esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Proposed Method
#### 3.3.3 Autoencoder-Based Data Generation Module
To achieve this, the MSE is used as the loss function, which is expressed as:  
\[\mathcal{L}_{\text{recon}}=\|\mathbf{X}-\mathbf{X}^{\prime}\|^{2}=\sum_{i=1}^ {n}(\mathbf{X}_{i}-\mathbf{X}^{\prime}_{i})^{2}, \tag{24}\]  
where \(\mathbf{X}_{i}\) and \(\mathbf{X}^{\prime}_{i}\) represent the original and reconstructed data of the \(i\)-th sample, respectively. By minimizing this loss function, the network learns an effective mapping that makes the reconstructed data as close as possible to the original data. The advantage of the autoencoder lies in its ability to automatically learn the latent structure of the data during the compression process and then precisely restore the data through the decoder. Compared to traditional noise addition methods, the autoencoder achieves a better balance between privacy protection and data utility by learning the low-dimensional representation of the data through training. Since the network weights are optimized via backpropagation, the autoencoder can precisely control both the reconstruction quality of the data and the privacy protection effects. In the adversarial training network framework of this study, the autoencoder-based data generation module is combined with the game mechanism between the generator (\(G\)) and the discriminator (\(D\)). Specifically, the generator \(G\) generates obfuscated data \(\mathbf{X}^{\prime}\) from the low-dimensional latent representation \(\mathbf{Z}\), while the discriminator \(D\) distinguishes between real and generated data. Through adversarial training, the generator can retain the key information in the data while ensuring privacy protection. The introduction of the autoencoder module further enhances the quality of the generated data, especially in terms of the separation and reconstruction of sensitive information, making it more accurate in achieving data privacy protection compared to traditional noise addition methods. In this framework, the input to the generator \(G\) is the low-dimensional representation \(\mathbf{Z}\), and the privacy-protecting data \(\mathbf{X}^{\prime}\) is generated through the encoding and decoding process of the autoencoder. The discriminator \(D\) is then used to optimize the privacy and authenticity of the generated data. Through this joint training, the generator not only learns how to generate privacy-protecting data but also enhances the privacy and authenticity of the generated data through adversarial training. The autoencoder module further refines the separation of sensitive and nonsensitive information, enabling the generator to better preserve data privacy while improving the effectiveness of the trained model.
# Creating a Regional MODIS Satellite-Driven Net Primary Production Dataset for European Forests
## 2 Materials and Methods  
We used two conceptually different methods to estimate NPP, (i) the MODIS NPP algorithm MOD17 and (ii) terrestrial forest inventory data and tree carbon estimation methods. Both have their respective strengths and weaknesses. MODIS NPP has the advantage of providing spatially continuous estimates with a consistent methodology, which is important for any large-scale studies. It incorporates biogeochemical principles in mechanistic modelling environment and the vegetation feedback to climate conditions through changes in Leaf Area Index and absorbed radiation [17]. It does not distinguish between different vegetation apart from general Land Cover types, has a coarse spatial resolution and might not be able to represent specific local conditions due to its calibration to global conditions. In contrast, terrestrial forest inventory NPP assesses the actual carbon allocation by trees and captures local small-scale effects (e.g., site conditions, tree age or forest management) as well as regional differences in estimating tree carbon [12; 18]. It covers only the increment of trees assessed by the inventory system and might not capture local specifics of litter fall and fine root turnover very well, since broad model assumptions have to be used.
# Millimetre-Wave Backhaul for 5G Networks: Challenges and Solutions
## 3 System Overview and Problem Statement
### Challenges and Design Goals
#### 3.2.2 Efficient Spatial Reuse  
The ultimate goal of backhaul transmission is improving throughput. Highly directional transmissions in mm-wave communication lead to much less severe interference compared to conventional wireless cellular networks, which paves the way for spatial reuse to effectively save frequency and time resources, as well as to enhance throughput gain. Spatial reuse is particularly suitable for the dense small-cell scenarios, because the densely distributed devices bring much greater opportunity for concurrent transmissions. Time division multiple access (TDMA) is adopted in the IEEE 802.11 ad standard, and spatial TDMA (STDMA) has attracted significant attention recently. Transmission is conducted in units of equal-length time intervals, termed time slots. The goal of scheduling is to allocate as many links as possible in the same time slot to fully exploit spatial reuse. Currently, most mm-wave-related scheduling studies are based on the half-duplex links in the single  
Figure 1: System overview for 5G mm-wave backhauling.  
user scenario, _i.e._, adjacent links having a common node cannot transmit simultaneously, and a device can only either send or receive a single data stream at a time. However, recent advances in physical layer techniques, such as full duplexing, hybrid beamforming and multicast beamforming, may bring changes to the scheduling to further improve spatial reuse gain.
