esa-sceva/satcom-chunk-collection huggingface dataset:
# DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks
## 4 Simulation
### Set-up Phase Convergence Time  
Figure 12 shows a global comparison of the average network convergence time (or set-up time) in small-, medium- and large-size scenarios for \(AODV\) (A), \(O\)-\(RPL\) (R) and \(DARAL\) (D). As long as the area size is directly related to the node degree, it is not considered in the graphical representation. As average convergence times are shown, smaller values mean better performances, specifically, Figure 12. Small and Table 3 show a small-size scenario with 100 nodes and the differences for each node density. For small-size scenarios with ND lower or equal to 10, \(DARAL\) shows a better performance than \(AODV\) and \(O\)-\(RPL\). Nevertheless, \(O\)-\(RPL\) performance is slightly better for scenarios with a high density, as can be seen in the case of \(ND=15\), even though the results of \(O\)-\(RPL\) are not significant due to their standard deviation. Figure 12. Medium shows a medium-size scenario with 200 nodes, where the results are similar to the small-size scenario. The performance of \(O\)-\(RPL\) in the \(ND=15\) case is so low that it cannot be observed in the graphical representation, although it can be seen in Table 3. Figure 12. Large shows a large-size scenario with 400 nodes, varying also in terms of density. As in the other scenarios, the different values for each protocol can be seen in Table 3. \(DARAL\) is repeated as the best solution for \(ND=5\) and \(ND=10\). For \(ND=15\), there is no doubt about \(O\)-\(RPL\), showing an extremely good performance in terms of convergence time and exceeding \(DARAL\) and \(AODV\). Globally, \(DARAL\) shows the best performance for low- and medium-densities.  
Under these conditions and depending on the scenario, \(AODV\) also improves \(O\)-\(RPL\), but under high-density conditions, \(O\)-\(RPL\) gives the best performance.
###### Abstract  
The scientific community has paid special attention to silver nanoparticles (AgNPs) in recent years due to their huge technological capacities, particularly in biomedical applications, such as antimicrobials, drug-delivery carriers, device coatings, imaging probes, diagnostic, and optoelectronic platforms. The most popular method of obtaining silver nanoparticles as a colloidal dispersion in aqueous solution is chemical reduction. The choice of the capping agent is particularly important in order to obtain the desired size distribution, shape, and dispersion rate of AgNPs. Gemini alkylammonium salts are named as multifunctional surfactants, and possess a wide variety of applications, which include their use as capping agents for metal nanoparticles synthesis. Because of the high antimicrobial activity of gemini surfactants, AgNPs stabilized by this kind of surfactant may possess unique and strengthened biocidal properties. The present paper presents the synthesis of AgNPs stabilized by gemini surfactants with hexadecyl substituent and variable structure of spacer, obtained via ecofriendly synthesis. UV-Vis spectroscopy and dynamic light scattering were used as analyzing tools in order to confirm physicochemical characterization of the AgNPs (characteristic UV-Vis bands, hydrodynamic diameter of NPs, polydispersity index (PDI)).  
silver nanoparticles; gemini surfactant; green synthesis +
Footnote †: journal: Applied Sciences
# Satellite Network Task Deployment Method Based on SDN and ICN
## 3 System Model and Problem Formulation
### Task Model
#### 3.2.2 Task Model  
After the task is decomposed into several subtasks, the resource consumption of each subtask is represented by \(task_{i}(C_{i},R_{i},G)\). We define \(C_{i}\) as the amount of computation required by subtask \(i\), and \(R_{i}\) is the cache space required by the task \(i\). The computing resources required by the task include the following: (1) The data of monitoring devices accessed in the IoT scenario need to be obtained from the access devices when performing the calculation; (2) In the case of providing computing services for access device terminals,part of the data required for computing comes from the access device, and the other part comes from the ground data center.
