esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Simulation of Dust Storm on 21 February 2015  
#### 3.4.1 Spatial Pattern of Vertical Dust Flux Using AFWA Wind Erosion Scheme  
The results of the simulated vertical dust flux of 21 February 2015 are shown in Figure 10.  
Figure 9: Average Storm Dust flux (\(\upmu\)g/m\({}^{2}\)s) pattern on 14 February 2018 with GOCART Scheme.  
Figure 10: Spatial pattern of dust flux (\(\mu\)g/m\({}^{2}\)s) with 3 h interval by AFWA scheme.  
Results show that the main dust source of this storm is the Arabian Desert; focusing on the internal sources, it appears that the Loot Desert, Jazmoorian and Daranjir desert have the highest potential to generate dust in the central Iranian plateau (Figure 11).  
Hence, the AFWA Scheme identified 4 sources of dust in the Loot Desert, Jazmoorian, Hirmand and Daranjir Desert. The VDF simulated by this scheme in the Loot Desert reaches 960 \(\upmu\)g/(m\({}^{2}\)s) (Figure 11).
## 3 Results
### The Factors Influenced the Slopes of SIF and GPP Light Response Curves  
Section 3.1 illustrated that the slopes of SIF light response curves were less influenced by EF and Ta than that of GPP, and were always related to the slopes of GPP light response  
Figure 5: The responses of half-hourly SIF (**a**) and GPP (**b**) to Ta under different PAR bins.  
Figure 6: The responses of SIF (**a**) and GPP (**b**) to SW in different EF bins.  
Figure 7: The responses of SIF (**a**) and GPP (**b**) to SW in different Ta bins.  
curves which were more sensitive to EF and Ta. Therefore, to explore whether the dynamics of the slopes of SIF light response curves were regulated by the environmental variables (EF and Ta) or induced by the changes of GPP light response curves related to EF and Ta, we calculated the importance of GPP/PAR(SW) ratio (slopes of GPP light response curves), EF and Ta in SIF/PAR(SW) ratio (slopes of SIF light response curves) for both TROPOSIF and site-observed data at DM site based on RF. The importance of SIF/PAR(SW) ratio, Ta and EF in GPP/PAR(SW) ratio were also calculated. The calculation of the importance (Figures 8 and 9) illustrated whether the dynamics in slopes of SIF light response curves were induced by GPP/PAR(SW) ratio related to EF and Ta or directly by EF and Ta, or which controlled it more. Figure 8 showed that at DM site, the GPP/PAR ratio had the highest importance in SIF/PAR at both half-hourly (43.84%) and daily (72.5%) scales, and the importance of Ta and EF were lower than that of GPP/PAR ratio (half-hourly: 30.25%, 25.91%; daily: 16.76%, 10.75%). While the environmental variables (EF and Ta) had the higher importance in GPP/PAR ratio at half-hourly scale which demonstrated that the slopes of GPP light response curves were more influenced by the environmental parameters. For the daily scale at DM site, though the importance of SIF/PAR ratio was higher than EF and Ta, the importance of Ta and EF was higher than that in SIF/PAR ratio. For the daily TROPOSIF and GPP, the results of importance calculation were similar to that of the DM site (Figure 9). The importance of GPP/SW ratio was higher than that of Ta and EF in SIF/SW. For the GPP/SW ratio, the EF had the most importance, followed by Ta, the SIF/SW ratio had the lowest importance.
