esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### The Impact of Recirculation  
[PERSON] et al. conducted acoustic measurements of small-scale rotors in the NASA Langley Structural Acoustic Loads and Transition acoustic chamber and reported that the build up of recirculation resulted in high amplitude fluctuations in the load cell signals [20]. [PERSON] et al. quantified the impacts of recirculation on rotor noise in closed anechoic chambers by examining the spectrogram of the acoustic pressure signal at several mic locations [4]. They showed that, after the flow develops in the chamber, not only does the thrust signal fluctuate wildly, but both the higher harmonic noise and broadband noise are amplified. This discovery somewhat conflicts with traditional acoustic measurement techniques in which the rotor is run at a constant operating condition until either the performance values or the acoustic signal reaches a steady state before data are collected. Therefore, care must be taken to record measurements prior to the effect of recirculation and after the rotor loads have stabilized. This window of opportunity can be very small depending on the rotor geometry, test equipment, and chamber size. They found that placing woven screen meshes in the wake could delay the effect of recirculation and enable a longer measurement duration. [PERSON] et al. similarly made attempts to mitigate the effects of recirculation using a similar mesh screen, but their results were inconclusive [5], emphasizing the difficulties in conducting aeroacoustics experiments.  
[PERSON] et al. were able to highlight the effect of recirculation [21]. They developed an experimental approach that ensured recirculation by maintaining the hovering operating condition until the aerodynamic loads were steady. They used the LBM solver Power-FLOW to simulate a confined and unconfined environment for a single rotor. They then showed that the confined simulation showed much better agreement than the unconfined simulation, especially at higher harmonics. They were able to examine the noise-generationmechanisms and identify leading-edge interactions with the recirculated vortices that amplified the higher harmonic peaks. However, the broadband components of the sound power watt level (PWL) of the confined and unconfined simulations were nearly identical. In addition, although neither the rotor rotation rate nor the absolute sound levels were reported, the PWL of the simulations underpredicted the experimental values by approximately 5 dBA/Hz.
## 5 Conclusions  
In this study, spatial patterns and temporal trends of cropland productivity and their relationships with climate variables from 2000 to 2013 in the Canadian Prairies were analyzed based on time series of MODIS-derived FAPAR and gridded climate datasets. The results effectively revealed the effects of climate variability on cropland productivity variation based on remote sensing observations in the Canadian Prairies with a better spatial coverage than using traditional _in-situ_ data. The main conclusions can be summarized as follows:  
1. The cumulative FAPAR during the growing season can be presented as a proxy of cropland productivity;
2. There was, in general, an increasing trend in cropland productivity during the period from 2000 to 2013 over most of the cropland area of the Canadian Prairies.
3. Temporal and spatial variabilities in cropland productivity are both connected to rainfall variability, with temperature being a negative factor in arid regions. The trend towards increasing cropland productivity was somewhat greater in the more arid regions of the Canadian Prairies.  
This technique shows how FAPAR can be used to evaluate productivity trends on large spatial scales in response to climate and other stressors for crop production, in particular in areas where sparse information is available from other data sources on productivity or yield.  
The authors would like to thank NASA for providing the MODIS FAPAR product (MOD15A2), land cover product (MCD12Q1) and the Modern Era Retrospective-analysis for Research and Application (MERRA) dataset. Dr. [PERSON] of Natural Resources Canada provided the gridded climate dataset ([[http://cfs.nrcan.gc.ca/projects/3/1](http://cfs.nrcan.gc.ca/projects/3/1)]([http://cfs.nrcan.gc.ca/projects/3/1](http://cfs.nrcan.gc.ca/projects/3/1))). This work was funded by Agriculture and Agri-Food Canada (Project #1130) and the Canadian Space Agency (Project #GRIP2013 GENSX).  
[PERSON], [PERSON] and [PERSON] contributed to forming the general idea of the paper, and helped conceive and design the experiments. [PERSON] performed the experiments, analyzed the data, and wrote the draft. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] helped edit the draft and provided critical comments to improve the paper. The authors declare no conflict of interest.
# Integrated Sensing and Communication Using Random Padded OTFS with Reduced Interferences
## 4 The System Model
### The Communication System Model
In this work, the Matlab code of the LMMSE equalizer [15], designed for ZP-OTFS, is used.
## 4 Results and Discussion
### Statistical Analysis on the Spectral Reproducibility of the Aggregates Coated with Asphalt Mortar  
Reproducibility is the closeness of the agreement between the measuring results of the same measurand [41], which is widely known as an important factor for the accuracy of measurement. Mean value (MV), standard deviation (SD), and coefficient of variation (CV) (CV = 100 \(\times\) SD/MV, %) are usually calculated to investigate the reproducibility [42,43], as shown in Figure 9, Tables 4 and 5.  
