esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Perceived Value of IoT Technology  
Even though IoT technology is still nascent, it can largely contribute to driving greater business value by using new and advanced analytic tools to process big data [1; 3; 11; 40; 60]. The actual value of the IoT can be reached when connected sensors communicate with each other to exchange data and knowledge without challenges appearing on the technology side, then convert knowledge into tangible benefits on the business side to reach an advanced approach or create a new value [8; 24; 52; 61]. The IoT perceived value represents an overall estimation of technology and how it might aid the MS in increasing its efficiency and achieving its goals.  
The IoT would help the MS to move to the next stage of development by affecting value-creation activities, such as organization framework, operations administration, and supply chains [18]. IoT applications can manage manufacturing activities with less time and effort, optimize operations performance, and enhance efficiency through real-time monitoring of employees, production and distribution lines, increase control and production capacity, reduce human errors, remote diagnosis for machines, and increase security and safety [38; 39; 60; 62], thus, contributing to adding significant _functional value_ to the MS [1; 11; 50; 60]. Furthermore, the IoT has a tremendous capability to link all products and customers with the organization, therefore understanding customers' business processes better and involving them in the decision-making process [52]. The consultations and direct interactions with customers lead to long-term and strengthened customer relationships [62]. This method would enable the establishment of significant _customer value_[1; 6; 54; 63] by providing swift updates on products and services, responding to customers' demands rapidly, and enhancing the personalized experience [10; 52; 54]; thus, the IoT can access the primary customer values, which are reliability and trust [6; 24].  
Moreover, the IoT has much potential for altering prevailing business processes [64] by delivering highly customized products [44], shortening the innovation cycles, capturing internal and external inventory movements, aiding in achieving better purchasing decisions, and providing dynamic pricing for bills based on consumption [18; 19; 38; 62], which means reducing overall costs and generating new revenue opportunities for the organization [28; 52; 53]. As a result, the IoT can contribute to adding significant _financialvalue_ to the MS [6]; in addition, the IoT enables resource allocation in a safe and environmentally responsible manner based on real-time data [10, 40, 60]. These data yield thorough knowledge to construct a prospective organizational map about raw materials, inventory, water, and energy [37], which lead to waste reduction, green procedures [1], harmful gas emissions reduction [6, 38, 60], and energy conservation [47, 49]; thus, these actions enable the achievement of sustainability targets and provide _environmental value_[6]. Figure 1 presents the summary of the IoT's perceived value.
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 3 System Model
### Security Requirements and Design Goals  
The proposed scheme aims to prevent the negative influence of malicious \(\mathcal{A}\) and hostile environment on the rescue and evacuation operation by guaranteeing the collected information's integrity and availability; it should prevent delaying or blocking the messages. Furthermore, the proposed scheme has to be lightweight in terms of communication and computation complexity because of the limited-battery drones.
## 4 Experimental Results
### Experiment 1: Data Augmentation  
All lung tumor images were classified using AlexNet and the training parameters of AlexNet and GAN listed in Table 6 were chosen by the user experiences based on MATLAB official default settings. In order to avoid producing confusing images, malignant and benign tumor images were generated separately. Figure 10 displays the generated images.  
Figure 9: CT images of (**a**) malignant and (**b**) benign tumors.  
The generated images were mixed into the original image data set for lung tumor identification. Thereby, 70% of mixed images were training data and 30% of mixed images were testing data, as presented in Figure 11.  
Figure 12 displays the number of the mixed images and the accuracy of experiments. The accuracy improves when the number of images is increased. Table 7 lists the number of mixed images and Table 8 presents the accuracy, specificity, and sensitivity of lung tumor classification. With data augmentation, both accuracy and sensitivity improved from 97.48% to 98.42% and from 95.10% to 99.40%, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**AlexNet**} \\ \hline Training Epochs & Mini Batch Size & Optimizer Type & Base Learning Rate \\
2 & 15 & SGDM & 0.0003 \\ \hline  & \multicolumn{3}{c}{**GAN**} \\ \hline Training Epochs & Mini Batch Size & Learning Rate \\
100 & 100 & Generator Network & Discriminator Network \\  & 100 & 0.0002 & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Training parameters of AlexNet and GAN.  
