esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Characteristic Signal Selection and Wavelet Denoising Processing  
### Characteristic Signal Selection  
As a kind of information carrier, the vibration signal generated in the indexing sharpening process of a single-crystal diamond arc cutter has the advantages of a wide frequency response range, sensitivity to grinding state, very little limitation by environmental conditions, flexible installation, convenient adjustment, and mature theory and easy realization. When the grinding process generates vibration signals, it also stimulates acoustic signals that contain rich information about the tool. When the tool is in different grinding states, the characteristics of the sound signal change, and the sound signal can be measured by non-contact, which is simple and convenient. Human senses are also very sensitive to the sound signal. Human intuition can effectively guide signal processing and feature extraction and avoid the blindness of analysis processing, so it has a strong application value. According to the literature [6], the acoustic emission signal generated during the grinding process of the tool can be transmitted and received without loss of truth; that is, its non-Gaussian characteristics will not be changed in the transmission and reception process, and the non-Gaussian characteristics of the signal can be found by reasonably setting the sampling period of the signal. The acoustic emission signal is closely related to the vibration phenomenon of the system, so the tool vibration signal and the sound signal are selected to represent the tool's state information.
## 4 Simulation and Experimental Results
### Experiment Validation  
In order to further verify the effectiveness and practicability of the proposed control method, a robot grinding experiment was carried out in the lab. As shown in Figure 11, the experimental setup was based on an automatic production line for the intelligent manufacture of high-end equipment, which was independently developed by the National Engineering Laboratory for Robot Vision Perception and Control Technology. The robot model was the MH-24 of Yaskawa, as shown in Figure 11 (left panel). Six drivers were connected to the industrial computer through the Ethercat bus, and each joint motor was controlled by an industrial computer. The workpiece for grinding was a cylinder block from an automobile engine. The sampling frequency was 100 Hz, and the grinding task for the robot was to grind 300 mm along a straight line in 5 s.  
Figure 10: (**a**) Control inputs of robust adaptive sliding mode control (RASMC); (**b**) Control inputs of sliding mode control (SMC).  
Figure 9: Position tracking performance of joint 1 (**left**) and joint 2 (**right**).  
In the experiment, a limiter was applied to the output of the first joint controller to simulate the dead zone. The upper limit was 0.8, and the lower limit was \(-2\). As can be seen from Figure 12, due to the fact of a certain delay, there was a large identification error at the moment of the upper and lower limit switching. When the system was in a steady state, the method based on the RBFNNs designed in this paper could identify the dead zone accurately. The peak value of the absolute value of the steady state error was 0.08.  
Figure 13 shows that all six joints were stable under the action of the controller. Taking the most fluctuating joint (4) as an example, the peak value of the steady-state error was 1.21 rad without compensation and 0.96 rad with compensation, which represents a decrease of 21%. The results clearly show that the system's steady-state performance and grinding accuracy were improved with the introduction of dead zone compensation. The same conclusion can also be determined from the other five joints. The last three joints fluctuated sharply because they were close to the polished end. The sixth joint (that is, the last joint) was directly connected with the grinding head. During the polishing process, the position of this joint remained basically unchanged, so the range of the joint displacement's change was very small.  
Figure 11: Experimental device diagram of the free form surface grinding robots(FFSGRs) (**a**) and engine cylinder block grinded in experiments (**b**).  
Figure 12: Dead zone non-linearity estimation performance of joint 1 (**a**) and estimation error (**b**).
