esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanomechanical Characterization of Bacterial Polyhydroxyalkanoates Using Atomic Force Microscopy
## 1 Introduction
In this work, we propose a nanomechanical characterization by force-distance curves obtained via AFM of different biopolymers belonging to the broad family of hemisynthetic and bacterial polyhydroxyalkanoateates (PHA). Parameters, methodologies and accessibility of AFM tests were studied and described in previous experiences in our research group [30]. First, a comparison of the mechanical properties measured via conventional tensile test and tip nanoindentation AFM test are presented, using polylactic acid (PLA) as a standard. Then, elastic moduli obtained by tip indentation AFM test are shown for different poly(hydroxybutyrate-co-hydroxyalerate (PHBV) copolymers, obtained by bacterial co-fermentation of different volatile fatty acid mixtures. Finally, a correlation of the obtained elastic modulus with composition, microstructure and thermal properties is proposed.
# EL-MTSA: Stock Prediction Model Based on Ensemble Learning and Multimodal Time Series Analysis
## 3 Model Construction
### Learning Optimization Module via Information Fusion Strategy  
Ensemble methods are widely used in machine learning and statistics, offering techniques to combine multiple base classifiers into an ensemble that produces more accurate and robust predictions than individual models. By leveraging the strengths of each classifier while mitigating their weaknesses, ensemble learning enhances overall predictive performance.  
The integrator module in our framework is designed to resample high-frequency data and aggregate predictions from multiple models to generate the final output. This module consists of two key components: a resampling mechanism and a CNN-linear layer model. The CNN and linear layer operate sequentially, with the linear layer featuring two branches. Branch 1 takes the prediction results of LSTM and GRU as input \(f_{1}(x_{1}(t))\), \(f_{2}(x_{1}(t))\), while branch 2 takes the prediction results of Bi-LSTM and CNN-GRU as input \(f_{3}(x_{2}(t))\), \(f_{4}(x_{2}(t))\). The linear layer captures temporal patterns and learns the reliability of each predictor under different conditions. The CNN then processes these inputs to identify relationships that can further enhance prediction accuracy. Finally, a fully connected layer integrates all information to produce the final output.  
From the experimental findings, it can be inferred that the robust architecture of our ensemble model consistently delivers dependable predictions across a wide range of scenarios. Regarding the fine-tuning of hyperparameters, including the learning rate, we utilize Bayesian optimization to systematically enhance their configuration.
# Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services
## 1 Introduction
At the end, the authors concluded that the radio parameters related with the transmission rate of the streaming session are the most important features in predicting QoE for the GTB algorithm. Therefore, our objective is to focus on the measurements on the end user terminals for collecting radio indicators such as the Reference Signal Received Quality (RSRQ) and Reference Signals Received Power (RSRP) for \(3G/4G\) networks in addition to some application metrics like the buffering time before playing a video on demand. We aim at estimating the QoE of some popular Internet services using different kinds of user terminals in a large covered region by many radio units and through several mobility test modes. We would like to understand the problem causes and, as a consequence, to come with helpful recommendations to mitigate the poor observed performance. To achieve this study, we proceed as follows. First, we survey some important and related crowdsourcing based studies, and we highlight the main goal and applied use case of the collected traces. Second, we collect our own crowdsourcing dataset composed of more than 230,000 traces from one of the major French mobile operators in the Ile-de-France region using two different \(3G/4G\) terminals. Third, we clean, process and annotate the collected dataset by implementing a known QoE model per considered service. In particular, we calculate the user's Mean Opinion Score (MOS) per service. Then, we use radio indicators to describe three interesting use cases: (i) Data statistical study in heterogeneous environments, (ii) Anomaly root cause analysis for the considered services, and (iii) we discuss the utility of estimating the MOS based on only the radio indicator (e.g., bitrate) as done in many previous works in the literature. We provide publicly in [9] the dataset with all the Python codes [10] for regenerating the analysis or reusing them on other datasets. The remainder of the paper is organized as follows: In Section 2, we introduce the related works including key existing mobile crowdsourcing works and the main applied use cases with the collected datasets. In Section 3, we introduce our cellular measurement campaign and the collected dataset. The pre-processing of the collected dataset is explained in Section 4. In Section 5, we describe three use cases of our proposal. In particular, we achieve first in Section 5.1 a statistical study in heterogeneous environments. Then, in Section 5.2, we evaluate the root cause of bad performance of key Internet services. Third, the impact of radio indicators on the video QoE is evaluated in Section 5.3. Finally, Section 6 concludes this work and presents future works. It is worth mentioning that the list of acronyms used in this work is presented in the abbreviations section at the end.
