esa-sceva/satcom-chunk-collection huggingface dataset:
# Gain-Scheduled Disturbance Observer-Based Saturated Controllers for Non-Linear First-Order System +
## 3 Illustrative Example
### Impact of the Measurement Noise  
To illustrate the effects of measurement noise, a random noise \(\delta\) was added to the output \(y\) (see Figure 3 below). Noise was generated in the simulation by the \"Uniform Random Number\" block in Matlab/Simulink with a sampling period \(T_{s}=0.001\). The fluctuations in performance measures caused by noise are mainly visible in the shape-related performance measures, which are represented by the values of \(TV_{0}(y)\) and \(TV_{1}(u)\) listed in Tables 3 and 4. In contrast to the performance measures shown in Figure 13, where the shape deviations for DOB0-5 and ARC0-5 were almost zero with the value \(T_{f}=5T_{c}\), the values for DOB0-X are always higher than those for ARC0-X, X = 1,5 under the measurement noise. If you increase the time constants \(T_{c}\) and \(T_{f}\), both shape deviations are significantly reduced. It is worth mentioning that, for ARC0, the increase in the filter time constant \(T_{f}=5T_{c}\) (i.e., from ARC0-1 to ARC0-5) does not lead to a significant reduction in the excessive controller effort \(TV_{1}(u)\) compared to DOB0-1 with \(T_{f}=T_{c}\) and DOB0-5 (\(K_{P}\) of ARC0 controllers does not depend on \(T_{f}\)). These results obviously show that the choice of the optimal controller depends on the actual circumstances.
# Energy Efficiency Perspectives of PMR Networks +
## 5 Technical and Economic Considerations on Energy Efficiency of Next Generation PMR Systems
### Business Models for Mobile Broadband PMR
#### 5.1.4. Build, Own and Operate a Dedicated Private Network  
In this model, the public safety agency finances, procures, builds and manages its own network, setting technical requirements for capacity, security, reliability, redundancy and robustness. It takes full responsibility for all network elements and software, and employs in-house personnel to build, manage, operate and maintain the network. The extent of upfront costs depends on the scale of deployment (local, regional or national), whether the network is shared among several entities and/or whether the deployment is scheduled gradually over years or within a shorter time period. The clear advantage with a dedicated LTE network is that it can be designed to match all mission-critical requirements, with the agency having full control over the procurement process. Specifications (such as site hardening, extended coverage and resilience to multiple faults, extreme events, and energy saving solutions) can be tailored to missions, as well as to the agency's future evolution strategy. As with the PPP model, a dedicated network can offset CAPEX and OPEX by operating as a wholesaler where regulations permit, or sharing with other critical users of the public sector such as defense, utilities and transportation agencies. On the other hand, specifying, building and maintaining a dedicated network requires significant upfront investments and technically skilled professionals for network operations. Acquiring dedicated LTE spectrum may require an initial investment or an annual fee. A dedicated spectrum must be cleared of any previous service, usually a slow process in countries where no dedicated spectrum currently exists. Both CAPEX and OPEX typically could be higher in this model. However, CAPEX depends significantly on the spectrum of operations (the lower the spectrum, the lower the number of sites to deploy) and can be planned over multiple years to deploy in critical areas first.
