esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Fault Classification Model
#### 2.2.4 Output Layer  
In order to realize the classification of input samples, an activation function is usually set after the full connection layer. Suppose the input is \(x\), and the probability of predicting that its category belongs to \(y\) is \(P\). The sum of the probabilities that \(x\) belongs to all possible types is 1. The type corresponding to the maximum probability value is selected as the output of the network. The expression is:  
\[P(x=y_{i})=\frac{e^{a_{i}^{(l)}}}{\sum\limits_{i=1}^{S}e^{a_{i}^{(l)}}} \tag{8}\]  
where \(S\) is the number of categories and \(a_{i}^{(l)}\) is the output of layer \(l\), that is, the input node of the softmax layer.
# Machine Learning Applications for Short Reach Optical Communication
###### Abstract  
With the rapid development of optical communication systems, more advanced techniques conventionally used in long-haul transmissions have gradually entered systems covering shorter distances below 100 km, where higher-speed connections are required in various applications, such as the optical access networks, inter- and intra-data center interconnects, mobile fronthaul, and in-building and indoor communications. One of the techniques that has attracted intensive interests in short-reach optical communications is machine learning (ML). Due to its robust problem-solving, decision-making, and pattern recognition capabilities, ML techniques have become an essential solution for many challenging aspects. In particular, taking advantage of their high accuracy, adaptability, and implementation efficiency, ML has been widely studied in short-reach optical communications for optical performance monitoring (OPM), modulation format identification (MFI), signal processing and in-building/indoor optical wireless communications. Compared with long-reach communications, the ML techniques used in short-reach communications have more stringent complexity and cost requirements, and also need to be more sensitive. In this paper, a comprehensive review of various ML methods and their applications in short-reach optical communications are presented and discussed, focusing on existing and potential advantages, limitations and prospective trends.  
machine learning; short-reach optical communication; optical performance monitoring; modulation format identification; equalization; indoor localization +
Footnote †: journal: _photonics_  
0
Footnote 0: Copyright** © 2022 by the authors. Liconssee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 4 Experimental Results and Analysis
### Threshold Determination and Distribution Fitting  
In the fourth experiment, the distribution fitting results and the calculated thresholds of the PRDV parameter were analyzed. The fitting results of the PRDV parameter are shown in Figure 11. Figure 11a-d shows the results of the Zhanjiang, Fujian, Berkeley, and Tianjin data, respectively, where the fitted gamma distributions are drawn with a red line. We can see that the histograms of the four data are all well fitted with a gamma distribution. The calculated thresholds are listed in Table 4, where the values of the first row stand for the average PV power of the sampling region selected by (8), the second row shows the calculated PV thresholds, the third row shows the average PRDV parameters of the extracted water region, and the fourth row shows the calculated thresholds of the PRDV parameter. The table shows that the PV thresholds of Fujian, Berkeley, and Tianjin, which are 0.0114, 0.0081, and 0.007, are almost equal, but the threshold of Zhanjiang is much larger. The reason for this is that there are many tanks on the sea in the Zhanjiang data. The thresholds of the PRDV parameter in Zhanjiang and Tianjin, which are 3.243 and 2.854, are larger than the thresholds in Fujian and Berkeley, which are 1.502 and 1.950. The reason for this is that the interference is more serious in Zhanjiang and Tianjin.  
Figure 11: The distribution fitting results of the ratio parameter in different scenes, where the fitted gamma distributions are drawn with a red line: (**a**) Zhanjiang; (**b**) Fujian; (**c**) Berkeley; (**d**) Tianjin.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**C** & **5 dB** & **7 dB** & **9 dB** & **FAR** & **0.02** & **0.05** & **0.09** \\ \hline IoU(\%) & 58.91 & 75.38 & 54.09 & IoU(\%) & 75.94 & 75.38 & 61.91 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The IoU indexes of the proposed method under different C and FAR parameters.
