esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Conventional Approaches
These images are then analyzed to identify any anomalous temperature distributions or thermal characteristics ([PERSON] et al., 2017). The advantage of this approach lies in its non-contact nature, as it does not necessitate direct contact with the blade surface, thereby avoiding production downtime. Additionally, it enables scanning of the entire blade surface rather than merely localized areas, making it suitable for the inspection of large-scale WTBs. [PERSON] et al. (2015) investigated the application of active and passive thermographic techniques for the detection of defects in WTBs, utilizing several image processing techniques on the unprocessed thermal data to increase the defect diagnosis process's signal-to-noise ratio. [PERSON] et al. (2016) proposed a Continuous Line Laser Scanning Thermography (CLLST) system that remotely detects internal delamination and surface damage in WTBs by analyzing the propagation patterns of laser-induced thermal waves.Thermographic techniques rely on the detection of temperature differences on the surface of the target. When there are reflective properties or contamination on the blades' surface, these surface characteristics can lead to misinterpretations, as thermography is sensitive to temperature variations. However, early-stage damage does not easily result in temperature changes, which means that thermography may not be suitable for the detection of early-stage damage.
# Privacy and Utility of Private Synthetic Data for Medical Data Analyses
## 4 Medical Use Case
### Legal Requirements  
Medical data processing requires the highest standard of data protection. The European GDPR considers medical data as sensitive data. Hence, processing is only allowed if one of few exemptions can be applied, e.g., when data processing is required for a medical treatment according to GDPR Article 9 (2)(a) and (h). For the secondary use of medical data, such as research purposes, the most common exception criterion is an explicit declaration of consent by the affected person. Nevertheless, strong privacy protection measures are required.  
In the United States, the Health Insurance Portability and Accountability Act (HIPAA) defines privacy rules for the usage of personal health data [28]. Like the GDPR, it follows the principle that patients must give their consent before their data are processed. Additionally, there are exclusions where processing is mandatory, such as for payment purposes or when the data are needed for treatment. HIPAA also regulates the secondary use of personal health data and provides guidance on the de-identification of data (SS 164.514). Included in this guidance is a list of 18 identifiers which should be removed so that the data can be considered de-identified. Such identifiers are the name of the subject, social security number, etc. However, HIPAA's de-identification guidelines show another issue regarding the privacy of personal medical data. While HIPAA considers data as de-identified when all specified identifiers have been removed, it does not address the issue of re-identification. The GDPR contains similar provisions for anonymized data without stating specific identifiers, but also does not consider the issue of re-identification. Potential re-identification can occur in many scenarios, especially if an attacker has background knowledge that can be used to link quasi-identifiers (e.g., zip code, year of birth) between different datasets. As such, an individual's identity can be revealed, potentially compromising their privacy. There are many studies that show the risk of re-identification in de facto anonymized datasets [29; 30; 31; 32]. This has led many legal scholars to demand that the state of the art must be considered in the case of anonymization. For this reason, PETs like the ones of Section 3 are used. In this article, synthetic differential private data are utilized to mitigate the re-identification risk.
# Reducing the Cost of Implementing Filters in LoRa Devices
###### Abstract  
This paper presents two methods to optimize LoRa (Low-Power Long-Range) devices so that implementing multiplier-less pulse shaping filters is more economical. Basic chirp waveforms can be generated more efficiently using the method of chirp segmentation so that only a quarter of the samples needs to be stored in the ROM. Quantization can also be applied to the basic chirp samples in order to reduce the number of unique input values to the filter, which in turn reduces the size of the lookup table for multiplier-less filter implementation. Various tests were performed on a simulated LoRa system in order to evaluate the impact of the quantization error on the system performance. By examining the occupied bandwidth, fast Fourier transform used for symbol demodulation, and bit-error rates, it is shown that even performing a high level of quantization does not cause significant performance degradation. Therefore, the memory requirements of LoRa devices can be significantly reduced by using the methods of chirp segmentation and quantization so as to improve the feasibility of implementing multiplier-less filters in LoRa devices.  
