esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Due to the physical characteristics of acoustic channels, the performance of underwater acoustic communication networks (UACNs) is more susceptible to the impacts of multipath and Doppler effects. Channel quality can serve as a measure of the reliability of underwater communication links. A cross-layer routing protocol based on channel quality (CLCQ) is proposed to improve the overall network performance and resource utilization. First, the BELLHOP ray model is used to calculate the channel impulse response combined with the winter sound speed profile data of a specific sea area. Then, the channel impulse response is integrated into the communication system to evaluate the channel quality between nodes based on the bit error rate (BER). Finally, during the selection of the next hop node, a reinforcement learning algorithm is employed to facilitate cross-layer interaction within the protocol stack. The optimal relay node is determined by the channel quality index (BER) from the physical layer, the buffer state from the data link layer, and the node residual energy. To enhance the algorithm's convergence speed, a forwarding candidate set selection method is proposed which takes into account node depth, residual energy, and buffer state. Simulation results show that the packet delivery rate (PDR) of the CLCQ is significantly higher than that of Q-Learning-Based Energy-Efficient and Lifetime-Extended Adaptive Routing (QELAR) and Geographic and Opportunistic Routing (GEDAR).  
cross-layer routing protocol; channel quality; Q-learning; underwater acoustic communication networks +
Footnote †: journal: Applied
# Effects of Water-Reducing Agents on the Mechanical Properties of Foamed Phosphogypsum
## 3 Results and Discussion
### Discussion  
It can be concluded that the types and dosages of water-reducing agents have a significant effect on the properties of foamed phosphogypsum; the effects vary greatly between PCE and FDN. Compared with PCE, the match between FDN and foamed phosphogypsum is better; the optimal content of FDN is 0.4%.  
Compared with the foamed phosphogypsum doped with PCE, the water consumption of the foamed phosphogypsum doped with FDN was significantly lower, which meant that the viscosity and shear stress of the slurry doped with FDN were larger, the foam was tightly wrapped by the gypsum slurry, and the foam flow range was relatively small. Therefore, the foamed phosphogypsum doped with FDN had a relatively low proportion of large pores and a large number of uniformly distributed medium pores; hence, the foamed phosphogypsum samples doped with FDN had a lower dry density and higher strength. The foamed phosphogypsum slurry mixed with 0.4% FDN consumed the least amount of water; hence, its viscosity and shear stress were relatively large. Moreover, the slurry mixed with 0.4% FDN had the lowest number of large pores and the highest number of medium pores; thus, it yielded the best mechanical properties. The water-reducing agents mainly reduced the water consumption of the phosphogypsum slurries; changed the viscosity and shear stress of the gypsum slurries; affected the stability of foam in the gypsum slurries; changed the pore size and distribution in the gypsum slurries; and influenced the strength, dry density, and other macro-properties of the foamed phosphogypsum samples. The viscosity of the gypsum slurry doped with 0.4% FDN better matched that of the foam; therefore, it had the highest macro-strength and the lowest dry density.
## 3 Results and Discussion
### Performance Prediction of a Notional Sensing System  
To predict the performance of bio-sensing using a non-linear tag, a notional system shown in Figure 9 is considered. This system consists of the proposed antenna and a passive frequency triplet circuit. By utilizing the measured peak gain of the proposed antenna and the antenna total efficiency obtained through simulations, we established the link budget of the notional system, as shown in Table 2. The system operates with a two-tone signal at a power level of 27 dBm (\(f_{1}=23.4\) GHz,\(f_{2}=23.7\) GHz) transmitted from the Tx/Rx module. The non-linear tag, positioned 1 m away from the radar, receives the transmit signal with an input power (\(P_{tag}\)) of \(-10.6\) dBm. The IM3 response signal (\(2f_{2}-f_{1}\)) generated by the third-harmonic generator is then re-radiated and received by the Tx/Rx module with a power of \(-68.9\) dBm. These findings provide valuable insights into the performance and feasibility of the proposed system for bio-sensing application.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Size (mm)** & **Peak Gain** & **Axial Ratio** & **Impedance Bandwidth** & **Dual CP** \\  & **(dBi)** & **(GHz)** & **Bandwidth** & **Bandwidth** & **(GHz)** & \\ \hline
[32] & \(76.2\times 76.2\times 0.5\) & 6 * & 18–18.55 & 18.35–20.18 & No \\ \hline
[33] & \(29.5\times 9.35\times 1.58\) & 6.56 * & 21.2–21.8 & 18.9–20.76 & No \\ \hline
[34] & \(12\times 12\,\times\,3.5\) & 4.9 & 18.4–19.8 & 17.5–20.5 & Yes \\ \hline
[35] & \(7.5\times 7.5\,\times\,0.7\) & 4 & 19.8–20.1 & 19.8–21.1 & No \\ \hline This work & \(30\times 16.4\,\times\,0.25\) & 11.5 & 23–25 & 23.16–24.92 & Yes \\ \hline \hline \end{tabular}  
* Simulated data  
\end{table}
Table 1: Comparison of proposed and reference antennas.  
