esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
In order to assess the validity of the 0D model, this section compares the results with the ones obtained from the 2D model. Afterwards, the impact of an LHTS system on the operational strategies of two relevant case studies is quantified through the application of the compact 0D model. In the former case, it shows how the LHTS allows achieving a significant reduction of the thermal power peak in a District Heating (DH) substation. In the latter case, it is used to include the latent thermal storage in an optimizer that finds the optimal performance of a multi-energy system.
# Blockchain-Based Internet of Things Access Control Technology in Intelligent Manufacturing
## 6 Security and Performance Analysis  
### Confidentiality  
The encrypted transmission of data is achieved with a symmetric cryptographic algorithm [20]. The data resource owner and the data requester use an identification password for authentication and implement the necessary key negotiation to generate a session key; \(K_{m}\), \(K_{m}\) is a symmetric key that is used to encrypt the transmission of the interaction information between the two parties during the subsequent interaction.
## 4 Model Validation  
In this section, the model proposed for estimating the RoI (mass and flow) is validated with the experimental data shown in the previous section.  
Figure 10 shows the comparison at an idle speed up to full load (\(\text{RoI}_{\text{m}}\left(\frac{\text{mg}}{\text{s}}\right)\)) with nozzle 2, which allowed for predicting the diesel fuel consumption behaviour, focusing on the original injection map of diesel vehicles with current technologies. It should be noted that the behaviour of the original injection map was a progressive stepwise behaviour of the injected fuel mass, while the presented model was a stepwise type for the injection rate variation, denoting a more accurate delivery for the prediction of the injected fuel mass consumption at 96% with an error factor of \(\pm 4\%\), discounting the stepwise step points and analysing the climb curve of the presented model with the measured data.  
Figure 9: Representativeness (**a**) and incidence (**b**) of variables in Equation (4).  
Figure 11 illustrates a comparison of the increase in injection flow through the analysis of a prediction model of the injected volumetric flow rate (\(\text{RoI}_{\text{Flow}}\left(\frac{\text{mm}^{3}}{\text{s}}\right)\)), which offers an advanced view of the behaviour of fuel consumption. This approach focuses on the original injection mapping of diesel vehicles to the operating parameters of the injector and CRDI pump calibration banks. In this case, the numerical equation presented fit more closely to the flow trend when nozzle 2 was used, which resulted in a notable improvement in the predictive capacity of the volume of fuel injected, considering variables such as pressure, number of holes, and the energization time of the Bosch injector.  
The test conditions were based on a gradual exponential progression, reaching a 93% approximation during the injection process up to the 110 MPa range and decreasing thereafter, with an error factor of 7% at the step points of the original injection map. This last value represents the maximum operating pressure in the working conditions of the vehicle, from which a decrease in pressure was observed.  
Using the examples shown in Figures 10 and 11, other methods for evaluating the fuel injection rate and volumetric flow were explored. This was achieved by using a current injection system, which allows for a detailed description of the operating conditions. The objective is to improve the understanding of diesel fuel behaviour by controlling key  
Figure 11: Comparison between \(\text{RoI}_{\text{Flow}}\) estimated and experimental obtained with diesel fuel.  
Figure 10: Comparison between \(\text{RoI}_{\text{m}}\) estimated and experimental obtained with diesel fuel.  
variables such as injection pressure, power delivery time, and the number of holes. These tools make it easy to predict and optimize performance, which is essential for checking and adjusting diesel injectors for optimal performance.
# Internet Video Delivery Improved by Super-Resolution with GAN
## 4 Video Super-Resolution with GAN
### Perceptual Loss Function  
In [56], the authors proposed mean-based loss functions that generate good quality pixel-wise images, according to PSNR and SSIM. However, the human eye quickly detects the smoothing artifacts of these images. In other words, mean-based loss functions are insufficient to capture human visual perception, which has inspired a new body of work on perception-oriented loss functions; see [21; 22; 33; 57; 58].  
