esa-sceva/satcom-chunk-collection huggingface dataset:
# The Feasibility of Shadowed Image Restoration Using the Synthetic Aperture Focusing Technique
## 2 Experimental Setup
### Laboratory Manipulation  
For the process of data acquisition, the model is soaked into a water tank to perform an immersed scan (Figure 1b). The P-wave velocity in water is preme  
Figure 1: (**a**) A picture of the created model. (**b**) Scales of the model and the relative positions of the targets to be scanned. Capital T stands for the target, and the scripts of OBS and the numbers denote the obstacle and the positions of different targets to be imaged. SP\({}_{\text{OBS}}\) and SP\({}_{\text{FR}}\) are two specific scan positions to perform obstacle and obstacle-free scans.  
active transducer used to facilitate our laboratory manipulation is an Olympus (V204-RM 2.25 MHz/6 mm) [18]. The dominant frequency of the active transducer thus makes the wavelength of the propagating P-wave 0.6 mm; hence, the dimensions of TOSB and TN are approximately ten times and equal to the dominant wavelength, respectively.  
For the process of data acquisition, a PANAMETRICS 5058 pulser-receiver is adjusted to be either in single probe or a pulse/echo mode to perform a B-mode scan and a pitch/catch scan, generate an electric pulse, receive the electric signal, and synchronize (Sync) the digital oscilloscope (Tektronix DPO3034) [19; 20]. The digitized radio frequency (RF) signal is then downloaded from the digital oscilloscope via a general-purpose interface bus (GPIB) and IEEE-488 communication to a personal computer for further processing. When the digitized echo signal is created, the transducer is automatically moved to the next location.
# An ERNIE-Based Joint Model for Chinese Named Entity Recognition
## 5 Discussion  
Firstly, using pre-training models can improve the performance of Chinese NER significantly without utilizing any external resources. The pre-training models have learned abundant prior semantic knowledge from the pre-training corpora (e.g., Baidu News) [8], which can also be known as the \"source domain\". The task-specific semantic knowledge will also be obtained during fine-tuning from the training set of the downstream task, which can also be known as the \"target domain\". The whole process can be regarded as transfer learning. However, the deep learning models only obtain the semantic knowledge from the \"target domain\". The training process is done from scratch, whether it is the baseline model (BiGRU-CRF) or other deep learning models.  
Secondly, ERNIE outperforms BERT in Chinese NER task. As mentioned before, BERT can only obtain the character-level representations of Chinese through the character-level masking strategy during pre-training. However, the knowledge masking strategy of ERNIE can learn the relationship between Chinese entities and long semantic dependency implicitly. Therefore, the representations of tokens generated by ERNIE contain the prior semantic knowledge of entities and phrases, which can make the model has better generalization and adaptability.  
Thirdly, the performance of Chinese NER can be improved when utilizing both the sentence-level and token-level features. ERNIE only uses the token-level features when conducting the NER task. The representation of [CLS] can be regarded as the sentence-level feature, but it does not participate in the computation of the cost function. The ERNIE-Joint model we proposed utilizes both the sentence-level and token-level features by a unique cost function. The experimental results show that introducing sentence-level features through joint training can improve the performance of the NER task. Moreover, the classification method we proposed makes the raw NER datasets suitable for text classification tasks, and these datasets can be applied to a joint training model without additional annotations.  
Finally, given that ERNIE-Joint introduces the cross-entropy errors in classification task when calculating the loss function, its running time will be higher than that of ERNIE. As shown in Figure 5, the running time of ERNIE-Joint is higher than that of ERNIE in each epoch except for the fourth epoch. In the fourth epoch, the running time of ERNIE may be affected by the running environment of a computer. The relatively high running time can be regarded as the drawback of our model.  
Figure 5: The running time of ERNIE-Joint and ERNIE.
# Gaze Tracking Using an Unmodified Web Camera and Convolutional Neural Network
## 4 Experiments and Results
### Discussion  
The experiments presented in Section 4.2 show that it is possible to achieve decent eye-tracking results even for unconstrained environments and using low-quality web cameras. Of course, the results are far from perfect--we checked only if one of the 20 areas could be determined based on eye image--but this outcome may be sufficient for many human-computer interface (HCI) applications.  