## 5 FSK Modulation and Demodulation  
FSK is one of earliest and most suitable modulation techniques for digital modulation. The principle of FSK modulation is to send binary data with two different frequencies and the resultant modulated signal is regarded as amplitude modulation (AM) of different carrier frequencies. There are two types of FSK modulation: _i.e._, non-coherent or discontinuous and coherent FSK modulation [55]. In the non-coherent types of FSK modulation different frequencies is represented by binary 1 and 0 as follows:  
\[\begin{split} S_{1}(t)&=A\;\cos(2\pi{f_{1}}t+\phi_{ 1})\;,kT\leq t\leq(k+1)T,for\;1\\ S_{2}(t)&=A\;\cos(2\pi{f_{2}}t+\phi_{2})\;,kT\leq t \leq(k+1)T,for\;0\end{split} \tag{6}\]where \(\phi_{1}\) and \(\phi_{2}\) are initial phases at \(t=0\) and they are not the same in general. \(T\) is the bit period of the binary data. This type can be generated by switching the modulator output line between two different oscillators. However, in the coherent types of FSK modulation, the two coherent signals have the same initial phase \(\phi\) at \(t=0\) as follows:  
\[\begin{split} S_{1}(t)&=A\cos(2\pi f_{1}t+\phi)\,,kT \leq t\leq(k+1)T,for\ 1\\ S_{2}(t)&=A\cos(2\pi f_{2}t+\phi)\,,kT\leq t\leq(k+1 )T,for\ 0\end{split} \tag{7}\]  
In this section, a detailed summary of FSK modulation in terms of frequency, data rate, power, sizes and applications of various telemetric wireless and implanted devices is listed in Table 10.
## 2 Materials and Methods
### Features Generation in Time-Frequency Domain  
For the time-frequency domain, the _MeanEnvelopeEnergy_ feature is used to analyze tool wear, capturing the mean energy of the upper and lower envelopes for each intrinsic mode function (IMF) [27; 28; 29; 30]. The generation involves computing the mean envelope energy of the IMFs derived from the input time-domain signals, for which the empirical mode decomposition (EMD) [31] method was applied. The final result was calculated by averaging the energy of the upper and lower envelopes for each IMF. By doing this, the signal's energy distribution is encapsulated in it. The resulting feature provides a compact yet informative representation of the signal's energy characteristics, pivotal for assessing tool wear in the milling processes. Each of the 11 original signals produced a 10-element vector, and a total of 110 potential features (_MeanEnvelopeEnergy_).  
Mathematically, the _MeanEnvelopeEnergy_ feature can be defined as follows:  
\[MeanEnvelopeEnergy=\frac{1}{N}\sum_{n=1}^{N}\left(\frac{1}{M_{n}}\sum_{m=1}^{M_{n }}\sqrt{U_{m}^{n}(t)^{2}+L_{m}^{n}(t)^{2}}\right) \tag{19}\]  
where:  
* \(N\) is the total number of IMFs considered for each signal.
* \(M_{n}\) represents the number of discrete time points in the \(n\)-th IMF.
* \(U_{m}^{m}(t)\) and \(L_{m}^{n}(t)\) denote the upper and lower envelope values at time \(t\) for the \(m\)-th time point in the \(n\)-th IMF, respectively.
