esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result Experiment
### Case Study: Estimation of Remaining Lifetime Using ACI  
The ACI is a key metric for quantitatively assessing the aging state of cable insulation and estimating the remaining lifetime under specific operating conditions. In this study, ACI is calculated based on aging models for tensile strength and elongation of thermoplastic cable insulation materials, with the goal of predicting the future service life.  
Using Equations (3) and (4), the aging behavior of tensile strength and elongation was modeled as functions of temperature and aging time. Based on these results, Equations (5) and (6) define the ACI formulations, which were then applied to predict the progressive mechanical deterioration of the insulation. The following section presents a step-by-step case study illustrating this approach.
## 4 Results and Discussion
### Distinguish and Precisely Evaluate Lift-off Effect  
Lift-off effect is one of the major problems of the ECT method and is caused by the electromagnetic field weakening or strengthening due to variation of the lift-off distance. Signals caused by such lift-off are usually difficult to distinguish from those caused by those defects. However, as discussed, the Lorentz force in the horizontal direction has a specific feature different than that in the vertical direction, this feature has different sensitivity to defects and lift-off factors such as the varied coating thicknesses, an irregular sample surfaces or an operator movement. This feature can be utilized to reflect the extent of the lift-off.  
The simulation results of the measured force in the \(z\) and \(x\)-direction are shown when a sample without and with a defect undergoes a \"lift-off\" as shown in Figure 9. Figure 9a, we see that in the \(z\)-direction a lift-off forms a signal which could be difficult to distinguish from signals refer to defect in practice. Nevertheless, in the \(x\)-direction no signal is detected, indicating that the x-component is resistant to lift-off. When a lift-off occurs with a defect (Figure 9b), the z-component is disturbed by the lift-off, note that in Figure 5a the experimental curve has a similar shape as the \(\mathbf{F}_{z}\) simulation result in Figure 9b. However, the signal of the x-component is still mostly unaffected as compare to that in Figure 5b. It is confirmed that a lift-off occurs in our experiment. The above results show that, horizontal components are much insensitive to lift-offs compared to vertical component, and some curves of the Lorentz force in the \(x\)-direction is invisible because there is much overlap among these forces, namely they vary very consistently. Consequently, we can take advantage of this feature to identify lift-offs.  
Figure 8: The voltage signal strength integral value in the \(z\)-direction as indicated in Figure 7a, varies approximately linearly with the different defect volumes.
## 5 Conclusions
\begin{tabular}{l l} \(A_{o}\) & external surface area _of_ heat exchange (m\({}^{2}\)) \\ \(C_{p}\) & specific heat capacity (of working fluid) (J kg\({}^{-1}\)\({}^{\circ}\)C\({}^{-1}\)) \\ \(d_{i}\) & inner diameter of the tube (m) \\ \(d_{o}\) & outer diameter of the tube (m) \\ \(d_{c}\) & coil diameter (m) \\ \(h\) & convective heat transfer coefficient (W m\({}^{-2}\)\({}^{\circ}\)C\({}^{-1}\)) \\ \(k\) & thermal conductivity (of working fluid) (W m\({}^{-1}\)\({}^{\circ}\)C\({}^{-1}\)) \\ \(k_{TS}\) & thermal conductivity of the tube in the test section (W m\({}^{-1}\)\({}^{\circ}\)C\({}^{-1}\)) \\ \(L\) & length of the tube in the test section (m) \\ \(\dot{m}\) & mass flow rate of working fluid (kg s\({}^{-1}\)) \\ \(n\) & empirical shape factor of nanoparticles \\ \(N_{De}\) & Dean number \(\left(=N_{Re}\sqrt{\frac{d}{d_{c}}}\right)\) \\ \(N_{Nu}\) & Nusselt number \\ \(N_{pe}\) & Peclet number \(\left(=N_{Re}N_{Pr}\right)\) \\ \(N_{Pr}\) & Prandtl number \\ \(N_{Re}\) & Reynolds number \(\left(=\frac{\rho\rho t_{i}}{\mu}\right)\) \\ ppm & parts per million \\ \(Q\) & heat transfer rate (W) \\ \(T_{0}\) & bulk fluid temperature at the inlet (\({}^{\circ}\)C) \\ \(T_{1}\)-\(T_{12}\) & bulk fluid temperatures at the intermediate points along the test section (\({}^{\circ}\)C) \\ \(T_{13}\) & bulk fluid temperature at the outlet (\({}^{\circ}\)C) \\ \(T_{b}\) & temperature of the heating bath water (\({}^{\circ}\)C) \\ \(\Delta T_{in}\) & temperature difference at the inlet (\({}^{\circ}\)C) \\ \(\Delta T_{out}\) & temperature difference at the outlet (\({}^{\circ}\)C) \\ \(\Delta T_{LMTD}\) & logarithmic mean temperature difference (\({}^{\circ}\)C) \\ \(U\) & overall heat transfer coefficient (W m\({}^{-2}\)\({}^{\circ}\)C\({}^{-1}\)) \\ \(U_{Y}\) & uncertainty in a calculated quantity \\ \(v\) & inlet velocity (m s\({}^{-1}\)) \\ \(v\)/\(\psi\)\% & volume percent concentration (\%) \\ \(w\)/\(w\)\% & weight percent concentration (\%) \\ \(X_{i}\) & a measured quantity for Equation (16) \\ \(U_{X_{i}}\) & uncertainty in a measured quantity \\ \end{tabular}   
**Subscripts** \begin{tabular}{l l} \(BF\) & base fluid \\ \(i\) & inner \\ \(NF\) & nanofluid \\ \(NP\) & nanoparticle \\ \(o\) & outer \\ \end{tabular}   
**Greek Symbols** \begin{tabular}{l l} \(a_{0}\), \(\beta_{0}\), \(\gamma\) & constants in Equation (13) \\ \(\lambda\) & curvature ratio \(\left(=\frac{d_{c}}{d_{c}}\right)\) \\ \(\mu\) & dynamic viscosity (kg m\({}^{-1}\) s\({}^{-1}\)) \\ \(\rho\) & density (kg m\({}^{-3}\)) \\ \(\phi\) & volume fraction of nanoparticles \\ \end{tabular}   
\(\phi_{L}\) nanoparticles volume fraction enhancement limit for the present study (\(\phi_{L}=0.015\)) \(\psi\) sphericity of nanoparticle
## 4 Experimental Results
### Rough Surfaces on both Sides
The relative nonlinearity parameter barely changes up to Ra = 2.2 \(\upmu\)m, as observed for the absolute nonlinearity parameter. It then increased sharply as the surface roughness increased; it increased to 1.17 on the roughest surface (Ra = 2.9 \(\upmu\)m). Consequently, the absolute nonlinearity parameter changed by a smaller degree than the relative nonlinearity parameter according to the variations in the surface roughness. This result is similar to that obtained for Al 6161-T6 (Figure 6). Although there is a small difference in the Ra at which the relative nonlinearity parameter changes (Al 6061-T6: Ra \(>\) 1.5 \(\upmu\)m; SUS 304: Ra \(>\) 2.2 \(\upmu\)m), comparisons of the measurement results for Al 6161-T6 and SUS 304 specimens indicate that the results are similar overall. Therefore, we believe that the variation in the nonlinearity parameter with surface roughness is less related to the material properties. These tendencies shown in Figures 5-7 can be explained using Figure 8, which shows the pulse-echo measurement results for Al 6061-T6 with a 10-MHz transducer used as the receiving transducer. Figure 8a shows the received signal obtained using the pulse-echo method with respect to the surface roughness. The amplitude of the received signal decreases with variations in the surface roughness, indicating that the signal reception sensitivity decreases. Figure 8b shows the spectrum obtained from the FFT results of the received signal. The resonance frequency was 7.7 MHz for the reference specimen (Ra = 0.5 \(\upmu\)m) and 6.8 MHz for a rough surface (Ra = 2.7 \(\upmu\)m) because the attenuation and scattering of the received signal caused by the surface roughness are more severe at a higher frequency than at a lower frequency. As the resonant frequency of the receiving transducer decreased, the relative sensitivity of the transducer changed at 5 and 10 MHz, i.e., the frequencies of interest [19]. Additionally, the received voltage amplitudes, \(A_{1}\)\({}^{\prime}\) and \(A_{2}\)\({}^{\prime}\), obtained from the relative nonlinear  
Figure 7: Variations in the absolute and relative nonlinearity parameters for SUS 304 with respect to the surface roughness. measurements decrease as shown in Figure 5, thus causing a change in the relative nonlinearity parameter as shown in Figures 6 and 7. However, because the relative nonlinearity parameter is inversely proportional to the square of the voltage amplitude of the fundamental wave component (\(A_{1}{}^{\prime 2}\)) and proportional to the voltage amplitude of the second-order harmonic component (\(A_{2}{}^{\prime}\)), the shifting of the resonant frequency of the receiving transducer to a lower value does not indicate a decrease in the relative nonlinearity parameter.
