esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Parametric Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Specimen** & \multirow{2}{*}{**\(R_{1\times D}\) (kN)**} & \multirow{2}{*}{**\(R_{3\times D}\) (kN)**} & \multirow{2}{*}{**\(R_{3\times D}\)/R\({}_{1\times D}\)**} & \multirow{2}{*}{**\(R_{max}\) (kN)**} & \multirow{2}{*}{**\(R_{max}\)/R\({}_{3\times D}\)**} & \multirow{2}{*}{**Det.**} \\  & **(\(F_{y}\)-2\(\gamma\)-\(\eta\)-1\(\Omega\))** & & & & & & \\ \hline
1 & 650-22.4-1-00 & 615.6 & 1312.4 & 2.13 & 1613.4 & (7.1) & 1.23 & F.P. \\
2 & 650-22.4-2-00 & 859.2 & 1786.7 & 2.08 & 2141.0 & (7.7) & 1.20 & F.P. \\
3 & 650-22.4-3-00 & 1110.9 & 2289.7 & 2.06 & 2681.4 & (7.9) & 1.17 & F.P. \\
4 & 650-22.4-4-00 & 1366.1 & 2808.3 & 2.06 & 3238.1 & (7.5) & 1.15 & F.P. \\
5 & 650-28.0-1-00 & 345.1 & 784.5 & 2.27 & 1018.8 & (7.7) & 1.30 & F.P. \\
6 & 650-28.0-2-00 & 461.5 & 1051.8 & 2.28 & 1343.1 & (8.9) & 1.28 & F.P. \\
7 & 650-28.0-3-00 & 587.7 & 1339.8 & 2.28 & 1665.2 & (9.0) & 1.24 & F.P. \\
8 & 650-28.0-4-00 & 715.5 & 1626.1 & 2.27 & 1999.8 & (9.0) & 1.23 & F.P. \\
9 & 650-37.3-1-00 & 165.5 & 404.5 & 2.44 & 552.7 & (7.9) & 1.37 & F.P. \\
10 & 650-37.3-2-00 & 210.8 & 526.5 & 2.50 & 736.8 & (9.8) & 1.40 & F.P. \\
11 & 650-37.3-3-00 & 261.4 & 657.1 & 2.51 & 904.8 & (11.9) & 1.38 & F.P. \\
12 & 650-37.3-4-00 & 313.5 & 795.4 & 2.54 & 1073.4 & (9.0) & 1.35 & P.W. \\
13 & 650-56.0-1-00 & 63.5 & 163.5 & 2.58 & 229.3 & (9.4) & 1.40 & P.W. \\
14 & 650-56.0-2-00 & 75.0 & 199.8 & 2.66 & 315.9 & (10.9) & 1.58 & P.W. \\
15 & 650-56.0-3-00 & 88.8 & 237.2 & 2.67 & 388.7 & (11.7) & 1.64 & P.W. \\
16 & 650-56.0-4-00 & 104.0 & 279.2 & 2.69 & 458.0 & (11.4) & 1.64 & P.W. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Parametric analysis results of HSA800 (\(F_{y}\) = 650 MPa). Figure 12: Load–indentation curves of S1100 (\(F_{y}\) = 1100 MPa): (**a**) 2\(\gamma\) of 22.4 and 37.3; (**b**) 2\(\gamma\) of 28.0 and 56.0.
# A Review of Optical Neural Networks
## 4 Discussion  
The demand for increased data processing volumes and operation speeds is constantly growing, and it is urgent to circumvent the structural von Neumann bottleneck and design new structures to accommodate efficient neural network training and testing. Optical processors, with their advantages of high speeds and low power consumption, have been gaining attention as a result of the rapid development of hardware dedicated to the inference and training of optical neural networks. Optical neural networks have shown great promise in terms of low-energy consumption and high-speed parallel computing, and the development trend of optical neuromorphic computing appears to be unstoppable. According to research findings, photonic neuromorphic computing is currently in its preliminary developmental stage, and various optimization solutions have emerged, even though photonic neural networks still have many challenges to overcome.  
In this paper, we reviewed the development of optical neural networks, emphasized optical neural networks with fiber dispersion, and provided some perspectives for the future development of optical neuromorphic computing (Table 1). Compared with other optical neural networks, optical neural networks with fiber dispersion have better nonlinear performance and neuron-like properties, thus exhibiting greater potential value. As optical technology matures, deep learning will further evolve with enhanced neuromorphic computing and optical neural networks as well as the application of increasingly high-performance integrated optics. In the future, hyper-scale and programmable optical neural networks will likely be implemented and applied to speech processing, image recognition, and target tracking, which will provide a bright future for neuromorphic photonics.  
