esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Monitoring and Compensation between EDM and ECM
## 1 Introduction
The four mainstream processing methods are ultrasonic-vibration EDM, dry EDM, powder-mixed dielectric EDM, and submergence EDM. These methods are used to improve processing performance, develop new material processing technologies, and achieve better quality [18; 19; 20]. Enhanced processes, materials, controls, and monitoring are also goals of such methodology. In the EDM process, discharge energy influences surface roughness and topography. The influence of the powder particles and capacitors connected in parallel in EDM must be determined, particularly in the gap, on the thermal spread in the dielectric, and on the influenced zone. A larger workpiece surface exerts greater discharge energy [21; 22]. Discharge energy and contact angle directly affect shape in the texturing process [23]. EDM's long processing time and fast pulse render defect detection in manufacturing difficult. Sensors can be used to collect voltage and current for analysis, but a high sampling rate of 100 MHz presents a problem; thus, an effective waveform is used for extraction [24], feature calculation [25], and dimensionality reduction to reduce data volume. Neural networks can be used to classify and predict processes [26; 27]. This is an effective method for a single process, but how the results are input in subsequent processes is crucial. [PERSON] et al. [28] compared ECM, EDM, and electrochemical discharge machining (ECDM). They reported the advantages of applying composite ECM and EDM to machining diamond tools combined with the effect of ECM erosion auxiliary discharge. However, this is difficult to control [29]. In industry, the controllability of a single process must be maintained using a fixture to regulate the process [30; 31; 32]. How to complete the processing and match the signal characteristics through preprocessing and postprocessing requires investigation. The same method can analyze the pulsed electrical signal and the two process models through the extraction of key process features. This study effectively lowered machining measurement errors and maintained more accurate geometric feedback.
## 4 Field Scale Test
### Model Construction  
According to the principle of similarity, under the same material conditions, the model size of the on-site scaled model test is reduced by 1:12. When physical force is not considered, the ratio of strength and elastic modulus between the prototype and the solid is 1:1, and the load ratio is 1:144.  
The scaled model test was conducted by comparing the terrain, physical and mechanical properties of the rock mass, and other conditions. It was selected at a relatively open and spacious platform in the northern part of the hill where the tunnel anchor is located. The test plan was arranged according to the actual double anchoring mode of the tunnel anchor. The test location is shown in Figure 4.  
Figure 4: Schematic diagram of scaled model test location and exploration audit location.  
In addition to scaling the geometric dimensions proportionally, consideration was also given to terrain similarity in this experiment. The terrain consistent with the solid was obtained through manual excavation, as shown in Figures 5 and 6.  
Through on-site investigation, it was found that the first section (K\({}_{2}\)I\({}^{1}\)) of the Luojingtan Formation, which was selected for the physical anchor, has a relatively deep burial depth and cannot be obtained on site. Therefore, the upper rock mass of the third section (K\({}_{2}\)I\({}^{2}\)) was ultimately chosen as the experimental stratum. The selected rock mass is consistent with the actual lithology of the stratum and also contains typical sandstone bands. The integrity of the model anchor surrounding rock is consistent with that of the actual bridge anchor surrounding the rock.  
According to the results of three sets of samples in the conglomerate (K\({}_{2}\)I\({}^{2}\)) where the model anchor is located, the average saturated uniaxial compressive strength is 15.6 MPa, which is basically consistent with the strength of the loaded surrounding rock of the actual  
Figure 5: Comparison diagram of terrain similarity analysis for scaled model test.  
Figure 6: 1:12 Field scaled model of tunnel anchorage. (**a**) Model hole; (**b**) site operation.  
bridge tunnel anchor. Through comparative analysis, the geological conditions of the surrounding rock selected for the model experiment are similar to those of the surrounding rock of the actual bridge anchorage, and the quality of the surrounding rock of the actual bridge anchorage is slightly better than that of the model anchorage.  
The anchorage should be made at a ratio of 1:12, and the corresponding model anchor hole size should meet similar requirements. Based on the future anchor surface, the anchor depth is 6.7 m, the axis spacing is 2.5 m, and the anchor length is 3.75 m. The angle between the axis of the model anchor and the horizontal is 40\({}^{\circ}\), which is the same as the burial angle of the actual bridge anchor, as shown in Figure 7.  
The anchor body is made of reinforced concrete, with five longitudinal steel arches and horizontally arranged stirrups, all of which are made of 10 mm threaded steel and poured with C40 concrete.
## 5 Simulation Results and Discussion
### Reception Quality Issue  
Targeted BER is the parameter that indicates the reception quality of the signal. The cooperative MIMO communication used in this paper is simulated with a fixed value of targeted BER. Figure 11 shows that the change in targeted BER changes the efficiency of cooperative communication over SISO transmission. Result shows that the cooperative communication is more energy efficient than SISO transmission in smaller targeted BER.
