esa-sceva/satcom-chunk-collection huggingface dataset:
# Forecasting Seasonal Sales with Many Drivers: Shrinkage or Dimensionality Reduction?
## 1 Introduction
Even though there are arguments in favour of the continued use of statistical methods for retail forecasting, a major challenge that needs to be overcome is the efficient selection and inclusion of the various potential demand drivers in forecasting models, not least because of their impact on operational decisions. This is the focus of this work which leads to the following contributions:  
* we propose a feasible solution to include relevant drivers, including promotions, into the statistical AutoRegressive Integrated Moving Average (ARIMA) and ExponenTial Smoothing (ETS) models based on automatically selected principal components;
* we propose an automatic approach to model the demand using Ridge regression. We investigate the encoding of seasonality using both stochastic terms, represented by seasonal lags, and deterministic, included as trigonometric indicator variables [32; 33];
* we comparatively evaluate dimensionality reduction and shrinkage approaches, identifying the benefits of each in the presence of promotion and prices changes in a retail setting;
* our approaches are completely automated and computationally efficient running without a need for human intervention and therefore scalable to address the retailers' requirements, offering modelling guidelines to both retailers and software suppliers. This paper is organized in five sections. We first briefly summarize the literature on two aspects of the problem: promotional modelling of retail sales and the forecasting models that have been developed. The third section considers the models we use in detail whilst the fourth presents a case study of supermarket SKU sales in Portugal. The final section contains our conclusions and reflections on further work of both practical and theoretical interest.
# A Head Control Strategy of the Snake Robot Based on Segmented Kinematics
## 1 Introduction
The stability of the motion is analyzed by the support polygon method and the zero-moment point method. Compared with the existing head control strategies, our proposed strategy has some advantages. [PERSON] et al. proposed the head control strategies of the wheeled snake robot on a plane or in a three-dimensional space [7]. However, these strategies are both based on the non-side slip constraint, and they are hard to be applied to the non-wheeled snake robot. Our proposed strategy can be applied to snake robots and non-wheeled snake robots. Moreover, the strategies proposed by [PERSON] and [PERSON] are hard to realize the accurate manipulation of the snake robot head since they are both based on the gait or the pure backbone curve of snake robots [9; 10]. However, our proposed strategy can realize the accurate manipulation of the head since it adopts segmented kinematics. In addition, the trajectory planning of the overall body in Cartesian space is proposed to avoid the collision with the ground. The article is organized as follows. The strategy of head control is introduced in the Section 2. The strategy includes the calculation of joint angles for the base part, the calculation of the dexterous workspace of the head part, choosing the dexterous workspace center and the calculation of joint angles for the neck part. The description of the process of head control is in Section 3. The stability analysis methods of the motion are given, and the trajectory planning method of the overall body is proposed in this section. In Section 4, the simulations of catching the spheres and getting through a hole on the wall are shown. In the Section 5, the discussions and conclusions are given.
# The Impact of Gravity on Different Height Systems: A Case Study on Mt. Medvednica
## 5 Results and Discussion
In Section 2.2.2, it is defined that the height difference between the geoid and the quasigeoid is correlated with the terrain elevation. Specifically, the difference between orthometric and normal heights is most pronounced in high-altitude regions, while at sea level, there is no difference at all. To verify this statement, the differences between the calculated normal and orthometric heights were determined at all the points along the leveling line (Figure 15). The figure shows that the smallest differences occur at the beginning of the leveling line, where the elevation is the lowest. However, even there, a difference of approximately 4 mm is present since the starting point is at an elevation of around 300 m. In the second half of the leveling line, where elevations are higher, these differences increase, reaching values of up to 1 cm. As can be seen, the increase in these differences is not linear, although it might be assumed at first glance. The reason for this is that orthometric heights are determined under the assumption of mass distribution, where the mass density is taken as constant, while the actual mass distribution cannot be precisely determined. Additionally, the topography in the vicinity of the leveling line significantly affects gravimetric measurements, which can also contribute to the nonlinear increase in the differences between normal and orthometric heights. Nevertheless, the figure clearly shows a tendency for this difference to increase with elevation, thus practically confirming the theoretical claim. Although the leveling measurements were performed with very high accuracy, the gravimetric data cannot be considered completely error-free. This is primarily because the measurements were taken during the day when vehicle traffic was present, which could introduce slight disturbances in the gravity measurements. Additionally, the influence of underground water was not accounted for, as no piezometers were available in the vicinity to measure groundwater levels. The eccentricity from which the absolute gravity value was  
Figure 15: Differences between normal and orthometric heights. obtained was observed nearly 25 years ago, which also introduces some uncertainty. These factors could affect the final gravity values at the leveling benchmarks. Therefore, while there may be some potential errors in the gravimetric data, these errors are minimized in relative gravimetry by subtracting the measurements from each other. As a result, the final gravimetric values should be sufficiently accurate, and the obtained heights can be reliably compared.
