esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Volatile Profile Analysis  
* HS-SPME GCMS analysis Twenty whiskey samples were analysed in triplicates for volatile components using headspace solid-phase microextraction gas chromatography-mass spectroscopy; sample IDs are shown in Table 11, Appendix A. The ABV% of these samples post-distillation was measured using an [PERSON] 5000 density meter ([PERSON], Dublin, Ireland), which is also recorded in Table 11.
* SPME: Sample preparation A 5 mL sample and 100 \(\upmu\)L standard (4-methyl, 2-pentanol, and 2-methyl, 3-heptanone at 10 ppm) sample were added to a 200 mL screw-capped amber SPME vial with a magnetic cap and silicone/polytetrafluoroethylene septa (Element, Maynooth, Ireland) and equilibrated to 40 \({}^{\circ}\)C for 10 min with pulsed agitation of 5 s at 500 rpm. The sample was introduced using a Gerstel MPS autosampler.
* GCMS Method A single 50/30 \(\upmu\)m CarboxenTM/divinylbenzene/polydimethylsiloxane (DVB/CAR/ PDMS) fibre was used (Agilent Technologies Ltd., Cork, Ireland). The SPME fibre was exposed to the headspace above the samples for 40 min at a depth of 1 cm at 40 \({}^{\circ}\)C. The fibre was retracted and injected into the GC inlet and desorbed for 2 min at 250 \({}^{\circ}\)C. Injections were made on a Shimadzu 2010 Plus GC (Mason Technology, Dublin, Ireland) with a DB-624 UI (60 m \(\times\) 0.32 mm \(\times\) 1.8 \(\upmu\)m) (Agilent Technologies Ltd.) column using a split/split less injector with a 1/10 split. A Merlin micro seal was used as the septum (Agilent Technologies Ltd.). The temperature of the column oven was set at 40 \({}^{\circ}\)C, held for 5 min, increased at 5 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C, then increased at 15 \({}^{\circ}\)C/min to 260 \({}^{\circ}\)C, and held for 15 min, yielding a total GC run time of 60 min. The carrier gas was helium, held at a constant flow of 1.2 mL/min. The detector was a Shimadzu TQ8030 mass spectrometer detector (Mason Technology), run in single quad mode. The ion source temperature was 220 \({}^{\circ}\)C, and the interface temperature was set at 260 \({}^{\circ}\)C. The MS mode was electronic ionisation (70 v) with the mass range scanned between 35 and 250 amu. Compounds were identified using mass spectra comparisons to the NIST 2014 mass spectral library, a commercial flavour and fragrance library (FFNSC 2, Shimadzu Corporation, Japan), and an in-house library created using authentic compounds with target and qualifier ions and linear retention indices (LRI) for each compound using Kovats index [18]. Spectral deconvolution was also performed to confirm the identification of compounds using AMDIS [19]. Batch processing of samples was carried out using MetaMS [20]. MetaMS is an open-source pipeline for GC-MS-based untargeted metabolomics. An auto-tune of the GCMS was carried out before the analysis to ensure optimal GCMS performance. A set of external standards was run at the start and end of the sample set, and abundances were compared to known amounts to ensure that both the SPME extraction and MS detection were performed within specifications.
## 2 Design and Methods
### Finite Element Analysis Set Up
These refinements ensure that the FEA model accurately captures the biomechanical behavior of the implant and screw fixation strategies, strengthening the study's reliability and applicability. A total of six loading scenarios were then analyzed using 1- and 2-screw configurations. An example of the baseline static structural analysis is shown in Figure 6. To simulate an average male standing, a force of 771.17 N was applied to the construct. To simulate an average female standing, a force of 760.44 N was applied to the construct. Based on conditions described in [PERSON] et al. in 2023, the model was subjected to four static loading events for gait analysis (Figure 7) [27]. The first scenario illustrates the loading (foot flat) response of the gait cycle, in which a total force of 1921.6 N was applied to the stance leg as the foot makes contact with the ground at a specific moment in time. The second scenario demonstrates the terminal stance (heel off) phase of the gait cycle, with a force of 1751.3 N applied to the model to simulate the moment at which the weight shifts  
Figure 5: Application of boundary conditions. Loading force was applied to superior surface of acetabular cup, and femoral stem was fixed at distal end to prevent translation and rotation. Figure 6: FEA deformation distribution with screw fixation in static case. (**A**) 1-screw configuration and (**B**) 2-screw configuration. forward from the stance foot to the forefoot. The third and fourth scenarios simulate going up and down stairs, applying 2399.1 N and 2490.1 N to the implant, respectively.