# Accurate Needle Localization in the Image Frames of Ultrasound Videos
## 3 Experimental Evaluation and Results
### Training the Classifier  
For the _ex vivo_ ultrasound video dataset, the LDA classifier was trained using a leave-one-out cross-validation strategy. In this strategy, each ultrasound video serves as a test set once, while the remaining videos are employed as a training set. This procedure was repeated seven times to guarantee that each video was tested once. The same leave-one-out cross-validation strategy was also employed for the _in vivo_ ultrasound video dataset.  
Evaluating the Effect of the Initial Estimation of \(\Theta\) in the First Image Frame of the Ultrasound Video  
As described in Section 2.1.1, the proposed method requires the operator to provide an initial estimation of \(\Theta\) for the first image frame of the ultrasound video. Therefore, a sensitivity analysis is performed to evaluate the effect of an incorrect estimation of \(\Theta\) on the method's ability to predict the needle angle in the first image frame. In this analysis, the first image frames of the fourteen ultrasound videos (seven _ex vivo_ and seven _in vivo_) are used. For each image frame, forty-nine initial estimations of \(\Theta\) are analyzed, in which the difference between the initial estimation of \(\Theta\) and the angle of the gold standard needle is varied between \(-46^{\circ}\) and \(46^{\circ}\), incrementing by \(2^{\circ}\). The angular error is computed for each tested value of the initial estimation of \(\Theta\). The angular error is defined as the deviation between the angle of the predicted needle trajectory and the angle of the gold standard needle.  
The results of the sensitivity analysis are provided in Figure 4. The mean angular error computed for the first image frame increases as the absolute difference between the initial estimation of \(\Theta\) and the angle of the gold standard needle is increased. However, the proposed method achieves a mean angular error below \(0.5^{\circ}\) for the first image frame of the ultrasound video, provided that the absolute difference between the initial estimation of \(\Theta\) and the angle of the gold standard needle is less than \(20^{\circ}\). Therefore, in the subsequent analyses, the absolute difference between the initial estimation of \(\Theta\) and the angle of the gold standard needle in the first image frame of the ultrasound video is maintained between \(10^{\circ}\) and \(20^{\circ}\).
## 4 Defense Strategies Against Deliberate Attacks on IECPS
### Post-Event Recovery and Summarizing
#### 4.3.2 Attack Traceability and Analysis  
After a deliberate attack occurs, regardless of whether it causes damage to the system, attack traceability should be immediately initiated. Traceability analysis is a key step in uncovering the nature and mechanisms of an attack event. By analyzing system logs, communication traffic, and system operational states in detail, it is possible to identify the attacker's intrusion path, attack method, and the system vulnerabilities exploited. A comprehensive analysis of the entire attack event provides a scientific basis for the formulation of subsequent defense strategies.  
Following the conduct of attack traceability, prompt optimization and upgrading of the system's security measures, based on the analysis results, is imperative. Firstly, the intrusion detection system should be updated by enhancing its rule base and behavioral analysis models, thereby enabling the identification of similar attack patterns and the issuance of early warnings. Secondly, the antivirus database should be supplemented, utilizing the latest malware signature libraries, thereby enhancing the system's capability to detect viruses and Trojans employed in the attack. At the same time, the security policy databaseshould be upgraded to strengthen system access control policies, improve data encryption mechanisms, and optimize network partition configurations, thereby minimizing potential attack paths. Forensic analysis, as a method for post-event collection and analysis of threat information related to cyberattacks, is widely applied in the fields of the internet, power systems, and IECPS security. It includes three steps: intrusion/attack evidence collection, threat information analysis, and evidence presentation. In [184], the role of deep packet inspection in malware detection, packet capture, and analysis was studied in depth, and a conceptual framework for deliberate attack data analysis and forensic analysis was proposed. In [185], data analysis of network traffic logs was performed to identify digital forensic traces, thereby studying the impact of FDIA on advanced metering infrastructure. Subsequently, a testing platform for advanced metering infrastructure was then designed and developed, with the aim of generating FDIA logs. In [186], considering that noise generated during system operation might interfere with forensic results, the recorded results were demodulated to accurately understand the specifics of the tampering.
