esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Datasets
### Experiment 2: Moving Antenna  
For the second experiment, data from a moving GNSS receiver were used. This receiver was installed together with two additional receivers aboard of a ferry, which sails from harbors on the German coast to Heligoland island on a daily basis. The installation consists of multiple GNSS Eclipse P307 receivers and Hemisphere A52 antennas. The equipment was installed as part of another project, aiming to measure the sea surface height from moving ships.  
Dual-frequency 1 Hz GNSS data were gathered between the beginning of July and the end of September 2017 (GPS Day of Year 186-268). For these 83 days, data from 147 ferry crossings were available for analyses (Figure 4). Due to several restrictions, the antennas had to be installed at the observation deck of the ship's superstructure. The antenna used for this work was fixed to the forward end of the rail. Hence, an azimuth mask cutting out the superstructure and the mast behind the antenna was used. The GNSS data were routinely analyzed in the frame of the previously-mentioned project. Therefore, the parameters \(h_{ref,t}\), \(h_{static}\), \(dh_{att,t_{i}}\), as well as \(heave_{t_{i}}\) from Equation (3) are known.  
Figure 2: Schematic diagrams of the geometries and connections between the parameters of Equations (2) and (3). (**a**) Geometry for a static antenna. (**b**) Geometry for a ship-based antenna.  
The squat correction was derived from an empirical model estimated previously from a calibration experiment [19].  
Figure 4: Overview of the investigation area. The orange hexagon highlights the position of the tide gauge station TGW2 and therefore the location of the static experiment. Ferry crossings are shown as red lines. Green triangles mark the positions of the wave buoys.  
Figure 3: Installation of the GNSS antenna atop the tide gauge station TGW2 (photo: BfG).
## 4 Evaluation and Results  
Making use of the techniques presented in the previous section, we proceed to evaluate our approach. As explained in Section 1, this work proposes extracting information from the CTG signal based on CEEMDAN and TV-AR modeling, whose feature extraction operation is performed separately on the complete FHR signal, deceleration episodes, and resting periods. The main idea is to determine whether by considering those segments independently, the CTG classification performance can improve compared with the traditional analysis based only on the complete signal.  
In this Section, we first explain the foundation of the proposed signal features qualitatively. Then, we present the results concerning their performance in CTG analysis by using a SVM based classification.
# Emerging Trends in Hybrid Wireless Communication and Data Management for the Industry 4.0
## 5 Hierarchical and Multi-Tier Communication and Data Management for the Industry 4.0
### Hybrid Communication Management
This function can be, for example, Admission Control or Inter-Cell Interference Coordination algorithms can be executed as the Global management decision function in the Orchestrator, Power Control, or Radio Resource Allocation within a cell can be executed as the Local management decision function in the LMs, and Scheduling or Power Control can be executed as the Communication management decision function at the end-devices. As shown in Figure 6, an end-device performs local measurements of the quality and performance experienced in its communication links. This local data (1) is processed by the reasoning functionthat provides high-level performance information (2a) that is reported to the LM in its cell (3). This high-level performance information can also be used by the end-device (2b) to get a management decision (4) and configure its communication parameters in the case that the end-device has management capabilities. In this case, the management decisions taken by different end-devices in the same cell are coordinated by the LM in the cell which can also configure some communication parameters of the end-devices (7b). Decisions taken by end-devices are constrained by the decisions taken by the LM (7c). If end-devices do not have management capabilities, the communication parameters for the end-devices are directly configured by the LM (7b). The Local management decisions taken by each LM are based on the performance information gathered by all end-devices in its cell (from 1 to \(n\) devices in the figure), and on local measurements performed by the own LM. This data (5a and 5b) is processed by the reasoning function in the LM, and the resulting high-level performance information (6b) is used to take a local management decision and configure the communication parameters of the end-devices in its cell (7a, 7b, and 7c). Each LM also reports to the Orchestrator the processed information about the performance experienced in its cell (8). The Orchestrator receives performance information from all the LMs (from 1 to \(m\) LMs in the figure). The performance information gathered by the LMs (9b), together with local measurements performed by the Orchestrator in its communication links with the LMs (9a), is processed by the reasoning function in the Orchestrator. The high-level performance information (10) is used by the Orchestrator to achieve a global management decision and configure radio resources to use at each cell (11a). The global management decisions taken by the Orchestrator constrain the local management decisions taken by the LMs (11b) to guarantee the coordination among the different LMs in the network, and finally ensure the communication requirements of the industrial applications and services supported by the network.
