esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Acoustic Emission Technique in Landslide Monitoring and Early Warning: A Review
## 5 Challenges and Recent Advances  
### Signal Detection Under High Attenuation Conditions  
Attenuation-induced amplitude decay impedes distant subsurface signal propagation, presenting a formidable challenge for long-range in situ monitoring applications employing the AE technique. Overcoming high damping capacities exhibited by geological media constitutes an active area of ongoing research and development.  
Key strategies focus on optimizing hardware designs and embedded sensor configurations tailored for specific material conditions to enhance sensitivity and detection efficacy under severe signal weakening scenarios. Notable developments include the following:  
1. Employing wideband piezoelectric transducers preferentially tuned to lower frequency ranges resulted in reduced damping compared to higher bands [113].
2. Deploying localized denser multi-sensor arrays configured in advanced geometries like concentric circles and nested quadrants to maximize detection through coincidence logic combining signals [131].
3. Coupling sensors and waveguides firmly using optimized viscoelastic materials with acoustic impedances closely matching both for minimal reflection losses and maximized transmission [132].
4. Implementing advanced low-noise pre-amplifier designs integrated very close to sensors to boost initial gains, countering losses through early-stage amplification [133].  
Overall, hardware customized according to specific propagation environments and application depths coupled with expertise in strategic sensor configurations holds immense potential for realizing long-range subsurface AE monitoring aspirations hitherto restricted by attenuation limitations.
# Recognizing Events in Spatiotemporal Soccer Data
## 4 Game Event Detection Pipeline
### Detecting Passes and Shots on Goal
```
1:ball enters vicinity of a non ball-possessing player \(p^{\prime}\)
2:\(\mathit{currentFrame}\leftarrow\textsc{currentFrame}()\)
3:\(\mathit{prevFrame}\leftarrow\textsc{getFrameAT}(\mathit{currFrame}-1)\)
4:\(\mathit{nextFrame}\leftarrow\textsc{getFrameAT}(\mathit{currFrame}+1)\)
5:\(\mathit{prevSpeed}\leftarrow\textsc{calculateBallSpeed}(\mathit{prevFrame}, \mathit{currFrame})\)
6:\(\mathit{nextSpeed}\leftarrow\textsc{calculateBallSpeed}(\mathit{currFrame}, \mathit{nextFrame})\)
7:\(\mathit{speedChange}\leftarrow\textsc{max}(\mathit{nextSpeed},\mathit{prevSpeed})/ \textsc{min}(\mathit{nextSpeed},\mathit{prevSpeed})\)
8:if\(\mathit{speedChange}>MinSpeedChangeFactor\)then\(\triangleright\) speed changed
9:returntrue
10:\(\mathit{prevDir}\leftarrow\textsc{calculateBallDirection}(\mathit{prevFrame}, \mathit{currFrame})\)
11:\(\mathit{nextDir}\leftarrow\textsc{calculateBallDirection}(\mathit{currFrame}, \mathit{nextFrame})\)
12:if\(\left|nextDir-prevDir\right|>MinTChangeAngle\)then\(\triangleright\) trajectory changed
13:returntrue
14:if ball is still possessed by \(p^{\prime}\) at \(\mathit{currFrame}+\mathit{GracePeriod}\)then
15:returntrue
16:\(\triangleright\) ball possessed by \(p^{\prime}\) after grace period
17:returnfalse \(\triangleright\) no change in ball possession so far
```  
**Listing 1**. Detecting changes in ball possession. As a result, in the current version of the system, we implement the following procedure for detecting passes and shots on goal (see Listing 2). ```
1:functionDetectPassOrShot()
2:if\(\left|\mathit{Ball}_{y}\right|>34.0\)then\(\triangleright\) ball crossed the touchline
3:returnVerifyFailedPass()
4:if\(\left|\mathit{Ball}_{x}\right|>52.5\)then\(\triangleright\) ball reached the goal line
5:if\(\textsc{Distance}(\mathit{Ball}_{y},0)<\mathit{GoalpostDistance}+\mathit{Goal Length}/2\)then
6:returnshot on goal
7:else
8:returnVerifyFailedPass()
9:if ball is within vicinity of another player \(p^{\prime}\)then
10:if\(\textsc{IsPOSsessionChanged()}\) is false then\(\triangleright\) see Listing 1
11:returnno event detected
12:if\(p^{\prime}\) is a teammate then
13:return successful pass
14:else
15:returnVerifyShot()
16:functionVerifyFailedPass()
17:ifpass distance is longer than \(MinFailedPassLength\)then
18:return unsuccessful pass
19:else
20:returnno event detected
21:functionVerifyShot()
22:if\(p^{\prime}\) is in the goal area and ball trajectory line crosses the goal line
23: not further than \(\mathit{GoalpostDistance}\) from the goalpost then
24:returnshot on goal
25:else
26:returnVerifyFailedPass()