## 2 The Experimental Platform  
### Test Facility and Test Section  
To assess the vibrations in a full-scale LBE cooled replica of the MYRRHA fuel assembly, we have used an experimental test facility that allows for characterizing the hydraulic and hydrodynamic behaviour of various full-scale MYRRHA components in LBE [18; 19]. An isometric view of the facility, with a height exceeding 7 m, is shown in Figure 2 (left). The set-up is an isothermal closed loop that can circulate LBE at a constant temperature. The maximum LBE temperature is 400 \({}^{\circ}\)C. The LBE is circulated by a vertical shaft sump type centrifugal pump via a cartridge filter vessel and through an air-cooler, sized to remove only the fluidic heating power from the pump. LBE flow rates up to 36 m\({}^{3}\)/h can be achieved, which is sufficient to cover the nominal flow rate through one MYRRHA fuel assembly. A vortex flowmeter measures the total volumetric flow rate entering the test section. For the experiments described in this paper, a full-scale mockup of the MYRRHA fuel assembly was installed into the vertical test section. The vertical test section in the set-up is representative of a single MYRRHA section at full height, with the LBE flowing upwards like in MYRRHA. To reduce the influence of piping loop components and to facilitate a fully developed flow at the inlet of the test section, an inlet box was installed at the bottom of the test section. A photograph of a part of the facility above the ground level is shown in Figure 2 (right). Overall the test facility allows experimenting with a very realistic mock-up of a full-sized fuel assembly with LBE temperatures and flow rates comparable to the situation in MYRRHA, whilst minimizing the influence of piping loop components. Note that the measurements in the loop are conducted in the absence of nuclear fission and associated nuclear radiation. Since the goal of the experiments was to evaluate the fuel assembly vibrations, the effect of nuclear radiation can be considered secondary in the context of this paper. In addition, irradiation induced swelling of the fuel assembly would lead to a reduction of the spatial tolerances and effectively closing gaps thereby possibly reducing the flow-induced vibrations.  
Figure 2: (**Left**) Isometric view of the experimental test facility in which the flow induced vibrations of a full-scale lead-bismuth eutectic (LBE) cooled fuel assembly were tested. (**Right**) A photograph of part of the facility above ground-level.
# Performance of Millimeter Wave Dense Cellular Network Using Stretched Exponential Path Loss Model
## 3 SINR Coverage Probability and Area Spectral Efficiency
### Area Spectral Efficiency  
To study ASE and to quantify the maximum number of transmitted bits per second per unit bandwidth per unit area (\(\text{bps}/\text{Hz}/\text{m}^{2}\)) the definition in [44] is used. It indicates the increase in achievable data rate as the network density is increased by adding more BSs. It is given by:  
\[ASE=\lambda log_{2}(1+T)\ P_{c}(T) \tag{17}\]  
By substituting the coverage probability expression obtained in (11) the above expression can be simplified as follow:  
\[ASE=\lambda log_{2}(1+T)\bigg{(}\bigg{(}2\pi\lambda\int_{0}^{ \infty}\exp\bigg{(}-2\pi\lambda\sum_{G\in\mathbb{G}_{z}}P_{G_{z}}\bigg{(}\int_{ r}^{\infty}\bigg{(}1-\frac{1}{1+\mu_{z}G_{z}t^{-\alpha_{z}}}\bigg{)}tp_{ LOS}(t)dt\bigg{)}\] \[\qquad\qquad\qquad\qquad\qquad r\exp(-\
## 3 The Cognitive Packet Network  
The cognitive packet network (CPN) introduced in [39, 40, 41] was originally proposed for multimedia networks with diverse end users [42]. As different classes of users may require different QoS, the traditionally best effort Internet, which satisfies a single QoS need, is difficult to adapt and can impede the performance. Therefore, CPN is presented to provide QoS-driven routing and realise self-improvement by using cognitive packets. Contrary to conventional routing protocols, in the CPN, \"cognitive packets\" play a predominant role in routing and flow control, other than routers and protocols. For example, cognitive packets can discover optimised routes with their predefined goals and improve QoS by learning from their own investigations and experience from other packets.  
Each node within a CPN hosts a recurrent random neural network, which has been applied to many domains [43] to determine the cognitive packets' next hop among neighbour nodes, and it maintains a routing list to reserve the routes discovered by cognitive packets. CPN carries three types of packets: smart packets (SPs), acknowledgements (ACKs) and dumb packets (DPs). SPs are responsible for information collection and routing discovery with respect to a specific QoS goal. They can either choose the neighbour node decided by random neural networks as the next hop or drift randomly to explore and update routes. When a SP reaches the destination, an ACK, which stores all the gathered information, will be generated and sent back to the source node through the reverse path. Because loops may be produced during route exploration, a loop remove algorithm is enacted on the reverse path to take out any sequences of nodes with the same origin and destination. When an ACK reaches a node, it will update the routing list and train the random neural network [44] by performing reinforcement learning (RL) with the neural networks [45, 46]. DPs are the packets that actually carry the payloads. It always selects the best path at the top of the routing list as the next hop.  
