esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Verification
Under this demodulation method, the MASK modulation bit error rate is inversely proportional to the signal-to-noise ratio SNR, which is:  
\[\text{SNR}=10\log\frac{S}{N}=20\log\frac{V_{\text{S}}}{V_{\text{N}}}, \tag{11}\]  
where \(S\) is the signal output power, \(N\) is the noise power and vs. and \(V_{\text{N}}\) are the demodulated signal and noise voltage values, respectively. Under the condition that the voltage source output is constant and the signal baud rate is constant, the signal-to-noise ratio and M value affect the bit error rate together. In the experiment, 16 ASK modulation is used, and the voltage fluctuation is 5%. Keeping the same signal-to-noise ratio, 8 ASK modulation is performed, as shown in Figure 12. With the increase in signal bandwidth, the error rate will be further reduced. If the system continues to increase the signal bit width, that is, to improve the signal bit rate, while maintaining the signal transmission error rate, this can be achieved by increasing the amplification factor of the differential amplifier in the demodulation circuit or adjusting the \(U_{\text{ref}}\) to improve the signal-to-noise ratio.
# Coupled Vibration Analysis of Multi-Span Continuous Cable Structure Considering Frictional Slip
## 4 Engineering Examples
### Analysis of Results
#### 4.3.1 Comparative Validation of Test Results  
In order to analyze the coupled vibration response of a multi-span continuous cable structure under moving load, the mid-span vertical displacement and cable force of the main cable are selected as parameters for comparison in this section. Since the main cable is supported by pulleys at the top of the cable tower, when the crane is traveling, the main cable will produce relative friction slip at the saddle, and under the action of friction and unbalanced force at both ends of the saddle, the cable tower will be deformed. Therefore, in this section, the deflection of the cable tower is again selected as a parameter for comparative analysis. The data obtained by calculating each datum from the moment when the crane travels to the middle of the span are taken to be analyzed for the error, as shown in Table 1 and Figure 14.  
From Figure 14 and Table 1, it can be seen that the simulation results and the program calculation results have the same trend. The calculation results of the self-programming program are more similar to the measured value, and the finite element calculation value results are large. Taking the maximum vertical displacement in the span as an example, the simulation calculation result is \(-\)9.631 m, and the measured value is \(-\)10.074 m. The program calculation result is \(-\)10.161 m, and the static calculation result is \(-\)8.674 m, and it can be seen that the simulation result and the program calculation result errors are \(-\)4.40% and 0.86%, while the static calculation result error reaches \(-\)13.90%, which causes this phenomenon. The reason for this phenomenon is that the coupled vibration caused by the non-uniform excitation of the main cable by the crane increases the vertical deflection, which also proves the correctness and feasibility of the deduced coupled vibration equations and the program calculation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Calculation Parameters**} & \multicolumn{2}{c}{**Finite Element**} & \multirow{2}{*}{**Programmed Values**} & \multirow{2}{*}{**Static Values**} & \multirow{2}{*}{**Dynamic Load**} \\  & & **Calculated Values** & & & & & **Test Values** \\ \cline{3-8}  & & **Values** & & & & & \\ \hline
1 & Vertical displacement/m & \(-\)9.631 & \(-\)4.40\% * & \(-\)10.161 & 0.86\% * & \(-\)8.674 & \(-\)13.90\% * & \(-\)10.074 \\
2 & Mid-span forces/kN & 4908.64 & \(-\)2.77\% * & 5218.57 & 2.12\% * & 4646.2 & \(-\)9.08\% * & 5110.12 \\