Figure 11: Flowchart of classifying mixed data set.  
Figure 10: Generated (a) malignant and (b) benign tumor images.
## 5 Architecture of the Proposed System  
A typical identification system structure includes three stages. The first one is signal recording, including a pre-processing stage, followed by parameter extraction and classification. A diagram of the method proposed by the authors is shown in Figure 1. The extraction stage, which also includes the selection of features, is the most important work stage in terms of designing each identification system. The process is aimed at choosing such parameters of a recorded signal, so as to achieve characteristic features for each class of acquired sounds. The descriptors obtained will be used to define the target feature vector describing a given signal. The objective of processing sound signals with the use of an appropriate algorithm is to achieve distinguishing features of a given model. It is worth noting that the selection and fusion of the features must be repeated to obtain the optimal solution. If the robust feature vector is determined, we can use an easy classification method. This is the main aim of the authors' research. The stage of parameterization of the signal is the most important because incorrect results at this stage cannot be corrected in further stages, even if complex machine learning methods were used.  
To highlight the diverse nature of human speech, the experimental studies proposed by the authors involved, first of all, creating three separate detection PD systems, for which separate descriptor methods were applied, using a speech signal generation model. The next level of research is an attempt to integrate individual feature vectors from the subsystem, through tight data integration.
# Utilizing Energy Storage for Operational Adequacy of Wind-Integrated Bulk Power Systems
## 2 System Model  
The operational adequacy evaluation of a bulk power system integrated with wind and ESS is carried out at a planning horizon of several hours, days, or weeks, for a selected evaluation period, as illustrated in Figure 1. The evaluation period is split into \(n\) number of unit commitment periods (UCP). Each UCP is divided into \(int\) number of time intervals of duration \(dt\). Time intervals and UCP are represented by index \(i\) and \(k\) respectively, during operational adequacy evaluation. The evaluation result helps the system operator to decide the resource requirements in the operational planning horizon.  
The proposed framework recognizes that the system operator estimates the capacity available from the wind and ESS and includes them in the unit commitment of a power system with high wind penetration. However, the degree of uncertainty in wind generation is relatively high considering the operational planning horizon. The state of charge (SOC) and thereby the capacity available from the ESS are dependent on the wind and load conditions. The generation model needs to sequentially capture these uncertainties and correlations.  
A generation model is developed incorporating the capacity available from the conventional thermal and hydro generation, wind generation, and ESS generation capacity. Similarly, a load model is developed by considering the duration and location of the additional demand on the bulk system required by the charging process of the ESS facilities.
# Dynamics Analysis of a Double-Plunger-Type Turbine Overspeed Protection Mechanism
## 1 Introduction  
The main shaft of a turbine rotates with a centrifugal force proportional to the square of the angular velocity. Once the design speed is exceeded, there is a high risk of damage to the unit and serious accidents [1]. Therefore, the overspeed protection device is of great importance for ensuring the safe operation of the turbine unit for a long time. There have been many studies on turbine overspeed protection devices, but the focus has been on electronic trip systems, which use speed sensors and electrical circuits [2; 3; 4]. For example, an electromagnetic system is used to monitor the system's operating status in real-time [5], and the timing of starting the overspeed protection program is determined by judging the current operating conditions. Mechanical overspeed protection devices usually use centrifugal force at high rotational speeds to drive the mechanism to generate tripping action. However, mechanical overspeed protection has the situation of \"mal-operation\" or \"refused operation of protection\" [6], which is more evident in traditional single-plunger type overspeed protection mechanisms. Compared with the traditional single-plunger type, the double-plunger type can act more promptly and accurately when the unit is running overspeed, has a more stable performance at higher speeds, and can adjust the trip speed by changing the position of the outer plunger. Additionally, compared to other principle-based overspeed protection devices such as electromagnetic and hydraulic types, the mechanical type can work in a liquid environment, is easier to disassemble, does not need to provide power during operation, and can still work in the case of loss of external power supply [7]. However, the motion process and force conditions of the double-plunger type overspeed protection mechanism are also more complicated than those of the traditional single-plunger type, which makes it difficult to directly obtain the corresponding relationship between the structural parameters and trip speed and adds challenges to the design and widespread use of this device. In actual use, the corresponding relationship between the adjustment value and the trip speed has to be determined byrepeated tests, which not only consumes considerable manpower but also reduces the operating efficiency of the turbine unit.  
