esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Complete characteristic curves of a pump turbine are fundamental for improving the modeling accuracy of the pump turbine in a pump turbine governing system. In view of the difficulty in modeling the \"S\" characteristic region of the complete characteristic curves in the pump turbine, a novel Autoencoder and partial least squares regression based extreme learning machine model (AE-PLS-ELM) was proposed to describe the pump turbine characteristics. First, a mathematical model was formulated to describe the flow and moment characteristic curves. The improved Suter transformation was employed to transfer the original curves into WH and WM curves. Second, the ELM-Autoencoder technique and the partial least squares regression (PLSR) method were introduced to the architecture of the original ELM network. The ELM-Autoencoder technique was employed to obtain the initial weights of the Autoencoder based extreme learning machine (AE-ELM) model. The PLS method was exploited to avoid the multicollinearity problem of the Moore-Penrose generalized inverse. Lastly, the effectiveness of the proposed AE-PLS-ELM model has been verified using real data from a pumped storage unit in China. The results demonstrated that the AE-PLS-ELM model can obtain better modeling accuracy and generalization performance than the traditional models and, thus, can be exploited as an effective and sufficient approach for the modeling of pump turbine characteristics.  
p 2019 9 3987 10.3390/app9193987
## 5 Real-Time Experiments
### Experiment on Solid-Oxide Fuel Cell  
The proposed tuning method was also tested on the temperature control of an air heat exchanger of a 10 kW solid-oxide fuel cell (SOFC) that comprised 80 cells. The process input was the flow of natural gas (NG) in standard litres per minute. The process output was the temperature of the heat exchanger in degrees Celsius. Control was performed with a Mitsubishi PLC controller [70] with a sample time of 1 s.  
The open-loop measurements are shown in Figure 26. Available measurements were not long enough to find the process model. However, since the process, at the beginning of its response (between 0 and 900 s), resembles the IP with delay, the controller parameters were calculated with the proposed tuning method.  
Figure 25: Process input and output signals during closed-loop autoclave experiment.  
The calculated areas were  
\[A_{0}=0.0204,\ A_{1}=4.667,\ A_{2}=610.96. \tag{49}\]  
The calculated controller parameters were  
\[K_{P}=0.122,\ K_{i}=1.517\cdot 10^{-4},\ T_{F}=804\ s, \tag{50}\]  
where \(T_{F}\) stands for the first-order filter placed in front of the reference signal.  
The closed-loop experiment was performed by increasing the Air HEX reference temperature from 652 to 642 \({}^{\circ}\)C; Figure 27 shows the closed-loop response is stable and smooth.  
Figure 27: Closed-loop response of Air HEX control loop.  
Figure 26: Open-loop response of Air HEX control loop.
### The Potential for Hydrogen  
With abundant land and renewable energy resources, a number of countries are advantageously poised to produce green hydrogen at low cost and to implement a hydrogen economy for domestic use or export to their neighbouring countries with high energy demands. This is because, unlike electricity, hydrogen can be stored and transported over long distances at lower cost. In particular for countries such as Australia and Chile, their geographic locations and well-established reputation as reliable conventional energy exporters could make them major hydrogen suppliers targeting their neighbours with net demands, such as China, Japan and Korea [18]. In fact, such collaboration is already underway with a joint project between Australia and Kawasaki Heavy Industries (Japan) to build a hydrogen supply chain between the two countries, wherein Japan is forecasted to import 900,000 tons of hydrogen by 2030 [19]. This strategic hydrogen export opportunity is estimated to add as much as AUD10 billion to Australia's economy by 2040 in a high hydrogen demand scenario [19]. European countries with a considerable production of renewable electricity, as well as mature infrastructure and transportation facilities for natural gas, such as the Netherlands, are also poised to benefit from an increased use of hydrogen energy [20]. The potentials to store excess electricity in the form of hydrogen and to adopt existing gas pipelines for hydrogen transportation as the production volume increases, place the Netherlands in an advantageous position as an energy exporter. The attractiveness is demonstrated by an estimated \(\sim\)EUR17.5-25 billion investment by 2025 to develop a carbon-free hydrogen economy in the Northern Netherlands [20].  
