esa-sceva/satcom-chunk-collection huggingface dataset:
# Human Activity Recognition Based on Deep Learning Regardless of Sensor Orientation
## 3 Methodology
### Madgwick Algorithm using Gradient Descent  
In the Madgwick algorithm, quaternion-based rotation is used to correct the orientation. This involves constructing reference vectors based on accelerometer and magnetometer measurements, then correcting the difference between these vectors and the measured gravity vector [24; 45]. As our dataset only included accelerometer and gyroscope sensors, gradient descent was used to minimize the error vector and update the quaternion values:  
\[f=\begin{bmatrix}2(q_{1}q_{3}-q_{0}q_{2})-acc_{\text{n}_{x}}\\ 2(q_{0}q_{1}+q_{2}q_{3})-acc_{\text{n}_{y}}\\ 2(0.5-q_{1}^{2}-q_{2}^{2})-acc_{\text{n}_{z}}\end{bmatrix} \tag{11}\]  
\[j=\begin{bmatrix}-2q_{2}&2q_{3}&-2q_{0}&2q_{1}\\ 2q_{1}&2q_{0}&2q_{3}&2q_{2}\\ 0&-4q_{1}&-4q_{2}&0\end{bmatrix} \tag{12}\]  
where \(f\) is a vector representing the error (residual). Equation (11) was used to calculate the difference between the current quaternion q and the accelerometer measurement. This error vector is the objective function of the gradient descent algorithm; we minimized this error through gradient descent. The Jacobian matrix \(j\) represents the partial derivatives of the error vector \(f\) with respect to the quaternion \(q\). It describes how changes in the quaternion affect the error vector. During gradient descent, we updated the quaternion values in the direction of the negative gradient of the Jacobian matrix, gradually reducing the error.  
Then, the direction of the quaternion update was calculated and normalized as follows:  
\[step=j^{T}\cdot f \tag{13}\]  
\[step=\frac{step}{\|step\|} \tag{14}\]  
where \(step\) is the dot product between the transpose of the Jacobian matrix and the error vector, representing the direction of the quaternion update.  
The gradient descent algorithm used here is an optimization method to minimize the objective function (in this case, the error vector) and find the variable values (in this case, the quaternion) that minimize the objective function. Gradient descent is an iterative method in which the values of the variables are updated in each iteration based on the gradient direction of the objective function in order to gradually approach the optimal solution.
# Dynamic Modeling and Optimization of Tension Distribution for a Cable-Driven Parallel Robot
## 4 Optimization Algorithm of Tension Distribution
### Cable Tension Optimization Process  
The process of solving and optimizing the cable tension of a cable-driven parallel robot includes two key steps. As shown in Figure 5, firstly, the force and moment equilibrium equations of the end-effector are established using the Newton-Euler method. By calculating the length and angle of the cable vector, a special solution for the cable tension is obtained. Then, to improve the minimum variance of tension distribution as the optimization goal, the tension average term related to the stiffness coefficient is introduced to ensure the uniformity and continuity of tension distribution. Specifically, the optimization goal is to further balance the tension distribution by introducing a stiffness coefficient so that the tension variance of each cable is minimized. In order to ensure the reliability of the system, the force and moment balance equation of the end effector and the protection against virtual traction and overload are used as constraints to keep the cable tension within the predetermined range. Based on these optimization objectives and constraints, an optimization model is constructed that takes into account the minimization of tension variance, the minimization of stiffness influence, and the minimization of force torque balance constraints. According to the firefly algorithm, the optimal solution is obtained by initializing variables, setting algorithm parameters, determining fitness function, initializing firefly position, updating position and brightness, and determining termination conditions.  
Figure 5: Global optimization flow chart.
## 5 MSC-EV and Wound Healing
### Proliferation  
This phase involves fibroblast proliferation, production of collagen III matrix and creation of granulation tissue. At this moment, angiogenesis is extremely important. Wound environments need new blood vessels to transport the necessary nutrients, oxygen and factors for correct healing [39]. Interestingly, MSC-EV are involved in endothelial cell proliferation, migration, secretion of growth factors and vessel formation. It has been described that MSC-EV could transfer some miRNA to endothelial cells and promote angiogenesis (Figure 2b). In particular, miR-125a, miR-126, miR-130a, miR-296 and miR-378 [57; 63]. Another study showed that MSC-EV contain miR-126, miR-210, miR-296 and miR-378 as well as some factors such as VEGF and FGF, necessary for this process [64]. In relation to proteins, it has been described that human umbilical cord-derived mesenchymal stem-cells (hUCMSC)-derived EV contain angiopoieint-2 (Ang-2), which is one of the main growth factors inducing new vessel formation [65].  
