esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Control Design and Stability Analysis
### Surge Control
\begin{split}\tau_{u}=& m_{0}\left(-k_{u}u_{e}+ \dot{\alpha}_{u}-\frac{F_{xD0}}{m_{0}}-vr-\hat{W}_{u}^{T}H_{u}\left(Z_{u} \right)-\bar{\delta}_{u}sgn\left(u_{e}\right)\right.\\ &-\frac{k_{ua}^{2}\left(t\right)-u_{e}^{2}}{k_{ua}^{2}\left(t \right)}\left(\cos\psi\frac{k_{xa}^{2}\left(t\right)x_{e}}{k_{xa}^{2}\left(t \right)-x_{e}^{2}}+\sin\psi\frac{k_{ya}^{2}\left(t\right)y_{e}}{k_{ua}^{2} \left(t\right)-y_{e}^{2}}\right)\\ &\left.-\frac{k_{ua}^{2}\left(t\right)-u_{e}^{2}}{k_{ua}^{2}\left( t\right)}\left(\frac{k_{ua}}{u_{e}}\log\frac{k_{ua}^{2}\left(t\right)}{k_{ua}^{2} \left(t\right)-u_{e}^{2}}-\frac{k_{ua}u_{e}}{k_{ua}^{2}\left(t\right)-u_{e}^{2 }}\right)\dot{k}_{ua}\right)\end{split}  \tag{29}\]  
\[\hat{W}_{u}=\Gamma_{u}\left(\frac{k_{ua}^{2}\left(t\right)u_{e}}{k_{ua}^{2} \left(t\right)-u_{e}^{2}}H_{u}\left(Z_{u}\right)-\sigma_{u}\hat{W}_{u}\right) \tag{30}\]  
where \(k_{u}\), \(\bar{\delta}_{u}\), and \(\sigma_{u}\) are positive constants. **Remark 6**. : _Using L'Hopital's rule, we have:_  
\[\lim_{u_{e}\to 0}\frac{k_{ua}\left(t\right)}{u_{e}}\log\frac{k_{ua}^{2}\left(t \right)}{k_{ua}^{2}\left(t\right)-u_{e}^{2}}=\frac{2k_{ua}\left(t\right)u_{e}}{ k_{ua}^{2}\left(t\right)-u_{e}^{2}} \tag{31}\]  
_Therefore, the surge control law \(\tau_{u}\) is well defined._Substituting (29) and (30) into (28) yields:  
\[ \begin{split}\dot{V}_{u}=&\frac{k_{ua}^{2}\left(t\right)u _{e}}{k_{ua}^{2}\left(t\right)-u_{e}^{2}}\left(-k_{u}u_{e}+\tilde{W}_{u}^{T}H_{u }\left(Z_{u}\right)+\delta_{u}-\tilde{\delta}_{u}sgn\left(u_{e}\right)\right)\\ &-u_{e}\left(\cos\psi\frac{k_{xa}^{2}\left(t\right)x_{e}}{k_{xa}^ {2}\left(t\right)-x_{e}^{2}}+\sin\psi\frac{k_{ya}^{2}\left(t\right)y_{e}}{k_{ya }^{2}\left(t\right)-y_{e}^{2}}\right)\\ &-\tilde{W}_{u}^{T}\Gamma_{u}^{-1}\left(\Gamma_{u}\left(\frac{k_{ ua}^{2}\left(t\right)u_{e}}{k_{ua}^{2}\left(t\right)-u_{e}^{2}}H_{u}\left(Z_{u} \right)-\sigma_{u}\hat{W}_{u}\right)\right)\\ &\leq-k_{u}\frac{k_{ua}^{2}\left(t\right)u_{e}^{2}}{k_{ua}^{2} \left(t\right)-u_{e}^{2}}+\sigma_{u}\tilde{W}_{u}^{T}\hat{W}_{u}-u_{e}\left( \cos\psi\frac{k_{xa}^{2}\left(t\right)x_{e}}{k_{xa}^{2}\left(t\right)-x_{e}^{ 2}}+\sin\psi\frac{k_{ya}^{2}\left(t\right)y_{e}}{k_{ya}^{2}\left(t\right)-y_{e }^{2}}\right)\end{split}  \tag{32}\]
## 5 Experiment Verification
### Experiment Equipment
#### 5.1.2 The Measurement System:  
1. The system uses the DAQ data measure interface which captures, processes, and converts the signal source from the sensor (thermocouple). It primarily consists of a high-performance measurement and controller card, signal processing module, filter amplifier, and charge amplifier. Combined with Signal Express included in the graphic control software, temperature data captured by the measurement system records complete data in real time.
