esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Modeling and Output Characteristics Analysis of the Hub-Drive Reduction System
## 5 Full-System Load Test and Validation of the Hub Drive System
### Installation Method and Sensor Arrangement for the Full-System Load Test  
In mining dump trucks, the hub frame is attached to the vehicle's chassis. Similarly, during the bench test, one side of the high-torque hub drive system was rigidly mounted to the test bench, while the other side was connected to a driving source. The driving source transferred power to the hub drive system through a transmission shaft coupled to the system's drive shaft, replacing the motor used in real applications. Torque sensors were installed at the driving source to monitor torque variations during the experiment and control the output speed as input boundary conditions. Figure 5 shows the installation schematic for one side of the hub drive system.  
The above describes the installation method for a single-side hub drive system. In this experiment, an identical hub drive system was fixed to the other side of the single-side hub drive system and installed on the test bench in the same manner, with reverse driving applied to serve as the system's load. Figure 6 provides the installation schematic for the load testing system, with the left side representing the load system and the right side representing the test hub drive system.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frequency Type** & **Frequency (Hz)** \\ \hline Primary Sun Gear Rotational Frequency & 16.67 \\ Secondary Sun Gear Rotational Frequency & 2.15 \\ Primary Planet Carrier Rotational Frequency & 2.15 \\ Secondary Planet Carrier Rotational Frequency & 0.38 \\ Primary Meshing Frequency & 275.75 \\ Secondary Meshing Frequency & 30.13 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Characteristic frequencies of the two-stage planetary gear system.  
Figure 5: Installation schematic of a single-side hub drive system.  
The experiment used model 8341 unidirectional accelerometers for vibration measurement, with data acquisition performed using a B&K system for real-time data recording. The vibration sensors were mounted at the same locations on both hub drive systems to monitor radial vibration acceleration at the ring gears in contact with the primary and secondary planetary gears. This was essential for observing the system's vibration behavior during operation. Figure 7 illustrates the sensor arrangement. Additionally, torque sensors (model JS338 PCB, Depew, NY, USA) were installed at the driving sources of both systems to measure input and load torques. Data from these sensors were recorded using a Yokogawa data acquisition system at five-second intervals to monitor torque fluctuations during the test.
# Demonstrating the Potential of a Low-Cost Soil Moisture Sensor Network
## 3 Results and Discussion
### Sources of Errors  
The accuracy and precision of the sensor data is dependent on sensor performance and sensor installation. Custom-designed sensor deployment tools are usually provided by high-end sensor manufacturers to reduce user errors. For low-cost sensors however, such tools are not provided. Furthermore, in most cases, no sensor deployment recommendations or guidelines are given. In this context, a series of installation configurations were investigated to determine sensor response variability and to provide sensor installation guidelines. Measurement errors were observed particularly when the times of the probe are partially exposed to air or air gaps are present. This can happen when the probe is not fully pushed into the soil, and given that the dielectric permittivity is a function of the volume influence, lower readings are observed (Figure 5c). Another example which is more common and requires considerable attention is the insertion of an air gap between the probe and the soil through probe disturbance (Figure 5b). This can happen immediately or during probe installation and can be caused by accidentally moving the probe from its original position or after covering of the probe with soil. Tines' deflection, caused by very dry compacted soil and tines angular off-set has also been observed. Figure 6d shows an example of data associated with this error in ethanol. Upon installation, it is not possible to know if the tines are parallel or angled, which reduces the volume of influence of the probe and causes an increase in \(\varepsilon\). A good approach to minimise this is to check and align probe tines to be parallel and/or to use a tool for piloting the holes.  
Figure 4: Comparison of Sense Cap sensors with the TDR instrument; relationship between the actual \(\theta_{\text{m}}\) and the \(\varepsilon_{\text{a}}\) measured with the Sense Cap and TDR sensors (see legend) in garden soil substrate (**a**) and potting soil substrate (**b**); error bars on the \(\varepsilon_{\text{TDR}}\) represent the standard deviation of at least 45 readings (5 readings/depth, 9 depths along the probe), \(\varepsilon_{\text{SenseCap}}\) is the average of 5 readings/unit (**c**) experimental set-up showing the soil sample holder and sensor placement in the same sample— example provided for garden soil substrate; (**d**) linear relationship between standardized \(\varepsilon_{\text{SenseCap}}\) and \(\varepsilon_{\text{TDR}}\) (see figure insets).
