esa-sceva/satcom-chunk-collection huggingface dataset:
# Link-State Aware Hybrid Routing in the Terrestrial-Satellite Integrated Network +
## 3 System Model
### Link-State Aware Hybrid Routing Algorithm  
Although the \(b\) and \(\vartheta\) are coupled through the capacity constraint, they can be decoupled by employing the special characteristic of our problem. Intuitively, with the given inter-satellite contact connection, minimizing the transmission delay for UTs gives the satellites the opportunity to delivery as much of their data as possible, which facilitates their mission completion velocity. Therefore, based on the time interval optimization, the ISL connection problem can be solved by minimizing the transmission delay for each satellite by optimally allocating the link resource of each satellite. When the optimal ISL connection planning during each time interval is obtained, a hybrid routing algorithm based on a bipartite graph is proposed to find the optimal ISL connection. To this end, the overall optimization of the hybrid routing problem (9) can be achieved by successively optimizing the ISL and TSL connection planning.
# Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions
[39] used domain-adversarial training based on the Wasserstein distance to  
Figure 1: Two kinds of main bearings in aeroengine. learn domain-invariant features from the raw signal. [PERSON] et al. [40] combined distance-based domain adaptation and adversarial-based domain adaptation to propose a multi-adversarial joint distribution adaptation network. This paper proposes an improved adversarial transfer network (IATN) for bearing fault diagnosis under variable working conditions, inspired by the effectiveness of distance-based and adversarial-based domain adaptation. Specifically, this paper combines an adversarial transfer network with a short-time Fourier transform (STFT) [41] to obtain satisfactory results with lighter networks. Then, this paper employs an attention module to enhance feature fusion. Moreover, this paper designs a novel domain discrepancy hybrid metric loss to improve model transfer learning performance. Finally, this paper verifies the method's effectiveness on a private dual-rotor dataset, a Case Western Reserve University (CWRU) dataset [42] and the Ottawa dataset [43]. The main contributions are as follows:  
1. This paper combines adversarial TL with the traditional signal processing method, STFT, which allows us to obtain satisfactory results with a much lighter network. 2. This paper employs a channel attention module to enhance feature fusion. 3. This paper designs a novel domain discrepancy hybrid metric to measure the discrepancy between features of the source dataset and target dataset features. 4. This paper evaluates the proposed approach on three test benches' single-source tasks and multi-source tasks. The results demonstrate that the proposed method performs better than other methods. The structure of this paper is as follows: In Section 2, related knowledge is introduced. Section 3 presents the proposed method in detail. Section 4 provides information about the experiment and analysis of the results. Section 5 provides further discussion, and Section 6 provides conclusions.
# Facility Layout Problem with Alternative Facility Variants
## 5 Experiments
### Evaluation  
One hundred independent runs were carried out with each tested algorithm's variant in each experiment. In the case of the single-objective optimization, the median best-of-run values were compared. In the case of multi-objective optimization, there is typically no single best solution produced by the MOEA. Instead, a set of so-called non-dominated solutions is delivered by the MOEA at the end of its run. We used a so-called _hypervolume_ performance metric [39], frequently used in the literature, to assess the quality of the set of non-dominated solutions. It calculates the hypervolume of the multi-dimensional region enclosed by the set of non-dominated solutions and a reference point; see Figure 4.  
The coordinates of the reference point were calculated as the worst value of the respective objective observed in the final non-dominated fronts over all 100 runs. The hypervolume expresses the size of the region that is dominated by the non-dominated set. Similarly to the single-objective case, we then compared the median of the hypervolume values.  
Note that the same random generator seeds were used for all series of 100 independent runs in all experiments. This implies that the runs with the cooling schedule followed the same search trajectory as those without the cooling schedule, up to generation 500 (i.e., \(G/2\)). Then, the two trajectories departed from each other and went their own way. This allowed us to accurately measure the effect of the cooling schedule.  
In order to assess the statistical significance of the differences among the methods, we analyzed them pair-wise using the Wilcoxon rank-sum test, which rejects the null hypothesis that the compared result sets are sampled from continuous distributions with equal medians at the 5% significance level.  
Figure 4: Illustration of the hypervolume calculation for a set of non-dominated solutions S, given the maximization objective maxFreeSpace and minimization objective minConnDist.
