esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Properties and Characteristics of LoRaWAN Technologies
### Modulation Technique  
The modulation methods that are supported by LoRaWAN are FSK and LoRa [15]. Chirp spread spectrum (CSS) modulation serves as the foundation for LoRa modulation, which can operate in a variety of spreading factors (SF) that allow a trade-off between transmission range and data throughput. LoRa uses an advanced modulation technique that enables low-power, long-range transmissions. LoRa modulation is resistant to fading and interference by design [16]. The signal is modulated onto a chirp waveform in LoRa modulation, which is a tone that changes in frequency in a linear manner over time. LoRa signals may be decoded even at a negative signal-to-noise ratio (SNR) because of the energy capture effect, which occurs as the receiver gradually gains more energy over time [17]. The architectural components of the modulation are mapped to the LoRaWAN protocol stack. A preamble or time-frequency synchronization sequence is transmitted by a device to determine the free channel capacity in LoRaWAN. The device then uses the measured channel capacity to select the correct SF. The selected SF is used to send the data payload in the next step. Another modulation method that LoRaWAN supports is FSK modulation with a maximum data rate of 50 kb/s. Short-range communications use FSK modulation, which benefits mostly from its low complexity. Binary data is communicated via FSK modulation, which shifts the carrier signal's frequency between two levels. The frequency difference between the modulating and carrier signals determines the data rate of the FSK signal [18].  
In certain regions, LoRaWAN also supports Long Range Frequency Hopping Spread Spectrum (LR-FHSS), which offers a reliable and robust connection technique. A pseudo-random hopping sequence over a group of sub-carriers is the foundation of LR-FHSS, which reduces the impact of interference [17].
# Formal Analysis of DTLS-SRTP Combined Protocol Based on Logic of Events
## 5 Formal Analysis of DTLS-SRTP Combined Protocol
### Results and Analysis  
This chapter employs an extended LoET to provide a security proof for the DTLS-SRTP protocol. Through an analysis of the protocol interaction process, a description of the protocol authentication procedure, and the formal modeling of the protocol's basic sequences, both sub-protocols are formally modeled. By deriving formulas, it is demonstrated that the sub-protocols satisfy mutual authentication and confidentiality. The proof results indicate that the DTLS-SRTP protocol is secure under the fulfillment of the underlying assumptions.  
We have chosen the classic theorem-proving method of Protocol Composition Logic (PCL) [19; 20] for comparison with LoET. The selection of PCL and Event Logic for comparison is motivated by the similarity of these two theories, both capable of proving the security of composite protocols. Through analysis and comparison, several conclusions can be drawn:  
1. PCL is limited in proving the authenticity of signature protocols only and cannot analyze other non-signature authentication protocols. In contrast, LoET can analyze the authenticity of both signature and non-signature protocols.
2. PCL lacks a definition for the pre-order behavior sequences of threads, leading to a lack of rigor in modeling the interaction actions among entities in cryptographic protocol instances. This deficiency fails to ensure the correlation between different basic sequences. In contrast, LoET explicitly defines the thread mechanisms for formal modeling of cryptographic protocols. The specification of thread states is achieved through atomic independence, ensuring the rigor of the modeling.
3. PCL, during the analysis of cryptographic protocols, can only capture a partial content of the security properties, demonstrating a lesser capability in characterizing protocol security properties compared to LoET.  
Model checking [21] is an automated method for verifying concurrent systems with a finite state space. It exhaustively explores the state space to determine whether safety goals can be achieved. The proof methodology in LoET treats the satisfaction of protocol specifications as logical propositions. Through a set of inference rules, it employs deductive reasoning to prove these propositions. Model checking often encounters issues such as state explosion and numerical limitations, making it suitable for relatively small-scalecryptographic protocols. On the other hand, LoET focuses more on the correctness of cryptographic protocols without the need to consider problems like state explosion.