3 & Left-bank cable tower deviation/m & 0.029 & \(-\)4.29\% * & 0.0311 & 2.64\% * & 0.027 & \(-\)10.89\% * & 0.0303 \\
4 & Right-bank cable tower deviation/m & \(-\)0.0386 & \(-\)3.02\% * & \(-\)0.0412 & 3.52\% * & \(-\)0.0369 & \(-\)7.29\% * & \(-\)0.0398 \\ \hline \hline \end{tabular}
# A Graph-Based Approach to Recognizing Complex Human Object Interactions in Sequential Data
## 4 Experimental Results
### Experiment III: Body Part Detection Rate
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Part** & **BB** & **BL** & **TS** & **PT** & **DS** & **HR** & **JV** & **SP** & **SB** & **CJ** & **AVG** \\ \hline HD & 92.23 & 90.34 & 90.03 & 90.12 & 92.24 & 93.4 & 94.32 & 90.45 & 88.02 & 90.12 & 91.13 \\ RE & 95.67 & 93.03 & 92.12 & 89.45 & 93.56 & 96.05 & 92.35 & 94.32 & 89.45 & 87.61 & 92.36 \\ LE & 87.61 & 95.67 & 91.78 & 94.38 & 87.61 & 89.45 & 93.62 & 87.61 & 93.56 & 89.45 & 91.07 \\ RH & 91.45 & 90.51 & 89.45 & 95.67 & 92.23 & 94.38 & 90.56 & 93.27 & 95.67 & 93.27 & 92.65 \\ LH & 89.45 & 90.12 & 88.02 & 91.45 & 92.35 & 88.02 & 92.03 & 93.56 & 96.05 & 92.23 & 91.33 \\ NK & 94.38 & 96.05 & 94.38 & 95.67 & 93.35 & 93.35 & 91.14 & 92.23 & 87.61 & 95.67 & 93.38 \\ TRS & 93.62 & 92.72 & 95.67 & 87.24 & 87.61 & 87.61 & 93.35 & 95.67 & 92.23 & 87.61 & 91.33 \\ BTR & 92.23 & 95.67 & 89.45 & 92.06 & 94.38 & 92.06 & 87.61 & 89.45 & 93.56 & 88.02 & 91.45 \\ RK & 93.35 & 91.39 & 94.38 & 92.23 & 88.02 & 92.23 & 93.24 & 93.56 & 94.38 & 92.23 & 92.50 \\ LK & 93.56 & 97.59 & 93.56 & 95.67 & 94.32 & 93.56 & 90.76 & 94.32 & 92.23 & 93.26 & 92.81 \\ RF & 93.27 & 91.45 & 92.23 & 91.45 & 94.38 & 92.23 & 91.09 & 94.38 & 97.59 & 94.32 & 93.24 \\ LF & 95.67 & 92.35 & 95.67 & 94.38 & 93.27 & 93.27 & 93.56 & 92.35 & 94.38 & 93.27 & 93.82 \\ \hline \end{tabular}
\end{table} 
Table 3: Body part detection rate achieved over Olympic Sports dataset.
# Rural Healthcare IoT Architecture Based on Low-Energy LoRa
## 5 Remote and Environment Triggering
### Sound Wake-Up  
Sound wake-up detector relies on detecting sound waves and can be based on frequency detection [55]. We consider two scenarios for sound wake-up: trigger from the person or the environment and device to device trigger.  
The first scenario is helpful for the user story in Section 2.3. A sound wake-up can be used to react to environmental changes. Loud noise can indicate an accident so that a sound amplitude trigger can wake up a specific sensor. In the first scenario, a patient could use voice to activate an IoT device to control the environment or provide feedback to the care providers, including calling for help. The suitable candidate for this is the piezoelectric device as described in [56]. It will only activate upon reaching the sound threshold and then process the sound. This microphone has sleep mode with a power consumption of only \(6\,\upmu\mathrm{W}\). While the wake-up signal is lost after the wake-up circuit is activated [55], the sound generated during an accident is one of the most valuable pieces of information. Losing this sound limits the system's ability to identify the event. However, the device described in [56] can go from sleep mode to full power in \(200\,\upmu\mathrm{s}\), thus allowing fast reaction time to record and process sound, including a call for help.  
In the second scenario, a sound beyond adult people's hearing abilities, an ultrasound, can be used for the wake-up signal between IoT devices or between a healthcare gateway and the IoT devices. In this application, the devices do not need a line of sight. Instead, they require proximity for the sound signal to travel from the source and be detected by the target device. However, the solution presented in [55], which meets the power requirements, is too complex and costly to be implemented in the simple sensor modules.
