esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 10 Future Research Directions
### Other Possible Future Research Directions
#### 10.4.3 Investigating Potential Risks  
To find vulnerabilities that could jeopardize system integrity, a thorough investigation and assessment are necessary when looking into potential threats in smart grid security. To detect and lessen new risks, preemptive steps, including thorough risk assessments, threat modeling, and scenario-based simulations, are used. Smart grid operators may put strong security measures in place to safeguard vital infrastructure and guarantee the dependable and secure functioning of smart grid systems by regularly assessing and mitigating possible risks.
## 4 Results and Discussion
### Muting of All Vehicles Except Target
#### 4.1.2. Muting of All Vehicles Except Vehicles Ahead of Target  
Finally, the focus was on vehicles on the same line, but specifically, only those that are ahead of the target vehicle at their last known position. It would be expected that this will pose an easier problem to model as, based on intuition, the vehicles in front of the target bus will have a more important role compared to those which are following the target. The results are shown in Figure 11 and are compared to the network-based predictor. For both directions, ETA predictions, were not affected by the muting compared to the full model. The muted trajectory prediction was superior to the full model in both directions. However, the generalisation errors for the eastbound direction are more negative than for the westbound direction, suggesting that the model is underfitting in this scenario, see Figure 11.
# A Membrane Modified with Nitrogen-Doped TiO2/Graphene Oxide for Improved Photocatalytic Performance
###### Abstract  
An improved photocatalytic microfiltration membrane was successfully prepared by the impregnation method with nitrogen-doped TiO2/graphene oxide (GO) (NTG). By utilizing the unique role of N and GO, the photocatalytic activity of the membrane in UV and sunlight was improved. Compared with the Polyvinylidene Fluoride (PVDF) microfiltration membrane which was modified by TiO2, N-TiO2 (NT) and TiO2-GO (TG), the NTG/PVDF membrane exhibited high photocatalytic efficiency and significantly improved photodegradation power to the methylene blue (MB) solution under ultraviolet light and sunlight, with the photocatalytic efficiency reaching 86.5% and 80.6%, respectively. Scanning electron microscopy (SEM), X-ray diffractometry (XRD) and Fourier transform infrared spectroscopy (FT-IR) were used to analyze the morphology, crystal structure and chemical bonds of the membrane surface. The hydrophilicity of the modified PVDF microfiltration membrane was significantly improved, the flux of the pure water membrane reached 1672 Lm-2h-1, the flux of the MB solution was also significantly improved due to photodegradation. Therefore, the nitrogen-doped titanium dioxide graphene oxide PVDF microfiltration membrane (NTG/PVDF membrane) has great development prospects in sustainable water treatment.  
nitrogen-doped TiO2; graphene oxide; photocatalysis; surface modification; microfiltration membrane +
Footnote †: journal: Applied Physics Letters  
_Article_  
A Membrane Modified with Nitrogen-Doped TiO2/Graphene Oxide for Improved Photocatalytic Performance
## 2 Materials and Methods  
The Cerberis3 QKD system 1.1.349 from ID Quantique, Rue Eugene-Marziano 25, 1227 Geneve, Switzerland, was used for the experiments. The system consists of two QKD nodes, each equipped with a quantum key distribution module, a classical key distribution module, and a monitoring module. The nodes were connected via a dedicated optical fiber link.
# Non-Contact SpO2 Prediction System Based on a Digital Camera
###### Abstract  
Patients with the COVID-19 condition require frequent and accurate blood oxygen saturation (SpO2) monitoring. The existing pulse oximimeters, however, require contact-based measurement using clips or otherwise fixed sensor units or need dedicated hardware which may cause inconvenience and involve additional appointments with the patient. This study proposes a computer vision-based system using a digital camera to measure SpO2 on the basis of the imaging photoplethysmography (iPPG) signal extracted from the human's forehead without the need for restricting the subject or physical contact. The proposed camera-based system decomposes the iPPG obtained from the red and green channels into different signals with different frequencies using a signal decomposition technique based on a complete Ensemble Empirical Mode Decomposition (EEMD) technique and Independent Component Analysis (ICA) technique to obtain the optical properties from these wavelengths and frequency channels. The proposed system is convenient, contactless, safe and cost-effective. The preliminary results for 70 videos obtained from 14 subjects of different ages and with different skin tones showed that the red and green wavelengths could be used to estimate SpO2 with good agreement and low error ratio compared to the gold standard of pulse oximetry (SA210) with a fixed measurement position.  
