esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Image-Based Human TRPA1 Assay System  
A detailed experimental method was previously described [4]. In brief, human TRPA1 was stably expressed in T-RexTM-293 cells (Invitrogen, Carlsbad, CA, USA), and the channel activity was evaluated by Ca2+ imaging. The TRPA1-encoding sequence was subcloned into pcDNA4/TO (Invitrogen). The plasmid was transfected into the T-RexTM-293 cells by using Lipofectamine 2000 reagent (Invitrogen), and the transfected cells were selected using 500 ug/mL zeocin and 10 ug/mL blasticidin to obtain cells that stably express TRPA1. The stable cell line was maintained in Dulbecco's modified Eagle medium--high glucose (Sigma-Aldrich, St. Louis, MO, USA) supplemented with 10% fetal bovine serum (Sigma-Aldrich), 100 unit/mL penicillin, 100 mg/mL streptomycin, and 250 ng/mL amphoteric (Nacalai Tesque, Kyoto, Japan), 500 ug/mL zeocin, and 10 ug/mL blasticidin. The cells were cultured at 37 \({}^{\circ}\)C in a 5% CO2 humidified incubator and passaged twice in a week. The intracellular Ca2+ concentrations were measured using a FlexStation III microplate reader system (Molecular Devices, Sunnyvale, CA, USA) at 37 \({}^{\circ}\)C. The cells were seeded in 96-well plates 24 h before each assay, and 1 ug/mL tetracycline was added to the cells to induce TRPA1 expression. The cells were loaded with 3 uM of Fluo-8 AM (Invitrogen) for 1 h at 37 \({}^{\circ}\)C in a measuring buffer (pH 7.4) which contained 5.37 mM KCl, 0.44 mM KH2 PO4, 137 mM NaCl, 0.34 mM Na2 HPO4, 5.56 mM D-glucose, 20 mM HEPES, 1 mM CaCl2, 0.1% bovine serum albumin, and 2.5 mM prophenect. The test compounds were dissolved in DMSO and added to the measuring buffer. All of the test compounds were administered at 1, 3, 5, 10, 30, 50, 80, and 100 uM to the TRPA1-expressing cells, because the compounds did not show nonspecific effects on parent cells, T-RexTM-293 cells not expressing TRPA1, at the concentrations. TRPA1 activity was calculated using the following equation: TRPA1 activity = \(\Delta\)F/F\({}_{0}\), where F\({}_{0}\) (baseline) is defined as the mean fluorescence value at 0-30 s before sample addition, and \(\Delta\)F is calculated by subtracting F (signal intensity, the highest fluorescence value at 30-150 s after sample administration) from F\({}_{0}\). For obtaining EC\({}_{50}\) values, curve fitting and parameter estimations were performed using GraphPad Prism 5 software (Graph Pad Software, San Diego, CA, USA). Each experiment was repeated at least three times, and each data point represents the mean \(\pm\) SEM. The assay results were divided into four categories (P1, P2, N1, and N2) as follows; P1: high-activity agonists (EC\({}_{50}\) was determined), P2: medium-activity agonists (EC\({}_{50}\) was too high to calculate), N1: difficult to evaluate activity due to its low solubility, N2: no activity. Because the TRPA1-expressing cells were seeded on the bottom of 96-well plates, test compounds need to be dissolved in the measuring buffer to reach the cells. Derivatization is a possible approach to improve the solubility of N1 compounds.
# Bounce: A High Performance Satellite-Based Blockchain System
###### Abstract  
Blockchains are designed to produce a secure, append-only sequence of transactions. Establishing transaction sequentiality is typically achieved by underlying consensus protocols that either prevent forks entirely (no-forking-ever) or make forks short-lived. The main challenges facing blockchains are to achieve this no-forking condition while achieving high throughput, low response time, and low energy costs. This paper presents the _Bounce_ blockchain protocol along with throughput and response time experiments. The core of the _Bounce_ system is a set of satellites that partition time slots. The satellite for slot \(i\) signs a commit record that includes the hash of the commit record of slot \(i-1\) as well as a sequence of zero or more Merkle tree roots whose corresponding Merkle trees each has thousands or millions of transactions. The ledger consists of the transactions in the sequence of the Merkle trees corresponding to the roots of the sequence of commit records. Thus, the satellites work as arbiters that decide the next block(s) for the blockchain. Satellites orbiting around the Earth are harder to tamper with and harder to isolate than terrestrial data centers, though our protocol could work with terrestrial data centers as well. Under reasonable assumptions--intermittently failing but non-Byzantine (i.e., non-traitorous) satellites, possibly Byzantine Ground Stations, and \"exposure-averse\" administrators--the _Bounce_ System achieves high availability and a no-fork-ever blockchain. Our experiments show that the protocol achieves high transactional throughput (5.2 million transactions per two-second slot), low response time (less than three seconds for \"premium\" transactions and less than ten seconds for \"economy\" transactions), and minimal energy consumption (under 0.05 joules per transaction). Moreover, given five more cloud sites of the kinds currently available in CloudLab, Clemson, we show how the design could achieve throughputs of 15.2 million transactions per two second slot with the same response time profile.  
