esa-sceva/satcom-chunk-collection huggingface dataset:
# Use of the Gene-Expression Programming Equation and FEM for the High-Strength CFST Columns
## 2 A Brief Background of Gene Expression Programming (GEP) and Finite Element Analysis (FEA)  
Recently, GEP has gained recognition among civil engineers. In fact, it has shown a good capability in prediction of some engineering problems with an acceptable accuracy [38]. Table 1 indicates a list of recent studies using AI approaches together with the numbers of samples and the analyzed parameters. In solving structural engineering problems, AI has become quite popular recently, specifically in prediction of the ultimate bearing capacity of composite columns [39; 40; 41].  
GEP benefits from two main chromosomes, and the expression tree (ET) provides solutions for removing the limitations of two older algorithms. The codifications are shown in the form of a string in GEP, which is in fact obtained from Karva programing language and can present a behavior like ETs. One of the interesting functions of GEP is that it can present its own models using mathematical equations. In fact, mathematical equations create relation between independent parameters. The GEP modeling process begins with the random creation of chromosomes for determined numbers, which follows Karva language (Karva is a symbolic language to introduce chromosomes). These symbolic chromosomes should be then defined as trees with different size and shapes (expression trees). These points are investigated by the functions that are responsible for controllingmodels and their adaptability. These functions have different types that can be defined by different criteria [10; 19; 38].  
On the other hand, a lot of scholars applied numerical methods to model and assess the mechanical behavior of CFST columns since experimental investigations are costly and time-consuming, and also have some limitations such as materials preparation [4; 43; 44; 45]. In some of these studies, comparisons were made between the results of ultimate bearing capacity achieved from the FEA and the outcome reached by the experimental tests. They indicated a good agreement with the results obtained by FEA methods [46; 47]. Nevertheless, it is believed that the GEP equation, generated from the previous study [10], is able to predict the maximum strength of CFST columns with a better accuracy compared to the FEA. Therefore, this study presents a valuable comparison between the methods which can be beneficial for the engineers to calculate the ultimate load bearing capacity faster and more accurately.  
Prediction of Bearing Capacity of Concrete-Filled Steel Tube (CFST) Columns by Finite Element Analysis (FEA)
## 3 Results and Discussion
### Design Complexity and Specialized Knowledge in Needed for Each DPI Prototype
#### 3.1.2 Type 2 DPI  
As shown in Figure 8, the total number of leaf springs and pressure plates is specified as a design parameter, and the needle part is assembled by inserting the needle body, cylinder, and spring. The number of leaf springs was reduced by one and three cases were analyzed. As shown in Figure 9, the Type 2 needle configuration allows fluid to move more freely than the conventional type. When hydraulic pressure is used in the conventional type, the needle moving distance is amplified and the needle can move with a small pressure force. However, the injection responsiveness is considered to be slow. Therefore, the design purpose of Type 2 is to increase the injection responsiveness by allowing pressureed fluid to flow freely without hydraulic pressure, by taking advantage of the mechanical characteristics.  
Case 2-1 in Figure 8 has an available maximum injection pressure of 130 MPa, as shown in Figure 10. The injection pressures of Case 2-2 and Case 2-3 were about 100 bar lower than Case 2-1. It was found that the available maximum injection pressure can be obtained, depending on the number of leaf springs. If one leaf spring in Type 2 is absent, the force for lifting the needle under CR high pressure is insufficient. However, when two leaf springs are provided, it becomes possible to lift the  
Figure 7: Actual number of injections according to the thickness of the steel wire; (**a**) 0.7 mm, (**b**) 0.8 mm.  
needle with a greater force, due to the increase in the elastic force. It is considered that the elastic force in the design of Type 2 is determined by the number of leaf springs.