## 2 Notations and Methodology
### Simulation of the Input Power Required \(P_{\text{REQ}}\)  
In a first step, we determine the input power required \(P_{\text{REQ}}\) as a function of the MZM parameters \(\left[\begin{array}{cc}V_{\pi}&\text{BW}_{\text{MZM}}\\ \end{array}\right]\) by using a split-step Fourier based simulator [11] running in Matlab(r) (R2015b, Mathworks, Natick, MA, USA). At this stage we do not consider that there is a link between \(V_{\pi}\) and \(\text{BW}_{\text{MZM}}\) in order to map the complete parameter space even if, as a result, some simulated MZM do not represent realistic situations. A transmission link at a data rate of 25 Gbps comprising a perfect continuous laser source followed by a MZM with a given \(V_{\pi}\) and \(\text{BW}_{\text{MZM}}\) is modelled. A variable optical attenuator is then used prior to the photo-receiver to vary the optical input power \(P_{\text{RX}}\), simulating the passive optical losses \(\Pi_{\text{PATH}}\). The photo-receiver (RX) itself consists of a PIN photodiode with responsivity \(\eta_{\text{ICE}}=0.9\) A/W, analogue bandwidth \(\text{BW}_{\text{PD}}=21\) GHz, a Trans-Impedance Amplifier (TIA) gain of 1 k\(\Omega\) and a Noise Equivalent Power (NEP) of 8 pW/\(\sqrt{\text{GHz}}\). These RX parameters are typical for 25 Gbps systems [12] and, as will be evident later, do not change the generality of the results or of the proposed method. The transmission quality is evaluated by means of the Q-factor with patterning effects of the two nearest neighboring bits being taken into account in the calculation [13]. Note that a Q factor of 6 corresponds to a BER of \(10^{-9}\).  
With the parameters given above, we first find that \(P_{\text{SEN}}\) is equal to \(-20.8\) dBm at 25 Gbps. Figure 2 shows the required power \(P_{\text{REQ}}\) computed by simulation as a function of \(V_{\pi}\) and \(\text{BW}_{\text{MZM}}\) for an absolute voltage \(V_{\text{data}}\) of 2.4 V.  
Figure 2: Evolution of the required receiver input for 25 Gbps On-Off Keying (OOK) modulation depending of the voltage and the electro-optic bandwidth of the Mach-Zehnder modulator (MZM). The receiver parameters are defined in the text.  
For the smallest value of \(V_{\pi}\) and the largest value of BW\({}_{\rm MZM}\) considered (2.4 V and 30 GHz respectively), we find that \(P_{\rm REQ}=P_{\rm SEN}=-20.8\) dBm as may be expected as the transmitter emits a \"perfect\" signal. When \(V_{\pi}\) increases (thus decreasing the extinction ratio) or when BW\({}_{\rm MZM}\) decreases (thus closing the eye diagram), the value of \(P_{\rm REQ}\) increases. This graph allows us to consider the different possible trade-offs between the three MZM key parameters \(\left[\begin{array}{cc}\rm{IL}_{\rm MZM}&V_{\pi}&\rm{BW}_{\rm MZM}\\ \end{array}\right]\) as IL\({}_{\rm MZM}\) directly impacts the RX input power.
# An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments
## 6 Simulation Study and Discussion
### Verification in Realistic Sea Environment with Multiple Moving Target Vessels
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Environment**} & \multirow{2}{*}{**Index**} & \multirow{2}{*}{\begin{tabular}{c} **Speed of the** \\ **USV (m/s)** \\ \end{tabular} } & \multicolumn{2}{c}{**Thruster**} & \multirow{2}{*}{\begin{tabular}{c} **Propulsion** \\ **Speed (t/min)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Computation** \\ **Angle (\({}^{\circ}\))** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Distance** \\ **Time (s)** \\ \end{tabular} } \\ \hline \multirow{3}{*}{static} & maximum & 11.0 & 4000 & 2.0 & 0.27 & 100.0 \\  & minimum & 0.0 & 0 & \(-\)18.0 & 0.14 & 60.8 \\  & average & 10.0 & 3731 & \(-\)1.9 & 0.16 & 92.8 \\ \hline \multirow{3}{*}{dynamic} & maximum & 11.0 & 4000 & 2.0 & 0.27 & 100.0 \\  & minimum & 0.0 & 0 & \(-\)18.0 & 0.15 & 25.4 \\  & average & 9.9 & 3690 & \(-\)1.9 & 0.17 & 85.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of evaluation index comprised of two tests in real environment. USV: unmanned surface vehicle. Figure 22: Evaluation results in the real environment. (**a**) Speed of the unmanned surface vehicle (USV); (**b**) Course of the USV; (**c**) Thruster speed; (**d**) Command propulsion angle; (**e**) Computation time; (**f**) Distance between the USV and the collision area.