# TSCH Multiflow Scheduling with QoS Guarantees:
## 1 Introduction
However, to compensate for the limitations they present, a disruptive approach such as Software Defined Networks (SDN) is needed. In contrast to centralised scheduling solutions that only have control over the MAC layer, an SDN solution allows control over all layers, which makes it possible to manage all decisions and processes that take place at the nodes. Hence, with this type of scheduler it is possible to meet the increased QoS requirements. However, SDN integration is not straightforward, due to the difficulty of managing control traffic in a resource-constrained environment. This has led to multiple implementations such as SD-WSN [11], TinySDN [12], Atomic SDN [13] and SDN-WISE [14]. However, these solutions use the CSMA protocol as MAC, which makes them unfeasible in industrial environments with deterministic requirements. To adapt to these environments, it is necessary to have solutions that use the TSCH protocol such as Whisper [15], uSDN [16] and SDN WISE-TSCH [17]. Whisper and uSDN are based on a traditional TSCH RPL network using the 6 TiSCH protocol stack, where in the case of Whisper the SDN controller injects \"fake\" messages that modify the metrics of the nodes in order to alter the operation of the routing and scheduling protocols, whereas uSDN injects configurations through the Constrained Application Protocol (COAP). However, in both cases the use of standardized upper layers limits the flexibility of software-defined networking. In the case of SDN WISE-TSCH extends the performance of SDN WISE that offers a complete design of all layers and protocols to allow full centralised management, therefore all layers and protocols are optimised to minimise control traffic. Because of this it has less control traffic than AMUS but still more than Orchestra. However, the control that is obtained over the entire network means that the adverse effects of this type of traffic are avoided [17]. This paper focuses on analysing the advantages of software defined networks to guarantee strict QoS parameters in multi-flow environments. Different scenarios with industrial level characteristics are simulated using different TSCH schedulers that are available and have been extensively studied [18; 19; 20]. Performance is evaluated in terms of end-to-end delay, packet delivery ratio, deadline satisfaction ratio and power consumption. In addition, a testbed is implemented to compare the two protocols that have shown the best performance in the simulations, evaluating the end-to-end delay and the Packet Inter-Arrival Time (PIAT). The rest of the paper is organised as follows. Section 1 introduces the TSCH and Routing Protocol for Low-Power and Lossy Networks (RPL) and the different scheduling protocols. Section 2 presents a description of the scenario and the metrics used in the tests. Section 3 contains the simulation results for each of the metrics, and the testbed results. Discussion and conclusions are shown in Section 4.
