esa-sceva/satcom-chunk-collection huggingface dataset:
# Coal and Gangue Recognition Method Based on Local Texture Classification Network for Robot Picking
## 2 Problem Analysis
### Poor Generalization Ability  
As shown in Figure 2, the classification of coal and gangue may be straightforward within homogenous datasets since the physical and optical property inherently differs between these two kinds of rocks. A model that give results merely based on raw numerical information (e.g., contrast, entropy, energy, and inverse different moment) of images may perform well on homogenous datasets, even if no feature of patterns is learned. However, these numerical properties could be vastly different between non-homogenous datasets, causing the performance to intensively deteriorate when a model is experimented on non-homogenous datasets to which it is trained. This conclusion is drawn based on the analysis in Section 4. We consider this as a kind of significant trait trap, because of which a model may lack the ability to generalize to various working conditions, limiting its application.  
For two-step indirect methods, such as Gray Value + SVM, GLCM + SVM, and MDFA, although decent accuracy can be reached on the training dataset and its homogenous datasets, a sharp drop occurs in both the accuracy and recall rate when tested on non-homogenous datasets. Even a total failure in recognition could occur. It will be proved in the following part that the inherent differences between non-homogenous datasets reflecting on significantly different features or characteristic numbers extracted in the first step lead to a less effective recognition model trained in the second step.  
For one-step direct methods like LeNet [21], an upturn in accuracy could be expected when tested on non-homogenous datasets. However, the result would also deteriorate with resolution degradation and can be adjusted and improved. Moreover, deep learning models trained under the homogenous datasets will also fall into a significant feature trap.  
In short, the existing methods lack generalization ability among complex application scenes and are strongly limited by the experimental environment. However, vision-based coal and gangue separation systems are expected to be prompt in efficiency and reduction in cost for coal yards. According to the field investigation, the lighting conditions, system structures, and camera installations differ among coal yards, failing to guarantee identical operating environments and homogenous images between yards. Under this circumstance, a non-generalization method could be limited from the wide application. A new solution is proposed to secure an acceptable recognition accuracy on non-homogenous datasets.
# Simple K-Medoids Partitioning Algorithm for Mixed Variable Data
## 3 Demonstration on Artificial and Real Datasets
### Data Simulation
#### 3.1.4 Different Numbers of Objects
A simulation of \(k=3\) and \(k=10\) with \(s=20\) in the SKM, however, had an insignificant gap from the SFKM when the number of clusters was large (Figure 2). Although the SFKM algorithm, which applied a specific formula for the initial medoid selection, gained speed, it performed badly with respect to the external validation, i.e., Rand index. With the current advancement in computation, in addition, the speed of the SKM algorithm could be increased. Due to the independent random seedings as many as \(s\) times, parallel computation was feasible to gain speed in the SKM. Hence, the SKM could be as fast as the SFKM when the number of cores was \(s\). Although it was suggested to repeat the algorithm as many times as possible to avoid the local optima problem [17], with 20 seedings, the SKM algorithm yielded a similar result to the popular PAM algorithm. Then, the SKM and PAM algorithms were compared with respect to their efficiency. The comparison took two different numbers of clusters (\(k=3\), \(10\)) in small and large numbers of objects (\(n=200\), \(1000\), \(3000\)). Each evaluation was averaged over 50 replications with seconds as the evaluation unit. The comparison also applied two different random number generators (\(set.seed\)). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(n\) & **Algorithm** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline \multirow{3}{*}{\(100\)} & PAM & 0.75 & 0.87 & 0.73 & 0.94 & 0.93 & 0.95 & 0.91 \\  & SFKM & 0.72  & 0.84  & 0.73 & 0.89  & 0.88  & 0.88  & 0.89  \\  & SKM & 0.74  & 0.88  & 0.73 & 0.89  & 0.88  & 0.89  \\  & SKM & 0.74  & 0.88  & 0.73 & 0.89  & 0.88  & 0.89  \\  & SKM & 0.74  & 0.88  & 0.73 & 0.89  & 0.88  & 0.89  \\  & SKM & 0.74  & 0.88  & 0.73 & 0.89  & 0.88  \\  & SKM & 0.74  & 0.88  & 0.74 & 0.86  & 0.87  & 0.84  \\  & SKM & 0.76  & 0.88  & 0.74 & 0.86  & 0.89  & 0.85  \\  & SKM & 0.76  & 0.91  & 0.76  & 0.97 & 0.95  & 0.97 & 0.92  \\  & SKM & 0.78 & 0.96 & 0.77 & 0.98 & 0.98 & 0.98 & 0.95 \\ \hline \multirow{3}{*}{\(2000\)} & PAM & 0.77 & 0.96 & 0.77 & 0.98 & 0.98 & 0.98 & 0.95 \\  & SFKM & 0.76  & 0.91  & 0.76  & 0.97 & 0.95  & 0.97 & 0.92  \\  & SKM & 0.78 & 0.96 & 0.77 & 0.98 & 0.98 & 0.98 & 0.95 \\ \hline \multicolumn{3}{c}{Significantly different (\(\alpha=0.05\)) from:   PAM and SKM,   PAM,   SKM.} \\ \hline \end{tabular}
\end{table} 
Table 7: Rand indices of the different numbers of objects. Figure 2: Benchmarking of the SKM and SFKM for \(k=3\) (**a**) and \(k=10\) (**b**). Figures 3 and 4 show that PAM had a faster run time than SKM when the number of clusters was small (\(k=3\)) and the number of objects was large (\(n>1000\)). With large \(k\) and \(n\) (\(k=10,n>1000\)), on the other hand, SKM required less time compared to PAM. Thus, SKM and PAM were on par for small \(n\) and \(k\).
