esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Setup
### Datasets  
We conduct experiments on three standard KGC benchmark datasets, including UMLS [40], FB15K-237 [41], and WN18 RR [42]. The descriptions of these datasets are summarized in Table 1. UMLS serves as a widely adopted structured knowledge resource in the biomedical domain, encompassing medical concepts and their interrelations, and is commonly employed to assess a model's capability for medical reasoning. FB15K-237 represents a handpicked collection derived from the Freebase knowledge graph, in which inverse relations have been eliminated to increase the complexity of the reasoning task. WN18 RR is derived from WordNet and focuses on evaluating the model's reasoning ability over lexical hierarchies. These datasets cover different domains and reasoning difficulties, providing a comprehensive evaluation of the model's performance. In this work, we follow the standard dataset splits for KGC tasks and evaluate the model performance on the test set.
## 3 Methodology
### Design of the Data Service Container  
In terms of technical implementation, a data service container is a lightweight hosting server for geographic data services, whose design goal is to provide data-access services to users through a series of common interfaces for heterogeneous geographic data resources.
## 2 Materials and Methodology  
### Obtaining Images and Creating Datasets  
#### 2.1.1 Data Gathering  
The research region is situated in southern Qinghai Province in China's Hainan Tibetan Autonomous Prefecture. The area is primarily mountainous and is situated at the center of the Tibetan Plateau, and it experiences a typical plateau continental climate, which is part of the Tibetan Plateau climatic system. Animal species that are widely distributed and raised in the alpine grasslands of the Hainan Tibetan Autonomous Prefecture include yaks, sheep, and cows. These animals are well-adapted to the local natural habitat and climatic circumstances. Data collecting was done in September and December of 2023 to guarantee data variety. The data collection targeted yaks, cattle, and sheep using a DJI Air 3 drone (DJI, Shenzhen, China). Livestock data were gathered using drone images at various times, places, perspectives, and elevations to guarantee the images' applicability to the actual world and the model's capacity for generalization.
# Cascaded Vehicle Matching and Short-Term Spatial-Temporal Network for Smoky Vehicle Detection
## 3 Smoky Vehicle Datasets
### Dataset Collection and Annotation  
To manually collect a large-scale smoky vehicle image dataset from scratch is pretty hard since there may only exist a few smoky frames in one day of surveillance footage. To this end, we created a cold boot process for data collection. First, we rented several smoky vehicles and drove them in the view of different surveillance cameras. Then, we captured all the smoky frames and used the LabelMe toolbox ([[https://github.com/CSAILVision/LabelMeAnnotationTool](https://github.com/CSAILVision/LabelMeAnnotationTool)]([https://github.com/CSAILVision/LabelMeAnnotationTool](https://github.com/CSAILVision/LabelMeAnnotationTool)), accessed on 1 July 2022) to annotate smoke bounding boxes. Specifically, we obtained 1000 smoke frames at this stage. After that, with the annotated smoke images, we managed to train a rough YOLOv5s smoke detector. Finally, we deployed this detector in several surveillance cameras and gathered these frames with a smoke detection threshold of 0.05.  
With the cold boot process, we managed to collect thousands of samples every day. To mine real smoky vehicle frames, we hired several people to carefully check whether the collected frames had a smoke region or not. To make the verification easy, the screen was adapted to a resolution of 1920 \(\times\) 1080 since all images were 1920 \(\times\) 1080. If a frame contained smoke, the people were asked to annotate the smoke bounding box using LabelMe. Within several months, we had annotated 75,000 smoke frames in total. We call this the Large-Scale SmOky Vehicle image dataset, or LaSSoV. LaSSoV includes light smoke frames and strong smoke frames, as well as crossroad smoke frames. Figure 2 illustrates several samples in LaSSoV.  
LaSSoV-video. Since temporal information is important for smoke recognition and existing private smoky vehicle datasets are comprised of videos, we further collected long-term smoke videos for temporal model exploration and performance comparison. We called this dataset LaSSoV-video. In total, we collected 163 one-minute videos with vehicle smoke, and used PotPlayer ([[http://potplayer.daum.net/](http://potplayer.daum.net/)]([http://potplayer.daum.net/](http://potplayer.daum.net/)), accessed on 1 July 2020) to annotate the initial smoke frame and the final smoke frame. Specifically, since we may use videos for temporal model training, we randomly split LaSSoV-video into two sub sets, i.e., a training set which includes 82 videos and a testing set which contains 81 videos.