Other cells involved in the proliferation phase are fibroblasts. MSC-EV promote their migration to the wound site, and improve granulation tissue formation. A study suggested that MSC-EV contain Jagged 1, which promotes fibroblast activity, via the Notch signaling pathway [66]. Additionally, lncRNA H19 has also been found in MSC-EV, inhibiting mir-19b expression and upregulating _SOX9_. This activates the Wnt/b-catenin pathway that promotes fibroblast proliferation, migration and invasion into wounds [67]. Also, the presence of miR-21, miR-29a and miR-132 in MSC-EV enhanced wound healing, promoting proliferation and migration of fibroblasts, in addition to extracellular matrix production [57].  
Moreover, a greater proliferation and migration of keratinocytes has been observed after being treated with MSC-EV. This effect may be mediated by the transfer of miRNA from EV into tissues. Thus, it has been demonstrated that the transfer of miR-27b from hUCMSC-EV into wounds can activate ITCH/JUNB/IRE1\(\upalpha\) signaling, accelerating re epithelialization processes [68]. In general, MSC-EV could have effects on the proliferation, migration and angiogenesis of keratinocytes, fibroblasts and endothelial cells, modulating different signaling pathways, among which AKT/HIF-1\(\alpha\) stands out, being related to cell proliferation [69].
# Review of Traffic Assignment and Future Challenges
## 3 Traffic Assignment: Theoretical Background
### Static Traffic Assignment (STA)  
The assignment is categorized as static when the assumption is made that users sharing the same road segment have the same experience during the designated time interval under study. This is exactly the assumption taken in the curves presented in Figure 2 used in the illustrative example (see Section 2.4), where the vehicles using the same road are assumed to experience the same travel time. This type of assignment aligns well with a macroscopic traffic model. In this context, flow-speed curves do not account for variations in speeds between the first and last vehicles traversing the segment during the specified time period. The same can be said for the \(BPR\) curves. This assumption shapes how the traffic supply is modeled. All that remains is to define the behavior of the decentralized demand, formulate the problem analytically, and solve it numerically. In the following, we present the two major assumptions about the demand behavior and the resolution methods. Within the context of \(STA\), both static and stochastic user equilibrium assumptions are the most used in the literature [15].
## 2 System Design  
Figure 1 shows the schematic diagram of the inserted pilot signal frequency 16-tupling MMW ROF system without the bit walk-off effect caused by MZMs.  
Figure 1: Schematic diagram of the structure of the frequency 16-tupling MMW ROF system.  
Figure 2 shows a schematic diagram of the three modules in Figure 1. These are the composite RF signal module, the \(\pm 8^{\text{th}}\)-order sideband generator module, and the data recovery module, respectively.  
The system is composed of two radio-frequency (RF) local oscillators (labeled LO1, LO2), electrical phase shifters (EPS) (labeled EPS1-EPS7), an electrical phase modulator (PM), an electrical gainer (EG), a continuous-wave (CW) laser, six 3 dB optical couplers(labeled OC1-OC6), a fiber Bragg grating (FBG), four MZMs (labeled MZM1-MZM4), an optical amplifier (OA) an electrical amplifier (EA), an intensity modulator (IM), a mixer (Mixer), a bandpass filter (BPF), two lowpass filters (labeled LPF1, LPF2), and two photodetectors (labeled PD1, PD2). The four MZMs are divided into two groups. Each group is constructed by two MZMs connected in parallel and is equivalent to a dual parallel MZM(DPMZM). The two DPMZMs are labeled as DPMZM1 (MZM1, MZM2), and DPMZM2 (MZM3, MZM4). When the initial values of EPS1-7 are set to \(180^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), \(45^{\circ}\), \(180^{\circ}\), \(90^{\circ}\), and \(180^{\circ}\), respectively, each MZM operates in maximum transmission point mode (MATP). The four MZMs that are connected in parallel can be regarded as a \(\pm 8^{\text{th}}\)-order sideband generator (SG). The dashed line and solid line in Figure 1 indicate the electrical path and the optical path, respectively.  
To compare our scheme with the conventional frequency 16-tupling MMW ROF system, we added an electrical switch to the module of the composite RF signal and an optical switch to the central station. When those switches are open, the system is the conventional frequency 16-tupling MMW ROF system.
