esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
[40] proposed an enhanced deployment algorithm based on Artificial Bee Colony (ABC) to extend the lifetime in TT-WSNs. [PERSON] and [PERSON] et al. [41] considered a GA to deploy RNs in ST-WSNs by following a C-RNPP approach to minimise the number of RNs while providing maximum connectivity. [PERSON] et al. [42] studied how to deploy RNs in ST-WSNs while optimizing energy cost and reliability, using to this end three MO metaheuristics. The proposal in this work differs from the papers introduced before in the following: (i) the network model considers a Single-Tiered (ST) approach, which is usual in medium-size networks assuming low-cost devices, such as occurs in intensive agriculture [43]. (ii) In this model, the RNs are similar to sensors, but without having electronics for capturing data, instead, they are specialized in communication. Moreover, RNs have harvested capabilities and then, they can be deployed without taking into account the existence of power sources, such as plugs. This fact results in an unconstrained RNPP, meaning that the search space is greater than in the C-RNPP focus. (iii) We address the RNPP through MO metaheuristics, providing a trade-off between the conflicting objectives, not as with heuristics, which is useful in the decision-making of network designers. As introduced before, many heuristics were proposed for addressing the ST-RNPP [24; 25; 27; 28; 29; 31; 32; 33; 35]. However, only a few papers considered metaheuristics to this end [36; 37; 39; 41; 42]. Regarding these latest works, the authors in [36; 39; 41] considered a single-objective focus and our proposal is MO. The authors in [37] considered a MO focus for the number of NRs and energy cost, but following a C-RNPP. Finally, the authors in [42] considered a similar focus to ours, but assuming three MO metaheuristics and an optimization problem with two objectives (reliability and energy cost). In our proposal, we consider a wide range (seven) of MO metaheuristics to solve two formulations of the RNPP, with two and three objectives, to identify a robust metaheuristic to solve the problem. This work is partially inspired by some early papers. The bi-objective RNPP was addressed in [44; 45; 46] for NSGA-II, SPEA2, MO-ABC, MO-GSA, MO-VNS, and MO-VNS*. The three-objective RNPP was addressed in [47] for NSGA-II, SPEA2, MOEA/D, MO-ABC, MO-VNS, and MO-FA. This paper presents a more complete development with an intensive statistical study comparing the performance of the metaheuristics solving the two RNPPs. As expected, this work includes experimental results never published before, such as the performance of MO-FA and MOEA/D solving the bi-objective problem and MO-GSA and MO-VNS* for the three-objective case.
## 5 Experiments and Results
### Performance Overhead Evaluation
#### 5.2.3 Optimized Effects of I-Cache  
As described above, the instruction monitor is also closely related to the hit rate of the I-Cache. In order to further explore the effects of the I-Cache hit rate on the performance overhead of SPU, we continued the benchmark evaluation experiments by maintaining the 8-KB D-Cache and M-Cache unchanged, and the size of I-Cache is reconfigured as 2, 4, and 16 KB, respectively. The performance overheads of ten selected benchmarks under different sizes of I-Cache are shown in Figure 12. The performance overhead incurred by the SPU decreased with the enlargement of I-Cache addressing space as well as the I-Cache hit rate improvement.  
Moreover, the space enlargement of I-Cache helps to reduce the number of times BB integrity verification. When executing the selected ten benchmarks with a 16-KB I-Cache, the performance overhead of the CPU-SPU collaborative SoC had significant reductions compared to I-Cache configured with 2-KB, where the highest performance overhead was 6.87% (OpenECC at 2-KB), and the lowest performance overhead was at 0.38% (quicksort at 16 KB). The mechanism of this trend is that, when the I-Cache hit rate increases, the number of times the CPU fetches instructions from the external memory decreases.  
Considering the applied practicality of the SPU in transplantation with reasonable resource consumption and low hardware complexity, we finally selected the 8-KB I-Cache to reach a good balance between the performance overhead and resource consumption; in addition, the size remains the same with the D-Cache and M-Cache depths, and it is easily configured in other instruction set architecture platforms.
