esa-sceva/satcom-chunk-collection huggingface dataset:
# Using a Cable-Driven Parallel Robot with Applications in 3D Concrete Printing
## 4 Dual Simplex Algorithm for the Optimization of Cable Tension, Taking into Account Cable Sagging
### Optimization Cable Tension
Hence, the equality constraints of \(\mathbf{P}\) are satisfied, since  
\[\mathbf{A}\mathbf{\tau}=\mathbf{B}\mathbf{B}^{-1}\mathbf{b}\mathbf{+}\mathbf{N}.0\mathbf{=}\mathbf{b}\]  
and the additional slackness is also satisfied, since  
\[(\mathbf{c}-\mathbf{A}^{\rm T}\mathbf{u})^{\rm T}\mathbf{\tau}=\mathbf{c}^{\rm T}\mathbf{\tau}-\mathbf{u} ^{\rm T}\mathbf{A}\ \mathbf{\tau}=\mathbf{c}^{\rm T}\mathbf{B}^{-1}\mathbf{b}-\mathbf{c}^{\rm T}\mathbf{B}^{-1}\mathbf{b}=0\]  
In this case, if \(\mathbf{x}_{\rm B}\geq\mathbf{0}\), then \(\mathbf{x}\) is the optimal solution for \(\mathbf{P}\) and all conditions of Theorem 4 are satisfied. If there is only a negative element in \(\mathbf{\tau}_{\rm B}\), it will be eliminated by changing the basic \(\mathbf{B}\), thereby reducing the primal infeasibility. This procedure is repeated until all elements in \(\mathbf{x}_{\rm B}\) are non-negative. This is the advantage of the dual simplex algorithm. In this study, the mobile platform interpolates a printing trajectory with small velocity and acceleration, and the nonlinear effects due to rapid turning of the load are omitted to speed up the calculation. The external force \(\mathbf{W}\mathbf{p}=-\mathbf{b}\) can be simplified as  
\[\mathbf{w}_{\rm p}=\left[ \begin{array}{c}\rm F_{\rm P}\\ \rm M_{\rm P}\end{array} \right]=\left[ \begin{array}{c}f_{x}\\ f_{y}\\ f_{z}\\ m_{x}\\ m_{y}\\ m_{z}\end{array} \right]=\left[ \begin{array}{c}0\\ 0\\ m_{g}\\ 0\\ 0\\ 0\end{array} \right] \tag{16}\]  
where \(m\) is the total mass of the concrete and the moving platform, and \(g\) is gravity acceleration. In the printing process, the mass of the concrete will decrease depending on the speed of the extruder. \[m=\rho_{c}flow_{ex}t \tag{17}\]  
where \(\rho_{c}\), \(flow_{ex}\), \(t\) are, respectively, the specific weight of the concrete, the flowrate of the extruder, and the interpolation time. With the Jacobian matrix \(\mathbf{A}\) defined in (8), the dual simplex algorithm is used to find the set of tension \(\mathbf{\tau}\) that satisfies the optimal conditions with cable structure constrains. The result of this part, used as the input of the cable length computation process, takes into account cable sagging due to the CDPR applied for the concrete printing having 8 cables--6 DOF.
## 2 Materials and Methods
### The Communication Between Server and MR Device  
In our approach, Unity is used to design the interaction interface and generate a virtual scene in the HoloLens2, and ROS is used to control the robot arm on the server. Therefore, data exchange is necessary between Unity and ROS. This section describes the communication setup between Unity in HoloLens2 and ROS on the server.  
The system uses the ROS-TCP-Connector and ROS-TCP-Endpoint to provide a network communication solution. ROS-TCP-Connector is used in Unity to receive and send ROS messages. ROS-TCP-Endpoint is used in ROS and receives messages sent from Unity. More specifically, in Unity, a WebSocket is established. The WebSocket allows Unity to use a specific IP address to connect to the ROS, and Unity transmits and receives data via the WebSocket connection. Relevant nodes are established in Unity, which can publish topics and subscribe to topics. In practice, ROS and Unity are configured to use the same network IP address. Then, ROS, which is running on the server, can publish any information about the objects in the form of topics and can subscribe to the information on the tasks in the form of topics published by Unity. On the other hand, Unity, which is running in the  
Figure 2: Flowchart of the teleoperation system by interacting with objects’ digital twins.  
HoloLens2, can subscribe to topics of information from objects published by the ROS and can publish topics about the task's information.  
