esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment
### Infrared Polarization Image Acquisition  
Because the circular polarization is very small in practice and can be ignored, assuming that the Stokes vector of the outgoing electroluminescence from the panel is \(S=\left[I,Q,U\right]^{T}\) (where \(I\), \(Q\), \(U\) denote the intensity, linear polarization component in horizontal direction, and linear polarization component in the diagonal direction of the electroluminescence), the energy received by the camera through the polarizer at the polarization azimuth \(\alpha\) can be presented as:  
\[I(\alpha)=\frac{1}{2}(I+Q\cos 2\alpha+U\sin 2\alpha) \tag{7}\]  
Figure 4: Experimental spot (**a**) Front view; (**b**) Side view.  
Figure 5: Experimental sample (**a**) Polycrystalline silicon panel; (**b**) Monocrystalline silicon panel.  
Figure 3: Schedule of the electroluminescence polarization experimental platform.  
\(\alpha\) is usually selected as 0\({}^{\circ}\), 60\({}^{\circ}\), 120\({}^{\circ}\) for the convenience of calculation in engineering. Then \(I\), \(Q\), \(U\) can be calculated as follows:  
\[\left\{\begin{array}{l}I=\frac{2}{3}[I(0^{\circ})+I(60^{\circ})+I(120^{\circ} )]\\ Q=\frac{2}{3}[2I(0^{\circ})-I(60^{\circ})-I(120^{\circ})]\\ U=\frac{2\sqrt{2}}{3}[I(0^{\circ})-I(120^{\circ})]\end{array}\right. \tag{8}\]  
where \(I\) (0\({}^{\circ}\)), \(I\) (60\({}^{\circ}\)), \(I\) (120\({}^{\circ}\)) denote the SWIR polarization images at the azimuth 0\({}^{\circ}\), 60\({}^{\circ}\), 120\({}^{\circ}\). Next, the degree of polarization \(DOP\) and the angle of polarization \(AOP\) images can be obtained:  
\[\left\{\begin{array}{l}DOP=\frac{\sqrt{Q^{2}+U^{2}}}{I}\\ AOP=\frac{1}{2}\arctan(\frac{U}{Q})\end{array}\right. \tag{9}\]
# Localized Necking Model for Punching Fracture Simulation in Unstiffened and Stiffened Panels
## 2 Plasticity and Fracture Models
### Fracture Model
#### 2.3.2 Localized Necking Model  
Shell element-based modeling for numerical analysis of thin-walled shell structures is beneficial for reducing time and simplifying modeling. The fracture model based on the equivalent plastic strain using shell elements is less accurate [17]. [PERSON] and [PERSON] [13] proposed a domain of DSSE model as an extended HC model for shell element application, which is called HC-DSSE. Figure 2 shows the concept of HC-DSSE in principle strain coordinates \(\{\varepsilon_{1},\ \varepsilon_{2}\}\). The fracture criteria in the HC-DSSE model were defined by the ductile fracture model and necking limit criterion for the fracture criterion. The DSSE model, which is valid in the biaxial tension region \(\left(\frac{1}{3}\leq\eta\leq\frac{2}{3}\right)\) under proportional loading, is similar to the HC model:  
\[\overline{\varepsilon}_{DSSE}^{pr}=b\Bigg{[}\left\{\frac{1}{2}\Big{(}(g_{1}-g_{ 2})^{d}+g_{1}^{d}+g_{2}^{d}\Big{)}\right\}^{\frac{1}{2}}\Bigg{]}^{-\frac{1}{ p}}, \tag{22}\]where the \(g_{1}\) and \(g_{2}\) are the functions of stress triaxiality:  
\[g_{1} =\frac{3}{2}\eta+\sqrt{\frac{1}{3}-\frac{3}{4}\eta^{2}}, \tag{23}\] \[g_{2} =\frac{3}{2}\eta-\sqrt{\frac{1}{3}-\frac{3}{4}\eta^{2}}. \tag{24}\]  
The recommended value of the coefficient \(p\) is 0.01 [13]. \(b\) is a parameter in the HC model. Therefore, the parameter in the DSSE model is only \(d\). The damage indicator in the DSSE model under non-proportional loading is expressed as  
\[D_{DSSE}=\int_{0}^{\bar{\epsilon}_{f}}\frac{\bar{\epsilon}_{p}}{\bar{ \epsilon}_{DSSE}^{p^{\prime}}[\eta,\overline{\theta}]}d\bar{\epsilon}_{p}. \tag{25}\]  
Localized necking occurs when \(D_{DSSE}\) reaches unity.
