esa-sceva/satcom-chunk-collection huggingface dataset:
# The Analysis of the Applications of Crop Seed Tape Sowing Technology and Equipment: A Review
## 5 Discussions and Prospection of Seed Tape Sowing
### Fundamental Issues in Seed Tape Sowing That Still Need Further Discussion
#### 5.1.4 Intelligent Issues of Seed Tape Equipment  
With the development of science and technology, agricultural machinery equipment has gradually become more intelligent. However, due to the low proportion of application of seed tape technology, seed tape seeding equipment has not been well developed, especially the seed tape laying machine. In addition, intelligent seed tape equipment is helpful to improve the seed tape quality of making and sowing. The technological advantages of seed tape would be fully exploited and people would pay more attention to seed tape seeding technology which would be a virtuous circle. Therefore, we can study the intellectualization of seed tape equipment from the following aspects.  
1. Precise control of seed hill distance in seed tape manufacture.
2. An unmanned seed tape planter based on the GNSS system.
3. Seed tape intelligent synchronous release and disconnection system.
4. Sowing depth control and monitoring system.
###### Abstract  
In this paper, we propose a numerical approach to simulate the degree of coherence (DOC) of a partially coherent beam (PCB) with a Schell-model correlator in any transverse plane during propagation. The approach is applicable for PCBs whose initial intensity distribution and DOC distribution are non-Gaussian functions, even for beams for which it is impossible to obtain an analytical expression for the cross-spectral density (CSD) function. Based on our approach, numerical examples for the distribution of the DOC of two types of PCBs are presented. One type is the partially coherent Hermite-Gaussian beam. The simulation results of the DOC agree well with those calculated from the analytical formula. The other type of PCB is the one for which it is impossible to obtain an analytical expression of CSD. The evolution of the DOC with the propagation distance and in the far field is studied in detail. Our numerical approach may find potential applications in optical encryption and information transfer.  
partially coherent beam; degree of coherence; numerical approach; free-space propagation 18 April 2019; 9, 2084; doi:10.3390/app9102084
## 4 Analytical Model
### Assumptions and Notations  
The network operation considered in the proposed analysis is a fully-connected single-hop network consisting of an AP and \(n\) STAs, as described in the previous section. This network setting implies that no hidden terminal exists. In addition, it is assumed that the AP and the STAs operate in saturation conditions, i.e., they always have data frames to be sent. The AP and the STAs employ the TXOP PSM-based mechanism with the RTS/CTS handshake enabled to contend for access to the channel, and \(\alpha\) data frames can be sent in each successful channel access attempt. Data frames of the same size are assumed. The channel is assumed to be error free and no capture effect is considered.  
Figure 1: Operation example of the TXOP PSM-based mechanism when the RTS/CTS handshake is enabled. First STA\({}_{1}\) and then the AP send a burst of data frames to each other, while other STAs sleep based on the duration carried in the RTS frame. The energy profiles of STA\({}_{1}\), the AP, and other STAs during transmission, reception (or overhearing), idle, and sleep periods are shown in the figure. The notations of time and power consumption included in the figure are used in the analytical model presented in Section 4.  
The Markov chain analysis presented in [21] is the basis of the analytical model proposed in this paper. The reader is assumed to be familiar with such analysis; a brief description of the derivation in [21] is provided next. In the analysis, ideal channel conditions as well as a finite and fixed number \(n\) of contending STAs in saturation conditions are assumed. The analytical model proposed therein is enabled by the fact that each single STA sees the events occurring on the channel according to a discrete, though not uniform, slotted time scale. In such model, the slot time can refer to either a constant value \(\sigma\) when the channel is sensed idle or a variable time interval when the channel is sensed busy. The key approximation in the model is that the probability \(p\) of collision of a transmitted frame is constant and independent of how many times the frame has been retransmitted. Based on the conditional collision probability \(p\), the probability \(\tau\) of an STA transmitting in a randomly chosen slot time is determined. Then, by studying the events that can occur within a generic slot time, the throughput of DCF under network saturation, hereafter referred to as saturation throughput, is obtained as a function of \(\tau\). In [24], the throughput expression in [21] is then refined to properly account for the backoff freezing operation in the DCF specification.