## 2 Related Work
One common strategy for obtaining good NER performance in the particular context of Russian text has been to modify pre-trained NER models to fit the unique characteristics of the Russian language. The authors of [12] presented a strong and effective fine-tuning approach that produced cutting-edge results on a range of NLP tasks. A few-shot fine-tuning framework for NER was presented by [13], demonstrating notable advancements over previous approaches. Their research [14] concentrated on low-resource NER challenges and introduced a pre-trained language model fine-tuning strategy that produced outstanding results on Hungarian and Uyghur datasets. Last but not least, the authors of [15] tackled the problem of zero-resource NER and suggested a target-oriented fine-tuning framework that produced cutting-edge outcomes on several benchmarks. Annotated datasets are essential for Named Entity Recognition (NER) model training and evaluation, as demonstrated by a collection of previous studies. Annotation optimization techniques such as cautious annotations, consensus annotations, and multiple annotations are covered in [16]. The authors of [17] present a framework that combines knowledge-based techniques and neural models to provide high-quality multilingual silver data for NER. In their work [18], they investigate the usage of Wikipedia to automatically generate a huge corpus of annotated text called entities, which performs better in cross-corpus evaluation than the gold standard corpus. The authors of [19] focus on the cross-language transfer of high-quality annotations, using NER models with clinical texts written in low-resource languages through neural machine translation. These studies focus on how to improve the development of NER systems by creating a high-quality annotated corpus and effective annotation tools. We acknowledge the significance of these discoveries in our study. We recognize that multilingual BERT models can improve our NER system for use with [PERSON]'s literary and historical heritage, and we take inspiration from their success. Our goal in fine-tuning such models is to create a customized NER system that balances the benefits of multilingual BERT with the particular requirements of the domain-specific material of [PERSON]'s literary and historical heritage. This method promises to provide an accurate and effective NER solution for the deep and varied world of [PERSON] and his literary heritage. We are also looking to build a database that utilizes the NER system, allowing us to conduct a comprehensive and deep study of [PERSON]'s works. We will also utilize this database to perform an extended analysis of how the articles within the Encyclopedia of A.S. Pushkin are connected. Proceeding from that, we will build a knowledge tree of all the connected relationships between the extracted entities, like [PERSON]'s contemporary writers, their writings, dates of publication, etc.
# Intelligent Neural Network Schemes for Multi-Class Classification
## 2 Proposed Network Schemes
### An Illustration  
An example is given here for illustration. Suppose we have a single-label application with 3 categories, having 12 training instances:  
