esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Problem Modeling  
The order of execution of a subtask-generated plan for a mobile application represented by \(G~{}=~{}(T,E)\) is \(A_{1:n}~{}=~{}\left\{a_{1},a_{n}\ldots a_{n}\right\}\), where \(a_{i}\) is made up of the offloading decision \(d_{i}\) of subtask \(t_{i}\) and the mobile device's WPT service time \(T_{i}^{WPT}\). The offload decision of subtasks and the WPT service of mobile devices are performed sequentially according to the generated plan, and all precursor tasks in the plan are completed before the subtasks due to the dependencies between tasks. Therefore, the completion time of task \(t_{i}\) depends on both the completion time of its precursor task and the resource availability time. In order to model the time delay of local processing and offloading processing, the uploadcompletion time, edge server processing time, result download time, and local processing completion time of task \(t_{i}\) are set as \(FT_{i}^{upload}\), \(FT_{i}^{compute}\), \(FT_{i}^{download}\), and \(FT_{i}^{MD}\), respectively. Moreover, if the WPT service shares a module with uplink and downlink, their available time has an impact, and the available time of uplink and downlink resources is denoted as \(AT_{i}^{upload}\) and \(AT_{i}^{download}\). \(AT_{i}^{compute}\) and \(AT_{i}^{MD}\) are used to describe the available time of edge server and mobile device computing resources. The available time of these resources is mostly determined by the end time of the preceding task that uses the resource, which in this paper is set to 0 if the resource is not utilized. According to the aforementioned definition, the task execution delay model, device energy consumption model, and device power model are created correspondingly.
# A Survey of Structure of Atmospheric Turbulence in Atmosphere and Related Turbulent Effects
## 3 Theoretical Studies of Turbulent Effect  
It is well known that both the amplitude and phase of optical wave experience random fluctuations caused by small random changes in the index of refraction as an optical wave propagates through atmospheric turbulence. The effect of atmospheric turbulence on optical wave propagation is fundamentally the destruction of its coherence, which includes manifested by various atmospheric turbulence effects, such as irradiance scintillation, angle-of-arrival fluctuations, beam wander, beam spread, spot distortion, and so on. In order to describe these random amplitude and phase fluctuations, one needs to solve the wave equation for the electric field of optical wave or for the various moments of the field. In the process of solving the above equations, the specific characterization of atmospheric turbulence was not considered. Therefore, the theoretical method of Kolmogorov atmospheric turbulence effect is also applicable to non-Kolmogorov atmospheric turbulence. Here, we first review the representative and classical theory for optical wave propagation in Kolmogorov turbulence and then elaborate the research results of non-Kolmogorov atmospheric turbulence effects thus far.
## 3 Results and Discussion
### Complementary Physical Characterization Tests  
In this section, Table 10 shows the results corresponding to the following properties analyzed: Shore D surface hardness, apparent density of the hardened mortar, adhesion and capillary water absorption. As shown in Table 10, mortars made with natural aggregate have a higher Shore D surface hardness compared to masonry mortars made with recycled concrete aggregate. In addition, it can be seen how the incorporation of aramid fibers does not produce an improvement in this physical property of the material [82]. Additionally, it is observed that the bond strength of the mortar applied on a brick masonry is reduced in the case of mortars made with recycled aggregate, with worse results in the dosages with a cement/aggregate ratio of 1:4 and with the incorporation of fibers being detrimental to this property [83]. Figure 5: Scanning electron microscopy: images (**a**,**b**) corresponding to mortar made with recycled concrete aggregate without fibers RA-1:3; images (**c**,**d**) corresponding to mortar with recycled concrete aggregate and aramid fiber RA-1:3-AF. Regarding the bulk density of the mortars, it can be seen that mortars made with recycled aggregate are lighter than traditional mortars. This greater lightness can be attributed to