esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Transcoding and Encryption for Live 360 CCTV System
## 2 Related Work
### Data Encryption for Surveillance System  
In information security systems to secure users data, different encryption algorithms are widely used. Users can access and interact with video streaming systems anywhere on any device such as home, office, video-conferencing, etc. Therefore, user's information is easily accessible by intruders due to tje lack of security protocols. The necessity of safety communications becomes vital as the cost of data loss is increasing. In the field of information security, especially in video surveillance systems, data encryption is used to protect the data.  
[PERSON] et al. [13] proposed lightweight encryption method based on automated cell subclassing to protect privacy in video surveillance. The authors extracted region of interests (RoIs) in the initial state of the eight-cell automata, which is controlled by randomly selected rules for the transition states of layered cellular automata (LCA). Therefore, all RoIs are encrypted synchronously and independently. Encrypted RoI is stored on the camera side and that can be interactively authenticated by the user through a required way. Video surveillance without RoI will be accessible in real time by the user. [PERSON] et al. [14] analyzed information leaking in video over IP traffic, including for encrypted payloads. The authors verified leakage by analyzing the metadata of network traffic including video bandwidth, packet size, and arrival time between packets. Event detection using metadata analysis is possible even if common encryption methods such as AES, and Secure Sockets Layer (SSL) are applied to video streaming. The leakage information has been observed on the camera and many codes. Through various experiments of the x264 codec [15], the basis for the ability to detect events through the package time was set. Based on this, event detection can be implemented on commercial streaming video applications. In this paper, the proposed method implements an encryption mechanism for specified Network Abstraction Layer (NAL) units based on ARIA cryptographic.
# Comparison of Genotoxicity and Pulmonary Toxicity Study of Modified SiO2 Nanomaterials
## 2 Materials and Methods
### In Vitro Genotoxicity Study  
#### 2.3.1 Ames Test  
Briefly, the Ames test is based upon _Salmonella typhimurium_ TA98 and TA100 strains that have mutations on the HIS operon. The mutation makes the TA strains unable to generate the histidine that is essential for bacterial replication [24]. TA98 and TA100 strains were used to detect the mutagenicity of SA, mA, SB, and mSB in the presence or absence of rat liver S9 fraction. The maximum bactericidal dose of test article was determined by the bactericidal test (data not shown), and the four doses obtained by twofold continuous dilution were added with the mixture of 0.2 mL of 0.5 mM histidine (Merck, kGaA, Darmstadt, Germany) and 0.5 mM biotin and 100 \(\upmu\)L TA strains. In the assay with S9 bioactivity, 0.2 mL of S9 mixture were added. Then, the solutions were added into 45 \({}^{\circ}\)C 2 mL 0.75\(\%\) soft agar containing 0.5\(\%\) NaCl, vortexed well, and poured into minimal glucose agar plates (MA) comprised of agar, glucose solution, magnesium sulfate,  
Figure 1: The TEM images of the test articles: sample A (**A**), modified sample A (**B**), sample B (**C**), modified sample B (**D**).  
citric acid monohydrate, potassium phosphate dibasic anhydrous, and sodium ammonium phosphate. The plate was incubated at 37 \({}^{\circ}\)C overnight. The number of colonies per plate was counted, and the ratio of the number of histidine-revertant colonies to the number of spontaneous revertant colonies for the negative controls was obtained.  
The results of the Ames test were analyzed by Student's \(t\)-test and Microsoft Excel to compare the data between groups. A significant difference was determined if there was a more than twofold difference in the numbers between the negative control and treated groups at \(p<0.05\).
## 3 The Human Role in Teleoperation
### Resolution  
Generally, higher display resolution leads to better teleoperation performances. Resolution impact is not as noticeable as other limiting factors like latency, but it is significant. Evaluating teleoperation driving tasks at different display resolutions (1600 \(\times\) 1200, 800 \(\times\) 600, and 320 \(\times\) 200 pixels per screen),[PERSON] et al. [51] observed a 23% reduction in the rover's average speed and an increase of 69 times in the average time that the operator stopped the rover for planning or other reasons while comparing the highest and the lowest resolutions.  
