esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results  
### Integration of RPR and WiMAX  
Fig. 10 depicts the mean aggregate throughput of SuperMAN versus the speed of the SS (given in km/h) for RPR background traffic only (no SSs) and RPR background traffic in conjunction with WiMAX traffic coming from and going to 25 attached mobile SSs for different terrain types A, B, and C, where type A describes an urban environment with maximum path loss, type B specifies a suburban environment with intermediate path loss, and type C describes a rural environment with minimum path loss. The considered RPR ring consists of 8 RPR nodes with 2.5 Gb/s line rate for each ringlet with an RPR RTT set to 0.5 ms. For RPR background traffic only, the mean aggregate throughput equals 15 Gb/s for each of the three traffic classes (voice, video, and data) independent of speed and wireless channel conditions. As shown in Fig. 10, for increasing speed the mean aggregate throughput decreases rapidly. This is due to the fact that for an increasing packet error rate, the number of required packet retransmissions increases, which in turn reduces the mean aggregate throughput of SuperMAN. This figure shows that deploying the proposed novel hierarchical scheduler at each RPR/WiMAX interface node improves the performance of SuperMAN in terms of mean aggregate throughput and mean delay for voice, video, and data traffic. The results shown in Fig. 10 prove that the proposed hierarchical scheduler with its multiple stages puts less backpressure on the RPR metro ring network and thereby achieves a higher mean aggregate throughput for all three traffic classes than a conventional weighted fair queuing (WFQ) scheduler for fixed users and mobile users with a speed of up to 120 km/h, under realistic wireless channel conditions. In the WFQ scheduler, the weights assigned to UGS (equivalent to A0 in RPR), rtPS (equivalent to B-CIR in RPR), and BE (equivalent to C in RPR) traffic are set to 50%, 30%, and 20%, respectively. For a more detailed discussion of Fig. 10 and the hierarchical scheduler, the interested reader is refered to [52].  
Figure 10: Mean aggregate throughput versus mobile SS speed with 25 attached mobile WiMAX SSs and 15 Gbps RPR background traffic for different terrain types using: (a) WFQ scheduler and (b) hierarchical WiMAX scheduler.
# A Petri Net Approach for Business Process Modeling and Simulation
## 4 PN Basics
### Relevant Aspects of the Construction of Petri Nets [37]  
A formal definition of a PN is presented below [38].  
A Petri net is a \(5-tuple\), \(PN=(P,T,F,W,M_{0})\) where:  
\(P=\{p_{1},p_{2},\cdots,p_{m}\}\) is a finite set of places,  
\(T=\{t_{1},t_{2},\cdots,t_{n}\}\)\(T\) is a finite set of transitions  
\(F\subseteq\{P\times T\}\cup\{T\times P\}\) is a set of arcs  
\(W=F\rightarrow\{1,2,3,\cdots\}\) is a weight function  
\(M_{0}=P\rightarrow\{1,2,3,\cdots\}\) is the initial marking  
\(P\cap T=\emptyset\) and \(P\cup T\
eq\emptyset\).  
The set of places connected to a transition is known as the input places, which is denoted as \(\bullet t\). On the other hand, the places connected from a transition are known as output places, and the set of output places is represented by \(t\bullet\). The movement of tokens through the PN represents the dynamic behavior of the system. To change the token position, the following transition firing rule is used [38]:  
1. A transition \(t\in T\) is enabled if every input place \(p\in P\) of \(t\) has \(w(p,t)\) tokens or more, with \(w(p,t)\) being the weight of the arc from \(p\) to \(t\).
2. When an enabled transition \(t\) fires, the event represented by \(t\) takes place.
3. When an enabled transition \(t\) fires, \(w(p,t)\) tokens are removed from every input place \(p\) of \(t\) and \(w(p,t)\) tokens are added to every output place \(p\) of \(t\), with \(w(p,t)\) being the weight of the arc from \(t\) to \(p\).
