esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Experimental Results and Discussion
### ML Models Prediction Accuracy  
#### 4.3.1 Data Preprocessing  
The outputs of the atmospheric modeling using WRF served as input for the ML model. Additionally, each measured wind vector U10/V10 was converted independently into five categorical classes. For each wind component, quantization into categorical classes was conducted, ensuring that the dataset's classes had equal sizes in terms of the number of observations. This step was crucial to prevent bias and represent all classes equally during the ML model training process. The dataset was split, with 80% allocated for training and the remaining portion for validation. Data preprocessing included data augmentation to expand the dataset with synthetic data by 4\(\times\)x, enhancing the training dataset size [83]. This augmentation method involves adding Gaussian noise to the input dataset, generating an extended dataset based on existing data. Data augmentation not only addresses overfitting concerns [84] but has also proven effective in weather and climatology studies [85; 86]. It should be noted that data augmentation increased the size of our training dataset by 4\(\times\), and thereby, the training time increased accordingly.  
Figure 7: Measured versus simulated wind velocity scatterplot with point density for the (**a**) WRF1 simulation and (**b**) WRF9. The x-axis shows measured direction, and y-axis shows simulated velocity (kts). The color map shows data point density, and each subfigure includes the regression line equation, \(r^{2}\), and \(p\) value. The diagonal black line indicates the 1:1 ratio between the simulation and measurement, and the red line is the regression line.
## 3 Analysis of Scale Effect of Simulation Data
### Inversion Analysis on Different IDSs  
On the different IDSs, 300 nm\(\sim\)700 nm unimodal PSD (true peak: 477.01 nm) and 100 nm\(\sim\)700 nm bimodal PSD (true peak: 220.01 nm, 575.01 nm) were taken for analysis. The unimodal PSD shares the parameters \(u\) = 0.4, \(\sigma\) = 2.0. The bimodal PSD shares the parameters \(u_{1}\) = \(-3\), \(\sigma_{1}\) = 2.4; \(u_{2}\) = 3, \(\sigma_{2}\) = 2.4. On 8 different IDSs, ACFs with the standard uniform distribution of noise level 0.001 are inverted by WRIM. In the inversion, the initial inversion range and wavelets base are [1, 2000] and \"db25\", respectively. The inversion results are shown in Figures 3 and 4 and Table 1.  
Figures 3 and 4 and Table 1 show that, with the change of IDS, \(RE\) is also changed. For the bimodal particles, WRIM cannot distinguish between the two peaks on IDS 1, and the bimodal characteristics of the inversion PSD are not obvious on IDS 2, 3, 5, and 6. On the contrary, the bimodal characteristics of IDS 4, 7, and 8 are obvious. However, the PSDs of IDS 7 and 8 have large errors, 0.8255 and 1.1538, respectively, and have also lost part of the information. The cause of this phenomenon can be explained as follows: When IDSs are 7 and 8, the inversion range of scale 1 is adjusted so it is so small that it is less than the true PSD range in WRIM. Overall, \(RE\) of IDS 4 is minimum. The inversion PSD and peak value of the IDS are the closest to true value. For the unimodal particles, the peaks recovered from IDS  
1 to 3 are wide and have burrs. In contrast to IDS 1 to 3, PSDs inverted from IDS 4 to 7 are the smooth single peak and coincide with the true PSD. Due to small inversion range of scale 1, the part inversion PSD is also lost on IDS 8. Considering \(\min[RE]\), the inversion PSD of IDS 6 is best. Therefore, through the above analysis, the choice of IDS has a great impact on the accuracy of the recovery PSD in WRIM. We should select the optimal IDS. The following research finds that the optimal IDS is related to noise level of ACF.  
Figure 3: Inversion PSDs of 300 nm–700 nm unimodal distribution particles on the different IDSs. **(a)** IDS 1, **(b)** IDS 2, **(c)** IDS 3, **(d)** IDS 4, **(e)** IDS 5, **(f)** IDS 6, **(g)** IDS 7, and **(h)** IDS 8.  
Figure 4: Inversion PSDs of 100 nm\(\sim\)700 nm bimodal distribution particles on the different IDSs. (**a**) IDS 1, (**b**) IDS 2, (**c**) IDS 3, (**d**) IDS 4, (**e**) IDS 5, (**f**) IDS 6, (**g**) IDS 7, and (**h**) IDS 8.