# Barrel Shifter Physical Unclonable Function Based Encryption
## 5 Circuit Implementation
### Output Logic  
Output logic measures and captures path delay. Output logic for each bit contains three parts: _Counter_, _Edge Detector Pulse Generator_, and _Entanglement Logic_.  
_Counter_ takes _CLK_ and _RST_ as input producing a 10-bit output; it counts the number of rising edges of _CLK_. Setting _RST_ high resets the counter to 0. The path delay is expressed as (input clock period) _es_ (counter value).  
_Edge Detector Pulse Generator_ generates a pulse in response to a voltage transition at its input. It includes an edge detector and a pulse generator. The edge detector converts a rising or falling edge into a rising edge at its output. The pulse generator converts the rising edge from edge detector into a pulse.  
The output logic works as follows: first, a rising/falling edge at input produces a pulse at the _Edge Detector Pulse Generator_ output. This pulse enables the transmission gate in Figure 9 for a short time period (2 ns). During this time, the counter output is captured; it must not change while being captured. Thus, enable time period must be shorter than clock period (4 ns).  
Finally, _Entanglement Logic_ takes the \(m\)th LSB of delay \(D(i,i^{{}^{\prime}})\). Computing XOR of this bit with the input signal \(x_{i}\) results in the entangled output bit.  
The output logic works by detecting a voltage transition. A voltage transition occurs when the current text bit differs from the previous text bit value. Thus, the output logic is incapable of detecting unchanging/stationary text values. A voltage transition is forced by providing \(\overline{x_{i}}\) before \(x_{i}\) at the text input.
## 3 Results and Discussion
### Interfacial Parameters and Gibbs Adsorption Isotherm
#### 3.5.3 Surface Pressure (\(\pi_{CMC}\))  
Surface pressure, i.e., \(\pi_{CMC}\) is the change in the surface tension caused by the solute molecules in amino acid tyramine with D-glucose, which was calculated as per Equation (5) of Section 2.3, where \(\gamma^{o}\) and \(\gamma_{CMC}\) are the surface tension of the solvent and solution at CMC, respectively. The surface pressure of glucose in taurine is found to increase with the increase in concentration at different temperatures. The values tabulated in Table 3 fall very close to each other, which indicates that the surface pressure is a temperature-dependent parameter.  
However, in the case of tyramine, the surface pressure of glucose is calculated by the difference in the surface tension of the solvent and that of the solute at CMC, i.e., \((\pi_{cmc}=\gamma^{o}-\gamma_{cmc})\)[37]. In addition, the calculated surface pressure (\(\pi\)/mNm\({}^{-2}\)) of D-glucose in aqueous taurine at different temperatures is tabulated in Table 3. Moreover, in determining surfactant efficiency, the role of the molecular structure is primarily thermodynamic, while its role in effectiveness is directly related to the relative size of the hydrophilic and hydrophobic portions of the adsorbing molecule. In recent years, several workers have determined the various thermodynamic properties of these model compounds in aqueous  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**m (mol\(\cdot\)kg\({}^{-1}\))**} & \multicolumn{5}{c}{**T/(K)**} \\ \cline{2-6}  & **298.15** & **303.15** & **308.15** & **313.15** & **318.15** \\ \hline Glucose + aq. Taurine & \multicolumn{5}{c}{Surface pressure (\(\pi\)/mNm\({}^{-2}\))} \\
0.01 & 2.00 & 2.07 & 2.03 & 2.06 & 2.06 \\
0.02 & 3.70 & 3.80 & 3.63 & 3.50 & 3.53 \\
0.03 & 5.30 & 5.30 & 5.03 & 4.79 & 4.76 \\
0.04 & 6.70 & 6.57 & 6.16 & 5.90 & 5.75 \\
0.05 & 7.33 & 7.03 & 6.56 & 6.27 & 6.16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Surface pressure (\(\pi\)/mNm\({}^{-2}\)) of aqueous taurine with D-glucose solution at different temperatures.  
solutions containing simple electrolytes having a hydrophilic nature [35]. Nevertheless, very few studies have been conducted on aqueous amino acids with glucose solutions.