```  
**Listing 2**. Detecting passes and shots on goal.
# A Survey from Real-Time to Near Real-Time Applications in Fog Computing Environments
## 2 Material and Methods
### Material
#### 2.1.4 Real-Time and Near Real-Time  
This subsection aims to present concepts of real-time and near real-time, which we understand to be the concept that applies most to unpredictable systems and with variable processing times, such as health monitoring.  
The [PERSON] et al. [15] survey shows that the term \"real-time\" has been presented and accepted, in the academic area, with different concepts. The authors present three concepts of real-time respectively applied to digital real-time signal processing, real-time systems, and real-time data stream:  
* Digital real-time signal processing: the hardware and software is designed to complete a task within a specified period of time.
* Real-time system: the system must present correct logical results, but it is imperative that it presents them within a deadline.
* Real-time data stream: the system must provide the processing high volumes of data, low latency, and fast response.  
On the other hand, according to [PERSON] et al. [16], [PERSON] et al. [17], [PERSON] et al. [18], applications that run the data stream can be classified according to the response time as real-time or near real-time. For [PERSON] et al. [17], near real-time can be understood as high response sensitivity, but without the hard time constraint. For [PERSON] et al. [16], [PERSON] et al. [18], the data are immediately processed upon arrival. In other words, near real-time is almost the time of the live event if subtracting the current time from the processing time.
# Preliminary Analysis of a Lightweight and Deployable Soft Robot for Space Applications
## 2 Robot Concept Design and Models
### Elastostatic Inverse Kinematics  
In the previous subsection, an algorithm to evaluate the deformations of the flexible structure and the forces acting on the EE, knowing only the value of motor torques and positions, has been proposed. Assuming the same hypotheses, the problem of positioning the EE from the starting point \(\mathbf{x}_{Start}\) to the end point \(\mathbf{x}_{d}\), with certain speed \(\mathbf{v}_{d}\), is approached. In this case, the load \(\mathbf{\gamma}_{e}\) is considered known and constant.  
Figure 6 shows how the algorithm works. It can be divided in two main parts: first, the motor joint variables \(\mathbf{q}_{m}\) are calculated to be used for the motor control; then, the virtual spring joint variables \(\mathbf{q}_{k}\) are estimated, given the external load \(\mathbf{\gamma}_{e}\) and the virtual spring stiffness \(k\), though a recurrent algorithm, based on the same elastostatic approach previously described.  
Figure 5: Load and Pose Estimation Algorithm.  
Once the deformations \(\mathbf{q}_{k}\) are estimated, the vector of all the joint variables \(\mathbf{q}\) is built to perform the forward kinematics and calculate the expected EE position \(x_{e}\), to be compared with its set value \(\mathbf{x}_{d}\). The error \(e\) can be calculated and multiplied by the symmetric positive definite matrix \(\mathbf{K}\), that can be tuned to enhance the performance.  
Having defined the right pseudo-inverse of the Jacobian matrix:  
\[\mathbf{J}^{\#}=\mathbf{J}^{T}.\Big{(}\mathbf{J}^{T}\Big{)}^{-1}, \tag{8}\]  
the joint velocity variables can be calculated with:  
\[\dot{\mathbf{q}}_{m}=\mathbf{J}_{m}^{\#}.(\mathbf{v}_{d}+\mathbf{K}.\mathbf{e}). \tag{9}\]  
where \(\mathbf{J}_{m}^{\#}\) is a \([3\times 6]\) dimension matrix composed by the 3 motor-associated rows of \(\mathbf{J}_{m}^{\#}\).  
