esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Determination of Volatile Compounds by Headspace SPME-GC/MS  
A small portion (approximately 5 g) of fish tissue was taken from six fish or fillets (three from each batch) in triplicates and cut quickly in small cubes, snap-frozen in liquid nitrogen to quench metabolism and grinded for 10-15 s in a pre-cooled A11 analytical mill (IKA, Wilmington, NC, USA) to obtain a fine frozen powder. Aliquots (2 g) of each powdered sample were accurately weighed (\(\pm\)0.01 g) in a porcelain mortar containing 2 g (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), homogenized for 20 s and transferred into 20 mL headspace glass vial. The vials were sealed with crimp caps with PTFE-lined silicone septa and equilibrated for 15 min at 40 \({}^{\circ}\)C in a water bath. The volatiles were extracted by exposing the SPME fiber (DVB/CAR/PDMS, length 2 cm, Sigma Aldrich, Taufkirchen, Germany) for 30 min under the same conditions. The volatiles were desorbed in the injection port of a GCMS-QP2010 Ultra (Shimadzu Inc., Tokyo, Japan) equipped with a SPME liner (0.7 mm i.d.) at 240 \({}^{\circ}\)C in splitless mode for 5 min. Afterwards, the fiber was retracted and conditioned for 5 min at 250 \({}^{\circ}\)C in the injection port of another GC to remove any volatile residues. The separation of the compounds was performed in a DB-WAX fused silica capillary column (30 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m film thickness, Agilent, Santa Clara, CA, USA) with He as carrier gas (constant linear velocity 36 cm/s). The oven temperature was programmed at 40 \({}^{\circ}\)C for 5 min, increased by 5 \({}^{\circ}\)C/min to 180 \({}^{\circ}\)C, and then by 30 \({}^{\circ}\)C/min to 240 \({}^{\circ}\)C (and held for 5 min). The mass spectrometer was operated in the electron ionization mode with the electron energy set at 70 eV and 40-300 \(m/z\) scan mass range. Source and interface temperatures were set at 230 \({}^{\circ}\)C and 240 \({}^{\circ}\)C, respectively. Annotation of the compounds was accomplished by comparing: (i) the retention indices based on the homologous series of n-alkanes (C8-C24, Niles, IL, USA) with those of authentic compounds (when available) and those of NIST14 library (NIST, Gaithersburg, MD, USA), (ii) MS data with those of reference compounds and MS data obtained from the NIST14 library. GCMS solution (ver. 4.30, Shimadzu), AMDIS (ver. 2.72, NIST) and NIST MS Search (ver. 2.2, NIST) software were used in the identification process. The reliability of identification (RID) was set at three levels. A-level: agreement of retention index (RI) and mass spectrum (MS) with those of an authentic compound analyzed under identical experimental conditions; B-level: agreement of retention index (ARI \(<\) 20) and mass spectrum (match \(>\) 900); C-level: at least \(\Delta\)RI \(<\) 20 or mass spectrum similarity match \(>\) 800. The percent normalized peak area of volatile components was calculated for each sample and further used in statistical analysis.
