esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Selective Encryption of DICOM Images
## 1 Introduction and Related Work
### Related Work
In this work, we present an automatic selective encryption approach in the spatial domain that aims to reduce the processing time overhead of the utilized encryption algorithms and to adaptively threshold DICOM images based on their modalities, anatomy part, and pixel intensity range, while maintaining the security of the encrypted DICOM image, in comparison with the Naive encryption approach, in which all pixel data are encrypted using sophisticated encryption algorithm, e.g., Advanced Encryption Standard (AES), as the bottleneck of securing a DICOM object is the processing time [24]. It is worthwhile to mention that AES is adopted by the DICOM standard. This paper introduces encryption approaches for single- and multi-frame DICOM objects while utilizing a two-region selective encryption approach based on an automatic statistical-based segmentation approach to select important pixel data in DICOM images [25]. The single-frame encryption approach was adopted after a thorough investigation of a mathematical solution based on a linear equation system to work out multi-region sizes inside the ROI of the DICOM image to encrypt them based on their importance. However, the saving time was not highly improved in comparison with the proposed approach in study [25]. Therefore, a promising approach was adopted based on the idea of improving the redundancy of the segmented pixel data. This approach has two proposed segmentation mechanisms that segment DICOM images into ROI and ROB based on pixel intensities. Thus, segmented regions are compressed using a fast-lossless compression algorithm. Consequently, the encryption process of the selected data can be significantly reduced. The proposed approach aims to reduce the processing time of multi-frame DICOM image encryption in comparison with the Naive encryption approach. Effective time performance and robustness of the proposed approach are verified by statistical assessments and cryptanalysis measurements. Therefore, the proposed approach can be exploited with various frameworks to attain data security requirements. This paper is structured as follows: Section 2 gives an overview of the motivations and limitations of encrypting DICOM images. The proposed approach is detailed in Section 3. Performance evaluation, analysis, and discussions are described in Section 4. Concluding comments, challenges, and future work are given in Section 5. Calculations of regions' sizes of the multi-region encryption approach are presented in Appendix A.
## 3 Results and Discussion
### Sieving of R4 and Optical Microscopy  
R4 was fractioned by sieving using sieves with 25-120 mesh, obtaining beads with the bulk of the material >96% of the original weight. The microstructure of R4 of these particles was investigated by acquiring optical microphotographs with a Zeiss Axioskop instrument on the sieved resin. In the optical images of the sieved dried R4, particles appeared as well-separated microspheular beads with the bulk of the material in the size range 125-500 \(\upmu\)m (Figure 4).  
Figure 3: ATR-FTIR spectra of R4 as obtained by polymerization (blue line) and R4 achieved after drying a sample of R4 swollen at its maximum swelling capability (pink line).  
Regarding this, the reaction vessel/stirrer combination adopted here was particularly convenient for the preparation of well-separated crosslinked polystyrene-based resin beads with well-defined average particle sizes, such as those of R4 shown in Figure 4[45]. It has been reported that other factors probably affected the average particle size and distribution during the suspension polymerization, including the type of suspension stabilizer used (SPAN 85), diameter of the vessel and stirrer, stirrer speed, and monomer-to-water ratio [45].
###### Abstract  
This article presents the experience of building a simulation scenario of the whole city of Duisburg using real traffic data. The establishment of the simulation scenario is based on road network and traffic volume. In most cases, it is hard to collect all data sources with high precision. Moreover, it is time-consuming to set up a realistic traffic scenario. Even with available data, conversion, calibration, and validation all take a large effort. With the increase of the respective simulation area, the difficulty and workload rise. In this study, a simulation scenario of the whole city of Duisburg with the road network area of 232 km\({}^{2}\) and Origin/Destination (OD) matrix area over 800 km\({}^{2}\) was established in the software package SUMO. Four cases with different networks and traffic volumes were built and compared with real traffic data collected from induction loops. The percentage of simulated traffic volume in real traffic volume range can be up to 72.22%.  
realistic traffic scenario; microscopic traffic simulation; traffic demand; scenarioy generation +
Footnote †: journal: Applied sciences
# Target Detection of Passive Bistatic Radar under the Condition of Impure Reference Signal
## 2 Signal Model and Problem Analysis  
In this section, the received signal model with the reference signal interfered by target echoes is established, and its effect on target detection, mainly including clutter suppression and RDCC, is derived and analyzed in detail.