These figures may have a significant growth potential, considering that industrial applications, for instance oil refining (33%), ammonia production (27%), methanol production (11%) and steel production (3%) currently dominates the use of hydrogen and drives more than 50% of the demand; while the use of hydrogen for power generation and propulsion only accounts for 1-2% of the total consumption [19; 21]. For the transportation sector, the low utilization could be attributed to the lack of hydrogen distribution infrastructure and the slow development of hydrogen powertrain that can efficiently and cost-effectively convert hydrogen into power. There is, therefore, significant room for growth for increased hydrogen usage in the transportation sector, as technologies surrounding hydrogen transportation and usage continue to improve and mature.
## 5 Discussion and Implications
### Application-Specific Recommendations  
Our analysis suggests that no single architecture uniformly dominates in every scenario, reflecting a broader consensus in the literature [19]. The results underscore the importance of aligning model selection with operational requirements:  
* Real-Time Applications: XCeption's high accuracy and low inference time make it the optimal choice for dynamic, high-volume scenarios such as live video streaming or social media monitoring.
* Forensic Investigations: VGG16's precision and calibrated predictions suit legal or investigative tasks where false positives must be minimized, and batch processing is feasible.
* Resource-Constrained Deployments: MobileNet V2 offers an efficient option for edge devices, balancing reasonable accuracy with minimal computational overhead.
* Adversarial Environments: None of the evaluated architectures demonstrate full resilience under adversarial attacks; adversarial training, input randomization, and layered defenses should be considered in contexts where adversaries may attempt evasion.
## 7 Discussion and Results  
Upon acquiring the given datasets, they are continuinated with noise and random samples. By definition, noise is small, unwanted forms of energy or samples; on the other hand, random samples are the unconscious and unspecified values within the range of normal values.  
The datasets with noise and random samples are processed using PCA-SRP with 98% selectivity (_Sc_), shown in Table 5, and then subjected to following tests:  
* PCA-SRP + ANN comparison accuracy testing compares the validation model accuracy with and without PCA-SRP in an ANN.
* Sensitivity vs. specificity testing is a diagnostic test to find the approximate _Sc_ range values (_Sctest_).
* Receiver operating characteristic (ROC) curve testing compares the methodology PCA-SRP in different datasets in terms of organisation and classification of samples. Moreover, ROC curves also provide a practical evaluation of machine learning techniques.
* Accuracy vs. additional random samples testing is a diagnostic test responding to the sudden increase in noise and random sample spikes.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 7 BackCom: Range Extension and Reliability
### Backscatter Sensor Network  
One of the important applications of BackCom is the backscatter sensor network. The passive sensors in this type of network can sense data, and BackCom can be used for data transmission [PERSON] et al. (2011). This network can be efficiently used in many battery-constrained IoT applications to extend the lifetime  
Figure 47: Vehicle health monitoring using BackCom.  
Figure 48: Vehicle safety using BackCom.  
of the network. A practical system implementation of such a network is shown to receive continuous backscattered signals reflected from wireless sensing nodes [212]. Moreover, [PERSON] et al. discussed the integration of RFID and wireless sensors that can be used in a wide range of applications [213]. Furthermore, three forms of network designs for integrated RFID-based WSN were proposed by [PERSON] et al. [214]. Researchers also presented a WSN based on RFID to eliminate the limitations of battery life [215]. Likewise, the feasibility of ambient-powered embedded sensors was proposed for soft nervous materials [216].  
**Discussion:** Table 12 highlights the summary of BackCom applications. Various applications of BackCom are enumerated below.  
1. Smart cities allow smart posters to communicate by reflecting nearby FM receivers using BackCom [96].
2. Smart homes enable robots in a room to use BackCom for sensing passages with the help of backscattered signals, received from different room items [193].
3. Smart homes can use BackCom to monitor the breathing rates and heartbeats of individuals without any physical contact [188].
4. UAVs along with BackCom can be used to extend the communication range [180] to supply power and to enhance the efficiency of the reader [195].
5. BackCom in bio-medical applications can check the emotions and heartbeats of patients using WiFi signals [198].
6. Backscatter-based neural tags were developed for the brain-machine interface and can be used to monitor neural signals [200].
7. Logistics is the most popular application of BackCom. Recent developments include the three-dimensional online tracking of items using backscattered signals [10].