# DFP-Net: A Crack Segmentation Method Based on a Feature Pyramid Network
## 2 Related Work
### Object Detection  
Due to issues like edge connection instability and sensitivity to noise in digital image processing, deep-learning-based crack recognition methods have achieved widespread use.  
In 2016, [PERSON] et al. [48] pioneered the application of object detection based on deep learning in pavement surface defect detection, demonstrating the superiority of convolutional neural networks over support vector machines in classifying crack images. However, the primitive network design and incomplete training data scene led to a slow final recognition speed and low accuracy. In 2018, [PERSON] et al. [49] designed an automatic road defect detection and analysis method. This method takes YOLOv2 as the core and uses the evaluation indicator F1-score as the rating standard to evaluate the condition of road surface defects.  
In 2020, [PERSON] et al. [50] presented an improved algorithm based on YOLOv3 for the bridge surface crack detection tasks. This algorithm combined MobileNets and a convolutional block attention module to achieve real-time detection of bridge surface cracks. Additionally, in 2020, [PERSON] et al. [51] used a single-shot multi-box detector as the basis, expanded the receptive field of convolutional layers, improved the feature extraction ability of the model's contextual information, and improved the accuracy of road crack detection. Furthermore, in 2021, [PERSON] et al. [52] used VGG16 as the feature extraction backbone network and designed a deformable SSD network. By introducing deformable convolution, they improved the detection ability of the model (Figure 1).  
Compared to traditional digital image processing, methods related to target detection offer advantages in multi-target processing and real-time crack detection [53]. However, challenges such as target category and shape limitations and sensitivity to complex backgrounds still exist.
# The Use of Underwater Gliders as Acoustic Sensing Platforms
## 4 Application of Gliders to Underwater Acoustics
### Ocean Ambient Noise Acquisition  
Since the Petrel II glider travels through the ocean, the ambient noises of different depths can be recorded by SCHs. Figure 6 shows the raw data and the ambient noise level of individual frequencies at different depths from a single profile in April 2018. The peaks in the left figure are caused by the roll motor, and the loud noises on the bottom are caused by the pump motor. After the noise induced by the two motors is removed, the profile data of the ambient noises can be obtained.  
As shown in the right-hand panel of Figure 6, the noise level at a higher frequency is lower, which is similar to the results of [22; 23]. Moreover, the general trend shows that the ambient noise levels of 2000-8000 Hz decrease by about 1-3 dB from 100 m to 700 m during the dive. Since the detection performance is highly correlated to the ambient noise [22; 24], the collected ambient noise data can be used as a reference for target detection performance.
# Research on the Path Planning Algorithm of a Manipulator Based on GMM/GMR-MPRM
## 4 MPRM Module
### Path Pruning Based on the Shortcut Algorithm  
The Shortcut path pruning process is shown in Figure 3, assuming that the planned path is composed of Points A, B, C, D, and E. Although the trajectory can avoid the obstacle, it can be further simplified. For example, Point B can be directly connected to Point D, then Point C can be removed. However, when Point B and Point E are connected, they will collide with an obstacle, then Point D cannot be removed. The pruning algorithm is shown in Algorithm 2.  
```
0: Path to be pruned path1
0: Path after pruning path2
1: path2\([1]\leftarrow\) path1\([1]\)
2:\(i=1\);
3:while\(i\
eq size(\text{path1})-2\)do
4:for\(j=i+2:size(\text{path1})\)do
5:if CollisionCheck\((\text{path1}(\text{i}),\text{path1}(\text{j}))\)then
6: path1\(=[\text{path2 path1}(\text{j})]\);
7:\(i=j\);
