esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Technologies in Data and Information Security
## 1 Introduction  
The protection of personal data and privacy is a timeless challenge which has intensified the modern era. The digitisation that has been achieved in recent decades has radically changed the way we live, communicate and work, revealing various security and privacy issues. Specifically, the explosion of new technologies and the continuous developments of technologies, such as the Internet of Things (IoT) and Artificial Intelligence (AI), have led to the increased value of data, while it has raised demand and introduced new ways to obtain it. Techniques such as data analysis and processing provide a set of powerful tools that can be used by both governments and businesses for specific purposes. However, as with any valuable resource, as in the case of data, the phenomena of abuse, unfair practices and even criminal acts are not absent. In particular, in recent years, there have been more and more cases of sophisticated cyberattacks, data theft and leaks or even data trade, which violate the rights of individuals, but also harm competition and seriously damage the reputation of businesses.  
With this in mind, the present Special Issue of _Applied Sciences_ on _\"Advanced Technologies in Data and Information Security\"_ provides an overview of the latest developments in this field. Nineteen papers were submitted to this Special Issue, and nine papers [1; 2; 3; 4; 5; 6; 7; 8; 9] were accepted (i.e., an 47.4% acceptance rate). The presented papers explore innovative trends of data privacy and information security that enable technological breakthroughs in high-impact areas and cover several topics, mainly regarding blockchain technology, secure multi-party computation, threat detection, trusted execution environment, as well as cyberawareness, security level estimation and security policy compliance.
# DkFD: Optimizing Common Pediatric Dermatoses Detection with Novel Loss Function and Post-Processing
## 1 Introduction
To the best of our knowledge, CPD-10 is the first clinical image dataset with the appropriate label and bounding boxes for common pediatric dermatoses. Then, we conduct a benchmarking by using the state-of-the-art CNN models such as ResNet [14], ResNeXt [15], Res2 Net [16], ConvNeXt [17], Swin Transformer [18], and PVTv2 [19] to learn its inherent characteristics. We discover that there are significant problems with training overfitting, inconsistent image quality, an imbalance of difficult and easy samples, and cross-sectionality of detection results when trying to identify common pediatric dermatological symptoms in natural scenes. Thirdly, we propose the DKFD algorithm, which employs Random Online Data Augmentation (RDA) and Selective Image Super-Resolution Reconstruction (SSR) techniques to address the issue of related small data size and low resolution of images. In DKFD, we also propose the cross-entropy-based loss function DK_Loss for two-stage object detection, to address the issue of imbalance between hard and easy samples in training by increasing the loss contribution of hard samples and allowing the model to concentrate more on hard samples. This problem is caused by intra-class variability and inter-class similarity in varying disease periods among various diseases. Furthermore, in order to improve the detection precision of the model, the Filter_nms technique is also developed for the intermingling of the detection boxes, which is based on the Non-Maximum Suppression algorithm [20] (NMS). The Filter_nms can efficiently remove the interference of some mis-specified detection boxes during the detection. We train on the CPD-10 dataset to demonstrate the effectiveness of the DKFD algorithm, and the experimental results show that using the RDA-SSR, DK_Loss, and Filter_nms can effectively reduce overfitting during training, enhance the robustness of the model, and improve the learning capacity of the hard samples, which leads to an improvement in the mAP of the model over 6%. Additionally, using a number of two-stage object detection algorithms and PASCAL VOC2007 datasets [21], we further confirm the effectiveness of the DK_Loss. According to the experiment results, the two-stage object detection algorithm uses the DK_Loss loss function, which not only improves the model's capability for learning from difficult samples, but also reduces the issue of overfitting, making it appropriate for object detection over the small dataset. The rest of this paper is organized as follows. Section 2 briefly reviews the existing object detection methods. The essential knowledge of this paper is given in Section 3. The proposed DKFD algorithm is described in depth in Section 4. The experimental study is presented in Section 5. Finally, Section 6 draws the concluding remarks and future works.