## 3 Security Issues And Challenges
### Challenges on CC
#### 3.2.1 Technological and Technical Challenges of the CC  
Setting up a CC on the Internet is a challenging endeavor. The awareness of these challenges would provide an understanding of the difficulties in ensuring comprehensive security for CC. The following are the technological and technical challenges that CSP may encounter:  
* _The heterogeneity:_ CC comprises an assortment of hardware manufactured by different manufacturers. For example, network switches, appliances, firewalls, and gateways could utilize proprietary communication protocols or operating systems.
* _The deployment model:_ The deployment model refers to a specific kind of CC, essentially featured by proprietorship, access, and size, so forth. Public CCs; community CCs; private CCs; and hybrid CCs.
* _The replica/DR:_ It should be borne in mind that there should be a duplicate of the original of the same CC in another geographical area, such as disaster recovery (DR) in the event of an earthquake or something similar.
* _Communication:_ Different technologies are utilized by CC devices, for example, wireless or wired communications.
* _The homogenization:_ For diversified CC devices to connect, team up and exchange data with one another, there ought to be a standardized and foreordained data interchange form.
* _Energy consumption:_ One of the major challenge of the CC. Any device operating on CC needs to be intended and prepared with the matter.
* _Energy continuity:_ An uninterruptible power supply (UPS) is an electrical device that supplies backup power when the main power supply source (generator or direct) fails.
# Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol
## 3 Results and Discussion
### Calculation of the Limit of Detection (LOD)  
The Limit of Detection (LOD) was calculated using the method described in Section 2.4.2 to evaluate the sensitivity of the analytical approach. Key parameters included the following:  
* Confidence level: \(\alpha=0.05\) (95% confidence);
* External validation dataset: \(n=13\) samples;
* Number of latent variables: \(p=3\), resulting in degrees of freedom \(df=n-p=10\);
* Critical \(t\)-value: \(t_{0.05,10}\approx 2.228\) (determined using Python's SciPy library);
* Root Mean Square Error of Prediction (RMSEP): \(1.154\cdot 10^{-4}\);
* Slope of the calibration curve: 0.997.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Number** & \
\begin{tabular}{c} **Theoretical Value** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Predicted Value** \\ **(\%)** \\ \end{tabular} & **Relative Error (\%) 1** \\ \hline
1 & 0.700 & 0.683 & 2.46 \\
2 & 0.500 & 0.489 & 2.23 \\
3 & 0.300 & 0.283 & 5.85 \\
4 & 0.200 & 0.209 & 4.57 \\
5 & 0.100 & 0.094 & 5.91 \\
6 & 0.070 & 0.076 & 8.42 \\
7 & 0.050 & 0.054 & 7.75 \\
8 & 0.030 & 0.032 & 5.91 \\
9 & 0.020 & 0.021 & 6.78 \\
10 & 0.010 & 0.011 & 11.46 \\
11 & 0.007 & 0.010 & 48.73 \\
12 & 0.005 & 0.008 & 55.32 \\
13 & 0.003 & 0.014 & 375.63 \\ \hline \hline \end{tabular}  
* Note: LOD (Limit of Detection) = 0.026%. For samples labeled with “\(\leq\)LOD” (Nos. 9–13), their predicted values are dominated by instrument noise. The relative error calculations are provided for reference only and should not be used to evaluate model performance.  
\end{table}
Table 6: Water content test results for samples from different batches.  
Figure 5: Scatter plot of predicted vs. measured water content for samples from different batches.  
These values are substituted into Equation (1):  
\[LOD=t_{\alpha,\,n-p}.\frac{RMSEP}{slope}=2.228.\frac{1.154\cdot 10^{-4}}{0.997}=0.026\%\]  
Notably, the calculated LOD (0.026%) integrates both prediction error (RMSEP) and confidence level (\(\alpha\) = 0.05), providing a statistically robust detection threshold rather than relying solely on experimental concentration limits. The derived LOD (0.026%) satisfies the rapid screening requirements for water exceedances in industrial ethylene glycol, where the quality control standard for premium-grade products is typically \(\leq\)0.1%.  