# The Risk Assessment of the Security of Electronic Health Records Using Risk Matrix
## 6 Proposed Work
### Firewalls  
Firewalls can provide security for stored data. Firewalls filter information according to policies configured in the firewall device. For instance, a firewall can have rules configured to accept traffic only from port 443 (HTTPS) and restrict traffic from all other ports. A healthcare institution needs a firewall to protect the EHRs from external and internal agents. A firewall must be configured to accept traffic from certain ports used by the hospital user interface and block all other traffic. Additionally, since healthcare professionals read only one patient's record in a given time, the firewall can be configured to block any request for large amounts of data by anyone, including employees with the right to access the data. Figure 2 below shows the concept of blocking outbound traffic.  
The firewall needs to block outgoing traffic based on several parameters. First, the firewall can limit the traffic based on the normal usage of the healthcare institution. Most healthcare institutions have a normal rate of accessibility of the EHRs, and a huge burst in traffic shows unintended and illegal activities in the EHRs. An external attacker accessing EHRs usually accesses large amounts of traffic in a short time, representing a burst in traffic at that time, which can be detected by the intrusion and detection capabilities of modern firewalls. The firewall can block such access and the IP address from which the attacker is accessing the EHRs.
# Implementation of a Lossless Moving Target Defense Mechanism
## 6 Implementation
### Environment Description
The start of using the new generation of IP addresses occurs when the SDN controller introduces information about the new generation into the appropriate flow table in at least one network node. In this scenario, the process of changing generations throughout the entire network lasts for a specified period. Unlike the wavelength-changing methods in NG-PON2 networks, it is impossible to precisely predict when information about the next generation of IP addresses will reach the switch. The change in the current generation in the switch can occur in two way: either by an entry into the flow table or by the switch detecting the new generation by the switch during packet processing. This necessitates storing information about the generation not only in the flow table but also in a structure the data plane's program code can modify. This structure could be a register. Each target of the P4 language offers several registers that can be utilized in the program code. One feature of registers is their ability to store data between subsequent packet processing events, meaning that changes introduced in the register during the processing of packet \(n\) are available during the processing of packet \(n+1\). Each switch, when detecting a new generation of IP addresses, modifies the contents of the register. If the contents of the register contain a newer (different) generation, it sends a message to the SDN controller. The removal of entries related to the outdated generation of IP addresses from flow tables is possible when the network reaches a specific state, i.e., when each switch in the network receives an entry from the SDN controller to the appropriate flow table about the new version or detects the new generation during packet processing and successfully informs the SDN controller about it. Immediately after removing outdated (old) entries from the flow tables, the network should be prepared for the use of the next generation of IP addresses by delivering new entries to all switches. This preparation involves an algorithm for controlling the core switch. Core switches aggregate traffic from many border switches. Their functions are limited to determining to which port of the switch the packet should be sent.
# Enhancing Telexistence Control Through Assistive Manipulation and Haptic Feedback
## 4 Augmentation Through Motion Blending  
Our study focused on improving object manipulation within telexistence systems by incorporating an advanced assistance framework to enhance user task efficiency through adaptable support levels. Unlike conventional telexistence systems that prioritize embodiment, body ownership, and agency, our methodology optimizes task performance via integrated assistance features. To achieve this, we employed the motion blending algorithm, which effectively merges user hand movements with system-generated motions to guide the robotic actions. This technique supports various tasks, including reaching, grasping, manipulating, and accurately placing objects in predetermined locations.  
The system was developed to aid users in physically manipulating objects through two key stages: (A) identifying and locating target objects and transmitting their data to the local environment and (B) improving the user's control over the robot by implementing adaptive assistance levels. The specifics of these steps are elaborated below.  
A. Detection and Localization of Objects of Interest at the Remote Site  
The system remotely identifies and determines the location of target objects to support robot motion planning and execution. To achieve this, we developed an object detection and localization approach that transfers relevant data to the local site (Figure 9). An Oak-D depth camera was installed in the robot's workspace at the remote site to detect and pinpoint the target object's position relative to the robotic arm. As shown in Figure 9, the left panel displays a depth image from the Oak-D camera, manufactured by Luxonis, a company based in Boulder, CO, USA, with the detected cube highlighted in a white rectangle, while the right panel shows an RGB image with the cube similarly marked.  
