esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Cell-Based In Vitro Assays
#### 2.6.3 Permeability Assay  
The permeability experiments were assessed using 12-well culture plates with Transwell inserts. TR146 cells were seeded into the inserts to mimic buccal mucosa epithelium, and Caco-2/HT29-MTX co-culture was used to mimic duodenum conditions. After 21 days, the medium was removed every two days. Guitar-gum films, chitosan microparticles, guargum film with chitosan microparticles, and free relaxing peptide were tested at 50 \(\mu\)g/mL. The substances were applied to the apical side of the cell layers, and the medium was replaced in the basolateral side (1.5 mL of PBS) and in the apical side (0.5 mL of PBS). Samples (1 mL) were withdrawn from the basolateral side at 0, 15, 30, 60, 120, 180, and 240 min. The volume was immediately replaced with fresh PBS. The Transwell culture plates were incubated at 37 \({}^{\circ}\)C in the atmosphere of 5% CO\({}_{2}\)/95% air for maintaining cell viability during permeation studies. The concentration of relaxing peptide was determined by the LC-ESI-QqTOF-HRMS method.  
The apparent permeability coefficient (Papp) was calculated from the measurement of the flow rate of relaxing peptide from the donor to the acceptor chambers, according to Equation (3).  
\[app\ (cm.s-1)=\frac{dQ}{dt\times A\times C0} \tag{3}\]  
where \(dQ\) is the total amount of permeated relaxing peptide (\(\mu\)g), A is the diffusion area (cm\({}^{2}\)), \(C0\) is the initial concentration in the donor compartment (\(\mu\)g/mL), and dt is the time of experiment (s). The coefficient \(dQ\)/\(dt\) represents the flux of the test compound across the cell layers.
# LBFT: An Asynchronous Committee-Based Blockchain Storage Strategy on Zero Trust Model
## 4 Design
### Detailed Construction
Figure 3 presents the details of the internal decoding process in each committee. The committees perform the decoding in parallel in order to further shorten system response time. After completing the decoding process, each leader inputs the recovered value of its committee into ABA protocol for the next consensus. The detailed steps of decoding a certain block \(B(h)\) in each committee and the consensus on the final output among the committees are illustrated in Algorithm 2. Firstly, as shown in Figure 3, the leader \(L_{i}\) of a committee \(C_{i}\) broadcasts a decode message \(<DECODE,h,c,n,id>\) to the followers in \(C_{i}\), where \(c\) and \(n\) are the principal parameters of the related RS scheme, and \(id\) is the unique identity of the committee (Line 2). ```
1:(Processing on leader \(L_{i}\) of Committee \(C_{i}\))
2:broadcast decode message \(<DECODE,h,c,n,id>\) to the followers
3:if receive \(c-1\) messages then
4:\(\mathcal{F}=\{f_{h}(1),...,f_{h}(c)\}\gets c\) values
5:\(\mathcal{B}_{i}(h)\leftarrow\) RSDecode(\(\mathcal{F},c,n,id\))
6: broadcast the recovered value \(\mathcal{B}_{i}(h)\)
7:endif
8:if receive \(\frac{k}{2}+1\) same recovered values \(\mathcal{B}(h)\)then
9: activate \(ABA\) with the input \(b=1\)
10:else
11: activate \(ABA\) with the input \(b=0\)
12:endif
13:if receive the output \(b=1\) from \(ABA\)then
14: return \(\mathcal{B}(h)\)
15:else
16: decoding failure, LBFT re-organize committees
17:endif
18:(Processing on followers \(N_{s}\))
19:if receive the decode message then
20:\(msg\leftarrow\)\(<f_{h}(s),\sigma_{h,s},h,s,id>_{\sigma_{s}}\)
21: SendMessage(\(L_{i},msg\))
22:endif
```  
**Algorithm 2** Data Recovery of a Certain Block. Receiving the decode message from the leader, each follower \(N_{s}\) replies with its stored value \(f_{h}(s)\), the aggregate signature \(\sigma_{h,s}\), the hash of block \(h\), its index \(s\), and the identity of the committee \(id\) to the leader \(L_{i}\) (Line 18-22). The leader \(L_{i}\) computes the recovered value \(\mathcal{B}_{i}(h)\) using the function \(RSDecode()\) if it has received all the encoded values from different followers in committee \(C_{i}\) and broadcasts the value to the leaders of other committees (Line 3-7). If the leader \(L_{i}\) receives more than \(\frac{k}{2}\) of the same computed values \(\mathcal{B}_{i}(h)\) (i.e., a majority of committees have recovered the same value), it activates the common \(ABA\) instance with the input \(b=1\); otherwise, it inputs \(b=0\) (Line 8-12). The common \(ABA\) outputs \(b=1\) if it has more than a half inputs are \(b=1\). If \(ABA\) instance outputs \(b=1\), indicating the recovered value \(\mathcal{B}_{i}(h)\) is correct unless a negligible possibility \(\varepsilon\), LBFT replies \(\mathcal{B}_{i}(h)\) to the client (Line 13-14). Oppositely, LBFT will re-select nodes to form new committees and repeat the above steps until the correct value is recovered (Line 15-17). Figure 3: The process of decoding operations in a committee \(C_{i}\).