## 3 Methods
### Time-Frequency Analysis Using the Choi-Williams Distribution  
In this paper, we employ the Choi-Williams distribution (CWD) to perform time-frequency analysis on radar jamming signals. The CWD is a bilinear time-frequency transform known for its ability to provide high-resolution representations of non-stationary signals in the joint time-frequency domain. It is important to note that the CWD is particularly effective at reducing cross-terms and enhancing concentration in the time-frequency plane, making it a valuable tool for analyzing non-stationary signals, including radar jamming signals. However, it should be acknowledged that the CWD may have limitations in cases where signals contain components that share similar time and frequency characteristics, as it may not be as effective in distinguishing such components. Nevertheless, for the time-varying spectral analysis of radar jamming signals, the CWD is a well-suited choice. By applying the CWD, we can effectively analyze the jamming present in the radar returns and develop algorithms for jamming identification. Figure 2 shows the CWD time-frequency representation of the 8 types of radar jamming signals in Section 2.  
The CWD belongs to the Cohen class of time-frequency distributions, which are bilinear transforms that reveal the joint time-frequency content of nonstationary signals. The general Cohen class is defined by:  
\[C_{x}(t,\omega)=\iint x(u+\frac{\tau}{2})x^{*}(u-\frac{\tau}{2})\psi(t-u,\tau)e ^{-j\omega\tau}dud\tau \tag{13}\]  
where \(\psi(t-u,\tau)\) is the time-frequency kernel function.  
By setting \(\psi(t-u,\tau)=\sqrt{\frac{\sigma}{4\pi\tau^{2}}}\exp(-\frac{\sigma(t-u)^{2}}{4 \tau^{2}})\), CWD is obtained as follows:  
\[\begin{split} CWD_{x}(t,f)=\iint\sqrt{\frac{\sigma}{4\pi\tau^{2}} }e^{-\frac{\sigma(t-u)^{2}}{4\tau^{2}}}x\left(u+\frac{\tau}{2}\right)\\ x^{*}\left(u-\frac{\tau}{2}\right)\mathrm{e}^{-j2\pi f}dud\tau \end{split} \tag{14}\]  
where \(CWD_{x}(t,f)\) represents the Choi-Williams distribution of signal x(t) at time \(t\) and frequency \(f,\sigma\) controls the width of the kernel function, \(u\) and \(\tau\) are integration variables.  
Figure 2: The CWD Time-Frequency Representations of Radar Jamming Signals.
## 1 Introduction
It was found that the material is the most significant factor that affects the roughness parameters values, and MDF recorded the highest value of the average roughness parameter (Ra) of the machined surface, compared to spruce edge-glued panel, in the same machining regime [16; 17]. A study on the surface quality of edge-glued-panels made of larch wood [18] has shown that the Ra roughness parameter has the lowest value for a spindle speed of 16,000 rpm and a feed speed of 2 m/min. As a general remark from judging the reports from the literature related to the evaluation of surface quality, it can be concluded that researchers have limited their studies to one or two roughness parameters and a comprehensive interpretation is lacking. Examining the surface quality is a complex process and the surface cannot be characterized by a single parameter. There are many parameters proposed by various standards, which must be well understood in order to be used for surface characterization and with a correct meaning and interpretation. The most used parameters in the literature cannot always be interpreted in isolation from other parameters [19]. The surface quality is most commonly characterized by the surface roughness. According to [20], roughness represents \"the finer irregularities of the surface texture that usually result from the inherent action of the production process or the material condition\" [21]. However, apart from surface roughness, other parameters which indicate the surface undulation, therefore characterizing longer wavelength irregularities, can also be useful for a more comprehensive understanding of the effect of processing on wood surface topography. The main problem in assessing the wood surface quality is the overlapping of the roughness components to the anatomical structure and surface irregularities due to machining, so it is recommended to analyze a large set of parameters for a good evaluation [22]. Therefore, the study in this paper will cover a comprehensive set of parameters for surface characterization, such as Ra, Rp, Rv, Rsk, Wa, Wt, Pa, Pt [23] and the Abbott-curve parameters: Rk, Rpk and Rvk [24]. Since no study was found in the literature simultaneously investigating the effect of CNC routing and laser cutting on the surface quality of network patterns of wood, such a research appears opportune. The purpose of this paper is to investigate and compare the quality of the surfaces obtained by laser cutting and CNC milling of larch wood (_Larix Decidua_ Mill.) network patterns cut at various angles with respect to the grain and at different feed speeds of the CNC routing. The interpretation of the surface quality results will be supported by the stereo-microscopic analysis of larch.
