esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Vacuum Freeze-Drying Process and Quality Evaluation of _Stropharia rugosanmulata_
## 3 Results and Discussion
### Analysis of Rehydration Rate Evaluation Results with Different Technological Parameters  
For freeze-dried fruits and vegetables, rehydration rate is one of the most important factors determining their quality. The rehydration rate is related to the freezing rate and drying rate of freeze-dried products and thus can be used as an indicator to measure the quality of freeze-dried _Stropharia rugosanmulata_. The rehydration rate is usually expressed in terms of the increase in weight. The rehydration rate is significantly influenced by the pre-freezing temperature and pre-freezing time. To some extent, the rehydration score can also reflect the effectiveness of optimizing the _Stropharia rugosanmulata_ freeze-drying process. From the data in Table 5, it can be concluded that groups A\({}_{1}\)B\({}_{1}\)C\({}_{1}\) and A\({}_{3}\)B\({}_{2}\)C\({}_{3}\) had better rehydration scores, with a significant difference in the scores ranging from 20.76 to 30 points. This indicates that different pre-freezing processes have a significant impact on the rehydration performance of freeze-dried _Stropharia rugosanmulata_[27].  
Figure 1: Changes in sensory evaluation of _Stropharia rugosanmulata_ samples under different treatments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**Moisture Content of**} & \multirow{2}{*}{**Rehydration**} & \multicolumn{2}{c}{**Moisture**} & \multirow{2}{*}{**Time**} & \multirow{2}{*}{**Drying**} & \multirow{2}{*}{**Rehydration**} \\  & & & & & & & \\ \cline{5-6}  & \multicolumn{1}{c}{**Fresh**_Stropharia_} & & & & & & \\  & \multicolumn{1}{c}{**Rate/\%**} & & & & & \\ \cline{5-6}  & \multicolumn{1}{c}{**Change/\%**} & & & & & \\ \hline A\({}_{1}\)B\({}_{1}\)C\({}_{1}\) & 94.10 & 13.15 & 93.57 & 60 & 1.56 & 30 \\ A\({}_{1}\)B\({}_{2}\)C\({}_{2}\) & 94.55 & 11.26 & 94.13 & 72 & 1.30 & 26.33 \\ A\({}_{1}\)B\({}_{3}\)C\({}_{3}\) & 94.52 & 10.65 & 93.79 & 84 & 1.12 & 24.3 \\ A\({}_{2}\)B\({}_{1}\)C\({}_{3}\) & 94.30 & 10.90 & 93.67 & 84 & 1.12 & 25.69 \\ A\({}_{2}\)B\({}_{2}\)C\({}_{1}\) & 94.69 & 10.61 & 94 & 60 & 1.57 & 20.76 \\ A\({}_{2}\)B\({}_{2}\)C\({}_{2}\) & 93.6 & 10.61 & 92.97 & 72 & 1.29 & 20.76 \\ A\({}_{3}\)B\({}_{1}\)C\({}_{2}\) & 93.69 & 11.99 & 92.98 & 72 & 1.29 & 27.35 \\ A\({}_{3}\)B\({}_{2}\)C\({}_{3}\) & 94.10 & 13.15 & 93.57 & 84 & 1.12 & 30 \\ A\({}_{3}\)B\({}_{3}\)C\({}_{1}\) & 94.19 & 11.54 & 93.68 & 60 & 1.56 & 27.05 \\ \hline \hline \end{tabular}
\end{table}
Table 5: One-minute rehydration rate, moisture content and drying rate results of _Stropharia rugosanmulata_ samples.