# Investigating the Adsorption Behavior of Zwitterionic Surfactants on Middle Bakken Minerals
## 1 Introduction
Understanding the adsorption behavior of surfactants on reservoir rocks is important, as it significantly impacts essential interface properties that can affect oil recovery outcomes [25; 26; 27]. Excessive adsorption can increase costs and reduce effectiveness, making it imperative to minimize adsorption while maximizing surfactant performance [19; 25]. Despite the extensive research on various surfactant types, there remains a significant gap in the understanding of zwitterionic surfactants, particularly under harsh reservoir conditions. Zwitterionic surfactants, known for their high stability, good compatibility, and impressive interfacial activity, show promise for EOR applications. However, their adsorption behavior, especially under conditions involving complex mineral compositions and high salinity levels exceeding 250,000 mg/L (with divalent ions), has not been thoroughly investigated. This study aims to address these gaps by exploring the adsorption properties of zwitterionic surfactants within tight reservoir rock samples. Our approach involved conducting static adsorption experimentsusing rock powder samples from the Bakken Oilfield. We systematically examined the effects of temperature, salinity, and mineral composition on the adsorption behavior of three betaine-type zwitterionic surfactants. In this study, the adsorption of three betaine zwitterionic surfactants onto Middle Bakken minerals was studied under Bakken conditions using the spectrophotometric iodine technique with KI-I\({}_{2}\) solution as the color-inducing reagent. This method offers many advantages, such as high accuracy, ease of use, acid stability, cost-effectiveness, and a broad linear detection range with strong regression coefficients [5]. By comparing the adsorption of these surfactants under Bakken conditions, the aim is to provide valuable insights that could inform the development of more effective and cost-efficient surfactant formulations for EOR applications.
###### Abstract  
Vision-based tracking systems enable the optimization of the productivity and safety management on construction sites by monitoring the workers' movements. However, training and evaluation of such a system requires a vast amount of data. Sufficient datasets rarely exist for this purpose. We investigate the use of synthetic data to overcome this issue. Using 3D computer graphics software, we model virtual construction site scenarios. These are rendered for the use as a synthetic dataset which augments a self-recorded real world dataset. Our approach is verified by means of a tracking system. For this, we train a YOLOv3 detector identifying pedestrian workers. Kalman filtering is applied to the detections to track them over consecutive video frames. First, the detector's performance is examined when using synthetic data of various environmental conditions for training. Second, we compare the evaluation results of our tracking system on real world and synthetic scenarios. With an increase of about 7.5 percentage points in mean average precision, our findings show that a synthetic extension is beneficial for otherwise small datasets. The similarity of synthetic and real world results allow for the conclusion that 3D scenes are an alternative to evaluate vision-based tracking systems on hazardous scenes without exposing workers to risks.  
