esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Modeling
### The Case of a Single External Source with a Varying Initial Phase
However, even in this case, with an increase in time by 10 times, the loss function stabilizes at a level less than \(10^{-14}\) without changing \(\mid\!X\!\mid\)), which indicates that the minimum has been reached, according to norm \(l_{1}\). Thus, like the case of using activation functions (18) and (20) and phase \(\varphi=72^{\circ}\), it is clear that the value of the loss function (3) turns out to be very small, but the parameters of the non-zero basis coefficients are very different from the parameters of the external source. In addition, the convergence process slows down. The activation function (19) made it possible to achieve a global minimum according to the \(l_{0}\) norm, with an increase in time, in the cases considered, by 2-3 times. Thus, all three options of the activation functions (18)-(20) allow, with a zero initial phase of the signal, the accurate representation of the external source signal with basis functions. However, if the initial phase is not zero, then options (18) and (20) may give an erroneous result.
## 4 Simulation Results  
This section presents the detailed simulation results. Extensive experiments are carried out on Google Colaboratory (Colab) platform. The Google Colab provides Intel Xeon CPU with a clock speed of 2.3 GHz and up to 16 GB of RAM. Moreover, the Google Colab also provides NVIDIA K80 or T4 GPU. We use Python V3.6 as a simulation tool for different vehicle datasets as described in subsequent sections. To investigate the performance of vehicle detection methods on different datasets, we select 14 state-of-the-art vehicle detector evaluations and comparisons with the proposed method in terms of accuracy and execution time. All of the compared approaches have been trained on the same training data from each of the PKU, COCO, and DAWN datasets.
## 4 An Adaptive Bayesian Melding Method
To avoid the concentration of posterior sets caused by large weights, a wider sample range was explored by splitting the larger weights into smaller ones, which also proved that the results remained unbiased ([PERSON] et al., 2016). The process of inferring a posterior based on the ABM method is shown in Figure 1 and listed as follows:  
1. Generate \(n\) samples \((\theta_{1},\theta_{2},\cdots,\theta_{n})\) from the subsystem prior distribution \(q_{1}(\theta)\) and generate \(m\) samples \((\alpha_{1},\alpha_{2},\cdots,\alpha_{m})\) from the beta distribution;
2. Nonparametric density estimation is used to estimate the induced prior distribution \(q_{1}^{*}(\phi)\);
3. Calculate the weight \(w_{ij}\) as \[w_{ij}=\left(\frac{q_{2}\left(\phi_{i}\right)}{q_{1}^{*}\left(\phi_{i}\right) }\right)^{1-\alpha_{j}}L_{1}\left(\theta_{i}\right)L_{2}\left(M\left(\theta_{ i}\right)\right);\] (13)
4. Select \(a\) weights randomly for \(w_{ij}\) and sort these weights in increasing order;
5. Determine the threshold weight \(w_{r}=w_{\lfloor r\times a\rfloor}\), where \(r\) is an empirical threshold percentage, and \(\lfloor r\times a\rfloor\) is the floor value of \(r\times a\);
6. Examine all importance weights and split any weight \(w_{ij}>w_{r}\) into \(k_{ij}=\left\lfloor w_{ij}/w_{r}+1\right\rfloor\) number of weights \(w_{ij}/k_{j}\) to re-sample the corresponding \(\theta_{j}\);
