esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Modelling
### Model Formulation  
#### 2.2.1 Basic Assumptions of the Model  
To better abstract this problem into a mathematical model based on the actual situation, this paper makes the following assumptions:  
1. The departure time, departure station, final station, and the required vehicle size for each departure task during the time period are determined and known;
2. Any basic information of any of the vehicles, including real-time passenger status of vehicles and so on, is known;
3. The locations of the yards and stations are known, and the driving distances between the stations are known;  
Figure 3: Representation of the vehicle scheduling problem in the form of DAG. (**a**) Representation of the vehicle scheduling problem. (**b**) The DAG after the transformation of scheduling problem.  
Figure 2: Diagram of a series of tasks during a time slot.  
To ensure the level of service, it is necessary to ensure an adequate number of vehicles of each size;
5. After the previous task has ended, the vehicle stays at the previous station and waits for dispatch.
6. During actual operation, vehicles are allowed to turn around.
7. Only a single trip for any vehicle during the time period.
8. When matching and connecting between departure tasks, certain scheduling time intervals or distance restrictions are required to prevent unrealistic vehicle scheduling in terms of time or distance.
9. The impact on vehicle scheduling due to unexpected events and other reasons is not considered.
# Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
## 8 Evaluation
### Performance Evaluation  
There are different metrics and measures to evaluate an ML model. Each learning task has a focus on various measures [42]. A good anomaly detector must have a high detection rate and a low false-alarm rate. Therefore, in this paper, the relevant measures from the confusion matrix are used to measure the model efficiency. The performance evaluation of anomaly detection algorithms based on time-series data uses recall, precision and accuracy in addition to the best F-Score. The percentage of positive samples predicted by using the model in the overall sample is reflected by the precision, recall reflects the proportion of true-positive samples among those predicted to be positive, F1-score combines precision and recall and accuracy is the number of correctly identified samples in the test sample as a percentage of the total number of samples tested in the database.  
* True Positive (TP): if an anomaly is classified by model as an anomaly, result is accepted as TP.
* False Positive (FP): if a normal instance is classified by model as an anomaly, result is accepted as FP.
* True Negative (TN): if an anomaly is classified by model as normal instance, result is accepted as TN.
* False Negative (FN): if a normal instance is classified by model as normal instance, result is accepted as FN.  
\[\begin{array}{l}\text{precision }=\frac{\text{TP}}{\text{TP}+\text{TN}};\\ \text{recall }=\frac{\text{TP}}{\text{TP}+\text{FN}};\\ \text{F1-score }=\frac{\text{TP}}{\text{TP}+\text{TP}+\text{FN}};\\ \text{accuracy }=\frac{\text{TP}}{\text{TP}+\text{TN}};\\ \text{LT}+\text{TN}+\text{FP}+\text{FN}}\end{array}\]  
As shown in Figure 13, we compare the performance based on the four metrics under different models with three KPIs. The experimental results show that the LNN-RLSTM model outperforms the other three models. In the three datasets, the anomaly feature of the average number of activated users is more prominent, so the F-Score is higher. The anomaly frequency of the average number of users in the cell is larger than that of the anomaly data, which can explain the phenomenon of \"anomaly aggregation\" in the cell PDCP data (4.48% of the anomalies and 1.38% of the anomaly windows), but its F-Score is the smallest.  
In fact, due to the complexity and diversity of data anomalies, it is still challenging to detect the anomaly frequency with very high accuracy unless the anomalies of data features are extremely obvious (obvious peaks or troughs in values).
## 2 Fundamental Review on Vibration Isolation Systems  
This section reviews the fundamental theory of vibration isolation systems and the key concepts of the QZS system.  
A vibration system consists of an inertial element (which could be represented as a translating block having mass equal to \(m\)) and a restoring element (which could be represented by a coiled spring having stiffness equal to \(k\)). The system vibrates in response to initial conditions or to a force, or to both initial conditions and force. The restoring element exerts on the mass by a restoring force, such as spring force; \(F_{spring}=kx\).  
Many systems vibrate due to a force exerted on the inertial element, and this force may have different shapes; it may be harmonic, impulse, periodic, or random. In this study, the harmonic force is used to simulate the vibration source of the system. The reason to choose the harmonic force comes from the fact that this force is one of the most common forms of force represented in the road profile and can be easily analyzed in comparison to other types of forces ([PERSON] et al., 2020). Thus, the theory presented here is dedicated to solving the problems of harmonic excitation.  
