esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Probe Measurement Method for Error Motion of Precision Rotary Stage Based on Reference Plate
## 2 Multi-Probe Measurement Method for Error Motion Based on Reference Plate
### Multi-Probe Measurement Method Based on Reference Plate
\right.. \tag{28}\]  
Reference [16] indicates that when the parameter \(k\) equals \(n(2\pi/\Delta)\pm 1\), both \(\alpha_{k}\) and \(\beta_{k}\) will be zero. This condition renders the coefficient matrix in Equation (26) singular, leading to an unsolvable problem: a phenomenon known as harmonic suppression. Here, \(\Delta\) represents the greatest common divisor (GCD) of \(\varphi\), \(\psi\), and \(2\pi\). Harmonic suppression engenders considerable measurement inaccuracies. To avoid or reduce these errors, meticulous selection of the probe placement angle is crucial so as to either minimize \(\Delta\) or eliminate it entirely. Figure 3 illustrates the behavior of determinant \(W_{k}\) for the coefficient matrix in Equation (26) when \(\varphi=40^{\circ}\) and \(\psi=160^{\circ}\). Since \(\Delta=40^{\circ}\) at this point, Figure 3 distinctly demonstrates the following two key observations: Firstly, when \(k\) takes values of \(9n\pm 1\) (where \(n\) is an integer), the determinant \(W_{k}\) consistently equals zero, confirming the prior mathematical conclusions. Secondly, the figure reveals that in instances when \(k=9n\), \(\beta_{k}\) becomes zero while \(\alpha_{k}\) remains non-zero, hence \(W_{k}\) retaining a non-zero value in these cases. Figure 3: Behavior of determinant \(W_{k}\) (\(\varphi=40^{\circ}\), \(\psi=160^{\circ}\)). Under current angular configuration, \(W_{k}\) equals zero whenever \(k=9n\pm 1\), indicating presence of harmonic suppression.
## 2 The State-of-Art
MIT scientists have recently created a piece of technology to acknowledge human emotion by transmitting Radio Frequency (RF) signals that can obtain emotional data from anyone using a heartbeat extraction algorithm where the RF signal can calculate group segments and detect a person's emotional state by classifying a subsystem [27]. Furthermore, some IoT systems are accessible to continually monitor a patient'scondition to identify their external and internal issues, such as blood pressure, blood sugar, oxygen, and diabetic levels. To create an advanced interactive healthcare system, researchers are currently continuing their research. We have presented an interactive healthcare design in this article that will be of assistance to all types of users.
## 3. Freestanding Position-Based Routing Protocol
### Beacon Rate  
The vehicles (nodes) choose the next hop for sending data or control messages based on information previously exchanged with neighbors. This information is exchanged by means of beacon messages. Consequently, there is a tradeoff between the overhead caused by the beacons and the age of the information in the one-hop neighbors table. As different VANET scenarios have different mobility patterns, the strategy to handle this tradeoff depends on the scenario specific characteristics [26]. Thus, setting an adequate beacon rate is an important task, since overly high beacon rates may lead to unnecessary overhead whereas overly low beacon rates may lead to erroneous next hop selections, (e.g., see [65]). Therefore, setting an adequate beacon rate is an important task that depends on the particular routing protocol features. As an example AODV, GyTAR, LORA-CBF and GPSR use a fixed beacon rate of 1 beacon per second, whereas DDOR uses a variable beacon rate ranging from 0.2 to 0.4 beacons per second.  
The same set of vehicles is available most of the times in highway scenarios, [26]. Therefore, it may be feasible to use a location prediction (LP) algorithm to achieve a relatively low beacon rate. Consequently, a LP algorithm has been implemented within FPBR in order to predict the gap between the stored and the actual position of neighbor vehicles. This LP algorithm makes possible to achieve beacon rates of 0.4 when considering a free flow vehicle density (this density implies that the vehicles will reach its maximum speed [66,67]), and beacon rates of 0.3 for other vehicle densities.
