esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Load Forecasting Using Gaussian Process Regression and Novel Residual Prediction
###### Abstract
Unlike traditional forecasting methods that are used to predict the load on a single node (e.g., a substation), the focus of this paper is on short-term total energy load forecasting, which yields greater accuracy by using the measured information in all nodes of interest. Another benefit of the proposed methodology over the classical methods mentioned above is the use of the residuals as predictors. This approach will customize the algorithm to better respond to the characteristics of the data. By doing this with each iteration the algorithm will better adapt to the data sets. The hybrid algorithm described in Figure 1, uses the autoregressive (AR) time series model in combination with Gaussian process regression (GPR), coupled with a data pre-processing algorithm, the empirical wavelet transform (EWT), for data optimization. This combination of parametric and non-parametric time series, AR and GPR, showed great results in predicting the energy load consumption characteristics [27]. For optimizing the forecasting model, the use of rational quadratic (RQ) kernel function is proposed. This covariance function was chosen after testing different other kernels, due to its compatibility with the non-linear profile of load data. The remainder of this paper is organized as follows: Section 2 presents the energy load data used to develop the forecasting method. The subsequent section deals with data preprocessing and presents the EWT used. Section 4 presents the GPR hybrid forecasting method proposed in this paper. Subsequently, the model performance indicators used to assess the algorithm are described in Section 5. Section 6 presents the results and evaluates the forecasting method's performance. The final section of the paper concludes the study with some of the authors' observations and recommendations for future research regarding the presented topic.
# Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment
## 5 Performance Evaluation
### Network Parameters and Settings  
We adopt Matlab as the simulator to test the performance of our presented TRP-MC. We also compare it with some similar work, such as LEACH, SHDGP, and MWR and analyze the simulation results. Some relevant parameters of the network are described in Table 3.  
We use the same network model to execute LEACH, SHDGP, and MWR to equally evaluate their performance. Following are some settings for the compared methods.  
**LEACH:** The number of cluster heads in LEACH is the same as nsp and a static sink is placed in the center of the network. We assume that the transmission range of sensors in LEACH is big enough that every two sensors in the network can communicate with each other.  
**SHDGP:** We only adopt the single mobile collector pattern in SHDGP. The speed of the mobile collector and the sojourn time of data uploading are the same as TRP-MC. Transmission range of sensors is also the same as that in TRP-MC.  
**MWR:** The number of polling points in MWR is the same as the number of SPs in TRP-MC. The transmission range of MWR and TRP-MC are the same. In the same way, the mobile data collector owns the same properties as the mobile collector in TRP-MC.
## 2 Methods and Materials
### Heuristic Algorithm for Minimum Energy Consumption  
To obtain the minimum energy consumption of the hexapod robot, we developed a heuristic algorithm (Figure 2). Since in Section 2.2 we only described experiments where we measured energy consumption dependence on separate robot movement speeds, the algorithm was developed to measure the energy consumption of robots in a realistic movement situation. The algorithm controls how a robot moves using both tripod and wave gaits at certain speed intervals without an additional load. This algorithm was developed for our hexapod robot model, but it could be used as a baseline algorithm for any type of hexapod robot. The only changes that would need to be done to the algorithm are changing the robot's maximum speed and gait changing speed.  
Figure 1: Hexapod robot model HexaV4 (**a**) and gait used in experiments time diagrams (black lines represent leg’s support phase, dashed gap—transfer phase) (**b**). Leg abbreviations: LF—left from; LM—left middle; LH—left hind; RF—right front; RM—right middle; RH—right hind; T—gait period. All dimensions are in millimeters.  
Tripod and wave gaits were selected after conducting a series of tests. During these tests, all four gaits were used with all possible step lengths along with different joint speeds to obtain various robot movement speeds. Each time, the robot's current was measured to estimate energy consumption. The results showed that neither tetrapod nor ripple gaits are energetically efficient. The results also showed that 0.046 m/s speed is the most optimal speed for changing from wave to tripod gait because otherwise, the energy consumption increases. The maximum speed of the robot is 0.33 m/s.  