# A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects
## 3 Methodology
### Long Short-Term Memory Network Model for Location Prediction
#### 3.2.1 Model Description
As shown in Figure 11, the current cell state \(\mathbf{c}_{t}\) is calculated by Equation (11). The output of LSTM \(\mathbf{h}_{t}\), as shown in Equation (13), is decided by \(\mathbf{o}_{t}\) and \(\mathbf{c}_{t}\). The training and parameter derivation process can be found in [30]. In conclusion, the conveyor belt-like structure allows LSTM to remove or add information from the very beginning to the current state. Therefore, LSTM is expected to perform well with long sequences. \[\mathbf{f}_{t}=\sigma\Big{(}W_{f}\cdot[\mathbf{h}_{t-1},\mathbf{x}_{t}]+\mathbf{b}_{f}\Big{)} \tag{8}\]  
\[\mathbf{i}_{t}=\sigma(W_{i}\cdot[\mathbf{h}_{t-1},\mathbf{x}_{t}]+\mathbf{b}_{i}) \tag{9}\]  
\[\widetilde{\mathbf{c}}_{t}=\tanh(W_{c}\cdot[\mathbf{h}_{t-1},\mathbf{x}_{t}]+\mathbf{b}_{c}) \tag{10}\]  
\[\mathbf{c}_{t}=f_{t}*\mathbf{c}_{t-1}+i_{t}*\widetilde{\mathbf{c}}_{t} \tag{11}\]  
Figure 11: The structure of the repeating module in the LSTM. \[\mathbf{o}_{t}=\sigma(W_{o}\cdot[\mathbf{h}_{t-1},\mathbf{x}_{t}]+ \mathbf{b}_{o}) \tag{12}\] \[\mathbf{h}_{t}=\mathbf{o}_{t}*\tanh(\mathbf{c}_{t}) \tag{13}\]
## 3 Methodology
### Algorithm Complexity Analysis  
In this section, we will further analyze the complexity of the proposed Joint-IF, which mainly consists of time complexity and space complexity. Concretely, the time complexity of the Joint-IF denotes the time consumed to complete the imputation and prediction tasks. Additionally, the additional space complexity indicates the space required to achieve the same task mentioned above, which is measured by the size of the space in which all data are stored. All in all, the complexity of Joint-IF can be denoted as a function whose domain is the size of the input values, and its range of values usually takes the number of steps to be executed (time complexity) or the storage space required (space complexity). In our study, the synthesis ability of Joint-IF relies on the premise that time and space resources reach a balance.  
Time Complexity: the time complexity of Joint-IF is expressed as \(\text{o}(n_{M}*n_{N}+nb)\), in which \(b\) is the number of parameters and \(n\) represents the number of samples. During the missing data imputation stage, the time complexity depends on the dimension of the input matrix \(\mathbf{Y}\), i.e., its time complexity is \(\text{o}(n_{M}*n_{N})\). In the prediction stage, the time complexity relies on the number of feature variables and the number of samples, i.e., its time complexity is \(\text{o}(nb)\). Therefore, the total time complexity of Joint-IF is the \(\text{o}(n_{M}*n_{N}+2 nb)\approx\text{o}(n_{M}*n_{N})\approx\text{o}(n^{2})\), where \(n\) is much larger than \(m\).  
Space Complexity: the space complexity of Joint-IF is the \(\text{o}\left(n^{2}+z^{2}k\right)\), where \(k\) is the number of neural network layers and \(z\) represents the number of neurons per layer. Inthe missing data recovery stage, the space complexity depends on the maximum space (the data size) required for data storage, i.e., its space complexity is \(\mathsf{o}\big{(}n^{2}\big{)}\). During the forecasting stage, the space complexity of Joint-IF relies on the number of layers and the number of neurons, i.e., its space complexity is \(\mathsf{o}\big{(}z^{2}k\big{)}\)). Thus, the total space complexity is the \(\mathsf{o}\big{(}n^{2}+2z^{2}k\big{)}\approx\mathsf{o}\big{(}n^{2}\big{)}\), where \(n\) is much larger than \(z\), \(k\).
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 4 Results and Evaluations  
The satellite access point based on our design (see Figure 3) was built and tested. We evaluated the power consumption of the system and verified the parameters of the solar module.