The path decision was negatively affected by low resolution conditions, as the teleoperator drove slower due to difficulties in distinguishing obstacles. A quality assessment suggested that high resolution improves operator confidence and contributes to the sense of realism and presence. An earlier study analyzed the impact of resolution in the periphery of an HMD in three conditions (64 \(\times\) 48, 192 \(\times\) 144, and 320 \(\times\) 240 pixels) for a simple search and identification object task [52]. [PERSON] et al. found that the lowest resolution was significantly worse than the two higher resolutions considering the criterion of accuracy and search time. Commodity game industry HMDs keep pushing resolution to higher levels of realism and fidelity (e.g., the HTC Vive display has 1080 \(\times\) 1200 pixels per eye, while the new model, HTC Cosmos, presents a resolution of 1440 \(\times\) 1700 pixels per eye). In [53], two HMDs with different display resolutions were evaluated for echography examinations in a pre-clinical and clinical study: the HM2-T2 (Sony Corp.) with a dual display with 1280 \(\times\) 720 pixels per screen and the Wrap 1200 (Vuzix Corp.) with a resolution of 852 \(\times\) 480 pixels for each eye. The study showed that the image quality and the diagnostic performance were significantly superior using the HMD with the highest resolution. Laparoscopic surgery using a high resolution HMD enabled superior image quality and faster task performances as opposed to a low resolution HMD model [54].
## 3 MTR Algorithm Based on Multi-Layer Sparse Structure
### Alternating Optimization  
Denote \(J(\mathbf{A},\mathbf{S},\mathbf{F})\) as the objective function in (9), hence we have  
\[\begin{array}{ll}J(\mathbf{A},\mathbf{S},\mathbf{F})=&\frac{1}{N}tr\Big{(}( \mathbf{Y}-\mathbf{S}(\mathbf{A}\mathbf{K}+\mathbf{F}))^{\top}(\mathbf{Y}- \mathbf{S}(\mathbf{A}\mathbf{K}+\mathbf{F}))\Big{)}\\ &+\lambda_{1}tr\Big{(}\mathbf{A}\mathbf{K}\mathbf{A}^{\top}\Big{)}+\lambda_{2} tr\Big{(}\mathbf{S}^{\top}\mathbf{D}\mathbf{S}\Big{)}+\lambda_{3}tr\Big{(} \mathbf{F}^{\top}\mathbf{F}\Big{)}\end{array} \tag{10}\]  
where \(\mathbf{D}\) is diagonal matrix, and the element on its diagonal can be calculated by Equation (5). Consider the objective function (10) is non-trivial to solve simultaneously for A, S and F due to the non-convexity of the objective function. We calculate \(\mathbf{A}\), \(\mathbf{S}\) and \(\mathbf{F}\) alternately by solving \(J(\mathbf{A},\mathbf{S},\mathbf{F})\) for one with the other fixed.
## 1 Introduction
The scalable MG-based architecture for BTSs described in this paper includes the following features:  
1. Transformation of BTSs in scalable and controllable DC Microgrids to reduce the OpEx. For this purpose, an Energy Management System (EMS) is developed. EMS manages the energy flow in the MG-based BTS based on different scenarios as described in Section 4. 2. The BTS is modeled on the IEC 61850 standard, which improves interoperability and scalability, supporting, in the future, the integration of new BTSs. The equipment used in BTSs are usually manufactured by different vendors overlooking the interoperability between these devices. Thus, a significant advantage of using the IEC 61850 is that it facilitates future extensions. 3. The data collection scheme is based on the cloud from where the information becomes available. This provides always-on, real-time data collecting and the possibility of cloud computing for real-time management. 4. The hardware/software (HW/SW) architecture within the BTS is implemented by using low-cost off-the-shelf hardware. This reduces the time for the return of investment and becomes an economically feasible solution for network operators. The main contributions of the paper are summarized as follows: (i) the design of a scalable architecture to turning BTSs into Scalable and Controllable DC Microgrids which can be applied to any type of BTS; (ii) the architecture is based on the IEC 61850 standard, which enables the distributed communication among the BTSs; (iii) the low-cost hardware implementation of the system, which decreases the time for return on the investment, making it more attractive for network operators; (iv) the proposed architecture lays the foundations to allow several BTSs to work cooperatively, sharing energy among them and injecting the surplus energy to the grid. In short, it allows a set of controllable MG-based BTSs to be aggregated through a centralized management in the shape of a Virtual Power Plant (VPP) [30; 31]; (v) implementation of an EMS which focuses on optimizing the monetary benefits obtained by managing the charging and discharging of a BESS along with the production of photovoltaic (PV) energy and the shedding of non-critical loads;(vi) the development of a smart sensing strategy, as all the sensors have communication capabilities, smart processing based on low-cost hardware and cloud computing facilities for PV forecasting. This allows several BTSs to work cooperatively. In the literature, there are virtually no works dealing with the transformation of a conventional BTS into a scalable and controllable DC MG. An experimental setup has been developed to validate the approach. The remainder of this paper is organized as follows. Section 2 describes the conventional BTS layout and the proposed BTS architecture. Section 3 described in detail the proposed MG-based BTS. Results are presented in Section 4. Finally, conclusions are drawn and future work is outlined in Section 5.