## 5 Results and Discussion
### Gap-Filling of the TWSA Between GRACE and GRACE-FO
This continuity indicates that the GAIN algorithm successfully captures the temporal dynamics of TWSA, contributing to a plausible reconstruction of missing values. Additionally, the correlation between TWSA and precipitation is preserved across the time series, both for original and filling values. For instance, peaks in TWSA often follow periods of higher precipitation, which is visible in the alignment between the TWSA trends and the precipitation bars. The filling data also maintain this relationship, suggesting that the GAIN algorithm has successfully captured the inherent relationships in TWSA development. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Missing Rate** & **Method** & **GAIN** & **LSTM** & **KNN** \\ \hline \multirow{4}{*}{10\%} & RMSE & 1.33 & 3.11 & 4.99 \\  & MAE & 0.59 & 2.54 & 4.22 \\  & ACC & 0.95 & 0.77 & 0.68 \\  & AUC & 0.97 & 0.90 & 0.68 \\ \hline \multirow{4}{*}{20\%} & RMSE & 1.85 & 3.48 & 5.32 \\  & MAE & 0.94 & 2.87 & 4.54 \\  & ACC & 0.91 & 0.77 & 0.64 \\  & AUC & 0.95 & 0.88 & 0.59 \\ \hline \multirow{4}{*}{30\%} & RMSE & 1.96 & 3.82 & 5.80 \\  & MAE & 1.21 & 3.13 & 4.83 \\ \cline{1-1}  & ACC & 0.86 & 0.77 & 0.56 \\ \cline{1-1}  & AUC & 0.96 & 0.89 & 0.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Different missing rate accuracy of three methods. Figure 7: The gap-filling TWSA data combined with precipitation.
## 8 Challenges and Future Directions
### Future Directions
#### 8.2.5 Standardisation  
For interoperability and reproducibility of HAR research, it is important to have industry standards. This is especially important with the use of AI in HAR.These standards should cover data collection and representation, communication, and model representation. It should also provide a guideline that guides the ethical use of HAR systems.  
Future research should be on developing metadata standards, clear annotation guidelines, API standards, data privacy guidelines, and ethical guidelines to improve the quality of HAR research and promote the adoption of HAR technologies. Creating a unified standard for HAR requires joint effort form government bodies, research institutes, companies, and industry experts. Sharing ideas and resources openly, through projects like open-source initiatives and industry consortium, can provide the needed momentum for developing an industry standard for HAR.
# Future Directions for Electric Propulsion Research
## 4 Challenges for Electric Propulsion Development
### High-Power Thrusters  
The low thrust produced by modern electric propulsion devices has created a large divide between the applications for which they are suited and those where chemical propulsion is more fitting. For example, crewed missions prioritize short travel times, and thus, the large thrust produced by chemical systems is needed even at the expense of payload mass. However, with more power, EP systems may produce enough thrust to break into this niche, while retaining the propellant efficiency characteristic of this class of propulsion. As a result, there is continuing research into scaling EP devices to higher power and exploring new concepts that may excel at \(\gg\)20 kW. We depict the s-curves for some of these devices in Figure 13.  
Some constraints in this direction of EP research have already been established by mass modeling and mission analysis. [PERSON] and [PERSON] developed a mass and cost model that indicated clustered Hall thrusters in the (individual) 20-100 kW range would be capable of supporting missions from 20 kW to 1 MW [103]. [PERSON] et al. examined Mars mission profiles in terms of the system mass-to-power ratio, \(\alpha\); they found that an \(\alpha\) of 1 kg/kW can allow for 40-day trips to Mars using variable-\(I_{sp}\) high-power electric propulsion [104]. With these studies in mind, it is clear that high-power EP development should focus on power levels 20-100 kW and with \(\alpha\sim\)1 kg/kW.  
Figure 12: A rotating magnetic field thruster developed at the University of Michigan, here mounted on a thrust stand for performance testing.
