esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This section summarizes the factors critical for the efficacy of the LEO launch assistance mission using LEO bolos, calculated in Section 6. Five directions for making such missions feasible are outlined, with most promising direction being the increase of spacecraft power/weight ratio.  
The development of a hypothetical space mission involving a satellite constellation delivered to orbit with the help of a LEO bolo has resulted in the following conclusions:  
Specific impulse (\(\sim\)1500 s) of modern ion motors is sufficient to deliver a 3:1 reduction of fuel mass spent for bolo-assisted momentum transfer (compared to chemical rockets). Development of MXER tethers is not critical for the momentum-transfer applications, unless tethers with tip speeds approaching 1 km/s will be deployed.  
Long-term strength of existing ropes is sufficient for LEO bolos. Tether maintenance crawler(s) may offer significant reduction of tether strength margin(s), but increasing tether strength does not significantly improve system performance, due to the added weight of the tether re-boost hardware and micrometeoroid shield.  
The power density of modern spacecrafts (1 W/kg) is inadequate for LEO bolos deployment. Development of higher power-density (up to 10 W of electrical power per kg) spacecrafts is necessary if realistic (\(\sim\)1-40 launches/year) launch rates are expected. Otherwise, launch rates of 50-200 launches/year are required to pay-off the initial cost of LEO bolo deployment (even if not taking into account R&D costs).  
The power-to-drag of modern solar arrays in \"night glider\" mode is inadequate for bolos operating below a 300 km altitude. Better power efficiency of solar cells or solar panels operating in \"sun slicer\" mode are desirable. However, operating in \"sun slicer\" mode still requires many advances in large structures and energy storage. A nuclear powered bolo for a 240-280 km catch orbit may be considered if politically acceptable.  
Plain tethers are vulnerable to micrometeoroid damage. Either shielded or redundant tethers must be used. Currently, shielded tethers seem to be the most developed solution, and are weight-effective for tethers below 10 km long.  
Payload capture requirements strongly favor high-agility upper stages of rocket boosters to reduce terminal guidance \(\Delta\)v loss.
# Context Analysis of Cloud Computing Systems Using a Pattern-Based Approach
## 2 Background
### Data Protection Goals  
Data protection goals are the following: _confidentiality_, _integrity_, _availability_, _unlinkability_, _transparency_ and _intervenability_[9]. _Confidentiality_ refers to the non-disclosure of data, i.e., keeping it private. _Integrity_ refers to avoiding the manipulation and corruption of data. _Availability_ means that the data is available for access at any time. _Unlinkability_ aims at the separation of privacy-relevant data from any other data, privacy-relevant or not. It should be impossible, or at least infeasible, to find a link between the privacy-relevant data and any other data that does not directly belong to that set of data. This also means that the privacy-relevant data should not be usable in any context other than the one it is intended for. Thus, assuring unlinkability also leads to purpose-binding, in which a set of data is only used for a specific purpose. An example would be the separation of account data like user-names and e-mail addresses from any Personally Identifiable Information (PII), i.e., real name and address. In case of a banking/payment system, the separation of login data and banking data is needed.  
_Transparency_ allows the involved parties to understand the privacy-relevant data handling processes and to be able to reconstruct data handling information at any time. It describes the transparency of the system, i.e., there is no black boxing allowed, as this would mean that the involved parties would not be able to understand the handling of the data. Transparency includes planned processing as well as the time after processing. A possible method of proving transparency is the availability of source code. An example is simple open source software. Due to the source code being available, the users of the software can find out what happens to their data. Thus, the software is transparent from a privacy protection point of view. If the software shares the data with some other service that is not an open source, transparency is no longer given.  
_Intervenability_ allows all involved parties to interfere with the data processing. This allows corrections in the system and countermeasures in the case something does not work as expected. Intervenability also allows the erasure of data and the withdrawal of consent to any privacy policy.  
Note that we carefully distinguish between data protection goals and the measures and mechanisms that are used to achieve them. For example, pseudonymity is a mechanism to achieve the privacy goal of unlinkability. Hence, we do not consider pseudonymity as a goal in our work.
