esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs +
## 3 System Model
### The Basic Concept of The Proposed Secure Routing and Clustering Protocol  
In this subsection, we present the basic concept of our proposed routing protocol. The proposed routing protocol can be divided into two processes, called clustering by using the DLC protocol and routing by using the DLSR protocol, that can avoid a blackhole attack. These processes can be summarized as follows:  
* **Clustering Process:** In the proposed DLC protocol, network nodes are grouped into clusters based on shared information such as distance, speed, direction, and remaining energy. CH is selected based on the highest remaining energy. DL is used to optimize the weights of the fitness function for selecting the most optimal CH so that a node can join a cluster as CM effectively and transmit its data efficiently. The DNN model takes input parameters such as cosine similarity, cosine distance, and remaining energy to calculate the optimized weight of the fitness function. GW is also considered to improve route connectivity and reduce control overhead in the clustering process.
* **Routing Process:** Following the clustering process, a source node broadcasts an RREQ packet to find the destination node. However, a blackhole node may send a fake RREP to deceive the source node into believing it has the optimal route. To overcome this, our proposed DLSR protocol uses DL to detect and select the optimal route for avoiding blackhole attacks. When a node receives RREP packets, it feeds the relevant feature data into the DNN model. The model then processes this information and generates an output, classifying the received data as originating from a blackhole node or a normal node. Then, based on the fitness value, the routing protocol can dynamically choose to operate in either secure or normal routing mode. Here, we also optimize the weight of the fitness function by using DL. Figure 1 shows an illustration when an intermediate node receives two RREP packets from the NN. One of the RREP packets received is a fake RREP from a blackhole node. In this case, the proposed DLSR protocol employs secure routing by detecting it through DL and calculating the fitness function. Therefore, the proposed DLSR protocol can establish the secure route from \(S\)-\(CH_{1}\)-\(CH_{2}\)-\(CH_{3}\)-\(CH_{m}\)-\(D\) to avoid a blackhole attack.
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 4 Experimental Results and Discussion
### Evaluation Metrics  
Our detection framework adopts the well-established average precision (AP) metric for holistic performance evaluation. AP quantification involves numerical integration of the precision-recall (PR) curve's enclosed area, providing a robust measurement of detection efficacy. Precision quantifies the ratio of true positive detections to total predicted positives, whereas recall assesses detection completeness by measuring identified positives against all annotated targets. These metrics are mathematically defined as:  
\[\text{Precision} =\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{28}\] \[\text{Recall} =\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{29}\]where true positive (TP), false positive (FP), and false negative (FN), respectively, denote correct detections, erroneous predictions, and undetected ground-truth instances. The intersection over union (IoU) metric calculates spatial alignment precision between detected and annotated bounding regions, establishing critical evaluation criteria for detection systems. For multi-object detection scenarios, we implement mean average precision ([EMAIL_ADDRESS]) as the principal performance indicator, computed by averaging category-specific AP values at the 0.5 IoU threshold. The mathematical formulation for mAP derivation is expressed as:  
\[\text{AP} =\int_{0}^{1}P(R)\,dR \tag{30}\] \[\text{mAP} =\frac{1}{n}\sum_{i=1}^{n}\text{AP}_{i} \tag{31}\]  
where \(P\) corresponds to the precision metric, \(R\) signifies the recall rate, \(n\) indicates the total categorical classes, and APi quantifies the average precision metric specific to class index (where \(i\)  in [1, n]) across all detection instances.
# Sample-Wise Aiding in GPS/INS Ultra-Tight Integration for High-Dynamic, High-Precision Tracking
## 4 Test Results and Discussion
### Test Setups and Scenarios
Finally the code phase errors and carrier phase errors can be obtained by subtracting the simulator recorded data from the receiver measurements.
# Design of Chaotic Interleaver Based on Duffing Map for Turbo Code
## 4 Empirical Tests in Software  
### Simulation of Communication System  
A communication system is simulated on the MatLab/Simulink software [41]. Figure 4 shows the implemented system, which comprises a turbo encoder and a decoder. A length constraint of four values and a generator are required for coding. Configurations are set up, so the system employs the following parameters: the interleaver indexes, code block length, number of decoding iterations, and the values of the \(E_{b}/N_{0}\) relation.  
