esa-sceva/satcom-chunk-collection huggingface dataset:
# Past, Present and Future Monitoring at the Vallcebre Landslide (Eastern Pyrenees, Spain)
## 2 Monitoring Methods Used in Valleebre and Sample Results
### The GPS-GNSS Surveys  
Following the discussion on the precision of the observations, the expected errors for the Global Positioning System (1 to 2 cm, depending on the method in use [5]) were lower when compared to theodolite and EDM errors. Additionally, the GPS precision was more balanced in the three axes [5]. These facts led us to apply GPS techniques to perform systematic monitoring of the Vallcebre landslide. In December 1995, a complete double survey (EDM and GPS) was carried out in order to link the measurements taken with classical methods with the first GPS campaign. The equipment used then was a Trimble 4000 SSi model with two dual frequency receivers (Trimble, Sunnyvale, CA, USA, 1995, Figure 8). Currently, we are using two Topcon HiperPro dual constellation receivers (Topcon Positioning Systems, Livermore, CA, USA, 2005).  
Figure 8: Examples of Vallcebre monitoring points with different antenna setup: telescopic pole (**a**); pole with mini-tripod (**b**); ground-plane antenna over a tripod, centered with an optical plummet over an inclinometer borehole (**c**).  
Figure 7: First tentative correlation between the displacement of a point in the lower unit (mm, black line) and the cumulative rainfall (mm, blue line), during 1994–1995.  
Most of the old targets were recovered with minor modifications. As new points have been added since 1996, the monitoring network has now around 50 points (Figure 3), consisting on engravings in rock blocks outcropping in the hillside, steel rods, stakes, and the top of the casing of the inclinometric boreholes (Figure 8); the radar Corner Reflectors vertexes were incorporated to the GPS network as of 2006. There are seven additional points on the limestone around the sliding zone. These were the fixed points used to check the GPS accuracy. This network allowed both the measurement of displacements and the comparison with movements obtained with the borehole equipment (inclinometers and wire extensometers) and the Radar measurements.  
Although the Fast-Static GPS method (more precise and robust) was partly applied in the first years, RTK (Real Time Kinematic) is currently in use for productivity reasons [5]. Currently, we can measure the entire network within a single day, travelling across the slope by car and on foot.  
Fourteen GPS campaigns were carried out from December 1995 to February 1998, one survey every 2 months approximately (Figure 9a). Later on, the campaigns were continued on a yearly basis (Figure 9b), 36 GPS surveys until now. Average displacement rates derived from GPS campaigns show a sustained regime of velocities: the annual displacement depends on the precipitation of the year (total value and peak periods). More details about the GPS and special considerations for its application to landslide monitoring can be found in [5; 18; 19].
# Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai
## 4 IPv6 Traffic Prediction Model
### The LS6 Model
#### 4.2.3 Integrated Predictor  
The integrated predictor combines the advantages of LSTM and SARIMA to build an integrated model which incorporates the deep learning approach with statistical prediction algorithm. The integrated predictor is an MLP model, which is a fully-connected class of feedforward neural networks. The MLP model consists of one input layer, one output layer, and one hidden layer using nonlinear activation, for example, the ReLU function. The input layer accepts the input information and then transforms it into a hidden layer. The output layer performs the prediction task with the output of the hidden layer. Due to the correlation between IPv4 and IPv6 traffic shown in Section 3.3, we consider the prediction of IPv6 traffic by LSTM and SARIMA using both IPv4 and IPv6 traffic data. In order to strengthen the weight of IPv4 traffic in predicting IPv6 traffic, we convert the predicted value of IPv4 traffic into the proportion of IPv6 traffic in the total traffic. The process is as follows:  
\[y_{prop}^{S}=\frac{ay_{1}^{S}}{y_{1}^{S}+y_{2}^{S}} \tag{11}\]  
\[y_{prop}^{L}=\frac{ay_{1}^{L}}{y_{1}^{L}+y_{2}^{L}} \tag{12}\]  
where \(y_{prop}^{S}\) is the proportion of IPv6 traffic in the total traffic with SARIMA results, \(y_{prop}^{L}\) is the proportion of IPv6 traffic in the total traffic with LSTM results, \(y_{1}^{L}\) and \(y_{2}^{L}\) are the IPv6 and IPv4 traffic predictions by LSTM, \(y_{1}^{S}\) and \(y_{2}^{S}\) are the IPv6 and IPv4 traffic predictions by SARIMA, and \(\alpha\) is a hyperparameter between 0 and 1, which limits the effect of proportion on the model.  
