esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Bluefin sea robin catches can be increased with the use of pots with LED lights in pot fishing grounds in the South Sea of Korea. The greatest catch improvement was seen in pots with green LED lights compared to those with red, blue, or white lights and the conventional pots containing bait only. The results indicated that, compared to the conventional method of using only the bait in the pot, attaching green, blue, or white LED lights to the pots could increase the efficiency of bluefin sea robin fishing. Hence, this method could increase the profitability of fishery operators. The difference in catch among the pots may be accounted for by the following reasons: the vision and behavior of fishery species to artificial light generally vary according to habitat conditions, target fish characteristics, and fishing gear composition. In certain species, the mechanism involves a combination of these factors [30; 31; 38; 39; 40]. The focus of previous studies on fishing gear with artificial lights, especially LED, has been a problem of bycatch. Low-powered LED lights were shown to reduce bycatch in studies on bottom shrimp trawl [23; 25; 35], while studies on trawls in the Barents Sea of Norway showed contrasting results [32; 33]. Currently, the mechanism of the influence of LED fishing lights on catch variation remains unclear. The LED lights are known to increase the catch by the direct or indirect luring of the target fish or prey organism [28; 29; 41]. Pot fishing is known to be affected by various conditions from the target fish density to bait type and characteristics, immersion time, fishing season, fishing gear shape, and fishing ground environment [13; 42]. The BSR pots, similarly, may capture fish as the artificial light directly affects fish entry, makes it easier for the fish to find the pot entrance, or as the prey organism of the target responds to the light. A study on cod (_Gadus morhua_) pot fishing in the Atlantic Ocean reported that green LED increased CPUE [27]. However, subsequent studies showed that the cod entered the pot to feed on the moving krill (_Thysanossa inernis_) gathered around the LED inside the pot and not because of any direct response to the light [29; 41]. Similarly to Atlantic cod, the bluefin sea robin catch may be attributed to the luring effect of the prey organism gathered around the light. This presumption is based on the results of stomach content analyses. In previous studies, bluefin sea robin was found to feed mainly on benthic shrimps as well as various other prey organism such as crabs, fish,
## 5 Evaluation
### Experimental Results  
#### 5.3.1 MSE with Respect to \(\epsilon\)  
Figure 3a-c show the estimation accuracy with respect to the privacy budget \(\epsilon\) as the MSE distributions of frequencies for the synthetic data and open datasets, MovieLens and Clothing, respectively. Note that the MSE values for emPrivKV were the minimum for both datasets and all \(\epsilon\). The MSE values of the estimated frequencies for the synthetic data and open datasets, MovieLens, and Clothing were 44.1%, 0.5%, and 0.5% of that of PrivKV, respectively, where \(\epsilon\) was 0.1.  
Similarly, the MSE of the mean estimation is shown in Figure 4a-c. The MSE values of the estimated mean values for the synthetic data, MovieLens, and Clothing were 24.8%, 0.2%, and 0.2% of that of PrivKV, respectively, where \(\epsilon\) was 0.1.  
Figure 3: \(MSE_{f}\) of mean with regard to \(\epsilon\). (**a**) Synthetic data. (**b**) MovieLens. (**c**) Clothing.
## 4 Evaluation Study
### Setup
#### 4.1.2 Environments  
* **Simulated environment**. To simulate the attacks on CAVs, we used the co-simulation tool of the nIoVe framework (Figure 4). The co-simulation tool is based on the CARLA open-source simulator that supports the development, training, and validation of autonomous driving systems. The interface of the co-simulation tool (i) enables the security expert to execute a specific attack scenario, (ii) provides a console to view the progress of the attack, and (iii) provides a live video stream of the CAV moving in a metropolitan area. * **Real-world**. This environment is a real smart city environment. The choice of this scenario is motivated by the fact that a real-life trial presents no danger for humans in this case (e.g., vehicle passengers, pedestrians, and other road users). It is mainly based on the communication between the road-side unit (RSU) and the CAV, with two on-board units (OBUs) installed (one responsible for the vehicle movement and the other responsibilities of the communication). * **Hybrid**. The deployment and the testing of the cybersecurity framework take part in a real-world-like environment of a setup that meets specific requirements. Like the real-work environment, it is based on the OBUs of the CAV.
# Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing
###### Abstract  
Rotating machinery, as a vital and inevitable component in industrial production and processing, plays a crucial role in ensuring the normal operation of production processes. However, most of the existing fault diagnosis methods for rotating machinery are either offline or cloud-based online approaches, which suffer from long latency and large data volumes, making them unable to meet real-time requirements. To reduce latency and data transmission volume, this research proposes a fault diagnosis method for rotating machinery based on edge computing. This research constructs an edge node that integrates signal acquisition, data preprocessing, feature extraction, and fault diagnosis classification to accurately and in real-time identify the fault status of equipment. To address the issues of low fault diagnosis recognition rate and data redundancy associated with single sensors under complex working conditions, this research proposes a fault diagnosis method based on dual-channel CNN decision-level fusion. To alleviate the computational pressure on edge nodes, the equipment fault status diagnosis model is trained on the upper computer, and the data preprocessing and diagnosis model are embedded into the edge nodes. The correctness and real-time performance of the proposed method were validated through comparisons with other methods and online fault diagnosis experiments.  
edge computing; fault diagnosis; data augmentation; data fusion Article  
1
# Unsupervised Domain Adaptive Person Re-Identification via Intermediate Domains
## 4 Experimental Results and Discussion
### Analysis of Intermediate Domains
#### 4.3.2 Quantitative Evaluation  
We conducted ablation experiments on several intermediate domains to evaluate the effectiveness of them. We first pre-trained the model on the source domain and then fine-tuned the model on several intermediate domains. The experiment results are shown in Figures 7 and 8.  
The results suggested that the model suffers a serious reduction in accuracy when we directly transfer the model trained on the source domain to the target domain. For example, the model pre-trained on the DukeMTMC-reID dataset only achieved 61.8% of rank-1 accuracy on the Market1501 dataset. The model achieved 83.9% and 68.9% rank-1 and mAP accuracy, respectively, after fine-tuning on the m2d_0.2 dataset. We then regarded the m2d_0.2 dataset as the source domain and the m2d_0.5 dataset as the target domain, and the rank-1 accuracy improved by 5.2%, which further improved by 0.5% from m2d_0.5 to m2d_0.8. Finally, the model was fine-tuned on the Market1501 dataset and achieved rank-1 and mAP accuracy of 92.4% and 80.2%, respectively. The results of Market1501 to DukeMTMC-reID are consistent with those of DukeMT  
Figure 8: The results of ablation experiments for Market1501 to DukeMTMC-reID dataset.  
Figure 7: The results of ablation experiments for DukeMTMC-reID to Market1501 dataset. Transfer represents the model trained on source domain and then directly evaluated on target domain. m2d_0.2 denotes that we conducted UDA baseline on intermediate domain with \(\lambda=0.2\).  
Furthermore, the performance of the model gradually improves with the increase in the domainness factor \(\lambda\). A large \(\lambda\) indicates that the image style is closer to the target domain. Hence, the intermediate domains do serve as a bridge between the source and target domain. The model smoothly adapts between the source and target domains and transfers the source knowledge more effectively in order to improve the model's performance on the target domain. That is, fine-tuning along the bridge can benefit the cross-domain performance of the model.  
The results of the Market1501 to MSMT17 dataset and DukeMTMC-reID to MSMT17 datasets are listed in Tables 2 and 3, respectively. All of the above results of the ablation experiments further verify the effectiveness of intermediate domains and the domainness factor in SPGAN.
