esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimal Design Formulation
The following inequality must be satisfied (see [26]):  
\[d^{T}=\left|h^{*}\;\;b_{i}\;\;t_{f,i}\;\;t_{f,o}\;\;\ell_{o}\right| \tag{17}\]4  
that, written as function of the design variables, reads:  
\[d^{T}=\left|h^{*}\;\;b_{i}\;\;t_{f,i}\;\;t_{f,o}\;\;\ell_{o}\right| \tag{17}\]5  
where \(I_{p}\) is the moment of inertia of the original beam element. The optimal design problem can be formulated as follows:  
\[d^{T}=\left|h^{*}\;\;b_{i}\;\;t_{f,i}\;\;t_{f,o}\;\;\ell_{o}\right| \tag{17}\]6  
Subjected to:  
\[d^{T}=\left|h^{*}\;\;b_{i}\;\;t_{f,i}\;\;t_{f,o}\;\;\ell_{o}\right| \tag{17}\]7  
\[d^{T}=\left|h^{*}\;\;b_{i}\;\;t_{f,i}\;\;t_{f,o}\;\;\ell_{o}\right| \tag{17}\]8\[d^{T}=\left|h^{*}\;\;b_{i}\;\;t_{f,i}\;\;t_{f,o}\;\;\ell_{o}\right| \tag{17}\]9 \[f(d)=A_{i}\ell_{i}+2A_{o}\ell_{o}=\left[2b_{i}t_{f,i}+t_{w,p}\left(h^{*}-t_{f,i }\right)\right]\ell_{i}+2\Big{[}2b_{p}t_{f,o}+t_{w,p}\left(h^{*}-t_{f,o}\right) \Big{]}\ell_{o} \tag{18}\]0 (34) \[f(d)=A_{i}\ell_{i}+2A_{o}\ell_{o}=\left[2b_{i}t_{f,i}+t_{w,p}\left(h^{*}-t_{f,i }\right)\right]\ell_{i}+2\Big{[}2b_{p}t_{f,o}+t_{w,p}\left(h^{*}-t_{f,o}\right) \Big{]}\ell_{o} \tag{18}\]1  
In the above reported relations, the adopted scalar functions, vectors and matrices have the following form:  
\[d_{low}^{T}=\left| \begin{matrix}0&3t_{w,p}&0&t_{f,p}&0\end{matrix} \right| \tag{36}\]  
\[d_{upp}^{T}=\left|h_{p}-t_{f,p}&b_{p}&t_{f,p}&\frac{h_{p}}{2}&\infty\right| \tag{37}\]  
\[A_{eq}=\left| \begin{matrix}1&0&0&1&0\end{matrix} \right|\text{; }\;\;b_{eq}=h_{p} \tag{38}\]  
\[A_{in}=\left| \begin{matrix}0&1&-18\sqrt{\frac{235}{f_{y}}}&0&0\end{matrix}  \right|\text{, }\;\;\;\;\;\;\;\;\;\;\;\;\;\;b_{in}=3t_{w,p} \tag{39}\]  
\[G_{eq}(d)=b_{i}\;t_{f,i}h^{*}+t_{w,p}\bigg{(}\frac{h^{*}-t_{f,i}}{2}\bigg{)} ^{2}\text{, }\;\;q_{eq}=\frac{N_{a}^{2}}{4\;t_{w,p}\;f_{y}^{2}}+\frac{M_{a}}{f_{y}} \tag{40}\]  
if the neutral axis cuts through the web cross section,  
\[G_{eq}(d)=\frac{N_{a}}{2}\Big{[}2t_{f,i}+\frac{t_{w,p}}{b_{i}} \Big{(}h^{*}-t_{f,i}\Big{)}-\frac{N_{a}}{2b_{i}\;f_{y}}-\Big{(}h^{*}+t_{f,i} \Big{)}\Big{]}+\] \[f_{y}\Big{[}b_{i}t_{f,i}+\frac{t_{w,p}}{2}\Big{(}h^{*}-t_{f,i} \Big{)}\Big{]}\bigg{[}\Big{(}h^{*}+t_{f,i}\Big{)}-t_{f,i}-\frac{t_{w,p}\big{(} h^{*}-t_{f,i}\big{)}}{2b_{i}}\bigg{]} \tag{41}\]  
\[q_{eq}=M_{a}\]  
if the neutral axis cuts through one of the flanges cross section,  
\[G_{in}(d)=\frac{\beta h_{p}}{\ell_{0}}-\frac{4b_{i}\;t_{f,i}^{3}+ 12b_{i}t_{f,i}h^{*2}+2t_{w,p}\big{(}h^{*}-t_{f,i}\big{)}^{3}}{2b_{i}\;t_{f,i} ^{3}+6b_{i}t_{f,i}h^{*2}+t_{w,p}\big{(}h^{*}-t_{f,i}\big{)}^{3}} \tag{42}\]  
\[q_{in}=0\]  
The above formulated problem is a non-linear programming one and for its solution a suitable solver is utilized by adopting an interior-point algorithm. In particular, the \"fmincon\" solver will be implemented in the application stage by utilizing the MATLAB Optimization Toolbox.
