esa-sceva/satcom-chunk-collection huggingface dataset:
# Practical V2I Secure Communication Schemes for Heterogeneous VANETs
## 2 Related Work
Unfortunately, it simply permits a user in a PKI system to transmit one message to the recipient in an IBC. Actually, it is not easy to design practical mutual many-to-one or one-to-many signcryption schemes for heterogeneous V2I communication because it needs to meet the security requirements and maintain high efficiency. Similar to the above schemes, we mainly consider how to realize the secure and efficient V2I communication of heterogeneous devices registered in PKI and IBC. Different from the aforementioned schemes, we consider the most common scenario that a static RSU often receives multiple messages from different mobile vehicles almost at the same time; it needs to verify the integrity and authenticity of these messages in a batch way for the potential traceability, which can save a lot of time compared with the traditional sequential verifications. That is the reason that we design many-to-one aggregate signcryption schemes (as in Figure 1). In turn, two one-to-many signcryption schemes are designed to meet an application scenario of an RSU that broadcasts a message to a great number of vehicles. Therefore, our schemes are quite suitable for the heterogeneous secure V2I communication. **Unsigncrypt**: Each receiver takes \(\sigma\) and the corresponding secret keys \(sk_{r_{i}}\) as inputs, then computes the broadcasting message \(m\) after a series of verifications. Here, we omit the security model of heterogeneous signcryption scheme because of the limited space.
# Microalgal Biomass Generation via Electroflotation: A Cost-Effective Dewaring Technology
## 5 Electroflotation-Facilitated Dewatering for Microalgal Biomass Generation
### Performance Estimation of Electroflotation-Based Dewatering Process  
The electroflotation dewatering performance is a function of several variables such as current density, voltage, electrolysis time, conductivity, initial solution pH, electrode materials, and flow velocity. That notwithstanding, the performance of electroflotation in generating microalgal biomass via dewatering can be estimated based on energy consumption, microalgae recovery efficiency, and/or the operational costs. The energy consumption is often in units of kWh/kg of recovered microalgae and computed from Equation (1).  
\[Energy\ consumption,\ E\left(\frac{\text{kWh}}{\text{kg}}\right)=\frac{U\times I \times t}{1000V\times\eta\times C_{i}} \tag{1}\]  
where \(U\) is the applied voltage (V), \(I\) is the current (A), \(t\) is the electrolysis time (h), \(V\) is the volume of the microalgal solution treated (m\({}^{3}\)), \(\eta\) is the recovery efficiency of the microalgae (%), and \(C_{i}\) is the initial concentration of the microalgae biomass (kg/m\({}^{3}\)) [73].  
The microalgae recovery efficiency, \(\eta\), is determined via indirect measurements of pre- and post-electroflotation concentrations of microalgae from turbidity or optical density data at specific wavelengths using Equation (2).  
\[Microalgae\ recovery\ efficiency,\ \eta\ (\%)=\left(\frac{OD_{f}-OD_{i}}{OD_{i}} \right)\times 100 \tag{2}\]  
where \(OD_{i}\) and \(OD_{f}\) are the initial and final optical densities, respectively.
## 3 Proposed System Model
### Proposed Methodology and Q-Value Updates
and performs, enables it to dynamically adapt to its environment, and permits the agent to learn efficiently under the guidance of the decision network's objectives. The loss function is the difference between current Q-value and expected (target) Q-value given by Bellman equation, which we want to minimize. This loss function guides the training of Q-values for the agent's timely evolution and learning of better decisions over time. Loss function (defined by Equation (19)):  
\[L(\theta)=\mathbb{E}\Bigg{[}\bigg{(}Q_{\theta}(s_{t},a_{t})-\bigg{(}r_{t+1}+ \gamma\max_{a^{\prime}}Q_{\theta^{\prime}}(s_{t+1},a^{\prime})\bigg{)}\bigg{)} ^{2}\Bigg{]}. \tag{19}\]  
where:  
* \(\theta\) represents the parameters of the current Q-network,
* \(\theta^{\prime}\) represents the parameters of the target Q-network, which is updated less frequently to stabilize the training process. It ensures that as the agent learns, the difference between the predicted Q-values and the optimum Q-values will guide the Q-network to eventually find optimal behaviour.
# Task-Oriented Motion Planning of a Space Manipulator under Multiple Constraints
## 4 Mission Profile Analysis of the Space Manipulator
### Typical Original Tasks and Operators  
According to the characteristics of the space manipulator and the analysis of on-orbit tasks, its original task set includes three basic original tasks: movement, capture and release. The corresponding operator contains the preconditions and completion status of the task.  
