esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Literature Review
### Construction Monitoring and Productivity Enhancement  
The study provides concrete examples of how drones have proven practical in monitoring construction processes, from residential apartments to large-scale facilities [15; 16]. Drones, when integrated with Building Information Modeling (BIM), potentially improve construction productivity by monitoring not only the construction process but also material delivery and equipment usage [16; 17].
## 2 Materials and Methods
### Model
This work aims at estimating the overpotentials related to the membrane and to the activation of the anode and the cathode. Therefore, the experimental values of \(E\) vs. \(j\) were analysed, removing the values outside of the Tafel region and calculating a monthly average for producing a pair of values per month. These values outside of the Tafel region are mostly related to shut-down and start-up occurrences; consequently, it was only considered the pair of values with a potential higher than 2.3 V and current densities of higher than 2 kA\(\cdot\)m\({}^{-2}\). Industrially, a set of cells or even the whole membrane electrolyser is normally considered for the potential versus current analysis. For this reason, \(E\) values considered are the average of all cells corrected for a reference current density value using Equation (14). In this equation, the reference current density was set to the average current density of the first operating month and \(E_{0}\) was obtained based on Equation (11), upon fitting to the experimental results for each month. Consequently, Equation (14) can thus be written for each operating month as:  
\[E_{ji,0}=E_{0}+(E-E_{0})\cdot\frac{j_{i,0}}{j} \tag{15}\]  
where \(E_{0}\) was estimated based on Equation (11) for each month of a membranes and electrodes set \(i\), \(j_{i,0}\) is the average current density of the first operating month, and \(j\) is the average current density of the actual month. For each month, the corrected potential outlier values, \(E_{ji,0}\), were removed based on the \"box-and-whisker\" statistical analysis [21]. From Equation (15) fitting it becomes possible to associate the increase of the corrected potential to the activation and ohmic overpotentials:  
Figure 1:0  
Figure 1:1  
where \(\eta_{\text{act}^{\prime}}\) is the activation overpotential for each set of electrodes operating month and \(\eta_{\Omega,i}\)\({}^{\prime}\) the ohmic overpotential associated for each set of membranes and electrodes \(i\).
# Quaternion Signal Analysis for Detection of Broken Rotor Fault Degrees in Induction Motors
## 3 Results
The polynomial method yields more stable values. Therefore, even when different samples are used to create the function that describes the behavior of the bars, the resulting functions adapt more effectively compared to those generated by the Fourier series. While a lower order in Fourier series with fewer samples may produce similar values in variation, the polynomial method generally demonstrates better adaptability. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Window**} & \multicolumn{5}{c}{**Order**} \\ \cline{2-6}  & **2** & **3** & **4** & **5** & **6** \\ \cline{2-6}
**Samples** & _Mean (min, max)_ & _Mean (min, max)_ & _Mean (min, max)_ & _Mean (min, max)_ \\ \hline
4000 & 0.263 (0.22, 0.39) & 0.315 (0.22, 0.47) & 0.270 (0.17, 0.47) & 0.301 (0.20, 0.56) & 0.357 (0.17, 1.29) \\ \hline
6000 & 0.267 (0.21, 0.34) & 0.330 (0.21, 0.66) & 0.290 (0.19, 0.56) & 0.285 (0.18, 0.66) & 0.310 (0.15, 0.66) \\ \hline
8000 & 0.259 (0.22, 0.32) & 0.315 (0.20, 0.80) & 0.278 (0.17, 0.74) & 0.273 (0.17, 0.68) & 0.355 (0.17, 0.88) \\ \hline
10,000 & 0.277 (0.22, 0.47) & 0.364 (0.21, 0.78) & 0.322 (0.18, 0.79) & 0.308 (0.18, 0.65) & 0.387 (0.15, 1.41) \\ \hline
12,000 & 0.274 (0.22, 0.47) & 0.341 (0.21, 0.83) & 0.307 (0.18, 0.85) & 0.303 (0.18, 0.68) & 0.387 (0.18, 1.08) \\ \hline
14,000 & 0.268 (0.21, 0.50) & 0.359 (0.20, 0.71) & 0.335 (0.19, 0.76) & 0.302 (0.18, 0.59) & 0.419 (0.14, 0.93) \\ \hline
20,000 & 0.256 (0.21, 0.43) & 0.334 (0.20, 0.81) & 0.343 (0.17, 0.86) & 0.312 (0.18, 0.79) & 0.409 (0.17, 0.96) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean, minimum, and maximum RMSE in each order using polynomial modeling. Figure 8: Mean RMSE behavior with Fourier series modeling. A lower order results in fewer operations during modeling and the QSA method demonstrates that a higher order is not necessary to achieve good adaptation results with the functions. Additionally, it has been shown that, given a sampling rate of 8.326 kHz, a duration of less than one second is sufficient in a real-world scenario to obtain a diagnosis of the wear degree in rotor bars.