The first experiment proved that sharpening the acquired image has a positive impact on further classification (accuracy increased from 77% to 81%). Therefore, all following experiments used the sharpened versions of the images. Not surprisingly, the results for the Dataset 2 were worse than for Dataset 1 for every experiment. However, it is worth noticing that these results are also quite good and had between 72% and 80% accuracy depending on the architecture. It should be remembered that random guessing for 20 classes provides about 5% accuracy, so the results are noticeably better than random ones.  
Interestingly, the best results (over 88% accuracy) were achieved for the architecture in which only images of the left eye were used (ARCH-L) (Table 5). The main reason for this phenomenon probably was that the network that uses two eye images and concatenates layers (ARCH-RL) was more complicated than ARCH-L, and 70 epochs were not enough to train it sufficiently.  
Another interesting finding was the good accuracy of the model that uses face images instead of eyes. The model obviously has more data to analyze, but a large percentage of this data is probably irrelevant. Due to more extensive input, this model was also more challenging to train; the training process lasted significantly longer than for other models. However, good results suggest that eye detection algorithms may be omitted in some applications.
# Wake-Up Receiver-Based Routing for Clustered Multihop Wireless Sensor Networks
## 4 Proposed Approach
### Wake-Up Packet Addressing  
In our approach, wake-up signals consisting of carrier burst, preamble, and address pattern are used and shown in Figure 4. We assign WuRx addresses made up of a pattern of 16 bits to the sensor nodes equipped with a WuRx. The first 8 bits of the address represent the assignment to a cluster. The remaining 8 bits represent the unique address of the individual sensor nodes within the cluster. This results in a two-part address for multicasting. In order to use the WuRx address required for multicast wake up, the Fog Node accesses a table in which the multicast WuPt addresses are stored. It should be noted  
Figure 3: Wake-up Receiver equipped sensor node used in this work.  
that 8 sensor nodes with WuRx are assigned to each cluster. This means that for each of the sensor nodes, there is a unique assignment of the rear 8 bits of the WuRx address.  
To illustrate this, based on the scenario outlined in Figure 2, in the first cluster using two cluster nodes as relay, Figure 5 is given. It can be seen that the Fog Node needs to wake up sensor nodes 2 and 4 for the data communication of cluster node 5, as these nodes must act as relays. This results in the address to be used for the WuPt for the Fog Node. According to the previous description, the bit corresponding to cluster 1, i.e., bit 8, is now set in the multicast WuPt and highlighted in the Figure with the blue outline. To wake up the individual sensor nodes 2, 4, and 5, bits 1, 3, and 4 are set to 1 in the rear half of the address pattern according to the assignment as illustrated with the blue arrows. This will generate the multicast address shown in the graphic.  
If a sensor node equipped with a WuRx receives the described WuPt, a bit-by-bit comparison of the addresses is started. Therefore, if the WuPt pattern for the cluster assignment bit (and the unique bit in the rear part if the address match those of the sensor node), an interrupt is generated and the entire sensor node is activated. If there is no positive bit matching, the sensor node goes into sleep mode.  
Our proposed smart addressing scheme offers the possibility to activate nodes in the routing path without increasing delays. Thus, we achieve a very prompt and energy-efficient sensor awakening.
###### Abstract  
Preharvest sprouting (PHS) is a key global issue in production and end-use quality of cereals, particularly in regions where the rainfall season overlaps the harvest. To investigate transcriptomic changes in genes affected by PHS-induction and ABA-treatment, RNA-seq analysis was performed in two wheat cultivars that differ in PHS tolerance. A total of 123 unigenes related to hormone metabolism and signaling for abscisic acid (ABA), gibberell acid (GA), indole-3-acetic acid (IAA), and cytokin were identified and 1862 of differentially expressed genes were identified and divided into 8 groups by transcriptomic analysis. DEG analysis showed the majority of genes were categorized in sugar related processes, which interact with ABA signaling in PHS tolerant cultivar under PHS-induction. Thus, genes related to ABA are key regulators of dormancy and germination. Our results give insight into global changes in expression of plant hormone related genes in response to PHS.  
