esa-sceva/satcom-chunk-collection huggingface dataset:
# Polyacrylamide Injection vs. Polylactic Acid in HIV Related Lipodystrophy: A RCT Systematic Review
### Results of Individual Studies  
[PERSON] in 2009 made a randomized and controlled open-label single-centre study and assessed the efficacy and safety of the treatment of HIV-associated facial lipoatrophy using facial injections of poly-l-lactic acid or polyacrylamide gel [31]. A total of 134 patients with lipoatrophy were randomly assigned to the immediate treatment arm or the delayed one. Using a facial lipoatrophy severity scale, they evaluated changes in dermal thickness. The follow up was after 27 weeks for the immediate group and 25 weeks for the delayed group. Secondary outcomes evaluated in the study were safety with adverse events, quality of life and anxiety.  
[PERSON] in 2013 made a randomized single-blinded trial comparing polyacrylamide gel and polylactic acid [32]. A total of 148 patients were included in the study and were randomly assigned to receive intradermal injection with PH or PLA. The total duration of the study was 96 weeks. The primary outcome was determining patient satisfaction at week 48, which was assessed using a visual analogue scale. The secondary endpoint of the study evaluated quality of life, cheek thickness and skin fold. Adverse events led the evaluation and safety.
## 1 Introduction
For that reason, three different NPASs of similar thickness (~60 \(\upmu\)m) were studied, one commercial asymmetric membrane (AnodiscTM, with nominal pore radii of 10 nm for one surface and 100 nm for the opposite surface) and two experimental samples with symmetrical structure and average pore radii around 10 nm (NPAS-Sf sample) and 100 nm (NPAS-Ph sample), similar to those corresponding to each surface of the commercial sample. Optical characterization was performed by spectroscopic ellipsometry (SE) measurements, and the comparison of results obtained for the different samples can give information on the influence of pore size/porosity and sample asymmetry on samples optical behavior. For that reason, chemical surface analysis by X-ray photoelectron spectroscopy (XPS) was also carried out. The refraction index and extinction coefficient were determined from SE results. Moreover, electrochemical characterization of the three selected samples by concentration potential measurements, using NaCl solutions at different concentrations, was also performed to estimate the effect of the films' geometry and asymmetry on characteristic electrochemical parameters such as ionic transport numbers and permselectivity. The obtained results, showing the predominance of porosity or pore size on optical or electrochemical parameters, respectively, might be of interest for different NPASs applications.
# Popularity-Aware Service Provisioning Framework in Cloud Environment
## 3 Popularity-Aware Service Provisioning Framework
### Popularity Estimation Model  
To estimate the popularity variation of each service, we constructed a general long short-term memory (LSTM) model based on Tensorflow and trained it with the Kaggle public dataset [18]. Within the dataset, we separated 80% into a training set and 20% into a test set. Note that the training set was utilized for the model design, and the test set was used for the validation and test. The details of the hyper-parameters are as follows. The number of layers and the hidden size are 5 and 16, respectively. The input and window sizes were set to 1 and 24, respectively. The learning rate was set to 0.01.  
Figure 2 shows the real and estimated results for the number of Amazon service requests as a function of time. As shown in Figure 2, the constructed model could catch the popularity variation well. Specifically, the accuracy of the constructed model became approximately over 95%, which means that the model is well generalized for the estimation. This indicates that the NWDAF can provide the accurate analytics estimating top services contributing most of the traffic change for the next time period. Thus, based on the analytics, the service provider requests the appropriate amount of resources for each service for the next time period to the cloud operator.  
Figure 2: The estimation result on the popularity variation.
# A New Parameter-Free Slope Unit Division Method That Integrates Terrain Factors
###### Abstract
To verify the effectiveness of the method, a comparison was made with the commonly used hydrological analysis approach. This new method aims to eliminate the need for manually setting parameter thresholds during slope unit division. It reduces the influence of subjective factors on the final results and improves the reliability and efficiency. This approach of slope unit division is particularly beneficial under different terrain conditions, especially in applications involving risk assessment and management in landslide-prone areas. By eliminating the cumbersome parameter setting process, the new method is expected to significantly improve the limitations of current slope unit extraction techniques. This advancement enhances geological disaster prediction, ecological protection, land use planning, and tasks in other fields.
# Multi-Scale Deep Feature Fusion with Machine Learning Classifier for Birdsong Classification
## 2 Research Method
### Step 1. Spectral Transformation Methods for Speech Processing  
The birdsong signals were transformed into three distinct spectral representations: log-power spectrum, log-Mel spectrum and log-wavelet spectrum. The extracted spectrograms are shown in Figure 4. The methods for spectral feature extraction are described below.
