esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Diagnosis for a Class of Robotic Systems with Application to 2-DOF Helicopter
## 3 Fault Isolation Based on Hamiltonian Structure
### Fault Decoupling  
Fault decoupling using a nonlinear transformation has been considered in ref. [8], see also in ref. [5]. However, as observed in ref. [30], a class of nonlinear systems can be decoupled from some additive faults using a constant state transformation.  
Consider the class of generalized Hamiltonian systems described as in (7) and (8) with the additive actuator and sensor fault representation:  
\[\dot{x} = [J(\bar{g})+S(\bar{g})]\frac{\partial H(x)}{\partial x}+F(x)+Gu+E _{f}f_{a}, \tag{15}\] \[y = C\frac{\partial H(x)}{\partial x}+f_{s}, \tag{16}\]  
where \(E_{f}\) is a constant matrix with \(n\) rows and as much columns as actuator faults. \(f_{s}\) is a vector whose elements represent sensor faults. \(\bar{g}\) represent the system output without faults (which is not available).  
Consider a decoupling transformation \(T\), which does not requires to be a square matrix (i.e., \(T\in\Re^{n_{1}\times n}\), with \(n_{1}<n\)):  
\[z = Tx\] \[\dot{z} = T\dot{x}=T\left([J(y)+S(y)]\frac{\partial H(x)}{\partial x}+F(x) +Gu+E_{f}f_{a}\right),\]  
and \(T\) should satisfy \(TE_{fi}=0\), with \(E_{fi}\) the i-th column of \(E_{f}\), in order to obtain a subsystem robust to the actuator fault \(f_{ai}\). Note that because \(\bar{g}\) is not available but \(y\) is, the subsystem will be sensible to sensor faults. Note that the Hamiltonian structure is not necessarily preserved after the decoupling  
Figure 1: Fault detection scheme.  
transformation, because of the constant transformation, it is always possible to represent the decoupled subsystem with a Hamiltonian structure.  
To make the analysis of fault dependencies easier, we filled a table with information about the designed properties of the residual with respect to fault dependency. If a perturbation is present, it needs to be included in the analysis to find out whether it is possible or not to distinguish not only between the faults but also the perturbation. A residual generator for the decoupled subsystems can be designed using the procedure described in 2.1.1. However, following in ref. [29] it is required an output observer instead of a state observer. Taking advantage of structure preservation after state transformation, a residual generator can be designed following the procedure presented in this Section 2.1.1.
# Characteristics of Ordinary Portland Cement Using the New Colloidal Nano-Silica Mixing Method
## 3 Results and Discussion
### Hydration Products
Therefore, the substitution of CNS during the initial hydration stage before 1-day reacts with portlandite to cause a rapid pozzolanic reaction. Therefore, portlandite consumption is accelerated during the first 1-day, and the reaction with nano-silica particles gradually slows as the amount of portlandite decreases. The peaks of ettringite and C-S-H did not show a sharp increase, even when the mixing ratio of CNS increased from 0% to 50%. This is because the amount of OPC (the main source of calcium and aluminum) required to form ettringite, and C-S-H does not change. Therefore, most of the portlandite is consumed in the initial hydration stage and the amount of hydration reactant changes little as the age increases. These results are supported by previous studies that have reported that the hydration acceleration caused by the PNS exhibits a high correlation with sufficient calcium-adsorption ability [39; 42]. In the present study, CNS and mixing water weight substitution methods show little change in the hydration reactant peak in XRD analysis after 1-day due to the rapid hydration reaction in the initial hydration stage. Figure 5: XRD results after 1-day and 28-day: (**a**) 0% CNS, (**b**) 50% CNS, (**c**) compared XRD with CNS, P: portlandite, E: ettringite, G: gypsum, C: calcite, M: magnesium oxide, S: quartz, A: alite, B: belite, CSH: calcium silicate hydrate.
# A Robust Statistical Methodology for Measuring Enterprise Agility
## 4 Results and Statistical Validation
### Difference of Variances Test (Fisher's \(F\)-Test)  
Before conducting the mean difference test, it is necessary to evaluate whether the variances of both samples are equal or not, which is achieved through a [PERSON]'s \(F\) test [36; 37]. Running the test shows that the variances differ significantly and that we significantly reduce variability in the Q4-FY22 quarter compared to the Q2-FY22 quarter.  
The [PERSON]'s method involves taking the ratio of the larger population variance, \(\sigma^{2}_{\text{Q2-FY22}}\), to the smaller population variance, \(\sigma^{2}_{\text{Q4-FY22}}\), and then looking up the ratio on an \(F\) distribution curve [36; 37; 50].  
