esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Anomaly Detection Based on Variational Deviation Network
## 5 Results
### Comparison Algorithm  
The variational deviation algorithm will be compared with the deviation algorithm [16], REPEN algorithm [5], Deep-SVDD algorithm [6], FSNet algorithm (algorithm for prototypical networks for few-shot learning) [32], and iForest (algorithm for isolation-based anomaly detection) [17], which were the five algorithms used for verification, because these five algorithms are currently the most advanced methods in the field of anomaly detection. The deviation algorithm is an end-to-end anomaly detection algorithm; the REPEN algorithm is a semi-supervised deep learning anomaly detection algorithm that solves a small amount of abnormal data; and the Deep-SVDD algorithm is a feature representation of learning normal data. The FSNet algorithm is an anomaly detection algorithm that solves small sample learning detection. The iForest algorithm is a typical anomaly detection algorithm in unsupervised learning. These algorithms are implemented in the python language. Among them, V-DevNet, DevNet, Deep- SVDD, FSNet, and REPEN are implemented using keras [33], and the iForest algorithm is called in scikit-learn. In the training, the first four algorithms all used the gradient optimization algorithm of [34] for training. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & \multicolumn{6}{c}{**AUC-ROC Performance**} \\ \hline  & V-DevNet & DevNet & REPEN & DSVDD & FSNet & iForest \\ donors & **1.000 \(\pm\) 0.000** & 1.000 \(\pm\) 0.000 & 0.975 \(\pm\) 0.005 & 0.995 \(\pm\) 0.005 & 0.997 \(\pm\) 0.002 & 0.874 \(\pm\) 0.015 \\ census & **0.856 \(\pm\) 0.017** & 0.828 \(\pm\) 0.008 & 0.794 \(\pm\) 0.005 & 0.835 \(\pm\) 0.014 & 0.732 \(\pm\) 0.020 & 0.624 \(\pm\) 0.020 \\ fraud & **0.988 \(\pm\) 0.003** & 0.980 \(\pm\) 0.001 & 0.972 \(\pm\) 0.003 & 0.977 \(\pm\) 0.001 & 0.734 \(\pm\) 0.046 & 0.953 \(\pm\) 0.002 \\ celeba & **0.967 \(\pm\) 0.009** & 0.951 \(\pm\) 0.001 & 0.894 \(\pm\) 0.005 & 0.944 \(\pm\) 0.003 & 0.808 \(\pm\) 0.027 & 0.698 \(\pm\) 0.020 \\ backdoor & **0.975 \(\pm\) 0.023** & 0.969 \(\pm\) 0.004 & 0.878 \(\pm\) 0.007 & 0.952 \(\pm\) 0.018 & 0.928 \(\pm\) 0.019 & 0.752 \(\pm\) 0.021 \\ URL & **0.985 \(\pm\) 0.016** & 0.977 \(\pm\) 0.004 & 0.842 \(\pm\) 0.006 & 0.908 \(\pm\) 0.027 & 0.786 \(\pm\) 0.047 & 0.720 \(\pm\) 0.032 \\ campaign & **0.914 \(\pm\) 0.015** & 0.807 \(\pm\) 0.006 & 0.723 \(\pm\) 0.006 & 0.748 \(\pm\) 0.019 & 0.623 \(\pm\) 0.024 & 0.731 \(\pm\) 0.015 \\ news20 & 0.948 \(\pm\) 0.002 & **0.950 \(\pm\) 0.007** & 0.885 \(\pm\) 0.003 & 0.887 \(\pm\) 0.000 & 0.578 \(\pm\) 0.050 & 0.328 \(\pm\) 0.016 \\ thyroid & **0.831 \(\pm\) 0.005** & 0.783 \(\pm\) 0.003 & 0.580 \(\pm\) 0.016 & 0.749 \(\pm\) 0.011 & 0.564 \(\pm\) 0.017 & 0.688 \(\pm\) 0.020 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: AUC-ROC performance of V-DevNet and five competitive methods. The bolded data in the table indicates that the accuracy of the algorithm is higher compared to other algorithms.