In [21; 22; 33], the authors proposed a perceptual loss function that has three components: (i) the perceptual component, (ii) the adversarial component, and (iii) the content component; see Equation (4).  
\[L_{G}=L_{percept}+\lambda L_{G}^{RaD}+\eta L_{1} \tag{4}\]The calculation of the perceptual loss component (\(L_{percept}\)) uses the mean-error loss defined by the space established using features extracted from the super-resolved frames \(f^{SR}\) and their high-resolution counterparts \(f^{HR}\). These features are the output of an image classification procedure of VGG19; a deep neural network presented by [59]. This feature classification occurs at the fifth block before the activation function in the fourth convolution layer of the VGG19, called \(VGG_{54}\). The coefficients \(\lambda\) and \(\eta\) weigh each component.  
Equation (5) presents the perceptual loss component.  
\[L_{percept}=\frac{1}{W.H}\sum_{x=1}^{W}\sum_{y=1}^{H}\bigg{(}VGG_{54}(f^{HR})_{ x,y}-VGG_{54}\Big{(}f^{SR}\Big{)}_{x,y}\bigg{)}^{2}, \tag{5}\]  
where \(W\) and \(H\) are the dimensions of the feature space.  
The adversarial loss component \(L_{\text{G}}^{R\text{{D}}}\) is a cross-entropy function as given by Equation (6).  
\[\begin{split} L_{\text{G}}^{R\text{{D}}}=&-\mathbb{ E}_{x_{r}}\Big{[}\text{log}\Big{(}1-D_{R\text{{D}}}(x_{r},x_{f})\Big{)}\Big{]}\\ &-\mathbb{E}_{x_{f}}\Big{[}\text{log}\Big{(}D_{R\text{{D}}}(x_{f },x_{r})\Big{)}\Big{]},\end{split} \tag{6}\]  
where \(x_{r}=f^{HR}\) and \(x_{f}=G(f^{LR})\).  
The content loss component (\(L_{1}\)) is given by Equation (7)  
\[L_{1}=\frac{1}{WH}\sum_{x=1}^{H}\sum_{y=1}^{W}\Big{|}f^{HR}_{x,y}-G(f^{LR})_{x,y}\Big{|}, \tag{7}\]  
where \(W\) and \(H\) are the dimensions of \(f^{HR}\).  
Figure 2: The generator architecture and relativistic average discriminator networks: (1) Pre-residual blocks in the generator; (2) Residual in residual blocks in the generator; (3) Upscaling blocks in the generator; (4) Discriminator blocks; and (5) The relativistic average discriminator component.
## 4 Methodology
### Dynamic Resource Scheduling and Task Offloading Based on Value Iteration  
To effectively address the issues of low resource utilization and high system latency in satellite networks, this paper proposes a collaborative optimization framework based on cooperative dynamic programming. This framework constructs delay optimization models and resource efficiency models, combining dynamic programming and value iteration techniques to achieve real-time updates of system states and decision variables. Specifically, task offloading decisions are represented by discrete matrices, which not only consider the priority and deadline of tasks but also evaluate each node's computational, storage, and bandwidth resources. By iteratively updating the value function based on the Bellman equation, this method optimizes resource allocation and task scheduling strategies at each time step until convergence to the optimal decision strategy.
## 11 Application of JADEDO in Attack-Response Optimization Problem  
This study investigates an attack-response optimization problem, in which a defender must select among multiple countermeasures (responses) to thwart or mitigate a threat. Each response has a cost, a risk, and a duration, while also contributing some effectiveness toward neutralizing the threat. The objective is to choose a combination of responses that maximizes overall effectiveness subject to constraints on total cost, total risk, and maximum time. To address the combinatorial nature of this problem, metaheuristic algorithms are employed to search for near-optimal solutions efficiently.  
In this case study, we consider five algorithms: JADEDO, FLO, GWO, SSOA, and Chimp. Each algorithm is run multiple times to account for randomness in both the threat factor and algorithm initialization. Summaries of their performance reveal how well they balance effectiveness against constraints and how quickly they converge on solutions.
