esa-sceva/satcom-chunk-collection huggingface dataset:
# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 3 Methodology of the Proposed Bangla Alphabet Detection Algorithm
### Improvements in YOLOv11 Network Structure  
As illustrated in Figure 1, the proposed YOLOv11 architecture incorporates an attention mechanism in conjunction with the ResNet50 backbone. The attention module enhances the model's ability to selectively focus on salient regions of interest within the input image, thereby refining feature maps before object detection. This is particularly beneficial in distinguishing visually similar Bangla characters with subtle variations.  
The resulting hybrid model offers an optimal trade-off between detection accuracy and computational efficiency. Its robust architecture enables real-time performance while maintaining high precision, making it well-suited for practical Bangla character recognition tasks in educational, archival, and OCR-related applications.
# 3D Acoustic Mapping in Automotive Wind Tunnel: Algorithm and Problem Analysis on Simulated Data
## 2 Volumetric Acoustic Inverse Problem Formulation and Data Preprocessing
### Pressure Data Preprocessing  
Before describing how to obtain a solution for the inverse problem, it is worth addressing in which form measured data can be used to set the problem. An inverse problem is solved for each spectral line, therefore frequency content must be extracted properly from time data. The term \(\mathbf{p}\) can be the vector of Fourier transform of the pressure time histories measured by the microphones. However, in aeroacoustic measurements, pressure data are generally processed to estimate the Cross Spectral Matrix (CSM) \(\mathbf{P}\), given the random nature of aeroacoustic noise. One possible strategy for source mapping is to extract coherent source components from CSM, solve an inverse problem for each of them, and then obtain the complete acoustic map summing all contributions. This aspect is not strictly linked to 3D mapping, but separation of coherent sources from noise helps to better condition the inverse problem, thus requiring less regularization. [PERSON] [17] proposes to decompose CSM in eigenmodes \(\mathbf{e}_{i}\) and use them as measured data:  
\[\mathbf{G}\mathbf{q}_{i}=\mathbf{e}_{i}\qquad i=1,\ldots,M_{0} \tag{2}\]  
where \(M_{0}\leq M\) is the number of expected components. Therefore, the generic vector of complex pressure \(\mathbf{p}\) is replaced by each eigenmode in turn, and the total map is obtainedfrom the energetic sum of all component maps \(\hat{\mathbf{q}}_{i}\). The eigenmode decomposition is based on the property of CSM that is Hermitian and non-negative definite. Therefore, it can be decomposed as  
\[\mathbf{P}=\mathbf{E}_{vec}\ \mathbf{E}_{val}\ \mathbf{E}_{vec}^{H}\, \tag{3}\]  
where \(\mathbf{E}_{vec}\) is a unitary matrix of \(M\) orthonormal eigenvectors and \(\mathbf{E}_{val}\) is a diagonal matrix containing the corresponding eigenvalues. It is possible to define the eigenmode \(\mathbf{e}_{i}\) as the eigenvector, including its amplitude:  
\[\mathbf{e}_{i}=\sqrt{e_{val,i}}\ \mathbf{e}_{vec,i}\ \ \ \ \ \ i=1,\ldots,M \tag{4}\]  
where \(\mathbf{e}_{vec,i}\) is the \(i\)-\(th\) eigenvector and \(e_{val,i}\) is the corresponding eigenvalue. Under the constraint of orthogonality, each eigenvector represents a coherent signal across the microphones. In this paper, the eigenmode decomposition is used, but any other method to extract a source component from CSM can be used. An example of this is provided by [PERSON] et al. [18] for aeroacoustic applications, where CLEAN-SC is proposed as a CSM decomposition tool.
## 2 Laryngeal Image Modalities
### Laryngeal Videostroboscopy
#### 2.1.1 Advantages  
* It can be used with Distal Chip Laryngoscopy (DCL) and Flexible Fiberoptic Laryngoscopy (FOL) [28] during articulated speech and singing. DCL is identical to FOL regarding diagnostic accuracy, but DCL is superior to FOL in image quality and interrater reliability. Despite flexible endoscope optics have lower quality than a rigid endoscope, it is possible to obtain an image quality comparable to the rigid laryngoscope using DCL [29; 30].  
