esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving the Manipulability of a Redundant Arm Using Decoupled Hybrid Visual Servoing
## 4 Results and Discussion
### Case Study 3: Manipulability Analysis of Four VS Methods Using Setup 1  
Four different visual servoing methods (DHVS, IBVS, HVS, PBVS) have been used to track the object. Figure 8 depicts the amount of manipulability for the VS methods. As shown in Figure 8, the manipulability of the DHVS method was mostly greater than the other three methods. The manipulability value at the time zero is the same since the robot started moving from the same position with an identical configuration during all trials. The minimum amount of manipulability for the DHVS method is 0.0681, while this amount is 0.0588 for IBVS, 0.0613 for PBVS and 0.0628 for the HVS method.  
Figure 7: The real world camera (EE) trajectory in different methods for the same scenario in Figure 6.  
In Figure 9, the manipulability ellipsoid of the robot in its minimum amount (lowest amount of controllability for the robot) for different VS approaches has been illustrated. Since the manipulability ellipsoid is a hyper ellipsoid in six dimensions and plotting this in 3D space is a complex task, the first three elements of the ellipsoid have been plotted (i.e., translational velocities). From the plots in Figure 9, it is obvious that the proposed hybrid method has better controllability on the robot movements, compared to IBVS, PBVS, and HVS methods. The more isotropic the ellipsoid, the higher the controllability of the robot.
# Intelligently Counting Agricultural Pests by Integrating SAM with FamNet
## 2 Materials and Methods
### Dataset  
The dataset employed in this study encompasses a collection of RGB images depicting a variety of plant diseases and pests from multiple perspectives and distances. The dataset is comprised of 803 RGB images, with a detailed breakdown as follows: 544 images of citrus psyllids, 26 images of lychee stink bugs, 57 images of aphids, 55 images of blackback beetles, 56 images of scale insects, and 65 images of ladybugs. It is important to note that these images were exclusively utilized for the purpose of model evaluation and were not part of the training dataset.  
The citrus psyllid RGB images were collected from the fruit orchards in the East Campus of South China Agricultural University and the orchards of the College of Engineering over a period from 25 June 2020 to 24 August 2020. To account for the variability in natural light conditions and to reduce the long-tail effect in image recognition, images were deliberately captured during three distinct time periods each day: early morning (9:00-11:00), midday (14:00-15:00), and late afternoon (17:00-18:00). This approach ensured a diverse dataset that better represents the range of environmental conditions that citrus psyllids may encounter.  
A Huawei Honor 20 handheld camera, manufactured in China, was employed to photograph adult citrus psyllids on citrus trees from a range of 4 to 6 cm. The initial collection yielded 2024 field images of citrus psyllids with varying resolutions: 3024 \(\times\) 4032, 2592 \(\times\) 1944, and 1200 \(\times\) 1600 pixels. To enrich the dataset, an additional 117 images, contributed by citrus experts and farmers from diverse regions across the nation, were incorporated. This supplementation culminated in a final dataset of 544 original citrus psyllid images, as illustrated in Figure 2.  
In addition to the RGB images of the lithi aphid, we supplemented the dataset with 259 images of insect damage using web crawling techniques. The images are as follows:  
To address the issue of insufficient training data leading to overfitting, a common approach of data augmentation was applied to the images [17]. This included random adjustments of contrast and brightness [18], adding noise from Gaussian and gamma distributions, and applying techniques such as contrast limited adaptive histogram equalization (CLAHE) [19], median blur, horizontal and vertical flipping, and transpose to modify the contrast. The final result is shown in Figure 3. Before each experiment, few random images were selected from the dataset as prior knowledge, while the rest of the dataset served as the test set.  
Figure 2: Images of citrus psyllids.
