esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Correct VMF Interpolation Model Based on Height Change Grid Model  
### Elevation Reduction of Grid Model Based on ERA5 Height Change  
By VMF analysis in Section 4, the elevation difference between the selected station and the surrounding four grid points is large, so there will be large errors in the tropospheric delay elevation correction, which will lead to the low accuracy of the interpolation results. Therefore, the elevation change grid model is used for tropospheric delay elevation  
Figure 7: Distribution of root mean square error in China.  
correction. First, the tropospheric delay grid elevation needs to be reduced to the station elevation.  
\[D(h_{s})=D\big{(}h_{g}\big{)}+\alpha_{s}e^{\beta_{s}h_{s}}-\alpha_{g}e^{\beta_{ s}h_{s}}+\varepsilon_{s}-\varepsilon_{g} \tag{16}\]  
where \(D(h_{s})\) and \(D\big{(}h_{g}\big{)}\) are ZTD of the site and grid point respectively; \(h_{s}\) and \(h_{g}\) are station height and grid height (unit:m); \(\alpha_{s},\beta_{s},\varepsilon_{s}\) are the fitting coefficients of the elevation change grid model at the interpolation points; \(\alpha_{g},\beta_{g},\varepsilon_{g}\) are the fitting coefficients of the elevation change grid model at the VMF grid points.
## 2 Background and Preliminaries
### Federated Learning Algorithm
#### 2.4.3 Federated Averaging: Peer-to-Peer (FedAVGP2P)  
FedAVGP2P is an extension or variation of the Federated Stochastic Gradient Descent (FedSGD) algorithm in federated learning. In the standard FedSGD algorithm, a central servercoordinates the federated learning process, where clients compute gradients on their local data and send them to the server for aggregation and model updates. In the FedSGDP2P variant, the communication process occurs directly between participating clients in a peer-to-peer manner, eliminating the need for a central server. Clients collaborate with each other to exchange gradient information and update their models collectively. This approach has the potential to enhance privacy, reduce communication overhead, and improve the scalability of federated learning. However, it may introduce challenges related to synchronization, security, and the management of peer-to-peer networks.
## 1 Introduction  
One of the biggest challenges that modern societies might face in the coming years is the aging of their populations. According to the European Commission report [1], the projected percentage of people aged 65 or higher in 2070 will be 42.1%, while in 2016 it was 25%. Additionally, the number of working-age persons for every person aged over 65 will fall from 3.3 to 2. It means that the traditional care system consisting in young, healthy people caring for the older ones will become ineffective. Therefore, there is an urgent need for solutions, which will support the elderly persons care and significantly reduce caregivers workload.  
The above problem can be partially solved by prolonging elderly people's independent living by using modern Internet-of-Things technological solutions. Ambient and Assisted Living (AAL) programme supports their development by funding various projects aiming to improve older people's health. Among many AAL projects, there are solutions intended to support elderly people living alone at their homes. Their main goal is to reduce the burden of the caregivers and allow many older people to independently live at their homes for as long as possible [2].  
The designed and implemented AAL platforms are usually comprised of a set of sensors and devices, which are used to monitor an older person's health state and behavior and help them in daily tasks. One of the functions allowing to monitor a user's activity and behavior is user localization, which is usually delivered by a localization system being a part of the platform [3]. The localization systems used in the AAL platforms should meet several requirements. First of all, their accuracy should be high enough to provide the caregivers with reliable data to detect abnormal behaviors [4] or life threatening situations. For example, the localization data should allow to detect presence near vital spots such as near a gas stove or in a shower, to properly react in case of the prolonged, unusual and possibly dangerous activities. Secondly, it should be energy efficient to avoid frequent charging of system devices. Unfortunately, meeting both of these requirements using conventional solutions is often hard.
# A Cross-Modal Hash Retrieval Method with Fused Triples
## 4 A Cross-Modal Hash Retrieval Method with Fused Triples  
### Model Framework  
This paper proposes a new cross-modal retrieval method with fused triples called the cross-modal hash retrieval method. The overall structure is shown in Figure 1. The method mainly consists of two parts: feature extraction and hash learning.  
In the feature extraction part, the data set with supervised information are preprocessing by a triples data selection method. Then, two deep learning neural network models are used to extract features from the image and text modalities, effectively using the global supervised information while better extracting the feature information of each modality. The triple data selection can form a supervised matrix by learning the feature space to complete intra-modal and inter-modal sample matching. Similar samples are close together, and different models are further apart [13]. It effectively uses supervised information and improves the network's cross-modal semantic similarity retrieval ability. The triples in the triples data selection method refers to the anchor, positive and negative, which are the baseline positive and negative samples. The triples approach determines whether images and text of the same semantic information belong to the same class by learning features in the feature space. Under the same category, the distance between the anchor and the positive is closer; under different types, the distance between the anchor and the negative is farther. Therefore, in triple training, not only is the training process of the network simplified, but also the training process can be controlled so that the global supervisory information can be used.  
