esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment  
### The Performance of Pedestrian Flow Statistics Algorithms in Real Scene  
Since there is no dataset specifically for pedestrian flow statistics, we use the image sequence captured by front-down cameras at the entrance of crowded scenic spots to verify the detection and counting effect of the proposed algorithm.  
To develop an algorithm with high robustness against the changes of illumination and fog concentration, we have manually detected and labeled the images captured by the camera, and made a new dataset. As a detection dataset, five half-hour video recordings were collected. There were intense changes in illumination in the videos, and some videos have dense fog. The frame rate of video recordings is 25 fps, and an image is saved every 12 frames. The total number of annotated images is 18,750, including 282,674 pedestrian bounding boxes. Among them, 2000 images from the end of two videos with different perspectives are used as the test set, while the remaining 16,750 images are used as the training set. After training, the detection effect of the algorithm is evaluated, and the most popular evaluation indicators in the detection field, such as precision, recall, mAP (mean Average Precision), are selected as the evaluation indicators. The detection performance is shown in Table 1. Compared with detectors trained only with coco datasets, the robustness of the detector trained with new images to the changes of illumination and fog concentration is greatly improved.  
For pedestrian counting effect, we selected five other videos to evaluate. By observing the counting results manually, the missing alarm and false alarm of the counting algorithm are counted. Finally, the counting algorithm is evaluated by the precision, recall and F1-Score of the counting algorithm. The evaluation results of the algorithm in five videos are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Precision\(\uparrow\)** & **Recall\(\uparrow\)** & **F1-Score\(\uparrow\)** & **TP\(\uparrow\)** & **FP\(\downarrow\)** & **FN\(\downarrow\)** & **mAP\(\uparrow\)** \\ \hline detector (coco) & 0.87 & 0.67 & 0.76 & 17786 & 2644 & 8887 & 72.88\% \\ detector (coco + new data) & 0.86 & 0.79 & 0.82 & 21156 & 3550 & 5517 & 83.39\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detection performance of detector trained with different data.  
Figure 6: Virtual block-based method, pedestrian counting method.  
It can be seen that the mean F1-score of the algorithm is over 95% in the five videos, which proves the effectiveness of the algorithm. The running speed of the algorithm on NVIDIA GTX1060 GPU is 28.4 FPS, which has good real-time performance. The actual running effect is shown in Figure 7.
## 5 Treatment
### Comparison among Treatments  
The systematic literature search, adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was performed across several databases for studies published from 2016 to 2021, focusing on diagnostic methods, prospective management trials, and innovative studies on MRON's pathogenesis [67]. The review emphasizes the importance of early and surgical intervention in MRONJ treatment, citing high long-term success rates exceeding 85% [67]. This assertion is further reinforced by additional research indicating that, although conservative treatment may occasionally result in healing for early-stage MRONJ, a significant portion of cases deteriorate over time. This trend suggests that opting for early surgical measures could be substantially more effective in curtailing disease progression and mitigating bone loss [68]. The review indicates that while non-surgical treatment options like antibiotic therapy, antiseptic rinses, and eliminating causative factors can be attempted for limited disease stages, complete healing of MRONJ lesions under conservative treatment is extremely unlikely [67].  
Another comparative review concludes that while conservative treatment can yield better outcomes for asymptomatic patients in early stages of MRONJ, surgical treatment is superior in patients with advanced stages of the disease [69]. It emphasizes the importance of individualized treatment decisions, regular communication, and collaboration between dental and medical professionals to ensure optimal care, tailored to the severity of the patient's condition, and to achieve the best possible clinical outcomes while minimizing potential complications [69]. However, it also acknowledges the limitations of the review, including the limited number of studies on conservative treatment and the small sample sizes, calling for further research to strengthen the evidence base and improve the significance of the findings [69].
