esa-sceva/satcom-chunk-collection huggingface dataset:
# Position Selection and Collision-Free Path Planning for Fruit Picking Robots
## 1 Introduction
A novel joint-space collision-free path planning method, the model predictive artificial potential field (MPAPF), is proposed to avoid singularity and to consider the joint limits and geometry of the manipulator. By optimizing potential values over multiple steps, it also reduces the risk of local minima. The remainder of this paper is structured as follows: Section 2 covers the method of selecting a position using the inverse reachability map. Section 3 explains the collision-free path planning method using the MPAPF algorithm. Section 4 validates the algorithm with applications in harvesting simulations. Section 5 demonstrates experiments with collision-free paths and the conclusions are provided in Section 6.
# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 4 Experimental Evaluation
### Results  
The experiments were performed at two levels: the first level is the classification of time slices by means of the NN-based representations presented in Sections 3.1 and 3.2, whereas the second level is the classification of routes, which is performed by means of the hybrid approach described in Section 3.4. In both levels, we perform comparisons with state-of-the-art approaches.
# A Wideband Non-Stationary 3D GBSM for HAP-MIMO Communication Systems at Millimeter-Wave Bands
## 5 Conclusions  
In this paper, we investigated a non-stationary 3D GBSM for HAP-MIMO communication systems at mmWave bands. We utilized a cylinder-based geometric modeling method to construct the channel and derived the channel impulse response. The Markov process was used to describe scatterers' birth-death process. The impact of large-scale parameters, such as free space loss and rainfall loss was considered in our proposed model. Furthermore, we derived the temporal ACF and obtained the effects of parameters such as elevation angles and scatterer distribution on the correlation of the model. Additionally, we obtained the spatial CCF and FCF and conducted simulation. Theoretical results were consistent with the simulation results, which showed that the constructed 3D GBSM model could effectively characterize the non-stationary characteristics of HAP-MIMO channels in the time, space, and frequency domains.  
The significance of this work lies in the fact that it provides a comprehensive and accurate model for HAP-MIMO communication systems at mmWave bands. The non-stationary 3D GBSM takes into account the large-scale parameters and can effectively describe the non-stationary characteristics in time, space, and frequency domains. This model can be used to optimize the design of HAP-MIMO systems and improve their performance. Our work can help fill the gap in existing literature on the non-stationary characteristics of frequencies at the mmWave band, and supplement some research on path loss. In future work, we plan to continue improving our non-stationary 3D GBSM by introducing more large-scale parameters and using machine learning methods to adjust the parameters to improve the accuracy of the model.  
Conceptualization, L.G. and W.Z.; methodology, L.G.; software, L.G. and K.Z.; validation, L.G. and K.Z.; formal analysis, L.G. and Z.J.; investigation, L.G.; resources, L.G.; data curation, L.G. and S.W.; writing--original draft preparation, L.G.; writing--review and editing, Y.Z.; visualization, L.G.; supervision, W.Z. and Y.Z.; project administration, W.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Comparison of stationary bandwidth [31; 32].  
**Funding:** This work was supported by the National Natural Science Foundation of China under Grant 62271051 and Grant 61871035, and Defense Industrial Technology Development Program, China under Grant JCKY2022209A001.  
**Data Availability Statement:** Data is contained within the article. The data presented in this study are available in Section 4.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling
## 1 Introduction
In Section 2, we detail the related work, including the software data plane architecture, the spin-polling mechanism, and the batch processing mechanism. We perform some experiments and use the experimental results to illustrate the motivation of our work in Section 3. In Section 4, we illustrate the experimental results of Section 3 with a simple model and lead to the starting point of our scheme. We present our latency optimize scheme based on the dynamic adjustment of Tx batch size in Section 5, and the details of scheme implementation are performed in Section 6. Finally, we conclude this paper and discuss the future works in Section 7.
## 6 Results and Discussion  
In this section, we present simulation results to demonstrate the efficiency of the proposed CBOSS and TLAC algorithms for energy-efficient data protection in CRNs. We first present simulation results to evaluate the performance of the proposed CBCSS technique compared with other fusion techniques, such as a half-voting rule [38], an energy detection (ED) method performed by a secondary user, and the Chair-Varshney rule [39]. We then investigate the potential of the TLAC solution for establishing an operation mode decision policy by comparing it with the POMDP-based solution from earlier work [20], the myopic scheme, and the fixed encryption methods, which will be described in detail later.
## 1 Introduction
### Contributions of the Study  
In summary, our contributions are as follows:  
