esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Length Measuring Capability and Three-Dimensional Measurement Capability
### Measurement with a Single Probe Does Not Define Three-Dimensional Measurement Capability  
It is also true that complete knowledge of CMM behaviour for a single probe is not sufficient to characterise its behaviour if multiple probes are used. The following example shows that completely characterising the behaviour of a CMM measurement using a single probe offset does not characterise CMM measurements using multiple probes. In particular, experiments to estimate the kinematic errors (see, e.g., [20]) of a CMM must involve multiple probe offsets.  
Suppose a CMM has an error behaviour determined by a roll about each axis that depends linearly on the length of travel along the axis. This behaviour can be modelled as  
\[\bar{\boldsymbol{x}}=\boldsymbol{x}+R(\kappa\boldsymbol{x})\boldsymbol{p},\]  
where \(\boldsymbol{x}\) is the true position of (a fixed point on) the probe assembly, \(\boldsymbol{p}\) is the probe offset (from the fixed point), and \(\bar{\boldsymbol{x}}\) is the CMM coordinate measurements (scale readings), \(R\) is the linearised rotation matrix corresponding to the roll about each axis given by  
\[R(\boldsymbol{x})=\left[\begin{array}{ccc}1&-z&y\\ z&1&-x\\ -y&x&1\end{array}\right],\]  
and \(\kappa\approx 0\) is a parameter determining the rate of roll. Then  
\[\bar{\boldsymbol{x}}=\boldsymbol{x}+R(\kappa\boldsymbol{x})\boldsymbol{p}= \boldsymbol{x}+\boldsymbol{p}+\kappa\boldsymbol{x}\times\boldsymbol{p}=R(- \kappa\boldsymbol{p})\boldsymbol{x}+\boldsymbol{p}, \tag{7}\]  
where \(\boldsymbol{x}\times\boldsymbol{p}\) is the vector cross-product of \(\boldsymbol{x}\) with \(\boldsymbol{p}\). The relationship Equation (7) shows that measurements of an artefact using a CMM with isotropic axis roll, i.e., having the same rate \(\kappa\) of roll along each axis, are the exact same as those of the same artefact rotated by \(R(-\kappa\boldsymbol{p})\) by a CMM with no axis roll (to first order). This equivalence means that, irrespective of measurement strategy and calibration information, a CMM cannot be completely characterised from the multiple measurements of calibrated artefacts such as ball plates and step gauges unless measurements are taken of the same artefact in the same position using more than one probe offset. In general, at least three probe offsets are required; by analogy, the location of three points are needed to track the position of a moving rigid body.  
The uncertainty evaluation method described in this paper attempts to characterise fully the three-dimensional nature of CMM measurement with multiple probes.
# Analysis of Pressure Effect on Three-Dimensional Flame Surface Density Estimation
## 3 Flame Surface Density
### Notations and 3D/2D Exact Relation  
For the sake of clarity on describing \(\Sigma\) and \(\Sigma^{2D}\), Figure 3a shows a surface given by three-dimensional Cartesian coordinates, measured by a two-dimensional plane. The 2D measurement of the surface is a curve, which is illustrated as the red curve in Figure 3a. **n** and **n\({}^{2D}\)** represent the normal vectors of the surface and the measured curve at location **p**, respectively. **n\({}^{\textbf{p}}\)** corresponds to the projection of **n** on the measuring plane. \(\theta\) is the angle between **n\({}^{p}(\textbf{n}^{2D})\)** and the \(y\) axis in the measuring plane (in-plane angle), denoted in Figure 3b. \(\phi\) is the angle between **n** and the measurement plane (out-of-plane angle), also denoted in Figure 3b. \(\theta\) is defined as \(0^{\circ}\) when **n\({}^{2D}\)** aligns with the positive direction of the \(y\) axis, and increases (decreases) counter-clockwise (clockwise) from \(0^{\circ}\) to \(180^{\circ}\) (\(-180^{\circ}\)), as illustrated in Figure 3c. \(\phi\) is defined as \(0^{\circ}\) when **n** is in the measuring plane, and ispositive (negative) when \(n_{z}>0(n_{z}<0)\). By definition, \(\theta\) and \(\phi\) are in the interval of \([-\pi,\pi)\) and \([-\pi/2,\pi/2)\), respectively, and are given by  
\[\theta=\arctan(n_{x},n_{y}),\quad\phi=\arcsin(n_{z}) \tag{5}\]  
