esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Motivation  
The shape of the bistable beams can be obtained by initial bucking or by pre-shaping during fabrication (Figure 1), resulting in bistable mechanisms with different shapes and specific characteristics, thereby expanding the possibilities to study bistable mechanisms. The flexible beams are mainly subjected to in-plane bending deformation deflections (often accompanied by axial deflections) [18]. Thus, out-of-plane deflections are rarely considered during design. In practice, however, compliant bistable mechanisms need to maintain consistent kinematic-static behavior when switching between two stable equilibrium positions [19]. When the compliant bistable mechanism is subjected to an out-of-plane load, as illustrated in Figure 2, its kinematic-static behavior will be significantly affected [15], resulting in motion inconsistency of the bistable mechanism. These changes will affect the behavior prediction of the entire mechanism. For the traditional straight-beam bistable mechanism (SBBM), when subjected to out-of-plane load, the shuttle will produce out-of-plane deflection, which will affect the motion consistency of the bistable mechanism, thus reducing the accuracy of the sensor. Therefore, improving the ability of a bistable mechanism to resist out-of-plane deformation has become a research focus.  
Figure 1: Fully compliant bistable mechanism.  
Figure 2: (a) Compliant bistable mechanism deflection unaffected by off-axis or eccentric load; (b) Compliant bistable mechanism deflection affected by off-axis or eccentric load.  
Inspired by the bistable buckled straight-beam mechanism, we select a circular beam instead of a straight beam to obtain a high-performance bistable mechanism. The shape of the bistable circular beams can be pre-shaped during fabrication. Thus, circular beam can provide advantages during beam buckling compared with the conventional straight beam. Given the increased length between the same endpoints, the circular beam shows a more even distribution of stress, greater flexibility during deformation, and higher bistable performance [20].
## 4 Experimentation on the Case Study
### Surveyed Area and Considered Assets  
Planning for a drone survey is crucial to ensure legal compliance, operational safety, and the collection of high-quality data. According to the European Union Aviation Safety Agency (EASA), different geographic zones (i.e., Excluded, Restricted and Facilitated geozones and U-Space airspace) have specific regulations that govern drone operations as follows [25]:  
Excluded geo-zones (marked red) prohibit flights for operations in all or certain classes; hence, drone flying is not allowed.  
* Restricted geo-zones (marked yellow) limit Unmanned Aerial System (UAS) operations which are subject to fulfilment of an imposed set of conditions, since these areas are usually near airports, heliports, national parks, military installations, hospitals, nuclear power plants or any kind of key industrial site.
* Facilitated geo-zones (marked green) rate UAS operations to 'Open', so drones can be flown without restrictions.
* U-Space airspace (marked blue) represents a portion of the lower space where operations are managed for drones and other vehicles that operate in it.  
Securing the appropriate flight permit ensures that the drone survey adheres to local and national aviation regulations, preventing legal issues and potential fines. Additionally, flight planning helps identify potential hazards, ensuring the safety of both the drone and the surrounding environment. It allows operators to clearly define the survey's scope and objectives and ensure optimal flight paths for comprehensive data collection. Effective planning ultimately leads to more efficient operations, better data quality, and more reliable survey results, aligned with the project's goals and requirements.  
The UAS (restricted and restrictable) geographical zones for Romania are available at the Romatsa Aeronautical Information Portal. Figure 7 displays UAS zones, with a detailed view of the area of interest valid from 1 February 2024. The regulation [26] issued on 24 May 2019 outlines the rules and procedures that govern the operation of UAS within the European Union (EU) airspace and dictate the rules that apply to all operators and remote pilots and are related to airworthiness, organisations, and personnel involved in UAS operations. Following these specifications, and based on the UAS flight area (Zone ID RZ 1030), the necessary flight permit was diligently obtained.