8:break
9:endif
10:endfor
11:endwhile
```  
**Algorithm 2** Shortcut path pruning algorithm.
## 3 Three-State Hidden-Markov Model
### Transition Probabilities  
From a visual inspection of the link BER time evolution we noted that, grouping the data on a per-state basis, a link in a state \(i\) is more likely to remain in that state in the successive time slot, rather than jump to another state. Once this was verified, we decided to model the PER time evolution of a generic link as a three-state Markov chain (Figure 4), with the three states \(S=\{G,M,B\}\) that stand for \"Good\", \"Medium\", and \"Bad\", respectively. Specifically, if we denote as \(X_{0},\ldots,X_{n},\ldots X_{N}\), a sequence of random variables where \(X_{i}\) takes values in the set \(S\) of the three states, \(\mathbb{P}(X_{n+1}=j|X_{n}=i)\) is the transition probability from state \(i\) to state \(j\) at step \(n\). Additionally, by the Markov property, we have that:  
\[\mathbb{P}(X_{n+1}=i_{n+1}|X_{0}=i_{0},\ldots,X_{n}=i_{n})=\mathbb{P}(X_{n+1}= i_{n+1}|X_{n}=i_{n}), \tag{3}\]  
which can be interpreted as the fact that, if the current state \(X_{n}=i_{n}\) is known, the probability of \(\mathbb{P}(X_{n+1}=i_{n+1})\) does not depend on the previous states. If the transition probabilities do not depend on \(n\) but only on \(i\) and \(j\), the Markov chain is _homogeneous_ and we  
Figure 3: PER vs. BER values considering a packet with 16 bytes payload and Hamming (\(7A\)) FEC for three different links: the “Good” link from node 4 to node 2 observed in topology 2 (**a**), the “Medium” link from node 2 to node 2 observed in topology 2 (**b**), and the “Bad” link from node 1 to node 3 observed in topology 1 (**c**).  
may compute every joint probability knowing only the initial distribution of the states \(p_{i}^{(0)}=\mathbb{P}(X_{0}=i)\) and the values of \(p_{ij}\), where:  
\[p_{ij}=\mathbb{P}(X_{n+1}=j|X_{n}=i),\forall n. \tag{4}\]  
Exploiting matrix calculus, since we knew the frequencies of the BER values of each link, we found the transition matrices \(P=(p_{ij})\), which have only non-negative elements, are row-normalized to 1 and, in our case, have a size 3 \(\times\) 3. In Figure 5 we show the matrix charts presenting the transition matrices for the three links discussed so far.  
A relevant result is that, given the transition matrix \(P^{n}\) at time \(n\), it is possible to compute the \(t\)-step transition probabilities by means of matrix exponentiation:  
\[\mathbb{P}(X_{n+t}=j|X_{n}=i)=(P^{t})_{ij},\forall n\geq 0. \tag{5}\]  
Figure 4: Three-state channel model.  
Figure 5: Examples of transition matrices: transition matrix \(\mathbb{P}\) of the links from node 4 to node 2 observed in topology 2 (**a**), from node 3 to node 2 observed in topology 2 (**b**), and from node 1 to node 3 observed in topology 1 (**c**).  
The averaged values of the BER in the three states for the links we are considering are reported in Table 2.
## 2 Materials and Methods
### Irrigation Scheduling Management  
#### 2.2.1 Irrigation Scheduling Managed by the Farmer (FarMan)  
This study is part of the \"Use of Innovative Systems based on Wireless Sensor Networks in the Irrigation Planning of Tomato Processing\" project (SIWIP), funded by the Italian Ministry of Agricultural, Food and Forestry Policies. The research was conducted in collaboration with a farm that has a longstanding association with processing tomato production and is part of a producer organization that manages around 500 hectares of processing tomatoes. According to the technique commonly used in the area, the farmer used fixed intervals (from 2 to 6 days) for irrigation in an attempt to reduce the water stress of the crops and make irrigation management easier [9]. However, this technique failed to consider the soil water content or the crop's water requirements and can be considered an empiric method. The total irrigation volumes applied by the farmer during the two tomato crop cycles and the number of irrigation events are reported in Table 2.
