esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Hardware Architecture
### Control Unit  
A dedicated Finite State Machine (FSM) is designed to efficiently manage all control functionalities while minimizing the total number of states required for precise control flow. In total, the designed FSM consists of 57 states, each with a distinct role in the execution of target cryptographic operations as presented in Figure 3. The control unit initiates its operation from an idle state (denoted as State 0). The triggers are received from the reset and start control signals, ensuring a controlled start to the cryptographic process. Subsequently, as the start signal is asserted, transitioning from State 0 to State 1 takes place. States 1 through 5 are designed for the efficient execution of projective to NAF conversions. States 6 through 35 generate signals essential for the Quad Block Itoh-Tsujii inversion operation. State 36 is responsible for counting the points on the specified BEC using the value of k. Based on this value, the FSM transitions either to State 47 or State 37. States 37 through 46 are dedicated to generating control signals that pertain to the \"else\" portion of Algorithm 1. Conversely, States 47 through 56 take responsibility for executing the \"if\" portion of Algorithm 1. Furthermore, States 46 and 56 serve as critical checkpoints  
Figure 2: Radix \(-4\) multiplier.  
for evaluating the value of m based on the specific condition of k. Based on the value of m, the FSM proceeds as follows: If the value of m is equal to 233, the FSM transitions to State 57. However, if the value of m is not equal to 233, State 36 becomes the next state in the sequence. By optimizing the control signals and state transitions, we enhance the overall efficiency and performance of our cryptographic accelerator. This optimization ensures the precise execution of Algorithm 1.
## 3 Experimental Verification
### Experimental Results during the Post-Tensioning Process  
#### 3.3.1 Experimental Validation of the Proposed Approach to Quantify Prestress Force  
In the experiment, during the post-tensioning process, the neutral axis location calculated from the recorded strain at II_t and II-b on segment S2 using Equations (1) and (2) is denoted as ENAL2. Similarly, ENAL3 denotes the neutral axis location calculated from the measured data at III_t and III-b on segment S3 of the beam. The prestress force recorded by the load cell in the experiment is denoted as 'EF'. The theoretical prestress forces of the tendon (i.e., TF2 and TF3) were calculated based on ENAL2 and ENAL3 by using Equation (5). The obtained neutral axis locations, the experimentally measured prestress force of the  
Figure 8: Location of linear variable differential transformers (LVDTs).  
Figure 7: Typical experimental setup for static loading test.  
beam, the calculated prestress force from strain measurements and neutral axis location, and the relative errors of the calculated prestress force against the measured ones in the test are presented in Table 2.  
Table 2 indicates that the theoretically predicted and experimentally measured prestress forces have differences of 4.22% and 15.20% when using strain measurements obtained from segments S2 and S3, respectively, when the prestress force is approximately 62 kN. At a higher prestressing level, i.e., 107 kN, the errors between the theoretical and experimental results based on the measured strains on S2 and S3 are slightly reduced to about 1.48% and 11.77%, respectively. These errors could be induced by a number of reasons. The error analysis of the theoretical calculation results is discussed in the following section.
# A Dataset of Pulsed Thermography for Automated Defect Depth Estimation
## 7 Conclusions  
In this study, a pulsed infrared thermography dataset with two distinct defect shapes and comprehensive defect depth information as the ground truth was introduced. This dataset makes a significant advancement over its predecessor, offering double the number of measurements and the advantage of using an innovative CAD-based labeling approach. This approach significantly enhanced the accuracy in defining the boundaries of the defect masks. The effectiveness of this novel labeling approach was further validated through two methods of labeling quality evaluation.  
Additionally, the study successfully trained a deep learning model based on the YOLOv5 architecture, capable of detecting defect locations and predicting their depths. The results underscored the potential of the YOLOv5 model, particularly when utilizing phase images generated through the PPT method. The combination of high efficiency and accuracy in this model makes it an invaluable tool in industrial settings for defect analysis.  
Looking ahead, it is worth exploring deep-learning-based recognition of defects with more-complex shapes, such as pyramids, wedges, or tapered cylinders. These shapes introduce novel challenges in thermal behavior analysis, presenting an opportunity to push the boundaries of current methodologies. Exploring these complex shapes could lead to significant advancements in the field of infrared thermography and defect characterization.  
