esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Analysis
### Method Accuracy Validation  
The reliability and accuracy of the proposed method were evaluated through validation experiments. This analysis assessed the performance of the SILSS in comparison to the Leica Pegasus Two Ultimate scanner system, a widely used alternative for tunnel deformation monitoring. Key metrics, including average point cloud displacement and ovality, were used to validate the precision of our method. To ensure fairness and consistency in the evaluation, identical datasets and processing workflows were applied to both systems. The results were analyzed to identify differences between the data measured by our method and the Leica Pegasus Two Ultimate scanner through the key metrics. This validation provides critical insights into the practical applicability of SILSS for high-precision monitoring tasks in tunnel engineering.  
The Leica Pegasus Two Ultimate scanner, equipped with a 360\({}^{\circ}\) panoramic camera, laser scanner, inertial measurement unit (IMU), and light sensor, was installed on a self-designed mobile rail car for railway dynamic measurement. The system collects 1 million points per second with a measurement accuracy of 1 mm operating at speeds of up to 20 km/h. [PERSON] et al. [39] utilized this system in their work, demonstrating its potential for the automatic extraction of rail point clouds from MLS data. The rail gauge measurements on the extracted rail point clouds further validate the method as a promising solution for extracting 3D rails from MLS point clouds.
# Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review
## 5 Discussion and Future Perspectives
### Deep Learning for 3-Dimensional Data  
The combination of deep learning and 3D data thus far has been infrequently explored despite the possibilities of improving assessment. Most studies have only used either one or two-dimensional data with the DL processing algorithm. Integration of data from soil, atmospheric and plant-based measurements would create a three-dimensional input that can improve the accuracy of assessment [14].  
Furthermore, there are sensors that can collect three-dimensional data that can generate more information for plant water stress assessment. For example, a hyperspectral image which contains spatial and spectral information can simultaneously be utilized for accurately assessing plant water status. Joint spectral and spatial information from remote sensing data has been utilized for improved the prediction task using a deep learning approach [124,125]. The application of 3D digital RGB image data to DL is also foreseeable in the future as a 3D image can be used to extract features like the bending angle of a leaf that can be related to water stress [99].  
Using advanced data can come with challenges. Firstly, the inaccuracies in 3D image reconstruction can affect the features extracted by the DL model resulting in subpar performance. Secondly, the high cost of the advanced sensors might limit the adoptability of such a method. This in turn will reduce the data production which then might further limit the much-needed data varieties pertinent to the successful learning of the DL model.
# GA-PSO Algorithm for Microseismic Source Location
## 3 Experiments and Results
### Simulation Validation of Microseismic Location Algorithms
#### 3.1.3 Comprehensive Comparative Experiments of Microseismic Location Algorithms
However, due to the random nature of GA's initial exploration, while the positioning accuracy improves, it does not surpass the effectiveness of PSO's early-stage local search. As the number of iterations increases, the velocity and position update mechanisms of PSO enable GA-PSO to converge rapidly within local regions, refine the solution towards a better optimum, and gradually reduce the positioning error. The PSO-ACO algorithm quickly identifies a promising initial solution through pheromone guidance, but its limited refinement capability results in slightly lower positioning accuracy compared to the other two hybrid algorithms. The NM algorithm excels in local optimization; however, when approaching the optimal solution, it makes excessive minor adjustments, leading to slower convergence speeds. The NM-PSO algorithm achieves higher accuracy than PSO-ACO, but its overall precision and iteration speed still lag behind those of the GA-PSO algorithm. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{ \begin{tabular}{c} **Wave Velocity** \\ **Fluctuate** \\ \end{tabular}  } & \multirow{2}{*}{**Sensors**} & \multicolumn{6}{c}{**P-Wave Initial Arrival time/ms**} \\ \cline{3-8}  & & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline \multirow{8}{*}{\(\pm 1\%\)} & 1 & 136.785 & 191.625  
In Figure 5b, after population initialization, GA performs a broad search across the entire solution space. However, it quickly converges to a relatively optimal solution, resulting in a larger optimal fitness value. In contrast, PSO directs all particles toward the global optimal solution through its particle update formula, which reduces population diversity in the later stages and further limits optimization accuracy. The convergence speed of the GA-PSO algorithm is between that of PSO-ACO and NM-PSO, but it can find the optimal solution within 50 to 70 iterations, with the optimal fitness value significantly outperforming the other two algorithms. GA-PSO introduces perturbations in the later stages through crossover and mutation operations, enabling refined local search optimization. This approach effectively avoids the \"particle clustering\" phenomenon commonly observed in standalone PSO optimization, resulting in fitness values that are closest to 0. Figure 4: (**a**) Positioning effects under \(\pm\)1% wave speed fluctuation; (**b**) positioning effects under \(\pm\)3% wave speed fluctuation; (**c**) positioning effects under \(\pm\)5% wave speed fluctuation.
