esa-sceva/satcom-chunk-collection huggingface dataset:
# A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
## 4 Analytical Models with Different Outlier Filtering Criteria
### Numerical Analyses  
To demonstrate the proposed analytical model, the following parameters were adopted to estimate the expected ranges of the longitude and latitude of the \(i\)-th cell-RSSI pair: \(\mu_{x,i}=120.3259728\), \(\mu_{y,i}=22.56716916\), \(\sigma_{x,i}=0.001271737\), \(\sigma_{y,i}=0.000940652\). Figure 3 shows the expected ranges of longitude with different outlier filtering thresholds \(t\). The expected range is approximately 0.001003427 (_i.e._, 103.03 m) when \(t=3\times\sigma_{x,i}\) (_i.e._, \(t=0.003815211\)). Moreover, the expected range is \(\frac{2\sigma_{y,i}}{\sqrt{2\pi}}=0.001014699\) (_i.e._, 104.19 m) when \(t=\infty\). Therefore, the improvement of location determination is 1.16 m after outlier filtering with \(t=3\times\sigma_{x,i}\). However, the availability \(p_{x,i}\) decreases when the outlier filtering threshold \(t\) is decreased. Therefore, there is a trade-off between the location error and availability.  
Figure 4: The expected ranges of latitude with different outlier filtering thresholds.  
Figure 3: The expected ranges of longitude with different outlier filtering thresholds.  
Figure 4 shows the expected ranges of latitude with different outlier filtering thresholds \(t\). The expected range is approximately 0.000742194 (_i.e._, 82.53 m) when \(t=3\times\sigma_{y,i}\) (_i.e._, \(t=0.002821957\)), and \(\frac{2\sigma_{y,i}}{\sqrt{2\pi}}=0.000750532\) (_i.e._, 83.46 m) when \(t=\infty\). Therefore, latitude location determination improves to 0.97 m after outlier filtering with \(t=3\times\sigma_{y,i}\).
## 3 Results
### Breakdown Results of High Voltage Capacitors  
Determining whether the capacitors can operate safely at their intended voltage levels is crucial for evaluating the success of the study. In this section, breakdown voltage measurements were conducted in the high-voltage laboratory to assess the suitability of the capacitors' operating voltage values. This testing ensures that the designed capacitors can reliably function under their specified operating conditions.  
In the measurements performed, the breakdown voltages were obtained as 14.02 kV for the PLA capacitor, 18.38 kV for the ABS capacitor, 12.34 kV for the ASA capacitor, and 16.37 kV for the PETG capacitor.
## 2 Related Work
### Ethernet Passive Optical Network (EPON)-Based Communication Network for WPFs  
EPON consists of an OLT located in the control center, multiple ONUs, and POS. In the downstream direction, EPON is a point-to-multipoint network; the OLT broadcasts control messages and data packets to all ONUs through a passive splitter. In the upstream direction, EPON is a multipoint-to-point network; multiple ONUs transmit data to the OLT through a passive combiner [11].  
Figure 2 shows a schematic view of the proposed EPON-based communication network for WPF [12]. The proposed network consists of an optical network device (ONU) deployed on the WT's side for collecting different data, including wind turbine operation, meteorological data, fault parameters, and security from different internal networks. All ONUs from different WTs are connected to an OLT unit, placed on the control center side. The path between WTs and the control center does not contain any active elements, which saves costs and reduces the complexity of maintenance and deployment, compared with current switched gigabit Ethernet.  
Figure 1: Schematic view of conventional wind power farms (WPF) network.  
In Figure 2, the proposed architectures (star, cascade, _etc._) represent the structure design of new planned WPFs. The hybrid configuration (Ethermet switch and ONU) represents modifying existing WPFs to support EPON technology, which is out of the scope of this work. In a star configuration, four wind turbines are connected with distributed fibers to the control center using one POS (\(1\times 4\)). In the cascade configuration, four POS (\(1\times 2\)) are connected in cascade; one port is connected to the next WT, while the other port is connected to the WT-ONU unit.  
