esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Verification
### Experimental Results with Parameter Variation  
The experimental results of the 2 DOF IMC method and 2 DOF IMC ADO method are given at 400 r/min and 3 N \(\cdot\) m under the inductance mismatch. Figure 10a,b shows the \(dq0-axes\) currents experimental results of both methods, where \(i_{d}^{*},i_{q}^{*},i_{0}^{*}\) are the reference current of \(i_{d},i_{q},i_{0}\). When the time is 2.7 s, the values of \(L_{dc}\) and \(L_{ac}\) are changed to 0.5 times the initial values. It can be seen from Figure 10a,b that after 2.7 s, due to the inaccurate inductance parameters, the \(dq0\)-axes current fluctuations of both methods begin to increase in varying degrees, which are the natural result of the poor relative stability under parameter variation. It is noted that when the inductance parameters start to vary at 2.7 s, the overshoot of the d-axis current based on both methods is 1.05 A and 0.64 A, respectively. In addition, the regulation time of the method in [14] is 0.105 s (\(d\)-axis), 0.12 s (\(q\)-axis) and 0.15 s (0-axis). By contrast, the \(dq0\)-axes current of the 2 DOF IMC ADO method can achieve the steady state in 0.08 s, 0.11 s and 0.146 s, respectively. Additionally, the \(dq0\)-axes current fluctuations of the proposed method are smaller than that of the 2 DOF IMC method in most instances. The reason is that the disturbances caused by parameter mismatch are quickly modified by the proposed ADO. The compensation of the ADO not only guarantees good current tracking performance but also enhances the system's disturbance rejection capabilities (Figure 10). The curves of the estimated disturbance voltages are given in Figure 10c. Although the ADO is also affected by the parameter mismatch, the ADO can quickly converge to real values.  
Based on the experimental results and analysis, it can be confirmed that by combining the ADO, the 2 DOF IMC ADO method can effectively estimate the disturbance caused by parameter mismatches to achieve good current control performance and strong robustness.  
Figure 10: Cont.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Speed (rpm)** & **Load Torque (N \(\cdot\) m)** & \(T_{r}\) \\ \hline Reference [14] & 400 & 1 & 65.01\% \\  & 400 & 4 & 85.73\% \\  & 400 & 1 & 56.49\% \\  & 400 & 4 & 67.73\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The torque ripple percentages for both methods when the load torque changes.
###### Abstract  
Frequency modulation continuous wave (FMCW) radio fuze is widely used in military equipment, due to its excellent range and anti-jamming ability. However, the widespread use of radio fuze jammers on modern battlefields poses a serious threat to fuzes. In this study, a classification method of targeting and sweeping frequency jamming signals of FMCW radio fuze based on wavelet packet transform features is proposed, which improves the anti-jamming ability of fuze. The wavelet packet transform of the output signal of the radio fuze detector is used to form a feature vector, which is fed into a support vector machine for targeting and jamming signal classification. The experimental results of the measured data show that the proposed method can achieve a high accuracy rate of classification and identification of FMCW radio fuze targets and frequency sweeping jamming signals. The highest recognition accuracy reached is 98.81% \(\pm\) 0.0037. The lowest false alarm probability is 0.57% \(\pm\) 0.0043, which indicates its potential application values in the near future.  
FMCW; radio fuze; anti-frequency sweeping jamming; wavelet packet transforms; support vector machine +
Footnote †: journal: Applied  
10 August 2022  
Accepted: 29 August 2022  
Submitted: 30 August 2022
## 2 Materials and Methods
### Data Analysis
In the present study, the angle \(\beta\) varied in the range of 0-60\({}^{\circ}\) in steps of 5\({}^{\circ}\), and \(\varphi^{\circ}\) (0\({}^{\circ}\) implies a horizontal plane, for which \(R_{e,\beta S}=0\)) is the latitude of the location. \(B_{e,0S}\) values were included in the TMYs-PV, while the rest of them (\(B_{e,\beta S}\), \(i\)\(\epsilon\)[5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60], \(\varphi\)] were calculated after Equation (6). In a similar way, \(D_{e,0S}=D_{e}\) was calculated as the difference \(G_{e}-B_{e}\), where \(B_{e}=B_{e,0S}\). All other values of \(D_{e,S}\), \(i\)\(\epsilon\)[5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 43; 45; 46; 47; 48; 49; 51; 52; 53; 54; 56; 57; 59; 60] were calculated after Equations (2) and (4). Similarly, \(R_{e,S}\), \(i\)\(\epsilon\)[5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 23; 24; 25; 26; 27; 28; 29; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60], \(\varphi\)] were computed after Equations (3) and (5). All above calculations were performed through a FORmua TRANslation (FORTRAN) programming code specifically developed for the purpose of this study. All analyses deployed in Section 3 refer to annual, seasonal, or monthly mean sums of received solar energy (in kWhm\({}^{-2}\)) under all-sky conditions.
