esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Underground Probe Design
#### 2.4.2 Varactor Selection  
Varactor diodes are utilized to create voltage-controlled variable capacitance in Radio Frequency circuits, providing cost-effectiveness and reliability advantages. The equivalent model of the SMV1801 varactor diode is illustrated in Figure 16, where \(C\) represents the capacitance of the varactor diode junction. Additionally, \(R\) and \(L_{s}\) denote the parasitic series resistance and inductance of the diode, respectively, while \(C_{p}\) represents the parasitic parallel capacitance [14; 23; 24; 25].  
The total capacitance of the varactor diode, \(C_{v}(V_{d})\), is expressed as follows:  
\[C_{v}(V_{d})=\frac{C_{j}}{\left(1+\frac{V_{r}}{V_{j}}\right)^{M}} \tag{8}\]  
where \(V_{d}\) is the applied reverse DC voltage, \(V_{r}\) is the zero-bias junction capacitance, \(V_{j}\) is the junction potential, and \(M\) is the grading coefficient. The parameters of the SMV1801 varactor diode model are presented in Table 5 below.  
Figure 17 depicts the equivalent circuit model of the varactor diode.  
Figure 18 illustrates the behavior of the SMV1801 varactor diode under reverse DC bias. The varactor capacitance, \(C_{v}\), varied from 0.4 pF to 85 pF, as the reverse voltage, \(V_{d}\), ranged from 0 to 30 V.  
Figure 15: Simulated reflection coefficient for different soil permittivities \(\varepsilon_{r}\).  
Figure 16: Reflection coefficient as a function of \(V_{r}\) for different dielectric permittivities \(\varepsilon_{r}\).  
Figure 17: Equivalent model of the varactor diode.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Description** & **Value** & **Unit** \\ \hline \(C_{j}\) & Zero-bias junction capacitor & 85 & pF \\ \(V_{j}\) & Junction potential & 10 & V \\ \(M\) & Grading coefficient & 4.4 & - \\ \(R_{s}\) & Series resistance & 1.1 & \(\Omega\) \\ \(L_{s}\) & Series inductance & 0.8 & nH \\ \(C_{p}\) & Parallel capacitance & 2.6 & pF \\ \hline \end{tabular}
\end{table}
Table 5: Model parameters of the SMV1801 varactor diode.  
Figure 18: The capacitance of the SMV1801 varactor diode.
## 4 Production of Plant Secondary Metabolites Using Microbial Chassis
### Pathway Establishment and Optimization
By optimizing the balance of these three strains and the composition of the culture medium, the rosmarinic acid content was increased from 4.5 mg/L in a mono-culture to 172 mg/L [199]. Sometimes, the low substrate specificity of upstream enzymes may lead to unwanted conversions into downstream products [194]. In such cases, enzyme engineering can be used to modify the enzymes, or the pathway can be split and expressed in different strains. [PERSON] et al. [224] separated the genistein synthesis pathways and placed them in _E. coli_ and yeast, achieving a higher production of 100 mg/L when mixed. Similar to plant chassis, microbial heterologous synthesis faces challenges in compound transport, such as precursor uptake, material exchange between subcellular compartments, intermediate loss, and feedback inhibition due to insufficient final product secretion [225]. [PERSON] et al. [190] discovered that overexpressing OmpF, among the ten native transport proteins screened, significantly increased the production of the stilbene resveratrol, indicating that the screening of native transport proteins can be valuable, even though most of them may not be conducive to improving yield or cell growth when overexpressed. Anthocyanins are often stored in the central vacuole in plants, where the pH is lower than in the cytoplasm, which is more favorable for the stability of the products. Lowering the pH of the culture medium and maximizing the secretion of anthocyanins into the extracellular space can boost production. In a study that aimed to synthesize anthocyanin in _E. coli_, it was found that simultaneously knocking out the catechin efflux transporter tolC and overexpressing the product C3G efflux transporter yadH significantly promoted product synthesis, with tolC knockout having a greater impact [195]. Alkaloids, due to their high pKa, carry a positive charge during microbial fermentation, making them unable to cross the membrane by diffusion. Overexpression a MATE transporter protein, AtDTX1, from _Arabidopsis_ in the yeast cell membrane, increases the secretion of (S)-reticuline, an intermediate in benzylisoquinoline alkaloids (BIAs) biosynthesis, and enhances its production 11-fold [226]. [PERSON] et al. [227] systematically screened and functionally identified a series of active transport proteins, BIA uptake permeases (BUPs), in opium poppy, which are involved in the intracellular transport of BIAs. Combining AtDTX1 and BUPs to regulate intermediate transport may make strategies involving the co-culture of different strains more promising for BIA production [201].
