esa-sceva/satcom-chunk-collection huggingface dataset:
# Electromagnetic Radiation Shielding Using Carbon Nanotube and Nanoparticle Composites
## 1 Introduction  
### Radiation Types: Ionizing and Non-Ionizing  
Electromagnetic radiation is a form of energy that travels through space as waves composed of oscillating electric and magnetic fields perpendicular to each other, comprising a spectrum of frequencies including radio waves, microwaves, infrared radiation, visible light, ultraviolet radiation, X-rays, and gamma rays. Electromagnetic radiation is produced by the movement of charged particles and can be characterized by its wavelength and frequency. Electromagnetic radiation can also be described as a stream of particles called photons, each carrying a specific amount of energy. The electromagnetic spectrum spansfrom low-energy long-wavelength radio waves to short-wavelength high-energy gamma rays. Radio waves are produced by oscillating electric charges in an antenna. Gamma radiation is produced from the nucleus of an atom. Electromagnetic or photon radiation can also be produced from the decomposition of unstable atomic nuclei. To become stable, the nucleus releases excess energy as radiation.  
The energy level of photons characterizes the types of electromagnetic radiation as being either ionizing or non-ionizing [1]. Ionizing radiation has high energy and includes gamma rays, X-rays, and high-energy particle beams. Non-ionizing radiation has lower energy and encompasses ultraviolet (UV), infrared (IR), microwave (MW), radio frequency (RF), extremely low frequency (ELF), and visible light [2,3]. The energy level of radiation strongly influences its effect on materials, affecting them at the atomic, molecular, and macroscale levels [4]. Ionizing radiation is harmful to humans and the environment [5]. Shielding non-ionizing RF and MW radiation is needed for protecting electronic equipment from electromagnetic interference (EMI) and for preventing damage to electronic semiconductor components. A new application area for MW shielding is countering high-power microwave directed energy weapons. Laser shielding is needed in industrial and manufacturing applications. An emerging application for laser shielding is countering high-power laser directed energy weapons. Another form of radiation is particle radiation such as proton beams. High energy protons are contained in space radiation [6]. Proton radiation is also produced by cyclotron linear accelerators for cancer therapy [7]. This perspective examines various shielding materials made from CNT composites or mixture materials, emphasizing the development of innovative combinations of different fillers and polymers with CNTs to shield the different types of radiation.
# Wireless Communication Technologies for Smart Grid Distribution Networks +
## 2 Available Wireless Technologies for Smart Grid Communications
### Power Line Communication (PLC)  
PLC uses existing power lines to transmit data at rates of up to 2-3 Mbps. It was the first choice in the implementation of AMIs, where it was used for data communication between smart meters and the data concentrator, whereas GPRS technology was used for transferring the data from the data concentrator to the utility's data center. The utilization of existing infrastructure reduces installation costs, making PLC an attractive solution for urban area applications. However, due to the harsh and noisy nature of power line networks, there are technical challenges. The channel is difficult to model. Also, the low-bandwidth characteristic of 20 kbps and sensitivity to disturbances make PLC unsuitable for data transmission. Medium-Voltage Broadband Power Line Communication (MV-BPLC) technology is considered an important candidate for communication with primary and secondary substations, and [8] proved that MV-PLC is effective in supporting grid management. However, it also suffers from deficiencies like cable length, PV DGs, industrial loads introducing high-frequency noise, and a lack of reliability with faults. Future work includes designing a hybrid model (wireless with MV-BPLC).
###### Abstract  
The mode superposition method has been widely used for the seismic response analysis of two-dimensional (2D) sites to enhance computational efficiency. However, this method lacks a guideline of modal truncation to control errors of acceleration responses. In this paper, the mode contribution coefficient of acceleration is proposed to be used as a criterion for modal truncation in the seismic acceleration response analysis of soil layers. Comparative analysis with the modal participation mass and modal contribution factor demonstrates the effectiveness of the proposed factor for the modal truncation of acceleration responses. The computational accuracy of the method for calculating acceleration from displacement using the central difference scheme is verified, which would further improve the computational efficiency in calculating site acceleration responses. A homogeneous soil site and a scarp topography site show that the proposed factor for modal truncation effectively controls the computational error of soil acceleration responses. Additionally, computing acceleration time histories from displacement time histories via the central difference method yields errors comparable to those from directly computing generalized coordinate accelerations. However, modal truncation based on modal participation mass or modal contribution factor results in fewer modes retained and larger computational errors.  