Figure 1: Overall structure of the cross-modal hash retrieval method with fused triples.  
The hash learning includes hamming distance loss, intra-modal loss, cross-modal inter-modal loss, and quantization loss. Cross-modal triple loss can reduce the distance between various modal positive samples for cross-modal sample pairs with the same semantic information, enabling the network model to acquire more comparable cross-modal features and enhancing the retrieval accuracy. The four loss functions are realized in feature space and hash space, respectively, taking advantage of their respective strengths, alleviating the differences in cross-modal similarity retrieval, and compensating for the lack of semantic similarity expression capability.
# Initial Assessment of the LEO Based Navigation Signal Augmentation System from Luojia-1A Satellite
## 2 The Principle of a LEO Navigation Augmentation System
With multiple visible LEO navigation augmentation satellite, users can benefit from the LEO signals and improve their positioning performance.
# AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks
## 4 Experimental Results
### Performance Evaluation
#### 4.2.3 Cost Analysis  
The main contributing factors towards total cost of proposed scheme include (1) pre-deterministic key distribution, (2) dynamic path routing, and (3) current attack information methods. As illustrated in Section 3.8, item (3) does not need any extra messages on the sensor nodes, so it does not contribute towards the cost of solution. Key distribution is performed at the initialization phase and does not incur cost towards performance evaluation. Dynamic routing method (2) is different from the fixed path routing as the former considers multiple parameters to select the next node as explained in route setup in Section 3.6. The relevant cost of these methods is trivial and have been included in performance measurements.
## 4 Discussion
### Study Strengths and Limitations/Implications for Clinical Practice  
This overview followed the PRISMA recommendations, including the development and prior registration of a protocol, an unrestricted search process (including grey literature), and a duplicate review process for the search, data extraction, and quality analysis. In addition, the outcome measures of the vast majority of SRs included in this overview (n = 71) were conducted by analyzing RCTs(the best evidence strength). However, this study is also subject to some limitations. First, some studies included in the SRs did not use the same protocol for making the PRF or did not present the centrifugation protocol. This can make it difficult to analyze and interpret the data. In addition, the quality analysis (AMSTAR 2) conducted demonstrated numerous methodological shortcomings in the performance of the SRs, which can increase the risk of bias.
# Interoperability between Building Information Modelling (BIM) and Building Energy Model (BEM)
## 1 Introduction
Due to the need to improve BIM-BEM interoperability in the design phase, this work aims to verify and evaluate whether there is an automated or semi-automated BIM to BEM workflow, comparing the importing of gbXML and IFC in three BEM software: Design Builder, Open Studio, and CYPETHERM HE. These software were chosen as they are all based on EnergyPlus [38], which is a notable and reliable energy simulation engine. EnergyPlus does not have a user-friendly interface; therefore, these three different software were chosen to run the engine. Firstly, Design Builder [39] is the most global tool, used by most professionals in the simulation sector. Secondly, Open Studio [40] was chosen because it is an open-source software, although it does not have the ability to model from scratch; instead relying on SketchUp to do so which, by contrast, is no longer a free software. Both Design Builder and Open Studio were created for energy simulations and they are the most popular tools for BEM creation. Finally, CYPETHERM HE [41] is a tool that has wide international outreach and is part of the complete CYPE package, made up of other software related to architecture, engineering, and construction. Furthermore, this software is BIM-oriented and should have a good interoperability process. Furthermore, we intend to lead to an improvement in the building design stage through this research, in terms of speeding it up, obtaining more precise energy data, and encouraging professionals in the construction field to gain confidence in the process and helping them to be aware of the limitations. This work could as well be a guide for BIM/BEM software developers, in order to find new paths to overcome the main problems that the process is now facing. For this purpose, two case studies at different scales were chosen: A single-family house and a warehouse. In both cases, we started from a BIM model and explored different paths to reach the BEM. The difficulties and the problems detected in the thermal envelope in the interoperability process were investigated. The rest of this paper is organized as follows: Section 2 explains the method, which consists of the BIM exporting formats and the BEM importing software. Section 3 analyses and discusses the results. Section 4 presents the conclusion and research directions for future works.
