esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Advanced Analysis Technologies in Precise Governance of Social Media Rumors
## 3 A Conceptual Framework of Social Media Rumor Precise Governance
### From the Perspective of Platforms and Agencies  
Internet platforms and agencies are the main participants in the process of rumor governance, mostly perform refutation for false rumors. False rumor refutation platforms mainly include official platforms of national and local governments as well as large platforms that rely on Internet companies, such as China Internet Joint Rumor Refutation Platform, Security Alliance Rumor Refutation Platform, Sina Rumor Refutation Platform, etc. Media organizations and some science popularization organizations are the backbone for rumor refutation, such as the \"Truth-Seeking\" column on www.People.cn (accessed on 16 April 2021), [PERSON], [PERSON], etc.  
For rumor governance platforms/institutions, it is necessary to conduct a scientific evaluation of the effectiveness and capabilities of rumor governance and grasp the key factors to improve the rumor governance. However, there is still a lack of indexes, evaluation systems, and multi-attribute evaluation methods that specifically target the effectiveness of refutation and rumor governance capabilities. If the key factors that affect the effectiveness and ability of rumor governance can be identified, such as the text characteristics, emotional characteristics, and auxiliary pictures/videos of the refutation information, it will help to make targeted improvements. The evaluation results of governance participants can distinguish the advantages and disadvantages of different platforms in terms of resources, credibility for different groups, science popularization capabilities, and technical capabilities. It helps to design a multi-platform coordination refutation response mechanism and refutation paths combination in order to maximize the function of rumor governance participants and improve the effectiveness of refutation.
## 5 Discussion
### Paleenvironment Reconstruction
#### 5.1.3 Salinity  
Salinity is a key determinant influencing the paleoenvironmental and paleoecological characteristics of lacustrine systems [45]. The solubility of Sr and Ba varies under different salinity conditions, making the Sr/Ba ratio a valuable proxy for discerning the salinity conditions within a lake's water column. Sr/Ba ratios below 0.2, between 0.2 and 0.5, and >0.5 typically indicate freshwater, brackish, and marine environments, respectively [51]. It should be noted that the Sr/Ba ratio may not serve as an accurate salinity proxy for samples containing more than 4% CaO because Sr can replace Ca\({}^{2+}\) in the CaCO\({}_{3}\) lattice [23]. The Sr/Ba ratio for the Da'anzhai Member shale in the study area ranges from 0.13 to 0.29, with an average of 0.20. Considering different lithofacies, the Sr/Ba ratio for ORA (average: 0.19) is slightly lower than OPA (average: 0.21). It is crucial to note that some ORM samples have higher CaO content, and as a result, ORM lacks Sr/Ba ratio data. In addition, under a microscope, it was observed that all three lithofacies contain bivalve debris shaped like elongated shell valves (Figure 7). The bivalves are adapted to freshwater-brackish water conditions, which further supplements the interpretation that the shale of the Da'anzhai Member in the study area was primarily deposited in a freshwater-brackish environment.  
Figure 6: Redox-sensitive trace element ratios for characterizing the redox condition among different lithofacies of the analyzed Da’anzhai Member shale (ORA: organic-rich argillaceous shale; OPA: organic-poor argillaceous shale; ORM: organic-rich mixed shale; \(n\) = 36, \(n\)—number of experimental samples; \(p\) < 0.05). (a) The cross-plot of V/Cr-Ni/Co; (b) the cross-plot of Ni/Co-U/Th.
