esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Aeromagnetic Compensation Method Robust to Geomagnetic Gradient
## 5 Conclusions  
The traditional aeromagnetic compensation method does not take into consideration the influence of the geomagnetic gradient. The interference field obtained after bandpass filtering still contains the information of the geomagnetic field, which results in errors during the estimation of compensation parameters. In this paper, an improved compensation method that is robust to the geomagnetic gradient is presented. By introducing the IGRF model, the geomagnetic gradient is modeled as a function of longitude, latitude, and flight height. This significantly improved the accuracy and the stability of the estimated compensation parameters. The experiment results show that the proposed method greatly improved the compensation effect. All the results demonstrate that the proposed method  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **Method** & \(\sigma_{u}\) & \(\sigma_{c}\) & **IR** \\ \hline \multirow{4}{*}{CF1} & Traditional & 0.1496 & 0.0309 & 4.8400 \\  & Ge method & 0.1496 & 0.0248 & 6.03 \\  & Proposed & 0.1496 & 0.0138 & _10.8406_ \\  & Traditional & 0.0699 & 0.0400 & 1.7469 \\  & Ge method & 0.0699 & 0.0311 & 2.24 \\  & Proposed & 0.0699 & 0.0194 & 3.6031 \\  & Traditional & 0.1835 & 0.0464 & 3.9599 \\ CF3 & Ge method & 0.1835 & 0.0391 & 4.6931 \\  & Proposed & 0.1835 & 0.0298 & 6.1577 \\  & Traditional & 0.1962 & 0.0339 & 5.7791 \\ CF4 & Ge method & 0.1962 & 0.0278 & 7.0576 \\  & Proposed & 0.1962 & 0.0222 & _8.8378_ \\  & Traditional & 0.0310 & 0.0109 & 2.8556 \\ LF1 & Ge method & 0.0310 & 0.0083 & 3.7349 \\  & Proposed & 0.0310 & 0.0033 & 9.3939 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of compensation results.  
Figure 15: The residual noise of level flight after compensation (after a bandpass filter).  
could significantly improve the effect of aeromagnetic compensation and the weak magnetic anomaly detection. In the future, more aeromagnetic survey tests will be carried out to verify the effectiveness of the proposed method.  
Conceptualization, Y.F.; methodology, Y.F. and X.Q.; software, Q.Z. and Y.Z.; validation, Y.Z. and F.W.; formal analysis, X.Q.; investigation, Y.F. and G.F.; resources, G.F.; data curation, Y.F. and Q.Z.; writing--original draft preparation, Y.F.; writing--review and editing, Y.Z.; visualization, Q.Z.; supervision, G.F.; project administration, X.Q.; funding acquisition, G.F. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by the National Natural Science Foundation of China under grant 61901441 and the National Key R&D Program of China under grant 2019 YFC1408102. The authors wish to express their gratitude to the editor and the anonymous reviewers.  
Not applicable.  
The authors declare no conflict of interest.
# Chinese Character Component Deformation Based on AHP
## 4 Control Point Selection and Component Deformation
### Hierarchical Single Ranking  
#### 4.2.1 Criterion Layer Weights  
First, we need to construct the judgment matrices of H, W, and N and then determine the weight of each. In general, the judgment matrix needs to be constructed by expert scoring, which is usually highly subjective. It may fail when performing the consistency tests mentioned later. Thus, the importance of the factors in criterion level is obtained by comparing the difference in width and height between the original component and the reference component in this study.  
Set \(\Delta\)w and \(\Delta\)h to represent the difference in width and height, respectively. We use r to denote the ratio of the larger to the smaller of \(\Delta\)w and \(\Delta\)h, rounded up. We stipulate that N has the same importance as the larger one between W and H.  
Suppose the importance of W relative to H is \(\rho\), and the importance of N relative to W is \(\varphi\). Then, the value of \(\rho\) and \(\varphi\) need to satisfy Equation (2).  
\[\left\{\begin{array}{ll}\rho=r,\,\varphi=1(\Delta w>\Delta h)\\ \rho=\frac{1}{r},\,\varphi=r(\Delta w<\Delta h)\end{array}\right. \tag{2}\]  
Thus, the judgment matrix of influencing factors is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **W** & **H** & **N** \\ \hline W & 1 & \(\rho\) & \(\frac{1}{\varphi}\) \\ H & \(\frac{1}{\rho}\) & 1 & \(\frac{1}{\rho\varphi}\) \\ N & \(\varphi\) & \(\rho\varphi\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Importance matrix of criterion level.  
