esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Many researchers found that the use of polyethylene glycol (P) as an organic additive in the sol-gel methods allows for protecting and altering the release rate of the drug embedded in the matrix [30; 31; 32]. In addition, the presence of P improves cell adhesion and growth by increasing the hydrophilicity of the materials and the controlled release of therapeutic molecules. Indeed, it has been demonstrated that the variation of the P content of polylactic acid/polyethylene glycol copolymers allows for controlling the adsorption of adhesion proteins and cell adhesion, due to the occurrence that cell adhesion takes place only in the presence of serum proteins [33]. In our previous paper, we demonstrated the feasibility to synthesize via the sol-gel route with hybrid organic/inorganic materials made up of silica and different weight percentages (wt%) of caffeic acid (from 0 to 20 wt%) [34]. Additionally, it has been demonstrated that the occurrence of some structural modifications, which nevertheless ensured a strong radical scavenging capacity of samples with 15 and 20 wt% of caffeic acid incorporated into the systems. In this paper, the release study of the entrapped drug was also investigated. Moreover, in such systems polyethylene glycol, another organic compound had been added to shed light on the antimicrobial effect and the influence on the release of the drug. Here, hybrid organic-inorganic materials were synthesized via acid-catalyzed sol-gel reactions, where the SiO\({}_{2}\) (labelled S) is the inorganic phase, whereas the organic precursors are the polyethylene glycol (labelled P) and the caffeic acid (labelled A). The SPxAy hybrids were composed of 0, 6, and 12 wt% (x) of P and 5, 10, 15, and 20 wt% (y) of A. The formation of new chemical bonds or weak interactions (e.g., van der Waals forces or hydrogen bonds) among the organic and inorganic phases inside the hybrid materials was evaluated using Fourier transform-infrared (FT-IR) spectroscopy. The bioactivity, as the ability to facilitate the hydroxyapatite layer formation on the hybrid materials, was analyzed using scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDS), FT-IR and X-ray diffraction (XRD) after the materials were soaked at 37 \({}^{\circ}\)C in SBF solution (SimulatedBody Fluid) for 21 days. The in vitro release study of caffe acid was evaluated using UV-Vis spectroscopy, whereas the antibacterial properties were assessed against both Gram-(_Escherichia coli_, _Pseudomonas aeruginosa_) and Gram+ (_Staphylococcus aureus_, _Enterococcus faecalis_) microbial strains.
# Analysis of Climate-Oriented Researches in Building
## 3 Results and Discussion
### Sub-Theme 5--Meteorological Data and Climate Change  
This fifth sub-theme shows nine papers linking energy conservation and meteorological data. To simulate energy consumption, meteorological data is needed, so the quality and selection of the data are important.  
Results of the use of typical meteorological year (TMY) data for energy simulation were compared with the use of real measurement data over a period of 30 years [164], with the use of typical principal component year (TPCY) data [165], and with the use of actual meteorological year (AMY) data [166]. It was concluded that, in the era of availability of meteorological data and the increasing power of computer technology, it is necessary to use real meteorological data from different years to simulate the energy consumption of households in order to obtain more realistic results or, as in Finland, to develop individual methodologies to create energy reference year data for energy simulations in different climate zones [167].  
The simulation of energy consumption for the present, meanwhile, has generated scientific interest in the study of future energy consumption. This interest has a basis in the development of climate models and forecasts, as well as the unification of climate change projections. Thus, in 2011, researchers presented construction methods for adapting designed residential buildings to climate change [168]. In 2012, other authors pointed out the importance of the ability to analyse how buildings will respond to future climate changes and assessed possible quantitative changes in energy use and carbon footprints. In addition, they discussed possible climate change mitigation and adaptation strategies [85].  
In the USA, changes in energy consumption in different types of buildings and climate zones were assessed according to IPCC climate change projections. It was found, by the 2080s, residential energy consumption will increase in climate zones 1-4 of ASHRAE and decrease in climate zones 6-7 of ASHRAE [25]. Similarly, in Greece, the effect of climate change on the energy consumption of buildings will be more noticeable in warmer climate zones [169].