1. We propose a unified GLR framework that constructs Graph-CoT prompts based on local subgraph structures, guiding LLMs to perform structured chain-of-thought reasoning along the graph paths. This effectively enhances the model's structural perception capability and its ability to model multi-hop relations.  
Figure 1: Illustration of two paradigms for LLM-based knowledge graph completion. The left diagram shows a paradigm where the graph structure is linearized into natural language input to guide the LLM for entity completion. The right diagram illustrates an enhanced approach that incorporates CoT prompting to improve structural modeling and multi-hop reasoning, enabling answer generation through structured path-based inference.  
We design a supervised fine-tuning strategy based on Graph-CoT prompts and apply LoRA to perform parameter-efficient fine-tuning on the base large language model (Qwen2-7B), thereby improving the model's adaptability to downstream domain-specific tasks.
* We introduce a P(True)-based confidence evaluation mechanism to guide the model in quantifying the confidence of candidate entities, enhancing its ability to assess the reliability of prediction results and improving controllability over the output.
## 2 Materials and Methods
### Derivation of a Solution for Arbitrary Dissipation Numbers  
As previously mentioned, the inverse Laplace transform (ILT) of the weighting function for both ends of the pipe in the case of an incompressible fluid is independent of the dissipation number \(Dn\) and was presented in Equation (41). The primary challenge in determining the general ILT of \(W_{1}^{*}\) is that the poles depend on \(Dn\). The \(k\)th pole is indirectly defined by the following formula, derived from Equation (32) for \(k>0\):  
\[1+\frac{\text{I}_{0}(\sqrt{\zeta_{k}})\zeta_{k}^{2}Dn^{2}}{k^{2}\text{I}_{2}( \sqrt{\zeta_{k}})\pi^{2}}=0. \tag{43}\]  
Rearranging for \(\zeta_{k}\) yields:  
\[\frac{\text{I}_{0}(\sqrt{\zeta_{k}})\zeta_{k}^{2}}{\text{I}_{2}(\sqrt{\zeta_{ k}})}=\left(\frac{k\pi}{Dn}\right)^{2}-1. \tag{44}\]  
Since it is not feasible to derive a solution applicable to all \(Dn\), the calculations will focus on discrete values of \(Dn\).  
Using the software Maple [36], the first ten complex conjugate poles of the weighting functions were computed for dissipation numbers ranging from \(1\times 10^{-7}\) to \(1\times 10^{-1}\), with 100 increments per order of magnitude. This produced a matrix of dimensions 20 by 700. The positions of the poles in the complex plane for three different dissipation numbers are depicted in Figure 6. It can be observed that as the dissipation number decreases, the poles move further from the origin in the complex plane. Note that poles positioned further to the left on the negative half of the real axis correspond to faster-decaying residues, while those further away from zero on the imaginary axis are associated with faster-oscillating residues.  
With the matrix of poles dependent on the dissipation number now in hand, we can calculate the residue for each pole across all considered dissipation number cases. The equation for the residues of simple poles is provided in Equation (45). Since all poles of the weighting function resulting from \(\sinh(x)\) have been confirmed to be simple poles (as shown in Equation (35)), this equation can be utilized for the automated computation of the residues:  
Figure 5: ILT using residue theorem for both weighting functions.  
\[\text{Res}(W_{1}^{*})\big{|}_{\xi_{k}}=\frac{\text{numerator}[W_{1}^{*}(\xi_{k})] \;\text{e}^{\xi_{k}t_{\text{n}}}}{\frac{\partial}{\partial\zeta}\left(\text{ denominator}[W_{1}^{*}(\zeta_{k})]\right)}. \tag{45}\]  
It is worth noting that in the exponent of \(\text{e}\):  
\[s_{k}t=s_{k}\frac{R^{2}}{\
u}\frac{\
u}{R^{2}}t=\xi_{k}\frac{\
u}{R^{2}}t=\zeta _{k}t_{\text{n}}. \tag{46}\]  
We can compute a matrix containing all residues using Maple and Equation (45). By combining this with the residues and poles from the incompressible case (as represented in Equation (41)), we can obtain the weighting function in the time domain for discrete values of \(Dn\). The rounding of actual \(Dn\) values to the nearest sampled values did not visibly impact the results.
# Creep Characteristics of Soil in the Sliding Zone of Huangtupo Landslide
###### Abstract  
The reservoir water level in the Three Gorges Reservoir (TGR) of the Yangtze River is adjusted between 145 m and 175 m throughout the year. The landslide below the reservoir water level bears periodic dynamic seepage pressure on the basis of the original steady-state water pressure. In the process of rising reservoir water levels in particular, the effective stress on the sliding zone soil below the reservoir water level line is reduced, and the sliding zone soil shows an unloading state. In order to study the creep characteristics of landslides in a reservoir area, direct shear creep tests of the sliding zone soil in the Huangtupo landslide in an unloading state were carried out in this study. The test results show that the creep characteristics of the sliding zone soil are obvious. The creep curve of the sliding zone soil presents an attenuation creep stage with low shear stress, which is mainly manifested as an elastic creep. However, with the increase in the shear stress, the creep curve of the sliding zone soil presents a steady creep stage, which is mainly manifested as viscoelastic creep. The nonlinear creep characteristics of the sliding zone soil are related to the creep time, stress level, and soil porosity. The longer the creep time, the greater the stress level and the denser the soil, the more obvious the nonlinear creep characteristics of the sliding zone soil. In this study, the Burgers model was used to fit the creep curve of the sliding zone soil, and the fitting effect was good, which indicates that the Burgers model can sufficiently describe the unloading state creep characteristics of the sliding zone soil in the Huangtupo landslide.  
