esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Noise Level to Detect Frame Repetition Forgery in Video Frame Rate Up-Conversion
## 2 Background
### Residual Detection  
Residual detection calculates residual variation between two adjacent frames, which is given by,  
\[v[t]\ =\ \|f[t]-f[t+1]\|_{2}^{2} \tag{2}\]  
where \(\|\cdot\|_{2}\) is the \(l_{2}\) norm. If \(f[t]\) is a repetitive frame, \(v[t]\) is zero; otherwise \(v[t]\) is not zero. Once these repetitive frames appear periodically, the residual variation will be zero periodically, so the fact that the residual variation periodically decays to zero can prove that the video is forged by FR. In general, when the original frame rate of video is increased by FR, in order to reduce the amount of data, H.264 [12] or high-efficiency video coding (HEVC) [13] is used to compress the forged video, which would cause some error between the compressed frame and the video frame as follows,  
\[\hat{f}[t]\ =f[t]+e[t] \tag{3}\]  
\[\hat{f}[t+1\ ]=f[t+1]+e[t+1] \tag{4}\]  
where \(\hat{f}[t]\) and \(\hat{f}[t+1]\) denote the compressed frames of the \(t\)-th frame and the \((t+1)\)-th frame respectively, \(e[t]\) and \(e[t+1]\) respectively denote the errors between \(\hat{f}[t]\), \(\hat{f}[t\ +\ 1]\)and their corresponding original frames. The residual variation between compressed frames can be expressed as follows,  
\[\hat{v}[t]\ =\ \|\hat{f}[t]-\hat{f}[t+1]\|_{2}^{2}=v[t]+\|e[t]-e[t+1]\|_{2}^{2} \tag{5}\]  
From Equation (5), it can be seen that when a forged frame suffers from lossy compression, the residual variation between compressed frames involves both the original residual variation and the error term. Because each compressed frame degrades at a different level, the error term can randomly change over time. So it can be seen that lossy compression can interfere with the periodicity of residual variation, thereby degrading detection performance.
## 5 Conclusions  
This paper presented an MCSS protocol designed to support a cluster-tree network topology and various traffic types in WPT-enabled WSNs. In addition, MCSS defines three types of TSCH concurrent slotframes: CM slotframes, HAP slotframes, and WPT slotframes, to schedule the CM traffic, inter-cluster traffic, and intra-cluster traffic for WPT-enabled  
Figure 11: Effect of WPT on aggregate throughput: (**a**) 12 and (**b**) 36 sensor nodes.  
WSNs, respectively. To address the problems caused by overlapped cells, MCSS sets each slotframe to a different length and transmission priority.  
In particular, the WPT slotframe length for each cluster was determined based on the total number of required power and data cells, including the calculated number of overprovisioned cells. The experimental simulation was conducted in an environment of varying network sizes. The performances of MCSS, legacy TSCH MAC, and TMSS were compared in terms of the average end-to-end delay, aggregate throughput, and average harvested energy to demonstrate the superiority of MCSS.  
Conceptualization, S.-B.L. and E.-J.K.; methodology, S.-B.L., S.N.-X., J.-H.K., and E.-J.K.; software, S.-B.L., J.-H.K., and E.-J.K.; validation, S.-B.L., S.N.-X., and E.-J.K.; formal analysis, S.-B.L. and E.-J.K.; investigation, J.-H.K. and E.-J.K.; resources, S.N.-X. and E.-J.K.; data curation, J.-H.K.; writing--original draft preparation, S.-B.L., S.N.-X., J.-H.K., and E.-J.K.; writing--review and editing, S.-B.L., S.N.-X., J.-H.K., and E.-J.K.; visualization, S.-B.L.; supervision, E.-J.K.; project administration, E.-J.K.; funding acquisition, E.-J.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Hallym University Research Fund, 2022 (HRF-202201-007).  
Not applicable.  