COVID-19; pandemic; non-contact SpO2 monitoring; SpO2; face detection; imaging photoplethysmography (iPPG); complete Ensemble Empirical Mode Decomposition (EEMD); Independent Component Analysis (ICA)
# Benefits of Advanced Air Mobility for Society and Environment: A Case Study of Ohio
## 3 Forecasting Approach  
In CBA, forecasting is an integral tool for estimating the costs that will be occurred before incurring those. As this tool gives a possible preview of the future, it is commonly used in the transportation field during the cost-benefit analysis [29]. To forecast the future values of 14 out of all variables (see Table 1) in our study, we performed a time-series analysis over 11 years period using an Auto-regressive Integrated Moving Average (ARIMA) model in RStudio. A time series is a sequence of data points that have been listed in time order. ARIMA model is considered as a powerful tool for doing forecasting in transportation [33]. The 'auto regressive' (AR) portion of ARIMA denotes that the dependent variable is regressed on its own lagged values. The middle part 'integrated' (I) implies that the data have been substituted with the difference between their current and former values. The'moving average' (MA) section of the model denotes a linear regression of errors whose values existed simultaneously and at different periods in the past [34]. Figure 2 shows the logical flow that we followed to build our ARIMA forecasting model. At first, we converted historical data of the corresponding variables into time series and then checked the stationarity of the time series. For stationarity, the time series must have constant mean, variance, and auto-correlation. Here, the auto-correlation of a time series measures the statistical relationship between its actual and lagged values. For checking the stationarity of the time series, we performed the Augmented Dickey-Fuller test (ADF test), where the null hypothesis (\(H0\)) is that the time series is not stationary; and the alternative hypothesis (\(Ha\)) is that the time series is stationary. If \(p\)-value is less than or equal to 0.05 (choosing the confidence level as 95%), we rejected the Null referring to the time series is stationary. Conversely, if the \(p\)-value is greater than 0.05, the time series is not stationary. Then, we did differencing time series and repeat the ADF test until we obtain a stationary time series. We selected the final \(d\) value. Next, PACF (Partial Auto-correlation Function) and ACF (Auto-correlation Function) were plotted, and the \(p\)- and \(q\)-values were selected accordingly. \(p\) indicates the values of correlation between observations of a time series with their lagged values, whereas \(q\) indicates the correlation between errors of a time series with their lagged values. The ARIMA(\(p\), \(d\), \(q\)) model means that it can describe the forecasted data by combining a \(p\) order auto-regressive model and a \(q\) order moving average model, when the input data have gone through \(d\) order differencing [35]. After that, we ran the ARIMA(\(p\), \(d\), \(q\)) model and performed a white noise test with a Ljung Box Test for residuals. Here,  
**Null Hypothesis (\(H0\)):** The residuals are independently distributed.
## 4 Simulation Results and Discussion
### Case 2: Sensitivity Analysis with Criterion Variation Within \(\pm 10\%\)
#### 4.3.3 Scenario 3: Variation in Upload Speed, Download Speed, and Ping  
In this scenario, in addition to the previous variation in upload speed and download speed, we randomly vary the initial ping values while keeping the values of packet loss unchanged. Table 18 shows the random variation rates applied to the initial ping values of each satellite. The resulting decision matrix is shown in Table 19.