* Movement \[move(v)\] indicates that the end of the manipulator moves from the current point \(w\) to point \(v\). The moving task only changes the position state of the manipulator itself and the position of the object grasped by the manipulator. Conditions: \[S^{\prime}=\left[\begin{array}{cccc}w&\cdots&w&\cdots\\ x&\cdots&x&\cdots\end{array}\right],\ M=\left[\begin{array}{cccc}x&\cdots &x&\cdots\\ x&\cdots&x&\cdots\end{array}\right]\] (5) Completion: \[S^{\prime}=\left[\begin{array}{cccc}v&\cdots&v&\cdots\\ x&\cdots&x&\cdots\end{array}\right],\ M=\left[\begin{array}{cccc}1&\cdots &x&\cdots\\ 0&\cdots&0&\cdots\end{array}\right]\] (6) where \(x\in\{0,1\}\). Since the manipulator can perform tasks in both unloaded and loaded states, the specific form of the operator of the mobile task is determined by the element \(x\) in the last row of the first column at the initial state matrix of the manipulator.
* Capture \[capture(u)\] means that the robot arm captures an object numbered \(u\) in the environment, and the position of object \(u\) is \(v_{u}\). Conditions: \[S^{\prime}=\left[\begin{array}{cccc}v_{u}&\cdots&v_{u}&\cdots\\ 0&\cdots&0&\cdots\end{array}\right],\ M=\left[\begin{array}{cccc}1&\cdots &1_{u}&\cdots\\ 1&\cdots&1_{u}&\cdots\end{array}\right]\] (7) Completion: \[S^{\prime}=\left[\begin{array}{cccc}v_{u}&\cdots&v_{u}&\cdots\\ 1&\cdots&1&\cdots\end{array}\right],\ M=\left[\begin{array}{cccc}0&\cdots&0_ {u}&\cdots\\ 1&\cdots&1_{u}&\cdots\end{array}\right]\] (8)
* Release \[release(u)\] indicates that the manipulator releases the object numbered \(u\) in the environment, and the end position of the manipulator is \(v_{u}\). Conditions: \[S^{\prime}=\left[\begin{array}{cccc}v_{u}&\cdots&v_{u}&\cdots\\ 1&\cdots&1&\cdots\end{array}\right],\ M=\left[\begin{array}{cccc}1&\cdots &1_{u}&\cdots\\ 1&\cdots&1_{u}&\cdots\end{array}\right]\] (9)Completion:  
\[S^{\prime}=\left[\begin{array}{cccc}v_{u}&\cdots&v_{u}&\cdots\\ 0&\cdots&0&\cdots\end{array}\right],\ M=\left[\begin{array}{cccc}0&\cdots&0_{u}& \cdots\\ 1&\cdots&1_{u}&\cdots\end{array}\right] \tag{10}\]
# Optimization of High-Performance Computing Job Scheduling Based on Offline Reinforcement Learning
## 4 Experimental Results and Analysis
### Computational Cost  
Lastly, we discuss the computational cost of our model, which includes both training and inference time: (1) training the model to learn the scheduling policy, and (2) using the trained model to generate scheduling decisions.  
The computational time is measured on an evaluation platform (Intel(R) Xeon(R) CPU E5-2620 v2, 64 GB DDR4 DRAM, and Nvidia Titan X). During the training phase, if the queue length is 128 and the job sequence length is 256, each epoch takes 22.3 s, assuming 100 iterations per epoch. Depending on the workload, convergence typically requires 80-90 epochs, resulting in a total training time of approximately 25.5 to 28.4 min. Our model consists of 12,531 trainable parameters, making its structure relatively simple. Once training is complete, the model can make scheduling decisions within 2 milliseconds, which is negligible in the context of batch job scheduling.
# An Efficient GNSS Coordinate Recognition Algorithm for Epidemic Management
## 4 Coordinate Recognition Algorithm
### The Proposed Algorithm  
In this section, a coordinate recognition algorithm will be introduced. We also assume that there is a large number of geographical points (targets) that are stored in table 'G' of a unique database, and there is also a unique polygon \(P=\{p_{0},p_{1},\ldots,p_{n}\}\) that refers to a  
Figure 3: Example of lines with intersections. Figure 2: Example of a polygon with point and space statuses. targeted area. This algorithm will be used to calculate the inner points of polygon \(P\) from table 'G', and several functions (SegSegInt, PtInPy, SgrInPy, RectOfPy, and RectPtnToSqr) are used. These functions are listed in Table 1.  