\[\mathbf{x}_{1} =(0.30,0.60),\mathbf{y}_{1} =(+1,-1,-1);\] \[\mathbf{x}_{2} =(0.70,0.35),\mathbf{y}_{2} =(-1,+1,-1);\] \[\mathbf{x}_{3} =(0.50,0.52),\mathbf{y}_{3} =(+1,-1,-1);\] \[\mathbf{x}_{4} =(0.35,0.38),\mathbf{y}_{4} =(+1,-1,-1);\] \[\mathbf{x}_{5} =(0.19,0.89),\mathbf{y}_{5} =(-1,-1,+1);\] \[\mathbf{x}_{6} =(0.78,0.20),\mathbf{y}_{6} =(-1,+1,-1);\]\[\mathbf{x}_{8} = (0.24,0.81),\mathbf{y}_{8}=(-1,-1,+1);\] \[\mathbf{x}_{10} = (0.40,0.65),\mathbf{y}_{10}=(+1,-1,-1);\] \[\mathbf{x}_{12} = (0.24,0.89),\mathbf{y}_{12}=(-1,-1,+1);\]  
Note that \(n=2\) and \(m=3\). We apply SCC to these training instances. Let \(v_{0}\) be 0.001. Three clusters are obtained:  
\[\mathbf{c}_{1}=(0.366,0.526),\ \mathbf{v}_{1}=(0.0892,0.1063);\]  
\[\mathbf{c}_{2}=(0.70,0.2667),\ \mathbf{v}_{2}=(0.081,0.0774);\]  
\[\mathbf{c}_{3}=(0.24,0.87),\ \mathbf{v}_{3}=(0.0418,0.041).\]  
Then, we build the M-RBF network with 2 input nodes (\(n=2\)), 3 hidden nodes (\(J=3\)), and 3 output nodes (\(m=3\)). From Equation (14), the settings for \(w_{k,0},\ w_{k,1}\,\ w_{k,2},\ w_{k,3},1\leq k\leq 3\), are optimally derived by  
\[\mathbf{w}_{1} = \ \left(\mathbf{O}^{T}\mathbf{O}\right)^{-1}\mathbf{O}^{T} \mathbf{d}_{1}\ = \ (1.0,\ 0.9337,\ -0.8453,\ -0.8740)\] \[\mathbf{w}_{2} = \ \left(\mathbf{O}^{T}\mathbf{O}\right)^{-1}\mathbf{O}^{T} \mathbf{d}_{2}\ = \ (1.0,\ -0.8208,\ 0.8395,\ -0.6254)\] \[\mathbf{w}_{3} = \ \left(\mathbf{O}^{T}\mathbf{O}\right)^{-1}\mathbf{O}^{T} \mathbf{d}_{3}\ = \ (1.0,\ -0.8981,\ -0.8065,\ 0.8659).\]  
The detailed SL-Scheme for this example is shown in Figure 5.  
For the input vector \(\mathbf{p}=(0.4,\ 0.5)\), we have \(o_{1}^{1}(\mathbf{p})\ =\ 1.8067\), \(o_{2}^{1}(\mathbf{p})\ =\ 1.1440\times 10^{-4}\), and \(o_{3}^{1}(\mathbf{p})\ =\ 4.3337\times 10^{-7}\). Consequently, we have  
\[o_{1}^{2}(\mathbf{p})=w_{1,0}+o_{1}^{1}(\mathbf{p})w_{1,1}+\,o_{2}^{1}( \mathbf{p})w_{1,2}+o_{3}^{1}(\mathbf{p})w_{1,3}=\ 2.6868,\]  
\[o_{2}^{2}(\mathbf{p})=w_{2,0}+o_{1}^{1}(\mathbf{p})w_{2,1}+\,o_{2}^{1}( \mathbf{p})w_{2,2}+o_{3}^{1}(\mathbf{p})w_{2,3}=-0.4829,\]  
\[o_{3}^{2}(\mathbf{p})=w_{3,0}+o_{1}^{1}(\mathbf{p})w_{3,1}+\,o_{2}^{1}( \mathbf{p})w_{3,2}+o_{3}^{1}(\mathbf{p})w_{3,3}=-0.6227.\]  
Since \(o_{1}^{2}(\mathbf{p})\) is the largest, through the competitive transfer function we have \(o_{1}(\mathbf{p})\ =\ +1\), \(o_{2}(\mathbf{p})=\ -1\), and \(o_{3}(\mathbf{p})\ =\ -1\). Therefore, \(\mathbf{o}(\mathbf{p})\ =(+1,\ -1,\ -1)\) and \(\mathbf{p}\) is classified to category 1.
## 3 Methodology  
### YOLOv8 Algorithm  
YOLOv8, as an important version of the YOLO series algorithms, has been widely used in various fields. Considering the deployment requirements of mobile platforms, this study has selected YOLOv8n, the smallest model in terms of size, as the baseline model. The schematic of YOLOv8n is presented in Figure 2[14].  
The Backbone network consists of standard convolution (Conv), C2f, and spatial pyramid pooling--fast (SPPF). The Conv module utilizes convolutional kernels to extract pixel-level information from images. Based on the C3 module in YOLOv5 and the ELAN module in YOLOv7, the C2f module incorporates extra branches to acquire more detailed gradient flow information [17]. C2f's structure is illustrated in Figure 3[14]. The SPPF module utilizes pyramid pooling to pool input features, generating a series of feature vectors corresponding to different pooling kernel sizes. Then, feature concatenation is performed.  
Figure 1: Categories in the eggplant disease dataset.  