# Interleaved Boost Converter with ZVT-ZCT for the Main Switches and ZCS for the Auxiliary Switch
## 5 Design of the Key Components
### Design of \(L_{1}\) and \(L_{2}\)  
The used converter operates in the continuous conduction mode (CCM) all over the input voltage range and the output current range. The worst case for the design of \(L_{1}\) is under the minimum input  
\begin{table}
\begin{tabular}{l l} \hline \hline
**System Parameters** & **Specifications** \\ \hline Operating mode & CCM \\ Input voltage (\(V_{in}\)) & \(24\)V \(\pm\) 10\% \\ Output voltage (\(V_{o}\)) & \(42\) V \\ Rated output current (\(I_{o,metal}\)) & \(6\) A \\ Minimum output current (\(I_{o,min}\)) & \(0.3\) A \\ Switching frequency (\(f_{s}\)) & \(25\) kHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: System specifications of the proposed converter.  
Figure 15: Proposed control block diagram.  
voltage and the minimum output current. It is assumed that as the auxiliary switch is turned on, the voltage across each input inductor is not affected by the resonant inductor. Hence, Figure 16 displays the current in \(L_{1}\) under the discontinuous conduction mode (BCM), whose direct current (DC) value is \(I_{LB1}\).  
Therefore, based on the following equation, the minimum value of the input inductor, called \(L_{1,min}\), can be figured out as below:  
\[L_{1,\text{min}}=\frac{V_{in,\text{min}}D_{\text{max}}T_{s}}{2\times 0.5I_{o, \text{min}}}=\frac{V_{in,\text{min}}D_{\text{max}}T_{s}}{I_{o,\text{min}}}=699.8 4\mu\text{H} \tag{21}\]  
Eventually, the value of \(L_{1}\) is set at 720 \(\mu\)H, which is also for the value of \(L_{2}\).
## 4 Simulation and Test Verification  
To validate the effectiveness of the proposed algorithm, relevant simulations and experiments were conducted. Without loss of generality, an Iridium satellite system in LEO constellation was selected for both simulations and experiments as the signal radiation source. The Iridium system consists of Polar Earth Orbit satellites at an orbit height of 780 km, with a total of six orbital planes, each containing 12 satellites (including 1 backup satellite) [41; 42]. The orbital inclination was 86.4\({}^{\circ}\), and the orbital period was 100.13 min, providing global coverage. The Iridium system has duplex and simplex operational channels, with duplex channels operating in the frequency range of 1616.0-1626.0 MHz and simplex channels operating in the frequency range of 1626.0-1626.5 MHz. Additionally, Iridium satellites achieve channel and time division by controlling the transmitted spot beams, enabling FDMA/TDMA/SDFA/TDD multiplexing for users [43]. Furthermore, the downlink transmission signal structure of the Iridium satellite primarily includes pilot signals and BPSK and QPSK modulation schemes. Users can receive signals from the seventh channel every 4.32 s.
# Thrust and Pressure Control in a Solid Propulsion System via Reinforcement Learning
## 3 Simulation and Experimental Results  
The control process comprises three levels, pressure control, thrust distribution, and opening closed-loop, that are managed by the pressure control agent, thrust distribution agent, and opening closed-loop agents, respectively. These agents operate in a cascaded manner, as illustrated in Figure 7, which shows the control architecture of the SADPS.  
Figure 7: Multi-agent control architecture.  
* Pressure Control: The pressure control agent at the top level regulates the total opening of the nozzle based on the combustion chamber pressure command.
* Thrust Distribution: The thrust distribution agent at the middle level allocates the opening commands to each valve according to the thrust command.
* Opening Closed-loop: At the bottom level, ten opening closed-loop agents control their respective motors to adjust the opening of each valve.  
