esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Risk Assessment Model
#### 2.1.1 Risk Assessment Indicators  
This study selects five meteorological and marine environment factors and then computes the absolute rate of information transfer from these factors to the damage caused by dangerous phenomena (based on historical marine data) to indicate any causal relationships [23].  
\[T_{j\to i}=\frac{1}{\text{detC}}\cdot\sum_{k=1}^{d}\Delta_{jk}C_{k,di}\cdot \frac{C_{ij}}{C_{ii}} \tag{1}\]  
where \(T_{j\to i}\) is the absolute rate of information transfer from variable \(X_{j}\) to variable \(X_{i}\), \(C\) is the covariance matrix, \(\Delta_{jk}\) are the cofactors of \(C\), \(C_{k,di}\) is the sample covariance between all \(X_{k}\) values and the Euler forward difference approximation of \(\frac{dX_{i}}{dt}\) (\(t\) is time), \(C_{ij}\) is the sample covariance between \(X_{i}\) and \(X_{j}\), and \(C_{ii}\) is the sample variance in \(X_{i}\).  
According to the reliability test rule, the standardized \(T_{x\to dangey}\) values of variables \(x\) (variables x are wind speed, wave height, ocean currents, and visibility) to the damage of dangerous phenomena are 0.1178, 0.2230, 0.0151, and 0.0521 in Table 1, respectively, all of which are greater than 1%, thus indicating that the results are statistically significant at a 95% confidence level. The standardized \(T_{temperature\to dangey}\) to the damage of dangerous phenomena is 0.0036, which means the causal link is weaker from sea water temperature to the damage of dangerous phenomena than the other four factors. Therefore, the variables of wind speed, wave height, ocean current, and visibility are selected to quantitatively calculate the hazard effect risk value.  
The vulnerability of a ship represents the risk-bearing capacity of the ship; i.e., the higher the vulnerability value of a ship, the higher the risk of navigation and the lower the level of safety of the ship. Since different ships have different probabilities of encountering danger in specific sea conditions mentioned in the IMO, the four factors selected in [PERSON]'s probability model for predicting accidents are adopted in this study [24].  
1. Vessel type and vessel size: During ship construction and design, classification can usually be based on vessel type and vessel size due to the different purposes of the ships being designed;
2. Vessel age: As one of the most important parameters, vessel age was mentioned in the Ship Risk Profile, and older age may increase the risk profile;
3. Vessel flag: The \"White, Grey and Black (WGB) list\" in the Paris MoU ranked countries' performance in different states from high to low as 'White', 'Grey', and 'Black' based on the total number of inspections and detentions over a 3-year rolling period for flags with at least 30 inspections in the period. To summarize, the risk assessment indicators established in this study are shown in Figure 1.
# Systematic Review: Malware Detection and Classification in Cybersecurity
## 4 Discussion
### Implications for Future Research
#### 4.6.3 Applications of Federated Learning Techniques  
Another promising direction for future research is the use of federated learning for malware detection in distributed networks and IoT environments. Federated learning allows machine learning models to be trained on data that remains localized on devices, without needing to transfer large volumes of data to a central server. This approach is particularly useful for cybersecurity applications in IoT devices, where sensitive data, such as malware signatures and network behavior, may need to remain on the device for privacy or security reasons.  
Federated learning not only enhances data privacy, but also enables the development of more robust models that can learn from distributed data across different environments. Instead of relying on a single centralized data source, models can learn malware patterns from data distributed across a network, allowing them to better adapt to specific threats faced by different devices in the network. Future research should explore how to integrate federated learning into malware detection systems, particularly in critical infrastructures and decentralized networks like IoT networks.Federated learning (FL) has emerged as a promising solution for training robust detection models without the need to centralize sensitive data. Its decentralized nature enables privacy preservation, bandwidth efficiency, and scalability across heterogeneous devices. A representative study in this domain proposes a clustered FL architecture tailored for large-scale, heterogeneous IoT environments ([PERSON] et al., 2016). The approach integrates unsupervised clustering into the FL pipeline, allowing model aggregation within device subgroups that share similar characteristics. This architecture enhances anomaly detection performance and addresses key challenges in IoT security, such as model heterogeneity, data isolation, and network constraints.