## 3 Methodology  
The validation of the low-cost SHM system was conducted by comparing and evaluating a set of small- and full-scale experimental tests using a reference SHM system. The experimental tests aimed to estimate the outreach, ascertain the performance, and identify the weaknesses of the low-cost monitoring system according to different ambient conditions. The analysis and the characterization of the dynamic properties of the structures were carried out considering a deterministic approach and white noise input excitation to ambient vibrations.  
Our methodology addressed data gathering, preprocessing, and processing and evaluation for every experimental testing case. The flowchart of this framework is shown in Figure 1.
# Simple HPLC-PDA Analysis to Determine Illegal Synthetic Dyes in Herbal Medicines
## 2 Materials and Methods
### Chemicals and Reagents  
The present study used three herbal medicinal products, including Hawthorn fruit (_Crataegus pinnatifida_ Bunge), Cornus fruit (_Cornus officinalis_ [PERSON] et Zuccarini), and Schisandra fruit (_Schisandra chinensis_ Baillon), which are variants of the color red, to develop a method to determine synthetic colorants. The standardized herbal medicines were purchased from a herbal medicinal store located in Daejeon, Korea, where the majority of samples were imported from China. Before conducting the experiments, the identities of the herbal medicines were confirmed through sensory tests performed by a specialist. Voucher specimens were deposited at the National Institute of Food and Drug Safety Evaluation in Osong, Chungbuk, Korea.  
The ten synthetic colorants, including Tartrazine (\(\geq\)99.0%, 1934-21-0), Sunset yellow (\(\geq\)95.0%, 2783-94-0), Metanil yellow (\(\geq\)98.0%, 587-98-4), Auramine O (\(\geq\)85.0%, 2465-27-2), Orange II (\(\geq\)98.0%, 633-96-5), Acid red 73 (\(\geq\)97.0%, 5413-75-2), Amaranth (\(\geq\)98.0%, 915-67-3), New Coccine (\(\geq\)99.0%, 2611-82-7), Azorubine (\(\geq\)98.0%, 3567-69-9), and Erythrosine B (\(\geq\)98.0%, 16423-68-0) were purchased from Sigma-Aldrich, Co. (St. Louis, MO, USA) (Table 1). HPLC-graded acetonitrile, methanol, and ammonium acetate were purchased from Merck KGaA (Darmstadt, Germany). Distilled water was prepared using a CascadaTM AN water purification system (Pall Corporation, New York, NY, USA) with specific resistivity of 18.0 M\(\Omega\).
# Part-Treatment Efficacy Prediction Using a CMA-ES-Based Dendritic Neuron Model
## 2 Materials  
The proposed dendritic neuron model consists of four parts: the synaptic layer \(Y\), the dendritic layer \(Z\), the membrane layer \(V\), and the cell body \(O\). Its logical structure is shown in Figure 1.  
The synapse in the synaptic layer receives the input signal \(x_{i}\) and outputs \(Y_{i,b}\) to the corresponding dendritic branch. The output \(Y_{i,b}\) of the \(i\)-th (\(i=1,2,\ldots,I\)) synapse at the \(b\)-th (\(b=1,2,\ldots,B\)) dendritic branch can be expressed as follows:  
\[Y_{i,b}=\frac{1}{1+e^{-k(w_{i,b}x_{i}-q_{i,b})}} \tag{1}\]  
where \(k\) is a predefined constant. \(w_{i,b}\) and \(q_{i,b}\) are the synaptic parameters to be optimized. Four different synaptic connection states can be identified according to the different \(w_{i,b}\) and \(q_{i,b}\) values, as shown in Figure 2. The different synaptic connection states affect the simplified pruning operation of the model. The determination of the different connection states can be found in the literature [35].  
Figure 1: Logical structure of the proposed dendritic neuron model.  
Figure 2: The random synaptic connection state is transformed into one of four synaptic connection states after training.  