# Autonomous Target Tracking of UAV Using High-Speed Visual Feedback
## 6 Experimental Results
### Target Tracking  
#### 6.3.1 Trajectory Simulation  
For the dynamic target tracking, the trajectory of the target was set beforehand for the convenience of setting the moving speed of the target. In the experiment, the target was moved back and forward between two postures of the robot arm with different moving speeds to verify the tracking results in different conditions. Figure 13 shows the positions set for the tracking tasks.  
Figure 11: Configuration of position hold flight.  
Figure 12: Positioning result of position hold flight.  
Figure 13: Simulation of target trajectory. (**a**) Target’s 1 st position. (**b**) Target’s 2 nd position.
## 3 Proposed LI2A Scheme  
This section presents a detailed breakdown of the LI2A concept and outlines its forward secrecy. The analysis comprises the processes of system initialization, registration, and authentication with key agreement. The registration step between users (IoT devices) and \(TA\) is presumed to take place on a secure channel, whereas the remaining components of the scheme function inside an open channel. The notations utilized in the LI2A system are presented in Table 1.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Description** \\ \hline \(TA\) & Trusted Authority/system administrator \\ \(C_{i}\) & \(i\)-th user (e.g., PLC or SCADA) \\ \(IIoT_{j}\) & \(j\)-th Industrial IoT device \\ \(ID_{i}\) & Identity of user \(C_{i}\) \\ \(ID_{j}\) & Identity of device \(IIoT_{j}\) \\ \(T_{i}\) & Current timestamp \\ \(K_{s}\) & Session key \\ \(K_{p/p}^{TA}\) & TA private and public master keys \\ \(K_{m}\) & Master private key generated by TA \\ \(r_{i}\) & Random number generated by \(C_{i}\) \\ \(r_{j}\) & Random number generated by \(IIoT_{j}\) \\ \(h()\) & One-way hash function \\ \(\oplus\) & XOR operation (bitwise) \\ \(T_{ACK}\) & Acknowledgement timestamp \\ \(h(T_{ACK}\parallel K_{s})\) & Integrity check hash \\ \(P\) & Prime number generated during initialization \\ \(\tau\) & Current timestamp included in messages \\ \(T_{m3}\) & Timestamp for message \(m_{3}\) \\ \(\Delta T\) & Expiration time for session keys \\ \(k_{i-1}\) & Previous session key during forward secrecy \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations utilized in LI2A.
# Software Operation Anomalies Diagnosis Method Based on a Multiple Time Windows Mixed Model
## 5 Result and Discussion
### Comparison of Integrated Methods  
The experimental results are shown in Tables 18-21.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **mixModel-Precision** & **mixModel-Sensibility** & **LSCP** \\ \hline SKAB & 0.6627 & 0.6621 & **0.6647** \\ ad-cpc & **0.7439** & 0.7250 & 0.7327 \\ occu & **0.9927** & **0.9927** & **0.9927** \\ speed & **1.0000** & **1.0000** & **1.0000** \\ twitter & **0.9941** & 0.9462 & 0.9900 \\ cardio & 0.9500 & 0.9500 & **0.9501** \\ vowels & 0.9518 & **0.9629** & 0.9621 \\ \hline \hline \end{tabular}
\end{table}
Table 18: AUC performance of the proposed algorithm and LSCP.  
Figure 17: (**a**) t-SNE visualizations on SKAB; (**b**) t-SNE visualizations on cardio; (**c**) t-SNE visualizations on vowels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **mixModel-Precision** & **mixModel-Sensibility** & **LSCP** \\ \hline SKAB & 0.4850 & 0.4686 & **0.4854** \\ ad-cpc & **0.0046** & 0.0021 & 0.0033 \\ occu & **0.0169** & 0.0139 & 0.0154 \\ speed & **0.0439** & **0.0439** & **0.0439** \\ twitter & **0.0794** & 0.0013 & 0.0571 \\ cardio & **0.6011** & **0.6011** & 0.5978 \\ vowels & **0.4576** & 0.2614 & 0.2945 \\ \hline \hline \end{tabular}
\end{table}
Table 19: Precision performance of the proposed algorithm and LSCP.  