## 3 Results and Discussion
### VFAs Production Using Immersed Membrane bioreactor
In this regard, a VFAs COD-equivalent level of 14 g/L was obtained. As can be seen in Table 4, for the provision of the same amount of COD-equivalent VFAs for complete replacement of methanol with permeate from tested food waste iMBR, an iMBR with 1.6-times greater volume and membrane area compared to the sludge iMBR is required. This imposes a great additional capital cost on the WWTP if iMBR retrofitting/integration is to be aimed at. As estimated, using a sludge iMBR compared to a food waste fed iMBR reduces the operational cost of VFA production by 30-35%. However, it should be considered that the presented scenarios suffer from the fact that the VFA solutions themselves carry an ammonium content that should be removed or converted prior to the addition of the VFA solution to the denitrification stage. Another future scenario can include an iMBR set-up that benefits from both sludge and food waste as co-substrates. According to our previous studies [9; 27], such a scenario would most likely yield higher VFAs and improve the C/N ratios of the recovered VFAs solution (Figure 13). However, regardless of which scenario to apply, without the application of the iMBR the plant has to provide about 300 kg fossil-based methanol per hour with an estimated cost of about 140 \(\upcels/\)h (based on estimated prices in 2018). It should be noted that the ability of this specific VFA solution to replace methanol for denitrification has not been verified by denitrification tests and the theoretical calculations are based on COD. Using the established flexible iMBR technology, surplus VFA solution can be easily produced by changing the process parameters. Considering the VFAs solution's average market value of 0.6-0.8 \(\upcels/\)kg, the WWTP or organic waste treatment facility can benefit from the obtained surplus VFAs. Using the proposed solution, the WWTP becomes more sustainable by circumventing the application of fossil-based methanol, recovers the nutrient content of the waste sludge and food waste to a high extent, and produces several value-added products, such as VFA and biogas, that can provide the basis for a WWTP biorefinery. However, to fully validate the proposed concept, further detailed study on the capital expenditures for integration or retrofitting the iMBR system into existing WWTPs (a biogas digester can be an option for conversion to iMBRs) should be conducted considering membrane filtration and process limitations, and the effect of ammonium nitrogen content of VFA solution on the final effluent quality. Figure 13: An overview of the proposed scenario with an MBR system treating sludge and food waste at Gryaab AB.
# Spatiotemporal Analysis of Evapotranspiration and Effects of Water and Heat on Water Use Efficiency
## 2 Materials and Methods
### Hurst Index  
The Hurst index is used to describe the sustainability of time-series data quantitatively and has extensive applications in hydrology, economics, and climatology [38]. Consider the \(ET\) (\(ET\) represents variables) time series \(ET(t)\) (\(t\) = 1,2,3,4,...,\(n\)), and define the mean sequence of the time series:  
\[\overline{ET}_{(\tau)}=\frac{1}{\tau}\sum_{t=1}^{\tau}ET_{(\tau)}\ \ \ \ \tau=1  
where WUEs (g C\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\)) represents the water use efficiency, \(ET\) is the evapotranspiration, and GPP is the gross primary productivity. Here, we define three similar water use efficiencies; eWUE represents the ecosystem water use efficiency (\(H\) stands for evapotranspiration); tWUE is the vegetation transpiration water use efficiency (\(H\) stands for transpiration); and pWUE is the precipitation of water use efficiency (\(H\) stands for precipitation). Here, GPP and precipitation data with a spatial resolution of 1 km and a time scale of month are resampled with a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\). Finally, monthly WUEs are calculated.  
This study used a linear trend estimation method to analyze the spatiotemporal change trend of WUEs. The \(x\) axis is the year (time series), and the \(y\) axis is the slope \(y=a+bx\) of the WUEs, the linear regression equation representing the trend rate. When the slope is greater than zero, the WUEs are growing, and vice versa [40; 41]. We have divide the trend change into five levels, as shown in Table 1.
## 3 A Review of Evaluation Criteria
### Evaluation Measures Related to Overhead and Efficiency  
In conjunction with the direct measures of energy, the other metrics related in evaluating the performance are overheads and efficiency of the WSN. The following is a list of possible metrics.  
* _Routing Protocol Message Cost_. This is one of the most common metrics used for evaluating the efficiency of the protocols. It measures the number of packets generated by a protocol/algorithm for each successful communication.
* _Message Loss_. It measures the percentage of messages not received by any node in the network.
* _Control Overhead_. It measures the ratio between control and data messages being transmitted in the network. Some authors also use the overall packets sent or packets received and others compare the application packet delivery rate with the routing packet rate.
* _Event Delivery Ratio_. This criterion is the ratio of the number of distinct event messages received by the sink to the number originally sent by the source. Some authors measure a related \"loss to collision\" ratio.
* _Transmissions to Query Ratio_. This is the ratio between the total number of packets to the number of queries injected into the sensor network.
* _Average Path or Route Length_. It measures the number of hops from source node to destination node. Although it is related to energy usage, each path or route can give very different results due to the nonlinear relationship between transmission power and range.