In this test, the null hypothesis states that the ratio is equal to 1:  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Statistic** & **Value Q2-FY22** & **Value Q4-FY22** \\ \hline Size & 28 & 28 \\ Mean & 0.51 & 0.29 \\ Std. dev. & 0.19 & 0.10 \\ Variance & 0.04 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample statistics Q2-FY22 and Q4-FY22.  
Figure 6: Anderson–Darling normality test. The normality test of data is an important step in data analysis that ensures the validity and reliability of the results obtained by selecting appropriate statistical methods for the data distribution.  
\[H_{0}:\frac{\sigma_{Q2-FY22}^{2}}{\sigma_{Q4-FY22}^{2}}=1\]  
and the alternative hypothesis states that the ratio is greater than 1:  
\[H_{1}:\frac{\sigma_{Q2-FY22}^{2}}{\sigma_{Q4-FY22}^{2}}>1\]  
In the Figure 7 and Table 3, we can see the results of the executed [PERSON]'s \(F\)-Test.  
Conclusion: For this one-tailed test, since the p-value of 0.0014 is less than the pre-established alpha (\(\alpha\)) of 0.05, or since the \(F\) statistic of the test equal to 3.31 falls outside the non-rejection zone (0.00 to 1.90), we proceed to reject the \(H_{0}\); therefore, we conclude that the variance of the Q2-FY22 period is statistically higher than the variance of the Q4-FY22 period. Then, upon evaluating the power of \(H_{1}\)[41], we see that the exhibited power of 0.92 is greater than the threshold of 0.80 and the relative effect size of 1.8204 is also highly relevant, so we conclude that in practical terms the variance of the Q4-FY22 period is significantly lower than that of the Q2-FY22 period, meaning that there is a significant and relevant reduction in the variability of the AgilityDebt(tm) data in CBO.
## 1 Introduction
One of the main concerns is the voltage and current spikes on the switching devices in a power module. Zero voltage switching (ZVS), zero current switching (ZCS), zero voltage transition (ZVT), and zero current transition (ZCT) are different approaches that can reduce these stresses and decrease the switching losses on power diodes and power switches [17; 18]. In these ways, to obtain higher efficiency, applying an additional switch is often inevitable and can need more control topologies and enhance the complexity and cost of the converter. Cascade structures can be employed for high step-up requirements. The authors in [19; 20] proposed sample topologies for this kind of converter. Higher DC gains can be achieved by applying more serial blocks of converters, but by increasing the number of blocks, the number of components will increase, especially extra power switches that have been applied, and the dynamic and frequency losses will increase, which will seriously affect the efficiency. Furthermore, the control process will be difficult for more power switches and the total cost will increase. Our study introduces a topology that can provide a pre-amplifier block containing an inductor, two power diodes, and a capacitor. This configuration can increase the input voltage, and according to the duty ratio, this amount can be greater. Next, it acts as a boost converter and increases the voltage to higher values. For high efficiency purposes, the diodes on the pre-amplifier part act in a complementary manner and when one of them is in ON mode, another is in OFF mode and vice versa. So, in comparison with a classical cascaded boost converter, the proposed configuration is more efficient. All mathematical analysis of the projected converter, calculations of the voltage gain, and the controller design and small signal analysis based on steady space matrixes are presented in Section 2. Section 3 presents the simulation results and the implemented prototype and the performance assessment of the proposed simple controller is presented in Section 4.
## 1 Introduction
As a matter of fact, the design of the PEF chamber, which must guarantee and impart uniform treatment to the food matrix with a minimum increase in temperature, and the design of electrodes, which must minimize the effect of electrolysis, may be listed as the two main critical aspects when PEF technology is intended to be employed at industrial scale for commercial purposes. The present study assessed the application of pulsed electric fields (PEF) for the stabilization of fresh blood orange juice. In alignment with contemporary consumption trends and the imperative to adopt sustainable food processing methods, the treatment was conducted continuously using a patented treatment chamber (Opticept Technologies AB, Lund, Sweden). This chamber ensures a consistent flow and uniform treatment, preventing the formation of \"hot spots\". The PEF-treated blood orange juice was evaluated for its physico-chemical, biochemical, nutritional, microbiological, and sensory properties. Additionally, its commercial refrigerated shelf-life was determined
## 4 Methodology  
### Spatio-Temporal Data Pre-Processing  
A univariate time series is a sequence of data points with timestamps representing contextual dimension. A multivariate time series is a set of univariate time series with the same timestamps. On the other hand, multivariate time series data can be thought as a dataset composed of many univariatetime series, which are measured at successive points in time and spaced at uniform time intervals. A spatio-temporal multivariate data point is a multivariate time series observation with a spatial attribute (a contextual attribute) attached to it.  