## 5 Model Validation  
The final hardness model can be considered generalisable if its prediction accuracy with a new (validation) dataset is similar to that achieved with the training dataset. Two validation datasets were created: one similar to the original training dataset where subjects rated the hardness of stimuli from multiple source types, called the _between-source-type_ dataset; and a second dataset where the ratings of hardness were collected where subjects rated the hardness of the same source type, called the _within-source-type_ dataset. This second dataset is relevant to a likely application of the model, where the user of an online sound-effect repository might search for sounds only of one particular type (e.g., kick drum) and wishes to sort the search results according to hardness.  
To identify source types for the within-source-type validation dataset, the five most commonly searched source types identified in Section 2.1 were used: kick, piano, cymbal, snare, and guitar. For each source type, the acquisition method described in Section 2.2 was employed, but only five stimuli per source type were selected. The within-source-type listening tests were designed with the five stimuli of a single source type on each test page. To prevent scale compression effects, subjects were asked to use the full range of the scale on each test page.  
The between-source-type validation dataset was compiled from stimuli gathered (using the same procedure) for related studies of each of the five other attributes (brightness, depth, reverb, metallic-nature, and roughness). The mid-rated stimulus for each source type for each of these attributes was used. These stimuli were supplemented with the mid-rated stimulus for each source type in the within-source-type hardness dataset. An independent expert then selected the most and least hard of these stimuli for use as hidden anchors on each test page. Each page of the between-source-type listening test had nine stimuli--the two hidden anchors and seven other stimuli randomly drawn from those remaining.  
Both the within- and between-source-type listening tests were completed by a group of 16 subjects (14 male, 2 female, mean age = 21.1 \(\pm\) 1.4 years), all of whom met the criteria set out in Section 2.3.  
Note that the training and validation datasets did not incorporate common anchor stimuli, so the meanings of the \"least hard\" and \"most hard\" scale end-point labels will differ between datasets. Consequently the model's performance should be evaluated in terms of linear correlation and rank order, and not in terms of absolute predicted hardness values.
# Three-Phase Unbalance Analysis Method Based on Three-Phase Motor Current Instantaneous Information
## 3 Simulation Analysis and Experimental Verification  
### Three-Phase Unbalance Simulation and Analysis under Amplitude Deviation and Phase Deviation  
In this section, simulation experiments are conducted to adjust the deviations of single-phase amplitude and phase and to analyze and discuss the effects of different amplitude and phase deviations on the three-phase unbalance. The simulated signal is shown in Equation (11). When there is no deviation, the three-phase current amplitude is set to 0.5 A, the signal fundamental frequency is 100 Hz, and the U-phase, V-phase, and W-phase phases are \(2\pi/3\), 0, \(-2\pi/3\), respectively. The sampling frequency is 20 kHz.  
\[\left\{\begin{aligned} & I_{u}=a\cdot\cos(2\pi f_{M}t+\theta_{u})\\ & I_{\text{\tiny{P}}}=a\cdot\cos(2\pi f_{M}t+\theta_{v})\\ & I_{\text{\tiny{W}}}=a\cdot\cos(2\pi f_{M}t+\theta_{w})\\ \end{aligned}\right. \tag{11}\]where \(a\) is the signal amplitude, \(f_{M}\) is the signal fundamental frequency, \(\theta\) is the signal phase, \(I_{u}\), \(I_{v}\), and \(I_{w}\) are the U-phase, V-phase, and W-phase current signals, respectively, and \(\theta_{u}\), \(\theta_{v}\), and \(\theta_{w}\) are the U-phase, V-phase, and W-phase current phases, respectively.  
The following subsubsections analyze the amplitude deviation effect and phase deviation effect, respectively.
## 3 Identification and Division of Urban Functional Zones
### Urban Functional Zone Division Based on Traditional Methods  
Initially, urban functional zone division was performed using qualitative methods, such as statistical surveys and expert knowledge. [PERSON], et al. [62] analyzed the questionnaire survey information of the public and experts, and finally realized the division of urban functional zones based on public will. [PERSON], et al. [19] made the function-oriented zone on NET and Geomedia platforms using cadastral data and Delaunay triangulation and Graham methods. Methods based on expert knowledge are limited by statistical data and may be time-consuming and labor-intensive, strong subjectivity, and difficult to prove the accuracy of results.  
