esa-sceva/satcom-chunk-collection huggingface dataset:
# Directly Matching an MMIC Amplifier Integrated with MIMO Antenna through DNNs for Future Networks
## 2 Proposed Optimization Method  
This section presents the optimization-oriented algorithm consisting of two sequential phases for: (i) modeling the antenna with the LSTM- based DNN, and (ii) modeling and optimizing the PA design in terms of linearity where it is in relation with the modeled antenna and it is optimizing with respect to the predicted load impedances from the antenna side. The DNNs are the networks that enable more accurate prediction of the targeted data than shallow neural networks (SNNs) [28], and LSTM-based DNN is employed for predicting the multi-segment frequency-series data in the determined bandwidth. Training DNNs with more than 90% testing accuracy requires two important challenging factors: one is to have a suitable amount of dataset, and the other one is to achieve optimal hyperparameters [29; 30]. This section provides detailed explanations about the proposed methodology, leading us to enhance the linearity specification in the communication systems. In the trained two DNNs (in phase 1 and phase 2), the used activation function and loss function are the rectified linear unit (ReLU) and mean squared error, respectively. Algorithm 1 (at the end of this section) describes the steps of applied method, and Figure 1 presents the general overview around the two phases of intelligent-based optimization method through DNNs.
## 3 Results
### Cell Proliferation Assays  
Subsequently, we evaluated the effects of _F. sellowiana_ acetonic extract on the GC cell line growth (AGS, KATOIII and SNU-1). In particular, cells were treated with three different concentrations of _F. sellowiana_ acetonic extract (5, 50 and 500 \(\upmu\)g/mL) or flavone (5, 50 and 100 \(\upmu\)g/mL) for 24 and 48 h.  
Figure 1: Effect of _Feijoa sellowiana_ acetonic extract and flavone on the activity of antioxidant enzymes in polymorphonuclear cells. (**a**) Superoxide dismutase, (**b**) catalase and (**c**) glutathione peroxidase. Data were presented as mean and standard error and were analyzed with a paired \(t\)-test. Bars not accompanied by the same letter were significantly different at \(p<\) 0.05.  
Flavone treatment was used as a reference to evaluate the efficacy and potency of the acetonic extract. The effect on cell proliferation was assessed by MTS assay. As shown in Figure 2, the _F. sellowiana_ acetonic extract induced a significant decrease in SNU-1 cell proliferation in a dose- and time-dependent manner, with a maximum effect at 500 \(\upmu\)g/mL and after 48 h of treatment, whereas no growth inhibitory effects were observed in AGS and KATOIII cells. The flavone effect was significant as early as after 24 h and at lower doses (50-100 \(\upmu\)g/mL) as compared to the _F. sellowiana_ acetonic extract. Furthermore, flavone seems to be more effective also to inhibit AGS and KATOIII cell proliferation.
## 3 Results and Discussion
### Effect of Catalytic Conditions
#### 3.3.3 Effect of Temperature  
The reaction temperature is an important factor for the activity of catalysts in the most reaction systems. Therefore, hydrolysis of inulin with PEK-C-ILs catalyst was performed at different temperature in the range of 50-70 \({}^{\circ}\)C to find out the optimum temperature of hydrolysis reaction. The effects of the TRS of inulin at different temperature were exhibited in Figure 6. The results suggested that the increment of temperature is apparently favorable to accelerate the hydrolysis reaction. However, when reaction temperature was above 65 \({}^{\circ}\)C, the hydrolysis rate had no marked further change after 25 min. At this moment, with the increase of temperature from 50 \({}^{\circ}\)C to 70 \({}^{\circ}\)C, the corresponding TRS yields increased from 35.3% to 97.3%.  
[PERSON] et al. [15] prepared a sulfonic acid functionalized ILs modified silica as catalyst for cellulose hydrolysis. Using 1-\(n\)-butyl-3-methylimidazolium chloride as solvent, a maximum TRS yield of 67% was obtained when 10 mol % catalyst was employed at 70 \({}^{\circ}\)C for 360 min. Meanwhile, [PERSON] et al. [28] investigated the hydrolysis of microcrystalline cellulose by ILs (3-sulfobutyl-1-(3-propyltriethoxy-silane) imidazolium hydrogen sulfate) supported silica and TRS reached 77% at 110 \({}^{\circ}\)C for 120 min. These catalytic processes require an elevated temperature or a longer reaction time. Herein, from the view of energy conservation and economy, it is advisable that the reaction temperature of inulin hydrolysis system should not be fixed at a higher temperature due to reducing sugars are easy to further degrade. Hence, the hydrolysis temperature at 65 \({}^{\circ}\)C was recommended.  