# Estimation of the Block Adjustment Error in UAV Photogrammetric Flights in Flat Areas
## 3 Results  
### Results of the Global Navigation Satellite System (GNSS)  
Table 4 shows the coordinates and uncertainties of the GCP and ChP, in addition to the four continuous stations located in the area near the study zone. The grid was finally adjusted with a loose restriction, taking into account the regional velocity field developed in [35,44]. The values of the uncertainties were calculated with a 95% confidence level.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Coordinates (m)**} & \multicolumn{3}{c}{**Std (m)**} \\ \hline
**Point** & **East** & **North** & **Altitude** & **East** & **North** & **Altitude** \\ \hline  
ALHA & 636,738.931 & 4,1852,311.011 & 201.790 & 0.003 & 0.003 & 0.004 \\ LORC & 615,840.139 & 4,168,225.450 & 313.952 & 0.003 & 0.002 & 0.003 \\ LRCA & 614,704.897 & 4,168,655.120 & 332.211 & 0.003 & 0.002 & 0.004 \\ MAZA & 649,154.772 & 4,162,049.757 & 55.060 & 0.002 & 0.002 & 0.003 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Coordinates of the GCP and the four continuous stations (ALHA, LORC, LRCA, and MAZA) for the observation period used for the adjustment; Microsearch GeoLab software, reference system UTM 30N ETTS89.
## 5 Case Study (Results (Step 2 to 5 in Figure 3))
### Gap of MWL in Each Factor (Step 4 in Figure 3)
#### 5.2.4 Level of Data Transmission to RCC (LoDT) (Visibility on the Screen)  
Table 16 shows the average weighted MWL of interviewees (\(n=10\)) depending on the screen's visibility. Every operator was at RCC (MO-4) in the coastal water in clear visibility (NC-2). The ship was assumed to have an autonomous navigation system (LoA-3). Figure 9 shows the scenarios considered to have a significant difference between data transmission levels (\(p<0.05\) of \(t\)-test). Cells (scenarios) at a start point and at an endpoint of an arrow line in the figure have a significant difference. Table 17 shows the difference of MWL in the scenarios extracted in Figure 9. Table 18 breaks down the difference into six sub-elements and describes the sub-elements considered to have a significant difference between them (\(p<0.05\) of \(t\)-test). These suggest the following findings:  
* MWL significantly increased when the responsible officers (ROs) could see only a static image on the screen at RCC (LoDT-1: (1) and (2) in Figure 9 and Table 17). On the other hand, a significant difference between rough video (LoDT-2) and clear video (LoDT-3) was not found;  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & \(p\) **\textless{} 0.05** & **No.** & \(p\) **\textless{} 0.05** & **No.** & \(p\) **\textless{} 0.05** & **No.** & \(p\) **\textless{} 0.05** \\ \hline (1) & MD/FR & (2) & MD/FR & (3) & MD/FR & (4) & MD/FR \\ \hline (5) & MD/TD/FR & (6) & MD/TD & (7) & TD & (8) & MD/FR \\ \hline (9) & - & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 15: Sub-elements of MWL that have a significant difference on each number in Figure 8 (\(p<0.05\) of \(t\)-test).  
Figure 8: Scenarios that have a significant difference between navigational conditions in the same MO according to \(t\)-test (\(p<0.05\)) (described by arrow line) (Number in parentheses is \(p\)-value of t-test).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
# Advanced Text Detection of Container Numbers via Dual-Branch Adaptive Multi-Scale Network
## 3 Methods
### Innovative Backbone Network Design  
#### 3.1.1 Dual-Branch Feature Extraction  
To enhance the effectiveness of feature extraction, the feature extraction module implements a dual-branch structure consisting of a main branch and an auxiliary information branch.  
In the main branch, the global image of the container number is input to capture global features. However, employing excessive convolutional layers in the network can result in many parameters, potentially leading to overfitting issues. To avoid this, we perform downsampling several times to reduce the size of the feature maps. Nonetheless, multiple downsamplings may cause the loss of valuable features. Moreover, the container number image often contains substantial irrelevant information. To address these challenges, we incorporate an auxiliary information branch in our model.  
