esa-sceva/satcom-chunk-collection huggingface dataset:
# Swin-MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN
## 2 Methods
### Swin Transformer Extracts Branches
#### 2.2.2 Multi-Scale Residuals and Dense Blocks  
Satellite remote sensing images contain feature compositions of different sizes, which may result in missing feature information if features are extracted using a single-scale convolution kernel. To address this problem, we refer to [PERSON]'s [34] design and introduce multi-scale convolution to extract feature information of different sizes. The multi-scale feature extraction block uses a CNN implementation that utilizes parallel connections of multiple-scale convolutional kernels. This allows for the extraction of features at different scales. As shown in Figure 4, the multi-scale residual and dense block contains four different sizes of convolutional kernels, and the underlying branch extracts small-scale features using multiple 3 \(\times\) 3 convolutional kernels, which reduce the losses incurred in feature extraction through the residual structure. Convolutional kernels of different sizes are chosen for each of the three upper feature branches to extract feature information of different sizes. This design allows the network to retain local information of different sizes. The structure of multi-scale residuals and dense blocks is shown in Equation (5).  
\[\begin{array}{c}top=Conv_{y\times 9}(HS/pan)\\ upper\ middle=Conv_{7\times 7}(HS/PAN)\\ lower\ middle=Conv_{5\times 5}(HS/pan)\\ bottom=DRB_{3\times 3}(HS/PAN)\\ MRDB_{hs/pan}=Conv_{1\times 1}(Concat(top,upper\ middle,lower\ middle,bottom))\end{array} \tag{5}\]  
where top, upper middle, and lower middle represent the top three feature extraction branches, respectively; \(Conv_{y\times 9}\), \(Conv_{7\times 7}\), \(Conv_{5\times 5}\), and \(Conv_{1\times 1}\) represent the size of the convolution kernel, respectively; \(DRB_{3\times 3}\) represents the dense residual block at the bottom; and \(MRDB_{hs/pan}\) represents the output of the multi-scale feature extraction block.  
Figure 4: Overall framework diagram of multi-scale residuals and dense blocks.
# Late Line-of-Sight Check and Prioritized Trees for Path Planning
## 6 Experiment
### Results and Analysis  
From the experiments in the previous three subsections, including the benchmark test, the complex street test, and the test in virtual scenes, we can find that LLA* ran the fastest among the four given planners, and it generated shorter paths than the Lazy Theta* and A* algorithms. LLA* employed the discriminatory updating strategy to explore the states that had the potential to improve the final solutions with priority, and this strategy was helpful in accelerating the planning algorithms.  
The planning results in the three tests showed that LLA* performed the least LoS-Check, and this could explain why LLA* ran the fastest, especially on the large size of the maps with a complex environment. Both the lazy evaluation strategy and the discriminatory updating strategy led to less LoS-Check, which sped up the planning procedure. Actually, LLA* was resolution complete like A*, which was bound to find a solution if it really existed.  
LoS-Check connected a state with its grandparent, but it did not work at a corner of the blocked cell because the line-of-sight was blocked by the obstacles. Meanwhile, LLA* further delayed LoS-Check, which reduced the risk that the line-of-sight was blocked. Thus, LLA* generated shorter paths than Lazy Theta* and sometimes generated shorter paths than Theta*. Of course, with the help of LoS-Check, LLA* always found a better solution than A*. LLA* did not change the framework of A*, and the worst case for LLA* was degrading into A*.  
Overall, the LLA* algorithm showed a good and robust performance in path planning, which outperformed Theta*, Lazy Theta*, and A* in the aspect of planning speed. LLA* also generated shorter paths than Lazy Theta* and A*. However, the planning algorithms that used LoS-Check, such as LLA*, Theta*, and Lazy Theta*, were not suitable in nonholonomic planning. In nonholonomic planning, if LoS-Check were used, some states in the configuration space would not be explored because of constraints (e.g., angle constraint), making the planning algorithm not complete. Thus, it is an interesting research topic how to employ path shortcutting methods in nonholonomic planning.
## 6 Discussion
### Low Power Consumption  
We conducted a series of experiments on active tags without specifically evaluating the power consumption of the tags. Though the tag structure is quite simple. [PERSON] et al. [17] discuss the performance of two methods for providing power to an ultra-low power, ambient backscatter tag, omitting the need for any battery. We believe that by designing reliable energy harvesting modules, it will ultimately be possible to achieve harmonic interference resilient frequency shifting on passive tags. Additionally, in our actual system, there are limitations in the selection of the MCU. We can choose a more power-efficient MCU to meet specific power requirements. Furthermore, when our system needs to be deployed on IoT devices with limited resources, a PWM controller within the MCU can be utilized to generate PWM signals. This PWM signals can be further leveraged to construct adaptive pulse-width square waves. This approach alleviates the computational burden on the main CPU, thereby enabling more efficient power management and improving response times.
# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
### Failure Modes of Railway S&C Systems
#### 2.4.4 Detection and Locking Subsystem  
The detection and locking subsystem is a major source of failure in railway S&C systems. [PERSON] et al. [51] showed that failures due to detection and locking accounted for 47.32% and 28.51% of total switch failure incidents for clamplock and HW switches, respectively, in the UK network during the study period of August 2008 to September 2011. The sources of failure are wear of the mechanical locks and detection bars, split pins, misalignment of the mechanical assembly, etc. HPSS locking is achieved by the electrical brake and non-backdrivable lead screw. Thus, any failure in the power source can result in failure of the electrical brake, and the locking then depends on the lead screw only, which makes HPSS prone to failure due to detection and locking failure (47.99% [51]).
## 3 Results and Discussion
### Properties of Sintered Aggregates
#### 3.2.2 Particle Density of the Sintered Aggregates  
The particle density of the sintered aggregate is plotted in Figure 13. It can be seen from Figure 13 that the particle density range of those that were sintered without adding textile sludge, i.e., those with only water purification sludge as the main raw material, ranged from 1.77 to 1.90 g/cm\({}^{3}\). During the sintering process, the particle density of the samples added with three kinds of textile sludge could be effectively reduced. Regardless of the type of textile sludge blended, the particle density of the sintered aggregates became smaller as the sintering temperature increased. At the sintering temperature of 1150-1200 \({}^{\circ}\)C, adding textile sludge from Sample A, Sample B, and Sample C resulted in the particle density ranges of the sintered aggregates being 0.54-1.1, 0.6-1.23, and 0.69-1.19 g/cm\({}^{3}\), respectively. Among them, after adding textile sludge from Sample A, the particle density of the sintered aggregate was lower than 1.1 g/cm\({}^{3}\). When the sintering temperature was increased to 1200 \({}^{\circ}\)C, the particle density of the sintered aggregate was even lower than 0.6 g/cm\({}^{3}\). However, at different sintering temperatures, the amount of added textile sludge from Sample A had little effect on the particle density of the sintered aggregates. Adding textile sludge from Sample B and Sample C could also significantly reduce the particle density of the sintered aggregate, and the higher the sintering temperature, the smaller the particle density of the sintered aggregates. Among them, the particle density obtained by adding 7.5% of textile sludge was the smallest. It can be seen from the test results that the particle density of the aggregates sintered by adding textile sludge to water purification sludge could meet the requirements of general lightweight aggregates (between 0.2 and 1.8 g/cm\({}^{3}\)).  
Figure 12: Relationship between the water absorption rate and sintering temperature for the water purification sludge blended with textile sludge: (**a**) Sample A; (**b**) Sample B; and (**c**) Sample C.  
Figure 13: Relationship between the particle density and sintering temperatures for water purification sludge blended with textile sludge: (**a**) Sample A; (**b**) Sample B; and (**c**) Sample C.
## 3 Results and Discussion
### Adsorption Kinetics  
The kinetics study depicts the uptake rate of an adsorbate, and obviously this rate controls the residence time of adsorption at the solid-solution interface in the whole adsorption process [59]. In the present study, the experimental data were fitted to the pseudo-first-order and pseudo-second-order kinetic models. The pseudo first-order equation can be expressed as follows [60; 61]:  
\[\log(q_{e}-q_{t})=\log q_{e}-\frac{k_{1}t}{2.303} \tag{16}\]  
where \(q_{e}\) and \(q_{t}\) are the amount of MG adsorbed (mg/g) on the adsorbent at the equilibrium and at time \(t\) (min), respectively, and \(k_{1}\) is the rate constant of adsorption (L/min). The pseudo-second-order equation is given as [62]:  
\[\frac{t}{q_{t}}=\frac{1}{k_{2}q_{e}^{2}}+\frac{t}{q_{e}} \tag{17}\]  
where \(k_{2}\) (g/mg\(\cdot\)min) is the rate constant of pseudo-second-order adsorption.  
