esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Dye Decolorization by the bacCTS-MB1 Complex  
Prior to dye decolorization using the bacCTS-MB1 complex, single factor tests for reaction time, bacCTS-MB1 loading, pH, temperature and shaking speed with 1 g L\({}^{-1}\) of the representative anthraquinone dye AG25 and the azo dye AR18 were performed to optimize the degradation reaction conditions. The time-course patterns of AG25/AR18 decolorization were initially determined using 2 g of the bacCTS-MB1s under the following reaction conditions: pH 2.5, 25 \({}^{\circ}\)C and a shaking speed of 180 rpm (Figure 5A). The results showed that the decolorization of both AG25 and AR18 occurred rapidly, with approximately 90% of the reaction equilibrium value attained within the first 20 min and an increasing trend maintained until 30 min, at which time the final decolorization reaction equilibrium was reached. Therefore, 30 min set as the reaction time for the other factor experiments. Figure 5B shows that the decolorization of AG25 and AR18 using 2 g of the bacCTS-MB1s reached its maximal value in a 30-min reaction. Figure 5C,D show that the bacCTS-MB1s were capable of decolorizing AG25 and AR18 across a wide pH range of 2.5-8.5 and a relatively broad temperature range of 15-85 \({}^{\circ}\)C. At an optimal pH value of 2.5 and an optimal temperature of 25 \({}^{\circ}\)C, the decolorization reached its maximal value in 30 min. Figure 5E shows that shaking increased the decolorization reaction compared to static conditions, with every shaking speed increasing the relative decolorization rate. Although shaking at 240 rpm exhibited faster decolorization of AG25 compared with that of 180 rpm, both reached similar decolorization values of AG25 and AR18 in 30 min. Thus, the optimized decolorization reaction conditions were defined as follows: a reaction time of 30 min with a loading of 2 g of the bacCTS-MB1s at a pH of 2.5, a temperature of 25 \({}^{\circ}\)C and a shaking speed of 180 rpm.  
The decolorization capacity of the bacCTS-MB1s on five industrial dyes was determined under optimized shake-flask incubation conditions. Figure 6 shows that the bacCTS-MB1 complex exhibited remarkable dye decolorization, with a maximum relative decolorization value of 96.2% for AG25, 95.6% for AR18, 96.3% for RB198, 95.7% for RB220 and 94.3% for DR243 in 30 min (Figure 6A); conversely, the cell-free CTS-MBs exhibited limited decolorization capacity with 29.0%, 28.7%, 16.2%, 36.0% and 21.9% decolorization values, respectively (Figure 6B). These results indicated that the immobilized _P. putida_ MB285 cells contributed to the decolorization of dyes and the CTS-MB matrix subsidiarity increased the total decolorization capacity via its biosorption activity.  
Figure 5: _Cont._  
Figure 5: Effect of reaction time (**A**), bacCTS-MB1 loading (**B**), pH value (**C**), temperature (**D**) and shaking speed (**E**) on AG25 and AR18 decolorization by the IBCC-B1s. Each reaction included 2 g (unless otherwise specified) of the bacCTS-MB1s (wet weight) and a final concentration of 1 g L\({}^{-1}\) of the dye substrate.
# Vessel Velocity Estimation and Docking Analysis: A Computer Vision Approach
## 7 Limitations and Further Research  
Although the results of the speed method were considered good by the port authorities, the speed estimation model results were obtained by analyzing videos, where the gap between the upper and lower regions was measured on-site. Unfortunately, there was a lack of other velocity measurement systems in the region where the data were collected. However, during the implementation of the system, the port would have a ground truth system in place. The radar and GPS data of the vessels would be provided by the port authorities, and the system would utilize this data to validate the velocity estimated by our computer vision approach. Subsequently, this will leverage this data source to make refinements to the model and enhance its errors. This will allow for the comparison of the developed model with other baseline methods and provide valuable insights into the model's effectiveness and potential areas for further improvement.  