# A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems
## 4 Two Case Studies of the Proposed Framework
### Traffic Light System for Smart Transportation
#### 4.1.3 Beyond Traffic Flow Optimization  
The benefits of this system extend beyond optimized traffic flow and reduced delays. By minimizing stop-and-go driving, a major contributor to urban vehicle emissions, the system indirectly contributes to a lower carbon footprint for smart transportation systems [32]. This aligns with sustainability goals and leads to improved air quality for urban residents.
## 3 Data Processing and Kinematic Analysis
### Object Tracking and Management
#### 3.4.2 State Prediction and Measurement Validation  
The next step is to predict the current states using the stochastic non-linear model. To deal with nonlinearities, UKF is implemented to predict the states and covariances.  
\[\begin{array}{c}\mathcal{X}_{k-1}=\left[\hat{x}_{k-1}^{*}\quad\quad\quad \gamma\sqrt{P_{k-1}^{*}}\oplus\hat{x}_{k-1}^{*}\quad\quad\quad-\gamma\sqrt{P_ {k-1}^{*}}\oplus\hat{x}_{k-1}^{*}\right],\\ \mathcal{X}_{i,k}^{*}=f(\mathcal{X}_{i,  
transition function \(f\), and the weighted sigma points are used to calculate the predicted state (\(\xi_{k}^{-}\)) and covariance (\(P_{k}^{-}\)). Similarly, the sigma points are projected by the observation function \(h\) in Equation (8) and then the weighted sigma points are involved in generating the predicted measurement (\(\hat{z}_{k}^{-}\)) and predicted measurement covariance (\(S_{k}\)).  
A measurement is regarded valid if it falls within the following elliptical validation gate \(\mathcal{G}(\gamma_{G})\). The validation gate of an object \(q\) is the same for all the models in \(\mathbb{M}\).  
\[\begin{split}\mathcal{G}(\gamma_{G})=\left\{y_{k}=z_{k}:\left(z_ {k}-\hat{z}_{i_{q},\,k}^{-}\right)^{T}\!S_{i_{k}}^{-1}\!\left(z_{k}-\hat{z}_{i_ {q},\,k}^{-}\right)\leq\gamma_{G}\right\}\\ i_{q}:=\operatorname{argmax}_{i\in\mathcal{M}}\,\det\!\left(S_{k}^{ i}\right)\end{split} \tag{13}\]  
where \(i_{q}\) is the index of the model in \(\mathbb{M}\), \(\gamma_{G}\) is the gate threshold, and \(y_{k}\) is the set of validated measurements.
## 2 Materials and Methods
### Experimental Evaluations
#### 2.3.3 Validation by dVRK  
The proposed prototype has been tested on the dVRK system. The experimental protocol followed for the tasks performed with the aid of the dVRK system, and the various phases, tools, and objects used are presented below. As shown in Figure 10, an MTM from the dVRK console was used to teleoperate the instrument mounted on the PSM. The activities were carried out by three users with the support of the visual feedback of the dVRK endoscope, designed with two separate optical channels capable of recreating the most important aspect of stereopsis: binocular disparity. Inside the viewer, therefore, users followed the operations thanks to the stereoscopic vision that provided three-dimensional images of the work area [16].  
The Arduino board with the integrated proximity sensor was positioned, using the support made by a 3D printer, on the EndoWrist instrument two centimeters from the tip of the tool, as shown in Figure 11. The positioning of the board at this distance from the tip of the instrument was in line with the results obtained during the sensor calibration phase; from the characterization curve, it is possible to see that the operating area of the liver sensor occurred after two centimeters, a distance at which the sensor returned the first non-null value.  
