esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Limitations and Future Directions  
While our proposed frameworks demonstrate promising results, several limitations warrant consideration. First, a fundamental challenge is the capacity mismatch between simple white-box explainers and complex black-box predictors. When the predictor is significantly more complex (e.g., deep neural networks), the linear explainer may provide oversimplified explanations that miss important non-linear patterns. This gap could limit the fidelity achievable between models of considerably different complexities. Second, our evaluation focuses on Factorization Machines as the black-box model and sparse logistic regression as the explainer. While effective for demonstrating our approach, a comprehensive evaluation with diverse model architectures (deep neural networks) and alternative explainer models (decision trees) can be pursued in future work. Each combination may require different optimization strategies and regularization strengths. Third, we evaluated exclusively on tabular binary classification tasks. Extending our frameworks to other data modalities (images, text, time series) and tasks (multi-class classification, regression) requires careful adaptation of the interpretable model choice and distance metrics. Finally, while we primarily compared our approach with LIME due to its similar local explanation method, a comparison with SHAP was omitted due to methodological differences (Shapley values vs. coefficient-based importance). Future work could explore incorporating game-theoretic concepts from SHAP into our in-training framework.  
Despite these limitations, our results on tabular data provide a strong foundation for future extensions. Key research directions include developing theoretical guarantees for the fidelity-accuracy trade-off, exploring alternative explanation types, and adapting the framework to domains where traditional feature-based explanations may not be applicable. As machine learning systems become increasingly utilized in critical applications, approaches that balance between performance and interpretability will be crucial for the responsible deployment of AI.
# Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images
## 3 Multiple Preprocessing Hybrid Level Set Model
### Multiple Preprocessing
#### 3.2.2 Channel Selection  
Tissues appear with different intensities in different color space; thus, selecting an appropriate color channel with a higher contrast is useful in obtaining more accurate OD segmentation results. The authors in [49; 50] proposed that the red channel in RGB color space is the most suitable for OD segmentation but without justification. This paper quantitatively analyzes the channels in RGB, HSV, and LAB color spaces through the contrast-to-noise ratio (CNR, value in Equation (12)).  
\[\text{CNR}=\frac{\left|\textit{mean}_{\textit{foreground}}-\textit{mean}_{ \textit{background}}\right|}{\textit{std}_{\textit{background}}} \tag{12}\]  
where \(\textit{mean}_{\textit{foreground}}\) is the average pixel values of the foreground, and \(\textit{mean}_{\textit{background}}\) and \(\textit{std}_{\textit{background}}\) are the mean and standard deviation pixel values of background, respectively. In this paper, the foreground is the OD area, while the background is the areas except OD.  
A large CNR value means that the difference of the average pixel values between the foreground and background is large, and the difference of the background pixel values is low. Therefore, if the CNR value of an image is large, it is a high-contrast and low-noise case.  
The images from the DRISHTI-GS dataset were used to calculate the CNR, and results are shown in Table 1. The average CNR in the value channel was 5.0408, and red channel was 4.9824, which were significantly higher than those of other channels, but the variance CNR in the value channel was 5.4443, which was slightly lower than that in the red channel, 5.4550. Thus, the value channel was selected to segment OD in this paper.  
Figure 11: Rough ROI with poor result.  
Figure 10: Results of each process of ROI detection in wide-angle fundus images.
## 3 Multi-Sensor Fusion
### System Overview  
In this paper, horizontal and vertical lidars are fused to make up for the shortcomings of lidar's narrow FOV. When indoors, accurate positioning information is obtained through the tight coupling of dual lidar inertial odometry. When outdoors, GPS measurements are added, providing a relatively accurate initial position for positioning, thereby further improving outdoor positioning accuracy.  
