esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This paper reviews the RF passive wake-up radio concept from hardware to the MAC layer design. Increasing attention has been given to traversing the different aspects of the passive wake-up radio design setting. We provided a comprehensive and systematic classification of the current state-of-the-art techniques available in the literature. From the literature review, it can be observed that there has been huge progress in the development of passive wake-up radio hardware. The associated performance parameters, such as sensitivity, range and power dissipation is evaluated and compared. The growing number of communication protocols that are utilized by the passive WuR features have also been analyzed.  
The study shows that RFID based wake-up radios are generally superior to other technologies based on the relative higher sensitivity (hence, offer high wake-up range), lower V\({}_{\text{in}}\)-to-V\({}_{\text{out}}\) ratios, implying that lower turn-on input voltage can produce a higher output voltage and hence, higher efficiency.  
The MAC protocols are also having issues of contention, channel access and delays caused by the time required to harvest enough energy to power it up. The key evolving application areas where the passive wake-up radios are utilized have been discussed, such as environmental monitoring, medical, smart grid and asset tracking. Open research issues and practical challenges in the passive wake-up radio have been highlighted, which include communication range, delay, channel access, application-specific protocols and mobility. In addition, several solutions and research opportunities have been highlighted to solve these challenges in order to reduce the technological barrier towards the realization of passive wake-up radio technology. Furthermore, most of the passive WuR MAC protocols evaluation is based on theoretical analysis and simulation, which can be further verified in the testbed prototypes and real implementation by researchers in the future. Our work has described the great potential of passive wake-up radios to improve the energy efficiency in wireless sensor networks in IoT applications.  
Conceptualization, H.B., Z.X. and J.X.; Methodology, all authors; Structure, H.B., R.N. and J.X.; Validation, Z.X., R.N. and J.X.; Investigation, H.B., Z.X. and J.X.; Resources, Z.X. and J.X.; Taxonomy/Classification of Previous Works, H.B., Z.X. and R.N.; Writing-Original Draft Preparation, H.B., Z.X. and J.X.; Writing-Review & Editing, all authors; Visualization, H.B. and R.N.; Supervision, Z.X. and J.X.; Project Administration, Z.X., and J.X.; Clarity & English Editing, H.B. and R.N.; Funding Acquisition, Z.X. This research was funded by the National Natural Science Foundation of China, grant number 61771080.  
The authors declare no conflicts of interest.
# State-of-the-Art Review of Structural Vibration Control: Overview and Research Gaps
## 3 General Limitations of Structural Control Systems
### Time Delay  
A standard structural control system for civil engineering structures comprises multiple components, including sensors, filters, controllers, and control devices. Since each element requires a finite amount of time to function, there is always a delay between measuring response quantities and applying control forces. In seismic control systems, these delays stem from the time required to measure state vector responses during earthquakes and to process this data using digital computer units. Furthermore, time delays are introduced when calculating active forces through control algorithms, activating control devices, and applying them to the plant or system.  
Experimental studies have shown that time delay is inevitable in operating control systems, which can deteriorate control performance. The existence of a time lag between sensing structural responses and applying control forces is a critical issue. From previous studies [39; 40; 41], it is observed that time delay can cause degradation in control performance. These time delays can occur in control system inputs, outputs, or states, and their characteristics can vary from being discrete to distributed, constant, time-varying, known, or unknown, and from deterministic to stochastic, depending on the specific features of the system [42].  
Study [39] demonstrated that the decentralized output feedback polynomial controller, the Lyapunov controller, and the simple passive controller are highly robust to time delay issues, even without implementing any compensation to mitigate the effects of the delays. Also, various compensation methods have been proposed to address the instability and performance degradation caused by these delays. Extensive real-scale semi-active tests and simulations were conducted [43] to estimate the time delay effects in the control electronics and the mechanical and electrical parts of the MR damper within a closed control loop using a Bingham model of 50 kN MR dampers. They identified three distinct time delays in the real-scale 50 kN MR damper: Control Electronics Time Delay, which included signal acquisition, processing of the acquired signal, and power supply operations; Electrical Time Delay, which pertains to the electrical part of the damper or its electromagnetic circuit; and Mechanical Time Delay, which is the interval between the moment the current to the device starts changing and the moment the device begins to adjust its mechanical behavior.
