esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Design and Implementation  
### Overall Architecture  
Figure 3 shows the overall architecture of the system, which is mainly composed of a W5500 Ethernet controller, STM32 microcontroller, and USART to RS485 modules.  
The W5500 serves as the core for Ethernet access, integrating a 10/100 M PHY and a hardware-based TCP/IP stack that supports IPv4, TCP, UDP, Internet Control Message Protocol (ICMP), Address Resolution Protocol (ARP), and Point-to-Point Protocol over Ethernet (PPPoE). It incorporates 32 KB of dual-port SRAM for transceiver buffering and manages up to 8 independent hardware sockets simultaneously. Utilizing an Serial Peripheral Interface (SPI) bus with speeds up to 80 MHz, its low-power design features a 1.8 V core voltage and operating current below 120 mA, meeting the stringent energy efficiency requirements of industrial IoT edge devices. W5500 is bound to the designated port through the pre-configured IP address, listens to and accepts Modbus UDP data messages from the host computer, and transfers the messages to STM32 for data processing through SPI communication after the Modbus UDP messages are received. After the Modbus UDP message is received, the message is transmitted to STM32 for data processing through SPI communication, which converts the Modbus UDP message into Modbus RTU message, and then sends the Modbus RTU message to the USART to RS485 module through USART to convert the serial signal into differential signal for RS485 transmission, and then finally transmits the data to the related equipment through RS485 bus. After receiving the data, the related device generates a Modbus RTU response frame and transmits it back to the gateway through RS485 bus, and finally converts the Modbus RTU message into Modbus UDP message and sends it back to the host computer through the signal conversion, data processing, and network connection of the three modules of the gateway.  
Figure 3: System overall architecture diagram.
## 3 Results and Discussion  
### Results of the ARIMA Model  
The ARIMA model was implemented stochastically based on the default values for the parameters \(p\), \(d\) and \(q\) for the different scenarios and individually for each cost object (\(Z^{labour}\), \(Z^{material}\)). The values assumed for each parameter in the scenarios were (1,1,1), (1,0,0), (1,0,1) and (2,0,1). For every scenario, all the cost data points of each object were included, covering a period of 97 months. The scenarios do not show a reasonable forecasting for a period of 20 months for each object. In this section, we present the cost object forecasting of the ARIMA model (1,1,1) as the default input parameters.  
Figure 2 shows the forecasting for the labour cost object with the polynomial trend to illustrate the relationship between the values over a timeline with monthly intervals. Before the vertical line, the historical labour cost data for 97 months are shown, and after the vertical line, the actual labour  
Figure 3: Material cost data forecasting based on the ARIMA model.  
Figure 2: Labour cost data forecasting based on the ARIMA model.  
cost data for 20 months are shown. The forecasted data for the 20-month period do not seem to be in sync with the actual data and are lower than the trend of the data. The forecasting based on the ARIMA model does not reflect the real data for the labour cost object.  
Figure 3 shows the forecasting for the material cost object with the polynomial trend to illustrate the relationship between the values over a timeline with monthly intervals. Before the vertical line, the historical material cost data for 97 months are presented, and after the vertical line, the actual material cost data for 20 months are presented. The forecasted data for the 20-month period do not seem to be in sync with the actual data and are lower than the trend of the data. The forecasting based on the ARIMA model does not reflect the real data for the material cost object.  
Overall, the forecasting based on the ARIMA model does not show sufficient accuracy over the 20-month period.
