esa-sceva/satcom-chunk-collection huggingface dataset:
# Industrial Image Anomaly Detection via Self-Supervised Learning with Feature Enhancement Assistance
## 4 Results
### Ablation Experiment  
#### 4.3.1 Features Layer Selection  
We investigated the effect of using features extracted from different network layers on anomaly detection results. ResNet18 consists of four main layers, of which we primarily utilized the first three. Table 5 displays the AUC scores obtained using the different feature layers. It can be seen that the highest image-level and pixel-level AUC scores are obtained when using all three layers of multi-scale features. It is worth noting that using only the third layer of features yields the same image-level AUC score of 95.7% as the multi-scale features, but a lower pixel-level AUC score of 94.2%. This discrepancy may be attributed to the fact that deeper features contain richer semantic and global information, which is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **SPADE** & **P-SVDD** & **PatchCore** & **VT-ADL** & **Ours** \\  & [5] & [31] & [7] & [46] & \\ \hline
01 & (91.4, 97.3) & (95.7, 91.6) & (90.9, 95.5) & (-, 99.0) & (97.3, 97.0) \\
02 & (71.4, 94.4) & (72.1, 93.6) & (79.3, 94.7) & (-, 94.0) & (84.1, 96.6) \\
03 & (99.9, 99.1) & (82.1, 91.0) & (99.8, 99.3) & (-, 77.0) & (98.7, 99.2) \\ \hline Average & (87.6, 96.9) & (83.3, 92.1) & (90.0, 96.5) & (-, 90.0) & (93.4, 97.6) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of (image-level, pixel-level) AUC% on BTAD with different methods.  
Figure 5: Anomaly visualization of our method on the BTAD dataset. BTAD has a total of three categories, 01, 02, and 03, corresponding to the first to third rows. From left to right, they represent test images, ground truth, and predicted heat maps, respectively.  
advantageous for the classification of anomalous images. However, their lower resolution may result in missing some local information, leading to lower pixel-level scores.  
Figure 6 shows the visualization results using different feature layers, comparing them with PaDiM. We specifically focus on multi-scale features, selecting combinations such as layers 1, 3; layers 2, 3; and layers 1, 2, 3. As can be seen from the results, our multi-scale feature aggregation achieves more accurate localization than directly using spliced multi-layer features (PaDiM). Meanwhile, the most accurate localization results are attained by using features that combine multi-layer semantic information with resolution (layers 1, 2, 3). Additionally, compared to PaDiM, our method exhibits a lower false detection rate. This is due to the fact that our self-supervised proxy task largely eliminates the domain bias with respect to natural image classification thus enhancing adaptability to anomaly detection tasks.
## 3 Optimization Empowerment Combined Model Based on Sliding Window  
We assume that the measured subsidence data for a point on the ground surface follows a time series \([t_{i},W(t_{i})]\). Where \(i=1,2,\cdots,N\), and \(t_{i}\) represents the time of the \(i\)-th observation relative to the first observation. We select \(l(l\geq 2)\) individual time function models for dynamic ground subsidence prediction. The prediction of subsidence at time \(t_{i}\) for the \(r\)-th \((r=1,2,\ldots,l)\) individual time function model is denoted as \(\tilde{W}_{rt_{i}}\). The weight corresponding to the \(r\)-th individual model is \(Q_{r}(0\leq Q_{r}\leq 1)\), where \(\sum\limits_{r=1}^{l}Q_{r}=1\). The vector of weighting coefficients is \(Q=[Q_{1},Q_{2},\ldots,Q_{r}]^{\mathrm{T}}\), and the expression for the combined prediction model is as follows:  
\[\widetilde{W}_{t_{i}}=\sum\limits_{r=1}^{l}Q_{r}\tilde{W}_{rt_{i}} \tag{12}\]  
The determination of weights directly impacts the predictive performance of the combined time function model. To accurately determine the model weights, this paper compares and selects the best weighting method among three approaches: entropy weighting, improved coefficient of variation weighting, and optimization empowerment.