The algorithm effectively identified the presence of tied cables on the mooring bits, demonstrating its capability in the docking process. However, it is important to note that certain challenges persisted despite the removal of images with high variations in illumination and saturation from the experimental dataset, as described in Section 5. Some images still exhibited occlusions and illumination issues, which impacted the performance of the algorithm. These challenging images led to a decrease in the recall of the \"tied\" class, highlighting the need for further refinements. Further research entails the advancement of robust algorithms capable of effectively dealing with occlusions and enhancing the accuracy of classification, particularly in real-world scenarios pertaining to mooring bits. Additionally, future studies may involve analyzing the algorithm's performance with different types of ropes made from various materials and assessing the accuracy of classification for each type. Moreover, incorporating a phase to classify rope tension can be applied to the model to detect incorrect ties on the mooring bits. The application of concepts such as Siamese Networks, harnessing the power of Artificial Neural Networks, holds promise for improving the metrics of the developed system.  
Another limitation of the mooring bitt analysis arises from the utilization of SSIM. Results obtained by [18] demonstrate that SSIM may not always accurately determine the similarity between images of the same scene. The authors further suggest that Pearson linear correlation could serve as a more suitable tool for similarity analysis. Therefore, future research can explore alternative metrics such as Pearson correlation coefficient to overcome this limitation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Precision** & **Recall** \\ \hline Tied & 1.00 & 0.62 \\ Not Tied & 0.78 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Precision and Recall of the SSIM strategy.
# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 4 System Design and Implementation
### User Interface  
The user interface depicted in Figure 3 is based on the one provided in the _geemap_ geospatial package [60; 61] and deployed via Streamlit. This approach not only offers additional capabilities apart from the ones we implemented but also highlights our software's openness and compatibility with existing ones. For the applications adapted to our needs, but not originally created by us, you can refer to the repository ([[https://github.com/giswqs/streamlit-geospatial](https://github.com/giswqs/streamlit-geospatial)]([https://github.com/giswqs/streamlit-geospatial](https://github.com/giswqs/streamlit-geospatial)), accessed on 1 September 2023). When the user first lands on the main page, they are greeted by a short description of the app. On their left side, they can choose between different applications, including Random Forest classification of abandoned buildings, One-Class SVM classification of abandoned buildings, Map Visualization, Timelapse, Marker Cluster for Greek Cities, and Population Heat-map for Greek Cities.  
In the case of our applications regarding abandoned building detection, the user is asked to provide their desired _gejson_ file. This file contains the spatial information for the area of interest (AoI). If no such file is available, they can generate one by simply drawing a polygon on the provided map, exporting it, and then uploading it. In addition, we have been experimenting with other ways (e.g., APIs for _landviewer_ and _geodj_) to further automate the generation of the _gejson_ file with success.  
After the AoI is selected, we can specify the number of equally sized grids to discretize the AoI. However, the number of polygons that the area can be split into is limited in the current implementation due to GEE's computational limits. During the computation, the signal for each operation will be displayed on the main screen. When complete, the grids identified by our method will be displayed on a map as green or red polygons depending on the absence or existence of abandoned buildings, respectively. Moreover, the user could create time-lapses for the defined AoI by simply specifying their desired imagery source (e.g., VIIRS, Landsat, etc.), as well as the time frame. Moreover, the user should be able to just enter the desired location, such as country, city, etc., and let our system take care of the rest.  
The user may in addition select one or more additional types of analysis related to Land Use and Land Cover (LULC) prediction, crop monitoring (we used NDVI/EVI or similar indexes to show current vegetation health and predict possible changes if possible),  
Figure 3: User interface snapshot.  
and finally checking nighttime lights and predicting the GDP of the desired area, inference population density, and other interesting facts about the AoI. The system could return related graphs (see, for example, Figure 4) of these Business-Intelligence related products or just give the user a simple answer.
## 3 Hybrid Detection Approaches  
A hybrid-based approach takes the advantage of both misuse based and behavioral based detection approaches. This ransomware detection approaches detect the ransomware by combining the use of signature and behavioral based detection approaches [77].  
Heldroid [72], a detection technique for android mobile systems used the misuse based approach to perform the detection. Heldroid analyzed the samples dynamically and statistically, which made this approach hybrid. It was also a state-of-art approach for unknown ransomware samples. Both static and dynamic analysis were carried out to categorize the samples into ransomware, goodware, and scareware.  