8. BackCom enables new conductive threads in textiles to produce interactive clothes in Project Jacquard [203].
9. Environmental monitoring can be done using backscatter-powered cameras and sensors [130].
10. BackCom enables the body area sensor to use an eye lens for the measurement of glucose in tears [10].
11. BackCom can be used in traffic monitoring to reduce the number of accidents, and BackCom allows for the communication of signboards to nearby vehicles using FM signals [96].
12. A backscatter sensor network can be made by integration of WSN and RFID to eliminate limited battery life constraints [213].
# Measurement-Device-Independent Two-Party Cryptography with Error Estimation
## 2 Preliminaries
### Joint Measurement  
Joint measurement and phase-matching are widely used in QKD, and we introduce them to our two-party cryptographic protocol. Next, we explain these two methods.  
Prior to 2012, most quantum cryptographic protocols, including QKD and many two-party cryptography protocols, used single-state measurement. The earliest application of joint measurement to quantum protocols is introduced by [PERSON] [19]. In Ref. [19], he presented the idea of MDI-QKD using joint measurement. The measurement method is shown in Figure 1.  
In Figure 1, [PERSON] and [PERSON] will prepare a single quantum state and send it to the third party, [PERSON]. [PERSON] will measure those quantum states in Bell basis. The state \(|\phi^{-}\rangle=\frac{1}{\sqrt{2}}(|HV\rangle-|VH\rangle)\) is joint by a click in \(D_{1H}\) and \(D_{2V}\) or \(D_{1V}\) and \(D_{2H}\), and \(|\phi^{+}\rangle=\frac{1}{\sqrt{2}}(|HV\rangle+|VH\rangle)\) is joint by a click in \(D_{1H}\) and \(D_{1V}\) or \(D_{2H}\) and \(D_{2V}\). Therefore, [PERSON] and [PERSON] can get the raw key based on measurement outcomes and prepared basis, which is shown in Table 1.  
Another joint measurement method uses phase coding, which is generally used in the continuous variable QKD. The representative protocols are PM-QKD [22] and TF-QKD [23]. The measurement method is shown in Figure 2. Phase-matching QKD uses coherent state to send information. We define that \(\delta_{a}=|\sqrt{\mu_{a}}e^{i(\phi_{a}+\pi k_{a})}\rangle\) and \(\delta_{b}=|\sqrt{\mu_{b}}e^{i(\phi_{b}+\pi k_{b})}\rangle\), where \(\phi_{a},\phi_{b}\in\{0,\frac{\pi}{2}\}\) are the basis phase chosen by [PERSON] and [PERSON].  
\begin{table}
\begin{tabular}{c c c} \hline
**[PERSON] \& [PERSON] Basis** & **Relay Output \(|\phi^{-}\rangle\)** & **Relay Output \(|\phi^{+}\rangle\)** \\ \hline \(+\) & Bit flip & Bit flip \\ \(\times\) & Bit flip & No bit flip \\ \hline \end{tabular}
\end{table}
Table 1: [PERSON] or [PERSON] flip their key based on the outcomes of measurement and announced prepared basis [19].  
Figure 1: The basic setup of a measurement-device-independent QKD (MDI-QKD) protocol is in Ref. [19]. [PERSON] and [PERSON] use three devices to prepare their photons, and the third party will make a joint measurement and announce measurement output.  
Figure 2: Measurement setup used in phase-matching QKD (PM-QKD).  
According to Mach-Zehnder interference, the detector \(D_{1}\) clicks when the phase difference of \(\delta_{a}\) and \(\delta_{b}\) is an even multiple of \(\pi\), and the detector \(D_{2}\) clicks when the phase difference of \(\delta_{a}\) and \(\delta_{b}\) is an odd multiple of \(\pi\). When a phase difference of \(\delta_{a}\) and \(\delta_{b}\) is not a multiple of \(\pi\), a random click occurs. [PERSON] will flip his key when detector \(D_{2}\) click because only \(|k_{a}-k_{b}|=1\) will cause the phase difference to be an odd multiple of \(\pi\).
## 5 Experimental Measurement and Analysis  
Measurements using the FDS method with the Megger IDAX-350 instrument constructed in Danderyd in Sweden were conducted on seven 22/0.4 kV distribution transformers rated at 100, 630, and 1000 kVA, having varying insulation conditions. These tests were performed at a constant ambient temperature of 7 \({}^{\circ}\)C, eliminating temperature as a variable.  