# Photon Counting Statistics of a Microwave Cavity Coupled with Double Quantum Dots
## 2 Model and Theoretical Formalism
### Photon Emission Counting Statistics
#### 2.3.2 Large-Deviation Statistics for Net Photon Current  
If we investigate the net photon current statistics between the cavity and the thermal reservoir, Equation (18) can be rewritten as  
\[\begin{array}{c}\frac{d}{dt}\mu(s,t)=\frac{1}{2}\gamma[1+\overline{n}]\left[ 2e^{s}a\mu(s,t)a^{\dagger}-\left\{a^{\dagger}a,\mu(s,t)\right\}\right]+\frac{ 1}{2}\gamma\overline{n}\left[2e^{-s}a^{\dagger}\mu(s,t)a-\left\{a^{\dagger}a,\mu(s,t)\right\}\right]+\\ i\delta_{m}\left[\text{a}^{\dagger}a,\mu(s,t)\right]+\frac{1}{2}A_{+}\left[2a^ {\dagger}\mu(s,t)a-\left\{a^{\dagger}a,\mu(s,t)\right\}\right]+\frac{1}{2}A_{- }\left[2a\mu(s,t)a^{\dagger}-\left\{a^{\dagger}a,\mu(s,t)\right\}\right]\end{array} \tag{27}\]  
Then, the moment generating function can be obtained as  
\[\mathcal{M}(s,t)=\frac{2\xi e^{(\gamma+W)t/2}}{2\xi\cosh\left[\frac{\xi( \gamma+W)t}{2}\right]+(1+\chi^{2})\sinh\left[\frac{\xi(\gamma+W)t}{2}\right]}, \tag{28}\]  
where  
\[\begin{array}{c}\xi=\sqrt{1-4[A_{+}(1+\overline{n})(e^{s}-1)+A_{-} \overline{n}(e^{-s}-1)]/(1+W/\gamma)^{2}}\\ \chi=\sqrt{1-4[(1+\overline{n})\overline{n}_{c}(e^{s}-1)+(1+\overline{n}_{c}) \overline{n}(e^{-s}-1)]/(1+W/\gamma)}.\end{array} \tag{29}\]  
For the long time limit, the cumulant generating function \(\Theta(s)\) for the net photon current is  
\[\Theta_{net}(s)=\lim_{t\to\infty}\frac{\ln\mathcal{M}(s,t)}{t}=\frac{\gamma+W }{2}\left\{1-\sqrt{1-4\frac{\gamma}{\left(\gamma+W\right)^{2}}k(s)}\right\}, \tag{30}\]  
where \(k(s)=A_{+}(1+\overline{n})(e^{s}-1)+A_{-}\overline{n}(e^{-s}-1)\). The probability \(P(J,t)\) can be obtained as  
\[\frac{\ln[P(J,t)]}{t}\approx\Theta_{net}(s_{0})-Js_{0}, \tag{31}\]  
where \(\Theta_{net}^{\prime}(s_{0})=J\).
# Panchromatic and Hyperspectral Image Fusion Using Ratio Residual Attention Networks
## 3 Methods  
### Framework Overview  
In panchromatic and hyperspectral image fusion, effectively combining the high spatial resolution of panchromatic images with the rich spectral information of hyperspectral images remains a key challenge. To address this, this study proposes a fusion method that combines ratio transformations with residual networks. The method consists of three key components: first, a ratio transformation method is used to generate an initial ratio image, which captures the difference information between the panchromatic and hyperspectral images through ratio computation. Second, a residual attention network is employed to fine-tune the initial ratio image, using an attention mechanism to automatically focus on important regions of the image and generate a refined ratio image. Finally, spectral preservation and spatial preservation loss functions are designed to constrain the spectral information and spatial details of the fused image, ensuring a balance between spectral and spatial resolution in the final fusion result. A flowchart of the overall method is shown in Figure 1.  
Figure 1: The flowchart of the proposed panchromatic and hyperspectral image fusion method.  
In the following subsections, we provide a comprehensive explanation of the method, with a detailed discussion of the three key stages: ratio transformation, residual attention network, and loss functions.
## 4 Review and Status of Cryogenic Laser Technology
### Other Cryogenic CW Lasers
#### 4.2.2 Er Cryogenic Lasers  
A number of Er based cryogenic lasers have also been reported in the literature [141, 142, 143, 144, 145, 146, 147]. A group at the U.S. Army Research Laboratory has investigated Er:Y\({}_{2}\)O\({}_{3}\)[141, 142], Er:Sc\({}_{2}\)O\({}_{3}\)[144], and Er:YAG [144, 145] crystals operated at cryogenic temperatures as potential candidates as eye-safe  
\begin{table}
\begin{tabular}{c c c c} \hline
**Year** & **CW Output Power (W)** & **Laser Material** & **Reference** \\ \hline
1998 & 40 & Yb:YAG & [1] \\
2001 & 110 & Yb:YLF & [109] \\  
139] \\ \hline \end{tabular}
\end{table}
Table 7: Reported Cryogenic CW Yb:YAG and Yb:YLF Demonstrations From 1998–2015.  
