esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment
## 3 Overview of Flood Sensor Development  
Flood sensors are used in all stages of flood management (early warning, flood mapping, flood monitoring, flood assessment, and flood recovery). Generally, sensors can be classified into ground-based in situ sensors and space-based sensors [50]. Floods can be categorized by their causes, each with distinct characteristics. We summarized these types and identified suitable sensors for monitoring key indicators to emphasize the importance of sensors in flood management (Figure 4).  
In situ and remote sensing sensors are used for flood early warning systems [51]. Common sensors include rainfall, temperature, water level, and flow sensors, which offer more accurate and reliable data than radar and satellite imagery [52]. Remote sensing is essential for precipitation estimation, and ground-based sensors also require a rapid hydrological response to precipitation [53]. Studies have proven that sensor-based river monitoring systems are increasingly important for early warning of river flooding [54] where integration of remotely sensed data can further improve the accuracy and reliability of warnings. Permafrost melting is a major source of water supply for many river and lake systems around the world. The monitoring of snowmelt floods has typically used in situ multispectral sensors to collect data on the water column, but satellite-borne SAR radar altimeters have become a more suitable option due to the limitation of harsh conditions [55]. Coastal floods, such as storm surge floods and tidal floods, are a serious threat to the safety of people and urban infrastructure in coastal areas [56]. Tidal level sensors and wave sensors are widely used in flood warnings, and satellite remote sensing provides an effective method for obtaining large-scale data on sea surface temperature, sea level height, wind speed, and wind direction [57]. Glacial lake outburst floods have a complex mechanism and are usually predicted by four types of indicators: climatic factors, glacier parameters, glacial lake parameters, and dam parameters [58], which rely heavily on satellite remote sensing data for monitoring [59]. Rapidly growing cities are prone to pluvial flooding due to improper urban expansion and inadequate drainage systems. Water sensor-based methods are expensive and susceptible to environmental influences [60; 61], whereas remote sensing and social media data can be used for large-scale monitoring  
Figure 3: Burst analysis of flood sensor keywords. (The graph is sorted by the start of the burst, with dark blue indicating the timeframe in which the keyword appeared, and red indicating the keyword with the highest citation intensity in a given time period).  
and warning instead. Tsunamis are closely linked to seismic activity, and seismographs, along with deep-water tsunami sensors, are key components of the early warning system. The remote sensing sensors do not directly measure tsunami waves, but they can provide communication support and assist in monitoring for early warning [62].
## 2 The Equivalent Model of MMC Considering AC Side Power and MMC Control
### The Model of MMC Controllers  
The control strategy of MMC is relatively mature. In this paper, the classic inner and outer loop control methods are used for calculation [4; 27; 28]. The specific control algorithm is not repeated in this article. All variables in Equations (7)-(9) are unit values. The positive direction of the AC currents is injecting into the converter.  
For the station with constant power control, the instantaneous expression of the outer loop controller is:  
\[\left\{\begin{array}{l}\Delta p^{*}(t)=P_{\text{ref}}^{*}-p^{*}(t)\\ i_{\text{kp}}^{*}(t)=k_{\text{pp}}\Delta p^{*}(t)\\ i_{\text{ki}}^{*}(t)=k_{\text{ip}}(\Delta p^{*}(t)+\Delta p^{*}(t_{0}))\Delta t /2+i_{\text{ki}}^{*}(t_{0})\\ i_{\text{dref}}^{*}(t)=i_{\text{kp}}^{*}(t)+i_{\text{ki}}^{*}(t)\\ i_{\text{dref}}^{*}(t)=0\end{array}\right. \tag{7}\]  
For the station with constant voltage control, the instantaneous expression of the outer loop controller is:  
\[\left\{\begin{array}{l}\Delta u_{\text{dc}}^{*}(t)=U_{\text{dref}}^{*}-u_{ \text{dc}}^{*}(t)\\ i_{\text{kp}}^{*}(t)=k_{\text{pp}}\Delta u_{\text{dc}}^{*}(t)\\ i_{\text{ki}}^{*}(t)=k_{\text{in}}(\Delta u_{\text{dc}}^{*}(t)+\Delta u_{\text{ dc}}^{*}(t_{0}))\Delta t/2+i_{\text{ki}}^{*}(t_{0})\\ i_{\text{dref}}^{*}(t)=i_{\text{kp}}^{*}(t)+i_{\text{ki}}^{*}(t)\\ i_{\text{dref}}^{*}(t)=0\end{array}\right. \tag{8}\]  
where, \(k_{\text{pp}}\), \(k_{\text{ip}}\) and \(k_{\text{pu}}\), \(k_{\text{iu}}\) are proportional and integral coefficient of constant power controller and constant voltage controller, respectively. \(u_{\text{dref}}\)' is the reference value of DC voltage. \(p_{\text{ref}}^{*}\) is the reference value of active power. \(p^{*}\) is the instantaneous value of active power. \(i_{\text{dref}}\)' is the reference current of \(d\)-axis, \(i_{\text{dref}}\)' is the reference current of \(q\)-axis.  