# Fall Detection Based on Continuous Wave Radar Sensor Using Binarized Neural Networks
## 4 Experiment Results and Discussion  
In this section, we analyze the results of the experiment and compare its performance with those of previous studies. Table 5 presents the confusion matrix for the test results for the proposed network. Four performance metrics were obtained from the confusion matrix. The performance metrics used are shown in Equations (3)-(6). For the 452 data point experiment, the numbers of true positives (TP), false negatives (FN), false positives (FP), and true negatives (TN) were 193, 14, 17, and 228, respectively. Thus, the proposed method achieved a precision of 93.2%, a recall of 91.9%, an F1-score of 92.5, and an accuracy of 93.1%.  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
\[F1-score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{5}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{6}\]  
Table 6 compares the performance of the proposed method with those of previous studies. Ref. [12] and the proposed method have almost similar performance with an accuracy difference of 0.3. However, our method has 2.75 times more actions and requires approximately 93% less memory. Ref. [16] has a 2.1% higher accuracy than the proposed method but has 36% fewer actions and requires approximately 91.9 times more memory. In addition, the cost of the overall system increases significantly owing to the use of FMCW radar. Compared with [18], the proposed method shows a higher accuracy, has 2.2 times more actions, and requires 99.4% less memory. Compared to [19], the proposed method is 2.7% less accurate and has 2.2 times more actions and requires approximately 99.9% less memory.  
Figure 8: Structure of the proposed network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Predicted Label**} \\ \cline{2-3}  & **Fall** & **Non-Fall** \\ \hline
**True label** & **Fall** & 193 & 14 \\
**Non-Fall** & 17 & 228 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix of experimental results.
## 4 Function Test  
To validate the feasibility and optimization ability of the ISSA, we selected nine benchmark functions for comparison and tested them against the SSA, PSO, SAPSO (self-adaptive particle swarm optimization), DE, and JADE (adaptive differential evolution with optional external archive). In the tests, the population size was set to 100, and the maximum number of iterations was 200. Finally, Table 1 summarizes the results.  
The above comparative experiments analyze the proposed algorithm's superiority from unimodal, multimodal, high-dimensional, and low-dimensional perspectives. The ISSA achieved the theoretical optimal value of 0 only when solving F1 among the three unimodal high-dimensional functions, F1, F2, and F3; however, for F2 and F3, the optimization performance difference between the ISSA, SSA, and JADE is relatively small, with the ISSA being the closest to the theoretical optimal. Among the three high-dimensional  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Serial** & **Function** & **Dimension** & **Range** &
\begin{tabular}{c} **Theoretical** \\ **Value** \\ \end{tabular} & **ISSA** & **SSA** & **DE** & **JADE** & **PSO** & **SAPSO** \\ \hline F1 & Sphere & 30 & \([-100,100]\) & 0 & \(0\times 10^{9}\) & \(4.5\times 10^{-82}\) & \(4.8\times 10^{-5}\) & \(1.8\times 1  
multimodal functions, F4-F6, the ISSA demonstrated a significant advantage in optimization performance. For the three low-dimensional multimodal functions, F7-F9, both the ISSA and JADE identified the theoretical optimal values. In contrast, the unimproved SSA and DE failed to find the theoretical optimal solutions in F9 and F7, respectively.  
To compare the algorithm's convergence speed, a convergence curve graph is provided for the above experiment, as shown in Figure 5. It is evident from the graph that ISSA's convergence speed is superior to other algorithms in most cases, particularly for high-dimensional function optimization. The ISSA is more likely to escape local optimal solutions than the SSA. Additionally, for optimizing low-dimensional multimodal functions (F7
## 3 Round Robin Test (RRT) and Reliability Analysis
### Comparison with RT  
For the application of PAUT in lieu of RT, the method should exhibit comparable or better flaw detection performance. For comparison, RT was performed on the same specimens, and the flaw detection performance of PAUT was comparatively evaluated. For the RT method, the double wall exposure technique was applied according to ASME Section I & V for flaw detection. The flaws detected via PAUT and RT are shown in Figure 7. In general, the flaw detection performance of PAUT for planar flaws (for which PAUT is advantageous) was significantly better than that of RT. For volumetric flaws, RT detected all the flaws, but PAUT missed one flaw in the dissimilar metal welds.  
Figure 5: Linear regression results obtained using the least-squares method for evaluating the sizing accuracy according to the material type.  
Figure 6: RMS result to evaluate the sizing accuracy according to the material.
