esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
### Motivations  
In recent years, wireless energy transfer (WET) has been regarded as a promising technology to increase the battery-lifetime of energy-constrained wireless sensor nodes [1; 2]. The WET technology allows sensor nodes to harvest energy from surrounding electromagnetic radiation instead of wired energy sources. Many works investigated the WET networks where the traditional ground base station (BS) or access point also acts as an energy station, which can charge the sensor nodes through WET [3]. However, such a configuration is still costly or even infeasible if the sensors are deployed in remote places with poor infrastructure (that may be used for forest fire detection, climate monitoring, etc.).  
Deploying massive infrastructure in these areas is apparently cost inefficient due to the low population density and hostile environment.  
A hybrid air-terrestrial network, where air platforms (APs) are deployed for WET, is a novel solution to deal with this problem. APs are aircraft such as balloons or unmanned aerial vehicles (UAVs) that can broadcast energy and provide wireless access for ground sensors at low cost. Recently, there has emerged a number of applications of AP in wireless networks, but mainly for information transmission [4; 5; 6; 7]. One example is Google Balloon [5], which employs high-altitude balloons to create a hybrid air-terrestrial network with up to an LTE data rate. Another example is \"Aquila\" proposed by Facebook. In this project, unmanned aerial vehicles are deployed to provide a novel and efficient method of access [6] for ground users. Other than the high-altitude APs, the deployment of low-altitude APs under 1 km for communications has also drawn much attention. In such applications, APs are employed in the high-capacity hot-spot scenario, which is one of the main technical scenarios for the fifth generation (5G) mobile communication systems [7]. In such a scenario, APs play the role of temporary BSs to provide ultra-high data rates for hot-spot users.  
Despite its successes in communication applications, AP is still difficult to apply in a WET system directly. One important reason is that, conventionally, AP is equipped with directional antennas that generate several spot beams toward the ground for data transmission [8]. However, when utilized for WET, such an antenna configuration cannot focus the energy to the intended users efficiently [8], and thus gives rise to poor energy efficiency (EE). A promising solution to this problem is to use massive multiple-input multiple-output (MIMO) technology [9] at AP. Through coherent processing for signals of large numbers of antennas, massive MIMO can concentrate the signal power to a compact area around the user efficiently [9; 10; 11; 12; 13]. Therefore, a very large power gain can be obtained. This property can be utilized to combat the path loss due to long-range propagation and greatly enlarge the wireless charging distance.
# Complex-Valued CNN-Based Defect Reconstruction of Carbon Steel from Eddy Current Signals
## 2 Related Work
### MRI Image Reconstruction  
In the domain of medical imaging, CNNs have demonstrated rapid and robust performance in magnetic resonance imaging (MRI) reconstruction from undersampled data. Although MRI data is inherently complex-valued, most traditional deep learning frameworks operate in the real domain, leading to a loss of important phase information. To address this issue, researchers have extended common architectures--such as Deep Residual Networks, U-Net [27], and unrolled networks [28]--into the complex domain to form CV-CNNs [29; 30; 31]. Notably, refs. [29; 30] applied complex-valued U-Net architectures and reported that they obtained improved structural similarity index (SSIM) scores compared to their real-valued counterparts and conventional methods. Deep complex MRI [31], a residual-based CVNN, further improves reconstruction quality with an SSIM of 0.920, exceeding the 0.907 achieved by the U-Net in [29]. This approach also reduces the sensitivity to auto-calibration signal (ACS) lines and reconstructs MRI images with less noise at higher acceleration rates than existing parallel imaging techniques.
# LogBD: A Log Anomaly Detection Method Based on Pretrained Models and Domain Adaptation
## 3 Methodology
### Anomaly Detection Based on Domain Adaptation  
Log anomaly detection is an important step to ensure stable and sustainable operation of software systems, and many machine learning based log anomaly detection methods are proposed to detect anomalous log sequences. However, due to limited anomalous logs, many methods detect anomalous log sequences on the basis of log normal patterns by learning log normal patterns from a large amount of normal log data. Most methods are applied to a system; when deploying to a new system, a large number of logs need to be collected in the new system to retrain the model [22]. For the cold-start problem, several solutions have also been proposed, such as LogTransfer, which assumes that anomalous log sequences in different systems may have similar patterns, and that log message words from different systems usually overlap. Moreover, there are some similarities in the normal workflow of different systems, and migration learning can be used to construct a cross-system anomaly detection model to detect anomalies in the source and target systems. However, LogTransfer requires a large amount of normal and anomaly log data from the source and target systems to construct the anomaly detection model, and it is actually difficult to collect enough log data in a new system [33].  
