esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The aim of this research was to develop software to automate the recognition of structural planes in borehole images by using deep learning. In comparison to previous studies, this work presents at least three original aspects, which are outlined as follows.  
1. Structural Plane Classification and Processioning: Borehole images from 30 coal mines were categorized into five distinct structural plane types based on morphological characteristics. Segmentation sampling and feature enhancement techniques were applied to preprocess the images, optimizing input data for deep learning models and reducing computational time. Additionally, color-coded annotations were implemented to differentiate between structural plane types during model training.
2. Domain-Adaptive CNN Architecture: A Deep CORAL (Correlation Alignment) architecture, built upon a Convolutional CNN, was designed to classify unlabeled borehole images in target domains by leveraging labeled source domain data. Model training demonstrated a classification accuracy of 86% on target domain images, indicating robust generalization capabilities for structural plane recognition.
3. Intelligent Recognition System Development: The Intelligent Recognition System for Surrounding Rock Fracture (IRSSRF) was developed using the Deep CORAL frame-work. This system incorporates five functional modules for automated fracture recognition, classification, and annotation in borehole images. Validation testing revealed structural plane recognition accuracy ranging from 0.76 to 1.0 across borehole test cases, meeting practical engineering requirements.  
Conceptualization, Q.X. and Z.X.; methodology, G.H.; software, X.G.; validation, X.L. and Y.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China [grant number 52404153] and the Natural Science Foundation of Jiangsu Province [grant number BK20241649].  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.
# Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems
## 3 Verification System
### Selecting the Similarity Threshold
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Set Similarity** & & & & & & \\
**Threshold** & **User No. 1** & **User No. 2** & **User No. 3** & **User No. 4** & **User No. 5** & **User No. 6** \\
**[\%]** & **([PERSON])** & **([PERSON])** & **([PERSON])** & **([PERSON])** & **([PERSON])** & **([PERSON])** \\ \hline
0 & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] \\
10 & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] \\
20 & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] \\
30 & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] \\
40 & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] \\
50 & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] \\
60 & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] \\
70 & [PERSON] & unknown & [PERSON] & [PERSON] & [PERSON] & [PERSON] \\
75 & [PERSON] & unknown & unknown & [PERSON] & unknown & [PERSON] \\
80 & [PERSON] & unknown & unknown & [PERSON] & unknown & [PERSON] \\
85 & [PERSON] & unknown & unknown & [PERSON] & unknown & [PERSON] \\
90 & [PERSON] & unknown & unknown & [PERSON] & unknown & unknown \\
95 & unknown & unknown & unknown & [PERSON] & unknown & unknown \\
100 & unknown & unknown & unknown & unknown & unknown & unknown \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Face recognition results with only Users No. 1, 4, and 6 introduced into the system and varying similarity thresholds. was also measured. As in previous tests, attempts lasting more than two minutes were considered unsuccessful. The results are summarized in Table 5. With a fixed similarity threshold of 85% and appropriate conditions for capturing test images, the system demonstrates effective user recognition within a satisfactory time frame. Based on the conducted tests, the similarity threshold has been consistently set at 85% for anthropometric distances. However, the system's reliable operation depends on maintaining suitable conditions during image capture.
## 8 System Design
### Onboard Processing Design, Software and Operations  
This section presents the design, software and operating plan for the OpenOrbiter spacecraft. The spacecraft autonomously executes ground controller requests, performing onboard task decomposition into specific jobs that must be performed (possibly across multiple orbits) for task completion.  
\begin{table}
\begin{tabular}{|c|c|c|} \hline
**Subsystem** & **SMAD \%** & **Power (Wh)** \\ \hline Payload & 43\% & 0.817 \\ \hline Thermal & 5\% & 0.095 \\ \hline Power & 10\% & 0.19 \\ \hline TT\&C & 11\% & 0.209 \\ \hline Onboard Processing & 13\% & 0.247 \\ \hline ADCS & 18\% & 0.342 \\ \hline
**Total** & 100\% & 1.9 \\ \hline \end{tabular}
\end{table}
Table 8: Power Budget [11].  
