esa-sceva/satcom-chunk-collection huggingface dataset:
# Soil Stabilization Using Waste Paper Fly Ash: Precautions for Its Correct Use
## 3 Results and Discussion  
### Raw Material Properties  
Particle size distribution for WPFA is shown in Figure 3. The WPFA contains uniformly distributed fine particle sizes within the range of approximately 0.05 \(\upmu\)m to 120 \(\upmu\)m.  
Figure 3: Particle size distribution of WPFA.  
Figure 2: Long time expansion setup: (**a**) water flow from top, (**b**) water suction.  
The density of WPFA was 2.68 g/cm\({}^{3}\), which is less than the density of the cement (3.05 g/cm\({}^{3}\)) and less than that obtained by Segui [14] (2.85 g/cm\({}^{3}\)). Its pH was 12.5. The principal elements of WPFA are Ca and Si, with a minor presence of Al, Fe, Mg, and Cl (Table 3). The number of other elements were less than 2%, and a loss of ignition (LOI) of 15.58% was obtained. The free lime content was determined in accordance with EN 1744-1. and 8.0% was obtained.  
The X-ray Diffraction (XRD) results showed the presence of calcite, lime, portlandite, quartz, halite, calcium silicate, gehlenite, and aluminum in the WPFA, as shown in Figure 4. Some of the minerals are similar to WSA, like gehlenite, calcite, and lime [4; 27; 28]. In cement, the presence of calcium silicate, silica, larnite, brownmillerite, tricalcium aluminate, and gypsum have been identified, as shown in Figure 5.  
The characterization of the soil is shown in Table 4.  
Figure 4: XRD diffractogram for VPFA.  
Figure 5: XRD diffractogram for cement IV.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Material** & **CaO** & **SiO2** & **Al2O3** & **Fe2O3** & **SO3** & **MgO** & **MnO** & **K2O** & **TiO2** & **Cl** & \(\sum\)**Others** & **LOI** \\ \hline Soil & 35.30 & 27.52 & 3.75 & 1.55 & 0.60 & 0.97 & 0.05 & 0.67 & 0.21 & 0.06 & 0.48 & 28.24 \\ WPFA & 48.86 & 12.58 & 12.55 & 1.01 & 0.97 & 1.82 & 0.03 & 0.36 & 1.25 & 2.33 & 2.63 & 15.58 \\ Cem IV & 35.55 & 38.11 & 10.90 & 5.59 & 2.61 & 1.51 & 0.05 & 1.66 & 0.42 & 0.06 & 0.98 & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Chemical composition of the raw materials.  
According to the ASTM and AASHTO classification systems, the soil was a GM with silt and sand, and A-1-b showing no plasticity, respectively. The particle size distribution of the soil is shown in Figure 6. The chemical composition is shown Table 3. The mineral phases found in soil are quartz, calcite, albite, biotite, chamosite, and mica.  
Additionally, according to Spanish guidelines (PG3) [16], the maximum allowable amount of organic matter and soluble sulfate must be less than 1.0% and 0.7% by mass, respectively. The organic matter in soil was determined according to UNE 103-204-93 [34], and resulted in 0.87%. The soluble sulfate was 0.27% [35].
## 2 Method
### Numerical Simulations
#### 2.2.2 Boundary Conditions  
Inlet boundary: As shown in Figure 3, wind velocity \(U\) and turbulent kinetic energy \(k\) were set as the same as the values in the wind tunnel experiment. More detailed information can be found from the wind tunnel experiment [39]. Turbulent energy dissipation rate \(\varepsilon\) could be calculated by Equation (1), where \(C_{\mu}\) is a constant with a value of 0.09; \(k(z)\) is the turbulent energy, which can be obtained from a wind tunnel experiment or an observation of corresponding surroundings; \(U(z)\) is the wind speed at any point in the computational domain; and \(z\) is the height of any point in the computational domain [43].  
\[\varepsilon(z)\cong\mathrm{P}_{k}(z)\cong-\overline{\mathrm{u^{\prime}w^{ \prime}}}(z)\frac{dU(z)}{dz}\cong\mathrm{C}_{\mu}^{\frac{1}{2}}k(z)\frac{dU( z)}{dz} \tag{1}\]  
Outlet boundary: The normal gradients of all variables were set to zero for the outflow [43].  