## 6 Two-Tier Data Source Authentication Scheme
### Design  
At the first tier, each mote is integrated with a small biometric scanner to capture a unique signature for the patient. The biometric signature must be validated by a base station before communication between the base station and mote can occur. We assume that the base stations have access to a list of valid patient biometric signatures (this could be obtained from each patient upon admission to the facility), and also that the space of possible signatures is sufficiently large that a brute-force attack is infeasible. There are several off-the-shelf miniature fingerprint readers and finger vein readers that could serve as the biometric scanner. One example is the MBF200 solid-state fingerprint sensor from Fujitsu [25] (also shown in Figure 2). It is small (24 mm \(\times\) 24 mm \(\times\) 1.4 mm), low in power consumption (20 mA active, \(<\) 200 \(\mu\)A sleep, 20 \(\mu\)A standby), and inexpensive (about $30 per unit in bulk quantities). Moreover, it can capture a fingerprint in less than one second. The fingerprint sensor simply captures an image (which would be inefficient to transmit directly), but off-the-shelf hardware (e.g., [26]) exists that integrates the Fujitsu sensor with a small processing board to allow feature extraction from the raw image.  
The first tier alone is insufficient for ensuring data authenticity because an attacker can capture a patient's mote after the patient has been accepted into the system. Therefore, we add a second-tier authentication system to _assert the identity of a patient based solely on the sensor data being collected from that patient_. More specifically, each patient will wear the sensors for a short period of time for the base station to \"learn\" the patient's profile. From then on, whenever the patient's data deviates substantially from that profile, the base station will raise an alarm. The alarm could be caused by forged data or by a medical emergency. In either case, it is important that the patient be checked on, so issuing the alarm is warranted. This approach does not require any extra bandwidth (the data being used is sent anyway) and is completely passive from the viewpoint of the patient.
# A Review of Blast Loading in the Urban Environment
## 4 Experimental Methods and Instrumentation
### Pressure Measurements
#### 4.2.5 Extrinsic Fabry-Perot Interferometric Pressure Sensors  
[PERSON] et al. [139] propose and demonstrate the use of an extrinsic Fabry-Perot interferometric (EFPI) pressure sensor. The EFPI sensor measures changes in the silicon cavity length caused by a pressure differential between the internal and external pressures. The sensor has low intrusiveness, with face dimensions of 4.3 mm \(\times\) 4.3 mm. The pressure sensor was tested experimentally, although the nature of the pressure application was static, so a direct comparison to other pressure sensors that are tested dynamically cannot yet be made. The pressure sensor underwent a calibration test where it was exposed to a gradual increase in pressure from 0 MPa to 1 MPa. The results showed a strong linear relationship between the cavity length and the applied pressure, achieving a regression coefficient of 0.99993. Additionally, the pressure resolution was 18.77 Pa from these results, less than 0.002% error at full scale. Clearly, further testing is required to determine how this particular pressure sensor would perform in dynamic pressure tests.
## 3 Field Geological Investigations
### Distribution Characteristics of Retrogressive Landslides  
From 2013 to 2023, landslides have occurred in the study area almost every year. As shown in Figure 4a, there were a relatively high number of landslides occurring in 2012, 2014, and 2018, with 9, 11, and 15 incidents, respectively. In 2006, 2011, 2013, 2015, and 2017, there were seven, five, six, seven, and six landslides respectively, which is close to the average number of annual landslides. In 2003, 2010, 2016, 2021, and 2022, the number of landslides was relatively low, with fewer than three incidents per year. However, it is important to note that despite fluctuations in individual years, the overall trend since 2016 indicates a gradual decrease in landslide frequency. This decrease is likely associated with the stabilization of groundwater levels due to changes in irrigation practices implemented by the local government.  
From a monthly perspective, as shown in Figure 4b, landslides occur most frequently in April, with 21 incidents, accounting for 27.63% of the total. Additionally, there were more than 10 landslides each in June and December. In contrast, landslides occurred less frequently in January, February, March, and November, with less than five incidents each. Additionally, there were no landslides occurring in August and September. These occurrences are related to the irrigation practices in the study area. Irrigation occurs annually in March, April, June, July, and November. From August to October, there is a prolonged dry period due to crop harvesting and reduced irrigation. Additionally, this period corresponds to summer in China, characterized by high temperatures and increased surface evaporation, which stabilizes the groundwater level and reduces the occurrence of landslides [3,7].  