Figure 2: TT&C Board Antenna Configuration.
# Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery
## Materials and Methods
### Preprocessing  
The raw data of the NTUH dataset contains images of different resolutions and fields of view (FOVs). We first used the skull stripping function of Brain-Suite [33] to locate the brain, then utilized the information possessed by the brain masks for centration and cropping of the MRI to make sure the images contain fewer extracranial areas. Brain-Suite was used only to locate the brain center for better cropping. Everything from the scalp to skull remained on the cropped images for reasons that some of our lesions may locate extra-axially. The final input images size was 200 \(\times\) 200 \(\times\) 200 mm\({}^{3}\). Finally, we normalized them by the z-scores.  
Images in the BraTS dataset were already registered, cropped, and normalized with bias field corrections. We only normalized the data by the z-scores for every pulse sequence (T1, T2, T1+C, FLAIR).
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 2 Materials and Methods
### Condition Monitoring Algorithm
#### 2.2.7 Probabilistic Monitoring  
The deviations of calculated samples from the identified distributions are monitored by calculating the squared Mahalanobis distance in the inference process depicted in Figure 2. The squared Mahalanobis distance from a monitored sample \(r_{m}\) to a distribution with the mean (\(\mu\)) and the covariance matrix (\(\Sigma\)) can be defined by  
\[D=(r_{m}-\mu)^{T}\Sigma^{-1}(r_{m}-\mu). \tag{7}\]  
The monitored sample \(r_{m}\) consists of the \(d\) regression residual values, which are normalized based on the means and standard deviations identified in the training set.  
The squared Mahalanobis distance follows the chi-square distribution with \(d\) degrees of freedom, and therefore, each value can be converted into a probability [44; 45]. An appropriate threshold value can be selected from the distribution to detect outliers [46] or to monitor the system health [18]. Figure 8 demonstrates the probabilities associated with \(D\) values using \(d=3\) and 5. In this study, the _chi2 cdf_ function in Matlab(r) was used for calculating the probability associated with the squared Mahalanobis distance.  
The probabilistic values defined by \(\alpha\) could be used as the reference limits to indicate if the new samples belong to the identified distribution. [PERSON] et al. [18], for example, recommend the use of 99.9 th percentile (\(\alpha=0.001\)) as the limit but inferred that the limit depends on the risk of system malfunction. [PERSON] [15] used a certain number of consecutive values (=3) exceeding a probabilistic control limit as the sign of health state change in the monitored machine. In addition, the \(D\) values can be monitored in moving windows. The moving median with window size \(n=5\) was used in this study as an addition to the monitoring of individual values.
# Surface Roughness Analysis in the Hard Milling of JIS SKD61 Alloy Steel
## 3 Design of Experiments
### Desirability Function  
To find the optimal process conditions for output response, numerical optimization was performed using the desirability function. [PERSON] [24] proposed a multi-objective optimization approach which is considered to be the desirability function. This method is widely applied in engineering to solve inherent multiple objective issues. Each of the responses \(y_{i}(x)\) corresponds with a desirability function \(d_{i}(y_{i})\) assigning a number in the range from zero to one. One describes the ideal case and zero depicts a condition whereby one or more responses fall outside its acceptable limit. Then, the individual desirability functions are combined by the geometric mean which produces the overall desirability D as follows:  
\[D=\left(d_{1}\times d_{2}\times d_{3}\times\ldots\times d_{n}\right)^{1/n}= \left(\prod\limits_{i=1}^{n}d_{i}\right)^{1/n} \tag{6}\]  
Figure 5: Graphical representation of roughness parameter \(Ra\).  
where \(n\) is the number of responses in the measure. If any of the responses are outside their desirable range, the overall function becomes zero.  
In order to reflect the possible difference in the importance of each response, Equation (6) can be extended as follows:  
\[D=\left(d_{1}^{\omega_{1}}\times d_{2}^{\omega_{2}}\times d_{3}^{\omega_{3}} \times\ldots\times d_{n}^{\omega_{n}}\right) \tag{7}\]  
where \(w_{i}\) is weight and satisfies conditions such as 0 \(<w_{i}<1\) and \(w_{1}+w_{2}+...+w_{n}=1\).  