Not new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
6P 6 top Protocol  
6 TSCH IPv6 over the TSCH mode of the IEEE 802.15.4e  
6 top 6 TSCH Operation Sublayer  
Ack  Acknowledgment  
CH  Cluster Head  
CM  Control Message  
CSMA/CA  Carrier Sense Multiple Access with Collision Avoidance  
DIO  DODAG Information Object  
DODAG  Destination-Oriented Directed Acyclic Graph  
EB  Enhanced Beacon  
HAP  Hybrid Access Point  
IoT  Internet of Things  
LCM  Least Common Multiple  
MAC  Medium Access Control  
MOSS  Multiple Concurrent Slotframe Scheduling  
OF  Objective Function  
PHY  Physical  
PRU  Power Receiving Unit  
PTU  Power Transmitting Unit  
RF  Radio Frequency  
RPL  Routing Protocol for Low-power and Lossy Networks  
RSSI  Received Signal Strength Indicator  
Rx  Reception  
TDMA  Time-Division Multiple Access  
TMSS  TSCH Multiple Slotframe Scheduling  
TSCH  Time-Slotted Channel Hopping  
Tx  Transmission  
WPT  Wireless Power Transfer  
WSN  Wireless Sensor Network
## 4 Discussion
### LF and High Field Subspectra  
JCS can also provide additional spectral details not captured in high field spectra. Consider the high field spectra of 1,2,4,5-tetrafluorobenzene, which consists of a relatively simple spectral pattern showing a triplet for \({}^{19}\)F and a quintet for \({}^{1}\)H (Figure 12). A cursory analysis of the spectra would suggest there are four magnetically equivalent F atoms coupled to two magnetically equivalent H atoms with a \(J\)-coupling of +8.65 Hz. However, since the mixing coefficient of \({}^{3}\!J_{\rm HF}\) and \({}^{4}\!J_{\rm HF}\) is small, albeit non-negligible, this results in an \"oversimplified\" spectrum where the high field resonances appear at an averaged position for the two \(J\)-couplings [71]. JCS provides additional insight into this proposed phenomenon since the JCS of both the \({}^{19}\)F and \({}^{1}\)H portions of the spectra have additional features that exclud the possibility of magnetic equivalence between the \({}^{19}\)F and \({}^{1}\)H environments. At high field, these spectral features overlap, which results in a relatively featureless spectrum. Simulation of the high field spectra at 9.4 T, using the same \(J\)-couplings and chemical shifts used to fit the experimental JCS, clearly shows that there is a triplet in the \({}^{19}\)F spectrum and a quintet in the \({}^{1}\)H spectrum. However, the simulated spectra are considerably narrower than the experimental spectra, causing additional features to emerge in the simulation of the \({}^{1}\)H spectrum. On the other hand, the relaxation rate required to fit the \({}^{19}\)F spectrum was 10 times larger than the relaxation rate used to fit the LF spectra. This is likely due to different relaxation mechanisms, where the high field relaxation results from chemical shift anisotropy rather than dipolar relaxation, as observed in LF [11]. The increased linewidth at high field obscured any spectral fine structure from being observed. Therefore, the high field spectral features did not result from a small mixing coefficient, but rather from a complex splitting pattern caused by magnetically inequivalent nuclei where any fine structure is obscured by the linewidth of the spectrum. A similar argument can be made for 1,4-difluorobenzene, which had the same, albeit reversed, Pople notation as 1,2,4,5-tetrafluorobenzene. Previous reports measuring fluorobenzene spectra, notably monofluuorobenzene and 1,4-difluorobenzene in the strong coupling regime, have lacked the spectral resolution and SNR to differentiate signals in the strong and weak heteronuclear \(J\)-coupling regimes [23; 22]. However, as demonstrated in this study, the strong heteronuclear \(J\)-coupling regime can, in most cases, provide additional details about structures that are masked at high field.