## 2 Results and Discussion
### Phenolic Composition of Viura Wines Treated with PS Extracts and CM  
Table 3 shows the effect of the PS extracts and CM on the monomeric phenolic compounds after one month (T1) and twelve months (T12) of bottling. The extracts, WPP, DWRP, and CP, reduced the concentration of total monomeric phenolics in Viura wines at both times. These results agreed with those previously reported by our workgroup [16], who observed that WPP extracts reduced the phenolic content in _Verdejo_ wines during deposit storage, and with research which has reported that MPs interact with phenolic compounds, reducing the content of flavonoids in wine [42; 43; 44; 45]. The addition of WPP and DWRP produced a loss of 9% of total monomeric phenolic after one month of bottling. After 12 months of bottling, this reduction was around 19% for WPP, DWRP, and CM. These results do not demonstrate a gradual desorption of the monomeric polyphenols adsorbed by the PS extracts and CM added to the wine, as described by [35], who points out that non-covalent and reversible MP-polyphenol interactions can present a gradual desorption of polyphenols to wine during aging. Probably, most of the monomeric polyphenols joined irreversibly with the PS of WPP, DWRP extracts, and CM. This loss is probably favored by the molecular weight of the PS of the grape-derived extracts and CMs that are susceptible to interact with the monomeric molecules of the polyphenols. WPP and DWRP extracts were mainly composed of medium- and low-molecular-weight PS, and CM was composed of both low- and high-molecular-weight PS [24]. However, it is important to note that the content of monomeric phenolics in white wines is low. Therefore, studies in red wines with higher contents of these compounds are needed to confirm these results. A Multivariate analysis of variance (MANOVA) of phenolic compounds of Viura wines after one month (T1) and twelve months of bottling (T12) with the Percentage of variance attributable (%) of the independent effect of Aging time and PS extract, and the interaction of both (Aging \(\times\) PS extract) can be consulted in the supplementary material (Table S1).
# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 1 Introduction
The acoustic emissions of the ships were recorded under various operational and machinery circumstances, that is, several ship runs for different boats. Data were collected at two different moments: a stationary scenario with the anchored ship, denoted as static, and, subsequently, in a moving scenario within the area of the acoustic ray, denoted as dynamic. A hydrophone, which was positioned at a depth of 45 m, recorded the sound using a 22,050 Hz sampling frequency and 8-bit resolution. Recording began when the ship was 1000 m before passing the hydrophone, and the recording ended when the ship was 500 m after the hydrophone. This study implemented both static and dynamic data characterization for ships, providing more accurate insights for analysis. An acoustic signature of a ship refers to the unique sound patterns it produces, which is used for detection and identification. There are two types: static and dynamic. The static acoustic signature consists of sounds generated when the ship is stationary or moving at a constant speed, including noises from the hull, onboard machinery, and propulsion system. These signatures are relatively constant and easier to analyze. In contrast, the dynamic acoustic signature involves sounds produced while the ship is in motion, such as noise from the propulsion system, interactions between the hull and water, and changes in operational activities. Dynamic signatures vary with speed, course, and operational conditions, making them more complex and harder to predict. Additionally, we advanced the use of other ML techniques, such as XGBoost and topic modeling through LDA, which transforms data into a domain where classes can be separated. Another contribution of this work was the data preprocessing stage, in which different dimensionality reduction techniques were applied. This study explored the impact of decimation and non-decimation on the performance of these techniques and analyzed their effects on both the effectiveness and efficiency of the proposed characterization. In summary, this study offered a comprehensive critical view of the various stages of SoNaR data processing for ML models and this paper presents alternatives for optimizing the modeling process.
# A Multi-Level Fuzzy Evaluation Method for the Reliability of Integrated Energy Systems
## 3 Methods
### Framework for Reliability Assessment of IES  
Compared with the single evaluation method, The combined evaluation method combines quantitative and qualitative analysis with feasibility and operability, thus enabling IES to reduce unnecessary overhead and improve the accuracy of assessment work in condition assessment, fault prediction, overhaul and maintenance.  
Based on the combined method, the hierarchical analysis, entropy weighting and multi-level fuzzy comprehensive evaluation are integrated to assess the reliability of IES, as shown in Figure 4. Firstly, the IES system model is constructed based on the historical data and real-time state, and then the multi-granularity indicator system is constructed. Next, the combination of AHP and EWM is used to determine the weight of each indicator, and then the fuzzy evaluation method is used to define the affiliation degree of single factor to each indicator, to calculate the fuzzy evaluation matrix and obtain the single-level evaluation results, and finally the multi-level fuzzy comprehensive evaluation of IES is carried out to obtain the final evaluation results.  