Figure 8: Simulated and measured axial ratios. The shaded area is the operational bandwidth.
## 3 LDPC Coding--Preliminaries
### Raptor-like LDPC Codes  
In contrast to block coding with a fixed code rate, rateless coding is an error correction scheme that, given a fixed information vector, generates a (potentially unconstrained) series of redundant bits [39], making use of IR decoding on the receiver side. However, constructing good rateless codes with the possibility of efficient encoding and decoding requires some effort. One of the recently achieved solutions is protograph-based Raptor-like (PBRL) LDPC coding [12; 13].  
The PBRL-LDPC code can be considered to be a concatenation of a high-rate code (HRC), which is an LDPC code with a parity check matrix \(\mathbf{H}_{\text{HR}}\) of size \(M\times N\) and an incremental redundancy code (IRC) characterized by a low-density generator matrix (LDGM) \(\mathbf{G}=[\mathbf{I},\mathbf{H}_{\text{IR}}^{\top}]\)[13]. In this paper, we employ the QC subclass of PBRL-LDPC codes, which will be called QC-RL-LDPC codes.  
The matrices defining the QC-RL-LDPC code \(\mathbf{H}_{\text{HR}}\) and \(\mathbf{H}_{\text{IR}}\) both have a structure consisting of CPMs, as in (2). The size of matrix \(\mathbf{H}_{\text{IR}}\) is \(LP\times N\), which means that it has \(L\) macrorows of \(P\times P\) circulants, giving a total of \(LP\) parity checks in the incremental redundancy scheme. The Tanner graph of such a QC-RL-LDPC code can be illustrated as presented in Figure 3, where the blocks denoted by \(\mathbf{H}_{\text{HR}}\) and \(\mathbf{H}_{\text{IR}}\) are edge permutators according to the matrices' structures.
## 4 Experimental Results
### Performance Verification Experiment
#### 4.2.3 Ablation Study
To further validate the impact of using original age labels on the accuracy of age estimation models, we compared the accuracy of three baseline models after layer-wise feature reconstruction, both before and after fine-tuning with age labels. Table 5 presents the MAE values of the models after global fine-tuning. The results indicate that all three models achieved further accuracy improvements, demonstrating that fine-grained tuning better adapts to the age distribution within the dataset, enabling the models to provide more accurate estimations across different age groups. Although feature reconstruction enhances the model's capability, it still retains some noisy features. By incorporating age labels for global fine-tuning, the models effectively eliminate these noise features, resulting in more precise final estimations. Therefore, the fine-tuning process not only improves model performance but also significantly enhances the model's adaptability to complex data characteristics, thereby boosting overall model accuracy. \begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MAE**} \\ \cline{2-3}  & **Baseline** & **Layer-Wise Feature Reconstruction** \\ \hline CORAL & 6.62 & 4.59 \(\downarrow\) 2.03 \\ DLDL & 6.88 & 4.91 \(\downarrow\) 1.97 \\ MWR & 6.85 & 5.08 \(\downarrow\) 1.77 \\ \hline \hline \end{tabular} Note: Lower MAE values indicate higher accuracy in age estimation. The red arrows show the reduction in MAE after layer-wise feature reconstruction.  
