esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Measurement Approach
### Wedge Detection Algorithm
#### 2.3.3 Determination of the Wedge Area
Therefore, additional templates with sizes in the vicinity of the initial template size are investigated by calculating the weighted correlation according to Equations (7)-(9). The maximum value of these correlations yields the optimal size parameters \((h_{i},w_{i})\) of the wedge at the position \(x_{i}\), which are then inserted into Equation (6) to obtain the wedge area.
## 3 Eye-Tracking Technology
### Commonly Used Methods and Metrics  
Most cognitive processing and information acquisition occur during fixations, which is why fixation-based metrics are commonly used in research [29; 78; 79]. Analyzing the locations and durations of longer fixations is a standard approach for assessing web processing difficulty [80], as fixation duration can serve as an indicator of cognitive load [24]. Researchers typically define AOIs to focus on specific regions of eye movement data and Time of Interest (TOI) to isolate relevant time intervals. The use of qualitative and exploratory analysis, especially through visualization techniques, has become more prominent [56]. These methods include string-edit and sequence-based representations [56; 81; 82], Markov models [83; 84], and classifications of raw eye-tracking data [85; 86]. Additionally, algorithms have been developedto analyze individual scan-paths, identifying commonly followed visual patterns [82] of interest for the purpose of this study.  
In brief, ET technology offers an innovative approach to assessment by capturing real-time data on visual attention and cognitive processes, providing insights that traditional methods often miss. While research underscores ET's potential in DC assessment, it is crucial to critically assess the methodologies and findings to fully understand their implications. Studies by [PERSON] et al. and [PERSON] [87; 88] reveal that ET offers detailed insights into users' attention and interaction patterns, enhancing the precision of DC assessments by evaluating both outcomes and cognitive processes. Additionally, studies like those by [PERSON] et al. and [PERSON] et al. suggest ET can validate test score interpretations by analyzing how individuals engage with tasks [21; 39; 53].  
However, significant challenges remain. The high cost and technical complexity of ET, as noted by [PERSON], restrict its accessibility and large-scale use [29]. ET data collection is resource-intensive, requiring specialized tools and expertise, and visual attention, measured through ET, does not always correspond to actual understanding [89]. Moreover, while ET may show if examinees engage with tasks as intended, it does not necessarily confirm that these cognitive processes mirror real-world actions. Variability in study outcomes, due to the lack of standardized protocols, further complicates its implementation [88; 89].  
Despite these obstacles, integrating ET into DC assessment presents a promising opportunity to enhance the validity and reliability of assessments. As research by [PERSON] et al. highlights, ET can offer a more comprehensive evaluation of DC [90].
# Utilizing Multi-Dimensional MmWave MIMO Channel Features for Location Verification
## 5 Numerical Results
### Impact of the Number of Antennas on the Authentication Performance  
We show in Figure 3 the ROC results under different receiver antenna numbers with the settings of (SNR = 10 dB, \(L\) = 20, \(k_{\phi}\) = 2 dB, \(k_{\theta}\) = 2 dB, \(k_{\alpha}\) = 2 dB). From Figure 3, we can see that the authentication performance (\(P_{f}\), \(P_{d}\)) under 64 receiver antennas outperforms that of the others, and the performance under 16 receiver antennas is poorest. This is because multiple antennas can improve feature dimensions and further improve the authentication performance. When \(P_{f}\) is fixed, \(P_{d}\) increases when the number of receiver antennas increases. Moreover, the authentication performance (\(P_{f}\), \(P_{d}\)) under 64 receiver antennas can satisfy the practical authentication requirements.
# Smart Healthcare Network Management: A Comprehensive Review
## 3 Smart Healthcare Remote Monitoring Contexts  
The Internet of Things (IoT) extends Internet connectivity beyond conventional devices such as desktops, laptops, tablets, and smartphones, enabling a broader range of computing and communication-enabled objects. These objects, equipped with sensing, computing, and communication capabilities, can be integrated with both living and non-living entities. In the context of smart healthcare, such devices facilitate the collection of vital signs and physiological parameters, enabling continuous patient monitoring and timely medical interventions.  
