esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Processing of GNSS Data
### Analysis of the Shift Error  
As mentioned above, the co-seismic displacement is calculated as the difference in the averaged position of the station before and after the earthquake. An important variable to compare with the result determined from the seismic model is its mean square error (RMSE). The RMSE defines whether the size of the co-seismic displacement is statistically significant. In our case, it was calculated as the \(RMSE\) of the difference between the two values.  
However, we can use two approaches to calculate the mean error value:  
**The first approach:**  
Assume that the mean quadratic errors of the positions determined from the application of the PPP method are real, and that they characterise the estimated actual variance. We can then assume that something happens with the GNSS before and after the earthquake and that the subsequent analysis of the time series gives us a realistic approximation of the station's behaviour after the earthquake. The mean square error of the approximation curve is considered a real estimate of the \(RMSE\) 1 st approach.  
**The second approach:**  
We assumed that the mean quadratic errors (RMSE) determined from the application of the PPP method are considered to be overestimated, and that they can only be considered internal mean errors. The assumption is that nothing happens with the GNSS station before or after the earthquake (it is not affected by post-seismic displacements). The dispersion of the approximation data from the time series analysis is given by the real errors of the individual daily results. This differs from the determination of RMSE from the PPP method. We also need to make the assumption that the time series is a random statistical process. When calculating the RMSE of the difference before and after the earthquake, in the RMSE 2 nd approach, it is taken into account that the resulting mean square error of the difference is calculated based on the variance of individual observations.  
In Tables 1 and 2, the RMSE calculated using the 2 nd approach was greater than the RMSE calculated by the 1 st approach. It is usually 2-3 times larger, but in some cases (NS component in Table 1), it is even 24 times greater. We found that the RMSE calculated according to the 1 st approach is not applicable for evaluating the statistical significance of the calculated co-seismic displacement.
# Grasping Assisting Algorithm in Tele-Operated Robotic Gripper
## 4 Experimental Setup and Tests  
### Overview  
In the following section we will describe the purpose of our tele-operated system, its structure, components and interactions. The next section describes the test area, and the last section describes the tests themselves.  
The tele-operated system allows us to simulate the daily usage of different tools (hammer, screwdriver, tweezers), their manipulation in space, holding and grasping--all controlled by an operator from a long distance. In this study we used the system to grasp specified objects with a robotic gripper and arm, with the goal of assisting the action with output from our algorithm.  
The experimental system was composed of an anthropomorphic gripper and long-distance control devices (tele-operated devices). Figure 2 shows the interaction between the different components in the system. The haptic control allows the human operator to preform the grasp of an object from a long distance. He/She controls the movement of both the arm and gripper with control devices, which translate the commands to movement in the physical world. While the robotic gripper grasps the object, necessary data for the calculation of the grasp quality are sent to the computer, which in turn computes and presents the achieved score. Lastly, the human receives visual output on the quality of their grasp, which helps him/her decide their next actions (improving their grasp or continuing with the specified task).
## 4 Selection of FEC Codes for the Railway Application
### POLAR Codes  
As we read in [43], the channel polarization transform is used to create POLAR codes. The notion is that by merging and splitting channels at infinite lengths, the channels (bits' positions) will polarize, with some being highly dependable and others being unstable. The channel capacity can be obtained if the information bits are exclusively put into the reliable channels and foreknown bits (typically zeros) are put into the unreliable channels. The goal of POLAR code creation is to identify the Frozen Set, which is a collection of the least trustworthy channels. Multiple building techniques exist, each with a different level of complexity, and due to the non-universal behaviour of POLAR codes, these algorithms require a parameter, namely Design-SNR. However, universal constructions exist.  
Figure 3: FEC scheme.