While the model achieves reliable predictions for water content \(\geq\) 0.26% (R\({}^{2}\) = 0.997, RMSEP = 0.0115%), performance degrades significantly below this threshold due to spectral noise dominance. Relative errors for samples < 0.026% should be interpreted cautiously, as they exceed the model's intended operational range. These results emphasize the system's suitability for rapid screening of non-compliant ethylene glycol (H\({}_{2}\)O > 0.1%), while highlighting the need for advanced signal processing to address low-concentration limitations.
## 4 Analysis of Simulation Results
### Energy Conversion  
The variation curves of the various energies after the stress wave was transmitted to the specimen are shown in Figure 16. The strain energy was mainly stored in contact bonding. Each decrease indicated a break in the contact bond and micro-failure inside the specimen. At 212 us, the stress wave began to be input into the specimen, the vibration speed of the specimen increased, and the kinetic energy increased abruptly. Simultaneously, the particles in the specimen were compressed compactly. This resulted in an increase in the strain energy of the contact between particles. After 260 us, the strain energy first attained a peak and then began to fall. This indicated that the local accumulated strain energy attained the storage limits of the specimen and that microcracks appeared. Part of the strain energy released owing to the breakage of contact was converted into kinetic energy. Thereby, the kinetic energy increased, attained a peak at 274 us, and then began to fall. At 277 us, the strain energy began to increase because of the local stress concentration, and the local particles of the specimen were compacted. This was accompanied by the transformation of part of the kinetic energy into strain energy. Subsequently, the strain energy decreased abruptly during 300-338 us, internal failure of the specimen occurred again, and the strain energy was released. This resulted in an increase in kinetic energy during 306-336 us. This process occurred repeatedly until the failure of the sample stopped. Apparently, a mutual transformation between strain energy and kinetic energy occurs during the process of specimen failure, and the decrease in one type of energy would result in an increase in another type of energy. However, the decrease in energy should be larger than the increase in energy owing to the damping and friction energy consumption in the conversion process. The trends of the damping energy and friction energy maintain the level after increasing to a maximum value with the incidence of the stress wave. As a result, the final kinetic energy gradually tends to zero.  
Figure 16: Energy conversion during stress wave incidence.
# Assessment of _Bacillus pumilus_ Isolated from Fresh Water Milieu for Bioflocculant Production
## 3 Results and Discussion
### Optimization of Culture Conditions for Biofoculant Production
#### 3.2.5 Effect of Cations on Flocculating Activity of Crude Biofoculant
This implies that the electrostatic repulsive forces between Fe\({}^{3+}\) and charged groups of the biofloculant will be higher as compared with Al\({}^{3+}\) and this consequently leads to the inhibition of flocculating activity of the biofloculant in the presence of Fe\({}^{3+}\). Monovalent cations could produce bonds that are loose in structure resulting in decreased floc density and size, which in turn caused reduction in flocculating activity [55]. This might probably account for the poor flocculating activity observed in the presence of Na\({}^{+}\) and K\({}^{+}\). Excessive adsorption of the ions might be the cause of inhibition of the flocculation process in the presence of Fe\({}^{3+}\)[55]. The flocculating activity of the biofloculant produced by the consortium of _Cobettia_ sp. and _Bacillus_ sp. was enhanced by all of the divalent metal ions tested (Ca\({}^{2+}\), Mg\({}^{2+}\), and Mn\({}^{2+}\)) but it was completely inhibited by Li\({}^{+}\) and K\({}^{+}\)[56]. The flocculating activity of BPB4 biofloculant produced by _Arthrobacter_ sp. B4 was Ca\({}^{2+}\)-dependent and unaffected by other metal ions tested, no flocculation occurred in the absence of Ca\({}^{2+}\) ions [53]. On the other hand, [PERSON] et al. [47] reported a cation-independent biofloculant MBF-W6 produced by _Chrysobacterium dageuense_ since it was observed that none of the cations added could evidently improve its flocculating rate. The function of cations is to stimulate the flocculating activity by neutralizing and destabilizing the residual negative charge of functional groups and to form bridges between particles [10]. However, metal ions have also been reported to inhibit flocculating activity or have no effect at all on the flocculating activity of biofloculants [53; 56].
