esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 UAN Generic Architecture
### Sensor and Actuator Nodes  
Sensor and actuator nodes are typically simple computing devices with sensing and/or actuation capabilities that exhibit significant constraints in terms of memory and processing power. Because in Smart Cities sensing nodes generally outnumber actuator nodes, in this paper we use the term _sensor nodes_ for the sake of simplicity. For deployment flexibility and cost efficiency, sensor nodes are usually provided with wireless communication technologies. In urban scenarios, sensor nodes may not rely on mains power availability in their intended location, and therefore they commonly need to use batteries as their energy source, although a tendency towards exploiting energy harvesting solutions is gaining popularity. In order to allow multiyear lifetime for the sensor nodes without mains power, they must be in sleep mode by default and operate under low duty cycle regimes (either by waking up periodically or upon event detection).
# Nutritional Analysis of Red-Purple and White-Fleshed Pitaya (_Hylocereus_) Species
## 2 Materials and Methods
### Phenolic Compounds  
For the extraction and hydrolysis procedure of phenolics, 0.5 g of homogenized samples were used boiled using a reflex unit for 1 h [49]. After it was cooled, the mixture was filtered and made up to 10 mL with the extraction solvent (acetone:water; 3:1). These samples were directly used for HPLC analyses. The liquid chromatographic apparatus (HPLC, Shimadzu LC-20A, Tokyo, Japan) consisted of an in-line degasser, pump, and controller coupled to a photodiode array detector equipped with an automatic injector (20 \(\upmu\)L injection volume) interfaced to a PC running ChemStation chromatography manager software. Separations were performed on a 150 mm \(\times\) 4.6 mm i.d., 5 \(\upmu\)m, reverse-phase Nucleosil C18 analytical column (Supelco, Bellefonte, PA, USA) operating at room temperature with a flow rate of 1 mL /min. Detection was carried out with a sensitivity of 0.1 a.u.f.s. between the wavelengths of 280 and 360 nm. Elution was affected using a nonlinear gradient of the solvent mixture 2.5% HCOOH in water (solvent A) and 2.5% HCOOH in acetonitrile (solvent B). The composition of B was increased from 5 to 13% in 15 min, increased to 15% in 5 min and to 30% in a further 5 min and held for 3 min, increased to 45% in 4 min and held for 3 min, increased to 90% in 5 min and held for 5 min, and then returned to initial conditions in 5 min.  
Gallic acid, myriectin, caffeic acid, \(p\)-coumaric acid, ellagic acid, quercetin and kaempfer (Sigma-Aldrich), were used as phenolic standards. Gallic acid, caffeic acid, \(p\)-coumaric acid, ellagic acid, myriectin, quercetin and kaempfer were dissolved in methanol at a concentration of 1000 ppm samples were directly injected into the reverse phase chromatography column. Five dilute solutions from these stock solutions were used for calibration curves and calibration curves were obtained for caffeic acid y = 49,033.3x + 0, for ellagic acid y = 31,923x + 0, for quercetin y = 21,275x + 0 and for kaempfer y = 27,358.73x + 0, respectively. Recoveries were measured by extracting the recovered amounts of pure substances added to frozen pitaya samples before the experiment. Three replicates from each sample were used for HPLC analyses. All the samples and standards were injected three times.
# Improvement of Network Flow Using Multi-Commodity Flow Problem
## 2 Previous Research  
Many modified algorithms based on [PERSON]'s algorithm have been proposed in studies considering shortest paths. [PERSON] et al. reduced the computational complexity by reducing the number of iterations [12]. [PERSON] et al. improved the algorithm so that the maximum load path can be found [13]. However, these proposals do not achieve load balancing.  