## 3 Test Details
### Enabling Load TH Test Legs
#### 3.1.4 Enabling Load TH Test Results  
The data of \(R_{\mathrm{LLCR}}\) for the tested units were collected at three different readouts (RO), 192 h, 312 h, and 408 h. They were plotted in the variability chart below, together with \(R_{\mathrm{LLCR}}\) at time zero (before units being put into chamber), as shown in Figure 7. In the chart, the resistances greater than 100 milliohms were not plotted for scale consideration. It can be seen that the TH tests under high load was good; the TH tests at time zero were good; but for the TH tests under min load, there were many failures. Additionally, note that at each RO, failed units (no more than three) were taken out for failure analysis (FA). These failed units did not go back to chamber to next ROs. This is why the resistances appear to get better with time for the min load case (since some failed units were taken out).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**\# of Contacts per Chain** & **Max Chain LLCR Limit** \\ \hline
2 & 59.50 \\
4 & 39.25 \\
6 & 32.50 \\
8 & 29.12 \\
10 & 27.10 \\
12 & 25.75 \\
14 & 24.78 \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(R_{\mathrm{LLCR-max}}\) per chain.  
Figure 7: Variability chart for low-level contact resistance.  
The measured LLCRs were plotted against the daisy chain layout (as illustrated in Figure 5) for each unit and at each RO. Figure 8 below shows two selected \(R_{\text{LLCR}}\) maps. The selected maps were from the units at 192 h RO under min load leg. These two units were taken out for FA due to failures.  
The LLCR map of a unit from another TH leg is shown in Figure 9. The test setup for this leg was the same as the previous min load leg, but the socket supplier was different. This unit was not used for any comparison in this paper, but only to demonstrate that this TH failure could happen at the inner perimeter and could exhibit as an open failure.  
From the LLCR distribution, it can be seen that there was no obvious pattern in terms of the failure location distribution. With step load on the North and South sides, the contact force distribution map is shown in Figure 10 (Note: NFORC3 divided by 10,000 is in gram force). With the package warped upwards in the center, the contact force had a tendency to decrease from the exterior perimeter to the  
Figure 8: Selected \(R_{\text{LLCR}}\) map of failing units. (**a**) Failing unit No 1; (**b**) failing unit No 2.  
Figure 9: Example of temperature-humidity (TH) open failure.  
interior perimeter, whereas the failures were found both around the outer perimeter and the inner perimeter. These were typical TH failure symptoms.
## 5 Pre-Treatments for Anaerobic Digestion of Food Waste
### Biological Pre-Treatments  
Biological pre-treatments often consist of aerobic or anaerobic treatment before the main AD process, in order to increase the enzymatic population or increase the process development [4].  
Aerobic pre-treatment, for example, [244] subsequently increases hydrolysis potential, resulting in higher biogas creation up to 70%. However, an excessive aireation can lead to a biogas production decrease due to VFA formation and accumulation [245].  
Anaerobic pre-treatments, on their own, consist of separating various phases in two different reactors (hydrolysis-acidogenesis from acetogenesis-methanogenesis) [246], so optimal conditions are achieved in both processes and key parameters, especially pH, can be controlled and optimized to lead to higher biogas formation. In one reactor, pH is lowered to acidity conditions (from 4 to 6), and hydrolysis-acetylogenesis is carried out, even breaking down inhibiting chemicals for the second stage microorganisms [260]. In the other reactor, the pH value is maintained from 6.5 to 8 for a proper growth of methanogenic population [262]. Also, less VFA are found in this reactor as they are easily converted into methane, avoiding VFA accumulation. This stage separation achieves biogas increases up to 20% [262], as many researchers found when comparing single stage and two-stage anaerobic digestion, revealing bioaugmentation and its effects [263] However, two-stage digestion is not so commonly used in full-scale plants, as single stage reactors are simpler to operate [199].
# Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks
## 4 Deep Reinforcement Learning-Based Algorithm
### Deep Reinforcement Learning Model
#### 4.1.2 Action Space  
At time slot \(t\), the action of UE \(i\) is represented as  
\[A_{m,t}=\{a_{m,t}|a_{m,t}=(\alpha_{m},\beta_{m})\}, \tag{17}\]  
It consists of two vectors: the task offloading and resource allocation vector \(\alpha_{m}\), and the video frame resolution scale vector \(\beta_{m}\). Vector \(\alpha_{m}\) contains two actions: the resource allocation action \(x_{m,n}\), and the offloading decision action \(\sum_{n=1}^{N}x_{m,n}\). \(x_{m,n}\) represents whether UE \(m\) is assigned to the NOMA cluster \(n\), and \(\sum_{n=1}^{N}x_{m,n}\) represents whether task \(D_{m}\) needs to be offloaded to the MEC server. Vector \(\beta_{m}\) represents the action for the video frame resolution compression ratio selection, where \(\beta_{m}\in[0.5,1.5]\).  
