esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 6 Recent Advancements in Clustering Optimization Algorithms
### Hybrid Techniques
#### 6.3.1 Hybrid Meta-Heuristics
The improved Cuckoo Search (CS) algorithm was developed to address the issue of WSN clustering; an improved Harmony Search (HS) was also incorporated into the protocol to address the routing problem. The improved HS served in the transfer of the aggregated data from the CH to the BS. The role of the improved CS was to establish the optimal set of CHs among the normal SNs, while the HS found the optimal tree that would connect the CHs to the BS. A problem with this method is that it did not balance the load of the SNs among the CHs. Hence, the CS-HS hybrid was evaluated based on the average power loss, path of inactive nodes, path of active nodes, and network lifespan. This algorithm balanced network lifespan with energy usage. * Hybrid GGWSO: Hybrid model for security-aware cluster head selection The hybrid GGWSO [87] was presented as a framework for improved data collection based on relevant data. The status of the gathered data is updated using the anchoring nodes while the overall gathering values are minimized to improve link capacity, capability, and data flow conservation. This protocol aimed to improve network lifetime by ensuring that the algorithm addressed some of the salient issues related to energy, delivery delay, security, and distance. The equalization procedure was used to partition the entire dataset into smaller components. The performance of the GGWSO was compared with that of various traditional methods such as fractional ABC, GWO-based CHS, GSO, and ABC. * DESA: Lifetime improvement using hybrid differential evolution in WSNs The DESA protocol was presented by [88] as an integrated SA-DE protocol for CH selection during WSN clustering. This protocol aimed to prevent the earlier failure of CHs to achieve an extended network lifetime. The four phases of the proposed DESA are population initialization, application of the mutation operation, crossover operation, and selection of the next generation. After the population is randomly initialized, the opposite point technique generates another population set known as the opposite population. Then, number of fittest individuals are selected from the opposite population set for the current generation. The selected mutation strategy is determined by the value of the chosen random number; if the selected random number is greater than the threshold value, DE/rand/1 will be performed. If not, DE/current-to-best/1 will be performed. The new approach outperformed LEACH, HSA, MHSA, and DE by 70%, 50%, 40%, and 60%, respectively.
## 2 Methods
### Experimental Procedures  
After the baseline assessment, participants were randomized in the EG and CG as follows: Each of the 27 enrolled and eligible participants received a progressive number. Then, a random number list was generated using online software ([[https://www.random.org/sequences/](https://www.random.org/sequences/)]([https://www.random.org/sequences/](https://www.random.org/sequences/)) (accessed on 2 October 2023) Dublin, Ireland), which contained numbers from 1 to 27 with no repetition. The list was used to rearrange the participants in a random order and allocate them into different groups in blocks of two participants per group, following the order of CG and EG. After randomization, the homogeneity of the two groups was assessed in terms of all the primary and secondary outcomes. A familiarization session was performed one week before the intervention in order to allow participants to learn the correct exercise technique. The training protocols were performed two days per week on nonconsecutive days, with 72 h of rest between them. Both groups performed 10-15 min of a moderate-intensity warm-up, including skips, push-ups, squats, and crunches and mobility exercises for hips, shoulders, arms, and trunk, such as. \begin{table}
\begin{tabular}{c c} \hline
**Variable** & **Mean \(\pm\) SD** \\ \hline
**Experimental group (\(n\) = 13)** & \\ \hline Age & 56.46 \(\pm\) 2.14 \\ \hline Training experience (years) & 12.85 \(\pm\) 2.54 \\ \hline Training volume (h/week) & 3.40 \(\pm\) 0.44 \\ \hline Experience with resistance training (years) & 15.24 \(\pm\) 5.45 \\ \hline
**Control group (\(n\) = 14)** & \\ \hline Age & 55.14 \(\pm\) 3.06 \\ \hline Training experience (years) & 13.21 \(\pm\) 2.33 \\ \hline Training volume (h/week) & 3.35 \(\pm\) 0.47 \\ \hline Experience with resistance training (years) & 14.91 \(\pm\) 5.82 \\ \hline \end{tabular}
\end{table} 
Table 1: Sample characteristics. The EG program consisted of 3 sets of 6-12 repetitions of 7 exercises, all performed with the device positioned behind the athlete (see Table 2). The Flyconpower conical machine was employed in this study (Flyconpower SRL, Cuneo, Italy). The EG participants were asked to reproduce the proposed technical movements; in particular, they were encouraged to execute the concentric phase as quickly as possible, resist the inertial force during the initial third of the eccentric action, and conclude the movement at the maximum range of motion with maximal effort. The requirement for maximum effort, maximum execution speed, and adherence to the joint angles applied during the concentric phase of the movement was in accordance with procedures used in a recent previous study [30]. It was recommended to the athletes to delay the braking action during the eccentric phase. The method used to vary the intensity involved in increasing the speed of the cord release, which was managed by the athlete based on their level of fatigue and fitness. Moreover, the number of overloads was incrementally raised while the volume was reduced. The EG protocol exercises are shown in Figure 1.