## 3 Data  
In this research we consider operating data generated by the SCADA system of wind turbines. The data set is gathered from an onshore wind farm consisting of 12 turbines with a nominal power of 2 MW commissioned in 2017 and located in the UK. The investigated period covers 15 months of data collection, hence seasonality effects should be limited.  
Various signals that measure and monitor operational and ambient conditions are available with a temporal resolution of 1 second. Signals that represent counters are excluded in this analysis. In total, 27 signals are evaluated including temperatures, pressures, speeds, voltages, currents, and pitch angles. Table 1 provides an overview of the investigated signals, partitioned into functional groups. Please note that in this study only operating data is considered.  
The time stamps of the original dataset are not always exact to the tick of a second. Sensors, for various reasons, might record their values slightly early or late. For computational reasons we decided to adjust the timestamp downward to seconds with the typical floor-functions available. Therefore, in a few cases two different records were assigned to the same timestamp. In these cases only the earlier one of the two values is kept. Furthermore, for missing data no replacement, i.e., no imputation, was performed and generally no treatment for outliers was performed, except for the descriptive statistics in Section 4.1.1.  
In order to examine the effect of temporal aggregation, throughout this paper downsampling of high-resolution data to a lower temporal resolution is accomplished by averaging if not stated otherwise. Table 2 depicts an example for the temporal aggregation of the wind speed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & **Control** & **Electrical** & **Environmental** & **Mechanical** \\
**Temeratures** & **Variables** & **Characteristics** & **Variables** & **Characteristics** \\ \hline Generator bearing 1 & Pitch angle blade 1 & Active power & Ambient temperature & Generator speed \\ Generator bearing 2 & Pitch angle blade 2 & Current phase A & Nacelle temperature & Rotor speed \\ Generator stator & Pitch angle blade 3 & Current phase B & Wind direction & \\ Gearbox oil & Yaw angle & Current phase C & Wind speed & \\ Gearbox shaft bearing 1 & & Grid frequency & & \\ Gearbox shaft bearing 2 & & Power factor & & \\ Main bearing & & Voltage phase A & & \\ Top box & & Voltage phase B & & \\  & & Voltage phase C & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of SCADA signals analyzed in this study.
## 3 Results
### Cluster Analysis  
To further investigate the performance difference between the programs, a cluster analysis [26] was conducted to group the programs based on the similarities in their discrimination ability. Using the EQ Scores and Flat Scores of the programs, an agglomerative hierarchical clustering (AHC) analysis with [PERSON]'s method [26] was performed. The clustering process was illustrated in the dendrogram, as shown in Figure 4. Two major clusters were derived, marked as C1 and C2 separately on the left side of the dendrogram. Among the 20 programs tested, C1 contained 14 programs and C2 contained the remaining six programs.  
