esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Diagnosis of RIS and BS for RIS-Aided Millimeter-Wave System
## 4 Simulation Results  
In this section, we show the performance of the proposed Algorithm 1 and fast Algorithm using simulations. The system parameters are set as follows: We consider that the RIS is equipped with 100 elements and the BS is equipped with 10 antennas; The number of the BS-RIS channel path is 1, and the number of the RIS-user channel path is 1; The AoA from RIS to BS \(\phi_{l_{1}}^{\text{BR}}\), AoD from RIS to BS \(\overline{\phi}_{l_{1}}^{\text{BR}}\) and AoD from the user to BS \(\phi_{l_{2}}^{\text{RU}}\) are randomly chosen from \([-\pi/2,\pi/2]\); We set the probability of blocked RIS and blocked BS as 0.2. We define the normalized mean-squared error (NMSE) of vector \(\mathbf{x}\) as \(\text{NMSE}_{\mathbf{x}}=\frac{E(||\mathbf{\tilde{x}}-\mathbf{x}||_{2}^{2})}{ E(||\mathbf{\tilde{x}}||_{2}^{2})}\), where \(\mathbf{\tilde{x}}\) denotes the estimation of \(\mathbf{x}\).  
Figure 2 shows the NMSE performance of the RIS blockage coefficient and BS blockage coefficient against SNR. We set the time slots \(T\) as 30. The curve \(NMSE\) of \(\mathbf{b}\) with [9] denotesthe NMSE of BS blockage coefficient \(\mathbf{b}\) estimated by the traditional diagnosis method for BS proposed in [9], which ignores the effect of RIS blockage. The curve _Ideal_\(\mathbf{b}\) denotes the NMSE of BS blockage coefficient \(\mathbf{b}\) estimated by the method proposed in [9], where the RIS blockage is known in advance. The curve _NMSE_ of \(\overline{\mathbf{b}}\) with [11] denotes the NMSE of RIS blockage coefficient \(\overline{\mathbf{b}}\) estimated by the traditional diagnosis method for RIS proposed in [11], which ignores the effect of BS blockage. The curve _Ideal_\(\overline{\mathbf{b}}\) denotes the NMSE of RIS blockage coefficient \(\overline{\mathbf{b}}\) estimated by the method proposed in [11], where the BS blockage is known in advance. From Figure 2, we observe that the proposed Algorithm 1 can jointly estimate the RIS blockage and BS blockage, accurately. The NMSE performance of method [9; 11] is poor, due to the effect of RIS blockage and BS blockage, respectively.  
Figure 3 shows the NMSE of RIS blockage coefficient and BS blockage coefficient against time slots \(T\). The SNR is set as 20 dB. We observe that the estimated performance increases as the time slots \(T\) increase.  
Figure 4 shows the probability of successful diagnosis against the SNR, where we only consider the completely blockage scenario. We observe that the proposed fast Algorithm can detect the location of completely blockage, accurately.  
Figure 2: NMSE performance against SNR.  
Figure 4: The probability of successful diagnosis.  
Figure 3: NMSE performance against time slots \(T\).
# Effect of Extraction Method and Thermosonication on Apple Juice Quality
## Materials and Methods
### Chemical Testing  
#### 2.5.1 Total Phenolic Content (TPC)  
Total phenolic content (TPC) of apple juice was determined according to the Folin-Ciocalteu (FC) method [29] with slight modification. Gallic acid was used as a standard, and methanol gallic acid solution (1 mg/1 mL) was diluted with methanol to give appropriate concentrations for a standard curve. Sample extract (0.2 mL) was mixed with 2 mL of methanol in a 25 mL volumetric flask. Then Folin-Ciocalteu reagent (2 mL, diluted 1:10) was added and allowed to react for 3 minutes. Next, 2 mL of Na\({}_{2}\)CO\({}_{3}\) solution was added and the mixture was made up to 25 mL with distilled water. After 30 minutes at room temperature in a dark place, absorbance at 760 nm was measured using a spectrophotometer (UV-1800, Shimadzu, Japan). The results were expressed as mg gallic acid equivalent/100 mL fresh juice (mg GAE/100 mL).
