esa-sceva/satcom-chunk-collection huggingface dataset:
# Speaker-Independent Spectral Enhancement for Bone-Conducted Speech
## 4 Experiment
### Experimental Dataset  
We used three BC speech datasets to verify the model effect, namely, the TMHINT dataset proposed in [8], the AEUCHSAC&BC-2017 dataset proposed in [33], and the TMBCS dataset we established.  
The TMHINT dataset is a balanced corpus consisting of 1920 statements. The statements in the dataset were recorded by six speaker participants in a standard recording room at a sampling rate of 44.1 kHz using AC and skull conduction microphones at the same time [8]. Each speaker was required to record 320 sentences, and the corpus of each sentence is composed of 10 Chinese characters.  
For the AEUCHSAC&BC-2017 dataset, the adopted corpus consists of 1000 short sentences, and the sentence time after each recording was required to be 3-5 s. A total of 16 speakers were asked to read 200 sentences in the corpus randomly. In the professional recording and broadcasting room, the recording personnel needs to read 200 corpora in the corpus at random and use an AC microphone and throat microphone (TM) equipment to record voices. In this paper, we will use some open-source BC speech datasets obtained from this dataset for relevant experiments.  
For the TMBCS dataset, 1600 Chinese corpora were selected as the corpus. The dataset was recorded by 10 speakers with a total of 3000 statements. Each speaker needs to use the AC microphone and throat microphone (TM) equipment to record 300 BC and AC speech, respectively, and the duration of each sentence is about 2-4 s. The dataset can be divided into two categories according to its purpose. The first category is suitable for BC speech enhancement of specific people; the second type is BC speech enhancement for speaker-independent systems. For the needs of this study, subset II is adopted.
# High-Durability Concrete Using Eco-Friendly Slag-Pozzolanic Cements and Recycled Aggregate
## 4 Concrete Applying Recycled Aggregate and Slag-Pozzolanic Cement
### Concrete Mixtures and Mechanical Characterization
#### 4.2.2 Inner Lining Concrete  
In order to use concrete with slag-pozzolanic binder and recycled aggregate, a concrete strength level of at least 50 N/mm\({}^{2}\) and a low permeability were defined as the crucial requirements. As calcareous schists are quantitatively dominant at the Brenner Base tunnel, cf. [28], concrete testing was performed using standard quartz aggregate for the grain fraction 0/4 mm and calcareous schists for 4/8, 8/16, and 16/31.5 mm. Cement content using (2) CEM V/B (S-V) 32.5 N was increased to a level of 330 kg per m\({}^{3}\) of fresh concrete, and the water-binder-ratio was set to 0.52. Superplasticizer was used to reach a flow spread of class F4 according to [57]. Test specimens were stored for 7 days under water with subsequent air storage until 28 days of concrete age. Fresh and hardened concrete properties are illustrated in Table 7, whereby fracture energy \(\mathrm{G_{F}}\) was determined by the cut-through-tensile splitting test.  
Figure 13: Compressive strength of various concrete mixtures varying cement and aggregate type (each value as mean value of 3 individual tests) after 28 days.  
The results from Table 7 show encouraging results concerning basic mechanical properties of the concrete composition, achieving good concrete density and workability of the fresh concrete, as well as a good strength level reaching concrete strength class C40/50 according to [57]. Figure 14 illustrates a concrete cube specimen after compressive strength testing.  
Water penetration testing was performed according to [64] with an initial water pressure of 175 kPa for the first three days, subsequently increasing the water pressure to 700 kPa up to the 14 th day. A water penetration depth of 19 mm could be determined, demonstrating a very low permeability comparable to the findings illustrated in Figure 8.
