esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Formulations
### Nussbaum Function  
In order to manage the unknown control direction, the Nussbaum gain approach is investigated in this study. A Nussbaum-type function possesses the following properties:  
\[\begin{array}{l}\underset{s\rightarrow\infty}{\limsup}\underset{s_{0}}{ \sup}N(\zeta)d\zeta=+\infty\\ \underset{s\rightarrow\infty}{\liminf}\underset{s_{0}}{\sup}N(\zeta)d\zeta=- \infty\end{array} \tag{7}\]  
Nussbaum functions are commonly used as \(k^{2}\sin(k)\), \(k^{2}\cos(k)\), and \(\exp(k^{2})\cos(\frac{\pi}{2}k)\). In this study, the Nussbaum function \(\exp\big{(}\zeta^{2}\big{)}\cos(\frac{\pi}{2}\zeta)\) is exploited.  
**Lemma 1**[29; 30; 31]: _if \(V(t)\) and \(\zeta_{i}(t)\) are smooth functions defined on \(\Big{[}0,t_{f}\Big{)}\) with \(V(t)>0\), \(\forall t\in\Big{[}0,t_{f}\Big{)}\), if the following inequality satisfies:_  
\[V(t)<c_{0}+e^{-C_{1}t}\underset{0}{\int}\underset{i=1}{\overset{n}{\sum}}\,( g_{i}N(\zeta_{i})\pm 1)\dot{\zeta}_{i}e^{C_{1}t}d\tau,\forall t\in\Big{[}0,t_{f}\Big{)} \tag{8}\]  
_where \(g_{i}\) is a non-zero constant, \(C_{1}>0\), and \(c_{0}\) expresses some appropriate constants, then \(V(t)\), \(\zeta_{i}(t)\) and \(\int_{0}\underset{i=1}{\overset{n}{\sum}}\,(g_{i}N(\zeta_{i})\pm 1)\dot{ \zeta}_{i}d\tau\) must be bounded on \(\Big{[}0,t_{f}\Big{)}\)._  
**Definition 1:**_Given a nonlinear system, \(\dot{x}=f(x,u)\in R^{n\times 1}\) with the equilibrium \(x_{e}\) is semi-globally uniformly ultimately bounded (SGUIUB) if for any \(\Omega\), a compact subset of \(R^{n}\) and all \(x(t_{0})=x_{0}\in\Omega\), there exists a \(\mu>0\) and a number \(T(\mu,x_{0})\) such that \(\|x(t)\|<\mu\) for all \(t>t_{0}+T\)._
# Optimization of Distribution Network Current Protection for Inverter-Based Distributed Power Access
## 5 Current Protection Optimization Enhancement Program
### Judgment 2  
Although Criterion 1 has the potential to extend the protection range to the entire length of the line by relying on threshold Ih1, which is lower than the traditional setting value and is not affected by the system operation mode and fault type, the low setting value is susceptible to causing a short circuit in the neighboring line, thereby triggering the protection error in this interval. Therefore, relying on Criterion 1 in isolation is an inadequate method of ensuring the selectivity of the protection action. It is thus proposed that Criterion 2 be implemented in order to enhance the selectivity of the proposed protection method.  
Figure 3 depicts a representative distribution network wiring schematic, with the BC section line serving as a case study. In a state of normal operation, the line's current relationship is theoretically consistent with the conditions set forth in Equation (20).  
\[|I_{1}|=|I_{2}+I_{3}| \tag{20}\]  
Satisfaction \(\Delta I_{BC}\) is calculated as follows:  
\[\Delta I_{BC}= |I_{1}-(I_{2}+I_{3})| \tag{21}\]  
In a state of normal operation, the current values of the BC section lines are relatively low. However, in the event of a short circuit fault occurring at a given point along the BC section line, the value of the current in question will increase significantly. Concurrently, the current difference observed in the remaining normal operating intervals will remain at a low level. In light of the aforementioned principle, the current difference of the intervals can be defined in accordance with the expression detailed in Equation (22).  
\[\Delta I_{i}= |\sum I_{i-in}-(\sum I_{i-out})| \tag{22}\]  
where \(\sum I_{i-in}\) denotes the sum of the currents in the inflow zone, and \(\sum I_{i-out}\) denotes the sum of the currents in the outflow zone.  