## 3 Results
### Countermovement Jump Performance
The CMJ group presented a trivial change (0.3%; d = 0.04) and a 20% responder rate (2 out of 10), whereas the CON group showed a small decrease (\(-\)4.3%; d = \(-\)0.41) and no responders. For peak power, the AEL group improved by 6.7% (d = 0.31, small effect) with all participants classified as responders (90%). The CMJ group exhibited a trivial increase (1.0%; d = 0.08) and no responders, while the CON group showed a small decrease (\(-\)6.4%; d = \(-\)0.53) with zero responders. Finally, in relative peak power, the AEL group demonstrated a moderate improvement (6.7%; d = 0.64) with an 80% responder rate (8 out of 9). The CMJ group showed a trivial increase (1.0%; d = 0.05) with only one responder, whereas the CON group experienced a small decrease (\(-\)6.4%; d = \(-\)0.47) and no responders. These findings highlight the superior responsiveness of the AEL group across all CMJ performance variables, while the CMJ and CON groups showed minimal or no meaningful individual adaptations. Figure 5: Effects of a five-week AEL training program on countermovement jump height, propulsive impulse, peak power, and relative peak power in the AEL, CMJ, and CON groups. Bars represent mean \(\pm\) standard deviation. AEL: accentuated eccentric loading; CMJ: countermovement jump; CON: control; N: newton; W: watt; kg: kilogram. *: \(p\) = 0.01; **: \(p\) = 0.03; ***: \(p\) = 0.04 ***: \(p\) = \(<\)0.001. Comparisons between Rs and NRs subject groups indicated significant differences (\(p\) = 0.01) between the AEL and the CMJ group and between the AEL and the CON group (\(p\) = 0.001) for jump height, while no significant differences were reported between the CMJ and the CON group (\(p\) = 0.21). For propulsive impulse, significant differences were found between the AEL and the CMJ group (\(p\) = 0.05) and for the AEL vs. the CON group (\(p\) = 0.001), while for the CMJ vs. the CON group (\(p\) = 0.47), no significant differences were found. For peak power, significant differences were found between the AEL group and the CMJ group (\(p\) = 0.001) and between the AEL group and the CON group (\(p\) = 0.01), while no significant differences were found between the CMJ group and the CON group (\(p\) > 0.99). Finally, for relative peak power, significant differences were found between the AEL group and the CMJ group (\(p\) = 0.005) and between the AEL group and the CON group (\(p\) = 0.07), while no significant differences were found between the CMJ group and the CON group (\(p\) > 0.99).
# Joint Content Recommendation and Delivery in Mobile Wireless Networks with Outage Management
## 2 System Model  
### Interactive Content Retrieval Protocol  
In this paper, we propose a novel interactive content retrieval protocol for personalized multi-user content retrieval in mobile communication networks. As illustrated in Figure 1, the proposed protocol includes the following four steps: (1) The BS monitors the channel state information (CSI) of each active user and sends the CSI information to the content server; (2) The content server continuously runs a recommender algorithm and maintains a user interest matrix \(\mathbf{R}\) (users are arranged in a column and contents in a row). Each element of \(\mathbf{R}\) is a real number indicating a user's interest in a piece of content. Based on \(\mathbf{R}\) and the CSI information feedback, the server generates a list of recommended content for each user and sends the lists to users. The list is a small file that includes the title or short abstract of the contents; (3) Each user browses the recommended list and chooses interesting files from the list to download and view; (4) The chosen files are transmitted to users via a shared wireless channel. The basic idea of our protocol is to jointly consider user interest, channel condition and content file size in the content retrieval process, so that only files that are likely to be delivered in time will be recommended to users.  
The proposed protocol is different from existing protocols. Compared with the \"push-type\" proactive caching protocols (e.g., [8; 9]), our protocol will not start transmitting a file before an actual user request occurs. Therefore, it is a \"pull-type\" protocol and does not have the inherent drawbacks of proactive caching. Compared with conventional \"pull-type\" protocols (e.g., the hypertext transport  
Figure 1: Illustration of the proactive caching protocol, the conventional pull-type protocol and the proposed content retrieval protocol.  
protocol), our protocol is more advance in that it jointly considers the physical layer constraint (i.e., channel information) and application layer semantics (e.g., user interests) to manage the overall system performance. In this way, our protocol can not only give users better experiences in content retrieval, but also avoid network congestion and ensure better coexistence with other applications. The typical signaling procedures of the push-type protocol, conventional pull-type protocol and the proposed protocol are illustrated in Figure 1a-c, respectively. Moreover, the advantages and disadvantages of these three types of protocols are summarized in Table 1.
