esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 5 Simulation and Results
### Performance Analysis of Tbrl with Dbr and R-ERP\({}^{2}\)R
#### 5.4.3 Energy Consumption  
Each time, DBR and R-ER\(P^{2}\)R calculate nodes depth, physical distance, residual energy, and link quality of receiver nodes for the selection of best one among them. Multiple receivers are entertained that cause unnecessary energy utilization in the form of packet receiving, change nodes state from sleeping to alive, and idle to alive power. Whereas, in TBRL, multiple receivers could not be entertained; therefore, its energy consumption is less as compared to DBR and R-ER\(P^{2}\)R as shown in Figure 15.  
Figure 14: Packet Delivery Ratio of TBRL, DBR and R-ER\(P^{2}\)R.  
Figure 13: End to End Delay of TBRL, Depth Based Routing (DBR), and Reliable Energy-efficient Routing Protocol (R-ER\(P^{2}\)R).
# Mechanical Behavior of Steel Fiber-Reinforced Lightweight Concrete Exposed to High Temperatures
###### Abstract  
The mechanical characteristics of steel fiber-reinforced lightweight concrete (SFLVC) under high temperatures are studied in this paper. Different concrete matrices, including all-lightweight concrete (ALWC) and semi-lightweight concrete (SIWC), and different steel fibers with hooked ends and crimped shapes are considered as objects. In addition, normal-weight limestone aggregates concrete (MWC), no-fiber ALWC, and SLWC were tested after high-temperature treatment as a control group. The temperature effects on the splitting tensile strength, ultrasonic pulse velocity, compressive stress-strain curve, elastic module, peak strain, and axial compressive strength of the SFLWC were investigated. The results showed that, with increasing exposure temperature, both the axial compressive strength and the elastic modulus decreased, while the axial peak strain has a certain increase, and hence the stress-strain curves were gradually flattened. The toughness of all the concretes increased first and then reduced with increasing temperature, while the specific toughness of all the concretes increased with the increase in temperature. Compared with NWC and SLWC, ALWC had a better capacity to resist high temperatures, especially temperatures > 400 \({}^{\circ}\)C. Adding steel fibers can improve the capacity of energy absorption, specific toughness, and residual splitting tensile strength of lightweight concrete (LVC) before and after it is exposed to high temperatures. Based on a regression analysis, a segmented constitutive equation for LWC and SFLWC under uniaxial compression was derived from fitting the experimental findings, and the fitting curve agrees well with the test results.  
steel fiber-reinforced lightweight concrete; mechanical properties; high temperature; constitutive model +
Footnote †: journal: Applied Sciences
## 4 Experiment and Evaluation
### Quantitative Evaluation Index
The calculation formula is as follows:  
\[R_{LCS}=\frac{LCS(C,S)}{len(S)} \tag{8}\]  
\[P_{LCS}=\frac{LCS(C,S)}{len(C)} \tag{9}\]  
\[Rouge-L=F_{LCS}=\frac{\left(1+\beta^{2}\right)R_{LCS}P_{LCS}}{R_{LCS}+\beta^{2 }P_{LCS}} \tag{10}\]  
\[P_{Rouge-L}=\frac{L}{ALL\ num-G} \tag{11}\]  
\[R_{Rouge-L}=\frac{L}{ALL\ num-R} \tag{12}\]  
\[Rouge-N=\frac{\sum_{S\in\{Ref\ Summaries\}}\sum_{n-gram\in S}Count_{match}(n- gram)}{\sum_{S\in\{Ref\ Summaries\}}\sum_{n-gram\in S}Count(n-gram)} \tag{4}\]0  