The blocks used in the Simulink software corresponding to the communication system are described below:  
* Bernoulli binary generator, which enables the generation of random binary numbers using Bernoulli's distribution;
* Unipolar-to-bipolar converter, which enables the conversion of the unipolar signal input into a bipolar output signal;
* Additive white Gaussian noise (AWGN) channel, which allows for adding white Gaussian noise into a real or complex input signal;
* Error rate calculation, which compares input data from a transmitter with output data from a receiver while calculating error rate;
* Turbo encoder, which enables the coding of a binary input signal using the scheme of coding concatenated in parallel. This coding scheme uses two identical convolutional coders and an internal interleaver. This internal interleaver is modified through the application of the interleavers presented above;
* Turbo decode, which decodes input signals through concatenated decoding in parallel. The scheme of iterative decoding employs an a posteriori probability (APP) algorithm for this purpose. The same trellis structure and decoding algorithm are used by the two decoders;
* Model parameters that allow for specifying the execution parameters for the simulation, which are the SNR in decibels, the block length in bits, and the decoding iteration number.
## 3 Methodology
### Outlier Detection Based on MAH  
Multivariate outliers can be identified using the MAH distance, which is the distance of a data point from the calculated centroid in other cases where the centroid is calculated as the intersection of the means of the assessed variables. Each point is recognized as an (X, Y) combination, and the multivariate outliers lie at a given distance from the other cases. The distances were interpreted using \(p\)\(<\) 0.001 and the corresponding \(\chi^{2}\) value, with degrees of freedom equal to the number of variables.  
Multivariate outliers can also be identified using leverage, discrepancies, and influence. Leverage is related to MAH distance but is measured on a different scale; therefore, the \(\chi^{2}\) distribution does not apply. Large scores indicate cases in which they are farther out; however, they may still lie along the same line. Discrepancy assesses the extent to which a case is consistent with others. Influence is determined by leverage and discrepancy and assesses changes in coefficients when cases are removed. Cases \(>\) 1.00 are likely to be considered outliers. The formula for computing the MAH distance is [11; 12]:  
\[D^{2}=(x-m)^{T}C^{-1}(x-m) \tag{1}\]  
where \(D^{2}\) is the square of the MAH distance; x is the vector of the observation (row in a dataset); m is the vector of the mean values of the independent variables (mean of each column); \(C^{-1}\) is the inverse covariance matrix of the independent variables; T denotes the transpose of the vector; and (x \(-\) m) is the distance of the vector from the mean, which is divided by the covariance matrix (or multiplied by the inverse of the covariance matrix). The \(p\)-value probability is given by the following equation:  
\[P=1-X^{2}(MAH,df) \tag{2}\]  
In this study, outliers were removed based on the MAH distance to detect multivariate outliers. We selected 37 diabetes-related features. The descriptions of these features are presented in Table 2. Figure 3 shows the data with and without outliers from the dataset, using several values based on the MAH method.  
Default training and testing splits of 70% and 30% were applied to the target dataset, respectively. Descriptive statistics for the target variables with and without FS and MAH are presented in Table 3. In this study, the diagnostic criteria typically involved the assessment of fasting blood glucose levels. Diabetes is usually diagnosed with a fasting blood glucose level of 126 milligrams per deciliter (mg/dL) or higher, or when the hemoglobin A1c (HbA1c) level reaches 6.5% or higher. Pre-diabetes is often identified when fasting blood glucose levels fall between 100 and 125 mg/dL or when HbA1c levels range between 5.7% and 6.4%. Normal ranges were defined as fasting blood glucose levels of \(<\) 100 mg/dL and HbA1c levels of \(<\) 5.7%. These values are used to diagnose diabetes and pre-diabetes and are based on guidelines from organizations such as the American Diabetes Association (ADA).