# Comparison of Cognitive Differences of Artworks between Artist and Artistic Style Transfer
## 3 Results and Discussion
### Key Factors for Distinguishing the Stimuli
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Stimuli**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{6}{c}{**Attributes**} \\ \cline{3-8}  & & **Only “Intuition”** & **Line** & **Color** & **Light and Shade** & **Stroke** & **Detail** \\ \hline \multirow{2}{*}{G01/B} & 497 & 49 (9.86\%) & 25 (5.03\%) & 25 (5.03\%) & 6 (1.21\%) & 62 (12.47\%) & 12 (2.41\%) \\  & (66.3\%) & & Only by ”intuition” \(>\) Stroke \(>\) Line \(=\) Color \(>\) Detail \(>\) Light and Shade & & & \\ \hline \multirow{2}{*}{G02/A} & 511 & 56 (10.96\%) & 36 (7.05\%) & 19 (3.72\%) & 19 (3.72\%) & 33 (6.46\%) & 25 (4.89\%) \\  & (68.1\%) & & Only by ”intuition” \(>\) Line \(>\) Stroke \(>\) Detail \(>\) Color \(=\) Light and Shade & & & \\ \hline \multirow{2}{*}{G03/B} & 499 & 52 (10.42\%) & 14 (5.38\%) & 9 (3.59\%) & 16 (3.21\%) & 24 (9.56\%) & 11 (4.38\%) \\  & (66.5\%) & & Only by ”intuition” \(>\) Stroke \(>\) Light and Shade \(>\) Line \(>\) Detail \(>\) Color & & & \\ \hline \multirow{2}{*}{G04/B} & 499 & 59 (10.30\%) & 17 (2.97\%) & 20 (3.49\%) & 31 (5.41\%) & 47 (8.20\%) & 18 (3.14\%) \\  & (66.5\%) & & Only by ”intuition” \(>\) Stroke \(>\) Light and Shade \(>\) Color \(>\) Detail \(>\) Line & & & \\ \hline \multirow{2}{*}{G05/A} & 559 & 66 (11.81\%) & 30 (5.37\%) & 31 (5.55\%) & 17 (3.04\%) & 41 (7.33\%) & 15 (2.68\%) \\  & (74.5\%) & & Only by ”intuition” \(>\) Stroke \(>\) Color \(>\) Line \(>\) Light and Shade \(>\) Detail & & & \\ \hline \multirow{2}{*}{G06/A} & 559 & 51 (10.41\%) & 27 (5.51\%) & 16 (3.27\%) & 15 (3.06\%) & 24 (4.90\%) & 36 (7.35\%) \\  & (74.5\%) & & Only by ”intuition” \(>\) Detail \(>\) Line \(>\) Stroke \(>\) Color \(>\) Light and Shade & & & \\ \hline \hline \end{tabular} n = 750; The attribute with the highest number of votes is marked in red.  
\end{table} 
Table 10: The number of votes for the attributes selected by the audience. It can be said that P05 has more characteristics of paintings completed by human artists. In addition, the audience preferred the paintings created by amateur artists. Therefore, we can speculate that when P05 appeared alone, the accuracy of the audience in distinguishing its creative mode was low compared with other stimuli, which may have just been an accidental event.
# The Loop-Mediated Isothermal Amplification for the Rapid Detection of Porphromonas Gingivalis
## 2 Materials and Methods
### Detection of Tested Periodontal Pathogens
#### 2.5.3 QPCR (Real-Time PCR)  
QPCR reactions were performed using the QuantiFast SYBR Green PCR kit (Novazym, Poznan, Poland), designed for use in a two-step protocol in which the process of annealing primers and product synthesis (elongation) are linked together. The reaction used Hot Start Taq Plus DNA polymerase, the activation of which lasted 5 min at a temperature of 95 \({}^{\circ}\)C. The process of denaturing the template DNA lasted 10 s at a temperature of 95 \({}^{\circ}\)C, and then for 30 s. The DNA sequence was elongated at 60 \({}^{\circ}\)C. At this stage, fluorescence detection was performed, which made it possible to measure the value of the relative fluorescence intensity and the melting point of the reaction products for individual bacterial strains. The process of verifying positive results was performed using the FAM Green fluorescence channel (Figure S2). Depending on the original concentration of the bacterial DNA template, the number of reaction cycles was between 35 and 40. The specially designed buffer contains the addition of the PCR Q-Bond, which significantly accelerates the course of the reaction by shortening the time of denaturation, renaturation, and elongation. Denaturation was carried out at 95 \({}^{\circ}\)C followed immediately by an extension step at 60 \({}^{\circ}\)C. This protocol was used for primers with a melting point below 60 \({}^{\circ}\)C. In order to obtain the maximum efficiency of the reaction, the amplicon lengths were in the range of 60-200 bp.  
