esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Performance Evaluation of Information Extraction Compared to Other Models
#### 4.3.3 Evaluation System Construction
The cosine similarity score for this element is defined as  
\[S_{C,i}=\frac{v_{i,m}\cdot v_{i,r}}{\parallel v_{i,m}\parallel\parallel v_{i,r}\parallel} \tag{2}\]  
where \(S_{C,i}\) represents the cosine similarity score for element \(i\); \(\parallel v\parallel\) denotes the L2 norm of the vector; and the cosine similarity ranges from \([-1,\,1]\), where values closer to 1 indicate a higher semantic similarity between the model-extracted text and the manually annotated text. Furthermore, considering that different requirement categories place varying levels of emphasis on different elements, applying uniform weights to all elements would fail to accurately reflect the model's actual information extraction capability across different requirement types. Therefore, to more precisely evaluate the effectiveness of the model's extraction performance, this study adopts a differentiated weight allocation strategy for the core elements of each requirement category. For example, in the structured data of functional requirements, the primary elements include subject, attribute, object, action, function, condition. However, in practical requirement analysis, functional requirements place greater emphasis on the subject performing the function and its specific functional content, while elements such as action receive relatively less attention. Therefore, assigning equal evaluation weights to all elements may lead to an inaccurate assessment of the model's performance in extracting key elements. To optimize the evaluation framework, we establish distinct weight allocation strategies for the elements of different requirement categories, ensuring that the assessment results align more closely with real-world application scenarios. This approach enhances the scientific rigor and reliability of the information extraction model. The weight allocation for each requirement category is presented in Table 9.  
The final evaluation scores for the structured requirement data are as follows:  
\[S_{f}=\sum_{i}w_{i}\cdot S_{i} \tag{3}\]  
where \(S_{f}\) represents the final score of the requirement data entry; \(S_{i}\) denotes the score of element \(i\), where, for node elements, \(S_{i}=S_{EM,i}\), and for textual elements, \(S_{i}=S_{C,i}\); \(w_{i}\) represents the weight of element \(i\) within the given requirement category; and \(\sum_{i}w_{i}=1\), ensuring that the weighted sum of all elements equals 1, thereby achieving normalization.
# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
## 5 Experimental Evaluation  
### Parameter Configuration  
In this subsection, the generalization performance of the proposed model under different SNR, channel model and velocity will be explored respectively. The simulation is carried out on a computer with Intel (R) Core (TM) i9-9900 kf CPU @ 3.60 GHz and NVIDIA GeForce GTX 2060 SUPER configuration under 64-bit Windows10 operating system. Among them, the data sets used are randomly generated by 5G channel model on  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Number of Parameters** & **Number of FLOPs** \\ \hline NDR-Net & 1231K & 67K \\ KDML [22] & 112K & 60K \\ ChannelNet [23] & 678K & 358K \\ CBDNet [24] & 6792K & 166k \\ \hline \hline \end{tabular}
\end{table}
Table 4: The computational complexity of different DL networks.  
the Matlab simulation platform. The network model is implemented in Keras in Tensorflow. The optimization algorithm is chosen as Adaptive Moment Estimation (Adam).  
The main parameters of the communication system used in the simulation are shown in Table 5. Normal CP refers to the 7 OFDM symbols in a time slot, the first OFDM symbol with 160 CP, and the 2-7 OFDM symbols with 144 CP. The movement rate of the simulated multipath channel is 3-30 km/h in TDL-A, 30-120 km/h in TDL-B, and 120-350 km/h in TDL-C unless otherwise specified. The parameters of the proposed network model are shown in Table 6.
## 4 Simulation  
In this section, we carried out a series of experiments to evaluate the performance of our proposed multicast approach. Multicast methods can be divided into SBT and ST. ST assigns a core/RP to each multicast group and builds a multicast tree for each multicast group, such as PIM-SM. In order to balance the load of RP, PIM-SM can use multiple candidate RPs and select one RP from the candidate RPs for each multicast group. MCT is an improvement of ST. MCT allocates multiple cores and establishes multiple independent multicast trees for each multicast group. MCT provides two mechanisms: sender-to-all and member-to-all.  