## 3 Analysis of Test Results
### Response Surface Analysis
#### 3.3.2 The Interaction Effect of Multiple Factors on Slurry Bleeding Rate  
The interactive effects of multiple factors on the slurry bleeding rate are shown in Figure 13. Following the contour line trend in Figure 13a, the sequence of the influence of various factors on the slurry fluidity is as follows: water-binder ratio > soil-binder ratio > particle size > binder-sand ratio = fly flash-cement ratio. As depicted in Figure 13b-e, significant interactions exist between the water-binder and soil-binder ratios, fly flash-cement and soil-binder ratios, and water-binder and particle size, as well as soil-binder and particle size, all of which modulate the slurry bleeding. It is evident that the muck content and much particle size exert substantial independent effects on the bleeding rate, while also interacting with each other and other factors. When the soil-binder ratio is high, the impact of the water-binder ratio on the bleeding is minor. However, when the soil-binder ratio falls below 0.6, the water-binder ratio's impact becomes more significant, displaying a near-linear relationship, as shown in Figure 13b and Equation (12). Additionally, the fly flash-cement ratio inversely correlates with the slurry bleeding rate when the soil-binder ratio is less than 0.6, and directly correlates when it exceeds 0.6, as illustrated in Figure 13c and Equation (13). The water-binder ratio's influence on the bleeding intensifies with larger particle sizes, exhibiting a linear relationship. Higher water-binder ratios result in higher bleeding rates, as demonstrated in Figure 13d and Equation (14). When the particle size is greater than 0.3 mm and the soil-cement ratio is less than 0.4, the response surface is extremely steep, and the change in the particle size and soil-cement ratio can more significantly affect the slurry bleeding rate. Furthermore, in scenarios where the particle size exceeds 0.3 mm and the soil-cement ratio is below 0.4, the response surface steepens significantly, enhancing the sensitivity of the bleeding rate to changes in the particle size and soil-cement ratio. Smaller particle sizes and larger soil-cement ratios result in lower bleeding rates, as demonstrated in Figure 13e and Equation (15).  
Figure 12: Interaction of factors on the slurry density. (**a**) The diagram of the density contour; (**b**) response surface of the w/b and the so/b; (**c**) response surface of the f/c and the so/b; (**d**) response surface of the f/c and the particle size; (**e**) response surface of the b/s and the particle size.  
\[f_{21}=6.272x_{4}^{2}+17.954x_{1}+5.222x_{4}-20.317x_{1}x_{4}-8.657 \tag{12}\] \[f_{22}=6.272x_{4}^{2}-17.871x_{4}-1.947x_{3}+3.420x_{3}x_{4}+9.521\] (13) \[f_{23}=1.208x_{1}-32.676x_{5}+50.925x_{1}x_{5}-0.406\] (14) \[f_{24}=6.272x_{4}^{2}+19.157x_{5}-9.414x_{4}-18.488x_{4}x_{5}+3.95 \tag{15}\]  
Figure 13: _Cont._
# Impact of Retention Agents on Functional Properties of Recycled Paper in Sustainable Manufacturing
## 4 Conclusions  
Overall, the addition of a retention agent can significantly enhance papermaking efficiency by improving key process parameters such as fiber retention, drainage, sheet formation, and uniformity. Optimized retention systems can lead to achieving higher-quality paper products with reduced raw material losses, minimized environmental impact, and improved operational cost-effectiveness. These benefits are crucial for maintaining competitiveness in an industry where cost and sustainability are critical considerations.  
However, the conducted analyses demonstrate that while the cationic polyelectrolyte based on acrylamide and a cationic derivative of acrylic acid contributes positively to process efficiency, it can adversely affect certain mechanical and surface properties of recycled paper. Specifically, this study revealed a marked decline in tensile strength and strain at break, with reductions of up to 36% for breaking length and 51% for strain compared to reference samples. Additionally, increased roughness parameters observed in the study suggest that the retention aid adversely affects surface smoothness, an important property for applications requiring high-quality printability or tactile smoothness, such as sanitary papers.  
The observed trade-offs highlight the importance of optimizing retention aid dosages to balance production efficiency and the functional properties of recycled paper products. While lower dosages improve tensile properties and surface quality, they may compromise retention and drainage efficiency. Conversely, higher dosages enhance retention but can - degrade mechanical properties and smoothness. The optimal dosage range identified in this study (0.8-1.0%) appears to strike a balance, providing acceptable mechanical recovery without exceeding the reference properties.  
These findings emphasize the complexity of wet-end papermaking, where numerous factors, including fiber type, stock preparation, and additive interactions, must be carefully managed. In conclusion, while the use of retention agents offers significant benefits for papermaking processes, their impact on recycled paper properties, such as tensile strength and smoothness, cannot be overlooked. Striking the right balance between process efficiency and product quality remains a key challenge, requiring ongoing research and collaboration between industry and academia.  
