esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Dependence of DDER on the FF and FFF Photon Beams  
When the FF is removed from the linac, the FFF beam contains more low-energy photons which would enhance the photoelectric effect and therefore the dose [24]. This can be seen in Table 1a-c for the Au NPs that the DDER for the FFF photon beam was larger than the FF. At a deep depth of 20 cm, the beam attenuation effect was significant as most of the low-energy photons were absorbed in the shallow depth. This caused the DDER of the FFF beam to be smaller than the FF. For the Fe\({}_{2}\)O\({}_{3}\) NPs, however, DDER was lower than Au NPs. Therefore, the FFF photon beam was not so effective on the DDE for the Fe\({}_{2}\)O\({}_{3}\) NPs compared to Au NPs.  
For the depth range of 10-20 cm and NP concentration equal to 18 mg/mL, which is the typical concentration used in preclinical model [37], the DDERs (Figure 1a,b) were found decreased at rates of \(1.0\times 10^{-3}\) cm\({}^{-1}\) and \(1.3\times 10^{-3}\) cm\({}^{-1}\) for the Au NPs, and \(0.8\times 10^{-3}\) cm\({}^{-1}\) and \(0.9\times 10^{-3}\) cm\({}^{-1}\) for the Fe\({}_{2}\)O\({}_{3}\) NPs, using the FF and FFF photon beams, respectively. It is found that the DDER of Au NPs decreased at a higher rate than the Fe\({}_{2}\)O\({}_{3}\) NPs, and the FFF photon beams decreased at a higher rate than the FF beams. This can be explained by the higher atomic number of gold (79) than iron (26), and the greater number of low-energy photons of the FFF beams than the FF beams [16; 24].
# Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet
## 2 Vibration Signal Data Preprocessing and Feature Extraction Modelling
### Sine-Cosine Optimisation Algorithm (SCA)  
The sine-cosine optimisation algorithm (SCA), as a new stochastic optimisation algorithm based on the population technique, contains two phases, namely, exploration and exploitation, and has the advantages of simple structure, easy implementation and fast convergence [26], which can be used in conjunction with the VMD algorithm to adaptively search for the optimal decomposition parameters K and \(\alpha\).  
In the SCA, the following two equations are used for the particle value position update:  
\[X_{j}^{t+1}=X_{j}^{t}+\alpha_{1}\times sin(\alpha_{2})\times\abs{\alpha_{3}D_ {j}^{t}-X_{j}^{t}} \tag{4}\]  
\[X_{j}^{t+1}=X_{j}^{t}+\alpha_{1}\times cos(\alpha_{2})\times\abs{\alpha_{3}D_ {j}^{t}-X_{j}^{t}} \tag{5}\]  
where \(X_{j}^{t}\) is the position of the current solution in the i-th dimension in the t-th iteration; \(\alpha_{1}\), \(\alpha_{2}\) and \(\alpha_{3}\) are random numbers; and \(D_{j}^{t}\) is the position of the i-th dimension endpoint.  
These two equations are usually used in the following combination:  
\[X_{j}^{t+1}=\begin{cases}X_{j}^{t}+\alpha_{1}\times sin(\alpha_{2})\times \abs{\alpha_{3}P_{j}^{t}-X_{j}^{t}}\abs{\alpha_{4}<0.5}\\ X_{j}^{t}+\alpha_{1}\times cos(\alpha_{2})\times\abs{\alpha_{3}P_{j}^{t}-X_{j}^ {t}}\abs{\alpha_{4}\geq 0.5}\end{cases} \tag{6}\]  
where \(\alpha_{4}\) is a random number in [0,1].  
From Equation (6), SCA has \(\alpha_{1},\ \alpha_{2},\ \alpha_{3}\) and \(\alpha_{4}\) main parameters. \(\alpha_{1}\) defines the direction of motion, which may or may not lie in the space between the solution and the endpoint; \(\alpha_{2}\) determines the distance travelled in the direction of the target or towards the target; \(\alpha_{3}\) denotes the stochastic weight of the target; and lastly, the parameter \(\alpha_{4}\) toggles between the sine and cosine components in Equation (6).  
