esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Datum Transfer
### Multi-Source Data Datum Alignment  
The principle of the cubic prism method of creating mirror plane vectors is shown in Figure 4a. The laser beam is reflected by the cubic prism to point \(A\), the coordinates of the virtual point \(B\) and the actual point \(A\) in the mirror are measured, and the line \(AB\) is the normal coordinate system vector [30].  
It is easy to prove from the reflection theorem that the mirrored reflection point \(A\) (\(x_{a},y_{a},z_{a}\)) and the virtual point, obtained after measurement, form the vector \(\overset{\rightarrow}{AB}(e_{1},e_{2},e_{3})\), which is the normal direction of the cubic mirror at that surface, where:  
\[e_{1}=\frac{x_{a}-x_{b}}{\left|\begin{array}{l}\overset{\rightarrow}{AB} \\ \overset{\rightarrow}{AB}\end{array}\right. \tag{7}\]  
Figure 4: Datum alignment of theodolite and laser tracker. (**a**) principle of cubic prism to create mirror vectors; (**b**) alignment of the cubic prism coordinate system with the datum platform coordinate system.  
\[e_{2}=\frac{y_{a}-y_{b}}{\left|\overrightarrow{AB}\right|} \tag{8}\] \[e_{3}=\frac{z_{a}-z_{b}}{\left|\overrightarrow{AB}\right|} \tag{9}\]  
Similarly, the normal vector \(\overrightarrow{CD}\) (\(m_{1}\), \(m_{2}\), \(m_{3}\)) in the direction of the other perpendicular of the cubic mirror is obtained. The above-normal vectors are cross multiplied to obtain the normal vectors \(\overrightarrow{P}\) (\(p_{1}\), \(p_{2}\), \(p_{3}\)) perpendicular to the vectors \(\overrightarrow{AB}\) and \(\overrightarrow{CD}\) to establish the cubic prism coordinate system \(L_{1}\). The simulation of this method is described in Section 4.1.  
As in Figure 4b, in the cubic prism coordinate system \(L_{1}\), use the laser tracker to aim at the surface feature point \(C_{n},n\geq 3\) on the datum platform. Using (\(x_{n}\), \(y_{n}\), \(z_{n}\)), \(n=1\), \(2\), \(3\)...\(n\), to fit the plane equation.  
\[E=\sum_{i=1}^{n}\left(a_{1}x_{i}+a_{2}y_{i}+a_{3}-z_{i}\right)^{2} \tag{10}\]  
To minimize the target variable \(E\), we make the partial derivatives equal to zero.  
\[\begin{cases}\sum{2(a_{1}x_{i}+a_{2}y_{i}+a_{3}-z_{i})x_{i}}=0\\ \sum{2(a_{1}x_{i}+a_{2}y_{i}+a_{3}-z_{i})y_{i}}=0\\ \sum{2(a_{1}x_{i}+a_{2}y_{i}+a_{3}-z_{i})z_{i}}=0\end{cases} \tag{11}\]  
By solving the above equation, we can get \(\overrightarrow{Q}\) (\(a_{1}\), \(a_{2}\), \(-1\)). After the unitization of the vectors, the surface normal vector of the datum platform is obtained. Similarly, the datum platform coordinate system L2 can be established. The angle between the cubic prism and the datum platform can be described by calculating the cosine matrix \(M\) between the cubic prism coordinate system L1 and the datum platform coordinate system L2.  
As shown in Figure 5, photogrammetry and laser trackers are used to illuminate the target sphere holders at the corners of the reference platform. Multiple source data datum alignment can be achieved by replacing the laser tracker target ball and the photogrammetric target ball. The simulation of this method is described in Section 4.2.
