esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Odor Profiles of Grain During the Ripening of Rice
1-Nonanol made a significant contribution to the aroma of brown rice, especially aromatic rice. It was also found in wild rice, providing floral and grassy aromas [43]. Therefore, 2-AP, heptanal, and 1-nonanol are key-marker metabolites that differentiate aromatic from non-aromatic brown rice. The remaining 37 VOCs (Table S7), which also varied by grain maturity but did not significantly differ between aromatic and non-aromatic varieties, form the fundamental VOC profile contributing to the natural aroma of rice. Among these, 5-ethyl-3-hydroxy-4-methyl-2(5H)-furanone and 2-methyltetrahydroofuran-3-one had the highest rOAVs across all four stages, imparting green-fruit, sweet, maple-sugar, and nutty notes, which are characteristic of food flavors [44]. Aldehydes are among the most important volatiles in rice; a total of 15 aldehydes contribute to the flavor of rice. Previous research has indicated that hexanal, pentanal, and (E)-2-octenal are the primary compounds responsible for the flavor differences between aromatic and non-aromatic rice [6; 8]. Furthermore, the hexanal content in ground rice is higher that than in whole rice [45]. Other VOCs, such as heterocyclic compounds, esters, alcohols, terpenoids, and ketone, also affect the aroma of rice. The heterocyclic compound 2-pentylfuran, which imparts a strong nutty and bitter-like aroma, has also been identified in rice and cooked rice [17; 46]. It is produced from an oxidation product of methyl linoleic [47]. Alcohol contributed greatly to the odor of rice. 1-Octen-3-ol, known for its raw mushroom-like odor, was found to decrease in content as grains matured [34]. 1-Heptanol provides a sweet and nutty taste only in ripening grains. The rOAV of linalool ranges from 15.51 to 20.4 in brown rice, and linalool has also been detected in rice bran [48], suggesting that it may be a major component of brown rice flavor. Overall, most VOCs in aromatic and non-aromatic rice were the same, except for some special substances.
## 5 Conclusions  
In this work, the environmental impact of the addition of batteries into a hybrid energy system is evaluated along with their complete life using the LCA approach. As expected, the results show that the overall impact is mainly related to the assembly process and the disposal phase. The reuse process of raw materials can drastically reduce the impacts in all the analyzed endpoint indicators. Sodium ion batteries are promising for all environmental aspects. The potential for improvement for this technology is still significant and refers to the possibility of further reducing the environmental impacts associated with the production of hard carbon for the anode and the nickel content in the active cathode material.  
This work also highlights that for a proper evaluation of the environmental benefits of the battery implementation, there is the need to consider (i) the battery operational life and (ii) the complete energy system where the battery works.  
The investigation outcomes reveal that the environmental convenience of battery installation is always proved for the analyzed scenarios, except for some impact categories with very low values in this case study. However, the results are different for different installation locations, even if the batteries are the same, depending on the energy mix feeding the battery.  
Finally, the analysis also demonstrates that the results are very sensitive to the functional unit. Therefore, it is important to select the proper functional unit and use it in a suitable evaluation of the battery performances.  
Future developments of this work will focus on the influence of energy mix and on battery operation strategy on the results of the LCA of energy storage technologies.  
Furthermore, even if the economic aspect is not considered in an LCA, the relationship among this aspect and environmental impact will study as a continuation of the work presented in this paper. The goal is to obtain an optimization of the battery size in a specific energy system based on both LCA and economic results.  
Conceptualization, A.S., A.B. and F.D.V.; Data curation, A.S., A.B. and F.D.V.; Formal analysis, A.S., A.B. and F.D.V.; Methodology, A.S., A.B. and F.D.V.; Writing--original draft, A.S. and A.B.; Writing--review and editing, A.S., A.B. and F.D.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Reference [10]** & **Present Work** \\ \hline LFP & 147–168 g Wh\({}^{-1}\) & 143 g Wh\({}^{-1}\) (143 t MWh\({}^{-1}\)) \\ NCM & 109–116 g Wh\({}^{-1}\) & 77 g Wh\({}^{-1}\) \\ VRFB & 183 g Wh\({}^{-1}\) & 150 g Wh\({}^{-1}\) \\ NIB & - & 36 g Wh\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Global warming potential (GWP) for the different batteries. The functional unit refers to the storage capacity.
