esa-sceva/satcom-chunk-collection huggingface dataset:
# State Estimation of Over-Sensored Systems Applied to a Low-Cost Robotic Manipulator
## 4 State Estimation  
The two different descriptions of pose, the link model and the joint model, are used with different filters. The link model has its state estimated using the MEKF as described in Section 2.4.1, whereas the UKF, which is described in Section 2.4.2, is used to estimate the state of the joint model. The covariance matrices in the algorithms were diagonal since the cross variance between different variables was negligible. The empirical values obtained for the variances of the state variables were:  
* \(4\times 10^{-5}\) for \(e_{\eta_{ij}}\), with \(i=\{1,2,3\}\) and \(j=\{x,y,z\}\) (link model);
* \(1\times 10^{-3}\) for \(e_{\omega_{ij}}\), with \(i=\{1,2,3\}\) and \(j=\{x,y,z\}\) (link model);
* \(1\times 10^{-7}\) for \(e_{\theta_{i}}\), \(e_{\omega_{ij}}\) and \(e_{\alpha_{i}}\), with \(i=\{1,2,3\}\) (joint model).  
The sensors' variances were obtained experimentally and are presented in Table 2 in their respective units (\((\text{rad}/s)^{2}\) for gyroscopes and encoders and \((\text{m}/s^{2})^{2}\) for accelerometers).  
After implementing the filters, each of the sensor models were tested individually (along with the manipulator model) to verify that they were working correctly. Most of these tests were performed with the manipulator stationary or while following this set of movements:  
* \(\theta_{1}=0^{\circ}\) \(\theta_{2}=0^{\circ}\) \(\theta_{3}=0^{\circ}\) @0s
* \(\theta_{1}=0^{\circ}\) \(\theta_  
These movements were controlled using a PD controller with a proportional gain of five and a differential gain of 0.2. The error that served as the input to this controller was the difference between the references described above and the dead-reckoning of the encoders' measurements. On their own, the encoders do not provide an accurate ground truth. However, their measurements are reliable enough to verify the correctness of the models. Once this process was complete, all the sensors were used to test the accuracy of the estimators at finding the position of the tip of the manipulator. This accuracy test was performed with the aid of a structure with points in specific locations. This structure is displayed in Figure 5.  
By manually driving the tip of the manipulator to each of the structure's points of interest and calculating the position from the estimated state, the error of the methods can be found for the different points. The tip was guided to the points in alphabetical order, starting at Point A, going through all the others, and finishing at A again. The coordinates of the presented points, in millimeters, are:  
* A: \((0;0;545.5)\)
* B: \((371;0;174.5)\)
* C: \((340.5;141.5;174.5)\)
* D: \((0;224;200)\)
* E: \((0;224;450)\)
* F: \((100;224;450)\)
* G: \((100;224;200)\)  
This analysis is performed for the estimator of the link model in Section 4.1 and for the estimator of the joint model in Section 4.2.
## 3 Experiments and Results
### Crosstalk Calibration
Therefore, crosstalk compensation effectively reduces errors due to force. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & \(f^{ext}_{x}\) & \(f^{ext}_{y}\) & \(f^{ext}_{z}\) & \(\tau^{ext}_{x}\) & \(\tau^{ext}_{y}\) & \(\tau^{ext}_{z}\) \\ \hline \multirow{3}{*}{MSE} & _No Calib._ & 1.086 & 2.607 & 1.932 & 0.009 & 0.012 & 0.000 \\  & _LSM Calib._ & 0.480 & 1.142 & 1.652 & 0.009 & 0.012 & 0.000 \\  & Improved (\%) & 55.8 & 56.2 & 14.5 & - & - & - \\ \hline \end{tabular}
\end{table} 
Table 5: The table of the MSE of force and torque with respect to 0 for each axis when calibration is performed using two methods on a non-inclined base. Figure 6: The robot path in the pick-and-place scenario. The robot sequentially moves from posture 1 to posture 4, with posture 2 set at an angle of approximately 30 degrees to simulate a torque-generating situation.
# On Mitigating the Effects of Multipath on GNSS Using Environmental Context Detection
## 4 Context-Aware Navigation (CAN)
### Environment Detection and Characterization Using CAN
This is usually the case where the minimum number satellites required for positioning are either inadequate or the available satellites have been affected by multipath/NLOS such that there is no positioning solution or, if the positioning exists, then there may be large errors. When using a multi-constellation GNSS receiver, there is a low probability of facing a highly degraded environment due to a large number of satellites available from different constellations. This is why it can be seen in Figure 7c that the highly degraded environment is identified by CAN at the 36 th, 38 th, 39 th and 46 th min in the P3 observation window.