Users were asked to perform a simple transfer task; a suitably sized ball was picked up from the worktable with the ProGaspr tool supplied with the dVRK, and it was brought within eight centimeters of the liver simulation object. The sequence of images below shows the steps just mentioned in performing the task (Figure 12). Finally, the user placed the ball on the object by approaching it slowly (Figure 13). During the execution of the operations, the user was able to estimate the distance of the instrument from the object based on what could be perceived by the viewer. During the entire execution phase of the task, the proximity values read by the sensor were recorded and compared with the values visually estimated by the users.  
Figure 10: Functionality demonstration. MTM from the dVRK console was used to teleoperate the instrument mounted on the PSM.
## 3 Results
### Output Potential and Voltammetry  
#### 3.2.1 Alkaline Fuel Cell  
For all the electrode combinations in the Alkaline Electrolyte, the cell output potential values are summarized in Table 5. The open circuit potentials for the electrodes that generate high cell output potentials can be found in Figure 5. The potentials were adjusted by +209 mV for the Ag/AgCl reference electrode. Figure 6 summarizes the cyclic voltammograms for all the electrodes in the alkaline electrolyte.  
Figure 5: Open circuit potentials of electrodes in Alkaline Electrolyte. This depicts which electrodes have the largest potential when attached to a common reference electrode. Tb and Ta have the highest open circuit potential.  
Figure 6: _Cont._  
Figure 6: Cyclic voltammograms of electrodes in Alkaline Electrolyte. Graphs (**a–c**) show overlaps of electrode voltammograms in Alkaline Electrolytes containing hydrogen peroxide. Graphs (**d–j**) show voltammograms for each electrode in Alkaline Electrolyte with and without hydrogen peroxide. Ta and Ho had the largest current densities during the potential sweep, demonstrating their favorable reactivity in alkaline electrolytes.
# Secure Trust-Based Blockchain Architecture to Prevent Attacks in VANET
## 6 Results and Discussion
### Transmission Overhead  
Transmission overhead refers to the number of overhead bytes divided by the total number of bytes in a packet transmission. Longer data packets are heavier, as they have to carry more data in a packet, resulting in higher transmission overhead.  
\[O_{T}=\ \frac{\mathit{bytes}_{\mathit{overhead}}}{\mathit{bytes}_{\mathit{ total}}}. \tag{4}\]  
Equation (4) shows the formula of transmission overhead calculation, where \(O_{T}\) denotes transmission overhead, \(\mathit{bytes}_{\mathit{overhead}}\) denotes overhead bytes, and \(\mathit{bytes}_{\mathit{total}}\) denotes the total number of bytes in a transmission. The results of transmission overhead when the attacker or malicious node was non-existent are illustrated in Figure 18, which clearly shows that the transmission overhead increased with an increase in the number of nodes. However, it is clear in the figure that the proposed solution incurred a much lower transmission overhead, with a difference of 6 kB and 5.1 kB as opposed to ESPA and SPACF, respectively. At 100 nodes, the average transmission overhead was 4.5 kB, with a standard deviation of 0.149443 and a confidence interval of 0.106905. Similarly, when there was an attack, as shown in Figure 19, the proposed solution incurred a steady increase in transmission overhead as compared to ESPA and SPACF, suffering a drastic increase in transmission overhead, with a difference of 15.5 kB and 7.6 kB, respectively. At 100 nodes, the average transmission overhead was 5.0 kB, with a standard deviation of 0.207913 and a confidence interval of 0.148722. The proposed solution incurred a lower transmission overhead compared to the benchmark protocols because of its implementation of a signatureless public key infrastructure authentication technique to maintain a lighter and shorter packet header.  
Figure 18: Transmission overhead without denial of service attack.
## 3 System Model Design  
In opportunistic social networks, when measuring the node similarity between a pair of nodes, most traditional routing algorithms based on node similarity mainly focus only on a single factor, such as the number of common neighbors, the position similarity, or the number of common social attributes. Additionally, none of them takes the mobility similarity between nodes into consideration. It should be noted that mobility similarity needs to consider both the time and location information of the node in the network, while position similarity just takes the location information into account. Consequently, we present a fuzzy routing-forwarding algorithm exploiting a comprehensive node similarity, which is the combination of the mobility and social similarities of nodes. Next, we will give a detailed introduction to the proposed algorithm.
