esa-sceva/satcom-chunk-collection huggingface dataset:
# Rao and Wald Tests for Moving Target Detection in Forward Scatter Radar
## 3 Design of Adaptive Detectors without Secondary Data
### Adaptive Complex Parameter Wald Detector without Secondary Data  
The complex parameter Wald test without secondary data is given by  
\[\left(\stackrel{{<}}{{\Theta}}_{r1}-\stackrel{{ <}}{{\Theta}}_{r0}\right)^{H}\left\{\left[\mathcal{I}^{-1}\left( \stackrel{{<}}{{\Theta}}_{1}\right)\right]_{\stackrel{{ <}}{{\Theta}}_{r0}}\right\}^{-1}\left(\stackrel{{<}}{{ \Theta}}_{r1}-\stackrel{{<}}{{\Theta}}_{r0}\right)\stackrel{{ H_{1}}}{{\gtrless}}\rho \tag{54}\]  
where \(\rho\) denotes the detection threshold, \(\stackrel{{<}}{{\Theta}}_{1}\) and \(\stackrel{{<}}{{\Theta}}_{r1}\) are the MLEs of \(\stackrel{{<}}{{\Theta}}\) and \(\stackrel{{<}}{{\Theta}}_{r}\) under \(H_{1}\), and \(\stackrel{{<}}{{\Theta}}_{r0}\) is the value of \(\stackrel{{<}}{{\Theta}}_{r}\) under \(H_{0},\stackrel{{<}}{{\Theta}}_{r0}=\left[0,0\right]^{T}\).  
The second term of the Wald test is obtained according to the four blocks of \(\mathcal{I}\left(\stackrel{{>}}{{\Theta}}\right)\), namely, (49)-(51) and the MLEs of unknown parameters under \(H_{1}\): \(\stackrel{{<}}{{\alpha}}_{1}=\left(\widetilde{r}_{d}^{-H} \widetilde{r}_{d}\right)^{-1}\left(\widetilde{r}_{d}^{-H}\widetilde{z}- \stackrel{{<}}{{\beta}}_{1}\widetilde{r}_{d}^{-H}\widetilde{r}_{t}\right)\), \(\stackrel{{<}}{{\beta}}_{1}=\widetilde{r}_{t}^{H}\widetilde{z}/ \left\|\widetilde{r}_{t}\right\|^{2}\), and \(\stackrel{{<}}{{\sigma}}_{1}^{2}=\left\|\mathcal{P}_{\widetilde {r}_{t}}^{\bot}\widetilde{z}\right\|^{2}/N\):  
\[\left\{\left[\mathcal{I}^{-1}\left(\stackrel{{<}}{{\Theta}}_{1} \right)\right]_{\stackrel{{<}}{{\Theta}}_{r0}}\right\}^{-1}= \frac{N}{\left\|\mathcal{P}_{\widetilde{r}_{t}}^{\bot}\widetilde{z}\right\|^{ 2}}\left(\left\|\widetilde{r}_{t}\right\|^{2}-\frac{\left|\widetilde{r}_{d}^{ -H}\widetilde{r}_{t}\right|^{2}}{\left\|\widetilde{r}_{d}\right\|^{2}} \right)\mathcal{I}_{2} \tag{55}\]  
where \(\mathcal{P}_{rd}^{\bot}=\mathcal{I}_{N}-\frac{\widetilde{r}_{d}^{-H} \widetilde{r}_{d}^{-H}}{\left\|\widetilde{r}_{d}\right\|^{2}}\widetilde{r}_{t }=\mathcal{P}_{rd}^{\bot}\widetilde{r}_{t}\), \(\widetilde{z}=\mathcal{P}_{rd}^{\bot}\widetilde{z}\), \(\mathcal{P}_{\widetilde{r}_{t}}=\widetilde{r}_{t}\left(\widetilde{r}_{t}^{H} \widetilde{r}_{t}\right)^{-1}\widetilde{r}_{t}^{H}\), \(\mathcal{P}_{\widetilde{r}_{t}}^{\bot}=\mathcal{I}_{N}-\mathcal{P}_{ \widetilde{r}_{t}}\).  