## 3 Results and Discussions
### Effects of Precipitation Route of Laves Phase on Creep Behaviors
#### 3.4.2 Particle Stability in the Late Stages of Creep Tests  
It is generally known in the creep of ferritic/martensitic heat-resistant steels that initial lath/block structures gradually recover to form cell structures with creep deformation [3; 4]. This recovery process is understood to soften the material and causes creep acceleration. A high density of second-phase particles act to inhibit the motion of boundaries, thereby retarding recovery and recrystallization processes in the matrix phase [12]. The quantitative  
Figure 12: Measured number density (**a**) and area fraction (**b**) of Laves phase particles and calculated mean radius (**c**) and Zener pinning force (**d**) of the particles taken from the creep-interrupted and creep-ruptured samples, as plotted against their Larson-Miller parameter (LMP) values.  
analysis for the Laves phase particles in the present study shows that IP particles tend to keep their fine size and spatial distribution and Zenner pinning force for a long time, compared to CP particles (see Figure 12). Microstructural observation for the matrix phase in the creep tested samples demonstrates that recovery and recrystallization processes take place in the late stages of creep tests. The results from the observations with the creep rate analysis indicate that the recovery and recrystallization processes trigger the rapid acceleration of creep and creep rupture in the creep tests, and IP particles are more effective in retarding the rapid creep acceleration and rupture, especially under lower stress conditions.  
The susceptibility to particle coarsening in CP particles should be discussed. A possible reason for the higher susceptibility originates from their heterogeneous precipitation on the grain/subgrain boundaries and dislocations. This may cause faster growth and particle coarsening kinetics due to the diffusion on the defects, resulting in the formation of a wide distribution of particle sizes, thereby increasing the driving force for particle coarsening. In fact, a larger size distribution is qualitatively observed for CP particles compared to IP particles in the early stage of creep tests (compare Figure 8a,b). This aspect reminds us of the degradation of long-term creep life in conventional ferritic steels where the particles are precipitated in the transformed ferrite grain/subgrain boundaries as well as within the grains. An introduction of both CP and IP particles may be effective to improve the long-term creep rupture strength.
## 3 Results and Discussion
Which causes significant improvement in the SNR of the output signal of the detector. The SNR of the fiber-coupling electro-optic detector which appears in Figure 6 is 15.66 dB. The \(\tau_{d}\) of the electro-optic detector is bigger than the Schottky diode by 135.7 \(ps\). The \(\tau_{d}\) difference between the Schottky diode and the fiber-coupling electro-optic detector almost doubled from the \(\tau_{d}\) difference between the Schottky diode and the free-space electro-optic detector. This indicates that the fiber-coupling process was able to increase the bandwidth of the electro-optic detector. Figures 5 and 6 show that the fiber-coupling electro-optic detection system is more clear and quieter than the free space electro-optic detection. This means the fiber-coupling system allows for an increase in the bandwidth and the SNR of the detectors that are based on the electro-optic process. Therefore, the fiber-coupling electro-optic method can present an easy way for detecting short THz pulses up to 6 THz, and 1 fs response can be achieved by using a long fiber to stretch the THz pulse [23; 24]. The response time of the electro-optic detector can be improved by using a more sensitive and fast photodetector. **Author Contributions:** Conceptualization, A.H.Y., Methodology, N.B. and A.H.Y., Data curation, A.K., Investigation J.G., supervision, A.F., Writing--original draft, A.H.Y., All authors have read and agreed to the published version of the manuscript. This research was supported by the [PERSON] family center for compact accelerators, Ariel University, Israel. Not applicable. **Data Availability Statement:** Data is contained within the article. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Integration of Visual Servo Control  
### Model of Macro-Micro Manipulator  
When the macro-micro manipulators are in series, the flexibility of the macro-manipulator is more important. Considering that the micro manipulator is stiff, the dynamic model is set up by the Lagrange equation with the generalized coordinates of the joints, as shown in Formulas (4) and (5).  