## 3 Results
### Degradation Study  
The mean weight loss of each group of samples after five days of enzymatic degradation is shown in Table 2. Non-significant differences (p \(>\) 0.05) were obtained among the groups tested, resulting in a total mean value of around 5.5%. The weight of the PLA scaffolds used as control (immersed in Tris-HCl buffer solution with sodium azide but without enzymes) showed no variation during the degradation test.  
Regarding the pH of the degradation media during the experiment, its value decreased remarkably from day 2 for all the group of scaffolds tested (Figure 3). This reduction was less drastic for the plasma-treated samples and for the samples coated with aloe vera at pH 3, which maintained a pH value greater than 6 at day 3. However, at days 4 and 5, similar results were obtained for all the groups of samples tested. While the coating at pH 3 seems to influence the maintenance of the pH at a higher level, the samples coated with aloe vera at pH 4 or 5 showed a degradation profile that nearly matched the one of the non-treated PLA scaffolds.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Group of Scaffolds** & **Weight Loss \%** \\ \hline PLA & 5.62 \(\pm\) 0.37 \\ PLA plasma & 5.37 \(\pm\) 0.85 \\ PLA aloe vera pH 3 & 4.81 \(\pm\) 0.79 \\ PLA aloe vera pH 4 & 6.01 \(\pm\) 0.90 \\ PLA aloe vera pH 5 & 5.85 \(\pm\) 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average weight loss (%) of the scaffolds after five-day enzymatic degradation.  
Figure 2: Mechanical properties of non-treated, plasma-treated and aloe vera-coated PLA 3D-printed scaffolds under compression testing.  
In Figure 4, SEM images obtained for each type of scaffold tested are shown, including non-degraded PLA samples for comparison (Figure 4a,g). A slight reduction of the diameter of the filaments is observed after 5 days of enzymatic degradation. There is also an evident increment of the microporosity of the scaffolds' surface after the degradation test. This was an expected result, as while immersed in the buffer solution the hydrolytic degradation of PLA takes place via a bulk erosion mechanism; in the presence of the _proteinase K_ enzymes the degradation of the structure is enhanced via a surface-erosion mechanism [40]. The evaluation of the high magnification SEM images (Figure 4g-l) suggests that the enzymatic degradation of the PLA samples followed a different mechanism than the plasma-treated or coated samples, as for the latter, the presence of newly formed voids over the surface can be observed (especially in Figure 4l), which correspond to the scaffolds coated with aloe vera at pH 5). In contrast, the non-treated PLA scaffolds surface showed some small fractures, from which the degradation of the structure progresses.
## 5 Experimental Results: Comparison and Analysis
### Strain Distribution Comparison  
The strain values measured using eight strain gauges installed in the ceiling and floor beams are shown in Figure 8 for each test specimen, for the first cycles of the loading schedule at the major drift angles (0.005, 0.01, 0.02, 0.03, 0.04 rad).  
Figure 8: _Cont._  
For all four test specimens, the ceiling and floor beams showed strain distributions similar to individual members, rather than composite behavior; reversal of strain distribution was observed due to local beam flange buckling or plastic hinge formation. The yield strain was calculated to be approximately 1300 \(\upmu\)m (= 263/205,000) on the basis of the average yield strength (263 MPa) of the beam, obtained through material testing; it was observed that, for most test specimens, yielding of the ceiling and floor beams began at a drift angle of around 0.02 rad. Therefore, the ceiling and floor beams must be modeled as independent members in structural analyses; when modeled as a single member, the cumulative performance values of each cross-section must be considered.
## 4 Applying the GDCAC Algorithm for EM Modeling Considering Renewable Power Penetration  
As mentioned in Section 3, both of the state and action spaces in EM with renewable power penetration are continuous, which means it is not suitable for applying table-based reinforcement learning algorithms (TBRLAs) (e.g., SARSA, Q-learning, Roth-Erev reinforcement learning, etc.) in EM modeling. That is because TBRLA can only deal with the Markov decision-making problem with both discrete state and action spaces; otherwise, a problem called \"curse of dimensionality\" [2] would be caused.  
