esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Passive Islanding Detection Solution Based on Accumulated Phase Angle Drift
## 3 Composite Islanding Detection Solution
### Phase Angle Drift Calculation Criterion  
A special status of \"Abnormal Status\" is defined as \"Balanced Abnormal Status\" when the three phase voltages shifts are well balanced. If Equations (9)-(11) are satisfied, the state of the power system is \"Balanced Abnormal Status\".  
\[\left\{\begin{array}{c}\Delta Ang_{Min}(n)>\Delta Ang_{set}\\ \Delta Ang_{Min}(n)=Max\{|\Delta Ang_{A}(n)|,|\Delta Ang_{B}(n)|,|\Delta Ang _{C}(n)|\}\end{array}\right. \tag{9}\]  
\[Sign[\Delta Ang_{A}(n)]=Sign[\Delta Ang_{B}(n)]=Sign[\Delta Ang_{C}(n)] \tag{10}\]  
\[\left\{\begin{array}{c}\Delta Ang_{Max}(n)-\Delta Ang_{Min}(n)<50\%\cdot \Delta Ang_{Average}\\ \Delta Ang_{Average}=(\Delta Ang_{A}(n)+\Delta Ang_{B}(n)+\Delta Ang_{C}(n ))/3\end{array}\right. \tag{11}\]  
Equations (10) and (11) ensure that the voltage vector shifts are three-phase balanced.  
If the power system is in \"Balanced Abnormal Status\", the calculation of the accumulated phase angle drift will be initiated as Equation (12).  
\[\left\{\begin{array}{c}\sum Ang(n_{0}+1)=0+2\pi\cdot[F(n_{0}+1)-F(n_{0})] \cdot\Delta T\\ \sum Ang(n)=\sum Ang(n-1)+2\pi\cdot[F(n)-F(n-1)]\cdot\Delta T\end{array}\right. \tag{12}\]  
In Equation (12), \(F(n_{0})\) is the last frequency measurement calculated in the \"Normal Status\"; \(\Delta T\) is the phase angle calculation interval (such as 20 ms in 50 Hz system and 16.67 ms in 60 Hz system); \(n_{0}+1\) is the index number of the first power cycle after \"Balanced Abnormal Status\" is detected. If the accumulated phase angle drift \(\sum Ang(n)\) is higher than the setting \(\sum Ang_{set}\), the islanding condition is claimed.
## 3 Dependency Graph  
To attain a more profound comprehension of the computational interdependencies and structures outlined in Equation (6), a dependency graph (DG) can be utilized for illustration. In examining the three matrix-vector products \(H_{1}\), \(H_{2}\), and \(H_{3}\) delineated in Equation (6), it is evident that they operate within a common processing framework, yet they exhibit distinct variations in the organization and presentation of input components to the computational system. Figure 1 exhibits the DG corresponding to the matrix-vector products detailed in Equation (6), employing a two-dimensional integer domain \(\mathcal{D}\) characterized by indices \(i\) and \(j\). The DG comprises \(r\times r\) nodes, with each node signifying the operations specified by the formula in Equation (6). Importantly, the coefficients for the matrix-vector products \(H_{1}\), \(H_{2}\), and \(H_{3}\) are calculated sequentially, commencing with the coefficients for \(H_{1}\). The resultant product \(H\) can be obtained after a latency of \(r+2\) clock cycles, by aggregating the outcomes from the three product vectors \(H_{1}\), \(H_{2}\), and \(H_{3}\).  