## 2 Materials and Methods
### Step 4: 10-Fold Cross-Validation  
We used 10-fold cross-validation to prevent bias from the test-set split of the entire dataset, which helps segment the entire dataset into 10 equal size subgroups. Each time, a subgroup was selected as the holdout set, and the rest of the 9 subgroups were used for training. At the end of 10 rounds of training, the average performance of the 10 models was obtained.  
For the machine-learning algorithm, we benchmarked random forest with MLP, C4.5, AdaBoost, and bagging:  
1. MLP is a classifier that uses backpropagation to learn a multi-layer perceptron to classify instances.  
Figure 2: Sample distribution of each characteristic specific to SPSC (red: SPSC; blue: non-SPSC).  
C4.5 develops a decision tree by splitting the value of the features at each node, including categorical and numerical features. The information gain is calculated, and the feature with the highest gain is used as the splitting rule.
3. AdaBoost with C4.5, which is part of the group of ensemble methods, i.e., boosting, adds newly trained models in a series, where subsequent models focus on fixing the prediction errors made by previous models. In this study, we selected C4.5 as a base classifier.
4. Bagging (bootstrap aggregation) with C4.5 is an ensemble skilled method that uses the bootstrap sampling technique to form different sample sets based on replacements. We used C4.5 as a base classifier to derive the forest.  
The random forest classification model helps develop parallel decision trees that decide on a category based on votes for a given instance and provide the final decision as a prediction. Cost-sensitive learning is important in the case of risk management, in which we pursue better flexibility of trade-offs among metrics. For example, we may focus more on the recall rate of the SPSC category by loosening the performance of the precision rate. In this section, we assign false negative (FN) cost errors to the SPSC categories 1, 3, and 5; however, we maintain the false positive (FP) cost error at 1.  
In our study, we set the SPSC category as positive and then evaluated several metrics. True positive (TP) is the number of instances of positive categories predicted as positive. True negative (TN) is the number of instances of the negative class predicted as negative. False positive (FP) is the number of instances of negative classes predicted as positive. False negative (FN) is the number of instances of positive classes predicted as negative. Accuracy: (TP + TN)/(TP + TN + FP + FN). Precision: TP/(TP + FP). Recall: TP/(TP + FN). F1-score: 2 \(\times\) (Recall \(\times\) Precision)/(Recall + Precision). FP rate: FP/(FP + TN). TP rate: TP/(TP + FN). The receiver operating characteristic (ROC) curve uses the TP rate as the y-axis and the FP rate as the x-axis and plots points with corresponding thresholds.
## 2 Safety and Security
However, a common attack against IIoT devices is the so called denial-of-service (DoS) attack, which--by applying huge work load--prevents the device from providing services, and leads to it becoming temporarily unavailable. A common variation of this attack is the distributed DoS (DDoS) originating from a number of sources. Using a couple of network and log features, ref. [59] proposes a hybrid deep learning framework (deep belief networks, autoencoders, etc.) to classify the type of attack reaching the device, e.g., DoS attacks, among others. Using the game-theory approach, a reinforcement learning technique was proposed in [60] to identify DDoS attacks. Bayesian networks can also be successfully used to predict traffic delays and DDoS attacks; for example, in [61] the solution was inspired by the portfolio theory used in economics. Industrial IoT systems have a special security issue, called _offload security_. Using machine learning algorithms in IIoT systems sometimes requires different calculations to be offloaded to edge devices, called edge or fog computing. This offloading gives rise to new kinds of security problems, since offloading tasks to the cloud or to the edge is vulnerable to security issues due to malicious devices. A typical solution uses blockchains and the reinforcement learning method to avoid the security problems of computation offloading, while implementing a double-dueling Q-network [62]. Other solutions typically use reinforcement learning methods, such as the solution in [63; 64].