a greater porosity of the mortars made with recycled concrete aggregate, which in turn has been manifested in a decrease of the flexural and compressive mechanical properties [84]. Finally, and in agreement with the aggregate characterization tests, it can be observed that mortars made with recycled concrete aggregate have a higher capillary water absorption than mortars with natural aggregate, although this higher capillary absorption is reduced in the recycled mortar mixes made with aramid fiber. This higher absorption can have a negative impact on the formation of pathologies derived from dampness that can be difficult to correct [85]. After analyzing Table 11, it can be seen that for the properties of adhesion and water absorption by capillarity, there are statistically significant differences for the two factors included in the study, since they have a \(p\)-value below the significance level (\(\alpha=0.05\)). However, in the case of bulk density and surface hardness, the only significant factor is the cement/aggregate ratio. Thus, the results of the ANOVA analysis support the discussion in Table 10. The results obtained for the multiple range tests are presented in Tables 12 and 13. \begin{table}
\begin{tabular}{c c c c c} \hline
**Type** & **Hardness (Ud. Shore D)** & **Bulk Density (kg/m\({}^{3}\))** & **Adherence (MPa)** & **Absorption (kg/mm\({}^{2}\)min\({}^{0.5}\))** \\ \hline NA–1:3 & 81.7 & 2093 & 0.52 & 0.53 \\ NA–1:4 & 77.0 & 1993 & 0.57 & 0.58 \\ RA–1:3 & 72.7 & 1831 & 0.42 & 0.67 \\ RA–1:4 & 70.7 & 1754 & 0.38 & 0.76 \\ RA–1:3-FA & 71.7 & 1855 & 0.36 & 0.61 \\ RA–1:4-FA & 71.0 & 1776 & 0.32 & 0.72 \\ \hline \end{tabular}
\end{table} 
Table 10: Results of complementary physical characterization tests.
# Opportunistic Multipath Routing in Long-Hop Wireless Sensor Networks
## 4 Performance Evaluation
### Packet Delivery Ratio Versus End-to-End Distance and Link Reliability  
In this section, to investigate the effect of the link reliability on the packet delivery ratio, we present the performance evaluation results while increasing the end-to-end distance when the link reliability is 80% and 90%. In this simulation, the failed packets during the transmission process do not retransmit to confirm the change in the packet delivery ratio.  
Figure 6 shows the packet delivery ratio versus the end-to-end distance when the link reliability is 90%. The proposed scheme could can satisfy the required packet delivery ratio by path bifurcation and high packet delivery ratio based on opportunistic routing. RGMR also satisfies the required packet delivery ratio based on several radio-joint paths generated by the required packet delivery ratio. However, REER and RRDLA cannot satisfy the required packet delivery ratio because they have a limitation on the number of paths. That is, two paths of REER are not sufficient to satisfy the required packet delivery ratio in a long-hop network. Similarly, although RRDLA could find the best path through the learning algorithm, it cannot satisfy the required packet delivery ratio in the long-hop network as it exploits only one path to transmit data.  
The packet delivery ratio is further degraded when the link reliability is degraded. Figure 7 shows the packet delivery ratio versus the end-to-end distance when the link reliability is 80%. The packet delivery ratio is slightly degraded; however, the proposed scheme can satisfy the required packet delivery ratio for the abovementioned reasons. Similarly, RGMR can satisfy the required packet delivery ratio; however, the packet delivery ratio decreases when the scale of the network increases to greater than 800 m\({}^{2}\). This is because RGMR cannot construct more paths to satisfy the requirement. That is, the radio-joint multipath scheme RGMR requires a wide path to prevent collisions and interference between each path. Owing to this, the packet delivery ratio is degraded because it cannot construct a sufficient number of paths. For the REER and RRDLA cases, the degradation of the packet delivery ratio is more severe. When the network size is 1000 m\({}^{2}\), the packet delivery ratio of REER sharply drops from approximately 60% to approximately 20% for a link reliability of 90%. Likewise, the packet delivery ratio of RRDLA drops from approximately 34% to approximately 10% for a link reliability of 90%.  
Figure 6: Packet delivery ratio vs. end-to-end distance with 90% of link reliability.
