esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Shovel Force of the Shovel Step in Typical Working Conditions
### Empirical Calculation of Shovel Force  
The operational efficiency of an EWL is influenced by various factors. During the process of material loading, the loading time of an EWL primarily depends on its power performance. Shovel force is a parameter directly affecting the power performance of the EWL. Previously, shovel force has mainly been predicted by empirical, formula-based methods. The empirical formula for shovel force \(F\) is as follows [17]:  
\[F=9.8K_{1}K_{2}L^{1.25}BK_{3}K_{4} \tag{1}\]  
Figure 1: V-type operational cycle of a large-tonnage EWL.  
\(K_{1}\)**--Impact coefficient of material block size and looseness. \(K_{2}\)**--Impact coefficient of material type. \(K_{3}\)**--Impact coefficient for the pile height of loose materials. \(K_{4}\)**--Impact coefficient of shape (typically ranging from 1.1 to 1.8). \(L\)**--Depth of bucket insertion into the material stack. \(B\)**--Bucket width. This formula includes multiple parameter-values. The bucket width (\(B\)) can be measured. The bucket's insertion depth into the material pile (\(L\)) can be determined through experiments [18]. Other parameters need to be established based on empirical values. The method exhibits significant calculation errors. Due to the difficulty in obtaining the parameters in the formula, further discussion is required.  
\[L=kL_{1} \tag{2}\]  
\[K=k_{1}k_{2}k_{3}k_{4} \tag{3}\]  
\(K\)--Coefficient of composite. \(k\)--Coefficient of bucket depth. \(L_{1}\)**--Depth of bucket. Based on the test results, the measured shovel force of the 5-ton EWL is 160 kN. The EWL's width (\(B\)) is 2.88 m, the bucket depth (\(L_{1}\)) is 1.1407 m, and the bucket's insertion depth into the material pile (\(L\)) is 1.156 m. By plugging these values into the above formula, the bucket depth coefficient (\(k\)) is determined to be 1.013, and the comprehensive coefficient (\(K\)) is 4.7318. The bucket depth coefficient (\(k\)) and the comprehensive coefficient (\(K\)) have been adjusted based on the measured shovel force from 7-ton, nine-ton, 12-ton, and 15-ton EWLs. Table 1 summarizes the calculated shovel force. From Table 1, the errors of shovel force in the cases of medium-to-small tonnage EWLs (3-12 ton) can be controlled to within 10% or less. There is a larger error of the shovel force for large-tonnage EWLs (15-ton and 35-ton). Using empirical, formula-based methods to predict shovel force is imprecise. New research methods are needed to predicate the shovel force.
# Temporal Modeling on Multi-Temporal-Scale Spatiotemporal Atoms for Action Recognition
## 4 Evaluation
### Performance Evaluation  
In addition to the evaluation of the improvement by optical flow and multiple temporal scales in Section 3.5, in this section, we conducted extensive experiments to evaluate the improvements by atoms mining and temporal modeling. The experimental results are illustrated as a matrix in Table 6. The temporal modeling on the spatiotemporal feature of sparse clips was not evaluated because the \(max\_num\) of an action in the dynamic LSTM is 221, which requires so much memory that an 'out of memory' error occurred with our 4x GeForce GTX TITAN X GPU. Atoms mining reduces the \(max\_num\) to 32 and ensures that the dynamic LSTM consumes less memory and lower computational load, making the temporal modeling possible. The unordered modeling on atoms performs slightly worse than on sparse clips but the temporal modeling on the atoms resulted in the best performance in the evaluation matrix. The temporal modeling improves the performance of the atoms significantly. These results show that temporal modeling on the atoms is an effective method to represent and recognize actions.
# Smart Healthcare Network Management: A Comprehensive Review
## 5 Reference Architecture for Smart Healthcare System Network Management
### Resource Management Layer
#### 5.4.6 Mobility Management  
It provides the necessary functions that allow the patient to switch between different contexts on different IP networks.
# Exploring Distributed Deep Learning Inference Using Raspberry Pi Spark Cluster
## 2 Related Work  
### DL Inference on Pi  
DL on Pi is a recently explored field. As seen in [13], a performance review involving an NVIDIA Jetson TX2, NVIDIA Jetson Nano, and Pi 4B in DL workloads was carried out. The results of the study have shown that the Pi 4B took between 5 times and 9 times longer than the Nano and the TX2 in executing inference on datasets while accuracy remained similar with a deviation of 3%. The authors stated that even though Pi is the most cost-effective hardware of the three, the lack of DL acceleration makes it unfavorable for DL and AI. They concluded that \"while the Jetson TX2 held the highest DL performance and bore the highest cost of the three SBCs, it is impossible to attain a highly performant SBC in DL with the price tag of a Pi at $35\".  
