esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on Design Requirement Development and Satisfaction for Future Virtual World Systems
## 2 Trend Analysis
### Diversity  
Virtual worlds have been designed to serve different purposes of use, as shown in Figure 3(a) (data source: [22]). Along this direction, the market/industrial evidence shows the increasing range of investment, which is illustrated in Figure 3(b). A shift of investment from traditional sectors (gaming, marketing, and military simulation) to new sectors (retail and manufacturing) can also be observed in Figure 3(b) (data source: [23]). To better serve diverse business purposes, some virtual worlds now allow users to create their own virtual objects with primitive building blocks to facilitate user innovation and collaboration [24], which has already been implemented in Second Life (secondlife.com) and Minecraft (minecraft.net). There are some typical examples of content creation and diverse usage of virtual worlds. In education, for example, immersive learning environments (Immersive learning environments are the virtual learning environments which employ VR, AR, or mixed reality (MR) to enhance community, presence and authentic situated learning experiences [25]) provide simulated and controlled situations to improve student's performance and motivation in studies [25]. Additionally, dynamic content generation can decrease development efforts for teachers to customize learning games [26]. In medicine, the realistic models created in virtual environments can facilitate surgical training [5]. In tourism, virtual world technologies can convert heritage relics to digital assets, which can avoid erosion in long-term preservation [27]. In e-commerce, user involvement in product cocreation [28] can improve product satisfaction.  
Figure 2: Trends of mobility: (**a**) number of mobile gamers in North America from 2014 to 2017; (**b**) worldwide mobile social gaming revenue from 2010 to 2014.
# Confidence Learning for Semi-Supervised Acoustic Event Detection
## 2 Proposed System
### Pooling Functions  
In C-SAED, we use different pooling functions between two-level predictions in two stages, as weak labels are only used in the first stage. To simplify, we briefly introduce two pooling functions for single event detection. Polyphonic SED can be considered as multiple binary classification problems.  
In the first-stage training, the pooling function needs to complete two tasks simultaneously: one is to generate weights for frame-level predictions to form the clip-level prediction. The other is to generate frame-level gradients from clip-level gradients.  
Because of the gradient backpropagation and the adaptability for sound events with various time scale, power pooling function [25] is the state-of-the-art method. Its formula and gradient formula are  
\[y_{c}=\frac{\sum_{i}y_{f}(i)\times y_{f}^{n}(i)}{\sum_{i}y_{f}^{n}(i)}, \tag{8}\]\[\frac{\partial y_{c}}{\partial y_{f}(i)}=\frac{(n+1)\times y_{f}^{n}(i)-n\times y _{f}^{n-1}(i)\times y_{c}}{\sum_{j}y_{f}^{n}(j)}, \tag{9}\]  
\(y_{f}\) is frame-level output probability, \(y_{c}\) is clip-level output, and parameter \(n\) represents the exponent which should be non-negative. Here, \(n\) is a free parameter to be learned alongside the model parameters, which allows Equation (8) to automatically adapt to and interpolate between separate pooling functions for different sound events. For instance, when \(n=0\), Equation (8) reduces to mean pooling. When \(n=1\), Equation (8) simplifies to linear pooling. When \(n\rightarrow\infty\), Equation (8) approaches the max aggregation.  
The gradient backpropagation of power pooling is quite suitable for SED. For positive clips, larger \(y_{f}\) is driven to 1 and smaller \(y_{f}\) is driven to 0, benefiting the timestamps detection. For negative clips, \(y_{f}\) is pushed towards \(y_{c}\times n/(n+1)\). Considering that \(y_{c}\) is trained towards 0, all the \(y_{f}\) will converge to 0 as desired after enough iterations.  
As for the second-stage training, only strong labels are applied. Then, max pooling is the default choice. After frame-level predictions are smoothed by the median filter, if at least one frame is positive, we regard that the clip is positive.
