esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Transfer Function of the Measurement System
### Wheel Dynamics
\begin{bmatrix}\Lambda_{y}\\ 0&1&0\\ -\varphi_{yWa}&0&1\end{bmatrix} \,\] (10) \[[\Lambda_{z}]= \begin{bmatrix}1&-\varphi_{zWa}&0\\ \varphi_{zWa}&1&0\\ 0&0&1\end{bmatrix} \] \begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Symbol** & **Value** \\ \hline Wheelset mass & \(M_{wh}\) & 1054 kg \\ Axle mass & \(M_{axle}\) & 283 kg \\ Axlebox mass & \(M_{ax}\) & 78 kg \\ Wheel mass & \(M_{whed}\) & 251 kg \\ Brake disk mass & \(M_{brake}\) & 135 kg \\ Primary suspension stiffness (vertical) & \(k_{ps,z}\) & \(2\times 0.5\) kN/mm \\ Primary suspension stiffness (lateral) & \(k_{ps,y}\) & \(2\times 2.1\) kN/mm \\ Primary suspension stiffness (longitudinal) & \(k_{ps,x}\) & \(2\times 4.15\) kN/mm \\ Primary suspension damping (vertical) & \(c_{ps,z}\) & 11 kNs/m \\ Primary suspension damping (lateral) & \(c_{ps,y}\) & 50 kNs/m \\ Primary suspension damping (longitudinal) & \(c_{ps,x}\) & 85 kNs/m \\ Wheel nominal radius & \(R_{uhed}\) & 0.41 m \\ Point mass & \(M_{aw}\) & 605 kg \\ \hline \end{tabular}
\end{table} 
Table 1: Wheelset data and list of symbols. are rotation matrices obtained by considering small displacement assumption and \(R_{w}\) is the wheel radius. The vertical direct receptance \(A_{Wz}(f)\) of the wheel is directly the term \(z_{W}\) of Equation (9), since a unit contact force is considered. The comparison between the receptances \(A_{Wz}(f)\) obtained with point mass (dashed blue line) and flexible wheelset (continuous red line) models are shown in Figure 2. Differences between the two models are obviously associated with the presence of resonances in the model accounting for wheelset flexibility. The highly damped resonance peaks at about 6 and 8 Hz are related to the rigid vibration of the wheelset on the primary suspensions, corresponding to the vertical bounce and roll motion, respectively. The peaks at higher frequencies are associated to the flexibility of the wheelset, in particular to its bending modes. Four bending modes are observed in the 0-800 Hz frequency range, whose natural frequencies are 76 Hz, 185 Hz, 370 Hz and 580 Hz.
## 4 Discussion
### Maintenance Strategies and Optimization  
The study has highlighted the crucial importance of reorganizing maintenance activities, considering the high proportion of defects in the three analyzed components (cutter, tooth, chute), which represent approximately 65% of the total defects. The annual maintenance planning, while necessary for resource allocation and scheduling major interventions, should be complemented by a more dynamic approach that takes into account the specific repair times for each component and the unforeseen fluctuations in production volume. The monthly maintenance schedule, updated according to the various uncertainties, should include scheduled interventions on equipment, correlated with the annual plan and the estimated sales volume.  
Studies conducted in similar production units [56] where equipment also operates for 8 h/day have led to the conclusion that a major overhaul (RK) cycle will include a specific number of technical revisions (Rt) and current repairs of grade 1 and 2 (RC1 and RC2). The authors achieve efficiency improvements in such a repair cycle by shifting maintenance activities (e.g., from RC2 to RC1 or vice versa) based on the results of reliability analyses. In the Patars quarry, the operators perform technical revisions (Rt) after 50 operating hours, current repair grade 1 after 500 operating hours, current repair grade 2 after 2000 operating hours, and major overhauls (RK) every two years. The authors obtained results indicating an 80% reliability, corresponding to 216 operating hours for the bucket knife, 480 operating hours for the bucket tooth, and 840 operating hours for the sorting station chute. They recommend the following:  
* For the Komatsu WA470 front loader bucket knife, the bucket knife should be replaced at every fourth scheduled technical revision (Rt);
* For the Komatsu HB365 excavator bucket tooth, the bucket tooth should be replaced at every scheduled current repair grade 1 (RC1);
* For the sorting station chute, the chute should be replaced at every second scheduled current repair grade 1 (RC1).  