where \(LCS(C,S)\) is the length of the longest common subsequence between the generated text \((C)\) and the reference text \((S)\). The \(F_{LCS}\) value is the weighted harmonic average of the precision and the recall and is used to balance the weight between the two. \(ALL\ num-G\) denotes the total number of words in the generated text, and \(ALL\ num-R\) denotes the total number of words in the reference text. BERTScore's calculation is based on a pre-trained BERT model that has been extensively trained and optimized to understand the meaning of words in context, so it is able to capture semantic similarities. When candidate texts and reference texts are converted into a BERT vector, the cosine similarity of the two vectors can be calculated. The precision, recall, and F1 scores were calculated as follows:  
\[Rouge-N=\frac{\sum_{S\in\{Ref\ Summaries\}}\sum_{n-gram\in S}Count_{match}(n- gram)}{\sum_{S\in\{Ref\ Summaries\}}\sum_{n-gram\in S}Count(n-gram)} \tag{4}\]1  
\[Rouge-N=\frac{\sum_{S\in\{Ref\ Summaries\}}\sum_{n-gram\in S}Count_{match}(n- gram)}{\sum_{S\in\{Ref\ Summaries\}}\sum_{n-gram\in S}Count(n-gram)} \tag{4}\]2  
\[Rouge-N=\frac{\sum_{S\in\{Ref\ Summaries\}}\sum_{n-gram\in S}Count_{match}(n- gram)}{\sum_{S\in\{Ref\ Summaries\}}\sum_{n-gram\in S}Count(n-gram)} \tag{4}\]3where \(x\) is the reference text, and \(\hat{x}\) generates the text for the retrieval model. The embedded cosine similarity is a common method to measure the similarity between two vectors, especially in the evaluation of large models in NLP tasks [33]. The method determines the similarity of two vectors by calculating the cosine angle. The formula is as follows:  
\[Rouge-N=\frac{\sum_{S\in\{Ref\ Summaries\}}\sum_{n-gram\in S}Count_{match}(n- gram)}{\sum_{S\in\{Ref\ Summaries\}}\sum_{n-gram\in S}Count(n-gram)} \tag{4}\]4  
where \(A\cdot B\) represents the dot product of two vectors, and \(\left\|A\right\|\left\|B\right\|\) represents the norm of \(A\) and \(B\) (i.e., the length of the vector). Taking the content about ocean front detection as an example, a summary of ocean front detection methods by professionals was used as the reference text.
## 3 Discussion of the Proposed ZVS ITLDC
### Voltage and Current Stress Analysis  
The circuit of the proposed ACAC does not operate in a resonance mode, the voltage of the auxiliary capacitor \(C_{\rm A}\) is stable and the current of auxiliary inductor \(L_{\rm A}\) is controllable. Thus, the proposed ACAC added to the ITLDC will not result in additional voltage stress. The voltage stress of the main switches S\({}_{1}\)-S\({}_{4}\) is equal to \(V_{\rm in}/2\) and the voltage stress of the auxiliary switches is equal to \(V_{\rm CA}\). When the output current is large enough to realize natural ZVS, the auxiliary circuit does not work; thus, the ACACs do not increase the current stress of the main switches in the ITLDC. The current stress of the main switches is equal to \(I_{\rm ON}/n\), where \(I_{\rm ON}\) is the designed maximum output current. The current stress of the auxiliary switches is equal to \(I_{\rm o\_natural\_ZVS}/2n\), which can be calculated from (6).  
The voltage and current stress of the main switches do not increase with the proposed ACACs. The voltage and current stress of the auxiliary switches are smaller than the stress of the main switches.
# QoE Optimization in a Live Cellular Network through RLC Parameter Tuning
## 4 Services  
In this section, the main characteristics of the services used in this paper are presented. In the same way, the metrics used to evaluate the QoE perceived by users in each of these services are defined. The services considered are real-time video streaming and file transfer.
