esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Wear Coefficient  
Figure 8a,b show the wear crater for samples N-H and N-H-PN, respectively, created by the ball during the wear resistance test. To determine the radius of the crater and apply it in Equations (1) and (2) a circle was fitted on the crater, represented in red in the figures. Figure 9a,b show the wear crater for samples N-H-PVD and N-H-PN-PVD, respectively.  
Figure 7: The cross-section microscopy of Nital etched (**a**) N-H-PVD test sample and (**b**) N-H-PN-PVD test sample.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Thickness** & **N-H-PVD Test Sample** & **N-H-PN-PVD Test Sample** \\ \hline Maximum thickness (\(\upmu\)m) & 1.984 & 1.896 \\ Minimum thickness (\(\upmu\)m) & 1.159 & 1.172 \\ Average thickness (\(\upmu\)m) & 1.660 & 1.465 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Surface layer thicknesses of N-H-PVD and N-H-PN-PVD samples.  
The calculated wear coefficient results are based on Equation (4) and presented in Table 6. Nitridation reduced the wear coefficient compared to the hardened sample by \(\sim\)70%, from 6.23 \(\times\) 10\({}^{-9}\) mm\({}^{3}\)/Nm to 1.95 \(\times\) 10\({}^{-9}\) mm\({}^{3}\)/Nm. This is a significant result; however, when PVD coating was applied, the results decreased by one order of magnitude. This illustrates the effectiveness of PVD coating in contrast to the nitride layer. It can also be noticed that, when PVD coating was applied onto the nitride layer, the wear coefficient was close to six times lower than when it was applied only on the hardened sample. The above-mentioned result correlates well with the elastic modulus of the surface layers,  
Figure 8: The wear crater of (**a**) N-H and (**b**) N-H-PN test samples.  
Figure 9: The wear crater of (**a**) N-H-PVD and (**b**) N-H-PN-PVD test samples.  
indicating the importance of elastic behavior, thus dampening effect of the coating system. The more energy can be dissipated by the surface layer the better the wear resistance. This also suggests that surface hardness is a necessary by not satisfactory parameter when determining the wear resistance. The data in Table 6 also indicate the necessity of a complex surface layer system to improve the wear resistance. The previously applied hardening process or surface treatment significantly influences the wear properties of the final layer.  
The work performed provides valuable information for practicing designers and engineers regarding the wear properties of the applied materials. Special attention should be attributed to determine the correct hardening conditions and surface treatment. The hardening temperature and time should be determined based on chemical composition and geometrical parameters. It is advisable to perform the hardening under vacuum or in inert atmosphere to eliminate decarbonization and oxidation of the surface. The lifetime of tools can be significantly increased by the combined application of nitriding and PVD surface modification techniques.
# Using Fluorescence Spectroscopy to Detect Rot in Fruit and Vegetable Crops
## 1 Introduction  
Population growth opens new challenges for improving efficiency and productivity of the agriculture and food production industry, which can be solved with the development of new sensing technologies capable of providing in vivo or/and onsite monitoring. One of the high-tech solutions in this field is fluorescence spectroscopy. Fluorescence is used to test authenticity of different categories of food products [1; 2; 3], analyze the quality of fruits [4; 5], diagnose of plant diseases [6; 7], and obtain information about the influence of environmental stress factors on agricultural crops [6; 8; 9; 10; 11].  
The kinetics of the development of apple disease at the early stage of fungal infection is studied under laboratory conditions using chlorophyll fluorescence [12]. In addition, fluorescence under laboratory conditions was used to detect patulin toxin in apples [13] and diagnose fungal diseases or substances harmful to humans in potato tubers [14; 15].  
On the other hand, the development of inexpensive, sensitive methods for detection of fluorescence leads to the development of portable devices that can diagnose the contentof chlorophyll and some other compounds in fruits and leaves with accuracy comparable to laboratory studies [16].  
The study of fluorescence spectra of the main diseases of fruit and vegetable crops are of interest not only from a scientific point of view, but also for solving many applied problems in agriculture. Therefore, the main goal of the work is the application of inexpensive methods of fluorescence spectroscopy for rot disease diagnosis and separation/quantification of healthy and diseased fruits and vegetables.