# Key Technologies in the Context of Future Networks: Operational and Management Requirements
## 4 Future Trends and Challenges
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Requirement** & **Challenge** & **Future Trends/Enabler Technologies** \\ \hline \multirow{7}{*}{System Performance} & \multicolumn{2}{p{142.3 pt}}{* Provide efficient mechanisms regarding to radio resource provisioning.} & \multicolumn{2}{p{142.3 pt}}{* Evolution of radio-access technologies (RATs).} \\  & \multicolumn{2}{p{142.3 pt}}{* Improving the capacity of radio resources.} & \multicolumn{2}{p{142.3 pt}}{* Decreasing the cell size.} \\  & \multicolumn{2}{p{142.3 pt}}{* Provide super wide bandwidth.} & \multicolumn{2}{p{142.3 pt}}{* Millimeter-wave communication} \\  & \multicolumn{2}{p{142.3 pt}}{* Better management of data traffic, interference and mobility levels.} & \multicolumn{2}{p{142.3 pt}}{* Intelligent resource allocation via SDN or SDN.} \\ \hline \multirow{7}{*}{Composite Wireless Infrastructures} & \multicolumn{2}{p{142.3 pt}}{The 5G device can choose the most appropriate wireless or mobile technology according their needs (Change between systems).} & \multicolumn{2}{p{142.3 pt}}{* Enhancement of user devices (Muti-Band-Multi-Mode support).} \\  & \multicolumn{2}{p{142.3 pt}}{* Improving the capacity of radio resources.} & \multicolumn{2}{p{142.3 pt}}{* Introduction of intelligent mechanisms and SDN control.} \\ \hline \multirow{7}{*}{Facilitating very dense deployments (Hetnets)} & \multicolumn{2}{p{142.3 pt}}{Operators must provide effective mechanisms to deploy cells of different sizes according to user needs.} & \multicolumn{2}{p{142.3 pt}}{* Improving the resource capacity through decreasing the cell size.} \\  & \multicolumn{2}{p{142.3 pt}}{* Improving the spectrum utilization in order to operate in some bands or channels, while reducing interferences.} & \multicolumn{2}{p{142.3 pt}}{* Introduction of Cognitive Intelligent mechanisms to exchange traffic between users.} \\ \cline{1-1}  & \multicolumn{2}{p{142.3 pt}}{* Reduce the average service creation.} & \multicolumn{2}{p{142.3 pt}}{* Resource sharing (Exploring Cloud-RAN, Cloud computing, NFV)} \\ \cline{1-1}  & \multicolumn{2}{p{142.3 pt}}{* Smart allocation of functional mobile components (SDN, NFV).} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Current Trends and Challenges. It is important to note the current efforts of initiatives such as 5G Americas [46], 5G-PPP or NGMN to develop future network. They promote not only SDN, NFV and cloud computing adoption but also the study of transversal concepts such as carrier aggregation, massive MIMO, Multi-RAT convergence, spectral and signaling efficiency, among others. It is imperative that telecommunication and network service providers find a consensus to develop solutions, architectures, technologies and standards for the next generation of infrastructures. The communication paradigm of anytime, anyhow and anywhere will become a reality in the future society.
## 4 Results
### Use Case Results  
From the previous analysis, we found that the worst option was the use of the Kubernetes and Docker Engine stack, which in the different comparison test figures, with results given as percentages (Figures 3-7), is the reference baseline, as it underwent no improvement in performance. Our purpose here is to check this option for a use case with intensive processing.  
To evaluate the performance and scalability of the use case given in Section 3.8, we conducted an experiment using a drone flight that captured 48 images. The data were processed using four ODM nodes configured in a cluster, with each node equipped with four virtual CPUs (vCPUs). The hardware specifications for the ODM nodes were based on  
Figure 10: Tests with 2-node cluster compared to previous 1-node architecture.  
Figure 9: Comparison of all 24 tests (20 min) performed on the Raspberry Pi testbed.  
the recommendations provided in the official documentation ([[https://docs.opendronemap.org/installation/](https://docs.opendronemap.org/installation/)]([https://docs.opendronemap.org/installation/](https://docs.opendronemap.org/installation/)) (Visited on 29 September 2024)).  
The results of the experiment are summarized in Table 4. The table presents the average preprocessing time per image, the average time taken to create each transformation matrix, and the total time required to generate the pointclouds for the RGB, Red Edge (RE), Red (R) and Green (G) spectral bands.  
The experimental results, summarized in Table 4, highlight the effectiveness of our distributed computing approach for pointcloud generation. The average preprocessing time per image was 3.67 s, and the creation of the warp matrix took an average of 24.72 s. We processed the pointclouds for the four spectral bands in parallel. The processing times were 912.28 s for RGB, 868.09 s for RE, 875.95 s for R and 858.71 s for G. This parallelization significantly accelerated the overall processing workflow, demonstrating that distributing computational tasks across multiple nodes reduces processing time and enhances efficiency in generating pointclouds from drone images.
## 2 Proposed Method  
Suppose that a dynamic load \(f\) is applied to an aerospace structure, time series signal of \(f(t)\) in the time domain could be a discrete signal \(f(0),f(\Delta t),f(2\Delta t),\cdots\), where \(\Delta t\) is a sampling time interval. The load would cause the vibration on the structure and the vibration response are measured at \(N\) different measure points on the structure. Suppose that the \(N\) measured responses are the acceleration signals, which are relatively easy to measure and widely used in different load identification works [23; 25]. Let \(a^{i}(t)\) be the measured discrete acceleration response in the time domain of the \(i\)-th measurement point. The goal of load identification is to identify \(f(t)\) using \(a^{i}(t),i=1,2,\cdots,N\).  
