esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Active Load Alleviation Performance
### Static Aeroelastic Analysis
#### 4.1.1 Model Definition  
Steady structural response is assumed in the formulation of the dynamic equations for static aeroelastic analysis [46], this means that only the degrees of freedom associated with the rigid body response will appear in dynamic equations, while the elasticity will provide a correction of the rigid body matrices, leading to a set of motion equations written as:  
\[\overline{M}_{bb}\dot{v}_{b}+\overline{C}_{bb}\mathbf{v}_{b}+\overline{F}_{c}\mathbf{ u}_{c}=\overline{F}_{g}\]  
where \(\overline{M}_{bb}\) and \(\overline{C}_{bb}\) are the rigid-body mass and damping matrices corrected with elastic and aerodynamic effects, \(\mathbf{v}_{b}\) is the vector containing the rigid body degrees of freedom, \(\mathbf{u}_{c}\) is a vector containing the control surface rotations and \(\overline{F}_{c}\) is the matrix defining the rigid body forces associated with the control surface rotation. \(\overline{F}_{g}\) is the weight force. No relationship between \(\mathbf{v}_{b}\) and \(\dot{\mathbf{v}}_{b}\) is assumed in the solution of the dynamic equations.  
The system of equation described above is underdetermined, and a set of additional relationships need to be introduced to fully define the manoeuvre condition, such as by  
Figure 18: Morphed winglet: Moment diagrams. (**a**) Cruise: \(\mathrm{C}_{\mathrm{n}}(\mathrm{C}_{\mathrm{L}})\) curve, (**b**) Cruise: \(\mathrm{C}_{\mathrm{L}}(\mathrm{C}_{\mathrm{m}})\) curve, (**c**) Climb: \(\mathrm{C}_{\mathrm{n}}(\mathrm{C}_{\mathrm{L}})\) curve, (**d**) Climb: \(\mathrm{C}_{\mathrm{L}}(\mathrm{C}_{\mathrm{m}})\) curve.  
imposing the angle of attack, the sideslip, the load factor or other quantities. All the constraints are expressed in the form  
\[\mathbf{V_{1}}\mathbf{\dot{v}}_{b}+\mathbf{V_{2}}\mathbf{v}_{b}+\mathbf{V_{c}}\mathbf{u}_{c}=\mathbf{b}.\]  
As stated in [47], when there are more control surfaces available than unconstrained degrees of freedom it is possible to reformulate the manoeuvre problem as a constrained optimization problem, where a quadratic cost function of the form is minimized. The dynamic equations is then used as a constraint equation in the optimization, along with the other constraint equations defining the manoeuvre defined above and additional constraints that limits the control surface rotations, expressed as:  
\[J=\ \frac{1}{2}\Big{[}\ \mathbf{\dot{v}}_{b}^{T}\ \ \ \mathbf{v}_{b}^{T}\ \ \ \mathbf{u}_{c}^{T}\ \ \Big{]}\left[\begin{array}{ccc}S_{aa}&S_{av}&S_{ac}\\ S_{ua}&S_{vv}&S_{vc}\\ S_{ca}&S_{cv}&S_{cc}\end{array}\right]\left[\begin{array}{c}\mathbf{\dot{v}}_{b} \\ \mathbf{v}_{b}\\ \mathbf{u}_{c}\end{array}\right]\]  
\[\delta_{lower}<\delta<\delta_{upper}\]  
where \(\delta_{lower}\) and \(\delta_{upper}\) represents the lower and upper limits for the rotation of the control surface.