Resources, D.Z.; data curation, D.Z.; writing--original draft preparation, D.Z.; writing--review and editing, D.Z. and Z.T.; supervision, Z.T.; project administration, Z.T.; funding acquisition, Z.T. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (61875008).  
The authors declare no conflict of interest.
# Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning
## 6 Implementation
### Adversarial Defense Mechanism  
The below schematic representation shows how the authors performed the adversarial attack. Different models, namely, random forest, convolution neural network and EfficientNet B0, were initially trained using the MaleVis dataset by traditional methods, after which an adversarial attack using the FGSM method was performed. The images in the dataset were mixed with some noise of three different epsilon values, which were 0.01, 0.1, and 0.15. When these adversarial images are fed into the classifiers, they fail to classify them into their proper classes correctly. This can be a threat to the malware detection system, as a malicious file can be classified as a benign file and can cause huge damage to the computer system [32; 66].  
To mitigate this, an adversarial defense technique made the classifier robust against the FGSM attack. The technique proposed by the authors is an adversarial training defense mechanism. In this technique, the adversarial samples are included in the training set and then fed to the model to learn the different features of adversarial samples and clean images [67]. In various recent studies and articles, this type of defense technique proved to be one of the most effective defensive mechanisms against a particular attack. In this research work, the authors have particularly performed the FGSM adversarial training method, including the adversarial samples generated using the FGSM method explained in the previous section. This can help to improve the error rate on the samples generated using the MaleVis dataset and the FGSM attack [53]. Figures 10 and 11 demonstrate the process of establishing the adversarial attack and defense.  
Figure 10: Schematic representation of adversarial Attack performed.
# On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
## 6 Conclusions  
In this paper, the BECPBs of OOK transmitters using realistic batteries are derived and compared. Two types of batteries with different BUFs are considered, with one depending on the instantaneous discharge current, while the other depending on the mean discharge current. Results show that the BECPB is related not only to the transmission and circuit energy consumption, but also to the battery  
Figure 4: BECPB for two types of batteries over \(d\).  
Figure 5: BECPB for two types of batteries over \(B\).  
characteristic parameter \(\gamma\). Specifically, the BECPB for each type of battery increases with \(\gamma\) and distance; the BECPBs for the two nonlinear batteries first decrease and then increase with bandwidth. There exists the optimal bandwidth corresponding to the minimum BECPB; the battery with IDC-based BUF corresponds to a higher BECPB than the one with MDC-based BUF. When \(\gamma\) and distance are large, the nonlinear effect of battery has significant influence on BECPB, which cannot be ignored. The comparative study of the energy consumption of the two battery utilization models can provide guidance in energy-efficient design of wireless sensor nodes.  
This work is supported by NSFC 61671011, 61401266, 61501289.  
[PERSON] made mathematical derivations and conducted the simulations; [PERSON] first proposed the research problem as well as solution methods, then supervised [PERSON] to carry out the research; [PERSON], [PERSON] and [PERSON] participated in the discussion and provided valuable suggestions.  
The authors declare no conflict of interest.
# Linked Data Triples Enhance Document Relevance Classification
## 4 Energy-Hub
### Topic-Entity Triples  
The extracted named entities \(E\) and LDA topics \(T\) from a document \(D\) can be used to form triples (linked data), and these often constitute atomic facts. A dependency parser that identifies \"subjects\" and \"objects\" in each sentence, while a POS (Part-of-Speech) tagger marks each token in the sentence with an appropriate part-of-speech, as shown in Figure 2. As the text from web pages is mostly semi-structured, namely not always grammatically well-formed, some sentences cannot be rendered as triples. Mapping the extracted named entities against subjects and objects helps resolve this. For any document \(D\), either the subject (or objects) of a sentence must be in its list of entities \(E\), or from the list of keywords from the dominant topics \(T\), to be considered as a candidate triple as defined in Algorithm 1. A well-formed formula in predicate calculus needs a subject, an object and a verb as a predicate. Likewise, a triple must have all three of these elements: namely object, subject, and predicate. These triples are used as an additional feature set as inputs into the training of the NN-classifier to classify relevance/irrelevance documents arriving to the news-feed. The question to answer is, do these triples contribute in a positive way to a relevance/irrelevance classifier?  
