esa-sceva/satcom-chunk-collection huggingface dataset:
# Interaction Strength Analysis to Model Retweet Cascade Graphs
## 3 The Interaction Strength-Based Network (ISN) Approach to Generating Retweet Cascade Graphs  
An information cascade \(C\) is defined as a directed graph \(C=(\mathds{V},\mathds{E})\) in which each node \(u\in\mathds{V}\) represents a user \(u\) and each edge \((u,y)\in\mathds{E}\) represents the link from user \(u\) to user \(y\). A retweet cascade graph is a class of information cascade characterized by a tree structure where the root node is the author of the original tweet (\(root_{author}\)), which was posted at time \(root_{time}\).  
Our purpose is to estimate the retweet information cascade graph using the following information:  
* The retweet's creation time \(t_{r}\);
* The interaction strength between each couple \((u,w)\in\mathds{V}\), which reflects the trust between users;
* The friend lists \(L_{\mathds{F}}\) for the remaining nodes \(\mathds{F}\subset\mathds{V}\), for which no interactions were found (e.g., \(IS_{\mathds{F}}=Null\)).  
A link in the cascade between any two nodes in \(\mathds{V}\) has to meet the following condition:  
\[\begin{split}\exists\quad E(u,w)\in\mathds{E}\quad\forall\quad u,w\in\mathds{V}&\to t_{u}>t_{w}\quad\land\quad\exists\quad IS_{u,w }\\ s.t.\quad IS_{u,w}&=\max(IS_{\mathds{V} \setminus\{u\}})\end{split} \tag{1}\]  
In other words, the user \(u\) is connected to the user \(w\) if \(w\) retweeted the message before \(u\) and if the IS of \(u\) with respect to \(w\) is the maximum of all the IS among user \(u\) and the other accounts that retweeted before \(u\). The procedure to find the IS value is described in the next subsection. Whenever the user \(u\in\mathds{V}\) has no interactions with any other accounts, the proposed method adopts the approach based on social networks [11] collecting user \(u\)'s friends list \(L_{u}\) such that:  
\[\begin{array}{ccccc}\exists&E(u,w)\in\mathds{E}&\forall&u,w\in\mathds{V}\to w \in L_{u}&\land\\ t_{w}=min(t_{i}-t_{u})&\forall&i\in L_{u}\cap\mathds{V}\setminus\{u\}&\land&t_ {i}>t_{u}\end{array} \tag{2}\]  
If there is no information about interactions, the node \(u\) is connected to the user in his/her friend list \(L_{u}\) that retweeted the tweet \(root_{author}\) before \(u\), using an inverse chronological order, i.e., minimizing the difference between the user \(u\) posting time \(t_{u}\) and \(t_{i}\), where \(i\) belongs to the set of users in \(L_{u}\). In the following, Sections 3.1 and 3.2 describe the steps performed to find the interaction strength (IS) and to evaluate different sets of possible weights. Then, Section 3.3 explains how to connect remaining users according to social network information.
## 4 Results and Discussion
### Effect of Vibration on the Shear Stress  
#### 4.2.1 0.25 g Vibration at Different Frequency  
Figure 8 shows the time-domain shear stress at the internal wall of the extrusion liquefier obtained by DMMFB and FEA without and with different-frequency vibration applied, of which the amplitude is 0.25 g and the frequency is, separately, 100, 200, 300, 400 and 500 Hz. With vibration applied, the shear stress in time domain changes periodically as the pressure drop changes to a periodic variation. From the comparison, it can be seen that both the values and trend of the analytical shear stress are in good agreement with the simulated ones.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Frequency (Hz)**} & \multicolumn{4}{c}{**Effective Value**} \\ \cline{2-5}  & **DMMFB (MPa)** & **Drop (\%)** & **FEA (MPa)** & **Drop (\%)** \\ \hline
0 (No vibration) & 16.1 & - & 15.7 & - \\
100 & 10.9 & 32.3 & 10.7 & 31.8 \\
200 & 9.5 & 41.0 & 9.2 & 41.4 \\
300 & 8.3 & 48.4 & 8.2 & 47.8 \\
400 & 7.6 & 52.8 & 7.5 & 52.2 \\
500 & 6.9 & 57.1 & 6.8 & 56.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effective value of the pressure drop without and with 0.35 g vibration applied at different frequencies.  