Let \(X=\left\{x^{(n)}\right\}_{n=1}^{N}\) denote a multivariate time series dataset composed of \(N\) subsequences of multivariate observations (data points). Each subsequence has the same number of time steps, or window size, which is generated from a long multivariate time series data. Let each subsequence \(x^{(n)}\) has \(T\) time steps, and each observation at time step \(t\), is a \(d\) dimensional vector. The dataset \(X\) has dimensions of \((N,d,T)\), where \(x^{(n)}\in\ \mathbb{R}^{d\times T}\). Each sample from multivariate time series dataset can be represented as:  
\[x^{(n)}=\left(\begin{array}{ccc}x_{11}^{n}&\cdots&x_{1T}^{n}\\ \vdots&\ddots&\vdots\\ x_{d1}^{n}&\cdots&x_{dT}^{n}\end{array}\right) \tag{10}\]  
In a spatio-temporal dataset, each multivariate data point \(x^{(n)}\) comes from a different spatial location, or region, which has different spatial attributes (such as latitude and longitude). Specifically, each multivariate data point has a spatial attribute \(s^{(n)}\) attached to it. We denote multivariate spatio-temporal dataset as \(\text{D}_{ST}=\left\{(X^{(i)},s^{(i)})\right\}_{i=1}^{S}\) which contains \(N^{(i)}\) multivariate data points from \(S\) different spatial regions. The multivariate spatio-temporal data matrix \(X_{ST}\) can be represented as a three-dimensional matrix, as shown in Figure 2. It is considered as a group of many multivariate subsequences related to multiple spatial regions and representing observations from the same time window with the same timestamps. \(T\), which is called the \"input window-size\", represents the number of timestamps in the multivariate subsequence, and \(d\) represents the number of observed features (number of univariate time series). \(m\) represents the number of spatial neighborhoods to include in the anomaly detection process. The best \(m\) can be found empirically for each problem domain. \(m\) number of neighboring regions are selected from \(S\) different spatial regions by a distance based nearest neighbor algorithm using pairwise spatial distance between regions.
## 3 Automated Negotiation Techniques for Channel Selection
### Negotiation Domain  
For the scope of this work, we assume a multi-attribute negotiation domain, where a deal or solution to the problem is defined as the set of attributes (issues), and each one of them can be in a certain range. In our case, for a channel assignment problem with \(n_{AP}\) access points, a solution or deal \(S\) can be expressed as \(S=\{s_{i}|i\in 1,...,n_{AP}\}\), where \(s_{i}\in\{1,\ldots,11\}\) represents the assignation of a Wi-Fi channel to the \(i\)-th access point. Although there are 13 channels in the 2.4-GHz frequency band, they are not used everywhere in the world. For instance, only the first 11 channels are used in North America. In this work, we will use only those 11 channels, as the co-channel interference computation, referred in the previous section, has been done after the work in [31], where only 11 channels are used.  
In this work, we assume that there are a number \(p\) of network providers offering surveillance services; thus, APs can belong to any of the providers \(P_{i}\). Each provider only has control over the frequency assignment for its own access points. According to this situation, \(P=\{p_{i}\}|i=1,...,p\) will be the set of agents that will negotiate the frequency assignment.  
Finally, each one of these agents will compute its utility for a certain deal or solution according to the model described in the previous section, but taking into account only the interference affecting its own access points. Our hypothesis is that the problem settings (high cardinality of the solution space and attribute interdependence) will make the utility functions highly complex, with multiple local minima and maxima. Figure 3 shows an example of such a complex utility function for two issues. Unfortunately, the dimensionality and cardinality of the utility spaces derived from the setting of this problem will prevent us from providing a graphical representation of the actual utility functions (or even sampling them). Nevertheless, in Section 4.3, we will present some results that support this assumption.  
Figure 3: Utility function with multiple local maxima.