The expression of the inner loop controller is:  
\[\left\{\begin{array}{l}\Delta i_{d}^{*}(t)=i_{dref}^{*}\\ \Delta i_{q}^{*}(t)=i_{qref}^{*}\\ \Delta v_{dkp}^{*}\\ \Delta v_{qk}^{*}\\ \Delta v_{qk}^{*}(\Delta i_{q}^{*}(t)+\Delta i_{q}^{*}(t_{0}))/{}^{*_{0}}_{ \text{dki}}\\ \Delta v_{qk}^{*}(\Delta v_{qk}^{*})\\ \Delta v_{qk}^{*}(\Delta i_{q}^{*}(t)+\Delta i_{q}^{*}(t_{0}))/{}^{*_{0}}_{ \text{dki}}\\ \end{array}\right. \tag{9}\]  
where, \(k_{\text{p2}}\) and \(k_{\text{u2}}\) are the proportional and integral coefficients of the inner loop controller. \(i_{d}\)' and \(i_{q}\)' are the \(d\)-axis and \(q\)-axis of the AC current. \(u_{d}\)' and \(u_{q}\)' are the \(d\)-axis and \(q\)-axis of the AC voltage \(u_{\text{s}}\).
## 8 Conclusions  
Sixth-generation communication networks will connect critical infrastructures. Therefore, the dependability of 6G communication networks is extremely important. Since 6G exacerbates the merging of the physical and digital worlds beyond the current traditional cyber-physical systems, dependability in terms of reliability, availability, safety, and security needed a thorough investigation. Therefore, in this article we have shed light on the dependability of 6G networks, first to highlight its importance and relevance in 6G, and then to bring forward existing challenges and potential solutions. The main challenges that persist in all dimensions of dependability arise from the distributed nature of 6G. The solutions, thus, must also be targeted at distributed network architectures. Therefore, edge computing, FL, and movable softwarized network functions, to name a few directions, related to reliability, availability, safety, and security, need to be researched. In summary, this article opens up interesting research questions and highlights research gaps to improve the dependability of 6G networks and systems.  
All the authors participated in conceptualization. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the VTT Technical Research Centre of Finland and in part by the Business Finland through the SUNSET-6G and the AI-NET-ANTILLAS projects.  
The authors declare no conflict of interest.  
List of most common abbreviations:  
\begin{tabular}{l l}
**Acronym** & **Full Term(s)** \\
3D & Three Dimension \\
5G & Fifth Generation \\
6G & Sixth Generation \\ AI & Artificial Intelligence \\ AR & Augmented Reality \\ CPS & Cyber-Physical System \\ D2D & Device to Device \\ DLT & Distributed Ledger Technology \\ DoS & Denial of Service \\ DPI & Deep Packet Inspection \\ eMMB & enhanced Mobile Broadband \\ FL & Federated Learning \\ ICT & Information and Communication Technologies \\ IDS & Intrusion Detection System \\ IoT & Internet of Things \\ IPS & Intrusion Prevention System \\ IT & Information Technology \\ MCA & Mission-Critical Application \\ MEC & Multi-access Edge Computing \\ MIMO & Multi-Input Multi-Output \\ mIoT & massive IoT \\ ML & Machine Learning \\ NFV & Network Function Virtualization \\ QoS & Quality of Service \\ \end{tabular}
## 3 Use-Case-Based Categorisation of Technologies
### Use Case Examples and Derived Requirements
#### 3.2.3 Long-Range Underground Monitoring  
Underground monitoring applications, such as IoT sensor networks, are characterized by low power consumption and long range. LPWAN technologies meet these requirements, but the wireless technologies are also characterized by a low data rate. LPWAN applications can be found in many fields, such as asset tracking, smart city applications, smart metering and smart agriculture [27].  