Previous studies have attempted to achieve load balancing. HiQoS [14] computes multiple paths for all pairs using [PERSON]'s algorithm for early recovery from link loss. However, this is not designed for large traffic flows. The distributed flow-by-flow fair routing (DFFR) algorithm [15] routes so that each switch has an equal load on all equal-cost paths. DFFR does this without rerouting or splitting flows, thus preserving TCP performance. However, DFFR assumes that all links are homogeneous and is not optimal in network topologies where this is not the case. Sorted-GFF [16] uses a fixed threshold set for the bandwidth of the link and recalculates the path if that threshold is exceeded. However, a fixed threshold cannot handle variability, such as differences in device performance. Moreover, when large traffic flows exceed the threshold value, the path is recalculated frequently, which places a heavy burden on the controller. When the threshold is increased, recalculation can be suppressed, but the load on the link cannot be detected. Therefore, a large amount of traffic cannot be assumed to flow. The proposed method uses a multi-product flow problem to compute paths using the entire network, which improves performance and allows the system to handle large amounts of traffic.
## 2 Materials and Methods
### Hyperspectral Imaging (HSI) Systems  
To acquire HSI data, we used three modalities in the reflectance mode: fluorescence (420-730 nm at 365 nm excitation), VNIR (400-1000 nm), and SWIR (900-1700 nm). The method of acquiring fluorescence and VNIR hyperspectral data from the same imaging system is described as follows. For VNIR and fluorescence data, a line-scan camera creates an image of size, 310 \(\times\) 502 pixels (MegaLuca, Andor Technology, Belfast, Northern Ireland), a hyperspectral imager with CMOS focal-plane array for VNIR wavelengths (VNIR Hyperspec, Headwall Photonics, Fitchburg, MA, USA) and linear motorized sample carrier (XSlide, Velmax, Bloomfield, NY, USA). The fluorescence HSI system comprises a UV-A (365 nm) LED lightning system (in-house product) for radiation emission (Figure 1a). The SWIR HSI system consists of an imaging spectrograph (HyperspecTM NIR, Headwall Photonics, MA, USA) covering a spectral range of 900-1700 nm (spectral resolution of 3.2 nm); an InGaAs focal-plane-array camera (640 \(\times\) 512 pixels, Xeva-1.7-320, Xenics Infrared Solutions, Inc.; Leuven, Belgium), and a 25-mm zoom lens (OB-SWIR25/2, Optec SpA.; Parabiago, Italy). Figure 1b presents the images obtained through the HSI system.
# A Security-Oriented Data-Sharing Scheme Based on Blockchain
## 3 A Security-Oriented Data-Sharing Scheme Based on Blockchain  
### Formal Model  
In the context of data sharing, consider a scenario involving distributed intrusion detection. Within a network, there exist multiple entities (hosts and security devices), each of which generates its own security data (e.g., alert logs). Intrusion detection systems require the utilization of these data to train a global intrusion detection model. It is important to note that these entities are not inclined to share their data. However, the peculiarity of the network topology necessitates that some entities must transmit data through other entities to reach the central server of the intrusion detection system. This scenario has motivated the data-sharing scheme proposed in this paper.  
Based on this specific scenario, the problem is formally defined as follows:  
Problem statement: Suppose there are a set \(U=\{u_{1},u_{2},\ldots,u_{n}\}\) comprising \(n\) users, where each user can be both a data owner and a data requester (transmitter). The data set owned by user \(u_{i}\) is denoted as \(D_{i}=\left\{d_{i}^{1},d_{i}^{2},\ldots,d_{i}^{m}\right\}\). Additionally, there exists a central server \(C\), which serves as the final data consumer. The global intrusion detection model in the example provided above is trained by \(C\). A data request \(R=<u_{i},u_{j},d_{i}^{m}>\), indicates that data requester \(u_{j}\) is requesting data \(d_{i}^{m}\) from the data owner \(u_{i}\). Data sharing is the process of fulfilling such requests \(R\). Moreover, it should be noted that once the data sharing is completed, \(u_{i}\) will no longer have ownership of the data \(d_{i}^{m}\), while \(u_{j}\) acquires ownership of \(d_{i}^{m}\); i.e.,  
\[D^{\prime}{}_{i}=\left\{d_{i}^{1},d_{i}^{2},\ldots,d_{i}^{m-1}\right\} \tag{1}\]  
\[D^{\prime}{}_{j}=\left\{d_{j}^{1},d_{j}^{2},\ldots,d_{j}^{k},d_{j}^{k+1}\right\} \tag{2}\]  
where \(d_{j}^{k+1}=d_{i}^  
then mint an NFT with the digest of the shared data via the blockchain. The data demander can obtain the data storage information by acquiring the NFT, thereby gaining access to the data shared by the data owner. In the process, some critical information is encrypted with the identity key of the data owner and data demander. With the proposed scheme, we have designed three important components: a separated storage architecture, a lightweight encryption solution, and a tracking mechanism. Those components are discussed in detail in the subsequent sections.