# High-Precision Chip Detection Using YOLO-Based Methods
## 3 Results
### Experimental Results
#### 3.3.4 Results of Video-Level Post-Processing Algorithm  
Ten videos that had not been included in training were used to test the results of all algorithms. The details of the six videos are shown in Table 5. The number of chips in the videos was counted using all versions of the YOLO algorithm combined with two post-processing algorithms. The results obtained by GM-YOLOv11+DNMS+VPPA are shown in Table 4. The number of crack chips in these videos was 436, while GM-YOLOv11+DNMS+VPPA counted 393 crack chips, resulting in overall accuracy of 89.9%. The comparison results with other models are shown in Table 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Error Proportion (\%)** \\ \hline YOLOv5n+DNMS+VPPA & 11.93 \\ YOLOv8n+DNMS+VPPA & 15.37 \\ YOLOv9t+DNMS+VPPA & 13.07 \\ YOLOv10n+DNMS+VPPA & 13.30 \\ YOLOv11n+DNMS+VPPA & 9.63 \\ GM-YOLOv11-backbone+DNMS+VPPA & 10.55 \\ GM-YOLOv11-neck+DNMS+VPPA & 11.47 \\ GM-YOLOv11+DNMS+VPPA & 9.86 \\ Literature [29] + VPPA & 15.14 \\ Literature [28] + VPPA & 15.60 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Chip quantity counting experiment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **F1-Score** & **[EMAIL_ADDRESS]** \\ \hline YOLOv5n+DNMS & 93.85 & 94.11 & 93.98 & 92.99 \\ YOLOv8n+DNMS & 93.30 & 90.78 & 92.02 & 90.85 \\ YOLOv9t+DNMS & 90.83 & 91.80 & 91.31 & 93.34 \\ YOLOv10n+DNMS & 92.46 & 88.33 & 90.35 & 88.89 \\ YOLOv11n+DNMS & 97.05 & 96.81 & 96.93 & 96.48 \\ GM-YOLOv11-backbone+DNMS & 95.13 & 96.18 & 95.65 & 95.08 \\ GM-YOLOv11-neck+DNMS & 94.81 & 94.37 & 94.59 & 95.01 \\ GM-YOLOv11+DNMS & 97.04 & 96.38 & 96.71 & 95.56 \\ Literature [25] & 92.27 & 91.69 & 91.98 & 90.12 \\ Literature [24] & 91.22 & 90.63 & 90.92 & 90.29 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative analysis of chip detection for different models with DNMS.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Serial Number** & **Duration (s)** & **Actual Number of Crack** \\  & & **Chips** \\ \hline
1 & 36 & 13 \\
2 & 103 & 47 \\
3 & 158 & 61 \\
4 & 210 & 82 \\
5 & 213 & 79 \\
6 & 213 & 80 \\
7 & 51 & 25 \\
8 & 49 & 18 \\
9 & 35 & 24 \\
10 & 28 & 7 \\ Total & 1096 & 436 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Details of the ten videos.
## 3 Verification and Results
### Results of Crack Classification Network  
As mentioned above, this study used 40,000 concrete images for the training and evaluation process of the artificial neural network, of which 36,000 images were used as training data and 4000 as test data. For the training, batch size was set to 50. Initial learning rate and decay were 10-3 and 10-2, respectively. The learning rate was set to be decayed continuously. Loss function was set to the softmax cross entropy. The Adam optimizer [30], which is a gradient-based optimization algorithm, was used to minimize total loss and update the network. Training was stopped at 12,140,400 iterations (337 epochs).  
As shown in Figure 8, training loss and evaluation accuracy were gradually decreased and increased without overfitting. And they almost converged around the epoch of 100. It was found that the speed of convergence is slow due to the reduced learning rate. As a result of evaluating the accuracy, we obtained a final verification accuracy of 99.98% as shown in Figure 8.  
Figure 7: Database of concrete cracks: (**a**) concrete images without cracks (top), (**b**) concrete images with cracks (middle), and (**c**) segmented resultant images with crack (bottom).
## 5 Communication in Vehicular Networks
### Routing
Geocast routing refers to the delivery of messages to a subset of nodes outlined by a geographical region [104]. The implementation of these protocols in vehicular networks is deemed to be problematic due to the high mobility of nodes [105]. However, the employment of the particle swarm optimization algorithm may alleviate some hindrances, as demonstrated in [96]. The discussed protocol employs the particle swarm optimization in the selection of a next-hop vehicle, which is a crucial task in geocast routing because of frequent changes in topology, interference, and the high speed of nodes. The protocol disposes of a fitness function maximizing delivery ratio and throughput, as well as diminishes the delay in delivery, dropped packets, or communication overhead. In the case of broadcast routing [106], the data packets must be delivered from one node to all other nodes present in the network [107]. Such a protocol includes, for example, the optimized link state routing protocol [92], which utilizes the particle swarm optimization algorithm to select multipoint relay nodes responsible for broadcasting messages, thereby consequently reducing the communication overhead in the network.