\(y_{prop}^{S}\), \(y_{prop}^{L}\), \(y_{1}^{L}\), and \(y_{1}^{S}\) are fed to the MLP model to learn the weights of features. The output of the MLP model is the final predicted volume of the IPv6 traffic \(\tilde{y}\).
## 2 Related Work and Motivation
### Federated Learning for Intrusion Detection  
_Intrusion Detection Systems (IDS)_[1] are security solutions designed to monitor, analyze, and detect malicious activities or policy violations within a network or host system. IDSs are an essential component of cybersecurity, designed to analyze data packets as they travel across the network to detect potential threats before they reach critical systems. By monitoring for suspicious patterns, unauthorized access, or known attack signatures, IDS helps organizations prevent security breaches and maintain network integrity. Traditional IDS approaches, such as rule-based and signature-based systems, rely on predefined heuristics to identify known threats. While computationally efficient, these methods are ineffective against zero-day attacks and novel threat patterns [17; 18]. To overcome these limitations, recent research has implemented Machine Learning (ML) into IDS [17; 18; 19; 20], which enables the automatic identification of previously unseen attacks and complex behaviors. ML-based IDS can generalize from historical traffic patterns, making them more adaptive and scalable. However, most ML-based systems require centralized data collection, raising privacy concerns and scalability issues in distributed environments. In the context of IDS, these systems are often distributed across routers, IoT gateways, or edge servers [6; 21; 22], where each node only observes a local slice of traffic. Raw logs (such as PCAP or DNS) often contain sensitive information. FL is naturally suited to this context because it enables training a global model without transferring raw logs. Specifically, each IDS node trains locally on its own traffic and only uploads model weights (updates) to the server [4; 6; 7]. For example, consider a smart home with an IP camera, a thermostat, and a smart TV. The camera primarily handles video streaming, the smart TV sends DNS queries to over-the-top services, while the thermostat connects periodically to a control server. These flows exhibit non-IID traffic patterns, yet with FL, all three devices can contribute to a shared \"IDS brain\" without transmitting images or raw packets. If the camera detects a suspicious domain like agsdjasa98.org (potentially DGA-related) that the smart TV does not, the global model can still learn this attack pattern and trigger alerts across the entire network. This approach helps handle data heterogeneity across different networks by allowing models to be trained on site-specific traffic patterns while contributing to a broader, more robust intrusion detection framework. FL also reduces communication costs by transmitting only model parameters instead of raw network logs. As a result, the amount of communicated data does not scale proportionally with the size of the client's local dataset, leading to relatively low bandwidth consumption. At the same time, FL enhances security through techniques such as secure computation, making it more resistant to model poisoning attacks and reducing the risks associated with data breaches. Moreover, deep learning-based IDS
## 4 Experimental Principles
### LACGAN-Based Small-Sample Data-Augmentation Method
#### 4.1.3 Sample Evaluation of Sample Evaluation Metrics  
This paper evaluated and screened the jamming samples generated by a LACGAN using the Pearson correlation coefficient. In the process of improving the success rate of DQN anti-jamming decision-making through data augmentation, if the similarity between the generated signal and the real signal is extremely high and the added training data fails to bring diversity to the recognition model then the augmentation effect exerted by the generated signal will be limited. Conversely, if the similarity between the generated signal and the real signal is too low then it will mislead the DQN model. Not only will it fail to achieve an augmentation effect but it will also reduce the decision-making accuracy of the DQN.  
This paper applied the Pearson correlation coefficient [28] to evaluate the linear correlation between the original samples and the generated jamming samples. Its value range is \([0,1]\), where the closer the absolute value is to \(1\) the higher the similarity between the two. The calculation formula is as follows:  
\[r(x_{l},y_{l})=\frac{\sum\limits_{i=1}^{N}{(x_{l_{i}}-\overline{x_{l}})(y_{l_{i} }-\overline{y_{l}})}}{\sqrt{\sum\limits_{i=1}^{N}{(x_{l_{i}}-\overline{x_{l}})^ {2}\sum\limits_{i=1}^{N}{(y_{l_{i}}-\overline{y_{l}})^{2}}}}}. \tag{10}\]  
where \(r\) represents the Pearson correlation coefficient; \(x_{l_{i}}\) and \(y_{l_{i}}\) are the original sample of category \(l\); and \(\overline{x_{l}}\) and \(\overline{y_{l}}\) are the mean values of the generated samples of category \(l\).  