## 2 Resolution, Scale, Scope and Other Properties of Descriptions  
### Resolution  
Resolution is a human created artifact to split system descriptions in regular, equally shaped and sized, pieces. It results from the process of discretizing the description of a system. The original description can be discrete or can be an analogous depiction taken directly from physical reality. Resolution is typically regarded as the number of equally sized pieces in which we divide the original description and thus it refers to the size of the smallest piece of information of a description, or equivalently, to the density of elementary-pieces of information. The resolution is commonly specified as the number of smallest information pieces that fit into each dimension of the description. Considering all dimensions conforming the description, the resolution can be regarded as the total number of elementary information pieces included in the whole description. In this case we use the letter \(R\) to refer to it. When resolution is specified as the density of information contained in a physical dimension, we use the number information pieces \(r_{j}\) that fit into the dimension considered, thus \(r_{j}=\ R_{j}/Dim_{j}\), where \(Dim_{j}\) refers to the absolute size of the physical dimension used. As an example, we can consider a \(16^{\prime\prime}\times 9^{\prime\prime}\) computer screen with 1920 pixels in the horizontal longitudinal dimension and 1080 pixels in the vertical longitudinal dimension. The resolution \(R\) is regarded as \(1920\times 1080\) (pixels \(\times\) pixels) and \(r\) would be \(120\times 120\) (pixels/in \(\times\) pixels/in). In another situation, if the description refers to a 60-seconds long sequence of 36,000 very short sounds, then \(R=\ 36,000\) (sounds) and \(r=600\) (sounds/s).  
Resolution, as a concept, loses meaning when the mesh of information elements is not regular as it would be an information structure formed by a set of symbols with a diversity of sizes and shapes. Such a situation can hardly be described using the resolution as a characteristic parameter because the density of the resolution would not be a constant.
## 1 Introduction
The comparison of ultrasonic NDT techniques is provided in Table 1 considering the overall capabilities and limitations of different techniques used in this study. In general, the bulk wave technique is more accurate because the insonified area is usually right beneath the transducer; however, that requires a time-consuming measurement process to scan a large structure. On top of that, immersion testing requires preparation of the setup, which includes a big water tank for inspection. Bulk waves in general are highly sensitive to the alignment of the transducers in case of the through-transmission technique. This is where the guided wave technique can excel because it is possible to use long-range inspection in short time. This study aims to compare contact, immersion, and air-coupled testing techniques with the case study on single lap joints containing different bonding quality. Hence in this work, metal-adhesive bonded structures specifically, aluminium-epoxy-aluminium single lap joints having release film inclusions at the bonding interface have been investigated with different ultrasonic non-destructive testing techniques. Particularly, immersion ultrasonic NDT with through-transmission and pulse-echo techniques, scanning acoustic microscopy (SAM), air-coupled through-transmission, guided wave inspections with air-coupled excitation and a contact receiver, and guided wave inspections with a contact transmitter and receiver have been employed. Guided wave inspections have been validated via numerical simulations.
## 3 OTFS Channel Estimation
### Latency/Complexity Comparison
P-IPIC RCF: during the \(i\)-th iteration, the number of required operations is (a) \(\mathbf{r}^{H}(\tau,\boldsymbol{\
u})\mathcal{E}^{(i-1)}\), \(\boldsymbol{\tau}\in\mathbb{C}^{MN\times 1}\), \(\mathcal{E}^{(i-1)}\in\mathbb{C}^{MN\times 1}\) requires \(MN\) multiplications and \(MN-1\) additions. After that, an additional multiplication is needed to compute the squared module. (b) \(\|\mathbf{r}(\tau,\boldsymbol{\
u})\|^{2}\) requires \(MN\) multiplications and \(MN-1\) additions. (c) One final division is required to compute the ratio between numerator and denominator. The computation of the RCF is independent of the iteration index \(i\), while the computational complexity of the cost function in Equation (19) increases as \(\mathcal{O}(i^{3})\) due to the inversion of a \(i\times i\) matrix. In the end, the search cost of the proposed P-IPIC is identical to that of the DDIPIC, since coarse and fine estimates are performed in the same way apart from the cost function. Hence, as in [11], the search cost depends on the number of points in the integer DD grid during the coarse estimate, or the number of points in the fractional search space \(F_{DD}^{(s)}\) during the fine estimate. In particular, during the coarse estimation phase, both algorithms perform \((L+1)(2K+1)\) cost function maximizations, while during the fine estimation phase, they perform at most \(\big{(}2\lfloor\frac{m_{\tau}}{2}\rfloor+1\big{)}\big{(}2\lfloor\frac{m_{ \tau}}{2}\rfloor+1\big{)}\) maximizations.
