esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Spectral Feature Visualization and Intra-Class Consistency Analysis  
From Figure 7a, it can be found that the various ground objects of the original image are completely mixed together and completely dispersed in the 3D space. From Figure 7b, it can be found that the various ground objects in the TCT image are separated compared with the original image, but the mountains, roads, bare soil and buildings are interspersed and not concentrated enough. From Figure 7c, there is a slight overlap of bare soil, roads and buildings in the 0123-combination image and the water bodies and shadows are closer together. The ground objects are not only separated but also relatively concentrated and unified in direction. Bare soil, shadows, water bodies, mountains, roads and vegetation are more concentrated, indicating that the intra-class consistency of these six ground objects was enhanced after pseudo-TCT processing. Overall, it seems that the ground objects in the 0123-combined image are most concentrated in the spectral feature space, with the best separability of the ground objects and more uniformity in direction.  
Table 3 shows that the typical ground objects in the 0123-combined image have the least number of sampling points, with only 40.38% of the original image and 94.61% of the TCT image. Figures 8 and 9 show that the interior of the buildings and roads in the 0123-combined image are more consistent and the edges of the buildings and roads are more clearly defined. The standard deviation of the intra-class consistency images for the 0123-combination image of region 4 and image B is the highest at 30.15, which is approximately three times that of the original image. A larger standard deviation indicates  
a higher image contrast and sharper edges. All of these points show that the pseudo-TCT can enhance the intra-class consistency of the images and make the edges of the ground objects clearer. Although the edges of the ground objects are sharper and the intra-class consistency within the ground objects is higher, it is not possible to determine whether these features are more conducive to deep learning or feature extraction algorithms and whether these features can be learned as we have not tested them using deep learning or feature extraction algorithms.
###### Abstract  
Machinery diagnostics and prognostics usually involve the prediction process of fault-types and remaining useful life (RUL) of a machine, respectively. The process of developing a data-driven diagnostics and prognostics method involves some fundamental subtasks such as data rebalancing, feature extraction, dimension reduction, and machine learning. In general, the best performing algorithm and the optimal hyper-parameters suitable for each subtask are varied across the characteristics of datasets. Therefore, it is challenging to develop a general diagnostic/prognostic framework that can automatically identify the best subtask algorithms and the optimal involved parameters for a given dataset. To resolve this problem, we propose a new framework based on an ensemble of genetic algorithms (GAs) that can be used for both the fault-type classification and RUL prediction. Our GA is combined with a specific machine-learning method and then tries to select the best algorithm and optimize the involved parameter values in each subtask. In addition, our method constructs an ensemble of various prediction models found by the GAs. Our method was compared to a traditional grid-search over three benchmark datasets of the fault-type classification and the RUL prediction problems and showed a significantly better performance than the latter. Taken together, our framework can be an effective approach for the fault-type and RUL prediction of various machinery systems.  
Keywords:data-driven; diagnostics; prognostics; genetic algorithm; ensemble; fault-types; remaining useful life +
Footnote †: journal: Applied Sciences
# Multi-Objective Evolutionary Neural Architecture Search with Weight-Sharing Supernet
## 2 Related Works
### Search Space  
In this framework, we adopt the search space designed in DARTS [21]. Since the DARTS search space contains a variety of optional operations, and the constructed architectures can be applicable for both CNNs and RNNs, many existing NAS studies have adopted this search space as well.  
The macro design of the DARTS search space refers to the most classic works, like ENAS [43] and NASNet [44]. Figure 2a illustrates the macro network architecture designed for the image classification task using the CIFAR-10 dataset [45]. In fact, the macro architecture in DARTS belongs to the cell-based search space [8], where the neural network is composed of a series of network units. There are two types of units: normal cells and reduction cells. Specifically, normal cells return a feature map of the same dimension, while reduction cells double the number of channels and reduce the height and width of the feature map by half [44]. These two types of network units can have the same architecture or different architectures. For the same type of units, the architectures are identical, but the weight parameters are different. As for the micro architecture in each network unit, the search space is as illustrated in Figure 2b. Intuitively, a network unit can be represented as a directed acyclic graph with seven nodes. Each node stands for a feature map, and the directed edges between nodes represent operations. For network architectures designed for image classification tasks, there are eight optional operations:  
* Convolution: \(3\times 3\) and \(5\times 5\) separable convolutions, \(3\times 3\) and \(5\times 5\) dilated separable convolutions;
* Pooling: \(3\times 3\) max pooling, \(3\times 3\) average pooling;
* Others: Identity, zeroize.  