## 1 Introduction
Mixture of Gaussian (MOG) can be seen as a combination of k single Gaussian models; MOG is widely utilized to mimic non-Gaussian density because it enhances the real-time state estimation performance while maintaining the estimation performance [21]. [PERSON] and [PERSON] [22] extended MOG and PF to dynamic space models with non-Gaussian noise; [PERSON] et al. [23] used MOG in multi-path-assisted positioning to improve the positioning accuracy. A recursive estimator for non-Gaussian problems was derived in [24]; any probability density can be approximated by a mixture model of finite Gaussian functions [25]. MOGKF outperforms PF and IMM-KF in non-Gaussian issues, as [PERSON] showed in [26]. In this paper, we address the challenging problem of ROV state estimation in an underwater environment that is nonlinear and non-Gaussian. Due to the complexity of the underwater environment, the process noise of ROV sensors is non-Gaussian, which poses a significant challenge to state estimation. To overcome this issue, we propose a state estimation algorithm that operates effectively in a non-Gaussian noise environment. Specifically, we apply the CKF to the PF framework to compute the mean and variance of the importance density function, generate a new posterior probability density distribution, and regenerate new particles. The problem of particle degradation is alleviated by using MOG instead of a single Gaussian noise distribution in the EM algorithm, which also reduces the amount of calculation required during resampling. Simulation trials are used to confirm the algorithm's efficacy and noise resistance. The rest of this paper is as follows. Section 2 analyzes the ROV motion state and establishes a four-degree-of-freedom dynamic model. Section 3 selects state quantities and observations and reviews the CKF algorithm. Section 4 introduces the proposed EM-MOGCPF algorithm. Section 5 presents the simulation results. Then, Section 6 summarizes the main work of this paper.
# Absolute Capacitance Measurement by Direct Digital Fitting of Proportional Coefficient
## 4 Analysis of Measurement Results
### Stability Testing
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Nominal Value of** & **Commercial** & \multirow{2}{*}{**Measured Average**} & \multirow{2}{*}{**Standard Deviation**} & **Relative Standard** \\
**Capacitance/pF** & **Instrument** & & & **Deviation of** \\
**Measurements/pF** & & & & **Capacitance** \\ \hline
60 & 58.2682 & 58.6755 & 0.522 & 0.89 \\
600 & 604.660 & 603.995 & 1.47 & 0.24 \\
800 & 819.997 & 825.072 & 1.57 & 0.19 \\
1800 & 1873.41 & 1871.08 & 2  
in terms of measurement bias. Additionally, in terms of measurement range, the proposed method's measurable capacitance range of 60 pF to 60 nF significantly outperforms other methods. This makes the measurement instrument particularly suitable for a wide range of capacitance measurement applications.
# Characterization of Chromatic Dispersion and
## 2 Theoretical Background  
The refractive index of the material of the fiber core varies with the light wavelength \(\lambda\). This variation can be approximated by the following power series, which is based on the Cauchy equation [19]:  
\[n(\lambda)=A+B\lambda^{-2}+C\lambda^{-4}+\ldots+M\lambda^{2}+N\lambda^{4}+\ldots, \tag{1}\]  
the constants \(A\), \(B\), \(C\), \(M\), \(N\),... must be determined for the interval of wavelengths considered. Often, the first two terms are sufficient to represent the refractive index properly, which simplifies the expression to [19]:  
\[n(\lambda)=A+B\lambda^{-2}, \tag{2}\]The group-time delay undergone by a light pulse as it propagates through a POF of length \(L\) can be derived from Equation (2), obtaining the following expression for a pulse whose spectrum is centered at a certain wavelength \(\lambda\):  
\[t_{g}(\lambda)=\frac{L}{c}\bigg{[}n(\lambda)-\lambda\frac{dn(\lambda)}{d\lambda }\bigg{]}=\frac{L}{c}(A+3B\lambda^{-2}), \tag{3}\]  
where \(c\) is the speed of light.  
Therefore, the corresponding material-dispersion coefficient is given by [20]:  
\[D(\lambda)\approx\frac{1}{L}\frac{dt_{g}}{d\lambda}=-\frac{\lambda}{c}\left( \frac{d^{2}n(\lambda)}{d\lambda^{2}}\right)=\frac{-6B\lambda^{-3}}{c}, \tag{4}\]  
In this expression, the symbol \(\approx\) means that the equation is only valid when the interval of wavelengths considered (\(\Delta\lambda\)) is small enough as to be able to use \(\Delta t=\big{(}dt_{g}/d\lambda\big{)}\Delta\lambda\).