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Supervised Learning
#### 4.1.5 Caching/Computing  
In [56], the authors use an ANN-based approach to address the issue of code caching, with results showing the effectiveness of the model, In [57], a supervised DNN is proposed to address the issue of caching in IoT systems, with results being close to the optimal of conventional ones.
# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
## 4 Results and Discussion
### Discussion  
#### 4.2.1 Comparison of Different Pre-Training Models  
To compare the performance of different pre-trained models in identifying UFZs, ST-CNN selected AlexNet, VGG16, ResNet50, and MobileNet-V2 as pre-training models, respectively [27, 63, 64, 65]. The classifier is softmax. In order to integrate with the self-built network, we delete the network structure behind the first fully connected layer of the pre-trained model and add a fully connected layer that outputs 64 dimensions. Furthermore, two training patterns were employed for the pre-trained model: training with all full parameter layer and training with only the full connection layer. A comparison was made between these two patterns. As shown in Figure 10, the training pattern that involved all parameter layers yielded superior results compared to training with only full connec  
Figure 9: UFZs recognition results of different classifiers: (**a**) ground truth, (**b**) SVM recognition results, (**c**) KNN recognition results, and (**d**) RF recognition results.  
tion layers. Specifically, when using pre-trained VGG16, the OA and kappa coefficient increased by 5.77% and 7.23%, respectively. In the model of all parameter layer training, the OA of the pre-trained VGG16 increased by 7.56%, 2.79%, and 5.37% compared with pre-trained AlexNet, ResNet50, and MobileNet-V2, respectively, and the kappa coefficient increased by 9.96%, 3.60%, and 6.85%, respectively. Therefore, ST-CNN can obtain the optimal classification results of UFZs by using the pre-trained VGG16 and all parameter layer training.  
Compared to AlexNet, VGG16 utilizes a series of continuous 3 \(\times\) 3 Convs instead of 7 \(\times\) 7 Convs. Due to the small parameter number of 3 \(\times\) 3 Convs, VGG16 has a deeper and wider structure, which can extract more features and fit the network effectively. In addition, ResNet50 and MobileNet-V2 do not show obvious advantages. On the one hand, gradient dispersion may occur in deep networks, making it difficult to optimize the model. On the other hand, there are more parameters in the deeper network, and the number of images in the dataset in this paper is small, so the whole network cannot be trained effectively.
## 3 System Performance
### Test Bench  
In order to check the performance of our system, we have simulated a network with 8 athletes, and all of them are connected to an AP. This AP connects to the cloud all the users for saving data and making the data fusion process. Finally, the cloud have a secure web server, which show the information processed in the cloud to the doctors, coaches, etc. The architecture of the network is shown in Figure 4. All patients or athletes send information to a private cloud, which can be accessed from external networks. Access is protected through a user authentication based on RADIUS and the information use HTTPS. In this way, we ensure that only registered users can access information and the encrypted information travels securely across the network.  
In this test we have used Riverbed Modeler as network simulator [21]. Riverbed Modeler Suite is a set of tools for designing, modeling and simulating local and global networks, computer systems, applications and distributed systems. This software gives end users--designers and engineers--a graphical environment for performance evaluation and creation of event simulation networks. This software can be used for different tasks, including model creation, testing communication protocols, interaction protocol analysis, network optimization and also it is possible to implement using packet validation of analytical models. This means that the user can create separate models of network nodes and packet formats to define their own components and sequence statistics simulations and their own characteristic servers [22].  