Figure 10: Hierarchical structure. The top level is the target level, the middle level is the criterion level, and the bottom level is the scheme level.  
The distribution of weights for W, H, and N by column normalization using the arithmetic mean method is shown in Equation (3). \(n\) represents the order of the matrix, and \(\omega_{i}\) represents the weight value of the \(i\)th element.  
\[\omega_{i}=\frac{1}{n}\sum_{j=1}^{n}\left(\frac{a_{ji}}{\sum_{k=1}^{n}a_{kj}}\right) \tag{3}\]
# Evidence of Carbon Uptake Associated with Vegetation Greening Trends in Eastern China
## 3 Results  
### Trends in Regional Vegetation Parameters  
Here, we show mean growing period (GP) vegetation trends for MODIS NDVI and AVHRR NDVI from 2003 to 2016 and AVHRR LAI and tree canopy cover trends from 2003 to 2016 (Figure 4). These vegetation indices are unitless because NDVI and EVI range 0-1 and LAI is in m\({}^{2}\) of leaf area per m\({}^{2}\) land surface area. MODIS EVI, LAI, and GPP trends during the GP from 2003 to 2016 are shown in Figure S9. Significant greening trends were observed in EC (Eastern China with red edge) with similar patterns in MODIS NDVI, AVHRR NDVI, and MODIS EVI (Figure 4a,b and Figure S9a). The trends in mean GP values of MODIS NDVI, AVHRR NDVI, and MODIS EVI over EC from 2003 to 2016 were 0.38 \(\pm\) 0.27 \(\times\) 10\({}^{-2}\) yr\({}^{-1}\), 0.38 \(\pm\) 0.30 \(\times\) 10\({}^{-2}\) yr\({}^{-1}\), and 0.32 \(\pm\) 0.21 \(\times\) 10\({}^{-2}\) yr\({}^{-1}\), which accounted for 0.70 \(\pm\) 0.61% yr\({}^{-1}\) and 0.71 \(\pm\) 0.75% yr\({}^{-1}\) of NDVI (MODIS and AVHRR, respectively) and 0.86 \(\pm\) 0.60% yr\({}^{-1}\) of EVI relative to the mean values over this 14-year period. The standard deviation reported here is the variability in all grid cells trends across the EC domain and not an indicator of within-grid cell trend uncertainty. AVHRR LAI also showed significant increasing trends in EC with 0.013 \(\pm\) 0.010 m\({}^{2}\) m\({}^{-2}\) yr\({}^{-1}\) (Figure 4c). In addition, increasing trends (0.009 \(\pm\) 0.010 m\({}^{2}\) m\({}^{-2}\) yr\({}^{-1}\) and 0.11 \(\pm\) 0.19 g C m\({}^{-2}\) yr\({}^{-2}\)) of LAI and GPP from MODIS were also significant in most area of EasternChina (Figure S9). Total increases of NDVI and GPP over Eastern China from 2003 to 2015 could be around 8.40% to 8.52% and 9.24 g C m\({}^{-2}\), respectively.  
Tree canopy annual trends from 2003 to 2016 showed a strong net increasing trend (\(0.65\pm 0.34\%\) yr\({}^{-1}\)) in the southern part of EC (Figure 4d, latitude \(<34^{\circ}\)N). Large total tree canopy increase from 2003 to 2015 (7.80%), potentially resulting in enhanced photosynthesis rates, may have increased the net carbon sink in this region.
# A Long-Distance Communication Architecture for Medical Devices Based on LoRaWAN Protocol
###### Abstract
The aim of this paper is to design, develop and analyze a long-distance communication architecture for medical devices based on the LoRaWAN protocol. In this paper, the sections are structured as follows: Section 2 contains a brief related work about the LoRa technology presented in the specialty literature; Section 3 presents the proposed medical device architecture based on LoRaWAN protocol; in Section 4, the hardware and software implementation for the proposed medical device are described, and finally, the conclusions are drawn in Section 5.
# An Efficient Encoding Algorithm Using Local Path on Huffman Encoding Algorithm for Compression
## 3 Materials and Methods
### Proposed Algorithm
When the example text data is rearranged by the developed encoding algorithm, the resulting bit sequence is as follows:  
\[``000000000101010100111010101001011101100010111011101110010111101000000000101\".\]  
As mentioned previously, special text was used to better understand the developed algorithm. This text was represented by 98 bits when compressed by the Huffman encoding algorithm. When the developed encoding algorithm was used to encode the text, it was represented by 83 bits. For this specific example, the developed coding algorithm achieved a better compression ratio of 15.30% compared to the Huffman encoding algorithm. In the next step of the study, detailed tests are performed on the data used in the literature to better evaluate the proposed encoding algorithm.