A deep learning based automated ransomware detection tool was proposed in [78]. This detection tool was introduced for the email filtration. It separated the emails with suspicious downloads. Both static and dynamic analysis were carried out to gather the information about ransomware activity. The proactive monitoring system monitored (PMS) the downloads to identify the ransomware attack.  
An early detection system named PEDA was proposed in [79] for the ransomware detection. Two level detection was performed with help of hybrid analysis. System calls were collected from the samples after analyzing the samples for 30 s in cuckoo sandbox. This system was also able to detect zero-day attacks. However, this system was not able to detect the ransomware that used its own encryption code.  
A hybrid analysis was carried out in [80] to perform the feature analysis. Two different datasets were used to perform static and dynamic analysis. Ransomwares were detected by performing the feature engineering technique to extract the static and dynamic features. Deep convolutional neural networks and machine learning were used to detect the ransomware.
## 3 Results  
### PVs and AVs of Frying Medium  
To analyze basic deterioration processes in frying fats, the acid values (AVs) and peroxide values (PVs) were determined. In this study, after each cycle, a sample of the frying medium was collected for further analysis. In all of the frying media, rapeseed oil and its mixtures with waxes, an increase in the AV was observed during frying. However, it remained at a low level throughout the entire frying period (9 h), taking into account that the limit of AVs for frying fats is 2.5 mg KOH/g according to Polish regulations (Regulation of the Minister of Health of 25 September 2012). The highest value of this parameter for pure rapeseed oil was 0.36 mg KOH/g fat, while the highest value of 0.55 mg KOH/g fat was observed for the BW 1 frying medium after 9 h of heating (Table 1). Moreover, it was observed that the AV increased with the addition of waxes. The reason for this may be because of the presence of free fatty acids in natural wax mixtures. In the study by [PERSON] et al. [13], it was reported that with the increase in beeswax content in the frying medium, the free fatty acid content was higher in comparison to pure sunflower oil. However, after 7 h of heating, there was no significant difference in free fatty acidity content between the oleogel with 3% beeswax and the control (sunflower oil). In a study conducted by [PERSON] et al. [25], an increase in the content of free fatty acids was observed from 0.09 to 0.15% after 16 h of frying potatoes in high-oleic sunflower oil.  
The PV indicates the presence of primary fat oxidation products. In the analyzed frying media, the PV was at a level of 9.01-14.62 meq O\({}_{2}\)/kg. The number of peroxides fluctuated during frying, probably due to their low stability. According to Codex Alimentarius, the maximum value for refined oils is 8.0 meq O\({}_{2}\)/kg. This value was exceeded after the first frying cycle (3 h). In studies conducted by [PERSON] et al. [25], after 16 h of frying potatoes, the PV of high-oleic sunflower oil was 4.63 meq O\({}_{2}\)/kg. The decrease in the PV during frying is caused by the degradation of primary oxidation products (hydroperoxides) and the formation of secondary oxidation products (alcohols, ketones, aldehydes, and acids) ([PERSON] et al. 2016) [26].
# DNN Beamforming for LEO Satellite Communication at Sub-THz Bands
###### Abstract  
The 6G communication system will be designed at sub-THz frequencies due to increasing demand in data rates, emerging new applications and advanced communication technologies. These high-performing systems will heavily rely on artificial intelligence (AI) for efficient and robust design of transceivers. In this work, we propose a deep neural network (DNN) beamformer that will replace the use of phase shifters for a massive array of antenna elements employed at the ground station for wideband LEO satellite communication at sub-THz bands. We show that the signal processing algorithm employed using DNN is capable to match the performance of a true-time delay beamformer as the angle of arrival of the received wideband signal at the ground station is changing due to rapid movement of the LEO satellite. The implementation of DNN beamformer will be able to reduce the cost of receiver and provide a way for the efficient and compact design of the massive array beamforming for wideband LEO satellite applications.  