The data in bold in the table indicate the best performing integrated algorithm with that dataset.  
The experimental results provide compelling evidence for the effectiveness of the proposed integrated method in this paper. The performance analysis on key datasets reveals both advantages and disadvantages when considering AUC. The integrated models and LSC exhibit comparable AUC values, with minimal differences numerically. However, when evaluating precision and F1 performance, the integrated methods outperform the others. The precision-based integrated model and LSCP achieve better performances overall, except for the SKAB and speed datasets, where the precision-based integrated model performed better compared to LSCP.  
On the other hand, the recall indicator is where the integrated model based on sensitivity excels. This model focuses more on identifying possible abnormal results, leading to higher recall rates. However, this process may generate numerous false alarms, resulting in lower AUC and precision values.  
For the speed dataset, the effects of the three methods are similar. This outcome aligns with previous research on the speed dataset, which attributes the results to the dataset's distinct abnormal characteristics. Consequently, the basic detector selected in the initial step is closer to its prediction results, promoting consistency in the integrated process.
# Exploring Vitamin D Synthesis and Function in Cardiovascular Health: A Narrative Review
## 6 The Impact of Processing on Vitamin D Levels in Foods and Mitigation Strategies
### Mitigation Strategies to Improve Vitamin D Content in Processed Foods
#### 6.2.2 UV Irradiation and Biofortification  
Besides post-processing fortification, alternative fortification methods can be explored to enhance the Vitamin D content of processed foods. Among them, the ultraviolet (UV) irradiation of dairy products post-pasteurization can stimulate the synthesis of Vitamin D, effectively increasing its levels without compromising food safety. The EU Novel Food Regulation oversees the approval and safety evaluation of UV-treated foods, considering them as novel foods. EFSA, under EC regulation no. 258/97, endorsed UV radiation for post-pasteurization milk processing to enhance shelf life and Vitamin D levels [113]. The European Commission specifies allowed UV wavelengths for treating different foods to increase Vitamin D. Ultraviolet (UV) irradiation, predominantly UVB and more recently UVC radiation, stimulates the synthesis of Vitamin D in certain foods, particularly those containing precursor molecules such as ergosterol or proVitamin D2. After irradiation with UVB light (290-315 nm), 7-dehydrocholesterol undergoes a photoisomerization reaction, followed by a thermal isomerization reaction, resulting in the formation of Vitamin D3. In this context, the European Commission (EC) allowed specific wavelength ranges for different foods (200-800 nm for mushrooms, 240-315 nm for bread, 200-310 nm for milk, not specified for baker's yeast) [112]. Within the realm of dairy products, UV irradiation is strategically employed post-pasteurization to bolster their Vitamin D content. By subjecting pasteurized dairy items to UV irradiation, manufacturers can catalyze the conversion of precursor molecules inherent in the product, such as ergosterol in milk, into active Vitamin D2 (ergocalciferol) [114; 112]. Studies have shown that Vitamin D2 levels in fungi can be significantly enhanced by exposing them to UVB light [114]. As reported by [PERSON] et al., UV irradiation significantly increased Vitamin D2 content in mushrooms, reaching up to 57 \(\upmu\)g/g dry weight in _A. bisporus_, and up to 37 \(\upmu\)g/g dry weight in _P. ostreatus_[115]. Additionally, UV irradiation circumvents the need for heat or chemical additives, thereby preserving the sensory and nutritional integrity of the food product, rendering it an appealing option for Vitamin D enhancement without compromising food safety or consumer acceptance.
# Online Privacy Fatigue: A Scoping Review and Research Agenda
## 3 Findings
### The Conceptualization of Online Privacy Fatigue
#### 3.2.1 A Cynical Means of Coping  
The articles reviewed conceptualized privacy fatigue as a state of mental and psychological exhaustion that requires a cynical means of coping. The analysis also revealed additional (and more specific) conceptual details as to why users often need to cope with privacy fatigue. These are detailed below.  
Figure 3: Visual summary of the study designs by analysis method.  