In [2], a fuzzy Q-learning algorithm was proposed to model the unilateral hour-ahead EM considering renewable power penetration. Although the approach proposed in [2] can effectively make the EM state space continuous, the action space of every NRGenCO is still assumed to be a discrete scalar set. Therefore, in this paper, a modified reinforcement learning algorithm called a GDCAC algorithm [40] is applied for bilateral hour-ahead EM modeling considering renewable power penetration.
## 3 Comparative Analysis of Monitoring Data and Numerical Simulation
### Space-Time Deformation Analysis of Foundation Pit Structure
#### 3.3.2 Foundation Pit Is Connected to the Wall  
In the early earthwork excavation process, the deformation of the top of the northern wall is more stable than that of the southern wall, and it shows obvious \"lag\". The final settlement value of the top of the northern wall is half that of the final settlement value of the southern wall, and the displacement modes of the northern and southern wall are, respectively, translational mode (T) and rotation mode around the bottom (RB) [25; 26] (Figures 8 and 9). The main reason is that the uplift and subsidence deformation of the ground connecting wall is caused by the potential sliding body at the foundation pit bottom, while the earth unloading operations are carried out on both sides of the northern ground connecting wall, and the deformation amplitude of the sliding body at the bottom of the foundation pit is smaller than that of the southern ground connecting wall with only one excavation side, and the northern foundation pit has stronger anti-uplift and deformation ability. It can be seen that controlling the displacement and deformation mode of the wall can control the deformation of the envelope structure. In the construction stage of roof and floor pouring, the integrity of the structure is enhanced because of the large roof and floor pouring, and the settlement deformation of the wall is caused by the self-weight of the wall.  
As can be seen from Figure 10, the vertical displacement of the ground connecting wall ranges from 0.3% to 0.9%. By comparing the deep foundation pit project in Hangzhou soft clay area [24], Shanghai Metro station project [7; 27], and Shanghai ultra-deep shaft excavation [28], the vertical deformation of the wall top caused by foundation pit construction is significantly greater than that of the existing foundation pit project. The main reasons for this phenomenon are as follows: (1) the excavation load of the super-deep foundation pit is large, the unloading modulus of soil in the foundation pit is low, and the soil outside the wall will slide and deform. On the other hand, the supporting structure of the foundation pit converges and deforms from the middle to the corner of the pit, resulting in an obvious corner effect. At the same time, because the embedment of the ground connecting wall is  
Figure 8: The monitoring deformation value and numerical simulation value of the north ground connecting wall.  
relatively small, the supporting structure produces translation mode (T), and the ascending settlement value of the ground connecting wall is small.
## 3 Design of an Adaptive Compound Controller for Reference Model  
### Dynamics of the Reference Vehicle Model  
A 2-DOF single-track vehicle model is developed in Equations (14) and (15), which includes both the lateral and yaw motions, as shown in Figure 5. The steering angles of the front left and front right wheels are assumed identical and denoted by \(\delta_{f}\). This model provides the desired yaw rate and the side slip angle for the predictive model. Under  
Figure 4: Design of the vehicle speed tracking controller.  
Figure 3: Efficiency map of the permanent magnet synchronous motor.  
the assumption of a small side slip angle at the CG, we have \(\beta=\arctan(v_{y}/v_{x})\approx v_{y}/v_{x}\). Therefore, the equations governing the vehicle dynamics can be written as  
\[mv_{x}(\hat{\beta}+\gamma)=F_{y,f}\cos(\delta_{f})+F_{y,r} \tag{14}\]  
\[I_{z}\dot{\gamma}=F_{y,f}\cos(\delta_{f})I_{f}-F_{y,r}I_{r} \tag{15}\]  
Assuming that the front wheel steering angle is small, we have \(\cos(\delta_{f})\approx 1\). The lateral forces acting on tires and tire side slip angles for the front and rear axles can be expressed as follows:  
\[F_{y,f}=K_{f}\alpha_{f} \tag{16}\]  
\[F_{y,r}=K_{r}\alpha_{r} \tag{17}\]  
\[\alpha_{f}=\beta+l_{f}\gamma/v_{x}-\delta_{f} \tag{18}\]  
\[\alpha_{r}=\beta-l_{r}\gamma/v_{x} \tag{19}\]  
where \(K_{f}\) and \(K_{r}\) denote the cornering stiffness of the front and rear tires, \(K=K_{f}+K_{r}\) represents the total cornering stiffness of the vehicle, and \(\alpha_{f}\) and \(\alpha_{r}\) represent the tire slip angles for the front and rear axles, respectively.  
