esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Method for Detecting Contradictions
### Process  
The following Figure 5 shows how the types of contradiction can be recognized based on simple, but specific questions.  
The first three questions refer to the effects of the requirements to be compared. The following three questions refer to the causes, if any. The questions are elaborated on below. For a contradiction to be identified, all questions must be answered as specified in the corresponding column. The check mark stands for \"yes\" and the cross for \"no\". The circle stands for questions, that do not apply in that case. Condition 1 and condition 2 are the respective conditions of the effects of requirement 1 and requirement 2. The same applies for cause 1 and cause 2.  
Effect-related questions:  
1. Are the variables from condition 1 and condition 2 the same or a subset of each other? Two statements can contradict each other in the sense of LNC only if the variables, i.e. the object in question, are the same or one is a part of the other, for example, table and table leg.
2. Does one condition include the other one? If one condition includes the other, it could be a subaltern contradiction, for example, \"... between 15 m and 30 m\" and \"... between 20 m and 22 m\". The range of the second  
statement is included in the range of the first statement and therefore the former is the superalternr of the latter.
3. Are condition 1 and condition 2 mutually exhaustive and mutually inconsistent? This question aims at finding contradictory opposites, for example, \"the car is ready\"/ \"the car is not ready\". If one is true, the other must be false and vice versa. Cause-related questions:
4. Is there a condition? If there is a condition, any form of Simplex-contradiction can be excluded.
5. Can cause 1 occur at the same time as cause 2? Two statements can only contradict each other, if they can theoretically occur at the same time. The statements \"If it rains,... \" and \"If it does not rain,... \" cannot occur at the same time and are therefore not contradicting each other. If \"it rains\" in the first statement were to be replaced with \"it's hot outside\", the two statements could theoretically contradict each other.
6. Are cause 1 and cause 2 the same? This question simply aims at detecting Idem-contradictions, who must have the same cause, for example, \"If I am here, you are there\" and \"If I am here, you are here\".
# Purification of Produced Water by Solvents to Enhance Oil Recovery and Reuse Separated Droplets
## 4 Discussion
### Alcohols  
#### 4.2.1 Monohydric Alcohols  
Monohydric alcohols with one to four carbon atoms are notably effective in recovering oil from produced water. In contrast, 2-phenylethylal exhibits moderate efficiency. The size of its aromatic ring appears to create steric hindrance, reducing its effectiveness compared with its counterparts with one to four carbon atoms (Figure 3a). It has also been observed that the position of the hydroxyl group within the carbon chain does not impact the efficiency of oil recovery: both 1-propanol and 2-propanol demonstrate identical effectiveness. Figure 3b illustrates oil recovery percentages as a function of the carbon atom count, revealing that the efficacy of monohydric alcohols is inversely proportional to the number of carbon atoms [37].
# Surface Topographical Control of a Liquid Crystal Microlens Array Embedded in a Polymer Network
## 2 Materials and Methods  
### Materials  
The constituent chemical structures used in this study are shown in Figure 1. A nematic host LC, 5 CB (4-cyano-4\({}^{\prime}\)-pentylbiphenyl), was obtained from Merck Ltd., Tokyo, Japan. The oligothiophene dye molecule, TR5 (5,5\({}^{\prime\prime}\)-bis-(5-butyl-2-thienylynyl)-2,2\({}^{\prime}\),5\({}^{\prime}\),2\({}^{\prime\prime}\)-terthiophene), was synthesized as previously reported [7]. The photopolymerizable monomer, A6 CB (4\({}^{\prime}\)-[6-(acryloyloxy)hexyloxy]-4-cyanobiphenyl), was synthesized according to a previously reported procedure [31]. The crosslinker, HDDMA (1,6-bis(methacryloyloxy)hexane), was purchased from Tokyo Chemical Industry Co., Ltd., Tokyo, Japan. The photoinitiator, Irgacure 651 (2,2-dimethoxy-2-phenylacetophenone), was obtained from Tokyo Chemical Industry Co., Ltd., Tokyo, Japan, and used without further purification for photopolymerization. A silane coupling agent, octadecyltrimethoxysilane, was purchased from Tokyo Chemical Industry Co., Ltd., Tokyo, Japan. Tetrahydrofuran (THF with stabilizer, 99.5%) and 2-propanol (IPA, 99.5%) were purchased from FUJIFILM Wako Pure Chemical Corp., Osaka, Japan.