## 3 Results
### Experiments
#### 3.2.3 Experiment C  
In order to further test the effectiveness of the proposed method in detecting defects at different tilt angles and in different locations, experiment C was carried out. The specimen used in this experiment was a standard B-type specimen for phased array ultrasonic testing, produced by Shandong Ruixiang Mould Company (China, Shandong Province, Jining City)., as shown in Figure 10(a). It was made of 20\(\mu\) steel, with a measured sound velocity of 6012 m/s. The defects tested consisted of a set of SDHs with diameters of 1 mm arranged along a circular arc of radius 25 mm, as marked by the red rectangle in Figure 10(a). In this experiment, we used a tilted wedge with an angle of 20 degrees for coupling, and the distance between the first array element center and the upper surface of the specimen was 39.28 mm, as shown in Figure 10(b). The sound velocity of the wedge was 2330 m/s. The rest of the experimental parameters remained the same as in the former experiments. The raw dataset size was 4096 \(\times\) 64 \(\times\) 64, and the data were imported into MATLAB 2018b for post-processing.  
To compare the imaging results, we used the TFM and the proposed method for image reconstruction. As shown in Figure 12, both the TFM and the proposed method could achieve imaging of the measured specimen and could compensate for the tilt angle of the linear array very well. However, it can be seen from the figures that the reconstructed images from both methods have shortcomings. The result of the TFM still had a distortion problem, especially for the defects near to the upper surface of the specimen. Furthermore, the background noise of the TFM result was very large. Although the proposed method could overcome the image distortion problem and suppress the background noise, the echo  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Items** & **FWHM: SDH1** & **FWHM: SDH2** & **FWHM: SDH3** & **FWHM: SDH4** & **Average FWHM** & **SNR** & **Time Cost** \\ \hline Values & 1.038 mm & 0.999 mm & 0.987 mm & 1.023 mm & 1.012 mm & 26.18 & 3.02 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: FWHMs and time costs of the proposed method in experiment B.  
Figure 10: The experimental results for the SDHs in a two-layer structure. (**a**) The image reconstructed by the proposed method. (**b**) The normalized amplitudes of four SDHs.  
energy of the defects near to the upper surface of the specimen was not as good as that of the TFM. Furthermore, neither of the two methods could reconstruct the images of the lowest defects well, because the refraction angle of the wave entering the specimen was too large, resulting in too little energy reaching the lowest defects. However, the proposed method still showed a great efficiency advantage, since the TFM cost 299.89 s to reconstruct this image with a size of 300 \(\times\) 450, while the proposed method only cost 5.01 s.
# An Assessment Scheme for Road Network Capacity under Demand Uncertainty
## 2 Network Capacity Problem under Demand Uncertainty  
### The Uncertainty of Network Capacity  
In this section, an easy-to-understand numerical example is offered to demonstrate the effect of travel demand uncertainty on network capacity assessment results. Given a road network (illustrated in Figure 1) consisting of seven road links and six nodes, the initial demands for O-D pairs (1, 3) and (2, 4) are 30 and 20, respectively. In this numerical example, the following BPR-type link performance function is assumed for the link:  
\[t_{a}(v_{a})=t_{a}^{0}(1+0.15(v_{a}/C_{a})^{4}), \tag{1}\]  
where \(t_{a}^{0}\) and \(C_{a}\) are the free flow time and capacity of link \(a\), respectively. Table 1 shows the free flow time and capacity of each link.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Link** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline \(t_{a}^{0}\) & 10 & 4 & 12 & 4 & 5 & 5 & 4 \\ \(C_{a}\) & 100 & 80 & 80 & 50 & 120 & 50 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Link information for the example network is shown in Figure 1.  
Figure 1: The example network.  
Figure 2 shows the variation in network capacity with different O-D demand patterns and illustrates fluctuations in the value of the capacity. The capacity of the network is also shown to be uncertain with uncertain O-D travel demands.
