esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reinforcement Learning Based Intercell Interference Coordination in LTE Networks
## 5 The Proposed ICIC Scheme  
The flowchart of the whole scheme is shown in Figure 1.  
Real time services are sensitive to delay and packet loss. The proposed scheme aims to keep delay and packet loss under acceptable values specified by the QoS Class Identifier (QCI). According to the standardized QCI [39], the acceptable delay for conversational voice service is 100 ms while the acceptable packet loss is 1%.  
In **Step 1**, users are selected based on user priority. The objective of the step is to minimize delay, thus packet loss. We use HoL to give priority to users given that, if a packet wait for more than acceptable delay, it will be discarded and packet loss will increase. Once users have been selected, RBs are allocated in **Step 2**. In **Step 3**, the transmit power of each enodeB on each RB is determined to maximize SINR value for each user on allocated RBs, which means minimizing intercell interference.  
In **Step 4**, packets to retransmit are identified based on the SINR value. There is no need to transmit a packet if SINR value on allocated RB is so low that it will not be recovered. The packet is then transferred to retransmission queue.  
Each enodeB is responsible of RBs allocation within a cell while transmit power determination is centralized. Position of selected users is subject to changes, which makes reinforcement learning a good choice for its flexibility to topology changes [15].  
We use GA [32; 40] as a policy search-based reinforcement learning for both resource allocation and power control steps. GA is a method used in evolutionary algorithms (EARL) [41; 42; 43; 44; 45; 46; 47; 48; 49] for reinforcement learning as a policy search method.  
Reinforcement learning is learning what to do--how to map situations to actions--to maximize a numerical reward signal [50]. A reinforcement learning system can be described by the following elements: an agent, an environment, a policy, a reward function, a value function, and, optionally, a model of the environment [50].  
Genetic algorithm (GA) is an optimization and search technique based on the principles of genetics and natural selection [42; 51; 52]. Genetic algorithms belong to meta heuristics family approaches of solving problems and are inspired by the evolution.
## 4 Preparation of Membranes
### Fabrication of Microporous Separators  
Unlike ion-exchange membranes, separators have no ion selectivity and function principally as physical barriers to prevent electrical short circuiting of the positive and negative electrodes while allowing the free movement of electrolyte to complete the circuit. Separators are a key component in many electrochemical systems, including batteries and fuel cells. Microporous membranes consist of a solid matrix with defined holes or pores with diameters ranging from less than 5 nm to more than 50 \(\upmu\)m [27]. Microporous separators are often fabricated from inorganic and organic materials [50]. The simplest method of preparation involves the stretching of a homogenous polymer film of particular crystallinity. The stretching leads to a partial fracture of the film and relatively uniform pores [27]. This technique is commonly applied to polyolefin or polytetrafluoroethylene films [27]. Transport through these separators generally occurs by hydrodynamic flow, and hence the discrimination of specific species is not often possible. Selectivity through the micropores is obtained through the different transport rates of various ionic species [50]. Semipermeable microporous membranes have a slightly smaller pore diameter, and are able to restrict passage of certain ionic species. An interaction between the permeating species and the membrane may occur, and properties of selectivity of ion exchange membranes may exist.  
Ion exchange functionality may also be introduced to microporous separators by impregnating with soluble polyelectrolytes. The separator is immersed in the aqueous polyelectrolyte in which it swells and sorbs it [48]. The separator is then dried to trap the polyelectrolyte. Although this method of impregnation is relatively simple, during constant use, the capacity of such membranes has been known to fade [48]. This has been attributed to a lower chemical stability than ion exchange membranes [48].
