esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Complexity Progressive MIMO-OFDM Receiver for Underwater Acoustic Communication
## 3 ICI Unaware Cost Function Based Soft Feedback Iterative Receiver  
Threshold controlled and uncontrolled soft and hard decision feedback methods are already in practice in iterative receiver systems [19]. The soft symbol estimates are claimed to be better than the hard symbol estimates, especially for iterative channel estimation as they provide more statistical information about the transmitted data [20]. First, we design an iterative receiver which is unaware of ICI and treats ICI as an additive noise as shown in Figure 2. The receiver uses a novel cost based soft decision feedback method which will be explained in detail later.  
The initial processing of MIMO-OFDM iterative receiver is the same as that of a non-iterative receiver processing, i.e., the channel estimation is performed first, then equalization, and finally decoding. OMP sparse estimation, MMSE equalization, and LDPC decoding algorithms are adopted here. The initial channel estimate is obtained from a training sequence contained within the transmitted signal. After the first equalization and decoding of a block of data, the output is fed back to the channel estimator, which then produces a better channel estimate which is passed on to the equalizer and subsequently to the decoder. In the decoding process, if the parity condition is satisfied or the number of iterations \(I\) reaches the preset maximum value \(I_{\max}\), the algorithm stops and gives us the output.  
Figure 2: ICI unaware Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) Iterative Receiver block diagram.
# Experimental Study on Permeability Evolution of Sandstone during Triaxial Compression Damage
## 3 Analysis of Test Results
### Permeability Characteristics
#### 3.3.2 Relationship between Permeability and Volumetric Strain
Figure 12: Permeability–axial stress–volumetric strain relationship curves for sandstone under different conditions. For pore pressures of 4 and 8 MPa, as well as the volumetric expansion stage, the permeability evolution patterns with volumetric strain are illustrated in Figure 13b. The relationship between permeability and volumetric strain is modeled using an exponential function:  
\[K=\exp\left(E\epsilon_{v}^{2}+F\epsilon_{v}+G\right) \tag{9}\]  
where, \(E\), \(F\), and \(G\) are fitting parameters. Table 6 demonstrates the equation coefficients of the relationship between permeability and volumetric strain at different void pressures. As can be seen from the table, the permeability of sandstones is affected by stress during both the volumetric compression and expansion phases, but there is some variation in the manner and extent of this effect.
# Occlusion Vehicle Target Recognition Method Based on Component Model
## 2 Occlusion Vehicle Target Segmentation and Recognition Scheme Design
### Design of Vehicle Component Feature Mask Module Based on Swin Transformer  
After obtaining the region of interest, it will not only segment the vehicle target but also segment other significant objects located in the foreground. It will interfere with the positioning of the vehicle target and need to filter it out. Considering that the features of the object components have better stability than the traditional corner points, colors, and other features, the regional features of the vehicle target components are selected for extraction. The components [24] in a vehicle target are defined as parts with different contour structures. How to effectively extract these parts of the vehicle is the key to subsequent vehicle target recognition. Therefore, a Vehicle Component Feature Mask (VCFM) module based on deep learning is designed, as shown in Figure 3.  
As shown in Figure 3, the overall architecture of the vehicle component feature extraction and classification module includes four parts. First, the backbone net part uses the Swin Transformer unit to extract the vehicle component features of the saliency segmented image; then, the component perception enhancement (CPE) unit is used to extract the multi-scale features of the components, and the up-sampling fusion is performed through the deep to shallow component feature transfer to enhance the features of the weak contrast region. Then, it is sent to the efficient attention (EA) unit for filtering processing to reduce the interference of irrelevant regions, and the weighted regional features are fused to obtain the features of each vehicle component. Finally, a Mask Decoder is introduced to remove the damaged areas that cannot be classified as vehicle component, and the Component Mask Recognition (CMR) unit is used to determine whether these parts features belong to vehicle parts features, and finally, the vehicle is identified.  
Figure 3: Vehicle component feature mask module structure.