The values of parameters obtained from these two models are presented in Table 9. From the values of \(R^{2}\), it can be concluded that the adsorption data is well fitted to the pseudo-second-order kinetic model. Nevertheless, its calculated removal efficiency has a slight deviation compared with the experimental removal efficiency. As shown in Figure 18, the full removal process reached an equilibrium within 120 min.
###### Abstract  
The feasibility study of a 24 GHz industrial, scientific, and medical (ISM) band Doppler radar antenna in electromagnetic aspects is numerically performed for near-field sensing of human respiration. The Doppler radar antenna consists of a transmitting (Tx) antenna and a receiving (Rx) antenna close to the human body for a wearable device. The designed slot-type Doppler radar antenna is embedded between an RO4350B superstrate and an FR-4 substrate. To obtain the higher radiation pattern of the antenna towards the human body, a ground plane reflector is placed underneath the substrate. The measured \(-\)10 dB reflection coefficient (S\({}_{11}\)) bandwidth is 23.74 to 25.56 GHz and the mutual coupling (S\({}_{21}\)) between Tx and Rx antennas is lower than \(-\)30 dB at target frequencies. The Doppler radar performance of the proposed Doppler radar antenna is performed numerically by investigating the signal returned from the human body. The Doppler effect due to human respiration is investigated through the I/Q and arctangent demodulation of the returned signal. According to the results, the phase variation of the returned signal is proportional to the displacement of the body surface, which is about 0.8 rad in accordance with 1 mm displacement. The numerical experiments indicate that the proposed Doppler radar antenna can be used for near-field sensing of human respiration in electromagnetic aspects.  
d 2020 10 6159 10.3390/app10186159
## 3 System Model and Problem Statement
### Radio Model  
Making realistic modeling of radio wave propagation between low-power sensing devices is greatly challenging. Based on the previous discussions about the radio model [36; 37; 38; 39], two modes are adopted to analyze this work: the free space model and two-ray ground propagation model [40]. The free space model assumes that in the ideal propagation condition, there is only one clear, unobstructed line-of-sight path between the transmitter and receiver. However, two-ray ground propagation model predicts path loss when the signal received consists of the line-of-sight component and multi path component formed predominately by a single ground reflected wave. Two-ray ground propagation model is reasonably accurate for predicting the large-scale signal strength over a long transmission distance. In addition, for a shorter transmission distance, free space model gives a better prediction. Then, a distance threshold \(d_{0}\) is introduced to determine which model should be adopted. If the distance between the transmitter and receiver is shorter than this threshold, we adopt the free space model; otherwise, we adopt the two-ray ground propagation model.  
At the transmitter, the energy spent to transmit a _k_-bit packet can be expressed as follows [38]:  
\[E_{Tx}(k,d)=\left\{\begin{array}{ll}kE_{elec}+k\varepsilon_{fs}d^{2}&d<d_{0} \\ kE_{elec}+k\varepsilon_{amp}d^{4}&d>d_{0}\end{array}\right. \tag{1}\]  
where \(d\) is the distance between the transmitter and receiver; \(E_{elec}\) is the amount of energy spent for a one-bit packet by the transmitting or receiving circuit; \(\varepsilon_{fs}\) and \(\varepsilon_{amp}\) are the amplifier characteristic constants corresponding to the free-space propagation model and two-ray ground reflection model, respectively; and \(d_{0}\) is the distance threshold which can be calculated as follows [39]:  
\[d_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{amp}}} \tag{2}\]  
At the receiver, the amount of energy spent for receiving a _k_-bit packet can be calculated as follows [20]:  
\[E_{Rx}(k)=kE_{elec}+k\left(\frac{1}{a}-1\right)E_{DA} \tag{3}\]  
where \((1/a\)-\(1)E_{DA}\) is the amount of energy spent to aggregate one bit of data and \(a\) is data compression ratio within the range of (0,1], when \(a=1\) means no compression.  
As concentric ring-based network model is adopted in this paper, we assume that each CH has a short inter-cluster data transmission distance and employs free-space propagation model for its inter-cluster communication.
## 5 Performance Evaluation of the Charging Circuit
### Temperature-Correction Algorithm for the Power System  
As mentioned above, the evaluation reveals that the low-temperature performance of the detection circuit maintained a strong stability at a temperature range of \(-\)50-30 \({}^{\circ}\)C. However, we still developed an approach to correct and minimize deviation, because the accuracy of the detection circuit directly affects the operation effect of the charging strategy. The relationship between output current of the detection circuit \(I_{D}(T)\) and corrected current \(I_{out}(T)\) could be approximated as follows:  
\[I_{out}(T)=a(T)I_{D}(T)^{3}+b(T)I_{D}(T)^{2}+c(T)I_{D}(T)+d(T) \tag{5}\]  
where \(a(T)\), \(b(T)\), \(c(T)\), and \(d(T)\) are the coefficients of Equation (5).  