Finally, throughout numerical integration, the inverse kinematics is completed by calculating the motor joint variable \(\mathbf{q}_{m}\).  
Therefore, this algorithm allows the robot to reach a point in the workspace by calculating the deformation of the inflatable links and consequently considering them to modify the set rotations of the actuated joints.
## 5 Experimental Results  
The 3D detection and localization performance of our algorithm on KITTI is shown in Table 1.  
Figure 7: Different difficulty levels of samples. The objects pointed by the yellow arrows belong to the “Easy” difficulty category, which are mostly unoccluded or untruncated, and relatively close to the camera; the objects pointed by the blue arrows belong to the “Moderate” difficulty category, which are slightly occluded or truncated, or slightly farther away from the camera. The objects pointed by the red arrows belong to the “Hard” difficulty category, which are severely occluded or truncated, or far away from the camera.
## 2 Materials and Methods
### Statistical Analysis  
Demographic and clinical characteristics were compared between frailty groups using one-way analysis of variance (ANOVA) for continuous variables and [PERSON]'s chi-squared test for categorical variables. The Shapiro-Wilk normality test (\(p<0.05\)) was used for a test of data normality. Normally distributed data were compared utilizing the parametric statistical method, i.e., with one-way ANOVA (\(p<0.05\)) or [PERSON]'s chi square. One-way ANOVA was used to compare the frailty group scores on physical performance tests and gait parameters derived from sensor data. The presence of overall statistically significant results in the ANOVA was followed with post-hoc Tukey analysis to identify significant pairwise associations. Effect sizes were calculated as partial eta-squared. The guidelines [38] for interpreting this value are: 0.01 = small effect, 0.06 = moderate effect, and 0.14 = large effect.  
Multinomial logistic regression [39], with the robust group as the reference, was then used to investigate the gait parameters that discriminate the three frailty levels. The dependent variable was frailty, modeled as two indicator variables of prefrail and frail referenced to the category of robust. The independent variables were TUG time, DGI score, and eight different sensor-based gait parameters. The independent variables assessed had partial eta squared effect sizes \(\geq\)0.14 for both prefrail versus robust and frail versus robust [38]. Each of the independent variables was fitted in a separate univariate logistic regression model for a total of ten models. Each model estimated the odds ratios for prefrail relative to robust and frail relative to robust. Linear regression diagnostics were performed to evaluate multicollinearity and normality. There were no major deviations from normality and multicollinearity.  
We used receiver operating characteristic (ROC) curves to calculate the area under curve (AUC) to estimate the predictive validity of each parameter. The cut-off values were calculated based on the Youden index. Sensitivity and specificity were calculated based on the cut-off values. All analyses were performed using IBM SPSS for Windows software, version 20.0. Statistical significance was set at a \(p\)-value less than 0.05.
## 2 Materials and Methods
### Activity of Defense Substances in the PaP Response to MeJA Treatment  
Selected healthy (uninfested) PaP seedlings (two years old) were divided into three groups. Each group comprised fifteen trees. We first sprayed three replicate trees, each one treated with five concentrations (10\({}^{-3}\), 10\({}^{-4}\), 10\({}^{-5}\), 10\({}^{-6}\), 0 mol/L) of MeJA aqueous solutions [24; 25]. After five days, quantities of H\({}_{2}\)O\({}_{2}\), POD, and T-SOD were determined in 5 g of bark (3 cm \(\times\) 3 cm) sampled from each tree in one group (group I) as a baseline control. Since taking samples was detrimental to the tree trunk, we did not sample this group again and regarded the other group (group III) as control. We then caged mated ALB adults on the trunks of PaP in the second group (group II) and allowed oviposition to proceed. Five adult females and two adult males per trunk were held together for 7 days previously to ensure they were mated and ready to oviposit. The third group (group III) of trees were maintained as controls with no ALB. Samples were taken 5 days after adult oviposition had been initiated in group II. A total of 5 g of bark (3 cm \(\times\) 3 cm) was taken using a knife from 1.5 cm above the oviposition site in group II trees and, at the same time, 5 g of bark was taken from the group III controls at a similar position on the tree trunk. Since the determination and sampling were carried out twice, before and after pest infestation, two controls, I and III, were set. All samples were stored at \(-\)80 \({}^{\circ}\)C before further evaluation.  
