esa-sceva/satcom-chunk-collection huggingface dataset:
# Space-Ground Remote Sensor Network for Monitoring Suspended Sediments in the Yellow River Basin
## 2 Data and Methods  
### Study Area  
Using ground-based and drone-based hyperspectral technology, researchers have achieved high prediction accuracy in monitoring complex optical and non-optical water quality parameters [28; 29; 30; 31]. The integrated monitoring of air, space, land, and sea has been applied in monitoring and evaluating water and sediment transport, transboundary disasters, etc., with good results [32; 33]. Figure 2 shows the schematic diagram of the integrated remote sensing monitoring network for water and sediment in the Yellow River basin. The upper and middle reaches of the Yellow River are mostly sediment production areas, such as the Loess Plateau, focusing on monitoring and predicting the processes of soil erosion and sediment production, while the middle and lower reaches of the river, especially the Hukou section, pay attention to the accumulation and flooding of the river channel. The lower reaches, including the Yellow River delta and estuary, focus on the ecological replenishment of wetlands, the accumulation of sediment in the estuary, and the impact of suspended sediment on water masses and the marine ecosystem [34; 35; 17; 36]. This  
Figure 1: The traditional regular sampling and measurements of water and sediments in the Yellow River. (**a**) On-site water sampling operations conducted by staff at the Lijin hydrological station, and (**b**) On-site flow velocity measurement operations by staff at the Luokou hydrological station during the water and sediment regulation period before the Yellow River flood in 2023. (Photo credits Xinhua News, 12 July 2023 16:43, [[https://www.news.cn/2023-07/12/c_1129745575.htm](https://www.news.cn/2023-07/12/c_1129745575.htm)]([https://www.news.cn/2023-07/12/c_1129745575.htm](https://www.news.cn/2023-07/12/c_1129745575.htm)), accessed on 25 April 2024).  
study presents an integrated remote sensing monitoring network for water and sediment, using the Qingshui River basin in Ningxia, the Xiaolangdi reservoir, and the adjacent Yellow River estuary as case studies.
# PneumoNet: Artificial Intelligence Assistance for Pneumonia Detection on X-Rays
## 2 Background and Related Work
[PERSON] and [PERSON]'s research findings also demonstrate that employing a VGG16 backbone with neural networks (NN) as a classifier yields superior performance compared to utilising VGG16 with Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF), and Naive Bayes (NB) for both datasets [8,10]. In the analysis of [PERSON] et al. [20], a DL model is showcased to distinguish between normal and severe pneumonia cases. The entire proposed system comprises eight pre-trained models: ResNet50, ResNet152V2, DenseNet121, DenseNet201, Xception, VGG16, EfficientNet, and MobileNet. These models were tested on two datasets, [9,21], containing 5856 and 112,120 chest X-ray images. In [PERSON] et al., the MobileNet model achieves the highest accuracy, scoring 94.23% and 93.75% on the respective datasets. Various crucial hyperparameters, such as batch sizes, epochs, and different optimisers, were carefully considered when comparing these models to identify the most suitable one. The work of [22] introduces PneuNet, a diagnostic model based on Vision Transformer (VIT), aiming for precise diagnosis leveraging channel-based attention within lung X-ray images. In this approach, multi-head attention is employed on channel patches rather than feature patches. The methodologies proposed in this study are tailored for the medical use of deep neural networks and VIT. Extensive experimental findings demonstrate that the approach achieves a 94.96% accuracy in classifying three categories on the test set, surpassing the performance of prior DL models. The research of [23] explores deep learning techniques for the automated analysis of chest X-ray images. The study employs two DL architectures: VGG16 and DenseNet121, and in Dataset #3 [10], the VGG16 emerged in the research as the most effective, achieving a training accuracy of 93.00% and a testing accuracy of 90.00%. In comparison, DenseNet121 exhibited slightly lower performance, with a training accuracy of 92.00% and a testing accuracy of 88.00%. The accuracy of each model is significantly influenced by its parameters. Based on the parameters utilised in this study, VGG16 demonstrates high accuracy and proves to be a reliable method for predicting pneumonia in chest X-ray images of patients.
## 4 Security Analysis of the Proposed Protocol
### Heuristic Analysis
#### 4.2.12 Man-in-the-Middle (MITM) Attack  
In our scheme, we suppose that the adversary [26] listens and blocks the user's login message \(F_{U_{i}}(t),\ PID_{i},MIS_{j},\ B_{1},B_{2},\) and \(T_{1}\) and response messages \(F_{U_{i}}^{new}(t),\ B_{10},B_{11},\) and \(B_{12}\) from \(GWN\). To issue a man-in-the-middle (MITM) attack, the adversary must create a flow of new login and response messages or replay old messages. As discussed above, the proposed protocol can resist impersonation and replay attacks. That is, the adversary cannot be authenticated by both the user and the gateway. Hence, the proposed scheme can be used against MITM attacks.