construction productivity; construction safety; deep learning; synthetic data; tracking +
Footnote †: journal: Applied Sciences
## 3 Engineering Applications  
The further to validate the rationality and accuracy of the in situ application of the stress field inversion method, in situ strain measurement tests and inversion analysis were conducted at Macheng Iron Mine. This mine is located in Macheng Town, Luannan County, Hebei Province, China. The mine has a construction scale of 25 million tons of iron ore per annum and was developed using a combination of vertical shafts and inclined ramps. The rock mass shows good stability, low fragmentation, and no significant large-scale joints. The mine is currently in the development stage of the \(-930\) level tunnel, primarily using drilling and blasting methods for excavation. To optimize the blasting design and achieve controlled contouring of the underground tunnels while ensuring safe tunnel excavation, it is necessary to determine the distribution of the stress field in the surrounding rock mass of the Macheng Iron Mine tunnel. For this purpose, the surface stress relief method was employed in the \(-930\) level tunnel of the mine to conduct in situ borehole tests to obtain the surface strains of the tunnel walls. Then, the proposed algorithm was used for stress inversion to identify the stress field distribution around the tunnel.
## 2 Results and Discussion
### Identification of Aroma Compounds in Young and Aged Nongxiangxing Baijiu by GC-O/MS  
The odorants present in Baijiu samples are low in concentration yet highly complex. Liquid-liquid extraction (LLE) effectively isolates and concentrates these aroma compounds. The concentrated extract was further analyzed using cAEDA to determine the flavor dilution factor (FD) value of each detectable odorant via gas chromatography-olfactometry (GC-O). This complexity will be investigated further through a detailed analysis of the individual compounds involved. As shown in Table 1, a total of 94 distinct aroma compounds were identified in the Baijiu samples, which include 27 types of esters, 16 types of alcohols, 11 types of acids, 13 types of aldehydes and ketones, 4 types of terpenoids, 3 types of lactones, 5 types of aromatics, 4 types of furans, 3 types of sulfur-containing compounds, 2 types of pyrazines, and 6 types of phenolic compounds. Esters are important to Baijiu, and they typically impart a floral, fruity, and sweet character to Baijiu [12]. The flavor attributes of alcohol compounds were observed to vary significantly depending on their functional group configurations. Saturated alcohols were predominantly associated with alcoholic notes, while unsaturated alcohols contributed to floral, fruity, and mushroom-like aromas. Complex compounds containing hydroxyl groups were found to impart multifaceted sensory characteristics, such as honey, almond, grassy, and rose nuances [13]. Acids play a vital role in the aromatic presentation of alcoholic beverages, often introducing a sour note. However, certain substances, like butanoic acid, can present a distinctly sour aroma. Figure 1: Sensory profiles of Baijiu with different aging times.
## 2 Materials and Methods
### Experiment
As we measure the normalized Stokes parameters in this experiment, the overall intensity and any global loss should not have an impact on the results. Tomography measurements on the polarization matrix can be conducted using a polarimeter to obtain the Stokes parameters of our beam. However, we use an intensity detection scheme, illustrated in Figure 1, that only requires a QWP, linear polarizer, and photodetector to obtain polarization projection measurements [35; 36]. Four intensity measurements are performed: \(I(0^{\circ},\,0^{\circ})\), \(I(0^{\circ},\,90^{\circ})\), \(I(0^{\circ},\,45^{\circ})\), and \(I(45^{\circ},\,45^{\circ})\). \(I(\psi,\,\phi)\) is the intensity measured by the photodetector with the fast axis of the QWP (in the detection scheme) at angle \(\psi\), and the axis of transmission of the linear polarizer at angle \(\phi\) (both with respect to the horizontal axis). With these intensity measurements, we can calculate the Stokes parameters [36]:  
\[ \begin{split} s_{0}&=I(0^{\circ},0^{\circ})+I(0^{ \circ},90^{\circ}),\\ s_{1}&=I(0^{\circ},0^{\circ})-I(0^{\circ},90^{\circ }),\\ s_{2}&=2I(45^{\circ},45^{\circ})-s_{0},\\ s_{3}&=2I(0^{\circ},45^{\circ})-s_{0}.\end{split}  \tag{5}\]  
Then from the Stokes parameters, we obtain the DOP of the states using  
\[\text{DOP}=\frac{\sqrt{s_{1}^{2}+s_{2}^{2}+s_{3}^{2}}}{s_{0}}. \tag{6}\]  
