esa-sceva/satcom-chunk-collection huggingface dataset:
# Innovative Approaches for Minimizing Disinfection
## 4 DBP Minimization Approaches
### Alternative to Chlorine Disinfectants
#### 4.7.5 Solar Water Disinfection (SODIS)  
The WHO has identified infections caused by pathogens resistant to antimicrobials, such as antibiotic-resistant bacteria (ARB), as a global health threat. The situation worsens when pathogenic bacteria carrying antibiotic resistance genes (ARG) transfer this resistance trait, via ARGs, to other bacteria in the water, thereby preserving an antimicrobial property even after disinfection. Hence, there is an urgent need for new water disinfection techniques capable of combating these emerging pathogens while being cost-effective and environmentally sustainable [42].  
The WHO has acknowledged SODIS as a simple and effective method for treating water contaminated with pathogenic microorganisms and reducing the occurrence of diarr diseases through sunlight exposure. The SODIS technique involves placinguntreated water in transparent plastic containers (typically 1 to 2-L PET bottles) and exposing them to direct sunlight for a minimum of 6 h (on cloudy days, the exposure time should be extended to 48-72 h). Subsequently, the water should be kept in the SODIS bottles and consumed within the next 24 h. The microbicidal action of SODIS is attributed to generating reactive oxygen species when the chromophores of microorganisms absorb UVA and UVB photons. The process's effectiveness is enhanced by the rise in temperature resulting from sun exposure. SODIS is particularly appealing from both an economic and environmental standpoint for several reasons: it has minimal operational costs for users (primarily involving bottle replacement), is extremely easy to use and relies solely on sunlight, preserves the organoleptic properties of water, does not necessitate additional chemicals, and does not leave behind residues [42].  
Furthermore, SODIS has been demonstrated to be effective against numerous waterborne pathogens, including _Escherichia coli_, _Salmonella_ spp., _Vibrio cholerae_, _Enterococcus faecalis_, _ bacteriophage MS2_, _hepatitis A virus_, and _Cryptosporidium parvum_. However, SODIS does have several drawbacks that have been the focus of recent studies aimed at addressing them: (i) water treated with SODIS, as mentioned, must be consumed within 24 h to prevent the reactivation of pathogenic organisms after sun exposure; (ii) the sunlight's effect on the plastic material can lead to the release of chemical compounds into the drinking water. SODIS is of significant interest from an economic and environmental perspective for several reasons: it has low operational costs for users (primarily involving bottle replacement), is extremely easy to use, and relies solely on sunlight. It does not alter the taste or odor of water, requires no additional chemicals, and leaves no residues. Additionally, SODIS has been demonstrated to be effective against a wide range of waterborne pathogens, including _Escherichia coli_, _Salmonella_ spp., _Vibrio cholerae_, _Enterococcus faecalis_, _bacteriophage MS2_, _hepatitis A virus_, and _Cryptosporidium parvum_[42].
## 2 Methodology  
### Noise Reduction of Prototype Dam Displacement Data  
#### 2.1.1 Complete Ensemble Empirical Mode Decomposition with Adaptive Noise  
[PERSON] et al. [31] proposed the empirical mode decomposition (EMD) algorithm. As a time-frequency domain processing method, the EMD algorithm can decompose an original signal into a set of intrinsic mode functions (IMFs) and is effective for analyzing nonlinear and nonstationary signals. However, the EMD algorithm has problems such as modal aliasing and significant reconstruction errors [42]. To alleviate this modal aliasing problem, [PERSON] and [PERSON] [32] proposed the ensemble empirical mode decomposition (EEMD) algorithm. In the EEMD algorithm, Gaussian white noise with a zero mean is added for auxiliary analysis. Because of the characteristics of the zero mean, after multiple averaging, the noise will cancel each other, and the final result is the result of the integrated mean. Although the EEMD algorithm has alleviated the modal problem to a certain extent, due to its low decomposition efficiency, problems such as modal aliasing in the low-frequency region and  
Figure 2: Flowchart of the proposed CSSKEE method.  
high reconstruction error remain. [PERSON] et al. [33] developed the complete ensemble empirical mode decomposition (CEEMD) method, which involves adding the positive and negative relative auxiliary white noise to the original signal and phase canceling them in the pooled average, thereby effectively improving the decomposition efficiency and overcoming the problems of large reconstruction error and lack of completeness of the EEMD algorithm. However, there remains a difference in the number of IMFs generated by the CEEMD algorithm during the EMD algorithm decomposition, leading to difficulties in aligning the IMF components in the final ensemble averaging, resulting in an ensemble averaging error. Researchers developed the CEEMDAN algorithm to solve the IMF component alignment problem at ensemble averaging. The CEEMDAN algorithm lowers the noise residual in the final reconstructed signal compared with the CEEMD algorithm result and reduces the screening time. The method also alleviates the problems of difficult alignment of the IMF components due to the differences in the decomposition results and the impact of poor IMF decomposition results on subsequent decomposition sequences. The specific principles can be found in the literature [43].