The use of remote sensing image data can provide detailed information on ground objects [63; 64], and dynamic monitoring of urban land-use change can be achieved by analyzing remote sensing images in different periods [65]. A remote sensing intelligent geo-interpretation model (RSIGIM) that includes multi-source remotely sensed data and ancillary geographic data were used to perform land-cover/land-use classification in Hong Kong [66]. In addition, some scholars used object-oriented segmentation and hierarchical classification to extract information on intra-urban land use, detect urban land-use change, and classify intra-urban land based on the functional characteristics using visual interpretation [67]. Remote sensing images provide rich information on the physical features of cities but lack information on functional interaction patterns and the socioeconomic environment. In addition, the timeliness of the images may not be adequate.
## 3 Materials and Methods
### Forward Noise Reduction Module (FNRM)  
Considering the low discrimination between polyps and background boundaries, and the edges of polyps are often disturbed by noise such as light, bubbles, and intestinal cleanliness, this may lead to inaccurate segmentation. In order to reduce these noise interferences, this section designed a FNRM that gradually integrates low-level implicit features such as texture into high-level features to enhance the boundary feature response of polyps.  
The FNRM gradually enhances from the lowest level \(D_{1}\) to \(D_{4}\) through 3 BEBs (boundary enhance blocks), as shown in Figure 3. Each BEB first conducts global average pooling (GAP) on \(D_{i}(i\in\{1,2,3\})\) to reduce its spatial dimension to \(1\times 1\), and then uses a \(1\times 1\) convolution to obtain the channel weight information \(r\). Next, it performs a \(3\times 3\) convolution operation on \(D_{i+1}\) to map it to the same number of channels and conducts point-wise multiplication with the weight information \(r\) to obtain \(D_{i+1}^{\prime}\). In this way, the noise information in the feature maps is cancelled out to reduce the model's sensitivity to noise. Finally, \(D_{i}\) is added to \(D_{i+1}^{\prime}\) after a \(3\times 3\) convolution operation with a stride of 2 to finally output \(N_{i+1}\). The composition of BEB is shown in Figure 4, and this process can be represented by Equations (3) and (4).  
\[r=Conv_{1\times 1}(GAP(D_{i})) \tag{3}\]  
\[N_{i+1}=Conv_{3\times 3 stride\to 2}(D_{i})+r\otimes Conv_{3\times 3}(D_{i+1}) \tag{4}\]  
Figure 2: Structure of SAM.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 1 Introduction
### Organization  
Section 2 introduces existing research related to the IoD environment. Next, Section 3 describes the threat model, PUF, and system model. Then, Sections 4 and 5 discuss the review and informal security analysis of [PERSON]'s protocol. Section 6 proposes a lightweight and security-enhanced key agreement protocol using the PUF for the IoD environment, and Section 7 presents the formal security analysis, including BAN logic, the RoR model, AVISPA simulation, and an informal security analysis of the proposed protocol. Furthermore, Section 8 presents a performance analysis of the proposed and related protocols regarding communication cost, computational cost, and security features. In Section 9, we conclude this study and discuss future research.
## 4 Model-Based Calibration
### Phase Center Positions and Troposphere
The parameter set \(\mathcal{O}\) comprises the tropospheric propagation correction \(\Delta c\), the corrections \(\Delta\vec{L}_{e}\) for all antenna elements \(e\) as well as channel dependent, constant range offsets \(\Delta r_{pq}\).