# Terahertz Inspection of Buildings and Architectural Art
## 2 Terahertz Uses in Architectural Art  
The characterization of historical buildings is the key task for fields, such as conservation of architectural heritage, building science, and building archaeology. Today's architectural conservation practice involves scientific survey based on specialized diagnostic methodologies and techniques aimed at understanding the conditions of conservation of architectural pieces as well as construction techniques. When dealing with culturally valuable objects, non-destructive testing is, of course, preferred. The use of non-destructive methods of scientific investigation have rapidly expanded thanks to the development and refinements of portable instruments which allow to collect large quantities of highly relevant data without the need for physical sampling. In many cases such portable devices, have capabilities analogous to larger laboratory-based systems. The most common techniques used in these fields include photogrammetric and geodetic endoscopy, ultrasound and thermographic analysis, and laser scanning. Other studies, like multispectral and hyperspectral tests, special photographic techniques (IR- and UV-based photography), and X-ray fluorescence spectrometry (XRF) for elemental analysis [19] are used, and also Raman spectroscopy, near-infrared spectroscopy (NIR) [20], among others, have been incorporated in the conservators toolboxes.  
Terahertz spectroscopy and imaging is a powerful analytical method that can provide unique information about the inspected object non-invasively and contactelessly. As a spectroscopic technique has the merit of contributing to material characterization, while as imaging technique with depth profiling capabilities can reveal finer stratigraphy and evaluate the structural characteristics and conservation conditions of the investigated target. The technique has been tested for the characterization of cultural heritage, architectural heritage, and decorations made with different types of building materials and from different regions. This section aims to illustrate the results obtained, so far, from these first investigations. Wall painting is, by far, the typology of cultural heritage most studied with this technique, and this theme will therefore be widely discussed.
## 5 Discussion
In fact, we can list a few of SDVN system's analysis as summarized in Table 2: placement of SDN controller [11, 13, 14, 39, 40], communication control VANET-based or cellular network-based [32], local knowledge of surrounding nodes via beacon or geo-broadcast messages [39, 42], network simulator tools such as Mininet-WiFi [14], trace of overhead messages between vehicles and SDN controllers [1]. Comprehensive surveys on the software-defined networks in [41, 43] lack a comparative study on the system analysis of existing SDVNs to point out SDVN components, architectures and algorithms investigated to tackle SDVN drawbacks. Authors in [41, 43] investigate SDVN architectures to identify their benefits and challenges against the VANETs regarding communication in [41], and security in [43]. Within the existing SDVN solutions, technology for SDN controllers, implementation tool for the OpenFlow protocol have been proposed, yet a comprehensive study on SDVN architectures based on the modeling will provide the required insights on the components needed for further enhancements. Since the system analysis of SDVN systems provide key enabling technologies for investigating SDVNs' challenges, the solution model proposed in the implementation based the system analysis entities in SDVN shows potential research opportunities towards an efficient SDVN that could allow a huge number of next-generation VANET applications.
## 3 Results and Discussion
### Comparisons and Disscussion  
In order to evaluate the performance of the proposed method, including robustness and accuracy, comparison studies are conducted in this section. The hyper-parameters of the established KNN, DTR and ANN are provided in Table 5. The comparison results are presented and discussed in detail as follows.  
Figure 11: The results of benchmark values and reasonable intervals in six testing regions.  
Figure 12: Pass percentage analysis of the line loss rates based on robust neural network (RNN). (**a**) Analysis of data points; (**b**) analysis of transformer regions.