# Time Segmentation-Based Hybrid Caching in 5G-ICN Bearer Network
## 3 Path Segmentation-Based Hybrid Caching  
### 5G-ICN Bearer Network  
The ICNoIP mode is used by the 5G-ICN BN in Figure 2, and it makes use of the LTE framework's architecture and the ICN methodology for service systems. This approach is based on the integration of ICN architecture with 5G BN and collaborative service with DN, as opposed to the academic deployment of ICN on IP BN. Between the next-generation Node B (gNodeB) and UPF at the network's edge, the BN applies 5G-ICN network. It offers network features including in-network caching, boosts network performance 5G-ICN BN is closer to users than 5G-ICN DN. Cache and other services can be arranged on the 5G-ICN BN to improve user experience by significantly reducing access time and link load of the upper network, such as the DN.  
5 GC includes access and mobile management functions (AMF), session management function (SMF), unified data management (UDM), UPF, and so on. Important functions including user management, uplink and downlink data notification, and UE mobility management notification are provided by 5 GC to gNodeB and ICN-GW in the 5G-ICN BN architecture. ICN can handle and uphold mapping relationships between content identifiers and locators thanks to NRS. When a request for content is made again, NRS checks the cached content in the area and promptly replies with the results. Inherent support for name-based networks, network memory, edge computing, security, mobility, and other 5G capabilities is provided by the model.  
Figure 2: 5G-ICN BN architecture.
# New On-Line MFL Testing Method and Apparatus for Winding Mine Hoist Wire Rope
## 3 Analysis of the Novel Probe
### Preliminary Experimental Verification  
In order to verify the simulation results, several magnetizer sizes were focused on, selecting for experimental comparison: (1) ID = 70 mm, OD = 100 mm, H = 15 mm, L = 10 mm; (2) ID = 70 mm, OD = 100 mm, H = 15 mm, L = 30 mm; (3) ID = 70 mm, OD = 100 mm, H = 15 mm, L = 60 mm; (4) ID = 70 mm, OD = 100 mm, H = 15 mm, L = 60 mm, and (5) ID = 70 mm, OD = 100 mm, H = 30 mm, L = 30 mm. As illustrated in Figure 13, a detection sensor was machined to size and an experimental system was developed to detect damage to the wire rope. A wire rope is magnetized to saturation by an open sensor. A leakage magnetic field is created on the surface of the magnetized wire rope when there is a defect. And the leakage magnetic field is captured by the inductor and passed through the data acquisition card, which converts it into electrical signal that can be analyzed by a PC analysis system. After the signal has been analyzed and processed by the data acquisition and test software, the curve of the detected signal is displayed on the screen in real-time.  
Figure 11: Defect signal characteristics: (**a**) Eigenvalues of the axial component of the signal; (**b**) Eigenvalues of the radial component of the signal.  
Figure 12: Defect signal characteristics under different variables: (**a**) defect signal characteristics under the variable L; (**b**) defect signal characteristics under the variable H; (**c**) defect signal characteristics under the variable OD; (**d**) defect signal characteristics under the variable ID.  
Figure 13: Experimental testing device.  
Figure 14 shows two different types of damage on a wire rope with multiple broken wires and a single broken wire, highlighted with red circles. The detection device is fixed, and the wire rope is moved back and forth to spot sweep the damage of the wire rope. Figure 15 shows the experimental results of damage detection of wire rope under different magnetizer sizes. When ID = 70 mm and OD = 100 mm are unchanged, (1) the value of H = 15 mm remains unchanged, while L is 10 mm, 30 mm and 60 mm in order, then when L = 30 mm, the maximum amplitude of the damage signal is detected; (2) when L = 30 mm, while H is 15 mm and 30 mm, it is obvious that when H = 30 mm, the maximum amplitude of the damage signal is detected. Therefore, when designing the size of the magnetizer, the inner diameter of the magnet should be determined by the diameter of the wire rope, and the thickness and outer diameter of the magnet should be increased appropriately to reduce the length of the armature.  