To address the limitations of these previous studies, we propose a domain-adaptive cross-system log anomaly detection method based on LogBD. LogBD establishes an adversarial training framework by domain-adversarial training of neural networks (DANN) [34].  
Figure 4: Vector representation by BERT.  
This approach simultaneously makes anomalies in logs and attribution domains, and then achieves the effect of detecting anomalies with confusing data domains to achieve cross-domain (system) log anomaly detection, which requires only a small amount of target system log data to achieve better performance. DANN is a deep learning model for domain adaptation, which achieves feature alignment between source domain systems and target domain systems by adversarial training. It learns feature representations that can effectively classify both source and target domains, and it improves the generalization performance of the model. The network architecture of the DANN model is shown in Figure 5. It contains three parts: feature extractor, anomaly classifier, and domain discriminator.
# Fast Deployment of a UWB-Based IPS for Emergency Response Operations
## 5 Performance Evaluation  
The proposed UWB-based IPS was subjected to different tests in the aforementioned building and surroundings to evaluate not only its performance but also its suitability to be employed in ER operations. This section compiles the main obtained results according to three categories: anchor self-location, static tag localization, and tag tracking.
## 3 Drilling  
### Gas Kick  
Intricacies such as elevated pore pressure and unyielding lithology appear to be endemic features of the Vaca Muerta formation, as evidenced by the drilling outcomes across multiple boreholes in the area. The Parva Negra Oeste site, which aims to exploit this formation, has been particularly problematic, manifesting a 40% well abandonment rate, due to operational complexities. In the surrounding area, wells have encountered issues ranging from gas influxes to fluid losses at disparate stratigraphic levels. During the drilling process, two primary geological obstacles emerged: the actual lithological hardness surpassed projections by more than half, and the pore pressure at shallower strata exceeded initial estimates by 5-20% [43].  
To optimize well design, data from adjacent boreholes within a 15 km radius were scrutinized. This analysis informed decisions on setting casing depth, defining the operational mud weight window, and planning for emergency casing scenarios, all while prioritizing wellbore stability and risk mitigation. Managed pressure drilling (MPD) techniques were implemented to counteract the gas influxes encountered at multiple stratigraphic intervals [44]. The unexpected lithological hardness necessitated a reassessment of the formation's mineralogical composition. The presence of carbonate cement in various strata was found to contribute to the increased lithological hardness, prompting modifications to drilling parameters. MPD strategies were also effective in mitigating the elevated pore pressures, although some wells still experienced isolated gas influx events.
# A Review of the Optimal Design of Neural Networks Based on FPGA
## 4 Neural Network Optimization Technology Based on FPGA
### Optimization of Data and Its Operations
#### 4.1.1 Fixed-Point Quantitative
In the same year, [PERSON] et al. [77] optimized the YOLOv3 network structure through pruning and the Int8 (Integer8) quantization algorithm with the trade-off between speed and accuracy. Good acceleration was achieved with limited and acceptable loss of accuracy. Some scholars proposed the use of the quantization algorithm to map matrix elements participating in matrix multiplication operations from single-precision floating-point data to half-precision floating-point data. Under the condition of ensuring the accuracy of the algorithm model, the consumption of on-chip storage resources on FPGA is greatly saved and the computing speed is improved [78].
## 4 Results and Discussion  
Upon completion of the misalignment fault experiment, the collected vibration datasets of the baseline and faulty states were first analyzed at the full stroke length. As illustrated in Figure 7, it can be observed that typical time domain features used in vibration analysis such as RMS (a) and standard deviation (b) have a percentage error of less than 10% when comparing both health states.  
Figure 6: Signal segmentation technique flowchart.  