The object detection model YOLOv4-Tiny [33] was trained to recognize a 3 \(\times\) 3 \(\times\) 3 cm cardboard cube used in the experiments. The training dataset, comprising 1144 images, was divided into 70% for training and 30% for testing, achieving 98% accuracy, precision, and recall on the test data. During operation, the Oak-D camera [34] provides depth data for the target object, which is sent to the local control system (Figure 9). This information is processed within the Unity3D control system to compute the cube's position relative to the robotic arm, and the cube's real-world location is replicated as a 3D model in the Unity3D environment.  
Furthermore, two types of cameras were employed at the remote site. The Oak-D camera identifies the target object's position for manipulation, while the ZED-M camera  
Figure 9: Oak-D camera feeds, (**left**) depth image, (**right**) RGB image, with the cube detected on both images.  
captures the surrounding environment and streams the visual data to the HTC VIVE VR head-mounted display (HMD), as described earlier.
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 5 Outlook on Key Technologies for Lunar Constellation Navigation and Orbit Determination
### Data Processing Filter Algorithm  
Quantity measurements in navigation systems are affected by environmental noise, measurement technical errors, sensor noise, etc., and errors are inevitable. There are also errors in information estimated recursively using orbital dynamics models. It is necessary to design a proper navigation filter algorithm to deal with various errors. At the same time, in order to comprehensively utilize the advantages of various navigation methods, it is necessary to combine angle measurement, speed measurement, and ranging information and perform information fusion by means of filters.  
For offline orbit determination, batch filter algorithms, such as the least squares filter, are commonly used to calculate the state quantity with the smallest sum of squares of the residuals of the objective function as the optimal estimate of the navigation parameters. The process is simple and efficient, and does not require prior statistical information of the state quantity. However, it is not applicable for the scenario of obtaining complex navigation parameters under the orbital dynamics model in real-time exploration missions. Traditional sequential filter algorithms for nonlinear systems are commonly used for real-time orbit determination tasks in astronomical autonomous navigation systems, including the extended Kalman filter, unscented Kalman filter, Cubature Kalman filter and other algorithms, which have the advantages of not requiring repeated iterations and fast processes. For information fusion in integrated navigation, federated filters are commonly used. Effective fusion of the valuations of each navigation subsystem via the information distribution principle can improve the failure tolerance of the integrated navigation system to a certain extent.  
Regarding the errors in the navigation system, the key to future research should satisfy the characteristics of the lunar exploration mission. Filter methods that achieve a balance between convergence, adaptability, stability, estimation accuracy and calculation amount should be designed.
# Light Propagation and Multi-Scale Enhanced DeepLabV3+ for Underwater Crack Detection
## 4 Results
### Qualitative Results  
Figures 8-11 illustrate the qualitative performance of the original DeepLabV3+, DeepLabV3+ with light propagation compensation, and the enhanced DeepLabV3+ model. These figures show segmentation results on various underwater structures, including concrete piles, pipelines, and submerged bridges, with a focus on the model's ability to detect fine cracks.  
Figure 8: The original DeepLabV3+ model fails to detect several small cracks, leading to fragmented segmentation. The model with light compensation performs better, but still misses some smaller cracks. In contrast, the enhanced model accurately segments both large and small cracks, providing a clean and continuous segmentation.  
In Figure 9, the original model struggles with crack boundaries due to low contrast, while the model with light propagation slightly improves the detection. However, the enhanced model accurately identifies both small and medium-sized cracks, thanks to the integration of multi-scale feature extraction and curvature flow guidance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Water Condition** & **Original DeepLabV3+ IoU** & **Enhanced DeepLabV3+ IoU** & **Degradation (\%)** \\  & **(\%)** & **(\%)** & **Degradation (\%)** \\ \hline Clear Water & 71.9 & 82.5 & \\ \hline Mildly Turbid & 67.4 & 78.9 & \(-\)3.6 \\ \hline Highly Turbid & 61.5 & 73.7 & \(-\)8.8 \\ \hline Low Light & 64.1 & 76.2 & \(-\)6.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results of different turbidity.  
Figure 10: The enhanced model performs exceptionally well in detecting large cracks in this highly textured underwater structure. The original model and the light compensation model provide less accurate segmentation, with the enhanced model clearly delineating the crack boundaries, even under challenging lighting conditions.  
