esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Evaluation Parameters and Numerical Modeling  
#### 2.2.1 Kinematic Parameters and Mechanical Parameters  
In the hydrodynamic performance study of hydrofoil, the mean thrust coefficient \(\overline{C}_{t}\) and mean lift coefficient \(\overline{C}_{y}\) can effectively represent the force of the hydrofoil, which is a key parameter to measure the hydrodynamic performance of the hydrofoil; the formulas for calculating the mean thrust coefficient \(\overline{C}_{t}\) and mean lift coefficient \(\overline{C}_{y}\) are as follows [13]:  
\[\mathbf{C_{p}}=\frac{1}{kT}\int_{(n-k)T}^{nT}\mathbf{C_{s}}dt, \tag{4}\]  
where \(\mathbf{C_{p}}\) is the average force coefficient matrix, \(\mathbf{C_{p}}=[\overline{C}_{t},\,\overline{C}_{y}]^{T}\); \(\mathbf{C_{s}}\) is the instantaneous force coefficient matrix, \(\mathbf{C_{s}}=[C_{t}(t),\,C_{y}(t)]^{T}\); n is the total number of oscillation cycles of the calculation; \(k\) is the number of oscillation cycles, taken as \(1\); \(C_{t}(t)\) is the instantaneous lift coefficient, and the calculation formula is as follows:  
\[\mathbf{C_{s}}=\mathbf{F_{s}}\cdot\frac{2}{\rho\overline{U}^{2}cs}, \tag{5}\]  
where \(\mathbf{F_{s}}\) is the instantaneous thrust matrix. \(\mathbf{F_{s}}=[F_{x}(t),\,F_{y}(t)]\) where \(F_{x}(t)\) is the instantaneous thrust in the horizontal direction, N; \(F_{y}(t)\) is the instantaneous lift in the vertical direction, N; and \(s\) is the spreading length of the hydrofoil, which was taken to be 1 m by default for the two-dimensional calculations in this paper.  
Figure 3: Schematic diagram of the flow channel.  
Furthermore, the propulsion efficiency \(\eta_{\rm pt}\) and pumping efficiency \(\eta_{\rm pb}\) are frequently employed in the domain of pumping research to assess the efficacy of hydrofoils. The underlying mathematical relationship can be expressed as follows:  
\[\left\{\begin{array}{ll}\eta_{pt}=\frac{\overline{F}_{T}\cdot\overline{U}}{ \overline{B}_{in}}\\ \eta_{pb}=\frac{\Delta\overline{P}\cdot\overline{Q}}{\overline{P}_{in}}\end{array} \right., \tag{6}\]  
where \(\Delta\overline{P}\) is the average pressure difference between the outlet and inlet of the flow channel; \(\overline{F}_{T}\) is the average thrust generated by the hydrofoil on the fluid; \(\overline{Q}\) is the average flow rate at the outlet; and \(\overline{P}_{in}\) is the average input power; \(\overline{F}_{T}\), \(\overline{Q}\) and \(\overline{P}_{in}\) are calculated as shown in Equations (7)-(9):  
\[\overline{F}_{T}=\frac{1}{kT}\int_{(n-k)T}^{nT}F_{x}(t)dt, \tag{7}\]  
\[\overline{Q}=\overline{U}bS, \tag{8}\]  
\[\overline{P}_{in}=\frac{1}{T}\left(\left|\int_{t}^{t+T}F_{y}(t)\overset{ \bullet}{h}(t)dt\right|+\left|\int_{t}^{t+T}M(t)\overset{\bullet}{\theta}(t) dt\right|\right), \tag{9}\]  
where \(M(t)\) is the instantaneous driving torque of the hydrofoil; \(T\) is the period time; \(h(t)\) is the displacement in the Y-direction; \(\theta(t)\) is the pitching angle; and \(b\) is the flow channel width, taken as 2.8 m.
## 2 ToU Pricing Dataset and Synthetic Theft Cases Generation
### New Types of Pricing Models for Smart Grid  
Traditionally, consumers can only be billed for the electricity they used via static pricing models such as flat rates or tiered rates [1]. However, in the SG, utilities using smart meters are now capable of using new forms of time-based (or dynamic) pricing models such as [1]:  
1. Fixed Time-of-Use pricing (f-ToU): this typically applies to usage over broad blocks of hours (e.g., for a weekday, on-peak = 6 h at afternoon; off-peak = 6 h at night; normal = rest of the hours), where the price for each period is predetermined and constant.