Keywords:two-dimensional site; seismic response; modal superposition method; modal truncation; time-history analysis +
Footnote †: journal: Applied sciences
# Classification of Heart Sounds Using Convolutional Neural Network
## 3 Results
### Performance Comparison between the Default Loss Function and the Loss Function with Class Weights  
We set class weights in the loss function during the training process to tackle the problem of class imbalance and to improve the classifier's Macc, especially for abnormal heart sounds. The same stratified five-fold cross-validation was applied to evaluate the performance of the training model with class weights and without class weights. The values of class weights for the normal and abnormal classes were calculated using Equation (14). For each fold of the cross-validation, the total number of training samples was 2523 with 1991 normal samples and 532 abnormal samples. Therefore, the class weights for the normal and abnormal samples were 0.63 and 2.37, respectively. The average performances are shown in Figure 5.  
Figure 4: Performance comparison between the fully connected layer and the global average pooling layer. FC, fully connected layer.  
Figure 5: Performance comparison between the default loss function and loss function with class weights. The default loss function is with the equal class weights for normal and abnormal samples, while the class weights-based method sets 2.73 for abnormal samples and a small value 0.63 to the normal samples.  
As shown in Figure 5, the classification mean accuracy obtained by setting the class weights in the loss function during the training process is 5 percentage points higher than that of the default equal weights loss function. The sensitivity of the model based on the class weights loss function is much higher than that the default loss function (up to 16 percentage points). The default equal weights loss function obtained a specificity of about 93%, which is higher than that of class weights-based loss function. Besides, the proposed model with different class weights obtained MCC of 72.1% which is higher than the default equal class weights. With the improvement of the sensitivity, the specificity of the model is still above 85%, which indicates the validity of this method.
# Photocatalytic Phenol Degradation by Silica-Modified Titanium Dioxide
###### Abstract  
Titanium dioxide (TiO\({}_{2}\)) has been widely applied as a photocatalyst for wastewater treatment due to its high photocatalytic activity and it can remove various harmful organic pollutants effectively. Under heated system, however, TiO\({}_{2}\) is prone to agglomeration that decrease its abilities as a photocatalyst. In order to overcome the agglomeration and increase its thermal resistance, addition of silica (SiO\({}_{2}\)) as supporting material is proposed in this research. Silica or silicon dioxide can be extracted from natural resources such as beach sand. Here, we report the application of a composite photocatalyst of TiO\({}_{2}\)/SiO\({}_{2}\) to remove phenolic compounds in wastewater. The photocatalyst was synthesized by adding SiO\({}_{2}\) from beach sand onto TiO\({}_{2}\) through impregnation methods. The results of the X-ray diffraction (XRD) showed that TiO\({}_{2}\) was present in the anatase phase. The highest crystallinity was obtained by TiO\({}_{2}\)/SiO\({}_{2}\) ratios of 7:1. SEM results showed that the shape of the particles was spherical. Further characterizations were conducted using Fourier-transform infrared spectroscopy (FTIR), Brunauer-Emmett-Teller (BET) analysis, and a particle size analyzer (PSA). By using the optimized condition, 96.05% phenol was degraded by the synthesized photocatalyst of TiO\({}_{2}\)/SiO\({}_{2}\), under UV irradiation for 120 min. The efficiency of the TiO\({}_{2}\)/SiO\({}_{2}\) is 3.5 times better than commercial TiO\({}_{2}\) P25 for the Langmuir-Hinshelwood first-order kinetic model.  
impregnation; phenol; photocatalyst; silicon dioxide; titanium dioxide +
Footnote †: journal:
## 1 Introduction  
Estimating ego-motion in autonomous vehicles is critical for tasks such as localization, navigation, and obstacle avoidance [1, 2, 3, 4]. Traditional pose estimation methods often rely on direct calculation techniques, which can be computationally expensive due to the need to process large volumes of data and the complexity of calculating incremental movements between consecutive frames. For example, these methods require an exhaustive search for all possible states in high-dimensional spaces, leading to high computational costs, even in environments with limited degrees of freedom (x, y, and yaw) [5, 6]. In such scenarios, achieving both accuracy and efficiency becomes a challenge.  