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 2 SAR Interferometric Processing
### Phase Unwrapping Operations
Some 3-D [80; 81] and hybrid space-time (e.g., [82; 83]) PhU algorithms have been developed in the last 20 years. While using conventional or hybrid space-time PhU algorithms, the PhU solution is not generally time consistent. It means that the curl of the unwrapped phases over closed loops is not typically zero. Some scholars have recently used such phase non-closure on triplets of unwrapped SAR interferograms to identify and partially correct the time inconsistent PhU errors of a sequence of unwrapped multi-temporal SAR interferograms [84]. Efficient solutions for the phase unwrapping of large swath interferograms have also developed (see [85; 86]).
## 3 FGRL for Mapless Navigation
### RL-Based Mapless Navigation
#### 3.1.1 General Framework  
Figure 2 depicts the RL-based framework adapted from the TD3 algorithm for mapless navigation. We can see that two critic networks \(Q_{\theta 1}\) and \(Q_{\theta 2}\), and one actor network \(\pi_{\phi}\) are parameterized by \(\theta_{1},\theta_{2}\), and \(\phi\), respectively. We can also find three corresponding target networks parameterized by \(\theta^{\prime}_{1}\), \(\theta^{\prime}_{2}\) and \(\phi^{\prime}\). The target networks allow a stable convergence of the training procedure. In each learning round, the agent selects an action \(a\) to perform based on actor network \(\pi_{\phi}\) and takes account of exploration noise \(\epsilon\) such that \(a\sim\pi_{\phi}(s)+\epsilon\), where \(\epsilon\) is drawn from a normal distribution \(\mathcal{N}(0,\sigma)\) with a mean of 0 and standard deviation \(\sigma\). Similarly, the target actor network \(\pi^{\prime}_{\phi}\) also includes noise \(\epsilon\), but it is drawn from a truncated normal distribution \(\epsilon\sim clip(\mathcal{N}(0,\sigma),-c,c)\), with the noise clipped to the range \([-c,c]\) to ensure stability. State transitions \((s,a,r,s^{\prime})\) are stored in the replay buffer. In order to smooth the value estimate, a small amount of random noise is added to the target policy, and the noise is clipped so as to stabilize the target within a small range. During  
training, a batch of size \(N\) is sampled from the replay buffer. The loss function for the critic network is defined as follows:  
\[Loss=N^{-1}\sum_{j=1}^{N}(y_{j}-Q_{j}(s_{j},a_{j}))^{2}, \tag{1}\]  
where  
\[y_{j}=r_{j}+\gamma\min_{i=1,2}Q_{\theta_{i}^{\prime}}(s^{\prime},\bar{a}), \tag{2}\]  
and \(\bar{a}\sim\pi_{\phi^{\prime}}(s^{\prime})\). The actor network is updated in the direction guided by the critic network. Specifically, the update of the actor network is carried out by computing the gradient via the chain rule, starting from the initial distribution \(J\):  
\[\
abla_{\phi}J(\phi)=N^{-1}\sum\
abla_{a}Q_{\theta_{1}}(s,a)|_{a=\pi_{\phi}(s) }\
abla_{\phi}\pi_{\phi}(s). \tag{3}\]
# Flash Flood Early Warning System in Colima, Mexico
## 5 Conclusions  
This document has presented an early warning system to alert areas that can suffer from flash floods in real time. Flash floods represent a serious natural hazard that a significant number of persons worldwide must face in terms of loss of life and long-term societal effects. This proposal offers a solution that integrates an early warning system, notifications, and real-time monitoring of flood risk areas. The platform was successfully implemented in the State of Colima, Mexico, covering the metropolitan area of Colima and Villa de Alvarez. This platform is comprised of eight hydrological monitoring stations, eight meteorological stations, mobile monitoring stations called \"drifters\", and a sniffer to help in the location of the drifters.  