The analysis was divided into two categories: transformers with insulation at normal moisture levels and those with extremely high moisture content. The IDAX-350 system provided modeled values for the loss factor at 50 Hz, paper moisture percentage, and specific oil conductivity.  
The analysis will be more readable and explanatory if we classify the transformers into two groups according to power and insulation properties.
# A Study on a Scenario-Based Security Incident Prediction System for Cybersecurity
## 2 Related Work  
Recent studies on security incident prediction systems have increasingly leveraged frameworks such as MITRE ATT&CK to enhance threat detection and response capabilities. [PERSON] et al. (2018) utilized the ATT&CK framework to identify defensive gaps and develop behavior-based analytic detection techniques through adversary emulation [4] Similarly, [PERSON] et al. (2013) explored the use of attack graphs to aggregate vulnerability metrics in enterprise networks, emphasizing systematic modeling of adversarial behaviors. However, these studies primarily rely on manual processes for scenario creation and analysis, limiting their scalability and real-time applicability [5].  
Traditional approaches to threat management often depend on tools like SIEM (Security Information and Event Management) and ESM (Enterprise Security Management). While SIEM excels at centralized management of structured log data, it struggles with processing unstructured or diverse data formats. Additionally, both SIEM and ESM are highly reliant on manual analysis by security personnel, which introduces bottlenecks in scalability and precision, especially for advanced threats such as APTs and ransomware. For instance, the 2021 KISA report highlighted that manual processes in SIEM systems hinder the timely correlation of security events and logs, reducing their effectiveness against rapidly evolving threats [6].  
Despite these advancements, limitations persist. Current frameworks often overlook the integration of unstructured data sources and fail to automate the generation of attack scenarios. [PERSON] et al. (2020) addressed this gap by incorporating automated adversary emulation, yet their methodology lacked comprehensive integration with legacy systems or the ability to process heterogeneous data formats [7].  
In contrast, this study aims to overcome these challenges by introducing a scenario-based security incident prediction system that integrates both structured and unstructured logs, automates the profiling of security scenarios, and bridges the gap between legacy systems and advanced threat modeling. Unlike traditional SIEM or ESM systems, the proposed framework leverages the predictive capabilities of the MITRE ATT&CK framework while addressing their inherent limitations through automation and enhanced data integration. For example, this study addresses the weaknesses identified by [PERSON] et al. by minimizing manual effort through automated scenario generation and real-time log analysis [8].  
Furthermore, while prior studies such as [PERSON] et al. (2021) have analyzed vulnerabilities in specific industrial settings, they often lack a systematic approach to mapping adversarial tactics to actionable security measures. This research builds upon their findings by integrating scenario-based simulations to enhance proactive threat management and mitigate risks in dynamic environments.  
By synthesizing the strengths and addressing the weaknesses of existing methodologies, this study contributes to the advancement of predictive cybersecurity frameworks.
## 3 Deliberate Attacks on IECPS
### Cyberattacks
#### 3.1.3 False Data Injection Attacks
In the power tampering part, the attack vectors need to be designed to ensure the data matching, i.e., the attack vectors need to be made to ensure that the power remains balanced before and after the attack: \[\Delta P_{l}=-S^{\rm PF,\it J}_{(0)}\mathbf{K}^{\rm PD,\it J}_{(0)}\mathbf{P}^{\it J}_{( 0)},\forall l\in C\] (16) \[\Delta P_{l}=-S^{\rm PF,\it J}_{(1)}\mathbf{K}^{\rm PD,\it J}_{(1)}\mathbf{P}^{\it J}_ {(1)}+S^{\rm PF,\it J}_{(0)}\mathbf{K}^{\rm PD,\it J}_{(0)}\mathbf{P}^{\it J}_{(0)}, \forall l\in L-C,\] (17) where \(\Delta P_{l}\) is the false data of injected branch volume measurement; \(S^{\rm PF,\it J}_{(0)/(1)}\) is the transfer factor matrix of the lines before and after the attack; \(\mathbf{k}^{\rm PD,\it J}_{(0)/(1)}\) is the node-load correlation matrix of the lines before and after the attack; \(\mathbf{P}^{\it J}_{(0)/(1)}\) is the node power measurement matrix of the lines before and after the attack; \(C\) is the set of lines to be attacked; and \(L\) is the set of system lines.
