esa-sceva/satcom-chunk-collection huggingface dataset:
## Animal Studies
### Cannabidi Slows Symptom Onset in EAE Mice  
CBD has been shown by several authors to reduce the severity of EAE in mice. The main questions to be asked are: what seems to be the clinically effective dose needed to see an improvement; what molecular changes occur after treatment; and most importantly, is CBD efficient in reducing the neuroinflammation associated with an animal model of MS? A conflicting aspect of the literature are the CBD doses administered to the animals. Some researchers found that administering lower doses of CBD, between 5-20 mg/kg [25; 26] is effective in reducing signs of the disease whereas other researchers found that a much higher dose of 50 mg/kg is the dose that most greatly improved results of scoring methods and preserved some paw sensitivity (see Table 1 below for a summary of drug doses and effects in animal models). In 2018, [PERSON] and colleagues demonstrated that 20 mg/kg of CBD administered intraperitoneally (IP) significantly reduced the amounts of pro-inflammatory infiltration of interferon \(\gamma\) (IFN-\(\gamma\)) and interleukin-17 (IL-17) in female C57 BL/6 mice who have been administered MOG (35-55). They found that treatment with CBD leads to the induction of myelin derived suppressor cells and the suppression of MOG specific T cell proliferation. Similarly, by depleting myelin derived suppressor cell levels, the beneficial effects of CBD were completely reversed. [PERSON] and colleagues used an imaging approach to assess the benefits of CBD in EAE infected C57 BL/6 mice. They found a reduced apparent diffusion coefficient in the basal ganglia, hippocampus, and the corpus callosum in all CBD-treated mice, but most notably in the higher dose group which was treated with 50 mg/kg. From the histological analysis, it was found that CBD alone significantly reduced axonal damage, cell infiltration and demyelination while also decreasing the levels of IL-6, a pro-inflammatory cytokine. Reduced microglial activity was also observed. Results showed that CBD caused decreased amounts of upregulation of ionised calcium-binding adaptor molecule 1 (IBA-1) labelling in adoptive transfer EAE mice and CB\({}_{2}\) receptors were expressed in high levels in the microglia suggesting that the upregulation of CB\({}_{2}\) receptors is important in MS. Similarly, [PERSON] et al. have demonstrated that CBD is anti-apoptotic against the neurodegenerative aspects of MS [66]. These authors used the clinical disease score (1-6) to measure the beneficial effects of CBD and found
## 3 Results and Discussion
### Predictive Performance of the Initialized Model
In order to comprehensively evaluate the performance of the initialized model, the data set was randomly divided in the ratio of 8:2, and a total of 100 training and testing processes were performed on the model, and then the evaluation results of each metric were calculated and recorded; the results are shown in Figure 5. The mean values of MSE, RMSE, MAE, and R\({}^{2}\) of the initialized model are 44.02, 41.81; 6.63, 6.47; 2.91, 2.86; 0.940, 0.943 (test set, training set), respectively. Figure 4: Regression analysis of the initialized XGB. Figure 5: Evaluation results of the initialized XGB.
## 3 Channel Model
### Pointing Error Model
The average power collected by the finite aperture at the receiver plane is expressed as [30]:  
\[\langle{\rm{P}}({\bf{r}},z)\rangle=\int_{-\infty}^{\infty}\int_{-\infty}^{ \infty}\left\langle I({\bf{p}}-{\bf{r}},z)\right\rangle\exp\Biggl{[}-\frac{2 }{a^{2}}\left(p_{x}^{2}+p_{y}^{2}\right)\Biggr{]}dp_{x}dp_{y}, \tag{14}\]  
Substituting Equation (13) into Equation (14), for the integral domain in the integration calculation, the circular plane with the received aperture radius of \(a\) is equivalent to the rectangle plane with a side length of \(\sqrt{\pi}a\). Then, the analytic formula for the average power can be derived as follows:  
\[\langle{\rm{P}}({\bf{r}},z)\rangle\approx\pi k_{1}W_{0}^{2}\exp\biggl{(}-2k_{ 1}\frac{r^{2}}{a^{2}}\biggr{)}{\rm{erf}}\biggl{(}\sqrt{\frac{\pi}{2(1-k_{1})}} \biggr{)}\left[\Phi\biggl{(}\frac{\sqrt{\pi}a}{2}-k_{1}r\biggr{)}+\Phi\biggl{(} \frac{\sqrt{\pi}a}{2}+k_{1}r\biggr{)}\right], \tag{15}\]  
where \(k_{1}=1/\left[1+2(W_{\rm{z}}/a)^{2}\right]\), and the other parameters' meanings are the same as above.
