esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Method
### Method
#### 2.2.2 Signal Acquisition Program Design  
The system is equipped with dual-channel A/D acquisition capabilities. However, due to the short execution cycle of a single program iteration, sequential execution of all processes--including the A/D acquisition, spectral analysis, speed calculation, LCD display, and host computer communication--cannot be completed within the available timeframe.  
To address this temporal constraint, an alternating dual A/D acquisition algorithm was implemented. During initialization, two ADC channels were configured to simultaneously acquire voltage signals from the two-stage amplification circuit. The acquired data are transferred to memory via Direct Memory Access (DMA).  
Two dedicated buffer spaces were allocated in the memory for optimal data handling. During DMA transfer to buffer space 1, the data stored in buffer space 2 are processed. Conversely, during DMA transfer to buffer space 2, the data in buffer space 1 undergo processing.  
Since DMA operations are executed without CPU involvement, the processor resources are exclusively dedicated to data-processing tasks. This architecture ensures that A/D acquisition is never interrupted by computational operations, thereby significantly improving overall system efficiency. The complete program workflow is illustrated in Figure 3.  
Figure 2: Acquisition circuit diagram.  
This alternating buffer strategy shares conceptual parallels with the diversity techniques in hybrid communication systems, where multiple signal paths (e.g., FSO/RF channels [37]) are leveraged to enhance reliability under dynamic conditions. Similarly to the Switch and Stay Combining (SSC) method used to mitigate atmospheric turbulence in wireless systems, the dual-buffer architecture ensures continuous data processing by toggling between independent acquisition streams, thereby avoiding the latency bottlenecks inherent in sequential workflows.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 5 Publicly Available Datasets for Medical Video Analysis
### EchoNet Dynamic Dataset  
[PERSON] et al. [30] created the EchoNet-Dynamic dataset, containing 10,030 echocardiography videos and covering a variety of typical lab imaging acquisition conditions. Allimages have labeled measurements, including LV volume at end-systole and end-diastole, ejection fraction, and expert tracings of the left ventricle. The dataset contains apical-4-chamber echocardiography videos from patients who underwent echocardiography tests between 2016 and 2018 at Stanford University Hospital. Each video was cropped to delete the text and information outside of the scanning sector, and the resulting images were downsampled into standardized 112 \(\times\) 112 pixel videos. The videos are in.avi format, containing an average of 50 frames per second and 176 in total.
## 6 Limitations and Future Perspectives
For example, manufacturers should be required to disclose potential vulnerabilities and provide timely security updates. Regulators should also encourage consumer education programs that help users to identify and respond to early signs of a breach. From a risk management perspective, organizations and users should implement strategies that encompass both technical safeguards and psychological support. Understanding the emotional impact of cybersecurity breaches enables companies to create comprehensive risk management approaches that mitigate not only technical vulnerabilities but also the emotional and psychological stress that these incidents cause. Addressing both aspects fosters a more user-centered approach to cybersecurity, reducing the broad range of risks associated with cyberattacks. Following ISO 27000 guidelines can help manufacturers to enhance the security of IoT devices, decreasing the likelihood of unauthorized access and alleviating users' concerns. These measures are essential for maintaining trust in IoT systems, particularly in homes where security breaches can lead to significant emotional and psychological harm. Conceptualization, S.B., J.R.J.F., N.M.A.H., A.H., G.L. and E.B.R. ; methodology, S.B., J.R.J.F., N.M.A.H. and E.B.R. ; software, F.S., R.H., A.B., A.F., I.R. and E.B.R. ; validation, E.B.R. ; formal analysis, S.B. ; investigation, S.B., J.R.J.F., N.M.A.H., A.H., W.A.I., A.-M.O., G.L., F.S.,R.H., A.B., A.F., I.R. and E.B.R. ; resources, S.B., J.R.J.F., N.M.A.H., A.H., A.-M.O., G.L., F.S., R.H., A.B., A.F., I.R. and E.B.R. ; data curation, S.B., J.R.J.F., N.M.A.H., A.H., G.L., F.S., R.H., A.B., A.F., I.R. and E.B.R. ; writing--original draft preparation, S.B., J.R.J.F., G.L. and E.B.R. ; writing--review and editing, N.M.A.H., A.H., A.-M.O., F.S., G.L. and E.B.R. ; supervision, J.R.J.F., A.H., G.L. and E.B.R. ; project administration, S.B., J.R.J.F., N.M.A.H., A.H., G.L. and E.B.R. ; funding acquisition, J.R.J.F., A.H. and E.B.R. All authors have read and agreed to the published version of the manuscript. This research was funded by the EU FP7 CHIST-ERA funding scheme (European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-NET, corresponding to grant: FWO project G0H6416N (FWOOPR2016009701) and NWO project 651.002.002). This study was conducted in accordance with the Declaration of Helsinki and approved by the ethical committees of all involved partners for this research project (Ghent University, University of Reading, Eindhoven University of Technology and University of Greenwich). Informed Consent Statement: Informed consent was obtained from all subjects involved in this study. The raw data supporting the conclusions of this article will be made available by the authors on request. The authors declare no conflicts of interest.
