esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Rain Rate Evaluation via Satellite Downlink Signal Attenuation Measurement
###### Abstract  
We present the NEFOCAST project (named by the contraction of \"Nefele\", which is the Italian spelling for the mythological cloud nymph Nephele, and \"forecast\"), funded by the Tuscany Region, about the feasibility of a system for the detection and monitoring of precipitation fields over the regional territory based on the use of a widespread network of new-generation Eutelsat \"SmartLNB\" (smart low-noise block converter) domestic terminals. Though primarily intended for interactive satellite services, these devices can also be used as weather sensors, as they have the capability of measuring the rain-induced attenuation incurred by the downlink signal and relaying it on an auxiliary return channel. We illustrate the NEFOCAST system architecture, consisting of the network of ground sensor terminals, the space segment, and the service center, which has the task of processing the information relayed by the terminals for generating rain field maps. We discuss a few methods that allow the conversion of a rain attenuation measurement into an instantaneous rainfall rate. Specifically, we discuss an exponential model relating the specific rain attenuation to the rainfall rate, whose coefficients were obtained from extensive experimental data. The above model permits the inferring of the rainfall rate from the total signal attenuation provided by the SmartLNB and from the link geometry knowledge. Some preliminary results obtained from a SmartLNB installed in Pisa are presented and compared with the output of a conventional tipping bucket rain gauge. It is shown that the NEFOCAST sensor is able to track the fast-varying rainfall rate accurately with no delay, as opposed to a conventional gauge.  
s 2017 17 1864 10.3390/s17081864 10.3390/s17081864 10.3390/s17081864
## 3 Results
### Case Studies of Fog Detection
#### 3.2.2 Low-Performing Cases  
On the night of 20 January 2021, warm air advected from the Yellow Sea by south-westerly to west-southwesterly winds ascended as it converged with a temperature trough over the Korean Peninsula. During this ascent, adiabatic cooling led to condensation of saturated air, forming widespread low-level clouds over the west coast and inland regions. From 21:00 on 20 January to 05:00 the following morning, the GK2A_FDA falsely detected these low-level clouds over the western region as fog (Figure 8a,b).  
Ground-based visibility meters reported no fog, while ceilometer data showed cloud base heights between 1.0 and 1.5 km (Figure 8b,c). This case represents a typical example of false fog detection during winter when stratiform low clouds frequently develop over the western Korean Peninsula. The GK2A CTH was estimated at approximately 2.5 km at night and around 1 km after 07:00 the next morning (Figure 8c). Surface RH remained consistently low at around 40%, with only minor fluctuations during the event, and air temperature, after decreasing following sunset, gradually increased once the low clouds were observed, indicating thermodynamic conditions unfavorable for fog formation (Figure 8d). One possible reason for misclassifying low clouds as fog in this case is related to an issue with \(\Delta\)FTs, the variable used to distinguish between fog and clouds. This misclassification is likely due to inaccuracies in the clear-sky brightness temperature field derived from the numerical model used to estimate the background surface temperature. This case highlights the algorithm's limitation in distinguishing fog from low stratiform clouds under certain conditions, underscoring the need to incorporate additional ground-based data, such as RH and wind, to reduce false alarms.
## 3 Occurrence of Microl plastics in Soils  
Microlastics are increasingly being detected in terrestrial environments, including soils. The sources, prevalence, and implications of microplastics in soils are areas of active research. The typical abundance of microplastics in soil varies widely depending on factors such as land use, the source of microplastics, and regional practices. Studies have reported a range of concentrations from a few hundred to thousands of microplastic particles per kilogram of soil [20].  
The use of plastic much films in agriculture is a significant source of microplastics. Over time, these films degrade into smaller particles that become incorporated into the soil. The application of biosolids (treated sewage sludge) and composts as fertilizers can also introduce microplastics into agricultural soils. These amendments often contain microplastics derived from wastewater and organic waste processing. Also, irrigation with water from sources contaminated with microplastics, such as rivers and lakes, can introduce these particles into agricultural soils [20]. Therefore, agricultural soils often contain higher concentrations compared to natural soils. However, the data are still sparse and not uniformly collected, making it challenging to provide a precise global average. For instance, data range from hundreds [21] to tens of thousands of particles per kilogram [22]. The most frequent shapes are fibres (from synthetic textiles), followed by fragments and then films (mulching) [23]. It is estimated that their concentrations could be 4-23 times higher in terrestrial compared to aquatic ecosystems [23]. Among the most prevalent polymers detected in agricultural soils are polypropylene (PP) and polyethylene (PE), while in industrial soils, polyvinyl chloride (PVC) is more common. These polymers are common in plastic films used in agricultural practices, such as mulching, which contribute significantly to microplastic pollution when they degrade or are mechanically broken down.  
