esa-sceva/satcom-chunk-collection huggingface dataset:
### Contributions  
To this end, this paper formulates the problem as a Partially Observable Markov Decision Process (POMDP) and proposes a stochastic MADRL algorithm in SAG-MCS environment, to perform data collection and task allocation simultaneously. The main contributions of this article are summarized as follows:  
1. We design a realistic SAG-MCS environment with obstacles and charging stations for simulation. To further enhance exploration of the global area, the UAV swarm can acquire multi-source observation inputs from embedded sensors and satellites.
2. We propose a DRL algorithm based on graph attention mechanism, namely _Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN)_. It integrates a multi-scale convolutional encoder to process different sizes of observations. This method also utilizes  
Figure 1: Proposed SAG-MCS Scenario Schematic.  
graph attention network (GAT [26]), gated recurrent unit (GRU [27]), as well as a maximum entropy method.
3. Although UAV swarm can receive parts of global observations from satellites, SAG-MCS is formulated as a practical partially-observed problem, and UAVs cannot have access to the overall system states during training. Therefore, we propose a heuristic reward function that only utilizes local observed information, but manages to train the UAV swarm to properly act for optimizing several global metrics.
4. We have designed and conducted several simulation case studies to verify the effectiveness of our stochastic MADRL method and reward function. Additionally, we validate the robustness of the trained model and the multi-scale CNN encoder under different communication conditions and at different environment scales.  
The remaining part of this paper proceeds as follows: Section 2 reviews the related research efforts about MCS and DRL approaches. Section 3 introduces the SAG-MCS problem definition and the 2D simulation environment in detail. Section 4 presents the proposed solution ms-SDRGN for SAG-MCS problem. We introduce simulation settings and present the experimental results and analysis in Section 5. Then, Section 6 discusses the practical implementation issues and limitation of the proposed approach. Finally, conclusions are made in Section 7.
## 4 Validation
### Ethernet Parser Resource Utilization  
Regarding the hardware resources used by the developed switch (REDSwitch), and considering that the main modification to the ONetSwitch (which served as the starting point) was the Ethernet parser, Table 6 presents the post-synthesis results of resource utilization on the Zynq-7000 XC7Z020 CLG484-1 board (which has 106,400 flip-flops, 53,200 LUTs, 200 I/Os, and 32 BUFGs) for the Ethernet parsers in both ONetSwitch and REDSwitch. The global buffer (BUFG) utilization increased from 3% to 16%, whereas the look-up table (LUT) usage increased from 1% to 3%. The input/output (I/O) utilization appears excessively high in both cases (96% and 160%) because it refers to a block extracted from within the switch, where multiple internal connections exist. This I/O count does not correspond to the number of board-connected I/O but reflects internal switch connections.
# Future Scenarios for Software-Defined Metro and Access Networks and Software-Defined Photonics
## Reasons for Software Defined Metro and Access Networks
### Metro and Access Network Traffic Evolution  
In order to evaluate the need for reconfigurable and programmable metro and access networks, it is necessary to analyze how the traffic typologies and distribution are changing, especially in the edge network segments.  
First of all, it is worth noting that the growth of bandwidth hungry services (e.g., ultra-high definition 4K video), made available by the optical access network deployed in recent years, is driving the explosion of traffic in the access segment according to the well-known Nielsen's law (high-end user's connection speed grows by 50% per year) [10]. For example, near-term pressures for capacity scaling include: 4G LTE-Advanced mobile networks that drive backhaul capacity from Mb/s to Gb/s and even 10-Gb/s channels; fixed access speeds moving from Mb/s to Gb/s with G.fast access networks and to 10-Gb/s and higher with time- and wavelength-division multiplexed passive optical networks (TWDM-PONs); data-center-interconnect demands moving to 10-Gb/s [11]. Figure 2 shows an increased forecast for 100 Gb/s or higher channels in the metro portion of the network [12].  
Figure 1: Main reasons for software-defined metro and access networks.  
Figure 2: Evolution towards higher capacity channels in the metro network [12].  