# Dynamic Analysis of a Delta Parallel Robot with Flexible Links and Joint Clearances
## 1 Introduction
The effect of link flexibility is analyzed for different parallel robots by using ADAMS software ([[https://hexagon.com/products/product-groups/computer-aided-engineering-software/adams](https://hexagon.com/products/product-groups/computer-aided-engineering-software/adams)]([https://hexagon.com/products/product-groups/computer-aided-engineering-software/adams](https://hexagon.com/products/product-groups/computer-aided-engineering-software/adams))) compared with rigid link case [27; 28; 29]. An alternative approach to obtaining the analytical model of a DPR with flexible links is presented in [30]. An optimal trajectory planning for a DPR is carried out in [31] aiming to suppress robot vibration by developing an elasto-dynamic model assuming flexible links, and a polynomial function in the operating space was considered. The dynamical model of a parallel robot considering link flexibility was developed in [32] based on co-rotational and rigid finite elements. Several scholars have analyzed the phenomenon of friction in spherical joints. The stability analysis of a ball joint based on Coulomb and Stribeck-type model was addressed in [33]. Nonlinear periodic solutions were obtained depending on the ball joint friction parameters. The error modeling for a DPR has been analyzed in [34] by considering joint clearances. Thus, there are works that separately deal with the influence of these factors on the kinematic and dynamic behavior of the DPR, some through analytical modeling and most through numerical simulation using specialized software, but without identifying relevant results regarding the cumulative effect of these three factors (link flexibility, friction and joint clearance). The main problems of DPR highlighted in the literature are systematized in the Introduction:  
1. kinematic and dynamic modeling and simulation of DPR;
2. optimizations of the dynamic model;
3. new methods for solving the dynamic model;
4. analytical modeling with experimental verification;
5. the influence of the flexibility of DPR elements using different software;
6. the analysis of the friction in joints of DPR;
7. errors produced by joint clearances of DPR. This paper addresses the following gap identified in the literature: to the best of the authors' knowledge, there is a lack of significant scientific works dealing with the cumulative effects of link flexibility, joint friction and joint clearance on the dynamic behavior of parallel robots. Therefore, the proposed research is conducted on a Delta parallel robot using a CAD model obtained in the CATIA software ([[https://en.wikipedia.org/wiki/CATIA](https://en.wikipedia.org/wiki/CATIA)]([https://en.wikipedia.org/wiki/CATIA](https://en.wikipedia.org/wiki/CATIA))) and deriving specific simulations in the ADAMS software. The rest of the paper is organized as follows: Section 2 presents the problem formulation; Section 3 proposes seven simulation scenarios and discusses the obtained results; and Section 4 draws final conclusions.
# Evaluation of Diversification Techniques for Legal Information Retrieval +
## 5 Conclusions  
In this paper, we studied the problem of diversifying results in legal documents. We adopted and compared the performance of several state of the art methods from the web search, network analysis and text summarization domains so as to handle the problems challenges. We evaluated all of the methods using a real dataset from the common law domain that we objectively annotated with relevance judgments for this purpose. Our findings reveal that diversification methods offer notable improvements and enrich search results around the legal query space. In parallel, we demonstrated that that web search diversification techniques outperform other approaches, e.g., summarization-based, graph-based methods, in the context of legal diversification. Finally, we provide valuable insights for legislation stakeholders though diversification, as well as by offering balance boundaries between reinforcing relevant documents or information space sampling around legal queries.  
A challenge we faced in this work was the lack of ground-truth. We hope for an increase of the size of truth-labeled dataset in the future, which would enable us to draw further conclusions about the diversification techniques. To this end, our complete dataset is publicly available in an open and editable format, along with ground-truth data, queries and relevance assessments.  
In future work, we plan to further study the interaction of relevance and redundancy, in historical legal queries. While access to legislation generally retrieves the current legislation on a topic, point-in-time legislation systems address a different problem, namely that lawyers, judges and anyone else considering the legal implications of past events need to know what the legislation stated at some point in the past when a transaction or events occurred that have led to a dispute and perhaps to litigation [57].  
