esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Models Evaluation  
There are two sets of studies: those that compared different models and those that used a single model. The latter should provide a detailed bibliographic analysis that serves as a comparison and justified the development carried out and the results obtained. Delving into the evaluation of the models, it is worth noting that both the architecture of the models and the training/validation methods were not specified in all articles. Regarding the architecture, even if it was specified, the information provided for different models within the same study exhibited a different degree of specificity, turning the architecture into heterogeneous information, which made it difficult to establish conclusions from a comparison between the articles. These facts are quite remarkable, since as shown in [41], the parameters that define the model architecture can greatly influence the results of the precision metrics, so it is important to specify them to increase the quality of the study.  
The validation method is key to evaluate the training process and to obtain information on the generalizability of the model, facilitating a possible application in the clinical setting. The most widely used validation method was k-fold cross-validation, considered by 55.81% of the articles. Among them, 13 studies (30.23% of the total set) used 10-fold cross-validation and five studies (11.63%) used a five-fold cross-validation. The articles [37; 58; 59] did not specify the validation process and therefore the results cannot be generalized. This validation method allows for the evaluation of the model by minimizing the bias produced by the choice of the data. Hence, it is especially appropriate in those cases in which datasets are considered small or difficult to expand, much like when dealing with clinical data. Even though most of the studies specified the validation method, which constituted a good practice, it should be noted that it was not specified for all models whether the results obtained belonged to the training, validation, or test sets, although the three provide crucial information about the training process of the model and its ability to generalize to blind data. This was an inconvenience when comparing the results of the models since the value of the metrics may be rather different in each of these scenarios.  
Finally, it was striking that the accuracy metric was the most widespread metric among the selected articles, being present in 90.70% of the studies, followed by the sensitivity metric considered in 69.77%. It should be noted that in the most recent articles, the evaluation of the models was carried out by means of other metrics. In the studies that work with patients, especially in a problem of diagnosis of a disease, the sensitivity or recall, specificity, and precision metrics are also very useful to evaluate the results of the model, as they provide a measure of the correctly classified positives, the correctly classified negatives, and the hits among the positives predictions, respectively.
## 3 Numerical Scheme
### Framework
#### 3.2.2 Vector-Matrix Notation  
The grid functions \(w_{l}^{n}\) and \(u_{l}^{n}\) are defined over the discrete domain \(\mathfrak{D}\), containing \(N_{\mathfrak{D}}+1\) grid points. The discrete position of the whole string can therefore be described with vectors of length \(N_{\mathfrak{D}}+1\). The first set of boundary conditions, Equations (41a) and (42a), ensure that the two extreme values of such vectors (_i.e._, the string displacement at the bridge and nut boundary) are 0 at all times. One now only needs to store the state of the string in a vector of size \((N_{\mathfrak{D}}-1)\), omitting these two extreme values:  
\[\mathbf{w}^{n}=\begin{bmatrix}w_{1}^{n},\ldots,w_{N-1}^{n}\end{bmatrix}^{ \mathsf{T}} \tag{43a}\] \[\mathbf{u}^{n}=\begin{bmatrix}u_{1}^{n},\ldots,u_{N-1}^{n}\end{bmatrix}^{ \mathsf{T}} \tag{43b}\]  
The action of spatial FD operators on the grid functions is then equivalent to a matrix-vector multiplication. For simply supported boundary conditions, the notation of spatial FD operators in matrix form naturally follows as:  
\[\mathbf{D}_{x-}=\frac{1}{h}\begin{bmatrix}1\\ -1&1\\ &\ddots&\ddots\\ &&-1&1\\ &&&-1\end{bmatrix} \tag{44a}\] \[\mathbf{D}_{x+}=-\mathbf{D}_{x-}^{\mathsf{T}}\] (44b) \[\mathbf{D}_{xx}=\mathbf{D}_{x+}\mathbf{D}_{x-} \tag{44c}\]  
of size \(N_{\mathfrak{D}}\times(N_{\mathfrak{D}}-1)\), \((N_{\mathfrak{D}}-1)\times N_{\mathfrak{D}}\), \((N_{\mathfrak{D}}-1)\times(N_{\mathfrak{D}}-1)\), and \((N_{\mathfrak{D}}-1)\times(N_{\mathfrak{D}}-1)\), respectively.  
