esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Analytical Biochemistry Experiments  
Expression of the NMDA receptors subunits NR1, NR2A, and NR2B in the frontal cortex were quantified using Western blotting. In brief, all samples were suspended in a loading buffer (63 mM Tris; 10% glycerol; 2% sodium dodecyl sulphate, 5% 2-mercaptoethanolanol and 0.01% bromophenol blue). The re-suspended material was used for the electrophoresis in the 7.5% polyacrylamide gel (Bio-Rad, Hercules, CA, USA) followed by electro-blotting in the criterion blotter (Bio-Rad, Hercules, CA, USA). Nonspecific binding was blocked with 3% non-fat milk (Merck, Kenilworth, NJ, USA) and dissolved in 0.1% Tween in phosphate-buffered saline (PBS buffer)/Tris-buffered saline (TBS buffer). Blots were incubated with anti-NMDA-NR1 (Merck, Kenilworth, NJ, USA, 1:1000 in 5% bovine serum albumin in 0.1% Tween in TBS buffer, overnight incubation), anti-NMDA-NR2A (Merck, Kenilworth, NJ, USA, 1:500 in 0.1% Tween in TBS buffer, 2 h incubation at room temperature) and with anti-NMDA-NR2B (Merck, Kenilworth, NJ, USA, 0.1% Tween in TBS buffer, 2 h incubation at room temperature) as primary antibodies and the loading control with an anti-\(\alpha\)-tubulin antibody (Exbio, Prague, Czech Republic, 1:1000 in 0.1% Tween in PBS buffer, 1 h incubation at room temperature). After, all blots were washed in PBS buffer/TBS buffer and incubated for 1 h with a horseradish peroxidase-conjugated secondary antibody (Dako, Santa Clara, CA, USA, 1:3000, for \(\alpha\)-tubulin--dilution of 1:5000 was used). Detectionswere performed with a chemiluminescent substrate (ThermoFisher Scientific, Waltham, MA, USA) and evaluated by the Gel Doc Analysis system (Bio-Rad, Hercules, CA, USA), in accordance with our previous studies [29; 36].  
The activities of nNOS, eNOS, and iNOS were estimated in the R and L parietal cortex separately using chemicals (except for radiolabelled arginine) from Merck, Kenilworth, NJ, USA. In brief, the parietal cortices were homogenized (1:10) in homogenization buffer (1 mM EGTA, 1 mM dithiothreitol, 20 mM HEPES, 0.32 M sucrose, 14.6 mM pepstatin and 21 mM leupeptin, pH = 7.4) and the resulting homogenates centrifuged at 1200\(\times\)_g_ for 10 min at 4 \({}^{\circ}\)C. Supernatants were added to the reaction buffer (homogenization buffer containing also 200 mM \(\beta\)-nicotinamide adenine dinucleotide phosphate, 50 mM tetrahydrorobiopterin and 4.6 mM [14C]arginine (ARC, St. Louis, MO, USA,)) and incubated for 30 min at 37\({}^{\circ}\)C. Some samples also contained 1 mM CaCl\({}_{2}\) (nNOS and eNOS) and specific inhibitors (1 mM spermidine for nNOS, 190 mM Na\(\omega\)-nitro-l-arginine methyl ester for nNOS/eNOS and 1 mM aminoquanidine for iNOS). Final protein concentrations determined by the Bradford method equalled 0.5 mg/mL in all incubation mixtures. The reaction was terminated by adding the stop buffer (30 mM HEPES, 3 mM EDTA, pH = 5.5) and by rapid cooling. DOWEX 50 WX8-200 was used to separate citrulfine from arginine, in accordance with our previous studies [29; 38; 45].
being highly efficient and eco-friendly with a distinguished performance on the decomposition of NOx [42; 43; 44; 45; 46].  
SCR assisted by DBD provides synthesis effects to reach significantly higher activity than SCR when the temperature is lower than 673 K. However, there are some drawbacks to using NH\({}_{3}\) as a reducing agent in the DBD-SCR process. On the one hand, the negative effect of ammonia escape is unavoidable as a result of excess NH\({}_{3}\) in SCR. On the other hand, the excited oxygen atom activated by DBD in an excess of O\({}_{2}\) can react with NH\({}_{3}\) in the feed gas to produce secondary products and then weaken the reduction of NOx in DBD-SCR [47; 48; 49; 50]. Some studies have shown that the use of hydrocarbons as reducing agents in DBD-SCR could be of particular interest [51; 52], not only for avoiding the production of ABS (ammonium bisulfate), but also for controlling the by-product of NO abatement. However, some limitations [53] are unavoidable in HC-SCR assisted by a DBD system, such as the production of coking.  
