esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A hierarchical distributed control scheme integrated with inverter ACs and conventional generators is established. Adjustable control performance indices, namely UPCE and the associated coordinated control strategies, are developed to refine frequency control performance. In order to achieve the consensus of power tracking and SOC balancing, the distributed pinning control algorithm coupled with a finite-time consensus protocol has been proposed to aggregate numerous inverter ACs in the same control area.  
The simulation results of numerous examples have demonstrated that the frequency response of the power system can be improved by the proposed coordinated control scheme. The proposed distributed control framework is effective for both power system contingency and wind fluctuations. Meanwhile, the tracking of frequency control signals and the SOC consistency of individual ACs can be achieved in finite-time. Different allocation strategies are introduced for comparison to verify the effectiveness and necessity of the proposed coordinated control method. It is validated by the simulation results that the proposed distributed pinning control framework is an effective way to incorporate widespread inverter ACs for secondary frequency control of a power system.  
Future focus will be on the hierarchical control strategies incorporated into more dispatchable resources. Novel allocation strategies and the uncertainties of frequency regulation will be explored.  
[PERSON] proposed the research topic, designed the research, carried out data analysis and prepared the manuscript. [PERSON] polished the manuscript and corrected spelling and grammar mistakes. This research received no external funding. This work is supported by National Key R&D Program of China (2017 YFB0902600), State Grid Corporation of China Project (SGJS0000 DKJS1700840) and the Fundamental Research Funds for the Central Universities (2019 QN121). The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
AC Air conditioning SOC State-of-charge DR Demand response TCL Thermostatic control load ETP Equivalent thermal parameters UPCE An adjustable control error based on unit capacity AGC Automatic generation control ACE Area control error  
Figure 20: The tie-line power between Area 1 and Area 2.
# Advances in Piezoelectric Jet and Atomization Devices
## 4 Advances in the Application of Piezoelectric Jets and Atomizers
### Drug Delivery  
In using microneedles for drug delivery, the main working principle is to destroy the skin layer, generate micron level channels, and direct the drug to the upper layer of the epidermis or dermis, so that the drug can be directly taken into the microcirculation without passing through the cutin barrier. This is so that the drug can pass through the skin efficiently [82], which has been widely used in the fields of vaccine injection [83; 84; 85], insulin delivery [86; 87; 88], skin beauty [89; 90; 91], and other aspects. Piezoelectric micro spray technology has the advantages of high precision and small amounts of droplets in a single injection, which can be used to spray drugs or macromolecules onto the microneedles. [PERSON] et al. [92; 93] fabricated drug microneedles based on piezoelectric micro jet technology, which can realize transdermal drug delivery, and analyzed the influence of composition ratio and pulse excitation parameters on the jet performance, as shown in Figure 5a. [PERSON] et al. [94; 95; 96] proposed to spray miconazole on the degradable microneedles by piezoelectric micro spray technology to achieve micro drug delivery for the treatment of Candida albicans infection. As shown in Figure 5b, [PERSON] et al. [97] used piezoelectric micro spray technology to realize drug spraying of the intravascular stent and proved the feasibility of this technology. [PERSON] et al. [98] used piezoelectric micro spray  
Figure 4: Spray cooling: (**a**) a zoomed view of heater assembly by [PERSON] et al. [77], reprinted with permission from Ref. [77]. Copyright 2014 Elsevier. (**b**) schematic diagram of experimental system by [PERSON] et al. [79], reprinted with permission from Ref. [79]. Copyright 2017 Elsevier.  
technology to spray three different kinds of anti-cancer drugs on metal microneedles, and realized the transdermal drug delivery method to transport micro amounts of anti-cancer drugs as shown in Figure 5c.