# Cm-Level Photonic-Crystal-Like Subwavelength Waveguide Platform with High Integration Density
###### Abstract  
In this paper, the cm-level photonic-crystal-like subwavelength waveguide platform is developed and analyzed by using the finite-difference time-domain method. The configuration can be considered as a hybrid waveguide combining with the advantages of a metal-dielectric-metal waveguide and a photonic crystal waveguide. The symmetric and high reflection effect of metallic sidewall and the effect of the photonic crystal structure on the light-guiding mechanism and integration characteristics of the waveguide are systematically investigated. The results reveal that the cm-level photonic-crystal-like waveguide platform provides subwavelength confinement and very low propagation loss with the isolation more than 30 dB, which are promising for high-density photonic integration. The tradeoff between integration density and propagation loss is optimized. In addition, a T-shaped power splitter based on the waveguide platform is proposed. The excess loss of the T-shaped power splitter is less than 0.4 dB. A set of passive components can be exploited on the proposed cm-level photonic-crystal-like subwavelength waveguide platform in future work to constitute the large-scale integrated photonic systems.  
Keywords:integrated optics; subwavelength waveguides; photonic crystals; waveguide splitter +
Footnote †: journal: Applied Physics Letters
## 3 Kinematic Analysis
### Position and Attitude Analysis  
The positions of spherical joints \(\mathbf{B}_{i}\) and \(\mathbf{A}_{i}\) relative to their respective coordinate frames \(\{B\}\) and \(\{A\}\) can be expressed by the constant vectors, \(\mathbf{B}_{i}\) and \(\mathbf{A}_{i}\), which can be de described in homogeneous coordinates as:  
\[\begin{array}{l}\mathbf{A}_{i}=\left[\begin{array}{ccc}0&y_{Ai}&z_{Ai}&1\\ 0&y_{Bi}&z_{Bi}&1\end{array}\right]^{T}_{T}\\ \mathbf{B}_{i}=\left[\begin{array}{ccc}0&y_{Bi}&z_{Bi}&1\end{array}\right]^{T} \end{array} \tag{3}\]  
The layout diagram of thrust cylinders is shown in Figure 2, and \(y_{Ai},z_{Ai},y_{Bi},z_{Bi}\) can be calculated as follows:  
\[\begin{array}{l}y_{Ai}=R_{0}\cos\eta=R_{0}\cos\frac{2(i-1)\pi}{\eta}&y_{Bi}= R_{0}\cos\eta=R_{0}\cos\frac{2(i-1)\pi}{\eta}\\ z_{Ai}=R_{0}\sin\eta=R_{0}\sin\frac{2(i-1)\pi}{\eta}&z_{Bi}=R_{0}\sin\eta=R_{0} \sin\frac{2(i-1)\pi}{\eta}\end{array} \tag{4}\]  
where \(R_{0}\) denotes the radius of the distribution of the cylinders; \(\eta\) denotes the angle between the \(i\)th hydraulic cylinder and the \(y\)-axis; \(i=1,2,\,\dots\,\,n\).  
According to the geometric characteristics of the parallel mechanism and the homogeneous transformation matrix, the position vector of \(B_{i}\) with the reference in global frame \(\{A\}\) can be described as:  
\[^{A}\mathbf{B}_{i}=\frac{A}{B}T\mathbf{B}_{i} \tag{5}\]  
Then, the following relationship can be established by using the vector construction method,  
\[\mathbf{A}_{i}{}^{A}\mathbf{B}_{i}={}^{A}\mathbf{B}_{i}-\mathbf{A}_{i} \tag{6}\]  
\[(\mathbf{L}+\mathbf{l}_{i}){}^{A}\mathbf{u}_{i}=\frac{A}{B}T\mathbf{B}_{i}-\mathbf{A}_{i} \tag{7}\]  
where \({}^{A}\mathbf{u}_{i}=\frac{A}{B}\pi^{B}\mathbf{B}_{i}-\mathbf{A}_{i}\) denotes the unit direction vector of the \(i\)th hydraulic cylinder branch chain in the global frame \(\{A\}\).  
Considering Equations (6) and (7), the stroke of the \(i\)th cylinder under a given pose of shield machine can be obtained as:  
\[\mathbf{l}_{i}=\sqrt{\binom{A}{B}T\mathbf{B}_{i}-\mathbf{A}_{i})^{T}\binom{A}{B}T\mathbf{B}_{ i}-\mathbf{A}_{i})}-L \tag{8}\]  
Thus, the strokes of cylinders can be determined from Equation (8) when the structural dimensions of the thrust mechanism and pose parameters (\(x,\,y,\,z,\,\psi,\,\theta,\,\phi\)) are given.  
