esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries
### Encryptions and Digital Signatures  
Encryptions can be categorized into symmetric and asymmetric encryptions. In this study, we focus on public-key (asymmetric) encryption; that is, public keys are used for encryption (\(e=Enc(m,\ pk)\), where \(m\) is the message, \(Enc\) is the encryption scheme, and \(e\) is the ciphertext), while secret keys are used for decryption: \(m=Dec(e,\ sk)\)[31]. In the smart vehicle context, a pair of public and secret keys can be created for each vehicle, with the public keys revealed to all as an identity and the secret keys kept by the vehicle itself. Messages sent to certain vehicles can be encrypted using their public identity/key, such that only the vehicle with the corresponding secret key can decrypt them.  
Similar to public-key encryptions, digital signature schemes also use public/secret key pairs to sign signatures and verify signatures. However, in the context of digital signatures, secret keys are used for signing (\(sig=Sign(m,\ sk,\ r)\), where \(sig\) is the digital signature, \(Sign\) is the signing scheme, and \(r\) is the randomness added to the message to prevent the signature from being re-used), while public keys are used for verification, \(Verify(m,\ pk,\ sig)\)[32]. In this way, vehicles can sign on to the messages they want to disseminate with their secret keys so that anyone who received the messages can validate the authenticity and integrity of messages with the senders' public keys.
## 3 Results and Discussion  
### Characterization of the Initial Powder  
The powder synthesized via the precipitation route described in Section 2.1 is shown in Figure 3. In particular, the SEM image is presented in Figure 3a, and the XRD diagram is provided in Figure 3b.  
The homogeneity of powder morphology can be noted from the SEM image (Figure 3a). It can be expected that the highly uniform particle size distribution contributed to the improved sinterability of the zirconia powder. The XRD diagram (Figure 3b) exhibits the presence of both phases, tetragonal \(t\)-ZrO\({}_{2}\) and monoclinic \(m\)-ZrO\({}_{2}\). Thus, the sample under investigation exhibits a biphasic structure.  
Figure 4 shows the BET (Brunauer-Emmett-Teller) isotherm of the initial powder. Its specific surface area \(a_{sBET}\) was found to be 6.3. Compared to the powder obtained by other means, this powder was of significantly higher quality. The specific surface area for the second powdered sample was \(a_{sBET}=3.7\), and this powder was of poor quality and thus excluded from further studies.  
The hysteresis shown in Figure 4a belongs to a Type IVa isotherm, according to the classification provided in [40]. Its two branches represent adsorption and desorption associated with capillary condensation. Capillary condensation typically occurs in granular or porous media and can strongly alter the properties of the materials. Its presence suggests the van der Waals interactions between the molecules assemble crystals to create atomic-scale capillaries in the condensed state.  
Although the final saturation plateau is very small, this indicates a mesoporous material. The increase in mesoporous can be attributable to the augmented number of grain boundaries, consequent to the formation of phase-separated nanocomposites.  
For nitrogen, adsorption took place in cylindrical pores at 77 K, and hysteresis started to occur for pores above 4 nm in width. The pore volume versus pore diameter shown in Figure 4b reveals three local extremes at diameter values of about 2.5, 7.5, and 11 nm.  
The observed morphology and the results of the BET analysis indicated that the initial powder exhibited good sinterability.  
Figure 4: The BET isotherm for the synthesized nanopowder ZrO\({}_{2}\) stabilized with 5 wt.% CeO\({}_{2}\): (a) BET (Brunauer–Emmett–Teller) isotherm hysteresis; (b) diagram of differential d\(V_{p}\)/d\(r_{p}\).  
Figure 3: The synthesized nanopowder ZrO\({}_{2}\) stabilized with 5 wt.% CeO\({}_{2}\): (a) SEM image; (b) XRD diagram specifying tetragonal \(t\)-ZrO\({}_{2}\) and monoclinic \(m\)-ZrO\({}_{2}\) phases.
