esa-sceva/satcom-chunk-collection huggingface dataset:
# Natural Photosensitizers in Clinical Trials
## 5 Reported Toxicity and Adverse Side Effects of Natural Photosensitizers
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Photosensitizer** & **Characteristic Information Described in the Article about the Mechanisms of Action of Each Photosensitizer** & **Article** \\ \hline Pheophorbide \(a\) & Significantly increases the levels of pro-apoptotic proteins while decreasing the levels of anti-apoptotic proteins. Inhibits proliferation, migration, and invasion of cancer cells. It works by inhibiting the EMT process and reducing the expression of matrix metalloproteinases, which are key in the process of tumor invasion. & [93] \\ \hline Alkaloids & It readily binds to DNA, increasing the efficiency of singlet oxygen generation. Upon photoexcitation, berberine causes guanine-specific oxidation in DNA, leading to DNA damage that can induce apoptosis or cell death by necrosis. & [105] \\ \hline Chlorophyllin & Decreases the expression of the anti-apoptotic protein BCL-2 and increases the level of the pro-apoptotic protein Bax, which alters the BCL-2/Bax ratio, further promoting apoptosis of cancer cells. & [115] \\ \hline Hypocrellin & It can affect mitochondrial oxidative stress-related signaling pathways regulated by proteins such as JunB and BAG-4. & [118] \\ \hline Cercosporin & Causes cell death mainly by necrosis mechanism through damage to mitochondria and ER cell membranes. & [133] \\ \hline Toliporphins & Nuclear membrane damage by transferring photo-oxidative damage from photoactivated TPs bound to the rough ER through inactivation of acyl-CoA:cholesterol-0-acyltransferase, a sensitive marker of ER membrane integrity. & [137] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._side effects of PDT with berberine. The only available in vitro study found less than 10% mortality of healthy HaCaT keratinocytes after administration of berberine without irradiation and about 53% mortality of these cells after irradiation [103]. In an in vitro study, chlorophyllin did not induce cytotoxicity against RAW 264.7 macrophages both in the dark and after illumination [115]. The in vivo safety of PDT with hypocrellin alone has also not been evaluated. Liposomal hypocrellin B used as a photosensitizer on a rat model of choroidal neovascularization led to little damage to the retina and retinal pigment epithelium. In addition, treated mice showed low skin phototoxicity under simulated sunlight 24 h after administration [141]. The lack of safety data is also apparent with cercosporin. In the case of loliorphins, the only in vivo study conducted did not describe the side effects of PDT with this PS in the mice tested. However, TPs were found to have relatively low blood levels and more uniform distribution in muscle, liver, and tumor tissues, which may lead to an increased risk of damage to healthy tissues [137]. The toxicity and side effects of photodynamic therapy with natural photosensitizers are summarized in Table 4.
## 3 Experiments and Results
### Configuring the Experimental Environment
#### 3.1.2 Experimental Platform  
For the quantitative evaluation of the theoretical verification proposed in this study, as shown in Figure 10, the intelligent wheelchair used in the experiment consisted of a PC for calculation and UWB, encoder, and IMU sensors for position estimation. The DWM1000 module of Decawave was used as the UWB sensor and two modules were installed at 65 cm intervals, at a height of 96 cm and 20 cm in front of the drive shaft in the horizontal direction. The UWB module exchanges distance data with each AN every 15 Hz period. A rotary encoder module with a resolution of 3600 was used, which also transmits data to a PC at a rate of 15 Hz. The IAHRS RB-SDA-v1 of ROBOR was used as the IMU sensor with a high accuracy using its own filter, and data were collected in a 15 Hz cycle to design all data cycles from each sensor based on 15 Hz.