## 1 Introduction
In addition, due to the re-transmission of data during communication, its energy consumption is generally high. Therefore, a novel, energy-efficient algorithm based on data scheduling is proposed. This algorithm aims to optimize the energy usage within smart beekeeping systems. By efficiently incorporating data integration techniques and scheduling data transmissions, unnecessary power consumption can be minimized. To provide a comprehensive assessment, we compare the performance of the proposed 5G-supported D2D communications in terms of their energy efficiency and throughput with the existing systems commonly used in smart beekeeping. Through this analysis, valuable insights are provided on the benefits of adopting 5G and D2D communication technologies in terms of their energy efficiency and overall system performance. These contributions aim to advance the field of smart beekeeping, ensuring sustainable practices, optimizing honey production, and mitigating the challenges faced by beekeepers, such as power consumption. This work is structured as follows: Section 1 presents the general introduction; Section 2 discusses the related works. Section 3 presents the system model and architecture design. Section 4 describes the energy-efficient algorithm for smart beekeeping D2D communication based on data scheduling and discusses the numerical results, while Section 5 summarizes the conclusion. Table 1 introduces the key mathematical notations and abbreviations used in this paper.
## 1 Introduction
Its effectiveness is based on the production of in situ oxidative species (OH; H; O, O, H\({}_{2}\)O\({}_{2}\), H\({}_{2}\), O\({}_{2}\), O\({}_{3}\)), as well as its ability to initiate a variety of chemical and physical processes (a high electric field, intense ultraviolet radiation, overpressure shock waves) in water for decomposing toxic compounds, while further advantages include its operational simplicity (realized under the conditions of ambient temperature and pressure), its variable reactor configuration, and its good performance degradation of low-concentration organic compounds [23; 24; 25; 26]. Among these techniques, dielectric barrier discharge (DBD) has the advantages of simple structure, low cost, and the ability to be operated at room temperature and atmospheric pressure [27], and has been widely used for the treatment of pollutants such as organic pollutants, induced surface modification, and microbial inactivation [28; 29; 30]. [PERSON] et al. [31] found that under a transmission voltage of 45 kV, the residual amount of dichloros in tomato juice decreased by 47% and 65% after being treated with DBD plasma for 4 and 5 min, respectively; [PERSON] et al. [32] found that under a discharge voltage of 80 kV, the residual levels of malathion and chlorpyrifos on the surface of lettuce were reduced by 59.0% and 57.9%, respectively, after being treated by nonthermal plasma for 180 s; [PERSON] et al. [33] found that under a discharge voltage of 13 kV, the residual levels of diazmon on the surface of fresh-cut apples and cucumbers were reduced by 87.38% and 82.24%, respectively, after being treated by nonthermal plasma for 10 min, and the residual levels of chlorpyrifos were reduced by 86.96% and 33.74%, respectively. [PERSON] et al. [34] found that under a discharge power of 25 W, the residual levels of chlorpyrifos and imidalcoprid on the surface of corn were reduced by 91.5% and 73.1%, respectively, after being treated by nonthermal plasma for 60 s. However, the use of nonthermal plasma for the degradation of acaricides on fruit and vegetable surfaces is still not well established. Kumquat cuimi, a new cultivar of kumquat developed in China, has gained significant popularity among consumers. This is attributed to its fruit characteristics, including a reduced number of oil cells in the peel, abundant juicy flesh, high content of soluble solids, and absence of a spicy taste. This study utilized kumquat cuimi to investigate five common acaricides: avermectin, bifenazate, spirodiclofenac, etoxazole, and lufenuron. A nonthermal air plasma device was designed to treat pesticide residues on the surface of kumquat cuimi and the degradation effects of five commonly used acaricides were investigated. The study explored the degradation efficiency of these five acaricides using the nonthermal plasma technique and analyzed the impact of various factors on the degradation effectiveness. This research provides insights for the practical application of nonthermal plasma in the degradation of pesticide residues on fruits and vegetables.
# Flash Flood Early Warning System in Colima, Mexico
## 2 Materials and Methods
### Responsive Web-Based Data Acquisition Platform
#### 2.8.3 Drifter Data Retrieving Section  
The drifter section allows users to upload the JSON files generated by the drifters and displays the data in it, which consist of the distance travelled, number of samples, top speed, average speed, maximum altitude, and average altitude. By using the JSON format, different devices can publish useful weather data to the web system, regardless of the hardware used.