esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Experimental Settings  
A total of three different runs were performed, using a transfer learning technique, for each filter and each model by randomly selecting the training, validation, and test sets. Different scenarios can be considered for transfer learning. The first scenario would be to have a large dataset (in our case images), but different from the pre-trained model's dataset and to be able to train the entire model, being useful to initialize the model from a pre-trained model, using its architecture and weights. The second scenario would be when the set of images that you want to train has a certain similarity with the images that were used to train the model, or when you have a small set of images to train the new system. In this case, it is usually chosen to train some layers and leave others frozen. Finally, the third scenario would be to have a small set of images, similar to the pre-trained model's dataset. In that case, they usually freeze the convolutional base. The scenario used in this article has been the first scenario: the model is initialized from a pre-trained model (using its architecture and weights), and the entire model is trained/adjusted (no layer was frozen). Therefore, the procedure that was followed for each filter and deep learning model is the following. First, the model pre-trained with the Imagenet dataset is loaded, removing the last default layers, and replacing them with layers that offer an adequate solution to the problem to be solved (flatten layer). On the other hand, after some experiments in cases where the models suffered from overfitting, measures would be incorporated to avoid its behavior, so we embed regularization kernels and a dropout layer that 20% of the neurons were removed. Finally, a fully connected layer whose activation function is a sigmoid. This results in a single output. Additionally, in this last layer, \(l_{1}\) and \(l_{2}\) kernels have been applied to regulate training. These kernels are penalty factors added to the loss function so that abrupt changes in the values of the weights are penalized during training to avoid over-fitting the models. Therefore, the usual transfer learning procedure was used. Relying on prior learning, it avoids starting from scratch and allows us to build accurate models in a time-saving manner [77; 78; 79]. The input to the different deep learning models is the breast cancer histopathology images. These images received the appropriate scaling for the expected input to each neural network. An Adam optimizer with a learning rate of \(10^{-5}\) was used. During training, the binary cross entropy was used as the loss function and a total of 50 epochs were iterated for each model. However, in general, not so many iterations were performed because early stopping with a latency of 15 was used in terms of validation accuracy, with the weights of the best result recovered at the end of training. A batch size of 128 was used.
###### Abstract  
Reliability is one of the major requirements for power and opto-electronic devices across all segments. High operation temperature and/or high thermomechanical stress cause defects and degradation of materials and interconnects, which may lead to malfunctions with costly or even life-threatening consequences. To avoid or at least reduce failures, nondestructive testing (NDT) methods are common within development and production of power and opto-electronics. Currently, the dominating NDT methods are X-ray, scanning acoustic microscopy (SAM), and transient thermal analysis (TTA). However, they have different strengths and weaknesses with respect to materials and mechanical designs. This paper compares these NDT methods for different interconnect technologies, i.e., fellow soldering, adhesive, and sintered interconnection. While X-ray provided adequate results for soldered interfaces, inspection of adhesives and sintered interconnects was not possible. With SAM, evaluation of adhesives and sintered interconnects was also feasible, but quality depended strongly on the sample under test. TTA enabled sufficiently detailed results for all the interconnect applications. Automated TTA equipment, as the in-house developed tester used within this investigation, enabled measurement times compatible with SAM and X-ray. In the investigations, all methods revealed their pros and cons, and their selection has to depend on the sample under tests and the required analysis depth and data details. In the paper, guidelines are formulated for an appropriate decision on the NDT method depending on sample and requirements.  
reliability; nondestructive testing; power electronics; X-ray; scanning acoustic microscopy; transient thermal analysis; TTA; sintering; LED; MOSFET +
Footnote †: journal: Applied Sciences
## 1 Introduction
[11], their dimensions are likewise constrained. [PERSON] et al. [14] proposed a deterministic sparse measurement matrix based on Euler's square; however, its size is constrained to \(M\times c\left(M\mu\right)^{2}\) where \(M\) represents the measurement number, \(\mu\) represents the mutual coherence, and \(c\in\left[1,2\right).\) [PERSON] et al. [15] constructed the deterministic bipolar measurement matrix utilizing pseudo-random sequences, utilizing pseudo-random sequences; however, the number of measurements is constrained to \(~{}2^{r}-1\) where \(r\) is an integer. [PERSON] et al. [16] proposed a more flexible deterministic bipolar measurement matrix based on Legendre sequences; however, the number of measurements is constrained to prime integer or even integer. [PERSON] et al. [17] proposed a deterministic sparse measurement matrix with a flexible measurement number using the bipartite graph. However, based on our experimental results, its performance still requires further improvement. There are also many other deterministic sparse measurement matrices based on coding theory [18; 19], expander graphs [20], balanced incomplete block designs [21], etc. However, due to the specific construction methods of deterministic sparse measurement matrices, their dimensions are often constrained to certain values, highlighting the need for more flexible designs. Inspired by [9] and the observation that pseudo-random sequences share similar properties with random sequences, we constructed a deterministic sparse measurement matrix using an pseudo-random sequence known as the Legendre sequence. The primary contributions of this paper include the following:  
1. A simple deterministic sparse measurement matrix is proposed utilizing the Legendre sequence. The proposed measurement matrix has a flexible measurement number, exhibits low computational capability, and requires small storage space. 2. Empirical analysis of the phase transition is carried out and an evaluation of the practical features of the proposed measurement matrix is performed. 3. The performance of the proposed measurement matrix is compared with that of several state-of-the-art measurement matrices using random signals and images. This paper is organized as follows: In Section 2, we review the theories of CS and the Legendre sequence. Section 3 introduces the methodology used for constructing the measurement matrix. Additionally, the phase transitions and practical features of the proposed measurement matrix are analyzed. In Section 4, experiments on random signals and images are conducted. Finally, in Section 5, the conclusion is presented.