The reaction mixtures for LAMP, PCR, and qPCR are presented in Tables S3-S5. The amount of DNA samples used for the analyses was below 1 ng/\(\upmu\)L.
## 1 Introduction
In [22], a differential evolution optimization algorithm was combined with the GWO algorithm to eliminate inferior individuals and accelerate the optimization speed, but it is still easily fell into a local optimum because the fundamental idea of the algorithm was not considered. In [23], an embedded optimization operator was used to update the population position, and a differential evolution strategy was used with adaptive parameters to further improve the optimization functions. The performance of the hybrid algorithm was improved by introducing the worst individual, but the role of other individuals was not considered. Although these improved algorithms optimized the performance of the GWO algorithm in some aspects, it was still difficult to achieve a balance between convergence speed, local optimum, and search accuracy. The aim of this paper is to provide an effective control method for aircraft engine systems with constraints. The primary contribution of this paper is as follows:  
1. By introducing logistic and chaotic mapping, an individual optimal search mechanism, and cross operation, a novel hybrid grey wolf optimization (HGWO) algorithm is proposed. The presented HGWO algorithm can provide faster convergence speed and better search accuracy and avoids falling into a local optimum. 2. The GWO is used to improve the selection of initial parameters in an elastic BP neural network. The accuracy of network modeling is increased, and the established engine neural network model has a better prediction performance as a result. 3. A nonlinear model predictive control method based on a hybrid grey wolf optimizer is proposed for the control of aircraft engines with constraints. The remainder of this paper is organized as follows. Section 2 describes the considered aircraft engine and the corresponding GWO-based elastic BP neural network prediction model. Section 3 presents the HGWO algorithm. In Section 4, the design of the NMPC controller is shown. Section 5 verifies the simulation of the designed NMPC controller. Section 6 draws conclusions.
## 4 Experiments
### Pitch and Speed Modifications  
The pitch and the elocution speed were two variables we informally observed to be changing between neutral and stressed speech. As a consequence, we performed an analysis to measure the differences between the mean pitch from neutral to stressed audio frames for each speaker  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Set** & **Test Set** & **Mean (\%)** & **Std (\%)** \\ \hline \multirow{2}{*}{Neutral} & Neutral & 96.73 & 0.33 \\  & Stressed & 79.21 & 0.90 \\ \hline \multirow{2}{*}{Stressed} & Stressed & 95.87 & 0.28 \\  & Neutral & 90.89 & 0.49 \\ \hline Mixed & Mixed & 96.05 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results for matched and mismatched settings.  
using VoiceBox [41]. An estimation of the average elcution speed for each user was calculated as well, computing the mean number of words per second of each speaker by obtaining an automatic transcription of each of the recordings using Google Speech Recognition [44] and dividing it over the length of the audio signals after having removed silent audio frames with the VAD module.  
The differences in pitch from neutral to stressed speech were in a range between a relative percentage of \(-2\%\) and \(+7\%\) for all speakers, increasing on average 2.2%. In regard to the elcution speed, subjectively, it seems to rise in stressed speech, however our analysis gave us the opposite conclusion. The number of words per second was higher when the user was reading a text, 2.2 words/s on average, in comparison with when the speaker was performing an oral presentation, 1.85 words/s. By listening to the signals, we determined that the words were pronounced faster but there were many short pauses and pause fillers--words like 'ehm', 'um', 'ah'--in between them, that did not count as words for the transcription but were not removed by the VAD either. Those causes lead to an overall lower elcution rate.  
