esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Hardware Realization
### Experimental Response of the Speed and Current with Varied External Load  
The experimental response to varying external loads is presented in Figure 18. The motor was started using the Volt/Hz control technique; the generator was not loaded. The speed was then gradually increased from 0.1 to 0.25 p.u-speed and finally to 0.4 p.u-speed by using the proposed method. At \(t\) = 26.26 s, the generator was connected to an electrical load by using a 3 \(\Omega\) resistor. The speed dropped temporarily but quickly recovered to 0.40 p.u-speed. At \(t\) = 26.39 s, a minimum speed of 0.367 p.u-speed was reached. After the load was connected, the currents \({i_{q}}^{*}\) and \({i_{q}}\) increased by 0.033 p.u-current from 0.013 to 0.046 p.u-current. At \(t\) = 35.64 s, the load was disconnected. A short spike in speed was observed on the speed response graph to a maximum at 0.440 p.u-speed. The currents \({i_{q}}^{*}\) and \({i_{q}}\) immediately returned to 0.013 p.u-current.  
A similar result was observed when the speed was increased to 0.65 p.u-speed and a 3-\(\Omega\) load was applied. The speed sharply decreased to a minimum of 0.590 p.u-speed and sharply increased to a maximum of 0.710 p.u-speed when the load was applied (\(t\) = 50.90 s) and removed (\(t\) = 65.85 s). Applying the load also caused increases in the currents \({i_{q}}^{*}\) and \({i_{q}}\). These experiments revealed that the motor speed can recover quickly in response to a load, demonstrating that the proposed system is also robust when implemented in hardware.  
Figure 17: Experimental result for current response in the _d-q_ axis.  
The next part discusses clarifying and verifying the roles of and linkages between the previous sections in realizing the PMSM sensorless control with ML-based observers. The proposed ML-based observer use modified the ENN as a basic algorithm of the ML model. The development of the ML model needs several steps. Section 3.2 outlined the steps. Then in Section 3.3.2, the ML model was trained and validated offline using a data-driven method, which was previously recorded from the simulation (Section 3.3.1). The validated ML model was attached to the ML function block (Section 3.4.1). The output of the function block was linked to the PLL function block (Section 3.4.2). The combination of these two function blocks formed the proposed ML-based observer. Then, the proposed ML-based observer was implemented in a full-control process environment to verify its performance. As a result, the simulation of the full-control process environment (Section 4) and experimental hardware (Section 5) confirmed that the proposed ML-based observer could be used in sensorless PMSM drive control. It can follow the transition of speed command changes well and is also robust to load changes.
## 4 Experiment and Results
### Experiment Design
#### 4.2.2 Dataset Division  
In this paper, the augmented dataset was selected to train and test the YOLO model, and the detection results were analyzed (Section 4.3). Meanwhile, the same operations were performed on the original dataset as a comparison to study the effect of dataset size on the detection accuracy (Section 5.1).  
Figure 5: Labeling components in the image of structural drawings.  
To prevent overfitting, the original and augmented datasets were divided into two parts, respectively. Eighty percent of each dataset were used to train the YOLO model while the remaining 20% was applied as a test set to evaluate the performance of the trained neural network of YOLO. The numbers of training and testing images in each dataset are shown in Table 2.
