esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Magnetoelectric Effects on Coaxial Fibers of Ferrites and Ferroelectrics
## 4 Spinel Ferrite-Ferroelectric Coaxial Fibers  
Studies on fibers with spinel ferrites mainly focus on cobalt ferrite and pure and Zn-substituted nickel ferrite [37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59]. Such spinel ferrite-ferroelectric systems are preferred, since past studies on bulk composites with spinel ferrites and BTO or PZT indicate the absence of any impurities when both phases are co-sintered at high temperatures [4]. Fibers with hexagonal ferrites and PZT or BTO are considered in Section 5.
# Evaluation of NOx Reduction Effect and Impact on Asphalt
## 2 Materials and Test Methods
### Photo Reactors Test  
To evaluate the NOx removal performance according to the type of reactor, photoreactors were designed, as shown in Figure 4. Two evaluation systems were established based on this design. The bed flow photoreactor type tester complying with ISO 22197-1 (2007) is shown in Figure 5. The evaluation system manufactured using the mixed tank photoreactor method is shown in Figure 6. A calibrator was installed to control the concentration of NOx flowing into the reactor, and a hydraulic system and valve device were configured to control the NOx inflow. To check the inlet NOx concentration, the T-connection of the photoreactor was connected to the NOx analyzer. A gas mixture of NOx and zero air entered and filled the photoreactor at a controlled humidity, flow, and NOx concentration. Before entering the photoreactor, the inlet jet stream continued through the humidifier to control humidity. The size of the bed flow photoreactor was 100 \(\times\) 50 \(\times\) 10 mm, whereas the size of the photoreactor of the mixed tank was 500 \(\times\) 500 \(\times\) 500 mm. Both methods were hermetically sealed to prevent the inflow of outside air and to maintain a controlled environment for the sample. A UV lamp was installed on the upper part of the photoreactor  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties** & **Results** & **Test Method** \\ \hline Penetration (1/10 mm) & 71 & ASTM D 5 \\ Density (g/cm\({}^{3}\)) & 1.036 & ASTM D 70 \\ Flash point (\({}^{\circ}\)C) & 338 & ASTM D 95 \\ Softening point (\({}^{\circ}\)C) & 44 & ASTM D 158 \\ Ductility (15 \({}^{\circ}\)C) & 150+ & ASTM D 113 \\ Solubility in trichloroylene (\%) & 99.78 & ASTM D 2042 \\ Mass change after thin-film oven test (\%) & \(-\)0.02 & ASTM D 2872 \\ Retained penetration after thin-film oven test (\%) & 69.0 & ASTM D 2872 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specifications of the asphalt binder.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Nominal Maximum** & **Density** & **Absorption** & **Abrasion** & **Flat or Elongated** \\
**Aggregate Size (mm)** & **(g/cm\({}^{3}\))** & **(\%)** & **(\%)** & **Particle Ratio (wt.\%)** \\ \hline
20 & 2.72 & 0.52 & 13.8 & 7.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Properties of the aggregate.  
to simulate ultraviolet light for the photoactivation reaction. The experiment was carried out to measure the amount of NOx reduction by installing a test specimen prepared in each reactor and setting the concentration of NOx input into the reactor to 1.00 ppm. In the state where NOx continuously flowed through the reactor, the light source of the UV lamp was irradiated at 10 W/m\({}^{2}\) for more than 5 h, and the change in NOx concentration according to the light source was measured in units of 1 min. All tests were performed at a temperature of 25 \(\pm\) 2 \({}^{\circ}\)C and a humidity of 45 \(\pm\) 5%.  
Figure 3: Images of the surface treated specimens.  
Figure 2: Coating mechanism of TiO\({}_{2}\)
## 4 Results and Analysis
### Transmission Delay  
The data transmission delay of a node is calculated when a node has data to transmit until it successfully transmits its data in its allocated time slot. If a data-requesting node is not entertained in the current session then it will resend the GTS request again in the next session. However, its data transmission time will be calculated from the time when it originates its first GTS request to the fog node. The data transmission time in a session is calculated for only those nodes that are successfully allocated GTSs in the current communication session. The results are observed for varying numbers of GTS-requesting nodes, for varying channel bandwidth, and varying transmission power, as shown in Figures 11, 12 and 13, respectively.  