AHP is a systematic hierarchical method combining qualitative and quantitative, which is applicable in the case of complex structure and lack of data, but it is subject to too strong human subjective factors and requires several experts to make judgments. EWM [47; 48] applies the idea of information entropy to the process of determining weights to make up for the defects of too strong subjectivity of determining weights in the hierarchical analysis method, i.e., the objectivity of the data makes up for the subjectivity of the evaluator [49].
# Compressive Sensing-Based Sparsity Adaptive Channel Estimation for 5G Massive MIMO Systems
## 5 Conclusions  
This paper proposes a BSAMP algorithm with adaptive sparsity-based on the joint sparsity of sub-channels in massive MIMO systems. The algorithm chooses the support elements as the first choice by setting the threshold and finding the maximum backward difference position. The element is secondarily selected by regularization to improve the accuracy of the selected elements. The MSE, BER and throughput analysis is performed against the SNR and number of pilots. The proposed BSAMP algorithm is compared with LS, OMP, SP, SAMP, ASSP, and reference [32,33] algorithms, and the corresponding system parameters are analyzed for performance evaluations. The algorithm complexity analysis was also performed, which clearly estimated that the proposed BSAMP algorithm has a 0.01284 s average runtime, which is much smaller than the other algorithms such as the average runtime of SAMP algorithm, which is 93.3930 s and the ASSP algorithm which has an average runtime of 15.3610 s. With such a computationally-efficient behavior, the proposed BSAMP algorithm provides efficient sparse channel estimation capability for 5G massive MIMO systems which also enables us to deploy it in practical usage scenarios. Theoretical analysis and simulation results show that the BSAMP algorithm has good channel estimation performance, high throughput and low computational complexity as compared to other algorithms. This research work can further be extended by incorporating the proposed BSAMP algorithm in TDD, FDD Massive MIMO for Energy Efficiency Analysis versus different performance metrics such as the distance between the BS and mobile users, antenna element spacing and hardware impairments.  
[PERSON] conceived and designed the main idea, perform the experiments and wrote the paper; [PERSON] provided extensive technical support in literature survey and theoretical analysis; [PERSON] provided extensive support in analysis, validation and performance evaluation of the proposed work with existing works. He also provided support in research funding.  
This research work is supported by Hankuk University of Foreign Studies research fund 2018.  
The authors declare no conflict of interest.
## 4 Overview of GNSSs
### BeiDou
#### 4.4.4 User Segment BeiDou  
The BeiDou user segment consists of BeiDou user terminals that receive BeiDou navigation signals, determine pseudo-ranges, and solve navigation equations to obtain their coordinates. There is also international cooperation on compatibility and interoperability between BeiDou and other GNSSs, with the goal of bringing terminals compatible with other GNSSs [91].
# Low-Cost Digitalization Solution through Scalable IIoT Prototypes
## 4 Case Study  
The case study presented in this paper is the digitization of a broaching machine in Toscana Spazzole Industriali S.r.l (TSI). TSI is an SME with 40 employees and an annual turnover of 65 million. It is based in Tuscany, Italy, and manufactures industrial brushes, which are mainly used in the textile industry. The installation and implementation of an IIoT system to collect and analyze relevant machine data have been performed on a hydraulic broaching machine on the company's shop floor. Before the retrofitting of the broaching machine, performance indicating data, if at all, has solely been displayed analogically and not registered.
## 5 Experimental Results and Discussion
### Characterization of SEB Arrays
#### 5.2.3 Experimental Characterization of SEB Arrays with Different Matching Networks
(**a**) Comparison of derivatives \(d|T|\)/\(dV_{g}\); (**b**) Comparison of derivatives \(d\Theta\)/\(dV_{g}\). Comparison of the magnitude (**c**) and phase (**d**) characteristics of the two MNs.
###### Abstract  
Large flexible solar panels have the properties of light weight, low stiffness, and weak damping, which leads to low-frequency and large-amplitude vibrations. The existing vibration control methods of solar panels mainly adopt intelligent piezoelectric structures. However, the disadvantage is that the large stroke drive and control are limited. In the present study, a semi-active vibration control approach is proposed for flexible space solar panels based on magnetically controlled joints. The magnetic stiffness comes from the linear relationship between the joint output torque and rotation angle. The magnetic damping stems from the eddy current damping resulting from the relative motion between the permanent magnet rotor and the stator core of the joint. Firstly, the coupling dynamic modeling of a flexible plate and magnetic joints is established by adopting the Lagrange equation and the assumed mode approach. Secondly, semi-active vibration control simulations of the coupled system are performed. Meanwhile, the influence of the variable joint stiffness on the system frequency-shift effect is studied. Finally, the experimental platform is built, and simultaneously, non-contact permanent magnets and airflow are used to simulate single- and multi- frequency excitations, respectively. The experimental results indicate that, in the range of 0.06-0.343 Hz, magnetic damping is the leading factor with magnetic stiffness being the auxiliary. Additionally, it is also experimentally verified that the dual joint actuation has good synchronization. This study provides a new solution for the low-frequency vibration control of large flexible space structures.  