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 7 Conclusions  
This article helps the research community to invent or re-invent the most appropriate MM mechanisms by following the capabilities concerning gaps/challenges and possible solutions provided for the complex scenario of 5G and 6G networks. In Section 2 and Table 1, we suggested the critical design criteria and functional requirements that need to be measured at the time of developing 5G and future MM solutions. Next, in Section 3 and Table 2, we highlighted the various parameters required to be fulfilled by future MM mechanisms. These parameters were adaptability, dependability, extensibility, and power consumption. Similarly, in Section 4, we concluded that the assessed legacy MM mechanisms are unable to fulfill the criteria of adaptability, dependability, extensibility, and power usage as a whole. However, the present standards, which were investigated in Section 5, are capable of offering improved performance (according to our defined criteria) to future MM solutions. From this investigation, we have inferred a comparison and presented the results in Tables 3 and 4 in the form of a qualitative analysis. From this qualitative analysis, a number of advantages and drawbacks of the legacy and current state-of-the-art mechanisms were identified, which can certainly be understood by the researchers. Next, we inferred from the findings that there is no existing MM mechanism that can be used entirely (as a whole) to fulfill the stated parameters of 5G and 6G MM mechanisms. Hence, it is evident from the finding that a generic MM mechanism for 5G and future networks may be undefinable. Therefore, Section 5.4 represents the gaps and challenges that are still required for planning, designing, developing, and deploying next-generation MM mechanisms. Additionally, in Section 6, we also briefly discussed the possible gaps and challenges that exist in MM techniques. Furthermore, the worth/efficiency of our current research was mentioned in Table 5, in which we drew a state-of-the-art mapping between possible strategies and challenges. Finally, we have proposed an innovative framework for the 6G MM mechanism based on our findings and defined parameters.  
This work was funding supported by the Programme: SME Growth Romania-Priority ICT, Contract Greensoft No. 2020/548467, Project name: AI FERODATA.  
The authors declare no conflict of interest.
# Review of Communication Technologies for Electric Vehicle Charging Management and Coordination
## 4 Cellular Networks
### UMTS and LTE  
The feasibility of a UMTS-based network to support vehicular communication is evaluated in [66], in which a highway scenario is simulated. Results show that the UMTS network can support V2I communications for non-safety-critical applications. Nevertheless, according to the analysis performed by [67], UMTS offers a round trip time of 350 ms for cooperative awareness messages (CAM) at intersections, which is more than three times  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Feature** & **Channel Utilization** & **Collisions** & **Throughput** & **Packet Loss** \\ \hline Contention-based & Inefficient & High & Medium & High \\ (CSMA/CA) & & & & \\ Contention-free & Efficient & Low & High & Low \\ (TDMA) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between contention-based and contention-free MAC protocols [61].  
larger than the 100 ms offered by LTE. This makes UMTS questionable for safety-related message exchange at traffic intersections at which quasi-dynamic charging is anticipated to be deployed. In general, LTE is envisioned to well-support V2I and I2V communications and is selected as the underlying network for ETSI ITS communication standards [65]. Furthermore, the simulation results reported in [68] demonstrate that LTE enhances the system throughput while maintaining approximately the same cell coverage compared to 3G networks.  