## 4 Results and Discussion  
### Characterization of Macro-Deformation Behavior  
Figure 5 shows the homogenized Young's modulus and yield strength calculated at each temperature using the 15-\(\upmu\)m-sized models of Cu-IMC-resin and Cu-SAC-resin, where the resin region is PI, epoxy, or air; the values are normalized with respect to that of the Cu-IMC-resin at 22 \({}^{\circ}\)C. The normalized homogenized Young's modulus of the Cu-IMC-resin is stable against temperature, whereas that of the Cu-SAC-resin decreases with the increase of temperature. The yield strength shows a similar trend (Figure 5b). These trends indicate the stability of properties in Cu and IMC against temperature variations as compared to SAC. In addition, the Young's modulus and yield strength in the models with PI resin and air exhibit a minor difference. Thus, these FE results suggest that the IMC inhibits \"softening\" at high temperatures accompanied by a reduction in the Young's modulus and yield strength, which is a significant advantage over SAC solder. Moreover, the load-bearing capacity of PI resin is very small due to its softness.  
Figure 6 shows the homogenized nominal strain-stress curves and mechanical properties calculated with the 50-\(\upmu\)m-sized model. Here, the PI resin part is treated as air for reducing the calculation elements of the model based on the results shown in Figure 5. Although the IMC is considered as a linear elastic material, the curves show a deformation behavior with a yield point. The Cu-IMC-resin microstructure exhibits isotropicity, i.e., there is a negligible difference between the obtained properties along the three load-directions. The Young's modulus of the Cu-IMC-resin composite (11.3 GPa) is significantly smaller than that of IMC (Cu\({}_{6}\)Sn\({}_{5}\), 102 GPa). Moreover, the modulus of the Cu-IMC-resin microstructure is 1/4 times smaller than that of the solder (SAC305, 46 GPa) also. This implies that the Cu-IMC-resin microstructure shows significantly low stiffness compared to the IMC and solder. In addition, CTE is \(17.8\times 10^{-6}\)/\({}^{\circ}\)C. The temperature dependence of the homogenized yield strengths was also evaluated using the same method as described before at various ambient temperatures from \(-55\) to \(175\)\({}^{\circ}\)C. The calculated yield strengths at \(-55\), \(22\), \(100\), and \(175\)\({}^{\circ}\)C are \(17.4\) (+\(0.6\)%), \(17.3\) (reference),  
Figure 4: Silicon carbide-direct bonded copper (SiC-DBC) FE model for temperature cycling simulations. The sizes of the SiC chip and DBC substrate are \(8\times 8\) mm, and \(12\times 12\) mm, respectively.  
17.0 (\(-\)1.9%), and 16.7 MPa (\(-\)3.7%), respectively. For comparison, those of the SAC305 solder at 22, 100, and 200 \({}^{\circ}\)C are 45 (reference), 15 (\(-\)66.7%), and 3 MPa (\(-\)93.3%), respectively [40; 41]. These results confirm that the Cu-IMC-resin microstructure exhibits remarkably stable mechanical characteristics against temperature variation.
## 2 Materials and Methods
### Characterization of Spray-Dried Microspheres
#### 2.3.3 BTA Release Studies  
For the quantification of BTA, released from CMC-Na microspheres, calibration curves were performed on a Perkin Elmer LAMBDA 650 UV/Vis Spectrophotometer by measuring the absorbance of BTA standard solutions at 258 nm. The correlation coefficient, from five standards, was higher than 0.999.  
BTA release profiles were performed by dispersing 10.0 mg of CMC.BTA-MS in 50.0 mL of distilled water, giving rise to a 0.2 mg mL\({}^{-1}\) suspension. At different time points (5 min-96 h), an aliquot of the capsule's suspension was extracted with a syringe, filtered through PTFE membrane filters (0.20 \(\upmu\)m pore), and measured after appropriate dilution with distilled water (dilution factor: 10). UV-Vis measurements were performed, in triplicate, following the same time scale for all of the samples, for all of the spray drying conditions tested (standard deviation was lower than 3%). The same experimental procedure was performed with empty CMC microspheres (CMC-MS) to investigate whether CMC-Na would interfere in the quantification of BTA (results not shown).  
\begin{table}
\begin{tabular}{c c c} \hline
**Condition Number** & **Inlet Temperature (\({}^{\circ}\)C)** & **Drying Gas Flow Rate (L/h)** \\ \hline
1 & 170 & 440 \\ \hline
2 & 170 & 600 \\ \hline
3 & 180 & 440 \\ \hline
4 & 180 & 600 \\ \hline
5 & 190 & 440 \\ \hline
6 & 190 & 600 \\ \hline \end{tabular}
\end{table}
Table 1: Experimental conditions for the preparation of CMC.BTA-MS by spray drying.  