## 2 Materials and Methods
### Research Area and Data Source
#### 2.1.2 Image Data Set and Preprocessing  
Taking Sentinel-2 and MODIS satellite image data stored on the GEE cloud platform as a data source, this study extracts the walnut planting area in Ganquan Town and monitors its growth. Equipped with an MSI multispectral imager, Sentinel-2 covers 13 wavebands, including 2 satellites A and B, with a reentry cycle of 5 days and a spatial resolution of 10 m [24]. The higher spatial resolution reduces the impact of mixed pixels on the extraction of the walnut planting area. MODIS MO9 GQ is a product of surface reflectance, with a reentry cycle of 1 day and a spatial resolution of 250 m; it has high temporal resolution and can continuously track the growth of walnuts [25].  
To improve the availability of the images, the image's time range was selected with respect to the walnut phenology in Ganquan Town, as shown in Table 1. The QA60 band cloud-bearing information of Sentinel-2 images was marked for de-clouding and then filtered to obtain images with less than 30% cloud content. Image pre-processing operations, such as image de-clouding, cropping, and fusion, were performed in GEE. After the de-clouding process was completed, the images were supplemented with images from adjacent years, a time window was set, the maximum value method was used to synthesize the images, and the images were mosaicked and cropped according to the extent of the study area; specific image information is shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Month** & **Growth Period of Walnuts** \\ \hline Between April and June & Fruit development period \\ July & Hardcore period \\ Aug & Oil conversion period \\ Mid-September & Mature period \\ \hline \end{tabular}
\end{table}
Table 1: The walnut growth cycle in Ganquan Town.  
Figure 1: Topographic map of the research area.
## 3 Results and Discussion
### Coral Health Predictions from Field Data
Because this degree of confidence was low, we carried out a similar desirability analysis with the top-performing neural network for CHD-field\(\blacktriangleright\)CHD-field(e) (Table 5), and the top 10 most influential proteins from the associated dependent resampled inputs analysis are shown in Figure 5 (as raw data & not _z_-scores). In this instance, the conditions portrayed in the plot would reflect a bleaching-resistant coral at 99% confidence. The most influential protein was, again, the activoxin, with high concentrations of this protein being associated with bleaching-resistant corals (desirable mean of \(\sim\)1.7=\(\sim\)2-fold higher than that of an average coral). Bleaching-resistant corals would also demonstrate higher-than-average concentrations of protein OFAVBQ_DN220777_c1_g3_i5, a titin-like protein with a wide variety of hypothesized functions. In contrast, a coral with a 99% chance of being bleaching-resistant would express low levels of OFAVBQ_DN220189_c1_g1_i3 (50% less than an average coral), an uncharacterized protein with a putative DNA-binding domain (Table 4). Increasing efforts by biologists to elucidate key facets of anthozoan-dinoflagellate endosymbiosises [20] means that we may soon better understand the role of these (& other) proteins in coral thermo-adaptation, as well as which of the underlying genes are under selection [21; 22]. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model Input Type** & \multicolumn{5}{c}{**Field Corals Sampled across Bleaching Event in 2019 (Figure 1)**} \\