As shown in Figure 4c, the total area affected by these landslides is relatively small, with nearly 90% of the landslides having an accumulated area of less than 50,000 m\({}^{2}\), 24 incidents with an accumulated area of less than 5000 m\({}^{2}\), and accounting for 31.58% of the total landslides. The number of landslides with an accumulated area between 25,000 m\({}^{2}\) and 30,000 m\({}^{2}\) is the lowest, with only two incidents. On the other hand, Figure 4d shows that half of the landslides have a maximum sliding distance within 50 m, and over 70% have a maximum sliding distance within 100 m. Only 10 landslides have a maximum sliding distance exceeding 200 m. Considering the accumulated area and sliding distance, most of the loess landslides in the area are small to medium-sized, falling into the category of slides and falls.  
Figure 3: Landslide distribution map in the study area.
# Time Classification Algorithm Based on Windowed-Color Histogram Matching
## 2 Related Works
### Deep Learning-Based Models  
Deep learning-based image analyses have shown a large progress in computer vision [23; 24; 25; 26; 27; 28; 29; 30; 31]. Deep models can recognize various features such as low-level as well as high level features. [PERSON] et al. and [PERSON] et al. have used deep learning models to get more complex feature and classified time into two or more classes [30; 31].  
[PERSON] et al. first attempted at predicting time classification in outdoor scenes [30]. They employed pooling layers that provide better features than fully connected layers for these tasks. They obtained performance, especially at month and season level. However, the accuracy on the time of day was relatively low.  
[PERSON] et al. have proposed a model to extract a visual condition such as dawn/dusk, day, and night for time detection. They trained a model relying on residual learning using the ResNet50 architecture [31]. It gave a little lower accuracy of time classification. We also conducted an experiment employing ResNet152 architecture and fine-tuning our own dataset. The proposed algorithm achieves better performance than ResNet, which has been used in [31].
# Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks
## 1 Introduction
Within METEOR, a tDBN is acquired to shape the general behavior of the data (a set of MTS). Each MTS is then scored according to a sliding window mechanism capable of capturing compelling patterns encoded by temporal dependencies amid variables, absent in existing literature. Outliers are ruled out as those with lower scores; these scores are based on the likelihood given by the joint probability distribution induced by the tDBN transition networks. The system can detect as outlier both an entire MTS, called a _subject_, or only some subject _transition_ (a partial MTS comprising contiguous observations of the subject variables in a certain period of time). In detail, data is comprised of a set of MTS known as subjects. For example, time series taken at discrete time with monthly temperature and humidity measurements at major cities would be composed of observations of both variables (temperature and humidity) where each city depicts a different subject. In its turn, each subject encompasses several observations of these two variables along time, where contiguous observations define transitions. For simplicity, consider a transition with lag 2, i.e., covering both temperature and humidity observations at time \(t\) and previous lagged observations at time \(t-1\) and \(t-2\). Low scores depict transitions that are not explained by observation at time \(t\) and its lagged observations, according to the tDBN model. Likewise, whole subjects are scored using the average of all its transition scores. Hence, METEOR is adapted to detect anomalous portions or entire MTS, fitting into numerous scenarios. A score-analysis phase is available to classify each score. Two main strategies are studied, namely Tukey's Method [17; 18] and Gaussian Mixture Models (GMM) [19]. A threshold is automatically selected to determine the outlierness disclosure boundary. The system is validated through synthetic and real-world data sets demonstrating its performance in multiple scenarios. Furthermore, a multivariate probabilistic suffix tree (PST) technique is built and compared with METEOR, illustrating the contrast of the current system with typical univariate existing techniques. Due to the increasing demand for data science-related appliances aspiring not only promptness but also easily adaptable mechanisms, the current implementation of METEOR is made entirely free and accessible through a web application [20] available at [[https://meteor.jorgeserras.com/](https://meteor.jorgeserras.com/)]([https://meteor.jorgeserras.com/](https://meteor.jorgeserras.com/)) (accessed on December 2020). The latter does not require any download and is accompanied by a tutorial video. This paper is organized as follows. Theoretical background regarding dynamic Bayesian networks modeling is made available in Section 2 before the description of each phase of the proposed system from pre-processing to score-analysis in Section 3. The developed web application and software along with experimental validation are showcased in Section 4. Finally, we draw some conclusions in Section 5.