The search principle of SCA is shown in Figure 1, where the model updates the response location by varying the range of values of the sine and cosine, and the exploration of the search space is guaranteed by determining whether the random location is in space or out of space by determining the defined \(\alpha_{2}\in[0,\ 2\pi]\). In each iteration, the range of values of the sine and cosine functions in the equations are adaptively adjusted to achieve a balanced exploration and utilisation to find promising regions of the search space, and ultimately, the global optimisation is achieved using Equation (7):  
\[\alpha_{1}=\
u-t\frac{\
u}{T} \tag{7}\]In Equation (7), \(\
u\) is a constant, t is the current iteration and T is the maximum number of iterations.
## 4 Results
### Time Efficiency  
In this section, the time efficiency of the main modules of the AR system is determined: Detection and estimation of the initial pose based on LINEMOD (with and without YOLOV4), the 6 DOF pose tracking method, and registration of the IRT image with the RGB-D one. To quantify the algorithm execution times, the hardware device used was a Microsoft Surface Book 2 with a 1.90 GHZ i7 processor, 16 gigabytes of RAM, and an NVIDIA Geforce GTX 1060 graphics card. The images from the RGB-D camera were captured at 60 fps with a resolution of \(640\times 480\). The LINEMOD 3D model data set was used to carry out the tests, as well as three 3D models generated in this work: An electronic board, an airplane combustible filter, and a water pump. A series of observations must be made regarding the different modules. First, for the initial pose estimation method, the same number of templates per 3D model was used as those cited by [PERSON] et al. (2015); in particular, 1235 templates. Second, the same similarity score was used in all cases, \(\epsilon_{s}=80\). It should be noted that the higher the similarity score, the faster the algorithm works to find the most similar LINEMOD template, as the search is reduced to a smaller number of candidates. The average time execution values are compiled in Table 5. The time efficiency of the optimized LINEMOD method ([PERSON] et al., 2017) without using the ICP algorithm was 0.069 s (14.49 fps). On the other hand, as mentioned in Section 3.2.2, incorporation of the YOLOV4 detection algorithm into the preprocessing phase sped up the LINEMOD calculation to 0.035 s but, to this time, the YOLOV4 execution time of 0.036 should be added, such that the LINEMOLO total execution time was 0.071 s. In summary, the overall initial detector and pose estimation phase execution time was 0.122 s (8.19 fps), including the Parallel ICP using LINEXOLO (option 2), or 0.12 s without YOLOV4 (option 1). As can be seen, use of the YOLOV4 algorithm did not greatly influence the computational cost, as a similar performance was obtained in both cases.  
Moreover, regarding the 6 DOF pose tracking module, an average rate of 43.4 fps was obtained. It should be noted that this time depends on several factors, including the number of polygons of the 3D object and the size that the object takes up in the image captured by the camera. On the other hand, the registration of the IRT and the RGB-D images was performed at 83.3 fps. Taking into account that most of the time the AR system will only use the 6 DOF pose tracking and the registration modules, one of the main objectives set out in this work is met: The proposed AR system works in real time.
# A 3D Vision-Based Weld Seam Extraction Method for Arbitrarily Placed Spatial Planar Weldments
## 2 Improved RANSAC for Plane Segmentation
### Algorithm Implementation
#### 2.4.1 Implementation for Improved RANSAC Algorithm  
The pseudo-code for the improved RANSAC algorithm for a single segmentation is provided in Algorithm 1 in Python style.  
Line 5 initiates the main loop (lines 5 to 24) that segments a single plane. Lines 8 to 14 implement Rule 1 (sample validation) in Section 2.2.1, where unqualified samples are bypassed before calculating the candidate plane. Line 15 calculates a candidate plane from the sampled points. Lines 16 to 17 implement Rule 2 (candidate plane validation) in Section 2.2.2. Lines 18 to 20 implement Rule 3 (inlier validation) in Section 2.2.3. Lines 21 and 22 update the current best candidate plane and its inliers by implementing Rule 4 (update criterion) in Section 2.2.3. Lines 23 and 24 check whether the termination condition in Formula (6) is satisfied. Lines 25 and 26 implement Rule 5 in Section 2.2.4, performing Euclidean clustering on the inliers and choosing the best cluster for final segmentation.  
Figure 7: Normal vector clustering: (**a**) the original point cloud; (**b**) the temperature map representing the probability density of the normal vectors; (**c**) normal vector clustering using Euclidean clustering.  