2. Real-Time Pricing (RTP): pricing rates generally apply to usage on an hourly basis.
3. Dynamic Time-of-Use pricing (d-ToU): a hybrid of fixed time-of-use and real-time pricing, where the different periods for pricing are defined in advance, but the price established for the on-peak period varies by utility and market conditions.
# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
###### Abstract
The findings from this study are crucial for refining RS monitoring, managing variability, and improving predictive models for grapevine development.
# A Power Processing Circuit for Indoor Wi-Fi Energy Harvesting for Ultra-Low Power Wireless Sensors
## The Proposed Power Processing Circuit for the Radio Frequency Energy Harvesting System (RFEHS)  
### The General Block Diagram  
An illustration in Figure 1 shows a general block diagram of the proposed power processing circuit together with the power management unit (PMU) of the full RFEHS system. In the block diagram, \(P_{INC}\) is the incident power at the energy harvester. \(P_{REC}\) is the input power of the bridgeless rectification circuit, after the losses due to the energy harvester connector is subtracted from \(P_{INC}\). Typically, when integrating a radio frequency component with passive devices, an impedance mismatch between them will occur, and this contributes to some of the incident power is reflected. The reflected power is denoted as \(P_{REF}\). The power processing circuit consists of a bridgeless rectification circuit and a push-pull circuit. The function of the circuit is to process the received instantaneous power before the power is transferred to the PMU. The bridgeless rectification circuit and the push-pull circuit function are for generating a control signal for the boosting operation of the power processing circuit. The power needed to activate the push-pull circuitry is labelled as \(P_{Control}\). \(P_{Loss}\) is the power lost during the proposed circuit operation which is from the microcontroller and circuit themselves. The output power of the power processing circuit is labelled as, \(P_{OUT}\), where it uses the input power of the PMU. The PMU monitors and manages the output power of the circuit before storing it in an energy storage device.
# Sentinel: Automated Detection of Android Vishing Apps Using Optical Character Recognition
## 1 Introduction
Then, through OCR, Sentinel checks for the Display Overlay Attack that could be used alongside Call Redirection Attack by comparing the number displayed on the screen with the number dialed by the user. Finally, Sentinel examines the user's contacts and cross-references the phone numbers stored in the recent call log and SMS records to identify unused contacts, thereby defending against Duplicated Contacts Attacks. Sentinel stores the identified phone numbers in the blacklist and, after confirming the user's intention to delete them, proceeds with deleting the contacts. In the process, Sentinel does not collect or store unnecessary data and only accesses the minimum data needed to monitor duplicate contacts by asking for explicit permission from the user. This method emphasizes pre-incident detection and operates entirely within the Android application, ensuring user convenience. Furthermore, even if the APIs used to execute the same malicious actions are changed, the Sentinel remains capable of detection. Sentinel demonstrated a 100% detection rate for Display Overlay, Call Redirection, and Duplicated Contacts, achieving higher scores compared to commercial apps available on the Android Play Store. Additionally, we conducted a user study using a benchmark that we developed, which revealed that, when using Sentinel, only approximately 8.9% of users responded on average. We present the following contributions:  
* We analyze APIs provided in Android level 29 and above that exhibit behaviors similar to malicious activities. * We identify and highlight the limitations of currently available commercial vishing defense applications. * We examine existing attack techniques used by vishing applications and propose novel attack methods. * We implement a defense mechanism within an application to counteract vishing attacks, specifically focusing on Display Overlay Attacks, Call Redirection Attacks, and the newly defined Duplicated Contacts Attack, all without requiring OS modifications.
# NMR Analysis of Pulegone in Food Products
## Materials and Methods
### Quantification  
Quantification was performed using the pulse length-based concentration determination (PULCON) method. Here, the determination of the analyte concentration is based on the correlation between a separately measured reference solution and the intensity of the analyte signal [35]. When using the PULCON method, an external reference solution and the sample are measured using the same instrument [36]. A so-called Quantification Reference Solution (QuantRef) is used as the external reference substance. The QuantRef used here consists of p-xylene and cyclohexane in chloroform.  