Keywords:LoRa; chirp spread spectrum (CSS); Internet of Things (IoT); pulse shaping filter; multiplier-less filters; sample quantization +
Footnote †: journal: Sensors
###### Abstract  
To address the issues of low efficiency and lengthy running time associated with trajectory planning for 6-degree-of-freedom manipulators, this paper introduces a novel solution that generates a time-optimal path for a manipulator while adhering to its kinematic limitations. The proposed method comprises several stages. Firstly, the kinematics of the manipulator are analyzed. Secondly, the manipulator's joint-space path points are interpolated via the quintic B-spline curve. Subsequently, the non-dominated sorting genetic algorithm II (NSGA-II) is improved by applying reinforcement learning to optimize its crossover and mutation probabilities, and the variable neighborhood search (VNS) algorithm is integrated to enhance its local search capability. Finally, the joint increments and running time of the manipulator are optimized using the improved NSGA-II, and the time-optimal trajectory is then determined by simulation on MATLAB. Furthermore, compared with other conventional optimization methods, the proposed approach has reduced the total running time by 19.26%, effectively improving the working efficiency of the manipulator.  
time-optimal trajectory planning; manipulator; NSGA-II algorithm; reinforcement learning; VNS algorithm Article
## 3 Results  
In March, all webs receiving the two control treatments had been substantially restored to their previous structure 24 h later, whereas none of the permethrin-treated webs were substantially restored (\(p\) = 1.69 \(\times\) 10\({}^{-10}\); \(X^{2}\) = 30; 2 df). Six of the 15 permethrin-treated webs had been partially restored, and the rest were in the same torn condition as the day before. Of those webs that had been rebuilt, the mesh areas were, on average, just 42% of their original size (\(p\) = 0.005, t = 4.13, df = 6.8, paired T-test, unequal variance, 2-tailed). Visual inspection revealed that spacing of the adhesive spiral mesh strands was wider than on a normal web (Figure 2F). Most spiders on the permethrin-sprayed webs remained on the web, though several disappeared, having either abandoned the web or been predated.  
In June, when we treated webs in preparation for mosquito capture, all control webs were substantially restored within 24 h, whereas just 29% (6 of 21) of the spiders on permethrin-treated webs had substantially restored their webs (\(p\) < 0.0001; [PERSON]'s Exact Test). All permethrin-treated webs had been restored after three days, showing the interference at this dose was temporary.  
The day after treating the webs in June, we tested the webs and spiders for mosquito-catching ability. Permethrin reduced the number of mosquitoes captured by 71%, independent of the reduced web area (Figure 3). Permethrin-treated webs were less effective at retaining the mosquitoes that struck them than the control webs; (\(p\) = 0.00024; t = 4.44, 15 df). Mosquitoes adhered to each of the 16 control webs for at least three seconds, and the resident spider seized a mosquito in each web. In the 21 permethrin-treated webs, we managed to stick a mosquito in what remained of 86% (18) of the webs; three webs could not retain a mosquito at all. Spiders on treated webs were less likely to seize a mosquito that did stick than spiders on control webs (\(p\) < 0.0001; Fisher's Exact test). In 67% of the webs that held a mosquito for at least 3 sec but that had not been substantially repaired (12 of 18), none of the spiders seized the mosquito. In the 21% of webs that were substantially reconstructed, each spider seized the mosquito immediately. Mosquitoes not seized by the spiders eventually wiggled free.  
Figure 2: Side by side comparisons of spider webs before (**A**,**C**,**E**) and after (**B**,**D**,**F**) the residing orchard spiders were exposed to treatments. The control treatments (**A**–**D**) had no adverse effect on web reconstruction. For the first day after application, permethrin treatment (**E**,**F**) interfered with the resident spider’s ability to reconstruct a normal web. The few webs that were partially reconstructed (6 of 15) had a significantly smaller web area, and irregular and wide spacing of adhesive spiral strands visible in the permethrin treatment after 24 h (**F**).
# Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity
## 6 Results and Discussions
### Comparison with Existing Works  
The proposed work **Fed-Hetero** addresses quantity skew, label distribution skew, and image skew. FL-Hetero explored a client-based approach where clients are informed about the type of data heterogeneity, enabling them to perform either data augmentation or clustering with other clients of similar distributions. Rather than excluding clients with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Clients** & \(R_{Q}\) & \(R_{l}\) & \(R_{i}\) & **Cluster** & **Augmentation** \\ \hline client 1 & 0 & 0 & 1 & ✓ & \\ client 2 & 1 & 1 & 1 & ✓ & ✓ \\ client 3 & 0 & 0 & 1 & ✓ & \\ client 4 & 0 & 0 & 1 & ✓ & \\ \hline \hline \end{tabular}
\end{table}
Table 13: Recommendations by Fed-Hetero system to the clients.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Clients** & \(JSD_{d}\) & \(D_{l}S,0\) & \(D_{l}S,1\) & **SSIM** \\ \hline client 1-client 2 & **0.70** & 0.34 & **0.57** & 0.6 \\ client 1-client 3 & 0.22 & 0.00 & 0.00 & **0.8** \\ client 1-client 4 & 0.17 & 0.00 & 0.00 & 0.7 \\ client 2-client 3 & **0.52** & **0.77** & **0.77** & 0.6 \\ client 2-client 4 & **0.56** & **0.77** & **0.77** & **0.8** \\ client 3-client 4 & 0.04 & 0.00 & 0.00 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Evaluation of various skews across clients in Fed-Hetero.  
limited data, we focused on incorporating them effectively into the FL process and, instead of sharing information with all clients, it is shared only with servers.  
In the work [22], the clients are selected for the FL process based on the similarity in the data distributions. The Federated Feature distillation (FedFed) [19] tackles the data heterogeneity by generating and sharing performance-sensitive features. FedFed raises potential privacy concerns as important features are shared. In federated daisy chaining [23], a daisy chain of local datasets enables more efficient training in data-sparse domains. The clients are sharing information with other clients that do not maintain privacy. Table 14 shows the comparison of Fed-Hetero with the existing works. Most existing works are focused on quantity skew and label distribution skew.  
Fed-Hetero additionally addresses image skew and incorporates a strategy for including clients with limited data into the FL process where the existing works exclude the limited data clients [22]. Fed-Hetero does not address feature skew, which refers to the potential mismatch or imbalance between the features of training and testing data that could affect the model's generalization performance. The scalability of this approach presents an opportunity for further exploration, particularly in scenarios with a growing number of clients and increased communication rounds in federated learning.
## 5 Machine Learning for the Prediction Model
### Results from Machine Learning
#### 5.3.3 Feature Importance  
The utilized XGB in this study is a tree-based algorithm that has the advantage of quantitatively analyzing the effects of each feature in the preference model.  
Figure 15 shows the normalization of feature importance derived through the XGB algorithm in each model. As shown in Figure 15a, \(P_{L}\) had the greatest impact on prediction model_S\({}_{R}\) with 0.234, and T\({}_{\rm F}\) and O\({}_{\rm V}\) were 0.174 and 0.165, respectively. O\({}_{\rm H}\), E\({}_{\rm G}\), P\({}_{\rm CTC}\), B\({}_{\rm P}\), and P\({}_{\rm N}\) were 0.118, 0.09, 0.074, and 0.071, respectively, and \(B_{r}\) was 0.0. However, this should not be interpreted as \(B_{r}\) having no effect on raft settlement due to tunneling.  
\(B_{r}\) is a variable that intuitively determines the adequacy of the model in the selection of variables, which is highly correlated to real B\({}_{\rm P}\) and \(P_{L}\). Therefore, the feature importance of \(B_{r}\) being 0 should only indicate that this model is rational and does not mean that it does not affect raft settlement induced by tunneling.  