# Multi-Topology Routing Algorithms in SDN-Based Space Information Networks
## 6 Discussion and Conclusions  
Aiming at the complex structure of the SIN and the dynamic change of network topology, this paper firstly established a SIN management framework based on SDN. By establishing three kinds of network controllers and resource management and dispatching centers of the SIN on the ground, the burden of satellite nodes is reduced, which is beneficial to the unified management of global resources. On this basis, the implementation steps of the SIN routing algorithm based on a snapshot sequence are designed. Aiming at the different QoS requirements of different spatial tasks in the SIN, taking full account of dedicated bandwidth, end-to-end delay, delay jitter, packet loss rate, node busyness and delay forwarding time, the calculation methods of the initial link weight and integrated link weight are studied. Then the Warshall-Floyd algorithm is used to design the optimal routing strategy for different spatial tasks. Finally, a TBA allocation algorithm is proposed for different spatial tasks running on the same link at the same time, which effectively guarantees the timely transmission of important task data. The research content of this paper provided a scheme for efficient and reliable transmission of the SIN. The research in this paper is based on the condition that the resource requirement of space task is unchanged, and lacks the consideration of the dynamic characteristics of the SIN. In the follow-up study, the dynamic routing algorithm of the SIN will be analyzed.  
[PERSON], [PERSON], and [PERSON] completed the overviews and methods; [PERSON] structured the evolution model; [PERSON] accomplished the theory and simulation verification; [PERSON] wrote the paper.  
This work was primarily supported by China Equipment Named Research Funded Project with grant number 6142010010301.  
The authors declare no conflicts of interest.
# Single-Shot Coherent X-Ray Imaging Instrument at PAL-XFEL
## 2 Materials and Methods
### Overview of the Whole System
#### 2.1.3 Detector Part  
The detector part contains the metal beam stopper with an XY manipulator, a photodiode, a Ce:YAG crystal, and the multiport charge-coupled device (MPCCD) detector, as shown in Figure 4. All components are mounted on the rails, and the sample-to-detector distance extends to \(\sim\)3.8 m. The beam stop chamber is connected to the sample chamber through a vacuum beam path, and it is terminated by a Kapton film with 120 \(\upmu\)m thickness to separate the vacuum chamber and the MPCCD detector. The pressure of the whole system, the sample chamber, and the beam stop chamber is maintained in the vacuum range of \(\sim\)10\({}^{-5}\) torr. The photodiode and the Ce:YAG crystal are mounted together by a linear manipulator. The photodiode is used to measure the focused beam size and check the flux of XFEL pulses. The position and shape of the XFEL beam are monitored by Ce:YAG crystal. The beam stopper is manipulated using an XY stage to block the direct beam. The beam stopper is made of 2-mm tantalum, and its edges are beveled at 5 degrees and polished. Alignment of the beam stopper is confirmed by a PD or a Ce:YAG crystal, which are mounted on the side of the MPCCD. The MPCCD is sealed by a 280-\(\upmu\)m thick beryllium window and operates in a vacuum environment independently. This MPCCD consists of two sensors with a 300-\(\upmu\)m overlapping area. Each sensor has 512 by 1024 pixels. The size of a pixel is 50 \(\upmu\)m\({}^{2}\). The specification of the MPCCD has been reported elsewhere [23].  
Recently, an adJUstiNg Gain detector FoR the Aramis User station (JUNGFRAU) 4 megapixel detector was readied for this XFEL experiment at PAL-XFEL. This JUNGFRAU detector has a 3.3 mm by 3.3 mm central hole to avoid damage by a direct X-ray beam. This detector can be connected to the sample chamber through a vacuum beam path without an air gap. It consists of eight sensors with 1.80 mm or 2.85 mm spacing. Each sensor has 512 by 1024 pixels. The size of a pixel is 75 \(\upmu\)m by 75 \(\upmu\)m [24].
# Three-Dimensional Elastodynamic Analysis Employing Partially Discontinuous Boundary Elements
## 4 Numerical Examples
### Response of a Plate with a Square Hole under Dynamic Load  
In this example, a more realistic problem, which is the dynamic response of a plate with a square hole under an impact load, is computed. The geometric model used in this example is shown in Figure 17. The bottom of the plate is fixed, and the top is subjected to an impact loading \(p(t)=-8.4\times 10^{10}t^{2}+6.3\times 10^{6}t\) Pa. The material parameters of the plate are [PERSON]'s modulus \(E=6.9\times 10^{4}\) N/mm\({}^{2}\), density \(\rho=2.7\times 10^{-9}\) t/mm\({}^{3}\), and [PERSON]'s ratio \(\
u=0.3\). The dynamic responses at points S1 and S2 in Figure 17 are computed in this example.  