They have proposed reducing the interval for performing a current repair grade 1 from 500 to 400 h to further eliminate the risk of a defect occurring between two scheduled maintenance activities.
# Cicada Species Recognition Based on Acoustic Signals
## 1 Introduction  
The 17-year periodical cicadas are cicada species with life cycles as long as 17 years. These cicada species spend most of their lifetime underground and only emerge from the ground once every 17 years to reproduce before dying in four to six weeks. Coming from the singing insect family, cicada species emit sounds that are species-specific for communication purposes [1]. Male cicadas can produce deafening clicking sounds with frequencies up to 7 kHz at a 100 dB sound pressure level [2]. Their sounds are produced from the vibration of drum-like membranes, called timbals, which can be found on both sides of their body [3, 4]. Female cicadas, on the other hand, produce a less loud sound using their wings to respond to the male's calling.  
For a long time, detecting and classifying cicada species have been conducted by numerous researchers. There are mainly two approaches for this task. The first one is based on traditional method where the type of cicada species is determined by the domain experts manually. This process is time-consuming, costly, and dependent on the observer's ability. Following the advancement of technology, researchers have utilized machine learning algorithms to develop systems for cicada species identification and classification. In general, cicada species recognition systems can be created using two sources: (1) visual images of the cicadas, and (2) species-specific acoustic signals emitted by cicadas. However, under conditions where the visual images are not clear or for cicadas that live in complex environments, acoustic signals are a more favorable source than images.  
From the study on related works, it was found that the current research is more inclined to use conventional methods, such as support vector machine (SVM) and random forest (RF), to build the classification model instead of deep learning methods that can be more effective [5]. Therefore, we propose to develop a cicada species recognition system using a deep learning algorithm. With the use of a deep learning-based cicada species recognition system, monitoring of cicada species can be carried out more efficiently in terms of time and effort. Three 17-year periodical cicada species were selected for the acoustic signal recognition task, namely _Magicada cassinitii_, _Magicada septendecim_, andMagicicada septendecula. Audio recordings of their different sound types such as calling and courtship sounds were collected.  
This paper is organized in the following order. Section 2 presents the study on related works. Section 3 details the methods used and the proposed solution for cicada species detection and classification, while Section 4 shows the experimental results. Section 5 summarizes the important findings in the experiments, and lastly, Section 6 provides the conclusion and future works.
## 6 Recent Studies on CRSN Routing
### Open Issues  
Based on the above discussion, it becomes clear that there is a need for extensive research efforts to address the discussed challenges and to develop effective network layer solutions along the open issues outlined below.  
* There is an urgent need for research efforts to incorporate reactive energy-efficient data transfer components into proactive strategy-based protocols. This effort should be aimed at replacing the usual route repair via the broadcast of route requests used in recent studies.
* Because constant periodic spectral monitoring is nearly impossible for constrained nodes, there is a need to study analytical frameworks that implement energy-aware, cooperative, opportunistic spectrum access for the optimization of routing.
* There is a need for the development of adaptive schemes that consider various QoS requirements for application-specific purposes.
* An optimal method of employing multipath or flooding methods must be investigated for data mining applications.
# Tire Bubble Defect Detection Using Incremental Learning
## 2 Proposed Method
### Data Sample Categorization and Enhancement  
Speckle images with dimensions of 1360 \(\times\) 1024 pixels are used as input for inspection. Defects can be classified into different categories based on their location, size and shape. Using only a binary classification to separate images with and without bubbles would likely result in higher misjudgment rates. In this research, we divided the bubble defects into six categories, as listed below in Figure 2.  