Note that each intermediate node can receive the outputs from two preceding nodes as its input.
## 3 Results and Discussion
### Engineering Applications  
The traditional ice-resistant design of bridges usually adopts the static ice-loading method, whose crude assumptions ignore the influence of factors such as porosity, leading to a bipolar dilemma: over-conservative design triggers material redundancy or underestimation of the extreme ice conditions buries the hidden danger of structural safety. The dynamic ice-load function model constructed in this paper establishes a quantitative correlation between the physical properties of the ice sheet and the dynamic load by introducing the porosity correction coefficient, which provides a theoretical basis for design optimization. The risk-grading strategy based on this model can differentiate the design of bridges: additional floating steel-covered composite collision avoidance facilities are installed in the high-risk river section (low porosity, fast ice speed, thick ice), and structural simplification is carried out in the low-risk river section (high porosity, slow ice speed, thin ice) to realize lightweight construction and save the construction cost. In addition, by monitoring changes in porosity, the maintenance cycle of bridges can be dynamically adjusted, realizing the shift from empirical maintenance to data-driven predictive maintenance.
# An Architecture for the Performance Management of Smart Healthcare Applications
## 4 The PRIMUS Architecture
### PRIMUS Architecture  
Figure 3 illustrates the PRIMUS architecture that follows the modules: (1) Decision-Making, (2) Network Slice Management, and (3) Resource Management. PRIMUS considers the application context to create and allocate network slices. Thus, different tenants \(u\) (users) generate network traffic. Each one belongs to a use case and demands specific requirements \(Q\). Tenant 1 (\(u_{1}\)), 2 (\(u_{2}\)) and 3 (\(u_{3}\)) refer to devices from industry 4.0, smart hospitals, and augmented reality, respectively. While tenant \(u_{1}\) requires ultra-low-latency, tenant \(u_{3}\) demands high bandwidth. Hence, PRIMUS needs to monitor the network traffic to establish and control the slice to support the requirements, especially the requirements of s-health. The identification and monitoring of s-health applications follow our previous work [20,21]. Based on the knowledge about the application context, PRIMUS creates the slices routes and adapts resources through NFVs. It enables devices to logically and separately share numerous network services without overloading switches. Therefore, PRIMUS considers the highest virtualization level, coordinating devices, network functions, and resources with greater flexibility. The next subsections detail the three modules of PRIMUS.
## 3 Proposed Algorithms  
We start by introducing the idea of the combined step-size APSA (CSS-APSA) algorithm. Then, we analyze the behavior of the mixing factor for CSS-APSA, and explain the reason why the mixing factor can be simplified. Finally, we derive the proposed algorithms.  