Let the condition threshold \(I_{h2}\) be satisfied as follows:  
\[I_{h2}=K_{2}\sum I_{i-in} \tag{23}\]  
In summary, Criterion 2 can be expressed as follows: once Criterion 1 has been satisfied, if the current difference between the existing intervals exceeds the set threshold, it is deemed that a fault has occurred in the interval and that the protection of the interval is initiated. Conversely, if the threshold is not exceeded, it is considered that the system is operating normally and that the protection is not activated. This is illustrated in (24).  
\[\left\{\begin{array}{l}\Delta I_{i}<I_{h2},\text{protection does not operate}\\ \Delta I_{i}>I_{h2},\text{protection action, output }I_{h1}\end{array}\right. \tag{24}\]  
Figure 3: Schematic diagram of a standard distribution network configuration.  
Finally, the real-time adjustment factor \(K_{0}\) can be derived from Equation (25).  
\[K_{0}=\frac{I_{set-c}(Z_{\text{Smin}}+Z_{L})}{E}=K_{rel}\frac{I_{H1}}{I_{load}} \tag{25}\]
## 6 Conclusions  
1. From the tolerance contribution degree, it can be seen that \(T_{3}\) is the output shaft 2 large gear tooth thickness deviation and \(T_{8}\) is the input shaft 1 two gear phase angle deviation. Tolerance \(T_{3}\) is the center distance error of the first stage gear; tolerance \(T_{8}\) is the phase angle error of the first and second stage gear; the error value of the center distance and phase angle is consistent with the direction of the backlash, and at the same time, due to the large value of the center distance error, it has the greatest impact on the backlash, followed by the phase angle error. These two design tolerances can be reduced appropriately in the theoretical design stage to obtain a more reasonable equal load factor.
2. The small-displacement-based Jacobian spin theory was used for three-dimensional tolerance modelling, and a three-dimensional tolerance analysis method was used to derive the backlash distribution law, which is of great significance for the optimization of gear system parameters. Meanwhile, the load balancing performance of the system can be improved by reducing the torsional stiffness of the branch elastic shaft with large tooth backlash.
3. The three-dimensional tolerance theory analysis method can calculate the backlash range more accurately, and utilizes the backlash range to calculate the uniform load coefficient range. The test-measured uniform load coefficient exceeded the theoretical range by 5.08%. The error was 6.61% compared to the uniform load coefficient calculated from the measured backlash. This method of calculating the uniform load coefficient has a greater reference value.  
**Replication of Results:** Both the backlash results and the mean load factor results in this paper can be reproduced using the data and equations in the research.  
[PERSON] collected and organized the data, conceived and designed the analysis, and conceived and wrote the paper; [PERSON] collected and organized the data and revised the paper; [PERSON] contributed to writing and revisions. All authors have read and agreed to the published version of the manuscript.  
The author declares that no funds, grants, or other support were received during the preparation of this manuscript.  
Not applicable.  
Figure 14: Input torque versus equal load factor.
## 2 Point Cloud Registration Algorithm
### Iterative Closest Point Accurate Registration  
The initial transformation matrix is the key to improved matching for accurate registration. An optimized rotational translation matrix \([\mathbf{R}_{0},\mathbf{T}_{0}]\) was obtained by initial registration, which is used as an initial value for accurate registration to obtain a more accurate transformation relationship by the iterative closest point algorithm.  
