esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Details  
### Materials and Electrode Fabrication  
A total of five types of Al\({}_{2}\)O\({}_{3}\) powder with different shapes and sizes were used as hypothetical active materials: (1) a small spherical shape (Al\({}_{2}\)O\({}_{3}\), 99.8%, 8-12 \(\mu\)m, Lumi-M Co., Seoul, Republic of Korea), (2-3) medium and large spherical shapes (Al\({}_{2}\)O\({}_{3}\), 99.8%, 90 \(\mu\)m, Lumi-M Co., Republic of Korea; powder was sieved and classified into two sizes), (4) plate shape (Al\({}_{2}\)O\({}_{3}\), primarily \(\alpha\) phase 99.5%, Sigma-Aldrich, St. Louis, MO, USA), (5) fibrous shape (Al\({}_{2}\)O\({}_{3}\), with a diameter of 2-6 nm and length of 200-400 nm, Sigma-Aldrich, St. Louis, MO, USA). The shape and size of the Al\({}_{2}\)O\({}_{3}\) powder were observed using a field emission scanning electron microscope (FESEM; MIRA3, TESCAN, Brno, Czech Republic). In particular, the average sizes of the spherical particles were determined by laser diffraction particle size analyzers (LS 13 320 SW, Beckman Coulter, Brea, CA, USA). Argyrodote (Li\({}_{6}\)PS\({}_{5}\)Cl, 3 \(\mu\)m, CIS Solid Electrolyte, Seoul, Republic of Korea) and carbon black (acetylene, 100%, compressed, Thermo Fisher Scientific, Oxford, UK) were used as the electrolyte and conductive material, respectively. At this time, no binder was added, in order to clearly observe the tortuosity change in relation to the active material's shape. In other words, the electrode was composed of an inert active material, a solid electrolyte, and a conductive material (the term \"electrode\" used hereinafter means \"electrode composite\" unless otherwise specified). When analyzing the tortuosity in relation to the active material ratio or solid electrolyte ratio, the solid electrolyte volume ratio was varied between 0.32 and 0.72.  
To ensure a uniform dispersion of the mixture (active material + solid electrolyte + conductive material), it was mixed and dispersed evenly for 10 min at 2000 rpm in a Thinky mixer (AR-100 Planetary centrifugal mixer, Thinky, Tokyo, Japan). Then, 80 mg of the dispersed mixture was hydraulically pressed in a \(\Phi\)13 mm mold at approximately 109 MPa to obtain a thick film pellet having a thickness of approximately 250-350 \(\upmu\)m. To analyze the tortuosity degree in relation to the shape of the active material, the pressure condition was fixed at 109 MPa, and the hydraulic pressure condition was varied between 44 and 655 MPa to analyze the tortuosity degree according to the pressing pressure.
# Nonlinear Maximum Power Point Tracking Control Method for Wind Turbines Considering Dynamics
## 7 Conclusions  
The conventional MPPT method is based on steady states and ignores the dynamic performance. The conflict between rapid wind variations (especially for the wind conditions with a low average value and high turbulent density) and slow dynamic response of the Large-scale VSWTs owing to the large inertia becomes the main challenge for MPPT dynamic performance.  
The wind turbine inertial response time \(\tau\) and the tracking bandwidth \(\omega_{\text{b}}\) are analyzed to indicate the dynamic performance of the wind turbine MPPT method. Both the numerical and analytical analysis reveal the nonlinear property of conventional MPPT. The inertial response time \(\tau\) will be larger under low-speed wind conditions than that under high-speed wind conditions.  
To solve the nonlinear problem and alleviate the conflict between rapid wind variations and slow dynamic response, an MPPT control strategy based on torque error feed-forward is proposed for wind turbines. Gain parameters in the feed-forward path are nonlinearly regulated by the estimated generator speed. Meanwhile, the blade-pitch angle is also regulated according to the non-optimal TSR, which improves the wind power capture under a wider non-optimal TSR range.  