sliding zone soil; Huangtupo landslide; unloading creep; porosity ratio; Burgers model +
Footnote †: journal: Applied Sciences  
1 Faculty of Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China 1
# An Unambiguous Delay-And-Multiply Acquisition Scheme for GPS L1C Signals
## 3 Results
### Results of the Performance Analysis for the Proposed DM Acquisition Method
#### 3.3.2 Probability of Detection for the Proposed DM Method  
For the proposed DM acquisition method as in Figure 5, the probability of detection is shown with respect to the probability of false alarm in Figure 10, where each point in the plot is obtained from 10,000 simulation runs.  
Figure 11 shows the probability of detection according to C/N\({}_{0}\) with P\({}_{\text{fa}}\) = 0.001. As discussed in Section 3.2.4, the detection probability can be increased by sampling more fast as well as by using longer samples like non-coherent integration. Figure 11 shows the detection probability with using longer samples, i.e., non-coherent integration. If we use \(KT_{c}\) long samples, where \(K\) is the number of non-coherent integration and \(T_{c}\) is the coherent integration time, the detection performance of DM method can be improved. As \(K\) increases, P\({}_{\text{d}}\) also increases as in Figure 11.  
Figure 10: Probability of detection obtained for the proposed DM method (C/N\({}_{0}\)): 33-chip delay.  
Figure 9: The CCF for the DM method (33-chip delay); (**a**) Correlation plot for 10,230 chips (600,000 samples); (**b**) Correlation plot for \(\pm 1\) chip (59 samples) around the correlation peak.
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 6 Technology for Healthcare: Challenges/Pitfalls
### How to Incentivize 5G Healthcare?  
The objective of a digital healthcare system, empowered by 5G across the globe, is the benefit of consumers (patients and doctors), businesses and inclusive of the economy. 5G will pave the new ways for the provision of specialized healthcare services, in particular services by IoT (telecare and telehealth). Currently, healthcare providers hardly receive reimbursement for telemedicine and at-home health therapies over video conferencing or mobile phone [48], which discourages the use of such services. This calls for new policies that would promote appropriate incentives and reward (investment) for telemedicine like services and 5G orchestration platforms. These investments and incentives should be enough to operate 5G-based health monitoring services and should remain productive enough to finance continuous upgradations for speed and capacity. In addition, for 5G-based digital healthcare system, value-based reimbursement, rather than fee-for-service, should be adopted to reward physicians by linking health outcomes with the treatment results and diagnosis records suggested by physicians. In this way, both service providers and patients will be able to observe the results of treatments that can directly be correlated with the worth of expenditure.
## 2 The Valle d'Aosta Region, Northern Italy
### Challenges for Retrieving Surface Deformation with DInSAR of VDA  
The geomorphological and meteo-climatic characteristics make VDA a challenging environment for the analysis of surface deformation with DInSAR, including (i) a high topography gradient, (ii) complex orography, (iii) a predominance of forest or abundant vegetated areas, and (iv) unsuitable valley flank orientations, specifically due to the alternatively west- and east-facing orientation of the tributary valleys.  
By assuming ERS-1/2 and ASAR-Envisat acquisitions, we aim at understanding the theoretical distribution of scatters in the VDA region. This analysis has been carried out by computing the R-Index [82], which considers slope and aspect angles of the ground surface, as well as the satellite's incident acquisition geometry (incident angle, i.e., 23\({}^{\circ}\), and an azimuth angle of the satellite orbit from north (clockwise), i.e., 345\({}^{\circ}\) in ascending orbit and 195\({}^{\circ}\) in descending orbit, respectively). The resulting map reveals, for the north-south oriented valleys, layover (thus less exploitable scatters) on the west-facing slope in ascending orbit (Figure 2a) and layover on the east-facing slope in descending orbit (Figure 2b). On the other end, east-west oriented valleys experience layover or foreshortening on the north-facing slopes in ascending orbit and the south-facing slope in descending orbit.  
Figure 2: R-Index map (**a**) of the ascending orbit and (**b**) of the descending orbit for the VDA region. Layover/foreshortening cases are present for values lower than zero (**blue** portion).  
Furthermore, land use is also an important factor that has effects on the SAR result distribution (Figure 3). The prevalence of natural and high-vegetated areas for the entire VDA regional territory generates temporal decorrelation effects [47]. In addition, temporal coherence loss may occur due to the meteo-climatic conditions of the investigated area, characterized by a prevalent snow cover during the winter season.
# Salp Swarm Algorithm Optimized A* Algorithm and Improved B-Spline Interpolation in Path Planning
## 2 Path Planning Algorithm
### Improvement of the A* Algorithm
#### 2.2.2 Optimization Using the SSA
 \tag{7}\]\[k>1,B_{i,k}(t)=\frac{t-t_{i}}{t_{i+k}-t_{i}}\ B_{i,k-1}(t)+\frac{t_{i+k+1}-t}{t_{i +k+1}-t_{i+1}}\ B_{i+1,k-1}(t) \tag{8}\]

