esa-sceva/satcom-chunk-collection huggingface dataset:
# An Indirect Identification Method for Train Basic Resistance Parameters
## 3 Algorithm  
### Objective Function  
From a modeling perspective, if the parameters can make the train operation process calculated by the train dynamics model closer to the real value, it will be considered more accurate. Therefore, the objective of TBRPI-SF is to obtain the best set of TBRPs. After selecting them for traction calculation, the square mean of speed error between the fitting position speed curve and the actual position speed curve is the smallest.  
Train operation data is obtained through two methods: Timed and event-triggered. Divide the train operation process into \(I\) stages using the acquisition interval of operation data. The operation data can be organized into: \(\widetilde{\mathbf{F}},\widetilde{\mathbf{s}},\widetilde{\mathbf{v}}\), where \(\widetilde{\mathbf{F}},\widetilde{\mathbf{s}},\widetilde{\mathbf{v}}\in\mathbf{R}^{I+1}\), they are the control force, position, and speed information in the actual train operation data. Let \(\mathbf{F}=\widetilde{\mathbf{F}}\), \(\mathbf{s}=\widetilde{\mathbf{s}}\), the fitting curves \(\mathbf{v}\) for this set of operational data under a set of basic resistance parameters \(\mathbf{\theta}\) is obtained by Equation (7).  
The accuracy of the parameter is assessed using the mean square error between the fitted curve and the actual curve, which is expressed as:  
\[f(\mathbf{\theta})=\left\|v(\mathbf{\theta})-\;\widetilde{\mathbf{v}}\right\|_{2}^{2}= \frac{1}{I}\sum_{i=1}^{I}\left(v_{i}(\mathbf{\theta})-\widetilde{v}_{i}\right)^{2} \tag{8}\]  
Figure 2: The fitting curves under three sets of TBRPs.  
As a result, the optimization problem of BRPI-SF can be described as:  
\[\min_{\theta\in\Theta}f(\theta) \tag{9}\]  
where, \(\Theta\) is the definition domain of \(\theta\). Each basic resistance parameter has a corresponding physical significance. Therefore, there exists a boundary \(\Theta=[\theta_{\text{min}},\theta_{\text{max}}]\) that satisfies \(\theta_{\text{min}}<\theta<\theta_{\text{max}}\). From Equation (9), it can be seen that BRPI-SF is a constraint estimation problem.  
In summarize, the main steps of the parameter identification method proposed in this paper are: (1) Configure train formation and line information, set train operation data, (2) establish the train dynamics model to obtain fitted curves for any set of basic parameters, (3) optimize to find the best set of TBRP.  
For this optimization problem, heuristic algorithms are easy to apply to solve, but their solving efficiency is relatively low. In order to meet the requirement of high efficiency when this method is applied to the train controller, this paper uses the [PERSON]'s method to solve this problem.
# A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data
## 3 Methodology
### Overview of the Improved Algorithm
#### 3.2.3 Potential Fire Pixel Judgment  
MWIR band of HJ-IRS is sensitive to BT changes and can be used to eliminate obvious non-fire pixels. According to [24], a pixel is identified as a potential active fire pixel if the BT of band 7 meets the criterion T\({}_{7}\) > 325 K in the daytime. In our study, the HJ-1B satellite passing time was approximately 10:30 a.m.
## 3 Results and Discussion
### Mechanical Properties  
#### 3.1.1 Young's Modulus  
Figure 1 shows \(E\) of neat PLA, random copolymer, and the PLA\({}_{\text{x}}\)/poly(S-_co_-MMA)\({}_{\text{y}}\) blends. The \(E\) values were 1.57, 1.58, and 1.60 GPa for PLA\({}_{\text{50}}\)/poly(S-_co_-MMA)\({}_{\text{50}}\), PLA\({}_{\text{75}}\)/poly(S-_co_-MMA)\({}_{\text{25}}\), and PLA\({}_{\text{90}}\)/poly(S-_co_-MMA)\({}_{\text{20}}\) blends, respectively. The increasing \(E\) of blends can be due to compatibility between the polymer and random copolymer. The term \"compatibility\" refers to the blend behavior in terms of mechanical properties, and the \"miscibility\" is related to a homogeneous system formation at a molecular level. Usually, a miscible mixture is compatible; however, a compatible mixture is not necessarily miscible. The compatibilization of polymer blends containing PLA has been reported. For example, [PERSON] et al. [38] produced PLA/high-density polyethylene blends obtaining an optimal \(E\) = 1.88 GPa for PLA\({}_{\text{50}}\)/HDPE\({}_{\text{50}}\). In another study, [PERSON] et al. [39] improved the flexibility and \(E\) (2.2 GPa) of a PLA/low linear density polyethylene (LLDPE) blend when adding up to 15 wt.% of LLDPE. Additionally, the PMMA \(E\) has been reported around 3.3 GPa, a higher value than the poly(S-_co_-MMA) used in the present investigation [40]. It is essential to mention that amorphous PLA has a lower \(E\) than semi-crystalline PLA, with values close to 2 GPa [31; 41]. In addition, the \(E\) reported in the literature is a function of processing conditions for amorphous and semi-crystalline PLA [27]. A further example, an amorphous PLA processed (4060D from Nature Works(r) at 160 \({}^{\circ}\)C for 10 min, in an internal mixer at 100 rpm, yielded 1.79 GPa of _E_[41].  