## 4 Deep Learning Methods for HDR Reconstruction
### CNN for HDR Reconstruction
#### 4.1.1 Single LDR to HDR Reconstruction with CNN  
Single-frame HDR image reconstruction methods generate their HDR output using only a single exposure as input. From this perspective, [PERSON] et al. [8] proposed the HDR-CNN architecture, which utilizes skip connections introduced by the Unet network [44], resulting in a hybrid network with an LDR image encoder and an HDR image decoder. This classic architecture is shown in Figure 5a. Conversely, HDRUnet [41] proposed merging the encoding and decoding stages, enabling 8- and 16-bit image processing simultaneously. This model was designed to reduce noise and quantization error, comprising three subnetworks related to a base network, a conditional network, and a weight network, alongside the Tanh L1 loss function. In a different vein, the HDRTV [113] model introduced a structure organized into three stages: Adaptive Global Color Mapping (AGCM), Local Enhancement (LE), and Highlight Generation (HG). Building on these results, the DCDR-UNet [45] architecture, with its novel Deformable Convolution Residual Block (DCRB)implementation and use of a VGG and Tanh L1 combined loss function, achieved promising performance in generating high-quality HDR images.  
Under a different methodological approach for generating brackets to form the HDR, the DrTMO [23] architecture establishes a neural network that leverages multiple exposures for training, enabling synthetic exposure generation, as illustrated in Figure 5b. This work served as a foundation for developing subsequent models [9; 114; 115; 116]. In a similar vein, [PERSON] et al. [9] applied an architecture following the traditional pipeline of generating HDR images. The structure comprises three CNNs: one for encoding and two for decoding (up and down exposure). The goal is to create multiple exposure values (EVs) from a single LDR image. Along these lines, [PERSON] et al. [115] implemented continuous exposure learning, in which the network can produce EVs with continuous values, leading to results with more detail compared to using fixed EVs.
## 3 Results and Discussion
### Mineral Content  
The inorganic composition of biochar samples is given in Table 1. The XRF results reveal the presence of CaO as the major element in the ash, which increases from 53.88 wt% to 60.90 wt%, followed by MgO (from 9.40 wt% to 10.75 wt%). However, the K\({}_{2}\)O and Cl decrease with rising pyrolysis temperatures, from 16.13 wt% to 8.16 wt% and from 8.75 wt% to 2.89 wt%, respectively. The ash fraction includes other minerals at low concentrations, in particulars P\({}_{2}\)O\({}_{5}\), Na\({}_{2}\)O, Al\({}_{2}\)O\({}_{3}\), and Fe\({}_{2}\)O\({}_{3}\). These findings highlight the fact that CC biochar has rich mineralogical characteristics, which makes it a good candidate for adsorption process. For instance, the presence of these minerals, particularly Ca, is expected to favour chemical interactions, ion exchange, and/or electrostatic adsorption when the selected dye interacts with the adsorbent [30]. Many research studies have focused on Ca-rich biochar for dye removal and have reported a high adsorption efficiency [30; 32; 33].
## 4 Performance Evaluation
### Number of Repetitions  
Figures 13 and 14 show the results in terms of number of repetitions (including collisions and packet loss). Results show that when the number of generated packets increases the number of collisions increases as well.  
These results show, as stated earlier, high repetition rate for the protocols that do not apply a rendez-vous mechanism. Indeed, when a node sends a packet to a node which is trying to send a packet to another node on a different channel, this packet will be lost because the receiver is currently busy on a different channel. HMC-MAC protocol does not suffer from that problem, and thus, reduces the number of frame repetitions compared to the other methods. Consequently, HMC-MAC avoids wasting energy by reducing the number of repetitions.  
Figure 12: Packet loss due to queue overflow for burst traffic.  
Figure 13: Number of repetitions for periodic traffic.