In [14], an experiment for performance analysis of edge computing platforms which are intended to serve as backup systems for autonomous drones was carried out. The devices used for testing are a Pi 3B with 1 GB RAM, an Intel Neural Compute Stick (NCS)2 AI accelerator, and a laptop PC powered by an Intel Core i5-8250U 4-core processor clocked at 1.6 GHz with 8 GB RAM. For their test, they tested three configurations: the Pi 3B with and without the NCS, and the laptop PC with the NCS. To test each configuration, they executed applications deemed to be safety critical in the backup system. The applications are Single Shot MultiBox Detector (SSD)-based image recognition, State Lattice Planner (SLP) for routing flight paths locally, and Pix2 Pix (P2P) for generating an aerial image of the drone into a map image to function as a fail-operational alternative for GPS. While the results showed a tremendous speedup of 38.51 times when the Pi 3 was paired with the NCS for the SSD-based image recognition application, the difference in performance between the two Pi configurations (with and without NCS) for SLP and P2P was not apparent. However, since SSD is the only application using a single deep neural network in contrast to SLP and P2P, the use of a neural accelerator helps to increase the DL performance of the Pi in DL applications. Even with an AI accelerator, the Pi is not guaranteed to perform at the level of an x86 computer, as some applications in the experiment were not entirely dependent on AI and leaned more toward conventional processing workloads. Pi development boards, while being affordable, have a low-end System-on-a-Chip (SoC) that is not meant for extensive processing workloads such as model training. This was apparent when other researchers resorted to offloading model training tasks to a conventional computer, and the trained model was then moved to the Pi for inference as seen in [8]. The authors hypothesize that most applications are written for the x86 computer architecture and the lack of processing power and optimization for the ARM architecture results in a huge performance deficit between the two platforms.
## 5 Low-Complexity Hybrid Beamforming
### Hybrid Beamforming Based on Spectral Efficiency Maximization
So, a hybrid beamforming optimization method based on Phase Extraction Alternating Minimization is introduced.
# An Efficient Location Verification Scheme for Static Wireless Sensor Networks
## 6 Performance Evaluation
### Discussion on the False Rejection Rate  
In the MSRLV, only smart attackers cause false rejections because they may cause honest sensors to fail to be verified as explained in Section 4.4. When there are only dumb attackers, the proposed D-filtering procedures filter out those kinds of malicious sensors at the first stage of the protocol. Thus, only the smart attacker scenarios are considered in this section. The frr according to the number of sensors and fake-angle under 10% of attackers is depicted in Figure 15. Even though this evaluation result does not show the false rejection rate for the honest sensors with no attackers around them, the MSRLV can suffer from packet loss in the real-world environment, which leads to the false rejection.  
Figure 15: False rejection rates.  
In the uniform random deployment scenarios, the frr is almost maintained the same as the portion of the attacker in the network. This result is related to D-filtering that may not successfully filter out the malicious sensors, as discussed in Section 5.1.3.  
In the simulation, every honest sensor performed the verification on each of its neighbors. The false rejection occurs when a malicious sensor \(M\) fakes its location by targeting an honest sensor \(A\), and the verification involves \(A\) as a verifier (or a claimant) and \(M\) as a neighbor. Since the proportion of that kind of verification is the same as the proportion of malicious sensors, the frr is almost maintained even though the number of sensors or fake-angle is changed.  
In grid deployment scenarios, the frr increases very slightly as the density increases. In a relatively low-density network of 200-400 sensors, the frr was relatively lower than at other densities because the fake location of a malicious sensor does not fall into the MSR of the honest sensors around it. As the number of sensors increases, even a small fake in the location can be included in the MSR of honest sensors in the vicinity of the attacker.  
In conclusion, the frr is affected by the amount of fake locations in the location of the malicious sensors whether they are the claimant or the neighbor of the verifier or claimant. Since the positions of the malicious sensors cannot be controlled by the system, the frr is not a controllable factor in the MSRLV. Fortunately, this result is not of any benefit to the attacker. However, the uncontrollable nature of the frr is the cost for achieving a higher srr.