In order to better identify the optimization problem, the cost functions need to be known. The CSS-APSA algorithm is derived by minimizing the \(\ell_{1}\)-norm of _a posteriori_ error vector \(\mathbf{e}_{p}(n)\) with a constraint on the filter coefficients, as  
\[\min_{\mathbf{w}(n)}\|\mathbf{d}(n)-\mathbf{U}^{T}(n)\mathbf{w}(n)\|_{1}, \tag{15}\]  
subject to \(\|\mathbf{w}(n)-\mathbf{w}(n-1)\|_{2}^{2}\leq\mu_{12}^{2}\), (16)  
where  
\[\mu_{12}=\lambda(n)\mu_{2}+(1-\lambda(n))\mu_{1}. \tag{17}\]  
Similar to the APSA [19], we can obtain the following:  
For the SCSS-APSA:  
\[J_{s}(\mathbf{w}(n))=\|\mathbf{d}(n)-\mathbf{U}^{T}(n)\mathbf{w}(n)\|_{1}+ \varphi[\|\mathbf{w}(n)-\mathbf{w}(n-1)\|_{2}^{2}-\mu_{s12}^{2}] \tag{18}\]  
For the MCSS-APSA:  
\[J_{\beta}(\mathbf{w}(n))=\|\mathbf{d}(n)-\mathbf{U}^{T}(n)\mathbf{w}(n)\|_{1 }+\varphi[\|\mathbf{w}(n)-\mathbf{w}(n-1)\|_{2}^{2}-\mu_{\beta 12}^{2}] \tag{19}\]  
For the SMCSS-APSA:  
\[J_{\beta\beta}(\mathbf{w}(n))=\|\mathbf{d}(n)-\mathbf{U}^{T}(n)\mathbf{w}(n) \|_{1}+\varphi[\|\mathbf{w}(n)-\mathbf{w}(n-1)\|_{2}^{2}-\mu_{s\beta 12}^{2}] \tag{20}\]where \(\varphi\) is a Lagrange multiplier, \(\mu_{s12}=\lambda_{s}(n)\mu_{2}+(1-\lambda_{s}(n))\mu_{1}\), \(\mu_{\beta 12}=\lambda_{\beta}(n)\mu_{2}+(1-\lambda_{\beta}(n))\mu_{1}\), \(\mu_{s\beta 12}=\lambda_{s\beta}(n)\mu_{2}+(1-\lambda_{s\beta}(n))\mu_{1}\), \(J_{s}(\cdot)\), \(J_{\beta}(\cdot)\), and \(J_{s\beta}(\cdot)\) represent the cost functions and discuss the computational complexity.
# Adaptive Handover Decision Using Fuzzy Logic for 5G Ultra-Dense Networks
###### Abstract
We performed simulations using the well-known ns-3 simulator to demonstrate the effectiveness and superiority of the proposed FLDHDT. The performance measures include the number of handovers, overall system throughput, and ping-pong ratio. The remainder of this paper is organized as follows. Section 2 explores the related literature. Section 3 describes the operating procedures of the proposed FLDHDT. Section 4 analyzes and compares the simulation results. Finally, our concluding remarks and an outline of future works are summarized in Section 5.
## 3 Analysis of Queue Length Policy at a Coding Node
### Computation of Threshold Policy
For the expression for \(P_{r}(N,L)\)  
\[P_{r}(N,L)=1-(1-P_{coding}(N))^{L-1}=1-[1-\frac{1+0.586(N-1)(N-2)}{N(N-1)}]^{L -1} \tag{14}\]  
Since we consider only the coding opportunity of two packets, the value of \(N\) is at most four. We have  
\[P_{r}(3,L)\leq P_{r}(N,L)\leq P_{r}(2,L) \tag{15}\]  
According to (13), we have  
\[P_{r}(N,L)<P_{r}(N,L+1) \tag{16}\]  
\[P_{r}(N,\infty)=1 \tag{17}\]  
Under the condition that  
\[P_{r}(2,11)=1-0.5^{10}\approx 0.999 \tag{18}\]  
\[P_{r}(3,11)\approx 1-0.638^{10}\approx 0.989 \tag{19}\]  
we have \(0.989\leq P_{r}(N,L)\leq 0.999\) according to (15). Then, according to (16) and (17), when \(L\geq 11\), the coding opportunity probability \(P_{r}(N,L)\) approximates 1. When \(P_{r}(N,L)\) is 1, coding opportunities are always available, and waiting for such opportunities can cause substantial delay and jitter in transmission. The negative influence of packet  
Figure 5: Average packet jitter _versus_ threshold. delay on jitter due to waiting for future coding opportunities may outperform the positive influence of network coding. Then, the searching range can be reduced from \([0,K-1]\) to \([0,\min(11,K-1)]\) in training phase, where \(K\) is the capacity of the coding queue. After establishing the coding-aware route, the coding node is selected and the network enters a training phase. The coding node decides packets according to the queue-length based threshold policy and records the value of average variation in packet waiting delay (AVPWD) for the successfully transmitted packets of the specific real-time flow in a fixed sample interval corresponding to each threshold ranging from 1 to \([0,\min(11,K-1)]\).Among these values of average variation, we can find the minimum value. The corresponding threshold is the optimal threshold for the network. This can result in very low implementation complexity. The control policy at the coding node is easy to implement, and there is no computational overhead when packets arrive. This makes these policies particularly attractive for practical implementation. Once the network parameters are given, the optimal threshold can also be calculated offline in advance.
