esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Experimental Approach
### Evaluation Metrics  
Precision (P) and recall (R) were the evaluation metrics chosen to assess the performance of the experimental methods, including the proposed method (R). The efficiency of the defective wind turbine blade region object detection must be carefully tested and analyzed. Average Precision (AP) is used in this article to represent the 11 interpolated points on precision and recall curves [26]. It is also used for performance evaluation of associated tests with Intersection over Union (IoU) 50% for the proposal Bounding Box (BBox) and labeled BBox. The mean Average Precision mAP is used to reflect the average classification performance of the models for all classes. Furthermore, the average test time of each image (\(t^{ATE}\)) is used to assess the efficacy of defective region object detection. The following are the formulae for the evaluation metrics used in the experiments:  
\[t^{ATE}=t^{e}/K^{te} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**WTG Blade Defect Type** & **Training Set** & **Test Set** & **Total No. Images** & **Total No. Labels** \\ \hline Structural crack & 546 & 215 & 761 & 854 \\ \hline Top coat flaking (Fatigue damage) & 350 & 190 & 540 & 709 \\ \hline Top coat chips (Leading Edge erosion) & 467 & 209 & 676 & 816 \\ \hline Top coat flaking (Delamination) & 189 & 67 & 256 & 302 \\ \hline Total & 1552 & 681 & 2233 & 2681 \\ \hline \end{tabular}
\end{table}
Table 1: PV module defects region object detection with different image scale transforms.  
where \(t^{ATE}\) is described by the total test time (\(t^{te}\)) and iterations \(K^{te}\).  
\[P=\frac{TP}{TP+FP} \tag{4}\]  
where TP is True Positives and FP is False Positives.  
\[R=\frac{TP}{TP+FN} \tag{5}\]  
where FN is False Negatives.  
\[AP^{IoU\geq 50\%}=1/11\sum P_{smooth}(k)\Big{|}IoU\geq 50\% \tag{6}\]
# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 2 Analysis of Modern-Day Smart Services
### Recommender Systems
So, raw data always have to be refined to purge such misrepresentations. Second, recommender systems rely on knowledge about the community. Only if large amounts of information about users' preferences and usage behavior are available can meaningful recommendations be made. Even if an individual's opinion cannot be inferred directly from the recommendations, such information is present in the base data. Moreover, statistical inferences can also be derived from the processed and accumulated data, which reveal information about the preferences of individuals. Therefore, privacy issues have to be addressed in recommender systems as well [100]. The inherent opportunities and privacy threats of IoT-supported recommender systems can be broken down as follows:  
Opportunities:  
ToT-supported recommender systems are able to provide search results that are tailored to the user (e.g., product recommendations) based on contextual information. With the help of collaborative filtering, users can also be presented with completely new recommendations, which can expand their horizons, as they were previously unaware that they might be interested in the suggested items. Searches become much more efficient, as irrelevant items can be excluded early on, and more effective, as relevant items can be suggested even if they were not directly included in the search query. Privacy Threats:  
The recommender systems have to collect and analyze a lot of data about a user's interests in order to make suitable suggestions. As a result, they also gain privacy-relevant insights into the life of the data subject. In collaborative filtering, the data of several users are combined, and profiles are created, which can be used to derive additional information about a data subject. For instance, knowledge about a data subject can be transferred to the other data subjects in the same cluster with a certain probability. A recommender system can also deliberately influence users by making one-sided recommendations.
# A Secure Link-Layer Connectivity Platform for Multi-Site NFV Services
## 4 Implementation and Validation
### Performance Evaluation
#### 4.2.2 Parallelization of L2S Operations
With an MTU of 1450 bytes, the computation and delays induced by fragmentation and reassembly reduce this fraction to 34.2%. We want to note that these results represent a first approximation to evaluate the support of parallel operations in LzS. An extension of the methodology described in this section is subject to further study, to consider aspects related to the virtual nature of the CPUs, as well as to the load of the server computers that host LzS VNFs. For the sake of reference, Table 1 summarizes the parameters and results of our calculations.
## 3 Ecological Impacts of Energy Development
### Persistent Linear Corridors  
The clearing of forest for linear corridors represents one of the major challenges for conservation science in ecology. Linear corridors have at least three major impacts on forest ecosystems: access by people; travel by predators; and edge effects. Linear corridors directly remove trees and often understory vegetation to make travel easier through forested ecosystems. However, the impacts to vegetation and ecosystem dynamics often exceed the spatial boundary of the clearings.  