The input required by this method is a polygon, a set of geographical points, and the side length of a square. Because the size of geographical points is very large, these geographical points are stored in a database and can be retrieved through the query command of the database. Our method will first define a rectangle that can cover this polygon. Then, it will partition the range of this rectangle into several squares whose side length is SL. Then, for each square, it will calculate the space-overlap status between the square and the polygon. When the range of the square is inside the polygon or is intersected with the polygon, it will retrieve the geographical points within the square range from the database. In the inner status, the retrieved points are the polygon's inner points without being confirmed through PtInPy. In the intersected status, these points must be confirmed again through PtInPy. Algorithm 1 is the SegSegInt algorithm. It is a segment-segment intersection algorithm whose input comprises four points: \(g_{a}\), \(g_{b}\), \(g_{c}\), and \(g_{d}\). The four points compose the segment from \(g_{a}\) to \(g_{b}\) and the segment from \(g_{c}\) to \(g_{d}\). Applying it will result in a value of 1 or 0 to represent that the two segments intersect or do not intersect. ```
1.\(\alpha\):= \((g_{a,x}-g_{b,x})\times(g_{c,y}-g_{d,y})-(g_{a,y}-g_{b,y})\times(g_{c,x}-g_{d, x})\);
2.if\(\alpha=0\)thenreturn0;
3.else
4.\(\kappa_{1}\):= \(((g_{a,x}-g_{c,x})\times(g_{c,y}-g_{d,y})-(g_{a,y}-g_{c,y})\times(g_{c,x}-g_{d, x}))/\alpha\);
5.\(\kappa_{2}\):= \(((g_{a,x}-g_{b,x})\times(g_{a,y}-g_{c,y})-(g_{a,y}-g_{b,y})\times(g_{a,x}-g_{ b,x}))/\alpha\);
6.if\((\kappa_{1}\geq 0\) and \(\kappa_{1}\leq 1\)) and \((\kappa_{2}\geq 0\) and \(\kappa_{2}\leq 1)\)thenreturn1;
7.elsereturn0;
8.endif
9.endif
```  
**Algorithm 1**SegSegInt(point \(g_{a}\), point \(g_{b}\), point \(g_{c}\), point \(g_{d}\))  
Algorithm 2 is the PtInPy algorithm. It is a point-in-polygon algorithm whose inputs are a point \(g\) and a polygon \(P=\{p_{0},p_{1},\ldots,p_{n}\}\). This algorithm is used to evaluate whether a point \(g\) is an inner point of polygon \(P\) or if that point \(g\) is an outer point of polygon \(P\)
## 2 Materials and Methods
### Adsorption on Bare Magnetic Bead  
Depending on the surface characteristics of the magnetic bead, lead may be adsorbed onto the bare bead itself, which hinders the evaluation of the adsorption performance of the pure peptide. Thus, several types of commercially available magnetic beads were compared, to identify the bead adsorbing the lowest amount of lead on its bare surface. Four kinds of beads with a functional carboxyl group, namely Dynabeads\({}^{\text{TM}}\) M-270 Carboxylic Acid (Thermo Fisher Scientific, Oslo, Norway), AccuBead\({}^{\text{TM}}\) COOH magnetic beads (Bioneer Inc, Daejeon, Korea), Cytodiagnostic Zepto\({}^{\text{TM}}\) Mag Carboxyl Microspheres (CytoDiagnostics Inc., Burlington, ON, Canada), and \(\text{B}\text{M}\text{ag}^{\text{TM}}\) long-arm carboxy-terminated magnetic beads (Bioclone Inc., San Diego, CA, USA), were purchased and compared to each other. First, 3 mg of each bead was washed with 25 mM MES buffer twice and then incubated in 5 mL of 60 mg/L Pb(NO\({}_{3}\))\({}_{2}\) for 1 h. After magnetically separating the beads, the lead concentration remaining in each supernatant was measured.
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 6 Security Analysis
### Continuous Authentication  
The continuous authentication strategy is conducted in our scheme, which could periodically detect the anomalies during the entire user session [26]. The analysis about behavioral biometrics is adopted, revealing the real-time human physical status. The personalized model for individual participant is constructed locally, where the training data characterize unique behavioral patterns within a relatively long interval. The biometric parameter \(\Psi_{i}=\{\Phi_{\Delta_{1}},\ldots,\Phi_{\Delta_{N}}\}\) is processed for anomalies detection. Additionally, after each validation, the stored parameter set \(\Psi_{i}\) is updated using newly acquired value so as to timely reveal the recent biometric status of user. Noting that the parameter checking process is performed at set intervals, thus continuous authentication is provided in our scheme.