## 3 Results and Discussion
### Fluorescence Characteristics of Stainless Steel and Fruits Samples
In the 650-690 nm range, the original juice, and the 1:5 and 1:10 dilution samples exhibit intensities much higher than the #4 SSS does, whereas the spectra of the remaining dilution samples are more similar in intensity and in their trend with concentration. In the 450-600 nm range, the spectra of the honeybee residue on #4 SSS show a decrease in fluorescence peak intensities with each sequential dilution of the original juice. Although the spectra of the original juice and the first three dilutions are very clearly distinguishable, but those of the two most dilute samples (1:50 and 1:100) are not. In the 600-730 nm range, the original juice and the 1:5 dilution can be distinguished but the four remaining dilutions cannot. The spectra of the orange residue on #4 SSS show decreasing fluorescence intensity with increasing dilution in the 450-600 nm range, similar to that of the honeybee residue spectra. In the 600-730 nm range, clearly distinguishable fluorescence peaks are observed for the original juice and for the 1:50 and 1:100 dilution samples. Figure 4: Average spectra of fruit juice residues on the 2B-finished stainless steel: (**a**) honeybee; (**b**) orange; (**c**) apple; (**d**) watermelon; and (**e**) raw pixel spectra extracted from one droplet of orange juice residues on the 2B-finished stainless steel. Figure 5: Average spectra of fruit juice residues on the #4-finished stainless steel: (**a**) honeydew; (**b**) orange; (**c**) apple; (**d**) watermelon; and (**e**) raw pixel spectra extracted from one droplet of orange juice residues on the #4-finished stainless steel. The spectra of apple residue on #4 SSS show a similar pattern of decreasing fluorescence intensity with increasing dilution in the 450-600 nm range, with the original juice and the 1:5 and 1:10 dilutions being the most distinct. The remaining samples have spectral properties similar to those of stainless steel. In the 600-730 nm range, the high intensity fluorescence peak decreases as the dilution increases for the six juice concentrations. The spectra of watermelon residue on #4 SSS show a similar pattern to those of honey-dew and orange in the 450-600 nm range, with decreasing fluorescence peak intensities with each sequential dilution of the original juice and distinguishable spectra for the original juice and the first three dilutions, but non-distinguishable spectra for the two most dilute samples (1:50 and 1:100). In the 600-730 nm range, the original juice and the 1:10 dilution clearly show a high intensity spectral peak but the spectra of the four remaining dilutions are not distinguishable. All the fruits show features in common on the 2B-finished as well as #4-finished surfaces. The broad peaks in the 450-600 nm range are presumed to be due to the complex composition of components that affect sugar content, such as glucose, or the components of flavonoids and carotenoids. The peak between 600-730 nm may to be due to chlorophyll [6; 38; 39; 40; 41; 42; 43].
## 2 Related Work
One of the future avenues for research in epilepsy detection [33; 34] is seizure localization. It can assist clinicians in identifying the specific region of the brain that is impacted by seizures, which is crucial in the treatment of some types of epilepsy. Raw EEG data usually contain artifacts and noise and may not provide the necessary patterns or signatures in the signal that are required to effectively classify seizures. Therefore, it is imperative to select the most suitable statistical features for efficient detection and dimensionality reduction [35; 36]. This research proposes a novel RNN-based epileptic seizure detection approach employing two commonly used \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Features** & **Classification Model** & **Dataset** & **Result** \\ \hline [PERSON] et al. [14] & Hybrid feature set & Gaussian mixture models & CHB-MIT & Accuracy: 86.26\% \\  & & Gaussian mixture models & CHB-MIT & Sensitivity: 87.58\% \\  & & & Specificity: 86.83\% \\ \hline [PERSON] et al. [16] & Time and frequency & Random forest & EPILEPSIAE & Sensitivity: 93.84\% \\ \hline [PERSON] et al. [17] & DWT & SVM and other neural networks & BONN & Accuracy: 99.1\% \\ \hline [PERSON] et al. [24] & & CNN & BONN & Accuracy: 88.7\%, \\  & & CNN & BONN & Specificity: 90\%, \\  & & Sensitivity: 95\%. & \\ \hline [PERSON] et al. [20] & Higuchi fractal dimension HFD, EAM & ANN+k-NN & BONN & ANN 94\%, k-NN 98\% \\ \hline [PERSON] et al. [21] & Time–frequency, statistical features & Random forest, SVM, KNN, and Adaboost & Bern-Barcelona & Sensitivity: 97.6\% \\  & & KNN, and Adaboost & Barcelona & Specificity: 94.4\% \\ \hline [PERSON] et al. [22] & coif3 mother wavelet + 7 statistical features & SVM & CHB-MIT & 92.30\% \\ \hline [PERSON] et al. [25] & Wavelet Synchrosqueezing Transform & 2D Deep-CNN & BONN & Accuracy: 99.70\% \\ \hline  & Hybrid features & SVM & BONN & Accuracy: 99.30\% \\ \hline  & Hybrid features & 1D-CNN & BONN & Accuracy: 99.40\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Machine Learning methods used for epilepsy detection sub-bands. datasets (CHB-MIT and BONN) that contain multi-channel and single-channel EEG data. The RNN-based epilepsy detection model has been optimized in multiple scenarios with different parameters to find the optimal configuration for the efficient detection of seizures. The proposed RNN is fundamentally different from the memory-based algorithms such as recurrent neural network architecture. Therefore, the RNN does not possess memory or temporal context, meaning that it does not consider or retain any information from previous inputs.
