esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of Producer Mobility Management Approaches in Named Data Networking
## 3 Analysis of Scheme Designs
### Kite
The producer of prefix /A/B is at PoA B and has established a verified trace via the exchange of TI or TD with the anchor. Thus, the routers in the branch from the anchor to the producer have the entry /A/B in their FIB tables associated with the face from which the TI arrived, \"which is the face leading to the producer\". If consumer2 is interested in /A/B, then it issues an interest with the name /A/B. This interest reaches router D, which preforms LPM and results in forwarding the interest through face D1, as specified in the FIB. We can see that router D only has an entry for the routing prefix /A advertised by the anchor. The interest reaches the anchor and thus is forwarded through F1. Eventually, this interest reaches the producer by traversing routers A and B. It can be seen that since consumer2 is not in the same sub-tree of the producer (rooted at the anchor), the interest must go through the anchor to find the producer. On the other hand, consumer2 is in the same sub-tree as the producer (rooted at the anchor), and therefore, the interest path intersects with the established trace path leading to the producer. Specifically, as the interest /A/B reaches router A, it is forwarded through face A3, because it resulted from the LPM. It is also forwarded through face B2 at router B by the same logic, eventually reaching the producer. In Figure 4b, the producer has relocated from router B to router D. Since the previous trace path was not refreshed by the periodic TI/TD exchange, the entry associated to /A/B expired and was thus removed from the FIBs of routers A and B. Therefore, the interests of consumer2 are correctly directed toward the anchor rather than the producer's old location. However, if consumer1's interest arrives before the expiry of the entry /A/B at router A, then it will be led to the producer's old location, thus resulting in a NACK at routers A, B, and C. Upon the arrival of NACK, and based on the forwarding strategy, A should resend the interest to face A1 this time, which leads to the anchor, and then to the producer at its new location. In addition, the producer establishes a trace path from its new location to the anchor. Hence, consumer2's interests are now directly forwarded to the producer without going through the anchor.
_Applied_  
_sciences_  
_Article_  
**Real-Time GPU-Based Digital Image Correlation**  
**Sensor for Marker-Free Strain-Controlled**  
**Fatigue Testing**  
**[PERSON]** 1,*, [PERSON]** 1**, [PERSON]** 2**, [PERSON]** 2**, [PERSON]** 1**,  
**[PERSON]** 1** and [PERSON]** 2**  
1 Fraunhofer Institute for Physical Measurement Techniques IPM, 79110 Freiburg, Germany;  
[EMAIL_ADDRESS] (D.J.R.); [EMAIL_ADDRESS] (A.B.);  
[EMAIL_ADDRESS] (D.C.)  
2 Fraunhofer Institute for Mechanics of Materials IWM, 79108 Freiburg, Germany;  
[PERSON]] (S.E.); [PERSON]SS] (M.S.);  
[PERSON]ESS] (C.E.)  
* Correspondence: [PERSON]SS]; Tel.: +49-761-8857-328  
Received: 18 March 2019; Accepted: 10 May 2019; Published: 16 May 2019  
**Featured Application: Strain in material testing is defined as relative elongation between two points of interest on the specimen surface. Digital image correlation (DIC) is a non-contact method to measure this deformation without slip and without unwanted normal pressure on the surface of the test specimen--in contrast to mechanical extensometers. However, most DIC sensors require special sample preparation with markers and they are too slow to meet the recommendations of ASTM E606 for strain-controlled low cycle fatigue (LCF) testing, i.e., cyclic fatigue testing in the elastic-plastic range.**  
**Abstract: Digital image correlation (DIC) is a highly accurate image-based deformation measurement method achieving a repeatability in the range of \(\sigma=10^{-5}\) relative to the field-of-view. The method is well accepted in material testing for non-contact strain measurement. However, the correlation makes it computationally slow on conventional, CPU-based computers. Recently, there have been DIC implementations based on graphics processing units (GPU) for strain-field evaluations with numerous templates per image at rather low image rates, but there are no real-time implementations for fast strain measurements with sampling rates above 1 kHz. In