## 3 Results and Discussion  
### Pressure Drop  
Figure 3 shows pressure drop for three certified mask materials as a function of face velocity for three RH levels. The P100 3M filtration media had the highest pressure drop, and the surgical mask had the lowest. Several materials (muslin, knitted cotton, and rayon cotton blend) had lower pressure drops than a surgical mask. A complete list of materials' pressure drops can be found in the Supplemental Material Table S1. Pressure drop has a linear relationship to the face velocity through the material, as expected for the laminar airflow through the filter media. Not considering the dust load, the pressure drop (\(\Delta\)P, Pa) is proportional to the air velocity at the face of a filter (U\({}_{0}\), m/s). \(\Delta\)P = \(\beta\)U\({}_{0}\), where \(\beta\) is the air resistance coefficient (Pa\(\cdot\)s/m) [11].  
Figure 4 shows the change in pressure drop of single-layer materials when the RH is varied. The effect of RH on single-layer materials' pressure drop is minimal or, in some cases, statistically insignificant. The minor pressure-drop increase on some hygroscopic materials such as silk, coffee filter, and cotton can be attributed to fiber swelling, a decrease in fabric porosity, and a possible increase in fabric thickness. A similar change in the resistance to convective flow at varying humidity levels was reported by [PERSON], where the largest change was observed at higher RH above 0.8 [67]. Commercial surgical face masks typically have a three-layer structure. The middle layer is made of a melt-blown material that serves as the filter media, whereas the inner layer is for absorbing moisture, and the outer layer repels water. The moisture repelling by the outer layer might explain the small change in pressure drop and FE of the surgical mask at elevated RH levels. For multiple material layers, humidity has minimal to no effects on the breathability of the layered materials, as shown in Figure S2 of the Supplemental Information. As expected, the material's pressure drop increased with each additional textile layer.  
Figure 3: Pressure drop for single-layered certified materials as a function of face velocity. Facets represent the relative humidity during the experiment. Results are plotted as the average pressure drop from three runs, with the error bars representing the standard deviation.
## 2 Materials and Methods
### Data Processing
#### 2.3.3 Pwsi  
The _PWSi_ was calculated as follows [17]:  
\[PWSi=\frac{dT-dT_{ll}}{dT_{ul}-dT_{ll}} \tag{4}\]  
where \(dT\) is the measured temperature difference between the canopy and the air (\({}^{\circ}\)C), \(dT_{ll}\) is the lower boundary representing non-water-stressed conditions (\({}^{\circ}\)C), and \(dT_{ul}\) is the upper boundary representing non-transpiring conditions (\({}^{\circ}\)C). Theoretically, the numerical value of the PWSI varies from zero to one, representing the water status from well-watered to non-transpiring conditions.  
\(dT_{ll}\) and \(dT_{ul}\) were calculated as follows [17]:  
\[dT_{ll}=\frac{r_{a}(R_{n}-G)}{\rho c_{p}}\times\frac{\gamma\left(1+\frac{r_{cp }}{r_{a}}\right)}{\Delta+\gamma\left(1+\frac{r_{cp}}{r_{a}}\right)}-\frac{VPD }{\Delta+\gamma\left(1+\frac{r_{cp}}{r_{a}}\right)} \tag{5}\]  
\[dT_{ul}=\frac{r_{a}R_{n}}{\rho c_{p}} \tag{6}\]  
where \(R_{n}\) is the net radiation (W m\({}^{-2}\)), \(G\) is the soil heat flux (W m\({}^{-2}\)), \(r_{a}\) is the aerodynamic resistance (s m\({}^{-1}\)), \(\rho\) is the air density (kg m\({}^{-3}\)), \(c_{p}\) is the heat capacity of air (1013 J kg\({}^{-1}\)\({}^{\circ}\)C\({}^{-1}\)), \(\Delta\) is the slope of the saturation vapor pressure-temperature relation (kPa \({}^{\circ}\)C\({}^{-1}\)), \(\gamma\) is the psychrometric constant (kPa \({}^{\circ}\)C\({}^{-1}\)), \(VPD\) is the vapor pressure deficit (kPa), and \(r_{cp}\) is the canopy resistance at potential evapotranspiration (s m\({}^{-1}\)).  
