esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Coding Based Dimming Scheme with Constant Transmission Efficiency in VLC Systems
## 4 Simulation Results  
To evaluate the performance of the proposed encoding and decoding scheme, simulations have been carried out. The indoor environment was designed based on [1] with a room size of 5 m \(\times\) 5 m \(\times\) 3 m. The LED was installed at a height of 2.5 m from the floor, and the PD was put on the desk at 0.85 m under the LED. The semi-angle at half-power of the LED chip was 60 deg, and the field of view was 60 deg. The effective detected area of the receiver PD was 1.0 cm\({}^{2}\). The gains of the optical filter and the refractive index of an optical concentrator were set as 1.0 and 1.5, respectively.  
Next, to compare the error performance of the various dimming schemes, we fixed the the peak light intensity of transmitted signal \(P\) for a fair comparison according to the literature [11, 14]. The signal-to-noise ratio (SNR) can be defined as \(\text{SNR}=10\log_{10}[\frac{1}{R_{c}}\frac{HP}{\sigma^{2}}]\), where the code rate \(R_{c}\) can be regarded as transmission efficiency here, and \(\sigma^{2}\) is the variance of the AWGN. The bit error rate (BER) was calculated by the Monte Carlo method, and the length of the transmitted data was set to be \(10^{8}\) in the simulations. Simulations of BER have been carried out for the proposed dimming scheme with the different dimming levels where the SNR gain (in dB) was utilized, and three representative dimming levels (i.e., 0.25, 0.375, and 0.5) with different parameter \(K\) values were considered to examine the performance of the proposed scheme.  
As shown in Figure 3, the red lines and blue lines represent the proposed scheme with parameter values \(K=2\) and \(K=4\) respectively. As the parameter \(K\) increased, the code rate \(R_{c}\) decreased accordingly. We can see that the BER performance with \(K=2\) outperformed \(K=4\) under the same dimming levels. In terms of the same parameter \(K=2\), the error performance with dimming level \(\gamma=0.25\) was better than dimming levels \(\gamma=0.375\) and \(\gamma=0.5\), 1.7 dB and 2.7 dB SNR gains at \(\text{BER}=10^{-4}\), respectively.
# Towards Feasible Capsule Network for Vision Tasks
## 3 Methods
### Preliminaries
\] (4)  
Activation of a parent capsule, \(j\), occurs when there is a substantial consensus among the votes with the parent capsule. This consensus leads to the formation of a compact cluster (\(\sigma_{j}\to 0\)) in the \(D\)-dimensional space. 3. Max-min routing [50]: Instead of using SoftMax, which limits the range of routing coefficients and results in mostly uniform probabilities, this study proposes the utilization of max-min normalization. Max-min normalization ensures a scale-invariant approach to normalize the logits. \[ \begin{split} c_{ij}\leftarrow\frac{b_{ij}-\text{min}b_{ij}}{ \frac{1}{\max\lambda_{j}-\text{min}b_{ij}}}\\ b_{ij}\gets b_{ij}+\left\langle S_{j},V_{j|i}\right\rangle \end{split} \] (5)
4. Fuzzy routing [21]: To address the computational complexity of EM routing, [PERSON] et al. introduce a routing method based on fuzzy clustering, where the coupling between capsules is represented by fuzzy coefficients. This approach offers a more efficient alternative to EM routing, reducing the computational demands, while still enabling effective information flow between capsules. \[w_{ij}=\frac{c_{ij}\leftarrow\frac{w_{ij}^{m}}{\sum_{k}w_{ij}^{m}}}{\Sigma_{k} \left(\frac{\left\|v_{ij}-\hat{s}_{j}\right\|}{\left\|v_{ij}-\hat{s}_{k}\right\| }\right)^{\frac{m}{m-1}}} \tag{6}\]  
In the Fuzzy C-means algorithm, the parameter \(m\) controls the degree of fuzziness in the clustering process. Larger values of \(m\) lead to fuzzier clusters, where the membership values \(w_{ij}\) can approach either 0 or 1. When \(m\) is set to 2, the objective of Fuzzy C-means aligns with that of the traditional K-means algorithm. Routing in CapsNets filters out contributions from submodules with noisy information, suppresses output capsules with high variance predictions, and promotes consensus among capsules. However, the assumption of spherical or normal distribution of prediction vectors may not hold in real-world data with variability and noise. It also has inherent weaknesses, the unsupervised clustering nature of routing requires repeated computations, increasing computational complexity compared to one-pass feed-forward CNNs.