The mathematical formula of the B-spline curve is typically expressed as (9). \[P(t)=\sum_{i=0}^{n}P_{i}B_{i,p}(t) \tag{9}\]

where \(C(t)\) is the curve, \(P_{i}\) are the control points, \(B_{i,p}(t)\) is the basis function of the \(i\)-th control point with degree \(p\), and \(t\) is the parameter. ### Control Point Adjustment

The B-spline interpolation method generates a smooth path based on the number and position of control points. However, this method lacks inherent obstacle avoidance capabilities. If control points are not properly selected, the planned path might not stay safely away from obstacles, which could cause collisions [31]. Therefore, the path nodes generated by the SSA-A* cannot be directly used as control points for B-spline smoothing. Instead, these nodes must be adjusted in terms of quantity and position before serving as control points for the B-spline smoothing process. This adjustment ensures that the generated smooth path avoids obstacles effectively. To achieve appropriate control point adjustment, we propose a two-stage optimization strategy: corner avoidance and path obstacle avoidance. The corner avoidance strategy adjusts the number and position of control points at path corners based on the spatial relationships of path nodes, enabling the path to navigate turns without colliding with obstacles. First, we define a diagonal judgment function \(Diagonal()\) as (10) and two midpoint functions \(C()\) and \(D()\) as (11). \[Diagonal(A,B)=|x_{A}-x_{B}|\wedge|y_{A}-y_{B}| \tag{10}\]

\[C(A,B)=(x_{A},y_{B})\ \ \,\ \ \ D(A,B)=(x_{B},y_{A}) \tag{11}\]

Next, we define the path insertion function. For a given path \(P=(p_{1},p_{2},\ldots,p_{n})\), inserting a point \(q\) at position \(i\) results in the following updated path, as expressed in (12). \[Insert(P,i,q)=(p_{1},\ldots,p_{i},q,p_{i+1},\ldots,p_{n}) \tag{12}\]

Similarly, we define the path deletion function. Given a path \(P=(p_{1},p_{2},\ldots,p_{n})\) and a set of points \(R\) to be removed, the updated path after deletion is given by (13). \[Remove(P,R)=\{p\in P\mid p\
ot\in R\lor p\in\{p_{1},p_{n}\}\} \tag{13}\]