## 2 Materials and Methods  
### Soil Collection and Characterization  
Stamp sand was collected from the shoreline of Torch Lake in Lake Linden, Michigan (USA). The shoreline was divided into plots 25 m in length, two meters from the water table. Six sites were randomly selected for metal analysis. After the initial metal analysis, soil was collected in bulk from the site with the highest level of Cu (47 deg11'17.39'' N, 88 deg24'26.05'' W). The site for collecting the control soil was selected from a nearby site using the Michigan State University Extensions soil survey map from Atlantic Mine, MI (coordinates N 47.18529, W 88.69616). The basis for control soil selection was minimal Cu content (<50 mg/kg), but similar physico-chemical properties as those of the stamp sand, including particle size distribution, pH, and organic matter content. The physiochemical characterization of the stamp sand and control soil were performed in triplicates and included particle size distribution (ASTM method D6913), water content using an oven drying method [18], soil organic matter (SOM) content by loss on ignition [19] and water holding capacity described by Harding & Ross [20]. pH and electrical conductivity were measured using an Orion pH meter and conductivity probe. Toxicity characterization leaching procedure (TCLP) was performed according to EPA method 1311 [21]. [PERSON]'s reagent extraction for plant available iron and aluminum [22], sequential extraction for metal speciation [23], anion analysis by ion chromatography, and metal content by acid digestion (EPA Method 3050B) [24] were performed. Metals were analyzed by inductively coupled plasma mass spectrometry (ICP-MS).
# Efficient Small-Object Detection in Underwater Images Using the Enhanced YOLOv8 Network
## 2 Related Work
### Lightweight Networks  
Following AlexNet's [19] triumph in the 2012 ImageNet [20] Challenge, the rapid advancement of graphics processing units (GPUs) has propelled deep neural networks (DNNs) to show significant potential in various AI domains. Meanwhile, resource-constrained devices such as mobile phones and edge devices have become increasingly common. These devices often have limited computational power, as well as limited energy resources and a limited storage capacity, which pose challenges when deploying DNNs. Therefore, reducing model parameters and computational complexity while maintaining model accuracy has become an urgent task.  
At present, lightweight research commonly utilizes two main approaches: network architecture design and model compression. The former focuses on exploring and designing efficient network structures that reduce model parameters and the number of floating-point operations (FLOPs) while maintaining a good performance. Some notable examples include SqueezeNet [21], ShuffleNet (V1, V2) [22; 23], MobileNet (V1, V2, V3) [9; 24; 25], EfficientNet [26], GhostNet [27], MobileViT [28], and the FasterNet [11] used in this paper. These networks have significantly contributed to the development of deep learning on mobile devices. The latter involves various techniques to reduce the size and computational complexity of deep neural networks. Recent popular methods include pruning, quantization, knowledge distillation, and a neural architecture search. Achieving model compression while meeting specific performance constraints (e.g., accuracy and latency) can be challenging. Researchers are now looking into joint research on hardware, software, and algorithm optimization as the next trend in model compression. This holistic approach considers not only accuracy but also energy efficiency and hardware costs during the design phase, leading to more efficient and effective optimizations for real-world applications.  
In the field of object detection, a common approach to model lightweighting is to use a lightweight backbone and replace the convolutional layers. [PERSON] et al. [29] used the ImageNet pre-trained FasterNet as a backbone and integrated it with the popular Mask R-CNN detector [30], resulting in a faster and better backbone compared to others. Depthwise Separable Convolutions [10], Pconv [11], and similar convolutional techniques have also shown significant effectiveness in reducing model parameters. Currently, lightweight networks have found widespread applications in embedded systems, such as surface scratch detection [31]. In this paper, we approach the design of a lightweight underwater network by applying FasterNet as the backbone for YOLOv8. Although this leads to a slight decrease in accuracy, subsequent targeted optimizations for underwater scenarios have improved the model accuracy.
## 4 Q3: What Are the Methodologies Used to Overcome the Challenges in UIoT?
### Methods to Overcome the Security Challenges in UIoT
#### 4.2.5 Methods to Prevent Wormhole Attacks  
The existing techniques to prevent wormhole attacks in UIoT are discussed herewith. In [49], [PERSON] et al. used the HELLO message based on packet timing analysis to control the wormhole attack, which can be used in UIoT networks. In [50], [PERSON] et al. proposed a two-tire-based localization method to identify the wormhole attack in a short time in UIoT networks. In [51], [PERSON] et al. proposed a hop-count-based analysis method to prevent the wormhole attack, which can be applicable in UIoT networks. Finally, in [52], [PERSON] et al. proposed a distributed method to identify the wormhole attack in UIoT networks.