## 3 Materials and Methods
**Second Layer LSTM**: LSTM Units: 50  
return_sequences=False: This parameter indicates that the LSTM layer returns only the last output of the sequence, which is used as input for the final dense layer. **Second Layer Dropout**: Dropout Rate: 0.2  
Similarly to the first Dropout layer, it helps prevent overfitting. **Dense Layer**: Units: 1  
This is the output layer that produces a single prediction. Filling in the Model. **Optimizer**: Adam. **Loss Function**: Mean Squared Error (MSE). In this study, we utilized LIDAR (Light Detection and Ranging) data. LIDAR is a high-resolution remote sensing technology used to model flood-prone areas with remarkable precision [35]. It employs an IMU (Inertial Measurement Unit) platform, emitting laser pulses to measure distances between the sensor and ground objects, enabling three-dimensional georeferencing. By scanning terrain, LIDAR generates a point cloud, distinguishing digital terrain models (DTMs) from digital surface models (DSMs). This data allows for precise elevation mapping, with a detection density exceeding 1.5 points/m\({}^{2}\) and an altimetric accuracy within 15 cm. LIDAR-derived digital elevation models (DEMs) play a critical role in hydraulic modeling, particularly in urban environments where high-resolution topography is necessary for assessing flood susceptibility. DEM resolution directly impacts flood propagation accuracy, with research confirming LIDAR-generated DEMs outperform other models [36; 37]. This methodology provides a reliable framework for hydrological predictions, aiding in the identification of risk areas and supporting effective land management strategies.
## 4 Background Study
### Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Dataset** & **Model** & **Challenges and Limitations** \\ \hline \multirow{3}{*}{[21]} & ADNI-like MRI data (T1w), & \multirow{3}{*}{Network eigenmode diffusion model} & Small sample size; no conventional ML metrics (accuracy, F1); assumes static connectivity; limited resolution in tractography; noise in MRI volumetrics. \\ \cline{2-3}  & ADNI-1 \& ADNI-2 & Two-stage: PET synthesis (3D-cGAN) + classification (LM3 IL) & Requires accurate MRI–PET alignment; patch-based learning may limit generalization. \\ \hline \multirow{3}{*}{[24]} & \multirow{3}{*}{OASIS-3} & WGAN-GP-based unsupervised reconstruction + anomaly detection using L2 loss & Region-limited detection (hippocampus/amygdala); may miss anomalies outside selected areas. \\ \hline \multirow{3}{*}{[25]} & ADNI & GAN + Classifier & Binary classification was not better than CNN-only models; requires more tuning and architectural exploration. \\ \hline \multirow{3}{*}{[26]} & ADNI & DCGAN & Trained separate GANs per class; used 2D slices only; lacks unified 3D modeling approach. \\ \hline \multirow{3}{*}{[27]} & ADNI & Bidirectional GAN & Limited fine detail in some outputs; latent vector injection mechanism could be improved for better synthesis. \\ \hline \multirow{3}{*}{[28]} & ADNI-1, ADNI-2 & DCGAN + DenseNet & Used 2D image generation; manual filtering of outputs; lacks 3D modeling and automation. \\ \hline \multirow{3}{*}{[29]} & ADNI-GO, ADNI-2, OASIS & mi-GAN + DenseNet & Lower performance on gray matter prediction; limited short-term progression prediction; improvement possible with better feature modeling. \\ \hline \multirow{3}{*}{[30]} & \multirow{3}{*}{OASIS-3, Internal dataset} & \multirow{3}{*}{7-SA MADGAN} & Reconstruction instability on T1c scans; limited generalization; fewer healthy T1c scans; needs optimized attention modules. \\ \hline \multirow{3}{*}{[31]} & ADNI, AIBL, NACC & GAN + FCN & Small sample size for GAN training (151 participants). Limited to AD vs. normal cognition (no MCI). \\ \hline \multirow{3}{*}{[32]} & ADNI-1, ADNI-2 & TPA-GAN + PT-DCN & Requires paired modalities; model trained/tested on ADNI-1/2 independently; limited generalization. \\ \hline \multirow{3}{*}{[33]} & ADNI-1 & THS-GAN & Requires careful TT-rank tuning; performance varies with GSP block position; validation limited to ADNI dataset. \\ \cline{2-3}  & ADNI (268 subjects) & GAN + Transformer with Bi-Attention & Limited dataset size, dependency on predefined ROIs, potential overfitting; lacks validation on other neurodegenerative disorders. \\ \hline \multirow{3}{*}{[35]} & ADNI (13,500 3D MRI images after augmentation) & WGANGP + Inception v3 + DBN & Heavy reliance on data augmentation, complex pipeline requiring multiple preprocessing and tuning steps. \\ \cline{1-1} \cline{2-3}  & & & \\ \end{tabular}
\end{table} 
Table 4: Overview of generative models used in literature to diagnose Alzheimer’s disease research highlighting their key challenges and limitations.