## 3 Results and Discussion
To promote easy recognition and association with the function of the flow cell, we have chosen to render the battery design to draw on existing consumer understanding and associations with common, and widely known household batteries. Figure 9 shows a 5 kW/25 kWh VRB concept with 415 L tanks of 1.8 M electrolyte. This is comparable to the power of a Tesla Powerwall 2, with almost double the capacity, in a package 50% wider and higher. A system of two such batteries would be capable of providing the electricity requirements for an average Australian household. The capital cost of vanadium flow batteries is currently higher than lithium-ion systems but is expected to decrease more quickly through increased manufacturing scale [38]. Figure 8: Rendering of 4 \(\times\) 40 toroidal redox flow cell stack concept incorporating auxiliary plant (motor and pumps) capped and centrally mounted internally. Figure 7: Prototype two-element mixers additively manufactured using (**a,b**) stereolithography (SLA) and (**c**) material jetting (MJP) [35]. The (**a**) blade-style and (**c**) helical mixers are shown on cubes made with fused filament fabrication (FFF) for contrast and on (**b**) felt, with an SLA printed flow frame for future experimental studies. Early adopters of devices such as solar panels, electric vehicles and smart appliances are motivated by technical innovation [39], while more widespread dispersion is facilitated by simplicity and observability. It is for this reason that the design resembles a D cell battery. Other advantages of this design include modularity and possible improvements to heat management with airflow through the center aided by convection. Figure 10 shows a 20 kW/100 kWh concept with 1660 L tanks of 1.8 M electrolyte for an alternative configuration, which would have the power and capacity to meet Level 2 Fast charging specifications [40] for a full charge of two mid-size electric vehicles, such as a Hyundai Kona Electric. Electrolyte energy density improvements [41] could lead to increased capacity or more compact systems. These future developments would be readily facilitated by the modular design.
# Structural Damage Detection through Dual-Channel Pseudo-Supervised Learning
## 2 Methodology
### Dual-Channel Pseudo-Supervised (DCPS) Learning  
To improve the noise immunity of the network model, a learning strategy based on DCPS learning is proposed in this paper. DCPS learning consists of two parallel channels whose inputs are different data-augmented versions from the same signal. In this paper, the inputs to the dual-channel network are signals with different levels of noise introduced. Subsequently, segmentation confidence maps for signals with two levels of noise are obtained through the same network architecture with different initial weights or two different network architectures, as follows:  
\[\mathbf{p}_{a}=f(X_{a};W_{a}) \tag{8}\]  
\[\mathbf{p}_{b}=f(X_{b};W_{b}) \tag{9}\]  
where \(X_{a}\) and \(X_{b}\) denote the input signal \(X\) with different levels of noise, respectively, \(W_{a}\) and \(W_{b}\) are the parameters of two networks, and \(\mathbf{p}_{a}\) and \(\mathbf{p}_{b}\) represent segmentation confidence maps after softmax normalization. According to the segmentation confidence maps, the predicted values \(\mathbf{y}_{a}\) and \(\mathbf{y}_{b}\) of two networks, expressed as one-hot vectors, can be further obtained. There is a supervised loss \(L\) and a DCPS loss to jointly drive model network learning to improve the noise immunity, which are defined as follows:  
\[L=\frac{1}{|B|}\sum_{i=0}^{|B|}(\mathbb{J}_{ce}(\mathbf{p}_{ai},\mathbf{y}_{i}^{*})+ \mathbb{J}_{ce}(\mathbf{p}_{bi^{*}},\mathbf{y}_{i}^{*})) \tag{10}\]  
\[L_{d}=\frac{1}{|B|}\sum_{i=0}^{|B|}(\mathbb{J}_{ce}(\mathbf{p}_{ai},\mathbf{y}_{bi})+ \mathbb{J}_{ce}(\mathbf{p}_{bi^{*}},\mathbf{y}_{ai})) \tag{11}\]  
where \(B\) is the batch size, \(\mathbb{J}_{ce}(\cdot)\) denotes the cross-entropy loss function, and \(\mathbf{y}_{i}^{*}\) indicates the ground truth. The supervised loss \(L\) represents the degree of inconsistency between the network predictions and the true values corresponding to the original data in two parallel channels with different noise levels. Meanwhile, the DCPS loss \(L_{d}\) is a manifestation of the mutual supervision and constraint of the different channels, which encourages the prediction of the same input signal at different noise levels to be consistent across channels. In this case, the one-hot predictions output from one network are used to supervise the confidence map of the other network.  
Next, the supervised loss \(L\) and the DCPS loss \(L_{d}\) are combined to form the complete loss function:  
\[LOSS=\alpha L+(1-\alpha)L_{d} \tag{12}\]  
where \(\alpha\) denotes a linearly decreasing parameter ranging from 0 to 1. It determines the weight of the supervised loss \(L\) and the DCPS loss \(L_{d}\). As the number of training iterations increases, the weight of the supervised loss \(L\) decreases, while the weight of the DCPS loss \(L_{d}\) increases. This adjustment is made to reflect the increasing stability of DCPS learning during the later stages of training.