# A Computer-Vision Based Application for Student Behavior Monitoring in Classroom
## 2 Proposed System
### Gaze Estimation  
As described in many pieces of research [12; 13; 15], eye-gaze and face-gaze are of great importance for assessing the cognitive engagement or inattention of students. Some methods [42; 43] have been developed to estimate eye-gaze. However, the ability to extract eye-gaze might be limited due to blurry images, camera resolution, etc. This raises the requirement for using the head-pose as an alternative approach. For head-pose estimation, 3 DDFA [44] and KEPLER [45] detect facial landmarks then fit them via a convolutional neural network (CNN) or its modified version. However, using landmarks could be a minus point. As with low-resolution images, the incorrect detection of landmarks could lead to worsening results. Hopenet [46] combined ResNet50 with a multi-loss architecture. Each loss contained a binned pose classification and regression, corresponding to yaw, pitch, and roll individually. It showed that it can directly predict head rotation and highly outperform landmark-to-pose methods using state-of-the-art landmark detection methods. FSA-Net [47] provides attention for pose estimation and even proved to be a slight improvement over Hopenet [46].
## 4 Improvement of Thermal Performance
### Form-stable PCMs
#### 4.2.1 Polymer Form-stable PCMs  
In polymer-based form-stable PCM composites, the PCM is entrapped inside a cross-linked polymer matrix. The synthesis of the polymer matrix can be achieved with many different methods, providing structural diversity and, as a result, the ability of fabricating a product with special specifications, properly designed for each application. Additionally,polymeric cross-linked materials, as substrates, have shown great encapsulation rate results, which is a very important property [5].  
There is a wide variety of the polymeric materials that can be used for this purpose. Materials that have been commonly used are polyethylene [35], polypropylene [36], polyurethane [37], poly methyl methacrylate [38], poly vinyl chloride [39], styrene-butadiene-styrene triblock copolymer [40], etc. Nevertheless, due to environmental concerns, there is great necessity for reducing the utilization of nonrenewable petroleum-based polymers. This has led to research for raw materials that can be received from natural sources, such as biomass, and they are renewable, biodegradable, environmentally friendly, of high abundance, and low cost. Such materials are celluloses, starches, and vegetable oils. Specifically, cellulose, agarose, chitosan [41], castor oil [42], etc. have been used for the fabrication of form-stable PCMs.
## 5 MTM Antenna Performance Analysis and Microwave Imaging
From the graph, it can be seen that the resultant value and the attainable values are close to the antenna parameters, which proves that the antenna design is optimum. Figure 12 shows the simulation model of a breast with a proposed MTM antenna. The antenna is kept with the breast phantom at a distance of 5 mm and the effects of breast tissues are studied. To study the power coupled to the breast tissue, we compute the near field directivity (NFD) for the proposed MTM antenna. The surfaces of the breast model with a radius of 90 mm and a height of is 100 mm, as shown in Figure 12. The phantom contains four layers, namely, the skin layer, the breast tissue layer or fat and the normal air layer. The skin layer has the following properties: dielectric constant = 38, thickness = 2.5 mm, and conductivity = 1.49 S/m. The breast tissue layer has a maximum width of 8.75 cm, with conductivity = 0.141 S/m and dielectric constant = 5.14. The work uses real breast phantom and image reconstruction algorithms. Figure 11: Theoretical limits and calculated Qa and (Brr)ub. Figure 7:0 Cross section of breast model simulation setup. The near field directivity (NFD) factor is computed using the formula in [24], which is the ratio of radiated power through the front of the antenna (Pf) and the total power radiated through the surface of the breast model (PT). Figure 7:1  
Figure 13 shows the NFD factor for a proposed side slotted Vivaldi antenna (SSVA). About 65% of the total power is radiated via the front side of the breast tissue. The radiation efficiency of the proposed prototype with the breast model is shown in Figure 7:2 The radiation efficiency is defined as \({}_{rad}=P_{T}/P_{in}\), where Pin is the fed power to the antenna. The radiation efficiency is more than 60% over the bandwidth with breast model. The coupling efficiency is also computed as \({}_{c}={}_{rad}\times NFD=\frac{P_{f}}{P_{in}}\), which is the function of radiated power and fed power. The average radiation efficiency over the bandwidth is more than 60%, which indicates that the overall coupling efficiency of the proposed MTM antenna is much better than that of existing, low-directive UWB antennas.