## Materials and Methods
### Antimicrobial Activity  
Antimicrobial capacity was assessed as per the Kirby-Bauer test, following an agar well diffusion method described by [PERSON] and [PERSON] [30]. This method is based on the relation between the concentration of the substance (in our case, the antimicrobial peptides) necessary to inhibit the bacterial strain and the growth inhibition halo on the surface of an agar plate with a suitable culture medium, which is inoculated homogeneously with the specific bacteria to be tested. A total of 6 strains were selected in this study. The nutrient broths employed were _Pseudomonas_ agar base for _P. aeruginosa_, Baird-Parker agar medium for S. aureus, streptomycin thallous acetate (STAA) for _B. thermosphacta_, and trypticase soy agar (TSA) for _L. monocytogenes_, _S. enterica_ and _E. coli_. Strains were prepared in saline solution following the commercial instructions. Then, 0.2 mL (_B. thermosphacta_, _P. aeruginosa_, and _S. aureus_) or 1 mL (L. _monocytogenes_, _S. enterica_, and _E. coli_) of broth was spread with a sterile Drigralsky spatula or poured onto plates, respectively. The bacterial concentration of the inoculum should be approximately 108 colony-forming units (CFU)/mL to ensure the highest growth of the microorganism inoculated in the growth medium. Seven wells measuring 6 mm in diameter were made using a sterile cork-borer. About 25 \(\upmu\)L of each hydrolysate was added into 5 wells onto the solid media in the nutrient agar. The rest of the wells contained 25 \(\upmu\)L of controls, one for the positive control and the other for the negative. All components were sterilized before their addition onto the plates through a sterile 0.22 um pore cellulose acetate filter. Positive controls were 50 ppm of gentamicin and 105 IU nisin/mL of Nisin for Gram-negative bacteria and Gram-positive bacteria, respectively. Sterile water was the negative control employed for both assays. The incubation conditions were 24 hours at 37 \({}^{\circ}\)C. The diameter of the inhibitory zone surrounding the wells (mm) was measured using a manual Vernier caliper. The percentage of inhibition was calculated following the formula shown below:  
\[\text{{Inhibition }}(\%)=\frac{(\text{{Diameter }}\text{{hydrolysate-Diameter }}\text{{blank}})}{(\text{{Diameter }}\text{{positive }}\text{{control-Diameter }}\text{{blank}})}\times 100\]
# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 5 Evaluation Scenario
### Simulation Design for Pollution Use Case  
The simulations for the pollution use case resulted in a batch of scenarios divided into two sets. The first set of simulations consists of testing standard speed limit modifications in each of the 16 sections of the highway shown in Figure 4, as if a pollution episode were present in each section. The speed limits tested range from 120 km/h down to 50 km/h, as  
Figure 4: C-32 highway sections and location of incidents simulated with the digital twin.  
discussed in Section 6.2.1, where the results of one simulation of this type are presented and analyzed.  
In the second set of simulations, the same approach is used, but speed limit modifications are applied not only to the incident section, but also to up to three previous sections. In this case, as shown in Section 6.2.2, the speed limits tested range from 90 to 70 km/h, as these speeds are considered the most effective for this use case.  
The performance evaluation has been carried out using the default scaling factor (i.e., 1), since high congestion situations are not considered in the pollution use case. However, to gather enough results for evaluating the decision-making algorithm, these simulations were repeated using three extra scaling factors, resulting in a batch of 64 simulations.
# Depth Retrieval Procedures in Pulsed Thermography: Remarks in Time and Frequency Domain Analyses
## 4 Evaluation of Analysis Parameters for Depth Prediction
### Selection of Sampling Frequency  
From the results presented above it, was observed that a low sampling resolution can be a strong limitation especially in the case of measurements over time for defects close to the surface. For this purpose, new measurements were performed to evaluate the effect of the sampling resolution on the depth results. In particular, a new dataset was collected, monitoring the heat diffusion into the material bulk with a frame rate of 88 Hz. The new sampling frequency contributed to the increase of the temporal resolution reducing the time step from 25 ms to 11 ms. Indicative results from the time domain analysis of the defects in the Series A and B, which produced the largest deviations, are summarised in Table 2. Apart from the change of the sampling resolution, all other experimental parameters were the same as in the previous measurements (i.e., selection of the sound area next to each detected defect).  