## 3 Results and Discussion
### Integrated Centrality and Essential Protein Targets
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**No.** & **Target** & **Description** & **Uniprot** & **IC** & **Module** & **Cannabinoid Ligands** \\ \hline
1 & CAT & Catalase & P04040 & 0.63 & 1 & CBD, CBDA \\
2 & COMT & Catechol-O-methyltransferasease & P21964 & 0.57 & 1 & \(\Delta^{9}\)-THC, CBN \\
3 & CYP17A1 & Cytochrome P450 family 17 subfamily A & P05093 & 0.56 & 1 & CBD, CBDA \\  & member 1 & & & & & \\
4 & GSTA2 & Glutathione 5-transferase alpha 2 & P09210 & 0.51 & 1 & \(\Delta^{9}\)-THC, CBC, CBN \\
5 & GSTM3 & Glutathione S-transferase mu 3 & P21266 & 0.64 & 1 & \(\Delta^{9}\)-THC, CBC, CBN \\
6 & GSTP1 & Glutathione S-transferase pi 1 & P09211 & 0.72 & 1 & \(\Delta^{9}\)-THC, CBC, CBN \\
7 & HMOX1 & Heme oxygenase 1 & P09601 & 0.52 & 1 & \(\Delta^{9}\)-THC, CBC, CBN \\
8 & AKT1 & AKT serine/threonine kinase 1 & P31749 & 0.81 & 2 & CBD, CBDA \\
9 & CASP9 & Caspase 9 & P55211 & 0.67 & 2 & CBD, CBDA \\
10 & PLCG1 & Phospholipase C gamma 1 & P19174 & 0.51 & 2 & CBGA \\
11 & PRKCA & Protein kinase C alpha & P17252 & 0.65 & 2 & \(\Delta^{9}\)-THC, CBC, CBD, CBDA, \\
12 & PRKCB & Protein kinase C beta & P05771 & 0.56 & 2 & \(\Delta^{9}\)-THC, CBC, CBN \\
13 & CYCS & Cytochrome c, somatic & P99999 & 0.59 & 3 & CBG \\
14 & TNF & Tumor necrosis factor & P01375 & 0.55 & 3 & \(\Delta^{8}\)-THC, CBD, CBDA \\
15 & CNR1 & Cannabinoid receptor 1 & P21554 & 0.20 & 4 & \(\Delta^{8}\)-THC, \(\Delta^{9}\)-THC, CBC, CBD, CBDA, CBG, CBGA, CBN \\
16 & CNR2 & Cannabinoid receptor 2 & P34972 & 0.20 & 4 & \(\Delta^{8}\)-THC, \(\Delta^{9}\)-THC, CBC, CBD, CBDA, CBG, CBGA, CBN \\
17 & CREB1 & cAMP-responsive element binding & P16220 & 0.63 & 4 & \(\Delta^{9}\)-THC, CBN \\
18 & GRIN2B & Glutamate ionotropic receptor NMDA & Q13224 & 0.68 & 4 & \(\Delta^{9}\)-THC, \(\Delta^{8}\)-THC \\ \hline \end{tabular}
\end{table} 
Table 2: The essential protein targets and their corresponding cannabinoid ligands. have shown that GRIN2B is significantly associated with depression and disruptive behavior, mood disorders, and bipolar disorder [61]. Besides, various studies have shown that GRIN2B is associated with cocaine and alcohol abusers, which may be related to the shared pathway of addiction [62]. CASP9 belongs to a family of caspases that play essential roles in programmed cell death. Cannabinoids like CBD and \(\Delta^{9}\)-THC have been proved to play role in the modulation of tumor proliferation, cell cycle, and apoptosis in various cancer types through activating CASP9 leading to apoptosis [63; 64]. CREB1, which encodes cyclic adenosine monophosphate reactive element-binding protein 1, plays a core role in intracellular signal transduction and plays a significant character in a variety of cellular functions. It has been implicated in anxiety, depression [65], mood disorders [66], and drug addiction [67]. PRKCA and PRKCB have been mentioned in substance dependence [68; 69].