Keywords:wheat (_Tritcium aestivum_); pre-harvest sprouting (PHS); phytohormones; differentially expressed genes (DEGs); transcriptomes +
Footnote †: journal: Applied Sciences
## 5 Performance Evaluation and Analysis
### Learning Curves  
The learning performance is illustrated through two key graphs. The first graph (Figure 8(a)) shows the loss curves for both the critic and actor components. The critic  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer (Type)** & **Output Shape** & **Parameters** \\ \hline Conv1d & \([-1,32,6]\) & 128 \\ SiLU & \([-1,32,6]\) & 0 \\ MaxPool1d & \([-1,32,3]\) & 0 \\ Conv1d & \([-1,64,5]\) & 6208 \\ SiLU & \([-1,64,5]\) & 0 \\ MaxPool1d & \([-1,64,2]\) & 0 \\ GRU & \([[-1,64,8],[-1,64,8]]\) & 0 \\ Flatten & \([-1,512]\) & 0 \\ Dropout & \([-1,512]\) & 0 \\ Linear & \([-1,64]\) & 32,832 \\ ReLU & \([-1,64]\) & 0 \\ Dropout & \([-1,64]\) & 0 \\ Linear & \([-1,32]\) & 2080 \\ ReLU & \([-1,32]\) & 0 \\ Linear & \([-1,2]\) & 66 \\ Linear & \([-1,32]\) & 1056 \\ ReLU & \([-1,32]\) & 0 \\ Linear & \([-1,1]\) & 33 \\ \hline \hline \end{tabular}
\end{table}
Table 6: A2C model architecture.  
Figure 8: A2C model training and testing datasets. (**a**) A2C Model Performance on Training Dataset. (**b**) A2C Model Performance on Testing Dataset.  
loss consistently decreases over 100 epochs, indicating enhanced accuracy in evaluating state-action pairs, while the actor loss remains low and stable, reflecting effective policy learning. The second graph (Figure 9b) presents the reward progression, which indicates the model's success in maximizing network efficiency. The rewards steadily increase, demonstrating the model's improving performance in optimizing resource allocation. This upward trend signifies the model's growing ability to enhance V2X communication reliability and minimize resource wastage and collision.  
The convergence of the proposed DRL method is demonstrated in Figure 9, where we observe a significant decrease in critic loss over the course of 100 epochs, stabilizing near zero. The actor loss remains consistently low, further supporting the convergence of the model. Additionally, the rewards graph shows a steady increase, indicating that the model is learning effectively and approaching optimal performance. The reduction in losses and the increase in rewards over the epochs confirm that the model has converged successfully based on the number of trials conducted.
## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Feature Sets** & **Dataset and Methodology** & **Classifiers** & **Notable Results and Remarks** \\ \hline [PERSON] and [PERSON] [30] & MFCCs; normalized pitch frequency; normalized phase cepstral coefficients & ITU-T P-Series (10 speakers, 16 sentences and phone recordings)—SV with added noise & CNN & Combination of other features with MFCCs leads to better performances on noisy data and state-of-the-art performances on the dataset considered. \\ \hline [PERSON] et al. [31] & MFCCs, RASTA, LPCC, PLP, Mel-PLP, BFCC & \begin{tabular}{c} 25 speakers, \\ 4500 utterances—speech recognition \\ \end{tabular} & HMM & \begin{tabular}{c} Features only considered separately: \\ MFCCs provide the best accuracy. \\ No assessment of noisy data. \\ \end{tabular} \\ \hline [PERSON] et al. [32] & MFCCs, LPC & \begin{tabular}{c} Various, studying \\ augmentation techniques for \\ children speech recognition. \\ \end{tabular} & General & \begin{tabular}{c} The union MFCCs + LPC brings \\ better performances; proposal of \\ frame concatenation for the temporal \\ evaluation of features. \\ \end{tabular} \\ \hline [PERSON] and [PERSON] [29] & MFCCs, BFCC, PLP, 198 utterances—SR & SVM & \begin{tabular}{c} Best-performing system on noisy \\ data is the one trained on MFCCs, \\ BFCC and RASTA. \\ \end{tabular} \\ \hline [PERSON] et al. [33] & MFCCs, RASTA & \begin{tabular}{c} MEPCC (60 speakers, \\ 360 utterances)—SV \\ \end{tabular} & GMM & \begin{tabular}{c} RASTA + MFCCs are found to be \\ better in noisy environments; \\ proposal of a quadrilateral filter bank for GMMs. \\ \end{tabular} \\ \hline [PERSON] and [PERSON] (this paper) & MFCCs, RASTA & \
\begin{tabular}{c} DECMS (56 speakers, \\ 8547 utterances)—SV with \\ added noise and reverb \\ \end{tabular} & CFS + SVM, RF, Adaboost &
\begin{tabular}{c} Accuracy of SV decays with noise or \\ reverb in validation data. MFCCs \\ worsen sensitivity on noisy data, and \\ RASTA features worsen specificity. A \\ merged set is the optimal compromise. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Relevant studies from the recent years tackling the problem of noisy speech data in recognition tasks, and the usage of alternative feature domains alongside MFCCs.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 1 Introduction
### Motivation  
We note that there is extensive research on the behavior of millimeter-wave propagation in indoor environments, while for UHF radio waves, we rely mainly on the results of work performed in the 1990s and mostly with continuous wave (CW) transmissions. In addition, the research on LoRa and Zigbee propagation is sparse and not very specific on path loss models. Similarly, research on the behavior of LoRa and Zigbee BER (Bit Error Rate) with distance is scarce, and its possible correlation with a path loss model has not yet been studied. Furthermore, the relationship between the propagation of a 5G transmission with a LoRa and Zigbee transmission has also not been studied in depth.  