The objective of this study is to establish the equations of the dynamics of the inner and outer plungers and further explore the mechanism of the plunger to achieve the overspeed protection function, clarify the motion process of the inner and outer plungers, and verify it by simulation.
## 2 Results
### Effect of Low-Sodium Salts on the Physicochemical Properties of Dry-Cured Meat
#### 2.1.2 pH, Shear Force, and Color  
As shown in Table 1, the pH of samples was between 5.90-6.15, which was within a good range of pH value for dry-cured meat products. Among them, the pH values of the low-sodium salt groups (SPMA, SPM, SP, and SM) were slightly lower than those of S. The pH of S (6.11) was significantly higher than SPMA (5.86) and SM (5.90), because the binding of divalent salts to proteins resulted in a decrease in the pH value of the isoelectric point [20].  
Muscle is mostly composed of myofibrillar protein, and the effect of myofibrillar protein on tenderness depends not only on the cross-linking degree between protein molecules but also on the interaction between protein molecules. Regarding the shear force, the shear force values of SPMA, SPM, SP, and SM were significantly lower than that of S, which was related to their ionic strength and pH. As shown in Table 1, the pH and ionic  
Figure 1: The distribution curves of transverse relaxation time (\(T_{2}\)) (**a**) of peak area proportions of \(T_{2b}\), \(T_{21}\), and \(T_{22}\), (**b**) of dry-cured meat with the addition of different low-sodium salts. S: 100% NaCl; SPMA: 59.375% NaCl+28% KCl+12% MgCl\({}_{2}\)+0.625% L-arginine (Arg); SPM: 60% NaCl+28% KCl+12% MgCl\({}_{2}\); SP: 72% NaCl+28% KCl; SM: 88% NaCl+12% MgCl\({}_{2}\). Different letters in the same group indicate significant differences (\(p<0.05\); Tukey test).  
strength of the S group were higher, which was beneficial to inhibiting the degradation of muscle fibers, resulting in greater shear force.  
Color is one of the main attributes of meat and meat products which reflects freshness and influences consumer purchase intentions. In this study, when sodium was partially replaced by other cations, it had a significant impact on the color parameters, as shown in Table 1. As for \(L^{*}\), S \(>\) SPMA \(>\) SPM \(>\) SM \(>\) SP, the \(L^{*}\) value of S was significantly higher than that of SP and slightly higher than that of SPMA, SPM and SM. [PERSON] et al. (2018) also reported that the \(L^{*}\) value of dry-cured bacon salted with a mixture containing KCl in the formula was relatively lower. Mg\({}^{2+}\) was not contained in SP, indicating that the absence of Mg\({}^{2+}\) can reduce the brightness of the meat and have an adverse effect on its color. For \(a^{*}\), the order of samples was SPMA \(>\) S \(>\) SP \(>\) SM \(>\) SPM. SP was significantly higher than SM and SPM; this is attributed to Arg's capacity to reduce brown metmyoglobin into bright red oxymyoglobin, improving the quality of dry-cured meat's \(a^{*}\) value ([PERSON] et al., 2018), which is consistent with the findings of [PERSON] et al. (2018) and [PERSON] et al. (2018). For \(b^{*}\), S \(>\) SPMA \(>\) SP \(>\) SPM \(>\) SM; the \(b^{*}\) value of S (26.00) was significantly higher than that of SM (21.72). The presence of Mg\({}^{2+}\) reduced the yellowness of dry-cured meat, which was good for consumer acceptability.
## 2 Background Research
### Adversarial Training  
Adversarial Training, as the name implies, is the process of training a model with adversarially perturbed images. The training process might use a dataset containing both clean and adversarial examples, as in [2], or a dataset with only adversarial examples, as in [13]. There have been various improvements and additions to the method; essentially, however, the algorithm should be able to augment or transform the training dataset with adversarial examples. Research has shown that training the model with perturbed examples significantly improves performance when an attack occurs [12; 13; 14; 15].  