The dendritic layer receives signals from the synaptic layer and outputs \(Z_{b}\) to the membrane layer by performing a cumulative multiplication operation. The \(b\)-th dendritic branch can be expressed as follows:  
\[Z_{b}=\prod_{i=1}^{I}Y_{i,b} \tag{2}\]  
The membrane layer gathers the signals of all of the dendritic branches and transmits them to the cell body. The membrane layer can be represented by a large-scale summation operation, which is expressed as follows:  
\[V=\sum_{b=1}^{B}Z_{b} \tag{3}\]  
The cell body receives the output \(V\) of the membrane layer and transforms the signal \(V\) into the probability \(O\) using a sigmoid function, which is expressed as follows:  
\[O=\frac{1}{1+e^{-k(V-\gamma)}} \tag{4}\]  
where \(\gamma\) is defined as the threshold of the cell body.  
The pruning strategy of the dendritic neuron model is based on the effect of the synapses in the constant 0 connection state. The constant 0 connection causes the output value of the dendritic branch to be close to zero, according to Equation (2). Since this dendritic branch has a minimal effect on the calculation of the membrane layer according to Equation (3), this dendritic branch connected with a synapse in the constant 0 connection state can be pruned. An example of the specific dendritic pruning mechanism is shown in Figure 3. Figure 3a shows the trained DNM before pruning. Figure 3b shows the structure of the pruned DNM where the dendritic branches connected with the synapses in the constant 0 connection state are pruned.
## 4 Airborne Balloon Movement Simulation
### Assessment of the Simulated Backward Trajectory Accuracy  
Figure 5 shows the comparison between the observed trajectories and the backward forecast trajectories of the airborne balloon, and Table 6 presents the forecast biases of the backward motion trajectories of the airborne balloon. The high agreement between the forecast and observed trajectories of airborne balloons, both in the horizontal and vertical directions, demonstrates the exceptional accuracy and reliability of FLEXPART in predicting their paths (Figure 5). Utilizing forecast data, FLEXPART predicts a minimum trajectory bias of 5.0 km and a maximum trajectory bias of 39.4 km in the horizontal direction of airborne balloons within 11 h (Table 6), whereas the results reported by [PERSON] et al. indicate that it takes 6 h to reach a mean MAE of 40 km [35]. As the forecast time increases, the biases rise simultaneously, but the RMSE and the MAE remain below 30 km. Therefore, conclusions can be obtained that FLEXPART has remarkable reliability and accuracy on short time scales, making it a powerful tool for forecasting short-term sounding balloon trajectories. In terms of vertical motion, the model forecasts the trajectories of airborne balloons with small biases within an 11 h period, ranging from the minimum bias of 192.4 m to the maximum bias of 804.6 m. Both the RMSE and the MAE are within a 500 m range. It is worth noting that unlike changes in horizontal biases with the increasing forecast time, there are no notable variations in trajectory bias in the vertical direction, possibly due to the complexity and uncertainty of atmospheric vertical motion.  
Further analysis shows that the pattern of changes in the forecast bias of backward trajectory predictions for airborne balloons is similar to that of forward trajectory predictions. This similarity arises because both are influenced by comparable atmospheric dynamics and physical processes. For instance, atmospheric turbulence, variations in wind speed, and changes in wind direction can all contribute to the forecast biases. Although FLEXPART demonstrates a certain level of accuracy in handling these complex factors, there remains room for the improvement in forecast accuracy through ensemble forecasting methods or parameterized methods.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Times** & **Bias Classification** & \(-\)**1 h** & \(-\)**2 h** & \(-\)**3 h** & \(-\)**4 h** & \(-\)**5 h** & \(-\)**6 h** & \(-\)**7 h** & \(-\)**8 h** & \(-\)**9 h** & \(-\)**10 h** & \(-\)**11 h** & **RMSE** & **MAE** \\ \hline Forecasting & Horizontal/km & 5.0 & 5.9 & 4.3 & 2.5 & 7.6 & 19.8 & 23.4 & 24.2 & 26.4 & 33.6 & 39.4 & 20.5 & 16.0 \\ tests & Vertical/m & 192.4 & 227.7 & 804.6 & 481.7 & 196.2 & 317.4 & 366.1 & 398.7 & 432.6 & 475.3 & 561.3 & 421.2 & 371.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Airborne balloon backward trajectory prediction biases.  
