esa-sceva/satcom-chunk-collection huggingface dataset:
# An Equal Precision Programmed Cymometer Design Using Low-Power Technique
## 2 Working Principles  
### Basic Structure  
Figure 1 describes the control structure of the equal precision digital frequency meter. The structure is mainly composed of a pulse signal processing circuit, pulse width measurement circuit, frequency, and cycle measurement circuit, controlling and data-processing circuit, and displaying circuit. Among them, the pulse signal processing circuit is used to generate the measured signal [13]. After receiving the detecting signal from the duty cycle measuring circuit, the pulse width measuring circuit begins to measure the pulse width generated by the pulse signal processing circuit. During the process of pulse width detection and frequency processing, the reference frequency is supplied by the standard frequency signal generation circuit. By employing the waveform-shaping circuit, the input signal amplified by the preset amplifier module will be shaped for easy frequency acquisition. After the frequency input signal to be tested enters the signal amplification and shaping circuit for processing, it is distributed to the frequency and period measurement circuit together with the standard frequency signal for counting operation and measurement. At the same time, the input frequency signal to be tested and the standard frequency will also be linked to the pulse signal processing circuit for duty cycle measurement. Finally, the processed data are transmitted into the controlling and data-processing circuit for decoding and data processing, which will be displayed in real time by the display circuit [14]. The working process above is the basic equal precision measurement method. It should be noted that the biggest feature of the equal precision measurement method is that the preset gate time is not a fixed value, but a value related to the signal to be tested. Moreover, it is exactly an integer multiple of the period of the signal under test [15; 16; 17].
## 3 Research of the Key Elements
### Injection of eNB Deviation by Fiber Optical Repeater
The eNB noise floor is increased by the fiber optical repeater, as shown in Table 3.  
In the case that every one of the fiber optical repeaters has the same path loss \(L_{p}\), then the fiber optical repeater gain in n fiber optical repeaters is smaller than that of one fiber optical repeater. In n fiber optical repeaters, the dropped value of fiber optical repeater gain is approximately \(10 logn\). In standard practice, the uplink gain of a fiber optical repeater is set in the eNB receiver, following below considerations:  
1. The path loss \(L_{p}\) from eNB transmitter to fiber optical repeater. 2. The number of fiber optical repeater in parallel to eNB receivers. In standard use, the fiber optical repeater directly couples to the communication traffic signal, which comes from the eNB, and the path loss of the fiber optical repeater coupling to the coupler is \(L_{p}\). Applying the same approach for the uplink of the fiber optical repeater gains gives a small coupling loss of approximately 8 dB. Two parallel fiber optical repeaters would be needed in practice. In general, these can be chosen for a high coupling ratio coupler, where the input optical fiber traffic signal of \begin{table}
\begin{tabular}{c c c} \hline \hline  & \(10 log\left[1+n10^{\frac{N\!F_{rep}+N\!F_{eNB}+-L_{p}}{10}}\right](\text{dB})\) \\ \hline \multirow{2}{*}{\(n\)} & \multirow{2}{*}{\(\Delta N_{eNB}\) (\(n\))\(N\!F_{rep}\)\(-N\!F_{eNB}\)\(+G_{rep}\)\(-L_{p}\)(dB)} & \multirow{2}{*}{\(P_{eNB}\): Thermal Noise Power of eNB Receiver (dB)} \\  & & \\ \hline
2 & \(-9\) & \(0.98\) \\
1 & \(-6\) & \(1.77\) \\
0 & \(0\) & \(3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The eNB noise floor increases due to the fiber optical repeater. fiber optical repeaters = 0 dBm. Therefore, the path loss \(L_{p}\) from eNB to the fiber optical repeater = 39 dB. The uplink gain of the fiber optical repeater = 27 dB, so the fiber optical repeater injects the eNB receiver port, whose sensitivity are not influenced. The calculation of uplink budget for the fiber optical repeater is shown in Table 4.