Plugging (55) and the MLEs of the unknown parameters under \(H_{1}\) into (54), we have the complex parameter Wald test without secondary data based on the FSR (referred to as Wald-FSR):  
\[T_{Wild-FSR}=2N\left\|\mathcal{P}_{\widetilde{r}_{t}}\widetilde{z}\right\|^{2 }/\left\|\mathcal{P}_{\widetilde{r}_{t}}^{\bot}\widetilde{z}\right\|^{2} \stackrel{{ H_{1}}}{{\gtrless}}\rho \tag{56}\]
# GPS/BDS RTK Positioning Based on Equivalence Principle Using Multiple Reference Stations
## Validation and Analysis
### Static Experiment  
#### 3.1.1 Experiment Setup  
A one-day dataset on 1 July 2018 of four stations (CUT00, CUTA0, CUTB0, and CUTC0) located at Curtin University in Australia, are collected for the data processing. The positions of four stations are precisely known, and each station is equipped with a TRM59800.00 SCIS antenna, which is connected with Trimble NetR9 receiver (Trimble Navigation Limited, Sunnyvale, CA, USA). Single-frequency code and carrier phase observation of the GPS L1 signal is adopted, and the sampling interval of the observation is 30 s. The satellite cut-off elevation angle is set to 10\({}^{\circ}\). The distribution of four stations and the sky plot of station CUT00 is depicted in Figure 3. Three schemes are designed to calculate the precise position of CUT00, which are summarized in Table 2.
## 3 Results
### Adsorption Experiments of Polymers Towards Methylene Blue
#### 3.3.2 Effect of pH  
The modification of \(\beta\)-CD with ethylene glycol and polyethylene glycol leads to a low value of specific surface area. It was reasonable to deduce that high adsorption performance could not be obtained. Therefore, the nature of the absorbent and target molecule may participate in the adsorption ability. The study of the pH remains essential, especially when the adsorbent and target molecule are subject to protonation or deprotonation as a function of pH. It is well known that pH is the most important parameter in the adsorption process due to its effect on the dye molecule and on the surface of the adsorbent [69]. In our study, pH values can affect not only MB but also the linker and \(\beta\)-CD.  
Since most of the studied parameters might be depending on the contact time, it has been established that MB adsorption was practically instantaneous [70], depending on the substrate. The time was fixed to 24 h to improve the stability of the \(\beta\)-CD/MB complex and confirm the equilibrium.  
The protonation of the OH group of the adsorbent forms a cation that pushes away the protonated MB. In these experiments, the sorption capacity of the polymers was evaluated by measuring the remaining amounts of MB in the supernatant. A fixed amount of MB (50 mg/L) was stirred in the  
Figure 4: X-ray diffraction (XRD) spectra of \(\beta\)-CD, EGCD, PEGCD1 and PEGCD2.  
presence of 30 mg of each polymer at room temperature during 24 h. The effect of pH on MB adsorption was investigated between pH 2 and 12, and the results are illustrated in Figure 6.  
We can observe that the MB adsorption was less effective at pH 2 for EGCD, PEGCD1 and PEGCD2. Indeed, at a low pH, the dimethylamino groups in the MB and hydroxyl groups of \(\beta\)-CD and the linker were probably positively charged, which reduced the formation of inclusion complexes due to the weak interaction of MB with the hydrophobic cavity. Between pH 2 and 4, the adsorption ability enhanced with increasing pH. In this case, the hydrophilic and hydrophobic groups in each MB molecule simultaneously exist, allowing the inclusion of MB. From pH 4, PEGCD1 adsorbs twice as much as EGCD and PEGCD2. No change was observed between pH 6 and 8. Then MB removal increased  
Figure 5: UV–vis spectra of the supernatant solution after sorption on the polymer compared to the initial solution of methylene blue (MB).  
