esa-sceva/satcom-chunk-collection huggingface dataset:
# Transmission Reduction for UV and IR Radiation with Dyed Lyocell Knitted Textiles
## 5 Conclusions  
Knitted fabrics produced from Lyocell yarn can be used as an effective basis to realize textile materials offering protective properties against UV and near IR radiation. The use of Lyocell fibers spin-doped with 2% titanium dioxide leads already to a significant transmission reduction for UV radiation. However, in the spectral range of 385 nm to 400 nm for UV light the transmission reduction should be further improved. As well, for the transmission of IR radiation still, optimization is necessary. By application of dyestuffs, the transmission of UV light for these Lyocell knitted fabrics is drastically decreased. Further, the proof-of-concept is complete, that similarly the transmission for near-infrared light can be decreased by dye application. By this, a first step is complete for the development of textile and clothing materials offering additional UV protection also a certain capability for protection against infrared radiation.  
Conceptualization, K.K., B.M. and K.R.; Methodology, M.K. and E.B.; validation, K.K., K.R. and B.M.; investigation, P.W., B.M. and K.R.; visualization: P.W., B.M. and K.K.; resources: M.K., B.M. and K.R.; writing--original draft preparation, K.K., B.M. and P.W.; writing--review and editing, B.M., K.R., E.B. and M.K.; supervision, B.M.; project administration, B.M. and E.B.; funding acquisition, B.M. and M.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the German Federation of Industrial Research Associations (AiF) was funded as part of the program for the promotion of industrial joint research IGF by the Federal Ministry for Economic Affairs and Energy based on a resolution of the German Bundestag, grant number 21077 BG.  
Not applicable.  
Further data are available from the corresponding author on request.  
The authors owe many thanks to [PERSON] and [PERSON] for their helpful advice during the dyeing experiments and colour analytics done in the finishing laboratory (Niederhtein University of Applied Sciences, Faculty of Textile and Clothing Technology). Many thanks are owed to [PERSON] for help during funding acquisition and to [PERSON] for support in the spinning lab (Niederhtein University of Applied Sciences, Faculty of Textile and Clothing Technology). All product and company names mentioned in this article may be trademarks of their respected owners, even without labelling.  
The authors declare no conflict of interest.
## 5 Experimentation
### Scenarios  
We use four scenarios which use different mobility patterns representing different network conditions. These scenarios are classified into two groups: (1) the scenarios based on real-world mobility traces, available at the Crawdad database [34] and (2) the synthetic scenarios generated by a random waypoint model (RWP) in a grid with reflective barriers where the nodes move at a configured speed for a configured distance. The nodes keep changing direction each time they cover that distance. The scenarios based on real mobility traces are very convenient for evaluating this proposal under real network conditions. In contrast, the synthetic scenarios help us to recreate particular network conditions as emergencies, not yet covered with real mobility traces samples. The considered scenarios are:  
**Taxis:**: tracks 304 Yellow Cab taxis in the San Francisco Bay area for one week. The traces are available at [35].
**Info5:**: tracks the movement activity of 41 students attending the Infocom conference in 2005 over three days [36].
**Campus:**: a synthetic map-based scenario that simulates the mobility activity of 80 students at Autonomous University of Barcelona campus, covering an area of 4.5 km \(\times\) 3.4 km with defined points of interest (POI) corresponding to eight faculties and the railway station. The students walk throughout the campus arriving/leaving their faculty and the railway station with a certain probability.
**Emergency:**: a synthetic RWP-based scenario with 100 nodes randomly walking in an area of one square kilometre. In this scenario, the pattern of message generation changes abruptly in the second half of the simulation, aiming to roughly simulate network conditions under the disrupting events of an emergency.  
Each scenario has different node densities. A sparse nature in a scenario entails fewer contact opportunities among the nodes, whereas dense natures are prone to more contacts between nodes. The Taxis scenario is a sparse scenario, and the Campus is a sparse scenario with POI promoting a temporary high density of nodes. Info5 and Emergency are both dense scenarios.
