esa-sceva/satcom-chunk-collection huggingface dataset:
# Empirical Approach to Defect Detection Probability by Acoustic Emission Testing
## 3 POD Estimation
### Empirical AE Signals Modeling  
To assess the POD, we have developed an algorithm for a priori estimation of AE data sampling parameters. The main stages of this algorithm are the probabilistic model of the AE source and the deterministic model of the waveguide.  
During industrial AE testing, when the distance between sensors and source can reach several meters, AE source parameters do not significantly affect the AE signal waveform. Therefore, we may assume that the AE source emits short impulses (8-impulses) with duration less than the sampling period. Thus, the AE source emits \(N_{\Sigma}\) signals with amplitudes \(A_{k}\), \(k\in[1,N_{\Sigma}]\). \(N_{\Sigma}\) and \(A_{k}\) may be considered as random variables. A detailed description of the AE source is presented in paragraph 3.1.1.  
Signal waveform is mainly determined by the dispersive propagation along the waveguide. Thus, to calculate the AE signal waveform, a waveguide model is necessary. In this study, we use an analytical model of the waveguide, which allows calculating the dispersive AE signals propagation. The waveguide model is described in paragraph 3.1.2.  
The flowchart of the simulation algorithm is shown in Figure 3. To make the model more realistic, noise may be added to the AE signal.  
As a result of the modeling, we obtain the AE data set which is regarded as a separate AE signal. The number of signals \(N_{\Sigma}\) and their amplitudes \(A_{k}\) are determined by AE source and the waveform \(f(t)\) is the function of the waveguide.
# Evaluation of BLE Star Network for Wireless Wearable Prosthesis/Orthosis Controller +
## 1 Introduction
Operating in the high frequency range (with a carrier frequency of 13.56 MHz), NFC is able to both transmit data and harvest power wirelessly between the NFC tags, which is ideal for small, low-power, battery-less wearable sensors [15]. Additionally, NFC is commonly built into smartphones, making it accessible to medical Internet of Things (IoTM) applications [16]. These NFC-based biosensor applications tend to be limited to relatively low bandwidths or low-power electrochemical, capacitive, and colorimetric applications such as a wireless pH sensor to monitor for the rejection of osseeintegrated implants [17], skin hydration-sensing patches [18], and an ultra-compact, battery-free sweat glucose monitor [19]. There are a number of benefits associated with NFC, including its small footprint, easy pairing process, and ability to harvest power wirelessly from the NFC transmitter, the Poller [15; 16]. NFC is ideal for very close-range applications in which the distance between the central and peripheral nodes is less than 10 cm [20]. When compared to BLE, NFC is a lower power option but has insufficient data transmission rates (424 kbps) and ranges (distance between the devices) for prosthesis control [21]. In this work, BLE was selected as the wireless communication protocol, as it offers a high data throughput at relatively low power consumption. Additionally, it can be configured for use in low latency applications (\(\leq\)100 ms) and guarantees interoperability with existing BLE devices. The goal of this work was to evaluate how the configuration parameters of the connection interval and event length influence power consumption,latency, and maximum network size of the biosensor network. The results of this evaluation are intended to demonstrate whether BLE would be a suitable protocol for a wireless, wearable EMG-driven prosthesis controller. This article is a revised and expanded version of a paper entitled _Power Consumption, Latency, and Maximum Number of Supported Nodes for BLE Biosensor Applications_, which was presented at the Myoelectric Controls Symposium in Fredericton, New Brunswick, on 12-15 August 2024 [22].
## 3 The Proposed Methodology
### Extraction of Skeleton Features  
We propose that once the key frames have been extracted, human pose estimation algorithms should be applied to those key frames to extract the skeleton features, which can then be used for the classification of different actions. The skeleton features are nothing but the pixel coordinates of the human skeletal joints in the key frame. They are also referred to as human keypoints. Human pose estimation schemes try to identify the relative placement or layout of different human joints and body parts in an image, which can then be used for action recognition, as we demonstrate in this study.  
There are two popular approaches to human pose estimation (i.e., the _top-down_ and _bottom-up_ approaches). In the _top-down_ approach, first a human body is detected in an image and bounded by a bounding box. Then, the skeletal joints and body parts are discovered within each bounded box (i.e., for each human body in the image). The coordinates of these joints and body parts are called the keypoints, which can be used to identify a human pose. Meanwhile, in the _bottom-up_ approach, first the skeletal joints and body parts are discovered in an image, and then they are joined together to constitute different poses. The _top-down_ approaches can be computationally expensive, as they first involve the identification of human objects in an image and then pose estimation for each human object in that image. This also entails that separate keypoint configurations be maintained for every human object or bounding box in the image. In contrast, _bottom-up_ approaches are generally faster as they directly identify all the keypoints in an image without first performing any human object detection.  
