esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Evaluation of Healing Efficiency
The PCL only partially remains on the fractured surface, while the greatest part flows out, over the Teflon film. The reason for this phenomenon is that the healing temperature is too high, thus enabling the PCL to melt and flow even outside the specimens. Although PCL could be an effective healing agent, its adhesion with the PA6 matrix is very poor, and therefore the healing efficiency of the prepared PA6/PCL systems is rather limited. Therefore, this work demonstrates the possibility to partially heal PA6 matrices utilized in TPCs with the introduction of a proper amount of PCL and the selection of a proper healing temperature. However, further improvements will be necessary to increase the efficiency of the healing process. For instance, the overall healing  
Figure 11: Evaluation of the healing efficiency in quasi-static and impact mode for the PA6_30 PCL blend. (**a**) Quasi-static mode, (**b**) impact mode. efficiency could be increased by means of a proper compatibilizer. For instance, the use of a glycolidyl methacrylate compatibilizer could significantly improve the adhesion between PA6 and PCL constituents. The epoxide groups present in this compatibilizer could react with the hydroxyl group of PCL and the carboxyl groups of PA6, thus producing ester linkages at the interface between PA6 and PCL [73]. This could lead to an increase in the overall healing efficiency, both in quasi-static and impact mode.
# Failure Identification Method and LMI-Based Control System for Small Spacecraft
## 2 System Dynamics
\]  
where \(\alpha\) is the angle between the x-axis and the first reaction wheel, and \(\beta\) is the angle between the plane xy and each reaction wheel, as shown in Figure 1. For this study, we considered \(\alpha=0^{\circ}\) and \(\beta=60^{\circ}\). The reaction wheels dynamics can be expressed in the body reference frame including the rotation matrix \(Z\) as  
\[Z\tau=\dot{h}_{med}+\omega\times Zh_{med}^{w}\]  
Finally, the attitude dynamics, Equation (1), is combined with MED dynamics, and it is given as  
\[\dot{\omega}=J^{-1}[u_{ext}-Z\tau-\omega\times(J\omega+Zh_{med}^{w})] \tag{2}\]  
Figure 1: The RWs model and configuration are shown in this figure. Pyramidal configuration with \(\alpha=0^{\circ}\) (**left**). Reaction wheels model (**right**). where \(J\in\mathbb{R}^{3\times 3}\) is the inertia matrix, \(u_{ext}\in\mathbb{R}^{3}\) are the external disturbances, \(\omega\in\mathbb{R}^{3}\) is the spacecraft angular velocity, \(Z\in\mathbb{R}^{3\times 4}\) is the rotational matrix from the wheels to the body reference frames, \(\tau\in\mathbb{R}^{4}\) is the torque applied to each reaction wheel, and \(h^{w}_{med}\in\mathbb{R}^{4}\) is the angular momentum of each reaction wheel. In order to simulate reaction wheels dynamics, a first-order filter is included in the model as a transport delay, as shown in Figure 1. \(\tau_{cmd}\in\mathbb{R}^{4}\) is the control torque given by the controller and designed as  
\[\tau_{cmd}=Z^{+}u\]  
where \(Z^{+}\in\mathbb{R}^{4\times 3}\) is the pseudo-inverse matrix of \(Z\). Note that, except for the NASA configuration with three RWs, it is necessary to allocate the three-axis control torque to the four wheels assembly. To accomplish this, it is common to use the Moore-Penrose pseudoinverse matrix, \(Z^{+}\), as the non-square control allocation matrix \(Z\in\mathbb{R}^{3\times 4}\) cannot be inverted.
# S seaweed Polysaccharides in Agriculture: A Next Step towards Sustainability
## 2 Seaweed Polysaccharides
### Alginate  
Alginate (Figure 1) is a polysaccharide naturally found in brown seaweed in the form of alginic acid. This anionic polymer is based on monomers of \(\beta\)-D-mannuronic acid (M) and 1,4 \(\alpha\)-L-gluturonic acid (G) [19]. Depending on the position of the monomeric units in the chain, the molecular weight of the polymer, and the nature of its associated counter ions, the properties of this polysaccharide can differ [1; 20].  