this article, a GPU-based 2D-DIC system is described achieving a strain sampling rate of 1.2 kHz with a latency of less than 2 milliseconds. In addition, the system uses the incidental, characteristic microstructure of the specimen surface for marker-free correlation, without need for any surface preparation--even on polished hourglass specimen. The system generates an elongation signal for standard PID-controllers of testing machines so that it directly replaces mechanical extensometers. Strain-controlled LCF measurements of steel, aluminum, and nickel-based superalloys at temperatures of up to 1000 \({}^{\circ}\)C are reported and the performance is compared to other path-dependent and path-independent DIC systems. According to our knowledge, this is one of the first GPU-based image processing systems for real-time closed-loop applications.**  
**Keywords: digital image correlation; experimental mechanics; real-time image processing; closed-loop control; high-speed deformation measurement; general-purpose computing on graphics processing units (GPGPU)**
# Hybrid Locomotion Evaluation for a Novel Amphibious Spherical Robot
## 4 The On-Land Locomotion Experiments
### Sliding Locomotion Using the LSWM  
Sliding is appropriate on smooth flat terrain. Below a certain frequency, the velocity of the sliding gait varies with the distance by which the LSWM falls. As the mobile platform descends, the toe can touch the ground with the knee joint rotated clockwise, reducing the horizontal distance \(\Delta l\) from the knee joint to the toe. As shown in Figure 11, the blue state (\(\Delta h=11\) mm) of the leg reflects the situation where the toe only just touches the ground, and the red state (\(\Delta h=36\) mm) reflects the situation when the stance is vertical. Therefore, the fall distance \(\Delta h\) is 11-36 mm. We used HS-5646 WP servos, rotating at a rate of 0.18 \(s/60^{\circ}\) at 7.4 V. We set the rotary angle \(\theta_{1}^{i}\) and the rotary speed \(w_{s}\) of the horizontal servo to \(90^{\circ}\) and 0.5 s/\(60^{\circ}\) respectively, in case a slice developed. We obtained the velocity of the sliding gait by Equations (13) and (14).  
\[v=\frac{l}{t}=\frac{l_{2}+\Delta l}{t}=\frac{l_{2}+\sqrt{R^{2}-\left[r_{3}-(36 -\Delta h)\right]^{2}}}{t} \tag{13}\]  
\[t=2\theta_{1}^{i}w_{s} \tag{14}\]  
where \(R\) is the distance from the knee joint to the toe (\(R=63.25\) mm).  
To evaluate sliding locomotion using the LSWM on smooth flat ground, we modeled a synchronous sliding gait using two legs. We measured the sliding velocities at different descent values \(\Delta h\) of the mobile platform. In Figure 12, the blue curve shows a simulation of sliding velocity based on Equations (13) and (14). The deep-sky-blue circles indicate the experimental sliding velocities. We set 1 mm as the step size, and obtained 25 groups of experimental results from 11 to 36 mm. The maximum velocity was 10.2 cm/s at \(\theta_{2}^{i}=37.97^{\circ}\) and \(\Delta h=11\) mm. As the descent distance of the mobile platform increased, the velocity declined to 66.5 mm/s. The experimental and simulated results  
Figure 10: The relationship between the duty factor and the velocity. The falling velocity is positive, and the rising velocity negative.  
differed by about 10 mm, which may reflect installation error. However, the trends of the two curves were similar.
## 2 Materials and Methods
### Determination of Tested Elements by Flame Atomic Absorption Spectroscopy (F-AAS)
#### 2.4.3 Analysis of Phenolic Acids Flavonoids, and Caffe  
The RP-HPLC-DAD method was employed to determine the levels of phenolic acids, flavonoids, and caffeine in the materials under examination. The liquid chromatography equipment used in this study consisted of an HPLC analyzer (Merck Hitachi, Tokyo, Japan), a DAD-L2455 detector, an L-2350 thermostat, an L-2130 pump, a 4 \(\times\) 250 mm RP-18 column (LiChrosfer, particle size 5 \(\upmu\)m), and an L-2200 autosampler. The content of the analyzed substances was analyzed using gradient elution. Two solvents (A and B) were used as the mobile phase. Solvent A consisted of methanol and a 0.5% acetic acid solution in a 1:4 volume ratio, whereas solvent B was methanol. The following gradient was established: 100:0 for 0-25 min, 70:30 for 35 min, 50:50 for 45 min, 0:100 for 50-55 min, and 100:0 for 57-67 min. The flow rate was 1 mL/min, and measurements were carried out at 254 nm.