Figure 6: Effect of initial pH solution on the adsorption capacity of MB by EGCD, PEGCD1 and PEGCD2.  
gradually until pH 12. At pH 12, the yield of MB removal was higher for all the studied polymers and pretty closed to the same value. All the MB molecules with ammonium salt are neutralized at a higher pH, thus reinforcing interactions between MB and CD and the dissemination of MB through the polymer network.
# Automatic Clustering for Improved Radio Environment Maps in Distributed Applications
## 2 Wireless Requirements and Applications  
This section describes the requirements for improving new generation wireless technologies, such as those in 5G and 6G networks. It also describes the great benefits of multiple applications, such as IoT and edge computing, which have become possible due to new and improved wireless technologies.
# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 3 Deep Keypoint-Based Monocular SLAM
### Coarse-to-Fine Keypoint Tracking  
Accurate keypoint matching is essential for visual SLAM performance. Traditional systems like ORB-SLAM assume uniform motion between adjacent frames, often leading to challenges in finding correct matches and complicating optimization when this assumption fails. Unlike ORB features, which are straightforward low-level image descriptors, learned features generate complex high-dimensional embeddings, making matching and outlier filtering more difficult. To address these issues, we propose a two-stage deep keypoint tracking strategy to enhance matching robustness and accuracy.The overview of proposed tracking method as Figure 2 shown.
# SCATTER PHY: An Open Source Physical Layer for the DARPA Spectrum Collaboration Challenge
## 3 Scatter Phy
### COT-Based Filtering  
Due to their poor spectral localization, OFDM based wave-forms are not suited for spectral coexistence [24]. This issue is due to the rectangular pulse-shape intrinsically employed by the OFDM wave-form, which leads, in the frequency domain, to a sync-pulse property with a very low second-lobe attenuation of approximately \(-13\) dB [25].  
Therefore, in order to guarantee lower OOBE, i.e., improved spectral-localization, and still maintain the orthogonality of the OFDM symbols, it is necessary to filter the generated subframes before their transmission [6]. The filtering process is applied to each COT of each PHY independently. The subframes comprising a COT are generated at the SW level and then filtered at the HW level, by a 128 order FPGA based FIR filter. The COT based filtering improves the closer coexistence with other radios (either belonging to our team or others), allowing radio transmissions to be closer in frequency. The filter used in SCATTER PHY was designed and explained in [6].  
This co-design SW/HW is used so that fast processing high order filters can be implemented adding up very low latency to the transmission chain and still allowing the flexibility of the software defined PHYs.  
The coefficients of the filters applied to the COT are automatically selected in real time based on the Tx PHY BW parameter carried by the Tx control message. The coefficients are automatically chosen as the filters' cut-off frequencies have to match and exactly filter the desired signal's bandwidth, i.e., the configured PHY transmit BW.  
The normalized FIR filter's coefficients used in the COT based filtering are given in time domain by [6]:  
\[f(n)=\frac{p(n)w(n)}{\sum_{k}p(k)w(k)}. \tag{2}\]  
where \(p(n)\) is the impulse response of the sincsignal and \(w(n)\) is the window used to truncate the sinc signal. These two signals are given as:  
\[p(n)=\left\{\begin{array}{cc}\sin\left(\frac{\pi[N_{1}+N_{c}]n}{N_{\rm{FFT}} }\right)/\frac{\pi[N_{1}+N_{c}]n}{N_{\rm{FFT}}},&n\
eq 0,\\ 1,&n=0.\end{array}\right. \tag{3}\]  
\[w(n)=\left\{\frac{1}{2}\left[1+\cos\left(\frac{2\pi n}{L-1}\right)\right] \right\}^{0.6}, \tag{4}\]  
Figure 3: SCATTER PHY frame structure. COT, Channel Occupancy Time.  
where \(N_{\rm U}\) is the number of useful subcarriers (see Table 1), \(N_{\rm FFT}\) is the length of the FFT used in the OFDM modulation (see Table 1), \(N_{e}\) is the excess bandwidth in the number of subcarriers, \(L\) is the length of the FIR filter, and \(-\frac{(L-1)}{2}\leq n\leq\frac{(L-1)}{2}\). The excess bandwidth is used to extend the flat region of the filter so that the subcarriers at the left and right borders of the OFDM symbols suffer less attenuation.  