Figure 5: Comparison of the observation trajectory of the airborne balloon with the backward forecast trajectory (red: forecast trajectory; blue: observation trajectory).
## 2 Methods
### Machine Learning Approach for Stress Prediction Modeling  
In this study, a machine learning approach was employed to optimize and assess several regression models for predicting stress values in single, tandem, and tridem axles. The procedure began by loading the dataset and applying preprocessing steps, such as a log transformation to the target variable (stress) to stabilize its variance. The dataset was then divided into training and testing subsets, with 80% used for training and 20% for testing and evaluation. Various regression models were trained, including linear regression, ridge, lasso, support vector regression (SVR), decision tree, random forest, gradient boosting, LightGBM, and CATBoost. To improve model performance, polynomial features were added. The models were evaluated using performance metrics like mean squared error (MSE), R\({}^{2}\), mean absolute error (MAE), and root mean squared error (RMSE). The best-performing model was chosen based on these metrics. Additionally, to assess model
## 2 Related Work  
This chapter provides a detailed introduction to the related work mentioned in the first chapter. This work is classified into the following three categories based on signals.  
GSM and LTE: [PERSON] et al. proposed the concept of GSM passive radar for road vehicle monitoring and verified its feasibility, providing an example for other GSM-based applications [1]. As for LTE, the feasibility of using LTE signals for target detection has been explored [4].  
WiFi: [PERSON] et al. designed a passive radar data processing method based on WiFi signals and verified the feasibility of the system through experiments [2]. In 2017, [PERSON] et al. applied the WiFi passive radar system to the detection and monitoring of targets such as aircraft and vehicles at small airports, proving the potential of the system in such scenarios [3]. In terms of human perception, [PERSON] et al. proposed a respiratory rate monitoring system based on WiFi signals, verifying the ability of passive radar based on WiFi [5]. These studies reveal the potential of passive radar for short-range applications and also provide different signal-processing methods. For example, [PERSON] et al. obtained target information by setting signal monitoring channels and reference channels, while [PERSON] et al. obtained WiFi channel state information ratio(CSIR) for target detection.  
5G: [PERSON] conducted a simulation analysis on the method of using 5G signals for target localization and evaluated the performance of applying 5G in radar systems [6]. [PERSON] et al. provided a detailed introduction to the basic principles of 5G sensing and conducted concept validation, explaining the feasibility and limitations of using 5G signals for passive radar and providing guidance for further research on passive radar based on 5G [7]. [PERSON] et al. also studied the use of Synchronization Signaling Block (SSB) signals for target detection in addition to classical passive radar signal processing methods, and validated this method with cars as experimental targets, providing a new approach for target detection [8].
## 3 Results
### Deployment Site-Xuejia Fish Pond  
1. Geographic Location: Xuejia District, southwestern Taiwan, characterized by a freshwater or brackish water environment.
2. Experiment Purpose: To investigate the effectiveness and stability of aquaculture technology in freshwater and brackish water environments.
3. Experiment Content: The underwater ROV monitored fish pond water quality parameters in fish ponds, including conductivity and dissolved oxygen, as well as the growth conditions of cultured organisms. The integration of multiple remote control technologies facilitated remote operation and real-time data transmission.  
In the tests conducted at the Xuejia fish ponds, the system exhibited excellent adaptability, comparable to that of cage environments. Thanks to improved network coverage in terrestrial settings, all control platforms functioned seamlessly. Notably, the tablet control mode proved particularly valuable in this context (Figure 12), as its large screen enabled operators to simultaneously monitor multiple water quality parameters and underwater images, thereby facilitating precise operations. Researchers successfully operated the underwater ROV from the central control center to perform water quality monitoring and biological observations, obtaining and analyzing critical data such as dissolved oxygen levels, pH, and conductivity in real-time. This data was compared with traditional manual sampling methods, resulting in a significant reduction in labor input and sampling time.