Figure 1: [PERSON]’s modulus, elongation at break, and ultimate strength of (**a**) PLA\({}_{\text{50}}\)/poly(S-_co_-MMA)\({}_{\text{50}}\); (**b**) PLA\({}_{\text{75}}\)/poly(S-_co_-MMA)\({}_{\text{25}}\); (**c**) PLA\({}_{\text{90}}\)/poly(S-_co_-MMA)\({}_{\text{10}}\); (**d**) neat poly(S-_co_-MMA); (**e**) neat PLA; and (**f**) overall.
## 4 Experimental Results and Discussion
### Comparison of Average Volumetric Air Flow Rate  
Figure 7 shows the average volumetric air flow rate measured by the ASTM-recommended triaxial permeameter used by [PERSON] and the triaxial permeameter consisting of the new gas pressure supply equipment proposed in this study [24]. It should be noted that the soil specimens used by [PERSON] and the ones used in this experiment have the same materials and compositional ratios, so the results can be compared and analyzed. In Figure 7, hollow and solid symbols represent the results measured by this study and [PERSON], respectively. The data obtained from the tests is fitted by a linear function with a fixed intercept of zero. And the range of \(\pm 5\%\) error is obtained from the fitted straight line, with upper and lower limits indicated by dot-dashed lines. The equations and variances of the fitted line and the dot-dashed line characterizing the error range are presented in Figure 7. Firstly, the test results of [PERSON]'s gas permeameter are very close to the best-fit line, indicating that the results comply with [PERSON]'s law. Secondly, the data measured by the triaxial permeameter proposed in this paper are within \(\pm 5\%\) error, which demonstrates that the new gas supply equipment is accurate, reliable, and of scientific value.
# A Review on Upper Limb Rehabilitation Robots
## 3 Rehabilitation Robots and Their Classifications
### According to the Robot Structures  
Rehabilitation robots are also classified as end-effectors and exoskeleton robots [29]. End-effectors are simple robots that have a distal movable handle, and the patient attaches his/her hand to this handle and follows a specific trajectory [30]. This kind of robot is characterized by its ability to adapt to different sizes and shapes of movements, as the rehabilitation process requires [31]. End-effector robots have the disadvantage of not providing a rotation movement, which makes it not suitable for pronation and supination movements [24]. End-effectors have been developed recently to provide bilateral rehabilitation training, where the impaired limb copies the movement of the unimpaired limb in a synchronized behavior [22]. Some researchers have reported that bilateral rehabilitation has the feature of activating the impaired hemisphere by making the left side and the right side of the body follow the same trajectory [21].  
Exoskeleton robots are characterized by encapsulating the limb with a splint or bionic structure [32]. Exoskeleton robots calculate the required torque for each joint and control the limb movements [13]. In comparison with end-effector robots, exoskeletons require a smaller working environment. Exoskeleton robots, however, comprise the limb joint axes as they provide a very specific movement [24].  
In addition, exoskeleton robots are not feasible for bilateral rehabilitation, as the right limb exoskeleton cannot be used for the left limb, and eventually, it is expensive to design right and left exoskeleton robots to perform bilateral rehabilitation training [22].
# Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
###### Abstract  
This paper evaluates the performance of an integrity monitoring algorithm of global navigation satellite systems (GNSS) for the Kalman filter (KF), termed KF receiver autonomous integrity monitoring (RAIM). The algorithm checks measurement inconsistencies in the range domain and requires Schmidt KF (SKF) as the navigation processor. First, realistic carrier-smoothed pseudorange measurement error models of GNSS are integrated into KF RAIM, overcoming an important limitation of prior work. More precisely, the error covariance matrix for fault detection is modified to capture the temporal variations of individual errors with different time constants. Uncertainties of the model parameters are also taken into account. Performance of the modified KF RAIM is then analyzed with the simulated signals of the global positioning system and navigation with Indian constellation for different phases of aircraft flight. Weighted least squares (WLS) RAIM used for comparison purposes is shown to have lower protection levels. This work, however, is important because KF-based integrity monitors are required to ensure the reliability of advanced navigation methods, such as multi-sensor integration and vector receivers. A key finding of the performance analyses is as follows. Innovation-based tests with an extended KF navigation processor confuse slow ramp faults with residual measurement errors that the filter estimates, leading to missed detection. RAIM with SKF, on the other hand, can successfully detect such faults. Thus, it offers a promising solution to developing KF integrity monitoring algorithms in the range domain. The modified KF RAIM completes processing in time on a low-end computer. Some salient features are also studied to gain insights into its working principles.  
Kalman filter; GNSS; RAIM; [PERSON] filter; weighted least squares +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Data Analysis  
To evaluate the effect of the combined exercises using the developed system, the body pressure distributions and trunk muscle activities were analyzed between before (BF) and after (AF) exercise. Body pressure distributions were analyzed for mean force and mean pressure using Novel software (Novel GmbH., Munich, Germany). The MyoResearch 3.6 software (Noraxson Inc., Scottsdale, AZ, USA) was used to analyze trunk muscle activation. Electromyographic (EMG) data were rectified and normalized using the maximum voluntary contraction method. All data were divided into two regions such as dominant side (DS) and non-dominant side (NDS) under four conditions: no pelvic tilt (NPT), lateral pelvic tilt (LPT), anterior pelvic tilt (APT), and posterior pelvic tilt (PPT).  
Statistical analysis was performed using SPSS software version 20.0 (IBM Corporation, Armonk, NY, USA). Shapiro-Wilk test was utilized to assess the normality of all variables. An independent sample t-test was used to analyze the differences in body pressure distribution and trunk muscle activity between DS and NDS. Changes in measured variables in experimental results between periods were assessed using paired t-test. Statistical significance was set at 0.05 and 0.01.
## 2 Mesh Stiffness Calculation of Gears
### Mesh Stiffness Calculation of Gears with Tooth Spalling  
The calculation method of mesh stiffness of gears with spalling teeth is similar to that of healthy gears. Both of these calculation methods consider the axial compressive stiffness, shear stiffness, bending stiffness, and Hertzian contact stiffness. According Reference [20], spalling is molded as a circular shape and occurred near the gear pitch line of the pinion, as shown in Figure 3.  
Figure 3: Geometric model of the spalling tooth.  
For a gear tooth with spalling, the value of \(B\), \(A_{j}\), and \(I_{j}\) are reduced. Here, \(\Delta B\), \(\Delta A_{j}\), and \(\Delta I_{j}\) represent the reduction of tooth contact width, area, and area moment of inertia of the tooth spalling section, respectively, which are given as follows.  
\[\Delta B_{j}=\left\{\begin{array}{ll}2\sqrt{r^{2}-\left[u-s_{j}\right]^{2}},& x_{j}\in\left[u-r,\ u+r\right]\\ 0,&\text{others}\end{array}\right. \tag{8}\]  
\[\Delta A_{j}=\left\{\begin{array}{ll}sh\Delta B_{j},&x_{j}\in\left[u-r,\quad u +r\right]\\ 0,&\text{others}\end{array}\right. \tag{9}\]  
\[\Delta I_{j}=\left\{\begin{array}{ll}\frac{1}{12}s\Delta B_{j}u^{3}+\frac{A _{j}\Delta A_{j}(Y_{j}-h/2)^{2}}{A_{j}-\Delta A_{j}},&x_{j}\in\left[u-r,\ u+r \right]\\ 0,&\text{others}\end{array}\right. \tag{10}\]  
where \(r\), \(h\), and \(s\) are the radius, depth, and number of spalling points, respectively. \(u\) is the distance between the center point of spalling and the tooth root. \(s_{j}\) denotes the distance between the gear contact point and the tooth root. Details of \(u\) and \(s_{j}\) could be found [20].  