## 3 Numerical Simulation and Results
### Simulation Results and Discussion  
#### 3.3.1 Strength Analysis of Elastomeric Bearing  
ANSYS Mechanical APDL, a commercial structural analysis software, was used to evaluate the effect of the three nonlinear M-R models (2P, 5P, and 9P) on the shear modulus of the elastomeric bearing. Figures 8-10 show the stress distribution with 2P (Figure 8),  
Figure 6: Material behavior curve for the Mooney–Rivlin hyperelastic model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Constants** & **Units** & **2-Parameter** & **5-Parameter** & **9-Parameter** \\ \hline C10 & MPa & 0.302 & \(-\)0.063 & 0.590 \\ C01 & MPa & 0.076 & 0.436 & \(-\)0.272 \\ C20 & MPa & N/A & 0.017 & 0.004 \\ C11 & MPa & N/A & \(-\)0.014 & 0.374 \\ C02 & MPa & N/A & \(-\)0.003 & \(-\)0.295 \\ C30 & MPa & N/A & N/A & 0 \\ C21 & MPa & N/A & N/A & \(-\)0.107 \\ C12 & MPa & N/A & N/A & 0.109 \\ C03 & MPa & N/A & N/A & \(-\)0.001 \\ D1 & 1/MPa & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mooney–Rivlin constants for neoprene sheet (Shore 50A).  
Figure 7: Loads and boundary conditions for analysis.  
5P (Figure 9), and 9P (Figure 10) M-R models when the elastomeric bearing subjected to vertical pressure of 6 MPa (compression) and the shear strain of 0.5 (as shear displacement is 65 mm). The von-Mises stresses (equivalent stress) are plotted, so the values have only positive signs. In Figures 8-10, the large equivalent stress of a steel reinforcing plate occurs in the center of the reinforcement plate during compression due to the resistance caused by the chemical bonding between the reinforcing plate and the elastomer. In contrast, the large stress of an elastomer is generated at both ends of the elastomer due to the resistance of the reinforcing plate.  
The maximum equivalent stresses and their difference rate of the elastomeric bearing with respect to the three M-R models are shown in Table 6. In Table 6, the difference between stress obtained with 2P and 9P was more than 10 times larger than that between 2P and 5P. This indicates that the 2P and 5P M-R models predict the similar values, and the 9P M-R model predicts a larger value compared with 2P and 5P models. In other words, in the case of compression test, the 9P M-R model has a larger percentage error than other models.
## 4 Model Testing and Results Analysis
### Data Description  
This study uses six actual pedestrian and bicycle traffic flow datasets to verify the investigated deep learning methods' forecasting performance. These hourly traffic flow datasets are created and maintained by the Seattle Department of Transportation in the USA. The data is gathered using sensors that record people riding bikes and pedestrians from 2014 until now in different Seattle locations (Table 1). In our experiment, the training is conducted using 90% of each dataset. The k-fold cross-validation technique has been considered in constructing these models based on the training data as recommended in [53; 54]. Specifically, we applied a five-fold cross-validation technique in training the investigated models.  
Figure 4 displays an example of the time evolution of bicycle and pedestrian traffic flows recorded over two weeks. The descriptive statistics of the six traffic flow datasets are given in Table 2. Table 2 indicates that the pedestrian and bicycle traffic datasets are non-Gaussian distributed with positive support and exhibit different intervals of variability.
## 2 Experimental Section  
### Materials  
Poly(nonamethylene azelate) (PE99) was synthesized by thermal polycondensation of azelaic acid with an excess of 1,9-nonanediol (2.2:1 molar ratio), as shown in Figure 2a. Titanium tetrabutoxyde was used as a catalyst and the reaction was first performed in a nitrogen atmosphere at 150 \({}^{\circ}\)C for 6 h and then in a vacuum at 180 \({}^{\circ}\)C for 18 h. The polymer was purified by precipitation with ethanol of a chloroform solution (10 wt. %). The average molecular weight and polydispersity index determined by GPC and using poly(methyl methacrylate) standards were 13,200 g/mol and 3.1, respectively.  
Preparation of silica micro/nanoparticles (Figure 2b): Deionized water (0.6 mL) and 1.2 mL of a 28% aqueous ammonium hydroxide (1.2 mL) (Aldrich, Madrid, Spain) were added to a flask of 100 mL containing absolute ethanol (50 mL). The mixture was stirred vigorously for 20 min at room  
Figure 2: Synthesis schemes for PE99 (**a**); silica spheres (**b**); and functionalization process (**c**).  
temperature using a magnetic agitator. Next 1.2 mL of tetraethoxysilane (Aldrich, Madrid, Spain) were quickly added and the resultant solution was purged with dry nitrogen and stirred for another 16 h. Spheres with a homogeneous diameter close to 600 nm were obtained after filtration. The same protocol except for increasing the stirring speed to 250-1000 rpm was employed to prepare nanoparticles with diameters close to 25 nm.  