Simulation results of a 5 MW wind turbine show that the novel MPPT control strategy has assuredly shortened the dynamic process between two steady states. Compared with the conventional MPPT method about the nonlinear performance, the novel MPPT strategy endows VSWTs with good dynamic performance at most of the working points. Furthermore, utilizing the novel strategy, VSWTs are able to track the wind components of higher frequencies and extract more energy from wind than that using the conventional method.  
Future works may include the application of the novel MPPT method in real VSWTs and experiments on large-scale wind turbines.  
Methodology--L.Q. and L.Z.; software--L.Q. and X.B.; writing--Review and editing--L.Q., Q.C. and J.C.; project administration--Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Planning Project of Guangdong Province, grant number 2015B02024003, the National Natural Science Foundation of China, grant number 51976113, the National High-tech R&D Program of China (863 Program), grant number 2012 AA051301.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications
## 5 Strategies for QoE Measurement in CIAs
### Strategies Based on Indirect Measures
[35] measured CPU and GPU usage in their study of cloud gaming. [PERSON] et al. designed and developed [53] a platform to evaluate QoE in any CIA. The objective was to check whether the parameters offered by the service were consistent with the service level agreement (SLA). The values compared were the CPU, RAM, and GPU usage of the client, as well as the bit rate of the connection between the client and the CIA cloud server. The problem with these proposals is that none of the authors established which values of the measurements they obtained are sufficient to guarantee QoE. Although there was a relationship identified between QoS and QoE, these studies did not use real users subjected to different boundary conditions to verify and evaluate the correlation between QoS and QoE. [PERSON] et al. [54] developed a model to guarantee QoE in any type of CIA. The authors argued that by optimising different metrics, the CIA user would have a good QoE. Their model is based on fuzzy logic and optimises two indirect measures called the Rating of Expectation (RoE) and Capacity Class Score (CCS). Using the values of these metrics, the authors determined the ideal location for the CIA's cloud server to guarantee an accurate QoE result. The RoE categorises the CIA's requirements based on its access rate (slow, normal, fast), expressed in user accesses per second; the computational resources it needs (small, regular, large), expressed in CPU cores; and the processing time required (stringent, moderate, flexible), expressed in milliseconds. The CCS, on the other hand, reflects the state of the cloud server, categorising the cloud server requirements by the RTT (short, typical, lengthy), expressed in milliseconds; the availability of computational resources (poor, standard, rich), expressed in CPU cores; and the processing speed (least, average, intense), expressed in thousands of instructions per second (TIPS). The [PERSON] et al. model allows for selecting the best location for the cloud server and for understanding in real time whether the QoE requirements of a particular CIA can be met by thresholding the RoE and CCS metrics. However, the proposal did not study the relationship between the selected metrics used to calculate the RoE and CCS and the QoE perceived by users. Therefore, proposals based on indirect measures have some drawbacks. Some studies lack details on how to reproduce their QoE assessment methods. Sometimes, the relationship between QoE and the proposed measures is not specified. In addition, there is a risk of confusing QoS measures with QoE in certain cases.
## 2 Standard Fire Tests  
Concrete structural members may be exposed to different types of fires and subjected to different intensities during service life [6]. In a standard fire test, structural members (beams, slabs, prisms) are exposed to a controlled fire condition in a specially constructed furnace. Fire intensity and the rate is defined in various codes and standards for evaluating any structural element. Fire intensity and the rate at any specific time are defined with the help of time-temperature curves, which are called standard fire curves. The purpose of conducting a fire test is to determine the time duration for the endurance of fire by a structural element within the prescribed limits (maximum allowable strength).  
During exposure to fire, the temperature gradient in the test specimen is obtained by measuring the temperature from the exposed surface and inner part of the specimen by installing thermocouples. The selection of the standard fire curve depends on the application of structure and intensity and type of fire that the building may receive. The most common standard fire curves are used for structural testing are ISO 834 [17] and ASTM E119 [18] standard fires. Hydrocarbon standard fire is also used to investigate the effects of high-intensity fire on structures. Figure 1 shows the time-temperature curves for the most commonly used standard fire test.  