Figure 7: Effective value of the internal pressure drop inside the extrusion liquefier without and with different-amplitude vibration applied.  
Table 6 summarizes the above effective value of the shear stress for each condition. The shear stress is decreased with the application of vibration and further decreased with the increase of vibration frequency. For example, when no vibration is applied, the effective value of the shear stress predicted by DMMFB is 0.89 MPa, when the frequency of the applied vibration is 100 Hz, the value is reduced down to 0.75 MPa, decreased by 15.7%, and when the frequency is 400 Hz, the value is further reduced to 0.44 MPa, decreased by 50.6%. This is because the shear stress is directly influenced by the pressure drop, see Equation (A5). Its changing situation and trend are similar to those of the pressure drop, as discussed previously.
## 4 Formalization of the Integration Approach
### Algebraic Specification of the Abstract Data Types for Earth Observation
An observation result such as a tweet could be generated by using the \"Message\" constructor, which has been introduced above, as part of the 'Value' ADT:  
**data** Volunteer = Volunteer {vgiOffering:: ObservationResult}  
In order to be later able to align our formal theory (ontology) for VGI sensing with DUL and its SSN extension, we also introduce an element that explicitly represents a piece of VGI as a senseable quality of a volunteer (Section 4.3 will provide more insides on the reasons behind this decision):  
**data** VGI = VGI Volunteer  
Now, a VGI sensor can be defined as the entity capable of translating the sensed offerings of volunteers into a new value-added observation result. **data** VGISensor = VGISensor {vsQuale:: ObservationResult, vsResult:: ObservationResult}  
For an earth observation (EO) satellite, the sensor can translate the stimuli that it perceives related to a particular quality--electromagnetic radiation from the surface of the ear--into an observation result that is an image in this case. Again we rely on the extension of the initial ontology as introduced above. In this case we depend on availability of the 'Image' constructor function in order to encode data received from cameras. Accordingly, the earth surface transmits radiation (here represented as a Float) and--vice versa--the electromagnetic quality can be associated to the earth surface:  
**data** EarthSurface = EarthSurface {radiation :: Float}  
**data** ElectromagneticRadiation = ElectromagneticRadiation EarthSurface  
**data** EOSatellite = EOSatellite {eoQuale:: [[Float]], eoResult:: ObservationResult}
# Advances in Graphene Field Effect Transistors (FETs) for Annine Neurotransmitter Sensing
## 3 Amine Neurotransmitter Detection via GFETs
### Histamine  
Although substantial progress has been made in histamine detection using graphene-based electrochemical sensing, the application of GFET sensors is still in its infancy. When a histamine molecule undergoes oxidation on a modified gate electrode, it releases four electrons, altering the potential drop fluctuations of the double electron layers and producing a Faraday current [252] similar to DA and EP. This process modifies the effective \(\mathrm{V_{G}}\), leading the channel current to respond sensitively to histamine concentrations [47]. [PERSON] et al. [180] proposed a solution-gated FET sensor based on a CVD-grown gate channel for histamine detection. The modified channel with gold nanoparticles and MWCNTs resulted in an n-type FET response, with measurement repeatability demonstrated after ten days. Their real-time investigations demonstrated a sensitive concentration-dependent FET response against 100 nM histamine, which was also highly selective against the interfents. Three different sensor batches revealed the excellent reproducibility of these findings.
# Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies
## 3 Evolving Computing Paradigms and Related Concepts
### Internet-of-Things
#### 3.2.2 New Challenges in IoT  
As billions of devices are connected globally, data expands exponentially and accumulates 24/7, driving big data to become the current buzzword [41]. The International Data Corporation (IDC) estimates that by 2025, IoT devices may reach 41.6 billion and create 79.4 zettabytes of data [42]. The five Vs of big data [43], namely, volume, variety, velocity, veracity, and value, pose distinct challenges. In the current scenario, the majority of data resulting from IoT devices are managed by the cloud. The resulting cloud IoT synergy poses demands that cannot be tackled by the existing cloud computing model alone [44]. A concise outline of challenges that IoT encounters [13; 45; 46; 47; 48] drives the need for edge and fog computing as a solution to manage demands [36] and is outlined as follows:  
* Low latency--IoT applications [44] and industrial control systems [49] demand low latency within a few milliseconds that can hardly be met by the existing cloud model.
