Networks by Combining Source Routing and Automatic Labelling
## 5 Evaluation
### Test 1: Flow Routing Analysis  
In this first test, we used the VL2 topology (Figure 7) to study the routing results obtained by the implemented eTori application. In particular, we generated traffic according to the following traffic matrix: h1\(\rightarrow\)h16, h2\(\rightarrow\)h15, h3\(\rightarrow\)h14,..., h14\(\rightarrow\)h3, h15\(\rightarrow\)h2, h16\(\rightarrow\)h1, with random interarrival times. This matrix was designed to have flows that equally traverse the whole topology, i.e., all switches in the network.  
The results obtained from executing this scenario are shown in Figure 9, which shows the percentage of paths grouped by common source ToR for certain destination ToR switches. Ideally, for a balanced distribution of traffic, this percentage should be uniformly distributed for each existing path.  
For example, the green grouping in Figure 9 depicts the paths from ToR 1 to ToR 4. As four available paths exist between these two ToR switches, the traffic distribution should have a value around 25% per each of the possible routes, and we can see their values are indeed around 15% and 35% (with a deviation of 10%), which is quite close to the ideal scenario, particularly considering this values are measured only with 10 executions (a value that is not divisible by 4). A similar behaviour can be observed for the paths originating at ToR 2 and ToR 4, and only the ones with source in ToR 3 have a bigger deviation, but the actual percentage never surpasses the 50% of use in any case. This observation is because the different available paths (HLMACs) are randomly selected and the results depend on the quality of the random number generator.  
Therefore, the results illustrate how eTorii balances the load among the diverse available paths between any source and any destination, which will improve the overall performance of the DCN.  
Figure 8: Spine-Leaf topology (parameters 4 \(\times\) 4).  
Figure 7: VL2 topology (parameters 2 \(\times\) 4 \(\times\) 4).
## 3 Solar Arrays Pointing Algorithms
### Determination Algorithm  
The determination algorithm is dedicated to computing the Sun angle from the current measurements coming from the EPS, detecting the eclipse condition, and providing a good representation of the Sun angle time derivative. Concerning this last point, current measurements are affected by the signal and processing noise and, thus, the time derivative cannot be computed from the raw data. For this reason, the time derivative is practically substituted by a direct numerical comparison on the time-averaged value of the Sun angle at two different times:  
\[\alpha_{s}\leq 0\text{ if }\quad\bar{\alpha}_{s}(t_{0},\ldots,t_{-K+1}) \leq\bar{\alpha}_{s}(t_{-K},\ldots,t_{-2K+1}), \tag{16}\]  
where  
\[\bar{\alpha}_{s}(t_{0},\ldots,t_{-K+1}) =\frac{1}{K}\sum_{i=-K+1}^{0}\alpha_{s}(t_{i}) \tag{17}\] \[\bar{\alpha}_{s}(t_{-K},\ldots,t_{-2K+1}) =\frac{1}{K}\sum_{i=-2K+1}^{-K}\alpha_{s}(t_{i}). \tag{18}\]  
The time average of the Sun angle values is performed over the last two batches of \(K\) samples of the raw measurements, which are sampled with a fixed and constant time step. Good performances have been achieved with \(K\) equal to 100, which corresponds to the last sampling batches across 10 s, for and ADCS running at 10 Hz. The pseudo-code of the determination algorithm is reported in Algorithm 1, which is the first section of the operative state. It elaborates the current measurements preparing the input for the successive control section.  
```
Data:\(I_{Si}\) with \(i=1,\ldots,N\) Result: Determination algorithm \(\rightarrow\alpha_{s},\dot{\alpha}_{s},ecl\)  Equation (11) \(\rightarrow\alpha_{s}\);  Equation (12) \(\rightarrow\)\(ecl\); if\(\alpha_{s}\geq 90\deg\) AND \(ecl=0\)then \(\alpha_{s}=180\deg\) ; // Sun is not in front the solar arrays for\(i\gets 0\) to \(-2K+1\)by \(-1\)do  Equations (17) and (18) \(\rightarrow\bar{\alpha}_{s}(t_{0},\ldots,t_{-K+1})\) and \(\bar{\alpha}_{s}(t_{-K},\ldots,t_{-2K+1})\); if\(\bar{\alpha}_{s}(t_{0},\ldots,t_{-K+1})\leq\bar{\alpha}_{s}(t_{-K},\ldots,t_{-2K+1})\)then \(\dot{\alpha}_{s}\leq 0\); else \(\dot{\alpha}_{s}>0\);