In this work, EPON uses a 1-Gbps single fiber, with a transmission range of 20 km. Two different wavelengths are used to support downstream and upstream transmission, 1490 nm and 1310 nm, respectively. Each WT transmits data in its time slots, to avoid data collisions. EPON must employ a medium access control (MAC) mechanism, to arbitrate access to the shared medium, to prevent different ONU's data from colliding in the upstream direction. OLT efficiently shares the upstream transmission bandwidth among all turbines\({}^{\prime}\) ONUs. Failure to gain access to this shared channel in a timely manner could negatively impact the communication and should be considered as an aspect of reliable communication. There have been many studies and frameworks for managing the medium access control in [13; 14; 15; 16].
## 3 Characteristics of FESS  
The principal characteristics of flywheels are high energy density and power density [7; 9], high charge and discharge rates, high round trip efficiency, rapid response, durability (greater than 20 years) and high cycle life (hundreds of thousands) with low environmental impacts [2; 3; 9; 28; 29; 48]. The rotational speed is used to easily measure the state of charge, as it is unaffected by temperature or wear [9]. There are drawbacks, however, such as a higher self-discharge rate compared to other storage mediums and the hazardous nature of flywheel rotors, if not safely designed. In comparing the self-discharge of a flywheel to other storage technologies, it is important to also account for energy of auxiliaries required to maintain the whole system, i.e., a maintenance loss. For instance, many battery systems need to be heated or cooled, and so this incurs a loss to be added to any actual self-discharge, which is relatively small for batteries.  
Flywheels have a long lifespan, with very low maintenance and operational requirements. Compared to other energy storage systems, flywheel cycle life is quite high as there is no requirement for long charge-discharge cycles. The charge-discharge cycle can occur rapidly based on the functionality and application, and is unaffected by the depth of discharge (DoD). It is estimated that FESS can last more than 20 years, and that there would be no performance deterioration, despite a charge-discharge cycle life of hundreds of thousands [3; 28; 29]. The technology has the capability to transfer large amounts of power (in seconds) with a high 90-95% range of roundtrip efficiency [1; 2; 35]. Its stored energy can be delivered, followed by a rapid recharge, within a matter of seconds. Further, since the construction material in FESS is not harmful to the environment and there are no emissions produced when in operation, it can be considered as environmentally friendly [29; 35]. Flywheel energy and power ratings are independent, and can thus be optimised for a specific energy storage application. The size of the MG and related power electronics help to determine the power rating of a flywheel, whereas the speed and size of the rotor contributes to the determination of the energy rating [3; 8].  
Flywheels are widely used in applications where a high power output for short periods is required. The overall dimension of a FESS unit is a vital characteristic that puts it in competition with some other energy storage technologies. The power densities of batteries can be as much as five to ten times less than that of flywheels. Because flywheel systems take up less volumetric space and have a longer operational life, they can be a worthy replacement for batteries in applications such as space vehicles and transportation [8]. Another area yet not explored is the application of flywheels in residential solar photovoltaic systems, where they can operate as a standalone storage system or supplement chemical batteries.
## 3 Graph Representation of the IoT Network  
An IoT network can be depicted as a graph, where the nodes represent the devices and the edges represent the relationships between the devices. The foundation of graph transformations and their applications are resumed in [16; 17; 18]. To specify the IoT behavior, we use graph transformations as they have been successfully applied a few times to specification of the behavior of large parallel systems [19; 20] or IoT networks [21].  
To accurately represent the network, a predefined set of metadata needs to be applied to define its characteristics. Metadata encompasses the static properties of the network, which are used to qualify and quantify specific elements within the network.  
An example of metadata could be the physical distance between sensors. This distance can impact other properties, such as the network latency between elements. Unlike static properties like physical distance, network latency is a dynamic property that is calculated based not only on the distance but also on the number of network hops between two elements.  
Translating this concept into the graph mechanism is not a straightforward process, and thus it is divided into several steps.  
Firstly, we introduce the representation of local capabilities, which consists of a node- or edge-attributing function and a set of capability verification rules. These verification rules are evaluated after any modification of the attribute parameters.  