According to the cluster results, Figure 5 demonstrates the EQ Score, Flat Score, and listeners' subjectively perceived difficulty for each program in the two clusters. For the Flat Score, it can be seen that programs in both clusters had high values (all above 85%). While for the EQ Score, the differences between the two clusters were apparent. Programs in C1 showed much higher EQ Scores than the programs in C2. Even the lowest EQ Score in C1 (73.5% for PNO (\"Pink Noise\")) was higher than the highest EQ Score in C2, which was 72.0% for HHU (\"Heaven Help Us All\") and SGB (\"Sweet Georgia Brown\"). This result suggested that when the programs in C2 were equalized, listeners usually had trouble detecting the equalizations that applied to these programs. In other words, these programs were poor signals for revealing the low-frequency equalizations.  
Figure 4: Dendrogram of the agglomerative hierarchical clustering (AHC) analysis of programs. C1 and C2 represent the two clusters.  
It was also worth noting that the six programs in C2 were all nominated multiple times as being difficult to make judgments on, as the histograms in Figure 5 shown. In order to examine whether there was a significant difference between the subjectively perceived difficulty of the two program clusters, a one-way analysis of variance (ANOVA) was performed. The result showed that the effect of the cluster was significant at a significance level of 0.01 (Difficult: F(1,18) =10.821, \(p\) = 0.004; Easy: F(1,18) = 10.665, \(p\) = 0.004), indicating that programs in C2 were significantly distinguished from programs in C1 in the listeners' subjective assessment of the task difficulty.  
Considering both the EQ Score performance and listeners' assessment of the task difficulty for each program, programs in C1 were regarded as the more revealing programs for detecting low-frequency spectral irregularities whereas those in C2 were not.
# Estimating Eddy Dissipation Rate with QAR Flight Big Data
###### Abstract  
Air turbulence (AT) is a typical risk that seriously threatens civil aviation safety. It is typically measured via vertical overload, derived equivalent vertical gust velocity (DEVG), and eddy dissipation rate (EDR), while the last one is the most ideal index. In this study, we combined two traditional methods of EDR estimation and proposed a new procedure to estimated EDR with quick access recorder (QAR) data. It enables us to use the parameters only from QAR data for EDR estimation, which fully reflects the great value of QAR data. We estimated EDR values with QAR data collected by the Civil Aviation Administration of China (CAAC), to present the intensities of air turbulences happened within China from 1 January 2016 to 31 December 2018. This study could also be helpful in evaluating and analyzing causes and early warning of AT risks.  
Keywords:QAR data; air turbulence; turbulence intensity; eddy dissipation rate +
Footnote †: journal: Applied Sciences
# Flexural Behavior of Corroded High-Speed Railway Simply Supported Prestressed Concrete Box Grider
## 4 Results and Discussion
### Load-Deflection Curve at Midspan  
Figure 10 compares the load-deflection curves for all the scaled beams. It can be found that the curves can be divided into three stages. Before the concrete in the tension zone cracked (point A), the load and midspan deflection were approximately linear and the scaled beam was in elastic working condition (stage 1). All the scaled beams exhibited a similar load-deflection behavior at stage 1, demonstrating that the strand corrosion had a negligible effect on the initial stiffness of PC beams. Stage 2 was the cracking elastic phase, where the concrete in the tension zone cracked and stopped working. This stage terminated when the non-prestressed tensile reinforcements yielded. Stage 3 was the plastic stage, where the cracks extended to the upper flange, and the flexural stiffness of the beam section decreased rapidly. This stage ended with the concrete being crushed in the compressed zone when the strand mass loss was low, or with the strand being ruptured when the mass loss was high. The inflection points in the curve were, sequentially, concrete cracking (point A), yielding of non-prestressed tensile reinforcement (point B), concrete crushing (point C), and strand rupture (point D).  
Figure 10: Comparison of the load–deflection curve.  
Figure 9: Cracking and ultimate loads vs. mass loss.  
Under general conditions, the corroded PC beam is usually working with cracks, i.e., in an approximately elastic stage. The deflection calculations of corroded PC beams are aimed at both elastic and cracking elastic phases. Therefore, a homogeneous elastic material model was identified as the object of study. The stiffness of the purely bending segment was taken for the study based on the \"principle of minimum stiffness\" [40]. Given the short period of the test loading, the short-term stiffness of the corroded PC beam was studied.  