In the auxiliary information branch, to effectively reduce the interference of background noise in the container number text detection task, we adopt the Otsu [31] thresholding method to binarize the original container number text image. Then, the binarized image is used as the input for the auxiliary information branch. Within the convolutional layers, local features are extracted by locally connecting multiple convolutional kernels to the input image and sharing weights. These feature maps not only contain geometric information such as edges and corners of the text but may also capture higher-level semantic information such as the relative positional relationships between text characters. Through the alternating processing of multiple convolutional and pooling layers, the auxiliary information branch can generate feature maps containing rich feature information.  
The main branch and the auxiliary information branch extract rich and complementary feature information independently through parallel learning. Fusing the features of these two branches not only enhances the model's ability to capture container number features but also improves the model's detection accuracy and robustness in complex scenarios.
# Transformer-Based Distillation Hash Learning for Image Retrieval
###### Abstract
Transformer can obtain the global sense field by self-attention mechanism, and can enhance the weight of unique discriminable features for image retrieval tasks to improve the retrieval quality. However, Transformer is computationally intensive, and it is challenging for it to meet real-time requirements when used for retrieval tasks. Knowledge distillation can replace a teacher model with a lightweight student model to improve speed and ensure accuracy. Therefore, it is necessary to design a framework based on knowledge distillation [13] to improve the retrieval speed for the Transformer-based image retrieval model. In recent years, the parameter size of models has become larger and larger, which often requires a large number of memory resources and is time-consuming to run during the deployment phase of the model. By model distillation, the model with huge parameters is compressed into a small parametric number model, which can make the model occupy fewer resources and be less time-consuming without losing accuracy. This paper proposes a Transformer-based image hash learning framework, and the constructed framework is compressed for efficient image retrieval using knowledge distillation. First, Resnet-based Backbone is used to extract the image features. Then, the features are input to multi-head of Transformer as query, key, and value, respectively, and the mutual attention weights are calculated using Transformer. Then, the features fused by Transformer are mapped to Hamming space to perform more compact hash learning. Finally, the performance of this model is distilled into a smaller and faster student model for real-time retrieval. The main contributions of the proposed method include: (1) by introducing the self-attention mechanism of the Transformer model, it makes the image features a global perspective, which can strengthen the weights of unique discernible features and improve the image retrieval quality. (2) By introducing knowledge distillation, the computationally heavy Transformer model is compressed into an efficient lightweight model, which reduces the computation and also has the advantage of features learned by the Transformer. (3) The experimental results on two public datasets achieved encouraging results, demonstrating the effectiveness of the proposed method.
# Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data
## 3 Results  
### Physical State Classification and Psychological Stress Detection  
The classification accuracy was calculated with following formula, where true positives (TPs), true negatives (TNs), false positives (FPs), and false negatives (FNs) define the accuracy as:  
\[Accuracy(\%)=(TP+TN)/(TP+TN+FP+FN). \tag{7}\]  
The various ML algorithms were tested for PS classification. The EL algorithm achieved the best classification accuracy at 99.3% (Figure 8a). The accuracies reported are based on 963 min (approximately 16 h) of testing data (15% of the whole data). The results are presented with a confusion matrix (Figure 8a). Various ML algorithms, SVM, LD, DT, NB, and k-NN, yielded accuracies of 99.0%, 97.7%, 96.6%, 95.6%, and 89.5%, respectively.  
The ML algorithms were tested for APS classification (NS, EAS, and MS) during SS activities. The LD algorithm achieved the best accuracy with 98.3% (Figure 8c). The accuracies reported are based on of 473 min (approximately 8 h) of testing data (15% of the whole data), and are presented using a confusion matrix (Figure 8c). Various ML algorithms, SVM, LD, NB, DT, and k-NN, yielded accuracies of 97.6%, 97.7%, 94.0%, 92.6%, and 88.3%, respectively.  