## 5 Geometric Implications for Optimizing PD Arrays for Received Power
### Parameters and Simulation Design
Modulation schemes were selected that were popular for DLOS links such as NRZ-OOK, L-pulse position modulation (PPM, for L-values 4, 8, and 16), and binary-phase shift keying (BPSK). The results were compared against a forward-error correction (FEC) limit of \(3.8\times 10^{-3}\). Their respective BERs were evaluated using Equations (20)-(22), where \(erfc(\cdot)\) refers to the complimentary error function [30]. \[BER_{NRZ-OOK}=\frac{1}{2}erfc\bigg{(}\frac{\sqrt{SNR}}{2\sqrt{2}}\bigg{)} \tag{20}\]  
\[BER_{L-PPM}=\frac{1}{2}erfc\bigg{(}\frac{1}{2\sqrt{2}}\sqrt{SNR\times\frac{L} {2}\times\log_{2}L}\bigg{)};\{L=4,\ 8,\ 16\} \tag{21}\]  
\[BER_{BPSK}=\frac{1}{2}erfc\bigg{(}\sqrt{SNR}\bigg{)} \tag{22}\]
# An Improved Image Filtering Algorithm for Mixed Noise
## 2 The Filtering Algorithm for Mixed Noise with Salt and Pepper + Gaussian Noise
### The Improved NLM Denoising Algorithm  
[PERSON] [27] et al. proposed the Non-Local Means (NLM) denoising algorithm in 2005, which takes advantage of the repeatability and self-correlation in an image. Firstly, it calculates the weighted average value of the neighborhood of all pixels in an image and the neighborhood of the current pixel. It then takes the average value as the Euclidean distance between the two points and assigns weights to them through the weight function. Finally, the sum of the product of the weight and all pixels is the denoised value of the current pixel.  
Many scholars have made improvement on the problems that exist in NLM, but as for the current research status, there still exists problems such as the running time being too long, unreasonable weight distribution, and the failure to highlight the role of central pixels when using Euclidean distance to calculate the similarity between neighborhoods.  
To solve the problems mentioned above, we improved the NLM algorithm. The improved algorithm uses integral image technology to change the neighborhood similarity disposal of each pixel to the unified disposal of the whole image matrix. Moreover, it uses the accumulation of Gaussian kernel functions with different radii to increase the weight of the central pixel in the neighborhood, aiming to enhance the weight role of the central pixel in the neighborhood. Finally, the weight function to measure the similarity between neighbors is improved by the power operation of the Gaussian function. Experiment results show that the improved algorithm can significantly save the running time of the algorithm and can better retain the edge and detail information of the image after denoising, and the denoising effect is significantly improved.
## 3 Results and Discussion
### Anti-inflammatory Activity  
Inflammation is a complex biological process that involves many signaling biomolecules at the intracellular level as well as coordinated communication between different immune cells. It appears when healthy tissues are exposed to biological, physical, or chemical stimuli. Protein denaturation is a well-documented cause of inflammation; thus, it is considered as a marker for inflammatory disease [72]. The stimulation and activation of immune cells, such as macrophages and neutrophils, induce the generation of proinflammatory cytokines such as tumor necrosis factor (TNF-\(\alpha\)), interleukin 1\(\beta\) (IL-1\(\beta\)), and mitogen-activated protein kinase (MAPK). TNF-\(\alpha\) can be involved in several crucial events for the initiation of inflammation [72]. Interleukin 1\(\beta\) may stimulate signal transduction by activation of the nuclear factor-\(\kappa\)B (NF-\(\kappa\)B) and the MAPK. NF-\(\kappa\)B, when activated, may, in turn, induce further expression of multiple cytokines, including TNF-\(\alpha\) and IL-1\(\beta\), and promote the inflammatory process.  
Microalgal anti-inflammatory compounds may act through one or more mechanisms involved in inflammation, including the release of chemicals such as leukotrienes, prostaglandamtions, bradykinin, histamine, platelet-activating factor, and interleukin-1 from tissues and migrating cells [14].  
The denaturation of proteins corresponds to proteins losing their secondary, tertiary, and quaternary structures, resulting in loss of biological function [73]. This process could be induced by the application of external stress, such as heat, or by exposure to chemicals, such as organic solvents [15]. Hence, to investigate the anti-inflammatory activity, the ability of protein extract and peptide hydrolysates to inhibit the heat-induced denaturation of albumin was evaluated.  
As seen in Figure 5, crude protein exhibited an inhibiting effect of about 67% at 0.5 mg/mL. The anti-inflammatory activity was found to be improved after enzymatic hydrolysis by the acidic protease: pepsin. In fact, at 0.5 mg/mL concentration, pepsin hydrolysate revealed a much higher capacity to inhibit albumin denaturation (89%). The in vitro anti-inflammatory activity of pepsin hydrolysate was similar to that of the standard drug diclofenac used at the same concentration. In addition, the anti-inflammatory activity of pepsin hydrolysate was much higher than that of the aqueous extracts of the medicinal plants _Cannabis sativa_ (\(<\)70%) and _Ficus racemose_ (\(<\)80%) [74].  