## 2 Materials and Methods
### Mathematical Model
#### 2.2.4 Incident Solar Irradiance  
The total incident solar irradiance \(G_{tot}\) [W/m\({}^{2}\)], as shown in Equation (12), is the sum of the incident beam \(G_{b}\) [W/m\({}^{2}\)], derived from Equation (13), diffuse \(G_{d}\) [W/m\({}^{2}\)], derived from Equation (14), and reflected \(G_{r}\) [W/m\({}^{2}\)], derived from Equation (15), solar irradiance [49; 50; 51]:  
\[G_{tot}\ =\ G_{b}+G_{d}+G_{r} \tag{12}\]  
\[G_{b}\ =\ I_{bs}\frac{cos\theta_{\beta}}{cos\theta_{z}} \tag{13}\]  
\[G_{d}\ =\ I_{ds}\frac{1+cos\beta}{2} \tag{14}\]  
\[G_{r}\ =\ f_{all}\,I_{rs}\frac{1-cos\beta}{2} \tag{15}\]  
where \(I_{br}\) [W/m\({}^{2}\)] is the incoming beam solar irradiance (Section 2.2.8), \(cos\theta_{\beta}\) [-] is the solar incident angle for a surface with an inclined N-S axis (Section 2.2.7), \(cos\theta_{z}\) [-] is the solar incident angle for a horizontal surface (Section 2.2.7), \(I_{gr}\) [W/m\({}^{2}\)] is the incoming diffuse solar irradiance (Section 2.2.8), \(I_{r}\) [W/m\({}^{2}\)] is the incoming reflected solar irradiance (Section 2.2.8), \(\beta\) [rad] is the inclination angle (adopted \(\beta\) = 34\({}^{\circ}\)--optimal angle for Kragujevac) and \(f_{allb}\) [-] is the surface albedo (adopted \(f_{allb}\) = 0.2).
###### Abstract  
Many adsorbent materials are now commercially available; however, studies have focused on modifying them to enhance their properties. In this study, an activated carbon (AC) and hydroxyapatite (HAp) composite was synthesized by the immersion of ACs in a simulated body fluid solution, varying the AC oxidation degree along with the addition of CaSiO\({}_{3}\). The resulting composites were characterized by ash %, X-ray fluorescence (XRF), Fourier-transformed infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and point of zero charge (PZC). The characterization results indicated that the addition of CaSiO\({}_{3}\) and the oxygenated functional groups in the AC surface are key factors for HAp growth. The composites were tested on methylene blue (MB) adsorption as a potential application for the synthesized materials. Adsorption isotherms were modeled with Langmuir and Freundlich isotherms, and the composites were fitted to a Langmuir model with the highest q\({}_{\text{max}}\) value of 9.82. The kinetic results indicated that for the pseudo-second-order model, the composites fitted, with a contact time of 180 min to remove a 95.61% average of the MB. The results indicate that composite materials can be an efficient adsorbent for the removal of MB from aqueous solutions at low concentrations since the material with the highest amount of HAp growth removed 99.8% of the MB in 180 min.  
