esa-sceva/satcom-chunk-collection huggingface dataset:
# Wear Model of Silicon Nitride Ceramic Balls in Three-Body Coupling Grinding Mode
## 3 Analysis of the Wear Performance of Precision Spheres
### Material Removal Mechanism of Precision Spheres  
Ball grinding is a process of removing materials by using abrasive particles to decrease ball diameter, improve sphericity, and reduce surface roughness. Any cross-section of an  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Grinding Mode** & **Grinding Efficiency** & **Grinding Accuracy** & **Mechanical Structure** \\ \hline Traditional V-groove grinding mode & Low & Low & Simple \\ \hline Double V-groove grinding mode & High & High & Complicated \\ \hline Magnetic fluid grinding mode & Very high & Low & Complicated \\ \hline Rotation angle actually controlled grinding mode & High & High & Complicated \\ \hline Three-body coupling grinding mode & High & High & Relatively simple \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the advantages and disadvantages of different grinding modes.  
Figure 8: Schematic of the magnetic fluid structure. 1—grinding groove; 2—ceramic ball blank; 3—driving shaft; 4—mixture of magnetic fluid and abrasive; 5—floating plate; 6—magnet.  
Figure 9: Schematic of the non-magnetic fluid structure.  
ideal sphere is a perfect circle, which is the starting point of the design of ball grinding methods. The grinding tool moves relative to the ball (Figure 10) such that the grinding trajectory is evenly distributed on the surface of the ball blank. This situation is the geometric condition for grinding materials into a ball. A physical condition for balling is that the amount of material removed from the surface of the ball can vary with spherical deviation or can be expressed as the following two points [20; 21; 22; 23]:  
(1) Equal probability of cutting: The cutting probability of every point on each spherical surface is the same.  
(2) Size selectivity: In machining, large balls are ground, whereas small ones are not ground or are ground minimally. Grinding is performed along the direction of the long axis and is not performed or is lessened along the direction of the short axis.  
Combining the basic balling conditions and the machining process shows that the factors that affect balling accuracy can be classified into two aspects: the uniformity of the grinding trajectory and MRR. Previous research shows that the uniformity of the grinding trajectory is more important than other factors and that MRR is subordinate [23]. Therefore, this study explored the influencing factors of grinding uniformity and their final influence on sphericity with the expectation of providing theoretical and technical support for actual production.
# Reliable Link Level Routing Algorithm in Pipeline Monitoring Using Implicit Acknowledgements
## 4 Energy and Transmission Time Analysis
### Energy Consumption
Percentages depend on payload, node address length, and mode of operation. The percentage decrease in power consumption at the RXN node is better with small payload as opposed to the TXN node, where it is better with large payload. Table 4 shows the percentage of energy saved when using iACK compared to the use of eACK. The reduction in energy consumption in the RXN node using iACK to retransmit the frame depends on interframe spacing. With SIFS it is about 22.5%, and with LIFS is about 8%. In TXN mode, the reduction in consumption to transmit two frames using iACK depends on the payload. With a payload of 18 bytes, it is possible to save 36% of energy, and with a payload of 114 bytes about 14%. In the RXN node the use of iACK to detect the loss of frames implies an increase of energy consumption that depends on the payload. With a payload of 18 bytes the increase is about 8% of the total energy consumed by the node and with a payload of 114 bytes the increase is about 2%. Thus, by confirming the reception of the information using iACKs we can reduce the consumption of energy [28]. When the transmission reliability is left for superior layers, where the \(v_{n+1}\) node confirms the message reception to the node \(v_{i}\), the energy consumption affects all the intermediate nodes that transmit the ACK confirmation.
## 3 Materials and Method
### Fault Tolerance  
Given the reduced version of the logical nodes from the previous step, fault tolerance for each path that is constituted from the combination of all nodes will be estimated depending on the typical DR-tree performance [13]. If a \(p\)-node fails, all the sub-trees are substituted in a non-default configuration (\(p\)-node by \(p\)-node) to maintain an invariant DR-tree. The proposed model maintains fault tolerance and retains the DR-tree architecture with its invariants utilizing non-leaf \(v\)-node replication. A fault-tolerant solution that utilizes non-leaf node replication preserves tree connectivity when crashes arise. The pattern for replication of \(v\)-nodes is: \(p\)-root has no replica, and each \(p\)-node has a replica of its top \(v\)-node's \(v\)-father.  