## 3 CFD Numerical Simulation Setup
### Boundary Conditions  
The simulation is based on a compositional transport model, in which diesel and the antioxidant additive are the only two chemical species. The antioxidant is represented by butylated hydroxytoluene (BHT, C\({}_{15}\)H\({}_{24}\)O) [22], with material properties sourced from the Fluent built-in database. The k-epsilon realizable turbulence model was employed to simulate diesel flow. All three nozzles were defined as velocity inlets. The inlet velocity, as well as the multiphase parameters of diesel and antioxidants, was imported from external data. The nozzle flow direction is determined as follows: a positive velocity denotes an inlet, a negative velocity indicates an outlet, and zero velocity defines a sealed wall. The domain was initialized such that only diesel exists inside the tank at the beginning, conforming to real operating conditions. A User-Defined Function (UDF) was applied to monitor the volume fraction of antioxidants at the three sensor points, acting as a termination criterion: the simulation halts once all monitoring points fall below the specified concentration threshold, and the final residence time is recorded. The input data used in our simulation were obtained from an ongoing nuclear engineering project focused on diesel conditioning system optimization. Specifically, the inlet velocity corresponds to the rated flow rate of the pump under standard operating conditions (measured at 20 \({}^{\circ}\)C), and the physical properties of the diesel-antioxidant mixture were derived from laboratory analyses conducted on-site using actual fluid samples. The fluid used in this study was a homogeneous diesel-antioxidant mixture, treated as a single-phase, incompressible liquid. Its physical properties (e.g., density and viscosity) were assumed to be constant and were derived from experimental measurements conducted under standard operating conditions. No phase change or compressibility effects were considered in this simulation.
## 3 Background and Definitions
### How Consistent Global Snapshots (CGS) Are Build  
There exist diverse ways to take checkpoints for a distributed system such as synchronous checkpoint, asynchronous checkpointing and quasi-asynchronous checkpointing, where process take checkpoints independently, and systems gain the ability to restart their computation after those checkpoints. However, not all saved checkpoints guarantee to return to a consistent global snapshot (CGS). [PERSON] and [PERSON] [13] gave the following definition:  
**Definition 4**.: _A CGS cannot contain any causally related checkpoints (see below); for any pair of checkpoints \(C_{i}\) and \(C_{j}\) we have:_  
\[\
eg(C_{i}\to C_{j})\wedge\
eg(C_{j}\to C_{i})\]  
_where \(\rightarrow\) is now extended over checkpoints in a conventional manner._  
CGS must avoid dangerous patterns as stipulated by [PERSON] and [PERSON] a _zigzag path_ or \(z\)-_path_) generalizes the Happened-Before Relation (HBR) in the following way:  
**Definition 5**.: _Let \(P_{i},P_{j}\in P\). There exists a \(z\)-path from \(C_{P_{i}}^{x}\) to \(C_{P_{j}}^{y}\) if there are messages \(m_{1},m_{2},...,m_{l}\in M\) such that:_  
1. \(m_{1}\) _is sent by process_ \(P_{i}\) _after_ \(C_{P_{i}}^{x}\)_._
2. _if some process_ \(P_{r}\) _receives_ \(m_{k}(1\leq k<l)\)_, then_ \(m_{k+1}\) _is sent by_ \(P_{r}\) _in the same or at a later checkpoint interval (although_ \(m_{k+1}\) _can be send before or after_ \(m_{k}\) _is received), and_
3. _Process_ \(P_{j}\) _receives_ \(m_{l}\) _before_ \(C_{P_{j}}^{y}\)__  
**Definition 6**.: _A checkpoint \(C_{i}^{x}\) is in a zigzag cycle or \(z\)-cycle, if there is a \(z\)-path from \(C_{i}^{x}\) to itself._  
A z-path was defined by [PERSON] et al. [14] in the following way:  
**Definition 7**.: _A \(z\)-path \([m_{1},...,m_{q}\in M]\) is a causal z-path if for each pair of consecutive messages \(m_{k}\) and \(m_{k+1}\), \(delivery(P_{i},m_{k})\to send(m_{k+1})\), with \(P_{i}\in P\). Otherwise, it is a non-causal z-path._  
**Definition 8**.: _A local checkpoint \(C_{j}^{y}\) z-depends on a local checkpoint \(C_{i}^{x}\), denoted \(C_{i}^{x}\overset{z}{\rightarrow}C_{j}^{y}\), if:_  
1. \(j=i\) _and_ \(y>x\)_, or_
2. _there is a_ z-path _from_ \(C_{i}^{x}\) _to_ \(C_{j}^{y}\)__
## 5 On the Robustness of the Proposed Feedback Power Control Strategies
### Non-Orthogonal Links: Multiple Access Interference
Finally, for \(\epsilon=0.3\) the performance with the LDPC code is better than that with the SCCC. This suggests that channel code optimization, in the presence of multiple access interference, remains an open problem. In Figure 10, the outage probability is shown, as a function of the average SNR at the sources, in a scenario with non-orthogonal links, considering: (a) DD LDPC-coded schemes and (b) SCCCed schemes. As before, two possible values for \(\epsilon\) are used: (i) 0.1 (moderate interference) and (ii) 0.3 (strong interference). The same considerations carried out considering the average BER still hold and, therefore, the proposed optimized unbalanced SNR power control strategy is effective for all considered values of \(\epsilon\). Moreover, the choice of the channel code is important to guarantee a desirable performance level in high interference scenarios.