Hydrocarbon fire occurs due to the burning of hydrocarbon-based fuel. In fire protection studies, hydrocarbon fire is usually referred to as fire with a high-temperature rate, which means that hydrocarbon fire achieves peak temperature very quickly. It takes only 8 min to achieve 900 \({}^{\circ}\)C, as shown in Figure 1. Usually, ASTM E1529 [19] standard is used to determine structural member fire performance under hydrocarbon fire. The rapid increase in temperature is hazardous, especially for a brittle material like concrete. High temperature develops a high amount of pore pressure, which can exert on the concrete in the cover zone and hence cause spalling [20].
## 3 Preliminaries
### Battery Consumption and Privacy Loss
#### 3.5.1 Case 1: Device Participates in Training  
When a device participates in FL training, its battery decreases based on workload and device efficiency. The battery at the next time step, \(bat_{t+1}\), is given by the following equation:  
\[bat_{t+1}=bat_{t}-\alpha\cdot bs\cdot C_{m}\cdot W_{d}\cdot\text{steps per epoch} \tag{4}\]  
where  
* \(bs\): batch size;* \(C_{m}\): model complexity;
* steps per epoch: number of steps per epoch;
* \(W_{d}\): data size weight;
* \(\alpha\): device-specific energy cost constant (based on mAh per operation).  
This formula captures the compute load and energy cost per training step, in line with edge-AI energy modeling [25; 26].
## 5 Simulation Setup  
A well-known and open source network simulation tool NS-3 [45; 46] was used for experiments. The EGCIR protocol was simulated against the existing work as discussed in [37; 38]. The various parameters used in the simulation are highlighted in Table 2. The sensor nodes were dispersed in random order within the range of \(300\times 300\) m\({}^{2}\) over the squared sized area. The simulation time was set to 1000 s. The amount of sensors and attacker nodes was arranged from 100 to 500 and 10, respectively, which were deployed randomly. In simulation experiments, the value of k varies in different odd series to check the accuracy and avoid the ties. The size of the key was set to 64 bits and all nodes transmit data based on constant bit rate (CBR). After deployment, the energy resource of all nodes was set to 5 J. The BS had no limitations in terms of constraints.
## 5 Conclusions  
In this paper, we proposed a low-complexity, IDE-based detection algorithm in uplink massive MIMO systems. The proposed MIDE algorithm avoided the calculation of the Gram matrix, the matrix inversion, and LDL decomposition to reach a low computational complexity. In addition, a self-updating damping method was provided with the damping factor estimated and updated at each iteration based on the Euclidean distance between the latest two detection solutions, which accelerated the convergence of the IDE-based detection algorithms. Simulation results showed that the proposed MIDE algorithm performed better than the conventional LMMSE, AltMin, and ADMM detection algorithms in terms of the BER performance and the computational complexity.  
[PERSON] and [PERSON] contributed the idea generation; [PERSON] and [PERSON] conducted the study design and paper writing; [PERSON] helped the analysis of the simulation results.  
This work is supported in part by the National Natural Science Foundation of China (No. 61571108, No. 61701197, and No. 61801193), the open research fund of National Mobile Communications Research Laboratory of millimeter wave, Southeast University (No. 2018D15), the open research fund of the National Key Laboratory of millimeter wave, Southeast University (No.K201918), the Open Foundation of Key Laboratory of Wireless Communication, Nanjing University of Posts and Telecommunication (No. 2017 WICOM01), and the Wuxi Science and Technology Development Fund (No. H20191001).  
The authors declare no conflict of interest.  
Figure 6: Computational complexity comparison against the number of users.
## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### Patient Rights and Informed Consent in AI Healthcare
#### 5.5.1 Adapting Consent Processes to Address Reidentification Risks in AI Healthcare  
The risk of reidentifying anonymized patient data using advanced AI algorithms is a pressing concern that underscores the need for clear communication with patients during the consent process. Despite efforts to anonymize data, AI's ability to identify individuals presents a significant privacy threat. This challenge is compounded by the dynamic nature of AI, which continuously evolves and uses data in novel ways. Consequently, the consent process must be equally dynamic, enabling patients to reassess and modify their consent as AI systems and their applications change. The rapid advancements in AI technology have outpaced current regulatory frameworks, necessitating updated regulations prioritizing patient agency and consent. This could involve implementing technologically facilitated recurrent informed consent mechanisms for new data uses, thereby respecting patients' privacy and agency in the face of AI's dynamic nature. Additionally, the development of generative models for creating synthetic patient data has been proposed to mitigate ongoing privacy concerns. Such models would generate data that are not linked to actual individuals, allowing for the benefits of machine learning without the risks associated with using actual patient data [1,2,22,38].