###### Abstract  
Satellite communication is inevitable due to the Internet of Everything and the exponential increase in the usage of smart devices. Satellites have been used in many applications to make human life safe, secure, sophisticated, and more productive. The applications that benefit from satellite communication are Earth observation (EO), military missions, disaster management, and 5G/6G integration, to name a few. These applications rely on the timely and accurate delivery of space data to ground stations. However, the channels between satellites and ground stations suffer attenuation caused by uncertain weather conditions and long delays due to line-of-sight constraints, congestion, and physical distance. Though inter-satellite links (ISLs) and inter-orbital links (IOLs) create multiple paths between satellite nodes, both ISLs and IOLs have the same issues. Some essential applications, such as EO, depend on time-sensitive and error-free data delivery, which needs better throughput connections. It is challenging to route space data to ground stations with better QoS by leveraging the ISLs and IOLs. Routing approaches that use the shortest path to optimize latency may cause packet losses and reduced throughput based on the channel conditions, while routing methods that try to avoid packet losses may end up delivering data with long delays. Existing routing algorithms that use multi-optimization goals tend to use priority-based optimization to optimize either of the metrics. However, critical satellite missions that depend on high-throughput and low-latency data delivery need routing approaches that optimize both metrics concurrently. We used a modified version of [PERSON]'s power metric to reduce delay and packet losses and verified it with experimental evaluations. We used a cognitive space routing approach, which uses a reinforcement-learning-based spiking neural network to implement routing strategies in NASA's High Rate Delay Tolerant Networking (HDTN) project.  
delay-tolerant networking; spiking neural network; satellite communication; QoS; AI; ISL
# Application of Riemannian Seismic Ray Path Tracing in Salt Dome Prospecting
## 5 Discussion
### Comparison between Techniques in Geophysical Exploration  
In the context of geophysical exploration, particularly in mapping salt domes, both differential geometric methods and traditional Euclidean techniques like ray tracing using the Eikonal equation have their unique advantages and limitations. To provide a comprehensive understanding, it is essential to compare these approaches, highlighting their respective applications, strengths, and weaknesses, and relating them to relevant studies in the field.
## 2 Signal and Channel Model
### Multipath Channel Transfer Function  
Multipath fading effect can be characterized by four parameters: the number of reflected signals, amplitude, time delay, and phase difference of each path relative to LOS. Assuming the incoming signal is an arbitrary BOC-modulated signal, it can be modeled as:\[s(t)=s_{0}(t)+\sum_{i=1}^{P}s_{i}(t)=\sum_{i=0}^{P}\alpha_{i}Ac(t-\tau_{i})sc(t- \tau_{i})\cos(w_{0}t+q_{i}) \tag{1}\]  
where \(s_{0}(t)\) is the LOS, \(s_{i}(t)\) denotes the \(i\)-th multipath signal, \(P\) is the total number of multipath signals, \(A\) is the amplitude of the LOS, \(c(t)\) represents the pseudo-random code and \(sc(t-\tau_{i})\) is the square-wave subcarrier of BOC modulation defined by \(sc(t)=sign[\sin(w_{m}t)]\) with subcarrier rate of \(w_{m}\). Besides,\(\alpha_{i}\), \(\tau_{i}\), and \(\varphi_{i}\) denote the amplitude attenuation, time delay and phase difference for path \(i\) relative to \(s_{0}(t)\), respectively. Here we can easily get that the time delay \(\tau_{0}\) and carrier phase difference \(\varphi_{0}\) of the LOS are both zero and amplitude attenuation factor \(\alpha_{0}\) is 1. For the sake of brevity and clarity, we just ignore the noise here and will consider its effect in the next section. Then the complex form of the signal after down-conversion can be expressed as:  
\[I+jQ=\sum_{i=0}^{P}\alpha_{i}Ac(t-\tau_{i})sc(t-\tau_{i})e^{i(\varphi_{e}+ \varphi_{i})} \tag{2}\]  