The results show that this system is affordable, technologically functional, and provides the few minutes of warning. Having these types of systems in place, together with hydrological forecasts, can help communities, decision-makers, and government officials properly plan or avoid urban development in at-risk areas and provide persons with a few valuable minutes to evacuate and possibly save their lives.  
Thanks to the data generated in this project, along with the coordination with the local authorities, we can conclude that a complete Emergency Water Information Network EWIN network may shortly reduce the impact of floods in the City of Colima, providing information for city planning and, in a worst-case scenario, offering citizens several minutes to evacuate from flash flood zones.  
The presented system is still under development. The integration of the weather data into the warning model is a key feature yet to be implemented. In the future, the proposed method with the deployed hardware will continue improving adding precision and features suggested by the current users.  
[PERSON] worked on article investigation, writing, and the development of data acquisition software. [PERSON] worked writing the article and project administration. [PERSON] worked on funding acquisition, review and planning. [PERSON] worked on writing, review, and validation. [PERSON] worked on validation and hardware configuration. [PERSON] worked writing the article as an expert hydrologist [PERSON] worked on embedded software development. [PERSON] worked on developing the web platform. [PERSON] worked on server validation and configuration. [PERSON] worked on electronic design and manufacturing. [PERSON] worked reviewed the article as an English native speaker. [PERSON] worked on simulation analysis. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Engineering and Physical Sciences Research Council (EPSRC) with grant number [EP/P029221/1]; the APC was also funded by EPSRC.  
The authors declare no conflict of interest.
# Testing an \"IoT\" Tide Gauge Network for Coastal Monitoring
## 3 Field Experiment Locations and Configurations
### Experiment 1: River Mersey, Liverpool  
This site near to the Sandon Half-tide Dock entrance (53 deg25'/37.9'' N, 3 deg0'/22.9'' W, WGS; British National Grid 333,227 392,738) is approximately 2.6 km upstream of the research-quality tide gauge at Gladstone Lock and about 2.6 km downstream of a port authority tide gauge across the river at Alfred Lock (see Figure 3). There are steps down to the water at this location (no public access), and the edge of the river bed is exposed at low water spring tides but not exposed at low water neaps.  
The IoT tide gauge consisted of a Blue Robotics pressure sensor connected by cable to an enclosure containing the Bosch BME 280 sensor, Arduino micro-controller, I\({}^{2}\)C differential converter and antenna. The enclosure was attached to safety railings on the dock wall along with a separate Voltaic Systems V44 battery (12,000 mAh) (see Figure 4). This battery supports the low-power sleep mode micro-controllers (i.e., it does not turn off if the current draw becomes small during the Arduino's sleep cycles), and Voltaic Systems solar panels can be added to provide continuous power. A valve was fitted to the top of the enclosure to allow for changes in air pressure. The Blue Robotics pressure sensor was located at the end of an external 10 m long ethernet cable at the bottom of the steps extending down the dock wall, to a couple of meters above the low watermark, in order to capture as much of the tidal range as possible. The installation was leveled to the local vertical datum by measuring the vertical distance from the pressure sensor to the top of the dock wall, the height of which was found using LiDAR data.  
Configuration 1 (see Figure 2c) was used for this experiment: set to wake up every 15 min (to be within the 140 daily message limit for Sigfox and to be comparable with the 15 min sampling of the nearby Gladstone lock tide gauge (see Figure 3), record for 40 s (to allow for averaging out of high-frequency wind-generated waves), and then send the averaged pressure and temperature data for each sensor via the Sigfox network. The data package contained the status, BME temperature, BME pressure, BME humidity, Blue Robotics pressure and Blue Robotics temperature. Date/time was added at the Sigfox backend as soon as each message had been received. To save power, no SD card was installed, and hence no data were stored internally during the deployment period.  