\[\mathbf{H}_{b}\mathbf{\ddot{q}}_{b}+\mathbf{H}_{bm}\mathbf{\ddot{q}}_{m}+\mathbf{C}_{b }+\mathbf{D}_{b}\mathbf{\dot{q}}_{b}+\mathbf{K}_{b}\Delta\mathbf{q}_{b}= 0 \tag{4}\] \[\mathbf{H}_{bm}^{T}\mathbf{\ddot{q}}_{b}+\mathbf{H}_{m}\mathbf{\ddot{q}}_{m}+\mathbf{ C}_{m}+\mathbf{D}_{m}\mathbf{\dot{q}}_{m}= \mathbf{\tau} \tag{5}\]  
where \(\mathbf{q}_{b}\in\Re^{nb}\) denotes the local coordinates of the macro-manipulator, \(\mathbf{q}_{m}\in\Re^{nm}\) stands for the joint coordinates of the micro-manipulator. The states of \(\Delta\mathbf{q}_{b}\) are the current angles minus initial states. \(nb\) and \(nm\) are the DOF of the micro/macro manipulator, respectively. \(\mathbf{H}_{b}(\mathbf{q}_{b},\mathbf{q}_{m})\in\Re^{nb\times nb}\), \(\mathbf{D}_{b}\in\Re^{nb\times nb}\), and \(\mathbf{K}_{b}\in\Re^{nb\times nb}\) denote base inertia, damping, and stiffness, respectively. \(\mathbf{H}_{m}(q_{m})\in\Re^{nm\times nm}\) is the inertia matrix of the arm. \(\mathbf{H}_{bm}(\mathbf{q}_{b},\mathbf{q}_{m})\in\Re^{nb\times nm}\) denotes the so-called inertia coupling matrix. \(\mathbf{C}_{b}(\mathbf{q}_{b},\mathbf{q}_{b},\mathbf{q}_{m},\mathbf{\ddot{q}}_{m})\in\Re^{nb}\) and \(\mathbf{C}_{m}(\mathbf{q}_{b},\mathbf{\dot{q}}_{b},\mathbf{q}_{m},\mathbf{\dot{q}}_{m})\in\Re^{nm}\) are velocity-dependent nonlinear terms, \(\mathbf{D}_{m}\in\Re^{nm\times nm}\) denotes arm joint damping and torques, \(\mathbf{\tau}\in\Re^{nm}\) are considered the control inputs. It can be seen that no external forces are present, and this assumption holds throughout the paper.  
The stiffness of the base joint \(\mathbf{K}_{b}\) is non-constant due to the gear backlash, as shown in Figure 4. The stiffness is not zero in the backlash due to the friction and lubrication. Outside the backlash, the stiffness is much more significant due to the flexibility of the gear reducer.  
The working condition of the manipulator in orbit is complicated, the dynamic model given above is an ideal model, in order to specify the dynamic characteristic, the following two essential assumptions are needed.  
**Assumption 1**.: _No constrained motions are considered, which means the manipulator is under free space._  
**Assumption 2**.: _The joint friction is not considered, which means the deflection of the macro-manipulator will oscillate around zeros._
## 2 Target Capture Scheme for AUV Formation  
When operating in waters where the target location is unknown, AUVs can sense the surrounding environment through various sensors that are carried by themselves (suchas multibeam forward-looking sonar and underwater camera), and use multi-source data fusion and target identification technology to obtain information regarding the obstacles and targets in the environment, providing data support for collision avoidance and target capture in AUV formation.  
The process of AUV formation when capturing targets in an unknown environment is shown in Figure 1, and the whole operation process is divided into two major parts, cooperative search and target capture, based on whether the robot formation finds the target. After the initialization of the formation, the robots keep the preset structure to detect the operating water in the forbidden search mode; if an obstacle is detected, they will judge whether they need to change the formation, and, if so, they will cross the obstacle domain with the optimal formation after the change, and then revert to the original formation to continue the search. When the formation senses the location of the target, the system enters the capture phase. After the task assignment, the system informs the corresponding robot of the capture location, and then combines with the path-planning algorithm to complete the containment of the target. Since the formation change strategy, task assignment and path-planning method directly affect the efficiency and safety of the operation, they are the focus of this paper.
# Hierarchical Fine Extraction Method of Street Tree Information from Mobile LiDAR Point Cloud Data
## 2 Materials and Methods  
### Technical Process  
In this paper, a region growth algorithm based on Gaussian distribution (GDRG) is designed to extract the point clouds of street trees in the road environment based on the mobile LiDAR point cloud data. Firstly, the original mobile LiDAR point cloud data is pre-processed, including ground point filtering and filtering the building point cloud based on the projected area of the building facade; then, the location of a single tree is located based on the vertical linear features of the tree trunk and the trunk point cloud is extracted by clustering based on the GDRG algorithm; secondly, the pseudo-trunk is removed based on the simple geometric rules of the trunk; then, the GDRG algorithm is applied again to extract the canopy point cloud of each tree separately. Finally, the Voronoi diagram constraint is used to partition the tree group and calculate the geometric features of the individual trees. The detailed technical flow of the method is shown in Figure 1.