In the context of emergency evacuation, because the environment is highly dynamic, SPs are used to resolve the \"optimised QoS routes\" and collect information, such as the hazard intensity or congestion level of a node. ACKs backtrack with collected information and update the excitation level of neurons at the traversed nodes. Evacuees are considered as DPs and always follow the top ranked path in the routing list. To apply the concept of CPN to emergency evacuation, the following requirements must be satisfied: a predefined graph-based layout of a confined place with information, such as the length of edges and node capacity, are available. The edges of the graph depict the paths, and the vertices represent the physical areas where sensors are installed. The sensors could communicate with their neighbour nodes and detect typical hazards, such as fire, smoke, water, _etc_. Additionally, in order to perform the congestion-aware algorithm, we also assume that the sensors can sense the arrival of a civilian and record the number of evacuees congested in proximity.
# Investigations on Millimeter-Wave Indoor Channel Simulations for 5G Networks
###### Abstract  
Due to the extensively accessible bandwidth of many tens of GHz, millimeter-wave (mmWave) and sub-terahertz (THz) frequencies are anticipated to play a significant role in 5G and 6G wireless networks and beyond. This paper presents investigations on mmWave bands within the indoor environment based on extensive simulations; the study considers the behavior of the omnidirectional and directional propagation characteristics, including path loss exponents (PLE) delay spread (DS), the number of clusters, and the number of rays per cluster at different frequencies (28 GHz, 39 GHz, 60 GHz and 73 GHz) in both line-of-sight (LOS) and non-LOS (NLOS) propagation scenarios. This study finds that the PLE and DS show dependency on frequency; it was also found that, in NLOS scenarios, the number of clusters follows a Poisson distribution, while, in LOS, it follows a decaying exponential distribution. This study enhances understanding of the indoor channel behavior at different frequency bands within the same environment, as many research papers focus on single or two bands; this paper considers four frequency bands. The simulation is important as it provides insights into omnidirectional channel behavior at different frequencies, essential for indoor channel planning.  
5G; indoor propagation; millimeter-wave; ray tracing +
Footnote †: journal: Applied  
1
Footnote 1: institutetext: Department of Communications and Computer Engineering, Faculty of Engineering, Jadara University,  
Irbid 21110, Jordan; [EMAIL_ADDRESS]
# Application and Evaluation of the Gaofen-3 Satellite on a Terrain Survey with InSAR Technology
## 4 Discussion  
### Elevation Accuracy Evaluation with GCPs and 1:2000-Scale DEM Data  
Six GCPs (Ground Control Points) in the Dengfeng area were adopted to evaluate the height accuracy derived from the Gaofen-3 SAR images. Those GCPs were obtained from six corner reflectors, which were placed in the study area in advance. Figure 13 shows the distribution of six corner reflector control points on Gaofen-3 SAR image.  
The elevation value obtained by Gaofen-3 was compared with six GCPs, as shown in Table 4. The difference is from \(-1.694\) to \(6.494\) m and the root mean square error is about \(3.3\) m.  
In order to carry out more accurate verification work, 1:2000-scale DEM data obtained from an aerial survey. The visual difference between the two DEMs in Figure 12 is not significant, and the difference between the obtained DEM and the 1:2000-scale DEM was therefore calculated to measure the elevation accuracy (Figure 14a,b). According to the statistics of elevation residuals, the height measurement accuracy of InSAR technology for the Gaofen-3 satellite data in the Dengfeng area was better than \(4\) m under control conditions, whereas the height measurement accuracy of InSAR under non-control conditions was better than \(7\) m.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Elevation Value** & **Elevation Value from Gaofen-3** & **Difference (m)** \\  & **from GCPs (m)** & **SAR Images (m)** & **3.702** \\ \hline
101 & 470.257 & 468.563 & \(-1.694\) \\
102 & 425.266 & 423.952 & \(-1.314\) \\
103 & 406.783 & 413.267 & 6.484 \\
104 & 482.970 & 485.012 & 2.042 \\
105 & 364.747 & 368.449 & 3.702 \\
106 & 372.336 & 378.83 & 6.494 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Elevation accuracy evaluated by six GCPs.  
Figure 13: Distribution of corner reflector control points on Gaofen-3 SAR image.
## 3 Results  
### Main Operation Area  
All 15 sets of experimental results are presented in the Figure 6a (P10\(\times\)5, P50\(\times\)5, P90\(\times\)5). Distinct from the model's base color, the painted areas have a clear boundary line and cover the bottom of the panel, indicating there is an upper limit of the thumb's touch range in the MOA.  