# Blind Recognition of Forward Error Correction Codes Based on a Depth Distribution Algorithm
## 6 Depth Distribution Algorithm Based on Parallel Convolutional Turbo Codes
### Recognition Algorithm of Parallel Convolutional Turbo Coding
From what is discussed above, when \(n_{e}\
eq\theta(n_{1}+n_{2}-k)l^{*}\
eq(n_{1}+n_{2}-k)(D_{1}+1+\varphi)\):  
\[\rho(S(C(n_{e},v_{e},D_{e},l_{e})))=1 \tag{41}\]  
when \(n_{e}=\theta(n_{1}+n_{2}-k)l^{*}\) and \(d_{e}=d\):  
\[\rho(S(C(n_{e},v_{e},D_{e},l_{e})))=1-\sum_{i=1,2 when \(n_{e}=\theta(n_{1}+n_{2}-k)l^{*}\) and \(d_{e}\
eq d\):  
\[1-\sum_{i=1,2}\biggl{(}\frac{n_{i}-k}{\theta k(D_{i}+a_{i}+1)k(n_{1}+n_{2}-k)} \biggr{)}\leq\rho(S(C(n_{e},v_{e},D_{e},l_{e})))\leq 1 \tag{43}\]  
when \(n_{e}=(n_{1}+n_{2}-k)(D_{1}+1+\varphi)\
eq\theta(n_{1}+n_{2}-k)l^{*}\) and \(d_{e}=d\):  
\[\rho(S(C(n_{e},v_{e},D_{e},l_{e})))=1-\frac{n_{1}-k}{(n_{1}+n_{2}-k)}\biggl{(} \frac{1+\varphi}{D_{1}+\varphi+1}\biggr{)} \tag{44}\]  
when \(n_{e}=(n_{1}+n_{2}-k)(D_{1}+1+\varphi)\
eq\theta(n_{1}+n_{2}-k)l^{*}\) and \(d_{e}\
eq d\):  
\[1-\frac{n_{1}-k}{(n_{1}+n_{2}-k)}\biggl{(}\frac{1+\varphi}{D_{1}+\varphi+1} \biggr{)}\leq\rho(S(C(n_{e},v_{e},D_{e},l_{e})))\leq 1 \tag{45}\]  
Equations (41)-(45) are the distribution function of the equivalent normalized depth distribution characteristics of the Turbo code word matrix, which can be used to realize blind recognition of the coding types of Turbo code.
## 3 PUB-SalNet
### Unsupervised Backpropagation  
The proposed unsupervised backpropagation algorithm is shown in Algorithm 1. For the salient segmentation task, we consider two aspects: (1) predicting salient objects through current network parameters; and (2) training network parameters through current salient predictions. Accordingly, we can obtain salient regions by a forward pass through the neural network and use the gradient descent to optimize the backward pass of the network at the same time. In order to update the parameters, we adopt stochastic gradient descent (SGD) with momentum to backpropagate results of the cross-entropy loss calculated between outputs of the model \(\{Y^{\prime}_{n}\}\) and the refined salient object labels \(\{y^{\prime}_{n}\}\). In the backpropagation step, we use the SLIC algorithm [25] implemented in scipy for the \(GetSuperpixels\) method. \(\{y^{\prime}_{n}\}\) is unified within every superpixel in an image, which is obtained through voting of labels inside a single superpixel and taking the majority of labels as the result. Different from Ref. [2]'s work, their prediction results are random and often fail on biomedical images. For solving these problems, our network parameters are initialized with the pre-trained method mentioned above. Furthermore, two attentional modules of the U-SalNet are combined to further refine the results, which achieve self-awareness. Finally, the forward-backward process is iterated \(I\) times to generate the final prediction of salient objects \(\{y_{n}\}\). The probabilities \(\{Y^{\prime}_{n}\}\) were trained in a self-supervised manner using \(\{y^{\prime}_{n}\}\). The orange dashed-line box in Figure 1(c) illustrates the proposed algorithm to train our U-SalNet model.  
