esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Literature Review
### Machine Learning for Routing in SDVN  
Machine Learning (ML) technology has been trending in recent years, and it has been applied for routing optimization [54], trust-based routing optimization [55], etc. in SDVNs. In the method in [56], the routing database is updated using reinforcement learning, and the most stable routing path is determined using multilevel greedy routing with link stability. Recently, distributed Q-learning has been applied to VANETs to update a Q-table maintained in each vehicle node by exchanging messages between the vehicles [57]. [PERSON] et al. [58] extended the preceding concept by eliminating the need for routing tables and replacing them with by Q-tables formed by traffic flow in neighboring grids, which divide the VANET into a set of grids and find the optimum sequence of grids from source to destination using Q learning.  
We summarize the existing studies for routing in SDVN in Table 1. However, as evident from Table 1, none of the existing work for routing in the SDVN in the reviewed literature effectively considers both link lifetime and link delay in finding optimum paths for routing, nor is there exploitation of the heterogeneity that exists in the links of the SDVN. Furthermore, none of the reviewed frameworks have used deep learning to predict heterogeneous one-hop channel delays and link lifetimes.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline
**Routing Framework** & **Routing Technique** \\ \hline Hierarchical SDVN [42] & Traditional VANET routing protocols with hierarchical controllers \\ \hline Resource scheduling scheme [43] & Greedy routing with objective of minimizing communication cost \\ \hline On-demand routing protocol [44] & Two-level packet forwarding using Bellman Ford algorithm and improved AODV \\ \hline Spray-and-pray multiple copy routing [45] & Graph-based least communication steps to minimize latency \\ \hline Globally optimized routing [46] & Minimum optimistic time-based shortest path routing algorithm \\ \hline Link stability-based routing [47] & Routing based on shortest stable path \\ \hline Cooperative data routing and scheduling [48; 49] & Routing by prioritizing traffic type to minimize service latency while maximizing packet delivery ratio \\ \hline Tribrid routing protocol [50] & Stable shortest path routing satisfying QoS parameters \\ \hline Cognitive routing protocol [51] & Stable path routing with spectrum sensing \\ \hline Cluster-based routing (ICDRF-F-SDVN) [52] & Clustering algorithm with overhead reduction approach with backup VANET routing \\ \hline Highway routing [53] & Optimal routing path selection by predicting destination vehicle location using hidden Markov model \\ \hline \end{tabular}
\end{table}
Table 1: Summary of existing studies for routing in SDVN.
# A Gentle Introduction to Applications of Algorithmic Metatheorems for Space and Circuit Classes
## 1 Introduction
### Related Work  
Except for the next introductory section, the theorems and applications presented in this paper all concern _small space and circuit classes_, even though most algorithmic metatheorems in the literature concern _time classes_. The reasons that I chose these theorems are, firstly, that there are already a number of excellent surveys on algorithmic metatheorems and their applications regarding time-efficient computations [7; 8; 9]. Secondly, the presented theorems are more recent and their applications may thus also be of interest to readers already familiar with algorithmic metatheorems for time classes.  
Thirdly, the presented theorems can be used to establish _completeness results_ for many problems for which the classical algorithmic metatheorems do not yield an exact complexity-theoretic classification: using Courcelle's Theorem and the tricks hinted at earlier, the even cycle problem can be solved in linear time and, clearly, this is also a tight lower bound. However, from a structural complexity-theoretic point of view, the problem is most likely not _complete_ for linear time; indeed, it _is_ complete for logarithmic space and the theorems presented in this paper are useful for establishing such results.