## 2 Materials and Methods
### Statistical Analysis  
Appropriate summary statistics (means and SDs for approximately normally distributed variables, geometric means and SDs for approximately log-normally (positively skewed) distributed variables, and medians and IQRs for other continuous variables; and counts and percentages for categorical variables) were presented. Using baseline data, intra-class correlation coefficients were calculated overall, by TMD status, and for each TMD subgroup, using the two-way mixed effects model for absolute agreement to assess reliability over the five measurements retained (as described above). Linear mixed models were then used to examine measurement sequence number effects to determine if there was evidence of fatigue. The measurement sequence number was examined for non-linear effects through the addition of a quadratic term and effect-modification by each of the TMD statuses and TMD subgroups through the addition of interaction terms. Binary logistic regression was used to examine the discriminating ability of the mean force (of the five measurements, as described above) for TMD at baseline, and multinomial logistic regression was similarly used for the discriminating ability in terms of subtypes. Initially unadjusted models were investigated, followed by likelihood ratio tests to assess whether adding each participant characteristic (age, sex, height, and weight) improved these models. This was followed by models adjusting for all of these variables simultaneously. A Chi-squared test was used to compare loss to follow-up between the two groups and between the sexes, with [PERSON]'s exact test used where more than 20% of cells had expected counts below 5. Given the skew in age and baseline forces, Mann-Whitney U tests were used to compare these between those retained and lost to follow-up. Spaghetti plots were used to visualize the changes in jaw-opening forces for the two groups (TMD patients and TMD-free participants) and by TMD subgroups. Linear mixed models were used to estimate and compare the changes in forces in the two groups, with a random participant effect included to accommodate the repeated measures. Similar models using only the TMD patients compared the three subgroups of TMD. Standard model diagnostics were used, including inspecting histograms of residuals and scatter plots of residuals against fitted values and continuous predictors and examining the approximate normality of the random effects. Where positively skewed model residuals were improved by log-transformation, as was the case here, this transformation was retained, and differences are reported as ratios of geometric (reflecting the positive skew) means (being back-transformed differences on the log-scale). Statistical analyses were performed by using Stata 16.1, with two-sided \(p<0.05\) considered statistically significant, except for Chi-squared tests, which were one-sided.  
Figure 1: (**A**) Jaw-opening device consisting of a 3D-printed headgear, load cell, and chin cap connected to a data capturing system. (**B**) During maximum opening.
## 2 Materials and Methods
The current consumption, I, can be decomposed into a fundamental frequency current, I\({}_{1}\), made up of an active and a reactive component (I\({}_{1a}\) and I\({}_{1r}\), respectively), and a harmonic or distortion current, I\({}_{D}\), associated with all non-fundamental frequencies, as expressed in Equation (1):  
\[\mathrm{i(t)}=\mathrm{i_{1}(t)+i_{D}(t)=i_{1a}(t)+i_{1r}(t)+\sum_{n\
eq 1}^{ \infty}i_{n}(t)} \tag{1}\]  
[PERSON] [24; 25] and [PERSON] [26] in the IEEE Standard 1459/2010 establish that each of these currents are associated with physical phenomena present in the electrical system. These phenomena are the useful, the reactive and the distortion energy. The same is established by [PERSON] in the unified power measurement (UPM) [27]. This author quantifies these phenomena through active power (P), reactive power (Q) and distortion power (D), orthogonal to each other, as are the associated currents. Starting from P, Q and D, the apparent power (S) is determined, which is the real power that must be supplied to the receiver for it to work. The expressions that allow us to calculate these powers are shown in Equations (2)-(5). \[\mathrm{P}=\mathrm{V_{1}\cdot I_{1}\text{-}\text{\text{\text{\text{\text{ \text{\text{\text{  
P \(<\)15 kW are considered as such). When the efficiency value is less than one, a part of the energy that is consumed is not counted in the billing, but it must be generated and distributed to the consumer. For example, if the value of \(\varepsilon\) was 0.5, the receiver would have to be supplied with twice the power it uses for it to work. The part that is not used is that related to reactive (var) and distortion (VAd) powers, and this energy is not measured for the billing of small consumers.  
The current distortion rate has also been calculated for each lamp with the expression shown in Equation (7), as defined by the EN-61000-2-2 standard [28]:  
\[\mathrm{THD_{I}(\%)}=\frac{\sqrt{\sum_{n=2}^{40}I_{n}^{2}}}{1_{1}} \tag{7}\]  
According to some of the literature [29; 30; 31], the distortions generated individually by certain non-linear devices compensate each other, so it is suggested that, despite the fact that the distortions determined individually are significant, when they work jointly, the distortion generated in the distribution system is not. To evaluate whether there is a compensation effect in the case of the lamps, in addition to analyzing the behavior of each one individually, groupings of lamps working on the same network were evaluated. For each grouping, the same parameters as those obtained for the lamps individually were measured. Groups only of LEDs, only CFLs and two combinations of mixed type have been included. Table 2 shows the type of association, the lamps that constitute it, and the keys with which they will be designated in the document. For the calculation of the powers associated with the groups, the result of the phasor sum of the currents for each of the devices within each group has been taken.