Here, \(B_{j}^{\prime}\), \(A_{j}^{\prime}\), and \(I_{j}^{\prime}\) denote the tooth contact width, area, and area moment of inertia of the tooth spalling section, respectively. They can be expressed as follows.  
\[B_{j}^{\prime}=B-\Delta B_{j} \tag{11}\]  
\[A_{j}^{\prime}=A_{j}-\Delta A_{j} \tag{12}\]  
\[I_{j}^{\prime}=I_{j}-\Delta I_{j} \tag{13}\]  
Substituting Equations (2) to (4), (8) to (10), (11) to (13) into Equation (1), the stiffness of the tooth bending, shear, and axial compressive of gear with spalling can be obtained as follows.  
\[\frac{1}{k_{bj}^{\prime}}=\sum\
olimits_{i=1}^{n}\frac{1}{E_{e}}\left\{ \begin{array}{ll}\frac{L_{j}^{3}+3L_{i}^{2}S_{ij}+3L_{i}S_{j}{}^{2}}{3(I_{j} -\Delta I_{j})}\cos^{2}a_{j}-\\ \frac{L_{j}^{2}Y_{j}+2L_{i}Y_{j}S_{ij}}{2(I_{j}-\Delta I_{j})}\cos a_{j}\sin a _{j}+\\ \frac{12(1+v)L_{i}}{5(A_{j}-\Delta A_{j})}\cos^{2}a_{j}+\frac{L_{i}}{(A_{j}- \Delta A_{j})}\sin^{2}a_{j}\end{array}\right. \tag{14}\]  
Substituting Equations (2) to (4), (8) to (10), (11) to (13) into Equation (5), the Hertzian contact stiffness of gear with spalling can be obtained as follows.  
\[\frac{1}{k_{hj}^{\prime}}=\frac{4(1-v^{2})}{\pi E(B-\Delta B_{j})} \tag{15}\]  
Substituting Equations (14) and (15) into Equation (6), the total effective stiffness is expressed as the following.  
\[k_{pg}^{\prime}=\frac{1}{\frac{1}{k_{h}^{\prime}}+\frac{1}{k_{bp}^{\prime}}+ \frac{1}{k_{bg}^{\prime}}} \tag{16}\]
## 2 Islanding Detection Methods
### Active Methods
#### 2.2.1 Active Frequency Drift Method (AFD)  
By using the inverter, this technique injects a slightly distorted current signal into the PCC. Because the voltage and frequency at the DG terminal are constant when the main grid is connected, the system is stable. The injected signal creates a zero crossing of the voltage when the islanding situation arises, which increases the phase error between the output voltage and the inverter current. The observed frequency drift of the inverter output current thereby eliminates the phase inaccuracy. When the frequency reaches the threshold value and the frequency drifts again, the islanding condition is detected [16].  
The chopping fraction describes the distorted current signal of the inverter which is defined in the following equation:  
\[\mathrm{CF}=2\mathrm{t}_{\mathrm{z}}/\mathrm{T}_{\mathrm{vutil}}, \tag{5}\]  
where, \(\mathrm{T}_{\mathrm{vutil}}\) is voltage period and \(\mathrm{t}_{\mathrm{z}}\) is the dead time.  
This method's key benefit is that it is simple to use and has a minimal NDZ. Because different inverters have distinct deviations in frequency bias, this approach might not be able to detect islanding when there are many inverters present. The efficiency of this strategy is also significantly influenced by the type of loads. The detection time and NDZ will rise if the load is not a resistive load. As a result, this approach works best for islanding detection in systems with just one inverter and resistive loads.
# Roles for Exosomes in the Pathogenesis, Drug Delivery and Therapy of Psoriasis
## 2 The Biogenesis of Exosomes  
Exosomes play an important role in intercellular communication under both physiological and pathological conditions. The biogenesis of exosomes is a complex and multistep biological process. Firstly, the plasma membrane is inwards invaginated to produce a primary endosome, which surrounds the biological molecules. Secondly, the multivesicular bodies (MVBs) are generated via inward budding of the endosomal membrane towards the lumen. During this process, proteins and cytosolic components are engulfed and enclosed within the intraluminal vesicles (ILVs). Finally, the MVBs are fused with lysosomes for degradation or the plasma membrane for releasing of ILVs, which are then called \"exosomes\" [14, 15, 16, 17]. Although the factors determining the fate of MVBs are not fully understood, previous studies have reported that it depends on the amount of cholesterol in MVBs. Cholesterol-rich MVBs tend to secret exosomes, but those with lower cholesterol levels are more prone to lysosomal degradation [18; 19].  