## 2 Materials and Methods
### ICP-OES Instrumentation and Method Analytical Characteristics  
A Thermo Scientific IRIS Intrepid II XDL inductively coupled plasma-atomic emission spectrometer (Thermo Electron Corporation, Waltham, MA, USA) was used for the elemental analysis. The emission wavelength (nm) was: 309.3, 455.4, 455.4, 313.0, 393.3, 228.6, 267.7, 324.7, 259.9, 766.5, 670.8, 279.5, 257.6, 202.0, 589.0, 221.6, 178.3, 206.8, 196.0, 251.6, 190.0, 334.9, 190.8, 292.4, 213.9, for Al, Ba, B, Be, Ca, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Sb, Se, Si, Sn, Ti, TI, V and Zn, respectively. The operational parameters for the instrument were in accordance with a previous work [17]. For the determination of each amount of mineral, calibration curves were prepared. These showed correlation coefficients (\(R^{2}\)) in the range of 0.9967-1.000 for soil and 0.997-1.000 for fruit juice samples, respectively (Supplementary Tables S1 and S2). Furthermore, the analytical method developed in the present study showed a satisfactory percent recovery for both soil and prickly pear juice samples at different spiking concentrations (six replicates) (Supplementary Tables S3 and S4). The limit of detection (LOD) and limit of quantification (LOQ) were estimated by spiking a blank sample (ultrapure water) three times with the standard mineral solution at low concentrations and the signal-to-noise ratio was determined. The LOD was defined as 3:1 and the LOQ as 10:1. The LOD and LOQ values for each mineral determined in soil and fruit juice samples are provided in the supplementary material (Supplementary Tables S5 and S6). Finally, the coefficient of variation for all minerals determined in prickly pear juice samples (\(n=36\)) was \(\leq\)5.10% (Supplementary Table S7).
## 4 Results
### Cluster Analysis
#### 4.2.3 North Aegean Sea Area  
The first seismic excitation with \(N\geq 30\) events (\(N1\) in Figure 7b) is a sequence of interest since two moderate events (\(M=5.2\) and \(M=5.3\)) occurred in 3 weeks, both producing their own aftershocks (Figure S12). The 2013, January 8 \(M=5.8\) North Aegean earthquake [60] along with its aftershock activity (cluster \(N3\) in Figure 7b) is also detected. The aftershock activity is temporally divided into two clusters (Figure S13). The 24 May 2014 \(M=6.9\) Samothraki main shock was followed by aftershock activity confined to three major clusters (\(N4,\ N5,\ N6\) in Figure 7b) and some secondary clusters with \(N\geq 10\) events (Figure S14), which are in accordance with the ones observed by [PERSON] et al. [61]. The seismic activity that took place near the Aegean coast of NW Turkey during January-October 2017 [67] is divided into three clusters with \(N\geq 30\) (\(N10,\ N11\) and \(N14\) in Figure 7b) and two minor clusters with 22 and 23 events, respectively (Figure S15). The strong main shock (\(M=6.4\)) that occurred on the 12 th of June 2017 offshore, south of the SE coast of Lesvos Island, along with its intense aftershock activity, is revealed and illustrated in Figure 7b (\(N12\)). Two major (\(N\geq 30\)) secondary outbursts of clustered activity occurred concurrently on the west (\(N17\)) and east (\(N16\)) side of the sequence (Figure S16). A thorough analysis revealing multiple spatial clusters of the sequences is conducted by [PERSON] et al. [62].  
Figure 6: (**a**) Spatial distribution of the centroids of the identified clusters for the area of Central Ionian Islands along with the trace of the Kefalonia Transform Fault Zone (yellow lines). The size of the circles is proportional to the earthquake number in each cluster, whereas the duration is represented by the color scale. (**b**) Spatial distribution of the clusters with \(N\geq 30\) events. The index of each cluster is given in the inset box.
## 3 Saudi Dialect Identification Approach
### Deep Learning Character-Based Model  
Our classification problem is a multiclass problem, where each dialect represents a separate class. Therefore, given s and its label l, the task is to predict the Saudi dialect language that s is written in, using only its character sequence.  
The CNN architecture described in [10] was adapted. The architecture consists of two main steps, as shown in Figure 2: the main feature of the first part uses the CNN layer as a feature extractor, and then the convolution output directly feeds to the long short-term memory (LSTM) layer to capture the long-term dependencies in the second step.  
The input layer for the convolution layer was a sequence of vectors, which was the output of the previous step. In the convolution layer, two 1D convolutions were applied in parallel to each of our character input layers to map this sequence \(x\) into a hidden sequence \(h\), with two different filters of size 64 and 100, each with a kernel size equal to 3 and a pool size of 2.  
After each convolution operation, a nonlinear activation of the rectifier linear unit (ReLU) [32] type was applied. Then, a temporal max-pooling layer was applied that strongly activated some nodes in the sequence. Next, a dense layer with a size of 128 and a ReLU activation function was applied. The two parallel 1D convolution outputs were concatenated into a sequence and passed to a dropout layer, in which 0.4 of the input units were dropped to reduce overfitting. Afterwards, a TimeDistributed layer was applied to make the sequence suitable for the bidirectional LSTM (BiLSTM) layer.  