The same ML algorithms were also tested for APS classification (NS, EAS, and MS) during SB exercise. The EL algorithm achieved the highest accuracy at 83.2% (Figure 8d). The accuracies reported are based on of 131 min (approximately 2 h) of testing data (15% of the whole data) and are presented  
Figure 8: Confusion matrix for PS and APS classification: (**a**) PS classification with SVM, (**b**) APS classification during TR exercise with SVM, (**c**) APS classification during SS activities with EL, and (**d**) APS classification during SB exercise with k-NN.  
in a confusion matrix (Figure 8d). Various ML algorithms, LD, SVM, DT, NB, and k-NN, yielded accuracies of 81.6%, 80.9%, 71.6%, 70.2%, and 60.3%, respectively.
## 2 Materials and Methods
### EO Composition Analysis and Sample Preparation  
For each 10 \(\mu\)L of the EO sample, 990 \(\mu\)L of CH\({}_{2}\)Cl\({}_{2}\) grade HPLC (Thermo Fisher Scientific, Waltham, MA, USA) was added. Gas chromatography/mass spectrometry (GC/MS) was used for identification, and gas chromatography with a flame ionization detector (GC/FID) was used for quantification.  
For qualitative analysis, the samples were analyzed using a Thermo Fisher Scientific GC/MS (model Trace 3000 series) coupled to a mass spectrometry detector (ISO 7000 series) operating at 70 eV with a scan rate of 2 scans/s and a mass range of \(m/z\) 40-350 using a non-polar column DB-5 ms (5% phenyl-methylpolysiloxane, 30 m \(\times\) 0.25 mm i.d., 0.25 \(\mu\)m film thickness; J&W Scientific, Folsom, CA, USA) with helium ultra-pure as carrier gas (1 mL/min). The GC oven temperature was kept at 60 \({}^{\circ}\)C for 5 min, then increased by 2 \({}^{\circ}\)C/min to 100 \({}^{\circ}\)C, 3 \({}^{\circ}\)C/min to 150 \({}^{\circ}\)C, 5 \({}^{\circ}\)C/min to 200 \({}^{\circ}\)C and 15 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C for 5 min. The split ratio was adjusted to 40:1.  
The quantitative analysis was carried out using the same Thermo Fisher system. The flame ionization detector (FID) temperature was 250 \({}^{\circ}\)C. The relative amounts of individual components were determined on the basis of their GC peak areas, without corrections for FID response factors. The analytical parameters were the same as for the GC/MS analysis. A calibration curve was established (0.6, 1.8, 4.3, 8.3, 16.8 and 34.3 mg isopropyl caproate in 10 mL cyclohexane) and n-nonane (7 mg) were used as calibration standards to generate internal standards and establish the calibration curve, respectively. The LOD (0.4 \(\mu\)g/mL) and LOQ (1.2 \(\mu\)g/mL) were measured, and a correlation coefficient of 0.995 was obtained from the calibration curve.