The IoT tide gauge was deployed for seven days between 1 and 8 November 2019, spanning the transition from spring to near tides (peak spring tide occurred on 28 October, while lowest neap tide occurred on 6 November).  
Figure 3: Fieldwork and local tide gauge locations in the Mersey Estuary; local tide gauges (red dots), experiments (blue dots).
Large public spaces such as airports, shopping malls, subways, and stadiums account for a large portion of the market size. These has mainly been serviced by passive distributed antenna systems (DAS). Large office spaces owned by a single enterprise have also been significant adopters of DAS solutions. Repeaters have been a signal source for many years, together with enhanced Node B, which, as of now, is the main signal source for any indoor distributed antenna system. This structure of this paper is based on the hierarchy of Self-organizing Networks, as depicted in Figure 3. The design rules of SCE for SoN [12] are described in Section 2. Regarding subsequent sections, research into the key factors and solutions for the fiber optical repeater (FR) over the SCE system of D2D architecture for a SoN are described in Sections 3 and 4, respectively. Lastly, conclusions are drawn in Section 5.
## 4 Experiments and Results
### Results and Analysis
#### 4.4.2 Results of the Comparison Experiment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Faster RCNN** & **SSD** & **YOLOv2** & **YOLOv3** & **YOLOv3-SPP** & **YOLOv4** & **Ours** \\ \hline Class 1 & 54.5 & 60.1 & 58.5 & 76.2 & 76.7 & 79.1 & 80.2 \\ Class 2 & 70.2 & 61.8 & 52.4 & 66.9 & 67.2 & 72.7 & 76.8 \\ Class 3 & 63.6 & 67.5 & 70.6 & 72.0 & 71.4 & 73.2 & 72.7 \\ Class 4 & 82.4 & 59.2 & 66.2 & 85.6 & 86.2 & 88.4 & 89.1 \\ Class 5 & 43.1 & 34.5 & 37.1 & 34.2 & 39.6 & 40.2 & 43.4 \\ Class 6 & 74.7 & 66.0 & 70.0 & 73.6 & 75.3 & 76.3 & 76.9 \\ Class 7 & 59.1 & 46.2 & 51.4 & 55.2 & 62.4 & 66.5 & 72.3 \\ Class 8 & 65.4 & 57.8 & 55.7 & 56.7 & 55.1 & 58.8 & 59.8 \\ Class 9 & 62.8 & 54.3 & 55.9 & 55.2 & 53.9 & 56.0 & 56.4 \\ Class 10 & 74.9 & 66.8 & 68.9 & 64.1 & 68.3 & 68.1 & 74.3 \\ Class 11 & 75.3 & 70.1 & 66.2 & 71.4 & 72.8 & 72.4 & 71.6 \\ Class 12 & 44.2 & 26.3 & 42.1 & 51.6 & 52.4 & 57.5 & 63.1 \\ Class 13 & 52.9 & 47.2 & 50.9 & 54.3 & 56.0 & 57.2 & 58.7 \\ Class 14 & 72.2 & 58.4 & 66.2 & 75.2 & 79.6 & 78.8 & 81.5 \\ Class 15 & 57.1 & 51.7 & 51.3 & 37.4 & 42.9 & 38.8 & 40.1 \\ Class 16 & 51.2 & 50.2 & 49.6 & 66.2 & 62.1 & 70.7 & 74.2 \\ Class 17 & 79.8 & 64.5 & 67.4 & 84.3 & 85.5 & 85.4 & 85.8 \\ Class 18 & 51.3 & 42.3 & 39.3 & 50.7 & 58.7 & 64.4 & 73.6 \\ Class 19 & 45.0 & 37.2 & 40.2 & 41.5 & 42.0 & 46.6 & 49.7 \\ Class 20 & 80.7 & 62.2 & 55.8 & 75.8 & 79.1 & 83.5 & 86.5 \\ mAP (\%) & 63.02 & 54.22 & 55.79 & 62.41 & 64.37 & 66.73 & 69.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the AP of the different methods on the DIOR datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Faster RCNN** & **SSD** & **YOLOv2** & **YOLOv3** & **YOLOv3-SPP** & **YOLOv4** & **Ours** \\ \hline Class 1 & 90.4 & 70.6 & 69.3 & 86.2 & 90.6 & 94.3 & 97.6 \\ Class 2 & 89.2 & 81.3 & 84.8 & 88.7 & 87.2 & 92.1 & 97.8 \\ Class 3 & 87.6 & 77.5 & 70.7 & 86.1 & 91.5 & 94.6 & 93.3 \\ Class 4 & 73.2 & 69.2 & 66.1 & 74.6 & 80.3 & 84.0 & 79.3 \\ mAP (\%) & 85.1 & 74.7 & 72.7 & 83.9 & 87.4 & 91.3 & 92.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the AP of the different methods on the RSDO datasets.