## 2 Configuration
### Pitch and Yaw Measurement  
As shown in Figure 2, the plane mirror (M1) is used as a sensitive element in the moving unit to measure pitch and yaw. The reflected s-polarized light by BS1 is converted into p-polarized light by the half wave plate (HWP) and is totally transmitted through the polarization beam splitter (PBS3). The transmitted light is reflected by the plane mirror (M1) and passes through the quarter wave plate (QWP2) twice to be transformed into s-polarized light. The light reflected by PBS3 is finally collected by lens L1 and focused on the image plane, where the CMOS3 camera is placed.  
When the moving unit undergoes pitch and yaw movement, the position of the spot changes on the CMOS3 camera. The pitch and yaw are expressed as  
Figure 1: Schematic of the system measuring 5 DOF motion errors.  
\[\alpha =\frac{\Delta Y_{CMOS3}}{2f_{1}}, \tag{1}\] \[\beta =\frac{\Delta X_{CMOS3}}{2f_{1}},\]  
where \(\Delta X_{CMOS3}\) and \(\Delta Y_{CMOS3}\) are the displacements on the COMS3 camera in the x and y directions, respectively; and \(f_{1}\) denotes the focal length of L1.
# Assessment of Water Consumption Behavior in Single Households Using Smart Water Meters
## 3 Results  
### Temporal Trends in Water Consumption among All Households  
Smart water meter data were chosen for three weeks (Monday through Sunday) in September 2022, the first week from the 5 th to 11 th, the second week from the 12 th to 18 th, and the third week from the 19 th to 25 th. A duration of three weeks was chosen to obtain water consumption patterns on weekdays and weekends. This duration also allowed this study to observe fluctuations in household routines and determine the categorization of each household. Figure 1 shows the average daily consumption of all 1871 households in the district for seven days of three weeks. The highest consumption was an average of 1596 m\({}^{3}\)/day for Monday, whereas the lowest consumption was approximately 1436 m\({}^{3}\)/day for Thursday. Both the highest and the lowest consumption rates occurred on weekdays, but the average daily water consumption was higher on weekends than on weekdays. On weekends, Sundays had a higher value than Saturdays and were closer to the average. A one-way ANOVA test was run over three weeks. ANOVA confirmed that there was no significant difference in daily water consumption among the first, second, and third weeks (F = 1.88, \(p\) = 0.53 \(>\) 0.05).  
Water consumption levels typically vary among households on weekdays (Figure 2). Water consumption was monitored on Tuesdays and exhibited a fluctuating pattern, often associated with users' daily routines. The peak water consumption was within an average of 36 L/h from 7:00 to 9:00 in the morning hours. Typically, water consumption for 3 h increases in the morning due to individual activities, as people often begin their day with general hydration by preparing breakfast, a common part of their daily routine. Water consumption decreases during the day until the late afternoon at 17:00, averaging 16 L/h; consumption then starts to increase again due to individuals' activities in the evening at approximately 19:00-22:00, averaging 46 L/h. The average daily water consumed over 24 h was 587 L/day. The difference in the consumed water between the evening and morning hours was approximately 10 L/h. The peak water consumption in the evening instead of the morning peaks occurs because daily activities, such as showering, cooking, and other routines, typically occur during evening hours. Additionally, the time spent consuming water in the morning is typically much shorter than that in the evening. In the morning, households are usually busy preparing for the day, which limits the available time for consumption. Additionally, many households engage in similar activities during this time, leading to almost concurrent water consumption. In contrast, various activities compete for water consumption during the evening, leading to longer and more varied individual activities that consume water. The evening peaks might also be related to Japanese evening bathing culture.  
Figure 1: Total weekly water consumption in the Chibata district of 1871 households in September 2022.
## 2 Data and Methods
For our study, the data of the so-called PPN-SOR meridional station chain, namely the SOR, MAS, MUO, PEL, RAN, OUJ, HAN, NUR, TAR, BRZ, SUW, PPN stations, were used (Figure A10b). Swarm satellite observations [43; 44] provided a global context for the local ground-based data. During the analyzed period, Swarm C orbited at -476 km altitude, and Swarm B at -516 km, both with a 95-min orbital period. The study examined in-situ plasma electron density and temperature from Langmuir probes, vertical total electron content (vTEC) integrated between Global Navigation Satellite System (GNSS) and Swarm satellite altitudes, small-scale FAC (ssFAC) density from magnetic field data [26], and the MIT location, which aligns with the nightside plasmapause footprint [24]. Detailed information on these data products is available in the Swarm Product Data Handbook at [[https://swarmhandbook.earth.esa.int](https://swarmhandbook.earth.esa.int)]([https://swarmhandbook.earth.esa.int](https://swarmhandbook.earth.esa.int)) (accessed on 8 July 2025). Global Ultraviolet Imager (GUVI) measurements from the Thermosphere, Ionosphere, Mesosphere, Energetics and Dynamics (TIMED) satellite were used to investigate the changes in the thermospheric O/N\({}_{2}\) ratio. GUVI measurements provide dayside O/N\({}_{2}\) composition (note that this is a column integrated value for an altitude range) and temperature profile of the Mesosphere and Lower Thermosphere/Ionosphere (MLTI) region, as well as the auroral energy inputs (see more in [20]).