To improve the accuracy for the hard-to-spot defects that are likely to be overlooked during the inspection, we used an image enhancement technique called Contrast Limited Adaptive Histogram Equalization (CLAHE) [12] to increase the visibility of the subtle bubbles present in the image. As shown in Figure 3, the bubbles in the red frame are much more obvious than the original image.  
As deep learning network training requires a large amount of sample data to attain a higher detection rate, insufficient defect samples in the dataset provided by the tire manufacturer may affect the effectiveness of the model. To address this issue, we combined the original and enhanced images in the model training set to increase sample data [13].  
Figure 3: Image enhancement (**a**,**b**) original speckle images, (**c**,**d**) Contrast enhanced images.  
Figure 2: Six types of bubble images (**a**) Tire tread defects and (**b**) Tire sidewall defects.
# A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference
## 2 Problem Description  
In this section, first, we establish the system models of INS/GNSS integration. Then, the influence of time-varying noise on the system is analyzed, and the VBAKF estimation method of MNCM is introduced. Finally, we analyze the distribution type of time-varying noise and the problems related to the existing VBAKF algorithm.
## 4 Optimization and Analysis
Typically, \(N_{iter}(\delta_{PIA})\) is no larger than 10 [28]. Therefore, the overall computing complexity of policy iteration algorithm scales as \(O(e_{max}N_{iter}(\delta_{PIA}))\). For the bisection method, the maximum iterations to achieve the expected accuracy \(\delta_{BM}\) are \(log_{2}(U/\delta_{BM})\). Thus, the overall complexity of Algorithm 1 is about \(O(e_{max}N_{iter}(\delta_{PIA})log_{2}(U/\delta_{BM}))\)._  
```
1 Initialization: Initialize the accuracy of policy iteration algorithm \(\delta_{PIA}>0\) and of bisection method \(\delta_{BM}\!>\!0\), \(\gamma_{low}^{[i]}\!=\!0\) and \(\gamma_{up}^{[i]}\!=\!U\), \(\eta^{[i]}(e)\!\in\! (0,min\ \{\beta(B\geq l),\eta_{PLR_{th}}\})\); \(i\!=\!j\!=\!0\);
2 Policy Optimization: Let \(\gamma^{[i]}=(\gamma_{up}^{[i]}+\gamma_{low}^{[i]})/2\) and determine \(\eta^{(\gamma)}\) by the following policy iteration steps: \(\bullet\) Policy Evaluation: Calculate the value function \(v_{\eta}(e)\) for \(e\!>\!0\), where \(v_{\eta}(e)\) is the solution of the linear system \(v_{\eta|i}(e)\!-\!\sum_{e\in\mathbb{E}}\mathbb{P}_{\eta|i}(e\,|\,e)\!-\!v_{ \eta|i}(e)\!=\!d_{\gamma^{[i]}}(\eta^{[i]}(e))\!-\!D\gamma^{[i]}(\eta^{[i]}),e\!\in\!E\), where \(D_{\gamma}(\eta)\!=\!MG(\eta)\!-\!\gamma P(\eta),d_{\gamma}(\eta)\!=\!U\eta(e) \!g(\eta(e))\!-\!\eta(e)\) and \(\mathbb{P}_{\eta|i}(e\,|\,e)\) is the Markov transition probability from energy level \(e\) to \(e\). \(\bullet\) Policy Improvement: Determine the new policy by solving the following optimization problem \(\eta^{[j+1]}(e)\!\!=\!\arg\max_{\eta\in\!\left(0,min\left\{\beta(B\geq l), \eta_{PLR_{th}}\right\}\right)}d_{\gamma^{[i]}}(\eta^{[i]}(e))\) \(+\mathbb{P}_{\eta|i}(e\,|\,e)\) If \(\eta^{[j+1]}(e)\!>\!min\left\{\beta(B\geq l),\eta_{PLR_{th}}\right\}\), then \(\eta^{[j+1]}(e)\!