Functionalization of silica micro/nanoparticles (Figure 2c): 1 g of micro/nanospheres previously dried under a vacuum was introduced into a vessel together with 66 \(\upmu\)L of pure ethanol. The mixture was sonicated for 15 min to enhance dispersion using a VWR Ultrasonic cleaner bath (UWR International, New York, NY, USA) at 100 watts, and subsequently 66 \(\upmu\)L of ammonium hydroxide was added under stirring for 15 min. Finally, 297 \(\upmu\)L of (3-aminopropyl) triethoxysilane (AMPS) was quickly added and the reaction was allowed to progress under stirring for 24 h at room temperature. The resulting solid was centrifuged, repeatedly washed with ethanol, and vacuum dried.  
Nanocomposites will be denoted by polymer abbreviation, size (micro or nano), and wt. % of added silica (e.g., PE99-M 6 and PE99-N 6 indicate composites having 6 wt. % of micro- and nanospheres, respectively).
# Innovative Approaches for Minimizing Disinfection
## 4 DBP Minimization Approaches
### Alternative to Chlorine Disinfectants
#### 4.7.3 Ultraviolet (UV) Irradiation  
Compared to chlorination, UV irradiation is a physical treatment method that does not leave residual disinfecting capability. As a result, UV disinfection is typically complemented with other oxidizing processes such as chlorination, chloramination, or the addition of hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) to ensure adequate disinfecting power in drinking water distribution systems. Combining UV irradiation with another oxidant is often utilized as an AOP to eliminate persistent organic pollutants. A novel AOP, the UV/chlorine process has effectively removed various contaminants, including trichloroethylene, desethylatrazine, sulfamethoxazole, carbamazepine, and diclofenac. Additionally, more \(\cdot\)OH radicals can be generated in the UV/chlorine process compared to the UV/H\({}_{2}\)O\({}_{2}\) process due to the higher molar absorptivity and quantum yield of HOCI. The formation potential of DBPs in the UV/chlorine system may significantly differ from those formed during UV irradiation or chlorination alone, owing to distinct reaction mechanisms [2].  
AOPs, such as UV/H\({}_{2}\)O\({}_{2}\), UV/O\({}_{3}\), and UV-chlorine, are recognized as promising methods for removing organic pollutants from water solutions by generating powerful hydroxyl radical oxidation species (HO-). Among these AOPs, the UV/H\({}_{2}\)O\({}_{2}\) process has been implemented in full-scale water treatment facilities, but it requires large amounts of H\({}_{2}\)O\({}_{2}\) to achieve the desired efficiency due to the low molecular absorption coefficient of H\({}_{2}\)O\({}_{2}\). The ultraviolet-chlorine process has been considered as an emerging alternative solution to the UV/H\({}_{2}\)O\({}_{2}\) process, as, in addition to HO-, reactive chlorine species (RSC) such as Cl-, Cl\({}_{2}\)-, and ClO- are also produced by the UV-chlorine process [40].
###### Abstract
To affirm the efficacy and the supremacy of our proposed approach, an extensive study, test, and benchmarking are conducted across other machine learners and anti-ransomware tools that have been recently adopted in the anti-ransomware domain. Also, a critical qualification is achieved to distinguish and justify their limitations in terms of the type of machine learning algorithms, the employed traits of ransomware, the family attribution, the robustness against big datasets, and the performance outcomes. Hence, the addressed limitations demonstrate what is overlooked, which our proposed model boosts for the best detection performance? Precisely, this paper makes a six-fold contribution as follows:  
* Revisiting state-of-the-art anti-ransomware technologies, particularly those assisted by machine learners to highlight the issues that they have overlooked in ransomware detection. * Deploying an informative compact set of static and dynamic traits to holistically characterize the ransomware versions of both crypto and locker families. * Enhancing numeral and statistic metrics to attribute a ransomware version to its ancestor family, and to fuse ransomware versions of multi- descent families, respectively. * Proposing a machine learner that unearths the 0-Day versions of ransomware into a generalized class model. * Designing a multi-tier streaming analytics model to implement in a realistic environment and operate on manifold platforms by integrating the compact set of traits, the numeral and statistic metrics, and the proposed machine learner. * Significance of our proposals is manifested through testing, evaluating, and benchmarking on a big dataset consisting of 35,000 ransomware versions of 14 families, 500 versions of 10 malware, and 500 good-ware apps aggregated at a different time from different data archives. For the aforesaid contribution, the remaining of this paper is organized as follows: Section 2 describes the ransomware life cycle, types, activities, and families. Section 3 revisits the state-of-the-art anti-ransomware tools and appraises them critically. In Section 4, the materials and methodology are elaborated, including the proposed machine learner, numeral, and statistic formulas. Results of the test, evaluation, and benchmarking are presented in Section 5. Then, Section 6 discusses the observationsand addresses the pivot issues of the proposed model and machine learner. Finally, Section 7 concludes the overall facets investigated in this paper, along with future outlook.