## 3 Mathematical Formulation  
This article deals with the optimal scheduling strategy of a VPP, including RES units, ESS, and demand, which aims to participate in both the BC and the energy markets. Based on the forecasted data of demand and RES available power output, the VPP operator calculates its reserve capacity and the amount of energy that can be sold or purchased each hour. Consequently, the RESs operating schedules and the ESS charge/discharge states are determined to maximize the total revenue of the VPP obtained in both the BC and the energy markets.  
The VPP decisions under uncertainty are handled by two-stage chance-constrained programming. In the first stage, VPP reserve capacity \(SR^{t}\) is decided for every hour during a 24-h horizon. Other operational parameters of the VPP, such as the energy trading with the main grid or the ESS charge/discharge power, are determined in the second stage. As can be seen, the first-stage decision has only one value held until the actual operation time. By contrast, the second-stage decisions are expressed as a range of values to ensure the availability of these decisions if the actual data differ from the predicted value. In addition, the second-stage decisions can be adjusted as soon as the latest short-term forecast data with high accuracy are available. Using the chance-constrained model ensures that the constraints in this problem are met with a chosen probability even if the predictive errors are quite high.
# Photocatalytic Degradation of Selected Non-Opioid Analgesics Driven by Solar Light Exposure
## 3 Results
### Photocatalysis and Photoelectrocatalysis--Systems with Tungsten Oxide and Iron Oxide  
WO\({}_{3}\) nanomaterial is a promising semiconductor to enhance photocatalytic reaction under solar light irradiation thanks to a band gap energy in the range of 2.7-3.0 eV, large surface area, porous structure, and absorbs radiation in the visible range (\(\lambda\) = 480 nm) [48; 49]. It is also very stable in low-pH aqueous solutions. However, its photoactivity is hindered by the rapid recombination of photogenerated electrons and hole recombination. One of the proposed methods for improving the photocatalytic performance of WO\({}_{3}\) is the formation of heterojunctions with other semiconducting materials [46; 49]. Several papers demonstrated the utilization of photoactive films containing WO\({}_{3}\) and Fe\({}_{2}\)O\({}_{3}\) prepared layer-by-layer method [30; 31; 50].  
In this study, the photocatalytic degradation of studied pharmaceuticals using WO\({}_{3}\) and Fe\({}_{2}\)O\({}_{3}\) applied individually and in their hybrid system WO\({}_{3}\)/Fe\({}_{2}\)O\({}_{3}\) under solar irradiation was investigated. Additionally, the evaluation of these photocatalysts was performed by the photoelectrochemical process, a new technology to overcome the limitations of conventional photocatalysis [46; 48; 49; 51]. It combines the strengths of both heterogeneous  
Figure 1: Chromatograms obtained during UV irradiation (**a**) ketoprofen, (**b**) naproxen, (**c**) diclofenac, and (**d**) decomposition of the tested analytes under the influence of UV radiation over time (§—area of signal). A total of 10 mL of the standard solution was irradiated with UV \(\lambda\) = 254 nm for 60 min at 22 °C.  
photocatalysis and anodic oxidation for the effective degradation of organic pollutants. Both experiments were conducted in a special cell.