7. Repeat steps 4-6 until the weights reach a certain deviation;
8. Re-sample from the initial samples by the final weights of step 7.
## 3 Models and Methods
### Models  
#### 3.1.1 Physical Model of Light Detection and Ranging (Lidar) Echoes for Forest  
We developed a physical model to derive the TEP for forest canopies, which linked lidar echoes to forest scenes based on the 1-D radiative transfer theory. In this model, foliage was thought to be the main cover to intercept solar radiation, and only the first collision between the laser and canopy was considered. The mathematical expression of this model is shown as follows:  
\[R_{n}=I_{n}[1-exp(-k\cdot LAI_{n})]\rho \tag{1}\]  
where \(R_{n}\) is the echo energy of the recorded layer \(n\), \(k\) is the extinction coefficient, \( LAI_{n}\) describes the foliage between two successive recorded layers, \(\rho\) is the reflectance of the observed materials (e.g., forest canopy), and \(I_{n}\) is the lidar transmitted energy received by recorded layer \(n\) and is defined as follows:  
\[I_{n}=\exp(-k\cdot LAI_{n-1})I_{n-1} \tag{2}\]
# Non-Invasive and Accurate Blood Glucose Detection Based on an Equivalent Bioimpedance Spectrum
## 8 A Non-Invasive Blood Glucose Detection Experiment
### Single-Person Blood Glucose Experimental Design  
The experimental conditions of the single-person blood glucose experiment include the following: (1) the physical condition of the tested person, (2) the test time, (3) the test attitude control, and (4) the test environment and other related information. Among them, the selected subjects are as follows: male, 26 years old, height 174 cm, weight 83 Kg, and BMI value 23.5. The test time starts from the fasting state in the morning, and the experiment is carried out every 1 h. The control of the test posture includes keeping the body stable during the test, emotional stability, no drinking water 20 min before the test, etc. The test environment requires that the external test environment temperature is stable, e.g., at room temperature, and there is no obvious temperature change during multiple measurements.  
The specific test steps are as follows:  
1. Before taking the equivalent bioelectrical impedance readings, ask the tested person to sit still for 10 min before starting the test. The research protocol and experimental methods are explained to each volunteer, and then they are asked to sign a consent form.
2. During the experiment, the tested persons continue to keep their bodies in a stable state. The impedance acquisition platform designed in this paper is used to conduct the experiment. The inner side of the forearm of the tested person is selected as the test point, and the corresponding electrode pads are pasted on the skin to start the test. The complete test time is about 2 min.
3. After the impedance collection test is completed, biochemical blood glucose collection is performed. The Ulite URI0-26 blood glucose analyzer was selected as the biochemical blood glucose collection instrument for blood collection experiments.
4. Arrange the impedance data obtained from the above experiments and the biochemical blood glucose data at the same time and label them.
# Robust Transceiver Design for IRS-Assisted Cascaded MIMO Communication Systems
## 3 Achievable Rate (Full-Duplex Relay)
### Algorithm Description
Algorithm 2 requires the SVD for matrices \(\mathbf{H}_{1}\), \(\mathbf{H}_{2}\) and \((\mathbf{H}_{IR}*\mathbf{H}_{RI}^{T})\), with the complexity \(\mathcal{O}(N_{t}K_{r}\min(N_{t},K_{r}))\), \(\mathcal{O}(N_{r}K_{t}\min(N_{r},K_{t}))\) and \(\mathcal{O}(MK_{r}K_{t}\min(M,K_{r}K_{t}))\), re

Figure 5: Examples of multilevel water-filling for two different cases. (**a**) No subchannel with optimum power equal to zero. Note that due to the power cap constraint (37d), water does not have the same level for all subchannels. (**b**) Subchannel \(i=13\) receives the optimum of zero as its input power. Notice that in this case, due to the additional power cap constraint (37d), all the remaining subchannels \(i>13\) also have zero power. spectively. Furthermore, the Khatri-Rao multiplication \(\mathbf{H}_{IR}*\mathbf{H}_{RI}^{T}\) is needed that has the complexity \(\mathcal{O}(N_{r}K_{t}M)\). As a result, the overall complexity of our method is \(\mathcal{O}(MK_{r}K_{t}\)\(\min(M,K_{r}K_{t})+N_{r}K_{t}M+N_{t}K_{r}\min(N_{t},K_{r})+N_{r}K_{t}\min(N_{r},K_{t})+I_{t} (I_{w}\min(N_{t},K_{r})))\), where \(I_{t}\) is a constant independent of the system parameters. Interestingly, our method has a super linear complexity with respect to the number of IRS elements which is better than the state of the art works, e.g., [58]. This means that our algorithm in more energy efficient and suitable for latency sensitive applications. It should also be noted that our algorithm does not provide the optimal IRS pattern; instead, it provides analytical bounds for the performance of the IRS that can be used as a benchmark. In other words, our work provides a tool with which one can evaluate the efficiency of their robust design. A comparison between our method and previous works is summarized in Table 2

## 4 Achievable Rate (Half-Duplex Relay)

We consider a simple HD relay where the source and the relay transmit in two subsequent time instances. Notice that for the case of HD, IRS can be used to assist both the source-relay and the relay-destination channels as the signal is being sent over each of these channels in a different time slot.