## 3 Our Proposed Method  
The objective of the downlink SCMA system receivers is to precisely reconstruct the \(\log_{2}(M)\) data bits of each of the \(J\) users from the received signal \(\mathbf{y}_{j}\) at the \(K\) REs, which is defined in Equation (2). The non-orthogonality between user carrier frequencies makes the design of SCMA system receivers more challenging compared with OMA-based systems. At the same time, noise interference and unstable channel characteristics in the wireless channel furtsher increase the difficulty of designing receivers with excellent performance for SCMA systems. In order to improve the downlink SCMA systems' decoding performance, we propose a novel image-classification-based decoding strategy that allows image classification technology to take the role of the MPA in SCMA receivers to accomplish signal detection tasks.  
In this section, we provide a detailed introduction to the algorithm design of the proposed \(K\)-Polygon diagram classification decoder (KDCD). We take into account both the effect of the AWGN and the downlink Rayleigh fading channel on signal transmission. A modified MobileNet model is adopted as the classification network. With the exception of the first layer of the convolutional layers, which is a fully convolutional layer, all remaining convolutional layers in the classification network are depthwise separable convolutional layers. Figure 2 depicts the difference between depthwise separable convolutional layers and standard convolutional layers [17]. The network parameters are learned from a large amount of training data. In order to transform the intricate process of classifying received overlapping codewords disrupted by noise and fading into an image classification task and obtain sufficient \(K\)-Polygon diagrams for training, we originally propose the concepts of sub-coordinate systems and dynamic datasets with automatic labeling.  
Figure 1: A basic downlink SCMA system model.
## 2 Related Work
### Factorization Machine-Based Models  
As deep learning advances, FM models are gradually evolving. The FNN model proposed by the University of London [31] mainly solves the problem of training convergence with embedding, for which it uses the hidden vectors of individual feature vectors trained by the FM model as the initialization parameters of the embedding layer. It is understood as the initialization of the network parameters with the addition of a priori information, accelerating the neural network's convergence. The structure is simple, and there is no separate feature cross layer. Harbin University and Huawei researchers proposed DeepFM optimized for the Wide&deep model to replace the original wide layer with the FM model. The resulting model has automatic feature combination capability. Deep&cross is different in its use of multi-layer cross-network feature combination, while DeepFM uses FM for combination. xDeepFM [32], on the other hand, improves on CrossNetby proposing a compressed interaction network, which avoids the situation in which the output of each hidden layer and the initial vectors are scalar multiples by making the interaction of the vectors bit-level. However, it ignores the combination of lower-order and higher-order interactions. The AFM [33] model proposed by Zhejiang University, on the other hand, introduces an attention mechanism to measure valuable information; however, it is limited to low-order feature interactions and ignores the importance of high-order interactions. The current deep learning technique based on the FM model is a more advanced recommendation model, which has the advantage of modeling the complex relationship hidden in the higher-order and lower-order interactions [34; 35].  
In summary, current recommendation models either have only low-order interaction modeling, neglecting the nonlinearity and intricate structure inherent in actual data, or focus only on high-order feature interactions while ignoring the mutual complementarity of explicit and implicit interactions, making them unable to better integrate low-order and high-order feature interactions. In real complex application scenarios, different interaction components exhibit different predictive capabilities; thus, a new model is needed to fit the application scenario in which a model captures high-order explicit and implicit interactions and infers which low-level feature interactions contribute more significantly to the predictions. On the other hand, for the recommendation problem of service components in industrial software it is necessary to improve the existing deep learning models and build an efficient recommendation architectures in accordance with the actual application to improve the recommendation accuracy and component utilization, which is the research motivation of this paper.
## 3 MIMO Antenna Configuration Analysis  
### Measurement Setup  
The sample fabricated prototype of the unit element, as well as the MIMO antennas, are utilized for measurement purposes. The performance is analyzed in terms of return loss and gain as well as axial ratio performance. Figure 5a,b shows the antenna prototype under  
Figure 4: Parametric study of the extended feed width variation.  
Figure 3: Parametric study of the stub width variation.  
s-parameters testing; the nearfield circular chamber for gain is shown in Figure 5c. The details about the circular chamber can be found in [26]. The close-up shots of the antenna under testing for gain measurements are depicted in Figure 5d,e.