# A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications
## 3 Inertial Based Systems
### Simultaneous Localization and Mapping
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Cost**} & \multirow{2}{*}{**Scalability**} & \multirow{2}{*}{**Anchors**} & \multirow{2}{*}{**Area (m\({}^{2}\))**} & \multicolumn{2}{c}{**Error**} \\ \cline{5-8}  & & & & & & & **Type** & **Value** \\ \hline [PERSON] et al. [12] & Time & Expensive & Limited & 100 & 280 & 95 th & 0.09 m \\ [PERSON] et al. [13; 95] & Time & Medium & Limited & 6 & \textless{}10 & 90 th & 0.3 m \\ [PERSON] et al. [22] & RSS & Low & Yes & 6 & 530 & RMSE & 1.4 m \\ [PERSON] et al. [23] & RSS & Low & Yes & 8 & 36 & 75 th & 1.8 m \\ [PERSON] et al. [19] & RSS & Low & Yes & 5 & 3400 & median & 3 m \\ [PERSON] et al. [27] & Proximity & Low & Yes & 12 & 300 & room detection & 97.2 \% \\ [PERSON] et al. [25] & Proximity & Low & Yes & 24 & 8 & RMSE & 0.32 m \\ [PERSON] et al. [30] & Fingerprinting & Medium & Limited & 3 & 980 & 75 th & 4.69 m \\ [PERSON] et al. [31] & Fingerprinting & Medium & Limited & 3400 & 192,200 & 75 th & 3-9 m \\ [PERSON] et al. [35] & Fingerprinting & Medium & Limited & 21 & 1700 & 90 th & 1.4 m \\ [PERSON] et al. [34] & Magnetic fingerprinting & Low & Limited & 0 & 4000 & 90 th & 2.5 m \\ [PERSON] et al. [8] & Inertial & Low & Yes & 0 & 75 & \% travelled path & 0.3 \% \\ [PERSON] et al. [39] & Inertial & Low & Yes & 0 & 3600 & \% travelled path & 0.3-1.5 \% \\ [PERSON] et al. [44] & Inertial & Low & Limited & 0 & 600 & RMSE & 1-2 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of Indoor Positioning Systems.
## 4 Analysis of Influencing Factors of Crossing Cable
### Number of Crossing Cables  
On the basis of two three-tower cable-stayed bridges, two pairs, four pairs, and six pairs of crossing cables are added, respectively, and the crossing cables are set near the position with the largest constraint stiffness. The anchorage positions of different numbers of crossing cables are set as shown in Figure 15. The red dots in the figure are the theoretical anchorage positions with the largest constraint stiffness of the crossing cables.  
For three-tower cable-stayed bridges with main spans of 1000 m and 1500 m under a uniformly distributed load, the vertical deflection of the deck is shown in Figures 16 and 17. When no crossing cables are installed in the main span, the maximum downward deflection of the deck for the two bridges is 3.33 m and 5.05 m, respectively. After adding two, four, and six pairs of crossing cables, the maximum downward deflection of the deck for the 1000 m main span bridge reduces to 2.04 m, 1.66 m, and 1.48 m, respectively, while that for the 1500 m main span bridge decreases to 3.67 m, 3.13 m, and 2.83 m, respectively. Compared to the case without crossing cables, the vertical deflection of the deck is reduced by 55.55% and 43.96% after installing six pairs of crossing cables. In kilometer-scale three-tower cable-stayed bridges, the installation of crossing cables significantly reduces deck  
Figure 14: Tower bottom bending moment under different crossing cable area ratios.  