Figure 5: Effect of PEK-C-ILs dosage on the TRS of inulin. Hydrolysis conditions: inulin concentration 20%, DCM 1.67, temperature 65 \({}^{\circ}\)C. PEK-C-ILs concentration (wt %, the weight ratio of the PEK-C-ILs to water and PEK-C-ILs).
## 3 System Model and Problem Formation
### Interference Model  
We use the interference range model [49], which is a special case of the protocol model [50]. Similar model with conjunction to Request to Send/Clear to Send (RTS/CTS) has been used in [51] to form a WMN. Interference range model states that link \(e_{i,j}\) between nodes \(v_{i}\) and \(v_{j}\), and link \(e_{k,l}\) between nodes \(v_{k}\) and \(v_{l}\) could not be assign same channel if the sender or receiver of one of them is in the interference range of the sender or receiver of the other one; more specifically, if \(d_{v_{l},v_{k}}\), \(d_{v_{l},v_{l},v_{l}}\), \(d_{v_{j},v_{k}},d_{v_{j},v_{l}}\leq I_{R}\), where \(d_{v_{l},v_{k}}\), \(d_{v_{l},v_{l}}\), \(d_{v_{j},v_{k}}\), and \(d_{v_{j},v_{l}}\) is Euclidean distance between subscripted nodes. Two links out of their interference ranges can be assigned the same channel.
## 4 Experimental Verification and Discussion
### Comparison of the Experimental Results and Theoretical Results  
The time-displacement curves and the vibration frequency of the piezoelectric cantilever beam can be obtained by the above-mentioned experiments. In the following, we compared the experimental results with the theoretical results. Before the comparison, we needed calculate the time-displacement curves of the theoretical solution under three initial displacements, namely, \(l=0.05\) m, \(b=0.02\) m, \(h=0.0001\) m, \(\xi=0.03\), and \(\overline{m}=0.015\) kg/m. Thus, from Equations (55) and (56), the vibration frequency and the time-displacement curves can be obtained, as shown in Table
## 2 Materials and Methods
### Crystal Phase Analysis  
The crystal phases of CAP granules were analyzed using X-ray diffraction (XRD; D8 Advance; Bruker AXS GmbH, Karlsruhe, Germany). The set granules were ground to a fine powder prior to XRD. The XRD measurement was conducted with a CuK\(\alpha\) radiation source that operated at 40 kV and 50 mA. The diffraction angle was continuously scanned in a 20 range from 10\({}^{\circ}\) to 40\({}^{\circ}\) at a scanning rate of 6\({}^{\circ}\)/min.
## 5 Discussion and Conclusions
### Conclusions  
This study developed a computer-aided diagnosis system to differentiate between benign and malignant lung cancer cases from EBUS images. Bagging ensemble learning and down-sampling techniques were applied to our CAD system to resolve an imbalanced dataset and enhance results. As there were far more malignant than benign cases, the malignant cases were down-sampled to consider the entire dataset. Malignant cases were equally divided into three, and each was combined with all the benign cases to create three small datasets. Models were trained with each small dataset and aggregated with the bagging ensemble learning method. Thus, the three trained models contained knowledge for reliable benign and malignant classification. In addition, the three fusion methods were majority voting, output average, and performance weighting. The performance weighting fusion method provided the best bagging ensemble result, achieving a 0.7 accuracy, 0.63 F1-Score, 0.75 AUC, 0.84 PPV, 0.68 NPV, 0.56 sensitivity, and 0.85 specificity.  
In conclusion, our proposed computer-aided diagnosis system based on deep learning achieved promising results for diagnosing benign and malignant peripheral pulmonary lesions from endbornchial ultrasonography. However, the dataset used in this study was imbalanced. Excluding data augmentation, we will gather additional PPLs data to verify the CAD system's efficacy in future studies.  