The transmission process of the control signal is shown in Figure 8. The upper part represents the four divert control valves, and the lower part represents the six attitude control valves. The outer layer of the flowchart represents the pressure control loop, with the combustion chamber pressure command (\(P_{ccmd}\)) and pressure (\(P_{c}\)) as inputs, and the valve opening area command (\(A_{tsumcmd}\)) as the output. The middle layer represents the thrust control loop, with the throat area command (\(A_{tsumcmd}\)), thrust commands (\(F_{ycmd}\), \(F_{zcmd}\), \(F_{pitchcmd}\), \(F_{yurcmd}\), \(F_{rollcmd}\)), and pressure (\(P_{c}\)) as inputs and the valve openings as the output. Finally, the thrust vector synthesis of the thrusts output by each nozzle yields \(F_{y}\), \(F_{z}\), \(F_{pitch}\), \(F_{yur}\), and \(F_{roll}\). As a result, the external system only needs to input the pressure command and the five-axis thrusts command into the algorithm, which then outputs the five-axis thrusts, achieving closed-loop engine pressure control and thrust distribution. Due to confidentiality principles regarding the technology, the following simulation results are presented using normalized units.
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 2 Related Work
The third type is clustering, in which the network is partitioned into a number of clusters, and each cluster selects a CH for data gathering from the MNs. After receiving data from MNs, the CH forwards them to the sink. A comprehensive overview of the discussed protocols is given in Table 2 for better understanding.
## 3 Influence of Key Parameters on the Response
### Craig-Bampton Reduction: Influence of the Retained Contact Nodes
#### 3.3.3 On the Cross-Influence between Retained Nodes Number and FE Model Mesh Refinement  
This subsection aims at finding out whether the number of retained nodes and the mesh refinement have independent effects on the forced response prediction. To reach this goal different cases have been investigated by varying the number of retained nodes along X direction (i.e., number of columns) and, at the same time, the mesh size. It was decided to address the node density variation along X direction  
Figure 10: Blade-strip contact patch top view: relative displacements along X direction for two different combinations of condensation nodes: 15 node columns along X, and 2 or 6 rows along Y direction.  
since, from the investigation in Sections 3.3.1 and 3.3.2, it was observed to be the most influential. Pattern A (see Figure 8b) has been analyzed for different values of Mesh Multiplying Factor. The results of this investigation are shown in Figure 11. It is observed that the two effects (mesh size and total number of retained contact nodes) are independent and additive in nature. Also, the influence of the \(MMF\) is much smaller than the influence of the number of retained contact nodes. In fact, if the blade mesh is fine enough (\(MMF>0.5\)), a further decrease of the mesh size does not induce a variation in the natural frequency value (see overlapping red and blue curves in Figure 11).
## 5 Discussion
### Subsurface Conductivity Structure and Associated Interpretations
#### 5.3.1 Hydrothermal System and Fluid Circulation
Considering both the low geothermal gradient temperature of A5 well (between 500 and 1200 m depth; see Figure 2 in [6] located in the center of the rift [12; 68] and associated interpretation of the negative SP anomaly in the same area [5], it is possible that the high conductivity of C1 layer can be interpreted as a saline (implying high conductivity) aquifer with fluids flowing toward Asal Lake through the faults and fracture networks at the center of the rift.
# Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology
## 3 Materials and Methods
### The Perception Layer  
#### 3.2.1.  
With the requirement of LTE-M communication established, the Eagle datalogger (Digital Matter, South Africa) was selected as the platform for the development of the REAMIT solution. The logger is an IP67 rated rugged cellular IoT device, supporting a range of inputs for various IoT applications. The Eagle runs on either 4 \(\times\) C Alkaline or Lithium Thionyl Chloride (LTC) batteries, or it can be wired to permanent power (6-16 V DC). It contains I2C, SDI-12 and RS-485 interfaces as well as 2 \(\times\) analogue inputs, 3 \(\times\) digital inputs, 2 \(\times\) switched ground inputs, and 2 \(\times\) 4-20 mA inputs, thus supporting a vast array of sensors to connect to the device. Additionally, it has an onboard GPS module and an accelerometer for geofencing and movement detection, and it is equipped with a SIM card allowing the device to run on the IoT low power LTE-M (CAT-M1) network [80]. The Eagle offers third-party cloud integration via an HTTPS webbook allowing for the convenient retrieval of recorded data for visualisation and analytics.