###### Abstract
Academics, industry, and business professionals are constantly seeking business and technical use cases for IoT to prove the transformational power of IoT to larger audiences [3]. This paper gives a detailed view of the main radio communication technologies that are used today in IoT applications. Studying the core aspects and the features of these technologies, as well as the market trends, it is possible to give a general comparative conclusion on which of the studied technologies fit the best for most IoT use cases. Usually, the candidates enabling the sensor node communication are mobile communications technologies such as the Global System for Mobile Communications (GSM), the General Packet Radio System (GPRS), Universal Mobile Telecommunication System (UMTS/3G), Long-Term Evolution (LTE/4G), satellite communications, licensed or unlicensed radio networks, and power line communications (PLC). When transmitting or receiving data, the sensor nodes establish radio links, making them IoT nodes [1]. For now, NB-IoT radio communication technology as an extension of LTE seems to be the leading technology in this field, so one chapter is dedicated to giving a detailed explanation of NB-IoT and its architecture. After this, IoT platform models and architectural designs used today are discussed. Generally, the architecture of an IoT system is layered, comprising the application, common service, and network service layers. The application layer is where the business and operational logic is implemented, while the common services and network services include machine-to-machine connection (M2M) functionalities (such as management, discovery, and policy enforcement) and the connectivity for data transfer. The design of an IoT application can be simplified into 3 layers horizontally starting from the IoT platform, to the connectivity layer, and down to the sensor layer. To deeply understand how the sensors communicate with the IoT platform, a chapter is dedicated to explaining communication protocols used to enable sensor-node communication through the IoT infrastructure. Various IoT networks are created with numerous sensors and actuators, including homogeneous and heterogeneous ones. Some to mention include the sensors used for machine vision or optical ambient light applications; the sensors used for acceleration, motion, position, presence, humidity, temperature, moisture, leak, and level sensing; as well as the electric and magnetic sensors [1]. After providing a detailed view of the above concepts, in the context of a smart city, a smart parking solution using NB-IoT radio communication technology is given to showcase a concrete implementation example of IoT concepts. In a smart city, the infrastructure comprises various data collection sources (sensors, microcontrollers, and system provider networks). The upper layers include data orchestration, service enablement, and application layers. The IoT platform aims to manage the devices and the applications and services are in the application layer or on top of the core platform [4].
###### Abstract  
As the demand for electric vehicles (EVs) rises globally, ensuring the safety and reliability of EV battery systems becomes paramount. Accurately predicting the state of health (SoH) and state of charge (SoC) of EV batteries is crucial for maintaining their safe and consistent operation. This paper introduces a novel approach leveraging deep learning methodologies to predict battery SoH, focusing on implementing a system prototype for real-world applications. The proposed system integrates an extended Kalman filter (EKF) with a deep learning framework, forming a system prototype known as FELL, aimed at EV battery diagnosis and prediction. We devise an algorithm utilizing the EKF to estimate the SoH of the battery. We present a detailed overview of the system architecture and implementation, showcasing its predictive capabilities. Experimental results demonstrate the effectiveness of the system in accurately estimating battery SoH with notable improvements in prediction accuracy. Additionally, the FELL system provides users with real-time predictions and comparative analysis across multiple prediction models, offering valuable insights for EV battery management.  
deep learning; electric vehicles; system implementation; state of health; state of charge +
Footnote †: journal: Applied sciences
# Evaluation of Methods Based on CPTu Testing for Prediction of the Bearing Capacity of CFA Piles
## 2 Materials and Methods
### Cone Penetration Testing and Pile Bearing Capacity Prediction
Because of uncertainty at bearing capacity calculation based on strength and deformation characteristics of soils, direct correlation between the pile bearing capacity components and CPT results is the most practically utilized approach. This paper is dedicated to the direct methods of pile design based on CPT results, namely the UniCone method, Laboratoire Central des Ponts et Chaussees method (LCPC) and the method involved in Eurocode 7--2 [56; 57].
# Optical Pupil Shift Correction Method for Large Ground-Based Optical Telescopes
## 5 Conclusions  
We investigated the degradation of adaptive optical image correction in a ground-based large optical telescope due to the optical pupil shift caused by gravitational deformation and temperature gradient. Methods to estimate the optical axis shift and actively compensate the secondary mirror adjustment platform were also investigated. A simplified optical model of the telescope was established based on ray tracing. The causes of the optical pupil shift and compensation process were meticulously studied. Based on the coma-free theory of the Cassegrain system, a method to compensate for the calibration of the optical pupil was proposed. The experimental results proved that both gravitational deformation and temperature gradients caused the disorientation of the optical axis of the entire system, and the pupil shift of the conjugate surface of the primary mirror reached 18 mm. The secondary mirror bend compensation and rotation compensation about the coma-free point remarkably reduced the pupil shift to less than 1 mm. Finally, the effectiveness of the proposed model and compensation method were verified by the pupil shift of effective sub-aperture on the Hartmann detector in the actual calibration process of an adaptive optical imaging system.  
The improvement of the image resolution of the adaptive telescope is related to the number of wavefront detection sub-apertures. Due to the optical pupil shift caused by the number of invalid and effective sub-apertures having limited differences, the method outlined in this paper will not only essentially improve the image resolution of the telescope but fundamentally solve the environmental reasons for the instability of correction. Thus, the theory presented in this paper has specific limitations for adaptive telescopes.
# Characterizing Growing Season Length of Subtropical Coniferous Forests with a Phenological Model
## 4 Discussion
### Limitations and Challenges  
We found that the use of phenological models in describing growing season characteristics with daily GPP still leaves uncertainty in our understanding of longleaf pine ecosystems; during short-term summer drought and post-drought recovery, the phenological model may not be able to appropriately reflect dynamic changes with GPP which reduces performance [48]. Improving the prediction accuracy of phenology models and developing piecewise logistic functions to deal with natural and human induced disturbances are still areas of critical research need that need to be examined in future studies. This will aid in advancing our predictive capacity of key forest phenological periods [24].  