The discretized mesh model with an element size of 5 mm is shown in Figure 18a. The exact solution of this example cannot be derived, so we adopt the results computed by FEM software to be the reference solution. The FEM mesh model is shown in Figure 18b, which uses a finer mesh model to ensure the reliability of the results.  
Figure 16: The condition numbers of matrix \(\mathbf{A}\) under a different inward shrink ratio \(\lambda\).  
Figure 17: The geometric model and the boundary conditions of the plate: (**a**) geometric model; (**b**) load curve.  
Figure 21: Displacement response at point S2 computed by using partially discontinuous elements in terms of (**a**) the cases of unstable results and (**b**) the cases of stable results.  
Figure 22: Traction response at point S2 computed by using partially discontinuous elements in terms of (**a**) the cases of unstable results and (**b**) the cases of stable results.  
BEM for elastodynamic problems are discontinuous piecewise functions, which leads to the element integral accuracy being low. The result error accumulates continuously as the number of analysis steps increases, resulting in lower and lower computational accuracy. The above two points are the difficulties to be overcome when using the time-domain BEM. They are also the reasons why the time-domain BEM is more suitable for calculating short-term dynamic responses such as explosions and shocks rather than long-term dynamic responses. The algorithm proposed in this paper can improve the computational stability and accuracy of the time-domain BEM to a certain extent. However, further research is needed to thoroughly solve the above two problems.
## 2 Named Data Networking: Nuts and Bolts
### Packet Forwarding  
Instead of using IP addresses to forward packets, NDN routers utilize application namespaces [19]. NDN routers employ stateful forwarding, which implies that routers store details of received requests until they are satisfied or timed out. Upon the arrival of an _Interest_ packet, the router checks whether it holds a copy of the _Data_ in the CS. The NDN node forwards the _Data_ downstream if it finds a copy of it. Otherwise, it checks the PIT for similar requests. If a pending request already exists, the router updates the incoming interfaces field. If no pending _Interest_ exists, a new entry is created. The NDN node then forwards the _Interest_ packet to the upstream neighbor(s) using a forwarding strategy and  
Figure 2: Example of the data authentication process.  
the FIB table [20]. The NDN node may respond with a NACK or drop the _Interest_ packet if no route is found.  
Once a _Data_ packet is received by a router from a producer or in-network cache, the router checks for a matching pending _Interest_ by examining the name of the received _Data_ packet. If there is no match in the PIT, the router drops the received _Data_ packet. However, if a match is found, the router caches (or not, depending on its caching strategy) the received _Data_ and forwards them downstream to all the interfaces associated with the pending _Interest_. As a result, NDN routers provide a built-in multicast mechanism. Figure 3 illustrates a scenario of packet forwarding in NDN.
## 4 Conclusions  
We propose the first photonic technology based on a mode-locked fibre laser with non-linear properties in both power and spectrum width controlled by a power threshold. Both properties are induced by the mode-locking effect. We distinguish two operating regimes dependent on input signal power with a distinct power threshold, below and above which the output signal follows the spectral shape of a self-excited eigen-signal and an input signal, respectively. The spectral power-dependent re-shaping with a threshold is a unique advantage of the proposed setup and, together with the non-linear power transfer function, paves the way for a new type of optical neuron with two-dimensional non-linearity in power and frequency, similar to biological neurons, which exhibit a frequency-adaptable response depending on the type of input stimulus. Thus, this work opens new directions in the study of artificial neurons with spectrum frequency-adaptable non-linear responses in multiple dimensions, stimulating the development of novel types of neural spectrum coding and neural networks with variable bandwidth for applications in optical communications and networks, 5G and emerging 6G communications.  
Conceptualization, M.S.; investigation, Z.X., J.P. and M.S.; supervision, J.P., M.S. and H.Z.; writing--review and editing, M.S. All authors have read and agreed to the published version of the manuscript.  