System Equation (39) can now be written as a pair of vector equations:  
\[\mathbf{L}\mathbf{w}^{n}=0 \tag{45a}\] \[\mathbf{L}\mathbf{u}^{n}=0 \tag{45b}\]  
where \(\mathbf{L}\) is the matrix form of the difference operator \(\mathsf{L}\) defined in Equation (40):  
\[\mathbf{L}=\rho\delta_{tt}-T\mathbf{D}_{xx}+EI_{0}\mathbf{D}_{xx}\mathbf{D}_{ xx}+\lambda_{1}\rho\delta_{t}.-\lambda_{2}\rho\delta_{t-}\mathbf{D}_{xx} \tag{46}\]
# Methods for Noise Event Detection and Assessment of the Sonic Environment by the Harmonic Index
## 2 Materials and Methods
### Noise Monitoring Sites and Data Set
#### 2.1.1 Andorra la Vella, Principality of Andorra  
Three locations were selected; they were already under consideration by the Ministry of Environment for future urban noise interventions and noise mitigation measures (Figure 1). The locations were [21]:  
* Site \"Aa\" (Figure 1a), connecting the three main roads of Andorra's capital, showing the heaviest and loudest road traffic during rush hour time, especially on weekdays; it is the main noise source, with rather constant, high sound levels, and low variations over time, compared to the other areas during the rush hours.  
* Site \"Ab\" (Figure 1b), being the intersection between the main road from the capital to the north valley and a wide pedestrian and commercial area. This is one of the most common promenade points for residents and tourists. In this area, the noise sources are \"wider\" than site (a) throughout the day; it recently became the largest pedestrian area in the country.
* Site \"Ac\" (Figure 1c), the crossing of a main road and the final part of a pedestrian and commercial street, similar to site (b), but not totally pedestrianized, since it depends on traffic lights for people to cross the road. Thus, a high variability of noise sources during the day was observed, as in site (b), but with the additional--and presumably not usual--street works occurring during the noise monitoring days.  
At each site, the environmental noise was monitored by an operator on two days (Wednesday, 21 March 2018 and Sunday, 15 April 2018), in order to obtain data on a weekday and during weekend, considering that specially traffic presents different intensities, depending on traffic to school and work or leisure. At all the three sites, the source recognition provided by the ANED procedure, since it was in its preliminary application, was manually validated by an expert.
## 4 Results
### Thermal Insulation  
#### 4.2.1 Reference Concept  
Based on the microporous thermal insulation material, the storage medium solution and the maximum temperature, variation calculations were performed on the permitted maximum surface temperature (\(T_{W}\)). In an iterative way, the thermal insulation thicknesses (\(\Delta\)s) are determined, resulting in the thermal insulation dimensions and with respect to the storage medium solution to the systemic storage densities. Those results together with the associated heat losses are shown in Figure 5 for permitted maximum surface temperatures from 10 \({}^{\circ}\)C to 200 \({}^{\circ}\)C.  
The characteristic of the systemic storage densities illustrates decreasing systemic storage densities with higher requirements for the permitted maximum surface temperatures. At maximum surface temperatures of 60 \({}^{\circ}\)C, gravimetric and volumetric systemic storage densities of 169.2 Wh/kg and 56.3 kWh/m\({}^{3}\) are reached, whereas moderately higher permitted surface temperatures of 120 \({}^{\circ}\)C result in a significant increase in systemic storage densities to 229.7 Wh/kg and 124.5 kWh/m\({}^{3}\). Simultaneously, the improvements are associated with increasing thermal losses. For the maximum permitted surface temperature of 60 \({}^{\circ}\)C as specified in Table 4, heat losses of 155.3 W are resulting at a required insulation thickness of 134 mm. Central results are summarized in Table 6 and serve as a base for comparing the reference with the alternative concepts.  
Even with conventional thermal insulation solutions, the characteristics show significant improvements in systemic storage densities, when safety-related restrictions regarding the permitted maximum surface temperatures are neglected. To use these potentials without exceeding the safety-related restrictions, the alternative concept is investigated in the following.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(Q\)/\(m_{tot}\) [Wh/kg] & \(Q\)/\(V_{tot}\) [kWh/m\({}^{3}\)] & \(\dot{Q}_{loss}\) [W] & \(\Delta\)s [mm] \\ \hline
169.2 & 56.3 & 155.3 & 134 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Reference concept—central results at maximum permitted surface temperature of 60 \({}^{\circ}\)C.  
