esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Signal Quality Analysis  
Signals are continuously broadcast by satellites. The broadcast signal strengths are the same for all satellites. However, the user receives discontinuous and varying-strength signals from satellites due to free-space path loss and interference in the transmission path. For a given user, each GNSS satellite faces a different environment when transmitting a signal to the receiver. The transmission distance and the interference in this path vary with satellite. When the received signal power is significantly below specified levels, ranging error increases because the autocorrelation function causes error in the code tracking loop [21; 22; 23; 24]. Carrier-phase measurements are more precise than pseudorange measurements, but integer ambiguity is a problem. If the integer ambiguity can be resolved, centimeter-level positioning can be achieved [25]. However, sometimes a GNSS receiver cannot continue to generate carrier-phase measurements due to the temporarily loss of lock on the carrier of a GPS signal caused by signal blockage. A satellite signal may be interrupted by radio interference, irregular ionospheric activity, or objects such as buildings or trees. High receiver dynamics can also cause signal lock loss. When receiver lock on satellite signals is lost, the receiver needs to recalculate the integer ambiguity for the satellite. If the integer ambiguity changes for a satellite, there will be a discontinuity or jump in the carrier-phase measurement. A cycle slip is an indicator of GNSS receiver loss of satellite signals. Receivers need to assign extra time for calculation of the new ambiguity value. As a result, availability is decreased. Analyzing the signal continuity for each satellite can provide useful information for precise positioning. This research analyzes the sensitivity of the received BDS and GPS signals for a given user. The signal strength and continuity differences between BDS and GPS are given. The carrier-to-noise density ratio (C/N\({}_{0}\)) is used to show the signal strength and quality. The C/N\({}_{0}\) output by a receiver is used as an indicator of signal strength and quality of the tracked satellite and the noise density as seen by the receiver's front-end [26]. The cycle slip number of the carrier-phase measurements for each satellite is used to analyze the continuity for these two systems. SQA is divided into three parts:  
1. SQA1: Signal strength for various times and satellite elevation angles.
2. SQA2: Signal continuity analysis.
3. SQA3: Signal discontinuity analysis.  
The data used in this section were recorded on the 22 nd of July 2014 for 24 h. The raw data from the NovAtel receiver were recorded at a rate of 1 Hz. The total numbers of data points for GPS and BDS were 870,874 and 899,137, respectively.
# Research on Real-Time Monitoring and Performance Optimization of Suspension System in Maglev Train
## 3 Modelling and Nominal Controller of Suspension System
### Model of Suspension System  
Each carriage of the maglev train has five bogies, each bogie has two suspension modules, and each suspension module has two suspension units, so the entire suspension system is composed of twenty sets of single suspension systems controlled independently [35]. The single suspension system is composed of a suspension control box, a set of sensors and an electromagnet. Taking a suspension point as an example, the single suspension point of a medium-speed maglev train is simplified to obtain the single point suspension model, as shown in Figure 2.  
Figure 1: Structure of real-time monitoring and performance optimization.  
Figure 2: The single point suspension model.  
The system model in Figure 2 is:  
\[\left\{\begin{array}{l}m_{B}\frac{d^{2}z}{dt^{2}}=m_{B}g-F(i,z)\\ F(i,z)=K\left(\frac{i}{z}\right)^{2}\\ u=Ri+\frac{2K}{z_{0}}\cdot\frac{di}{dt}-\frac{2 Ki_{0}}{z_{0}}\cdot\frac{dz}{dt} \\ m_{B}g-F(i_{0},z_{0})=0\end{array}\right. \tag{6}\]  
where \(m_{B}\) is the equivalent mass of the electromagnet; \(F(i,z)\) is the electromagnetic attraction exerted on the electromagnet; \(i\) is the current in the electromagnet coil; \(z\) is the gap between the electromagnet and the track and \(i\) and \(z\) are both variables that change over time; \(g\) is the acceleration of gravity; \(K=\frac{\mu_{0}AN}{4}\); \(N\) is the number of turns of the solenoid winding, \(A\) is the pole area of the iron core, and \(\mu_{0}\) is the vacuum permeability rate; \(i_{0}\) and \(z_{0}\) are the coil current and the gap at equilibrium point, respectively. \(R\) is the equivalent resistance of electromagnet; \(L\) is the inductance of coil; \(u(t)\) is the control voltage of the coil.  