The flow of signals in the DG depicted in Figure 1 can be characterized as follows: From the left side, the signals \(f_{i}\), \(f_{i}\), and \(f_{r-i+1}\) are introduced sequentially, with \(i\) taking values from 1 to \(r\). In addition, signals \(e_{r-i+1}\), \(e_{i-1}\), and \(e_{r-i}\) are introduced from the left corners of the nodes along the DG's left edge, following a similar sequential order. From the top of the DG, the signals \(e_{j-1}\), 0, and \(e_{r-j}\) are introduced in succession, where \(j\) ranges from 1 to \(r\). Additionally, the zero values of the signals \(h_{j}\) are introduced from the top of DG. It is essential that these signals are entered in the correct sequence from the upper part of the DG at designated timings. As processing occurs at each node, the intermediate coefficient results of the matrix-vector products \(H_{1}\), \(H_{2}\), and \(H_{3}\) are computed and transmitted to the subsequent row of nodes. Ultimately, the result \(H\) is derived by summing the coefficients from \(H_{1}\), \(H_{2}\), and \(H_{3}\), with this summation executed using XOR gates.
# Development of Multi-Channel Seismic-Electrical Combined Rolling Coverage Measurement System
## 2 Design of the Combined Measurement System
### Design of Analog Channel Circuit  
The analog channel filters and amplifies signals from geophones or electrodes, then digitizes them via an ADC chip before passing them to the main control unit. As shown in Figure 7, each channel is equipped with an independent amplification and filtering circuit, along with a dedicated 24-bit ADC. The system uses the AD7175 [29], a 16-channel, 24-bit, 250 ksps \(\Sigma-\Delta\) ADC featuring a 20 ms settling time and a true rail-to-rail buffer, which communicates with the main controller via SPI.  
As shown in Figure 8, the SoPC control circuit adjusts the preamplifier gain via an analog switch to accommodate varying input signal amplitudes during seismic and electrical data acquisition. Compared to seismic signals, electrical signals are more susceptible to power-frequency interference. Considering the versatility of the analog channels, we have added a twin-T notch filter circuit to each analog channel. As shown in Figure 9, the SoPC uses an analog switch to connect or bypass the twin-T notch filter circuit, allowing the power-frequency interference to be filtered from the signal when needed. The twin-T notch filter uses 0.1%-tolerance resistors and low-temperature-drift COG capacitors to ensure a stable frequency response, achieving a 50 dB notch at 50 Hz.  
Figure 6: The schematic diagram of the channel input/output control circuit during seismic method data acquisition.  
Figure 7: Block diagram of data acquisition.  
The front-end filter of the ADC converter is used to eliminate high-frequency components from the input signal, preventing frequency aliasing during sampling. Each channel is equipped with an independent preamplifier, filter, and ADC, enabling simultaneous data acquisition from all channels and thereby enhancing efficiency. The SoPC processes and stores the voltage and current data acquired by the ADC. The computer retrieves the data from the instrument's storage via Wi-Fi or Ethernet.
# CNT-PUFs: Highly Robust and Heat-Tolerant Carbon-Nanotube-Based Physical Unclonable Functions +
## 4 Fabrication and Implementation of CNT-PUFs
### CNT-PUF Characterisation  
After CNT integration, the wafer was characterised by evaluating AFM topography images to determine the realised CNT density depending on the different integration process parameters. This way, the number of CNTs crossing arbitrary cross-sections drawn within the device channel in the parallel direction of the later-defined source and drain electrode edges was counted, and a CNT line density was extracted and used for the studies contained in Section 5.  
After finishing the fabrication, each individual cell of the final CNT-PUF was electrically characterised using a Cascade PA200 semi-automated probe station and a Keithley 4200A-SCS semiconductor parameter analyser. These data provided the basis for the evaluation in Section 5. A representative transfer curve of a CNT-FET reflecting a conductive cell is shown in Figure 3. Such ambipolar transfer curves were captured in particular for the investigations in Section 5.3. There, pulsed transfer curves were measured in order to gain a complete understanding of the temperature dependence for the CNT-FETs. In this measurement procedure, a voltage pulse with \(-V_{\text{GS}}\) was applied before the actual measurement at \(V_{\text{GS}}\) to unload the charge traps and, thus, suppress hysteresis effects. The maximum current for the p-branch \(I_{D,\text{max},\text{p}}\) was extracted from these curves. Extracting multiple transfer characteristic measurements requires a considerable time investment. Therefore, for all investigations, fast measurements using a single set of bias voltages were performed to ensure statistically profound results within an acceptable amount of time. There, the maximum drain current \(I_{D,\text{max},\text{p}}\) was captured at \(V_{GS}=-2.5\,\text{V}\) and \(V_{DS}=-1\,\text{V}\). For the robustness measurements, we limited ourselves to 28 cycles. After this number of measurements, the PUF response remained essentially unchanged, so this was found to be the best compromise between time consumption and the number of repeated measurements.