# A Measure Based on Beamforming Power for Evaluation of Sound Field Reproduction Performance
## 1 Introduction
Thus, the effect of the reflections can be suppressed by a directional filter that gives a higher weighting around the intended direction of a reproduced source. After applying the directional filter, the error of the beam-power between the desired and the reproduced fields is quantified and termed the beam-power error. The effect of the difference in the amplitude decay vanishes when the beam-power is normalized (See Section 3). A typical circular loudspeaker array with 2.5D HOA reproduction is used as an example. The research problems of this paper are defined in Section 2. The proposed measure and its basic properties are investigated via simulations in Section 3. In Section 4, the beam-power error is compared with the spatial error in a simple pre-experiment and experiments with 2.5D HOA with a circular array of 16 loudspeakers in a damped room.
# Non-Classic Atmospheric Optical Turbulence: Review
## 2 Measurements and Synthesis
### Non-Kolmogorov Power Law  
In addition to anisotropy effects, the Kolmogorov power law (\(11/3\)) of the 3D power spectrum of the refractive index in the inertial range of scales might be violated. In the 1990s, [PERSON] et al. measured non-Kolmogorov turbulence in velocity and temperature statistics in the upper troposphere and lower stratosphere [41; 42] and found that substantial qualitative discrepancies are possible between them. Such discrepancies could lead to optical power spectra, with the exponent differing from the Kolmogorov value. The non-Kolmogorov turbulence was also recently measured in an experiment involving urban paths [43].
## 3 Materials and Methods
### Data Analysis  
First, we verified the preconditions necessary for the inferential analyses of the experiment's data. For the continuous variables that were used, the normality of the distribution was assessed through the analysis of asymmetry and kurtosis values, and their averages and standard deviations were produced. Then, we proceeded to the inferential analyses using a general linear mixed model (GLMM) approach [40] due to the repeated measures structure of the experimental data. GLMM allows seeing, in a robust way, the existence of net combined effects of the variance explained by the factors taken individually.
## 3 Proposed Status-Byte-Assisted RDMA Transmission Mechanism
### Design of the Status-Byte-Assisted RDMA Transmission Mechanism
#### 3.2.7 Data-Processing Module  
During the data-receiving phase, the data from the same sensing device are stored in the same directory based on the device number and arranged sequentially by packet number. By using container volume mounting technology for data sharing, the data from different devices are mounted into separate containers for processing. By allocating different resources to different containers, the containers are treated as the smallest computing units for data processing.  
To enhance the efficiency and scalability of container management, K8s is utilized to automate the deployment, scaling, and operations of containers, ensuring optimal resource utilization and fault tolerance. By managing the lifecycle of containers, K8s simplifies the orchestration of containerized data processing applications and thus allows the system to handle dynamic workloads more effectively. Thus, this approach facilitates collaborative data processing by the edge server and enhances the scalability of the system.
## 3 Attacks in Blockchain
### Classification of Blockchain Attacks
#### 3.1.1 The 51% Attack  
Also known as majority attacks, 51% attacks remain one of the most critical threats to blockchain networks [31]. They occur when an attacker or a group of miners gains control of more than 50% of a network's computational power, thereby compromising the consensus mechanism. This control enables malicious actors to manipulate blockchain data and execute attacks such as the following:  
1. Reversing transactions, enabling double spending (spending the same coins multiple times);
2. Altering the order of transactions;
3. Disrupting the activities of other miners;
4. Preventing confirmation of legitimate transactions.  
For instance, Ethereum Classic suffered a series of 51% attacks in 2019, causing financial losses and undermining the network's integrity. Indicators of a 51% attack often include abrupt changes in network behavior or transaction anomalies.