Based on the optimal rotation translation matrix obtained from the initial registration, the source point cloud \(O_{s}\) is transformed into \(O_{s}\), and it is used together with \(O_{t}\) as the initial set for accurate registration. For each point in the source point cloud, the nearest corresponding point in the target point cloud is determined to form the initial corresponding point pair and the corresponding point pair with the direction vector threshold is deleted. The rotation matrix \(\mathbf{R}\) and translation vector \(\mathbf{T}\) are then determined. Given that \(\mathbf{R}\) and \(\mathbf{T}\) have six degrees of freedom while the number of points is huge, a series of new \(\mathbf{R}\) and \(\mathbf{T}\) are obtained by continuous optimization. The nearest neighbor point changes with the position of the relevant point after the conversion; therefore, it returns to the process of continuous iteration to find the nearest neighbor point. The objective function is constructed as follows:  
\[f(R,T)=\frac{1}{N_{P}}\sum_{i=1}^{N_{P}}\left|O_{t}^{i}-R\cdot O_{s}^{i}-T \right|^{2}\, \tag{5}\]  
when the change of the objective function is smaller than a certain value, it is believed that the iterative termination condition has been satisfied. More precisely, accurate registration has been completed.
## 3 Challenges to be Overcome in an ICT--Based Diabetes Management System
Multiple access points have to be available to check data and processes and to change settings, which can be done not only via the patient's smartphone, but also remotely via the Internet or another smartphone (which could be useful for monitoring children or aged/impaired patients). * User-oriented environment. In a health care context, the focus must always be on the user. A proper interface, from a cross-platform perspective, is essential to enable patients to take advantage of this work's concept. Furthermore, a friendly way to provide information is necessary when considering a population sector with special requirements, such as people with disabilities, the elderly, or children. Customizable options will result in a good user experience. * Privacy, security, integrity. These usual concepts in ICT frameworks are also critical in the proposal of this work, and in fact, personal data are being processed. Therefore, control mechanisms need to be provided in order to assure a proper management of such sensitive information [53]. Once more, it is necessary to remark that, although some previous proposals offer some of these points, to the best of the authors' knowledge, there is not one that is complete with all of them.
## 3 Results
### Spatial Coverage and Distribution of Land Cover Categories
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Island**} & \multirow{2}{*}{**Land Cover**} & \multicolumn{2}{c}{**Agricultural Area**} & \multicolumn{2}{c}{**Buffer Zone**} & \multicolumn{2}{c}{**Total**} \\ \cline{3-8}  & & **Ha** & **\%** & **Ha** & **\%** & **Ha** & **\%** \\ \hline \multirow{8}{*}{**Santa Cruz**} & Native Vegetation & 1500.40 & 13.12 & 1405.95 & 76.92 & 15,566.35 & 52.37 \\  & Invasive Vegetation & 2474.12 & 21.63 & 3061.52 & 16.74 & 5535.64 & 18.62 \\  & Pastures & 3726.33 & 32.58 & 185.67 & 1.02 & 3911.99 & 13.16 \\  & Transitory and Permanent Crops & 2549.13 & 22.29 & 118.93 & 0.65 & 2668.07 & 8.98 \\  & Mixed Forest and Pioneer & 1090.52 & 9.53 & 948.41 & 5.19 & 2038.93 & 6.86 \\ \hline \multirow{8}{*}{**San**} & Native Vegetation & 2535.61 & 30.40 & 6981.65 & 87.10 & 9517.25 & 58.18 \\  & Invasive Vegetation & 2766.56 & 33.17 & 270.02 & 3.37 & 3036.58 & 18.56 \\  & Pastures & 790.09 & 9.47 & 67.67 & 0.84 & 857.76 & 5.24 \\  & Transitory and Permanent Crops & 972.11 & 11.65 & 81.63 & 1.02 & 1053.73 & 6.44 \\  & Mixed Forest and Pioneer & 1175.85 & 14.10 & 468.43 & 5.84 & 