For quantification of pulegone, either the dt at \(\delta_{\text{H}}\) = 2.74 ppm, the doublet at \(\delta_{\text{H}}\) = 1.81 ppm, or the mean value of both was selected. Generally, these signals were well separated from other signals for all essential oils containing pulegone. In case of overlap, which happened in less than 10% of the cases, the quantification relied on the signals that did not show overlap. The recovery rate can be determined, the linearity checked, and the real concentration of the analyte in the matrix determined [37]. The LOD and LOQ were also determined from the calibration curve [37]. In this study, standard additions of pulegone to peppermint, Chinese mint, corn mint, buchu, pennyroyal, and muna oil were performed.
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 1 Introduction
The contributions of our work are threefold:  
* We introduce a specialized FSKG that models the relationships between food categories and visual safety attributes, providing structured prior knowledge for zero-shot hazard detection. * We adapt and extend the Knowledge-Enhanced Feature Synthesizer framework to the food safety domain, addressing the unique challenges of fine-grained visual hazard detection through multi-source graph fusion and region feature diffusion. * We present a new Food Safety Visual Hazards (FSVH) dataset with rich attribute annotations, establishing a benchmark for evaluating zero-shot food safety hazard detection. The experimental results demonstrate that our approach achieves 63.7% mAP in zero-shot hazard detection, outperforming state-of-the-art general zero-shot detection methods by 6.9%. Furthermore, our framework demonstrates robust generalization to fine-grained novel hazard categories while maintaining a high detection performance (59.8% harmonic mean) in generalized zero-shot scenarios where both seen and unseen hazards may occur simultaneously. The remainder of this paper is organized as follows: Section 2 reviews related work on food safety inspections, zero-shot learning, and knowledge graphs. Section 3 details our proposed approach, including the design of the Food Safety Knowledge Graph, the adaptation of KEFS to food safety, and the training methodology. Section 4 presents the experimental results and comparisons with baseline methods. Finally, Section 5 concludes with a discussion of the implications and future directions.
## 6 Weld Inspection Application
### Experimental Verification  
The double probe penetration method can be used to detect the crack of rib-to-deck by finite element analysis simulation. Three rib-to-deck specimens with prefabricated cracks were fabricated to verify the applicability of ultrasonic penetration techniques in this section. The thicknesses of the deck roof parts were 12 mm. The thickness of the U-rib was 8 mm. The width of the specimen was 60 mm and 120 mm. The prefabricated cracks were initiated in the weld root of the specimen and expanded in the direction of the weld root inside the weld. The crack length was 20 mm, 35 mm and 50 mm respectively. The specimen size was shown in Figure 12 and Table 7.  
Figure 11: Wave propagation in a rib-to-deck specimen. (**a**) Waves travel through the probe; (**b**) waves reflect off the bottom of the component; (**c**) waves travel through the weld and (**d**) waves travel through the U-ribs.  
Figure 12: Sample details. (**a**) The dimension of the cracked sample and (**b**) artificial crack.  
The \(K\) value of probes was selected as 0.8 here. The transmitting probe and receiving probe were placed on the deck and U-rib respectively, and the two probes were kept moving in the direction of the vertical welding line. The wave heights at the crack-free and crack tip of the same specimen were measured, and the results were shown in Figure 13. It can be seen from the figure that the wave height of the crack tip was about 30% for the three kinds of cracks of different lengths and the maximum wave height at the crack-free position was 90%.  
There is a certain proportional relationship between the wave heights at the crack tips and the wave heights without cracks. The ratio of the two was approximately 0.33. The conclusion was consistent with the test results from flat plate specimens.
## 4 Discussion  
### DeepESN Processing for PAM and QAM-OFDM Signal  
The unusual behaviour of DeepESN processing performance for PAM signals can be attributed to the fact that higher-data-rate PAM signals, compared to lower-data-rate cases, feed more symbols to DeepESN with a fixed number of training states. From an optical communications perspective, after analogy-to-digital conversion, higher-data-rate signals have a lower oversampling factor; thus, they provide more symbols for DeepESN training. For instance, for a training period of 2 milliseconds, a 100 Mbps PAM signal could provide 100,000 symbols, while a 167 Mbps signal provides approximately 167,000 symbols for DeepESN training. Additionally, in a slow time-varying channel, there is less change between \(\mathbf{x}^{(i)}(t-1)\) and \(\mathbf{x}^{(i)}(t)\) in higher-data-rate signals, allowing DeepESN to better learn and adapt to the slow time-varying factors in UWOC channels. Consequently, DeepESN can offer improved signal processing performance due to its inherent properties of state dynamics in layered recurrent neural architectures.  