# A Low-Complexity Deep Learning Model for Predicting Targeted Sequencing Depth from Probe Sequence
## 3 Results
### Performance Comparison with Other Methods  
To better evaluate the performance of our model, Figure 7 demonstrates a performance comparison of different networks on three different datasets. F2 err and F3 err were applied to determine the proportion of sequences for which the error between the predicted and observed sequencing depth exceeded twofold and threefold, respectively, where RMSE represents the root mean square error across the dataset. The DLM and the linear model were employed for comparison with the proposed model. The DLM used the GRU network were used to process the target and probe sequences for sequencing depth prediction. The linear model used four biochemical features calculated by Nupack as input to predict the sequencing depth. The sequencing depth prediction performance for all competing methods was obtained from their respective papers.  
As demonstrated in Figure 7, our model outperformed the DLM and the linear model in terms of RMSE, F2 err, and F3 err metrics on both the SNP and SynDNA datasets (F2 err of 18.3%, F3 err of 5.7%, and RMSE of 0.295 on the SNP dataset and F2 err of 1.5%, F3 err of 0.3%, and RMSE of 0.109 on the SynDNA dataset), indicating that better predictions were achieved for different types of probes. Our model also performed slightly better than DLM in predicting the sequencing depth of the lncRNA dataset using a onefold model trained on the SNP dataset (F2 err of 29.5%, F3 err of 10.8%, and RMSE of 0.316 on the lncRNA dataset). This suggests that it could achieve a better prediction performance for probes that use similar library preparation methods to predict their sequencing depth. The performance improvement can be attributed to the distributed representation of the sequence, as well as the utilization of Bi-LSTM and attention modules, to capture the long-range and short-range interactions in the sequences.
## 4 Future Perspectives
Several in vivo studies exhibited excellent results in drug delivery [189; 190; 191]; thus, they have possible applications in wound care. For instance, to treat skin inflammation, [PERSON] et al. encapsulated capsaicin and anti-TNF \(\alpha\) in novel cyclic cationic head LPN. The study showed that this formulation was able to efficiently penetrate deep skin layers, delivering active molecules such as anti-TNF \(\alpha\) and capsaicin with no cytotoxic effects [192]. Another work designed a new approach for fusidic acid using LPNs in order to encounter MRSA burn wound infections. This study showed excellent results regarding permeability, microbial activity and wound closure compared with conventional formulations [193]. The fusidic acid was also encapsulated in LPNs by other authors, with similar results [194]. The applicability of LPNs to overcome burn wound infection has been reinforced by another study that used them to deliver nonfloxacin [195]. Scaling up the nanosized-lipid-based drug delivery systems is another challenge. Reported data showed that vesicular systems, such as liposomes, are difficult to scale up or require expensive methods compared with lipid nanoparticles, which are easy to scale up [196,197]. However, a suitable process for this is represented by hot or cold high-pressure homogenization [162]. Another important topic for future development is to translate nanosized-lipid-based drug delivery systems into clinical practice and provide reliable large-scale manufacturing. Thus, the comprehensive research on clinical trials in different types of wounds comparing the efficiency of different topical formulations with conventional treatments is imperative to obtain more information about possible adverse reactions in human subjects and develop newly approved therapies in wound care.