## 2 Discussion
### Conclusive Remarks
This implies that interactions at the channel's recognition site correspond to increased diffusion of particular compounds through OmpF [84; 99; 181; 183], as shown by previous studies indicating that mutations of critical residues in the porin affect \(\beta\)-lactam uptake and susceptibility [7; 84; 89; 99; 181; 183]. An alternative method involves introducing the charged compound of interest to one side of the channel to create a concentration-driven flux, which can be assessed by a diffusion potential [43; 44; 88; 181]. The permeation assay, based on the conventional electrophysiology method, primarily entails the application of concentration gradients of charged molecules with different electrophoretic mobilities through a membrane channel [26; 39; 54; 153]. Consequently, the unequal flux generates an electrostatic potential that provides direct evidence of relative ion fluxes [37; 38; 40; 54; 153]. The investigative methodology utilized here involves measuring zero-current-reversal potentials and the subsequent channel con ductance of a single pore [37; 38; 40; 54; 153]. Combined with single-channel conductance measurement, this technique allows for more quantitative analysis [37; 40]. In summary, electrophysiology and its integration with other techniques have yielded valuable insights into the uptake of drugs through porins, enabling the development of new methods to differentiate between molecular binding and transport [37; 40]. These techniques can potentially aid in discovering new scaffolds to enhance the permeability of antibiotics [37; 39; 40; 54; 165; 164; 37]. Recently, the efficacy of these approaches has been validated using in vivo susceptibility and accumulation assays on intact bacterial cells by comparing different methodologies to measure the transport of ceftazidime and cefotaxime [38]. Likewise, these new methodologies offer the opportunity to study membrane porins and their role in influx and environmental stressors affecting them [7; 29]. In real-time, measurements of the interbacterial accumulation of individual molecules, such as antibiotics, inhibitors, antimicrobials, and more, provide valuable insights into the early phases of envelope changes during bacterial life [7; 29]. These studies are essential for understanding the relationships between bacterial adaptation and various environmental stressors, which could provide new targets for developing antibiotics [7]. In conclusion, the advances toward understanding bacterial porins can contribute to a deeper understanding of the transport of hydrophilic molecules across lipid bilayers, a fundamental process in living cells [7]. By investigating the molecular events that regulate the efficiency of diffusion rate, these studies will aid in our efforts to understand how gram-negative bacteria manage to transport essential molecules while adapting to changing environmental conditions [7; 15; 24; 29]. This research received no external funding. Not applicable. The author declares no conflict of interest.
## 4 Comparison Results
### Analyzing Local Metrics on Real-World Datasets
#### 4.3.3 Class Stability Map  
As explained earlier in Section 3.1, the global average value for class stability \(\bar{S}\) indicates whether a decision map is robust to (inverse) projection errors. The class stability map \(S\) reveals _where_ the decision map is susceptible to such errors. Figure 7 shows the \(S\) maps for the three studied decision-map techniques. As explained in Section 4.2, pixels with low \(S\) values were expected to appear primarily near the decision boundaries. This assumption held true, especially for simpler datasets, such as the HAR dataset.  
Figure 6: Gradient maps \(G\) of the studied decision-map methods. Gradient values are scaled to \([0,1]\) within each dataset. The number in the bottom-right corner of each plot is the average \(\bar{G}\) of each map.  
Figure 7: Class stability map \(S\). The number in the bottom-right corner of each plot is the \(\bar{S}\) of each map.  
However, some anomalies can be seen for each decision-map technique: the DBM exhibited large zones with low \(S\) values. Referring to the decision map (Figure 5), most of these zones were _no data zones_ (NDZs), in which no data points were projected--see the zones circled with dotted green lines in Figure 7 (leftmost image). Another interesting observation is that the most unstable pixels tended to change their labels _immediately_ after the first round trip in the DBM. This observation was also made for DV. Unlike these two methods, the SDBM continued to be affected by round-trip errors. This can be observed by the gradual changes in the \(S\) values for the SDBM in Figure 7. After several rounds, most of the pixels in the SDBM changed their labels, except for those with the most robust labels, which almost never changed. DV exhibited another salient pattern: given the blob-like patterns in this decision map, a particular class often formed the 'base' of the blob-like shapes in some cases, e.g., the MNIST dataset using the random forest and neural network classifiers. The pixels in these areas were more prone to round-trip errors. In summary, the SDBM performed the worst regarding \(S\), as also indicated by the results for the global metrics (Figure 3) on the synthetic blob dataset (Figure 4). The DBM and DV exhibited similar patterns for \(S\), with the DBM slightly outperforming DV on the MNIST and FashionMNIST datasets. However, _all_ three methods were prone to instabilities, and many such instabilities existed along their decision boundaries, which, as explained earlier in Section 4.2, is an undesirable aspect of trustworthy decision maps.
