esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Spatial Audio Recording Techniques for Soundscape Design
### Recording Techniques
#### 2.1.3 Binaural Recording  
Binaural recording is an extended form of stereo recording. Ideally, binaural recording only captures the sound received at the left and right ear positions (eardrum for the dummy head and blocked or open entrance of the ear canal for human listeners). Therefore, binaural recording is the format that is the closest to the human hearing, when it is played through calibrated headphones. By recording sound at the eardrum positions, it automatically embeds all the cues needed for sound localization in the 3D space and the natural alteration of the sound timbre due to propagation, reflection, and scattering of sound as it interacts with our body.  
The different binaural recording setups can be classified into four main types: (1) binaural microphones inserted in the ears of the human listeners; (2) microphones in the dummy head with torso, head and ears; (3) microphones in the simulator with head and ears; (4) microphones in the simulator with only ears.  
First, a miniature microphone is usually placed at the entrance of the ear canal of human beings since it is impractical and difficult to be placed inside the ear canal near the eardrum. Examples of binaural microphones vary from professional, calibrated types (e.g., from Bruel & Kjaer [43], shown in Figure 3) or low-cost types. Some other binaural recording microphones are integrated into a headset. An appropriate recording device is required to obtain the binaural recording.  
In addition to the use of human beings for binaural recording, dummy heads are also commonly used in academia and the industry to obtain consistent and comparable results. A dummy head usually consists of a torso, head and ears, which are made up of special materials whose acoustic properties are similar to human body and its anthropometry generally follows closely with the average of the whole or a certain part of the population [44]. Several dummy heads that consist of a torso, head and ears include KEMAR (Knowles Electronics Manikin for Acoustic Research) [45], Bruel & Kjaer 4128 HATS (head and torso simulator) [46], Head acoustics HMS IV [47], as shown in Figure 4. An example for dummy heads with only a head and two ears is the Neumann KU-100 [48]. As shown in Figure 5, the 3 Dio binaural microphones [45] offer superb portability for binaural recording when compared to the bulky dummy heads, but at the expense of lacking head shadowing effects due to the absence of a head. Furthermore, the 3 Dio omni binaural microphone records sound from four different head orientations, which could probably be more useful in interactive spatial audio in VR applications.  
Figure 3: Bruel & Kjaer 4101 Binaural microphone worn on the ear.
## 4 Simulation
### Impact of Node Density  
The connectivity of DTMSN is closely related to the density of sensor nodes. The following experiments show the network performance of the four protocols with different sensor node densities. As shown in Figure 5(a), the epidemic protocol almost achieves the upper bound of the data delivery ratio when the node density is very low, since low node density means low transmission load and a small amount of wireless collisions. As the node density increases, the number of message copies increases dramatically in epidemic routing, which results in an increasing number of collisions and the  
Figure 4: Impact of buffer size. **(a)** Average delivery ratio; **(b)** Average delay.  
reduction of the data delivery ratio. FAD shows a slightly better data delivery ratio than DRADG when the node density is very low. This is due to poor connectivity resulting from very low node density, which influences the performance of DRADG. With the increment of node density, the connectivity of the network is enhanced, and thus the performance of DRADG improves rapidly. When the node number reaches 300, DRADG outperforms the other three protocols. As far as the performance of FAD and Spray-and-Wait are concerned, when the node density is high, both of the protocols will generate large numbers of message copies, which expends the limited resources of bandwidth and buffer quickly and results in the decrease of their performance.  
In Figure 5(b), we can find that the delivery delay of DRADG reduces as the node density is increasing in the initial stage, because its performance is improved as the network connectivity is enhanced, but the delay of DRADG begins to go up due to the increasing communication overhead when the node number is more than 250. The delivery delays of the other three protocols show sustained trends with the increase of the node density. The reason is that the three protocols above have very high demand for network resources, and an increase of the node density aggravates the situation. Of course, epidemic routing has the fastest increase speed of the delivery delay among all four protocols.
# UVSQ-SAT, a Pathfinder CubeSat Mission for Observing Essential Climate Variables
## Appendix A
This subsystem uses a power supply for deployment in orbit and will be automatic. Each antenna element can be deployed separately through a dual redundant deployment system. The antenna is designed to cover the amateur satellite band with a UHF selection of 437.020 MHz and VHF of 145.830 MHz being validated by International Amateur Radio Union (IARU).
## 2 Materials: System Description and Experimental Set-Up
### Experimental Facility  
In order to be able to evaluate and characterise the solution experimentally, a test facility was built that simulated outdoor summer conditions. In this way, it was possible to have control over the excitations. The integration of the solution in a real pilot has been developed in Section 4.  
