esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 ML Techniques for Adaptive Plasma System
### Gaussian Process Regression  
[PERSON] et al. [125] used Model Predictive Learning Control (MPLC), an extension of MPC that incorporates machine learning techniques for self-adaptive CAP jet optimization.  
Figure 3: Adaptive Plasma Framework for Precision Cancer Treatment: integration of GP, MDP, Safe Q-Learning, and Softmax [119].  
While MPC typically uses a fixed mathematical model and optimization algorithm-based control strategy [71], MPLC integrates machine learning algorithms to capture system dynamics and adapt the control strategy based on real-time data. In the study, MPLC is implemented using GP regression, a statistical machine learning technique, to model the dynamic behavior of cancer cell viability in response to CAP treatment. After each treatment, the GP model is continuously updated with real-time feedback from the actual cancer cell responses, measured using real-time electrochemical impedance spectroscopy (EIS). Cancer cell response measurement after CAP treatment using EIS analyzes changes in the electrical impedance of the cells. The EIS response is directly proportional to cell viability, making it a quantitative indicator of the treatment's effectiveness in affecting the health and viability of the targeted cancer cells. This real-time feedback enables the MPLC algorithm to adapt and improve its predictions and treatment plans over time (Figure 4).  
By dynamically adjusting the plasma treatment parameters based on actual system responses, the self-adaptive plasma jet can optimize its treatment strategy for a wide range of biomedical applications and variations in the system.  
While the study demonstrates its use for cancer cell viability in response to CAP treatment, the transition to clinical applications may require additional research and validation. Pros include the adaptability of MPLC with GP regression, which enhances precision and effectiveness through real-time learning and feedback via EIS. However, potential cons involve computational complexity and the reliability of GP models. Future directions should focus on clinical validation, scalability, and addressing computational challenges for practical healthcare adoption.
### Life Cycle Assessment  
LCA provides a robust, structured, and standardized method for evaluating the environmental impacts of systems and quantifying potential environmental emissions and impacts. In LCA, energy and material inputs and associated waste and emissions outputs are evaluated in terms of possible environmental impacts. LCA, according to the ISO 14040/44, consists of four phases: goal and scope definition, life cycle inventory (LCI), life cycle impact assessment (LCIA), and interpretation [13; 14]. In the goal and scope definition, the functional unit (which is the reference for the results of the assessment), system boundaries, data sources, and impact assessment methods are defined. This phase is followed by the LCI, in which all inputs and outputs in the form of material and energy flows within the system boundaries are collected. Furthermore, the inputs and outputs of the LCI are then classified in different impact categories and characterized according to the chosen impact assessment method in order to calculate the potential environmental impacts produced by the analyzed product (system). Finally, the results of the LCIA are interpreted (interpretation phase) based on the defined goals and recommendations which can be made. Furthermore, sensitivity analyses can be carried out to assess the impact of robustness on the results and assess further life cycle scenarios.
## 1 Introduction
The second-order design (SOD) optimizes the weight matrix (determining measurements to be taken and the number of repetitions of these measurements) and the third-order design attempts to optimize the network through its densification (i.e., the addition of new point(s) or observation(s)) [23]. From the perspective of practical use, SOD (determining what measurements need to be taken and how many times) is the most important of these designs. However, the available literature [20; 22; 24; 25; 26; 27; 28] mostly deals with determining weights of individual metrics (typically slope distance, zenith angle, and horizontal direction) separately. It should be strongly emphasized that in terms of the operation of geodetic instruments, when performing terrain measurements, the total station provides all these values at the same time and, in effect, recording only one or two of these values would not make much sense. Also, many of these studies work with measurement weights in the optimization procedure, which is also suboptimal as the weights must be eventually converted to the actual number of measurements. This usually does not yield integers and the numbers must be rounded up, which negatively affects the optimality of the solution (the number of measurements actually taken may then be higher than what could be sufficient). The same issues can be found in many other papers as well [29; 30; 31; 32; 33]. In this paper, we aim to propose a method of second-order design optimization that would be applicable in everyday geodetic practice. The requirement of practicability disqualifies all previously discussed methods as it implies the following: (i) the numbers of measurements are integers; (ii) total station always simultaneously measures three values (slope distance, zenith angle, and horizontal direction), which must be considered as a group (it would make no sense to discard one or two measurements from the same position if they are taken anyway); (iii) all points measured from the same position of the total station are measured with the same number of repetitions (which is the way the software in total stations work); and (iv) an optimized network is defined as a network, whose points are determined with an accuracy parameter equal or better than a set (required) value and which is obtained using the minimum effort (i.e., number of measurements). For ease of use, it would also be optimal if the accuracy characteristic could be chosen from a pool of metrics (such as standard deviation of the coordinate, of the position, or the longest semiaxis of the error ellipsoid (hereinafter abbreviated as LSEE)).