We take 120,000 intensity measurements over 24 s for each polarization projection, then take a time average of all measurements to obtain an average intensity to be used with Equation (6). It is important to recognize that our results rely on many measurements being recorded, then time averaged to show DOP is preserved. The setup in Figure 1 only allows us to take the intensity measurements independently at different times. We take all input measurements in which we remove the tank from the setup, then reproduce all states when the tank is placed in the path of the light in order to take output measurements. This is done \begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Symbol** & **Value** \\ \hline Power transmitted & \(P_{trans}\) & \(\approx\)24 dBm \\ Transmitter gain & \(G_{trans}\) & \(\approx\)0 dB \\ Transmitter loss & \(L_{trans}\) & \(\approx\)15 to 18 dB \\ Free-space loss & \(L_{fs}\) & \(\approx\)0 dB \\ Turbulence related loss & \(L_{turb}\) & \(\approx\)13 dB \\ Receiver gain & \(G_{rec}\) & \(\approx\)0 dB \\ Receiver loss & \(G_{rec}\) & \(\approx\)0 to 20 dB \\ \hline \end{tabular}
\end{table} 
Table 1: Link parameters for the experimental setup. All values are approximate. to keep the nature of the underwater turbulence as consistent as possible when measuring different states. Due to the irreproducibility and chaotic nature of bubbles, enough data points to recover the entire intensity distribution must be taken. More details regarding the intensity distribution of the experimental results are presented in Appendix B.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 6 Results
### Experimental Results
#### 6.3.1 Scenario 1  
In scenario 1, 72 experiments were performed and packet loss occurred in seven cases, resulting in 90.78% of tests without losses. In such cases, the total amount of lost packets was equal to 15, where four packets are the maximum number obtained in a single experiment. 500 packets per experiment were sent, comprising a total of 36,000 packets, where 99.96% of the transmitted packets were successfully received. The average throughput was 15.56 kbps, with an average transmission time of 67 s and local and remote RSSIs equal to \(-50.00\) and \(-50.00\) dBm, respectively. The best result  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline PTx—transmission power & 23 dBm \\ GTx—transmission antenna gain & 1.7 dBi \\ GRx—receiving antenna gain & 1.7 dBi \\ \(\lambda\)—wavelength & 0.33 m \\ \(X_{\sigma}\)—random variable & 6.0 dB \\ \hline \hline \end{tabular}
\end{table}
Table 7: Frequencies and respective impedances.  
Figure 17: Simulated behavior of the power received by the XBee transceiver.  
was obtained in 62 s, where no packets were lost. The aforementioned results are summarized in Tables 8 and 9.
## 3 Research Methodology
### Applications of Hybrid CNN and LSTM  
The utilization of a hybrid approach facilitates a more all-encompassing comprehension of the data, thereby potentially enhancing performance across diverse applications. The efficacy and precision of intrusion detection systems (IDSs) in safeguarding network security can be significantly improved through the implementation of the CNN-LSTM approach [40]. The model's capability to acquire knowledge from both approaches renders it highly valuable in intricate real-world situations where both aspects are crucial. The hybrid model, which integrates the CNN and LSTM architectures, demonstrates the ability to effectively capitalize on the respective advantages of both approaches. This amalgamation renders the model versatile and strong for a wide range of tasks that entail the analysis of spatial and temporal data [41].  
Several studies have employed a combination of various deep learning architectures to create hybrid architectures, while others have combined deep learning with shallow algorithms to form hybrid models [42; 43; 44]. The prediction generated by the CNN-LSTM model holds significant importance, taking a multitasking approach for predicting various network traffic loads through combining CNN-LSTM. The CNN-LSTM model has been observed to effectively extract temporal features [42]. The CNN-LSTM model demonstrates superior performance compared to baseline algorithms in terms of accurately forecasting the minimum, maximum, and average traffic loads within a network [43]. A convolutional neural network (CNN) is employed for the offline prediction of the q-function prior to the implementation of online deep q-learning, which is utilized to explore the control strategy [44]. The methodology has been discovered to optimize power transmission and enhance the quality of service.  
The reason for adopting CNN-LSTM for this study primarily lies with the fact that the integration of CNN and LSTM architectures in a hybrid model enables the efficient capture of spatial patterns and temporal dependencies within the data [45]. The conventional architecture of a hybrid CNN-LSTM model entails employing CNN layers as the initial component to extract spatial characteristics from the input data. The output generated by the CNN layers is subsequently inputted into LSTM layers in order to effectively capture the temporal dependencies that occur over a given period of time. Additionally, via capitalizing on the respective advantages of CNN and LSTM networks, the proposed model has the potential to enhance the precision, resilience, and versatility of intrusion detection systems. Consequently, it can offer heightened security measures for the identification and mitigation of network intrusions and attacks.