## 4 Data and Experimental Setting  
As a case-study, we address the spatio-temporal interpolation of satellite-derived SST fields associated with infrared sensors, which may involve high missing data rates (typically from 50% to 90%). We consider the same region and dataset as in [22] to make easier benchmarking analysis.
# Recent Advances in Volatiles of Teas
## 2 Aromatic Volatile Compounds in Various Kinds of Teas
### Formation of Tea Aromatic Volatiles
It was later confirmed that the major glycosides in tea leaf were primeverosides and glucosides, among which primeverosides were predominant [15; 16]. Biosynthesis of glycosides from free volatile compounds is catalyzed by glycosyltransferases [14]. Glycosidically-bound volatiles are located in the vacuoles, but glycosidases are present in the cell walls and cavity areas among cells [17]. The primeverosides and glucosides were hydrolyzed by the enzymes glucosidase and primeverosidase during tea processing, especially in the stage of rolling. Primeverosides are the major black tea volatile precursors, because they are abundant in fresh tea leaf, but almost disappear in the final tea products, whereas the glucosides are not substantially changed during tea processing [18]. Non-enzymatic hydrolysis of the glycosidically-bound volatiles takes place during tea processing. During the high temperature processing of ready-to-drink green tea and black tea, glycosidic precursors of damascenone are hydrolyzed [19]. The Maillard reaction is defined as a chemical reaction between reducing sugars and amino acids at high temperature, which produces brown pigments with caramel flavor. Roasted and pan-fired green teas contain high levels of Maillard reaction products, such as 1-ethyl-3,4-dehydropyrrolidone, pyrazines, pyrroles, pyrrans and furans. Pyrazines and 1-ethyl-3,4-dehydropyrrolidone play an important role in developing a roasted flavor in both roasted and pan-fired green teas [20; 21]. Figure 2: Formation of fatty acid-derived volatiles: (**A**) geranyl pyrophosphate; (**B**) linalool; (**C**) linalool-6,7-epoxide; (**D**) geraniol; (**E**) linalool oxide (puranoid); (**F**) linalool oxide (furanoid).
# MeLa: A Programming Language for a New Multidisciplinary Oceanographic Float
## 4 Experiments
### Detection of Earthquakes
#### 4.1.2 The Seismic Detection Algorithm  
The seismic wave detection algorithm is presented in detail in [PERSON] et al. [14]. Here, we give a brief and simplified overview of this algorithm. The first step of the algorithm is the detection of a signal amplitude increase. The signal is filtered by a high pass filter to suppress the micro seismic noise at frequencies below 1 Hz. A Short Term Average over Long Term Average (STA/LTA) is used to detect an elevation of the absolute signal amplitude. In this example the STA/LTA is the mean of the last 10 s of the signal over the mean of the last 100 s. When the result of the STA/LTA exceeds a threshold, it triggers the discrimination part of the algorithm, which decides whether the signal is a seismic wave.  
The discrimination algorithm computes the wavelet transform of the signal, equivalent to a bank of six bandpass filters. The six frequency bands are averaged over time and a normalization process is done between the noise part, before the trigger, and the signal part, after the trigger. This leads to a representation of the signal that is similar to a power spectrum, with six values for each frequency band. A criterion is computed from the distance between the measured powers and the center of six reference distributions. The Signal over Noise Ratio (SNR) is also computed. Both values are used in a decision to trigger the recording of the signal and eventually the ascent of the float to obtain a precise position with the GPS at surface.
# Size and Shape Optimization of a Guyed Mast Structure under Wind, Ice and Seismic Loading
## 3 Load Analysis
### Variable Loads  
In this section, the detailed load modeling phase, for each variable load considered, is described. With specific reference to the wind action evaluation, the drag and lift forces are calculated according to the CNR-DT 207 R1/2018 [43]. The relationship between inertia and viscous forces, i.e., how wind load impacts to the surface, is taken into account with the Reynold's number \(R_{e}\) with the following expression:  
\[R_{e}(z)=\frac{l\cdot v_{m}(z)}{\
u} \tag{1}\]  
where \(z\) is the elevation, \(l\) is the characteristic length, \(v_{m}\) is the averaged wind speed, while \(\
u\) is the kinematic viscosity of air (\(\
u=15\times 10^{-6}\) m\({}^{2}\)/s).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **qk [kN/m]** & **Qk [kN]** \\ \hline Steel ladder, & 0.3 & - \\ other & & \\ Antenna & - & 1.53 \\ Antenna & - & 1.19 \\ Parabolas & - & 0.52 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Non-structural dead loads.  