Therefore, the main contribution of this work, motivated by the lack of similar studies in the literature, is to design a path loss propagation model for the WSN radio link for different technologies in an indoor environment, specifically a corridor, employing the license-exempt ISM (Industrial, Scientific and Medical) 868 MHz frequency band, and relate it to the BER value measured in order to be able to predict the BER with the measured power, and vice-versa. This is possible because, as discussed in [34], there is a strong relationship between the received power, Zigbee RSSI and the BER value.  
To achieve this aim, it will be necessary to accurately model the relationship between power and distance for the different transmissions (narrowband, LoRa, Zigbee and 5G), so two adjustment models (CI and FI) will be proposed, and the accuracy and behavior of each one will be analyzed. After that, the power of the most accurate model will be correlated with the BER value measured at each point, and some received power margins will be established for each technology for which there will be a BER higher than 5%.  
The paper is further organized as follows. Section 2 describes the environment where the measurements are carried out and the equipment that was used. Section 3 describes the methodology carried out for the measurements with each technology and the metrics used. Section 4 describes the radio channel models used. Section 5 details the results obtained from the measurements and discusses their interpretation, and finally, Section 6 contains the conclusions of this research work.
## 3 Results and Discussion
### Dietary Fiber  
Dragon fruit has a relatively high level of dietary fiber, so the increasing proportion of dragon fruit in white bread formulations, steamed buns, and donuts results in increased fiber levels; see Figure 2a. The dietary fiber of the control sample without red dragon fruit puree was 1.27%, 1.25%, and 1.25% for white bread, steamed buns, and donuts, respectively. The presence of red dragon fruit puree increased the dietary fiber of white bread (1.32-2.03%), steamed buns (1.27-1.96%), and donuts (1.27-1.95%), which was considerably higher than their respective controls. The statistical analysis shows significant differences  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Puree Concentration**} & \multicolumn{4}{c}{**Parameters ***} \\ \cline{3-6}  & & **L*** & **a*** & **b*** & **C** & **Hue** \\ \hline \multirow{6}{*}{White bread} & 10 & 89.9 \(\pm\) 0.2 f  & 26.8 \(\pm\) 0.5 a  & 13.5 \(\pm\) 0.5 a  & 29.3 \(\pm\) 0.9 a  & 24.8 \(\pm\) 0.7 a  \\  & 20 & 75.3 \(\pm\) 2.5 e  & 24.9 \(\pm\) 1.5 b  & 10.3 \(\pm\) 0.5 b  & 22.7 \(\pm\) 1.3 b  & 10.5 \(\pm\) 0.4 b  \\  & 30 & 72.7 \(\pm\) 1.7 d  & 26.8 \(\pm\) 1.3 c  & 15.5 \(\pm\) 1.1 c  & 28.3 \(\pm\) 1.5 a  & 23.2 \(\pm\) 2.4 c  \\  & 40 & 64.5 \(\pm\) 1.02 c  & 34.9 \(\pm\) 1.2 d  & 7.6 \(\pm\) 1.4 d  & 35.8 \(\pm\) 0.9 c  & 12.4 \(\pm\) 1.2 d  & 50 \\  & 50 & 60.2 \(\pm\) 1.9 b  & 32.1 \(\pm\) 2.9 e  & 11.8 \(\pm\) 0.9 e  & 33.5 \(\pm\) 1.3 d  & 19.9 \(\pm\) 1.3 e  \\  & 60 & 58.6 \(\pm\) 1.4 a  & 33.7 \(\pm\) 1.4 f  & 9.1 \(\pm\) 0.4 f  & 34.7 \(\pm\) 1.5 d  & 14.7 \(\pm\) 1.3 f  \\ \hline \multirow{6}{*}{Donut} & 10 & 87.9 \(\pm\) 0.2 f  & 24.8 \(\pm\) 0.7 a  \\  & 20 & 75.3 \(\pm\) 2.5 e  & 22.7 \(\pm\) 1.3 b  & 10.3 \(\pm\) 0.5 b  & 22.7 \(\pm\) 1.3 b  &in the dietary fiber content as affected by red dragon fruit puree addition. The findings are consistent with previous research [38], which indicated that the fiber content in biscuit products increased with the increased dragon fruit use. Additionally, fortifying oat-wheat bread with red dragon fruit has been shown to elevate dietary fiber levels [39]. Although the presence of fiber in bakery products may negatively affect certain quality parameters, e.g., increasing hardness, decreasing specific volume, and others [40], fiber is beneficial for human health, such as reducing the risk of obesity, diabetes, cancer, and intestinal diseases [41]. Therefore, the incorporation of dragon fruit into bakery products has the potential to position them as functional foods.