# Dynamic Structural Response of a Corrugated Blast Wall Under Hydrogen Blast Loads
## 1 Introduction
The review focused on full papers and conference proceedings published after 2000 that were accessible through institutional resources. In addition, the AI-based tool Scite.ai was utilized to identify five studies that were most relevant and influential to this research. These studies were already included in the literature review, thereby confirming the comprehensiveness and relevance of the review. However, most of the target structures investigated for dynamic structural response have been fundamental structures, such as storage tanks, simple plates or walls, and tunnels. In contrast, studies on hydrocarbon explosions have extensively investigated dynamic structural responses of both fundamental and practical structures, including steel frames, stiffened plates, reinforced concrete slabs, blast walls, nuclear power plants, and topside structures. Moreover, various studies have been conducted on diagrams, such as Pressure-Impulse (P-I) diagram and resistance curve, which can be utilized in the design of structures subjected to hydrocarbon blast loading. Unfortunately, such studies on hydrogen explosions are extremely rare, particularly in the shipbuilding and shipping industries. In this study, a literature review was first conducted to collect and analyze experimental data on explosions, and blast load characteristics of hydrogen were examined. The trend of hydrogen blast loads and the correlations between load characteristics were analyzed and compared with those of hydrocarbons. Moreover, the dynamic structural response of a target structure was investigated under various hydrogen blast load scenarios that reflect the examined load characteristics, using a numerical model created in LS-DYNA [23]. A corrugated blast wall, commonly employed in offshore structures to prevent the propagation of blast loads, was selected as the target structure. Lastly, a new type of diagram and deformation modes for analyzing the dynamic structural response of corrugated blast walls were proposed. The objective of this study is to investigate the dynamic structural response of a corrugated blast wall, originally designed for hydrocarbon explosions, when subjected to hydrogen blast loads. The findings are expected to contribute to the advancement of safety standards and structural design practices in the shipbuilding and offshore industries.
# A Robust Multi-Watermarking Algorithm for Medical Images Based on DTCWT-DCT and Henon Map
## 3 The Proposed Algorithm  
The proposed algorithm is based on DTCWT-DCT and a henon map. It is a multi-watermark scheme for medical images, which meets the requirements of zero watermark and blind extraction. It has strong resistance to geometric attacks and displays good robustness. The algorithm consists of five parts: feature extraction of the medical image, preprocessing of multi-watermark, embedding of the multi-watermark, extraction of the multi-watermark and recovery of the multi-watermark. In the feature extraction stage, based on human visual features, a new feature sequence based on perceptual hashing was found in the DTCWT-DCT domain to participate in the watermark operation. In the design of the algorithm, common watermark technology is organically combined with henon chaotic encryption, cryptography and the third-party concept, which not only allows the digital watermark to resist conventional and geometric attacks, but also makes the algorithm strong and robust. Meanwhile, the use of multiple watermarks also enhances the security of medical image transmissions, which can better protect the privacy of patients.  
Figure 6 shows the working principle of the proposed algorithm. First, DTCWT-DCT transform was performed on an original medical image for a vector \(VF(j)\) conforming to human visual features found in the transformation domain. And binarization was performed on multiple watermarks to obtain multiple corresponding watermarks \(W_{n}(i,j)\). Then, different initial values were taken for henon chaos to generate multiple different chaotic sequences \(X_{n}(j)\). For these chaotic sequences, the symbol operation was applied to obtain the binary encryption matrixes \(C_{n}(i,j)\), and the encrypted watermarks \(BW_{n}(i,j)\) are obtained using \(C_{n}(i,j)\) and \(W_{n}(i,j)\), \(W=\{w(i,j)|w(i,j)=0,1;1\leq i\leq M1,1\leq j\leq M2\}\). Next, logical operations are performed on \(BW_{n}(i,j)\) and \(VF(j)\). According to the encrypted watermarks \(BW_{n}(i,j)\) and the visual feature vector \(VF(j)\) of the image, binary logic sequences \(Key_{n}(i,j)\) are generated through the Hash function. These binary logic sequences \(Key_{n}(i,j)\) can be stored in a third-party platform. When testing, the same method was performed to the test medical image to extract the visual feature vector \(VF(j)\), and the watermarks \(BW_{n}t(i,j)\) contained in the image were extracted by \(VF(j)\) and the \(Key_{n}(i,j)\) generated earlier. The chaotic sequences \(X_{n}(j)\) and the encryption matrixes \(C_{n}(i,j)\) were generated using the same henon initial value as the above method. Then, the restored watermarks \(W_{n}t(i,j)\) were obtained by hashing \(C_{n}(i,j)\) and \(BW_{n}t(i,j)\).  
A detailed algorithm description is shown in Figure 6.  
Figure 5: Two domain of attraction.
# Silver Nanowires: Synthesis, Antibacterialativity and Biomedical Applications
## 2 Synthesis of AgNWs
### Hard-Template Synthesis  
The hard-template method is based on the use of nanoporous membranes, such as carbon nanotubes, nanoporous silica and alumina membranes, zeolites and track-tetched polycarbonate, to direct the growth of AgNPsWs [19; 20; 21; 43; 44]. The greatest benefit of this method is that the synthesis can be carried out in a controlled manner, obtaining AgNPs that are uniform in size; furthermore, the obtained nanowires are already immobilized on a substrate, which is ideal in the production of nanodevices; on the other hand, this is not desirable when AgNPs are produced for biomedical applications [45; 46]. Nanoporous membranes are commercially available, and easy-to-use hard templates with the wires synthesis occur within the cylindrical pores of the membrane, leading to the formation of silver nanowires with a controlled length and diameter. Furthermore, the production yield can be modified by choosing the desired membrane porosity [45]. Despite these advantages, the purification of AgNPs from the membrane requires dissolution of the nanoporous support in harsh conditions, which often results in damage to the nanowires [25].