More complex capabilities, known as global capabilities, are associated with subgraphs of the IoT network representation. They take the form of predicates linked to specific capabilities and are evaluated after any modification of the predicate parameters. To avoid exponential complexity in implementation, certain limitations need to be imposed on this concept.  
The graph presented in Figure 1 represents an IoT network where the following definitions apply:  
* TS represents the temperature sensor;
* HS represents the humidity sensor;
* LS represents the light sensor;
* CE represents the management and communication unit.  
The other elements of the IoT are based on the work of the second author, described in [22]:  
* MS represents the movement sensors;
* LC represents the lighting control elements;
* TC represents the temperature control elements;
* Other definitions apply.  
It is important to note that node attributes encapsulate elementary capabilities, such as verifying that a temperature sensor's correct output falls within the range from 19 to 23 degrees Celsius. Additionally, certain capabilities may pertain to static attributes, such as numerical values, while others may involve attributes derived from computations.
## 5 Experimental Verification of Phase-Modulated Spectra
Thus, it is experimentally verified that the GTF \(t_{GTF2}\) can be arbitrarily phase-modulated by properly controlling (\(\theta_{Q1}\), \(\theta_{H1}\), \(\theta_{Q2}\), \(\theta_{H2}\)), specifically, appropriately selecting (\(\theta_{Q1}\), \(\theta_{H1}\), \(\theta_{Q2}\), \(\theta_{H2}\)) that satisfies the WOA sets suggested in Figure 4. Ultimately, we conclude that any GTF generated in the proposed comb filter can be phase-modulated, or wavelength-tuned, continuously by taking advantage of WOA sets that can always be drawn out with our reverse tracing algorithm. Figure 6: Actual experimental setup for measurement of phase-modulated transmission spectra of GTF (\(t_{GTF2}\)).
# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 5 Discussion
### Limitations of Deep Learning for Medical Image Cryptography and Future Prospects
#### 5.2.4 Data Availability and Quality  
Large-scale high-quality datasets are required for the training of deep learning models. Access to large labeled datasets, however, may be constrained in medical image cryptography because of data scarcity and privacy issues. Future developments could include the creation of privacy-preserving methods that permit the training of models on encrypted or decentralized data sources while retaining the integrity and security of the data.  
In a nutshell, despite deep learning's potential for medical image cryptography, it has drawbacks related to generalization, adversarial attacks, processing demands, and the availability of high-quality data. Future possibilities include overcoming these constraints through research and development initiatives targeted at enhancing generality, robustness, efficiency, and accessibility of high-quality data.
# A Novel Variable Step Size Incremental Conductance Method with an Adaptive Scaling Factor
## 4 Simulation and Experimental Results
### Simulation Result
As shown in Figure 6, the irradiance level and panel temperature are 500 W/m\({}^{2}\) and 30 \({}^{\circ}\)C during 0-5 s, which increases to 800 W/m\({}^{2}\) and 50 \({}^{\circ}\)C during 5-10 s, and decreases to 300 W/m\({}^{2}\) and 20 \({}^{\circ}\)C during 10-15 s. From Figure 6, the performance indexes such as tracking time, tracking accuracy, and tracking energy loss can be calculated. The obtained results are summarized in Table 4. As shown in Figure 6 and Table 4, the proposed method has the best performance for the  
Figure 4: Simulation results of different \(M\) values (a) \(M\) = 0.65, (b) \(M\) = 1.08, (c) \(M\) = 1.08/0.65 under increasing irradiance level. transient tracking speed and steady-state tracking accuracy. Moreover, the proposed method has a minimum tracking energy loss. Figure 5: Simulation results of different \(M\) values (**a**) \(M=0.65\), (**b**) \(M=1.8\), (**c**) \(M=0.65/1.8\) under decreasing irradiance levels. Figure 6: Power tracking curves of different \(M\) values.
# IoT Helper: A Lightweight and Extensible Framework for Fast-Prototyping IoT Architectures
## 5 Experiments  
In this section, we report a number of experimental results with IoT Helper. Given the low cost and the limited hardware profile of the Arduino module, we are especially interested in investigating two essential questions:  
* Our first goal is to investigate the capability of IoT Helper to collect potentially large volumes of data in real-time applications.