## 4 Flow of the Deep Mixing Project and QC/QA
### QC/QA for Stabilized Soil-Current Practice
#### 4.2.4 Quality Verification  
After the improvement, the quality of field-stabilized soil columns should be verified in advance of the construction of superstructures to confirm the design quality, such as continuity, uniformity, strength, permeability, or dimension. Core samples were taken throughout the depth to verify the uniformity and continuity of the stabilized soil column (Figure 6). The engineering properties of the stabilized soil are evaluated by the unconfined compressive strength of the core samples.  
The reliability and accuracy of the unconfined compressive strength of the core sample depends upon the quality of the core sample, and the quality of the sample depends upon the drilling and coring method and drillers' skill. The Denison-type sampler, double tube core sampler, or triple tube core sampler can be used for stabilized soil columns whose unconfined compressive strength ranges from 100 to 6000 kPa. It is advisable to use samplers of a relatively large diameter to take high-quality samples. The quality of the retrieved core is evaluated by visual observation and the rock quality designation (\(RQD\)) index. The \(RQD\) index is defined by Equation (5). The \(RQD\) index measures the percentage of \"good rock\" within a borehole and provides the rock quality. The \(RQD\) index was originally applied for evaluating the quality of rock sample, as shown in Table 1.  
\[RQD\ =\ \frac{\sum\text{length of core pieces}\ >\ 10\text{ cm}}{\text{Total core run length}}\cdot 100\ (\%) \tag{5}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline \(RQD\) & **Description of Rock Quality** \\ \hline
0–25\% & Very poor \\
25–50\% & Poor \\
50–75\% & Fair \\
75–90\% & Good \\
90–100\% & Excellent \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(RQD\) index and rock quality ([PERSON], 1989).  
Figure 6: Core boring machine.  
A variety of verification test procedures to examine the engineering characteristics of stabilized soil have been proposed [9; 10; 11]. However, actual practices rely upon the unconfined compression test on core samples. Most of the other procedures seem to be used only for research purposes or settling noncompliance.
## 5 Simulations and Comparison  
The thruster saturation effect is sometimes taken into consideration in the control algorithm [58]. However, when the thrust saturation effect is not serious, then the problem is omitted [8; 52]. Such a situation occurs when the trajectories set for tracking are not complicated and the saturation effect for thrusters occurs only temporarily or occasionally.
## 6 Challenges and Future Development
### Hybrid Fault Diagnosis Model  
The sensor system in Ag-IoT is frequently dynamic, uncertain, open, vulnerable to attack, and concurrently susceptible to multiple faults. If only a single fault diagnosis method is used, issues such as low accuracy, poor generalization ability, and incomplete diagnosis arise, making it difficult to achieve accurate diagnosis results. The method for fault diagnosis based on the fusion of multiple technologies can be studied. Combining a model-based approach and deep learning can enhance a diagnostic system's robustness, accuracy, and dependability while decreasing uncertainty. This method can not only expand the fault ability based on deep learning but also fully utilize the interpretability advantage of the model-based method, which aids technicians in understanding the fault type and origin.
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 3 GAN-Enhanced CNN for Voiceprint Recognition  
The methodology described here proposes an algorithm combining both CNN and GAN in an innovative way to address the challenge of limited data in voiceprint recognition, named GAN-enhanced CNN for voiceprint recognition (GEC-VR). The CNN is used for classification, while the GAN model augments the dataset by generating new audio samples, thus enhancing the training process, as Figure 1 shows. The detailed description is as follows:  
Input:  
* Audio samples (e.g., from Common Voice dataset);
* Number of original samples \(N\);
* Number of GAN-generated samples \(G\);
* CNN model initialized.  
Output:  
* Trained CNN model;
* Accuracy metrics for voiceprint recognition;  
Algorithm Steps:  
1. Data Collection  
* Collect \(N\) audio samples, ensuring diversity in gender, language, and voice characteristics.
* Split the data into training and test sets, where \(N=N_{train}+N_{test}\):  
\(N_{train}=50\);  
\(N_{test}=10\).
2. Preprocessing  
* Convert all audio files into Waveform Audio File (WAV) format.
* Extract MFCC from audio:  
Use librosa.feature.mfcc to compute MFCC features.  
