esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Malicious Document Classification
### Model Construction
#### 3.4.2 Svm  
SVM is another classical classification model. SVM can achieve a high level of classification accuracy in a variety of fields and build sophisticated classification models [38; 39]. It seeks an ideal hyperplane or a group of hyperplanes that distinguishes the class of data in a given dataset correctly and clearly. The ideal hyperplane is the one with the widest margin between classes, which helps the classifier in reducing future errors. Classification using SVM predicts the class of a given sample by determining the side of the found hyperplane to which the given sample belongs. The training complexity of nonlinear SVM is generally between \(O(N^{2})\) and \(O(N^{3})\).
## 3 Results
### Influence of Slag and Graphene on the Heating Efficiency of the Asphalt Mixtures  
The effect of adding slag or graphene to the mixture is an increase in the heating rates, as shown in Figure 11. The higher the amount of additive, the faster the temperature increase with energy. Even the mixture without additives can be heated by microwaves, although more energy, and consequently more heating time, must be applied to reach the same temperature. This means that microwave heating technique can also be used for existing pavements without additives, as also reported by other authors [56]. It can be observed in Figure 11 and Table 3 that the lineal models fit well the data, in terms of R\({}^{2}\). Nevertheless, it is interesting to analyze the effect of adding slag or graphene in terms of energy saving. The addition of 3 %, 6 % and 9 % (o/aggregates) of slag allows to save, respectively, 29%, 37% and 45% of the heating energy, respect to the ordinary asphalt mixture, while the addition of 1% and 2% (o/binder) of graphene allows to save 29% and 50% of the heating energy. This improvement of energy efficiency can produce several benefits in terms of CO\({}_{2}\) emissions and maintenance costs.
## 1 Introduction
The reliability scores of new products, expressed as fuzzy numbers or linguistic variables, are combined and processed by fuzzy inference methods so that experts can better judge the reliability level through their experience. To fuse experts' judgments and limited failure data, Bayesian inference is used, which is a common method that is used to combine multiple sources of information. A practical framework is proposed to evaluate the reliability of a new product; this framework fully considers the capture of expert information and the fusion of all prior information, and these priors can be aggregated at once without needing to consider the weights of priors. First, an expert system is derived by the Bayesian best-worst method and fuzzy logic inference, which aggregates a group of expert decisions. Then, an adaptive Bayesian modeling (ABM) method is investigated and modified to integrate experts' judgments and limited test data, and a modified sampling importance resampling algorithm is employed to obtain the posterior distribution. Finally, this framework is applied to the evaluation of a new type of servo turret, which is an important subsystem of a CNC lathe. The paper is organized as follows. Section 2 presents a review of the existing literature and further justifies the need for our approach. Section 3 establishes an expert system for collecting and aggregating experts' judgments. Section 4 proposes an adaptive Bayesian melding method to generate the posterior distribution. Section 5 presents a case example and discusses the rationale for this approach. Section 6 draws some conclusions and discusses possible future research.
# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 3 Sensor Localization
### Distance Estimation Methods  
The node position inside any network is determined by knowing the distance between the various nodes that make up the network. There are two ways to evaluate distance:  
Range-Based: The techniques belonging to this family are based on the Euclidean distance separating the different nodes.  
The best known are the time-based methods, divided into time of arrival (ToA) and time difference of arrival (TDoA) [3]. To use these methods, it is required to know in advance the propagation speed of the involved signal.  
The Angle-Based methods also belong to this family, based on the determination of the arrival angle of the signal transmitted by the Anchors nodes and resorting to trigonometric formulations to estimate the distance of the Unknowns.  
Finally, RSSI methods are based on the measurement of the power of the signal transmitted by the Unknown and received by Anchors.  
Range-Free: These techniques are independent of Euclidean distance.  
The most important and possible implementations of this method are Centroid methods: the position of Unknowns is determined by knowing only the coordinates of neighboring Anchors, regardless of their distance. When an Unknown is within the coverage radius of several Anchors, it will assume the coordinates of the centroid of the geometric figure with the Anchors themselves as vertices.  
