esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Algorithmic Basis for Urban Residential Vacancy Rate Observation
### Application of the Algorithm in Urban Residential Vacancy Rate Observation
In addition, neural network algorithms, especially convolutional neural networks (CNNs) in processing remote sensing image data, and long short-term memory networks (LSTMs) in time series prediction, provide new perspectives and possibilities for observing residential vacancy rates. CNN can extract the characteristics of light and dark changes in residential areas from nighttime light remote sensing images, thereby inferring the vacancy rate, while LSTM can predict future vacancy trends by analyzing time series data of residential rental or sale. In terms of the integration of diverse data sources, multi-source data fusion algorithms play a vital role [71]. In a large-scale urban study, principal component analysis (PCA) extracted key features from remote sensing images, census data and real estate transaction records, reducing data dimensionality. Then, the K-nearest neighbor (KNN) algorithm fused the data by calculating data-point similarities. For example, in Shenzhen's 2023 vacancy rate monitoring, high-resolution satellite imagery, real estate transaction data and census data were integrated. PCA was used to extract core features such as building density, population density and transportation accessibility, while KNN classified and predicted vacancy rates across different urban areas. The results showed that the fused model improved accuracy by 15-20% compared to single-source data, identifying Qianhai New Area as having a lower vacancy rate due to its robust commercial infrastructure. This insight enabled the government to optimize public rental housing allocation policies, effectively reducing resource waste [72]. This improved vacancy rate observation accuracy and revealed that areas with high population density and good transportation had lower vacancy rates, providing insights for urban planning. Data from different sources, forms and periods, such as drone aerial images, census data and real estate transaction records, can be effectively integrated through data preprocessing, feature extraction and similarity measurement. This method not only improves the accuracy of vacancy rate observation, but also reveals the regional distribution of residential vacancy, providing a scientific basis for policy making. The combination of drone aerial photography technology and machine learning provides high-precision and high-resolution geographic information data for observing residential vacancy rates, making the observation results more accurate.
## 3 Results
### Storage Experiment 1--Microbiological and Metabolic Analysis
#### 3.1.4 Short-Chain Fatty Acids Levels  
The concentration of acetic acid, propionic acid, butyric acid and the branched SCFA (isobutyric and isovaleric acid) in algae samples without added acetic acid was significantly influenced by storage time, temperature, the way of closing the storage recipient (\(p<0.001\) for all main effects) and their interactions (\(p<0.05\)). Storage temperature was by far the major factor affecting SCFA concentrations with much higher levels after 20 \({}^{\circ}\)C storage than after 4 \({}^{\circ}\)C and 8 \({}^{\circ}\)C storage (Figure 4). Algae stored at 20 \({}^{\circ}\)C had higher SCFA concentrations when stored in closed tubes than when stored in open tubes and higher concentrations were observed after 14 days than after 7 days of storage. n-Valeric acid (C5:0) was only detected after 14 days of storage in open tubes at 20 \({}^{\circ}\)C (6.9 \(\pm\) 1.6 mg/L) and in closed tubes stored at 20 \({}^{\circ}\)C for 7 and 14 days (1.9 \(\pm\) 0.2 mg/L and 22.5 \(\pm\) 2.1 mg/L, respectively, results not in figure).  
By adding acetic acid at the start of preservation, the propionic acid concentration was seriously reduced (about 30 to 60 times) in algae suspensions stored at 8 \({}^{\circ}\)C in open tubes (Figure 5), whereas butyric acid and branched SCFA concentrations were kept low (<1 mg/L, not shown for branched SCFA). In addition, acetic acid addition drastically reduced the pH of the algae suspension (Supplementary Materials Figure S2).  
Figure 4: _Cont._  
Figure 3: O\({}_{2}\) levels in the headspace of closed storage recipients - storage experiment 1.  
Figure 4: Short-chain fatty acid (SCFA) levels in algae concentrates to which no acetic acid was added—storage experiment 1. (**a**) Acetic acid concentrations, (**b**) propionic acid concentrations, (**c**) butyric acid concentrations and (**d**) concentrations of branched SCFA (sum of isobutyric and isovaleric acid) in algae concentrates to which no acetic acid was added. Separate graphs for isobutyric acid and isovaleric acid are included in Supplementary Materials Figure S3. Error bars represent standard deviations. Lowercase letters (a,b,c,d) denote significant differences. Values within one figure that are not labelled by the same letter are significantly different.
# Research and Analysis of MEMS Switches in Different Frequency Bands
## 3 The Research Status of MEMS Switches in Different Frequencies
### Research on Reliable Contacts of MEMS Switches
The contact resistance was 1-2 \(\Omega\), the power handing capacity was better than 10 W, and the lifetime was 1 \(\times\) 10\({}^{9}\) cycles at 2-5 W of RF power. [PERSON] et al. [104] presented a novel Au-Au contact design employing ball-grid array (BGA) dimples, which limited the actual contact area to a few tens of nanometers in diameter, and the power handling was greater than 1 W under the condition of hot switching in excess of 100 million cycles.