## 2 Methods and Data
### New Algorithmic Ateps
#### 2.2.1 SENet_v2 Module
In terms of application and performance, SENet_v2 performs well in several computer vision tasks, including image classification, object detection, image segmentation, and more. In addition, its lightweight design makes it ideal for deployment in mobile devices and embedded systems. By optimizing computing and storage requirements while ensuring high performance, SENet_v2 can effectively operate under limited resources, so it has a wide range of applications in edge computing, real-time image processing, etc. SENet_v2 proposes a more efficient and powerful attention mechanism by improving the original SENet. While maintaining the advantages of the original model, SENet_v2 further improves the modeling efficiency of inter-channel relationships, increases the expressive power of the network, and significantly improves the computational efficiency through lightweight design. SENet_v2 provides a more efficient solution for a variety of computer vision tasks and has a wide range of academic research and practical application value.
## Key Rheological Models
### Empirical constitutive models  
These models are derived from experimental data and establish functional relationships between stress, strain, and time. Common forms include power function, logarithmic, exponential, and polynomial relations. Rock creep typically progresses through three stages: (1) deceleration creep, where the strain rate decreases over time; (2) steady-state creep, where the strain rate stabilizes; and (3) accelerated creep, where the strain rate increases rapidly, often leading to failure. Although empirical models are widely used, they tend to provide only a simplified view of the creep process and are often limited by their dependency on experimental data.
These models are built by combining basic mechanical elements such as the Hookean spring (elastic), Newtonian dashpot (viscous), and Saint-Venant element (plastic). Through creep and relaxation tests, the elastic, viscous, and plastic characteristics of the rock are evaluated to establish a rheological constitutive model. Popular models in this category include the Burgers model, Nishihara model, Bingham model, Maxwell model, and Kelvin model [176]. These combination models provide a more nuanced understanding of the stress-strain-time relationship by accounting for different mechanical behaviors. Researchers are focusing on integrating nonlinear components into rock rheology models to capture the nonlinear characteristics of rock rheology. This modification is crucial for accurately describing the full creep process of rocks, from the initial deceleration stage to the eventual failure [177].
## 4 Results and Discussion
### Robustness Evaluation
#### 4.2.2 Cropping Attacks  
The three sets of experimental data are attacked with different degrees of cropping. The cropping scale is about 20%, 25%, 50%, and 75% of the original area of the map. Based on this, the watermark images are extracted and restored, and the number of watermark images that can be restored in each set of data is counted. Table 3 displays the results, showing that all three data sets preserve a substantial number of complete sub-blocks and can extract a full watermark image at the 20%, 25%, and 50% crop stages, respectively. When the cropping scale hits 75%, there are just a handful of sub-blocks left from which to extract the watermark, whereas data (a) keeps the entire sub-block and extracts the watermark in its entirety so that it can be restored in full. Data (b) and data (c) both have NC values above 0.95 of extracted watermark images, indicating that the watermark images are recognized with a high degree of accuracy, despite
## 4 Comparative Analysis  
### Example APDPs  
We denote the PDP averaged over the five spatial points (see Figure 4) as the averaged PDP (APDP) [23]. In Figure 5, we observe the APDPs for both frequency bands. For the example APDPs, the TX and RX are placed in the middle of the corridor and the TX-RX distance is 1.2 m. For visual comparison, we have normalized the line-of-sight (LOS) contribution to 0 dB.  
It is observed that the main components fit the same delays. For both frequency bands, the delay of the largest component is about 4 ns, which, multiplied by the velocity of light, equals the LOS distance (1.2 m).  
Figure 5 clearly illustrates multipath propagation. From basic trigonometry, the excess distances (compared with the LOS path) for the ground reflected path, the ceiling reflected path and the wall reflected paths are 1.85 m, 1.85 m, 1.43 m and 1.43 m, respectively. The distance differences divided by the velocity of light are from 4.77 and 6.16 ns. The components (area highlighted by red box in Figure 5) are likely the combination of all the four reflected paths since the relative excess delay is also between 4.77 and 6.16 ns.  