# Review of Advancement in Variable Valve Actuation of Internal Combustion Engines
## 5 Continuous VVL (CVVL) and Associated VVA Systems  
A continuous VVL (CVVL) system includes a mechanism capable of changing the lift profile continuously, and it becomes a cam-based VVA (or CVVL + VVT) system when a VVT mechanism is further incorporated. The applications of the CVVL and CVVL + VVT systems include, but are not limited to, those by BMW, PSA, Hyundai, Nissan, and Toyota, as shown in Table 1.  
In 2001, BMW launched the world's first CVVL + VVT system, as well as the first CVVL system called the Valvetronic system [45,56]. The Valvetronic system combines its double VANOS variable cam timing system for intake and exhaust valves with their CVVL system for lift control of the intake valve. The CVVL system includes an eccentric shaft moved by an electric stepper motor and the camshaft, with the camshaft being driven by the VANOS phaser. The valve lift can be varied from 0.18 mm to 9.9 mm. Later, in 2002, PSA Peugeot Citroen and BMW jointly developed a variable valve lift and timing injection (VTi) engine based on the Valvetronic concept [23].  
In 2007, Toyota launched, in the Noah, the Valvematic system, which is essentially a combination of VVT-i and a continuously variable valve lift (CVVL) mechanism for the intake valve. This system is functionally similar to and structurally simpler and more compact than BMW Valvetronic. It varies intake valve lift in the range 0.9 mm to 10.9 mm, with a corresponding or coupled valve opening duration range of 106\({}^{\circ}\) to 260\({}^{\circ}\) in crank angle [26].  
In 2007, Nissan launched a cam-based VVA system by combining its continuous variable valve timing control (CVTC) and variable valve event and lift (VVEL) systems, which are VVT and CVVL mechanisms, respectively [24,25]. It performs similarly to BMW's Valvetronic system but with desmodromic control of the output cam, allowing VVEL to operate at higher engine speeds. The Nissan VVEL system includes a rocker arm and two types of links that open the intake valves by transferring the rotational movement of a drive shaft with an eccentric cam to the output cam. The movement of the output cam is varied by rotating the control shaft with a direct current (DC) stepper motor and changing the fulcrums of the links.  
In 2012, Hyundai launched a CVVL system [20] characterized by its compactness, i.e., no increase in engine height, using a unique six-linkage mechanism. In 2018, Great Wall became of the first Chinese OEMs launching a CVVL + VVT system [21].