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 4 Channel Models for Over-the-Sea Wave Propagation
### Marine Communication Channel Modeling Based on the Finite Difference Time Domain (FDTD)
\(X_{f}\) denotes fast fading effects, which are also Gaussian random contributors. The attenuation of typical transmission confronts was characterized by the perfection and accuracy of knowledge about the path loss exponent. This knowledge provided a universal means of characterizing such attenuation, regardless of the specific features of physical communication channels. The multipath characteristics of the wireless communication channel over the sea surface are also of interest in [9]. Signals at TX and RX locations with variable physical pathways experience different propagation delays. Regardless of the carrier frequency of a wireless communication channel, ISI is a key consideration in the realistic bandwidth that the channel can support. Therefore, characterizing a wireless communication channel required particular attention to the mean excess delay. The loss exponent, standard deviation, mean excess delay, and RMS delay were calculated for the development of the simulation engine to implement a new wireless channel model for UAVs to be deployed over the sea surface using equations (1-3) and (4) from [9]. For propagation analysis, it was necessary to generate a random sea surface, as the channel modeling was based on the sea surface environment. In [9], wireless channel simulation was performed using a PM spectra model representing a North Atlantic area with a deep sea. The authors chose this area because the fading effects are high and severe there. They chose the [PERSON]'s formulation [36] to represent the spectral content of the North Atlantic sea region as follows:  
\[S_{m}\ (\omega)[4\cdot\pi^{3  
The channel modeling in [9] is highly suitable for creating UAVs for ocean surface area-based communications, but it has some limitations. The entire modeling process was carried out using the 2-D approximation, but it would be more realistic if the 3-D approximation was used. In addition, the simulations of channels were conducted taking into account the sea surface up to 12 m. Simulations could be performed with a wider range. It was also claimed in [9] that the channel modeling is well suited for other marine surface vessel communication systems. However, they do not indicate what kind of communication systems are suitable, other than the deployment of UAVs, especially when UAVs have high mobility and there are other types of communication systems where the nodes are stationary.
## 3 Results and Discussion
### Optical Absorption  
One of the main characterizations for semiconductors is the energy bandgap. Figure 5 exhibits the result of Tauc procedure to calculate the direct energy bandgap for each involved material deposited on glass: (a) CdS and (b) PbSe. To measure the energy bandgap for CdS and PbSe, it was necessary to use two different spectrophotometers, one for UV-Vis and one for medium-far infrared regions, respectively. For CdS thin film, the value obtained was 2.42 eV, which agrees with values reported in the literature; for the PbSe film, the bandgap was 0.34 eV, which is slightly higher than the 0.27 eV reported value for PbSe bulk bandgap.  
Figure 4: (**a**) SEM micrograph showing the cross-sectional view of the CdS/Pb\({}_{5}\)(CO\({}_{3}\))\({}_{3}\)O(OH)\({}_{2}\) heterojunction to estimate its thickness, and (**b**) perspective view of EDS elementary mapping of the CdS/PbSe heterojunction to analyze its elemental composition and their distribution.
# Hybrid Positioning for Smart Spaces: Proposal and Evaluation
## 4 Hybrid Positioning Approach
### Proximity Algorithm  
The proximity algorithm detects the closeness to a radio tag or a short/medium-range base station. In a previous phase, all base stations were registered in the system database, taking into account its geographical position and the signal strength received at a one-meter of distance (this is not required for BLE tags due to this 1-meter reference is included in the beacon messages). Depending on the technology and the communication range configured, when the signal strength of the detected base-stations/beacons are obtained in a scan process, the distance is approximated using the 1-meter reference. This calculation is finally translated into distances in meters, which let us know which base-station is the closest one. At this point we can estimate that the mobile device is positioned at the installation point of the base station or tag, with an estimation error equal to this calculated distance. The stronger the detected base-station signal, the more accurately the mobile device is located.
# An Audio-Based Motor-Fault Diagnosis System with SOM-LSTM
## 3 Methodology
### Same-Frequency-Ratio Axis Correction  
Due to the running sound of the motor, in the spectral data of FFT, it can be known from Figure 2 that, when the motor is running, for example, the no-load, half-load, and full-load conditions of 128 Hz, the maximum amplitude of the spectral data is not at the same frequency. This paper uses frequency-ratio axis correction, namely Equation (10), to calculate the position of the maximum amplitude of different loads at the same frequency in the spectral data.  
\[Fm_{A}=\frac{\sum\limits_{n=1}^{16}maxF_{A}}{M} \tag{10}\]  
where \(max\)\(Fm_{A}\) is the spectral position of the maximum amplitude of different loads at the same frequency, \(m=1\) is the spectral position of the maximum amplitude of no load, \(M=16\) is the spectral position of the maximum amplitude of a full load, and \(Fm_{A}\) is the average value of the maximum amplitude of the same frequency. The maximum amplitude of different loads at the same frequency is moved to the spectral position of \(Fm_{A}\) in order to achieve the same frequency-ratio axis correction.  