Figure 2: Structure of YOLOv8n.  
The Neck network utilizes the FPN-PAN architecture for feature fusion. The feature pyramid network (FPN) [18] adopts up-sampling to cascade strong semantic features from top to bottom, while the path aggregation network (PAN) [19] uses down-sampling to recover and enhance localization information from bottom to top. This design effectively integrates the information flow within the network, empowering the model with enhanced multi-scale perception.  
The Head module employs three different detection heads to extract the category and location information of targets in feature maps of different scales. It adopts an anchor-free method, directly utilizing the attributes of Neck output features to predict the coordinates of bounding boxes. Additionally, YOLOv8 replaces the coupled head with a decoupled head, explicitly separating detection and classification tasks, thereby better adapting to objects of different sizes and shapes.
# Driver Intention Recognition for Mine Transport Vehicle Based on Cross-Modal Knowledge Distillation
## 2 Materials and Methods
### Data Preprocessing  
EEG data preprocessing was conducted using the EEGLAB toolbox in MATLAB 2022a. First, electrode positions were imported from location files. The EEG signals were then notch-filtered at 50 Hz to remove power line noise. A bandpass filter with a 1-49 Hz range was applied to retain relevant signals while removing high-frequency noise. After filtering, the EEG signals were re-referenced using a common average reference to ensure comparability and independence across electrodes. Independent Component Analysis (ICA) was used to separate the EEG signals into neural activity and artifact-related components. Artifacts were excluded, and the remaining components were used to reconstruct the clean EEG signals.  
The clean EEG data corresponding to the 4 s intervals for each driving intention label were extracted and baseline corrected to eliminate drift. The first second of each trial was discarded to avoid interference from the gray screen before the video stimulus and potential confounding effects from the labeled video segments. The video data and corresponding EEG data were aligned in terms of time intervals, ensuring that the time ranges of the video data and EEG data were consistent, thereby ensuring overall temporal alignment. This alignment of time intervals ensured that the overall understanding of the video and the EEG data analysis were within the same time frame, providing a guarantee for the validity of subsequent data analysis. Ultimately, 3 s video data and corresponding 3 s EEG data were extracted for further analysis, with the 3 s EEG data containing 768 data points across 16 channels.
## 5 Results and Discussion  
### Plackett-Burman Experimental Results  
Through Plackett-Burman experimental and variance analysis (Tables 3 and 4), the significant influencing factors affecting theabrownins production were screened from six factors.  
Using Design Expert 10 software (Statease Inc. Minneapolis, MN, USA) for data processing, the \(F\)-value of the model was 61.91, which is very significant (\(p<0.01\)). The total determination coefficient of the model was \(R^{2}=98.41\%\), and the correction determination coefficient was \(R^{2}_{\text{adj}}=99.39\%\), indicating that the test data can be explained by the model. The single factors affecting the production of theabrownins were \(X_{2}>X_{3}>X_{6}>X_{5}>X_{4}>X_{1}\), among which \(X_{2}\), \(X_{3}\), and \(X_{6}\) had significant effects on the theabrownins content (\(p<0.01\)). Therefore, three significant influencing factors, \(X_{2}\), \(X_{3}\), and \(X_{6}\), were selected for the Box-Behnken test.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variance Source** & **SS** & **d.f.** & **MS** & **\(F\)-Value** & **\(p\)-Value** & **Significance** \\ \hline Model & 96.78 & 6 & 16.13 & 61.91 & 0.0002 \({}^{\star}\) & **—** \\ X\({}_{1}\) & 4.083 \(\times\) 10\({}^{-4}\) & 1 & 4.083 \(\times\) 10\({}^{-3}\) & 1.567 \(\times\) 10\({}^{-3}\) & 0.9700 & \\ X\({}_{2}\) & 72.87 & 1 & 72.87 & 279.65 & -0.0001 & **—** \\ X\({}_{3}\) & 17.84 & 1 & 17.84 & 68.46 & 0.0004 & **—** \\ X\({}_{4}\) & 0.075 & 1 & 0.075 & 0.29 & 0.6141 & \\ X\({}_{5}\) & 0.63 & 1 & 0.63 & 2.42 & 0.1806 & \\ X\({}_{6}\) & 5.37 & 1 & 5.37 & 20.62 & 0.0062 & **—** \\ Residual & 1.30 & 5 & 0.26 & & & \\ sum & 98.08 & 11 & & & & \\ \hline \hline \end{tabular} (1) \(R^{2}=98.41\%\), \(R^{2}_{\text{Adj}}=99.39\%\). (2) \({}^{\star}\) indicates significant (\(p<0.05\)). ** indicates extremely significant (\(p<0.01\)). (3) SS, d.f., and MS represent the sum of squares, degrees of freedom, and mean square, respectively.  