**Author Contributions:** [PERSON] conceived of the idea, designed and performed the experiments, analyzed the results, drafted the initial manuscript and revised the manuscript. [PERSON] analyzed the results, helped to draft the initial manuscript and revised the final version. [PERSON] provided feedback and revised the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 7 Overview of the Data Security and Privacy Risk Management Framework (Beta Version)  
ISO 62304 is a widely known standard which provides guidelines for developing healthcare applications [16]. This standard states that organizations need to implement a risk management process while developing healthcare software to assure security and privacy. ISO 62304 refers to AAMI TIR57 for managing security and privacy risks during development. The framework proposed in this paper is based on the guidelines provided by AAMI TIR 57. Furthermore, security and privacy activities in the healthcare application lifecycle guidance provided by IEC 80001-5-1 were also taken into consideration. The framework consists of three different stages: (1) Security and privacy risk assessment, (2) Security and privacy risk controls and (3) Evaluation of overall residual security and privacy risk acceptability. These stages are similar to AAMI TIR57, but is differentiated as follows:  
* AAMI TIR57 does not clearly define how to conduct the security and privacy risk assessment at both the requirements analysis and the system architecture phases. This framework provides the steps to conduct security and privacy risk assessment at both phases. Additionally, the framework also provides a list of assets, threats and vulnerabilities which are specific to WBAN applications, which can be used as a starting point for conducting risk analysis.
* AAMI TIR57 does not provide any design review guidelines at the system architecture phase. The proposed framework added the design review guidelines recommended by IEC 80001-5-1.
* TIR57 does not include risk treatment to identify unacceptable risks which require controls to mitigate. This framework provides risk treatment steps as part of the risk assessment.
* This framework also consists of a mapping of possible threats and vulnerabilities with respective controls along with implementation details for the controls.
* This framework provides steps and tools to conduct in-house vulnerability scans and penetration testing.  
The framework takes initial product requirements as an input but does not perform any validation or verification of the quality of the product requirements. To develop quality product requirements, guidelines provided by ISO/IEC 62304 can be utilized. Toimplement this framework an organization needs to gather a team. Table 4 outlines the respective tasks of each role related to the implementation of the framework. In the case of limited resource in an organization, a single resource can carry out multiple roles and conduct more than one task.  
The three different stages of the beta version of the framework are outlined as follows; Section 8 presents the steps to conduct the security and privacy risk assessment at both the requirement analysis and system architecture phases. Section 9 outlines the steps to implement risk controls. Finally, Section 10 outlines the steps to evaluate the effectiveness of the controls.
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 3 New Energy-Aware Routing Protocols
### Wireless Sensor Networks (WSN) Framework  
Our WSN monitoring system is organized as a network with a multi-tier architecture [6] with low-tier animal tag devices operating on a tight energy-harvesting budget, the middle-tier gateways acting as a data aggregation layer, and high-tier cloud services offering seamless delay-tolerant presentation of data to end users (see Figure 3).  
Figure 3: Network architecture of the wildlife tracking framework.  
Figure 2: Energy module implementation in the ONE simulator.  
This architecture allows the end devices to focus on sampling the data in an energy-efficient way and be agnostic when the users access the data. The data aggregation layer supports intermittent connectivity of the mobile devices. Gateway nodes in the aggregation layer act as data sinks and they share a network-wide state of the downloaded data for each individual end device. Consequently, the data from the end devices does not need to be re-downloaded when they connect through another gateway. The top tier in our system consists of several web services running on a server. This layer presents real-time updated information about the download status for each individual node, node configuration information, and finally the time series of the collected data, such as GPS locations or animal activity.  
The main criteria in our system design are energy efficiency at the end device layer and delay tolerance of the data collection process to support a case where animals are not in contact for an extended time period. Energy efficiency of data communications is achieved through an asymmetric low-power listening protocol [29], where gateways keep their radios on at all times and end devices beacon short packets when they have data to be transmitted. The delay tolerance is achieved through buffering the data in the on-board flash memory on the end devices, until the time when the node connects to a gateway node. Our buffer was designed to hold data over a two-week period, after which old data will be overwritten.