Figure 3: Wind speed and turbulent kinetic energy measurements.  
Figure 2: High-rise building and computational domain.  
Building-surface boundary and ground boundary: Rough wall conditions and the default parameters of wall roughness in simulation software were used to define the building surface and ground boundary.  
Side boundary and top boundary: The smooth wall condition and the default wall treatment in simulation software were used to define the side and top boundaries.
# Quantum Leap from Gold and Silver to Aluminum Nanoplasmonics for Enhanced Biomedical Applications
## 3 Applications
### Biosensing
Polyelectrolyte layer-by-layer assembly with Al NPs was used to enhance the fluorescence intensity of BSA and IgG [127]. Figure 3: Biosensing using Al NSs. (**a**) Fluorescence intensities of biomolecules show the enhancement on Al nanostructured substrates compared to quartz. (Adapted from [PERSON] et al. [129]). (**b**) Fluorescence spectra of adenine on Al NP array show large enhancement compared to bare quartz. (Adapted from [PERSON] et al. [130]). (**c**) Surface-enhanced Raman scattering (SERS) of adenine on Al nanovoids. (Adapted from [PERSON] et al. [118]). (**d**) Enhancement factors of biomolecules on various substrates show largest resonance Raman enhancement on ALFON. (Adapted from [PERSON] et al. [119]).
## 3 Methodology
### Using FFT-Attention to Extract Parallel Features in Frequency Domain  
For the encoder section, embedding is carried out to enhance the information of the input time series for more effective feature capture utilizing FFT-Attention. Firstly, these vectors can be obtained from Equation (13):  
\[X_{EDE},X_{ETE}=Embedding(X_{ED},X_{ET}) \tag{13}\]  
where \(X_{EDE}\) and \(X_{ETE}\in\Re^{b\times s\times d}\), \(d\) denotes the feature dimension, and \(Embedding\) indicates the embedding structure.  
In the high-frequency details section, based on signal decomposition and an adapted mechanism, FFT-Attention was utilized to exact the feature information in terms of frequency. In particular, the input vectors \(X_{EDE}\) are transformed to query \(\vec{Q}\), key \(\vec{K}\), and value \(\vec{V}\) vectors by utilizing the feature augment method, as in Equations (14)-(16):  
\[\vec{Q}=X_{EDE}\times\vec{W_{Q}} \tag{14}\]  
\[\vec{K}=X_{EDE}\times\vec{W_{K}} \tag{15}\]  
\[\vec{V}=X_{EDE}\times\vec{W_{V}} \tag{16}\]  
where \(\vec{W_{Q}}\), \(\vec{W_{K}}\), and \(\vec{W_{V}}\) denote the related parameter matrices, respectively. Then, these vectors are transformed multiple times to obtain multiple self-attention sub-layers, i.e., \(\vec{Q}=\{\vec{Q}_{1},\vec{Q}_{2},\cdots,\vec{Q}_{h}\}\), where \(h\) is head size and \(\vec{Q}_{i}\in\Re^{b\times s\times(\frac{d}{h})}\) indicates the \(ith\) sub-layer. Similarly, \(\{\vec{K}=\vec{K}_{1},\vec{K}_{2},\cdots,\vec{K}_{h}\}\) and \(\vec{V}=\{\vec{V}_{1},\vec{V}_{2}  
where \(d_{f}\) denotes the feature dimension after the fast Fourier transform, and \(\hat{Q_{i}},\hat{K_{i}},\hat{V_{i}}\in\Re^{b\times s\times d_{f}}\) (\(FFT\) denotes fast Fourier transform). Moreover, every sub-layer can be calculated using Equation (18):  