Given that the maximum lateral acceleration is constrained by road friction conditions, the yaw rate should satisfy the condition \(|\gamma|\leq\mu g/v_{x}\)[39]. Note that high-friction road surfaces lead to a larger effective region for maintaining vehicle lateral stability. When the vehicle reaches a steady state, both the accelerations \(\hat{\beta}\) and \(\dot{\gamma}\) are equal to zero. The reference yaw rate and side slip angle can be computed as follows:  
\[\gamma_{\rm ss}=\min\left(\left|\frac{v_{x}\delta_{f}}{L(1+\kappa v_{x}^{2})} \right|,\frac{\mu g}{v_{x}}\right)\text{sign}(\delta_{f}) \tag{20}\]  
\[\beta_{\rm ss}=\gamma_{\rm ss}v_{x}\left(\frac{l_{r}}{v_{x}^{2}}+\frac{ml_{f} }{K_{r}L}\right) \tag{21}\]  
where \(\kappa=m(l_{f}K_{f}-l_{r}K_{r})/\left(LK_{r}K_{f}\right)\) represents the understeer gradient.
## 2 Related Works
### Non-EH Clustering Routing Protocols  
Non-EH clustering routing protocols are further categorized by cluster radius into uniform and uneven clustering protocols. Uniform clustering protocols often see CHs near the sink tasked with more data transfers, leading to quicker energy depletion and potential energy holes or network fragmentation. To address this, uneven clustering protocols that adaptively adjust cluster radius sizes have been proposed. Such protocols divide the network into clusters of varying sizes, with smaller-radius clusters located closer to the sink and larger-radius clusters situated farther away.
## 2 Materials and Methods
### Air Change Rate Estimation  
The steady-state or equilibrium method was applied in order to roughly estimate the air exchange rate through window opening in the different case studies. This method is used when the CO\({}_{2}\) level has reached a steady-state concentration (the occupancy conditions remain fixed for at least three complete air changes). In this case, two air exchange rates were estimated for each measured day: one during the first half of the school day (before the break, when the classroom is empty) and another during the second half.  
This method is documented in various guidelines and standards [36; 37], where the calculation of the air change rate (\(A_{s}\)) is performed according to Equation (3).  
\[A_{s}\left[\mathrm{h}^{-1}\right]=\left(6\times 10^{4}\times n\times G_{p} \right)\div\left\{V\times\left(C_{i}-C_{o}\right)\right\} \tag{3}\]  
where \(n\) is the number of occupants; \(G_{p}\) is the average CO\({}_{2}\) generation rate per person [l/min]--according to [PERSON] [38]; \(V\) is the volume of the classroom [m\({}^{3}\)]; \(C_{i}\) is the steady-state indoor CO\({}_{2}\) concentration [ppm]; and \(C_{o}\) is the outdoor CO\({}_{2}\) concentration [ppm].
## 2 Methodology
### Statistical Analysis of Data  
A statistical analysis of the acquired data was carried out. Both speech intelligibility values and subjective listening difficulty scores were investigated, considering their dependency on different noise conditions, type of mask, and individual factors such as gender and age. Generalized linear mixed models (GLMM), performed using the software R3.6.0 and the lme4 package, were applied to analyze the database.
# An Interactive Method for Detection of Process Activity Executions from IoT Data +
## 4 Method to Identify Activity Executions
### Method  
From the previous elaborations in Section 4 we derive the method depicted in Figure 18, which describes the general process of analyzing raw IoT data for activity executions. We derive this method as a sequence of the essential steps taken by the process analyst to analyze a given IoT data set following the visual information-seeking mantra as described in Sections 4.2-4.6.  
* _Step 1: Visualize all IoT data over time_ to get on overview of the data set, the involved CPS components and their sensors and actuators (cf. Section 4.2).
* _Step 2: Identify relevant CPS components and time frames_ to determine the parts of the IoT data set that need to be further analyzed (cf. Section 4.3).  
Figure 17: All data (except irrelevant EC1 component) with activity and process annotations.  
Figure 18: Derived method for detecting activity executions from IoT data.  
* _Step 3: Filter by CPS component and time frame_ to reduce the amount of data to analyze at a time (cf. Section 4.3).
* _Step 4: Find activity start and end patterns_ to detect the points in time in the IoT data where an activity started and ended (cf. Section 4.4.1). This step may need to be repeated to refine the detected activities according to the required level of granularity [48].
* _Step 5: Determine activity signature_ to make a detected activity identifiable in other parts of the IoT data set (cf. Section 4.4.2).
* _Step 6: Find and label similar activities_ to provide labels for unknown parts of the IoT data set (cf. Section 4.5).