## 3 Materials and Methods
### Vehicular CO Prediction Model
#### 3.4.5 GIS Modelling  
Collecting traffic CO data, especially on the highways, is dangerous and expensive. Therefore, predicting traffic CO concentrations on highways helps to generate traffic CO data that can be used for further studies. In this research, the measured and predicted traffic CO concentrations are used for GIS modelling based on a grid analysis. GIS modelling and mapping are mainly applied to assess affected people and environments due to inappropriate traffic emissions from traffic activities. The observed traffic CO samples are an important factor in the model, for computing the relationship between the predictive factors and carbon concentrations which were applied in the training data, and for the validation process. After training the NN model with the CO measurements from the field, the NN model produced a regression equation based on weighted values for each predictor factor, in order to calculate the predicted values based on the predictor factors that can be easily applied in a GIS platform; this is the main contribution of the proposed model. This equation has been applied using GIS to produce a spatial prediction of traffic CO concentration in the study area. This step was applied  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Search Domain** \\ \hline Type of network & MLP, RBF \\ Number of hidden units & (3–40) \\ Training Algorithm & BFGS, RBFT \\ Hidden Activation & Identity, Logistic, Tanh, Exponential, Gaussian \\ Output Activation & Identity, Logistic, Tanh, Exponential, Gaussian \\ Learning rate & (0.1, 0.5) \\ Momentum & (0.1–0.9) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyper parameters of the proposed model for traffic CO prediction and their search spaces used for fine-tuning.  
by implementing the final prediction equations on the 5 \(\times\) 5 m GRID, using ArcGIS tools which are very efficient for spatial representation [23].
## 2 Non-Orthogonally Multiplexed Cognitive Radio (NOMCR) System
### Fast Convolution Filtered OFDM Based-NOMCR System  
Fast convolution processing based filtered multicarrier systems can provide enhanced data rates as well as lower out-of-band (OOB) radiation, the latter being attributed to the use of suitable pulse shaping filters. Fast convolution filtered OFDM (FC-F-OFDM) seems to be an attractive choice for the design of NOMCR system. Hence in this work, we propose to apply fast convolution-based filter-bank processing instead of the conventional OFDM modulation.  
In FC-F-OFDM transmitter, multiple narrow-band low-rate signals denoted by \(\mathbf{x}^{\mathrm{su}}\mathrm{m}\), where \(\mathrm{m}=0,1,\ldots,\mathrm{M}-1\), are filtered by pulse shaping filters with adjustable frequency responses and adjustable sampling rates. The filtered signals are then multiplexed into a single wideband signal, denoted by \(\mathbf{s}^{\mathrm{su}}\). This is referred to as a synthesis filter bank (SFB). At the receiver side, the dual operation called analysis filter bank (AFB) takes place. In AFB, the incoming high rate wideband signal is split into multiple narrow-band signals and reverse operations to the transmit side processing take place to recover the transmitted data. Details of the transmitter and receiver side processing operations in FC-F-OFDM system can be obtained from References [9; 10]. In this section, the mathematical equations describing the FC-FB transmitter and receiver are mentioned briefly.