The filter, \(f(n)\), exhibits the following properties: (i) a time duration that is comparable to a small fraction of an OFDM symbol's duration, (ii) a sharp transition-band, which minimizes the necessary guard-bands, (iii) a flat pass-band over the useful subcarriers, and (iv) fair stop-band attenuation [6].
# Deep Learning Methods for Space Situational Awareness
## 3 Case Study
### Radar Processing  
At the receiver, the electromagnetic echoes reflected by a target are split into their I and Q (in-phase and quadrature) components by using coherent demodulation. As a propagation form, we assumed a two-rays ground reflection model, while targets have been characterized in terms of distance (i.e., range) from the radar, velocity, and Radar Cross Section (RCS). Once the I and Q components are extracted, digital sampling is performed after a signal windowing that controls the sidelobes level caused by the sampling operation. Afterward, pulse-Doppler signal processing separates reflected signals into channels by means of a set of filters for each ambiguous range. The maximum unambiguous range is related to the inverse of the Pulse Repetition Frequency (PRF) of the radar. More in-depth, the I and Q signals are filtered by the following scheme: the samples are reshaped into a time domain matrix; columns correspond to range samples (fast time), while rows correspond to pulse intervals (slow time). Convolved with the matched filter by means of a Fast Fourier Transform operation, the output matrix provides a power spectral density estimate of the returned signal in function of the range and Doppler frequency. This matrix is also known as a range-Doppler map. In a classical pulse-Doppler radar processing chain, the estimation of target position and velocity is performed by thresholding the range-Doppler map and finding the range and the Doppler bins in which the energy exceeds the given threshold. In our experiments, these maps are the input of the neural network architectures that we want to compare. For further details on radar signal processing, the reader may refer to [113].
## 3 Model Complexity and Space Reduction Techniques for System-Level Optimization
### Surrogate Modeling
#### 3.1.1 Kriging Model
The authors chose the DROM for its efficiency in managing computationally intensive tasks like finite element simulations. Unlike conventional global optimization techniques (e.g., genetic algorithms or particle swarm methods), the DROM employs a two-stage process--starting with a coarse search to identify promising regions, followed by a refined search within those areas. This strategy reduces computational cost while avoiding local optima. This reduction is performed iteratively at each step until a near  
Figure 6: Illustration of the Kriging model structure and its fundamental components. optimal point is found, progressively narrowing the range of each parameter. The Kriging model is employed using data extracted from the FEM simulations in ANSYS/Maxwell, simulating four different driving conditions. The experimental results from the protype's validation demonstrate errors below 1%. After each simulation, a Pearson correlation analysis is applied to simplify the optimization process and eliminate redundancies. The study highlights that the proposed method significantly reduces the computational time--where a full FEM-based approach would require 750 simulations. The results indicate an increase in torque and a reduction in ripple across all driving conditions. Finally, in [40], a study is conducted on the design of an SRM using the FEM and the Transient Lumped-Parameter Thermal Model (TLPTM), considering the thermal analysis of the motor. Additionally, a sensitivity analysis is performed through the Sobol method, and the Kriging model is constructed using the FEM in Ansys/Maxwell 2D, with 500 simulations, and the NSGA-II is employed for optimization. The control optimization is performed through the turn-on, \(\theta_{on}\), and turn-off, \(\theta_{off}\), angles, resulting in a reduction in the torque ripple, an increase in the torque, and a decrease in the temperature of the windings and the stator, all of which are optimized to a satisfactory degree. Experimental validation using a scaled-down prototype of the 12/10 SRM confirms the effectiveness of the proposed design, showing improvements in the torque, temperature reduction, and losses that closely match the simulation results.