1. _Fault Tolerance Estimation_ Replicas are generated at join operations and updated to join both split processes. When changing a \(v\)-node, its holder can alert the \(p\)-nodes containing its replicas. Consider an \(N\)\(v\)-node DR-tree with degree \(m\): \(M\). Presume that upgrading a replica costs one post (message). To calculate the cost of replica changes, determining how many \(v\)-nodes are updated throughout a split process is also important. Let the cost be described as the value of split-time updating reproductions. Since each \(v\)-node has \(M\)-\(1\) replicas, each update requires one post, so it costs:  
\[cost_{s}=(m+2)(M-1), \tag{2}\]  
A \(p\)_-node_ associated with the system may change between \(0\) and \(\lfloor log_{m}(N)\rfloor\) splits, adding its \(v\)_-leaf_ to the \(v\)_-children_ of another \(v\)_-node_ that is denoted in the sequence of the joined \(v\)_-node_. The latter has between \(m\) and \(M\)_v-children_; \(\lceil log_{m}(N)-1\rceil\)_v-ancestors_; between \(m-1\) and \(M-1\) replicas. A \(v\)_-node_ can have \(m\) to \(M\)_v-children_ and therefore has \(M-m+1\) possible \(v\)_-children_. The split will happen only when it has exactly \(M\)_v-children_. The probability for a \(v\)_-node_ to split is \(p\), where:  
\[p=\frac{1}{M-m+1}, \tag{3}\]  
The likelihood of a \(p\)-node producing \(k\) splits is that the corresponding \(v\)-node and its \(k\)-\(1\) first \(v\)-ancestor would have practically \(M\)\(v\)-children, although their \(k\)-th ancestor would not split, therefore:  
\[p_{k}=p^{k}(1-p). \tag{4}\]  
Let \(cost_{r}\) denote the estimated price of updating replicas when associating a \(p\)-node with a DR-tree:  
\[cost_{r}=p_{o}(M-1)+\sum_{k=1}^{\lfloor\log_{m}(N)\rfloor}(p_{k}k\,cost_{s}), \tag{5}\]The first term describes the case in which no splits are made, i.e., \(M-1\) replicas of the merged \(v\)-node must be modified, whereas the second corresponds to the other cases. The proposed approach should have defined the case where the \(v\)-root splits, for a specific possibility, since it has \(M-1\)\(v\)-children. Even so, for \(m>2\), this likelihood is lower than \(p\), so an upper limit is believed to simplify estimation.
## 4 Proposed LOADng Enhancement for IoT Networks
### LOADng-IoT Required Increments and New Features  
The proposed LOADng-IoT requires several increments in the existing default LOADng structure. Table 3 shows the fields added to the LOADng to make possible the use of IoT enhancement. Basically, a new field was inserted in both the RREQ and RREP to allow the nodes to identify the messages as IoT and realize special handling. In the Routing Set, a new field was included to enable a route to be identified as an Internet route. Hereafter, in this paper, the term Internet route will be used to refer to a route whose destination is a node with an Internet connection.  
In addition to the increments previously presented, the LOADng-IoT also presents several new features required to improve the performance of the studied IoT networks. Table 4 presents these features. The data structure of the proposed IRC is composed of two fields to store the address related to the route entry removed from the Routing Set. The memory required by the IRC structure varies according to the adopted addressing scheme (IPv6, IPv4, or Rime). However, it is important to note that the IRC is optional and could be used just in nodes with slightly greater memory capacity. The option to use or not use the IRC should be configured in each node at the deployment moment using the configuration parameters dedicated to it. In addition, it is possible to define the number of entries in the IRC set according to the memory restriction of the nodes. Considering that, in the RoutingSet, the Internet routes may require a valid time that is different from that of common routes, a new parameter was created to allow this feature. Finally, a new error code was created to be used with the LOADng-IoT. The new specific error code allows an Internet node to inform that a received Internet message was not forwarded due to loss of Internet connection. Thus, the Internet data message originator can find a new node to forward its messages.