Finally, according to the calculation results of the Pearson correlation coefficient, the signals in the generated sample set \(S_{coarse-grained}\) are sorted in descending order. The five samples with the highest similarity and the five samples with the lowest similarity are selected, and the final generated jamming sample result \(S_{fine-grained}\) is obtained.
## 3 Muscle Activity and Disorders
### Muscle Electrodiagnostic Methods
#### 3.2.2 EMG Processing and Reproducibility  
The EMG signals of string players are often processed in the time domain, e.g., using the average rectified EMG (AREMG) from different muscles, as detected by surface electrodes [67]. Another method consists of quantifying the EMG amplitude and duration distributions in defined time domains, using the exposure variation analysis (EVA) [57; 69; 70]. This processing method is based on the simultaneous assessment of amplitude variability, as well as the time period corresponding to the different amplitudes. This method has been successfully used to determine the effectiveness of ergonomic solutions in various assembly workplaces [69].  
Rectified EMG signals are also used to study muscle synchronicity among the proximal/distal musculature of the limb by means of cross-correlation analysis [71; 72], or among antagonistic muscles to reveal synchronicity and co-contraction in drum players [73].  
EMG amplitudes as such have no significance on their own, and cannot be compared to one another unless referenced to some standard measure, such as EMG corresponding to the maximum voluntary contraction (MVC) of the actually measured muscle. Another option is to use as a reference the root mean square value of a 10 s voluntary electrical activity corresponding to 30% of the maximum voluntary contraction (MVC) [69].  
It should be noted, however, that true MVC is not always measurable, such as in cases of weakness or other muscle deficiencies, and additional ways of normalizing should be sought [74; 75]. For instance, dividing the EMG amplitude by the maximal amplitude value of the measured muscle within the same testing set has been used in children with cerebral palsy [76]. The average rectified EMG may also serve as a basis of normalization, as reported for the upper trapezius muscle of upper string players [77].  
The individual reproducibility of EMG has been demonstrated in the trapezius muscle of string players [57]. This suggests that EMG measurements can be used to analyze differences in individual pattern in the course of a musical performance, and to determine the efficacy of alternative ways of playing. Inter-player variability of the trapezius has, however, been reported with greater variability when playing a difficult music piece than when playing an easy one [57; 67; 77; 78].  
The processing of the EMG in the frequency domain is associated with the frequency content of the signal, and specific quantities, such as mean and median frequencies, and is used to express fatigue of the muscle [79; 80].
# Development and Prospect of UAV-Based Aerial Electrostatic Spray Technology in China
## 3 Rapid Progress of UAV-Based Electrostatic Spray Technology  
### Research Background  
In 2014, China's agricultural pesticide application was still dominated by large fixed-wing aircraft, supplemented by rotary-wing UAVs [28]. However, since 2015, with the urbanization process in China, the rural labor force population has become less and less. Meanwhile, with the improvement of living standards, automatic pesticide application tools have gradually been accepted by farmers [29]. Nowadays, plant protection UAVs have maintained a booming trend in the field of agricultural plant protection. China has taken an internationally leading position in terms of technology, quantity, and product types of agricultural plant protection UAVs [30].  
Figure 1 shows the increasing number of plant protection UAVs and operating areas covered in China from 2014 to 2020. The number of plant protection UAVs has reached 106,000 units with an operation area covered of 64 million \(\mathrm{hm}^{2}\) in 2020. In this context, Chinese scholars began to put forward the idea of applying aerial electrostatic spray technology to UAVs, while there are few reports on this topic worldwide.
# Enhanced Steganography for High Dynamic Range Images with Improved Security and Capacity
## 3 Proposed Scheme  
To address the aforementioned issues, an improved steganography scheme composed of three phases is presented. The corresponding flowchart is depicted in Figure 3. The proposed approach involves three phases: pre-processing, embedding, and post-processing. In the pre-processing phase, the original cover image undergoes modification to generate additional pixels with homogeneity values, increasing embeddable pixel capacity. The subsequent embedding phase entails the incorporation of secret data into the cover image. In the post-processing phase, a pseudo-random number generator (PRNG) generates a random bit stream, which is utilized to adjust the pixel values of the embedded image, resulting in a stego-image with a histogram similar to that of typical natural images.  