## 4 Sensitivity Analysis of Random Forest Surrogate and Influence of Physics-Guided Features
These surrogates have better accuracy as the band averaging smooths the STL curve while keeping the information valuable for practical purposes. Figure 8: MDI-based sensitivity of the STL of finite plates evaluated with FEM: (**a**) the inputs of the surrogate only consist of the STL variables (\(RMSE=5.76\pm 0.06\) dB); (**b**) physics-guided features \(m\) and \(D_{R}\) are included as input for the surrogate (\(RMSE=5.60\pm 0.06\) dB). Figure 9: MDI-based sensitivity of the STL of finite plates including mass density \(m\), the real part of bending stiffness \(D_{R}\) and the resonance coefficient term \(R\) as physics-guided features: (**a**) sensitivity indices from MS dataset (\(RMSE=2.67\pm 0.02\) dB); (**b**) sensitivity indices from FEM dataset (\(RMSE=4.86\pm 0.05\) dB). The results presented in this section show evidence of the physical consistency of the surrogate models as the STL sensitivity indices have a behavior coherent with the literature of each STL physics-driven model. Furthermore, the sensitivity indices of the width and length of the plate are consistently equivalent in all analyses, as expected for isotropic materials. Physical inconsistencies could indicate regions where the ML model is not accurate. The investigation also demonstrates that physics-guided features can be readily included to improve surrogate accuracy and lead to a clearer distinction of STL regions in the sensitivity analysis. However, the interpretation of the resulting feature importance is not straightforward when physics-guided features are included, as the input features are not independent anymore. In summary, the MDI-based sensitivity analyses presented in this section are a cheap-to-evaluate method that improves the surrogate interpretability and reliability and allows for the use of data-based information to deepen physical discussions on variable importance and interactions.
## 5 Experiments
### Experimental Results
#### 5.3.1 Time-to-Accuracy Performance  
Our experiments evaluated the time taken to achieve accuracy levels of 60%, 70%, and 80%. Table 3 presents the experimental results concerning the FEMNIST dataset. The first row represents the performance of the ResNet34 model without the Oort selection algorithm. It took approximately 66,761 s to achieve 60% accuracy and 101,072 s to achieve 70% accuracy. In contrast, the MB_ResNet34 model achieved the same level of accuracy in significantly less time than the original ResNet34 model.  
However, it should be noted that the scenarios without the client selection algorithm faced poor overall accuracy due to the impact of non-iid data. To address this issue, we integrated the selection algorithm, as shown in the third and fourth rows of the table. It is evident that, compared to the random selection method, the time to accuracy decreased, and the overall accuracy exceeded 80%.  
For the CIFAR-10 dataset, as shown in Table 4, we observed similar trends to those in Table 3. This observation further supports the effectiveness of our proposed method in alleviating the negative impact of non-iid data on the MB_ResNet34 model in [11] (rows 2 and 4) and reducing the training time compared to the original ResNet34 model in [17] (rows 3 and 4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy 60\%** & **Accuracy 70\%** & **Accuracy 80\%** \\ \hline
**ResNet34** & 246,102 & 837,236 & — \\
**MB_ResNet34** & 23,036 & 48,558 & — \\
**ResNet34 + Oort** & 225,743 & 811,432 & 2,432,746 \\
**MB_ResNet34 +Oort** & 20,365 & 43,812 & 124,677 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training time versus accuracy for different models on the CIFAR-10 dataset (in which the symbol “\(-\)” means that accuracy was not achievable due to the impact of non-iid data during the experiment).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy 60\%** & **Accuracy 70\%** & **Accuracy 80\%** \\ \hline
**ResNet34** & 66,761 & 101,072 & — \\
**MB_ResNet34** & 6,799 & 13,097 & — \\
**ResNet34 + Oort** & 47,701 & 68,869 & 286.324 \\
**MB_ResNet34 +Oort** & 5,555 & 10,113 & 36.854 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training time versus accuracy for different models on the FEMNIST dataset (in which the symbol “\(-\)” means that accuracy was not achievable due to the impact of non-iid data during the experiment).
## 2 Land Subsidence in the Study Area  
The study area, Hwajeon, is located in the city of Taebaek, South Korea (Figure 1), at 37\({}^{\circ}\)11'07''-37\({}^{\circ}\)11'07'' N, 128\({}^{\circ}\)56'40''-128\({}^{\circ}\)57'43'' E. Underground coal mining activities were carried out in Taebaek for nearly 20 years. The coal seams in this area were irregularly disturbed and inclined with various widths by reverse and thrust faults [22]. Therefore, the slant-chute block caving method was mainly used. About 10 million tons of coal were mined from the study area between 1953-1991 [22], and coal was transported to other areas by railroad beginning in 1973 (Figure 1). Since 1990, most of the coal mines have been closed due to reduced coal demand. However, the abandoned underground coal mines are currently causing land subsidence in the study area [11; 21; 22; 23]. Additionally, infrastructure has been damaged by the land subsidence, as shown in photographs in a previous report [11].  