For the four-point bending test, deflection can be obtained using the principle of superposition. The flexural stiffness of scaled beams is obtained as follows:  
\[B=\frac{Q}{\delta}(\frac{aL^{2}}{8}-\frac{a^{3}}{6}) \tag{20}\]  
where \(Q\) is the external load applied at one loading point; \(\delta\) is the midspan deflection, which is taken as a positive value when moving downwards; \(a\) is the distance from the loading point to the support; and \(L\) is the calculated span of the scaled beam.  
Figure 11 compares the load-flexural stiffness curves calculated using Equation (20). It can be found that the general trend of relative stiffness variation was decreasing, with two obvious stages: the stiffness remained unchanged during the period from loading to cracking, and decreased sharply after the test beam crack. The load-flexural stiffness curves of the scaled beams still satisfied the bifold variation law. After cracking, the slope of the second stage increased as the mass loss increased; that is, the flexural stiffness decreased more and more sharply. This implies that strand corrosion has a significant effect on flexural stiffness after cracking.
# Routing Protocol for Heterogeneous Wireless Sensor Networks Based on a Modified Grey Wolf Optimizer
## 4 Proposed Algorithm
### Energy Consumption Model  
In this paper, the energy consumption model is similar to that used in [51]. In WSNs, nodes are randomly deployed, and the locations of the nodes are not pre-designed. Most of the energy of a node is dissipated due to communication between nodes, depending on the distance between the nodes. Both data transmission and reception consume energy. Therefore, to transmit an \((m-bit)\)-long data packet over the distance \(d\), the required energy is  
\[E_{TX}(m,d)=\left\{\begin{array}{ll}m\times E_{elce}+m\times\varepsilon_{fs} \times d^{2},\;if\;d\leq d_{\textit{crossover}}\\ m\times E_{elce}+m\times\varepsilon_{mp}\times d^{4},\;if\;d>d_{\textit{crossover}} \end{array}\right., \tag{6}\]  
where \(E_{TX}\) is the energy consumed when the node transmits data, \(E_{elce}\) is the energy dissipation of the process of transmitting 1 bit of data and the process of receiving 1 bit of data, \(\varepsilon_{fs}\) is the coefficient of energy dissipation in the free-space model, \(\varepsilon_{mp}\) is the coefficient of energy dissipation in the multi-path attenuation model, and \(d_{crossover}\) is the threshold of the transmission distance, which is calculated as  
\[d_{crossover}=\sqrt{\varepsilon_{fs}/\varepsilon_{mp}}. \tag{7}\]  
However, the energy consumption required by the receiving node to receive an \(m\) bit data packet is calculated as follows  
\[E_{RX}(m)=m\times E_{elac}. \tag{8}\]  
The energy consumption of CH can be calculated by the above model. The energy consumption of CH mainly includes three aspects: the energy consumption of receiving data packets of member nodes, fusing data and transmitting fusing data to the base station. The calculation formula is as follows:  
\[E_{CH}=E_{RX}(m)\times CM_{num}+m\times E_{DA}\times(CM_{num}+1)+E_{TX}(m,d) \tag{9}\]  
where \(CM_{num}\) is the number of member nodes in the cluster and \(E_{DA}\) is 1 bit of data aggregation energy cost; \(m\) is the packet length.  
The energy consumption of the non-CH node is only the energy consumption of sending data to the CH, and its mathematical expression is as follows:  
\[E_{non-CH}=E_{TX}(m,d) \tag{10}\]  
The total residual energy in the \(r^{th}\) round is calculated as follows:  
\[E_{totR}(\mathrm{r})=E_{totR}(\mathrm{r}-1)-\left(\sum\
olimits_{i=1}^{CH_{ num}(r)}E_{CH}(i)+\sum\
olimits_{j=1}^{N_{alive}(\mathrm{r})-CH_{num}(\mathrm{r})}E_{ non-CH}(j)\right) \tag{11}\]  
where \(E_{totR}(\mathrm{r}-1)\) is the total residual energy in the \((r-1)^{th}\) round, \(CH_{num}(\mathrm{r})\) represents the number of CHs in the \(r^{th}\) round, \(N_{alive}(\mathrm{r})\) represents the number of alive nodes in the network in the \(r^{th}\) round, \(E_{CH}(i)\) is the energy consumed by the i\({}^{th}\) CH, and \(E_{non-CH}(j)\) is the energy consumed by the j\({}^{th}\) non-CH.
