esa-sceva/satcom-chunk-collection huggingface dataset:
# Correlation Between Sunlight Exposure Time and Cataract Prevalence in Korean Adults
## 3 Results  
To verify the difference in the ratio of cataract types according to the sunlight exposure time, a cross-tabulation was created. As a result, in the 2-5 h group, there were 52 cortical (30.1%), 115 nuclear (66.5%), 3 anterior subcapsular (1.7%), and 3 posterior subcapsular cataracts (1.7%), and in the 5 h or more group, there were 48 cortical (24.5%), 135 nuclear (68.9%), 10 anterior subcapsular (5.1%), and 3 posterior subcapsular cataracts (1.6%). As a result, both groups showed that nuclear cataracts were more prevalent in Koreans regardless of sunlight exposure time (\(p\) = 0.250) (Table 1). Table 2 shows the demographic and sociological characteristics of the study subjects as follows. The amount of sunlight was 2-5 h in 482 people (50.6%), and more than 5 h in 470 people (49.4%), showing little difference in the proportions of the two groups. As for their gender, 522 (54.8%) were male and 430 (45.2%) were female, and 259 (27.2%) were under the age of 45 and 693 (72.8%) were over the age of 45. As for their educational level, 354 (37.2%) had graduated from elementary school and 136 (14.3%) from middle school. And there were 268 (28.2%) who had graduated from high school and 138 (14.5%) from university. Their residence data included 139 people in Gyeonggi Province (14.6%) and 137 in North Gyeongsang Province (14.4%), 15 in Jeju (1.6%), and 13 in Daejeon (1.4%). The refractive error was myopia in 303 patients (34.8%) and hyperopia in 566 patients (65.2%). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Sun Exposure**} & **Total** & \(\mathbf{x}^{2}\) & \(p\) \\ \cline{2-6}  & & **2-5 h** & **Over 5 h** & & \\ \hline \multirow{4}{*}{Cataract} & Cortical & 52 (30.1) & 48 (24.5) & 100 (27.1) & & \\ \cline{2-6}  & Nuclear & 115 (66.5) & 135 (68.9) & 250 (67.8) & & \\ \cline{2-6}  & Anterior subcapsular & 3 (1.7) & 10 (5.1) & 13 (3.5) & & \\ \cline{2-6}  & Posterior subcapsular & 3 (1.7) & 3 (1.6) & 6 (1.6) & & \\ \hline \multicolumn{6}{c}{Total} & 173 (100) & 196 (100) & 369 (100) & & \\ \hline \multicolumn{6}{l}{Unit: Frequency (\%).} \\ \end{tabular}
\end{table} 
Table 1: Types of cataracts according to sunlight exposure time. Cross-tabulations were calculated to verify differences in cataract incidence rates depending on sunlight exposure time. As a result, in the 2-5 h group, there were 275 people (57.1%) without cataracts and 207 people (42.9%) with cataracts. In the 5 h or more group, there were 213 people (45.3%) without cataracts, and the number of people with cataracts was 257 (54.7%). As a result of conducting a chi-square test to determine the statistical significance of the difference in the cataract incidence rate according to the sunlight exposure time, it was found that there was a statistically significant difference (Figure 1, Table 3).
# Dual-IoTID: A Session-Based Dual IoT Device Identification Model
## 3 Proposed Method
### System Overview and Methodology
#### 3.1.1 Traffic and Session Splitting  
Our method first acquires traffic from the IoT network, where traffic from multiple devices is mixed together. Therefore, the purpose of this step is to separate the traffic from different IoT devices and reorganize it into sessions for feature extraction. In IoT device identification, the granularity of traffic splitting includes packets, TCP connections, flows, and sessions. Different granularities can be divided into different traffic units. Flows and sessions focus on the complete communication interaction process, helping to better understand the relationships between packets. Therefore, we chose to split traffic based on flows and sessions. A flow is defined as packets with the same five tuples (source IP, destination IP, source port, destination port, and transport protocol), while a session consists of bidirectional flows containing both directions of traffic. In this step, there are two operations: (1) IoT device traffic splitting: In the dataset we use, each device's MAC address is fixed. Therefore, we first use the SplitCap tool (SplitCap is a free, open-source pcap file splitting tool that can split traffic based on flows, sessions, MAC addresses, etc.) to segment traffic based on MAC addresses, separating the traffic generated by each IoT device. (2) Session splitting: For the obtained traffic data from different IoT devices, we again use SplitCap to split each type of traffic generated by each IoT device into multiple files based on TCP or UDP flows, with each file representing a set of sessions for IoT devices.  