The equation of motion of a spring-mass system that is subjected to harmonic force has the following form:  
\[m\ddot{x}+kx=F_{0}\sin\omega t \tag{1}\]  
where \(m\) is the equivalent mass of the system, \(k\) is the stiffness of the restoring element (spring), \(F_{0}\) is the amplitude of the harmonic force, \(\omega\) is the frequency of excitation, \(x\) is the position of the inertial element, and \(\ddot{x}\) is the second derivative of the position of the inertial element in terms of the time, in other words, it is the acceleration of the inertial element.  
The solution of the equation of motion consists of two parts: a homogeneous part and a complementary part. It can be expressed as  
\[x(t)=A\sin\omega_{n}t+B\cos\omega_{n}t+X\sin\omega t \tag{2}\]  
where \(\omega_{n}\) is the natural frequency of the system, which depends on the specifications of the system itself, regardless of the type of the force, \(\omega_{n}=\sqrt{\frac{k}{m}}\); \(X\) is the amplitude of the steady-state solution, which depends on the amplitude of the force, the natural frequency, and the excitation frequency; \(A\) and \(B\) are the amplitudes of the transient solution, which can be calculated with the aid of initial conditions.  
The excitation may happen at the inertial element itself, which can be expressed by the equation of motion; the excitation may also be due to a force exerting on the base of the system.
# A Python-Based Indoor Channel Model with Multi-Wavelength Propagation for Color Shift Keying
## 1 Introduction
channel models have been identified: (i) the models do not consider the spectral response of emitters, detectors, and walls when computing the received power; (ii) the models do not compute the photometric and colorimetric characteristics of the illumination; and (iii) there are no open-source computational tools to simulate these kinds of indoor WDM-VLC systems. To address these limitations, we implement a simple indoor channel model considering multi-wavelength propagation. This model was designed as a Python package and is available at PyPI. Using the recursive algorithm presented in [17], the steady-state gain for each color channel and the normalized interference gain matrix were computed for a rectangular empty room. The same model was used to compute the illuminance, correlated color temperature, and color rendering index at the receiver position. The model includes a module for estimating the symbol error rate based on the interference gain matrix and the dark current of the photodetector. The following section defines the model of the transmitter, receiver, and light propagation, while Section 4 describes the implementation of the model in Python, Section 5 presents the simulation results and model validation, and Section 6 presents a discussion of the results.
# CycleDiffusion: Voice Conversion Using Cycle-Consistent Diffusion Models
## 4 Experiments  
To evaluate the effectiveness of the proposed method, we used a subset of the VCTK dataset [30], which is commonly employed in voice conversion research. This subset comprised two female speakers (P236 and P239) and two male speakers (P259 and P263), who served as both source and target speakers. For simplicity, we will refer to these speakers as F1, F2, M1, and M2, respectively, in the subsequent discussion. Each speaker contributed 461 training utterances and 10 test utterances. With 12 conversion directions and 9 target speaker embedding vectors utilized for each speaker, the overall total of testing samples amounted to 1080 utterances.  
We utilized 80-dimensional mel-spectrograms as the speech feature vectors, which were extracted from the downsampled speech waveforms at 22.05 kHz. These spectrograms were computed every 256 samples using a Hanning window of 1024 samples in length. For the mel-spectrogram inversion to waveforms, we employed a pre-trained universal HiFi-GAN vocoder [31].  
An adam optimizer with a learning rate of 0.00003 was used for the stochastic gradient descent training in lines 6, 12, and 15 of Algorithm 1. The total number of training epochs was set to 270 after monitoring the reduction of loss values. Following [25], a noise schedule of \(\beta_{t}=\beta_{0}+(\beta_{T}-\beta_{0})t\) with \(\beta_{0}=0.05\) and \(\beta_{T}=20.0\) was employed.  