Figure 2: Layout diagram of thrust cylinders.
## 4 Discussion  
In the IR range, losses are below 6 dB (measured at 1550 and 2640 nm), while, in the visible range, we observe an increment of about 20 dB (measured at 532 and 635 nm). Insertion losses are the sum of two contributions: propagation losses, probably due to different scattering processes along the WGs; coupling losses, due to spatial mode mismatch; and Fresnel reflections at input/output facets. Coupling losses cannot be easily estimated, due to the complexity of the WGs' geometry, preventing reliable numerical calculations. Nevertheless, we can set an upper limit for the coupling losses, assuming them to be equal to the coupling losses at 2640 nm, since at this longer wavelength, propagation losses are expected to be the lowest ones. As we indicate from the results obtained in Section 3.2, the lengths of the guides used in the test were below the effective length (6 mm), and, therefore, we assume that the propagation losses were negligible, and the losses measured are dueto coupling loss only. Losses measured at 1550 nm were close to those reported in other studies, as such 5.6 dB for Si WGs at 1500 nm with a 106 nm \(\times\) 475 nm core [27] or 8 dB for SiN WGs at 1540 nm with a cross-section of \(1.11\times 1.11\) um\({}^{2}\)[28].  
Using FEM, we simulated different lengths for every WG and, consequently, we evaluated the propagation constant. In these calculations, the number of layers, the width, or the length could be fixed. In particular, considering 6-mm guides with three SiO\({}_{2}\) interlayers of different widths, a good agreement in terms of order of magnitude was obtained between simulation (Figure 8) and experimental evaluation of propagation losses (Figure 11). The measured values show a loss reduction at longer wavelengths. Furthermore, a slight increase in transmission for wider WGs was observed. Such increments are associated with better light coupling, due to the wider lateral dimensions.  
Furthermore, as can be seen in Figures 7 and 11, this particular geometry, optimized for single-mode waveguiding between 1 and 3 um, shows low losses and single-mode behavior also at shorter wavelengths, around 1.5 um. This is due to a trade-off between the width of the guide, the number and thickness of the multilayers, and the scattering losses. The FEM simulations proved to be a very powerful tool to design this kind of photonic structure. Indeed, a careful modelling of multi-wavelength guides will enable modal phase-matching towards more efficient non-linear processes or for integrated photonic circuits useful in quantum optics, communication, spectroscopy, and sensing experiments operating simultaneously among NIR and MIR regions.
# Wideband Vibro-Acoustic Modulation for Crack Detection in Wind Turbine Blades
## 2 VAM Model for Detecting Cracks in WT Blades
### Extract Non-Linear Parameter Derived from the Published Data  
There are many publications about the amplitude frequency response measured for blades, both with and without cracks, and some of them are presented in the Table 1. For the demonstration of the suggested model ability to predict VAM parameters, we have chosen several examples demonstrating clear frequency response variations in the damaged blades.  
Figure 5 presents the frequency response measured in a test using an 8-foot blade section [8]. The interpolations of the measured response by Equation (3) are represented by dashed lines. The resonance frequency of the model was chosen to be the same as the measured resonance frequency, and the Q factor of the model was chosen as it provided the minimal averaged difference between the experimental frequency response and the interpolation.  
This interpolation allows the determination of the resonance frequency and Q factors for a blade without a crack \((Q_{2},f_{2})\) and with a crack \((Q_{1},f_{1})\)  
For rough estimation of the non-linear parameters, it is assumed that the non-linear parameters of an uncracked blade are closely aligned with those of a cracked blade when the crack is nearly completely closed. It is speculated that under particularly high loads, both the resonance frequency and the Q factor could exhibit variations, which reside in the ranges \(f_{1}-f_{2}\) and \(Q_{1}-Q_{2}\), respectively. To account for the partial closure of the crack due to oscillating blade rotation loads, a minor parameter \(\gamma\) is introduced. This parameter indicates which part of the crack is closed under the influence of the alternating load. In this model, \(\gamma\) is considered to be quite small, having an estimated value of around 0.1 for the estimation of the non-linear parameters presented in Table 2. This methodology allows the provisional estimation of the parameters \(\alpha\) and \(\beta\) of the non-linear model.  
\[\alpha=\gamma(f_{1}-f_{2})/f_{2}, \tag{7}\]  
Figure 5: The frequency response changes for the test using an 8-ft blade section [8]. The blue solid lines show experimental data for intact and damaged states, and red dash lines are interpolated by Equation (3) for the intact and damage status damaged of the blade.  
\[\beta=\gamma(Q_{1}-Q_{2})/Q_{2}, \tag{8}\]  
The same interpolation of the resonance responses measured for the 52-m WT blade [6] is presented in Figure 6.  