Figure 2 provides a brief overview of our proposed framework. Firstly, after obtaining the IMU data, the IMU state prediction and pre-integration are updated, as detailed in Section 2.1. IMU pre-integration is used to construct a factor graph constraint, which will be used for joint optimization. Secondly, the dual lidar point clouds are obtained through external calibration and an adaptive timestamp synchronization algorithm, as detailed in Section 3.3. Since lidar has continuous measurement, the lidar measurement is obtained in continuous motion and it is almost certain that motion distortion will occur. When dual lidar point cloud is received, deskewing is applied on the dual lidar raw point cloud to obtain deskewed dual lidar point cloud [14]. To be ground-optimized and reduce the amount of calculation, point cloud segmentation is applied to filter out noise (e.g., moving people or objects) and the planar points and edge points are distinguished by calculating the curvature of the lidar point, as detailed in Section 2.2. After feature extraction, the dual lidar point cloud is registered to construct the lidar odometer factor. Then, the sliding window factor graph optimization method of local finite frame is used, with the IMU pre-integration factor and lidar odometer factor jointly optimized to realize the mutual parameter optimization of the dual lidar and IMU. Joint optimization is taken to obtain a maximum a posteriori (MAP) estimation of the IMU states, as detailed in Section 3.4, avoiding the drift from IMU propagation. To further improve outdoor positioning accuracy, GPS is introduced in this framework. When GPS is loosely coupled with IMU through an Unscented Kalman Filter (UKF), GPS measurements are used to further improve the positioning accuracy of IMU, with the optimized IMU state then used for the state estimation of IMU at the next time step. GPS measurements are added, providing a relatively accurate initial position for localization, thereby further improving outdoor localization accuracy [25]. However, the  
Figure 1: A CAD (Computer Aided Design) model of the scanning arm is shown on the left, with a photograph of the scanning arm shown on the right.  
drift of lidar inertial odometry grows very slowly; in practice, GPS measurements are used when the estimated position covariance is larger than the received GPS position covariance. Finally, the output of the figure is the global map and localization.
## 2 Demonstration of Ternary Logic Gates
Note that in our circuit design, the output voltage is always represented as V\({}_{\text{out}}\) = V\({}_{\text{dd}}\)*R\({}_{1}\)/(R\({}_{1}\) + R\({}_{2}\)), where R\({}_{1}\) and R\({}_{2}\) is the resistance of Part 1 and Part 2, respectively. If the ratio of R1/R2 is constant, then the output voltage will also be a constant. Therefore, it will show a middle logic output (\"1\") when V\({}_{\text{in}}\) is middle. In addition, it is easily to get the other two states, namely \"highest logic level\" (\"2\") and \"lowest logic level\" (\"0\"). Thus, the output voltage will show three stable states. The NTI and PTI differ from STI only when the input is middle. Note that R\({}_{1}\) and R\({}_{2}\) are mainly controlled by the BP1 and BP2, and the MoS\({}_{2}\) resistance is almost negligible when input is middle [19; 20]. Therefore, we can tune the middle output logic level to be very high for the PTI (Figure 2c) or very low for the NTI (Figure 2d) in our design. Figure 2: Demonstration of different ternary logic gates. (**a**). Schematic view of a BP-MoS\({}_{2}\) standard ternary inverter. V\({}_{\text{out}}\)-V\({}_{\text{in}}\) characteristic curves of the (**b**) STI, (**c**) PTI and (**d**) NTL. (**e**). Schematic view of a BP-MoS\({}_{2}\) decrement cycling ternary inverter. (**f**). V\({}_{\text{out}}\)-V\({}_{\text{in}}\) characteristic curves of the DCI. Furthermore, beneficial from the ambipolar behavior of a BP transistor with high mobility of both hole and electron, we design and fabricate a decrement cycling inverter (DCI) with a quite simple device structure (Figure 2e,f). There is only one BP transistor, together with one N-type MoS\({}_{2}\) transistor used for the design of the DCI, which is just the same as a CMOS binary inverter [24; 25]. When V\({}_{\text{in}}\) is low, MoS\({}_{2}\) FET is off while BP FET is on, then the output is high (logic \"2\"); when V\({}_{\text{in}}\) is middle, MoS\({}_{2}\) FET is on while BP FET is off, then output is low (logic \"0\"); when V\({}_{\text{in}}\) is further higher, both MoS\({}_{2}\) FET and BP FETs are fully turned on as the n-type transistor [23], then there will be a voltage distribution in between the resistance of MoS\({}_{2}\) and BP. We will get a middle output if the R\({}_{\text{MoS2}}=\) R\({}_{\text{BP}}\) on its n-type which forms the third state of the inverter (logic \"1\"). So far, we have designed and fabricated four types of inverters (STI/NTI/PTI/DCI) by using 2D materials. Compared to the existing silicon ternary [4; 5] and CNT ternary [9; 10; 11; 12; 13; 14; 15], our design has largely simplified the ternary logic design complexity. There are only two or three FETs involved in one ternary logic gate, which is only about half of that in silicon ternary or CNT ternary. Therefore, it is possible to accomplish simplicity in modern digital design by using ternary logic due to the reduced circuit overhead and chip area.