## 2 Background
### Estimation of Covariance Matrix  
Observing (6)-(9), we can see that it is necessary to acquire the interference and noise covariance matrix to calculate the weight vector \(\mathbf{\omega}\). However, in practice, \(\mathbf{\rm R}\) is usually unavailable and estimated with the sample covariance matrix \(\mathbf{\rm R}\), defined as [28]:  
\[\overset{\mathbf{\rm A}}{\mathbf{\rm R}}=\mathbf{\rm X}(\rm k)\mathbf{\rm X}(\rm k)^{\rm H} \tag{12}\]  
For medical ultrasound systems, echo data are broadband and coherent signals, so that formula (12) cannot be directly used in the data processing. To acquire a reliable estimation of the covariance matrix \(\overset{\mathbf{\rm A}}{\mathbf{\rm R}}\), we should eliminate the correlation first. Usually, subaperture averaging is applied. As shown in Figure 2, subaperture averaging divides the array into P overlapping subarrays with length L, and the covariance matrix is obtained by averaging the covariance matrices from all subarrays [29]. The estimation of the sample covariance matrix \(\overset{\mathbf{\rm A}}{\mathbf{\rm R}}\) can be expressed as:  
\[\overset{\mathbf{\rm A}}{\mathbf{\rm R}}=\frac{1}{\rm P}\sum_{\rm p=1}^{\rm P}\mathbf{\rm G }_{\rm p}(\rm k)\mathbf{\rm G}_{\rm p}^{\rm H}(\rm k) \tag{13}\]  
where P denotes the number of subarrays and P = M\(-\)L + 1, and L denotes the length of subarrays. The larger L is, the smaller P is, the better beamformer is, however, the worse the stability of the beamformer will be [30]. Usually, L is less than M/2.
# Deep Learning-Based Acoustic Echo Cancellation for Surround Sound Systems
###### Abstract  
Surround sound systems that play back multi-channel audio signals through multiple loudspeakers can improve augmented reality, which has been widely used in many multimedia communication systems. It is common that a hand-free speech communication system suffers from the acoustic echo problem, and the echo needs to be canceled or suppressed completely. This paper proposes a deep learning-based acoustic echo cancellation (AEC) method to recover the desired near-end speech from the microphone signals in surround sound systems. The ambisonics technique was adopted to record the surround sound for reproduction. To achieve a better generalization capability against different loudspeaker layouts, the compressed complex spectra of the first-order ambisonic signals (B-format) were sent to the neural network as the input features directly instead of using the ambisonic decoded signals (D-format). Experimental results on both simulated and real acoustic environments showed the effectiveness of the proposed algorithm in surround AEC, and outperformed other competing methods in terms of the speech quality and the amount of echo reduction.  
acoustic echo cancellation; surround sound; ambisonics +
Footnote †: journal: _Applied_  
0
Footnote 0: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. 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/))).
# Device Management and Data Transport in IoT Networks Based on Visible Light Communication
## 2 Related Work on VLC and IoT
Release 1 includes the common service platform for IoT, which defines the Common Services Function (CSF) for data saving/sharing, device management, group management, subscription and notification, location, billing, and so on. This release also contains fundamental security features such as authentication and access control. In addition, it defines message primitives and protocol bindings. Release 2 includes the interoperation with AllJoyn, OCF, Lightweight M2M (LWM2M), 3 GPP Rel-13. It also includes various features needed by industries. As described previously, much work has been done on how to improve the performance of VLC transmission and on the protocols and platforms for IoT, such as 6 LoWPAN, CoAP, MQTT, and oneM2M. However, how to effectively integrate VLC services into IoT networks has not been studied enough. That is, VLC has not been considered as a wireless access medium for IoT. Thus, in this paper, we propose a framework for VLC-based IoT networks and also discuss the operations for device management, data transport and handover in IoT networks using VLC.