# A Deep Learning Model to Forecast Solar Irradiance Using a Sky Camera
## 5 Conclusions  
In this study, a model to forecast solar irradiance from sky images was proposed and evaluated. The images recorded using a sky camera were designed to capture a hemispherical image each minute. Image processing using the RBR method was used to improve the quality of the image in detecting the cloud pixels in the image. The LSTM, as a deep learning model, was applied to forecast the cloud cover for several minutes ahead. Then, the physical model was used to estimate the GHI by using the forecasted cloud cover.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Forecast Horizon**} & \multicolumn{3}{c}{**Clear Days**} & \multicolumn{3}{c}{**Overcast Days**} \\ \cline{2-7}  & **Proposed** & **[PERSON] [16]** & **Persistence** & **Proposed** & **[PERSON] [16]** & **Persistence** \\ \hline
**(minute)** & **(Wm\({}^{-2}\))** & **(Wm\({}^{-2}\))** & **(Wm\({}^{-2}\))** & **(Wm\({}^{-2}\))** & **(Wm\({}^{-2}\))** & **(Wm\({}^{-2}\))** \\ \hline
1 & 20.59 & 35.44 & 6.20 & 17.74 & 74.32 & 2.52 \\
2 & 31.59 & 39.15 & 7.79 & 16.73 & 90.66 & 3.66 \\
3 & 37.80 & 31.78 & 14.04 & 18.17 & 98.26 & 4.67 \\
4 & 45.70 & 41.40 & 20.92 & 19.41 & 99.86 & 9.43 \\
5 & 45.56 & 57.56 & 28.26 & 19.34 & 104.51 & 6.64 \\
6 & 29.69 & 59.68 & 56.24 & 20.20 & 138.79 & 5.48 \\
7 & 34.76 & 54.05 & 41.74 & 20.46 & 119.06 & 8.30 \\
8 & 34.15 & 53.76 & 38.28 & 17.33 & 158.65 & 7.26 \\
9 & 40.27 & 58.20 & 49.26 & 15.60 & 155.47 & 6.48 \\
10 & 43.08 & 63.74 & 52.49 & 17.37 & 169.86 & 9.98 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Root mean square difference (RMSD) between the proposed, the Yoo, and the persistence models for clear days and overcast days.  
Figure 9: Comparison between proposed and persistence models using the mean values of RMSD and UVI for 10-min forecast horizon in partly cloudy, clear, and overcast days.  
When the sky is partly cloudy, i.e., the UVI is larger than 20, the proposed model is able to forecast GHI for 10 min in advance with RMSD, rMSD, MBD, and rMBD values of 199.75 Wm\({}^{-2}\), 25.1%, \(-60.65\) Wm\({}^{-2}\), and \(-12.70\%\). These evaluation metrics demonstrate that the proposed model has a better performance than the Yoo and the persistence models. On the other hand, for clear and overcast sky conditions when the UVI is smaller than 5, performances of the proposed and the persistence models are comparable.  
Formal Analysis R.A.R.; Conceptualization R.A.R. and R.K.; Writing--original draft R.A.R.; Writing--review and editing H.-J.L.; Funding acquisition H.-J.L. All authors have read and agreed to the published version of the manuscript.  
This study is financially supported by grants from the National Research Foundation of Korea (NRF), Ministry of Science and ICT (2018M1A3A3A02065823, 2019R1A2C1009501).  
Not applicable.  
Data Availability Statement: Data are contained within this study.  
The authors declare no conflict of interest.