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 5 Performance Analysis
### Distance Error  
Consider the right triangle in Figure 1. The squared Euclidean norms of the angle error \(a_{k}\) and the codeword \(b_{k}\) scaled by the optimal factor \(v_{k}\) give  
\[a_{k}^{2}+v_{k}^{2}\|b_{k}\|_{2}^{2}=1 \tag{28}\]  
Figure 1: Decomposition of the approximation error.  
for a target vector of unit norm. The distance error \(d_{k}\) is maximal, if the magnitude of the optimum scale factor \(v_{k}\) is exactly in the middle of two adjacent powers of two, say \(p_{k}\) and \(2p_{k}\). In that case, we have  
\[d_{k}=\|b_{k}\|_{2}(|v_{k}|-p_{k})=\|b_{k}\|_{2}(2p_{k}-|v_{k}|) \tag{29}\]  
which results in  
\[|v_{k}|-p_{k}=\frac{|v_{k}|}{3}. \tag{30}\]  
Due to the orthogonal projection, the magnitude of the optimal scale factor is given as  
\[|v_{k}|=\frac{1}{\|b_{k}\|_{2}}\sqrt{1-a_{k}^{2}}. \tag{31}\]  
Thus, the distance error obeys  
\[|d_{k}|\leq\frac{1}{3}\sqrt{1-a_{k}^{2}} \tag{32}\]  
with equality, if the optimal scale factor is three quarters of a signed power of two.  
It will turn out useful to normalize the distance error as  
\[\delta_{k}=\frac{d_{k}}{\sqrt{1-a_{k}^{2}}} \tag{33}\]  
and specify its statistics by \(\mathrm{P}_{\delta}(\delta)\), to avoid the statistical dependence of the angle error. The distance error is a quantization error. Those errors are commonly assumed uniformly distributed [13]. Following this assumption, the average squared distance error is easily calculated as  
\[\overline{d^{2}}=\frac{1-\overline{a^{2}}}{27}. \tag{34}\]  
Unless the angle \(\alpha\) is very small, the distance error is significantly smaller than the angle error. Their averages become equal for an angle of \(\mathrm{arccot}\sqrt{27}\approx 11^{\circ}\).  
Note that the factor \(1/27\) slightly differs from the factor \(1/28\) in Example 2. Like in Example 2, the number to be quantized is uniformly distributed within some interval. Here, however, the interval boundaries are not signed powers of two. This leads to a minor increase in the power of the quantization noise.
## 2 Modeling and Problem Formulation
### Problem Statements  
The path planning problem for a mobile manipulator in this paper can be defined as finding a collision-free path that starts from the initial configuration and reaches the goal configuration where the end-effector reaches the target position and satisfies the requirements of the pose of the end-effector, as shown in Figure 2.  
The configuration of the mobile base can be represented by a vector \(q^{mb}=[x,y,\varphi]\), where \(x\) and \(y\) are the position of the mobile base and \(\varphi\) is the heading angle of the mobile base. The configuration of the manipulator can be represented by another vector \(q^{m}=[\theta_{1},\,\theta_{2},\,\theta_{3},\,\theta_{4},\,\theta_{5},\,\theta _{6},\,\theta_{7}]\), where \(\theta_{i}\) is the \(i\)th joint angle of the manipulator. The configuration of the mobile manipulator is \(q^{mm}=[q^{mb},q^{m}]\). The path for the mobile manipulator can be expressed as follows:  
\[\Pi^{mm}=\Big{[}q^{mm}_{s},q^{mm}_{1},\cdots,q^{mm}_{n},q^{mm}_{g}\Big{]}. \tag{2}\]  
\(\Pi^{mm}\) is an ordered set of mobile manipulator configurations. \(q^{mm}_{s}\) is the starting configuration of the mobile manipulator for path planning. \(q^{mm}_{i}(1\leq i\leq n)\) is a waypoint in the path. \(q^{mm}_{s}\) is the goal configuration of the mobile manipulator for path planning. Unlike  
Figure 2: Mobile manipulator path planning description.  
the start configuration, the goal configuration of the mobile manipulator is not directly given. In pick and place tasks, the position and rotation constraints of the end-effector are often given as known conditions. The constraints of the end-effector can be represented as \(X_{goal}=[R,\,T]\), where \(R\) is the rotation constraint and \(T\) is the position constraint. So, \(q_{g}^{mm}\) that can satisfy the constraint \(X_{goal}\) and not collide with the environment should be calculated first in path planning for a mobile manipulator.  