2. Actual testing was conducted on types I and II as they performed as the top candidates in the simulation as shown in Figures 13 and 14.  
Figure 12: A schematic of system planning for the active heat dissipation device.  
Figure 13: Specim Type I.
# Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks +
## 6 Numerical Results
### Link Budget Simulation
The time of 6.5 h is 27% out of 24 h, and 5 h is 20%, which is about a 7% improvement in performance. However, about 73% of the time one is still unable to communicate, so the impact is insignificant in one orbit. Figure 18b presents the \(T_{S}\) result in six orbits. The maximum \(T_{S}\) is 23.55 h for \(N_{dntrs}=1\), and the maximum \(T_{S}\) for \(N_{dntrs}=2,3\) are 24 h. \(T_{S}\) does not make a large difference, but the proposed method performs better with fewer satellites. For example, when \(N_{dntrs}=2,3\), the maximum (24 h) \(T_{S}\) is possible using 114 satellites, but when \(N_{dntrs}=1\), the maximum \(T_{S}\) is 23.55 h using 162 satellites. By adding _DMRS_ symbols, one can achieve good performance  
Figure 16: The maximum persistent service time (_MPST_) for different numbers of _DMRS_ symbols per slot. (a) one orbit, (b) six orbits. with 48 fewer satellites. As a result of the analysis in this study, the addition of _DMRS_ symbols shows that it can satisfy existing performance standards by using fewer satellites.
## 3 Materials and Methods
### MFCC and RASTA Features  
As the gold-standard method of SR, MFCCs are the acoustic features of choice for this study, along with RASTA-PLP features, which are used as noise-robust alternatives.  
Using OpenSMILE, version 3.0 (audEERING GmbH, Gilching, Germany [37]), we extracted 1400 features from the MFCC domain (14 mel bands \(\rightarrow\) coefficients) and 2600 features from the RASTA-style filtered auditory spectrum (26 Bark bands \(\rightarrow\) PLP coefficients, from 0 to 8 KHz) of each audio signal.  
Each feature is the result of a smoothed moving average over the whole signal and represents a specific metric such as the mean, max, and min, or quartiles. Delta (first derivative) and delta-delta indicators are also extracted for both MFCC and RASTA features.  
From a starting number of 4000 (1400 + 2600), the features endured a feature selection procedure. We implemented [PERSON]'s correlation-based feature selector (CFS) on Weka(r), version 3.8.5 (University of Waikato, Hamilton, New Zealand [38]) in order to find reduced optimal subsets in accordance with the principles behind the \"curse of dimensionality\" [39]. The algorithm is based on the computation of a minimum redundancy, maximum relevance merit factor for a given subset of features according to the following formula [40]:  
\[M_{S}=\frac{kr_{cf}}{\sqrt{k+(k-1)r_{ff}}}, \tag{3}\]  
where k is the number of features in the subset \(S\); \(r_{cf}\) is the mean correlation between features and class; and \(r_{ff}\) is the mean cross-correlation among the \(k\) features within the set.  
A forward greedy stepwise search algorithm [41] was employed to select the best subset: as an optimizer, the number of features retained was different for each classification problem and/or feature family.  
Feature selection was applied to MFCC and RASTA datasets as standalone groups, as well as to the complete set of the two families united.  
As a further refinement, the features were ranked according to [PERSON]'s Information Gain [42], which is a measure of the discriminative power of a feature in terms of the entropy difference before and after the dataset has been split according to that specific feature. The top 50 features were retained after ranking to create consistent, even more reduced sets.