# Literature Review of Digital Twins Applications in Construction Workforce Safety
## 7 Discussion
### Challenges and Future Works
#### 7.2.1 Sensor-Based Indoor Positioning  
The first step in the realisation of the DT concept is the synchronisation of construction activity information; the first step in the synchronisation of construction activity is the synchronisation of spatial positions. However, both sensor- and vision-based positioning are still limited. Trilateration, triangulation and scene analysis are the basic principles of sensor-based positioning, which are formed based on the characteristics of signal transmission [98]. The former two principles use the time difference and phase difference generated in the signal transmission at different positions. With construction activities, these two principals have always been used in an outdoor environment with no shielding. However, it has rarely been used in indoor environments because they are inaccurate when the signalis blocked or interference occurs. Scene analysis, also known as fingerprinting, is considered the most feasible method for indoor positioning [54]. This method determines the location of the target in the space by comparing the characteristics of the positioning signal data with the recorded environmental signal data. However, as the signal characteristics constantly change during the construction activities, considerable human effort is required to update the environment signal characteristics, so the method has rarely been used to locate an on-site individual during the construction stage. The current solution to deal with sensor-based indoor positioning is increasing the number of signal base stations to reduce the impact of walls and floors on signals. This is expensive and labour-intensive.
# Adjustable Structure for Feedback Active Headest System Using the Virtual Microphone Method
## 3 ANC Algorithms for Active Headrest
### Auxiliary Filter Method  
The AFM consists of the tuning stage and control stage, as shown in Figure 3. During the tuning stage, additional filters are trained by modeling the transfer matrix  
Figure 2: Block diagram of the multichannel feedback FxNLMS algorithm.  
between the reference signals and the monitoring error signals after the control filters converge to the optimum, as shown in Figure 3a. The reference signals are synthesized from the monitoring error signals based on the IMC. The modeled additional filters \(\mathbf{g}(n)\) can be observed as a matrix with \(M\times L_{\text{AF}}\) rows and \(M\) columns. The additional filter from the \(m_{x}\)th reference signal to the \(m\)th physical monitoring signal is given by \(\mathbf{g}_{m_{x}m}(n)=\left[g_{0,m_{x}m}(n),g_{1,m_{x}m}(n),\ldots,g_{L_{ \text{AF}}-1,m_{x}m}(n)\right]^{\text{T}}\), and the \(m\)th residual error signal \(e_{\text{T},m}(n)\) can be described as  
\[e_{\text{T},m}(n)=e_{m}(n)+\mathbf{g}_{m_{x}m}^{\text{T}}(n)\mathbf{x}_{m_{x}} (n), \tag{7}\]  
where \(e_{m}(n)\) denotes the \(m\)th physical monitoring error signal. The updated equation for this additional filter vector obtained using the NLMS algorithm [24] is as follows:  
\[\mathbf{g}_{m_{x}m}(n+1)=\mathbf{g}_{m_{x}m}(n)-\frac{\mu_{\text{AF},\text{T} }\mathbf{x}_{m_{x}}(n)e_{\text{T},m}(n)}{\left\|\mathbf{x}_{m_{x}}(n)\right\| ^{2}+\beta_{\text{AF},\text{T}}}, \tag{8}\]  
where \(\mu_{\text{AF},\text{T}}\) and \(\beta_{\text{AF},\text{T}}\) denote the step size and the regularization factor of the NLMS algorithm, respectively. During the control stage, pre-trained additional filters are used to approximate the physical monitoring error signals to the estimated values of these signals in the tuning stage. This implies that the disturbances at the virtual microphones are minimized when the mean square of the effective residual error signal \(e_{\text{C},\text{m}}(n)\) is the least [23]. Thus, the cost function can be stated as  
\[\text{J}=\min\,e_{\text{C},\text{m}}^{2}(n), \tag{9}\]  
where \(e_{\text{C},\text{m}}(n)=e_{m}(n)+\mathbf{g}_{m_{x}m}^{\text{T}}(n)\mathbf{x} _{m_{x}}(n).\) The updated equation for the control filter is given by  
\[\mathbf{w}_{\text{AF}}(n+1)=\mathbf{w}_{\text{AF}}(n)-\mu_{\text{AFC}}\sum_{m =1}^{M}\frac{\mathbf{r}_{m}(n)e_{\text{C},m}(n)}{\left\|\mathbf{r}_{m}(n) \right\|^{2}+\beta_{\text{AF},\text{C}}}, \tag{10}\]  
where \(\mu_{\text{AF},\text{C}}\) and \(\beta_{\text{AF},\text{C}}\) represent the step size and the regularization factor, respectively.