\[Attn^{(i)}=Attn(\hat{Q_{i}},\hat{R_{i}},\hat{V_{i}})=Softmax(\frac{\hat{Q_{i}}\hat {K^{\intercal}}}{\sqrt{d_{f}}})\times\hat{V_{i}} \tag{18}\]  
Therefore, all sub-layer outputs are concatenated to acquire the feature vectors \(X_{EDEA}^{\rightarrow}\) in Equation (19):  
\[X_{EDEA}^{\rightarrow}=Concat(Attn^{(1)},Attn^{(2)},\cdots,Attn^{(h)})\vec{W_{h}} \tag{19}\]  
where \(\vec{W_{h}}\) indicates the related parameter matrix. Furthermore, inverse fast Fourier transform is performed to make the signal return to the time domain in Equation (20):  
\[X_{EDEA}^{\rightarrow}=iFFT(X_{EDEA}^{\rightarrow}) \tag{20}\]  
where \(X_{EDEA}^{\rightarrow}\in\Re^{b\times s\times d}\), and \(iFFT\) denotes the inverse fast Fourier transform. Next, one feed-forward layer and two residual links are created, as in Equation (21):  
\[X_{EDEA}^{\rightarrow}=Norm(ReLU(Norm(X_{EDEA}^{\rightarrow}\vec{W_{n}}+b_{n1} )+\vec{W_{n2}})+b_{n2})\vec{W_{n3}}+b_{n3} \tag{21}\]  
where \(Norm\) indicates one residual links function, \(ReLU\) denotes one feed-forward layer function, and \(\vec{W_{n1}},\vec{W_{n2}},\vec{W_{n3}}\in\Re^{d_{f}\times d_{f}}\) represent learnable parameters.
## 2 Positioning in the Forest
### Global Navigation Satellite Systems (GNSS)
#### 2.1.1 Gps  
The GPS is maintained and financed by the US Department of Defense and currently consists of 31 operational satellites orbiting at an altitude of about 20,200 km [21]. These satellites transmit signals primarily at two frequencies called L1 and L2. In the future, GPS satellites will also transmit a signal called L5. Two types of pseudorandom codes are modulated in GPS signal transmission onto the carrier signal: a civilian Coarse Acquisition (C/A) code is modulated to carrier L1 and this is used for coarse positioning, and Precise (P) code is modulated to carriers L1 and L2 for precision positioning. In addition, a navigation message is modulated to the signal containing information about the satellites' orbits, clocks, and health status. Generally, only the C/A code is utilized in civilian receivers for obtaining the satellite-to-user distances and then the user location. Because the true difference between the satellite's and receiver's clocks is not known, the measured distance is called pseudorange. The C/A code enables the code's pseudorange to be derived with an accuracy of about 3 m. When the phase of the carrier signal is used, the phase pseudorange can be determined with an accuracy of a millimeter, but then the number of full waves, the ambiguity, must be either resolved by using the code pseudorange or estimated. The loss of signal lock between a GPS satellite and the receiver is referred to as cycle slip, following which the ambiguity must be resolved. The use of a dual frequency receiver, utilizing both L1 and L2, allows the removal of the first-order effects of the ionosphere. [22] The more precise P code is mostly intended for military purposes. A GPS receiver obtains and processes signals simultaneouslyfrom multiple satellites. When the satellite locations can be determined from the navigation message modulated to the signal, the different satellite-to-user distance measurements provide the means for calculating the user's receiver's position, velocity, and time [20].