Encapsulation efficiency and loading capacity were calculated according to Equations (2) and (3), respectively, and estimated from the release study results, assuming a complete release of the encapsulated material at 96 h.  
\[\text{Encapsulation efficiency (EE\%)}=\text{(M}_{\text{final}}/\text{M}_{\text{ initial}})\times 100\% \tag{2}\]  
where M\({}_{\text{final}}\) is the amount of encapsulated BTA in carboxymethylcellulose microspheres determined from the release profiles, and M\({}_{\text{initial}}\) is the initial amount of BTA added in the feed solution (before the spray drying process).  
\[\text{Loading capacity (LC\%)}=\text{(M}_{\text{encapsulated}}/\text{M}_{\text{ microspheres}})\times 100\% \tag{3}\]  
where M\({}_{\text{encapsulated}}\) is the amount of encapsulated BTA in carboxymethylcellulose microspheres determined from the release profiles, and M\({}_{\text{ microspheres}}\) is the total amount of microspheres obtained.
# Providing Email Privacy by Preventing Webmail from Loading Malicious XSS Payloads
## 3 Webmail XSS
### Attack Vector  
The Webmail system sets basic defenses to resist XSS attacks [24]. Therefore, when conducting cross-site scripting attacks, some mutated attack vectors are usually used to launch these  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variant Type** & **Example** \\ \hline Sender injection & From: \(<\)“ciframe onload=alert(/xss/)\(>\)@attacker.com“\(>\) \\ Reciever injection & To: [EMAIL_ADDRESS];<img src=a onerror=alert(“xss”)\(>\)@victim.com \\ Subject injection & Subject: hello\(<\)script>alert(“xss”)\(<\)/script\(>\) \\ Content injection & Content-type: text/html xss=cimg src=“foo alt=“onerror=alert(“xss”)\(>\) \\ Attachment injection & Content-Disposition: attachment; filename=“cimg src=x onerror=alert(“xss”)-a.jpg” \\ \hline \hline \end{tabular}
\end{table}
Table 1: Examples for different Email XSS injection type.  
attacks. We classify mutated XSS vectors into 4 categories--encoding, keyword substitution, position/morphology transformation, and special characters. Table 2 shows the example for these four categories.  
* Encoding. The browser can actively decode and escape for some special circumstances, which allows hackers to use this feature to encode the attack vector. In this way, the original attack feature of the XSS vector during transmission can be effectively hidden. For example, HTML, Unicode, UTF-7 encoding for special tags or attributes.
* Keyword substitution. Many tags and keywords are blacklisted by various XSS detection software. Therefore, in order to obtain a better attack effect, it is necessary to replace sensitive words. For example, one could invent sensitive word substitution, \"Alert\" popup sensitive word replacement, white space replacement, parenthesis replacement, and label replacement for general events.
* Position/morphology transformation. Regular matching and detection models tend to be sequential and usually read strings from left to right. Therefore, the attribute position change or the attribute status change can be performed within the rules allowed by the script tag to achieve the purpose of avoidance detection. For example, the positional transformation of attribute expressions and event expressions, keyword case transformations, alerts, and other bullet-box string morphological changes.
* Special characters. An attacker can add special characters to the constructed XSS code that does not affect code execution but may disrupt the detector. For example, add an assignment expression before the event; add a blank character between the event and the trigger code; add any letter or number in the standard room; add any calculation expression between the function name; add a comment between the function name and the parenthesis; add before and after the attack vector to any character.
# Multilevel Coding for the Full-Duplex Decode-Compress-Forward Relay Channel
## 6 Conclusions  
This work studies the decode-compress-forward protocol, highlighting scenarios where it exceeds the rates available via decode-forward and compress-forward in the constellation constrained full-duplex relay channel. Furthermore, this work presents a multilevel coding strategy for the decode-compress-forward protocol. The proposed multilevel coding was shown to approach the rates achieved by decode-compress-forward in the constellation constrained AWGN full-duplex relay channel.  
This work was supported in part by the grant CIF1219065 and ECCS1546969 from the National Science Foundation.  
