esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Chipless Hybrid RFID Sensor for Metal Crack Detection
## 3 Results and Discussion
Advanced signal processing techniques, such as multi-frequency analysis and machine learning algorithms, will significantly improve the system's ability to distinguish between different sources of frequency shifts for precise defect identification. The chipless RFID tags have the potential to be printed on a metallic surface using a flexible, adhesive substrate. Three-dimensional printing of the RFID antennas on metal surfaces can be performed by inkjet printing using conductive ink. Silver/copper nanoparticle ink can be used for inkjet printing for prototyping. Conductive inks must maintain a low resistance despite bending or deformation, while the adhesive substrate should resist printed coil cracking under mechanical stress. The impacts of the material and electrical properties of conductive inks on crack detection need to be investigated. For practical SHM applications, fully printable chipless RFIDs can be applied as metal coatings in the future for continuous monitoring to detect cracks. These tags are low-cost, with simple designs. The reduced size of the proposed tag makes it more attractive to mount in hard-to-reach areas and small areas. Another challenge is to have a portable reader set up to monitor the tag. Machine learning algorithms can be used to provide a better quantitative approach to classify the defects and characterize them efficiently based on feature extraction methods. These algorithms will help in separating the desired signal from the noise that might be caused due to reflections from other metallic objects in large structures. The proposed tag is used for sensing; more bits in terms of the resonant frequency can be added to identify multiple tags in the same environment. Table 1 shows the work done on chipless sensors for crack detection, designed on different substrates, and their respective results.
## 2 Problem Formulation and Modeling
### Convex Function Construction
(i) Property 1, which establishes the intersection condition between parametric circles and their common point \((x_{f},y_{f})\), where the first term of the objective function in Equation (14) corresponds to the 2-norm of geometric congruence errors; (ii) Property 2, which specifies that \((x_{f},y_{f})\) coincides with the trajectory's terminal waypoint, where the second 2-norm term quantifies terminal state deviation. \[V_{s_{2},s_{1},s_{0}}=\underbrace{\sum\limits_{I^{s}_{i,j},I^{s}_{j,k}\in \mathbb{C}_{s_{2},s_{1},s_{0}}}\frac{1}{2}||I^{s}_{i,j}-I^{s}_{i,j}||^{2}}_{ \text{$V^{\prime}$-Norm with other intersections}}+\underbrace{\sum \limits_{I^{s}_{i,j}\in\mathbb{C}_{s_{2},s_{1},s_{0}}}\frac{1}{2}||(x_{f},y_ {f})-I^{s}_{i,j}||^{2}}_{\text{$V^{\prime}$-Norm with desired position}} \tag{14}\]  
Thus, the parameter estimation problem becomes the one shown in Equation (15), which is solved so that the sum of two squares of the 2-norms is taken as the smallest combination \(s_{2},s_{1},s_{0}\) of intersection points \(I^{s_{12}}_{1,2},I^{s_{1}}_{2,3},I^{s_{0}}_{3,1}\) and the corresponding target end  
Figure 5: The form of the intersection is affected by the parametric circle (there may be 2 intersections, 1 double root, or a conjugated double root). state \(x_{f},y_{f}\), and \(\theta_{f}\). Obviously, the optimization goal \(V\) is positively definite, and if \(\hat{V}\) is negatively definite, and the problem model is a convex optimization problem; this would ensure that any local minimum is also a global minimum. Thus, the optimization problem for parameter estimation takes the form shown in Equation (15). That is, the sum of the distances between the intersections is minimal, and the sum of the distances between the intersections and the final state positions is also minimal. To make the sum of squares of the norm representing the distances equal to 0, two conditions need to be met simultaneously: 1. the selected intersection group \(I_{1,2}^{s_{1}},J_{2,3}^{s_{1}}\), \(I_{3,1}^{o}\) is correct; 2. the solution is precisely the corresponding target ending states \(x_{f},y_{f}\), and \(\theta_{f}\). \[\arg\min_{x_{f},y_{f},\theta_{f}}V_{s_{2},s_{1},s_{0}}=\arg\min_{x_{f},y_{f}, \theta_{f}}(V^{d}+V^{o}) \tag{15}\]