Therefore, our evaluation offered a comparison with Source-Based Tree, PIM-SM using a single RP, PIM-SM using multiple RPs, Multiple-Core Trees using the senders-to-all mechanism (MCT-Sender), and Multiple-Core Trees using the members-to-all mechanism (MCT-Member). In the experiment of PIM-SM using a single RP, the center node of the network was selected as the RP. In the experiment of PIM-SM using multiple RPs, five network nodes were randomly selected as RPs, and each multicast group was assigned an RP through the hash function to achieve load balancing. In the experiment of MCT, 5 network nodes were randomly selected as cores too.  
The experimental environment was created in Python 3.8 running on windows 10 with 16 GB RAM. We used a static model, in which we generated a random graph, deployed multicast groups, and then calculated the multicast trees. We used the BRITE [41] topology generator to generate a random network topology under the Waxman model. We randomly deployed multicast groups, each of which contained several multicast sources and multicast receivers. In the simulation, the multicast source continuously sent the multicast flow, and the receivers joined the multicast service in turn. NRS was implemented as a program that maintains the mapping between MSEIDs and NAs. Then, we calculated the multicast tree and counted the relevant experimental results. We tested different multicast approaches under the same conditions. The evaluation setting is shown in Table 3. Each experiment was repeated 100 times and the average value was taken as the result. The metrics we evaluated include core load, number of join requests, link load, traffic concentration, and routing state.
## 1 Introduction
The SPL increased for some cases, at power \(P<12\) W, but in the case of the chevrons with the same height and width, the noise reduction was obtained at each investigated actuator power. [PERSON] and [PERSON] (2019) investigated the actuators with circular, diamond, and oval shape orifices. The highest noise was generated by the actuator with the circular orifice. The oval office was even about 13 dB quieter than the circular orifice at Reynolds number \(Re=10,000\). The impact of the orifice shape on the generated noise was also investigated by [PERSON] et al. (2018), [PERSON] et al. (2019), and [PERSON] et al. (2019). Based on these papers, it can be observed that the circular orifice shape is one of the least favorable in terms of the noise generated by SJA. [PERSON] (2019) and [PERSON] et al. (2019) investigate also the impact of the orifice length and showed that the increase of orifice length causes the SPL to decrease. The problem of noise reduction in SJA was widely discussed in (2019). In the paper (2019), it was noticed that the sharp edge in the bottom of the chevron orifice can cause an increase in the SPL. So, if it is true, the rounding of this orifice can decrease the SPL generated by SJA. Of course, it may not be a cause-and-effect relationship, but it is an interesting and important point for the investigation of noise reduction in the SJA and other use of chevron orifice and nozzle. For this reason in this paper, the acoustic and flow parameter of SJA with a different type of chevron orifice was tested. The cutouts of the chevron orifice were sharp or rounded and the chevron number was different. Figure 1: Generation of synthetic jet (Sf): (a) injection of fluid; (b) expulsion of fluid and SJ formation; (c) moving away of SJ and injection of a new volume of fluid to cavity (1—deformable diaphragm; 2—cavity; 3— orifice; 4—fluid injected to cavity; 5—synthetic jet).
## 4 Machine Learning Based Mimetic Whistle Receiver
### Machine Learning Network Structure for Biomimetic Receiver  
The proposed machine learning network is designed to extract the features of whistle subgroups from the received whistles with the link information. For the training data, two test sets with classified real dolphin whistles are made: one set is obtained by only adding the noise, and the other set is made by passing the UWA channel and adding the noise. The test whistles are transformed into the 2-D time-frequency plane using the spectrogram. In this paper, the DAG-net that is composed of the parallel convolutional layers (CLs) and the merging layers is proposed, and is shown in Figure 4.  
