esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
## 2 Automatic Feature Extraction
### Image Classification
[PERSON] et al. [41] performed a comparative study between the application of transfer learning concept and trained from scratch in the crack detection area with an IC setting. Several studies with unique research objectives have been published in the IC category. [PERSON] et al. [23] used MatConvNet [42] to perform crack detection in concrete images. They proposed the application of a sliding window technique resulting in scanning images with any resolution in the testing phase. The specific suggested architecture was employed in [43] to propose a UAV-based crack detection system. As the novelty in this study, in addition to crack detection in the images, the geo-localization of cracks was also performed. [PERSON] et al. [44] considered a large data set (5000 3D pavement images) including diverse examples to help the architecture to learn the possible complexity and diversity of the road surface. The German asphalt pavement distress (GAPs) data set was collected and presented as a publicly available data set in [45]. LeNet [35] and ANIVOS (inspired by both  
Figure 4: Typical output of a deep learning-based crack detection approach based on the IC setting [23]. VGG-16 [38] and AlexNet [34]) architectures were employed to perform crack detection on the proposed data set. In the crack detection area, in addition to the detection of crack images/image patches using an IC setting, the localization of cracks in the images has high importance. Although a considerable research has been conducted on the application of this setting for crack detection, the main drawback is that the overall skeleton of cracks in the images, obtained by stacking the positive patches together (Figure 4), is coarse and fails to provide a detailed localization of the cracks. A summary of the studies of deep crack detection approaches based on the IC setting is shown in Table 1.
## 3 Results and Discussion
### Weight and Cost Estimation  
The aluminium pressure vessel was lighter than the 304 stainless steel and the Grade 5 (Ti-6 Al-4V) titanium pressure vessels, as seen in Figure 9. The 6061-T6 aluminium is the least dense material at a 25 mm wall thickness, weighing 512.24 kg, followed by the titanium vessel, at 854.34 kg, and the 304 stainless steel vessel at 1433.14 kg. However, when a 25 mm wall-thickness vessel is designed for 70 MPa, the weight of aluminium is reduced to 311.18 kg, the weight of 304 stainless to 870.64 kg, and titanium to 519.02 kg. As might be expected, as the wall thickness increases, the weight of the vessel increases. However, it has been found that the weight of the vessel decreases when the storage pressure increases; this is because when hydrogen is compressed to a higher pressure, the hydrogen volume required will be reduced, and this will reflect on the reduction in the size and weight of the vessel. The cost of the aluminium is low in contrast with the 304 stainless steel and titanium. An aluminium vessel costs around GBP 717 for a 35 MPa design, followed by stainless steel, which is GBP 1935, and titanium, which is around GBP 61,640. Meanwhile, for the 70 MPa design, the weight and cost of the vessel materials are presented in Figures 9 and 10.  
Figure 9: Weight of each vessel at 35 MPa and 70 MPa design for 25 mm thickness.
# Implementation of an IoT Based Radar Sensor Network for Wastewater Management
## 2 Methodology
### Selection of Measurement Device
#### 2.1.2 Ultrasonic  
Ultrasonic sound waves are used to send a signal down to the surface of the flow at the bottom of the manhole. The sound waves travel at a speed of approximately 343 m/s in air, which bounce back to be detected by the device. The main drawback with ultrasonic sensors is that false-positives can often occur in humid environments with steam, mist and water condensation. Similarly, where soft objects such as foam are present, the sound energy is absorbed instead of being reflected and the recorded return signal is minimal. Further, ambient temperatures greatly affect the speed of sound, \(c\). For example, in air, \(c\) = 343 m/s at 20 \({}^{\circ}\)C, however \(c\) = 322, 352 m/s at \(-\)15 \({}^{\circ}\)C and 35 \({}^{\circ}\)C, respectively. Thus, in most industrial applications additional temperature compensation sensors are required for ultrasonic measurements.