# Forest Resistance and Resilience to 2002 Drought in Northern China
## 2 Materials and Methods
### Data
#### 2.2.3 Drought Index  
The standardized precipitation evapotranspiration index (SPEI) was used for drought detecting in this study. The SPEI is a state-of-the-art meteorological drought index. It considered not only temperature and/or precipitation as the traditional drought index (e.g., PDSI and SPI), but also considered evapotranspiration (ET), which described the average water balance of the terrestrial ecosystem [24]. In this study, we downloaded the 12-month windowed SPEI (SPEI-12) dataset from the Global SPEI Database ([[https://spei.csic.es/database.html](https://spei.csic.es/database.html)]([https://spei.csic.es/database.html](https://spei.csic.es/database.html)), 18 January 2021),since the annual water balance has been proved to be strongly correlated with forest growth [25]. The spatial resolution of this dataset is 0.5 degree. To make the pixels location fit to the MODIS EVI and GPP for spatial statistical analyses, we resampled the SPEI pixel into 500 m following the nearest neighbor rule.
## 3 Results and Discussion
### Analysis of Molecular Weight and Chain Length Distribution  
Compared to the control starch group with a weight-averaged molecular weight (M\({}_{w}\)) of 72,080.96 kDa, the M\({}_{w}\) of the ultrasound-treated starch decreased (US: 70,744.22 kDa), indicating that the ultrasonic treatment led to the degradation of molecular chains in the starch, further confirming that both amylose and amylopectin molecules were disrupted. This finding has also been observed in other studies [32]. However, it is likely to cause starch degradation by shear force in the SEC separation system. This is a common phenomenon when using SEC to separate giant molecules, such as amylopectin. To avoid this, a low flow rate may not result in a full or partial change in the apparent size distribution, or cause artifacts via shear fracture [44]. Herein, the flow rate of 0.3 mL/min was used to separate the broken rice starch samples before and after ultrasonic treatment, which is suitable for the SEC or GPC of native starch in an eluent system [45].  
Figure 3: (**a**) Infrared spectroscopy images of broken rice starch (black line), ultrasound-modified starch (red line), and modified starch composite (blue line), as well as quercetin (**b**).  
After complexation with quercetin, the starch exhibited a higher molecular weight (US-Q; 85,141.95 kDa), which could be attributed to the added quercetin forming complexes with the starch. Additionally, the ultrasonic treatment processing method had a significant impact on the formation of the complexes [46]. Furthermore, the chain length distributions of C, US, and US-Q are shown in Figure 4. The two peaks in the chain length distribution represent amylopectin (branched polymer) and amylose (linear polymer). Peak 1 represents A chains and short B chains in amylopectin, peak 2 represents long B and C chains in amylopectin, and peak 3 represents amylose. As a result, compared to the broken rice starch serving as the control (15.94%), both the ultrasonically modified starch (31.76%) and the modified starch composite (26.76%) exhibited a significant increase in relative amylose content. The degree of branching of amylopectin in the ultrasonically modified starch was noticeably decreased, accompanied by a significant increase in the average degree of polymerization. The degree of branching of amylopectin in the modified starch composite also demonstrated a significant reduction. However, in contrast to the ultrasonically modified starch, the degree of branching of amylopectin in the modified starch composite exhibited a slight recovery, while the average degree of polymerization underwent a sharp and abrupt decline.
## 4 Results and Discussion
### Validation with SSH of Spaceborne Radar Altimeter  
Spaceborne radar altimeters can offer centimeter-level instantaneous SSH measurements [44]. In this section, the data for February and March 2021 are utilized as an example. The SSH retrieval precision of the new MRMDL model was evaluated using the SSH of Jason3, HY-2C, and HY-2B spaceborne radar altimeters [45]. The SSH results of Jason3, HY-2C, and HY-2B altimetry satellites are calculated in this paper using the following equations [46]:  
\[\begin{split} SSH=&\textit{alt }-\textit{ range }-\textit{ model\_dry\_tropo\_corr }\ldots\\ &-\textit{inv\_bar\_corr }-\textit{ocean\_tide\_sol1 }-\textit{solid\_earth\_tide }-\textit{pole\_tide}\ldots\\ &-\textit{sea\_state\_bias\_ku }-\textit{ino\_corr\_alt\_ku }-\textit{rad\_wet\_tropo\_corr}\end{split} \tag{10}\]  
where _alt_ represents altimeter track height; _range_ represents altimeter measurement distance; _model\(\_\)dry\(\_\)tropo\(\_\)corr_ represents dry troposphere correction; _inv\(\_\)bar\(\_\)corr_ represents inverse atmospheric pressure height correction; _ocean\(\_\)tide\(\_\)soll_ represents ocean tide correction; _solid\(\_\)earth\(\_\)tide_ represents ocean tide correction; _pole\(\_\)tide_ represents polar tide correction; _sea\(\_\)state\(\_\)bias\(\_\)ku_ represents sea state bias correction; _iono\(\_\)corr\(\_\)alt\(\_\)ku_ represents ionosphere correction; and _rad\(\_\)wet\(\_\)tropo\(\_\)corr_ represents wet troposphere correction.  