# Loop Closure Detection Based on Multi-Scale Deep Feature Fusion
## 3 Loop Closure Detection Algorithm Based on Multi-Scale Deep Feature Fusion
### Feature Extraction Layer  
The feature extraction layer is composed of two identical CNNs to extract features for two inputs separately. Compared to the early CNN, AlexNet [27] uses a much deeper network model to acquire features. Additionally, it adds modules such as the ReLU activation function, local response normalization (LRN), Dropout, etc., which can reduce the risk of over fitting. Moreover, it takes advantage of multi-GPU to improve the training speed of the network model. In view of these advantages, this paper refers to AlexNet.  
The network model has a total of eight layers, consisting of five convolution layers and three fully connected layers. Only the first five layers of AlexNet are needed, as shown in Figure 2. Here, we set the input of the feature extraction layer to be an RGB image of 227 \(\times\) 227 \(\times\) 3, and obtained 256 feature maps with a size of 6 \(\times\) 6 through five convolution layers. (The output data size of each layer is indicated in Figure 2). The convolution layer contains the ReLU activation function and LRN processing, as well as max-pooling. Among them, LRN, which draws on the idea of \"lateral inhibition\" in neurobiology, is used to locally suppress neurons. When the activation function is ReLU, this \"lateral inhibition\" is very useful. It can prevent the model from over-fitting prematurely and speed up the training of the model. Its calculation formula is  
\[b_{x,y}^{i}=a_{x,y}^{i}/(k+\alpha{\sum}_{j=\max(0,i-n/2)}^{\min(N-1,i+n/2)}(a_{x,y}^{i})^{2})^{\beta}, \tag{1}\]  
where \(a_{x,y}^{i}\) denotes the value of the \(i\)-th convolution kernel after applying the ReLU activation function at position (x, y); n is the number of convolution kernels adjacent in the same position, and N represents the total number of convolution kernels. \(k,n,\alpha\) and \(\beta\) are tunable parameters; we set \(k=2,n=5,\alpha=0.0001,\beta=0.75\) according to the empirical value.
## 4 III-V-on-Silicon Devices for Optical Communication Applications
### III-V-on-Silicon Electro-Absorption Modulators for Optical Communication Applications  
Wavelength division multiplexing (WDM) modules are of key importance for realizing high aggregate bitrate optical interconnects. To imprint data on the individual optical carriers, both direct modulation and external modulation can be used. In the case of external modulation, for shorter distance links III-V EAMs are often used. We developed a 5 \(\times\)20 Gbps WDM transmitter consisting of an array of 5 EAMs heterogeneously integrated on a silicon PIC used for wavelength multiplexing (using an AWG) and fiber-chip interfacing, operating around 1.55 \(\upmu\)m [56]. The lumped III-V EAMs show a 3-dB E/O bandwidth of 17 GHz and can operate up to 28 Gbps. A module with a compact footprint (1.5 \(\times\)0.5 mm\({}^{2}\)), a low driving voltage (\(\sim\)2.5 V\({}_{\text{pp}}\)), and a large extinction ratio (4.9-6.9 dB) is obtained with 100 Gbps capacity. Figure 18a shows the schematic of the chip layout. On each channel of the AWG, a separate EAM is integrated. The AWG is formed using 220 nm height silicon rib waveguides and it has five channels with 200 GHz channel spacing. The top view of the EAMs is shown in Figure 18b. The passive silicon waveguide underneath is a rib waveguide with 1.5 \(\upmu\)m width and is 160 nm etched into the 380 nm silicon layer. The optical coupling between the silicon device layer and the III-V epitaxial stack is realized using a multi-level taper consisting of two linearly tapered sections in the III-V structure, as discussed in Section 3.1. The coupling efficiency from the passive silicon waveguide to the III-V waveguide using this 45 \(\upmu\)m long taper can be more than 95%.  