## 3 Research Methodology
### Transfer Learning in an Inductive Setting  
To train and validate a classifier that can achieve accuracy on image classification tasks near or above the human level, a lot of training data, heavy computational power, time and resources are required. It becomes challenging to train and validate an image classifier from scratch until having a large data set. In contrast, TL is a method that uses the gained knowledge of a trained model and applies it to solve other related problems [19]. The aim is to employ a wide training dataset provided to the model with more image feature information before adapting it to a new data field. TL networks are intended to acquire spatial features using convolutional, pooling and FC layers. Moreover, traditional CNN requires a lot of training data, time and computation resources for training. Therefore, in the case of limited data (such as brain tumor recognition, where training image samples are scarce, then classifier performance suffers significantly) and computational resources, the TL of pre-trained deep neural networks is a faster and more cost-effective approach for classification tasks. When there are limited data to learn from, common information  
Figure 2: Transfer learning setting.  
is transferred from old tasks to new ones, and some specialized knowledge is produced throughout the problem-solving process. The model learns high-level features specific to the target domain, e.g., brain tumor classification, whereas the pre-trained layers learns low-level features of the original networks. Depending on the type of task and the nature of the data accessible at the source and destination domains, several parameters for TL are offered [36]. The TL approach is known as inductive TL [37] when labeled data are available in the source and target domains for a classification task. TL algorithms enhance classification performance even with limited data available for training and validation. The key task in TL is selecting a pre-trained deep neural network among the available TL algorithms. This selection is based on the related problem relevant to the target problem. The chances of overfitting are high in the case of limited target data, similar to the source training dataset. In contrast, the chances of overfitting are low if the target dataset is larger and similar to the source dataset, and then it only needs fine tuning of the pre-trained deep neural network. For this purpose, we selected nine pre-trained TL algorithms to identify their performance in detecting and identifying meningioma, pituitary and glioma brain tumors. Figure 2 represents the basic framework of the TL method employed in our work. We changed the last three models' layers to adapt them to the brain tumor classification domain.
## 2 Description of Compressor and Computational Considerations  
### The Geometry of Hydrogen Compressor with Radial Inlet  
Figure 1 illustrates the hydrogen compressor in a methanol process which was used to compress the recycled gas. The composition of the recycled gas is summarized in Table 1, and the main component was hydrogen, which was about 89 in terms of mole fraction. The parameters of the recycled gas were calculated by the Refprop program (National Institute of Standards and Technology, Gaithersburg, MD, USA) based on the GER-2004 natural gas mixture model, which was used as the working fluid in the subsequent simulation.  
The compressor is a double-sided symmetrical structure. Figure 1a shows the compressor meridian plane, which comprises four components: the radial inlet, the first stage impeller, the valeness diffuser, and the return channel. Figure 1b shows the radial inlet with 18 unevenly distributed guide vanes. Figure 1c presents the first stage with 13 shrouded impeller blades where the diffuser is valeness and there are 28 vanes in the return channel. The parameters of the compressor are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & **Nitrogen** & **Hydrogen** & **Carbon Monoxide** & **Carbon Dioxide** & **Methane** & **Argon** \\ \hline \hline
\begin{tabular}{c} Mole fraction \\ Mass percent (\%) \\ \end{tabular} & 0.042 & 0.891 & 0.039 & 0.009 & 0.004 & 0.015 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the recycled gas for the studied hydrogen compressor.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline \hline
\begin{tabular}{c} Radius of the impeller inlet \(R_{1}\) \\ Radius of the impeller outlet \(R_{2}\) \\ Impeller outlet angle \\ Impeller inlet angle \\ Chord length of the guide vane \(b\) \\ Height of the guide vane \(h\) \\ Maximum thickness of the guide vane \\ Radius of the guide vane outlet \(R_{v}\) \\ \end{tabular} & 0.17 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: The geometrical parameters of the hydrogen compressor.  
Table 3 summarizes the compressor performance at the design condition, and the parameters refer to the first stage, not taking into account the seal leakage loss. Since hydrogen was the main component of the recycled gas, which is hard to compress, the compressor pressure ratio was low, with a value of 1.045. Under the hydrogen brittleness effect and high pressure, the impeller was working in a bad environmental condition, which can easily raise the issue of impeller failure.  