Set \(n_{\text{mfcc}}=13\), i.e., extract 13 feature variables.
* Format the MFCC features to ensure consistent input length for CNN.
3. CNN Model Construction
* Initialize a CNN model with the following layers:  
Convolutional layers: Use four convolutional layers with ReLU activation.  
Pooling layers: Apply max-pooling after each convolutional layer.  
Fully connected layers: Include three fully connected layers for classification.  
Dropout layers: Introduce dropout between fully connected layers to prevent overfitting.
* Compile the model using:  
Loss function: sparse_categorical_crossentropy;  
Optimizer: Adam.
4. GAN Model Construction
* Generator:  
Generate new samples based on random noise (e.g., 600-dimensional noise).
* Discriminator:  
Classify the generated samples as real or fake.
* Use a GAN training cycle to improve the quality of the generated audio samples.
5. Training and Evaluation
* Combine the original samples \(N\) with the GAN-generated samples GGG.
* Train the CNN model:  
Perform forward propagation and backpropagation.  
Train for a predefined number of epochs (e.g., 30 epochs).
* Evaluate model accuracy using the test set.
* Repeat the training cycle multiple times (e.g., 50 rounds) to ensure stable performance.  
6. Iterative GAN Reduction and Re-training * Reduce the number of GAN-generated samples by \(\Delta\). (\(\Delta\) can be determined by user, e.g., five). * Re-train the model and evaluate accuracy. * Repeat the reduction and re-training process for 10 cycles. * Track accuracy across different GAN sample sizes to determine the optimal balance between real and GAN-generated data. 7. Final Evaluation * Record the final model's accuracy metrics for voiceprint recognition. * Analyze the impact of GAN sample generation on model performance.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 6 Validation of the Proposed Protocol  
In this section, SMTrust is validated using the routing protocol requirements specified in [73; 74]. RPL uses source routing or hop-by-hop forwarding to send packets to the root node. Additionally, RPL uses the Bellman-Ford algorithm to calculate path cost, because RPL is a distance vector-routing protocol [75]. The shortest path is computed by this algorithm from source to destination in a weighted directed graph. Thus, the SMTrust and trust index metric is analysed to fulfil the routing protocol requirements considering the theoretical framework by [74; 76].
## 6 Effects of Roasting on Polyphenol Content  
Polyphenols possess various beneficial properties for human metabolism. These properties are attributed to their antioxidant, anti-inflammatory, neuroprotective, and anticancer effects. Chemically, polyphenols contain one or more aromatic rings bearing one or more hydroxyl groups. They are synthesized through the secondary metabolism of plants. Generally, polyphenols are unstable during food processing methods. This instability arises from their reactivity with other molecules and their susceptibility to epimerization and oxidation when exposed to alkaline conditions, heat, and oxygen [74].  
Nevertheless, in contrast to previous studies, the thermal process of roasting evaporates intracellular water, initiating a series of chemical and physical reactions within the cell structure. These reactions, including the degradation of lignocellulosic and proteins,result in the increased availability of polyphenolic compounds in nuts. This ultimately enhances their antioxidant activity and facilitates their extraction from the matrix [75].  
Alternatively, [PERSON] et al. (2016) investigated the impact of different roasting conditions, including conventional, infrared, and hybrid methods, on peanut kernels. Their study revealed an increase in phenolic compounds after roasting. This increase was attributed to the Maillard reaction, which can synthesize compounds with phenolic structures, such as furans and pyrroles. These compounds may contribute to enhanced antioxidant activity and an increased total phenolic content [76].  
Table 6 presents a comparison of polyphenol content changes in nuts before and after roasting.
# Modeling and Assessment of GPS/BDS Combined Precise Point Positioning
## 3 Data Processing
### Position Differences between Static PPP and IGS Daily Solutions  
We compared GPS/BDS daily position estimates with the IGS daily solutions covering the same period through a seven-parameter Helmert transformation [28; 36]. The RMS of each station is showed in Figure 2 and the mean value is shown in Table 1. GPS/BDS combined PPP achieves the best performance compared with GPS-only PPP and BDS-only PPP. The improvement of combined PPP over GPS-only PPP is not very obvious. BDS-only PPP has the worst accuracy, which may be due to the less accurate BDS orbit and clock products and a regional satellite constellation with less satellites compared with GPS. We notice that stations JFNG (Wuhan, China), CUT0 (Perth, Australia), GMSD (Guts Masda, Japan), and REUN (Le Tampon, La Reunion, France) have the best accuracy in BDS PPP, which is due to the fact that more BDS satellites are tracked for these sites under the current BDS constellation. Also, the GPS/BDS combined PPP using a simplified and traditional model is nearly the same and their values differ at the level of \(\upmu\)m, which will be discussed in detail in Sections 3.3 and 4.  