blockchain; consensus protocol; performance analysis; satellite; space-terrestrial network
## 4 Simulation Results and Discussions
### Saturation Throughput  
Figure 6 shows the behavior of saturation throughput growth of network during simulation rounds, and suggests that the cumulative packets collected at the sink are higher in CTP-SEEC. This is attributed to the improved lifespan of the network with the capacity policy of controlled transmission. In addition to this, CTP-SEEC's expanded throughput also results in a high number of live nodes during the network lifespan. Packets obtained per round in the EEDBR result in high residual energy, while a high packet distribution ratio in the sparse area cannot be accomplished by choosing nodes and by considering residual energy. In DBR, only the low-depth node is considered for packet routing, which often results in packet replication, allowing DBR to reach the lowest throughput compared to other protocols. Under the SEEC protocol, the number of packets received at the sink node is less than the suggested CTP-SSEC routing protocol. The theory behind this is that the transmitting power stays steady in SEEC and the network dies early. Figure 7 shows that the packets received per round with controlled transmission power are better than SEEC.  
Figure 6: Saturation throughput.  
Figure 7: Packets Received Per Round.
# A 3D Numerical Model to Estimate Lightning Types for PyroCb Thundercloud
## 3 Simulation Results and Discussion
### Tripole Structure with Large Lower Positive Charge
of \(3.5\leq\text{x}\leq 26.5\) km and \(3.5\leq\text{y}\leq 26.5\) km on the projected ground surface beneath the pyroCb thundercloud. This would trigger the start of \(-\)CG flashes. This corresponds to the theory that says the presence of an LP charge layer below the pyroCb thundercloud amplifies the electric field and triggers \(-\)CG flashes [15; 19]. A similar simulation for \(\sigma\) is carried out for the second configuration of the pyroCb thundercloud by enlarging the lateral and vertical extents of the LP charge region, along with adjusting its charge magnitude. As depicted in Figure 6b, the value of \(\sigma\) turns negative across the entire simulation domain, reaching its peak of 0.2454 nC/m\({}^{2}\) at the thundercloud's core. This simulated negative peak is larger in magnitude than the maximum value of \(\sigma\) (0.2087 nC/m\({}^{2}\) at \(x=14\), 14.5 \(\leq\text{y}\leq\text{15.5}\) km) that appeared in the first configuration of the pyroCb thundercloud. The negative value of \(\sigma\) in the second configuration could trigger the initiation of a downward positive leader, ultimately causing a \(+\)CG flash to form as it reaches the ground. These projected outcomes align with the findings in [29], which concluded that the existence of a large LP charge layer hinders negative lightning leader progression to the ground. Consequently, this terminates the incidence of \(-\)CG flashes while raising the likelihood of positive \(+\)CG lightning. This phenomenon takes place when a negative electric field other than a positive or zero value appears at the thundercloud's bottom.
# Applications of Mueller Matrix Polarimetry to Biological and Agricultural Diagnostics: A Review
## 2 Principles of Mueller Matrix Analysis
### Reflection Mueller Matrix Ellipsometry  
Ellipsometry is a highly sensitive method for determining the optical parameters of reflecting samples based on the relative change, as a result of reflection, in the amplitudes and phases of the components of the electric field of an electromagnetic wave, parallel and transverse to the plane of incidence. Simultaneous measurement of the amplitude and phase changes of an electromagnetic wave, expressed by the so-called ellipsometric parameters \(\Psi\) and \(\Delta\)[93], for example, allows precise determination of both the thickness and optical constants of the film material on the surface. Ellipsometry generalized to obtain the full MM (MM ellipsometry) provides a higher stability of determining the material optical properties in the case of diffusely reflecting samples than ellipsometry measuring only two ellipsometric parameters [94]. A general scheme of an MM ellipsometer is shown in Figure 6.  
Spectral ellipsometers (spectroellipipsometers) have significant capabilities in the characterization of organic molecular structures and biological systems on a solid substrate. For example, MM spectroscopic ellipsometry was used to study the polarization and depolarization properties of chiral films [95]. As a light source, lamps of various types, LEDs and lasers are employed in different spectral regions. Multi-laser ellipsometers have the potential to be well conditioned [96]. In spectroellipsometers, optical monochromators and filters can be used in analogy with other spectral methods [94].  