Based on the superiority of hydrocarbons in DBD-SCR, using CH\({}_{4}\) as an assistant reducing agent in DBD-NH\({}_{3}\)-SCR probably overcomes the negative effect of NH\({}_{3}\), which conduces the good performance of NO abatement for DBD-NH\({}_{3}\)-SCR with exceeded O\({}_{2}\). Nevertheless, there are few reports concerning the effects of and mechanisms involved in plasma enhanced NH\({}_{3}\)-SCR using methane as the assistant reducing agent in such a hybrid reaction system.  
In this work, a new hybrid reaction system using CH\({}_{4}\) as an assistant reducing agent in DBD-NH\({}_{3}\)-SCR has been studied in detail. The reaction products of NO abatement in DBD-SCR with different reducing agents were detected by Fourier transform infrared spectroscopy (FTIR). The selectivity of NO abatement for two hybrid systems has been researched and the involved chemical mechanism was also revealed.
## 4 Cryptographic Analysis  
An important part of the encryption algorithms is their reliability. It is determined by cryptographic analysis. In this section, we present the results of empirical tests performed using proposed audio encryption algorithm described in the previous section.  
Using the C++ programming language, the proposed audio encryption method was implemented. All experimental results discussed in the following subsections were taken by using one iteration of the novel technique.  
Nine 2-bytes per sample audio files were encrypted for the security tests. The files were randomly selected from the huge collaborative database Freesound, accessed 1 September 2021 ([[http://freesound.org](http://freesound.org)]([http://freesound.org](http://freesound.org))). The chosen files are currently stored in WAV format with an audio sample rate of 44.1 kHz.
## 4 Radar Observations of the Downslope Wind Storm and Turbulence
### Flight-Level Measurements
WCR-based spectra can be produced up to 500 m AGL only, because of a lack of radar echoes at higher levels (Figure 8). This lack of radar echoes and the resulting noisy Doppler velocity field (with remaining velocity unfolding uncertainties) explain why we did not attempt to use WCR data to estimate the spatially resolved turbulence spectrum and EDR as in [23]. The spectral peak near 4-6 km in the last two transects in Figure 10 may be related to a hydraulic jump (see below). TKE tends to be higher at 500 m AGL than closer to the ground or at flight level. This is unlike typical shear-driven boundary-layer turbulence, which weakens with height in the boundary layer (e.g., [30]). It is also unlike the resolved turbulence in the WRF simulation, which generally weakens with height (Figure 7). The cause of this high TKE at 500 m AGL will become apparent in the next section. Figure 10: Power spectra of WCR vertical velocity (color lines, representing heights ranging from 100 to 500 m AGL) and flight-level vertical velocity (black line) for along-wind transect (**a**) #1, (**b**) #2, and (**c**) #4. Only the lee-side portion is included, a total distance of \(\sim\)30 km. Frequency is converted to distance using the average ground-relative speed of the aircraft. The dashed lines show the \(-5/3\) inertial subrange slope at increasing levels of TKE.
## 5 Discussion
### Limitations and Future Research  
A rigorous approach was adopted for the selection of studies in this review. However, there is still a chance that there are some missing studies that could enhance this literature review's findings. The literature review has presented a process model that needs to be validated. In the future, the research team intends to perform some query-based analysis to validate this process model. Short queries will be generated in the BPMN-Q language for every activity to perform validations. The results showed that the existing compliance checking approaches are not enough to solve the problem. The researchers intend to solve this problem with the help of process management tools and techniques. In contrast, this research is the first step towards modeling and behavioral compliance towards information security policies. In future studies, researchers should focus on transforming employees' behaviors rather than measuring compliance and noncompliance. One must identify the reasons for the noncompliance then apply compliance techniques accordingly. Second, there is much more literature available on compliance behaviors than noncompliance; there is a need to explore more theories and factors incorporated with noncompliance behaviors. Third, information security policy development is another area that needs attention. ISP makers design ISPs without thinking about their own organization's environment and needs. For instance, health employees or IT employees have a more stressful job than many other sectors, so the design of an ISP must focus on the sector or organization's needs. Researchers should research the organization's needs and then suggest how an organization can enhance its ISP according to its needs. Fourth, technology-based solutions are needed, for example, compliance management systems, compliance support systems, and compliance reporting systems. Researchers must see how to incorporate technology in this area and make ISPC more efficient. Fifth, there is still a need to explore more about actual compliance than intention. This literature review revealed that few studies are focusing on actual compliance behavior.