Figure 44: CW output power at 1029 nm from demonstrated Yb:YAG and Yb:YLF cryogenic lasers in the years 1998–2015.  
lasers. In [144], a 1530 nm pumped Er:YAG crystal at 78 K was operated quasi-CW, producing 375 mJ energy/pulse at a 2 Hz repetition rate. The output wavelength was 1618 nm for temperatures <90 K, and 1645 nm for >110 K. Further results for Er:YAG were later reported in [145], where a careful and thorough study of the spectroscopic and lasing properties of the material are discussed. Over 25 W of quasi-CW output power was obtained. A 1525 nm pumped Er:Sc\({}_{2}\)O\({}_{3}\) crystal was demonstrated to operate at 1558 nm, resulting in a very low quantum defect of only 1.5% [143]. The CW output power was 3.3 W. An Er:Y\({}_{2}\)O\({}_{3}\) crystal diode-pumped with a 974 nm source was also demonstrated with output at 2710 nm and CW output of 14 W [142]. One higher average power demonstration has been discussed in the literature as well, at cryogenic temperatures. A 400 W Er:YAG cryogenic laser has been described [146] that produced 386 W of CW output power at 1645 nm, 420 W of quasi-CW power, and was also operated Q-switched. Most of the results described here were obtained in \"proof-of-principle experiments\", using relatively recently developed InGaAsP/InP (1000-1650 nm) diode laser sources that are not as technologically or commercially as well-developed as InGaAs/GaAs and InGaAs/GaAs diode sources operating in the 720-850 nm and 900-1100 nm ranges respectively. As a consequence, beam-quality is seldom mentioned, and that the transverse mode distributions are multi-mode. As discussed in the aforementioned, most Yb:YAG cryogenic laser sources routinely produce \(M^{2}\) values of <1.3, and the transverse mode-structure is a single Gaussian mode. This is largely due to the availability of more advanced high-brightness diode sources.  
Figure 45 shows CW and QCW power output against the year demonstrated. The data and references are summarized in Table 8. While undoubtedly higher CW power outputs will be reported in the future to address a number of commercial and military applications, at the present time most cryogenic laser research is centered on the development of high-average-power nanosecond, picosecond, and femtosecond laser sources.
###### Abstract
Satellite remote sensing technology offers a good way to obtain high-resolution spatial and temporal continual surface parameter information and data that reflect the true history of land cover change; hence, such data have become the main source for climate change research on regional and global scales [23; 24]. Since the 20 th century, with new socioeconomic developments, complex land use changes like urbanization, cropland expansion, and afforestation have occurred in China, and these changes have had a significant impact on the regional and global climate. In the early 21 st century, large-scale cropland expansion occurred in northern China. Though there are many local-scale studies in China about the effects of cropland expansion on the climate, there are few studies on the larger region of northern China. In this paper, by using remote sensing data, land use change data, and reanalysis radiation data, we quantitatively assessed the change in radiative forcing due to land cropland expansion as mediated by changes to the surface albedo and emissivity in different climate zones in northern China from 2000 to 2015. We also assessed changes in the heat flux from the surface to the atmosphere caused by different cropland expansion trends in different climate zones and estimated the net effect of the cropland expansion on the surface radiation energy balance. Overall, this research aimed to reveal the effects of land cropland expansion on biophysical processes that can affect regional climate change.
# Machine Learning in Detecting COVID-19 Misinformation on Twitter
## 3 Methods
### Performance Metrics  
Performance or evaluation metrics play a vital role in identifying an optimal model for classification. They evaluate the performance of a model while training the classifier. It is important to select a suitable metric for evaluating classifiers. There are various available evaluation metrics for determining the performance of a model. It is important to carefully select the most adaptable metric when classifying imbalanced data [89]. The majority of the data in an imbalanced dataset belong to a particular class, and the minority to another class in the case of a two-way classification scenario. There is a possibility of bias towards the majority category. So, we need to more carefully evaluate the classifier. Therefore, accuracy, precision, recall, F measure, and PR-AUC are used for evaluating classifier performance.  