Also, there are the DV-Hop methods [12]. In this approach, the distance that separates an Unknown from an Anchor is not expressed in terms of Euclidean distance but in the number of hops that divide the two nodes. The length of such hops is determined a priori by the Anchors of the network by calculating the value of the Euclidean distance that separates them and dividing it by the number of hops that the packet has made.
## 2 Materials and Methods
### Electrode Areal Arrangements in Square Surfaces  
Considering the typical multi-channel instruments on the market today, the availability of an instrument with 96 channels, i.e., capable of acquiring up to 96 measurements simultaneously, was hypothesized.  
In a square surface with a side equal to 120 m, three different areal electrode arrangements were considered, each having a total number of 96 electrodes (Figure 1). The first simulated electrode arrangement consisted of positioning the electrodes on the perimeter of the square that surrounds the investigated area, with a minimum electrode spacing \(a=5\) m (electrode arrangement \(A\), Figure 1A). The second consisted of two concentric squares: the external square with a side of 120 m in which 48 electrodes are placed with a minimum spacing of 10 m, and the internal square with a side of 60 m, in which the other 48 electrodes are placed with minimum electrode spacing \(a=5\) m (electrode arrangement \(B\), Figure 1B). The third electrode arrangement considered the same external square, cut in four by a cross (with a minimum electrode spacing \(a=5\) m) to simulate four contiguous areas (electrode arrangement \(C\), Figure 1C).
# A Critical Review of Supersonic Flow Control for High-Speed Applications
## 5 Critical Analysis of Literature
#### Challenges in the Flow Control Method  
Some major issues can be found when the active flow control method is used:  
* **Experimental test:** One of the major issues during the test is how to reduce the flow disturbances in the connecting pipe from the main settling chamber.
* **Instrumentation:** With its sensing hole facing the flow, the pitot probe mounted on a rigid 3D traverse with a resolution of 0.1 mm was used for pressure measurement. Deficient Reynolds numbers based on the probe diameter significantly influenced the pressures measured by pitot probes. However, this effect is seldom a problem in supersonic streams because a probe of reasonable size will usually have a Reynolds number above 500, which is above the range of troublesome Reynolds numbers.
* **Data accuracy:** Pressure distribution along the jet centerline with a normal to the tab is difficult to measure and needs a proper location and observation.
* **The nozzle calibration:** The measured pitot pressure can determine the Mach number by treating the flow through the nozzle to be isentropic, thus leading to the total pressure at the nozzle exit being the same as the settling chamber pressure.
* **Simulation:** The 2D model was developed in several studies but this can only be suitable when it is uncontrolled or passive controlled. Indeed, a microjet controller needs a 3D model for more accurate results and it is difficult to design, also requiring a supercomputer to simulate/analyze.
* **Base pressure:** Based on the previous result, the base pressure either increases or decreases when it is controlled and also it varies by varying the nozzle parameter. However, it is critical to increasing the supersonic or hypersonic ranges due to high-speed formation in the setup that has a chance to incur breaks/damages; hence, it needs a very high configured/quality setup for testing.
* **Microjet location:** It was found that the microjets were located in the PCD of 13 mm but there is no other location that is reported in the literature; this can be explored well in future studies.
* **Soft computing:** As compared to DOE, only a few studies are reported with machine learning algorithms and the Fuzzy logic approach. These methods can be explored well in future investigations.