# An Image Processing Method for Extraction of the Stress Wave Reflection Period
## 3 Experimental Verification
### Platform Construction  
The experimental setup is shown in Figure 3. Prior to testing, the piezoceramic sensor probe was attached to the exposed end of the buried metal piles coated with a coupling agent to ensure good contact between the probe and the end face, thus reducing the acoustic impedance. The PZT probe was used for receiving multiple periodic reflection signals. A hand-held excitation hammer was used to tap the end face of the pile to generate a stress wave. The hand-held excitation hammer was made of iron and weighs 0.5 kg, and the cone design was adopted to reduce the contact area so as to form better pulse characteristics. The magnetic PZT sensor probe was connected to the data acquisition system. The PZT-based accelerometer has a sensitivity of 20.21 mv/(m\(\cdot\)s\({}^{2}\)). All experimental data were displayed on a notebook. Data were acquired at a sampling frequency of 500 kHz with 2048 sampling points.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Materials** & \(a\) **(mm)** & \(h\) **(mm)** & \(g\) **(kg/m\({}^{3}\))** & \(P\) **(GPa)** \\ \hline GB/T(Q235) & 114 & 4.5 & 7800 & 210 \\ \hline \hline \end{tabular} Note: \(a\) is the external diameter, \(h\) is the wall thickness of the pipe, \(P\) is the Young’s modulus of the pipe material, and \(g\) is the density of the pipe material.  
\end{table}
Table 1: Materials and geometric parameters of the experimental pipe.  
In Tables 2 and 3, the processes include setting up seven different buried-depth conditions for the buried metal piles with a design length of 1.00 m, and eight different buried-depth statuses for the 2.00 m buried metal pile. The measurement was repeated 15 times for each working condition. In this experiment, MATLAB [32] and Python [33] were used for signal processing.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Test No.** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline Buried depth (m) & 0.00 & 0.10 & 0.20 & 0.30 & 0.40 & 0.50 & 0.60 \\ \hline \end{tabular}
\end{table}
Table 2: Buried-depth status of the buried metal pile (full length 1.00 m).  
Figure 3: Schematic diagram of the experimental device.  
Figure 2: Schematic diagram of measuring the length of the buried column by the stress wave method.
# Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete
## 3 Materials and Methods
### Collection of Data  
In this study, the target output is the compressive strength (both cubic and cylinder) of MSC at different curing ages ranging from 3 to 388 days. The database was collected from experimentaltests in the available literature [14; 81; 82; 83]. A number of 289 MSC samples have been mixed by raw materials such as: (i) ordinary silicate cements; (ii) admixture consisted of fly ash, slag and silica fume; (iii) crushed stone and (iv) manufactured sand. The properties of the mentioned materials (i.e., compressive strength and tensile strength of cement at 28 days), the mix proportions as well as MSC's compressive strength were detailed in the database, as summarized in Table 1. A factor of 0.82 was used to convert the cylinder and cubic compressive strength of MSC as suggested by [PERSON] et al. [83]. As indicated, the cubic compressive strength of MSC, denoted by Y, ranges from 19 to 96.3 MPa, with an average value of 55.80 MPa, a standard deviation of 1670 MPa (corresponding to a coefficient of variation CV of 29.93%). The characteristics of sand such as stone powder content and fineness modulus are also indicated. As reported by [PERSON] et al. [13], stone powder content in sand was most influenced factor on the compressive performance of MSC.  
Initial data analysis (histogram) was performed and showed that most inputs, except I\({}_{3}\), I\({}_{4}\) and I\({}_{9}\), are well distributed and suitable for the training of the models (i.e., the histogram is close to a Gaussian distribution). For that reason, PCA was performed in this study to reduce the noise in the 11-dimensional input space. As mentioned above, two datasets were prepared for training the models using the raw and PCA pre-processed inputs. The detailed analysis of PCA technique are indicated in the results and discussions section.
# Modelling of a Non-Transferred Plasma Torch Used for Nano-Silica Powders Production
## 4 Results and Discussion  
In this section, the simulation results of the DC non-transferred arc plasma torch used for the synthesis of nano-silica are presented. Furthermore, the influence of the working current on the plasma distribution fields is analyzed, and the efficiency of the torch is discussed for different cases based on comparisons with experimental results. At the nozzle exit, the distributions of temperature and velocity plasma jet are represented. Moreover, the nanosilica particles elaborated in the experimental device were characterized by X-ray diffraction and transmission electron microscopy. For all representative results, the operating gas introduced in the torch was nitrogen gas with flow rates varying in the range (78-240) sccm and current varying in the range (100-200 A).