## 1 Introduction  
Efficient warehouse management directly influences critical performance metrics such as operating costs, throughput, and customer satisfaction. Among all warehouse activities, order picking is one of the most resource-intensive and cost-sensitive processes, particularly in spare parts supply chains where timely and accurate order fulfillment is paramount [1]. In order to address the challenges, many warehouses employ a forward-reserve area configuration. The forward area serves to stock a limited quantity of spare parts for rapid order picking, while the reserve area holds bulk inventory to replenish the forward area as needed. This internal replenishment practice aims to balance accessibility and storage capacity, but it also introduces operational complexities.  
The forward-reserve replenishment process often faces significant challenges, such as delays, congestion, and capacity constraints, particularly in high-demand environments. To alleviate these issues, some warehouses utilize buffer areas as interim holding spaces. The usage of a buffer area is a well-established practice in warehouse management, valued for enhancing overall warehouse flexibility. Despite the benefits of reduced handling times and improved responsiveness, buffer areas can contribute to congestion if not properly managed. This study introduces a replenishment policy that strategically incorporates buffer areas into forward reserve operations. The policy prioritizes order picking from the buffer area. Additionally, it restricts returning stock to the reserve area to avoid operational disruptions. By focusing on reducing excess buffer inventory, the approach ensures a more efficient flow of goods, maximizing the benefits of buffer areas. Thus, buffer areas not only relieve pressure on reserve areas but also enable faster replenishment of forward areas, enhancing throughput and operational fluidity.  
The significance of this study lies in its ability to address a critical gap in warehouse management practices: the absence of structured strategies for buffer area utilization. Although buffer areas are commonly used across industries, they are often implemented reactively as short-term solutions rather than as complementary components of a well-designed replenishment strategy. This study's novelty revolves around the development of a tractable, integrated, and adaptive framework for this complex, real-world problem. The methodological novelty lies in the synthesis of three components: (1) the explicit mathematical modeling of unstructured buffer areas within the replenishment problem, (2) the use of a rolling-horizon mixed-integer linear programming (MILP) model as a high-quality policy evaluation engine, and (3) its integration with a dual-loop, adaptive forecasting module. This combination provides a practical approximate dynamic programming (ADP) framework for optimizing a high-dimensional, stochastic system where traditional dynamic programming is computationally infeasible.
## 2 Simultaneous Localization and Mapping
### FastSLAM 2.0
#### 2.1.4 Landmark Estimation  
Given the new proposal distribution, each landmark position in the map is corrected based on the new observations in an EKF-like fashion, meaning that a separate EKF is applied to each observed landmark. New landmarks are initialized based on observations not associated with any preexisting map feature. The initialization happens according to the inverse observation model, which is defined as follows:  
\[x_{l} =x_{r}+\rho\cos(\theta_{r}+\beta),\] \[y_{l} =y_{r}+\rho\cos(\theta_{r}+\beta)\]  
The landmark estimation step is summarized in Algorithm 3, where the Jacobian \(H_{a}\) is derived as described in Section 2.1.3.  
Figure 2: Observation model—graphical representation.  
In this step, a weight is assigned to the particles. If at least one unmatched observation exists, the particle weights are reinitialized as in line 21 in Algorithm 3. Otherwise, the weight is assigned according to what is stated on line 24.  
The complexity of this step depends on the number of particles and the number of observations.
## 3 Results and Discussion
### Performance of BDS-3 SAIM Based on On-Orbit Data
#### 3.1.2 Pseudo-Range Monitoring  
In Figure 8, the upper and lower subfigures are the pseudo-range measurement monitoring results of the B1C and B2a signals, respectively. The results show that the standard deviations of the pseudo-range measurement monitoring data of C30 satellite's B1C signal and B2a signal are 0.08 m and 0.23 m, respectively. The standard deviation of the pseudo-range measurement monitoring data of the C34 satellite's B1C signal is 0.06 m and the standard deviation of the B2a signal is 0.07 m.  
Figure 8a shows that the pseudo-range measurement of the C30 satellite's B2a signal has a long-term slow peak-to-peak fluctuation phenomenon, as the peak-to-peak value has changed from 0.6 m to 0.8 m, and the average value has also changed by about 0.1 m. This value is caused by the slight change in the temperature of the single machine caused by the operation of the satellite, which is the same to the power data.  