## 4 Electrochemical bAA Sensors and Biosensors
### Metal Nanoparticles and b AA Electrochemical (Bio)Sensing
The proposed sensor had good sensitivity,a wide dynamic range, and good long-term stability. The response to Leu was studied by the current-voltage (I-V) technique. The calibration plot was linear between 0.1 nM to 0.1 mM. The sensitivity was equal to 2.53 nA\(\cdot\)\(\upmu\)M\({}^{-1}\)-cm\({}^{-2}\), and the limit of detection was calculated and found equal to \(37.5\pm 0.2\) pM. The sensor was applied to real samples such as L-Leu spiked urine, milk, and serum, giving acceptable results. Another useful tool on b AA analysis is multiwall carbon nanotubes. On this matter, [PERSON] and [PERSON] and [PERSON] (2018) developed a simple and sensitive leucine voltammetric detection assay in blood and urine samples. In their study a GCE was used and modified with MWNTs. The CV measurements revealed that MWNTs enhanced the oxidation of Leu GCE. They revealed that Leu was oxidized following multistep mechanism on the proposed electrode. A calibration curve was plotted under the optimum condition, and the sensor had a linear response in the range \(9.0\times 10^{-6}-1.5\times 10^{-3}\) mol L\({}^{-1}\). The LOD was found equal to \(3.0\times 10^{-6}\) mol L\({}^{-1}\) and a relative standard deviation (RSD%) was estimated below 3.0% (n = 5). The use of metal nanowires (NWs) gives some advantages in clinical diagnosis such as simplicity, rapid sensor response and short total analysis time, and low sample consumption. Recently, [PERSON] et al. [PERSON] et al. (2018) demonstrated the fabrication of vertically aligned nickel nanowires-based electrochemical sensors (v-NiNWs) for fast determination of b AA, aiming to noninvasive screening of MSUD. v-NiNWs. The analytical features of the proposed methodology (for Leu as representative b AA in MSUD) such as LOD (8 mM) and linear range (25-700 mM) demonstrate that v-NiNWs are suitable disposable features for monitoring b AA in MSUD due to their ability to differentiate healthy and MSUD penitents. In addition, the results pointed an excellent intra and inter-electrodes repeatability. Total b AAs were also determined in positive samples with accuracy in just 5 min and using only 250 mL. Furthermore, NiO NPs were electrochemically immobilized on a GCE and a platinum electrode [PERSON] et al. (2018). In the particular study, CV in a flow cell was used, evaluating the sensors' capability to detect Val among other amino acid. The LOD was estimated to be 4 mM. In this work it was found that Val was electroactive sporadically.
# Development of a CFD-Based Wind Turbine Rotor Optimization Tool in Considering Wake Effects
## 3 Results and Discussion
### Computational Setup  
As sketched in Figure 3a, the two rotors are placed in tandem. The uniform inflow is applied at inlet, and the distance to the first rotor is about 10D (rotor diameter). The distance between downstream wind turbine and outlet is about 30D. The inlet and outlet width are chosen as 10D. The distance between the two rotors is not necessarily fixed which provides flexibility to be further optimized.  
With a non-dimensional form, the uniform flow at the inlet and far field is Vo = 1. The change of wind turbine operational condition is through the Tip Speed Ratio (TSR). For the turbulence inflow conditions, the initial k and \(\omega\) are computed through Equations (6) and (7) which are determined by the selected turbulence intensity. The mesh points are clustered around the rotor as seen in Figure 3b where only half the symmetrical plan is displayed. The mesh is block-structured with a total number of 14 blocks and 64 \(\times\) 64 points in each block. The thick white lines indicate the two rotors whereas the mesh lines are clustered towards the rotors. With an OpenMPI (Message Passing Interface), the parallel simulation (on a Dell work station with Intel Xeon CPU E5 series) takes about 30 s for each simulation depending on the convergence rate. The computational time varies according to different TSRs as well as initial k and \(\omega\) values. The simulation time using an AD method on a full 3D mesh will be more than hundred times slower than the 2D axisymmetric solver. On the other hand, the 2D CFD method is still slower than the classical steady BEM, but the 2D CFD method provides detailed flow field around the wind turbines.
## 5 Results
### Empirical Mode Decomposition Results  
Using monthly data, it was generally observed that the second IMF had a high VCR value. This measure essentially evaluates the explanatory power of the trend component on the original series and provides insights into the significance of the trend. One of the key features distinguishing the trend component obtained by EMD from other trend analysis methods is its nonlinear structure and its ability to provide information about changes over time. Figure 8 presents an example of the IMFs and trend component obtained after EMD analysis. This graph shares insights from the analysis of the MuSyQ dataset values from the Konya region. The related time series was decomposed into a total of six sub-series, with the last sub-series representing the trend. The sub-series that explained the most variance of the original data was IMF1 with 57.1%, followed by the trend component with 16.5%. Examining the trend component overlaid on the original time series reveals a decreasing trend during the first 100 months, followed by an increasing trend up to the 400 th month, and then a slight decrease thereafter. However, the overall trend from the beginning to the end points is increasing.  