beamforming: deep neural network; LEO satellite; sub-THz bands 1
## 2 Materials and Methods
### Experiments
#### 2.3.1 Oulu-CASIA NIR Facial Expression Database  
Oulu-CASIA NIR facial expression database [11] includes facial expression videos captured in three different illumination conditions: normal, weak, and dark. The database was collected in an experiment room. The subjects were asked to sit on a chair in the observation room that he/she was in frontal direction to the camera. The images/videos of the database were captured by using a USB 2.0 PC Camera (SN9C201&202) that includes a VIS camera and a NIR camera. Eighty NIR light-emitting diodes with wavelength of 850 nm were used as active illumination sources. The NIR camera thus mainly receives NIR light at around 850 nm. The camera-face distance is about 60 cm. The subjects were asked to make a facial expression according to an expression example shown in picture sequences. The imager worked at a rate of 25 frames per second and the image resolution was \(320\times 240\). The usability of Oulu-CASIA NIR facial expression database has been confirmed by many researchers and widely used in FER research [33; 34]. [PERSON] et al. [11] demonstrates that NIR FER under dark illumination is most challenge in all three illumination conditions since the facial images captured in the dark illumination often lose much useful texture information. Therefore, in this paper, we tested our methods on the subset of the database (dark illumination condition). This subset consists of 6 expressions (anger, disgust, fear, sadness, surprise, happiness) of 80 persons between 23 and 58 years old. The subjects differ in sex, age, background, and region with glasses or without glasses. There is a collection of 480 (80 \(\times\) 6) video sequences. Each video sequence starts at neutral state and ends at the expression's apex. Figure 2 shows six facial expression images of one person in the database in apex condition.
# A Photo Identification Framework to Prevent Copyright Infringement with Manipulations
## 3 Copyright Photo Identification Framework  
### Overall Framework  
As shown as Figure 5, the copyright photo identification framework and searches photo assets in the database with an image query through three steps: _Image RoI Detection_, _Image Hashing_, and _Image Verification_. The first step, _Image RoI Detection_, detects image regions and object regions from an input image. The image detector separates an image collage into individual images, and the object detector recognizes object regions to reduce the effect of image cropping in backgrounds. The largest object is selected as an RoI to represent each separated image. If the object size is less than a certain percentage of the separated image or any object is not detected, the whole separated image is selected as an RoI. Through this module, a query image is refined to be similar to the RoI of the original photo.  
In _Image Hashing_, binary descriptors are generated from the RoIs selected in the previous step. The backbone network of _Image Hashing_ is pretrained by ImageNet to utilize its representation power, and the SimSiam [35] is employed as the head network to fine-tune on copyright photos. The output vector of the head network is utilized as a binary descriptor, which constructs the photo copyright database with the information of an input photo. The binary descriptor helps to search quickly the most similar photo with Hamming distance.  
The matched photo with the binary descriptor is verified with the query image by measuring their similarity in _Image Verification_. The query image and the matched photo are aligned geometrically by the affine matrix, and the overlapping region is derived according to their geometric relationship as shown in 3-(2) of Figure 5. The similarities of patch pairs sampled from the overlapping region are calculated by the proposed Siamese similarity measure network, and aggregated into the image similarity by multi-layer perceptron (MLP). This image similarity determines whether two photos are identical based on the threshold value. Details of each step is described in following Sections.  
Figure 5: Overall framework for copyright photo identification. The source of these photos is ImageNet [29].