## 2 Related Work
### Shadow Compensation  
Physical models-based shadow compensation methods rely on radiometric measurements and geometric information. These methods compensate for the spectral quality of shadows by analyzing the basic principle of shadow formation. [PERSON] et al. [29] used spectral decomposition to compensate for shadows. This method optimizes the linear mixture model to better distinguish and compensate for shadow regions. [PERSON] et al. [30] proposed a method to compensate for shadows in hyperspectral images through nonlinear spectral decomposition, which distinguishes between non-shadowed and shadowed endmembers and uses weighted non-shadowed spectra to compensate for shadow spectra. [PERSON] et al. [31] developed a shadow correction method that adjusts radiance values based on variations in sunlight strength and wavelength. This method calculates the radiance ratio between non-shadowed and shadowed areas, applying linear regression to compensate for the radiance of shadowed pixels across different spectral bands. [PERSON] et al. [32] introduced a method for shadow compensation, progressing from a 1-D illuminant invariant representation to a full-color 3-D image. This process includes shadow edge recognition and repair, effectively compensating for shadows while preserving texture details.  
Deep learning methods extract shadow features from large datasets, and the trained model effectively preserves the spectral features of materials. [PERSON] et al. [17] developed a method to generate shadow-invariant hyperspectral image features using deep learning and physics-based illumination modeling, eliminating the need for labeled data or extra sensors. [PERSON] et al. [16] introduced an unsupervised method for shadow compensation in hyperspectral images, effectively transforming shadowed regions to non-shadowed areas without the need for paired samples or prior shadow detection.  
Most of the existing hyperspectral shadow compensation methods are based on physical models [33]. This is because deep learning-based methods require a large amount of training data [34; 35], and hyperspectral datasets are generally collected in a single pass, without shadow-free reference data, making them difficult to train. The methods of [PERSON] et al. [36] and [PERSON] et al. [37] inspire us to compensate for shadow spectra while maintaining the original ground material details when constructing physical models.
# GIS-Based Identification of Locations in Water Distribution Networks Vulnerable to Leakage
## 2 Problem Statement  
Water authorities are responsible for providing a safe and reliable water supply to the end users. NRW is a significant challenge for water authorities in most countries, especially the developing ones where control mechanisms are often cost prohibitive. Recent statistics show that the worldwide NRW volume is about 126 billion cubic meters annually, which translates into 346 million cubic meters daily [26]. Studies have shown that 25-50% of treated water is considered NRW due to deteriorating water distribution infrastructure, leakages, faulty billing systems, illegal connections, inaccurate water pressure management, and errors in metering, among other factors. This surpasses the World Bank recommendation of 25% of the total water produced, with many countries recording NRWs exceeding 60% [27]. In developing countries, about 45 million m\({}^{3}\) of water is lost daily, representing over USD 3B annually [28; 29]. NRW values are the highest among low-income and lower-middle-income regions, and relatively lower among the upper-middle-income and high-income countries [30]. The high NRW level poses severe economic and environmental implications [31]. Therefore, measures should be taken to reduce water losses in WDNs, even in developing countries.  
