esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Convergent Validity  
To determine whether convergent validity is acceptable, the measurements must fulfill three conditions [93; 94]: (1) all item factor loadings with reflective measures must be significant and greater  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Construct**}} & \multirow{2}{*}{**Mean**} & **Std.** & **Factor** & **Lambda** & **Composite** & \multirow{2}{*}{**AVE**} & \multirow{2}{*}{**Cronbach’s \(\alpha\)**} \\  & & **Dev.** & **Loading** & **Stand.** & & & **Reliability** & \\ \hline \multirow{4}{*}{Autonomy (AUTO)} & AUTO1 & 2.97 & 1.337 & 0.907 & 0.900 & & & \\  & AUTO2 & 2.99 & 1.368 & 0.902 & 0.901 & & & \\  & AUTO3 & 2.75 & 1.339 & 0.931 & 0.934 & & & \\  & AUTO4 & 2.77 & 1.327 & 0.891 & 0.900 & & & \\ \hline \multirow{4}{*}{Competence (COMP)} & COMP1 & 2.64 & 1.315 & 0.939 & 0.941 & & & \\  & COMP2 & 2.69 & 1.275 & 0.929 & 0.932 & & & \\  & COMP3 & 2.50 & 1.274 & 0.916 & 0.917 & & & \\  & COMP4 & 2.73 & 1.265 & 0.917 & 0.921 & & & \\ \hline \multirow{4}{*}{Relatedness (REL)} & REL1 & 3.01 & 1.225 & 0.908 & 0.910 & & & \\  & REL2 & 2.91 & 1.278 & 0.923 & 0.922 & & & \\  & REL3 & 2.83 & 1.226 & 0.936 & 0.931 & & & \\  & REL4 & 2.80 & 1.237 & 0.903 & 0.906 & & & \\ \hline \multirow{4}{*}{Perceived Ease to USE (PEU)} & PEU1 & 3.13 & 1.248 & 0.931 & 0.936 & & & \\  & PEU2 & 3.11 & 1.253 & 0.958 & 0.952 & 0.952 & 0.87 & 0.951 \\ \cline{1-1}  & PEU3 & 3.18 & 1.258 & 0.905 & 0.909 & & & \\ \hline \multirow{4}{*}{Perceived Usefulness (PU)} & PU1 & 2.94 & 1.227 & 0.907 & 0.912 & & & \\ \cline{1-1}  & PU2 & 2.99 & 1.328 & 0.903 & 0.926 & 0.939 & 0.837 & 0.94 \\ \cline{1-1}  & PU3 & 2.92 & 1.296 & 0.906 & 0.907 & & & \\ \hline \multirow{2}{*}{Behavioral (Intention to USE (BI)} & BI1 & 3.18 & 1.301 & 0.935 & 0.938 & & & \\ \cline{1-1}  & BI2 & 3.21 & 1.342 & 0.955 & 0.955 & 0.969 & 0.912 & 0.96 \\ \cline{1-1}  & BI3 & 3.24 & 1.356 & 0.974 & 0.972 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Item loadings and measurement reliability.  
than 0.70, (2) the composite reliability for each construct must surpass 0.70, and (3) the Average Variance Extracted (AVE) for each construct should be higher than 0.5. In this study (Table 2), the factor loadings for all constructs substantially exceeded the threshold of 0.70, and they were statistically significant at the 0.001 level. In addition, composite reliability scores showed good internal consistency for all constructs (Table 2), and the AVE values were between 0.826 and 0.912, considerably higher than the recommended value of 0.5.
## 4 MSaaS Reference Architecture
### Structure of the MSAaS Reference Architecture  
The proposed MSAaS RA, shown in Figure 3, consists of the following building blocks:  
* **User interface:** Provides a web-based visual interface that allows users to make use of the capabilities provided by other RA building blocks.
* **Discovery service:** Provides the capability to perform a federated service discovery, in order to retrieve the existing M&S services made available by the federation's participant.
* **Composition Service:** Provides the capability to create a MSAaS application by composing the needed services.
* Configuration of each M&S service included in the composition;
- Specification of the computing nodes constituting the execution platform;
- Description of how the M&S services have to be deployed onto the execution platform.
* **Repository service:** Provides the capability for storing actual M&S services implementations.
* **Registry service: Provides the capability for storing M&S services descriptions.**
* **Orchestration service:** Provides the capability to run the executable artifacts included in the deployment description.