# All-Optical Modulation and Ultrafast Switching in MWIR with Sub-Wavelength Structured Silicon
## 5 Demonstration of the Ultrafast Switching
The \(\Delta R/R_{0}\) recovery rate was determined by the combination of the development of both \(\Re(\epsilon_{eff})\) and \(\Im(\epsilon_{eff})\) as a function of time, and their values determined the level at which \(\Delta R/R_{0}\) would saturate. We noted that the carriers did not recombine during this short time period, and therefore, the modulator switching speed did not depend on the recombination process. However, the modulator operation/repetition frequency was limited by the recombination time required for the modulator to return to the unexcited state before the next switching event happened. In our previous work, we showed that the complete recombination of the excited carriers occurred on the nanosecond time scale [35]. Figure 3: (**a**) The change of the reflectance \(\Delta R/R_{0}\) as a function of the arrival time difference, \(\Delta t\), between the pump and signal pulses, for a pump fluence of 10 mJ/cm\({}^{2}\). The solid blue, red and dashed black lines are the experimental results for the bulk, structured silicon and the best fitting, respectively. (**b**) The evolution of the real (blue) and imaginary (red) part of the dielectric function as a function of \(\Delta t\), reconstructed from the experimental data. The black dashed curve reproduces that shown in Panel (**a**) to assist with the interpretation. (**c**) The variation of the carrier concentration \(N\) (blue) (at the sample surface) and scattering rate \(\gamma\) (red) as a function of \(\Delta t\). The vertical dotted line indicates the condition of \(\epsilon\to 1\) when the reflectance reaches its minimum.
## 3 Experimental Validation of the Mechanical Amplifier
### Preliminary Dynamic Simulations  
The final step is to analyze the dynamics of the entire amplifier in order to determine its resonant frequencies. They must be outside the application's range of interest (i.e., much higher than 15 Hz) in order for the structure to fulfill its purpose. Figure 17 illustrates the resonance frequencies of the mechanical amplifier, with its fixed constraint positioned at the lower section. This constraint corresponds to the attachment point intended for a future fixed platform, which is designed to incorporate three such mechanisms. The first frequency occurs at 156.02 Hz, which is well above the frequency range of the application, thus confirming the capability of the structure to perform its scope.  
One must observe that a complete dynamic model includes the impedances of the piezo actuator, the compliant mechanism, and the upper section of the device. The resonance frequency of the piezo actuator is specified in the technical data sheet at approximately 35 kHz. According to the piezo actuator theory, for materials with significant damping, the system behaves purely as a compliance up to at least 50% of the resonance frequency. If the damping is minimal, this behavior extends up to 70% of the resonance frequency.  
The simulation shown in Figure 17 indicates a first resonance frequency of 156 Hz. Because the utilized material is steel with a low internal damping, the mechanism could be operated up to a frequency of 70-80 Hz, assuming it behaves purely as a compliance. However, our primary area of interest is limited to a maximum of 20 Hz, specifically for tremor compensation.  
We must point out that these results are currently based solely on software simulations. Therefore, they require experimental validation, which is a primary focus of future research. Additionally, the differences between the dynamic response of the single mechanical amplifier and the combined dynamic response of the actuation subsystem (including the mechanical amplifier, end caps, and piezoelectric stack) must be thoroughly investigated, as well. However, the above simulation confirms that the resonant frequency is well above the required frequency band. Finally, compensation tests are necessary in order to evaluate the performance of the proposed solution.