## 2 Background
### Defenses against Adversarial Attacks
#### 2.2.2 Data Preprocessing Defenses  
Data preprocessing defenses, such as input transformations [15] and randomization [16], work by transforming the adversarial examples before feeding them into the model. They aim to either remove or distort the adversarial perturbations so that the model can classify the altered input correctly. These methods are partially effective but are often circumvented by adaptive attacks that anticipate such transformations [10].  
While data preprocessing defenses have previously been employed, none have leveraged clustering to understand the inherent structure of adversarial examples, thus missing out on a more strategic, targeted, and effective training regimen.
## 2 Action Mechanism and Calibration of the Optical-Fiber-Embedded Beam (OFEB)
### Test Results  
Figure 6 presents the fiber Brillouin frequency of five calibration tests. Based on Formula (4), the Brillouin frequency shift (BFS) is transformed into displacement changes at different positions.  
Figure 7 shows the strain and displacement curves obtained for Tests 1 to 5. The upper and lower surface strains of the OFEB measured in different tests were generally symmetrically distributed, which is consistent with the mechanical characteristics of the beam unit under a point displacement load, indicating that the distributed optical fiber can accurately measure the strain at each point in the beam unit.  
Figure 8 compares the measurements of the optical fiber and the displacement sensor for the five tests. The measured settlements at B in Tests 1, 2, 3, 4, and 5 were 9.7 mm, 15.5 mm, 22.4 mm, 54.9 mm, and 57.4 mm using the optical fiber, respectively, and 10.3 mm, 15.6 mm, 22.1 mm, 52.6 mm, and 55.1 mm using the displacement sensor, respectively. The relative error between the two measurement methods ranged from 0.6% to 5.8%; the test error can be mainly attributed to the manufacturing error of the OFEB and the deformation  
Figure 5: Calibration of the OFEB deflection measurement: (**a**) schematic and (**b**) photographic image.  
transfer error between the optical fiber and the pipe, which can be mitigated by factory manufacturing. The measured settlement at A ranged from 0.2 mm to 1.5 mm using the optical fiber and from 0.3 mm to 1.5 mm using the displacement transducer. Thus, the results at the measurement points A and B indicate that the embedded optical fiber can accurately measure the vertical deflection of the OFEB, therefore realizing the continuous distribution measurement of beam deflection.  
Figure 6: The Brillouin frequency shift measured in five calibration tests.  
Figure 7: The distribution of the strain and displacement measured in five calibration tests: (a) Test 1; (b) Test 2; (c) Test 3; (d) Test 4; and (e) Test 5.
## 3 Results and Discussions
### Toxicity Model Predicting the ROUR under Different Conditions  
As discussed above, the ROUR was significantly affected by the concentration of toxic substances, MLSS content, and HRT. It is therefore important to develop a mathematical model simulating the effects of both toxic substances and environmental variables to compare the ROUR in different WWTPs with distinct operation parameters. In the present study and considering the microbial activity inhibition by toxic substances as described in Equation (1), the integral effects of these parameters were expressed in the following Equation (7) to predict the ROUR of the activated sludge.  
\[ROUR=1-\frac{\int_{1}^{t}OUR_{t}\cdot dt}{OUR_{0,0}}=1-\frac{\int_{0}^{t}\left( \frac{k_{\textit{[PERSON]}}^{-1}}{k_{\textit{[PERSON]}}^{-1}+[\textit{Toxic}]/[ MLSS]}\times OUR_{0,t}\right)\cdot dt}{OUR_{0,0}} \tag{7}\]  
Here, \(OUR_{0,0}\) and \(OUR_{0,t}\) represent the OUR in the absence of toxic substances at time = 0 and \(t\), respectively. If the OUR of the activated sludge is consistent along with time, the equation can be converted as Equation (8) and fit well with Equations (3) to (7) above.  
\[ROUR=1-\frac{k_{\textit{[PERSON]}}^{-1}}{k_{\textit{[PERSON]}}^{-1}+[\textit{Toxic }]/[MLSS]}\times HRT \tag{8}\]  
Although numerous studies have reported the impacts of toxin concentration and other parameters on the OUR or other indicators representing the activated sludge activities [27; 28; 32], our work is the first attempt to explain the toxic effects in a theoretical model, and the results suggested that toxic substances, MLSS and HRT were all key factors affecting the ROUR.