In Figure 4, the parallel CLs of the DAG-net are constructed to simultaneously extract the nonlinear contours and the detail whistle features from the input spectrogram. The extracted features are merged  
Figure 4: Directional acyclic graph (DAG)-net structure.  
and transferred to the next stage [25; 26; 27]. The final stage extracts the detailed features and estimates the subgroups. The parallel CL structure of the DAG-net shows a better feature extraction performance than the conventional Deep Neural Network (DNN) in the simulation section.  
The misdetection performance of the designed DAG-net for the distorted whistles shows approximately a \(10^{-2}\) order, which is better than that of the ML detector and may be enough for some applications. However, this detection performance may not be acceptable to the communication systems [28; 29; 30; 31; 32]. In this case, the link information plays an important role in increasing the detection performance, and the LSTM utilizes the link information. Among many LSTMs, Bi-directional Long Short Term Memory (Bi-LSTM) stores the input information in both long and short term memories and effectively analyzes the link information back-and-forth [33; 34]. Thus, we design the D-LSTM that consists of the DAG-net and the Bi-LSTM, in which the DAG-net is for extracting the features of whistles and the Bi-LSTM is for utilizing the link information between whistles. The proposed D-LSTM method shows a lower BER than the DAG-net without the link information. The proposed D-LSTM structure is shown in Figure 5.  
In Figure 5, the spectrogram sequence of \(\mathbf{Y}\) is input and the estimated bits are output. The analyzed features of the whistles by the DAG-net are transferred to the Bi-LSTM, which extracts the link information and demodulates received bits. The following section describes the training and detection methods of the proposed D-LSTM.
###### Abstract  
The aim of the study was to investigate the effect of using various by-products (orange and apple pomace, tomato peel, pepper peel, prickly pear peel, and prickly pear seed peel) on the dough rheology and properties of gluten-free bread. The by-products were incorporated into a gluten-free bread formulation based on corn and chickpea flows (2/1 \(w/w\)). Different levels of each by-product (0, 2.5, 5, and 7.5% in the basic replacement) were tested. Wheat bread and gluten-free bread without the addition of by-products were used as controls. The results indicated that the by-products increased the maximum dough height, the total CO\({}_{2}\) production, and CO\({}_{2}\) retention coefficient compared to unenriched gluten-free dough. The highest K-value consistency coefficient was observed for the dough enriched with the prickly pear peel. The addition of by-products significantly improved (_p_ < 0.0001) the specific volume of gluten-free bread, with values increasing from 1.48 to 2.50 cm\({}^{3}\)/g. The hierarchical cluster analysis and the constellation plot showed four groups: the wheat bread group, the second group containing the gluten-free control bread, the group with bread enriched by pomace, and the group with bread enriched with peels, exhibit the same effect on gluten-free bread and the peels exhibit the same effect on gluten-free bread.  
gluten-free bread; pomace; peel; rheology; bread properties Article
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 7 Discussion and Algorithm Variations
### Look Up Table Predictions  
A technique which requires significant amounts of on-board memory, but results in potentially incredibly fast estimates of key tracking parameters (specular point, code phase delay difference and Doppler) is to include one or more of these parameters in an on-board look up table (LUT).