# Frequency-Shifted Optical Feedback Measurement Technologies Using a Solid-State Microchip Laser
## 2 Experimental Setup and Theoretical Analysis
### Laser Frequency-Shifted Optical Feedback
For the weak feedback level, there are two peaks at the relaxation oscillation frequency  
Figure 1: Schematic diagram of the laser frequency-shifted optical feedback interferometer. LD: laser diode; ML: microchip Nd:YVO\({}_{4}\) laser; BS: beam splitter; PD: photon detector; L: lens; AOM\({}_{1}\), AOM\({}_{2}\): acousto-optic modulators; ATT: attenuator; T: target. \(f_{r}\) and shifted frequency \(\Omega\). When the feedback level is increasing, the signal to noise ratio (SNR) of the measuring light increases as well. However, the harmonic peaks (\(2\Omega\dots\) ) and parametric peaks (\(\Omega\)+\(f_{r}\), \(\Omega\)-\(f_{r}\)... ) appear. Especially, once the \(\kappa\) increases to the strong regime, the oscillating peak at \(f_{r}\) disappears, and the signal at \(\Omega\) and its harmonics (\(2\Omega\), \(3\Omega\), \(4\Omega\)... ) oscillate. In summary, the laser power spectrum with two peaks at the relaxation oscillation frequency \(f_{r}\) and shifted frequency \(\Omega\) is the desirable case for actual measurement. The appropriate \(\kappa\) can be adjusted by the ATT in the measurement. For strong feedback, the power spectrum is raised and disturbed by the shifted frequency signal, which cannot be utilized as a sensor. The relative laser output power with the frequency of \(\Omega\) is given as:  
\[\frac{\Delta I(\Omega)}{I_{\mathrm{s}}}=\kappa G(\Omega)\cos(2\pi\Omega t-\phi +\phi_{\mathrm{s}}) \tag{6}\]  
where \(\Delta I\) denotes the intensity modulation of the measuring light, \(\phi_{\mathrm{s}}\) is a fixed phase, and \(\phi\) is the phase related to the external cavity length. \(G\) is the frequency-dependent amplification factor, which can be expressed as:  
\[G(\Omega)=2\gamma_{c}\frac{\left[\eta^{2}\gamma^{2}+4\pi^{2}\Omega^{2}\right] ^{1/2}}{\left[4\eta^{2}\gamma^{2}\pi^{2}\Omega^{2}+\left(4\pi^{2}{f_{r}}^{2}- 4\pi^{2}\Omega^{2}\right)^{2}\right]^{1/2}} \tag{7}\]  
The simulation based on the parameters of a Nd:YVO\({}_{4}\) laser is as follows. The nearer the shift frequency \(\Omega\) is to the relaxation oscillation frequency \(f_{r}\), the larger the amplification factor \(G\) is, and it can be as high as \(10^{6}\) according to Figure 3. Therefore, the laser frequency-shifted optical feedback has ultrahigh sensitivity and excellent performance in the detection of weakly scattering light. However, it should be noted that the shift frequency \(\Omega\) cannot coincide with the relaxation oscillation frequency \(f_{r}\) in the actual experiment. Otherwise, the optical power spectrum will be in chaos, similar to that under a strong feedback level. Both the stability of the power spectrum and the signal amplification should be considered in the laser feedback interferometry. Figure 2: Numerical power spectrum of the Nd: YVO\({}_{4}\) laser with different feedback levels: (**a**) weak feedback, (**b**) moderate feedback, and (**c**) strong feedback.
# Development of a Robotic Airboat for Online Water Quality Monitoring in Lakes
## 6 Results
### The Lagoon of Condominium Jardins de Alice e Pereira  
The second official autonomous mission occurred in the lagoon of the condominium Jardins de Alice e Pereira (Latitude: -3.81068166667, Longitude: -38.506945) on 15 July 2017 (ID Mission: 20170715171002). The paths configured and traversed by the boat are displayed in Figure 15.  
The mission began at 21:15 and ended at 21:28, totaling 50 collections. The ambient temperature had a minimum value of 27 \({}^{\circ}\)C and maximum of 27.75 \({}^{\circ}\)C, reaching a mean value of 27.11 \({}^{\circ}\)C, with a standard deviation of 0.21 \({}^{\circ}\)C.  
Figure 16 shows the ambient temperature variations during the course of the 50 waypoints. A slight variation can be observed from waypoint 25 to 39, with a slight variation at waypoint 48, and then increasing from waypoint 49.  
Concerning water temperature, a minimum value of 28 \({}^{\circ}\)C and maximum of 28.73 \({}^{\circ}\)C was observed, reaching a mean value of 28.05 \({}^{\circ}\)C with a standard deviation of 0.12 \({}^{\circ}\)C. During the route, there were some minor variations, the largest was at waypoint 14, as shown in Figure 17.  
Figure 16: Ambient temperature graph for Jardins Alice e Pereira lagoon.  