Figure 5: Systemic storage densities (\(Q\)/\(m_{tot}\), \(Q\)/\(V_{tot}\)) and heat losses (\(\dot{Q}_{loss}\)) as a function of the permitted maximum surface temperature (\(T_{W}\)).
## 5 Discussion  
The main objective of the proposed ultrasonic data processing approach is to estimate the depth of shallow delamination in concrete. Additionally, the proposed approach characterizes material properties (bulk wave velocities) by solving an optimization problem (Equation (7)). Bulk wave velocities are directly related to the elastic properties of the tested  
Figure 10: Objective history for experimental results: (**a**) objective history for Step 1 in the data processing approach and (**b**) objective function for Step 2 over the search space. The actual delamination depth is 0.06 m.  
Figure 9: Comparison between dispersion curves extracted from the numerical simulation data (Figure 7b) and those theoretically computed with the estimated delamination depth.  
material. The dynamic Poisson's ratio (\(\
u\)) can be computed using the obtained bulk wave velocities as  
\[v=0.5\left(1-\frac{c_{S}^{2}}{c_{P}^{2}-c_{S}^{2}}\right). \tag{11}\]  
If the mass density (\(\rho\)) of the material is known, the dynamic Young's modulus (\(E\)) can be computed by  
\[E=\rho c_{S}^{2}\left(\frac{3c_{P}^{2}-4c_{S}^{2}}{c_{P}^{2}-c_{S}^{2}}\right). \tag{12}\]  
Obtaining bulk wave velocities is particularly important for concrete, considering the high variability and uncertainty of its material properties.  
Although the proposed ultrasonic data processing approach accurately estimates the concrete delamination depth from numerical simulation data, it reveals a nontrivial error (15.7%) for the case of experimental data. A possible source of the estimation error is the variability in the material properties of the tested concrete specimen. To be specific, the elastic/shear modulus (or P- and S-wave velocity) of concrete affects the dispersion relation in a plate-guided wave. While the applied mechanical wave approach has an advantage of a direct relation to the mechanical properties of concrete, unmeasured velocities from the specimen affect the accuracy of the estimation in the proposed method. In Step 1 of the proposed approach, bulk wave velocities are obtained from the reference ultrasonic scanning data, which correspond to a full-depth pristine region of the concrete specimen. The obtained wave velocities represent the material properties of the full-depth region of the tested concrete specimen, but they may be slightly different from those for the delamination region. This discrepancy can be a source of error in Step 2 of the data processing approach.  
Lamb waves need to be generated and propagated within the delamination region of concrete for a successful implementation of the proposed approach. In addition, the length of delamination needs to be several wavelengths of the S0 and A0 modes to support Lamb wave propagation. The mode separation within the applied frequencies is required for the proposed approach. The applicable range of delamination depth is around 0.03-0.08 m with respect to applied frequencies (30-60 kHz) in this study. This condition imposes a limitation on the proposed approach in that early-stage delamination damage to a small extent may not be effectively evaluated.
## 2 Basic Principles
### Feature Engineering  
In general, the selection and extraction of features during _feature engineering_ is crucial for the success of machine learning applications. The term _selection_ refers to the identification of strongly influencing measurable properties with regard to the mathematical task, whereas _extraction_ deals with dimensionality reduction when using compositions of features. For example, in [40], PCA was used to determine the histogram of the most influencing PC for a given time window, from which the mean and variance were used as features. Another example is [37], where the number of discrete frequency components was determined using spectrograms that contain micro-Doppler signatures, providing useful information about the locations of small-scale motions.  
Classical techniques, e.g., _Linear Regression_, _Decision Trees_, _Random Forests_, _k-Nearest Neighbors_, etc., rely heavily on handcrafted feature engineering, which implies certain experience and domain knowledge, whereas DL methods use algorithms that automatically select useful features, which, as their main drawback, are barely interpretable by humans and difficult to evaluate indirectly.
## 2 Materials and Methods
### Preprocessing  
Before reaching the model, all images (both from training and from test datasets) were filtered to include only frontal images (PA or AP), removing the remaining projections (including lateral projection). We also filtered out all pediatric images (<18 years old) from the datasets containing age.  