The typical abundance of microplastics from biodegradable plastics in soil is not well documented, partly due to the complexity and variability of soil environments, the relatively recent focus on these materials [2], and the lack of analytical techniques for their detection [2]. However, biodegradable plastics, such as polyhydroxyalkanoateates (PHAs), polylactic acid (PLA), polybutylene succinate (PBS), and polybutylene adipate terephthalate (PBAT) starches and their blends are increasingly used in applications such as mulch films in agriculture, which will lead to their presence as microplastics in soils [24]. The fate and biodegradation of most typical bioplastics in soil is extensively reviewed in work of [PERSON] et al. [24].
# Multi-User AF Relay Networks with Power Allocation and Transfer: A Joint Approach
## 2 Preliminaries
### Energy Harvesting and Power Transfer Model  
At the \(i^{th}\) time slot, \(E_{k,i}\geq 0\), \(k=1\ldots N\) and \(E_{r,i}\geq 0\) are the amounts of harvested energy at \(S_{k}\) and \(R\) nodes, respectively, and we assume the energy profiles are non-casually known before data transmission [14]. Furthermore, during the \(i^{th}\) slot, each source node \(S_{k}\), transfers \(\delta_{k,i}\geq 0\) amount of energy to \(R\) with an efficiency of \(\beta_{k}\), \(0\leq\beta_{k}\leq 1\) by utilizing a dedicated control channel with channel coefficients \(f_{k,i}\). Thus, when node \(S_{k}\) has enough energy, it can share a part of its harvested energy with node  
where \(\delta\in\mathbb{C}^{N\times N_{T}}\). Since the objective function in (9) is non-concave, to resolve this, we adopt the following change of variables: \(\bar{P}_{k,i}=\ln P_{k,i}\) and \(\bar{W}_{r,i}=\ln W_{r,i}\). After the change of variables, we can rewrite the problem **(P.1)** as follows:  
\[\textbf{(P.2)} \max_{\bar{P}_{s},\bar{W}_{r},\delta}\bar{f}_{R}(\bar{\textbf{{P}} }_{s},\bar{\textbf{{W}}_{r}})\] (10) subject to \[\text{(C.1)}\ \sum_{i=1}^{m}e^{\bar{P}_{k,i}}\leq\sum_{i=1}^{m} \left(E_{k,i}-\delta_{k,i}\right)\ ;\] \[\text{for}\quad m=1,\ldots,N_{T}\,k=1,\ldots,N\ ; \tag{11}\] \[\text{(C.2)}\ \sum_{i=1}^{m}e^{\bar{W}^{r,i}}\leq\sum_{i=1}^{m} \left[E_{r,i}+\sum_{k=1}^{N}\beta_{k}\delta_{k,i}\right]\ ;\] \[\text{for}\quad m=1,\ldots,N_{T}\ ; \tag{12}\]  
\[\text{(C.3)}\ \sum_{i=1}^{m+1}(E_{k,i}-\delta_{k,i})-\sum_{i=1}^{m} \left(e^{\bar{P}_{k,i}}+P_{k,i}^{c}\right)\leq B_{k,max}^{s}\ ;\] \[\text{for}\quad m=1,\ldots,(N_{T}-1)\ ;\ k=1,\ldots,N\ ; \tag{13}\] \[\text{(C.4)}\ \sum_{i=1}^{m+1}\left[E_{r,i}+\sum_{k=1}^{N}\beta_{k} \delta_{k,i}\right]-\sum_{i=1}^{m}\left(e^{\bar{W}_{r,i}}+P_{r,i}^{c}\right) \leq B_{r,max}\ ;\] \[\text{for}\quad m=1,\ldots,(N_{T}-1)\, \tag{14}\]  
where \(\bar{\textbf{{P}}}_{s}\in\mathbb{C}^{N\times N_{T}}\), \(\bar{\textbf{{W}}}_{r}\in\mathbb{C}^{N\times N_{T}}\), \(\bar{f}_{R}(\bar{\textbf{{P}}}_{s},\bar{\textbf{{W}}}_{r})=\frac{1}{2}\sum_{ j=1}^{N}\sum_{i=1}^{N_{T}}\log_{2}(1+\bar{\Upsilon}_{j,i})\) and \(\bar{\Upsilon}_{j,i}=\frac{e^{\bar{P}_{j,i}+\bar{W}_{r,i}}|\bar{S}_{j,i}h_{j,i }|^{2}}{\bar{\Upsilon}_{1}+\bar{\Upsilon}_{2}}\), where \(\bar{\Upsilon}_{1}\) and \(\bar{\Upsilon}_{2}\) are defined as  
\[\bar{\Upsilon}_{1} =e^{\bar{W}_{r,i}}\sum_{k=1,k\
eq j}^{N}e^{\bar{P}_{k,i}}|g_{j,i} h_{k,i}|^{2}+e^{\bar{W}_{r,i}}|g_{j,i}|^{2}\sigma_{r}^{2}\ ; \tag{15}\] \[\text{(C.1)}\ \sum_{i=1}^{m}e^{\bar{P}_{k,i}}\leq\sum_{i=1}^{m} \left(E_{k,i}-\delta_{k,i}\right)\ ;\]0  
After applying the above change of variables, \(\eqref{eq:P.2}\) and \(\eqref{eq:P.2}\) in **(P.2)** are convex, while \(\eqref{eq:P.2}\) and \(\eqref{eq:P.2}\) become non-convex.