Figure 2: Illustration of the stroboscopic sampling. Adapted from [27]. (1) and (3) are the rigid and flexible endoscope, respectively; (2) are the vocal folds; (4) represents the real vibratory pattern; (5) illustrates the strobe light; (6) is the estimated version of the vibratory pattern.  
* It can be coupled with high-definition cameras, providing a higher spatial resolution of the vocal fold structures involved in phonatory vibration (e.g., mucosa, superficial vasculature, etc.) [18]. The High-Definition Digital Stroboscopy System by KayPENTAX Model 9310 HD, for example, records interlaced video frames with a spatial resolution of 1920 \(\times\) 1080 full-HD letting real-time viewing of exam video in uncompressed HD. 4K video format with a spatial resolution of 3840 \(\times\) 2160 pixels has been tested in VS. Despite the fact that its clinical implementation is feasible, its usefulness must be proven [31].
# A Fading Suppression Method for \(\Phi\)-OTDR Systems Based on Multi-Domain Multiplexing
###### Abstract  
The phase-sensitive optical time domain reflectometry (\(\Phi\)-OTDR) has been widely applied in various fields. However, due to fading noise, false alarms often occur; this limits its engineering applications. In this paper, a fading suppression method for \(\Phi\)-OTDR systems based on multi-domain multiplexing (MDM) is proposed. The principles and limitations of existing suppression methods such as spatial-domain multiplexing (SDM), polarization-domain multiplexing (PDM), and frequency-domain multiplexing (FDM) are analyzed. The principle of MDM is explained in detail, and an experimental system is established to test the fading noise suppression capabilities of different parameter combinations of the PDM, FDM, and SDM methods. Experimental results show that it is difficult to comprehensively suppress fading noise with single-domain multiplexing. Through optimizations of different parameter combinations, MDM can comprehensively suppress fading noise by appropriately selecting the number of FDM frequencies, the spatial weighting intervals, and using PDM, thus obtaining the optimal anti-fading solution between performance and hardware complexity. Through MDM, the fade-free measurement is achieved, providing a promising technical solution for the practical application of the \(\Phi\)-OTDR technology.  
Keywords:phase-sensitive optical time domain reflectometry (\(\Phi\)-OTDR); fading noise; spatial-domain multiplexing (SDM); polarization-domain multiplexing (PDM); frequency-domain multiplexing (FDM); spatial weighting; multi-domain multiplexing (MDM) +
Footnote †: journal:  
_Article_
# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
###### Abstract  
Energy conservation is one of the most critical problems in Internet of Things (IoT). It can be achieved in several ways, one of which is to select the optimal route for data transfer. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized routing protocol for IoT. The RPL changes its path frequently while transmitting the data from source to the destination, due to high data traffic in dense networks. Hence, it creates data traffic across the nodes in the networks. To solve this issue, we propose Energy and Delay Aware Data aggregation in Routing Protocol (EDADA-RPL) for IoT. It has two processes, namely parent selection and data aggregation. The process of parent selection uses routing metric residual energy (RER) to choose the best possible parent for data transmission. The data aggregation process uses the compressed sensing (CS) theory in the parent node to combine data packets from the child nodes. Finally, the aggregated data transmits from a downward parent to the sink. The sink node collects all the aggregated data and it performs the reconstruction operation to get the original data of the participant node. The simulation is carried out using the Contiki COOJA simulator. EDADA-RPL's performance is compared to RPL and LA-RPL. The EDADA-RPL offers good performance in terms of network lifetime, delay, and packet delivery ratio.  