1644.28 & 10.05 \\ \hline \multirow{8}{*}{**Isabela**} & Native Vegetation & 657.99 & 12.63 & 3555.66 & 47.22 & 4213.66 & 33.08 \\  & Invasive Vegetation & 1871.86 & 35.93 & 2149.56 & 28.55 & 4021.42 & 31.57 \\  & Pastures & 1047.19 & 20.10 & 467.18 & 6.20 & 1514.37 & 11.89 \\  & Transitory and Permanent Crops & 1017.21 & 19.52 & 92.31 & 1.23 & 1109.52 & 8.71 \\  & Mixed Forest and Pioneer & 620.51 & 11.91 & 1302.27 & 17.30 & 1922.78 & 15.09 \\ \hline \multirow{8}{*}{**Floreana**} & Native Vegetation & 15.81 & 5.84 & 1044.70 & 71.43 & 1060.52 & 61.17 \\  & Invasive Vegetation & 76.63 & 28.28 & 204.68 & 13.99 & 281.32 & 16.23 \\  & Pastures & 56.48 & 20.84 & 12.95 & 0.89 & 69.42 & 4.00 \\  & Transitory and Permanent Crops & 74.22 & 27.38 & 95.22 & 0.65 & 83.73 & 4.83 \\  & Mixed Forest and Pioneer & 39.18 & 14.46 & 181.43 & 12.40 & 220.62 & 12.73 \\ \hline \multirow{8}{*}{**All Islands**} & Native Vegetation & 4709.81 & 18.64 & 25,647.96 & 72.67 & 30,357.77 & 50.13 \\  & Invasive Vegetation & 7189.17 & 28.46 & 5685.79 & 16.11 & 12,874.96 & 21.26 \\ \cline{1-1}  & Pastures & 5620.08 & 22.25 & 733.46 & 2.08 & 6353.54 & 10.49 \\ \cline{1-1}  & Transitory and Permanent Crops & 4612.67 & 18.26 & 302.39 & 0.86 & 4915.06 & 8.12 \\ \cline{1-1}  & Mixed Forest and Pioneer & 2926.06 & 11.58 & 2900.54 & 8.22 & 5826.60 & 9.62 \\ \hline \hline \end{tabular}  
*Shaded cells group land cover types by island.  
\end{table} 
Table 11: Extent and percentage cover by general vegetation cover categories. *
## 3 System Architecture
### Match Filter  
In Section 4, we will show the relationship between SNR and communication. With the higher communication distance, the SNR values will be smaller. Therefore, the receiver side will be challenging to define the threshold between ON and OFF values. We should find another technology to replace the zeros crossing technology to increase the communication distance.  
The matched filter, known as a linear filter technology, maximizes the SNR value with additive random noise in the communication system. It is widely applied in wireless communication systems to optimize the SNR values at a low cost. The 802.15.7-2018 standard [7] recommends using the matched filter in an OCC system. This paper proposes using the matched filter to optimize the SNR value and increase the communication distance. As shown in Figure 2, the signals from 48 LEDs were combined based on the intensity of 48 areas, which were determined from four positioning anchor corners. By generating known patterns such as those in Figure 3, the received signals from two adjacent LEDs will multiply with the 1 pulse pattern and the 0 pulse pattern via the convolution algorithm. Therefore, we can easy to define the signal value (1 or 0).  
Figure 2: An experimental results of 2D-MIMO scheme from 48 LEDs (without 16 corner LEDs) in \(8\times 8\) matrix.
## 5 Conclusions  
This work has analysed the contribution of the GSS and VT cues to the generation of happy and aggressive emotional vowels by means of GlottDNN-based analysis-by-synthesis methodology, considering a parallel neutral-expressive speech database. The experiments have considered the Spanish \([\mathrm{a}]\), \([\mathrm{i}]\) and \([\mathrm{u}]\) vowels from aggressive, happy, and neutral paired utterances. Results from the objective evaluation show that both GSS and VT cues have a statistically significant contribution to convey these tense voice emotions when compared with the baseline reference (with expressive prosody, \(\mathrm{GSS_{N}}\) and \(\mathrm{VT_{N}}\)). The subjective evaluations show that VT cues significantly affect the perceived emotion of vowels \([\mathrm{a}]+[\mathrm{a}]\), while GSS cues have a significant effect on the vowels \([\mathrm{i}]+[\mathrm{i}]\) and \([\mathrm{u}]+[\mathrm{u}]\), especially on the former.  