Figure 14: Wire rope damage: (**a**) multiple broken wire damage; (**b**) single broken wire damage.  
Figure 15: Experimental results: (**a**) damage signal of multiple broken filaments; (**b**) damage signal of single broken filaments.
## 3 Results and Discussion
### Answering RQ1 and RQ2
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Quality Category**} & \\ \cline{2-9}
**Ref.** & **Year** & **AR as a** & **AR** & **AR-Based** & **Application** & **Technology** & **Sample** & **Results** \\  & **Virtual** & **Assisted** & **Actised** & **No-Line Quality** & & & & & \\  & **Local** & **ogy** & **Control** & & & & & & \\ \hline
[0] & 2021 & & x & x & Shop floor procedures & Monitor, Markerless tracking & 4 inoperienced users and 3 experienced users & 36\% reduction of execution time besides reducing the risk of human errors \\ \hline
[188] & 2021 & & x & Inspection activities & \
\begin{tabular}{c} HHD, \\ Hybrid tracking (market-based tracking, \\ markerless tracking, \\ sensor-based tracking) \\ \end{tabular} &
\begin{tabular}{c} 16 engineers and factory \\ workers of Baker \\ Hughes plant \\ \end{tabular} & High satisfaction from the selected user \\ \hline
[189] & 2021 & x & & \begin{tabular}{c} Car body \\ alignment \\ \end{tabular} & \
\begin{tabular}{c} HHD, HMD, \\ Markerless tracking \\ \end{tabular} & N/A &
\begin{tabular}{c} Immediate detection of alignment errors \\ Reducing the gap and flushness from 12.77 mm and 3.05 mm to 7.17 mm and 0.33 mm, respectively \\ \end{tabular} \\ \hline
[190] & 2021 & x & \begin{tabular}{c} Machines process \\ monitoring \\ \end{tabular} & \
\begin{tabular}{c} HMD, \\ Hybrid tracking (markeries tracking, \\ sensor-based tracking) \\ \end{tabular} & N/A &
\begin{tabular}{c} Robust registration method \\ User-friendly AR interface to support the \\ integration of operators with digital twin data \\ \end{tabular} \\ \hline
[191] & 2021 & x & \
\begin{tabular}{c} Aviation system \\ inspection \\ \end{tabular} &
\begin{tabular}{c} HMD, \\ Model-based tracking \\ \end{tabular} & N/A & Achieved accuracy of pin-detection up to 99\% \\ \hline
[192] & 2020 & x & PCBA inspection &
\begin{tabular}{c} HHD, HMD, \\ Hybrid tracking (marker-based tracking, \\ markerless tracking) \\ \end{tabular} & 31 users from university & High usability of the system while ensuring the quality of inspection of PCBAs \\ \hline
[193] & 2020 & x & \begin{tabular}{c} Casting process \\ \end{tabular} & \
\begin{tabular}{c} Monitor, \\ Marker-based tracking \\ \end{tabular} & N/A &
\begin{tabular}{c} AR marker-tracking method can be used to \\ measure the worker’s pouring motion \\ Revealing the relations between workers’ \\ motion and the casting defect \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Categories of current AR-assisted quality.
# A Review: Using Multiple Templates for Molecular Imprinted Polymer: Is It Good?
## 2 Multi-Template Strategy
### Influencing Factors
#### 2.3.4 Porogen  
In the MIP process, the nature and volume of the solvent play important roles. The porogen influences how the target and the monomer interact. During polymerisation, the porogen acts as a dispersion medium and helps to form a homogeneous cavity. For MIP synthesis, toluene, chloroform, dichloromethane, or acetonitrile are the most often utilised solvents. In the polymerisation process, the solvent serves to combine all of the constituents (monomer, template, initiator, and crosslinker) into one phase and is what provides macroporous polymers with their pores. To ensure that the resultant MIP has good flow-through properties, the solvent should produce large pores; as the volume of the solvents increases, so do the polymer's pore sizes. For this reason, the term porogen is frequently used to describe the solvent [77; 82].  