## 3 Decision Mechanism for Collaborative Search  
### Path Grid Determination Algorithm  
To calculate the situation information cost along the UAV's movement path, it is necessary to acquire the grids traversed by the path. As shown in Figure 3, assume that the center positions of the UAV's current grid \(g_{L}\) and the next candidate waypoint \(WP_{1}\) are \((x_{L},y_{L})\) and \(\big{(}x_{up1},y_{up1}\big{)}\), respectively. The line connecting these two points is denoted as \(L_{0}\). By connecting the vertices of grid \(g_{L}\) and grid \(g_{wp1}\) (where \(WP_{1}\) is located), two lines parallel to \(L_{0}\), namely \(L_{1}\) and \(L_{2}\), are obtained:  
\[\begin{cases}L_{1}:Ax+By=C_{1}\\ L_{2}:Ax+By=C_{2}\end{cases}. \tag{7}\]  
For the coordinates \(\big{(}x_{g,i},y_{g,i}\big{)}\) of any grid \(g_{i}\), the following holds:  
\[C_{g,i}=Ax_{g,i}+By_{g,i}. \tag{8}\]  
When the center position of grid \(g_{i}\) satisfies Equation (9), it is considered to lie on the UAV's future movement path:  
\[\begin{cases}C_{g,i}\in\left[\min\{C_{1},C_{2}\},\max\{C_{1},C_{2}\}\right]\\ x_{g,i}\in\left[\min\{x_{L},x_{wp1}\},\max\{x_{L},x_{wp1}\}\right],\\ y_{g,i}\in\left[\min\{y_{L},y_{wp1}\},\max\{y_{L},y_{wp1}\}\right]\end{cases}, \tag{9}\]  
by substituting \(x_{wp1}\), \(y_{wp1}\), \(x_{L}\), and \(y_{L}\) in Equation (9) with \(x_{wp2}\), \(y_{wp2}\), \(x_{wp1}\), and \(y_{wp1}\), the grids along the path from waypoint \(wp_{1}\) to \(wp_{2}\) can be determined.
## 1 Introduction
The comparative analysis focuses on three scenarios--hanging, object-to-object collisions, and self-collisions--under identical conditions involving node counts and time-lapse-based fps measurements. Our results demonstrate that VBD achieves superior fps, outperforming PBD by up to 82.9% in hanging scenarios, 31.1% in object-to-object collision scenarios, and 8.8% in self-collision scenarios when operating with a large number of nodes. This performance comparison is based on CPU-only computations, while future \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \multicolumn{2}{c}{**Advantages**} & \multicolumn{2}{c}{**Disadvantages**} \\ \hline Mass-Spring & - & Simple and intuitive implementation & - & May produce unrealistic deformations (lack of physical accuracy) \\ System & - & Lightweight and efficient calculations & & Can be unstable in complex simulations \\  & - & Suitable for small-scale simulations & - & Can be unstable in complex simulations \\ FEM & - & Physically accurate and realistic deformations & - & Computationally expensive and slow \\  & - & Can account for various material properties & - & Challenging to implement parallelization \\  & - & Stable and fast calculations & - & Focuses on stability over accuracy (less physically \\ PBD & - & Well suited for parallelization & & accurate) \\  & - & Excellent performance in real-time simulations & - & Performance may degrade with high node density \\  & - & High stability and accuracy & & High implementation complexity \\ VBD & - & Better performance in complex simulations & - & May not show significant performance gains in \\  & - & compared to PBD & & small-scale simulations \\  & - & Suitable for parallelization & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparing the advantages and disadvantages of algorithm. work will involve optimization via GPU-based parallel processing using compute shaders, with the expectation of further enhancing performance.
# Sustainable Removal of Cr(VI) by Line Peel and Pineapple Core Wastes
## 3 Results and Discussion  
### Screening of Biosorbents  
The selected PP wastes (lignin 5.78, cellulose 24.53, and hemicellulose 28.53 g/100 g dry weight [18]) and LM wastes (lignin 1.73, cellulose 12.72, and hemicellulose 6.30 g/100 g dry weight with around 46% w/w of carbon content) are lignocellulosic-rich materials; this fact provides these biosorbents a quite complex structure that contains a wide range of active sites that makes them able to adsorb different pollutants from wastewater [5]. Thus, the adsorption ability of the selected residues was analyzed using a synthetic Cr(VI) solution at a natural pH, around 4.  
As can be seen in Figure 1, for both biosorbents Cr(VI) removal levels are higher than Cr adsorption, showing PP waste the best performance in terms of Cr(VI) removal (around 70%) after reaching the equilibrium at 24 h (Figure S1). These facts can be explained by the \"adsorption-coupled reduction\" reaction that takes place and is fully recognized as the Cr(VI) adsorption mechanism in natural biosorbents operating at acidic conditions [12]. According to [PERSON] et al. [19; 20], this Cr(VI) to Cr(III) reduction can happen through two distinct reduction mechanisms (direct and indirect). [PERSON] and [PERSON] [12] found that a biomaterial with a reduction potential lower than Cr(VI) can reduce Cr(VI) in an aqueous phase due to the contact with electron-donor groups (such as thiol, phenolic, and carboxylic functional groups). In addition, indirect reduction includes 3 stages: (i) binding of anionic Cr(VI) onto the biomaterial positively charged groups (amino and carboxyl groups), (ii) reduction of Cr(VI) by adjacent electron-donor groups to Cr(III) and (iii) release of the generated Cr(III) directly into aqueous phase or complexation with adjacent groups.  