This research was financially supported by the projects RPDS.01.02.01-02-0051/20 by Polish Regional Funds and POIR.01.01.01-00-0084/17 funded by the National Centre for Research and Development (NCBR, Poland).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the author upon request.  
Author [PERSON] was employed by the company Natural Fibers Advanced Technologies. The author declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Anomaly Detection for IOT Systems Using Active Learning
## 7 Conclusions  
In conclusion, our active learning approach outperforms previous methods by accurately detecting anomalies in IoT systems. Additionally, the precision and recall measurements of our approach further validate its effectiveness in correctly identifying anomalies. One of the key contributions of our research is the development of a unique uncertainty-based sampling strategy. By selecting the most informative instances for labeling, we were able to significantly reduce the labeling costs associated with anomaly detection in IoT systems. This not only saves time and resources but also improves the overall performance of the model. Furthermore, our framework, which combines active learning with random forest ensemble classifiers, proved to be highly effective in identifying previously unnoticed anomalies. It demonstrates the robustness and adaptability of our approach, making it a valuable tool for protecting IoT devices from potential vulnerabilities. It is worth noting that our findings have broader implications beyond IoT systems. Furthermore, through the utilization of various methodologies and the UNSW-NB15 dataset, we were able to compare and evaluate the performance of different algorithms. Our approach, which incorporated active learning, outperformed all other methods with an impressive accuracy rate of 99.75%. This highlights the effectiveness of active learning in enhancing the identification of anomalies in IoT systems. The integration of active learning successfully addressed the challenge of limited annotated data in IoT systems, as it allowed for the identification and labeling of the most informative examples.  
Exploring the scalability and effectiveness of active learning techniques in large-scale IoT environments is essential for future research. A fascinating area for further investigation is looking into how advanced anomaly detection algorithms can be combined with active learning to improve the security and dependability of IoT systems.  
Conceptualization, M.Z. and A.S.A.; methodology, M.Z.; software, M.Z.; validation, M.Z. and A.S.A.; formal analysis, M.Z.; investigation, A.S.A.; resources, A.S.A.; data curation, A.S.A.; writing--original draft preparation, M.Z.; writing--review and editing, A.S.A.; visualization, M.Z.; supervision, M.Z.; project administration, A.S.A.; funding acquisition, A.S.A. All authors have read and agreed to the published version of the manuscript.  
The research is funded by the Researchers Supporting Program at King Saud University, (RSPD2023R809).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors extend their appreciation to King Saud University for funding the publication of this research through the Researchers Supporting Project number (RSPD2023R809), King Saud University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.  
The area Under the ROC Curve  
Mean Squared Error
## 2 Related Works and Contribution  
In the literature several methods have been proposed to extend lifetime of WSNs by properly managing CH nodes. This section briefly reviews the state-of-the-art methods for CH selection in WSNs and discusses the main contribution of the paper.  
The basic method for organizing data transfers in WSN is direct transmission [14]. This method assumes that all sensor nodes have the same role and their task is to send all sensors readings to a base station (sink). In this case, the CH nodes are not present. It should be noted that the sensor nodes consume energy mainly when they are accessing the communication channel and transmitting data. Thus, the transmission of all sensed data to base station results in high energy consumption and reduced lifetime of sensor nodes. Moreover, scalability of this method is low as the base station must collect and process large amounts of data from all sensor nodes. If workload of the base station is too high, the sensor data may be lost. The complexity of direct transmission protocol is negligible, and its implementation is simple. However, the low energy efficiency of this protocol makes it not suitable for most applications.  
Limitations of the direct transmission have motivated the development of more sophisticated data collection methods that divide the WSN into clusters. These methods reduce the number of data transmissions and save energy of sensor nodes. The sensor nodes in a given cluster send information to CH node, which aggregates the data and transmits them further to the sink or to another CH. However, as already mentioned in Section 1 the CH role has to be rotated among sensor nodes to balance their energy consumption and avoid fast energy depletion of the nodes that takes CH role for a longer time.  
The available CH rotation algorithms can be divided into two categories. The first category includes time-driven algorithms. According to these algorithms the sensor nodes take the role of CH for a predetermined time period. The second group consists of energy-driven algorithms that take into account residual energy of sensor nodes and change CH node when a predetermined portion of the energy is consumed.
## 4 Analysis of the Field Measurement Data
### Analysis of the Monitoring Data
#### 4.3.1 Vertical Displacement
For the final phase of the stop test, the middle part of the down-line of the existing tunnel eventually returned to the initial state; yet, the two ends of the tunnel remained in the settlement state. Generally, the vertical displacement law of the down-line is most likely to be the up-line, and the polynomial curve has the same shape. Still, the interval range of its displacement value is more moderate than the up-line, and it always changes around the initial value.