In Figure 9, it can be seen that the CV values of the original spectra across the entire fingerprint region are higher, almost above 50%, than those of the other region. Though the CV values of original spectra in the range of 3400-3700 cm\({}^{-1}\) are lower than 20%, this is meaningless because characteristic absorption peaks do not exist in this region. Furthermore, the CV values at the 12 characteristic peaks of original spectra in Table 4 all show a high level higher than 30%, and the CV value at 712 cm\({}^{-1}\) is as high as 88.5%.  
Figure 8: Normalized spectra of the aggregates coated with asphalt mortar with ATR-FTIR.  
Figure 9: CV values at different wavenumbers.  
Therefore, it can be concluded that the original spectral reproducibility of aggregates coated with asphalt mortar with ATR-FTIR spectroscopy is greatly affected by the heterogeneity of the coated aggregates.  
However, the CV values at the 12 characteristic peaks of normalized spectra in Table 5 all show a relatively low level, mostly lower than 20%. And compared to the original spectra in Figure 9, the CV values of normalized spectra in the range of 1500-1000 cm\({}^{-1}\) decrease to less than 20%, indicating that norm normalization could improve data quality. Hence, the absorption peaks at 2952 cm\({}^{-1}\), 2920 cm\({}^{-1}\), 2851 cm\({}^{-1}\), 1453 cm\({}^{-1}\), 1419 cm\({}^{-1}\), 1377 cm\({}^{-1}\), 1018 cm\({}^{-1}\), and 873 cm\({}^{-1}\) in Table 3 might be considered useful peaks due to their low CV values.
# Design of a SIMO Deep Learning-Based Chaos Shift Keying (Dlcsk) Communication System
## 3 SIMO DLCSK System Model
### Receiver
The goal of the training process is to minimize the categorical cross-entropy loss function,  
\[i_{t}=\mu\ (w_{i}\cdot[h_{t-1},r_{t}]+b_{i}), \tag{10a}\]2  
where \(a_{t}\) is the mini-batch size, \(\mathbf{\theta}\) indicates set of network parameters corresponding to the different layers, \(p_{n,j}\) is the Softmax's layer output probability for output class \(j\) and observation \(n\), and \(p^{\prime}_{n,j}\in\{0,1\}\) represents the binary indicator if class label \(j\) is the correct classification for observation \(n\). A popular algorithm to obtain \(\mathbf{\theta}\) is the Stochastic Gradient Descent (SGD) method [79], which starts with a random initial value \(\mathbf{\theta}=\mathbf{\theta}_{0}\), and iteratively updates \(\mathbf{\theta}\) as  
\[i_{t}=\mu\ (w_{i}\cdot[h_{t-1},r_{t}]+b_{i}), \tag{10a}\]3  
where \(\eta>0\) is the learning rate, and \(\mathbb{L}(\mathbf{\theta}_{k})\) is an approximation of the loss function which is computed for a random mini-batch of training examples of size \(a_{t}\) at each iteration. Through offline training, a network with optimized weights and biases that can be used for online signal demodulation is formed. The proposed training algorithm is summarized in Algorithm 1. ```
> At the Transmitter side:
1: Input parameters: Number of generated signals (\(2N\)), Spread factor (\(\beta\)). 2: Generate two sets of the chaotic signals, each with length \(N\), using the Chebyshev and the Logistic maps, i.e., \(\{\mathbf{\hat{x}}^{(n)}\}_{n=1}^{N}\) and \(\{\mathbf{\hat{x}}^{\prime(n)}\}_{n=1}^{N}\). 3: Normalize chaotic signals. 4: Transmit all generated signals over the channel: \(\mathbf{for}\ n=1:2N\)  Generate a random \(\sigma^{(n)}_{tr}\in[\sigma_{tr,min},\sigma_{tr,max}]\);  Generate a random \(\mathbf{\hat{x}}^{(n)}_{n,j}\);  Transmit \(n\)th symbol; Endfor. > At the receiver side:
5: The \(m\)th antenna receives altered signals \(\{\mathbf{r}^{(n)}_{m}\}_{n=1}^{2N}=\{\mathbf{\hat{r}}^{(n)}_{m},\mathbf{\hat{r}}^{(n)}_{ m}\}_{n=1}^{N}\) with known labels \(\{j^{(n)}\}_{n=1}^{2N}=\{0^{(n)},1^{(n)}\}_{n=1}^{N}\). 6: Separate \(\mathbf{r}^{(n)}_{m}\) into real and imaginary parts to form the training set: \(\mathcal{D}_{m}=\{|\Re\{\mathbf{r}_{m}\},\Im\{\mathbf{r}_{m}\}|^{(n)},j^{(n)}\}_{n=1}^{2N}\). 7: Train \(m\)th NN-based receiver including: - Sequence input layer; - LSTM/BiLSTM layer; - Fully connected layer; - Softmax layer; - Classification layer;