\begin{table}
\begin{tabular}{c l l l l l l} \hline \hline
**Typeology** & **Model** & **No** & **Elevation [m]** & **H\(\times\)W\(\times\)D [mm]** & **Self-Weight [kg]** & **Clamps [kg]** & **Total [kg]** \\ \hline Antenna & AOC4518R7v06 & 3 & 29.25 & \(1509\times 469\times 206\) & 39.3 & \(2\times 5.8\) & 153 \\ Antenna & 6888670N & 3 & 26 & \(1997\times 305\times 163\) & 32 & \(2\times 3.9\) & 119 \\ Parabola & n.d & 3 & 23.15 & Diameter = 300 & 15 & 2.2 & 51.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Weight of equipment, H, W, and D stand for height, width, and depth.  
\begin{table}
\begin{tabular}{c l l l l l} \hline \hline
**Computation of Dead Loads** & & & & & \\ \hline Profile [mm] & w & Length [m] & n\({}^{\circ}\) & Wtot \\  & & [kg/m] & & & [kg] \\ \hline \multirow{3}{*}{Circular} & D168.3 \(\times\) 12.5 & 48 & 6 & 5 & 1440 \\  & D168.3 \(\times\) 12.5 & 48 & 5.65 & 2 & 543 \\ \hline \multirow{3}{*}{Rectangular} & 60 \(\times\) 40 \(\times\) 3 & 4.35 & 3.16 & 9 & 124 \\  & 60 \(\times\) 40 \(\times\) 3 & 4.35 & 1.8 & 9 & 71 \\  & 100 \(\times\) 40 \(\times\) 3 & 6.13 & 0.45 & 6 & 17 \\ \hline \multirow{3}{*}{Rope} & D16 & 1.3667 & 12.45 & 3 & 51 \\  & D16 & 1.
# Energy-Water Management System Based on MPC for a Greenhouse in a Mapuche Indigenous Community
## 6 Case Study
During the simulation, the system was used to provide 250 L of irrigation each day, with a minimum tank volume of 200 L. The irrigation volume was set based on the upper limit of daily irrigation for a full-capacity greenhouse given the information provided by the farmer (the 500 L tank of the previous greenhouse was filled every two days). In addition, four metrics were used to evaluate system performance: reference deviation, the number of battery cycles \(BC\)[28], the number of times the pump was activated \(\#REC\), and the average number of times the system performed irrigation per day. The following formula was used to calculate \(BC\):  
\[BC=\frac{E_{tot}}{SoE_{max}}, \tag{50}\]  
where \(E_{tot}\) is the total energy from the battery used during the simulation. The resulting \(BC\) value was compared with that in a case in which all pump activation occurred at night (and therefore no solar energy was used). Additionally, this simulation was compared with a manual irrigation scenario in which the 250 L objective was distributed between two irrigation instances (from 6:00 to 7:30 and from 9:00 to 10:30). In this scenario, the water tank was refilled whenever the water volume dropped below 200 L, and filling stopped when the tank began to overflow. Finally, to test the complete EWMS, three simulations with summer data were performed. All scenarios were performed with the full summer dataset, starting with an initial crop age of 14 days. The first scenario was denoted as \"manual irrigation\", and the same manual irrigation scheme described previously was used to irrigate half of the greenhouse, with the difference being that the daily irrigation objective was 150 L instead of 250 L. This scenario was designed as an approximation of real manual irrigation based on interviews with the farmer (estimated daily irrigation of 100 L to 200 L for a half-capacity greenhouse depending on observable factors). The second scenario was the \"EWMS\" scenario, and the proposed EWMS was used to irrigate half of the greenhouse. In this scenario, the minimum tank volume was set to 200 L. In both cases, the other half of the greenhouse consisted of crops of the same type and age but without stress. These conditions were only considered for the greenhouse climate simulation. The final scenario was the \"combined scenario\", in which each half of the greenhouse was irrigated with one of the previous strategies. In this case, pump activation was managed by the EWMS, and the minimum tank water volume was 50 L because of the higher demand. To study the controller's performance in each scenario, the average daily irrigation, total relative yield \(Y_{r}\), and battery cycle \(BC\) were evaluated. For the third scenario, the irrigation and relative yield were only evaluated for the half of the greenhouse that was controlled by the EWMS.