# A Method Improves Speech Recognition with Contrastive Learning in Low-Resource Languages
###### Abstract  
Building an effective automatic speech recognition system typically requires a large amount of high-quality labeled data; However, this can be challenging for low-resource languages. Currently, self-supervised contrastive learning has shown promising results in low-resource automatic speech recognition, but there is no discussion on the quality of negative sample sets in speech contrastive learning. In this paper, we propose the false negatives impact elimination (FNIE) method to filter false negative samples and improve the quality of the negative sample set in speech. FNIE compares the support vector with the negative sample vector set and optimizes the corresponding loss function, allowing the model to learn better speech representations and achieve superior results in low-resource speech recognition. Experiments demonstrate that FNIE effectively filters negative samples, enhances the quality of the negative sample set, and improves the accuracy of speech recognition. The quality of the negative sample set significantly affects the model's learning ability, and using too many negative samples can deteriorate it. In a low-resource setting, our FNIE method achieved a relative improvement of 2.98% in WER on the English dataset, 14.3% in WER on the [PERSON] dataset, and 4.04% in CER on the Mandarin dataset compared to the baseline model.  
contrastive learning; negative samples; wav2 vec 2.0; low-resource +
Footnote †: journal: _Applied_  
0023
# Performance Analysis of Troposphere Scattering Communication Channel with Chirp-BOK Modulation
## 3 Theoretical Analysis for Bit Error Rate  
### Rayleigh Flat Fading  
The average bit error rate (BER) (\(\overline{p}_{e}\)) of chirp-BOK modulation over a flat fading channel can be calculated by the instantaneous BER integrated over an AWGN channel \(p_{e}(\gamma)\) with a signal-to-noise ratio (SNR) \(\gamma\) as follows:  
\[\overline{p}_{e}=\int_{0}^{\infty}p_{e}(\gamma)f_{\gamma}(\gamma)d\gamma, \tag{8}\]  
where \(p_{e}(\gamma)\) is given by [31]  
\[P_{e}(\gamma)=Q\Bigg{(}\sqrt{\frac{E_{b}}{N_{0}}(1-\rho_{ij})}\Bigg{)}=\frac{1 }{2}erfc\Bigg{(}\sqrt{\frac{1}{2}\gamma}\Bigg{)}. \tag{9}\]  
where \(E_{b}\) is symbol energy and \(N_{0}\) is noise power spectral density. The Gaussian Q-function is defined by \(Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}e^{-t^{2}/2}dt\), and \(\rho_{ij}\) is the normalized cross-correlation coefficient between the up/down-chirp signals, as follows:  
\[\rho_{ij}=\frac{1}{\sqrt{BT_{b}}}\sqrt{(C(\frac{\overline{\gamma}}{2}BT_{b}))^ {2}+(S(\frac{\overline{\gamma}}{2}BT_{b}))^{2}}. \tag{10}\]  
For Rayleigh fading, the probability density function (PDF) of the received SNR \(\gamma\) and the cumulative distribution function (CDF) of the received SNR \(\gamma\) can be described, respectively, as  
\[f_{\gamma}(\gamma) =\frac{1}{\overline{\gamma}}exp(\frac{-\gamma}{\overline{\gamma} }), \tag{11}\] \[F_{\gamma}(\gamma) =1-exp(\frac{-\gamma}{\overline{\gamma}}), \tag{12}\]  
where \(\gamma=\frac{|h|^{2}E_{b}(1-\rho_{ij})}{N_{0}}\) is the instantaneous SNR per bit, \(h\) is the fading coefficient with the Rayleigh distribution, and \(\overline{\gamma}=\frac{|\overline{h}|^{2}E_{b}(1-\rho_{ij})}{N_{0}}\) is the average SNR per bit. Additionally, since Equation (9) is a complementary error function, its original function cannot be easily expressed by a simple elementary function, so the integral by parts method is used to calculate Equation (8). Thus, Equation (8) can be deduced by  
\[\overline{p}_{e}=-\int_{0}^{\infty}p_{e}^{\prime}(\gamma)F_{\gamma}d\gamma, \tag{13}\]where  
\[p_{e}^{\prime}(\gamma)=-\sqrt{\frac{1}{8\pi}}(\gamma)^{-\frac{1}{2}}exp(-\frac{1}{ 2}\gamma). \tag{14}\]  
By substituting Equations (12) and (14) into Equation (13), we can get  
\[\overline{p}_{e}=\frac{1}{2}\bigg{(}1-\sqrt{\frac{\tilde{\gamma}}{2+\tilde{ \gamma}}}\bigg{)} \tag{15}\]  
Hence, the average BER of the chirp-BOK modulation over the Rayleigh flat fading channel can be written in a closed form as  
\[\overline{p}_{e}=\frac{1}{2}\bigg{(}1-\sqrt{\frac{\tilde{h}^{2}E_{b}\big{(}1- \rho_{ij}\big{)}/N_{0}}{2+\tilde{h}^{2}E_{b}\big{(}1-\rho_{ij}\big{)}/N_{0}}} \bigg{)}. \tag{16}\]
# Routing Protocols for Delay Tolerant Networks:
## 2 Reference Architecture
### Basic Components
Observe that a message that has been selected by the FW policy to be delivered to the current contact is normally deleted from the queue, but the R policy can enqueue it again in order to have multiple copies in the network. * Single copy: Messages are never replicated. Once a message has been delivered to an encounter, it is deleted from the queue. * Limited: The total number of copies of a message in the network is bounded. * Controlled: A message is replicated only if a condition holds. * Unlimited: There are no constraints on the number of replicas in the network.