The frequency-domain data volume of operational sound directly increases the complexity of the SOM calculation. Because, in each iteration, the distance between each input vector and the weight vectors of all neurons on the map need to be calculated, the data volume makes the calculation take more time. In the high-dimensional space, it becomes more difficult to find meaningful local structures that may affect the mapping quality learned through SOM. Therefore, this paper proposes the selection of the frequency-feature window of operational sound, which is the feature-window selection for FFT spectral data. The spectral data volume is reduced using the feature window. The ratio-frequency components in the signals are enhanced or highlighted to improve the accuracy and performance of spectral analysis. A window method is used to get the signal characteristics, and the requirements and the performance characteristics of the window function are analyzed, as well as the possible computational costs. Under normal circumstances, it is most effective to conduct experiments and comparisons based on specific requirements and select the most suitable window method. The selection of the frequency-feature window after the same-frequency-ratio axis correction is shown in Figure 4.
# Ammonia Gas Sensing Characteristic of P3 HT-rGO-MWCNT Composite Films
## 3 Results and Discussion  
### Morphology and Structure of the Films  
FESEM images of the synthesized rGO, MWCNTs, and PGC composite films were shown in Figure 2. In Figure 2a, the rGO layers are well separated from each other smooth surfaces. MWCNTs have an average diameter of about 25 nm and are also separated from P3 HT layers (Figure 2b). These figures indicate that P3 HT is able to be utilized as a smooth surface of the MWCNTs layer, especially when P3 HT was mixed with rGO (Figure 2c). The presence of both rGO and MWCNTs resulted in the formation of multiple nanostructured heterostructures of rGr/P3 HT and CNT/P3 HT in the PGC films.  
Figure 1: The schematic draw of the drop-casting technique for preparing resistance sensors.  
XRD characterization of the PGC film shows four peaks. Among them two distinctive peaks (002) and (101) belong to MWCNTs [32]; two other peaks are unidentified (Figure 3). The representative peak of rGO made by the modified Hummers method are immersed in the (002) peak of MWCNTs and obviously, a P3 HT amorphous cluster dispersed in a rGO matrix did not exist in XRD pattern.
## 4 Modeling Setup and Realistic Experimental of Simulation Scenarios
### RFI Emitter Geolocation Using CAF  
This section covers the accurate RFI emitter geolocation scenario using the CAF approach based on extracted data from the Orolia Skydel Simulator. In this scenario, we have considered two LEO satellites as sensors that receive emitted RFI signals from the motionless UAV as an emitter in this approach. The two sensors (\(s_{1}\) and \(s_{2}\)) and emitter \(\mathbf{u}_{k}\) are located at the coordinate positions shown in the grid. The symbols \(d_{1}\) and \(d_{2}\) are the relative position vectors between each of the sensors and the emitter, while \(\dot{\mathbf{s}}_{1},\dot{\mathbf{s}}_{2}\) and \(\dot{\mathbf{u}}_{k}\) are the respective velocity vectors. dm is the required resolution to achieve the best CAF-Map image. In this derivation, the emitter is considered stationary, so there is no time-variant (\(k\)), and the emitter's velocity equals zero. Figure 10 illustrates the geometry of the geolocation scenario.  
Figure 9: Three instances created by the Orolia Simulator. (**a**) Instance-1. (**b**) Instance-2. (**c**) Instance-3.  
For your information, this figure represents one instantaneous snapshot \((Num_{Snaps})\) of the system geometry. Since the sensors are moving at their respective velocities, the geometry changes with each passing instant of time. That is why the TDOAs and FDOAs in a system are time-varying in nature. Therefore, CAF can be computed based on the TDOA and FDOA measurements. Consequently, it can assume the number of snapshots within a fixed gap in the considered scenario [56]. The steps of emitter geolocation using CAF are detailed as  
1. **Input parameters:**\(f_{c}\); \(f_{s}\); \(R_{sym}\); dm; \(Num_{Snaps}\); \(S/N\); and create XY grid
2. **Snapshots Loop** for break up the signals into snapshots. **for**\(i=1,\,2,\cdots,Num_{Snaps}\)**do**,
3. **Import I/Q files for UAV and LEO satellites trajectory, as well the delay time and Doppler shift of receiving signal.