Figure 6: (**a**) Experimental results of MOA; (**b**) normalized analysis based on Cartesian coordinate system established using the midpoint of the diameter as the origin. Commencing from the starting point, points are uniformly sampled along the positive x-axis at intervals of 50 pixels. (**c**) Fifteen sets of experimental data were collected. Different colors are used to distinguish the results of different participant operations.  
The paint-marked upper edge was irregular and rough, likely influenced by individual habits, physical condition, and the interaction between the paint and materials, which could cause operational resistance. To extract the universal features of the upper edge curves of each experimental result, the study involved sampling feature points from the images and then performing polynomial fitting (see Figure 6b).  
In Figure 6c, boundary curves in the same hand length percentile group are in roughly similar positions. With the increase of hand length, the boundary curves are waving up. The 15 boundary curves exhibit a similar trend, reaching the peak of the operable range near the origin. The curve on the origin's right side descends more slowly than the left.
## 2 Male Infertility  
There is increasing evidence of the progressive decline in human fertility in both developed and developing countries, with a variable prevalence [2]. For instance, the prevalence estimate of infertility is 6% in the United States of America (USA), 10-15% in the United Kingdom (UK) and 20-46% in sub-Saharan Africa, with the African continent having an overall infertility rate of 41.91% among males and females, in which male infertility contributes to 22.26% [3,6,24].  
Male infertility is commonly caused by reduced semen quality characterised by low sperm count (oligozoospermia), impaired motility of the sperm (asthenozoospermia), decreased vitality of the sperm (necrozoospermia), impaired morphology of the sperm (teratozoospermia) or a combination of these parameters termed oligoasthenoteratoozospermia and azoospermia [2]. In total, 90% of male infertility problems are linked to sperm count, and there is a link between reduced sperm count and reduced semen quality [6]. In addition, a cohort study of South African men of reproductive age indicated in 34.2% of the sub-fertile men, 11.9% had severe azoospermia [25].  
Several risk factors can disrupt semen quality leading to semen-related abnormalities [6]. Oxidative stress has been linked to male infertility due to enhanced reactive oxygen species (ROS) production or decreased antioxidants. ROS leads to infertility through two fundamental mechanisms. Firstly, by inducing sperm membrane damage, which decreases sperm motility and its fertilisation capacity, as well as by altering the DNA molecule of sperm, resulting in the passage of defective paternal DNA on to the conceptus [7].
## 2 Results and Discussion
### Extraction Efficiency for the Polar Compounds  
Four kinds of Ti wires were inserted into the extraction tube for the comparative investigation of extraction efficiency. Benefiting from the much larger surface area of the subsequent TiO\({}_{2}\) around their surface and different surface energy after hydrothermal etching and chemical modification, the superhydrophilic wire (b) and UV-irradiated superhydrophilic wire (d) exhibited higher extraction efficiency than the pristine and superhydrophobic wires in Figure 6 (left). Two kinds of superhydrophilic Ti wires showed a small enrichment for the first four compounds, including bisphenol A, estradiol, ethynestradiol, and estrone, and a particularly large enrichment for the last two compounds, including diethylstilbestrol and heexstrol. The comparative peak areas for each target compound are illustrated in Figure 6 (right). Superhydrophilic materials, due to their intrinsic character of polar nature, are expected to be the perfect option for the extraction of organic species with more polarity.  
Figure 5: WCAs of the pristine Ti mesh (**a**), the etched Ti mesh (**b**), the FOTS-modified Ti mesh (**c**) and the UV-irradiated superhydrophilic mesh (**d**).  
The enrichment factor (EF) values were accurately calculated as follows [3; 4; 7]: After 20 \(\mu\)L of standard solutions with different concentrations of 0, 0.25, 0.5, 1, 2.5, 5, 10, 25, and 100 mg/L were injected using the sample loop, the calibration curve with a slope of k\({}_{1}\) was then calculated according to the peak area vs. concentration. Similarly, another series of standard solutions with a constant sampling volume of 60 mL and different concentrations of 0, 0.5, 1, 2, 5, and 10 \(\mu\)g/L were prepared. After they were pumped into the extraction tube for preconcentration, each analyte in the standard solution was desorbed and detected through the UV detector. The calibration curve was then achieved with a slope of k\({}_{2}\). The final EF can be calculated according to the following equation: EF = k\({}_{2}\)/k\({}_{1}\).