###### Abstract  
In order to explore the effects of planting two economic crops in Moso plantations on the composition of soil phosphorus-functional microbial community, this study collected soil samples of Persimmon and Tea-oil plantations cultivated on the original bamboo soil for 3 years for comparison. Soil physical and chemical measurements and metagenomic sequencing were used to evaluate the effects of crop cultivation on the diversity of soil phosphorus-functional microorganisms. Results show that (1) Moso forests are converted to different crops after the soil pH values decline, and other physical and chemical properties of soil and microbial biomass phosphorus (MBP) content rise. (2) Soil microbial community structure changed with crop planting. The number of phosphorus-functional bacteria in Persimmon soil was higher than Tea-oil and Moso soils, with the total number of phosphorus-functional bacteria and unique phosphorus-functional bacteria in Persimmon soil being the highest. (3) The relative abundance of _phoU, phoR, ugpA, ugpp, gcd_ and _ppaC_ genes was significantly increased, while the abundance of _pstA, pstB_ and _pstC_ genes was decreased by crop replanting. (4) The dominant phosphorus-functional microorganisms under different crop cultivation were closely related to basic soil properties. _Bradyrhizobium_ and _Camelia_ abundances were significantly positively correlated with soil total phosphorus (TP), while _Sphingomonas_ was significantly negatively correlated with soil TP. Soil electrical conductivity (EC), soil total nitrogen (TN) and soil MBP were positively correlated with the _ppx-gppA_ gene. AP, EC and TN were positively correlated with the _phoB_ gene, while TN and MBP were negatively correlated with the _phoP_ gene. These results suggested that land use patterns could directly change soil environmental conditions, thereby affecting phosphorus-functional microbial communities. In conclusion, the conversion of Moso plantations to commercial crops is beneficial for the optimization of the soil system, promoting the activation and release of soil phosphorus to maintain the dynamic balance of soil microbial community.  
Moso; persimmon; tea-oil; phosphorus-functional microorganisms; soil
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 3 Materials and Methods
### Improvement and Refinement of Flood Maps  
The semi-automatic or manually drawn flood maps based on remote sensing data were improved and refined by using DEM modelling and ancillary data. For such support data, we also followed the criterion of using free-of-charge data. As a final step, manually drawn flood maps were produced using QGIS software.
## 4 The Preliminary Experiment of Atmospheric Density Inversion for SWARM-A
### Inversion Results and Comparative Validation  
The atmospheric density on 28 April 2024, can be calculated following Equation (5), contingent on the acquisition of the \(C_{D}*A\) sum. Figure 4 illustrates the time-dependent atmospheric density obtained from the inversion. It can be observed that on 28 April 2024, the atmospheric density around the orbit of the SWARM-A satellite was approximately \(10^{-12}\) kg/m\({}^{3}\). This cycle is closely related to the flight path of the SWARM-A satellite.  
The inversion results were compared with the atmospheric density simulated by the Nrlmisise00 model. Based on the daily measured F10.7 and Ap indices provided by GFZ officials and their corresponding averages, combined with the geographic latitude and longitude positions obtained from precise orbit data, the data are input into the Nrlmisise00 model to obtain the corresponding atmospheric density. The time-averaged atmospheric  
Figure 4: On 28 April 2024, the atmospheric density inversion results of the SWARM-A satellite were obtained, with red indicating the semi-long axis attenuation method and blue indicating the accelerometer inversion. The atmospheric density varied from 1.6 to 3.2, and there was a minimum value at 15 UT (h) in the morning and noon.  
density obtained through calculation was compared with the actual inversion results, and the analysis results are shown in Figure 5. From the graph, it can be seen that a review of the time series indicates that the atmospheric density of the two is highly consistent, and the overall trend of change is also similar. After linear fitting, the correlation coefficient between the accelerometer method and nrlmisise00 reached 0.9204, and the SSE was 5.888 \(\times\) 10\({}^{-13}\) in Figure 6a. The correlation coefficient between the semi-long axis attenuation method and nrlmisise00 reached 0.9158, and the SSE reached 6.108 \(\times\) 10\({}^{-13}\) in Figure 6b. The inversion accuracy of the accelerometer method was slightly higher than that of the semi-long axis attenuation method [53].