Nevertheless, while LTE can meet the requirements of safety services under low and medium traffic scenarios, its performance may experience significant degradation under heavy load conditions [50]. To reduce latency and improve the system throughput, the authors in [69] suggest utilizing LTE femto access points (FAPs) as relay nodes that can be deployed in buses and large trucks to provide better connectivity. Accordingly, the authors divide the spectrum between cellular users and vehicles, instead of sharing resources, to avoid interference and propose a power control algorithm to maximize the total throughput while mitigating the co-channel interference. With the help of low-cost FAPs and the proposed sub-channel power control, the scheme proposed in [69] provides better throughput and queuing delay compared to traditional DSRC vehicular network in IEEE 802.11p. Another approach to improve the latency performance of LTE networks in vehicular communications is to shorten the transmission time interval (TTI) [70], as shown in Figure 4. However, this approach increases the communication overhead due to additional scheduling and retransmissions.
## 3 Results and Discussion
### The Relationship between Ecospatial Network Topology Index and NPP
#### 3.3.2 Correlation Analysis between Topological Indices and NPP of Different Land Cover Types
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Land Cover** & \multicolumn{2}{c}{**Degree**} & \multicolumn{2}{c}{**Clustering Coefficient**} \\ \cline{2-5}
**Type** & **Correlation Coefficient** & **\(p\)-Value \({}^{*}\)** & **Correlation Coefficient** & **\(p\)-Value \({}^{*}\)** \\ \hline Woodland & \(-0.444\) & \(<\)0.01 & 0.084 & 0.254 \\ \hline Grassland & \(-0.217\) & \(<\)0.01 & \(-0.070\) & 0.139 \\ \hline Water & \(-0.076\) & 0.607 & \(-0.116\) & 0.433 \\ \hline \hline \end{tabular} \({}^{*}\)\(p\)-value is the abbreviation of the significance level, which is the result of the significance test. \(p\)-value \(<0.01\) means significance at the 0.01 level.  
\end{table} 
Table 3: Correlation analysis between NPP and topological indices of different land cover types. Figure 14a shows the fitting results between the degree of ecological nodes in woodland and NPP, and the regression equation is \(y=-112.3\ln x+418.634\). Between the degree of ecological nodes in grassland and NPP, as shown in Figure 14b, the regression equation is \(y=-65.932\ln x+300.15\). The results show that the degree of nodes in both woodland and grassland has a negative effect on NPP, and the negative effect of nodes in woodland is even stronger. With the increase in degree, NPP gradually decreased, but its decline rate gradually decreased; that is, with the increase in degree, the negative influence of degree on NPP gradually decreased. Therefore, in terms of improving the carbon sequestration and oxygen release capacity of the ecosystem in the study area, under the premise of taking into account the network connectivity, reducing the average degree of the ecospatial network is the key direction of optimization, and the nodes with moderately low degrees in woodlands and grasslands are the key areas for ecospatial network optimization.
## 5 Evaluation
### Evaluation Metrics
#### 5.2.3 Different Detection Period  
At the end of each monitoring period, the controller will compute the three variables P\({}_{1}\), P\({}_{2}\) and P\({}_{3}\). Different detection periods can make different effects on the detecting accuracy of the DDoS attack. The detection period was set to 1 s and 3 s. When it was set to 1 s, the changes of the variables P\({}_{1}\), P\({}_{2}\) and P\({}_{3}\) are as shown in Figure 13. We can see that, when the monitoring period is low, the computing frequency of the three variables will become high. In this way, the proposed approach can detect the DDoS attack in a timely manner but may lead to too much computing overhead and the flow statistics collection overhead. When it is set to 3 s, the changes of the variables P\({}_{1}\), P\({}_{2}\) and P\({}_{3}\) are as shown in Figure 14. We can see that, when the monitoring period is high, on the one hand the DDoS detection will lack the timeliness, while, on the other hand, the accuracy of P\({}_{1}\), P\({}_{2}\) and P\({}_{3}\) will be also decreased. Therefore, choosing an appropriate monitoring period is also important to the proposed DDoS detection.