## 2 Study Site  
The Bay of Arcachon is located in the South-western French Atlantic coast (Figure 1). This coastal lagoon is separated from the open ocean by the Cap Ferret sand spit. Its main flow entrance is divided into two channels, with sandy banks and shoals between them [35]. The presence of a mouth bar and inter-channel banks limits the internal propagation of external ocean waves. Therefore, inside the lagoon, waves are mainly wind generated and fetch limited, especially by the presence of the \"Ile aux Oiseaux\". Wind speed and wind direction vary seasonally; they are variable and weak during summer and mostly westerly or northerly in winter, reaching speeds \(>\) 10 m/s. Tides are semidiurnal, ranging from 0.8 m during neap tides to 4.5 m during spring tides [36], and the high tide surface can reach an area of 174 km\({}^{2}\), mainly composed of a network of narrow channels, which is reduced to 40-50 km\({}^{2}\) at low tide [37]. The main source of freshwater is the Leyre river in the South-east, and the Porge Canal in the North. The total freshwater input represents less than 1% of the tidal prism [37].  
For this study, we chose to consider a study area located on the intertidal zone at Andernos (Figure 1), on the north-eastern side of the Bay. The particularity of the marsh in this zone is that, even though it presents a stable niche of native vegetation, there is a significant presence of the exotic _Spartina_ in small random patches. This is representative of an initial stage of the invasion and is an ideal scenario for our objective of identifying this species from its early stage  
Figure 1: Location of the Andernos’ saltmarshes (white rectangle) in the study site (Bay of Arcachon, SW France).
# Retrieval of Biophysical Crop Variables from Multi-Angular Canopy Spectroscopy
## 2 Materials and Methods
### Radiative Transfer Modelling
Biophysical parameters were varied via uniform distribution, with typical min & max values. Parameters that control the sun–target–sensor-geometry were varied in discrete steps, covering all conditions that were observed in the field. \(\mathrm{n_{lut}}\) = 25,200,000 for each soil and senescence background. A LUT-size of 12,600,000 (\(\mathrm{n_{para}}\) = 50,000) turned out to perform equally well (\(\mathrm{\Delta_{RMSE}}\) < 1%), while allowing a quicker inversion and thus the conduction of more experiments in the same period of time. Accounting for the different potential background signals (soil or senescent material respectively) the LUTs are duplicated and only varied by a different background signal. This method, therefore, shall be called duplex LUT. Finally, artificial noise can be applied to make simulated spectra more realistic and to improve the inversion accuracy (overview given by [6]). The best performing LUT settings have been varied in noise type (Gaussian additive/Gaussian inverse multiplicative) and noise level (0.0%, 0.1%, 1.0%, 2.0%, 5.0%, 10.0%) respectively.
# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 2 Materials and Methods
### Part 2: Domain Data Preparation and Splitting
#### 2.4.2 Noise Embedding  
In a workshop environment, various ambient noises influence recorded data. The following are the most common types of disruptions in a production setting and the synthetic noise types used to represent them.  
* White Gaussian Noise (WGN): Represents broadband random noise that introduces non-repetitive disturbances in the signal. This can arise from electronic interference, environmental factors, or other unpredictable disruptions in the measurement system [23].
* Electrical Noise (EN): A 50 Hz power line interference caused by electrical components and circuits in the surroundings [5].
* Impulse Noise (IN): Sudden, short-duration spikes or impulses in the signal, typically resulting from unexpected external impacts or mechanical shocks [24].
* Combined Noise (CN): A mixture of all the mentioned noise types, reflecting complex interference in a production environment [5].  
To ensure noise variability, the synthetic noise samples were randomly regenerated for each data sample. This prevented the introduction of repetitive patterns into the simulated disruptions. Within the different scenarios, the effects of each noise type and their combination were analyzed under both weak and strong noise variations with characteristics selected through prescreening tests. Examples and exact characteristic parameters of the added noise across various scenarios are provided in Section 2.5.