## 3 Key Challenges
### Networking  
In the context of the CPNT application, a flexible and reliable network architecture is needed that can ensure efficient end-to-end data delivery [45]. Tailed network protocols for underwater applications are essential for different functionalities under different topological structures. Following the classical terrestrial network architecture, we segment the network into layered structures. However, it is not suitable for underwater networking due to the instinctive difference between radio and acoustic communications [46]. A customized architecture is needed, and each layer's protocols face unique challenges dictated by the specified requirements.  
Efficiency in coordinating network nodes for channel occupancy and conflict avoidance is crucial for the MAC (Medium Access Control) layer due to the narrow band of acoustic channel [7]. At the network layer, it is crucial to consider the high delay dynamic topology [47], ensuring that the data can be transmitted to the destination node with minimal overhead. With respect to the transport layer, particular attention should be paid to rational congestion control and effective retransmission mechanisms [48]. For the application layer, it is important to balance efficiency, reliability, and energy consumption, while embracing cross-layer design and new technologies [49; 50].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Acoustic Communication** & **Radio Wave Communication** \\ \hline Propagation Speed [31] & 1500 m/s & \(3\times 10^{8}\) m/s \\ \hline Drawbacks [32] & Doppler, latency and shadow zones & High attenuation \\ \hline Reliable Communication Distance [32] & kilometers & Few meters \\ \hline Energy Efficiency & low energy-efficiency (\(\approx\)100 bits/Joule for several km) [33] & \(\approx\)9850 bits/Joule for 10 m range [31] \\ \hline Data Rate [32] & 1.5 to 50 kbps (@0.5 km); 0.6 to 3.0 kbps & 1 to 10 Mbps (@1–2 m); 50 to 100 bps \\  & (@28–120 km) & (@200 m) \\ \hline Bandwidth & 100 kHz (!1 kHz for very long distance, \(>\)100 kHz for very short distance) [34] & 3–30 MHz [35] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of acoustic vs. radio communication in underwater environment.
## 2 Materials and Methods
### Field Data Collection
#### 2.5.2. Micrometeorological Data  
Micrometeorological data were obtained at the EC heat flux or weather tower in each field. An HMP45C sensor probe (Vaisala, Helsinki, Finland) measured air temperature (T\({}_{\mathrm{a}}\), \({}^{\circ}\)C) and relative humidity (RH, %) at 3.5 m AGS. The HMP45C probe has a 1000-ohm thermometer with measurement uncertainty within 0.20 to 0.30 \({}^{\circ}\)C at ambient temperatures varying from 20 to 40 \({}^{\circ}\)C. Within the HMP45C sensor, a HUMICAP H-chip measures RH with an uncertainty of approximately \(\pm\)1% when T\({}_{\mathrm{a}}\) equals 20 \({}^{\circ}\)C [63]. Wind speed and direction were measured using a three-dimensional (3D) sonic anemometer installed at each research site, 3.5 m AGS. At IIC, the 3D sonic anemometer was a CSAT (Campbell Scientific Inc., Logan, UT, USA). In contrast, at LIRF, the 3D sonic anemometer was a Gill WindMaster 3D sonic anemometer (Gill Instruments, Lymington, Hampshire, UK). Incoming shortwave solar radiation was measured using an LI-200X pyranometer (LI-COR, Lincoln, NE, USA) at LIRF (3.3 m AGS) and the CNR01 net radiometer at the IIC site. The on-site weather data were recorded every minute and averaged every 15 min in CR1000 and CR3000 datalogers (Campbell Scientific Inc., Logan, UT, USA) at the IIC and LIRF sites, respectively.
## 4 Conclusions  
In conclusion, we have proposed a scheme to generate an attosecond gamma-ray source by the structural fingerprints of micro bunches from LWFA. In the near-threshold ionization injection scheme, by using a linearly polarized laser at an intensity of a \(a_{0}=1.5\), the phase-dependent ionization causes the injection of micro-bunched beams with dispersive comb-like longitudinal density distributions. These periodic modulations imprint onto secondary gamma-ray sources with attosecond temporal resolution upon head-on collision with the reflected laser pulse. The internal fine structures of the generated ICS source are consistent with the micro-bunches. The time intervals between sub-pulses range from 1.10 fs to 1.45 fs, which is about half of the wavelength of the driving laser, and the FWHM length of each single pulse can reach 260 as. The critical energy of the gamma-ray source is about 2 MeV, with the cut-off energy extending to 10 MeV, which yields a peak brilliance of \(\sim\)10\({}^{22}\) photons s\({}^{-1}\) mm\({}^{-2}\) mrad\({}^{-2}\) 0.1% BW.  