Access into forests is one of the greatest impacts of forest land use. Most corridors are roads, but increasingly, many corridors are now related to the exploration and development of oil and gas [20,24]. In addition to the direct mining or extraction site, ancillary infrastructures often accompany non-renewable resource development that might otherwise not be present on the landscape. For example, airports may be constructed for distant locations and railways, roads and pipelines for transporting the resources to market. Ecologically, the conversion of forest to access corridors could be considered a permanent change since any known recovery interval can exceed the lifecycles of many forest-dwelling species [38].  
Corridors related specifically to oil and gas infrastructure have increased significantly on the continent. An estimated US$29.1 billion will be spent annually over the next two decades on oil and gas infrastructure in both the U.S. and Canada [39]. Future pipelines are proposed to connect Northern Canada and U.S. refineries and ports in the U.S. Midwest and Gulf Coast [40]. Reflection seismology is also increasingly being used on forested landscapes more than during any prior time. Cut lines are cleared forest corridors used to explore subterraneous deposits of minerals and hydrocarbons beneath forests. Densities of cut lines on forested landscapes have reached as high as 2.7 km\(\cdot\)km\({}^{-2}\) in Alberta [38]. Like well sites, many cut lines can persist on landscapes without tree cover for more than three decades and many are converted to roads and other permanent access corridors [20].  
Linear corridors can modify ecosystem structure through predator-prey pathways. Travel and mobility are markedly enhanced by linear corridors resulting in increased predation rates [35] and concomitant top-down trophic cascades on community structure [41]. Additionally, habitat use can be severely attenuated due to increased predation [41, 42, 43, 44]. Many of these effects are not discrete and it can be challenging to characterize these anthropogenic impacts using traditional landscape pattern indices and patch-based statistics [45]. For example, cut lines can have the largest footprint of any anthropogenic disturbance when a spatial buffer is applied [46].
## 4 Conclusions  
The absorbing effect of three kinds of composite absorbers in the frequency range 8.2-18.0 GHz is compared and studied. In summary, we concluded that 7 wt% graphene/PLA composite absorber has better absorbing property than 5 wt% and 9 wt% graphene/PLA composite absorbers. Compared with 10 wt% and 30 wt% nano-Fe\({}_{3}\)O\({}_{4}\)/PLA composites, the 20 wt% nano-Fe\({}_{3}\)O\({}_{4}\)/PLA composite absorber has better absorbing property. The absorbing property of the graphene/nano-Fe\({}_{3}\)O\({}_{4}\)/PLA composite absorber consisting of 5 wt% graphene and 20 wt% nano-Fe\({}_{3}\)O\({}_{4}\) are better than those of 5 wt% graphene/PLA composite absorber and 20 wt% nano-Fe\({}_{3}\)O\({}_{4}\)/PLA composite absorber. The absorbing mechanism is analyzed by analyzing the electromagnetic parameters and SEM. Due to the existence of the three-dimensional conductive network, the graphene/PLA composite absorbers absorb electromagnetic waves through the conductive and sheet structure. The nano-Fe\({}_{3}\)O\({}_{4}\)/PLA composite absorbers consume electromagnetic waves mainly through magnetic loss and the protrusions formed by the adsorption of nano-Fe\({}_{3}\)O\({}_{4}\) magnetic microspheres on the surface of the substrate. Graphene/nano-Fe\({}_{3}\)O\({}_{4}\)/PLA composite absorbers combine the advantages of graphene/PLA and nano-Fe\({}_{3}\)O\({}_{4}\)/PLA composite absorbers. In addition, the synergistic effect of the multiple loss mechanism attenuated the electromagnetic waves effectively and achieved an efficient absorbing effect.  
Conceptualization, H.W.; [PERSON] wrote the paper. [PERSON], L.L., E.H., B.L. and X.T. offered useful suggestions for the preparation. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (grant no.51575313); the State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University (grant no. sklms2018001); and The Major Technological Innovation Project of Hubei Science and Technology Department (2019 AAA164).  
The authors declare no conflict of interest.  