# An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments
## 2 Materials and Methods
### The \(A^{*}\) Search Algorithm  
The optimal visiting sequence obtained from the traveling salesman problem optimization does not consider irregular terrain elevations. Furthermore, the UAV needs to fly at a constant height above ground level, such that some points cannot be reached using a simple straight line path. To deal with this issue, the \(A^{*}\) search algorithm was used to find the minimum cost path between the sequence of points of interest. Here, the goal of the \(A^{*}\) algorithm is to find the path that minimizes the following cost:  
\[F_{cost_{i}}=g_{cost_{i}}+h_{cost_{i}}, \tag{3}\]  
where \(g_{cost_{i}}\) represents the accumulated travel cost between the current node and the previous nodes, and \(h_{cost_{i}}\) represents the heuristic cost, which is an estimation of the cost between the current and the goal nodes. The spatial resolution of the raster data grids was considered in the cost function of the \(A^{*}\) algorithm. In this case, the length and width of the Keswick region were divided by the number of rows and columns, respectively, of the  
Figure 3: Mutual viewshed space for successful relaying.  
raster grid to obtain the longitudinal and latitudinal grid resolutions. The final resolutions for longitude and latitude are \(reslon=35.988\) m and \(reslat=30.922\) m. The cost index used by the \(A^{*}\) algorithm is, therefore, given by  
\[\text{Cost}_{2-1}=\sqrt{reslon(x_{2}-x_{1})^{2}+reslat(y_{2}-y_{1})+(z_{2}-z_{1}) ^{2}}. \tag{4}\]  
The data used by the \(A^{*}\) algorithm were stored in a two-layer map while the search was performed. The first layer contained the digital elevation model raster, whilst the second layer contained the binary occupancy map that prevents the UAV from entering blockage and hazardous weather areas.
# Development of Digital Training Twins in the Aircraft Maintenance Ecosystem
## 2 Materials and Methods
### Digital Training Twin Model
Apache Kafka supports high-throughput event streaming between orchestration modules, simulation servers, and analytics dashboards, while MQTT provides a lightweight protocol optimized for low-latency messaging between edge devices and cloud nodes in bandwidth-constrained environments. This tightly integrated architecture supports a continuous real-time loop of observation, orchestration, and feedback. The orchestration engine processes incoming learner performance data, selects optimal training content, deploys resources through the cloud-edge network, and updates competence profiles based on observed outcomes. The hybrid deployment structure ensures scalable operation across both centralized institutional training centers and decentralized or remote educational environments, while maintaining regulatory compliance, instructional efficiency, and data security. The cloud component of the DTT ecosystem is built upon a containerized microservices architecture using Docker and orchestrated through Kubernetes. This allows for scalable deployment of simulation engines, orchestration services, and learner data analytics modules. High-performance computing tasks such as federated model aggregation and reinforcement learning policy evaluation are executed on GPU-enabled nodes hosted in the cloud, utilizing platforms such as NVIDIA CUDA and TensorFlow. The edge layer supports real-time content delivery through lightweight clients deployed on local workstations or XR headsets. These clients are implemented using Unity or Unreal Engine for immersive training modules, and standard HTML5/JavaScript frameworks for CBT delivery. Secure communication between cloud and edge layers is maintained via TLS-encrypted MQTT for real-time telemetry and Kafka for high-throughput streaming of xAPI logs and orchestration events. This hybrid deployment model ensures seamless interaction between centralized orchestration and decentralized learning environments while maintaining performance, scalability, and data protection.
# On the Octonion Cross Wigner Distribution of 3-D Signals
## 3 Octonion Wigner Distribution  
We devote this section to the new concept of the octonion Wigner distribution. In addition to the definitions and basic properties (which are analogs of the corresponding properties of the classical Wigner distribution), we also focus on the important (from the point of view of signal theory and applications) theorems concerning Moyal's formula and the uncertainty principle.
# Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image
## 4 Proposed Method: Adopting a Signal-Processing Technique
### First-Time Derivative Attribute (Contrast Attribute)  
The first-time derivative attribute is calculated as an envelope rate change of time series data. This attribute shows the strength of energy changes in the medium. It also indicates the strength of absorption of energy in the medium. The first-time derivative attribute is defined as  
\[C=\left\{\frac{\sum_{k--K}^{+K}\left(\left[\frac{\partial a_{jk}}{\partial x} \right]^{2}+\left[\frac{\partial a_{jk}}{\partial y}\right]^{2}\right)}{\sum_{ k-K}^{+K}\sum_{j=1}^{l}a_{jk}^{2}}\right\}^{1/2} \tag{9}\]  
where \(a_{jk}\) is the envelope at the \(j\) and \(k\) position, while \(x\) and \(y\) are the spatial coordinates. This attribute can be directly implemented into the image processing, where the a is taken from the grayscale image of the CT scan and \(x\) and \(y\) are the coordinates of the pixel, and where \(C\) is the value located in the center of the calculated pixels.  
The contrast attribute is directly related to amplitude changes, where the amplitude anomalies indicate the layers' internal properties. However, due to ambiguity in the amplitude of the time series, the interpretation of the contrast amplitude also leads to ambiguity or non-unique interpretations. This can also occur in X-ray imaging; a density change in human tissue can cause HU contrast. However, the value of the HU contrast itself will lead to ambiguity due to the dependency of HU on X-ray energy used during recording, resulting in difficulties when one cut-off/ threshold value is applied for different scanning modalities. Some statistical studies are needed to determine the cut-off or threshold value for osteoporosis determination.
# Structural Correlation Based Method for Image Forgery Classification and Localization
## 6 Experimental Results
### Forgery Detection and Localization Results on CASIA 2 Dataset  
Table 4 summarizes the 3-class classification results of the proposed method on the CASIA 2 dataset. To the best of our knowledge, all of the previous researches on forgery detection of this dataset are binary classification. Therefore, only the results of our work are reported. The detection accuracy of authentic images achieve 96.9%, which is higher than two image forgery types. 6.7% of copy-move images are classified as spliced images. By contrast, 4.4% of spliced images are mistakenly detected as copy-move images.  
Table 5 and Table 6 compare the proposed method with other researches on localization performance of spliced images and copy-move images of CASIA 2 dataset, respectively. Examples of CMFL results of the proposed method are shown in Figure 9. Since CASIA 2 is the most challenging dataset in our experiments with many small and smooth tampered regions, the proposed method occasionally fails to search for matching regions.
# Estimate of Passive Time Reversal Communication Performance in Shallow Water
## 2 Parameter \(E_{q}\)  
During passive time reversal communication, the probe signal and the subsequent communication stream are transmitted by a source and are received at multiple receivers after propagating through a multipath ocean channel. The CIR \(h_{i}(t)\) at each receiver position is estimated by a matched filtering of the received probe signal at each receiver element with a replica of the emitted probe signal. The \(q\)-function is estimated by summing the autocorrelation of \(h_{i}(t)\) as given inside the bracket of Equation (1).  
[PERSON] and [PERSON] [22] suggested the parameter \(E(t)\) to describe a shallow-water multipath channel. \(E(t)\) is a normalized, cumulative, time-integrated form of the channel intensity impulse response function. The parameter \(E(t)\) estimates the relative energy contribution of the multipath with respect to time. After that, [PERSON] et al. [23] suggested the \(E_{1}\) parameter by applying the concept of the \(E(t)\), which is an energy fraction of the channel impulse response over one symbol duration. They showed that the overall BER performance improved as \(E_{1}\) increased.  
The new parameter proposed here (\(E_{q}\)) uses an idea similar to \(E_{1}\) in which the channel intensity impulse response in \(E_{1}\) is replaced by the right-hand side of the \(q\)-function as given by  
\[E_{q} = \frac{\int_{0}^{T_{s}}q_{r}(\tau)d\tau}{\int_{0}^{\infty}q_{r}( \tau)d\tau}, \tag{2}\]  
Here, \(q_{r}\) is the right-hand side of the \(q\)-function and \(T_{s}\) is one symbol duration. In general, communication BER increases as the side-lobe magnitude and the main-lobe width of the \(q\)-function increase [18; 19]. The higher side lobes and wider main-lobe width augment the energy fraction of \(q\)-function positioning outside of one symbol duration and consequently create a significant ISI. In this case, a decrease in \(E_{q}\) is expected. Therefore, \(E_{q}\) might be used as a parameter to describe the performance of time reversal communication and can be applied to determine the symbol rate and/or optimal receiver number and spacing using time reversal communication. In addition, it can be used as a parameter to determine the need for additional equalization for a given ocean environment and the fixed receiver array system.