# Gain-Scheduled Disturbance Observer-Based Saturated Controllers for Non-Linear First-Order System +
As  
\[R^{00}(s)=\frac{U(s)}{E(s)}=K_{P00}=\frac{1}{K_{s}T_{00}}, \tag{32}\]  
the equivalent disturbance-compensating controller in the proportional zone of the control is equivalent to PI controller  
\[C^{00}(s)=K_{P00}\frac{1+T_{f00}s}{T_{f00}s}=\frac{1}{K_{s}T_{00}}\left(1+ \frac{1}{T_{f00}s}\right)=\frac{1}{K_{s}T_{00}}+\frac{1}{K_{s}T_{00}T_{f00}s}. \tag{33}\]  
Since ultralocal process models are used, they are expected to be sufficiently accurate only in the narrow neighborhood of the operating point. When using the simplest GS-ARC0, it should therefore be noted that:  
* The simplest reconstruction of \(d_{i}\) with \(a=0\) is achieved for a sufficiently large \(T_{f}\), i.e., when approaching the steady-states. The disturbance reconstruction and compensation is reduced to a positive feedback from the saturation non-linearity to the offset of the stabilizing controller;
* The scheduling parameter \(\alpha\) (26) influences the operating point \(X\) (14) and the gain \(K_{P00}\) (32) based on the model parameter \(K_{s}\) (17);
* The saturation limits when calculating the output \(\Delta u_{r}\) from \(\Delta u\) correspond to those of \(u\) reduced by the offset \(u_{p}\) (18) added to the controller output, if \[\Delta u_{r}\in[U_{min}-u_{p},U_{max}-u_{p}].\] (34)
* If the output correction by the offset \(u_{p}\) according to (18) is omitted, the controller output \(\Delta u\) with the saturation limit defined for \(u\) compensates for an equivalent disturbance \(d_{i}+u_{p}\). As with ADRC, such a cumulative disturbance can then be referred to as a \"lumped\" or \"total\" disturbance. * As will be shown below, simplifications based on ultralocal models with \(a=0\) can only guarantee stable processes for limited tuning parameters \(T_{00}\) and \(T_{f00}\).
## 3 Energy-Saving Optimal Control Strategy of Refrigeration Station
### Bidirectional Variable Flow Technology of Primary and Secondary Chilled Water Pump  
There are two supply-demand relationships in the refrigerated water cycle of air conditioning: the supply and demand of the cooling capacity, and the supply and demand of the refrigerated water flow as the carrier of the cooling capacity. Any imbalance between supply and demand will affect the effect of air conditioning, and if the way to achieve the balance is not reasonable, it not only will increase energy consumption but also cannot achieve the goal. As shown in Figure 2, the secondary pump structure system is commonly used today. The working principle is as follows: When the load is high, meaning the demand at the end increases, the secondary pump provides a higher flow. As a result, part of the chilled water bypasses the primary pump, flowing through valve 5 along the red arrow to the secondary pump, and does not pass through the chiller. When the load is low, meaning the demand decreases, the secondary pump provides a lower flow, and some chilled water flows directly from valve 5 along the blue arrow to the primary pump. This configuration cannot achieve both thermal balance and flow balance for the chiller system, which is unfavorable for improving energy efficiency and reliability.  
As shown in Figure 3, according to the research results of [PERSON], [PERSON] et al. [16], the transformed secondary pump system adopts bidirectional variable flow technology. The working principle is as follows: A bypass balancing pipe with an electric control valve (valve 5) is installed at the chiller side, and a bypass load balancing pipe with an electric control valve (valve 6) is installed at the terminal side. Based on changes in the terminal load, the control system adjusts valves 5 and 6 to balance cooling capacity and chilled water flow, thereby improving the system's reliability and energy efficiency. This is achieved within a specific range, where the pressure difference across different terminal branch circuits remains relatively consistent, optimizing the secondary pump system's performance. After applying bidirectional variable flow technology, the number of pumps is significantly reduced, the number of pipeline valves is reduced, and resistance is reduced by utilizing idle pipeline diameters. The flow and head of the pump group are effectively distributed, the conveying efficiency of the pump group is improved, and the failure rate is greatly reduced.
## 3 Numerical Model
### Convective Losses of the Thermal Storage Tank  
The thermal storage tank is assumed to be of cylindrical form, and in particular, of that cylinder, proportions that minimize the external area. This is verified, regardless of the volume, when the cylinder height, \(\bar{H}\), equals its diameter, \(D\). The tank is also assumed to be thermally insulated with a layer of polyurethane having a thermal conductivity of \(k=0.03\) W/mK at 80 \({}^{\circ}\)C [23] and that the cylinder leans on the floor. Hence, convective thermal losses with the environment are computed accounting for the lateral and top surface only. Since heat losses depend on tank temperature and this, in turn, varies with time, heat loss coefficients must be computed at every time step.  