## 2 Materials and Methods
### Methods
#### 2.5.3 Scenario Using Spearman + RFE  
1. The first step is to find Spearman correlation coefficients ranging from 0 to 1 for each of the 48 features in the dataset. Furthermore, the Spearman correlation value is determined using MICtools provided by [26]. Then, depending on the correlation value's results, issue ten features. The Spearman rank correlation coefficient is calculated by first rating the X elements in the paired sample data from 1 to n and then ranking the Y elements separately from one to n, assigning rank 1 to the minor component and n to the largest while retaining the original pairings. As a consequence, [rank (Xi), rank (Yi)] pairings are obtained. Then, compute a difference d equal to the difference between the X and Y variables' ranks for each pair. As mentioned in Equation (1), the test statistic, rs, is defined as a function of the sum of squares of these differences, d [38]. At this stage, the correlation value between two features is rated, and then ten features with a high correlation value between features are eliminated.  
\[\mathrm{r_{s}}=1-\frac{6\sum_{i=1}^{n}\mathrm{d_{i}}^{2}}{(\mathrm{n^{3}-n})} \tag{1}\]
2. Step two: After deleting ten features, there are 38 remaining features for dataset 1 and 77 for dataset 2. The remaining characteristics are obtained by conducting a ranking search using RFE and selecting rankings 1 to 10.
3. Step three: The ten highest-ranked features are evaluated for accuracy using the RF algorithm.  
Figure 2: Procedures for finding MICe TICe values.  
SubdomainLevel, PctExtResourceUrls, UrLength, SubdomainLevelRT, PathLength, NumAmpersand, NumQueryComponents, and PctExtNullSelfRedirectHyperlinksRT. For dataset 2, as seen in Table 6, the deleted features from those findings and their effect on the output variable for dataset 2 are ratio_extHyperlinks, shortest_word_path, web_traffic, char_repeat, nb_extCSS, domain_in_brand, external_favicon, domain_registration_length, links_in_tags, and ratio_intHyperlinks.
## 2 Materials and Methods
### Data Sources
#### 2.2.4 Normalized Difference Vegetation Index (NDVI)  
NDVI data from 2001 to 2020 in Hainan Island were downloaded from NASA Earth Data; the product is MOD13A2 ([[https://doi.org/10.5067/MODIS/MOD13A2.006](https://doi.org/10.5067/MODIS/MOD13A2.006)]([https://doi.org/10.5067/MODIS/MOD13A2.006](https://doi.org/10.5067/MODIS/MOD13A2.006)), accessed on 7 April 2022) [51] at a per-pixel basis at 1 km spatial resolution and 16-day temporal resolution. MOD13A2 chooses the best available pixel value from all the acquisitions from the 16-day period; in this study, the criterion used to calculation monthly value was the maximum value composition (MVC).
# A Single Motor-Driven Focusing Mechanism with Flexure Hinges for Small Satellite Optical Systems
## 1 Introduction
In addition, a material with properties that resist fatigue, thermal expansion, hysteresis, and other failures should be further studied [34]. In contrast to thermally driven systems, [PERSON] et al. presented a direct transmission-based refocusing mechanism [35]. The secondary mirror (M2) was controlled by linear motion, and the Tilt was achieved by controlling rotational motion. The proposed design allowed not only the De-space to displace 25 \(\upmu\)m in a longitudinal direction with a resolution of 0.5 \(\upmu\)m, but also the Tilt to rotate \(\pm\)3\({}^{\circ}\) with 6 s of arc of resolution. However, by using two actuators, the energy consumption of such mechanisms could be quite high, in contrast to a single actuator-driven mechanism. Based on the introduced scope, the main objective of the focusing mechanism is to achieve a simple yet reliable control system that integrates a single-geared DC motor. The proposed single motor-driven focusing mechanism can save the energy consumption and control the De-space precisely using a Flexure Hinge (FlexHe) that features zig-zag patterned slits in a radial direction. The FlexHecan ensure its structural elasticity, and its novel design was conceived to reinforce bending stiffness, as well as to provide relatively compliant axial stiffness. First, the mechanical behaviors (i.e., elasticity, stress concentration, strain energy, etc.) of the FlexHe were investigated. Through a Finite Element Method (FEM) simulation, optimization was performed. For further investigation, a fatigue analysis was carried out, and the available life cycle was determined. Then, we fabricated the FlexHe, and a novel focusing mechanism was proposed by integrating three FlexHe, radially arranged at 120\({}^{\circ}\) from each other. The so-called Single Motor-driven Focusing mechanism with Flexure Hinges (SMFH) was successfully assembled. By using a single-geared DC motor and LVDT (Linear Variable Differential Transformer) sensors, SMFH demonstrated their potential to expand into practical use in space as well as to provide more insights into the future of small satellites. To demonstrate its feasibility, the targeted optics type is Schmidt-Cassegrain, which has been widely addressed as a suitable optical system for small satellites [36; 37]. Its specifications are summarized in Table 1.