Figure 11: SEM cross-section of pure PLA and composite absorbers with different absorbing agent composition. (**a**) Pure PLA absorber; (**b**) 5 wt% graphene/PLA composite absorber; (**c**) 20 wt% nano-Fe\({}_{3}\)O\({}_{4}\)/PLA composite absorber; and (**d**) 20 wt% nano-Fe\({}_{3}\)O\({}_{4}\) + 5 wt% graphene/PLA composite absorber.
# Detection of Welding Defects Tracked by YOLOv4 Algorithm
## 1 Introduction
[16] addressed significant shape and size variations by designing multi-scale feature extraction modules, proposing the LF-YOLO method, which balances performance and computational cost. [PERSON] et al. [17] introduced a gray value curve enhancement module and the WD-YOLO model to handle large shape and size variations. [PERSON] et al. [18] proposed an end-to-end detection model with bidirectional convolutional LSTM blocks to optimize shortcuts, addressing the lack of time-based information in the existing methods. To address the weak generalization of anchor-based detectors for large-scale defects, [PERSON] et al. [19] proposed an efficient anchor-free detector with Dynamic Receptive Field Allocation (DRFA) and task alignment, improving both localization and classification. Additionally, [PERSON] et al. [20] designed a Multilevel Attention Feature Fusion Network (MAFFN) to enhance prediction accuracy for multi-scale defects. [PERSON] et al. [21] proposed an enhanced Faster-RCNN model for detecting weld defects. The model employs a multi-layer feature extraction network and incorporates multiple sliding windows to improve detection performance. [PERSON] et al. [22] primarily investigated the impact of different activation functions, including Mish, Swish, and Leaky-ReLU, on the detection results of the YOLOv4 model. Their findings indicate that the simultaneous use of the Swish and Mish activation functions in the YOLOv4 model yields the best detection performance. Inspired by these studies, this manuscript introduces improvements to the original YOLOv4 model. Compared with existing algorithms, the improved model demonstrates superior performance metrics in defect detection, providing more accurate results across various types of defects. This paper is organized as follows: Section 1 introduces deep learning models and their applications; Section 2 details the prediction principles, activation functions, and overall framework of the YOLOv4 algorithm; Section 3 presents the improved YOLOv4-cs1 and YOLOv4-cs2 models, along with diagrams of their frameworks; and Section 4 describes the experimental conditions, hardware setup, and analysis of the experimental results. Finally, a comparative chart of the different models' performance in defect detection is provided.
# Optical Wireless Communications Using Signal Space Diversity with Spatial Modulation
###### Abstract  
A signal space diversity (SSD) scheme was proposed to be incorporated with spatial modulation (SM) in an intensity-modulation/direct-detection-based multiple-input-single-output (MISO) indoor optical wireless communication (OWC) system to improve bit-error-rate (BER) performance and system throughput. SSD was realized via signal constellation rotation and diversity interleaving using different channel gains to improve the BER. With SM incorporated, the MISO-OWC system throughput increased. Theoretical BER expressions of the SSD scheme were established for the first time by investigating the distance of neighboring constellation symbols upon maximum-likelihood detection. Such BER expressions were further verified by numerical results. The results showed that, except for the slightly-lower-accuracy performance brought by comparable distances of neighboring constellation symbols in cases of low signal-to-noise ratios, these BER expressions were accurate in most scenarios. Moreover, theoretical investigations of channel gain distributions were performed at different signal constellation rotation angles to show the capability of the SSD scheme to improve the BER. The results showed that a significantly improved BER by two orders of magnitude could be achieved using a reasonably high channel-gain ratio and a larger constellation rotation angle. The SSD-SM scheme provides a promising option to achieve transmitter diversity with an enhanced throughput in high-speed indoor OWC systems.  
optical wireless communications; spatial modulation; signal constellation rotation; diversity interleaving; channel gain difference +
Footnote †: journal: _Photonics_
## 4 Implementation and Experiment Results
### Parameter Analysis
#### 4.3.1 Dimension Reduction Using the DAE
Besides, the experiment shows that applying DAE for feature reduction in the proposed system can significantly shorten the item similarity evaluation time while keeping the essential characteristics of the original features. Figure 4: Experiments for the number of nodes in the hidden layers of the DAE. Figure 5: Experiments for the number of nodes in the latent representation layer of the DAE.