In the mining sector, LPWAN technologies have a great potential due to increased need for connectivity and the potential of an increased range in underground spaces compared to existing technologies. These properties could enable data transmission with sparse installations in dynamic and widespread areas. Research underground is limited, but first investigations and tests (e.g., [37; 38; 39]) show a promising application in the underground area.  
Use case example are:  
* Ubiquitous coverage for safety
* Transmission of health/process status and/or position of worker mobile equipment
* Environmental monitoring of rock stress, gas concentration, etc.
* Asset tracking
* Predictive maintenance
* Supervision of certain areas  
In conjunction with these usages, the following data must be transmitted:  
* Health/process status and position
* Environmental data
* Single (status) images
* Operational or process data  
A wireless technology must fulfil the following requirements to be appropriate and function effectively for the use cases mentioned above:  
* Softly bounding time delays for a single path (<1000 ms) for supervision (single point-to-point connection)
* Support of numerous devices
* Very low-frequency (from per minute to days) transmission of data
* Not dependent on electric infrastructure; sufficiently high battery lifetime of extending stations and sensors
* Very wide and penetrating coverage of single stations
## 2 Smart Homes
### Home Application Areas
#### 2.2.3 Health Care Systems  
Having advanced technology in our homes will lead to various opportunities in the near future in this area. One of the most important is the monitoring of a person's cognitive and physical health and, as a consequence of an aging population, an area of critical need is eldercare. The Health Smart Home concept, which can meet this challenge, has been extensively researched by many authors [25].  
Suggestive kinds of smart healthcare technologies contain simple devices (blood glucometers, oximeters, blood pressure monitors, _etc._) which deliver standardized outputs for specific physiological conditions, smart applications or software able to analyse and process body signals, sensor integrated smart devices (gaming devices, smartphones and pads), wearable sensors (e.g., wrist straps, T-Shirts) and additional devices entirely manufactured for the purpose of body signal monitoring/processing (e.g., mainframe computers, tablets). The proposed solutions can be applied to several healthcare technological solutions, including smart homes [26]. Each of these categories poses different challenges when their designers try to comply with the Health Care Smart Home requirements.  
The patients utilize components (e.g., sensors), which may be invisible and transparent to the user. Their constantly increasing storage and communication capabilities coupled with their small size enable collection, processing, and potential disclosure of personal health information. Whether at home, work or traditional settings (physician's office, hospital), healthcare IT infrastructures transfer sensitive patient health information and, as such, this issue faces several constraints and information security threats. Security safeguards and controls and data quality and integrity are classified as top priority, mostly because they arise by different fields of information security, but protecting the patient's location and purpose specification, remain the least addressed requirements [26].  
[PERSON] and [PERSON] reported a mechanism for the estimation of elderly well-being condition based on usage of household appliances [27]. [PERSON] _et al._ proposed a computer vision-based posture recognition method for home monitoring of the elderly [28], while [PERSON] _et al._ implemented an enhanced fall detection system based on on-body smart sensors that successfully detect accidental falls in a consumer home application [29]. [PERSON] _et al._ proposed a general purpose home area sensor network and monitoring platform intended for e-Health applications [30].  
The outdated devices commonly used to monitor body parameters like heart beat rate or exertion level are not fit for real-time measurements [31]. Nonetheless, a continuous monitoring [13] of such parameters as diabetes, hypertension, and cardiac diseases could allow for constant control of elderly people's physical conditions and provide valuable information since these chronic diseases are more common among this age group.