Figure 1: Description of (**a**) the compressor meridian profile, (**b**) the radial inlet with guide vane, and (**c**) first stage of the compressor.  
The efficiency and pressure ratio of the compressor are defined [32], respectively, as:  
\[\eta=[(\gamma-1)\ln(P_{out}/P_{in})]/[\gamma\ln(T_{out}/T_{in})] \tag{1}\]  
\[\varepsilon=P_{out}/P_{in} \tag{2}\]  
where \(\gamma\) represents the specific heat ratio, and \(P_{out}\) and \(T_{out}\) represent the discharge total pressure and the discharge total temperature, respectively.
# Classification of Human Motion Data Based on Inertial Measurement Units in Sports: A Scoping Review
## 2 Results
### Classification Performance  
The classification accuracies ranged up to 100% [35; 88; 105] in the analysed work. Due to the different classification tasks for the distinct disciplines, the different experimental set-ups (including different numbers of subjects and trials, different numbers and positions of sensors etc.), and the different validation procedures (e.g., cross-validation, leave-one-subject-out validation etc.), a direct comparison between the different works was not carried out.  
However, to emphasize a better understanding of the performance of machine learning algorithms, we compared the results of the studies where multiple machine learning approaches were compared with each other. This analysis showed that the best model differs between the works and also within the works, depending on, e.g., different validation procedures or different training data sets. In [100], the best model varied between the different types of features. While NB performed best when all features were used, KNN performed best in a leave-one-subject-out feature selection. In [102], the best performing model depended on the number of classes to classify. Ref. [56] found differences between person dependent and person independent classification tasks. While DTW performed better in the person-dependent case, SVM performed better in the person-independent case [56]. Ref. [58] compared random forests, linear SVM, polynomial SVM, NN and a gradient boosting algorithm and found that no model was better than the others. Ref. [66] compared different classifiers with different window sizes and found that the SVM with a cubic kernel with 600 ms windows and a 10% overlap performed best. In 15 out of the 36 times (41.7%) where SVMs were used in comparative works, SVMs achieved the highest accuracy. Random forest achieved the highest accuracy values in 8 out of 14 (57.1%) works, neural networks in 10 out of 20 (50.0%), and boosting approaches in 4 out of 8 (50.0%) works where more than one classifier was compared. Table 15 in the Appendix A marks the best performing method with the highest classification accuracy for each work that applied more than one classifier.  
Figure 7: The number of validation methods in the literature. (Acc: Accuracy; Conf. M.: Confusion Matrix; Rec.: Recall; Pr.: Precision; F1: F1 Score; ROC: Receiver Operating Characteristic; AUC: Area Under the Curve; Comp: Computational)
## 2 Numerical Method and Validation  
The numerical analysis was conducted using the ANSYS Fluent 2021 R1 commercial software program. Flow analysis of the brush seal requires the bristle pack to be modeled. However, analyzing each bristle element individually is impractical as it would require excessive computational resources. Therefore, the bristle pack was modeled using the Porous Media model provided by ANSYS Fluent. However, the porous media approach does not account for 3D effects, hence, since the geometry is axisymmetric, an axisymmetric 2D CFD analysis was conducted. This significantly reduced the computation time, enabling the examination of various geometric parameters and flow conditions. ANSYS Fluent has been validated extensively in the context of fluid flow analysis and is commonly used for brush seal modeling [31]. The porous media model, frequently employed in brush seal studies, effectively approximates the flow resistance of the bristle pack while maintaining computational efficiency [32]. Although 2D axisymmetric CFD analysis simplifies the problem, studies have shown that it can produce results closely aligned with experimental data for similar applications [33]. The 2D geometry was created as the computational domain, and the mesh was generated using ANSYS Meshing. The mesh for the brush seal was designed with reference to the model created by [PERSON] [34]. Mesh generation was performed using the MultiZone Quad/Tri method to produce a structured