# Data-Driven Test Scenario Generation for Cooperative Maneuver Planning on Highways
## 2 Materials and Methods
### Simulation Environment
#### 2.4.2 Cooperative Behavior Planner
Negative accelerations can reach any value up to full braking, but the range of a comfortable deceleration is given by parameter \(b\). The action set must therefore reflect the normal driving range between \(a\) and \(b\) and still be able to provide stronger braking if necessary. To achieve this with a limited number of actions, the situation-dependent IIDM acceleration is also an element of the action set in the cooperative planner. The limits of normal driving behavior are represented by constant accelerations with \(a\) and \(b\). Within these limits, the vehicles can maintain their speed, coast with slight deceleration, or follow their leading vehicles according to IIDM acceleration. Lane changes to the left or right form the actions in the lateral direction. In summary, each vehicle can choose from the following options at each state transition:  
* [noitemsep,topsep=0 pt]
* Accelerate: constant acceleration of value \(a\)
* Maintain speed: constant acceleration of value \(0\)
* Coast: deceleration according to driving resistance forces, at least \(-0.5\,\mathrm{m}\,\mathrm{s}^{-2}\)
* Decelerate: constant deceleration of value \(b\)
* Car-following: acceleration according to IIDM model
* Lane change to left lane
* Lane change to right lane  
When implementing the A* method, the heuristic used is essential. It estimates the minimum expected future costs for each node. As long as these future costs are not overestimated, the A* method is guaranteed to find the best possible solution within the applied discretization. The speed of the vehicles in their current state can serve as the basis for this best-case estimation. Since the maximum acceleration \(a_{max}\) is limited upward by parameter \(a\) and downward by full braking, the desired speed cannot be reached immediately. The minimum costs \(h(n)\) incurred by deviating from the desired speed \(v_{des}\) (compare Section 2.3) can therefore be calculated as:  
\[h(n)=\int_{0}^{t_{max}}\left|\frac{v_{des}-v}{v_{des}}\right|\mathrm{d}t= \begin{cases}(1-\frac{v_{start}}{v_{des}})t_{max}-\frac{a_{max}}{2v_{des}}t_{ max}^{2}&\text{if $v_{start}<=v_{des}$}\\ (\frac{v_{start}}{v_{des}}-1)t_{max}+\frac{a_{max}}{2v_{des}}t_{max}^{2}&\text{ else}\end{cases}  \tag{3}\]  
where \(v=v_{start}+a_{max}t\) describes the future velocity \(v\) obtained by constant acceleration with \(a_{max}\) from current speed \(v_{start}\) and \(t_{max}=\frac{v_{des}-v_{start}}{a_{max}}\) represents the time until \(v_{des}\) is reached. Depending on the size of a scenario in terms of duration, number of vehicles and discretization in time, it may not be solved with the available amount of working memory or within reasonable computing time. In this case, a timeout fixes the parent node of the best-rated node of the deepest tree level. The previous states of this node are not changed anymore and the search continues from the fixed node. In such cases, the computed solution is not guaranteed to be optimal.
## 3 Results
### Temperature Difference and Responsivity  
One of the main goals of this paper is to evaluate the responsivity of the detector. For this purpose, one should know the amount of power scattered and transferred to the detector working surface. The amount of power absorbed will differ from the power on the excitation port. The difference between excitation power and power on the detector surface can be expressed in terms of S-parameter (scattering parameter). The part of S-parameter expressing the transmission is \(S_{21}\). The \(S_{21}\) value differs within the change of \(h_{pert}\) and has a dependence on frequency. Parameter \(S_{21}\) was evaluated using the following expression:  
\[S_{21}=\frac{\int_{port2}(\mathrm{E_{c}\cdot E_{2}^{*}})\mathrm{d}A_{2}}{\int_ {port2}(\mathrm{E_{2}\cdot E_{2}^{*}})dA_{2}}, \tag{20}\]  
where E\({}_{c}\)--the computed electric field which consists of the excitation and reflected field, \(A_{2}\) is the port 2 surface area, and * symbol means that the calculation is to be made at a specific mode number.  