Recent advances in artificial intelligence (AI) have led to the development of deep learning-based methods that improve the accuracy of pose estimation. However, these methods often rely on large-scale training datasets, which can be difficult to obtain, particularly in dynamic environments where vehicles must adapt to unknown and evolving obstacles [7, 8]. But, AI-based methods may struggle to perform reliably without prior training under specific conditions, which poses a challenge in real-time autonomous vehicle systems where environments change rapidly. In contrast, our approach mitigates the reliance on large datasets by leveraging genetic algorithms, which can work effectively even with limited or no prior training data.  
To address these limitations, in this work, we propose a brute-force-based approach that uses genetic algorithms to explore possible vehicle movements. By generating an initial population of random motion candidates, we iteratively evaluated and selected the best-performing individuals based on image similarity between frames. This method not only improves accuracy but also enhances computational efficiency through parallel processing with CUDA [9; 10]. Our application domain is for an autonomous vehicle scenario. Thus, our algorithm is specifically designed for this case, that is, it takes advantage of the constraints of autonomous vehicles, which are assumed to have only three degrees of freedom (x, y, and yaw). Our primary contribution lies in the efficient integration of genetic algorithms and CUDA to achieve real-time performance while maintaining high accuracy. Experimental results demonstrate that our approach effectively estimates ego-motion with significant improvements over traditional approaches.
## 2 Materials and Methods
### Proposed Model Architecture
#### 2.6.2 LSTM Encoder  
For sequence modeling, long short-term memory (LSTM) has been considered a highly successful RNN architecture. When time is a relevant feature, the easiest way to handle it is to concatenate the time features with the input and use the LSTM model. Due to their ability to learn how and when to forget and when not to use gates, LSTMs and their bidirectional variants are popular.  
When sequential data are processed, the LSTM often ignores future information. Based on LSTM, BiLSTM processes the data in the series forward and reverse, connecting the two hidden layers to the same output layer [47] and storing previous and subsequent information as the current time basis for the time series data [48; 49]. Accordingly, multi-directional LSTM is more accurate than unidirectional LSTM. In BiLSTM [50], the hidden layer output includes both forward and backward activation outputs.  
\[\begin{split}\overrightarrow{h_{t}}&=\sigma(W_{x \overrightarrow{h}}x_{t}+W_{\overrightarrow{h}}\overrightarrow{h_{t-1}}+b_{ \overrightarrow{h}})\\ \overleftarrow{h_{t}}&=\sigma(W_{x\overleftarrow{h }}x_{t}+W_{\overleftarrow{h}}\overleftarrow{h_{t-1}}+b_{\overleftarrow{h}}) \\ &\quad\quad H_{t}=W_{x\overrightarrow{h}}\overrightarrow{h}+W_{ \overleftarrow{h}}y\overrightarrow{h}+b_{y}\end{split} \tag{8}\]  
where W and b are weight matrices and vectors, t is the current iteration of the recurrent network, \(\sigma\) is the activation function, \(H_{t}\) is the input of the hidden layer, and the output is generated by updating the forward structure \(\overrightarrow{h}\) and the backward structure \(\overleftarrow{ht}\).