* High network bandwidth--The escalating amount of IoT devices produce sizable data [50], which may be rendered useless due to high bandwidth usage to transfer it to the cloud or denied due to privacy concerns; hence, entails to be dealt with locally.
* Limited resources--numerous IoT-connected devices possess limited resources to interact directly with the cloud, demanding intensive computation and complex protocols.
* IT and OT convergence--In industrial systems, the confluence of information technology (IT) and operational technology (OT) creates new needs. As offline systems may cause business loss or consumer annoyance, contemporary cyber-physical systems demand continual and safe operation. Thus, the upgradation of the system software and hardware causes concern.  
* Intermittent connectivity--When IoT devices have intermittent network connectivity, it is difficult to provide uninterrupted cloud services to those devices.
* Geographical distribution--the majority of IoT devices entail services of computing and storage that are dispersed across large geographic areas, and it is highly challenging to position them at a location that meets IoT demands [51].
* Context-awareness--Local contextual data must be accessed and processed by IoT applications (vehicular networks and augmented reality), for which the physical distance between IoT devices and the centralized cloud is a hindrance.
* Security and privacy--The existing cybersecurity solutions prove to be unsatisfactory to manage IoT applications due to the evolving security challenges [52; 53].
## 3 Results and Analysis
### Relationship Between Moisture Content and Settlement Deformation  
In this section, Surfer software is used to analyze the humidity of the embedded sensor section in the model, and the trend in moisture content at two stages of seepage, the mid-point (18 December) and the end (23 December), is examined. Figure 15 illustrates the changes in moisture content during these two periods. Table 6 shows the settlement fitting equation.  
Figure 14: Contour of the moisture content of this model. (**a**) Mid-phase seepage; (**b**) later-phase seepage.  
Figure 15: Moisture content and settlement deformation. (**a**) At a depth of 40 cm in the foundation; (**b**) at a depth of 20 cm in the foundation; (**c**) interface between the subgrade and foundation.  
As shown in Figure 15a, during the initial stage of seepage, the water flow primarily diffuses horizontally. Defining a 1% change in moisture content as the effective influence area, the impact is mainly concentrated in a circular arc centered at the slope foot with a radius of 40 cm, where the maximum change is 4.2%. As shown in Figure 15b, with continuous water injection, the water flow gradually diffuses in all directions. The influence area expands to an arc with a radius of 60 cm, centered at the slope foot, with the maximum change reaching 7.3%. By the end of the test, the moisture content of most regions of the foundation fluctuates, while some areas of the subgrade show minimal changes. This indicates that in practical engineering, the water in the ditch at the foot of the slope mainly affects the wetting of the foundation and the interface between the subgrade and the foundation, eventually contributing to the collapse of the subgrade slope.  
Comparing Figure 15a,b, it is evident that the seepage diffuses in a wave-like pattern, gradually intensifying with time and temperature. The diffusion trend is significant, with changes in the moisture content primarily concentrated at the foundation, while the subgrade remains largely unaffected.  
The relationship of accumulated settlement and deformation at each measuring point is shown in Formula (4).  
\[S=aw^{2}+bw+c \tag{4}\]  
where \(S\)--cumulative settlement value;  
\(w\)--cumulative moisture content value;  
\(a\), \(b\), and \(c\)--undetermined coefficients.
## 3 Results and Discussion
As shown in Figure 7, the alignment of NFs did not change significantly with the increase of needle-to-collector distance. As stated before, with the increase of needle-to-collector distance, the electric field acting on the NFs decreased, and consequently, whipping instability, which is responsible for bending and allows the electrical forces to elongate the jet, decreased. Therefore, the as-spun NFs did not lose their aligned morphology with the increase of needle-to-collector distance. For the nanoscale applications, where the diameter of TiO\({}_{2}\) NFs directly affects the device performance, the difference observed among the synthesized TiO\({}_{2}\) NFs in this study can be significant. In this study, the air gap distance between the two Al collectors was remained constant. However, the change in the air gap distance can greatly influence the morphology and alignment of NFs [16; 18].
