esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Improvement of ROS-Based Control for Teleoperated Yaskawa Robots
## 2 Kinematic Modeling for ROS-Based Teleoperation  
The system studied in this paper is a serial \(m\)-DoFs robot. It is composed of a sequence of \(n\) links. The notations of Khalil and Dombre are used [34] to describe the structure with only revolute joints. We assign a frame \(\mathcal{F}_{i}\) attached to the \(i\)th link, \(i\in[1\ldots n]\). The \(z_{i}\)-axis is coincident with the \(i\)th joint axis. The \(x_{i}\) is along the common normal between \(z_{i}\) and \(z_{i+1}\). The \(y_{i}\)-axis is formed by the right-hand rule to complete the coordinate system.  
The transformation matrix from \(\mathcal{F}_{i}\) to \(\mathcal{F}_{i+1}\) is expressed as a function of the following geometric parameters:  
* \(\alpha_{i}\): the angle between \(z_{i-1}\) and \(z_{i}\) about \(x_{i-1}\).
* \(d_{i}\): the distance between \(z_{i-1}\) and \(z_{i}\) along \(x_{i-1}\).
* \(\theta_{i}\): the angle between \(x_{i-1}\) and \(x_{i}\) about \(z_{i}\).
* \(r_{i}\): the distance between \(x_{i-1}\) as \(x_{i}\) along \(z_{i}\).  
The variable of \(i\)th joint, defining the relative orientation between \((i-1)\)th and \(i\)th links is expressed by:  
\[\theta_{i}=q_{i}+q_{i0}, \tag{1}\]  
where \(q_{i0}\) is a constant offset and \(q\) is the \(i\)th joint position. The homogeneous transformation matrix \({}^{i-1}\mathbf{T}_{i}\in\mathbb{R}^{4\times 4}\), defining \(\mathcal{F}_{i}\) relative to \(\mathcal{F}_{i-1}\) is expressed as follows:  
\[{}^{i-1}\mathbf{T}_{i}=\begin{bmatrix}C\theta_{i}&-S\theta_{i}&0&d_{i}\\ C\alpha_{i}S\theta_{i}&C\alpha_{i}C\theta_{i}&S\alpha_{i}&-r_{i}S\alpha_{i}\\ S\alpha_{i}S\theta_{i}&S\alpha_{i}C\theta_{i}&C\alpha_{i}&r_{i}C\alpha_{i}\\ 0&0&0&1\end{bmatrix} \tag{2}\]  
The direct kinematic model of a robot manipulator expresses the end-effector (EE) velocity \(\dot{\mathbf{x}}\in\mathbb{R}^{m}\) as a function of the joint velocities \(\dot{\mathbf{q}}\in\mathbb{R}^{n}\).  
\[\dot{\mathbf{x}}=\mathbf{J}(q)\dot{\mathbf{q}}, \tag{3}\]  
where \(\mathbf{J}(q)\in\mathbb{R}^{m\times n}\) is the Jacobian matrix. It also appears in the direct differential model, which provides the differential EE displacement \(\dot{\mathbf{\delta x}}\) as a function of the joint differential variation \(\dot{\mathbf{\delta q}}\):  
\[\dot{\mathbf{\delta x}}=\mathbf{J}(q)\dot{\mathbf{\delta q}}. \tag{4}\]  
The Jacobian matrix is derived from the direct geometric matrix (DGM), where the EE pose \(\mathbf{x}=[x,\ y,\ z,\ r_{x},\ r_{y},\ r_{z}]^{T}\in\mathbb{R}^{6}\) is expressed as \(\mathbf{x}=f(q)\). It is obtained by the differentiation of the DGM using partial derivative \(\frac{\partial\mathbf{f}}{\partial\mathbf{q}}\), such that:  
\[J_{kj}=\frac{\partial f_{k}(\mathbf{q})}{\partial q_{j}},\ \ k\in[1..m],\ j\in[1..n]. \tag{5}\]  
\(J_{kj}\) is the (\(k\),\(j\)) element of \(\mathbf{J}\). The inverse kinematic model is then:  
\[\dot{\mathbf{q}}=\mathbf{J}(q)^{-1}\ \dot{\mathbf{x}}, \tag{6a}\]\[\delta\mathbf{q}=\mathbf{J}(q)^{-1}\,\delta\mathbf{x}. \tag{6b}\]
## 3 Linear and Non-Linear Predictive Analytics Comparison Framework: L&NL-PAC
### Predictive Methods
#### 3.2.4 Tree-Based Ensembles  
The class of tree-based ensembles contains non-linear methods whose basic building blocks are regression trees. A regression tree is a particular model that approximates the relationship between predictors and response variables by a piece-wise constant function. Furthermore, regression trees are very flexible, which often lead to a high variance in their predictions (i.e., small changes in the training set could lead to significant differences in the predicted values). A common solution is then to use ensembles of regression trees, in order to decrease the variance by aggregating the pools of models.  