# Algorithms for Calculating Generalized Trigonometric Functions
## 6 Construction of Discrete _Ateb_-Transforms
### Algorithms for Calculation of Discrete Ateb-Transform  
Here, we will describe the algorithm for the calculation of discrete _Ateb_-transform with two parameters according to Formula (42).  
Define parameters \(m\) and \(n\);  
Control periodic condition;  
Define dimension of discrete signal \(N\);  
Define two \(N\)-dimensional arrays \(S\) for input signal and \(X\) for output signal;  
Calculate \(\Pi(n,m)\) period of function. Define other index k = 0 as array index.  
Define \(SumA=0,SumB=0\) the sums for calculation coefficients \(A(m,n,k)\) and \(B(m,n,k)\) and current index for sum \(p=0\).  
Calculate elements \(Ap=S(p)ca^{m}(m,n,-i\frac{2\Pi pk}{N})\) and \(Bp=S(p)sa^{n}(n,m,-i\frac{2\Pi pk}{N})\) as current elements for summarizing;  
Calculate \(SumA=SumA+Ap,SumB=SumB+Bp\);  
Calculate \(p=p+1\);  
If \(p=N\), then \(X[k]=sumA-isumB;k=k+1\); else go to step 6;  
If \(k=N\), then output array \(X\).  
As the results of the algorithm realization we have the spectrum \(X(n,m,k)\) of discrete signal \(S(p)\) created with discrete \(Ateb\)-transform.
## 4 Wireless Access for Vactrain
### Simulation
#### 4.3.1 Phase Distribution along Tube's Axial Direction  
The simulation of phase distribution along the tube is carried out in this subsection. The overall configuration of leaky waveguide and receiving antenna in the simulation is shown in Figure 2a. The distance between receiving antenna and leaky waveguide is 40 cm. The antenna moves from right to left to collect phase data right below the waveguide. The length of the leaky waveguide is 7 m, and it operates at 3.3 GHz. Due to the field volatility at the ends of the waveguide, the data is collected in the 3 meter long center section. Note that the attenuation constant of the leaky waveguide \(\alpha\) is set to zero and the receiving antenna with conventional gain is adopted since the Doppler frequency shift is mainly determined by the phase characteristics of the received signal and independent from the receiving signal level.  
The simulation results are shown in Figure 2b. The phase basically floats around 105\({}^{\circ}\) with a limited fluctuation range. The flat phase distribution observed above indicates that the Doppler frequency shift could be suppressed effectively. The little variation of the phase is mainly due to the reasons as follows. First, limited by the computing resources, only the simulation for finite-length leaky waveguide could be conducted. However, this will introduce the truncation effect, thus then the edge current will be generated at both ends of the waveguide, resulting in the fluctuations in the field distribution. Second, the field distributions of the leaky wave could be affected by the power attenuation of the propagating electromagnetic wave along the leaky waveguide. Note that the problems caused by finite simulation will no longer exist in practical applications. As for the power attenuation, we could further improve the design of the leaky waveguide to reduce the leakage rate and keep the distribution of leakage field distribution unchanged after attenuation. Thus, compared with the antennas methods in Section 4.1, a more flat phase distribution along the tube's axial direction would be retained and the Doppler could be further suppressed.
# Link Load Correlation-Based Blocking Performance Analysis for Tree-Type Data Center Networks
## 4 Simulation Results
Figure 10: Network blocking performance of different methods with \(K=4\), \(F_{\text{OS}}=2\), and \(M_{0}=30\). Figure 9: Network blocking performance of different methods with \(K=4\), \(F_{\text{OS}}=2\), and \(M_{0}=20\). For cases with tree factor \(K=5\), the simulated and analytical blocking rates under different over-subscription ratios and sub-channel numbers are compared in Figures 11 and 12, respectively. Similar to the cases with \(K=4\), the dominant blocking rate range of the LLC-based method is slightly different for \(F_{OS}=1.5\) and \(F_{OS}=2\). With \(F_{OS}=1.5\), the LLC-based method curve matches best with the simulation curve especially in the blocking rate range below 4%; while the EFPA method still achieves the most accurate blocking rate in the range below 1% with \(F_{OS}=2\). Therefore, it can be concluded that, after the load correlation between adjacent layer links is taken into consideration, the network blocking rate analysis accuracy can be improved in a two-layer tree-type DCN through use of the proposed method, especially with over-subscription ratio 1.5.