# Learning State-Specific Action Masks for Reinforcement Learning
## 4 Approach
### A Perfect Mask Model  
The main contribution of this work is to learn a mask model \(\textit{mask}_{\phi}(I_{t}|s_{t})\) with two objectives.  
Firstly, the action masks \(I_{t}\sim\textit{mask}_{\phi}(I_{t}|s_{t})\) should filter out all invalid actions that do not influence state \(s\), e.g., the \"up\" action when an agent in Maze goes to the top-right corner. Formally, given state \(s\), an invalid action \(a^{i}\) is the action that leads to \(p(s|s,a^{i})=1\). Therefore, the perfect mask \(I_{t}\sim\textit{mask}_{\phi}(I_{t}|s_{t})\) should satisfy:  
\[I_{t}^{i}=0\quad if\quad p(s_{t}|s_{t},a^{i})=1 \tag{8}\]  
where \(\epsilon\) is a small value.  
Secondly, the action masks should aggregate actions with the same behavioral consequences and only reserve actions distinct from each other. We use soft BRAS to illustrate this relation. Formally, given state \(s\), the mask model \(\textit{mask}_{\phi}(I_{t}|s_{t})\) should satisfy:  
\[\begin{split}&\sum_{i\in\mathcal{I}}\textit{mask}_{\phi}(I_{t}^{i }|s_{t})=1\wedge\prod_{i\in\mathcal{I}}\textit{mask}_{\phi}(I_{t}^{i}|s_{t})= 0,\\ &\mathcal{I}=\textit{index}(A_{B_{s}}),\forall A_{B_{s}}\in\hat{ \mathcal{A}}_{B_{s}}\end{split} \tag{9}\]  
where \(\textit{index}(\cdot)\) is the index of all actions in the action space.  
Different from the mask model commonly used in many computer vision (CV) works [20; 21], the mask \(I\) on action space for RL serves a more intricate role than a simple attention layer. In prior research, [PERSON] et al. [17] delved into the impact of action masks on the learning process through extensive experiments on \(\mu\)RTS2. Their findings revealed that even if an RL agent samples actions within a masked action space but updates the policy using the original gradients, it still experiences accelerated exploration. This suggests that the primary role of an action mask is to prevent the agent from sampling invalid actions, necessitating a binary mask.  
Therefore, to ensure the mask model outputs a binary vector, we design the mask model as a categorical model, as illustrated in Figure 1.  
The mask model outputs real numbers of \(2|\mathcal{A}|\) dimensions indicating logits of the masks and \(I_{t}\) is sampled from them. This design allows the model to achieve an exact binary mask and a differentiable output simultaneously.
## 2 Dynamic Model of the Feed System
### Calculation of Restoring Force for Ball Screw  
To deduce the restoring force function at the screw-nut interface, the position relationship of the raceway centers of the screw and nut needs to be described, and then the transformation relationship between the different coordinate systems should be established. As shown in Figure 2, the global coordinate system \(\text{CS}_{0}(x,y,z)\) is fixed at the geometric center of the nut chassis; the coordinate system \(\text{CS}_{1}(x_{1},y_{1},z_{1})\) is located at the screw shaft centerline corresponding to the \(i\)th ball center, and its axes are parallel to the \(\text{CS}_{0}\) axes; the coordinate system \(\text{CS}_{2}(x_{2},y_{2},z_{2})\) is located at the \(i\)th ball center, and its \(y_{2}\) axis is perpendicular to the screw shaft centerline; the coordinate systems \(\text{CS}_{\text{W}}(x_{W},y_{W},z_{W})\) and \(\text{CS}_{5}(x_{5},y_{S},z_{5})\) are located at the geometric centers of the workable and the screw shaft for the screw-nut interface, respectively, and their three axes are parallel to the \(\text{CS}_{0}\) axes. The homogeneous coordinate transformation matrix \(\mathbf{T}_{1-2}\) from \(\text{CS}_{1}\) to \(\text{CS}_{2}\) can be expressed as:  
\[\mathbf{T}_{1-2}=\text{Trans}(R_{\text{snm}}\cos\theta_{\text{sni}},R_{\text{ snm}}\sin\theta_{\text{sni}},0)\cdot\text{Rot}_{2}\Big{(}\theta_{\text{sni}}+ \frac{\pi}{2}\Big{)}\cdot\text{Rot}_{y}(-\lambda) \tag{1}\]  
where \(R_{\text{snm}}\) is the pitch radius; \(\theta_{\text{sni}}\) is the \(i\)th ball azimuth angle; \(\lambda\) is the pitch angle, given as:  
\[\lambda=\tan^{-1}\bigg{(}\frac{L_{p}}{2\pi R_{\text{snm}}}\bigg{)} \tag{2}\]  
where \(L_{p}\) is the lead of ball screw.