\end{table} 
Table 4: MAE of the models with and without layer-wise feature reconstruction on the MORPH-2 dataset with random occlusion. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Occlusion Type**} & \multicolumn{3}{c}{**MAE**} \\ \cline{2-4}  & **CORAL** & **DLDL** & **MWR** \\ \hline Mask & 5.51 & 5.72 & 6.44 \\ Sunglasses & 6.21 & 6.44 & 6.32 \\ Mask + Sunglasses & 7.52 & 7.71 & 7.82 \\ Mouth Color Block & 4.96 & 4.85 & 5.57 \\ Eyes Color Block & 5.09 & 5.22 & 5.98 \\ Mouth + Eyes Color Block & 6.62 & 6.31 & 7.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of MAE under different occlusion types. \begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MAE**} \\ \cline{2-3}  & **Baseline** & **Layer-Wise Feature Reconstruction** \\ \hline CORAL & 6.62 & 4.59 \(\downarrow\) 2.03 \\ DLDL & 6.88 & 4.91 \(\downarrow\) 1.97 \\ MWR & 6.85 & 5.08 \(\downarrow\) 1.77 \\ \hline \hline \end{tabular} Note: Lower MAE values indicate higher accuracy in age estimation. The red arrows show the reduction in MAE after layer-wise feature reconstruction.  
\end{table} 
Table 4: MAE of the models with and without layer-wise feature reconstruction on the MORPH-2 dataset with random occlusion.
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
## 5 Performance Evaluation  
To study the properties of the classification method presented in this work, several experiments are performed in two phases. In the first phase, the ns-2 simulator version 2.31 is used to capture thepotential performance with different scenarios. In the second phase, real trace data are used, which were generated by the medical asset tracking system deployed in a real emergency room environment. This simulation evaluates the environmental changes, which affect the performance of the proposed classification scheme.
## 3 Results and Discussion
### Antidiabetic Activity  
Oxidative stress in the body is one of the most serious factors in the development of diabetes [52]. Free radical scavengers are used to manage the oxidative damage and to constrain enzymes like \(\alpha\)-amylase and \(\alpha\)-glucosidase, which are responsible for diabetes [53]. The inhibition of carbohydrate-hydrolyzing enzymes like \(\alpha\)-amylase can be an important approach to lower postprandial blood glucose levels. In this study, the activity of \(\alpha\)-amylase was potentially inhibited in CuO NP-elicited CSC extracts (Figure 5a). Marked suppression of \(\alpha\)-amylase in a dose-dependent manner was observed  
Figure 4: Effect of CuO NPs on antioxidant activities in CSC of _G. sylvestre_. (**a**) Free radical-scavenging activity by the 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH) assay, (**b**) total Fe\({}^{3+}-\) Fe\({}^{2+}\) reductive potential reference antioxidants (butylated hydroxytoluene), (**c**) total antioxidant capacity (TAC) by the phosphomolybdenum method. TAC is expressed as equivalents of \(\alpha\)-tocopherol (\(\upmu\)g/g of extract). Different letters indicate a significant difference at \(p\leq 0.05\).  
after incubation with different amount of extracts. CuO NP-elicited CSC extracts (100 \(\upmu\)g/mL) displayed an 86.75% rate of \(\alpha\)-amylase enzyme inhibition compared to the rate of 70.50% for non-elicited CSC extracts. The inhibition rate achieved using acarbose was 92.00% (Figure 5a). Similarly, the GA fraction displayed a significant reduction in amylase activity (14.25%) using the extract of _G. sylvestre_ leaves [54]. Non-enzymatic glycosylation of hemoglobin was increased in CuO NP treated and non-treated CSC extracts. The inhibition of glycosylation was concentration-dependent with CuO NP treated and non-treated CSC extracts andtocopherol, which was used as a standard. The inhibition of glycosylation in CuO NP treated (3 mg/L) and non-treated CSC extracts were 90.50% and 75.25%, respectively (Figure 5b). The results offer clues that effective treatment for diabetes may be possible using the bioactive elements present in plant extracts. GA obtained from _G. sylvestre_ inhibits glucose absorption in the small intestine and suppresses hyperglycemia and hyperinsulinemia in oral glucose tolerance test. The efficiency of GA in inhibiting glucose absorption in the small intestine was increased by a combined effect with acarbose and voglibose [55]. GA and PC have potent inhibitory effects on \(\alpha\)-amylase and \(\alpha\)-glucosidaseidase [55,56]. We hypothesize that the presence of GA II and PC in CuO NPs elicited by CSC extracts of _G. sylvestre_ could significantly inhibit \(\alpha\)-amylase and glycosylation activities.