## 1 Introduction
UCS not only reflects the reinforcement effectiveness of bio-cemented sand, but its accurate prediction is crucial for engineering design, construction control, and quality assessment. Therefore, establishing a multi-factor model capable of accurately predicting the UCS of MICP-treated sand holds significant reference value for promoting the engineering application of MICP technology. In recent years, machine learning (ML) technology has demonstrated tremendous potential in geotechnical engineering predictive modelling, providing new technological approaches for addressing complex engineering problems [18; 19; 20]. Compared to other traditional methods, ML models can automatically learn complex patterns and nonlinear relationships within data without requiring predetermined mathematical forms, making them particularly suitable for handling complex engineering problems involving multi-factor coupling [18]. For instance, [PERSON] and [PERSON] [6] proposed a predictive strategy integrating multi-expression programming (MEP) with Monte Carlo methods for modelling the complex relationships between bio-cemented sand UCS and multiple influencing factors. [PERSON] [21] employed gradient boosting algorithms to model the UCS of MICP-treated sand samples, with results demonstrating superior predictive accuracy compared to various common ML models. The research also identified key factors affecting strength and proposed relevant recommendations regarding environmental factor mechanisms. Although individual ML models perform well in specific problems, they often suffer from algorithmic bias and insufficient generalisation capability, making it difficult to comprehensively capture multi-dimensional features in complex systems such as MICP [22]. To enhance the robustness and predictive performance of engineering models, ensemble learning techniques have gained widespread attention [22; 23]. By integrating multiple diverse learners, ensemble methods can effectively synthesise the advantages of individual models, thereby significantly improving overall prediction accuracy and stability. To enhance the accuracy and practicality of predicting unconfined compressive strength in MICP-treated sand, this study constructs a multi-model ensemble prediction framework, EnML, based on the LARO algorithm. Additionally, the SHAP (SHapley Additive explanations) interpretability analysis method is introduced to systematically analyse the influence mechanisms and relative importance of various variables on prediction results, providing theoretical support and decision-making guidance for understanding the dominant factors in MICP processes, optimising treatment process parameters, and guiding practical engineering applications.
# Design and Implementation of Virtual Security Function Based on Multiple Enclaves
## 5 Implementation
### Performance Optimization
#### 5.3.4 Shared Memory  
In Click architecture, the transmission of data packets in elements is based on the mechanism of copy-on-write. When a packet needs to be copied, only the header of the data packet needs to be copied, because most of the processing of the data packet is only necessary to read the header of the packet instead of the data of the packet. This mechanism greatly improves the efficiency of Click's execution.  
Based on the above observation, we design our shared memory mechanism for data packet transmission. In our approach, the DPDK caches packets from network devices in a memory pool via polling and maintains a pointer queue to the packet buffer. Enclave does not need to transfer a large number of copied packets through the secure channel established by local attestation, instead of passing directly the address of the packet. The enclave can directly query the DPDK and retrieve the pointer to the next batch of packet buffers, then directly process the packets without any copy. This solution greatly reduces the frequent conversion of data packets between multiple enclaves and the frequent copying of data packets, so as to improve system performance.
# A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm
###### Abstract
Traditional feature extraction methods only focus on the local neighborhood information of nodes, and they cannot effectively capture the information in the global graph structure, and for large-scale graphs, the traditional feature extraction methods increase the computational and storage costs and reduce the generalization ability of the model intending to extract high-dimensional feature vectors; 3. In some cases, link prediction must rely on network structure information. Therefore, it is important to obtain accurate node attributes. However, in a real network, the information of many users is private, and it is difficult to obtain node information. Given the above problems, the purpose of the method in this study is to extract and apply the high-order information existing in the graph structure, and this paper starts from the initial input data of the experiment. Firstly, the HWA algorithm is proposed to construct the hypergraph. This involves constructing a hypergraph through a general graph with basic information about the network structure. Secondly, based on the GCN model proposed in this paper, the feature vectors of nodes are extracted according to the input hypergraph correlation matrix. At the same time, residuals are added to solve the problem of gradient disappearance due to the increase in the graph data. Finally, MLP is used for link prediction, and the model is evaluated by AUC and F1 values to comprehensively evaluate the performance and effects of the model and to verify the effectiveness and reliability of the proposed method. The sections of this paper are organized as follows. Section 2 introduces an overview of the foundational ideas that support this article. Section 3 reviews in detail the related work on graph convolutional neural networks and multilayer perceptrons. Section 4 describes the HWA data modeling method and the node feature extraction method proposed in this paper. Section 5 focuses on the experimental steps and experimental results. The conclusions of this paper are presented in Section 6.
###### Abstract  
The recently deployed Sentinel 2 satellite constellation produces images in 13 wavelength bands with a Ground Sampling Distance (GSD) of 10 m, 20 m, and 60 m. Super-resolution aims to generate all 13 bands with a spatial resolution of 10 m. This paper investigates the performance of DSe2, a proposed convolutional neural network (CNN)-based method, for tackling super-resolution in terms of accuracy and uncertainty. As the optimization problem for obtaining the weights of a CNN is highly non-convex, there are multiple different local minima for the loss function. This results in several possible CNN models with different weights and thus implies _epistemic uncertainty_. In this work, methods to quantify epistemic uncertainty, termed _weighted deep ensembles_ (WDESen2) and its variants), are proposed. These allow the quantification of predictive uncertainty estimates and, moreover, the improvement of the accuracy of the prediction by selective prediction. They involve a consideration of deep ensembles, and each model's importance can be weighted depending on the model's validation loss. We show that weighted deep ensembles improve the accuracy of prediction compared to state-of-the-art methods and deep ensembles. Moreover, the uncertainties can be linked to the underlying inverse problem and physical patterns on the ground. This allows us to improve the trustworthiness of CNN predictions and the predictive accuracy with selective prediction.  