# A Wireless EEG System for Neurofeedback Training
## 4 Materials and Methods
### NF-EEG Experiment  
The reinforcement technique (neurofeedback) for the magnocellular pathways in DD children consisted of visual stimuli with different contrasts vertically flicking in an external noise field (Figures 5 and A3C).  
The discrimination of different contrast Gabor gratings with illusionary doubling motion (LSF illusion: flicker with a 15 Hz counter phase and low spatial frequency 2 cycles  
Figure 4: Forty sensor locations of the EEG system: 10-20 (green circles) and 10-10 systems (yellow circles). The 12 NF sensors are written in red.  
Figure 5: Scheme of experimental set-up. (**A**) EEG signals and frequency analysis at the EEG signals. Presentation of pre-training neurofeedback sessions: six sessions (for each hemisphere). (**B**) Visual training (VT) tasks, described in Supplementary Materials. (**C**) Post-training neurofeedback.  
per visual angle degree) consisted of the following conditions: a low-contrast level 6% and high-contrast level 12% of the threshold level of 50%, defined in previous psychophysics experiments [33]. The stimuli were presented in the center on a monitor and were arranged in a 40-trial pseudo-randomized series with 1.5-3.5 s intervals between stimuli (20 trials/condition). A fixation white cross appeared for 100 ms at the start of each trial. After 200 ms, the LSF illusion appeared for 200 ms at a viewing distance of 210 cm from the monitor (Figure 5). A colored cross was presented on the monitor for 100 ms in red when the EEG prints were below the set threshold and in a green sign when the EEG stamps were above it. The child determined the illusion with low contrast by pressing a button with the left hand and the illusion with high contrast by pressing a button with the right hand. The trial's end was marked with a brief beep. A response time period comprised the period of 100-1500 ms from the stimulus onset. The acquisition of the EEG data was synchronized with the stimuli onset. Trial-by-trial neurofeedback was perceived as a different-colored cross.
## 3 Proposed Methodology
### Feature Extraction
#### 3.1.1 Wavelet Transform  
The Wavelet Transform is a common image processing approach for extracting features. It divides the image into frequency sub-bands that capture varying levels of detail and texture. The Haar wavelet transform was utilized in this study to extract characteristics from reference and distorted images. This is how the Haar wavelet transform is defined,  
\[W_{l,m}=\frac{1}{\sqrt{2^{l}}}\sum_{q=0}^{2^{l}-1}h_{q}\cdot x_{p-q}\quad\text{ where }l,p=0,1,2,\ldots \tag{1}\]  
where \(W_{l,p}\) denotes the wavelet coefficient at scale \(l\) and position \(m\), \(h_{q}\) denotes the Haar wavelet filter, and \(x_{p}\) denotes the input image pixel value.
## 3 LCC Implementation in GPP in Greece--Methodology
### The EU LCC Tool  
Several authorities have developed tools with the main purpose of comparing LCC between different products. A comprehensive overview of LCC tools, approaches and good practices is included in the EU-funded SPP Regions report [17], for various categories of products. In this paper the EU developed LCC tool is selected. It is a well-established and widely-acknowledged approach, which aims at supporting real-life decision-making. As it is officially backed by the EU, it has gained acceptability and recognition from the academia, practitioners and policy-makers, referring to key product categories such as Vending Machines, Computers and Monitors, Outdoor Lighting, Indoor Lighting and Imaging Equipment. It may be applied: (a) _before tendering_, in order to assess the LCC of alternative options and to strengthen the market engagement procedures prior to the tendering process, (b) _during tendering_, in order to compare offers during the evaluation and award of contracts, as foreseen in Directives 2014/24/EU on public procurement and 2014/25/EU on procurement by entities operating in the water, energy, transport and postal services sectors and (c) _after tendering_, in order to evaluate the performance of the awarded solution compared to other tenders or the previous situation, but also to contribute to the preparation of future tenders for similar products or services [25].  
The life cycle impact assessment method used in EU LCC tools is ReCiPe. According to January 2021 Biodiversity Measurements Approaches and summary descriptions, ReCiPe is one of the most recent and updated impact assessment methods available to LCA practitioners [34]. The environmental impacts are transformed into externalities by applying monetization factors. EU LCC tool initially assessed four environmental impact categories: human health, ecosystem, resource availability and climate change. Regarding the final version of this tool, due to little consensus on human health and ecosystems and the lack of available methods for their evaluation and monetization, the European Commission decided to use monetization only for climate change.