## 4 Numerical Simulation
### Simulation Results  
Observation points A, B, and C were placed sequentially at a depth of 5 m, 10 m, and 15 m along the central axis of the model, as indicated in Figure 9. The seismic response of EXPPR at the observation points during earthquakes of different peak accelerations were plotted and compared with the measured results in the centrifuge model (see Figure 10).  
Figure 10: \(Cont.\)  
With regard to the numerical results, it can be seen that with the increase of the peak acceleration of the input ground motion, the EXPPRs at all the depths reach up to a higher maximum value with faster accumulation and slower dissipation. Under the earthquake with peak acceleration of 0.3 g, the EXPPRs at all the observation points reach 1.0 after about 9 s, even for point C located at 15 m depth, implying a high risk of liquefaction for the simulated site. Moreover, the EXPPRs remain in a high platform and do not dissipate during the loading of the earthquake. The earthquakes with peak acceleration of 0.1 g and 0.2 g both result in EXPPRs at different depths lower than 0.6, indicating that the liquefaction potential of the site is relatively low.  
Compared with the experimental results, the numerical results generally present a faster process in both accumulation and dissipation but relatively consistent peak values of the EXPPRs. The only noticeable discrepancy occurs at the depth of 15 m under PGA of 0.3 g. The numerical results largely overestimated the EXPPR, which may be attributed to the overburden stress effect that is not well incorporated in the constitutive model. Moreover, it is obvious that the numerical results hardly have the \"spikes\", which is distinct in experiments. Generally, both the numerical and experimental results imply that the saturated calcareous sand site with relative density of 50% will have a high liquefaction potential for the sand at shallow depth when subjected to the earthquakes having peak acceleration of 0.3 g or even higher. In addition, the dynamic FE analysis framework adopting \(u\)-\(p\) formulation and PDMY02 was proved to effectively predict the peak values of EXPPR during earthquake.
## 4 Discussion
### Grain Clusters and Scattering  
From the attenuation measurements in Figure 5, we can observe a hump at 3.5 MHz in D1, D2 and at 4 MHz in D3. Similarly a hump at 4 MHz and 5 MHz can be observed in the FOM results in Figure 7. A hypothesis for this hump is presented below: (A) The 22 dB difference in attenuation between conventional sample and DED sample suggests, the average grain size must be significantly larger in the DED sample. From the micrographs in Figure 9, we can notice that the macrograins are spread between 100-500 \(\upmu\)m, whereas conventional samples typically have grain sizes on the order of 10-20 \(\upmu\)m. The micrograins on the other hand are on the order of 5-10 \(\upmu\)m. Based on these observations, it can be argued that the scattering and attenuation observed in the DED sample can be mainly due to the macrograin. For the macrograin to act as a single grain, the assumption is that the macro-grain has a local texture, which has been shown earlier by other authors using EBSD imaging [52]. (B) For conditions when the scatterer size is approximately equal to the wavenumber, i.e., \(ka=1\), where \(k\) is wavenumber and \(a\) is the average scatterer size, [PERSON] and [PERSON] [53] have shown that a transition in the scatter mechanism from Rayleigh to stochastic occurs. The power law of frequency dependence also changes at this transition according to S-K theory. Depending on various factors, a hump can be observed [54; 55] at this transition. The reason for the hump like behavior can be due to grain size distribution [54], or grain morphology [55]. If the hump observed at 3.5 MHz is a transition, then \(ka=1\) will give us an average grain diameter of \(\approx\)250-300 \(\upmu\)m based  
Figure 9: Micrographs of (**a**) \(Z\) plane of conventional sample, (**b**) X-Y plane of DED sample, and (**c**) \(Z\) plane of DED sample.  
on S-K theory. From the micrographs in Figure 9, this macrograin size is possible. If the grain scattering were to occur from the micrograin, then the transition is expected around hundreds of MHz-GHz, which is not feasible with the current setup.  