# Low-Cost, Distributed Environmental Monitors for Factory Worker Health
## 2 Materials and Methods
### Sensor Performance
#### 2.4.5 Gas Sensor Bias and Precision  
For the gas sensors, bias and precision experiments were conducted outside the chamber using a shroud and a reference gas for one minute. NIOSH evaluation for gas sensors requires that the test be performed with a known gas concentration and that the measurements be conducted at steady state concentrations due to response time differences between instruments. Therefore, due to the unavailability of ozone calibration gas, we were only able to deliver CO and NO\({}_{2}\) gas at stable conditions to the sensors, using calibration gases. For the CO sensors, a 10-ppm carbon monoxide span calibration gas (Gasco, part number 103L-375-10, Oldsmar, FL, USA) was used. For the oxidizing gas sensors, a 2-ppm nitrogen dioxide span calibration gas (Gasco, part number 58L-112-2, Oldsmar, FL, USA) was used because an O\({}_{3}\) reference gas was not available, and we were unable to generate known steady-state O\({}_{3}\) concentrations. To convert sensor signal to NO\({}_{2}\) concentration for this experiment, we applied the sensors' relationship with NO\({}_{2}\). Measurements were collected from each low-cost sensor for one minute. The bias (\(B\)) was calculated as [45]:  
\[B=\frac{1}{n}\sum\frac{y_{i}-x_{i}}{x_{i}} \tag{1}\]  
where \(y\) is the calculated mass concentration for the sensor, \(x\) is the reference gas concentration, and \(n\) is the number of data pairs measured in one minute. Bias ranges, minimum, and maximum values were calculated for each sensor.  
Precision represents the variation among sensors and was calculated as the measure of the coefficient of variation (\(CV\)) of the calculated mass concentrations. Based on the one-minute data, the \(CV\) was calculated as [46]:  
\[CV=\ \frac{\sigma}{\mu} \tag{2}\]  
where \(\sigma\) is the standard deviation and \(\mu\) is the mean of the mass concentration measurements from the 30 replicate sensors of the same type.
# Automated Methodology for Dependability Evaluation of Wireless Visual Sensor Networks
## 3 Problem Formulation and Background
### Node Modeling
#### 3.2.1 Hardware Modeling  
We consider that the hardware of the node is composed of the following electronic components: processor, memory, radio and sensing unit (camera). For these elements, it is assumed that a failure is permanent, which means that a component replacement or a repair action is required to return the component to an operational state. It is also considered that hardware failures have a random nature and occur according to a Poisson process, i.e., with a constant rate during the useful life period of the node [55].  
Regarding the repair processes, we assume that the repair time can be approximated by an exponential distribution (i.e., a constant repair rate). This kind of approximation is reasonable when failure and repair rates differ from each other by several orders of magnitude. We also assume thata repair action repairs all faulty components and that the number of repair actions is not bounded [56]. Moreover, failure and repair actions are assumed i.i.d. (independent and identically distributed) random variables [6]. That way, it is possible to summarize the hardware failure rate of a node as a single and constant failure rate \(\lambda_{hw}\), resulting in the sum of failure rates of each component. In an analogous way, hardware repair actions of a node can be summarized by a single and constant repair rate \(\mu_{hw}\).  
The hardware behavior with respect to availability is described as a binary relationship, which can be operable (UP) or failed (DOWN). In the former case, the components are operational, and, in the latter, they are failed. This behavior can be represented by a CTMC with two states (\(UP_{hw}\)) and (\(DOWN_{hw}\)). Transitions between these two CTMCs' states are described by the failure and repair rates, \(\lambda_{hw}\) and \(\mu_{hw}\), respectively. Figure 4 shows the proposed hardware model. Under the stated assumptions, the hardware availability, \(hw=A_{hw}(t)\), can be computed as the probability of being on state \(UP_{hw}\).