Simultaneously, in addition to traffic growth, the dramatic increase of new generation mobile networks and cloud-computing traffic is causing traffic patterns to become ever more dynamic and unpredictable [9]. The traffic related to cloud-based services is extremely time-varying. On the other hand, mobility traffic is highly volatile. Just to describe an exemplary scenario, the daily traffic pattern in a big city can be so considered: during the morning, an high traffic peak can be imagined in business quarters and industrial areas; then, the 'traffic storm' would shift to commercial centers and free-time zones (public parks, sport facilities, etc.); in the evening, bandwidth demand would increase in residential areas. In addition to this, the emerging trend of Internet of Things (IoT) is changing where and how bandwidth is being consumed: on one hand, IoT is causing a significant change in the traffic characteristics with the proliferation of'short-burst' type communications [6]; on the other hand, the number of connected devices will exponentially grow as the fully-digital connected homes will become an everyday reality.  
To support this highly dynamic environment and to avoid congestion phenomena due to tremendous traffic growth, reconfigurable capabilities are required to metro and access optical networks according to different service requirements in terms of downstream and upstream bandwidth and latency [10]. Programmability is necessary as well to manage connectivity services in real-time through a high-level intent-based orchestration covering potentially multiple network domains.
## 3 Related Methodologies
### Unsupervised Learning Model Based on Improved Neighbor2 Neighbor  
Aiming at the sampler description in Section 3.1, training image pairs are extracted from the single noise image to be used as input to the self-supervised training algorithm in this section. Given a pair of subsampled images \((g_{1}(y),g_{2}(y),g_{3}(y),g_{4}(y))\) derived from noisy image \(y\), we train the denoising network using the structural similarity loss and the regularization loss proposed in Section 3.2.  
\[L=L_{rec}+\gamma\cdot L_{reg}+\eta\cdot L_{ssim} \tag{15}\]  
where \(\gamma\) is a hyperparameter that regulates the intensity of the regularization term and \(\eta\) is a hyperparameter of the structural similarity loss function. For better ablation experiments, \(\gamma=2\) and \(\gamma=1\) are used for the synthetic experiments and the practical experiments as in the Neighbor2 Neighbor model, respectively. We stop the gradients of \(g_{1}(y)\), \(g_{2}(y)\), \(g_{3}(y)\), and \(g_{4}(y)\) during training and gradually raise \(\eta\) to the appointed value.  
\(L_{rec}\) is the built loss function, where \(L_{rec}\) is defined as:  
\[L_{rec}=\left\|f_{\theta}(g_{1}(y))-g_{3}(y)\right\|^{2}+\left\|f_{\theta}(g_{ 2}(y))-g_{4}(y)\right\|^{2} \tag{16}\]  
The regularized loss function \(L_{reg}\) is defined as:  
\[L_{reg} =\left\|f_{\theta}(g_{1}(y))-g_{3}(y)-(g_{1}(f_{\theta}(y))-g_{2} (f_{\theta}(y)))\right\|^{2} \tag{17}\] \[+\left\|f_{\theta}(g_{2}(y))-g_{4}(y)-(g_{3}(f_{\theta}(y))-g_{4} (f_{\theta}(y)))\right\|^{2}\]  
where \(f_{\theta}\) is a denoising network design, and the training framework is described in Algorithm 1. The specific improved model of this paper is shown in Figure 3.  