The QQ analysis charts of the pseudo-range measurement monitoring data of the C30 and C34 satellites are given in Figures 9 and 10, respectively.  
Figure 6: Signal power monitoring data of the C30 satellite: (a) B1C signal; (b) B2a signal.  
Figure 7: Signal power monitoring data of the C34 satellite: (a) B1C signal; (b) B2a signal.  
Figure 8: SAIM pseudo-range measurement monitoring values of the B1C and B2a signals of (**a**) the C30 satellite and (**b**) the C34 satellite.  
Figure 10: The QQ analysis chart of the pseudo-range measurement monitoring data of the C34 satellite: (**a**) B1C signal; (**b**) B2a signal.  
Figure 9: The QQ analysis chart of the pseudo-range measurement monitoring data of the C30 satellite: (**a**) B1C signal; (**b**) B2a signal.  
The distribution deviation of the B1C signal pseudo-range monitoring data of the C30 satellite is \(-0.08\) and the kurtosis is \(2.85\). The distribution deviation of the B2a signal pseudo-range monitoring data of the C30 satellite is \(0.22\) and the kurtosis is \(1.87\). The distribution deviation of the B1C signal pseudo-range monitoring data of the C34 satellite is \(0.01\) and the kurtosis is \(2.87\). The distribution deviation of the B2a signal pseudo-range monitoring data of the C34 satellite is \(-0.1\) and the kurtosis is \(2.12\).  
It can be seen from the figures that the pseudo-range measurement monitoring data of the C30 satellite's B1C signal have a distribution close to the normal distribution, while a certain deviation from the normal distribution is seen in the B2a signals, with larger skewness and kurtosis, indicating that the deviation and dispersion between the sample data and the mean are also large. The B1C signal of the C34 satellite basically presents normal distribution characteristics. The B2a signal occasionally has a large level of measurement noise, and the tail dispersion can be seen clearly in the QQ analysis chart. The reason for the deviation characteristics of B2a delay data is the same as that of power data, and the description will not be repeated here.
# Different Configurations of the Non-Minimal Prismatic Tensegrities
## 3 Verification
### Physical Models  
Here, two physical models are presented--the minimal quartex module (Figure 10a) and the non-minimal quartex module for \(\theta=-10^{\circ}\) (Figure 10b). The geometrical lengths are given in Table 2. Please note that the lengths are called geometrical here, since they are calculated for the unstressed module--for assembling a physical model this approach is accurate enough. Bars are made out of CHS 10x1 mm, with the internal M10 thread crafted on each end. Bar endings are filled with M10 screws. Cables are made out of 3 mm diameter braided polypropylene lines, which were attached to M12 pads on both ends. Similar line dimensions were assured by using a simple device presented in Figure 10c, where two bolts with adjustable lengths are blocked with one screw. Polypropylene lines are attached to the bolts by M12 pads and are further attached to them in order to create the cables of the tensegrity structure.  
The minimal quartex physical model is a well-known tensegrity (Figure 10a). The modified one is an example of the stable configuration of the \(2t\)-bar system (Figure 10b). Please note that both models have similar lengths of horizontal cables and bars. In order to obtain similar lengths of bars with the change in the rotation angle \(\theta\), the height \(h\) needed to rise. Also, small changes in lengths of cross cables were made.  
The presented models are simple, yet they show that the stability of these systems is possible.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Angle \(\theta\)** & **Horizontal Cable** & **Cross Cable** & **Bar** & **Additional Member** & **Height** \\  & **[deg]** & **[mm]** & **[mm]** & **[mm]** & **[mm]** & **[mm]** \\ \hline Minimal & 0 & 226.3 & 420.2 & 499.0 & 420.2 \({}^{\star}\) & 402.0 \\ \hline Non-minimal & \(-10\) & 226.3 & 421.5 & 499.0 & 436.2 & 410.4 \\ \hline \hline \end{tabular}  
* This member is not mounted in the minimal configuration, and therefore it can be omitted.  
\end{table}
Table 2: Geometrical dimensions of quartex physical models (calculated based on (7)).  
Figure 9: Prestressing the non-minimal quartex by shortening the additional cables for \(e/l_{0}\) equal to (**a**) 0.056, (**b**) 0.093, (**c**) 0.187 and (**d**) 0.4632.
# Solution to Solid Wood Board Cutting Stock Problem
## 2 Mathematical Model  
In practical production, there are often different types of defects on the surface of solid wood boards; these have an impact on the structural strength and aesthetic degree. After obtaining the characteristic information of surface defects using machine vision technology, the defects are removed by the cutting saw, and multiple boards with different lengths are obtained [36; 37; 38]. These boards must be segmented according to the standard length for subsequent processing.  