Subsidence is caused by a variety of contributing factors, including geological discontinuities, presence of water, mining depth, and weak overburden [24; 25]. The two forms of subsidence caused by underground coal mining are trough and sinkhole subsidence [25]. In the study area, a very irregular sinkhole occurred due to many complex underground coal mine pits excavated via slant-chute block caving in combination with the aforementioned factors [22]. After a mine cavity is excavated, roof stability becomes unstable over time due to changes in the strength and stress of the roof strata. Under such conditions, additional contributing factors can lead to the occurrence of sinkholes [25]. The Coal Industry Promotion Board [11; 26] has reported 24 land subsidence events within the study area. Figure 1 shows a representative land subsidence from location S1 to location S6 of a subsidence event reported in 1999. Table 1 provides a description of the land subsidence. Locations S1 to S5 of this land subsidence mainly occurred along railways and at elevations above 800 m. Location S6 occurred in residential areas and at a lower elevation than S1-S5. Also, the depth of subsidence of S6 is the deepest (508 mm). Some photographs providing evidence of the land subsidence have been published [11; 23].
# Multiuser Equalizer for Hybrid Massive MIMO mmWave CE-OFDM Systems
### Previous Work on Constant Envelope Orthogonal Frequency Division Multiplexing (CE-OFDM)  
Regarding CE-OFDM, some designs were addressed in [24; 25; 26] for sub-6 GHz systems, and specifically for mmWave in [27; 28; 29; 30]. The authors of [24], proposed to incorporate a spectral precoder in the CE-OFDM block. The goal is to suppress the side lobe powers, and then to obtain a more efficient system, preserving the advantages of CE-OFDM. In [25], it is proposed a receiver using amplitude-phase demodulator with an iterative detection, for the demodulation of the dual-stream CE-OFDM. In [26], it is proposed a data-aided carrier frequency offset estimation strategy based on the frequency domain pilot symbols.  
Regarding the works on mmWave, the authors of [27] designed an iterative space-frequency domain equalizer for a full-digital system, by minimizing the overall mean square error (MSE) of all data streams at each subcarrier. The aim was to exploit the high space-frequency diversity order inherent to CE-OFDM. In [28], it is proposed a substrate integrated waveguide slotted array with a squared cosecant pattern at 73 GHz, where it can be seen that for low modulation indexes, the use of trellis coding and interleaving greatly increases the system performance. The authors of [29], proposed to integrate space-time shift keying with CE-OFDM. The aim was to transmit streams without nonlinear amplitude distortion and to exploit the flexible diversity/multiplexing tradeoff that is inherent to space-time shift keying. Finally, in [30], it is done a study and comparison, for downlink transmission in outdoor picocells, between the CE-OFDM and constant-envelope single-carrier OFDM (CE-SC-OFDM). It was demonstrated that constant envelop multicarrier waveforms enhance robustness and increase coverage and capacity in the proposed scenario, as compared to conventional OFDM and SC-OFDM counterparts. However, to the best of our knowledge, CE-OFDM based systems employing hybrid analog-digital architectures for broadband mmWave mMIMO systems, were not yet addressed in the literature.
## 3 Envelope Demodulation  
During the operation of bearings, complex modulation effects arise from interactions among multiple moving components, including rollers, cages, inner rings, and outer rings [3]. The characteristic information of the fault is predominantly embedded within the envelope components of the modulation signal. Consequently, performing effective envelope demodulation on collected vibration signals to extract envelope signals that contain fault characteristics precisely constitutes a critical step in achieving accurate bearing fault diagnosis. The envelope information of the amplitude modulation signal can be effectively extracted through the Hilbert transform, thereby achieving envelope demodulation, which can be expressed as follows [21]:  
\[d(t)=H[c(t)]=\frac{1}{\pi}\int_{-\infty}^{+\infty}\frac{c(\tau)}{t-\tau}d\tau \tag{1}\]  
Then, the complex functions can be expressed as  
\[z(t)=c(t)+id(t)=A(t)e^{i\theta(t)} \tag{2}\]  
where the instantaneous amplitude can be expressed as \(A(t)=\sqrt{c^{2}(t)+d^{2}(t)}\) and the instantaneous phase can be expressed as \(\theta(t)=\arctan\left(\frac{d(t)}{c(t)}\right)\). In addition, the instantaneous frequency can be obtained by differentiating the instantaneous phase and can be expressed as  
\[f(t)=\frac{1}{2\pi}\frac{d\theta(t)}{dt}. \tag{3}\]