The minimum insertion loss of the EAM at 0 V bias is measured to be 1.2 dB. The spread in insertion loss is attributed to device-to-device fabrication variations. Figure 19a illustrates the measured static extinction ratio of the 100 \(\upmu\)m long EAMs under different reverse biases. More than 12 dB extinction ratio can be achieved when the bias is changed from 0 V to \(-\)2.5 V. Figure 19b-f shows the eye diagrams after the AWG multiplexer at 20 Gbps for the five different channels. All EAMs exhibit clean and open eyes. The dynamic extinction ratios vary between 4.9 dB and 6.9 dB.  
Figure 17: (**a**) Schematic cross-section of the oxide-confined hybrid-cavity VCSEL. (**b**) Measured light-current-voltage characteristics for hybrid-cavity VCSELs with oxide aperture diameters of 3–9 \(\upmu\)m. Inset: laser spectrum for a 7 \(\upmu\)m aperture VCSEL operated at 3.0 mA. Reproduced from [55].
## 3 Capacity Analysis of the IWMSNs
### The Linear MMSE Capacity  
Another important performance for the capacity of IWSSMSNs is the linear MMSE. The work in [57] proposed a framework to describe the linear MMSE performance of the return link for the multibeam satellite communication systems, which analyzed average MMSE spectral efficiency per user of the multibeam satellite systems and did not give a closed approximation for the MMSE capacity. The work in [58] investigated the impact of spatially-correlated rain attenuation on the performance of a multibeam satellite return link above the K-band. The work in [58] also did not give the closed approximations of MMSE capacity for the multibeam satellite systems under Rician fading. Thus, the arithmetic mean over the \(K\) sinks per beam of the MMSE is given by [49]:  
\[ \begin{array}{l}\mathrm{MMSE}_{\mathrm{avg}}((\mathbf{T}\otimes\mathbf{1}_ {K})\mathbf{R};\alpha,\beta,\gamma)\\ =\frac{1}{NK}\min\mathbb{E}\left[\left\|\mathbf{x}-\mathbf{M}\mathbf{y} \right\|^{2}\right]\\ =\frac{P}{NK}\text{tr}\left\{\left(\mathbf{I}_{N}+K\gamma\mathbf{T}\Theta \mathbf{T}^{\dagger}\right)^{-1}\right\}\\ =P\left(1-\frac{1}{NK}\gamma\frac{d}{d_{7}}\left(\text{logdet}(\mathbf{I}_{N} +K\gamma\mathbf{T}\Theta\mathbf{T}^{\dagger})\right)\right)\end{array}  \tag{30}\]  
where \(\mathbf{M}\) is the linear MMSE filter and \(\mathbf{M}\mathbf{y}\) is the linear MMSE filter output. The linear MMSE capacity has been studied in [49], and we also adopt the Haar approximation to obtain the MMSE capacity, given by:  
\[ \begin{array}{l}\lim\limits_{N\to\infty}\mathbf{C}_{\mathrm{ MMSE}}((\mathbf{T}\otimes\mathbf{1}_{K})\mathbf{R};\alpha,\beta,\gamma)\\ =K\log(\frac{P}{\mathrm{MMSE}_{\mathrm{avg}}((\mathbf{T}\otimes\mathbf{1}_{K}) \mathbf{R};\alpha,\beta,\gamma)})\\ \approx-K\log\left(1-\frac{1}{NK}\gamma\frac{d}{d_{7}}\left(\text{logdet}( \mathbf{I}_{N}+K\gamma\mathbf{T}\Theta\mathbf{T}^{\dagger})\right)\right) \end{array}  \tag{31}\]  
From Equation (19), we have \(\text{logdet}(\mathbf{I}_{N}+K\gamma\mathbf{T}\Theta\mathbf{T}^{\dagger}) \approx\left.\frac{d\mathbf{F}(u)}{du}\right|_{u=0}\); thus:  
\[ \begin{array}{l}\lim\limits_{N\to\infty}\mathbf{C}_{\mathrm{ MMSE}}((\mathbf{T}\otimes\mathbf{1}_{K})\mathbf{R};\alpha,\beta,\gamma)\\ \approx-K\log\left(1-\frac{1}{NK}\gamma\frac{d}{d_{7}}\left(\frac{d\mathbf{F} (u)}{du}\right)\right)\end{array}  \tag{32}\]  
In order to solve \(\mathbf{C}_{\mathrm{ MMSE}}\), we must derive \(\frac{d}{d\gamma}\left(\frac{d\mathbf{F}(u)}{du}\right)\):  
\[
## 4 Discussion
### Bed Resistance Associated with Bedforms  
This study extensively explored the challenging problem of bed resistance prediction in river dynamics. Utilizing the data from movable-bed dune experiments and applying [PERSON]'s formula, this study quantitatively analyzed bed resistance to elucidate the inherent relationship between bed resistance and dune morphology. The findings indicate a clear positive correlation between the dune height and bed resistance. However, the influence of dune length on bed resistance appeared less significant, aligning with the conclusions from prior research [3; 62].  