When \(q_{s}^{mm}\) and \(q_{g}^{mm}\) are known, a path \(\Pi^{mm}\) between them can be searched and smoothed for the mobile manipulator. Some sampling-based methods could solve this problem, but they are not suitable for pick and place tasks in a confined space.
###### Abstract  
The aim of this study was to severely reduce energy availability (EA) in controlled conditions in trained male endurance athletes to observe any effects on health, performance, and psychological and energy markers. EA was reduced by 50% over 14 days in athletes by maintaining identical energy intake and increasing exercise energy expenditure. Blood was drawn, performance was measured by three specific tests (endurance, explosive power and agility) and two psychological questionnaires were used. Reduced EA (17.3 \(\pm\) 5.0 kcal/kg FFM/day) resulted in lower body fat% (t(12) = 3.36, \(p\) = 0.006), lower power output and relative power output (t(12) = 2.69, \(p\) = 0.021 t(12) = 2.34, \(p\) = 0.036), explosive power was reduced (t(12) = 6.41, \(p\) = 0.000), lactate metabolism was altered (\(p\) = 0.001). EA was negatively correlated with haemoglobin and testosterone (r = \(-\)0.557, \(p\) = 0.30 and r = \(-\)0.532, \(p\) = 0.037), anaerobic threshold (r = \(-\)0.597, \(p\) = 0.02) and respiratory compensation point (r = \(-\)0.575, \(p\) = 0.025). There were significant differences in Well-being (t(12) = 4.11, \(p\) = 0.002) and the Three Factor Eating Questionnaire (t(12) = \(-\)2.71, \(p\) = 0.020). This is the first study to demonstrate that endurance performance and explosive power can be affected before detrimental health effects occur in male athletes. Drastic reductions of EA could lead to poor eating behaviours. The two psychological questionnaires seem to be more sensitive to EA changes than blood markers.  
energy availability; performance; endurance athletes; explosive power; lactate metabolism; relative energy deficiency in sport 2021  
outcomes can provide the threshold for LEA in men. This remains to be determined [3]. Finally, the theory about the performance effects of LEA is lacking in direct evidence [1; 2; 4].  
We set this research to find direct evidence on how performance, health and mental state are affected when EA is reduced progressively by 50% in trained well-trained and elite endurance male athletes. The study was designed to measure EA with objective methodology, meaning that EA was determined by using direct tools. The EA was measured in strictly controlled conditions by including athletes without any pre-existing signs of LEA to determine if changes can be found after 14 days.
# Dynamic Stability Analysis of Isolated Power System
## 4 Impact of the Isolated System's Response on Settings of HVDC BtB Converter
### Impact of HVDC BtB Converter Filters on Active Power Transmission  
During the simulation, active power transmission was analyzed for the cases:  
1. Without HVDC BtB converter filters;
2. With one HVDC BtB converter filter;
3. With two HVDC BtB converter filters.  
The impact on active power transmission is examined by increasing the load of the HVDC BtB converter by 30 MW. The filters are of capacitive type, so their rated reactive power is 92 MVar. The simulation results are shown in Figure 10.  
Figure 10: Influence of filters on the active power transfer of the HVDC BtB converter.  
Figure 9: Frequency change in the isolated power system caused by a load decrease (case B).  
Analysis of the results showed that the HVDC BtB converter filters reduce active power oscillations in the presence of sudden changes in active power in the isolated EPS. Both harmonic filters must be kept on during operation of the isolated EPS.
## 4 Truncation Error Estimation  
Suppose \(y(x)\) is sufficiently smooth at \([a,b]\). By substituting \(x_{i-1/2}\) into (1), we have  
\[M_{i-1/2}^{\prime}+P_{i-1/2}y_{i-1/2}=Q_{i-1/2}, \tag{19}\]  
where \(M_{i-1/2}^{\prime},y_{i-1/2}\) denote \(y^{\prime\prime}(x_{i-1/2}),y(x_{i-1/2})\) respectively. To estimate the truncation error, we have to consider all these nine cases in Section 2, here we only analysis parts of these cases due to space limitations. Without loss of generality, we discuss the truncation errors of case 1 and 2 in detail, the results of the others are also listed. For convenience, the local truncation error is denoted by \(t_{i}^{j}(i=1,2,\cdots,n-1;j=1,2,\cdots,9)\).