Most of the articles reviewed (\(n=17\)) conceptualized privacy fatigue by linking the resultant fatigue and exhaustion to a loss of control, specifically in relation to users' personal information, which is disclosed (or seized) in a variety of technological and behavioral contexts. These contexts included modeling the influence of personality traits [13], uncontrollable interactions with chatbots [14], interactions on the Internet [15; 16; 17], unclear (or nontransparent) privacy information/agreements [18], and the use of smart voice assistants [7]. In quite a few instances (\(n=5\)), the loss of control is linked to the use of social media [8; 13; 19; 20; 21]. Two articles conceptualized online privacy fatigue as a form of exhaustion in response to a loss of control within the context of app use, either general apps [22] or mHealth apps [23]. The remaining articles within this subtheme conceptualized privacy fatigue as exhaustion caused by the loss of control of personal information stored and transmitted via smart devices [24], online vendors [5], and the Internet in general [25].  
The Futility of Privacy Protective Behavior  
The remaining articles (\(n=6\)) indicated that privacy fatigue is also conceptualized as a feeling of exhaustion brought on by powerlessness. As such, users resign themselves to the fact that it is only a matter of time before they fall victim to a privacy breach--something that is not within their power to prevent [26; 27; 28]. Such views of eventual victimization, as a function of privacy invasions and breaches, further fuel beliefs that the adoption of privacy-protective behavior is futile [29]. This perpetuates the belief that privacy awareness or knowledge is something best left to researchers and academics. Furthermore, one article [6] attributes such views to privacy generalizations whereby users justify their views  
Figure 4: Sample size analysis by SEM technique.  
by using highly publicized privacy breaches to demonstrate the ineffectiveness of adopting protective behavior [30].
# Fooling in Membrane Distillation, Osmotic Distillation and Osmotic Membrane Distillation
## 4 Characterization Techniques for Fouling Analysis
### Microscopy Techniques
#### Confocal Laser Scanning Microscopy (CLSM)  
CLSM technique is used to analyze both the surface and internal structure of the membrane based on the fluorescence emitted by a sample after its irradiation by a laser beam, obtaining three dimensional images of the samples. It permits us to increase the optical resolution and contrast by means of eliminating the out-of-focus light, achieving a controlled and highly limited depth of focus. CLSM is a non-destructive technique allowing in situ visualization of membranes and one of the most important techniques in the field of fluorescence imaging and scanning to obtain high-resolution optical images [15; 104].  
CLSM is basically used to take images of membrane biofouling, combining the laser scanning method with the 3D detection of biological objects labeled with fluorescent markers. It runs by focusing a laser beam onto a small sample of the fouled membrane and then the reflected light is detected by a photodetection device. The images are then acquired point-by-point and reconstructed with a computer, allowing three-dimensional reconstructions of topologically complex objects [15; 104]. More details on this technique can be found in [105; 106].  
It is worth noting that few authors used this technique to characterize the membrane biofouling. [PERSON], et al. [106] used CLSM to observe the growth of biofouling layer in osmotic membrane bioreactors (OMBRs). The CLSM images showed that during OMBRs operation the variation of the quantity and distribution of polysaccharides, proteins and microorganisms in the biofouling layer were significantly different. [PERSON], et al. [107] used CLSM to determine the fouling produced during the filtration of protein solutions. Other authors considered this technique to validate the efficiency of membrane cleaning [108; 109]. However, this technique is not applied yet in MD, OD and OMD fouling analysis.
# Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer
## 2 Methods
### Algorithm Principle  
#### 2.5.1 Principle of Convolutional Neural Network  
Convolutional neural network (CNN) is a classic deep learning model typically composed of multiple convolutional layers, pooling layers, and fully connected layers. It is widely used in geophysical well logging to extract features. The convolutional layer extracts feature information between well logging curves and core analysis porosity. The pooling layer reduces the sequence size, lowering computational complexity, and the fully connected layer is used for classification or regression tasks. The core idea is to map input data to a high-dimensional feature space through convolution and pooling operations to extract features effectively [25; 26; 27]. The primary characteristics of CNN are weight sharing and local connections, which significantly reduce the number of model parameters, lower the risk of overfitting, improve generalization ability, and enhance feature extraction capabilities. The convolution operation is defined by the following formula:  
\[y_{t}=\sum_{k=1}^{K}\omega_{k}x_{t-k+1}+b \tag{5}\]  
where  
\(y_{t}\) represents the output feature data;  
\(\omega_{k}\) is the convolution kernel (filter);  
\(x_{t-k+1}\) denotes the input data;  
\(b\) is the bias term;  
\(K\) is the length of the data (or kernel size).  
The convolution operation integrates information from neighboring regions by sliding the kernel across the input feature map, effectively capturing local spatial dependencies.