# Impact Load Sparse Recognition Method Based on Mc Penalty Function
## 2 Impact Load Sparse Identification Model  
### The Governing Equation of Single Impact Load Identification  
For a linear time-invariant system with single impact load identification, the system vibration equation is as follows:  
\[m\ddot{y}+c\dot{y}+ky=f(t) \tag{1}\]  
where: \(m\), \(c\), \(k\) are the mass, damping and stiffness of the system, respectively; \(\ddot{y}\), \(\dot{y}\), \(y\) are acceleration, velocity, and displacement, respectively, \(\dot{y}\) and \(y\) are known; and \(f(t)\) is the position load function to be identified. By further refining Equation (1), we can get:  
\[\begin{cases}\dot{y}(t)\\ \ddot{y}(t)\end{cases}=\begin{bmatrix}0&1\\ -\omega^{2}&-2\xi\omega\end{bmatrix}\begin{cases}y(t)\\ \dot{y}(t)\end{cases}+\begin{cases}0\\ \frac{f(t)}{m}\end{cases} \tag{2}\]  
where, \(\omega\), \(\xi\) is the natural frequency and damping. Let \(z(t)=\begin{cases}y(t)\\ \dot{y}(t)\end{cases}\) be the state vector, and construct the state space equation:  
\[\left\{\dot{z}(t)\right\}=\mathbf{R}\{z(t)\}+\{F(t)\} \tag{3}\]\[R=\begin{bmatrix}0&1\\ -\omega^{2}&-2\xi\omega\end{bmatrix} \tag{4}\]  
\[\{F(t)\}=\begin{cases}0\\ \frac{f(t)}{m}\end{cases} \tag{5}\]  
the general solution of Equation (3) is as follows:  
\[\{z(t)\}=e^{R(t-t_{0})}\{z(t_{0})\}+e^{Rt}\int_{t_{0}}^{t}e^{-Rs}\{F(s)\}ds \tag{6}\]  
Assuming that at each integration step \([t_{k},t_{k+1}]\), let \(T=e^{R(t-t_{0})}\{z(t_{0})\}\), Equation (6) can be further deduced and simplified to obtain:  
\[H=\begin{bmatrix}H_{11}&\cdots&H_{1P}\\ \vdots&\ddots&\vdots\\ H_{N1}&\cdots&H_{NP}\end{bmatrix}=\begin{bmatrix}\mathbf{X}_{11}&\cdots&\mathbf{X}_{1P }\\ \vdots&\ddots&\vdots\\ \mathbf{X}_{N1}&\cdots&\mathbf{X}_{NP}\end{bmatrix} \tag{7}\]  
where,  
\(H_{11}=\left(\mathbf{H}^{-1}-\mathbf{H}^{-1}-\mathbf{H}^{-1}\mathbf{H}^{-1}+\mathbf{H}^{-1}\mathbf{H} ^{-1}+\mathbf{H}^{-1}\right)_{11^{\prime}}\)  
\(H_{N1}=\left(T\mathbf{H}^{-1}+\mathbf{H}^{-1}-\mathbf{H}^{-1}\mathbf{H}^{-1}-\mathbf{H}^{-1}+\mathbf{H }^{-1}\right)_{11^{\prime}}\)  
\(H_{1P}=\left(\mathbf{H}^{-1}\mathbf{H}^{-1}+\mathbf{H}^{-1}\mathbf{H}^{-1}-\mathbf{H}^{-1}\right)\),  
\(H_{NP}=\left(\mathbf{T}\mathbf{H}^{-1}\mathbf{H}^{-1}-\mathbf{H}^{-1}\mathbf{H}^{-1}-\mathbf{H}^{-1}\right)\).  
\(P\) represents the number of features of matrix \(\mathbf{H}\), Equation (2) can be rewritten from Equations (3)-(7):  
\[[\mathbf{H}]\begin{cases}f(t_{k})\\ f(t_{k+1})\end{cases}=\{\{z(t_{k+1})\}-[T(\eta)]\times\{z(t_{k})\}\}\times m \tag{8}\]  
Equation (8) can be simplified to obtain:  
\[y=\{\{z(t_{k+1})\}-[T(\eta)]\times\{z(t_{k})\}\}\times m \tag{9}\]  
\[\mathbf{H}\mathbf{f}=\mathbf{y} \tag{10}\]  
where \(\mathbf{H}\) represents the response transfer matrix of the system, which is related to the positions of excitation points and response points. \(\mathbf{y}\) is the response vector of the system.