## 4 Experiments and Result Analysis
### Ablation Experiment  
To verify the effectiveness of the three proposed improvements in enhancing the detection performance of the YOLOv5 model, a series of ablation experiments were designed using the original YOLOv5 model as the baseline. In these experiments, the EMA, ECA attention mechanisms, and WIoU loss function were progressively incorporated into the original model. The detailed integration process is illustrated in Figure 8. The detection accuracy of the model was evaluated using the AP values of each defect category and the [EMAIL_ADDRESS] score, with the results shown in Table 3. After adding the ECA attention mechanism, the model's detection accuracy for unpenetrated and cracked defects significantly improved, resulting in a 1.5% increase in the optimized [EMAIL_ADDRESS] value compared to the original model. Adding both the ECA and EMA attention mechanisms further enhanced the detection accuracy for all defect types, leading to a 5.1% increase in the optimized [EMAIL_ADDRESS] value. Finally, when both attention mechanisms were incorporated alongside the replacement of the loss function with WIoU, the model's detection accuracy for all five defect types showed notable improvements, with the optimized [EMAIL_ADDRESS] value increasing by 6.9% compared to the original model. These results further quantitatively validate the harmonious integration of the EMA and ECA attention mechanisms without structural conflict. Specifically, EMA contributes in the early stages by providing global contextual information, which expands the receptive field and enhances the initial feature representation. This enables the model to dynamically adjust feature focus across different scales and defect types, thereby improving its ability to detect multi-scale targets. In contrast, the ECA module refines local features in the later stages by adaptively reweighting channel-wise information, enhancing the representation of small objects and critical local features. This highlights key information related to weld defects while suppressing irrelevant background noise, leading to improved detection of small-sized and low-contrast defects.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**ECA**} & \multirow{2}{*}{**EMA**} & \multirow{2}{*}{**WIoU**} & \multicolumn{5}{c}{**AP/\%**} & \multirow{2}{*}{**[EMAIL_ADDRESS]/\%**} \\ \cline{3-3} \cline{5-8}  & & & **Circular** & & **Linear** & **Non-Fusion** & **Non-Penetration** & **Crack** \\ \hline — & — & — & 93.1 & 90.8 & 98.0 & 79.9 & 76.7 & 87.7 \(\pm\) 0.2 \\ \(\surd\) & — & — & 91.2 & 92.5 & 95.5 & 85.3 & 81.7 & 89.2 \(\pm\) 0.1 \\ \(\surd\) & \(\surd\) & — & 95.2 & **94.3** & 98.8 & 91.5 & 84.3 & 92.8 \(\pm\) 0.3 \\ \(\surd\) & \(\surd\) & \(\surd\) & **95.7** & 91.3 & **99.5** & **94.2** & **92.0** & **94.6 \(\pm\) 0.2** \\ \hline \hline \end{tabular} Note: “\(\surd\)” represents the addition of the scheme, while “—” represents the absence of the scheme.  
\end{table}
Table 3: Comparison of ablation results.
# Fault Feature Extraction of Parallel-Axis Gearbox Based on IDBO-VMD and t-SNE
## 4 Fault Diagnosis Process for Parallel-Axis Gearboxes Based on WOA-VMD and t-SNE
### Feature Extraction  
Time domain features of vibration signals can reflect the overall state of the gearbox and can be used for fault detection and trend forecasting. Frequency domain features are useful for identifying the location and cause of faults. The combination of time and frequency domain feature information can effectively determine the current condition of the gears. In this paper, a variety of time domain and frequency domain feature parameters are selected to form the fault information feature matrix.  
Information entropy describes the degree of uncertainty in a system and is used to analyze the complexity of a signal. Single entropy characteristics may not fully reflect the signal's feature information. Therefore, multiple entropy values are extracted for each IMF to ensure data completeness and diagnostic accuracy. This paper extracts permutation entropy, fuzzy entropy, and sample entropy.  
According to the above introduction, the specific steps for gear fault diagnosis based on IDBO-VMD decomposition, selection of IMF features, and t-SNE are as follows:  
1. Obtain vibration signals for various states using an accelerometer sensor.
2. Use the IDBO algorithm to search the optimal parameters of VMD for each state. After parameter optimization, apply VMD to decompose the signals from various states, resulting in K IMF components.
3. Apply the kurtosis criterion to filter the obtained IMF components, selecting the best IMF.  
Perform feature extraction on the chosen IMF, recombine the extracted 20 feature values to create a new feature vector.