Adversarial attacks can be divided into several classes based on their training techniques. According to [PERSON] et al. (2021) [12], adversarial training is generally divided into six main types, with other variants not classified under the main branches. Most variations here are based on the initially proposed adversarial training model in [2]. Examples such as [15] have suggested using augmented adversarial examples generated from different target models to generate different types of adversarial samples, while other research, including [51; 52; 53], has changed the perturbation with respect to each pixel, i.e., epsilon (\(\epsilon\)), in an adaptive fashion that allows for better generation. These attacks can be implemented on regression models, as done by [PERSON] et al. [54]. Although it is outside of our research scope in this paper, applying the proposed method to regression models is an interesting future direction.  
The concept of using generative models for adversarial training is a relatively recent concept that has been gaining traction. In this paper, we compare [PERSON]'s adversarial training method proposed in [9] with our adversarially robust models based on Unrestricted Example generation using the AC-GANs proposed in [20] improved for Adversarial training. Research by [PERSON] et al. (2022) [55] proposed using a generative classifier to boost adversarial performance, which is very different from our approach; similar research was conducted by [PERSON] et al. (2020) [56]. In this latter case, the authors used autoencoders to generate adversarial examples instead of GANs. Our reasoning for using GANs is that they can be conditioned to produce the required results while generating clear samples. Research has shown that GANs have superior performance in generating well-defined quality images [57]. This paper proves that GANs have better potential to generate universal adversarial examples and, as a result, boost defenses. This improvement in robustness results from better coverage of adversarial samples generated from scratch. This generative nature adds a variety of samples that attackers might exploit; such samples are not usually discovered through traditional algorithms such as PGD and FGSM.
## 3 Estimating Sinusoidal Parameters from 3 Bins of Spectrum
The parabolic fitting is shown in Figure 1. The thick dashed blue line is the DTFT of a length-4096 Hann-windowed sinusoid and the thick solid green line is a parabola fit to the top three bins (\(k_{m-1}\), \(k_{m}\), and \(k_{m+1}\)) of the magnitude DFT, shown as stemmed circle. The vertical dot-dashed lines mark the true fractional bin index \(\mathcal{K}\) and the estimate \(\hat{\mathcal{K}}\); the horizontal dot-dashed lines mark the true amplitude \(\mathcal{X}\) and the estimate \(\hat{\mathcal{X}}\). The QIFFT can be considered a perfect match to a truncated (order-2) Taylor series expansion of the window transform. QIFFT-derived methods are very attractive, as they all have the following desirable properties: (1) they can greatly reduce estimation error; (2) they are inexpensive (requiring only a few multiplies to find the refined peak frequency and amplitude estimates); (3) they can be used with _any_ window type with the notable exception of the non-zero-padded rectangular window as its main lobe width less that three bins [30]; and (4) they can be combined seamlessly with zero padding to further increase their accuracy. The property of being applicable to any window type is one of the main advantages of QIFFT-derived methods. Other popular direct parameter estimation techniques are derived from properties of the rectangular window (no windowing) [2; 8; 18; 19; 36] or a particular window (e.g., [1], Cosine window) or class of windows (e.g., [16], Rife-Vincent class I windows). A few state-of-the-art methods are fully general and can be applied to any window, including methods by [PERSON] [14] and [PERSON] [15]. General methods have the distinct advantage of being portable to any window type, even those without analytic descriptions such as the Slepian (DPSS) window [4]. For instance, [PERSON] gives examples of applying his method to Dolph-Chebyshev and Kaiser-Bessel windowed signals [14], which can only be approximated with Rife-Vincent class I windows and hence can't take advantage of the approaches of, e.g., [16].
# Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning
## 4 Proposed Method
### Feature Extractor and Prototype Classifier  
#### 4.1.1 Feature Reuse Structure  
The increase of the number of network layers will strengthen the feature extraction ability of the feature extractor, but a degradation problem may be exposed [35], i.e., the performance of a deep network is lower than that of a shallow network. To avoid the problem, we propose a feature reuse structure, which is modified by a simplified version of a DenseNet structure [36]--that is, the version without the transition layer. Since the interference signal is a one-dimensional sequence, we use a one-dimensional convolution kernel to replace the 2D convolution kernel in the original DenseNet, and the feature reuse structure is shown in Figure 2.  