In a preliminary experiment, we compared two cycle consistency training schemes: the first scheme updated all parameters in the dotted and dashed lines in Figure 1, and the second scheme updated the parameters in the dashed lines only. We found that both schemes yielded almost similar performance, but the second method was faster. Therefore, during the training of parameters via gradient descent of the cycle consistency loss, the parameters associated with the dotted lines are frozen, and those involved in the dashed lines are adjusted for faster training. To determine when the conversion path training should become effective in Algorithm 1, we conducted multiple experiments with varying starting points. It was generally observed that initiating the conversion path training yields better results when the reconstruction path training has converged to a certain extent. Therefore, \(\lambda_{i}\) was set 0 for \(i\in[1,50]\) and 1 for \(i\in[51,270]\).  
DiffVC [23], recognized as the leading VC method using DMs, served as a baseline for comparison. To ensure fair comparisons, the same pretrained encoder was used for both DiffVC and CycleDiffusion.
# Machine Learning-Based Garbage Detection and 3D Spatial Localization for Intelligent Robotic Grasp
## 4 Experiments and Results
### Model Training and Testing Results
Yolactedge achieves a near 2\(\times\) speedup by converting the modules in Yolact to TensorRT, as shown in Table 2, with the frame rate increasing from 35.76 to 110.16 for Res101 However, there is a slight 2% decrease in both localization precision and mask segmentation precision. As shown in Figure 1a, Yolactedge-Res50/Res101 can reach 100 FPS with 30% _mPA_ accuracy, which is suitable for high-speed and high-accuracy detection tasks. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Backbone**} & \multirow{2}{*}{**Weights** **(MB)**} & \multirow{2}{*}{**FPS**} & \multicolumn{2}{c}{**Bbox**} & \multicolumn{2}{c}{**Mask**} \\ \cline{5-8}  & & & _mAP_ & _mIoU_ & _mPA_ & _PA_ \\ \hline Yolact & Res50 & 123.5 & 50.23 & 13.29 & 25.03 & 37.40 & 95.77 \\ Yolact & Res101 & 199.8 & 35.76 & 15.16 & 22.94 & 34.13 & 96.17 \\ Yolact & Mobilenetv2 & 34.9 & 55.72 & 11.38 & 18.79 & 28.93 & 95.05 \\ Yolactedge & Res50 & 123.5 & 110.16 & 13.40 & 22.00 & 31.72 & 95.31 \\ Yolactedge & Res101 & 199.8 & 106.84 & 14.26 & 20.43 & 29.06 & 95.80 \\ Yolactedge & Mobilenetv2 & 34.9 & 148.42 & 13.10 & 14.94 & 23.19 & 94.39 \\ Mask & VGG16 & 256.4 & 8.93 & 16.43 & 10.55 & 15.80 & 94.88 \\ R-CNN & VGG16 & 353.5 & 5.62 & \(-\) & 4.61 & 5.67 & 94.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison each model’s performance in TACO-28. The _PA_ values in Table 2 are above 94% because the model correctly classifies a large proportion of the background pixels. Figure 4: Comparison mask segmentation results for each model. All of these models can detect garbage instances from images, but there are cases of misclassification and inaccurate mask segmentation. Firstly, lightweight Mobilenetv2 has the lowest mask segmentation accuracy. When comparing the prediction results of Yolact and Yolactedge, the _mIoU_ and _mPA_ of the predicted masks decrease by about 2% and 5%, respectively, after converting the model component to TensorRT, although the frame rate increases.
## 3 Modeling the Relationship between Field AGB and Satellite Data
### Machine Learning Methods
#### 3.2.2 Support Vector Machines for Regression  
SVR is a regression version of support vector machines that project the training dataset from a lower dimensional space into a higher dimensional feature space using kernel functions to separate groups of input data in a linearized manner, based on the Vapnik-Chervonenkis (VC) dimension theory and structural risk minimization [81; 82; 83]. An SVR function for AGB estimation is defined as [84]:  
\[AGB=\sum\limits_{k=1}^{P}{(\alpha_{k}-\alpha_{k}^{*})\cdot K(x_{k},x_{j})+b} \tag{4}\]  
where \(x\) is a vector of the input predictors; \(K(x_{k},x_{j})\) is a kernel function; \(b\) is a constant threshold; and \(\alpha_{k}\) and \(\alpha_{k}^{*}\) are the weights (Lagrange multipliers), with the constraints given in Equation (5):  
\[\left\{\begin{array}{c}\sum\limits_{k=1}^{P}{(\alpha_{k}+\alpha_{k}^{*})}=0 \\ 0\leq\alpha_{k},\alpha_{k}^{*}\leq C\end{array}\right. \tag{5}\]  
where \(C\) is the regularization parameter for balancing between the training error and model complexity. The sequential minimal optimization (SMO) algorithm was used to solve the quadratic programming optimization problem step-by-step and to update Equation (4) to reflect the new values until the Lagrange multipliers converged [66; 85].  