Future work will focus on developing further analyses to validate the obtained results on other phonemes and will also consider other expressive speaking styles and/or phonation types, such as the ones present in sadness. Furthermore, we also plan to input the obtained results within our 3D-based numerical synthesizer by introducing the observed relevant variations of both the glottal source and the vocal tract to improve the current naturalness of the happy and aggressive speaking styles generated.  
Conceptualisation, methodology, writing--original draft preparation, writing--reviewing, and editing, M.F., J.C.S. and F.A.; software, formal analysis and investigation, M.F. and J.C.S.; validation, data curation, and visualisation, M.F.; supervision, J.C.S. and F.A. All authors have read and agreed to the published version of the manuscript.  
This research has been partially funded by the Agencia Estatal de Investigacion (AEI) through the FEMVoQ project (PID2020-120441 GB-I00/AEI/10.13039/501100011033).  
Not applicable  
The data generated in this study are available on request from the corresponding author.  
The authors would like to thank the participants on the perceptual test for their collaboration in this work.  
The authors declare no conflict of interest.  
The data generated and analysed during the current study is available from the corresponding author on reasonable request.  
The following abbreviations are used in this manuscript.
# Elastic Computing in the Fog on Internet of Things to Improve the Performance of Low Cost Nodes
## 5 Test Bed for Soundscape Monitoring and Its Performance Analysis  
As a proof of concept and in order to highlight the advantages of the proposed fog computing scheme, in this section we analyze a deployment that requires high computational costs applied to psycho-acoustic annoyance (PA) monitoring, or soundscape monitoring, as shown in Figure 2. We used this scenario as a real example to performance elastic computing on a cluster of RPi, without loss of generality in our results. This IoT application for soundscape monitoring is an example of the new trends on IoT, as mentioned before. Thus, in this case we see the challenges imposed by these low cost devices (RPi) when requiring enhanced computational power that these devices alone are unable to provide, and that they need to collaborate in a fog computing paradigm.  
Soundscape monitoring is characterized by requiring tough signal processing algorithms. These algorithms are explained and analyzed in [22; 23]. Behind this monitoring process, there are several rules and standards such as Environmental Noise Directives (ENDs) 2002/49/EC and ISO 12913 (soundscape) [24; 25]. In particular, END 2002/49/EC requires main cities (with more than 250,000 inhabitants) to gather real data on noise exposure in order to produce local action plans and to provide accurate real time mappings of noise pollution levels.  
The evaluation of this annoyance (or PA) is mainly based on the work by [PERSON] and [PERSON] [26], defining a set of parameters such as loudness (L), sharpness (S), fluctuation strength (F), and roughness (R) that will let us measure PA. In other words, PA is given by a function knowing L, S, F, and R, and we need all of them before calculating PA. The flow diagram of these algorithms is shown in Figure 3. Due to the complexity of their implementation, we cannot perform this monitoring process with conventional hardware (such as RPi) in a simple IoT deployment, and less so in real time. We must highlight that we refer to real time as when the time required to process an audio chunk is shorter than the chunk itself, which is by default 1 second.  
It must be stressed in these scenarios that due to stringent laws related to personal privacy, it is essential that PA calculations must be performed in real-time and as close as possible to the source within the communication range, because the data are not allowed be to sent the recorded audio out.  
First, we carry out the performance analysis in two steps. First, we analyze parallelization and granularity issues of the containers, and secondly, we analyze its throughput.