inverse problems; uncertainty quantification; deep ensemble methods; physics-informed ML; Bayesian inference +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 1 Introduction
One such approach is the reaching law method, which encompasses techniques like constant rate reaching, constant plus proportional rate reaching, and power rate reaching [18]. Among these, incorporating fast reaching and low chattering strategies within the reaching law method has shown promise for reducing chattering effects in SMC. Nonetheless, permanent magnet synchronous motors face an array of challenges from external disturbances, encompassing load variations, thermal effects, rotor harmonic flux, inverter non-linearity, parameter uncertainties, and other unknown factors [19]. These disturbances can be collectively termed mismatched disturbances. The traditional technique of SMC may struggle to fully mitigate the impact of these disturbances, due to its sensitivity and susceptibility to the chattering phenomenon [20]. Furthermore, combining controllers with observers proved effective in several works utilizing disturbance observer control strategies to boost controls specifically for addressing mismatched disturbances [17; 21; 22; 23; 24]. However, it is worth noting that, while linear strategies have their merits, they may struggle to fully account for the inherent nonlinearities that arise during faults. In comparison, nonlinear controllers and observers tend to offer more robust solutions in such scenarios. Therefore, this paper introduces a comprehensive control strategy that integrates three key elements: optimal current references for the FPPMSM model in both healthy and faulty conditions; a simple SMC design that combines proportional and power rate reaching properties to achieve robust and efficient motor control; and a nonlinear extended state observer (NESO) employing a hyperbolic tangent function (HTF) for feed-forward compensation to the SMC, specifically designed to address mismatched disturbances. After this introduction, this paper will explore the following subjects: Section 2 details the modeling of FPPMSM under both normal and fault conditions. Section 3 covers the determination of optimal control references for these models. Section 4 outlines the combination of NESO with SMC design. Section 5 presents an analysis of the outcomes of the proposed method. Lastly, Section 6 offers concluding remarks.
## 3 Materials and Methods
### Statistical Analysis
#### 3.6.2 Statistical Analysis on Depth Difference Estimation  
Depth estimation by means of Deep Learning models exploiting an RGB 2D image can be quite noisy and can be affected by light condition. However, we wanted to test if such models can be useful for estimating the depth difference of a pair of objects in the scene, thus allowing us to infer which object is closer to the camera, and whether this depth estimate is consistent and to what extent it is affected by lighting conditions. To get an answer to this question, we calculated the median values of the averaged depth difference estimates under different lighting conditions for each possible combination over different distances (8 cm to 64 cm) for all three objects. By plotting the median values over different distances, we can fit a line to the points and obtain the coefficient of determination (R-squared) of such linear regression from the perfect line with _p_-values less than 0.0005 for each method. The perfect diagonal line (with a slope of 45 degrees) indicates that the average of the depth differences is stable for closer and further distances (the difference in distances between each median point is 8 cm).  
To evaluate the usefulness of the capability of the MiDaS and Alhashim algorithms to estimate depth differences, we calculated the success rate in discriminating depth differences of each pair in the scene ( distinguishing further distances from closer distances), starting from \"d + 16\", which corresponds to 34 cm. Thus, we calculated the correct differentiation of depth estimation in the method for each scene where the object is at different distance and under different lighting condition. In a simple way, we measure the performance of each method in differentiating between, for example, 8 cm (closer) and 16 cm (farther) distances. To measure the performance of each method, we calculated how many times the method was successful in estimating the relative depth between three objects, and divided this by all combinations to determine the method's success percentage. In this way, we indirectly account for each lighting condition in each scene.
## 3 Presentation of Developed Interface
### The End-User Mobile Phone Interface
#### 3.2.2 The End-User Mobile Phone Interface for HEM System  
The main target of HEM system is to defer the working hours of some appliances taking into account various criteria such as hourly energy pricing, user comfort preferences and desired power threshold limit value. For higher efficiency, the HEM module has the capability of updating its existing power plan based on the changes in energy prices or consumer preferences.  
The proposed HEM interface can be effectively used for monitoring the power consumption of the appliances in detail and therefore controlling the appliances for energy and cost savings. It should be however noted that the energy management plan of the appliances is re-arranged with the incoming signal from the system operator at the starting times of DR events.  
The proposed HEM interface controls the appliances through a module including an ARM Development board (Tiva C 1294 XL), which is an easily accessible, cheap and energy-efficient tool. In the proposed structure, HEM module is connected to home network and accesses internet via Ethernet. Therefore, any smart appliance connected to the home network can communicate with HEM unit. Also, the appliances in the house can be connected to HEM module easily using various communication protocol thanks to its connection flexibility. In other words, HEM units  
Figure 14: The appliance selection and confirmation screen on the end-user interface.  