## 3 Key Technologies of UAV-Assisted SAGINs
### Path Planning  
Path planning is a critical component of UAV-assisted SAGINs [75], aiming to determine the optimal 3D path for UAVs from an initial point to a target destination while satisfying various complex constraints, such as kinematic constraints, energy limitations [76], and collision avoidance. From an optimization perspective, this task is essentially an NP-hard multi-objective optimization problem [77] and finds it hard to figure out polynomial-time exact solutions. With continuous advancements in science and technology, heuristic algorithms, machine learning algorithms, and hybrid approaches combining both have been proposed, offering an increasing number of viable solutions for the path planning problem in SAGIN environments. This section will comprehensively review the latest research progress of UAV path planning algorithms, and the review's results are presented in Table 5.
# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 5 Applying Deep Learning Algorithms in Healthcare
The CT scan image input is given to the Restnet101 deep convolutional neural network model that has 101 layers deep with 33 residual blocks. Then the result is passed to a logistic regression classifier. The logistic regression classifier classifies the result as COVID-19, normal, or pneumonia. The test produces 99.15% accuracy with COVID-19-positive patients. The COVID19 diagnosis has threshold values of 0 and 1. 1 indicates the person is affected with coronavirus or pneumonia, and the result zero indicates the person is not affected with coronavirus. To identify the patient's condition from the initial stage to a severe stage, the threshold value is utilized for CT image classification. Threshold value 0.5 indicates the initial stage, and 1 indicates a severe condition [103]. One of the latest techniques for COVID-19 diagnosis and classification is the depthwise separable convolution neural network (DWS-CNN) with a deep support vector machine (DSVM) enabled by the Internet of Things (IoT). The procedure consists of two stages, training and testing. It starts with collecting data from patients using IoT devises, and sending them to the cloud server via 5G networks. The aim of DWS-CNN is to determine the binary and multiple class labels of COVID-19 using CXR images. CNN is involved as the basis of new models used for predicting diseases, due to its efficiency at detecting structural abnormalities. This model was developed to improve the accuracy of COVID-19 detection from chest X-ray scans, and it minimizes manual interaction dependent on radiologists [112]. [PERSON]'s disease (PD) is an illness which could be diagnosed and detected by a CNN from drawing movements, using the Digitized Graphics Tablet dataset. This CNN includes two parts: feature extraction (convolutional layers) and classification (fully connected layers). The fast Fourier's transform has the range of frequencies between 0 and 25 Hz, which are used as inputs to the CNN [113; 114; 115]. Skin cancer is one of the most widespread causes of death, and early detection could increase the survival rate to 90%. For that purpose, deep convolutional neural networks (DCNNs) have been developed and applied to classify the color images of skin cancer into three types: melanoma, atypical nevus, and common nevus [116; 117; 118].
# Toward High Bit Rate LoRa Transmission via Joint Frequency-Amplitude Modulation
## 2 Preliminaries
### Basic Idea of QR\(-\)LoRa  
The LoRa chirp is a sinusoid with a constant envelope whose frequency increases linearly with time. It carries information by modulating \(f_{0}\). The key idea of QR\(-\)LoRa is to exploit the constant modulus property of the amplitude to carry additional information alongside the frequency domain. Thus, we design a ramp signal whose amplitude increases linearly from 0 to 1. Then, we achieve the amplitude modulation by performing a cyclic right-shift operation on it. The cyclic right-shift makes the amplitude jumps appear at distinct time points, achieving the encoding in the amplitude domain. Figure 1 presents an example of a QR\(-\)LoRa chirp with four amplitude encoded values, 0, 127, 255, 383, corresponding to four cyclic right shift operations.  
Then, multiply the encoded ramp signal by the standard LoRa chirp. As a result, a QR\(-\)LoRa chirp with the frequency encoded \(F_{0}\) and amplitude encoded \(T_{0}\) is achieved, as shown in Figure 2.  
With the four amplitude encoded values, we provide an example of four QR\(-\)LoRa chirps with the same frequency encoded values, as shown in Figure 3  
Figure 1: QR\(-\)LoRa chirp envelopes with different amplitude encoded values.  
Figure 3: Four QR\(-\)LoRa chirps with same frequency encoded value but different amplitude encoded values.  
Figure 2: QR\(-\)LoRa symbols.
# Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network
## 3 Cellular Traffic Prediction Model
### Convolution Module  
In recent years, the improvement of convolutional neural network performance has been mainly divided into two major areas. One area is depth, such as ResNet, which solves the problem of gradient disappearance when the network is too deep [21]. The other area is the width, such as the Inception network, which uses a multi-scale convolution kernel to extend the model's generalization capabilities. In STDenseNet, the prediction module consists of two densely concatenated convolutional networks with the same structure. Similar to ResNet, DenseNet also establishes a dense connection between the front and back layers; that is, each layer accepts the output of all the previous layers as input and implements feature reuse in turn. DenseNet relies on the ultimate use of the network architecture to achieve fewer parameters and leading performance compared to traditional models [19].  
Cellular traffic has a strong spatiotemporal autocorrelation. This work uses DenseNet to extract the spatiotemporal features of historical data, which can achieve better results than the traditional single channel convolutional neural network. The convolution module in HSTNet contains two  
Figure 5: The hybrid spatiotemporal network’s (HSTNet) framework structure.  
separate DenseNets for processing two sets of historical data. Each DenseNet consists of three layers, each consisting of a unit block we call the DenseBlock. The outputs of the two DenseNets are multiplied by the learnable parameter matrix and then added.
## 4 Results
### Analysis of the Correlations between Features  
Detecting causality is the key step to construct the causal feature network. As causal relationships only exist based on correlations, the existence of correlations is the prerequisite for the study of causal relationships. Therefore, in this section, we base our analysis on both correlations and causal relationships.  
We first draw upon Pearson product-moment correlation coefficients (PCCs) to verify the correlations between composite fault features [34]. We undertake correlation analysis and hypothesis testing on the statistical features of 20 signals in the feature set and obtain the correlation coefficient r and its \(p\) value among statistical features. Experimental results are as shown in Figure 4. At the assumed significance of \(\alpha\) = 0.05, features are mostly correlated with the exception of few features. Especially, strong linear correlations exist among fea1-7 and fea8-12. Therefore, correlations exist and thus can serve as the foundation for our exploration of causal relationships in the composite fault feature set.  
Through IAMB algorithm, we further calculate the Markov blanket of every feature, obtaining the subset of features sharing causal relationships with the features in the original feature set. In this case, we get the causality between two random features, and the results are shown in Figure 5. The results show that the feature set has much fewer causal relationships than correlations, and a large amount of correlations are false due to data co-existence. Such false correlations will result in feature redundancies and confuse the interpretation of composite fault diagnosis. Therefore, incorporating causality into the selection of composite fault features can effectively control the size of the optimal feature subset.  
Figure 4: Heat map of correlations among composite fault features. The output ranges from \(-1\) to \(+1\). Blanks represent no correlation; negative values, negative correlation; and positive values, positive correlation.
# Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems
## 6 Conclusions  
In this paper, a novel Q-Learning based anti-jamming algorithm for networked UAV radar systems is proposed for optimizing the information acquisition capabilities, which is captured by the radar information representation. Due to the payload size and energy constraints of UAVs, the beam synthesis method under low bit phase condition is adopted. Both the angle measurement and range measurement scenarios are constructed to evaluate the effectiveness of the proposed algorithms. Simulation results show that the algorithm may facilitate effective anti-jamming and adapt to different tasks. In this paper, the Q-Learning based algorithm is selected due to its stability and interpretability, while it also discretizes the state variables in space, frequency and antenna beamforming, which may be continuous in general. In our future work, we will consider the reinforcement learning algorithm to generate continuous policies, such as the deep deterministic policy gradient (DDPG).  
Methodology, Q.W.; Supervision, H.W., X.L. and J.P.; Writing--original draft, Q.W.; Writing--review and editing, B.Z.  
This work is funded by the National Natural Science Foundation of China (No. 91648204, No. 61601486), Research Programs of National University of Defense Technology (No. ZDYYJCYJ140601), and State Key Laboratory of High Performance Computing Project Fund (No. 1502-02).  
The authors declare no conflicts of interest.
# Impact of Cooking Processes on Volatile Flavor Compounds and Free Amino Acids in Fish Sauce
## 3 Results and Discussion
### Qualitative and Semi-Quantification Analysis of Volatile Compounds in the Fish Sauces
#### 3.3.2 Esters and Alcohols  
Carboxylic esters are formed through the esterification of alcohols and acids, while lactones are produced by the cyclization of hydroxycarboxylic acids [23]. Esters generally impart floral and fruity notes (short-chain esters) or fatty flavors (long-chain esters), while \(\gamma\)-lactones are sweet compounds that enhance aroma. Seven esters, including three lactones, were identified and quantified in the fish sauce samples. As cooking temperature and time increase, the number of esters in fish sauce decreases, likely due to the decomposition of heat-sensitive esters. However, the levels of heat-resistant esters increased during cooking. Alcohols are produced through lipid oxidation, carbohydrate metabolism, and amino acid decarboxylation [25]. As shown in Figure 3, phenylethyl alcohol had the highest concentration in B30, while trans-1,2-cyclopentanediol was most abundant in F10, followed by B30. The alcohol concentration in boiled samples was higher than in unheated samples. However, alcohol content decreased after stir-frying, except for 1,2-cyclopentanediol and phenylethyl alcohol in F10. Therefore, the cooking process should be optimized to enhance the flavor of fish sauce.