Then, a BiLSTM was used, resulting in two sequences in the forward and backward directions with hidden sizes of 128 each. The final output of the BiLSTM layer for both sequences was concatenated to yield 256-dimensional hidden units, and the fully connected layer (Dense) with a size of 128 and a RelU activation function was applied. To reduce overfitting, two dropout layers with a drop rate of 0.4 were applied after and before the Dense layer. Finally, the resulting vectors passed to the Softmax layer to produce the final probability distribution over our dialect classes \(k\).
# Evolution and Testing of NB-IoT Solutions
## 4 NB-IoT Evolution
### TCP Performance in High Latency Scenarios  
One of the conclusions of the study carried out in TR 45.820 [7] about Cellular system support for ultra-low complexity and low throughput Internet of Things is that the current E-UTRAN/EPC architecture based on the S1 interface presents an inefficient performance when transporting infrequent messages. In addition, one of the architectural enhancements identified by the study carried out in 3 GPP TR 23.720 [11] to support ultra-low complexity, power constrained and low data rate, is the efficient support of infrequent small data transmissions. As a result, the Non-IP Data Deliver (NIDD), a term coined to refer to this modification in the standard, has been included in the general speciation of the LTE radio interface 3 GPP TS 23.401 [22]. Concretely, two different mechanisms are supported:  
* Delivery using SCEF (Service Capability Exposure Function). SCEF was introduced in Release 13 as part of AESE specification for the provision of service capabilities to 3 rd parties. In this proposal, the SCEF is connected directly to the MME and provide the delivery of NB-IoT services. The service provided by SCEF can be based on IP or non-IP protocol.
* Delivery using a Point-to-Point (PtP) SGi tunnel.  
The mechanisms proposed don't guarantee the delivery in the order of the data packages, a higher protocol layer should ensure if it is required. However, the security, a key factor in IoT because the entire amount of private and sensible data that is transported is improved, is also due to control plane integrity mechanisms that are also applied to the data.  
The third alternative for the transport of the Non-IP data is the SMS service. SMS service is supported in the NB-IoT specification, and can be used simultaneously with Non-IP data and IP-data transport.  
Data can also be delivered on a user plane like in LTE. In this case, data are delivered in order, but there is a higher overhead due to the signaling.
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 4 Wireless Network Architectures for Energy-Efficient DFL
### DFL in Satellite Constellations Communication Networks  
CFL relies on a central server to aggregate model parameters, which is impractical for distributed LEO satellite constellations due to the difficulty in selecting a suitable central satellite. Moreover, reliance on ground stations introduces latency and limits operational flexibility due to the intermittent visibility of satellites. With the increasing deployment of LEO constellations by organizations such as SpaceX, OneWeb, and Amazon, there is a growing need for solutions like SFL that integrate scalability, efficiency, and robustness into satellite-based operations [116]. To address these challenges, a DFL framework for LEO satellite constellations was proposed in [94], as shown in Figure 5b. This framework enables efficient model aggregation across LEO satellite networks without requiring a central server, thereby mitigating reliability concerns and communication bandwidth limitations inherent in CFL approaches. Optimizing energy efficiency in DFL over satellite constellations involves enhancing ISLs to minimize communication overhead, utilizing energy-aware data exchange protocols, and applying dynamic resource allocation strategies to reduce power consumption. The study in [117] introduced a novel decentralized SFL framework for Walker Delta, where satellites  
Figure 7: DFL over UAV networks. communicate via ISLs across orbital planes. This approach addresses a mixed combinatorial optimization problem by jointly optimizing power control, bandwidth allocation, and routing to minimize energy consumption. The authors in [118] leveraged decentralized operations and energy-aware communication strategies to reduce communication congestion and improve fault tolerance, aligning with DFL principles to optimize energy consumption while maintaining system performance. The authors in [119] focused on minimizing convergence time in satellite-based DFL by optimizing the number of satellites per orbit and the number of orbits. This approach balances energy constraints, communication distance, and DFL performance, ensuring efficient and reliable operation. These studies highlighted the innovative strategies for enhancing energy efficiency in DFL-enabled satellite constellation systems. Table 3 summarizes the research issues and limitations of the existing energy-efficient FL-enabled wireless network architectures, providing a comprehensive reference for the topics discussed in Sections 3 and 4.