The interaction between the template molecule and the functional monomer can be impacted by the porogen's polarity. Organic solvents that tend to be non-polar, with low dielectric constants, such as toluene, acetonitrile, and chloroform, are often used for polar non-covalent printing because they enhance the formation of complexes that facilitate polar non-covalent interactions such as hydrogen bonding. The interaction force between the analyte and the polymer tends to decrease when a more polar solvent is used because more polar solvents tend to detach the non-covalent interactions in the prepolymer complex, especially protic solvents which leads to a high degree of hydrogen bond disruption in the monomer template [77; 86; 88; 89].  
[PERSON] et al. [90] listed the requirements that must be fulfilled before a substance may be utilised as a porogen. (1) The porogen must be inert, have a high boiling point, and not polymerise or react with monomers, other materials, or other substances in the polymerisation mixture. (2) If a thermal initiator is to be used, a solvent with a boiling point higher than the initiator's decomposition temperature is typically chosen. The porogen must also be compatible with the initiation method. (3) When combined with monomers and crosslinkers, porogens must be miscible with one another and capable of forming homogenous solutions. The partition coefficient (log P), polarity index (PI), and solubility factors are also additional selection criteria for pyrogens. MIPs with small diameters and substantial surface areas can be made by solvents with strong dipole moments, and low log P. MIPs with small pores can also form when solvents with a high polarity index are used. The Hildebrand solubility parameter (8) of the solvent can be used in selecting the combination of porogens by comparing the value of \(\delta\) in each solvent with the value of \(\delta\) in the monomer. In general, organic solvents are considered good if the difference in the value of \(\delta\) < 1.7 MPa\({}_{\frac{1}{2}}\)[90].
# Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet
## 3 Cross-Guide-UNet (CG-UNet)
### Cross-Guide Module
#### 3.1.2 Spatial Attention  
ECA-Net solely utilizes channel attention and does not incorporate spatial attention. Spatial attention is pivotal for highlighting essential features within the image, particularly to help the model focus on the ice edge. Consequently, the CGM calculates spatial attention after extracting channel attention from the feature map. Figure 4 illustrates the diagram of spatial attention.  
Figure 3: Diagram of channel attention.  
Figure 2: Cross-guide module.  
Spatial attention in the CGM draws inspiration from the calculation method of channel prior convolutional attention (CPCA) [43]. It employs a dynamic allocation method and replaces the convolution kernel with strip convolutions, rendering it more appropriate for strip images like transmission lines. Unlike channel attention, spatial attention does not aggregate feature maps in a specific direction since it assigns identical spatial attention information to all channels. This ensures the preservation of distinctions between various channels. Attention is computed through strip convolutions using convolution kernels of different scales. Finally, spatial attention weight is derived through addition.  
\[Y_{sa}^{i}=C_{1}(\sum_{k=0}^{3}B_{k}(C_{dw}(Y_{ca}^{i}))), \tag{5}\]  
Equation (5) presents the formula expression of spatial attention. \(Y_{ca}^{i}\) is the channel attention weighted image feature, \(C_{dw}\) represents deep convolution, \(B_{k}\) is the k-th branch, and \(C_{1}\) represents the convolutional layer with kernel 1. By calculating the spatial attention of different branches, the final feature vector is obtained.
## 6 Simulation Evaluation  
### Parameter Settings and Evaluation Metrics  
In analyzing and verifying the effectiveness of the NDACS algorithm, it is compared with VBDA in this study. Because VBDA is a node depth-adjustment deployment algorithm based on computational geometry, nodes can only move along the vertical direction, and VBDA's various hypotheses and preliminary conditions are similar to those of the NDACS algorithm. The algorithms are compared according to network connectivity rate and network coverage rate, energy consumption of communication and movement, and network reliability in this study.  