The model of the suspension system consists of a set of nonlinear equations and it is difficult to design the nominal controller directly. Considering that during the operation, the current and gap of the electromagnet fluctuate up and down at the equilibrium point \((i_{0},z_{0})\), \(F(i,\;z)\) can be expanded by Taylor series at the equilibrium point, and higher-order terms can be ignored, and the system model is linearized as [36]:  
\[\left\{\begin{array}{l}m_{B}\ddot{z}=\frac{2 Ki_{0}{}^{2}}{z_{0}}z-\frac{2 Ki_ {0}}{z_{0}}i\\ u=Ri+\frac{2K}{z_{0}}i\end{array}\right. \tag{7}\]
## 2 Materials and Methods
### Animals, Experimental Treatments, and Management  
Thirty male lambs (\(\%\) Santa Ines \(\times\)\(\%\) Dorper breed) at 68 \(\pm\) 13 days (mean \(\pm\) SD) of age and of average body weight (BW) of 22.56 \(\pm\) 2.72 kg (mean \(\pm\) SD) were used. After weaning, the lambs underwent 15 days of adaptation on a Tifton-85 pasture under their respective experimental supplements, and then started the experimental evaluation for a period of 60 days. The lambs were placed in a grazing experimental area and the treatments consisted of three different supplements. The control group (n = 10) received a base supplement/concentrate consisting of soybean meal (867 g/kg DM), ground corn (99 g/kg DM), mineral premix (28 g/kg DM), and dicalcium phosphate (6 g/kg DM). The MO group (n = 10) received the same concentrate as the control group, supplemented with 0.1% of the lambs' live weight in macadamia oil. The MOVE group (n = 10) received the same concentrate as the MO group, with 745 IU of Vitamin E in the dry matter (DM) (Table 1). Data provided for Control treatment have been used in another context of analysis [13]. Base supplements were offered daily in the proportion of 1.6% of BW with the aim of promoting weight gain in the lambs. The proportion of fatty acid in relation to the total ether extract of macadamia oil was: C14:0 (1.95%), C16:0 (3.19%), C16:1 c9 (22.78%), C18:0 (11.98%), C18:1 c9 (40.16%), C18:2 n6 (7.62%), and C18:3 n3 (0.60%). The amount of macadamia oil in the supplement was adjusted every two weeks according to the BW of the lambs. The oil was weighed daily according to the established amount for each animal in individual bottles using an analytical balance (Sartorius BA11 OS, Gottingen, Germany). Subsequently, the oil was manually mixed with the amount of concentrate offered on a particular day to each animal. Vitamin E was homogenized within the concentrate in a 300 kg vertical mixer (Incomagri 300P, Itapira, Sao Paulo, Brazil). Tifton-85 (_Cynodon_ spp.) pasture comprising a total area of 9000 m\({}^{2}\) was equally subdivided into five paddocks with fresh water and a rest area with shade (a polyethylene fabric of 20 m\({}^{2}\) was used to provide 80% shade). A fixed stocking rate with a grazing cycle of 35 days (7 days of grazing and 28 days of rest) was applied. The lambs were managed daily from 7 a.m. to 6 p.m. in the pasture. From 6 p.m. to 7 a.m., the lambs were taken to a covered shed which contained thirty individual pens measuring 1.3 m\({}^{2}\) each and provided with a feeder, water trough, and woodchip bedding; this was where the lambs individually received the experimental supplements. Every morning the remnants of the supplements were collected, weighed, and individually sampled for 60 days to determine supplement intake. Pasture samples of Tifton-85 (_Cynodon_ spp.) were taken manually, simulating the grazing technique described by [PERSON] [15]. The sampling (400 g forage) was performed in the morning on days 1, 3, and 7 in each paddock [16]. The Tifton-85 (_Cynodon_ spp.)