Regarding the Cr adsorbed, it is concluded that although the best adsorption capacity was also displayed by the PP waste, the total Cr concentration measured in the supernatant was greater than Cr(VI). This fact corroborates that the elimination of Cr(VI) in PP and LM takes place by both mechanisms, direct and indirect, as previously described by [PERSON] et al. [19; 20].  
Figure 1: Screening of wastes pineapple core (PP) and lime peel (LM) for the removal of Cr(VI) and total Cr. Operational conditions: 0.5 g of biosorbent, 50 mL of a solution 50 mg/L Cr(VI) at 25 \({}^{\circ}\)C and 150 rpm during 24 h.  
The best performance was attained when the adsorption was carried out with PP as a biosorbent with an adsorption uptake of 2.49 mg/g increasing 2.8-fold the values attained for LM. This is also in consonance with the \(K_{D}\) values obtained for the biosorbents: 99.20 and 21.19 mL/g for PP and LM, respectively. Therefore, the next assays were carried out using this PP biosorbent.
## 3 Design Examples for the Proposed Synthetization Approach
### Comparison and Summary
 Table 5: Typical simplification examples with detailed parameters, under the condition of \(Z_{S}=1.5\,\Omega\), \(Z_{L}=1.0\)\(\Omega\), \(k=1.5\), \(\theta=90\) at\(f_{0}=2\) GHz, \(\theta_{c}=45^{\circ}\), \(RL=20\) dB. Figure 9: Relationships among characteristic impedance, \(k\) and \(\theta_{c}\) of case 21 under the condition of \(RL=20\) dB. (**a**) Relationships about \(Z_{\text{cr2}}\) and \(Z_{\text{cr4}}\). (**b**) Relationships about \(Z_{\text{cr4}}\) and \(Z_{\text{cr4}}\). (**c**) Relationships about \(Z_{1}\) and \(Z_{5}\). (**d**) Relationships about \(Z_{3}\) and \(k\). (1) Although all the topologies (25 cases) are quite different, by using the proposed synthetization approach, the same Chebyshev frequency responses (\(S_{11}\), \(S_{21}\) and group delay) can be derived. (2) Symmetrical or asymmetrical topology will not affect the Chebyshev frequency responses, which are only determined by all the characteristic impedances of CLSs and TLs. (3) When the category is determined, considering coupling strengths and impedance ratio, CLSs are preferred to connect the source and load terminals in symmetrical topology. (4) More variable parameters will provide more flexible design procedures and wider realization ranges. When the number of variable parameters is zero, the most simplification topology appears with the unique solution of characteristic impedances. Although the realization range is quite limited, the circuit is highly convenient for fabrication.
# Measurement and Analysis on Magnetic Field Influence of Substation for Magnetic Shielding Device
## 3 Principle of Measurement  
Shielding factor of magnetic shielding device can be calculated as follows (single-layer shielding cylinder) [31]:  
\[\mathrm{S_{s}}=\frac{2\mu R\mathrm{\,R}^{\frac{1}{2}}}{L^{\frac{3}{2}}} \tag{1}\]  
where \(\mathrm{S_{s}}\) is the single-layer shielding factor; \(\mu\) is the relative permeability; \(R\) is the radius of the shielding layer; \(t\) is the thickness of the shielding layer; \(L\) is the length of the shielding layer.  
The formula for calculating the shielding factor of the multi-layer magnetic shielding cylinder is:  
\[\mathrm{S_{z}}=\mathrm{S_{n}}\prod_{i=1}^{n-1}\mathrm{S_{i}}\left[1-\left( \frac{D_{i+1}}{D_{i}}\right)^{j}\right] \tag{2}\]  
where \(\mathrm{S_{z}}\) is the n-layer radial shielding coefficient; \(\mathrm{S_{i}}\) is the i-th layer shielding coefficient (the outermost is the first layer).  
