esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Evaluation of Pavement Crack Damage by Deep Learning
### Crack Damage Evaluation and Mapping to GIS  
Firstly, we defined the percentage value of images with cracks in the segmented target images as \(CR\) defined by Equation (6).  
\[CR=\frac{\sum\limits_{i=1}^{n}p_{i}}{n} \tag{6}\]  
\(p_{i}\) is the ratio of images that contain cracks among the overlapped input images in the pixel of interest (for example, if three of the four overlapping images are judged to be cracked, the value is 0.75), and \(n\) is the total number of pixels. The results of the overlap analysis are given, for example, in Figure 9 below. This image is designed to have a pixel value of \(p_{i}\times 255\). That is, it is white for \(p_{i}=1.0\) and black for \(p_{i}=0.0\). In Figure 9, a semi-transparent overlay of the original image and the analysis results is shown for clarity.  
Figure 8: Comparison of accuracy between the proposed and conventional methods.  
Figure 7: Improvement of the analysis results with an increase in the number of steps.  
Although CR is the quantitative value, we should consider the applicability of our method to the existing inspection guidelines. Since the general inspection guidelines stipulate that the degree of crack damage should be evaluated in multiple grades, the thresholds for converting CR into multiple grades of evaluation were calculated as follows.  
We evaluated the degree of crack damage of the target images first according to the existing inspection guidelines. Then, in order to reproduce the results of the existing inspection guidelines from the CR, the mean absolute error of the following Equation (7) is minimized.  
\[\text{Mean absolute error}=\frac{1}{N}\sum_{i=1}^{N}\lvert r_{CR}-r_{h} \rvert_{i} \tag{7}\]  
In the above equation, \(N\) represents the number of target images, \(r_{h}\) represents the multiple grade evaluation result under the existing inspection guidelines, and \(r_{CR}\) represents the multiple grade evaluation result by CR. Specifically, \(r_{CR}\) is calculated from CR by determining the threshold between grades. Note that the pixels trimmed in the image are also counted in the total number of pixels. This is because it trimmed in the image is excluded from the calculation, some images with a very small denominator will appear, and as a result, even small cracks are often misidentified as serious cracks.  
In this study, CR are compared with the Japanese pavement inspection procedure, which is judged in three grades (grade 1 to grade 3). Grade 1 means sound, and grade 3 means severely damaged. The threshold values were calculated according to Equation (7) by the data taken in this study; the threshold between grade 1 and grade 2 was 0.33 and the threshold between grade 2 and grade 3 was 0.70. Lastly, we conducted mapping of the resulting data on GIS. In this study, the latitude and longitude information obtained by the vehicle is linked to the acquired images; therefore, it is easy to plot the analysis results on the GIS based on the information.
# HFDF-EffNetV2: A Lightweight, Noise-Robust Model for Fault Diagnosis in Rolling Bearings
## 1 Introduction
Aiming to address the challenges of achieving light weight, high accuracy, strong robustness to noise, and real-time responsiveness in practical industrial applications, we propose HFDF-EffNetV2, a lightweight FD model, with the following main contributions:  
1. The improved EfficientNetV2 was constructed based on multiplicity factors combined with the Mish activation function. Additionally, Fused-MBPyConv and BSMBConv-MLCA were designed to combine multi-scale shallow features with multi-dimensional deep features for efficient extraction of representative fault features from time-frequency feature maps (FMs). 2. The hierarchical fine-grained decision fusion (HFDF) strategy is proposed to perform weight updating in phases to alleviate the tendency of the lightweight model to fall into local overfitting when dealing with confusable fault types and small samples. The proposed model was evaluated under noisy and small-sample conditions and compared with six other models. The results indicate that the proposed model outperforms these others in terms of robustness to noise and stability while remaining lightweight. The remainder of this study is structured as follows. Section 2 presents the materials and methods; Section 3 describes the results, providing detailed architectural parameters;Section 4 discusses the robustness to noise and stability of HFDF-EffNetV2 in two cases; Section 5 concludes this study.
# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 2 Related Research and Background
### Challenges in IoT
#### 2.1.4 IoT Security
In fact, even over normal networks, asymmetric or public-key cryptography is only employed in the key exchange process, after which encryption proceeds with symmetric cryptography, which is much less complex. Lightweight cryptographic algorithms are more suitable for the IoT, as they have smaller block sizes, key sizes, and fewer encryption rounds. These algorithms also have applications in Wireless Sensor Networks WSNs. A lightweight cipher EXPer for IoMT in Reference [27] was based on Selective Encryption (SE), which encrypts part of the encoded bitstream (in the case of compressed video), while making the decoded video stream unwatchable without decryption. The study in Reference [27] demonstrated a lower Encryption Space Ratio (ESR) on application of the lightweight EXPer cipher on selected syntax elements arising from H.264/AVC entropy coding. EXPer works best with the CABAC entropy coder works best, with less data selectively encrypted, while still maintaining visual security as a result of distorted video streams (after decoding without decryption). Hence, EXPer leads to reduced computational overhead, making it suitable for an IoMT.
## 4 Conclusions  
The main parameter of the various dielectric materials for the DBD devices is the capacitance, \(C\propto K/T\), with the dielectric constant, \(K\), and the thickness, \(T\). The characteristic of the current-voltage (I-V) depends on the C, which is proportional to the slope of the I-V curve. A large \(C\) of a high \(K\) and thin \(T\) provides a low firing voltage and a high amount of the plasma current. For the solid plates with the \(T\) of 500 \(\upmu\)m, the \(K\) of the ceramic alumina is 9.0, which is larger than that of the glass (\(K=5.3\)) or quartz (\(K=3.6\)). The \(K\) of the thin flexible polymer films is \(\approx\)(2-3), which is smaller than those of the solid plates. Since the solid plates are fragile, they are limited to the thickness reduction of less than a few-hundred \(\upmu\)m. Alternatively, the commercialized polymers are available to form the thin films with the \(T\) of less than 100 \(\upmu\)m and with a high \(C\), and this is achieved by reducing the film \(T\). Therefore, the \(C=2.55\) pF with the \(K=2.8\) and the \(T=125\)\(\upmu\)m is comparable to that of the alumina with the \(C=2.40\) pF, the \(K=9\), and the \(T=500\)\(\upmu\)m.  
In the prolonged tests involving the discharge samples, the solid plates remained strong against the plasma erosion, while the polymer films were readily etched by the chemical reaction of the  
Figure 12: Infrared (IR) spectra of the original silicone (Si) film (lower black line) and the Si PI-surface film with the plasma discharge (upper red line).  
air-plasma species. The glass polymer (GP) film is somewhat stronger than the polymer film; however, its film surface was also damaged after the long-run test of 10 h. The polyimide (PI) shows the dielectric breakdown and the surface degradation after the 30-min operation. The plasma erosion of the polypropylene (PP) is serious; its film did not withstand the damage even after the short-time operation of approximately 19 min. In the IR spectra of the polymer films, the structural change pf the PP chain was observed due to the plasma; however, such a change was not found for the PI. The films coated with the Si were reinforced, and they hardly showed any plasma-erosion damage, even after the prolonged test that lasted for more than 10 h.  
[PERSON] conceived and designed the experiments; [PERSON] performed the experiments and wrote the manuscript; [PERSON]-j.K. and [PERSON]-N.L. performed the experiments; [PERSON]-T.K. analyzed the data.  
This work was supported in part by the Kwangwoon University under a Research Grant in 2018 and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2018R1A2B6008642).  
The authors declare no conflict of interest.
# Synthesis of Oligomeric Silicone Surfactant and its Interfacial Properties
## 4 Conclusions  
The synthesis of the oligomeric silicone surfactant and the examination of the interfacial properties of low permeability gas-condensate reservoirs gave the following conclusions:  
1. A new kind of oligomeric silicone surfactant (OSSF) containing sulfonic acid groups was synthesized. The critical micelle mass concentration was 0.980 g/L and the critical surface tension was 20.631 mN/m. The surface tension of OSSF increased with increasing hot rolling temperature and decreased with the addition of NaCl, KCl, or CaCl\({}_{2}\).
2. OSSF adsorption transferred the wettability of cores from water-wet to preferential gas-wet. A change in the OSSF adsorption layers' surface chemical composition occurred and exhibited lower interface energy than that of the cores. The adsorption isotherm of quartz sands changed from Langmuir type (L-type) to \"double plateau\" type (LS-type) in the OSSF solution due to the chemical adsorption at high temperature.