# Temperature Dependence of Density and Viscosity of Biobutanol-Gasoline Blends
## 2 Materials and Methods
### Viscosity Measuring  
To determine the dynamic viscosity, a DV2T rotary viscometer (Brookfield, Middleboro, MA, USA) with standard spindle 18 (according the Brookfield) was used. The shear strain rate was set to a constant 200 s\({}^{-1}\). During the experiment, the similar procedure that was already used for gasoline-bioethanol and diesel-biobutanol blends in the papers [19; 24; 25] was followed. All the samples were measured in a temperature range from \(-\)10 to 40 \({}^{\circ}\)C (\(\sim\)263-313 K). Each sample was cooled down to \(-\)10 \({}^{\circ}\)C and gradually heated by 5 \({}^{\circ}\)C steps and after 2 min of stabilising the temperature, the viscosity was measured. A closed measuring cuvette was used to prevent evaporation of the sample during the measurement.  
The kinematic viscosity was calculated from the dynamic viscosity and volumetric mass density using the equation:  
\[\
u=\frac{\eta}{\rho} \tag{1}\]  
where \(\
u\) is the kinematic viscosity, \(\eta\) is the dynamic viscosity, and \(\rho\) is the volumetric mass density.
## 3 UWOC System Model
### Digital Signal Evaluation Cycle from Optical to Electronic--A Mathematical Viewpoint
Therefore, the filter electronic signal output \[r_{f}(t)\] is \[r_{f}(t)=\sum_{j=1}^{N(t)}\ h_{d}(t-t_{j})\otimes h_{f}(t)+r_{th}(t) \tag{3b}\]  
If we assume that \(h_{d}(t)=\delta(t)\), then Equation (3b) takes the following form:  
\[=\sum_{j=1}^{N(t)}h_{f}(t)+r_{th}(t) \tag{3c}\]  
We should note that the assumption of \(h_{d}(t)=\delta(t)\) is valid for modern fast-response PIN detectors. The first term is the signal component \(r_{sig}(t)\), and the second term \(r_{th}(t)\) is the AGTN. In Equation (3), \(\{t_{j}\}\) is the set of photoelectrons' arrival times governed by Poisson statistics. \(N(t)\) represents the stochastic counting process, which is an inhomogeneous process with a time-varying rate intensity of \(\{a_{k}\}\) in Equation (1). Figure 4: Typical direct detection optical receiver model. It is expected that the DU in Figure 4 will be able to estimate \(r_{f}(t)\) as an accurate replica of \(s(t)\). The accuracy and speed of decision-making, which is comparable to the function \(s(t)\), heavily relies on the computational processing strategy of the DU. This strategy minimises the BER to meet the receiver's performance goals.
## 8 Results and Discussion  
This section compares the performance of the vector-based SDR with its counterpart scalar-based SDR in degraded signal environments, including attenuation of the signal strength of one or more satellites, and the presence of the jamming signal. Both static and dynamic scenarios were considered.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Acquisition Search Space & 14 & kHz \\ Coherent Integration Time & 5 & ms \\ Non-Coherent Integration & 3 & blocks \\ Acquisition Threshold & 2.5 & \\ PLL Damping Ratio & 0.7 & \\ PLL Noise Bandwidth & 25 & Hz \\ DLL Damping Ratio & 0.7 & \\ DLL Noise Bandwidth & 2 & Hz \\ DLL Correlator Spacing & 0.5 & chip \\ \hline \hline \end{tabular}
\end{table}
Table 3: Software receiver parameters.  
Figure 6: Sky plot. Static scenario (**left**) and dynamic scenario (**right**).  
The first three scenarios are based on the static scenario described in the Section 7, while the last scenario was based on the dynamic scenario. The performance is evaluated in terms of signal tracking and navigation solution accuracy. Despite the availability of eleven visible in both static and dynamic scenarios, the number of tracked satellites inside the SDR was set to eight to assess the performance in case of the availability of fewer satellites. The selected satellites to be tracked were the ones with the highest acquisition metric, which is defined as the ratio between the first-highest peak to the second-highest peak in the acquisition search space.