* **Adaptation service:** Provides the capability for adapting the deployment description, in order to make it compliant with the Orchestration Service.
* **Modeling and simulation services:** Provide the capability for building a MSAaS application. When a new M&S service is registered on the MSAaS infrastructure, the corresponding entry, which includes the service description, is created in the concrete registry managed by the registry service. Moreover, the service implementation is made persistent due to the capability provided by the repository service.
* **Simulation management and execution control (SMEC) Service:** Provides the capability for overseeing the simulation execution.
* **Infrastructure control service:** Provides a set of cross-functional capabilities including logging capability, infrastructure monitoring capability, resource monitoring capability, etc.  
Figure 2: Reference architecture, concrete architectures, and systems implementations.  
As discussed in Section 1, along with the MSaaS RA, this paper's contribution also includes ArTIC-MS, a conceptual architecture derived from the RA, which specifically introduces TOSCA as an enabling technology for ensuring the actual interoperability and portability of M&S services in a federated infrastructure.  
The next section introduces ArTIC-MS and clarifies how the proposed approach ensures the interoperability of concrete architectures derived from ArTIC-MS or directly from the RA.
## 1 Introduction
### A Considerably Low Computational Cost of Protocol Process Operations  
With the awareness that nodes with low-power features contribute to the network, our protocol not only preserves its security features but also substantially reduces the computation time required for its operations. The suggested protocol has effectively offered significantly reduced computational time compared to the previously mentioned protocols.
## 4 Simulation Results and Discussion
### Case 2: Sensitivity Analysis with Criterion Variation Within \(\pm 10\%\)
#### 4.3.4 Scenario 4: Variation in Upload Speed, Download Speed, Ping, and Packet Loss  
In this last scenario, we randomly vary the initial packet loss values, in addition to the previous variation in upload speed, download speed, and ping. Table 20 shows the random variation rates applied to the initial packet loss values of each satellite. The resulting decision matrix is shown in Table 21.
# Incorporating New Technologies in EEIO Models
## 2 Methodology
### Augment/Modify Make and Use Tables
#### 2.1.2 Make Table  
The production, in physical units, of the new commodity for each new industry was calculated in Equation (8). Now, the future production, in physical units of the similar commodity, is calculated according to Equation (13).  
\[P^{1}s_{2},s_{1}=(x_{s1}^{c,1}\div p_{s1})-\sum_{j\in\{1,\ldots,n\}\setminus\{ s_{2}\}}P^{0}_{j,s_{1}} \tag{13}\]  
where the first term, in parenthesis, corresponds to the total consumption of the similar commodity in physical units. On the other hand, \(P^{0}_{j,s_{1}}\) (Calculation details in Appendix A.2) is the current production of similar commodity \(s_{1}\) by industry \(j\) and thus the second term of Equation (13) refers to the current production of \(s_{1}\) by all industries different from the primary producer \(s_{2}\). It is assumed that the production of the similar commodity by all its secondary producers remains unaffected. Therefore, all decreases in similar commodity production are decreases in the similar industry (its primary producer) output.  
To modify the Make table, \(k\) rows and one column are added. Column \(m+1\), the one for the new commodity, will be modified according to Equation (14). The column corresponding to the similar commodity, \(s_{1}\), will be modified according to Equation (15) and the rows for the new technologies according to Equation (16). These changes correspond to the colors yellow and gray in Figure 1.  
\[V^{1}_{j,m+1}=\begin{cases}0,&\text{if }j\in\{1,\ldots,n\}.\\ (P^{1}_{j,m+1}\cdot p_{m+1,tech-i}),&\text{if }j\in\{n+1,n+2,\ldots,n+k\}. \end{cases} \tag{14}\]  
\[V^{1}_{j,s_{1}}=\begin{cases}V^{0}_{j,s_{1}},&\text{if }j\in\{1,\ldots,n\} \setminus\{s_{2}\}.\\ 0,&\text{if }j\in\{n+1,n+2,\ldots,n+k\}.\\ (P^{1}_{j,s_{1}}\cdot p_{s_{1}}),&\text{if }j=s_{2}.\end{cases} \tag{15}\]  
Note that Equation (16) indicates that new industries do not produce any of the existing commodities, and Equation (14) indicates that existing industries do not produce the new commodity. These are the assumptions in gray in Figure 1. In addition, since the Make and Use tables are in monetary units, transformation from physical units is required. To transform the similar commodity \(P^{1}_{s_{2},s_{1}}\) into \(V^{1}_{s_{2},s_{1}}\) the price for the similar commodity is used. To transform the new-commodity production by each of the new industries, the minimum selling price (MSP) of the new commodity for each new industry is used. The rest of the Make matrix/Make transactions remain unchanged.  