## 7 Results and Discussion
### Results of Experiments  
Given the dataset's inherent imbalance, with overload events constituting less than 2% of total instances, we adjusted the test set to achieve a balanced 1:10 ratio of overload to non-overload cases. This adjustment mitigates bias towards the majority class, ensuring a comprehensive evaluation of the model's generalization capabilities in diverse scenarios. The proposed model was evaluated on 4632 test cases using a 20-fold cross-validation strategy [46], resulting in subsets of approximately \(S=231\) cases per fold. This approach provided robust insights into the model's predictive performance and stability across different data partitions. Performance metrics included sensitivity, specificity, and F1-score. On average, the model achieved a sensitivity (recall) of \(0.935\pm 0.014\), indicating a high ability to detect overloads. Specificity was \(0.995\pm 0.001\), reflecting strong identification of negative instances. The F1-score was \(0.99\pm 0.002\), demonstrating a balance between precision and recall. Low standard deviations across all metrics confirmed consistent performance across data subsets. Table 2 compares the proposed model with benchmarks, including [PERSON] et al.'s locally linear embedding (LLE) and linear discriminant analysis (LDA) model [16], as well as our prior models from 2021 and 2023 [10]. The results show that our current model, enhanced with a physics-informed loss function, outperforms previous approaches. That highlights the value of integrating domain-specific knowledge into the architecture, improving predictive accuracy and generalizability.  
In addition to the reported metrics, the accuracy and precision of the model were also evaluated to validate its performance further. The model achieved an accuracy of 0.990 (\(\pm 0.0016\)), indicating that 99.0% of all instances, both overload and non-overload, were correctly classified. This high accuracy reflects the model's overall reliability across the test set. Furthermore, the precision was 0.964 (\(\pm 0.0073\)), demonstrating the model's ability to identify overload events with minimal false positives. These metrics, coupled with low standard deviations across sensitivity, specificity, and F1-score, reinforce the model's robustness and confirm its suitability for practical applications in SAG mill overload prediction.
## 5 Discussion
### Limitations of This Study  
This review has some limitations. First, despite extensive search on several digital journals, only 22 studies were finally included and fully evaluated. Considering the heterogeneity in the sample size, objective, methods and results, a direct and comprehensive comparison of similar studies is not always possible. This is particularly evident for studies focusing on activities different from gait and finger tapping, for which it is not possible to provide robust conclusions. In addition, the distribution of study objectives is very unbalanced, with most studies focusing of diagnosis and therapy condition estimation, while the severity of motor symptoms and overall motor condition were poorly addressed. Finally, this review focused specifically on DB-MS-PD. This led to consider motor symptoms such as gait impairment, postural instability, tremor, bradykinesia, and dyskinesia. Other motor symptoms such as rigidity, fatigue, and hypomimia were not addressed in the reviewed studies.  
Another limitation that may be encountered is the replicability, as each study uses a different wearable device with its own sampling frequency and filtering techniques (not all of which are indicated), using a wide variety of devices.  
A high percentage of the assessed studies (84.8%, n = 168 of the 198 articles screened) are from 2020 or later. Furthermore, the observable trend in recent years has been that more articles are being produced each year, so it can be deduced that the definition of biomarkers using wearable technology is a subject on which numerous research projects are being carried out and that new results and DB-MS-PD will be published in the coming years. Therefore, this work can be a reference to know the current trends and future literature reviews on this topic can expand the information shown and know in further detail the way to the definition of DB-MS-PD.  
In addition, 6 of the 22 articles use public databases, therefore, the variability in the results is conditioned to a certain extent, since the results of these databases are similar. It would be interesting to extend the results obtained by using other different databases or by proposing new activities or measures.