## 2 Functional Specification Model and Synthesis of Real-Time Systems
### Mapping Functional Specifications to Real-Time IoT Architecture  
In a typical approach to mapping a system specification into a distributed information system architecture, three main steps are performed [52]:  
* Resource allocation: At this stage, the target hardware architecture of the system is determined. Resources can be processors, specialized hardware modules, and communication channels (buses, communication processors, etc.). At the same time as the allocation, the connections between system components are determined.
* Assignment of tasks to resources: This step involves assigning individual tasks to resources. Tasks are assigned to computing modules, and transmissions are assigned to communication channels. Task allocation is closely related to resource allocation. For specialized resources, only tasks corresponding to the function performed by the resource can be assigned. Transmissions must only be assigned to communication channels between resources with assigned communicating tasks. When two tasks are assigned to the same resource, transmissions between them are ignored.
* Task prioritization: Task scheduling is necessary to determine the order in which tasks are performed when more than one task is assigned to a resource. Scheduling must consider the sequential relationships between tasks. During scheduling, speed optimization is performed. In the case of real-time systems, the main goal is to arrange the execution of tasks  
Figure 1: Sample task graph.  
and transmissions in such a way that all timing constraints are met (hard-constrained systems) or that timing constraint overruns are minimized (QoS maximization).  
IoT systems are characterized by a specific architecture, and for this reason, it is not possible to directly apply existing methods of synthesizing real-time systems to IoT applications. The main distinguishing features of these systems are:  
* A distributed architecture based on internet infrastructure.
* Communication through internet links, thus not ensuring that time constraints are met.  
From the above-mentioned properties of IoT systems comes the need to develop a specialized method for mapping specifications to the target architecture, as well as the need to develop solutions to ensure the implementation of transmission that meets real-time requirements.
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 6 Discussion  
This paper surveyed the use of USVs and their role in the DM process. This section presents recommendations for USVs in the DM process. Table 5 presents the current work on disaster robotics involving USVs. We classify each operation according to the DM phase and maturity level, where \"mature\" means the technology is ready to be used, \"deployed\" means it was at least tested in the field, and \"experimental\" means preliminary experiments were performed. Figure 3 presents a bubble chart for the visual representation of the number of works in Table 5, classified by the operation and disaster management phases. The number of works is directly proportional to the size of a bubble, while the colors represent the maturity level for each operation. Please note that the use of USVs for DM is maturing across different applications. Finally, this section presents a series of technological and non-technological guidelines which have an important role in the DM with USVs.  
Figure 3: Visual representation for the number of works in Table 5.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Operation** & **DM Phase** & **Maturity** \\ \hline Storm and Hurricane Forecast & Preparedness, Mitigation \& Response [61; 62; 63; 64; 65; 66; 67; 68; 69] & mature \\ \hline Structural Inspection & Preparedness, Mitigation \& Response [19; 61; 62; 63; 64; 79; 83; 175; 176; 177; 178; 179; 180; 181; 182; 183; 184] & mature \\ \hline Search \& Rescue & Response [21; 66; 189; 190; 191; 192; 193; 194; 197; 204; 207; 209; 214; 216; 224; 226; 227; 228] & mature \\ \hline Assess Water-Life Impacts after Disaster & Recovery [93; 94; 101; 107; 108] & mature \\ \hline Online Detection of Water Contaminants & All [102; 103; 104; 105; 106] & mature \\ \hline Tsunami Forecast & Preparedness, Mitigation \& Response [53; 54; 55; 56; 57; 58; 59; 60] & deployed \\ \hline Landslide and Erosion Detection & Preparedness \& Mitigation [76; 77; 91] & deployed \\ \hline Detection \& Monitoring HAB & Preparedness, Mitigation \& Response [146; 147; 148; 149; 150; 151; 152; 153; 154; 155; 156; 157; 158; 159; 160; 161; 162] & deployed \\ \hline Flood Risk Monitoring & Preparedness, Mitigation \& Response [70; 71; 72; 73; 74; 75] & deployed \\ Tracking \& Containment of Water Plumes & All [109; 110; 111; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121] & experimental \\ \hline \hline \end{tabular}
\end{table}
Table 5: USVs Capabilities for Disaster Robotics.
