esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
To conclude, this work, within the limits of its lab-scale, offers insights for future research lines toward the development of cellulose-based food contact materials, with special attention to a greater sustainability compared to actual commercial solutions. The SEM analysis of the developed coated samples showed the disappearance of the typical fiber network of paper and allowed to observe a continuous layer of coating. The smoothness of the coating surface was used as an indicator of good biopolymer solubilization. Coated samples developed in this work showed a significant improvement of water vapor barrier compared to uncoated paper and also to commercial fluorinated paper. The significant reduction of WVTR is a promising feature of the developed coated paper samples, according to the importance of this parameter for food quality preservation. Biopolymer-coated paper samples showed improved grease resistance, still not comparable with fluorinated and polyethylene-coated commercial samples. Among samples, PCL-coated offered the best resistance, ranging from 4 to 12 h. PHBV and PCL coatings with PEG at 20% showed good water contact angles, thus a low wettability. The measured oil contact angles were generally much lower compared to fluorinated paper, however certain coating formulations (i.e., PCLpeg20, PCLpeg10 and PHBV) showed significantly higher surface oleophobicity compared to commercial polyethylene-coated paper.  
Chlorogram utilized in this study is a poorly biocompatible solvent; therefore, future developments should include the evaluation of more eco-friendly solvents, when possible, or other coating techniques, such as extrusion coating. The increasing availability and commercial variety of compostable biopolymers allow a more concrete technical feasibility. The real compostability of biopolymer-coated paper should, however, be verified.  
Conceptualization, F.L. and P.F.; methodology, F.L., P.F., E.L.F., and C.M.; formal analysis, E.L.F. and C.M.; investigation, E.L.F. and C.M.; data curation, F.L. and C.M; writing--original draft preparation, C.M. and E.L.F.; writing--review and editing, F.L. and P.F.; supervision, F.L. and P.F. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Oil contact angle of fluorinated paper (Fluo).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample Code** & **Mean Contact Angle Value** \\ \hline UCP & 15.1 \(\pm\) 3.8 de \\ Fluo & 101.9 \(\pm\) 1.5 a \\ Poli & 16.9 \(\pm\) 0.2 d \\
80d & 12.6 \(\pm\) 1.8 e \\ PHBV & 24.7 \(\pm\) 1.6 c \\ PHBVpeg5 & 19.3 \(\pm\) 1.0 d \\ PHBVpeg10 & 17.2 \(\pm\) 1.7 d \\ PHBVpeg20 & 17.6 \(\pm\) 2.3 d \\ PCL & 18.9 \(\pm\) 1.9 d \\ PCLpeg5 & 16.6 \(\pm\) 0.8 de \\ PCLpeg10 & 27.0 \(\pm\) 2.3 c \\ PCLpeg20 & 37.3 \(\pm\) 1.9 b \\ \hline \hline \end{tabular}
\end{table}
Table 5: Oil contact angle values of second set samples.  
Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflict of interest.
###### Abstract  
In recent times, a significant amount of power loss and system instability due to high voltage deviation experienced by modern power systems, in addition to the pressing issues challenging the power industry such as pollution--especially the emission of greenhouse gases--and aging infrastructures, have posed a serious threat to system operations. Distributed generation has been identified as one main solution capable of reducing pollution when solar and wind power are used and, hence, rejuvenating dilapidated infrastructures and redeeming climatic changes. This paper presents a novel two-stage approach for the identification of suitable locations for DG placement and the sizing of DG for loss reduction and voltage stability enhancement. The first stage explored the use of a network structure to develop a coupling factor (CF) approach that was non-iterative in nature to determine suitable DG locations. In the second stage, the size of the DG was determined using the particle swarm optimization (PSO) algorithm. The main objective was to obtain an optimal voltage profile of the system under consideration while lowering the power loss in the system and ensuring network stability amidst DG incorporation. The model design, optimization and simulation were carried out using the MATLAB 2016a environment and the IEEE 33-bus test system, in which DG was integrated. The influence of increasing the level of DG placement in the system was then investigated. The forward/backward sweep method was applied to monitor the optimization process. The voltage profiles for both the base case when no DG was integrated and the case of incremental DG integration were considered. The results obtained for both single and multiple DG integration are compared with those obtained using the existing methods. The results show the efficiency and applicability of the new non-iterative scheme in the quick identification of DG locations for voltage profile enhancement and network real power loss reduction in radial distribution networks.  