Regardless of their original resolution or bit depth, all images were rescaled to a fixed size of 456 \(\times\) 456 pixels with three channels. Grayscale images were replicated across channels to create RGB representations, enabling compatibility with models pretrained on natural images. Pixel intensities were normalized to \([0,1]\) range based on their original bit depth.  
Training time augmentation was used by applying random transformations to the input images at training time, as it is known to improve model performance [62]. We used horizontal flipping, random rotations (up to \(\pm 10^{\circ}\)), brightness adjustments (80-120%), horizontal and vertical translations (up to 3%), and shearing (up to 3\({}^{\circ}\)).  
To create training and validation datasets, each individual dataset was applied a function to split individual patients into training (95%) and validation (5%) sets preserving the label distribution of the dataset for the pneumothorax label. Then, to partially mitigate the label imbalance of pneumothorax across all datasets, we applied 2\(\times\) oversampling on pneumothorax images and random undersampling to non-pneumothorax images to obtain a ratio of 4:1 (pneumothorax to no pneumothorax) in the training set.  
Table 2 shows the final number of unique images used in the training set, where pneumothorax images are oversampled, so each image is used twice as input for each epoch. Table 3 shows the number of images in the test set.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **\# Images** & **\# Pneumothorax** \\ \hline NIH & 52,157 & 5036 (9.66\%) \\ CheXpert & 93,797 & 16,962 (18.08\%) \\ MIMIC-CXR & 112,806 & 10,794 (9.57\%) \\ PadChest & 49,100 & 393 (0.8\%) \\ VinDr-CXR & 8006 & 84 (1.05\%) \\ SIIM-ACR & 6703 & 2572 (38.37\%) \\
**Total** & 322,569 & 35,841 (11.1\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training datasets used in our work.
## 1 Introduction
Furthermore, this study used the Raspberry Pi, a single-board computer capable of running Linux or Windows-based operating systems. The Raspberry Pi has a CPU, RAM, and I/O ports suitable for IoT-based projects [15]. Hence, in this research, Raspberry Pi was the server running the Mosquito IoT Broker application, an Apache Server, and the MySQL database [16; 17; 18]. Subsequently, sensor measurements from the hardware components are sent to the IoT broker using a WiFi module with an MQTT communication protocol. MQTT is a protocol that offers three different quality of service (QoS) levels and employs publish/subscribe (pub/sub) terminology for communication [19; 20]. The transmitted data enter the IoT broker and are further processed by the system, being stored in the database. The data stored in the database are then displayed in real-time on the web application. Furthermore, this web application is responsive and can be accessed on various screen sizes, including mobile devices. This feature allows for flexible control and monitoring of the incubator from anywhere and anytime over the Internet using the MQTT protocol. The data stored in the database are then used as a dataset for creating a predictive model for electricity consumption. The process of creating this model utilizes a combination of methods from deep learning, namely long short-term memory (LSTM) and a 1-dimensional convolutional neural network (1D-CNN) [21]. Combining these two methods aims to increase the accuracy of predicting electrical energy consumption [22]. The choice of these two methods is based on the temporal or sequential data type. Therefore, the decision was made to employ the LSTM method to understand the data sequence that will occur next. The output from the 1D-CNN will be used as input for LSTM. Generally, the analysis process begins by inputting data into the 1D-CNN. The output results from the 1D-CNN are then used as input for LSTM. Following this, the final result of this model will be evaluated to measure the level of prediction it produces. In the context of the incubator, numerous studies have been conducted previously, but most of them utilized control methods such as PID and Fuzzy [6; 7; 8; 9], as shown in Table 1. Our research aims to extend the development of the incubator with artificial intelligence to monitor energy usage based on temperature conditions inside the incubator. In this work, our main contributions are:  
* web application for real-time monitoring and control;
* visualization of the temperature data distribution inside the incubator;
* prototype of an electronic hardware incubator; and
* hybrid model utilizing 1D-CNN and LSTM methods for predicting energy consumption models.