After augmenting the Make table, the total commodity output \(q^{M,1}\) and total industry output \(x^{M,1}\) can be recalculated following input-output basic accounting Equations (17) and (18). Note that these equations are in matrix notation.  
\[q^{M,1} =\mathbf{1}^{\prime}V^{1} \tag{17}\] \[x^{M,1} =V^{1}\mathbf{1} \tag{18}\]  
where \(\mathbf{1}\) is a vector of ones of the appropriate size.
## 2 Results
### Expression Analysis of Genes Related to ROS Production and Scavenging  
The staining results suggest that the occurrence of petiole spot is correlated with cell death (Figure 2). As a burst of reactive oxygen species (ROS) consistently coincides with cell death, we analyzed the expression of genes involved in ROS production and scavenging. Notably, 53 differentially expressed genes strongly correlated with ROS production and scavenging were identified (Pearson correlation coefficients > 0.8, \(p\)-values < 0.05) at the transcriptomic or proteomic level (Figure 6).  
Among the identified DEGs, 10 genes were contributors to ROS production, including BraA10g005020.3C, BraA09g043070.3C (acyl-CoA oxidase, ACX), BraA02g009250.3C (aldehyde oxidase, AAO), BraA09g016610.3C (superoxide dismutase, SOD (Cu-Zn)), BraA09g006630.3C (SOD (Fe)), and BraA01g039910.3C (SOD (Mn)). Compared to the resistant counterpart, the susceptible line showed higher expression levels in BraA01g021740.3C, BraA08g008660.3C, BraA09g032560.3C (ferredoxin, Fd), and BraA09g012740.  
As for ROS scavenging, forty-three genes were identified, sixteen of which exhibited increased expression in CKB compared to those in CKA, whereas the remaining genes displayed the opposite pattern of expression. The same trend is present in TB compared to CKB. The development of petiole spot in Chinese cabbage appears to be intricately linked to ROS production in the leaves. The delicate balance between ROS production and scavenging emerges as a crucial factor in differentiating susceptibility from resistance to petiole spot.  
Figure 5: Analysis of expression of genes related to nitrogen metabolism. (**A**) Heat map of expression of nitrogen metabolism pathway and related enzymes. (**B**) The content of ammonium NH4\({}^{+}\)-N in different samples. (**C,D**) Activity of GS and GDH enzymes in different samples. Values are expressed as the mean \(\pm\) SE (n = 3). Different letters denote statistically significant differences among the different groups (one-way ANOVA \(p<0.05\), Tukey’s test).
# A Privacy-Preserving Protocol for Utility-Based Routing in DTNs
## 4 System Design of PPUR
### Collection of Encounter Record Information and Calculation of Real Utility Value  
Nodes generate the encounter record information anonymously upon encounter. A node forwards encounter record information to the TA when connecting the AP. The TA receives the encounter record information, generates the session key, and then decrypts the information through its main key \(s\). The TA obtains the node's real ID and then calculates the node's routing utility value according to Equation (1) in [2] (other routing utility value calculating methods can be adopted when needed):  
\[U_{ij}=U_{ij(old)}+\left(1-U_{ij(old)}\right)\ \times\ U_{init},\ \ 0<U_{init}\leq 1, \tag{1}\]  
where \(0\leq U_{ij}\leq 1\) denotes the new utility value of nodes \(N_{i}\) and \(N_{j}\), \(U_{ij(old)}\) is the utility value before update, and \(U_{init}\) is the initialization constant. The TA returns the utility value to the nodes. In this way, a node cannot get any privacy information except the real utility values.  
If multiple pseudo-IDs only correspond to one real ID in the encounter record information sent to the TA, a malicious node can infer the corresponding relationship between the pseudo-IDs and real ID based on the returned utility value's changing result. To prevent the corresponding relationship from being inferred, TA will return the true utility value to a node only when the pseudo-IDs in encounter record information correspond to multiple real IDs. If there are \(k\) different pseudo-IDs, the probability that a node can infer other nodes' true ID from the utility value's changing result is only \(1/k\).