Results in Figure 11 show the accumulated transmission time of all GTS-allocating nodes in a session for varying numbers of nodes. The results show that the nodes' transmission time in TMPAD is longer than the other three schemes. This is because the TMPAD prioritizes GTS-requesting nodes that could not transmit their data in the previous session/s, and the longer time a node has to wait, the greater will be its priority towards the GTS allocation. This results in increased data transmission time of the nodes during that session. However, all other algorithms decide to scrutinize the GTS-requesting nodes by considering their current session request. The results show that in \(TMPAD\), data transmission time is more than the other three standards. The same trend is observed in Figure 12 when 100 nodes are sending GTS requests to a fog computing node in a session with varying channel bandwidth. With an increase in channel bandwidth, nodes can transmit more data in a slot, resulting in less requirement of GTSs in transmitting the same amount of data. This allows more nodes to be allowed to transmit their data in the allocated time slots in a session, prioritizing the waiting nodes and, consequently, increasing the accumulated transmission time of nodes.  
Results in Figure 13 are observed for 100 GTS-requesting nodes with varying transmission power. The results show that with the increase in transmission power, more nodes can transmit their data to the fog node in fewer time slots. This allows more GTS-requesting nodes to transmit their data in a session, resulting in more accumulating transmission time for all successfully allocated GTS nodes.  
Figure 10: Number of GTS-allocating nodes for varying transmission power of GTS-requesting nodes.  
Figure 11: Transmission time of legitimate nodes for varying GTS-requesting nodes.  
Figure 12: Transmission time of legitimate nodes for varying channel bandwidth.
# Accurate Tracking Algorithm for Cluster Targets in Multispectral Infrared Images
###### Abstract  
To address the issue of poor tracking accuracy and the low recognition rate for multiple small targets in infrared images caused by uneven image intensity, this paper proposes an accurate tracking algorithm based on optical flow estimation. The algorithm consists of several steps. Firstly, an infrared image subspace model is established. Secondly, a full convolutional network (FCN) is utilized for local double-threshold segmentation of the target image. Furthermore, a target observation model is established using SIR filtering particles. Lastly, a shift vector sum algorithm is employed to enhance the intensity of the infrared image at a certain time scale in accordance with the relationship between the pixel intensity and the temporal parameters of the detected image. Experimental results demonstrate that the multi-target tracking accuracy (MOTA) reaches 79.7% and that the inference speed frame per second (FPS) reaches 42.3. Moreover, the number of ID switches during tracking is 9.9% lower than that of the MOT algorithm, indicating high recognition of cluster small targets, stable tracking performance, and suitability for tracking weak small targets on the ground or in the air.  
optical flow estimation; infrared image; cluster objectives; image intensity; target tracking 2023
## 2 Thematic Review and Analysis
### Novel Concepts
#### 2.7.2 Decentralized Pumps  
A transformative innovation in DH systems involves the deployment of small-scale, demand-driven decentralized pumps at end-user sites, replacing traditional large-scale central pump stations. These small pumps operate on an on-demand basis, adjusting the required flow by regulating the pump speed, thereby eliminating the need for flow control valves. This approach offers several benefits, including the removal of thermostatic radiator valves and differential pressure valves (or pressure-independent thermostatic radiator valves). In conventional DH systems, the central pump speed is set to accommodate the most distant consumers, leading to excessive throttling of flow by nearby consumers due to high pressure. The new demand-driven consumer pumps effectively mitigate this issue by preventing flow loss from throttling and maintaining better control over the flow. Improving flow regulation to match exact heat demand is crucial for reducing highreturn temperatures, making the decentralized pump concept a promising solution in this regard [29; 140; 141; 142; 143].  
[PERSON] [29], referencing [PERSON] [144], advocates for a configuration that includes direct connections between the main and sub-networks, coupled with demand-driven decentralized consumer pumps, to achieve low return temperatures. For indirect substations, the configuration involves two demand-driven pumps positioned on either side of the heat exchanger [143].  
[PERSON] et al. [143] present a simulation comparing traditional branched ('tree-like' or 'Y-type') DH networks with a novel ring network incorporating decentralized demand-driven consumer pumps. The results indicate that the ring network, utilizing decentralized pumping, achieves lower return temperatures compared to the traditional DH system, particularly when the outdoor temperature ranges between \(-5\)\({}^{\circ}\)C and \(+5\)\({}^{\circ}\)C. Specifically, the average return temperatures are 25 \({}^{\circ}\)C and 20.4 \({}^{\circ}\)C for the branched and ring network configurations, respectively, with decentralized pumps. In contrast, the traditional control system, employing a central pump station, yields return temperatures of 26 \({}^{\circ}\)C and 24.2 \({}^{\circ}\)C for the same network types.