Internet of Things; data aggregation; compressed sensing theory; residual energy 18 October 2019; Published: 12 December 2019
## 5 Analysis of Link Up/Down Dynamics
### Analysis of Density Change  
To advance the investigation, let \(f_{D_{u}}(t)\) be the node density in the communication group of vehicle \(u\) and \(N_{\text{in}}^{(u)}(t)\) and \(N_{\text{out}}^{(u)}(t)\) represent the number of nodes moving in and out of group \(u\) at time \(t\), respectively. Therefore, at time \(t+\triangle t\), in group \(u\), the node density change \(\triangle f_{D_{u}}(t+\triangle t)\) can be expressed by:  
\[\triangle f_{D_{u}}(t+\triangle t)=\frac{N_{\text{in}}^{(u)}(t+\triangle t)-N_ {\text{out}}^{(u)}(t+\triangle t)}{A_{u}} \tag{16}\]  
where \(A_{u}\) is the area size of group \(u\). According to the criteria for reforming the communication/routing group (as described in Section 3.3), the relationships between the node density change and the operation of group reformation are:  
1. If \(|G_{u}|\geq UB\), update \(G_{u}\) and \(R_{u}\). \[\int_{t_{0}}^{t_{1}}\triangle f_{D_{u}}(\tau)d\tau>\frac{UB-N_{GM}^{(u)}(t_{0} )}{A_{u}}\] (17)
2. If \(UB>|G_{u}|\geq LB\), go to Phase II, monitoring and updating the routing group members \(R_{u}\). \[\frac{LB-N_{GM}^{(u)}(t_{0})}{A_{u}}<\int_{t_{0}}^{t_{1}}\triangle f_{D_{u}}( \tau)d\tau<\frac{UB-N_{GM}^{(u)}(t_{0})}{A_{u}}\] (18)
3. If \(|G_{u}|<LB\), increase \(\theta\) and go to Phase I (_i.e._, reforming \(G_{u}\)). \[\int_{t_{0}}^{t_{1}}\triangle f_{D_{u}}(\tau)d\tau<\frac{LB-N_{GM}^{(u)}(t_{0 })}{A_{u}}\] (19) where \(N_{GM}^{(u)}(t_{0})\) is the number of group members in group \(u\) at time \(t_{0}\) and \(t_{1}\) is the time stamp to trigger the process of group reformation (\(t_{1}>t_{0}\)). Accordingly, the above criteria can be applied to perform group reformation.
_ISPRS Int. J. Geo-Inf._**2015**, \(4\), 2769-2791; doi:10.3390/ijgi4042769  
[1]  
ISPRS International Journal of _Geo-Information_**ISSN 2220-9964**  
www.mdpi.com/journal/ijgi  
_Article_  
**Combining 2D Mapping and Low Density Elevation Data in a GIS for GNSS Shadow Prediction**  
[1]  
**[PERSON]**  
National Centre for Geocomputation, Maynooth University, Co. Kildare, Ireland;  
E-Mail: [EMAIL_ADDRESS]; Tel.: +353-1-708-6204  
[2]  
Academic Editor: [PERSON]  
_Received: 22 September 2015 / Accepted: 30 November 2015 / Published: 10 December 2015_  
**Abstract:** The number of satellites visible to a Global Navigation Satellite System (GNSS) receiver is important for high accuracy surveys. To aid with this, there are software packages capable of predicting GNSS visibility at any location of the globe at any time of day. These prediction packages operate by using regularly updated almanacs containing positional data for all navigation satellites; however, one issue that restricts their use is that most packages assume that there are no obstructions on the horizon. In an attempt to improve this, certain planning packages are now capable of modelling simple obstructions whereby portions of the horizon visible from one location can be blocked out, thereby simulating buildings or other vertical structures. While this is useful for static surveys, it is not applicable for dynamic surveys when the GNSS receiver is in motion. This problem has been tackled in the past by using detailed, high-accuracy building models and designing novel methods for modelling satellite positions using GNSS almanacs, which is a time-consuming and costly approach. The solution proposed in this paper is to use a GIS to combine existing, freely available GNSS prediction software to predict pseudo satellite locations, incorporate a 2.5D model of the buildings in an area created with national mapping agency 2D vector mapping and low density elevation data to minimise the need for a full survey, thereby providing savings in terms of cost and time. Following this, the ESRI ArcMap viewshed tool was used to ascertain what areas exhibit poor GNSS visibility due to obstructions over a wide area, and an accuracy assessment of the procedure was made.  
**Keywords:** GNSS shadowing; GNSS prediction; mobile surveys; viewshed
# PEDRO: Privacy-Enhancing Decision suppoRt tOol
## 2 Background
### Current State of Research
#### 2.1.2 Adoption of Privacy-Enhancing Tools  
Existing research on privacy self-protection has focused on awareness or education [11; 12; 13]. Specifically, research has been conducted on a possible learning taxonomy for PETs [14; 15], as well as privacy awareness and knowledge [16; 17], but does not address other factors that contribute to PET adoption such as barriers to adoption and challenges faced by adoptees.  