## 4 Conclusions  
The goal of this paper was the thermodynamic analysis of a thermal-pumped storage system for trigeneration. The basic idea was to exploit volatile electricity from renewables in order to produce cold storage for cooling, medium thermal storage for space heating and high-temperature thermal storage for utilization in an ORC for electricity recovery. The present work used a thermodynamic approach that aimed to present the present idea and to determine its performance. The following points summarize the most significant conclusions of the simulation study:  
* The exergy efficiency of the trigeneration scenario was maximized for \(T_{heat,h}\) = 115 \({}^{\circ}\)C at 45.28%, while for the cogeneration scenario it was maximized for \(T_{heat,h}\) = 125 \({}^{\circ}\)C at 45.17%. * The energy efficiency ranged from 242.58% to 378.29% for the trigeneration case, while it varied from 146.72% to 213.09% for the cogeneration case. * The increase of the ambient temperature reduced the system exergy efficiency while leading to higher system energy efficiency. * The increase of the cooling load and of the space heat load led to higher energy and exergy efficiencies. However, the augmentation of the high heating temperature reduced both energy and exergy efficiencies. * It was found that the high heating temperature storage must exceed a minimum limit and it is preferable, thermodynamically, for it not to be designed in a very high capacity in order to have high energy and exergy efficiency rates. In the future, the proposed configuration will be investigated in detail by placing emphasis on the storage system and the determination of the storage tank volumes, as well as on the selection of the proper phase change materials. Moreover, it is important to examine this system coupled to an application (e.g., a building). Lastly, this system has to be examined experimentally in order to check the long-term performance of the unit under real conditions. E.B. : Conceptualization, investigation, software, methodology, writing and reviewing the original paper; P.L. : software, methodology, writing and reviewing the original paper; C.T. : software, supervision, writing and reviewing the original paper. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Figure 20: System operation margins for different combinations of cooling loads. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data available after request. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**
## 2 Related Works
### Enhanced Autoencoder-Based Neural Network Architecture Combining RVQ and CM
Subsequently, the QIV is reconstructed into a latent vector through the RVQ demodulator and then passed to the decoder for further processing. The reconstructed latent vector is fed into the decoder, with an additional CM employed to correct errors introduced during the quantization process. The CM consists of shallow 1D convolution layers, designed to compensate for errors arising during the quantization and reconstruction stages, thereby improving the quality of the final reconstructed signal. The correction signal generated by the CM is integrated with the input to the decoder, allowing the decoder to ultimately reconstruct the signal in a form closely resembling the original acoustic signal. Moreover, the proposed method achieves a higher compression ratio compared to the conventional approach [6]. In the conventional method, the latent vector in the latent space is represented using 16 bits, whereas the proposed RVQ method improves the compression ratio by representing the signal as a 10-bit QIV. RVQ sets the size of the index vector to 1024 (10 bits) at each stage and progressively reduces the residuals over a total of 10 stages. This facilitates more efficient acoustic signal transmission between DIFAR sonobuoys and MPAs.
# Changes of Air Pollution between Countries Because of Lockdowns to Face COVID-19 Pandemic
## 3 Results and Discussion
### South America Region  
Results for countries of South America are in Figure 3.  
Figure 2: Average concentrations of air pollutant in countries of the North American geoeconomic region.  
Figure 3: _Cont._  
In Brazil, a partial lockdown started in March 2020 and ended in June 2020 [88]. The influence of lockdown policies on the air quality of Brazil, considering the average air pollutant concentrations in March 2020 compared to the pre-COVID-19 period, showed that the highest decrease was from 22 to 112% for CO, followed by NO\({}_{2}\) (\(-\)24%, see Figure 3, Brazil). [PERSON] et al. [88], during the partial lockdown in Rio de Janeiro, Brazil, showed that CO concentration reduced significantly because of the decrease in the traffic density, while O\({}_{3}\) concentration increased, most probably due to a decline in primary air pollutants. In contrast, PM\({}_{10}\) concentration did not exhibit a remarkable variation. In short, the partial restrictions improved the air quality of Rio de Janeiro.  