## 3 Basic Principles of 3 MA
### Correlations to Microstructure and Material Properties  
Coercive field, Hc, remanence, Br, as well as initial and maximum permeability, \(\upmu_{\rm init}\) and \(\upmu_{\rm max}\) are \"hysteresis parameters\", i.e., measuring parameters that are determined from the major hysteresis curve. Variations in the microstructure of a ferromagnetic material are reflected by changes in the hysteresis morphology and in changes of these parameters. Therefore, the hysteresis parameters show correlations to microstructure data. In an early work, [PERSON] and [PERSON], who reported a linear correlation between Hc and the grain size or the concentration of nonmagnetic inclusion in steels, showed this [24]. Researchers at Fraunhofer IZFP have shown correlations between Hc and the approximate value of skin depth in grinded parts [25]. Later, such correlations were used for to determine the quantitative hardening depth in steel and cast iron components, while using 3 MA [26].  
Besides \"conventional\" measuring parameters such as coercive fields (H\({}_{\rm CO}\), H\({}_{\rm CM}\), H\({}_{\rm CH}\)) or maximum amplitudes (M\({}_{\rm MAX}\), \(\upmu_{\rm MAX}\)), additional measuring parameters have been defined, as already described in Tables 1-4. Such \"unconventional\" parameters could be useful, as it becomes apparent in observing the diagrams in Figure 3. The diagram (a) shows hysteresis measurements on a steel sample before (I) and after (II) plastic deformation. Measurements with the Incremental Permeability (IP) method on the same sample are shown in diagram (b). The shape of hysteresis drastically changes from I to II due to the increased dislocation density and altered residual stress level. Especially noticeable is some kind of \"bulging\" around the coercive field in hysteresis II. This corresponds to a distinct change in the width of the IP signal. Therefore, the widths of the IP signal at 25, 50, and 75% of the maximum amplitude contain valuable additional measuring information.
# Distribution and Prediction of Incremental Cutter Flank Wear in High-Efficiency Milling
## 6 Wear Prediction of Flank Face of Cutter Tooth
### Validation of the Predictive Model for Flank Wear
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Calculation Methods and Characteristics** \\ \hline \multirow{4}{*}{Square sum of residuals} & \(\text{SSR}=\sum\limits_{i=1}^{q}w_{ij}(y_{j}-y_{j}^{\text{out}})^{2}\) \\ \cline{2-3}  & To calculate the sum of the square of the difference between the actual value and the predicted value of the flank incremental wear of the cutter tooth. \\ \hline \multirow{4}{*}{Total sum of square} & \(\text{SST}=\sum\limits_{i=1}^{q}w_{ij}(y_{j}-\overline{y})^{2}\) \\ \cline{2-3}  & It reflects the dispersion of all predicted value errors. \\ \hline \multirow{4}{*}{Regression sum of squares} & \(\text{SSE}=\sum\limits_{i=1}^{q}w_{ij}(y_{j}^{\text{out}}-\overline{y})^{2}\) \\ \cline{2-3}  & It reflects the fitting of multivariate linear regression lines to sample observations. \\ \hline \multirow{4}{*}{Mean square error} & \(\text{MSE}=\frac{\text{SSR}}{m}=\frac{1}{m}\sum\limits_{i=1}^{q}w_{ij}(y_{j}- y_{j}^{\text{out}})^{2}\) \\ \cline{2-3}  & The difference between the actual value and the predicted value is reflected. \\ \hline \multirow{4}{*}{Mean absolute error} & \(\text{MAE}=\frac{1}{m}\sum\limits_{i=1}^{q}w_{ij}\Big{|}y_{j}-y_{j}^{\text{ out}}\Big{|}\) \\ \cline{2-3}  & The offset between different errors could be avoided. \\ \hline \multirow{4}{*}{Root mean square error} & \(\text{RMSE}=\sqrt{\text{MSE}}=\sqrt{\frac{1}{m}\sum\limits_{i=1}^{q}w_{ij}(y_ {j}-y_{j}^{\text{out}})^{2}}\) \\ \cline{2-3}  & The deviation between the predicted value and the true value is calculated and is not affected by the number of samples. \\ \hline \multirow{4}{*}{Goodness of Fit} & \(\text{R}^{2}=\frac{\text{SSE}}{\text{SST}}=1-\frac{\text{SSR}}{\text{SST}}= \frac{\sum\limits_{i=1}^{q}(y_{j}^{\text{out}}-\overline{y})^{2}}{\sum\limits _{i=1}^{q}(y_{j}-\overline{y})^{2}}\) \\ \cline{2-3}  & When the value of goodness of fit R\({}^{2}\) is greater than 0.8, it shows that the regression line is better to fit the predicted value of the instantaneous incremental wear of the flank. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical indicators and their calculation methods and characteristics. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mean Square Error** & **Mean Absolute Error** & **Root Mean Square Error** & **Goodness of Fit** \\
**MSE** & **MAE** & **RMSE** & **R\({}^{2}\)** \\ \hline
4.02 \(\times\) 10\({}^{-16}\) & 1.06 \(\times\) 10\({}^{-8}\) & 2.00 \(\times\) 10\({}^{-8}\) & 0.82659 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Test set errors. meta learning model. The experiments carried out in this study prove that, when compared to the existing neural network learning method reported by previous researchers, using the instantaneous contact stiffness of the cutter tooth flank to predict the instantaneous incremental wear of a specific position has better pertinence and accuracy.