Thus, we applied modifications in the locution speed and the pitch on the original database, to produce synthetically stressed samples of speech. The pitch was modified by the following relative percentages [\(-6\%\), \(-3\%\), \(+3\%\), \(+6\%\)] and the speech signals were slowed down--with the aim of extending the duration--by the following percentages [\(-20\%\), \(-15\%\), \(-10\%\), \(-5\%\)]. All of these modifications were applied to the original audio signals and resulted--in what we would name--a new synthetically stressed set per modification. In this manner, we augmented our data by a factor of 9, the original dataset plus 8 modifications. Figure 4 presents a block schematic of the original data and the synthesized one. Synthetic Stress (SS) and Synthetic Super Stressed (SSS) represent the synthetically stressed collections obtained from N and S sets respectively.
## 3 System Model and Problem Formation
### Problem Statement and Formation  
The problem is to reconstruct the optimal mesh backbone topology and identify extra physical layer resources for multimedia-enabled WMN modeled as an undirected graph \(G=(V,E)\) where, \(V=\{v_{1},v_{2},v_{3},....,v_{n}\}\) is the set of the WMRs and \(E=\{e_{1},e_{2},e_{3},....,e_{m}\}\) is the set of the edges among WMRs, \(e_{i}\) is a link between two WMRs say \(v_{i}\) and \(v_{j}\) whose Euclidean distance is less than their transmission ranges; thus, the link \(e_{i}\) could also be represented as \(e_{(i,j)}\). \(I_{(v_{i})}\) is the set of incident edges of any WMR \(v_{i}\) where, \(F=\{f_{1},f_{2},f_{3},....,f_{y}\}\) is the set of network flows and each flow represents a bandwidth demand. \(C=\{c_{1},c_{2},c_{3},....,c_{k}\}\) is the set of orthogonal channels. A central static/ permanent channel allocation approach to minimize the interference and maximize the network capacity similar to [4] is adopted. QoS-based routing is considered separately to channel assignment. Static channel allocation is given as matrix \(a_{(i,j),k}\) of \(m_{(i,j)}*k\) size, where \(m\) is the total number of edges and \(k\) is the total number of channels, where matrix value can be either 0 or 1 showing whether a channel is assigned to an edge or not. A comprehensive survey on channel assignments techniques was done in [53], which provides good references and background for interested readers. \(CC=\{cc_{e_{1}},cc_{e_{2}},cc_{e_{3}},...cc_{e_{m}}\}\) is the set of channel capacities where \(cc_{e_{(i,j)}}\) is the total capacity assigned to any link \(e_{i,j}\) and \(R_{c}=\{r_{e_{1}},r_{e_{2}},r_{e_{3}},...,r_{e_{m}}\}\) is the set of residual capacities of each link. All possible end-to-end paths for all flows are known and \(P_{f_{y}}=\{p_{1},p_{2},p_{3},...,p_{z}\}\) is the set of all possible paths for any flow \(f_{y},D=\left\{d_{f_{1}},d_{f_{2}},d_{f_{3}},...d_{f_{y}}\right\}\) is the set of maximum affordable delay and \(PLR=\left\{Pl_{f_{1}},Pl_{f_{2}},Pl_{f_{3}},...,Pl_{f_{y}}\right\}\) is the set of \(PLR\) requirement of network flows \(f_{y}\).
## 3 Experimental Results
### In Vitro Experiment
Figure 10 shows the fluorescence microscopy results obtained on the zirconia substrates (ZTA and Y-TZP show similar results). On the uncoated substrate, it can be observed that the three signals for the non-collagenous bone matrix proteins in osteoblasts: osteoclastin and osteeopontin, and for cell nuclei, are barely visible and are only localized in a few spots. Moreover, the increased distribution of blue DAPI signals indicates that more nuclei than bone tissue formed. When the coating is applied, osteepontin and osteoclastin are strengthened and more uniformly distributed over the entire area. Figure 11 shows the fluorescence analysis of substrate surface bone and cell nuclei quantities before and after treatment. Figure 11: Relative surface substrate amounts assigned to the fluorescent probes used for osteoclastin, osteeopontin, and cell nuclei based on fluorescence micrograph images. Figure 10: Y-TZP fluorescence micrograph samples after exposure to SaOS-2 cells. [PERSON] and red stains identify osteoclastin and osteeopontin, respectively, while blue shows cell nuclei. For the LDPE substrate, the bone tissue increase is around 2%, but the percentage of cell nuclei still present on the surface is nearly identical to that of the untreated substrate. For the Ti6 Al4V, the number of nuclei is higher than that of the untreated samples, but bone formation is virtually the same for both samples. The only change is a more homogeneous distribution on the substrate surface. For the Y-TZP substrate, the histogram confirms a high amount of surface bone tissue formation. An increase of 5-6% of the bone product and 3% of nuclei on the coated surface is evident.