For instance, consider the two triples formed from the sentence _\"UL.S. scientists call for fight against climate change\"_, namely <U.S. scientists, call, against climate change>, <U.S. scientists, call, for fight> presented earlier. _\"UL.S. scientists\"_ is identified as a named entity (by NER), and _\"climate change\"_ is a keyword from the most dominant topic of that document. This triple contains a named entity in its subject and a topic keyword in its object; hence, it can be considered as topic-entity triple. On the other hand, _\"fight\"_ is neither represented in the topic list for the document nor is it a named entity (by NER). Hence, the triple <U.S. scientists, call, for fight> is discarded; it is not a topic-entity triple.  
Preliminary analysis (using JSD above in Section 4.3) on the proposed natural language features shows named entities \(E\) and LDA-derived topic distributions \(T\) have significant information to distinguish the two classes, relevant versus irrelevant documents. Triples formed from these features are also inferred as a distinguishing feature between two classes, hence being considered as an additional feature for text classification.  
```
Input : Given a document \(d\) from the corpus, with abstract topics \(T\) and named entities \(E\) Output : A list of Topic-Entity Triples <Subject, Verb, Object>  TripleExtraction(\(d\), \(T\), \(E\)) I=newlist fors in d.sentencesdo \(\tau=ExtractTriple(\)s if\(\tau\_subject\) in \(\texttt{filter}(T\), \(E\)) or \(\tau\_object\) in \(\texttt{filter}(T\), \(E\)) then  I\(\leftarrow\)\(addtlist(<\tau\_subject,\tau\_verb,\tau\_object>,I)\)  end for  end for return \(I\)
```  
**Algorithm 1**Triple Extraction Algorithm from Raw Text
# Infrared Fault Classification Based on the Siamese Network  
[PERSON]  
1 Department of Computer Science and Technology, China Jiliang University, Hangzhou 310018, China 1  
[PERSON]  
1 Department of Computer Science and Technology, China Jiliang University, Hangzhou 310018, China 1  
Qi., 2  
which the most popular are model-based analysis and data-driven methods [7]. However, some faults that may occur in solar power modules can cause changes in the I-V curve, which are almost impossible to detect. Although this makes fault classification difficult, it also increases the time and installation costs of fault detection systems. Using visual fault classification makes the system monitoring and maintenance simpler, with lower operating costs. With the development of artificial intelligence, the automatic detection and classification of abnormal thermal imaging PV images based on deep learning have become more efficient and accurate [8]. Unlike machine learning techniques, deep learning has higher feature extraction and learning capabilities, allowing for more accurate and robust classification performance. Moreover, deep learning models can provide nonlinear representations and generalization capabilities for big data.  
In this study, an ACA-FFM Siamese convolutional neural network model was proposed. The model performs feature extraction followed by similarity logical regression. The key objective of this technology is to reduce the dependence on large-scale datasets. The similarity comparison of images is used to classify equipment heat image faults. The main contributions of this paper include the following three aspects:  
1. A novel feature extraction method based on adaptive coordinate attention (ACA) is proposed, which dynamically adjusts the scope of module attention based on input images, thereby enhancing the ability of multi-scale feature extraction.
2. A novel feature fusion module (FFM) is designed to enhance feature learning ability and improve the accuracy of fault detection.
3. Advancements in terms of the average correct classification rate on an open-accessed infrared failure dataset. Experimental results show that the ACA-FFM Siamese network, compared to existing methods, demonstrates higher recognition rates in scenarios of high similarity.  
The remainder of this paper is structured as follows: In Section 2, the related technologies are summarized. In Section 3, the proposed network is described in detail. The experimental results are stated in Section 4. Finally, in Section 5, the results of this study are summarized and future work is discussed.