# Parallelizing the Computation of Grid Resistance to Measure the Strength of Skyline Tuples
## 4 Parallel Algorithms
### Computing the Indicator  
Finding gres requires recomputing the dominance on grid-projected datasets for various values of the grid interval \(g\). Luckily, the gres operator is stable, i.e., it does not depend on dominated tuples, and therefore we can focus on skyline tuples alone. Algorithm 1 shows the pseudo-code illustrating this idea. The grid interval varies between 2 (smallestmeaningful value) and an upper bound \(\bar{g}\) that depends on the dataset. In particular, we have the guarantee that no new dominance relationship may occur when \(g>\bar{g}=\lfloor\ell^{-1}\rfloor\), where \(\ell\) is the absolute value of the smallest non-zero difference on the same attribute between any two tuples (line 2). It suffices then to compute, for each value of \(g\) among \(\bar{g},\bar{g}-1,\ldots,2\) (line 3), the skyline \(\textsc{Sky}(\textsc{groj}(r,g))\) (line 4) and, for each tuple \(t\in\textsc{Sky}(r)\), to test whether \(\textsc{groj}(t,g)\in\textsc{Sky}(\textsc{groj}(r,g))\); the inverse of the first value of \(g\) for which membership does not hold is \(\textsc{gres}(t,r)\) (lines 5-6). If we ignore the dependence on \(\bar{g}\), which is dataset-dependent, the complexity of computing gres for a given tuple is \(\mathcal{O}(S^{2})\), where \(S=|\textsc{Sky}(r)|\in\mathcal{O}(N)\), i.e., in the worst case, \(\mathcal{O}(N^{2})\). It is strikingly evident that computing gres requires several rounds of computation of the skyline (although on a potentially much smaller dataset than the starting one, since dominated tuples can be disregarded completely). In this respect, adopting the partitioning strategies that typically quicken skyline computation may be beneficial for gres too.  
```
Input: \(\textit{skyline}\)\(s=\textsc{Sky}(r)\) Output: \(\textit{a map from every tuple }t\in s\textit{ to }\textsc{gres}(t,r)\)
1.\(\textit{map}:=\emptyset\)// the result map, initially empty
2.\(\bar{g}:=\lfloor\ell^{-1}\rfloor\)// where \(\ell\) is the minimum possible value for gres
3.for each \(g\)in \(\bar{g}.\bar{g}-1,\ldots,3,2\)do
4.\(s^{\prime}:=\textsc{Sky}(\textsc{groj}(r,g))\)
5.for each \(t\)in \(s\)do
6.if\(\textsc{groj}(t,g)\
otin s^{\prime}\wedge\textit{map}(t)=\textbf{nil}\)then\(\textit{map}(t):=g^{-1}\)
7.for each \(t\)in \(s\)do
8.if\(\textit{map}(t)=\textbf{nil}\)then\(\textit{map}(t):=1\)//\(t\) never exited the skyline
9.return\(\textit{map}\)
```  
**Algorithm 1**Algorithm pattern for computation of gres
## 5 Studies on the Effects of _M. oleifera_ Leaf Extracts on Male Reproductive Function
### Cryptorchidism  
Cryptorchidism is characterised by the failure of one or both testes to descend into the bottom of the scrotum. Instead, the testes are located along the usual route of testicular descent, which may have an intra-abdominal, inguinal, suprascrotal or high scrotal position. The abnormal positioning of the testis induces hyperthermia, which is detrimental to the process of spermatogenesis [115]. There is evidence that 10% of infertile men are cryptorchid, and 98% of these cryptorchid men are azoospermic. In patients with cryptorchidism, there is also a prominent depletion of the Leydig cells each month due to insufficient secretion of gonadotropic hormones [116]. There is also impairment of germ cell maturation, reduced sperm cell density and tubular and interstitial damage [117].  
The temperature in undescended testicles might influence the testicular environment and spermatogenesis through oxidative stress induction [118]. Fortunately, _M. oleifera_ leaves exhibit enormous antioxidant properties. In male Wistar rats induced with bilateral cryptorchidism, _M. oleifera_ leaf extract increased SOD activity, reduced GGT activity [95] and the levels of MDA [94]. This leads to increased sperm count, germ cell count, testicular testosterone and testicular weight [95]  
Histological evaluation of the testes of cryptorchid rats indicated a widened semiiferous tubule lumen with an absence of spermatozoa strands and degenerative alterations in the epithelium of the semiiferous tubules, which was suggested by indistinct Sertoli cells and loss of germ cells. Administration of a low dose of _M. oleifera_ improved the appearance of the testes. At the same time, a high dose of _M. oleifera_ attenuated the appearance of the semiiferous epithelium and interstitium with mild degenerative changes characterised by very few tubules with normal germ cell layers visible [95].