# Software-Defined Networking: Categories, Analysis, and Future Directions
## 2 Network Testing and Verification
### Summary and Lessons Learned  
The summary of network testing and verification studies are presented in Table 1, and the following conclusions are drawn based on these studies. NDB [70] helps to debug SDN networks via breakpoints and packet backtraces. These primitives help in locating the order of events that led to error conditions. Though NDB can identify error conditions, it does not fix the error conditions. VeriFlow [72] checks network invariants in real time and generates an alarm or blocks these events from occurring. It checks network invariants in real time. However, it does not work in multi-controller architecture, and its verification process does not support delay-sensitive applications in which flow rule installation is continuously in flux, which is most likely desired for the least delay in forwarding devices. NICE [81] uses model checking and symbolic execution to detect bugs and invariant conditions in SDN applications. However, it is unable to test a controller implemented in the same language. FPB [82] provides an efficient buffer management mechanism at the switch level to avoid per-flow packet disorder, which helps to minimize the packet drop ratio. HSA [83] facilitates network administrators to statistically analyze their networks for network-wide invariants. However, it only works for static networks and lacks the ability to detect network policy change with dynamic change in ACL policies.  
PyResonance [84] utilizes the composition operator of the Pyretic language to implement state-based network policies to predict a possible network's forwarding behaviour. PGA [85] provides automatic and conflict-free policies by examining various network policies that are individually stated for any conflict. In PGA, the focus is on implementation of various policies in such a way that conflict does not occur. The research works [75,76] debug traditional as well as SDN environments to detect network anomalies, ensure consistency of the data forwarding plane [77,78], and allow several applications to run in parallel in a non-conflicting way [79]. However, all these mechanisms lack the ability to detect network policy change and delete conflicting flow rules along with installation of new flow rules as per new network policies to avoid packet violations. There is still a need to develop tools that can detect bugs in real time, along with the mechanisms to correct those bugs. This will help to avoid network inconsistencies, which will result in increasing network efficiency and QoS.
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Design Related Problems in WSNs
#### 3.1.1 Network Lifetime  
Network lifetime is very critical parameter related to sensor network performance and has been tackled at various levels namely, design, operation and deployment. For example in [44], a stochastic multi-objective algorithm for WSNs has been proposed to maximize the aggregate utility and to extend the lifetime of the network. Space-based applications of wireless sensor networks are considered in [45], where authors have proposed a multi-objective formulation to address the problems of maximization of lifetime, minimization of energy consumption and maximization of the coverage. In [46], authors have formulated a multi-utility function to represent various performance metrics of the WSN and then jointly optimized the utility function and the lifetime maximization. A multi-objective routing protocol design has been proposed in [47,48] to maximize the lifetime while considering other conflicting objectives like, minimization of energy consumption, minimization of delay and secure routing.
# A Spatial Prediction-Based Motion-Compensated Frame Rate Up-Conversion
## 3 Experimental Results
### Subjective Evaluation  
In order to evaluate the subjective visual quality of the proposed SP-MCFI algorithm, we select the video sequences _Foreman_ and _Stefan_ to perform contrast experiments among which _Foreman_ contains moderate scene motion while _Stefan_ has a mass of sharp movements. Figures 4 and 5 shows the interpolated frames of the comparing algorithms and proposed algorithm, and the 78 th frame of _Foreman_ and the 88 th frame of _Stefan_ are respectively captured as examples. Observing the interpolated frames, we can see that obvious blur and block artifacts appear in the face part when using the
## 2 Description of the Proposed System
### Transmission System  
The transmitter system consists of four beacons (see Figure 2), each one incorporating an infrared LED and a sync detector. The infrared emitters located in the transmission system are the same as those used in the synchronism module [22]. The general operation of the system for the beacon \(b_{i}\) is presented in Figure 2, where each transmitter is individually driven by a microprocessor [24], whose input signal is the pulse generated by the infrared detector whereas the output is the sequence \(c_{i}\) to be transmitted. Every time the synchronism detector receives the carrier from the synchronization beacon, the transmission begins.  