###### Abstract
One type of ABE called ciphertext-policy attribute-based encryption [4] or CP-ABE is a technique in which the user's secret key is generated for their attributes and an access policy is coupled with ciphertext. Access policies are typically expressed by threshold gates or AND-OR gates over the attributes. Users receive their attribute keys in terms of secret keys issued by the attribute authorities. CP-ABE is a kind of public key encryption (PKE) technique. It provides access control for a large group of users because it allows decryption only when the user carries the attribute set that matches the policy. Credentials or attributes are issued to the users by various attribute authorities, which may be from single or multiple domains. Several ABE techniques for cloud-based data sharing are given [5; 6; 7; 8]. However, these schemes consider a single domain authority that manages all the attributes. In practical scenarios, users from multiple domains may request a download of the shared data and different administrating authorities may issue the attributes. Additionally, normal cipher-policy ABE schemes [4; 9] store the access policy with outsourced encrypted data, but in plain text. This may result in the disclosure of sensitive information regarding the encrypted data. Let us understand cloud-based data sharing with the help of a healthcare use case.
## 4 Results and Discussion
### Radiological Hazards Parameters
#### 4.3.2 External and Internal Hazard Indices (Hin and Hex)  
The significant health effect can be predicted by the Hex and Hin hazard indices. The guidance safety level of Hex and Hin data must be smaller than unity [47,48]. According to the \({}^{238}\)U, \({}^{232}\)Th, and \({}^{40}\)K activity concentrations in the investigated Wadi sediments, the values of Hex and Hin are computed using the relations (2) and (3) [47,48] and listed in Table 3:  
\[\text{H}_{\text{ex}}=\frac{A_{U}}{370}+\frac{A_{Th}}{259}+\frac{A_{K}}{4810} \ \leq\ 1 \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Country** & \({}^{238}\)**U** & \({}^{232}\)**Th** & \({}^{40}\)**K** & **References** \\ \hline Egypt & 62 & 84 & 949 & Present study \\ Egypt & 137 & 82 & 1082 & [33] \\ Nigeria & 63.29 & 226.67 & 832.59 & [34] \\ Saudi Arabia & 28.82 & 34.83 & 665.08 & [35] \\ Saudi Arabia & 11 & 22 & 641 & [36] \\ Palestine & 71 & 82 & 780 & [37] \\ Jordan & 41.52 & 58.42 & 897 & [38] \\ Oman & 17 & 18 & 379 & [39] \\ Iran & 77.4 & 44.5 & 1017.2 & [40] \\ Malaysia & 184 & 165 & 835 & [41] \\ India & 25.88 & 42.82 & 560.6 & [42] \\ China & 112 & 72 & 672 & [43] \\ Spain & 84 & 42 & 1138 & [44] \\ Greece & 74 & 85 & 881 & [45] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of \({}^{238}\)U, \({}^{232}\)Th and \({}^{40}\)K activity concentration in Wadi Rod Elsayalla area with numerous studies from around the world.  
\[\mathrm{H_{in}}=\frac{A_{U}}{185}+\frac{A_{Th}}{259}+\frac{A_{K}}{4810}\ \leq\ 1 \tag{3}\]  
Table 3 reveals the data of \(\mathrm{H_{ex}}\) altered from 0.3 to 1.2 with a mean value of 0.7, which is less than the approved level (\(\mathrm{H_{ex}}<\) 1). While the mean value of \(\mathrm{H_{in}}\) (0.9) was comparable with the approved level (\(\mathrm{H_{in}}<\) 1), the minimum and maximum values are 0.4 and 1.5, respectively. Figure 5 illustrates the high values of \(\mathrm{H_{ex}}\) and \(\mathrm{H_{in}}\) in the studied Wadi sediments, which were 3.2% and 18.9%, respectively, from all their registered values. This means the Wadi sediments in the studied area may contribute significantly to health effects associated with gamma radiation, radon gas, and its daughters [49].
# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 4 Model Architecture
### Deep Learning Experiments  
In this research, we implemented CNN and RNN models. There are many researchers [41; 42] who have experimented using a combination of RNN and CNN. They have clearly indicated that as compared to the machine learning models, this mixture is much more effective.
## 5 Numerical Examples
### An Example of a Large-Sized Experiment  
Table 3 shows the original and repositioned data of a large-sized experiment with \(N=50\) ships. The 1 st column shows the ship ID (SID); the 2 nd column shows the ship length; the 3 rd column shows the Estimated Time of Arrival of ship \(j\) (\(ETA_{j}\)); the 4 th column shows the desired berthing position of ship \(j\) (\(d_{j}\)); the 5 th column shows the original coordinates of ship \(j\) (\(X_{0}\), \(Y_{0}\), \(X_{1}\), \(Y_{1}\)); the 6 th column shows the repositioned coordinates of ship \(j\) (\(X_{0}\), \(Y_{0}\), \(X_{1}\), \(Y_{1}\)); the 7 th column shows the increased waiting time of ship \(j\) (\(c_{1}\Delta W_{j}\)); the 8 th column shows the increased handling time of ship \(j\) (\(c_{2}\Delta H_{j}\)). Figure 13 shows a berth plan that is unfeasible due to the overlapping of the ships, based on the original coordinates of ships. To derive a feasible solution, it is necessary to resolve all overlaps of ships. Figure 14 shows the feasible berth plan obtained from the ISFLA after resolving all overlaps among the ships. In Table 3, the bolded figures indicate updated coordinates for those target ships that were repositioned to avoid overlapping. Figure 14: The feasible berth plan after resolving all overlaps of ships (\(N=50\) ships). Figure 13: The infeasible berth plan before resolving all overlaps of ships (\(N=50\) ships). Table 4 shows the results obtained from different approaches with different problem sizes. The \(Z\) indicates the objective function value; the T indicates the computational times; the GAP indicates the gap in percentage compared to the ISFLA. The experimental results show that, on average, the ISFLA exhibits better performance in terms of solution quality. Figure 15 gives a graph with the average \(Z\)s obtained from different approaches under different problem sizes (\(N=10\), 20, 30, 40, 50 and 60). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**SID**} & \multirow{2}{*}{\(I_{j}\)} & \multirow{2}{*}{\(ETA_{j}\)} & \multirow{2}{*}{\(d_{j}\)} & \multicolumn{4}{c}{**Original**} & \multicolumn{4}{c}{**Repositioned**} & \multirow{2}{*}{\(c_{1}\Delta W_{j}\)} &  
+++ ==WARNING: Truncated because of repetitions==
\multirow{2}{*}{\(c_{2}\Delta H_{j}\)} \\ \cline{5-12}  & & & & \(X_{0}\) & & \(Y_{0}\) & \(X_{1}\) & & \(Y_{1}\) & \(X_{0}\) & \(Y_{0}\) & \(X_{1}\) & \(Y_{1}\) \\ \hline  
++
# Low Power Multi-Hop Networking Analysis in Intelligent Environments
## 4 Methodology
Finally, the effect of the delay by retransmissions due to interference was extracted from the sets of samples after analyzing them and not considering the outliers of the series. Such retransmissions are usually a result of the influence of a series of external factors, as the presence of other equipment in the same frequency band or the other processes run by the operating system in the computer. On the other hand, the resolution of the instrumentation used in the tests was about tens of milliseconds and it was enhanced by repeating the tests hundredths of times for each one of the situations.
# The Role of Ultrasonography in the Diagnostic Evaluation of Patients with Polyneuropathy
###### Abstract  
Background: The role of ultrasonography (US) in the practical management of polyneuropathies, particularly axonal, remains unclear. The present study aimed to explore the contribution of the US examination of polyneuropathies in daily clinical practice. Methods: We performed a retrospective chart review of patients with clinical and electrophysiological diagnoses of polyneuropathy referred to our US laboratory over eight years. The contribution of US examination in this patient population was evaluated. Results: We analyzed 201 consecutive patients (66% men), aged 12-90 years (mean (SD), 62 (15) years). The most common referral questions were differentiation of hereditary from acquired demyelinating polyneuropathies (71 (35%) patients, sensitivity 63%, specificity 88%), and additional focal neuropathies in patients with generalized neuropathies (51 (25%) patients, sensitivity 75%, specificity 34%). The US examination was pathological in 158 (79%) of patients. The most common US finding was nerve enlargement at typical entrapment sites (73 (36%) patients), followed by proximal nerve thickening (34 (17%) patients). The US provided new diagnoses in 7 (3.5%) patients, contributed to diagnoses in 39 (19%) patients, and confirmed diagnoses in 50 (25%) patients. Conclusion: Our study demonstrated the ability of peripheral nerve US to provide useful additional diagnostic information in about half of the referred patients with polyneuropathy.  