# Analytical Solution for the Problem of Point Location in Arbitrary Planar Domains
## 5 NRA Crossing in Parametric Paths
### Comparison to the State-of-the-Art Algorithm  
For several years, Matlab has been using a version based on the algorithm from [PERSON] [8], valid for point location in arbitrary polygons. According to the original paper, that algorithm is based on three steps, which, on the optimized variant, includes the following operations:  
* Evaluation of the determinant: performs 2 multiplications and 1 subtraction per segment;
* Quadrant classification: uses 6 comparisons per vertex;
* Determination of winding number: performs 2 subtractions/sums and up to 4 + 1 comparisons per vertex.  
Algorithm 4 has more steps (though simpler) than Algorithm 3 and requires:  
* Initial test: 1 sign comparison per vertex;
* Coordinate differences: 2 per vertex;
* Y coordinate conditions: up to 3 comparisons per vertex;
* Test NRA: 2 multiplications and 2 comparisons per vertex;
* Test special cases end points: 2 comparisons with zero;
* Crossing counter: 1 addition per NRA crossing.  
The comparison is given side-by-side in Table 1, where it can be seen that Algorithm 4 has fewer comparisons (1 + 2 + 2 + 2 = 7 vs. 10 + 1 = 11) and potentially much less if tests dismiss early; there is a similar number of additions/sums: 2 + 1 vs. 3; the multiplications are in equal number but the advantage is that in Algorithm 4 they are needed only when x-axis intersection occurs. In the previous analysis, 2\(M\) subtractions were omitted, reporting to the relocation of the polygon around point \((0,0)\), but which is a common operation to all.  
An illustrative example of simple benchmarking is shown in Figure 12 with a polygon with more than 2200 sides and thousands of testing points in two situations: on the left, 5000 completely random points, and on the right more than 2200 points near the border. Results are shown for Algorithm 3 and the state-of-the-art algorithm in Matlab [8]. The result shown is the average of 100 runs and had the same outcome in both algorithms. In both cases, Algorithm 3 proposed in this paper outperforms  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Algorithm 3** & **Algorithm 4** & **Algorithm from [8]** \\ \hline Comparisons & 1 + 3 + 2 & 1 + 2 + 2 & 10 + 1 \\ Sums/subtractions & 2 + 1 & 2 + 1 & 3 \\ Multiplications & 1 & 2 & 2 \\ Divisions & 1 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of estimated number of operations per vertex/segment in the algorithms.  
Figure 11: Example of vectorization of Algorithm 4 in Matlab code.
## 4 Natural Antioxidants and Their Role in Cosmetics
### Taxifolin
#### 4.3.4 Bakuchiol  
Bakuchiol (Figure 4) is a plant-derived merotterpene, meaning it is partly a terpenoid due to its conjugation with a compound of a different class--a phenol, in this case. It was isolated from the seeds and leaves of _Psoralea corylifolia_ L. (Fabaceae) [109; 110].  
Bakuchiol exhibits potent antioxidant properties [111; 112] and retinol-like activity, as it regulates gene expression in a manner similar to retinol, stimulates the production of type I, III, and IV collagen; inhibits melanogenesis; and improves skin photoaging--all while offering better skin tolerance compared to retinol [113; 114; 115]. For these reasons, it is widely used in cosmeceuticals as an anti-aging, anti-inflammatory, and antibacterial agent [116; 117; 118].  
The production of bakuchiol for cosmetic use involves traditional extraction methods, advanced biotechnological approaches, and precise formulation techniques to ensure safe and effective skincare products. Ongoing research and technological advancements continue to optimize these processes, ensuring that bakuchiol remains a valuable ingredient in the cosmetic industry [119].
## 4 Results and Discussion
### Evaluation of SPPs
#### 4.1.2 Case Evaluation  
Since the flood discharge simulation focuses on the five cases we selected, it is essential to evaluate the original IMERG and GSMaP and their best-corrected ones (IMERG-RBC, GSMaP-RBC) for the period of each case. Figure 5 shows the Taylor diagram [59] for five flood cases. It can effectively show three indicators (correlation coefficient, normalized standard deviation (\(NSD\)), and normalized root mean square deviation (\(NRMS\))) in one chart. Generally, the corrected NRT SPPs have a better performance than the original data. In the case of October 2004, Figure 5c, the original and corrected NRT SPPs show the best agreement with gauge rainfall among five flood cases. In the case of October 2009, Figure 5e, GSMaP performs the worst with a CC around 0.6. The evaluation for these cases could also provide some implications for flood simulation in Section 4.2.