The remanence of the magnetic shielding device \(B\) can be calculated by the magnetic shielding factor \(\mathrm{S_{z}}\):  
\[B=\frac{B_{0}}{\mathrm{S_{z}}} \tag{3}\]  
where \(B_{0}\) is the background magnetic field.  
According to the above formula, when the material permeability, size, and other parameters of the magnetic shielding device are determined, the background magnetic field strength is an important factor affecting the internal remanence of the magnetic shielding device.  
The environmental magnetic field is tested by a three-axis fluxgate magnetometer, where \(\mathrm{B_{X}}\) is the east-west magnetic field (facing west is positive), \(\mathrm{B_{Y}}\) is the vertical to the ground magnetic field (facing the ground is positive), \(\mathrm{B_{Z}}\) is the north-south magnetic field (facing north is positive), and \(\mathrm{B_{T}}\) is the total magnetic field:  
\[\mathrm{B_{T}}=\sqrt{B_{x}^{2}+B_{y}^{2}+B_{z}^{2}} \tag{4}\]  
Follow the test steps:  
1. Determine the test distance, take the substation to be tested as the center, and use a tape measure to determine the test point;
2. Set the three-axis fluxgate at the test point through the tooling;
3. Turn on the three-axis flux gate to preheat for 10 min, and open the acquisition board and computer;
4. Read and store test data through computer;
5. Calculate the magnetic field with Formula (4);
6. Repeat (1)-(5) to test the next test point.  
Use a tripod to fix the fluxgate magnetometer and its clamp, as shown in Figure 4. Use a tape measure to measure the distance from the test point to the measured substation, and test according to the above steps.
## 2 Background and Related Work  
Data security means ensuring that data are protected while the data are being collected, processed, stored and transmitted. The data confidentiality, integrity and availability (CIA) triad is a common concept to ensure data security. Confidentiality ensures that data are not made available or disclosed to unauthorized individuals or entities. Integrity provides assurance that data are not modified accidentally or deliberately. Availability ensures the reliable accessibility of the system for authorized entities. Data privacy governs how data are collected, shared and used; it also ensures that only authorized persons can access the data [7]. However, data privacy cannot be achieved by securing only personally identifiable information (PII). As PHR data include both PII and patient health record data, privacy needs to be assured for both PII and health record data.  
WBAN applications are vulnerable and open to many types of attacks and threats as sensor nodes operate in an environment where the sensor node uses low powered radio signals for communication. These attacks make the security and privacy of PHR data one of the primary challenges for WBAN systems. We have previously conducted a structured literature review and identified a total of 11 types of attacks on WBAN applications, in addition to identifying 22 security and privacy requirements for WBAN applications [8].
# Trends in Quantification of HbA1c Using Electrochemical and Point-of-Care Analyzers
## 7 Strategies for Measurement of HbA1c
The study concluded that both the PoC and HPLC methods exhibited certain biases and overestimated HbA1c in a certain window when compared to NGSP standards [104]. Interestingly, in a comparative study of the PoC analyzer Cobas b 101 (Roche Diagnostics) and HPLC [105] for HbA1c quantification from EDTA whole blood and capillary blood, both techniques yielded similar results, thus giving the PoC system an edge owing to its ease of usage. Recently, in 2018, another study was conducted to evaluate the performance of four PoC analyzers, which was compared with four secondary reference measurement procedures (SRMPs), recognized by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) and the National Glycohemoglobin Standardization Programme (NGSP). The four PoC were Afinion2(tm) (Abbott, boronate affinity separation), Quo-Lab (EKF Diagnostics PLC, boronate affinity separation), HbA1c 501 (HemoCue Diagnostics, boronate affinity separation), and A1 Care (i-SENSE, enzymatic determination). The results revealed that the Afinion2 and Quo-Lab devices performed the best. It is evident from these studies that most of these PoCs are limited by their sensitivity, response time, need for expensive reagents, and lack of specificity [106]. Hence, there is an acute need for new detection strategies that can overcome the disadvantages of the conventional methods and offer rapid, sensitive, and cost-effective measurement of HbA1c.