# DFT Characteristics of Charge Transport in DBTP-Based Hole Transport Materials
## 3 Results and Discussions  
### Geometry and Frontier Molecular Energy Level  
Chemical structures of HTMS based on mDPA-DBTP and pTPA-DBTP, and energy levels of perovskite, TiO\({}_{2}\), and HTMS, are shown in Figure 1. Compared with mDPA-DBTP, two benzene rings have been added to the conjugated chain of pTPA-DBTP. Therefore, pTPA-DBTP displays obviously aromaticity, which results in a strengthened delocalization effect. As shown in Figure 1, the highest occupied molecular orbital (HOMO) increased, and the lowest unoccupied molecular orbital (LUMO) reduced owing to the introduction of the symmetry of the two benzene rings. The change in the substituted group (methoxy) location from meta to para increases the planarity and symmetry of pTPA-DBTP. The substitution of meta does not form a stable resonance structure, which caused a decline in the HOMO level [25]. The trend in HOMO levels is pTPA-DBTP \(>\) mDPA-DBTP, and a deeper HOMO leads to a larger open-circuit voltage (V\({}_{\text{OC}}\)). A larger gap in HOMO and the Fermi level of TiO\({}_{2}\) can cause a higher open-circuit voltage (V\({}_{\text{OC}}\)) [26]. The HOMO of HTMs is higher than the valence band (VB) of CH\({}_{3}\)NH\({}_{3}\)PbI\({}_{3}\), which is advantageous for hole-transport [27]. CH\({}_{3}\)NH\({}_{3}\)PbI\({}_{3}\) is considered a classic perovskite material, so mDPA-DBTP, with better hole mobility, will improve the charge transfer ability and further result in a higher power conversion efficiency (PCE) The LUMO level is also a factor affecting the performance of PSCs. The LUMO levels of HTMs should be higher than the conduction band (CB) of CH\({}_{3}\)NH\({}_{3}\)PbI\({}_{3}\) for preventing electron backflow from CH\({}_{3}\)NH\({}_{3}\)PbI\({}_{3}\) to the metal electrodes [28]. LUMO levels are in the order: mDPA-DBTP \(>\) pTPA-DBTP. Therefore, mDPA-DBTP has a greater ability to avoid electron back flowing.  
Figure 1: Chemical structures of the investigated molecules and energy level diagram, where FTO, A, D, e and h represent fluorinated tin oxide, acceptor, donor, electron and hole, respectively.
# Using Connected Vehicle Data to Evaluate National Trip Trends
###### Abstract  
The National Household Travel Survey (NHTS), conducted by the Federal Highway Administration, has historically been used for documenting personal mobility trends. Current techniques using surveys to collect this data are labor-intensive and difficult to scale. Emerging connected vehicle (CV) data can provide an alternative data source to potentially provide a more scalable method to measure the temporal and spatial usage of passenger vehicles in near real-time. With an impending shift in the automobile industry towards alternative fuel vehicles (AFV), agile monitoring of trip trends is important to help guide state and national investments in AFV infrastructure. This study presents methodologies and visualizations summarizing observed trip characteristics using a sample of more than 500 billion CV records and nearly 1 billion CV trips for December 2022 in the United States. The analysis found very close agreement between trip lengths for internal combustion engine vehicles (ICEV) for CVs and those reported by the 2017 NHTS. Mean trip lengths and trip durations from CVs and NHTS for ICEVs are within 7.8% and 6.6% of each other. The 85 th percentile comparison was similarly close, within 0.7% and 8.3%. A comparison of trip trends among states for ICEVs and AFVs as well as US census places and temporal trends for a selection of states, including Indiana, Texas, Wyoming, and California, is provided. The paper concludes that CV data is an important source to monitor trip characteristics across ICEVs and AFVs in near real-time, which will be particularly important to track during the anticipated change to AFVs.  
connected vehicles; big data; trip trends; travel patterns +
Footnote †: journal: Applied  
applications, benefits, and emerging opportunities for using connected vehicle data to augment the current state of the practice in this domain.