Figure 15: Data collection in the lagoon of the Jardins de Alice e Pereira condominium. Source: Adapted by Google Maps, 2017.  
The minimum DO value was 8.76 ppm and the maximum was 9.97 ppm, reaching an average of 8.87 ppm with a standard deviation of 0.07 ppm. During the route, there was a discrete growth reaching the maximum value of 9.97 ppm, at waypoint 21 (where there was an aerator) and a decrease in value (reaching 8.76 ppm) was observed when the boat returned to the initial waypoint, as shown in Figure 18.  
The Oxidation-Reduction Potential (ORP) values obtained were dispersed in certain regions of the lagoon; the minimum value was 32.6 mV, at waypoints 42 to 48, and the maximum was 51.15 mV, at waypoint 2, with a mean value of 38.45 mV and a standard deviation of 4.52 mV, as shown in Figure 19.  
Figure 19: ORP graph in the Jardins Alice e Pereira lagoon.  
Figure 17: Water temperature graph for the Jardins Alice e Pereira lagoon.  
Figure 18: The DO graph for the Jardins Alice e Pereira lagoon.  
The Potential of Hydrogen (pH) had slight variations during the course and obtained a minimum value of 7.5, at waypoint 30, and a maximum value of 7.8, at waypoint 1, with a mean value of 7.6 and a standard deviation of 0.06, as shown in Figure 20.
# Exploitation of Antimicrobial Nanoparticles and Their Applications in Biomedical Engineering
## 2 Nanomaterials
### Intrinsic Properties and Characteristics of Nanoparticles
For example, Ag NPs at 10 nm produced more antimicrobial activity towards _Methylobacterium spp._ than Ag NPs at 100 nm; however, an increase in cytotoxicity was also seen towards human peripheral blood mononuclear cells [19]. As biological processes happen on the nanoscale, nanomaterialswith unique properties, as mentioned above, are expected to have great potential in biomedical applications by appropriately involving them in these processes [64].
# Spoofing Detection Using GNSS/INS/Odometer Coupling for Vehicular Navigation
## Abbreviations  
\begin{tabular}{l l} \(DS\) & Detection Statistics \\ \(\sigma_{i}^{o}\) & speed from the odometer \\ \(d_{i}^{o}\) & acceleration from the vehicle odometer \\ \(f_{i}^{x}\) & x accelerometer outputs \\ \(f_{i}^{y}\) & y accelerometer outputs \\ \(\omega_{i}^{x}\) & vertical gyroscope output \\ \(\mathbf{x}_{k}\) & Mechanization vector \\ \(\{\varphi_{k},\lambda_{k},h\}\) & position vector in the geodetic coordinate (latitude, longitude and height) \\ \(\{\frac{v_{k}^{x},v_{k}^{y},v_{k}^{y}\}\}\) & velocity vector in the East-North-Up (ENU) coordinate frame \\ \(\{p_{k},r_{k},A_{k}\}\) & pitch, roll and azimuth angles \\ \(g\) & gravity \\ \(\delta\mathbf{x}_{k}\) & error state vector \\ \(\delta S_{k}^{pd}\) & scale factor of odometer \\ \(\delta\omega_{k}^{x}\) & vertical gyroscope drift \\ \(\mathbf{\Phi}_{k-1}\) & state transition matrix \\ \(\mathbf{G}_{k-1}\) & shaping matrix or noise coupling matrix \\ \(\mathbf{\Psi}_{k-1}\) & zero mean unity variance white noise \\ \(\gamma_{cd}\) & inverse of autocorrelation time for odometer stochastic errors \\ \(\gamma_{uz}\) & inverse of autocorrelation time for gyroscope stochastic errors \\ \(\sigma_{\mathrm{ad}}^{2}\) & variance of odometer noise \\ \(\gamma_{uz}^{2}\) & variance of gyroscope noise \\ \(\delta\mathbf{z}_{k}\) & measurement vector \\ \(\mathbf{H}\) & design matrix \\ \(\epsilon_{k}\) & measurement noise \\ \end{tabular}