where \(\varphi_{\rm e}\) denotes the difference of carrier phase between the LOS and the locally generated carrier. Generally, without using multipath mitigation methods, the direct signal and multipath signals are all correlated with the local replica in the tracking loop. The final correlation function is written as:  
\[R(\tau)=\sum_{i=0}^{P}\alpha_{i}AR_{0}(\tau-\tau_{i})e^{i(\varphi_{e}+\varphi_ {i})} \tag{3}\]  
where \(R_{0}(\tau)\) is the normalized autocorrelation function for BOC signal with multi-peak. According to the Wiener-Khinchin theorem, we obtain the CPSD between incoming signal and local replica as follows:  
\[G(f)=\sum_{i=0}^{P}\alpha_{i}Ae^{i(\varphi_{e}+\varphi_{i})}G_{0}(f)e^{-j2\pi f \tau_{i}}=Ae^{j\varphi_{e}}G_{0}(f)\left(1+\sum_{i=1}^{P}\alpha_{i}e^{j\varphi _{i}}e^{-j2\pi f\tau_{i}}\right) \tag{4}\]  
where is the power spectral density (PSD) function of an ideal BOC signal. It should be noted from (4) that the CPSD is expressed as a product of two parts: The first item is the PSD of an ideal incoming signal with amplitude \(A\) and carrier phase difference relative to local generated carrier; whereas the second item is exactly the multipath channel transfer function (CTF) given by:  
\[\text{CTF}=1+\sum_{i=1}^{P}\alpha_{i}e^{i\varphi_{i}}e^{-j2\pi f\tau_{i}} \tag{5}\]  
CTF is the Fourier transform of the time domain channel impulse response (CIR) function [27]. It is a function of \(\alpha_{i}\), \(\tau_{i}\) and \(\varphi_{i}\) which mainly depends on the current multipath channel characteristics and contains all the parameter information of the total \(P\) multipath signals. So, we can estimate these parameters by carefully analyzing the CTF.
## 2 ACS-SSI Algorithm and Its Verification  
Considering the advantages and disadvantages of covariance-driven stochastic subspace identification (COV-SSI), this paper improves the covariance-driven subspace method using the noise suppression characteristics of the average correlation function. The use of multiple average correlation functions to replace the original vibration signal as the input can theoretically reduce the influence of noise signals on the identification accuracy. Therefore, the average correlation signal based stochastic subspace identification (ACS-SSI) is applied to the identification of modal characteristics of dynamic systems. The effectiveness of the improved method is initially verified based on a multi-DOF dynamic model.
## 3 Our Proposed Method
### MIFS and SWAP Subroutines  
Our proposed MIDNI algorithm employs two subroutines, MIFS and SWAP, similar to MIBNI [7]. However, in the MIBNI algorithm MIFS and SWAP only work with the binary expression. Thus, we modified them to adapt both two-level and three-level discretized expressions, as shown in Algorithms 1 and 2. For each target gene \(v\) of the network, the MIFS subroutine returns an initial set of candidate regulatory genes \(S_{v}\subseteq V\) by evaluating an approximated multivariate mutual information. Subsequently, the SWAP subroutine is used to increase the dynamic accuracy by swapping between selected candidate regulatory genes \(S_{v}\) and the set of unselected genes \(V\setminus S_{v}\). This swapping process is repeated until there is no improvement in the consistency of the gene-wise dynamics. Finally, MIDNI predicts a set of regulatory genes for each target gene of the input network. Based on this result, the inference method is applied to reconstruct the original network.  
```
0:\(k\geq 1\), \(|W|\geq 1\) \(S\leftarrow\) Initialize to empty \(v\gets argmax_{w\in W}I(v_{0};w)\) \(S\gets S\cup v\) \(W\gets W\setminus v\) while\(|S|\leq k\)do \(v\gets argmax_{w\in W}(I(v_{0};w)-\Sigma_{s\in S}I(w;s))\) \(S\gets S\cup v\) \(W\gets W\setminus v\) endwhile return\(S\)
```  
**Algorithm 1**\(MIFS(v_{0},W,k)\) subroutine, where \(v_{0}\) is the target variable, \(W=\{w_{1},w_{2},...,w_{M}\}\) is the set of variables, and \(k\) is the desired number of input variables  
```
0:\(k\geq 1\), \(|W|\geq 1\) \(S\leftarrow\) Initialize to empty \(v\gets argmax_{w\in W}I(v_{0};w)\) \(S\gets S\cup v\) \(W\gets W\setminus v\) while\(|S|\leq k\)do \(v\gets argmax_{w\in W}(I(v_{0};w)-\Sigma_{s\in S}I(w;s))\) \(S\gets S\cup v\) \(W\gets W\setminus v\) endwhile return\(S\)