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Groups**} & \multicolumn{2}{c}{**Av. Sim. within**} & \multirow{2}{*}{**Main FA**} & \multirow{2}{*}{**\% Contribution to Sim.**} & \multirow{2}{*}{**\% Cumulative Contribution to Sim.**} \\  & & **Each Group** & & & **to Sim.** \\ \hline \multirow{4}{*}{A} & \multirow{4}{*}{95.92} & C16:0 & 31.71 & \multirow{4}{*}{65.00} \\  & & C18:1 & 19.64 & & \\  & & EPA & 13.65 & & \\  & & C16:0 & 27.92 & & \\ B & 95.00 & C18:1 & 24.70 & & 64.32 \\  & & EPA & 11.70 & & \\  & & C16:0 & 26.97 & & \\ C & 92.76 & EPA & 20.48 & & 60.22 \\  & & C18:1 & 12.76 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: SIMPER results regarding similarities (sim.) within the groups identified (A—_Laminaria ochroleuca_; B—_Saccharina latissima_; C—_Saccharizina polychides_ and _Undaria pimmatifida_). Similarities within groups are provided by the average similarity within each group (Av. Sim. within each group)-the higher the similarity (tending to 100), the higher the similarity within each group. The three FAs that contribute the most to the similarity in each group are shown, as well as the percentage of that contribution and the cumulative contribution of the three FAs for the total similarity of each group, in percentage. Replicates SL_R2 and SP_R3 were considered outliers of the analysis.
## 4 Evaluation Model Based on AH-Entropy and Normal Cloud Model
### Flow of Evaluation System for Combination Weighting and Normal Cloud Model  
The construction process of the evaluation model for existing asphalt pavement in highway renovation and expansion projects, based on AHP-entropy and the normal cloud model, is as follows:  
1. Determination of the comprehensive weight of evaluation indicators.
2. Determination of performance ratings for the indicators.
3. Calculation of the comprehensive certainty degree of the normal cloud.
4. Determination of the performance rating for existing asphalt pavement.  
The flow of the evaluation system for combination weighting and the normal cloud model is shown in Figure 4.
## 4 Experimental Setup
### Evaluation Methodology  
The experiments were divided into three categories:  
* First, the effectiveness of the proposed approach was evaluated separately on the IoT-23 and SIMARGL2021 datasets. A classical random split approach was used, where 70% of the data was used for training and the remaining 30% was used during testing. The recall, precision, and F1-score were measured for two cases, namely, anomaly detection and threat identification.
* Subsequently, the transferability capabilities of the proposed approach in a zero-shot manner were measured. The models were trained on the SIMARGL2021 dataset and evaluated on the IoT-23 dataset. The results for two cases were provided, namely, when the domain adaptation module was turned off and on. This enabled highlighting of the importance of domain adaptation for the proposed method.
* Finally, the transferability capabilities were tested using a varying number of samples drawn from the other domain. Specifically, the models were trained on the SIMARGL2021 dataset with \(N\) additional samples from IoT-23, and evaluated using the models for the remaining part of the IoT-23 dataset.
# A Bandwidth-Efficient Dissemination Scheme of Non-Safety Information in Urban VANETs +
## 3 Design of a Scheme for Urban Dissemination
### Store-Carry-Forward Mechanism
#### 3.2.1. Acknowledging and Taking over as Relay  
When a vehicle forwards a message, it may reach new recipients or none at all. If there is any, it will start a new contention, and there will eventually be a retransmission. Otherwise, no one will forward the message again. Therefore, if disconnections cannot be ignored and we need to apply a healing mechanism, silence after a retransmission will be the sign that it did not reach any new node. A relay that does not hear a subsequent retransmission will have to forward again in the future in the hopes of finding new neighbors. This retransmission will happen at some point in the following interval:  
\[(W,W+max(t_{w},t_{j})]=(W,W+max(T_{max},T_{j}))=(W,W+T_{max}] \tag{4}\]  
After the assessment time for receiving almost simultaneous duplicates, \(W\), nearby vehicles will start one of two types of contention, depending on the applied algorithm and their position: a regular distance-based contention (given by \(t_{w}\) in Equation (1)) or an intersection contention (given by \(t_{j}\) in Equation (2) or Equation (3)). Both of them have a maximum length of \(T_{max}\) and \(T_{j}\), respectively. We want vehicles in intersections to wait, at most, the same as vehicles applying the general distance-based contention equation. Thus, \(T_{j}\) must be less than or equal to \(T_{max}\). Hence, the maximum time a relay will wait for a new one is \(W+T_{max}\).  
Conversely, a new duplicate with lower TTL in said interval acts as an implicit acknowledgment of reception and taking over as the relay.