## 2 Materials and Methods  
### Crops and Fertigation System  
Three lettuce crops in an aeroponic growth chamber (Figure 1) were developed in this work. The chamber was installed inside a 16 m\({}^{2}\) greenhouse with arch-type roof and perimeter anti-aphid mesh cover with average room temperature of 25.52 \({}^{\circ}\)C, average relative humidity of 62.26% and average light intensity of 16,515.08 lux throughout an approximate photoperiod of 12 h. In the three crops the lettuce seedlings were transplanted into the system 30 days after germination. The first, second and third transplantation were conducted on 16 September, 14 November and 26 November, respectively, 2021. The system fertigation time was 24 min off and 24 s on. Such time values were determined by experiments in three testing crops carried out previously to the crops described in this paper. In addition, the operation of the plant fertigation system is shown in the following link [[https://drive.google.com/file/d/1](https://drive.google.com/file/d/1) sdxU4 vET4x3F5 ebqwKuc2 fW5 Nbdp3 Goi/view?usp=sharing]([https://drive.google.com/file/d/1](https://drive.google.com/file/d/1) sdxU4 vET4x3F5 ebqwKuc2 fW5 Nbdp3 Goi/view?usp=sharing) (accessed on 17 June 2022), see also Supplementary Materials. The nutrient solution for the system fertigation was prepared in water with a pH level of 6 and vegetable nutrients used in commercial Steiner-type hydroponics [5]. The mineral content and the electric conductivity of the nutrient solution was monitored and adjusted every third day to maintain a concentration and conductivity range of 1726-1880 ppm and 3128-3290 \(\upmu\)S/cm, respectively. Such parameter values were measured with an HM Digital meter model TDS-3.  
After the germination stage in crop 1, the Iceberg lettuce seedlings (_Lactuca sativa_) were transplanted to the aeroponic growth chamber. No significance plant stress presented in the crop during the first three weeks, but at the beginning of the fourth week, water stress  
Figure 1: Aeroponic growth chamber.  
was suffered for approximately 6 h due to programming flaws in the automatic fertigation system. However, the culture could be recovered 48 h later. Crops 2 and 3 started with the transplantation of the same lettuce seedling types. No complications presented in the lettuce root and foliar growth throughout the growing period of 30 days.  
The environment variables of temperature and relative humidity inside of the greenhouse were daily registered throughout the growing period of 30 days for each crop. The measured values are depicted in Figure 2.  
The average ambient temperature and relative humidity inside the greenhouse throughout the growing period were 25.33 \(\pm\) 2.36 \({}^{\circ}\)C and 59.66 \(\pm\) 4.83% (mean \(\pm\) standard deviation), respectively. The ambient temperature behavior was different for the three crops. However, the temperature and relative humidity values were nearly within the value range for lettuce growing, 17-22 \({}^{\circ}\)C for daytime and 3-12 \({}^{\circ}\)C for nighttime [45], the suggested average ambient temperature is 23.4-24 \({}^{\circ}\)C with relative humidity of 65% [46].
# Defect Detection on Rolling Element Surface Scans Using Neural Image Segmentation
## 7 Conclusions  
Based on only a limited, unlabeled, and unbalanced dataset, we developed a classification system that reliably detects defects on the surface scans of rolling elements. Using heavy augmentation and windowing, the size of the dataset could be multiplied until sufficient for training of a neural network. The chosen convolutional encoder-decoder architecture was trained to segment its input data. Simulated defects bypassed the need for rare, real defective parts and lowered the manual labeling efforts required for training. While simplistic, experiments showed that the simulated defects were viable in training the network to perform well on real data. It is unlikely however, that the presented approach would work on a task where defects are too complex to reliably simulate. This limits the proposed system's application to domains, where defects are well understood, simple and clearly defined.  