The parameters of the algorithm are set to the minimum: step length is set to 1 cm and servo speeds are set to 12 rpm. These parameters set robot movement speed to 0.00042 m/s. Gait is set to wave gait. After setting initial parameters, the robot starts to move forward. After making 3 full gait cycles, the algorithm increases the robot's movement speed by increasing servo speeds or step length. If movement speed is equal to or greater than 0.046 m/s, the gait type is switched to the tripod gait, otherwise, the robot continues moving forward with the wave gait and further increases speed in the same manner. Gait switching is done using a fixed threshold and is initiated at the end of the gait period to ensure that the previous gait is complete. When tripod gait is selected, the robot moves forward, increasing speed until it reaches a speed of 0.33 m/s, which is maintained until the end of the experiment. Each experiment took 2 min and during the whole time the robot's current was measured using the current shunt sensor mounted on the robot's control system. Over the 2 min, 1500 current values were obtained. This means that the instantaneous current value was measured every 80 ms. Such a sampling rate is adequate to measure current consumption during robot locomotion.
## 3 Bispectrum Analysis of EEG Signals for an Emotion Recognition System
### Feature Extraction Based on Bispectrum Analysis and 3D Pyramid Filter
#### 3.4.3 Feature Types Based on the Bispectrum  
Bispectrum analysis has also been used for an EEG-based emotion recognition system by [PERSON] [20], with several entropies, values and moments taken as the features, which we adapted as one of the feature extraction modes in our experiments (Mode #1 feature extraction); however, this feature produces a high dimension of feature vectors and thus requires a high computation cost. In this work, we propose as simpler feature, which is the mean of the bispectrum value, as Mode #3 feature extraction, producing only one feature value for each area of the filter, thereby reducing the dimension of the feature vectors.  
In previous work, we have found that the energy percentage performed well as the feature of an EEG signal [49]; therefore, here, we considered the percentage value as the feature. The percentage value of entropies and moments of Mode #1 becomes the Mode #2 feature extraction, and the percentage of the mean becomes Mode #4 feature extraction. Suppose \(F_{m}\) is a feature and \(M\) is the number of features per channel; then, the percentage value of the feature \(FP_{m}\) is defined as:  
\[FP_{m}=\frac{F_{m}}{\sum_{m=1}^{M}F_{m}}\times 100. \tag{5}\]
# Early Fire Detection on Video Using LBP and Spread Ascending of Smoke
## 2 Proposed System
### Texture Analysis and Classification  
Among the most important attributes of an object is its texture, which is related to the visual properties of the observed object. These properties are derived from the surface roughness, the structure or composition of the material from which it is made. The visual texture is defined as the characteristic variation in the intensity that allows describing and recognizing a particular region of a given image. That is, texture does not exist in an area in which the image has a uniform intensity, while, if the intensity change does not have enough regularity, the texture cannot be characterized properly, thus an accurate recognition and segmentation is not possible. These regularity and randomness degrees should be measured and compared when a texture is characterized.  
The visibility of smoke texture mainly depends on the concentration and distribution of the smoke cloud, which in turn depend on many factors such as the chemical composition of the fuel and combustion rates, the magnitude and direction of the airstream, room temperature, atmospheric pressure and if it is a stratified or uniform environment. Sometimes the texture is more visible in the boundary layer of the cloud of smoke than inside it because of its density, thus it is not possible to capture the texture of the brightly lit and transparent smoke region in the image under analysis. The texture is itself a complicated to measure characteristic. In addition, textures in the real world are affected by variations in lighting, perspective, viewing distance, additive noise, resolution, shadows, possible occlusions, and mutability in appearance over time, among others. Moreover, a robust operator invariant to lighting, rotation, scaling and observation points, among others, is required. The Local Binary Pattern (LBP) is an operator that combines features of statistical and structural texture analysis methods, and can conveniently characterize the regularity and randomness of the pixels in an image. It is independent of linear changes in the average intensity, robust to rotation and supports multi-scale analysis. In addition, it is complemented with contrast information, which enhances the description. These qualities make it suitable for describing smoke texture.