## 3 Results  
### Calculation of the Topographical Index  
The topographical index is calculated using the single-flow direction algorithm based on the digital elevation model (DEM). Due to the topographical index's similarity across grids, it is unnecessary to simulate it in every grid. The distribution function in the whole basin is generated by a statistical method with different topographical index values (Figure 2). The study region is segmented into 30 sections according to the topographical indexes. The relationship of area ratios of the topographical index is shown in Figure 3. The topographical index's minimum, maximum, mean, and standard deviations were 0.86, 14.84, 7.85, and 4.17, respectively.  
Figure 3: The plot of topographical index versus area ratio in the Chengcun basin.
## 3 Design and Implementation of the WSN Based AST System
### Wireless Sensor Network Node Performance Tests
The function generator is used to simulate the sensor input signal, and is connected directly to the ADC input channel of the wireless sensor node. A sine wave with 250 mV the peak value is generated for the test input signal. The test wireless sensor node collects the wave signal and transmits a data packet per second with the radio channel set to 2,400 MHz. Some EMI factors such as Bluetooth devices, mobile phones and human bodies were considered in the anti-EMI test. Figure 6 shows the signals received by another immobile node connected to the monitor server by the serial 232 port when the EMI environment is set by no EMI factors adding, a Bluetooth phone call, a GSM mobile phone call and human body obstacle separately. From the received signal, it can be found that data can be collected and transmitted correctly in the EMI environment.
# A New Hybrid Prime Code for OCDMA Network Multimedia Applications
## 3 Code Construction
### Correlation Properties  
Equation (1) represents the auto- and cross-correlation functions of the HPC sequences according to Table 3.  
\[R_{C_{\mathrm{m}}C_{\mathrm{e}}\Rightarrow}\left\{\begin{array}{ll}2n&for\ m=a\ and\ z=b\\ 0,1&for\ m\
eq a\ \frac{and}{or}\ z\
eq b\end{array}\right. \tag{2}\]  
This equation illustrates that:  
1. The peak value of the auto-correlation property is \(2n\), where \(n\) is an integer number equal to the number of code words used to construct the code sequence in each tree.
2. The value of the cross-correlation property is \"0\" or \"1\" between any two different code sequences in the coding of Table 3 and is independent of whether these two codes share the same code index or not.