As shown in Figure 3, before constructing the virtual scene, the information about objects in the scene needs to be published in the form of a topic. This information includes the objects' category and coordinates in the robot arm base coordinate system. The information is obtained by the server through processing the RGB image and depth image. When ROS publishes object information, the application in HoloLens2 receives the information and generates the virtual objects in the virtual scene.  
In HoloLens2, Unity can subscribe to the topics published by ROS to obtain object information through the ROS-TCP-Connector. After the user indicates the target object and the destination position of the target object via HoloLens2, Unity publishes a topic about the target object and destination position through ROS-TCP-Connector. Then, ROS subscribes to this topic through ROS-TCP-Endpoint and subsequently performs the grasping operation.
## 2 Basic Model Assumptions in Conventional Thin Film Optics  
### System Geometry  
Let us shortly summarize basic model assumptions concerning system and illumination geometry characteristics in thin film optics [9; 10]:  
* We assume stratified media only. Consequently, the optical properties of the media shall depend on one coordinate (here, the \(z\)-coordinate, as seen in Figure 1) only. The optical parameters describing the materials may exhibit a discontinuous \(z\)-dependence, and in this case, the discontinuities in the optical parameters describe what we will further call interfaces. The interfaces are perpendicular to the \(z\)-axis.
* Consequently, the model system extends to infinity along the \(x\)- and \(y\)-axes.
* We further assume optical isotropy of all media. In addition, any magnetic response is neglected in our model.
* The semispace above the stratified medium is filled with a homogenous medium, called the incidence medium. As a postulate, light propagation in the incidence medium should be free of damping. On its bottom, the stratified medium faces a semispace filled with a further homogeneous medium, called the exit medium.
* It is assumed that a plane monochromatic electromagnetic wave is incident (from the incidence medium) on the stratified medium. In this case, an incident wavevector may be unambiguously defined. On this basis, an incidence angle may be introduced, which is zero for the particular case of normal incidence.
* At oblique light incidence, the wavevector of the incident wave and the \(z\)-axis allow the defining of an incidence plane.
* We further assume a three-wave scenario. That means that the incident wave gives rise to the generation of two other plane waves, propagating either in the exit medium (the transmitted wave) or in the incidence medium (the reflected wave).
* The materials are described in terms of linear optical constants only. As a consequence, reflectances and transmitances may be introduced that do not depend on the light intensity.  
The set of requirements I-VIII defines what we will further call the standard model of thin film optics (see Figure 1). In many situations, the exit medium is associated with the substrate material. We further note that sometimes, the incidence medium is called the superstratum. Note that in this article, we will not explicitly consider the back side of the substrate (see [10; 11]).
## 3 Results and Discussion
### Synthesis of Pigments  
Finally, in the tested strains, similar levels regarding the content of total chlorophyll and carotenoids (i.e., TCh/x% = 3.2-5.0%, wt/wt, and TC/x% = 1.3-1.6%, wt/wt) were observed. The _Picochlorum_ strains and _M. gaditana_ VON5.3 synthesized pigments in amounts comparable to other studies. For example, the chlorophyll content of _N. oceanica_ was 1.84 \(\pm\) 0.11% and carotenoids 0.42 \(\pm\) 0.02% in the study of [PERSON] et al. (2022) [5]. _N. oulata_ used by [PERSON] and [PERSON] (2017) [6] produced chlorophyll a, which is almost equal to the chlorophyll content of the present study, and carotenoids at 4.9 \(\pm\) 0.1%-11.6 \(\pm\) 1.1% and 0.8 \(\pm\) 0.01%-2 \(\pm\) 0.2% during the winter season and 3.3 \(\pm\) 0.5%-6.2 \(\pm\) 0.6% and 0.5 \(\pm\) 0.1%-1.1 \(\pm\) 0.1% during the spring season, respectively. These results align with the findings of [PERSON] et al. (2022) [7]. Specifically, total chlorophyll content dropped from 5.1 \(\pm\) 1.2% to 2.7 \(\pm\) 0.05% and carotenoid content from 0.51 \(\pm\) 0.10% to 0.27 \(\pm\) 0.05% when _Tetraselmis striata_ (Chlorophyta) CTP4 was cultivated at 25 \(\pm\) 1 \({}^{\circ}\)C and 28 \(\pm\) 1 \({}^{\circ}\)C, respectively. These findings reveal a correlation between pigment synthesis and temperature of incubation. However, pigment synthesis depends on many factors, with the most significant being the provided illumination and \"mutual shading,\" which should be taken into consideration as it affects the cells of the low layers of the cultivation. On the other hand, _N. pyriformis_ PAT2.7 pigment content was lower in all relevant measurements. However, it should be mentioned that _Nephroselmis_ strains are well-known producers of carotenoids like neoxanthin, lutein, and siphonaxanthin, which can be used in the pharmaceutical and food/feed industries [8]. Therefore, it is crucial to find ways to optimize carotenoid synthesis.