In the class of tree-based ensembles, two approaches were selected: random forests (RF) and boosting of regression trees (BoRT). In RF, for each regression tree in the ensemble, only a small percentage of randomly chosen predictors are selected for the model instead of utilizing all the predictors available. On the other hand, BoRT is based on the boosting idea, where models are fit to residuals from previous models in order to improve the prediction ability. Both RF and BoRT have several tunable parameters (also called hyperparameters, such as the number of trees in the ensemble and the minimum number of samples in each leaf). For RF, the number of trees in the ensemble (\(T_{RF}\)) is often the most important hyperparameter for controlling overfitting, thus, it is optimized by k-fold cross-validation. The other parameters are left at their default values. For BoRT, two parameters are often more relevant: the number of trees (\(T_{RF}\)) and the learning rate (\(u\)). As they are inversely related (lower learning rates require more trees and vice-versa), we opted to set a low value for the learning rate (\(u=0.02\)) and to optimize the number of trees in the ensemble.
# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 2 Theoretical Background
### Long-Term Evolution (LTE)  
In the context of the 3 rd Generation Partnership Project (3 GPP), LTE can be described as the flexible radio technology behind the 4G cellular network. During its first release, data transfer rates of 300 Mbps were recorded in different conditions [33]. These speeds had poor radio delays of less than 5 ms. LTE is operational across a wide variety of frequency spectrum allocations and operates across varying frequency or time-division duplexes (FDD or TDD). This makes it possible for LTE to provide solutions for wide-range system bandwidths [13]. In addition, LTE and other recent network technologies, such as 5G, bring to light the advantages of wireless technology, such as lower cost, mobility, and ease of installation for users. In 2015, [PERSON] and [PERSON] [34] compared the effective coverage of several mobile broadband operators (MNOs) in Nigeria. The study found that one operator's network was comparatively better and that optimization could improve its performance. [PERSON] and [PERSON] [35] also compared multiple input multiple outputs (MIMO) transmission techniques over 4G networks and found that optimized data quality produced the best image quality. Additionally, in [36], [PERSON] conducted a comparative analysis of MIMO, OFDMA, and synchronized channel frequency division multiple access and found that OFDMA provides the best quality of service. This performance was linked to its minimal signal-to-noise ratio at the least considered bit error rate. 4G networks have always had better quality compared to 3G. In 2011, this was clearly illustrated in a study by [PERSON] and [PERSON] [37] in which they used the universal mobile telecommunication service (UMTS) and mobile worldwide interoperability for microwave access (WiMAX) as the bases for 3G and 4G, respectively. Interestingly, while trying to improvise on better ways to improve packet data forwarding between multiple base stations, [PERSON] and [PERSON] [38] found that frequent polling during handover was the most efficient technique. [PERSON] et al. in 2015 [39], studied the relationship between four LTE measurements with emphasis on the effect of signal-to-noise ratio on the transfer rate of a network. They detailed their findings in [23], and this work laid the foundation for further studies, such as one conducted by other researchers in mobile broadband technologies. Notable researchers who have cited this work include [PERSON], [PERSON], and [PERSON], who in [40] provided solutions for improving data transfer rates by optimizing some propagation parameters. These studies provide valuable insights into the performance and optimization of all the network generations and their LTE networks. Thus, these empirical studies and other works provide quality grounds for examining the efficacies of existing mobile networks and comparing the network technologies. A summary of the related work is given in Table 1.