In the time domain, for a linear vibration system, the relation between the dynamic response \(a^{i}(t)\) and the dynamic response \(f(t)\) can be modeled by a convolution of the unit impulse response function \(h(t)\). The calculation of the inverse of the response function often suffers ill-posedness, and the accuracy of the identification of the loads would be affected by the noises in the measurements. In the frequency domain or the transform domain, though the conventional relation between the load and the response is replaced by a multiplication, a similar ill-posed problem would happen as well.
## 4 Point Cloud Uncertainty Evaluation: Explicit Models
### Scale and Squareness Effects (S)
For measurements involving multiple probe offsets \(\mathbf{p}_{k}\), \(k=1,\ldots,n_{P}\), the measured coordinates \(\mathbf{x}_{i}\) are related to the true point coordinates \(\mathbf{x}_{i}^{*}\) through a model of the form  
\[\mathbf{x}_{i}=B(\mathbf{b})\mathbf{x}_{i}^{*}+\mathbf{p}_{k(i)}.\]  
For this model, the sensitivity of \(\mathbf{x}_{i}\) with respect to \(\mathbf{b}\) is approximated by  
\[G_{i}=G(\mathbf{x}_{i}-\mathbf{p}_{k(i)})\]  
with \(G\) defined as in Equation (11). In two dimensions, the model has the form  
\[\mathbf{x}_{i}=\left[ \begin{array}{cc}(1+b_{aa}+b_{xx})&b_{xy}\\ 0&(1+b_{aa}+b_{yy})\end{array} \right]\mathbf{x}_{i}^{*}, \tag{15}\]  
depending on effects \(\mathbf{b}=(b_{aa},b_{xx},b_{yy},b_{xy})^{\top}\). The \(2m\times 4\) sensitivity matrix \(G_{X|B}\) for this model is assembled from \(2\times 4\) matrices of the form  
\[G_{i}=\left[ \begin{array}{ccc}x_{i}&x_{i}&0&y_{i}\\ y_{i}&0&y_{i}&0\end{array} \right]\!, \tag{16}\]  
variance matrix \(V_{B}\) associated with the scale and squareness effects typically of the form  
\[V_{B}=\left[ \begin{array}{cccc}\sigma_{S}^{2}&0&0&0\\ 0&\sigma_{S,x}^{2}&0&0\\ 0&0&\sigma_{S,y}^{2}&0\\ 0&0&0&\sigma_{Q}^{2}\end{array} \right]\!. \tag{17}\]
# IRONEDGE: Stream Processing Architecture for Edge Applications
## 5 Results and Comparison
### Actual Throughput and Log Time  
Recall that, for both K0-WC and K1-NC, the Data Reports all originated in a single Data Source and were sequentially inserted into the Data Collection. Figure 8 shows the actual throughput of the system, measured as the rate at which Events were written into the Cloud Layer. Figure 8 shows the log time, the average time spent delivering all valid Events to the Cloud Layer.  
For both figures, the implementations displayed similar behavior for the test case of 1000 Data Reports per second, but differed significantly for all higher rates. In the case of actual throughput, both implementations initially matched the Data Report rate, where both had a 0% loss. For higher rates, K0-WC clearly showed low throughput resulting from high losses. K1-NC, in contrast, showed an increase in throughput, but never matched the input rate for the Data Reports. For example, for 12,000 and 14,000 Data Reports per second, the throughput was limited to 4610.3 and 4146.2 Events per second, respectively. Coupled with the low loss rates already verified in Figure 7, this displays a spreading effect in internal processing. Due to the fact that the implementation consumes Data Reports at a  
Figure 8: Implementation performance measured as throughput and log time, for each implementation and Data Reports’ rate. (**a**) Actual throughput. (**b**) Log time.  
much faster rate than it can process and deliver them to the Cloud Layer, as such, the total time taken to deliver Events will tend to be the same as or higher than the time taken to consume Data Reports. As already noted for the recorded latency, this appears to be due to the internal batching size set by Apache Storm to 5000 Data Reports per batch.  