```
1 Input: the point cloud \(points\) and the corresponding normal vectors \(normals\);
2 Output: segmented plane model \(plane\) and its inliers \(inliers\)
3\(max\_rho=0\)
4\(inlier\_indices=[]\)
5 for\(i\)in\(\text{range}(max\_iteration)\):
6\(inlier\_indices=[]\)
7\([a,b,c]=[-1,-1,-1]\)
8 for\(j\)in\(\text{range}(max\_sampling)\):
9\([a1,b1,c1]=\text{sample\_3\_random\_points\_indices}(points)\)
10 if\(\text{valid\_sample}(normals[a],normals[b],normals[c])\):
11\([a,b,c]=[a1,b1,c1]\)
12 break
13 if\(a<0\):
14 continue
15\(plane=\text{get\_plane}(points[a],points[b],points[c])\)
16 if\(\text{not}\)valid\_candidate\(\_plane\)(plane):
17 continue
18 for\(j\)in\(\text{range}(\text{len}(points))\):
19 if\(\text{valid\_inlier}(\text{j})\):
20\(inlier\_indices\).append\((j)\)
21 if\(\text{get\_rho}(inlier\_indices)>max\_rho\):
22\(max\_rho=\text{get\_rho}(inlier\_indices)\)
23 if\(i>\text{get\_k}(\text{len}(inlier\_indices))\):
24 break
25\(clusters=\text{euclidean\_cluster}(points,inlier\_indices)\)
26\(plane\), \(inliers=\text{choose\_from\_clusters}(clusters,points,normals)\)
27 return\(plane\), \(inliers\)
```  
**Algorithm 1** Improved RANSAC Algorithm
# An Intelligent Cluster-Based Routing Scheme in 5G Flying Ad Hoc Networks
### Fanet
#### 1.2. 5g
Our proposed framework enables CC and DCs to manage long-lifetime (i.e., with long expiry due to low dynamicity) and short-lifetime data (i.e., with short expiry due to high dynamicity) in order to reduce end-to-end delay under ultra-densified and highly heterogeneous network scenarios. Frequent link disconnections and network partitions are commonplace in highly dynamic FANETs. The scheme consists of vertical routing over a clustered network. By _vertical_, we refer to mechanisms that involve different network cells (or network planes). While existing routing schemes for FANETs are only horizontal-based and mainly reduce the average number of hops between the source and destination UAVs [14; 15], our proposed framework focuses on vertical routing and supports horizontal routing. Table 3 presents the brief functions of various network elements, and we explain the detailed working of each network element based on the use-case scenario in Figure 1. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Terminologies** & **Abbreviations** & **Functions** \\ \hline Unmanned aerial vehicle & UAV & UAVs are autonomous, small-sized, lightweight flying nodes moving at high speed at low or high altitudes in a three-dimensional space. \\ \hline Central controller & CC & CC makes decisions and manages global tasks (i.e., vertical routing). \\ \hline Distributed controller & DC & DC makes decisions and manages local tasks (e.g., vertical clustering) in a particular network plane. \\ \hline Cluster head & CH & CH, which serves as the cluster leader, manages and handles cluster-level operations (e.g., routing), and performs intra- and inter-cluster communications. \\ \hline Cluster member & CM & CM, which is associated with a CH, performs intra-cluster communication. \\ \hline Cluster gateway & CG & CG, which is associated with a CH, interacts with neighboring clusters through inter-cluster communication. \\ \hline Vertical cluster gateway & VCG & VCG enables interactions among UAVs in different clusters across different network planes, which is conveniently known as inter-plane communication. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The functions of various network entities are shown in Figure 1.
# A Performance Evaluation for Software Defined Networks with P4
## 1 Introduction
Hardware Offload: In some cases, P4 programs can be used to offload certain packet processing tasks to specialized hardware accelerators or programmable ASICs. These hardware components can operate in parallel with the CPU, further increasing packet processing efficiency. 4. Load Balancing: P4 can be used to implement load balancing mechanisms, where incoming packets are distributed across multiple processing units or network paths, enabling parallel processing of packets. P4 has three main properties: re-configurability, protocol independence, and target independence. These allow a network administrator to determine the functions and capabilities of a switch rather than adhering to vendor specification [7]. Utilising a common open interface, the administrator can leverage P4 to program a flexible parser to match new header fields as opposed to working with a fixed parser in OpenFlow which would require a specification update in order to process new header fields. Extensive research carried out by [8; 9] indicates that the majority of future network traffic is expected to be the result of using hand-held smart devices. It is claimed that the increased traffic due to hand-held devices has resulted in approximately 52% of all consumer Internet traffic [9]. A study conducted by Ericsson [10] reports that mobile traffic for 2024 reached 1.3 zettabytes, supporting conjectures from earlier studies. The increase in end-user IP traffic threatens to lead to network congestion, resulting in a reduced Quality of Service (QoS) in terms of, for example, jitter, packet loss, and throughput [11] with service providers trying to maintain an acceptable level of service. In thus context we note, the research by [PERSON] et al. [12], in which they suggest that the majority of the delay in data communication occurs at the core of the network due to an exponential growth in periodic updates used to maintain the network state, [13; 14].