distribution networks; distributed generation; power loss; optimization; voltage profile +
Footnote †: journal: Applied Sciences
# A Combined Full-Reference Image Quality Assessment Method Based on Convolutional Activation Maps
## 3 Experimental Results
### Cross Database Test  
Cross database test refers to the procedure of training on one given IQA benchmark database and testing on another to show the generalization potential of a machine learning based method. The results of the cross database test using KADID-10k [44], TID2013 [53], TID2008 [46], VCL-FER [47], MDID [48], and CSIQ [49] are depicted in Figure 9. From these results, it can be concluded that the proposed method loses from its performance significantly in most cases, but is still able to achieve the performance of traditional state-of-the-art FR-IQA metrics. Moreover, there are some pairings, such as trained on KADID-10k [44] and tested on CSIQ [49], trained on TID2013 [53] and tested on TID2008 [46], trained on TID2008 [46] and tested on TID2013 [53], where the performance loss is rather minor.  
\begin{table}
\begin{tabular}{c|c|c|c|c|c|c} \hline \hline  & \multicolumn{3}{c}{**MDID [48]**} & \multicolumn{3}{c}{**CSIQ [49]**} \\ \cline{2-7}  & PLCC & **SROCC** & **KROCC** & **PLCC** & **SROCC** & **KROCC** \\ \hline MDSI [57] & 0.829 & 0.836 & 0.653 & 0.953 & 0.957 & 0.812 \\ CSV [58] & 0.879 & 0.881 & 0.700 & 0.933 & 0.933 & 0.766 \\ GMSD [19] & 0.864 & 0.862 & 0.680 & 0.954 & 0.957 & 0.812 \\ DSS [59] & 0.870 & 0.866 & 0.679 & 0.953 & 0.955 & 0.811 \\ VSI [60] & 0.855 & 0.857 & 0.671 & 0.928 & 0.942 & 0.785 \\ PerSIM [61] & 0.823 & 0.820 & 0.630 & 0.924 & 0.929 & 0.768 \\ BLESS-SR-SIM [62] & 0.805 & 0.815 & 0.626 & 0.892 & 0.893 & 0.718 \\ BLESS-FSIM [62] & 0.848 & 0.847 & 0.658 & 0.882 & 0.885 & 0.701 \\ BLESS-FSIMc [62] & 0.878 & 0.883 & 0.702 & 0.913 & 0.917 & 0.743 \\ LCSIM1 [40] & - & - & - & 0.897 & 0.949 & 0.799 \\ ReSIFT [63] & 0.905 & 0.895 & 0.716 & 0.884 & 0.868 & 0.695 \\ IQ(\(\mathcal{L}_{T}\)) [28] & 0.867 & 0.865 & 0.708 & 0.915 & 0.912 & 0.720 \\ MS-UNIQUE [64] & 0.863 & 0.871 & 0.689 & 0.918 & 0.929 & 0.759 \\ SSIM CNN [41] & 0.904 & 0.907 & 0.732 & 0.952 & 0.946 & 0.794 \\ RVSIM [65] & 0.884 & 0.884 & 0.709 & 0.923 & 0.903 & 0.728 \\
2 stepQA [66] & 0.753 & 0.759 & 0.562 & 0.841 & 0.849 & 0.655 \\ SUMMER [67] & 0.742 & 0.734 & 0.543 & 0.826 & 0.830 & 0.658 \\ CEQI [68] & 0.863 & 0.864 & 0.685 & 0.956 & 0.956 & 0.814 \\ CEQIc [68] & 0.864 & 0.863 & 0.684 & 0.956 & 0.955 & 0.810 \\ VCGS [69] & 0.867 & 0.869 & 0.687 & 0.931 & 0.944 & 0.790 \\ DISTS [70] & 0.862 & 0.860 & 0.669 & 0.930 & 0.930 & 0.764 \\ \hline DeepFL-IQA [42] & - & - & - & 0.946 & 0.930 & - \\ BLINDER [43] & - & - & - & 0.968 & 0.961 & - \\ RankIQA [71] & - & - & - & 0.960 & 0.947 & - \\ BPSOM-MD [72] & - & - & - & 0.860 & 0.904 & - \\ NSSADNN [73] & - & - & - & 0.927 & 0.893 & - \\ \hline _ActMapFeat_(ours) & **0.930** & **0.927** & **0.769** & **0.971** & **0.970** & **0.850** \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._
## Materials and Methods
### Attention-Based Feature-Fusion VGG19
#### 2.1.4 Training Parameters  
The following parameters are selected for the specific task. Their selection require fine-tuning, performed before network training and evaluation.  
1. The dense layer at the top connects each of the neurons from the previous layers and allows the network to extract features from the input data. AF-VGG19 consists of one dense layer of 512 nodes.