For this simplified analysis, the tank surface temperature is assumed to be equal to the average temperature between the start and end of the time step (30 min); water stratification due to temperature gradients in the tank are not taken into account. For every time step, one Rayleigh number for natural convection is computed for the lateral surface and for the top of the cylinder:  
\[Ra_{1,2}=\frac{g\beta}{\
u\alpha}(T_{s}-T_{\infty})L_{1,2}^{3}\quad\begin{cases} L_{1}=\bar{H}&\text{lateral surface}\\ L_{2}=D&\text{top surface}\end{cases} \tag{19}\]  
where \(g\) is the gravity acceleration, \(\beta\) is the air thermal expansion coefficient, \(\
u\) is the kinematic viscosity, \(\alpha\) is the thermal diffusivity, \(T_{\infty}\) and \(T_{s}\) are the environment and the surface temperature, respectively, and \(L\) is a characteristic length, equal to \(D\). The heat transfer coefficient can be computed as \(h_{1,2}=kNu_{1,2}/L_{1,2}\), where \(Nu\) is the Nusselt number. Finally, the convective energy losses of the cylindrical water tank over the time step \(\Delta t=30\) min are given by:  
\[E_{loss,i}=E_{lat}+E_{top}=\big{(}h_{1}\pi D\bar{H}+h_{2}\pi(D/2)^{2}\big{)}( T_{s}-T_{\infty})\Delta t. \tag{20}\]  
The energy stored in the tank at every time step \(i\) can be written as:  
\[E_{tank,i}=(m_{H_{2}O}\ c_{p,H_{2}O}\ T_{H_{2}O}\ \Delta t+E_{th}-\overline{E}_{ heat}-\overline{E}_{shv}-E_{loss})_{i} \tag{21}\]  
where \(T_{H_{2}O}\) is the water temperature, \(E_{th}\) is the thermal energy produced by the system and is given either by a steady-state operation (see Table 1) or by a maneuver, depending on SP and \(T_{34}\) (Figure 4), \(\overline{E}_{heat,i}\) and \(\overline{E}_{shv,i}\) are the thermal demands described by Equations (16) and (18). A scheme of the numerical solver is reported in Figure 6 for clarity.  
Figure 5: Non-dimensional energy demand profiles. Effect of weekday ((**a**) vs. (**b**)), season ((**c**) vs. (**d**)), and weather ((**e**) vs. (**f**)) on time and intensity distributions for a single-family building.
# A Review of the High-Mix, Low-Volume Manufacturing Industry
## 4 Optimisation of the HMLV Layout and Flow
### Layout and Flow Optimisation
#### 4.2.2 Flexible Manufacturing Systems (FMS)  
The flexible manufacturing system (FMS) is one of the concepts which manufactures a variety of products from a selected range of parts without batch production or time lost in reprogramming [40], while minimising reconfiguration time. The typical row layout with a straight-line flow is suitable for an automated assembly system with a well-defined work sequence for all work units. However, the length of the line may limit the movement of operators and materials if it is too long. The U-shaped line was developed to solve this by allowing the supply of material from outside to the operator inside. It also enables multi-functional workers to be mobile, performing tasks on both sides of the U-shaped layout.  
Another approach is the loop layout, which is based on the straight-line flow that allows to serve as loading and unloading location at the same place: this is useful for pick-and-place on the material handling system. However, in the HMLV industry, there is no fixed material flow pattern. An open field layout is therefore presented as the random order form of FMS, which allows flexibility in the process sequence. This layout requires a greater number of machines and material handling. Therefore, an open field circular FMS layout is being proposed by [PERSON] and [PERSON] [40], with the integration of robot-centred cells for more flexibility while being cost-effective at the same time. The face centred composite design (FCCD) approach is used to generate 40 scenarios that are used to numerically investigate the effects of layout, production quantity, variety, and complexity with minimal production costs. Then, linear programming (LP) is used to run optimisation models with layout-dependent machine distance algorithms individually. From there, a regression model is developed to provide quick estimates that negate the need to run intensive computational numerical models.  
Meanwhile, [PERSON] et al. (2020) investigated the sustainability of the FMS. A four-level architectural model was used to describe the optimisation problem of HMLV production, using sustainability parameters in their mathematical modelling. The model optimised energy consumption and scrap percentage with a simulation study of a flexible job shop scheduling problem (FJSSP).