Previous studies have revealed that the formation of MVBs and sorting of cargoes into exosomes can occur through the endosomal sorting complex required for transport (ESCRT)-dependent and independent pathways [20; 21; 22]. The ESCRT-dependent pathway is critical for MVB formation, membrane budding and sorting of protein cargoes [17; 23]. Four separate protein complexes (ESCRT 0, -I, -II, -III) along with accessory proteins, such as Vps4, VTA1 and ALIX, participate in the ESCRT-dependent pathway [18]. Firstly, ESCRT-0, -I and -II complexes sort ubiquitinated membrane proteins into the invagination in the endosomal delimiting membrane. In addition to recognizing ubiquitinated cargoes, ESCRT-I and ESCRT-II are also involved in membrane germination. Then, the ESCRT-III complex removes the ubiquitin in the shipments and drives vesicle scission, leading to the formation of ILVs. Lastly, Vps4 separates and recycles the ESCRT protein machineries [24; 25; 26]. Moreover, exosomes can be formed in another way, which is the ESCRT-independent pathway. This pathway involves ceramide- and CD63-dependent processes, heat shock proteins and tetraspanin for sorting cargoes into the exosomes [17; 27]. The ceramide can induce membrane budding, resulting in the formation of ILVs [14], while tetraspanin-enriched microdomains are related to the loading of cargo molecules, grouping of particularly targeted receptors, and processing of the essential molecular components into exosomes [28; 29].
## 3 Low-Complexity Decoding Algorithm
### Modified 2-Bit MSA
By doing so, the min1 and min2 of all 32 \(B_{m}\) can be obtained efficiently. This approach effectively reduces the hardware resources required for the CNU implementation, while ensuring high performance and accuracy of the decoder. There are four steps in VNU. First, four 3-bit 2v messages are inputted based on the column weight. Next, four 2-bit \(R_{mn}\) are recovered with the help of \(L_{n}^{(k-1)}\), which means the \(L_{n}\) in the \(k-1\)-th iteration. Then, these four \(R_{mn}\) and the \(I_{n}\) will be converted into integers according to Equation (6), which will be added together later. At last, the summation will be converted back to 2 bits and saved as v2c. Before going into more detail about VNU, two auxiliary functions will be introduced first. Function \(f(\cdot)\) and \(g(\cdot)\) are used in the VNU to convert 2-bit messages into integers for addition operation, and to transform the integer results back into 2-bit messages for storage, respectively. Specifically, \(f(\cdot)\) is formulated in Equation (5) and \(g(\cdot)\) is formulated in Equation (6). The magnitude bit of the integer result in \(f(\cdot)\) corresponds to the confidence coefficient \(B_{m}\), with a value of \(w\) for \(B_{m}=0\) indicating low confidence, and a value of \(W\) for \(B_{m}=1\) indicating high confidence. These values are determined through simulations, along with the threshold \(T_{y}\) for \(f(\cdot)\) and \(T_{L}\) for the VNU process. \[f(B_{s}B_{m})= \begin{cases}w,\text{ if }B_{s}B_{m}=00,\\ W,\text{ if }B_{s}B_{m}=01,\\ -w,\text{ if }B_{s}B_{m}=10,\\ -W,\text{ if }B_{s}B_{m}=11.\end{cases}  \tag{5}\]  
\[g(x)=\begin{cases}00,\text{ if }x\geq T_{L},\\ 01,\text{ if }T_{L}>x>0,\\ 10,\text{ if }0>x>-T_{L},\\ 11,\text{ if Our \(w\) and \(W\) in \(f(\cdot)\) are chosen as \(1\) and \(5\), so we need four bits to represent the four potential integers, which are \(-5,-1,1,5\). Furthermore, during the conversion, we summarized the following connection between c2v messages' two bits(\(B_{s}\), \(B_{m}\)) and integers' four bits(\(b_{1},b_{2},b_{3},b_{4}\)) as Equation (9) shows. In this way, we are able to substitute simple logic circuit for comparers. \[ \begin{split} b_{1}&=B_{s},\\ b_{2}&=B_{s}\oplus B_{m},\\ b_{3}&=B_{s},\\ b_{4}&=1.\end{split}  \tag{9}\]  
The last step is the summation, which we realize using a 2-stage pipeline. At last, the first bit of the result, which is the sign bit, will be kept as \(B_{s}\), and the absolute value of the result will be compared with \(T_{L}\) and converted into \(B_{m}\). \(B_{s}\) and \(B_{m}\) will make up the \(L_{n}^{(k)}\), which is also the v2c messages.