## 3 Results
### First Stage
After cultivation in the control medium, the volumetric efficiency amounted to an average of 1.56 mg/L, while after cultivation in the medium containing 300 mg/L aluminum, it increased to 2.32 mg/L. Supplementation of the culture medium with barium, aluminum, and manganese cations or niacin caused changes in the content of synthesized carotenoids (Tables 3 and 4). The proportions of torularhodin, torulene, and \(\beta\)-carotene determined after cultivation in the control medium were 22.79%, 31.85%, and 43.08%, respectively. With an increase in the concentration of barium cations in the medium from 50 to 300 mg/L, a decrease was observed in the proportion of torularhodin from 21.29% to 7.32%, respectively. However, an increase in torulene was noted from 34.34% to 68.24%. The proportion of \(\beta\)-carotene in the biomass obtained after cultivation in the medium supplemented with 300 mg/L barium cations was 21.74%, which was twofold lower than that in the control sample. The addition of aluminum cations to the media containing potato wastewater and glycerol caused similar changes in the percentages of carotenoids, such as barium. With an increase in the concentration of Al\({}^{3+}\) ions in the medium, the proportion of torulene increased from 31.85% to 75.20%. This was due to a decrease in the biosynthesis of torulene (from 22.79% to 4.50%) and \(\beta\)-carotene (from 43.08% to 17.80%). Similarly, the addition of manganese at doses of 250 and 300 mg/L resulted in a decrease in torularhodin to 14.20-14.35%, while the content of \(\beta\)-carotene increased (49.30-51.13%). Among the examined B group vitamins, only the addition of niacin to the medium caused significant changes in the composition of carotenoids. This vitamin stimulated the biosynthesis of torularhodin, the percentage of which increased from 22.79% (control culture) to 29.79-33.79% in the media containing niacin at concentrations from 100 to 1000 \(\mathrm{\SIUnitSymbolMicro g}\)/L. At the same time, an increase in torulene content was also noted in the niacin-supplemented media (from 31.85% in the control culture to 40.63-45.89%).
## 1 Introduction
Through various simulations, we prove that the proposed approach can reduce the dynamic failure ratio without regard to the background traffic as well as extend network lifetime. The remainder of this paper is organized as follows. In Section 2, we discuss existing research on WMSNs and real-time routing protocols. In Section 3, we explain the real-time model, new QoS routing protocol, and traffic handling scheme. The results of the performance evaluation are provided in Section 4. Finally, the proposed scheme is summarized and further work is mentioned in Section 5.
## 3 Methodology
### Data Preparation for Classification and Parameters for Performance Measurement  
In the dataset used for this study, each sample from individual sleep stage consists of a 30 s epoch length. However, there is a significant imbalance in the data in terms of the number of samples for different sleep stages of individual subjects. To balance the dataset prior to its use by the ResNet, 70 data samples from each sleep stage are randomly selected. Thus, a total of 350 samples are considered for each healthy subject (each class comprises 70 data samples, and in total 5 signal classes were considered in this study). To compute the classification performance of ResNet, K-Fold cross validation (CV) [57] is used. It should be noted that the choice of K, in K-Fold CV is usually arbitrary. To lower the variance of the CV results, it is recommended to repeat/iterate CV procedure with several new random splits. Therefore, in this experiment, the number of K in the CV is 4. For executing the 4-Fold CV, all the samples from the individual datasets are divided in the ratio 75:25 for training and testing, respectively. For example, dataset 1 consists of 5600 samples (350 samples from each of the 16 female subjects), of which 4200 samples are used for training whereas the remaining 1,400 are used for testing. The details of datasets used for training and testing the ResNet are given in Table 2.  
The performance of the proposed framework is evaluated using several parameters, i.e., (a) F1 score (F1) [58], (b) accuracy score (AS), and (c) confusion matrix [59]. The F1 score and AS can be calculated using Equations (3), and (4), respectively.  
\[F1\ =\ \frac{2 TP}{2 TP+FN+FP}\times 100\% \tag{3}\]  
\[AS\ =\ \frac{TP+TN}{TP+FP+TN+FN} \tag{4}\]  
In Equations (3) and (4), the terms TP, TN, FN, and FP represent the number of true positives, true megetives, false negatives, and false positives, respectively. For measuring the class-wise performance, F1 score is considered. The classification accuracy for each gender is determined by calculating the mean AS. Similarly, the overall accuracy can be determined by calculating the mean accuracy for both genders. During training, the loss function of the proposed deep learning framework is monitored to avoid both overfitting and underfitting [60]. The details of the performance analysis are given in Table 3.  
Figure 4: Block diagram of the proposed architecture.