Such a micro-sized attosecond gamma-ray source is expected to achieve unpredicted high brilliance with ultra-cold high-brightness electron beams by applying the plasma photocathode technique (the so-called trojan horse scheme) [38; 39] under the near-threshold ionization injection. Experimentally, it is valuable to reveal the sub-micrometer structure of electron beams from plasma wakefield acceleration [40], which also critically influences gain in free-electron lasers or particle yield in colliders. Further optimization of such space-time-correlated structures of the beams and the temporal-spatial characteristics of the tabletop plasma wakefield-based X-ray sources could pave the way for new avenues in sub-femtosecond probing.  
Conceptualization, A.D. and Z.L.; methodology, Y.L. and Y.W.; software, Y.L., Y.W. and Z.L.; writing--original draft preparation, A.D., Y.L., Y.W. and Z.L.; writing--review and editing, A.D. and X.Y.; visualization, A.D., Y.L. and Y.W.; supervision, A.D. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 12174343, 12005187), and the Zhejiang Provincial Natural Science Foundation of China (Grant No. LQ21A050002).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 4 Statistical Analysis
### Vif  
Multicollinearity refers to the correlation between explanatory variables. If multicollinearity exists between variables, it distorts the estimated regression coefficients of the multiple regression model. Therefore, multicollinearity must be checked in order to build a multiple regression model. In general, the variance inflation factor (VIF) is used to test for multicollinearity. A VIF of less than 10 means no multicollinearity is present [24]. Equation (4) shows how to calculate the VIF for \(\beta\_\)j, the estimator of the jth regression coefficient [25]. R_jis the Coefficient of Determination calculated from the regression model with xj as the dependent variable and the remaining variables as explanatory variables.  
\[VIF_{j}=\frac{1}{1-R_{j}^{2}} \tag{4}\]  
Table 4 shows the VIFs of the data variables. All factors except the plastic limit had VIFs greater than 10. Accordingly, the initial void ratio and plasticity index, which were suspected to overlap with the natural water content and liquid limit in the linear regression analysis, were excluded. The initial void ratio is calculated using the natural water content, and the plasticity index is the numerical difference between the liquid limit and plastic limit.  
Figure 2: Correlation distribution between compression index and influencing factors from (**a**) Wn-Cc, (**b**) LL-Cc, (**c**) PI-Cc, (**d**) e0-Cc, (**e**) PL-cc.  
Therefore, considering the amount of data, a linear regression analysis was conducted with the compression index using the natural water content, liquid limit, and plastic limit and excluding the initial void ratio and plasticity index. Table 5 shows the VIFs of the final data variables.
## 9 Design and Manufacture of Robotic Prostheses with 3D Printing
### Printing and Post-Processing Phase  
Once the digital model has been designed and the materials selected, the next step is the printing of the prosthesis. At this point, 3D printing technologies such as fused deposition modeling (FDM), stereolithography (SLA), and selective laser sintering (SLS) are applied to manufacture the prosthesis layer-by-layer. Each technology has its own advantages and challenges: while FDM is more accessible and economical, SLA and SLS offer greater accuracy and better surface finishes, which is critical in prostheses that require an ergonomic and aesthetic fit [11].  
Post-processing is an important stage that is often overlooked. This includes cleaning the part, sanding to improve surface smoothness, and, in some cases, assembling multiple parts if the prosthesis has multiple components. In robotic devices, this stage also includes the integration of sensors, motors, and electronic systems that allow for movement control. For example, in a robotic hand, servo motors can be integrated to provide precise finger movements, while pressure sensors allow the user to regulate the force with which an object is gripped [12].