# Analytical Model and Feedback Predictor Optimization for Combined Early-HARQ and HARQ
###### Abstract  
In order to fulfill the stringent Ultra-Reliable Low Latency Communication (URLLC) requirements towards Fifth Generation (5G) mobile networks, early-Hybrid Automatic Repeat reQuest (e-HARQ) schemes have been introduced, aimed at providing faster feedback and thus earlier retransmission. The performance of e-HARQ prediction strongly depends on the classification mechanism, data length, threshold value. In this paper, we propose an analytical model that incorporates e-HARQ and Hybrid Automatic Repeat reQuest (HARQ) functionalities in terms of two phases in discrete time. The model implies a fast and accurate way to get the main performance measures, and apply optimization analysis to find the optimal values used in predictor's classification. We employ realistic data for transition probabilities obtained by means of 5G link-level simulations and conduct extensive experimental analysis. The results show that at false positive probability of \(10^{-1}\), the e-HARQ prediction with the found optimal parameters can achieve around 20% of gain over HARQ at False Negative (FN) of \(10^{-1}\) and around 7.5% at FN of \(10^{-3}\) in terms of a mean spending time before successful delivery.  
Keywords:Markov chain model; stationary distribution; performance measures; 5G mobile communication; HARQ; early-HARQ +
Footnote †: journal:  
_Article_  
Analytical Model and Feedback Predictor Optimization for Combined Early-HARQ
# Reconstruction of Rainfall Field Using Earth-Space Links Network: A Compressed Sensing Approach
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Situations** & **Rainfall Fields** & \multicolumn{2}{c}{**RMSE (mm/h)**} & \multicolumn{2}{c}{**MB (mm/h)**} & \multicolumn{2}{c}{**CC**} \\  & **IDW** & **CS** & **IDW** & **CS** & **IDW** & **CS** \\ \hline \multirow{3}{*}{No noise} & Rainfall field 1 & 0.86 & 0.08 & 0.07 & 0.01 & 0.975 & 0.999 \\  & Rainfall field 2 & 1.20 & 0.10 & 0.08 & 0 & 0.960 & 0.999 \\  & Rainfall field 3 & 1.03 & 0.12 & 0.03 & 0 & 0.934 & 0.999 \\  & Rainfall field 4 & 0.56 & 0.07 & \(-\)0.12 & 0.01 & 0.966 & 0.999 \\ \hline \multirow{3}{*}{With noise} & Rainfall field 1 & 0.86 & 0.22 & 0.07 & 0.06 & 0.974 & 0.998 \\  & Rainfall field 2 & 1.21 & 0.25 & 0.08 & \(-\)0.05 & 0.960 & 0.998 \\ \cline{1-1}  & Rainfall field 3 & 1.03 & 0.13 & 0.03 & 0 & 0.934 & 0.999 \\ \cline{1-1}  & Rainfall field 4 & 0.57 & 0.15 & \(-\)0.12 & 0.03 & 0.965 & 0.997 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The quantitative results of reconstructing the rainfall fields of the rainfall event by ESL network 1.
## 2 Transient Temperature Field Model of Wear Band on the Flank of End Mills
### Transient Temperature Field in Wear Band on the Flank of End Mills  
(1) When the edge is in contact with the workpiece, there will be heat sources in the PSZ, RCZ and FWZ, which will have a temperature rise effect on the transient temperature field of wear band on the flank of end mills. Therefore, considering the effects of heat sources in the PSZ, the RCZ and the FWZ on the transient temperature field of wear band on the flank of end mills, the temperature \(T_{in\_flank}\) (\(x^{\prime\prime}\), 0, 0) at any point \(P\) (\(x^{\prime\prime}\), 0, 0) along the \(X^{\prime\prime}\) direction can be calculated using Equation (40).  
\[T_{in\_flank-fl}(x^{\prime\prime},0,0)=\Delta T_{fank-fl}(x,0,0)+\Delta T_{ fank-fl}(x^{\prime},0,z^{\prime})+\Delta T_{fank-ll}(x^{\prime\prime},0,0)+T_{0} \tag{40}\]  
Further, the temperature at any point \(P\) (\(x^{\prime\prime}\), 0, 0) along the \(X^{\prime\prime}\) direction of the wear band on the flank of end mills can be obtained from the coordinate system transformation and can be calculated using Equation (41).  