## 3 Results and Discussion
### SEM Analyses  
SEM produced two-dimensional data, which allowed visual identification of metal slag particle surfaces without sputter coating. According to the results of SEM-EDS analysis, the slag from mine tailings mainly contained SiO2 (22.2%), oxides (46%), and carbon (17.8%), in addition to Zn (9.08%) and Pb (4.22%), as shown in Figure 4c. Figure 4a,b show SEM images at 400\(\times\) and 3000\(\times\) magnification, respectively.  
* MPC, maximum permissible concentration.  
\end{table}
Table 4: HM concentration of soil samples from mine tailing slag at different depths (5 and 15 cm).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **HM** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Certified Values (mg/L)** \\ \end{tabular} } & \begin{tabular}{c} **Measured** \\ **Values (mg/L)** \\ \end{tabular} & \
\begin{tabular}{c} **Recovery (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Mean Recovery** \\ \(\pm\) **SD (\%)** \\ \end{tabular} \\ \hline Pb & 5.0 & 4.98 & 99.6 & 99.6 \(\pm\) 0.15 \\  & 10.0 & 9.97 & 99.7 & 99.6 \(\pm\) 0.15 \\ Zn & 5.0 & 4.95 & 99.0 & 99.4 \(\pm\) 1.51 \\  & 10.0 & 9.98 & 99.8 & \\ Cd & 5.0 & 5.01 & 100.0 & 100.5 \(\pm\) 0.57 \\ Cd & 10.0 & 10.05 & 101.0 & 100.5 \(\pm\) 0.57 \\ \hline \hline \end{tabular}  
* SD, standard deviation.  
\end{table}
Table 3: Reference standard certified and measured values and the recovery.  
An atomic absorption spectrometer was used to estimate the distribution of heavy metal concentration at each site. The average Pb concentration of the soil samples from the three experimental (Fields 1, 2, and 3) and control sites were 4.94 \(\pm\) 0.07, 2.72 \(\pm\) 0.03, 5.34 \(\pm\) 0.06, and 2.20 \(\pm\) 0.03 mg/kg, respectively (Table 5). These data showed that Pb concentrations were different at the three sites and varied by as much as 51%. The mean Zn concentrations at the sites were 3.87 \(\pm\) 0.02, 24.63 \(\pm\) 0.02, 109.69 \(\pm\) 0.03, and 3.70 \(\pm\) 0.03 mg/kg, respectively. Zn concentrations were very different at the three sites. In addition, the Cd concentrations were 4.04 \(\pm\) 0.01, 5.97 \(\pm\) 0.04, 14.56 \(\pm\) 0.04, and 3.18 \(\pm\) 0.01 mg/kg, respectively (Table 5).
###### Abstract  
The integration of Software Defined Networking (SDN) technologies in Passive Optical Networks (PONs) would provide great advantages to Internet Service Providers (ISPs) and Network Operators, since they can optimize the network operation and reduce its complexity. However, some tasks regarding online service and network configuration strategies are difficult to move to external SDN-controllers since they are time-critical operations. However, the control of some of these policies by SDN techniques could lead to better network and management configuration in a centralized and automatic way. As a consequence, we propose and experimentally test the integration of an OpenFlow approach over legacy Gigabit Passive Optical Networks (GPONs), which allows moving some global service configuration policies to an external SDN controller implementing an SDN management layer that adjust these strategies according to dynamic Quality of Service (QoS) requirements of services in residential users. The viability and efficiency of our approach are demonstrated using a GPON testbed and proposing a new business scenario for ISPs and Network Operators.