Royal Academy of Engineering (RF/201718/17154).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 3 Interference Management
### Recent Advances in Interference Management
#### 3.1.2 Interference Coordination  
Interference coordination schemes have significant advantages in Inband D2D communication. For the Centralized Interference Coordination (CIC) scheme, this involves the monitoring from the BS. However, for the Decentralized Interference Coordination (DIC) scheme, the monitoring from the BS is minimized due to the participation of D2D nodes in the coordination mechanism. Interference coordination techniques are used to mitigate interference between cellular links and D2D links. [PERSON] et al. in [41] used the full-duplex communication technique in D2D communication environment. The authors presented the interference issue for devices operating in full duplex mode. To solve the joint optimization problem with a minimum complexity, the authors proposed a graph-coloring-based resource sharing (GCRS) scheme. The scheme was adopted to optimize the spectrum utilization. [PERSON] et al., in [42], presented the interference management for D2D communication in heterogenous networks. The authors proposed a feasible set scheme to handle the interference in the D2D communication link and to guarantee the QoS requirements. The proposed scheme shows that it can reduce the average outage probability by up to 92 percent. In [43], [PERSON] et al. proposed a D2D management scheme that is based on the interference limited area (ILA) combined with a power control algorithm to mitigate the interference that may be inflicted on the cellular users caused by activated D2D links. Under the proposed ILA scheme, the authors produced a mathematical expressions of the average coverage probability for D2D links and cellular links. Then, the authors proposed a resource allocation algorithm that depends on the changes in the power control mechanism. The results show that the proposed scheme is able to outperform the conventional scheme especially in a high D2D density and signal-to-interference-plus-noise ratio in terms of the sum data rate and coverage probability.
# Integrated GNSS/IMU-Gyrocompass with Rotating IMU. Development and Test Results
## 4 Methods
### Error Model of Inertial Sensors  
Accelerometer error model is described as a sum of biases \(\delta\mathbf{f_{0s}}\) described by Wiener processes (random walk) with generating noises \(\mathbf{w_{f_{0}}}\) and measurement noises \(\mathbf{w_{f}}\), i.e.,  
\[\begin{split}\delta\mathbf{f_{s}}&=\delta\mathbf{f_ {0s}}+\mathbf{w_{f}},\\ \delta\dot{\mathbf{f_{0s}}}&=\mathbf{w_{f_{0}}}.\end{split} \tag{2}\]  
Gyro error model in s-frame axes bound with the inertial sensor axes is set as follows, with account for IMU rotation about vertical axis \(Z_{s}\):  
\[\begin{split}\delta\omega_{X_{s}}&=\delta\omega_{0X_ {s}}+\delta\omega_{RX_{s}}+w_{gx},\\ \delta\omega_{Y_{s}}&=\delta\omega_{0Y_{s}}+\delta \omega_{RY_{s}}+w_{gy},\\ \delta\omega_{Z_{s}}&=\delta\omega_{0Z_{s}}+\delta S _{gY_{s}}\omega_{Z_{s}}+w_{gz},\\ \delta\dot{\mathbf{\omega_{0}}}&=\mathbf{w_{\omega _{0}}},\\ \delta\dot{S_{gY_{s}}}&=w_{S_{g}},\end{split} \tag{3}\]  
where \(\delta\mathbf{\omega_{0}}=\left[\delta\omega_{0X_{s}},\delta\omega_{0Y_{s}}, \delta\omega_{0Z_{s}}\right]^{T}\) are the gyro biases characterizing instability from run to run and in run and presented by Wiener processes with generating noises \(\mathbf{w_{\omega_{0}}}\); \(\delta S_{gZ_{s}}\) is the scale factor error of the gyro with measurement axis parallel to \(Z_{s}\) rotation axis with generating noise \(w_{S_{g}}\); \(\delta\omega_{R_{i}}(i=X_{s},Y_{s})\) are the rhumb drifts of the gyros with measurement axes orthogonal to the rotation axis; and \(\omega_{Z_{s}}\) is the angular velocity projected on IMU and antennas' rotation axis \(Z_{s}\).  