```
0: Original biomedical image
0: Salient segmentation results
1:\((W,b)=Init()\) // Initialize backbone parameters
2:\((W^{\prime},b^{\prime},nClass)=Init()\) // Initialize classifier parameters
3:\(\{S_{k}\}_{k=1}^{K}=GetSuperpixels(\{p_{n}\}_{n=1}^{N})\)
4:for\(iter=1\to I\)do
5:if\(nClass>2\)then
6:\(\{F_{n}\}_{n=1}^{N}=GetFeatures(\{p_{n}\}_{n=1}^{N},\{W,b\})\)
7:\(\{GA_{n}\}_{n=1}^{N}=GlobalAttention(\{F_{n}\}_{N=1}^{N})\)
8:\(\{LA_{n}\}_{n=1}^{N}=LocalAttention(\{F_{n}\}_{n=1}^{N})\)
9:\(\{Y_{n}\}_{n=1}^{N}=\{W^{\prime}(GA_{n}\bigoplus LA_{n})+b^{\prime}\}_{n=1}^{N}\)
10:\(\{Y_{n}\}_{n=1}^{N}=BatchNorm(\{Y_{n}\}_{n=1}^{N})\)
11:\(\{y_{n}\}_{n=1}^{N}=\{\operatorname{argmax}Y^{\prime}_{n}\}_{n=1}^{N}\) //predict salient labels
12:for\(p=1\to P\)do
13:\(y_{\max}=\operatorname{argmax}\lvert y_{n}\rvert_{n\in S_{p}}\)
14:\(y^{\prime}_{n}=y_{\max}\)for\(n\in S_{p}\)
15:endfor
16:\(L=CrossEntropyLoss(\{Y^{\prime}_{n},y^{\prime}_{n}\}_{n=1}^{N})\)
17:\(\{W,b\},\{W^{\prime},b^{\prime}\}=Update(L)\)
18:endif
19:endfor
```  
**Algorithm 1** Unsupervised Backpropagation Algorithm
## 3 Results  
### Improved LMS Algorithm  
The LMS algorithm is only suitable for single frequencies, and hence, a complex multifrequency signal must be decomposed into a single-frequency signal suitable for LMS processing. ICEEMDAN solves the mode-aliasing problem of the EMD method while reducing the reconstruction error after the CEEMDAN calculation. Complex multi-frequency signals are decomposed into a single IMF component, and noise reduction is then carried out on each IMF component using the adaptive LMS algorithm. That is, the collected original noisy signals are first decomposed into multiple IMF components using ICEEMDAN, and the measured noise signals are also decomposed into multiple IMF components using ICEEMDAN. If the actual noise is difficult to detect, simulated noise can be used instead. After the decomposed multi-scale signals have been obtained, the input signals and noise are decomposed into each frequency band, and then the decomposed components are processed by the improved LMS algorithm to obtain the denoised IMF components. These IMF components are summed to update the signals at different frequency scales. Finally, the denoised signal is reconstructed. Using this signal processing and decomposition, the signal and noise of each frequency band are simplified. Therefore, by adopting this method, the filter-order selection will not be too high, thereby reducing the convergence time and improving the corresponding denoising results. A flowchart of ICEEMDAN-LMS is presented in Figure 1.  
Figure 1: ICEEMDAN-LMS noise reduction process.
###### Abstract  
Cyber attacks bring key challenges to the system reliability of load frequency control (LFC) systems. Attackers can compromise the measured data of critical variables of the LFC system, making the data received by the defender unreliable and resulting in system frequency fluctuation or even collapse. In this paper, to detect potential attacks on measured data, we propose a novel attack detection scheme using the dual-source data (DSD) of compromised variables. First, we study the characteristics of the compromised LFC system considering potentially vulnerable variables and different types of attack templates. Second, by designing a variable observer, the relationship between the known security variables and the variables which are at risk of being compromised in the LFC system is established. The features of the data obtained by the observer can reflect those of the true data. Third, a Siamese network (SN) is designed to quantify the distance between the characteristics of measured data and that of observed data. Finally, an attack detection scheme is designed by analyzing the similarity of the DSD. Simulation results verify the feasibility of the detection scheme studied in this paper.  
load frequency control (LFC); cyber attack; Siamese network; attack detection scheme; unknown input observer
## 3 Stability Analysis
### Time-Delay Dependent Stability  
From the damping determined in Section 2.2, we obtain \(\zeta_{2}=0.0876\). If the feedback gain \(g^{*}\) falls within the orange region of Figure 3, the system switches in stability as the time-delay changes. Assuming that the polynomial \(F(\omega_{0})\) has positive real roots [37], a set of trigonometric equations can be derived as  
\[\begin{array}{l}\cos(\omega_{0}\tau^{*})=-\frac{P_{R}(\omega_{0})Q_{R}( \omega_{0})+P_{I}(\omega_{0})Q_{I}(\omega_{0})}{Q_{R}(\omega_{0})^{2}+Q_{I}( \omega_{0})^{2}}\\ \sin(\omega_{0}  
The critical time-delay can be obtained:  
\[{\tau_{\rm i,j}}^{*}={\tau_{0}}^{*}+\frac{2{\rm i}\pi}{\omega_{0}},\left\{\begin{array} []{l}{\rm i}=1,2,\cdots\\ {\rm j}=0,1,\cdots\end{array},\right. \tag{20}\]  
where \(i\) denotes the \(i\)-th positive real root of the polynomial, and \(j\) denotes the \(j\)-th critical time delay corresponding to the \(i\)-th positive real root \(\omega_{0}\). In this way, the relationship between \(\omega_{0}\) and \({\tau_{\rm i,j}}^{*}\) can be obtained as \({\tau_{\rm i,j}}^{*}=T(\omega_{0})\). Then, the stability interval is determined according to the stability switches theory. The relation between the gain \(g^{*}\) and \(\omega_{0}\) can be obtained from Equation (16), \(\omega_{0}=R(g^{*})\). According to the above, the relationship between \(g^{*}\) and \({\tau_{\rm i,j}}^{*}\) can be obtained as \({\tau_{\rm i,j}}^{*}=T(R(g^{*}))\). Therefore, the time-delay dependent stability region can be drawn as shown in Figure 4.