```  
**Algorithm 1**Determination Algorithm.
# DCO-MAC: A Hybrid MAC Protocol for Data Collection in Underwater Acoustic Sensor Networks
## 4 Proposed DCO-MAC Protocol
### CT-MAC Protocol
When level-2 node with data packet(s) to be transmitted receives an \(x\)RTS or \(x\)NOTICE packet, it sets the QUIET_1 timer, and enters the QUIET_1 state. The duration of QUIET_1 timer is \(T_{\text{QUIET\_1\_timer}}=3\tau_{\text{max}}\). When the QUIET_1 timer expires, the level-2 node goes back to the IDLE state. When the level-2 with data packet(s) to be transmitted receives \(x\)INVITE or \(x\)ORDER packet, it sets the QUIET_2 timer, and enters the QUIET_2 state. The duration of the QUIET_2 timer is \(T_{\text{QUIET\_2\_timer}}=(2N_{\text{neighbor}}+2)\tau_{\text{max}}\). When the \(x\)ACK packet is received or the QUIET_2 timer expires, the level-2 node goes back to the IDLE state. Figure 7: The state transition diagram of a level-2 node in CT-MAC protocol. In the DCO-MAC protocol, the transmission of the level-1 sub-network and level-2 sub-network is performed alternatively. In a data collection period, the CT-MAC protocol is first carried out in the level-2 sub-networks, the level-1 nodes broadcast a short message to the level-2 nodes to inform them about the start of the level-2 transmission phase, and the sink is in the IDLE state. In each level-2 sub-network, the \(N_{\text{trans}}\) times data collection are performed during the data transmission phase of the level-2 sub-network. After the \(N_{\text{trans}}\) times data collection is finished, the corresponding level-1 node sends a short message to the sink and waits for the response message. If the response message is not received, the level-1 node sends the short message again, until it receives the response message. After receiving the short messages from all the level-1 nodes, the sink broadcasts an RTR packet to the level-1 nodes, which means that the procedure of the RSV-MAC protocol starts. During a data collection period, the RSV-MAC protocol is carried out at the same time. In the end of the data transmission of the level-1 sub-network, the level-1 nodes announce a short message to inform the new data collection period. In this paper, we assume that there is only one mobile node in each sub-network. However, the proposed MAC protocol can be extended to the network with one more mobile node. If several mobile nodes exist in the network, their transmission order should be set before their deployment. Then, the sink or level-1 node reserves \(2\tau_{\text{max}}\) time for each mobile node to receive the ATS or NOTICE packet. After receiving the RTR or INVITE packets, the mobile node, \(I\), waits \(2\tau_{\text{max}}(O_{\text{mobile},i}-1)\) and sends an ATS or NOTICE packet, where \(O_{\text{mobile},i}\) is the transmission order of the mobile node, \(i\). The sink or level-1 node calculates the propagation delay of the mobile nodes after receiving the ATS or NOTICE packet.
## 4 Experimental Setup
### Comparison with SOTA Methods  
The proposed model exhibits superior performance compared to existing state-of-the-art (SOTA) methods across all datasets, as evidenced by the results presented in Tables 1 and 2. In the Hephaestus and xView3-SAR datasets, our model achieved higher accuracy, recall, F1-score, and AUC, indicating a strong advantage in both detection and classification tasks. For example, on the Hephaestus dataset, our model reached an accuracy of 93.12% with an AUC of 92.63%, outperforming the closest competitor, InceptionTime, by a margin of nearly 2%. This improvement underscores the effectiveness of our model's architecture, which integrates both temporal and spatial features through attention mechanisms, enabling it to capture intricate details in SAR images that are challenging for conventional models like LSTM and GRU. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Hephaestus Dataset**} & \multicolumn{4}{c}{**xView3-SAR Dataset**} \\ \cline{2-9}  & **Accuracy** & **Recall** & **F1-Score** & **AUC** & **Accuracy** & **Recall** & **F1-Score** & **AUC** \\ \hline LSTM [40] & 87.35 \(\pm\) 0.03 & 85.29 \(\pm\) 0.02 & 83.47 \(\pm\) 0.02 & 88.92 \(\pm\) 0.03 & 86.54 \(\pm\) 0.02 & 83.10 \(\pm\) 0.02 & 82.24 \(\pm\) 0.02 & 87.63 \(\pm\) 0.03 \\ GRU [41] & 89.12 \(\pm\) 0.02 & 87.30 \(\pm\) 0.02 & 86.19 \(\pm\) 0.02 & 89.45 \(\pm\) 0.03 & 87.88 \(\pm\) 0.03 & 85.12 \(\pm\) 0.02 & 83.75 \(\pm\) 0.02 & 88.15 \(\pm\) 0.02 \\ Transformer [42] & 90.58 \(\pm\) 0.03 & 88.91 \(\pm\) 0.02 & 87.43 \(\pm\) 0.02 & 90.31 \(\pm\) 0.03 & 89.71 \(\pm\) 0.03 & 86.84 \(\pm\) 0.02 & 85.64 \(\pm\) 0.02 & 89.42 \(\pm\) 0.03 \\ TCN [43] & 88.62 \(\pm\) 0.02 & 86.77 \(\pm\) 0.02 & 85.39 \(\pm\) 0.02 & 88.74 \(\pm\) 0.03 & 88.71 \(\pm\) 0.02 & 85.45 \(\pm\) 0.02 & 84.29 \(\pm\) 0.02 & 88.10 \(\pm\) 0.03 \\ InceptionTime [44] & 91.34 \(\pm\) 0.03 & 89.23 \(\pm\) 0.02 & 88.47 \(\pm\) 0.02 & 90.82 \(\pm\) 0.03 & 90.03 \(\pm\) 0.03 & 87.58 \(\pm\) 0.02 & 86.31 \(\pm\) 0.02 & 89.75 \(\pm\) 0.02 \\ ResNet [45] & 89.77 \(\pm\) 0.02 & 88.12 \(\pm\) 0.02 & 86.85 \(\pm\) 0.02 & 89.33 \(\pm\) 0.03 & 89.34 \(\pm\) 0.02 & 86.91 \(\pm\) 0.02 & 85.62 \(\pm\) 0.02 & 89.02 \(\pm\) 0.03 \\ Ours (Proposed Model) & **93.12 \(\pm\) 0.02** & **91.45 \(\pm\) 0.02** & **90.17 \(\pm\) 0.02** & **92.63 \(\pm\) 0.03** & **92.48 \(\pm\) 0.03** & **90.34 \(\pm\) 0.02** & **89.02 \(\pm\) 0.02** & **91.27 \(\pm\) 0.02** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of proposed model with SOTA methods on Hephaestus and xView3-SAR datasets for time-series prediction. Bold indicates the best value.