At first glance, this small difference shown from the results may mislead its interpretation by believing that the misalignment had no effect on the vibration signal. Therefore, the signal segmentation was applied to both datasets, and the same features were evaluated to identify how they changed. To conclude the studies, the segmentation technique was applied to the repair state dataset to determine how the features improved when compared to the faulty state and if they changed when compared to the baseline.
# Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265
## 5 Quality of PEDBF for HEVC/H.265  
The optimization techniques employed in the hardware architectures of the DBF filter should not deteriorate the quality of the reconstructed video for any reason. Degradation in the quality of the filtered video will lead to poor performance and coding inefficiency. The DBF algorithm is highly adaptive and data-dependent, as the current edge to be filtered depends on the previously filtered pixel blocks. Since different filter ordering is followed, these changes in the ordering should not affect the quality of the filtered video. Hence, the quality assessment of the dual-edge DBF for H.265 coding standard is measured using _PSNR_.
###### Abstract
By identifying the operations that affect the performance of the system, the bottleneck problem can be identified and solved more accurately. [PERSON], [PERSON], and [PERSON] have already made bottleneck predictions with the help of statistics, time series analyses, and deep learning. The bottlenecks have also been solved by employing process redesign, resource reallocation, and technology upgrades [15]. Many experts have also applied bottlenecks to different subject areas such as supply chain management [16], transportation systems [17], and computer networks [18] to explore solutions to bottlenecks in specific areas. Along with the rise in Industry 4.0, the integration and application of advanced manufacturing technologies such as Digital Twins, deep learning, and interpretable artificial intelligence have revolutionized bottleneck identification and prediction in systems [19; 20]. In the presence of increasingly diverse production structures, existing bottleneck research methods have made it difficult to satisfy the needs of modern manufacturing systems. Therefore, it is necessary to review the current status of bottleneck research to further clarify the research trends, explore new research paths, promote the development of bottleneck identification and prediction techniques, and provide more forward-looking technical guidance for manufacturing enterprises. In this review, the first section briefly reviews the current stage of research on bottlenecks in manufacturing systems. The second section introduces the definitions of various types of bottlenecks in manufacturing systems and the bottleneck extended concepts. The third section focuses on methods for bottleneck identification, including static model-based, simulation-based, and data-driven-based approaches. This section also provides a detailed explanation of the causes of bottleneck shifting, the consequences of a bottleneck, and the bottleneck management strategies, with the current research stage and existing challenges of the above fields. The fourth section explores the integration of bottleneck issues with advanced technologies such as Digital Twins and neural networks, offering a perspective on future research trends in manufacturing system bottlenecks. The final section summarizes the future directions and trends in bottleneck research within manufacturing systems.
## 5 Numerical Results and Discussion
### Detection Performance under Phase Noncoherent Channel  
Under phase noncoherent channel, the BER, PER, and SER performance of the proposed simplified scheme, uncoded scheme, and the exact LLR scheme are verified, where \(\rho=1\), \(N=1\). As can be seen from Figure 6, compared with the uncoded scheme, the detection performance of the coding scheme is significantly improved. In addition, the performance of the simplified LLR scheme is extremely close to that of the exact LLR scheme. As shown in Figure 6c, when PER = \(1\times 10^{-3}\), compared with the uncoded scheme, the proposed simplified scheme obtains a gain of nearly 5.7 dB. Further, compared with the exact LLR scheme, the simplified LLR scheme in (13) only has a performance loss of about 0.04 dB, and the simplified LLR schemes in (16) and (17) has a performance loss of about 0.16 dB. Therefore, the simplified scheme has little performance loss and friendly-complexity. In particular, the simplified LLR scheme in (16) do not require CSI when using MS decoding. In addition, as shown in Figure 7, we can draw similar conclusions under \(\rho\
eq 1\), which however is not illustrated here. In a word, our simulation results in Figures 6 and 7 indicate that our simplified LLR scheme depicts good performance under the phase noncoherent channel. Note that no CSI and process for zero-order Bessel function of the first kind is needed in our simplified scheme.