Among the various kernel functions, the radial basis function (RBF) shows a superior performance and robust results [86; 87], and was used in this study [88]:  
\[K(x_{k},x_{j})=\exp\ \left[\frac{{(x_{k}-x_{j})}^{2}}{\sigma^{2}}\right] \tag{6}\]  
where \(\sigma\) is a scale parameter chosen based on the training data, and a unit vector could be concatenated with kernels as the intercept. In a word, the training of the SVR model required finding the best values for the two meta-parameters: the regularization parameter (\(C\)) and the kernel width (\(\sigma\)).  
Figure 3: Schematic representation of an example multi-layer perception neural network (MLPNN) model structure to predict forest above-ground biomass. Shown are the inputs with their neurons \(o_{i}\), and interlinked connections from each input to all hidden layer neurons \(o_{j}\), along with the selected weightings. The weighted outputs were then merged and fed into the output neuron \(o_{k}\) to form the output values.
# The Effect of Ageing on Chemical and Mechanical Properties of Asphalt Mortar
## 3 Results and Discussion
### Dynamic Shear Rheometer
#### 3.2.2 Uniaxial Tension Tests  
Uniaxial tension tests were performed using DSR. To obtain the effect of ageing on the tensile behavior of asphalt mortar, at least three samples were tested for each ageing condition. Tensile strength and failure strain are influenced by varying the temperature and strain rate due to the time-temperature dependency of bituminous materials. The tensile strength is higher for lower temperatures and higher strain rates. Therefore, uniaxial tensile tests in this study were performed at 0 \({}^{\circ}\)C with 0.2% (0.024 mm/s) tensile rate to make sure all tests can be conducted in a suitable testing range of the device. The normal force in relation to the displacement of the machine for mortar_AM at different ageing times was recorded and is plotted in Figure 10.  
Figure 8: Master curve of mortar_AM and mortar_MA at different ageing times.  
Figure 9: Complex shear modulus and phase angle at 20 \({}^{\circ}\)C, 10 Hz of bitumen, mortar_AM, and mortar_MA.: (**a**) complex shear modulus; (**b**) phase angle.  
Figure 10 shows that higher peak normal forces and smaller displacement at failure were obtained for longer ageing times. In other words, aged samples tended to become more brittle. Meanwhile, the steeper initial slopes of the curves can be seen at the longer ageing times, which mean higher moduli for the aged sample. Tensile strength is the maximum normal force divided by the crossing area of the cylinder sample. It denotes the maximum stress that a material can withstand while being pulled before breaking. Figure 11 shows the tensile strength of asphalt mortar at different ageing times. It can be observed that the strength of mortar increased with ageing time. Another observation was that under the same ageing conditions, the strength of mortar_AM was higher than that of mortar_MA.
## Materials and Methods
### Polymerase Chain Reaction and Library Preparation for Next-Generation Sequencing  
Bacterial 16S rRNA gene and fungal ITS region were amplified using primer pair sequences for the V3 and V4 region [25] and ITS region [26]. We used 16S Amplicon PCR Forward Primer (5'-TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG CCT ACG GGN GGC WGC AG-3'), 16S Amplicon PCR Reverse Primer (5'-GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA GGA CTA CHV GGG TAT CTA ATC C-3'), ITS1 (5'-TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG CTT GAT TTA GAG GAA GTA ATA-3') and ITS4 (5'-GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA GTC CTC CGC TTT ATG ATA TGC-3'). All primers contained Illumina adapter regions. PCR mixture contained HotStartTaq(r) Plus Master Mix (Qiagen, Hilden, Germany) (10 \(\mu\)l), template DNA (20 ng), and amplicon primers (2 \(\mu\)L, 1 \(\mu\)M) in the total reaction volume of 20 \(\mu\)l. Reaction conditions consisted of an initial 95 \({}^{\circ}\)C, 3 min; 35 cycles of 95 \({}^{\circ}\)C for 60 s, 60 \({}^{\circ}\)C, 45 s, and 72 \({}^{\circ}\)C, 60 s, and final extension of 72 \({}^{\circ}\)C, 7 min. After Amplicon PCR, samples were purified with DNA Clean & Concentrator(tm-25 (Zymo Research, Irvine, CA, USA).  