The code \(c_{i}\) used for the \(i\)th LED beacon is an 1151 bit LS sequence with a BPSK modulation. The frequency carrier is \(f_{LED}=25\) kHz, whereas the sampling frequency is \(f_{SLED}=250\) kHz. The four codes \(c_{i}\) are emitted simultaneously by each LED, with a relative delay of 10 samples regarding the previous one, giving a total duration of 46.3 ms for the whole transmitted signal. The suitability of an 1151 bit LS sequence with a 10-sample displacement was studied in [20]. The use of LS sequences with a 10-sample relative displacement is preferable to Kasami sequences in order to minimize the multiple access interference (MAI) effect, produced by the simultaneous transmission and reception of all the sequences.
# Research on a Decoupling Algorithm for the Dual-Deformable-Mirrors Correction System
## 3 Numerical Simulation Analysis
Based on the influence function and control voltage of the FSM and DM, the correction amount of the FSM and DM is calculated. Substituting these correction amounts into the coupling coefficient expression [4], the value of the coupling coefficient between the FSM and DM is solved. The coupling coefficient diagram is presented in Figure 5, where the black line represents the coupling coefficient curve for 2 nd-order aberrations corrected by the FSM, and 2 nd-order and 30 th-order aberrations corrected by the DM. It is observed that there exists a coupling effect between the FSM and DM with a coupling coefficient of approximately \(4.8\times 10^{-1}\), indicating an order of approximately \(10^{-1}\). In the proposed algorithm, a finite matrix is used to constrain the Zernike coefficients. By modifying the elements on the diagonal of the matrix defined by Equations (12) and (16), it is ensured that only the 2 nd-order aberration is corrected by the FSM, while only the 30 th-order aberration is corrected by the DM. As shown in the red line, this results in a coupling coefficient value around \(1.3\times 10^{-2}\), indicating that the algorithm effectively suppresses the coupling effect between the FSM and DM.
## Appendix D Description of General Purpose Mechanisms
### Satellite-Aided Consensus [141]  
This mechanism targets the astronautics domain, specifically, geostationary earth orbit satellites. It constitutes a permissionless consensus protocol for space-terrestrial blockchains. Sybil attack resistance in the protocol is achieved through random oracles and a principle similar to PoS.
## 2 Definition of Tensegrity  
With regard to structural mechanics, the presence of self-stress states (understood as the system of self-equilibrated normal forces which satisfies homogeneous equations of equilibrium) and infinitesimal mechanisms which are stabilized by these states are the most important features of tensegrity structures. In this paper, considerations are limited to \"pin-jointed plane structures\" and the definition of tensegrity formulated by [PERSON] [20] is used. Based on the definitions known from the literature, tensegrity structures demonstrate the following characteristic features:  
* \(T\)--the structure is a truss,
* \(S\)--there is a self-stress state,
* \(M\)--there is an infinitesimal mechanism stiffened by the self-stress state,
* \(D\)--the extremities of compressed components (struts) do not touch each other and struts constitute a discontinuous set,
* \(I\)--the set of compressed components is included inside the set of tensile components,
* \(C\)--tensile elements have no rigidity in compression--these are cables.  
The characteristics above are used to classify structures as \"pure tensegrities\" or \"structures with tensegrity features\".  
\"Pure tensegrity\" is represented by a structure that closely meets all the requirements arising from the definition of tensegrity and defining its specific properties. A tensegrity structure is a truss in which there is an infinitesimal mechanism stiffened by a self-stress state. Components in compression create a discontinuous system inside a continuous system of components in tension, which have no compressive rigidity. The structures that have all of the six features (\(T+S+M+D+I+C\)) are classified in the \"pure tensegrity\" group.  
\"Structures with tensegrity features\" include a wider range of systems in which some properties of tensegrity can be ignored, e.g. a truss in a self-stress state, with tensile elements having no compressive rigidity, demonstrating at least one of the following features: The presence of an infinitesimal mechanism stabilized by self-stress state, the discontinuity of the system of compressed components, and/or the use of compressed elements inside a set of tension components. Structures assigned to this group fulfil three obligatory criteria (\(T+S+C\)) and have at least one of the features: \(M\), \(D\), or \(I\).  
From an engineering perspective, it is very important that the structures have all six features. Only pure tensegrities possess all the benefits of tensegrity structures, including the ability to control their stiffness and natural frequency by modifying the self-stress level.  
Singular value decomposition of the compatibility matrix is used to determine the states of self-stress (\(S\)) and mechanisms (\(M\)). The nonlinear analysis with the use of geometric stiffness matrix, which takes into account the impact of the self-stress state, allows establishing whether the mechanism is infinitesimal or finite. The other characteristic features of tensegrity structures like \(D\), \(I\), and \(C\) are determined indirectly by the identified self-stress state.