# Cybersecurity and Medical Imaging: A Simulation-Based Approach to DICOM Communication
### Related Works  
Several works have contributed to understanding DICOM communications and PACS integration in medical imaging. [PERSON] et al. [28] identified a lack of tools for PACS training and developed a Radiology Information System (RIS) simulator. However, advancements in medical imaging, DICOM communication, PACS integration, and cybersecurity have occurred subsequently. [PERSON] et al. [21] introduced a cybersecurity methodology and tools for healthcare operational information systems with a focus on cybersecurity, for which they used Orthanc [29] and ONIS [30]. Other researchers have provided extensively details on the advantages of the Fast Healthcare Interoperability Resources (FHIR) protocol while outlining a user-friendly approach for individuals who are new to these concepts [31].  
In the present research, a DICOM simulator was developed in a virtual lab context for use in conducting security tests. This study emphasizes in-depth analysis and learning aspects related to the fundamental attributes of the DICOM protocol, and the approach remains flexible for subsequent extensions, providing openings for further utilization by researchers according to their particular requirements.  
[PERSON] et al. [32] discussed the significance of the DICOM Validation Toolkit (DVTK) [33], which offers potential for individuals working with the DICOM standard. Other researchers [34] have utilized DVTk and highlighted the benefits of HIS, RIS, and PACS systems in healthcare management. Additionally, [PERSON] et al. [35] presented a collection of tools, including DVTK [33] and HAPI [36], to explore their development and integration with PACS. [PERSON] et al. [37] conducted a comparative study of DICOM API libraries, while NIST [38] identified software and managed risks within the PACS ecosystem. [PERSON] et al. [39] introduced covert channels for DICOM transport mechanisms, offering possibilities for covert communications, data exfiltration, and privacy leaking attacks.  
Table 1 showcases the comprehensive differentiations and additions from the existing pertinent research. Within this study, a DICOM simulation and a topology are introduced to showcase the interaction between DICOM and PACS. This research is valuable to a broader audience interested in DICOM communication and for the practical application of DICOM simulations in real-world scenarios for learning and testing purposes.
## 4 Experiments
### Ablation Study  
To further measure the contributions of each component of the proposed model, the results of ablation experiments depicted in the IEMOCAP dataset, which is shown in Table 3.  
From the results in Tables 3 and 4, there is a clear measurement of the contributions of each component. The forward fusion provides the fusion of multi-scale deep features for robust semantic feature leaning in progressive downsampling. The backward fusion mainly focuses on the salient emotional periods in multi-scale deep features and aggregates them into a high-resolution feature map. The CSA in MSCNN is important for local correlation learning, which effectively enhances the expression of emotion in semantically strong features. The core component in our proposed framework is the MSCNN, which extracts multiple deep features and enables the fusion mechanism to appropriately capture emotional-related features.  
\begin{table}
\begin{tabular}{c c} \hline
**Model** & **UA** \\ \hline Chakhtouna [40] & 82.6\% \\ [PERSON] [41] & 82.3\% \\
**MSFPN** & **86.5\%** \\ \hline \end{tabular}
\end{table}
Table 2: Comparisons of UA with state-of-the-art methods on RAVDESS. The best results are highlighted in bold.  
Figure 8: The t-SNE visualization of the proposed framework. (**a**) MSFPN; (**b**) DRN.  
\begin{table}
\begin{tabular}{c c c} \hline
**Method** & **UA** & **WA** \\ \hline w/o CSA & 66.89\% & 65.35\% \\ w/o MSCNN & 70.95\% & 69.05\% \\ w/o forward fusion & 71.85\% & 70.09\% \\ w/o backward fusion & 72.72\% & 71.26\% \\
**MSFPN** & **73.39\%** & **71.79\%** \\ \hline \end{tabular}
\end{table}
Table 3: Performance of ablation studies on IEMOCAP dataset. ‘w/o’ denotes the vanilla MSFPN framework without certain components.