In this study, we use two different pose estimation models (i.e., the high-resolution network (HRNet) [22] and OpenPose [12]) for the extraction of skeleton features from key frames. Both of these models delivered the best performance on the _COCO_ keypoint dataset [41], which includes around 200,000 images with about 250,000 instances of persons labeled with 17 joints. OpenPose [12] uses the bottom-up approach for the extraction of keypoints, whereas HRNet [22] works in a top-down fashion. OpenPose can be used for the detection of multiple persons in a single image in real time and can extract up to 135 keypoints. The running time complexity of OpenPose is not affected by the number of persons in the image, as it first recognizes the body parts in an image and then estimates the pose from those body parts. In either case, the extracted features are used to construct training vectors for the classifier, which is discussed in more detail in Section 3.3. The trained classifier can then be used for the recognition of the action represented by a vector of skeleton features extracted from the key frames alone.
# A Malware Detection Framework Based on Semantic Information of Behavioral Features
## 2 Related Work
### Dynamic Analysis  
The dynamic analysis approach entails running the test program in a secure simulation environment as a way to capture the behavior of the program's execution. Activities like registry, API call, and network communications are examined to identify if the program is malicious.  
[PERSON] et al. [37] used a data mining algorithm to capture the difference between malicious and benign programs from the dependency graph. This method was able to detect the obfuscated malware, but it was time consuming. [PERSON] et al. [38] used Long Short Term Memory (LSTM) to construct a behavioral model to transform APIs into feature vectors and generate feature images and then used Convolutional Neural Network (CNN) to perform malware detection. It was observed that API call sequences could cause performance loss if they were too long. However, the dataset used in this method is too small, and the model tends to be overfitted. [PERSON] et al. [39] dynamically obtained API calls in a virtual environment and performed similarity calculations on API sequences to distinguish different malware with similar behavioral contexts. [PERSON] et al. [40] obtained API-Graph to characterize the internal relations among entities by constructing API relationship diagrams. Then, they transformed the API in the relationship graph into the embeddings of Random Forests, Support Vector Machine (SVM), and Deep Neural Networks (DNNs), so that the semantic feature similarity was preserved even if the malware evolves. [PERSON] et al. [41] separated the original API sequence into several subsequences and used Recurrent Neural Network (RNN) to detect each subsequence separately. The whole sequence would be identified as malicious whenever a subsequence was marked as malicious. [PERSON] et al. [29] extracted features from API names and runtime parameters, used a hashing method to encode API names, categories, and parameters separately, and set different dimension sizes based on the feature characteristics. Then, these characteristics were further connected and fed into the CNN model and applied Bi-LSTM to capture the connections between APIs. The results showed that the method achieved an accuracy of over 0.96 with an AUC of 0.99, which is higher than other similar methods. [PERSON] et al. [42] suggested a DNN-based detection of malware for learning parameter-enhanced API calls. For part of the API, a set of empirical rules containing four categories totaling more than 100 were constructed manually, together with statistical rules to identify if the parameter is malicious. Different categories were classified based on the GSDMM clustering algorithm for the rest of the calls to obtain their sensitivity labels.
# Optical Code Construction of 2D Spectral/Spatial BIBD Codes for SAC-OCDMA Systems
## 1 Introduction
Simulations are carried out by setting different fiber related parameters and activating wavelength-dependent nonlinear effects such as cross-phase modulation, four-wave mixing, and self-phase modulation at different data rates. We expected that the proposed code could provide better cross-correlation properties with increased cardinality. A simple code construction method and a low-cost receiver architecture compared to existing 2D OCDMA codes were introduced within the proposed model. The contributions of this paper are the following:  
1. Design of novel 2D-BIBD code. 2. Analytical and simulation validation of the 2D-BIBD numerical model. 3. Accommodation of many active users with a high bit rate.
# ScientIST: Biomedical Engineering Experiments Supported by Mobile Devices, Cloud and IoT
## 5 Discussion  
Biosignal acquisition has quite strict hardware requirements [16], and instruments need to be designed with accurate sampling rates and high noise rejection capabilities. Although the noise specifications depend mostly on the sensor frontend, the sampling rate depends on the acquisition system. As such, we evaluated the behavior of the Arduino SCB when used in conjunction with the GSJ.  
To assess the sampling rate accuracy, we deployed the case study described in Section 3, revealing performance limitations in some of the technologies. The sampling rate obtained, as summarized in Section 4.1, is fairly low for most biosignal acquisition applications; even disregarding the non-uniform sampling, according to the Nyquist rate only signals of up to 50 Hz could be sampled. Considering that signals such as EMG, ECG and EEG have higher bandwidths, the sampling rate achieved by the SCB and GSJ enables experimentation with signals such as EDA, PPG, force, respiration, motion, pressure, or temperature, for example.  