###### Abstract  
Orbit determination for non-cooperative low Earth orbit (LEO) objects undergoing continuous low-thrust maneuvers remains a significant challenge, particularly for large satellite constellations like Starlink. This paper presents a method that integrates the unscented transformation into a batch filtering framework with an optimized rho-minimum sigma points sampling strategy. The proposed approach uses a reduced dynamics model that considers Earth's non-spherical gravity and models the combined effects of low-thrust and atmospheric drag as an equivalent along-track acceleration. Numerical simulations under different measurement noise levels, initial state uncertainties, and across multiple satellites confirm the method's reliable convergence and favorable accuracy, even in the absence of prior knowledge of the along-track acceleration. The method consistently converges within 10 iterations and achieves 24 h position predictions with root mean square errors of less than 3 km under realistic noise conditions. Additional validation using a higher-fidelity model that explicitly accounts for atmospheric drag demonstrates improved accuracy and robustness. The proposed method can provide accurate orbit knowledge for space situational awareness associated with continuously maneuvering Starlink satellites.  
orbit determination; low-thrust maneuvers; Starlink satellites; orbital dynamics model; unscented transformation; batch filtering Article
# New Insights into the Symbiotic Relationship between Orchids and Fungi
## 3 Nutrient Exchange between Orchids and Mycorrhizal Fungi
This tripartite relationship among orchid, fungus and autotrophic shrub was further confirmed by investigation of _Rhizanthella gardneri_, a unique underground orchid that is entirely subterranean even during flowering, using isotopically labelled tracers, \({}^{13}\)CO\({}_{2}\) and double-labelled [\({}^{13}\)C-\({}^{15}\)N]glycine [47]. [PERSON] et al. (2010) reported that the mycorrhizal fungus _Ceratobasidium_ sp. associated with _R. gardneri_ can simultaneously form ectomycorrhizas with autotrophic _Melaleuca scalene_, and C in _R. gardneri_ was originally from photosynthetic carbohydrates in _M. scalene_. In addition, _Ceratobasidium_ sp. is able to acquire C from _M. scalene_ and access soil nutrients saprophytically, and subsequently provide both C and N to _R. gardneri_ and N to _M. scalene_[47]. Increasing evidence indicates that different mycorrhizal types can be developed by the same mycorrhizal fungi in different host plants, especially by those fungi which can form ectomycorrhizas. Therefore, a tripartite system of nutrient exchange among orchids, mycorrhizal fungi and the adjacent trees may be more common than the relationship established by only orchids and their symbiotic fungi [32; 47; 48; 49; 50].
## 2 Materials and Methods
### Universal GNSS Channel Validation
#### 2.2.2 Frequency Bands Compatibility  
Although this is not a feature related to the universal channel per se, being able to acquire and track signals on all bands has a net advantage in terms of both frequency diversity for improved ionosphere error correction in an autonomous receiver, as well as enhanced resistance to interference. In the current implementation, a super-heterodyne RF front-end approach is used, where a configurable Local Oscillator (LO) takes the Radio-Frequency (RF) signal down to 70 MHz, which is then processed by a 24 MHz wide band-pass filter and down-converted to IF with a common 55 MHz LO. All LOs and clock are synchronized through an external 10 MHz reference clock.
## 3 Results and Discussion
### Local Particle Characteristics Probed by TS-TEM  
In the second part of our study, the question is addressed whether TS-TEM experiments can be used to examine a local carbon formation with a certain temporal resolution. Given the powder analysis (Section 3.1), one may not expect a considerable carbon formation in situ. However, when processing 0.1 M INN in EtOH + EHA using SpraySyn1 (cf. Figure 1b) atomization, mixture formation and ignition are temporally unsteady [23; 24], which may promote a temporal and local formation of carbon in the flame center. Hence, TS-TEM experiments are conducted at 5 cm HAB, and samples are extracted either from flame flickering or flame pulsing, allowing the examination of particle structures momentarily present during different flame activity. TS-TEM is also conducted at 15 cm HAB to investigate weather carbon can be found downstream of the flame.