## 4 Discussion
### Biogas Production  
Generally, biogas constituting hydrogen (H\({}_{2}\)), methane (CH\({}_{4}\)), and carbon dioxide (CO\({}_{2}\)) are the biologically digester end-products of the wastewater substrates. In essence, reducing the CO\({}_{2}\) content into a more ecofriendly gas involves a series of mechanism [15; 28]. The methanation reaction between the CO\({}_{2}\) and H\({}_{2}\), was being influenced by the Fe-TiO\({}_{2}\) induced with the activated energy of the UV-light. Herein, the hydrogenation process or hydrolysis of the water molecules splitting (H\({}_{2}\) and O\({}_{2}\)) was being ignited by coupled photocatalysis [29]. In addition, the incorporation of the magnetic field influenced the agglomeration of the action of the hydrogenotrophic methanogens in conversion of the CO\({}_{2}\) into methane [5]. This reaction mechanism is expressed in Equation (5).  
\[\text{CO}_{2}+4\text{H}_{2}\rightarrow\text{CH}_{2}+2\text{H}_{2}\text{O} \tag{5}\]  
Figure 4 shows the cumulative biogas production volume at different levels of the three bioreactors operated at the same conditions for 30 days. The maximum cumulative biogas estimated for the biogas produced by the bioreactors (BP = 1125 mL; BPM = 1330 mL) with the Fe-TiO\({}_{2}\) additives were found to be significantly different from that of the control (AD = 625 mL). The BPM system, where 2 g of Fe-TiO\({}_{2}\) additives was added on an average basis, was estimated to be 788 mL/d, followed by the BP system with 671 mL/d and the AD system with 345 mL/d. Kinetically, the cumulative data obtained were well-fitted on the modified [PERSON] model (2), where the operating variables obtained are presented in Table 3. There exists a significant correlation (95% confidence level) between the degradation of the carbon content of the wastewater to biogas with a high regression coefficient (R\({}^{2}\) > 0.9). These findings correlate with other studies, whereby the Fe-TiO\({}_{2}\) additives increased the surface area of the sludge. This enhanced the microbial activity and hence resulted in high biogas production as compared to the AD system [20; 25].  
After the HRT of 30 days for methanogenesis, the biogas composition was characterised as showing a high methane yield greater than 65% in the increasing order of the systems as AD < BP < BPM. As shown in Figure 5, there was an impact in both the biogas and methane of the BP and BPM systems. There was partial degradation in the AD system, which is consistent with previous studies that showed the AD process to be kinetically unstable with a general methane potential of 50-65% [7; 20; 25].
## 5 Trust Adaptation
### Topology Modelling
#### 5.1.2 Capabilities Types  
We model trust capabilities for the security components as follows: From the server's perspective, an SP may define whom it can trust for providing service. In Figure 5b, the \"capabilities.Trust\" is an abstract trust model. It describes that a _trustor_ (i.e., an SP) trusts a _trustee_ (to be defined) to delegate permissions for an _actor_ subject when the actor subject satisfies the trustor's security policy. If the trust evaluation is correct, the SP continues to execute under the actor's identity. We model trust with three sub capabilities:  
1. _DirectTrust_: An SP (trustor) trusts a service consumer (trustee) directly. When using this trust model, the service consumer indicates the SP to execute further using the service consumer's identity itself (actor). This trust model may generate a security policy that builds a mutual trust relationship between Web services such as the \"SSL transport binding\" [57].
2. _DirectBrokerTrust_: An SP (trustor) trusts a service consumer (trustee) to generate a self-signed identity for a user. The service consumer indicates the SP to execute further using the user identity (actor). This trust model may generate a security policy that builds a mutual trust relationship between Web services and transmits a self-signed user identity such as the \"SAML assertion Sender Vouches over SSL\" [57].