# Handover-Driven Interference Management for Co-Channel Deployment of Femto- and Macro-Cells
## 2 Motivation
### Limitations of Existing Techniques
The proposed scheme of this paper outperforms ARCHoN due to its finer control granularity (as compared in the evaluation section).
## 3 Methods
### Large-Kernel Convolution Feature Extraction Block (LKE Block)  
In the decoder design, we initially chose to employ a large convolutional kernel to extract features from medical images. However, large kernels typically increase the number of parameters. To maintain network efficiency, we employed an inverse bottleneck structure combined with depth-separable convolution [36] for the decoder architecture. Depth-separable convolution involves two principal stages: depth-wise convolution (DW-Conv) and point-wise convolution (PW-Conv). Depth-wise convolution is a form of group convolution where each input channel is convolved with a separate convolution kernel, and the outputs from the different channels are then concatenated together. Point-wise convolution involves convolution using a 1 \(\times\) 1-sized kernel, enabling the adjustment of channel numbers and linear combinations between channels. Initially, the depth-separable convolution performs depth-wise convolution to extract spatial information by processing each channel independently. This is followed by point-wise convolution, which fuses spatial and channel features. As depicted in Figure 3, our decoder module comprises three sequential components: a 7 \(\times\) 7 depth-wise convolution followed by two 1 \(\times\) 1 point-wise convolutions. The initial 7 \(\times\) 7 layer captures global features from each channel. The subsequent 1 \(\times\) 1 layer enhances feature representation by expanding the channel dimensions. The final 1 \(\times\) 1 convolution reduces the channel count while retaining the essential features. Additionally, each convolutional stage in the decoder includes residual connections [2], normalization layers [34], and activation functions [35]. Specifically, a Global Response Normalization (GRN) [37] layer follows the second 1 \(\times\) 1 convolution to boost feature diversity and enhance the overall quality of the extracted features. Our decoder module can be represented as:  
\[y_{1}=\sigma(BN(DW\_Conv_{7\times 7}(x))) \tag{4}\]  
\[y_{2}=\sigma(GRN(PW\_Conv_{1\times 1}(y_{1}+x))) \tag{5}\]  
\[y_{3}=BN(PW\_Conv_{1\times 1}(y_{2})) \tag{6}\]  
\[Out=\sigma(y_{2}+x) \tag{7}\]  
Figure 3: Detailed structure of the large-kernel convolutional feature extraction module, which is the primary component of the decoder. This module uses a depth-wise convolution with a large kernel size to extract the global information from each channel, followed by a residual connection. Two point-wise convolutions are applied after the depth-wise convolution, and an inverted bottleneck design is implemented to comprehensively integrate spatial and channel information.  
Among them, \(x\) represents the input feature map, and \(y_{1}\), \(y_{2}\), and \(y_{3}\), respectively, represent the outputs of the three parts of the decoder. \(Out\) represents the final output of the decoder. \(\sigma\) is the GELU activation function, \(BN\) stands for Batch Normalization layer, and \(GRN\) represents Global Response Normalization, DW-Conv stands for depth-wise convolution, while PW-Conv refers to point-wise convolution.