# A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals
## 2 Methods
### Scalar Tracking
#### 2.2.3 Optimizing the Loop Filter
In real-time GNSS tracking scenarios, these conditions are often difficult to satisfy due to the complexity of signal environments. As such, continued research into robust and adaptive filtering techniques, as well as improved state modeling under dynamic and uncertain conditions, is essential to further enhance GNSS receiver performance. Table 4 provides a structured comparison of the main loop filter optimization techniques used in GNSS tracking systems. It highlights each method's strengths and drawbacks, helping to contextualize their applicability across different scenarios.
## 2 Experimental Section
### Measuring Device
#### 2.1.2 Infrared Camera  
The digital camera used for measurements is shown in Figure 3. It was a commercial ordinary infrared camera consisting of an optical lense, a CMOS (complementary metal oxide semiconductor) image sensor, and a signal processing circuit board. The camera model was KS1.3A142, produced by Shenzhen Kingsen Technology Co., Ltd. of China, and the maximum resolution of the camera was 1280 \(\times\) 960 pixels. The lense was equipped with an 850 nm narrow-band filter, which could manually adjust the focal length to increase the clarity of the suspension image. The image acquired by the infrared camera was a single-channel image. These images are similar to grayscale images and avoid color interference from the test solution.  
With the designed PC software, we could change the properties of the image by adjusting the camera parameters, such as the hue, saturation, and white balance, and ensure that the whole  
Figure 1: Structure diagram of the vitamin C (VC) measuring device based on an infrared camera.  
Figure 2: Light-emitting diode (LED) constant current driving circuit diagram.  
measurement range had a good discrimination by adjusting the camera parameters of exposure, brightness, and contrast. These parameters of the camera were automatically saved to the registry of the system after setting. The same parameters should be used in the calibration and measurement process to ensure the accuracy of the measurement. Regarding camera selection, as long as the camera can manually change the focal length, disable the function of automatic exposure and brightness, and can adjust the exposure to ensure the consistency of the parameters during the measurement process, it can be used in the measurement system.  
The CMOS sensor used in the digital camera was a semiconductor component that was used to record light changes. Each pixel of the camera is equivalent to a photo detection element. When the light from the light source passed through the suspension, it was filtered by the 850 nm filter, and then projected onto the CMOS sensor. The signal processing component integrated in the camera processed the voltage data from the CMOS sensor to obtain the digital signal, and the digital signal was transmitted to the upper computer software through the USB port for further processing. The use of the digital camera replaces the photoelectric sensor, optical system, signal amplification, signal acquisition, analog-to-digital conversion circuit, and signal processing circuit that are required by other optical instruments. The design of the instrument is simplified, and measurement process can be visualized.
# An Ensemble Model for Multi-Level Speech Emotion Recognition
## 3 Proposed Method
### Expert 2: GRU Model Combining the Attention Mechanism
#### 3.3.2 Design of GRU Model Combined with the Attention Mechanism  
GRU is an enhanced version of the LSTM network, which is simpler and has a very streamlined structure. As a variant of LSTM, GRU can also solve the problem of long dependence in RNN networks. Unlike LSTM, there are 2 types of gates in the GRU model: update gate and reset gate.  
The GRU model combines the forgotten gate and input gate into a single update gate, and merges the cell state with the hidden state. The update gate is used to control the extent to which the status information of the previous moment is brought into the current state. The amount of information that the previous state is written to the current candidate set is controlled by the reset gate. The update gate also mixes the cell state and hidden state, and simplifies the standard version of the LSTM model through formal changes.  