Figure 15: Anchorage position of crossing cable.  
deflection. However, as the main span length increases, the effectiveness of crossing cables in enhancing the vertical stiffness of the deck slightly diminishes.  
Figures 18 and 19 give the bending moment distribution in the mid tower of two three-tower cable-stayed bridges after adding two, four, and six pairs of crossing cables. It is observed that significant bending moments occur in the anchorage zone of the mid tower for the bridge with a 1000 m main span. This is because the crossing cables are densely concentrated near the tower top, imposing strong longitudinal constraints that result in an \"S\"-shaped deformation of the tower. The bending moments at the tower base for the two bridges are 629.46 MN\(\cdot\)m and 5190.73 MN\(\cdot\)m, respectively. After installing two, four, and six pairs of crossing cables, the bending moment at the mid-tower bottom of the 1000 m main span bridge decreases by 28.41%, 38.28%, and 44.16%, respectively, while that of the 1500 m main span bridge decreases by 28.16%, 39.85%, and 46.47%, respectively. The addition of crossing cables in the main span significantly reduces the bending moment at the mid-tower bottom. For both span lengths, the reduction magnitudes of the tower base bending moment induced by crossing cables are comparable.
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 7 Current and Future Research Directions
### Current Work in the Field
#### 7.2.2 Data Processing  
Words such as \"feature\", \"high\", \"big\", \"label\", and \"outlier\" demonstrate that researchers work on challenges of (pre-)processing tasks such as outlier detection, labeling, feature selection, and high dimensional data. A current example is feature selection for multi-dimensional time series data. Within [189], relevant features are filtered by calculating their correlation with classes of the decision problem. The authors evaluated the algorithm on six (mainly motion-capturing related) data sets and reported significantly lower computational costs in contrast to other methods in the field. Ref. [190], on the other hand, take the approach of splitting up data streams in a meaningful way and processing them further based on this. Their main contribution is that the date's origin (here called entity) contains information useful for solving the decision problem. IoT data is split per sensor into so-called sub-streams, for example. The approach also proposes a sampling approach called k random entities, which selects suitable sub-streams for the classification problem. The authors evaluate the approach on three data sets of different domains. They conclude that the approach has potential in both the medical and sensor data fields, but the sub-stream selection part needs further investigation.  
The cleaning of highly noisy data streams is another important task for future work. Ref. [191] enhance online stream classification algorithms by using prediction uncertainty and active learning. Conflicting opinions of the classifiers of the ensemble are stored as features, and instances are sent to an expert oracle from time to time. Evaluations of IoT, cloud task and face recognition data sets show a significant increase in classification accuracy and the cross-domain suitability of the algorithm.
## 3 Results and Discussion
### Mechanical Properties
#### 3.3.2 Peak Strength  
The triaxial compressive strength of the grouting reinforcement body is a critical parameter for assessing its engineering characteristics, reflecting its maximum bearing capacity under various practical engineering conditions. Variations in the triaxial compressive strength of the grouting reinforcement body following freeze-thaw cycles effectively indicate the extent of freeze-thaw damage, providing valuable reference points for the design and construction safety of geotechnical engineering in cold regions. The test results have been organized, and the effect of freeze-thaw cycles on the peak strength of the grouting reinforcement body is depicted in the curve shown in Figure 21.  
As shown in Figure 21, the peak strength of grouting reinforcement bodies with different crack dip angles follows a largely consistent variation pattern as the number of freeze-thaw cycles increases. As the number of freeze-thaw cycles increases, the peak strength of the grouting reinforcement body decreases, with the rate of decline progressively accelerating. This trend is consistent with a negative exponential function. The relationship between peak strength and freeze-thaw cycles for the grouting reinforcement body, under varying confining pressures, is individually fitted to a negative exponential function. The detailed fitting equations are provided in Figure 21, with correlation coefficients ranging from 0.94 to 0.99, indicating a strong fit.  