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 3 Expt. 1: Spectral Reallocation Preferences for Full-Bandwidth Speech
### Stimuli  
Speech stimuli were derived from the Sharvard Corpus [70], which consists of Spanish sentences spoken at a normal speaking rate. The linguistic complexity of this corpus is similar to that of the original English Harvard Corpus [71]. Each sentence contains five keywords used for scoring. Target sentences were drawn from the male Sharvard talker. Sentence numbers 1-380 and 381-540 were used in the adjustment and test phases, respectively (these phases are defined in Section 3.3 below).  
Stimuli were presented in quiet and in three additive stationary speech-shaped noise (SSN) conditions at SNRs of \(-6\), \(-3\) and 0 dB. The masker was generated by filtering random uniform noise with the long-term spectrum of 700 concatenated sentences of the female talker of the [PERSON] corpus.  
Four kinds of variation (for brevity, referred to as 'features' below) were tested in Expt. 1, namely, changes in spectral tilt and changes in the energy of octave bands in the low-, mid- and high-frequency parts of the spectrum. For each feature, the entire sentence set (both adjustment and test phase sentences) was processed at each level of adjustment available to the listener, resulting in multiple examples of each sentence that differed only in the degree of modification applied to them. Collectively, the sentence sets formed the precomputed input to the SpeechAdjuster tool. The number of modification steps was chosen in pilot testing as the minimum number required to give the impression of continuous change to the listener. Different numbers of steps (indicated below) were used for different feature types. The amplitude of each sentence was normalised using a fixed root-mean-square criterion following modification to ensure that each sentence had the same presentation level as every other sentence, and to remove any contribution to intelligibility from changes in the overall energy.
# Gait Kinematics Analysis of Flatfoot Adults
## 3 Results
### Kinematics Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Maximum and Minimum Value**} & \multicolumn{3}{c}{**Range of MOTION**} \\ \cline{3-8}  & & **FF** & **NF** & **\(p\)-Value** & **FF** & **NF** & **\(p\)-Value** \\ \hline \multirow{8}{*}{Ankle (\({}^{\circ}\))} & Dorsiflexion & \(12.49\pm 3.52\) & \(13.58\pm 6.94\) & \(0.029\) & \multirow{8}{*}{\(27.87\pm 6.28\)} & \multirow{8}{*}{\(29.29\pm 8.47\)} & \multirow{8}{*}{\(0.163\)} \\  & Plantarflexion & \(-15.67\pm 6.61\) & \(-16.09\pm 8.36\) & \(0.541\) & & & \\  & Abduction & \(0.38\pm 4.09\) & \(1.59\pm 9.95\) & \(0.033\) & & & \\  & Adduction & \(-16.61\pm 5.20\) & \(-16.43\pm 6.43\) & \(0.398\) & & & \\  & External rotation & \(-33.35\pm 5.48\) & \(-7.05\pm 8.08\) & \(<\)0.001 & & & \\  & Internal & \(-19.36\pm 5.42\) & \(-19.63\pm 23.56\) & \(<\)0.001 & & & \\  & Flexion & \(60.60\pm 4.68\) & \(56.87\pm 12.41\) & \(<\)0.001 & & & \\  & Extension & \(-50.4\pm 4.53\) & \(-5.16\pm 10.71\) & \(<\)0.001 & & & \\  & Abduction & \(18.04\pm 5.71\) & \(21.21\pm 9.60\) & \(<\)0.001 & & & \\ Knee (\({}^{\circ}\)) & Adduction & \(-0.81\pm 5.61\) & \(-1.92\pm 8.34\) & \(0.236\) & & & \\  & External rotation & \(29.19\pm 7.94\) & \(33.71\pm 15.30\) & \(<\)0.001 & & & \\  & Internal & \(5.42\pm 10.37\) & \(0.15\pm 32.89\) & \(0.342\) & & & \\  & Flexion & \(30.67\pm 8.82\) & \(27.36\pm 10.90\) & \(0.002\) & & & \\  & Extension & \(-10.21\pm 8.34\) & \(-12.42\pm 10.36\) & \(0.006\) & & & \\ Hip (\({}^{\circ}\)) & Abduction & \(18.18\pm 14.48\) & \(17.79\pm 13.60\) & \(0.552\) & & & \\  & Adduction & \(-9.27\pm 5.99\) & \(-9.34\pm 6.31\) & \(0.883\) & & & \\  & External rotation & \(7.48\pm 7.21\) & \(11.91\pm 12.71\) & \(0.012\) & & & \\  & Internal & \(-7.79\pm 