## 1 Introduction
If the techniques for an optimal LMI-based observer parameterization of continuous-time systems, published in [14], are used, the domains around the system's equilibrium states can be identified for which no contractivity, that is, stability in a stochastic sense, can be verified. To perform the analysis, the Ito differential operator [15] was applied to a Lyapunov function candidate to perform its temporal differentiation in the corresponding stochastic setting. In addition, we introduced an LMI-based numerical optimization of the observer gains in [14] to minimize the domains for which the contractivity of the observer's error dynamics cannot be proven by the technique above. This paper proposes a novel iterative solution that (i) solves the nonlinear matrix inequalities for a combined control and observer design of linear continuous-time processes with polytopic parameter uncertainty in an iterative manner and (ii) generalizes the procedures from [14] to optimize the controller and observer gains so that the sensitivity against actuator, process, and sensor noise on the closed-loop trajectories and the observer's error dynamics is minimized by means of the simultaneous control and observer design. Besides the design of observer-based full state feedback controllers, our approach is also capable of designing output feedback control laws, where the measured outputs and fed-back quantities do not necessarily have to coincide. Due to the inclusion of the state observer in the design procedure, the proposed approach is more general than the robust output feedback design that was proposed by [PERSON] and [PERSON] in [16] for discrete-time systems. Finally, the work of [PERSON] et al. in [17] should be mentioned. There, a combined control and observer design was performed. However, the authors do not aim at directly minimizing the domain for which stability in a stochastic sense cannot be proven. Instead, they aim to achieve the stability of the overall closed-loop control structure by suppressing the influence of disturbances by means of a frequency response shaping approach. This paper is structured as follows. After a short introductory example in Section 2, Section 3 reviews our results published in [14] to turn them into a novel iterative LMI approach for an optimized, guaranteed stabilizing combined control and observer synthesis in Section 4. The efficiency of this approach is demonstrated in Section 5 in numerical simulations for the stabilization of the [PERSON] catastrophe machine [18]. The stabilization is performed along the curve of input-dependent unstable equilibria. In addition, a representative tracking control task is presented, in which the feedforward control signal is determined by a P-type iterative learning control approach [19; 20]. Finally, conclusions and an outlook on future work are provided in Section 6.
# Edge-Aided Reliable Data Transmission for Heterogeneous Edge-IoT Sensor Networks
## 2 Related Works
### Summary  
The basic idea of the existing works is to employ the computing resources of the edge servers to empower sensor networks and help sensor networks to fulfill more complex tasks. Different from the existing works, our basic idea is to seize the opportunity to exploit the additional communication abilities of edge servers to improve the reliability and efficiency of data transmissions in sensor networks. As edge servers are not constrained by battery capacity, it can use much larger radio power to transmit data packets, which covers more nodes than traditional low power radios on sensor nodes.  
Specifically, we exploit both forwarding and retransmissions to improve the reliability and energy efficiency of data transmission in sensor networks. The packet forwarding can save a number of transmissions of sensor nodes. In addition, for those poor links with very low packet reception rates, the retransmissions from edge servers can also reduce delay overheads and transmission to a great degree.
## 2 Potential Application Areas of CR-WSNs
### Bandwidth-Intensive Applications  
Multimedia applications, such as on-demand or live video streaming, audio, and still images over resource constrained WSNs, are extremely challenging because of their huge bandwidth requirements [41; 42; 43]. Other WSN applications, such as WSNs in a hospital environment, vehicular WSNs, tracking, surveillance, _etc._, have vast spatial and temporal variations in data density correlated with the node density. These applications are bandwidth-hungry, delay intolerable and bursty in nature. Because in CR-WSN, SUs can access multiple channels whenever available and necessary, CR-WSN is very suitable for these types of bandwidth-hungry applications. [PERSON] _et al._[44] reported channel bonding in CR-WSNs for such bandwidth intensive applications.  
Figure 3: Wireless body area network with CRWS.