Considering that the energy of speech is a process of concentrated explosion in a short time, it is possible to focus on the key areas of the outbreak for feature selection and training, and to increase the contribution of such areas. Therefore, on the basis of the GRU model, the attention mechanism is added, which is a selection mechanism for assigning limited information processing capabilities that helps to quickly analyze target data and cooperates with information screening and weight setting mechanisms to improve the computing power of the model.  
For each vector \(x_{i}\) in the sequence of input \(x\), attention weight \(\alpha_{i}\) can be calculated according to Equation (2), where \(f(x_{i})\) is the scoring function:  
\[\alpha_{i}=\frac{\exp(f(x_{i}))}{\sum_{j}\exp(f(x_{j}))} \tag{2}\]  
The output of the attention layer, _attentive_x_, is the sum of the weights of the input sequences, as shown in Equation (3):  
\[\textit{attentive\_x}=\sum_{i}\alpha_{i}x_{i} \tag{3}\]  
The second expert chooses to introduce the attention mechanism after the 2-layer bi-directional GRU model. The specific model structure is shown in Figure 2.  
As shown in Figure 2, the spectrum map is first processed in order to reduce the dimension, merge vertically, and average the PCA. After experiencing the 3-layer bi-directional GRU model, the attention mechanism is fed to obtain the salient features. Then, the full connection layer is experienced, and the judgment result of each emotion is obtained as the discrimination result of the second expert.
## 4 Experimental Setup
### Experimental Details  
**Evaluation metrics**: For DailyDialog, since the \"neutral\" label is the majority in the dataset, we follow the method of [PERSON] et al. [47] and [PERSON] et al. [25] to ignore the label \"neutral\" when calculating the results, and use micro-F1 as the evaluation metric for this dataset. For MELD, EmoryNLP and IEMOCAP, we use weighted-average F1 as the evaluation metric.  
**Parameter setting**: In our experimental setting, to extract word embeddings, we use \(BERT_{base}\) as a pre-trained model, and the dimension of all hidden vectors is equal to 256 for easy calculation. In order to learn hyperparameters faster and avoid gradient disappearance or gradient explosion, the optimizer adopts Adam [41] to optimize the parameters in the model with a learning rate of \(1\times 10^{-4}\). The dropout rate for all datasets is 0.5, we set the epoch to 100 and the batch size to 128. We stop training if the validation loss does not decrease for 20 consecutive epochs. For all experiments, we choose the model that performs best on the development set and then evaluate it on the test set.  
For the implementation, we used PyTorch, a full-featured framework for building deep learning models written in python. In addition to that, we use Python programming language [48]. Furthermore, as hardware, we use two systems; GPU is RTX 3090 and the CPU is Intel i7-12700.
# Improvement for Convolutional Neural Networks in Image Classification Using Long Skip Connection
## 4 Experiments and Results Analysis
### Case Study on the Reduced Version of ImageNet  
We also test the M-DenseNet and DenseNet families without/with long skip connection and s-conv. We use another dataset, which is a reduced version of ImageNet [27] with 100 classes and about 500 images in each class. The dataset is also accessible via the same github link above (Section 4.1.1). All models are trained by the same technique with extra augmentation [28; 29; 30] and normalization [31]; batch size is 32, Adam optimizer, 60 epochs and learning rate is \(10^{-3}\).  
Table 3 presents the value of accuracy and FLOPs in comparison between DenseNet and M-DenseNet. By observation, models with long skip connection converge faster, as shown in Figure 11 and has a better overall result. Parameters and FLOPs of M-DenseNet family equal roughly 80% of original DenseNet, while the accuracy increases about 3-5%.  
Figure 10: Image prediction final result.  
Testing usage with other models: We make modifications to other models, similar to what we have done to create MDenseNet in Section 3.7. The models are EfficientNet [23] and MobileNet v1, v2, v3 [13; 18; 19]. We use a reduced version of ImageNet and training technique the same as that explained in Section 4.2. Below is the detail of comparison.  
Figures 12 and 13 point out that our modifying models perform better than the original ones with small decrease in FLOPs (6-3% for MobileNet and 2-1% for EfficientNet). This proves the effectiveness of the long skip connections to performance of CNN.  