## 3 Background and Preliminaries
### Anomaly Detection in Wireless Sensor Networks
#### 3.3.2 Motivations for Anomaly Detection in Wireless Sensor Networks  
One of the most important motivations for anomaly detection in WSN is to provide data reliability and quality since sensor data can be corrupted and damaged due to many reasons such as reading errors, faulty sensors, or malicious attacks. Event reporting is another motivation for building anomaly detection since many WSNs have been used recently for monitoring different kinds of phenomena, for example, weather changes and fire detection [36; 37]. The use of anomaly detection for event detection helps in detecting such a disaster or serious problem in its early stage and helps in making decisions accordingly. Furthermore, an event may be of interest for further analysis by scientists. Malicious sensor nodes that are compromised by some adversaries in WSNs are another strong motivation for building anomaly detectors. Such adversaries may gain access and control of some nodes and then start launching attacks that either can drain the limited resources of the network or inject false and corrupted data. Subsequently, this data may be used later by to make false decisions. Some works [9; 38; 39; 40; 41] have tackled this problem and use anomaly detection to detect such kinds of attacks so that a suitable action can be taken. A comprehensive survey of intrusion detection schemes in WSNs can be found in [42].
## 1 Introduction  
With the increasing availability of digital microscopy scanners and whole slide imaging, digital pathology (DP) will continue to successfully grow into our daily routine diagnostic practice. Whole-slide images, as they are digitized slides, provide the intriguing opportunity for the application of image analysis techniques for advanced tasks, such as disease classification. Deep learning (DL) is the most commonly applied technology in the realm of feature learning. The process involves the iterative improvement of learned representations of regions of interest to achieve maximum class separability. Medical (and nonmedical) image classification tasks have been remarkably successful utilizing DL. The area of computational image analysis of DP images has been already addressed by some previous works. Successful examples range from utilization of different types of cancer detection, classification, or grading [1; 2]. Recent work has shown that the differentiation of histologically similar lesions in Focal Cortical Dysplasia in human focal epilepsies is possible [3]. What is more remarkable is that these pathologies differed only in genotype and not in phenotype. Classification of liver cirrhosis, heart failure detection, and classification of Alzheimer's plaques [4] have also been successfully tackled [5]. Lymph node screening to search for metastatic breast cancer has been successfully performed with the help of deep convolutional neuronal networks. Classification of skin lesions has also been successfully performed with the help of DL and elegantly distributed to smartphones for easy daily use of non-expert users [6]. Disease grading, prognosis prediction, and imaging biomarkers for genetic subtype identification are more challenging tasks but have also been successfully established [7; 8; 9].  
All of these works have shown that deep learning in the context of pathology is becoming more and more common.  
However, a prerequisite to successfully apply deep learning requires domain-associated knowledge in the field of DL and DP. Whereas many pathologists are not familiar with the problem-specific tasks and technical issues for applying DL techniques, DL developers most often have little experience with histology and histopathology-associated workflows. In addition, currently available open-source tools and tutorials do not provide guidance for the needs of both groups, and available programming libraries and tools (either open- or closed-source) are not targeted for an application by a pathologist or clinician with little experience in DL programming routine. This is a major obstacle for researchers to use or extend the available technology and investigate their clinical use-case and hypotheses. We developed, therefore, an open-source library specifically tuned and adjusted to the special needs of digital pathology-associated analysis tasks in the context of DL. We showcase the potential of our library by outlining two specific projects, each driven by a unique clinical hypothesis.