activated carbon; CaSiO\({}_{3}\); oxidation; hydroxyapatite; methylene blue; adsorption Article
## 3 Results
### Spatiotemporal Coverage
On the other hand, the results of (c) and (d) compared with (a) and (b) show that the ground coverage pattern in the case of a Moon-based platform is not impacted by the halo motion, which is more likely to play a role in extending the intensively covered area and slightly increasing the coverage time of the equatorial region and polar area. As mentioned above, the halo motion can contribute to the revisiting cycle of the nadir point, which is an advantage for ground coverage frequency compared to the Moon-based platform due to the fact that the repeating period of the nadir-point track for the Moon is as long as about 27 days. Thus, without loss of generality, the following section takes several locations on prime meridians with different latitudes as examples to analyze the differences in coverage frequency during two typical years. The segments of lines in Figure 8 depict the annual coverage cycle and frequency of eleven typical locations of different latitudes during the annual period from March 2024 to March 2025 (a) and from March 2033 to March 2034 (b). The black segments of each line with different latitudes indicate the moment able to be covered while the white intervals between segments indicate a non-covered period. In a similar definition, the segments of lines with different colors and intervals under the black ones indicate the same in the case of halo orbit. Moreover, the grid of the time axis refers to the orbital period of the lunar, which is regarded as a sidereal month inconsistent with the commonly used Gregorian month. Note that due to the density of the scatter, there are still invisible intervals in what seemingly looks like a segment, thus the line of Moon in (b) with a latitude of 28\({}^{\circ}\) is not a completely continuous line. As is presented, the total pattern of the coverage cycle for the Moon and halo orbit is almost the same both in (a) and (b). However, (a) shows that the shortest gap period occurs in the equatorial region and the gap length increases with hoisting latitude, while (b) shows that the shortest gap period occurs at low latitudes around 28\({}^{\circ}\) and decreases towards high latitudes and the equator. Similarly, the halo motion does not influence the overall trend of the coverage cycle in the dimension of latitude; instead, it plays a role in extending the coverage segment under a monthly scale, especially in areas with shorter coverage time, such as the polar region and slightly reducing the interval inter-monthly. Figure 7: Accumulated coverage time of a Moon-based platform (**a**,**b**) and halo orbits with a z-direction amplitude of 20,000 km (**c**,**d**) during one year from March 2024 to March 2025 (**a**,**c**) and from March 2033 to March 2034 (**b**,**d**).
## 2 Wavelet Denoising Technique  
Wavelet analysis is an effective and widespread time-frequency signal processing tool. The continuous wavelet transform (CWT) of signal s is given by (1), where the signal is transformed into shifted and scaled versions with respect to a mother wavelet \(\psi(i),a\) and \(b\) are the scaling and translation parameters, respectively.  
\[W(a,b)=\frac{1}{\sqrt{a}}\int s(t)\cdot\psi^{*}((t-b)/a)dt \tag{1}\]  
Given a sequence of noised signal \(s=\{s(i)\}\), \(i\in\){1, 2,..., \(N\}\), discrete wavelet transform (DWT) is expressed by (2), where \(j\) and \(k\) are the discrete versions of \(a\) and \(b\), respectively.  
\[W(j,k)=2^{-j/2}\sum_{i}s(i)\cdot\psi^{*}\left(2^{-j}n-k\right) \tag{2}\]  
[PERSON] produced a fast decomposition and reconstruction algorithm for DWT, which is known as a two-channel sub-band coder using quadrature mirror filters [23]. Starting from the signal \(s\), two sets of coefficients are computed: approximation coefficients \(cA_{1}\) and detail coefficients \(cD_{1}\). The sets are obtained by convolving \(s\) with the low pass filter \(h\) for approximation and with the high pass filter \(g\) for detail, followed by dyadic decimation. The approximation coefficients \(cA_{1}\) are then split into two parts using the same scheme by replacing \(s\) with \(cA_{1}\) and producing \(cA_{2}\), and so on. The wavelet tree containing the terminal nodes for \(j\) level DWT decomposition is portrayed in Figure 1[24].  
PD pulses and white noise show different behaviors in the frequency sub-band. The white noise energy is uniformly distributed in the wavelet coefficients among each scale, while the PD pulse energy concentrates on a limited number of coefficients with amplitudes above those of the corresponding noise coefficients [19]. The wavelet denoising is based on the assumption that coefficients with large amplitudes are mainly generated by signal and coefficients with small amplitudes are mainly generated by noise. Wavelet threshold shrinkage denoising involves three steps: decomposition, thresholding, and reconstruction.  
1. Decomposition Select a wavelet, determine decomposition level \(J\), and perform the \(J\) level DWT decomposition as Figure 1.
2. Thresholding Estimate the threshold for each level and apply it to the detail coefficients.  
Figure 1: j level DWT decomposition.  
Reconstruction  
Reconstruct the signal using the approximation coefficients and the thresholded detail coefficients of all scales.