5. Utilize the t-SNE method for dimensionality reduction, obtaining a two-dimensional feature vector.
6. Input the feature vectors of the training dataset into an SVM for training, creating an SVM classification model.
7. Input the feature vectors of the testing dataset into the trained SVM model to perform fault diagnosis.
# Quantum Approximation for Wireless Scheduling
## 5 Performance Evaluation
### Experiments  
This experiment demonstrates the possibility of a novel quantum approach via QAOS by focusing on simple and light methods to wireless scheduling problems. In the classical approaches, a message-passing algorithm consisting of linear programming relaxation, maximum product method, and maximum a posteriori estimation is mainly used to solve the MWIS-based wireless scheduling problem [14; 16; 17]. However, if the network requires a more simple and lighter algorithm, the random and greedy-based algorithms are also used for wireless scheduling [29; 30]. Thus, the performance of the proposed QAOS, based on QAOA, one of the simple and light quantum algorithms that intuitively express the state with qubit rotation, is compared with the random search and greedy search, which are simple and light classical algorithms [31; 32]. In addition, the QAOS algorithm is executed with different \(p\) value settings, where the \(p\) value means the number of alternations of \(U_{P}(\gamma_{\zeta})\) and \(U_{M}(\beta_{\zeta})\) in Equations (33) and (34), i.e., \(\zeta\in\mathbb{Z}^{+}\) and \(1\leq\zeta\leq p\).  
For the performance evaluation, random conflict graphs with 10 nodes are generated; then, random search, greedy search, and QAOS algorithms are performed for the given random conflict graphs. The measurement of each QAOS is performed 1000 times in each simulation (i.e., in each randomly generated conflict graph). The performance of each algorithm is quantitatively measured with \(\eta\) as follows:  
\[\eta\triangleq\frac{a}{b}, \tag{35}\]  
where \(a\) and \(b\) are the summations of weights of the scheduled nodes by the used algorithms and the summations of weights of the scheduled nodes by brute-force search (i.e., exhaustive search), respectively, for the given randomly generated graphs. As shown in Figure 3, the cumulative distribution functions (CDF) of \(\eta\) for each algorithm are computed.  
As presented in Figure 3, QAOS algorithms with \(p\geq 8\) present a better performance than random search and greedy search, in any kind of randomly generated conflict graph. In these repeated simulations, the performances of QAOS algorithms are improved as the \(p\) value increases. In particular, the performance of the QAOS algorithm with \(p=10\) is much better than the QAOS algorithms with \(p=8\) and \(p=9\). As shown in Table 1, the QAOS algorithm with \(p=10\) returns optimal solutions (i.e., equivalent to the solutions obtained by brute-force search) with a ratio of 69.50%. Through these results, we have verified that the proposed QAOS algorithm presents beautiful results in terms of the accuracy of the solutions.  
Figure 2: Parts of Python codes using Cirq and TensorFlow-Quantum for solving the maximum weight independent set (MWIS)-based scheduling problem.
# Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data
## 3 Model Construction of Crude Oil Futures Price Fluctuation Based on Mixed-Frequency Data
### GARCH-MIDAS-Skewed-t Model  
In the traditional GARCH-MIDAS model, the residual items \(\varepsilon_{i,t}\) obey the standard normal distribution; that is, \(\varepsilon_{i,t}|\Phi_{i-1,t}\)-N(0,1) the traditional normal distribution of which does not well deal with the \"spikes and thick tails\" and biases that exist in financial time series data. [PERSON] (1994) et al. introduced the skewed-t distribution into the ARCH family model and found that, when the residual term distribution is assumed to be a skewed-t distribution, the fitting effect significantly outperforms the fitting effect when the residual term is a normal distribution [47]. With reference to [PERSON] et al., this paper introduces the skewed-t distribution as the distribution state of the residual items in the GARCH-MIDAS model. The density function of the standard Skewed-t distribution is as follows:  
When \(Z<-a/b\), the density function of the skewed-t distribution is  
\[g(z|\eta,\lambda)=b\bigg{(}1+\frac{1}{\eta-2}\bigg{(}\frac{bz+a}{1-\lambda} \bigg{)}^{2}\bigg{)}^{-(\eta+1)/2} \tag{16}\]  
When \(Z\geq-a/b\),the density function of the skewed-t distribution is  
\[g(z|\eta,\lambda)=b\bigg{(}1+\frac{1}{\eta-2}\bigg{(}\frac{bz+a}{1+\lambda} \bigg{)}^{2}\bigg{)}^{-(\eta+1)/2} \tag{17}\]  
where \(2<\eta<\infty\), and \(-1<\lambda<1\).  