# GPRS Sensor Node Battery Life Span Prediction Based on Received Signal Quality: Experimental Study
## 3 Material and Methods  
In this work, we did three types of experiments: RSSI recording experiment, current consumption recording experiment, and data transmission experiment. We used SIM800L GSM/GPRS module which was supplied from a 12 V DC power source through an MP1584 adjustable step-down DC to DC converter which helped us to get 4.2 V from 12 V and enough current for the module as it can consume up to 2000 mA [19]. Basically, the experimental set-up has two parts as per Figure 2.
## 3 Results and Discussion
### Sensory Analysis (Trained Panel)  
In general, the sensory profiles of RDI tomatoes at weeks 1-3, were characterized by higher intensity of key attributes, such as sweetness, sourness, fruit and green notes, aftertaste, and hardness (Figure 4). On the other hand, this trend was reversed at week 6, where control fruits presented a more intense tomato flavor (higher intensity of sweetness, sourness, and tomato-ID) than RDI fruits, probably at this week the stress suffered by RDI plants was extremely strong and plants were highly affected. In this way, the proposed deficit irrigation treatment led to tastier cherry tomatoes from 1 to 5 weeks of water deficit. [PERSON] et al. (2012) also reported that the most characteristic descriptors were sweetness, fruity, hardness, in a study carried out for samples of cherry tomatoes (pear and round) of different varieties.  
Figure 5a shows that the component (F1) explained 28.64% of the total variance of the data and the second (F2) explained 24.85% of the total variance. While in the dendrogram presented in Figure 5b it can be observed how the samples are grouped into 3 main groups.  
Figure 4: Descriptive sensory analysis in cherry tomato samples * \(p<0.05\), ** \(p<0.01\) and *** \(p<0.001\).  
The PCA analysis is by far the most used technique for dimensionality reductions, meant to reduce the number of variables while preserving as much information as possible, and provide an accurate and simple view of total results. In this case, PCA basically showed that the \"harvesting time\" was more relevant than the factor \"irrigation treatment\". In this way, all samples (both control and RDI treated) from weeks 1 and 2 were grouped together and characterized by high contents of lycopene, \(\beta\)-carotene, high size, intense hardness, juiciness, tomato-ID, and long aftertaste. On the other hand, all samples collected  
Figure 5: (**a**) PCA and (**b**) cluster maps prepared using, (\(\bullet\)) Stress Integral, (\(\bullet\)) color, (\(-\)) TSS, pH, titratable acidity, maturity index, organic acids, and sugars, (\(\blacksquare\)) antioxidant activity, lycopene, \(\beta\)-Carotene and total polyphenol content, (\(\Delta\)) sensory profile, and (\(\bullet\)) volatile compounds in cherry tomato samples.  
at weeks 4-6, independently of the RDI treatment, were characterized by high values of TPC, antioxidant activity (ABTS\({}^{\star+}\), DPPH\({}^{\star}\), and FRAP), high contents of both sugars (also TSS) and organic acids (also titratable acidity) leading to red, sweet, and sour fruits with a high content of pulp. However, it can be seen that RDI tomatoes were mainly surrounded by tomato ID, hardness, juiciness, sweetness and redder color.
## 2 Materials and Methods
### Methods
#### 2.3.3 Training and Testing Data Selection  
To evaluate the susceptibility models, the data set for training and testing was divided in a 70-30% ratio [7; 12]. The importance of these data sets lies in the fact that they teach the model the classes to predict, check its degree of fit through training data and verify its predictive capability through testing data [60]. In addition, several papers emphasize the use of the 70-30% data ratio in the generation of landslide susceptibility maps, mainly because this approach allows training the models with most of the data in the set and reserving a subset to independently validate the model, which guarantees robustness in the models [40; 61; 62].