In the MEC system network proposed in this paper, the MEC server distributes the offloading and resource allocation policy to the UEs, however, the selection of the video frames resolution should also be determined.
# Parameter Optimization for Ship Antiroll Gyros
## 3 Study of the Antirolling Characteristics of the Gyro
### Influence of Gyro's Damping Coefficient on Roll Reduction Rate  
By substituting values of gyro's damping coefficients into Equation (11) to solve the differential equation, the variation in the ship roll reduction rate under corresponding gyro's damping coefficients could be obtained. Where the roll reduction rate means the rate at which the roll angle decreases when the gyro is working compared to that when the gyro is not working, and it would be defined qualitatively in Equation (18). As displayed in Figure 5, when the gyro's damping coefficient increased,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Total length \(L\) (m) & 44.8 & Ship’s roll-damping coefficient \(c_{3}\) (Ns/m) & 843 \\ Molded breadth \(b\) (m) & 8.9 & Ship’s roll-damping coefficient \(c_{3}\) (Ns/m) & 6589 \\ Molded depth \(H\) (m) & 4 & Roll-restoring moment coefficient \(k_{1}\) (Ns\({}^{2}\)/m) & 10,791 \\ Displacement \(d\) (t) & 594 & Roll-restoring moment coefficient \(k_{3}\) (Ns\({}^{2}\)/m) & -9284 \\ Initial metacentric height \(h\) (m) & 1.09 & Roll-restoring moment coefficient \(k_{5}\) (Ns\({}^{2}\)/m) & 894 \\ Total moment of inertia \(h_{s}\) (Nms\({}^{2}\)) & \(6\times 10^{6}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ship’s parameters.  
Figure 4: Structure of supporting antiroll gyro rotor.  
Figure 3: Marine surveillance ship and antiroll gyro.  
the roll reduction rate gradually increased. When the gyro's damping coefficient \(C=16,000\) Ns/m, the roll reduction rate reached the maximum value and then began to decline.  
Considering that the mean value period has little influence on the wave slope angle, we only studied the influence of gyro's damping coefficients under different significant wave heights on roll reduction rate. Usually, when the wave height is higher than 5 m, the surveillance ship will not cruise. So the roll reduction rate under different gyro's damping coefficients was solved in five cases with wave heights of 1-5 m, and the significant wave height of 5 m was taken as the subsequent optimization condition. The five coordinates in Figure 6 were, respectively, the maximum values of the roll reduction rate when the wave height was 1-5 m. Figure 6 illustrates that as the significant wave height changed, the optimal interval of gyro's damping coefficient changed very little. Therefore, the gyro's damping coefficient was taken as a constraint condition in the subsequent simulation.
# Design of a Wideband Antenna for Wireless Network-On-Chip in Multimedia Applications
## 4 Comparison of the Designed Antenna vs. the State-of-the-Art
With respect to these designs, the bow-tie antenna proposed in this work stands out for its large bandwidth, which is an added value of the selected bow-tie approach. More in detail, [PERSON] et al., in [85], discuss the impact of on-chip metal interference structures, such as a power grid, local clock trees and data lines, on the performance of on-chip antennas. In Ref. [86], [PERSON] et al. propose on-chip wireless interconnections for clock signal distribution. In Ref. [87; 88; 89; 90; 91; 92], [PERSON] et al. present several on-chip antennas operating at 24 GHz, 5.8 GHz and 60 GHz. Particularly, a communication range of 10 m operating at 100 kb/s is possible using on-chip antennas at 24 GHz (a 3-mm long zig-zag dipole antenna fabricated on a 20-\(\Omega\)-cm substrate and having an efficiency of 25%). At 5.8 GHz, using a pair of 6-mm long monopoles, [PERSON] at al. demonstrated that a communication range of 30 m can be achieved. A bond wire antenna, operating at 60 GHz, has also been proposed with an efficiency of about 15%.