We put the whole detection circuit into the GDJS series high-low-temperature test chamber over a range of \(-\)50-30 \({}^{\circ}\)C and measured the accuracy of the output current. During the experiment, the interval of the measured temperature was set as 5 \({}^{\circ}\)C from \(-\)50 \({}^{\circ}\)C to 30 \({}^{\circ}\)C. We measured the output of the detection circuit by the oscilloscope. Oscilloscope results were compared with the output current of the detection circuit. At each measured temperature, the high-low-temperature test chamber maintained the temperature for 20 min in which measurement was repeated 300 times. We took the average values to minimize statistical error and uncertainty. The coefficients \(a(T)\), \(b(T)\), \(c(T)\), and \(d(T)\) could be simultaneously calculated.  
The results of measured currents of detection circuit and ideal currents are shown in Figure 15. We observe that the discrepancy between actual currents and measured values remained in the minimal range. The maximum and minimum errors were 0.0061 and 0.0015 A, respectively.  
The coefficients \(a(T)\), \(b(T)\), \(c(T)\), and \(d(T)\) decreased approximately in a parabolic manner with increase of temperature (Figure 16). The maximum and minimum \(a(T)\) were \(-\)3.4 \(\times\) 10\({}^{-7}\) at 30 \({}^{\circ}\)C and \(-\)5.83 \(\times\) 10\({}^{-7}\) at \(-\)50 \({}^{\circ}\)C, respectively. The maximum and minimum values of \(b(T)\) were 1.28 \(\times\) 10\({}^{-5}\) at \(-\)50 \({}^{\circ}\)C and 7.47 \(\times\) 10\({}^{-7}\) at 30 \({}^{\circ}\)C, respectively. The maximum and minimum values of \(c(T)\) were 0.0298 at \(-\)50 \({}^{\circ}\)C and 0.0017 at 30 \({}^{\circ}\)C, respectively. The maximum and minimum values of \(d(T)\) were 0.211 at \(-\)50 \({}^{\circ}\)C and 0.012 at 30 \({}^{\circ}\)C, respectively. The value of coefficients \(a(T)\), \(b(T)\), \(c(T)\), and \(d(T)\) at the nonmeasured temperatures could be predicted by interpolation.  
Figure 15: Temperature dependence of the current of the detection circuit.
# Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing
## 1 Introduction
### Preparation of Data for the System
#### 1.3.3 Classes of Disease  
* **19--**Lyme disease
* **27--**Infectious mononucleosis
* **47--**Dermatophytosis (skin fungus infection)
* **51--**Hemiplegia
* **112--**Kidney stones
* **125--**Diabetes
* **126--**Type II diabetes
* **135--**Pneumonia
* **177--**Influenza (flu)
* **178--**Osteoarthritis
* **184--**Herpangina
* **188--**Bacterial middle ear infection
* **191--**Giardiasis
* **194--**Iron deficiency anemia
* **196--**Benign prostatic hyperplasia  
Each of the above classes represents a specific disease entity. These classes are used in a diagnostic system that generates response vectors based on a set of questions.
## 4 Experimental Results
### Performance Evaluation
#### 4.2.4 Detailed Connection Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **PSO** & **GA** & **Hybrid** \\ \hline Coverage Area (km\({}^{2}\)) & 25.01 & 24.62 & 26.57 \\ \hline Connection Counts & 19.0 & 21.0 & 20.0 \\ \hline Average Connection Distance (km) & 1.74 & 1.69 & 1.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of optimization methods. Figure 9: Optimized layout from the hybrid GA + RL method, achieving the best trade-off between coverage and connectivity. Overall, the hybrid optimization framework surpasses traditional GA and PSO techniques in critical performance metrics, establishing its potential for real-world applications requiring adaptive and robust drone network deployment.