## 3 Materials and Methods
### Metabolite Extraction  
VOC metabolites were extracted from each rice sample using HS-SPME as follows: 100 mg of the rice sample was transferred into a 20 mL headspace bottle, and 10 \(\upmu\)L of 2-octanol (>99.5%, TCI Chemical, Shanghai, China,(10 mg/L stock in dH\({}_{2}\)O) was added to the headspace bottle as the internal standard; the automatic heating incubator was preheated for 15 min and then extracted at 60 \({}^{\circ}\)C for 30 min using an SPME fiber (DVB/CAR/PDMS, 2 cm, 50/30 \(\upmu\)m; Supelco, Bellefonte, PA, USA). For every five samples, an empty vial analysis was performed to check for any carryover.
# Abnormal Ship Behavior Detection Based on AIS Data
## 3 Materials and Methods
### Detection of Abnormal Ship Behavior Based on Spatial Attributes
#### 3.2.2 Detection of Spatial Abnormal Behavior of Ships Based on [PERSON]'s Criterion  
The basic assumption of [PERSON]'s criterion is that a group of detection data contains only random errors, the calculated standard deviation of which determines an interval according to a certain probability and considers that errors beyond this interval are coarse errors and should be eliminated. Due to the influence of environmental factors and the navigation price, the ship usually follows specific route information. Therefore, we assumed that a ship was traveling on its planned route, and the lateral distance between the ship and the route should meet the needs of a normal distribution; that is, the probability that the ship belonged to the route could be calculated by using the normal probability density distribution function. Therefore, we could detect the yaw behavior of the ship by calculating the Gaussian distribution of the ship's lateral distance. Shown in Figure 5 is an illustration of the maritime routes and trajectory points displayed by real data.  
It can be observed from Figure 5 that the lateral distance of the ship's trajectory points conformed to the characteristics of a Gaussian distribution. Additionally, the characteristic of a Gaussian distribution is that the standard deviation of a set of data averages can reflect the distribution of this set of data values. Therefore, it was feasible to detect the ship's spatial displacement based on [PERSON]'s criterion. [PERSON]'s criterion takes k (k = 1, 2, 3) times the standard deviation as the threshold value. A value beyond this range can be judged as abnormal if it does not belong to the category of random errors. The value of  
Figure 5: Distribution of ship trajectory points along the maritime routes.  
the multiple of the standard deviation can be adjusted according to the required situation in practical applications. There is an inference in experimental science that when a set of data is normally distributed, almost all the values are within three standard deviations of the mean. The percentages within one standard deviation, two standard deviations, and three standard deviations from the mean can be expressed as 68.27%, 95.45%, and 99.73%, respectively. Based on this inference, this study detected abnormal trajectory points based on the ship's spatial information.  
The pseudocode for [PERSON]'s criterion is shown in Algorithm 2.  
```
Input: Trajectory points x, standard deviation multiple k Output: outlier set y
```  
1. Calculate the mean num \(\overline{x}\) of \(\text{x}_{i}(\text{i}=1,\,2,\,\ldots,\,n)\)
2. Calculate the standard deviation of \(\text{x}_{i}\) based on \(\overline{x}\): \(\delta=\sqrt{\sum_{i=1}^{n}(x_{i}-\overline{x})/(n-1)}\)
3. If \(|x_{k}-\overline{x}|>k\,\delta\) : Then, put \(\text{x}_{k}\) into y ```  