## 4 Results  
We evaluated the proposed method using simulations in the stage simulator [28]. We studied the proposed method under the condition of obstacle avoidance, formation with turns, and large populations. The safe distance \(d_{s}\) is set to 5 m while the desired distance \(l_{d}\) is set to \(1.2d_{s}=6\) m, the desired formation angle is set to \(\gamma=100^{\circ}\), and the target forward speed of the swarm leader is set to 1 m/s. Figure 7 shows the configuration of each member. The large sector with field of view \(250^{\circ}\) is its communication range. The agent is able to exchange simple data to others in this area. This radius is set to 30 m for simulations with swarms of fewer than 50 members, and 100 m for larger swarms (\(N=200\)). The small sector is the coverage of nine ranger sensors with \(30^{\circ}\) FOV spread on the \(250^{\circ}\) with some overlaps for anti-collision and obstacle avoidance. With those configurations, the simulation process and other details are given in following subsections.  
Figure 7: Member configurations in stage simulator.
# Infrared Dim and Small Target Detection Based on Local-Global Feature Fusion
## 3 Experiments
### Experiments and Analysis
#### 3.2.2 Comparison Experiments on Drone Vehicle
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **Data1** & **Data2** & **Data3** & **Data4** & **Data5** & **Data6** & **Data7** \\ \hline IPI & 0.322 & 0.294 & 0.312 & 0.291 & 0.309 & 0.296 & 0.315 \\ NRAM & 0.145 & 0.146 & 0.151 & 0.153 & 0.162 & 0.164 & 0.157 \\ TLLCM & 0.183 & 0.185 & 0.188 & 0.193 & 0.185 & 0.186 & 0.221 \\ PTSNN & 0.082 & 0.073 & 0.072 & 0.071 & 0.081 & 0.079 & 0.0734 \\ New Top-Hat & 0.026 & 0.027 & 0.023 & 0.026 & 0.024 & 0.022 & 0.028 \\ LIG & 0.131 & 0.132 & 0.136 & 0.137 & 0.125 & 0.124 & 0.236 \\ NOLC & 0.137 & 0.134 & 0.154 & 0.153 & 0.145 & 0.143 & 0.162 \\ SSD & 0.066 & 0.069 & 0.056 & 0.058 & 0.076 & 0.079 & 0.080 \\ RFBnet & 0.019 & 0.021 & 0.022 & 0.023 & 0.021 & 0.020 & 0.021 \\ ISTRU-net & 0.285 & 0.287 & 0.276 & 0.277 & 0.289 & 0.278 & 0.351 \\ Proposed & **0.017** & **0.018** & **0.017** & **0.018** & **0.018** & **0.017** & **0.018** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average time consumption (sc) of data1–7 obtained by 11 methods. Figure 7: The detection results of each algorithm for typical images in data1 to data7 by 11 methods: (**a**) the detection results of data1; (**b**) the detection results of data2; (**c**) the detection results of data3; (**d**) the detection results of data4; (**e**) the detection results of data5; (**f**) the detection results of data6; (**g**) the detection results of data7. Specifically, the red rectangles indicate the targets in the images. To further demonstrate the detection performance of our method, the ROC curves for the real data are shown in Figure 8. Under the same false alarm rate, a higher detection rate indicates better performance and robustness of the detection method. Our method achieved a higher \(\mathrm{P_{d}}\) and lower \(\mathrm{F_{a}}\) than other methods when applied to real data. Furthermore, the ROC curve of our proposed method is closer to the upper left, indicative of its superior performance. Additionally, our method maintains a satisfactory detection rate even under conditions with an extremely low false alarm rate, demonstrating that it is well suited to meet the stringent requirements of dim and small target detection in infrared imaging.
## 2 Models and Methods
### Principles of Acoustic Black Hole  
The wave equation of flexural waves in a thin plate structure with a power function \(h(x)=\varepsilon x^{m}\ (m\geq 2)\) variation in thickness is [4]  
\[\
abla^{2}\Big{(}D\
abla^{2}w\Big{)}-(1-v)\bigg{(}\frac{\partial^{2}D}{\partial x ^{2}}\frac{\partial^{2}w}{\partial x^{2}}-2\frac{\partial^{2}D}{\partial x \partial y}\frac{\partial^{2}w}{\partial x\partial y}+\frac{\partial^{2}D}{ \partial y^{2}}\frac{\partial^{2}w}{\partial y^{2}}\bigg{)}=\rho h\frac{ \partial^{2}w}{\partial t^{2}} \tag{1}\]  
where \(w\) is the transverse displacement of the structure, \(\rho\) is the density, and \(t\) is the time variable; \(D(x)=Eth^{3}/12\ (1-v^{2})\) is the flexural stiffness, where \(E\) is the Young's modulus, \(h\) is the thickness of the structure, and \(v\) is [PERSON]'s ratio. The amplitude of \(Z\) at any point during wave propagation can be expressed as [4]  
\[U(x)=A(x)e^{i\phi(x)} \tag{2}\]  
where \(\Phi\) is the cumulative phase; \(A(x)\) is the amplitude of the wave at \(x\).  