```
0: A set of noisy images Y, Denoising network \(f_{\theta}\), Hyperparameter \(\gamma\) and \(\eta\) Operation
1. while not converged do
2. Sample a noisy image \(y\in\)Y;
3. Generate Sampler \(G=(g_{1}g_{2}g_{3}g_{4})\);
4. Desire the sub-sample images \((g_{1}(y),g_{2}(y),g_{3}(y),g_{4}(y))\), where \(g_{1}(y),g_{2}(y)\) is the network input, and \(g_{3}(y)\), \(g_{4}(y)\) is the network target;
5. Calculate \(L_{ssim}\);
6. For the network input \(g_{1}(y)\), \(g_{2}(y)\), derive the denoised image \(f_{\theta}(g_{1}(y)),f_{\theta}(g_{2}(y))\);
7. Calculate \(L_{rec}\);
8. For the original noisy image \(y\), derive the denoised image \(f_{\theta}(y)\) with no gradients;
9. Use the same sub-sampler \(G\) to derive the images \((g_{1}(f_{\theta}(y)),g_{2}(f_{\theta}(y)),g_{3}(f_{\theta}(y)),g_{4}(f_{ \theta}(y)))\);
10. Calculate \(L_{reg}\);
11. Update the denoising network \(f_{\theta}\) by minimizing the objective \(L_{rec}+\gamma\cdot L_{reg}+\eta\cdot L_{ssim}\)
12. end while Output: denoise images \(\gamma^{\prime}\)
```  
**Algorithm 1** Based on improved Neighbor2 Neighbor
# Inverse Coupled Simulated Annealing for Enhanced OSPF Convergence in IoT Networks
## 2 Main Contribution
### Design Overview
Additionally, the routing table calculation phase, based on [PERSON]'s algorithm for shortest path computation, also operates within a limited time frame. Therefore, this paper restructures the protocol based on the traditional OSPF protocol, dynamically optimizing the Hello packet transmission frequency and fault detection time, aiming to accelerate fault detection speed, thereby enhancing routing convergence speed and, to some extent, reducing routing overhead, as illustrated in Figure 2.  
The specific improvements are as follows:  
Utilize the statistical characteristics of Hello packets to obtain the link status, and characterize the node state based on the aggregated features of the link state, thereby assessing the state of links and nodes in the network and providing data support for the reverse coupled annealing algorithm. Design an OSPF interval synchronization and node state propagation mechanism (OSPF-IS-NSP) to synchronize the sending intervals of Hello packets and fault detection times of nodes within the same subnet and propagate the node state of the local node. Propose a reverse coupled annealing algorithm, which consists of two coupled annealing algorithms: the upward optimization annealing algorithm and the downward optimization annealing algorithm. When one annealing algorithm is executed, the temperature of the other gradually rises, and vice versa. Through this mechanism of alternating heating and cooling, and based on the link and node status, the two algorithms collaboratively optimize the Hello packet transmission interval and fault detection time.
# Biomass and Cellulose Dissolution--The Important Issue in Renewable Materials Treatment
## 3 Dissolution of Biomass and Its Components
### Inorganic Salts in Organic Solvents  
Cellulose can be effectively dissolved in an \(N\),\(N\)-dimethylacetamide solution of lithium chloride (LiCl/DMAc) [67; 68; 69]. It is possible to dissolve up to 16% of the polymer present in various biomass sources, e.g., wheat straw, wood waste, and sugar cane bagasse, at 85 \({}^{\circ}\)C [70]. There are two proposed mechanisms of this process, both based on hydrogen bonding occurring between glucose monomers and chlorine anions, which results in the formation of the Li\({}^{+}\)(DMAc)\({}_{\rm x}\) cation complex. The first mechanism (Morgenstern) suggests direct linkages between lithium cation and hydroxy group oxygen atoms. The second one (McCormick) states that this type of hydrogen bond does not occur. Instead, the cellulose structure is charged negatively because of interactions with the chlorine ion. It results in cellulose fibers spreading apart and polymer solvation. Figure 8 presents both of these simplified mechanisms [67; 70; 71].  
Figure 8: Proposed mechanisms of cellulose dissolving in LiCl/DMAc solution: (**a**) McCormick mechanism, (**b**) Morgenstern mechanism.  
The combination of LiCl with DMAc is unique because only this certain mixture is able to dissolve cellulose effectively. By changing lithium to another metal or replacing DMAc (e.g., with DMF), the efficiency of the solvation effect decreases. Despite many advantages of LiCl/DMAc (the limitation of side effects, no need for applying additional chemicals), it was not introduced into the industrial scale due to the long processing time, high price, toxicity, corrosivity, and vapor pressure of solvent [70].  