# Review of Flexible Piezoresistive Strain Sensors in Civil Structural Health Monitoring
## 4 Optimization of Sensor Performance  
To evaluate the working performance of the flexible strain sensor, several parameters are indispensable, including sensitivity, stretchability, linearity, hysteresis and dynamic durability. As mentioned in Section 3, the selection of the appropriate material type is es  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Materials** & **Maximum** & **Gauge Factor/GF** & **Linearity** & **Cyclic Times for** \\  & **Filler + Substrate** & **Detectable Range** & **(Strain Range)** & **(Strain Range)** & **Durability** \\ \hline
[58] & CuNWs + PDMS & 7.5\% & & Nonlinear & \\
[65] & CB + Knitted Fabric & 60\% & 1–100 & Nonlinear & (\(\varepsilon<60\%\)) \\
[67] & CNT + TPU & 400\% & 4–69 & Nonlinear & 1000 \\
[68] & MWCNT + SBC & 45\% & 5.39–8.58 \(\times\) 10\({}^{7}\) & Nonlinear & 600 \\ CNT/Graphene & & & Linear & 3000 \\
[69] & + PDMS & 10\% & 0.4 & (\(\varepsilon<10\%\)) & (\(\varepsilon<10\%\)) \\
[73] & AuNWs/ AgNPsNWs & 70\% & 236 (\(\varepsilon<5\%\)) & Nonlinear & (\(\varepsilon<10\%\)) \\
[74] & MWCNT + PU & 200\% & 5–1.4 \(\times\) 10\({}^{5}\) & Nonlinear & (\(\varepsilon<10\%\)) \\
[75] & CNT/CB + PMVS & 60\% & 1–192 & Nonlinear & 10,000 \\
[76] & CNT/CB + PVDF & 10\% & 0.6 & Nonlinear & (\(\varepsilon<30\%\)) \\
[77] & CNT/Graphene + TPU & 30\% & 152 & Nonlinear & \\
[79] & CNT + PDMS & 30\% & 3 & (\(\varepsilon<30\%\)) & 600 \\
[80] & CNT + Ecoflex & 500\% & 2.4 & Linear & 2000 \\
[82] & SWCNT + TPU & 25\% & 0.2 & (\(\varepsilon<25\%\)) & (\(\varepsilon<20\%\)) \\
[85] & Graphene + PVA & 55\% & 271 (\(\varepsilon<35\%\)) & Nonlinear & 10,000 \\
[87] & CrNP + PET & 3\% & 20 (\(\varepsilon<0.36\%\)) & Nonlinear & (\(\varepsilon<55\%\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Conductive materials and flexible substrates (‘\(\varepsilon\)’ is strain).  
sential to the working performances of strain sensors; nevertheless, there are still problems beyond that. For instance, the incomplete stress transfer between the conductive layer and the substrate layer will reduce the sensing accuracy because of the elastic mismatch and weak interfacial adhesion between them [91]. Moreover, the prevalent resistance hysteresis between the loading and unloading processes is ascribed to the internal friction between the material layers and the inherent viscoelastic character of the polymer substrates [92; 91]. Additionally, the destruction and reconstruction of the conductive networks are unstable during deformation process, leading to unstable sensitivity and low linearity. Besides, the sensing performance usually shows a degradation trend during cyclic stretching/releasing loadings. This is attributed to plastic breaks of the polymer substrate and the buckling of the conductive layer during large cyclic deformation [76]. As a result, improved structural design and fabrication technology are requested to deal with the above problems for better sensing performance.
# Multi-LEO Satellite Stereo Winds
## 4 Discussion
### Applications
#### 4.2.2 Polar Meridional Transport Processes  
The multi-LEO stereo cloud winds provide tracking of cloud development and life cycle in the polar region. Atmospheric Rossby waves produce periodical poleward meridional transport, or Warm Air Intrusions (WAIs), as well as equatorward transport, or Cold Air Outbreaks (CAOs), which often lead to different cloud formation and atmospheric responses. During the WAI events in the northern hemisphere, the poleward transport of warm humid air into the Arctic can enhance or prolong sea-ice melt. On the other hand, large-scale CAO events export cold dry air from the Arctic to the midlatitudes, which can cause severe weather in the winter [71].
## 1 Introduction  
A number of techniques are known to qualitatively convert phase changes to observable amplitude variations. Developed in the 1930s, the Phase Contrast (PC) microscope [1], uses a spatial filter and a phase plate to translate phase into intensity modulation. Since then, PC microscopy and its derivatives, Differential Interference Contrast (DIC) microscopy and Hoffman Modulation Contrast (HMC) microscopy, have become widely adopted as techniques for cellular observation. DIC uses two polarized light beams, which take slightly different paths through the microscopic sample. As their optical path lengths differ and as the beams are recombined, they interfere and thereby create a shadow effect at refractive index gradients [2]. HMC uses an off-axis slit plate and a spatial modulator to alternately attenuate or enhance scattered light, thus creating a bright and dark shadowing effect at refractive index gradients, whereas image areas of constant refractive index appear grey [3]. However, the images are non-quantitative, neither optimized for visualizing cells nor for performing measurements. Quantitative phase imaging (QPI) methods use interference techniques to convert the phase information into pixel intensity, and in that way directly record the quantitative phase delay. Using QPI, studies associated with both thickness and refractive index fluctuations can be performed [4]. Previous work has been reviewed by [PERSON] et al., who described new ways of monitoring the cellular morphology changes in response to drugs [5], and by [PERSON] et al. who presented a review of digital holography (DH), with emphasis on microscopy techniques and applications [6]. There are a number of different technical implementations of QPI; and, by convention, a variety of names have been used to describe what is here collectively referred to as QPI. Terms differ even in some cases for the same commercial product (Table 1). In this review, we use the term QPI to comprise all, and we let other notations denote specific variations of hardware configuration.