# High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method
## 4 Experimental Design  
### Experimental Data  
For direction forecasting and trading simulation, the 1-h frequency trading data of rebar futures are chosen and prepared for the experiments. It should be noted that before separating the whole dataset into the training and testing datasets, the 5 min frequency data are transformed into 1-h frequency data, and they are normalized afterward. The whole dataset includes five years data from 4 January 2016 and ending on 31 December 2020, which are derived from the Choice database (The formal website of Choice Database is [[http://choice.eastmoney.com/](http://choice.eastmoney.com/)]([http://choice.eastmoney.com/](http://choice.eastmoney.com/)), accessed on 1 May 2022). Specifically, the historical transaction data consist of the opening, closing, high, and low prices. In addition, the trading volume and open interest of rebar futures are also included. Finally, the hourly returns (_Return_), trading volume change rates (\(TVCR\)), and open interest change rates (\(OICR\)) from the (\(i-9\))-th period to the \(i\)-th period are utilized as the input features for the classification model. The calculation formulas for the three input features at time \(t\) are shown in Equations (10)-(12), respectively:  
\[Return_{t}=\frac{Close_{t}-Open_{t-9}}{Open_{t-9}} \tag{10}\]  
\[TVCR_{t}=\frac{TV_{t}-TV_{t-9}}{TV_{t-9}} \tag{11}\]  
\[OICR_{t}=\frac{OI_{t}-OI_{t-9}}{OI_{t-9}} \tag{12}\]  
where \(Close\) and \(Open\) refer to the closing price and opening price of rebar futures in Equation (10). In Equations (11) and (12), \(TV\) and \(OI\) denote the trading volume and open interest of rebar futures, respectively.  
Figure 2 shows a slide window method for the dynamic arrangement of the training and testing periods. The primary purpose of dividing the whole dataset into four consecutive subdatasets is to better test the effectiveness of the proposed method. The training period is designed for XGBoost model training and trading rule optimization, while the testing period is utilized to test the prediction and trading performance. After the experiment of one subdataset is done, the slide window is moved forward by three months to perform a new subdataset of model training and testing. The specific date ranges and sample sizes of four training and testing datasets are reported in Table 1. Note that the whole testing period lasts for one year.  
Figure 2: The slide window method for the dynamic arrangement of the training and testing periods.
# Changes in Spinal-Reflex Excitability during Static Stretch and/or Explosive Contraction
## 1 Introduction
Therefore, the purpose of this study was to examine the immediate effects of static stretch and/or two-legged maximal vertical jump on quadriceps spinal-reflex excitability, and the latency time of the H-reflex and M-response, compared with no activity which served as the control. Based on previous reports on the soleus H-reflex [15; 16; 17; 18], it can be hypothesized that quadriceps spinal-reflex excitability would be decreased after stretching and increased after contracting (jumping). Since change in quadriceps spinal-reflex excitability after stretch or contraction is unknown, it is difficult to predict the result of the combined condition (stretch and contraction). If the magnitudes of change after stretch or contraction were similar, the effects of two stimuli would be cancelled out, resulting in no change. While change in the latency time can be indicative of temporal change in neural activation, a previous study [21] reported that static stretch or vibration did not change the H-reflex and M-response latency time in the soleus. According to that, we hypothesized no change in the H-reflex and M-response latency time across the four conditions.
# An Adaptive and Secure Holographic Image Watermarking Scheme
## 4 The Proposed Watermarking Method  
### Selection of Embedding Positions  
As described in Section 2.1, to maintain imperceptibility and robustness to attacks, the entropy and the edge entropy are employed to select the embedding positions adaptively in our method. In addition, to strengthen the robustness, the watermark signal will be superimposed on the low frequency sub-band of the SFLCT-transformed original image in this work. The detailed steps that select the suitable blocks for watermark embedding are shown as follows.  
(1) Divide the low frequency sub-bands of the SFLCT-transformed original image into non-overlapping blocks with \(z\times z\) pixels first. Then compute the entropy and the edge entropy of each block by use of Equations (1) and (2), respectively.  
(2) Sum up the two measure of entropy of each block according to the following equation.  
\[ETPS_{i}=ETP_{i}+ETPE_{i}, \tag{13}\]  
where \(ETP_{i}\) and \(ETPE_{i}\) are the entropy and the edge entropy of the \(i^{\text{th}}\) block.  