# Flocking of Multi-Agent System with Nonlinear Dynamics via Distributed Event-Triggered Control
## 2 Preliminaries and Problems Formulation
### System Model  
Consider a group of \(N\) agents moving in \(n\) dimensional space, the dynamics of agent \(i\) is described by  
\[\left\{\begin{array}{l}\dot{x}_{i}(t)=v_{i}(t),\\ \dot{v}_{i}(t)=f(v_{i}(t))+u_{i}(t),\quad i=1,2,\cdots,N,\end{array}\right. \tag{1}\]  
where \(x_{i}\in\mathbb{R}^{n}\), \(v_{i}\in\mathbb{R}^{n}\) and \(u_{i}\in\mathbb{R}^{n}\) are the position, the velocity and the control input of the \(i^{th}\) agent, respectively. The function \(f(v_{i}(t))\in\mathbb{R}^{n}\) is the nonlinear dynamics of agent \(i\), which is Lipschitz continuous.  
**Assumption 1**.: _The nonlinear function \(f(z)\) is Lipschitz in \(z\) over the time, which means that there exists a positive constant \(\rho\), such that \(f(z)\) satisfies_  
\[\left\|f(z_{1})-f(z_{2})\right\|\leq\rho\left\|z_{1}-z_{2}\right\|,\quad \forall z_{1},z_{2}\in\mathbb{R}^{n}. \tag{2}\]  
Supposed that all the agents have the same sensing radius \(r>0\), then the neighboring set of agent \(i\) is defined as \(\mathcal{N}_{i}(t)=\left\{j\right|\left\|x_{i}(t)-x_{j}(t)\right\|<r,j=1,2, \ldots,N,j\
eq i\right\}\). A minimum allowable distance \(r_{0}\) (we also called collision distance) is considered in the model since that the size of agents cannot be ignored usually. Let \(\tau\in(0,r)\) be a given constant. Then, the graph \(\mathcal{G}(t)\) is a dynamic undirected graph with a time-varying set of links \(\mathcal{E}(t)\) such that  
(i) Initial links are generated by \(\mathcal{E}(0)=\left\{(i,j)|r_{0}<\left\|x_{i}(0)-x_{j}(0)\right\|<r\right\}\);  
(ii) If \((i,j)\
otin\mathcal{E}(0)\) and \(\left\|x_{i}(t)-x_{j}(t)\right\|\leq r-\tau\), then \((i,j)\) is a new link to be added to \(\mathcal{E}(t)\). It was called hysteresis effect and \(\tau\) is the hysteresis distance, which is crucial in preserving connectivity of the network [26; 37];  
(iii) If \(\left\|x_{i}(t)-x_{j}(t)\right\|\geq r\), then \((i,j)\
otin\mathcal{E}(t)\).  
The neighboring set of agent \(i\) is divided into four regions, named collision region, separation region, alignment region and attraction region (see Figure 1), in which \(r>r-\tau>\bar{d}\geq\underline{d}>r_{0}\). If \(\underline{d}\leq\left\|x_{ij}(t)\right\|\leq\bar{d}\), it is said that agent \(i\) and agent \(j\) are in desired distance.
# Improved ISR for IEEE 802.11 ah Nonlinearity Compensation via Adjustable Constellation Borders
## 1 Introduction  
IEEE 802.11 ah [1], also known as Wi-Fi HaLow, is a low-power wide-area (LPWA) version of the IEEE 802.11-2007 wireless networking standard [2]. It supports various Internet of Things (IoT) services based on low-power connectivity, such as sensor networks and wearables [3, 4]. Although the usage of license-free bands and well-established infrastructures makes 802.11 ah more competitive than other LPWA techniques, its orthogonal frequency division multiplexing (OFDM)-based architecture still challenges the necessity for low power consumption due to the high peak-to-average power ratio (PAPR) issue over power amplifiers (PAs) [5]. Therefore, the improvement in power efficiency has become a critical issue for 802.11 ah [6, 7, 8, 9, 10, 11].  