## 4 Load-Similar Node Distribution  
### Energy Model  
In our model, each sensor node is assumed to have the same initial energy, whereas the sink has unlimited energy to consume. Assume that any sensor node is eligible to be elected as a cluster head. The energy consumed (referred as _load_ in later text) by each sensor node majorly consists of three parts:  
Figure 3: Illustration of gradually growing overlap in MOL scheme. **(a)** Initial Structure. **(b)** changed structure with dynamic layer boundary.  
\(E_{i}\): the energy consumed for transmitting data generated from all sensor nodes in its cluster and the data relayed through all outer layers; \(E_{r}\): the energy consumed for receiving data collected from all outer layers; \(E_{c}\): the energy consumed for network formation in each round.  
Following the free space channel model [2], the energy consumed for transmitting _l_-bit data over the distance of \(r\) is given by:  
\[l(E_{elec}+\sigma^{2}), \tag{1}\]  
where \(E_{elec}\) and \(\varepsilon\) represent the electronic energy and amplifier energy respectively. The corresponding energy consumed in receiving _l_-bit data is \(lE_{elec}\). The system parameters used in this paper are set as, \(E_{elec}\) = 50 nJ/bit, \(\varepsilon\) = 10 pJ/bit/m\({}^{2}\).  
The energy consumed in network formation phase is majorly composed of the energy consumed for receiving control packets, including Control Packet (CP), Header Selection Packet (HP), Broadcast Packet (BP), and Cluster Control Packet (CCP) [16]. Here, the energy consumed in sending these control packets is neglected due to the small volume of such packets. Assume the total number of layers in the network is \(m\), then the energy consumed during each network formation at layer \(i\), \(E_{ci}\), can be calculated as:  
\[E_{cl}=(2*l_{cp}+l_{hp}*n_{hp})E_{elec}, \tag{2}\]  
\[E_{cl}=(l_{cp}*n_{cp}+l_{bp}*n_{bp}+l_{bp}*n_{hp}+l_{ccp}*n_{ccp})E_{elec},\ \ 1<i<m, \tag{3}\]  
\[E_{ci}=(l_{cp}*n_{cp}+l_{bp}*n_{bp}+l_{ccp}*n_{ccp})E_{elec},\ \ \ i=m, \tag{4}\]  
where \(l_{cp}\), \(l_{hp}\), \(l_{bp}\) and \(l_{ccp}\) represent the packet length of a CP, HP, BP and CCP, respectively, while \(n_{cp}\), \(n_{hp}\), \(n_{bp}\) and \(n_{ccp}\) represent the respective average number of CPs, HPs, BPs and CCPs received by each node during each network formation. As we can see from Section 3.1, each node participates in the formation of its own layer and its outer layer, the energy consumed during network formation phase for each node has no relation to its distance to the sink.
## 3 Experimental Results and Analysis
### Triaxial Shearing Strength Tests  
For the triaxial shearing strength test, a GDS triaxial testing apparatus is used. This instrument utilizes three controllers to independently control and monitor the confining pressure, back pressure, and base pressure (pore water pressure) in real time. Triaxial shearing was carried out using the GDS reaction frame loading system. The sample dimensions and saturation preparations are the same as those for the unconfined compressive strength test. Conventional triaxial tests are conducted under confining pressures of 100 kPa, 200 kPa, and 300 kPa, with the loading rate controlled to a strain rate of 0.1% per minute, corresponding to an actual loading rate of 0.1 mm/min.  
Figure 2: The unconfined compressive strength over time.  
[PERSON] and [PERSON] (2016) [26] investigated the effects of varying bacterial concentrations on intermediately compressible clay and highly compressible clay. Their results indicated that the unconfined compressive strength (UCS) of both soil types increased with higher bacterial cell concentrations. In contrast, for MICP-treated soft clay, the cohesion increases notably, while the internal friction angle remains essentially unchanged. Therefore, the carbonate cementation generated after MICP treatment significantly enhances the cohesion of the soil. This cementation effect is primarily reflected in the increased cementation of soil particles rather than in the friction and occlusion resulting from the interlocking effect among the particles. According to previous research, for MICP-treated sandy soil, both the internal friction angle and cohesion increase significantly [17; 27; 28; 29]. Their results indicate that as the casting time increases, the volume of generated calcium carbonate also increases. Consequently, the sandy soil cohesion can increase from 40 kPa to 160 kPa, and the internal friction angle can increase linearly from 32\({}^{\circ}\) to 35.5\({}^{\circ}\).  