The results of these estimated parameters are presented in Table 2.  
In this exploration of VAM effects, we focus on the following two distinct scenarios:  
Active VAM, in which the external excitation is produced by a harmonic wave. This method could involve a shaker, as presented in [6], or a piezoceramic actuator, as used in [8]. Passive VAM uses the natural excitation of blade vibrations, which forms the basis for the OMA, as described in [23].
## 3 Preparation for Fault Localization
### Process of Fault Localization  
As shown in Figure 5, the overall objective of this study is to provide developers with test results that include the potential memory location of each fault. This is provided with the symbol list in each partition in the form of a ranking according to fault suspiciousness. Figure 5 relates to the section displayed within the dotted line in Figure 1; it describes a method by which a tester can indicate the fault candidate region to developers. There are two memory-partition methods in the overall process of fault localization: 1 the memory-map method, and 2 the stack method without a memory map (Figure 5). We firstly explain memory-map partitioning, and then that by the stack.  
Figure 4: Example of memory partitioning by memory map and stack.  
The fault-localization method comprises four steps (Figure 6). As mentioned previously, the third of the four steps (highlighted by a dashed line) is the memory-partitioning step, by either the memory map or the stack. As shown by the solid line in the fourth step in Figure 6, the final result is a fault-suspiciousness ranking based on that of each partition. The individual steps are described below.
* **Capturing memory information**: this is done by CAN communication during program execution in the black-box testing environment using the HiL simulator; memory information refers to the set of values at each address [20]. In addition, files are generated related to the configuration information of the memory section (section file), the allocation information of each symbol in the memory (symbol file), and the function call relationship (call file) from the automotive software via the binary utility [25]. These files are used in the memory-partitioning step.
* **Identifying the set of updated addresses**: a list of addresses updated each time is extracted based on the acquired memory information.
* **Partitioning the data section**: this step can be performed in parallel with the second stage; the memory map and the stack are used to partition the memory.
* **Fault localization by FS**: fault suspiciousness (FS) is calculated by applying the partitioned memory obtained in the second step to the memory-update information obtained in the third step.  
Figure 5: Fault localization by partitioning memory in two ways for automotive software.  
Figure 6: Process of fault localization.  
The FS ranking of partitions is then determined based on the calculated FS. The number of fault candidates is reduced because the final result is provided in terms of the FS ranking of the top 10 partitions, is provided with the symbol list of fault candidates.
## 4 Performance Evaluation in Edge Scenarios
### Performance Under Realistic Workloads  
In this section, we aim to evaluate the performance and applicability of SGX in the context of IoT workloads. IoT systems often involve extensive data caching and fast access requirements, which impose stringent demands on both performance and data security. SGX offers hardware-based TEE that ensures the confidentiality and integrity of sensitive data while maintaining system flexibility and scalability. To simulate a representative IoT workload, Memcached is selected as the target application for evaluation. Memcached, a lightweight and widely adopted in-memory caching system, is commonly utilized for efficient key-value storage and retrieval in distributed systems with high-speed data access requirements. Its workload characteristics closely align with the caching and access patterns frequently observed in IoT scenarios. By employing Memcached as the benchmark, this study replicates typical IoT workloads to systematically analyze the impact of SGX on both performance and security under realistic operating conditions. The results aim to provide meaningful insights into the feasibility and practicality of deploying SGX in real-world IoT environments.  