## 3 Experimental Setup and Results
### Results for Image Classification  
#### 3.4.1 Transfer Learning Models' Results  
Four different models for this study generated comparatively high results with the transfer learning approach. Resnet50, VGG16, Xception, and MobileNet achieved an accuracy of 90.6%, 92.2%, 85.6%, and 93.8%, respectively. Table 4 summarizes the details of the results of each of the pre-trained models and baseline CNN model from the previous subsection in terms of _accuracy_, _precision_, _recall_, _f1-score_, _selectivity_, and AUC score. Figure 5 visualizes the confusion matrix for each model in binary classification. Finally, Figure 6 depicts the ROC curve for pre-trained models.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Value** \\ \hline CPU & Intel ® Xeon ® Silver 4210 Processor \\ Total Cores & 10 \\ Total Threads & 20 \\ Max Turbo Frequency3 & 3.20 GHz \\ Processor Base Frequency2 & 2.20 GHz \\ Cache & 13.75 MB \\ RAM size & 64 GB \\ Maximum Memory Speed & 2400 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of the machine where computation was conducted.  
Figure 5: Confusion matrix of transfer learning approach models (**a**) ResNet50 (**b**) Xception (**c**) VGG16 (**d**) MobileNet.  
Figure 6: ROC curve for all models.  
Figure 8: BN structure from unsupervised learning with node strength evaluated with normalized KL divergence and Arc strength evaluated with mutual information.  
Figure 9: BN structure from unsupervised learning with node strength evaluated with “node force” and Arc strength evaluated with mutual information.  
Figure 11: Tornado effects (conditional probabilities) for the target variable “tumor”.  
Figure 10: Total effects for the target node tumor.  
Figure 12: BN influences model for the target variable with supervised learning.  
Figure 13: ROC curve for the expert model.  
Both expert models were also evaluated with the K-fold validation method. No weaknesses were detected. The results show that both BN models are competitive with the pure CNN diagnostic tool. This means that the BN-constructed expert models can be safely used to understand which factors play a key role in the final successful diagnosis of whether a patient has a tumor (something that is not possible with the pure CNN system).
###### Abstract  
Landslides are a common natural hazard that causes casualties and unprecedented economic losses every year, especially in vulnerable developing countries. Considering the high cost of in-situ monitoring equipment and the sparse coverage of monitoring points, the Sentinel-1 images and Interferometric Synthetic Aperture Radar (InSAR) technique were used to conduct landslide monitoring and analysis. The Muyubao landslide in the Three Gorges Reservoir area in China was taken as a case study. A total of 37 images from March 2016 to September 2017 were collected, and the displacement time series were extracted using the Stanford Method for Persistent Scatterer (StaMPS) small baselines subset method. The comparison to global positioning system monitoring results indicated that the InSAR processing of the Muyubao landslide was accurate and reliable. Combined with the field investigation, the deformation evolution and its response to triggering factors were analyzed. During this monitoring period, the creeping process of the Muyubao landslide showed obvious spatiotemporal deformation differences. The changes in the reservoir water level were the trigger of the Muyubao landslide, and its deformation mainly occurred during the fluctuation period and high-water level period of the reservoir.  
Keywords:landslide deformation; InSAR; reservoir water level; Sentinel-1; Three Gorges Reservoir area (China) +
Footnote †: journal: Remote Sensing, 2020, 12, 3385; doi:10.3390/rs12203385
# Feasibility of Optical Flow Field Measurements of the Coolant in a Grinding Machine
### Aim and Outline  
For this reason, the article investigates the measurability of the MWF flow velocity field in a grinding machine with SIV and PIV for various volume flows. For the quantification of the velocity field of the continuous free MWF jet between nozzle and grinding wheel, SIV and PIV are applied and studied to serve as complementary measurement techniques to evaluate the usability of flow characteristics as flow tracers. For the MWF flow behind the interaction zone with the grinding wheel, the droplets of the MWF are used for a respective velocity field measurement with SIV.  
The measurement approaches for the velocity field of the MWF flow with SIV and PIV are explained in Section 2. Section 3 gives details about the experimental setup, which is implemented in a grinding machine. The flow field measurement results are shown and discussed regarding the measurement feasibility in Section 4. At first, SIV and PIV measurements of the supply flow are compared. Subsequently, measurements of the MWF flow in interaction with the grinding wheel are presented and assessed regarding the measurability of the MWF flow field. The article closes with a summary and an outlook in Section 5.