Figure 5 also indicates that the normalized amplitudes of the reflected paths are similar for the two bands. The received power for the LOS component at RX is  
\[P_{RX,LOS}=\frac{P_{t}G_{t}}{4\pi d_{LOS}^{2}}A_{e}, \tag{4}\]  
where \(P_{t}\) is the transmitted power, \(G_{t}\) is the transmitter antenna gain, \(A_{e}\) is the RX antenna effective area, and \(d_{LOS}\) is the distance for the LOS path as shown in Figure 6. For a reflected component, the received power at the RX is [24]  
\[P_{RX,r}=\frac{P_{t}G_{t}|\Gamma|^{2}}{4\pi(d_{r1}+d_{r2})^{2}}A_{e}, \tag{5}\]  
where (\(\Gamma\)) is the reflection coefficient, \(d_{r1}\) is the distance between the TX and the reflecting point, and \(d_{r2}\) is the distance between the RX and the reflecting point. The power ratio between the LOS component and reflected component is  
\[\frac{P_{RX,r}}{P_{RX,LOS}}=\frac{|\Gamma|^{2}d_{LOS}^{2}}{(d_{r1}+d_{r2})^{2 }}. \tag{6}\]  
The reflection coefficient (\(\Gamma\)) is determined by the complex dielectric constant, \(\delta\), and \(\delta\) is defined as [19]  
Figure 5: (color online) Normalized APDPs (averaged power delay profile) at 3.6 GHz and 14.6 GHz.  
\[\delta=\epsilon-j\frac{\sigma_{\epsilon}}{2\pi f}, \tag{7}\]  
where \(j\) is the imaginary unit, \(\epsilon\) is the dielectric constant, and \(\sigma_{\epsilon}\) is the conductivity. As \(\sigma_{\epsilon}\) is very small [25], the imaginary part in Equation (7) becomes negligible. In addition, \(\epsilon\) stays constant with a large frequency range (1-100 GHz) for many typical building materials (e.g., concrete, wood, glass and chipboard) [25]. Thus, the reflection coefficient is almost independent of the frequency. Then, via Equation (6), the power ratio between the LOS component and reflected component (i.e., the normalized power for the reflected path) is almost independent of the frequency.  
The similar delays and normalized amplitudes should yield similar RMS delay spread.
# Coverage, Rate, and Last Hop Selection in Multi-Hop Communications in Highway Scenarios
## 4 Numerical Results
### Impact of System Parameters  
Having studied the main dependencies, we now proceed with investigating the impact of the system parameters including road traffic conditions and the last hop selection scheme on the probability of establishing a connection to the BS. This metric basically specifies the ISD between BSs corresponding to a certain bridge availability.  
We start with Figure 6a showing the bridge availability as a function of the ISD for different vehicle densities. The variable parameters are set as \(M=2\), \(N_{max}=\infty\), while the applied last hop selection strategy is FO. By analyzing the presented data, we observe one important counter-intuitive takeaway--the higher the value of \(\lambda\), the higher the bridge availability for a fixed value of ISD. The rationale is that the single-hop communications distance is rather large, especially when operating over the LoS path. However, eventually, there is the density of vehicles that is too small even for direct LoS communications. We also observe that lowering the vehicle density further would eventually result in lower bridge availability.  
Figure 5: Trade-off between the considered metrics. (**a**) bridge length vs. data rate; (**b**) number of hops vs. data rate.  
The maximum allowed number of hops is another system design metric. To this aim, Figure 6b shows the bridge availability as a function of the ISD for the different maximum number of hops in normal traffic conditions (\(\lambda=1/30\)) for the FO strategy. By observing the data, we note that linearly increasing the maximum number of hops results in an almost linear increase of the bending point, where the bridge availability starts to sharply drop. Still, as one may notice, the impact of the maximum number of hops is of secondary importance compared to the vehicle density for large targeted values of bridge availability. This is because even allowing for just a few hops allows for reliable bridge extension for small distances. However, as the number of allowed hops becomes larger, the whole \"ad hoc\" construction becomes fragile, reducing the considered metric. The number of lanes is even of less importance as evident from Figure 7 illustrating the bridge availability as a function of ISD for different values of \(M\). Recall that typically \(M=2\) corresponds to the highway scenario with multiple lanes, \(M=1\) to the rural road, while \(M=0\) to the case on one-way roads. Here, the difference between the three considered cases is minimal barely reaching a few percent between \(M=2\) and \(M=0\). The rationale is that side-reflections are not that heavily utilized as compared to other propagation paths resulting in a small impact of \(M\).  
Figure 6: Bridge availability as a function of the inter-site distance. (**a**) different traffic conditions; (**b**) different number of hops.  
Figure 7: Bridge availability as a function of the number of traffic lanes.
## 2 Model Setup
### Model Establishment
#### 2.2.3 Rigid Zones  
The T-shaped segments (T1 and T2 in Figure 2) have a transition function to connect the neighboring segments with the interior column, which does not exist in circular tunnels. A concept of rigid connection is proposed in the model. A proper method to consider the mechanical properties of this area is needed, similar to the calculation of a column and beam joint in frame structure buildings. As such, this concept is introduced into the quasi-rectangular tunnel model and its effects will be discussed later. Based on the Chinese Technical Specification for Concrete Structure of Tall Building [JGJ3-2010][36], an illustration of the rigid zone is shown in Figure 5 and its size can be determined  
Figure 4: Illustration of a general spring element.  