## 3 Methods  
As shown in Figure 1, there were three steps in this study.  
* Step 1: Underground Utility Tunnel Digital Twin: Create a virtual replica (digital twin) of the underground utility tunnel to bridge the physical and digital worlds. This involves using sensor data, 3D modeling, and simulation software to create a detailed digital model for monitoring, planning, and a predictive analysis.
* Step 2: Classification of Tunnel Database: Classify the data collected about the underground utility tunnels based on attributes like function, materials, location, and size. Also, categorize the tunnels as point, linear, or planar objects. Point objects represent specific locations, e.g., junctions or sensors.  
Figure 1: Research methodology.  
Linear objects have a length but a negligible width and height, e.g., pipelines or cables. Planar objects have two dimensions (length and width), representing sections or larger features within the tunnel.
* Step 3: Object Segmentation Algorithm: Apply object segmentation algorithms to the digital twin of the underground utility tunnel to identify and distinguish different elements within the model. This involves dividing the digital image into meaningful segments for an easier analysis, enabling tasks such as identifying utility lines, structural components, and sections of the tunnel to support decision making and predictive maintenance.
# Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
## 5 Conclusions
The ANN model depicted a mean R\({}^{2}\) of about 0.86 and a PBIAS of about 3%. A comparison between LLR and ANN models showed that ANN was slightly ahead of LLR, but the difference was not significant and both may be considered as equally good alternatives for the prediction of SSL. Performance-wise, the WANN models in the testing phase showed a mean R\({}^{2}\) of 0.92 and a _PBIAS_ of \(-0.59\%\). Overall, the results indicated that the WANN models for both stations had higher R\({}^{2}\) and lower RMSE and PBIAS values, thus, establishing that they were more accurate than the other models in the prediction of daily SSL in rivers. Further, the list of models, in decreasing order of accuracy in the prediction of daily SSL, was as follows: WANN, ANN, LLR model and SRC approaches. Similarly, the WANN model was most accurate in reproducing the SSL peak flow rates compared to the rest of the models. The soft computing methods (ANN, LLR, and WANN) performed better than the traditional technique (SRC), as they made use of non-linear techniques for data reconstruction. It can be concluded that, among all the models, the WANN models, which used decomposed data that captured the dynamic features of the non-linear and non-stationary SSL time series data, performed better than other models, which used simple raw data. Conceptualization, M.A.K. and J.S. ; methodology, M.A.K., J.S. and S.H. ; software, M.A.K. ; validation, M.A.K. ; formal Analysis, M.A.K. ; investigation, M.A.K. ; data curation, M.A.K. and J.S. ; writing--original draft preparation, M.A.K. ; writing--review and editing, J.S. and S.H. ; visualization, S.H. ; supervision, J.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. No new data were created or analyzed in this study. Data sharing is not applicable to this article. The first author was financially supported by the doctoral scholarship from the Higher Education Commission of Pakistan (HEC), Pakistan, and the German Academic Exchange Service (DAAD), Germany. The authors wish to acknowledge the Department M3--Fluvial morphology, Sediment dynamics and Management, Federal Institute of Hydrology (BfG), Germany and [PERSON] for providing the data used in this study. The first author is also thankful to [PERSON] from IWD, TU Dresden for his constructive guidance, suggestions and cooperation during research work. We extend our thanks to the \"Gesellschaft der Forderer des Hubert-Engels-Instituts fur Wasserbau und Technische Hydromechanik der Technischen Universitat Dresden e.V.\" to support this work in the Open Access Publishing Program framework. The authors declare no conflict of interest.