* Our second goal is to study scalability in terms of number of sensors, actuators and clients that IoT Helper can handle.  
To find answers to the previous questions, we conducted two main groups of experiments in a controlled environment.  
**Pull-Mode Experiments**: These experiments measure performance in a data-collection scenario where a client application uses IoT Helper to collect increasingly high-volumes of data from sensors. We measured the number of messages sent from the embedded system to a client subscribed to the data channel.  
We fixed seven different frequencies of data collection. Then, we measured the number of the messages received by the client in a period of fixed time (10 min) with different numbers of sensors. For each experiment, we have an _expected number of messages_, corresponding to the total number of data items collected by the sensors in the time interval and the _actual number of messages_ received by the client. We report the comparison of these two numbers to measure the effectiveness of IoT Helper.  
For example, in the scenario with the frequency of one message per second, we expect that after 10 min 600 data items have been collected ad published on the channel. If the actual number of messages received by the client is lower than 600, IoT Helper is dropping values and, therefore, generating errors.  
**Command-Mode Experiments**: These experiments measure the performances of IoT Helper in a scenario where clients send commands and receive data. Our goal is to evaluate the number of clients and commands that the embedded system is able to effectively handle.  
We fixed the number of sensors and actuators plugged into the Arduino shield. We then simulate four different scenarios with 2 to 5 clients. Clients sent both read and write commands with different time intervals. We measured the average time of response of the embedded system. We also measured the errors, i.e., messages lost or handled after 10 s.  
This section is organized as follows: (i) First, we introduce the setting in which experiments were conducted. (ii) Then, we discuss the results of Pull-Mode Experiments and (iii) the Command-Mode Experiments, respectively. (iv) Finally, we conclude this section by discussing some insights.  
Figure 7: Quality control scenario architecture.
## 2 Methods and Data
### Data Source and Pre-Processing
#### 2.2.9 Data Pre-Processing  
In this study, the data were separately pre-processed before they were inputted into the PLS regression model. The spatial resolution of these data was not wholly consistent; this paper, therefore, employed the nearest interpolation method to unify their spatial resolution into 8 km. These data have a consistent temporal resolution of monthly. The period of 1982-2018 was selected as the study period.
## 2 GNSS Signal Characteristics  
This study uses a Huawei Mate30 as the main experimental object, as shown on the right in Figure 1. Huawei Mate30 is an Android smartphone with a Kirin 990 chip developed by Huawei Technologies Co., Ltd., in September 2019, and it supports the L1 and L5 dual bands of GPS, B1 band of BDS, G1 band of GLONASS, and E1 and E5a dual bands of Galileo. Referring to the calibration of the GNSS antenna L1 phase center of the smartphone [9], the antenna phase center is located at the top of the equipment with a small offset to the right, and there no millimeter-accurate phase center exists. Therefore, the Huawei Mate30 is placed on the reference point SYS2. The top point of the smart phone is on the axis that is orthogonal to the plane and crosses the reference point, and the distance between the two points is used as the height of the antenna on the smartphone. On the left in Figure 1, there is a reference station with a CR3-G3 geodetic antenna on the reference point SYS1 northeast of the point SYS2. It should be noted that the Huawei Mate9, Huawei Mate20, and Huawei P30 were also employed in part of the subsequent experiments.  
Figure 1: View of the experiment site. Left: global navigation satellite system (GNSS) antenna of the reference station on the reference point SYS1, working all days; right: the Huawei Mate30 is placed on the instrument base above the reference point SYS2 and secured with tape.  
The experiments were conducted in the open area on the roof, and the GNSS observation data of the smartphone were collected and stored by Android app GEO++ RINEX Logger V2.1.3 with RINEX VERSION 3.03, without significant signal obstructions and strong multipath reflectors. The test data were collected over 2 h, and we mainly used the GNSS observations for the first 100 min. The coordinate values of reference points SYS1 and SYS2 were calculated on the NTRF14 in 2020 and treated approximately as an accurate coordinate for comparison in subsequent experiments.