MATLAB is used to simulate and analyze experiment results, and the simulation result is the mean of 30 experiments. In verifying the feasibility of the results, the 3D volume of water monitored is 500 m \(\times\) 500 m \(\times\) 400 m; the sensing radius \(Rs\) of the node is 50 m; the initial communication radius \(Rc\) is 60 m; and \(Bu\) is 5 m. When the network is deployed, the network coverage rate and connectivity rate are compared. Energy consumptions are compared based on the total energy consumed during the deployment process, and the rest of the parameters are set as shown in Table 2, with other settings derived from [30].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**H** & **Depth of Monitoring Space** \\ \hline v & Movement speed of node \\ K & Number of times of depth-adjustment \\ Rc & Communication radius of node \\ P & Speed of sound \\ T\({}_{\mathrm{d}}\) & Propagation delay of acoustic signal \\ T\({}_{\mathrm{c}}\) & Transition time from active to sleep or inversely \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbolic description of complexity analysis.
###### Abstract
[15] employed information such as train speed, train position, and road surface roughness as inputs for the artificial neural network to predict the dynamic response of railway bridge systems. The previous studies proved that the design of the input data is the critical problem during the process of constructing an artificial neural network to predict the train-induced bridge dynamic responses; however, existing studies struggled to accurately incorporate the time history information of train loads. To address the abovementioned issues, a novel framework for predicting the structural dynamic responses of railway bridges was proposed by combining the deep long short-term memory network, denoted as LSTM, and vehicle-bridge coupling theory. Specifically, the framework considers the load of each node of the bridge structure at any moment as the input information of the neural network to establish a deep LSTM network to predict the train-induced dynamic responses of the railway bridge. A numerical example of a single-supported bridge under a moving train is utilized to validate the proposed methodology and illustrate the potential influence factors.
## 2 Materials and Methods
### Analysis of Free Volatile Aroma Compounds  
Volatile aroma compounds were isolated from wine samples using solid-phase microextraction (SPME) with a divinylbenzene-carboxen-polydimethylsiloxane (DVB-CAR-PDMS) fiber. The method was detailed in previous research [21]. A 4 mL wine sample was prepared in a 10 mL glass vial. The sample was diluted fourfold with the addition of 1 g of (NH4)2 SO4 (Kemika d.d., Zagreb, Croatia) and 50 mL of internal standard solution (2-octanol at a concentration of 0.84 mg/L of wine for monoterpenes, C13-norisopenroids, and alcohols; methyl nonanoate at a concentration of 0.82 mg/L of wine for esters; and heptanoic acid at a concentration of 2.57 mg/L for fatty acids). After conditioning for 15 min at 40 \({}^{\circ}\)C, headspace extraction was performed using a DVB-CAR-PMS fiber (Stable-Flex, 50/30 \(\mu\)m, 1 cm; Supelco, Bellefonte, PA, USA) at the same temperature for 40 min with continuous stirring using a magnetic stirrer (800 rpm). Following extraction, the SPME fiber was placed in the gas chromatograph injector for the desorption of volatile compounds at 248 \({}^{\circ}\)C for 5 min (3 min splitless mode). Identification and quantification were conducted using a gas chromatograph-mass spectrometer (GC-MS) system consisting of a Varian 3900 gas chromatograph coupled to a Varian Saturn 2100 T mass spectrometer with an ion trap analyzer (Varian Inc., Harbor City, CA, USA) and the associated Star Chromatography Workstation software, version 6.6 (Varian, Inc.). A capillary Rtx-WAX column (60 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\mu\)m d.f.) (Restek, Bellefonte, PA, USA) was used. The oven temperature program was as follows: the initial temperature was set at 40 \({}^{\circ}\)C, then increased by 2 \({}^{\circ}\)C per minute to 240 \({}^{\circ}\)C, where it was held for 10 min. The injector temperature was set to 245 \({}^{\circ}\)C, the transfer line to 80 \({}^{\circ}\)C, and the ion trap to 120 \({}^{\circ}\)C. Mass spectra were obtained by electron ionization (EI, 70 eV) with 1 s/scan, and the detection range was set for ions with a mass-to-charge ratio of 30-450. Helium was used as the carrier gas with a flow rate of 1.2 mL/min. Calibration curves were prepared based on the analysis of standard solutions. Linear retention indices (relative to retention indices of n-alkanes from C10 to C28) were calculated and compared with the literature values. Compound identification was performed by comparing the retention times and mass spectra with those of pure standards when available, and with mass spectra from the NIST05 spectral library. For other compounds, semi-quantitative determination was conducted, and concentrations were expressed as equivalents of compounds with similar chemical structures for which standards were available, assuming a response factor of 1.