The variables that affected preference model_S\({}_{P}\) are represented in Figure 15b.  
\(P_{L}\) resulted in 0.222, showing the highest influence in prediction model_S\({}_{P}\), and T\({}_{\rm F}\) and O\({}_{\rm V}\) were 0.152 and 0.148, respectively. O\({}_{\rm H}\) was the next important feature in prediction model_S\({}_{R}\); however, P\({}_{\rm N}\) was the next high F score in prediction model_S\({}_{P}\), with 0.106. E\({}_{\rm G}\), O\({}_{\rm V}\), B\({}_{\rm P}\), and P\({}_{\rm CTC}\) were 0.099, 0.097, 0.091, and 0.085, respectively.  
The feature importance of prediction model_dS is shown in Figure 15c.  
In this model, O\({}_{\rm H}\) scored the highest with 0.183, followed by T\({}_{\rm F}\), O\({}_{\rm V}\), \(P_{L}\), E\({}_{\rm G}\), P\({}_{\rm CTC}\), B\({}_{\rm P}\), and P\({}_{\rm N}\) with 0.170, 0.165, 0.150, 0.108, 0.093, 0.066, and 0.065, respectively. This shows that T\({}_{\rm F}\) was the most common influential variable in all models. Thus, it is an important variable to consider when analyzing the settlement behavior of piled rafts due to tunneling.  
Figure 14: Verification of prediction model_S\({}_{R}\).  
Figure 15: _Cont._
# Novel Motion Sickness Minimization Control via Fuzzy-PID Controller for Autonomous Vehicle
## 2 Methodology
### Motion Sickness Incidence (MSI) Quantification  
The efficiency of the suggested control structure is evaluated by calculating the passenger's MS level, before and after the inner loop system was applied to the control structure. Here, the MS is calculated through a 6 degrees-of-freedom (DOF) subjective vertical conflict (SVC) [32]. The model can quantify motion sickness incidence (MSI) which represents the severity level of MS. The mathematical 6-DOF-SVC model calculates the MSI based on the lateral acceleration and head roll responses. Therefore, this model is assumed to be a suitable method to quantify the MSI in this study.
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 4 Applications and Target Industry
### E-Commerce  
E-commerce is the buying and selling products and services over the Internet [125]. It enables customers to access a wide range of products, services, and information from businesses worldwide. The increase in E-commerce activity has led to an increased usage of recommendation systems [177; 178]. To this end, FRSs can allow different data sources to collaborate in order to provide personalized recommendations to customers. A literature review on the application of FRSs in E-commerce revealed the various advantages [179; 180; 181]. First, FRSs can help reduce the cost of data access and storage. By sharing data across different sources, FRSs can provide a more efficient and cost-effective way of accessing and storing data. Furthermore, FRSs can improve the accuracy of recommendations by providing more relevant and personalized recommendations [182]. This can increase customers' satisfaction and loyalty by providing customers with better recommendations that are tailored to their individual needs.
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 6 Evaluation
### CPS Hub with Heterogeneous Data Planes  
The operation capabilities of the CPS hub with heterogeneous data planes are clarified here using three environments realized for the validation of the data plane negotiation capability of the CPS hub. The first system includes the use of MQTT as the data plane protocol, whereas in the second system, HTTP/CoAP entities are used. The third evaluation system contains endpoints that use the OneM2M protocol as the data plane protocol.  
Figure 21: Experimental system of the automated dynamic microgrids. EMSP, energy market service platform; ADM, automated dynamic microgrid.