The experimental prototype carried out to test the radiant solution took place in a test cell of 12 m\({}^{2}\) (see Figure 2), located at the Eduardo Torroja Institute of Construction Sciences in the city of Madrid, Spain. It had the following characteristics:  
1. Test cell cover: The radiant solution covered 10.5 m\({}^{2}\) of the total gross surface, where the remaining 1.5 m\({}^{2}\) corresponded to the space occupied by the impulsion and return pipes. The space between the roof and the radiant solution was fully insulated by 12 cm thick extruded panels.
2. Walls of the test cell: The space walls were made of wood up to 1 m high and glass up to 2.5 m high. All walls were insulated from the outside by 8 cm thick extruded honeycombs.  
Figure 1: Technical detail of the studied system.  
3. Test cell floor: the cement floor was entirely covered by 8 cm thick extruded insulation panels.  
Figure 3 shows the outline of principles used for the tests.  
The proposed experimental prototype consisted mainly of the radiant solution, a heat pump to cool or heat the water accumulated in the buffer tank, and a pump in charge of  
Figure 3: Hydraulic scheme of active surface.  
Figure 2: Test cell.  
driving the water from the buffer tank to the radiant solution through a collector of supply and return of the two available circuits. The inertia accumulator had a protective coating, with a 160 mm thick synthetic wool thermal insulation and a capacity of 491 L. The heat pump had a nominal heat output of 15.1 kW to heat the water in the tank to use the radiant solution in heating mode and a nominal cooling capacity of 17.4 kW.
## 2 Background and Related Work
### Cache Attacks
#### 2.2.1 Evict+Time  
This technique consists of three steps: First, an encryption is triggered and its execution time measured. Second, an attacker evicts some line. Third, the encryption time is measured again. By comparing the second time with the first measure, an attacker can decide whether the cache line was accessed, as higher times will be related to the use of the mentioned line.
# Development of an Automatic Rock Mass Classification System Using Digital Tunnel Face Mapping
## 2 Literature Review and Case Study
### Traditional Rock Mass Classification
This digital mapping system ensures applicability not only in domestic tunnel projects in Korea but also in initiatives within the Middle East, Southeast Asia, and Europe. Prevalent tunnel digital mapping systems exhibit a notable limitation, as they require the rock quality characteristics, such as joint surface conditions, overall rock quality, and groundwater discharge rates, to be manually documented on the tunnel face. Subsequently, the RMR rating or rock quality evaluation must be conducted again post-mapping. To alleviate the inefficiencies associated with this duplicative process, this study proposes a solution that involves recording the parameters essential for rock quality evaluation during mapping. These data points can then be leveraged to automate the scoring of the RMR value during subsequent rock quality evaluations, thus enhancing operational efficiency and reducing the need for repetitive tasks.
## 1 Introduction  
### Motivation  
Electrical energy created by wind turbines has become an increasingly important part in providing clean power. However, wind turbines are exposed to many environmental influences (e.g., hail [1], rain or insects [2]) that contribute to defects such as erosion and contamination of the rotor blade, especially at the leading edge. Defects influence the geometry and surface quality of the rotor blade and may lead to premature transitions from laminar to turbulent flow in the boundary layer. Premature transitions create distinctly wedge-shaped areas of turbulent flow in otherwise laminar flow regions, the so-called turbulence wedges [3]. The increase in the overall surface area with turbulent flow due to the existence of turbulence wedges can amplify acoustic emissions [4] as well as aerodynamic imbalances [5]. Furthermore, an increase in area with turbulent flow negatively affects the aerodynamic properties (i.e., decrease in lift, increase in drag), and thus reduces the annual energy production [6]. Therefore, it is necessary to monitor the condition of the blade's surface that influences the flow to quantify the amount of additional turbulent flow area. Infrared thermography is an established contactless, in-process measurement technique to visualize laminar and turbulent flow regions on operating wind turbines without blade modifications [7; 8; 9]. A temperature difference exists between different boundary layer flow regimes due to varying local heat transfer coefficients. Using this technique, areas with turbulent flow such as the turbulence wedges can be visualized. A heating of the rotor surface is desirable to increase the temperature difference between the blade surface and fluid, which increases the contrast between laminar and turbulent flows. However, often, no active heating is available when operating wind turbines and the blade is only heated passively by sunlight. For this reason, an image processing algorithm is required which is capable of reliably detecting turbulence wedges under low contrast conditions. This would enable extensive field studies of the boundary layer flow state around the blade of operating turbines. In addition, the required algorithm has to determine the wedges' features such as position and size with a low uncertainty to finally quantify the resulting total area with turbulent flow.