## Materials and Methods
### Field Data
#### 2.2.2 Leaf Biochemistry  
A set of leaves from five QUDO individuals were collected from the upper, sunlit portion of the canopy. Sampling started within an hour of the timing of the overflight. Leaf samples were collected from open grown trees that were in full sunlight, as high into the canopy as possible to reach, and from branches on the east and west side of the tree. Leaves were placed in foil packets so that light would not reach them and stored in a container on blue ice and transferred to a lab refrigerator at the field station until lab measurements could be made, no later than 48 hours after data collection. Leaf samples were frozen in liquid nitrogen for a short time until lyophilized.  
The analysis extraction was done in 90% acetone (14 mL) for 48 hours, which provided enough solvent extract to make 3.5 mL samples for each individual. Lambda 25 UV/Vis spectrophotometer runs were done with standards with a concentration of 23 mg/mL for chlorophylls and 8 mg/mL for carotenoids. Absorbance \(A\) of the solution was measured at 0.470, 0.662, 0.645, and 0.710 \(\upmu\)m. Chlorophylls and carotenoids concentrations of the solution (\(c_{a}\), \(c_{b}\), \(c_{(x+c)}\)) were computed using Equations (1)-(3) [40,41] with a dilution factor equal to 3.5, and further processed to get chlorophylls a + b and carotenoid concentration on the leaf area (\(\upmu\)m/cm\({}^{2}\)). Information concerning the final number of validation points is given in Table 1.  
\[c_{a}(\upmu\mathrm{g/mL}) =(11.24\,(A_{0.662}-A_{0.710})-2.04\,(A_{0.645}-A_{0.710}))\times \mathrm{dilution\ factor} \tag{1}\] \[c_{b}(\upmu\mathrm{g/mL}) =(20.13\,(A_{0.645}-A_{0.710})-4.19\,(A_{0.662}-A_{0.710}))\times \mathrm{dilution\ factor}\] (2) \[c_{(x+c)}(\upmu\mathrm{g/mL}) =\left(\frac{1000(A_{0.470}-A_{0.710})-1.90c_{a}-63.140c_{b}}{214} \right)\times\mathrm{dilution\ factor} \tag{3}\]  
While leaves from fives trees were originally collected for summer 2013, biochemistry results from two trees were rejected as they showed lower C\({}_{\mathrm{ab}}\) and Car values than those from the same trees in fall 2013 (20 to 37 \(\upmu\)g/cm\({}^{2}\) and 34 to 38 \(\upmu\)g/cm\({}^{2}\) from summer to fall, respectively). This is contrary to the expected behavior of these pigments, and it was assumed that the leaf samples from those trees suffered degradation between collection and laboratory analysis.
# Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks
## 1 Introduction
As per the existing CSMA/CA mechanism of the IEEE 802.15.4e standard, a node has to go for the CCA twice in order to avoid the collision due to acknowledgment transmissions. Normally, collisions occur in WSN either due to simultaneous channel assessment or a hidden terminal problem. When two nodes are not present in the communication range of each other and transmit to another node, which is present within both's communication range, it is known as the hidden terminal problem. The hidden terminal problem can be avoided by allocating a common shared slot in the LLDN superframe to the nodes present within the same communication range and different shared slots to the hidden nodes. The random backoff and CCA in the CSMA/CA mechanism avoid the collisions and are only effective for the nodes that are not hidden. However, they cannot minimize the number of collisions due to simultaneous transmissions, and therefore, these problems need to be addressed for better channel utilization. To mitigate the problems, a new channel access mechanism is proposed in this paper. The originalities in our work are that a new prediction model is designed to predict the optimal numbers of retransmission slots based on the historical data of the previous transmissions and performance analysis of different network parameters of the LLDN superframe such as reliability, throughput, energy consumption and delay, which have not been studied yet. The main contributions of our work can be summarized as follows. * In order to reduce the power consumption of sensors, a new CSMA/CA mechanism is designed for the shared slots of the LLDN superframes. * In order to avoid collision due to simultaneous transmission by nodes, a new channel access mechanism is designed. * Mathematical models are designed to predict the optimal numbers of retransmission slots based on the historical data of the previous transmissions. The rest of the paper is organized as follows. The survey of related works of the existing IEEE 802.15.4 standard are given in Section 2. The overview of LLDN superframe is given in Section 3. The network model and proposed MAC mechanisms are presented in Section 4. Various analytical models are designed in Section 5. The performances of various network parameters are studied in Section 6. Simulation results are given in Section 7, and concluding remarks are made in Section 8.
# Effect of Cohesive Properties on Low-Velocity Impact Simulations of Woven Composite Shells
###### Abstract  
The effect of the interface stiffness and interface strength on the low-velocity impact response of woven-fabric semicylindrical composite shells is studied using finite element (FE) models generated with continuum shell elements and cohesive surfaces. The intralaminar damage is accounted for using the constitutive model provided within the ABAQUS software, while the inter-laminar is addressed utilising cohesive surfaces. The results show that the interface stiffness has a negligible effect on the force and energy histories for values between \(10^{1}\) N/mm\({}^{3}\) and \(2.43\times 10^{6}\) N/mm\({}^{3}\). However, it has a significant impact on the delamination predictions. It is observed that only the normal interface strength affects the maximum impact force and the delamination predictions. Increasing its value from 15 MPa to 30 MPa resulted in an 8% growth in the maximum force, and a substantial reduction in the delaminated area. The obtained results serve as guidelines for the accurate and efficient computation of delamination. The successful validation of the FE models establishes a solid foundation for further numerical investigations and offers the potential to significantly reduce the time and expenses associated with experimental testing.  
low-velocity impact; cohesive behaviour; interlaminar properties; finite element method; composite shells +
Footnote †: journal: Applied Sciences
# Enhanced Speech Emotion Recognition Using Conditional-DCGAN-Based Data Augmentation
## 2 Related Works
### Research on Speech Emotion Recognition  
#### 2.2.1 Generative Adversarial Nets  
GAN (Generative Adversarial Nets) [20] is a framework where two neural networks, a generator and a discriminator, compete against each other while learning simultaneously. GANs are used to generate various types of data, especially images, music, and text, and several variants have been proposed to adapt them to different fields. For example, CGAN (Conditional GAN) [18] generates data based on specific conditions, while DCGAN (Deep Convolutional GAN) [19] enhances the generation quality by applying convolutional layers, preserving the spatial information of high-dimensional data like images.  
GANs are also useful as an oversampling technique to address imbalanced data problems; in particular, GAN oversampling methods based on Markov Random Field (vMRF) have been shown to outperform traditional techniques such as SMOTE (Synthetic Minority Over-sampling Technique), which synthesizes samples from a small number of classes [22]. GANs have also been actively applied in speech emotion recognition research. [PERSON] et al. [23] applied DCGAN to augment emotion data and combined it with existing data to improve performance. The datasets used were RAVDESS [14] and Emo-DB [13], with Mel-spectrograms used as features. As a result, RAVDESS showed an improvement in WA from 64.8% to 72.3% and UAR from 64.2% to 72.3%, while Emo-DB showed an improvement in WA from 80.6% to 90.4% and UAR from 82.6% to 91.3%. Additionally, [PERSON] et al. [24] addressed class imbalance in emotional data by applying Cycle-GAN (Cycle-Consistent Adversarial Networks) [25] and compared it with other techniques like adding random noise, SMOTE [26], and 3D CRNNs feature generation methods [27]. By selecting five emotion categories from the Emo-DB dataset, they achieved WA of 84.49% and UAR of 83.33%.  