2. Loss function: The categorical cross-entropy loss function is commonly used in supervised learning tasks with multiple classes. It is used to measure the dissimilarity between the predicted probabilities of a model and the true class labels.
3. Optimiser: An optimiser plays a crucial role in training a CNN [9]. It updates the model weights based on the gradients calculated during the forward and backward passes. The present version of our network uses the Adam optimiser.
4. Early Stopping: We implemented two early stopping rules. Suppose the validation accuracy reaches 0.91 and does not improve for ten epochs, while the training accuracy remains above 0.91. In that case, the training stops, and the weights of the best epoch are restored.
5. Data Augmentation: Data augmentation is an essential training technique, even when a large amount of data are available. Data augmentation aids in overfitting reduction when the model's performance on the training data is substantially better than on unseen data. Data augmentation creates new data points by transforming existing data points to preserve the original data information. The latter helps improve the model's generalisation ability and performance on unseen data. As advised by similar studies [27; 28; 29], slight data augmentations are applied for particular classification tasks. These include slight height and width shifts (by 10 pixels), random rotations (by a maximum of 10 degrees), and Gaussian noise injections.
# Thermal Fatigue Life Prediction of Thermal Barrier Coat on Nozzle Guide Vane via Master-Slave Model
## 4 Thermal Cycle Analysis and Life Prediction of NGV.
### Temperature Test  
To verify the thermal-elastic simulation of the NGV, temperature-sensitive paint (TSP) technology was adopted to measure the surface temperatures of the NGV under the maximum operation conditions of a turbojet engine. The surface temperature tests for the NGV were performed inside an indoor aero-engine test rig at the China Gas Turbine Establishment, the structure of which is shown in Figure 12. The technical parameters of the engine are listed in Table 3.  
As each TSP material has one dedicated temperature sensitive range, only temperatures can be recorded in this range in the TSP test. Two typical TSP were selected to show the temperature distributions in the NGV. Isothermal lines (also called discoloration lines) were drawn to calibrate and explain the TSPs instead of the colors after heating. The calibrated temperatures of the thermal-indicate standard models under the constant peak temperature for 180 s are shown in Figure 13. In the coming work, the colors of the TSP-M02 and TSP-M05 were referred to determine the temperature distributions gained in the thermal-elastic simulation of the NGV in Section 4.  
Figure 11: Variation trend and temperature curves with heating time. (**a**) Change of temperature contour with heating time; (**b**) Changing curves of maximum and minimum temperature.  
Figure 12: Indoor aeroengine test rig [(26)].  
For the high-integrity approach with the master-slave model, the accuracy of the TFL depends highly on the prediction precision of the temperature field. According to the simulation, we found that the temperature distribution of the NGV was stable after 60 s, at which the temperature fields were compared with the result of the TSP test as shown in Figure 14.  
As revealed in Figure 14, the temperature range of the leading edge and trailing edge were 1021 K to 1126 K and larger than 1070 K, respectively. The maximum temperature of the TSP test was a bit higher than the simulation. The film cooling technology was used to reduce the surface temperature. Therefore, the simulation temperature was reasonable. Compared with traditional methods, the prediction accuracy of the temperature field was improved by using the fully coupled technology in the heat transfer simulation [18]. Furthermore, the temperatures at the center of the leading edge and the trailing edge were 1177 K and 1205 K, respectively, which were similar to that of the ceramic metal tube test and indicate that the parameters in the oxidation weight gain model are also applicable to the NGV.
# Development of IoT-Based Real-Time Fire Detection System Using Raspberry Pi and Fisheye Camera
## 3 Methods
### Deep-Learning-Based Fire Image Detection and Location Tracking
Also, the blue semicircle shows the 180-degree angle of view of the fisheye camera, and the orange sector shows the 90-degree angle of view of the normal camera. Normal cameras only take pictures of the sky when looking vertically upward, whereas fisheye cameras can take pictures in all directions. This is because fisheye cameras have a wider shooting range than normal cameras. The greater the change in the elevation of the topography, the more accurate the distance estimation, and the smaller the change in elevation, the easier it is for errors to occur. The term error here does not mean an error in the direction between the device and the fire, but a distance error between two points, that is, a position error due to a distance error. For example, on flat land, both near and far points in the same direction can be located at the same point in polar coordinates. On the other hand, if the relief of the terrain is large, the difference in elevation angle is large, so the location of the fire can be accurately calculated. This error is fundamentally difficult to solve with only one device, but it is not a big problem in an extensive mountainous terrain, which is the main target area of this study. Another type of error can be caused by not installing the device properly. To minimize this error, the fisheye camera should be installed so that the direction of the lens is perpendicular to the horizon (i.e., level the device), and the north should be accurately identified before installation. Figure 5: An example of (**a**) a topographical map where two forest fires occurred, (**b**) the position of the forest fire in the polar image obtained through the fisheye camera, and (**c**) a cross-sectional diagram from the observation point to the forest fire point.