## 2 Literature Review
### Benefits of IRS-Aided Wireless Communication
* Applications: The key applications of the IRS are in the area of non-line-of-sight (NLoS) transmission and blockages, smart wireless power transfer, enhanced security, interference cancellation, etc., by intelligently controlling the signal propagation. Summary of literature survey can be observed in Table 2. \begin{table}
\begin{tabular}{c p{213.6 pt}} \hline \hline
**Reference** & **Contribution** \\ \hline \([11,34,36]\) & Application and potential use case of an IRS in 5G and FWNs \\ \hline \([33]\) & Application of 2D MS for controllable smart surfaces and EM at various frequency bands. \\ \hline \([34]\) & Theory and the design of an IRS to achieve a smart radio environment and further discussions on the deployment in FWNs. \\ \hline \([11]\) & Theoretical performance limit of an IRS using mathematical techniques and the discussion of fundamental research issues needed to be addressed and elaborates the potential use cases in FWNs. \\ \hline \([36]\) & Discussion on HMIMO surfaces, a technology similar to IRS which leverages on the subwavelength metallic or dielectric scattering particles. \\ \hline \([70]\) & Discussion on the overview of the IRS, advantage when compared to similar technologies, design challenges and implementation of IRS-assisted FWNs. \\ \hline \([58]\) & Discussions on backscatter principles and communication, reflective relay and introduction to large intelligent surface/Antenna (LISA). \\ \hline \([24]\) & Survey on IRS, highlighting the basic concept of IRS. Reconfigurability and its most recent applications and performance metrics to characterize the improvement in IRS-assisted FWNs. \\ \hline \([71,72]\) & Application of a HS approach to achieving a programmable control over the behavior of a FWN. \\ \hline \([35]\) & Review of Reconfigurable Intelligent Surface Myth and reality. \\ \hline \([73]\) & IRS enhanced OFDMA system is proposed. \\ \hline \([37]\) & Challenges and Opportunities of an IRS in FWNs. \\ \hline \([74]\) & Amalgamate IRS and relay to improve the system performance of FWNs. \\ \hline \([75]\) & Buffer aided relays to enhance system secrecy rate with a delay constraint. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Related Works on the Review on IRS Technology.
# Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models
## 4 Experimental Results
### Dataset Description  
This subsection describes the different characteristics of the utilized datasets in this study, as follows:  
Figure 8: General structure of RSU block.  
1. **The open crack detection dataset [7]** is a benchmark dataset, consisting of a total of 537 images with manual annotation maps. It is divided into 300 and 237 images as training and testing datasets, respectively.
2. **The CrackForest dataset [31]** is another benchmark crack detection dataset, consisting of a total of 118 images.
3. **The primary dataset** consists of a total of 263 crack images of historical surfaces with ornaments, carvings, wood patterns, separators, and corrosion on walls.  
All models were trained on a mixed dataset, consisting of the augmented CrackForest [31] dataset and an open crack [7] training dataset, using rotation and flipping, with a total of 2508 crack images.  
Moreover, the proposed approach was subsequently tested on a mixed dataset of two different datasets (the open crack detection testing dataset and the primary dataset of the historical building) using 500 crack images.
## 3 Results and Discussion
### Analysis of Differentially Accumulated Metabolites (DAMs)  
To analyze the constituents responsible for the three tea samples clustering, OPLS-DA analysis was performed on a random combination of tea samples to find components with variable importance in the projection (VIP) scores >1 and T-test values of \(p\) < 0.05. A total of 105 DAMs were identified by comparison with authentic standards and elucidation of MS\({}^{2}\) spectra. Figure 3 indicated the presence of 105 DAMs, including 25 flavonoids and flavone glycosides, 5 catechins, 14 amino acids, 21 organic acids, 15 heterocyclics, 5 saccharides, and 20 other components. As displayed in the heat-map, the relative abundance of the DAMs were significantly different in the three tea samples, and these may be the important reasons for the differences in taste, liquor color, and health function of the three tea samples. The following portion discuss several significant variation in each chemical category.  
Figure 3: A heat map of the relative amounts of the differentially accumulated metabolites (DAMs) in the three tea samples. The scale (ranging from 5.0 to 10.0) represents the log10 (Abundance) of the DAMs. Color-coding is graded from green to red with the relative intensity shift from low to high, respectively.