In many existing studies, separate GANs are applied to each emotion category, which imposes the burden of creating new models for each emotion. Therefore, this study proposes a method to improve speech emotion recognition performance by using Conditional-DCGAN, which combines CGAN and DCGAN to augment emotion data. CGAN enables the generation of data based on specific conditions, while DCGAN preserves the spatial information of high-dimensional data, making it suitable for generating emotion data in a flexible and scalable manner. A new loss function is also introduced to reflect emotion-specific characteristics, and its impact on emotion recognition performance is evaluated.
## The Lighting System in Urban Area
### Urban Lighting as Development Indicator  
Researches on this topic showed as remotely sensed lighting data can be assumed to predict both urban extension [55] considering borders and occupied territory [56] encouraging agreement between lighted areas and various definitions of urban extent [57]. For example, conurbations greater than 80 km of diameter account for \(<\)1% of the analyzed settlements but describe about half the total lighted area worldwide. The area/perimeter distributions designate that urban areas become progressively dendritic while growing. Studies about lighted areas with built areas estimated from Landsat imagery in a sample of 17 cities showed that lighted areas are regularly larger than even maximum estimated built areas for almost all cities in every light dataset. Thresholds \(>\)90% can often reunite lighted area with built area in the datasets dating from 1994/1995 nevertheless there is not one level that fits the majority of the sample cities.  
Outdoor lighting can also provide an estimation of the population distribution, when rough spatial scales are accepted; the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS)-based research provided an approximated assessment of the world population that could demonstrate a 6% of discrepancy with the correct value, using the night-time satellite imagery and the rate of urbanization of the different nations [58].  
With increased urbanization, the spatial coverage and intensity of artificial light pollution seem to be growing [21; 59]; furthermore, the night sky spectrum is correspondingly shifting due to the replacement of urban lighting infrastructure [60]. Lighting enables citizens to experience the built environment and research into the effect on urban lighting on behavior are trying to unveil the outcomes related to environmental psychology and social interaction [61].  
Lighting has strong cultural link to ideas of modernity and safety whilst developing land-use strategies to contain lighting pollution can lead to energy and economic saving, mitigation of climate change as well as preservation of biodiversity and ecosystem services dependent on natural darkness [62] (Figure 1).  
Thus a multi-layered evaluation is needed. The artificial lighting is a demonstration of progress, giving people wider options about where, when and how long their activities can be performed. Nevertheless, lighting has direct effects on health [63] and sector studies demonstrate that the amount of pollution is strongly dependent on the spectral characteristics of the lamps [64].
## 5 Discussion
### Effectiveness of the Mish Activation Function  
In Section 2.2, we explain why we chose Mish as the activation function instead of ReLU. Here, we will test the two functions separately to further verify the much better performance of HDCDF based on Mish. As you can see from Figure 19, HDCDF based on Mish surpasses HDCDF based on ReLU. Specifically, there was an increase of 2.27%, 1.97%, and 2.84% on the IP, UP, and SV datasets, respectively. Thus, the Mish activation function is more suitable than ReLU to deal with the nonlinear relationship between the spectral and spatial dimensions of hyperspectral images.
# Mechanical Performance Characterization of Lignin-Modified Asphal Mixture
## 3 Materials and Methods  
### Materials  
In the current study, basalt aggregates and limestone fillers were used, the properties of which met the requirements of \"Test method of Aggregate for Highway Engineering\" (JTG E42-2005). Physical and mechanical properties of aggregates are summarized in Table 1. The used asphalt binder was neat Pen-70 asphalt from Esso (Singapore). Physical and mechanical properties of asphalt are listed in Table 2.  