## 3 Wireless Body Area Network (WBAN)
### EMG sensors  
Electromyography (EMG) sensors detect the electrical activity produced by muscles. These devices are commonly used in medical and research fields to assess muscle function, diagnose neuromuscular disorders, and control prosthetic limbs. EMG sensors consist of electrodes placed on the skin surface above the muscles of interest. These electrodes pick up the electrical signals generated by muscle contractions. When you move a muscle, electrical impulses are sent from your brain through your nerves to the targeted muscle fibers, causing them to contract. The EMG sensor detects these electrical signals and converts them into measurable readings [57]. There are two types of EMG sensors: surface EMG and intramuscular EMG. Surface EMG sensors use adhesive electrodes placed on the skin, while intramuscular EMG sensors involve inserting fine needle electrodes directly into the muscle [58]. EMG sensors are valuable in various applications, including medical diagnostics, sports science, rehabilitation, and human-computer interfaces. They enable researchers and clinicians to assess muscle function, monitor progress during therapy, and develop innovative technologies that interface with the human body [59]. Medical professionals use EMG sensors to keep track of a patient's nerve and muscle problems. Wearable gadgets that monitor a patient's behavior use such sensors as well. In systems that use emotion-based intelligent information sensing, EMG sensors prove to be essential. There are systems that use EMG signals and other body vital monitoring biomedical sensors to evaluate face muscle variability and classify each change with its related emotion [60]. Table 4 presents the common market available IoT medical sensors. Also, the table introduces the main specifications of each sensor [61].
## 5 Results and Discussion  
### Machine Learning Model  
#### 5.1.1 Establishment of Models  
As mentioned above, two data sources were used to build the two ML models: (1) vibration and RCOU signals obtained from the flight data files, as shown in Figure 5d-f; and (2) data based on the audio sensor signal, as shown in Figures 6 and 7. These features reveal information inside the data, and we built two vectors that contain the time-domain and time-frequency domain features. Then, using the two training datasets, two classification models were built to analyze their accuracy. Both of these models used the nearest neighbor (kNN) classification model, but their parameters were different. Through repeated experiments, the model parameters that are most suitable for the flight data parameters of the quadcopter were found. In this research, we used the MATLAB(r) nearest neighbor classification model function library.  
The kNN machine learning algorithm was used because we aimed study the efficacy of the features. Thus, by using the simple ML algorithm, the results could be as clear as possible. In the process of building the kNN model, several parameters were tuned to identify the best classification model.  
Additionally, we used confusion matrixes to evaluate the model. In the field of machine learning, a confusion matrix is a comparison table for evaluating classification models, which shows the accuracy of the model and the individual discrimination rate. The confusion matrix is mainly evaluated through the proportional relationship between actual and predicted values. The accuracy formula  
settings, the three main tuning parameters were the distance metric, distance weighted, and the number of nearest neighbors. For the model built for the non-fault state and the damaged propeller, the second type of parameter setting had the highest accuracy for the damaged propeller state. The accuracy was 91 percent and the running time was 1.32 s (Table 3).  
The same steps were used to find the kNN classification model of the other fault states. The best fault kNN classification model could then be identified based on time-domain (flight controller) data analysis. The same process was also able to be used in the analysis of double and triple faults.
## 6 Result and Discussions
### Evaluation Metrics
#### 6.3.3 Word Error Rate  
The WER is the metric used to assess the language model's performance. A lower WER indicates better performance, which means fewer words are predicted wrong. We have obtained the WER of 0.16 given in Table 6, which is relatively low and indicates the model is performing well in predicting the next word.
## 3 Results and Discussion
### ERT Field Acquisition and Data Analysis
#### 3.1.4 Comprehensive Interpretation  
According to the measurement results of three survey lines, L1, L2, and L3, there is a high resistivity region in the earth resistivity profile. In order to analyse whether the high resistivities are correlated with each other in space, a 2.5D simulated diagram of the three survey lines in relative positions is drawn, as shown in Figure 6. It is observed that if the high resistivity regions of various profiles are selected and connected in line, the selected region and path correspond to the tunnel location and path in the literature.  
Figure 5: L3 result.  
Figure 6: 2.5D simulated diagram of three survey lines.
## 2 Circuit Model and Theoretical Analysis
### Analysis of CCO Characteristics and ZPA Operation  
Based on the above analysis, when \(R_{\rm ac}\) is not included in the expression of \(\mathbf{I_{\rm s}}\), CCO characteristics can be realized. It is clear from Formula (4) that if \(A=0\) holds, \(\mathbf{I_{\rm s}}\) is independent of \(R_{\rm ac}\), namely  
\[Z_{1}Z_{\rm m}+Z_{1}Z_{\rm p}+Z_{\rm m}Z_{\rm p}=0 \tag{6}\]  
When Equation (6) holds, the input impedance \(Z_{\rm in}\) of the proposed system can be expressed as  
\[Z_{\rm in}=\frac{\mathbf{I_{\rm in}}}{\mathbf{I_{1}}}=\frac{\omega^{2}M^{2}(Z_{1}+Z_ {\rm m})}{\omega^{2}M^{2}+Z_{\rm m}R_{\rm ac}+Z_{\rm p}R_{\rm ac}+Z_{\rm m}Z_{ \rm s}+Z_{\rm p}Z_{\rm s}} \tag{7}\]  
According to Formula (7), if the following Formula (8) is established, the input impedance \(Z_{\rm in}\) is pure resistance, and the ZPA input of the system is realized.  