## 4 Case Study
### Data Fault Recognition and Analysis  
From Figure 7a, it can be found that the data near the sampling point 300-350 appear to be of a more serious fault type. Other sampling points, such as 500, have a different degree of fault data in the vicinity of the sampling point. Compared to Figure 7b, it is known that the No. 3 detector and No. 7 detector have the greatest contribution to the fault data, of which the number 3 detector has a relatively more serious fault. Among them, the real line represents the \(T^{2}\) control limit of 99% of confidence.  
In order to verify the advantages of the proposed fault data detection model, each vector of the data to be detected is first decomposed into a wavelet packet, and the principal component analysis is used to model the matrix composed of each node of the wavelet packet decomposition. The wavelet packet energy difference of each node is calculated, and the component of the fault is found to be analyzed by the PCA model, and the location where the data fails under the component can be determined.
# Research on the Dynamic Characteristics Analysis and Power Control Method of Heat Pipe Reactors
## 2 Dynamic Modelling of HPR
### Equivalent Thermal Conductivity Model of Core  
The arrangement of heat pipes, the monolith, and fuel rods in the MegaPower core is shown in Figure 2. Excluding the edge of the core, each heat pipe is surrounded by six fuel elements, and each fuel element is surrounded by three fuel rods and three heat pipes. In Figure 3, each heat pipe and six fuel elements are regarded as a hexagonal assembly. Each fuel element in the hexagonal assembly gives \(1/3\) of its own heat to the central heat pipe, so the central heat pipe corresponds to two fuel elements in view of heat transfer (\(1/3\times 6=2\)). To simplify the heat transfer analysis, a single hexagonal component can be equivalent to a cylinder component [29], as shown in Figure 3.  
In the cylindrical thermal conduction model, the equivalent fuel element on the periphery is equated from two actual fuel elements to keep the thermal power constant. The monolith is equivalent to cylindroid from an irregular shape, but its own volume and heat transfer area with heat pipe remain unchanged. The thickness of the helium gap needs to be changed before and after equivalence as both contact areas of the monolith and the fuel to the helium gap are changed after equivalence. Since the contact area between the air gap and other materials changes after equivalence, the principle of helium gap equivalence is to keep the thermal conductivity resistance unchanged, so the thickness of the helium gap needs to be changed. The heat pipe is in the center of the assembly and does not change after equivalence, but it is worth noting that the monolith also acts as the wall of the evaporation section of the heat pipe in the \"MegaPower\" reactor design.  
Figure 3: A single group of hexagonal components is equivalent to a cylinder heat conduction model [29].  
Figure 2: Arrangement of heat pipes, the monolith, and fuel rods in the core [2].
# Rapid and Accurate PPA Prediction for the Template-Based Processor Design Methods
## 3 PPA Prediction Method
### Prediction Model Derivation  
Having the effect of each parameter of the parameter set on PPA is not enough to predict the PPA of a certain parameter set design. Moreover, the relationship between the multifactor influence and a single-factor effect on the same system is a black box. Therefore, multivariate regression is utilized to fit the multiparameter influence on the PPA via a single parameter effect. The PPA estimate of the parameter set can be calculated via this method and the PPA samples of each parameter. In this study, the fit model is built separately based on a weight model and Amdahl's law.
# Effective Seed Scheduling for Directed Fuzzing with Function Call Sequence Complexity Estimation
## 5 Evaluation
### Ablation Study (RQ2)  
We conducted ablation experiments using the same dataset to understand the impact of each module in SEZZ on performance. To assess the influence of reaching seed scheduling on performance, we removed the reached seed scheduling and implemented a variant called SEZZ-R. Correspondingly, to evaluate the effect of reached seed scheduling on performance, we removed the reaching seed scheduling in SEZZ and implemented a variant named SEZZ-R*. The result is displayed in Figure 4, which presents a heatmap illustrating the time required to expose vulnerabilities. The color gradient reveals that the SEZZ row consistently displays lighter green blocks than SEZZ-R and SEZZ-R*, demonstrating its greater efficiency in reproducing vulnerabilities. The detailed experimental data is recorded in Table 3.  