## 2 Methodology
### Ensemble Approach to Anomaly Detection Using Spherical Detectors
#### 2.5.4 Anomaly Detection with Ensemble Voting  
Given a point \(x\in\mathbb{R}^{n}\), determine whether or not it is an anomaly as follows:  
1. For each model, \(D_{k}\), in the ensemble: 1. Initialize detected\({}_{k}\leftarrow\) False. 2. For each detector \((c_{k,j},r_{k,j})\in D_{k}\): * If \(\|x-c_{k,j}\|\leq r_{k,j}\): - Set detected\({}_{k}\leftarrow\) True. - Break the inner loop. 2. Collect the detection results \(\{\text{detected}_{k}\}_{k=1}^{K}\). 3. Apply majority voting to decide if \(x\) is an anomaly: \[\text{Anomaly}=\begin{cases}\text{True}&\text{if }\sum_{k=1}^{K}\mathbb{I}(\text{ detected}_{k})>\frac{K}{2},\\ \text{False}&\text{otherwise},\end{cases}\] where \(\mathbb{I}(\text{detected}_{k})=1\) if detected\({}_{k}=\) True, and 0 otherwise.
## 3 Spectrum Management Issues
### Small Cell
#### 3.3.2 Throughput Improvement  
The researchers in [143] have accomplished the task of delivering high QoS to each user by presenting a cache-enabled small cell network. In this scenario, optimal memory size is obtained in closed form for a provided density of a small BS. Hence, the outcomes indicate that equipping the optimal size of memory can minimize the capacity of backhaul and increases the throughput efficiency. Another RRM scheme is discussed in [144] for enhancing the performance of a small cell network. The idea is to propose a cooperative game radio resource-sharing scheme to improve the results for user throughput; moreover, spectral efficiency is better than that in the no-game scenario. The author has suggested that scaling up the environment for larger small cell networks requires additional distributed learning approaches that can be applied for efficient coalition formation.
## 1 Introduction
### 1.4. This Study: Optimus  
In the field of computer science, different types of metaheuristic algorithms have been suggested to solve real-parameter optimization problems by researchers and engineers in many years. As a common approach, the performance of each developed algorithm is tested by using a set of the standard benchmark problems such as Sphere, Schwefel's, Rosenbrock's, Rastrigin's, etc. For real-world problems, this test is done by using benchmark instances. The main reason for comparing algorithms is based onthe no free lunch theorem (NFLT) [39]. The performance of an optimization algorithm depends on the nature of the problem. In other words, one algorithm can outperform another algorithm in a specific problem. Thus, NFLT argues that there is no global optimization algorithm, which can present the best results for all real-world and benchmark problems.  
In the field of architecture, testing different types of algorithms for the same architectural design problem is not a common approach. One of the most important reason of this fact is that computer aided design (CAD) tools of architects does not include optimization algorithms in a wide range. According to the current state of the art [40], only 3% of total users of optimization tools are architect in the domain of building performance optimization. This fact clearly shows that there is a little attention on using optimization methods within the CAD programs. Therefore, this paper introduces a new optimization solver, called Optimus, with significant features listed below:  
* Compatible with parametric design models created in GH [16] algorithmic modeling for Rhinoceros [41] CAD software.
* Supports PCA framework outlined in previous section.
* Implements a self-adaptive [42] differential evolution algorithm with ensemble of mutation strategies [43] (jEDE), explained in Section 3.
* Presents the highest performance when compared to other optimization algorithms available in GH reviewed in Section 1.3.  
* The performance of the Optimus is tested by benchmark suite, which consists of standard single objective unconstrained problems and some of the test problems proposed in IEEE Congress on Evolutionary Computation 2005 (CEC 2005) [44]. Problem formulations and optimization results of these benchmarks are given in Section 4.1.
* Finally, Optimus is tested with a design optimization problem. The problem formulation and optimization results are given in Section 4.2.  
The development process of the Optimus is illustrated in Figure 3.