## 8 Comparative Review of CIPP and ET
### Emissions from CIPP and ET
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Impact Category** & **Unit** & **Glass Fiber-Heinforced** & **Dummy Plastic** & **Polyserer Resin** & **System E** & **Peter (Amorphous)** & **Polyethylene (Linear Low-Density, Resin, at Plant, CTR/kg/RNA)** & **Total Emissions** \\ \hline Ozone depletion & kg CFC-11 eq & 0.00153 & N/A & 0.00032 & N/A & N/A & \(5.656\times 10^{-6}\) & 0.0021 \\ \hline Global warming & kg CO\({}_{2}\) eq & \(2.24\times 10^{4}\) & N/A & \(3.79\times 10^{3}\) & \(2.517\times 10^{3}\) & 311 & \(1.77\times 10^{3}\) & \(4.545\times 10^{4}\) \\ \hline Smog & kg O\({}_{3}\) eq & 941 & N/A & 110 & 86.72 & 17.6 & 57.29 & \(1.62\times 10^{3}\) \\ \hline Acidification & kg SO\({}_{3}\) eq & 81.25 & N/A & 9.615 & 7.75 & 1.44 & 5.44 & 141 \\ \hline Eutrophication & kg N eq & 33.81 & N/A & 6.31 & 0.182 & 0.0447 & 0.106 & 46.15 \\ \hline Carcinogenics & CTUh & 0.000735 & N/A & \(9.87\times 10^{-5}\) & \(5.67\times 10^{-6}\) & \(8.06\times 10^{-8}\) & \(4.68\times 10^{-6}\) & 0.00107 \\ \hline Non-carcinogenics & CTUh & 0.00471 & N/A & 0.000542 & \(4.78\times 10^{-6}\) & \(2.44\times 10^{-7}\) & \(4.96\times 10^{-5}\) & 0.0063 \\ \hline Respiratory effects & kg PM2.5 eq & 5.97 & N/A & 0.78 & 0.345 & 0.0632 & 0.324 & 10.43 \\ \hline Ecotoxicity & CTUe & \(6.52\times 10^{4}\) & N/A & \(9.68\times 10^{3}\) & 94.46 & 11.78 & 818 & \(9.21\times 10^{4}\) \\ \hline Fossil fuel depletion & MJ surplus & \(3.867\times 10^{4}\) & N/A & \(6.78\times 10^{3}\) & \(9.58\times 10^{3}\) & 967 & \(1.025\times 10^{4}\) & \(9.74\times 10^{4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results for 12-inch-diameter CIPP Renewal. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Impact Category** & **Unit** & **PVC Pipe E** & **Excavator** & **Total Emissions** \\ \hline Ozone depletion & kg CFC-11 eq & N/A & \(2.167\times 10^{-6}\) & 0.00347 \\ \hline Global warming & kg CO\({}_{2}\) eq & \(4.278\times 10^{4}\) & 987 & \(2.035\times 10^{5}\) \\ \hline Smog & kg O\({}_{3}\) eq & \(2.014\times 10^{3}\) & 98.7 & \(1.52\times 10^{4}\) \\ \hline Acidification & kg SO\({}_{2}\) eq & 187 & 4.656 & 735 \\ \hline Eutrophication & kg N eq & 14 & 0.265 & 108 \\ \hline Carcinogenics & CTUh & 0.00885 & \(5.12\times 10^{-7}\) & 0.01225 \\ \hline Non-carcinogenics & CTUh & 0.00357 & \(3.14\times 10^{-6}\) & 0.0216 \\ \hline Respiratory effects & kg PM2.5 eq & 8.49 & 0.256 & 38.25 \\ \hline Ecotoxicity & CTUe & \(2.04\times 10^{3}\) & 31.89 & \(3.53\times 10^{5}\) \\ \hline Fossil fuel depletion & MJ surplus & \(1.01\times 10^{5}\) & \(1.95\times 10^{3}\) & \(4.49\times 10^{5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results for 8-inch-diameter ET. Figure 4: EIA processes for 8-inch-diameter ET. Figure 5: EIA processes for 10-inch-diameter ET. Figure 3: EIA processes for 12-inch-diameter CIPP Renewal.
# Machine Learning Subjective Opinions: An Application in Forensic Chemistry
## 2 Background
### Limitations to Machine Learning Predictions  
The ML methods referred to in Section 2.2 can assist the analyst in assigning evidential strength in the form of a likelihood ratio. Decision theory, ROC analysis and ML can assist the analyst in making a decision; however, as discussed above, the analyst should be reporting an opinion [23], not a \"forensic decision\". Complex ML methods, when presented with previously unseen data for the purpose of prediction, typically give a single answer (e.g., the posterior probability that a sample contains ILR). If the new data lies outside the distribution of the training data, or if the training set was too small to accurately represent the state space, the predicted probability may lead to an erroneous categorical classification due to uncertainty in the prediction. Uncertainty falls into three categories, aleatoric, epistemic and distributional [2]. Aleatoric uncertainty is comprised of data uncertainty, such as label noise and class overlap. Epistemic uncertainty arises from model complexity, and distributional uncertainty is due to training-testing set distribution mismatch. The epistemic uncertainty can be reduced by increasing the size of the training data set; however, the aleatoric uncertainty is not reduced by increasing the size of the training data set. If the model's probabilistic output is converted to a categorical value for forensic reporting (e.g., reporting an absolute opinion as ILR present or not), the effects of uncertainty are more significant. Opinions reflecting unacceptable levels of uncertainty should not be used for making decisions, and therefore a knowledge of the uncertainty associated with each opinion is required.  