As previously mentioned, the Arduino SCB lacks a \(V_{\text{CC}}/2\) reference voltage source, however, this can be generated at the sensor using a resistive voltage divider. There was also evidence of missing data when using the GSJ and sensors embedded in the smartphones. The results suggested that the computational capability of the smartphone is a major factor determining the loss of samples during acquisition. Future work might thus include a comparison analysis of different mobile phone models. Nevertheless, this work enabled us to determine the receptiveness of the students to the proposed approach, resulted in the understanding of several technical limitations with the base tools, and in identifying future work perspectives.  
Software limitations include the lack of reporting capabilities and adapting the features to the higher education needs. One of the features to be optimized for higher education is the creation of advanced lab guides, and experiment templates, which would benefit the whole efficiency of use for students and teachers. Furthermore, Android users are not able to export reports as PDF, and students report the lack of a computer-based application, which would benefit the experiment process.  
Hardware-wise, the time and amplitude resolution limit the set of sensors that can be used, and should be improved. Regarding external communication, the system is not able to communicate with the computer, and the interfacing of external sensors should become more user-friendly.
## 8 Performance Evaluation
### Recognition  
To validate the proposed recognition model, the accuracies of two network modes, CNN and LSTM, were compared to the proposed CNN + LSTM model. CNN and LSTM comprise the first and second parts of the network, respectively. It can be observed from Figure 11 that the proposed model provided the best accuracy of 98.4% for one target and 98.1% for two targets.
## 4 Experiments  
### Datasets and Preprocessing  
**Segmentation Dataset**  
The network was trained and validated using a clinical dataset obtained from the First Affiliated Hospital of Dalian Medical University. This dataset consists of contrast-enhanced MRI (CE-MRI) scans, including arterial phase, venous phase, and delayed phase images, collected from 192 patients diagnosed with hepatocellular carcinoma (HCC). The regions of interest (ROIs) of the liver and tumors were manually annotated by two radiologists with more than five years of clinical experience. Each image slice has a resolution of 512 x 512 pixels, with 60-140 slices per scan and a voxel spacing of [0.78 mm, 0.78 mm, 2.2 mm]. Based on patient-level grouping, the dataset was divided into a training cohort (128 patients, 384 MRIs), a validation cohort (32 patients, 96 MRIs), and a test cohort (32 patients, 96 MRIs). MRI scans from different phases of the same patient were kept within the same cohort to prevent data leakage.  
To improve the accuracy of liver tumor segmentation, all images underwent several preprocessing steps. First, the MR images were cropped to remove irrelevant background regions. Next, the images were resampled to an isotropic resolution of [1.00 mm, 1.00 mm, 1.00 mm], and pixel intensities were normalized to the range [0, 255]. Furthermore, to enhance the model's generalization and robustness, several image augmentation techniques were applied during training. These included random rotations, horizontal and vertical flips, scaling, elastic deformations, and intensity variations such as Gaussian noise and contrast adjustment. These augmentations help simulate real-world variability and reduce the risk of overfitting.  
**SSL Dataset**  
Our self-supervised learning dataset was created by gathering chest MRI scans from 594 patients, comprising 192 annotated cases and 402 unannotated cases. During pretraining, only the unannotated cases were used, with no reliance on the existing annotations. To maintain consistency, the preprocessing applied to the unlabeled patient MRIs mirrored that of the segmentation dataset.  
The study was approved by the ethics committee of the First Affiliated Hospital, Dalian Medical University (PJ-KS-KY-2019-167).
# Optical Boundaries for LED-Based Indoor Positioning System
## 4 Investigations Showing the Effect of Defined Optical Boundaries
### Boundary Based Positioning Protocol  
The three major modules which describe the transmitter are LED ID generation, data encoding, and modulation as shown in Figure 2. For investigation purposes in this section, the LED ID is generated using normal random variables with equal probability of ones and zeros. The generated binary data is encoded and then modulated to a 38 kHz frequency. The optical energy content in the signal is dependent on the encoding protocol and type of modulation scheme used. Encoding not only marks start and stop bits for frame synchronization, it also maps ones and zeros to pulses of different high and low duration depending on the scheme used. In the design of an encoding protocol for a frame, pulses of duration \(L\) are used to encode the data such that a one in bi-phase coding (BPC) as explained in Reference [85] is a high pulse of duration \(L\) followed by the zero of duration \(L\), and a zero is encoded as a low pulse of duration \(L\) followed by a high pulse of duration \(L\). With pulse width modulation (PWM) based encoding, three different relationships can be established between the representation of ones and the representation of zeros. They can be additive, where the widths of pulses are designed to be in linear increments of \(L\). For instance, one is represented by \(L\) and zero by \(L+L\). Pulses can also be designed to operate in gains, where the widths of pulses are designed to be in multiplicative increments. Finally, pulses can be represented in exponents where the widths are in the form \(L\) and \(L^{L}\). If \(\theta_{1}(t)\) and \(\theta_{2}(t)\) are two orthonormal basis functions, a signal space representation for each of the above-mentioned schemes can be written as represented in Table 3.  