The numerical optimization is performed to determine a point that represents the maximizing desirability function. Adjustment in importance or weight can change the characteristics of an objective. For many responses, all objectives combine into a desirability function. Each goal is assigned to a response which has low and high values.  
The goal of this study is to optimize the input variables for minimizing the surface roughness, so the goal field must be selected as follows:  
Minimum:  
\(d_{i}=1\) if response \(<\) low value  
\(0\leqslant d_{i}\leqslant 1\) as response varies from low to high  
\(d_{i}=0\) if response \(>\) high value
## 3 Method  
The solver presented in this paper is based on the principle of ray tracing. Following this approach, each ray is represented by either discrete FRFs or discrete transmissibility functions--one function for each discrete frequency of interest. For each feature (e.g., defects or rivet holes) within the plate, several parameters need to be evaluated in an offline phase, which is described in Section 3.1, followed by a section on the use of these parameters in the online stage (Section 3.2). At this point, the following assumptions are put in place:  
1. The material is linear-elastic and isotropic.
2. The features are assumed to be of circular shape, and therefore, the response is not affected by the direction of the incident wave.
3. The size of the features is small compared to the size of the distances between actuator, sensors and features.  
As the assumption of circular features introduces a strong limitation, it should be stated here that it is assumed that this assumption can be relaxed in future work. At this point, this assumption allows for training the Reduced Order Model (ROM) with very few simulations. A higher number of simulations is expected to allow the ROM to be trained for small features of any form.
## 2 Experimental and Statistical Methods
### Corrosion Potential  
Rebar corrosion experiments are usually performed by burying reinforcements in concrete and measuring the potential [8; 11; 12; 25]. However, the aim of this study was to remove the temperature effect from the corrosion potential. Therefore, all the specimens had to be in an uncontrolled air environment, and the corrosion potential data was collected for two months continuously. The reason for arranging the specimens in an uncontrolled air condition was to get more temperature effect at the temperature variation. Our methodology was similar to that of [PERSON] et al. [13], who studied a nondestructive corrosion detection method and performed their experiments with unembedded rebars. They used UPV method on corroded rebars in open-air conditions and were able to confirm the corrosion rate. Similarly, [PERSON] et al. [15] used an optical sensor to monitor the corrosion of steel rebars in an uncontrolled air environment, and they documented the specimen corrosion. The experimental conditions in this study were similar to those two experimental setups, but the temperature was not controlled, and the corrosion speed was slower than in those studies.  
Our experimental setting is presented in Figure 2. Specimens were set as the separate cases of corroding and non-corroding like Figure 2. Measuring interval was set as mentioned before and temperature was also logged simultaneously. The corrosion was triggered by evaporating a 10% NaCl solution so that the corrosion proceeded as slowly as possible; the NaCl purity was 99%. In addition, copper-copper(II) sulfate solution setup was used as an electrode with a copper line (CSE) [16; 25].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fe** & **C** & **Si** & **Mn** & **Cu** & **Ni** & **Cr** \\ \hline
98.22 & 0.31 & 0.29 & 1.09 & 0.02 & 0.04 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The composition of the rebar.  
Figure 2: Experimental setting to measure corrosion potential.
# Reversible and Plausibly Deniable Covert Channels in One-Time Passwords Based on Hash Chains +
## 2 Fundamentals and Related Work
### Fundamentals
In our case, _intrinsic_ data-hiding is applied, in which the covert receiver is able to achieve full reversibility without guessing (_implicit_ reversibility techniques) or embedding of additional information that describe the original message (_explicit_ reversibility techniques) [11]. Plausible deniability for a synthetic record is achieved when _there exists a set of real data records that could have generated the same synthetic data with (more or less) the same probability by which it was generated from its own seed_[12]. Plausible deniability is usually achieved by replacing pseudo-random data with encrypted covert data that also appear pseudo-random. Sometimes, it is also achieved by nesting secret information inside multiple layers of hidden information, where the surrounding layer is revealed to an observer in order to show cooperation. For instance, a steganographic filesystem might contain multiple layers of steganographic filesystems nested inside each other. Only outer layers are revealed to a warden while inner layers remain hidden.