* _Steps 4-6_ have to be repeated for all unlabeled parts of the IoT data set for the current CPS component.
* _Steps 3-6_ have to be repeated for all relevant CPS components and time frames (cf. _Step 2_) of the IoT data set.
* _Step 7: Visualize all detected activities_ to get an overview of all identified activity executions in the IoT data set (cf. Section 4.6).
* _Step 8: Find repeated activity sequences_ to identify candidates for processes (cf. Section 4.6).
# Improving Passband Characteristics in Chebyshev Sharpened Comb Decimation Filters
## 3 Passband Compensation of Chebyshev Sharpened Combs from [8; 10; 11]
### Chebyshev Sharpened Comb Filters [10; 11]
#### 3.3.1 Narrowband Compensation  
We chose \(\omega_{p}=0.22\pi/M\) and the compensator \(C_{1}\) in (3). The optimum parameter \(C_{11\text{o}}=0.7213\), and \(\delta_{0}=0.0111\)dB. The SPT coefficients are given in Table 4, changing \(N_{1}\) from four to eight. For \(N_{1}=8\), we obtain approximately the optimum compensation.  
Figure 3: Wideband compensation of Coleman sharpening filter [8], \(M=16\), using filter \(C_{3}\). Passband magnitude responses of non-compensated, optimal compensated and SPT compensated Coleman sharpening comb filters, with \(N_{3}=12\).  
\begin{table}
\begin{tabular}{c c c c} \hline \(C_{31}\) & \(C_{32}\) & \(\delta_{3}\) [dB] & \(N_{3}\) \\ \hline \(2^{0}\)-\(2^{-2}\) & \(2^{0}\)-\(2^{-2}+2^{-6}\) & 0.0616 & 9 \\ \(2^{0}\)-\(2^{-2}\)-\(2^{-8}\) & \(2^{0}\)-\(2^{-2}+2^{-6}\) & 0.0532 & 11 \\ \(2^{0}\)-\(2^{-2}\)-\(2^{-5}+2^{-7}\) & \(2^{0}+2^{-2}+2^{-4}\) & 0.0474 & 12 \\ \(2^{0}\)-\(2^{-2}\)-\(2^{-5}+2^{-7}\) & \(2^{0}+2^{-2}+2^{-4}\)-\(2^{-9}\) & **0.04591** & **13** \\ \(2^{0}\)-\(2^{-2}\)-\(2^{-5}+2^{-7}\)-\(2^{-10}\) & \(2^{0}\)-\(2^{-2}+2^{-4}\)-\(2^{-14}\) & **0.0452** & **16** \\ \hline \end{tabular}
\end{table}
Table 3: SPT compensator \(C_{3}\) coefficients, passband deviations and the number of adders.
# Detection and Evaluation of Construction Cracks through Image Analysis Using Computer Vision
## 2 Materials and Methods
### Manual Image Classification  
LabelImg was used for manual classification. LabelImg is an open-source graphical image tool on GitHub that provides image annotation mechanisms by labeling bounding boxes of objects. This tool is available for Linux, Windows, and macOS operating systems. It is written in Python and uses Qt as a graphical interface [25].  
According to [26], cracks can be classified according to their origin and moment of appearance:  
* Cracks originated in the plastic state.  
Figure 6: Example of images resulting from the frame extraction process.  
* Cracks caused by plastic contraction. The leading causes from which this type of crack usually originates are hydraulic shrinkage during setting and excessive vibration or towel.
* Cracks originated from the plastic settlement due to four factors: little coating and excessive diameters in the steel, changes in consistency in continuous pours, displacement of the formwork, and deformation of the supporting ground.
* Cracks originated in the hardened state, among which cracks originated by spontaneous movements caused by: contraction due to carbonation and thermal shrinkage; numbness due to thermal expansion, excessive oxidation of reinforcing steel, or excess expansive in cement; and alkali-aggregate reaction.  
Another case of cracks in the hardened state is produced by loads caused by compression, traction, bending, shear, and torsion stresses. Figure 7 shows the types of compression failures that can be generated in concrete.  
The images obtained from the process indicated in Section 2.5.1 were saved in a folder with the classes.txt file containing the classified classes. The manual image classification process was conducted with 1132 images and five classes (Beam_bending_failure, Column_type2_failure, Column_type3_failure, Column_type4_failure, and Column_type5_failure) to classify the types of cracks in beams and test tubes within the images. Table 2 shows the five classified classes and the region of interest where the compression failures were generated. Figure 8 shows how the region of interest was selected using the software Labellmg.