Stageless, multifactor technology acceptance modelling has a long tradition. Researchers have investigated the influence of a variety of factors on technology adoption. Examples include the technology acceptance model (TAM) [18] and the unified theory of acceptance and use of technology (UTAUT) [19]. Influential factors on adoption include perceived usefulness and perceived ease of use. The technology acceptance model has been applied and extended to help understand users' acceptance of privacy-enhancing tools [20; 21], but does not address the PET adoption process stages.  
Another line of technology adoption research has used technology diffusion theory [22], which distinguishes a knowledge stage from a persuasion stage. This research has focused on (workers in) organisations rather than on personal adoption. Influential factors include relative advantage, ease of use, compatibility, image, result demonstrability, visibility, voluntariness, and trialability ([23], p. 507). However, this work has not addressed the adoption of privacy protection tools.  
According to stageless protection models such as Protection Motivation Theory (PMT) [24], the Health Belief Model (HBM) [25], and the Theory of Planned Behaviour (TPB) [26], intention to protect oneself has a positive effect on self-protection behaviours, and intention itself is influenced by other social-cognitive variables such as threat and coping appraisal (in Protection Motivation Theory). The Theory of Planned Behaviour [27] and Protection Motivation Theory [28] have been applied to understand the determinants of the adoption of privacy-enhancing tools. However, by their nature, stageless models do not address the adoption process.  
In staged protection models, such as the Transtheoretical Model of Change (TTM) [9], \"_The stage dimension defines behaviour change as a process that unfolds over time and involves progress through a series of stages_\" (p. 845). The TTM has mainly been used in health, but also in other domains such as reducing energy consumption [29]. Nevertheless, it has not been applied to PET adoption.  
In sum, missing from the existing research is a model that explicitly represents the staged PET adoption process. The current study contributes to support for PET adoption by proposing such a model and uses this as a basis for developing the PEDRO decision support tool to encourage the adoption of PETs by removing barriers to adoption.
# A New Approach to Detect Hand-Drawn Dashed Lines in Engineering Sketches
## 4 Non-Solid Line Clustering  
Algorithm 1 identifies potential dashed strokes by chaining dashes that are short, isolated, consecutive, and aligned. The general flow is explained by the next pseudocode:  
```
Input: Set of strokes in a sketch drawing. Output: Identified dashed stroke chains.
```
1:Initialization: * Put short and isolated strokes in a list of candidate dashes. * Label dashes of the list as non-visited.
2:Main Loop: * While there exist non-visited candidate dashes: 2.1: Select one non-visited candidate dash from the list. 2.2: Start a new chain with the selected candidate dash. 2.3: Label the candidate dash as visited. 2.4: Explore adjacent candidate dashes: * For each candidate dash not yet visited: * If the current candidate dash is consecutive to the chain: * Evaluate its consecutively merit. * If the current candidate dash is aligned with the chain: * Evaluate its alignment merit. * Evaluate its overall dash merit by combining consecutiveness and alignment merits. * If the dash merit is greater than the current best merit: * Select the current candidate dash as the best-continuation. * While a best-continuation was found: * Add the best-continuation dash to the chain. * Label the best-continuation dash as visited. * Repeat the exploration from step 2.4. ```
**Algorithm 1**Dashed Stroke Identification Algorithm  
Terms like \"short\", \"isolated\", \"consecutive\", and \"aligned\" pose computational challenges due to their unprecise nature. They are qualitative criteria that depend on a mixture of geometric and perceptual concepts. Therefore, it is important to highlight that the algorithm depends on certain configuration parameters that must be tuned for the dashed stroke search as they significantly impact the efficiency and quality of the process. When used along this section, these parameters are denoted in bold and italics for easy identification. They are further analyzed in Section 5.  
Next, we first detail the input data; then, we describe the criteria to select candidate dashes. We subsequently describe the recursive procedure to search for chains of consecutive dashes, to finally explain the output information.
# Experimental Analysis of Ultrasonic Multiple Scattering Attenuation through the Air with Fine Dust
## 4 Experimental Details
### Fine Dust Materials  
Standard particles (ISO 12103-1, A1 ultrafine test dust) were adopted in this multiple scattering experiment. The standard particles were composed of silicon, aluminum, iron, calcium, potassium, sodium magnesium, and titanium, and the density of the composite was 500 kg/m\({}^{3}\). To determine the shape of the particles, micro-scale images for a scoop of the particles were captured using a scanning electron microscope (Zeiss Gemini 300). It was observed that a surface coating procedure with magnetron sputtering was required to prevent image distortion caused by charged electrons. Secondary electron imaging was performed to obtain surface information. As shown in Figure 3, although the nominal diameter of the standard particles is less than 10 um; the shape is irregular. The irregularities possibly affect the scattering cross-section, which is conventionally assumed to have a circular shape with a constant diameter. Further details on the influence of the shape are discussed in Section 6.