## 3 Results and Discussion
### Three-Point Bending Test
#### 3.2.2 Test Results  
As a result of the 3-point bending test, the flexural strain and stress were obtained as shown in Figure 11. Based on ASTM, the test was carried out up to the point of flexural strain of 5%, with no fracture occurring in any of the specimens. The test was repeated 5 times for each specimen and the flexural stress at 5% flexural strain is shown in Figure 12 as the average and standard deviation. For materials A and B, which were printed with only a single composite material, the difference in mechanical properties of the base resin was very large, so the differences were about 20 times greater with flexural stress levels of 4.746 MPa and 0.228 MPa, respectively. For the half-and-half structure, in which the material was changed only once and printed with different materials for half of the total thickness, the value was slightly lower than the median value of the flexural stress of materials A and B. In addition, the flexural stress levels differed by about 25% depending on the printing order and bending direction of materials A and B. As mentioned above,  
Figure 10: Specimens in the 3-point bending test.  
this was because the type of force received by each material varied depending on the printing order and bending direction of the materials. Accordingly, when hard material A received tensile force it showed about 25% higher flexural stress than when soft material B received tensile force. On the other hand, for the laminar composite structure in which the material was changed for each printed layer, a force of 2.326 MPa, which was similar to the median value for materials A and B, was shown regardless of the printing order or bending direction of the materials. In other words, the half-and-half structure and the laminar composite structure contained the same ratio of materials A and B, although their bending properties were different. Unlike the half-and-half structure, which was a simple bond of two plates with different properties, the laminar composite structure showed mixed properties from a macroscopic point of view due to the plate-reinforced effect caused by continuously changing the printed material for each layer over a microscopic area.
# Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer
## 4 Analysis of Experimental Results
### Comparative Experiment
#### 4.5.3 Australian Electricity Load Dataset Experiment  
In the visualization of the results, we present the models other than ARIMA, which have relatively small differences. As shown in Table 5 and Figures 14-17, in the comparative experiments of different prediction steps for the Australian power load dataset, the MSGNet-MLLA-Transformer method proposed in this paper is lower than all baseline models in both RMSE and MAE, while R\({}^{2}\) is higher than the baseline, with the best performance. The ranking of model performance is as follows: Ours > MSGNet > Mr-CAN > TCN-Attention/GRU-Attention > TCN/GRU is comparable to MSGNet in single-step prediction) > ARIMA. Regarding this phenomenon, we analyze and believe that capturing the spatio-temporal characteristics and long-term dependencies of data is crucial for improving prediction performance. The method proposed in this paper achieves the best performance by effectively decoupling spatio-temporal features and optimizing multi-scale dynamic interaction. Although MSGNet is good at multi-scale spatio-temporal modeling, it has long relied on insufficient capture. MrCAN lacks a clear multi-scale decomposition mechanism; GRU/TCN and its improved attention mechanism may have worse performance due to weak spatial modeling ability, limited receptive field, or insufficient decoupling ability of spatio-temporal features. These results highlight the crucial role of collaborative optimization, spatio-temporal feature decoupling, and multi-scale interaction in the performance of the power load forecasting model.  
Figure 13: Comparison chart fitting the 12-step prediction experimental model of the ETTm1 dataset, listed as (**a**) GRU; (**b**) TCN; (**c**) GRU-Attention; (**d**) TCN-Attention; (**e**) MrCAN; (**f**) MSGNet; and (**g**) Ours.  
Figure 14: Comparison chart fitting the 1-step prediction experimental model of the Australian electricity load dataset, listed as (**a**) GRU; (**b**) TCN; (**c**) GRU-Attention; (**d**) TCN-Attention; (**e**) MrCAN; (**f**) MSGNet; and (**g**) Ours.  
Figure 15: Comparison chart fitting the 3-step prediction experimental model of the Australian electricity load dataset, listed as (**a**) GRU; (**b**) TCN; (**c**) GRU-Attention; (**d**) TCN-Attention; (**e**) MrCAN; (**f**) MSGNet; and (**g**) Ours.  
Figure 16: Comparison chart fitting the 6-step prediction experimental model of the Australian electricity load dataset, listed as (**a**) GRU; (**b**) TCN; (**c**) GRU-Attention; (**d**) TCN-Attention; (**e**) MrCAN; (**f**) MSGNet; and (**g**) Ours.  
Figure 17: Comparison chart fitting the 12-step prediction experimental model of the Australian electricity load dataset, listed as (**a**) GRU; (**b**) TCN; (**c**) GRU-Attention; (**d**) TCN-Attention; (**e**) MrCAN; (**f**) MSGNet; and (**g**) Ours.