As can be seen from the results in Table 2, by increasing approximately twice the temporal monitoring resolution, the error production is dramatically reduced. In particular, when studying the heat diffusion with a time step of 25 ms the error production for the defects of Series A was 15%, while by reducing this step to the value of 11 ms the error was calculated to be 1.5%. On the other hand, larger deviations were observed for series' B defects which however are also reduced if compared with the previous analysis. As discussed in the previous analysis, the large deviations observed on series' B defects can also be a result of the 3D heat conduction effect, nonetheless from the results in Table 2 it can be seen that a higher sampling frequency can partially overcome the above negative effect.
# Axial Load Transfer Mechanism in Fully Grounded Rock Bolting System: A Systematic Review
## 3 Analysis and Results
### Influential Parameters on the Axial Behaviour of FGRBSs
#### 3.3.4 The Interaction between Grouting Process and Rock Mass Condition  
The quality of rock mass can heavily affect grouting process. For instance, weak and fractured rocks can pose challenges to achieving optimal grouting results. Studies have shown that voids and fractures conditions within the rock mass can contribute to significant grout loss [11; 94]. Additionally, even non-continuous discontinuities can play as open fractures, leading to inaccurate predictions of both hydraulic conductivity and grout flow [95]. Also, it is suggested to apply a resin-grouted rock bolt in rocks without widely open fractures or discontinuities [96]. To address these issues in situations where internal connections within the rock may impede grout flow, innovative techniques like forensic excavation of rock mass (FERM) could prove valuable in detecting these problematic connections [97].  
Along with rock mass conditions, other several key parameters, such as the grouting layer and grouting pressure, significantly impact the quality of the grouting reinforcement system [98]. For instance, the fractures' sizes gradually decrease by increasing the depth of the grouting procedure. As a result, by applying moderate pumping pressure, the grout properly spreads in the voids, which results in suitably compacting the surrounded rock mass. Conversely, applying high pumping pressure allows for the grout to not only penetrate even microfractures but also potentially induce new fractures in the rock itself, achieving a more comprehensive \"fracture grouting\" effect [98].
## 3 Materials and Methods
### Data Preprocessing  
The categorical information features are transformed to nominal type. The Cleveland dataset has five target classes (0, 1, 2, 3, 4) while Statlog has 1 and 2. In reference to Cleveland, the numbers in the range 1 to 4 are altered to 1 because the major goal of this study is to forecast whether a patient is at risk of coronary heart disease or not. The Statlog dataset target classes 1 (No heart disease), and 2 (Presence of disease) are converted to 0 and 1, respectively. As a result, the 'target' attribute in the datasets only has two classes: 0 and 1.  
Data imputation is the procedure of substituting missing values of the attributes in the input data before training ML algorithms for the prediction problem. There are six missing valued instances in the Cleveland dataset. There are no missing values in the Statlog dataset. The missing values are replaced with the majority mark of that attribute and remain 573 total observations in which 314 accounted for 55% are target class 0 and 259 accounted for 45% are target class 1, as illustrated in Figure 4. In terms of the target class, the dataset appears to be balanced.  
From Figure 4, the blue color bar indicates class 0 observations, i.e., absence of coronary heart disease, and the red color bar indicates class 1 observations, i.e., presence of coronary heart disease.
# Towards a Real-Time Description of the Ionosphere:
## 6 Validation Results for _fo_F2 Based on Radio Occultation Observations
At mid and high latitudes, the seasonal dependence of the error associated with both models is remarkable, with lowest errors during summer months and highest during winter months. Figure 23 shows the IRI and IRTAM _RMSPE_ maps obtained as a function of modify and the solar activity. This figure highlights a clear improvement of the IRI performance made by IRTAM only for MSA conditions. The poor results observed for LSA from the IRI side were expected, because the conditions characterizing the solar minimum in 2008/2009 [64; 65], much lower and prolonged than earlier minima, represented a chal  
lenge for IRI [66; 67; 68]. At the same time, it is somewhat unexpected that IRTAM cannot improve the IRI output for LSA, even though the model assimilates _fo_F2 values that well represent the very low solar activity conditions of that period. As seen before, most of the improvement made by IRTAM is narrowed to mid latitudes.