Discussing the impact of weather and forest management on ecosystem phenological processes still requires a longer series of environmental observations. This study, while encompassing nine years, shows only a subset of possible conditions experienced in this site. Finally, the study is only a preliminary discussion of the phenological processes of subtropical evergreen coniferous forest ecosystems under human and weather disturbances and their responses to disturbances and climate changes. To analyze the influence of a single climatic factor and the threshold of the ecosystem's demand for precipitation after short-term drought, more environmental observations and physiological experiments are needed.
## 3 Results and Discussion
The measurements of the electron temperature and the density of the atmospheric plasma are carried out in research with atmospheric argon plasma generated by a dielectric barrier discharge. The determined electron temperature is in the range of 1-10 eV and the estimated electron density is 10\({}^{19}\)-10\({}^{24}\) m\({}^{-3}\) under the assumption of a partial local thermodynamic equilibrium [59]  
The electron density and temperature of the cold argon plasma jets are determined at normal atmospheric pressure using the optical emission spectroscopy method and the Stark expansion analysis method for two emission lines in the study [60]. Depending on the applied high voltage, the electron temperatures were up to 1.32 eV and the electron density was in the order of magnitude of 10\({}^{23}\) m\({}^{-3}\). In the experimental investigation of the cold atmospheric pressure plasma jet and its application, the authors used argon as the working gas. The optical characterisation of the discharge was carried out by means of Stark broadening and the Boltzmann plot method using optical emission spectroscopy. The results are 1 eV for electron temperature and 10\({}^{22}\) m\({}^{-3}\) for electron density [61]. A parametric study of the electron temperature of an industrially produced plasma jet was carried out using optical emission spectroscopy. The results for the electron temperature (1 eV) and the density (10\({}^{21}\) m\({}^{-3}\)) show that it is a non-thermal plasma with numerous applications [52]. A comparison of the experimentally determined parameters for non-thermal plasmas is shown in Table 2. These experiments were not performed for food applications but show that the values for electron temperature and electron density are comparable in plasmas generated in the same way as they are generated for food applications, i.e., by gas discharge or plasma jet. A specific contribution of this work is the monitoring and diagnostics of non-thermal plasma generated by high-voltage electrical discharge, a well-established method for plasma generation in the food sector. Optical emission spectroscopy as an established diagnostic tool in plasma physics is usually only used for the detection of spectral lines in the food industry. Coupling a carefully selected optical emission spectrometer with a high-voltage electrical discharge is recommended for the daily control of food processing, especially to confirm that the processes are non-thermal and suitable for food industry applications such as extraction of bioactive components or microorganism inactivation. The parameter values obtained in our study show that it is a non-thermal plasma that can be successfully used as a non-thermal technology, especially for the inactivation of microorganisms [3; 26; 33; 37; 43; 55; 56; 57; 62; 63; 64; 65; 66; 67; 68; 69].
## 6 Conclusions  
In this study, we demonstrated the effectiveness of SMDT as a practical feature selection method for identifying significant features, validated using the MQTTset dataset. Among the models evaluated, DT, RF, GB, and XGBoost emerged as top performers in both binary and multiclass classification, with DT standing out for its superior computational efficiency. This was evident as it outperformed more complex models like DNN, which, despite its sophisticated architecture, did not deliver proportional improvements in detection rates and instead highlighted the computational cost challenges, especially in IoT settings. These findings underline the trade-offs between model complexity, performance, and computational demand, suggesting that simpler and more efficient models could be more suitable for real-time applications in resource-limited IoT environments.  
While the findings underline the importance of lightweight and efficient models for IoT applications, the study also identified areas for further research. Future work will address the limitations outlined, such as validating SMDT with real-world IoT datasets and expanding the range of attack types analyzed. Additionally, refining DNN architectures to improve efficiency and exploring cost-sensitive learning methods to mitigate class imbalance could further enhance model effectiveness.  
Conceptualization, M.M., E.T. and V.P.; methodology, E.T. and M.M.; software, E.T.; validation, M.M., E.T., P.A.C., V.P. and D.T.C.; formal analysis, M.M., P.A.C., and D.T.C.; investigation, V.P. and A.R.; data curation, E.T. and A.R.; writing--original draft preparation, E.T.; writing--review and editing, E.T., M.M., P.A.C., V.P., D.T.C., and A.R.; visualization, E.T. and A.R.; supervision, P.A.C. and D.T.C.; project administration, M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The dataset used in this study is publicly available in Kaggle at [20].  
The authors declare no conflicts of interest.