# Improved Hybrid A* Algorithm Based on Lemming Optimization for Path Planning of Autonomous Vehicles
## 3 Improved Hybrid A* Algorithm
### Steering Penalty Term  
In the hybrid A* algorithm, the steering penalty term is used to restrict unnecessary sharp turns in the path, ensuring smoother and more feasible trajectories that comply with the vehicle's kinematic constraints, as shown in the following Equation (22):  
\[g(n)=g(n-1)+s+P_{\theta}\cdot\sum\lvert\theta_{n}-\theta_{n-1}\rvert \tag{22}\]  
where \(g(n)\) represents the total cost of the current node; \(g(n-1)\) denotes the total cost of the previous node; \(S\) indicates the step length (unit movement distance); \(P_{\theta}\) is the steering penalty coefficient (range: [0.1, 5]); while \(\theta_{n}\) and \(\theta_{n-1}\) represent the heading angles (in radians) of the current and previous nodes. During path searching, excessive steering angle changes result in higher costs, prompting the algorithm to prioritize smoother paths.  
Different types of penalty functions are used at different stages of the algorithm. Compared with hybrid A*, the penalty functions are as shown in Table 3.  
Therefore, as can be seen from Table 3, the penalty function in hybrid A* restricts drastic changes in the turning angle when calculating the cost. If the angle changes too much, the cost increases, preventing the algorithm from making large, frequent direction changes during obstacle avoidance, thereby avoiding excessive sharp turns.
## 2 FSO Communication System
### Channel  
In the FSO communication system, the channel is defined as the free space or atmospheric pathway through which the optical signal is transmitted. Unlike conventional fiber optic systems that use physical cables to guide the light signals, FSO systems depend on a direct LoS between the transmitting and receiving devices. This LoS requirement makes the FSO channel highly susceptible to various environmental influences.  
The quality and reliability of the FSO channel can be significantly impacted by several external factors. For terrestrial applications, weather conditions such as fog, rain, and snow can cause attenuation of the signal, reducing its strength as it travels through the atmosphere. Additionally, AT, which includes variations in air density and temperature, can cause the light beam to scatter, further degrading signal quality. Physical obstacles that obstruct the LoS can also interrupt the signal transmission. These environmental factors pose considerable challenges to the stability and performance of FSO communication systems. A comprehensive discussion on these challenges, including detailed analyses of how different conditions affect the FSO channel, is provided in Section 3. The section elaborates on the mechanisms of signal degradation and the strategies employed to mitigate these adverse effects, ensuring more reliable FSO communication.
## 3 Mathematical Model of Cable Force Optimization
### Mathematical Model  
After determining the objective function and corresponding constraints, the optimization model of the cable force can be expressed as follows:  
\[\left\{\begin{array}{l}\min f=[\min D,\min R]=[\min f_{1},\min f_{2}]\\ s.t.g_{w}(\sigma_{j},T_{i})\leq 0,w=1,2,\ldots,\varepsilon\end{array}\right. \tag{14}\]  
where \(f\) is the objective function, including the objective term \(D\) of the horizontal displacement of the bridge tower and the vertical displacement of the main beam, \(R\) is the range of cable force concentration per unit length, \(g_{w}(\sigma_{j},T_{i})\) is the unified form of constraints, and \(\varepsilon\) is the number of constraints.
## 3 Mathematical Model
### Objective Function  
Our objective is to minimize the total cost while meeting various load demands in the zero-carbon building system. Specifically, the total cost comprises five components: electricity cost, gas cost, carbon cost, penalties for curtailed wind and solar energy, and cost of ES when charging and discharging.  
\[\min E=E_{E}+E_{G}+E_{C}+E_{W}+E_{ES} \tag{21}\]  
The operation and maintenance costs of facilities within the building are negligible, so the electricity and gas costs only account for the portions purchased from external markets.  
\[E_{E}=\sum_{t}\lambda_{t}^{E}P_{buy,t}^{E} \tag{22}\]  
\[E_{G}=\sum_{t}\lambda_{t}^{G}G_{buy,t} \tag{23}\]  
Here, \(\lambda_{t}^{E}\) and \(\lambda_{t}^{G}\) represent the prices of electricity and gas, while \(P_{buy,t}\) and \(G_{buy,t}\) denote the purchased quantities in the corresponding markets at time t.  