Z.W.: methodology and manuscript drafting, A.O.: methodology and supervision. B.V.: supervision, X.M.: supervision. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the European Regional Development Fund (ERDF) and LUFO-Projekt RapidEF-Enablers from Fraunhofer IZFP institute.  
Not applicable.  
The dataset can be found on the website of Kaggle with the URL: [[https://www.kaggle.com/datasets/ziangwei/irt-pvc-depth](https://www.kaggle.com/datasets/ziangwei/irt-pvc-depth)]([https://www.kaggle.com/datasets/ziangwei/irt-pvc-depth](https://www.kaggle.com/datasets/ziangwei/irt-pvc-depth)) (accessed on 21 November 2023). The dataset is available for academic and research use only. Papers using this dataset are kindly requested to refer to this paper.  
Support from Fraunhofer IZFP is gratefully acknowledged. [PERSON] is acknowledged for the data acquisition. [PERSON] is acknowledged for support in the method's implementation. [PERSON] is acknowledged for supporting the publication of the dataset.  
The authors declare no conflict of interest.
## 4 Characterization of Real-Life Signals
### Experiments  
In subsequent experiments, the Wi-Fi signal is captured in an office environment, including two access points (AP's) at 5540 MHz (Channel 108) and on average 20 associated work stations; the LTE signal is captured from a nearby base station, operating in FDD (Frequency Division Duplex) mode at 806 MHz, around the Ghent area of Belgium; finally, the DVB-T signal is collected from the local TV broadcasting station at 482 MHz.
## 2 Signal and Frequency Offset Model  
For the frequency estimation of complex exponential signals in noise, the received signal can be written as [11]  
\[\begin{split} y(n)&=x(n)+w(n)\\ &=Ae^{(j(2\pi\frac{f}{f_{s}}n+\phi))}+w(n),n=0,1,\ldots,N-1,\end{split} \tag{1}\]  
where \(x(n)=Ae^{(j(2\pi\frac{f}{f_{s}}n+\phi))}\) is the useful signal, \(A\) is the signal magnitude, \(\phi\) is the initial phase, \(f_{s}\) is the sampling rate, \(N\) is the number of signal samples, and \(f\) is the signal frequency that needs to be estimated. \(w(n)\) represents complex Gaussian white noise following a normal distribution \(N(0,\sigma^{2})\).
# Using Statistical Shape Models to Optimize TKA Implant Design
## 2 Materials and Methods
### Use of the SSM
#### 2.3.2 Use of SSMs for Design Input  
SSMs were used as follows to obtain information for design input. To define the best possible contour for a tibial implant, the averaged sized Caucasian tibia SSM was cut orthogonal to the mechanical axis at different heights (8, 10, and 12 mm). To determine the optimal trochlea design, the depth and path of the native trochlea (deepest points of the trochlea groove) were extracted from different statistical shape model sizes. The resulting splines were projected on the sagittal plane. To evaluate femoral curvature, a spline at the center of the femoral canal was computed for different femur sizes.
###### Abstract  
This paper addresses the problem of noncooperative spectrum sensing in very low signal-to-noise ratio (SNR) conditions. In our approach, detecting an unoccupied bandwidth consists of detecting the presence or absence of a communication signal on this bandwidth. Digital communication signals may contain hidden periodicities, so we use Recurrence Quantification Analysis (RQA) to reveal the hidden periodicities. RQA is very sensitive and offers reliable estimation of the phase space dimension \(m\) or the time delay \(\tau\). In view of the limitations of the algorithms proposed in the literature, we have proposed a new algorithm to simultaneously estimate the optimal values of \(m\) and \(\tau\). The new proposed optimal values allow the state reconstruction of the observed signal and then the estimation of the distance matrix. This distance matrix has particular properties that we have exploited to propose a Recurrence-Analysis-based Detector (RAD). The RAD can detect a communication signal in a very low SNR condition. Using Receiver Operating Characteristic curves, our experimental results corroborate the robustness of our proposed algorithm compared with classic widely used algorithms.  