## 3 Results
Among the women, Wrapper_LR's AUC, AUPRC, R2, and RMSE were the predictive power with 0.932 [0.926-0.938], 0.851 [0.843-0.859], 0.986 [0.983-0.988], and 0.308 [0.298-0.318], respectively. The wrapper_LR showed the best performance among other prediction models in both men and women. For men, the MS prediction model (the wrapper-based logistic regression) showed the strongest predictive power (AUC = 0.868), as detailed in Figure 5. In women, the wrapper based logistic regression model showed the highest predictive power (AUC = 0.932). Figure 6 shows the calibration curve for each prediction model. The x-axis of the calibration plot is a predicted class, and the y-axis is plotted as the true class. In men, Wrapper_LR suggests a better calibration plot than Wrapper_NB. In women, Wrapper_LR suggests a better calibration plot than Wrapper_NB. In the feature selection method, the wrapper showed better results for prediction model development than CFS. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sex** & **Prediction Model** & **AUC** & **AUPRC** & **R2** & **RMSE** \\ \hline \multirow{4}{*}{Men} & Wrapper NB & 0.830 [0.821–0.839] & 0.616 [0.604–0.628] & 0.899 [0.892–0.907] & 0.396 [0.384–0.408] \\ \cline{2-6}  & Wrapper LR & 0.868 [0.860–0.876] & 0.712 [0.701–0.723] & 0.960 [0.955–0.965] & 0.358 [0.346–0.370] \\ \cline{2-6}  & Filter NB & 0.830 [0.821–0.839] & 0.604 [0.592–0.616] & 0.802 [0.792–0.812] & 0.407 [0.395–0.419] \\ \cline{2-6}  & Filter LR & 0.863 [0.854–0.872] & 0.703 [0.692–0.714] & 0.952 [0.947–0.958] & 0.361 [0.349–0.373] \\ \hline \multirow{4}{*}{Women} & Wrapper NB & 0.908 [0.902–0.914] & 0.788 [0.779–0.797] & 0.969 [0.965–0.973] & 0.341 [0.330–0.352] \\ \cline{2-6}  & Wrapper LR & 0.932 [0.926–0.938] & 0.851 [0.843–0.859] & 0.986 [0.983–0.988] & 0.308 [0.298–0.318] \\ \cline{2-6}  & Filter NB & 0.911 [0.905–0.917] & 0.783 [0.774–0.792] & 0.915 [0.908–0.921] & 0.352 [0.341–0.363] \\ \cline{2-6}  & Filter LR & 0.929 [0.923–0.935] & 0.843 [0.835–0.851] & 0.986 [0.983–0.988] & 0.311 [0.301–0.321] \\ \hline \multicolumn{6}{c}{The results are shown as each values with corresponding 95% CI. NB: [PERSON], LR: logistic regression, R2: coefficients of determination for calibration curve.} \\ \end{tabular}
\end{table} 
Table 7: Performance analysis results of the four models in men and women. Figure 5: AUC of metabolic syndrome prediction models in men and women.
## 3 Results and Discussion
### The Antioxidant  
Non-thermal plasma has the ability to affect the antioxidant activity of fresh foods, resulting in the oxidation of antioxidants and a change in respiration rate [62]. Figure 2B depicts the effect of SDBD plasma on the antioxidant capacity of palm date varieties. This graph illustrates that the antioxidant capacity of untreated samples varied from 1.06 \(\pm\) 0.09% to 49.77 \(\pm\) 1.75%, while the antioxidant capacity of treated samples ranged from 1.28 \(\pm\) 0.04% to 55.41 \(\pm\) 1.45%. The antioxidant capacity of all palm date cultivars is enhanced after plasma treatment. There was a statistically significant difference (\(p\) < 0.05) between treated and untreated samples in their antioxidant capacity. The lowest rise in antioxidant capacity was documented for the plasma-treated _Shalbi_ variety, which increased by 10.56 percent compared to the untreated type. In contrast, the treated _Ajwa_ variety had the greatest increase in antioxidant capacity; it increased by 67.69% in comparison to the untreated variety. The increases in total phenolic content and antioxidant activity between untreated and treated palm dates followed a similar pattern, as seen by these data.  