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 3 Verification Methods for Quantum Money in Quantum Wallets
### Entanglement-Based Verification  
In entanglement-based verification, the quantum states within the wallet are entangled with ancillary particles held by a trusted institution, such as a bank. This setup allows the bank to verify the presence and amount of quantum money without directly accessing the quantum states stored in the wallet. The entanglement ensures that the quantum properties of the wallet remain intact while enabling remote verification.  
A maximally entangled state between the quantum wallet and the bank's ancillary system can be described using a maximally-entangled Bell state [12; 28]:  
\[\ket{\Phi^{+}}=\frac{1}{\sqrt{2}}(\ket{00}+\ket{11}). \tag{9}\]  
In general, an entangled quantum money state shared between the wallet and the bank can be represented as:  
\[\ket{\Psi}=\sum_{i}c_{i}\ket{i}_{W}\otimes\ket{i}_{B}, \tag{10}\]  
where \(\ket{i}_{W}\) are the basis states of the quantum wallet, \(\ket{i}_{B}\) are the corresponding states held by the bank, and \(c_{i}\) are complex coefficients satisfying the normalization condition:  
\[\sum_{i}|c_{i}|^{2}=1. \tag{11}\]  
The verification process relies on quantum correlations between these entangled particles. When the bank measures its part of the entangled system using an appropriate observable \(\hat{O}\), the state of the quantum wallet is projected accordingly. For example, if the bank measures in the computational basis and obtains outcome \(k\), then the state remaining in the wallet collapses to \(\ket{k}_{W}\). This ensures that any attempt to tamper with the quantum money (e.g., through cloning or unauthorized measurements) will disrupt the entanglement and be detected.  
To further enhance security, the verification process can utilize Bell inequalities [29; 30] or quantum state tomography to ensure that the quantum correlations remain intact.  
By relying on quantum entanglement, this method enables a verification protocol that ensures authenticity while preventing unauthorized duplication or tampering, making it a powerful tool for quantum money authentication.
# Effect of Holes on Dynamic Crack Propagation under Impact Loading
## 4 Conclusions  
The effect of the holes on dynamic crack propagation was investigated using a experimental-numerical method. SHPB experiments were conducted with a crack propagation gauge  
Figure 14: Numerical (AUTODYN) results of B16-D30 specimen. (**a**) Contour plot of the maximum principal stress. (**b**) Contour plot of the compressive stress \(\sigma_{yy}\).  
Figure 15: Numerical (AUTODYN) results of maximum compressive stress curves. (**a**) Specimens with a constant distance of D = 15 mm. (**b**) Specimens with a constant distance of D = 30 mm. (**c**) Specimens with a constant distance of D = 45 mm.  
test system, and the finite difference code AUTODYN was applied in determining the mechanism of the holes' effect. The conclusions are listed below.  
1. The crack may be arrested by two symmetrical holes when propagating between the holes. Furthermore, the main crack may bifurcate to two sub-cracks and connect with the holes when the distance between the holes is very small, or the extending crack may be transitorily restrained by the holes when the propagation velocity decreases sharply.
2. The effect of the holes has a sensitive relationship with the distance between the holes and the distance of the holes to the crack tip.
3. The effect of the holes is dependent on the compressive stress field caused by the deformation of the holes during the crack propagation process. Moreover, the compressive stress perpendicular to the crack is the dominant factor influencing the crack propagation behavior.
4. The finite difference code AUTODYN has an advantage in solving dynamic fracture problems, and it can be applied in investigating the crack propagation behavior of brittle materials.  
Conceptualization, F.W. and M.W.; methodology, F.W. and M.W.; software, F.W. and M.W.; validation, F.W. and M.W.; formal analysis, F.W.; investigation, F.W.; resources, F.W. and M.W.; data curation, F.W. and M.W.; writing--original draft preparation, F.W.; writing--review and editing, F.W. and M.W.; visualization, F.W. and M.W.; supervision, M.W.; funding acquisition, M.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 11702181), the China Postdoctoral Science Foundation (grant number 2016M602689), the Fundamental Research Funds for the Central Universities (grant number 2018 SCU12047), and the China Scholarship Council (grant number 201806245024).  
The authors declare no conflicts of interest.