Figure 1: Convolutional prototype learning architecture.  
Conv1d represents a one-dimensional convolutional layer. The \(Y\times 1\) convolution operation can be expressed as:  
\[\mathbf{X}^{out}(t,c)=\sum_{i=-\frac{Y-1}{2}}^{\frac{Y-1}{2}}\sum_{j=0}^{Ch-1}G_{i,j, c}\times\mathbf{X}^{in}(t\times s+i,j) \tag{4}\]  
where \(G_{i,j,c}\) represents the weights of the \(c\)-th convolution kernel, \(\mathbf{X}^{out}(t,c)\) and \(\mathbf{X}^{in}(t,c)\) represent the convolution output and input of the corresponding position, \(Ch\) is the channel number of \(\mathbf{X}^{out}(t,c)\) and \(s\) is the stride of the convolution kernel.  
1 dAvgPool represents a one-dimensional average pooling layer, and its operation can be expressed as:  
\[\mathbf{X}^{out}(t,c)=\frac{1}{Pn}\sum_{i=0}^{Pn-1}\mathbf{X}^{in}(t\times s+i,c) \tag{5}\]  
where \(Pn\) is the size of pooling kernel and \(s\) is the stride of the convolution kernel.  
\(\mathbf{X}_{T_{1}\times C_{1}}\) indicates the input feature map, which has \(T_{1}\) time points and \(C_{1}\) channels. \(\mathbf{X}_{T_{2}\times C_{2}}\) indicates the middle feature map, which is obtained from \(\mathbf{X}_{T_{1}\times C_{1}}\) passing through two convolution layers.  
The input feature map \(\mathbf{X}_{T_{1}\times C_{1}}\) should be aligned with \(\mathbf{X}_{T_{2}\times C_{2}}\) in the time domain by a time alignment module (TAM) when \(T_{1}\
eq T_{2}\), and then, \(\mathbf{X}_{T_{1}\times C_{1}}\) and \(\mathbf{X}_{T_{2}\times C_{2}}\) are implemented channel-wise concatenation to get the output feature map \(\mathbf{X}_{T_{2}\times(C_{1}+C_{2})}\). The TAM is composed of a one-dimensional convolution layer and one-dimensional average pooling layer, and the number of input and output channels of the TAM is the same.  
The subscript dimension of the feature map is omitted below for simplicity.  
On the one hand, because the shallow features are directly connected to the final classifier like the deep features in the feature reuse structure, supervision information can be provided by the shallow layers directly, and the middle layers are forced to learn usable features [36]. On the other hand, feature reuse can greatly reduce the number of parameters without performance deterioration.
## 4 Results and Discussion
### Comparative Classification Results  
This section includes a summary of the results for RFI classification and characterization. Figure 5 demonstrates the comparative analysis of deploying the proposed approaches for the first level classification to recognize the type of received signals. All the three approaches reach performance accuracy more than 90%, while MLP + GA with five features slightly outperforms other techniques.  
Figure 6 indicates the results of the proposed classifiers for the second classification process to distinguish four modulation types (QPSK, 8 APSK, 16 APSK and 32 APSK). As the results present, in no jamming case, the highest accuracy is achieved using MLP + GA (\(NoF=1\)) with an accuracy of 90.83%. For AMC2, MLP + PCA (\(NoF=1\)) outperforms other techniques with a precision of 89.77%. In the presence the two other jammers such as MCWI and CI, MLP + GA performs more precisely.  
Figure 7 illustrates the computation times of the presented techniques for the RFI classification. To this end, we used the \"tic toc\" function in MATLAB to calculate the consumed training time. As the results show, GA + MLP is computationally more expensive, and as a result, the computation time is comparatively longer, while MLP + PCA is the most efficient one.  
Figure 5: Comparative results of the RFI classification results with considering BS = 1 and HLN = 30 MLP (with all features), MLP + GA (\(NoF=5\)) and MLP + PCA (\(NoF=5\)).  
Figure 6: Comparative results of the RFI classification results with considering BS = 1 and HLN = 30 MLP (with all features), MLP + GA (\(NoF=5\)) and MLP + PCA (\(NoF=5\)).