The quantities of H\({}_{2}\)O\({}_{2}\), POD, and T-SOD in PaP bark in group I (different concentrations versus 0 mol/L MeJA aqueous solutions), group II (after MeJA treatment and ALBinfestation), and group III (after MeJA treatment but no ALB infestation) were determined using a manufacturer's kit (Nanjing Jiancheng Biotechnology Co., Ltd, Nanjing, China). The contents of H\({}_{2}\)O\({}_{2}\), POD, and T-SOD were carried out according to the operation instructions of the reagent kit. Quantities were calculated according to the corresponding standard curve.
# Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM +
## 2 System Model  
Figure 1 shows a MU-MISO system with \(N\) transmit antennas and \(K\) UEs that each have a single antenna. The base station has one message per UE and each antenna has a resolution of \(1\) bit for the amplitude (on-off switch) and \(b\) bits for the phase per antenna. All other hardware components are ideal: linear, infinite bandwidth, no distortions except for additive white Gaussian noise (AWGN).  
Figure 1: Multi-user MIMO downlink with a low resolution digitally controlled analog architecture.
## 2 Materials and Methods  
### Choosing the Right Optical Fiber  
There are a variety of silica-core silica-clad optical fibers of different core and cladding diameters available on the market. The silica core and cladding constitute a monolithic silica member. The tensile strength of a silica-core silica-clad optical fiber depends on the overall diameter of the silica member, \(d_{2}\), as well as on the ultimate stress of the fiber, \(\sigma_{u}\). In practice, the tensile strength of 20 m fiber samples averages about 4 GPa [65]. The average ultimate strength, \(\sigma_{u}\), increases with a decrease in the length of fiber samples, since the probability of a flaw in a sample is inversely proportional to its length.  
However, the type of coating material affects the strength of optical-fiber bonding to a structural element. Based on the above considerations and previous research [61], and with the aim of detecting structural cracks in the mm range [43,44], acrylic-coated optical fibers of the smallest available cladding diameter \(d_{2}\) = 125 um were chosen for this study. Among the fibers in this group, the optical fibers with the largest available core diameter \(d_{1}\) = 105 um were chosen and used (a large core diameter facilitates the efficacious coupling of inexpensive light sources--light-emitting diodes (LEDs) and optical fibers). The optical fibers chosen had an acrylate coating with a diameter of \(d_{3}\) = 250 um, numerical aperture of \(NA\) = 0.22, and optical attenuation of \(\alpha\) = 15 dB/km @ \(\lambda\) = 633 nm.  
The suitability of these optical fibers for the present application was verified by a simple tensile test, similar to that in [62], on 20 samples of these optical fibers. The test consisted of bonding an optical fiber sample to two separate ceramic bricks and pushing the two bricks apart at a strain rate of 0.25/min until the breakage of the fiber [63]. This led to a steep decrease in the pilot optical signal transmitted through the fiber to almost zero. The test yielded a minimum detected crack of about 1 mm wide, a mean structural crack width of \(M\) = 2.34 mm, a variance of \(\sigma^{2}\) = 0.79 mm\({}^{2}\), and a standard deviation of \(\sigma\) = 0.89 mm (see Figure 2). (The zero probability of failure at a structural crack width range of 0 < \(\delta_{c}\) 1 mm is due to the screening out of optical fibers that failed the proof tests of \(\sigma_{pt}\) = 0.7 GPa at the production facility.) These cracks fitted a range of 1 mm to 5 mm wide, which corresponds to a medium-to-high structural hazard for masonry [43,44]. Therefore, the above-named optical fiber was used in this work.