# The Impact of Eclipsing GNSS Satellites on the Precise Point Positioning
###### Abstract  
When satellites enter into the noon maneuver or the shadow crossing regimes, the actual attitudes will depart from their nominal values. If improper attitude models are used, the induced-errors due to the wind-up effect and satellite antenna PCO (Phase Center Offset) will deteriorate the positioning accuracy. Because different generations of satellites adopt different attitude control models, the influences on the positioning performances deserve further study. Consequently, the impact of three eclipsing strategies on the single-system and multi-GNSS (Global Navigation Satellite System) Precise Point Positioning (PPP) are analyzed. According to the results of the eclipsing monitor, 65 globally distributed MGEX (Multi-GNSS Experiment) stations for 31-day period in July 2017 are selected to perform G/R/E/C/GR/GREC PPP in both static and kinematic modes. The results show that the influences of non-nominal attitudes are related to the magnitude of the PCO values, maximum yaw angle differences, the duration of maneuver, the value of the sun angle and the satellite geometric strength. For single-system, using modeled attitudes rather than the nominal ones will greatly improve the positioning accuracy of GLONASS-only and BDS-only PPP while slightly contributions to the GPS-only and GALILEO-only PPP. Deleting the eclipsing satellites may sometimes induce a longer convergence time and a worse solution due to the poor satellite geometry, especially for GLONASS kinematic PPP when stations are located in the low latitude and BDS kinematic PPP. When multi-GNSS data are available, especially four navigation systems, the accuracy improvements of using the modeled attitudes or deleting eclipsing satellites are non-significant.  
Keywords: attitude control model; yaw steering mode; orbit normal mode; multi-GNSS; precise point positioning
## 1 Introduction
The results are validated by comparing them with those from recent approaches, such as ACO [32], PGSA [33], DSA [34], GA [35], and BFA [36]. This article is organized as follows: the motivation, literature review, and the contributions of the work are detailed in Section 1, followed by the optimization problem formulation in Section 2. The implementation strategy of the proposed method is explained in Section 3. This section also elaborates on the losses in RDNs, the LSIs, the PSO algorithm, and the steps involved for implementation of the proposed method. The results are summarized in Section 4. Finally, the conclusion is presented in Section 5.
# PEiD: Precise and Real-Time LOS/NLOS Path Identification Based on Peak Energy Index Distribution
## 5 Experimental Evaluation  
In this section, we conduct experiments to evaluate the performance of PEiD. Three aspects of issues are concerned. First, we investigate the relationship between the packet number (also known as the window length) and identification accuracy. Second, we compare PEiD with previous works to demonstrate the improvement of our approach. Finally, we perform LOS/NLOS identification in a mobile scenario. The problem of time delay is discussed to evaluate our strengths and limitations. We conduct experiments in two different environments. One is a conference room, while the other is an office. The detailed information about implementation and floor plans are shown in Figure 7.  
Figure 6: Handle the mobility using a sliding window.  
Figure 7: Two different indoor scenarios: (**a**) Lab 1: conference room; (**b**) Lab 2: office.
## 2 Study Area and Methods
### Methods
#### 2.3.4 Partial Correlation Analysis and Lag Analysis  
A partially correlated analysis and lag study examined the consequence of seasonal precipitation and temperature with ET for TVDI at different scales and TVDI's lag time on the three variables. The partially correlating coefficient equation is [32].  
\[r_{ab.cd}=\frac{r_{ab.c}-r_{ad.c}r_{bd.c}}{\sqrt{(1-r_{ad.c}^{2})(1-r_{bd.c}^{ 2})}} \tag{4}\]  
where \(a\) is TVDI; \(b\) is precipitation; \(c\) is temperature; \(d\) is ET; \(r_{ab.c}\) represents the partial coherence coefficient that controls temperature TVDI with precipitation; \(r_{ad.c}\) is with ET; \(r_{bd.c}\) is precipitation; and \(r_{ab.cd}\) is ET and temperature. Using analogies, the test of association was used to determine the partially correlated coefficient significance.  
First, the partially correlated coefficients during TVDI with temperature, precipitation, and ET are computed for the current month, one month ahead, two months ahead, and six months ahead. Next, a maximum number of the corresponding image pixels' partial correlation at various scales is synthesized; an absolute number is taken to obtain the greatest partial correlation coefficients and their corresponding lag times. These are the fundamental steps of the lag analysis. Below is the expression [33]:  
\[S=max\{|R_{0},R_{1},R_{2},\ldots,\ldots,R_{n-1},R_{n}|\} \tag{5}\]  
The greatest partial correlation value is \(S\), and its lag time is \(n\). The partial correlation values of TVDI are \(R_{0},~{}R_{1},R_{2},\ldots,\ldots,R_{n-1}\), and \(R_{n}\) for the current month, 1 month forward, 2 months forward, and \(n\) months forward, respectively. When \(R=R_{n}\), the lag time is \(n\) months. In this study, the seasonal division scale is based on 3 months; that is, the seasonal scale only calculates the partial correlation coefficient forwarded to 2 months, and the whole year scale calculates the partial correlation coefficient to 6 months forward.