# Combined Sweeping and Jumping Method to Enhance Node Insertion Algorithm for Wi-Fi Sensor Networks
## 5 Conclusions  
For the development of the efficient MAC protocol for wireless sensor networks, the connectivity among sensors should be taken into account for the design of the MAC protocol. The connectivity-based multipolling MAC protocol modifies the PCF protocol, which is a basic MAC protocol for Wi-Fi networks, and optimizes the MAC efficiency by minimizing the polling transmissions based on the connectivity among sensors. For the connectivity-based multipolling MAC protocol to be easily applied to wireless sensor networks, an efficient algorithm for deriving the optimal number of serially connected multipolling sequences of sensors is needed. The node insertion algorithm was proposed to efficiently derive the near-optimal number of serially connected multipolling sequences by eliminating the retracting process in composing the sequences of sensors, and inserting the sensors that have the greatest connectivities with the recently inserted sensors.  
In this paper, to enhance the performance of the node insertion algorithm, the combined sweeping and jumping method was proposed to reduce the search space required for the node insertion algorithm. The computational complexity for the node insertion algorithm has been significantly reduced by the proposed combined sweeping method. By numerical results, it has been verified that the time for the node insertion algorithm to take for deriving the near-optimal number of serially connected multipolling sequences can also be significantly enhanced by the proposed combined sweeping and jumping method.  
To tackle the issue of the energy consumption of sensors under the connectivity-based multipolling MAC protocol, the connectivity-based multipolling MAC protocol should be improved so that only the sensors having data to transfer are polled by APs. Furthermore, the security issue for detecting fraudulent sensors and authenticating genuine sensors should be addressed for the connectivity-based multipolling MAC protocol. These problems are left for future researchers.  
This work was supported by the Dong-A University research fund.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The author declares no conflicts of interest.
# Investigation on Reference Frames and Time Systems in Multi-GNSS
## 6 Conclusions
### Reference Frames  
Reference frames adopted by GPS and Galileo FOC broadcast orbits, in the week taken into account, are aligned with ITRF since the translation of the origin is less than 0.10 m and rotations are less than 2 milliarcsec. For Galileo IOV, GLONASS and BeiDou instead translations at dm-level and rotations higher than 4 milliarcsec have been found.  
The Galileo IOV broadcast reference frame is offset to ITRF by at most 0.23 \(\pm\) 0.04 m in Y.  
The GLONASS M broadcast reference frame is offset to ITRF by at most 0.27 \(\pm\) 0.05 m in Y and the maximum rotation is 4 \(\pm\) 2 milliarcsec about Y. The GLONASS K broadcast reference frame is offset to ITRF by at most 1.06 \(\pm\) 0.18 m in Y and the maximum rotation is 19 \(\pm\) 2 milliarcsec about X.  
The BeiDou IGSO broadcast reference frame is offset to ITRF by at most 0.31 \(\pm\) 0.08 m in Y and the maximum rotation is 3.6 \(\pm\) 2.0 milliarcsec about Z. BeiDou MEO broadcast reference frame is offset to ITRF by at most 0.38 \(\pm\) 0.35 m in X and the maximum rotation is 4.3 \(\pm\) 2.9 milliarcsec about Z.
###### Abstract  
Conventional soil classification methods are expensive and demand extensive field and laboratory work. This research evaluates the efficiency of various machine learning (ML) algorithms in classifying soils based on [PERSON]'s soil behavioral types. This study employs 4 ML algorithms, including artificial neural network (ANN), random forest (RF), support vector machine (SVM), and decision trees (DT), to classify soils from 232 cone penetration test (CPT) datasets. The datasets were randomly split into training and testing datasets to train and test the ML models. Metrics such as overall accuracy, sensitivity, precision, F1_score, and confusion matrices provided quantitative evaluations of each model. Our analysis showed that all the ML models accurately classified most soils. The SVM model achieved the highest accuracy of 99.84%, while the ANN model achieved an overall accuracy of 98.82%. The RF and DT models achieved overall accuracy scores of 99.23% and 95.67%, respectively. Additionally, most of the evaluation metrics indicated high scores, demonstrating that the ML models performed well. The SVM and RF models exhibited outstanding performance on both majority and minority soil classes, while the ANN model achieved lower sensitivity and F1_score for minority soil class. Based on these results, we conclude that the SVM and RF algorithms can be integrated into software programs for rapid and accurate soil classification.  
c pacs: **Keywords:** cone penetration test; soil classification; machine learning; artificial neural network; support vector machine; random forest; soil behavioral type