Smart healthcare is poised to transform the healthcare industry by offering personalized and adaptive healthcare environments. However, for the effective deployment and management of a robust smart healthcare system, it is crucial to understand the various contexts in which a patient may exist. This section categorizes the different contextual scenarios that a patient might encounter.  
Most smart healthcare solutions advocate the use of a gateway between sensors and the backend server. This gateway can be either a dedicated device or a smartphone equipped with IoT and Wireless Sensor Network (WSN) protocols. By performing preliminary data processing and filtering, these gateways help minimize the volume of data transmitted to the backend. [PERSON] et al. [PERSON] et al. (2018) proposed a framework designed for various healthcare settings, including hospitals, specialized clinical facilities, home care, senior citizen residences, gyms, and mobile healthcare environments such as ambulances, mobile clinics, and travel health services.  
While the primary settings for smart healthcare systems are typically homes, nursing homes, and hospitals, additional contexts can be considered to provide patients with greater mobility and enable them to fully benefit from the IoT framework. Figure 1 presents both conventional and unconventional contexts where smart healthcare systems can be utilized. These contexts are categorized into three main groups, as outlined below.
## 3 Experiments  
### Dataset Selection and Preprocessing  
In recent decades, the importance of seafloor maps has become increasingly evident. Despite their relatively coarse resolutions, global digital bathymetric models (DBMs) like ETOPO_2023 [50] and GEBCO_2024 [51] have been extensively utilized to clarify complex oceanic processes and geological features. In contrast, some countries, including Australia, have released regional high-resolution (HR) seafloor datasets--for example, the 1-arc-second digital elevation models (DEMs) of the Torres Strait and Bass Strait. These datasets integrate multi-beam echo sounding, airborne LiDAR, and satellite-derived bathymetry and are standardized under the WGS84 horizontal datum and mean sea level vertical datum to ensure geospatial consistency. In this study, we formulate a super-resolution reconstruction task based on the aforementioned HR DEMs, with detailed data information provided in Table 1. To enhance training efficiency, large-area DEMs were divided into fixed,non-overlapping sub-blocks (256 \(\times\) 256) to serve as supervision signals, yielding a total of 16,960 sub-blocks from the two regional DEMs. Low-resolution input data (64 \(\times\) 64) were generated via uniform down-sampling to create paired input-output datasets. For model optimization, 90% of the data (15,264 pairs) were allocated for training, with the remaining 10% (1696 pairs) reserved for performance evaluation. Moreover, to eliminate scale differences in water depth across various marine regions, all data were linearly normalized [52] to a range of -1 to 1, thereby enhancing model convergence and generalization.
## 1 Introduction
Therefore, it is important to consider the hygothermal properties of earth-based materials, as these are influenced by temperature,  
Figure 1: Tons of plant residues per crop in Almeria, Spain. Modified from [PERSON] et al., 2016 [2]. relative humidity and wind speed [22]. Furthermore, it should be noted that consolidation treatments designed to enhance the weathering resistance of earth-based building materials have been evaluated by [PERSON] et al. (2022) [23]. In contrast, the performance of polypropylene fibre-reinforced materials has been the subject of numerous studies. These studies have analysed the improvement of the bonding between polypropylene fibres and the matrix in compromised concrete composites, primarily due to the hydrophobic nature of polypropylene. In this regard, methodologies such as surface modification of fibres with calcium nanocarbonate [24] or copolymerisation of polypropylene with non-ionic surfactants [25] have been investigated. These treatments have been shown to enhance interfacial adhesion, consequently leading to improved mechanical properties of composites [26]. Consequently, there is a necessity for innovation of polypropylene-reinforced construction materials to achieve greater strength, ductility and crack resistance. Another potential alternative is the utilisation of fibres in the fabrication of insulation panels as building material. The consumption of virgin wood for insulation panels compromises biodiversity; the quality and quantity of forests and their protection are part of global and regional policies, for example, in the EU Biodiversity Strategy 2030 or the Sustainable Development Goals (SDGs) [11; 27]. Consequently, the wood-based panel industry has shifted its focus towards exploring alternative raw material sources, including the utilisation of recycled materials and by-products. It is important to emphasise the extant research in the direction of biopolymer-based materials and to draw parallels with the present research. The research of [PERSON] et al. (2024) and [PERSON] et al. (2025) [28; 29], as well as other research in this field, is centred on the development of biodegradable, sustainable and high-performance materials. Numerous studies have been conducted on the potential of agricultural residue for panel development [30; 31], and no studies have been found specifically using tomato, pepper, zucchini, cucumber and aubergine fibres for the development of this building material. All of this justifies this research through the study of the physical, mechanical and chemical properties to characterise these by-products as raw materials and analyse the partial or total replacement of wood in insulating panels.