At the level of food certification, the molecular weight of alginate is not considered. However, it is contemplated for good practices in the extractive industry associated with the food industry [21].  
The industrial extraction of this polysaccharide involves several steps: washing the seaweed to remove impurities and pre-treatment with heated acid (usually hydrochloric acid for 24 h) to remove pigments, proteins, and lipids [22]. Next, the solid-liquid extraction takes place, where the solid residue is subjected to an alkaline treatment (sodium carbonate) followed by a centrifugation or filtration process. After this process, hydrochloric acid is added to the liquid extract to precipitate the alginate dissolved in the solution in the form of sodium alginate. After precipitation, the solution with the precipitate is centrifuged /filtered to obtain the precipitated alginate. Afterwards, the alginate is dried and milled for later application [23].  
Alginate is classified as a non-organic compound and is approved by the Food and Drug Administration (FDA, USA) and the European Food Safety Authority (EFSA, EU) as a food ingredient [24]. In this context, the application and labeling of food products containing alginate are regulated according to the European Union Commission Regulation (1333/2008) as E400 (alginic acid), E401 (sodium alginate), E402 (potassium alginate), E403 (ammonium alginate), E404 (calcium alginate), and E405 (propylene glycol alginate) [21].  
The main characteristics of alginate are its high degree of viscosity and absorption, which make it possible to thicken food products, such as jellies, marmalades, sauces (e.g., mayonnaise), syrups, and ice cream [25; 26].  
The FDA has approved alginate for human consumption after toxicological testing. However, the FDA requires evidence of good practices in alginate extraction and the use of alginate at threshold concentrations, which vary according to the type of food product [24].
# A Dual-Branch Self-Boosting Network Based on Noise2 Noise for Unsupervised Image Denoising
## 2 Related Work
### Nonresidual- and Residual-Learning-Based Denoising Models  
To provide a more comprehensive description of our work, we have reformulated Equation (1) for clarity as follows:  
\[I_{\text{noisy}}=I_{\text{content}}+I_{\text{noise}} \tag{6}\]  
where \(I_{\text{noisy}}\), \(I_{\text{content}}\), and \(I_{\text{noise}}\) represent the noisy image to be denoised, the clean image, and the noise, respectively. Based on the diverse mapping objectives, current deep-learning-based denoising models can be categorized into two methodologies: nonresidual (content) learning and residual (noise) learning. Both methodologies possess distinct advantages while being capable of predicting the latent clean image. Nonresidual-learning-based denoising models, such as N2N (Noise2 Noise) [21] and FFDNet (Fast and Flexible Denoising Network) [27], typically employ neural networks denoted as \(f_{\text{content}}\), which aim to transform a given noisy image \(I_{\text{noisy}}\) into an approximation \(\hat{I}_{\text{content}}=f_{\text{content}}(I_{\text{noisy}})\) of the clean image \(I_{\text{content}}\). Residual-learning-based denoising models, such as the DnCNN (Denoising Convolutional Neural Network) [15] and Restormer [19], first employ neural networks designed to learn residual mappings. The final clean image is obtained by subtracting the predicted noise from the noisy image, formulated as \(I_{\text{content}}=I_{\text{noisy}}-\hat{I}_{\text{noise}}=I_{\text{noisy}}- f_{\text{noise}}(I_{\text{noisy}})\). Generally speaking, the advantage of nonresidual learning lies in its simplicity and straightforwardness as the model is relatively lightweight and easy to train and understand. However, without residual connections, deeper networks may face issues such as vanishing or exploding gradients, leading to training difficulties. On the other hand, residual learning addresses the vanishing or exploding gradient problem more effectively by introducing residual connections, ensuring more stable training. Additionally, residual learning can learn deeper relationships between input and output, thereby improving the model's representational and generalization capabilities. Note that most of existing denoising models predict a single task while neglecting the potential of dual learning with two tasks, i.e., estimating residuals and estimating latent clean images [28; 29].