# PDF Malware Detection Based on Fuzzy Unordered Rule Induction Algorithm (FURIA)
## 3 Research Methodology  
This study aims to develop a FURIA-based model for PDF malware detection. The overall research methodology is presented in Figure 1, which starts from data acquisition to comparison and performance analysis of each employed model. The dataset used in this study has been taken from the University of New Brunswick (UNB), Canadian Institute for Cybersecurity, [[http://205.174.165.80/CICDataset/CICEvasivePDFMal2022/Dataset/](http://205.174.165.80/CICDataset/CICEvasivePDFMal2022/Dataset/)]([http://205.174.165.80/CICDataset/CICEvasivePDFMal2022/Dataset/](http://205.174.165.80/CICDataset/CICEvasivePDFMal2022/Dataset/)) (accessed on 5 February 2023). The dataset consists of 33 features, of which 32 are independent and 1 is dependent. The first 11 features are removed because they do not act in the analysis phase. These attributes are known as general features, including PDF size, metadata size, encryption, header, page number, text, image number, font objects, object number, number of embedded files, and the average size of all the embedded media. For extracting such features, we have used ClassifierAttributeEvaluator methods using a ZeroR classifier and the Ranker searching method. The selected features are ranked as follows:  
Selected Features: 21,7,8,10,6,5,4,3,2,9,11,20,18,19,12,17,16,15,14,13,1:21. These features are titled as: colors, startxref, pageno, ObjStm, trailer, xref, end-stream, stream, endobj, encrypt, JS, XFA, launch, EmbeddedFile, Javascript, RichMedia, JBIG2 Decode, Acroform, OpenAction, AA, and obj, respectively. Table 1 presents the description of each selected feature. Figure 1: Methodology workflow [22]. For model training and testing, a standard method of K-fold validation [23; 24] is used. Here, the value of K is selected as 10. The performance of each employed model is evaluated using some of the standard evaluation metrics, including mean absolute error (MAE), recall, precision, Matthew's correlation coefficient (MCC), FM, and classification ACC. These measures can be calculated as follows:  
\[\text{MAE}=\frac{1}{2}\sum_{\text{j=1}}^{\text{n}}\lvert\text{y}_{\text{i}}- \text{y}\rvert \tag{1}\]  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN}) \tag{2}\]  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}) \tag{3}\]  
\[\text{MCC}=\frac{\text{TP}*\text{TN}-\text{FP}*\text{FN}}{\sqrt{(\text{TP}+ \text{FP})(\text{TP}+\text{FN})(\text{TN}+\text{FP})(\text{TN}+\text{FN})}} \tag{4}\]  
\[\text{FM}=\frac{\text{TP}}{\text{TP}+\frac{1}{2}(\text{FP}+\text{FN})} \tag{5}\]
# The BCL Method for DSSC: Basis and Applications +
## 7 The Role of Lithium on the Photo Conversion Efficiency of Ruthenium Dyes
### Adduct Generation
#### 7.2.4 Effect on the Performance Index  
The main objective of this section is to calculate the effect of lithium on the theoretical index GEI. As mentioned in previous chapters, the global efficiency index is obtained through the product of three local indexes associated with the transmission of the absorbed energy (F1) the injected charge (F2) and the regeneration efficiency (F3).  
Table 8 displays the calculated values for the ruthenium dye 1(S0) and the adduct 1(S0)Li. The most important result of this table is that the GEI confirms that the lithium adduct has a higher global efficiency yield than the dye alone. The causes of this increase in efficiency can be determined through the variations of the three indices. First, it can be observed that in the case of the adduct the values of the indices F1 and F3 increase above the values of the free complex. Looking to the enhancement of the energy obtained by light harvesting, it is due to an increase in the energy delivered to the anchor ligand, and because of a greater level of participation from the anchor group.  
The second indexes measuring the amount of charge injected diminishes because the SOMO orbital is stabilized, and the energy gradient is lower.  
In the previous analysis of molecular orbital energies, it was mentioned that the presence of lithium causes an increase in the oxidation potential of ruthenium dye. The enhancement of the redox potential caused by the absorption of lithium on the surface of the dye could lead to a pseudo-capacitance effect [23; 24] which increases the current transport and favors the regeneration process.  
According to the value of the GEI displayed in the last column of Table 8, it can be concluded that the presence of the metastable Li-adduct has a positive impact on the Global Efficiency Index, which can be related to a higher value of the PCE of the cell, caused mainly  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**COMPLEX** & **EA** & **ED** & \(\mathbf{\Phi_{t}}\) & **F1** & \(\mathbf{\Delta\varepsilon}\) & \(\mathbf{\Delta V_{DA}}\) & **F2** & **F3** & **\%GEI *** \\ \hline
1(S0) & 0.96 & 0.36 & 0.06 & **0.021** & 1.31 & \(-\)0.26 & **0.12** & **4.43** & **0.27** \\
1(S0)Li & 0.94 & 0.46 & 0.08 & **0.037** & 0.83 & \(-\)0.14 & **0.07** & **5.64** & **0.35** \\ \hline \end{tabular} (*) %GEI = F1F2F3/Esun, Esun = 4.13 eV.  