## 3 Air Pressure Compensation Algorithm  
### ILC Algorithm  
The iterative learning control (ILC) algorithm [33; 34] is often used to solve the control accuracy problem of highly repeatable systems. It only needs the input data of the system from the previous or the previous few times, and can accurately track the reference curve after multiple iterations of learning.  
The governing equation of the \(i\)-th iteration of the second-order repeated operation of the continuous nonlinear system can be expressed as:  
\[\left\{\begin{array}{l}\dot{x_{i}}(t)=f(x_{i}(t),u_{i}(t),t)\\ \quad y_{i}(t)=g(x_{i}(t),u_{i}(t),t)\end{array}\right.  
The tracking error of the system at the \(i\)-th iteration can be defined as:  
\[e_{i}(t)=y_{d}(t)-y_{i}(t) \tag{24}\]  
The control goal of ILC can be described as using the last iteration error \(e_{k-1}(t)\) and the control input information \(u_{k-1}(t)\) to learn according to the set learning law. When the number of iterations \(k\) tends to infinity, the control input \(u_{k}(t)\) of the system can make the control output \(y_{k}(t)\) infinitely approach the target reference trajectory \(y_{d}(t)\). That is, the tracking error \(e_{k}(t)\) converges to zero in a finite time. The iterative learning principle of ILC is shown in Figure 6.  
The analytical expression of ILC can be derived from Figure 6:  
\[u_{k}(t)=u_{k-1}(t)+U(e_{k-1}(t)), \tag{25}\]  
where \(U(\cdot)\) represents the learning law of the last iteration to control the output error.
# Multiple Network Fusion with Low-Rank Representation for Image-Based Age Estimation
## 4 Experimental Evaluation  
### Settings and Datasets  
Images in traditional face datasets are low-resolution and ages are not labeled. Therefore, [PERSON] et al. collected a large dataset of face images with age information [30], which has been made available for academic research purposes (Available at [[https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/](https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/)]([https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/](https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/)) (accessed on 8 May 2012)). To achieve this, they crawled the data of popular actors on the IMDb  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Network** & **Structure** \\ \hline Autoencoders & Stacked denoising autoencoders with 0.3 corruption level and 5 layers \\ CNN & CNN with 3 convolutional layers and 2 fully-connected layers \\ RNN & RNN with 3 layers \\ \hline \hline \end{tabular}
\end{table}
Table 1: The structures of different networks implemented by the proposed method.  
website and fetch their date of birth, name, and gender in their profiles. Besides, they crawled the same meta information and profile images of these people on the Wikipedia website. In this way, 460,723 face images were collected from 20,284 celebrities on IMDb, and 62,328 on Wikipedia were obtained. Sample images of these two datasets are shown in Figure 2. In our experiments, we used the two datasets individually. When we used IMDB, we randomly chose 100,000 images as the training samples and the rest as testing samples. When we used WIKI, we randomly chose 10,000 images as the training samples and the rest as testing samples. This process was repeated 20 times. Then, average performance and standard deviation were recorded. The evaluation was conducted on a desktop with NVIDIA 1080 Ti.  
For evaluation, we used mean absolute errors (MAEs), which was computed by  
\[MAE=means(|\hat{Y}-Y|) \tag{26}\]  
where \(\hat{Y}\) is the estimation results. For regression methods, results can be directly computed. For classification methods, we simply treated the age estimation problem as a classification task of 100 classes. \(Y\) is the ground truth.
## 6 Conclusions  
To solve the problem that it is difficult to extract useful fault features directly from vibration signals because of the non-stationary, non-linear characteristics and strong noise interference, a method of feature extraction based on refined multi-scale symbolic dispersion Entropy (RMSDE) is defined in this paper. This method can extract vibration characteristics that most effectively reflect the mechanical health state and, combined with a self-organizing fuzzy logic classifier (SOF), can achieve high-precision fault diagnosis. Specific conclusions are as follows.  
A series of simulation tests are constructed to analyze the method in terms of complexity estimation performance, noise immunity performance and computational efficiency. The results show that the proposed RMSDE has the best discrimination performance for sequences with different complexity, the best anti-noise interference ability, and the obvious advantages in computing efficiency compared with the traditional entropy calculation method.  