## 3 Results and Discussion
### Long-Term Trend of LDV and HDDV Emission Factors
Studies in the 1990s observed HDDV/LDV NOx emission ratios in the range of 3-6, and this ratio increased in the mid-2000s to ~13-15, as LDV emissions were reduced relative to HDDV vehicles when Tier-1 and Tier-2 LDV entered the vehicle fleet. Our study is the first among those listed in Table 2 with appreciable penetration of SCR-equipped HDV, and we observed a HDDV/LDV emission ratio of 6, consistent with a rapid drop in HDDV emissions relative to LDV emissions. HDDV emissions of EC showed a similar rapid drop. The HDDV/LDV EC emission ratio fell by at least 50%, and perhaps more, between the mid-2000s and 2013-2014 when our measurements were acquired. The relative emissions of OC, on the other hand, have been more stable. In Figure 6, we also compared the vehicle emission factors measured in tunnels with the national level vehicle emission rates simulated by the EPA MOVES. Unlike tunnel studies that only measure the vehicle emissions under high-speed driving, MOVES estimates the vehicle emission factors under a wide range of conditions, including vehicle cold starts and transient operation. The emission factors estimated by MOVES are used for national emission inventory development. As shown in Figure 6, MOVES consistently has larger estimates for LDV emissions of CO, NOx, and OC than tunnel studies. For 2014, our LDV emission factors for both NOx and CO were roughly half of what is predicted by MOVES. MOVES and the tunnel studies agree better for LDV EC, though for 2014, our measurements suggest a larger EF than MOVES. However, the trend in LDV EC emissions does not agree between MOVES, which shows a smooth decrease with time, and tunnel measurements, which show roughly constant emissions. Overall, MOVES and the tunnel measurements agree better for HDDV emissions. For HDDV NOx emissions, the tunnel measurements largely agree (within error bounds) with MOVES from 1997 \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**References** & **Tunnel** & \begin{tabular}{c} **Year of** \\ **Measurement** \\ \end{tabular} & \
\begin{tabular}{c} **NOx** \\ \end{tabular} & **OC** & **EC** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} [PERSON] et al. \\ \end{tabular} } & Fort McHenry Tunnel, & \multirow{2}{*}{1992} & \multirow{2}{*}{\(4.0\pm 0.4\)1  \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ratio of HDDV-to-LDV emission factors measured in traffic tunnels in the United States. onward. The same is true for CO since 1992, albeit for only three points in time. Agreement for OC and EC is more variable. MOVES predicts higher emissions than tunnel measurements for the 1990s and early 2000s. MOVES also predicts higher emissions than our 2014 data for both OC and EC, though agreement is better than in previous years.
# A Long-Distance WiFi Network as a Tool to Promote Social Inclusion in Southern Vercarruz, Mexico
## 3 Methodology
### Strengths, Weaknesses, Opportunities, and Threats Matrix
\begin{table}
\begin{tabular}{c l l} \hline \hline  & **Strengths** & **Weaknesses** \\ \hline  & \(\cdot\) Take advantage of state investment & \(\cdot\) Allocate state resources to social \\  & for the development of tourism, & impact investments allowing to \\  & seeking to attract a greater number & improve the quality of life of the \\  & of the of visitors. (O1, O5, S1) & inhabitants. (O1, O2, W1, W5) \\  & \(\cdot\) Improve the main development & \(\cdot\) Promote public and private \\  & programs to increase agriculture & investment to generate employment \\  & and livestock production. (O2, S3) & and development in Mecayapan. \\  & \(\cdot\) Manage private, states, and & (O1, O2, W2, W3, W4) \\  & government investment to close & \(\cdot\) Schedule training aimed at small \\  & the gaps in transportation and & entrepreneurs, guiding them to \\  & communications. (O3, O4, S5) & formalization. (O2, W3) \\ \hline  & \(\cdot\) Generate regulations to promote & \(\cdot\) Direct the resources of the \\  & agricultural and livestock production & municipality to improve basic \\  & in a responsible manner, in terms of & services (health, education) and \\  & the use of natural resources. & employment to reduce the \\  & (S2, S3, T5) & (W1, W4, W5, T2) \\  & \(\cdot\) Improve the living conditions of & \(\cdot\) Formalize local businesses to \\  & the inhabitants to mitigate the risks & improve tax collection to mitigate \\  & of weather effects. & the effects of project suspension \\  & (S2, S4, T1, T2, T3) & due to lack of financial resources. \\  & & (W2, W3, T4) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: SWOT Matrix. According to the matrix, the strategy that generates the greatest impact for the project and that allows the project to spread in other territories is to manage private, state, and government investment in order to close the gaps in transportation and communications. Taking this into account, in the medium to long term, it will be necessary to optimize the transport infrastructure, through the creation of new roads or highways, in order to generate greater spatial mobility between Mecayapan and the other territories in the area.