## 3 Theoretical Analysis of the Anchoring Action of Oil Wells on the Movement of Overburden Rock
### Mechanics of Oil Wells in the Formation
#### 3.2.2 The Effect of Oil Wells on the Cohesion of the Rock Formations Surrounding Them  
As the casing itself has a certain bending capacity, it will provide some restraint to the horizontal misalignment between the rock layers. From the perspective of the surrounding rock casing increases the shear strength of the rock mass. From the analysis of the tangential force of the casing, the tangential anchoring force of the casing originates from the binding force of the casing on the horizontal displacement of the surrounding rock, and the maximum value of the horizontal displacement binding force is the shear strength of the casing. The casing-enclosed rock bond exhibits specific cohesive properties, and the casing anchorage cohesion \(c\) is:  
Since the casing possesses a certain bending capacity, it provides restraint against horizontal misalignment between rock layers. From the perspective of the surrounding rock, the presence of the casing enhances the shear strength of the rock mass. Analyzing the tangential force of the casing, the tangential anchoring force originates from the constraint imposed by the casing on the horizontal displacement of the surrounding rock. The maximum horizontal displacement constraint force corresponds to the shear strength of the casing.  
The casing-enclosed rock bond exhibits cohesive properties, and the casing anchorage cohesion \(c\) is expressed as follows:  
\[c=c_{r}+nS(c_{b}-c_{r}) \tag{4}\]  
The increment produced by the casing of an oil well on the cohesive force of the formation surrounding is expressed as follows:  
\[\Delta c=c-c_{r}=nS(c_{b}-c_{r})+\sigma_{b}tan\varphi \tag{5}\]In the formula, \(c_{r}\)--rock cohesion, \(c_{b}\)--cementing cement cohesion, \(\sigma_{b}\)--casing axial stress acting on the geotechnical body, and \(\varphi\)--rock internal friction angle.  
Based on the geological data, it can be determined that the muddy siltstone on the 3-2 coal seam has a maximum internal friction angle of 37\({}^{\circ}\). Considering the maximum effect of the oil well casing on this cohesion, the cohesion of the rock body is calculated using the cohesion of the muddy siltstone, which is 14.14 MPa. The axial stress of the oil well casing and cement ring on the surrounding rock body is similar to that generated by the casing's self-weight, and based on the calculations in Equations (4) and (5), the cohesive force of the anchoring solids for a single casing in the 3-2 coal seam is 19.65 MPa, and the increase in the cohesive force is 5.51 MPa.
# A Review of Fault Diagnosis Methods for Key Systems of the High-Speed Train
## 2 Fault Diagnosis of High-Speed Train Bogie System
### Fault Diagnosis of Train Bearing
In addition, the results of references [22-24] show that fault identification of train bearings can be achieved based on morphological component analysis, data-driven analysis, and improved SVDP. Furthermore, the time-frequency features of the empirical modal decomposition of the signal are input to a support vector machine, which can achieve fault diagnosis of train bearings [25-27]. To improve the accuracy of fault diagnosis across domains, [PERSON] et al. [28] proposed an adaptive convolutional ResNet-50 network model and validated it with train bearings. [PERSON] et al. [29] proposed an effective adaptive Gabor sub-dictionary approach that can extract fault features from the vibration signals of trains. With the autoencoder as the feature learning model, bearing fault types can be accurately identified through the weighted voting strategy and generative adversarial network (GAN) [30,31]. [PERSON] et al. [32] introduced a time-frequency domain feature extraction method based on sound signals and vibration signals and applied it to condition monitoring and service life prediction of train bearings. Therefore, the bearing fault diagnosis method for high-speed trains is mainly achieved by fault feature extraction and fault pattern recognition. In feature extraction, the main methods are EWT, singular value decomposition, VDF, empirical modal decomposition, MED, wavelet transform, VMD, TFD, etc. In pattern recognition, the main methods used are principal component analysis, support vector machines, dictionary learning, self-encoders, generative adversarial networks, etc.
## 5 The MRLG Routing Protocol
### Managing Link Failures  
When the link to reach a specific node is lost (after 3 unacknowledged transmission attempts), the routing entry for that node is removed. Afterward, the algorithm will select all the potential next_hop entries from the list of downhill and peer nodes, picking one of the entries with fewest hops to reach the sink. Then, a broadcast message will be generated, informing about the new hop_count and the new next_hop to neighbor nodes. If no valid next_hop neighbors exist, the sensor node inhibits itself from transmitting data until the routes are again refreshed by the sink, to avoid resource wasting.