# Low Thermal Expansion Machine Frame Designs Using Lattice Structures
## 1 Introduction
### The Finite Element Method  
Concept lattice designs were modelled in SolidWorks 2018 to obtain representative CAD models, and their performances were simulated using the FE method implemented in Abaqus 2018 and 2020. The initial lattice unit cell dimensions (i.e., in their un-heated state) were (20 \(\times\) 20 \(\times\) 20) mm. The temperature of all elements in all models was raised from 19-23 \({}^{\circ}\)C, which represents the temperature variation in a typical temperature-controlled metrology laboratory. All nodes on three of the lattice's cubic edges were constrained in space, as shown in Figure 6, to enable the calculation of thermally induced expansion. The XYMM (Symmetry about a plane X = constant (U1 = UR2 = UR3 = 0)), YYMM (Symmetry about a plane Y = constant (U2 = UR1 = UR3 = 0)), and ZSYMM (Symmetry about a plane Z = constant (U3 = UR1 = UR2 = 0)) boundary conditions were applied to the nodes on the surfaces of the YZ plane in the -X direction (labelled 'YZ (-X)' in Figure 6), the XZ plane in the -Y direction ('XZ (-Y)'), and the XY plane in the -Z direction ('XY (-Z)'). The thermal expansion of the lattice was calculated by examining the displacement of the nodes on the surfaces opposite to those that were constrained. The in-plane CTE (\(\alpha_{X,Z}\)) was found by taking the average of the CTEs in \(X\)-axis and \(Z\)-axis over four orientations of the unit cell or lattice around the Y-axis: 0\({}^{\circ}\) (origin), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\). In a preliminary investigation, the CTE of a unit cell was calculated using the mesh dependency at different mesh densities to determine the required mesh for accurate CTE modelling. The elements used were ten-node tetrahedral elements (C3D10) because, in comparison with hexagonal elements, they provided a more accurate description of the complex lattice geometry [34; 35]. The number of elements per unit cell was increased from 200,000 to 1,100,000 as shown in Figure 7. A mesh density of 440,000 elements per unit cell was sufficient to determine the CTE of the lattice with a deviation of less than \(-0.4\%\) from the 1,100,000 element result, indicating the result is well converged. Figure 6 shows the unit cell model meshed with 440,000 elements.  
Figure 6: Representative example of the converged mesh of the low-CTE concept lattice unit cell and its boundary conditions applied on a lattice unit cell design.
# An Insight into the Chemistry of Cement--A Review
## 3 Hydration Step Involving OPC
### Chemical Reactions Occurring during the Hydration Process
#### 3.2.1 Reaction Involving C\({}_{3}\)S  
The principal constituent of the OPC is the tricalcium silicate (C\({}_{3}\)S, or alite). Alite largely affects the cement mechanical properties (strength) and the hardening process. During the alite hydration, four stages may be distinguished. The first one is the pre-induction period (duration time: a few minutes, just after being exposed to water) where the rapid hydration of C\({}_{3}\)S occurs. When C\({}_{3}\)S is exposed to water, at the surface level, ions start to a rapidly dissolve, with release of hydroxyl and silicate ions in the liquid phase. As a result, the positive charge of Ca\({}^{2+}\) is counterbalanced by the negative ones due to silicate and hydroxyl anions. Since the dissolution rate of C\({}_{3}\)S is faster than the diffusion rate of ions at the surface level, the liquid phase rapidly turns oversaturated considering the silicate hydrated phase. The direct consequence of this phenomenon is the precipitation of a first layer of C-S-H (i.e., tobermorite) nearby the surface. A high heat of hydration is produced in this first step of reaction [53]. Immediately after, the rate of hydration slows down and then increases, with a second heat release. Finally, the hydration process slows down again, entering the deceleration step [54]. The explanation of this specific behavior is the object of several theories, and some considerations regarding kinetic aspects are reported in the literature [55].  
C-S-H is the main relevant binding phase, and is chemically defined as CaO \(\cdot\) SiO\({}_{2}\)\(\cdot\) H\({}_{2}\)O. The stoichiometry of C-S-H in the cement paste is variable (and this is particularly true for the bound water). Additionally, a secondary product, calcium hydroxide or portlandite (i.e., Ca(OH)\({}_{2}\), or CH), is generated in the case of a CaO/SiO\({}_{2}\) molar ratio which is smaller than the alite one (namely, 3:1).