Figure 1: Overall framework of Dual-IoTID contains four components. (**a**) traffic and session splitting; (**b**) Session communication feature extraction; (**c**) frequent item extraction and initial classification; and (**d**) Second Classification.
# Quantitative Analysis of Forest Water COD Value Based on UV-vis and FLU Spectral Information Fusion
## 2 Materials and Methods
### Spectra Acquisition  
The measurement of the UV-vis spectrum was conducted using an American PerkinElmer Lambda 950 spectrophotometer and the usable wavelength range was from 175 to 3300 nm. The optical system has a SiO\({}_{2}\)-coated holographic ruled grating with the highest wavelength accuracy of 0.08 nm. Before detecting the UV-vis absorbance of the solution, deionized water was used as a reference to eliminate the absorption of light by water. We measured the spectral absorbances of samples from 200 to 500 nm when the optical path was adjusted to 1 nm. Due to the complex composition of the actual water samples and the presence of noise interference during some tests, the collected spectral data may fluctuate and deviate from the baseline. Before modeling, we preprocessed the collected spectral data using Savitzky Golay (SG) filters. As this is a commonly used data smoothing method, the shape of the original spectrum will not change after SG processing, but the signal-to-noise ratio of the spectrum can be significantly improved.  
As shown in Figure 1a, due to the presence of unsaturated structural organic pollutants (conjugated systems containing aromatic hydrocarbons, double bonds, and carbonyl groups) and some inorganic ions in water, we can observe that samples have significant absorption peaks at around 210 nm. For the FLU experiments, we used a USA PerkinElmer (Waltham, MA, USA) LS 55 fluorescence spectrophotometer. The variable range of the excitation light path slit (spectral passband) is from 2.5 to 15 nm, and the variable range of the emission light path is from 2.5 to 20 nm. We set the excitation wavelength of the FLU spectrometer to 285 nm and measured the emission spectra of the samples. As shown in Figure 1b, the mapping relationship between the FLU intensity and the content of organic matter has been established. The water samples have obvious emission peaks in the wavelength range of 300 to 500 nm. Compared with UV-vis spectroscopy, FLU spectroscopy has a wider effective range of spectral information. Even at COD = 3.2 mg/L, the FLU spectra can be well characterized.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sampling Area** & **Number of Samples** & **Actual COD Value (mg/L)** \\ \hline Dongshuiguan & 3 & 20.6749 \\ Bonsai Garden & 3 & 17.4284 \\ Tsui Chau & 3 & 14.3955 \\ Pingjiang Bridge & 3 & 14.1392 \\ Diaoyutai & 3 & 13.8402 \\ Baima Lake & 3 & 12.1315 \\ Hamzhongmen & 3 & 11.8325 \\ Xianne Bridge & 3 & 10.3374 \\ Shuiximen & 3 & 7.9026 \\ Caochang Gate & 3 & 7.1337 \\ Laiyan Bridge & 3 & 6.6211 \\ Zhonghua Gate & 3 & 6.4502 \\ Xuanwu Gate & 3 & 6.1512 \\ Xi’an Gate & 3 & 5.6813 \\ Front Lake & 3 & 3.2037 \\ \hline \end{tabular}
\end{table}
Table 1: The numbers, sampling locations, and COD values of actual water samples measured by the dichromate method.
# Parameter Estimation of the Bishop Photovoltaic Model Using a Genetic Algorithm
## 2 Bishop PV Model  
Figure 1 shows the circuital model of a PV cell proposed by [PERSON] [39]. It consists of a current source, associated with the generation of photovoltaic current (\(I_{ph}\)); a diode, representing the energy level threshold so that photons can trigger a significant production and circulation of electron-hole pairs across the \(PN\) junction; a series resistance (\(R_{s}\)), representing losses due to contact resistance; and a shunt resistance (\(R_{sh}\)), representing leakage currents along the cell edges.  