The SSH of the Jason3, HY-2C, and HY-2B altimetry satellites were spatiotemporally matched (time: 0.5 h, latitude and longitude: 0.25\({}^{\circ}\)) with the CYGNSS spaceborne datasets. A total of 865 results were obtained.  
Figure 8 compares the SSH of traditional spaceborne radar altimeter and the new MRMDL model. The retrieval results of the new MRMDL can well fit the sea surface height of the radar altimeter. This demonstrates that the retrieval results of the proposed new MRMDL model can well describe the variation of the real SSH. This further validates the effectiveness of the new MRMDL model.  
Figure 9a,b present the scatter density and probability distribution plots of SSH for the altimetric satellite and DTU18, respectively. The SSH of the DTU18 validation model has tiny difference and strong correlation with the SSH of the radar altimeter.  
Figure 10a,b present the scatter density plots and error distribution histograms of SSH for the radar altimeter and MRMDL models, respectively. The retrieved SSH of the new MRMDL has an extremely strong correlation for the SSH of the radar altimeter, with a PCC of about 0.97. However, because CYGNSS satellites are primarily used for ocean wind speed measurement, they are not optimized for ocean altimetry, due to their own data quality limitations, the _MAD_ and _RMSE_ are 4.32 m and 6.50 m, respectively.  
Figure 8: Comparison of SSH curves between traditional spaceborne radar altimeter and the new MRMDL model.
# Investigation of a Solar-Driven Organic Rankine Cycle with Reheating
## 3 Results and Discussion
### Discussion  
The present work compares two different types of solar-driven ORCs. The conventional system is compared to the reheat design, which is a novel cycle. This cycle is found to be more efficient than the conventional one, an important result that indicates that the reheat ORC can be a sustainable technology. It has been found that the energy optimization of the reheat ORC system leads to a global maximum efficiency of 7.33% on a yearly basis, compared to 5.45% of the conventional system. This enhancement is important and about 34.4%. Financial optimization leads to optimal systems financially, with the reheat system to present 6.98% yearly system efficiency and the simple system 5.09%. Again, there is a significant enhancement in efficiency with the reheat design which is 37.1%.  
At this point, it is useful to state the performance of other similar configurations in the literature in order to discuss the results of the present work in energy terms. [PERSON] et al. [31] conducted a dynamic analysis on solar ORC with Dymola and found efficiency of 3.5%. In another study, [PERSON] et al. [32] conducted a thermoeconomic optimization about a solar-driven ORC with ETC and concluded a 5.5% efficiency. In another work, [PERSON] et al. [33] conducted an optimization of a unit with ETC coupled to ORC and found 5.5% efficiency for the city of Athens in Greece. In addition, they indicated that the use of evacuated tube collectors is a better choice than the use of flat plate collectors.  
The aforementioned literature indicates that the obtained values of the present work for the conventional system (5.45%) are very close to the values of the references [32; 33] for the system efficiency. This fact verifies that the present work estimates with high accuracy the simple ORC performance and is a very important point of verification. Therefore, the obtained enhancement with the reheat ORC (7.33%) provides clear enhancement, compared to the usual values of the conventional ORCs, taking into account the present work, as well as other works from the literature.
# Cryptographic Encryption and Optimization for Internet of Things Based Medical Image Security
## 4 Performance Measure  
The robustness and security of the proposed cryptosystem are assessed using a number of tests. Peak signal-to-noise ratio (PSNR), mean square error (MSE), bit error rate (BER), structural similarity index (SSI), and coefficient are a few examples of statistical evaluations.