**Model Name** & **CHD-Field-\(\Phi\)** & **CHD-Field-\(\Phi\)** & **CHD-Field-\(\Phi\)** & **CHD-Field-\(\Phi\)** & **CHD-Field-\(\Phi\)** & **CHD-Field-\(\Phi\)** \\
**CHD-Field(a)** & **CHD-Field(b)** & **CHD-Field(b)** & **CHD-Field(c)** & **CHD-Field-\(\Phi\)** & **CHD-Field-\(\Phi\)** \\ \hline Training \# & 19 & 18 & 11 & 12 & 15 \\ \hline Training data months & Jul, Aug, Dec. & Jul. and Dec. & Jul. and Dec. & Jul. & Jul. and Aug. \\ \hline Validation \# & 8 & 12 & 4 & 12 & 6 \\ \hline Validation data months & Jul, Aug, Dec. & Aug. & Jul. and Dec. & Aug. & Jul. and Aug. \\ \hline Test sample \# & 3 & NA & 3 & Not applicable (NA) & 3 \\ \hline Test data months & Jul, Aug, Dec. & NA & Jul. and Dec. & NA & Jul. and Aug. \\ \hline Training proteins & 16 & 16 & 16 & 28 & 28 \\ \hline Model type \#1 (accuracy [\%]) & SVM: 90\%a  & Naive Bayes: 94\% & Gen-reg lasso: 95\% & NN: 95 \(\pm\) 6\% (n = 20) & SVM: 94\% \\ \hline Model type \#2 (accuracy [\%]) & NN: 84 \(\pm\) 95 (n = 40) a  & NN: 87 \(\pm\) 13\% (n = 40) a  & NN: 87 \(\pm\) 14\% (n = 20) & Naive Bayes: 92\% & NA \\ \hline Most important proteins(s) & OFAVBQ\_DN221258, & OFAVBQ\_DN222422, c2 & OFAVBQ\_DN222422, c2 & OFAVBQ\_DN186152, & OFAVBQ\_DN222422, c2 [FOOTNOTE:a]Footnote a: Included all 30 samples in calculation of misclassification rate. \({}^{\rm 3}\) excluded from Figure 5 since it was not featured in every simulation.
# Evaluation and Comparison of Spatial Clustering for Solar Irradiance Time Series
## 2 Solar Irradiation Time-Series Analysis
### Data Quality Control
#### 2.3.2 Validation of the SIAR GHI Time Series  
Even if the data series were verified by the SIAR's quality check, it seems that it is useful to realize a new checking by using the visual procedure proposed by Reference [45] and completed by some tests on the kt values. The solar irradiance data that are used here are the average horizontal global solar irradiance for 30 min on the period 1 January 2017 to 31 December 2020. For each station, the following steps are realized:  
Visual validation: Measured data (SIAR) are plotted in a 2D graph (Figure 6); in the same graph are drawn the theoretical sunrise (red color) and sunset hour (green color). This  
Figure 5: Clear-sky modeling corrected with 20 min offset in A Capela Station (C01) during 2020.  
analysis allows for a visual identification of errors over time to visualize missing values, issues in time reference, and abnormal values. The two meteorological stations taken as an example passed the test, although an asymmetry is noted between the measurements above the green dotted line and below the red dotted line. Moreover, the measure begins before theoretical sunrise. This may indicate a little measurement error or sensor calibration error. This test allows, by its nature, to point out only the big measurement errors and is extremely time-consuming but necessary if one wants to use clean data. The other visual tests performed are not detailed here, but interested readers can refer to Reference [45].  
In what follows, a filtering of the time series is systematically performed to eliminate the night hours. GHI and GHIcs irradiance values are removed for the hours of the day related to a solar angle less than 5 degrees.  
Over-irradiance test: This test is based on the exploitation of kt histograms, as seen in Figure 7. Although easily automated, it is interesting to look at the shape of the histogram. The experienced user will be able to easily point out measurement errors and stations to remove from the study. Another way is to quantify the number of occurrences where kt is greater than 1. We have arbitrarily chosen a limit of 30%. Thus, if for a series, there is more than 30% of over-irradiance data (which is rare or impossible for 30 min time granularity), we consider that it is preferable to not use the station.