**Phase 1: Pre-Processing**  
The statistical analysis conducted prior to embedding reveals that, with the exception of 0, the maximum value of the three color channels for most pixels is not less than 127. This finding indicates that the maximum pixel value cannot be divided more than once. To address this limitation, the proposed scheme assumes that the embeddable pixels can be divided or multiplied once for each of the three color channels while increasing the exponent channel by 1. To implement this assumption, we propose a straightforward scheme that involves two steps.  
First, we identify specific pixels using one of four cases. Second, we adjust the pixel values using one of four different methods, depending on the case. These two steps are further elaborated below.  
Step 1. The pixels with \(\max(R,G,B)=127||128||254||255\), where \(||\) means the _or_ operation, are selected.  
Step 2. The selected pixels are further modified as follows.  
Case 1: If a pixel with \(\max(R,G,B)=127\), the individual \(R\), \(G\), and \(B\) values of the pixel are unaltered.  
Figure 2: The histograms are the number of embeddable “irregular” pixels in 101 HDR images. (**a**) The cover images and (**b**) the embedded images, as per [PERSON] et al.’s scheme.  
Case 2: If a pixel with \(\max(R,G,B)=128\), the maximum value, i.e., 128, undergoes a decrement of 1, and the new maximum value becomes 127.  
Case 3: If a pixel with \(\max(R,G,B)=254\), the odd pixel values can be converted to even values by subtracting 1.  
Case 4: If a pixel with \(\max(R,G,B)=255\), either the maximum value of 255 or any odd values will decrease by one to become even values.  
Figure 3: The flowchart of the proposed steganography scheme.
## 3 Results
### Boundary Layer
#### 3.1.1 Relationship Between Flow Velocity and Boundary Layer  
At low Re, the simulations produced a laminar boundary layer, as shown in Figure 7, indicative of a viscous-dominated flow. As Re increased, corresponding to higher fluid inertia, the laminar boundary layer extended further along the surface profile. Concurrently, the separation points were observed to shift downstream (Figures 7-10). This downstream displacement resulted in a reduction in the wake region, consequently minimising the pressure drag inflicted on the O-ring. This behaviour aligns with the established relationship between Re, boundary layer development, separation and pressure drag in external flows [13; 17; 18; 19; 20].
# SPEEDY Quantum Circuit for Grover's Algorithm
## 2 Related Work
### Quantum Background
#### 2.1.2 [PERSON]'s Algorithm for Key Search  
An exhaustive key search using [PERSON]'s algorithm described in Figure 2 can recover an \(n\)-bit key with only \(2^{n/2}\) searches. The classical key search performs \(2^{n}\) searches in the worst case (i.e., \(O(2^{n})\)). Grover key search always repeats \(2^{n/2}\). The [PERSON]'s algorithm operates with oracle and diffusion operation and increases the probability of finding the correct key through repetition. The oracle in [PERSON]'s algorithm finds the correct key. The quantum circuit of the target cipher is used. The diffusion operation in [PERSON]'s algorithm operates to increase the probability of measuring the correct key. The following describes the operation process for [PERSON]'s algorithm:  
1. \(n\)-qubits are prepared to find a key of length \(n\). \(|0\rangle^{\otimes n}=|0\rangle_{0}\otimes|0\rangle_{1}\otimes\cdots\otimes|0 \rangle_{n}\).
2. All \(n\) qubits are placed in a superposition state by a Hadamard gate. \(|\psi\rangle^{\otimes n}=|\psi\rangle_{0}\otimes|\psi\rangle_{1}\otimes\cdots \otimes|\psi\rangle_{n}\).
3. If the ciphertext generated by the input \(n\)-qubits (i.e., key) matches the known ciphertext, the sign of the key in that state is inverted.
4. The amplitude of the solution key is amplified through the diffusion operator.
5. Steps 3 and 4 are repeated \(\sqrt{2^{n}}\) times to increase the key search probability.  
Figure 1: Quantum gates.