## 2 Basic Equations for EM Wave Scattering by a Linear Array of Spheres
\right) \tag{8}\]  
are expressed in terms of Wigner 3-j symbols,  
\[\mathcal{H}(l,m;\
u,-m;p)=\sum_{s=-1}^{1}\mathcal{G}_{s}(l,m;\
u,-m;p) \tag{9}\]  
with  
\[\mathcal{G}_{0}(l,m;\
u,-m;p)=-2m|p|\mathcal{G}(l,m;\
u,-m;p-1),\] \[\mathcal{G}_{\pm 1}(l,m;\
u,-m;p)=\mp\sqrt{(\
u\pm m)(\
u\mp m+1)p(p-1) }\mathcal{G}(l,m;\
u,-m\pm 1;p-1), \tag{10}\]  
and  
\[s_{p}=\lambda_{p0}\sum_{j=1}^{\infty}h_{p}(k_{0j})(e^{j\beta j}+(-1)^{p}e^{-i \beta j}), \tag{11}\]where \(\lambda_{lm}\) is normalization factor given in Appendix A. The next step is to account for an incident plane wave which can be expanded over vector spherical harmonics [1; 6]. \[\mathbf{E}^{\sigma}(\mathbf{r})=\sum_{l=1}^{\infty}\sum_{-l}^{l}[q _{lm}^{\sigma}\mathbf{M}_{l}^{m}(\mathbf{r})+p_{lm}^{\sigma}\mathbf{N}_{l}^{m} (\mathbf{r})],\] \[\mathbf{H}^{\sigma}(\mathbf{r})=-i\sum_{l=1}^{\infty}\sum_{-l}^{ l}[p_{lm}^{\sigma}\mathbf{M}_{l}^{m}(\mathbf{r})+q_{lm}^{\sigma}\mathbf{N}_{l}^{m} (\mathbf{r})]. \tag{12}\]  
Here, index \(\sigma\) stands for plane TE/TM wave. \[p_{lm}^{TE}=-F_{lm}\tau_{lm}(\alpha),\;\;q_{lm}^{TE}=F_{lm}\tau_{ lm}(\alpha),\] \[p_{lm}^{TM}=-iF_{lm}\tau_{lm}(\alpha),\;\;q_{lm}^{TM}=iF_{lm}\tau _{lm}(\alpha), \tag{13}\]  
\(k_{x}=-k_{0}\sin\alpha,k_{y}=k_{0}\cos\alpha\),  
\[F_{lm}=(-1)^{m}i^{l}\sqrt{\frac{4\pi(2l+1)(l-m)!}{(l+m)!}},\] \[\tau_{lm}(\alpha)=\frac{m}{\sin\alpha}p_{l}^{m}(\cos\alpha),\] \[\mathcal{G}_{0}(l,m;\
u,-m;p)=-2m|p|\mathcal{G}(l,m;\
u,-m;p-1),\]0  
For a particular case of normal incidence \(k_{z}=0\), \(\alpha=-\pi/2\) we obtain from Equation (14)  
\[\mathcal{G}_{0}(l,m;\
u,-m;p)=-2m|p|\mathcal{G}(l,m;\
u,-m;p-1),\]1  
The general equation for the amplitudes \(a_{l}^{m},b_{l}^{m}\) which describe the scattering by a linear array of spheres takes the following form:  
\[\mathcal{G}_{0}(l,m;\
u,-m;p)=-2m|p|\mathcal{G}(l,m;\
u,-m;p-1),\]2 \[\mathcal{G}_{0}(l,m;\
u,-m;p)=-2m|p|\mathcal{G}(l,m;\
u,-m;p-1),\]3  
Here the left hand term formulates explicitly the matrix \(\widehat{L}\) in Equation (1) and the right hand term corresponds the vector of incident wave \(\Psi_{inc}\) in the space of vector spherical functions notified by two integers \(l,m\) and polarization \(\sigma\).
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Congestion Control Mechanisms in CoAP
#### 4.1.7 Improved Adaptive Congestion Control  
The previously proposed methods do not consider the problem of choosing a right RTO value in case of burst traffic. In addition, these methods also do not consider the packet loss ratio. Packet loss ratio is defined as the number of packets received at the receiver end over number of packets sent by the sender. The packet loss ratio is used to evaluate the loss performance of method. [24] proposes an improved congestion control algorithm based on the packet loss ratio and RTT values of previous transmission. The method proposes two scenarios using packet loss ratio as key parameter and adjusts the RTO value accordingly based on the previous RTT values.  
Case 1: The RTO value is updated according the formula presented in Equation (5) in case when packet loss ratio is lower than 50% in order to prevent unnecessarily long idle delays, whereas the RTO value is updated according to the formula presented in Equation (1) to correct the loss value.  
\[RTO_{recent}=RTT*packetlossratio+(1-packetlossratio)*RTO_{previous} \tag{5}\]  
\[RTO_{recent}=RTO_{previous}*packet_{1}oss_{r}atio+(1-packet_{1}oss_{r}atio)*RTT \tag{6}\]  
Since the RTO value is updated in each transmission based on the packet loss ratio, there is no need for RTO aging mechanism. Calculation of RTO value in each transmission causes too much overhead and can cause delay in transmission.