The amount of power \(P\) transmitted to the material can be found in terms of the following expression:  
\[P=P_{ex}\cdot S_{21}^{2}, \tag{21}\]  
where \(S_{21}\)--transmission parameter, \(P_{ex}\)--emitted power.  
In Figure 7, the square of \(S_{21}\) absolute value is presented. The results for different \(P_{ex}\) values were equal. In our case, distances between wave excitation port and antenna from several tens of microns to several millimetres are allowable. At the distance equal to 2 mm, the part of scattered radiation is from 0.81 to 0.97. The wave with the frequency of 0.3 THz scatters more intensively than with 0.4 and 0.5 THz. The lowest scattering rate can be seen at the frequency of 0.5 THz.  
The oscillations in transmission parameter dependence on the distance between THz radiation source and detector are related to wave interference between source and detector. The troughs in the transmission occur at distances given by \(h_{pert}=(c/(2f))\cdot N\), where \(c\) is the speed of light, \(f\) is the frequency, and the integer \(N\) is the order of the trough.  
Figure 7: Transmission parameter dependence on the distance between THz radiation source and detector. The results are obtained for the power input of 50 mW.  
The responsivity of the antenna at different frequencies is presented in Figure 11. The dependence is nonlinear for all the frequencies and changes its value from 1 to 2.5 mVW\({}^{-1}\). The responsivity at 0.3 THz has the lowest values, no higher than 1.5 mVW\({}^{-1}\). The responsivity at 0.5 THz is the highest--from 1.25 to 2.5 mVW\({}^{-1}\). No dependence of responsivity on the excitation power was observed.  
Figure 11: Responsivity of antenna on \(h_{port}\) in frequency range from 0.3–0.5 THz.  
Figure 10: Temperature difference on \(h_{port}\) at 0.5 THz; \(P_{ex}\) values are listed in the legend.
# Motion Response of the Submersible Underwater Towed System as Cable Length Changes
## 4 Results and Discussion
### Simple Maneuvering Motion  
Before investigating the motion response of the system under composite maneuvering motions, it is first necessary to analyze the motion response of a simple maneuvering motion that does not enter into the cable retraction and deployment behavior. The submersible's speed ranges from 2 kN to 4 kN, and the speed change takes 1500 s. The circular rotational motion speed is 4 kN with a rotational radius of 400 m.  
The course of the towed body depth and towed cable tension during the accelerated and circular rotational motions are shown in Figures 3 and 4. In this paper, towed cable tension refers to the tension at the point where the towed cable connects to the submersible. This is the point where the forces on the entire towed system are most concentrated, and understanding and monitoring the tension at this point is critical to ensuring the stability and safety of the system. During accelerated motion, the towed body's depth stabilizes gradually from one initial depth to another. The inertial effect of the towed body significantly impacts the system's maneuverability. Once the submersible reaches a constant speed, the tension in the cable stabilizes at a specific level. When the submersible undergoes circular rotational motion, the change in the depth of the towed body and tension in the towed cable can be divided into two phases: an initial phase of a continuously changing transient state, followed by a stable long-term variation. Figure 5 displays the changes in the shape of the towed cable when the submersible undergoes accelerated motion. The cable pattern was recorded at intervals of 100 s, starting from 0 s and continuing throughout the entire 1500 s acceleration duration. The alteration in the shape of the towed cable during the circular rotation was documented at intervals of 25 s, starting from 3000 s and continuing until 3500 s, as depicted in Figure 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Cable Retraction and Deployment** & **No Cable Retraction and Deployment** \\ \hline
**Acceleratedmotion** & Composite maneuvering motions & Simple maneuvering motion \\ \hline
**Circular rotational motion** & Composite maneuvering motions & Simple maneuvering motion \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification of maneuvering motion.  
Figure 3: The course of the motion response of a towed system in accelerated motion. (**a**) The trend of tension in the towed cable over time; (**b**) The trend of the depth of the towed body over time.