# An Efficient and Reliable Algorithm for Wireless Sensor Network
## 7 Result Comparison and Performance Evaluation  
In this experiment, wireless motion sensors are used. The motion sensor is divided into a base station and a sensor.  
The base station is shown in Figure 6 consists of an antenna and AC adapter with 4 AA batteries, LED indicator for power on/off, low battery LED indicator, 4 receiver LED indicator, power and volume. The antenna covers an area of 1/4 miles. Hence, nodes in this defined radio range are entertained and communication takes place within this area. The AC adaptor is used for indoor detection. However, in remote places such as mountains and battlefield scenario and vehicle detection, 4 AA batteries can also be used. LED indicator shows whether the power is on or off. Likewise, when the battery is low, then LED light is ON, which indicates the recharging. Each base station can entertain four receivers at a time, and there are four LED lights on each receiver. However, the number of a receiver can be increased by connecting the base station with each other.  
The sensor as shown in Figure 7 is consisting of Sensor Eye, Switch 1 and Switch 2. Sensor Eye is used for the detection of nodes. Switch 1 is composed of HI, MID1, MID2 and Low. In HI mode the detection range is 30 ft. Likewise, the detection ranges for MID1, MID2 and Low are 25 ft, 20 ft and 15 ft consecutively. The detection range for small and large areas is defined for the need of the user and situation. Switch 2 having the capability to form four cluster head, i.e., CH1, CH2, CH3, CH4. Each cluster head defines Zone 1 to Zone 4, i.e., its own zone. The sensor having 18,650 batteries, when fully charged, will operate for up to 1 year.
## 3 Building Blocks of Vehicular Networks
### Sensors  
This category of sensors mounted directly on a vehicle can aid in two primary tasks: motion control and data acquisition. In movement control, air and ground vehicles frequently utilize accelerometers and gyroscopes to determine the position and orientation of a vehicle, which enables the control system to maintain a desired path or position of a vehicle, particularly in cooperation with other sensory equipment such as modules for global positioning systems or cameras [34]. Several factors may determine the selection from a range of available sensors. Primarily, it is necessary to consider constraints on energy, weight, available space, connection, and a motion control system. Furthermore, an intended application must be taken into consideration. Analogously to the sensors for movement control, the use of data acquisition sensors is determined by available energy, weight, available space, and connection performance [30]. This category may include cameras, LiDARs, ultrasonic sensors, or radio frequency identification. Ideally, the selected sensors would function for movement control and data acquisition.
## 4 Integrated Positioning  
This section describes the between-platform processing involving relative positioning between two platforms equipped with arrays of GNSS receivers/antennas. The array-aided positioning described in the following is a novel positioning concept improving between-platform positioning using an array of antennas on the platforms [15, 18, 19, 20]. Unlike the formulations in [18, 19, 20], the formulation in this contribution explicitly considers different numbers of antennas on the reference and user platforms. Unlike the parameter space formulation in [15], the current contribution considers a simplified, double-difference observation space formulation elegantly demonstrating the advantages of array-aided positioning. First, the combined observation model for all independent baselines among all receivers on both platforms is described. Then, we describe attitude-bootstrapping, showing how platform arrays improve the between-platform baseline estimate.  
Figure 2: Multi-sensor experiment set-up on the roof of the building of the Geodetic Institute (Messdach), Leibniz Universität Hannover, Germany. The MSS is mounted on Pillar 5, while the reference station is located on Pillar 8. (**a**) Location; and (**b**) the MSS used in the experiments.  
Let us consider two platforms carrying \(n_{1}+1\) and \(n_{2}+1\) receivers/antennas. The functional and stochastic models for the between-platform baseline formed by the first antennas (pivot antennas) read:  
\[\mathrm{E}(y^{12}) = Az^{12}+Gb^{12}\qquad z^{12}\in\mathbb{Z}^{fm} \tag{24}\] \[\mathrm{D}(y^{12}) = Q_{yy} \tag{25}\]  
where \(y^{12}\) is the between-platform double-difference observables, \(z^{12}\) is the unknown between-platform double-difference ambiguities and \(b^{12}\) is the unknown between-platform baseline. Note that atmosphere delays are not considered in this formulation, as troposphere delays and ionosphere delays can be ignored for the short baseline (\(<\)10 km) considered in this work. However, these atmosphere delays must be taken into account for general long baseline scenarios [19]. In standard positioning, the LAMBDA method yields the optimal estimates for the ambiguities and, hence, for the baseline.