A confusion matrix is a way to visualize classifier performance. Most evaluation metrics are based on the number of correctly classified evaluated documents. Each row represents the predicted category in a confusion matrix, whereas each column represents the actual category. The matrix compares the actual values with the predicted ones and obtained four matrices: TN, TP, FN, and FP.  
* TP or true positive: classifier correctly predicted the observation as positive.
* TN or true negative: classifier correctly predicted the observation as negative.
* FP or false positive: classifier wrongly classified the observation as positive, but it is actually negative.
* FN or false negative: classifier wrongly classified the observation as negative, but it is actually positive.  
The confusion matrix is effective in measuring other evaluation metrics such as precision, recall, and accuracy. Various evaluation metrics are displayed in Table 1.  
Area under precision-recall curve (PR-AUC): The precision-recall curve is similar to the ROC curve, which is also a performance evaluation metric, especially when the supplied data are heavily imbalanced. PR-AUC is generally used to summarize the precision-recall curve into a single value. If the value of PR-AUC is small, it indicates a bad classifier; a higher value such as 1 indicates an excellent classifier.
## 4 Confidence Determination of Multi-Sensor Data  
In order to improve the fusion efficiency and filtering accuracy of multi-sensor data of the unmanned ship, the confidence distance judgment and credibility assignment of ZigBee-GPS/BDS multi-sensor data was carried out in this paper. Firstly, the credibility of the positioning data was determined by performing a confidence interval test on the sensor positioning data. Then, the credibility was divided into the corresponding confidence intervals according to its size, and the corresponding confidence factors were assigned. Finally, the confidence factor was associated with the TCPF algorithm to improve the filtering accuracy of the algorithm.
# Precise Point Positioning Using Dual-Frequency GNSS Observations on Smartphone
###### Abstract  
The update of the Android system and the emergence of the dual-frequency GNSS chips enable smartphones to acquire dual-frequency GNSS observations. In this paper, the GPS L1/L5 and Galileo E1/E5a dual-frequency PPP (precise point positioning) algorithm based on RTKLIB and GAMP was applied to analyze the positioning performance of the Xiaomi Mi 8 dual-frequency smartphone in static and kinematic modes. The results showed that in the static mode, the RMS position errors of the dual-frequency smartphone PPP solutions in the E, N, and U directions were 21.8 cm, 4.1 cm, and 11.0 cm, respectively, after convergence to 1 m within 102 min. The PPP of dual-frequency smartphone showed similar accuracy with geodetic receiver in single-frequency mode, while geodetic receiver in dual-frequency mode has higher accuracy. In the kinematic mode, the positioning track of the smartphone dual-frequency data had severe fluctuations, the positioning tracks derived from the smartphone and the geodetic receiver showed approximately difference of 3-5 m.  
GNSS; Android smartphone; precise point positioning +
Footnote †: journal: Sensors
## 2 Results and Discussion
### Antioxidant Activity
#### 2.4.2 HO\(\bullet\) Scavenging Activity  
HO\(\bullet\) is a very dangerous and highly reactive radical to the organism because it can destroy virtually all types of macromolecules including carbohydrates, nucleic acids (mutations), lipids (lipid peroxidation), and amino acids (e.g., conversion of Phenylalanine (Phe) to m-Tyrosine and o-Tyrosine). In addition, HO\(\bullet\) only can be eliminated by endogenous and dietary antioxidants. The abilities of MCPE-A, MCPE-B, and MCPE-C were investigated, and the dose-related effects were observed at different peptide concentrations ranging from 0 to 5.0 mg/mL (Figure 5B). EC\({}_{50}\) values of MCPE-A, MCPE-B, and MCPE-C were 0.25, 0.34, and 0.06 mg/mL, respectively, and MCPE-C exhibited the highest radical scavenging ability among all samples including the positive control of ascorbic acid at the same concentration. EC\({}_{50}\) of MCPE-C was lower than those of peptides from protein hydrolysates of conger eel (Leu-Gly-Asn-Gly-Asp-Asp-Val-Asn (LGLNGDDVN): 0.687 mg/mL) [39], weatherfish loach (PSYV: 2.64 mg/mL) [33], mussel sauce (HFGBPFH: 0.50 mg/mL) [37], Chinese cherry seeds (Phe-Pro-Phe-Leu-Ile (FPSLLI): 0.57 mg/mL; Val-Phe-ala-Ala-Leu (VFAAL): 0.31 mg/mL) [4], blue mussel (Tyr-Pro-Pro-Ala-Lys (YPPAK): 0.228 mg/mL) [40], skate cartilages (FIMGPY: 3.04 mg/mL; GPAGDY: 3.92 mg/mL; IVAGPQ: 5.03 mg/mL) [24], grass carp skin (PYSFK: 2.283 mg/mL; GFGPL: 1.612 mg/mL; VGGRP: 2.055 mg/mL) [34], and giant squid (Asn-Gly-Leu-Glu-Gly-Leu-Lys (NGLEGLK): 0.313 mg/mL; Asn-Ala-Asp-Phe-Gly-Leu-Asn-Gly-Leu-Glu-gly-Leu-Ala (NADFGLNGLEGLLA): 0.612 mg/mL) [37]. MCPE-C showed strong HO\(\bullet\) scavenging ability, which indicated that it could serve as a HO\(\bullet\) scavenger for decreasing or eliminating the damage caused by HO\(\bullet\) in food industries and biological systems.  