## 3 Mathematical Model and Numerical Simulation  
### Thermal Analysis of TEM  
The commercialized TEM is normally composed of a series of thermocouples electrically connected in series and thermally converged in parallel. When direct current flows through a thermocouple which is composed of an N semiconductor element and a P semiconductor element, the phenomenon of heat absorption and release happens at the contact surface. The current flows from the N element to the P element above the contact surface and the surface absorbs heat from the outside, becoming the cold side. The current flows from the P element to the N element below the contact surface and the surface releases heat to the outside, becoming the hot side [12]. The cooling capacity (\(Q_{c}\), heat absorption power on the cold side) of the TEM is  
Figure 6: Diagram of the hardware circuit.  
\[Q_{c}=S_{m}IT_{c}-\frac{1}{2}I^{2}R_{m}-K_{m}(T_{h}-T_{c}). \tag{3}\]  
The quantity of heat production, \(Q_{h}\), on the hot side of the TEM is  
\[Q_{h}=S_{m}IT_{h}+\frac{1}{2}I^{2}R_{m}-K_{m}(T_{h}-T_{c}). \tag{4}\]  
In Equations (3) and (4), \(S_{m}\) is the Seebeck coefficient of TEM, \(T_{c}\) is the temperature of the cold side of the TEM and \(T_{h}\) is the temperature of the hot side of the TEM, \(I\) is the direct current through the module, \(R_{m}\) is the electrical resistance of the TEM and \(K_{m}\) is the thermal conductance of the TEM.  
The performance parameters, \(S_{m}\), \(R_{m}\) and \(K_{m}\), can be figured out by the TEM parameters of \(I_{\text{max}}\), \(\Delta T_{\text{max}}\), \(T_{h}\) and \(V_{\text{max}}\) given by the manufacturer through Equations (5)-(7) [19; 20], of which \(I_{\text{max}}\) is the direct current flowing through TEM that causes the maximum temperature difference, \(\Delta T_{\text{max}}\)-\(\Delta T_{\text{max}}\) is the maximum temperature difference between the cold and hot sides of the TEM at a certain temperature, \(T_{h}\). \(V_{\text{max}}\) is the direct voltage that diverges the \(\Delta T_{\text{max}}\).  
\[R_{m}=\frac{(T_{h}-\Delta T_{\text{max}})\times V_{\text{max}}}{T_{h}\times I _{\text{max}}} \tag{5}\]  
\[K_{m}=\frac{(T_{h}-\Delta T_{\text{max}})\times V_{\text{max}}\times I_{\text {max}}}{2\times T_{h}\times\Delta T_{\text{max}}} \tag{6}\]  
\[S_{m}=\frac{V_{\text{max}}}{T_{h}} \tag{7}\]
# Textured and Hierarchically Constructed Polymer Micro- and Nanoparticles
## 3 Results
### Hierarchically Constructed Particles of Poly-Arylamid/TPGDA/MMA
#### 3.2.1 Synthesis of PMMA Nanoparticles  
Surface charged polymer nanoparticles were synthesized similar to the description in Section 3.1 using hydrophilic polycationic polymer PDADMAC in the aqueous carrier phase and hydrophobic monomer MMA in the monomer phase according to [7]. PDADMAC acts as an interface stabilizing agent for emulsification of the dispersed phase in order to control particles size and surface charge.  
* The monomer phase consisted of 97% MMA, 3% EGDMA (volume fractions), and 30 mM AIBN. When indicated, Nile red was introduced to enable detection by fluorescence microscopy.
* The carrier phase consisted of water with variable amounts of poly-DADMAC.
* By means of the microfluidic hole-plate device, an emulsion was produced at variable flow rates and piped into the thermostat held at 97 \({}^{\circ}\)C.
* After 60 min, the reaction mixture was removed from the thermostat and treated as described.  
Polymer particles with a mean size of about 800 nm were obtained at a 3.23 g/L PDADMAC (20 mM per monomer unit) concentration and 1200/70 \(\upmu\)L/min flow rate ratio of carrier to monomer. By changing the flow rate ratio or PDADMAC concentration, it was possible to tune the mean size of the nanoparticles from 800 to 100 nm (Figure 5). By reducing the monomer phase flow rate (carrier flow constant at 1200 \(\upmu\)L/min), the particle size decreased, but their polydispersity increased from 8-15%. The particle surface charge was positive (zeta potential +35 mV) because of the positively charged surface layer of PDADMAC on the PMMA cores. At a higher PDADMAC concentration of 6.47 g/L (40 mM per monomer unit), the zeta potential increased to +50 mV.