###### Abstract  
The use of Wireless Sensor Network (WSN) technologies is an attractive option to support wide-scale monitoring applications, such as the ones that can be found in precision agriculture, environmental monitoring and industrial automation. The IEEE 802.15.4/ ZigBee cluster-tree topology is a suitable topology to build wide-scale WSNs. Despite some of its known advantages, including timing synchronisation and duty-cycle operation, cluster-tree networks may suffer from severe network congestion problems due to the convergecast pattern of its communication traffic. Therefore, the careful adjustment of transmission opportunities (superframe durations) allocated to the cluster-heads is an important research issue. This paper proposes a set of proportional Superframe Duration Allocation (SDA) schemes, based on well-defined protocol and timing models, and on the message load imposed by child nodes (_Load-SDA_ scheme), or by number of descendant nodes (_Nodes-SDA_ scheme) of each cluster-head. The underlying reasoning is to adequately allocate transmission opportunities (superframe durations) and parametrize buffer sizes, in order to improve the network throughput and avoid typical problems, such as: network congestion, high end-to-end communication delays and discarded messages due to buffer overflows. Simulation assessments show how proposed allocation schemes may clearly improve the operation of wide-scale cluster-tree networks.  
Keywords:IEEE 802.15.4; ZigBee; cluster-tree; wide-scale, allocation scheme; superframe duration; congestion; buffer overflow +
Footnote †: journal: Sensors
## 3 Experiments and Results
### Experiment 2
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Pearson** & \multicolumn{2}{c}{**Kendall**} & \multicolumn{2}{c}{**Spearman**} \\ \hline Stat parameter & r & P & \(\tau\) & P & \(\rho\) & P \\ \hline Radial Length & 0.16 & \(\varepsilon\) & 0.12 & \(\varepsilon\) & 0.17 & \(\varepsilon\) \\ \hline Working Distance & \(-\)0.14 & \(\varepsilon\) & \(-\)0.08 & 0.003 & \(-\)0.13 & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Correlation coefficients of non-uniform model for radial measurement error. The symbol \(\varepsilon\) denotes a p < 0.0001.  
the systematic nature of the error. Finally, based on Table 4 the error of the uniform method is on average 395-times higher than the non-uniform approach. This result confirms a recent finding suggesting the presence of significant errors in horizontal measurements if nonlinear distortion of fiberoptic endoscopy is not compensated [14].
# Determination of a Complete Conversion Model for Gasification of Lignite Char
## 3 Experimental
### Experimental Setup  
The gasification tests were conducted in a TGA (Netzsch STA 449 F3 Jupiter) at atmospheric pressure, which allows the injection of two dry reaction gases and is fitted with a steam generator. The flow rate of gases is controlled with mass flow controllers (MFCs).  
The crucibles used were plate-shaped and had a diameter of 17 mm. On these plates, 10 \(\pm\) 2 mg of the fuel samples was evenly distributed for ideal gas exchange. The temperature of the sample was monitored with a thermocouple in the sample carrier. The systematic error of the mass measurement was mitigated by a correction run for every gas composition with an empty crucible. The correction measurement was then subtracted from the actual measurement.  
In the first step, prior to the actual gasification tests, the samples were heated in the TGA to approximately 1100 \({}^{\circ}\)C with 20 K/min in a nitrogen atmosphere to ensure a complete drying and devolatilization of the samples. Then, the samples were cooled down to 500 \({}^{\circ}\)C and stabilized at this temperature for 30 min. During this time period, the reactive purge gases were injected into the oven to ensure enough time for gas mixing and gas distribution with the oven. Then, the experiments were performed under non-isothermal conditions with a heating rate of 20 K/min up to a temperature of 1100 \({}^{\circ}\)C. Finally, this temperature was held constant for 20 min for a complete reaction of the carbon.  
The experiments were conducted for CO\({}_{2}\) gasification as well as steam gasification. For both sets of experiments, the gasifying agent was introduced into the oven with 20%, 25%, 33% and 50% of the gas flow. For each gas concentration, a configuration with and without gasifying product, H\({}_{2}\) or CO respectively, was tested. In total, 16 different atmospheres were used for the CO\({}_{2}\) and steam gasification. Tables 2 and 3 display the matrix of the experiment configurations. For each of the configurations, three separate experiments were performed to test the reproducibility of the results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Proximate Analysis (wt\%)**} & \multicolumn{3}{c}{**Ultimate Analysis (wt\%, daf)**} \\ \hline
**Water** & **Ash** & **Volatile Matter** & **Fixed Carbon** & **C** & **H** & **O (Calculated)** & **N** & **S** \\ \hline
15.3 & 17.05 & 37.1 & 30.55 & 70.1 & 4.84 & 23.12 & 0.75 & 1.19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proximate and ultimate analyses of the char samples, oxygen calculated by difference.  