# Adversarial Sample Generation Method Based on Frequency Domain Transformation and Channel Awareness
## 8 Conclusions  
To sum up, this study successfully proposed a new channel estimation method based on deep learning, giving full play to the advantages of deep learning in data processing and feature extraction, and achieving high-precision estimation of sparse channels in OFDM systems. On this basis, further innovation was carried out and a channel-aware adversarial sample generation method based on frequency-domain transformation was proposed. It effectively integrates channel estimation and frequency-domain transformation techniques, significantly improving the generation effect of adversarial samples.  
Judging from the phenomena revealed by the research results, deep learning models can capture channel features more accurately. In the complex OFDM system environment, they show stronger adaptability and accuracy compared with traditional algorithms. This theoretical achievement not only enriches the research methods in the field of channel estimation, but also provides new ideas for the optimization of communication systems. In terms of practical significance, high-precision channel estimation and more effective adversarial sample generation methods contribute to enhancing the stability and security of communication systems, providing more reliable technical support for practical communication applications.  
Although this study has achieved remarkable results, there is still room for further research. In the future, the performance optimization of deep learning models under different channel conditions can be deeply explored, and studies can be conducted on how to further enhance the universality and concealment of adversarial samples. Meanwhile, studies can apply these methods to more complex communication scenarios, verify their effectiveness and feasibility, and contribute more to the development of communication technology.  
Conceptualization, Y.G.; methodology, Y.G.; software, Y.G.; validation, Y.G. and H.Z.; formal analysis, Y.G. and H.Z.; investigation, Y.G.; resources, Y.G.; data curation, Y.G.; writing--original draft preparation, Y.G.; writing--review and editing, Y.G.; visualization, Y.G.; supervision, D.X.; project administration, Q.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The first dataset was random. Relevant data files can be obtained from the corresponding author upon reasonable request.The second dataset used in this study is a publicly available dataset RadiOML 2016.10A, which can be accessed via [[https://www.deepsig.ai/datasets](https://www.deepsig.ai/datasets)]([https://www.deepsig.ai/datasets](https://www.deepsig.ai/datasets)) (accessed on 29 April 2025). Detailed information about the dataset is provided in the Methods section. Experimental results supporting the findings of this study are presented in the manuscript (Tables and Figures). Additional raw data or analysis results can be provided by the authors upon request.  
The authors declare no conflicts of interest.
# Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
## 2 Related Work
The network life can be lengthened by applying a probabilistic dissemination algorithm among those relay nodes. Some fuzzy logic-related articles have also been proposed to improve the energy efficiency in WSNs. [PERSON] et al. [33] proposed a fuzzy logic and black widow optimization clustering protocol. However, the black widow optimization's ideal performance is modest. Later, the authors proposed a strategy for designing the optimal interval type 2 fuzzy logic by involving the evolutionary algorithms [34]. This solution technique is suitable for WSNs with limited energy since it helps to extend the network's lifespan. In reference [35], a trust-aware energy-saving stable clustering algorithm based on the fuzzy type-2 algorithm was devised to solve the constraint of the shortening lives of the cluster heads in clustering algorithms. Various studies have proposed game-theoretic approaches for the establishment of a tradeoff between the desired signal-to-noise ratio (SNR) and energy consumption [36; 37]. These approaches focus on optimal route selection while considering communication quality. The game-theoretic approach is effective in the sense of getting a payoff for individual nodes. However, the entire network's performance cannot be optimized by these approaches. Moreover, the nodes are self-focused in such approaches, and these do not give any length to the network life. The node selection mechanisms such as those in [27; 38] were also proposed for choosing the best nodes among others for energy optimal efficiency in the network. Various nodes and network scoring mechanisms have been proposed by many articles mainly focused on energy efficiency, node behavior, and security. The GoNe scheme, proposed in [39], was designed for enforcing data security and privacy in WSNs. Nodes are given some scores based on their reputation in the network. These reputation scores are managed by CHs, which are later used to manipulate the behavior of nodes. In another score-based load management scheme [40], the authors proposed a mechanism to compress the data through CHs to reduce the load on the nodes with low scores. The best nodes are chosen based on their remaining energy and distance from the BS. The CHs use compressive sensing to compress data and then forward information towards the sink through the best nodes. The authors claim that in this way, the load is balanced among all the nodes. The SBRR protocol [41] considers many factors to score paths for nodes. The parameters are the hop count, the remaining energy of nodes, link quality, and the buffer sizes on the nodes. All the parameters are integrated to form the path score. The main focus of the work was to reduce the pack loss in the transmission. Still, there is space for load balancing and energy efficiency in the work.