# Dual-Band Single-Layered Frequency Selective Surface Filter for LTE Band with Angular Stability
## 4 Simulation Results and Discussion of C-FSS
### Transmission/Reflection Coefficient of Double Band with TE Mode Polarization  
The graph shown in Figure 9 illustrates the reflection and transmission coefficients (in dB) versus frequency (in GHz) for C-FSS, highlighting its band-pass filter characteristics under the condition of \(|S_{11}|<-10\) dB. The desired frequency points include a reflection coefficient in dB of approximately \(-29.59\) and \(-31.11\) dB at 1.92 and 2.08 GHz, respectively. The graph identifies two distinct passband regions obtained as 40 MHz (1.9-1.94 GHz) bandwidth and 60 MHz (2.06-2.12 GHz) bandwidth, where the transmission coefficient is significantly higher, allowing effective signal passage. In contrast, the stop-band regions exhibit higher reflection and lower transmission, indicating the device's selective frequency filtering capability.  
Figure 8: (**a**) Electric field distribution at 1.9 GHz. (**b**) Surface current distribution at 1.9 GHz. (**c**) Electric field distribution at 2.2 GHz. (**d**) Surface current distribution at 2.1 GHz.
# FMT: Unsupervised RGB-D Point Cloud Registration via Fusion Feature Matching with Transformer
## 4 Experiments, Results, and Ablation Study
### Experimental Settings  
#### 4.1.1 Datasets  
ScanNet [19] and 3 DMatch [20], which consist of RGB-D images, camera intrinsic parameters, and ground truth poses, were extensively experimented on in our study. The dataset partitioning followed the setup described in [21; 22; 23; 24], with ScanNet utilizing 1594k/12.6k/26k RGB-D pairs and 3 DMatch utilizing 122k/1.5k/1.5k RGB-D pairs for training, validation, and testing, respectively.
## 2 Methods
### Phase Compensation Error Analysis  
In this section, the error between PC method and the actual propagation will be analyzed theoretically.  
As the propagation from object plane to WRP is calculated by plane diffraction theory, where little error is introduced, we will mainly discuss the propagation error between WRP and SCGH. From the perspective of a diffraction process, the complex amplitude of SCGH can be calculated precisely by PS method, which is given by  
\[\begin{split}& G_{PS}(x_{p},y_{q},z_{s})=\sum_{m=1}^{M}\sum_{n=1}^{N }\frac{1}{L_{mn}}F(x_{m},y_{n},z_{2})\exp(jkL_{mn}),\\ & L_{mn}=\sqrt{(x_{p}-x_{m})^{2}+(y_{q}-y_{n})^{2}+(z_{s}-z_{2}) ^{2}},\end{split} \tag{6}\]  
where \((x_{m},y_{n},z_{2})\) and \((x_{p},y_{q},z_{s})\) are discrete coordinates of arbitrary points \(F\) and \(G\) on the WRP and SCGH, respectively, shown in Figure 1c,d. \(M\times N\) is the resolution of WRP.  
In the proposed PC method, the complex amplitude of a point \(G_{PC}(x_{p},y_{q},z_{s})\) on the SCGH is calculated by  
\[G_{PC}(x_{p},y_{q},z_{s})=F(x_{m},y_{n},z_{2})\cdot\exp[-jk(z_{s}-z_{2})], \tag{7}\]  
Figure 2: Schematic diagram of nonuniform sampling. Panel (**a**) is the 3D model of sampling, where \(N\) is the vertical projection point of \(M\) on SCGH; panel (**b**) is the top view of arc \(AB\) on the SCGH (\(R^{\prime}\) is the projection on the x-z plane of the radius \(R\) of SCGH).  
where \(F(x_{m},y_{n},z_{2})\) is the vertical projected point of \(G_{\text{PC}}\).  
The error of the PC method \(Err(x_{p},y_{q},z_{s})\) can therefore be described as follows,  
\[Err(x_{p},y_{q},z_{s})=G_{PS}(x_{p},y_{q},z_{s})-G_{\text{PC}}(x_{p},y_{q},z_{s}). \tag{8}\]  
Clearly, there are errors in both amplitude and phase. Moreover, the error is obviously increased with the increase of the curvature of SCGH due to the longer distance between WRP and SCGH. However, the PC method still works well when the curvature of SCGH is limited and the distance between WRP and SCGH is relatively short.