Among them,  
\[\Phi=\int_{0}^{x}k(x)\ dx \tag{3}\]  
when the thickness of the structure changes according to the above, the cumulative phase \(\Phi\) of the flexural wave tends to infinity, which means that the wave will not reach the boundary with the smallest thickness, nor can it reflect at the boundary with the smallest thickness, so the flexural wave will gather at the boundary with the smallest thickness. At this time, without considering the effect of the moment of inertia and shear force, the wave number \(k\) is used to describe the relationship between the wavelength and the physical quantity of the medium:  
\[k=\Big{(}\rho h\omega^{2}/D\Big{)}^{1/4} \tag{4}\]  
where \(\omega\) is the angular frequency, and then the phase velocity of the flexural wave \(c=\omega/k\). This is represented as  
\[c=\sqrt{\omega h}\bigg{[}\frac{E}{12\rho(1-v^{2})}\bigg{]}^{1/4} \tag{5}\]  
It can be seen from Equation (5) that the phase velocity \(c\) of the flexural wave with frequency \(\omega\) in the homogeneous thin plate is proportional to \(h\). Therefore, for a two-dimensional acoustic black hole plate with a thickness change that satisfies \(h(x)=\varepsilon x^{m}\)  
Figure 1: Radial elastic metamaterial: (**a**) schematic of the structure of an acoustic black hole (ABH); (**b**) unit lattice of acoustic black hole radial elastic metamaterial (AREM); (**c**) schematic diagram of AREM three-dimensional (3D) model; (**d**) formation of radial elastic metamaterial; (**e**) unit lattice of the wedge-shaped radial metamaterial structure (WREM); (**f**) schematic diagram of the WREM 3D model.  
\(m\geq 2\), as the plate thickness decreases, the flexural wave velocity also gradually decreases, and energy accumulation effects occur at the boundary with the smallest thickness.
## 1 Introduction
This concept offers the possibility to describe the manufacturing process in an abstract way on a semantic level and to assign concrete services of devices and manufacturing modules right at the moment the product enters the manufacturing line. The abstract description of the process could even be attached directly to the product (e.g., stored on a digital product memory [5]) turning the product into an active component of the manufacturing line. When dynamically orchestrating web services contextual information can be used to influence the choice or the parameterization of the respective service leading to the concept of context-based orchestration. Contextual information can be information about current environmental influences or the current state of products, machines, etc. One of the most common definitions of context describes it as \"any information that can be used to characterize the situation of an entity\" [6]. In the domain of production automation, the production process and the associated task that needs to be fulfilled are the most important aspects. All production entities that are relevant for the execution of this task, either by actively participating in the process or by supplying input (e.g., material, information) can deliver contextual information about themselves. Influencing the discovery of device and module services by taking advantage of contextual information at deployment or run time significantly increases the agility of smart factories and boosts productivity when manufacturing highly individualized products. This article describes how semantic technologies can be applied to the dynamic context-based orchestration of technical manufacturing processes based on the concept of service-oriented architectures. Whereas existing syntactic approaches (e.g., using web services) are sufficient to realize a technical interoperability, a semantic interoperability can only be achieved by using sophisticated semantic models. Therefore, we discuss how a structure of modular ontologies can be developed and which upper ontology is suited to act as a common interoperability model for our approach. After a brief introduction to the basic paradigms and concepts of service-oriented architectures and semantic web services, the article shows how these approaches can be transferred to the field of production automation (Section 2). Based on three central use cases, the authors derive which kind of semantic models are needed for the semantic description of web services provided by field devices, their discovery, selection and orchestration. The development of respective ontologies is illustrated in Section 3. In addition, a system architecture is presented and the central aspects of implementation are discussed. In an experimental setup the functionality of the theoretical approaches is proven by means of the scenario of resource-efficient manufacturing of a smart product (Section 4). The article concludes with a short summary and an outlook for future work.
## 3 Results
### Scoping Review
#### 3.2.3 Monitoring Based on Computer Vision  
Computer vision-based surveillance involves monitoring areas of interest using only a sequence of images, making it possible to track multiple targets within a field using a single camera. Advances in computer vision have made this approach a prominent research topic. With the development of modern deep learning (DL) algorithms, such as convolutional neural networks (CNN), region-CNN (RCNN), fast RCNN, and You Only Look Once (YOLO), vision-based tracking has become increasingly accurate and reliable [7; 48; 49; 70; 71]. Computers can track objects in the field based on their characteristics using models trained with deep learning (DL) algorithms, which effectively capture the complex relationship between input data, such as images, and output reasoning, such as object recognition.  