## 2 FSM Control System  
### Dynamic Characteristics of Piezoelectric (PZT) Actuator  
In this study, the high-precision FSM system consists of a PZT actuator as the mechanical part, an actuator amplifier, and a sensing signal processing unit as the electrical part. In addition, the mirror-mounted piezoelectric (PZT) actuator is used to drive the FSM system to achieve the tip-tilt motion. In general, it is necessary to identify the frequency response performance of the real plant in order to design the control system. Here, the frequency response of the real plant (i.e., FSM system) was analyzed using a dynamic signal analyzer (35670A). The identified dynamic characteristics of the real plant are summarizedin Table 1. In addition, from the measurement of identification specifications for the plant, the frequency response of the real plant and modeled plant are illustrated in Figure 1.  
Moreover, the nominal plant is modeled on a second-order transfer function and can be expressed as follows:  
\[P_{n}(s)=\frac{3,891,000}{s^{2}+1651s+4,911,000} \tag{1}\]  
The configuration of the FSM platform for high-precision tip-tilt motion control is shown in Figure 2. It consists of a real plant and a real-time control platform used to realize the tip-tilt motion system. A Scalexio autobox (dSPACE) is used as the real-time control platform. In addition, a mirror-mounted PZT actuator (S-340) and an amplifier module (PI Ceramic GmbH, Lederhose, Germany) are used in the tip-tilt motion system.
## 2 Model Construction for an SCCNMT
### The Capacity Vector  
An SCCNMT exists in more than two suppliers and retail stores. The vector \(X=(x_{1},\ x_{2},\ \ldots,\ x_{q})\) is defined as the capacity vector of \(G\), where \(x_{q}\) represents the current used truck number of \(a_{q}\). Here, \(a_{q}\) is an integer random variable with a maximum value of \(M_{q}\). An SCCNMT can be derived into two areas by logistic companies. One area focuses on supplying logistics companies with products to meet the demand of retail stores. The other area is for logistic companies to deliver products to retail stores. Due to the pressure of time, the capacity must consider both delivery time and demand simultaneously. If the flow vector \(F\) satisfies the following Equations (1) and (2), it is said to be feasible under \(M_{q}\),  
\[\left[\sum_{\sigma=1}^{w}\sum_{k=1}^{p}\Bigl{\{}f_{\sigma}^{k}|a_{q}\in P_{ \sigma}^{k}\Bigr{\}}\right]\leq M_{q},\ \text{for}\ q=1,\ 2,\ \ldots,\ z, \tag{1}\]  
Figure 1: Service time concept in a cold chain.  
\[\begin{array}{l}\max\Big{\{}f_{o}^{k}|a_{q}\in P_{o}^{k}\Big{\}}\leq\Big{[} \frac{l-\Delta}{U}\Big{]}\text{for}o=1,\;2,\;\ldots,\;w;\;k=1,\;2,\;\ldots,\;p; \\ q=s+1,\;s+2,\;\ldots,\;z;\;b=1,\;2,\;\ldots,\;s,\;\text{where}\;\Delta=\max\{v_{ b}\}+(U+L)+\max\{v_{q}\}.\end{array} \tag{2}\]  
Equation (1) represents that the demand cannot exceed its maximal capacity \(M_{q}\). Equation (2) is inverted by the total delivery time to ensure the number of trucks that can satisfy the flow on time.  