The use of databases containing records of IL and substrate pyrolysis data is supported under the current ASTM standard [12]. Databases are retained by most fire debris analysis laboratories. A set of data is also freely available online from the National Center for Forensic Science (NCFS) at the University of Central Florida (UCF) [24]. The NCFS site also contains fire debris data sets generated in-silico, 240,000 samples in total, for use in ML applications. Experimental ground truth fire debris data set for ML validation is also available on the NCFS site. The work reported here uses 60,000 in-silico training samples and 1117 ground-truth validation samples from the NCFS site. The ML method predictions from an ensemble of models are fused using the principles of subjective logic to provide the analyst with an \"opinion\", not a single answer [1; 5; 25; 27]. In this approach, the formalism of subjective binary opinions is used as a fusion method for the ML outputs in a way that is similar to the fusion step in ensemble methods.
# A New Cascade-Correlation Growing Deep Learning Neural Network Algorithm
## 3 New Proposed Cascade-Correlation Growing Deep Learning Neural Network Algorithm (CCG-DLNN)
### Notations and Assumptions  
* \(N_{HL}\): number of hidden layers as in Figure 2;
* \(N_{ii}^{l}\): number of units within the lth layer as in Figure 2;
* \(S\): is the attributes number at the training data set;
* \(l\): Layer number, \(l=0\) is the input layer, \(l=N_{HL}+1\) is the output layer and numbers of hidden layers is defined as \(1\leq l\leq N_{HL}\);
* \(w_{ij}^{l}\): is the weight between the ith node at lth layer to the jth node at the (lth \(-\) 1) layer \(1\leq l\leq N_{HL}+1\) as in Figure 2;
* \(W^{l}=(w_{ij}^{l})_{N_{ii}^{l}\times N_{ii}^{l-1}}\): is the matrix of weights which links the lth layer to the (lth \(-\) 1) layer as in Figure 2;
* \(\varphi=(w_{ij}^{l})_{n}\): is all the neural network weight connections \(n=\sum_{l=1}^{N_{HL}+1}N_{u}^{l}\times N_{ii}^{l-1}\);  
Figure 1: Traditional CCNN.  
\(\phi\): is the new weights when the network is increased;
* \(L(\varphi)\): loss function;
* \(E^{m}(\varphi)\): is the output error resulting from the mth input \(x^{m}\);
* \(\
abla L(\varphi)\): the gradient of \(L(\varphi)\) based on the parameter \(\varphi\) and given by Equation (1): \[\
abla L(\varphi)=\frac{\partial E^{m}(\varphi)}{\partial w_{ij}^{l}}\] (1) To train the deep neural network, we use an online backpropagation algorithm and update the weights using Equation (2): \[\Delta w_{ij}^{l}|_{m}=-\eta\
abla L(\varphi)\] (2) where the learning rate is \(\eta\):
* \(x^{m}\): is the mth input sample of the training input data set \(X=(x^{m})_{S}\), \(1\leq m\leq S\);
* \(o^{m}\): is the mth desired output of the training output data set \(O=(o^{m})_{S}\), \(1\leq m\leq S\);
* \(y^{m}\): is the output of the NN obtained for the input \(x^{m}\), \(Y=(y^{m})_{S}\) as in Figure 2;
* \(h_{i}^{l,m}\): the output of the ith hidden unit related to the lth hidden layer related to the mth input \(x^{m}\) and given by Equations (3) and (4): \[h_{i}^{l,m}=f(g_{i}^{m})\] (3) \[g_{i}^{m}=\sum_{j=1}^{N_{i}^{l-1}}(w_{ij}^{l}h_{i}^{l-1,m})\] (4) where \(g_{i}^{m}\) is the summation of the product of previous layers' neurons outputs and weights and \(f(.)\) is the nonlinear activation function that could be sigmoid, tanh or ReLU; and \(y^{m}=g_{i}^{m}\) when \(l=N_{HL}+1,i=1\).