# Experimental Study of Imbibition Characteristics of Silica Sol in Coal-Measure Mudstone Matrix
## 3 Basic Characteristics of Coal-Measure Mudstone and Silica Sol  
### Coal-Measure Mudstone  
Mudstone samples were taken from the Permian stone box set of the Huaibei coalfield, from the floor of the 8\({}_{2}\) coal seam in Tongting Mine. The sample was grey-black with substantial texture and contained siderite. The test for composition characteristics was conducted at the Advanced Analysis & Computation Center of China University of Mining and Technology using a D8 Advance X-ray diffractometer (XRD), FEI Quanta(tm) 250 Environment Scanning Electron Microscope (SEM) (Hillsboro, OR, USA), and mercury porosimeter. The result is shown in Figure 2.  
Based on XRD and energy dispersive spectrometer (EDS) analysis, the samples were composed of quartz, kaolinite, and barite. Kaolinite contains a small amount of Fe\({}_{2}\)O\({}_{3}\). Semiquantitative statistical results showed compositions of 33.90% quartz, 63.60% kaolinite, and 2.50% other. The sample had a low permeability with 5.8687 mdarcy.
## 3 Performance Metrics and Application Scenarios in PLS
### Secrecy Capacity Metrics
It should also be noted that the secrecy capacity and secrecy rate that are discussed above do not consider wireless fading channels. More precisely, it is assumed that [PERSON]'s channel is more substantial than the eavesdropper channel. However, in fading channel environments, the channel gains for both legitimate and eavesdropper channels change randomly over time while remaining constant in each time slot [70]. When considering the features of fading channels, the average secrecy capacity must be assessed to improve the resulting security. Consequently, the secrecy rate or secrecy capacity is a suitable metric for these fading channel scenarios [66]. In order to assess the security within the actual transceiver design, an achievable ergodic secrecy rate was proposed in [69] as a security metric under the assumption of fading channels. When the transmitting power constraint is P, the attainable secrecy rate can be defined as the maximum gap between the secrecy rates of the main transmission and eavesdropper channels. When considering average power, the secrecy rate of a secure transmission system is described as:  
\[R_{S}=log_{2}\bigg{(}1+\frac{\mathbf{H}_{AB}\,P}{\sigma_{B}}^{2}\bigg{)}-log\bigg{(} 1+\frac{\mathbf{H}_{AE}P}{\sigma_{E}}^{2}\bigg{)} \tag{17}\]where \(\sigma_{B}{}^{2}\) and \(\sigma_{E}{}^{2}\) are the variance of noise vectors in the Bob and eavesdropping channels, respectively. Therefore, the attainable rate of transmission is assumed to evaluate the security performance of the designs for efficient transmission strategies under the consideration of fading channels. However, the secrecy rate is different for different optimization and signal design techniques. In order to evaluate better secrecy rate performances in multi-antenna techniques, a novel approach to the optimization technique was discussed in [74]. Generally, the secrecy rate can be maximized through the allocation of power according to the knowledge of the CSI of the transmitter. Therefore, when the combinations of CSI are different at the source Alice, the attainable secrecy rate may be different. In summary, the secrecy rate or secrecy capacity of an Alice-Bob-Eve channel is mainly calculated using the difference between the capacities of the legitimate and eavesdropper channels and the availability of the CSI of the transmitter. Although the secrecy rate and secrecy capacity are well established and commonly used in information-theoretic security, these metrics only reflect the achievable bounds of the random channel characteristics and do not indicate the real security performance in practical design scenarios with various service applications. In order to measure and improve the resulting secrecy in fading channel environments, these metrics are extended to outage secrecy rate probability and outage secrecy capacity [75].