# COSMIC-2 RO Profile Ending at PBL Top with Strong Vertical Gradient of Refractivity
## 4 Results
### Influence of Clouds  
In addition to the COSMIC-2 RO profiles that cannot reach near the surface due to a strong vertical gradient of \(N-\overline{N}^{obs}(r)\) and/or strong horizontal gradient of refractivity, there are still some RO profiles whose lowest observing altitudes do not reach near the surface. These are the points left of the dashed line in the scatter plot Figure 12. Since the absorption and scattering effects of clouds contribute to the atmospheric refractivity [38,44], we may examine whether the presence of clouds might have affected the penetration depth of the RO profiles. The cloud contribution to the refractivity can be calculated as:  
\[N_{cloud}=1.45w_{liquid}+0.69w_{ice} \tag{5}\]  
where \(w_{liquid}\) is the liquid water content in g m\({}^{-3}\), and \(w_{ice}\) is the ice water content in g m\({}^{-3}\). [PERSON] et al. [45] provided a detailed derivation of the two terms in Equation (5). For convenience, we will call \(N_{cloud}\) the cloud refractivity. The cloud refractivity values at the lowest heights of the RO profiles are indicated in colors in Figure 12. It can be seen that the majority of the cloudy RO profiles have high lowest altitudes in both the SEP and SCP areas.
# Feasibility of a Reusable Radiochromic Dosimeter
###### Abstract  
The current practice for patient-specific quality assurance (QA) uses ion chambers or diode arrays primarily because of their ease of use and reliability. A standard routine compares the dose distribution measured in a phantom with the dose distribution calculated by the treatment planning system for the same experimental conditions. For the particular problems encountered in the treatment planning of complex radiotherapy techniques, such as small fields/segments and dynamic delivery systems, additional tests are required to verify the accuracy of dose calculations. The dose distribution verification should be throughout the total 3D dose distribution for a high dose gradient in a small, irradiated volume, instead of the standard practice of one to several planes with 2D radiochromic (GAFchromic) film. To address this issue, we have developed a 3D radiochromic dosimeter that improves the rigor of current QA techniques by providing high-resolution, complete 3D verification for a wide range of clinical applications. The dosimeter is composed of polyurethane, a radical initiator, and a leuco dye, which is radiolytically oxidized to a dye absorbing at 633 nm. Since this chemical dosimeter is single-use, it represents a significant expense. The purpose of this research is to develop a cost-effective reusable dosimeter formulation. Based on prior reusability studies, three promising dosimeter formulations were studied using small volume optical cuvettes and irradiated to known clinically relevant doses of 0.5-10 Gy. After irradiation, the change in optical density was measured in a spectrophotometer. All three formulations retained linearity of optical density response to radiation upon re-irradiations. However, only one formulation retained dose sensitivity upon at least five re-irradiations, making it ideal for further evaluation as a 3D dosimeter.  
radiation therapy verification; dosimetry; radiochromic; reusability +
Footnote †: journal: Applied  
0021
# Efficient and Robust Arabic Automotive Speech Command Recognition System
## 3 Methodology
### Speech Feature Extraction
#### 3.2.2 Weighted Mel-Frequency Cepstral Coefficient (WMFCC)  
In addition to MFCC, which provides static features, researchers demonstrated that considering dynamic features will further enhance the performance of SR systems [30]. Dynamic features are extracted from MFCC derivatives. The first-order derivative of MFCC is a delta coefficient, which provides information about the temporal variations in the speech signal. The second-order derivative is a double-delta coefficient, which holds the acceleration or rate change of the delta coefficients over time.  
However, the inclusion of these features will be expensive in terms of computational complexity as it creates a higher-dimensional feature vector. For instance, if we extracted 40 feature coefficients of MFCC, delta, and double delta, we would end up with a feature vector of 120 dimensions. Therefore, we use the innovation of the WMFCC method. As illustrated in Figure 3, WMFCC is a method that averages the MFCC coefficients with its derivatives, thus containing both static and dynamic information of the signal while maintaining the initial vector dimension (40 in our example). Furthermore, researchers in [24; 25; 26] conducted several experiments comparing MFCC and WMFCC features where they found that WMFCC performs better in noisy environments. Therefore, this prompted us to include it in our experiment.  
We extracted 40-dimensional WMFCC coefficients using the equation proposed in [24]:  
\[\text{wc (n)}=\text{c (n)}+\text{p }\bullet\triangle\text{c (n)}+\text{q }\bullet\triangle\triangle\text{c (n)},\text{q }<\text{p}<1 \tag{1}\]  
where c (n) is a 40-dimensional MFCC feature vector, \(\Delta\)c (n) is an MFCC delta feature vector, and \(\Delta\Delta\)c (n) is an MFCC double delta feature vector. p and q represent the weights assigned to the delta and double-delta features, respectively.
# Assessment and Categorization of Biological Effects and
## 4 RF-EMF Effects Categorization  
### Direct Simple Effects on Biological Tissues  
There are different effects owing to exposure to RF-EMF. Biological interactions of RF-EMF and the exposed body are related to the nature of exposure and to tissue compartment that depends on the signal frequency. These interactions can produce different effects.  