# The Potential of Aquafaba as a Structure-Shaping Additive in Plant-Derived Food Technology
## 5 Functional Properties of Aquafaba in Food Technology  
### Foaming Properties  
The foaming properties of aquafaba were discovered first. It turns out that the foaming of aquafaba is similar to egg white (1:1 ratio) [17]. The foaming properties of aquafaba result from the presence of albumin as well as polysaccharides and saponins. Thanks to its protein content, aquafaba can foam, and thanks to the carbohydrate content, it remains stable. Saponins are believed to be responsible for facilitating the formation of air bubbles because they have an amphiphilic structure. [PERSON] et al. [33] showed in their research that the higher the protein content in aquafaba, the higher the foaming efficiency. It was observed that extending the foaming time of aquafaba did not adversely affect the foaming properties, as is the case with egg white foam [17]. The foaming ability of aquafaba can be used in mousses, meringues, biscuits and meat roasts. Foamed chickpea aquafaba is shown in Figure 2.  
Other authors report that ultrasonic treatment can be used to enhance the foaming properties of aquafaba. Ultrasonication also improved foam stability, color and texture, as well as emulsifying capacity [31; 45]. The foaming properties of aquafaba have been fairly well studied; however, the results of individual scientists vary depending on the other parameters of aquafaba (Table 3). Based on the examples of the collected publications, it can be concluded that one of the best yields and stability of aquafaba foaming can be obtained using chickpea seeds, which were previously soaked in water and then boiled in a ratio of 1:4 with water for 60 min.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Legume Seeds**} & **Foaming Capacity,** & **Foaming Stability,** & **Emulsifying Capacity,** & **Emulsibility Stability,** & **Emulsifying Activity Index, m\({}^{2}\)/g** & **Reference** \\ \cline{2-7}  & **521** & -685 & -99 & - & - & - & [14] \\ \cline{2-7}  & 182** & -480 & 74** & -92 & - & 60** & - & [17] \\ \cline{2-7}  & **-** & - & - & 72** & - & - & [20] \\ \cline{2-7}  & **89** & - & - & - & - & [24] \\ \cline{2-7}  & **-** & - & - & - & 39** & [26] \\ \cline{2-7}  & **162** & -324 & 3.4** & -93 & -72 & -76 & - & [27] \\ \cline{2-7}  & **127** & 95 & - & - & - & - & [29] \\ \cline{2-7}  & **40** & -290 & 7** & -58 & - & - & - & [30] \\ \cline{2-7}  & **259** & -548 & 42** & -77 & - & - & - & [31] \\ \cline{2-7}  & **505** & -611 & 81** & - & - & - & [34] \\ \hline \end{tabular}
\end{table}
Table 3: Foaming and emulsifying properties of aquafaba.  
Figure 2: Meringue mass based on aquafaba (photo by [PERSON]).
# A Ship Trajectory Prediction Method Based on an Optuna-BILSTM Model
## 2 Methods
### Deep Learning Prediction Models
#### 2.2.2 BILSTM Model
The LSTM has three fully connected nonlinear cells with a gate structure for nonlinear transformation according to different weight matrices W and bias terms b, combined with a \(\rm{\sigma(sigmoid)}\) activation function. The computational principle is as follows:  
Figure 2: The unfolding structure of an RNN. For the input gate:  
\[\text{i\_t}=\sigma(\text{W}_{\text{hi}}\text{h}_{\text{t}-1}+\text{W}_{\text{xi}} \text{x}_{\text{t}}+\text{b}_{\text{i}}) \tag{12}\]  
For the forget gate:  
\[\text{f\_t}=\sigma(\text{W}_{\text{hf}}\text{h}_{\text{t}-1}+\text{W}_{\text{xf}} \text{x}_{\text{t}}+\text{b}_{\text{f}}) \tag{13}\]  
For the output gate:  
\[\text{o\_t}=\sigma(\text{W}_{\text{ho}}\text{h}_{\text{t}-1}+\text{W}_{\text{ xo}}\text{x}_{\text{t}}+\text{b}_{\text{o}}) \tag{14}\]  
For g\_t:  
\[\text{g\_t}=\tanh\Bigl{(}\text{W}_{\text{hg}}\text{h}_{\text{t}-1}+\text{W}_{ \text{xg}}\text{x}_{\text{t}}+\text{b}_{\text{g}}\Bigr{)} \tag{15}\]  
For cell\_state:  
\[\text{c\_t}=(\text{f\_t}\text{.}\text{.}\text{h}_{\text{t}-1}+\text{W}_{\text {xf}}\text{x}_{\text{t}}+\text{b}_{\text{f}}) \tag{16}\]  
Output values:  
\[\text{h\_t}=\text{o\_t}\text{.}\text{.}\text{.}\text{tanh}(\text{C}_{\text{t}}) \tag{17}\]  
The distinction between LSTM and the RNN primarily lies in their handling of sequence data. Unlike RNN, which performs monomial multiplication, LSTM introduces a more complex mechanism involving polynomial operations. LSTM employs the sigmoid activation function within the forgetting gate to scale the input values between 0 and 1, determining the extent of cell-state information retention. This mechanism allows for selective memory preservation, effectively addressing the issue of vanishing gradients common in RNNs. Consequently, the long short-term memory model is better equipped than traditional RNNs to process extended time-series data, offering enhanced memory capabilities and sequence handling.