## 3 Numerical Results  
The sequence of steps resulting from the mathematical framework defined in the previous section is summarized in Algorithm 1. After the initialization, the gPCE surrogate model is built taking into account only the most significant parameters, whose selection is based on the computation of the total Sobol' index \(S_{T}\). We retain in the final model only the parameters with \(S_{T}\) larger than a user-specified threshold \(\varepsilon_{S}\), which can be conveniently adjusted so as to provide a good balance between model representativeness and cost. Whenever a parameter is excluded from the uncertainty analysis, the gPCE model has to be recomputed. The most expensive task, that is, the \(n_{q}\) full model \(\mathcal{S}\) runs for a variable number of sweeps, is carried out at this stage, which must be therefore carefully designed. Once the surrogate model is available, the uncertainty quantification step can be completed by means of the classical ES or ES-MDA algorithms, where a very large number of MC realizations can be now computed at a low computational cost.  
```
1:% Initialization
2:Define \(n\) uncertain parameters in the vector \(\mathbf{p}\)
3:Set \(N\), \(\varepsilon_{S}\), \(n_{MC}\), \(n_{MDA}\) and \(\alpha_{MDA}\)
4:Input \(\mathbf{d}\) and \(H\)
5:% gPCE model and sensitivity analysis
6:Check = True
7:while Check do
8: Check = False
9: Set the gPCE basis functions \(\Phi_{\mathbf{\alpha}}\)
10:\(n_{q}=(N+1)^{n}\)
11:\(\mathbf{c_{\mathbf{\alpha}}}=\mathbf{0}\)
12:for\(j=1,...,n_{q}\)do
13: Set the nodes \(\mathbf{z}^{j}\in\Sigma\) and the weights \(w(\mathbf{z}^{j})\)
14:\(\mathbf{U}^{j}=\mathcal{S}(\mathbf{z}^{j})\)
15:\(\mathbf{c_{\mathbf{\alpha}}}\leftarrow\mathbf{c_{\mathbf{\alpha}}}+\mathbf{U}^{j}\Phi_{\mathbf{\alpha}}( \mathbf{z})w(\mathbf{z}^{j})\)
16:endfor
17: Set \(s\) as the total number of Sobol' indices
18:for\(i=1,\ldots,s\)do
19: Compute \(S_{i}=V_{i}/V\)
20:endfor
21:for\(k=1,...,n\)do
22: Compute \(S_{T,k}\)
23:if\(S_{T,k}<\varepsilon_{S}\)then
24: Remove the \(k\)-th parameter from \(\mathbf{p}\)
25:\(n\gets n-1\)
26: Check = True
27:endif
28:endfor
29:endwhile
30:% Constrain the model via Data Assimilation
31: Set the prior parameter set \(\mathbf{p}^{p}\)
32:for\(i=1,...,n_{MDA}\)do
33: Compute \(D\) and \(R\) using \(\alpha_{MDA}\)
34:for\(j=1,...,n_{MC}\)do
35:\(\mathbf{u}^{f}=\sum_{\left|\mathbf{\alpha}\right|}c_{\mathbf{\alpha}}\Phi_{\mathbf{\alpha}}( \mathbf{p}^{p})\)
36:endfor
37: Compute \(\mathcal{C}^{f}\)
38:\(K=\mathcal{C}^{f}H^{T}(H\mathcal{C}^{f}H^{T}+R)^{-1}\)
39:\(X^{u}=X^{f}+K(D-HX^{f})\)
40:endfor
41:\(\mathbf{u}^{i}=\sum_{\left|\mathbf{\alpha}\right|}c_{\mathbf{\alpha}}\Phi_{\mathbf{\alpha}}( \mathbf{p}^{p})\)
```  
**Algorithm 1** gPCE-ES and gPCE-MDA for the forward model \(\mathcal{S}\).
## 3 Proposed Security Framework for RPL-Based IoT Networks (SRF-IoT)
### Topology  
IoT networks usually are deployed in mesh topology. Devices in mesh topology route packets with each other directly. In our case, the topology is shown in Figure 2. Our approach takes advantage of RPL specification that allows operation of one DODAG with two different RPL instances [6]. This is already supported in operating systems (OS) such as Contiki-NG that implement RPL protocol, so no extra modifications are needed. It is a different architecture from our initial topology shown in the first version of SRF-IDS. This is because having two RPL instances allows the operation of two networks at the same time. The RPL instance with ID equal to zero is the one that is monitored for suspicious activity and we call it a monitored network. It has one sink/router that acts as BR, and several devices that can be benign or malicious.  