There are two types of lignin products used in this study: Lignin powder (LP) and lignin fiber (LF), as shown in Figure 1. The wood lignin powder was extracted using the organosolv method provided by Chemical Point UG (Oberhaching, Germany). The density of the lignin powder measured by a helium pycnometer was 1.3774 g/cm\({}^{3}\), which is much lighter than aggregates and close to asphalt.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specific Gravity** & **Water** & **Crushing** & **Asphalt** & **Abrasion Loss (\%)** \\
**(g/cm\({}^{3}\))** & **Absorption (\%)** & **Value (\%)** & **Affinity (\%)** & **(Los Angeles)** \\ \hline
2.82 & 0.72 & 12.8 & \(>\)85 & 14.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical properties of basalt aggregates.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Properties** & **Value** \\ \hline Flash point (\({}^{\circ}\)C) & 272 \\ Rotational viscosity (60 \({}^{\circ}\)C, Pa\(\cdot\)s) & 203 \\ Wax content (\%) & 1.6 \\ Density (15 \({}^{\circ}\)C, g/cm\({}^{3}\)) & 1.032 \\ Penetration (25 \({}^{\circ}\)C, 100 g, 5 s, 0.1 mm) & 71 \\ Ductility (5 cm/min, 5 \({}^{\circ}\)C, cm) & 32.2 \\ Softening point (R\&B, \({}^{\circ}\)C) & 47.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic properties of asphalt binder.  
The specific surface area measured by a dynamic vapor sorption system was 147.0593 m\({}^{2}\)/g. The pH value of lignin fiber was 7.5, the moisture content was 1.8%, and the length range was 3 to 6 mm. The environmental scanning electron microscope (ESEM) images of the used lignin powder and fiber are shown in Figure 2. It can be seen from Figure 2a that lignin powders contain smaller fractions of particles that are crushed from larger particles. Lignin particles have a diameter ranging from 10 to 200 \(\upmu\)m. The angularity of the particles was also observed.
# A Tweak-Cube Color Image Encryption Scheme Jointly Manipulated by Chaos and Hyper-Chaos
## 6 Conclusions  
In this paper, we devise a novel 1D chaotic map LFHCM with increased parameter space. The simulation results exhibit that the LFHCM has better chaotic properties than congeneric chaotic maps. In addition, combining LFHCM with a 4D hyperchaotic system, we also exogitate a color image encryption scheme based on tweak-cube theory. On the one hand, the secondary key streams updated by the plain image guarantee that the image withstands known-plaintext attack and chosen-plaintext attack. On the other hand, fast disorganizing at the bit-level and pixel-level allows our solution to be applied in crucial scenarios for real-time communication, such as telemedicine, authentication, and so forth. Compared with some state-of-arts, our work has achieved excellent encryption effects.  
Conceptualization, L.-L.H. and S.-M.W.; methodology, L.-L.H.; software, S.-M.W.; validation, L.-L.H., S.-M.W. and J.-H.X.; formal analysis, J.-H.X.; investigation, S.-M.W.; resources, L.-L.H.; data curation, L.-L.H.; writing--original draft preparation, S.-M.W.; writing--review and editing, L.-L.H.; visualization, J.-H.X.; supervision, L.-L.H.; project administration, S.-M.W.; funding acquisition, L.-L.H.  
This research was funded by National Natural Science Foundation of China (No. 61203004, 61306142) and Natural Science Foundation of Heilongjiang Province (Grant No. F201220).  
The authors declare no conflict of interest.
## 5 Experiments
### Experiments in an Industrial Scenario
#### 5.3.1 Communications Latency  
Figures 21 and 22 show the communication latency of messages sent using the developed protocol at 9 s intervals from the two scenarios selected for the shipyard workshop. Figure 21 shows that latency remained more stable for the first scenario (i.e., with nodes at a distance of 43 m). In fact, latency variability is small, losing only one packet. The typical delay oscillates between 8 and 9 s. Figure 22 shows more variation than the second scenario (i.e., for a distance between nodes of 117 m) suffered more latency oscillation and spikes. In such a second scenario, latency usually ranged between 8 and 9 s, with peaks of up to 11 s and a total of five packets lost, which are related to the increase in the communications distance and to the existence of more obstacles in the signal propagation path inside the workshop. As with the home automation tests, a peak occurs (in this case, around packet 50). As it was previously mentioned, such a spike is due to the fact that, after a certain number of minutes, the protocol checks whether the communication is still active, causing a delay in the remaining packets.