## 3 Methodology
### Extending to Local Explainability
#### 3.5.1 Local Explainability with Neighborhood Information  
Local explainability refers to understanding and interpreting a model's predictions at an individual instance level. We leverage neighborhood information and the Jensen-Shannon divergence to achieve local explainability. By considering each instance's local neighborhood and comparing the predictions of the black-box model with those of the white-box model within this neighborhood, we can capture the regional variations in predictions and ensure that the explanations are faithful to the model's local behavior.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**ML-100k**} & \multicolumn{2}{c}{**ML-1M**} & \multicolumn{2}{c}{**HELOC**} \\ \cline{2-7}  & **AUC \(\uparrow\)** & **Fidelity \(\uparrow\)** & **AUC \(\uparrow\)** & **Fidelity \(\uparrow\)** & **AUC \(\uparrow\)** & **Fidelity \(\uparrow\)** \\ \hline Explainer (WB) & 0.7655 \(\pm\) 0.0042 & - & 0.7882 \(\pm\) 0.0038 & - & 0.7616 \(\pm\) 0.0051 & - \\ Original (BB) & 0.7784 \(\pm\) 0.0039 & 0.8287 \(\pm\) 0.0156 & **0.8078 \(\pm\) 0.0041** & 0.8875 \(\pm\) 0.0143 & 0.7703 \(\pm\) 0.0048 & 0.7728 \(\pm\) 0.0187 \\ \hline Pre hoc (BB) & 0.7801 \(\pm\) 0.0037 & 0.9094 \(\pm\) 0.0098 & 0.8033 \(\pm\) 0.0044 & 0.9404 \(\pm\) 0.0076 & 0.7698 \(\pm\) 0.0046 & 0.8454 \(\pm\) 0.0134 \\ Co hoc (BB) & **0.7816 \(\pm\) 0.0035** & **0.9194 \(\pm\) 0.0087** & 0.8036 \(\pm\) 0.0042 & **0.9484 \(\pm\) 0.0065** & **0.7743 \(\pm\) 0.0044** & **0.8572 \(\pm\) 0.0121** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model comparison in terms of prediction accuracy and fidelity on three real-world datasets. All metrics are computed with respective regularization parameters \(\lambda_{1}\) selected via validation. Results show mean \(\pm\) standard deviation over 5 runs. The best results are in **bold**. \(\uparrow\) means higher score is better, \(\downarrow\) means lower score is better.  
We define the neighborhood fidelity objective function as follows:  
\[\min_{f_{\theta}\in\mathcal{F}}\frac{1}{N}\sum_{i=1}^{N}D_{local}\big{(}f_{ \theta}(\mathcal{N}(x_{i})),g_{\phi}(\mathcal{N}(x_{i}))\big{)}, \tag{16}\]  
where \(\mathcal{N}(x_{i})\) denotes the set of instances in the local neighborhood of instance \(x_{i}\), and \(D_{local}\) is a divergence measure such as the Jensen-Shannon divergence computed over the neighborhood.  
The Jensen-Shannon divergence for local explainability is given by  
\[\begin{split} D_{\text{JS}_{\text{local}}}(g_{\phi},g_{\theta})& =\frac{1}{2}\big{(}\sum_{x_{j}\in\mathcal{N}(x_{i})}\ln\bigg{(} \frac{\hat{y}_{j_{\phi}}}{\hat{y}_{j_{\theta}}}\bigg{)}\hat{y}_{j_{\phi}}\\ &+\sum_{x_{j}\in\mathcal{N}(x_{i})}\ln\bigg{(}\frac{\hat{y}_{j_{ \theta}}}{\hat{y}_{j_{\phi}}}\bigg{)}\hat{y}_{j_{\theta}}\big{)}\end{split} \tag{17}\]
## 2 Materials and Methods
### Statistical Analysis  
We summarized the distribution of urinary phthalate metabolites using median and interquartile range. For statistical analysis, t-test, ANOVA, and Spearman correlation were performed using software from the R Foundation for Statistical Computing (version 4.0.4) and SPSS statistical software (version 26.0, Chicago, IL, USA). The urinary concentrations of four phthalate metabolites (i.e., MiPrP, MHkP, OH-MiNp, and cx-MIDP) and alternative plasticizers were not fully analyzed from all study participants (only \(n=21\) were available for those), attributable to insufficient urine samples. Thus, correlation and regression analyses were performed with <30 samples for the listed metabolites above. Phthalate metabolites that were detected at concentrations of more than 70% were selected for further analyses. All of the phthalate metabolites data were assessed by the Kolmogorov-Smirnov test to evaluate whether they were normally distributed. Since the distribution of phthalate metabolite concentrations was most likely right-skewed, all the phthalate metabolite measurements were logarithm-transformed to approximate normal distribution. The total concentration of phthalate and alternative plasticizer metabolites was creatinine standardized using the following formula to address urinary dilution as stated in previous studies: Total [phthalate metabolites] = 100\(\times\) phthalate (ng/mL)/creatinine (mg/dL) [29; 30].  