## 3 High-Capacity DWDM PDM-16 QAM Optical Switch
### Experimental Results and Discussion  
Figure 9a-c shows the bit-error-rate (BER) for the 64 wavelength channels after the transmitter (back-to-back, measured at point A on Figure 8), after label-switching (measured at point B) and after packet addition (measured at point C), respectively, for every individual packet P1 to P8 and each measurement averaged over three packets (best three of 10 experimental trials). Discontinuities in the plots arise from error-free measurements. The observed saw-tooth shape is due to uneven biasing of the inphase-quadrature (IQ) modulator between odd and even channels and also to modulator BIAS instabilities. These plots show that PDM-16 QAM payload BER is below the FEC limit of \(3\times 10^{-3}\). Moreover, the soft horizontal lines show the total BER averaged for all odd packets (cyan) and even packets (magenta), showing a very small BER penalty after label-switching. However, after the addition of packets, even packets in the packet train suffer an additional penalty due to increased DGD originating in the optical switch.  
Next, we optimized the OPS transmitter by placing the packet carving MZMs in front of the modulator (instead of after the modulators) and optimized the DPMZMs BIAS. The average was computed over 10 traces. Figure 10 shows a significant BER improvement compared to Figure 9 and demonstrates the successful switching with a capacity of 10.24 Tbit/s/port. We confirmed that the OPS node processing using PLZT optical switches did not adversely affect the multi-level PDM-16 QAM mod  
Figure 9: (**a**) Back-to-back payload BER for optical packets P1-P8 as a function of the wavelength channel measured at point A in the setup of Figure 8; (**b**) payload BER after optical switching and label swapping measured at point B of Figure 8; (**c**) payload BER after packet addition measured at point C on Figure 8.
## 4 Applications of Heterogeneous Photocatalysis in Water Purification
### Pilot-Scale and Real Water Matrix Applications  
While most photocatalytic studies are conducted under laboratory conditions using synthetic water, there is growing interest in evaluating photocatalytic systems in real or natural water matrices. These environments contain competing ions, dissolved organic matter, and turbidity, which can affect the photocatalytic performance [124,125].  
Several pilot-scale photocatalytic reactors, including solar-driven systems and those powered by artificial light sources such as UV lamps and visible-light LEDs, have demonstrated promising results in degrading emerging contaminants under realistic operating conditions. Figure 8e illustrates the implementation of photocatalytic systems at pilot scale under real-world conditions, including the treatment of surface water, secondary effluents, and drinking water. It emphasizes the influence of complex water matrices, such as the presence of ions, organic matter, and turbidity on system performance, while showcasing the potential of solar-driven reactors for practical, decentralized water purification [126; 127]. However, further development is needed to address operational challenges and ensure regulatory compliance.
# Semi-Supervised Learning for Intrusion Detection in Large Computer Networks
## 3 Methodology
### Proposed Semi-Supervised Learning Models
#### 3.3.7 Combining These Concepts  
In semi-supervised learning with logistic regression and self-training, the workflow typically includes the following steps:  
1. **Initial Training**: Train the logistic regression model on a small labeled dataset.
2. **Prediction**: Use the trained model to predict probabilities for the unlabeled instances.
3. **Confidence Selection**: Select the instances with the highest confidence (i.e., the highest predicted probabilities) for which the model is most certain.
4. **Labeling**: Assign the predicted labels to these selected instances.
5. **Model Update**: Add the newly labeled instances to the labeled dataset.
6. **Retraining**: Retrain the logistic regression model with the expanded labeled dataset.
7. **Iteration**: Repeat the process of prediction, selection, labeling, and retraining until a stopping criterion is met (e.g., a certain number of iterations, achieving a performance threshold, or exhausting the labeling budget).  
The workflow is summarized in the Algorithm 2.  