[PERSON] conceived the multilevel approach to multi-node coded modulation and contributed to mathematical formulation and proofs. [PERSON] contributed to mathematical formulation, proofs, and simulations.  
The authors declare no conflict of interest.
# Design and Development of an SVM-Powered Underwater Acoustic Modem
## 3 Results
### Robustness Analysis of Signal Classification  
The robustness of the developed SVM-based signal classification system was evaluated through a comprehensive noise analysis and a K-fold cross-validation to ensure its efficacy under different underwater acoustic conditions.
# Profiles of Volatile and Phenolic Compounds as Markers of Ripening Stage in Candonga Strawberries
###### Abstract  
Volatile compounds, quality traits (total phenols and antioxidant capacity) and High-performance liquid chromatography (HPLC)-isolated polyphenols of strawberries, variety Sabrosa, commercially referred to as \"Candonga\", harvested at three different times (H1, H2 and H3) and at two different ripening stages, namely half-red (Half-red-H1, Half-red-H2 and Half-red-H3) and red (Red-H1, Red-H2 and Red-H3) were evaluated. Dominant anthocyanins, namely cyanidin-3-O-glucose, pelaponidin-3-O-glucose and pelaponidin-3-O-rutinoside, as well as \(p\)-counaryl hexoside increased during harvesting, differently from flavonoids, such as quercetin-3-O-glucose, kaempfer-3-O-glucuron and quercetin 3-O-glucuron, that declined. Samples clustered in different quadrants of the principal component analysis (PCA) performed on volatiles, quality traits and phenolic compounds, highlighting that only the red samples were directly correlated to volatile components, as volatiles clearly increased both in number and amount during ripening. In particular, volatiles with a positive impact on the consumers' acceptance, including butyl butyrate, ethyl hexanoate, hexyl acetate, nonanal, terpen and lactones, were positively associated with the Red-H1 and Red-H2 strawberries, while volatiles with negative coefficients related to consumer liking, including isopropyl butyrate, isoamyl butyrate and mesifurane directly correlated with the Red-H3 samples. Accordingly, strawberries harvested at Red-H1 and Red-H2 ripening stages could be preferred by the consumers compared to the Red-H3 fruit. Altogether, these results could help to individuate quality traits as putative markers of the ripening stage, and optimize the process of post-harvesting ripening to preserve or improve the desirable aromatic characteristics of strawberries.  
Fragaria \(\times\) ananassa Duch.; Sabrosa; ripening stage; headspace solid phase microextraction (HS SPME GC/MS); HPLC-MS/MS principal component analysis +
Footnote †: journal: 10 3300
## 4 Experimental Results
### The Computational Complexity Analysis  
In order to analyze the computational complexity, we calculate the number of ear pictures detected per second when the batch size is 1 (namely, FPS) and the calculation formula is shown in Equation (10).  
\[\text{FPS}=1/\text{average inference time per image}, \tag{10}\]  
All experiments were performed under the same conditions and the FPS of all methods are shown in Table 13. The best and second-best running times are displayed in red and blue, respectively.  
From Table 13, we can see that the proposed YOLOv5s-MG method has the highest detection speed. On the CCU-DE ear dataset, FPS of the improved YOLOv5s-MG model increased by 116.4%, 20.8%, 38.1%, 81.3%, and 1350% compared with that of YOLOv5s,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Human Ear Dataset**} & \multicolumn{6}{c}{**Model**} \\ \cline{2-7}  & **YOLOv5s** & **YOLOv5s-V3** & **YOLOv5s-V2** & **YOLOv5s-G** & **YOLOv5s-MG** & **YOLOv7** \\ \hline CCU-DE (epoch = 150) & 6.7 & 12 & 10.5 & 8 & 14.5 & 1 \\ \hline USTB (epoch = 150) & 4.6 & 7.5 & 6.2 & 5.5 & 8.5 & 1.3 \\ \hline EarVN1.0 (epoch = 150) & 5.7 & 9.6 & 8.1 & 6.7 & 11.2 & 1.1 \\ \hline EarVN1.0 (epoch = 1000) & 5.8 & 9.7 & 8.3 & 6.8 & 11.4 & 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 13: FPS of the six methods on the testing set of three datasets.  