```  
**Algorithm 2**\(SWAP(v_{0},S,W)\) subroutine, where \(v_{0}\) is the target variable \(S=s_{1},s_{2},...,s_{k}\) is the set of selected variables such that \(I(v_{0};s_{i})\geq I(v_{0};s_{j})\) if \(i<j\) for all \(s_{i},s_{j}\in S\), and \(W=\{w_{1},w_{2},...,w_{M}\}\) is the set of unselected variables such that \(I(v_{0};w)\geq I(v_{0};w_{j})\) if \(i<j\) for all \(w_{i},w_{j}\in W\) \(v_{0}\gets search\_update\_rule(v_{0},S)\) \(E_{MAX}\gets E(v_{0},v_{0}^{\prime})\) \(i=1\) while\(i\leq M\)do \(j=1\) while\(j\leq K\)do \(v_{0}^{\prime}\leftarrow search\_update\_rule(v_{0},S\cup\{w_{j}\}\setminus\{s_{j}\})\) if\((E(v_{0},v_{0}^{\prime})>E_{MAX})\)then \(S\gets S\cup\{w_{i}\}\) \(S\gets S\setminus\{s_{j}\}\) \(W\gets W\cup\{s_{j}\}\) \(W\gets W\setminus\{w_{j}\}\) \(E_{MAX}\gets E(v_{0},v_{0}^{\prime})\) endif endwhile endwhile return\((S,E_{MAX})\) ```  
**Algorithm 2**\(SWAP(v_{0},S,W)\) subroutine, where \(v_{0}\) is the target variable \(S=s_{1},s_{2},...,s_{k}\) is the set of selected variables such that \(I(v_{0};s_{i})\geq I(v_{0};s_{j})\) if \(i<j\) for all \(s_{i},s_{j}\in S\), and \(W=\{w_{1},w_{2},...,w_{M}\}\) is the set of unselected variables such that \(I(v_{0};w)\geq I(v_{0};w_{j})\) if \(i<j\) for all \(w_{i},w_{
## 4 Discussion
### Clinical Perspective  
Real-life data provide insights into the actual activities of patients, and analyzing data from diverse populations can reveal patterns in wheelchair ambulation, in terms of speeds, distances, turns, and inclines, that may exacerbate or mitigate shoulder pain. This knowledge will help clinicians identify specific strategies that minimize shoulder strain and inform patients about optimal wheelchair use.  
Additionally, tailored programs can be designed that consider individual out-patient characteristics and daily activities. For instance, a patient who regularly engages in frequent long-distance propulsion may benefit from different interventions than someone using a wheelchair primarily for short distances. As an example from this study, participant 6 showed the highest WCMM values among all participants. He took his dog for a longer walk, covered almost 11 km on the day of measurement, made around 200 turns in the range of 180\({}^{\circ}\) to 210\({}^{\circ}\) when looking for his dog (background information from observation of video recordings), and covered more than 1100 m over inclines of 3\({}^{\circ}\) or more. When this is a regular ambulation pattern for this person, and this person presents to a clinician with shoulder pain, the WCMM could be very helpful in formulating some advice to reduce shoulder load that could ignite the shoulder pain. Possible advice could be to keep moving, but for shorter distances or with longer breaks in between, to reduce the number of turns, and to try to find another route with less elevation gain. Or, when unavoidable, consider the use of power assist technology. By aligning rehabilitation strategies with actual usage patterns, clinicians can enhance the effectiveness of their interventions. In fact, monitoring wheelchair use before and after any intervention can also help in evaluating the effectiveness of such an intervention.  
Data-driven insights might also help identify specific risk factors associated with shoulder pain in wheelchair users. The analysis of longitudinal real-life wheelchair mobility behavior can reveal an eventual relation between the several aspects of wheelchair use and development of shoulder problems over time. For example, excessive use of the upper extremities without proper ergonomic adjustments may lead to chronic pain [5, 55, 56]. But, for a person with a low ambulation distance, other risks and priorities might exist. Based on the real-life data, clinicians can pinpoint these risks and develop preventative strategies, such as recommending ergonomic modifications, discussing alternative routes, etc. Furthermore, real-life data serve as a powerful tool for patient education and shared decision making. When clinicians present data on how specific wheelchair mobility behavior impacts shoulder health, patients can better understand the importance of adopting recommended practices.