7.08\) & \(-2.61\pm 13.63\) & \(<\)0.001 & & & \\  & Anterior Tilt & \(-4.13\pm 12.49\) & \(-4.23\pm 10.90\) & \(0.905\) & & & \\  & Posterior Tilt & \(37.00\pm 10.93\) & \(4.02\pm 11.91\) & \(0.900\) & & & \\  & & \(5.09\pm 3.63\) & \(4.71\pm 2.83\) & \(0.489\) & & & \\ Pelvis (\({}^{\circ}\)) & Lateral Tilt & \(-5.18\pm 3.14\) & \(-5.10\pm 3.15\) & \(0.909\) & & & \\  & Rotation & \(10.66\pm 4.70\) & \(8.79\pm 6.33\) & \(0.017\) & & & \\  & Rotation & \(-10.67\pm 8.20\) & \(-9.22\pm 5.94\) & \(0.125\) & & & \\ Center of Mass & Vertical Maximum & \(55.07\pm 1.23\) & \(55.67\pm 0.85\) & \(<\)0.001 & & & \\ (height \%) & Vertical Minimum & \(52.68\pm 1.40\) & \(53.04\pm 0.91\) & \(0.243\) & & & \\ \hline \multicolumn{8}{c}{Mean \(\pm\) Standard Deviation; NF = Neutral Foot; FF = Flatfoot; Negative value = extension/internal rotation/adduction/anterior tilt; positive value = flexion/external rotation/abduction/posterior tilt.} \\ \end{tabular}
\end{table} 
Table 2: Groups kinematics characteristics. Figure 1: Kinematics of various NF lower limb joints during gait. Figure 2: Kinematics of various of FF lower limb joints during gait. The Center of Mass variation over gait is illustrated in Figures 3 and 4 (NF and FF subjects respectively).
## 4 Simulation Analysis
The time-domain data in the workspace is processed using the Fast Fourier Transform (FFT) with a rectangular window function to obtain the frequency spectrum of the vibration signal. The frequency spectrum is used to evaluate whether the lateral vibration control results meet the requirements. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **SSA** & **GA** \\ \hline Mutation probability & \(\backslash\) & 0.3 \\ \hline Proportion of discoverers & 0.7 & \(\backslash\) \\ \hline Proportion of followers & 0.1 & \(\backslash\) \\ \hline Proportion of vigilantes & 0.2 & \(\backslash\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 8: Fitness curve of two algorithms. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Optimal Parameters** \\ \hline Translational motion damping controller parameter \(C_{s1}\) (N/m) & \(3.74\times 10^{3}\) \\ \hline Pitching motion damping controller parameter \(C_{s2}\) (N/m) & \(1.06\times 10^{3}\) \\ \hline Roll motion damping controller parameter \(C_{s3}\) (N/m) & 514.74 \\ \hline Sky – hook damping controller parameter (N/m) & \(\begin{array}{c}C_{\min}:1.93\\ C_{\max}:1.32\times 10^{5}\end{array}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The final parameters after optimization by SSA. In order to further demonstrate the effectiveness of the proposed lateral vibration control method and the advanced nature of the SSA optimization algorithm, simulations are conducted according to the original parameters of the high-speed elevator (without vibration control) and optimized parameters of GA and SSA. During the simulation, the same guide rail excitation is applied, and the frequency spectrum graphs of the simulation results are shown in Figure 9.  
From Figure 9, it can be observed that within the human sensitivity range of 1-2 Hz, the maximum amplitude of vibration without vibration control is 0.63 m/s\({}^{2}\), significantly exceeding the comfort standard of 0.2 m/s\({}^{2}\). However, after adjusting the parameters based on intelligent optimization algorithms, the vibration amplitude within 1-2 Hz decreases by two orders of magnitude, far below the comfort standard of 0.2 m/s\({}^{2}\). Moreover, most of the results from optimal parameters obtained through SSA are lower compared to those from optimal parameters by GA within 1-2 Hz. This demonstrates the effectiveness of introducing electromagnetic active roller guide shoes and sky-hook damping control in vibration control for high-speed elevators, as well as the efficacy of seeking optimal parameters through SSA.