## 3 Methodology
### Multi-Feature Fusion Distance Measure Model of MFF-SLIC
#### 3.2.2 Local Attitude Feature Calculation of TLS Point Cloud  
The attitude of rock surface is usually described as dip angle \(\theta\) and dip \(\alpha\), the value range of which is respectively \(\left[0^{\circ},90^{\circ}\right]\) and \(\left[0^{\circ},360^{\circ}\right]\). The values of \(\theta\) and \(\alpha\) could be calculated with the normal vector according to space geometry relation of rock surface. The normal vector of any point in a point cloud is calculated by the local plane fitted by points in neighborhood. The normal vector of the point is approximately equal to the one of the fitted local plane. According to the above, the local attitude of the point cloud could be obtained by the normal vector of the corresponding point.  
The geometric relation of normal vector and local attitude is shown as Figure 7. In Figure 7a, the normal vector of the point cloud is denoted as \(\mathbf{n}=\mathbf{OA}=\left(n_{x},n_{y},n_{z}\right)^{\mathrm{T}}\), and \(\theta\) and \(\mathbf{OA}^{\prime}\) respectively means the dip angle and the direction of the dip in the local rock surface. In Figure 7b, the projection of the normal vector in horizontal plane is denoted as \(\mathbf{n}^{\prime}=\mathbf{OA}^{\prime}\), and \(\alpha\) and \(\mathbf{OA}^{\prime}\) respectively means the magnitude and direction of the dip. The local attitude could be calculated as the following two ways because of the different values of the normal vector.  
Figure 7: The sketch map for geometric relation of rock surface: (**a**) the spatial relation of the normal vector and the local attitude; (**b**) the geometric relation of the normal vector and the local dip in projection plane.  
All three components of the normal vector are not equal to 0.  
Under this condition, the relation of the normal vector \(n\), dip \(\alpha\) and dip angle \(\theta\) is described as Equation (17).  
\[\left\{\begin{array}{l}n_{x}=n_{z}\cdot\tan\theta\cdot\cos\alpha\\ n_{y}=n_{z}\cdot\tan\theta\cdot\sin\alpha\end{array}\right., \tag{17}\]  
A calculation formula for dip \(\alpha\) and dip angle \(\theta\) derived from Equation (17) by using the elimination method is shown as Equation (18).  
\[\left\{\begin{array}{l}\theta=\tan^{-1}\Bigl{(}\sqrt{n_{x}^{2}+n_{y}^{2}}/| n_{z}|\Bigr{)}\\ \alpha=\tan^{-1}\bigl{(}n_{y}/n_{x}\bigr{)}\end{array}\right., \tag{18}\]  
where the sign of \(n_{x},n_{y}\) and \(n_{z}\) determines the quadrant of dip \(\alpha\) in the horizontal plane. The four geographic directions of east, south, west and north are respectively represented by E, S, W and N, which is shown as Figure 6b, then the relation of three components of the normal vector and the dip is listed in Table 1.  
2. No less than one component of the normal vector is equal to 0.  
For some certain special rock surfaces, such as those developed horizontally and vertically, it is not suitable for Table 1. The calculation of local attitude of rock surface could be achieved by Table 2.