## 4 Performance Evaluation
### Performance Evaluation Using MATLAB Simulation
For Arrow-Curve, average position \(\text{error}_{\text{std}}=1.4\). For H-Curve, average position \(\text{error}_{\text{std}}=1.5125\). For R-Curve, average position \(\text{error}_{\text{std}}=1.55\). R-Curve achieves the highest values  
Path length. As noted from Figure 15, the path length is affected by the network size and the path model. Arrow-Curve and R-Curve have a shorter path model length than H-Curve because of the nature of the two paths, and H-Curve path as described extensively in previous sections. Energy consumption. As noted from Figure 16, Arrow-Curve achieves lower energy consumption results than H-Curve by 62.65%. R-Curve achieves energy consumption results lower than H-Curve by 24.12%. The energy consumption in simulation tool is affected by the network size, path length, and the communication process, where Arrow-Curve and R-Curve dissipate less energy, while H-Curve has high energy consumption because of the nature of the path length of H-Curve. The energy dissipation on simulation tools is calculated using two parameters, the energy dissipation through movement and the energy dissipation through sending messages from the mobile node to the unknown nodes. To reduce power dissipation, we should choose the path model with small length and reduce energy dissipation through sending by reducing the number of transmitted messages and reducing the time of experiment. Figure 14: Localization coverage ratio corresponding to resolution (L) in Arrow-Curve path model, R-Curve path model, and H-Curve. Figure 15: Path length (PL) corresponding to resolution (L) in Arrow-Curve model, R-Curve path model, and H-Curve.
# Blockchain-Based Multistage Continuous Authentication for Smart Devices
## 2 Related Work
### Biometric Authentication
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Research** & **Method** & **Accomplishment** & **Result** & **Drawbacks** \\ \hline \multirow{4}{*}{[20]} & Uses CM, AEAD & \multirow{4}{*}{A new authentication protocol for} & This protocol allows for & \multicolumn{1}{c}{Cannot realize a} \\  & algorithm, and a secure & & & mutual authentication & distributed architecture \\  & hash algorithm to & & & between the user and & and has the problem of \\  & design CMAP-IoTs & & & server and establishes a & single-point attack. Not \\  & & & & session key for & physically deployed in \\  & & & & encrypted transmission & IoTs devices. \\ \hline \multirow{4}{*}{[21]} & Uses an authenticated & \multirow{4}{*}{An authenticated SK establishment mechanism} & Provides sound protection to the patient’s data and & There is a security problem \\  & & & & to the patient’s data and & with keys during \\  & & & & mitigates the overall cost & long-term sessions, and it \\  & & & & and delay & has a \\  & & & & centralization problem. \\ \hline \multirow{4}{*}{[22]} & Ethereum & \multirow{4}{*}{A novel browser} & Describes a & \multirow{4}{*}{The solution is vulnerable} \\  & blockchain ecosystem & & & & to phishing attacks. \\  & & & & \\  & & & & user authentication & \\ \hline \multirow{4}{*}{[23]} & Local and global & \multirow{4}{*}{Decentralized blockchain-based permission delegation and access control} & Provides strong & \multirow{4}{*}{Does not realize formal modeling and formal verification} \\  & & & & crossdomain & \\  & & & & formal verification \\ \hline \multirow{4}{*}{[24]} & Ethereum & \multirow{4}{*}{A trust management method for reputation evaluation and a credit penalty mechanism} & The scheme offers flexible and scalable trust management for large-scale IoTs devices and storage pressure \\  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1} Proposed & Blockchain technology and the IPFS & A multistage authentication & The first work that applies a blockchain to multistage continuous authentication. & Smart contracts and consensus algorithms for authentication can be improved \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the related works. Identification systems based on gait generally consist of an enrollment phase and an authentication phase. The system mainly consists of the following modules: signal acquisition, data preprocessing, gait feature extraction, feature matching, and creating a gait database. These are shown in Figure 2. During the registration phase, the user's behavioral data are recorded by built-in sensors in the mobile device while the user is using the phone. The noisy behavioral data are preprocessed and fed into the feature extraction module to build a feature dataset. During the authentication phase, behavioral data are randomly collected from users. After the data preprocessing and feature extraction module, the model is used directly to authenticate the user's identity and determine whether the current user is a legitimate one.