# Structural Adversarial Variational Auto-Encoder for Attributed Network Embedding
## 4 Experiments
### Main Results
#### 4.3.1 Link Prediction  
The experimental results on the link prediction task are reported in Table 3. We can observe that SAVGAE achieves outstanding performance: consistently performs better than all baseline models. Compared with all the baselines, SAVGAE increased the AP score from around 2.9% compared with ARVGE, 3.9% compared with VGAE, 13.1% compared with VGAE without node features; 14.3% and 12.3% compared with Spectral Clustering and DeepWalk respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **\#Nodes** & **\#Edges** & **\#Attributes** & **\#Lables** \\ \hline Cora & 2708 & 5429 & 1433 & 7 \\ Citeseer & 3327 & 4732 & 3703 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Cora**} & \multicolumn{2}{c}{**Citeseer**} \\ \cline{2-5}  & **AUC** & **AP** & **AUC** & **AP** \\ \hline SC & 84.6 & 88.5 & 80.5 & 85.0 \\ DW & 83.1 & 85.0 & 80.5 & 83.6 \\ node2 vec & 84.1 & 87.2 & 83.7 & 85.3 \\ GAE* & 84.3 & 88.1 & 78.7 & 84.1 \\ VGAE* & 84.0 & 87.7 & 78.9 & 84.1 \\ GAE & 91.0 & 92.0 & 89.5 & 89.9 \\ VGAE & 91.4 & 92.6 & 90.8 & 92.0 \\ ARGE & 92.4 & 93.2 & 91.9 & 93.0 \\ ARVGE & 92.4 & 92.6 & 92.4 & 93.0 \\ SAVGAE(ours) & **95.0** & **95.3** & **94.6** & **95.7** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results for Link Prediction. GAE* and VGAE* denote the variants of GAE and VGAE, which do experiments without using input features. Bold font indicates the best result.
## 5 Discussion
### Controlling Factors on Acoustic Velocity
#### 5.2.3 Permeability, Bioturbation, and Compressional Velocity  
A series of studies investigated the relationship between acoustic velocity and permeability and tried to define the factors controlling acoustic velocity and permeability in carbonates [64; 71; 72; 73; 74; 75], and found that pore structure, size, connectivity, and distribution are the main factors controlling acoustic velocity and permeability. The majority of these studies have shown an inverse relation between permeability and acoustic velocity, either as direct or indirect relation. The aforementioned studies demonstrated that large pores with a simple pore structure will have higher velocities and lower permeabilities compared to small pores with a complex pore structure with lower velocities and higher permeabilities.  
Similarly, our data show an inverse relationship between velocity and permeability, where the samples with relatively higher permeability (>0.1 mD) have lower velocities compared to samples with lower permeability values (<0.1 mD) (Figure 16). Our results agree with findings of the aforementioned studies and the inverse relation between velocity and permeability is evident in Figure 10B. However, the data discussed for this study are different as bioturbation is controlling permeability and thus plays a significant role in permeability enhancement of mud-dominated samples. In other words, bioturbation burrows (empty or coarse-filled) act as fractures [32] by enhancing permeability and reducing the acoustic velocity [71; 76].  
Figure 16: Velocity–porosity relationship with permeability values superimposed. Most of the samples with higher permeabilities (>0.1 mD) lie below the line of 5 km/s velocity.  
Likewise, and as discussed in Section 5.1, the _Thalassioides_ burrows enhance permeability of the mud-dominated units. Hence, it can be concluded from the inverse relation between velocity and permeability that bioturbation will increase permeability while decreasing the velocity of bioturbated muddy strata that are dominated by _Thalassioides_ burrows and filled by coarse grain material.
# Laser Absorption Sensing Systems: Challenges, Modeling, and Design Optimization
## 4 Strategies for Noise Reduction and Design Optimization
### Strategies based On Signal Processing Schemes
#### 4.3.2 In Situ Parameter Fitting  
In Section 3, we have discussed the limitation of calibration-free WMS when it is important to perform accurate assessments of laser characteristics. It is often assumed that the magnitude of wavelength modulation is constant over the entire scanning range [58] and the differential current-to-wavelength tuning behavior is linear for small current variations around a bias point [100; 101]. Such assumption may nevertheless introduce different degrees of uncertainty under different circumstances. [PERSON] et al. presented an improved methodology for assessing the wavelength response of a DFB laser by using a high-order empirical formula [102].  