Rhumb drifts are the components of gyro bias whose projections on geographical axes _ENU_ remain constant. These error components may arise due to the effect of the Earth's magnetic field to which FOGs are sensitive [41]. FOG rhumb drifts cannot be compensated by IMU rotation through angle \(u\) and increase the heading error. Rhumb drifts are presented by the first harmonic of Fourier expansion of heading angle and rotation angle \(\mathrm{u}\)  
\[\begin{split}&\Delta\omega_{RX_{s}}=A_{X_{s}}\cos(K-u)+B_{X_{s}} \sin(K-u),\\ &\Delta\omega_{RY_{s}}=A_{Y_{s}}\cos(K-u)+B_{Y_{s}}\sin(K-u),\\ &\left[A_{X_{g}},B_{X_{g}},A_{Y_{S}},B_{Y_{S}}\right]^{T}= \mathbf{w_{R}},\end{split} \tag{4}\]  
where \(A_{i},B_{i}(i=X_{S},Y_{S})\) are the amplitudes of rhumb drift components for the gyros with measurement axes orthogonal to the rotation axis presented by the relevant Wiener processes; \(\mathbf{w_{R}}\) is a four-dimensional vector of generating noises.
## 3 Numerical Simulation of the Effect of Deep Excavation on Adjacent Tunnels
### Constitutive Model and Parameters of Material  
#### 3.2.1 Constitutive Model and Parameters of Soil  
It is known from the statistics in actual projects that most of the collected case histories occurred in the soft soil represented by silty clay. Therefore, silty clay was chosen as the soil for numerical simulation, and it was assumed to be a single homogeneous layer to simulate the thick, soft ground.  
The hardening soil model with small strain stiff (HSS model) is used for the soil [28; 29; 30; 31]. In addition to the basic physical parameters such as unit weight \(\gamma\) and void ratio \(e\), the HSS model includes 13 constitutive model parameters, as listed in Table 1.
## 3 Phase Sensitive Detection
### Median Retrieving Algorithm  
Calculate the median \(\overline{\Psi}_{0}\) of the yaw angle sequence \(\Psi_{0}\) as \(\overline{\Psi}_{0}\)\(=[\max{(\Psi_{0})}+\min{(\Psi_{0})}]/2\); the sequence \(\Psi^{\prime}\) is defined by  
\[\Psi^{\prime}=\Psi_{0}-\overline{\Psi}_{0}I_{1\times N_{0}}=\big{\{}\psi^{ \prime}(1),\psi^{\prime}(2),\cdots,\psi^{\prime}(i),\cdots,\psi^{\prime}(N_{0} )\big{\}}, \tag{2}\]  
where \(I_{1\times N_{0}}\) denotes a \(1\times N_{0}\) row vector. The median of the sequence \(\Psi^{\prime}\) is zero.  
Let \(T_{0}\) be the set of zero-crossing marks. For \(\forall\psi^{\prime}(i)\in\Psi^{\prime}\)\((i=2,3,\cdots,N_{0})\), we set Criterion 1:  
If \(\psi^{\prime}(i-1)\psi^{\prime}(i)<0\) or \(\psi^{\prime}(i)=0\), then the yaw angle signal \(\Psi^{\prime}\) passes through a value of zero one time and \(i\in T_{0}\).  
That is, if the sign of the yaw angles \(\psi^{\prime}(i-1)\) and \(\psi^{\prime}(i)\) are opposites, it can be inferred that \(\Psi^{\prime}\) passes through a zero point, and the number \(i\) (the time mark in this case) is put into set \(T_{0}\).  
The number set \(T_{0}\) yielded by Criterion 1 is annotated as \(T_{0}=\{t(1),t(2),\cdots,t(i),\cdots,\)\(t(n_{0})\}\), where \(t(i)\) is the \(i\)th term in the sequence and \(n_{0}\) is the length of \(T_{0}\) and the number  
Figure 1: Time history of yaw angles: (**a**) profile 1; (**b**) profile 2.  
of zero-crossing points in \(\Psi^{\prime}\). Due to the number of zero-crossing points must be less than the length of the yaw angle time history (\(n_{0}<N_{0}\)), we know that \(T_{0}\) belongs to the natural number field with \(T_{0}\subset\{1,2,3,\cdots,N_{0}\}\).  
Through these operations, PSD retrieves the median terms and can count the number of median-crossing marks of the original signal \(\Psi_{0}\).