## 3 Results and Discussion
### I-Tasser Studies  
In order to predict the structures of the peptides, we carried out I-TASSER studies. As shown in 3, I-TASSER studies predicted exclusively coiled structures for all peptides except Mut2, for which Trp3 and Tyr4 regions were predicted to form strands based on C-scores. This secondary structural change is likely a result of the change of His to Ile, imparting elevated hydrophobicity to the peptide. Solvent accessibility ranged from 6-8 for N- and C-terminal residues and from 2-4 for interior residues. Solvent accessibility for the original peptide (Pep) was found to be 7-4-3-4-4-3-4-4-7-8 corresponding to each residue of the sequence. For Mut1, the N-terminal solvent accessibility decreased slightly, giving a value of 6-4-3-4-4-3-4-4-7-8. For Mut2, where His2 was changed to Ile, scores were found to be 6-3-2-4-4-3-4-4-7-8. Mut3 and Mut4 both had identical solvent accessibility to Pep, 7-4-3-4-3-4-4-7-8. These results indicate that the peptides had amphiphilic properties. The C-scores, or confidence scores, for I-TASSER predictions were \(-0.58\) for Pep, \(-0.56\) for Mut1, \(-0.54\) for Mut2, \(-0.68\) for Mut3, and \(-0.46\) for Mut4. Thus, the I-TASSER predictions can be expected to have the most variability for Mut3 and the least for Mut4 based on a higher confidence level for these predictions.
###### Abstract  
Recently, owing to the high mobility and low cost of drones, drone-based delivery systems have shown considerable potential for ensuring flexible and reliable parcel delivery. Several crucial design issues must be considered to design such systems, including route planning, payload weight consideration, distance measurement, and customer location. In this paper, we present a survey of emerging drone routing algorithms for drone-based delivery systems, emphasizing three major drone routing aspects: trajectory planning, charging, and security. We focus on practical design considerations to ensure efficient, flexible, and reliable parcel delivery. We first discuss the potential issues arising when designing such systems. Next, we present a novel taxonomy based on the above-mentioned three aspects. We extensively review each algorithm for drone routing in terms of key features and operational characteristics. Furthermore, we compare the algorithms in terms of their main idea, advantages, limitations, and performance aspects. Finally, we present open research challenges to motivate further research in this field. In particular, we focus on the major aspects that researchers and engineers need to consider in order to design effective and reliable drone routing algorithms for drone-based delivery systems.  
drone-based delivery; drone logistics; drone routing; package delivery; unmanned aerial vehicle; trajectory planning; charging; security +
Footnote †: journal: Sensors
## 3 Results
### Comparison with GRACE-FO  
A high correlation (R = 0.96, \(p\) value < 0.01) was found between RadWet-L mapped inundation extent and GRACE-FO water equivalent thickness [57] at the whole-basin level, as shown in Figures 10 and 11. This demonstrates that RadWet-L is able to capture the temporal fluctuations in inundation over the hydrological year shown in Figure 11.  
The extent of open water remains relatively static over the hydrological year at a basin-wide scale, as seen in Figure 11. Conversely, inundated forest extent was shown to vary considerably with dry to wet season variations in extent in the order of 285%.  