3. \(m\in\{0,\ 1,\ 2,\ \ldots\ldots,\ (Pi+2 Pj)\}\) and \(z\in\{0,\ 1,\ \ldots,\ (P_{i}-2)\}\)
# Identification of Terahertz Link Modulation in Atmospheric Weather Conditions
## 3 Simulation and Discussion
### Performance at Water Vapor Absorption Peaks  
One of the key characteristics of terahertz wireless links is the atmospheric absorption they suffer. This is very strongly frequency-dependent, especially when links are at frequencies close to the absorption resonances of the water vapor molecule [4; 5]. What about the efficiency of this method if the link operates at such frequencies? In this sub-section, a preliminary study is conducted on a wireless link at 183 GHz, which corresponds to an absorption resonance [4; 34]. Simulation results based on both CNN and LSTM networks in clear weather and rain are shown in Figure 4, for a high SNR level of 80 dB.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**SNR** & **Network** & **Clear Weather** & **Rain** & **Fog and Haze** & **Wind** \\ \hline \multirow{2}{*}{13 dB} & CNN & 61.5\% & 51.32\% & 54.32\% & 55.76\% \\ \cline{2-6}  & LSTM & 79.16\% & 75.20\% & 72.00\% & 74.76\% \\ \cline{2-6}  & CNN (Voting) & 64.32\% & 56.68\% & 58.96\% & 58.56\% \\ \hline \multirow{2}{*}{80 dB} & CNN & 80.48\% & 78.60\% & 80.80\% & 79.84\% \\ \cline{2-6}  & LSTM & 99.88\% & 99.92\% & 100\% & 99.64\% \\ \cline{2-6}  & CNN (Voting) & 82.04\% & 80.96\% & 80.84\% & 80.36\% \\ \hline \end{tabular}
\end{table}
Table 4: Results from CNN and LSTM networks.  
Figure 4: Simulation results by CNN enhancement in (**a**) clear weather and (**b**) rain; by LSTM network in (**c**) clear weather and (**d**) rain for a link operating at 183 GHz. (The value of SNR is 80 dB for all the simulations, and the value in each grid represents the number of real labels identified to be other labels with 500 labels considered).  
The identification accuracy of the CNN enhancement algorithm in rain is close to that in clear weather, with an identical average of 79%. Moreover, a 100% accuracy in both weather conditions can always be achieved by using the LSTM network, no matter the modulation scheme. This means this modulation identification method can overcome the deterioration caused by water vapor absorption by increasing the SNR levels, which is consistent with our discussion in Table 4.
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 3 Results and Experiments
### Reasons for Yolo V8 to Outperform the Other Models  
In comparison to previous iterations and other prevalent models like Inception, Xception, VGG16, and AlexNet, the superior capabilities of the pretrained YOLOv8 model may be attributed to several pivotal enhancements and optimizations:  
* Advanced Architecture: YOLOv8 incorporates more sophisticated architectural changes, building upon the qualities of its predecessors. Improved feature extraction layers and complicated multi-scale object detection handling techniques are probably among these improvements, which lead to a more powerful and nuanced comprehension of complex visual inputs.
* Enhanced Generalization: There has been a noticeable improvement in the model's generalization capacity across different datasets and circumstances. Because agricultural surveillance frequently deals with changing situations and unknown components, this resilience is essential. Regardless of changes in the environment, YOLOv8 constantly achieves great performance thanks to its enhanced generalization.
* Streamlined Efficiency: Efficiency, which is a defining feature of the YOLO series, is still emphasized in YOLOv8, but in a more simplified manner that cuts down on duplication and concentrates on critical calculations. This modification probably leads to substantially quicker inference times without sacrificing detection granularity or model fidelity.
* Precision in Localization: It is anticipated that YOLOv8 has even more accurate object localization, which is crucial for real-time monitoring applications like agricultural surveillance. A proactive approach to managing agricultural resources can be substantially improved by a model having the capacity to precisely identify and categorize possible risks or incursions.
* State-of-the-art Training Techniques: It is likely that YOLOv8 has upgraded its training methods to take advantage of the most recent developments in deep learning. These include innovative regularization techniques, loss functions, and data augmentation tactics that work together to enhance the model's remarkable learning ability.
* Hardware Optimization: It is possible that YOLOv8 keeps optimizing for GPU acceleration, making the most of contemporary technology. This optimization guarantees that the model may be deployed in field applications with strict real-time requirements and that it functions well in research settings as well.  
YOLOv8's excellent performance may be attributed to its sophisticated architecture, enhanced generalization, increased efficiency, accurate localization, state-of-the-art training methods, and optimized utilization of hardware. All of these elements work together to guarantee that YOLOv8 not only builds upon the achievements of its predecessor, YOLOv7, but also brings about important new developments that push the limits of object detection technology--particularly in demanding and dynamic domains like agricultural monitoring.