# Audio, Image, Video, and Weather Datasets for Continuous Electronic Beehive Monitoring
###### Abstract
Our field experiments in 2016-2017 [7; 8; 14] convinced us that solar power was not a viable option in northern Utah. While some experiments ran to completion and allowed us to acquire audio, video, and temperature data, we found solar power harvesting to be unreliable for continuous data collection over longer time periods (e.g., at least one calendar month without interruptions). Rechargeable batteries also had notable drawbacks. In particular, we discovered that many UPG batteries stopped holding charge in cold (\(\leq\)\(-15\)\({}^{\circ}\)C ) or hot (\(\geq+25\)\({}^{\circ}\)C) temperatures. We found the Anker battery's performance to be significantly better than that of the UPG battery. However, a fully charged Anker battery can power one deployed BeePi monitor for \(\approx\)24 h, after which it must be replaced with a new fully charged battery, which is not acceptable to many researchers and practitioners who drive long distances to their apiaries. For these reasons, all deployed BeePi monitors have been powered from the grid since 2017. In the subsequent sections of this article, we specify how we have curated our datasets, describe the experiments in which we have used them, and briefly summarize our findings. We refer interested readers to the citations of our prior EBM research in the introduction and the subsequent sections for detailed formal and experimental treatments of our results. In Section 2, we present our audio datasets we have used in our research on audio beehive monitoring. In Section 3, we describe our image datasets we have used in our research on omni-directional bee traffic. In Section 4, we describe our video datasets we have used in our research on omni-directional and directional honeybee traffic. In Section 5, we describe our weather dataset curated in 2020 that we are currently using in our experiments to align the weather data with video-based honeybee traffic curves. In Section 6, we summarize our data curation efforts and provide information on the availability of our datasets.
# A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet
## 3 Results
### Performance of the Proposed Method with Different Epoch Lengths and Selected Channels  
The EEG data of the dataset used in this manuscript is 64 channel EEG data. However, MI tasks only activate the sensorimotor areas for the MI task. Therefore, we selected the EEG channels corresponding to the motor cortex region. These include FC1, FC2, FC3, FC4, FC5, FC6, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, and CP6. We used different epoch lengths, and the selected channels applied the proposed methodology (see Table 2). There was a significant improvement in accuracy, training, and testing times with the selected channels.
# Multi-Scale Feature Learning for Language Identification of Overlapped Speech
## 4 Experiments and Discussion  
### Dataset  
1 The Oriental language dataset used in this paper was provided by the AP17-OLR competition [8]. In this paper, we used the following five languages for our experiments: Mandarin (zh-cn), Vietnamese (vi-vn), Indonesian (id-id), Japanese (ja-jp), and Korean (ko-kr). For each language, 1800 speech data were extracted and divided into a dataset with a ratio of 7:2:1 (training set/validation set/test set), and the structure of the dataset is listed in Table 2.  
2 Multilingual cocktail party dataset: We created a multilingual cocktail party dataset based on the Oriental language dataset to simulate a real multilingual overlapping speech scene. The process was as follows: First, we cut the original data into 4s segments. Second, we randomly selected some speech for each language and split it into target and non-target speakers. Third, we mixed them with different overlap rates according to the scenario needs. The result was a multilingual cocktail party dataset. For the language identification task, we assigned numerical labels to each language, such as \"0\" for \"id-id\", \"1\" for \"ja-jp\", and so on.