# A Dynamic Multi-Layer Steganography Approach Based on Arabic Letters' Diacritics and Image Layers
## 3 The Proposed Approach
### The Metrics of Evaluating Steganography
#### 4.2.2 Capacity  
The maximum amount of data that can be hidden within a cover represents another crucial aspect of steganography [47]. In the case of the Arabic language, there are two proposed scenarios: the Hamza and diacritic scenarios. The capacity for the Hamza scenario is limited as it relies on the natural distribution of Hamzas within Arabic text. While Alif is the most common letter, it only accounts for up to 10% of the text, resulting in a relatively small amount of information that can be stored, which is also dependent on the size of the cover text. On the other hand, the diacritic scenario offers greater capacity, as one is able  
Figure 16: Correlation value vs. message length.  
to hide data in all letters of text by decorating letters with diacritics, which represents the maximum capacity for text-based methods. In terms of images, techniques based on the LSB principle can typically conceal up to 12.5% of the image size (number of pixels), which is adequate considering the considerable size of digital images.  
The following equations represent the relationship between the maximum number of bits that can be embedded and the corresponding factors in each scenario. We represent the maximum number of bits that can be hidden as \"\(M\)\".  
**Diacritic Scenario:** The number of diacritics that can be used in the text is represented as \"D\". The equation for Scenario 1 can be written as:  
\[M=D\]  
**Hamza Scenario**: The number of Hamzas used in the text is represented as \"\(H\)\". The equation for Scenario 2 can be written as:  
\[M=H\]  
**Hybrid Scenario:** The total maximum number of bits that can be embedded in the hybrid scenario is equal to the sum of the maximum number of bits for Scenario 1 and Scenario 2. The equation for the hybrid scenario can be written as:  
\[M=D+H\]
## 5 Design and Verification of Vertical Steel Damper
### Evaluations of Energy Dissipation Performance
Considering that Type 316 SS material used in this study has a total elongation of 40% or more, it is expected that sufficient plastic strain margin can secure the structural integrity of the steel damper without fracture during the strong vertical earthquakes. Figure 15 presents the analysis results of hysteretic behavior for five cycles, which is the vertical displacement-reaction force relationship of the steel damper. As shown in the figure, the effective stiffness increases in order of SD1, SD2, and SD3, and energy dissipation area increases inversely, and it can be seen that all shapes of steel dampers exhibit hysteretic behavior in which all isotropic hardening properties are quickly stabilized after about three cycles. Figure 13: Analysis results of equivalent plastic strain distributions and deformation shapes of steel damper, SD2 during \(\pm\)30 mm stroke: (**a**) After 1/4 cycle; (**b**) after 1(3/4) cycles; (**c**) after 3(1/4); (**d**) after 5 cycles. Table 11 presents the results of calculating the energy dissipation performance of the steel dampers from the following relational expression [16], assuming idealized bilinear hysteretic behavior extracted from the third cycle. \[\zeta_{d}=W/\left(2\pi K_{eff}D^{2}\right) \tag{7}\]  
In the above equation, \(W\), \(K_{\text{eff}}\), and \(D\) represent the energy dissipation area per cyclic load, the effective stiffness, and the maximum displacement of the hysteresis curve, respectively. As shown in the table, all steel dampers designed in this study were evaluated to have damping performance with a critical damping ratio of 30% or more. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Steel Damper** & \(K_{\mathbf{t}}\) & \(K_{\mathbf{e}}\) & \(F_{\text{slide}}\) & \(W\)1  & **Critical Damping** \\ _ID_ & **(kN/m)** & **(kN/m)** & **(N)** & **(N-m)** & **Ratio (\%)** \\ \hline SD1 & 7.21 & 100.82 & 330.64 & 25.44 & 35.17 \\ SD2 & 9.00 & 127.21 & 413.24 & 31.87 & 35.25 \\ SD3 & 37.00 & 695.77 & 1064.61 & 90.60 & 31.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Evaluation results of energy dissipation capacity of steel dampers. Figure 14: Analysis results of equivalent plastic strain time history during cyclic load for SD2. Figure 15: Analysis results of hysteretic response for steel dampers: (**a**) SD1; (**b**) SD2; (**c**) SD3.