## 7 Future Research Directions
### Mixed Testing Combining Simulation-Based and Real-World Environments  
Continuing in the same vein, one could model and simulate a certain environment with the real-time input from that environment, including ships. For example, we could add a simulated ship to study, e.g., autonomous navigation systems, and develop rules and methods for future connected vessels in realistic settings. Our own simulator with implemented 5G connections and intelligent connectivity management is one possible platform for this type of study.
## 2 Data Preparation
### Data Processing  
The obtained trajectory data for manned aircraft and the drone are processed and represented as time-series data at equal intervals. The detailed procedure is illustrated as follows.  
1. Data processing for manned aircraft
2. Step 1-1: Coordinate system transformation
3. The longitude and latitude in the collected aircraft trajectory data based on the space spherical coordinate system are converted into the Earth-centered, Earth-fixed coordinate system (ECEF), which is shown in Equation (1). \[\left\{\begin{array}{l}X=(h+N)*\cos\varphi*\cos\lambda\\ Y=(h+N)*\cos\varphi*\sin\lambda\end{array}\right.\] (1)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Longitude (\({}^{\circ}\))** & **Latitude (\({}^{\circ}\))** & **Altitude (m)** & **Ground Speed (m/s)** & **Monitoring Time** \\ \hline
121.3286514 & 31.18910980 & 7.62 & 209.21 & 18 November 2017 14:35:13 \\
121.3322525 & 31.20203972 & 114.30 & 209.21 & 18 November 2017 14:35:19 \\
121.3318024 & 31.20713043 & 167.6  
In the formula, \(h\) is the height; \(N\) is the radius of curvature; \(\varphi\) is the radian corresponding to latitude; \(\lambda\) is the radian corresponding to longitude. The unit of latitude and longitude after coordinate transformation is meter (m). \(X\) is the longitude after transformation; \(Y\) is the latitude after transformation.  
Step 1-2: Time series data generation  
For the altitude and ground speed in the aircraft trajectory data and the converted longitude \(X\) and latitude \(Y\) in Step 1-1, time series data at equal intervals are generated through linear interpolation.  
Step 1-3: Dimensional processing and sample segmentation  
The equally spaced time series data in Step 1-2 are normalized to eliminate the influence of dimension, and the normalized equally spaced time series data are divided into fixed-length time series slices via a sliding time window. The coordinate value of the trajectory point contained in the slice sample is subtracted from the initial position value of the sample, and the slice sample set containing the input and output data of the trajectory prediction model in the take-off stage and the landing stage are generated, respectively. Each slice sample set includes four variables: latitude, longitude, altitude, and ground speed.
## 6 Discussion
### Effectiveness on Session Cold-Start Problem  
In addition to the overall recommendations we reported in Sections 5 and 6.1, we evaluate the effectiveness of ASLM-LHS on recommendations for the first \(n\) time steps, for \(n=1,...,5,L\), where \(L\) is the maximum session length and \(L=19\) in our experiment.  
Figure 8 shows the comparison of performances of Recall@5 at the first \(n\) recommendations of each session for Reddit and Last.fm datasets between ASLM and II-RNN.  
Notice that, for Reddit and Last.fm, where abundant user history behavior is given, our attention-based ASLM-LHS model achieves \(R@5>0.3\) and \(R@5>0.099\) when \(n=1\), respectively, and,  
Figure 8: Comparison of cold-start effects at the start of a new session for Reddit and Last.fm datasets between ASLM and II-RNN.  
under most circumstances, each \(R\)@5 score of the ASLM-LHS model increases throughout the session. ASLM-LHS has an advantage over II-RNN-LHS and II-RNN-AP at the start of a new session in Reddit and Last.fm datasets, with the improvements of 7.00% and 3.12%, respectively. When more events have been investigated in the current session, for the Reddit dataset, the advantage becomes more obvious, while, for the Last.fm dataset, since the performances of ASLM-LHS and II-RNN-AP are identical when \(n=2\), the advantage becomes greater when \(n\) increases from 3.  