## 3 Results and Discussion
### PAH Origin Identification  
Diagnostic Isomer Ratio  
Different diagnostic ratios, such as Fl/(Fl + Pyr) and LMW/HMW, were used to identify the original sources of PAHs, either petrogenic or pyrogenic. The pyrolytic sources are a result of incineration processes, while petrogenic PAHs are a result of incomplete combustion of petroleum products. When the ratio (Fl/(Fl + Pyr)) is less than 0.40, the PAHs are said to be due to petrogenic sources and when greater than 0.40 are a result of pyrogenic sources ([PERSON] et al., 2020). When the LMW/HMW ratio is greater than 1, the PAHs are typically a result of combustion of petroleum contaminants, and when less than 1 due to combustion of grass, wood or coal.  
The Fl/(Fl + Pyr) was determined to be greater than 0.4 for all sites, indicating that the PAHs originated from incineration processes (Table 2). The LMW/HMW ratio was also found to be less than 1 for all sampling sites in uMsunduzi river and Darvill sludge, which suggests that the PAH were indeed a result of emissions of high-temperature combustion or incineration processes, which typically results in PAH mixtures with higher proportion of HMW PAHs ([PERSON] et al., 2020).
# Log Volume Measurement and Counting Based on Improved Cascade Mask R-CNN and Deep SORT
## 7 Conclusions  
This paper presented a method for log volume measurement and log counting based on an enhanced Cascade Mask R-CNN and Deep SORT. We integrated the CBAM attention mechanism into the feature extraction network and the FPN network to improve the extraction of essential information of the image. We combined with the three cascade detection heads of the Cascade Mask R-CNN to reduce the false detection rate of the model. The detected results are subsequently utilized for calculating log volume and log statistics. Experimental results demonstrate that the improved Cascade Mask R-CNN shows good performance, the \(AP\) reached 82.3%, the \(AP_{s}\) reached 75.3%, the \(AP_{m}\) reached 70.9%, and the \(AP_{l}\) reached 86.2%. Compared to Mask R-CNN, it improved the \(AP\) by 1.8%, the \(AP_{s}\) by 3.7%, the \(AP_{m}\) by 2.6%, and the \(AP_{l}\) by 1.4%. Compared to manual measurement, our method also has good performance in volume, an errors as minor as 4.07% using Deep SORT to track the log ends to resolve the redundant counting problem in video. The results indicate that our method has high precision in log volume measurement. It is a feasible approach to automatically measure the log volume in timber trade activities, enabling a more rational utilization of log resources.  
Conceptualization, C.Y. and Y.S.; methodology, Y.S.; software, C.Y.; validation, C.Y., Y.S. and X.Z.; formal analysis, C.Y.; investigation, Y.C.; resources, Y.S.; data curation, Y.S.; writing--original draft preparation, C.Y.; writing--review and editing, Y.S., Y.C. and L.L.; visualization, X.Z.; project administration, Y.S.; funding acquisition, Y.S. and Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Agricultural Joint Project of Yunnan province (202301 BD070001-241), Scientific Research Fund of Yunnan Provincial Department of Education (2023)0699), Scientific Research Project of Southwest Forestry University (110222011), and National Natural Science Foundation of China (61962055).  
Data will be made available upon request.  
The authors declare no conflicts of interest.
# Web Mapping for Farm Management Information Systems: A Review and Australian Orchard Case Study
## 6 Conclusions
For example, the conversion of geographic coordinates to projected coordinates is only possible within the GDA94 datum, i.e., (GDA94)\({}_{\text{geographic}}\Leftrightarrow\) (GDA94)\({}_{\text{projected}}\) and (GDA2020)\({}_{\text{geographic}}\Leftrightarrow\) (GDA2020)\({}_{\text{projected}}\) or (WGS84)\({}_{\text{geographic}}\Leftrightarrow\) (WGS84)\({}_{\text{projected}}\). 4. **Coordinate transformation**: Geographic coordinates can be transformed from one datum to another. Datum could be local or global. If coordinates are in the projected coordinates system, they should be converted into the geographic coordinates system first. For example, (WGS84)\({}_{\text{geographic}}\Leftrightarrow\) (GDA94)\({}_{\text{geographic}}\) or (WGS84)\({}_{\text{projected}}\Rightarrow\) (WGS84)\({}_{\text{geographic}}\Rightarrow\) (GDA94)\({}_{\text{projected}}\).
# Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures
## 4 Conclusions  
In this paper, we have developed a multistage clustering framework, termed CLUS-BPT, for unsupervised HSI classification. The proposed clustering framework contains the Watershed algorithm as a pre-processing stage, segmentation and feature extractions stages, and final clustering stage. In the first stage, an initial over-segmentation map of the image is obtained. The regions of the obtained segmentation map are then modeled while using first-order parameteric modelling. According to their spatial and spectral features, the regions are merged to form a BPT representation of the HSI cube. Subsequently, PCA is applied to the input HSI cube. Finally, each HSI pixel is clustered to one class by using a filtering algorithm with a feature vector as input containing information that is extracted from the closest neighborhood (spatial information) of the pixel and its transformed spectral value. As future work, other feature extraction techniques can be explored for improving the accuracy. For the publicly available HSI datasets that are used in the analysis, the proposed method results in competitive accuracy when compared to the other state-of-the-art clustering frameworks.  
All authors made significant contributions to the manuscript: conceptualization, M.I. and M.O.; methodology, M.I.; software, M.I.; validation, M.I.; formal analysis, M.I.; investigation, M.I. and M.O; resources, M.O.; data curation, M.O.; writing--original draft preparation, M.I.; writing--review and editing, M.O.; visualization, M.I.; supervision, M.O.; project administration, M.O.; funding acquisition, M.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 10: Computational time measured at different number of regions \(N_{P_{k}}\) for the proposed method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Set** & **Framework** & **PCMNN CT(s) [7]** & **FSC CT(s) [6]** & **CLUS-BPT CT(s)** \\ \hline Salinas & 134.63 & 1.62 & 7.48 \\ PaviaU & - & 1.34 & 58.2 \\ Indian Pines & - & 0.53 & 2.72 \\ Salinas-A & - & 0.06 & 1.56 \\ Samson & - & 0.10 & 1.52 \\ Urban & - & 0.41 & 2.15 \\ Jasper Ridge & - & 0.11 & 2.78 \\ PaviaC & - & - & 18.26 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computational time for projected clustering using mutual nearest neighbor (PCMNN), fast spectral clustering (FSC), and clustering using BPT (CLUS-BPT) obtained for the best purity and NMI results for hyperspectral image data sets.  
**Acknowledgments:** This work was supported by the Norwegian Research Council through the MASSIVE project (grant no. 291270959).  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Millimeter-wave radar has demonstrated its high efficiency in complex environments in recent years, which outperforms LiDAR and computer vision in human activity recognition in the presence of smoke, fog, and dust. In previous studies, researchers mostly analyzed either 2D (3D) point cloud or range-Doppler information from radar echo to extract activity features. In this paper, we propose a multi-model deep learning approach to fuse the features of both point clouds and range-Doppler for classifying six activities, i.e., boxing, jumping, squatting, walking, circling, and high-knee lifting, based on a millimeter-wave radar. We adopt a CNN-LSTM model to extract the time-serial features from point clouds and a CNN model to obtain the features from range-Doppler. Then we fuse the two features and input the fused feature into the full connected layer for classification. We built a dataset based on a 3D millimeter-wave radar from 17 volunteers. The evaluation result based on the dataset shows that this method has higher accuracy than utilizing the two kinds of information separately and achieves a recognition accuracy of 97.26%, which is about 1% higher than other networks with only one kind of data as input.  
human activity recognition; millimeter-wave radar; deep learning; feature fusion; point cloud; range-Doppler +
Footnote †: journal: Physics Letters A  
1
Footnote 1: institutetext: College of Communication Engineering, Jilin University, Changchun 130000, China;  
[EMAIL_ADDRESS] ((Y.H.); [EMAIL_ADDRESS] (W.L.); [EMAIL_ADDRESS] (Z.D.); [EMAIL_ADDRESS] (W.Z.); [EMAIL_ADDRESS] (A.Z.)