Similarly, to be feasible under the capacity vector \(X=(x_{1},\,x_{2},\,\ldots,\,x_{q})\), the flow vector \(F\) must satisfy the following Equations (3) and (4),  
\[x_{q}=\Bigg{[}\sum_{o=1}^{w}\sum_{k=1}^{p}\Big{\{}f_{o}^{k}|a_{q}\in P_{o}^{k} \Bigg{\}}\Bigg{]},\;\text{for}\;q=1,\;2,\;\ldots,\;s, \tag{3}\]  
\[\begin{array}{l}x_{q}=\Bigg{[}\frac{U\max\{f_{o}^{k}|a_{q}\in P_{o}^{k}\}}{ l-\Delta}\Bigg{]}\text{for}o=1,\;2,\;\ldots,\;w;\;k=1,\;2,\;\ldots,\;p;\\ q=s+1,\;s+2,\;\ldots,\;z;\;b=1,\;2,\;\ldots,\;s,\;\text{where}\;\Delta=\max\{v _{b}\}+(U+L)+\max\{v_{q}\}.\end{array} \tag{4}\]
### Ultrasound Diagnostics in Dermatology and Cosmetology
High-frequency ultrasound makes it possible to obtain high-resolution images of surface structures in dermatology with the possibility of storing and forwarding them, especially when diagnosing and monitoring patients suffering from chronic skin diseases [39]. High-frequency ultrasound has been widely used for diagnostic purposes in the study of the skin changes in hormonal, inflammatory, and autoimmune diseases (scleroderma, psoriasis, atopic dermatitis, skin striae) [40; 41; 42]; for monitoring the treatment [43]; and for the objective assessment of the severity of various chronic skin diseases [44; 45]. High-frequency ultrasound is used as a non-invasive diagnostic method to diagnose and determine the stage of scleroderma [46; 47] in its localized forms [48]. Diagnostic criteria and the classification of the scleroderma severity have been developed based on ultrasound data [49]. The introduction of transducers with a higher frequency has made it possible to assess in more detail the processes of wound healing and burns as well as the scar formation process in dermatology [50] to noninvasively differentiate between the inflammatory and fibrous phases of wound healing and determine the degree of atrophy of skin appendages [51]. High-frequency ultrasound has also found its place in trichology, diagnosing the stages of inflammation and fibrosis, the disruption of the hair growth cycle, and changes in the difference in the echogenicity of the dermis in both the scar and non-scar hair lesions [52]. Potentially metastatic skin tumors are determined using ultrasound in dermatoontology; treatment is also monitored and targeted percutaneous puncture biopsy of lymph nodes is performed [53; 54; 55]. A special place is occupied by high-frequency ultrasound in the case of the preoperative evaluation of skin neoplasms to determine the size of the lesion, location in relation to other tissues and structures, and the potential invasion and its depth [56; 57]. Thus, the use of high-frequency and ultra-high-frequency ultrasound with excellent visualization capabilities for the epidermis and dermis has realized significant progress in the use of ultrasound in dermatology and cosometology in the diagnosis, treatment, and evaluation of its results. Accordingly, it is obvious that ultrasound will be used increasingly in clinical practice for an expanding list of conditions and pathologies of the skin and its appendages.
# Muscle Activity and Biomechanics of Sprinting: A Meta-Analysis Review
## 2 Materials and Methods
### Search Strategy  
This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, specifically following the updated version published in 2020. PRISMA provides a standardized reporting framework to enhance transparency, reproducibility, and methodological quality in systematic reviews and meta-analyses. The 2020 update includes expanded items to better reflect advances in review methodology, such as structured database searching, reporting of inclusion/exclusion criteria, and flow diagram standards. In the context of this meta-analysis, adherence to PRISMA 2020 ensured rigorous selection, appraisal, and synthesis of included studies. Compared to the original 2009 version, the PRISMA 2020 guidelines introduce updated and expanded reporting items that better align with contemporary systematic review practices. These include more detailed requirements for reporting search strategies, eligibility criteria, data extraction procedures, and methods for handling missing data or assessing bias. The updated flow diagram format also enhances transparency in study se lection. By adhering to PRISMA 2020, this review ensures alignment with current standards of methodological rigor and reproducibility in meta-analytic research.  
This systematic review and meta-analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses PRISMA and PRISMA 2020 guidelines. A systematic literature search was performed in PubMed, Scopus, Web of Science, and SportDiscus. Keywords included: sprinting biomechanics, muscle activity, electromyography, sprint running, kinematics, kinetics. Search terms were combined using Boolean operators (AND, OR) to refine the query and ensure comprehensive retrieval of relevant studies.  
Two independent reviewers (both co-authors of this manuscript) screened titles and abstracts for relevance, followed by full-text evaluation. Any disagreements were resolved through discussion and consensus, or by consulting a third and fourth co-author. Studies were included based on methodological rigor, sample size, and data reporting quality. Any discrepancies were resolved through discussion or consultation with a third reviewer.
## 4 Overview of GNSSs
### Quasi-Zenith Satellite System
#### 4.5.1 QZSS Architecture  
The QZSS consists of a space segment and a ground segment. The space segment initially consists of a single QZS. The ground segment consists of master control station (MCS), a monitoring station (MS), tracking and control stations (TCSs), and systems from other national research institutes. About 10 MSC stations have been deployed in Japan and overseas to more accurately estimate and predict the QZS orbit and clock [92].
## 2 Applications of HREM  
HREM has been employed in various areas of the life sciences and for visualising a number of different organic materials. Its chief domains of application are phenotyping of embryos of biomedical model organisms and structural analysis of human biopsy material. Besides those, there are a number of unconventional applications.