The receiver-side remedies for PA nonlinearity in OFDM systems have been widely investigated in the literature [12, 13, 14, 15, 16]; however, the increase in processing time makes such iterative methods infeasible due to the high data rate requirement of mobile broadband. Nevertheless, these techniques may come in handy for LPWA uplinks to support machine-type communications, which need to leave all possible complicated operations at base stations for power saving [17, 18]. Based on this framework, iterative subcarrier regularization (ISR) [19] has recently been introduced for 802.11 ah power-efficient uplink. ISR can compensate for the PA-distorted signals in two ways: regularizing subcarriers iteratively with deterministic prior knowledge only (ISR scheme I, ISR-I) or with both deterministic and probabilistic prior knowledge (ISR scheme II, ISR-II). In ([19], Figure 12), ISR-II outperforms ISR-I in terms of bit error rate (BER) and convergence under a fair channel; however, only ISR-I can further improve the BER by consuming more iterations when the signal-to-noise ratio (SNR) consistently increases.  
In this paper, we propose an improved ISR to overcome the saturation of BER improvement and concurrently retain its fast convergence through adjustable constellation borders. The proposed scheme shrinks the decision region of the modulation for ISR only, and, hence, computational complexity is not increased. By using the experimentally optimized hard-decision range, the 802.11 ah-based simulation results demonstrate that the proposed scheme can significantly enhance the performance of BER and convergence compared to that of the original ISR-I or ISR-II.
# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
## 3 Proposed Methodology: DCBA Fault Diagnosis
### Integrated Diagnosis Model
#### 3.2.3 BLSTM Layer  
A Bidirectional Long Short-Term Memory (BLSTM) network consists of two independent LSTM networks: one for processing the forward input sequence and the other for processing the backward input sequence. Each LSTM network internally includes a forget gate, an input gate, and an output gate. The forget gate determines which information  
Figure 5: Convolution operation with multiple convolution kernels.  
needs to be forgotten, the input gate determines which information needs to be retained, and the output gate decides which information needs to be outputted. The specific computational processes of these three gates are shown in Figure 6.  
In the forget gate, the information that needs to be discarded can be identified through the following process:  
\[F_{t}=\sigma\Big{(}X_{t}W_{xf}+H_{t-1}W_{hf}+b_{f}\Big{)}. \tag{4}\]  
Here, \(X_{t}\) represents the data at time step \(t\), \(H_{t-1}\) represents the output at the previous time step \(t-1\), \(W_{xf}\) and \(W_{hf}\) are the weight matrices associated with \(X_{t}\) and \(H_{t-1}\) respectively, and \(b_{f}\) is the corresponding bias vector.  
In the input gate, the information that needs to be input can be determined as follows:  
\[I_{t}=\sigma(X_{t}W_{xi}+H_{t-1}W_{hi}+b_{i}), \tag{5}\]  
and  
\[\widetilde{C}_{t}=tanh(X_{t}W_{xc}+H_{t-1}W_{hc}+b_{c}). \tag{6}\]  
Here, \(W_{xi}\), \(W_{hi}\) and \(W_{xc}\), \(W_{hc}\) are the weight matrices associated with \(I_{t}\) and \(\widetilde{C}_{t}\), respectively; \(b_{i}\) and \(b_{c}\) is the corresponding bias vector.  
In the output gate, the information that needs to be output can be determined as follows:  
\[O_{t}=\sigma(X_{t}W_{xo}+H_{t-1}W_{ho}+b_{o}). \tag{7}\]  
Here, \(W_{xo}\) and \(W_{ho}\) are the weight matrices associated with \(O_{t}\), while \(b_{o}\) is the corresponding bias vector.  