Figure 7: _Cont._  
All the GPP values for each region were analyzed in this manner, and the sub-series obtained through EMD along with their VCR values are shared in the Supplementary Materials (Table S8). Upon examining Figure 9, for the Antalya region, an increasing trend can be observed in all datasets except for FLUXCOM. In TL-LUE and PMLV2, a nearly linear increase is observed, whereas in GOSIF, MODIS, and MuSyQ, a trend of initial decrease followed by an increase is detected. The lowest VCR, at 0.13%, is calculated for FLUXCOM, while the highest, at 5.5%, belongs to GOSIF; the GOSIF and MODIS trend bands exhibit similar behavior. In Erzincan, an increasing trend is apparent in most datasets with the highest VCR (4.1%) in TL-LUE. In the Izmir region, a slight increasing trend is observed in the FLUXCOM dataset with a VCR of 1.73%, while TL-LUE, MuSyQ, GOSIF, and PMLV2 show an initial decrease followed by an increase. In Konya, TL-LUE and MuSyQ exhibit similar behavior, as do MODIS and GOSIF. The most pronounced VCR, at 16.45%, is calculated for the trend in the MuSyQ data series. In Samsun, a near-linear increase is seen in TL-LUE and a decrease followed by an increase is observed in GOSIF, MODIS, and MuSyQ; this pattern is even more pronounced in PMLV2. In Sanliurfa, the most distinct trend behavior belongs to the MuSyQ dataset with a VCR of 11.61%. The TL-LUE trend series follows a near-linear course; the trend in the PMLV2 dataset can be interpreted as showing an overall trendless movement, decreasing until the midpoint and then increasing. All the time series and trend components obtained with EMD are shared in the Supplementary Materials (Figure S10).  
Figure 7: ITA results for (**a**) Antalya, (**b**) Erzincan, (**c**) Izmir, (**d**) Kirklareli, (**e**) Konya, (**f**) Samsun, and (**g**) Sanliurfa.  
Figure 8: Example of EMD subseries for MuSyQ GPP estimates from Konya, Türkiye.  
Figure 9: _Cont_.
# Random PWM Technique Based Two-State Markov Chain for Permanent Magnet Synchronous Motor Control
## 4 Experimental Results and Analysis  
In order to verify the correctness of the theoretical analysis and the effectiveness of the proposed strategy, the experimental platform shown in Figure 9 is built. The hardware circuit is built with TMS320F28335 DSP manufactured by Texas Instruments in Texas, the United as the core, and the speed/torque loading is realized based on the dynamometer driven by the AVL control system.  
The experimental motor was operated under two steady-state conditions: at a speed of 3820 r/min, a load torque of 5 N\(\cdot\)m, and a modulation index of 0.5; and at a speed of 3820 r/min, a load torque of 150 N\(\cdot\)m, and a modulation index of 1.0. The traditional SVPWM, traditional RPWM, RPWM based on a two-state Markov chain, and the method proposed in this paper were experimentally verified. The expected carrier frequency \(f_{c}\) is 10 kHz, the random gain \(r\) is 2 kHz, and the randomized carrier frequency \(f_{r}\) varies randomly within the range of [8 kHz, 12 kHz].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Sign** & **Value** & **Unit** \\ \hline Rated current & \(I_{N}\) & 135 & A \\ Rated voltage & \(U_{N}\) & 382 & V \\ Rated load & \(T_{N}\) & 150 & N\(\cdot\)m \\ Rated speed & \(n_{N}\) & 3820 & r/min \\ Rated frequency & \(f_{N}\) & 255 & Hz \\ Rated efficiency & \(\eta_{N}\) & 96 & \% \\ Rated power factor & cos\(\varphi_{N}\) & 0.7 & - \\ Stator resistance & \(R_{s}\) & 32.6 & m\(\Omega\) \\ d-axis inductance & \(L_{d}\) & 0.437 & mH \\ q-axis inductance & \(L_{q}\) & 1.119 & mH \\ Poles pairs & P & 4 & - \\ Moment of inertia & \(J\) & 0.04739 & Kg\(\cdot\)m\({}^{2}\) \\ Permanent magnet & & & \\ flux linkage & \(\psi_{f}\) & 0.09398 & Wb \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of the PMSM.  
Figure 9: Experimental system.  
In order to analyze the harmonic suppression effect of PMSM during operation, the experimental results are analyzed using frequency domain analysis, and due to the adoption of the RPWM, the Power Spectral Density (PSD) analysis method is further used.