=\!min\left\{\beta(B\geq l),\eta_{PLR_{th}}\right\}\). \(\bullet\) Termination Test for Policy Iteration Algorithm: If \(\left|D\gamma^{[i]}(\eta^{[j]})-D\gamma^{[i]}(\eta^{[j+1]})\right|<\delta_{PIA}\), \(\eta^{[j+1]}\) is the optimal policy and \(\eta^{(\gamma^{[i]})}=\eta^{[j+1]}\). Otherwise, \(i:=i+1\), and repeat from the policy evaluation step. \(\left(3\right)\) Calculation \(f(\gamma^{[i]})\): Calculate \(f(\gamma^{[i]})=\Gamma(\eta^{(\gamma^{[i]})})-\gamma^{[i]}\) under the policy \(\eta^{(\gamma^{[i]})}\). \(\bullet\) Termination Test for Bisection Method: \(\bullet\) If \(\left|f(\gamma^{[i]})\right|\!<\!\delta_{BM}\), \(\eta^{*}\!=\!\eta^{(\gamma^{[i]})}\); \(\bullet\) If \(f(\gamma^{[i]})<-\delta_{BM}\), update the bounds \(\gamma_{up}^{[i+1]}:=\gamma^{[i]}\) and \(\gamma_{low}^{[i+1]}:=max\left\{\gamma_{low}^{[i]},\Gamma(\eta^{(\gamma^{[i]})})\right\}\) respectively, repeat from step (2) and update the counter \(i:=i+1\); \(\bullet\) If \(f(\gamma^{[i]})>\delta_{BM}\), update the bounds \(\gamma_{low}^{[i+1]}:=\gamma^{[i]}\) and \(\gamma_{up}^{[i+1]}:=min\left\{\gamma_{max}^{[i]},\Gamma(\eta^{(\gamma^{[i]})})\right\}\) respectively, repeat from step (2) and update the counter \(i:=i+1\). \(\bullet\)
```  
**Algorithm 1** (Optimal \(\gamma^{*}\) via bisection method )
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 7 2.5-Dimensional FSSs  
Miniaturized 2.5D frequency selective surfaces (FSSs) represent an innovative approach that bridges the gap between traditional two-dimensional (2D) and three-dimensional (3D) structures. These surfaces typically consist of a planar arrangement of elements with a limited vertical dimension, combining the benefits of both 2D and 3D designs. The  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Pros** & **Cons** \\ \hline Single-Layer FSS & Simple planar & Dependent on polarization and angle of incident. Bandwidth is narrow. \\ \hline Multi-layer FSS & Bandwidth is wider. & Coupling effect. \\  & Multiband & Costly and difficult to construct. \\ \hline Multiband responses & More Selective FSS & Fabrication process needs strict requirements. Concentric elements have unexpected coupling. \\ \hline Tunable FSS & Frequency properties varies & Numerous active elements required. \\  & Frequency properties varies & High cost and complex. \\  & & More potential for failure. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the Pros and Cons of Current FSSs [140].  
Figure 9: Frequency selective surface made of Jerusalem cross elements. (**a**) Jerusalem cross unit cell, (**b**) Simulated transmission and reflection coefficient [131].  
\"2.5D\" designation highlights the incorporation of a third dimension, albeit in a constrained manner, allowing for improved performance in various applications.  
The 2.5-Dimensional frequency selective surfaces (FSSs) are compact and lightweight designs that integrate multi-layer configurations, allowing for functionalities such as filtering, impedance matching, and reconfigurability without increasing bulk. These surfaces often incorporate tunable elements, like MEMS switches or varactor diodes, enabling real-time adjustments to their frequency response, which is particularly beneficial in dynamic environments. Applications of 2.5D FSSs span various fields, including wearable devices, where they minimize electromagnetic interference while ensuring efficient wireless communication; smart textiles that enable advanced functionalities like environmental sensing; Internet of Things (IoT) devices that require efficient and compact communication solutions; and healthcare, where they filter noise in medical implants and wearable health-monitoring devices, thus enhancing data reliability and performance.