In this paper, the validity of the proposed RMSDE in fault feature extraction is verified by the test signals of rolling bearings and bevel gears under various health conditions. Compared with the traditional entropy calculation method, the fault features extracted by this method are more stable, with minimum error and fluctuation, and have the best separability, which has a certain engineering application value.  
A variety of machine learning classifiers have been used to demonstrate the superior performance of the proposed RMSDE. Among them, RMSDE-SOF obtained the ideal diagnosis result, and the standard deviation was also the lowest. RMSDE also has good adaptability to various machine learning classifiers. It can be seen that it has the best consistency as well as the best feature extraction effect.  
Conceptualization, Y.H.; methodology, Y.H.; software, Y.H.; validation, Y.H. and S.L.; formal analysis, Y.H. and S.L.; investigation, S.L.; resources, S.L.; data curation, J.L.; writing--original draft preparation, Y.H.; writing--review and editing, M.F.; visualization, Y.H.; supervision, S.L.; project administration, S.L.; funding acquisition, S.L. 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 (51975276).  
Not applicable.  
The datasets in Section 5.1 can be found on this website: [[https://mb.uni-paderborn.de/kat/forschung/datacenter/bearing-datacenter/](https://mb.uni-paderborn.de/kat/forschung/datacenter/bearing-datacenter/)]([https://mb.uni-paderborn.de/kat/forschung/datacenter/bearing-datacenter/](https://mb.uni-paderborn.de/kat/forschung/datacenter/bearing-datacenter/)) (accessed on 18 March 2023).  
The authors declare no conflict of interest.  
Figure 19: Average diagnostic accuracy and standard deviation to different classifiers.
# Influences of Brass Surface Morphology on Leidenfrost Effect during Liquid Nitrogen Cooling
## 3 Results
### Leidenfrost Indexes  
To explore the influence of brass surface morphology on the Leidenfrost effect in liquid nitrogen cooling process, the indexes \(\Delta T_{\text{LP}}\) and \(\Delta T_{\text{CHF}}\) of the Leidenfrost effect were associated to the two parameters \(Sq\) and \(D_{\text{RSE}}\) of surface morphology. First, the \(Sq\) and \(D_{\text{RSE}}\) values calculated by using the WLI data shown in Figure 3 are illustrated in Figure 6a, which imply that there was a strong correlation between \(Sq\) and \(D_{\text{RSE}}\). \(D_{\text{RSE}}\) monotonically decreased from 2.664 to 2.287 when \(Sq\) increased from 0.192 m to 1.961 m, and such a trend was consistent with previous studies on fractal analysis of various surfaces.  
Figure 4: Comparison of \(Sq\) and \(Rq\) roughness values of brass samples, which were obtained by using WLI and roughness meter, respectively.  
Figure 5: SEM images of brass samples surface at 700\(\times\). The sub-figures from (**a**–**f**) correspond to the grit densities of 180, 400, 800, 1200, 1500 and 2000, respectively.  
\(\Delta T_{\text{CHF}}\) could vary from 94.1 K to 104.3 K, but there was no clear relationship of \(\Delta T_{\text{CHF}}\)-\(Sq\) or \(\Delta T_{\text{CHF}}\)-\(D_{\text{RSE}}\), indicating that brass surfaces morphology could not directly influence \(\Delta T_{\text{CHF}}\) within the experimental scope of this study.  
The relationships of \(\Delta T_{\text{LP}}\)-\(Sq\) and \(\Delta T_{\text{LP}}\)-\(D_{\text{RSE}}\) were shown in Figure 6b,c, respectively. \(\Delta T_{\text{LP}}\) generally increased within a range from 102.74 K to 107.58 K along with the \(Sq\) increasing within the range from 0.192 \(\upmu\)m to approximate 1 \(\upmu\)m, but there was a significant fluctuation of \(\Delta T_{\text{LP}}\). It could also be noticed that when \(Sq\) was larger than 1 \(\upmu\)m, its further increasing could cause a slight decreasing of \(\Delta T_{\text{LP}}\).