We used the 16S Metagenomic Sequencing Library Preparation protocol (Illumina Inc., San Diego, CA, USA) to create libraries. Index PCR was performed following the manufacturer's instructions using 2x KAPA HiFi HotStart ReadyMix (25 \(\mu\)L, Kapa Biosystems, Wilmington, MA, USA), Nextera XT Index Primers (5 \(\mu\)L per sample, Illumina Inc., San Diego, CA, USA), PCR grade water, and 5 \(\mu\)L of template DNA/cDNA solution in the total reaction volume of 50 \(\mu\)L. PCR Clean-Up 2 was performed as described in the protocol, followed by library validation using Qubit dsDNA high sensitivity assay (LifeTechnologies, Eugene, OR, USA) and Agilent(r) HS DNA Chip (Agilent(r) Technologies 2100 Bioanalyser, Santa Clara, CA, USA). Finally, DNA libraries were normalized to 4 nM concentration, denatured and processed to the sequencing run on MiSeq instrument (Illumina Inc., San Diego, CA, USA) using a MiSeq Reagent kit v3 (Illumina Inc., San Diego, CA, USA) with the paired-end setting of 2 \(\times\) 300 bp reads.
# Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series
## 6 Conclusions  
Our study presents a novel rice mapping product for the years 2002, 2005, 2010 and 2014 at higher resolution than currently available maps for continental China. In our approach, we trained a one-class support vector machine with training data from the year 2014 and employed it to map rice paddies based on MODIS time series data. We extracted time series features that are robust in regards to data gaps induced by frequent cloud cover, as well as possible temporal overlaps in our synthetic eight-day time series. Our classification has an overall accuracy of 0.90 and a kappa coefficient of 0.77, compared to the 2014 reference dataset. Our results correlate highly with sown area data from the agricultural census at the province level, with R\({}^{2}\) ranging from 0.90-0.93. Our approach should be transferable to other sensors that deliver dense time series with medium to low spatial resolution over paddy rice areas, such as MERIS or the recently-launched Sentinel-3.  
With our study, we aim to fill a gap in the currently available rice mapping products for China that are either spatially or temporally limited or at a coarser resolution. The delivery of such products in a timely and accurate manner is important for decision makers in the context of food security and international rice trade. Knowledge about the location and extent of paddy rice areas is also valuable for the Earth systems and hydrological modelling scientific community, as rice is a large consumer of water and a producer of green-house-relevant methane emissions. In a time of ever-increasing amounts of openly available high resolution satellite data from missions, like Copernicus Sentinel-1, Sentinel-2 and Landsat-8, medium resolution rice maps are useful for the remote sensing community to reduce the amount of data and processing time necessary for creating high resolution rice maps.  
This research has been partially funded by the German Ministry of Education and Science (BMBF) in the context of the DELIGHT project, as well as the National Natural Science Foundation of China with Grant No. 41430861.  
[PERSON] and [PERSON] designed the research concept. [PERSON] processed and analyzed the data. [PERSON] shared the AMS reference data and helped with the accuracy assessment. All authors jointly discussed the results. [PERSON] authored the first version of the manuscript, and [PERSON] and [PERSON] commented and worked on the manuscript.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AMS & Agricultural Meteorological Station \\ EVI & Enhanced Vegetation Index \\ LSWI & Land Surface Water Index \\ MIR & Mid-Infrared \\ MODIS & Moderate Resolution Imaging Spectroradiometer \\ NDVI & Normalized Difference Vegetation Index \\ NIR & Near-Infrared \\ OCSVM & One-Class Support Vector Machine \\ RBF & Radial Basis Function \\ SPOT-VGT & Satellite Pour l'Observation de la Terre-Vegetation \\ SVM & Support Vector Machine \\ TS & Time Series \\ \end{tabular}