# IoTactileSim: A Virtual Testbed for Tactile Industrial Internet of Things Services
###### Abstract  
With the inclusion of tactile Internet (TI) in the industrial sector, we are at the doorstep of the tactile Industrial Internet of Things (IIoT). This provides the ability for the human operator to control and manipulate remote industrial environments in real-time. The TI use cases in IIoT demand a communication network, including ultra-low latency, ultra-high reliability, availability, and security. Additionally, the lack of the tactile IIoT testbed has made it more severe to investigate and improve the quality of services (QoS) for tactile IIoT applications. In this work, we propose a virtual testbed called IoTactileSim, that offers implementation, investigation, and management for QoS provisioning in tactile IIoT services. IoTactileSim utilizes a network emulator Mininet and robotic simulator CoppeliaSim to perform real-time haptic teleoperations in virtual and physical environments. It provides the real-time monitoring of the implemented technology parametric values, network impairments (delay, packet loss), and data flow between operator (master domain) and teleoperator (slave domain). Finally, we investigate the results of two tactile IIoT environments to prove the potential of the proposed IoTactileSim testbed.  
5G/6G; URLLC; tactile Internet; industrial IoT; network emulator; robotic simulator; virtual testbed +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Equations used
Increasing the weight of the satellite allows for the application of more advanced and heavier optic systems that offer a larger field of vision and higher quality of imaging. Therefore, in the first analysis (Section 3.1), the authors used a width of the swath width that increases with the weight of the satellite, to reflect the realistic structural and technological possibilities of nanosatellites. Specific values of swath width assigned to specific weights of satellites result from the assumptions concerning the configuration of the optic systems and the technical parameters of this class of devices. Specific values are presented at the further stages of the experiment to ensure consistency with the assumptions adopted for the purposes of the analysis. In the other analyses (Sections 3.2-3.5), the authors decided to use the parameters of the MultiScape 100 CIS sensor (Simera Sense Europe BV, Technologielaan 9, 3001 Leuven, Belgium), which are listed in \[\Delta v=v_{e}\cdot\ln\frac{m_{p}}{m_{k}}, \tag{9}\]1  
The MultiScape100 CIS was designed with a specific goal in mind, i.e., to record images of the Earth with a higher level of detail and a broader range of spectral bands, while fitting inside the CubeSat format of the 3U and 6U size. The sensor is based on a CMOS matrix with a shutter and a multi-spectral filter in the visible and near-infrared spectrum (VNIR). Additionally, each channel has up to 32 digital TDI (\"Time Delay Integration\") stages to improve the sensitivity and quality of the acquired images [29]. **Duration of the satellite's flight over a given area**  
* Case with a variable weight of the satellite \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline GSD at the altitude of 500 km & 4.75 & m \\ Swath width at the altitude of 500 km & 19.4 & km \\ Number of channels in the VNIR band & 7 & - \\ Spectral range & 450–900 & nm \\ Number of pixels & 4096 & pix \\ Pixel size & 5.5 & \(\mu\)m \\ Sensor type & “pushbroom” & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the MultiScape 100 CIS sensor. Source: [29]. Table 2 shows the key parameters related to the orbital motion of the satellite and its observation capacity. It includes the weight of fuel, thrust force, and the altitude of the orbit, as well as orbital velocity and acceleration of gravity that influence the satellite. * Case with variable fuel weight Table 3 presents the initial weight of the satellite, orbit altitude, orbital velocity, and the active acceleration of gravity that influence the dynamics of the flight.