The RadWet-L mapped total wetted area is also highly correlated to GRACE-FO water equivalent thickness within the Amazon River Basin measured at the GRACE-FO native resolution of 3\({}^{\circ}\)\(\times\) 3\({}^{\circ}\), as shown in Figure 12. The Pearson-R correlation between RadWet-L and GRACE-FO is shown across the Amazon Basin, with the 2017-2019 maximum total wetted area shown for reference. The correlation is generally high, and greater than 0.7 (\(p\) value < 0.01) where RadWet-L is detecting inundation. Areas of lower correlation are usually in herbaceous-dominated areas where the ALOS-2 PALSAR-2 ScanSAR system is unable to map inundation.  
The annual median date of peak high water measured by RadWet-L was April 27 th, with a mean date of peak high water on the 6 th of May. Both these estimated dates of peak high water occurred earlier than measured by GRACE-FO, with a difference of 19 and 10 days, respectively.  
The annual median estimated date of low water occurs on the 6 th of November, with a mean low water occurrence on the 28 th of October. This is later in the hydrological year than measured by GRACE-FO, with a median and mean low water occurrence on the 16 th of October.  
Figure 10: Pearson R correlation between RadWet-L total wetted area classified extent and GRACE-FO. Gravity Anomaly [57] where measurement and observation dates are within 10 days.
## 2 Methods
### Development of hydro-optical models
#### 2.4.3 Lake Erie hydro-optical model
Then, the retrieved _doc_ concentrations supplemented the measured concentrations of \(chl\) and \(sm\). This set of data was then processed like it was done in the previous step. This allowed tuning not only the CPA vectors \(a^{*}\) and \(b_{b}{}^{*}\) but also the _doc_ concentration values. Having tuned \(a^{*}\) and \(b_{b}{}^{*}\) with adjusted _doc_ concentrations, _doc_ concentrations are to be retrieved anew with the tuned model. A single iteration proved to be sufficient: the CPA spectral specific coefficients established in the first iteration didn't change with further iteration steps. Thus the established spectral specific coefficients were assumed as the best fit. Our comparison of \(chl\) and \(sm\) measured _in situ_ with the respective concentrations obtained by applying the LM-based algorithm to \(R_{rsw}(\lambda)\) values concurrently measured from board a ship showed (Figure 3) that correlation is high (r\({}_{chl}\) = 0.74, r\({}_{sm}\) = 0.82), absolute difference is low (RMSE\({}_{chl}\) = 0.98 \(\mu\)g/L, RMSE\({}_{sm}\) = 0.68 mg/L) and regressions are practically without any offset (\(chl_{\rm{retrieved}}\) = 0.98 \(chl_{\rm{measured}}\) - 0.1, \(sm_{\rm{retrieved}}\) = 0.58 \(chl_{\rm{measured}}\) + 0.159) indicating that the established dependence is governed exclusively by the parameter in question. The results obtained indicate that firstly, the established hydro-optical model describes fairly well the present hydro-optical conditions in Lake Erie, and secondly, the developed LM-based algorithm operates satisfactorily in the case of a formerly eutrophic but then oligotrophicated water body.
# An Earthquake Forecast Model Based on Multi-Station PCA Algorithm
## 1 Introduction  
An earthquake is a kind of natural disaster that is represented by the activation of faults as a consequence of the regional stress field resulting from plate movement. When the fault zones between plates slide, huge energy is released and then causes an earthquake. China is located at the junction of the Indian Ocean plate, the Pacific Plate, and the Eurasian plate. The three plates create a seismic belt named the Pacific Rim seismic belt, which is an area with lots of earthquakes. For these reasons, many earthquakes in China such as the Xingtai earthquake in 1968, the Tonghai earthquake in 1970, the Tangshan earthquake in 1976, and the Wenchuan earthquake in 2008 caused huge losses, while the earthquake has become an important disaster to humans' lives and property.  
This situation raises the demand for an earthquake prediction method to guide people to avoid the earthquake disaster, and the efforts have never stopped. Earthquake prediction research could be divided into the short-term prediction of a few days, the medium-term prediction of under one year, and the long-term prediction of years. The short-term prediction is most difficult and valued for avoiding disaster.  