## 2 Materials and Methods
### Data Processing  
From each of the 15-day filtered data sets, the greenest pixel composite was prepared and incorporated into the individual data sets. All the greenest pixel layers were stacked together and scaled so that the image size and processing time might be reduced. For each of the greenest pixel composites, overall local maxima in the NDVI were identified. In each greenest pixel composite layer, if NDVI is higher than before or after that time, it is considered the peak value. In the present study, during the Kharif time series data set, the peak NDVI was higher than 0.52, while the minimum value was between 0.18 and 0.34. Hence, if the peak NDVI was identified as more than 0.52, it was reaching its maximum growth in the Kharif season. In the similar way, for the Rabi season, the peak NDVI was identified as more than 0.68, and the minimum value was between 0.26 and 0.41. Hence in the Rabi season, the peak NDVI heading toward 0.68 indicated the highest growth stage of crops.  
The presence of clouds and atmosphere are major noise sources in the NDVI data sets. For the smoothening of the temporal greenest pixel NDVI curve, the Savitzky-Golay filter (S-G filter) was applied. The filter adopts the local polynomial least square fitting method to smoothen the temporal curve [40]. The S-G filter for the smoothening of multi-temporal NDVI was performed in the Origin software (Figures 4-6).  
The Savitzky-Golay process can be represented as follows:  
\[\beta_{j}^{*}=\frac{\sum_{i=-n}^{n}\ \gamma_{i}\beta_{j+i}}{W} \tag{2}\]  
where \(\beta_{j}^{*}\) is the reconstructed dataset, \(\gamma_{i}\) is the coefficient of the filter, \(\beta_{j+i}\) is the original value, \(n\) is the window width, \(W\) is the number of data in the moving window (\(2n+1\)). Once the maximum NDVI greenest pixel dataset was identified, cropped areas were depicted, and signatures were generated for the cropped and non-cropped areas, followed by image classification using the random forest technique. After the first iteration of classification, the entire area was thoroughly assessed for misclassification through visual interpretation and validated using Google Earth images. The misclassified pixels were further classified correctly, and the final iteration was processed. The generated classified map also calculated the total cropped area for Kharif and Rabi seasons. The generated classified image was downloaded from the Google Earth Engine code editor. For Kharif and Rabi seasons, a similar process was adopted.
## 3 Background: Models
### BiGRU  
A variant of the gated recurrent unit (GRU) consists of only two gates: an update gate that acts as a catalyst for combining the forget and input gates and controls the information flowing to the current state, and a reset gate that determines when to ignore the previous hidden state [31]. The update and reset gates are computed similarly to LSTM as follows [31]:  
\[r_{t}=\delta(W_{t}h_{t-1}+U_{r}x_{t}+b_{r}), \tag{4}\]  
\[z_{t}=\delta(W_{z}h_{t-1}+U_{z}x_{t}+b_{z}), \tag{5}\]  
\[h_{t}=(1-z_{t})\odot h_{t-1}+z_{t}\odot\tilde{h_{t}}, \tag{6}\]  
\[\tilde{h_{t}}=tanh(W_{\tilde{h_{t}}}(h_{t-1}\odot r_{t})+U_{\tilde{h_{t}}}x_{ t}). \tag{7}\]  
In the formulas above, \(\delta(.)\) signifies the logistic sigmoid function, \(W\) and \(U\) are gate weight matrices, and \(h_{t}\) and \(b\) are the hidden state and bias vectors, respectively. Basic RNNs simply consider the previous context and lack the ability to capture future context. As a result of the breakthrough design, bi-directional long short term memory (BiLSTM) and bi-directional gated recurrent unit (BiGRU) are suitable options for accounting for the future and preceding contexts. To do this, the forward and backward hidden layers are integrated, controlling the temporal information flow in both directions and leading to better learning.
## 4 Results
### Model Validation and Comparative Analysis
#### 4.1.2 Limitations of Data-Driven Approaches  
The systematic failure of machine learning approaches in extrapolation scenarios (R\({}^{2}\) < 0) reveals fundamental incompatibilities between statistical learning paradigms and  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **RMSE (kg/ha)** & **R\({}^{2}\)** \\ \hline DSSAT & 190.95 & 0.871 \\ BP & 519.57 & \(-\)0.606 \\ XGBoost & 465.02 & \(-\)1.637 \\ Random Forest & 409.35 & \(-\)4.549 \\ KNN & 371.04 & \(-\)0.052 \\ \hline \hline \end{tabular}  
Negative R\({}^{2}\) values indicate worse performance than horizontal mean prediction. Mechanistic modeling advantage persists despite identical training data due to biophysical constraints enforcement.  