## 2 Materials and Methods  
For dual-channel SAR, the complex images that are processed are highly correlated, regardless of whether the two channels are along-track or cross-track. Interferometric images of the two channels contain certain information about the height and velocity of the imaging area, and the two intensity images are almost identical if the amplitude-phase errors are neglected or compensated for. On the other hand, the jamming signals that the two channels receive have a small time lag and a corresponding phase shift, which change slowly according to the geometrical relationship between the SAR channels and the source of radiation. If the IPSR is estimated precisely, then the jamming signals can be eliminated by phase compensation and cancellation.
## 4 Physical Audio Features Extraction Techniques
### Time Domain Physical Features
#### 4.1.3 Power-Based Features  
The following paragraphs describe the most relevant and classic temporal audio features based on signal power.  
* **Short-time energy:** using a frame-based procedure, short-time energy (STE) can be defined as the average energy per signal frame (which is in fact the MPEG-7 audio power descriptor [45]). Nevertheless, there exist also other STE definitions in the literature that compute power in the spectral domain (e.g., see [PERSON] _et al._[55]). STE can be used to detect the transition from unvoiced to voices speech and _vice versa_ ([PERSON] and [PERSON] [56]). This feature has also been used in applications like musical onset detection ([PERSON] _et al._[57]), speech recognition ([PERSON] and [PERSON] [58]), environmental sound recognition ([PERSON] _et al._[18], [PERSON] and [PERSON] [47], [PERSON] and [PERSON] [48]) and audio-based surveillance systems ([PERSON] _et al._[24]).
* **Volume:** according to the work by [PERSON] _et al._[59], volume is defined as the Root-Mean Square (RMS) of the waveform magnitude within a frame. It has been used for speech segmentation applications, e.g., see [PERSON] _et al._[60].
* **MPEG-7 temporal centroid:** it represents the time instant containing the signal largest average energy, and it is computed as the temporal mean over the signal envelope (and measured in seconds) [45]. The temporal centroid has been used as an audio feature in the field of environmental sound recognition, like in the works by [PERSON] and [PERSON] [47], and [PERSON] and [PERSON] [48]).
* **MPEG-7 log attack time:** it characterizes the attack of a given sound (e.g., for musical sounds, instruments can generate either smooth or sudden transitions) and it is computed as the logarithm of the elapsed time from the beginning of a sound signal to its first local maximum [45]. Besides being applied to musical onset detection ([PERSON] _et al._[57]), log attack time (LAT) has been used for environmental sound recognition (see [PERSON] and [PERSON] [47], and [PERSON] and [PERSON] [48]).
# Research on the Pavement Performance of Slag/Fly Ash-Based Geopolymer-Stabilized Macadam
###### Abstract  
The substitution of slag-based geopolymer materials for cement-stabilized macadam in road bases is in line with the demand for green and sustainable development in the transportation industry. Thus, slag/fly-ash-based geopolymer materials were prepared to stabilize macadam of road bases in this study. The mechanical properties, freeze-thaw resistance, and dry shrinkage durability of slag/fly-ash-based stabilized macadam materials were studied to analyze the influence of geopolymer dosage on these properties of stabilized macadam. Microscopic tests such as XRD, FITR, and SEM were carried out to explore the formation mechanism of strength and the characteristic of interface transition zone (ITZ). Results show that the 28 d compressive strength, compressive elastic modulus, and tensile strength of slag/fly-ash-based stabilized macadam increase linearly with the increase of geopolymer content. When the dosage of slag-based geopolymer is 4%, the 7 d unconfined compressive strength of slag/fly-ash-based stabilized macadam reaches 8.76 MPa, and the strength still reaches 14.84 MPa after five freeze-thaw cycles (28 d), which satisfy the application requirements of expressway and first-class highway base (JTC/T F20-2015). The dry shrinkage property of slag/fly-ash-based stabilized macadam is better than that of cement-fly-ash-stabilized macadam. When the amount of geopolymer is 3%, the dry shrinkage strain of slag/fly-ash-based stabilized macadam is 231.2 \(\upmu\)e, which is smaller than that of cement-fly-ash-stabilized macadam (261.3 \(\upmu\)e). No obvious porosity around the ITZ was detected, indicating good binding between the geopolymer and the aggregate.  
slag; fly ash; geopolymer; semi-rigid road base; pavement performance