## 4 Quadrotor Control
### Control Law Design  
For an appropriate control of the UAV quadrotor system described by Equations (1)-(6), PID controllers are used. These controllers are simple to design and robust. The complete control scheme for the quadrotor is represented in Figure 2. It can be clearly seen from this figure that three PID controllers are used for positions (\(x,y,z\)) and three PIDs for attitudes (\(\phi\), \(\theta\), \(\psi\)). The inputs are divided to two parts, the desired and the sensor signals. Desired signals for positions and yaw angle (\(x_{d}\), \(y_{d}\), \(z_{d}\), \(\psi_{d}\)) are fixed by the pilot or the autopilot program. Sensor signals for desired roll and pitch angles (\(\phi_{d}\), \(\theta_{d}\), and \(U_{1d}\)) are computed from the three virtual inputs \(U_{x}\), \(U_{y}\), and \(U_{z}\).  
The transfer functions \(C_{s}(p)\) of PID controllers are given in Equation (21), where \(s=\{\phi\), \(\theta\), \(\psi\), \(x\), \(y\), \(z\)}. Their gains (\(K_{ps}\), \(K_{is}\), \(K_{ds}\)) should be optimally tuned for the quadrotor in order to ensure stable, fast, precise, and robust responses. For this objective, PSO, CS, PSO-CS, and RM methods are applied to realize an optimal setting of these parameters.  
\[C_{s}(p)=K_{ps}+\frac{K_{is}}{p}+K_{ds}p \tag{21}\]  
Figure 2: Complete control scheme for quadrotor.
# A Multisensor Approach to Satellite Monitoring of Trends in Lake Area, Water Level, and Volume
## 1 Introduction
### Study Region
(**a**) Landsat image mosaic from the 1980s, prior to lake formation; (**b–c**) Landsat images of the eastern half of the Toshka Lakes region, 1998–1999; (**d–l**) Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) images from odd-numbered years 2001–2017. [PERSON] et al. (2002) used Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery from 2002 and SPOT-4 imagery from 2006 to measure the extent of the lakes in those two years. Lake areas were derived from a manual thresholding process in the near-infrared band of each sensor, resulting in a binary land/water image. A DEM was also digitized from pre-inundation topographic maps, and used to estimate water level and volume in each lake on the dates of the ASTER and SPOT-4 images. The 2002 total surface area was reported as 1591 km\({}^{2}\), and was 937 km\({}^{2}\) in 2006. Volume (of all lakes) was 25.26 \(\times\) 10\({}^{9}\) m\({}^{3}\) in 2002 and 12.67 \(\times\) 10\({}^{9}\) m\({}^{3}\) in 2006. The rate of decline in water level during the four-year interval was reported as (approximately) 7 mm/day. [PERSON] et al. also predicted that, in the absence of additional inflows, the larger lakes would disappear in 2012 (Lake 3), 2014 (Lake 1), and 2020 (Lake 4). [PERSON] et al. (2002) used a one-class parallelepiped image classification approach to map lake extent on approximately 14 dates from 1998-2007. They reported a maximum area of \(\sim\)1586 km\({}^{2}\) in August 2001, the closest of their image dates to the actual maximum. The projected date for the disappearance of the lakes was given as March 2011. The difference between this and the 2020 projection from ([PERSON] et al., 2002) was due to the fact that ([PERSON] et al., 2002) projected each lake's evolution individually, while ([PERSON] et al., 2002) projected only the aggregated total area of all lakes, using a linear model. Finally, [PERSON] (2013) analyzed 14 MODIS images, one per year from 2000-2013 during the month of March, to estimate lake surface area. Atmospherically corrected (surface reflectance) versions of the MODIS red, near-infrared, and shortwave-infrared bands were extracted from the MOD13Q1 vegetation index product, and used in two spectral indices: the land surface water index (LSWI; ([PERSON] et al., 2002)) and the NDLI (([PERSON] et al., 2002)). A threshold in each index was used to separate land and water, in a binary fashion as in ([PERSON] et al., 2002). The largest surface area reported (in March of 2002) was 1664 km\({}^{2}\), three to four months after the actual maximum area was reached in December 2001. In addition to reporting the surface area of the lakes, ([PERSON] et al., 2002) also examined the extent of irrigated agricultural development between the Toshka Lakes and Lake Nasser, a large undertaking supported by water withdrawn from the Nile.