###### Abstract  
Companies building products with a usage phase of 25 years and more, have suffered from obsolescence for decades. Software and hardware components inside of the supporting systems, such as avionics test benches, are particularly affected. They consist of components built for 3-7 years but expected to operate for decades. The lack of standardized, open and modular systems for special timing constraints increase the pressure and impede the interoperability in this field. Industry 4.0 systems offer exactly this, but do not support aviation standards. The research in this article aims to show that it is possible to use widespread Industry 4.0 standards and technology at the field level to achieve transferability and maintainability in the operation of test benches. This article describes an implemented and evaluated proof of concept of the implementation of an industrial ethernet standard EtherCAT for the ARINC 429 I/O for an aviation test bench. As a baseline, market available EtherCAT components were evaluated and selected. For the client development, standard components for the EtherCAT logic and ARINC 429 were used. Additionally, the full interoperability at the syntax level, was achieved by implementing a configuration approach of the I/O, in order to encode and decode the messages. The evaluation was performed using an Airbus A350-1000 test bench for secondary flight controls--namely the HighLift system.  
industry 4.0; obsolescence; design support systems; automation; testing Article
# Integrated Sensing and Communication Using Random Padded OTFS with Reduced Interferences
## 8 Conclusions  
In this paper, an important enhancement of RP-OTFS modulation was introduced based on the repetition of random pilot samples. This small modification was shown to have a significant impact on reducing the ICI between pilot carriers (stronger) and data carriers (weaker), and consequently, cause BER reduction (Figure 16) in the ISAC system. An additional step (sinc extrapolation) has been proposed to be added to the OTFS signal processing chain that allows to reduce the level of interference in scenario where a large number of relatively weak reflections are present (Figure 15). The comparison of OFDM, ZP-OTFS, and RP-OTFS without and with these two techniques has demonstrated that the methods proposed here offer the smallest BER for a carrier frequency of 70 GHz. The ability of the dynamic to change the location of the pilot interference in the Doppler direction allows adjusting the grid to different Dopplers without changing processing on the receiver side.  
At the same time, as was shown, the new method also allows for a more noise-robust detection of moving vehicles in mm-wave communication (Figure 17). Finally, it was shown how to choose the parameters of the RP-OTFS for efficient work in the millimeter wave bandwidth (Table
## 1 Introduction
Additionally, fabrication of complex 3D structures is still challenging with this material. On the other hand, glass seems to be a more appropriate material for some applications, in particular when long-time cellular observation or high-resolution cell imaging is required. Thus, glass biochips fabricated by FLAE represent innovative platforms that are extensively explored to advance not only fundamental studies, but also clinical applications in nanomedicine for overcoming the limitations of traditional biomedical approaches [7; 41; 42]. They can further represent miniaturized micropatterned devices, including but not limited to microfluidics, microwell chips, and microarrays proposed for specific bioassays. Their main advantages as compared with conventional bioassays are related to the use of low sample volumes; accurate control over sample manipulation and analyses; high spatial resolution down to a single cell, high-throughput screening; and broad spectrum of in situ measurements [7]. In the field of ultrafast laser processing for the development of biochips, fs lasers are mostly used as described above, and few works have been done with ps lasers in spite of the fact that the ps lasers offer some advantages such as higher power, higher reliability, and higher stability, which are essential for practical use. We recently introduced the possibility of precise microfabrication of PG volume by high repetition rate ps laser-assisted etching (PLAE) [28]. Along with the determination of critical irradiation doses and etching rates for glass processing with either ultraviolet (355 nm) or visible (532 nm) laser wavelengths, the study revealed the very fast and controlled synthesis of embedded 3D microfluidic channels with high aspect ratio, reduced processing time, and lower fabrication cost. The main advantage of PLAE over FLAE was found to be the short laser processing interval, up to 10 times faster, which is beneficial for fabrication of large-area (cm sizes) biochips, while keeping a microscale processing resolution. In the present study, we evaluated three distinct approaches of applying PLAE to large-area PG processing: (1) microfabrication of complex 3D interconnected glass channels, (2) laser surface patterning to fabricate PG molds for PDMS replication of numerous microchambers, and (3) PG masks consisting of microholes with different sizes to be used for the synthesis of nanomaterial patterns by subsequent additive processes. The customized micropatterns were evaluated, and the fabricated microfluidic devices were applied for cell cultures and single cell immobilization.