The data sent by the nodes (RPi) are the data collected in the previous section. We have selected several periods, where we can observe various states of fatigue. Here, we show the key values measured for the network undertaken to evaluate the performance of the monitoring system when eight patients or athletes were monitored over one hour period, as previously mentioned. These values enable us to make an estimation of the technological resources needed to implement the system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **BPM/SPO2 (\%)** & **Temp. (\({}^{\circ}\)C)** & **GSR (V)** & **Air Ampl. (V)/Rate (bps)** \\ \hline \(E_{5}\) & 120–220/85–90 & 36.5–38.2 & 2.0–2.5 & \textgreater{}1.7/\textgreater{}0.6 \\ \(E_{4}\) & 90–120/90–95 & 36.2–37.5 & 1.1–1.9 & 0.8–1.7/0.3–0.6 \\ \(E_{3}\) & 70–90/95–98 & 35.1–37.1 & 0.5–1.8 & 0.5–1.4/0.4–0.6 \\ \(E_{2}\) & 60–70/98–100 & 35.1–37.1 & 0.3–0.8 & 0.3–1.1/0.2–0.4 \\ \(E_{1}\) & 40–60/98–100 & 35.1–37.1 & 0.1–0.5 & 0.3–0.6/0.1–0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Range of values for health-status classification in adult athletes (bps = breathes per second; Air ampl. is limited by the system to 2.5 V).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **BPM/SPO2 (\%)** & **Temp. (\({}^{\circ}\)C)** & **GSR (V)** & **EMG(\(\mu\)V)** & **Air Ampl. (V)/Rate (bps)** \\ \hline Avg. & 82/97.2 & 35.33 & 0.57 & 453 & 0.7/0.42 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average values of measurements with different sensors (bps = breathes per second)
# Detection of Volatiles by HS-SPME-GC/MS and Biological Effect Evaluation of Buddha's Hand Fruit
## 3 Results  
### Vapor Phase Chemical Composition  
SPME-GC/MS analysis was performed to describe the volatile composition of fresh Buddha's hand exocarp and mesocarp. In total, eighteen compounds were identified and listed in Table 1. In general, the monoterpene content largely prevailed over the sesquiterpene content in both the exocarp and mesocarp, and limonene (75.8%; 76.2%) and \(\gamma\)-terpinene (16.5%; 15.0%) were the most abundant compounds. \(\beta\)-Pinene (2.5%; 2.4%), \(\alpha\)-pinene (1.6%; 1.4%), and cis-\(\beta\)-ocimene (1.2; 1.1%) were also present with lower and comparable relative percentages. Small qualitative differences were found between the two matrices; \(\beta\)-thujene (0.2%) and \(\alpha\)-farnesene (0.2%) were present only in the exocarp while carved (0.1%) and \(\alpha\)-citral (0.1%) were present only in the mesocarp.