## 4 Experimental Results and Analysis
### Effectiveness of Each Module  
In this section, we design ablation experiments illustrated in Table 4 to evaluate the effectiveness of each module in our proposed method. As shown in the comparison between rows 1 to 3, the CNN-LSTM architecture significantly outperforms the individual CNN and LSTM architectures in terms of classification metrics. This is because CNN and LSTM possess complementary advantages in the particle classification task. When used jointly, they can extract more discriminative local features through the CNN module, while capturing long-term dependencies from the LSTM module to process sequential information and obtain more generalized global representations.
# VANET Secure Reputation Evaluation & Management Model Based on Double Layer Blockchain
###### Abstract  
Vehicle ad-hoc network (VANET) is interconnected through message forwarding and exchanging among vehicle nodes. Due to its highly dynamic topology and its wireless and heterogeneous communication mode, VANET is more vulnerable to security threats from multiple parties. Compared to entity-based security authentication, it is essential to consider how to protect the security of the data itself. Existing studies have evaluated the reliability of interactive data through reputation quantification, but there are still some issues in the design of secure reputation management schemes, such as its low efficiency, poor security, and unreliable management. Aiming at the above-mentioned issues, in this paper we propose an effective VANET model with a secure reputation based on a blockchain, and it is called the double-layer blockchain-based reputation evaluation & management model (DBREMM). In the DBREMM, we design a reputation management model based on two parallel blockchains that work collaboratively, and these are called the event chain and reputation chain. A complete set of reputation evaluation schemes is presented. Our schemes can reduce observation errors and improve evaluation reliability during trust computation by using direct trust calculation based on the multi-factor Bayesian inference. Additionally, we propose an indirect trust calculation based on the historical accumulated reputation value with an attenuation factor, and a secure a reputation fusion scheme based on the number threshold with the fluctuation factor, which can reduce the possibility of attacks, such as collusive attacks and false information injection. Theoretical analysis and extensive simulation experiments reflect the DBREMM's security algorithm effectiveness, accuracy, and ability to resist several attacks.  
VANET; blockchain; reputation evaluation +
Footnote †: journal: Applied Sciences
# A High-Dynamic-Range Optical Remote Sensing Imaging Method for Digital TDI CMOS
###### Abstract  
The digital time delay integration (digital TDI) technology of the complementary metal-oxide-semiconductor (CMOS) image sensor has been widely adopted and developed in the optical remote sensing field. However, the details of targets that have low illumination or low contrast in scenarios of high contrast are often drowned out because of the superposition of multi-stage images in digital domain multiplies the read noise and the dark noise, thus limiting the imaging dynamic range. Through an in-depth analysis of the information transfer model of digital TDI, this paper attempts to explore effective ways to overcome this issue. Based on the evaluation and analysis of multi-stage images, the entropy-maximized adaptive histogram equalization (EMAHE) algorithm is proposed to improve the ability of images to express the details of dark or low-contrast targets. Furthermore, in this paper, an image fusion method is utilized based on gradient pyramid decomposition and entropy weighting of different TDI stage images, which can improve the detection ability of the digital TDI CMOS for complex scenes with high contrast, and obtain images that are suitable for recognition by the human eye. The experimental results show that the proposed methods can effectively improve the high-dynamic-range imaging (HDRI) capability of the digital TDI CMOS. The obtained images have greater entropy and average gradients.  
Keywords:optical remote sensing; digital TDI CMOS; high-dynamic-range imaging; EMAHE; gradient pyramid decomposition; image fusion +
Footnote †: journal: Applied
## 3 Results
### Phase 1--GNSS and Behavioural Recordings of Alpacas and Ewes
#### 3.1.2 Sheep and Alpaca Diurnal Patterns  
When examining the median hourly speeds of the two alpacas over the observation period (10 days), we found that there were a maximum of 11 separate one-hour periods (minimum = 6; mean = 8) on any one day where hourly median speed was significantly different between the two animals (paired Wilcoxon Tests, \(p<0.05\)). Other than these periods the alpacas demonstrated little to no differences in daily speed. Alpacas demonstrated a peak morning activity from 05:00 h followed by periods of intermittent activity ceasing at 17:00 h, coinciding with sunset (Figure 4). Animals also appeared to have an increase in activity occurring between 21:00 and 22:00 h (Figure 4).  