cognitive radio; dynamic spectrum access; spectrum sensing; embedding parameters; false nearest neighbors; recurrence quantification analysis Article
# A Wideband Termination Based on Laser-Scribed Lossy Microstrip Line Structures
## 3 Fabrications
### Characterization  
The morphology and microstructures were characterized by a scanning electron microscope (SEM, Gemini500, ZEISS Microscopy, Jena, Germany). The Fourier transform infrared spectroscopy (FTIR) spectra were obtained using a Nicolet Nexus 670 FTIR spectrometerr (Thermo Electron Corporation, Madison, WI, USA). The sheet resistance was obtained from the formula Rs = \(\textit{Rw}/l\), where the resistance R was measured using a source meter (2400 SourceMeter, Keithley Instruments, Cleveland, OH, USA), and \(w\) and \(l\) are the width and length of the electrode, respectively. The RF performance of the terminal was examined by a vector network analyzer (ZVA67, Rohde & Schwarz, Muenchen, Germany) from 0.2 GHz to 30 GHz.  
Figure 5: The S-parameters of the terminations as a function of the sheet resistance of rGO at double-end configurations, (**a**) reflections \(\mid\)S111\(\mid\), and (**b**) transmissions \(\mid\)S211\(\mid\).
## 3 Formulation of Finite Element Model
### Finite Element Model  
Figure 2 illustrates the finite element (FE) model for the fiber-metal laminates (FMLs) comprising a polypropylene (PP) fiber/PP matrix composite (Curv from Propex Fabric), subjected to impact by a hemispheric-nosed projectile at varying impact energies. A nitrogen gas gun was utilized to evaluate the high-velocity impact response of FMLs. The cross-sections were polished and subsequently analyzed using optical microscopy [23]. The impactor has a mass of 46.7 g with 12.7 mm diameter. The panel (\(100\text{ mm}\times 100\text{ mm}\times 5.73\text{ mm}\)), clamped in a fixture with an 80 mm \(\times\) 80 mm opening, was composed of 4 layers of woven PP fiber-reinforced PP composite (PP/PP) and 5 layers of 2024-T3(from Alcan) aluminum alloy [23], arranged as [Al/PP/Al/PP/Al/PP/Al/PP/Al]. The total thickness of the panel was 5.73 mm, with each layer of 2024-T3 aluminum alloy being 0.8 mm. To enhance computational efficiency, a fine mesh with an element size of 1.0 mm \(\times\) 1.0 mm was employed in the impact region. The general contact algorithm was employed to simulate the contact interactions, incorporating a friction coefficient of 0.2 within the contact formulation. Assuming the impactor maintains its rigidity throughout the collision, C3D8R solid elements were utilized for all components. A fixed boundary condition was applied to the supporting area in the finite element models.
# Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things
## 4 Markov Decision Process
### Transition Probability  
\(P[S^{\prime}|S,A]\) is the probability of transition from the current state \(S\) to the next state \(S^{\prime}\) when the mobile device chooses action \(A\). The transition probability from the current state \(S=[D,M,A]\) to the next state \(S^{\prime}=[D^{\prime},M^{\prime},A^{\prime}]\) can be denoted by  
\[P[S^{\prime}|S,A]=P[D^{\prime}|D]\times P[M^{\prime}|M,A]. \tag{6}\]  
When the mobile device \(i\) has no connection to the relay node (\(A=0\)), data in the memory state space \(M\) do not decrease. On the other hand, data in the memory state space \(M\) decrease if the mobile device connects to the relay node. Therefore, the corresponding probabilities can be represented as  
\[P[D^{\prime}|D,M,A=0]=\begin{cases}1,&\text{if }M^{\prime}=M\\ 0,&\text{otherwise}\end{cases} \tag{7}\]  
and  
\[P[M^{\prime}|D,M,A=1]=\begin{cases}1,&\text{if }M^{\prime}=M-1\\ 0,&\text{otherwise}.\end{cases} \tag{8}\]
# Human-Like Walking with Heel Off and Toe Support for Biped Robot
## 2 Methods
### The Biped Robot Model  
The model considered in this paper is a planar seven-link biped robot of which the movement is constrained in the sagittal plane. The biped robot has two three-segmented legs connected by the right and left hip joints at the torso. Each leg is composed of a thigh, shank, and foot that are interconnected by the knee joint and the ankle joint. Each joint is regarded as an independently actuated ideal or theoretically frictionless revolute joint with only one degree of freedom (DOF). Therefore, the biped robot has a total of six internal DOFs, namely, one in each hip joint, one in each knee joint, and one in each ankle joint.  