Multiple regression analyses were performed to identify the association of methylation changes (by leiomyoma and myometrium tissues type) with each urinary phthalate and alternative plasticizer metabolite level. A separate regression model was conducted for each log-transformed urinary phthalate and alternative plasticizer metabolite due to a possible correlation with each other. The regression model was adjusted for age, dysmenorrhea,menstrual phase, smoking status, alcohol drinking, secondhand smoking, and BMI. The \(p\) values less than 0.05 were considered statistically significant.
## 4 Results and Analysis
### Analysis of Meteorological Factors on Historical Forest Fire Occurrences
#### 4.1.3 Analysis of Annual Precipitation's Impact on Forest Fires  
Figure 5c,d demonstrate the correlation between annual average precipitation and the frequency and area affected by forest fires in Yunnan Province from 1991 to 2021. The analysis indicates that in years such as 1992, 1997, 2004, and 2010, a higher number of fires and larger affected areas were closely related to lower annual average precipitation, suggesting that reduced rainfall is a key climatic factor in escalating forest fire risk. The decrease in precipitation is not only linked to an increase in the frequency of fires but also significantly correlates with the expansion and severity of fires. These findings emphasize the importance of including annual average precipitation as a key variable in forest fire risk assessment and early warning systems to enhance the accuracy of forest fire predictions [50]. In formulating policies and management measures, the potential impact of reduced rainfall due to climate change on fire risk should also be considered to develop effective adaptive management strategies.  
Figure 5: _Cont._
# Composite Kernel Method for PolSAR Image Classification Based on Polarimetric-Spatial Information
## 4 Experiment
### Data Description
#### 4.1.2 San Francisco Bay Data Set  
The second data set regarding San Francisco Bay is comprised of NASA/JPL AIRSAR L-band PolSAR data. The size of the image is 900 \(\times\) 1024. In Figure 4a, the color image obtained with Pauli decomposition is shown. This image is manually labeled with three classes: city, vegetation, and water. The obtained reference map is shown in Figure 4b, in which different colors stands for different land cover types. Similar to the Flevoland data set, we randomly split pixels into the training set and the testing set. Detailed information about the training and testing set is listed in Table 4.  
Figure 4: San Francisco Bay data set collected by AIRSAR system. (**a**) RGB image obtained by Pauli decomposition. (**b**) Reference map. A total of three classes are identified. Color-coded class label: yellow—city area, blue—water, and green—vegetation.  
Figure 3: Flevoland data set collected by RadarSat-2. (**a**) RGB image obtained by Pauli decomposition. (**b**) Reference map. A total of four classes are identified. Color-coded class label: red—building area, green—woodland, orange—farmland, and blue—water.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Building** & **Woodland** & **Farmland** & **Water** \\ \hline Number of Samples in the Reference Map & 71,331 & 85,539 & 184,920 & 59,504 \\ Number of Training Samples & 713 & 855 & 1849 & 595 \\ Number of Testing Samples & 70,618 & 84,684 & 183,071 & 58,909 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of samples of the Flevoland data set used for quantitative evaluation.
## 4 Staphylococcus aureus Inactivation by HPP  
Food poisoning outbreaks in pasteurized milk products (e.g., chocolate milk, milk) were reported due to the ingestion of foods containing staphylococcal enterotoxins (SEs) produced by _S. aureus_ ([PERSON] et al., 1988; [PERSON] et al., 2003; [PERSON] et al., 2009; [PERSON] et al., 2010; [PERSON] et al., 2012) [42; 43; 44; 45; 46]. _S. aureus_ infection symptoms begin with diarrhea, but vomiting, nausea, dizziness, and abdominal pain associated with moderate fewer can also be registered ([PERSON] et al., 2012) [46]. At _S. aureus_ concentrations of \(\geq\)10\({}^{5}\) cells/g of food, detection of the enterotoxin in the contaminated food can confirm food poisoning by this bacterium ([PERSON] et al., 2012) [46].  
Table 1 shows HPP inactivation studies carried out with different _S. aureus_ strains and specific foods including milk, cheese slurry, poultry meat, pork slurry, and raw beef ([PERSON] et al., 1991, [PERSON] et al., 2021, [PERSON] et al., 2000, [PERSON] et al., 2020, [PERSON], 1998, [PERSON] et al., 1999, [PERSON] et al., 2022, [PERSON] et al., 2021; [PERSON] and [PERSON], 2000; [PERSON] et al., 2002) [47; 48; 49; 50; 51; 52; 53; 54; 55; 56].  