To calculate the output current of the cell (\(I_{cell}\)), a summation of currents is performed as shown below in Equation (1).  
\[I_{cell}=I_{ph}-I_{d}-I_{sh} \tag{1}\]  
The current-voltage relationship of the diode (\(I_{d}\), \(V_{d}\)) can be represented by the Shock-ley equation as given in Equation (2), where \(I_{sat}\) is the saturation current; \(\eta\) is the ideality factor; \(k\) is the Boltzman constant; \(T\) is the cell temperature in kelvin degrees (\(K\)); and \(q\) is the electron charge.  
\[I_{d}=I_{sat}\Big{(}e^{(qV_{d}/\eta kT)}-1\Big{)} \tag{2}\]  
Finally, the current through the shunt resistance (\(R_{sh}\)) can be written as reported in Equation (3), where \(a\) is the fraction of the ohmic current related to the breakdown of the semiconductor; \(V_{br}\) is the breakdown voltage; and \(m\) is the avalanche exponent.  
\[I_{sh}=\frac{V_{d}}{R_{sh}}\left\{1+a\cdot\left(1-\frac{V_{d}}{V_{br}}\right)^ {-m}\right\} \tag{3}\]  
The voltage across the diode can be expressed as follows:  
\[V_{d}=V_{cell}+R_{s}\cdot I_{cell} \tag{4}\]  
In conclusion, eight model parameters should be estimated to reproduce the behavior of a PV cell using the Bishop model: \(I_{ph}\), \(R_{s}\), \(R_{sh}\), \(I_{o}\), \(\eta\), \(a\), \(V_{br}\) and \(m\). Moreover, the relationship between the output current \(I_{cell}\) and the voltage at the cell terminals \(V_{cell}\) is implicit and non-linear; thus, it requires iterative techniques for its evaluation.
## 4 Discussion
The hourly analysis confirms that 98% of the rain gauge data used in this study registered precipitation amounts below 1.2 mm/h and that a large portion of these data may consist of no-precipitation (0 mm/h) cases. Precipitation above the value of 1.2 mm/h are more likely to be underestimated by IMERG. The latter is in agreement with [PERSON] et al. [3]. Daily analysis precipitation above 27 mm/day were found to be underestimated by IMERG. However, Figure 8b suggests that this underestimation could still be produced for precipitation intensities below this threshold. In addition, the daily analysis of precipitation intensities and the elevation shows no relation (Figure 7). IMERG shows no significant deviation when daily precipitations are lower than 10 mm/day; the same as in [PERSON] et al. [4]. However, when precipitations are higher or extreme, deviations of IMERG can be over 15 mm/day per station regardless of its altitude. In Figure 6, it can be noted that IMERG deviates during particular days when precipitation might be considered extreme or intense. _RMSE_ values in Figure 2, Tables 2-4 also confirm that the larger the amounts of precipitation, the larger the deviations. [PERSON] et al. [5] consistently stated that IMERG overestimates rainfall intensities higher than 9 mm/day and attributed it to the fact that extreme rainfall events evolve rapidly, reducing the probability of the direct detection of such events given the approximate 3-h sampling of the whole GPM satellite constellation. Furthermore, the accuracy of the IMERG estimates also improves with the time scale. Spatial values of _RBIAS_ (Figures 4a and 9a) and _RMSE_ (Figures 4b and 9b) are also better for the daily analysis but the trend for IMERG to overestimate mean values is maintained. Also, the _POD_ and _CSI_ values are closer to 1 for the daily precipitation than for the hourly precipitation. However, the _ACC_ is better for the hourly than for the daily precipitation. This happens because _ACC_ is sensible to the amount of no-precipitation events which decreases with the time scale. In addition, results found here show that the _FAR_ decreases with increasing altitude while the _POD_ and the _CSI_ increase with increasing altitude (Figures 5 and 9). The mean values of these categorical and the other continuous statistics are consistent with earlier studies [3; 4]. Finally, the seasonal analysis showed good results, particularly for the dry season. For the wet season, positive deviations of IMERG may be expected.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 2 Applications for VLC-Based IPS
### Smart Homes and IoT  
VLC-based indoor positioning can indeed be integrated into smart home systems to enable location-aware automation. By using visible light signals emitted from LED lights, VLC technology can accurately determine the position of individuals within a house. This information can then be used to trigger various automation actions based on the user's location [28]. For example, when a user enters a room, the system can automatically adjust the lighting, temperature, and sound settings to provide the best comfort experience.  