## 3 Dataset and Methodology
### Input Parameters
#### 3.1.2 Sea Level Variation  
The Fraser River Valley and its surrounding area have experienced significant sea level variations, in particular, close to the end of the last glaciation period [35; 36]. At around 15,000 BP, the Fraser Valley was covered by \(\sim\)1.5 km thick glaciers [35]. Glacial retreat resulted in approximately 150 m relative sea level increase at around 14,000 BP. Between 14,000 and 10,000 BP, the relative sea level rapidly dropped to about 12 m below its present level, due to isostatic rebound [36; 37]. The delta deposition started approximately 10,000 years BP (within the modelling interval herein). Sea level variation since the delta progradation initiated has received some attention, and there are various opinions. Several studies suggest that during delta progradation, relative sea level increased a noticeable 12 m until 2000 BP, when the relative sea level reached its current level [3; 13; 38; 39]. Other studies indicate that sea level fluctuations were reported to be minor (\(\sim\)1 m) [37]. Therefore, in this study, two main scenarios are considered: (1) a 12 m increase between 10,000 and 2000 BP, and (2) a constant (zero) sea level throughout the modern Fraser River Delta evolution (step 1-2). Some recent studies also suggest that relative sea level might increase up to 2 m in the next 100 years, mainly due to anthropogenic effects [33; 40]. The potential results of this relative sea level increase are also tested in this study.
# Dynamic Routings in Satellite Networks: An Overview
## 3 System Model and Characteristics
### System Model  
The space-air-ground integrated network has become the inevitable trend of the future network. It consists of a satellite network, an air network of various flying probes, and aground network. A model of the space-air-ground integrated network is shown in Figure 2.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Network Type** & **Reference** & **Feature** & **Main Contributions** \\ \hline \hline \multirow{6}{*}{Satellite Networks} & [10] & Multi-layer & Summarized routing methods in multi-layer satellite networks \\ \cline{2-5}  & [15] & & Summarized the main issues faced by large LEO satellite networks and analyzed the main directions for the future development of the network \\ \cline{2-5}  & [9] & & Reviewed the routing algorithms based on virtual nodes in single-layer satellite networks \\ \cline{2-5}  & [11] & & Summarized the development routing algorithms for single-layer satellite networks \\ \cline{2-5}  & [12] & & Investigated a partial load balanced routing algorithm based on link state \\ \cline{2-5}  & [16] & & Investigated a deep-reinforcement-learning-based routing scheme for satellite routing \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 2: Space–air–ground integrated network.  
Since they are highly dynamic network environments, most existing space-air-ground integrated network routing strategies are insufficient for interactive information transmission between heterogeneous layers. In particular, satellite networks are characterized by high dynamics. The satellite networks are constructed by integrating several satellite networks and hierarchical structures. The satellite networks can be divided into single and multi-layer satellite networks. The single-layer satellite network consists of one or more orbital planes. Each satellite is generally equipped with an inter-satellite link to communicate with neighboring satellites. Meanwhile, the satellite can interact with the ground gateway station and the user station via the feeder and user links, creating a complex communication system with multiple links.  
The multi-layer satellite networks are usually composed of two or more satellites with different orbits. Different altitudes of satellites have different effects on service performance. In addition, there are many various types of applications, and each has its QoS. Due to the quick movement of different satellites, dynamic routing is one of the most fundamental issues for multi-layer satellite networks. Many existing multi-layer satellite networks work mainly on GEO/LEO or MEO/LEO two-layer satellite networks and GEO/MEO/LEO three-layer satellite networks. Table 3 shows a comparison of the characteristics of the GEO/LEO or MEO/LEO two-layer satellite networks and the GEO/MEO/LEO three-layer satellite network.