## 4 Feature Extraction and Classification
### Classification  
In this study for classification, we investigated three classification techniques with different characteristics: AdaBoost, random forests (RF) and support vector machine (SVM). We employed three classifiers to avoid biasing the results by selection of a single classification method. The reason we choose them is that the first two classifiers both include a feature selection mechanism. By applying these two classification techniques, we are seeking the best features that would separate responders from non-responders. SVM is reported to outperform several of the most frequently used machine learning techniques in structure-activity relationship (SAR) analysis. [34] In this study, all classifiers were implemented in the open source data mining software Weka [41]. Their performance was evaluated using 10-fold cross validation method.  
Three experiments were performed. First, the conventional method of using mean ADC for treatment response classification was applied [2]. Second, the AdaBoost, RF classifier, and SVM were applied to the difference feature vectors of general statistical histogram features without GMM features, and results from the three classifiers were compared. Finally, the three classifiers were applied using all statistical features including the GMM features, and the results were compared, and the results of accuracies from different classification techniques were compared with conventional method of ADC mean changes by the test of proportion.
# An Energy-Efficient Integration of a Digital Modulator and a Class-D Amplifier
## 5 Experimental Setup and Results
### Discussion  
A qualitative comparison with the state-of-the-art is presented in Table 1. The input signal type in most of the designs is analog, while our proposed amplifier design accepts a digital signal of 1 V. We have tested our design on a 2 kHz input frequency range compared to 1 kHz in many cases and 5 kHz in [6]. The switching frequency used in the state-of-the-art is in the range of 200-2000 kHz, while in our case, it is much lower, i.e., 19.8 kHz, which is a plus point. Mostly, PWM or, in some cases, PDM is used inside the class-D amplifier followed by an LPF. We used BPF that not only filters but provides PSK modulation without using any modulator block. This unique feature is the novelty of our design that reduces the cost as we did not use any modulator, as well as increases the power efficiency of the overall system. The proposed design can deliver up to 6 W under a controlled environment at room temperature.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## Appendix A LIDAR in Space Missions for Extraterrestrial Exploration  
Tables A1-A3 provides a summary of selected spaceborne missions that utilize LiDAR as a scientific payload to explore celestial bodies other than Earth. It includes mission parameters such as launch year, operating agency, target object to study, launch year, name of the LiDAR instrument, status, and description. This overview highlights the technological evolution and diverse applications of LIDAR in planetary and small-body exploration. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Mission** & **Agency** & **Target Object** & **Launch Year** & **LiDAR** & **Status** & **Description/Objectives** \\ Apollo 15, 16, 17 & NASA & Moon & 1971–1972 & Apollo laser altimeter & Completed & Measure the lunar shape parameters and infer its structure \\ Clementine & SDL & Moon, 1620 & 1994 & The Clementine LiDAR & Ended in 1994 (mal-function) & Technology demonstration (carry and test 15 advanced flight-test components and nine science instruments), create global topographic Model of the lunar landscape including polar regions. Attempt a rendezvous with the asteroid 1620 Geographos. & & & \\ NEAR & NASA & Mathilide, & 1996 & NLR & Landed on EROS 12 February 2001, last contact on 28 February 2001 & Flyby of 253 Mathilide. Land on asteroid Eros, Gather data on its physical properties, mineral components, morphology, internal mass distribution, and magnetic field \\ MGS & NASA & Mars & 1996 & MOLA & Last contact 2006 & Precise topographic map of Mars, study of geophysics, geology, and atmospheric circulation, measurement of the radiance of the MARS surface. Study the formation and evolution of surface features such as volcanoes, basins, channels, and polar ice caps. Measure the altitude and distribution of water and carbon dioxide clouds to understand the volatile budget in the Martian atmosphere. & & \\ Hayabusa & JAXA & 2003 & Asteroid & Hayabusa Li- & Reached the asteroid in 2005, released in 2005, released in 2001 & Reached the asteroid in 2005, released in 2001 & Reached the asteroid in 2005, released in 2005, released in 2006, released in 2005, released in 2006, released in 2007, released in 2008, released in