The results are depicted in Figure 3. The left axis of the figure represents the cohesive force, while the right axis indicates the internal friction angle. According to the experimental data, as the cultivation period increases, the soil cohesion markedly improves. After 7 days, soil cohesion increased by 30.5% compared to the control group. After 14 days, soil cohesion increased by 53% and then stabilized. The internal friction angle, however, remained essentially unchanged at approximately 23\({}^{\circ}\).
## 3 Results and Discussion
### Membrane Resistance  
Electrochemical impedance spectroscopy is an important tool to elucidate the electrochemical parameters of a membrane, allowing for quantifying the resistance of the membrane matrix. As can be observed in Figure 7, for M-0 membrane, the membrane resistance increased by a factor 1.5 for MgCl\({}_{2}\) when compared to the NaCl solution. The increased resistance for NaCl solution indicates that the presence of the MPD/TMC layer near the cation-exchange membrane hinders the ionic transport. Since the similar IEC and water uptake yield a relative constant resistance, which means that the transport of Na\({}^{+}\) is not much affected by the ZIF-8 incorporation. In the case of Mg\({}^{2+}\), the reduction of membrane resistance was observed, because carboxylic acids are inert diluents that the interaction between Mg\({}^{2+}\) was greatly mitigated. As a consequence, Mg\({}^{2+}\) permeates through the cation-exchange membrane becomes easier and the membrane resistance reduced. The variation of resistance with ZIF-8 content on the membrane surface was large. Since the incorporation of ZIF-8 would increase the water uptake, in this regard, the further enhancement of membrane conductivity is produced. However, with the increase ZIF-8 loading to a certain content, the membrane resistance continuous increasing. In this condition, the steric hinderance effect of ZIF-8 and enhanced crosslinking play a much more important role, rather than electrostatic effect. In contrast, while ZIF-8 loading reached 0.8%, higher filler concentration caused the unselective voids with a significant drop in rejection, as a result, membrane resistance in MgCl\({}_{2}\) solution drops again.
## 3 Results
### Enzymatic Degradation  
Figure 6 presents the diagram corresponding to enzymatic (trypsin) degradation of native (unmodified) membranes compared to PRGF-modified collagen membranes, monitored during the 72 h incubation time. Within the first 9 hours, only a small degradation was noticed for all the specimens. By comparing the results at each specific incubation time (9, 18, 36, and 72 h), the diagram reveals that PRGF-modified membranes exhibited a slower degradation compared with the native ones, in all the three cases. After the first 9 hours, only a small degradation was noticed in all the three cases, but the degradation occurred gradually during the entire interval (72 hours). The highest degradation rate was noticed for Biocollagen(r). Jason(r) had a moderate degradation rate, and \(\mathrm{Cova}^{\mathrm{TM}}\mathrm{Max}\) had a slow one. This effect was even more evident after 72 h, in the case of unmodified Biocollagen(r), exhibiting more than 50% degradation. Overall, a significant difference between the three membranes was observed during the enzymatic degradation process (\(p<0.05\)).  
Figure 5: Young modulus calculations with respect to the three collagen membranes before (**a**) and after PRGF treatment (**b**). Legend: MPP1/MPP2 = \(\mathrm{Cova}^{\mathrm{TM}}\mathrm{Max}\) membrane before/after PRGF treatment; MC1/MC2 = Jason® membrane before/after PRGF treatment; MCP1/MCP2 = Biocollagen® membrane before/after PRGF treatment.
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 5 Taxonomy for Task Scheduling Algorithms in Fog Computing
### Optimization Criteria
#### 5.1.3 Latency  
Latency in fog computing task scheduling signifies the time delays during different stages of task execution. It is a vital performance metric influencing overall system responsiveness and efficiency. Reducing latency is essential for meeting real-time requirements, enhancing user satisfaction, and optimizing resource use in fog computing. Scheduling algorithms strive to minimize latency by efficiently assigning tasks, handling communication, and adapting to dynamic system changes [46].