With the notation defined above, \(\mathbf{n^{p}}\) and \(\mathbf{n^{2D}}\) has the relation of  
\[\mathbf{n^{p}}=\mathbf{n^{2D}}\cos\phi \tag{6}\]  
For a scalar \(c\), \(|\
abla c|^{2D}\) is the measured value of \(|\
abla c|\) in the measuring plane, which can be established mathematically as follows:  
\[|\
abla c|^{2D}=\cos\phi|\
abla c|. \tag{7}\]  
Applying Equation (1) in its 3D and 2D form to Equation (7) yields [15],  
\[\Sigma=\frac{1}{\langle\cos\phi\rangle_{s}}\Sigma^{2D} \tag{8}\]  
where \(\langle Q\rangle_{s}\) is the surface-averaged quantities, defined as  
\[\langle Q\rangle_{s}=\frac{\overline{Q|\
abla c|\delta(c-c^{*})}}{|\overline {\
abla c|\delta(c-c^{*})}} \tag{9}\]  
For detailed derivations on Equation (8), interested readers can refer to Refs. [13; 15]. Specifically, Equation (8) is the exact relation of the 3D/2D flame surface densities. It is readily seen that \(\Sigma^{2D}\leq\Sigma\) and both flame surface densities are identical if a two-dimensional flame front is assumed (no flame front wrinkling perpendicular to the measurement plane). The models of \(\langle\cos\phi\rangle_{s}\) are summarized in [15], and are briefly described in the following section.
## 1 Introduction  
### Motivation  
Recently, the interest in direct current distribution networks has increased due to their advantages in terms of efficiency and controllability and the advances in semiconductor technologies [1; 2]. In addition, several distributed energy resources, such as storage devices, photovoltaic panels, and loads, operate directly in DC, reducing the presence of power electronics [3; 4; 5]. DC networks are a competitive alternative to AC transmission systems at both high and low voltage levels [6]. Systems at high voltage levels are called HVDC systems (i.e., high-voltage DC), and those at low voltage levels are called LVDC systems (i.e., low-voltage DC) [7]. These grids do not require synchronization and pose economic advantages due to their elimination of the adverse effects caused by reactive power requirements in AC networks [8]. Furthermore, it has been reported that DC networks have low power losses and improved voltage profiles [9].  
Models, methods, and algorithms for analyzing conventional AC networks must be extended to DC networks [10; 11]. Among these methods, the power flow is perhaps the most important, since it is part of every operation, control, or planning model of electric systems. Despite its apparent simplicity, a power flow for DC networks brings challenges, especially for bipolar networks, namely, networks that include positive, negative, and neutral wires [12]. It has been reported that bipolar LVDCs are more efficient and flexible than the most known monopolar structure [13; 14]. While most of the current investigations involve dynamic studies from the power electronic perspective [14; 15], the static case concerning the power flow has received little attention, and the utility perspective and steady-state analysis have been neglected.
## 7 Results and Discussions
### Optimal Configuration from the HPRALC, MPRAMC, and LPRAHC-Based Configurations
#### 7.5.2 Medium Efficiency Appliances Usage-Based Scenario (MPRAMC) (with DSM)  
In the HPRALC scenario, the Ni-Fe-battery-based IRES (base case) was found to have the lowest LCC when compared to other battery-based IRESs. This configuration was then thoroughly examined using a scenario based on the use of appliances with an MPRAMC-based scenario (i.e., with DSM). The MPRAMC scenario describes a situation in which consumers use medium-power-rated appliances of moderate cost. Table 2 reveals that the LCC provided by the MPRAMC-based scenario is 33% lower than the base case LCC of the HPRALC-based scenario, i.e., $635,271. Moreover, the optimal values of the NPV and N\({}_{\text{BAT(Ni-Fe)}}\) components, in this case, are 1005 and 612, respectively. This demonstrates that the use of MPRAMC appliances has a remarkable effect on the system performance, resulting in a lower LCC and fewer components required when compared to the optimal values obtained using the HPRALC-based scenario.
# An FPT Algorithm for Directed Co-Graph Edge Deletion
## 3 Preliminaries  
For an integer \(i\), \([i]\) denotes the set of all positive integers no greater than \(i\).  