## 2 The Seismic Reliability Analysis Process for the Substation System
### Building the System Model  
To perform the calculations of the system model, the fragility of the unit should be defined first, and the fragility curve or function in the reliability evaluation is usually selected to define fragility. The function \(F_{R}(x)\) represents the probability that the response, \(D_{EDP}\), of the selected engineering demand parameter, \(EDP\), exceeds a selected structural limit state, \(LS\), for a specific intensity measure, \(IM\), of seismic excitation [31], as shown in Equation (2). The specific calculation process can be found in [32; 33].  
\[F_{R}(x)=P[D_{EDP}\geq LS|IM=x] \tag{2}\]  
Substation systems have a large number of power supply facilities, which have a complex connection form. To study the seismic reliability of the substation system, the substation system is abstracted as a geometric figure composed of many functional units, and these units are connected through related links. The functional unit represents a series-parallel system composed of power supply facilities, such as equipment and architecture. An edge represents a bus, and differences in its electrical parameters and physical structure are ignored. The substation system is abstracted as a directed topology without weights [34; 35], which is represented as a set, as shown in Equation (3).  
\[G=\{V,E\} \tag{3}\]where the nonempty set \(\mathbf{V}=\{v_{1},v_{2}\cdots v_{n}\}\) is called the node set of \(\mathbf{G}\) and the set \(\mathbf{E}=\{e_{1},e_{2}\cdots e_{n}\}\) is called the edge set of \(\mathbf{G}\). An edge \(e_{i}=\{v_{s},v_{i}\}\) corresponds to a binary relation on the set.  
This work uses the adjacency matrix of the directed topology to describe the structure. If the number of substation units in the directed topology \(G(V,E)\) is \(n\), the connections between units are represented by an adjacency matrix \(\mathbf{A}\) of order \(n\times n\)[36; 37].  
\[\mathbf{A}=[a_{ij}] \tag{4}\]  
where if \(\mathbf{A}[i,j]=1\), there is an adjacent edge between the functional unit \(v_{i}\) and functional unit \(v_{j}\); \(\mathbf{A}[i,j]=0\) indicates that there is no adjacent edge between the function unit \(v_{i}\) and function unit \(v_{j}\).
## 4 Experimental Discussion
### Accuracy Analysis
#### 4.2.1 Results on the Indian Pines Datasets  
Compared to Figure 8, SFFNet has almost no obvious classification errors. Specifically, the other frameworks have also achieved good classification results, but are prone to classification errors at the boundary of different categories. The AMGCFN errors are more obvious, and these errors are basically uniformly distributed. This shows that our framework is more advantageous in distinguishing the edge boundaries of different categories.  
Table 5 shows the quantitative results of different frameworks on the Indian Pine dataset. It can be seen that our framework achieves top-level and optimal single classification accuracy up to 100.00%. Although the suboptimal structure (WFCG) can achieve an accuracy of 100.00% in some single category classifications, the overall accuracy (OA) gap with the optimal framework (SFFNet) reaches about 4%. Compared with the 3D-CNN framework using only convolutional neural networks, the other three models combining GCNs all have better classification results, which suggests that exploiting the topology of HSIs is necessary.  
Figure 7: False-color image and ground truth of the Houston 2013 dataset.
# Thermal Accelerometer Simulation by the R-Functions Method
## 2 Thermal Accelerometer and Its Principle of Operation  
The principle of operation of a TA is based on the effect of fluid or gas convection in a closed cavity under the influence of external forces of inertia. The device (Figure 1) includes a heating element H that creates around itself a symmetrical thermal field. The thermal sensors are located on opposite sides of this element.  
In the absence of an external acceleration along the sensitive axis \(X\), the system is balanced and the heat detectors generate the same signal (Figure 2a). In presence of an external acceleration, the temperature profile changes, which results in the temperature difference d\(T\) between the sensors S1 and S2, depending on the amplitude of the acceleration (Figure 2b). Nonzero temperature difference between the heat sensors converts the input impedance to the output electrical signal.  
This type of accelerometer has several important advantages over conventional sensors for acceleration based on microelectromechanical systems (MEMS). In particular, due to the absence of moving mechanical parts, convective accelerometers have high reliability, low cost of production, as well as the ability to withstand and measure high loads caused, for example, by shock action.  
To find the optimal parameters of the device: the type of gas and its pressure, the size and geometry of the cavity, the material and the size of the heating element and heat sensors, etc., it is necessary to solve the boundary value problem described by a system of the Navier-Stokes differential equations. This problem can be solved analytically only for a limited class of simple regions (cylinder, sphere), and in the general case, a numerical simulation is required.  
Figure 1: The general scheme (**a**) and microstructure design (**b**) of a thermal accelerometers (TA).  
Figure 2: The principle of operation of a TA. (**a**) without acceleration, (**b**) with acceleration.