The data required and the low training times of the neural network allow the system to be quickly retrained. This enables it to adapt to changes in the production process and different classification circumstances. Thanks to a GPU implementation of all necessary pre- and post-processing operations, the system allows rapid inference during its working phase. Preliminary laboratory tests resulted in satisfactory classification rates. In the future, a bigger test in a production environment is required to confirm these results.  
Conceptualization, N.P. and M.B.; data curation, A.L.; methodology, N.P. and M.B.; project administration, G.B.; software, N.P.; supervision, G.B. and F.D.; validation, N.P.; visualization, N.P.; writing--original draft, N.P.; writing--review and editing, M.B. and G.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Free State of Bavaria and Fertigungsgeratebau Adolf Steinbach GmbH & Co. KG, 97616 Salz, Germany, grant number IUK514/002.  
The authors would like to thank Fertigungsgeratebau Adolf Steinbach GmbH & Co. KG for providing the steel rolling elements for the above-mentioned data collection process and providing feedback and input for the hardware design.  
The authors declare no conflicts of interest.
## 4 Experiment and Result Analysis
### Experimental Results
The results of the proposed drivable area estimation are depicted in Figure 4. The drivable areas for both ego-lane and alternative drivable lanes are sufficiently well segmented in a complex environment, for example, highway, city, night, curve, parking lot, single lane. The light reflection can causes errors where light is insufficient at night, but the drivable area is well preserved. The areas in the opposite lane are excluded from the drivable area, and it was successfully differentiated from the opposite driving lane even on a road with sparse lanes. Free space between the cars parked on the road was also well detected. Figure 5 shows the lane line segmentation result. In this experiment, following the ground truth of the BDD dataset, the crosswalk is considered as lane line. Some lane lines are occluded by other objects on the road. However, the result shows that multiple lanes lines are well estimated under various conditions even on a rainy day. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **DAE** & **Lane** & **Scene** & **Multi** & **Speed** \\ \cline{2-5}  & **mIoU(\%)** & **mIoU(\%)** & **Acc.(\%)** & **Yes/No** & **fps** \\ \hline IBN\_PSA/P [26] & 86.18 & - & - & no & 3.81 \\ Mapillary [27] & 86.04 & - & - & no & 0.153 \\ DiDiLabs [28] & 84.01 & - & - & no & 3.35 \\ Asgarian [9] & 83.50 & - & - & no & 322 \\ ENet-SAD [29] & - & 47.72 & - & no & 12.1 \\ SCNN [14] & - & 47.34 & - & no & 123.6 \\ ERFNet [13] & - & 51.77 & - & no & 7.3 \\ DLT-Net [3] & 72.10 & - & - & yes & 9.30 \\ MultiNet [1] & 71.60 & - & - & yes & 8.6 \\ VisLabs (DAE) [2] & 83.35 & - & - & yes & 23.86 \\ VisLabs (Scene) [2] & - & - & 77.73 & yes & 27.58 \\ VisLabs (DAE+Scene) [2] & 82.62 & - & 76.53 & yes & 22.59 \\ \hline Ours (Lane + Scene) & - & 74.46 & 76.76 & yes & 96.34 \\ Ours (DAE + Scene) & 82.55 & - & 76.51 & yes & 106.38 \\ Ours (DAE + Lane) & 83.34 & 75.66 & - & yes & 104.43 \\ Ours (DAE + Lane + Scene) & 84.56 & 78.57 & 78.4 & yes & 93.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative evaluation on the BDD dataset. DAE, Lane, Scene, Multi, and Speed refers to the drivable area estimation task, lane line segmentation task, scene classification task, multi-task learning, and running time, respectively.
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 5 Results  
This section presents the evaluation of the developed system, with a particular focus on fall detection performance. While the system may have the capacity to recognize various human activities, the primary objective of this project was to accurately detect fall events. To assess this, we analyzed the system's behavior using standard classification metrics derived from the confusion matrix, which specifically reflect the system's ability to distinguish between fall and non-fall scenarios.  
Figure 5: Emergency alert message sent via Telegram when no user response is detected after a fall.