# Analysis of Human Body Shadowing Effect on Wireless Sensor Networks Operating in the 2.4 GHz Band
## 2 Materials and Methods
### Analysis of Shadowing Effect with Finite-Difference Time-Domain Based Program
For further analysis, the distance, \(a=2\) m, was selected, because in indoor environments, larger distances between transmitting sensors and human bodies are less likely. The analysis of the body shadowing effect using the FDTD method in this study was limited to only the regions in closest proximity to the human body. Due to the high requirements of the simulation for computer memory, it was possible only to model the human body and two antennas at a maximum distance of 7 m from each other (the maximum values for parameters \(a\) and \(b\) were 3 m and 4 m, respectively). In XFdtd\({}^{\text{TM}}\), it is possible to adjust the voxel size to the length of the wave in a particular object, but even with this possibility, the requirements for computer memory were high. The voxel sizes used in this study were 2 mm for the body model and 10 mm for the air volume. The two half-wave dipole antennas used to identify the path loss in the numerical experiment were modeled using 0.5 mm voxels suitable for thin wires. The computer memory required to model the entire geometry of the experiment was 9.8 GB. The simulations were run on a computer equipped with two Nvidia Tesla 2070 GPU cards with multi-core (448 cores) processors capable of running massively parallel computations and with 6 GB of memory each. With this setup, for each position of the receiving antenna, the simulation time lasted from 20 min up to 85 min. This shows the limitation of FDTD-based software for simulating the body shadowing effect on wireless sensor networks inside buildings. To simulate such networks in modern buildings, it is necessary to model not only much larger volumes, but also the walls and other obstacles. For this, FDTD-based programs require far more than 12 GB of computer memory. The time needed for the simulations would also be impractically high, especially for wireless sensor network designs demanding numerous simulations. These limitations motivated the author to develop a numerical model of the human body applicable in the numerically effective method that can be used for large scale simulations of indoor wireless sensor networks, including the body shadowing effect. Figure 4: Path loss between transmitting and receiving dipoles simulated in XFdtd\({}^{\text{TM}}\) program as a function of receiving antenna distance \(b\) defined in Figure 2. Results for free space (no human body model and parameter \(a\) equal to 2 m) and for the shadowed case with different transmitting antenna spacings (\(a=1,2,3\) m).
## 3 Results and Discussion
### Screening of Phase Ratio in ATPS System
#### 3.1.2 Selection of Ammonium Sulfate Mass Fraction  
It can be seen in Table 2 that as the ammonium sulfate mass fraction increased, the yield of flavonoids showed a trend of rising first and then falling. However, the yield of flavonoids changed little in the range 17-22% of ammonium sulfate. When the ammonium sulfate mass fraction reached 18% (\(w/w\)), the highest yield of total flavonoids was obtained.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Mass** & **Mass Fraction of** & **Concentration** & **Enzymolysis** & **Enzymolysis** & **Ultrasonic** & **Liquid–** & **Yield of Total** \\  & **Ethanol** & **Sulfate** & **of Enzymes** & **Time** & **Temperature** & **Time** & **Solid Ratio** & **Flavoroids** \\  & **(\%)** & **(\%)** & **(\%)** & **(\(\mathrm{U}/\mathrm{g}\))** & **(\(\mathrm{min}\))** & **(\(\mathrm{c}^{*}\mathrm{C}\))** & **(\(\mathrm{min}\))** & **(\(\mathrm{mL}/\mathrm{g}\))** & **(\(\mathrm{m}/\mathrm{g}\))** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 2: Effects of different influencing factors on the yield of total flavonoids.  
This occurred in view of the mass fraction of ammonium sulfate directly affecting the phase ratio in the ATPS system. The volume ratio of the upper and lower phases and the total extraction capacity of the solvent were the factors that affected the extraction rate of total flavonoids [31]. Therefore, we selected 18% (\(w\)/\(w\)) of ammonium sulfate concentration as the optimal parameter.