## 4 Experiments and Results
### Experimental Results of the Number of Chargers  
This experiment compares the relationship between the number of sensors, different algorithms, and the number of chargers required. The average number of chargers used by the SMRU algorithm is less than that used by RPRO and PBAD. Figure 7 shows the average number of chargers used. RPRO selects the charger position and the charging direction randomly. As the charging angle is not selected, the number of chargers required is significantly large. PBAD searches for 360\({}^{\circ}\) angles that can cover most of the sensors for charging after randomly selecting positions. Additionally, PBAD repeatedly calculates the same sensor position map and selects the best position using a minimal charger as the deployment location of the charger. In this experiment, PBAD was repeated 10 times to select the best result. Therefore, the experimental results of PBAD are superior to those of RPRO. SMRU first selects a random position and then searches for 360\({}^{\circ}\) of the charging radius at that position. If there are more than three sensors (three candidate positions) within the charging radius at this position, it chooses the candidate position that can cover the largest number of sensors and deploy the charger. Additionally, the elimination of the excess charger positions that may be generated in the previous steps means that the number of charger requirements can be reduced to less than that of PBAD.  
Figure 7: The average number of chargers.
## 3 Simulation Results
### Weighted Broadband MUSIC  
As the high computation time burden of the time-domain DAS beamformer conflicts with the real-time localization and separation requirements, an alternative localization approach, the so-called BW-MUSIC method, was utilized to balance the broadband feature of the speech signal and the real-time operation performance. In the simulation cases, the sampling rate of the speech signals from the ST-CMDS data set was 16,000 Hz; therefore, the analysis frequency band was set to 50-8000 Hz, and the step length was 50 Hz for the BW-MUSIC method. The total processing duration for one sound source was 78.037 s, measured by MATLAB with an Intel i7-6700 central processing unit (CPU) and 44 GB of random access memory (RAM).  
The improved localization results obtained with the BW-MUSIC method are shown in Figure 9, in which the locations of \(s_{1}\), \(s_{2}\), and \(s_{3}\) are denoted in the beamforming maps conspicuously by the virtual upgraded measurement sound signal. Compared with Figures 6, 7b and 8b of the DAS beamformer, the localization results of BW-MUSIC in Figure 9a-c are much more accurate, with minor main-lobe-to-side-lobe ratios (MSRs) and higher resolution. More specifically, owing to the error elimination by the weighting summation and averaging calculation in the BW-MUSIC method, the sound sources could be significantly identified by the main lobes, which are the distinct hotspots in all the three figures, while the areas of the hotspots are smaller.
## 3 Case Study
### OSP in Seamless Condition
#### 3.3.3 Optimal Sensor Placement  
In this section, comparison and optimization are performed using six intelligent algorithms, including PSO, IPSO, SSA, ISCAMS, AHA, refs. [49; 50; 52; 53; 54], and IAHA. Considering the stochastic nature of intelligent algorithms, each algorithm is run 10 times, and the optimal value is selected. The sensor placement results are illustrated in Figure 11, where sensors are indicated by red dots in Figure 11. From Figure 11, It can be clearly judged from the spatial layout that the sensor layout placed using intelligent algorithms is relatively scattered. This suggests that introducing intelligent algorithms for sensor placement can effectively reduce the issue of sensor concentration, thus avoiding significant redundancy in modal information. Additionally, most sensors are concentrated in the upper-middle part of the downstream surface of the dam, which is consistent with the characteristic that the vibration mode information of arch dams is mainly concentrated in the upper-middle part of the dam body. The sensor placements obtained from the above  
Figure 10: MAC-MAX variation curve with the number of sensors.  
Figure 9: Initial candidate points on the downstream surface of the arch dam (indicated by red dots in the figure).  
results are consistent with the results in references [7; 30; 32], verifying the rationality and effectiveness of the method.