###### Abstract  
The progression of liver fibrosis and the presence of portal hypertension are two key points in the follow-up and severity assessment of patients with chronic liver disease. Objective evaluation of such aspects has proven to be difficult due to the lack of reproducible and standardized non-invasive methods. Therefore, the aim of this study was to evaluate whether spleen stiffness (SS) can rule out the presence of high-risk varices (HRVs) in patients with non-alcoholic fatty liver disease (NAFLD). We designed a prospective follow-up of a cohort of 48 consecutive patients diagnosed with compensated advanced chronic liver disease (cACLD) due to NAFLD, between January 2020 and January 2021. After clinical evaluation, laboratory testing, ultrasonography (US), and shear-wave elastography (2D-SWE.GE) of both the liver and the spleen, patients were endoscopically screened for esophageal varices, gastric varices, and portal hypertensive gastropathy. Correlations and predictors were assessed. After univariate, multivariate, and predictive analyses, SS could be referred to as an independent predictor for high-risk varices (AUROC 0.987, \(p<0.001\), OR 4.985, 95% CI: 1.57-15.73, \(p=0.006\)), with a calculated cutoff value of 17.95 kPa. These results are consistent with those of other, similar studies using both 2D-SWE.GE and a similar module (2D-SWE.SSI) in patients with metabolic liver disease. When confirmed by subsequent larger studies, SS could potentially become a useful non-invasive tool in the assessment of clinically significant portal hypertension in patients with advanced fatty liver disease.  
s +
Footnote †: journal: Medical Informatics, 70011581
## 4 Results and Analysis
### Results of the Simulation Data Set
The general results demonstrate that scheme 2 is more suitable to estimate the receiver clock offset than scheme 1. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**10 min**} & \multicolumn{3}{c}{**30 min**} & \multicolumn{3}{c}{**60 min**} & \multicolumn{3}{c}{**90 min**} \\ \cline{2-13}  & **Sch1** & **Sch2** & **(\%)** & **Sch1** & **Sch2** & **(\%)** & **Sch1** & **Sch2** & **(\%)** & **Sch1** & **Sch2** & **(\%)** \\ \hline BRUX & 0.44 & 0.14 & 68.64 & 0.43 & 0.14 & 67.99 & 0.46 & 0.16 & 65.22 & 0.45 & 0.18 & 59.93 \\ IENG & 0.40 & 0.11 & 71.43 & 0.41 & 0.17 & 58.38 & 0.39 & 0.18 & 53.52 & 0.41 & 0.22 & 46.34 \\ NIST & 0.40 & 0.15 & 61.71 & 0.40 & 0.14 & 64.49 & 0.39 & 0.16 & 60.31 & 0.44 & 0.21 & 51.67 \\ OPMT & 0.50 & 0.19 & 61.99 & 0.51 & 0.18 & 64.89 & 0.49 & 0.20 & 58.70 & 0.51 & 0.20 & 59.71 \\ PTBB & 0.52 & 0.15 & 71.22 & 0.53 & 0.15 & 72.06 & 0.52 & 0.18 & 65.38 & 0.51 & 0.23 & 54.10 \\ WAB2 & 0.36 & 0.15 & 58.48 & 0.37 & 0.15 & 58.72 & 0.36 & 0.23 & 36.11 & 0.37 & 0.26 & 30.96 \\ \hline \hline \end{tabular} Where sch1 and sch2 represent scheme 1 and scheme 2, respectively, and (\(\%\)) is the improvement of scheme 2 over scheme 1.  
\end{table} 
Table 4: RMS of the clock difference between the solutions obtained with two different schemes with respect to the solutions obtained with the complete data when data interruption occurs (ns). Figure 7: Allan deviation of scheme 1 (**blue**) and scheme 2 (**red**) at six stations for observation interruptions of 10 and 30 min. The inspection of Figure 9 can reveal more about the improvement of the frequency stability at a specific averaging time. The data indicate that all the involved stations are improved at each averaging time. Furthermore, as the averaging time increases, the improvement percent decreases. Before 10000 s, the improvement extent of the six stations was up to 50% from data interruptions of 10 min to 90 min; some points even improved by 100%. However, after 10000 s, the improvement extent of the six stations fell below 50%; some points being at only 1%. Briefly, compared to scheme 1, scheme 2 works well for short-term stability. The Allan deviation is a common measure of the frequency stability. Although the definition for short-term stability and long-term stability is the same, they reflect different characteristics of the signal stability. Short-term stability mirrors the signal's fluctuations, and long-term stability represents the signal's drift as time goes by. The between-epoch constraint model merely lowers the noise of the clock offset time series; however, it cannot change the physical trend of the atomic clock. Hence, there are enough reasons to certify that the random-walk model is an effective approach for improving stability at each averaging time.