## 2 Supervisory and Control System Based on Real-Time Power Management
### Energy Cost Optimization  
The energy cost optimization layer interacts with the prediction layer and the HMI to run the optimization. The objective of the optimization is to find the lowest energy cost and the maximum PV power for each EV. The sharing power between the stationary storage and the grid is represented by the power distribution coefficient \(k_{D}\) that is calculated from this layer using the power references obtained in the optimization. The benefits of optimization lie in many aspects: reducing the grid peak power consumption, minimizing the energy cost, deciding which of the stationary storage or grid may have the better contribution, and avoiding EV and PV shedding. The communication with the smart grid informs the system about grid power limits for injection and supply, which are set by a contract with the grid operators, and the energy pricing in real-time. Additionally, stationary storage physical limits should be known. The objective is to minimize the total energy cost with respect to different constraints [45].  
The constraints and the objective function are represented in the following subsections.
# Experimental Study on the Desalination of Waste Leachate Using the Combined Freezing Method
## 2 Materials and Methods  
### Experimental Materials  
The experimental treatment water samples were of two types: one was a 5% high-saline simulated wastewater prepared with NaCl (AR, Tianjin Dengfeng), and the other was waste leachate taken from the sewage treatment station of a local waste incineration plant. The freezing generator uses a polypropylene container, and the freezing unit was a commercial freezer model DB/BC-303 KEM (Midea, Foshan, China), with a refrigeration temperature range of 40-0 \({}^{\circ}\)C and a standard power consumption of 0.69 (kw-h) for 24 h.  
The ice crystal desalting device included funnels (wall thickness 2 mm, diameter 18 cm, cylinder height 13 cm, and cone height 4.3 cm); a 2 L stainless steel container (316 austenitic steel); a hair dryer (model KF-8894, Guangdong Huanengda Electric Appliance Co., Ltd., Jieyang, China); a filter centrifuge (TD5F, Shanghai Huazhou Filtration Equipment Co., Ltd., Shanghai, China); and an ice crusher (TH-168, Vismi, Foshan, China). Other major instruments included a portable conductivity meter (DDBJ-350, Beijing Oriental Sail Technology Co., Ltd., Beijing, China), a total organic carbon analyzer (TOC-2000, Shenghan Technology Co., Ltd., Shanghai, China), and an artificial climatic chamber (MGC-850 HP, Ningbo Haishu Saifu Experimental Equipment Co., Ltd., Ningbo, China, temperature control range 4-50 \({}^{\circ}\)C).
# Design of an SVM Classifier Assisted Intelligent Receiver for Reliable Optical Camera Communication
## 3 OCC System Overview  
### OCC System Architecture  
In Figure 3, the block diagram delineates the overall OCC architecture in addition to the proposed classifier model. The structure mainly consists of two parts: the transmitter and the receiver, but, unlike the traditional receiver system, the receiver part includes some additional features that assist with differentiating the accurate data transmitting LED over other LEDs. On the transmitter side, numerous LED regions were present. The LED optical signal was modulated by FSOOK modulation at a frequency level between 2-4 kHz before sending to the receiver. While the modulation is performed, the LED blinking should not be perceivable to the human eye. Since the perceivable flickering rate is up to 200 Hz [29], the modulation frequency used in FSOOK attenuates the flickering issue in a significant margin. With regard to the transmitting LEDs and other sources of interference, they are not modulated in that particular frequency. The projection of all these light-emitting sources was captured by the image sensor. Interference sources were dispelled when a trained CNN model is used. All the possible LED sources were detected and segmented using image processing techniques. Due to the rolling shutter effect of the CMOS based image sensor, each image frame gets striated of white and black shade. Afterwards, necessary geometrical features have been extracted to classify and recognize accurate data transmitting regions from the range of all possible regions.