## 3 Results  
### The Demographic Characteristics of Participants  
Table 1 shows a comparison of the characteristics of participants by gender. Out of 350 participants, 165 (46%) were men and 185 (53%) were women. The mean age of the participants was 69 years (\(\pm\)SD = 7). Most participants were or had been married (97%), while 69% had a low literacy level. The majority of participants lived in brick houses (85%), had electricity (97%) and access to municipal water (100%) but used pit toilets (87%). Significant differences in proportions were observed for level of education (\(p\) = 0.002), household headship (\(p\leq 0.0001\)), household income (\(p\) = 0.015), alcohol use (\(p\) = 0.001) and family history of hypertension (\(p\) = 0.024) between men and women.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **All, \(n\) = 350** & **Men, \(n\) = 165** & **Women, \(n\) = 185** & **\(p\)-Value** \\  & \(n\) (\%) & \(n\) (\%) & \(n\) (\%) & \\ \hline Age (years) & & & & \\ Mean & 69 (\(\pm\)SD = 7) & 70 (\(\pm\)SD = 7) & 68 (\(\pm\)SD = 7) & 0.017 \({}^{\star}\) \\ \(<\)69 & 184 (53) & 79 (47) & 105 (57) & 0.097 \\ \(\geq\)69 & 166 (47) & 86 (52) & 80 (43) & \\ \hline Marital status & & & & \\ Ever married & 341 (97) & 158 (96) & 183 (99) & 0.062 \\ Single & 9 (3) & 7 (4) & 2 (1) & \\ Level of education & & & & \\ Low literacy & 240 (69) & 100 (61) & 140 (76) & 0.002 \({}^{\star}\) \\ High literacy & 110 (31) & 65 (39) & 45 (24) & \\ \hline Receiving old age grant & & & & \\ No & 13 (4) & 3 (2) & 10 (5) & 0.093 \\ Yes & 337 (96) & 162 (98) & 175 (95) & \\ \hline Employed & & & & \\ No & 329 (94) & 159 (96) & 170 (92) & 0.113 \\ Yes & 21 (6) & 6 (4) & 15 (8) & \\ \hline Household headship & & & & \\ Self & 224 (64) & 131 (79) & 93 (50) & \\ Spouse & 71 (20) & 8 (5) & 63 (34) & \(\leq\)0.0001 \({}^{\star}\) \\ Child of the participant & 51 (15) & 24 (15) & 27 (15) & \\ Relative & 4 (1) & 2 (1) & 2 (1) & \\ \hline Living with & & & & \\ Alone & 23 (7) & 8 (5) & 15 (8) & 0.219 \\ Family members & 327 (93) & 157 (95) & 170 (91) & \\ \hline Dwelling place & & & & \\ Brick-house & 301 (86) & 143 (87) & 158 (85) & 0.734 \\ Non-brick house & 49 (14) & 22 (13) & 27 (15) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the characteristics of participants by gender.
## 1 Introduction
In addition to its static applications, the GPS technique can be employed in continuous monitoring, and the analysis results of GPS time series and inclinations can be converted each other and hence provide a double check for continues structural movements. In the results of this study, although the inclination sensors were proved self-sufficiency in monitoring structural deformations, their use together with GPS sensors provide a more reliable results and detailed insight of structural behaviors. The results of this study should also be considered to emphasize the importance and necessity of structural health control using appropriate monitoring and analyzing techniques in active tectonic regions like Istanbul.
## 3 Case Study--The ANN Models for Load Forecasting
### Training ANN Models  
The selected ANN models are trained for both using univariable and multivariable inputs. The previous 24 h are used as the input sequences for training, and the Adam optimizer with learning rate 0.001 is used for all the ANN models. All the models have trained on a workstation with CPU: dual intel Xeon gold 6248R (35.75 MB cache, 24 cores, 48 threads, 3.00 GHz to 4.00 GHz Turbo, 205 W) and GPU: NVIDIA RTX(tm) A5000, 24 GB GDDR6, 4 DP. Figure 15 shows the accuracy of the ANN models in training and validation datasets for 100 training epochs in 25% of the dataset. The accuracy of the ANN models in 100% and 50% of the datasets can be seen in Appendix B.  
As can be seen, the LSTM, BiLSTM, ConvLSTM, and GRU models, which were originally developed for time series analysis, are more stable during the training as the error does not change dramatically from the previous step compared to the other models developed for including the time series analyses as well. In most of the models, the error decreases in 100 epochs of training in both validation and training datasets, except for the bidirectional LSTM (BIiLTM) multivariable model. In other words, this might be a sign of overfitting in the BiLTM multivariable model. Thus, the performance of this model is expected to be worse than the other models. Otherwise, this might be relevant to the probabilistic nature of ANN models, complexity and many other hyperparameters to be considered. This argument is true for the ConvLSTM2D univari  
Figure 15: The performance of the ANN models in training and validation datasets for 100 training epochs in 25% of dataset.