Figure 11: The comparison results of YOLOv5s-MG (proposed), YOLOv5s-V3, YOLOv5s-V2, YOLOv5s-G, YOLOv7, and YOLOv5s models on three datasets: (**a**) [EMAIL_ADDRESS]; (**b**) the parameter quantity (params); (**c**) the calculation amount (GFLOPS); and (**d**) the model size.  
YOLOv5s-V3, YOLOv5s-V2, YOLOv5s-G, and YOLOv7, respectively. On the USTB ear dataset, FPS of the improved YOLOv5s-MG model increased by 84.8%, 13.3%, 37.1%, 54.5%, and 553.8% compared with that of YOLOv5s, YOLOv5s-V3, YOLOv5s-V2, YOLOv5s-G, and YOLOv7 respectively. On the USTB ear dataset when epoch = 1000, FPS of the improved YOLOv5s-MG model increased by 96.6%, 17.5%, 37.1%, 37.3%, and 776.9% compared with that of YOLOv5s, YOLOv5s-V3, YOLOv5s-V2, YOLOv5s-G, and YOLOv7, respectively.
# Simulation and Catastrophe Detection of Spontaneous Combustion Processes in Suffide Ores
## 3 Results
### Catastrophe Detection of the Whole Unsteady Process of Spontaneous Combustion of Suffide Ore Heap
However, as one moves towards the central region of the heap, the ore becomes denser, resulting in increased ventilation resistance and reduced oxygen supply. This reduction in oxygen availability impedes the spontaneous combustion effect. Therefore, the combination of ventilation and heat storage effects significantly contributes to the observed spatial differences in the spontaneous combustion process of the sulfide ore heap. Figure 11: Catastrophe detection results. Figure 11 shows that the sample entropy variation at the four measurement points exhibits a similar pattern: a transition from relative stability to a catastrophic state, followed by a period of brief stability and a final sharp decrease. Initially, during the early stages of spontaneous combustion in the sulfide ore heap, the system remains relatively stable. Heat accumulation and gradual temperature increases result in minimal changes in sample entropy. As the temperature rises significantly and spontaneous combustion becomes evident, the system transitions from a safe to an unsafe state, leading to a sharp increase in sample entropy. This rise in sample entropy reflects the growing complexity and unpredictability of the combustion process. During the peak of spontaneous combustion, the sample entropy remains high with significant fluctuations due to the chaotic nature of the reactions. In the final stage, as the ore particles within the heap reach full oxidation and the system approaches equilibrium, the sample entropy decreases, indicating a stabilization of the system. The analysis of catastrophe points from measurement points A, D, E, and H in Table 5 reveals that the critical temperatures at which catastrophic events occur during the unsteady process of spontaneous combustion differ across these points. This variability can be attributed to several factors, including the ore's intrinsic thermal conductivity, particle size, and environmental conditions, such as ventilation and void ratios within the ore heap. Furthermore, the mean sample entropy values for regions (I) and (III)--which represent stages before and after the catastrophic region (II)--were calculated for these measurement points. It was observed that the mean sample entropy values for region (III) are consistently higher than those for region (I). This suggests that the high-rate oxidation or combustion stage (region III) is more complex and exhibits greater variability compared to the earlier stage of spontaneous combustion development (region I). The increased sample entropy in the high-rate oxidation stage reflects the heightened complexity and chaotic nature of the reactions occurring during this period.
## 3 Feature extraction and dimension reduction
### Feature subset selection  
The output of sensor array or even a single sensor can be composed of a high dimensional vector, with each element representing a physical variable or a chemical compound. However, it is very likely that only some of these variables contain pertinent information, so feature subset selection not only reduces signal dimension but also excludes irrelevant variables from system modeling. More importantly, the selected feature subset can provide the most direct and pertinent information for system analysis. For example, detecting biomarkers from the signals of a biosensor designed for disease diagnosis can provide the kernel information for pathology research.  
In general, selecting a feature subset is an optimization problem which aims to find a subset that can yield an optimal solution such as the highest classification accuracy. Because the objective function could be represented by nontraditional models like neural network models, stochastic optimization methods such as genetic algorithms [42], differential evolution [43], and simulated annealing [44] are popularly used in this aspect.