A directed graph (digraph) is a tuple \(G=(V,A)\), where \(V\) is the set of vertices of \(G\) and \(A\) is the set of arcs of \(G\). An arc from a vertex \(v\) to another vertex \(v^{\prime}\) is denoted by \((v,v^{\prime})\). For a digraph \(G=(V,A)\), we define \(E(G)=\{\{u,v\}:\{(u,v),(v,u)\}\cap A\
eq\emptyset\}\) as the set of edges in the underlying undirected graph of \(G\). For notational brevity, we denote an edge between two vertices \(v\) and \(u\) by \(vu\). For a graph \(G\) (be it directed or undirected), we use \(V(G)\) to denote the vertex set of \(G\). Deleting an edge \(vu\) from \(G\) means deleting all arcs between \(v\) and \(u\). Thus, if we delete an edge \(vu\) from \(G\), we obtain a graph with vertex set \(V\) and arc set \(A\setminus\{(v,u),(u,v)\}\). For \(E^{\prime}\subseteq E(G)\), \(G-E^{\prime}\) denotes the digraph obtained from \(G\) by deleting all edges in \(E^{\prime}\). A graph \(G\) is considered to be \(F\)-_free_ if it does not contain an induced subgraph that is isomorphic to \(F\). In this case, \(F\) is also referred to as the forbidden graph for \(G\). In this paper, we use the branch vector \((\tau_{1},\tau_{2},\ldots,\tau_{r})\) to represent a branching rule that branches into \(r\) instances, where the maximum number of new parameters is \(k-\tau_{1}\), \(k-\tau_{2}\), \(\ldots,k-\tau_{r}\). The linear recurrence of the maximum number of leaves for this branch vector is \(T(k)\leq T(k-\tau_{1})+T(k-\tau_{2})+\ldots+T(k-\tau_{r})\). The DCGED problem is formally defined as follows:  
Directed Co-Graph Edge Deletion (DCGED)  
**Input:** A directed graph \(G\) and an integer \(k\).  
**Question:**\(\exists E^{\prime}\subseteq E(G)\) so that \(|E^{\prime}|\leq k\) and \(G-E^{\prime}\) is a directed co-graph?
## 5 Conclusions  
This study investigates the full-field strain characteristics of anisotropic coal samples under creep loading using the DIC testing system to investigate the anisotropic deformation characteristics. The full-field strain evolution and crack propagation characteristics under creep loading were obtained, revealing the influence of anisotropic bedding angles on the strength and deformation characteristics of coal samples. The main results are summarized as follows:  
1. The peak strength of coal under creep loading is significantly affected by the bedding angle. As the bedding angle increases, the peak strength first decreases and then increases. Under the same stress level, coal samples with bedding angles of 0\({}^{\circ}\) and 90\({}^{\circ}\) exhibit stronger resistance to deformation, while coal samples with a bedding angle of 60\({}^{\circ}\) show the weakest deformation resistance, making them more prone to creep sliding failure along the bedding planes.
2. For coal samples with different bedding orientation, creep failure is characterized by a process of instantaneous deformation, decelerating creep, steady-state creep, accelerating creep, and eventual failure. Under multi-stage loading conditions, coal samples show significant creep behavior under higher loading levels in the final stage.
3. The bedding angle significantly affects the strain field evolution of coal samples. Regions of concentrated displacement changes typically occur at the intersections between bedding planes, and cracks tend to initiate and propagate at bedding interfaces. During the early stages, coal samples remain in a compressed and densified state. Just before failure, within seconds of reaching the peak stress, the strain range shows dramatic changes, followed by the formation of crack bands and overall macro failure.
4. The post-peak stress-strain curve behavior is closely related to the failure modes of the coal structure. For coal samples with bedding angles of 0\({}^{\circ}\) and 90\({}^{\circ}\), the final macroscopic failure mode is characterized by longitudinal tensile splitting failure; for coal samples with bedding angles of 30\({}^{\circ}\) and 60\({}^{\circ}\), failure tends to occur along the bedding interfaces, forming tensile or tensile-shear failure, leading to single-shear plane failure or X-shaped conjugate shear failure.  
**Author Contributions:** Experiments and writing, X.L.; funding acquisition, Y.W.; methodology, X.Y.; software, X.B. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Geological Survey Project of the China Geological Survey (DD202030437).  
Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Overview of LPaMI
### Features of LPaMI  
LPaMI allows a user to either consider selected photographs from his/her collection or download in bulk from cloud storage. The retrieved photographs to be processed further can be seen in the \"Show Images\" screen, as shown in Figure 2a. As soon as the photographs are selected, the back-end process of OOI recognition starts. The retrieved data on OOIs is written to the disk, and the data migration routines start processing to clean, filter, and then load the data in the persistent storage. We then perform graph modelling based on different configuration settings like granularity level, the set of photographs to consider for mining in certain period of time, modelling as either a single large graph or as a set of graphs, visualisation layout, and basic quantitative information of the graphs. Finally, the data of OOIs takes up the shape of a graph and is visualised for analysis, as shown in Figure 2b.  
As soon as the OOIs are modelled as a graph, pattern mining can be applied. We show the screen for this purpose in Figure 2c. It allows a user to select the type of patterns to be mined. The user is provided a control using a slider to set a minimum support threshold for pattern mining. Resultantly, all the patterns satisfying the input threshold criteria are displayed. LPaMI also provides a \"Search\" screen to the user as shown in Figure 2d. Here, a user can select some nodes of interest using a dialogue box and can create a query graph. The query graph is then used to mine patterns containing or similar to the input pattern with the aim of analysing the behaviour in a customised manner. In this way, LPaMI provides an interactive procedure to understand the details of the data.  
Figure 1: Pipeline of Lifestyle Pattern MIning (LPaMI) from personal image collections in a smartphone.  
LPaMI also describes the data using a storyboard, shown at the bottom of the Figure 2b,c. The storyboard explains the data in a textual format and presents the details of data and of mined patterns too. Our idea is to make LPaMI a user-friendly product, so this feature is a great facilitation option. To explain OOIs in terms of a graph, the storyboard reads data from persistent storage along with its metadata to explain the situation. On the other hand, it explains each pattern obtained as a result of applying pattern mining. To build a story, we have a database of different statements, and each is randomly picked and concatenated with the name of every OOI along with the metadata information of the concerned graph. Finally, the statements built using metadata and other statements, describing OOIs, are combined to shape a story. We understand that such procedure of generating the storyboard may be a naive approach, however, we plan to make it richer in semantics in next release of LPaMI.
## 5 Simulation and Experimental Results
### Experimental Parameters  
The two different image sensors (camera) supporting HD (1280 \(\times\) 720) and full HD (1920 \(\times\) 1080) resolutions videos with 30 FPS are used to capture real-time vehicular environment. A YOLOv3 [48] is exploited to object detection and classification. We consider 5.9 GHz DSRC network for V2V communication. For compatibility, we consider the proposed packet header contains detection results encapsulated with the basic payload of 500 bytes [75]. The packet is transmitted in 10 Hz period, which is applicable for safety message formats such as Cooperative Awareness Message (CAM), Decentralized Environmental Notification Message (DENM), and BSM used in DSRC. We consider the weighted similarity matrix which has elements such that \(Q(\text{E},\text{U})=100/101,Q(\text{N},\text{U})=1/101\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Data Type**} & \multicolumn{2}{c}{**Object**} & \multirow{2}{*}{**Trajectory**} \\ \cline{2-3} \cline{3-4}  & **Existence** & & **Classification** \\ \hline Value [s] & \(4.8\times 10^{-4}\) & \(5.0\times 10^{-4}\) & \(2.8\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Time consumptions of the proposed data fusion schemes for 10 data points.  
Figure 9: Comparisons of the false negative rates.  
The collision occurrence scenarios are modeled by MATLAB on Intel(R) Core(TM) Xeon 2.4 GHz with NVIDIA GeForce 1080 Ti GPU. The detailed experimental parameters are presented in Table 2.
# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 2 Related Work
### Degraded Image  
Degraded images are typically those affected by factors such as low contrast, occlusion, noise, or blur [16; 18]. The low quality of these images may arise for a variety of reasons, including suboptimal shooting conditions, the limitations of the equipment's performance, or interference from external environmental factors. Currently, researchers have proposed various methods to deal with degraded images. For example, some studies use image enhancement techniques to improve image quality, while others focus on reconstructing degraded images. These representative research works are displayed in Table 1.  