elecrodiagnosis; nerve cross-sectional area; peripheral nerves; polyneuropathy; ultrasonography
# Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack
###### Abstract  
Low Earth orbit satellite constellation networks (LSCNs) have attracted significant attention around the world due to their great advantages of low latency and wide coverage, but they also bring new challenges to network security. Distributed denial of service (DDoS) attacks are considered one of the most threatening attack methods in the field of Internet security. In this paper, a space-time graph model is built to identify the key nodes in LSCNs, and a DDoS attack is adopted as the main means to attack the key nodes. The scenarios of two-satellite-key-node and multi-satellite-key-node attacks are considered, and their security performance against DDoS attacks is also analyzed. The simulation results show that the transmission path of key satellite nodes will change rapidly after being attacked, and the average end-to-end delay and packet loss are linearly related to the number of key-node attacks. This work provides a comprehensive analysis of the security performance of LSCNs under a DDoS attack and theoretical support for future research on anti-DDoS attack strategies for LSCNs.  
LSCNs; DDoS; space-time graph model; security performance; anti-DDoS
# OQPSK Synchronization Parameter Estimation Based on Burst Signal Detection
## 3 Performance Evaluation  
### Performance Simulation of Burst Signal Detection  
Tables 3 and 4 are the comparisons of the false alarm rate and detection rate of OQPSK burst signal detection under local \(UW\) being up-sampled four times and not up-sampled in the case of Equation (5). Simulation conditions: in the case of being up-sampled, the added normalized frequency offset is 0.04 (relative to the symbol rate and the same below); the total length of \(UW\) after being up-sampled four times is \(N=108\); the number of segments is \(M=9\); and the local \(UW\) length of each segment is \(L=N/M=12\); in the case of not being up-sampled, the added normalized frequency offset is 0.04, \(N=27\), \(M=9\), and \(L=N/M=3\). We performed 10,000 simulations. From Tables 3 and 4, it can be seen that the performance of the local \(UW\) when it is up-sampled four times is much better than not being up-sampled. This is because there are more points after being up-sampled, so the value of the correlation result is larger, but the more important reason is that the OQPSK signal can stagger its in-phase component and quadrature component by half a symbol period only after being up-sampled; otherwise, the partial correlation will be lost.
## 3 Results
### Form of the Motion Trajectories  
Figure 8 depicts representative trajectories of the markers on the MF joints for all three experimental conditions for one of the subjects. The trajectories are depicted in the sagittal plane--along the \(x\) and \(z\)-axis, showing how each marker was moving as the subject performed the protocols. The solid lines represent the trajectories obtained from each protocol--the blue colour represents the P0 protocol, the red colour represents the P1 protocol, and the yellow colour represents the P2 protocol. The dashed lines of the same colours are used to help visualise the corresponding positions of skeletal structures of the hand at the specific points along the trajectories--the markers were used as a representation of the wrist and finger joints positions. The point \((0,0)\) in Figure 8 represents the wrist, and the lines along the negative \(x\)-axis represent the forearm. From left to right, the trajectories in Figure 8 correspond to the movement of the markers on the MCP, PIP and DIP joint, and the tip of the MF.  
The shape of the trajectories in Figure 8 shows a similarity of the wrist and finger movement across different experimental conditions. The shapes of trajectories in the P1 and P2 protocol, especially when moving into flexion, are expectedly similar, as the device constrained the subject's movement. We can also observe a substantial difference between P0 and the other two protocols regarding the amount of flexion--during the P0 protocol the subjects could, in general, achieve a greater amount of flexion because they were able to flex the wrist and fingers completely, i.e., making a fist. That was not possible during the other two protocols, P1 and P2, as the device has a limited amount of flexion to prevent the bottom of the distal finger rest from compressing the thumb, which is fixated at the bottom of the proximal finger rest. Compared to P0 or P1, we can also observe a greater extension of the MF joints during P2 protocol, which could mean that the device moved the MF joints past their active ROM and into the passive ROM.