# Model-Based Design and Simulation of Paraxial Ray Optics Systems
## 4 Results and Discussion
### Automatic Focusing System
As shown in Figure 6c, for the system disturbed with an abrupt displacement of 10 mm at 0.2 s, the linear stage position signal \(z_{S}\) follows \(\delta\) to correct the distance \(d_{3}\) and compensate \(d_{2}\). Monitoring the height \(y_{7}\) of output ray \(\mathbf{y_{7}}\), Figure 6d, indicates that the light is initially deviated at 0.2 s due to \(\delta\), then the control loop adjusts \(y_{7}\) to 0 (focusing on the target surface) in less than 0.1 s with a negligible steady-state error. Ray tracing for \(y_{0}=\pm 10\) mm and \(\theta_{0}=0\) is presented in Figure 6e at 0 s (initial condition, similar to the depicted in [5]), 0.2 s (disturbance), 0.21 s (regulation), and 0.5 s (final position), wherein the automatic focus is evidenced: firstly, the target departs from its original position and defocus the light, then, the objective moves toward \(+z\) until the height of output rays become zero, indicating that the focus was restored. The modular project allows for refining or changing the controllers to enhance the system performance, whereas the electrical and mechanical modules can be replaced by more realistic approaches to meet the implementation requirements. Another possible application is the design of three lenses zoom system, in which one lens is fixed, whereas the other two elements are moved to adjust the focal length (magnification) without changing the image plane [33]. The strict requisites of settling time and overshoot demand sophisticated control strategies for displacing the two lenses simultaneously [34]; therefore, the proposed model-based design tool is convenient for implementing control loops and evaluating the results in terms of ray tracing through a unique simulation environment.
# Computational Load Analysis of a Galileo OSNMA-Read Receiver for ARM-Based Embedded Platforms +
## 4 Software Profiling Analysis of the OSNMA Functions  
The software profiling, already performed to evaluate the OSNMA additional computational cost for the NGene receiver in [17] and eNGene on ODROID-X2 in [16], has been extended to the two additional embedded boards, i.e., Raspberry Pi4 and ODROID-C4.
## 5 Simulation Results and Analysis
### Simulation 1: Performance of 2D DOA Estimation with Respect to SNR  
In the first simulation, the performance with respect to SNR is studied. The SNR is varied form \(-21\) dB to \(9\) dB with an interval of \(3\) dB, while the number of sensors \(M\) is fixed at \(7\) and the number of snapshots is fixed at \(500\). As shown in Figures 3 and 4, the performance of the proposed method coincides with the ML estimator and the CRB over most areas of SNR, and only slightly deviates from the CRB when SNR is less than \(-15\) dB.  
In addition, Table 1 shows the average time consumed per run of three estimators, and it is obvious that the computational efficiency of the proposed method is much higher than the ML and MVATA estimator.  
Figure 4: Scatter plot of 2D DOA estimates of the proposed method.  
Figure 3: RMSE of 2D DOA estimation versus SNR from \(-21\) dB to \(9\) dB. (a) RMSE of elevation angle, (b) RMSE of azimuth angle.
# An Overview of Position Sensorless Techniques for Switched Reluctance Machine Systems
## 3 An Overview of Recent Development in Position Estimation Methods of SRM
### Magnetic-Model-Free Method Position Sensorless Methods
#### 3.2.3 Methods Based on Electromagnetic Characteristics  
This type of method does not require mathematical modeling or a three-dimensional look-up table like the model-based method, but uses certain characteristics of the motor to perform position detection.  
Current is an inescapable variable for all rotor position estimates. The rotor position can be extracted by the characteristics of current, which has a good versatility in low speed and start-up [143]. The critical position is based on the chopping current time width [144], based on the lowest point of the inductance [145], and on the inductance start to rise point [146]. Using the current gradient sensorless (CGS) scheme method [147] of the current slope in the wide speed range, the rotor position estimation performance remains stable. One of the more typical formulas uses the slope of the current to detect the position of the minimum inductance. The basic Equation (7) is satisfied on the left and right sides of the minimum inductance point. Equation (12) is obtained by subtracting (7), and the special point of the inductance is obtained by derivation of the rotor through the current.  
\[\frac{di_{0-}}{d\theta_{ph}}-\frac{di_{0+}}{d\theta_{ph}}=i_{0+}\frac{dL_{0+} /d\theta_{ph}}{L_{\min}}>0 \tag{11}\]  
where \(i_{0-}\) and \(L_{0-}\) represent the current and inductance values approaching the left of the inductance inflection point, respectively. \(i_{0+}\) and \(L_{0+}\) represent the current and inductance values approaching the right of the inductance inflection point.  