# Chaotic Communication System with Symmetry-Based Modulation
## 2 Materials and Methods  
### Synchronization of Chaotic Systems  
Synchronization of two or more chaotic systems in a master-slave configuration can be performed using the different methods such as Pecora-Carroll common signal linking [12], Hamiltonian forms [13] or the Open-Plus-Close-Loop approach (OPCL) [14]. In this study, the communication system is implemented using the classical Pecora-Carrol method. This technique involves the addition of the synchronization error multiplied by a parameter, so-called the synchronization coefficient, into one or more lines of the slave equation system.  
Let us consider an autonomous \(n\)-dimensional chaotic system:  
\[\dot{x}=f(x). \tag{1}\]  
Split the system into two parts:  
\[\begin{array}{l}\dot{u}=g(u,w),\\ \dot{w}=h(u,w),\end{array} \tag{2}\]  
where  
\[\begin{array}{l}u=(x_{1},\ldots,x_{m})^{\top},\\ w=(x_{m+1},\ldots,x_{n})^{\top},\\ g=(f_{1}(x),\ldots,f_{m}(x))^{\top},\\ h=(f_{m+1}(x),\ldots,f_{n}(x))^{\top},\end{array}\]  
and \(m,n\in\mathbb{N},m\leq n-1\). Now, one can construct new subsystem \(\hat{h}\) with independent variable subset \(\hat{w}\), and involving variable subset \(u\) from the first system:  
\[\begin{array}{l}\dot{u}=g(u,w),\\ \dot{w}=h(u,w),\\ \dot{w}=\hat{h}(u,\hat{w}).\end{array} \tag{3}\]  
Then, the difference \(\Delta w=\hat{w}-w\) tends to zero \(\Delta w\to 0\) as \(t\to 0\) due to the chaotic synchronization phenomenon [12], that is the generalization of the [PERSON]'s slaving principle [15].  
Applying Pecora-Carroll synchronization [12] to the well-known Rossler system [16] one can obtain the following equation:  
\[\begin{array}{l}\dot{x}=-y-z;\\ \dot{y}=x+ay;\\ \dot{z}=b+z(x-c).\end{array} \tag{4}\]  
Here, \(a\), \(b\), and \(c\) are the system parameters. In the study, let nonlinear parameters be \(a=0.2\), \(b=0.2\), \(c=5.7\). The ordinary differential equation (ODE) for master Rossler system is as follows:  
\[\begin{array}{l}\dot{x}_{1}=-y_{1}-z_{1};\\ \dot{y}_{1}=x_{1}+a\cdot y_{1};\\ \dot{z}_{1}=b+z_{1}\cdot(x_{1}-c);\end{array} \tag{5}\]  
and the slave equation is:  
\[\begin{array}{l}\dot{x}_{2}=-y_{2}-z_{2}+k\cdot(x_{1}-x_{2});\\ \dot{y}_{2}=x_{2}+a\cdot y_{2};\\ \dot{z}_{2}=b+z_{2}\cdot(x_{2}-c);\end{array} \tag{6}\]  
where \(k\) is a synchronization coefficient. For further examples of the Rossler systems synchronized by the Pecora-Carroll method refer to [17].
## 3 Experiments and Results
### Data Sets
#### 3.1.2 WHU Aerial Image Data Set  
The WHU data set [38] consists of an aerial image sub-dataset and two satellite image sub-datasets. In this experiment, only the aerial image data sets were selected as the test data. The WHU aerial dataset covers 450 km\({}^{2}\) in Christchurch, New Zealand, and about 220,000 independent buildings are contained in it. The spatial resolution of the WHU arterial image data set is 0.3 m. In the data reprocessing stage, images were cropped into 512 \(\times\) 512 pixels. All of the cropped images were sliced into training, test and validation sets numbering 4736, 2416 and 1036 images, respectively. Some samples of them are shown in Figure 7.  
Figure 7: Samples of the WHU aerial image data set.