To show the effects of pulse duration on BER and PDR, the BPC in Table 3 is used to form packets for the transmission of positional information. The packets are transmitted considering the Lambertian channel model for LEDs as described in (1), where the transmitted power is based on the energy signal. Noise from Section 3.1 is used to calculate the SNR, and the BER is calculated using (9). The effect of the encoded pulse duration \(L\) on the BER and delay in positioning is estimated in Section 5.4.
# Dual Clustering-Based Method for Geospatial Knowledge Graph Partitioning
## 2 Methods
### Geographic Node Partitioning Based on Density Peak Clustering
#### 2.1.1 Definition of Local Density and Relative Distance  
As mentioned above, local density and relative distance are the basis of choosing cluster centers and determining the cluster boundaries. In this study, we used the definition of local density proposed in DPC-KNN [53]. The local density of a node is defined as the exponent of the mean square distance between the node itself and its \(k\)-nearest neighbors, as follows:  
\[\rho_{i}=exp\bigg{(}-\frac{1}{k}\sum_{j\in KNN_{i}}d(i,j)^{2}\bigg{)} \tag{1}\]  
where \(\rho_{i}\) represents the local density of node \(i\), \(k\) denotes the number of nearest neighbors, \(KNN_{i}\) represents the set of the \(k\) nearest nodes to node \(i\), and \(d(i,j)\) represents the Euclidean distance between node \(i\) and node \(j\). Calculating the local density based on the \(k\)-nearest neighbors can assess the local structure of nodes and is helpful for identifying the clusters with different densities [53]. The relative distance is defined as the minimum distance between a node and the set of nodes with higher local density. For the node with the highest local density, the relative distance is defined as the maximum possible distance between two nodes (Equation (2)).  
\[\delta_{i}=\begin{cases}min_{j:\rho_{i}>\rho_{i}}(d(i,j))\ \rho_{i}<max(\rho)\\ max_{j:i}(d(i,j))\ \ \ \ \rho_{i}=max(\rho)\end{cases} \tag{2}\]  
where \(\delta_{i}\) represents the relative distance of node \(i\), and \(max(\rho)\) represents the maximum local density.
## 2 Materials and Methods
### Description of Finite Element Calculation  
Finite element analysis was performed to investigate the feasibility of using the SASW method to evaluate the lining-bedrock interface condition. The plane model had a dimension of 8 m wide and 2 m deep, and a uniform element size of 2 cm was adopted, as shown in Figure 4. Four different model schemes were created, as shown in Table 1. The material properties are listed in Table 2. The weak interlayer was used to simulate a real case scenario when there was very poor bonding between the lining and the surrounding rock, which often happened at the two sidewalls and the bottom of the lining due to insufficient treatment of the excavated rock surface. The weak layer intended to simulate the case where there was a total separation (void) outside the lining, which typically happened at the top arch of the lining due to over-excavation of the bedrock. The layered concrete-weak interlayer-bedrock model is shown in Figure 5. The hammer impact was simulated by applying a 0.5 KN-magnitude impulse point-load with a half-cycle sine curve of force-time history at the top middle node of the model, in which the time span of the impulse represented the impact duration.  
Figure 3: Matlab implementation procedures of the SASW method.  
Linear transient modal time history analysis was conducted. Vertical accelerations were picked up at two receivers shown in Figure 4. The sampling interval at the receiver locations was 5 us and the total number of sampling points was 1024. Cross-power spectrum analysis of the signals of these two channels was then performed using Matlab to generate phase spectra and the corresponding R-wave velocity dispersion curves.  
The theoretical R-wave velocity of concrete can be obtained from Equation (7), which is calculated to be 2059 m/s using the parameters in Table 2.  
\[V_{R}=\frac{0.87+1.12\mu}{1+\mu}\sqrt{\frac{E_{d}}{2\left|\hat{\rho}\right.(1+ \mu)}} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Material** & **Thickness (cm)** & **Model Type** \\ \hline
1 & concrete & 200 & Uniform \\
2 & concrete & 40 & Layered model 1 \\  & bedrock & 160 & \\  & concrete & 40 & \\
3 & weak interlayer & 2 & Layered model 2 \\  & bedrock & 158 & \\
4 & concrete & 40 & \\  & weak layer & 160 & Layered model 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model schemes.  
Figure 4: Numerical model and schematic layout of impact and receivers.  
Figure 5: Layered model 2 (concrete-weak interlayer-bedrock).  
where \(E_{d}\) is the dynamic elastic modulus, \(\underline{\rho}\)\(\underline{\rho}\) is the density, and \(\mu\) is the Poisson's ratio of concrete.