## 3 Results and Discussion
### Chemical Treatment  
The effect of the disinfectant \"Bunker\" in doses of 0.5 and 1.5 L/t on the shoot and root growth of the winter wheat green seedlings (_Tritcium aestivum_ L., variety Irkutskaya, grown by imitation day /night on Knop's solution) on the third, seventh, and ninth days of development was compared in [22]. In particular, the shoot length, total root length, raw mass of shoots, and wet and dry weight of shoots and roots were determined. A comparison of the results for the seventh and ninth days with our results is given in Table 5. Treatment with a disinfectant increased germination on the third and seventh days of development. On the seventh day of development, the main tendencies found in [22] were reproduced during germination in the dark. The disinfectant inhibited the shoot length; with an increase in the dose of the disinfectant, the inhibition became stronger. The length of the root system was also inhibited in the control variant. There was no difference in the fresh mass of shoots and roots. The dry mass of the shoot and roots did not respond to the treatment with the disinfectant. By the ninth day, on seedlings grown in the dark, tendencies towards the dose-dependent inhibition of the shoot length and the length of the root system and the invariability of the dry mass of shoots and roots persisted. It could be seen that this was not the case for green seedlings, where the root systems of plants treated with the disinfectant developed more intensively. The disinfectant increased the relative frost resistance (Table 3) at a dose of 0.5 L/t at temperatures of \(-16\) and \(-18\)\({}^{\circ}\)C and at a dose of 1.5 L/t over the entire range of considered negative temperatures. This is consistent with literature data [19; 23]. Thus, the plant, after treating the seeds with the disinfectant at a dose of 0.5 L/t, still retained the potential for a further increase in frost resistance. Based on these results, we carried out a combination of treatments with plasma and an electric field with the disinfectant treatment at the dose of 0.5 L/t. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(U_{RMS}\), kV & 0.5 & 0.75 & 1 & 1.5 & 1.75 & 2 & 2.1 & 2.4 \\ \hline Frequency & \multicolumn{8}{c}{Power, W} \\ \hline
4.4 kHz & 0.3 & 0.7 & 1.3 & 3.2 & 4.3 & 6.3 & 7.7 & 9.4 \\
16 kHz & 0.7 & 1.9 & 3 & 7.4 & 12.6 & 15.9 & 22.4 & 32.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Discharge power for various plasma treatment regimes.
# A Modular IoT Hardware Platform for Distributed and Secured Extreme Edge Computing
## 3 Security on the Extreme Edge
### The Chain of Trust on the Edge  
When two members do not know each other, they need to establish a root of trust. This technique is based on the fact that the manufacturer of the equipment or a Certificate Authority (CA) acts as a third member that provides confidence by giving legitimacy to the relationship between the public key and the member who claims to have it.  
This process (known as Public Key Infrastructure, or PKI) is a combination of different elements and procedures that allow the execution of the encryption, signature and non-repudiation of transactions or electronic communications using asymmetric cryptography, with guarantees during the whole process. Using PKI, members that do not know each other can authenticate and trust among them before starting a communication. This is done by means of using signatures and certificates. The process consists of the creation of certificates, by the CA, for each device. Subsequently, each member has the public key of the CA with which it is possible to check the validity of the member's certificate with the one a communication (and thus an authentication process) has to be performed. The certificate is a data structure that contains relevant information about the device including its public key, and it is signed by the CA.  
This concepts have been brought to the Extreme Edge by the design and implementation of the proposed new Cookie platform, combining the main processing core with a so-called Hardware Security Module (HSM). This dedicated accelerator allows providing the chain of trust with enhanced security capabilities in a transparent and efficient fashion, thus creating a protected modular and trustable hardware node for the Extreme Edge of IoT, as described in the following paragraphs.
# A Novel RPL Algorithm Based on Chaotic Genetic Algorithm
## 3 RPL-Cga
### Proposing Composition Metric and Objective Function
In this way, RPL-CGA can easily choose the optimum candidate parent (neighbor) as the preferred parent (the next hop), and achieves significant improvement on the network performance of LLNs in the aspect of average end-to-end delay, average success rate, etc.
# IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices
## 4 IoTDS: A Host-Based Botnet Detection System for IoT
### Threat Model  
Our threat model is based on the typical behaviour of IoT botnets such as Mirai. These botnets are usually composed of the following elements: bots, C&C server, scanner, reporting server, loader, and malware distribution server. As already mentioned in this work, bots are compromised devices that are used to carry out powerful attacks against selected targets. The C&C server is the central console the attacker uses to control the entire botnet operation. The scanner is a software agent responsible for scanning the Internet looking for devices that are potential bots. These devices have open ports that can be accessed through the Internet and some well-known vulnerability. Devices that can be accessed through Telnet using default credentials are a typical example. When the scanner finds a device that matches the desired characteristics, it sends the device's basic information to the reporting server, which stores it. The loader uses the information in the reporting server to log into the vulnerable devices. Then, it makes the vulnerable devices download the malware code from the malware distribution server. The malware is installed in the device's RAM, and its downloaded binary is deleted. From this point on, the malware keeps listening to a device's port, waiting for commands from the C&C server [8; 10; 33].  
IoT botnets are known by the simplicity of their operation. Based on our observation of IoT botnet malware functioning and literature reports [8; 10; 33], we assume that they can try to kill competing malware at the same device and other processes that use the same ports as them. However, they do not bring other legitimate applications down. Moreover, as they are installed in the device's RAM, they do not persist after the device's reboots. Installing the malware only in RAM sounds as if attackers made a design mistake, but it is a matter of choice. IoT botnet malware is designed to be very aggressive at scanning, being able to recruit many bots quickly. Therefore, they do not need to put a great effort in persisting in the devices.
# Stochastic Weight Averaging Revisited
## 4 Periodic SWA
### On the Performance of PSWA  
The PSWA algorithm was a byproduct of our experimental findings in Section 3. The aim of our experiments was to test whether our hypothesis in Section 4.1 could be confirmed. We compared PSWA with the backbone SGD on datasets CIFAR-10 and CIFAR-100, based on DNN architectures VGG16, PreResNet-164, and WideResNet-28-10. The codes for reproducing the experimental results are available at [[https://github.com/ZJL](https://github.com/ZJL)]([https://github.com/ZJL](https://github.com/ZJL)) AB-AMMI/PSWA (accessed on 22 February 2023). The momentum factor for the SGD was 0.9, and the weight-decay parameter was 0.0005. The PSWA initialized after the 40 th epoch with a period of 20 epochs. Within one period of the PSWA, a full SWA procedure was conducted. In a SWA procedure, we sampled one NN weight per epoch and then averaged the weights that had been sampled within this SWA procedure, as the current output of PSWA.  
The experimental results are shown in Figure 4. We found that PSWA provided a remarkable performance gain, as compared to its backbone SGD, during the early stage of the training process. We also conducted an experiment in which we compared SWA with DSWA and TSWA, and we conducted the same number of iterations to guarantee that their computational budgets were approximately the same. We did not use the momentum and weight decay methods to prevent their influences on the comparison.  
We found that if the backbone SGD that ran before SWA did not converge or converged to a bad local optimum, corresponding to Case II in Appendix A.1.2, DSWA and TSWA found flatter optima that led to a better generalization than SWA, as shown in Tables 4 and 5, and Figure 5. If the backbone SGD converged well, corresponding to Case I in Appendix A.1.2, then DSWA and TSWA failed to find flatter optima than SWA, as shown in Figure 6. Note that Figures 5 and 6 were obtained using the same procedures as those used to obtain Figure 5 in [5].  
Figure 5: Cross-entropy training loss and testing error as a function of a point on the line connecting the SWA and DSWA (or TSWA) solutions on CIFAR-100. DSWA and TSWA were initialized by a non-converged preceding SGD procedure. Left: PreResNet-164. Right: VGG16.  
Figure 6: Cross-entropy training loss and testing error as a function of a point on the line connecting the SWA and DSWA (or TSWA) solutions on CIFAR-10. DSWA and TSWA were initialized by a converged preceding SGD procedure. Left: PreResNet-164. Right: VGG16.