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 3 Related Work
### Static Approaches
#### 3.1.1 Alice  
The autonomous link-based cell scheduling for TSCH (ALICE) [7] is one of the first schedulers that improves the performance of Orchestra. It keeps the structure of the slotframes dedicated to EBs and RPL control traffic but changes the structure of the slotframe dedicated to unicast traffic. For the latter, the authors of ALICE imagined a link-based approach. Instead of allocating one cell per node, the scheduler allocates one cell for each direction of communication between nodes. This way, the scheduler separates upstream and downstream traffic and avoids collisions. Additionally, the scheduling of cells changes in time to avoid repeating possible overlaps in the time slot allocation between the same nodes. The overlaps could appear because of the hashing function yielding the same cell in the slotframe to send or receive on. The authors break the repetition by introducing a new variable, the absolute slotframe number (ASFN), to compute the time offset. Other improvements to Orchestra are the early drop of packets and the impromptu matching between the packet and cell. The early drop of packets is achieved by checking if the packets' MAC address in the queue corresponds to one of the existing neighbors' addresses. The impromptu matching between cell and packet is performed by allocating the communication cell at the moment the packet is ready to be sent and not when the packet is added to the queue, which is the case for Orchestra. ALICE, implemented in Contiki, is compared to Orchestra, demonstrating its superiority in latency, reliability, and energy consumption. One of the disadvantages of ALICE is that it does not adapt its schedule to traffic changes.  
Figure 3: Overview of discussed schedulers.
# Time-Specified Adaptive Robust Control Framework for Managing Nonlinear System Uncertainties
## 2 Background and Related Work
### Adaptive Control  
Adaptive Control is a method employed to manage uncertainties in system parameters and environmental variations. For uncertain nonlinear systems, Adaptive Control adjusts the controller parameters in real-time to achieve the desired performance. Adaptive Control encompasses two main strategies: Model Reference Adaptive Control (MRAC) and Self-Tuning Regulator Control (STRC). In Model Reference Adaptive Control (MRAC) [47], a reference model is established to describe the desired behavior of the system. The controller parameters are adjusted so that the actual system output aligns with the reference output. These parameters are continuously updated in real-time to accommodate any changes in the system, with stability typically ensured by employing Lyapunov-based methods. Self-Tuning Regulator Control (STRC) directly estimates and adjusts the controller parameters to account for uncertainties within the system. Adaptive Control offers significant advantagesin handling uncertainties in system parameters and adapting to environmental changes, which makes it an extremely versatile approach. However, its design for nonlinear systems can be quite complex, and challenges may arise regarding the convergence of parameter estimation. Despite these challenges, Adaptive Control is extensively used across various industries, such as aerospace, autonomous driving, unmanned vehicles, and robotics, particularly for systems with dynamic or fluctuating parameters.
## 4 Discussion
Consequently, we believe that the 1 min-STST and CST are valid, reliable and reproducible tools for the evaluation of effort tolerance in people who have suffered from COVID-19. The MDC was also assessed in this study, which is the smallest measure change that can be objectified and cannot be attributed to a measurement error [28]. Based on the MDC established in the present study, the people who recovered from the effects of COVID-19 required a difference of 17 steps made during the CST to be considered a significant change (MDC\({}_{90}\): 16.96 (12%); MDC\({}_{95}\): 20.15 (15%)). Regarding the 1 min-STST, it was necessary to stand up from the chair at least two more times with respect to the baseline measurement in order to consider that the patient's situation has really changed (MDC\({}_{90}\): 1.89 (8%); MDC\({}_{95}\): 2.71 (12%)). The MDC of these tests has been previously established for other populations [51; 52], but not for post-COVID-19 patients, thus we cannot compare our data with those of previous studies. However, we consider that the values recorded by the participants in this study can be useful in the evaluation of the effectiveness of interventions applied to these patients. We would like to point out the importance of expressing the changes in the tests that evaluate functional performance as percentages, which is usual in the assessment of the respiratory function [53; 54], and not as an absolute value, since the changes caused by a treatment or a pathology occur in the context of changes caused by the natural deterioration of older people, growth, or very different morphologies and anthropometric values. Otherwise, the results may be misinterpreted, potentially overestimating the effect of an intervention, or undervaluing the deterioration caused by a pathology. An example of that is the typical inverted U-shaped evolution of [PERSON]'s muscular dystrophy, where a functional plateau occurs at the age of 7 years; at that point, the initial gains that can be attributed to growth are overcome by deterioration, due to the progression of the disease [55]. Lastly, although our results indicate that these tests can be an adequate alternative to evaluate tolerance to physical exercise, it is important to take into account that certain people with musculoskeletal pathologies, overweight or balance problems may not be capable of adequately performing the tests, which has a negative impact on performance. Therefore, it would be appropriate, in these cases, to resort to modified versions, such as the 30-second sit-to-stand test or the 5-times sit-to-stand test.
## 2 Approach and Implementation
### Framework for the Aggregation and Visualisation of Inhomogeneous Data Sources  
Initial requirements for the system revolved around the primary objective for the implementation of a common interface for the storage, collection and modification of inhomogeneous data sources. Generated from the requirements, several phases of the system were developed as a high level abstract framework for the system, visually represented in the context of the system framework (Figure 2). The framework consisted of the following high level data components, sources, transport, staging and transformation, data storage, analysis and retrieval and visualisation.  