## 5 Discussion and Conclusions
Future studies will incorporate datasets like TAO [70] and KITTI [71] to evaluate its generalizability in these contexts. Furthermore, the effect of integrating temporal smoothing or adaptive reinitialization techniques in stabilizing the tracks will be analyzed in addition to integrating temporal occlusion reasoning, such as interpolation-based ReID to improve recovering tracks after occlusions. Future work will focus on key improvements such as enhancing the ReID module to better handle identity switches in dense scenes, integrating advanced motion models for tracking complex behaviors, and optimizing computational efficiency for real-time applications through efficient architectures or hardware accelerators. Future work will also explore the integration of advanced optimization algorithms to enhance the parameter search space for motion prediction and ReID modules. Recently developed successful methods, such as the Mayfly Optimization Algorithm [72] and Improved Gorilla Troops Optimizer [73], offer exciting opportunities to refine module configurations further, especially in handling diverse tracking scenarios. This research received no external funding. Not applicable. Data Availability StatementData are contained within the article  
The authors declare no conflict of interest.
## 4 Results
### Data Preparation  
The first step was preparing the signals, as there were different sampling rates and amplitudes. In Figure 6a, the resampled signals corresponding to the current and vibration HLT class are shown to illustrate the acquired signals; the amplitudes of the signals correspond to the values obtained with the 12-bit ADC. In contrast, Figure 6b-e present the steady-state, normalized samples to be analyzed, corresponding to the CONT, C1 OR, C3 OR, and C5 OR classes. This figure shows that the changes in the signals due to the faults are not graphically appreciable.
# Farmed Insect Frass as a Future Organic Fertilizer
## 3 Results and Discussion
### Chemical Changes in Acid Peat
Frass had a more acidifying effect on the analyzed substrate than urea, most likely because it contains acid-producing compounds, such as sulfates. Frass has a high content of organic matter that undergoes mineralization with the release of CO\({}_{2}\). Therefore, frass could acidify the substrate to a greater extent than urea since the latter does not contain organic matter. According to [PERSON] et al. [21], the decrease in soil pH is due to the acidic pH of frass and its rapid mineralization, leading to the release of CO\({}_{2}\) and organic acids. Different results were obtained in deacidified peat: urea exerted an acidifying effect, whereas mealworm frass stabilized pH [37]. In addition to the pH of horticultural substrates (which affects the availability of nutrients for plants), EC should also be measured because its value provides information about the concentrations of dissolved nutrients in the soil solution and the salt content of soil. High rates of mineral fertilizers increase soil salinity due to excessive concentrations of the following ions: NO\({}_{3}\)\({}^{-}\), K\({}^{+}\), Na\({}^{+}\), Cl\({}^{-}\), and SO\({}_{4}\)\({}^{2-}\). The salimization of horticultural substrates is usually caused by excessive accumulation of K\({}^{+}\) and SO\({}_{4}\)\({}^{2-}\) ions and, to a lesser extent, Cl\({}^{-}\) and Mg\({}^{2+}\) ions, followed by Ca\({}^{2+}\), NH\({}_{4}\)\({}^{+}\), and phosphate ions. Plant tolerance to salt stress varies depending on the species, growth stage, soil moisture content, and cultivation method. Under standard conditions, the EC of peat used in the cultivation of ornamental trees and shrubs with low, moderate, and high nutrient requirements should be \(<\)0.8, \(<\)1.1, and \(<\)1.4 mS cm\({}^{-1}\), respectively [47; 48]. In this incubation experiment, EC was determined at 0.13 to 0.99 mS cm\({}^{-1}\), remaining within the above range (Table 4). Urea contributed to a significantly higher increase in acid peat's EC than frass. The noted increase was caused by higher concentrations of mineral N, particularly N-NH\({}_{4}\), in peat fertilized with urea, which indicates that mineral N was released more rapidly from urea than from frass. It should be noted that the content of the other analyzed nutrients (K, Na, Cl, S-SO\({}_{4}\), Mg, Ca, and P) in peat was lower after the application of urea than frass, and similar results were reported for deacidified peat [37]. The content of these nutrients in acid peat generally increased with increasing N rates. The only exception was Ca, and the decrease in its availability resulted from the fact that this element forms insoluble complexes with phosphate anions.