When comparing the alpaca and ewe hourly diurnal pattern averaged over the 10-day measurement period the interval between the hours of 05:00 to 17:00 h was found to be significantly different between species (F\({}_{23,435}\) = 4.29, \(p<0.05\)). This reflects differences in daytime speed between species and coinciding with sunrise and sunset (Figure 4). However, when the differen  
Figure 4: Median hourly speed (m/s) over 24 h of \(n=10\) ewes and \(n=2\) alpacas averaged over a 10-day observation period.  
Figure 3: Average daily distances (m) travelled and trend over time by alpacas and lambing ewes over the 41-day testing period.  
and mean daytime hourly median speed was converted to a proportion of the maximum speed for each species (daytime activity ratio), there were no significant interactions in hourly measurements between ewes and alpacas which was indicative of similarity of activity or movement. This similarity is also suggested in Figure 4 where both the alpacas and ewes exhibit similar peaks and drops in their activity levels. The results also indicate that alpacas were either more active, or move more quickly, than ewes during the daytime (mean hourly median speed 0.045 vs. 0.037 m/s, Students \(t\)-test \(t=-6.18\), \(\mathrm{df}=269\), \(p<0.0001\)), but were less active than ewes during the night time (mean hourly median speed 0.024 vs. 0.03 m/s, Wilcoxon Test W = 5648, \(p<0.0001\)).
## 3 Methodology
### Sampling and Partitioning Strategies
#### 3.1.4 Cross-Validation  
A large number of previous studies divided the original dataset into two parts for training and prediction [22]. However, cross-validation proves to be a more effective partitioning strategy when the sample size is not large enough [30]. In this paper, the dataset was divided into k parts to form k subsets; each subset was cyclically treated as a test set and the remaining k \(-1\) subsets were treated as the training sets. Finally, each sample was predicted k times as the test set. The average of the k prediction results was used to evaluate the model's performance. This method fully utilized all the data in the dataset, which can effectively prevent model overfitting and improve the accuracy of the model evaluation.
# Characterization of Dynamic Blockage Probability in Industrial Millimeter Wave 5G Deployments
## 3 System Model
### The Proposed Methodology  
The overall proposed methodology for LoS blockage analysis consists of two stages. At the first one, photogrammetry is utilized to build the so-called \"transparency map\" of the autonomous machines listed in the previous section. This map provides the probability of having an unobstructed LoS path \"through\" a certain geometric location of the autonomous machine. The snapshots taken at different yaw (normal axis) and pitch (transverse axis) angles are then superimposed to produce the final transparency map of a machine providing a probabilistic assessment of blockage for different geometric locations of LoS passing through a machine. Depending on the required accuracy, the LoS blockage probability can be further averaged over different angles to produce an averaged blockage probability. Alternatively, one may utilize the produced transparency maps without averaging. In this case, the LoS probability will depend on the geometric coordinates of the LoS path crossing a machine.  
Once the first stage is completed, an MPLP process with a certain composition of autonomous machines and site occupancy probability fitting a scenario of interest is defined. This process is then analyzed by utilizing conventional tools of stochastic geometry to produce the sought LoS blockage probability with a UE located at coordinates \((l_{x},l_{y})\). To produce the final average blockage probability we will integrate it over different locations of UE in the environment.
## 4 Result and Discussion
### Boundary Effect Model
#### 4.2.4 Influence Distance  
To understand the influence distance of the boundary effect, this study set the resistivity ratio \(n=0.1\) and electrode spacing \(dx=2\). These results are shown in the left image in Figure 12, where the resistivity profile obviously had an abnormal resistivity zone within 6 m from the boundary medium, which was obviously affected by the boundary effect. However, after 8 m away from the boundary medium, it was almost unaffected by the boundary effect. This result is similar to the influence distance of the 3D effect.  
Figure 11: The influence of the boundary effect with different medium depths. (**a**) medium depth 2 m; (**b**) medium depth 4 m.  
Figure 12: _Cont._  
influence distance does not become smaller as the electrode spacing becomes smaller. This result is similar to the 3D effect of the pipeline stratum, where neither were affected by the electrode spacing.