Finally, using the state value \(C_{t-1}\) at time step \(t-1\), the output \(H_{t}\) and the cell state \(C_{t}\) of the unit can be obtained as follows:  
\[C_{t}=F_{t}\odot C_{t-1}+I_{t}\odot C_{t}, \tag{8}\]  
and  
\[H_{t}=O_{t}\odot tanh(C_{t}). \tag{9}\]  
Here, \(\odot\) denotes the element-wise multiplication.  
Figure 6: Interactive layers within the repeating module of the BLSTM.
# Optimal Thresholding for Multi-Window Computed Tomography (CT) to Predict Lung Cancer
## 3 Experiments and Results
### Performance Evaluation and Comparison
Moreover, it works for both lung window scans and mediastinal window scans and achieves better accuracy. The proposed method was implemented in Python and its source code has been publicly released for research purposes at (accessed on 6 June 2023). [[https://github.com/Mufah-cloud/lung-cancer-detection](https://github.com/Mufah-cloud/lung-cancer-detection)]([https://github.com/Mufah-cloud/lung-cancer-detection](https://github.com/Mufah-cloud/lung-cancer-detection)). All experiments were performed on a laptop running macOS Big Sur with a 2.4 GHz Dual-Core Intel Core i7 and 16 GB RAM. The execution time of the proposed method was computed on the entire dataset and averaged to analyze the computational efficiency. Our algorithm takes 6.25 seconds on average to process one CT scan, which shows that the method is computationally efficient. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Recall** & **Precision** & **Dataset** \\ \hline [PERSON] [43] & 0.96 & — & — & — \\ [PERSON] [44] & — & 0.94 & — & LIDC-IDRI \\ [PERSON] [19] & 0.92 & — & — & — \\ [PERSON] [46] & 0.84 & 0.82 & 0.86 & — \\ [PERSON] [45] & — & 0.86 & — & LUNA16 \\ [PERSON] [31] & 0.86 & — & — & LUNA16 \\ [PERSON] [47] & — & 0.82 & 0.89 & LUNA16 \\ Proposed & 0.94 & 0.97 & 0.92 & LUNA16 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Nodule detection performance comparison of the proposed and the compared methods in terms of precision, recall, and accuracy. The dataset used in evaluation is listed under ’Dataset’. ’\(\rightarrow\)’ indicates the unavailability of the score.
## 2 Modeling of PMSG
### Torque Limit Control of PMSG  
Figure 4a shows the control strategy consisting of three sequential steps, indicated by the red line. The first step is the additional output activation step (A-B-C), the second step is the deloaded operation step (C-D-E), and the final step is the recovery rotor speed step (E-F-A). In step A-B-C, the operating point is larger than the mechanical power \(P_{m}\); thus, the rotor speed decreases during operation, which results in the extraction of rotational kinetic energy from the drivetrain. Therefore, it is necessary to restore the rotor speed to the MPPT operating point. For this recovery, the operating point is lowered below \(P_{m}\) in  
Figure 3: Torque–rotor speed curve on the MPPT.  
Figure 2: Drivetrain based on the two-mass shaft model.  
step C-D-E. When the operating point is lower than \(P_{m}\), D-E becomes the acceleration step, and the rotor speed increases. As the rotor speed increases, it meets the MPPT curve (point F) and performs the MPPT operation.  
The wind turbine generator based on the PMSG controls torque via a machine-side converter; the torque-rotor speed curve for a simple control strategy is shown in Figure 4b. The electrical torque will be at its maximum value during the deceleration period, that is, the step in which rotating energy is released. In the acceleration period, the electrical torque will decrease to below point D. The electrical torque necessary for the rotor speed to recover is shown in Figure 4a. This must be lower than the OD slope, that is, the torque at point D, and for constant torque control, it must be lower than the OA slope. Therefore, the electrical torque must be set to a value lower than that of point A, as shown in Figure 4b. The rotor speed can decelerate to \(\omega_{min}\), but the greater the deceleration, the longer the recovery period. Therefore, in this study, the deceleration period was set to 3 s.