Notably, at high flow intensities, the dune height does not consistently increase with bed resistance; for instance, the dune height on a 4% slope is smaller than that on a 2% slope [63; 64]. Given the resistance of uniform sediment remains constant, an increase in resistance from other sources must compensate for the diminished bed resistance due to the reduced dune height [27], potentially caused by increased mass bedload transport.  
To verify this hypothesis, this study examined the relationship between bedload transport intensity and bed resistance. Analysis demonstrated a significant positive correlation: bed resistance increases with heightened sediment transport intensity (Figure 11). This finding signifies that the mass bedload transport at high flow intensities significantly contributes to increased bed resistance. Understanding this relationship is crucial for comprehending sediment transport in rivers, highlighting the role of bed resistance in influencing the sediment transport capacity of actual rivers.  
This study both quantitatively and qualitatively assessed bed resistance in the presence of dunes using experimentally measured data, offering new insights into the understanding of bed resistance. With advancements in technologies, such as remote sensing [65], numerical simulation [66], and artificial intelligence [67], more precise measurements and predictions of bed resistance are anticipated. Integrating data from diverse sources, including topography, flow velocity, and the sediment size, will enhance the comprehensive understanding of bed resistance formation mechanisms, which will facilitate the development of more accurate and reliable prediction models.
# An Overview of Variants and Advancements of PSO Algorithm
## 4 Advances on Particle Swarm Optimization (PSO) Algorithm
### Extensions of Applications of PSO
[40] proposed a novel differential particle swarm optimization(PSO)-based support vector machine (SVM) classifier, namely (DPSO-SVM). The authors, through experiments on several benchmark functions, showed the superiority of the proposed algorithm (DPSO-SVM) in order to improvise the classifier's performance. In neuro fuzzy system (NFS), usually a gradient-based algorithm was used by the researchers, but [PERSON] et al. [41] used an improved PSO (iPSO) to introduce the first embedded high-speed, low-cost implementation of the neuro fuzzy network (NFN) hardware through online training on a field programmable gate array (FPGA). The authors tested its proposedimplementation method on practical problems. The results showed the efficiency of the proposed implementation over other approaches available in the literature. In order to solve a power consumption minimization problem, [PERSON] et al. [42] suggested a PSO-based algorithm, namely distribution PSO (DPSO). The improved algorithm of PSO was checked on a luminous control problem and the results showed the effectiveness of the modified algorithm, along with its suitability to be parallelized. The same optimization problem performed differently with different settings of the parameters. Based on the concept of the building block thesis, [PERSON] et al. [43] proposed two variants to the PSO algorithm, namely the PSO with a fixed phase (PSOFP) algorithm, and the PSO with a dynamic phase (PSODP) algorithm. These new variants were tested on benchmark functions to solve the single objective numerical optimization, and revealed the robustness of the proposed variant of the PSO algorithm. The areas of application for the PSO algorithm has been extended by many researchers, but there is still a need to explore more areas of application of the PSO algorithm.