# Design, Challenges and Developments for 5G Massive MIMO Antenna Systems at Sub 6-GHz Band: A Review
###### Abstract  
Massive multiple-input multiple-output (mMIMO) is a wireless access technique that has been studied and investigated in response to the worldwide bandwidth demand in the wireless communication sector (MIMO). Massive MIMO, which brings together antennas at the transmitter and receiver to deliver excellent spectral and energy efficiency with comparatively simple processing, is one of the main enabling technologies for the upcoming generation of networks. To actualize diverse applications of the intelligent sensing system, it is essential for the successful deployment of 5G--and beyond--networks to gain a better understanding of the massive MIMO system and address its underlying problems. The recent huge MIMO systems are highlighted in this paper's thorough analysis of the essential enabling technologies needed for sub-6 GHz 5G networks. This article covers most of the critical issues with mMIMO antenna systems including pilot realized gain, isolation, ECC, efficiency, and bandwidth. In this study, two types of massive 5G MIMO antennas are presented. These types are used depending on the applications at sub-6 GHz bands. The first type of massive MIMO antennas is designed for base station applications, whereas the most recent structures of 5G base station antennas that support massive MIMO are introduced. The second type is constructed for smartphone applications, where several compact antennas designed in literature that can support massive MIMO technology are studied and summarized. As a result, mMIMO antennas are considered as good candidates for 5G systems.  
5G systems; massive MIMO; base station; smartphone; 5G antennas; 5G applications; sub-6 GHz Band; 2023  
2022
## 5 Results and Discussion
### Self-Localization Accuracy  
Self-localization gives information on the location of the agent in the internal representation of the environment. Afterward, the localization, an agent, performs prediction based on the current location and movement. If the self-localization gives an erroneous location, then it will directly affect the above-performed recognition task accuracy results. The self-localization is dependent on the number of grid neurons used because fewer grid neurons prone to ambiguity problem as a result of this wrong location can be predicted. The result for the self-localization accuracy is shown below in Figure 22 with the help of a line graph, where the vertical axis represents the error percentage, the horizontal axis represents the number of grid neurons.  
The experimental result of the models validates the theory as the error rate has been decreased drastically with the increase of the grid neurons. Here, the used environment is unambiguous. By unambiguous, we mean that every unique sensation is present at only one location in the environment.
## 4 Pharmacological Potential
### Laurel Leaves
#### 4.1.6 Antidiabetic Activity  
The leaf extracts of _L. nobilis_ showed a positive impact on blood glucose levels. Diabetic rats treated with _L. nobilis_ showed a significant decrease in glucose concentration compared to untreated diabetic rats. Additionally, the extracts demonstrated a beneficial effect on pancreatic islet regeneration and restored liver enzymes, total protein levels, creatine kinase, calcium, urea, and ferritin to near-normal levels. Furthermore, _L. nobilis_ EO exhibited an inhibition of \(\alpha\)-glucosidase, which is an indication of its in vitro antidiabetic activity [55; 59]. These findings suggest that _L. nobilis_ leaf extracts possess noteworthy antidiabetic properties. In a different study, after 4 weeks of treating diabetic rats with bay leaf extracts at a concentration of 250 mg/kg body weight, there was a notable reduction in fasting blood glucose, triglyceride (TG), total cholesterol, low-density lipoprotein (LDL), very low-density lipoprotein, alanine aminotransferase, aspartate aminotransferase, and alkaline phosphatase, and a significant increase in high-density lipoprotein (HDL) andbody weights [60]. A study carried out on 40 people with type 2 diabetes had shown that consumption of ground bay leaves in the amount of 1, 2, or 3 g had reduced the serum glucose levels from 21 to 26% after 30 days [61]. Hence, _L. nobilis_ decreases the risk factors of diabetes [61].  