Conceptualization, H.W. and T.N. ([PERSON]); methodology, H.W., T.N. ([PERSON]) and Y.N.; software, H.W.; validation, H.W., T.N. ([PERSON]) and Y.N.; formal analysis, H.W., Y.N. and T.N. ([PERSON]); investigation, H.W., K.S. and T.N. ([PERSON]); resources, H.W., K.S. and T.N. ([PERSON]); data curation, H.W., K.S. and T.N. ([PERSON]); writing--original draft preparation, H.W.; writing--review and editing, T.N. ([PERSON]), Y.N., T.N. ([PERSON]) and K.S.; visualization, H.W. and T.N.([PERSON]); supervision, T.N. ([PERSON]); project administration, T.N. ([PERSON]); funding acquisition, T.N. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of Chiba University Graduate School of Medicine (M10393, 9 September 2022).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Proposed Methods
### Stereo Taillight Matching
#### 3.2.2 Features for Stereo Taillight Matching
However, it is important to note that VPR cannot be independently used to evaluate the matching probability, as the difference between the values of VPR in the matched and the unmatched cases is usually minimal in real scenarios. 3. **WR**: The ratio between the widths of the two taillights. To obtain the value of WR, we use the widths of \(L_{i}\) and \(R_{j}\), which are \(w_{i}^{L}\) and \(w_{j}^{R}\). Initially, their normalized value, \(\overline{w_{i}^{L}}\) and \(\overline{w_{j}^{R}}\), are estimated: \[\begin{cases}\overline{w_{i}^{L}}=\dfrac{w_{i}^{L}}{W_{L}}  
4. **HR**: The ratio between the heights of the two taillights. In the same way as calculating WR, HR is calculated using the height of each taillight region, which is \(h_{i}^{L}\) and \(h_{j}^{R}\), and the height of each image, which is \(H^{L}\) and \(H^{R}\): \[ \begin{cases}\overline{h_{i}^{L}}=\dfrac{h_{i}^{L}}{H_{l}}\\ \overline{h_{j}^{R}}=\dfrac{h_{j}^{R}}{H_{r}}\end{cases} ,\] (20) \[HR= \begin{cases}\dfrac{\mathbf{min}(\overline{h_{i}^{L}},\overline{h_{j}^{R}} )}{\mathbf{max}(\overline{h_{i}^{L}},\overline{h_{j}^{R}})}&\text{if }\overline{h_{i}^{L}}\leq \overline{h_{j}^{R}}\\ -\dfrac{\mathbf{min}(\overline{h_{i}^{L}},\overline{h_{j}^{R}})}{\mathbf{max}( \overline{h_{i}^{L}},\overline{h_{j}^{R}})}&\text{otherwise}\end{cases} ,\] (21) The values of WR and HR are both inside the range [\(-1\), 1]. When each of these values comes toward 0, it is more likely that the corresponding sizes of the taillight in the left and right images are far different from each other, highly indicating that the two taillights do not match. 5. **NCC**: Derived from the cross-correlation matrix, which is a popular tool for measuring the similarity between two arbitrary matrices. NCC is used for evaluating the similarity of the two taillights \(L_{i}\) and \(R_{j}\). First, the color of each taillight is converted into grayscale. After that, these two taillights need to be standardized by applying mean subtraction, as follows: \[ \begin{cases}L^{\prime}(k,l)=L_{i}(k,l)-\mathbf{mean}(L_{i}),&1\leq k\leq w_{ i}^{L},\\ &1\leq l\leq h_{i}^{L},\\ R^{\prime}(m,n)=R_{j}(m,n)-\mathbf{mean}(R_{j}),&1\leq m\leq w_{j}^{R},\\ &1\leq n\leq h_{j}^{R},\end{cases} ,\] (22) After subtracting the mean on each element of \(L_{i}\) and \(R_{j}\), we obtain two standardized matrices L' and R'. Then, the cross-correlation matrix of these two matrices can be identified as follows: \[C(k,l)=\sum_{a=1}^{w_{i}^{L}}\sum_{b=1}^{h_{i}^{L}}L^{\prime}(a,b)R^{\prime}( a-k,b-l),\hskip 14.226378 pt-(w_{j}^{R}-1)\leq k\leq w_{i}^{L}-1,\] (23) NCC is determined by the following equation, given that \(\|m\|\) denotes the matrix norm and function \(\mathbf{max}\) aims to get the maximum element of the matrix: \[NCC=\dfrac{\mathbf{max}(C)}{\|L^{\prime}\|\|R^{\prime}\|},\] (24) The value of NCC is within the range [0, 1], with a higher value indicating that the appearances of the two taillights are more similar, which also means these taillights are highly likely matched.
# Methods for Corrosion Detection in Pipes Using Thermography: A Case Study on Synthetic Datasets
## 1 Introduction  
### Thermography  
Thermography is a non-destructive testing (NDT) technique that employs infrared thermography (IRT) to evaluate the thermal characteristics of materials and structures. Infrared cameras measure the infrared radiation emitted by objects, capturing variations that correspond to different surface temperatures. The underlying principle is that materials with varying thermal conductivities emit different levels of infrared radiation. By analyzing these radiation patterns, IRT can determine the apparent surface temperature and detect anomalies or subsurface defects [1]. Thermography methods can be categorized into two types: active and passive. Active thermography involves applying an external heat source to the specimen, while passive thermography relies on the specimen's natural thermal emissions. The choice between active or passive thermography depends on the specific requirements of the testing scenario [2].