Previous research found no discernible differences in the antioxidant capacity of Abate Fetel pear [63], blueberries [64], and strawberries [65] after non-thermal plasma treatments. On the other hand, mandarin [54], strawberries [55], and bananas [66] are significantly increased after non-thermal plasma treatment. Due to an increase in total phenol and  
Figure 2: The effect of SDBD plasma on (**A**) total phenol \(p\) value = 0.00, (**B**) antioxidant, \(p\) value = 0.00, and (**C**) HMF, \(p\) value = 0.00 of palm dates, lowercase letters a–n in the same column represent significance analysis; the different letters mean significant difference among various treatments at \(p\) < 0.05 level.  
flavonoid content, the antioxidant activity values of non-plasma-treated samples increased. The increase in antioxidant activity of cloudy apple juice [67] and tomato pomac [68] after non-thermal plasma treatment has been attributed to the higher extraction of polyphenols. In addition, non-thermal plasma treatment induced the elevation of phenylpropanoids and ROS metabolism, as well as the augmentation of enzyme activities and the activation of related gene expression, hence increasing the antioxidant activity of fruit [56]. In addition, non-thermal plasma treatment increased phenolic accumulation and, therefore, antioxidant activity [55].
## 3 Results
### Sustainable Solutions  
Drones are a viable technology to facilitate sustainable practices in various sectors. Drones offer versatile solutions for tasks like ecological monitoring and environmental control over land and water surfaces, including the integration of small-class photography systems for aerial imaging [54]. Drones equipped with advanced sensors and image processing software emerge as a valuable enhancement for rangeland inventory and monitoring, offering higher confidence in landscape-scale indicator estimations and potential to complement traditional field methods [47]. [PERSON] and [PERSON] [55] found that drones equipped with multispectral sensors originally designed for terrestrial use are effectively employed to enhance water quality monitoring in coastal waters by accurately measuring and analysing radiance data, thereby facilitating improved assessment and management of water quality trends and risks. Sensors mounted on drones on a farm collect various environmental parameters and send them to a platform that can help farmers, government agencies or manufacturers to predict environmental data over the geographically large farm field [50]. Additionally, drones equipped with ground-penetrating radar can significantly aid in high-resolution mapping of soil moisture at a field scale, proving beneficial for precision agriculture and environmental monitoring [44]. [PERSON] et al. [43] found that drones can effectively predict corn forage yield and model plant height, enhancing precision farming through cost-effective, high-resolution data.  
Drones are equipped with a software system for efficient charging station placement and path specification, enhancing agricultural surveillance by optimising coverage of large, inaccessible areas with 70-90% optimal performance in minimising mission time [49]. Researchers used drones to capture aerial images to establish a digital surface model, combining with the soil sample test results in the laboratory in soil survey [59]. Drones can also assist in identifying soil erosion hotspots and assessing the vulnerability of different regions, thus contribute to the development of effective soil and water conservation strategies [63]. Drones have also been used for monitoring mine restoration and assessing the progression of mined sites [46]. Drone mapping provides land cover maps, drainage network evaluation maps, detection of erosion problems, and volume variation calculations. [PERSON], [PERSON], [PERSON] and [PERSON] [56] confirmed the efficacy of drone photogrammetry in monitoring changes and rehabilitation in mines, demonstrating its potential for widespread use in generating and analysing substantial spatial data.
## 2 Materials and Methods  
### Data Analysis of LMO Monitoring Project from 2009 to 2013  
Since 2009, the Ministry of Environment (MOE) of South Korea has conducted an LMO monitoring and post-management project in natural environments. Based on the LMO monitoring scheme of the MOE, six administrative districts (Gyeonggi-do, Gangwondo, Chungcheong-do, Gyeongsang-do, Jeolla-do, and Jeju-do) were surveyed [49]. The nationwide survey of unintentionally released LMOs was conducted from 2009 to 2013, and the sample collection site was recorded with a handheld GPS device. In accordance with the guidance manual, survey sites were monitored every four months to collect LMO suspicious plants. Suspicious samples were collected within a 100 m radius of each survey site. The collected plant tissues were dried with silica gel and used for LMO identification using an immunochemical strip kit and polymerase chain reaction (PCR). Suspicious LMO samples were collected nationwide, and 113 LMO crops were detected at 65 sites. The distribution of LMO collection sites was investigated for the intensive survey and management of LMO re-collection sites. For post-management, a re-investigation, accompanied by removal operations, was conducted more than twice a year in areas where suspected samples were found.