## 4 Discussion and Conclusions
### Research Limitations  
Application of the SLR method in this research allowed the generalizability and consistency of research findings, following the goal to systematize and comprehend the scope of application of AI in the creation of 3D-printed architectural structures. To provide transparency, clarity, integration, focus, equality, accessibility, and coverage of the study, the authors strictly followed the PRISMA statement [38; 39]. The previous approach aimed at minimizing bias or chance results and producing reliable findings. However, the paper has several limitations. Firstly, the analysis was performed on a data sample limited primarily to the WoS database. Additional papers were included in the sample through manual and reference list searches to provide a more thoughtful overview and overcome this limitation. Secondly, the review limitation could be induced by the study retrieval process and linguistic biases. Thirdly, the keywords applied in the search needed to be standardized, excluding authors who use distinct keyword variations. Finally, one should be mindful of the authors' bias and that no formal risk-of-bias evaluation was carried out when using the research's findings. Nevertheless, the author's expertise in the field provides context for interpreting bibliometric analyses.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## ZigBee (IEEE 802.15.4)
### Satellite Communication  
Satellite communication can play an important role in SG communication in rural areas without cellular coverage, or as a backup solution for other communication technologies [96]. Examples of areas of use for satellite communication are control and monitoring of remotely located substations [97].
# A Multi-Source Consistency Domain Adaptation Neural Network MCDANN for Fault Diagnosis
## 1 Introduction
[28] realizes domain alignment on the last two feature extraction layers of their networks. Adversarial-based methods learn invariant features between domains by introducing a domain discriminator to encourage domain confusion [29]. [PERSON] et al. proposed a Double-level adversarial network that simultaneously achieves domain alignment and class alignment [30]. Based on the use of domain classifiers, [PERSON] et al. enhanced the generalization of the learned features by using multiple class classifiers [31]. [PERSON] et al. learn domain-invariant features via a domain classifier and MMD distance [32]. [PERSON] et al. adversarial training and distance metric, and scale the vibration data to enhance robustness [33]. [PERSON] et al. get an accurate value of joint discrepancy by improving joint distribution adaptation [34]. In the work of [PERSON] et al. [35], a new framework WDCNN combined with adaptation batch normalization was proposed. The one-dimensional signal is used as input in the first convolutional layer, and convolution is performed with a wide kernel (64) to suppress high-frequency noise. Domain adaptation is then achieved by extracting the mean and variance of the target domain signal and passing them to AdaBN. Although the above domain adaptation methods have achieved good results, they all match the features in the domain from a global perspective, without considering more fine-grained information. This may allow the model to match different classes of data between the source and target domains, a phenomenon known as domain mismatch. In order to solve this problem, people are also studying how to introduce fine-grained (such as label) information into domain matching. [PERSON] et al. [36] proposed a Deep Subdomain Adaption Network (DSAN), which divided the original domain into several subdomains according to the similarity of the samples and then aligns the corresponding subdomains in the source domain as well as the target domain. However, this method only has a good effect on some specific data sets, and even has the opposite effect on the bearing fault data set. In order to effectively improve the accuracy of sub-domain alignment, this paper proposes a method of multi-source prediction consistency to improve the prediction accuracy of pseudo-labels, so as to achieve more accurate sub-domain alignment. The main contributions of this paper are as follows: (1) A domain adaptation network combining DANN [37] and LMMD is proposed; (2) Fine-grained alignment with LMMD method is greatly improved with Multi-source consistency and majority voting. (3) Experiments are performed on the CWRU and DIRG [38] dataset to demonstrate the effectiveness of the proposed method. The rest of the paper is organized as follows: Section 2 describes the proposed method and the structure of the model. Section 3 presents the experiments and results. Section 4 discusses the pros and cons of several distance metrics, as well as the effectiveness of multi-source methods. Section 5 provides the conclusion and possible future research directions.
# Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5
## 3 Materials and Methods
### Pre-Training  
At this stage, it is very difficult to obtain large datasets when users have to take pictures themselves. At the same time, if the dataset is too small, overfitting will occur when training the model, which will lead to the model having poor generalization ability and robustness. Therefore, users typically do not train network models from scratch for a given item. The amount of data in this experiment was also limited, and the training results are likely to exhibit overfitting. To solve this problem, we adopted the transfer learning method to improve model generalization [40]. We used the backbone of the COCO datasetto pre-train the network model and used the trained backbone to train the wildlife dataset. This method reduced the size of the training dataset, increased the training speed of the model, and effectively solved the problem of model overfitting. Since transfer learning allows the model to learn using different types of data, it is better at capturing the internal connections of the problem to be solved.