# Multi-Objective Real-Time Planning of Evacuation Routes for Underground Mine Fires
## 2 Related Works
### Fire Monitoring System  
A monitoring and control system is a software and hardware system with information collection and processing as well as sound and light alarms that can monitor the concentration of poisonous and harmful gases, wind speed, wind pressure, temperature, smoke, fan starting and stopping status, ground pressure, and other objects in underground mines. In coal mines, a monitoring and control system is one of the effective measures to prevent and control mine gas, mine fire, rock burst, and other serious accidents [12]. The current monitoring methods for exogenous fires in mines can be mainly classified into temperature monitoring, indicator gas monitoring, and image recognition methods based on visual features. Fire temperature monitoring mainly includes the use of temperature sensors such as RTDs, thermocouples, and infrared thermometers. Indicator gas monitoring mainly refers to the use of gas sensors to monitor CO, CO2, HCL, and other electrical equipment and tape pyrolysis combustion of a variety of iconic gases. Visible light and infrared video image detection methods are also widely used because of their large monitoring range and fast response speed [13]. [PERSON] et al. proposed using a deep learning algorithm to predict the heat release rate of transient fires based on fire scene images, providing an alternative method for fire heat release rate measurement [14]. [PERSON] et al. designed an autonomous patrol firefighting robot based on SLAM, infrared thermal imager, and YOLOv4 deep learning algorithm. This robot can perform autonomous inspections and execute firefighting actions in indoor spaces, thereby reducing the probability of fire occurrence [15].
## 4 Proposed Approach
### Priority Assignment Unit  
After detecting events of interest, with their respective priorities and areas of influence, the Priority Assignment Unit takes place. For that, the list \(Eo1\) is transmitted from the PCU to the PAU every \(\Delta_{t}\) seconds to allow the association of sensor nodes to the corresponding priorities.  
The operation of the PAU unit is complex and thus it is divided into two distinct modules: Priority Association and Priority Assignment Protocol. Both modules are required and they must operate in sequence, as described as follows:  
* **Priority Association**: This module processes a list of detected and classified events of interest and associates the priority of those events to a subset of sensors. _Input_: List of events associated to priorities (\(Eo1\)). _Output_: List of sensors to be assigned to priorities (\(PS\)).
* **Priority Assignment Protocol**: This protocol is proposed to define how priorities will be transmitted to sensors. Initially, this protocol specifies a single message type to be broadcasted over one or more WSN. _Input_: List of sensors to be assigned to priorities (\(PS\)). _Output_: PAP PM messages.
## 5 Monitoring Program and Results
### Monitoring of Soil Displacement
#### 5.2.2 Deep Lateral Displacement of Soil  
The highest lateral displacement of the soil body of the bridge pile is located close to the side of the pipe section, according to the monitoring data. Figure 15 shows the soil lateral displacement over a time curve of 18 m under the bridge pier. The positive value in the figure indicates the direction away from the pipe jacking. The soil lateral displacement of 20 m under the bridge pier increases with the advancement of the pipe jacking; Y156 is close to the pipe jacking I, and the soil displacement grows faster in comparison to the bridge piles on the right side.  
After the completion of pipe jacking I, the maximum horizontal displacements of the soil body of the soil on the left and right pile sides are 9.5 mm and 6.6 mm, respectively.  
Figure 14: Settlement-time curves of monitoring points.  
Figure 15: Settlement-time curves of monitoring points.  
After pipe jacking II begins to dig, the displacement of the soil body of Y156 on the right side rises more quickly. The maximum horizontal displacements of the soil 20 mm under the piles on both sides are 15.7 mm and 17.4 mm, respectively, and the construction of the pipe jacking II has a greater influence, which is consistent with the simulation results. After construction is complete, the final horizontal displacements of the piles on both sides are essentially the same.
# Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks
## 3 System Model
Then the signal received at terminal user can be expressed as  
\[\mathbf{y}=\sqrt{P}\mathbf{H}_{UIM}\mathbf{B}\mathbf{x}+N, \tag{7}\]  
where \(P\) is the transmission power of the macro BS, \(\mathbf{x}\in\mathcal{CN}(0,\mathbf{I})\) is the original message transmitted from macro BS with size \(N_{D}\) and \(N\in\mathcal{CN}(0,\sigma^{2}\mathbf{I})\). Since the equivalent channel fading matrix of the femto BS is zero-forced, we only need to observe the signal-noise-ratio (SNR) of the associated user. We define the average normalized SNR received at the user as  
\[\gamma=\frac{P}{\sigma^{2}N_{M}}. \tag{8}\]  
Classically, the receiver obtains the wireless CSI through pilot signals transmitted from the BS, and there exists a time difference between the channel estimation and the data packet transmission. Thus, the estimated CSI is an imperfect version of the instant of packet transmission. To reduce the analysis complexity, it is assumed that the estimation of \(\mathbf{H}_{FM}\), while that of \(\mathbf{H}_{UIM}\) is imperfect. Specifically, the imperfect equation is modeled as  
\[\hat{\mathbf{H}}_{UIM}=\sqrt{\xi}\mathbf{H}_{UIM}+\sqrt{1-\xi} \mathbf{N}_{UIM}, \tag{9}\]  
where \(\xi\) is the correlation factor of the imperfect version of channel matrix. The standard maximum likelihood detector (MLD) with imperfect CSI can be employed to detect the original message as  
\[\mathbf{H}_{UIM}(n)=\rho\mathbf{H}_{UIM}(n-1)+\sqrt{1-\rho^{2}}\mathbf{N}_{UIM}(n), \tag{2}\]0  
where \(\Omega\) is the all possible constellations set.