## 2 Extraction of Spatial Domain Features from Digital Print Images
### Printing Image Structural Features Based on the SSIM  
Structure refers to the layout and organization of textures, details, and patterns within an image. The quality of structure significantly influences the visual quality of the image and the overall perception of printed materials. Based on the premise that the CIELAB color  
Figure 7: The impact of different values of \(C_{2}\) on the similarity of data gradients.  
space is well suited for extracting structural information from the scene, [PERSON] et al. [14] introduced a novel concept for measuring image quality known as the SSIM. This concept defines separate functions to measure the luminance, contrast, and structural similarity between two images, \(I_{o}\) and \(I_{s}\), in the L channel. Their similarity is calculated as the product of the luminance similarity \(l(I_{o},\,I_{s})\), the contrast similarity \(c(I_{o},I_{s})\), and the structural similarity \(s(I_{o},I_{s})\), resulting in an overall measure of image similarity:  
\[S_{B}(I_{o},\,I_{s})=l(I_{o},I_{s})\times c(I_{o},\,I_{s})\times s(I_{o},\,I_{s }), \tag{13}\]  
where:  
\[l(I_{o},\,I_{s})=\frac{2\mu_{L_{o}}\mu_{L_{s}}+C_{3}}{(\mu_{L_{o}})^{2}+(\mu_{ L_{o}})^{2}+C_{3}}, \tag{14}\]  
\[c(I_{o},\,I_{s})=\frac{2\delta_{L_{o}}\delta_{L_{s}}+C_{4}}{(\delta_{L_{o}})^{ 2}+(\delta_{L_{s}})^{2}+C_{4}}, \tag{15}\]  
\[s(I_{o},\,I_{s})=\frac{\delta_{L_{o}L_{s}}+C_{5}}{\delta_{L_{o}}\delta_{L_{s}} +C_{5}}, \tag{16}\]  
Let the parameters \(C_{4}=2C_{5}\), and \(S_{B}(I_{o},\,I_{s})\) be expressed as follows:  
\[S_{B}(I_{o},\,I_{s})=\frac{2\mu_{L_{o}}\mu_{L_{s}}+C_{3}}{(\mu_{L_{o}})^{2}+( \mu_{L_{s}})^{2}+C_{3}}.\frac{2\delta_{L_{o}L_{s}}+C_{4}}{(\delta_{L_{o}})^{2}+ (\delta_{L_{s}})^{2}+C_{4}}, \tag{17}\]  
The equations demonstrate that \(\mu_{L_{o}}\) and \(\mu_{L_{s}}\) represent the luminance features using the mean value. Taking \(L_{o}\) as an example, the calculation is as follows:  
\[\mu_{L_{o}}=\frac{1}{N}{\sum\limits_{i=1}^{N}{L_{o}(i)}}, \tag{18}\]  
Similarly, the calculation for \(\mu_{L_{s}}\) is as follows.  
\(\delta_{L_{o}}\) and \(\delta_{L_{s}}\) represent the contrast feature using normalized variance. For instance, the calculation for \(L_{o}\) is as follows:  
\[\delta_{L_{o}}=\sqrt{\frac{1}{N-1}{\sum\limits_{i=1}^{N}{(L_{o}(i)-\mu_{L_{o} })}^{2}}}, \tag{19}\]  
Similarly, the calculation for \(\delta_{L_{s}}\) is as follows.  
\(\delta_{L_{o}L_{s}}\) represents the covariance used to characterize the structural feature, which is calculated as follows:  
\[\delta_{L_{o}L_{s}}=\frac{1}{N-1}{\sum\limits_{i=1}^{N}{(L_{o}(i)-\mu_{L_{o} })(L_{s}(i)-\mu_{L_{s}})}}, \tag{20}\]  
\(C_{3}={(k_{1}L^{\prime})}^{2}\) and \(C_{4}={(k_{2}L^{\prime})}^{2}\) are constants employed to ensure stability. Here, \(L^{\prime}\) denotes the number of grayscale levels in the image. For an 8-bit grayscale image, \(L^{\prime}=255\), with \(k_{1}=0.01\), and \(k_{2}=0.03\). Substituting these values, we obtain \(C_{3}=6.5025\) and \(C_{4}=58.5225\).