# A Distributed Approach to the Evasion Problem
## 1 Introduction
In [2], the authors prove that the sensor model used in previous work is insufficient for a full solution to the evasion problem, and further provide an algorithm that--using slightly increased sensor capabilities--provides a sufficient and necessary criterion for evasion. Another and more complex sufficient and necessary criterion using sheaf theory was proposed in [3]. Most of the models mentioned above are focused on deployment of sensors to achieve the goal of covering a certain region with static sensors. However, another interesting perspective of the problem is dynamic coverage, where mobile sensors are deployed or dispatched. In particular, in the scenario of tracking down a target when assuming the target is static, using mobile sensors may help increasing the covered region over time such that tracking scheme may require fewer sensors to be deployed. There has been research [16; 17] in the direction of sensor deployment, where mobile sensor were used to increase the coverage in addition to static sensors. In our work, we are more interested in determining if a target can be detected, rather than focusing on whether or not a certain region is covered completely by a sensor network. Therefore, in this work, we primarily focus on the Evasion problem as described above. After the evasion problem was introduced in [1], it took until [2] before a sufficient and necessary criterion was constructed for the existence of an evasion path. In this paper, building on the previous work, we make two important contributions to the field:  
1. We adapt the algorithm from [2] to a distributed computing setting, where each mobile sensor only needs to track their neighbors. 2. We propose an algorithm for enumerating possible evasion paths up to homotopy. The rest of this paper is structured as follows: Section 2 presents [PERSON] algorithm from [2], extends it to a distributed sensor network setting and proposes evasion path tracking with both forwards and backwards schemes. Section 3 summarizes this work and discusses potential directions for future work.
## 2 Methodology
### Orbital Parameters
#### 2.1.2 Eccentricity (\(e\))  
The eccentricity (\(e\)) of an orbit describes how far away from a perfect circle the orbit is, so it is a measure of the orbit's shape. Equation (1) defines eccentricity as the ratio of the distance between the center of the Earth and the satellite at its closest point (perigee) to the distance between the center of the Earth and the satellite at its farthest point (apogee).  
\[e=\frac{c}{a} \tag{1}\]  
where \(c\) is the center-focus distance of the ellipse and \(a\) is the semi-major axis. Additionally, an orbit that is very eccentric is closer to one, while a circular orbit has an eccentricity of zero [13]. Moreover, not the center of the ellipse, but one of its focal points, is where a satellite in an eccentric orbit moves. Figure 3 shows orbits with different eccentricity values.
## 3 Results and Discussion
### Encapsulation Efficiency, Drug Loading, and Recovery Yield of MPs  
Encapsulation efficiency (EE) is defined as the ratio between the weight of entrapped drug and the total original weight of the drug, while drug loading (DL) is defined as the ratio between the amount of entrapped drug and the total weight of the matrix [25]. The nature and concentration of the carrier, the formulation technique, and the nature of the active pharmaceutical ingredient are some of the factors that influence the EE and DL of MPs. The insulin-loaded MPs showed EE, DL, and recovery values (RV) that ranged over 81%-92%, 18%-39%, and 75%-89%, respectively (Table 2). These encouraging values were attributed to the method used for the inclusion of insulin; the insulin was not incorporated during the homogenization step because this could lead to degradation, as has been observed for protein drugs subjected to high homogenization forces [7]. The observed high EE% is ascribed to a high adsorption capacity for insulin in these polymeric matrixes. Additionally, an increase in PEG-4000 concentration caused increases in the EE, DL, and RV (e.g., see batch A5 vs. A1) (Table 2). The recovery values (RV) of batches loaded with insulin MPs ranged from 75% to 89%, being much higher than that of the unloaded batch A0 (66.5%). This could be attributed to good adsorption of insulin into the polymeric MPs. Furthermore, batch A5 showed the maximum RV value of 89%, while batch A1 and unloaded batch A0 displayed RV values of 75% and 66%, respectively. Hence, the recovery values increased with drug loading, which is in good agreement with the observations made in previous studies [26].