Because of the time-dependent nature of feedback, the efficient handling and flow of time series data was critical. In all applications, data transport required the processing of moving data from specific data sources, both from operators manually uploading or automatic transport from wearable devices through modern internet based protocols e.g., IPv6. Transformation includes the process of taking various distributed inhomogeneous data sources and compiling them into a converging uniform data structure--therefore, allowing the ability for autonomous and operator based metadata indexing for future processing. Storage provides an interface for the long-term storage and retrieval of indexed data sources including the file storage of archived original data sources and exported transformed data structures. Analysis includes the ability for the system to provide interfaces for the processing of transformed and indexed data sources. This allows for the retrieval of cached indexed homogeneous data structured through exported data sources as well as system visualisation and aggregation through integrated layers (Figure 2).  
The framework was developed using open source tools with platform independence and scalability as important factors. Initially implemented on a single workstation, a browser based interface was seen as desirable because of its platform independence and scalability to multi workstation and cloud based functionality [21]. It was considered that using open source enables resources that improve system development and reduce development time and dependency on proprietary systems. Java and in particular Node.JS [22] was selected due to its available packages, wide community and could be used both client and server side. It is reasonably portable and has favourable development times. Whilst legacy code exists within MATLAB, implementation in Python scripting language was seen as favourable because of similar processing power, popularity with the data sciences community in similar applications [23] and its backward compatibility with the data structures.  
Figure 2: Visualisation of a typical user workflow and application for aggregation of inhomogeneous datasets.
## Appendix A
### Computation of Results  
The curves for the band width \(\Delta\Omega\) (7) in Figure 2 are obtained by expressing the enclosed gas volume \(V=V_{\rm C}+A\cdot(R-r)\) and the radial product \(\overline{r}\Delta r=0.5\cdot(r_{0}+r)(r-r_{0})\) in terms of the radial positions of the menisci in the inlet reservoir \(r_{0}\), and the inbound segment of the siphon channel \(r\), respectively. For a distribution \(\Lambda\) throughout a contiguous segment of an incompressible (\(\varrho=\rm const.\)) liquid, \(r_{0}\) immediately derives from \(r\) while observing the continuity of volume for given parameters \(\{\gamma_{k}\}\); in this case, \(U_{0}\) and \(\Gamma\) are delineated by the depths, widths and heights of its constituent sectors.  
For the (deliberately) simplified geometry in Figure 1, the volume integral over \(\Lambda\) across \(\Gamma\) imposing the continuity of liquid volume hence reduces to the sum of the products of the (constant) cross section times the filling level of each cuboidal segment. With \(V\) fully determined through \(r\), \(R\) and \(\Gamma\) via \(V_{\rm C}\) and \(A\), and \(\overline{r}\Delta r\) via \(r\) and \(r_{0}=r_{0}(R,\Gamma,U_{0},r)\), \(\Delta\Omega/2\pi\) in Figure 2 can thus be directly expressed as a function of the main compression volume \(V_{\rm C,0}\) (a) and the radial position \(R\) (b).  
Design optimization in this work roots in the numerical minimization of a chosen target metric \(\mathcal{M}_{l}\) as a function of the parameters \(\{\gamma_{k},\Delta\gamma_{k}\}\); as per Figure 1, these \(\{\gamma_{k}\}\) mainly represent the dimensions of each constitutional segment of the fixed valve geometry \(\Gamma\) at the radial position \(R\), the loaded liquid volume \(U_{0}\) of density \(\varrho\), the ambient pressure \(p_{0}\), and the spin rate \(\omega\). For a given set of standard deviations \(\{\Delta\gamma_{k}\}\), e.g., reflecting machining or pipetting tolerances, the minimization (or maximization) of target metric \(\mathcal{M}_{l}\) is implemented by common numerical routines under a given set of constraints.  
For instance, to derive the band-width optimized basic and multi-segmented geometries in Figure 3, the value of \(\Delta\Omega\) (7) is numerically minimized over the geometrical parameter space \(\{\gamma_{k}\}\) of \(\Gamma\) with realistic assumptions for all experimental tolerances \(\{\Delta\gamma_{k}\}\), while imposing \(\Omega/2\pi=25\) Hz, and all parts of \(\Gamma\) remaining within the structurable area on the disc extending between \(R_{\rm min}\) and \(R_{\rm max}\). In Figure 4, the field strength \(f_{\omega}\) (8) is maximized while keeping \(\Delta\Omega\) constrained below a chosen upper value.