We make 3D RMS statistics of the 11 stations over the whole month and plot the histogram of the three scenarios in Figure 3. The GPS/BDS combined PPP achieves the best accuracy, with a mean 3D  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **N (cm)** & **E (cm)** & **U (cm)** \\ \hline GPS/BDS & 0.34 & 0.58 & 1.46 \\ GPS-only & 0.36 & 0.59 & 1.54 \\ BDS-only & 2.09 & 2.48 & 2.97 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean RMS of station coordinate differences of different scenarios.  
Figure 2: RMS of coordinate differences between daily static PPP and IGS daily solutions for each station in the North, East, and Up component, where PPP in different scenarios is shown in different color. The last column of each subplot is the mean value of the 11 stations.  
RMS of 1.5 cm, while those of GPS-only and BDS-only is 1.6 cm and 3.7 cm, respectively. The percentage of 3D RMS within the range of \([0,1.5]\) cm is 26%, 40%, and 59% for BDS-only, GPS-only and combined solutions, which demonstrates a more accurate solution is obtained by adding observations from different systems.
## 3 Results and Discussion  
### Vulnerable Areas for Emergency Medical Services (EMS)  
#### 3.1.1 The Distribution State of EMS in Urban and Rural Areas  
We examined the current states of medical facilities to identify whether the medical environment is balanced between rural and urban areas (Figure 2). The number of medical facilities includes 495 emergency medical centers (Figure 2a), 3161 community health centers (Figure 2b), and 1711 119-safety centers in Korea (Figure 2c). Of these facilities, 80.2% of the emergency medical centers (397 centers) are concentrated in urban areas. Since most emergency medical centers are concentrated in urban areas that are highly populated, emergency patients in rural areas are relatively vulnerable to health emergencies.  
Community health centers and 119-safety centers are also balanced between urban and rural areas because they are managed by the government to improve and promote residents' public health. They provide protective services for residents' health and property according to the Regional Public Health Act and the Framework Act on fire-fighting services [53]. Approximately 90.9% of community health centers seem to be concentrated in rural areas, but in fact one health center is located in each jurisdictional county in each urban and rural village. The distribution of 119-safety centers is relatively balanced between urban and rural areas with 41.4% (709 centers) in urban areas and 58.6% (1002 centers) in rural areas.  
Figure 2: Distribution of medical facilities in Korea. The yellow and gray areas indicate urban and rural areas, respectively. The individual medical facilities are represented by dots. Red dots in (**a**) are emergency medical centers, blue dots in (**b**) are community health centers, and green dots in (**c**) are 119-safety centers. There are 495 emergency medical centers, 3161 community health centers, and 1711 safety centers.
# Application of Machine Vision Technology in Citrus Production
## 2 Pest and Disease Detection  
The quality of citrus is not only affected by climate, environment, and planting conditions, but is also threatened by diseases and pests [2]. The quantity of citrus is huge, and there are many types of diseases and pests that are highly harmful. The diseased plants can quickly spread to other healthy plants, leading to a significant reduction in, or even death of, citrus trees. Citrus diseases and pests affect the development of the agricultural economy and are one of the main reasons for the reduction in citrus crop yield. Therefore, it is very important to help fruit farmers effectively and quickly judge and prevent citrus diseases before harvesting, and to control citrus diseases and reduce losses in a timely manner. At present, the main diseases commonly found in citrus include huanglongbing, canker, anthrax, sooty mold, fatty spot macular, scab, foot rot, resin disease, and so on [3], and the main insect pests include red spider, psyllid, stink bug, scale insect, leaf miner, long-horned beetle, and so on [4]. The disease occurs mainly in leaves and fruits, so we have a comprehensive discussion of the two parts.