SRF-IDS forms a second network inside the DODAG with RPL instance ID equal to one, which contains the SRF-IDS root and SRF-IDS detectors. These two networks belong to the same DODAG but are two different RPL instances. This helps SRF-IDS to distinguish packets coming from neighbour monitored network easily. We assume that the RPL instance of SRF-IDS is secured by allowing only authenticated SRF-IDS devices to participate and encrypting packet contents using symmetric encryption. Therefore, it is protected from malicious attackers.  
Figure 1: SRF-IoT framework high-level architecture.
## 3 Methodology
### Data Validation  
To ensure face validity, the questionnaire was first reviewed by a data analyst to confirm that the constructs were measured accurately. Data from the field survey were manually coded, except for the data collected online using Google Forms. To ensure the integrity of the data, a series of data verification steps were undertaken. Data collected via online platforms such as Google Forms were checked for any unusual response patterns or inconsistencies that might suggest automation or AI involvement. To ensure data validity and accuracy, responses were manually reviewed and subjected to item analysis using Cronbach's Alpha to assess internal consistency [37]. Construct validity was evaluated through the reliabilities of individual factors [38], also measured using [PERSON]'s Alpha coefficient and through Exploratory Factor Analysis (EFA) [39].
## 5 Analysis of the Monitored Results
### Lateral Deflections of the Enclosure Pile
It can be found that the lateral displacement of foundation pit B was larger than that of foundation pits A, C, and D. The relationship between the excavation depth of foundation pits A, B, C, and D and the maximum value of the lateral displacement of each measuring point are shown in Figure 12. The upper and lower limits of the ranges from previous studies ([PERSON] and [PERSON], 1990; [PERSON] et al., 2007; [PERSON] et al., 2013) [24; 25; 26] are also plotted in Figure 12 for comparison. For foundation pits A, C, and D, \(\delta_{\text{hm}}\) ranged from \(\delta_{\text{hm}}\) = 0.14% \(H_{\text{e}}\) to \(\delta_{\text{hm}}\) = 0.48% \(H_{\text{e}}\), which is in line with those empirical relationships. This shows that the deformation of foundation pits A, C, and D conformed with other single foundation pits in Shanghai. The main reason is that foundation pits A, C, and D were not adjacent and, thus, were not affected by each other in the excavation process. For foundation pit B, \(\delta_{\text{hm}}\) ranged from \(\delta_{\text{hm}}\) = 0.26% \(H_{\text{e}}\) to \(\delta_{\text{hm}}\) = 0.63% \(H_{\text{e}}\), which was higher than those empirical relationships. This may be caused by the disturbance of the construction of foundation pits A, C, and D in the early stages. By controlling the excavation sequence of adjacent foundation pits, the superposition effect of the foundation pit group excavation can be well controlled. Therefore, the excavation sequence design of this foundation pit group was reasonable and effective.
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 3 Description of the System Under Study
### Choice of Robot Under Study  
One of the central challenges in controlling robot manipulators is ensuring that the end-effector follows a desired trajectory, reaching a specific target position. The Scorbot robot, with its suitable link lengths and joint rotation degrees, offers an optimal workspace  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Symbol** & **Link 1** & **Link 2** & **Link 3** & **Unit** \\ \hline \(l\) & 0.32 & 0.22 & 0.22 & [m] \\ \(lc\) & 0 & 0.052 & 0.1376 & [m] \\ \(m\) & 7.1402 & 2.2483 & 1.957 & [kg] \\ \(Izz\) & 0.04624 & 0.02595 & 0.03616 & [kg\(\cdot\)m\({}^{2}\)] \\ \(Fv\) & 0.025 & 0.025 & 0.025 & [N\(\cdot\)m/rad] \\ \(Feca\) & 0.05 & 0.05 & 0.05 & [N\(\cdot\)m] \\ \(Feb\) & 0.05 & 0.05 & 0.05 & [N\(\cdot\)m] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scorbot model’s technical specifications [24,26].  
for such studies. Its structural and technical characteristics make it an ideal candidate for the examination and analysis of various control algorithms. Additionally, many large-scale machines used in the mining industry, such as excavators, drills, and rock breakers, share similar structural designs with the Scorbot. This makes the analysis of the Scorbot relevant to the future automation of heavy machinery used in mining operations like comminution processes.