4. **Calculation:** Calculating a CAF surface based upon input signals. * Calculating relative position vectors for each sensor and the current map location, * Start going through each map location in XY grid by creating a loop for XY index \(ii=1,\,2,\cdots,\) length (indexX) **do**, **for**\(jj=1,\,2,\cdots,\) length(indexY) **do**, 1. Calculate relative position vectors for each sensor and the current map location, 2. Calculate the TOA and FOA between emitter and sensors via (2) and (8), 3. Compute TDOA and FDOA from the relative position vectors via (4) and (10), 4. Compute the CAF value for the current MAP position via (29), **end** loop \(jj\) **end** loop \(ii\)
5. **Saving CAF mapping result of current snapshot.**
6. **end**\(i\)**, end Snapshots Loop. **Output result:** Save file of CAF mapping; and plotting CAF result with the peak point.
## 3 Results and Discussion
### Synthesis Development and Optimization
#### 3.1.2 GC-MS and NMR Analysis
The main peaks are highlighted, and particularly the molecular ion at 270 \(m/z\), cleavage of methanol at 239 \(m/z\), and progressive breaking of the aliphatic chain from 227 \(m/z\) to 101 \(m/z\) with peaks distances 14 \(m/z\) (corresponding to loss of -CH\({}_{2}\)).
# Adaptive Optimization Method for Piled Raft Foundations Based on Variable Pile Spacing
###### Abstract  
Stricter control of the differential settlement is always required in important buildings more than in ordinary buildings. It becomes a necessity to find a simple and efficient optimum design method for pile foundations in terms of performance and economy. In this paper, an adaptive optimization method (AOM) is proposed to reduce the differential settlement of the pile group and piled raft, in which the piles are located in appropriate locations according to the settlement characteristic of the raft. Piled raft foundations with different types of load and different raft shapes are optimized using this method; soil inhomogeneity and nonlinear characteristic are considered during this process. The optimization results show that the reductions of the differential settlement are more than 80%. The overall foundation performances are improved as the maximum settlements of the foundations are reduced. The maximum bearing capacity of the pile group is no more than its ultimate bearing capacity after optimization design, and part of the excess bearing capacity can be translated into economic savings (AOM-ES). By keeping a good optimization effect of the differential settlement, the number of piles can be reduced by AOM-ES compared with the initial design. The AOM is robust and can be applied to the piled foundations of various raft shapes in layered soils under complex vertical loads with no significant impact on optimization efficiency.  
piled raft foundations; adaptive optimization method; variable pile spacing; differential settlement; economic savings +
Footnote †: journal: Applied Sciences
# A Deep Learning Based Dislocation Detection Method for Cylindrical Crystal Growth Process
## 3 Experimental Results
### Tracking Strategy  
Sometimes it is difficult to judge the ingot state through one image or just looking into the furnace once, it can only be determined by looking at the observation window for a while during production. Therefore, we proposed a tracking strategy to judge the ingot state by the comprehensive results of multiple images continuously captured. The image was captured according to the rotation speed, for example, the ingot rotates at a speed of 8 r/min, we needed to continuously capture images while the ingot was making a turn, the acquisition frame rate was 7 FPS, therefore, 52 images needed to be captured. The state of the habit line was initialized to 0, when the first normal habit line was detected, it was updated to 1, if it was a disappeared one, and the state was updated to \(-1\). The corresponding coordinates were also be recorded, around 1/4 cycle, at the same position in the current image, to detect whether there was a second normal habit line or a disappeared one. Finally, the ingot state was determined according to the habit line state and position of a cycle of ingot images. To be clear, inference results of a tracking process are illustrated in Figure 10. Starting from the first image of the 52 images and find the first image where there was a normal habit line in it, such as line No. 1 in Figure 10a. If there was normal habit line in the current image and the two images before and after, and this condition still held for the next three 1/4 cycles (Figure 10b-d), then the habit line was classified as normal. On the other hand, if there were no habit lines in the current image and the two images before and after, then the habit line was classified as disappeared. Compared with determining the current ingot state based on just one image, tracking strategy could provide a more comprehensive and practical result.
# Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset
## Appendix A The Structure of the Model
### Stomatal Conductance  
In our study, the Ball-Berry-Leuning (BBL) stomatal conductance model was coupled with the photosynthesis process [50]. The stomatal conductance is expressed as follows:  
\[g_{s}=g_{0}+g_{1}\times\frac{A_{n}}{(1+\frac{VPD}{D_{0}})\times(C_{a}-\Gamma^ {*})} \tag{19}\]  
where \(A_{n}\) is the net leaf \(CO_{2}\) assimilation rate; \(VPD\) is the vapor pressure deficit; \(C_{a}\) is the \(CO_{2}\) concentration at the leaf surface; \(\Gamma^{*}\) is the \(CO_{2}\) compensation point; and \(g_{0}\), \(g_{1}\), and \(D_{0}\) are empirical coefficients, the values of which [111] are presented in Table 1.