\end{table}
Table 4: Analysis of variance in Plackett–Burman experimental analysis.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Run** & **SC** & **FLR** & **BC** & **FD** & **FT** & **RS** & **Theabrownins** \\  & **(X\({}_{1}\))** & **(X\({}_{2}\))** & **(X\({}_{3}\))** & **(X\({}_{4}\))** & **(X\({}_{5}\))** & **(X\({}_{6}\))** & **Content (\%)** \\ \hline
1 & 1 (8\%) & \(-1\) (1: 20) & \(1\) (\(1\times 10^{7}\)) & \(-1\) (5) & \(-1\) (28) & \(-1\) (120) & 27.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 2 Submerged Membrane-Filtration Applications and Benefits  
Compared to conventional water-treatment techniques, the most popular of which involves an integrated system consisting of coagulation, flocculation, sedimentation, and disinfection, the production of drinking water via membrane technology is acknowledged to be attractive, especially in terms of higher quality water and ease of implementation [29; 30; 31; 32; 33; 34]. The use of submerged hollow fiber membranes can be classified into three main application areas, namely, surface-water treatment for drinking purposes (Section 2.1), pretreatment for RO desalination and reclamation (Section 2.2), and membrane bioreactors (MBRs) (Section 2.3). The former two usually are operated in the dead-end filtration mode with intermittent backwashing, while the third is usually operated as a continuous filtration process with bubbling for inducing tangential shear to mitigate fouling. Table 1 summarizes the submerged membrane-filtration applications and benefits.
# Lower-Latency Screen Updates over QUIC with Forward Error Correction
## 3 Use Case: Pixel-Based Screen Sharing Protocols
Non-streaming updates: The client sends a request for the first update, then finishes downloading it, and after a while (e.g., 0.4 s) sends the request for the second update, and so on (Section 6.2). Real workloads over VNC can be a combination of these two specific use cases. For example, we may use a mouse and click on a link or a file to watch a video and then use the mouse to open a window for editing a text with the keyboard. We need to deliver the screen updates in the same order that they have been sent from the server. In other words, there is a consistency fence between every two consecutive screen updates. However, the rectangles within each update can be delivered in arrival order. In Section 6, we evaluate and compare the two synthetic workloads mentioned above using total ordering, partial ordering, and partial ordering+FEC over QUIC.
## 2 Development of the Pre-Tuning Machine
### Coaxiality Testing  
Coaxiality testing ensures that the cells of a multi-cell elliptical superconducting cavity are aligned along a common axis. This is essential for uniform electric field distribution and optimal performance. In this study, a coaxial testing ring is placed with its center aligned with the cavity's axis.  
Laser displacement sensors are mounted at equal angular intervals around the testing ring, as shown in Figure 4. These sensors measure the radial distance from the center of the testing ring to each cavity's equatorial position. As the cavity moves through the ring, the sensors measure the diameters of each unit cavity, which can detect any axial misalignment.  
The data from the sensors are used to calculate the axial deviation of each cavity. The radial distance at each sensor position is compared to the expected values from the ideal cavity geometry. These deviations help assess the coaxiality of the entire multi-cell cavity.  
To calculate coaxiality, the radial distances at each sensor position are analyzed. The deviations are averaged and used to determine the overall coaxiality. This method ensures precise alignment, minimizing field distortions that compromise the efficiency and stability of superconducting accelerator systems.