In Columbia, the government announced the quarantine restrictions on a national scale starting from 20 March to 31 August 2020. Variations of pollutant concentrations in March 2020, relative to the pre-COVID-19 period, had a reduction in CO (56%), SO\({}_{2}\) (39%), PM\({}_{2.5}\) (20%), and PM\({}_{10}\) (46%). [PERSON] and [PERSON] [57] compared the concentration levels of air pollutants in Bogota, Columbia, considering lockdown and previous periods. They observed a significant decline in pollutant concentrations by \(-\)13% and \(-\)22% in NO\({}_{2}\), \(-\)11% and \(-\)20% in SO\({}_{2}\), \(-\)23% and \(-\)34% in CO, \(-\)7% and \(-\)15% in PM\({}_{2.5}\), and \(-\)25% and \(-\)16% in PM\({}_{10}\), respectively. On the other hand, levels of atmospheric O\({}_{3}\) increased by 31% and 14% compared to the baseline period (Figure 3, Colombia). Finally, in Chile, lockdown restrictions started in March 2020; the highest variation of concentrations between the pre-COVID-19 period (2015-2018) and post-lockdown period was observed for SO\({}_{2}\) (\(-\)132%) in January 2022, whereas NO\({}_{2}\) (\(-\)29%), PM\({}_{2.5}\) (\(-\)21%), and PM\({}_{10}\) (\(-\)2%) showed decreasing trends in March 2020 (see Figure 3, Chile).  
Figure 3: Average concentrations of air pollutant in countries of the South America.
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline M\({}_{\text{a}200}\) & M\({}_{\text{a}250}\) & M\({}_{\text{a}300}\) & M\({}_{\text{a}350}\) & M\({}_{\text{e}200}\) & M\({}_{\text{a}250}\) & M\({}_{\text{a}300}\) & M\({}_{\text{a}350}\) & SS\({}_{\text{a}}\) & BG\({}_{\text{a}}\) & Assignment & H & C & L \\ \hline
3283.27 & 3287.20 & 3213.11 & - & 3285.20 & 3285.98 & 3210.38 & 3196.89 & 3278.21 & 3342.36 & OH stretching vibration & & x \\  & & & & & & & & & & & (H-bonded) & & \\  & & & & & & & & & & C-H stretching and & & & \\
2921.21 & 2919.62 & 2920.79 & 2923.13 & 2923.85 & 2923.44 & 2923.15 & 2923.59 & 2920.00 & 2920.46 & aliphatic alkanes & x \\  & & & & & & & & & vibration & & & \\  & & & & & & & & & & C-H stretching and & & & \\
2852.4 & 2851.32 & 2852.02 & - & 2853.96 & 2853.95 & 2853.20 & 2853.52 & 2851.98 & - & aliphatic alkanes & x \\  & & & & & & & & & & vibration & & \\  & & & & & & & & & & & C-O stretching & & \\
1709.66 & 1707.85 & 1708.69 & 1711.34 & 1709.49 & - & 1704.85 & 1705.27 & 1730.17 & - & (carbonyl)/carboxyl & x & x \\
1606.57 & 1646.05 & 1600.19 & 1600.58 & 1628.44 & 1659.52 & 1603.43 & 1600.90 & 1638.08 & 1633.33 & C=C stretching & & x \\
1431.50 & 1460.50 & 1428.10 & 1432.22 & 1440.97 & 1449.80 & 1439.18 & 1436.06 & 1426.75 & 1463.25 & C=H bond deformation & x & x & x \\  & & & & & & & & & & and deflection & & \\
1206.63 & 1164.95 & - & 1218.50 & 1165.07 & 1160.02 & - & - & 1243.49 & 1239.39 & - & C-O stretching of & & \\  & & & & & & & & & & & & & \\
1035.77 & 1031.20 & 1037.18 & 1036.27 & 1035.05 & 1030.47 & 1033.61 & 1033.08 & 1032.47 & 1026.17 & C=O, C=C and C-C-O stretching & x & x & x \\  & & & & & & & & & & & & & \\
692.18 & 667.40 & 768.78 & 772.38 & 531.45 & 528.58 & 695.13 & 694.18 & 663.49 & 529.92 & Out-of-plane O-H & & x & \\  & & & & & & & & & & bending & x & x & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fourier-transform infrared spectroscopy (FTIR) peaks and their corresponding functional groups for torrefied sewage sludge and sugarcane bagasse.