\end{table}
Table 7: Comparative performance of fertilization optimization approaches.  
agricultural system dynamics. Conventional ML architectures inherently assume temporal independence between observations, a premise violated by the 20-40 day latency of nitrogen application impacts on yield formation--a phenomenon rooted in crop phenological development and nutrient translocation kinetics. This temporal decoupling manifests as erroneous phase shifts in predicted response curves, particularly acute in neural network implementations (BP R\({}^{2}\) = \(-\)0.606). Furthermore, the non-convex optimization landscapes generated by threshold biological processes (e.g., nitrogen deficiency-triggered leaf senescence) create metastable local minima that trap gradient-based learners, exacerbated by the high-dimensional interactions between soil hydrology and root architecture. The path-dependent nature of nutrient allocation further compounds these issues, as standard regression architectures lack explicit memory mechanisms to track cumulative fertilization effects across growth stages--a critical limitation evident in XGBoost's nonsensical negative determination coefficients (R\({}^{2}\) = \(-\)1.637). Our hybrid framework circumvents these constraints through dual mechanisms: DSSAT's process-based modules enforce temporal coherence via mechanistic phenology clocks, while the genetic algorithm's population-based search escapes local optima through stochastic exploration of the fertilization decision space.
# A Cloud-Based Distributed Architecture to Accelerate Video Encoders
## 4 Experimental Setup and Results
### Scalability and Resource Usage
#### 4.2.1 Time to Encode and Speedup  
Table 2 shows execution time for each codec as the number of workers increases. VP9 is almost 5.5 times faster than H.265, which is maintained when the number of workers increases. Results denote a significant decrease of the execution time in comparison to the sequential version (1 worker), which ranges from 63% to 89% when using from 2 to 15 workers.  
Figure 2 depicts the speedup as the number of workers increases, which is obtained from data in Table 2 as the ratio of the sequential version using one worker to the time taken by increasing the number of workers. The speedup is almost ideal until the use of 9 workers (using a total of 12 vCPUs per compute node out of 16 physical cores) with an efficiency greater than 80%. However, there is a reduction for 12 and 15 workers, although the efficiency in these cases is still equal or greater than 70% for 12 workers and greater than 60% for 15 workers. For 12 workers, there are 4 workers per compute node demanding the use of 16 vGPU among the 16 physical existing cores, therefore some of them will be shared with the operating system and the applications running in the compute node. This is even worse for 15 workers, as there are 5 workers per compute node demanding the use of 20 vCPUs of the only 16 physical existing cores, thus some of them will be shared with the operating system, applications, and other encoding processes. A more detailed view of this effect is analysed in the next subsection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Workers** & **H.265** & **VP9** \\ \hline
1 & 131.40 & 23.98 \\
3 & 45.67 & 8.80 \\
6 & 24.93 & 4.72 \\
9 & 17.37 & 3.28 \\
12 & 15.81 & 2.73 \\
15 & 14.41 & 2.44 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Execution time (minutes) as the number of workers increases.  
Figure 3 shows the number of executed jobs by each worker when encoding using 15 workers.  
This number depends on the computational load of the chunk encoding. Although the chunk size is uniform in all jobs, the encoding time will depend on its content. For example, one worker encodes 5 chunks for H.265, while other encodes 12.  
Figure 4 depicts the job scheduling for H.265 when encoding using 15 workers.  
As can be seen in this figure, worker 4 performs 5 time-consuming encoding, while worker 12 performs 12 shorter encoding jobs. The total encoding time is determined by the last ending worker. In order to avoid congestion in the media network, when the workers download their video chunks, the master generates job messages at a rate of 1 message per second (this value is provided when the master application is started). Therefore, as can be seen in Figure 4, not all workers start simultaneously.  
Figure 3: Number of executed jobs per worker with a configuration of 15 workers for the two codecs with chunks of 50 frames, which generates 123 jobs.  