In contrast, this phenomenon was not observed for OFDM signals. This can be explained by the fact that an OFDM symbol is composed of a CP and a block of IFFT output, with each OFDM symbol occupying 264 samples. As a result, the number of reservoir units was insufficient to cover the entire OFDM symbol. Furthermore, QAM-OFDM modulation accommodates input data at different data rates by utilizing a varying number of subcarriers while maintaining the same OFDM symbol rate. Consequently, the reservoir units always cover the same portion of the OFDM symbol in different data rates. Although DeepESN processing cannot encompass an entire OFDM symbol, the enhancement in system resilience against signal deterioration with increasing data rates is evident in the power-saving plot in Figure 3b.  
Figure 5: The BER and calculating time against different numbers of recurrent layers (\(L\)) and units (\(U\)).
# Movable Platform-Based Topology Detection for a Geographic Routing Wireless Sensor Network
## 4 Evaluation
### Setting of Prior Conditions  
By comparing hops of all nodes of these three routing protocols from the station, we found 90% nodes of GEM and GEAR routing protocols are in similar hops from the station, although their nodes are selected up to different standards. We guessed for WSN using location-based routing protocol, the hops between their nodes and the station should be consistent. Through simulation, we found as long as \(\alpha/\beta>1.5\) in Equation (9), hops from the nodes of the simulated routing protocol to the station are over 90% similar with the hops from the nodes in GEM and GEAR routing protocols to the station.  
Through analysis we thought in Equation (9), \(\alpha/\beta>2\) (shown in Figure 8) is the precondition that the target network selects routing nodes in minimum hops, laying a foundation for predicting how many hops the nodes of location-based routing protocol are from the station. According to the simulation, we can obtain the target network that uses location-based routing protocols and same nodes are in similar hops from the station. Although the hops from a node to the station change due to lots of influencing factors, the main factor is the distance from node to station. In view of the above, we can know it applies to similar target networks to estimate node hops by taking advantage of the distance from node to station. In terms of fitting node hops, we found the distance from node to station is larger than node hops, but the distance, after quantified by the maximum communication distance of node (referring to as quantified distance from node to station) is always smaller than node hops (shown in Figure 9). Based on these two estimation values, we rounded off and averaged numbers again and again with the method of bisection, and finally fitted one estimation curve of node hops, which is roughly consistent with the actual curve of node hops (shown in Figure 10).  
Figure 8: \(x\)-coordinate is the value of \(\alpha/\beta\) and the \(y\)-coordinate is the proportion of all nodes of the simulated routing protocol and GEM routing protocol that are in same hops from the station. When \(\alpha/\beta=2\), the proportion is larger than 90%.  
Figure 10: \(x\)-coordinate is the codes of all nodes, and the 120 th node is the station. \(y\)-coordinate is the hops or distance from nodes to the station. The black curve is the estimated node hop curve after fitting and the red curve is the universal node hops.  
Figure 9: \(x\)-coordinate is the codes of all nodes, and the 120 th node is the station. \(y\)-coordinate is the hops or distance of nodes from the station. The black curve is the distance curve from nodes to the station, the red curve is the universal node hop curve, and the green curve is the quantified distance curve from nodes to the station.