# Optimal Particle Filter Weight for Bayesian Direct Position Estimation in a GNSS Receiver
###### Abstract  
Direct Position Estimation (DPE) is a rather new Global Navigation Satellite System (GNSS) technique to estimate the user position, velocity and time (PVT) directly from correlation values of the received GNSS signal with receiver internal replica signals. If combined with Bayesian nonlinear filters--like particle filters--the method allows for coping with multi-modal probability distributions and avoids the linearization step to convert correlation values into pseudoranges. The measurement update Equation (particle weight update) is derived from a standard GNSS signal model, but we show that it cannot be used directly in a receiver implementation. The numerical evaluation of the formulas needs to be carried out in a logarithmic scale including various normalizations. Furthermore, the residual user range errors (coming from orbit, satellite clock, multipath or ionospheric errors) need to be included from the very beginning in the stochastic signal model. With these modifications, sensible probability functions can be derived from the GNSS multi-correlator values. The occurrence of multipath yields a natural widening of the probability density function. The approach is demonstrated with simulated and real-world Binary Phase Shift Keying signals with 1.023 MHz code rate (BPSK(1)) within the context of a real-time software based Bayesian DPE receiver.  
GNSS; Bayesian direct position estimation; BDPE; software receiver; particle filter; optimal particle weight +
Footnote †: journal: Sensors
# _remote sensing_
## 2 Introduction and Altimeter Calibration Method of WSCS
### Altimeters and Calibration Methods  
The HY-2B, HY-2C, Jason-3, and S3A altimeters are calibrated in this research, and the SSH of the altimeters (SSH\({}_{\rm alt}\)) are defined using Equation (1).  
\[\rm{SSH}_{\rm alt}=H-(R+WZD+DZD+IC+SSB+SET+LT+PT) \tag{1}\]  
where the H is the height of the altimeter above the ellipsoid;  
R is the range from the altimeters to the surface of the ocean;  
WZD/Dry is the wet/dry zenith delay caused by the wet and dry atmosphere, respectively;  
IC is the ionosphere correction;  
SSB is the sea state bias which was obtained using empirical models;  
SET is the solid Earth tide;  
LT is the loading tide height;  
PT is the pole tide.  
The calibration of the SSH of the altimeters using the ATGs was defined using Equation (2).  
\[\rm{Bias}=SSH_{\rm alt}-SSH_{in-situ}-DMSS-DTidal \tag{2}\]  
where the \(\rm{SSH}_{\rm tg}\) is the sea level of the in situ measurements at the time of altimeter overflight which was described in Section 3.2;  
DTidal is the tidal differences between the in situ measurements and the altimeter footprints which was described in Section 3.3.  
DMSS is the MSS differences between the ATGs and the altimeter footprints, which were described in Section 3.4.
## 4 Modeling the Identity Registration Status
In the considered use case, the ILM service instance in FRMCS system 2 subscribes to notifications from the location service about the UE entering and leaving its serving area. The registration procedure starts by switching on the UE. 1. Once the UE is switched on, it sends a registration request to the RIM service in FRMCS system 1, as described in previous sections. The RIM service requests an update regarding the UE registration status using the ILM service in the CR by means of a PATCH request. 2. The CR acknowledges the update and stores the FRMCS system 1 identity as serving the UE. 3. The RIM service queries the CR for the UE serving profile and for its functional numbers if the UE is of the \"Cab radio\" or \"PA\" types by means of GET request. Figure 11: Flow of initial UE registration and subsequent registration in a new FRMCS system. The CR provides the requested information. * The RIM service acknowledges the UE registration. * The RIM service subscribes to notifications about UE registration status with the ILM service in the CR using a POST request. * The UE may register one or more of its functional numbers, and in the case of successful registration, the RIM service in the CR subscribes for their registration status. * While moving, the UE enters the serving area of FRMCS system 2. * The location service in FRMCS system 2 notifies the ILM service instance in FRMCS system 2 about the UE entering its serving area. * As a client of the ILM service, FRMCS system 2 retrieves information about the UE from the ILM service in the CR using a GET request. * The CR sends the requested information, including the identity of the FRMCS system where the UE is registered. * The ILM service in FRMCS system 2 queries the RIM service in FRMCS system 1 for information about UE serving profile and the registered UE functional numbers. * The RIM service provides information about the UE serving profile and registered functional numbers for different pieces of UE and their profiles. * Now that information about the registered UE and its registered functional numbers is available, and the ILM service client in FRMCS system 2 requests an update in the CR of the FRMSC serving area using a PATCH request. * The CR acknowledges the updated registration status of the UE. * The CR notifies the RIM service in FRMCS system 1 about the change in the registration status of UE and its functional numbers. * The RIM service terminates its subscription to the UE and to its functional numbers' registration status with the ILM service in the CR and considers the UE as being unregistered.