# SoK: Delegated Security in the Internet of Things
## 2 Application Domains of Security Delegation
### Computation Delegation  
In this application domain, the delegation of computationally intensive cryptographic or computational operations is addressed. Sixty-three papers from our literature review are classified under this domain (see Table 1).  
Here, the RC system queries the delegate to perform necessary mathematical computations or cryptographic operations such as key generation, encryption or filtering. After finishing the requested operations, the delegate returns the results to the RC system (see Figure 2d).  
Example  
In [15], a lightweight revocable hierarchical attribute-based encryption (LW-RHABE) scheme is presented that combines efficient encryption/decryption, flexible key delegation, and revocation.  
The solution consists of five main components (see Figure 6). Central and Domain Authorities (CA and DA, respectively) do not participate in delegation directly but generate  
Figure 5: Blockchain-based delegation process of [23]. DSC is a delegation smart contract.  
system parameters and user secret keys and maintain revocation lists. A Cloud Service Provider (CSP) serves as a delegate that provides storage and computational resources. Data owners and users form the RC system: while data owners are smart IoT devices that collect and encrypt data, users consume data. The workflow of the proposed LW-RHABE system has several steps. First, the CA generates the system's public parameters (a master secret key) and initialises several DAs. Then, the DAs generate secret keys for the users based on their attributes and secret/public keys for data owners. In the next step, data owners create an access control policy, encrypt collected data, and outsource it to the CSP. When the data users want to obtain some data collected by the data owners, they request it and the decryption from the CSP. According to the authors, LW-RHABE allows flexible key delegation and user revocation as well as lightweight encryption and decryption. Based on the results of the performance analysis, the described scheme is significantly more efficient than approaches without delegation.
# DFD-SLAM: Visual SLAM with Deep Features in Dynamic Environment
## 2 Related Works
### Dynamic SLAM  
Feature-based SLAM systems encounter challenges in dynamic environments due to the interference of dynamic object feature points. The mainstream strategy involves identifying and removing these dynamic feature points. Early methods used geometric approaches, leveraging dense optical flow and motion models to identify dynamic objects. For example, SUN proposed online motion removal [25], and [PERSON] introduced a method using Delaunay triangulation and frame comparison to identify dynamic points [26]. Although these methods operate in real-time, they do not offer the robustness and accuracy characteristic of semantic approaches.  
Advancements in deep learning have propelled the development of dynamic SLAM. DynaSLAM utilizes Mask R-CNN for instance segmentation to remove feature points associated with dynamic objects, along with background restoration [27]. However, DynaSLAM's reliance on PyTorch for model inference results in suboptimal real-time performance due to frame-by-frame instance segmentation. Moreover, DynaSLAM indiscriminately removes all predefined dynamic objects, lacking a precise mechanism for judgment. To improve real-time performance, RDS-SLAM segments keyframes and employs Bayesian probability propagation, enhancing real-time capability but potentially compromising removal accuracy [28]. For more precise removal of dynamic points, DS-SLAM combines semantic and geometric information, achieving accurate removal through optical flow tracking and epipolar line computation [29].  
In recent approaches, many systems incorporate acceleration techniques for model inference to obtain semantic information, meeting real-time requirements. SG-SLAM employs NCNN to accelerate semantic information model inference, blending geometric data with distinct threshold decisions for varied semantic areas to facilitate dynamic feature point removal [30]. However, this geometry-focused approach could result in diminished robustness, causing tracking losses across multiple RGBD-TUM sequences. CDS-SLAM employs TensorRT for accelerated inference and designs specific strategies for indoor human detection to achieve more accurate removal [9]. This approach aids in addressing the challenges of Crowd-SLAM, characterized by excessive removal through object detection, leading to the loss of tracking. Despite these advancements, precise removal of dynamic feature points remains a significant challenge.  
In fact, excessive removal can lead to performance decline, and when there are too many dynamic objects in an image, most feature points may be removed, leading to tracking loss and affecting robustness. Our method further detects each dynamic object and more precisely removes the moving parts on dynamic objects. Moreover, our system is a dynamic SLAM system based on deep features, and we also demonstrate that the application of deep features can enhance the tracking performance of dynamic SLAM.