## 4 Discussion
### Phyto-Active Compounds of _S. miltiorrhiza with AMFs_ Inoculation  
Secondary metabolites with different functions are produced during the growth and development of plants. Medicinal plants inoculated with AMFs would promote the accumulation of several secondary metabolites [31]. For example, [PERSON] et al. found that licorice plants that were inoculated with AM enhanced glycyrrhizic acid content by 0.38-1.07-fold after 4 months; also, after 30 months, it enhanced by 1.34-1.43-fold [35].  
There are two categories of phyto-active compounds in _S. miltiorrhiza_: fat-soluble tanshinones (e.g., dihydrogen tanshinone I, implicit tanshinone, tanshinone and tanshinone II\({}_{A}\)) and phenolic acid water-soluble compounds (salvianolic acid B). It is clear that tanshinones are synthesized in roots of _S. miltiorrhiza_ plants. Seedlings inoculated with AMFs could increase content of tanshinones significantly, especially with _G.m_ and _G.i_ (Table 7). Salvianolic acid B accumulation in leaves and roots were remarkably increased with AMFs inoculation, because the biomass with AMFs inoculation significantly increased (Tables 4 and 8). The results were in accordance with previous studies [30; 31; 35]. In addition, there was an interesting finding in this study. _S. miltiorrhiza_ seedlings' inoculation with _G.m_had the lowest biomass (Table 4) but it had the highest concentration of tanshinones and accumulation of salvianolic acid B among the four AMFs (Tables 7 and 8). There are two reasons to explain the relationship between biomass and secondary metabolites. First, secondary metabolites are positively correlated with mineral elements [43; 44]. However, there were few relations between tanshinones concentration and mineral concentration in the present study (Tables 4-6). This is because mineral nutrition was supplied for photosynthesis, and then photosynthate was transferred to maintain the AM symbiosis [45]. Consequently, carbon was not enough the increase the biomass. Second, it is a result of the plant reaction to AMFs inoculation. The AM symbiosis formation is mediated by gene expression and the production of chemical molecular signals both in plants and fungi [46]. Chemical defense is induced by gene expressions that manipulate their metabolism [1; 45]. Therefore, tanshinones were produced as defense chemicals to resist AMFs inoculation. That was the reason for the \(G.m\) infection rate; also, intensity was lower than the other three fungi (Table 2), while the concentration of tanshinones' concentration was higher than the others (Table 7), which is consistent with [PERSON] et al. [47].
## 5 Simulation Results and Discussions
### Comparison of Energy Consumption  
Finally, the energy consumption of each algorithm is compared. As shown in Figure 10, our proposed CSLT consumes more energy than the compared algorithms, e.g., MANCL, ReTrust, and RFSN. In order to detect malicious nodes, CSLT needs to collect trust evidences and calculates trust values for sensor nodes, which introduces more communication overheads and consumes more energy. However, CSLT can well protect the network and ensure node security. In addition, CSLT performs better than the three compared related works in terms of location security, average localization accuracy and localization ratio. That is to say, we sacrifice some energy to improve network security, and ultimately to improve performance of secure localization.  
Figure 10: Comparison of energy consumption.  
Figure 9: Comparison of localization ratio. (**a**) Impact of malicious node ratio; (**b**) Impact of anchor node ratio.
# Real-Time Human Activity Recognition on Embedded Equipment: A Comparative Study
## 5 Results of Implementation
### Results of Human Activity Recognition  
The initial phase in the proposed methodology involves the real-time dynamic segmentation method, as detailed in [11], employing a dynamic window size. Figure 5 illustrated an instance of real-time dynamic segmentation recorded on 4 November 2010. The segment is characterized by its start time at 05:40:51 and its end time at 05:43:30. When the spatial event correlation reaches unity and the duration between the beginning and end aligns with the typical time frame for the Bed to Toilet activity, it signifies the identification of the corresponding segment for the Bed to Toilet activity. An example of obtained segments using this approach on 4 November 2010 is given in Table 5.  