However, the methods mentioned above, for different classification problems, all depend on diverse domain knowledge to design new solutions. Additionally, in real life, the decline in image quality is not just due to a single adverse factor. Therefore, in the field of degraded image classification, a new method is needed. This method should not rely heavily on domain knowledge while being able to adapt to a variety of task requirements, effectively solving degraded image classification problems that are affected by multiple adverse factors.
## 1 Introduction
The parallelism measurement in industry commonly uses specific tools, such as micrometer, deflection instrument, coordinate measuring machines etc. In fact, micrometer has great randomness, which is greatly influenced by the operator's proficiency and experience. In the detection process, the operator can only judge the quality by observing the change range of the micrometer, and the detection data cannot be directly saved to the computer. The coordinate measuring machine can record the test data with high precision, but it can't measure the machine tool and large equipment on site. Therefore, from the above introduction, for machine tools with limited operating space, developing a simple  
Figure 1: Evaluation method of parallelism: (**a**). Line-to-line (**b**). Surface to surface. and easy-to-operate measuring instrument will be an effective method to on-site obtain the positional relation of two positioning surfaces. In this paper, a method of describing the positional relation of the two horizontal parallel surfaces and two vertical parallel surfaces on-site is proposed. This method can separate the shape contours of the two surfaces and obtain the real positional relation curve of the two surfaces. At the same time, the laser displacement sensor, which can record the detection data to the computer, is selected as the detection element, a continuous measuring mechanism suitable for manual operation is developed, and the corresponding algorithms are studied. Finally, the calculation results are drawn into a curve, which makes the description of the positional relation more intuitive. This method can also be used to measure two working surfaces and two guideways of large equipment. The rest of this paper is organized as follows. In Section 2 the principle of the measurement mechanism is depicted. In Section 3, the method for describing the positional relation between two surfaces is introduced. In Section 4, mathematical models of the algorithms are established. In Section 5, the measurement experiment is carried out and the results are analyzed. Finally, a conclusion is given in Section 6.
## 2 Principle of the Broadcast Ephemeris Correction with the PPP-B2b Service  
PPP-B2b has seven types of messages for correcting the broadcast ephemeris and pseudo-range observations. The application of these corrections usually comprises two parts. The first part is the matching of various corrections and the second is the correction of the broadcast ephemeris and code measurements.
## 1 Introduction
This sensor uses a proprietary algorithm based on empirical experimentation on multiple crops. [PERSON] et. [19] developed a new methodology based on a site-specific calibration. This methodology (Oklahoma State University algorithm, OSU) takes advantage of the GreenSeeker (Trimble Inc., Sunnydale, CA, USA) sensor. The amount of nitrogen to apply was decided according to the crop variability detected by the sensor, and it was calibrated on an area with no nitrogen limitations (N-rich strip) [20]. [PERSON] et al. [21] improved the accuracy of the OSU algorithm by using zero nitrogen strips combined with a N-rich strip to calibrate the sensor. In the very beginning, these sensors were used primarily for staple crops such as wheat, corn, canola, and potato, but in the last decade, they have been applied to other crops [22; 23]. Similarly, passive optical remote sensors have been used for VIs estimation. [PERSON] et al. [24] proposed a methodology to manage nitrogen fertilization using a SPOT satellite, based on the relationship between NDVI, LAI and chlorophyll content. [PERSON] et al. [25] used high-resolution satellites to evaluate the nitrogen status in winter wheat, finding a good correlation between chlorophyll and nitrogen content. [PERSON] et al. [26] improved the nitrogen status monitoring using a combination of mid-resolution and high-resolution satellites (RapidEye, Formosat, and WorldView-2). RapidEye images were also used by [PERSON] et al. [27] to calculate the normalized difference red edge index (NDRE) and map wheat nitrogen uptake. The correlation between remotely sensed NDVI and nitrogen uptake is used by several decision support systems for precision farming currently available (CropSat, Agrosat, Agrisat, etc) to provide nitrogen recommendations for farmers and final users [28]. More practically, satellite images can be used to delineate stable homogeneous zones when yield data are insufficient. [PERSON] et al. [29] proposed an algorithm to delineate stable management zones for precision agricultural use, based on automatic segmentation of RapidEye images. Variable rate nitrogen application can provide environmental and economic benefits, such as an increment in energy use efficiency and in the net return on nitrogen application [30]. According to [PERSON] et al. [31], variable rate nitrogen application led to an improvement in corn yield by 31% over ten years while quantity of nitrogen applied was reduced by 23%.