In Sharjah, UAE, Sharjah Electricity and Water Authority's (SEWA) water leakage percentage is approximately 20%--slightly lower than worldwide values [32]. However, even a small percentage of loss is financially significant, as potable water supply poses a considerable challenge in this part of the world due to freshwater scarcity. Sharjah overcomes this challenge by relying on expensive seawater and brackish groundwater desalination to produce potable water to meet the increasing water demand and compensate for the water losses. Potable water production by desalination consumes enormous energy and has numerous negative environmental impacts on both air and water bodies, thereby increasing the carbon and water footprint [33]. Thus, water leakage must be managed to save energy, money, and the environment. Water leakage management can be categorized into leakage assessment, detection, and control. Leakage assessment quantifies the amount of water leaked, leakage detection locates the leaking parts of the WDN, and leakage control regulates current and possible future leakage percentages in the WDNs [9]. This study aims to develop a leakage detection method to help identify locations in the WDN that are vulnerable to leakage, considering major conditioning factors. The innovative aspects introduced in this work with respect to the known literature include the use of logistic regression and vulnerability analysis, which is utilized to identify the parts of WDN that are susceptible to leaks.
# Satellite Network Task Deployment Method Based on SDN and ICN
## 4 Task Deployment Method Based on Genetic Algorithm
### Variation  
Variation refers to the change of gene values at some loci of individual strings in a population. The genetic algorithm introduces mutation for two purposes: one is to make genetic algorithm have local random search ability. When the genetic algorithm has approached the neighborhood of the optimal solution through the crossover operator, the local random search ability of the mutation operator can accelerate the convergence to the optimal solution. According to the different representation methods of individual coding, there can be the following algorithms: (1) Real value mutation; (2) binary variation. Generally speaking, the basic steps of mutation operator operation are as follows: (1) Randomly select mutation bits for mutation; (2) judge whether the variant is a feasible solution. According to the form of the solution in this paper, binary mutation is adopted, and the probability of individual mutation is set as 0.01.  
Therefore, the satellite network task deployment method based on genetic algorithm is shown in Algorithm 1:  
```
Input: List of available satellite nodes downstream of satellite nodes \(L\) and network topology diagram between nodes \(G_{L}\), network status diagram between available satellite nodes \(G_{T}\), task decomposition diagram, list of available resources of nodes \(L_{s}\), balance coefficients \(\alpha\), \(\beta\).
1. Initial population; Generate a matrix of \(m\times n\) individuals and a population of \(N^{c}C_{c}\).
2. Parameters of the genetic algorithm: iteration number MaxGen, iteration state Gen=0; The crossover probability is \(P_{c}\) and the mutation probability is \(P_{p}\).
3. While gen<MAXGEN then
4. Calculate the fitness \(Fit(A)\) of each individual, order the individuals according to Tfitness and copy the individuals with the highest fitness into the next generation population.
5. Select two individuals \(A^{1}\) and \(A^{2}\) with probability \(P(A)\) for crossover operation, and judge whether the individuals \(A^{1}_{new}\) and \(A^{2}_{new}\) generated after crossover meet resource constraints. Detect individual \(A^{1}_{new}A^{2}_{new}\).
6. If Individuals meet resource constraints then:
7. Insert \(A^{1}_{new}\),\(A^{1}_{new}\) into the population \(C_{c}\);
8. Else
9. Re-perform the crossover operation;
10. End If
11. The mutation operation is carried out with the probability \(P_{v}\), and the individual \(A\) generated after the mutation is \(A_{v}\);
12. If \(A_{v}\) meet resource requirements then:
13. Insert \(A_{v}\) into population \(C_{c}\);
14. Else
15. Delete \(A_{v}\);
16. End If
17. Gen = gen + 1;
18. Preserve the fittest individuals of each generation.
19. End While
20 Outputs: the individual with the highest fitness, the list of subtask deployment locations \(L_{d}\), and the value of the minimum utility function \(Q_{n}\).
```  
**Algorithm 1**********Satellite Network Task Deployment Method Based on Genetic Algorithm
# Methods for Determining Polycyclic Aromatic Hydrocarbons (PAHs) in Milk: A Review
## 3 PAH Detection Methods in Milk and Dairy Products
### Instrumentals Detection Methods  
The extracted and purified PAH analytes are then subjected to instrumental analysis, most often chromatographic. These methods include high-performance liquid chromatography (HPLC) and gas chromatography (GC), which allow for the effective separation of analytes. On the other hand, the identification and quantitative determination of individual polycyclic hydrocarbons is ensured by an appropriate detector. Table 3 presents examples of PAH research applications on specific devices, specifying the detectors used and analysis conditions [84; 85].  