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 5 Results  
This section presents the findings from our proposed protocol. We evaluated the protocol based on different metrics such as latency, scalability, and cryptographic overhead. Experiments were conducted on a high-end server equipped with an Intel Xeon 24-core processor operating at 2.0 GHz, 32 GB of RAM, and nodes of various specifications.
# State-of-the-Art Review on Determining Prestress Losses in Prestressed Concrete Girders
## 1 Introduction
Accordingly, there are difficulties in determining prestress losses related to factors including, inter alia, assumptions about the properties of prestressing systems and time-dependent phenomena, such as long term degradation processes, tendon relaxation, creep and shrinkage of concrete, and parameters of the real environment [29; 30]. Given the current state of quantitative and principled methodologies, this paper represents a state-of-the-art review of some important research works conducted worldwide on determining prestress losses in PC girders. At first, laboratory, numerical investigations, and testing methods are reviewed. Secondly, the article focuses on a static NDT method, and a comparison with dynamic ones is elaborated, since old unsolved problematics as well as new challenges exist because a variation in prestress force does not significantly influence the vibration responses of PC girders. Consequently, this makes uncertain the use of natural frequencies as appropriate parameters for prestress loss determinations. Thus, amongst emerging techniques, static identification based on vertical deflections has preliminary proved to be a reliable method with the aim to become a dominant testing approach in the near future. In fact, measured vertical deflections take accurately and instantaneously into account the changes of structural geometry of PC girders due to prestressing losses on the equilibrium conditions, in turn caused by the combined effects of relaxation of tendon, concrete creep and shrinkage, and parameters of the real environment as, e.g., temperature and relative humidity. Comments and recommendations are made at proper places, whilst concluding remarks including future investigations and field developments are mentioned at the end of the article.
## 3 Ice-Templating of Nanocellulose Suspensions
### Forming Processes
#### 3.2.3. Spray-Freeze Drying Processes  
The atomisation and spray-freeze-drying (SFD) process was used by [PERSON] et al. [128] for the fabrication of porous nanocellulose-based microspheres. Spray-freeze drying is a process that was originally developed for the production of biopharmaceutical powder [129]. In SFD, a suspension of CNFs or CNCs is sprayed with an air-atomisation nozzle into a cold vapour phase over a cryogenic liquid to form droplets (Figure 7). The droplets of the suspension may begin to freeze during the time of flight through the cold vapour phase and completely solidify upon contact with the cryogenic liquid phase. Then, the frozen droplets are dried in a lyophiliser.  
Figure 6: (**a**) SEM micrographs showing the microstructures of a TEMPO-oxidised CNF foam obtained using a sorbent-like process. In (**b**), the photograph inset shows the sorbent machine used to apply mechanical stirring during the freezing of CNF suspensions. (**c**) Horizontal cross-section of the TEMPO-oxidised foam obtained using X-ray microtomography (voxel size: 1.6\({}^{3}\)\(\upmu\)m\({}^{3}\)). (Adapted from [PERSON] et al. [56] with permission from Elsevier).  
Using this approach, the authors produced porous nanocellulose-based microspheres (Figure 7) with an average diameter of about 60-120 \(\upmu\)m [128]. In addition, the microbeads had a porous structure with pore sizes ranging from a few nanometers to micrometers. Microspheres are promising materials that can be used a template for drug delivery, superabsorbents, synthesising porous catalysts, cell culture growth templates, and targeted release systems.