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 3 Improved Security AODV Routing Protocol
### Elliptic Curve Cryptosystem
#### 3.1.3 Elliptic Curve Discrete Logarithm Problem  
The security of ECC is based on the difficulty of solving the elliptic curve discrete logarithm problem (ECDLP). The difficulty of ECDLP is that it is difficult to find the integer L for the discrete points P and Q on the curve, so that LP = Q. When applying an elliptic curve to a cryptosystem, assuming that P is the public key and Q is the private key, its security is that it knows P but cannot derive Q. For a and b on a finite group, if there is a positive integer \(n\), making \(a^{n}=b\), the problem of solving \(n=\log_{a}^{b}\) is called the discrete logarithm problem on a finite group. For the discrete points P and Q on the elliptic curve, solving L makes LP = Q, which is called the elliptic curve discrete logarithm problem.  
The attractive point of ECC is that its key length is shorter when the security is equal. For example, RSA uses a 1024 bit module length to obtain security, and in the elliptic curve cryptosystem, a 160 bit module length can obtain the same security. Table 1 shows the security analysis and comparison between ECC and RSA. Million instructions per second for one year (MIPS-a) in the table refers to the computer that executes 1 million instructions per second runs for one year. At present, it is considered that when the deciphering time is 1012 MIPS-a, it represents security. Compared with other public key systems such as RSA and DSA, ECC can provide better encryption strength, faster execution speed,and shorter key length. Table 2 shows the comparison of signature length and encrypted message length when the data length to be signed and encrypted is 2000 bit and 100 bit, respectively. A short key means a reduction in computing overhead, storage space, and bandwidth requirements. Therefore, ECC is more suitable for the UAV communication network with limited resources such as bandwidth, storage capacity, and CPU computing power.
## 5 Discussion  
### Classification Results of Training Sets with Varying Proportions  
In order to assess the resilience and consistency of the proposed method, we evaluate the classification performance under different proportions of training samples across the datasets. Specifically, 0.1%, 0.5%, 1%, and 1.5% of labeled samples from the PU dataset are utilized as training sets. Similarly, 1%, 3%, 5%, and 7% of the labeled samples are employed as training sets in the IP and KSC datasets.  
The results depicted in Figures 9-11 indicate that all methods exhibit enhanced classification performance with an increased proportion of training samples. Furthermore, as the sample size rises, the classification accuracy of the various methods tends to converge. Notably, the proposed method demonstrates robust classification results across all datasets when training on limited samples. Impressively, selecting merely 0.5% of the training samples in the PU dataset can yield an overall classification accuracy exceeding 90%. Similarly, achieving over 90% overall accuracy is feasible by utilizing only 3% of the training samples in the IP and KSC datasets. While the overall accuracy of the proposed method may slightly lag behind that of the DBDA and SSRN methods when employing  
Figure 8: Classification maps for IP dataset: (**a**) ground-truth map; (**b**) SVM; (**c**) DBDA; (**d**) SSRN; (**e**) CS2 DT and (**f**) G_A; (**g**) MSTViT; (**h**) proposed method.  
Figure 7: Classification maps for KSC dataset: (**a**) ground-truth map; (**b**) SVM; (**c**) DBDA; (**d**) SSRN; (**e**) CS2 DT and (**f**) G_A; (**g**) MSTViT; (**h**) proposed method.  
0.5% of the training samples in the PU dataset, it consistently attains an overall accuracy exceeding 90%. Noteworthy, the proposed method maintains its superior classification performance in both the IP and KSC datasets, showcasing its efficacy in handling scenarios with limited training data.  
Figure 10: The results of the overall accuracy (OA) for various methods with different proportions of training samples in the KSC dataset.  
Figure 9: The results of the overall accuracy (OA) for various methods with different proportions of training samples in the PU dataset.