Figure 2 illustrates the biped robot model. The right and left legs are supposed to be identical and exchange their roles between steps. Thus, the properties of the two legs are pairwise equal. \(l_{i}\) and \(m_{i}\) represent the length and mass of link \(i\), respectively. The distance between the local center of mass (CoM) and the lower end of link \(i\) is indicated by \(\eta_{i}l_{i}\) where \(0<\eta_{i}<1\). Having six actuated joints, the biped robot dynamics is driven by six joint driving torques, i.e., \(\tau=[\tau_{1},\tau_{2},\tau_{3},\tau_{4},\tau_{5},\tau_{6}]^{\mathrm{T}}\). At any time during a step, the configuration of the biped robot can be defined by a set of generalized coordinates, namely, \(q=[\theta_{1},\,\theta_{2},\,\theta_{3},\,\theta_{4},\,\theta_{5},\,\theta_{6 },\,\theta_{7}]^{\mathrm{T}}\). The base coordinate frame is attached to the base of the robot, i.e., the tiptoe of the stance foot. According to the kinematics theory of robot, \(\theta_{i}\) (\(i=1,\,2,\,3,\,4,\,5,\,6,\,7\)) indicates the amount of rotation around the \(z\)-axis needed to align the axis of link \(i-1\) with the axis of link \(i\). Specifically, \(\theta_{1}\) represents the angle around the \(z\)-axis from the \(x\)-axis of the base frame to the axis of the stance foot of the biped robot, and \(\theta_{2}\) to \(\theta_{7}\) represent the angles between each two adjacent links from the stance foot to the swing foot. Positive rotation follows the right hand rule. In this case, the direction of counterclockwise rotation about the \(z\)-axis is assumed positive.  
Figure 1: A single gait cycle of the human walking pattern. In the single support phase, the heel of the stance foot lifts off the ground and rotates around the toe, which is named “heel off and toe support”.
## 1 Introduction  
The passive Q-switched and mode locking operation of lasers using saturable absorbers to produce intense ultrashort pulses is a well-settled and understood technique [1], both from the theoretical and the experimental point of view. Among the systems that can act as saturable absorbers, we find dyes, SESAMs (semiconductor saturable absorber mirrors) [1] and, more recently, carbon nanotubes (CNTs)and graphene. One advantage of the last two in relation to dyes and SESAMS is that they are not wavelength specific, so you can use the same absorber for a large variety of lasers. Carbon nanotubes have already been used to produce Q-switched [2] and mode-locked [3; 4; 5; 6] laser pulses in bulk solid-state lasers. CNTs have been also used in fiber lasers using single-walled CNTs for the high-order harmonic mode locking [7] or single or double-walled CNTs for passive mode locking of erbium-doped fibers [8].  
More recently, graphene has been employed for both temporal regimes, too, both in monolayer [9; 10; 11] and multilayer forms [12; 13; 14], in bulk solid-state lasers. While in fiber lasers, multilayer graphene [15] or graphene nanoparticles [16] have been used to produce mode locking in erbium-doped fiber lasers.  
Both temporal regimes are not always easy to isolate experimentally from each other. Q-switch-modulated mode locking can appear or simply Q-switch can hinder mode locking. Hence, when looking for mode locking operation in a laser using a saturable absorber to produce ultrashort laser pulses, some attention should be paid to the possibility of Q-switched operation, which can make it more difficult to find pure mode locking. For example, the titanium:sapphire laser, which has optimal performance in mode locking, can be affected by the appearance of Q-switch in certain cavity designs [17; 18; 19]. In this paper, we want to evaluate the convenience of using carbon nanotubes or graphene in diode-pumped lasers, evaluating their performance as saturable absorbers for Q-switched operation. We will use as an example Nd:YLF laser. The choice of this type of laser is due, on one side, to the fact that it can be transversally pumped (side-pumped lasers have the advantage of a higher average output power) and, on the other side, because we have obtained good agreement between experimental and theoretical results in this type of laser in Q-switched operation using a monolayer graphene SAM [10].
# A Phase Error Correction System for Bioimpedance Measurement Circuits
## 6 Conclusions  
In this study, we introduced a Phase Error Compensated Synchronous Demodulation system (PECSD), a discrete component realization of the SD bioimpedance system featuring automatic phase error correction. Our work focused on eliminating a fundamental, systematic, and often unreported source of errors in bioimpedance measurement systems, particularly in phase shifts between the VCCS output current and I,Q demodulation signals in the Synchronous Demodulation/Detection (SD) architecture.  
The developed phase compensation method features an auto-adjustable delay block with an all-pass filter. The PECSD system achieved a substantial reduction in phase error at \(\leq\)5% across all RC loads compared with the uncompensated SD system, where errors  
Figure 15: Resense Lumped Impedance model.  
\begin{table}
\begin{tabular}{l l} \hline \hline Architecture & Discrete \\ \hline PCB Active area & 130 mm \(\times\) 162 mm \\ \hline Supply Voltage & \(\pm\)15 V \\ \hline Bandwidth & 1 kHz–300 kHz \\ \hline Phase Measurement accuracy & \\ \hline \% Error of Experimented RC loads @ full bandwidth. & \(\leq\)5\% with phase compensation \\  & \(\leq\)21.1\% without phase compensation \\ \hline Settling time @ 98\% of final value & 3.965 s with phase compensation \\  & 3.53 s without phase compensation \\ \hline Maximum output generated noise & 91 nv/Hz with and without phase compensation \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of PECSD measured performance.  
could reach up to \(\leq\)21.1%. This improvement in measurement accuracy will significantly improve SD systems' applicability to diagnostic applications, particularly in scenarios where tissue phase values serve as crucial diagnostic indicators, such as in cancer diagnosis and bioelectrical impedance myography. This system applies to a wide range of applications beyond bioimpedance (e.g., electrical impedance spectroscopy or tomography), and works for a wide range of loads. Specific loads were chosen for their relevance to specific biomedical applications (e.g., skin cancer and other skin conditions, i.e., rubber [2; 35]) and because 1-5 k\(\Omega\) load values are used in the vast majority of bioimpedance instrumentation research literature [27]. Moreover, with SD impedimetric instrumentation being widely applicable beyond bioimpedance, e.g., electrical impedance tomography and electrochemical impedance spectroscopy, this work is expected to have a wider impact on more accurate electrical impedance sensing systems.  
Conceptualization, I.I.N. and I.F.T.; methodology, I.I.N.; software, I.I.N.; validation, I.I.N.; formal analysis, I.I.N.; investigation, I.I.N.; writing--original draft preparation, I.I.N.; writing--review and editing, I.I.N. and I.F.T.; supervision, I.F.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no competing interests.
# Thermodynamic Analysis and Performance Evaluation of Microjet Engines in Gas Turbine Education
## 2 The Basis of Study  
In the performance evaluation and analysis of micro jet engines, the initial step typically involves creating an engine data library. This library compiles data from various engine manufacturers, different engine models, and different thrust classes, making it accessible and useful for performance calculation models. These models can then be used to calculate various parameters or performance metrics. Given that the engine data are experimental results from different engine models tested with various laboratory test benches equipped with dedicated instrumentation lines and specific measuring points for pressure and temperature at particular engine cross-sections [10; 19], the data must be standardized to a common reference.  
The second step involves organizing the engine data library using common parameters, thereby establishing a general model of the engine's main stations. Typically, the engine's main stations are defined by the cross-sections of the inlets and outlets of the primary components, such as the compressor, combustion chamber, turbine, and jet nozzle. Figure 1 illustrates the general model of the micro jet engine stations for the engines studied.  
In this context, station 1 refers to the compressor inlet, station 2 to the compressor outlet, station 3 to the turbine inlet, station 4 to the turbine outlet and also the jet nozzle inlet, and station 5 to the jet nozzle outlet.  
The third step is to establish an analysis methodology, as the performance evaluation is based on a series of ratios and differences of certain parameters or engine component performance, determined through an analytical calculation model. This calculation model is based on the real Brayton cycle, depicted in Figure 2, which is the reference cycle describing the thermodynamic operation of gas turbine engines. The thermodynamic cycle is characterized by a total expansion process, meaning the jet nozzle outlet static pressure is equal to atmospheric pressure p5 = p0 = 1.01325 bar.  
By following these steps, a systematic and standardized approach to performance evaluation can be achieved, ensuring consistency and reliability in the analysis of different micro jet engines.  
Figure 1: Microjet engine main stations.  
Figure 2: Microjet engine real cycle diagram.