## 5 Conclusions  
CMAC was specially developed for smallsat applications to provide accurate and precise SR retrieval and rapid calibration to enable its use. CMAC is empirically derived, and though it is a significant simplification compared to state-of-the-art RadTran applications, Sen2 Cor and LaSRC, it provides accuracy and stability for fully automated SR retrieval over ranges of atmospheric effects from clear to extreme, doing so in near real time. CMAC can be applied to calibrate and perform SR retrieval with commensurate or better accuracy for smallsats, as demonstrated in this paper and paper 1. With continued research and development, CMAC may address the goal of accurate AC of smallsat data for all environments and all times of the day, limited only by cloud cover.
## 2 Materials and Methods
### Methods
#### 2.3.4 Selection of Optimal Terrain Feature Parameters  
Six terrain characterization parameters (slope; slope direction; relief degree of land surface (RDLS); surface roughness; elevation coefficient of variation (ECV); and depth of surface cut) were selected in this study. In order to maximize the expression of terrain feature information, terrain feature parameters with low correlation coefficients were used. Therefore, the correlation analysis of the six selected terrain feature parameters was performed to ultimately select the best combination to reflect the geomorphic features.  
In order to eliminate the influence of the magnitude, the topographic feature parameters were first normalized via the polar deviation method (Equation (5)) so that their values were all in the range of 0 to 255.  
\[x_{ij}^{\prime}=\frac{x_{i,j}-x_{min}}{x_{max}-x_{min}}\times 255 \tag{5}\]  
Figure 4: Variation curve of \(S-S_{i}\).  
where \(x^{\prime}_{ij}\) is the normalized value, a constant; \(x_{i,j}\) is the calculated value of the topographic indicator; \(x_{max}\) is the maximum value; \(x_{min}\) is the minimum value.  
After the correlation analysis (Table 2), the correlation coefficients of the relief degree of the land surface, the elevation coefficient of variation and the depth of surface cut were all above 0.937, which were highly correlated. Most studies have selected the degree of relief degree of the land surface as the primary terrain characterization parameter for describing the overall morphology of the terrain. Therefore, the relief degree of the land surface was retained, and the elevation coefficient of variation and depth of surface cut were excluded. In this paper, we finally choose four topographic parameters: slope, slope direction, slope direction, relief degree of land surface, and surface roughness.
# A Lightweight and Efficient Detection Transformer for Highway Abandoned Objects
## 2 Methodology
### Proposed Methodology for the Detection of Abandoned Objects on Highways
#### 2.2.2 Principle of the Proposed TFusion Module  
While large-size feature maps are crucial for object detection tasks, the main approach of the RT-DETR encoder for fusion is upsampling of smaller feature maps and combining them with the features from the previous layer. In this process, the rich detail information contained in larger feature maps is ignored, consequently restricting the ability to detect abandoned objects accurately. To tackle this problem, we designed the TFusion module, which effectively handles features of different sizes (large, medium, and small) and greatly enhances the richness of detail by merging these features [23].  
Figure 5 shows the structure of the TFusion module. Initially, the spatial size and number of channels of different feature maps are adjusted to align with the dimensions of the intermediate-size feature maps. Large-size feature maps are processed through a convolution module and downsampled using a combination of max and average pooling. This step helps reduce spatial dimensions and enhances translational invariance. We employed nearest neighbor interpolation to perform upsampling of small-size feature maps. This method effectively preserves local features and prevents the loss of details in small objects by utilizing adjacent pixel values. Lastly, the following equation illustrates the concatenation of feature maps of large, medium, and small sizes along the channel dimension to facilitate subsequent fusion:  
\[F_{TF}=Fusion(Concat(F_{1},F_{m},F_{s})) \tag{1}\]  
where \(F_{1}\), \(F_{m}\), and \(F_{s}\) denote feature maps of large, medium, and small sizes, respectively. \(Fusion\) denotes the original Fusion module. The output \(F_{TF}\) is obtained by inputting the concatenated result of \(F_{1}\), \(F_{m}\), and \(F_{s}\) into the subsequent module. The resolution of \(F_{TF}\) is equivalent to that of \(F_{m}\).