The traditional inductance slope zero-crossing detection has a large number of interference signals. The crossing point of motional electromotive force (MEF) and the transformer electromotive force (TEF) are detected as a characteristic position, as shown in Figure 12a. The experimental results in the literature verify the correctness of the principle as shown in  
Figure 11: Schematic of the pulse injection method.  
Figure 12b. To use fewer sensors, the bus current is decomposed, and the current gradient is then used to estimate the rotor position [149]. The theoretical basis is to expand Equation (3) to obtain Equation (12).  
\[U_{k}=R_{k}i_{k}+L_{k}\frac{dL_{k}}{dt}+i_{k}\frac{d(\cdot L_{k})}{dt}=R_{k}i_{ k}+E_{MEF}+E_{TEF} \tag{12}\]  
where \(E_{MEF}\) and \(E_{TEF}\) represent motional electromotive force and transformer electromotive force, respectively  
As the rotational speed increases, \(E_{TEF}\) much larger than \(E_{MEF}\) affects the position-free estimation at high speed. The accuracy of this method is high in the low and medium speed range. The feature point-based method is efficient and, in particular, has good performance in a specific speed range. However, because the position corresponding to the feature quantity is less, this will limit the speed regulation performance and real-time performance of the motor.
# Multi-Resolution Analysis with Visualization to Determine Network Attack Patterns
## 5 Classification Analysis and Visualization
With the DWT features, the attack events become more visible in the CICIDS 2017 dataset on the right center of the PCA projection space (see red-colored attack events in Figure 1F). We provide a more detailed explanation of the generated visualizations with the DWT features in Section 6. With the user interaction techniques (i.e., selection and manipulation), the user can select and manipulate network instances to understand the network traffic data within the visualization. In detail, with the selection technique, interesting network patterns can be selected or highlighted by the user. The manipulation technique assists the user in controlling the visual representation of the data. For instance, the user can select and eliminate unwanted data instances from the projection. This approach is essential in MRA with visualization because it helps the user understand the overall contribution of the removed data instances compared to the rest of the data. This feature also leads to an outlier detection analysis, as minimally contributing instances can become possible outliers. Figure 1: Visual representations of intrusion detection datasets. (**A**–**C**) represent the original raw features of the datasets. (**D**–**F**) show the analyzed the DWT features (\(\alpha=150\) and \(\beta=10\)). All network traffic instances are mapped to blue- and red-colored glyphs to represent normal and attack events, respectively. (**A**) NSLKDD Raw Features; (**B**) Kyoto2006 Raw Features; (**C**) CICIDS2017 Raw Features; (**D**) NSLKDD DWT Features; (**E**) Kyoto2006 DWT Features; (**F**) CICIDS2017 DWT Features. During the visual analysis, similar network traffic instances can be identified using similarity measurements: Cosine similarity, Euclidean distance, extended Jaccard coefficient, and Pearson correlation coefficient. With the user-selected data item(s), statistically similar network traffic instances (\(p<0.05\)) are determined. It helps the user initiate an analysis of the selected network traffic instances and discover possible outliers if no similar events are detected. Additionally, the user can select \(k\) clusters with a hierarchical clustering method based on distance measures, such as Euclidean distance (L\({}^{2}\)), Chebyshev distance (L\({}^{\infty}\)), City-block distance (L\({}^{1}\)), and Pearson correlation coefficient (R\({}^{2}\)). This approach of identifying possible clusters may not be optimal in intrusion detection analysis due to the randomness in network traffic patterns [67]. However, understanding possible clusters is vital in MRA because it supports identifying different feature spaces through various DWT wavelet families and parameters.
# Modeling Network-Controlled Device-to-Device Communications in SimuLTE
## 5 Modeling Device-to-Device Communications in SimuLTE  
SimuLTE implements both P2P and P2 MP D2D communications. In what follows, we show the main modeling choices regarding both. These concern: modifications to the protocol stack to incorporate the new functions required by D2D; modifications to resource allocation at the eNB, to account for frequency reuse and mode selection; and modeling air transmission, i.e., identifying the set of possible receivers for a transmission and modeling its logical and physical effects.