# Routing Path Assignment for Joint Load Balancing and Fast Failure Recovery in IP Network
## 4 Numerical Results and Discussions
### Network Survivability
#### 4.2.2 Link Protection  
If we look at Figure 12 for a link failure event, we can see that when link (3,6) fails for destination node 12, node 3 has no LFA, node 6 has no LFA if link (6,9) fails, and so on. The network will not be 100% survivable in case of link failure. We define the survivability for link failure as:  
\[Survivability=\frac{\textit{Total no. of cases that does not have LFA}}{\textit{(Total destination nodes)}}\ast\text{(Total no. of links)}\]  
When the LBO problem experiences a link failure, we calculate the survivability ratios. When there is a link failure event in the network, as illustrated in Table 5, it is not 100 percent survivable. Figure 14 demonstrates that when the size of the network grows (in terms of the number of nodes and the degree of the network), the survivability ratio grows as well. These, however, are not 100% survivable. When a node fails, the survivability ratios are lower than when a link fails. Our LB&LFP optimization model ensures network viability at all times. In the LB&LFP problem, the 27 th constraint assures 100 percent survivability in the presence of a single link failure.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Degree** & **N:10** & **N:12** & **N:14** & **N:16** & **N:18** & **N:20** \\ \hline
**4** & 63 & 68 & 72 & 76 & 79 & 84 \\ \hline
**5** & 65 & 70 & 73 & 77 & 81 & 87 \\ \hline
**6** & 67 & 71 & 75 & 78 & 83 & 88 \\ \hline \end{tabular}
\end{table}
Table 4: Survivability ratio (%) when LBO model has node failure event.  
Figure 13: Survivability ratio (%) when LBO model has node failure event.
# UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
## 5 Experiment Evaluation
### UAV-Guest eNB Connection and Mutual Authentication  
Next, the UAV starts communicating with the guest eNB to facilitate the privacy-friendly service between the guest eNB and the UE in need of service. The UAV and the guest eNB are required to authenticate mutually, and then the UAV requests _accesstoken_ from the guest eNB, which the UAV sends along with the guest eNB identifiers to the UE.  
While communicating with its home network's subscribers, the UAV communicates with the nearby guest eNBs using its identity, \(DID\), along with its valid proxy signature. As the home core network, the public key is known to the guest eNBs; the UAV \(DID\) is easily verified to be authentic. Additionally, the guest eNB uses its identity signed by its home network to verify itself as an authentic eNBs to the UAV. Once each party verifies the other, the UAV and the guest eNB become mutually authenticated.  
To achieve this mutual authentication procedure, we had to modify the eNB module in the srsRAN library to handle different workflows. We have three workflows in total, one for the eNB network subscribers, another for mutual authentication with the UAV, and a third that accepts porting requests from the guest UE devices. Any connection request is sent using _send_con_request_ function from the _srsue: rrc_ module. The _send_con_request_ function has _cause_ parameter used as an indicator to specify which flow should be followed.  
Once the accommodation request is accepted, the guest eNB generates an access token with a specific expiration date stores it as a valid token. Then the guest eNB sends it to the UAV in _accommodation accept_ msg defined in the _dl_dcch_msg_ module. The accommodation requests data structure contain the _token_ and the rrc_transaction_id. Finally, the UAV sends this token to the UE, which will be used later during the service porting request.
# Current Applications of Ultrasound in Fruit and Vegetables Osmotic Dehydration Processes
## 3 Sonication
### Use of Sonication in Food Technology  
Ultrasound-enhanced processes are good alternative to conventional processing due to reduction of costs and often better quality of products, depending on the processing. Studies carried out over the past decades resulted in wide industrial applications of sonication in various food processing [91]. Among them, US is applied in the industry in the following processes: surface cleaning, extraction, degassing, foam removal, homogenization, crystallization, emulsification, extraction, filtration, sterilization, cutting, enzyme inactivation and desensitization [91; 92].  
Nonetheless, in recent years, research efforts are focused on exploitation of US in other heat and mass transfer processes, e.g. drying [50; 93], freezing [94], disinfection, cooking [95] or osmotic dehydration [3; 42; 96]. The use of US in OD causes a faster removal of water from the raw material, than when it is carried out in a traditional way due to turbulences of medium and structures changes [97]. US supports not only the removal of water from the material but also increases the dry matter content [31; 70]. The selection of appropriate conditions for the technological process contributes to the preservation of a better aroma, color and taste of the product as well as retention of valuable nutrients.