# Nonlinear Refractive Index in Rectangular Graphene Quantum Dots
###### Abstract  
Alongside its other favorable properties, the large refraction nonlinearity of graphene-related material makes it ideal for use in optoelectronics applications. Numerous experimental studies about nonlinear optical refraction have been conducted, but theoretical verification is lacking. In this paper the nonlinear refractive index for rectangular graphene quantum dots (RGQDs) was calculated using the relationship between nonlinear refractive index and the third-order nonlinear optical susceptibility. The third-order nonlinear optical susceptibility for third harmonic generation was derived employing the electronic states obtained from the Dirac equation around K point in RGQDs under hard wall boundary conditions. Results revealed that the calculated nonlinear refractive index, \(n_{2}\), was in the magnitude of \(10^{-14}\) m\({}^{2}\)/W in the visible region, which is nearly five orders larger than conventional semiconductor quantum dots, while in the infrared region the nonlinear refractive index reached up to the magnitude of \(10^{-11}\) m\({}^{2}\)/W for \(M=3M_{0}\) sized RGQDs where the resonance enhancement occurred. The nonlinear refractive index could be tuned both by the edges and sizes.  
nonlinear refractive index; graphene; quantum dot +
Footnote †: journal: Applied Physics Letters
## 2 Structure of the Monitoring and Control System
### Fuzzy-PID Control  
PID is a common method in engineering, and is the most widely used regulator control method and has a slight dependence on the accuracy of digital parameters. When the characteristics of the objects to be controlled are complicated and relatively non-linear or time-varying, the inappropriate parameter adjustment may cause oscillation in the system control. Compared with the traditional PID control, Fuzzy-PID controller has a relatively weak dependency on environmental factors, which is applicable in a complicated circumstance such as in a greenhouse. Based on the fuzzy logic principle [24], in most cases, a Fuzzy logic controller (FLC) shows a satisfying response capability for a non-linear system. The typical characteristic of an FLC is its incapability to generalize and only supply the response according to the existing rules. In this process, an expert in determining the inference logical rules can realize the function as a computation of desiring control value [25]. In practical application, in consideration of the relative data from the monitoring system for control purposes, an FLC is defined by a set of linguistic rules and fuzzy sets to compute the reasonable value for a greenhouse actuator. The actuator is the electromagnetic valve for relative regulation of CO\({}_{2}\) concentration in this paper, as shown in Figure 4. The monitoring sensor nodes collect the required input factors and send them to the FLC. The FLC derive the required PWM driving signal for the valve to control the CO\({}_{2}\) concentration. As the Fuzzy rules, a multiple inputs and multiple outputs (MIMO) are used in this system to regulate the CO\({}_{2}\) concentration.  
As shown in Figure 4, the multiple inputs come from the monitoring sensors, and the multiple outputs supply the required parameters for the following PID control. There are two inputs of the FLC, i.e., CO\({}_{2}\) concentration error (\(E\)) and changing rate of error (\(Ec\)). The outputs of the FLC are the required parameters, \(K_{\text{P}}\), \(K_{\text{I}}\) and \(K_{\text{D}}\), for PID controllers. After the second calculation by the PID controller, the output value is set to the range from 1 to 100, which corresponds to the driving frequency of the actuator.