# Semantic Image Segmentation Using Scant Pixel Annotations
## 2 Related Work  
Semantic segmentation [4] is one of the challenging image analyses tasks that has been studied earlier using image processing algorithms and more recently using deep learning networks; see [6, 10, 11, 18] for detailed surveys. Several image processing algorithms based on methods including clustering, texture and color filtering, normalized cuts, superpixels, graph and edge-based region merging, have been developed to perform segmentation by grouping similar pixels and partitioning a given image into visually distinguishable regions [6]. More recent supervised segmentation approaches based on [4] use fully connected networks (FCNs) to output spatial maps instead of classification scores by replacing the fully connected layers with convolutional layers. These spatial maps are then upsampled using deconvolutions to generate pixel-level label outputs. Other decoder variants to transform a classification network to a segmentation network include the SegNet [19] and the U-Net [20].  
Currently, deep learning-based approaches are perhaps the de facto choice for semantic segmentation. Recently, [PERSON] and [PERSON] [11], reviewed most of the popular learning algorithms (\(\sim\)120) for semantic segmentation tasks, and concluded the overwhelming success of deep learning compared to the classical learning algorithms. However, as pointed out by the authors, the need for large volumes of training data is a well-known problem in developing segmentation models using deep networks. Two main directions that were explored earlier for addressing this problem are the use of limited dense annotations (scant annotations) and the use of noisy image-level annotations (weakly supervised annotations). The approach proposed in this paper is based on the use of scant annotations to address manual labeling at scale. Active learning and semi-supervised learning are two popular methods in developing segmentation models using scant annotations and are described below.
## 8 Results and Discussion
### Convergence Analysis  
Figure 7 describes the convergence (cost) analysis of the developed model over traditional optimization schemes regarding cost. Here, analysis was performed for the varied number of iterations that measured 0, 5, 10, 15, 20 and 25. Upon observing the analysis outcomes, the proposed model had attained minimum cost values for all iterations when compared with the existing schemes. As shown in Figure 7a, the presented model accomplished a lower cost value from the starting iteration to the 100 th iteration i.e., until reaching convergence when compared with other optimization methods. Likewise in Figure 7b, the proposed scheme exhibits little higher value in the starting iterations; however, after the 13 th iteration, the suggested approach had attained minimal cost values when compared with all the other models. Furthermore, upon observing the cost function as shown in Figure 7b, the adopted scheme attained a reduced cost value (1.07), and was 19.16%, 35.51% and 33.64% superior to traditional MFO + DCNN, EHO + DCNN, and WOA + DCNN models, when the number of iterations was 25. Hence, the overall evaluation illustrates the impact of the HEM-AL + DCNN scheme with better results.
## 5 Conclusions  
The effectiveness of the proposed networking scheme for the awareness combat network in this paper is verified by computerized numerical simulation of the awareness combat network. Each node in the awareness combat network is assigned a unique node ID in the entire network and the IP addresses of the nodes in the camp are planned to be in the same network segment. By combining cellular networks with Ad hoc, node state routing and mobile cellular routing are designed. The routing protocol supports QoS. The priority of information transmission in descending order is: voice, data, video for fire control, data, and ordinary video. According to the service type transmitted by the node, the corresponding routing algorithm is automatically selected according to the priority. Node states of the awareness combat network can be divided into \"flat\" state and \"peak\" state; the DTN overlay is added to the AODV protocol layer. Node state routing and mobile cellular routing complement each other. Compared with the traditional wireless mobile Ad-hoc network, the routing design scheme of the awareness combat network greatly improves the packet delivery rate, shortens the time delay, obviously reduces the network control overhead compared with the existing protocols, has a better anti-interruption performance, and is suitable for the application environment of the perceptual combat network.  
C.-H.W.: contributed to the conception, modeling, and algorithm design of the study, contributed to the analysis of simulation data, and approved the final manuscript. K.-Y.Q.: contributed to the conception and acquisition of simulation. P.L.: contributed to the analysis of simulation data. G.-H.W.: contributed to the modeling and Numerical Simulation Scenario Design of the study.  
The authors declare that no conflicts of interest.  
Figure 15: Routing Control Overhead.