Figure 15: The auto-reply menu on the end-user interface.  
can be configurable to use any device which uses wireless communication such as Bluetooth, Xbee or any wired communication such as controller area network (CAN), universal asynchronous receiver/transmitter (UART), serial peripheral interface (SPI), and inter-integrated circuit (I2C).  
The proposed interface also has other features. For instance, end-users can view a summary of daily energy consumption through the link between the app and HEM unit. It is also possible for HEM unit to collect data of each connected device, to see the stored data and to intervene the working of appliances. Furthermore, the proposed module sends planned and realized consumption values to the main server at the end of each day, which might be valuable for estimating the possible future power consumption. Lastly, in addition to the power consuming units, the HEM module has also the ability to monitor or control the status of local generation units such as a wind turbine and photovoltaic (PV) panel, as well as of a storage system.
## 4 Experimental Results and Discussion
### Vision Capabilities Simulations
#### 4.2.3 Recognition in the Vineyard  
In this case too, the recognition accuracy of the **608@VOC+WA-WGISD** network was significantly higher than all the other implementations. Figures 8 and 9 show the detection results on some images taken in the vineyard, where the grape cluster replicas were placed on the vine shoots at different heights and degrees of occlusion caused by the foliage.  
Notice that occlusion in image detection is an open problem for which only experimental work has been performed so far with incomplete solutions. However, even if foliage occlusion is a problem to be dealt with, there is no need to solve the occlusion problem among bunches. In fact, the decision whether to spray or not (and specifically, where) does _not_ involve the most difficult task of isolating one bunch from another, as in the case of bunch counting.  
Figure 7: Bounding Box Inference from **608@VOC+WA-WGISD** in Open-Space Controlled Environment (BBI-OSCE).  
Figure 8: Bounding Box Inference of 608@VOC+WA-WGISD in Vineyard (BBI-V)-Part 1/2.  
Figure 9: Bounding Box Inference of 608@VOC+WA-WGISD in Vineyard (BBI-V)-Part 2/2.
## 5 Outage Probability Analysis
In the figure, when \(m_{k,1}=1\), \(m_{k,2}=2\), and \(m_{k,3}=3\), the performances of both PPS are similar for high SNR as the proposed PPS uses CSI feedback for the first hop like the conventional PPS. As shown in Figure 7, the proposed DF-based PPS generally attains considerable performance improvement as compared with the conventional AF-based PPS, although the proposed PPS requires more complexity for the CSI feedback process as well as DF relaying than the conventional PPS. Figure 7: Outage probability of the proposed PPS with \(l_{k}=1\) for all \(k\) and the conventional AF-based PPS, when \(m_{k,n}\)’s are unequal, \(N_{k}=3\) for all \(k\), \(K=3\), and \(R_{t}=1\) bps\(/\)Hz.
## 4 Discussion
The energy of the blasting waveform is mainly distributed in the sub-band of 100-700 Hz, which accounts for 97.2% of the total energy. The energy distribution ratio within 400-500 Hz reaches 35.5%, indicating that this range contains a significant amount of energy. In the frequency sub-band of 0 to 100 Hz, the proportion of Microseismic waveform 1 to Microseismic waveform 3 gradually increases, while the SNR of Microseismic waveform 1 to Microseismic waveform 3 gradually decreases. The energy of Microseismic waveform 1 is mainly distributed in the sub-band of 0-200 Hz, accounting for 99.1% of the total energy. However, its energy in the band above 200 Hz is very small, only 0.086% of the total energy. The proportion of energy distribution in the sub-band of 0-100 Hz reaches 60.7%. For Microseismic waveform 2, the proportion of energy distribution in each frequency band is roughly the same, fluctuating from 0.95% to 3.38%. In the band above 200 Hz, the proportion of energy of Microseismic waveform 2 is relatively low, accounting for 12% of the total energy. The energy of Microseismic waveform 2 is mainly distributed in the sub-band of 0-200 Hz, accounting for 87.9% of the total energy. The proportion of energy distribution in the sub-band of 0-100 Hz reaches 63.8%. The energy of Microseismic waveform 3 in the band above 100 Hz is very small, accounting for only 0.079% of the total energy. The energy distribution of Microseismic waveform 3, which has the highest distribution ratio, is mainly in the sub-band of 0-100 Hz, and the energy distribution ratio is 99.9%. The energy of water flow signal is mainly distributed in the sub-band of 0 to 200 Hz, accounting for 97.1% of the total energy. In the sub-band above 200 Hz, the energy of water flow signal is very low, amounting for only 0.029% of the total energy. The proportion of energy distribution in the sub-band of 0-100 Hz reaches 60.6%. Figure 14: The energy ratio of different microseismic waveforms in different frequency bands.