(3) Sort the values \(ETPS_{i}\) in an ascending order. Literatures state that the block with low \(ETPS\) value is suitable for embedding [2; 21]. Thus, the block with smallest \(ETPS\) value is chosen for embedding the watermark signal until the number of selected blocks is equal to the number of watermark bits.
## 5 Case Study: Practical Implementation of DT for HEMS
### Digital Twin for HEMS
#### 5.3.3 Thermal Comfort in the DT  
The system is designed to seamlessly integrate with other services and applications (Section 4.2), enabling enhanced functionalities such as estimating thermal comfort using the PMV index [45]. The PMV index provides a scientifically validated measure of thermal comfort; this index is estimated in real time based on environmental parameters such as temperature, humidity, and personal factors, and the results are stored in the system's database. Figure 12 showcases the PMV data as calculated in real time. The thermal comfort conditions are visualized for the three different rooms within the house. As seen, the PMV values for the living room and bedrooms exhibit slight variations throughout the day, reflecting different usage patterns and environmental influences. Despite these differences, the overall fluctuations remain within a moderate range, indicating stable comfort levels across the rooms.  
To represent thermal comfort visually within the Digital Twin, a color map is implemented based on the PMV scale as shown in Figure 13.  
Each wall and room area is dynamically updated to reflect its thermal comfort level, with colors ranging from blue (cooler) to green (neutral) and red (warmer). This visualization allows users to intuitively assess the thermal conditions of their home and make informed adjustments if necessary, such as modifying HVAC settings to maintain optimal comfort Figure 14. The integration of real-time PMV estimation, visualization, and actionable controls within the Digital Twin enhances the user experience and ensures energy-efficient home management while maintaining thermal comfort.  
Figure 11: Indoor conditions monitoring in DT.  
The integration of real-time PMV estimation, visualization, and actionable controls within the Digital Twin enhances the user experience and ensures energy-efficient home management while maintaining thermal comfort. The results of the case study are illustrated on the GitHub repository [46].
## 3 The Proposed System
### Classification Model  
Since various tree-based boosting algorithms have the advantages of strong interpretability, high speed, and high precision, XGBoost, LightGBM, and CatBoost algorithms have received great attention in the field of classification and detection. The XGBoost algorithm, a parallel implementation of a gradient boosting tree classifier that produced better performance than other algorithms in most cases, was mostly used by the top 10 teams in the recent Kaggle contest. Furthermore, as a fast and efficient parallel algorithm, XGBoost has fully adjustable parameters, which is the main motivation for using this algorithm. In this paper, we also utilized random forest, SVM, KNN, AdaBoost, etc., classifiers to assess the viability of the suggested fusion feature set and malware classification system. In the experimental section, categorization models will be covered in more detail.
## 4 Engineering Application Case
### Program Verification
#### 4.3.1 First Phase  
The proposed approach was performed only on video 2, not on video 1. The processing results are shown in Figure 16 and Figure 17, respectively.  
The recorded results of video 1 and video 2 show the following:  
Figure 16: Performance results of both videos. (**a**) Processing result from video 1; (**b**) processing result from video 2.  
Figure 17: The results of detected vessels.  
Figure 15: Original video image.  
In Figure 16a, the background noise and the impulse noise were not all eliminated, while this was executed well in Figure 16b. The results from video 2 are clearly better than those recorded from video 1.
* In video 1, the contrast of distant target vessels was too low, resulting in a relatively high false detection rate.
* From the analysis of the processing time, video 1 took 2.37 s to determine the number of vessels in the image. Video 2 only needed 1.23 s to obtain the number of vessels from the image. It can be concluded that the vessel detection method adopted in this study can meet the requirements of real-time video processing. The processing time was improved by 1.14 s.
* The original image and the multi-structure diagram were analyzed for significance, and the analysis results are shown in Figure 18.  
In the image, the following can be observed:  
* The pixels of the original image are evenly distributed in a wide range of gray levels.
* After the calculations were complete, the background pixels of the sea surface were mainly concentrated in a very narrow low gray level.
* The pixels corresponding to the target vessels are concentrated at the end of the high gray level, which is conducive to the image segmentation between the target vessels and the background.
* Comparatively, the improved open operations performed better than the traditional open operations.