**Algorithm 2** [PERSON]'s criterion
# Optimization of Slotted Steel Plate Shear Walls Based on Adaptive Genetic Algorithm
## 4 Performance Analysis of SSPSWs
### Elastic Buckling Performance  
An elastic buckling analysis of the optimized SSPSWs was conducted via ABAQUS's buckling module, revealing distinct failure modes across the connection types. The bilateral-joined configurations predominantly exhibited out-of-plane buckling in the steel plate's right quadrant, involving 3-4 inter-slot columns (Figure 9a,b). The quadrilateral-joined systems demonstrated dual buckling mechanisms: Type I (SPSW-FF, SPSW-FT10, and SPSW-FT14) combined upper-left plate buckling with inter-slot-column lateral-torsional deformations (Figure 9c,d), while Type II (SPSW-FB12, SPSW-FB36, SPSW-FB48, and SPSW-FT8) manifested in-plane inter-slot-column flexural buckling (Figure 9e,f). As quantified in Table 3, the quadrilateral connections exhibited 18-25% lower critical buckling loads compared to their bilateral counterparts, necessitating the preferential adoption of bilateral configurations to ensure that yielding precedes instability.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Frame** \\ **Configuration** \\ \end{tabular} & **No.** & \begin{tabular}{c} **Buckling Load** \\ **(N)** \\ \end{tabular} & \begin{tabular}{c} **Buckling** \\ **(N)** \\ \end{tabular} & \begin{tabular}{c} **Packling** \\ **Displacement** \\ **(mm)** \\ \end{tabular} & **No.** & \
\begin{tabular}{c} **Buckling** \\ **Load (N)** \\ \end{tabular} &
\begin{tabular}{c} **Displacement** \\ **(mm)** \\ \end{tabular} \\ \hline \multirow{7}{*}{B-J} & SPSW-TF & 1,753,150 & 17.648 & & SPSW-FF & 1,688,240 & 10.489 \\  & SPSW-TB12 & 4,247,120 & 39.689 & & SPSW-FB12 & 1,347,010 & 7.576 \\  & SPSW-TB36 & 2,020,080 & 22.069 & & SPSW-FB36 & 932,071 & 6.411 \\  & SPSW-TB48 & 2,659,400 & 31.152 & & Q-J & SPSW-FB48 & 478,802 & 3.655 \\  & SPSW-TT8 & 2,332,960 & 22.643 & & SPSW-FT8 & 493,331 & 3.669 \\  & SPSW-TT10 & 1,337,540 & 13.487 & & SPSW-FT10 & 1,185,110 & 8.017 \\  & SPSW-TT14 & 2,811,690 & 28.27 & & SPSW-FT14 & 2,194,360 & 11.916 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Buckling loads and displacements.  
Figure 9: Buckling mode of SSPSWs: **(a)** SPSW-TF, **(b)** SPSW-TB36, **(c)** SPSW-FF, **(d)** SPSW-FT10, **(e)** SPSW-FT8, and **(f)** SPSW-FB36.
# Research on Three-Dimensional Scanning Path Planning of Casing Parts Based on Industrial Robot
## 6 Conclusions  
In this paper, a non-contact automatic three-dimensional scanning measurement process system and process are proposed and established for the rapid and high-precision measurement requirements of aero-engine casing parts. The scanning trajectory is analyzed and verified by mathematical modeling, simulation analysis and process test. The main conclusions are as follows:  
(1) According to the relationship between the scanning area constraint of the scanner and the geometric size of the casing, a mathematical model of the annular scanning trajectory is established. The scanning path planning from part model to off-line robot programming was realized. The scanning path-planning method is suitable for the scanning measurement of cylindrical parts with tapering, such as casing parts;  
(2) It is proposed to evenly distribute the scanning repetition area by adjusting the scanning height, and introducing the square compensation coefficient to compensate for the repeated scanning area. Through the numerical calculation of the scanning path before and after the adjustment, it is found that the allocation method shortens the scanning path by 1.1%, and the scanning integrity is also improved;  
(3) The robot-studo software is used to conduct the motion simulation of the optimized scanning path and verify the scanning process experiment. The results show that the annular trajectory scanning method established in this paper can meet the scanning measurement requirements of casing parts. The efficiency of scanning measurement is more than 80% higher than that of artificial ultrasonic point-by-point measurement.  
Conceptualization, J.L. and M.W.; methodology, L.Q.; formal analysis, G.L.; investigation, J.L.; data curation, J.L.; writing\(-\)original draft preparation, J.L.; writing\(-\)review and editing, M.W.; supervision, L.Q.; project administration, G.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Liaoning Province 'Xingliao Talent Plan' project, grant number XLYC2002086; the Discipline Construction Fund, grant number 310121001 and the National Key Research and Development Plan by Liaoning, grant number 2022 JH2/101300211.  
Not applicable.  
The authors declare no conflict of interest.