## 3 Materials and Methods
### LLRs with Extrinsic Feedback  
The theory in Section 2.1 shows that when extrinsic feedback is being used, a weighted delay filter should not be utilized. At first sighting, these conclusions may seem counter intuitive given the significant performance gains that have been seen for the APP feedback case. We now present an insight into the reasons behind this finding, based on [29]. Consider the two LLR sequences:  
\[L_{k}^{(n)}=\frac{\Bigl{(}\lambda_{k}^{(n)}\Bigr{)}^{2}}{2}x_{k}+l_{k}^{(n)} \omega_{k}^{(n)} \tag{24}\]  
\[\Lambda_{k}^{(n-1)}=\frac{\Bigl{(}\lambda_{k}^{(n-1)}\Bigr{)}^{2}}{2}x_{k}+ \lambda_{k}^{(n-1)}\varphi_{k}^{(n-1)}. \tag{25}\]We may model \(\varphi_{k}^{(n-1)}\) as follows:  
\[\varphi_{k}^{(n-1)}=\rho\omega_{k}^{(n)}+\sqrt{1-\rho^{2}n} \tag{26}\]  
where \(n\sim N(0,1)\), \(\mathcal{E}\Big{[}\varphi_{k}^{(n-1)}\Big{]}=0\), \(\mathcal{E}\Big{[}(\varphi_{k}^{(n-1)})^{2}\Big{]}=1\) and \(\mathcal{E}\Big{[}\omega_{k}^{(n)}\varphi_{k}^{(n-1)}\Big{]}=\rho\), then we have the following;  
\[\Lambda_{k}^{(n-1)}=\frac{\Big{(}\lambda_{k}^{(n-1)}\Big{)}^{2}}{(l_{k}^{(n)} )^{2}}\Bigg{(}\frac{\Big{(}l_{k}^{(n)}\Big{)}^{2}}{2}x_{k}+\frac{\lambda_{k}^{ (n-1)}\rho\omega_{k}^{(n)}\Big{(}l_{k}^{(n)}\Big{)}^{2}}{\Big{(}\lambda_{k}^{( n-1)}\Big{)}^{2}}\Bigg{)}+\lambda_{k}^{(n-1)}\sqrt{1-\rho^{2}n}. \tag{27}\]  
Now by utilizing (21),  
\[\Lambda_{k}^{(n-1)}=\frac{\Big{(}\lambda_{k}^{(n-1)}\Big{)}^{2}}{(l_{k}^{(n)} )^{2}}L_{k}^{(n)}+\lambda_{k}^{(n-1)}\sqrt{1-\rho^{2}n}. \tag{28}\]  
In (28), we see that the signal \(\Lambda_{k}^{(n-1)}\), is simply a scaled noisy version of \(L_{k}^{(n)}\). \(\Lambda_{k}^{(n-1)}\) is not providing any extra information. Hence, there is nothing to be gained by combining the current signal with the signal from the previous iteration.
# Follow-Up Control and Image Recognition of Neck Level for Standard Metal Gauge
## 4 A recognition Algorithm for Scale Image
### Recognition of Scale Numbers and Scale Lines  
In order to realize the identification of the scale, the edge position search method is used to accurately identify the pixel position of the scale line, a binary tree multiclass support vector machine (MCSVM) classifier was chosen to identify scale numbers, and the correspondence between the scale and the position coordinates of the pixel is obtained.  
As is shown in Figure 10, for the scale line of scale image, according to the characteristics of different types of scale line length, we used edge features to detect the number of edges in each column of scale image, clustered and counted the number of edge features and edge coordinates under each classification according to the different number of edges in each column of 1 cm, 5 mm and 1 mm scale lines, and then recognized the 1 cm scale line.  
The edge identification steps for an M \(\times\) N input image can be summarized as follows:  
1. The number of upper and lower edge features were counted in each column.
2. These features were then divided into 1 cm, 1 cm + 5 mm, and 1 cm + 5 mm + 1 mm scale categories using K-means clustering. The column containing only 1 cm tick marks was identified as the scale edge.
3. Corresponding pixel coordinates for upper and lower edge features were sequentially detected and stored in the matrices E1 and E2, respectively.  
Figure 10: Upper and lower edge features in scale image.  
Figure 9: The result of image segmentation using the K-means clustering algorithm, including (**a**) the initial linear edge data, (**b**) the cluster data set, (**c**) clustering results and cluster centers, (**d**) clustering four, (**e**) an effect map based on clustering edge segmentationThe pixel coordinates corresponding to specific edge features in each column were acquired by averaging E1 and E2.
5. The corresponding element in the matrix Ei was used as the y coordinate corresponding to 1 cm tick marks.  
In this way, the number of columns and the characteristic coordinates were collected for all vertical edges. The column that met the required edge feature number was then identified, as demonstrated by the yellow line in Figure 11. The corresponding coordinates for the vertical edge were used as the y coordinates of the 1 cm scale, as shown in Figure 12. This technique was used to identify scale lines and a support vector machine (SVM) was included to read scale numbers [24]. A sample matrix containing digital scale and pixel coordinates is shown in Table 2.