# Equilibrium Inefficiency and Computation in Cost-Sharing Games in Real-Time Scheduling Systems +
## 3 Monomial Cost Functions
### Price of Stability  
In this section, we show that, independent of \(d\), for every monomial cost function, the price of stability is 1, that is, there exists an optimal stable assignment. On the other hand, we show that, for arbitrary cost function, the PoS is unbounded.  
Theorem 3.2: _For every monomial cost function, \(x^{d}\), \(PoS=1\)._  
Proof: We show that every social optimal is a NE. Let \(s^{*}\) be any social optimal solution. \(C(s^{*})=\sum_{t=1}^{T}l_{t}(s^{*})^{d}\). If \(d=1\), then, independent of the schedule, the cost of every job is 1, and \(s^{*}\) is a NE. Assume by contradiction that some player has a beneficial migration from a slot with load \(l_{1}\) into a slot with load \(l_{2}\). Thus, \(l_{1}^{d-1}>(l_{2}+1)^{d-1}\). The change in the total cost is \((l_{2}+1)^{d}-l_{2}^{d}+(l_{1}-1)^{d}-l_{1}^{d}\).  
If \(d>1\), then the migration is beneficial only if \(l_{1}>l_{2}+1\). For every \(d>1\), the function \(x^{d}\) is convex, thus the change in the total cost is negative for every \(l_{1}>l_{2}+1\).  
If \(d<1\), then the migration is beneficial only if \(l_{1}<l_{2}+1\). For every \(d<1\), the function \(x^{d}\) is concave, thus the change in the total cost is negative for every \(l_{1}<l_{2}+1\).  
We conclude that any beneficial migration contradicts the optimality of \(s^{*}\), implying that the social optimum is a NE.  
Theorem 3.3: _For arbitrary cost function, \(PoS=\Theta(n)\)._  
Proof: Given \(n,\epsilon\), we describe a game with \(PoS=(n-\epsilon)/2\). Let \(c(x)=1,\forall x<n\) and \(c(n)=n-\epsilon\). The set \(J\) includes \(n\) jobs where \(n-1\) jobs must be processed in slot 1, and job \(n\) can choose between slots 1 and 2.  
In the only optimal schedule, \(s^{*}\) all jobs except for job \(n\) are assigned on slot 1 and job \(n\) is assigned on slot 2. \(c(s^{*})=1+1=2\). However, \(s^{*}\) is not a NE. By migrating to slot 1, job \(n\) reduces its cost from 1 to \((n-\epsilon)/n<1\). The total cost of the resulting schedule, in which all the jobs are assigned on slot 1 is \(c(n)=n-\epsilon\). This schedule is the only NE for this instance. Thus, \(PoS=(n-\epsilon)/2\).
## 4 Results and Discussion
### CTAM Performances under Different Compaction Methods
#### 4.2.2 Maximum Dry Density and Optimal Moisture Content  
Aggregate A (moisture content of 4.0%, 4.5%, 5.0%, 5.5% and 6.0%) and Aggregate B (moisture content of 6.0%, 6.5%, 7.0%, 7.5% and 8.0%) with a constant cement content of 4.0% were compacted using a Proctor, vibration and gyration compactor, respectively. It should be noted that all the aggregates were saturated before the test. The optimal moisture content and maximum dry densities of Aggregate A and Aggregate B are shown in Figures 9 and 10, respectively.  
Figure 9 shows that the optimal moisture contents of Aggregate A and Aggregate B under gyration compaction were both lower than those under Proctor and vibration compaction. This is because during gyration compaction more cement grout was extruded from the specimen (Figure 11), which led to a lower measured moisture content of the mixture. It was also found that due to the lower moisture absorption of Aggregate A than of Aggregate B, the optimal moisture content of Aggregate A was lower than that of Aggregate B.  
Figure 8: Schematic diagrams of aggregate degradation for (**a**) Aggregate A and (**b**) Aggregate B.  
The maximum dry densities of Aggregate A and Aggregate B under gyration compaction were both higher than those under Proctor compaction, but lower than those under vibration compaction, as can be seen in Figure 10. A greater compaction effort, on the one hand, was applied to the specimen by SGC than by the Proctor tester. On the other hand, the cement grou  
Figure 11: Specim appearance under gyration compaction.  
Figure 10: Maximum dry densities of Aggregate A and Aggregate B.  