# Comparison of CAD Software for Designing Cellular Structures for Additive Manufacturing
## 3 Results
### Rhino 7/Grasshopper(r)
In Figure 8a, left, the resulting model with BCC cell topology can be observed after a series of Boolean operations, including the design space as well as the inner and outer solid structures, were performed. Another approach had to be used to formulate a conformal lattice structure inside the given design space or between two sets of surfaces when using the plugin Crystallon [56] (Figure 8a, right). The plugin is only able to formulate lattice structures between two non-trimmed surfaces. When using a BRep--the design space of the bike seat--as boundary volume, the Deconstruct BRep command only yields trimmed surfaces. It is not possible to convert trimmed surfaces to non-trimmed surfaces easily; thus, a detour through additional functionality was taken. The top surface of the bike seat's design space was extracted from the BRep and formulated as quadratic mesh, as can be seen in Figure 8b, right. In order to enable the formulation of a lattice structure using quadratic meshes, two identical surfaces were required. The previously extracted top surface was copied and moved by a thickness value of 15 mm, and voxels between the two resulting surfaces were created with Crystallon's MBM command and filled (CLF command) with a BCC unit cell (CT command). The resulting lattice was thickened with the Pipe command with a diameter of 1 mm (Figure 8a, right). Table 3 shows the calculated as well as the measured values for both the BCC and FCC lattice structures of the bike seat. These were the longest processing times among all the software tools investigated. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criteria** & **BCC** & **FCC** & **Relative Change** \\ \hline Lattice processing time & 33.6 s & 2 min 06 s & 275\% \\ Resolution & 0.1 mm & 0.1 mm & \\ Export time & 1.2 s & 7.8 s & 550\% \\ File size & 11.8 MB & 14.7 MB & 24.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: BCC and FCC lattice generation values of the helical gear in Rhino7/Grasshopper®. Figure 7: BCC simple lattice structure generation for parts of a gear’s solid domain: (**a**) 8 \(\times\) 8 \(\times\) 8 mm BCC base cell, (**b**) 7 \(\times\) 7 \(\times\) 2 lattice structure, (**c**) lattice structure after inner cutout, (**d**) lattice structure after solid intersection with BRep_design_space, (**e**) finished gear after solid intersection of BRep_Gear with lattice structure BRep_design_space, and (**f**) manufactured gear using LPBF.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 3 Techniques for Dense Video Captioning
### Discussion-Transformer Based Approaches  
Transformer-based approaches have shown significant promise in video captioning. Basic Transformer models have demonstrated improved performance over RNN-based methods due to their ability to capture long-range dependencies. Enhanced Transformer  
Figure 11: The proposed COST method’s network architecture (redrawn by the authors, adapted from [41]) comprises three transformer branches: Action-Text, Video-Text, and Detection-Text. A cross-granularity attention module aligns the interactions modeled by these branches, with \(Y\), \(H\), and \(X\) representing their respective interactions.  
models, such as VideoBERT, MART, and SBAT, further improve performance by incorporating additional mechanisms like memory augmentation, sparse attention, and boundary-aware pooling. Collaborative frameworks like COST and knowledge graph-augmented models provide additional context to generate more informative captions. Finally, efficient Transformer models like SnapCap and factorized autoregressive decoding enable video captioning in resource-constrained environments.
# Spatiotemporal Evolution and Impact Mechanisms of Gross Primary Productivity in Tropics
###### Abstract  
Gross primary productivity (GPP), representing organic carbon fixation through photosynthesis, is crucial for developing science-based strategies for sustainable development. Given that the tropical region harbors nearly half of all species, it plays a pivotal role in safeguarding the global environment against climate change and preserving global biodiversity. Thus, investigating changes in vegetation productivity within this region holds substantial practical importance for estimating global vegetation productivity. In this study, we employed an enhanced P model to estimate vegetation GPP in the tropical region from 2001 to 2020, based on which we quantified the spatiotemporal changes and associated mechanisms. The results reveal that the annual mean GPP in the tropical region ranged from 2603.9 to 2757.1 g cm\({}^{-2}\) a\({}^{-1}\), demonstrating an overall apparent increasing trend. Inland areas were mainly influenced by precipitation, while coastal areas were primarily influenced by temperature. Land cover changes, especially conversion to cropland, significantly influence GPP, with deciduous--everygreen forest transitions causing notable decreases. Climate change emerges as the dominant factor affecting GPP, as indicated by the contribution rate analysis. This research interprets the spatiotemporal pattern and mechanisms of GPP in the tropics, offering valuable insights for sustainable ecosystem management.  