# Sea Level Estimation Based on GNSS Dual-Frequency Carrier Phase Linear Combinations and SNR
## 2 Methods  
Figure 1 illustrates a scenario where direct and the reflected signals from the sea surface arrive at the zenith-facing GNSS antenna. It is shown that the antenna height above the sea surface is reduced by rising sea levels, and vice versa. The point in the GNSS antenna where the signals are detected is the antenna phase center (APC), the location of which is defined with respect to the antenna reference point (ARP). The vector between the averaged APC and the ARP is the modelled antenna phase center offset (PCO). The GNSS data used in this paper are all at low elevation, and the vertical errors caused by PCO are in the millimeter range. As a result, these effects will not be considered further in this paper. The geodetic height of the antenna can be obtained with high precision using Precise Point Positioning (PPP) or other geodetic positioning methods, and the vertical distance between the ARP and the specular point can be estimated by our approach described in the following parts of this paper. Then, after subtraction of the geodetic height of the antenna and the vertical distance is performed, the absolute sea surface height can be estimated.  
The algorithm theories used for retrieval of the antenna height from the sea surface using GNSS SNR, and the phase combination can be found in the literature [(26; 27)] and, for convenience, these are briefly summarized in the next section.
# Towards Efficient Wireless Body Area Network Using Two-Way Relay Cooperation
## 4 Simulation, Performance Evaluation and Optimal Packet Size
### Performance of Asymmetrical Link  
The impact of an asymmetrical link on the performance of the overall system is discussed in this subsection. Two different scenarios are considered in this regard. The placement of relay node i.e., unequal length of \(S-D\) link and the \(R-D\) link is considered as followed by the unequal amount of data on both links. The simulations are performed in MATLAB using multiple relay positions and by sending unequal data over the links. The results in terms of EE are shown in Table 4 for locating the relay node at distances other than \(d/2\). A comparison of EE shows that the link remains energy efficient over maximum distance when the relay node is placed midway between the source and destination nodes. The percentage decrement in efficient link length, compared with \(d/2\), is visible from Table 4 for all other relay locations. The placement of relay node at the midpoint for achieving maximum EE is also supported in [17] for an incremental one-way relay link.  
Table 5 shows the percentage decrement in EE when unequal data is transmitted over both links in two-way relay scenarios. The results show maximum EE when equal data is sent over both links. The percentage decrease in EE, compared to equal data, for different data ratios over the links is elaborated in Table 5.
# Machine Learning Methods in Weather and Climate Applications: A Survey
## 7 Discussion
### Challenge  
Although we found extensive work on machine learning frameworks that succeed in short-term weather prediction and even outperform traditional methods, climate prediction in the medium-to-long term mainly relies on traditional methods. The main challenges can be attributed to the limited data size and complex climate change effects.  
**Dataset.** The scarcity of seasonal meteorological data, particularly evident from the era around 1979, poses significant challenges for applying machine learning to climate prediction. While data from this period may be adequate for short-term weather forecasting,it falls short for medium-to-long-term climate models. This data limitation impacts machine learning algorithms, which rely on large, quality datasets for robust training. Consequently, the lack of seasonal data affects not only the model's performance and reliability but also complicates validation procedures. This makes it challenging to assess the model's generalizability and accuracy. Additionally, the sparse data hampers the effective fusion of machine learning with traditional physics-based models, affecting the overall reliability of climate predictions. Therefore, the limitations of historical meteorological data significantly constrain the application of machine learning in long-term climate studies.  
**Complex climate change effect.** A certain climate change may be related to hundreds or thousands of variables. It's difficult for us to use machine learning to capture their correlation. The intricate nature of climate change, influenced by hundreds or even thousands of interrelated variables, presents a daunting challenge for machine learning applications in climate prediction. Unlike simpler systems, where causal relationships between variables are straightforward, climate systems embody complex, non-linear interactions that are difficult to model. Machine learning algorithms, though powerful, often require clearly defined feature sets and labels for effective training, a condition seldom met in the realm of climate science. The sheer number of variables can lead to issues of dimensionality, where the complexity of the model grows exponentially, making it computationally intensive and difficult to interpret. Furthermore, capturing long-term dependencies between these myriad variables is particularly challenging, given the current state-of-the-art in machine learning techniques. This complexity often results in models that, while mathematically sophisticated, lack the interpretability necessary for scientific validation and policy implications.