# Effect of Optical Aberrations on Laser Transmission Performance in Maritime Atmosphere Turbulence
## 2 Numerical Simulation Method
### Construction of Zernike Polynomial Aberrations  
Zernike polynomials, due to their form being consistent with that of aberration polynomials, are often used to fit wavefronts and analyze wavefront characteristics [22; 34]. In wave optics, phase or wavefront is commonly used to quantitatively describe the aberrations of optical systems. The wavefront \(\Phi\) is defined as the distance traveled by the light wave (with the smallest unit being the wavelength \(\lambda\)), and the phase is given by the following:  
\[S=\frac{2\pi\Phi}{\lambda}=k\Phi \tag{14}\]  
In a circular domain, Zernike polynomials are orthogonal two-dimensional polynomials that include the radial coordinate \(\rho\) and the angular coordinate \(\theta\in[0,2\pi]\), typically expressed in polar coordinates \((\rho,\theta)\). This study adopts [PERSON]'s representation of Zernike aberrations and introduces a representation of Zernike polynomials that only includes a single order, which is given by [25]:  
\[Z_{i}(\rho,\theta)=\sqrt{n+1}\begin{cases}R_{n}^{m}(\rho)\sqrt{2}\cos(m\theta) &(even\quad i\,m\
eq 0)\\ R_{n}^{m}(\rho)\sqrt{2}\sin(m\theta)&(odd\quad i\,m\
eq 0)\\ R_{n}^{m}(\rho)&(m=0)\end{cases} \tag{15}\]  
Its orthogonal property is given by [35]:  
\[\int_{0}^{2\pi}\int_{0}^{1}Z_{i}(\rho,\theta)\cdot Z_{i^{\prime}}(\rho,\theta) d\rho d\theta=\pi\delta_{ii^{\prime}} \tag{16}\]  
where \(R_{n}^{m}(\rho)\) is the radial function, \(\delta_{ii^{\prime}}\) is the Dirac delta function, and the integers satisfy the condition: \(n\geq m\geq 0\) is an even integer.  
The optical aberrations of the system are simulated using orthogonal Zernike polynomials within the unit circular domain. Within an entrance pupil of a given radius, the wavefront of the light wave is described by [25]:  
\[\Phi(R\rho,\theta)=\sum_{i=1}^{\infty}a_{i}\cdot Z_{i}(\rho,\theta) \tag{17}\]  
Based on the orthogonal properties of Zernike polynomials, the coefficients of each term can be determined as follows [36]:  
\[a_{i}=\pi^{-1}\int_{0}^{2\pi}\int_{0}^{1}\Phi(R\rho,\theta)\cdot Z_{i}(\rho, \theta)\rho d\rho d\theta \tag{18}\]  
The corresponding phase can be given by the following:  
\[S_{i}=\frac{2\pi\Phi_{i}}{\lambda}=ka_{i}\cdot Z_{i}(\rho,\theta) \tag{19}\]  
where \(a_{i}\) represents the weight coefficient of the \(i\)-th order Zernike polynomial, and \(Z_{i}\) is the \(i\)-th order Zernike polynomial. By setting different values for \(a_{i}\), various types of aberrations and combinations of aberrations can be obtained.  
The light field with the introduction of initial aberrations is given by the following:  
\[u(\rho,\theta,z=0)=u_{0}(\rho,\theta,z=0)\cdot e^{S_{i}} \tag{20}\]  
where \(u_{0}\) and \(u\) are the original emitted light field and the initial light field modulated by aberrations, respectively.
## 2 Mathematical Model
### Boundary Conditions  
The non-slip and impenetrable boundary conditions (walls) were set for the walls of the upper vents and tunnel roofs. The Reynolds number in the near-wall region was quite low, and the standard wall function method was used to estimate the velocity distribution near the wall. The first grid node needed to be placed in the area where the laws of logarithm worked, thus ensuring that the dimensionless wall distance \(y+\) fell between 30 and 300. For this study, \(y+\) was guaranteed to be between 53 and 153 in all calculation conditions when the thickness of the first layer of the grid was 0.02102 m.  