3. The foaming volume and property of OSSF began to stabilize as the weight percentage increased. The presence of NaCl decreased the foaming volume and improved the OSSF foam stability. At the same time, OSSF decreased the initial foaming volume and stability in the induction period and accelerated sodium dodecyl benzene sulfonate (SDBS) formation.  
[PERSON] and [PERSON] was the main supervisor of the research, [PERSON] contributed in analysis of the results, [PERSON] contributed in experiments design and conducted the experimental work and analyzed the results. [PERSON] wrote the manuscript, [PERSON] contributed in analysis of the results. [PERSON] contributed in analysis and discussion of the results and reviewing and editing of the manuscript.  
This research was funded by thank Important and Special Project of China (No. 2016 ZX05020-004) sponsored by the Ministry of Science and Technology of China.  
The authors declare no conflict of interest.
## 4 Review of Mask-Face Detection Methodologies
### Three-Class Mask-Face Detection Methodologies  
The main defect of two-class based methods is that in the case of incorrect mask-wearing (i.e., wearing a mask without covering the nose, mouth or chin), the mask can still be detected in the image but it this not effective in virus prevention. Therefore, researchers recently paid more attention to performing mask-face detection with the additional class of incorrect mask-wearing. The detection categories used for three-class mask detection are roughly the same as the two-class methods. Due to the characteristics of three-class datasets, three-class mask detection is more challenging. Firstly, a majority of datasets used by existing three-class detection methodologies are unbalanced and the number of samples in the incorrect mask-wearing class is less than that in the class of wearing mask or the class of without mask. In addition, the feature of incorrect mask-wearing has a high similarity with the other two categories, and this directly leads to the lower accuracy of the class. In order to improve the accuracy of detecting incorrect mask-wearing, some studies [57; 58; 59; 60] added attention mechanisms to balance features from multiple dimensions to generate more salient features. Other studies [61; 62] searched for and labelled more incorrect mask-wearing data by themselves or used the data augmentation approaches to increase the small number of samples in the incorrect mask-wearing class, thus making the three classes in the training dataset more balanced as to achieve a better mask-face detection performance. Studies used for three-class mask-face detection are discussed in Table 3 after introducing representative studies from each category in the subsequent sections. \begin{table}
\begin{tabular}{c c c c c c} \hline
## 3 Results and Discussion
### Effects of Sub-Lethal Concentrations of NP/Derivatives on AF Production: A. parasiticus
Complete inhibition of fungal growth was achieved at 0.6 mM of SLD (3-day exposure), thus indicating SLD can exert potent antifungal activity on different food crops. Tree nuts in California, USA, are mostly contaminated with pathogenic fungi, such as _Alternaria_ sp., _Aspergillus_ sp., _Botrytis_ sp., _Botryosphaeria_ sp., etc. [65]. Of note, coinciding with the SLD antifungal activity was the inhibition of corn seed germination at \(\geq\)0.6 mM (2- to 3-day exposure) (Table 2; Supplementary Figure S3). Therefore, results indicated that SLD could be utilized as a potent antifungal fumigant especially for the foods/crops \"to be processed\", but not for the crop seeds for cultivation. A recent study also indicated that a benzaldehyde derivative, benzoic acid, which is a secondary metabolite released as a root exudate, is an inhibitor triggering plant autotoxicity, thus interfering with seed germination and root growth in _Lactuca sativa_ L. [66]. As mentioned above, natural phenolics, such as benzaldehyde analogs, are potent redox cyclers that destabilize cellular redox homeostasis [39; 40]. We surmised that, as determined in benzoic acid [66], SLD might also negatively affect redox homeostasis in corn seeds, thus resulting in the inhibition of seed germination. Other studies have described further the importance of redox homeostasis in seed germination/plant growth [67; 68; 69]. SLD has been used as a food flavoring agent and/or an intermediate for producing pharmaceuticals [64]. In the atmosphere, SLD exists as a vapor with vapor pressure value of 5.93 \(\times\) 10\({}^{-1}\) mmHg at 25 \({}^{\circ}\)C. Noteworthy is that SLD has been exempted from requiring a tolerance by the United States Environmental Protection Agency (EPA); hence, this compound can be applied as an inert ingredient up to 14% by weight of antifungal formulations [70]. Collectively, SLD could be applied as a fumigant during food/crop sanitation or storage. The identification of the precise mechanism of the structure-activity relationship, namely, differences in the antifungal/anti-aflatoxigenic efficacy between SLD and SA, warrants future in-depth investigation.