HPP at 600 MPa for 10-15 min achieved the minimum 5-log reductions recommended in milk and pork slurry. Cheese slurry required 20 min at 600 MPa for 4.5-log reductions in ATCC 6538 strain. Human milk required less stringent treatments of less time (4 min) at 600 MPa or 15 min at 450 MPa. ATCC 13565 _S. aureus_ was more resistant in poultry meat than in milk, as only 4.0-log reductions in poultry meat vs. 5.5-log reductions in milk were obtained after a HPP treatment of 15 min at 600 MPa. Ovine milk subjected to 500 MPa for 15 min resulted in only 3.2-decimal reductions. Applying heat assisted HPP at a moderate temperature of 50 \({}^{\circ}\)C increased the _S. aureus_ log\({}_{10}\) reductions to 6.0 in poultry meat and >7.0 in ovine milk. Although not so realistic, studies can be carried out using a cocktail of resistant strains to mimic the worst-case scenario in terms of inactivation. A HPP treatment of raw beef at 500 MPa for 7 min caused only 1.7-log reductions in a cocktail of 5 strains of _S. aureus_ previously inoculated in raw beef. Similar to thermal pasteurization, dry-cured ham contains a high content of salt and presents low water activity, thus making it very difficult to inactivate microorganisms. Overall, for similar processing conditions, there are some differences in the _S. aureus_ inactivation, depending on the food and the bacterial strain.
# Hiding Stealth Optical CDMA Signals in Public BPSK Channels for Optical Wireless Communication
## The Theories and Principles Background of the Proposed Stealth Communication Approach  
### Chromatic Dispersion  
Chromatic dispersion in an optical medium is a phenomenon in which the group velocity of light propagating through the medium depends on the light's wavelength. Let us consider a situation wherein a pulse is transmitted along a single-mode fiber. In an optical fiber, different spectral components of the pulse travel with slightly different group velocities. Consequently, they arrive at the fiber output at different times, even though they started at the same time. Figure 1 illustrates the phenomenon of chromatic dispersion caused by light propagation in a single-mode fiber. A pulse at frequency \(\omega\) passes through a fiber with length \(L\) after a time delay \(\tau=L/v_{g}\). The group velocity \(v_{g}\) is given by  
\[\
u_{g}=d\omega/d\beta, \tag{1}\]  
where \(\beta\) is the propagation constant. The range of pulse broadening for a fiber of length \(L\) can be expressed as [23]  
\[\Delta\tau=\frac{d\tau}{d\omega}\Delta\omega=\frac{d}{d\omega}(\frac{L}{v_{g} })\Delta\omega=L\frac{d^{2}\beta}{d\omega^{2}}\Delta\omega, \tag{2}\]  
where \(\Delta\omega\) is the spectral width of the pulse. The pulse spectral width \(\Delta\omega\) is usually replaced by the range of wavelengths \(\Delta\lambda\) emitted by the optical source. Then, Equation (2) can be written as  
\[\Delta\tau=-(2\pi c/\lambda^{2})\beta^{\omega}L\Delta\lambda=DL\Delta\lambda, \tag{3}\]  
by using the relations \(\omega=2\pi c/\lambda\) and \(\Delta\omega=(-2\pi c/\lambda^{2})\Delta\lambda\), where \(\beta^{\omega}\) is the second derivative with respect to \(\lambda\) and \(D\) is the _dispersion parameter_ with units ps/(nm\(\cdot\)km). A negative dispersion parameter indicates that lightwave with longer wavelength travels faster than lightwave with shorter wavelength.
## 3 Proposed Method
### Data Augmentation  
To increase the data features to obtain better results, data preprocessing is the building block of deep learning-based algorithms. We know that real world datasets might be contaminated with noise. Many times, these datasets are inconsistent, or some things may be missing. Sometimes, uneven and unbalanced classes appear. As can be seen in lines (9) to (18) of Algorithm-1, data preprocessing and augmentation is analyzed. As shown in Table 3, we preprocessed our dataset in distinct steps. One is to get the same size of each image of the HDT, the LDT, and video datasets. We make the dataset of 416 \(\times\) 416 pixels resolution of each image and video. Then the dataset is split into train, test, and validation set. To split the dataset, we used the RoboFlow ([[https://public.roboflow.com/](https://public.roboflow.com/)]([https://public.roboflow.com/](https://public.roboflow.com/)) (accessed on 12 January 2023)) (RFW) tool as described below.  