# Experimental and Numerical Evaluation of Calcium-Silicate-Based Mineral Foam for Blast Mitigation
## 2 Air-Blast Experiments  
### Main Structure and Front Skin Materials  
The main structure and the front skin are made from aluminum plates of the EN AW 1050A-H24 alloy. This alloy, composed of 99.5% pure aluminum, undergoes annealing and work hardening through rolling. Uni-axial tensile tests on samples of 2 mm thickness to study their static mechanical properties were conducted by [PERSON] et al. [43]. The material mechanical properties along the longitudinal (0\({}^{\circ}\)) and transversal (90\({}^{\circ}\)) directions relative to the rolling direction are compared. They found that the material exhibits nearly isotropic properties with slight variations in failure strain and yield stress. This aluminum alloy's mechanical properties are as follows: Young's modulus of 69 GPa, yield strength of 75 MPa and tensile strength of 115 MPa.
## 2 Related Works  
### Dynamic Satellite Routing  
The performance of traffic scheduling is directly influenced by the network quality [18]. To address this, dynamic routing strategies has been proposed. Some approaches involve continuous packet exchange to obtain the most up-to-date network state description. For instance, building upon the optimized link state routing protocol, [PERSON] et al. [19] proposed a method that senses the network state and detects any changes using specially designed packets. However, the complexity of networks may lead to a flood of detection packets. To tackle this issue, [PERSON] et al. [20] proposed a new protocol for adaptively selecting the optimal routing path in dynamically changing environments, using local and global pheromones based on the ant colony algorithm for QoS routing selection. Nevertheless, this method lacks an overall definition of the network and a comprehensive state tracking strategy.  
Meanwhile, in order to improve satellite networks, some scholars have started utilizing software-defined network (SDN) technology. This technology allows for the separation of the data layer and control layer within the SDN architecture [21]. The primary function of the SDN controller entity is to concentrate the control logic and update the forwarding rules of data layer devices in response to network resource changes. [PERSON] et al. [22] proposed an SDN-based composite architecture for a space fusion network, which integrates satellite backbone networks, ground mobile networks, and internet backbone networks for real-time signaling exchange, completing the transmission, storage, and processing of massive data and achieving the efficient utilization of network resources. [PERSON] et al. [23] explained the benefits of integrating SDNs into satellite networks and proposed a multipath routing strategy based on an SDN, which used a combination of delay, bandwidth, and node load as the calculation factors for the link transmission costs.
## 5 Discussion  
In the simulation experiments, we use three sub-training sequences whose lengths are \(20\) symbols to form the training sequence. As a matter of fact, the length of the sub-training sequence can be adjusted according to the communication overhead that is available in the MPDS and the complexity of the application environment. We can learn from the construction method for virtual array signals introduced in Section 3.1 that the length of the training sequence not only determines the aperture of the virtual array but also determines the number of virtual array snapshots to be constructed. For a fixed length of the training sequence, a large virtual array aperture means a small amount of virtual array snapshots. Generally, to ensure the effectiveness of the covariance matrix estimation in the root-MUSIC algorithm, the number of virtual array snapshots cannot be too small. Thus, under such a precondition, we need to make a compromise between the communication overhead of the MPDS and the performance of the proposed method to determine a suitable length for the training sequence. Although a long training sequence is beneficial to construct a large virtual array aperture that has more array degrees of freedom to suppress in-band interferences, it also brings problems of raising communication overhead and computational complexity. In general, while ensuring a certain level of communication quality, it is better to choose a shorter training sequence based on the complexity of environmental interferences in the passive detection scenario to lower the communication overhead, which is an appropriate way that we would like to suggest.