# Rate of Penetration Estimation with Parameter Correction
## 3 ROP Calculation  
The measured depth is an important quantity in drilling operations, as it represents the total depth along the wellbore from the surface to the drill bit. In addition, the true bit penetration (\(\Delta z_{\rm bit}(t)\)), dynamic elongation, and static elongation of the drill string also play crucial roles in determining the actual progress of the drill bit into the formation. The relationships among these quantities are given below.  
\[\mathrm{MD}(t)=\mathrm{MD}_{initial}+\Delta z_{\rm bit}(t), \tag{26}\]  
where \(\mathrm{MD}_{initial}\) refers to the initial measured depth at a specific reference point in time during operations. The ROP in drilling operations is determined by measuring the velocityat which a drill bit progresses through the rock or formation being drilled. It is commonly measured in units of depth per unit of time, or  
\[\text{ROP}(t)=\frac{d}{dt}(MD(t)). \tag{27}\]  
In fact, the calculation of the ROP shall consider the time derivative of the true bit penetration (\(\Delta z_{\text{bit}}(t)\)) as  
\[\text{ROP}(t)=\frac{d}{dt}(\Delta z_{\text{bit}}(t)). \tag{28}\]  
From Equation (25), it is easy to have  
\[\Delta z_{bit}(t)=MD_{measured}+\Delta L(t)-MD_{initial}\]  
Rearranging the equation, we have  
\[\text{ROP}(t)=\frac{d}{dt}(\text{MD}_{measured}(t))+\frac{d}{dt}(\Delta L_{ static}(t))+\frac{d}{dt}\Big{(}\Delta L_{dynamic}(t)\Big{)}. \tag{29}\]  
The depth interval \(\frac{d}{dt}(\text{MD}_{measured}(t))\) can be affected by sensor noise, which introduces random fluctuations in the readings and other errors such as inaccuracies due to drill string elongation or calibration drift. These factors can further cause deviations in the measured depth from the true depth, leading to potential errors in calculating the actual drilling progress. In the next section, the model-based filter is implemented to enhance the ROP calculation with consideration of sensor errors and elongation factors. While \(\Delta L_{static}(t)\) may change gradually over time due to evolving downhole conditions, its temporal variation is significantly slower than that of \(\Delta L_{dynamic}\). Hence, the derivative \(\frac{d}{dt}(\Delta L_{static}(t))\) possibly can be neglected in Equation (29) to simplify the model without sacrificing estimation accuracy. If needed, \(\Delta L_{static}(t)\) can be periodically updated in a slower outer loop.
# Many-Objective Optimization for Anomaly Detection on Multi-Layer Complex Interaction Networks
## 3 Method
### Assessing Anomalous Nodes  
For the final step in identifying a set of nodes that have no connection or have very low centrality in a high density layer, it is necessary to consider the density of the layer and to take the less important nodes, which are far from the mean in that layer, into account. In the following, we describe a special score to be applied, which we propose for indicating a node as an anomaly, the so-called ACE score (Anomaly candidate based on Centrality Evaluation).  
The ACE score considers the mean and standard deviation of the centrality of all nodes in a specific layer (of the multiplex network), e.g., one which has very high edge density compared to other layers. Using the ACE score for the set of nodes on the Pareto Front, we chose those with the highest ACE score or those that are very close to this score and are far from the mean \(\mu\) in each layer. Then, these nodes were indicated as anomalous _candidate nodes_. These candidates can then be further assessed and inspected in the described human-centered approach. More formally, the ACE score for node \(v\) in layer \(l\) is defined as follows:  
\[ACE(v,l)=\left(\frac{\mu_{l}-x_{vl}}{\sigma_{l}}\right)D_{l}\,,\]  
where \(\mu_{l}\) is the mean nodal centrality in layer \(l\), \(x_{vl}\) is the centrality of each node/vertex \(v\) in layer \(l\), \(\sigma_{l}\) is the standard deviation, and \(D_{l}\) denotes the edge density of layer \(l\).