# Risk-Based Selection of Inspection Method for External Post-Tensioning System of Bridges
## 4 Application of RBSIM for Selection of Inspection Method, a Case Study
### Determination of Consequences for Risk Analysis  
The consequences of a bridge failure can be devastating. The cost of reconstructing a structure is generally significant, but the loss of functionality that can have an impact on the surrounding area in terms of environmental harm and economic losses, among other things, can result in far larger expenses. However, in the structural risk and reliability analysis of the bridge of the case study, for simplicity, the bridge construction cost/bridge value is considered as the consequence of failure similar to some of the available literature [1,74]. To establish a correspondence between bridge failure and condition rating, the condition rating of equal or less than three (3) was basically interpreted as failed/obsolete bridge. Also, as described earlier, the probability of a false negative inspection report is equated with a condition rating of equal or less than three (3). Therefore, it can be assumed that the consequence of a false negative inspection report is the same as consequence of bridge failure.  
The risk and reliability analysis of bridges normally involves a combination of structural systems. For instance, in the bridge of the case study, three different structural systems most influential in the bridge load carrying capacity are external post-tensioning system, deck system (in main bridge and approaches), and cable stay system (in main bridge only). Therefore, the structural capacity and redundancy analysis of the bridge should include the combination of these systems. However, for cases such as that studied in this research, where one system is deemed to be most critical in load carrying capacity, or if the defects and damages are proven to be concentrated in one system, the focus can remain on the critical system, and the contribution of the other systems can be conservatively ignored. This will also simplify the analysis. This is why the study has focused on the external post-tensioning system. Therefore, the failure of the post-tensioning system is assumed as the failure of the whole bridge structure.
## 4 Experimental Setup  
### Electromechanical Test Bench Based-System  
The proposed methodology is validated under a complete set of experimental data. In this sense, an electromechanical test bench-based system is used to evaluate different fault severities in a bearing element. The electromechanical system is a self-designed laboratory test bench, and it is based on a pulley-belt system as the flow chart in Figure 4. shows. The electromechanical system includes a 971-W three-phase induction motor (IM) (model is WEG00136 APE48T) with one pair of poles, and the IM is fed through a variable frequency driver (VFD) (model WEGCFW08) with 220 VAC as a power supply; the use of the VFD allows controlling the rotational speed of the IM. Additionally, a conventional alternator is used as a mechanical load, and it is coupled to the IM using the pulley-belt system, as Figure 4 shows; the alternator comprises approximately 25% of the nominal load of the IM.  
Figure 3: The multi-layer structure of the proposed NN-based classifier for assessing the different bearing conditions under study.  
Regarding the acquired signals, the occurrence of vibrations in the IM and the stator current consumption of one of its three phases are continuously monitored and acquired by a data acquisition system (DAS), which is a proprietary low-cost design based on FPGA (field-programmable gate array) technology. The proprietary DAS has a serial-output sampling analog-to-digital converter with 12 bits and 4 channels (model ADS7841). Thus, the mechanical vibrations that are produced in the electromechanical system are acquired through an accelerometer sensor model LIS3L02 AS4 that is placed on the top of the IM case; in this sense, the acquired vibrations are those produced on the perpendicular plane of the rotating axis of the IM shaft, as illustrated in Figure 4. On the other hand, the stator current of one of the three phases of the IM is measured through a hall-effect sensor from Tamura Corporation (model L08P050D15); such a sensor is capable of measuring up to 50 Amps with a high linearity response (1%). Thereby, the hall effect sensor is placed between the VFD and the IM in power supply lines, as is shown in Figure 4. Each of the considered sensors is individually mounted on a PCB that includes the corresponding anti-alias filter and signal conditioning. The sampling frequencies used to acquire the occurrence of vibrations and the stator current consumption were set to 3000 Hz and 6000 Hz, respectively.