Specifically, personalized settings can be applied based on the occupant's location within the house. For example, the temperature, music, or preferred lighting ambiance can be adjusted according to the occupant's preferences in different areas of the house. Therefore, VLC-based indoor positioning can greatly enhance the overall smart home experience by making automation more context-aware and personalized. It adds an extra layer of intelligence to the system, allowing it to adapt and respond to the user's location in real time [29].
## 2 Methods
### Data Preprocessing  
To ensure the quality and consistency of the imaging data, preprocessing steps were performed using the AFNI 24.0.17 software suite. First, skull and non-brain tissues were removed to isolate brain tissues. The images were then normalized to a standard space using the Automated Anatomical Labeling (AAL) template [23]. This normalization process involved aligning the input data with the template, ensuring accurate mapping of anatomical structures for subsequent region-of-interest (ROI)-based analysis.  
Following normalization, a whole-brain ROI-based signal intensity analysis was conducted. A predefined AAL atlas was used to define the ROIs, and the mean signal intensity was calculated for each ROI. This preprocessing step facilitated the quantification of regional signal characteristics, laying the groundwork for group comparisons and statistical analyses in the later stages of the study.
# Comparative Assessment of Shear Demand for RC Beam-Column Joints under Earthquake Loading
## 4 Results and Discussion
### Comparison of Shear Demands  
The envelope values of shear demand, computed for different structural analysis methods, are reported in Table 4 and Figure 17.  
It is clearly recognized that NLSA: (i) gave larger values if compared with MRSA and NLTH; and (ii) generally exceeded the lowest predicted strength. The latter, which contradicts the experimental evidence, can be partly sustained by recalling what was claimed by [PERSON] for the estimation of plastic hinge rotations using the modal-pushover (MPA) [53]: _...All pushover analysis procedures considered do not seem to compute accurately local response quantities, such as hinge plastic rotations. Thus, the structural engineering profession should examine the present trend of comparing computed hinge plastic rotations against rotation limits established in FEMA-273 to judge structural performance. Perhaps structural performance evaluation should be based on storey drifts that are known to be closely related to damage and can be estimated to a higher degree of accuracy by pushover analyses._ In this context, element forces could also be assimilated to local response quantities, thus less accuracy should be expected when MPA is employed. As a proof of that, cases where the estimated shear demand overcame the yielding force were recognized. Specifically, according to Equation (4), a value of 0.88 MPa\({}^{0.5}\) was obtained for the hogging type moment, having considered an overstrength factor equal to \(\gamma_{Rd}\) 1.25. Although the sagging type moment gave half of the value, the comparison with envelopes of shear demand can be sustained only for the former case.  
While the adoption of MPA has been considered as the state-of-the art for the inelastic analysis of non-symmetric structure (possibly torsionally flexible) under bi-axial seismic input, the authors are aware of other procedures such as the \"modified\" N2 method [30; 31]. Although its computational effort is reduced with respect to MPA, the same lack of accuracy in predicting the element forces should be expected.  
Differences were recognized by comparing NLTH values with those given by [PERSON] [36]. The deviation trend is not easily discernible to the point that, aside from general differences in modeling, both (i) a different definition of shear demand and (ii) use of mean extreme values rather than absolute might have an influence.  
Finally, the shear demands are represented as a function joint type in Figure 19. The exterior joints with a transverse beam were characterized by the highest shear demand by using NLSA. Interior joints prevailed with MRSA. NLTH did not show a quite clear trend.  
Figure 19: Shear demand of beam–column joints of SPEAR frame as a function of the joint type.
# Mitigating Timing Side-Channel Attacks in Software-Defined Networks: Detection and Response +
## 3 Proposed Methodology
### Solution Blueprint
#### 3.3.6 Data Collection Module  
The data collection module collects and stores historical data on the network traffic and previously detected adversaries. This module is implemented using various data collection tools such as network traffic analysers, listener API, and sFlow [53]. The data collected via this module includes information about network traffic patterns, security events, and historical responses to security threats. We used the MongoDB platform for this collection. In addition to its primary functions, the module served as the initial stage of the response mechanism. It operates by receiving the output generated via the detection phase and then utilising these data as the input. When the Detection phase successfully identifies the source of the attack, it transmits the crucial information to the data analysis module of the response solution. Specifically, it contains the MAC addresses, IP addresses, and switch ports used by the intruder. This dynamic, real-time process allows for the solution to gather and evaluate key information quickly. Therefore, they form an integral element of the response mechanism by providing actionable information regarding potential threats.