# Electrochemical Control of Biofilm Formation and Approaches to Biofilm Removal
## 3 Biofilms in Food Industry  
Firstly, it is important to realize that biofilm-growing cells are fundamentally different from free-floating cells. In particular, they are highly resistant to external influences and therefore represent a source of serious complication in medicine. This is the main area where biofilm-induced diseases are registered, as opposed to food-borne diseases, where the origin is difficult to trace. For this reason, the chapter dealing with this topic is included.  
Biofilm in food processing plants is a major source of spoilage and food-borne microorganisms. The improper detection of biofilm formation or its subsequent removal can cause rapid deterioration of food and its quality during processing, retailing, or storing. In Europe and North America, per-capita food losses range from 280 to 330 kg/year [88]. The relative contribution of processing to overall food waste varies across food commodities. For instance, the beginning and end of the food supply chain (agriculture and consumption) is the main source of food waste for fruits and vegetables, while a relatively high contribution of processing has been observed for meat, tuber, or oilseed and pulses. In 2011, the European Commission estimated that the processing sector has released almost 88 million tons of food waste. This corresponds to 33 kg per person per year, including both the edible and inedible parts of processed food products [89]. A year later, when applying the language of money, the cost of EUR 13 billion was estimated for EU-28 (9.1% of overall costs). Biofilm control, or its successful eradication from food processing plant environments, is a great challenge for the food industry to decrease the costs as well as the health implication for consumers.  
Biofilms are deposited on the surface of processing equipment and can be the source of spoilage or pathogenic microorganisms. Once the food product is in contact with the processing tool on which the biofilm is present, the cells can be transferred from the biofilm to the food product. It was recently proven that biofilm-bacteria were transferred from freshly cut vegetable surfaces at a higher rate than planktonic bacteria [90]. There is no direct evidence that biofilm was the main cause of food spoilage or aimentary disease; however, it is very likely. [PERSON] et al. found that 80% of the microbial flora isolated from vegetable processing plants has the potential to form biofilm [91]. Food spoilage microorganisms were present in 70-80% of the biofilms collected from meat processing facilities [92]. In the meat industry, approximately 90% of microorganisms form biofilms and are responsible for the loss of USD 150 million per year [93].
## 3 Experimental Verification for the Proposed Plasmonic Metasurface
with the plasmonic metasurface in a manner similar to 3D structures, where metal bars aligned with the direction of the incident EM wave contributed to the absorption. When compared to the absorption spectrum obtained from simulations, the measured values exhibit some discrepancies. However, the broadband absorption characteristic and high absorption exceeding 90% of the plasmonic metasurface are still preserved. The observed differences might stem from the fabrication process of the plasmonic metasurface, since small fabrication errors can influence the interactions between the split rings in the unit cell structure. Nevertheless, these interactions might compensate for each other, ensuring that the quality of the absorption spectrum of the plasmonic metasurface is not significantly affected. Moreover, the discrepancies might also derive from the measurement process, since we used a VNA to measure the reflection coefficient (\(S_{11}\)) of the plasmonic metasurface, from which the absorption performance was calculated. The antennas might have been placed at slightly misaligned positions, and certain unavoidable unwanted interactions could also contribute to these differences. Figure 8b illustrates the absorption performance of the plasmonic metasurface for TM-polarized waves. Due to the design of the circular unit cell of the structure, the symmetry is relatively high, resulting in an absorption spectrum for the TM waves, which is quite similar to that of the TE mode. The metasurface provided an absorption over 90% in a frequency range of 12.40 to 14.90 GHz for the TM-polarized waves. Naturally, slight differences between the TE and TM modes remained, attributed to the inherent properties of the metasurface and the measurement inaccuracies discussed above. For both TE and TM modes, the absorption spectrum is not completely flat at a level of 90% across the frequency range. Instead, it reaches 99% at certain frequencies for all incident angles. This behavior can be explained by the unequal interaction of the split-ring pairs forming the metasurface. Nevertheless, the lowest absorption level remains above 90%, making the metasurface suitable for applications requiring a high absorption efficiency.