Spectroellipsometry in the IR range is relevant for the analysis of multicomponent organic materials with characteristic IR absorption lines. The reflected Mueller matrix elements can be measured in a spectral range of 9-11 \(\upmu\)m using an IR spectroellipsometer based on a tunable CO\({}_{2}\) laser [76]. To work with thick samples, an attenuated total reflection (ATR) device in combination with achromatic polarimetric elements can be installed into a standard Fourier-transform infrared (FTIR) spectrometer to measure the reflection MM of bulk materials in the spectral range from 3 to 14 \(\upmu\)m [97]. A scheme of an ATR-FTIR spectrometer for measuring the IR spectra of MM elements of the sample placed on the ATR surface is shown in Figure 7.  
An IR laser or FTIR MM spectroellipsometers are suitable for the complex analysis of organic bulk materials, films and liquids [98; 99; 100].  
The performance of an ellipsometer can be improved by particular technical solutions. Multichannel ellipsometry is increasingly used [101; 102; 103; 104]. For example, a coupled-phase polarization modulator, together with a multichannel polarimeter, allows Mueller matrix measurements without choppers and lock-in amplifiers [105]. Polarization modulation using photoelastic modulators (PEM) can provide a high measurement speed and sensitivity [106].  
Figure 6: General scheme of MM ellipsometer.
# On Evaluating IoT Data Trust via Machine Learning
## 2 Related Work
### Conventional Approaches  
A variety of techniques have been proposed for evaluating IoT data trust. Approaches utilizing weighted voting, Bayesian inference, and DST have demonstrated good performance. However, they often do not directly evaluate the trustworthiness of the data, but those of the sensors from which the data emanate [13]. This is limiting as IoT sensors may not always produce data that are solely trustworthy or untrustworthy. The trustworthiness of IoT data can vary depending on various factors such as sensor reliability, environmental conditions, data transmission integrity, and potential cybersecurity threats.  
The Bayesian and Gaussian reputation systems proposed in [15; 16; 17], which have been verified mostly using simulated network packet data, can provide some insights into data trust. However, reputation-based systems in the context of IoT typically focus on evaluating the trustworthiness of IoT sensors, rather than that of the data they capture [14].
## 4 Experimental Results and Discussion
### YOLO-T Deep Learning Network Target Recognition
Considering that the complexity of the YOLO-T method is not lower than the YOLO v3/v4 method, the results show that YOLO-T method has better detection efficiency. In actual engineering applications, the testing time within 30 milliseconds is acceptable. Therefore, the above discussion can show that the YOLO-T method has the advantage of high efficiency in the field of dangerous goods target detection.
## 3 Modeling of Various Stakeholders in the Distribution Network (DN)
### Objective Function at the Cloud Layer  
We assume there are \(N\)-normal customers, \(K\)-prosumers, and one DSO in the DN. The utility maximization of the normal customers and prosumers are chosen as the objective function at the cloud layer, which is expressed as a quadratic function:  
\[Maximize\ \ \sum_{i}^{N}[U(x_{i}^{t},\omega_{i})-P_{retail}^{t}x_{i}^{t}]+\sum_{t}^{ K}[U(x_{j}^{t},\omega_{j})-P_{retail}^{t}x_{i}^{t})]\]  
where the optimized variable is the total amount of consumption of customers and prosumers at each time slot based on the retail price announced by the DSO. The retail price \(P_{retail}^{t}\) is cited from [31]. The utility parameters \(\omega_{i}\) and \(\omega_{j}\) can be captured through the NN at the fog layer. The cloud layer function can be considered flexibly, and below is an example of the maximization utility of prosumers and customers. The models for other stakeholders are not used in the optimization but are used for the real-time revenue calculation after the optimal is found.
# A Self-Paced Multiple Instance Learning Framework for Weakly Supervised Video Anomaly Detection
## 2 Related Work
### Self-Paced Learning  
The self-paced learning (SPL) theory [12] is inspired by the cognitive process of human beings, where samples are gradually incorporated into the training process from easy to hard. In recent years, self-paced learning methods have been successfully applied to different computer vision tasks [17, 18, 19]. For example, [PERSON] et al. [17] proposed a deep self-paced learning algorithm for the person re-identification task, which utilized high-confidence samples while suppressing low-confidence noisy samples in the early stages of training. This approach allowed the neural network to be trained more stably by gradually involving reliable samples from easy to hard, thereby improving the accuracy of person re-identification. For weakly supervised object detection tasks, [PERSON] et al. [18] proposed a training method based on self-paced learning, which selects the most reliable subset of images and boxes in each iteration and trains the network in a fully supervised manner to develop robust detectors. [PERSON] et al. [19] proposed a self-paced fine-tuning network-based framework for the weakly supervised object segmentation task, which combined a self-paced learning mechanism with deep neural networks to enhance the model's segmentation capability.  