# Review of Indoor Positioning: Radio Wave Technology
## 4 Positioning Algorithms
### Filtering Approach
In [252], a grid-based Bayesian filter is applied to the trusty step length estimation from smartphone data for indoor localization. The main idea of the Bayesian filter is too outperformed as a seamless positioning estimation by considering a complex indoor environment situation. Therefore, in [242] are described efficient conditions and more accurate positioning based on wireless sensor networks in the complexity environment mixing with LoS and NLoS scenarios. In [244], linear and nonlinear models for the location estimation of sensor fusion are considered under a recursive Bayesian Filter by using the location data of dead reckoning and UWB. Moreover, recursive Bayesian filtering, called channel-SLAM, addresses a multipath effect by using a mobile sensor platform. A particle filter is the iterative estimation method that could take data from human motion, radio map information and RSSI measurements from location APs. In [248], it is proposed that the positioning and tracking algorithm be performed by using an interesting method of signal strength measurement with a particle filter. In addition, the pedestrian map-matching problem can be solved by an accurate positioning and tracking framework with a particle filter by using low-cost smartphone MEMS sensors [239, 248, 253]. The PF algorithm is a state-equation-based method that has been proven to be suitable for solving the nonlinear filtering problem. KF and EKF, according to [246], are involved in recursive Bayesian Filter, which has been performed for sequentially investigating positioning in the tracking system. In [254], KF is used for a navigation system upon the motion detection of dead reckoning provided by an MEMS-based INS. KF which solves the linear-quadratic model in real time, and particularly, is used to improve indoor tracking and in navigation applications [247]. It also provides optimal position estimation under accurate measurement modelling and Gaussian measurement noise distribution. Although traditional KF can be achieved for the positioning fusion system under a linear Gaussian model, it cannot solve for nonlinear barriers. It is presented in [255] that there is a hybrid constrained KF approach for the dynamic Gaussian model. Furthermore, EKF has been advocated in advanced nonlinear system processes. EKF is commonly used for the probabilistic mapping problem in SLAM. However, there have some assumptions in EKF-based SLAM that require an updated time for sensor data and for truly known mapping between the observation and landmark [256], leading to important solution methods of the Fast SLAM algorithm. The authors in [247] introduce Bluetooth-based positioning by using indoor map information with the EKF algorithm. EKF, used for the nonlinear models by using the Wi-Fi signal and PDR [257], are also applied to eliminate the noise and are used in linear Gaussian theory.
## 5 Capacity Trends Analysis  
In this section, we utilize the capacity model proposed earlier to assess the network capacity trends of representative sensor and satellite networks. First, the simulation tools and the environment are described. Second, we analyze the major constraints in the network capacity model, including the dynamics of nodes, long-term variations, the number of nodes and actualized factors. Third, we study the relationships between the capacity model and major constraints. From the analysis, we obtain the network capacity trends and find the opportunities to optimize the IWSSN network schedules.
# Experimental Evaluation of Collision Avoidance Techniques for Collaborative Robots
## 1 Introduction
When the distance between the obstacle and any point of the manipulator's kinematic chain is lower than a critical threshold, a repulsive speed is generated, which allows the robot to deviate from the path initially established, trying to avoid the collision. The region of influence of each link is defined by a radius \(r\) whose dimension must be set as a compromise of avoidance capability and easiness of motion and task execution. In addition to this, the region of influence may vary depending on the relative velocity between the obstacle and the manipulator. In fact, implementing a safety volume proportional to the speed of the obstacle is a technique widely used to ensure greater safety [22]. The algorithm provides three modes of motion re-planning [23]. The first mode allows both the position and orientation of the end-effector to be varied with 6-DOF. Often, however, it is necessary to limit the freedom of movement of the tool, so two other modes are available. The second mode allows 4-DOF to be varied so that all translations are free, such as rotation about the vertical axis. The third mode keeps the orientation of the tool fixed, while the position can vary with 3-DOF. The algorithm is briefly resumed in Section 2. Section 3 describes the implementation of the algorithm on a real system with a collaborative UR5e robot that can be controlled by an external reference at a maximum frequency of 500 Hz. Several methods are available in the literature for real-time control of robots, a very common problem [24; 25]. In the present application, the cobot was fixed upside down on a stand and flipped over from normal use conditions; in addition, a tool was added to make the application as real as possible. All three modes of the control law were tested. In Section 4, the results obtained in the tests are compared with data obtained from simulations, which were carried out under the same conditions. Kinematics data of the joints are then analyzed to verify the transferability of the algorithm on a real system. To do this, 3 case studies are proposed. In the first one, the robot moves between two points in a straight line, free from obstacles. In the second test, a stationary obstacle is placed on the planned trajectory, always straight between two points, forcing the robot to avoid it by deviating from the original path. In the third example, the robot stands still at a point in the workspace while an obstacle moves to interfere with the end effector. Results and insights on future works are then discussed in the concluding Section 6.