## 3 Results
### Santa Rosa Site
#### 3.1.2 Santa Rosa 2023 Dataset
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor** & **N** & **a** & **c** & **d** \\ \hline
1a & 39,011 & 0.004109 & 0.092604 & \(-\)0.614406 \\ \hline
1b & 39,011 & 0.003677 & 0.10811 & \(-\)0.685727 \\ \hline
2a & 39,010 & 0.00447 & 0.096386 & \(-\)0.76239 \\ \hline
2b & 39,010 & 0.00433 & 0.078342 & \(-\)0.959499 \\ \hline
3a & 39,011 & 0.00382 & 0.095303 & \(-\)0.678204 \\ \hline
3b & 39,008 & 0.003972 & 0.0986 & \(-\)0.728282 \\ \hline
4a & 39,001 & 0.004565 & 0.079921 & \(-\)0.92565 \\ \hline
4b & 39,001 & 0.004467 & 0.097416 & \(-\)0.848253 \\ \hline
5a & 39,001 & 0.003897 & 0.097416 & \(-\)0.848253 \\ \hline
5b & 39,001 & 0.003543 & 0.106866 & \(-\)0.612662 \\ \hline
6a & 38,992 & 0.002672 & 0.123556 & \(-\)0.775122 \\ \hline
6b & 38,992 & 0.003937 & 0.10173 & \(-\)0.64114 \\ \hline \hline
7 mean & 0.003955 & 0.098021 & \(-\)0.75663233 \\ \hline SD & 0.00052 & 0.012065 & 0.11799929 \\ \hline SE & 0.00015 & 0.003483 & 0.03406346 \\ \hline RSD & 0.131586 & 0.123084 & \(-\)0.15595327 \\ \hline RSE & 0.037986 & 0.035531 & \(-\)0.04501983 \\ \hline RSE (\%) & 3.8\% & 3.6\% & 4.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Individual best-fit models of CF_1 for 12 sensors. The general model is calculated as the mean values of a, c, and d.  
For the Redwood City site, the regressions of the individual models on observed CF_1 PM\({}_{2.5}\) all had intercepts below 0.2 ug/m\({}^{3}\) and slopes >0.96 (Table 11). The general model performed well, with intercepts between \(-\)0.32 and +0.74 ug/m\({}^{3}\) and slopes between 0.95 and 1.06. Thus, all models met the EPA requirement for an intercept absolute value less than 5 ug/m\({}^{3}\) and a slope between 0.9 and 1.1. Our second main objective is to compare the two algorithms cf_1 and pm2.5 alt for precision, accuracy, and limit of detection.
# Measurement of Absolute Acoustic Nonlinearity Parameter Using Laser-Ultrasonic Detection
## 4 Experimental Results
### Laser-Ultrasonic Detection Results
Figure 8 displays the relationship between \(A_{2}\) and the square of \(A_{1}\), which were extracted from their frequency spectra, as a function of increasing input displacement amplitudes in both specimens. The relationships indicate acceptable linearity with a high correlation coefficient (0.99) for both specimens. From the slope, ultrasonic wavenumber (\(k_{u}\)), and ultrasonic wave propagation distance (\(x_{u}\)) for each specimen described above, the absolute \(\beta\) parameters for the copper and Al6061-T6 specimens were determined and are listed in Table 2. The \(\beta\) value of the copper specimen obtained from the laser-ultrasonic detection method was \(\beta_{cu}=3.4\), and that of the Al6061-T6 specimen was \(\beta_{AI}=4.9\). The difference in \(\beta\) values between the two materials is attributed to the difference in the microstructural characteristics such as the lattice parameter, crystal structure, phase,  
Figure 8: Linear fitting plots of \(A_{1}^{2}\) and \(A_{2}\) for copper (blue circle) and Al6061-T6 (brown square) specimens. Figure 7: Typical frequency-domain signals for copper (blue solid line) and Al6061-T6 (brown dotted line) specimens. and dislocation density between the two materials. Generally, the \(\beta\) value of Al6061-T6 is higher than that of the copper because aluminum alloys such as Al6061-T6 are composed of various ingredients, which may result in higher lattice anharmonicity and dislocation density when compared to the pure copper [9].