# Pasteurization of Foods with Ultrasound: The Present and the Future
## 2 The Science behind Ultrasound
At higher temperatures, the vapor pressure also increases, affecting the surface tension and viscosity of the liquid, leading to a higher generation of bubbles [14]. If the temperature is not controlled during processing, the heat generated can influence the product's microbial inactivation and nutritional and overall quality. The generated heat during sonication depends on several variables. Some of them are intrinsic to the ultrasound generator itself, such as the power, the frequency, and the way to apply the sonication (constant or pulsed). Others are related to the process, such as the time, the product and its thermal properties, and any additional thermal treatment applied together, the volume of the treatment chamber. The type of microorganism also influences the inactivation. Larger cells are more susceptible to cavitation. Gram-positive bacteria are more resistant to sonication because of their thicker and tight cell wall. Cocooid cells are more resistant than rod-shaped ones [4; 13]. Acoustic energy is often used to measure the level of ultrasound applied during processing [15]. However, the conversion of electrical into acoustic energy is not always complete. Some energy is lost by thermal, chemical, sound, and other effects [16]. The acoustic power (AC) has been widely used in ultrasound and is measured based on a calorimetric method [16; 17]. Equation (1) allows the measurement of AC:  
\[AC=mC_{p}\left(\frac{dT}{dt}\right) \tag{1}\]  
where \(m\) is the mass of the product to be treated (kg), \(Cp\) (J/kg \({}^{\circ}\)C) is the specific heat of the product, and \(dT/dt\) (\({}^{\circ}\)C/s) is the change of temperature over time. The power density (\(PD\), W/mL) is a measurement of the AC per unit of volume [16; 17] and can be estimated using Equation (2):  
\[PD=\frac{Acoustic\ power}{Sample\ volume}=\frac{AC}{Volume} \tag{2}\]  
Figure 1: Comparison between stable and transient cavitation during ultrasound processing. In “stable cavitation,” bubbles grow and dissolve, and micro eddies are formed. In “transient cavitation,” bubbles grow and collapse violently, there is sonoluminescence, generation of hot spots and free radicals (OH\({}^{-}\), H\({}^{+}\)), and breakdown of macromolecules. Both parameters should be reported while working with ultrasound to compare treatments and reduce replication of experiments or lack of information.
## 4 Results and Discussion
### Charge Decay Plots for Homogenous Textiles  
The effect of the electrical conductivity of the raw material in the ESD protective textile on the charge decay plots was considered. For this purpose, a model was built, and the mesh shown in Figure 6a was generated. A specimen thickness of 1.8 mm was assumed (corresponding to the thickness of the nonwoven fabric tested in the laboratory). Simulations were carried out for conductivity (\(\sigma\)) from the set of \(\{1\times 10^{-13},1\times 10^{-12},1\times 10^{-11},\)\(3\times 10^{-11},\)\(1\times 10^{-10}\}\), all in S/m. Figure 6b-m show slice plots for \(\sigma=3\times 10^{-11}\) S/m. Figure 6b shows the slice in the \(y-z\) plane crossing the model midpoint for \(t=0\) s, and Figure 6c depicts \(t=15.7\) s, which corresponds to \(t_{50}\) in the EN 1149-3 standard. Figure 6d-m show slices in the \(x-y\) plane for five values of the \(z\) coordinate corresponding to distances (\(d\)) of 0.1 mm, 1 mm, 2 mm, 5 mm, and 50 mm from the bottom surface of the specimen at \(t=0\) s, and \(t=15.7\) s. Figure 7 shows the charge decay characteristics for all assumed conductance values. It can be seen that at a fixed specimen thickness, \(t_{50}<30\) s can be determined only for conductivities of \(3\times 10^{-11}\) S/m and \(1\times 10^{-10}\) S/m (curves (d) and (e)). For a conductivity value of \(1\times 10^{-10}\) S/m, the time is 4.3 s, slightly exceeding the 4 s allowed by the standard.