## 4 Results and Discussion
### Scenario 1  
Scenario 1 creates a metadata for the standard dataset MNIST which is clustered using _uCLUST_ and compares the performance of _ELM++_ with existing incremental learning algorithm _Learn++_[31]. MNIST dataset contains 10 classes representing digits 0-9. Metadata is created for the dataset with attributes _image_type_, _height_, _width_, _number_. This metadata of images is given to the clustering algorithm _uCLUST_ which clusters these images into 10 labeled clusters. Later, these labeled clusters were divided into 6 training sets (S1-S6) and 1 test set Each train set holds images belonging to classes 0 through 9, with 200 images in each class. Train set S2 will hold different images from S1. Train set S3 will hold images that are not present in S1 and S2, and so on. Test set contains images from all these 10 classes. Each train set was trained incrementally using _ELM++_ to create an individual model. When the test set is classified using the model creates by S1, an accuracy of 72% is obtained. When the test set is classified by combining models S1 and S2, the accuracy increases to 78.2%. By combining all these models S1-S6, the classification of the test images gets increased to 94%. The performance of _ELM++_ was compared with the existing algorithm _Learn++_. Table 4 and Figure 10 shows the performance comparison between _Learn++_ and _ELM++_ for MNIST dataset  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Incre. Testing**} \\ \cline{2-5}
**Set** & \multicolumn{4}{c}{**Accuracy (\%)**} \\ \cline{2-5}  & _Learn++_ & _ELM++_ & _Learn++_ & _ELM++_ \\ \hline S1 (0–9) & 94.2 & 92 & 82 & 72 \\ S2 (0–9) & 93.5 & 90 & 84.7 & 78.2 \\ S3 (0–9) & 95 & 90 & 89.7 & 85.3 \\ S4 (0–9) & 93.5 & 92 & 91.7 & 88 \\ S5 (0–9) & 95 & 96 & 92.2 & 90.8 \\ S6 (0–9) & 95 & 96 & 92.7 & 94 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of _ELM++_ and _Learn++_– MNIST Database.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S. No.** & **Dataset** & **Number of Classes** & **Number of Images Per Class** & **Total Number** \\ \hline
1 & MNIST & 10 & 2000 & 20000 \\
2 & STL-10 & 10 & 1300 & 13000 \\
3 & CIFAR-10 & 10 & 6000 & 60000 \\
4 & Caltech101 & 101 & 40 & 4040 \\
5 & Caltech256 & 256 & 119 & 30607 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Standard Datasets.
# A Flexible Localization Method of Multi-Constrained Free-Form Surface Based on the Profile Curves
## 3 The Localization Problem of the Near-Net Shape Blade Surface
### Reconstruction of the To-Be-Cut Blade Surface  
In the proposed localization optimization, the initial value is the profile curves on the sections of the design blade. By the Newton iteration method, the transformation parameters are determined as the optimization result. By the lofting method, the to-be-cut blade surface is constructed in terms of the profile curves in the sections (Figure 5). The process of lofting is as follows.  
Step 1: The profile curves are generated in the sections represented by expressions \(\mathbf{C}_{i}(v)\).  
Step 2: The number of control points of each profile curve are uniformed.  
Step 3: The node vector in \(v\) directions are uniformed.  
Step 4: The number of \(u\) directions on the surface, parameters, and node vector are determined.  
Step 5: The control points of the surface are optimized in terms of the proposed optimization model.  
Step 6: The expression of the surface is obtained in terms of the control points and the original B-spline basis function.  
The parameter equation of the to-be-cut blade surface is given by  
\[r(u,v)=\sum_{i=0}^{m}\sum_{j=0}^{n}N_{i,3}(u)\cdot N_{j,3}(v)\cdot V_{i,j} \tag{8}\]  
The above formula can also be written as  
\[r(u,v)=\sum_{i=0}^{m}\left(\sum_{j=0}^{n}N_{j,3}(v)\cdot V_{i,j}\right)\cdot N _{i,3}(u) \tag{9}\]  
It can also be written as  
\[r(u,v)=\sum_{i=0}^{m}c_{i}(v)\cdot N_{i,3}(u) \tag{10}\]  
where \(\mathbf{C}_{i}(v)\) are the profile curves of the to-be-cut blade surface. \(V_{i,j}\) are the control points of the profile curves. The translation and rotation operators in the optimization are applied on these control points.
## 5 Experimental Evaluation
### Performance Metrics
#### 5.2.3 Backhaul Traffic  
The backhaul traffic is the total size of all transmitted contents plus the total requested contents that could not be served by the clustered MAN servers. If a content request scenario necessitates content transfers from neighbor MAN servers or the ROC, the size of the data transferred is also recorded as the backhaul traffic.  
\[Backhaul\ Traffic=Total\ size\ of\ data\ transmitted\]
## 5 Discussion
### Future Directions  
Building on this foundation, several directions merit further exploration. First, adaptive risk scoring could allow AVI to adjust sensitivity dynamically based on user profiles, query history, or trust levels. Second, integration with audit trails and compliance reporting could help organizations meet documentation requirements for AI oversight. Third, deeper user interface integration, such as alerting end-users to moderated content and offering explanations, would enhance transparency and engagement [68].  