## 4 The Improved GA
### The Crossover Operator  
Using the selection operator, the chromosomes are picked for the crossover operation to produce the offspring according to the crossover probability. In this process, two chromosomes generate two new chromosomes by exchanging some parts of them, but it is noted that these two chromosomes (paths) should have one or more same genes (nodes) besides the source node and the destination node because it may produce infeasible routing paths easily otherwise. The places of the same genes in two chromosomes are where the crossing points lie. Two crossover methods are adopted in this paper: single-point crossover and two-point crossover, which differ from the traditional ones. The single-point crossover is carried out when there is only one common gene in the two chromosomes and they exchange the latter parts of themselves, which start from the crossing point to the destination node. Two new chromosomes are thus formed as demonstrated in Figure 3. Additionally, as shown in Figure 4, the two-point crossover is used when two common genes exist in the two chromosomes and they exchange the parts that are between the two same genes so as to form two new chromosomes. If three or more same genes exist in two chromosomes, the paper still adopts the two-point crossover method and the crossing points are selected randomly from the same genes.  
As illustrated in Figures 3 and 4, some of the new produced chromosomes are better than the original ones, which ensures that the preferable paths can be found. Therefore, the crossover operation can improve the ability of the path search, thus accelerating the algorithm convergence and finding the optimal path. However, sometimes the crossover operation may cause path loops, which is not desirable in the path search. Therefore, the repair mechanism is adopted to look for the loops and then wipe them out. The key point is that the proposed method is able to find the optimal path.  
Figure 4: The two-point crossover procedure.  
Figure 3: The single-point crossover procedure.  
is to find out whether one node exists in the different locations of one path. For example, there are two chromosomes representing the two paths:  
\[\begin{array}{l}\text{Path 1}:\ SN\to N_{1}\to N_{3}\to N_{2}\to N_{5}\rightarrow\cdots\to DN\\ \text{Path 2}:\ SN\to N_{2}\to N_{4}\to N_{3}\to N_{5} \rightarrow\cdots\to DN\end{array}\]  
The two crossing points are \(N_{3}\) and \(N_{5}\). After crossover operation, the produced paths are:  
\[\begin{array}{l}\text{Path 3}:\ SN\to N_{1}\to N_{3}\to N_{5} \rightarrow\cdots\to DN\\ \text{Path 4}:\ SN\to N_{2}\to N_{4}\to N_{3}\to N_{2}\to N_{5} \rightarrow\cdots\to DN\end{array}\]  
There exists a loop \(N_{2}\to N_{4}\to N_{3}\to N_{2}\) in path 4. After the loop is wiped out, the path becomes a feasible one: \(SN\to N_{2}\to N_{5}\rightarrow\cdots\to DN\).
###### Abstract  
In this study, a CVD (Chemical Vapor Deposition)-diamond coated tungsten carbide cobalt (WC (Co)) punch was trimmed to adjust its surface roughness and to significantly reduce its edge curvature for fine piercing by femtosecond laser processing. Through this laser trimming, the surface quality of the diamond coating and the punch edge profile were improved to less than 0.5 \(\upmu\)m at the maximum roughness and 2 \(\upmu\)m in the edge width, respectively. In parallel with this improvement of surface quality, the side surface of the diamond coating was modified to include nano-textures via the LIPSS (Laser Induced Periodic Surface Structuring) process. Through the fine piercing process, this nanotexture was transcribed onto the pierced hole surface together with fine shearing of the hole by piercing. WLI (White-Light Interferometry) and SEM (Scanning Electron Microscopy) were utilized to describe this transcription of nanotextures during the piercing process. These semiregular nanotextures with an LIPSS period of 300 nm on the pierced hole surface induced a blue colored surface plasmon.  
diamond-coated WC (Co) punch; femtosecond laser trimming; simultaneous nanotexturing; fine piercing; concurrent transcription of nanotextures; surface plasmon