## 2 Project Overview
Based on our experiences from previous GPS monitoring projects [21; 22; 23; 24; 25], we established a GPS monitoring network with one base station (BASE) and six rover stations named GPS1, GPS2, GPS3, GPS4, GPS5, and GPS6 (Figure 1b). The base station was installed at the headquarters of the project, about two kilometers away from the river (Figure 1a). The antenna pole of the base station was arched on a concrete block fixed on the floor of a park lot (Figure 3a). The antenna monument was further stabilized by three steel reinforced bars. There are tall trees, metal fences and roofs, and cars close to the antenna. Obviously, this site is not perfect for a continuously operating reference station (CORS). However, it is a security site and is able to provide reliable alternating current (AC) for power supplies and fast internet connection for data communication, which are critically important for real-time (or near real-time) monitoring. In practice, the selection of a reference site always needs to balance many issues, such as distances to rovers, multipath effects, power supplies, data communication, and site security. These six rover stations were installed within the river along the centerlines of the twin tunnels as exhibited in Figure 3b. The antenna poles are galvanized steel pipes with an outside diameter of 11 cm and a length of 5.5 m. Each antenna pole was built into a cast-in-place concrete cylinder. The concrete cylinder was constructed by first sinking a heavy steel tank (0.8 m in diameter and 1.2 m in height, Figure 2c) into the silt and clay below the water, and then pouring concrete into the tank. Each antenna pole was further enforced by three to four reinforcing bars (2.5 cm in diameter) as shown in Figure 3b. The rover stations were installed approximately three weeks before the first shield arrived to the river bank. The Trimble NetR9 GNSS receivers and Zephyr Geodetic II antennas were equipped on the base and rover stations. Specific parameters of the GNSS antennas and receivers are available at the website of the manufacturer ([[https://www.trimble.com/Infrastructure/Trimble-NetR9.aspx](https://www.trimble.com/Infrastructure/Trimble-NetR9.aspx)]([https://www.trimble.com/Infrastructure/Trimble-NetR9.aspx](https://www.trimble.com/Infrastructure/Trimble-NetR9.aspx))). The GPS units were configured to track GPS and BeiDou satellites. A solar power system and a cellular data communication system were equipped at each rover site (Figure 3b). The right line was excavated about one month earlier than the left line. Therefore, the right line is called the first tunnel, and the left line is called the second tunnel. GPS2, GPS4, and GPS6 were installed along the centerline of the first tunnel; GPS1, GPS3, and GPS5 were installed along the centerline of the second tunnel (Figure 1b). Figure 3: (**a**) The antenna monument of the reference GPS station (BASE); (**b**) the antenna monuments of rover stations GPS1 and GPS2, and the power and real-time data communication system.
## 5 Discussion
### Limitations  
This study presents certain limitations that should be acknowledged:  
1. The analysis is confined to publicly known vulnerabilities and exploits. APT groups, however, may possess undisclosed zero-day vulnerabilities or exploits that are not publicly documented, presenting a potential blind spot in the assessment.
2. This study focuses exclusively on general-purpose high-performance computers, neglecting the possibility that APT groups might leverage supercomputers designed for specific purposes. These specialized supercomputers could offer enhanced performance capabilities beyond those of general-use high-performance computers.
3. Physical attacks are not taken into account in this paper. APT groups may have the resources to execute physical attacks, gaining physical access to WLAN network equipment or devices. This aspect introduces a distinct dimension of risk that was not addressed in the scope of this study.
# An End-to-End Video Steganography Network Based on a Coding Unit Mask
## 3 Experimental Results
### Number of Kernel Parameters Comparison among Different Steganography Networks  
Model parameter is an index to measure the complexity of the model. The larger number of parameters the model has, the larger the graphics card memory it requires, and the more time one-time forward operation spends. Constructing an effective model with a small number of parameters plays an important role in the portability, run speed, and memory requirements of the algorithm.  
The parameters and occupied memory of different networks are displayed in Table 1. Note that PyraGAN_F32_2 is the model for which the hidden feature channels are 32 and the down sampling network channels are 2, as shown in Figures 4 and 5, where the channel of the blue box is removed, PyraGAN_F16_3 is the model for which the hidden feature channels are 16, and the down sampling network channels are 3. Model HiDDeN [21] has the maximum number of parameters on the treen components, and the total memory it occupies is up to 1.8 M. Proposed PyraGAN_F32_2 has the same number parameters as model SteganoGAN [22] on the discriminator component and almost equivalent parameters for the decoder and encoder components, and the total memory occupied of model is one fifth that of HiDDeN. By increasing the diversity of the down sampling and reducing the features of the hidden features, the proposed PyraGAN_F16_3 is a light-weight model that occupies only 220 K memory, as shown in bold in Table 1, and has minimum parameters among four networks.