# A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging
## 3 Explanation Methods for Deep Learning Cancer Detection Models
### Ad-Hoc
#### 3.2.2 Lung Cancer  
The authors of [135] proposed An explainable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification in 2018. The proposed network provides two levels of output. One level contains the low-level, explainable features for radiologists and the second level provides a high-level malignancy prediction score. They incorporate both of these tasks in their loss function therefore optimizing the network parameters for both levels mentioned above. The authors use the LIDC dataset which contains annotated lung nodule characteristics. The network achieves a test AUC of 0.856. For the semantic, explainable features task they achieved mean accuracy of 0.908, 0.725, 0.719, 0.834 and 0.552; mean AUC score of 0.930, 0.776, 0.803, 0.850 and 0.568; mean sensitivity of 0.930, 0.758, 0.673, 0.855 and 0.552; and mean specificity of 0.763, 0.632, 0.796, 0.636 and 0.554 for calcification, margin, subtlety, texture, and sphericity, respectively.  
Another approach is SISC: End-to-end explainable Discovery Radiomics-Driven Lung Cancer Prediction via Stacked explainable Sequencing Cells was proposed by the authors of [134]. They implement an end-to-end system that automatically discovers semantically meaningful radiomic features using radiomic sequencers. The radiomic sequencer being discovered possesses a deep architecture comprised of stacked explainable sequencing cells (SISC). The network produces an AUC score of 89.06.
## 5 Applicability on Various Technologies
### Evaluation Methodology  
Evaluating such a methodology is a difficult task, as it is generally not directly connected to identified performance indicators. Furthermore, such implementations are generally \"one-shots\", and concurrent uses of different methodologies for comparison purposes are generally impossible. The choice made in this study was to analyze different studies found in the literature that successfully integrated Industry 4.0 tools in a company, chosen because of their variety of technologies and application fields. One study was on reconfigurable manufacturing system in the automotive industry, one study was on additive manufacturing in aeronautics, and one was related to the use of multi-agent systems in a mail sorting center. As far as the authors know, no other publications were found documenting in enough detail the implementation process, except in the field of industrial agents [49]. However, as this technology has already been covered, these were not considered here.  
The objective is to present how these very different studies actually follow the IPSI framework. The variety of tools and application fields demonstrate the pertinence of the approach and its genericity for future deployments. Each study will be presented in the next sections, directly following the framework. At each step, numbered I1 to I3, P4 to P6, S7, S8, I9, and I10, the corresponding elements in the study are exhibited, as summarized in Table 2. The objective is not to emphasize the results of the studies themselves, but rather to focus on the methodology described. The first study is about manufacturing system designs, the second one is about product design, and the final one is about real-time control of manufacturing systems.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**IPSI Steps** & **Manufacturing Systems Design [50; 51]** & **Product Design [52]** & **Manufacturing Control [53; 54]** \\ \hline I1 & Variability of the demand & Complexity reduction & Variability of the demand \\ I2 & RMS & Additive manufacturing & HMS \\ I3 & One assembly line & One single part & One robotic \\ P4 & 5 KPIs & 4 KPIs & 2 KPIs \\ P5 & Dedicated manufacturing system & Classical design and machining & Expert-based model evaluation \\ P6 & Zero reconfiguration time & N/A & Zero operation times and infinite \\ S7 & Exact methods for optimization & DBAM methodology & Heuristics definition \\ S8 & Evaluation of optimization results by & Low cost materials & Discrete-event \\  & discrete-event simulation & & simulation-based evaluation \\ I9 & Investment strategy definition & Use of actual machine & Emulation-based \\ I10 & Integration of reconfigurability in future & Design framework integration in & Software development \\  & line design & design office & Extension of the control to \\ \hline \hline \end{tabular}
\end{table}
Table 2: Case study summaries.
## 3 Experimental Techniques
### Hot Wire Anemometry  
To obtain the power spectra that would reveal the most dominant frequencies in the flow, Hot Wire Anemometry (HWA) was applied. The system consisted of a Dantec Dynamics MiniCTA 5442 bridge with a National Instruments NI USB-9162 data acquisition system. The resultant of the streamwise and vertical velocity components was measured using a single-sensor 55P15 boundary layer probe which allowed to obtain measurements very close to the surface of the airfoil. A traversing system, using the coordinate system shown in Figure 5, was used to position the probe at the desired point of measurement. The lines of measurement on the airfoil were placed perpendicular to the surface of the airfoil at \(x/c=0.1\), 0.2 and 0.3, denoted by \(L1\), \(L2\) and \(L3\), respectively. Two measurement lines in the wake were also considered, placed perpendicularly to the freestream flow and denoted by \(W1\) and \(W2\). The position of \(W1\) was \(x/c=0.033\) and \(W2\) at \(x/c=0.1\), both measured from the trailing edge of the airfoil downstream. The positions of the measurement lines are shown in Figure 5.  