To help explore this problem, the Peking University Key Laboratory of Integrated Microsystems (IMS) presents a multi-component earthquake prediction system named AETA [1], which is a set of monitoring systems that integrates precursor signal collection, data transmission, storage and processing, analysis, and real-time display on a website [2], with a series of advantages contains high sensitivity, low cost, real-time, and large area monitoring. We set hundreds of monitoring stations in China and accumulated 50 TB data with 20 GB increased each day, and these data have shown excellent results in earthquake analysis. Electromagnetic and acoustic signals are both under the monitoring of the AETA. In this work, the PCA method is used to extract features from origin data, and a machine learning method LightGBM (LGB) is used to predict the earthquake.
## 3 Results and Discussion
### Model Monitoring Results and Analysis
#### 3.4.5 Comparative Analysis of Models
The unique contribution of this study lies in its innovative integration of vegetation indices into deep learning algorithms for vegetation change monitoring, a methodology that outperforms traditional models (random forest and PT-Former) in detection accuracy.
## 3 Results
### Access to Educational Centers
#### 3.2.3 Access to Public High Schools  
Unlike students from elementary and middle schools, the ones attending high schools are much more independent concerning their mobility (14 to 18 years old). As a result, they do not rely as heavily on other persons assisting them to go to school (such as their parents), so walking or cycling to schools stand as very attractive mobility alternatives. Both modes of transport can also be easily complemented with school buses or public transport if the commutes are long.  
Figure 16 shows the number of public high schools within the Stanford District. Immediately, it can be noticed that the density is very low, with only three destinations inside the district, two located at the center and one at the eastern corner. Consequently, only 7.59% of the aggregation areas have high accessibility levels, 91.14% medium levels, and 1.27% low levels (see Table 11).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Accessibility Level** &
\begin{tabular}{c} **Stanford District** \\ **(Block Groups)** \\ \end{tabular} & **Distrito Tec (AGEBs)** \\ \hline Low (\(>\)30) & 1/79–1.27\% & 0/11–0\% \\ Medium (16–30) & 72/79–91.14\% & 2/11–18.18\% \\ High (0–15) & 6/79–7.59\% & 9/11–81.81\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: Accessibility level to high schools traveling by bicycle per aggregation area.  
Figure 15: Distrito Tec average travel time to the 3 closest public middle schools by bicycle.  
Figure 17 shows that like in the Stanford District, in Distrito Tec, there is an extremely low density of public high schools, with only one destination available within the area. Nevertheless, 81.81% of the aggregation areas have a high level of accessibility and the remaining 18.18% a medium level (see Table 11).  
It is clear that the density of high schools for both areas is very low, and thus, there is a significant reduction in accessibility levels. Nevertheless, exactly as in the previous cases, it would be necessary to further assess the demand for such destinations to determine whether an increase in their density is required.  
To promote the use of bicycles by students, it is important to have safe streets, as well as parking spaces for bicycles at schools. For students with longer commutes, sustainable transport modes such as school buses or public transport can be used.
# An Enhanced Lightweight IoT-based Authentication Scheme in Cloud Computing Circumstances
## 2 Review and Security Analysis of [PERSON]'s Scheme
### Registration Phase
#### 2.1.2 Cloud Server Registration Sub-phase  
In this sub-phase, the server \(\left(S_{j}\right)\) is registered by the control server \(\left(CS\right)\). The communication between \(S_{j}\) and \(CS\) is through a secure communication channel. The steps involved in this sub-phase are as follows:  
Step 1: \(S_{j}\) selects an identity \(\left(SID_{j}\right)\) and pseudo-identity \(\left(PSID_{j}\right)\). Then, \(S_{j}\) sends the registration request message \(M_{3}=\left\{SID_{j},PSID_{j}\right\}\) to \(CS\).  
Step 2: Upon receiving the registration request message \(M_{3}\), \(CS\) computes \(B_{1}=h\left(PSID_{j}\parallel ID_{CS}\parallel x\right)\) and \(B_{2}=h\left(SID_{j}\parallel x\right)\). Then, \(CS\) stores \(SID_{j}\) and sends the registration response message \(M_{4}=\left\{B_{1},B_{2},ID_{CS}\right\}\) to \(S_{j}\).  
Step 3: After \(S_{j}\) receives the registration response message \(M_{4}\), \(S_{j}\) stores \(\left(B_{1},B_{2},SID_{j},PSID_{j},ID_{CS}\right)\).  
At this point, the cloud server registration sub-phase is over and \(S_{j}\) was registered by \(CS\).