The experimental results indicate that for most target vulnerabilities, SEZZ reaches optimal performance on most targets, demonstrating the effectiveness of our design. Notably, for easily reproducible vulnerabilities, such as PDF016, PNG003, and XML017, the improvements in efficiency from reaching seed scheduling and the reached seed scheduling are not very significant. However, for hard-to-reproduce vulnerabilities, such as PDF021, SND007, and SSL001, the seed-scheduling strategy significantly increases the likelihood of triggering these vulnerabilities. It is worth noticing that the improvement in SEZZ-R* is 1.09\(\times\), meaning that the effect of reaching seed scheduling is not significant. The main reason is that the reaching time accounts for a lower proportion of the total time. The second reason is that more samples can reach the target point in a short time. By observing the example where the TTR is greater than 1000s, such as PDF018, SQL018, and TIF002, we can find that the average improvement is 1.80\(\times\).  
Figure 4: The heatmap of the time to expose (TTE) vulnerabilities by SEZZ-R, SEZZ-R*, and SEZZ in seconds. TO denotes that a tool reaches the time limit (timeout) before triggering a vulnerability. The better result of a case is with lighter green.
# An SSI-Based Solution to Support Lawful Interception
## 4 Proposed Approach
### Setup  
Through this phase, the LEA starts a new LI procedure by leveraging the SSI framework. This phase is performed through the following steps:  
1. First, the LEA contacts the LEMF to establish a secure peer-to-peer channel according to the DIDComm protocol (see Section 2.2).  
In this connection, the LEMF provides the LEA with all the technical information needed for the interception procedure (for example, the phone number and the IMEI of the SIM of the intercepted subject).
3. The LEA releases the credential \(VC_{LEMF}\) to the LEMF certifying that the latter is in charge of this LI procedure. This credential will be presented by the LEMF in all the next interactions with LEA.
4. Finally, an interaction according to the standard [10] is performed between LEA and the NO, so that the latter can define the interfaces to be used in the interception process.  
The above steps are summarized in the sequence diagram of Figure 1.
# Optimal Learning and Self-Awareness Versus PDI
## 2 Materials and Methods
### Luenberger-Like Controllers (i.e., Nonlinear-Enhanced Proportional-Derivative-Integral, PDI)  
The input torque vector, \([T_{x},\,T_{y},\,T_{z}]\) is a signal generated by the trajectory block in Figure 1. However, this signal is not tuned to adjust to real world influences, where mechanical hardware can introduce errors due to incorrectly or un-modeled attributes, noise, etc. In order to overcome these losses, either a feed forward controller, a feedback controller, or a combination of both controllers can be used to counter errors. More specifically, proportional, integral, and derivative (PID) gains are correlated only to the position error generated when moving from one position to another position to correct the errors, as displayed in Equation (2). On the other hand, proportional-derivative-integral (PDI) control uses full-state feedback to eliminate virtual-zero references inherent to the cascaded topology of the classical PID form. The modern PDI form is augmented here with a nonlinear term \(\omega\times J\omega\) to counter the nonlinear coupling induced by the cross-product operation necessary to account for rigid body motion in moving reference frames, and the combined form is displayed in Equation (3) whose topology is illustrated in Figure 2.  
\[\text{{Classical PID feedback form}}:\ u_{fb}=k_{p}e_{\theta}+k_{d}\dot{e}_{ \theta}+k_{i}\int e_{\theta} \tag{2}\]  
\[\text{{Nonlinear -enhanced PDI}}:\ u_{fb}=-k_{p}(\theta_{d}-\theta)-k_{d}( \omega_{d}-\omega)-k_{i}\int(\theta_{d}-\theta)dt-\omega\times J\omega \tag{3}\]However, using only the position error \(\mathrm{e}_{0}\), its integral \(\int\mathrm{e}_{0}dt\), and its derivative \(\frac{d}{dt}e_{0}\) results in inaccuracies. This is due in part to noise amplification of the derivative calculation, which is both inefficient and inaccurate as a result of the virtual zero reference created in the cascaded topology of a PID controller. This inaccuracy can be prevented by calculating both the position error and the velocity error, which has been done in this experiment via a nonlinear-enhanced Luenberger proportional, derivative, integral (PDI) controller [15; 16; 17; 18] where the enhanced Luenberger Controller differs from the conventional PID controller which only receives a position error and does not have a nonlinear decoupling term to account for moving reference frames. The result is a controller that outputs a commanded torque to the actuator block in Figure 1. Topologies are shown of the overall feedback controller in Figure 4, and the enhanced Luenberger PDI controller in Figure 5. A further augmentation adds a nonlinear component: \(\omega\times J\omega\) accounting for coupled motion.