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 1 Introduction  
Industry 5.0 is an approach wherein production and manufacturing are inclined towards more sustainable and humanistic practices. Advanced technologies in AI, robotics, and IoT will mold the power demands of Industry 5.0-related buildings much more. This framework promotes total efficiency and sustainability, which is also apparent in smart energy management applications that concern themselves with the optimal use of any kind of energy consumption [1, 2, 3]. Renewable energy is harnessed in buildings with an energy-efficient design and real-time monitoring for minimum carbon footprints. Further,such a collaboration of humans and machines will require appropriately adaptive energy solutions so that energy supply matches the dynamic energy demands of the intelligent manufacturing processes. In Industry 5.0, energy demand will not address just operational needs, but will also play an integral role in creating a circular economy to protect the environment [4; 5; 6; 7].  
Traditional energy demand forecasting models are likely to experience severe shortcomings like sparsity of data, which can adversely affect the quality and reliability of forecasts due to the absence of adequate historic data; seasonality, whereby the demand patterns change based on the time of year, and, thus, establishing consistent trends is challenging; and computational complexity, since traditional methods may require extremely high computational time and effort to achieve reliable results [8]. AI is transforming energy management in buildings by facilitating smarter data-driven decisions. Through advanced analytics and machine learning algorithms, AI can forecast energy consumption trends, optimize resource distribution, and improve operational efficiency. This technology allows for the real-time monitoring and control of energy systems, enabling quick adjustments based on factors like occupancy, weather, and energy prices. As a result, buildings become more responsive and adaptable, significantly reducing waste and enhancing sustainability [9; 10]. Moreover, AI-driven predictive maintenance can identify potential issues before they escalate, further improving energy efficiency and lowering operational costs. This proactive approach to energy management enhances both performance and sustainability [11; 12; 13; 14; 15; 16]. The combination of Artificial Neural Networks (ANNs) and Random Forest (RF) models introduces a new method that leverages the strengths of each approach: ANNs detect nonlinear relationships and learn intricate patterns from large datasets, while RF contributes robustness and interpretability with its ensemble method, effectively managing overfitting and boosting predictive ability [17; 18; 19].
# Effects of Smartphone Size and Hand Size on Grip Posture in One-Handed Hard Key Operations
###### Abstract  
Greater understanding of the grip postures preferred by users is needed for the ergonomic design of smartphone user interfaces. The present study identifies user-preferred grip postures by smartphone size and hand size in one-handed hard key operations. Grip postures of 45 participants were photographed while major smartphone tasks were simulated in standing with smartphone mockups of nine screen sizes (3.0\"-7.0\"). The grip postures were encoded by the locations (left side: L, right side: R, top: T, bottom: B, front: F, back: K) of a smartphone and the number of fingers at each contact location. Three grip postures (L3-R1-K1: 70.0%, L4-R1: 13.3%, L3-R1-T1: 12.0%) were found dominant and the distribution of grip posture changed by smartphone size and hand size--the larger the smartphone size or hand size, the higher the frequency of L3-R1-K1. The grip posture frequency distribution by smartphone size would be of use to determine the optimal locations of hard keys on a smartphone of a particular size.  