# Optimizing Rural Highway Maintenance Scheme with Mathematical Programming
## 5 Findings and Research Extensions
### Comparison with Existing Research  
This study builds upon existing research in the field of rural highway maintenance and optimization. Previous studies have primarily focused on single-objective optimization, such as pavement management systems (PMS) aiming to minimize institutional costs. While these approaches have been successful in reducing maintenance expenses, they often overlook the negative externalities imposed on road users, such as traffic congestion and increased travel times. This study addresses this gap by incorporating user costs into the optimization framework, resulting in more comprehensive and socially responsible maintenance strategies.
# Efficient Coverage Path Planning for a Drone in an Urban Environment
## 5 SST--An Efficient Heuristic Algorithm for the GPP Problem
### Heuristics and Methods
#### 5.5.3 Nearest Neighbor  
Nearest Neighbor Search (NNS), in the context of proximity search, is the process of finding the point in a set \(S\) that is closest to a given point \(q\).  
In this case, the NN algorithm is used both to determine an upper bound for the minimum length of a tour that visits all graph vertices and as a heuristic algorithm for OLTSP. Given the first waypoint, this algorithm chooses the nearest yet unvisited waypoint as its next move.  
The time complexity of the NN algorithm is \(O(n^{2})\), where \(n\) is the number of points to visit and the following is guaranteed [34]:  
\[\frac{NN(I)}{OPT(I)}\leq 0.5(\lceil\log_{2}n\rceil+1) \tag{9}\]
## 3 Results
### Time-Domain HRV Measures
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **HRV** \\ **Measures** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Baseline** \\ **\(M\) (SD)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Mid-Term** \\ **(8 th Session)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **End of the** \\ **Intervention** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Baseline** \\ **\(M\) (SD)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Mid-Term** \\ **(12 th Session)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **End of the** \\ **Intervention** \\ \end{tabular} } \\  & & \(M\) (SD) & & & \(M\) (SD) & \\ \hline SDSD & 0.103 (0.213) & 0.094 (0.206) & 0.100 (0.179) & 0.137 (0.125) & 0.028 (0.028) & 0.054 (0.076) \\ SDNN & 0.075 (0.109) & 0.032(0.029) & 0.055 (0.070) & 0.132 (0.088) & 0.041 (0.024) & 0.059 (0.062) \\ RMSSD & 0.074 (0.130) & 0.027 (0.025) & 0.043 (0.078) & 0.137 (0.125) & 0.028 (0.028) & 0.054 (0.076) \\ pNN50 & 0.058 (0.097) & 0.027 (0.047) & 0.063 (0.133) & 0.166 (0.153) & 0.035 (0.039) & 0.114 (0.173) \\ VLF & 1.28 (1.54) & 1.15 (1.14) & 1.00 (0.999) & 0.343 (0.321) & 0.955 (0.646) & 0.432 (0.274) \\ LF & 0.974 (0.635) & 1.00 (0.701) & 1.21 (0.797) & 1.36 (0.942) & 1.27 (0.696) & 1.70 (0.985) \\ HF & 0.573 (0.537) & 0.751 (0.643) & 0.627 (0.323) & 0.698 (0.610) & 0.456 (0.463) & 0.604 (0.553) \\ LF/HF ratio & 5.56 (5.45) & 4.53 (6.83) & 7.15 (11.88) & 6.04 (9.71) & 6.04 (6.91) & 5.615 (5.96) \\ \hline \hline \end{tabular} Note: Standard deviation of successive differences (SDSD); the standard deviation of NN intervals (SDNN); not mean square of successive differences (RMSSD); percentile of NN intervals (pNN50); very low-frequency (ULF); low-frequency (LF); high-frequency (HF); ratio of low-frequency power to high-frequency power (LF/HF ratio).  
\end{table} 
Table 2: Mean and standard deviation of time and frequency-domain HRV measurements in the study. particularly marked among participants who underwent the extended multimodal training, while participants who did the reduced multimodal training showed a more balanced tendency over the different assessment periods.