# Focus Estimation Methods for Use in Industrial SFF Imaging Systems
## 2 Materials and Methods
### Considered Operators for Estimating Focus on a Digital Image  
This comparative study consisted of the evaluation of operators for focus estimation on digital images. The operators were selected based on the literature in the field of focus evaluation in both the SFF and autofocus approaches. The selection of operators was driven by two indicators: efficiency and time required to perform the calculations. The selected methods are listed in a table together with a classification into four groups (Table 3). The first group is based on calculating the derivative at a point. The study included modifications of the classic filters by averaging in the neighbourhood (Tenengrad algorithm). The additional averaging allowed extending the kernel size to values larger than 3 \(\times\) 3. The second group of filters are operators based on the computation of the Laplace transform together with a modification of neighbourhood averaging. The next group of filters are statistical filters. This group includes filters that consider the spread of pixel intensity values in the local window as the focus estimator. The last group of filters allows the estimation of focus directly on the intensity of a point or its neighbourhood without the need for greater computational complexity calculations. The symbols given in Table 3 will be used consistently throughout the article.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**No.** & **Group** & **Operator** & **Symbol** \\ \hline
1 & Derivative & Robert’s & DRB \\
2 & Derivative & Robert’s–Tenengrad & DRBT \\
3 & Derivative & Sobel & DSL \\
4 & Derivative & Sobel–Tenengrad & DSLT \\
5 & Derivative & Prewitt & DPT \\
6 & Derivative & Prewitt–Tenengrad & DPTT \\
7 & Laplace & Laplace & LLP \\
8 & Laplace & Laplace–Tenengrad & LLPT \\
9 & Laplace & LOG & LLOG \\
10 & Laplace & Modified Laplace & LML \\
11 & Laplace & Modified Laplace–Tenengrad & LMLT \\
12 & Statistical & Entropy & SETP \\
13 & Statistical & Standard Deviation & SSTD \\
14 & Statistical & Variance & SVAR \\
15 & Statistical & Variance Normalized & SVRN \\
16 & Intensity & Intensity Sum & ISUM \\
17 & Intensity & Intensity thresholded count & ICNT \\
18 & Intensity & Intensity Energy & IENG \\ \hline \hline \end{tabular}
\end{table}
Table 3: Operators used to assess the sharpness of a digital image.
# CCAIB: Congestion Control Based on Adaptive Integral Backstepping for Wireless Multi-Router Network
## 4 Performance Evaluation
### Results and Analysis
#### 4.2.3 Simulation Results for Different Propagation Delays  
In this group, \(T_{p}=0.05\), \(0.1\), \(0.15\) s, respectively. Figure 4 shows the performance of different propagation delays. Although all the queue lengths converge in 15 s with different propagation delays, a bigger propagation delay leads to more serious overshoot, as shown in Figure 4a. It is obvious in Figure 4b,c that a bigger propagation delay also produces larger window size and lower packet loss ratio. That is because when the packets arrive to the router slowly, the congestion can be alleviated. It should be noted that larger propagation delay decreases the effectiveness of the communication. So, there must be a tradeoff when the distance between routers is determined.  
Figure 3: Simulation results of multiple routers using CCAIB for different link capacities. Columns from left to right show the performance of CCAIB in (**a**) queue length stability, (**b**) window size and (**c**) packet loss ratio in the queue.  
Figure 2: Simulation results of single router using CCAIB and Hoo algorithm for case 1. Columns from left to right show the performance of CCAIB and Hoo in (**a**) queue length stability, (**b**) window size and (**c**) packet loss ratio in the queue.