## 4 Properties of Treated Liquids from Waste Cooking Oil as an Alternative Insulating Liquid
### Influence of WCO Treatment on Thermal Properties
#### 4.1.6 Oxidation Stability of WCO
In some cases, the antioxidant could be a donor type like butylated hydroxytoluene (BHT), which donates hydrogen, and could also be an acceptor type like superoxide dismutase (SOD), which traps the free radicals, changing them to a stable compound [167]. The study reported in Ref. [168] investigated the effectiveness of three antioxidants, butylated hydroxytoluene (BHT), tert-butylhydroquinone (TBHQ), and pyrogallol (PY), in improving the oxidative stability of waste cooking oil fatty acid methyl esters. Among them, TBHQ demonstrated the superior performance, as confirmed through comprehensive analyses using FTIR and DSC techniques. The oxidative stability of waste cooking oil can also be enhanced through chemical modifications such as epoxidation. This process involves the reaction of the carbon-carbon double bonds in unsaturated fatty acids with an oxidizing agent, typically forming epoxide groups (oxirane rings). By reducing the number of double bonds, which are highly reactive sites for oxidation, epoxidation effectively improves the oxidative resistance of the oil. Studies have shown that epoxidized vegetable oils exhibit higher thermal and oxidative stability, making them more suitable for long-term applications in transformer insulation systems [166; 169; 170; 171; 172; 173; 174]. It is therefore important to consider the optimization of antioxidants and chemical modification parameters on the enhancement of WCO methyl esters for efficient application in transformer application.
# Instability and Drift Phenomena in Switching RF-MEMS Microsystems
## 3 Instabilities Due to Dielectric Charging
In fact, the narrowing effect also included mechanical degradation phenomena, such as the progressive reduction of the spring constant and the residual stress of the beam [23; 25]. Figure 3: C–V curve of a dielectric-less capacitive switch before and after applying a positive bias at 60 V for 12 hours. Charging phenomena are strongly reduced (small shift of the central point) and narrowing effects become visible. Figure 2: Scheme of capacitance–voltage (C–V) curves of a capacitive MEMS switch with charging phenomena. When the bias is applied, the actuation and release voltages can drift to higher values (positive charging) or lower values (negative charging).
# Real-Time Location-Positioning Technologies for Managing Cart Operations at a Distribution Facility
## 5 Conclusions  
The applications of big data and IoT are becoming more and more popular and have been shown to be able to generate great value for businesses. While there are already numerous uses of RFID in supply chains, e.g., inventory control and reporting the current locations of the products, we propose an RFID-enabled locating system for managing cart operations for circulating mail bags in mail facilities. We believe that this application is of very high practical impact due to the increasing demand for postal services in the age of electronic retailing. This RFID-enabled technology is able to help the staff members to locate carts in real time so as to save their time and effort from searching for the right carts and hence to increase the efficiency and throughput of the mail facility. This platform is also capable of reducing cases that mail bags are forgotten to be loaded to ULDs and misplacements of carts to wrong ULDs. Our project team developed the locating system for the use in a large and high-traffic AMC. The application was shown to be successful in locating the tagged carts in real time in this dynamic environment. We also used the offline data about the cart locations collected by the RFID infrastructure to visualize and analyze their movements and observe some of the operational issues within the facility.  
Conceptualization, C.-H.C., Y.-H.K. and M.P.; methodology, C.-H.C., Y.-H.K., H.L. and M.P.; software, C.-H.C. and H.L.; validation, Y.-H.K.; formal analysis, C.-H.C., Y.-H.K. and M.P.; investigation, C.-H.C., Y.-H.K., H.L. and M.P.; resources, C.-H.C.; data curation, C.-H.C. and H.L.; writing--original draft preparation, Y.-H.K.; writing--review and editing, C.-H.C., Y.-H.K. and M.P.; visualization, Y.-H.K.; supervision, C.-H.C.; project administration, C.-H.C.; and funding acquisition, C.-H.C. All authors have read and agreed to the published version of the manuscript.  
This study was funded by Innovation and Technology Commission, Hong Kong SAR Government (Grant No. ITP/035/12 LP). The research of the second author was partially supported by the Germany/Hong Kong Joint Research Scheme Germany Academic Exchange Service (DAAD) and Research Grants Council (RGC) of Hong Kong (G-HKU703/19) and the 2019 Guangdong Special Support Talent Program--Innovation and Entrepreneurship Leading Team (China) (2019 BT025593).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data confidentiality related to the third party.  