Figure 3: Beam-spring model of the quasi-rectangular shield tunnel linings.  
through expressions (1), (2) and (3). Herein, \(b_{c}\) is the average thickness of the haunch (the top and bottom thicknesses of the haunch are 850 and 350 mm, then \(b_{c}\) equals 600 mm) and \(h_{b}\) is the thickness of the segment (450 mm). Two rigid zones are introduced at the upper and lower T-shaped segments with the following characteristics.  
\[l_{h1}=0.5b_{c}-0.25b_{b}, \tag{1}\]  
\[l_{h2}=0.5b_{c}-0.25b_{b}, \tag{2}\]  
\[l_{c1}=0.5l_{b}-0.25b_{c}. \tag{3}\]  
This results in \(l_{h1}=l_{h2}=187.5\) mm and \(l_{c1}=75\) mm.
# Low Power Consumption Signal Detector Based on Adaptive DFSD in MIMO-OFDM Systems
## 5 Simulation Results
### The Bit Error Rate Performance
As a result of Figures 13 and 14, the adaptive DFSD detection scheme has lower complexity than the conventional DFSD detection scheme. Also, as the num \begin{table}
\begin{tabular}{c c} \hline \hline
**Detection Scheme** & **Complex Multiplication Expression** \\ \hline Conventional DFSD & \(12N_{t}^{3}+4N_{t}^{2}+4\sum\limits_{i=1}^{N_{l}}L_{N_{l}-i+1}(i+1)\) \\ Adaptive DFSD & \(20N_{t}^{3}+4N_{t}^{2}+4(M-1)+4\sum\limits_{i=1}^{N_{l}}L_{N_{l}-i+1}(i+1)\) \\ DFSD using \(n\) nodes at the top layer & \(12N_{t}^{3}+4N_{t}^{2}+4\sum\limits_{i=1}^{N_{l}}L_{N_{l}-i+1}(i+1)\) \\ Adaptive QRD-\(M\) & \(12N_{t}^{3}+4N_{t}^{2}+4\sum\limits_{i=1}^{N_{l}}L_{N_{l}-i+1}(i+1)\) \\  & \(L_{i}:\) the number of nodes at the \(i\)-th layer \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The complex multiplication expression according to detection schemes. Figure 12: The complexity of the detection schemes in \(2\times 2\) MIMO-OFDM system with 16-QAM. increases, the increase rate of complexity for the adaptive DFSD detection scheme is lower than the increase rate of complexity for the conventional DFSD detection scheme. Especially, the increase rate of complexity for the adaptive DFSD detection scheme at the 24 dB of SNR is lower than the increase rate of complexity for the adaptive DFSD detection scheme at the 15 dB of SNR. Based on the above results, the adaptive DFSD detection scheme at low SNR has lower complexity and the lower increase rate of complexity according to the number of transmit antennas than the conventional DFSD detection scheme.
###### Abstract  
In order to improve the network layer of the Internet of things to improve transmission reliability, save time delay and energy consumption, the Internet of things cooperative communication and intelligent agent technology were studied in this paper. In cooperative communication, a new cooperative communication algorithm KCN (k-cooperative node), and a reliability prediction model are proposed. The k value is determined by the end-to-end reliability. After k cooperative nodes are selected, other nodes enter dormancy. In aggregate traffic allocation, game theory is used to model the traffic equilibrium and end-to-end delay optimization scenarios. In practice, the optimal duty cycle can be calculated, which makes some cooperative nodes enter an idle state to save energy. Under the premise of guaranteeing end-to-end delay, it is shown that the reliability of the proposed KCN algorithm is better than that of the other existing routing protocols. In the aspect of intelligent agent, a Directional source grouping based multi-Agent Itinerary Planning (D-MIP) is proposed. This algorithm studies the routing problem of multi-agent parallel access to multiple source nodes. A directed source packet multi-agent routing planning algorithm is proposed. The iterative algorithm of each source node is limited to a sector, and the optimal intelligent agent route is obtained by selecting an appropriate angle. Compared with other algorithms, it is shown through a lot of simulated results that energy consumption and time delay has been saved by the proposed D-MIP algorithm.  
intelligent agent algorithms; internet of things (IoT); cooperative communication; routing protocol; duty cycle; game theory; directional source grouping +
Footnote †: journal:  
_Future Internet_**2019**, _11_, _233_; doi:10.3390/f11110233