8: End of Training. ```  
**Algorithm 1** Training of \(m\)th receiver.
## 2 Architecture of the Multi-Park IEMG System
### CCHP Model  
The CCHP unit can output cooling, heating, and electrical energy according to different demands, and its mathematical expression can be described by the following formula:  
\[P_{\text{CCHP,t}}=F_{\text{g,t}}L_{\text{H}}\eta_{\text{CCHP}} \tag{1}\]  
\[H_{\text{CCHP,t}}=P_{\text{CCHP,t}}\frac{1-\eta_{\text{CCHP}}-\eta_{\text{L}}}{ \eta_{\text{CCHP}}} \tag{2}\]  
\[H_{\text{CCHP,H}}=H_{\text{CCHP,t}}\eta_{\text{He}} \tag{3}\]  
\[C_{\text{CCHP,t}}=H_{\text{CCHP,t}}\eta_{\text{HE}}COP_{\text{AC}} \tag{4}\]  
wherein \(P_{\text{CCHP,t}}\) represents the output electrical power of the micro CCHP, \(\eta_{\text{CCHP}}\) represents the power generation efficiency of the micro CCHP, and \(F_{\text{g,t}}\) represents the consumed amount of natural gas; \(L_{\text{H}}\) represents the calorific value of natural gas; \(H_{\text{CCHP,t}}\) represents the heat output of the micro CCHP, \(\eta_{\text{L}}\) represents the heat dissipation loss rate of the micro CCHP. \(H_{\text{CCHP,H}}\) represents the amount of waste heat recovered by the waste heat recovery boiler, \(\eta_{\text{He}}\) represents the waste heat recovery efficiency of the waste heat boiler equipment. \(C_{\text{CCHP,t}}\) represents the cooling capacity of the absorption refrigerator (AC) in the CCHP unit, and \(COP_{\text{AC}}\) represents the energy efficiency ratio of the AC in the CCHP unit.
## 3 Adaptive Real-Time Routing Protocol
### Adaptive Traffic Handling
This is, recovery procedure is invoked according to current positive DBP value. * If the two consecutive DBP values are positive, two different tasks are performed according to the current index according to current index value. If the current index reaches the maximum value and the packet size is changed, the previous payload size is restored. Otherwise, \((m_{i},k_{i})\) is set to close to \((m_{1},k_{1})\) by decreasing the index number. * If the DBP value becomes positive every time, the algorithm for adaptive traffic handling is not executed because the \((m_{i},k_{i})\)-firm requirement is met by adaptive QoS routing. As for all algorithms, Figure 2 shows the time sequence diagram for the proposed scheme. At first, path setup procedure is initiated. After receiving reply from the sink node, a sensor node starts delivering packets along the selected path according to \((m,k)\)-firm requirements. The timeliness of packet is measured in a sink node and reported to a sensor node. According to this DBP value, a node decides whether new path is demanded or traffic shaping is required. If a next node detects event, a packet is delivered to the previous node and new path is searched. A previous node delivers packet from the next node along the path between itself and sink node. After new path is established, packet is delivered from current node to sink.
## 4 Results and Discussion
### Cross-Validation Techniques  
Cross-validation techniques, particularly k-fold cross-validation, were employed to prevent overfitting. This method involves dividing the dataset into multiple subsets, training the model on some while testing on others to ensure robustness. Random forest models, while powerful, can still overfit if too many decision trees are used, or if the dataset lacks sufficient diversity. The relatively small sample size of 197 students limited the model's ability to generalize across different populations. Although random forest models reduce overfitting by averaging multiple decision trees, an excessive number of trees can still cause the model to fit too closely to the training data, especially with a small dataset. In conclusion, while the model reached 100% accuracy, it is important to question whether this performance would hold up in real-world applications or with larger, more diverse datasets. Although cross-validation and careful tuning were applied, the small dataset and the use of data from a single institution suggest that further testing on larger, more varied datasets is necessary to confirm the model's robustness and avoid overfitting.