Finally, the corner avoidance optimization function \(Optimize(P)\) is formulated as (14)-(17). \[Optimize(P)=Remove(InsertPoints(P),R) \tag{14}\]

\[InsertPoints(P)=\bigcup_{i=1}^{n-1}Insert(P,i,q_{i}) \tag{15}\]

\[q_{i}= \begin{cases}C(p_{i},p_{i+1})\ \ if\ C(p_{i},p_{i+1})\
ot\in O,\\ D(p_{i},p_{i+1})\ \ if\ D(p_{i},p_{i+1})\
ot\in O,\\ \varnothing\ \ otherwise.\end{cases}  \tag{16}\]

\[R=\{p_{i},p_{i+1}\mid Diagonal(p_{i},p_{i+1})\wedge(C(p_{i},p_{i+1})\in O \lor D(p_{i},p_{i+1})\in O)\} \tag{17}\]In (16) and (17), \(O\) represents the set of obstacles. The pseudo-code for corner avoidance is presented in Algorithm 2.
## 4 Results and Discussion
### ANN, SVR and RF
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Cutting Depth (um)**} & \multicolumn{2}{c}{**ANFIS**} & \multicolumn{2}{c}{**ANN**} & \multicolumn{2}{c}{**SVR**} & \multicolumn{2}{c}{**RF**} \\ \cline{2-9}  & **Predicted MRD (um)** & **Error (\%)** & **Predicted MRD (um)** & **Error (\%)** & **Predicted MRD (um)** & **Error (\%)** & **Predicted MRD (um)** & **Error (\%)** \\ \hline
92.49 & 93.77 & 1.38 & 95.31 & 3.05 & 92.35 & 0.15 & 81.25 & 12.15 \\
78.67 & 78.97 & 0.38 & 78.54 & 0.17 & 77.64 & 1.31 & 67.54 & 14.15 \\
74.35 & 72.87 & 1.99 & 74.62 & 0.36 & 76.27 & 2.58 & 68.32 & 8.11 \\
47.94 & 48.07 & 0.27 & 46.52 & 2.96 & 46.21 & 3.61 & 51.54 & 15.02 \\
133.21 & 132.14 & 0.80 & 131.67 & 1.16 & 130.26 & 2.21 & 117.36 & 11.90 \\
40.65 & 41.97 & 3.25 & 43.23 & 6.35 & 45.28 & 11.39 & 47.51 & 16.88 \\
120.67 & 118.70 & 1.63 & 117.65 & 2.50 & 122.34 & 1.38 & 105.24 & 12.79 \\
123.54 & 122.07 & 1.19 & 120.32 & 2.61 & 130.48 & 5.62 & 132.98 & 7.64 \\
106.74 & 105.93 & 0.76 & 108.94 & 2.06 & 101.36 & 5.04 & 99.21 & 7.05 \\
62.45 & 60.24 & 3.54 & 59.12 & 5.33 & 64.82 & 3.80 & 53.15 & 14.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Partial measured and predicted values of the four models. Figure 14: Absolute percentage error (APE) of the four models. As shown in Figure 14, the absolute percentage error (APE) of the ANFIS model was the smallest, compared to the other three models. ANN and ANFIS had similar APEs. The \(MAPE\) of the predicted value of the ANFIS model was 3.976% and the minimum APE was 0.27%. The findings indicate that the suggested ANFIS model is reliable and accurate for predicting material removal depth during grinding.
# LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection
###### Abstract  
The electrical losses in power systems are divided into non-technical losses (NTLs) and technical losses (TLs). NTL is more harmful than TL because it includes electricity theft, faulty meters and billing errors. It is one of the major concerns in the power system worldwide and incurs a huge revenue loss for utility companies. Electricity theft detection (ETD) is the mechanism used by industry and academia to detect electricity theft. However, due to imbalanced data, overfitting issues and the handling of high-dimensional data, the ETD cannot be applied efficiently. Therefore, this paper proposes a solution to address the above limitations. A long short-term memory (LSTM) technique is applied to detect abnormal patterns in electricity consumption data along with the bat-based random under-sampling boosting (RUSBoost) technique for parameter optimization. Our proposed system model uses the normalization and interpolation methods to pre-process the electricity data. Afterwards, the pre-processed data are fed into the LSTM module for feature extraction. Finally, the selected features are passed to the RUSBoost module for classification. The simulation results show that the proposed solution resolves the issues of data imbalancing, overfitting and the handling of massive time series data. Additionally, the proposed method outperforms the state-of-the-art techniques; i.e., support vector machine (SVM), convolutional neural network (CNN) and logistic regression (LR). Moreover, the F1-score, precision, recall and receiver operating characteristics (ROC) curve metrics are used for the comparative analysis.  
non-technical losses; electricity theft; smart meter; random under-sampling; imbalanced data; parameter tuning +
Footnote †: journal: Applied Physics Letters  
1
Footnote 1: _Appl. Sci._**2020**, _10_, 4378; doi:10.3390/app10124378