Computer vision has been used in several studies to confirm that workers are properly equipped with personal protective equipment, such as helmets and harnesses [49; 51; 52; 54; 56; 58; 61; 62; 64; 69], and to monitor their actions when using ladders [7; 48; 55]. This information is then processed using DL algorithms, which can issue an alert if the equipment is not being used correctly, increasing the risk to the worker. Five of these studies have been applied in a real construction context [49; 50; 53; 54; 56; 58; 59; 62; 65; 66; 67; 68; 69; 70], and their effectiveness when applied on a large construction site is unknown.  
Some studies have used computer vision to check for the absence of guardrails in an area with a risk of falling and to warn of this situation [60].  
It is also possible to use computer vision to track the location of workers and equipment on site, with the collected images used as cross-referenced BIM files to create a virtual construction site, while identifying the hazard and generating an alert in hazardous situations based on DL [53; 68; 71].  
Unmanned aerial systems (UAS) equipped with GPS and cameras have been used to collect images in a work zone, considering construction progress, site conditions, and the surrounding environment. This information is transferred to a digital platform that automatically cross-references the images collected by the UAS and runs a CNN-based DL algorithm to detect workers, vehicles, and equipment, helping to monitor safety conditions on site [72; 73].
# A Data-Driven Model for the Energy and Economic Assessment of Building Renovations
## 1 Introduction  
In recent decades, the growing awareness of the environmental impact of human activities has made the transition to sustainable energy one of the world's main challenges. Recent energy and environmental crises have emphasized the importance of accelerating the shift towards a sustainable development model that focuses on reducing greenhouse gas emissions, making efficient use of resources, and integrating renewable energy sources. Against this backdrop, the European Union has set ambitious targets: reducing net emissions by 55% by 2030 compared to 1990 levels and achieving climate neutrality by 2050, as set out in the European Green Deal [1, 2]. Within this strategic framework, the built environment sector, particularly the architecture, engineering, construction, and operation sector (AECO), plays a central role. According to data from the European Commission, buildings account for around 40% of energy consumption and 36% of CO\({}_{2}\) emissions in Europe [3]. A significant proportion of this consumption is linked to residential buildings and their daily use, as well as to the design, construction, and management phases throughout a building's entire life cycle. Reducing the environmental impact of buildings is therefore crucial for achieving climate targets and for the systemic transformation of the Europeanenergy sector. One of the most critical issues in this context is the poor energy efficiency of existing buildings, many of which were constructed before modern energy regulations were adopted. Most existing buildings are energy inefficient, often lacking efficient systems and being built with materials that are not thermally efficient [4]. The built heritage is extremely heterogeneous in terms of construction period, building techniques, intended use, climatic conditions, and energy requirements, making it difficult to adopt standardized retrofitting strategies that are truly effective [5]. Taking action on the built heritage through energy renovation measures is not only an obligation under environmental regulations but also an opportunity to reduce energy poverty, increase indoor comfort, enhance property values, and create skilled jobs in the construction sector [6]. However, the effective implementation of these measures on a large scale is hindered by several obstacles, including the fragmentation of available information, the uncertainty surrounding the economic costs and benefits of potential solutions, and the absence of digital decision-making tools capable of integrating available data and facilitating informed choices.
# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
## 2 The System Model
### The Oceanic Environment  
The main challenge in deploying wireless networks at sea is that hydrological and meteorological factors like wind and waves add stochastic fluctuations to the navigation status. The horizontal distances at the time point \(i\) between the two ships and between the relay ship and the BS are denoted by \(d_{1}^{1}\) and \(d_{2}^{2}\). Meanwhile, \(h_{1}^{1}\) and \(h_{2}^{2}\) represent the wave  
Figure 1: Relay ship-assisted system model.  
heights in the areas of the sea around the two respective ships. Thus, the exact distances are given by  
\[d_{i}^{1}=\sqrt{(d_{i}^{1})^{2}+(h_{i}^{1}-h_{i}^{2})^{2}} \tag{2}\]  
\[d_{i}^{2}=\sqrt{(d_{i}^{2})^{2}+(h_{i}^{2})^{2}} \tag{3}\]  
\(v_{i}^{1}\), \(v_{i}^{2}\) represent the scalar speeds of the two ships at the \(i\)-th time instant, which are expressed as follows:  
\[v_{i}^{1}=V_{i}^{1}+\Delta_{i}^{1} \tag{4}\]  
\[v_{i}^{2}=V_{i}^{2}+\Delta_{i}^{2} \tag{5}\]  
where \(\Delta_{i}^{1}\), \(\Delta_{i}^{2}\) are the associated random deviations and \(V_{i}^{1}\), \(V_{i}^{2}\) are the velocities recorded by the ship's log-based instruments. \(\theta_{i}^{1}\), \(\theta_{i}^{2}\) signify the angles by which the actual cruising directions of the two ships deviate from the pre-defined nominal direction at the \(i\)-th time step.