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 3 Error Bound Analysis
### Metric of Single Measurement
#### 3.1.3 Metric of AOA  
When the AOA is selected as the measurement to conduct the indoor localization, the arrived angle from the \(k\)-th AP is estimated by:  
\[\widehat{\alpha_{ik}}=\alpha_{ik}+\chi_{3}=\arctan\frac{{{y_{i}}-{y_{k}}}}{{{x _{i}}-{x_{k}}}}+\chi_{3} \tag{18}\]  
where \(\alpha_{ik}\) is the angle of direct signal path between the \(k\)-th AP and receiver located at \((x_{i},y_{i})\). \(\chi_{3}\) is a random variable that follows the Gaussian distribution with the mean zero and variance \(\sigma_{3}^{2}=\sigma_{\alpha}^{2}\), \(N\left(0,{\sigma_{3}}^{2}\right)\). \(\sigma_{\alpha}^{2}\) is the variance of angle of the received signal. The PDF of \(\alpha_{ik}\) with respect to \(\theta_{i}\) is:  
\[{f_{\theta_{i}}}\left({\boldsymbol{\alpha}_{ik}}\right)=\prod\limits_{k=1}^{m }{\frac{1}{{{\sqrt{2\pi}\sigma_{3}}}}\exp\left({-\frac{{{\xi_{i3}}^{2}}}{{2 \sigma_{3}}^{2}}}\right)} \tag{19}\]  
where \(\xi_{i3}=\widehat{\alpha_{ik}}-\arctan\frac{{{y_{i}}-{y_{k}}}}{{{x_{i}}-{x_{k }}}}\). Based on (5), we can calculate that:  
\[\left\{\begin{array}{l}J\left(\theta_{i}\right)=\left[{\begin{array}{*{20} {c}}{{{J}_{xx}}\left(\text{AOA}\right)}&{{J}_{xy}}\left(\text{AOA}\right)\\ {{{J}_{yx}}\left(\text{AOA}\right)}&{{{J}_{yy}}\left(\text{AOA}\right)}\end{array} }\right]\\ {{{J}_{xx}}\left(\text{AOA}\right)}={{\rho_{3}}\sum\limits_{k=1}^{m}{\left[{ \frac{{\sin\alpha_{ik}}}{{{d_{ik}}}}}\right]^{2}}}\\ {{{J}_{xy}}\left(\text{AOA}\right)}={{J}_{yx}}\left(\text{AOA}\right)=-{{\rho_ {3}}\sum\limits_{k=1}^{m}{\frac{{\sin\alpha_{ik}}\cos\alpha_{ik}}{{{d_{ik}}^{2 }}}}}\\ {{{J}_{yy}}\left(\text{AOA}\right)}={{\rho_{3}}\sum\limits_{k=1}^{m}{\left[{ \frac{{\cos\alpha_{ik}}}{{{d_{ik}}}}}\right]^{2}}}\\ \end{array}\right. \tag{20}\]  
where \(\rho_{3}=\left(\frac{1}{\sigma_{\alpha}}\right)^{2}\).  