## 4 Implementation
### Evaluation Metrics  
In this study, several metrics were used to evaluate the performance of the Multiple Object Tracking (MOT) model. The main evaluation metrics used are MOTA (Multiple Object Tracking Accuracy), MOTP (Multiple Object Tracking Precision), and IDF1 (Identification F1 score).  
MOTA (Multiple Object Tracking Accuracy) is a metric that evaluates the accuracy of multi-object tracking by comprehensively considering the impacts of false negatives (FN), false positives (FP), and ID switches (IDSW). It measures the tracking accuracy of multiple targets in a single camera and is calculated using the following Formula (6):  
\[MOTA=1-\frac{\sum_{t}(FN_{t}+FP_{t}+IDSW_{t})}{\sum_{t}GT_{t}} \tag{6}\]  
Here, \(FN_{t}\) represents the false negatives (undetected objects) at time \(t\), \(FP_{t}\) represents the false positives (incorrectly detected objects) at time \(t\), \(IDSW_{t}\) represents the ID switches (incorrect ID matching) at time \(t\), and \(GT_{t}\) represents the number of ground truth objects at time \(t\).  
MOTP (Multiple Object Tracking Precision) is a metric that evaluates the precision of multi-object tracking based on the distance error of each object and is calculated using the following Formula (7):  
\[\text{MOTP}=\frac{\sum_{i}\sum_{t}d_{t}^{i}}{\sum_{t}c_{t}} \tag{7}\]  
Here, \(d_{t}^{i}\) represents the distance error of object \(i\) at time \(t\), and \(c_{t}\) represents the number of matched objects at time \(t\). Additionally, \(d_{t}^{i}\) indicates the overlap area between the bounding box of target \(i\) and the matched bounding box at frame \(t\). An overlap area within  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Division** & **Train Data** & **Test Data** \\ \hline (a) Clear water, natural habitat & 528 & \\ (b) Fish farms with artificial structures & 344 & \\ (c) Fish farm with lighting & 181 & 1300 \\ (d) Cloudy water, inside the fish farm & 4737 & \\ (e) Natural habitat rich in seaweed & 1291 & \\ \hline Total & 7081 & 1300 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dataset configuration and quantity.  
the 50-100% range is considered a correct match, and a higher MOTP value indicates better location accuracy of the tracked objects.  
IDF1 (Identification F1 score) represents the F-value for identifying the target ID in each frame and is calculated using the following Formula (8):  
\[IDF1=\frac{2\cdot IDTP}{2\cdot IDTP+IDFP+IDFN} \tag{8}\]  
Here, IDTP (ID true positives) represents the number of correct object ID matches, IDFP (ID false positives) represents the number of incorrect object ID matches, and IDFN (ID false negatives) represents the number of missed object IDs. A higher IDF1 value indicates better consistency in maintaining object IDs, and this metric comprehensively evaluates the model's performance in terms of detection accuracy and object identification consistency. Through these evaluation metrics, the performance of the proposed model in this study was comprehensively evaluated.
# Electricity Usage Efficiency and Electricity Demand Modeling in the Case of Germany and the UK
## 4 Model Output
All factors combined, with GDP increasing by 2% points a year, industrial production in Germany increasing by 1% a year, and electricity usage efficiency improving by about 1% annually, we expected the German power consumption in 2020 to be about 3% lower compared to 2008. A reversal of this trend could be expected somewhere between 2020 and 2030 due to the increasing demand for new electric intensive industries. While we agree with the UK Department of Energy and Climate Change that electricity consumption is likely to be higher in 2050 than it is today, we do not think the increase could be anywhere close to 100%. Various consulting bodies, such as McKinsey and Company, considered that for achieving clean energy targets, EU electricity consumption will have to increase substantially by 2050 [26], by 48% between the years 2010 and 2050 [17] and by 1.1% points per year between 2010 and 2020, mentioning that this rate is still lower compared to a 1.5% electricity demand growth during the 1990-2007 period. It is visible from this analysis that such studies probably neglected the very important factor of electricity usage efficiency, which led to an approximately stable demand during the period analyzed, as is shown in Figure 5. These results are also important when analyzing electricity demand in EU countries in the future years. While it is hard to predict if the electricity efficiency can be gradually improved until 2050, it still has an important impact on electricity consumption during the present years and there is no reason to expect a crucial change any time soon. Table 3 provides reference data for the period 2013-2020. Based on this reference data, it is possible to further analyze the prediction accuracy of the proposed model (Equation (1)). Figure 5: Germany, consumption by year, real data, modeled forecast, and usual demand forecast based on [17]. Figure 6 Provides a more detailed comparison of the model output and the real data during the 2013-2018 period. As can be seen, the model managed to forecast the real market situation reasonably well except for the very high electricity consumption during the winter of 2017. It would be impossible for the model to predict 4 years in advance. A similar output, structured by year, is summarized in Table 4. This table also confirms that the model was able to provide results very close to the real market data. The highest deviation of the model came in the year 2015 and was at the level of 2%. The average yearly deviation of the model was at the level of 0.8%, and the difference between the model and reality for the whole six-year period of 2013-2018 was at the level of 0.1%.