Another mixture that could be applied to dissolve cellulose is DMSO with tetrabutylammonium fluoride (TBAF). Similar to the previous example, changing fluoride to other anions causes a decrease in the effectiveness of such a mixture [72]. Fluoride is a very strong hydrogen bond acceptor. It has the ability to efficiently reduce the interaction between cellulose chains by competing with the hydroxyl and acetal oxygen atoms of cellulose. The other anions (Cl\({}^{-}\), Br\({}^{-}\)) are not strong enough [73; 74]. Additionally, TBA\({}^{+}\) cations prevent cellulose aggregation, which additionally enhances the process [75]. The main problem with TBAF application is its high hygroscopicity. The presence of a small amount of water causes an increase in viscosity and gelation [73]. Additionally, water is able to remove the fluoride anions from the cellulose chains, which results in the reformation of hydrogen bonds between cellulose chains. Anhydrorous TBAF is unstable and expensive. The commercially available trihydrate TBAF\(\cdot\)3H\({}_{2}\)O is an alternative to be used in cellulose treatment [48; 76].
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 1 Introduction
### Main Contributions  
The main contributions of this manuscript involve:  
1. a new symmetric cryptography-based mutual authentication and key agreement protocol for cloud-based e-health/m-health/TMIS that is adapted to support D2D communications;
2. the guarantee offered by the proposed protocol of several security properties (e.g., confidentiality and anonymity) and resistance to attacks, such as replay, denial-of-service, and man-in-the-middle;
3. computational, communication and energy costs evaluation and comparison with other authentication protocols from the literature, which demonstrated that the scheme proposed provided the best results;
4. semi-formal validation of the proposed protocol, using Automated Validation of Internet Security Protocols and Applications (AVISPA) [1].
## 2 Materials and Methods
### Binding Free Energy Calculation Using MM-PBSA  
The binding free energy of protein-ligand complexes was calculated via the molecular-mechanics Poisson-Boltzmann surface area (MM-PBSA) method. In this instance, binding free energy was calculated using the most recent, stable 30 ns trajectories determined by the RMSD plot. For better structure-function correlation, the frames at 200 ps intervals encompassed a wide range of trajectories and a diversity of conformational spaces.  
In the single trajectory approach, the following equation represents the full process:  
\[\Delta\mathrm{G_{Binding}}\;=\;\mathrm{G_{Complex}}\;-\;(\mathrm{G_{Recpotor}} \;+\;\mathrm{G_{Ligand}})\]  
\[\Delta\mathrm{G_{MM-PBSA}}\;=\;\Delta\mathrm{E_{vdW}}\;+\;\Delta\mathrm{E_{ ele}}\;+\;\Delta\mathrm{G_{polar}}\;+\;\Delta\mathrm{G_{nonpolar}}\]  
The \(\mathrm{G_{Complex}}\) represents the total MM-PBSA energy of the protein-ligand complex, \(\mathrm{G_{Recpotor}}\) and \(\mathrm{G_{Ligand}}\) correspond to the total solution free energies of the isolated receptor and ligand, respectively. The \(\Delta\mathrm{G_{MM-PBSA}}\) value of the protein-ligand complex was determined from the sum of gas-phase electrostatic energy (\(\mathrm{E_{ele}}\)), van der Waals (\(\mathrm{E_{vdW}}\)), polar (\(\mathrm{G_{polar}}\)), and nonpolar (\(\mathrm{G_{nonpolar}}\)) components [40].
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
## 6 The 6G Network Infrastructure to Manage V2X Application Lifecycles
### Critical Evaluation  
Feasibility in 6G: The advent of 6G and its inherent capabilities, such as eMBB and URLLC, pave the way for efficient OTA software updates in vehicular networks. The expansive bandwidth of 6G facilitates the rapid transmission of large software update files, ensuring vehicles receive pertinent updates in a timely manner.  
Advantages: Using 6G for OTA updates ensures that vehicles are always equipped with the latest software features and security patches, enhancing functionality and safeguarding against potential vulnerabilities.  