## 2 System Model with Channel Crosstalk
Accordingly, the matrix **T** can be regarded as the identity matrix for the WDM-VLC system model. At the receiver side, the received signal corrupted by the shot noise and the interference caused by crosstalk can be expressed as:  
\[\textbf{r}=\rho\textbf{THP}+\textbf{n}, \tag{5}\]  
where \(\rho\) denotes the optical/electric (O/E) conversion efficiency of the PD, \(\textbf{H}=diag([h_{11},h_{22},\cdots,h_{NN}])\) denotes the channel gain matrix where \(diag(\cdot)\) is a diagonal matrix, and **n** can be modeled as signal-independent additive white Gaussian noise (AWGN) with zero mean and noise variance \(\sigma^{2}\)[18]. With the combination of Equations (1) and (5), the received signal can be rewritten as:  
\[\textbf{r}=\rho\textbf{THp}+\rho\textbf{TH\bar{P}}+\textbf{n}. \tag{6}\]  
After removing the DC component, the received signal can be rewritten as:  
\[\bar{\textbf{r}}=\rho\textbf{THp}+\textbf{n}. \tag{7}\]  
Figure 2: Emission spectra of LEDs with different colors. Here, we assume the \(i^{\text{th}}\) receiver is interested in the data from the \(i^{\text{th}}\) color chip. Accordingly, the received signal from the \(i^{\text{th}}\) color LED chip can be expressed as:  
\[\tilde{r}_{i}=\rho t_{i}h_{ii}p_{i}+\sum_{j=1,j\
eq i}^{N}\rho t_{i }jh_{ij}p_{j}+\sigma^{2}. \tag{8}\]
# Fresher Experience Plays a More Important Role in Prioritized Experience Replay
###### Abstract
The experiences are feedback of the environment to the agent, which inform the agent whether the current action is good or not. So, it is crucial to find an efficient way to sample the experiences. Prioritized Experience Replay (PER) [29] is proposed to assign the experiences priority according to the absolute value of temporal difference error (TD-error), where a larger absolute TD-error denotes higher priority. Hence, the agent is more likely to replay important experiences instead of uniformly choosing experiences from the replay buffer. In addition, Combined Experience Replay (CER) [30] is a modification of PER. It considers the freshness of experiences and always puts the latest experience into the mini-batch to train the neural network. Of note, fresh experiences are produced by virtue of the current networks i.e., they are more valuable for updating current network parameters than the past. In other words, the learning speed of the agent can increase as long as the sampled experiences are fresh enough. Unfortunately, this fact has been neglected by the community for a long time. In light of the above-mentioned issues, this paper focuses on the freshness of all experiences in the replay buffer instead of the latest one. Our objective is to figure out how the experience freshness affects the learning process of DRL. To be more concrete, a freshness discounted factor \(\mu\) is introduced for the process of calculating the experience priority. This method can decrease the priority of past experiences by recording the replay times of experiences. Therefore, the sampling probability of fresh experiences can enhance indirectly. The major contributions of this paper are summarized as follows:  
* Evaluation of Freshness. A freshness discounted factor \(\mu\) is introduced evaluate the freshness of each experience during the calculation of the priority of each experience. The constraint of \(\mu\) increases sampling probability of the fresher experience. We refer to this method as freshness prioritized experience replay (FPER) in the rest of this paper. * Lifetime Pointer. In order to make the learning process more stable and to accelerate the convergence process, a lifetime pointer scheme is proposed. The pointer always points to the position of the experience with the lowest absolute TD-error. When the latest experience enters the buffer, it overwrites on the position pointed by the lifetime pointer. It prolongs the lifetime of valuable experiences, reduces the lifetime of worthless experiences, and further breaks the data correlation. The mentioned FPER is not a complete DRL algorithm; it should be combined with other algorithms to form a complete learning system. In the experiments, we consider the combination of FPER with Dueling DQN and DDPG separately to validate its superiority in both discrete control tasks(e.g., CartPole-v1, LunarLander-v2) and continuous control tasks(e.g., HalfCheetah-v1, Ant-v1).
# A Vehicle-Infrastructure Cooperative Perception Network Based on Multi-Scale Dynamic Feature Fusion
## 2 Related Work
### LiDAR-Based 3D Object Detection  
LiDAR-based 3D object detection can be primarily classified into two categories based on how the point cloud data is represented: point-based methods and voxel-based methods. Point-based methods directly process raw point clouds, preserving the original structural information. However, they encounter challenges due to the unstructured and sparse nature of point cloud data. For instance, PointNet [25] processes the input point cloud directly and employs MaxPooling to aggregate both local and global features, effectively capturing the intrinsic structure of the data. Building on this, PointNet++ [26] enhances the original PointNet by addressing the issue of uneven point cloud density through a multi-scale approach. Further advancing this concept, PointRCNN [27] introduces a two-stage 3D object detection framework that utilizes point cloud data to generate high-quality region proposals, achieving precise 3D object detection.  
In contrast, voxel-based methods partition the point cloud into uniformly spaced 3D voxels, extracting features from the points within each voxel to create a structured representation akin to image pixels [4; 28; 29]. This structured approach simplifies subsequent feature extraction and detection processes, allowing deep learning models to process point cloud data more efficiently. Unfortunately, the voxelization process can introduce quantization errors, potentially resulting in the loss of detailed information.  