## 4 Discussions of the Results
### Perplexity Analysis  
The perplexity measurements, as presented in Tables 1 and 2, illustrate the capability of SBA-RoPE to maintain lower perplexity across extended context windows, surpassing the baseline methods. This is particularly significant in the context of long sequence language modeling, where maintaining coherence over longer spans of text is crucial. The minimal increase in perplexity within the original context lengths for models fine-tuned with SBA-RoPE suggests that our method successfully preserves the model's original performance while effectively extending its context window. This indicates a balanced approach to extrapolating high-frequency dimensions, which is critical for minimizing the loss of pre-trained information.  
Furthermore, the extended context window experiments, especially with the scaling factor \(s=4\), highlight SBA-RoPE's superior extrapolation performance. Despite the models never being exposed to context lengths between 4 k and 8 k during training, SBA-RoPE demonstrates a robust transfer learning capability, effectively leveraging learned representations to adapt to and perform within these expanded context windows. This underscores the potential of SBA-RoPE in enhancing model flexibility and generalization across various context lengths, a key advantage for applications requiring comprehension of long documents or conversations.
# Topology Inference and Link Parameter Estimation Based on End-to-End Measurements +
## 3 Topology Inference and Estimation of Link Parameters
### Distance-Based Agglomerative Hierarchical Clustering
Each node (either destination or intermediate created parent) enters the NN chain only once, where it remains until it is joined with another node. * At _reduction_ step, we create node \(u\) as the parent of the previously found RNNs \(i\) and \(j\), and we calculate its LCA depth with the remaining nodes \(\ell(u,k),\ \forall k\in D,\ k\
eq u,i,j\). The choice of the employed reduction update formula is a crucial issue, which we analyze in Section 3.3. * _Tree reconstruction_ regards the update of the data structures related to the part of the tree that has been recovered up to this moment. In greater detail, we remove the found RNNs \(i\) and \(j\) from set \(D\), \(D=D\setminus\{i,j\}\); we add the newly created parent node \(u\) to sets \(V\) and \(D\), \(V=V\cup\{u\}\) and \(D=D\cup\{u\}\); we add the two new edges to set \(E\), \(E=E\cup\{(u,i),(u,j)\}\); and we connect \(i\) and \(j\) to \(u\) with link lengths \(d(u,i)=\ell(i,i)-\ell(i,j)\) and \(d(u,j)=\ell(j,j)-\ell(i,j)\). * Finally, if there is only one node left in set \(D\) (_stopping condition_\(|D|=1\)), we connect that remaining node with the source and we terminate the algorithm, otherwise we return to the neighbor selection step and we repeat the process. Given that a pair of RNNs is selected as siblings at every iteration, the output of the algorithm will be a binary inferred tree. To successfully apply the algorithm to general routing trees, nodes can be grouped as if the tree was binary, and then the \"extra\" edges of approximately zero length (excluding the edge between the root and its single child) can be removed at a post-processing step.
# A Photonically-Excited Leaky-Wave Antenna Array at E-Band for 1-D Beam Steering
## 2 Leaky-Wave Antenna
### RF Choke  
Antennas integrated with photodiodes often require a polarization network to enhance the photodiode responsivity. Therefore, one must include an RF choke at the end of each row to separate the radiating section from the pads used to polarize the UTC-PD. The choke is developed in microstrip technology, and it features a simulated RF isolation better than 35 dB within the array operating frequency band. It consists of seven cascaded identical sections, as shown in Figure 2 (right). Each section is T-shaped, and the horizontal bar is composed of two parallel, \(\lambda\)/4 open-ended stubs. The stubs create a short-circuit, resulting in an RF choke. Besides, the length of the T's vertical bar is chosen to translate the shorted microstrip to a large inductive load, so that it presents high input impedance, and this effect is reinforced, cascading them. Figure 2 (left and center) represents simulated and calculated isolation, \(|S_{2I}|\), depending on the number of sections as well as the magnitude of the reflection coefficient, \(|S_{1I}|\), for the 7-section choke, which is better than -0.85 dB in the whole band. In practice, the stubs do not create an exact short-circuit, and several sections need to be cascaded. Calculations are performed retrieving the ABCD matrix of the parallel stubs from full-wave simulations, taking the vertical bar as a section of a transmission line, and cascading the sections.  