## 2 Materials and Methods
### Classification Methods
#### 2.6.1 Spectral Water Indices  
The Normalized Difference Water Index (NDWI) was used to detect lake systems in wetland areas and to establish pond dimensions. Aquatic systems usually reflect positive values, whereas vegetation and soil usually reflect zero or negative values [37]. The NDWI for Landsat 7 and 8 images was calculated by the following Equation (1):  
\[\text{NDWI}=\frac{\text{GREEN}-\text{NIR}}{\text{GREEN}+\text{NIR}} \tag{1}\]  
The Modified Difference Water Index (MNDWI) is efficient for extracting water bodies. The resulting values representing water features have positive values, and non-water features have negative MNDWI values [38]. The MNDWI for Landsat 7 and 8 imagery was calculated by the following Equation (2):  
\[\text{MNDWI}=\frac{\text{GREEN}-\text{SWIR}}{\text{GREEN}+\text{SWRI}} \tag{2}\]  
The Automated Water Extraction Index (AWEI) was used to differentiate water and non-water areas in shaded locations. Water sources reflect values above zero, while areas containing no water reflect negative values [39]. The AWEI for Landsat 7 and 8 images was calculated by the following Equation (3):  
\[\text{AWEI}_{\text{sh}}=\text{BLUE}+2.5\cdot\text{GREEN}-1.5\cdot(\text{NIR}+ \text{SWIR}_{1})-0.25\cdot\text{SWRI}_{2} \tag{3}\]  
where \(\text{SWIR}_{1}\) (Landsat 7: band 5; Landsat 8: band 6); \(\text{SWIR}_{2}\) (band 7); NIR is near infrared (Landsat 7: band 4; Landsat 8: band 5); GREEN (Landsat 7: band 2; Landsat 8: band 3); BLUE (Landsat 7: band 1; Landsat 8: band 2).
# Multi-Region OpenFOAM Solver Development for Compact Toroid Transport in Drift Tube
## 2 Models
### Implementation of the mhdF Solver Algorithm  
The mhdF solver numerically solves the full set of MHD equations using the PIMPLE algorithm--a hybrid of PISO and SIMPLE methods--which employs \(m\) outer iterations and \(n\) inner iterations for convergence. The main workflow is as follows:  
1. Momentum prediction: In this step, the pressure gradient \(\
abla p\) and Lorentz force term \(\mathbf{J}\times\mathbf{B}\) are treated explicitly to obtain an estimated velocity \(\mathbf{U}^{*}\) for the current time step.
2. Energy equation solution: Based on the estimated velocity \(\mathbf{U}^{*}\) and other explicitly treated variables, the energy equation is solved. Subsequently, the estimated temperature \(T^{*}\) is derived from the relationship between temperature and internal energy. This allows for the estimation of the compressibility coefficient \(\psi^{*}=1/(RT^{*})\) and the density \(\rho^{*}=\psi^{*}P\).
3. \(n\) inner loops for solving the pressure equation: The pressure equation is derived from the combination of the momentum and continuity equations using the previously estimated velocity \(\mathbf{U}^{*}\), density \(\rho^{*}\), and the compressibility coefficient \(\psi^{*}\). After \(n\) inner iterations, the corrected pressure value is obtained, the velocity flux and velocity \(\mathbf{U}^{**}\) are updated, and the density is recalculated using the equation of state.
4. \(n\) inner loops for solving the magnetic induction equation: The algorithm for solving the magnetic induction equation is analogous to the PISO algorithm and fundamentally follows [PERSON]'s projection method, aiming to enforce the divergence-free constraint \(\
abla\cdot\mathbf{B}=0\). First, the magnetic induction equation is solved to obtain an estimated magnetic flux density \(\mathbf{B}^{*}\). Subsequently, based on [PERSON]'s projection method and the relationship between the magnetic flux density and its vector/scalar potentials \(\mathbf{B}=\
abla\times\mathbf{A}+\
abla\phi\), a Poisson equation for the scalar potential \(\phi\) is formulated and solved. The scalar potential \(\phi\) is then used to correct the magnetic flux density, yielding \(\mathbf{B}^{**}\). After n iterations, the magnetic flux density approximately satisfies the divergence-free constraint.  
The magnetic diffusion solver solves the magnetic diffusion equation, which does not involve complex algorithms and can be implemented by modifying OpenFOAM's existing heat conduction solver. Detailed discussion will be presented in the following example to verify the computational accuracy of this part.