###### Abstract  
Data compression is a useful method to reduce the communication energy consumption in wireless sensor networks (WSNs). Most existing neural network compression methods focus on improving the compression and reconstruction accuracy (i.e., increasing parameters and layers), ignoring the computation consumption of the network and its application ability in WSNs. In contrast, we pay attention to the computation consumption and application of neural networks, and propose an extremely simple and efficient neural network data compression model. The model combines the feature extraction advantages of Convolutional Neural Network (CNN) with the data generation ability of Variational Autoencoder (VAE) and Restricted Boltzmann Machine (RBM), we call it CBN-VAE. In particular, we propose a new efficient convolutional structure: Downsampling-Convolutional RBM (D-CRBM), and use it to replace the standard convolution to reduce parameters and computational consumption. Specifically, we use the VAE model composed of multiple D-CRBM layers to learn the hidden mathematical features of the sensing data, and use this feature to compress and reconstruct the sensing data. We test the performance of the model by using various real-world WSN datasets. Under the same network size, compared with the CNN, the parameters of CBN-VAE model are reduced by 73.88% and the floating-point operations (FLOPs) are reduced by 96.43% with negligible accuracy loss. Compared with the traditional neural networks, the proposed model is more suitable for application on nodes in WSNs. For the Intel Lab temperature data, the average Signal-to-Noise Ratio (SNR) value of the model can reach 32.51 dB, the average reconstruction error value is 0.0678 \({}^{\circ}\)C. The node communication energy consumption can be reduced by 95.83%. Compared with the traditional compression methods, the proposed model has better compression and reconstruction accuracy. At the same time, the experimental results show that the model has good fault detection performance and anti-noise ability. When reconstructing data, the model can effectively avoid fault and noise data.  
wireless sensor networks; data compression; variational autoencoder; downsampling-convolutional restricted boltzmann machine
## 2 Biosensors in Patient Health Monitoring  
### Advancements and Innovations  
In recent years, there has been a remarkable transformation in biosensor technology, marked by significant advancements and innovative developments [17]. One of the key trends driving this evolution is the move toward miniaturization and the emergence of wearable biosensors.  
The traditional bulky biosensors have given way to compact, miniaturized versions, offering patients a novel means of continuous health monitoring [18]. This shift in form factor holds particular significance because it allows individuals to seamlessly integrate biosensors into their daily lives without hindering routine activities.  
Figure 1: Overview of this paper’s research.  
A noteworthy aspect of miniaturization is the rise in wearable biosensors. These devices, often designed as discreet patches or accessories, provide users with the convenience of constant health parameter monitoring [19; 20]. The reduced size not only enhances user comfort but facilitates prolonged usage, enabling uninterrupted data collection over extended periods.  
Another stride forward is the development of flexible and stretchable biosensors. Innovations in materials, such as flexible polymers and advanced composites, have empowered the creation of biosensors that can conform to the natural contours of the body. This adaptability ensures improved skin contact, thereby enhancing the accuracy of health parameter measurements, especially in dynamic environments [21].  
Intelligent data processing has become a cornerstone of biosensor innovation with the integration of artificial intelligence and machine learning (ML) algorithms [22]. These algorithms play a crucial role in transforming raw biosensor data into actionable insights. Real-time processing and analysis enable the detection of patterns and anomalies, contributing to early identification of health issues.  
Biosensors equipped with AI-driven decision-support systems are now at the forefront of healthcare technology [23]. These systems provide valuable assistance to healthcare professionals by offering intelligent interpretations of biosensor data. This not only aids in timely medical interventions but also contributes to a more nuanced understanding of patient health.  
The concept of multi-parameter biosensors represents a significant leap forward in health monitoring capabilities [24]. Modern biosensors are designed to measure multiple health parameters simultaneously, providing a holistic view of the patient's well-being. This comprehensive approach enhances diagnostic capabilities, allowing for more accurate health assessments and supporting a personalized healthcare strategy.  
However, the ongoing advancements in biosensor technology, encompassing miniaturization, flexibility, intelligent data processing, and multi-parameter capabilities, are ushering in a new era of remote and continuous healthcare monitoring. These innovations collectively contribute to making patient health monitoring more accessible, accurate, and adaptable to individual needs.