These effects were also reflected in the log time. After initially matching the behavior, K0-WC showed high log times, starting at 152,826.6 ms for 2000 Data Reports per second and lowering to 103,647.8 ms for 14,000 Data Reports per second. In addition to the high loss rate, this high log time indicates that the system is losing messages that it cannot process. However, as the internal state progressed, it accepted messages received later. Identifying which component, or set of components, is shedding messages would require more detailed delivery metrics taken at the boundaries of each component. For K1-N, the recorded log time showed a more stable behavior, with no particular trend to be identified.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 5 Illustrative Example
### Phase 2: Short-Term Anomaly Detection  
Once deployed in real time, the system scrutinizes each arriving multi-modal instance for abrupt deviations. A surge in reconstruction error prompts a Bayesian threshold mechanism to decide whether a short-term label is warranted (as shown in Figure 3), thereby adapting the decision boundary to fluctuating production contexts. The bottom portion of Figure 2 features purple dashed lines that denote short-term alerts. Machine 1 exemplifies this phenomenon, generating multiple warnings in the spring of 2024 tied to intermittent current spikes. Machine 3, owing to its high-throughput recipes, occasionally underwent sudden current surges, aptly captured by concurrent log references such as \"current fluctuations.\" For Machine 4, spikes often occurred during transitions between Recipe \(A\) and Recipe \(C\), creating short-term anomalies whenever process parameters briefly deviated from baseline norms. These minute-scale alerts empower operators to intervene preemptively, preventing minor anomalies from amplifying into more serious downtime.
# Mechanisms of Fluoride Toxicity: From Enzymes to Underlying Integrative Networks
## 3 Mechanisms of Fluoride Toxicity on Cellular Level
### Fluoride Inhibits Adenosine Triphosphatases by Multifactorial Mechanisms  
The membrane integral PPases couple the hydrolysis of PPi to sodium (Na+) and/or H+ pumping. Fluoride inhibits the membrane-bound, proton-pumping H+-ATPase, which is involved in the generation of proton gradients through the efflux of protons from the cell at the expense of ATP. Thus, fluoride has the dual action of dissipating proton gradients and preventing their generation through its action on H+-ATPase. The collapse of the transmembrane proton gradient, in turn, reduces the ability of cells to transport solutes via mechanisms involving proton driving force [23].  
P-type adenosine triphosphatases (ATPase) share common phosphorylation and dephosphorylation mechanism. The reactions of ATP binding, phosphoryl transfer and hydrolysis, and the transduction of the energy to the ion-binding site, are highly conserved. The transfer of a phosphoryl group regulates the activity of enzymes in the cell [24].  
Na+, K+-ATPase (NKA) is an integral protein in the plasma membrane that transports Na+ ions to the outside and K+ ions to the inside of the cell [25]. By using the energy from ATP to establish asymmetric distributions of ions across the cell membrane, NKA links metabolic energy to cellular functions and to signaling events both between and within cells. More than 60 papers describe the effects of fluoride on the NKA activity, for example, in erythrocytes, hepatocytes and the liver, neurons and the brain, kidney, cardiac muscle and the heart, and adipocytes in both laboratory animals as well as humans. [PERSON] explains the molecular mechanisms by which fluoride inhibits NKA [25]. He suggests that fluoride can inhibit NKA activity by lowering serum Mg\({}^{2+}\). Animal studies have also shown that fluoride exposure results in decreased serum Mg\({}^{2+}\) due to the decreased Mg\({}^{2+}\) absorption in the gastrointestinal tract. NKA activity is also downregulated by the activity of adenylyl cyclase, various hormones, cytokines, and neuropeptides [25]. NKA is a member of the P-type family of active  
Figure 1: The hydrolysis of pyrophosphate to inorganic phosphate.  
cation transport proteins, such as the Ca\({}^{2+}\)-ATPase or the gastric H\({}^{+}\), K\({}^{+}\)-ATPase [26]. Mechanisms of fluoride inhibition of ATPases activities are, therefore, multifactorial.
## 4 Semantic Understanding of Object Layer
### Spatial Structure Semantics
#### 4.3.2 Object-Oriented Method  
The basic unit of object-oriented processing is homogeneous objects (image blocks, homogeneous areas or patches) with certain semantic information in images [81]. The method needs to segment the image to obtain the objects to further describe the spatial structure of the objects in the images [82]. The advantage of this method is that it has more abundant spatial relationships for the objects themselves and is convenient for extracting spatial features [83, 84]. The deficiency of this method lies in its serious dependence on the quality of image segmentation. In fact, inaccurate image segmentation results in error accumulation when understanding spatial structure semantics [85].