\end{table}
Table 8: Calculated values for the Global Efficiency Index.  
by two factors: a more efficient harvesting of sunlight energy, and a faster regeneration of the starting photo catalyst.
## 5 Experiments
### First Experiment: Evaluate the Model for STS Tasks  
We evaluate the SelfCCL model on seven standard STS tasks: STS 2012-2016 [58; 59; 60; 61; 62], STS-Benchmark [63], and SICK-Relatedness [64]. STS 2012-2016 and STS-Benchmark assign gold labels between 0 and 5, and SICK-Relatedness assign gold labels between 1 and 5 to the sentence pairs. Gold-labels show the semantic relatedness of the sentence pairs. Spearman's rank correlation between the cosine-similarity of the sentence embeddings and the gold labels is reported for each benchmark in columns. A modified version of the SentEval toolkit from Ref. [12] is used to evaluate models on STS tasks. SentEval [65] is a general library for assessing the quality of sentence embeddings ([[https://github.com/facebookresearch/SentEval](https://github.com/facebookresearch/SentEval)]([https://github.com/facebookresearch/SentEval](https://github.com/facebookresearch/SentEval)) (accessed on 13 December 2022)). Ref. [12] released a modification of SentEval in their Github repository. They added the \"all\" setting to the original SentEval so that the overall Spearman's correlation was computed for all topics in sub-tasks. In addition, additional regressors were removed from SentEval by Ref. [12] for training frozen sentence embedding on the STS-B and SICK-R datasets. **Results:** As shown in Table 4, SelfCCL-BERT and SelfCCL-SBERT models outperform baseline SBERT, SBERT-base-nli-v2, and SOTA SimCSE. For SelfCC-BERT, the corresponding improvements are 0.23 point compared to the SOTA SimCSE and 6.91 compared to baseline SBERT. For SelfCCL-SBERT, the corresponding improvements are 0.17 points compared to SBERT-base-nli-v2 and 6.88 compared to baseline SBERT. It is clear that we could improve the performance of SOTA SimCSE to some extent. Although, our models are still behind the SOTA SupMPN on STS tasks, according to the Table 3, our models require low computational power compared to it, and so they are more cost-effective in terms of hardware requirements.
## 7 Discussion
Based on the simulation, the propagation and evolution process of disturbance in the system was explored, and the results of the simulation can be seen in Figures 4-6. The results in Figures 4 and 5 demonstrate the performance advantages of the proposed model compared to the FVD model, BLVD model, and SLV model. The comparison between pairs reveals the contribution of different information incorporated in these models to the traffic flow stability. Specifically, at t = 300 s and t = 1200 s after disturbance is applied, it can be observed that the traffic flow composed of vehicles under the control of the established model has a stronger capability to resist disturbance, suppressing the same disturbance within a smaller scale. On the other hand, the simulation results in Figure 6 employing the headway as the indicator reveal the same conclusion; that is, compared to the FVD model, BLVD model, and SLV model, the traffic flow controlled by the model established in this paper has stronger stability. A good agreement between the theoretical analysis and the numerical simulation suggests that the stability of traffic flow can be significantly enhanced by considering the motion state of the SLV and the rear vehicle, which also confirms the necessity of considering these items in the research of car-following behavior and traffic flow. The results of linear stability and nonlinear analysis collectively suggest that they demonstrate remarkable congestion mitigation efficacy while effectively enhancing traffic flow capacity in the urban expressway and highway environments by reinforcing traffic flow stability and improving its capability to resist, suppress, and absorb disturbance. The results of this work could provide a reference or basis for further investigation into car-following behavior and traffic flow under the conditions of the penetration of CAV technologies, such as V2X. Nevertheless, there are limitations in this work, such as not considering the impact exerted by the difference between the type of vehicle and its driver (the information on these factors is available for drivers under the conditions of penetration of CAV technologies, such as V2X), which we look forward to exploring in future work.