# Semi-Automatic Method of Extracting Road Networks from High-Resolution Remote-Sensing Images
## 2 Materials and Methods  
### Experimental Data  
Four different types of remote-sensing images were selected for road extraction experiments. One type is aerial images with a spatial resolution of 0.2 m of a certain rural area in Huizhou, Guangdong Province, as shown in Figure 1a, an image with a size of 5000 pixels \(\times\) 5000 pixels is selected and recorded as Data 1. The second type is GF-2 satellite images with a spatial resolution of 1 m of a certain suburban district in Wuhan, Hubei Province, as shown in Figure 1b, an image with the same size as Data 1 is selected and recorded as Data 2. The third type is a partial image of the Massachusetts Road Data Set [45], with a size of 630 pixels \(\times\) 600 pixels and spatial resolution of 1 m, which is recorded as Data 3 and shown in Figure 1c. For this image, a part of the image containing the road was left, and more details of the road can be seen by zooming in. The last image is one image in DeepGlobe Datasets [46], with a size of 1024 pixels \(\times\) 1024 pixels and a spatial resolution of 0.5 m, which is recorded as Data 4 and shown in Figure 1d. Data 1 has only 6 long roads and 4 simple intersections but at least 8 obvious occlusions on the roads. Data 2 includes 21 roads and 26 different intersections, together with 14 areas of buildings. Additionally, there are more than 15 roads and 20 intersections in Data 3 and Data 4, with many buildings on the roadside.  
Figure 1: Experimental data: (**a**) Data 1 in a rural area, with a size of 5000 pixels \(\times\) 5000 pixels and a spatial resolution of 0.2 m; (**b**) Data 2 in a suburban area, with a size of 5000 pixels \(\times\) 5000 pixels and a spatial resolution of 1.0 m; (**c**) Data 3 in an urban area with a size of 630 pixels \(\times\) 600 pixels and also a spatial resolution of 1 m; (**d**) Data 4 in a rural residential area with a size of 1024 pixels \(\times\) 1024 pixels and a spatial resolution of 0.5 m.
# Roller-Integrated Acoustic Wave Detection Technique for Rockfill Materials
## 3 Case Study
### Correlations between SCV and Compaction Quality of Rockfill Materials  
In the upstream cofferdam construction area, field tests at four strips were conducted to obtain the dry density, compactness, and other property parameters of the rockfill materials (\(P_{5}\) content, minimum dry density, and maximum dry density). In total, 16 groups of data were collected for correlation analysis. Figure 16 shows the correlation between the SCV and the dry density as well as between the SCV and the compactness when the number of compaction times is 6, 8, 10, and 12, respectively, in strip 1. The correlation between the SCV and the dry density as well as the correlation between the SCV and the compactness had similar trends, i.e., while the SCV increased, the dry density and the compactness also increased and vice versa. The relation between the SCV and the compactness is shown in Figure 17. It evidently exhibits a strong linear relation with the determination coefficient \(R^{2}\) ranging from 0.7371 to 0.8064. Since the SCV strongly correlates to the compactness, the SCV is an effective index that reflects the compaction status of rockfill materials, while the compactness is a key index of the compaction quality of the dam foundation and dam body.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Block** & **1** & **2** & **3** & **4** \\ \hline
1 & 0.8972 & 0.8085 & 0.8147 & 0.9096 \\
2 & 0.8461 & 0.8316 & 0.8237 & 0.9204 \\
3 & 0.8342 & 0.8775 & 0.9696 & 0.8416 \\
4 & 0.7803 & 0.8628 & 0.9232 & 0.8052 \\
5 & 0.9001 & 0.7961 & 0.8679 & 0.8714 \\
6 & 0.8010 & 0.8619 & 0.7955 & 0.8968 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specific determined coefficient (\(R^{2}\)) between the SCV and the number of compaction times for NGM.  
Figure 15: Correlation between the SCV and the number of compaction times in Strip 1.  
Figure 16: SCV, dry density, SCV, and compactness at each block along Strip 1.  
Figure 17: Linear correlations between SCV and compactness computed from all test strips.  
project in Luoyang, China. As a result, a new compaction detection technique is proposed in this paper. The new compaction detection technique can solve the existing problem and drawbacks in the CMV method. The SCV, which is used to detect the compactness of rockfill materials, can automatically be acquired by CAWDS developed in this study, and can directly characterize the compaction status of rockfill materials (Figure 19). Figure 19 shows the variation of the SCV as the number of compaction times in Strip 1. The overall compaction performance covering 100% of the work area can be clearly determined by the nephogram.