## 3 Optimization Methods
### Optimization Problem  
From Figure 1, we can see that the shape of the beam needs to extend as much as possible in the four directions determined by both streets, in order to maximize, in these directions, the received signal power \(P_{i}\), with \(i\in\{1,2,3,4\}\). Therefore, the optimization problem can be formulated as:  
\[P_{i}=\operatorname*{arg\,max}_{P_{i}}\left(\sum_{i=1}^{4}P_{i}\big{(}A_{j}, \phi_{j}\big{)}-\mu\left(\max_{l}P_{l}\big{(}A_{j},\phi_{j}\big{)}-\min_{n}P_{ n}(A_{j},\phi_{j})\right)\right), \tag{6}\]  
where \(l,n\in\{1,2,3,4\}:n\
eq l\), and \(\mu\) is a variable that controls the importance of distributing the power equally. Note that the algorithm must learn to control the values of the amplitude \(A_{j}\) and phase \(\phi_{j}\) of the \(j\)th element of antenna in an array with \(k\) elements. Consequently, the optimization problem to solve can be reformulated as:  
\[\hat{A}_{j},\hat{\phi}_{j}=\operatorname*{arg\,max}_{\begin{subarray}{c}A_{ j},\phi_{j}\\ \sum_{n=1}^{k}A_{m}<A\end{subarray}}\left(\sum_{i=1}^{4}P_{i}\big{(}A_{j}, \phi_{j}\big{)}-\mu\left(\max_{l}P_{l}\big{(}A_{j},\phi_{j}\big{)}-\min_{n}P_{ n}(A_{j},\phi_{j})\right)\right), \tag{7}\]  
where \(\hat{A}_{j}\) and \(\hat{\phi}_{j}\) are the optimal amplitudes and phases for the array, respectively.
# GAN-Based Image Dehazing for Intelligent Weld Shape Classification and Tracing Using Deep Learning
## 2 Methods
### Theoretical Background of YOLO Architecture  
You Only Look Once (YOLO) is a popular single-stage object-detection algorithm that uses a single neural network to perform both the classification and prediction of bounding boxes. The main working of YOLO is that the images are split into \(S\times S\) square grids where each cell will predict \(B\) bounding boxes of weld shapes with a confidence score for each box. YOLO finally predicts bounding boxes with \(x\) coordinates, \(y\) coordinates, box width \(w\), box height \(h\), and confidence \(c\). The confidence score varies between 0 to 1 where 0 means no object exists in that cell and 1 means weld shapes are present in that cell. The coordinates \((x,\ y)\) are the centroid of the predicted bounding box, and the width and height are fractions relative to the entire image size. The confidence is calculated based on the intersection over union (IOU), which is nothing but the area of overlap between the predicted and ground truth boxes divided by the area of the union [32; 33]. In this work, different versions of YOLO algorithms such as YOLOv4 DarkNet, Scaled YOLOv4 and YOLOv5 are used, and their performances are compared.
## 1 Introduction
In our numerical calculations, we explicitly consider the trap potentials by employing the real-space dynamical mean-field theory (R-DMFT) that includes the local quantum fluctuations beyond the mean-field theory and the site-dependent self-energies to present the density and polarization profiles in the trap in a self-consistent way. At the half-filling in the absence of the trap, a previous DMFT study with the numerical renormalization group [53] found a ferrimagnetic ordering where the staggered magnetization appeared while the system was ferromagnetic. Namely, while total up-spin and down-spin populations are not equal in an entire system, which is thus a ferromagnet, the majority spin species at a site alternates between up- and down-spin species across the lattice sites. A recent DMFT study with quantum Monte Carlo calculations [63] also found that at a low temperature, the double occupancy sharply drops at one of the sublattices because of the localization in the flat-band at the half-filling. We find that the system in the trap exhibits a double-shell structure composed of the incompressible core of the ferrimagnetic ordering and the surrounding outer shell of the normal metallic phase at a low temperature. The core at the trap center has a finite radius, inside which the particle density is uniform with half-filled lattices, showing the plateau in the density profile. In the magnetization profile, the core shows the staggered pattern, while one of the spin species is partly depleted, verifying the ferrimagnetic ordering previously expected at the half-filling. We also examine the incompressibility of the core by adding particles and increasing the total polarization. While the core area expands with more particles being included in the trap, the excess particles do not increase the density of the core, so that the plateau in the density profile does not change. In addition, we find that the signature of the phase separation is different between the two sublattices where the discontinuous change of the density is evident only at the interstitial sites at the shell boundary between the areas of the ferrimagnetic, incompressible, and normal metal phases. This paper is organized as follows. In Section 2, we briefly review the real-space dynamical mean-field theory and provide the numerical details. In Section 3, we present our numerical results of the density and polarization profiles across lattices in the trap. The explicit real-space results are compared with the estimates based on the local density approximation. The incompressibility of the ferrimagnetic core in the trap is presented with a change of the particle number and polarization. In Section 4, we summarize our results and provide our conclusions and outlook.