\[T_{in\_flank}(x^{\prime\prime},0,0)=\Delta T_{fank-l}(x^{\prime\prime}-VB_{1},0,0)+\Delta T_{fank-ll}(l_{e}-Z^{\prime\prime},0,VB_{1}-X^{\prime\prime})+ \Delta T_{fank-ll}(x^{\prime\prime},0,0)+T_{0} \tag{41}\]  
(2) When the edge is out of contact with the workpiece, the heat sources in the PSZ, RCZ and FWZ disappear, and then the dissipated heat sources will appear, which will have a temperature drop effect on the transient temperature field of the wear band on the flank of end mills. Therefore, considering the effect of dissipation source on the transient temperature field of wear band on the flank of end mills, the temperature \(T_{out\_flank}\) (\(x^{\prime\prime}\), 0, 0) at any point \(P\) (\(x^{\prime\prime}\), 0, 0) along the \(X^{\prime\prime}\) direction can be calculated using Equation (42).  
\[T_{out\_flank}(x^{\prime\prime},0,0)=T_{in\_flank}(x^{\prime\prime},0,0)-\Delta T _{fank-air}(x^{\prime\prime},0,0) \tag{42}\]  
Based on the two kinds of contact relationship between tool and workpiece, the transient temperature field of wear band on the flank of end mills, the temperature \(T_{fank}\) (\(x^{\prime\prime}\), 0, 0) at any point \(P\) (\(x^{\prime\prime}\), 0, 0) along \(X^{\prime\prime}\) direction can be calculated using Equation (43).  
\[T_{fank}(x^{\prime\prime},0,0)=\left\{\begin{array}{ll}T_{in\_flank}(x^{ \prime\prime},0,0)&(--)\\ T_{out\_flank}(x^{\prime\prime},0,0)&(--)\end{array}\right. \tag{43}\]  
In this part, the temperature field models of 2.1, 2.2, 2.3, and 2.4 were simulated and superimposed by MATLAB software (R2014a, The MathWorks, Natick, MA, USA, 2014). Consequently, the predicted results for the transient temperature field of the wear band on the flank of end mills were obtained. The modeling process is summarized in Figure 10.
## 2 Numerical Mean Echo Power Model for SAR Altimetry
### Radar Signal Model  
In this section, we derive the echo signal from a single scatterer located at point \((x,y,z)\) on the flat surface. To begin with, the radar transmit waveform is provided by  
\[s(t)=\sum_{n=0}^{N-1}s_{0}(t-nT_{r})e^{j2\pi f_{0}t}, \tag{3}\]  
where \(s_{0}(t)=1/\sqrt{T}rect(t/T)exp\left\{j2\pi f_{0}t+j\pi Kt^{2}\right\}\) is the baseband waveform in each pulse, \(f_{0}\) denotes the carrier frequency, \(T_{r}\) denotes the pulse repetition interval (PRI), and \(N\) is the number of pulses. Denoting the distance from the radar to the scatterer at time \(t\) by \(R(t)\), the echo signal from this scatterer after down-conversion can be written as  
\[s_{r}(t)=\sum_{n=0}^{N-1}A_{0}s_{0}\bigg{(}t-nT_{r}-\frac{2R(t)}{c}\bigg{)} \exp\left(-j4\pi f_{0}\frac{R(t)}{c}\right), \tag{4}\]  
Figure 1: Delay–Doppler mapping. Each delay–Doppler bin is associated with two delay–Doppler cells on the surface.  
where \(c\) is the propagation velocity, \(A_{0}\) is a complex amplitude factor, and  
\[R(t)=\sqrt{x^{2}+\left(y-vt\cos\mu\right)^{2}+\left(h-z-vt\sin\mu\right)^{2}}.\]  
Let \(t=t_{r}+nT_{r}\), where \(n\) is pulse index and \(t_{r}\) denotes the radar's fast time. Thus, the received signal during the \(n\)-th PRI is  
\[S_{r}(t_{r},n)=s_{r}(t_{r}+nT_{r})=A_{0}s_{0}\bigg{(}t_{r}-\frac{2R(t_{r}+nT_{ r})}{c}\bigg{)}\exp\bigg{(}-j4\pi f_{0}\frac{R(t_{r}+nT_{r})}{c}\bigg{)} \tag{5}\]  
for \(n=0,\ldots,N-1\) and \(0\leq t_{r}\leq T_{r}\).