mesh. [PERSON] [34] emphasized that the flow behavior within a brush seal is highly complex, requiring dense mesh placement near the walls, within the bristle pack, and in the fence height region to achieve accurate results. In this study, the minimum element size in the fence height region was set to 0.0254 mm for mesh generation [35]. Simulations were conducted for three brush seal geometries: those used by [PERSON] et al. [25], [PERSON] et al. [26], and [PERSON] et al. [27]. Among these, the first [25] and second [26] represent brush seals in a contact state without clearance, while the third [27] includes clearance and requires consideration of the blow-down effect. Figure 2 shows the mesh configurations for each of the three geometries. Figure 2b and Table 1 describe the element-sizing method for the three brush seal meshes, with all elements shaped as quadrilaterals. To simplify the calculations, regions other than the fluid flow paths and the bristle pack were excluded from the computational domain. In the bristle pack region, fluid velocity decreases, leading to laminar flow. Therefore, a laminar model was applied to the bristle pack section [36]. Figure 2: _Cont._The most widely used models for the flow analysis of brush seals are the k-\(\varepsilon\) and k-\(\omega\)-SST turbulence models [37; 38]. In this study, these two turbulence models were compared based on the brush seal geometry used by [PERSON] et al. [25]. The leakage flow rate of the brush seal was used as the evaluation metric, and the results are presented in Figure 3. In
## 4 Static Network Performance of a TFBN
### Fault Diameter  
Unlike the fault tolerance, the fault diameter helps to determine the communication delay and also to track the changes in network diameter if there are \(f\) faulty vertices (with \(f\) fault tolerance). Moreover, diameter estimation on networks is important if there is a faulty node or a link failure on the network.  
Theorem 1: _The fault diameter of TFBN is its network diameter + 4._  
Proof of Theorem 1.: TFBN(2, L, 0) has BM connectivity and at most two-higher level connectivity. So, the fault tolerance of TFBN(2, 1, 0) is five and TFBN(2, L, 0) is six. Now, if we consider a scenario that there is a faulty node(1, 0) exits in a TFBN(2, 1, 0) and we would like to transmit the packet from node(0, 0) to node(2, 2). Then, node(0, 0) can directly send packet to node(2, 0) and after that to node(2, 2) even if we follow the YX-order routing. On the other hand, let us consider the Figure 3, if there is a link failure between the node(0, 0) to node(2, 0) then the packet needs to be transmitted through node(1, 0). Hence, fault diameter for basic module of TFBN requires network diameter + 1. The higher level network of TFBN considers 2D-Torus network for its BM to BM connectivity. The Level-2 network of TFBN consists of 16 BMs and the Level-3 network consists of \(16\times 16=256\) BMs. Suppose a link failure occurs from BM(0, 0) to BM(1, 0) at Level-2 as shown in Figure 4. For transmitting the packet from BM(0, 0) to BM(1, 0) and considering the routing of XY-order (considering minimum hop distance), packets need to be transmitted to BM(0, 1) and then BM(1, 1) and finally, they will reach BM(1, 0), which results in a fault diameter equal to network diameter + 4.  
Table 1 shows the fault diameter for different networks. For 256 nodes, a 2D-Mesh network requires a network diameter of 30 and a fault diameter of 30 + 2 = 32. In case of 4096 nodes, the same network requires diameter of 126 and fault diameter of 128. Now, since the diameter of TFBN is 10, the fault diameter is 14 and it is less than the fault diameter of TTN (18), TESH (24), and 2D-Torus (18) with 256 nodes. Again, considering 4096 nodes, the fault diameter of TFBN is 23, which is also less than TTN (27), TESH (35), and 3D-Torus (26) networks.  
The question may emerge why we have taken Level-2 (256 nodes) and Level-3 (4096 nodes) for performance analysis. The performance is evaluated using the shortest path algorithm with the help of dimension order routing for all distinct pairs of nodes. If the number of the nodes increase, the number of pairs is also exponentially increased, which in turn takes huge time for evaluation. Since TFBN is a hierarchical interconnection network, it will yield better performance with the increase in hierarchy level as we have seen scaling up from Level-2 to Level-3, as depicted in Table 1. We have also observed the improvement of performance with the increase in hierarchy level for the TTN as presented in Ref. [29].