Figure 2: Speedup as the number of workers increases.
## 3 Result and Discussion
### Maximum Shear and Dilatation Strain Rate with Additional Campaign Observation  
In this study, the maximum shear strain rate is 0.0013 to 1.12 upstream/year. Figure 8b is the visualization result of the maximum shear strain rate. The visualization results show the high value of the maximum shear strain rate in the western and southeastern regions.  
Figure 8: (**a**) Distribution of the principal strain rate showing the compression and extension process. (**b**) Estimated maximum shear strain rate value indicating the presence of strike–slip faults. (**c**) Estimated dilatation rate value that shows the magnitude of compression and extension values around the Kendeng Fault.  
The high value of the maximum shear strain rate indicates the presence of strike-slip faults, so the potential for earthquakes in the region is quite high.  
In contrast with the regional scale of strain rate, based on additional campaign stations, we obtained a more localized maximum shear strain rate along the Purwodadi Fault, the northern and southern part of the Cepu Fault, the Waru Fault, and the Blumbang Fault. In this analysis, we also obtained an insignificant maximum shear strain rate across the Demak Fault and the central Cepu Fault, which needs to be investigated considerably by additional campaign stations.  
The dilatation rate in this study has a value range of \(-2.24\) to \(0.698\) strain/year. The distribution of the dilatation rate values is shown in Figure 8c. The visualization shows the magnitude of the compression and extension process. Negative values in the dilatation rate results of this research are more dominant than positive values, which indicates that the compression process is found more around the Kendeng Fault.  
The dilatation contrast was also observed across the Purwodadi Fault and the east and west sides of the Cepu Fault, a considerably compressional zone that implies high strain accumulation related to dip-slip faulting. Meanwhile, several spots show extensional regions with recorded seismicity. In this analysis, we observed that the maximum shear strain rate along the Demak and central Cepu Faults was insignificant. This finding underscores the necessity for further investigation to gain a deeper understanding of the geophysical processes at play. To achieve this, we recommend deploying additional campaign stations in these areas, allowing for more comprehensive monitoring and data collection. Such efforts could enhance our insights into the fault dynamics and contribute to a better understanding of regional tectonic activity.
# A Multi-Carrier Waveform Design for 5G and beyond Communication Systems
###### Abstract  
The next generation communication network (NGCN) is expected to provide higher spectral efficiency, low latency, large throughput and massive machine-to-machine type communications. In this regard, the design of the multi-carrier waveform (MCW) is posing a major research problem for the NGCN. To overcome the stated problem, a lot of state-of-the-art work exists that proposes various MCW alternative to the standard orthogonal frequency division multiplexing (OFDM) waveform. It is true that OFDM was used in a number of real-time communication systems of fourth generation (4G) networks. However, their use in the upcoming fifth generation (5G) network is not very feasible. This is because of the strict requirements of 5G communication systems, which also extend beyond 5G systems; hence rendering the use of OFDM infeasible for newer communication standards. To satisfy the requirements of upcoming communication networks, there is a dire need for MCWs with better flexibility. In this regard, a precoding-based MCW has been proposed. The proposed MCW fulfills the requirements of the NGCN in terms of low peak-to-average power ratio (PAPR), high spectral efficiency and throughput. The MCW proposed in this work uses power-domain multiplexing such as non-orthogonal multiple access (NOMA) and phase rotation by using the selective mapping (SLM) and generalized chirp-like (GCL) precoding of the input signal to the universal filtered multi-carriers (UFMC) modulations. Statistical analysis of the PAPR is presented by using the complementary cumulative distribution function (CCDF). The MATLAB(r) simulations have been carried out to implement the CCDF of PAPR and results show that a PAPR gain of 5.4 dB is obtained when the proposed waveform is compared with the standard NOMA-UFMC waveform at clip rate of \(10^{-3}\), using 4-QAM.  
Keywords:next generation communication networks (NGCN); multi-carrier waveform (MCW); complementary cumulative distribution function (CCDF); non-orthogonal multiple access (NOMA); universal filtered multi-carriers (UFMC) +
Footnote †: journal: _mathematics_