## 4 Statistical Quality Assessment of GNSS Signal  
In this study, we conducted a comprehensive analysis of GNSS signals by computing both time and frequency domain attributes. The time domain features included the mean, standard deviation, median, mean absolute deviation, root mean square error, 25 th and 75 th percentiles, inter-percentile range, skewness, kurtosis, entropy, and maximum-to-mean ratio. These time domain features were computed to capture the statistical properties of the signal. These metrics offer clear information about the patterns, central tendency, variability, distribution of power and shape of the spectrum in a specific band of interest, and overall complexity of the signal.
## 3 Proposed Protocol
### Key Generation Algorithm  
The first step in the proposed framework is to generate the keys for secure communication. In this step, we propose the use of the ECC algorithm for key generation. ECC is widely used in cryptography due to its smaller key size and higher security as compared to other cryptographic algorithms. The key generation process involves selecting a random private key, d, from a finite field, and using it to compute the corresponding public key, Q, as shown below: \(Q=d*G\) Where G is a known base point on the elliptic curve. The elliptic curve equation used in this process is defined as: \(y^{2}\cong x^{3}+ax+b(modp)\) Where a, b, and p are the parameters of the curve. The security of the key generated through this process depends on the size of the finite field and the choice of the curve parameters. The key generation process can be summarized as follows by below notations as shown in Table 2.  
\begin{table}
\begin{tabular}{c l c} \hline \hline
**S. No** & **Definition** & **Notations** \\ \hline
1 & A prime modulus & \(p\) \\
2 & Coefficients a and b of the curve equation & _a,b_ \\
3 & A known base point on the curve & \(\mathrm{Rp}\) \\
4 & Private key of sensor node & \(E_{C}P_{s}S_{n}\) \\
5 & Private key of Sink node & \(E_{C}P_{s}S_{n}\) \\
6 & Private key of Cluster-head node & \(E_{C}P_{s}C_{H}\) \\
7 & Public key of sensor node & \(E_{C}P_{u}S_{n}\) \\
8 & Public key of Sink node & \(E_{C}P_{u}S_{n}\) \\
9 & Public key of Cluster-head node & \(E_{C}P_{u}C_{H}\) \\
10 & The order n of G & G \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notation for Key Generation.  
Figure 3: Proposed Key management model for UWSNs.  
The key generation setup for the base station, sensor node and sink node are as below.  
\begin{tabular}{l} \hline \hline \multicolumn{2}{c}{**Key Generation Setup**} \\ \hline
**Base station:** \\ KeyGeneration(E): \\ // E = (p, a, b, 8p, n) \\  Generate a random integer d from the interval \([1,n-1]\). \\  d \(\sim\) RandomInteger(\(1,n-1\)) \\ \(E_{C}P_{n}C_{Hn}\)\(E_{C}P_{n}S_{n}\)d * G \\ Transmit(Q) \\ Return (d, \(E_{C}P_{i}CH_{n}\)) \\
**Sensor Node:** \\ E = (S_n_, a, b, G, n) be the elliptic curve parameters. \\  Generate random integer from the interval \([1,n-1]\). \\  A = a* \(E_{C}P_{n}S_{n}\) \\  Transmit(A) \\
**Sink Node:** \\ E = (p, a, b, G, n) be the elliptic curve parameters. \\  Generate a random integer from the interval \([1,n-1]\). \\  B = b* \(E_{C}P_{n}S_{n}\) \\  Transmit(B) \\ \hline \hline \end{tabular}  
The key size for the ECC algorithm can be varied to provide a balance between security and computational efficiency. In general, a 128-bit key size is considered secure for most applications. The proposed key generation algorithm can be expressed mathematically as \(\text{E}=(\text{P},\text{ a},\text{b},\text{ G},\text{n})\) where \"p\" is the prime modulus, \"a\" and \"b\" are the coefficients of the curve equation, \"G\" is the base point of the curve, and \"n\" is the order of \"G\". The algorithm provides a secure and efficient method for generating keys in UWSNs.
# A New Passive Islanding Detection Solution Based on Accumulated Phase Angle Drift
## 4 Application of Composite PAD Solution
### Sudden Phase Angle Change  
Unlike the conventional relay, in the new composite PAD method, the detection of voltage vector shifts in same direction is only to initiate the calculation of the accumulated phase angle drift. The main purpose of the setting \(\Delta Ang\_set\) is to differentiate the islanding condition from the system events, such as frequency excursions.  