Similar results are shown in Figure 9 for the Tmall dataset. In terms of the Tmall dataset, which is severely short of both long-term and short-term user behavior, ASLM-LHS still provides 0.5063 in \(R\)@5 with just a slight difference compared with the overall \(R\)@5 (i.e., 0.5074). Due to the lack of history information from each user in the Tmall dataset, its performance is not stable at the start of a session when \(n\) is between 1 and 3, however it gets better when \(n\) increases from 3. As a result, the attention-based short-term intent layer can thoroughly reveal the variation in the user's short-term intent between events from the current session \(S_{t}^{u}\). To sum up, ASLM significantly alleviates the cold-start problem.
# A Novel Searching Method Using Reinforcement Learning Scheme for Multi-UAVs in Unknown Environments
## 2 Materials and Methods
### Multi-Objective Function Establishment  
The main goal was to find as many targets as possible, on the premise of ensuring safety. Therefore, the optimization function \(J(s(k),u(k))\) is composed of the benefit of the target found \(J_{p}\), the benefit of searching environment \(J_{\chi}\), the cost of execution \(C\), and the cost of collision \(I\), which are described below.  
The benefit of discovering targets: \(J_{p}\) is the possibility of discovering the target by UAVs in flight. It can be described as the sum of the target probabilities at the area \(R_{i}^{n}\) occupied by the trajectories. It is defined as  
\[J_{p}(k)=\sum\limits_{i=1}^{N_{p}}\sum\limits_{(m,n)\in R_{i}^{n}}\left[(p_{D }-p_{F})p_{nm}(k)+p_{F}\right]. \tag{12}\]  
The benefits of searching the environment: as the task proceeds, the UAVs can obtain information on the search area, which means the entropy [6] search map is gradually reduced. Thus, \(J_{\chi}\) is defined as the decrement of the entropy of information.  
\[J_{\chi}(k)=H(k)-H(k+1), \tag{13}\]  
where \(H(k)=-\sum\limits_{(m,n)\in E}(1-\chi_{mm}(k))\ln(1-\chi_{mm}(k))\) is the entropy of information at instant \(k\).  
The cost of execution: The cost \(C\) is the consumption of time and fuel during the task, which can be estimated as  
\[C(k)=\sum\limits_{i=1}^{N_{p}}\|p_{i}(k)-p_{i}(k+1)\|/v_{i}(k). \tag{14}\]  
The cost of collision: Collision avoidance is the primary consideration of cooperative search for multiple UAVs in the same plane. Combined with the characteristics of the territory awareness information map, the concentration of pheromones is high at the locations which are current grid locations of each UAV or where flight tracks are dense. Therefore, when the \(i\)th UAV takes a control  
Figure 1: Airborne sensor detection model.  
decision, the direction of flight is where the pheromone is lower. This decision reduces the possibility of collision with other UAVs, and \(I\) is defined as  
\[I(k)=\sum_{i=1}^{N_{v}}\overline{H}_{inn}(k). \tag{15}\]  
According to the analysis, the overall efficiency function of the UAVs' cooperative search problem is as follows:  
\[J(s(k),u(k))=w_{1}J_{p}(k)+w_{2}J_{\chi}(k)-w_{3}C(k)-w_{4}I(k), \tag{16}\]  
where \(0\leq w_{i}\leq 1(i=1,2,3,4)\) are weights. Note that the above functions have different dimensions; thus, it is necessary to normalize them separately before summation.
# Naturally Occurring Flavonoids and Isoflavonoids and Their Microbial Transformation: A Review
## 1 Introduction  
Flavonoids and isoflavonoids are versatile natural compounds and subdivisions of polyphenols that represent a large proportion of secondary metabolites produced by higher plants and are a rich part of the human diet [1]. They are playing multiple roles in the physiology and ecology of individual plant species. Flavonoids are a type of yellow pigment derived from 2-phenyl chromogenone as the parent nucleus and a series of compounds with C6-C3-C6 as the basic skeleton. Isoflavonoids are a subclass of flavonoids characterized by possessing a benzene-ring connected to C-3 instead of C-2 [2]. Phenylpropanol and polyketone compounds are normally catalyzed by chalcone synthase to produce chalcones, and then cyclization of chalcones leads to generate flavonoids. Isoflavonoids, originating from the same biochemical pathway as flavonoids, are derived by aryl migration in a 2-phenylchroman skeleton under the catalysis of 2-hydroxisyoflavanone synthase [3; 4; 5; 6]. Flavonoids and isoflavonoids are well-known natural products with extensive pharmacological activities and extremely low toxicity and therefore have become the focus and hotspots of drug discovery and development [7; 8; 9]. Most plants contain isoflavonoids and flavonoids that play important roles in the growth and development of plants as well as in antibacterial and disease-preventing aspects [10; 11]. More importantly, they possessa wide range of biological activities, such as antibacterial, antifungal, antiviral [12; 13], antitumor [8], anti-inflammatory [14], and antiaging activities [15].  