## 5 Analysis of Co-Occurrence
### All Keyword-Based Co-Occurrence Analysis  
This section presents the analysis of co-occurrence for all keywords identified by WoS concerning publications in OT security. VosViewer's search identified a total of 1314 keywords, but only 69 of these met the threshold criteria. The top thirteen terms with the highest co-occurrence, as indicated by their TLS values, are shown in Table 6. Table 6 indicates that the keyword 'Anomaly Detection' appeared in 183 published articles (Np). Additionally, within these articles, it co-appeared with 139 WoS-indexed keywords. The term 'Security' achieved a TLS of 172, representing the total number of occurrences where this keyword coincided with other keywords, each appearing at least five times. In this context, when \"n\" WoS-indexed terms appeared together in a publication, the link strength for each pair was determined as 1/n based on the citing article. TLS is the total of the link strengths between a keyword and all other co-occurring keywords across all published articles. Since a keyword's fractional link strength adds up to one in every published article, the co-occurrence-based TLS of keywords is always a whole number rather than a fraction. Table 6 does not illustrate this pattern, as the TLS remains a whole number while consistently being less than the Np (total number of publications). This discovery implies that some authors of published articles did not refer to any additional OT security keywords. It might also indicate that in some published works, only OT security keywords appearing less than five times were present together; therefore, they were omitted from this study and were not deemed co-occurring keywords. The leading thirteen keywords indicate that numerous research papers focus on intrusion and anomaly detection using machine/deep learning-based classification in OT security.  
Figure 18 presents a co-occurrence network map that shows the interconnections between all keywords. The pattern indicates that no single research topic clearly dominates. Although some keywords have relatively higher popularity (e.g., security, ICSs, ML, etc.), they are not much more popular than relative outsiders. The graph also shows that there are no clear clusters, demonstrating still a low level of research maturity.
## 4 Experimental Results and Analysis
### Discussion
#### 4.5.6 Comparison with Other Bearing Datasets  
To further verify the effectiveness of the proposed method, bearing data from a gearbox at Southeast University were used for experimental verification [43].  
The data were taken from the Drivetrain Dynamic Simulator (DDS), which collects a total of eight channels of data in the rotating speed-load configuration set to 20 Hz-0 V and 30 Hz-2 V. This experiment used channel 2 data at a speed-load setting of 20 Hz-0 V. The sampling rate is 20 Hz and number of data points is 1024. There are five types of the bearing data: normal, inner ring fault, outer ring fault, rolling element fault, and combined failure of inner and outer rings. The 500 samples of each fault are selected, and the training data and test data are divided by the ratio of 9:1.  
The model structure used for this experiment was similar to that used for the Case Western Reserve University bearing dataset, and the specific model structure parameters are listed in Table 1. The results of the fourfold cross-validation of the different methods through multiple experiments are shown in Figure 24. To avoid the contingency of experimental results, the average results of 10 experiments are used, while the experimental results of the proposed method are compared with the SVM, LeNet-5, Resnet, and gcForest methods, and the results are shown in Table 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**The Serial Number** & **Model** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Accuracy (\%)** \\ \hline
1 & SVM & 36.08 & 39.11 & 37.53 & 37.60 \\
2 & gcForest & 92.83 & 92.15 & 92.49 & 90.80 \\
3 & LeNet-5 & 84.83 & 85.16 & 84.99 & 84.80 \\
4 & Resnet & 95.38 & 95.41 & 95.37 & 94.80 \\
5 & IGVMD– & 99.54 & 99.49 & 99.51 & 99.50 \\  & 1 DCNN & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison results with the different models using the gearbox dataset.  
Figure 23: The performance of five methods in noise environment.  
Figure 24 and Table 9 indicate that compared with other methods, the proposed method significantly improved the accuracy and stability of the model. SVM is a shallow model that has limited feature learning ability, and the learned features do not have good classification characteristics. The gcForest deep model has a better diagnostic effect but has large memory consumption and low operation efficiency. Compared with LeNet-5, the depth of the model proposed is greater, and the feature extraction and diagnosis ability are stronger. The accuracy is improved compared with Resnet because this model eliminates the interference of strong noise. The fault identification rate of the proposed method is 99.50%, and it has the highest recognition accuracy. Therefore, the method proposed can be used for bearing fault diagnoses of different types with good generalization ability.