Inspired by the successful application of SPL in related tasks, we propose to combine SPL with multiple-instance learning (MIL) for the WS-VAD task. This combination aims to aid in the selection of more reliable video segments, thereby facilitating the training of more robust models. To the best of our knowledge, we are the first to use SPL for the task of WS-VAD. The work most related to ours is [PERSON] et al. [10], in which the authors proposed an unbiased multiple-instance learning (UMIL) approach. In each training iteration, the video clips are first divided into confident and ambiguous sets. The confident set is used to train the classifier through segment-wise supervised learning, while the ambiguous set assists the classifier training by reducing the abnormal context bias within the confident set through unsupervised clustering. This method allows the classifier to be trained on a broader range of video clips, ultimately leading to a more reliable and unbiased classifier. However, their approach typically involves presetting the proportion of confident video clips used for training, which limits its flexibility in various practical scenarios, particularly for videos with differing durations of abnormal events. Therefore, in this paper, inspired by the success of self-paced learning, we propose a SP-MIL framework, the core idea being to adaptively select the subset of segments used for training in a easy-to-hard way and dynamically update the classifier to persistently enhance its performance through continuous self-training.
## 2 Literature Review
### Comparison of Prior Work and Research Gap  
While several studies have explored crash classification using either structured or unstructured data, and others have applied machine learning for severity prediction or report generation, few have directly addressed the challenge of detecting mislabeled crash records through a validation-oriented lens. Table 1 provides a comparative summary of representative prior studies, highlighting their core tasks, data types, methodological approaches, and limitations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Task Focus** & **Data Type** & **Method/Model** & **Validation** & **Identified** & **Gap This Study** \\  & & **Used** & **Approach** & **Limitation** & **Fills** \\ \hline [PERSON] et al. & Work zone & & & & & No use of AI or \\  & crash & & & & & LLMs for \\  & identification & Narrative + & & & & & multimodal data \\  & identification & & & & & fusion for \\  & & & & & & improved \\  & & & & & & detection \\ \hline [PERSON] et al. (2018) & Work zone & & & & & & & \\  & crash severity & & & & & & \\  & prediction & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: A review of prior studies and the novelty of this research.  
Notably, none of the existing works leverage fine-tuned large language models (LLMs) for retrospective mislabel detection using multimodal data fusion. This gap underscores the novelty of the current study, which introduces a precision-focused framework using GPT-3.5 to identify inconsistencies between narrative text and structured crash attributes, validated through expert review.  
The gap identified in prior research is significant, as it highlights the need for a methodological shift toward validation of labeled crash records using advanced data fusion techniques. This study aims to fill that gap by employing a fine-tuned LLM to enhance the accuracy and reliability of crash data classifications.
# Simulation Analysis of a Three-Degree-of-Freedom Low-Frequency Resonant Mixer
## 2 A Three-Degree-of-Freedom Low-Frequency Resonant Mixer  
### Principle of Operation  
This three-degree-of-freedom low-frequency resonant mixer consists of a core section and a framework. As depicted in Figure 1, the core part of the device is made up of three components: the payload assembly, the vibration-isolating assembly, and the excitation assembly. The excitation assembly is rigidly connected to the driving component, while the payload assembly is fixedly connected to the mixing container.  
Differential equations of motion for the three assemblies can be established based on the system dynamics model \(M\ddot{\textbf{X}}+\textbf{C}\dot{\textbf{X}}+\textbf{K}\textbf{X}=\textbf{F}\). **M**, **C**, and **K** denote the mass matrix, damping matrix, and stiffness matrix, respectively, while **X** and **F** represent the displacement matrix and the sine excitation force matrix, respectively.  