## 5 Experimental Results and Discussion
### Classification
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Study**} & \multicolumn{3}{c}{**Details**} & \multicolumn{3}{c}{**Details**} \\ \cline{2-13}  & **Sensor** & **Features** & **Classifier** & **Accuracy** & **Classes** \\ \hline Our & Sentinel-2 & BOA Reflectances & Pixel R-CNN & 96.50\% & 15 \\ [PERSON] [78], 2018 & Sentinel-2 & TOA Reflectances & Recurrent Encoders & 90\% & 17 \\ [PERSON] et al. [72], 2016 & Radarsaf+ Landsat-8 & Optical+SAR & NN and MLPs & 90\% & 11 \\ [PERSON] et al. [76], 2014 & RapidEye & Vegetation Indices & RF and OBIA & 86\% & 9 \\ [PERSON] et al. [80], 2018 & Sentinel-2 & Optical & RF & 91-95\% & 9 \\ [PERSON] et al. [30], 2015 & MODIS & Stat + phenological & RF & 89\% & 6 \\ JM[PERSON] [81], 2011 & ASTER & Vegetation Indices & OBIA+DT & 79\% & 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: An overview and performance of recent studies.
# Discrete-Time First-Order Plus Dead-Time Model-Reference Trade-off PID Control Design
## 2 Control System
### Objective Function  
The objective function is defined based on the sum of absolute errors (SAE) as follows:  
\[J_{d}=\sum_{k=1}^{\infty}|r(k)-y(k)| \tag{15}\]  
In a one-degree-of-freedom (1 DOF) system, the relationship between the servo and regulation performances is a trade-off relationship. Therefore, the objective functions in the reference tracking optimization and the disturbance rejection optimization are given by Equations (16) and (17), respectively:  
\[J_{d}^{s} =\sum_{k=1}^{\infty}\left|r^{s}(k)-\frac{C_{\epsilon}(z^{-1})P_{d }(z^{-1})}{1+C_{d}(z^{-1})P_{d}(z^{-1})}r(k)\right| \tag{16}\] \[J_{d}^{r} =\sum_{k=1}^{\infty}\left|r^{d}(k)-\frac{P_{d}(z^{-1})}{1+C_{d}(z ^{-1})P_{d}(z^{-1})}d(k)\right| \tag{17}\]  
where \(r^{s}(k)=1\), \(r(k)=1\), \(r^{d}(k)=0\), and \(d(k)=1\).  
In the discussed control system, the stability and the tracking performance are simultaneously decided because they are in a trade-off relationship.
## 2 Radio Propagation Measurement Method
### Radio Propagation Measurement Environment
#### 2.2.2 Complex Measurement Environment  
Figure 4a shows the photograph of the selected C environment, which is surrounded by buildings on all sides. The height of each building is as follows: Building A is 26 m, Building B is 18 m, Building C is 18 m, and Building D is 8 m. As shown in Figure 4a, these buildings have balconies and windows, and thus the diversity of the wall surface shapes is expected to cause complex reflections and scattering that will affect the propagation characteristics.  
Figure 4: Complex measurement environment: (**a**) photograph of measurement environment; (**b**) two antenna arrangements; (**c**) top view of the analytical model and (**d**) flight path of the UAV.  
Figure 4b shows two antenna arrangements to achieve the different directivity characteristics in terms of the radiation pattern and the polarization. Horizontal installation is when the antenna elements are set parallel to the ground and Building A, resulting in a horizontal polarization of omnidirectional nature, whilst vertical installation is when the antenna elements are set perpendicular to the ground, resulting in a vertical polarization of figure-8 shape. In this way, it is possible to evaluate in detail the effects of the directivity and polarization characteristics of the antenna on the propagation characteristics.  
Figure 4c depicts the top view of the measurement environment with a detailed diagram of each parameter. If the transmitting and receiving antennas have been set up for the vertical installation and \(D_{t}=D_{r}\), the radiation gain of the transmitting and receiving antennas in the direction of the direct path decreases rapidly, as can be seen from the directivity of the transmitting and receiving antennas drawn in Figure 4b, so \(D_{t}\) and \(D_{r}\) were set to different distances. The UAV equipped with the transmitting antenna was flown parallel to Building A, and the center of its path was 15 m from Building B and 5 m from Building A. The receiving antenna was placed at a height of 1 m above the concrete pavement surface, 15 m from Building B, and at a distance of \(D_{r}=15\) m from Building A. Half-wavelength dipole antennas were used for the transmitting and receiving antennas.  
Figure 4d illustrates the flight path of the UAV parallel to Building A at a flight distance of \(L=3\) m centered at \(D_{t}\). This migration length was set to twice that of the defined S environment to consider the effect of the C environment on the propagation characteristics. The UAV altitude was changed in 5 m increments between 10 m and 30 m above the ground. The movement speed of the UAV \(v\) was set to 0.3 m/s.