\[a=4\lambda c(\frac{\eta-2}{\eta-1}) \tag{18}\]  
\[b^{2}=1+3\lambda^{2}-a^{2} \tag{19}\]  
\[c=\frac{\Gamma(\frac{\eta+1}{2})}{\sqrt{\pi(\eta-2)}\Gamma(\frac{\eta}{2})} \tag{20}\]  
where \(\eta\) is the degree of freedom. When \(\lambda\) is greater than 0, the density function is skewed to the right; when \(\lambda\) is less than 0, the density function is skewed to the left; if \(\lambda\) is equal to 0, the skewed-t distribution is the standard t distribution, the mean is 0, and the variance is 1.  
As far as the characteristics of the distribution function are concerned, although the skewed-t distribution can better characterize the \"spikes and thick tails\" of financial data and its biased phenomenon than the standard normal distribution, this conclusion is not yet verified in the research on the GARCH-MIDAS model. Therefore, it is necessary to introduce the skewed-t distribution of the residual term in the GARCH-MIDAS model.
# An Optical Interference Suppression Scheme for TCSPC Flash LiDAR Imagers
## 4 LiDAR Optical Interference  
This section provides some required definitions and a description of optical interference in the case of TCSPC LiDAR imagers.  
In the context of this paper, a _victim_ is a TCSPC LiDAR system potentially subject to optical interference. We define optical interference as any optical signal possessing the following properties.  
1. The signal is modulated or pulsed, so as to appear distinctly different in its temporal evolution from ambient light or detector noise in the victim.
2. The signal is actively generated by a third party, potentially a TCSPC LiDAR of the same nature as the victim.
3. The signal is detected by the victim.  
The saturation of the detector is not considered as optical interference. Issues related to pile-up distortion and saturation, including strong sunlight and the intentional blinding of the victim, remain open and should be addressed by different methods.  
A third-party device able to generate optical interference is defined as an _aggressor_, regardless of the intent of the interference action. Figure 3 illustrates the concept of optical interference between two cameras imaging the same object.  
The worst possible case for a TCSPC LiDAR victim is to operate in an environment with an aggressor that uses the same functional parameters (emission wavelength, pulse repetition frequency, and pulse duration). In this case, the photons of the aggressor will appear as correlated to the victim. The corresponding pixel events will build up in a second histogram peak with an unknown delay that is dependent on the phase shift between the PRFs of the two devices and the difference in length between the victim-target-victim and aggressor-target-victim optical paths.  
Once in the histogram, the aggressor peak is indistinguishable from the victim's peak. This uncertainty in the interpretation of the LiDAR data is a source of risk as it is impossible to differentiate between a real object and an artifact of interference.
# Flash Flood Early Warning System in Colima, Mexico
## 4 Discussion  
Several important aspects that can be improved were identified during the final stages of project evaluation. First, close consultation with hydrologists and data scientists will be needed to implement machine learning algorithms and other forecasting methods that can be used in a more extended network formed by many of these devices, thus permitting scalability. It is also worth mentioning that if this model is replicated, artificial canals should be considered to deploy the water depth sensors, hence they will not change their characteristics and the model will remain valid; otherwise, frequent topographic surveys of each sensing site will be necessary.  
General maintenance is a must for this type of deployed hardware. Exposure to the environment can severely affect the functionality of the devices, and with that, the accuracy of the platform. The most affectable part of the fixed nodes are the ultrasonic sensors; any strange object in the sensing field will modify the data.  
Today, cellular networks are well developed in most countries, providing a suitable platform upon which to deploy monitoring networks. The communications technology should be chosen according to the needs of each implementation. Due to the difficult terrain where the infrastructure was deployed, other communication technologies were analyzed and discarded; taking advantage of the infrastructure of the service providers in the area was the most practical solution. This does not minimize the fact that cellular networks can fail during natural disaster situations, as with any other wireless technology. Because most bodies of water bodies are not equipped with any sensing technology, despite the potential risks they can represent, this work represents an important step in using state-of-the-art technologies in the area of hazard prediction in conjunction with fluid dynamics research. A significant limiting factor in employing technological solutions is their cost. This work shows that a low cost and reliable water level monitoring network is, indeed, feasible. The average cost for the whole system is approximately 60,000 USD.  
Figure 21: Drifter tracking system.