## 3 Metabolic Engineering to Improve Bio-Butanol  
### n-Butanol  
n-butanol can act as an alternative to gasoline, as the quantum of energy in them is almost same. n-butanol can also be blended with gasoline for usage, and its transportation is also easy [52]. Its precursor molecule is acetyl-CoA; after a cascade of reactions, acetyl-CoA is converted into butyryl-CoA before being converted to n-butanol (Figure 1). This pathway is known as the keto-acid pathway. Through acetone-butanol-ethanol fermentation, _Clostidium acetobutylicum_ produces high amounts of n-butanol [53]. Yet the sluggish growth rate as well as sporulation-related issues make it unfit for producing n-butanol at an industrial scale [54]. Attempts were made to engineer _E. coli_ so that it can produce n-butanol. One such attempt was reversing the \(\beta\)-oxidation pathway [53; 55; 56]. The acquired n-butanol-producing pathway in _E. coli_ creates an inequity in NADH levels, which can be increased by introducing the trans-enyl-CoA reductase enzyme, thereby increasing the flux for yielding high amounts of n-butanol [53; 57; 58]. For enhancing the n-butanol yield in _E. coli_, there is a need to increase NADP. By engineering the central metabolism of _E. coli_, flux towards the glycerol pathway was improved, and this in turn enhanced n-butanol production [57; 59]. In a metabolomics study, it was reported that for enhancing n-butanol production handling, the CoA levels are vital, and it can be achieved by the overexpression go alcohol dehydrogenase (adhE2) in _E. coli_[60].  
There a few organisms (like _S. cerevisiae_) which produce less amounts of n-butanol through their innate keto-acid pathway [61]. [PERSON] et al. have reported that the production of n-butanol in _S. cerevisiae_ can be enhanced by introducing and overexpressing genes like crotonase, adhE2, and 3-hydroxybutyryl-CoA dehydrogenase from _C. beijerinckii_, along with the crotonase gene from _Streptomyces collinus_[52]. According to [PERSON] et al., when the trans-enoyl-CoA reductase gene was expressed in _S. cerevisiae_, n-butanol production was improved [62]. In an another attempt by [PERSON] et al. to enhance n-butanol titer, alcohol dehydrogenase genes (adh1-5) were knocked out and the overexpression of the pantotheane kinase CoA gene (source--_E. coli_) gave encouraging results [61]. Moreover,when the NADH oxidase gene was expressed in the same strain under aerobic conditions, the n-butanol production was very much enhanced [63]. It was observed that co-culturing _S. cerevisiae_ and _C. acetobutylcium_ increased the n-butanol production [64]. At present, there a few companies like Green Biologics and Butamax that are producing commercial n-butanol [65]. Still, optimization for a better and more cost-effective n-butanol-producing host at the commercial level is necessary.
# Multi-Granularity Semantic Collaborative Reasoning Network for Visual Dialog
## 3 Methodology
### Mgscrn  
In this section, we formally describe our proposed method. Figure 3 illustrates the overall structure of MGSCRN. It consists of three main modules. First, in the multi-granularity textual representation (MGTR) module, initial token- and sentence-granularity semantic representations are obtained by adopting self-attention mechanisms of transformers and convolutional neural networks (CNNs), respectively. Second, the question-aware attention-refer (QAAR) module leverages the transformer-based cross-attention. It aims to build intersections between the question and dialog history encoded using the same granular semantic representations, which are obtained by the MGTR module. In this process, two types of dialog history distributions are learned: one representing the attention weights of all of the tokens in the dialog history, and another representing the attention weights of all of the question-answer rounds. To more accurately identify the historical information associated with the question, one distribution is modified by the other during training. Consequently, even if one distribution deviates from the truth, the degree of deviation will be reduced via the corrections offered by the other distribution. Thus, the QAAR module collaboratively reasons the question-related information from the dialog history by leveraging different semantically granular representations of the current question and historical dialog. Third,the visual-aware attention alignment (VAAA) module learns the semantic alignments between the visual features and each representation of the question-fused history obtained from the previous module; hence, two types of distributions over visual objects are learned simultaneously. In the same way as the QAAR module, the VAAA module allows these two distributions to collaboratively assess the question-related visual objects. The following subsections discuss each module in detail.