# Delineation of the Flooded Areas in Urban Environments Based on a Simplified Approach
## 3 Case Study and Mathematical Model
### Mathematical Model
The IDF curves provided by the Tulcea meteorological station were used for the case study. The rainfall duration (40 min) was considered to be equal to the concentration time of the catchment, and was obtained from the basin characteristics of slope and hydraulic length. Concerning the design storm structure, multiple methods have been developed over recent decades; consequently, selecting a suitable design storm can be seen as subjective. There are two main categories of methods for deriving the design storm [34; 35], namely frequency-based methods (derived from IDF curves) and mass curve methods (based on observed rainfalls). The most popular frequency-based methods are the Chicago design storm or Alternating Block Method (ABM). Among the mass curve methods, the most common are the SCS design storm, Huff's curves, and the Pilgrim and Cordery method. Although the frequency-based methods are widely used in hydrological modelling or for stormwater drainage system design, they do not represent the registered temporal patterns for a specific area. Hence, in the current paper, the design storm was based on the structure of the observed 2013 storm event in order to have a better temporal representation of the region. For this purpose, the recorded values of the 2013 event were scaled both on the abscissa and on the ordinate, while keeping the total amount of precipitation derived from the IDF curves unchanged, for the previously computed concentration time of the urban basin. In the present study, simulations for design rainfalls corresponding to the frequencies 1:2; 1:5; 1:10, and 1:100 were run.
## 3 Datasets
### Synoptic Stations  
Numerous precipitation observations from synoptic stations distributed throughout Iran (see Figure 1a) were acquired from the Islamic Republic of Iran Meteorological Organization (IRIMO) for over 20 years, from 2000 to the end of 2020, to evaluate the performance of selected GPPs. On average, daily observations from 229 synoptic stations were employed for validation, starting with 164 synoptic stations in 2000 and reaching 244 synoptic stations in 2020 (see Figure 2). A notable effort was made to select the most appropriate stations by considering the long-term and consistent daily observations. Consistency of the considered in situ observations for GPP validation allows for a robust evaluation and comparison. As is evident, a larger number of synoptic stations with consistent observations were available in the central to the western parts of the country, where higher precipitation rates occurred. On the other hand, relatively fewer synoptic stations were located in the central to the eastern parts of the country with lower precipitation rates, where unvegetated planes and deserts existed [59]. In total, over 1.7 million daily observations (see Figure 2) were employed to evaluate the selected GPPs. These daily observations were also aggregated to generate accumulated and average monthly and annual precipitation values for further investigations.  
One crucial issue in evaluating the GPPs is incorporating enough independent in situ observations [11]. In other words, the GPPs that have been subjected to calibration or bias correction used interpolated precipitation data from in situ observations. Therefore, it is necessary to determine the data independency rate to ensure an appropriate evaluation. In this regard, the underlying in situ data of GPCC, GHCN, GSD, and GTS were examined, and it was observed that, on average, over 81% (at least) of synoptic stations were not  
Figure 2: The number of synoptic stations and the corresponding daily precipitation observations between 2000 and 2020 that were used to validate and intercompare the selected Gridded Precipitation Products (GPPs).  
employed in the creation of these datasets, justifying the independent evaluation of the current study. However, it should be noted that such precipitation products, used for calibration and bias correction, are mostly monthly averaged interpolated values and are not used in daily scales. Accordingly, their values are not identical to the used in situ observations of this study, increasing the data independency rates.
# Tumor Volume Distributions Based on Weibull Distributions of Maximum Tumor Diameters
## 2 Materials and Methods
### Tumor Volume Distributions Determined from SEER for Breast, NSCLC Liver  
To apply the presented method of deriving volume distributions from diameter distributions, we extracted maximum diameters from the SEER [25] database for the three tumor types breast, NSCLC liver. The SEER database has generally contained tumor diameter at time of diagnosis data since 1975, and, according to SEER guidelines, tumor size is measured as the largest diameter or dimension of the tumor. The way tumor size is coded in SEER has changed over time. For this work, only post-1988 data were considered because pre-1988 data were categorized with much poorer accuracy. The data were queried using SEERS Stat [26].  
For each of the three tumor types, multiple diameter distributions were created, each depending on the year of diagnosis. This allowed us to test the diagnostic limit function for plausibility, as more recent diagnoses should also contain smaller tumors (small \(d_{c}\)) due to improved diagnostic capabilities and more frequent screening. Table 1 shows the distribution of patients over the different time periods. The data obtained from the SEER queries were filtered to include only the relevant tumor diameter ranges. For breast cancer, data were restricted to female patients older than 15 years. Weibull distributions were fitted to the data using PVWave (version 2017.0, Rogue Wave Software, Minneapolis, MN, USA) by minimizing the chi-squared. The parameters \(\lambda\) and \(k\) were fitted to all time periods, while \(d_{c}\) was fitted to the specific time period.