# Caching Placement Optimization Strategy Based on Comprehensive Utility in Edge Computing
## 2 Related Work
### The Comprehensive-Utility-Based Caching Placement Model  
To optimize the overall performance of data block placement, the placement decision is made by considering the retrieval cost, placement cost, and replacement cost. The placement problem is formulated as:  
\[\begin{array}{l}\max\ \sum\limits_{i=1}^{n}\sum\limits_{j=1}^{m}x_{i,j}* \left(Value_{i}^{j}-C_{i}^{j}-SC_{i}^{j}-Pepc_{i}^{j}\right)\\ s.t.\left\{\begin{array}{l}C1:\ x_{i,j}\in\left\{\left.0,1\right\},\forall i \in\left[1,n\right],j\in\left[1,m\right]\\ \forall j\in\left[1,m\right]\cup\left\{\left.m+1\right\}-\left\{j\right\} \right.\\ C2:\ 0\leq\sum\limits_{j=1}^{m}x_{i,j}\leq m\\ C3:\ 0\leq\sum\limits_{i=1}^{n}x_{i,j}\leq n\end{array}\right.\end{array} \tag{22}\]  
In Equation (22), constraint (\(C1\)) indicates whether a data block \(i\) is stored on a specific edge server \(j\), where \(m+1\) represents the cloud data center. Constraint (\(C2\)) allows the data block to be stored on multiple servers. Constraint (\(C3\)) indicates that all data blocks can be stored on an edge server, depending on the available storage space. Thus, the task of determining the optimal placement of data blocks can be expressed as a 0-1 multiple-choice knapsack problem, which is known to be NP-hard. At present, the traditional methods for solving the knapsack problem are the dynamic programming method and the branch-and-bound method. But these methods do not apply to the large-scale knapsack problem, and the main methods for solving the large-scale knapsack problem include the particle swarm optimization algorithm and the tabu search algorithm. Since the tabu search algorithm is efficient for solving combinatorial optimization problems, although it can easily fall into local optimality, this paper uses an improved tabu search algorithm to solve the cache placement problem and obtain an approximate optimal solution.
# Novel Internet of Things Platform for In-Building Power Quality Submetering
## 2 IoT Power Quality Sensor
### Sensor Signal Conditioning
#### 2.2.2 Current Sensor  
The current conditioning circuit has been implemented following the manufacturer recommendations [41] and is shown in Figure 4. The active phase wire passes through a current transformer (CT) with a transform ratio of 1:1000. The current seen in the secondary of the transformer depends on the load resistor (R\({}_{\text{L}}\)) used to measure a proportional voltage to the current of this secondary circuit. Thus, a suitable design of this resistor is determined to have an appropriated current range on the measured circuit. The input current range is limited due to functional limitations of the prototype. The CT bandwidth is 50 Hz so that the measured waveform energy in frequencies beyond the fundamental component will be attenuated.  
The final implemented design allows current measurements in a single phase from 0 to 5.8926 A\({}_{\text{RMS}}\). The resistor-capacitor circuit in each node of the differential analog input is used as an antialiasing filter, filtering frequencies above the sampling frequency at the ADC input.
# NOx Formation Mechanism and Emission Prediction in Turbulent Combustion: A Review
## 6 Turbulent Combustion Simulation Approach  
The computational fluid dynamics (CFD) method is a mathematical calculation method that makes the approximate-solution approach the real value by solving discrete functions in the domain. Because it can solve the Navier-Stokes equation effectively, it is widely applied in the calculation of hydrodynamics and thermal dynamics to predict flow patterns and temperature, which has obtained excellent achievements.  
In conventional CFD calculations, the chemical reaction of fuel combustion is usually considered as a source item, and the subsequent flow process is used to diffuse the reaction product. This approximation has been successful in predicting hydrocarbon combustion. This is based on the fact that the decomposition of hydrocarbon fuels could be completed in an extremely short time. However, in response to the rapid decomposition of hydrocarbon fuels, the formation of NOx is as long as the order of seconds, and the formation is accompanied by the flow of the entire flue gas in the combustion chamber. The chemical reaction rate of NOx should be calculated with the whole flow process. Therefore, it is difficult to calculate the NOx formation using traditional calculation methods. Several different calculation methods were proposed to solve this problem, such as the direct numerical simulation method (DNS), probability density function method (PDF), and flame-let model (FLM). These methods of CFD are applied in the field of NOx generation in turbine combustors due to their convenience [60].