# Navigation Path Extraction and Experimental Research of Pusher Robot Based on Binocular Vision
## 2 Materials and Methods
### Image Segmentation
#### 2.6.2 Principal Component Analysis  
For each pixel, the Gabor filter extracted 40 amplitude features using 40 different filters at 5 scales (\(\lambda\) = 2, 3, 4, 5, 6) and 8 directions (\(\theta\) = 0, \(\pi\)/8, \(\pi\)/4, 3\(\pi\)/8, \(\pi\)/2, 5\(\pi\)/8, 3\(\pi\)/4, 7\(\pi\)/8). A barn image has 1280 \(\times\) 720 \(\times\) 40 = 36,864,000 dimensional features, and it is more difficult to directly characterize such high-dimensional features; therefore, in order to minimize the computational requirements during processing, the principal component analysis (PCA) technique is used. The PCA technique is a common method of data analysis, is often used for dimensionality reduction of high-dimensional data and can be used to extract the main features of the data [31]. The principle of PCA is to reduce the dimensionality of a dataset with a large number of correlated variables while preserving as much of the variation present in the dataset as possible. PCA eliminates redundancy by transforming several interrelated variables to generate a new set of orthogonal features, called principal components.  
In this work, we implement the PCA algorithm based on the eigenvalue decomposition covariance matrix. For an input of 36,864,000-dimensional data, the eigenvalues and eigenvectors of the covariance matrix are calculated by subtracting the respective mean values for each part of the features, selecting the largest 500 of them, and forming the eigenvector matrix P with its corresponding 500 eigenvectors as row vectors, respectively. PX is our mathematical expression after dimensionality reduction, and the expression is as follows.  
\[X\ =\ \{x_{1},x_{2},x_{3},\ldots x_{n}\} \tag{17}\]  
\[D(X)\ =\ \frac{1}{n}XX^{T} \tag{18}\]  
where, \(X\) represents the input n-dimensional vector, \(n\) = 36,864,000, and \(D(X)\) represents the covariance matrix of the n-dimensional vector.  
The Gabor filter data were run through a PCA algorithm that identifies the principal components used as texture descriptors representing the cow field paths, thus allowing a more natural segmentation and reducing the dimensionality of the data.
## 3 Results
### Investigation of ROM and Predominant Impingement Type for the preoperatively Planned Situation  
The femoral stem antetorsion, which included n = 19 patients, preoperatively ranged from 0.4\({}^{\circ}\) to 40\({}^{\circ}\). The comparison of the ROM for the three motions is depicted in Figure 5A. After virtual implantation, 124.3\({}^{\circ}\)\(\pm\) 14.5\({}^{\circ}\) of mean flexion was reached. Internal rotation averaged at 37.5\({}^{\circ}\)\(\pm\) 12.9\({}^{\circ}\) and external rotation at 102.6\({}^{\circ}\)\(\pm\) 13.6\({}^{\circ}\). Compared to physiological ROMs [18,64], similar values were observed for flexion and internal rotation. Contrarily, the ROM for external rotation of the patients treated with THR exceeded the  
Figure 4: Comparison of the range of motion using implantation according to preoperatively planned situation (pps) and postoperatively reconstructed situation (pps) for each patient during maximum flexion (maxFlex), internal rotation at 90\({}^{\circ}\) of flexion (IR@90 Flex), and external rotation at 90\({}^{\circ}\) flexion (ER@90 Flex) for n = 6 patients. (**A**) Mean range of motion with standard deviations until impingement during the three movements for n = 6 patients. Note that the median of range of motion values are depicted as diamonds. (**B**) Quantity N of the impingement type (BBI, bone-to-bone impingement; II, implant-to-implant impingement; IBI, implant-to-bone impingement).  
physiological ROM [54]. Within the ROM analysis, the different types of impingement at maximum ROM was detected depending on the movement (Figure 5B). Regarding the type of impingement, a maximum flexion of 128.7 \(\pm\) 13.0\({}^{\circ}\) up to the implant impingement was detected. In the case of bone-to-bone impingement, the ROM slightly decreased to 122.3\({}^{\circ}\)\(\pm\) 14.7\({}^{\circ}\). The predominant type of impingement at the flexion movement (Figure 5B) was bone-to-bone impingement with N = 13, followed by implant-to-implant impingement with N = 6, while for internal rotation, implant-to-implant impingement with N = 10 was followed by bone-to-bone impingement with N = 8. Implant-to-implant impingement was the most dominant type of impingement during external rotation, which occurred in 17 out of 19 patients. It is apparent (Figure 5B) that bone impingement restricted the ROM more than implant impingement during all movements. It is also noteworthy that implant-to-bone impingement occurred only once in all 19 patients.