In another study, the aim of the research was to evaluate the potential hypoglycemic effects of _L. nobilis_, a traditional spice used by Benin's traditional healers for diabetes management. The study involved obtaining aqueous extracts of _L. nobilis_ leaves by maceration and performing acute oral toxicity tests on female Wistar rats at a dose of 2000 mg/kg body weight. Blood tests were conducted to determine animal weight, serum creatinine, alanine transaminase (ALT), and hemoglobin levels on day 0 and day 14 [62]. The efficacy test was performed on rats with a concentration of 300 mg/kg body weight for 21 days. Blood sugar, triglycerides, and cholesterol were monitored, and histological sections were taken from the liver, kidney, and spleen. Results showed the presence of tannins, mucilages, and flavonoids in the extract. No rat deaths occurred, and there were no significant changes in animal weight, serum creatinine, ALT, and hemoglobin levels during the toxicity test [62]. The extract significantly reduced mean blood sugar levels in rats at days 14 and 21, but mean triglyceride and total cholesterol levels did not change significantly during the experiment. _L. nobilis_ was found to have hypoglycemic properties in non-diabetic rats and no acute toxicity, indicating that it may be a potential treatment option for diabetes regulation [62].
## 4 Experimental Results
### Firewall Performance under Real Operative Conditions
#### Access to Websites with Different Restrictions on the Firewall Rules  
During this test, the PC tried to access three different websites, characterized by three different IP addresses: 146.75.61.50 for website #1, 13.226.175.71 for website #2 and 88.221.111.115 for website #3. The firewall was programmed with a blacklist approach to block data traffic to the IP address 13.226.175.71 of website #2. Access to website #1 was carried out from 0 to 65 s, access to website #2 was carried out from 75 to 125 s and access to website #3 was carried out from 130 to 185 s. The measured data throughput of TCP and UDP packets are presented in Figure 12 for the network traffic from Port A to Port B (i.e., from the PC to the Internet) and in Figure 13 for the network traffic from Port B to Port A (i.e., from the Internet to the PC).  
In the case of websites #1 and #3, which have no restrictions based on the firewall rules, control data are sent from the PC to the website and the website content is transferred back to the PC. These websites were correctly visualized. In the case of website #2, which is present on the firewall blacklist and, thus, must be blocked, some control data are sent to the Port A of the firewall, but these data are blocked and no response from the website is detected. The website #2 was not visualized on the PC.  
Figure 11: Data throughput measured by the proposed firewall during a file upload/download using the open-source software FileZilla (version 3.66.1).
## 2 Methods
### Phase 7: Discriminant Function  
Discriminant Analysis differs from Clustering Analysis as it is not meant to determine to which group each object belongs [43]. Instead, Discriminant Functions use a priori measured information (Oil-Slick Information Descriptors) and knowledge of the object's (oil slick) group membership (see or spill), to obtain the maximum discriminating power that minimizes the probability of erroneous discrimination: [DF(X) = (W\({}_{1}\)X\({}_{1}\) + W\({}_{2}\)X\({}_{2}\) + \(\ldots\) + W\({}_{n}\)X\({}_{n}\))\(-\)C\({}_{\rm off}\)]; in which DF(X) corresponds to the dependent variable (i.e., Discriminant Function); X\({}_{n}\) to the independent variables (i.e., Oil-Slick Information Descriptor value); W\({}_{n}\) to the independent variables' weight; and C\({}_{\rm off}\) to the constant offset [58; 59; 60; 61].  
The use of uncorrelated attributes (selected PCs from Phase 6), or at least with the lowest possible degree of dependence (UPGMA selected variables from Phase 5), is a pressing need for Discriminant Functions [62], and as such, this concerns a crucial development of the current EDA from our previous investigations [19; 20; 21; 22]: herein, we are not only using the PCA scores (PCs) as input to the Discriminant Functions, we are also testing the use of UPGMA dendrogram selected variables (see Phase 5: Section 2.5.1) without passing through the PCA.