# Cooperative Position Aware Mobility Pattern of AUVs for Avoiding Void Zones in Underwater WSNs
## 3 Proposed Schemes  
In this section, we present PAMP (position-aware mobility pattern) and Co PAMP (cooperative PAMP) for UWSNs. PAMP is proposed to minimize energy consumption and avoid void zone occurrence. Similarly, Co PAMP is presented to enhance the network throughput and to avoid void hole creation using cooperative routing. Details of both schemes are given in this section.
# A Fully Automatic DEXP Method for Gravity Data and Its Application on a Potash Salt Deposit
## 2 Methodology  
In this section, it will begin by introducing the theoretical background of the automatic DEXP method [25]. Subsequently, we will present the comprehensive details of our proposed improved DEXP method proposed in this paper. This enhanced approach offers two significant advantages: firstly, it effectively solves the issue of singularity or trapezoid lines encountered in the traditional DEXP method, and secondly, it successfully mitigates the effect of undulating terrain. The core of the improved DEXP method is based on two key techniques, the adding constant method and the hypothetical observed surface method. The details are as follows:
# Design of a Rotary Transformer for Installations on Large Shafts
## 2 Rotary Transformer: Model and Parameters
### Compensation Strategy
As a matter of fact, the imaginary part to be zeroed can be expressed as follows:  
\[\text{Im}\{\hat{Z}_{\text{in,2C}}\} =\frac{\omega^{2}C_{1}\Bigg{\{}L_{1}\Bigg{[}(R_{2}+R_{\text{L}})^ {2}+\bigg{(}\omega L_{2}-\frac{1}{\omega C_{2}}\bigg{)}^{2}\Bigg{]}-\omega M^{ 2}\bigg{(}\omega L_{2}-\frac{1}{\omega C_{2}}\bigg{)}\Bigg{\}}}{\omega C_{1} \Bigg{[}(R_{2}+R_{\text{L}})^{2}+\bigg{(}\omega L_{2}-\frac{1}{\omega C_{2}} \bigg{)}^{2}\Bigg{]}}+\] \[-\frac{(R_{2}+R_{\text{L}})^{2}+\bigg{(}\omega L_{2}-\frac{1}{ \omega C_{2}}\bigg{)}^{2}}{\omega C_{1}\Bigg{[}(R_{2}+R_{\text{L}})^{2}+ \bigg{(}\omega L_{2}-\frac{1}{\omega C_{2}}\bigg{)}^{2}\Bigg{]}} \tag{13}\]  
A smart way to solve this critical point is to focus back on Equation (10); it can be seen that \(C_{1}\) and \(C_{2}\) can be tuned according to \(L_{1}\) and \(L_{2}\), respectively, that is:  
\[C_{1}=\frac{1}{\omega^{2}L_{1}} \tag{14}\] \[C_{2}=\frac{1}{\omega^{2}L_{2}} \tag{15}\]  
Figure 3:0 Rotary transformer with series compensation of \(X_{\text{in}}\): (**a**) single-capacitor compensation and (**b**) dual-capacitor compensation. allowing us to obtain a purely real input impedance equal to:  
Figure 3:1  
This choice allows for an easy tuning of the capacitors, which are less subjected to variations of transformer parameters due to aging, temperature drifts and peculiar working conditions with respect to the other single- or double-capacitor options. Moreover, each capacitor is bouncing energy with the winding on the same side only. Otherwise, the compensation strategy foresees the flow of some energy between \(C_{1}\) and \(L_{2}\) or between \(C_{2}\) and \(L_{1}\). On the other hand, it must be observed that compensation with two capacitors does not necessarily imply that the active power increases with respect to the other cases with no capacitor or one capacitor only. It is recommended to study the input impedance in an accurate way before choosing any compensation strategy.