# Simulation of Wind-Battery Microgrid Based on Short-Term Wind Power Forecasting
## 2 Modeling of System Components
### Wind Power Forecasting Model  
This work employs an ANN-based approach for 24-h ahead wind power forecasting. The main characteristics of the proposed method include the use of statistical features as input of the multilayer perceptron model, as in [34], along with the consideration of a clustering approach for the output of the network [49]. As a result, an ANN-based clustering model is obtained that classifies the ANN output into power production intervals that are ruled by the power generation curve of the wind turbines (defined in Section 2.1).  
The factors forming the input vector \(I\) of the ANN are:  
\[I=\big{[}W_{avg},W_{sd},FOD_{\max},T_{avg},W_{dir},yd\big{]}, \tag{10}\]  
where \(W_{avg}\) is the average of the wind speed in the last 24 h, \(W_{sd}\) is the standard deviation of the wind speed in the last 24 h, \(FOD_{max}\) is the maximum of the first order difference of the wind speed time series, \(T_{avg}\) is the average of the temperature in the last 24 h, \(W_{dir}\) is the mode of the wind direction in the last 24 h, and \(yd\) is the day of the year (\(yd\in[1,365]\)) [34]. The statistical parameters \(W_{avg}\) and \(W_{sd}\) in the input vector \(I\) represent the dispersion of the wind speed data in relation to the sample mean, while \(FOD_{max}\) provides further information about the variation of the wind speed during the day. Moreover, the parameters \(T_{avg}\), \(W_{dir}\), and \(yd\) are included in the input vector \(I\) to improve the accuracy of the forecasts, as pointed out in [34].  
Moreover, the Fisher-Jenks natural breaks optimization method is applied to identify the optimal number of intervals \(N\) that classify the power production values based on historical series of wind speed and wind power production [50]. This guarantees that the values of the components of the output vector in Equation (11) fall into one of the Fisher-Jenks intervals.  
\[O=[O_{1},O_{2},\ldots,O_{24}], \tag{11}\]  
where \(O_{i}\in\{C_{1},C_{2},\ldots,C_{N}\}\) represents the forecasted power class \(C_{i}\) calculated by rounding down the numerical output value of the ANN. The output of the ANN model is a vector of classes that represent the expected power production within the next 24 h. Each class represents an interval that classifies a given value of the power production.  
Following the proposed multilayer perceptron architecture in [34], the ANN model employed for the purposes of this work consists of an input layer with six neurons, a hidden layer with 13 neurons, and an output layer of 24 components representing the estimated wind power production in the next 24 h. In addition, the same ANN training method is chosen, namely the Levenberg-Marquardt algorithm.
## 3 Finite Element Analysis of a Railway Track Slab with a Gap
### Analysis of Computed Results by Finite Element Analysis
#### 3.2.2 Comparison of Acceleration Level in Frequency Domain  
Figure 5 compares the variation of acceleration amplitudes generated on the concrete surface and leaked into the air, respectively. These were both obtained by FFT of obtained data computed by ABAQUS in the time domain. The amplitude graph obtained on the concrete surface produced peak modes clearly at certain higher frequency ranges in the case of with a gap compared to without a gap. The peaks of amplitudes are much higher than those of the non-gap case. In addition, at the second mode of peak that was generated at resonant frequency of around 170 H\({}_{\mathrm{z}}\), the peak value was seven times larger than that of the non-gap cases. At the first mode of resonance generated around 90 H\({}_{\mathrm{z}}\), the peak value in the case of with gap was not much bigger than that of the without gap. The first mode resonance was thought to be generated due to total body of the slab and soil ground. However, the second mode resonance was generated mainly due to the slab itself, which was interfaced directly with the gap. The leaked Rayleigh waves into air were also converted into the frequency domain. In the case of gap existence, peak amplitude was generated at 7 to 12 H\({}_{\mathrm{z}}\), which was much lower than those captured on the concrete surface. The peak values of the case of with a gap were two times larger than those of the without a gap case. In addition, amplitude generated in the gap case was distributed over a wider range of frequency than that in the non-gap case.  
Figure 4: Variation of acceleration amplitude with time in the air 0.4 m above the concrete surface. (**a**) Without gap. (**b**) With gap.  
Figure 3: Comparison of acceleration amplitudes on the concrete surface between without gap and with gap cases in time domain. (**a**) Without gap. (**b**) With gap.