Table 6 shows an extract of the results of classification with a minimum setup of sensors. Validation is an essential part of evaluating the algorithm's performance. Here, to validate the proposed algorithm, we verify whether it can accurately estimate occupant activities using the F-score indicator [11] In statistical analysis, the F-score defined by Equation (4) is an indicator of the accuracy of a defined test. It is calculated from the precision (P) and recall (R) of the test defined by Equations (2) and (3), where:  
\[P=\frac{TP}{TP+FP} \tag{2}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Activity** & **Ground Truth Segment** & **Simulated Segment** \\ \hline \multirow{2}{*}{Sleeping (1)} & begin: 00 H:03 Min: 50 S & begin: 00 H:03 Min:50 S \\  & end: 05 H:40 Min:43 S & end: 05 H:40 Min:44 S \\ \hline \multirow{2}{*}{Bed to toilet} & begin: 05 H:40 Min:51 S & begin: 05 H:40 Min:51 S \\  & end: 05 H:43 Min:30 S & end: 05 H:43 Min:24 S \\ \hline \multirow{2}{*}{Sleeping (2)} & begin: 05 H:43 Min:45 S & begin: 05 H:43 Min:45 S \\  & end: 08 H:01 Min: 12 S & end: 08 H:01 Min:09 S \\ \hline \multirow{2}{*}{Meal Preparation} & begin: 08 H:11 Min:09 S & begin 08 H:11 Min:15 S \\  & end: 08 H:27 Min:02 S & end: 08 H:24 Min:48 S \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of Ground Truth and Simulated Segments for Sleeping Activity. \[R=\frac{TP}{TP+FN} \tag{3}\]  
\[\text{F-score}=2\times\frac{P\times R}{P+R} \tag{4}\]  
* TP: represents the True Positive
* FP: represents the False Positive
* FN: represents the False Negative  
Table 7 shows the evaluation of the quality of segmentation for the sleeping activity using the F-score. In this work, only the example of Sleeping activity is presented. The evaluation of the performance of activities classification is presented in [11].
## 6 Assessment of the Platform
### Evaluating the Feature Satisfaction Index
The weights were determined by considering the relevance and impact of each feature on (1) diagnostic accuracy, (2) clinical significance, and (3) their contribution to the overall performance of the ML models. These considerations include expert domain knowledge, statistical analyses of feature importance, and empirical results from extensive testing and validation using diverse datasets. The compliance scores of the 42 features were determined mainly by the developers who implemented the case study system (the liver cancer diagnosis system). The determination was based on a combination of factors, including (1) clinical relevance, (2) statistical significance, (3) empirical testing, (4) expert consensus, and (5) technical feasibility. This multifaceted approach ensures that the compliance scores reflect a comprehensive and balanced assessment of each feature's value in diagnosing liver cancer accurately and effectively. As shown in the table, the overall value of Feature Satisfaction (FS) was computed as 91.4%, which we believe is considerably high for a software platform in the medical application domain. We noticed that the FS value for the feature category of Diagnosis Process measured at 86.6%, the lowest among the four categories. This was mainly due to the high variability in the diagnosis processes for (1) different diseases, (2) their relevant body organs, and (3) standard guidelines governing the diagnosis processes. In general, an FS rate above 90% is uncommon for software platforms. However, the high FS rate of this platform can be attributed to several objective factors and technical design strategies. The platform is focused on medical analytics using contrast-enhanced CT scans, which narrows its scope and leads to a high degree of feature commonality across applications. Additionally, there is substantial overlap in diagnostic tasks across international standards like LI-RADS, LUNG-RADS, BI-RADS, and PI-RADS, further contributing to the increased FS rate. The unified diagnosis process with CECT scans, paired with well-suited ML algorithms for each task, also enhances the FS rate. To further boost the platform's applicability, the Open-Closed Principle (OCP) design was applied to all variation points, including the ML model interfaces. By ensuring that software components such as classes, interfaces, and ML models are open for extension but closed for modification, the platform promotes flexibility and adaptability. This design approach allows for the extension of functionality without altering existing code, leading to enhanced reliability, easier maintenance, and ultimately contributing to the higher FS rate.