Considering Equation (3), the angle change is related to the active power imbalance and the sub-transient reactance of the generator. For the generator with damped winding, the typical sub-transient reactance is around 0.2. Taking the synchronization machine in the following simulation section as an example, the sub-transient reactance \(X_{d^{\prime\prime}}\) is 0.23. The angle change under different power imbalance is shown in Table 1.  
The default setting for \(\Delta Ang\_set\) is suggested as small as 1.0\({}^{\circ}\) with sensitivity better than 10%. The stable setting for \(\Delta Ang\_set\) can be 2.0\({}^{\circ}\) which can provide better stability. The setting for \(\Delta Ang\_set\) should be based on the size of the DG units and the analysis of possible power imbalance. Besides, the practical application should comply with the related requirements from the utility. If the sensitivity has higher priority, the setting for \(\Delta Ang\_set\) can be as small as 1.0\({}^{\circ}\) to minimize the non-detection zone. If the stability has higher priority, the worst case of power imbalance in the regional grid should be analyzed and then set \(\Delta Ang\_set\) suitably with relatively large value.
## 3 Results and Discussion  
### Microbiological Quality  
The results of the microbiological evaluation of the raw chicken meat treated with SC-CO\({}_{2}\) (7.4, 11.4 and 15.4 MPa) at 31 \({}^{\circ}\)C for 10 min during the seven-day storage period are shown in Table 1. Differences (\(p<0.05\)) are observed on the zeroth day of storage, where increasing the SC-CO\({}_{2}\) pressure caused the log CFU/g of the treated samples to decrease. In this case, 15.4 MPa resulted in lower bacterial, and yeast and mold count. The log\({}_{10}\) CFU/g of total yeast and mold also decreased (\(p<0.05\)) compared to the control sample on the seventh day of storage. However, no differences (\(p>0.05\)) were detected between the total plate count on the third and seventh day of storage. The muscle from a healthy animal that is turned into food could also be contaminated in the slaughterhouse environment, or as a result of dirty knives, intestinal content, or polluted water [21]. The high microbial count on the zeroth day might be the result of such contaminated conditions.  
[PERSON] et al [22] studied the microbial reduction of fresh pork and observed a reduction in microbes under high-pressure SC-CO\({}_{2}\) treatments. Solid foods, including meat and meat products, have a lower microbial reduction compared to liquid foods because of the partial diffusion of CO\({}_{2}\) into solid food [23]. Two stages were detected in the survival curves of microorganisms after the application of SC-CO\({}_{2}\). In the early stage, the microbial reduction was sluggish and then declined rapidly during the later stage [24]. The results of this research showed that the inactivation rate increases with increased pressure. The reasons for the increase in log\({}_{10}\) CFU/g on the third and seventh day of storage might be that the lower diffusion of CO\({}_{2}\) into the chicken meat's deep structure and the inactivation of vital enzyme and cellular metabolism resulted in the germination of some remaining spores.
## 4 Data Storage and Processing: Automated Data Processing and Storage  
Effective data processing is crucial for automated irrigation management systems to ensure optimal decision-making and water usage efficiency. In this section, various techniques and strategies employed in the cloud for handling data quality, preprocessing, scalable deployment, model inference, and online learning are outlined. These approaches are fundamental in transforming raw sensor data into actionable insights, enabling real-time adjustments to irrigation schedules based on current conditions.
# A Low-Loss Circuit with High-Pass Low-Pass Broadband Flat Negative Group Delay Characteristics
## 2 Circuit Design and Analysis with High-Pass and Low-Pass Negative Group Delay
### High-Pass Flat Negative Group Delay Circuit
#### 2.1.1 High-Pass Negative Group Delay Circuits with Different C\({}_{1}\) Values  
When the values of \(L_{1}\), \(R_{1}\), \(L_{2}\), \(R_{2}\), and \(L_{3}\) in the circuit remain unchanged, as shown in Table 1, we change the values of \(C_{1}\) to 5 pF, 15 pF, and 25 pF in sequence and observe the changes in the circuit transfer function and group delay. The circuit results in these three cases are shown in Table 2 and Figures 5 and 6. Through observation, it is found that changing the value of capacitor \(C_{1}\) does not change the group delay of the high-pass circuit at 0 MHz. However, as the frequency increases, the larger the value of capacitor \(C_{1}\), the greater the negative group delay value. The change in capacitor \(C_{1}\) also affects the flat negative