The Memcached environment is configured for both Gramine and bare-metal Linux, with a focus on evaluating the relationship between throughput and latency. Memcached runs inside the SGX enclave, and load testing is performed using the memtier_benchmark [71] from a separate terminal. The default configuration uses four threads, and the load is increased by incrementally raising the number of concurrent clients per thread. The resulting performance metrics are presented in Figure 10. It is observed that the maximum throughput of Gramine is only about 30% of that achieved on bare-metal Linux, and its relative performance is even lower under light workloads. This indicates that in the Memcached scenario, the use of SGX still introduces significant performance overhead. Frequent encryption and decryption operations, along with transitions in and out of the enclave, severely limit the performance of edge devices.
## 4 Methodology
### Network Bandwidth Prediction  
As a preprocessing step, data were scaled to ensure stable training of the network bandwidth prediction model. Given the large variations in network bandwidth across different scenarios, min-max scaling was applied to normalize the data range to values between zero and one. To determine the proposed model's hyperparameters, we measured the normalized root meant squared error (NRMSE) values for different layers, epochs, learning rates, and batch sizes. Figure 6 presents the performance measurement results for different hyperparameter settings. As shown in Figure 6a, the NRMSE value increased with the number of layers, whereas as shown in Figure 6b, it decreased with the number of epochs. As shown in Figure 6c,d, the highest prediction accuracy was achieved when the learning rate and batch size were 0.001 and 32, respectively. Based on these results, the proposed GRU model's structure was defined as shown in Figure 7, and the hyperparameters were set as shown in Table 1. The input size corresponds to the size of the input data, which were the network bandwidth data in this study. The hidden state refers to the dimensionality of the hidden state vector, which the GRU layer passes from each time step of the sequence data to the next. In this study, the hidden state was set to two. The number of layers indicates the depth of the model, with one layer used in this study. The epoch count and learning rates were set to 400 and 0.001. The sequence length defines the length of the input sequence used for predicting the next value using the previous 20 data points, and the batch size was set to 32. The output size refers to the number of output values, with the only output in this case being the predicted network bandwidth. We calculated the training phase time complexity of the GRU-based network bandwidth prediction model. According to [29; 30], the time complexity of one gradient step of the GRU is O(\({T_{h}}^{2}+Td_{h}d_{i}\)), where T is the length of the input sequence, \(d_{h}\) is the dimension of the hidden state, and \(d_{i}\) is the dimension of the input.  
Figure 4: Bandwidth measurement results.  
Figure 5: Ratio of measurements recorded as zero among total measurements.
## 2 Theoretical Background
### Continuity Conditions  
The continuity conditions regarding the displacement and the rotation of the bonded section of the system of the two sub-beams are different for each of the three models (denoted by \(m\)). In this study, three different models of joints are considered: the rigid joint model (\(m=1\)) in which both translations and rotation are not allowed, the semi-rigid joint model (\(m=2\)) in which only the translations are not allowed, and the flexible joint model (\(m=3\)) where both are allowed. Figure 3b presents the resulting moments and forces and the interface's boundary conditions. To distinguish the variables (displacements, rotations, etc.) among the three models, the superscript \((ms)\) is used when a variable of the uncracked part of a sub-beam \(s\) is considered using each model \(m\). The interface models refer only to the interface, and therefore, the variables that refer to the cracked part of the system do not depend on the assumptions of the model and are distinguished using the superscript \((s^{\prime})\). Finally, the value of \(s=0\) is used for variables that correspond to the total beam. The continuity conditions of the three models are based on [8,28,30].
# Prediction of Deflection of Shear-Critical RC Beams Using Compatibility-Aided Truss Model
###### Abstract  
This study proposes a method for predicting the deflection of shear-critical reinforced concrete (RC) beams. Shear deterioration of shear-critical RC beams occurs before flexural yielding. After shear deterioration occurs in the shear-critical RC beams, the deflection caused by shear is greater than the flexural deflection obtained from the elastic bending theory. To reasonably predict the deflection of shear-critical RC beams, it is necessary to evaluate deflections due to shear as well as flexure. In this study, the deflections produced by flexure and shear were calculated and superposed to evaluate the deflection of shear-critical RC beams. The method recommended by ACI 318-19 was employed to calculate the flexural deflection, and a compatibility-aided truss model able to calculate the shear stress and shear deformation at each load stage was used to consider the shear deflection. A comparison of the experimental and analytical results showed that the proposed analytical method can effectively predict the deflection of shear-critical RC beams.  
deflection; shear; flexure; truss model; RC beams +
Footnote †: 10.000