# 6G Mobile Networks: Key Technologies, Directions, and Advances
## 5 Role of Network Slicing in 6G Technology
### Optimization in 6G Network Slicing  
Next-generation wireless networks are planned to support a wide range of vertical industries and provide a plethora of new applications. Network-slicing, which divides the infrastructure into several logical networks to enable service-oriented resource allocation,was created to fulfil the stringent specifications of various services. A hierarchical resource management system introduces the application of deep reinforcement learning in processing requests for resource requirements from various tenants and resource management within authorised slices for each tenant. The E2E slice, which consists of numerous connected VNFs from the transport network, core network, and RAN, presents a challenge for the optimization of diverse resources. When compared to conventional model-based optimization techniques, ML systems can find the best solution more rapidly by repeatedly learning from the reward feedback of the surrounding environment. To function at its best, RL incorporates long-term system growth into its decision-making process and modifies choice strategies in response to feedback from earlier choices. In order to boost revenue, resource providers must control multi-domain resources efficiently and allot the right number of resources to E2E slice instances. RL has been extensively utilised to address a variety of resource management issues, including voltage regulation, spectral efficiency, and computing resource management, because of its ability to develop an optimal strategy fast. In terms of providing excellent networking choice optimization and satisfying constantly changing quality of service needs, the proposed strategy seeks to implement a global resource manager (GRM) to standardise the method differentiable towards resource demands from tenants and several local-resource-managers (LRM) to deal with fluctuating resource needs for separate tenants [63].
# Detection and Localization of Overlapped Fruits Application in an Apple Harvesting Robot
## 1 Introduction
[PERSON] [19] first used the K-means cluster segmentation method to extract the target fruit region and then used the Ncut algorithm to extract the outline of the target fruit. Finally, the contour was reconstructed by the Spline interpolation method. Experiments were performed on 20 overlapped fruit images to calculate the segmentation error, and the mean value of coincidence was 5.24% and 93.81%. [PERSON] [20] used an extremum method to achieve an overlapped fruit positioning algorithm. Firstly, the target fruit area was segmented by a color difference method or the K-means method and then hole filling and contour extraction were performed. Finally, the minimum distance from the inner point to the outline edge was calculated, determining the maximum value to determine the center and radius of the circle to complete the identification and positioning of the target fruit. All these cases achieved good effect to some extent, but there was room for improvement in the real-time performance or recognition accuracy of recognition and positioning algorithms. Generally, the recognition algorithm of overlapped apples is complex, which will lead to poor real-time performance or recognition accuracy of the system, and the effect of recognition or localization is not ideal. The color and shape of the fruit changes greatly during the growth process, and most existing algorithms are unable to recognize overlapped apples. In this paper, a new recognition algorithm based on maxima of the overlapped circles fit is presented. First, convert the overlapped image to Lab color space, which is devoted to reflect the uniformity of color. Then, the overlapped apple images are segmented by the \(K\)-means algorithm, and the contour of the target apple is extracted by some morphological processing. This method is simple and easy to implement. For massive data, the time complexity tends to be linear. Finally, in order to improve the localization speed, and recognition accuracy, a fast algorithm for calculating the minimum distance is applied to find the center coordinates of the apple, the radii are obtained by calculating the minimum distance between the center and the edge. The robot harvesting operation process is as follows: the robot approaches the plant through the walking device, the vision system recognizes and locates the target fruit, the robotic arm guides the end effector to avoid the obstacle and approaches the harvesting target, the end effector harvests the target fruit, and the robot arm and the end effector store the harvested fruit. In the whole process of harvesting, the accuracy and speed of the target fruit recognition and positioning become the bottleneck restricting the harvesting efficiency of the robot. In this paper, a fast localization method of overlapping apples based on \(K\)-means and extremum algorithms was studied, and its performance was verified through many experiments. Experimental results show that the new method can accurately and quickly locate the overlapped apples, the contours of the apples were complete.