Enhancing PII Detection Robustness: A key priority is to significantly improve the PII detection module. This involves expanding its evaluation and training (if applicable, depending on the methods used) on large-scale, diverse, real-world, and multilingual PII datasets. The aim is to ensure higher accuracy, reduce false positives/negatives, and adapt to various PII patterns encountered in global contexts.  
Cross-LLM Validation and Adaptability: A paramount future direction is the comprehensive validation of AVI's effectiveness and LLM-agnosticism across a diverse array of large language models. This will involve rigorous testing with leading commercial models (such as GPT-4 and Claude) and popular open-source alternatives (e.g., Llama and Mistral). Such testing will also explore how AVI's policy configurations may need to be adapted to optimally manage risks associated with different LLM architectures, their specific alignment strategies, and varying propensity for issues like hallucination or bias. This will also help to understand AVI's utility in complementing or fine-tuning the safety layers of more heavily moderated models.  
In addition, expanding AVI's capabilities to handle multimodal input--such as images, speech, or code--would greatly increase its applicability. While the current version focuses on text interactions, the same modular validation logic can be extended with domain-specific detection models (e.g., for visual toxicity or code vulnerabilities). Lastly, future iterations should explore collaborative filtering networks, where risk data and rule templates are shared across organizations to accelerate threat detection and policy refinement.
## 3 Data and Preprocessing
### Advanced Weak-Pair Data Input Strategy  
For the case where the axial position of the input MR image slice is close to the boundary, since the axial density of the reference MR images is lower there, the structural similarity of the weak-pair selection of the reference image may be lowered if the same random criterion is applied. Therefore, to make the selected reference MR image structurally similar to the input MR image and guarantee a certain randomness, an advanced weak-pair data input strategy is proposed. Firstly, the axial location of each brain MR image slice in Montreal Neurological Institute (MNI) space is extracted with SPM12 tool. Then, in every epoch, 30 MR images in the reference dataset axially closest to \(x_{n,m}\) is used to form a set \(Xsel1_{n,m}\). Meanwhile, MR images in the reference dataset within the range of \(\pm 3\) mm from \(x_{n,m}\) is used to form another set \(Xsel2_{n,m}\). One image \(x_{N,\overline{m}}\) in the larger set above is randomly selected to be paired for \(x_{n,m}\) in the training process. After that, a random number \(rand_{n,m}\) is generated according to a Gaussian distribution with \(\mu=256\) and \(\sigma=N_{epoch\ left}+64\). The augmented image size is  
\[size_{n,m}=round\left(256+abs\left(256-rand_{n,m}\right)\right). \tag{6}\]  
Therefore, the \(x_{n,m}\) and corresponding \(x_{N,\overline{m}}\) are scaled to \(size_{n,m}\times size_{n,m}\), randomly cropped to \(256\times 256\), and then randomly flipped. The above random procedures are all synchronized within an image pair to eliminate the structure disparity.
# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
## 1 Introduction
First, MLS point clouds obtained from the same device exhibit similar error distribution patterns, and these patterns remain consistent across multiple scans of the device. Second, if the error distribution pattern of each MLS device can be measured, it can be used as a correction factor to generate more accurate as-built BIMs from MLS-acquired point clouds. In order to validate these assumptions, the authors designed an experiment to identify error distribution patterns of MLS point clouds and designed a simple method to apply the patterns for improving the accuracy of as-built BIM. The experiment was based on the principle of iterative closest point (ICP) registration and involved the use of mock-ups with a closed shape in the xy-plane. The cloud-to-mesh (C2M) distance measurement method was employed to measure the distances between each point of the MLS point clouds and the reference planes. These reference planes were obtained from point clouds collected using TLS. The experiment was conducted using three MLS devices, resulting in a total of 11 datasets collected from an experimental room. The error distribution patternsfor each device were analyzed, and their potential for dimensional error correction was demonstrated through the proposed method and corresponding test. This paper is organized as follows: Section 2 reviews previous research on MLS analysis and the measurement of building component dimensions in point clouds. Section 3 describes the experimental design and the proposed analysis method. Section 4 presents the experimental results on MLS error distribution patterns. Section 5 discusses the proposed analysis method, the experiment, and its results. Finally, Section 6 provides a summary of the key findings and conclusions of this study.