## 3 Results
### Use Case: FZK-Haus  
The use case building for demonstrating the proposed IFC-based thermal comfort setup is the KIT FZK-Haus (FZK-Haus (IFC4), provided by Institute for Automation and Applied Informatics (IAI) / Karlsruhe Institute of Technology (KIT): [[https://www.ifcw.iki.org/index.php?title=KIT_IFC_Examples](https://www.ifcw.iki.org/index.php?title=KIT_IFC_Examples)]([https://www.ifcw.iki.org/index.php?title=KIT_IFC_Examples](https://www.ifcw.iki.org/index.php?title=KIT_IFC_Examples)), accessed on 12 November 2023) (total floor area: 208.55 m\({}^{2}\)) displayed in Figure 4. The simulation setup is based on the structure of the archetypal templates used for TEASER [46], which are also used in bim2 sim. For the choice of materials and constructions, the construction type is selected as heavy, and the materials and constructions are set for a building construction year between 1995 and 2015. The thermal zone usage templates are modified to match the use case of a residential house and further extended by the derived templates for activity and clothing (cf. Tables 2 and 3). The set points for heating and cooling (cf. Table 4) are derived from SIA 2024 [49] and do not include a night setback. Spaces that do not have cooling requirements according to SIA 2024 [49] are defined to have a maximum indoor temperature of 32 \({}^{\circ}\)C. The simulation setup uses ideal loads to meet the desired set points for heating and cooling in Section 3.2, and heating without cooling in Section 3.3.  
Figure 3: Monthly outdoor temperature for TMYx (2007–2021) and SSP5-8.5 for 2050 and 2080 weather data (Boxplots defined by median and Interquartile Range (IQR) from 25 th to 75 th percentile and whiskers limited by \(\pm\)1.5 IQR).  
The occupancy schedules are derived and adapted from [PERSON] et al. [67] and displayed in Figure 11. Schedules and loads for lighting and technical equipment are adapted from [PERSON] et al. [46] and SIA 2024 [49]. Natural ventilation set points are adapted from [PERSON] et al. [46] and DIN V 18599-10 [47].
# Utilization of Merge-Sorting Method to Improve Stitching Efficiency in Multi-Scene Image Stitching
## 2 Related Work
### Image Fusion Method  
After images are registered according to the method of image registration, they are fused. The linear weighted fusion method [27] can quickly produce fused images, but the overlapping areas often appear as ghosting and the sharpness of the images is reduced during fusion. Laplacian pyramid image fusion [14] not only solves the ghosting problem but also the problem of fusing a small number of images with different exposures; however, it is computationally intensive and time-consuming. The optimal seam line fusion algorithm [16] is an algorithm based on image cutting. In the overlapping area, the cutting line with the smallest energy intensity value obtained through the energy function is the optimal suture line. The optimal seam line algorithm not only has a good fusion effect but also lower computational complexity than the Laplacian pyramid algorithm. Therefore, we used the optimal seam line fusion algorithm as the image fusion algorithm in this study.
## 3 Results and Discussion
### Wilcoxon Matched Pairs Test of the NADES Samples
montana_ components can play the role of hydrogen bond donors and acceptors; hence, they can potentially form interactions with NADES components and thus slow down the movement of molecules, preventing oxidation and other degradation reactions and maintaining the overall stability. Also, for the further implementation of the established process on a large scale, an important aspect is the price of the used NADES (Table 1). Amino-acid-based NADES are evidently more expensive compared to sugar-based ones. Among the systems for which stability was established over time, the cheapest was Bet/Sor/W, followed by Bet/EG and Gly/Glu. Conventional procedures for obtaining and stabilizing aroma volatile components include Soxhlet or hydrodistillation, followed by spray drying. Soxhlet extraction involves the use of hazardous organic solvents, as well as the possibility of their remaining in the final product. On the other hand, hydrodistillation is performed at high temperatures that can lead to the degradation of thermally labile compounds, and, as a result, the smell of hydrodistilled essential oil is often not authentic to the source. With the application of supercritical CO2 at mild temperatures, the possibility of deterioration is reduced, there is no use or generation of toxic waste, and the obtained extract is clean and safe [42]. Furthermore, the spray drying technique is the most common method of preserving volatile components; however, it implies the use of carriers and additional processing of the sample, which, apart from additional costs, also suggests working at high temperatures and the possibility of losing thermo-sensitive components [5]. On the other hand, the preparation of NADES is simple, and dispersion in NADES avoids additional processing of samples at high temperature. The resulting systems represent products with numerous potential applications. Due to the presence of components with important pharmacological properties, such as carvacrol, thymol, thymoquinone, and others, they can be used in obtaining pharmaceutical products. Also, the components of _S. montana_ are highly relevant in the cosmetic industry as well as the perfume industry. Additionally, the properties of _S. montana_ components, such as the improvement of sensory characteristics and preservative and antimicrobial properties, are also important for the food industry. Moreover, the obtained systems can play an important role in creating repellent products [43; 44].