In contrast, the anti-inflammatory capacity was lost after hydrolysis by alcalase. Changing the protease modifies the resulting peptide's length, sequence, and consequently, biological activities.  
Figure 5: Inhibition of albumin denaturation by crude protein extract and peptides at different concentrations.
# Intelligent Reflecting Surface Aided Wireless Systems with Imperfect Hardware
## 4 Ergodic Capacity Analysis
### RIS-T Case  
The ergodic capacity in RIS-T case is calculated as follows.  
\[C_{T}=\frac{1}{\ln(2)\Gamma(N)}\int\limits_{0}^{\frac{\varpi_{1}}{\varpi_{2}}} \frac{\Gamma\left(N,\frac{(1+\delta_{k})d_{2}^{3}\varpi_{3}}{B\rho_{S}(\varpi _{1}-x\varpi_{2})}\right)}{1+x}dx. \tag{32}\]  
Finally, the closed-form ergodic capacity in RIS-T case can be obtained as follows.  
\[C_{T}\approx\frac{\pi}{I\ln(2)\Gamma(N)}\sum_{k=1}^{I}\sqrt{1-\delta_{k}^{2}} \frac{\varpi_{1}\Gamma\left(N,\frac{(1+\delta_{k})d_{2}^{3}\varpi_{3}}{B\rho_ {S}\varpi_{2}(1-\delta_{k})}\right)}{2\varpi_{2}+(1+\delta_{k})\varpi_{1}}. \tag{33}\]  
Since ergodic capacity is the second metric, we expect that some parameters can be adjusted at the RIS and the source to deal with the worse situation at the destination. For example, when the destination is located a far distance from the source, it is predicted that the phase factor of RIS elements and the number of RIS element will contribute to the system improvement.
## 1 Introduction
### Flexibility of Distribution Network with DG
#### 2.2.1 Objective Function of Multi-Objective Optimization Model
Considering the increase of future demands or the drastic fluctuation of DG output, the larger the \(F_{\text{LMC},k}\) is, the better the flexibility of branch \(k\) is. Here, \(F_{\text{LMC},k}<0\) indicates that the capacity margin of branch \(k\) is insufficient and congestion occurs.
## 2 Materials and Methods
### Cell Wall Disruption and Astaxanthin Extraction Using \(\alpha\)-Quartz NPLs and ILs  
Cell wall disruption and astaxanthin extraction from _H. lacustris_ cyst cells were performed using only IL or \(\alpha\)-quartz NPL-assisted IL treatment. The IL types and concentrations were selected based on a previous study on _H. lacustris_ biomass [17]. In the absence of \(\alpha\)-quartz NPLs, approximately 10 mg of lyophilized _H. lacustris_ cysts were treated with 1.5 mL of 33.3% (\(v/v\)) IL/water solution in a 12 mL Pyrex-glass tube with a Teflon-sealed screw stopper. The algal solutions were incubated for 2 h at five different temperatures (room temperature [\(\alpha\). 28 \({}^{\circ}\)C], 40, 60, 80, and 100 \({}^{\circ}\)C) using a heating block (HS R200, Humans, Daejeon, Korea). For astaxanthin recovery, the reaction mixture was mixed with 0.75 mL of ethyl acetate and incubated for 30 min in a tube rotator at 50 rpm (Fine PCR, Gunpo-si, Korea) at room temperature. The astaxanthin-containing ethyl acetate layer was separated by centrifugation (3000 rpm for 5 min). The astaxanthin concentration of the ethyl acetate solution was estimated based on the correlation between the optical density at 474 nm, as measured using UV/Vis spectrophotometry (Optizen 3220 UV, Mecasys Co., Daejeon, Korea), and data from HPLC-based astaxanthin quantification (refer to Section 2.3. Total Astaxanthin Content Estimation for the detailed method). For the control experiment, astaxanthin was extracted from the lyophilized _H. lacustris_ cyst biomass using only ethyl acetate without IL treatment.  