As demonstrated in Figure 21, confining pressure significantly influences the peak strength of the grouting reinforcement body, with its impact becoming more pronounced as the crack dip angle increases. For example, the effect of confining pressure on peak strength is analyzed for a grouting reinforcement body with a 60\({}^{\circ}\) crack dip angle. As illustrated in Figure 21d, under the same number of freeze-thaw cycles, the peak strength  
Figure 21: Effect of freeze–thaw cycles on the peak strength of sample (**a**) crack dip angle = 0\({}^{\circ}\); (**b**) crack dip angle = 30\({}^{\circ}\); (**c**) crack dip angle = 45\({}^{\circ}\); (**d**) crack dip angle = 60\({}^{\circ}\).  
of the grouting reinforcement body increases proportionally with rising confining pressure. Using 30 freeze-thaw cycles as a reference, compared to a sample with a confining pressure of 0 MPa, the peak strength increases by 410.32%, 479.13%, and 655.87% when the confining pressure is 3 MPa, 6 MPa, and 9 MPa, respectively.
# Configuration Design of an Upper Limb Rehabilitation Robot with a Generalized Shoulder Joint
## 2 Human Upper Limb Kinematics
### Human Upper Limb Anatomical Structure and Equivalent Mechanism Model
Thus far, the equivalent mechanism model of the human upper limb can be established as a 9-DOF series motion model, as shown in Figure 6.  
To facilitate the measurement of upper limb kinematic information and unified clinical application communication, the International Society of Biomechanics (ISB) developed a description method for upper limb movement [31]. The lifting surface angle \(\alpha\), uplift angle within the uplift plane \(\beta\) and internal rotation/external rotation angle \(\gamma\) are used to describe the current position of the upper arm relative to the GH joint. [PERSON] [32] et al. used experiments and synthetically obtained the kinematic coupling relationship between the SC and the GH rotation angle. The functional relationship between the uplift angle \(\beta\) and the forward/retracted angle \(\varphi_{fb}\) and the uplift/sink angle \(\varphi_{ud}\) of the SC is:  
\[\phi_{fb}=\left\{ \begin{array}{cc}-0.35\beta,&\beta\leq 0^{\circ}\\ 0^{\circ},&0^{\circ}\leq\beta\leq 70^{\circ}\\ -0.22\beta+15.4^{\circ},&\beta>70^{\circ}\end{array} \right. \tag{2}\]  
\[\phi_{ud}=\left\{ \begin{array}{cc}-0.3\beta,&\beta<0^{\circ}\\ 0^{\circ},&0^{\circ}\leq\beta\leq 30^{\circ}\\ 0.36\beta-10.8^{\circ},&\beta>30^{\circ}\end{array} \right. \tag{3}\]  
Figure 5: Simplified equivalent mechanism model of the scapula belt. Figure 6: Simplified, equivalent mechanism model of the upper limb.
###### Abstract  
This study evaluated the validity and reliability of the Fitnessmeter mobile application for measuring change of direction (COD) performance compared to a gold-standard photocell system. Forty-six trained adolescent soccer players performed the 5 + 5 m COD test, with simultaneous data collection using the Fitnessmeter app and photocells. A strong correlation was observed between the two instruments (r = 0.971), with no significant differences in COD times (\(p>0.05\)). Bland-Altman analysis indicated minimal systematic bias (0.02 \(\pm\) 0.05 s) and acceptable limits of agreement. Reliability analysis demonstrated high consistency for both devices (Fitnessmeter ICC = 0.66-0.72; Photocells ICC = 0.57-0.76), with reproducibility confirmed by a low coefficient of variation values (<3%). The Fitnessmeter app also effectively measured inter-limb asymmetries, showing results comparable to photocells (\(p=0.42\)). These findings validate Fitnessmeter as a reliable, portable, and cost-effective tool for COD assessment. Its accessibility makes it suitable for applications in sports, education, and rehabilitation, offering a practical alternative to expensive equipment. Further studies are recommended to confirm its reliability in other COD tests and contexts. Fitnessmeter's innovative motion detection technology has the potential to democratize performance assessment, making it accessible to a broader audience beyond elite sports.  
inter-limb asymmetry; change of direction test; mobile application; validity Article