Therefore, based on (8), the localization error bound by using the metric of AOA with respect to \(\theta_{i}\) equals:  
\[\begin{split} V_{m}\left(\text{AOA}\right)&=\frac{{{{ \int_{yy}}\left(\text{AOA}\right)+{{I}_{xx}}\left(\text{AOA}\right)}}}{{{{J}_{xx }}\left(\text{AOA}\right){{\int_{yy}}\left(\text{AOA}\right)-{{I}_{yy}}^{2} \left(\text{AOA}\right)}}}\\ &=\frac{1}{\rho_{3}}\cdot\frac{\sum\limits_{k=1}^{m}{\left[{ \frac{{\sin\alpha_{ik}}}{{{d_{ik}}}}}\right]^{2}}+\sum\limits_{k=1}^{m}{\left[{ \frac{{\cos\alpha_{ik}}}{{{d_{ik}}}}}\right]^{2}}}{\sum\limits_{k=1}^{m}{ \left[{\frac{{\sin\alpha_{ik}}}{{{d_{ik}}}}}\right]^{2}}\times\sum\limits_{k=1 }^{m}{\left[{\frac{{\cos\alpha_{ik}}}{{{d_{ik}}}}}\right]^{2}}}{\sum\limits_{k =1}^{m}{\left[{\frac{{\cos\alpha_{ik}}}{{{d_{ik}}}}}\right]^{2}}}-\left[{ \sum\limits_{k=1}^{m}{\frac{{\sin\alpha_{ik}}\cos\alpha_{ik}}{{{d_{ik}}^{2}}} }\right]^{2}}\end{split} \tag{21}\]
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
###### Abstract
Other promising technologies also emerged such as network-based and collaborative positioning and navigation that are readily available examples [16, 17, 18, 19, 20, 21]. In PNT technologies, the aforementioned approaches are of high interest for several applications in which size, power consumption, or computational constraints constitute the limiting factors of potential implementations. This paper aims at providing a timely contribution to feed both the interest and research efforts in PNT technologies applied to smart contexts [22, 23, 24]. Few example of pioneering projects have been pursued in recent years and documented in [9, 10] about the topic. The GHOST framework [10] aimed at implementing a centralised platform to address the emerging issues of modern cities and the relevant needs in terms of urban planning and maintenance, thus mostly supporting Intelligent Transportation Systems (ITS). The system described in [9] defined a similar, service-oriented approach based on mobile applications and a centralised back-end Database (DB) logic to support Location Based Services (LBS) in a university campus. Differently from such valuable studies this work presents a set of state-of-the art technologies that are expected to pave the evolution of urban indoor and outdoor positioning and navigation in the near future. The objective of this work was indeed the design, development, and testing of an open GNSS-centered positioning platform capable of supporting the requirements that Smart City users need in terms of localisation accuracy and precision. An innovative architecture is hence presented for a _testbed_ which was designed within HANSEL, a project funded by the European Space Agency to provide a flexible _testbed_ to assess the added value of the proposed promising paradigms. In parallel, the research and development activities pursued and presented in this work aim at highlighting cutting-edge solutions for the definition of a complex PNT ecosystem devoted to the support of LBS through GNSS-based and network-based solutions. The rest of the paper is organised as follows: In Section 2 the core technologies addressed by the project are introduced along with the relevant background and related previous works. Section 3 discusses the technical aspects related to the design, implementation, and deployment of the _testbed_ and its services. In Section 4, the services included in the _testbed_ are described while the mobile applications designed to actively interact with the services are detailed in Section 5. A summary of the project results is reported in Section 6, which also includes details about materials and the experimental setup for each proposed technology. Conclusions are eventually drawn in Section 7.
# Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence
## 2 Materials and Methods
### Dielectric Defects and PD Types  
Given the diverse array of defects present in the dielectric of HV electrical equipment, resulting in varying levels of uniformity in electric field distribution, an analysis of three discharge types was conducted based on statistical data pertaining to PD faults observed during the operation of HV electrical equipment [35; 36; 37]. To simulate these discharges induced by different electric field distributions, three discharge types were modeled, as depicted in Figure 1. These simulations encompassed tip discharge, plate discharge, and ball-plate discharge [38]. This was executed within the context of HV electrical equipment, with polyvinyl chloride serving as the inter-electrode insulation material and an insulation layer thickness of 4.5 mm.
# Edge Computing Architecture for the Management of Underwater Cultural Heritage
## 1 Introduction
This is further complemented by nonterrestrial networks (NTNs), including satellite constellations, extending connectivity to remote and hard-to-access maritime areas. By integrating these technologies, the platform supports a resilient, multilayered communication and localisation system that ensures continuous operation, even during intermittent cloud connectivity. This approach not only improves the accuracy and reliability of underwater localisation but also enhances overall system responsiveness, making it ideal for real-time conservation efforts. The proposed platform leverages AI models at the edge to provide real-time risk assessments based on data from public databases and in situ sensor measurements. This allows for the dynamic prioritisation of conservation efforts, making it possible to anticipate and mitigate the risks associated with environmental factors, such as sediment displacement and rising sea temperatures. UCH-related platforms have been identified in the literature [3; 4; 5; 6; 7]. However, to the best of our knowledge, none integrate the edge computing paradigm. This type of architecture leverages distributed processing and storage capabilities to capture, analyse, and monitor large amounts of multimodal sensor data in real time. This helps address the current limitations in centralised cloud-based solutions by performing initial data filtering, processing, and analysis close to the data source using edge nodes [8]. While edge computing introduces security and management challenges due to increased network nodes, it also enhances privacy and security [9]. Overall, the platform not only addresses the current limitations in underwater heritage conservation but also provides a scalable and adaptable foundation for integratingfuture technological advancements, ensuring the long-term protection and management of UCH sites. This paper is organised as follows: Section 2 reviews the existing IoUT solutions applied to UCH conservation, identifying current gaps and challenges. Section 3 details the edge computing platform's architecture, layers, and components, explaining their interaction within the proposed system. Section 4 presents a case study focused on the underwater localisation system, demonstrating how the platform's acoustic communication could facilitate precise real-time tracking and monitoring of submerged heritage assets. Section 5 concludes this paper and outlines future work.