# Characteristics of Ordinary Portland Cement Using the New Colloidal Nano-Silica Mixing Method
## 3 Results and Discussion
### Microstructure  
Figure 8 shows the SEM images of the specimens with 0%, 30%, and 50% CNS replacement. The presence of ettrinigite and C-S-H gel observed in the XRD results of Figure 5 was confirmed.  
Figure 8: _Cont._  
Figure 7: Thermal analysis: (**a**) 0% CNS, (**b**) 50% CNS.  
The CNS-substituted specimens exhibit a needle-like shape ettrigite in the reaction products [26]. As shown in Figure 8a, this ettringite is also observed inside the pores. However, when the CNS replacement rate increased to 30%, a thin bed ettringite was rarely observed, as shown in Figure 8b. As showed in Figure 8c, little ettringite was observed in 50% CNS specimens. However, the reaction product matrix exhibited a dense state with almost no pores. The increase in the CNS substitution rate promoted the formation of hydration reactants, such as C-S-H gel, by the reaction of nano-silica particles with portlandite. Therefore, the nano-silica particles of CNS improved the microstructure dencification by increasing the reactivity with the cement particles using the filler effect and the pozzolanic reaction [22; 31].  
Figure 9 shows the Ca/Si and Al/Si ratios by performing energy dispersive spectroscopy (EDS) analysis on the hydration products that are exhibited in Figure 8. The Ca/Si ratio of the reaction product C-S-H gel was 2.76, 2.31, and 1.68 in the 0%, 30%, and 50% CNS specimens, respectively. As the substitution ratio of CNS increased, the nano-silica particles increased, and the Si of C-S-H gel also increased. As a result, the Ca/Si ratio of the reactants decreased. Previous studies with PNS have shown that the Ca/Si ratio decreases with increasing nano-silica concentrations [21; 29; 34]. Furthermore, in the previous study, the increase in the amount of nano-silica increases the Al/Ca ratio of the hydration  
Figure 8: SEM images at 28-day: (**a**) 0% CNS, (**b**) 30% CNS, (**c**) 50% CNS.  
reactant [27] and the tendency of increasing the Al/Ca ratio shown in Figure 9 is similar. The method of substituting CNS for the weight of mixing water in this study showed that the Ca/Si and Al/Ca ratios of the hydration reactants were similar to those of the previous studies using PNS or CNS.
## 3 Monitoring and Service Area of the KOERI Tsunami Service Provider  
At the onset of its operations in 2012, the KOERI-TSP earthquake source monitoring area was bounded by 30\({}^{\circ}\) N to 48\({}^{\circ}\) N and 22\({}^{\circ}\) E to 44\({}^{\circ}\) E. Based on the experience gained from the 25 October 2018 (UTC) Mw 6.8 Zabruthos (Ionian Sea) earthquake and subsequent minor tsunami, starting from 1 January 2019, KOERI updated its earthquake source monitoring area for TSP purposes as 30-48\({}^{\circ}\) N and 19-44\({}^{\circ}\) E, excluding the Gulf of Corinth. KOERI monitors the earthquake activity in the earthquake source monitoring area, evaluates its tsunamigenic potential and sends tsunami messages to the subscribers for earthquakes of magnitudes larger than 5.5, according to the decision matrix in operational use. Messages include a set of tsunami forecast points (TFP), locations where the tsunami service provider, or another organization, provides an estimate of the tsunami arrival time, alert level or wave height. TFPs (Figure 1) may correspond to important coastal cities or populations or to the locations of sea level gauges [4]. For the selection of TFPs in Turkey, locations of ports, shipyards, marinas, oil refineries, coastal airports, touristic destinations, densely populated beaches and existing tide gauge stations have been considered based on the following criteria: (1) at least one point for each administrative division has been selected; (2) priority is given to the existing mareograph stations, and no limits are applied to the number of mareograph stations per administrative division. TFPs officially communicated to the IOC by NEAMTWS member states are included in KOERI-RETMC's messages. For those NEAMTWS member states where no TFPs have been officially communicated to IOC, KOERI has selected TFPs to ensure that no NEAMTWS Member State coastal zone is excluded from its services.