The data were acquired at a frequency of 8 kHz for a total test time of 5 s, which results in \(40,000\) data samples at each point along the measurement lines. The choice of frequency was such that the Nyquist criterion was satisfied [28]. For the measurement lines \(L1\), \(L2\) and \(L3\), 40-70 points were examined at \(\Delta z=0.5\) mm. Likewise, for the measurement lines \(W1\) and \(W2\), 39-65 points were examined at \(\Delta z=2.9\) mm.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 9 Conclusions and Future Work  
In order to develop strong and reliable communication systems for underwater sensor networks, this paper proposes adaptation layer techniques using a multi-band/multi-media method inside the MAC layer. For multi-band communication, the existing communication protocol for MAC layers such as OFDM, OFDMA, or FDMA can be used to divide the frequency bands. The medium selection mechanism is proposed to carry the data through multiple media, such as acoustic, VLF, or IR. In the medium selection mechanism, the proposed scheme is split into two phases: (1) Finding the distance of near and far nodes; and (2) Medium selection and data transferring. To find the distance, the Manhattan method was used, and for the medium selection, a new algorithm was proposed. In the adaptation layered multi-media/multi-band approach, the RSSI between nodes is considered as the main factor to find the distance, since distance is the main property used when transferring data. Also, this paper takes into account scenarios such as diver networks monitoring and tsunami monitoring based on multi-media communication technology. In our approach, a multi-media/multi-band modem was developed with: (1) Two transducers supporting bandwidths of 70 kHz and 140 kHz for acoustic communication; (2) a single bandwidth for Infrared (IR) communication at a wavelength of 700 nm to 1 mm; and (3) Visible light (VL) communication that uses the blue wavelength at a range of 450 to 485 nm. So, this modem setup combines multiple bands for different media such as acoustic, VLC, and IR. Currently, tests have been undertaken only for VLC and IR. In future, red and green light will be considered for VL communication. Also, the multi-media/multi-band adaptation techniques will be improved by considering more properties of underwater environments, such as temperature, pressure, pH, etc.  
Conceptualization, S.-H.P. and S.-Y.S.; Supervision, S.-H.P.; Validation and Data curation, J.-I.N.; Visualization, S.-Y.S.; writing--original draft, D.R.K.M.; Writing--review & editing, S.-H.Y. and E.K.  
Figure 16: Open seabed test.  
Figure 15: Salty water test using large water tank.  
**Funding:** This research is supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1D1A1B03028903).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Poisson Process-Based Random Access Channel for 5G and Beyond Networks +
## 5 Performance Evaluation
### Limitations of the Work  
In our current article, we focus on highlighting the issues related to the currently in-use RACH mechanism due to a uniform distribution-based channel access. We propose using a Poisson process-based RACH mechanism to enhance the channel access in terms of increased reliability and reduced end-to-end delays, which is evaluated based on several simulations and experiments. However, it is more convincing for the readers to see the match of analytical results with the simulation results. We understand the importance of comparing analytical results with simulation results to affirm the performance improve  
Figure 5: Performance comparison of 2 PRACH with existing RACH and CRB-RACH procedures with varying data frame sizes, where (**a**) network reliability (%), and (**b**) end-to-end latency (ms).  
Figure 6: Performance comparison of 2 PRACH with existing RACH and CRB-RACH procedures with varying packet interarrival rate, where (**a**) network reliability (%), and (**b**) end-to-end latency (ms).  
ment of the proposed solution. However, our current manuscript does not include such analytical modeling due to the related complexities in designing a Markov Chain-based analytical model for our proposed solution. We are already working to fix this limitation and hope to develop a novel analytical model for Poisson process-based channel access mechanisms.