# Towards a Hybrid Security Framework for Phishing Awareness Education and Defense
## 6 Education System Architecture--ph1 sher  
In this section, we will explore in detail the functionalities of our new phishing detection and training sub-system--ph1 sher. The system essentially contains three major functionalities:  
* User awareness: A comprehensive collection of a wide range of available resources for educating and creating awareness about phishing among users.
* Phishing campaign: A generative-AI-based system that can dynamically create phishing emails based on the specified scenario.
* URL detection: To identify malicious phishing URLs based on an AI detection model.  
The backbone of the ph1 sher tool is Python Flask, which communicates with the GPT API and Amazon Web Services (AWSs) server through our web portal or Chrome extension as shown in Figure 4.  
A user can access the system through our web portal to access one of the three services that we provide--User awareness, Phishing campaign, or Phishing URL detection. The User awareness option will redirect the user to a categorized section of resources related to phishing attacks and countermeasures. These educational resources can be used for general awareness about phishing.  
Figure 4: Phishing Training and Testing System Architecture.  
The major highlight of our tool is the use of generative AI to launch customized phishing campaigns that cater to various scenarios. The user can choose the campaign that he desires from the web portal. Our Flask backend will handle this HTTP request and redirect the external GPT API to generate context-aware customized phishing emails according to the targeted victim profile. Various prompts used in our phishing campaign portal are discussed in detail in Section 7.  
For URL detection, the Flask backend will make use of the Chrome extension and the backend detection deep learning model to classify the URL. The Chrome extension intercepts the URL visited by the user, and if it is classified as dangerous, then the user is redirected to a captive portal. The design and training of the detection model are discussed extensively in Sections 9-11. Based on the user input via the portal, our Flask backend invokes the appropriate functional unit to perform URL detection, Phishing campaign, or User awareness.
## 6 Simulation Verification
### Verification of the LVRT Control Strategy  
Figure 11 presents simulation results of wind turbine's LVRT based on switching the virtual resistor, where the grid-side filter inductor, L\({}_{\text{f}}\), is 0.038 mH, the short-circuit fault occurs at 24 s and the ground-connection resistor, R, is 0.05 \(\Omega\), the output active power of the wind turbine before short-circuit fault is 2 MW, the output reactive power is 0, the grid voltage phase, \(\varphi\), is 0, and the short-circuit ratio is 2.5, i.e., the line inductor, L\({}_{\text{line}}\), is 0.304 mH, and the line resistor, R\({}_{\text{line}}\), is 0.045 \(\Omega\). According to Equation (18), the value of virtual resistance, R\({}_{\text{v}}\), is set to 0.3 \(\Omega\). It can be seen from Figure 11 that the amplitude of the voltage at the point of common connection (PCC) decreases after the short-circuit fault, and the short-circuit fault flag changes from 0 to 1. The ground-connection resistor, R, is 0.05 \(\Omega\), which makes the voltage at the PCC drop to 0.2 p.u. The output current, i\({}_{\text{1 abc}}\), of the wind turbine increases in a short transient and then remains at the rated value. The DC-link voltage, u\({}_{\text{dc}}\), increases briefly and then remains at the rated value. The ESC first absorbs energy and then emits energy. The MSC reduces the rated output power to 0.1 p.u. The speed of the wind wheel increases, and the pitch angle increases correspondingly.  
At 26 s, the fault is cleared, and the voltage amplitude of PCC rises instantly. The short-circuit fault flag changes from 1 to 0. Since the virtual resistance, R\({}_{\text{v}}\), of the GSC slowly decreases to 0, the output current, i\({}_{\text{1 abc}}\), of the wind turbine is restored to the rated value gradually. The DC-link voltage fluctuates and then remains at the rated value. The  
Figure 10: Simulation results of the wind turbine’s output current with different grid voltage phase, \(\varphi\), when the short-circuit fault occurs.  
output power of the MSC gradually increases from 0.1 p.u. to the rated value. The ESC first absorbs the power and then the output power becomes 0. The rotor speed gradually recovers, and the pitch angle gradually decreases to zero. In Figure 11, the wind turbine maintains operation even during a 2 s short-circuit fault, which proves the feasibility of the proposed LVRT control strategy based on switching the virtual resistor.