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 3 Cooperative Control of Variable-Sweep Wing UAV via MADDPG
### Optimization of the MADDPG-VSC Algorithm for Control Design  
To address the limitations of traditional reinforcement learning algorithms in controlling variable-sweep wing UAVs, this study introduces the MADDPG-VSC algorithm. Unlike traditional algorithms that primarily target discrete action spaces, the MADDPG-VSC algorithm is specifically designed for continuous action spaces, enabling real-time adjustments of wing configurations according to flight states. This ensures the aerodynamic efficiency and stability of UAV formations.
# Study on Disturbance Mechanism of Squeezed and Non-Squeezed Soil Piles on Soft Soil Foundation
## 3 Methodologies
### Bag Grouting Pile  
(1) Mechanism  
A bag grouting pile is an effective method for strengthening a deep soft soil foundation. It involves the pre-embedding of geotextile bags into the soft soil foundation and then injecting grout from the bottom of the pre-drilled hole. This process causes the hole wall to expand from the bottom, forming regular cylindrical or gourd-shaped stones. These stones interact with the surrounding soil to form a composite foundation, achieving the goal of strengthening the soft soil foundation (Figure 4). Therefore, the bag grouting pile essentially relies on the compression and expansion of the pre-drilled hole wall to disturb the soil. Bag grouting piles are appropriate for fortifying deep soft soil foundations due to their stable pile performance, convenient construction, and high shear resistance. It has evident benefits for site-restricted or soft-soil foundations with solid interlayers.  
(2) Assumptions  
Based on the mechanism of bag grouting pile formation and the theory of column (or sphere) expansion, a model is established to simulate the disturbance effect of bag grouting pile formation on the soil by gradually expanding the borehole wall from bottom to top at predetermined pile positions. This simulation method is based on the following assumptions:  
1. The process of bag grouting pile formation is quasi-static during soil squeeze.
2. The variation of pile diameter within the vertical range of the pile body is ignored.
3. The variation of pile diameter caused by internal pressure adjustment following grouting expansion is ignored.  
(3) Simulative steps  
The main steps of the simulation process are as follows:  
i. Establishment of bag grouting pile reservation grids: this component consists of a cylindrical pile body and a hemispherical pile tip with a radius equal to one-fourth of the actual pile diameter.  
ii. Establishment of soil grids around the pile: the vertical and lateral lengths of the model are both twice the length of the pile, and the grids are slightly dense near the pile.  
iii. Calculation of initial soil state: define all grids as soil, assign material parameters, set gravity load, and complete the calculation of initial stress as well as initial modulus.  
iv. Gradual grouting and expanding from bottom to top: first, close the grid at the pile location and then apply horizontal displacement to the hole wall at the grouting location. The grouting process should start with an initial radius and gradually expand from bottom to top to achieve the actual pile diameter.
# A Conceptual Design of Deployable Antenna Mechanisms
## 2 Materials and Methods
### Working Principle  
The V-folding mechanism plays a role in supporting both the 6-bar linkage mechanism and the upper net fixed point while avoiding undesired movements by moving the longeron linkage (which is folded in a V-shape at the stowage stage) into a horizontal position while deployment. The 6-bar linkage mechanism serves as the driving mechanism. The single pantograph mechanism and the 6-bar linkage mechanism are connected at the bottom net fixed joint, operating independently in two planes: the yz plane and the xz plane, respectively. As will be detailed later, the linear movement of the driving joint of the 6-bar linkage mechanism along the z-axis causes the connected links and joints to move accordingly, initiating the deployment of the mechanism at the stowed stage. The single pantograph mechanism is composed of two links that are mutually connected by a single rotational joint. As the joint of the 6-bar linkage mechanism moves, the two links rotate around the rotational joint, leading to a variation in the height along the z-axis. This synchronized motion results in the full deployment of the mechanism.  
Figure 1: A conceptual design of the deployable antenna mechanism. (**a**) Sequential images showing the deployable antenna at different stages (from stowed (**left**) to fully deployed stage (**right**)). A hierarchical composition composed of (**b**) a 6-bar linkage mechanism, (**c**) a single pantograph mechanism, and (**d**) a V-folding mechanism (scale bar = 1 m).