The Dirichlet boundary condition was set for the pollutant discharge outlet of the upper vents. The pollutant discharge velocity and concentration were provided according to the actual tunnel ventilation and possible working conditions in the stage of engineering operation. Due to the general characteristics of pollutant emission at fixed points and fixed volume in urban tunnels, and based on the sewage discharge parameters of Zijingang Tunnel's upper vents in Hangzhou, this paper set the fixed discharge capacity (\(M\) = 40 mg/s) of the upper vents to study the rules of pollutant dispersion when the discharge velocity (\(V_{0}\)) was 3 m/s, 4 m/s, 5 m/s, and 6 m/s. The discharge concentrations (\(C_{0}\)) were then 0.83 mg/m\({}^{3}\), 0.63 mg/m\({}^{3}\), 0.5 mg/m\({}^{3}\), and 0.42 mg/m\({}^{3}\) at the corresponding discharge rates.  
The Neumann boundary condition and pressure outlet were adopted for the boundary of the external air area in the computational region without considering the ambient wind. Taking the condition of ambient wind into consideration, the inlet of the external air area in the computational region was set as the Dirichlet boundary condition. The ambient wind speed was provided according to meteorological conditions, and the outlet of the external air area was set as Neumann boundary condition. It is thought that the normal gradient of the physical quantity is zero. The specific boundary conditions are shown in Figure 4.  
Figure 3: Velocity profile of pollutant dispersion with different mesh numbers: (**a**) vertical discharge pattern; (**b**) horizontal discharge pattern.  
The ambient wind speed (\(U\)) at any elevation can be obtained by the following equation [25]:  
\[U=U_{10}\Big{(}\frac{z}{10}\Big{)}^{\alpha} \tag{6}\]  
\(U_{10}\) indicates the wind speed at 10 m elevation above ground, namely, the surface wind speed by the meteorological circle. The pollutant dispersion was computed when \(U_{10}\) was 0 m/s, 1 m/s, 2 m/s, 3 m/s, and 4 m/s. The ambient wind direction is the positive direction of the \(x\)-axis; \(p\) refers to the coefficient of roughness, which is related to landform and architecture environment. The wider and smoother a land is, the smaller \(p\) is. This study refers to the theses by [PERSON] et al. [26], where the \(\alpha\) = 0.287 in the laboratory. The exponential distribution of wind speeds in the vertical direction can be achieved in Ansys Fluent 2019 R3 [27] by preparing the user-defined function (UDF).
# SMS: A Secure Healthcare Model for Smart Cities
## 4 Proposed SMS Framework
### Components of SMS  
The Central Monitoring Unit (CMU) is the heart of the SMS framework. All the decisions about data capturing, routing, storage, retrieval, and blocking are taken by it. The Encryption and Standardization Unit (ESU) is responsible for converting the data captured by the sensors into a standard format so that it can be used for analysis. As the SMS employs multiple sensors of varied nature and configuration, the format in which they capture the data is largely different. Therefore, in order to perform a meaningful analysis, it is imperative to convert these intricate and heterogeneous data into a standard format.  
The SMS uses the OAuth 2.0 protocol to ensure that only the legitimate personnel get access to the sensitive and protected information like patient's records. The CMU validates the credentials of the users in real time before allowing access to the patients' data. These data can also be made available through an SMS app, which can be accessed by the doctors, hospitals, as well as the patients using valid authentication credentials. This can save a major amount of time and effort spent in travelling to the hospitals or doctor's premises. Furthermore, the Cloud Storage and Processing Unit (CSPU) serves several purposes. First, it acts as a repository for storing the patient's data generated over the observation period. Secondly, the data from the cloud can be used as reference for similar medical cases. The data stored in the cloud are made available to the CMU for centralized monitoring of the patient's current health status, which can be used for diagnosis and suggesting treatments. The doctors, as well as the hospitals, can be connected to the CMU to keep track of the patient's medical history and current state and suggest a treatment or help in diagnosis. In cases of \"Critical Care Units (CCU),\" \"Intensive Care Units (ICU),\" \"Intensive Critical Care Units (ICCU),\" etc., it is imperative to process the data on the edge of the network in order to provide real-time diagnosis and analysis of the data. The unit requires round-the-clock real-time monitoring of the patients. The edge nodes are responsible for performing the processing of the data captured by the sensors, which requires immediate actions and decision-making.  
Figure 1: Core components of smart medical system (SMS) in a network.  
The presented architecture aims to bring together the patients, doctors, and hospitals into a single umbrella for better medical facilities, and better diagnostics and treatments. The movement of data from one source to another in the SMS is governed by Software-Defined Networking (SDN) [65; 66]. As SDN provides network flexibility, the data traffic can be dynamically controlled by the CMU in real time. SDN ensures dynamic route management for selecting optimal paths for faster data transfer.