# New Four Points Initialization for Digital Image Correlation in Metal-Sheet Strain Measurements
## 5 Discussion  
The principal contribution of this work consists in the correct points-pairing between images (100%), in the specific case of almost-identical marks, to measure the deformation of a metal sheet with a small error. The method is semi-automatic due to the high complexity of the shown problem. This is evident when using the MATLAB function called \"imregcorr\" [41], and it is solved when the user indicates only two pairs of coinciding points. This works as an initial condition to begin a search of correspondent marks among the images. Basically, we noticed that the initial conditions of the search increased, in a notable way, the efficiency of the point-pairing system (with _efficiency_ referring to the amount of paired marks, divided between the total of marks in the space of analysis). We assumed that the quality of the mark stamping, lighting, and the initial conditions, also contribute to trustworthy effects. According to the results, it can be observed that the correlation applied to the detection of deformed circular patterns (ellipses) is not ordered. This is because the objective of the construction of the pattern is to make the marks identical and that, in opposition to this, the correlation uses the differences between marks to emit a factor of similarity. The system was also compared with a manual deformation measurement using a microscope, which achieved a low sensitivity of the camera system and a displacement. This was due to the resolution of the camera system. In terms of processing time, it is well known that the correlation has a computational complexity of m\({}^{2}\)n\({}^{2}\) for an nxn image and an mxm template. Meanwhile, the proposed method depends on the number of points on the analyzed sheet; which is many times less than the number of pixels of an image, and it also depends on the distance between the camera and the printed sheet that is to be studied.
# High-Resolution Spaceborne SAR Geolocation Accuracy Analysis and Error Correction
## 2 Materials and Methods
### Geolocation Procedure
\[row=\frac{t-t_{start}+\frac{\left|\overrightarrow{R}(t)\right|}{c}}{dt} \tag{10}\]  
For the Tianhui-2 secondary images, the single-satellite model in Equation (6) is not applicable. The range vector, \(\overrightarrow{R}\), and the velocity, \(\overrightarrow{V_{S}}\), of the reference satellite must be considered, as shown in Equations (11)-(13) and Figure 4b, where \(\Delta t=\frac{\left|\overrightarrow{R}(t)\right|}{c}\). The azimuth coordinate, \(row\), follows the same formula as the reference images, as shown in Equation (10) [40]. \[\frac{\overrightarrow{V_{S}}(t+\Delta t)\cdot\overrightarrow{R}(t+\Delta t )}{\left|\overrightarrow{R}(t+\Delta t)\right|}+\frac{\overrightarrow{V_{S} }(t-\Delta t)\cdot\overrightarrow{R}^{\prime}(t-\Delta t)}{\left| \overrightarrow{R}^{\prime}(t-\Delta t)\right|}=0 \tag{11}\]  
\[t_{k+1}=t_{k}+\frac{\overrightarrow{V_{S}}(t_{k}+\Delta t)\cdot\overrightarrow {R}(t_{k}+\Delta t)}{2\left|\overrightarrow{V_{S}}(t_{k}+\Delta t)\right|^{2 }}+\frac{\overrightarrow{V_{S}}(t_{k}-\Delta t)\cdot\overrightarrow{R}^{ \prime}(t_{k}-\Delta t)}{2\left|\overrightarrow{V_{S}}(t_{k}-\Delta t) \right|^{2}} \tag{12}\]  
Figure 4: The geometry for solving the imaging time, \(t\): (**a**) single satellite model; (**b**) Tianhui-2 secondary satellite model. \[col=\frac{\left|\overset{\rightarrow}{R}(t)\right|+\left|\overset{ \rightarrow}{R}(t)\right|}{2 dR}+\frac{-R_{0}+\Delta r_{tro}+\Delta r_{ion}}{dR} \tag{13}\]  
The final step is the sampling of the pixel (row and col) and placing it in the original geographic coordinate. The error, \(\Delta h\), in the elevation will lead to a geolocation error, \(\Delta s_{de}=\Delta hcos\theta\), in the range (Figure 5). If the elevation with error is more significant than the actual elevation, the range coordinate \(col\) with error will be relatively minor. We discuss the range geolocation accuracy in the image coordinate system for the rest of the article; so here, we use \(\Delta\mathrm{hcos}\,\theta\) to describe the range error. The distance \(|OB|\) on the ground is \(\Delta\mathrm{hcot}\,\theta\), which is more commonly seen in direct geolocation research.