# Comparison of Thermal Response for RF Exposure in Human and Rat Models
## 2 Models and Methods
### Computational Models
#### 2.2.4 Definition of Thermal Time Constant  
The thermal time constant is an empirical quantity that characterizes the time needed to reach steady-state temperature (the step response of the tissue). A small thermal time constant indicates a fast thermal response. The thermal time constant was calculated from the time evolution of temperature from continuous wave exposure as:  
\[\Delta T(t)=\Delta T_{max}\left(1-e^{-(t-\tau_{D})/\tau}\right), \tag{9}\]  
where \(\Delta T_{max}\) (\({}^{\circ}\)C) is the maximum temperature elevation, \(\tau\) (s) is the thermal time constant, and \(\tau_{D}\) (s) is the heat conduction time from the model surface.  
The time evolution of temperature was observed as a primary delay function in surface tissues; \(\Delta T(t)=\Delta T_{max}\) (\(1-\mathrm{e}^{-t/\tau}\)) can be obtained by substituting \(\tau_{D}=0\) in Equation (9). The temperature elevation at the thermal time constant is approximately 63% of the maximum temperature elevation, because \(\Delta T(\tau)=\Delta T_{max}\) (\(1-1/e\)) \(\approx 0.63T_{max}\) when \(t=\tau\). The twice and three times values of the thermal time constant approximately correspond to temperature elevations of 86% and 95%, which may be more relevant to human safety, as discussed in Appendix A of the ICNIRP draft guidelines [48].  
A secondary delay function, \(\tau_{D}\
eq 0\) in Equation (9), was observed in inner tissue (such as the brain). The heat conduction time was observed as the time taken for the heat source (the RF power absorption) to reach the observation point. The observation points of the thermal time constant were taken as the points of the maximum temperature elevation in the brain and tissues (excluding the pinna). Here, the brain tissue included gray matter, white matter, cerebellum, thalamus, pineal gland, and hypothalamus.
## 3 Test Results and Discussion
### Microstructural Analyses
#### 3.6.2 SEM Analysis  
Scanning electron microscopy (SEM) was utilized to examine untreated and 14% MgCl\({}_{2}\) treated samples for microstructural modifications in the soil's composition. Figure 13 displays SEM images of untreated and treated samples after 28 days of curing. Applying a 14% solution of MgCl\({}_{2}\) resulted in the reorganization of the particles, which led to a more cohesive structure and an improved structural integrity compared to the untreated sample. This aggregation transformed the geotechnical properties by reducing interactions between surface areas and water, with changes proportionate to the concentration of MgCl\({}_{2}\) solution. This observation can be attributed to the increased salt concentration after adding MgCl\({}_{2}\) solution, which facilitated the rapid flocculation and subsequent substantial particle size increase. In this regard, the sample treated with a 14% MgCl\({}_{2}\) solution exhibited the highest strengths, indicating improved structural integrity and larger particle size.  
Figure 12: FTIR spectrums for untreated and 14% MgCl\({}_{2}\)-treated soil.
# EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
## 1 Introduction
As a result, the BA-based optimization method is used to optimize the TEF characteristics in order to choose the efficient channels that improve the stationarity and resilience of the system. After we train a KNN classifier technique, we conduct a series of experiments to evaluate our proposed framework using 25 MI-based BCI sessions with follow-up assessment visits to examine the functional changes before and after EEG neurorehabilitation from public datasets from the BCI Competition. The hybrid time-entropy-frequency (TEF) attributes used in the AICA-WT-TEF-Chs framework for MI-BCI classification are intended to efficiently leverage the underlying information of the time, entropy, and frequency domains on classification performance. The approach suggested in this paper makes the following contributions. First, it looks at how time-entropy-frequency (TEF) variables affect classification performance. Second, it uses the effective EEG channels generated by a BA-based optimization method to detect changes both before and after rehabilitation. Third, it tests the effectiveness of the AICA-WT-TEF-Chs framework utilizing a variety of classification models and cutting-edge techniques. By eliminating extraneous dimensions, effective channel selection decreases the amount of data that must be stored and processed, speeds up classifier training by using simpler data, and prevents classifier overfitting by lessening the impact of the \"curse of dimensionality\".