Carbon costs can be divided into two components: the first corresponds to the variable costs associated with the carbon processing procedure, while the second reflects the expenditure in the carbon market to purchase carbon quota shortfalls. When actual carbon emissions exceed the carbon quota, this cost is positive; otherwise, it is negative.  
\[E_{C}=\sum_{t}\Bigl{[}\mu^{C}\left(C_{cap,t}+C_{emi,t}\right)+\lambda_{t}^{C} \left(C_{emi,t}-C_{quota,t}\right)\Bigr{]} \tag{24}\]  
\[C_{quota,t}=\sigma\Bigl{(}P_{buy,t}^{E}+P_{W,t}^{E}+P_{CHP,t}^{E}+P_{GT,t}^{E} \Bigr{)} \tag{25}\]  
where \(\mu^{C}\) is used to denote the operation cost, which is related to the total amount of carbon. \(\lambda_{t}^{C}\) represents the price in the carbon market. \(C_{quota,t}\) refers to the carbon quota of the building, and \(\sigma\) is the average marginal emission factor.  
To enhance the utilization of renewable energy, it is necessary to address potential curtailment of wind and photovoltaic power. \(\mu_{pen}\) is introduced as a penalty factor, leading to the following formula:  
\[E_{W}=\sum_{t}\mu_{pen}P_{cur,t}^{E} \tag{26}\]  
where \(P_{cur,t}^{E}\) denotes the curtailment of renewable energy in PV&WPP.  
Finally, the operation cost of ES is closely related to \(p_{cls,t}\) and \(p_{dis,t}\):  
\[E_{ES}=\sum_{t}\mu_{ES}(p_{cls,t}+p_{dis,t}) \tag{27}\]  
where \(\mu_{ES}\) represents the variable cost parameter of ES.
# Repulsive Force for Micro- and Nano-Non-Contact Manipulation
## 7 Discussion  
Controlling the localization of a high number of charges on surfaces may have a lot of applications. This paper illustrates an original way to induce high electrostatic forces on micro-objects along several micrometers and paves the way for the application of non-contact manipulation of micro-objects. The electrostatic force depicted in this article can be used in two different ways in non-contact manipulation.  
The first approach consists using the electrostatic force to place the object in levitation several micrometers up to a substrate, and the use of another actuator to move the object parallel to the substrate. In such a case, both the substrate and the micro-object have to be functionalized.  
The second approach is closer to electrophoresis, where a functionalized object can be moved in an electric field induced by microelectrodes. The possible performances (typically manipulation velocity) have been compared with more usual principles, such as the dielectrophoresis and our approach, which show a potential interest compared to the state-of-the-art.  
Moreover, both approaches (chemical and physical) could be combined in the same device in the future. As an example, the bottom face of the sorting microchannel can have a chemical pattern in order to generate the passive levitation of microparticles in the channel  
Figure 4: (**A**) Trajectory modeling for a microsphere functionalized by APTES by dielectrophoresis; and (**B**) the rotation modeled for a rectangular object (20 \(\upmu\)m) functionalized by amine and carboxylic groups, each on a side of the object.  
avoiding sedimentation. The top face could be structured with electrodes enabling to actively sort microparticles.  
The long distance of repulsion was already mentioned on several studies with a distance close to 200 \(\upmu\)m [32]. Indeed, a large exclusion zone (EZ) was observed in the vicinity of metals [40], hydrogels [41], ion exchange polymer [42], biological tissues [43], white blood cells [29], Nafion polymer [16], and self-assembled monolayer (SAM) [28,44]. An important research was performed to understand such a peculiar phenomenon during the last few years. Several hypotheses were developed as: (i) water structuring [45], (ii) a pH gradient, and thus a charge separation [46], (iii) chemotaxis driven by solute gradients (OH-, H+ and salts in solution) [30], and (iv) a combination of ion-exchange at the surface, diffusion of ions, and diffusiophoresis of particles in the resulting ionic gradients [31]. Recently, a review analyzed a different theory to explain the EZ and concluded several major problems, such as that the water in the EZ undergoes a phase change or significant reordering. They added that [PERSON]'s theory [30] of macroscopic chemotaxis presents a compelling alternative theory, which can explain experimental findings; however, there are still many open questions about exclusion zones.