# Analysis of Multi-Antenna GNSS Receiver Performance under Jamming Attacks
## 1 Introduction  
Applications of antenna arrays for mitigating different types of Global Navigation Satellite System (GNSS) interference have been widely studied [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. Unlike other wireless applications, the quality of GNSS measurements after interference mitigation is important for high precision applications. However, antenna array processing induces distortions in code and carrier phase measurements in the presence of electronic interference [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. The distortions in GNSS measurements caused by antenna array processing have been studied previously; for instance, [12; 20] demonstrated distortions in carrier phase measurements with actual data and showed the dependency of distortions on satellite direction. In [13], code phase biases on the order of one to two metres using Monte-Carlo simulations in the presence of interference and mutual coupling were observed using the Minimum Variance Distortionless Response (MVDR) technique. In [8], authors studied phase tracking loop performance for possible carrier phase distortions due to different array processing techniques such as minimum variance and MVDR; it was shown through simulations that the minimum variance or power minimization based beamformers suffer comparatively more phase distortion than a MVDR based technique. Global Positioning System (GPS) phase measurement distortions due to phase centre variations of individual antenna elements in an array are studied in [18; 19]. It has been shown that mutual coupling between antennas also causes phase distortions of a few centimetres;these are not tolerable for precise applications as they render carrier phase ambiguity resolution more difficult to achieve. In addition, code phase measurement biases due to the phase delay and group delay of antenna elements were observed. Although code phase and carrier phase measurement distortions were reported, a detailed analysis of these and navigation solution quality for high precision applications was not completed. In this research, the performance of a multi-antenna GNSS receiver is evaluated for different array processing techniques in terms of carrier tracking, measurement, and position distortions. Both narrow band and wide band interference sources are considered. Two array processing techniques, namely PM beamformer, which does not take Angle-of-Arrival (AOA) information of satellites into consideration, and MPDR beamformer, which uses the AOA of satellites [24], are considered. The methodology for evaluating possible measurement distortions is described and followed by a discussion of the results using simulated and actual data. Performance evaluations are carried out using GPS L1 Coarse/Acquisition (C/A) signals.
# Exploiting the Rolling Shutter Read-Out Time for ENF-Based Camera Identification
## 2 Related Works and Concepts
### ENF Basics  
The ENF is the supply frequency of an electric power grid. Generators rotating at a speed of 50 cycles per second in Europe, Australia, and most parts of the world and 60 cycles per second in North America generate an alternating current (AC) which travels along transmission lines [32]. In an ideal situation, the frequency is fixed at a nominal value of 50 Hz or 60 Hz, based on the region. However, as the generation of electricity is dependent on the demand for power, it must be produced proportionately. When the demand is high, the frequency drops, and when it is low, the frequency rises temporarily. The varying value of the frequency as the power generation and consumption rises and drops is regarded as the ENF signal. The following models may be used to describe the instantaneous voltage of the power grid:  
\[\begin{array}{c}W(t)=\sqrt{2}W_{0}\text{csc}(\sigma(t))\\ =\sqrt{2}W_{0}\text{cos}\left(2\pi f_{p}+\theta_{i}(t)+\alpha_{i}\right)\\ =\sqrt{2W_{0}}\text{cos}\left(2\pi f_{p}t+2\pi\int_{0}^{t}f_{i}(\tau)d\tau+ \alpha_{i}\right.\end{array} \tag{1}\]  
where \(f_{p}\) denotes the nominal frequency (50/60 Hz) and \(W_{0}\) denotes mains effective voltage, while \(\alpha_{i}\) denotes the initial phase offset [33]. \(f_{i}(t)\) is the instantaneous variation from the nominal frequency, while \(\theta_{i}(t)\) is the instantaneous phase that changes based on power imbalance caused by demand and supply. The instantaneous mains power frequency at time \(t\) can be stated from the equations above as:  
\[f(t)=\frac{1}{2\pi}\frac{d\sigma(t)}{dt}=f_{p}+f_{i}(t) \tag{2}\]  
As \(f_{p}\) remains unchanged, the ENF changes based on \(f_{i}(t)\) fluctuations. Utilizing the model in [33], \(f_{i}(t)\) can be expressed as:  
\[f_{i}(t)=\frac{f_{p}}{2K}(E_{s}(t)-E_{d}(t)) \tag{3}\]  
where \(E_{s}(t)\) is the amount of power supplied, \(E_{d}(t)\) is the total amount of power demanded plus losses, and \(K\) is an inertia constant. So, at each point in time, \(f_{i}(t)\) and \(f(t)\) change based on the difference between how much power is produced and how much is used.
# Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize
## 2 Materials and Methods
### Sensors on the UAS Data Ferry and Node Stations
#### 2.3.3 Unmanned Aerial System (UAS)  
The DJI Matrice 600 Pro Hexacopter (DJI, Shenzhen, China), a six-rotor flying platform specifically designed for professional aerial photography, industrial, and research applications, was selected as the multi-rotor UAS for this study (Figure 5a,b). The UAS is equipped with dedicated advanced intelligent flight functions, always ensuring safe and stable operation. The Matrice 600 Pro Hexacopter has a maximum takeoff weight of 15.5 kg and a patented battery management system to extend flight time and provide a safe and reliable power supply, suitable for the proposed data ferrying application in fields of approximately 50 ha.