Figure 11: Comparison between DenseNet121 and M-DenseNet121 in terms of model validation accuracy from epoch 0 to 30.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameter** & **FLOPS** & **Accuracy on** \\  & **(10\({}^{6}\))** & **(10\({}^{6}\))** & **Validation set** \\  & & & **(\%)** \\ \hline DenseNet121 & 7.14 & 14.11 & 73.72 \\ DenseNet169 & 12.80 & 25.30 & 74.54 \\ DenseNet201 & 18.51 & 36.57 & 75.65 \\ M-DenseNet121 & **5.50** & **10.83** & 78.56 \\ M-DenseNet169 & 10.41 & 20.50 & 78.86 \\ M-DenseNet201 & 15.71 & 30.94 & **79.42** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy and FLOPs achieved by M-DenseNet in comparison with DenseNet.  
Figure 12: Comparison between MobileNet and (M) MobileNet (V1-V3, right to left, respectively).
###### Abstract
They are not adaptive, requiring a previous off-line training using the backpropagation algorithm (BP). Some researchers directly claim limitations of the MLP by generating the reference signals, due to the diversity of loads and kinds of electric disturbances [24; 33]. Even the works which conclude supporting the capabilities of this ANN type show poor results [32] and weaknesses--it is difficult to apply a training capable of covering high order harmonics, as well as the possible practical frequency deviations. It is also worth mentioning the numerous methods combining fuzzy logic and ANNs in different ways. As an example, in [37], a combination of fuzzy and neural network techniques is used for improved DC voltage control by an APF, where a fuzzy logic controller extracts the training dataset for the neural network. In [38], an adaptive backstepping fuzzy neural network controller is designed to suppress the harmonics in a SAPF. In this paper, the main features are as follows. Firstly, a new MLP-based control method for three-phase shunt APFs, able to obtain the reference signals, is proposed. It will prove that this ANN type can provide an alternative tool, which will allow taking advantage of the benefits of MLPs, such as simplicity, no need of online training, fast response, and the absence of internal feedback connections within the network. Secondly, the strategy of use of the ANN consists of estimating the Fourier coefficients of the fundamental harmonic of phase currents and voltages, enabling higher MLP learning capacity than different methods tested by the authors. Thirdly, an effective training method was designed, in which the waveform patterns are automatically generated using random combinations of many different harmonics. Variable fundamental frequencies were included in the patterns, representing the possible nominal frequency deviations in the grid, ensuring a robust control response in different real cases. This method has shown to be more efficient for the MLP learning than any other previously applied. It provides a deep training with a high, but limited, number of patterns. Fourthly, for a compensation strategy that pursues harmonic distortion mitigation and power factor correction, the power theory was applied to develop the computation steps, from the coefficients of every phase current and voltage that lead to the reference currents. The method also will be able to correct the currents' unbalance, in the case of balanced supply voltages. The paper is structured as follows. In Section 2, the fundamentals of the control strategies are described. Section 3 addresses the explanation of the neural network configuration and the training steps carried out. In Section 4, practical cases are simulated and thoroughly tested using the MATLAB-Simulink platform, and a practical case is tested in laboratory. Finally, the conclusions highlight the contributions of the work.
# O-Band Grating Couplers Using Silicon Nitride Structures
###### Abstract  
To test silicon photonics component performances, a silicon (Si) grating coupler (GC) is used to couple the light from a single-mode fiber (SMF) into the chip. However, silicon nitride (Si\({}_{3}\)N\({}_{4}\)) waveguides have recently become more popular for realizing photonic integrated circuits (PICs), which may be attributable to their exceptional characteristics, such as minimal absorption and low back reflection (BR) in the O-band spectrum. Thus, to test the photonic chip, a waveguide converter from Si\({}_{3}\)N\({}_{4}\) to Si needs to be added to the photonic circuit, which can lead to more power losses and BR. To avoid this conversion, we propose in this manuscript a configuration of a GC based on Si\({}_{3}\)N\({}_{4}\) structures, which can be employed to minimize the footprint size and obtain better performance. The achievement of high efficiency was possibly obtained by optimizing the structural properties of the waveguide and the coupling angle from the SMF. The results demonstrated high efficiency within the O-band spectrum by using a wavelength of 1310 nm. Notably, at this specific wavelength, the findings indicated a coupling efficiency of \(-5.52\) db. The proposed design of the GC consists of a uniform grating that offers improvements regarding affordability and simplicity in manufacturing compared to other GC models. For instance, using a reflector or a GC with non-uniform grooved teeth introduces challenges in fabrication and incurs higher costs. Thus, the proposed design can be useful for improving the testing abilities of the Si\({}_{3}\)N\({}_{4}\) photonic chips used in transceiver systems.  
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Footnote †: journal: Applied Sciences  
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e-mail: [EMAIL_ADDRESS]