VoxelNet [28] is an end-to-end 3D object detection framework that begins by dividing point cloud data into fixed-size voxels. It employs a Voxel Feature Encoder to extract point features within each voxel, aggregating them into voxel-level feature representations before performing object detection using a Region Proposal Network. PointPillars [4], on the other hand, projects point cloud data into a two-dimensional pillar grid. By leveraging its efficient pillar projection mechanism and lightweight network architecture, PointPillars achieves real-time object detection while maintaining high accuracy. In this work, we select PointPillars as the backbone network for constructing the vehicle-infrastructure cooperative perception network.
# Circulating Tumor Cells in Metastatic Breast Cancer: Clinical Applications and Future Possibilities
## 4 Therapy Selection Based on CTCs  
So far, treatment decisions in metastatic breast cancer are based on the histologically verified predictive features of the disease. This makes invasive and potentially harmful biopsies of distant metastatic sites, such as lung or liver, necessary in order to recommend adequate systemic therapy. In patients where a metastatic biopsy is not possible (e.g., due to an untypical localization or patient's condition), the choice of therapeutic strategy is based on the properties of the primary tumor, which insome cases had been removed several years ago. However, the pheno-and genotype of tumor cells may change during the course of disease. According to a large meta-analysis, a switch of receptor status can be observed in up to one third of metastatic patients (Table 1, [11]). Therefore, current guidelines recommend a re-evaluation of predictive features of tumor cells via a metastatic biopsy. However, this approach does not address a potential heterogeneity between different metastatic sites (or even different parts of the same metastasis). Furthermore, serial metastatic biopsies are hardly feasible.
## 5 Performance Analysis
In our scheme, the average amount of messages that each node transmitted and received in one epoch is utilized to estimate the communication overhead. Figure 10 shows that the communication overhead changes over the number of nodes uniformly distributed in wireless sensor networks. The communication overheads under different communication ranges are compared. When the communication range of sensor node increases, the set of node' one-hop neighbors enlarges. The probability that two tracking nodes with same tracked node encounter increases, and the detection information forwarded to witness node for detection increases accordingly, so the communication overhead becomes higher. As is shown in Figure 12, the average amount of messages sent and received under \(R=100\) is 62, and the average amount of messages sent and received under \(R=15\) is 29, as the network size is 500 nodes. The average amount of messages sent and received under \(R=100\) is 113 and the average amount of messages transmitted and received under \(R=15\) is 56 when the network size is 1000 nodes, so the larger the communication range, the higher the communication overhead, further a gap of the average amount of messages transmitted and received under the two communication range becomes more and more large with the network pattern's augmentation.
# Extending Wireless Reschargeable Sensor Network Life without Full Knowledge
## 10 Simulation Results and Analysis
### Upper Bound Analysis  
We start by showing how close our simulation results are to the theoretical bound obtained by applying Equation (3). Figure 2 shows the actual percentage of counted nodes with power less than or equal to a given power level against the calculated theoretical upper bound for the same power level.  
Each point in Figure 2 represents counted and computed probability for a power level to exist. We generated 1000 random networks consisting of 300 nodes each. For each node in the network, we identify its residual power level \(Y\) and perform two tasks:  
* We count the number of nodes with residual power less than or equal to \(Y\), in the same network, to compute a percentage of lower or equal power nodes in the network.
* We compute the Chernoff Bound for \(Y\), using Equation (3).  
We then plot a point representing the two values. We repeat this process for all nodes in all 1000 networks, resulting in 300,000 points, as plotted in Figure 2.  
Figure 2 also shows the average count percentage found at each probabilistic value. We rounded the calculated probabilities to the closest second decimal digit, and consolidated similar points, then averaged the points and plotted them.  
It can be seen from Figure 2 that the theoretical upper bound is a true upper bound. Meaning each computed probability is always higher or equal to the counted percentage. It can also be noticed that, at least up to a probabilistic value of 0.9, the counted values are half the theoretical upper bound. When plotting averages in Figure 2, we find that the averages are below half the theoretical bound for all cases. We speculate that a reason for this could be that almost half the nodes in the network are at a power level above \(E(X)\) with the other half being below. This division is due to \(p\), the probability of discharge, being 0.5 as a default value selected for this plot and our network parameters Table 2.  
Next, we will address the results obtained by our algorithmic approaches that utilize this plotted upper bound equation (Equation (3)).