Most research on isoflavoids and flavonoids has focused on plant sources, and relatively less attention has been paid on microorganisms as the potential sources of these compounds. The presence of metabolic pathways of isoflavoid and flavonoid biosynthesis in microorganisms has been confirmed. In particular, the presence of isoflavoids and flavonoids in fungi and actinomycetes has been widely reported in studies [16; 17]. Both fungi and actinomycetes exhibit unique and unusual biochemical pathways, and many important drugs have been derived from their secondary metabolites, for instance, penicillin, cyclospor, paclitaxel, and statins are derived from fungi [18], and streptomycin, kanamycin, nystatin, and rifamycin are derived from actinomycetes [19; 20]. Therefore, isoflavoids and flavonoids derived from fungi and actinomycetes are of great significance. Fungi and actinomycetes are also the key biological sources of isoflavoid and flavonoid biotransformation [21; 22]. Microbial transformation is advantageous because of mild reaction conditions and high regio- and enantioselectivity. The literature suggests that almost all isoflavoids and flavonoids derived from actinomycetes are produced by _Streptomyces_ because the genes in this genus encode key enzymes such as phenylalanine ammonia-lyase and chalcone synthase (CHS) that catalyze isoflavone and flavone synthesis [23; 24].
# Service for Deploying Digital Twins of QKD Networks
## 3 Functional Overview
### QKD Networks Clients  
The clients of a network are the requesting party, in our case, the applications that request keys from the QKD modules. A real example of these applications could be routers that need to settle an encryption key or a malicious application that tries to obtain that encryption key. The digital twin deployed by our service enables the testing of these applications over a realistic QKD network environment, supporting research and experimentation in this field.  
In the presented service, any device with access to the digital twin of the QKD network can potentially be a client and run applications over the QKD network environment. This includes both external and internal QKD network devices. In order to make their requests and receive their responses, clients must use a specific API. In the first version of this service, the API corresponds to an implementation specifically designed based on the one defined by ETSI GS QKD 004 [27].
## 4 Performance Evaluation
### Performance Metrics Evaluation  
The main goal of the proposed protocol is to improve the system performance by reducing the energy consumption and extending network lifetime. Following are the metrics that used for evaluation in this paper:  
1. **Energy Efficiency**: This term can be declared as the ratio between total consumed energy over the number of packets received by the sink-node.
2. **Energy consumption**: The metric gives the energy consumption of nodes in the event area for transmitting a data packet to sink.
3. **Average Energy**: The metric gives the average of energy of all nodes at the end of simulation.
4. **The standard deviation of energy**: The metric gives the average variance between energy levels on all nodes.
5. **Throughput**: This metric is defined as the rate of the successful packets that can be transferred from a source to the sink node within a certain period of time.
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
## 2 Background
### Location Privacy
#### 2.3.1 Anonymity Set Size  
The authors of [18], presented the concept of anonymity that they provided was a selection of all users willing to submit a single message that is clear to the global observer. It is believed that the global intruder is someone who has corrupted a group of nodes within a network. The definition has been used to model the Dining Cryptographers (DC) Network protection. According to [17] the \(ASS\) calculates the number of users that could be the target individual p. It is defined as the size of the region where the target user p will blend. where \(\mid ASt\mid\) is the anonymity set of the targeted node. One of the drawbacks of this is that it does not bother taking up previous experience. This depends solely on the number of users in the program. The mixture of \(ASS\) and standardized entropy [19] does however provide a better privacy.  
\[Priv_{ASS}=\left|ASt\right| \tag{1}\]