**Acknowledgments:** Part of the work was conducted while the first three authors were working with the Chinese University of Hong Kong (CUHK) and the last author was visiting CUHK as a research scholar.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 System Model
### Antenna Model  
We implement the 3 GPP active antenna model used to model the channels above 6 GHz [9]. The model can be used on frequencies up to 100 GHz. We have implemented the 3D model (2D steering) with steering in both elevation and azimuth since both scanning directions will be exploited in mmWave antennas [28]. The total antenna radiation pattern is the combination of the radiating element's radiation pattern and the array factor. The modeling of the antenna array is explained in the following subsections.
# Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology
## 4 Results
### Battery Life Analysis
This is most likely because the device does not go to sleep, meaning components stay powered on during the 5-min intervals when recordings are not taking place. The original estimate of battery life with trip detection disabled can now be updated using the results from this analysis. Given that power consumption during the linear decay phase is \(\approx\)1.5 times greater than that of trip detection logger, the new estimate for the total number of recordings during the batteries' total useful life is 29,342/1.4907 = 19,683 data points. At 288 recordings per day, the life expectancy of the logger without trip detection enabled is estimated at 19,683/288 = 68.35 days. This is a 61.8% decrease in performance from the logger with trip detection enabled, which recorded data for 179 days in total before battery exhaustion. Figure 7 visualises the difference in battery degradation between the logger which has trip detection enabled (blue plot) versus the logger which has it disabled (purple plot). From this, the \(\approx\)2.5 times performance increase the algorithm has on battery life can be observed. It is worth noting that the logger deployed in the refrigerated van with trip detection enabled is additionally exposed to lower temperature levels which can negatively influence the battery voltage and capacity. This is due to the slowdown of electrochemical reactions and decrease in the ionic conductivity in certain battery components [83]. Therefore, the results of the trip detection algorithm may be exacerbated further if battery analysis testing was conducted on a logger deployed in the refrigerated van with trip detection disabled. Further research is required to see if this is the case. Figure 7: Battery degradation with trip detection enabled and disabled.
## 5 Conclusions  
Based on the GEE cloud platform, this study used Sentinel-2 multi-temporal image data as the data source, extracted the walnut planting area of Ganquan Town from 2017 to 2021 after feature construction and optimization using the random forest classification algorithm, and applied the NDVI difference calculation method to the extracted walnut planting area in 2021 to realize research on walnut growth monitoring. Combined with climatic factors, this study monitored and analyzed walnut growth in Ganquan Town from April to mid-September 2021, and the results show that the method adopted in this study not only allowed for obtaining high-precision walnut planting distribution information, but also allowed for high-frequency growth monitoring. However, there are some shortcomings in the monitoring of walnut growth in this study: there are still errors in the data due to the inevitable correlation between the mixed pixels in the images and the original spectral bands.  
Subsequent studies will perform feature operations on the raw spectra, combined with deep learning, to improve the accuracy of classification extraction. This study only focuses on the changes in walnut cultivation in Ganquan Town within the last five years, and subsequent studies will monitor the growth of walnut cultivation areas in the entire southern border region over a longer time period.  
Conceptualization, Z.S. and X.L.; data curation, Z.S.; formal analysis, Z.S.; funding acquisition, R.Z. and X.L.; investigation, X.L.; methodology, X.L.; resources, R.Z.; software, Z.S.; supervision, T.B.; validation, R.Z., T.B. and X.L.; visualization, X.L.; writing--original draft, Z.S. and X.L.; writing--review and editing, T.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Corps Financial Science and Technology Plan projects under grant 2021 AB022; the Bintuan Science and Technology Program under grant 2021 CB041; the President's Foundation of Tarim University under grant TDZKCX202101; Tarim University Graduate Research Innovation Project under grant TDGRI202148; and National Key Research and Development Program under grant 2020 YFD1000703.  
Not applicable.  
The experimental data used to support the findings of this study are available from the corresponding author upon request.  
The authors would like to show sincere thanks to those technicians who have contributed to this study.  
The authors declare no conflict of interest.