There could be a few arguments that can be made against this hypothesis as well: It is interesting to note that there is no change in power law dependence before and after the 3.5 MHz hump, which is expected for a transition based on S-K theory. Furthermore, the forward scatter noise overlapping the backwall could also result in aberrations in the spectral response. This will typically result in bi-modal spectrum as shown in Figure 4d,e, and taking the ratio of this spectrum with a smooth spectrum like the reference (in Figure 5b) will result in humps. Any local velocity changes will also result in phase aberrations as the beam propagates. Therefore, it could also be argued that these humps are purely spectral aberrations due to time-gating or the choice of time gate position. The origin of the humps remains debatable and uncertain and further work is necessary to understand them completely.
## 3 Dataset
### Example Data Entries  
To illustrate the dataset structure, we provide examples of data entries, using the first claim (Claims_Climate_B2\(0\)1) in the dataset as a representative example.  
* Claim_Topic_ID: Claims_Climate_B2\(0\)1
* Claim_Text: New Zealand has a carbon trading system
* Label: T
* Evidence_Text: (The following list contains the six pieces of annotated evidence associated with this claim:)
* Evidence Item 1: A number of other countries have, however, also implemented a carbon-trading system at a national or sub-national level, or have one in development, including Canada, China, Japan, New Zealand, South Korea, Switzerland, and the United States, according to the European Commission.
* Evidence Item 2: As of July, 46 countries are pricing emissions through carbon taxes or emissions trading schemes (ETSs), according to the International Monetary Fund.
* Evidence Item 3: NZ's agricultural emissions are not currently captured under the ETS (unlike other sources like industrial processes).
* Evidence Item 4: The number of emission units released for auction is designed to meet New Zealand's international obligations.
Evidence Item 5: With many New Zealand farms having been converted to forestry due to rising carbon prices in recent years, feedback last week closed on a proposal to change settings in the emissions trading scheme, where permanent plantings of exotic forests, like pine forests, would be excluded from the scheme from next year. - Evidence Item 6: China, South Korea, Canada, Japan, New Zealand, Switzerland and the US already have a number of national or regional systems; however, the international carbon market is said to develop through a bottom-up approach, whereby the EU ETS will be linked with other international systems, with a common aim to reduce the amount of emissions.
* Reason: New Zealand has an existing emissions trade scheme
* Reason_Type: Abstractive
## 4 Experimental Design and Results
### Comparative Experimental Analysis  
#### 4.4.1 Model Performance Comparison  
To establish the superiority of our proposed model, we conducted a comparative analysis with common image classification models, namely ResNet50 [40], DenseNet121 [41], LeNet [42], and ViT models. All models were trained and tested on the same schematic dataset used in this study, using identical model parameters and pre-trained weights obtained through transfer learning.  
The accuracy curves during the training process are illustrated in Figure 15. Upon completing the final iterations, the training accuracies for each model were as follows: Swin Transformer = 96.04%, ViT = 92.78%, LeNet = 91.03%, DenseNet121 = 91.96%, and ResNet50 = 93.02%. Remarkably, the Swin Transformer model exhibited significantly higher classification accuracy when compared to all other models.  
Figure 14: PR curves for each category.  
Figure 15: Accuracy of training for each model.  
The performance metrics of all models are compared in Table 2. It can be observed that the transformer models outperform the CNN models overall. This can be attributed to the following reasons: The transformer model utilizes self-attention mechanisms to capture global information in the schematic diagrams and model the relationships between different parts. This enables the model to better understand the importance and interactions of different features in the schematic diagrams, thereby improving classification accuracy. The self-attention mechanism in the transformer model allows the output at each position to be influenced by other positions, enabling parameter sharing. This characteristic of parameter sharing allows the transformer model to handle schematic diagram data more efficiently, reducing the number of model parameters and computational complexity. Transformer models are typically initialized with parameters from pre-trained models on large-scale datasets, providing better initial representation capabilities. This allows the transformer model to converge and adapt to the schematic diagram classification task more quickly, resulting in improved model performance.  
The proposed Swin Transformer model in this paper performs better in addressing the recognition problem among similar but distinct categories of schematic diagrams. By leveraging the influence of self-attention values on image patches, the Swin Transformer model can focus more on informative features that contribute to classification and avoid negative effects caused by the similarity between schematic diagrams. As a result, the Swin Transformer model achieves the best performance in the task of schematic diagram classification.