# A Lightweight Crop Pest Detection Method Based on Convolutional Neural Networks
## 1 Introduction
This lightweight sandglass block creatively uses group convolution to significantly reduce the number of parameters and the level of computation. Meanwhile, the channel shuffle operation is introduced to compensate for the insufficient inter-channel information fusion caused by group convolution. The lightweight sandglass block also uses a better loss function, _hard-swish_. The number of parameters of the lightweight sandglass block is almost one-third of that of the original sandglass block, and the number of computations is also significantly reduced, while still having a strong ability to extract spatial and channel information. As one of the main building blocks, the reduction in the number of parameters and computations in the sandglass block is important to maintain its light weight. Finally, we introduce a coordinate attention [28] mechanism for both classical residual blocks and lightweight sandglass blocks. The environment in which crop pests live is more complex. The coordinate attention mechanism can guide the feature extraction process and suppress the interference of noisy information, which can help extract a better feature representation. With the above improvements, this study builds a crop detection method with an extremely low number of parameters and computations, and high pest detection precision. Experimental results on the CP15 dataset demonstrate the advantages of the method in terms of the number of parameters, the number of computations and detection accuracy, meeting the requirements of pest detection tasks in agricultural environments. The method can be used to build lightweight and highly accurate crop pest detection systems in agricultural environments, such as being deployed in sticky traps to detect pests. The method can also be embedded in android applications to build a pest detection gadget. The basic building sequence of the method proposed in this study is the overall structure, the generation method of the prior boxes, and the feature extraction process. This clear sequence can be used as a reference for researchers to build task-specific detection methods and accelerate the progress of detection method research. Moreover, the method of reducing the number of parameters and computations adopted in the overall structure and building blocks can guide the following research on lightweight object detection methods.
# An On-Orbit Task-Offloading Strategy Based on Satellite Edge Computing
###### Abstract  
Satellite edge computing has attracted the attention of many scholars due to its extensive coverage and low delay. Satellite edge computing research remains focused on on-orbit task scheduling. However, existing research has not considered the situation where heavily loaded satellites cannot participate in offloading. To solve this problem, this study first models the task scheduling of dynamic satellite networks as a minimization problem that considers both the weighted delay and energy consumption. In addition, a hybrid genetic binary particle swarm optimization (GABPSO) algorithm is proposed to solve this optimization problem. The simulation results demonstrate that the proposed method outperforms the other three baseline algorithms.  
M 2023 23 2023  
latency. At the same time, they also investigated the on-orbit verification of the distributed cognitive service-oriented architecture for the 6G core network.  
However, satellite on-orbit processing tasks face the challenge of a single satellite's limited computing power, resulting in delayed processing of tasks and difficulties in meeting user requirements due to long task delays. Additionally, reducing energy consumption in satellite edge computing is a major concern for scholars [15; 16]. Therefore, utilizing multiple satellites for assisted computing is a valuable research direction to achieve smaller latency and energy consumption.  
The contribution of this research work includes the following:  
* This research proposes a computation offloading strategy based on satellite edge computing, where large dependent tasks can be divided into multiple subtasks and computed on other satellite nodes.
* On the basis of the shortcomings of computation offloading strategies for satellite edge computing of other literatures, study paper considers the scenarios where the satellite network topology changes, and high-load satellites are not involved in offloading. The optimization problem of weighted minimization of task completion delay and energy consumption is then investigated.
* To solve the optimization problem, the modified GABPSO algorithm is utilized. The proposed algorithm is extensively simulated, demonstrating its superior performance compared with other benchmark algorithms.  
The remainder of this paper is organized as follows: Section 2 summarizes the related work. Section 3 describes the system model and problem formulation. Section 4 describes the proposed hybrid genetic binary particle swarm algorithm and analyzes the simulation results. Section 5 concludes the paper.