## 1 Introduction  
Quantum Optical Coherence Tomography (QOCT) is a technique that incorporates Optical Coherence Tomography (OCT) principles with entangled photons to achieve enhanced imaging capabilities. Classical OCT is a well-established non-invasive imaging modality used in medical and biological applications. The technique utilizes low-coherence interferometry to image samples of interest with micron-scale resolution. A broadband light source is split into two arms: a reference arm and a sample arm. Measuring classical interference between the two beams reveals transverse and cross-sectional images of a sample [1]. In QOCT, entangled photons are generated via spontaneous parametric down-conversion in a non-linear crystal. QOCT employs fourth-order interference, otherwise known as the \"photon bunching\" effect, to capture and reconstruct the profile of the sample [2; 3; 4; 5; 6; 7; 8]. The quantum interference effect is inherently different from classical and offers a reduction of the wave dispersion and is expected to improve image quality and resolution [9; 10; 11; 12; 13; 14]. Polarization-Sensitive Quantum Optical Coherence Tomography (PS-QOCT) extends the capabilities of QOCT by providing additional information about polarization and enables measurement and visualization of tissue birefringence [15; 16; 17; 18]. In our paper, we present experimental PS-QOCT imaging and characterization of a biological sample.  
Classical optical techniques were used in [19; 20; 21] to image carcinoma cells of lung tissue via Polarization-Sensitive Optical Coherence Tomography (PS-OCT). The authors reported a birefringence signature. In this work, we use PS-QOCT to image and characterize birefringence in similar histological samples.  
Polarization-Sensitive Quantum Optical Coherence Tomography (PS-QOCT) is a fourth-order interferometric technique that uses quantum-entangled photons [15; 16]. PS-QOCT eliminates dispersion and enhances the resolution capability that is present in classical OCT [14]. A brief overview of the dispersion cancelation effect in quantum interference is outlined in the next section. The technique provides both axial and transverse optical sectioning capabilities that are also polarization-sensitive. In the experimental setup, entangled photon pairs undergo fourth-order quantum interference in a Mach Zehnder interferometer. In the presence of localized birefringence, interference patternschange, and these changes are used to image and characterize birefringence within the biological samples.
# Towards Autonomous Bridge Inspection: Sensor Mounting Using Aerial Manipulators
## 5 Motion Planning  
As discussed in Section 1.1, the main concept of this paper was to use a team of two UAVs, each applying one component of the adhesive. To apply the \"resin\" component, the UAV has to plan a collision-free trajectory and position itself in front of the target area to start spraying. This is fundamentally different from mounting a sensor coated with \"hardener\". In the latter case, apart from planning a collision-free trajectory, the manipulator-endowed UAV has to apply pressure for a certain amount of time for the two components to mix.  
From the perspective of motion planning, the planner needs to be augmented to include a manipulator with three degrees of freedom, contact force and the weighing parameter \(\beta\). To successfully maintain the pressure, the planner relies on the impedance controller described in Section 4.2. Furthermore, one of the requirements when mounting the sensor on the wall is for the sensor to be perpendicular to the wall. Therefore, it is necessary to take the underactuated nature of the multirotor UAVs into account during the motion planning. Namely, the errors in the planned end-effector configuration were mainly induced due to the roll and pitch angles while executing the planned motion. In our previous work [28], we developed a model-based motion planner for aerial manipulators that is capable of correcting the aforementioned end-effector deviations. In this paper, the idea from [28] was extended to consider the impedance control when obtaining the full state of the aerial manipulator.