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 3 Dummy Packets Techniques  
In this section, we present the existing techniques in the literature, which rely on dummy packets against the global adversary model. All techniques in this survey are utilized in monitoring and tracking applications. For example, tracking endangered species in a national park, a soldier on a battlefield, a patient in a hospital, etc. The location of the asset needs to be securely communicated to the base station, which means it is untraceable under any traffic analysis applied by an attacker. Traffic analysis can be rate monitoring, time correlation attacks, and size/structure correlation attacks.
# MPH-M, AODV-M and DSR-M Performance Evaluation under Jamming Attacks
## 6 Results
### System Throughput
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Re-Transmissions** & **CSMA Retries** & **Overhead (\%)** & **Delay End-to-End (s)** & **Energy (J)** \\ \hline  & \multicolumn{5}{c}{**Without Mitigation Mechanism**} \\ \hline
** AODV** & 2.51 & 4.06 & 60 & 1.256794 & 6.51 \\
**DSR** & 2.48 & 3.93 & 54 & 1.198735 & 6.23 \\
**MPH** & 2.21 & 3.60 & 38 & 0.716355 & 4.11 \\ \hline  & \multicolumn{5}{c}{**With Mitigation Mechanism**} \\ \hline
** AODV-M** & 2.28 & 3.91 & 51 & 1.209485 & 6.30 \\
**DSR-M** & 2.20 & 3.77 & 42 & 1.168756 & 6.12 \\
**MPH-M** & 1.98 & 3.39 & 27 & 0.589765 & 3.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics per node under an uniform random traffic rate generation. MPH has around 2.57 J, AODV-M has 6.12 J, and DSR-M has 5.81 J. Table 7 shows how much more efficient the performance metrics are for MPH-M than other protocols, when the network is attacked. This table helps to visualize improvements among the protocols. We can observe that for most of the metrics the MPH-M better adapts to the mitigation scheme, than AODV-M and DSR-M. Moreover, we can also note that under attack, the metrics improve when the mitigation algorithm is used. Table 8 shows how each protocol improves each metric when mitigation scheme is used. We can observe that for all metrics, the MPH adapts much better to the mitigation scheme. For example, with respect to the CSMA retries, MPH-M improves 32.27% compare with only 32.27% and 39.04% for AODV-M and DSR-M, respectively.
### Research Questions and Motivation  
This study aims to investigate object distance estimation using camera-LiDAR sensor fusion under varying levels of occlusion. While previous methods primarily focus on fully visible objects using raw point cloud data, our approach considers easy, moderate, and hard occlusion scenarios and uses a projected transparent sparse depth map to speed up the process. Based on this motivation, we address the following research questions.  
1. How can object distance be accurately estimated from fused camera-LiDAR data in the presence of different occlusion levels?
2. How do the bounding box and the segmentation-based depth extraction methods perform under easy, moderate, and hard occlusion conditions?
3. Can the transparent sparse depth map projected from LiDAR data provide sufficient information for reliable object distance estimation despite the lack of intermediate depth data?
## 2 Materials and Methods
### Materials and Its Characterization  
The properties of the PCM and the HTF are summarized in Table 2. The PCM filled in the modules is an organic commercial paraffin thermally stable and chemically inert. Some of its properties according to the manufacturer datasheet [24] are: phase change temperature between 2 \({}^{\circ}\)C and 4 \({}^{\circ}\)C, thermal conductivity \(k_{PCM}=0.2\) W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\), density \(\rho_{PCM,s}=880\) kg\(\cdot\)m\({}^{-3}\) in solid-state and \(\rho_{PCM,J}=770\) kg\(\cdot\)m\({}^{-3}\) in liquid-state. According to the manufacturer, the TES capacity between \(-\)8 \({}^{\circ}\)C and 7 \({}^{\circ}\)C is 180 kJ\(\cdot\)kg\({}^{-1}\). To obtain the PCM enthalpy and specific heat capacity over a wider range of temperature, experiments were carried out using DSC 3+ Toledo equipment with 0.1 K precision for temperature and \(\pm\)3 J\(\cdot\)s\({}^{-1}\) for enthalpy. The results obtained from the DSC measurements were reported in [25] and used to generate the PCM enthalpy curve \(h_{PCM}\) (in kJ\(\cdot\)kg\({}^{-1}\)) as a function of its temperature \(T\) (in \({}^{\circ}\)C), as shown in Equation (1) [15].  