\[\textbf{Wherein},\quad\textbf{M} = \begin{bmatrix}m_{1}&0&0\\ 0&m_{2}&0\\ 0&0&m_{3}\end{bmatrix},\quad\textbf{K} = \begin{bmatrix}k_{1}&-k_{1}&0\\ -k_{1}&k_{1}+k_{2}+k_{3}&-k_{3}\\ 0&-k_{3}&k_{3}+k_{4}\end{bmatrix},\] \[\textbf{C} = \begin{bmatrix}c_{1}&-c_{1}&0\\ -c_{1}&c_{1}+c_{2}+c_{3}&-c_{3}\\ 0&-c_{3}&c_{3}+c_{4}\end{bmatrix},\textbf{F}=\begin{bmatrix}F\sin(\omega t)&0& 0\end{bmatrix}^{\mathsf{T}}.\]  
The characteristic equation of the system  
\[\left|K-\omega_{ni}^{2}M\right|=\begin{vmatrix}k_{1}-\omega_{ni}^{2}m_{1}&-k_ {1}&0\\ -k_{1}&k_{1}+k_{2}+k_{3}-\omega_{ni}^{2}m_{2}&-k_{3}\\ 0&-k_{3}&k_{3}+k_{4}-\omega_{ni}^{2}m_{3}\end{vmatrix}=0 \tag{1}\]  
The final obtained vibration system responds to the excitation  
\[X=A_{N}\begin{pmatrix}f_{N1}\beta_{N1}\sin(\omega t-\psi_{N1})/\omega^{2}{}_{n 1}\\ f_{N2}\beta_{N2}\sin(\omega t-\psi_{N2})/\omega^{2}{}_{n2}\\ f_{N3}\beta_{N3}\sin(\omega t-\psi_{N3})/\omega^{2}{}_{n3}\end{pmatrix}= \begin{pmatrix}B_{1}\sin(\omega t-\psi_{1})\\ B_{2}\sin(\omega t-\psi_{2})\\ B_{3}\sin(\omega t-\psi_{3})\end{pmatrix} \tag{2}\]  
In the formula, \(A_{N}\) is the regular array matrix of the system; \(B_{i}\) and \(\psi_{i}\) are the amplitude and phase angle of each mass; \(\beta_{Ni}\) is the amplification factor; \(\psi_{Ni}\) is the phase angle, whose value is as follows:  
\[\beta_{Ni}=\frac{1}{\sqrt{\left(1-\omega^{2}/\omega^{2}{}_{ni}\right)+\left( 2\zeta_{ii}\omega/\omega{}_{ni}\right)^{2}}}\]  
\[\psi_{Ni}=\arctan\frac{2\zeta_{ii}\omega/\omega{}_{ni}}{1-\omega^{2}/\omega^{ 2}{}_{ni}} \tag{3}\]  
When [13],  
\[1-\omega^{2}/\omega^{2}{}_{ni}=0^{-},\ \psi_{Ni}=\psi_{Ni}+\pi \tag{4}\]  
Figure 1: Three-degree-of-freedom resonant mixing system dynamics model.  
\[f_{Ni}=A^{i}{}_{N1}F_{1}+A^{i}{}_{N2}F_{2}+A^{i}{}_{N3}F_{3}\]
# Quaternion Codes in MIMO System of Dual-Polarized Antennas +
## 3 Comparative Analysis of the Construction Techniques  
### Code Rates  
An important result, which gives us bounds on the upper limits of code rates of above QODs can be proved easily. For instance, the underlying CODs which we employ in the construction of such codes have upper bounds (Theorem 5 and Theorem 6 in [3]), therefore, result below follows immediately.  
**Theorem 5**.: _(a) For an even number of transmit dual-polarized antennas, the highest possible rate of QODs arising from rectangular CODs is bounded above by_  
\[\mathcal{R}_{\mathbf{Q}}\leq\frac{n+2}{2n}. \tag{16}\]  
_(b) For an odd number of transmit dual-polarized antennas, the highest possible rate of QODs arising from rectangular CODs is bounded above by_  
\[\mathcal{R}_{\mathbf{Q}}\leq\frac{n+3}{2n+2}. \tag{17}\]  
Note that there is substantial difference between the code rates of QODs obtained from the above approach, denoted by \(R_{\mathbf{Q}}\), and those which are based on recursive techniques represented with \(r_{\mathbf{Q}}\). Below figure clearly describes that as the number of dual-polarized transmit antennas increases the code rate \(r_{\mathbf{Q}}\) sharply declines. Quite contrary, the code rates \(R_{\mathbf{Q}}\) of QODs based on [PERSON]'s approach remain stable. In Figure 2, we compare the code rate curves from both approaches where red curve represent the code rate \(r_{\mathbf{Q}}\) of QODs based on recursive methods and code rates \(R_{\mathbf{Q}}\) of QODs based on [PERSON]'s approach are depicted with green curve. Regardless of the number of transmit antennas, we can always obtain a QOD with code rate higher than or equal to 0.5, as is clear from Figure 2. So we obtain a robust approach of developing QODs based on [PERSON]'s mechanism.