# Changes in Meteorological Elements and Its Impacts on Yunnan Plateau Lakes
## 3 Results  
### Annual Analysis  
Table 2 lists the statistical results of climate change for the Yunnan Plateau lakes from 1980 to 2019, based on the ITA method. Figure 4 shows the change trend of the annual average value of each of the assessed climate factors. The results indicate an upward trend in the air temperature for the plateau lakes, whereas the trends of wind speed and relative humidity are downward. With the exception of Lake Lugu, long-wave radiation and rainfall are also characterized by downward trends. In the case of short-wave radiation, a downward trend was detected for those lakes located in central Yunnan, whereas an upward trend was observed for the lakes in northwest Yunnan, and snowfall showed a mainly downward trend.  
Figure 4 shows that all air temperature-related data points are located above the 1:1 line, and all lakes show a significant upward trend in air temperature from 1980 to 2019, among which, the rising trend of air temperature for the lakes in northwest Yunnan is higher than that of the central Yunnan lakes, with Lake Lugu in the northwest being characterized by the largest upward trend. The short-wave radiation for lakes in northwest Yunnan showed a similar upward trend, whereas in the case of the lakes in central Yunnan, the trend was downward. As in the case of air temperature, Lake Lugu in northwest Yunnan was characterized by the largest upward trend. With the exception of Lake Lugu, which showed an upward trend, all other lakes were characterized by a downward trend for long-wave radiation, although the descending trend for lakes in central Yunnan was found to be more pronounced than that of lakes in northwest Yunnan. Contrastingly, we were unable to detect any clear trend with respect to snowfall. All lakes were, however, characterized by a downward trend for wind speed, although again this trend was more pronounced for the lakes in central Yunnan than for those in the northwestern region. Both relative humidity and rainfall also showed downward trends, with the latter being characterized by a more pronounced trend in the central than in the northwestern Yunnan lakes.  
Figure 3: The technical roadmap of this study.  
Figure 4: Innovation trend analysis of annual mean value of climatic factors in Yunnan Plateau lakes.
# On the Capacity of Optical Backbone Networks
## 1 Introduction
This behavior implies that the fiber channel capacity does not grow indefinitely; instead, it is limited and reaches a maximum value as the transmitted signal power increases [4; 7; 8; 9]. The problem of estimating the optical channel capacity has been the focus of many studies. Some rely on accurate numerical simulations [9], while others offer detailed analytical models based on either the Gaussian noise (GN) model [10; 11; 12] or a regular perturbation model [13]. On the contrary, the topic of network capacity has received considerably less attention in the literature, and only recently have some papers been published in this area [14; 15; 16; 17; 18]. All these works involved computing the signal-to-noise ratio (SNR) for each optical channel routed in the network, with the capacity of each channel being determined by selecting the modulation format that best suited the SNR and evaluating the corresponding bit rate, with the exception of [17], which though also requiring SNR computations, evaluated the channel capacity using [PERSON]'s theory instead. In this paper, we revisit the concept of capacity in optical backbone networks and examine the fundamental principles underlying their operation. We present an alternative approach to evaluate the capacity of optical networks. Rather than using the metric SNR as employed by the other authors, we propose to utilize the metric optical reach, which measures the maximum distance at which an optical channel can propagate effectively, to simplify the computational process. This approach avoids the need to calculate SNRs for all optical channels, which can be cumbersome, especially for large optical networks. Furthermore, we use the network-wide average channel capacity [14] as an intermediate step to assess the network capacity, whereas refs. [15; 16; 17; 18] employ more complex procedures. The paper also relies on an integrated treatment in the sense that it considers various interrelated aspects such as channel capacity, link capacity, and network capacity. A key goal of this work is to understand the trends in capacity evaluation, keeping the analysis as simple as possible, while also investigating how close our results are to the experimental ones reported in the literature. The rest of the paper is organized as follows: Section 2 provides an overview of the basic principles underlying optical backbone networks and their major physical limitations. Section 3 reviews the concept of channel capacity and introduces the necessary background for determining the spectral efficiency, capacity, and optical reach of an optical channel. In addition, it presents numerical results related to these topics. Section 4 evaluates the link capacity and presents a suitable methodology for computing the network capacity based on the capacitated routing, which is applied afterwards in three reference networks. Finally, Section 5 summarizes and concludes the paper.