# Unknown Traffic Recognition Based on Multi-Feature Fusion and Incremental Learning
## 4 Evaluation
### Comparison Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{Methods} & \multicolumn{6}{c}{Scenario A} & \multicolumn{6}{c}{Scenario B} \\ \cline{2-11}  & Acc & TPR & FPR & FTF & F1 & Acc & TPR & FPR & FTF & F1 \\ \hline CNN & 46.02\% & 55.87\% & 64.29\% & 53.74\% & 60.20\% & 55.35\% & 71.73\% & 68.89\% & 56.26\% & 64.17\% \\ \hline CNN-GR & 72.49\% & **98.16\%** & 33.41\% & 74.28\% & 70.31\% & 77.04\% & 96.04\% & 37.47\% & 70.21\% & 75.92\% \\ \hline OPEN-CNN & 82.66\% & 58.36\% & **11.34\%** & 67.04\% & **85.79\%** & 84.29\% & **98.37\%** & 70.30\% & 58.32\% & **94.67\%** \\ \hline HMCD & 68.83\% & 86.95\% & 20.34\% & 77.34\% & 75.28\% & 73.36\% & 85.37\% & 18.33\% & 80.16\% & 75.89\% \\ \hline MI-UTR & **86.15\%** & 90.88\% & 16.38\% & **80.48\%** & 83.78\% & **88.60\%** & 93.27\% & **12.89\%** & **87.93\%** & 81.38\% \\ \hline \multicolumn{11}{c}{Scenario C} & \multicolumn{6}{c}{Scenario D} \\ \cline{2-11}  & Acc & TPR & FPR & FTF & F1 & Acc & TPR & FPR & FTF & F1 \\ \hline CNN & 57.98\% & 50.24\% & 66.22\% & 40.86\% & 53.88\% & 55.32\% & 48.33\% & 67.28\% & 55.90\% & 58.17\% \\ \hline CNN-GR & 60.72\% & **98.02\%** & 43.60\% & 78.94\% & **93.70\%** & 64.38\% & 97.48\% & 65.42\% & 55.06\% & 64.78\% \\ \hline OPEN-CNN & 71.78\% & 62.82\% & 60.69\% & 74.56\% & 82.66\% & 76.85\% & **98.31\%** & 51.47\% & 61.25\% & 82.64\% \\ \hline HMCD & 66.05\% & 72.27\% & 64.28\% & 59.65\% & 74.02\% & 70.91\% & 82.31\% & 27.94\% & 73.59\% & 86.35\% \\ \hline MI-UTR & **90.36\%** & 95.30\% & **13.28\%** & **91.05\%** & 88.45\% & **91.88\%** & 93.39\% & **12.66\%** & **91.42\%** & **93.66\%** \\ \hline \multicolumn{11}{c}{Scenario E} \\ \cline{2-11}  & Acc & TPR & FPR & FTF & F1 & & & & & \\ \hline CNN & 75.05\% & 85.52\% & 35.28\% & 74.55\% & 81.67\% & & & & \\ \hline CNN-GR & 92.22\% & 90.78\% & 24.96\% & 87.27\% & 92.90\% & & & & \\ \hline OPEN-CNN & 84.65\% & 93.52\% & 10.45\% & 91.58\% & 86.15\% & & & & \\ \hline HMCD & 89.27\% & 91.33\% & 28.49\% & 87.34\% & 90.88\% & \\ \hline MI-UTR & **96.25\%** & **94.66\%** & **3.75\%** & **93.96\%** & **97.43\%** & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results of comparison of MI-UTR with other methods.
# Consumer-Driven Usability Test of Mobile Application for Tea Recommendation Service
## 2 Related Work  
Recommender systems are a key technology for suggesting appropriate content, services, or products that suit the user's needs based on the input of personal preference, area of interest, and purchase history [12]. For more than a decade, the use of recommender systems has increased, and giant global IT companies such as Google, Facebook, Netflix, and Amazon are actively using such systems for their marketing sales [3; 4; 13]. Recommender systems can be broadly classified into content-based filtering and collaborative filtering [12; 14; 15]. Content-based filtering measures the degree of similarity between item categories classified by pre-selected criteria and user-preferred items, and recommends items with high similarity. Collaborative filtering is a method of predicting preferences based on the assumption that consumers with similar preferences for certain items will have similar preferences for other items. Hybrid filtering is a combination of these two methods.  
While tea, especially blended tea, has rarely been a target of recommender systems, studies have proposed recommender systems for food. Food recommender systems provide personalized recommendations for food items such as recipes, meals, or ingredients [16; 17; 18; 19; 20; 21]. [PERSON] et al. present a food and personalized recipe recommender system to suit individual taste [18]. They also propose a health-aware recommender system that not only offers recipe recommendations that suit the user's taste preference but also provide nutritional information that promotes a healthy diet [22]. [PERSON] et al. propose a method to recommend healthier alternatives to routine recipes for commonly selected meals [19]. Different from these works, we aim to develop a recommendation service for blended tea. This paper investigates the feasibility of such a service through a mobile application usability test and a consumer acceptability test. It should be noted that the main focus of this study is not to propose a new recommendation algorithm. Rather, we adopt the well-known content-based approach to develop the mobile tea recommendation application.  