Although the laser characterization process is not difficult by itself, the task of accurately tracking its variations in real time is nontrivial. More recently, [PERSON] et al. have proposed a new calibration-free _2f_-WMS technique to measure gas concentration and pressure without the need for laser pre-characterization [103; 104; 105; 106]. Similar to the idea of DAS baseline fitting, the harmonic backgrounds, i.e., RAMs, are obtained by interpolating the non-absorbing wings of the X and Y components of the demodulated _1f_, _2f_, and _3f_ signals (Figure 13). The intensity modulation indices \(i_{n}\) can then be obtained from the RAMs, whereas the intensity-frequency modulation phase shifts \(\varphi_{n}\) can be obtained by taking the inverse tangent of the ratio of X and Ys.  
The _in situ_ and real-time characterization of relevant laser parameters ensures that the measurements are not affected by rapid non-absorbing laser intensity variations such as those due to light scattering, beam steering, vibrations, and window fouling or by slow variation effects such as temperature changes, calibration drift, and aging of the devices. However, even if it has been demonstrated that higher-order harmonic signals would have reduced spectral interference from neighboring lines, the requirement for non-absorbing baseline portions for the fitting still puts limitations to the applicable scope of this method.  
Figure 13: The 1650 nm DFB laser was modulated at m = 2.2, and the transmitted light through a 1% CH\({}_{4}\) sample at 1 bar pressure was demodulated by a lock-in amplifier (LIA) to obtain (**a**) _1f_ X-component along \(1\) H\({}_{1}\), (**b**) _1f_ Y-component orthogonal to \(1\) H\({}_{1}\), (**c**) magnitude of _1f_ Signal, (**d**) _2f_ X-component along \(1\) H\({}_{2}\), (**e**) _2f_ Y-component orthogonal to \(1\) H\({}_{2}\), (**f**) magnitude of _2f_ Signal, (**g**) _3f_ X-component along \(1\) H\({}_{3}\), (**h**) _3f_ Y-component orthogonal to \(1\) H\({}_{3}\), and (**i**) magnitude of _3f_ Signal. Figure adapted from [104].
###### Abstract
Our previous study demonstrated that the mass distribution of the anticancer agent in IAC is dominated by blood flow field and the flow of the agent into the targeted tumor-feeding artery is determined whether the blood flow that comes from the inlet of the CCA toward the artery contacts the catheter tip or not [13]. In the situations,  
Figure 1: Retrograde intra-arterial chemotherapy for oral cancer. (**a**) Superselective intra-arterial chemotherapy, (**b**) the FA and LA bifurcate from the common trunk, and (**c**) conventional intra-arterial chemotherapy. CCA, common carotid artery; ECA, external carotid artery; FA, facial artery; ICA, internal carotid artery; LA, lingual artery; MA, maxillary artery; OA, occipital artery; STA, superficial temporal artery; STha, superior thyroid artery. the anticancer agent flow in the linguofacial trunk is thought to be dominated by the blood flow field in the common trunk. However, in our past study, the vessel model was made from medical images of patients without the common trunk. Therefore, both blood and anticancer agent flow in the common trunk have not been fully addressed. In addition, in clinical practice, we have experienced some oral cancer residual or recurrence cases after SSIAC, which had been done through the linguofacial trunk. Therefore, this study focused on the flow of the anticancer agent and blood in the linguofacial trunk. This study hypothesized that the distribution of anticancer agents among the ECA branches with a common trunk can be affected by the catheter tip position and analyzed the agent flow. This study aimed to investigate the flow distribution of anticancer agents in the ECA with the linguofacial trunk. This article is structured as follows: The geometrical model creation, the mathematical methodology, the numerical condition, and the other settings for the CFD analysis are described in Section 2. The analyses with visualized data are provided in Section 3. The mechanism of the distribution of the agent in the linguofacial trunk and the mechanical effect of IAC on the vessels are discussed in Section 4. Finally, the conclusion is presented in Section 5.
## 2 Objectives and Hypothesis  
The perception of architectural spaces and the spatial skills used to represent them and manage their three-dimensionality are essential conditions that architecture students must have to successfully undertake their university studies. The frequent low levels of spatial perception and the poor development of spatial skills are not the only reason for high dropout rates amongst architecture majors, but they can also contribute to low professional performance in those who manage to complete their studies. This is because a fundamental task of the architect is the creation of spaces for the development of human activities.  