```
1:Labeled dataset \(\mathcal{C}\), Unlabeled dataset \(\mathcal{U}\);
2:Trained Logistic Regression Model \(\mathcal{M}\);
3:Initialize: Train initial logistic regression model \(\mathcal{M}\) on labeled set \(\mathcal{L}\);
4:while stopping criterion not met do
5:for each instance \(x\in\mathcal{U}\)do
6: Predict the probability \(P(y=1|x)\) for \(x\) using \(\mathcal{M}\);
7:endfor
8: Select instances from \(\mathcal{U}\) with the highest confidence (i.e., probabilities close to 0 or 1);
9: Assign predicted labels to these high-confidence instances;
10: Add newly labeled instances to \(\mathcal{L}\) and remove them from \(\mathcal{U}\);
11: Retrain the logistic regression model \(\mathcal{M}\) using the updated labeled set \(\mathcal{L}\);
12:endwhile
```  
**Algorithm 2** Semi-Supervised Learning Workflow Using Logistic Regression  
**(3) Co-Training with a Random Forest Base Estimator.** This approach uses a co-training model with a random forest estimator as the base. Co-training with a random forest base estimator integrates concepts from both co-training and ensemble learning, and is adopted here for its high accuracy, robustness to class imbalance, and reduced exposure to overfitting [34; 35]. A comprehensive overview is provided below.
## 4 Probability-Based ToF Method  
By assigning a target occurrence probability for each point in the virtual calculation domain, the probability-based ToF method can give an estimated target area, whether these points are located on the ellipsoids or not. [20; 21] Therefore, the two problems caused by ToF measurement errors could be solved.  
Figure 4: The four ellipsoids do not intersect at one point because of ToF measurement errors.  
Figure 5: (**a**) The disjoint ellipsoids. (**b**) The disjoint curves.  
In the probability-based ToF method, the virtual calculation domain in water was evenly meshed. For a certain measured ToF, \(T_{i}\), the possibility of each mesh node being the target, was estimated using a probability density function [4],  
\[f_{i}(x,y,z)=(\frac{1}{\tau}\sqrt{2\pi})\exp\left\{-\frac{\left[T(x,y,z)-T_{i} \right]^{2}}{2\tau^{2}}\right\}, \tag{2}\]  
where \((x,y,z)\) were the coordinates of each node. \(T(x,y,z)\) was the theoretical ToF at the node. \(T_{i}\) was the measured ToF from the actuator to the \(i\)-th receiving position of the sensor. \(\tau\) was a decay factor representing the decay rate of an exponential function. Equation (2) indicates that the possibility of target occurrence at each node is determined by the deviation from its theoretical ToF to the measured ToF. The mesh nodes right located on the established ellipsoid corresponding to \(T_{i}\) were estimated to be the highest degree of probability. For the others, the greater the deviation from the measured ToF, the lower the probability of finding the target at these nodes.  
All estimation results for each measured ToF were combined to give the localization result in a matrix form. Each element of the matrix represented the probability of the presence of target for one mesh node,  
\[F(x,y,z)=\sum_{i=1}^{12}f_{i}(x,y,z), \tag{3}\]  
where \(N\) was the total number of actuator-target-sensor paths, referring to the total number of receiving positions.  
In our experiment, ToFs were extracted from the scattered signals, which were recorded at 12 receiving positions of the sensor. The localization result estimated by the probability-based ToF method is shown in Figure 6. The yellow dot represents the real target location where ultrasonic waves interacted with the target. The gray dot represents the node meshed in the virtual calculation domain. The darker the grayscale, the higher the possibility of finding the target in that area.  
The probability-based ToF method assigned the probability of target occurrence to the nodes that were not located on the ellipsoids. The method solved the two problems caused by ToF measurement errors to give an area estimate. However, the estimated target area was dispersive, meaning that it was not accurate enough. Moreover, the estimation result was a target area, not a target point. A new localization method should be proposed to narrow down the target area and give an accurate target location.  
Figure 6: Target localization result using the probability-based ToF method.
# Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method
## 4 Conclusions  
In this paper, we presented the concept of a static triangulation positioning system (STS) that operates at a refresh rate of \(f_{update}=50\) Hz. We demonstrated the basic principles of the system's operation, described its main components and carried out a preliminary theoretical simulation analysis, which confirmed the correctness of the chosen concept and the hardware components used in the designed devices. In the following sections, we described the measurement experiment carried out, and based on the recorded data, we determined the accuracy of our positioning triangulation system, which was \(\Delta\varphi=0.51^{\circ}\), and translated to an accuracy of \(\Delta R=6.55\) cm in position. It turned out that the system we built, despite not containing any moving/rotating sensors (as is commonly used), had comparable measurement accuracy to other positioning triangulation systems, and moreover, is a couple of times faster in terms of the refresh rate of the determined position, which was \(f_{update}=50\) Hz.  
However, the system still needs further modification. Based on the measurement data, it was noted that for measurements with a tripod in a fixed position with the receiver, the calculated position is described by a dispersion \(\sigma_{R}\) of up to 5 cm. The likely reason for this non-isotropy due to the angular position of the receiver is the differences in the characteristics of the measurement photodiodes mounted on the receiver disk. In our future research, we want to investigate what accuracy and isotropy will be achieved by a receiver composed of photodiodes that do not contain factory-mounted plastic lenses, as in the BPV22 NF diodes (produced by Vishay Intertechnology, Inc, Malvern, PA, USA) we used. We suspect that the lenses in the diodes may be the source of discrepancies in their characteristics.  
Conceptualization, M.C.; methodology, M.C. and R.K.; software, M.C.; validation, M.C. and R.K.; investigation, M.C. and R.K.; data curation, M.C.; writing--original draft preparation, M.C. and R.K.; writing--review and editing, M.C. and R.K.; visualization, M.C. and R.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Bialystok University of Technology grant number WZ/WE-IA/4/2023.  
Not applicable.  
Data Availability Statement:Dataset available on request from the authors.  
The authors declare no conflicts of interest.
# Stents in Congenital Heart Disease: State of the Art and Future Scenarios
## 2 Stent Design
### Geometry and Biomechanical Properties  
According to the choice of the specific raw material form, stents can be fabricated in different geometrical configurations, such as a slotted tube, helical spiral, coil, woven, individual rings, and sequential rings [23]. Such a variety of designs seeks to balance  
Figure 1: Different stent geometries according to the choice of the raw material form: (**a**) revolution of a sheet, (**b**) coil, (**c**) braided wires, and (**d**) slotted tubes, with detail on closed-cell (above) and open-cell (below) designs. Adapted with permission from [23]. (Elsevier, 2023).  
the major requirements for a stent, meaning radial strength, flexibility, and low elastic recoil [24]. While slotted-tube-type designs have excellent radial strength, they sometimes lack flexibility. The opposite is true of coil designs, which are currently used mostly in non-vascular applications, such as urethral and esophageal stenting.  
Woven stents refer to many different designs that can be obtained from one or more strands of wire: they include braided stents, which are more common in self-expandable structures; and knitted stents. Braided stents can guarantee excellent surface coverage and kinking resistance, but are affected by a substantial longitudinal shortening during expansion [28]; indeed, their radial strength is strongly dependent on the fixation that can be achieved at the stent ends.  
Single Z-shaped rings are typical for supporting endovascular prostheses such as aortic grafts; such rings are individually sutured or attached to the graft tissue, and can provide good radial support.  
The majority of cardiovascular stents, both balloon-expandable and self-expandable, can be grouped in the category of sequential rings, meaning the structural elements (also known as struts) are connected by bridges, hinges, or nodes. These stent designs can be grouped into two main categories, namely open- or closed-cell designs. In open-cell stents, some or all of the internal inflection points of the struts are not connected by bridging elements, creating a periodic pattern, whereas in closed cells, all internal inflection points of the struts are connected by bridging elements into a more regular scheme [24]. Reasonably, the closed-cell stents increase the radial strength and better contain the atherosclerotic plaque [29] compared with the open-cell stents at the expense of flexibility, coverage ratio, and ability to access the side branches that are covered by the stent [23]. Even though closed-cell geometries were successful at the early stages of their introduction, open-cell structures are now preferred in stent manufacturing, allowing for greater versatility.