## 2 Results and Discussion
### Theoretical Investigation  
The geometries of five compounds (**1**, **2**, **3**, **6** and **11**) were optimized (Figure 5) using the HF (Hartree-Fock) method with the basis sets 3-21G. The calculation was performed with the Gaussian 03 program [31]. From Figure 5, the intramolecular hydrogen bond only existed in compound **6**, in which the distance between the hydrogen atom of the interacted site (-NH) and the oxygen atom of ketone group was 1.667 A. The intramolecular hydrogen bond did not exist in other compounds (**1**, **2**, **3** and **11**). According to the literature [32,33], the existence of an intramolecular hydrogen bond could improve the anion binding ability and so the anion binding ability of compound **6** was the strongest of the synthesized compounds. The above results indicate that multiple hydrogen bonds are important to anion binding ability.  
In addition, selected frontier orbitals for the synthesized compounds (**1**, **2**, **3**, **6** and **11**) were shown in Figure 6. We introduced molecular frontier orbital in order to explain the red-shift phenomenon of UV-vis absorption spectra in the host-guest interaction process by electron transition of the frontier orbital. For compound **1**, the highest occupied molecular orbital (HOMO) density was mainly localized on the 5-diaza-fluorenone moiety, while the lowest unoccupied molecular orbital (LUMO) density was localized on the whole molecular, which demonstrated that it was the electron transition of HOMO to arouse the red-shift phenomenon in the UV-vis spectra of host-guest. A similar result was also observed in compound **6**. For compounds **2**, **3** and **11**, the HOMO density was mainly localized on the 5-diaza-fluorenone moiety and 9-anthracene moieties, while the LUMO density was localized on the benzene cycle moiety, which demonstrated it was the electron transition of HOMO to arouse the red shift phenomenon. In addition, the frameworks of compounds **1-3** were identical based on 5-diaza-fluorenone derivatives. However, the electron cloudy densities were different due to the different substituent.  
Figure 5: The optimized structures of synthesized compounds (**1**, **2**, **3**, **6** and **11**).
# Time Series RUL Estimation of Medium Voltage Connectors to Ease Predictive Maintenance Plans
###### Abstract  
The ageing process of medium voltage power connectors can lead to important power system faults. An on-line prediction of the remaining useful life (RUL) is a convenient strategy to prevent such failures, thus easing the application of predictive maintenance plans. The electrical resistance of the connector is the most widely used health indicator for condition monitoring and RUL prediction, even though its measurement is a challenging task because of its low value, which typically falls in the range of a few micro-ohms. At the present time, the RUL of power connectors is not estimated, since their electrical parameters are not monitored because medium voltage connectors are considered cheap and secondary devices in power systems, despite they play a critical role, so their failure can lead to important power flow interruptions with the consequent safety risks and economic losses. Therefore, there is an imperious need to develop on-line RUL prediction strategies. This paper develops an on-line method to solve this issue, by predicting the RUL of medium voltage connectors based on the degradation trajectory of the electrical resistance, which is characterized by analyzing the electrical resistance time series data by means of the autoregressive integrated moving average (ARIMA) method. The approach proposed in this paper allows applying predictive maintenance plans, since the RUL enables determining when the power connector must be replaced by a new one. Experimental results obtained from several connectors illustrate the feasibility and accuracy of the proposed approach for an on-line RUL prediction of power connectors.  
remaining useful life; predictive maintenance; power connectors; ageing; ARIMA model; time series; degradation trajectory +
Footnote †: journal: Applied Sciences
# Transitioning Broadcast to Cloud
## 4 Transitioning Broadcast to Cloud
### Cloud-Based Broadcast and OTT Delivery System  
Finally, in Figure 11, we show an architecture, where pretty much all signal processing, transcoding, and multiplexing operations are moved to cloud.  
Figure 10: Hybrid architecture with ingest, playout and OTT/streaming workflow offloaded to cloud.  
In addition to running playout, this system also runs broadcast transcoders and the multiplexer in cloud. The final multiplex TS is then sent back to on-prem distribution system, but mostly only to be relayed to modulators and amplifiers or (via IP or ASI) to next tier stations or MVPD headends.  
To make this system work, in addition to all improvements mentioned earlier, what further needed is:  
* broadcast-grade transcoders and multiplexer should be natively implemented in cloud
* this includes implementation of statmux capability, generation and insertion of all related program and system information, possible addition of datacast service capability, etc.  
This architecture is indeed an extreme example, where pretty much all data- and processing- intensive operations are migrated to cloud. It is most technically challenging to implement, but is also most promising, as it enables best utilization of cloud and appreciation of all benefits that it brings.