## 2 Methodologies for the Synthesis and Characterization of Hyaluronic Acid-Catechol  
Carbodiimide chemistry is commonly used to prepare HA-catechol [37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81]. It involves the formation of an amide bond between the carboxylic group of HA and the primary amine of dopamine (Figure 1a). The important reaction conditions for HA-catechol synthesis include maintaining the pH between 4 and 6 to prevent oxidation of the catechol groups [37]. The reaction solution turns black at a pH above 6, indicating the oxidation of the catechol groups. The degree of catechol substitution (DS) in HA is 10-35%, identified using standard carbodiimide chemistry. Similar approaches have been used to prepare catechol-conjugated negatively charged polymers (i.e., alginate-catechol [82; 83], heparin-catechol [84], and polyacrylic acid-catechol [85]). In a previous study, a Schiff's base reaction between aldehyde-modified HA and dopamine was used to prepare HA-catechol [86]. HA was first oxidized using sodium periodate to prepare dialdehyde-functionalized HA. The formation of the Schiff base was achieved by the reaction between dialdehyde groups in oxidized HA and the primary amine groups in dopamine. The DS (25-45%) obtained using the Schiff base reaction was higher than that obtained using the carbodiimide coupling reaction (-10%), probably due to the dialdehyde groups of the oxidized HA [86].  
Synthesized HA-catechols can be characterized using \({}^{1}\)H-NMR and UV-Vis spectroscopy. Figure 1b shows the \({}^{1}\)H-NMR spectrum of HA-catechol. HA peaks have been previously assigned as follows: the N-acetyl peak at 2.0 ppm, and multiplets of disaccharide units and anomeric protons at 2.8-3.7 ppm [38; 39; 40; 41; 42; 43]. In addition, the catechol protons were assigned to the peaks at 6.5-7.0 ppm [38; 39; 40; 41; 42; 43]. The degree of catechol substitution was calculated by comparing the integrated area of the catechol protons (3H, 6.5-7.0 ppm, D\({}_{2}\)O) and acetyl groups (3H, -COCH\({}_{3}\), 2.0 ppm, D\({}_{2}\)O). In UV-Vis spectroscopic studies, catechol groups show maximum absorption at a wavelength of 280 nm (Figure 1c). Catechol content can be quantified using standard curves of catechol concentrations constructed using a standard solution of dopamine [37; 38; 44; 45]. Fourier transform infrared spectroscopy is also used to confirm the catechol conjugation of HA-catechol [38]. The peaks at 1720, 1630, and 1410 cm\({}^{-1}\) are assigned to C=O, amine bonds, and C-O stretching in HA-catechol, respectively. In addition, C-H vibration (2850 cm\({}^{-1}\)) and C=C ring stretching (1520 cm\({}^{-1}\)) indicate catechol conjugation on the HA backbone.
## 2 Materials and Methods
### SBF-SEM Imaging  
For SBF-SEM imaging, the specimen was first embedded in EM-dedicated epoxy resin to obtain a small and compact block, then such a block was mounted on an Al specimen. Silver paint was used to electrically ground as much as possible the edges of the block to the aluminum pin. The entire block was then sputter-coated with a layer of gold capable of reflecting light to perform the fine SBF-SEM alignment. The block containing NP Al was then imaged using a ThermoFischer (Waltham, MA, USA) VolumeScope (VS) device, mounted into a ThermoFischer Teneo FEG SEM equipped with a Schottky field emission electron source, operating at an accelerating voltage of 2 kV, with a beam current of 50 pA, and under low-vacuum conditions (50 Pa). Serial BSE-SEM images were acquired by a dedicated detector after cutting the specimen with a thickness of 100 nm at different magnifications, and with resolution and dwell times chosen adequately to prevent further local specimen charging. For 3D reconstruction, rendering, and analysis, serial SEM images were aligned and stacked into a volume using AVIZO software (version 2020.3) (ThermoFischer). Three-dimensional structures in image stacks containing hundreds or thousands of 2D ortho slices were traced individually in each plane and automatically surface rendered. From SBF-SEM stacks analysis, the features of larger pores (such as area, shape, characteristic lengths, and pore profile roughness) were estimated through a semi-automatic tool (Analyze Particles) using Fiji program (version 1.53c plus) [29]. Fractal dimension was estimated by using \"Fractal Box Count\" tool of Fiji [29].