3. _IndirectTrust_: An SP (trustor) trusts an IdP (trustee) for issuing a user identity. The service consumer may act on behalf of a user (i.e., identity impersonation) or act as the user (i.e., identity delegation) to request a user identity from the IdP. The consumer indicates the SP to execute further using the user identity (actor). This trust model may generate a security policy that establishes  
Figure 5: (a) Node types; (b) Capabilities types.  
trust with an unknown Web service and transmits a user identity such as the \"Symmetric binding with supporting token\" [57].  
In summary, by using this trust model, we can express both direct trust and indirect trust relationships between Web services, and cover all scenarios associated with identity propagation. Also, in our trust capabilities, the trustee and actor have a data type that gives more information about a given entity. For instance, the data type \"identity.User\" has two elements \"claims_mapping\" and \"group_mapping\". These data types allow the security architect to express which identities his application needs from the runtime environment (more details in the experimental results).
## 3 Applications of VI-Based Inverters  
### Grid-Connected RES  
In the future, the power system will be highly decentralized and comprised of massive small-scale RES that has its own generation paces and power quality. This phenomenon results in a different frequency, amplitude, and phase angle AC output. It contributes to AC power instability. Hence, VI-based inverters are crucial to regulate voltage and frequency stability by enabling RES to participate in power quality control [76]. VI control strategy can be adapted to converter integration to interface DC output of RES to the AC power grid. It performs not only grid feeding or following but also provides ancillary services including grid-forming capability, dynamic power support, and harmonics compensation. VI-based inverters are typically utilized and applied for grid-connected RES, specifically in wind turbine and solar PV. The electricity company is expected to utilize VI as an ancillary service. The interface of VI-based inverter for grid-connected RES eliminates the need for an isolating transformer to save cost since VI-based inverter acts as an isolating element. VI is also applicable to the integration of flexible loads. Generally, flexible loads are the loads that are designed according to customer requirements, needs, and production capability. A wind turbine known as a wind energy converter is a device that converts the wind's kinetic energy into electrical energy. In wind farms, the most commonly used wind turbines are permanent magnet synchronous generator (PMSG) and doubly-fed induction generator (DFIG). By modifying the control strategy and system assembly, a VSG-based wind turbine can deliver VI and power oscillation damping to the grid [77]. It works in a standalone scenario and responds to dispatch orders from the upper-level operator. The wind power system can be transformed into a grid-friendly wind turbine generator by integrating VI-based inverters [78]. It is applicable for both PMSG and DFIG machines to equip them with VI and fault-ride through capabilities.
# Microcrack Detection Using Spectral Response Data Alone
## 3 Crack Inspection with Simple Specim
These results indicate the feasibility of the crack indicator obtained using the response data alone for the sideband frequency with minor dependency on the measurement location, and the resonance frequency seems usable if the measurement allows location dependency. The difference in the crack indicator obtained using these two different methods may arise from the difference of the phase information in the frequency domain, because the proposed method assumed similar input forces even though it is impossible to control the phase of the impulse force in practice. Nonetheless, the crack indicator obtained using the response data alone is advantageous compared with the previous FRF-based method owing to its simple calculation process and the lack of the need for information of the input impulse force. Therefore, the crack indicator obtained using the response data alone should be used preferentially if the crack sensitivity can vary by a small margin with the measurement location. The limitation of this study was the dynamic dependency on the rectangular simple specimen. The orthogonality between two resonance frequencies in the cracked specimen was not explained, and both the in-plane and out-of-plane may not be valid for other shape of specimen, if the cross-section was round. In addition, the localization of microcrack was not possible from the proposed crack indicator. Both of issues will be future work from current studies.
## 4 Discussion  
The TS is a simple indicator that forecasts bias present in a forecast model. It is most often used when the validity of the forecasting model might be in doubt.  
If the value of the TS is between \(-4\) and \(+4\), the accuracy of the prediction method is regarded as acceptable and can be continuously applied. However, if it is outside of this range, there is a problem with the fitness of the currently applied prediction model, so it must be relearned with new data or new algorithms. If the TS is \(\pm 4\), it means that the cumulative error is \(\pm 4\) MAD, which corresponds to \(\pm 3.2\) s. Since s \(\leq\) 1.25 MAD, 1 MAD = 0.8 s. The probability that the normal TS value is within the range of \(\pm 4\) is 99.8%, and the probability that the abnormal TS value is outside of the range of \(\pm 4\) is 0.2%. Therefore, there is little possibility of an outlier.  
This means that 99.8% of normal prediction errors will be in the range between \(\pm 4\) MAD, and if the TS value of an inference model is outside of \(\pm 4\) MAD, it becomes an abnormal error, and it means that the machine learning algorithm needs corrective action.  