gross primary productivity; P model; tropics; climate change; land cover change +
Footnote †: journal: Physics Letters A  
1
Footnote 1: institutetext: Correspondence: [EMAIL_ADDRESS]; Tel.: +86-0989-6626-6469
## 3 Test Results and Analysis
### Rate of Mass Loss After Freeze-Thaw Cycles  
The mass loss rate was calculated for specimens with different numbers of freeze-thaw cycles. The formula for the mass loss rate is as follows:  
\[\eta=\frac{m_{0}-m_{1}}{m_{0}}\times 100\% \tag{1}\]  
where \(\eta\) is the mass loss rate after the freeze-thaw cycle, %; \(m_{0}\) is the specimen mass before the freeze-thaw cycle, g; and \(m_{1}\) is the specimen mass after the freeze-thaw cycle, g.  
The mass loss rate of specimens under different times of freeze-thaw cycles is shown in Figure 4. It increases with the increase in the number of freeze-thaw cycles. However, the mass loss rate of groups C-5 and D-5 is negative after 25 freeze-thaw cycles. This is because after 25 freeze-thaw cycles, both the surface and the interior of the specimen produce small cracks, which allows more water to enter the specimen. When the amount of water entering the specimen is greater than the amount of sand and cement paste shed on the surface of the specimen at the initial stage, the overall mass of the specimen increases, and the mass loss rate shows a negative value. The mass loss rate of group A-5 is significantly higher than that of the other three groups at all times, indicating that adding rubber to concrete effectively enhances the frost resistance of concrete. Comparing groups B-5, C-5, and D-5, it can be seen that the D-5 group has the best freezing resistance, followed by the C-5 group, and the B-5 group has the worst freezing resistance. Therefore, when the rubber mixing amount is the same, the smaller the rubber particle size, the more obvious the improvement in the freezing resistance of concrete. Also, the mass loss rate of all four groups after 125 freeze-thaw cycles is less than 5%. Thus, from the freeze-thaw cycle test phenomenon and the mass loss rate, it can be seen that the freeze-thaw cycle has a small impact on the surface damage of the specimen and mainly causes internal damage to the concrete.  
Figure 3: Changes in specimens before and after freezing and thawing.
# Fast 3D Transmission Tower Detection Based on Virtual Views
## 3 Methodology
### 3.3 3D Object Localization Based on Back Projection  
The locations of TTs detected by the local-global dual-path feature fusion network described in Section 3.2 are on the virtual view rather than in the LiDAR point cloud. Therefore, it is necessary to use the projection model, \(P\), to back-project the 2D detection results into the LiDAR point cloud to obtain the portions of the point cloud corresponding to the TTs. However, since the side view projection only provides nearly half of the side information of the TTs, direct back projection can only yield half of the 3D point cloud of the towers.  
To acquire the complete point cloud of the TTs from the LiDAR data, this paper employs a seed point-based 3D region growing algorithm. By using the detected 3D points belonging to the TTs as seed points, and taking the average Euclidean distance and local point cloud density in both planar and elevation directions as similarity criteria, the algorithm sequentially determines whether the neighboring points near the seed points meet the similarity criteria. If they do, the neighboring points are considered part of the TT and used as new seed points; otherwise, the points are discarded, and this region-growing step is continued until no new seed points are added, at which point the process stops. Consequently, the entire point cloud of TTs is extracted completely, as shown in Figure 5.