The aim of this research is twofold: to analyze the improvement of spatial skills in first-year architecture students (freshmen) by training them in life-size sketching of architectural spaces in virtual immersive environments, and to analyze the visuospatial perception in reference to the acquisition of spatial sensations in virtual immersive environments in last-year students in the same field of study.  
The proposed general objectives are:  
1. To determine if the sketching of architectural spaces in immersive 3D virtual environments using virtual reality glasses improves spatial skills.  
To confirm if the user immersion in architectural spaces artificially created in virtual reality environments allows for spatial perception and sensorial awareness as if they were performed in real spaces.  
In order to verify these objectives, the following hypotheses will be addressed:  
1. H1A: First-year architecture students improve the components of spatial skills (mental rotation, visualization and orientation) after being trained in the sketching of architectural spaces in immersive VR spaces.
2. H2A: First-year architecture students improve their spatial perception after being trained in the sketching of architectural spaces in immersive VR spaces.
3. H3A: Immersive spatial reality environments allow for the capturing of user sensations by visualizing and virtually interacting with architectural spaces.  
For this purpose, a pilot study has been developed with first-year architecture students at the University of La Laguna in Spain and with students in the fifth and final year in similar degrees at the National University of San Agustin in Arequipa, Peru.
## 3 Hybrid Business System Model
### Throughput Modeling  
URLLC services are typically characterized by an end-to-end transmission delay of less than 1 ms and a bit error rate \(\varepsilon\leq 10^{-5}\). However, the transmission time interval in LTE-based systems is fixed at 1 ms, which falls short of meeting the ultra-low latency transmission requirements of URLLC services. To address this problem, the 5G system leverages flexible carrier spacing to reduce time slots and accommodate ultra-low latency services. The objective of this chapter is to increase the amount of data transmitted by the network URLLC and mMTC services in a hybrid service system. That is correct. By increasing the total system capacity to meet the low-latency and high-reliability requirements of the URLLC link, more mMTC service information can also be transmitted, thus increasing the overall system throughput. Currently, mMTC services are mainly latency-insensitive services. Therefore, a mMTC business admission control model is built as follows:  
\[\Pr\{\sum\limits_{n=1}^{N}\sum\limits_{h=1}^{H}\rho_{j,h}C_{j,t}\geq \subseteq C_{mMTC,t}\}, \tag{6}\]  
where \(C_{j,t}\) is the link capacity of the \(j\)-th mMTC service in the time slot \(t\). \(C_{mMTC,t}\) denotes the traffic volume that needs to be transmitted by the mMTC service in the time slot \(t\). \(\rho_{j,h}\) is the \(h\)-th URLLC service bandwidth allocation coefficient and \(j\)-th mMTC service when they are in the same sub-bandwidth.  
In summary, the resource scheduling problem can be viewed as optimizing the bandwidth allocation strategy of the mMTC service to enhance the traffic volume of both URLLC and mMTC in the communication link. Therefore, the optimal bandwidth model in finite code is calculated as follows:  
\[\begin{array}{l}\underset{h,j}{Max}C_{sum}=C_{URLLC}+C_{mMTC}\\ \underset{s,t,\rho_{j}}{s.t.\rho_{j,n}},\rho_{j,n},\rho_{j^{\prime},n}\in\{0,1 \}\forall j,j^{\prime},n;\\ \underset{h}{\sum}\underset{j}{\sum}n_{x_{1}}+n_{x_{2}}\leq n_{\max};\\ \underset{n}{\sum}P_{h,n}\leq P_{h,\max},\forall h\in H;\\ \underset{n}{\sum}P_{j,n}\leq P_{j,\max},\forall j\in J;\end{array} \tag{7}\]  
where \(n_{\max}=14\) is the maximum number of symbols that can be used in a time slot, according to the definition in the 5G system. \(P_{h,n}\) is the transmission power of the \(h\)-th URLLC service in the \(n\)-th sub-bandwidth; \(P_{h,\max}\) is the maximum transmission power of all URLLC services in the total bandwidth. \(P_{j,n}\)is the transmission power of the \(j\)-th mMTC service within the \(n\)-th bandwidth. \(P_{j,\max}\) is the maximum transmission power of all mMTC services within the total bandwidth.