# Enhancement of Question Answering System Accuracy via Transfer Learning and BERT
## 4 Method
### Model For Entity Linking
#### 4.2.2 Model for Entity Disambiguation  
Next, since the entities mentioned in the natural language question may correspond to multiple entities saved in the knowledge base. After obtaining the entity identification of the subject entity in each question, it is essential to generate a set of candidate entities related to the entity mention from the knowledge base and disambiguate these candidate entity sets and to select the correct one. Accurately matching the subject entities asked in the question can also reduce the candidate set size for the next step of predicate matching and enhance the efficiency of the question answering system. We suggest a BERT-CNN model that introduces the predicate features of the entities once they are chained in order to boost the performance of the entity disambiguation task.  
In the entity disambiguation part, to obtain the set of candidate entities close to the subject entities in the question, we firstly generate the set of candidate entities by mapping the entity mentions identified in the previous step to the mention2 id library provided by the NLPCC- ICCPOL-2016 KBQA evaluation. For entity mentions that could not be mapped, we relied on the knowledge base to retrieve entities with similar character as the candidate entity set. Then, we input the interrogative sentences and the set of candidate entities into the BERT-CNN [35] model (as shown in Figure 8). This task can be viewed as a binary task, with the output label 1 if the candidate entity is a subject entity in a labeled triplet and 0 otherwise. The input data are in the form of [CLS], problem character sequences, [SEP], candidate entities for concatenation with predicate features, [SEP]. Among them, the predicate feature is the chained relationship from candidate entities in the knowledge graph to the connected predicates, as shown in Formula (16), where \(q\) represents the problem, \(e\) represents the candidate entity, and \(p_{i}\) represents the chained relationship starting from \(e\).  
\[x=[CLS],q,[SEP],e,p_{1},\dots,p_{n},[SEP] \tag{16}\]  
After BERT network coding, the hidden layer vectors of the last four encoder outputs are obtained, and the hidden layer outputs \(H\) after addition. Formula (17) illustrates the features of the convolution layer \(C\) :  
\[C=\sigma(H\otimes W+b) \tag{17}\]  
where \(\sigma\) is the sigmoid function, \(\bigotimes\) is the convolution operation,\(W\) is the weight in the convolution kernel, and \(b\) is the bias. \(H\) extract features through three convolution layers corresponding to stepsize 1, 3 and 5, respectively. Then the features feed into the highest pooling layer and the Softmax layer carries out classification after concatenation the three vectors, and the output label is 0 or 1. Formula (18) explains that the loss function is the cross-entropy loss function, which is minimized during training. In the prediction,the probability that the candidate entity is predicted as tag 1 is taken as the candidate entity's score.  
\[L=-[y\cdot\ln(x)+(1-y)\cdot\ln(1-x)] \tag{18}\]
## 3 Theoretical System Construction
### Application of Grounded Theory in Data Classification and Structure Construction
#### 3.2.3 Selective Coding Process  
As the concluding stage of the grounded theory analysis process, the core task of selective coding is to extract the main axis categories with the highest explanatory power and theoretical dominance from the previous large number of original concepts (level 1 coding) and category systems (level 2 coding) and then construct a logically self-consistent and structurally stable theoretical model. This stage marks the transition of the meaning construction of phoenix pattern image data from \"descriptive category induction\" to \"explanatory structural integration\", laying the foundation for subsequent model abstraction, database construction, and generative application.  
According to the statistical results of the level 2 coding shown in Table 1, this study identified 34 subcategories under 7 main categories, covering 1004 coding units. The frequency distribution shows the structural characteristics of \"configurational dimension dominance, semantic dimension support, and situational dimension supplement\". Among them, the \"element of ornamentation (EL)\" dimension has the highest total frequency (292 items, accounting for 29.08%), especially the \"structural component element (82 items)\" and \"main biological image element (49 items)\", which become the explicit skeleton of the image visual language, followed by the \"media and technology (MT)\" and \"symbolism and meaning (SM)\" dimensions, accounting for 12.05% and 17.43%, respectively, reflecting that the visual form of the phoenix pattern is highly dependent on craft support and cultural projection; the \"composition pattern (CP)\" and \"colour system (CYs)\" dimensions serve as supporting structural elements, providing a diversified basis for graphic organization and aesthetic style; and the \"application scope (AS)\" and \"theme context (TC)\", although slightly lower in frequency, play a key bridging role in scene distribution and cultural narrative.