* The most common category of such effects is the thermal effect where tissues are physically heated by absorbed electromagnetic energy produced by an EMF.
* Other types of these interactions can also be more complex, producing non-thermal effects involving various biochemical or bioelectrical consequences that affect cellular, molecular and chemical structures in living tissues [47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58]. These effects can be produced due to long-term exposures or excessive short-term exposure involving high SAR values. Indeed, electromagnetic fields of moderate intensity generally have no non-thermal effects on living tissue cells. On the other hand, excessive strength fields can display non-thermal effects such as membrane electroporation. Note that non-thermal effects can be used clinically for tumor treatment by applying RF-EMF with moderate strength (100-200 V/m) without risk. It can be noted that the mechanisms of the non-thermal effects remain undetermined [51].  
* The last category of effects owing to exposure to RF-EMF concerns exposed people endangering AS.
## 3 Results and Discussion
### Mass Calculation  
The fitting of the thickness calibration surfaces of Al and Mg are shown in Figure 13. The coefficients of the polynomial fitting function for thickness calibration of Al and Mg are shown in Table 7. The mean values of the material mass after the calibration calculation and the actual mass data measured by electronic scale are shown in Table 8. The mass calculation results of the actual materials in different positions and postures were stable (including overexposed and underexposed points), whose error was less than 5% compared with the mass by electronic scale. When the calculated mass of sample 1# was 172 g (about 5% higher than the actual mass of 163.81 g), and the preset falling point distance \(y_{n}\) was 600 mm, the calculated distance was 586 mm (\(\theta\) = 45\({}^{\circ}\), \(H_{b2l}\) = 0.8
# Switchable Chiral Metasurface for Terahertz Anomalous Reflection Based on Phase Change Material
## 2 Materials and Methods  
The performance of the switchable metasurface was numerically simulated by a commercial full-wave numerical software computer simulation technology (CST) Microwave Studio. The frequency-domain solver is selected as the simulated method. On a single periodic structural element, the boundary conditions in the x and y directions are set as unit cells, and the boundary condition in the z direction is open. When x- and y-polarized plane waves are incident, due to the existence of polarization conversion, we obtain the following reflection coefficients of linearly polarized light: \(r_{xx}\), \(r_{yx}\), \(r_{yy}\) and \(r_{xy}\). Then, the reflection coefficient of CPL can be obtained by Equation (1). Here, \(x\), \(y\), \(r\) and \(l\) in the subscript represent \(x\)-polarized light, \(y\)-polarized light, right-handed circularly polarized light and left-handed circularly polarized light, respectively. \(R_{ij}\) represents the reflection coefficient of \(i\)-polarized light reflected by the structure under the incidence of \(j\)-polarized light, \(i\), \(j\)\(\in\)\(\left\{x,\,y,\,r,\,l\right\}\). It should be pointed out that we will observe circularly polarized light facing the propagation direction of electromagnetic waves. At a fixed position, if the polarization direction rotates clockwise with time, it is RCPL; in contrast, it is LCPL.  
\[\left(\begin{array}{cc}R_{rr}&R_{rl}\\ R_{lr}&R_{ll}\end{array}\right)=\frac{1}{2}\left(\begin{array}{cc}R_{xx}-R_{ yy}+i\big{(}R_{xy}+R_{yx}\big{)}&R_{xx}+R_{yy}-i\big{(}R_{xy}-R_{yx}\big{)}\\ R_{xx}+R_{yy}+i\big{(}R_{xy}-R_{yx}\big{)}&R_{xx}-R_{yy}-i\big{(}R_{xy}+R_{yx} \big{)}\end{array}\right) \tag{1}\]  
Figure 1a,b shows a schematic diagram of the unit structure of ASRR_1. Here, a continuous uniform metal film \(rr\) with a thickness of 200 nm is used as the reflecting layer. The top of each unit is an ASRR composed of metal and phase change material GST. A 50 um thick polyimide (PI) dielectric layer separates the bottom metal film from the top ASRR. In the simulation, the metal structures were made of aluminum (Al) with a conductivity of 3.56 \(\times\) 10\({}^{7}\) S/m. The dielectric constant and loss tangent of PI are 2.93 and 0.044, respectively. The thickness, outer radius R and linewidth w of ASRR_1 are 200 nm, 65 um and 20 um, respectively. The angle of gaps \(\alpha\) and \(\beta\) are 20\({}^{\circ}\) and 40\({}^{\circ}\). \(\varphi\) is the angle at which ASRR_1 rotates around the z-axis. The period of the unit structure P is 160 um. Figure 1c shows the schematic of the electromagnetic responses of a gradient chiral metamirror, which is obtained by arranging ASRR_1 into a phase continuous metasurface.