Figure 4: Sample images of our collected dataset: (**a**) video dataset and (**b**) annotated images.  
**The RFW:** this tool hosts free public computer vision datasets in many popular formats. The RFW provides a streamlined workflow to identify edges of various objects in several iterations. With each iteration, the detection models become smarter and more accurate. We used the RFW tool to fragment the entire dataset into train, validation, and test sets. In this study, we keep the split ratio as 7:2:1 that is the image dataset of both categories and the video dataset has been divided into 70% train, 20% validation, and 10% test sets. Training a model on small number of images could result in overfitting [26]. Moreover, it also results in poor generalization despite the fact that the training results are good enough. However, the testing accuracy drops down and the model classifies the samples into one class. In short, the training accuracy is high, but the validation accuracy drops down. To overcome this issue, data augmentation is used, which modifies the data using different techniques and increase the samples of the dataset. Through empirical analysis, we applied the following augmentation techniques on our collected dataset.  
_Cropping:_ In this stage, we crop the image dataset of both categories between 0% minimum and 30% maximum zoom.  
_Saturation:_ To achieve better results, we change the color ranges of images of both categories and the video as well. In this study, we saturate images \(\pm\)25%.  
_Brightness variation:_ The brightness of the image dataset of both categories has been carefully varied. We darkened and brightened the images \(\pm\)25%. After applying the data augmentation technique to the image dataset, it is ready to use in a model for object detection. The statistics of the dataset after augmentation are shown in Table 3. As can be seen, we have a total of 123,232 training images, 5476 validation images, and 40,997 test images.
###### Abstract  
The Japanese National Institute of Information and Communications Technology (NICT) is currently developing a high-performance laser-communication terminal for CubeSats aimed at providing a high-datarate communication solution for LEO satellites requiring transmission of large volumes of data from orbit. A key aspect of the communication system is a high-power optical amplifier capable of providing enough gain to the transmitted signals to be able to close the link on its counterpart's receiver with the smallest impact in terms of energy and power on the CubeSat's platform. This manuscript describes the development of a miniaturized 2-W space-grade 2-stage erbium-doped fiber amplifier (EDFA) compatible with the CubeSat form factor, showing the best power-to-size ratio for a space-qualified EDFA to the best of the authors' knowledge. Performance results under realistic conditions as well as full space qualification and test are presented, proving that this module can support short-duration LEO-ground downlinks as well as long-duration intersatellite links.  
EDFA; CubeSat; satellite communication; free-space laser communications; optical amplifier; space qualification; thermal vacuum 2022
# Concentration in Saliva and Antibacterial Effect of Xylitol Chewing Gum: In Vivo and In Vitro Study
###### Abstract  
_Purpose._ The saliva concentration of xylitol released from two chewing gums was recorded, the first containing xylitol as the only sweetener (100% xylitol) and the second containing only 22% of the polyol. In addition, the in vitro antibacterial effect of the two chewing gums was evaluated. _Materials and Methods._ The salivary concentration of Xylitol in 32 subjects was determined before and at 0.30, 1.00, 2.00, 5.00, and 10.00 min while using the chewing gums, and at 15.00, 20.00, and 25.00 min after the gums were discarded. In vitro antibacterial activity was determined on a pooled subgingival plaque sample obtained from four patients with periodontal disease. Cariogenic and periodontal bacteria were evaluated before and 15 min, 60 min, and 24 h after gum contact. _Results._ Using the 100% xylitol chewing gum, saliva levels increased bimodally, one peak after 30 s (1.49 \(\pm\) 1.41 logug/L) and a second one at a 10-min evaluation (1.41 \(\pm\) 1.11 logug/L); the 22% chewing gum peaked only two minute after contact (1.21 \(\pm\) 1.24 logug/L). Overall, a statistically significantly higher salivary concentration of xylitol was detected using the 100% xylitol gum. All bacteria decreased after the addition of the two chewing gums; the 100% gum achieved a greater decrease than the 22% gum. _Conclusion._ The use of both chewing gums increased the concentrations of xylitol in saliva, with a statistically significantly higher concentration using the 100% xylitol gum. Cariogenic and periodontal bacteria were reduced by both chewing gums; 100% xylitol gum produced the highest and longest lasting effect. This study opens up to the use of xylitol against periodontal disease.  
xplicit; saliva; oral bacteria; sub-gingival plaque; antimicrobial effect; chewing gum +
Footnote †: journal: Applied Sciences