# Intelligent Total Transportation Management System for Future Smart Cities
## 2 Materials and Methods
### Sensing and Technologies in Total Transportation Management  
Smart city developments are driven by accelerating technology developments, defined by technology laws and studied by technology foresight and forecast techniques, as well as technology identification, evaluation, and selection methods [19; 48; 49; 50; 51]. The most crucial future and emerging technologies that enable the development of smart city solutions and especially smart city total transportation management are  
* disruptive technologies including new methods of design and production process planning, new optimized transport network planning, new (lightweight) materials (as full composite vehicles), new solutions as (as electric vehicles), new unconventional solutions (as an autonomous vehicle, pilot-less taxi drones);
* micro-electric-mechanical-system (MEMS)-based sensors and actuators, enabling traffic monitoring and active control;
* new info-communication technology based on wireless technology, Internet, cloud, and Internet of Things concepts;
* including further data processing, situation awareness-evaluation-decision making by the methods of soft computing and artificial intelligence; and
* improving the new concept of total transportation/total management. The new and emerging technologies enable the creation of new microsensors that might be integrated into the  
* vehicle structures and systems;
* operators' working environment, and more particularly in the drivers' cockpits and monitoring/control rooms;
* infrastructure of the transportation system; and
* general info-communication systems. The elements of the transportation monitoring and management systems include the sensors, data info-communication, data collection and data processing, situation awareness, and evaluation decision making. Sensors are items providing (mostly electric analog or digital) signals depending on the investigated objects' condition. They may use physical, chemical, and recently biological principles [52; 53]. Sensors are classified [52] as:  
* passive sensors--that do not require own or external power supports (like thermocouple, electric field sensing, piezoelectric sensors, chemical and infrared sensors, or video camera/infrared camera)
* active sensors--sensing devices that require an external source of power to operate (GPS, radar, ultrasonic detectors, Lidar, blood pressure sensors) This article recommends using passive and active condition monitoring and detection where
* passive system uses signals measured by sensors in case of out of control actions, in quasi-steady operation modes and
* active system applies predefined inputs, trajectories, special loads, or test signals (including, for example, recognition-decision action time for braking or delay in braking, deviation from predefined trajectories as curved lanes). By implementing this monitoring classification approach, the following methods can be applied.
# Financial Market Correlation Analysis and Stock Selection Application Based on TCN-Deep Clustering
## 3 Experimental Settings and Results  
### Data Description  
This paper focuses on the closing prices of two important index markets in China and the United States, the CSI 300 and the S&P 500 index. The historical ticker data of the CSI 300 index comes from the Joinquant platform and the historical ticker data of the S&P 500 index comes from Yahoo Finance. The collected data are shown in Table 1. Stocks with long-term suspensions and ST stocks were removed from the paper. The time period for the data collected in the experiment was from 1 January 2015 to 1 July 2018. We divided the time period into three parts. The first part was the training set; this period was mainly used to define the optimal number of clustering classes and the selection of the hyperparameters, as well as the application to obtain the clustering results. The second time period was the observation set, where the market return analysis was conducted for a similar set of different stocks that was obtained in the previous step. The third time period was the backtesting phase, where the empirical analysis was conducted mainly by the stock selection scheme obtained from the analysis. Figure 5a,b show the closing price movements of randomly selected constituents from the S&P 500 and CSI 300 indices, respectively.
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 3 Materials and Method  
In this section, we first present the idea and the overall process of our MTAD model. Then, the data preprocessing method is introduced. Finally, we show the details of each learning task and joint learning of the MTAD.
# Nighttime Image Dehazing Based on Point Light Sources
## 4 Experiment
### Main Results
#### 4.2.1 Subjective Evaluation  
The comparison experimental image of the nighttime defogging algorithm experiment is shown in Figure 2. DCP sets the ambient light as a global constant. Due to the special environment at night, the atmospheric light value of the artificial light source will be considered by DCP as the global atmospheric light term during the day, so the image after defogging is dark. As shown in the figure, the information of river surface, buildings and haystacks is unclear. This proves that the nighttime foggy ambient light should be a local variable rather than a simple fixed value. NDIM (the third row in Figure 2) tends to increase the overall brightness of the resulting image more, but it is worth noting that the result may add more noise. In addition, since the algorithm does not balance the brightness, the image is easily overexposed, e.g., there is still a lot of noise in the light source region in the sky. Although the OSFD algorithm can obtain better image sharpness, it also can not avoid the noise problem. The method in this paper effectively solves the defects of the above algorithm, makes the image more colorful while preserving the original colors. Effective defogging is performed in the dark areas of the image, as well as around the light source. Details can still be effectively maintained in distant depth of field. A comparison of the enlarged images in Figure 3 shows that the algorithm in this paper is smoother in the dark detail part than the OSFD algorithm. However, there is still residual fog in the results, and there is still some color bias in the light source region, which can be addressed in future work by glow effects (e.g., adding a color bias term to the glow term).  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Method** & **Atmospheric Light Values** & **Transmittance** & **Applicable Condition** \\ \hline \multirow{3}{*}{DCP} & Takes the maximum value of the first 0.1\% of pixels of the image & \multirow{3}{*}{dark channel prior} & \multirow{3}{*}{Daytime} \\  & & & \\ \cline{1-1} \cline{3-4} Proposed & & & \\ \cline{1-1} \cline{3-4} Proposed & & & \\ \cline{1-1} \cline{3-4} method & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The difference between proposed method and DCP.