In the results of this study, comparing the TS values of Scikit-learn using SLP machine learning and Keras using MLP machine learning, it was found that the learning results of MLP were more predictive. However, Scikit-learn also had a TS value within the range of \(\pm 4\), so there would be no problem in applying it to food processing equipment with few parameters. In this study, learning was conducted using the deep learning method using the data obtained by [PERSON] [18], and through TS verification it was confirmed that it was possible to create a predictive model with over 99.8% predictive power.  
Reliable predictive models can be applied to virtual tests to optimize electrical energy consumption, and the results can be predicted in advance by approaching areas that are not actually tested. The application of such a development method can reduce electrical energy to achieve the same effect in the food manufacturing equipment related industry. As a result, it will be possible to contribute carbon emission reductions without spending money.  
This research was funded by a 2020-2021 Research Grant from Sangmyung University.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Minimum** & **Maximum Electrical** & **Max.\(-\)Min.** \\  & **Electrical Energy** & **Energy** & **Max.\(-\)Min.** \\ \hline Sequence Number & 2054 & 10,088 & N/A \\ Target (\({}^{\circ}\)C) & \(-20\) & \(-20\) & N/A \\ Hysteresis (\({}^{\circ}\)C) & 1 & 5 & N/A \\ Compressor delay (s) & 620 & 0 & N/A \\ Fan speed (step) & 14,000 & 23,000 & N/A \\ Fan delay (s) & 0 & 0 & N/A \\ Average room temperature (\({}^{\circ}\)C) & 24 & 24 & N/A \\ Electrical energy prediction & 737,498 & 1,100,332 & 362,834 \\ results (kWh) & & & \\ \hline \end{tabular}
\end{table}
Table 3: Summary of predicted electrical energy values.
## 4 Optimization Methods of Base Placement  
### Wheelchair-Mounted Robotic Arm System  
The WMRA in our study is comprised of a mobile wheelchair, a robotic arm, various sensors, and a host computer, as shown in Figure 4. The host computer, running Ubuntu 18.04, is an Nvidia Jetson TX2, and the communication among the visual, SLAM, navigation, and control modules is facilitated by a robot operation system (ROS). An Arduino Mega 2560 serves as the lower-level controller of the mobile wheelchair, and two drivers control the PWM speed of the two DC motors that enable the wheelchair to achieve differential steering and forward and backward movements. To obtain color images and depth information, an Intel RealSense D435i depth camera sensor is used. The experimental platform utilizes both the eye-to-hand and eye-in-hand configurations to capture local and global information, respectively. The eye-in-hand camera is mounted on the Kinova Jaco 6 DOF robotic arm, while the eye-to-hand camera is equipped with a two-degree-of-freedom rotating cloud platform that expands the camera's field of view and enables it to better obtain environmental information.  
Figure 3: Algorithm flowchart: (**a**) operation process of the EL-E robot; (**b**) the operational process of the method proposed in this article.  
During operation, the user interface of the experimental platform displays the current image information from the global camera, the map of the home environment, and the mobile wheelchair's current location. The user selects a target object on the touch screen and the WMRA returns its coordinate value relative to the camera's position. Using a coordinate transformation, the platform computes the relative position between the target point and the wheelchair and sends it to the inverse reachability map as the target point for the robotic arm's end effector to calculate the next step.
## 3 Wireless Environmental Station  
The requirements of the wireless environmental station (WES) essentially constitutes it as a global system for mobile communications-based (GSM) multi-sensor device, used to obtain measurements of pollutants as well as other factors such as the environmental conditions of the monitored area. In particular, the WES should include a power supply and the options to include solar panels, GSM modules, and batteries of NP7-12 (6 batteries) as well as NCR 18650B (30 batteries) types. The environmental sensors available can measure CO, NO, NO\({}_{2}\), O\({}_{3}\), PM\({}_{1.0}\), PM\({}_{2.5}\), and PM\({}_{10}\). The environmental sensors include the temperature, relative humidity, pressure, wind direction, and wind speed. Hence, we selected the RAMP SENSIT devices [33], which cover the requirements. In Figure 1, the parts comprising the internal mechanism of the SENSIT device can be seen.  
The concept was to deploy a device to the port area of Igoumenitsa, in order to investigate pollution as well as environmental parameters. In Figure 2 we can see the SENSIT device deployed on a building in the port of Igoumenitsa. The collected data is transmitted to a server, where it is processed the comma-separated value (csv) file is produced. This file is then processed to be presented in a Web-GIS application, which is a real-time application, showing the parameters in the port area.  
Note that the deployment of such a device is a pilot for the deployment of more devices near the port area, in order to monitor the parameters in the broader area of the port, including residential areas, because the port resides very close to the city. As such, a comparison and correlation between the measured parameters can give us the necessary information to indicate whether the port's activities essentially impose pollution on the city of Igoumenitsa. Here we will show that we can use a state-of-the-art machine-learning method to show that we can predict parameters coming from the measured data.