Figure 1: Layout of the designed antenna with the different sections indicated, and unit-cell of the radiating section.
# Bootstrapped SSL CycleGAN for Asymmetric Domain Transfer
## 2 Preliminaries
### Unsupervised Training Procedure  
In the following lines of Algorithm 1 are formally summarized the main steps of the unsupervised training procedure that was discussed in more details in Section 2.2. The algorithm implements an iterative optimization of the objective function given by Equation (10).  
The presented steps are the basis for the proposed semi-supervised extension of the original CycleGAN algorithm, presented in Section 3.  
```
procedureCycleGAN (process in the left part of Figure 1) \(N\), number of iterations; \(m\), minibatch size; \(\eta>0\), learning rate; \(X\in\mathcal{X}\), \(Y\in\mathcal{Y}\), unpaired or unlabeld training sets, such that \(|X|\ll|Y|\);  Randomly initialize parameters of discriminators \(D_{X}\), \(D_{Y}\), and generators \(G_{X\to Y}\), \(G_{Y\to X}\): \(\theta_{D_{X}}\), \(\theta_{D_{Y}}\), \(\theta_{G_{X\to Y}}\), \(\theta_{G_{Y\to X}}\) for\(k=1\) to \(N\)do  Sample minibatch of unpaired training data \(\{x_{1},\dots,x_{m}\}\subset X\), \(\{y_{1},\dots,y_{m}\}\subset Y\) \(\widetilde{\mathcal{L}}_{adv}(G_{X\to Y},D_{Y})=\frac{1}{m}\sum_{i=1}^{m}\,\ln D _{Y}(y_{i})\;+\;\frac{1}{m}\sum_{i=1}^{m}\,\ln\big{(}1-D_{Y}(G_{X\to Y}(x_{i}) )\big{)}\) \(\widetilde{\mathcal{L}}_{adv}(G_{Y\to X},D_{X})=\frac{1}{m}\sum_{i=1}^{m}\,\ln D _{X}(x_{i})\;+\;\frac{1}{m}\sum_{i=1}^{m}\,\ln\big{(}1-D_{X}(G_{Y\to X}(y_{i}) )\big{)}\) \(\theta_{D_{X}}^{(k+1)}\;\longleftarrow\;\theta_{D_{X}}^{(k)}\;-\;\eta\, \
abla_{\theta_{D_{X}}}\widehat{\mathcal{L}}_{adv}(G_{Y\to X},D_{X})\) \(\theta_{D_{Y}}^{(k+1)}\;\longleftarrow\;\theta_{D_{Y}}^{(k)}\;-\;\eta\, \
abla_{\theta_{D_{Y}}}\widehat{\mathcal{L}}_{adv}(G_{X\to Y},D_{Y})\) \(\theta_{G_{Y\to X}}^{(k+1)}\;\longleftarrow\;\theta_{G_{Y\to X}}^{(k)}\;-\; \eta\,\
abla_{\theta_{G_{Y\to X}}}\Big{[}\,\widehat{\mathcal{L}}_{adv}(G_{Y \to X},D_{X})\;+\;\lambda_{cyc}\,\mathcal{L}_{cyc}(G_{X\to Y},G_{Y\to X})\, \Big{]}\) \(\theta_{G_{X\to Y}}^{(k+1)}\;\longleftarrow\;\theta_{G_{X\to Y}}^{(k)}\;-\; \eta\,\
abla_{\theta_{G_{X\to Y}}}\Big{[}\,\widehat{\mathcal{L}}_{adv}(G_{X \to Y},D_{Y})\;+\;\lambda_{cyc}\,\mathcal{L}_{cyc}(G_{X\to Y},G_{Y\to X})\, \Big{]}\) endfor endprocedure
```  
**Algorithm 1** CycleGAN training procedure