## 3 Results
### Comparison of Cyclic Peak Stress and Strain Variations  
As shown in Figures 6 and 7, the peak strain and peak stress under quasi-static and dynamic loading conditions were compared across varying confining pressures. With increasing cycle number N, the peak strain of the coal specimens under both loading regimes demonstrated a gradual upward trend. Notably, dynamic loading consistently produced higher peak strains than quasi-static loading, highlighting its pronounced strain rate effects. For instance, under initial dynamic impact (the first cycle), the peak strain increased from 0.020 at a confining pressure of 0 MPa to 0.026 at 20 MPa, indicating enhanced plasticity with elevated confining pressures. Furthermore, while the peak strain under quasi-static loading increased linearly with N, dynamic loading induced a nonlinear growth pattern.  
As shown in Figure 6, the 95% confidence and prediction bands decreased with increasing confining pressure. Similar to 3.2, this was due to the dynamic loading experimental data fluctuation and the increased dynamic impact cycles (data points) with rising confining pressure [36]. Under quasi-static loading, there were no obvious 95% confidence and prediction bands. This means the quasi-static experimental data fit the curve well at the 95% confidence level (with R\({}^{2}\) = 0.999).  
Figure 6: Comparison of peak strain under quasi-static and dynamic loading.  
Similar to peak strain evolution, Figure 7 shows that peak stress also rose with increasing N. However, unlike peak strain, peak stress cannot increase indefinitely due to material strength limitations, resulting in a nonlinear trend characterized by a gradually decreasing slope, followed by abrupt post-peak stress reduction. Notably, dynamic loading exhibited a more distinct \"rise-then-decline\" peak stress trajectory compared to quasi-static loading. This phenomenon likely stemmed from the larger loading increments in dynamic protocols and the accelerated failure processes inherent to high-strain-rate regimes [22].  
Figure 7 also shows how increasing the confining pressure affected the 95% confidence and prediction bands. Under dynamic loading, although the samples at 20 MPa were subjected to more impact cycles and thus a larger sample scale, the peak stress fluctuation was greater. This resulted in wider confidence and prediction bands than at 10 MPa. For quasi-static loading, the 95% confidence and prediction bands tended to increase with confining pressure. This was mainly because a higher confining pressure causes more significant stress changes in coal specimens, especially during the yielding and failure stage. The effect of these changes on increasing the confidence interval width outweighs the impact of a larger sample scale on reducing it.
## 4 Mathematical Formulations
### Flexible VRPSPD: MILP 3  
_MILP3_ represents a simultaneous pickup and delivery operation such as the one in \(MILP1\). However, the novelty in \(MILP3\) is its flexibility, which allows the dropping/releasing of some boxes. Thus, a partial collection of boxes is acceptable due to either a limited vehicle capacity and/or the high cost of collecting all boxes. Despite \(MILP3\) being more realistic in terms of collecting flexibility, several trips become necessary to collect all boxes, which could be high in terms of cost. To deal with this issue, we added a penalty cost to the objective function that penalizes the process of leaving boxes. Figure 4 represents an overview of \(MILP3\). We define the following additional variables for \(MILP3\). * \(Sc_{j}^{emd}\): boxes left at customer \(j\). * \(c_{ca}\): penalizing cost associated to the left boxes. Figure 4: Overview of the flexible VRPSPD: \(MILP3\).