In order to define the type of function for the mass influence \(g(m_{0})\), an additional set of experiments was performed for a constant gas composition but varying sample masses. The experiments are listed in Table 4.
# Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation
## 3 Method
### Review
#### 3.1.2 Hypergraph Definition  
A hypergraph is an important concept in discrete mathematics, which is a generalization of ordinary graphs [37]. In a hypergraph, the hyperedge can connect more than 2 vertices, unlike an ordinary graph where each edge connects only two vertices. Thus, a hypergraph can represent higher-order relations more flexibly. Hypergraph \(G=\{V,E,W_{H}\}\) consists of a vertex set \(V\), a set of hyperedges \(E\), and a diagonal matrix \(W\) of hyperedge weights. Here, each hyperedge \(e\in E\) is assigned a positive weight \(w(e)\). A hypergraph with \(N\) vertices can be represented by an incidence matrix \(H\in R^{N\times|E|}\) whose elements are defined as  
\[h(v,e)=\begin{cases}1,\ if\ v\in e\\ 0,\ if\ v\
otin e\end{cases} \tag{4}\]  
The degree of vertex \(v\in V\) based on \(H\) is denoted by  
\[d(v)=\sum_{e\in E}w(e)h(v,e) \tag{5}\]  
The degree \(e\in E\) of hyperedge is denoted by  
\[\delta(e)=\sum_{v\in V}h(v,e) \tag{6}\]  
Further, let \(D_{v}\) and \(D_{e}\) denote the diagonal matrices of the vertex degree and hyperedge degree, respectively.
## 4 Simulation Results
### Comprehensive Analysis of the Simulations  
This section reports a comprehensive analysis of the simulations carried out. As already mentioned, we varied the number of new PSs from 1 to 12. Figure 28 shows, in a single chart, the best fitness evolution through the 150 iterations. The trend confirms the effectiveness of the proposed approach. On the one hand, the left picture suggests that the more the number of PSs, the more the iterations needed to find a stable final solution.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PSs in** & **X** & **Y** & **Best Average** & **Best Average Weighted** & **Best Average Weighted** \\
**Construction Order** & **Coordinate** & **Coordinate** & **Distance [km]** & **Distance [km]** & **Distance Variation [km]** \\ \hline \multicolumn{5}{c}{AS-IS} & 1.82 & 1.48 \\ \hline
1 & 1,513,010 & 5,026,222 & & & 1.54 \\ \hline
2 & 1,513,752 & 5,039,173 & & & 1.44 \\ \hline
3 & 1,511,950 & 5,032,438 & & & 1.36 \\ \hline
4 & 1,508,634 & 5,033,486 & & & 1.29 \\ \hline
5 & 1,515,044 & 5,034,710 & & & 1.23 \\ \hline
6 & 1,512,006 & 5,037,262 & 1.73 & 0.98 & 1.18 \\ \hline
7 & 1,517,698 & 5,037,654 & & & 1.13 \\ \hline
8 & 1,516,543 & 5,040,503 & & & 1.08 \\ \hline
9 & 1,512,300 & 5,034,664 & & & 1.05 \\ \hline
10 & 1,517,821 & 5,034,554 & & & 1.01 \\ \hline
11 & 1,513,852 & 5,033,367 & & & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Timing, siting of the new eleven PSs, and best average weighted distance values.  
Figure 27: Best fitness through iterations and the global best fitness: eleven new PSs.  
On the other hand, the right picture shows that the more the number of PSs, the lower the fitness's final value.  
Finally, Figure 29 reports the evolution of the fitness's final value concerning the different number of PSs. The Y-axis reports the fitness, i.e., the average weighted distance, while the X-axis is the number of new PSs installed. Moreover, the regression curve is shown in dashed red together with the curve equation and the value of \(\mathbb{R}^{2}\).  
As justified by the correlation coefficient, the two trends are pretty similar. Thus, the regression curve was used to estimate the fitness value of an increased number of new PSs. As shown in Figure 30, the main advantages of the mean weighted distance are obtained by installing the first PSs: the first twelve PSs guarantee a reduction of about 42%, while, for instance, the following twelve guarantee a reduction of only 8%. The chart in Figure 30 can also be used by Unareti to estimate the optimal number of PSs that optimize capital and operational costs.  
Figure 28: Left picture: the trend of the best fitness varying the number of PSs from 1 to 12; right picture: Trend of the global best fitness varying the number of PSs from 1 to 12.  
Figure 29: Trend of the best mean weighted distance in blue; regression curve in red.