## 4 Conclusions  
Taking advantage of the sensitivity of the SPP resonance of metals to the refractive index of the dielectric layer in contact with the metal, we observed a shift in the resonance energy due to the Pockels effect of the water (in the EDL) on the Ag electrode surface when an \(E\) field was applied normally to the electrode surface. When an AC voltage was applied between the Ag film and the Pt electrode in the attenuated total reflection (Kretschmann) geometry, the SPP resonance shifted spectrally in synchronization with the frequency of the AC voltage, and the magnitude of the energy shift was proportional to the voltage. This indicates that the signal is due to the Pockels effect. To evaluate the Pockels coefficient, we calculated the SPP resonance spectrum of the Ag film covered with a thin electric double layer of thickness \(d_{w}\) and the \(\Delta n\) from bulk water, using the known complex dielectric constant of Ag. As long as the penetration depth of the SPP into the water layer, \(\delta_{w}\), is larger than the thickness of the EDL of water, \(d_{w}\), we showed that the spectral shift is proportional to the optical path length change \(\Delta nd_{w}\), and evaluated \(\Delta nd_{w}\) which reproduces the spectral shift of the experiment. Therefore, although the effective thickness of the EDL under AC \(E\) field is not precisely known, the Pockels coefficient was determined to be about 5 pm/V from \(-\frac{2\Delta nd_{w}}{n_{w}^{2}V}\) using the voltage \(V\) distributed in the EDL determined through AC impedance measurement.  
Figure 12: Cole–Cole plot from the impedance measurement for Ag electrode with the applied voltage of 2 V. The green circle shows the impedance at 224 Hz, closest to 222 Hz. The same equivalent circuit as in Figure 9b is assumed for fitting.  
**Author Contributions:** Conceptualization, E.T., methodology, E.T., Y.N., and S.S., software, R.W., validation, Y.N., K.S., and E.T., formal analysis, Y.N., A.O., and R.W., investigation, Y.N. and S.S., writing--original draft preparation, E.T. and Y.N., writing--review and editing, E.T., T.K., and K.S., visualization, Y.N., supervision, E.T., project administration, E.T., funding acquisition, E.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Grant in Aid for Scientific Research(C) (Grant Number JP15 K05134) and Scientific Research(B) (Grant Number JP20 H02659), Japan Society for the Promotion of Science (JSPS); Murata Science Foundation.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** The authors acknowledge [PERSON] and [PERSON] for their support in the preparation stage of the experiment.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Correlations Between Key Electrode Data and TE Analysis  
A TE analysis to compare neural connectivity in schizophrenia patients and HCs was also conducted. TE, an information-theoretic measure, quantifies the directional flow of information between different brain regions, making it particularly suited for studying the complex network of neural interactions associated with cognitive and emotional processing.  
This study focused on correlating EEG data from the nine key electrodes in the open EEG dataset (Fz, FCz, Cz, FC3, FC4, C3, C4, CP3, CP4) with the electrodes in the Muse 2 headband (AF7, AF8, TP9, TP10). Through the use of TE, distinct connectivity patterns can be identified in order to serve as biomarkers for schizophrenia. In particular, the TE analysis identified decreased connectivity between several pairs of frontal and central electrodes in schizophrenia patients, which confirms the speculations that disrupted information transfer might account for the cognitive dysfunctions observed in the disorder.  
The analysis also underscores the applicability of the Muse 2 headband for real-time monitoring, given its strategic placement over the frontal and temporal brain regions. Based on the TE results, the Muse 2 headband can effectively identify neural signals associated with schizophrenia, and it is thus useful for continuous mental health monitoring. Therefore, while using Muse 2 EEG data with the NeuroPredict platform, this study proposes that these connectivity insights based on TE processing could be applied to identify potential signs of cognitive decline or schizophrenia relapse. The continuous monitoring and real time neural connectivity analysis may lead to more proactive, personalized interventions, improving long-term outcomes for patients.
# Coordination of Macro Base Stations for 5G Network with User Clustering
## 4 Case Study
### Simulation on 3 \(\times\) 3 5G Macro BSs Network  
#### 4.1.1 System Description  
The simulated 3 \(\times\) 3 5G macro BS network is shown in Figure 6 with the geographic distribution of each cell. The dot in each subcell represents the cluster of all users in that subcell. The traffic load, renewable generation power, and electricity price profiles are shown in Appendix A.