# Optimization of Signal Detection Using Deep CNN in Ultra-Massive MIMO
## 2 Materials and Methods
### Traditional Method
#### 2.2.2 MMSE Signal Detector  
The MMSE signal detector is an algorithms referenced in many works. It is calculated similarly to channel estimation [18,19]. It is the second comparator that we used for signal estimation, because it is the most commonly used and most complex calculation method. As a result, it is more accurate than the ZF signal detection given by  
\[\widetilde{X}_{MMSE}=\text{argmin}\left\|Y-\widetilde{X}_{MMSE}H\right\|^{2} \tag{8}\]  
The detection strategy includes accounting for noise during the computation process, which is measured by  
\[\widetilde{X}_{MMSE}=YH^{H}(HH^{H}+\frac{{\sigma_{n}}^{2}}{{\sigma_{n}}^{2}}I) ^{-1} \tag{9}\]  
In this context, I denotes the identity matrix of size \(M_{T}\ \times\ M_{R}\), while \({\sigma_{n}}^{2}\) represents the noise variance, which is inversely proportional to the signal-to-noise ratio (SNR). Additionally, all signal response energies have been normalized, as exemplified by  
\[E\left\{\left|h_{M_{R},M_{T}}\right|^{2}\right\}={\sigma_{n}}^{2} \tag{10}\]
# Research on the Dynamic Characteristics Analysis and Power Control Method of Heat Pipe Reactors
## 2 Dynamic Modelling of HPR
### Control Drums Model  
The control drums are arranged around the core as in Figure 2. A layer of neutron-absorbing material is arranged at an angle within the control drum, and the core reactivity is controlled through rotating the drums to different angles to vary the neutron absorption rate. If \(\rho_{D}\) is the integral value of the control drum, and \(\theta\) is the angle of rotation of the control drums (\(0\leq\theta\leq 180^{\circ}\)), then the relationship between them can be expressed by Equation (3) [28].  
\[\rho_{D}=0.03581-\frac{0.09647}{1+\exp(\frac{\theta-94.8083}{27.09451})} \tag{3}\]
# Anomaly Detection and Early Warning Model for Latency in Private 5G Networks
## 2 Related Works
### Anomaly Detection  
Compared with traditional time-series-analysis-based anomaly monitoring models [9,10], most existing studies focus on machine-learning-algorithm-based approaches [6]. [PERSON] et al. [11] focused on the use of principal component analysis (PCA) for anomaly detection and constructed a prediction model to determine anomalies based on the principal and subprincipal components of normal instances. [PERSON] et al. [12] considered the concept of isolation and proposed the isolation forest (IF) model. They considered that anomalies are sparser than other normal data points and can be isolated and divided into leaf nodes using few splits, making the path between the leaf node where the anomaly is located and the root node shorter.  
The autoencoder network has also been applied to detect anomaly metrics using reconstruction errors [6,13,14]. [PERSON] et al. [6] proposed an autoencoder-based anomaly detection and diagnosis method by designing a scoring algorithm to calculate the root-cause occurrence likelihood ranking. [PERSON] et al. [13] proposed a method that combines a deep autoencoder (DAE) and Gaussian mixture model (GMM). The method performs well on several types of public data and outperforms many existing anomaly detection methods; however, it does not consider the temporal correlation and severity of anomalies in real scenarios and is not very robust against noise. [PERSON] et al. [14] used graph neural networks for the anomaly detection of temporal data using a combination of convolutional layers, convolutional long short-term memory networks (Conv-LSTM), and an autoencoder. This algorithm can eliminate the effect of noise between mutually uncorrelated features; however, the data size increases, leading to a high computation time and high computational load. [PERSON] et al. [15] trained normal data using a temporal convolutional network (TCN) and used Gaussian distribution fitting for anomaly detection. [PERSON] et al. [16] proposed a method that uses a convolutional neural network (CNN) for time-series prediction and then uses the Euclidean distance between predicted and true values for anomaly detection. In addition, Yahoo, Skyline, Netflix, and others have conducted related studies. Yahoo proposed a single-indicator anomaly detection system that combines three modules: timing modeling, anomaly detection, and alerting [17]. Netman Lab of Tsinghua University and Alibaba and Baidu also conducted related studies, such as the Donut algorithm for periodic temporal data detection based on the variational autoencoder (VAE) model [18] and the apprentice algorithm based on random forests [19].