Figure 5: DPPH\(\bullet\) (**A**); HO\(\bullet\) (**B**); superoxide anion radicals (O\({}_{2}^{-}\bullet\)) (**C**); and 2,2\({}^{\prime}\)-azino-bis-3-ethylbenzothazoline-6-sulfonic acid radicals (ABTS\({}^{+}\bullet\)) (**D**) scavenging activities of MCPE-A, MCPE-B, and MCPE-C from cartilage protein hydrolysate of spotless smoothhound. All data are presented as the mean \(\pm\) SD of triplicate results.
# CD44 Depletion in Glioblastoma Cells Suppresses Growth and Stemness and Induces Senescence
## 4 Discussion
It has been shown before that CD44-ICD stabilizes HIF2\(\alpha\), positively contributing to stemness [21]. Our results demonstrate partial inhibition of the hypoxia-related genes _VEGFA_, _LOX_, and _BCL2_, in CD44-depleted cells or when CD44 cleavage was abrogated by DAPT. The fact that _BCL2_ expression is dependent on CD44 links CD44 biology to pro-survival mechanisms. Interestingly, in the inner mass of the sphere the cells need to bypass limited accessibility to nutrients and oxygen. Therefore, CD44\({}^{+}\) cells with enhanced cleavage turnover would confer survival advantage compared to CD44-depleted cells or cells with decreased amount of CD44-ICD. According to our RNA-seq analysis and previous reports [39], CD44 positively regulates glycolysis and, interestingly, DAPT treatment suppresses glycolytic genes. Thus, CD44 and its cleavage product CD44-ICD might promote growth advantage characteristics by sustaining the metabolism of environmentally challenged cells. Moreover, we observed an impairment in hyaluron production and secretion in CD44 KO U251 MG, grown in spheres, compared to the control cells, which may be attributed to HAS2 suppression (Figure 4a,b). Furthermore, _HAS2-AS1_, which is required for HAS2 expression, was also downregulated. The reduction in version levels suggests global changes in the hyaluron-formed matrix in sphere-like conditions (Figure 4a). Noteworthy, HAS2 knockdown diminished the CD44 protein levels as well as pCREB and pERK1/2 levels (Figure S5), concurrently with decreased hypoxia-related genes _VEGFA_, _LOX_, and _HAS2-AS1_, implying a potential role for HAS2 mediating CD44-transcriptional responses. Such double-positive feedback loops have been reported before in breast cancer [40]. In summary, we provide evidence that depletion of CD44 in U251 MG cells leads to diminished cell growth and acquisition of senescence. Based on data from a transcriptomic analysis, we propose CD44-dependency for certain GBM hallmarks, such as hypoxia, enhanced glycolysis and cell cycle progression from the G2 to M phase. Our data support the notion that a CD44-HAS2 feedback circuit drives GBM progression.
## 3 Results and Discussion
### Interactive Application Prototyping  
#### 3.3.1 Platform Framework Design  
The visualization platform framework designed for this study is divided into four parts as follows:  
1. The algorithm model part is based on the ET_s_BG+p sentiment analysis model, which calculates the sentiment value of the front-end input text and stores it in the database.
2. The data storage part is implemented using Python's own lightweight database SQLite.
3. The interaction interface part uses the Flask framework to connect the front and back ends to achieve the interaction between data and the user interface.
4. The front-end application part uses HTML, CSS, JavaScript, and other technologies to build direct interaction functions with users.  
After the user enters text on the platform, ET_s_BG+p can perform sentiment analysis on the text and store the resulting sentiment index in the database. Then, the visualization tool can generate a visual chart through calling the sentiment index. The specific architecture of this visualization platform system is shown in Figure 8.  
Figure 7: Model \(F1\) difference comparison.