# Efficient Retrieval of Music Recordings Using Graph-Based Index Structures
## 4 Experiments
### Constructing, Saving, and Loading the Index
Without dimensionality reduction (\(K=240\)), storing the \(K\)-d tree requires 209.7 MB and 5161.2 MB of disk space for \(\mathbb{D}_{\text{sm}}\) and \(\mathbb{D}_{\text{lg}}\), respectively. The HNSW graph takes 53.9 MB and 1310.9 MB for the same data. In general, the required disk space scales roughly linearly with the dataset size as well as with the dimensionality in both indexing approaches. Furthermore, the graph-based index is generally more space-efficient than the tree-based structure in the given formats. To apply a pre-computed index for retrieval, we need to load it into the computer's main memory. We perform this step of loading the index with the functions required for the respective file formats used in the previous step (using functions from joblib and hnswlib, respectively). Columns 8 and 9 of Table 5 show the required time to load the index files. Loading a \(K\)-d tree without dimensionality reduction requires 131.5 ms for \(\mathbb{D}_{\text{sm}}\) and 3209.1 ms for \(\mathbb{D}_{\text{lg}}\). Using the same data, loading an HNSW graph takes 108.1 ms and 2680.0 ms, respectively. For smaller dimensions, loading a \(K\)-d tree is faster than loading an HNSW graph (e.g., \(K=12\) and \(\mathbb{D}_{\text{lg}}\): 167.0 ms for KD and 2071.5 ms for HNSW). The time to load an index scales linearly with the dimensionality in both KD and HNSW approaches (with a much flatter slope for HNSW). With our system (see Section 4.1 for specifications), we did not have any issues with loading the index structures into the main memory (31 GiB RAM) in all settings. On other systems with less memory (8 GiB RAM), we could not fully load the index structures into the main memory for \(K=240\). In this case, dimensionality reduction becomes crucial for practical reasons.
## 6 Conclusions  
This paper assesses the impact of various angular correction methods on the data acquired from the high-resolution, flexible side-view imaging satellite, Jilin-1 GP01/02. We have developed a correction method specifically tailored for the Jilin-1 GP01/02 PMS to enhance the precision of crop parameter extraction. Moreover, this study employed ground measured data to validate the accuracy of LAI, FV, and CHL retrieval using both pre- and post-correction reflectance. Compared with commonly used angular effect correction methods, the method introduced in this study, termed the Fixed-Jilin-1 method, effectively mitigates the influence of observation geometry on data accuracy. The findings indicate that the Fixed-Jilin-1 method markedly enhances the precision of crop parameter extraction, followed by the AFX method, the MODIS-based method, the Fixed-Landsat method, and the VJB method, respectively. Notably, the retrieval accuracy for the LAI and FVC saw an improvement of over 25%.  
Thus, by addressing the angular effect in Jilin-1 GP01/02 PMS data through the Fixed-Jilin-1 method, significant strides are made towards bolstering the inversion accuracy of agricultural vegetation parameters. This approach holds substantial promise for the monitoring of crops using CubeSat constellations and may pave the way for a variety of novel applications within the realm of precision agriculture.  
Conceptualization, [PERSON] and X.Z.; methodology, [PERSON]; validation, [PERSON] and Z.X.; formal analysis, [PERSON] and R.Z.; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and J.Y.; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China Postdoctoral Science Foundation, grant number 2022M720538, and the Major Science and Technology Program of Jilin Province, grant number YDZJ202303 CGZH023.  
Restrictions apply to the availability of these data.  
We thank the Inner Mongolia Autonomous Region Surveying and Mapping Geographic Information Center for supporting the field survey work. We are also grateful to the MODIS team for providing BRDF products that are available online. We also would like to thank the reviewers for their helpful comments.  
Authors [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] were employed by the company Chang Guang Satellite Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Networks and Protocols of Communication
### Network and Communications Protocols for IoT Applications
#### 3.3.2 Rest HTTP  
Due to the large volumes of data that are generated and sent across the new electronic devices that use the concept of IoT, it is necessary to build systems of communication and architectures that support various communications in different network environments. One of the most popular communication protocols in computer systems is REST HTTP (Representational State Transfer--REST and Hypertext Transfer Protocol--HTTP). Its use is especially popular in software development projects due to its good computational performance, tested in traditional computer cloud computing [37]. HTTP REST follows the client-server interaction model, which implies that each request is independent. REST uses different verbs; GET, POST, PUT, etc., for features and representations that can be transmitted in different formats such as HTML, XML, or JSON. Following the architecture defined by REST, the HTTP protocol has become widely used as a communication protocol for web services and also for the creation of REST APIs for computational distribution systems [38].