flexible plate; low-frequency vibration suppression; magnetically controlled joint stiffness/damping; non-contact excitations
## 6 Conclusions  
In this research, we introduced an integrated adversarial example detection and restoration framework for signal intelligent recognition using AE. Addressing the pervasive threats of adversarial attacks on deep learning, our framework effectively detects gradient-based and optimization-based attacks. Through the reconstruction error and KL divergence methodologies, we achieved a comprehensive detection rate of 88.48%. Beyond detection, our design excels in restoration, particularly for optimization-based attacks. Using AE, adversarial examples are reverted to their original state, leading to a recognition accuracy boost of over 30% against CW and Deepfool attacks compared to an undefended model. Validation on public datasets confirmed our method's robustness, with FGSM and PGD attacks detected at rates of 91% and 81%, and CW and Deepfool attacks detected at a remarkable 99%. In essence, our approach provides a unified and efficient defense mechanism, enhancing deep learning classifiers' resilience against adversarial threats.  
Looking ahead, we plan to delve deeper into understanding the distinct behaviors of adversarial examples and original samples within model decisions. This will offer critical guidance for further enhancing the accuracy of our detection framework. Concurrently, we are committed to developing new and more efficient detection methods to bolster the defensive capabilities of intelligent systems when faced with unknown attacks. Moreover, we will undertake a profound exploration of signal data features, probing the impact of adversarial perturbations on both shallow and deep signal characteristics. The goal is to discern adversarial examples from signals based on a comprehensive understanding of the data, without relying on model representations.  
Conceptualization, C.H.; methodology, C.H.; software, R.Q.; validation, C.H., L.W. and B.Y.; formal analysis, C.H.; investigation, C.H.; resources, W.C.; data curation, D.L.; writing--original draft preparation, C.H.; writing--review and editing, W.C.; visualization, R.Q.; supervision, W.C.; project administration, B.Y.; funding acquisition, B.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Defense Key Laboratory Fund grant number 6142413210003.  
Not applicable.  
The data used to support the findings of this study are included within the article.  
The authors declare no conflict of interest.
## 4 Sensing-Oriented Issues and Solutions
### Defect Types and Antenna Topologies
#### 4.1.3 Corrosion Detection and Characterization  
The interaction of a corrosive environment and tensile stress (e.g., directly applied stresses or in the form of residual stresses) can produce failure in the form of stress corrosion cracking (SCC) in susceptible metallic components [4]. This damage produced is not always obvious to casual inspection, for example, when under paint, so failures can be both unexpected and catastrophic. Thus, early detection of such defects is important in order to have sufficient time for condition-based maintenance. In the early stages of corrosion, a thin layer of oxides appears and causes changes in the conductivity, permittivity and permeability of the metal on the surface [109]. These changes variate with metal type and can be captured by the impedance change of tag antennas. The developments of corrosion detection based on passive antenna sensors are summarized in Table 3.  
The corrosion was demonstrated to be detectable using a LF RFID coil antenna by directly monitoring tag's response in time domain [47]. The feature of peak value is lift-off (or read range) dependent. In order to tackle this issue, a complex impedance measurement was conducted with the help of VNA; meanwhile, a PCA method was utilized to extract a lift-off independent feature [50]. However, the read range of this type of sensor system is limited because of the evanescent coupling. A 3D antenna was designed to be mounted on the metallic surface and the UHF band RFID technique was adopted to transfer the corrosion thickness induced variation by an AID in a 1-m read range [51]. In addition, one chipless antenna was developed to measure the corrosion under water using stub resonator in a 2-m read range; however, the occupied band of the system is not compatible with Gen2 standard and also a VNA was required to obtain the transmission coefficient of S\({}_{21}\)[48]. As Tables 1-3 show, different antenna design and development including configuration can be applied for different sensing applications. The optimal impedance match, gain, and measurement range remain challenges [110]. In addition with the interrogation using narrow-bandwidth RFID technique, the trade-off between sensitivity and dynamic range challenges the antenna-sensor design as well [99]. Meanwhile, the multiple-parameter signature of defects, for example, the crack profile, depth, and location, and the multi-physics procedures in EM modeling and co-simulation [6], complicates the design procedure and optimization including selection of materials.