Challenges: Key challenges encompass ensuring the integrity and security of software updates, managing the simultaneous update of numerous vehicles without overwhelming the network, and ensuring that updates are installed and implemented successfully without inducing system errors.
## 2 Background
### Sample Hardware  
Our MAV platform combines a miniature helicopter with an ultra-low-powered wireless node (Figure 1). The GINA board [14] consists of a 2.4 GHz IEEE 802.15.4 radio, Texas Instruments MSP430 microprocessor, and a number of inertial sensors. The GINA platform can be used directly as a MAV flight controller, or as a relay for control signals from a central location [15]. The OpenWSN project [16] has been ported to the GINA hardware to implement time synchronization, frequency channel hopping, mesh networking, and multihop communications. A MAV controlled by a GINA can be used to deploy additional GINA nodes for the stationary drop-off mesh infrastructure, as shown in Figure 2. With standards compliant networking interfaces, this hardware forms a testbed for the implementation of the protocols described in this paper.  
Figure 2: GINA hardware is used to implement an integrated MAV + WSN system.
## 1 Introduction
The scenario includes an excavator operating in an outdoor environment. Note that using only a stereo camera would be hazardous. Hence, two LiDAR sensors complement it (Section 2). The distance values acquired by each sensor are projected onto one coordinate system using a calibration board and a line laser for accuracy (Section 3). As the cargo box door resembles a plane shape, the 3D estimation of the position of the cargo box is achieved by determining the plane of the rear of the cargo box from the data received from the two LiDAR sensors installed vertically on the sensing device (Section 4.1). Furthermore, the 3D position of the box is determined by projecting the dense 3D location of the door acquired from the camera onto a plane and matching it with the actual 3D model of the cargo box. The 3D position of the box can be obtained by utilizing one virtual matching point and four matching points of the rear plane (Section 4.2). If the 3D position of the cargo box is estimated solely using the plane, errors may occur. Therefore, points along the long shaft of the box are used for more accurate determination. By matching the sampling of the points of the box's long shaft to the actual 3D model, the distance data of the shaft obtained from the stereo camera are fed to an iterative closest point (ICP) algorithm (Section 4.3). Lastly, after the 3D position of the cargo box is estimated, the volume of the load is calculated. Moreover, the 3D position information for the loading space is transmitted to the excavator. The general outline of this paper is as follows. First, Section 2 describes the sensing device developed for this study. In Section 3, the method of calibrating the sensors of the sensing device is discussed. Section 4 outlines the method for estimating the truck cargo box using information acquired from the sensing device. Section 5 examines the accuracy of the proposed method for estimating load volume and determining the loading space. The study concludes with Section 6.
## 4 Multi-Parametric MINLP Optimization
### Optimal Masses (MASS) of the Composite Floor Structures  
The optimal masses of CFSs are presented. Figure 6 shows diagrams of the achieved optimal masses (kg/m\({}^{2}\)) for the structure types CFSIPE, CFSHEA and CFSWIS. The diagrams were drawn for five alternative loads that ranged from 2 kN/m\({}^{2}\) to 10 kN/m\({}^{2}\).  
Figure 5: The optimized composite floor structures; (**a**) mass objective function; (**b**) direct cost objective function—DC; (**c**) energy life-cycle cost objective function of an energy efficient case—ELCC EEC; (**d**) energy life-cycle cost objective function of an energy inefficient case—ELCC IEC; (**e**) energy life-cycle cost objective function of a high energy consumption case—ELCC HEC.  
We found that the long-span CFSs with standard profiles exhibited very heavy and unrealistic designs. For this reason, the CFSIPE structures that are presented here are shown only for spans up to 20 m, and the CFSHEA structures are only shown for spans up to 25 m. The CFSWIS structures are drawn for all of the considered spans up to 50 m.  
The diagrams show that the CFSIPE structures are the lightest structures for spans up to 7 m, while the CFSWIS structures are the lightest structures for all spans longer than 7 m. The CFSHEA system is for spans above 17 m lighter than the CFSIPE system for all loads except 2 kN/m\({}^{2}\).