# Real-Time Behaviour of Dredged Slurry Treated by Air-Booster Vacuum Consolidation
## 5 Test Operation Procedures  
In practice, the air-boosting system is activated when the slurry has already consolidated to a certain degree. Correspondingly, at the beginning stage of our model test, only vacuum pressure was applied (\(-85\) kPa according to the vacuum pump). When the degree of slurry consolidation reached approximately 70%, the vacuum pump was turned off and the air compressor was turned on to provide pressurised air. The air-boosting duration was set to 10 min; then, the air compressor was turned off, and the vacuum pump switched on to continue the vacuum consolidation.  
The main operational steps of the model test are briefly described: (1) Fix the positions of the PVD, iron rig, PVP transducers and booster plate. (2) Connect the PVD and booster plate to the tubes via the hand-shaped connectors. (3) Lead the transducer wires and tubes out from the hole opened in the model box sidewall. (4) Connect the valve, water-gas separation bottle and vacuum pump in sequence to complete the vacuum system. Similarly, connect the valve, air-pressure transducer, vortex flowmeter and air compressor to complete the air-booster system. (5) Connect the PWP transducers, electronic balance and flow meter to computers for automatic data recording and spread the tracers evenly onto the inner wall of the transparent glass. (6) Uniformly mix the dredged slurry and pour it into the model box to a filling height of 0.45 m. (7) Place two layers of air-tight membrane on top of the slurry. Tighten the bolts to ensure that the flange clamps the membrane. (8) Start the vacuum pump to perform vacuum consolidation of the slurry. When the degree of consolidation reaches 70%, the vacuum system is stopped. (9) Activate the air-booster system to start the air pressurisation. Meanwhile, the digital camera takes high-resolution pictures of the slurry displacement field. (10) Turn off the booster pump after 10 min of pressurisation. Then, the vacuum pump is turned on again to continue the vacuum consolidation of the slurry. The flow chart of test operation procedures is showed in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline Water content (\%) & \(w\) & 70.7 \\ Liquid limit (\%) & \(w_{L}\) & 56.6 \\ Plastic limit (\%) & \(w_{p}\) & 32.5 \\ Specific gravity of soil grain & \(G_{s}\) & 2.74 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Index properties of the sampled slurry.  
Figure 5: Flow chart of test operation procedures.
# Safety Issues Referred to Induced Sheath Voltages in High-Voltage Power Cables--Case Study
## 3 Results of the Induced Voltages Computer Simulations
### Induced Voltages for Single-Point Bonding
Halfway along the cable line (in p.6), the ECC was transposed to the other side of the line (Figure 8b), according to the recommendations included in the guide [2]. Figure 6: Induced sheath voltages as a function of the cable line length for three values of the transferred power (1 MW, 10 MW, 80 MW), tg\(\phi\) = 0.4: (a) single-point bonding at point p.0 in Figure 2; (b) single-point bonding at point p.12 in Figure 2.  
Figure 7: Induced sheath voltages as a function of the cable line length, in case of the line-to-earth fault at the unearthed end; single-point bonding at: (a) supply service substation (point p.0 in Figure 2); (b) consumer substation (point p.12 in Figure 2). Induced sheath voltages at the unearthed end of the line reach values over 22 kV without the ECC and approx. 12 kV with the ECC. All respective values are similar, regardless, the single-point bonding and earthing are performed in p.0 or in p.12. Such high values of the induced voltages between the copper sheath and the earth may be a source of the damage of the non-metallic outer sheath of the cable (PE outer sheath in Figure 3). It is a common design practice to assume that this non-metallic outer sheath will not be damaged if the induced voltage does not exceed 5 kV. As it is seen in Figure 7, the value 5 kV is not exceeded only for the length of the cable line (from the earthing point) approx. 2.5 km without the ECC and approx. 5 km with the ECC. Thus, in the considered power line of the length 12 km, the single-point bonding is not acceptable--too high values of the induced voltages in case of the short-circuit conditions exist (despite the possible application of sheath voltage limiters (SVL); a high-level margin of reliability and safety is required).