Figure 11: In low-light and turbid water conditions, the original DeepLabV3+ struggles with crack detection, while the model with light compensation performs moderately well. However, the enhanced model maintains accurate segmentation, even in challenging visibility conditions, demonstrating its robustness.  
Figure 9: Medium crack boundary precision: (**a**) original images; (**b**) labels; (**c**) enhanced DeepLabV3+; (**d**) original DeepLabV3+.
## 1 Introduction
### Simulation Results of Proposed HG-SIQBC
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \(V_{in}\) = 75 V & & \(V_{n\!f}\) = 400 V & \\ \hline
**R (f)** & **Rise Time (s)** & **Setting Time (s)** & **Overshoot (\%)** & **Regulation (\%)** & **Measured Output** \\ \hline
160 & 0.035 & 0.15 & 10 & 0.05 & 399.8 \\
180 & 0.035 & 0.15 & 10.5 & 0 & 400 \\
200 & 0.035 & 0.155 & 10.875 & 0.025 & 399.9 \\
220 & 0.035 & 0.16 & 11.125 & 0.25 & 399 \\
240 & 0.034 & 0.17 & 11.45 & 0.25 & 399 \\
260 & 0.032 & 0.175 & 11.625 & 0 & 401.3 \\
280 & 0.032 & 0.175 & 11.95 & 0.175 & 399.3 \\
300 & 0.032 & 0.178 & 12.075 & 0 & 401.6 \\
320 & 0.033 & 0.178 & 12.2 & 0.05 & 399.8 \\
80 & 0.04 & 0.13 & 5.5 & 0.1 & 399.6 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance parameters of proposed HG-SIQBC for \(V_{in}\) = 75 V.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \(V_{in}\) = 150 V & & \(V_{n\!f}\) = 400 V & \\ \hline
**R (f)** & **Rise Time (s)** & **Setting Time (s)** & **Overshoot (\%)** & **Regulation (\%)** & **Measured Output** \\ \hline
80 & 0.004 & 0.03 & 1.375 & 0.075 & 399.7 \\
160 & 0.00346 & 0.05 & 3.4 & 0.05 & 399.8 \\
180 & 0.00346 & 0.0535 & 3.5 & 0 & 400 \\
200 & 0.00346 & 0.006 & 3.575 & 0.025 & 399.9 \\
220 & 0.00335 & 0.066 & 3.75 & 0.05 & 399.8 \\
240 & 0.00334 & 0.0662 & 4.2 & 0.05 & 399.8 \\
260 & 0.00335 & 0.07 & 4.375 & 0.05 & 399.8 \\
280 & 0.0034 & 0.08 & 4.55 & 0.05 & 399.8 \\
300 & 0.0035 & 0.085 During the time interval of \(t\) = 0 to 0.6 s, the input voltage was maintained at 75 V. At \(t\) = 0.6 s, an overshoot of 17.5% of \(V_{0}\) occurred with a step increase in input voltage from 75 V to 150 V, and it settled down to 400 V within 0.04 s. Similarly, an overshoot of 14% of \(V_{0}\) occurred with a step increase in input voltage from 150 V to 250 V, and the output voltage returned to the steady state within 0.04 s. The respective changes are depicted in Figure 13. Despite the input voltage variation, the output voltage became regulated and returned to the desired level within a fraction of seconds. The output voltage response for a step change in load resistance is given in Figure 14.  
During the time interval of \(t\) = 0 to 0.5 s, the output voltage \(V_{0}\) was in the range of 400 V for a rated load current of 2.5 A. When the load was changed from the rated value, there was a step increase in load current from 2.5 A to 5 A at \(t\) = 0.5 s. An undershoot occurred for a minimum duration of 0.04 s before returning to the steady-state value. Again, at \(t\) = 0.8 s, the load current increased to 7.5 A with an undershoot of 4.5% of \(V_{0}\) for 0.045 s.  
Figure 14: Output voltage response with step change in load resistance for proposed HG-SIQBC for \(V_{in}\) = 75 V.  
Figure 13: Output voltage response with input voltage variation for proposed HG-SIQBC. The switching sequence for the proposed MLI is given in Table 5.