# Evaluation of Pressure Effect on Accelerated Aging Tests of Polymer-Insulated Aircraft Wires
## 5 Experimental Results
### Comparative Analysis of the Three Proposed Methods
The results presented in Table 1 show, for the three methods analyzed, the mean slopes \(\mu\) (they describe the rate of change in the indicators with the number of degradation cycles) and their standard deviations \(\sigma\) averaged over the eight samples. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**SDIV**} & \multicolumn{2}{c}{**Image Intensity**} & \multicolumn{2}{c}{**UV Sensor**} \\ \cline{2-7}  & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) \\ \hline
16 kPa & \(-0.013\) & \(0.013\) & \(-1.67\cdot 10^{5}\) & \(6.16\cdot 10^{5}\) & \(0.304\) & \(0.575\) \\
44 kPa & \(-0.028\) & \(0.016\) & \(-1.06\cdot 10^{5}\) & \(8.46\cdot 10^{6}\) & \(0.430\) & \(0.461\) \\
72 kPa & \(-0.043\) & \(0.023\) & \(-2.15\cdot 10^{5}\) & \(6.51\cdot 10^{6}\) & \(0.463\) & \(0.373\) \\
100 kPa & \(-0.047\) & \(0.029\) & \(-2.32\cdot 10^{5}\) & \(6.90\cdot 10^{6}\) & \(0.457\) & \(0.372\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean slopes of the linear regressions over the 8 samples shown in Figure 6.  
Figure 7: Discharge images taken with the CMOS sensor for sample #8. The results in Table 1 show that the slopes of the SDIV, image intensity, and UV sensor outputs became steeper with increasing air pressure. This trend suggests that these indicators were more sensitive to the degradation effects when the tests were conducted at atmospheric pressure than at low pressure. The results presented in Table 1 also show that the mean slopes of the solar blind UV sensor output and the image intensity had more dispersion and thus higher \(\sigma/\mu\) values than did the SDIV, while the \(\sigma/\mu\) values of the SDIV indicator tended to decrease with increasing pressure. Therefore, it can be concluded that the most reliable method to quantify the degradation of the wires is the SDIV, which has a higher sensitivity at standard pressure. It is noted that despite the significant scatter in the data, the degradation trend over the degradation cycles can be observed using the evolution of the SDIV, indicating that all samples underwent a progressive degradation process that permanently damaged the insulation. This can be seen by the decrease in the SDIV value as the sample aged.
## 4 Discussion  
The synthetic WPs of the SH guided waves in 1D PC plates are investigated. Here a virtual 3D space is formed by the Bloch wave vector plus the two additional parameters. Base on the zone folding mechanism, the WPs can be obtained. Furthermore, we also obtain the WP by changing one structural parameter. In order to confirm the WP, the conic intersection in the bulk dispersions is also presented in \(p\)-\(q\) space. To further confirm the synthesized WPs, the frequency-dependent TS through 1D PC plates with different structural parameters are investigated. Besides, the RP of a semi-infinite SC with a rigid boundary is also studied, and a vortex as the signature of Weyl physics is found in the phase distribution. The vortex phase structure ensures the presence of interface states, and  
Figure 4: RP singularity and subsequent topology interface state. (**a**) The RPs of PC plates with different parameters are calculated at a Weyl frequency of 0.305 MHz. (**b**) The energy density spectrum normalized by the flat panel at \((p,q)=(0.35,0.45)\).  
Figure 5: The displacement distribution of the PC plate at a Weyl frequency of 0.305 MHz with \((p,q)=(0.35,0.45)\).  
the interface state is also shown in the TS. This study may facilitate several operational applications. For example, the interface states can enhance the elastic energy and thus can be combined into directional elastic antenna structures, providing a technical means for communication and detection.  
Conceptualization, H.Z.; Investigation, H.Z.; Methodology, J.L.; Software, S.Z.; Writing--original draft, H.Z.; Writing--review & editing, B.L. All authors have read and agreed to the published version of the manuscript.  
This research work is supported by the National Natural Science Foundation of China (NSFC) (Grant No. 51575288 Grant No. 11874034) and the Taishan Scholar Program of Shandong (Grant No. ts201712054). The authors gratefully acknowledge the supporting agency.  
All data that support the findings of this study are included within the article.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.