# A Coupling System for Prediction of Physiological Parameters in an Immersed Condition
## 3 Results
### Local Skin Temperature  
We compared the experimental test results when wearing the two life preservers of LP1 and LP2 with the local skin temperature prediction results of the thermal manikin and the human thermal model coupling system (see Figure 8a-f). There is variation in the coupling system's prediction for several body parts in both air exposure and water immersion situations. While some of the predictions for the other locations, particularly for the thigh and leg (\(p<0.05\)), were beyond the range of SD of the measured values, the mean predicted values for the chest and back were within SD of the measured values. When wearing LP1, the expected deviation values for the chest, thigh, and leg did not surpass 1 \({}^{\circ}\)C; when wearing LP2, they were 1.04 \({}^{\circ}\)C, 1.16 \({}^{\circ}\)C, and 1.27 \({}^{\circ}\)C, respectively, and did not exceed 1 \({}^{\circ}\)C, with the exception of the maximum predicted deviation value of 1.13 \({}^{\circ}\)C for the thigh. Furthermore, when wearing LP1, the RMSD was less than SD for the chest, upper arm, and arm, and when wearing LP2, it was less than SD for all locations. In conclusion, the coupling model could not accurately anticipate changes in local skin temperature, possibly as a result of the thermal manikin's constraints. However, compared to [PERSON]'s coupling system [21], the coupled model's predicted values were still within an acceptable limit.  
Figure 7: Predicted results for mean skin temperatures and measured data.
# Real-Time 3D Reconstruction Method for Holographic Telepresence
## 2 Real-Time 3D Reconstruction Method  
### Data Acquisition  
Microsoft Kinect V2 has been utilized to capture the local user in the data acquisition process. Data captured by depth sensors can be noisy and needs to be pre-processed to recover the missing depth values at some defective areas in the depth map. The depth holes can be filled by using a hole-filling algorithm [21], after which the depth map will be smooth, using a bilateral filtering algorithm [22]:  
\[D(p(x,y))=\frac{1}{w_{n}}\sum_{l=0}^{m-1}w(x_{l},y_{l})\times D_{l} \tag{1}\]  
The depth information of the depth hole in the depth map can be filled using the hole-filling formula shown with \(m\) be a constant, where \(p(x,y)\) will be the filling target point, \(D_{l}\) presents the \(l^{th}\) depth value, \(w_{n}\) denotes the normalization parameter, and the \(w(x_{l},y_{l})\) is the weight coefficient. The hole in depth map will be filled through this hole-filling processing step. After this process, the traditional bilateral filtering algorithm is used to smooth the depth map with holes that have been filled.  
Figure 2: Diagram flow for the 3D reconstruction method.  
Since the RGB-D cameras have been utilized, the instrument parameters for a general colour-depth sensor for 3D reconstruction are the distance difference and depth resolution. Distances between the reconstructed target points were compared to the ground truth measurements. Using Kinect, the object was then captured from two different distances to get measurements between 0.7 m to 2 m. RGB-D cameras and the corresponding depth used for scene reconstruction, then depth resolution was measured by moving the Kinect away (0.5-15 m) from a planar target in sufficiently fine steps to record all values returned in an approximately 5\({}^{\circ}\) view field around the image centre. Considering the depth resolution, when the depth image has been constructed by triangulation from the IR image, the depth resolution for triangulation-based devices such as the Kinect is expected [23]. However, this paper is limited to explaining the marching cube algorithm for 3D reconstruction, therefore it does not provide the evaluation to count on instrument parameter for its accuracy.