\[h_{PCM}(T)=\left\{\begin{array}{c}152.49+2.64\cdot(-4-T)\ \left[\text{kJ}\cdot\text{kg}^{-1} \right],\ \ \text{if}\ T<-4\ ^{\circ}\text{C}\\ -0.1139\cdot T^{3}-1.3116\cdot T^{2}-8.5545\cdot T+131.97\ \left[\text{kJ}\cdot \text{kg}^{-1}\right],\ \text{if}-4\ ^{\circ}\text{C}\leq T<6\ ^{\circ}\text{C}\\ -0.0985\cdot T^{3}+2.8732\cdot T^{2}-28.629\cdot T+99.839\ \left[\text{kJ}\cdot \text{kg}^{-1}\right],\ \text{if}\ 6\ ^{\circ}\text{C}\leq T\leq 12\ ^{\circ}\text{C}\\ -2.38\cdot(T-12)\ \left[\text{kJ}\cdot\text{kg}^{-1}\right],\ \ \text{if}\ T>12\ ^{\circ}\text{C}\end{array}\right. \tag{1}\]  
Figure 2: Experimental facility: (**a**) photo of the test-rig and (**b**) schematic diagram.  
The HTF used in the heating/discharging loop is a water-glycol mixture of 30% of glycol. Among the thermo-physical properties according to the manufacturer datasheet [26], one can mention a freezing temperature of \(-18\)\({}^{\circ}\)C, an average density \(\rho_{HTF}=1050.45\) kg\(\cdot\)m\({}^{-3}\), an average specific heat capacity \(Cp_{HTF}=3602\) J\(\cdot\)kg\({}^{-1}\).K\({}^{-1}\), and a thermal conductivity \(k_{HTF}=0.4275\) W\(\cdot\)m\({}^{-1}\).K\({}^{-1}\), in the temperature range of operation. Moreover, another relevant material in the module is aluminum (AI), which accounts for more than 70% of the total weight of each module when filled with PCM and HTF. The specific heat capacity of aluminum was obtained from the literature [27] as \(Cp_{AI}=900\) J\(\cdot\)kg\({}^{-1}\).K\({}^{-1}\).
## 4 Experiments and Analysis of Results
### Analysis of Results
#### 4.4.5 Analysis of Results for Different Triad Overlap Types  
To evaluate the MD-SERel model's ability to extract overlapping triples, experiments were conducted on the DuIE dataset using three subsets with varying degrees of overlap: Normal, SEO (Single-Entity Overlap), and EPO (Entity-Pair Overlap). The results are shown in Figure 7. The experimental results show that the extraction performances of models like GraphRel and CasRel declined across datasets with different levels of relationship overlap. However, the MD-SERel model maintained good performance, further improving the more complex SEO and EPO datasets. Compared to the TPLinker and BiRTE models, MF-SERel achieved improvements of 3.4 and 1.1 percentage points in F1 score on the Normal dataset, respectively, and demonstrated strong performances on the two other datasets with complex overlapping relationships. These performance enhancement results are attributed to the proposed HT-BES multi-dimensional labeling strategy. On one hand, the multi-label annotation scheme enhanced the model's generalization capability for entities, enabling it to recognize more entities. On the other hand, this strategy made use of independent relationship sub-matrices for each relation and mapped different relationships to their respective matrices during decoding, mitigating the confusion caused by overlapping relationships. This approach fully demonstrated the robustness and advantages of the model in handling complex contexts. In summary, the MD-SERel model exhibited a strong generalization capability, adapting to various complex contexts and showing excellent performance when extracting overlapping triplets.