# Stable Evaluation of 3D Zernike Moments for Surface Meshes
## 3 Algorithms for Computing the 3D Zernike Moments for a Surface Triangular Mesh
### Computing the Integrals \(c_{nl}^{m}(T)\)  
Recall that the triangle is defined as \(T=(A,B,C)\) and that we need to compute over this triangle the integral  
\[c_{nl}^{m}(T)=\frac{3V(\sigma_{T})}{S(T)}\int_{T}\frac{1}{r_{0}^{3}}Q_{nl}(r_{0 })\overline{Y_{l}^{m}(\theta,\phi)}\mathrm{d}P.\]  
Let us define  
\[g_{nl}^{m}(P)=\frac{1}{r_{0}^{3}}Q_{nl}(r_{0})\overline{Y_{l}^{m}(\theta,\phi )}. \tag{31}\]  
Note that there is a function \(g\) for all triplets \((n,l,m)\). We need to compute as exactly as possible,  
\[\frac{1}{S(T)}\int_{T}g_{nl}^{m}(P)\mathrm{d}P.\]  
This integral can be approximated with a 2D quadrature [59]  
\[\frac{1}{S(T)}\int_{T}g_{nl}^{m}(P)\mathrm{d}P\approx\sum_{i=1}^{L}w_{i}g_{nl }^{m}(\alpha_{i}A+\beta_{i}B+\gamma_{i}C), \tag{32}\]  
The sum is computed over \(N_{p}\) points on the triangle, with each point \(P_{i}\) defined by its barycentric coordinates \((\alpha_{i},\beta_{i},\gamma_{i})\) with respect to \((A,B,C)\), and \(w_{i}\) is a weight. The points and weights are said to define a quadrature rule. A rule is said to be of strength \(N\) if it is capable of exactly integrating any polynomial of maximal degree \(N\) over the domain (here the triangle). To apply such a scheme for our application, we need to consider two elements:  
1. _Exactness._ As mentioned above, a 2D quadrature may be exact if it is applied on a polynomial. This is our case. Indeed, recall that \(Q_{nl}(r_{0})\) is a polynomial function of degree \(n+3\), with \(r_{0}^{l+3}\) as a factor. The function \(g\) can then be rewritten as \[g_{nl}^{m}(x) =\frac{1}{r^{3}}Q_{nl}(r)\overline{Y_{l}^{m}(\theta,\phi)}\] \[=r^{l}U_{nl}\overline{Y_{l}^{m}(\theta,\phi)}\] \[=U_{nl}e_{l}^{m}(x),\] where \(e_{l}^{m}(x)\) are the harmonic polynomials (see Equation (5)). As \(U_{nl}\) is of degree \(n-l\) and \(e_{l}^{m}\) is of degree \(l\), the function \(g\) is a 2D polynomial function of degree \(n\). A quadrature rule of strength \(n\) will therefore integrate \(g\) exactly.
2. _Number of points for the quadrature,_ while it is well known that an \(n\)-point Gaussian rule is exact for all polynomials of degree up to \(2n-1\) in one dimension, the situation is more complex in higher dimensions. [PERSON] and [PERSON] [60] proposed an empirical rule for the minimal number of points \(N_{p}^{\min}\) to integrate exactly a polynomial of order \(n\): \[N_{p}^{\min}=\bigg{\lceil}\frac{(n+1)(n+2)}{6}\bigg{\rceil}.\]
## 2 Background
### Creating Rules for Filtering  
While the rules, e.g., for Bark as shown in Section 2.4 may be easy to read, [PERSON] et al., as the authors of the original work [26], admit that creating the mappings between human-readable names and the underlying entities (port numbers, sets of permitted IP addresses, domain names, API-specific constructs, etc.) may require knowledge and skills that not every user has. Hence, such mappings could be done by experts and be loaded when a new device is added [26].  
The end user could copy and use rules that have 'been defined once by an expert' [26] and published, e.g., by communities of users or the device manufacturers. The end user could also benefit from automated guidance, e.g., in [44] the authors [PERSON] et al. introduced a tool named _COPSEC_. COPSEC allows to evaluate whether an IoT device is compliant with security guidelines and privacy regulations, e.g., the European Regulation on Data Protection (GDPR) [45]. If the user would trigger such an automated evaluation for his own device and the result would be negative Bark-style filtering rules could be automatically suggested based on the analysis from COPSEC. Then, once filtering is in place, the user could re-run the COPSEC analysis framework to check if filtering has improved. Also, Intrusion Detection Systems-conventional or those based on artificial intelligence [46]-could also be used to prepare rules and present them to the end user as suggestions. Further, there is research in the human-computer interaction domain suggesting that end users, once empowered, could be more willing to try building rules on their own [47]. This would require a playful and dynamic interaction with visualisations of the data flows to enable and disable those, i.e., building a rule from a simple live or historic data flow visualisation [47].  
Moreover, it is assumed that the rules are restrictive enough to only allow the communication that is necessary, as depicted by the Data Flow Diagrams for our Smart Home communication scenarios.