smartphone; hard key location; grip posture; posture encoding; frequency analysis Article
# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 4 Datasets and Performance Evaluation Indices
### Datasets
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Source** & **Type and Format** & \begin{tabular}{l} **Nbr of** \\ **Values** \\ \end{tabular} & \begin{tabular}{l} **Nbr of** \\ **Channels** \\ \end{tabular} & \begin{tabular}{l} **Nbr of** \\ **Num of Parameters** \\ \end{tabular} & \
\begin{tabular}{l} **Nbr of** \\ **Anomalies** \\ \end{tabular} &
\begin{tabular}{l} **Approach(es)** \\ \end{tabular} \\ \hline \multicolumn{6}{c}{Real datasets} \\ \hline  & & & & \multicolumn{3}{c}{445 parameters of:} \\  & & & & \multicolumn{3}{c}{- Attitude Control Subsys (ACS)} \\  & & & & \multicolumn{3}{c}{- Electrical Power Subsys (EPS)} \\ JAXA SDS-4 & Multi-variate time series & — & — & \multicolumn{3}{c}{- Thermal Control Subsys (TCS)} \\  & & & & \multicolumn{3}{c}{- Command and Data Handling} \\  & & & & \multicolumn{3}{c}{Subsys Transmitter} \\  & & & \multicolumn{3}{c}{- Receiver Subsys (TRX)} \\ \hline JAXA EPS: MDS-1 & Multi-variate time series & — & — & — & — & MPPCA [50] \\ JAXA EPS: DRTS & Multi-variate time series & — & — & — & — & MPCA [50] \\ JAXA EPS & Multi-variate time series & 3000 & — & 120 & — & PCA, KPCA, MPPCA, \\ LEIVM [47] \\ \hline China Academy of & & & & & & & \\ Launch Whiche & Multi-variate time series & 27,928 & 1 & 19 & — & GRU+VAE, LSTM + GAN [60] \\ Technology: PSR & & & & & & \\ \hline China Aerospace & & & & & & \\ Science and & & & & & & \\ Technology & Multi-variate time series & 21,010 & — & Magnetometer parameters & — & Clustering [45] \\ Corporation (CASC): & & & & & & \\ \hline ADAPT-Lite EPS & Multi-variate time series & — & — & — & — & Improved KNN [43] \\ \hline Label & Multi-variate time series & — & — & — & — & NN [52] \\ \hline Korea Aerospace & & & & & & \\ Research Institute: & & & & & & \\ KOMPSAT-2 & & & & & & \\ \hline Military & & & & & & \\ communication & Multi-variate time series & 67,968 & — & 20 & — & Thresholding [24] \\ satellite & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Source** & **Type and Format** & \begin{tabular}{l} **Nbr of** \\ **Values** \\ \end{tabular} & \begin{tabular}{l} **Nbr of** \\ **Channels** \\ \end{tabular} & \begin{tabular}{l} **Nbr of** \\ **Channels** \\ \end{tabular} & \
\begin{tabular}{l} **Nbr of** \\ **Anomalies** \\ \end{tabular} &
\begin{tabular}{l} **Approach(es)** \\ \end{tabular} \\ \hline \multicolumn{6}{c}{Real datasets} \\ \hline Anonymized in-orbit & \multirow{2}{*}{Multi-variate time series} & — & — & — & — & LS-SVM [28] \\ spacecraft & & & & & & \\ \hline Anonymized power & \multirow{2}{*}{Univariate time series} & — & 6 & — & — & KNN [44] \\ subsetline & & & & & & \\ \hline Anonymized attitude & \multirow{2}{*}{Multi-variate time series in-orbit satellite} & \multirow{2}{*}{5000} & — & 14 & — & PCA [49] \\ \cline{1-1} \cline{6-6} \multicolumn{6}{l}{_Real telemetry system of in-orbit satellite_[67]} & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._
## 5 Developed Applications
### Prototype Three  
With the intention of testing the development process of the METUIGA methodology for another type of disability, it was decided to conduct an experiment to demonstrate that the generated software meets the characteristics of tangible user interfaces and gamification techniques in an application aimed at another type of target user; in this case, in people with Autism Spectrum Disorder (ASD).  
\"People with ASD have serious difficulties in communication and social interaction skills and behavioral flexibility\" [56]. \"These difficulties, which respond to variable expressions of severity, are related to the existence of barriers to learning mental states that hinder the understanding of reality from the perspective of another person. Therefore, it is important to develop teaching aimed at helping these people to acquire the relevant skills and abilities to achieve these competencies to provide them with the tools to enjoy a life as normal as possible\" [57].  
The third prototype is called Tangible Emotions, this application is designed to help in the recognition of basic emotions (surprise, anger, fear, sadness, happiness) for children with ASD. The application is composed of two main activities, the first activity consists of showing the main characteristics of each emotion. In the second activity the child receives a question related to a social event and with the support of an adult must deduce what emotion he/she would feel when that event occurs, once the emotion is decided the child must place the object that corresponds to that emotion on acrylic of tangible interface, then the system will evaluate his/her answer and gives the corresponding feedback.  
The system is enriched with a series of gamification elements such as challenges, levels and rewards that are obtained according to performance. Furthermore, since this application is aimed at children with ASD, it was decided not to implement the system of lives so as not to generate a certain level of frustration. For this reason, it is possible to use as many tangible objects as necessary to answer each of the activities correctly.  
Figure 11(a) shows the graphical interfaces of the tangible emotion's application showing the gamification elements and Figure 11(b) shows the tangible elements available for this application as well as a fiducial marker attached to one of them: