esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Evaluation of a Shaping and Pruning Machine for Dwarf and Densely Planted Jujube Trees
## 3 Results
### Results of the Quadratic Regression Orthogonal Test
#### 3.1.1 Analysis of Variance for Test Results  
Analysis of variance was conducted in Design-Expert V8.0.6.1 data analysis software [45; 46; 47]. After removing the non-significant items in the model, a final regression model was obtained, taking the missed shear rate \(P_{1}\) and stubble breakage rate \(P_{2}\) as response functions and each influencing factor as an independent variable. As shown in Equations (16) and (17):  
\[P_{1}=71.489-0.013X_{1}-0.365X_{2}+6.250X_{3}+2.667\times 10^{-5}X_{1}X_{2}+6.11 0\times 10^{-5}X_{2}^{2} \tag{16}\]  
\[P_{2}=58.277-9.720\times 10^{-3}X_{1}-0.323X_{2}+12.179X_{3}+2.667\times 10^{-5} X_{1}X_{2}-3.571\times 10^{-3}X_{1}X_{3}+5.260\times 10^{-4}X_{2}^{2} \tag{17}\]  
where, \(X_{1}\) is the rotation speed of the cutter, in r/min; \(X_{2}\) is the diameter of the cutter, in mm; \(X_{3}\) is the forward speed, in m/s; \(P_{1}\) is the missed pruning rate, in %; \(P_{2}\) is the stubble breakage rate, in %.  
Analysis of variance was conducted on the pruning test results for the shaping and pruning machine for jujube trees, as shown in Table 5. The result of \(P<0.0001\) could be obtained from the two regression equations of missed pruning rate \(P_{1}\) and breaking stubble rate \(P_{2}\), indicating that the two regression models were extremely significant. The \(R^{2}\) of the response index was 0.9803 and 0.9722 for missed pruning rate \(P_{1}\) and stubble breakage rate \(P_{2}\), respectively, indicating that the regression model could explain 98.03% and 97.22% of the experimental data variability, respectively. The results proved that the predicted value was highly correlated with the actual value. According to the results of the \(F\) test for missed pruning rate: \(F_{1\text{ regression}}=119.19>F_{0.1}(5,12)=2.39\), \(F_{1\text{ lack of fit}}=2.44<F_{0.1}(7,5)=3.37\), and \(F\) test of breaking stubble rate: \(F_{2\text{ regression}}=64.10>F_{0.1}(6,11)=2.39\), \(F_{2\text{ lack of fit}}=1.74<F_{0.1}(6,5)=3.40\). We concluded that the above two regression equations had a good fitting relationship with the actual situation and had practical significance. According to the regression coefficients of various factors in the models, it could be concluded that the order of significance for  
Figure 12: Field validation test.  
influencing the missed pruning rate \(P_{1}\) and stubble breakage rate \(P_{2}\) was \(X_{3}\), \(X_{1}\), \(X_{2}\) and \(X_{3}\), \(X_{1}\), \(X_{2}\), respectively.
# A Novel UWB Pulse Expander Using an Integrated Microstrip Splitter, Delay Lines, and a Combiner
## 2 Design and Modeling
### Initial Considerations
The time function of such a pulse \(s(t)\) can be described with Formula (1) [40], as follows:  
\[s(t)=A\cdot\exp\left(-\frac{1}{2}\cdot\frac{t^{2}}{t_{\rm P}^{2}}\right)\,, \tag{1}\]  
where \(A\) is the waveform amplitude, and \(t_{\rm P}\) is half of the pulse duration at level \(A/\sqrt{\rm e}\), s.  
If parameter \(t_{\rm P}\) is substituted with \(\frac{\tau_{1/p}}{2\sqrt{\ln p}}\) in Formula (1), we obtain Formula (2) for the unipolar Gaussian pulse, where the pulse duration is given at level \(A/p\):  
\[s_{A/p}(t)=A\cdot\exp\left(-4\cdot\ln p\cdot\frac{t^{2}}{\tau_{1/p}^{2}}\right)\,, \tag{2}\]  
where \(p\) is the coefficient, which describes the fraction of the waveform amplitude \(A\) at which the pulse duration is given; \(\tau_{1/p}\) is the pulse duration at level \(A/p\), s.  
The spectral density of the pulse can be described with Formula (3), as follows:  
\[S(\omega)=A\cdot\frac{\tau_{1/p}}{2\cdot\sqrt{\ln p}}\cdot\sqrt{\pi}\cdot\exp \left(-\frac{\tau_{1/p}^{2}\cdot\omega^{2}}{16\cdot\ln p}\right)\,. \tag{3}\]  
If the duration of the pulse is given at level \(A/2\), which can be denoted as \(\tau_{1/2}\), then from Formula (3), we can obtain the time function of the pulse, which can be described with Formula (4) as follows:  
\[s_{A/2}(t)=A\cdot\exp\left(-4\cdot\ln 2\cdot\frac{t^{2}}{\tau_{1/2}^{2}}\right)\,, \tag{4}\]  
The spectral density of the pulse is described with Formula (5), as follows:  
\[S(\omega)=A\cdot\frac{\tau_{1/2}}{2\cdot\sqrt{\ln 2}}\cdot\sqrt{\pi}\cdot\exp \left(-\frac{\tau_{1/2}^{2}\cdot\omega^{2}}{16\cdot\ln 2}\right)\,. \tag{5}\]  
For the graphical representation of the pulse spectral density in decibels, we can use Formula (6), as follows:  
\[\hat{S}(\omega)=20\lg\frac{S(\omega)}{S(0)}=20\lg\left[\exp\left(-\frac{\tau _{1/2}^{2}\cdot\omega^{2}}{16\cdot\ln 2}\right)\right]=-\frac{5\cdot\tau_{1/2}^{2} \cdot\lg\lg}{4\cdot\ln 2}\cdot\omega^{2}\,. \tag{6}\]  
If calculations are made for a time-shifted pulse, which is shifted to the right from \(t=0\) by the value \(t_{0}\), the pulse formulas \(t\) is replaced by \(t-t_{0}\) and spectral density is multiplied by \(\rm e^{-\rm\rm\cdot\omega\cdot\cdot\cdot_{0}}\). The energy of the Gaussian pulse, described with Formula (4), can be calculated by using Formula (7):  
\[E_{G}=A^{2}\cdot\frac{\tau_{1/2}\cdot\sqrt{\pi}}{2\sqrt{\ln 2}}\,. \tag{7}\]  
The device proposed in Figure 1 is designed for such a case when the input pulse at the pulse expander input is a unipolar Gaussian pulse with a duration of \(\tau_{1/2}=200\) ps (pulseduration at full width at half maximum of the amplitude--FWHM), and pulse waveform amplitude \(A=1\) V. Plots of the waveform of the pulse, which is used as the input pulse for the expander, as well as its spectral density, can be seen in Figure 1:0
### State of the Art in Proximal Sensing Sensors for SWC
#### 1.1.2 Interferometric Reflectometry Systems  
In general terms, Geographical Positioning System-Interferometric Reflectometry (GPS-IR) and Global Navigation Satellite System-Interferometric Reflectometry (GNSS-IR) use energy emitted elsewhere and capture the reflected signals from the ground, containing data about the surrounding environment [18]. The SWC changes the reflected signal from altered soil's permittivity and reflectivity [19]. This way, the information about the soil moisture can be indirectly measured by analyzing the reflected signal [18; 20].  
Generically, GPS signals are situated in the so-called microwave part of the Radio Frequency (RF) spectrum [11]. The particular frequency strongly influences the measurement depth of GPS-IR and GNSS-IR sensors; L-band microwaves reduce the penetration depth from 5 cm to a few millimeters (for high values of SWC) [14; 21; 22]. The radius of the measuring area varies with the antenna height, achieving around 300 m, with the antenna at 20 m height [21; 22].  
The benefits of GPS-IR and GNSS-IR approaches in agriculture are the ability to use a commercial receiver with a modified antenna, the measurements range, and the high correlation with soil moisture sensors, with values above 0.90 [23; 24]. Remote sensing GPR, such as GPS-IR and GNSS-IR, sensors have disadvantages such as the short depth measurements range, errors due to soil roughness, crop stubble, and vegetation biomass [9; 19; 25].
# Detecting IoT Anomalies Using Fuzzy Subspace Clustering Algorithms
###### Abstract  
There are many applications of anomaly detection in the Internet of Things domain. IoT technology consists of a large number of interconnecting digital devices not only generating huge data continuously but also making real-time computations. Since IoT devices are highly exposed due to the Internet, they frequently meet with the challenges of illegitimate access in the form of intrusions, anomalies, fraud, etc. Identifying these illegitimate accesses can be an exciting research problem. In numerous applications, either fuzzy clustering or rough set theory or both have been successfully employed. As the data generated in IoT domains are high-dimensional, the clustering methods used for lower-dimensional data cannot be efficiently applied. Also, very few methods were proposed for such applications until today with limited efficacies. So, there is a need to address the problem. In this article, mixed approaches consisting of nano topology and fuzzy clustering techniques have been proposed for anomaly detection in the IoT domain. The methods first use nano topology of rough set theory to generate CORE as a subspace and then employ a couple of well-known fuzzy clustering techniques on it for the detection of anomalies. As the anomalies are detected in the lower dimensional space, and fuzzy clustering algorithms are involved in the methods, the performances of the proposed approaches improve comparatively. The effectiveness of the methods is evaluated using time-complexity analysis and experimental studies with a synthetic dataset and a real-life dataset. Experimentally, it has been found that the proposed approaches outperform the traditional fuzzy clustering algorithms in terms of detection rates, accuracy rates, false alarm rates and computation times. Furthermore, nano topological and common Mahalanobis distance-based fuzzy \(c\)-means algorithm (NT-CM-FCM) is the best among all traditional or nano topology-based algorithms, as it has accuracy rates of 84.02% and 83.21%, detection rates of 80.54% and 75.37%, and false alarm rates of 7.89% and 9.09% with the KDDCup'99 dataset and Kitsune Network Attack Dataset, respectively.  
a 2024 14 1264 10.3390/app14031264
# Extending Appearance Based Gait Recognition with Depth Data
## 1 Introduction and Motivation
The height of a person is estimated from the silhouette using the additional depth information now available. The approaches were tested on populations with different sizes and height distributions derived from a TUM Gait from Audio, Image and Depth (GAID) [1] dataset, a large dataset containing both RGB and depth based silhouettes. The proposed methods were also applied to and compared with Backfilled GEI (BGEI) [2], another recent approach that harnesses depth information in gait recognition to construct a feature that is valid irrespective of the walking direction of the subject relative to the camera (front or side views). Since BGEI uses a depth stream as input, the extension of BGEI with one of the proposed methods in order to harness also the size of the silhouette is straightforward. The experimental results for the proposed approaches show that recognition rates are improved when compared to existing appearance based gait recognition methods which use only 2D data. Both approaches are suitable for real-time processing. This paper is structured as follows. In Section 2, the state-of-the-art research on appearance based gait recognition methods using depth video stream as input is listed and described briefly. Section 3 describes the key processing steps performed by gait recognition methods in general, and by the proposed methods in particular, starting from the depth and RGB input streams. In Section 4, the two proposed recognition methods that extend appearance based gait recognition with depth derived feature are proposed and described. In Section 5, the experimental setup is described with the presentation of the dataset used in the experiments, the selection of parameters for the methods, and the definition of the set of experiments. In the same section, the results are then presented, analyzed, and discussed. Finally, the concluding remarks are given in Section 6.
# Multicriteria Route Planning for In-Operation Mass Transit under Urban Data
## 3 Preliminary  
**Definition 1**.: _Grid. We divide the city into disjoint grids (0.1 km \(\times\) 0.1 km) [84] and store all features that are correlated with PF (e.g., population in this grid, whether existing routes appeared on this grid, etc.) into the corresponding grid._  
**Definition 2**.: _Grid-like graph. A grid-like graph is composed of disjoint grids, and based on the road network information from OpenStreetMap (OSM), it records the connectivity between each grid, similar to road networks in the real world. For each grid and its surrounding eight grids, if there are roads in the OSM that connect each other (the grid itself and adjacent grids), this information will be stored._  
**Definition 3**.: _Passenger Flow (PF). The passenger flow in the route here indicates the total number of passengers who travelled at any point along the route. That is, PF is counted once someone pays by smart card when either getting onto or getting off the mass-transit at a station of a route._  
**Definition 4**.: _Route-affecting region (RAR). The demand for mass transit depends on the urban functionalities and geographical environment of nearby areas instead of simply the origin and destination of the service [9; 85]. Consequently, the RAR considers features related to PF on the basis of the route. Explicitly, the algorithm draws a circle from each point of the targeted route so as to form the RAR, with the radius of each circle set to 400 m according the walking tolerance for pedestrians introduced in Design Manual for Urban Sidewalks [86]. Finally, the features can be harvested from the grids within RAR for PF inference. The PF inference module used with our proposed algorithm is applicable to feature extraction based on either Origin-Destination (OD) Matrix [87; 88] or RAR._
## 2 Numerical Model  
A horseshoe-shaped drill-and-blast tunnel designed and built in South Korea was employed in this study. The tunnel cross-section was 13 m wide and 8 m high, with a 0.3 m lining thickness. Figure 1 depicts the geometry and reinforcement in detail. The newly constructed tunnel runs parallel to the existing tunnel in a deep rock mass. The rock around the tunnel is mainly composed of granite, which is almost intact, with no visible joints or fractures along the tunnel alignment, which can only be excavated by blasting. The existing tunnel is 12.6 m away from the blasting excavation location.  
The explicit nonlinear program LS-DYNA, which has been utilized in several dynamic investigations of concrete components that have been subjected to blasting, was utilized to carry out the numerical simulation. A Lagrangian simulation algorithm was used in this work to simulate solids and beams. Within the scope of this part, the numerical model of the rock and reinforced concrete tunnel that was subjected to underground blast loading is shown in Figure 2. The dimensions of the three-dimensional model were set to \(55\text{ m }\times 30\text{ m }\times 1\text{ m }\)(width \(\times\) height \(\times\) depth). The concrete tunnel shown in Figure 3a and the rock mass were created using the hexahedron node solid element. The Hughes-Liu beam element (type 1) was utilized to represent the steel rebars depicted in Figure 3b.  
Figure 1: Cross-section and reinforcement of a horseshoe-shaped tunnel (unit mm): (**a**) Tunnel geometry and (**b**) reinforcement of tunnel.  
Figure 2: Numerical model.  
The Rayleigh damping formulation, defined in Equation (1), was used to model the damping of the rock media and structural components.  
\[[C]=\alpha[M]+\beta[K] \tag{1}\]  
where \([C]\) is the damping matrix, \([M]\) is the mass matrix, \([K]\) is the stiffness matrix, and \(\alpha\) and \(\beta\) are the mass and stiffness proportional damping constants, respectively, where \(\alpha=4\pi\zeta(f_{1}f_{2})/(f_{1}+f_{2})\), \(\beta=\xi/[\pi(f_{1}+f_{2})]\), and \(\xi\) is the target damping ratio. The damping ratio of the rock mass and structural members was set to 5%, as is most often used in numerical analyses ([PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2016) deemed that the predominant frequency (\(f_{p}\)) was not suitable for defining the impulse function since the blast load has a short rise time followed by a long decay curve. Instead, rise time was used to select \(f_{1}\). A new formulation was presented to select target frequencies, and \(f_{1}=1/(4t_{r})\) and \(f_{2}=10/(4t_{r})\) were selected. In this study, frequency range was used to consider the effect of damping. A frequency range of \(f_{1}=10\) and \(f_{2}=1000\) was used.
## 7 Conclusions  
In order to address the issue of the increased load volatility due to the large-scale integration of distributed renewable energy sources, this paper proposes a method for short-term load forecasting that utilizes GMM clustering analysis to classify daily load characteristics. The method also employs the MIC for feature reconstruction and trains a neural network using the reconstructed input features to improve the prediction accuracy. The proposed method can be summarized as follows:  
1. A feature selection and input feature reconstruction method based on MIC is proposed. For different types of days, the feature variables are rearranged according to sampling points, allowing the MIC to reflect the influence of different features at different times.
2. For regions with a large number of DPVS, solar irradiance is directly used as an input variable. Meteorological information represents the impact of PV generation, simplifying the model structure for load forecasting in such cases.
3. The analysis, selection, and reconstruction of feature influences are implemented at the sampling point granularity, replacing the original input features with an input matrix that reflects the degree of influence.  
The proposed short-term load forecasting method based on feature reconstruction can improve the accuracy of load forecasting in regions with DPVS. It can provide reliable references for local grid operators in formulating daily generation plans, thereby enhancing the economic and operational reliability of the power grid.  
However, it should be noted that this paper did not consider the impact of holidays on load classification. In reality, the variation in load types during holidays can increase prediction errors. Future work will involve enriching the load types and separately modeling holidays to improve prediction accuracy. Additionally, the influence of market factors was not considered in constructing the input features; so, future work will need to incorporate additional input features.  
Conceptualization, X.Z.; Methodology, X.Z. and C.W.; Software, X.Z., M.Y. and C.W.; Validation, X.Z.; Formal analysis, X.Z. and M.Y.; Investigation, X.Z and Y.Y.; Resources, X.Z. and C.W.; Data curation, X.Z and Y.Y.; Writing--original draft, X.Z.; Writing--review and editing, X.Z.; Visualization, X.Z., M.Y. and C.W.; Supervision, X.Z. and C.W.; Project administration, X.Z. and M.Y. All authors have read and agreed to the published version of the manuscript.  
This research is supported by a General Program from the National Natural Science Foundation of China (No. 52177095) (Setting: National Natural Science Foundation of China).  
Figure 10: Comparison of the predicted results for typical high-temperature-day load curves.
## 4 Evaluation and Discussion
#### Limitations of the Proposed Model  
In the context of indoor object identification, neutrosophic logic has several advantages. These include the following: the ability to handle data from multiple sources; the resolution of complex problems using only partial or imprecise information; and the production of trustworthy outcomes in numerous cases of multi-criteria decision-making. The neutrosophic controller is a revolutionary system that is more generalizable and tolerant of uncertainty than its fuzzy counterparts. In cases where there is a potential of indeterminacy and incompleteness in the acquired data, as in ours, a neutrosophic controller is employed as a means of dealing with the situation. Using neutrosophic logic has its benefits since it can tell the difference between a truth that holds true in only one or a few worlds and a truth that holds true in every world. To the same extent, neutrosophic logic delineates between relative and absolute falsehood.  
Although the suggested model makes use of neutrosophic logic, doing so presents a number of challenges that severely restrict its effectiveness. These restrictions are a result of how the neutrosophic inference engine was built. According to the proposed model, in neutrosophic sets, truth, falsehood, and indeterminacy are all independent; it is unknown how they influence one another in the decision-making process. Not only that, but the sets of inference rules and the membership functions were also developed by hand by a specialist in the field. It's preferable to produce them automatically based on data you already have. Finally, the variables used to characterize the distance to the obstacle have a significant impact on the performance of the proposed model. Selecting these characteristics requires an optimization method.
# Dynamic Characteristics of Urban Rail Train in Multivehicle Marshaling under Traction Conditions
## 4 Analysis and Discussion  
The working conditions studied in this paper adopt the linear traction working conditions, and the track spectrum is based on the measured data of a line in Beijing, the time is 40 s and the sampling frequency is 5000 Hz.  
The traction motor of an urban rail train adopts an AC asynchronous motor, and the working speed range of the motor is divided into two parts: a constant torque area and a constant power area. When the traction motor works in the constant torque range, the output torque remains unchanged, and the working range is between the starting speed and the synchronous speed; when the traction motor works in the constant power range, the output power remains unchanged, and the output torque and speed have an inversely proportional relationship. According to the data provided by the traction motor, the motor output torque is obtained as a piecewise function:  
\[T_{s}=\left\{\begin{array}{ll}T_{e}&n\leq n_{s}\\ 9550\frac{P_{e}}{n}&n>n_{s}\end{array}\right. \tag{1}\]  
where \(T_{s}\) is the driving torque; \(T_{e}\) is the rated torque; \(P_{e}\) is the rated power; \(n\) is the traction motor speed; and \(n_{s}\) is the motor synchronous torque.  
The traction characteristic curve of the urban rail train is shown in Figure 4a. The urban rail train will be affected by the traction force and basic resistance of the motor under traction conditions. According to the literature [9], the speed curve of the urban  
Figure 3: Comparison of the experimental data and simulation data in the vertical direction: the time series (**a**) on the axle box and (**b**) gearbox.  
rail train under traction conditions can be obtained, as shown in Figure 4b. The train performs a uniform acceleration motion in 0-15.7 s, the working range of the traction motor is the constant torque range and the speed reaches 42.6 km/h; the train performs variable acceleration motion in 15.7-70 s and the working range of the traction motor is the constant power range.  
The evaluation index of the train's dynamic performance is an important basis for evaluating the dynamic characteristics of the train. When evaluating the safety of train operation, the derailment coefficient, wheel load reduction rate and other indicators are generally comprehensively evaluated. In this section, based on the dynamic simulation results of urban rail trains under traction conditions, evaluation indicators such as wheel-rail vertical force, wheel-rail lateral force, the derailment coefficient and the rate of wheel load reduction in six carriages are compared and analyzed, and the influence of train dynamics is analyzed.