The \(r_{cp}\) and \(r_{a}\) were calculated as follows [24; 37]:  
\[r_{cp}=\frac{r_{min}}{LAI_{e}} \tag{7}\]  
\[r_{a}=4.72\times\left\{ln\left[\frac{(z-d)}{z_{0}}\right]\right\}^{2}/(1+0.54u) \tag{8}\]  
\[LAI_{e}=\left\{\begin{array}{ll}LAI&(LAI\leq 2)\\ 2+\frac{LAI-2}{3}&(LAI>2)\end{array}\right. \tag{9}\]where \(r_{min}\) is the minimum leaf stomatal resistance (90 s m\({}^{-1}\)), \(LAl_{c}\) is the effective \(LAl\), \(z\) is the reference height (m), d is the zero-plane displacement height (m) (d = 0.66 h), h is the tree height (m), z\({}_{0}\) is the roughness length (m) (\(z_{0}\) = 0.075 h), and \(u\) is the wind speed (m s\({}^{-1}\)).
# Modeling the Energy Performance of LoRaWAN
## 5 Evaluation
### End-Device Current Consumption  
#### 5.1.1 Unacknowledged Transmission  
We calculate the average current consumption of the end-device in unacknowledged transmission by using the values shown in Tables 5 and 6 and Equations (1)-(11). Figure 8 illustrates the obtained results, as a function of \(T_{notif}\) and the DR selected. Note that the minimum value for \(T_{notif}\) is given by the 1% duty-cycle limitation that applies for the mandatory, three-channel sub-band, and it depends on the DR.  
As expected, the average current consumption decreases as \(T_{Notif}\) increases, since then the sleep intervals have a greater duration, while the duration of the intervals related with communication remains constant. The average current consumption tends asymptotically to the sleep current as \(T_{Notif}\) grows.  
On the other hand, the average current consumption decreases with the DR, since the duration of transmit and receive intervals is inversely proportional to the bit rate of the DR used. For example, the highest relative difference in terms of current consumption between using DR0 and DR5 is a factor of 2.76, for a notification period of 5 min. However, differences due to the DR used decrease with \(T_{Notif}\).  
Finally, note that, in unacknowledged transmission, end-device current consumption is independent of the BER, as the end-device will perform a single transmission attempt of each data unit, regardless of whether it is correctly received by the network server.
## 3 Results and Discussion
### Discharge Bus Waveform Test  
Upon discharging, the output voltage of the push-pull circuit is the bus voltage, and the purpose of the constant-voltage discharge control is to maintain a constant bus voltage. Figure 12 shows the voltage and current waveforms of the loaded push-pull discharge busbar, where the bus voltage is 398 V, which is the same as the set output voltage. Figure 13 shows the ripple voltage of the busbar at this time, where the ripple amplitude is 4.2 V, which is 1% of the bus voltage. The fact that the bus ripple amplitude is low indicates the effectiveness of the designed bus capacitance and constant voltage control.  
Figure 11: Tracking results of different powers. **(a)** 500 W tracking effect; **(b)** 1 kW tracking effect; **(c)** 2 kW tracking effect; **(d)** 3 kW tracking effect.
# Re-Optimized Design for the ADR-Based Dual-Loop Controller of 100 m Class Aperture Radio Telescope
## 3 Re-Optimized Design of the Dual-Loop Controller
### Position Controller Simplification  
The amplitude of the closed-velocity-loop transfer function is approximated as unity within the position-loop bandwidth; therefore, a single integrator exists in the whole position system. A proportional controller for the single-integrator system is sufficient for step tracking without static error. The typical trajectory of a radio telescope for astronomical observation is a ramp, which means that utilizing a position-loop proportional controller will create a static tracking error. Therefore, a PID controller is used for non-static-error tracking for non-step signals and enhancing the response rapidity. However, for the adverse effect of the measurement noise and integrator saturation, the coefficients of the integral and the derivative terms in the PID are smaller than the proportional term coefficient, making the PID control manifest primarily proportional control; some radio telescopes even employ a pure proportional control in their position-loop system. Some radio telescope position-loops, such as GBT, utilize a lead-lag compensator. The nature of the lead-lag compensator is a PD cascading a first-order low-pass filter, and it also cannot achieve static-error-free tracking for non-step signals.  
In fact, a radio telescope does not need to achieve static-error-free tracking for a position signal; instead, it is sufficient to achieve command-tracking with a certain static error. Correction by a pointing model can eliminate the negative effect of the position tracking error. Therefore, a proportional controller in the position-loop is sufficient for a radio telescope.  
Finally, the block diagram of the re-optimized dual-loop control structure used for the 100 m class antenna is shown in Figure 4, which consists of a position proportional (P) controller and a velocity-controller-based LADRC with disturbance derivative (DLADRC).