## 4 High Average Power Short-Pulse Thin Disk Lasers at HiLASE
### Frequency-Stabilized Oscillator  
Some applications, such as the RF photocathode of an electron accelerator, require high stability of the repetition rate and a low timing jitter in order of 100 fs. The accelerated electron bunch needs to collide precisely with the laser pulse to generate, e.g., X-ray pulses via the inverse Compton scattering or high-gain harmonic-generation in free-electron lasers. We are developing an Yb-doped fiber mode-locked laser oscillator at the center wavelength of 1030 nm with active repetition rate stabilization. The system generates pulses with 1.9 nJ of energy, a 1.5-ps pulse width, a 20-nm spectral bandwidth and operates at a repetition rate of about 44.81 MHz. Additional frequency components in the phase noise can arise through the inner arrangement of the laser, external vibrations and air turbulences. The repetition rate is stabilized by changing the laser resonator length by altering the position of one of two collimators at the border between the fiber and non-fiber part of the oscillator (_cf._ Figure 10). The collimator is placed on a piezo-driven translation stage with a computer-controlled system. The repetition rate can be adjusted in the range from 44.807-44.812 MHz, which is mainly limited by the stability of mode-locking.  
A fast digital lock-in amplifier is used to characterize the timing jitter of the laser pulses detected by a photodiode. The signal is demodulated, which enables determining the amplitude and phase spectrum. Further, the phase difference between two oscillators at the same frequency, namely the laser oscillator and reference oscillator of the lock-in amplifier, is used as the feedback signal for the laser repetition rate stabilization. By controlling the piezo-driven translation stage, the phase difference is kept constant. Figure 12 shows the measured phase noise and accumulated timing jitter of the stabilized laser oscillator. To conclude, we have successfully developed the noise measurement procedure. The timing jitter will be improved using a fast voice coil actuator controlled by the direct analog feedback [48].  
Figure 12: Single side band (SSB) phase noise and accumulated timing jitter of the stabilized oscillator. The blue ellipse marks the frequency components caused by vibrations of the translation stage [48].
## 4 Modification of the Gaussian Model
As shown in Figure 11b, all the fitted coherence models of \(\zeta_{s}\) reach zero far before the separation distance reaches four kilometers. At this condition, \(\gamma_{\zeta}\) has reached its greatest possible variance. Still, this value is insufficient to realize the NPLVs of actual earthquake records. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{\(N_{\zeta}\)=6} & \multicolumn{3}{c}{\(N_{\zeta}\)=12} \\ \cline{2-9}  & **HV1** & **HV2** & **LW** & **HV1** & **HV2** & **LW** \\ \hline a\({}_{0}\) & 0.7515 & 0.9208 & 0.6941 & 0.1884 & 0.1214 & 0.4489 \\ a\({}_{1}\) & 0.8759 & 0.9016 & 0.5628 & 0.9736 & 0.6143 & 0.8229 \\ a\({}_{2}\) & 0.9619 & 0.5760 & 0.3026 & 0.8113 & 0.8905 & 0.1301 \\ a\({}_{3}\) & 837.1936 & 196.8346 & 613.1423 & 132.2794 & 62.9362 & 2.4242 \\ a\({}_{4}\) & 14.1450 & 55.6462 & 11.8553 & 10.1328 & 13.2031 & 852.6785 \\ a\({}_{5}\) & 0.0145 & 0.0914 & 0.0418 & 0.0595 & 0.2496 & 0.2499 \\ J & 0.1028 & 0.1370 & 0.0769 & 0.1305 & 0.2489 & 0.1046 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the searched parameters and fitness values. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Distance** **(km)**} & \multicolumn{6}{c}{\(N_{\zeta}\)=6} & \multicolumn{6}{c}{\(N_{\zeta}\)=12} \\ \cline{2-13}  & \multicolumn{3}{c}{**NPLV**} & \multicolumn{3}{c}{**Error Ratio (\%)**} & \multicolumn{3}{c}{**NPLV**} & \multicolumn{3}{c}{**Error Ratio (\%)**} \\ \cline{2-13}  & **HV1** & **HV2** & **LW** & **HV1** & **HV2** & **LW** & **HV1** & **HV2** & **LW** & **HV1** & **HV2** & **LW** \\ \hline
0.2 & 1.098 & 1.032 & 1.114 & 0.18 & 5.31 & 1.27 & 1.1058 & 1.045 & 1.203 & 0.53 & 6.63 & 9.36 \\
1 & 1.317 & 1.236 & 1.396 & 7.25 & 2.68 & 1.69 & 1.2969 & 1.191 & 1.307 & 8.67 & 6.22 & 7.96 \\
2 & 1.367 & 1.383 & 1.466 & 0.22 & 13.36 & 7.79 & 1.327 & 1.055 & 1.327 & 3.14 & 13.52 & 2.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Simulated averaged NPLVs for different spectral coherence models and different \(N_{\zeta}\). The search result when \(N_{\xi}=6\) is shown in Figure 11a. None of the fitted coherence reaches zero. For this condition, the fitting accuracy depends mainly on the functional form chosen in Equation (24). The characteristics of the fitted coherence in Figure 11a estimated from different spectra show no features in common, indicating that the coherence of \(\zeta_{s}\) should be calculated according to the spectral coherence model. Figure 12 shows the NPLVs realized by different spectral coherence models. As shown in Figure 12, the results obtained by the modified Gaussian model amplify the NPLV compared with those obtained by the Gaussian model. In this study, \(\zeta_{s}\) is assumed to be a frequency-independent random variable. However, the spectral coherence model has a significant influence on the shape of NPLVs between 0 and 5 Hz. Further studies should be carried out to reveal the dependency of \(\zeta_{s}\) on the frequency.