\[\omega^{2}M^{2}+Z_{\rm m}Z_{\rm s}+Z_{\rm p}Z_{\rm s}=0 \tag{8}\]  
The conditions for satisfying CCO and ZPA input can then be summed up as  
\[\left\{\begin{array}{l}Z_{1}Z_{\rm m}+Z_{1}Z_{\rm p}+Z_{\rm m}Z_{\rm p}=0\\ \omega^{2}M^{2}+Z_{\rm m}Z_{\rm s}+Z_{\rm p}Z_{\rm s}=0\end{array}\right. \tag{9}\]  
When Formula (9) is established, the current expressions can be simplified as  
\[\left\{\begin{array}{l}\mathbf{I_{1}}=\frac{(R_{\rm ac}Z_{\rm m}+R_{\rm ac}Z_{ \rm p})\mathbf{\mu_{\rm m}}}{\omega^{2}M^{2}(Z_{1}+Z_{\rm m})}\\ \mathbf{I_{\rm p}}=\frac{Z_{\rm m}(R_{\rm s}+Z_{\rm s})\mathbf{\mu_{\rm m}}}{\omega^{2 }M^{2}(Z_{1}+Z_{\rm m})}\\ \mathbf{I_{\rm s}}=-\frac{Z_{\rm m}\mathbf{\mu_{Obviously, according to Formula (11), the input impedance \(Z_{\text{in}}\) is related only to \(R_{\text{ac}}\), so the system input impedance is purely resistive, and ZPA input can be achieved under different loads. According to Formula (12), the transconductance gain \(G\) is independent of \(R_{\text{ac}}\). Therefore, the proposed system can achieve CCO functions.
## 3 System Model
### Communication Model  
The transmission process only takes place when the task is offloaded. The transmission process uses OFDMA, which is able to occupy multiple subcarriers simultaneously when the terminal device offloads data. Therefore, in epoch \(t\), the data uplink transmission rate \(R_{i,j,t}\) for the terminal device \(i\) sending tasks to the edge server \(j\) is given by  
\[R_{i,j,t}=w_{i,j,t}\text{Blog}_{2}(1+\frac{h_{i,j,t}p_{i,j,t}}{\delta^{2}}), \tag{2}\]  
where \(w_{i,j,t}\) is the number of subcarriers that have been selected by the terminal device for the data offloaded, \(B\) denotes the bandwidth of the subcarrier, \(p_{i,j,t}\) denotes the transmission power of each subcarrier, \(h_{i,j,t}\) denotes the channel gain of each subcarrier, and \(\delta^{2}\) is the channel noise power. The subcarrier channel gain \(h_{i,j,t}\) can be expressed as  
\[h_{i,j,t}=\alpha_{0}d_{i,j,t}^{-2}, \tag{3}\]  
where the channel gain at a reference distance of one meter is denoted by \(\alpha_{0}\), and \(d_{i,j,t}\) is the Euclidean distance between the terminal device \(i\) and the edge server \(j\), which is set at random according to a uniform distribution.  
In MEC systems, the size of the data provided by the edge server as a result of the computations is usually so small as to be negligible [12]. Therefore, the transmission delay of the downlink is not considered. The transmission delay of the uplink is given by  
\[T_{i,j,t}^{trans}=O_{i,j,t}\frac{I_{i,t}^{a}}{R_{i,j,t}}, \tag{4}\]  
where \(O_{i,j,t}\) denotes the offloading ratio of the terminal device \(i\) to the edge server \(j\). The energy required to offload a task from the terminal device to the edge server is equal to the energy consumed by the terminal device \(i\) to transmit data to the edge server \(j\). Then, the energy consumed by the terminal device during transmission can be expressed as  
\[E_{i,j,t}^{trans}=w_{i,j,t}p_{i,j,t}T_{i,j,t}^{trans}. \tag{5}\]  
The edge server is fixed, as it is wired to the base station. Data can be transferred between multiple edge servers over optical fiber links with negligible delay and energy loss during transmission.