For NPL-assisted IL-mediated extraction at the ambient temperature (\(\sim\)28 \({}^{\circ}\)C), 1 mL of the \(\alpha\)-quartz NPL solution (1000 mg/L; [13]) was added to approximately 10 mg of _H. lacustris_ cysts under the conditions. To facilitate sufficient physical contact between _H. lacustris_ cysts and \(\alpha\)-quartz NPLs, the algal solution was incubated in an ultrasonic bath (40 kHz, 100 W; 2510-DTH, Branson, Danbury, CT, USA) for 5 min, as described previously [13]. To extract astaxanthin, 33.3% (\(v/v\)) IL in an aqueous solution was supplied. As described above, the algal solution was incubated at room temperature for 2 h, mixed with ethyl acetate for 30 min, separated by centrifugation, and analyzed using a UV/Vis spectrophotometer. For the control experiment, astaxanthin was extracted from _H. lacustris_ cysts using only NPL or IL, with sonication for 5 min. Astaxanthin recovery efficiency was expressed as a percentage of the total astaxanthin content obtained, as described above.
## 4 IoT for Geospatial Research
### Security and Privacy  
Security in this context covers IoT devices, their services, the data measured by their sensors and the platforms involved for data collection. The aspect of security is particularly important in participatory sensing, since _malicious individuals may intentionally pollute the sensing data_[22].  
Since access to IoT devices is sometimes difficult due to security restrictions of local domains (e.g., firewalls, use of NAT or mobile network routers), relay infrastructures such as Yaler ([[https://www.yaler.net](https://www.yaler.net)]([https://www.yaler.net](https://www.yaler.net))) have been proposed in [9]. The need for secure network and encryption technologies is stressed in [19]. Encryption and digital signatures are considered critical in urban monitoring based on vehicular sensor networks [54]. Security of the IoT air quality platform used for data collection in [46] has been addressed by means of a sensor observation service (SOS-T).  
Privacy of IoT devices and their data is also a sensitive issue [77], and multiple privacy threats have been reported in mobile RFID applications [9] and mobile location-based services [19]. The Social Access Controller (SAC) has been mentioned in [9], as an authentication proxy between (mobile) users and IoT devices. In respect to participatory sensing, aspects of intellectual property and regulation are discussed after the lessons learned from the case study of the Haitian earthquake [61]. The AnonySense architecture is proposed in [22], for supporting the development of privacy-aware applications based on crowdsensing, and for privacy protection recommendations. At the noise map application [25], the user has the option to decide whether to make his measures public and thus contribute to the collective noise mapping. In addition, in the application targeting the monitoring of dengue fever [48], _users are expected to follow their organizations' rules and geo-mask the geospatial data accordingly_. Finally, in relation to sports-tracking data [45], a privacy-preserving diversity method (ppDIV) has been employed for deriving heat maps, adopting an approach based on k-Anonymity for privacy preservation.
# GAN-Based Image Dehazing for Intelligent Weld Shape Classification and Tracing Using Deep Learning
## 2 Methods  
In recent years, with advancements in computer vision and artificial intelligence, deep learning has gained popularity in industrial applications because it can handle larger data efficiently [12]. The methodology involved in deep learning-based weld seam detection is shown in Figure 1, and the detailed explanation is described in following sections. In Figure 1, images are captured using a Logitech c270 2D camera placed at an angle of 26\({}^{\circ}\) in eye-to-hand configuration. This camera is based on CMOS technology with a resolution of 720 p and 30 fps. If the images are clear without any fumes, then images are sent to the YOLO detector, but when it has fumes, these images are passed to the dehazing network where GAN is used to remove the nonhomogeneous weld fumes. Once the weld fumes are removed, images are sent to the YOLO model to detect the weld joints autonomously. These weld joints after classification are sent to contour detection block where binary thresholding is applied to extract the weld seam. These weld seam pixel coordinates were converted using an ANN model, as this is the simplest and easiest way for mapping input and output without complex calculations. The robot coordinates obtained were finally sent to robot for tracing the particular shape. In this work, the entire workspace is calibrated with backpropagation ANN model. This eliminates the need for camera calibration involving computation of transformation matrix. This matrix becomes complex when the number of robot joints increases. Hence, the ANN model is used for pixel-to-robot coordinate transformation for weld shape tracing.
# Numerical Analysis of Ultrasonic Multiple Scattering for Fine Dust Number Density Estimation
## 5 Results and Discussion
### Effects of Particle Volume Fractions  
To investigate the effects of particle volume fraction on the number density estimation accuracy, numerical simulation data for different particle volume fractions were used. Figure 8a-d present the number density estimation results (left) and estimation error (right) for volume fractions of 2%, 5%, 10%, and 15%, respectively. Along with Figure 7 (1% volume fraction), Figure 8 indicates that the estimated number density values over the frequency region of interest (1-10 MHz) become more deviant from the exact number density values as the volume fraction increases. In general, a higher estimation error is observed as the particle volume fraction increases. The higher errors observed in the cases with higher volume fractions are mainly because the scattering attenuation computed by the ISA is valid within lower volume fractions of scatterers [28].  