There have been many studies on usability evaluations of mobile applications [23; 24; 25; 26; 27; 28]. Some of these propose methodologies for usability evaluations of mobile applications in diverse application domains, such as mobile learning applications [24], mobile fitness applications [25], and mobile sale force automation applications [26]. In this paper, we evaluate the usability of our prototype mobile application for tea recommendation. The usability measure for our evaluation is based on previous works. However, our evaluation further covers other specific factors such as intention to use and brand attitude regarding consumer behavior for tea recommendation. In addition, we study consumer preference for the recommended tea, which is evaluated by users that have sampled the recommended tea.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 3 Overview of Telemedicine
### Forms of Telemedicine  
Telemedicine can be classified based on three considerations: the mode of operation, the communications technology employed, and the specific application area. Figure 6 shows the various classifications of telemedicine.  
Figure 5: Advantages of Telemedicine.  
When considering mode of operation, researchers in the field of telemedicine have typically classified telemedicine into two main classes, namely, real-time and store-and-forward telemedicine [71, 74, 75, 76, 77]. In the research of [3, 78], telemedicine was classified into two modes of operation, store-and-forward and two-way Interactive Television (IATV). Alternatively, [4, 43, 79, 80, 81] classified telemedicine into three classes: online/real-time, store-and-forward and remote health monitoring telemedicine. Adopting the three-class classification, Figure 7 depicts the forms or categorisation of telemedicine based on mode of operation.
# A Review of the Performance of Infilled RC Structures in Recent Earthquakes
## 2 Seismic Behaviour of Infilled RC Structures: Definition of Damage Type
### Representative Damage Type Observed in RC Buildings Due to Earthquakes
#### 2.1.1 Damage Type 1: Damage Associated with Stirrups and Hoops (RC Detailing)
Some experimental studies were carried out on evaluating and retrofitting existing RC building structures designed according to codes earlier than the 1970s [6; 17; 18; 19; 20]. Most of these studies are related to ribbed bars. However, only a minor number of research works were developed using smooth bars [21; 22; 23; 24; 25; 26]. The major conclusions from those works indicate that the strength capacity of the elements built with smooth bars is lower, followed by a more substantial degradation of the connection conditions along the longitudinal bars that significantly exceed and violate the plane sections theory. Furthermore, in most cases, their performance was characterised by a flexural behaviour followed by post-peak degradation, which was confirmed by the measurement of bar strains relatively lower than the steel yielding strain. This fact suggests that the code approach and the procedure to adopt in the seismic safety assessment to estimate the flexural strength capacity overestimate the actual flexural strength capacity of the RC elements (when executed with smooth bars).
## 3 Methodology
### Optimize General Fitting Methods by Wavelet Transform
#### 3.1.2 General Fitting Methods  
Elastic network regression (ER) [75] is a multivariate statistical regression model that uses two prior regularization terms from ridge regression and lasso regression. ER can eliminate the high collinearity between variables and reduce their dimensionality so as to obtain better fitting results than linear regression.  
Area-to-area regression kriging (ATARK, hereafter referred to as ATA) [36] follows the principle that geographical attributes are often spatially autocorrelated [76]. The implementation of ATA includes two steps: (1) constructing the coarse-scale trend surface with the regression process and obtaining residuals by subtracting the regressed trend surface from the original geospatial field; and (2) applying area-to-area kriging interpolation  
Figure 2: The block diagrams of the wavelet transformation in decomposing an \(n\times m\) image into four decomposed images.  
to reconstruct the residuals at a fine resolution and then superposing them onto the trend surface to obtain the final results.  
The random forest model (RF), introduced by [PERSON] [77], is an ensemble learning technique commonly applied to classification and regression problems. The basic principle is integrating results taken from different decision trees through ensemble learning. Multiple decision trees are first built during training, and then the individual results of the trees are generated as the prediction. RF has many advantages, including insensitivity to multivariate collinearity and default hyperparameter values, with anti-noise ability, and strong robustness when applied to high-dimensional data [78; 79].  
To ensure that the training model was suitable for dealing with long spatiotemporal sequences and dynamic movement (e.g., SM), nonlinear autoregressive models with exogenous-input feedforward neural networks (NNs) were used for SM prediction. NNs have been shown to perform well on problems involving long-term dependencies [80]. In this approach, output signals from one layer are regressed on the previous layer's results with current and past values of the input signals by configuring a tapped delay line (TDL) [81; 82].