# A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
## 2 Principles of Ensemble Learning Algorithms
### Boosting Algorithms  
Boosting algorithms use a forward stagewise process to transform weak learners into strong learners by increasing the weights of training samples that were mistakenly identified or wrongly calculated in a successive iteration. The final output of boosting is obtained by combining the results from all iterations using a weighted vote for classification or a weighted sum for regression [34]. Widely used boosting ensemble algorithms include adaptive boosting (AdaBoost), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), LightGBM, and categorical boosting (CatBoost).  
AdaBoost was initially developed to increase the efficiency of binary classifiers. In GBM, all the weak learners of GBM are decision trees. XGBoost is an improved version of GBM that implements parallel preprocessing at the node level, which makes it faster than GBM. XGBoost also introduces a variety of regularization techniques to reduce overfitting [35]. Studies have shown that of XGBoost is excellent in mapping plant species diversity [36], predicting PM2.5 concentrations [37], forest biomass estimation [38], and risk analysis for flash floods [39]. LightGBM has many of XGBoost's advantages, such as parallel training, regularization, and sparse optimization. The major difference between the two lies in the method of constructing trees. LightGBM uses a leafwise split: after the first split, the node with a higher delta loss is selected for the next split [40]. This technique enables LightGBM to easily handle huge amounts of data. A histogram-based method is often adopted to select the best split in LightGBM to reduce the time used in training. However, LightGBM cannot perform well with a small number of samples.  
CatBoost is an improved gradient boosting decision tree (GBRT) algorithm and an alternative to XGBoost. As the name suggests, CatBoost can internally handle categorical variables in the data, and is thus suitable for dealing with machine learning tasks involving categorical and heterogeneous data [41; 42]. Studies have found that CatBoost is superior to other machine learning algorithms, and it has been used to estimate forest biomass [28; 43] and reference evapotranspiration [44].
## 2 Methods and Performance Figures
### Partial Reset of Stored Information  
To overcome these limitations, the \(Q\) and \(K\) matrices are periodically reset by partially discarding the information therein stored. To clarify this point, it is understood that the information is _totally discarded_ when the two matrices are reset to zero. In this way, all the information that the system has gained in the previous period is forgotten and the algorithm restarts from arbitrary initial conditions. Totally discarding the accumulated knowledge is, however, not adequate for tracking slow changes of the system parameters such as fouling. Thus, a \"partial discard\" is more useful. The information in the two matrices is said to be _partially discarded_ when the new matrices in the learning are computed as:  
\[\left\{\begin{array}{l}\mathbf{Q}_{t+1}=\beta_{Q}\mathbf{Q}_{t}\\ \mathbf{K}_{t+1}=\left|\beta_{K}N_{e}\mathbf{O}\right|\end{array}\right. \tag{7}\]  
The parameters \(\beta_{Q}\) and \(\beta_{K}\) (\(\beta_{Q}=\beta_{K}=\beta\) in the following) span between 0 and 1 and are understood as a sort of \"exploration degree\" when a new explorative phase is started. \(O\) represents a matrix the same size as \(\mathbf{Q}\). and \(K\) composed entirely by identity elements. When the RL agent updates the matrices with Equation (7), a new exploration phase with a duration depending on the selected parameter \(\beta\) is started.