Multiple scattering models can be divided into first-order and second-order models with respect to the number density [29]. The effective wavenumber (i.e., phase velocity and attenuation) computed by the first-order model is valid under lower number density media (roughly volume fractions below 10%). A second-order model, in most cases, provides higher accuracy when computing phase velocity and attenuation for higher number density media (higher volume fractions) [28], with a cost of higher computation complexity. Note that the ISA used in this study is inherently a first-order model. The ISA is selected as the basis of our approach, considering that it is computationally simple and most real-world applications may have a low volume fraction of fine dust particles, possibly below 1%.  
In reality, the reported index of fine dust in the air is the mass concentration that can be obtained by multiplying the number density, mass density, and spherical volume of fine dust. The volume fractions (2-15%) considered in this study exceed the air quality guideline from the World Health Organization, which is 50 \(\upmu\)g/m\({}^{3}\) per day in the unit of mass concentration [30]. In other words, the volume fraction of fine dust, in reality, may be lower than 1%.  
Figure 7: Scatterer number density estimation results for the 1% volume fraction case: (**a**) computed number density and (**b**) estimation error. A zoom-in plot of the computed number density in the frequency region (1–10 MHz) is displayed in (**a**).
## 2 Numerical Models and Schemes  
### Pump-Turbine Model  
A low-specific-speed model pump-turbine was investigated. Table 1 reports the main geometrical data of the considered pump-turbine. [PERSON] [26; 27] has deduced the runaway stability criterion of pump-turbines at constant guide-vane openings and proved that the runaway stability depends on the slopes of the characteristic curves at the no-load point and the time scales of the hydraulic system. However, the length of the pipeline is a minor factor. To reduce the impacts of the hydraulic system and highlight the influence of transient flow in the pump-turbine on the runaway oscillation stability, the computational domains only include the flow passages from the spiral-case to the extension part of draft-tube (Figure 2a), which is like a short tube model.  
Hybrid meshes were constructed for different domains by the ANSYS ICEM 14.0 software. Tetrahedral meshes were used in the spiral-case; wedge meshes were employed in the guide-vanes; structured hexahedral meshes were applied in the runner and draft-tube (Figure 2b). Mesh independence checks for different GVO conditions have been conducted in our previous works [23; 24]. When the number of gird elements is more than 4.0 million, the relative error of macro parameters among each mesh generation was less than 0.3%, indicating that the numerical simulation results are reasonable. Considering the numerical accuracy and time cost, the total number of mesh elements was capped at  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Specific speed \(n_{\text{QE}}=n\sqrt{Q}/H^{3/4}\) & 29.17 \\ Runner inlet diameter \(D_{1}\) & 0.280 m \\ Runner outlet diameter \(D_{2}\) & 0.1409 m \\ Number of runner blades \(z_{b}\) & 9 \\ Number of stay vanes \(n_{\text{s}v}\) & 20 \\ Number of guide vanes \(n_{\text{g}v}\) & 20 \\ Rated rotational speed \(n_{0}\) & 1000 r/min \\ Guide-vane openings \(\alpha\) & \(6^{\circ},9^{\circ},15^{\circ},24^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the model pump-turbine.  
Figure 1: Different characterizations. (**a**) Runners. (**b**) Runaway oscillation processes [20].  
9.0 million. Finally, the total numbers of mesh elements for the four GVO conditions are 7.86 million, 7.78 million, 8.22 million, and 7.75 million. The simulated static performance curves in S-shaped region were compared with the results of a model test, and a good agreement was obtained [23]. The final mesh specifications are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Spiral-Case**} & **Guide/Stay** & **Guide/Stay** & **Guide/Stay** & **Guide/Stay** & **Runner** & **Draft-Tube** \\  & **Vane (6\({}^{\circ}\))** & **[PERSON] (9\({}^{\circ}\))** & **[PERSON] (15\({}^{\circ}\))** & **[PERSON] (24\({}^{\circ}\))** & **Runner** & **with Extension** \\ \hline \(1.01\) & 2.06 & 1.98 & 2.42 & 1.96 & 2.45 & 2.34 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of mesh elements (million).  
Figure 2: Computational domain and mesh. (**a**) Geometry; (**b**) mesh.