Other methods are less frequently used and appear to be of interest to researchers, e.g., spectrometric analysis (room-temperature phosphorimetry (RTP), molecular fluorescence analysis (MFA) or surface-enhanced Raman spectroscopy (SERS)), the chemiluminescence (CL) method and electrochemical method, as well as immunological detection methods (like enzyme-linked immunosorbent assay (ELISA)). The development of methods, especially those in which ready-made kits can be used, may be justified due to shortening the analysis time and reducing the complexity of the research procedure. However, the reference methods for them will still be chromatographic methods [25].
### Motivation  
While the traditional Private Set Intersection Cardinality (PSI-CA) protocols have been extensively explored in research, real-world applications continue to present unique challenges. Traditional approaches assume similar-sized datasets and equal computational power, overlooking practical imbalances. In real-world applications, dataset sizes and computational capacities often vary, particularly in Internet of Things and mobile scenarios where device limitations restrict computational types. Traditional PSI-CA protocols are inefficient here, as computational and communication complexities correlate with the size of larger datasets. Thus, adapting PSI-CA protocols to these imbalances is crucial.  
A compelling example is found in the collaboration between major medical institutions and small health app developers. In this scenario, a large medical institution with extensive patient data and robust computational capabilities collaborates with a small app developer who possesses minimal user data and limited computational resources. The primary goal is to analyze the coverage of health app users within the extensive patient database to assess market penetration and potential partnership opportunities. For instance, the medical institution might want to determine how many of its patients are using the health app to consider recommending it more broadly or collaborating on new features.  
This article uses the above scenario as an example to conduct research, but our scenario is not limited to this, and the data types are also diverse. As long as one party has a large number of dataset elements and strong computing and storage capabilities, while the other party has a small number of dataset elements and weak computing and storage capabilities, and both parties want to perform PSI-CA operations, our solution is applicable.  
To address the challenges outlined above, this paper delves into the unbalanced Private Set Intersection-Cardinality (PSI-CA) protocols and proposes three innovative unbalanced PSI-CA protocols. In practical applications, different solutions can be chosen based on varying performance and security requirements.
## 2 Array Design  
In radar applications, some antenna requirements are crucial. In particular, the half-power beamwidth (HPBW) is related to the spatial resolution of the radar, the sidelobe level (SLL) is related to false target detection, and the scan range defines the region of space where the antenna can properly detect a target without a beam-shape degradation. Even the radiating element polarization is crucial, and dual-polarized elements are interesting for multifunctional and reconfigurable systems.  
In this work, we aim at investigating a planar 40 \(\times\) 40 dual-polarization AESA capable of producing a pencil beam (about 3\({}^{\circ}\) of half-power beamwidth at broadside) over an angular range of 60\({}^{\circ}\) on the elevation plane and a full coverage on the azimuthal plane, with a sidelobe level of \(-\)30 dB to be maintained for the whole scanning range and over the design frequency range, also exhibiting polarization reconfigurability with a low cross-polarization level and high isolation. C-band patch antennas are considered for the design, as shown in Section 3, with a fractional bandwidth of 10% and equally spaced over a rectangular lattice by \(d_{x}=d_{y}=\lambda_{0}/2\). The main frequencies in the design band will be called \(f_{\text{min}}\) for the lower frequency, \(f_{\text{max}}\) for the higher frequency, and \(f_{0}\) for the center frequency. In particular, \(f_{\text{min}}=0.95f_{0}\) and \(f_{\text{max}}=1.05f_{0}\). The latter properties can be demanded by the element factor and will be discussed in Section 3. In the next subsections, we focus on the design of the array factor (AF) with analytical techniques to meet the constraints of having a pencil beam with controlled SLL over the entire scanning range.