# VFLD: Voxelized Fractal Local Descriptor
## 4 Experimentation
### Qualitative Evaluation of the Registration Pipeline on ViDRILO  
As a qualitative example of applying the VFLD to a registration pipeline, we adopted the ViDRILO dataset, which is described in Section 4.1. For this qualitative evaluation, we used parts of the first sequence. The parameters of the method were the best performers in the previous test: we used 18 radii, the radii starting value was set to 1, the radius increment was set to 0.5, and \(nIters\) was set to 5. The point clouds were downsampled using uniform sampling with about 3000 points obtained for each one. The first point cloud of the sequence was normalized such that the largest dimension was 100. The scale factor was applied to the subsequent point clouds of the same sequence. This was performed so that the parameters tuned could be applied without further tinkering.  
Figure 22 shows some of the results of the registration process. As can be seen, overall, the reconstruction is accurate in both examples. For the first one, which is a professor's office, some minor misalignment issues can be noticed in Figure 22f, in the part of the window. Another slight misalignment error can be seen in Figure 22g, in the multiple planes that correspond to the desk. Regarding the technician's lab, which is the second example, some minor misalignment issues can be seen at the corner of the room and in the wall of  
Figure 21: Number of scenes (in ordinate) for each error rate interval (in abscissa) of VFLD (in blue) and FPFH (in red) for the registration evaluation protocol on the voxelized environment.  
the background. These errors can be seen in Figure 22,n. It is worth bearing in mind that the point clouds are affected by the noise and error introduced by the sensor. In addition, the displacement error in this pipeline is accumulative, given that if the transformation between two point clouds fails, the subsequent transformation will be erroneous as well. We noticed that the pipeline provided cleaner outputs and was less error prone when there was a significant presence of cluttering in the scenes. On the other hand, when the environment was composed of mostly planes and regular surfaces, like walls and floor, the performance was negatively affected. This is because, in the latter, most of the descriptors were similar and thus the matching process became more challenging. A proper keypoint extraction step would help mitigate this issue. Nonetheless, the probability of having many similar features decreases in the presence of clutter, leading to better performance.
## 2 Materials and Methods
### Procedure
#### 2.3.4 Muscle Oxygen Saturation  
To measure SmO\({}_{2}\) levels in the _vastus lateralis_, a non-invasive near-infrared spectroscopy (NIRS) device was used (HUMON HEX, Dymmetries Inc., Cambridge, MA, USA). This device is equipped with six slots that emit LED radiation at two wavelengths (760-840 nm), which was used to calculate both hemoglobin content (g/dL) and oxygen saturation levels (%). The NIRS sensor was precisely and consistently positioned on the right _vastus lateralis_ of each subject, specifically two-thirds of the distance between the greater trochanter and lateral epicondyle of the femur. To ensure the stability of the sensor during physical activity, adhesive tape and a black neoprene band were used to ensure that the device remained fixed and, in position, marked with a marker pen to control any displacement during the test.  
The sensor was calibrated according to the manufacturer's specifications before each use. For real-time data collection and visualization, HUMON HEX (Dymmetries Inc., Cambridge, MA, USA) was connected via Bluetooth(r) to an Android tablet (Samsung Galaxy Tab A7 line; Samsung Electronics, Seoul, Republic of Korea). The MaxZones application (MoxZones SL, Madrid, Spain) was used to display, playback, and store the collected data at a data output rate of 1 Hz.