Assume that the total number of original boards after cutting defects is \(n\). The length of the \(i\)-th original board is \(L_{i}\) (\(i=1,2\cdots,n\)). \(m\) types of standard boards need to be placed on these original boards without overlapping. The length and demand of the \(j\)-th type of standard board are \(l_{j}\) and \(b_{j}\), respectively (\(j=1,2\cdots,m\)).  
On the premise of meeting the constraints, the optimization goal is to minimize the total length of the used original boards, namely, to maximize the board utilization rate. The objective function is expressed as  
\[max\ U=\sum_{j=1}^{m}l_{j}b_{j}/L_{C} \tag{1}\]  
where \(L_{C}\) is the total length of all the original boards selected in a layout scheme, \(U\) is the board utilization rate, and \(m\) is the number of types of standard boards.  
The solid wood board CSP must meet the following three constraints:  
(1) The total quantity of the \(j\)-th type of standard board placed on the \(i\)-th original board should be a nonnegative integer:  
\[a_{ij}\in N \tag{2}\]where \(a_{ij}\) is the total quantity of the \(j\)-th type of standard board placed on the \(i\)-th original board, and \(N\) is a nonnegative integer.  
(2) The total quantity of the \(j\)-th type of standard board placed in a layout scheme is equal to its demand:  
\[\sum_{i=1}^{n}a_{ij}=b_{j} \tag{3}\]  
where \(b_{j}\) is the demand of the \(j\)-th type of standard board, and \(n\) is the total number of original boards.  
(3) The total length of all the standard boards placed on the \(i\)-th original board cannot exceed the length of the \(i\)-th original board:  
\[\sum_{j=1}^{m}a_{ij}l_{j}\leq L_{i} \tag{4}\]  
where \(l_{j}\) is the length of the \(j\)-th type of standard board, and \(L_{i}\) is the length of the \(i\)-th original board.  
Figure 2 shows the mathematical model of the solid wood board cutting stock problem. 5, 6, 8, and 11 are the selected original board numbers in a layout scheme.  
Figure 2: Mathematical model of the solid wood board cutting stock problem.
###### Abstract  
Recent advancements in geophysical exploration have highlighted the importance of integrating electromagnetic (EM) and magnetic data to enhance subsurface target detection. Conventional inversion techniques often struggle with the non-uniqueness of solutions and sensitivity to noise when relying on a single data modality. In this study, we introduce a novel deep learning framework, MagEMNet, designed to jointly invert EM and magnetic responses. This convolutional neural network (CNN)-based model effectively combines these two complementary data types, improving the estimation of target characteristics such as location, orientation, and physical properties. Trained on synthetic datasets generated through forward modeling, MagEMNet leverages the adaptive moment estimation (Adam) optimizer and a dynamic learning rate strategy to enhance convergence. Our results show that MagEMNet not only outperforms traditional inversion techniques in terms of accuracy but also accelerates the inversion process, offering an efficient solution for real-world applications, including unexploded ordnance (UXO) detection and subsurface resource assessment.  
joint inversion; convolutional neural network (CNN); subsurface target detection; magnetic field response +
Footnote †: journal: Applied sciences  
0000-0001-5888-726  
0000-0001-5888-726
# A Reliable Handoff Mechanism for Mobile Industrial Wireless Sensor Networks
## 5 Effective Handoff Mechanism
### Registration  
To guarantee network stability, the scheduled superframe and registration need to be adapted for mobility. The infrastructure nodes connected to \(MN\) should also detect the broken link when \(MN\) moves to another location. As \(MN\)'s leaf nodes, they re-register the new links and resources same as the principle of handoff triggering and registration. When it comes to the parent node of \(MN\), once it detects that the link to \(MN\) has broken, it reports to the manager and releases such link resources. For instance, when \(g\) receives the upstream packets from \(MN\), it checks the parent address added by \(MN\) in the network header. If it is different from the registered parent address, \(g\) will record it temporarily for the downstream flow. Moreover, the allocated slot resource should be released, thus \(g\) issues a notification to \(MN\)'s original parent. Just as the \(MN\)'s packet arrives through \(v_{4}\) at \(t_{1}\), \(g\) sends a notification to \(v_{8}\) to release the relative resources related to links \(MN\longrightarrow v_{8}\) and \(v_{8}\longrightarrow g\).  
Figure 8: The structure of the superframe and the adjustment when \(MN\) moves from time \(t_{0}\) to \(t_{4}\).