Figure 9: Optimal moisture contents of Aggregate A and Aggregate B.  
reduced the possibility of aggregate redistribution, thus the specimen under gyration compaction was more difficult to compact than that under vibration compaction.  
In addition, we also found that the maximum dry density of Aggregate A was higher than that of Aggregate B, and the optimal moisture content of Aggregate A was lower than that of Aggregate B. The phenomenon was attributed to the lower void content (or lower moisture absorption) and higher density of Aggregate A than of Aggregate B.
## 7 Conclusions  
In summary, a single-layer, multiband and multifunctional polarization converter was designed in this paper. The design principle of the proposed polarizer, which is based on a simple semicircle embedded with a rectangular slot, was thoroughly investigated, and evaluated. The proposed polarization converter achieves LP-CP conversion at five frequency bands and CPC at four frequency bands, with high efficiency. The physical process of polarization conversion is proposed, while considering the surface current distribution. Experimental measurements were carried out to verify and show good agreement with the simulation results. Moreover, a comprehensive comparison of the proposed structure with recently published single-layer polarization converters highlighted its advantages.  
Conceptualization, J.Y.; Methodology, S.H.; Software, S.H.; Validation, W.Y.; Formal analysis, F.A.U.; Investigation, M.I.; Resources, J.Y.; Data Curation, J.Y.; Writing--original draft, S.H.; Writing--review and editing, S.H.; Visualization, J.Y.; All authors have read and agreed to the published version of the manuscript.  
Figure 11: The measured and simulated results: (**a**) co- and cross-polarization conversion, (**b**) PCR (%); (**c**) axial ratio (dB) (blue line indicates the 3 dB axial ratio line).  
Funding: This work was supported by National Natural Science Foundations of China (No. 62127802). Data Availability Statement: The original contributions presented in this study are included in the article, further inquiries can be directed to the corresponding author/s. Conflicts of Interest: The authors declare no conflicts of interest.
## 6 Results
### Cluster Topology  
In general, a cluster topology is used when there is the need to segregate partially a network. Each cluster may assume specific tasks, for example, monitoring a region, traffic prioritization, provide redundancy, _etc_. Figure 20 illustrates a typical cluster topology for a WSN, where the clusters communicate each other through router devices.  
\begin{table}
\begin{tabular}{c r r} \hline \hline \multirow{2}{*}{**Device**} & \multicolumn{2}{c}{**Increase in MTTF**} \\ \cline{2-3}  & **1 spare device** & **2 spare devices** \\ \hline \multirow{2}{*}{Gtw} & \(2.32\%\) & \(2.43\%\) \\  & \(1.98\%\) & \(2.40\%\) \\  & \(-1.95\%\) & \(14.05\%\) \\  & \(3.60\%\) & \(17.19\%\) \\  & \(10.44\%\) & \(20.90\%\) \\  & \(19.23\%\) & \(25.43\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensitivity analysis.  
Figure 19: Analysis of the component importance based on Criticality measure for the line topology.  
Consider for example an application where each cluster monitors an industrial control loop. The application will fail if at least one of the cluster fails. On the other hand, a cluster will fail if all field devices within the cluster fail. The dependability target is to maximize the availability of the application and consequently to minimize the application outage (measured in hours per year).  
We have analyzed the influence upon the network unavailability of maintenance operations. The results of this analysis are illustrated in Figure 21. It can be observed that changes in the repair rate of the gateway or the access point do not cause significant changes in the network unavailability. On the other hand, the repair rates of the field devices and the routers have strong influence on the network unavailability. If, for example, the repair rate of one these devices is doubled, the application outage is decreased by around 50% (131 hours per year to 66 hours per year).  
According to the results presented in Figure 20, a critical device is the head of cluster. This function is executed by a router device. Assuming that there are two spare router devices available, the bottleneck of  
Figure 20: Typical cluster topology for a Wireless Sensor Network.  
Figure 21: Influence of the maintenance of devices in the network unavailability.  
cluster can be minimized in two different ways: configuring a structural redundancy (scenario illustrated in Figure 20) for two router devices where each one receives a spare, or configuring the two spare devices as new router devices in the network as illustrated in Figure 22. The results from this analysis are presented in Table 2. In the first case, when redundancy has been used, the application outage time was around tree times more efficient than the scenario presented in Figure 20 (\(\mu_{gtw}\), \(\mu_{AP}=0.2\) and \(\mu_{router}\), \(\mu_{field\ device}=0.02\)). On the other hand, the use of two new routers can significantly decrease the application outage by around 65 times. This result is due to the creation of new paths to the gateway, when new router devices are added to the network.