# Case Study and Risk Assessment of Water Inrush Disaster in Qingdao Metro Line 4
## 2 Description of the Project and the Collapse
### Exavation and Support Methods  
The construction methods of the tunnel are the shield method and the drilling and blasting method, and the areas used are shown in Table 2. The diameter of the shield tunnel is about 6.3 m. The section and support parameters of the drilling and blasting method tunnel are shown in Figure 3. The tunnel excavation area is about 7.5 m high and 7.4 m wide. The tunnel is excavated by the benching method. It is about 3.7 m high for the upper bench, 3.85 m high for the lower bench, and 3-5 m long for each bench. The initial support of the tunnel is grid steel frame and shotcrete. The grid steel frame spacing is 0.5 m. The shotcrete is C25 concrete with a thickness of about 300 mm. The permanent lining of the tunnel is 300 mm thick molded concrete consisting of C45 waterproof concrete and steel reinforcement. When the tunnel passes through an area with poor surrounding rock properties, \(\Phi\)42 advanced small conduits (3.5 m in length and 15\({}^{\circ}\) inclination angle) are mounted into the rock in a 120\({}^{\circ}\) range around the extrados of the tunnel.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & _E/kPa_ & \(\mu\) & \(e\) & _K/(m/Day)_ & _C/kPa_ & \(\varphi\) \\ \hline Plain fall & 8000 & 0.2 & 0.9 & 30 & 0 & 15 \\ Silty clay & 5671 & 0.33 & 0.718 & 0.05 & 8.2 & 12 \\ Sand & 6070 & 0.33 & 0.5 & 0.05 & 13.9 & 12.5 \\ Strongly weathered tuff & 20,000 & 0.3 & 0.8 & 5.1 & 3.0 & 30 \\ Moderately weathered tuff & 50,000 & 0.25 & 0.8 & 1.728 & 3000 & 45 \\ Slightly weathered tuff & 5.00 \(\times\) 10\({}^{6}\) & 0.22 & 0.09 & 0.026 & 11,500 & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of different ground obtained from the geological report.  
Figure 2: Geological profile of Jing-sha section.  
In addition, due to construction needs, there is a construction shaft and cross passage at YDK25+460.532.
## 3 Numerical Uncertainty Analysis
### Evaluation of the Results
#### 3.2.2 Convergence  
To evaluate the quality of the reconstructed time series from the aspect of convergence, the divergence ratio \(\eta_{div}\) was used, which is defined as the ratio between the number of divergent reconstructions \(n_{div}\) and the total number of experiments \(n_{all}\) in one MCS,  
\[\eta_{div}=\frac{n_{div}}{n_{all}}. \tag{11}\]  
The divergence ratio \(\eta_{div}\) varies from 0 to 1. A lower \(\eta_{div}\) means a higher portion of the MCS represents the conservative character of the single-mass oscillator. The subsequent analysis is separated into two parts using different initial values, \(x_{1}^{\star}(0)\) and \(x_{2}^{\star}(0)\).  
With the initial value \(x_{1}^{\star}(0)\) and the LSST method, the reconstructed signals converge up to the polynomial order \(Ord=4\) with a low divergence ratio \(\eta_{div}\), as presented in Figure 5. A tendency can be identified that the divergence ratio \(\eta_{div}\) rises with higher \(Ord\) and \(\epsilon_{N}\). The maximum value in this figure equals 0.24 from the simulation, where \(\epsilon_{N}=0.01\), \(Ord=4\). In Figure 6a the divergence ratio \(\eta_{div}\) with \(Ord=5\) increases proportionally with the noise factor up to 0.4, which makes the curves of the first four polynomial orders in this figure unrecognizable. On the contrary, with \(x_{1}^{\star}(0)\) and LSPL, \(\eta_{div}\) equals zero in all MCSs; see Figure 6b.  
The second test initial value \(x_{2}^{\star}(0)\) is more challenging for the identified model than \(x_{1}^{\star}(0)\) due to its ten times larger amplitude; see Figure 2. In Figure 6c,d, the divergence ratio \(\eta_{div}\) of both methods up to a polynomial order of two remains zero. From \(Ord=3\), the divergence ratios of LSST increase to higher values. In particular, more than 80% of the reconstructions do not converge for \(\epsilon_{N}\geq 0.0025\). On the other hand, the divergence ratio of LSPL rises up to only 7.2% for \(\epsilon_{N}>0.0025\).  
Figure 5: Divergence ratio of LSST with \(x_{1}^{\star}(0)\) up to \(Ord=4\).
# Hierarchical Gradient Similarity Based Video Quality Assessment Metric
## 2 Hierarchical Video Quality Assessment  
As shown in Figure 1, the neuronal processing of visual information starts from the retina. Before the visual information reaches the visual cortex, it projects to a visual area named lateral geniculate nucleus (LGN). This stage is called _precortical processing_[14]. The occipital part of the primary visual cortex covers area V1-V4 and middle temporal (MT) area [15]. In the early visual areas, simple image features are extracted over small local regions. Then, this information is transmitted to the higher visual areas, in which more complex features are extracted covering larger and larger regions. The occipital part gives input to the _ventral pathway_ (VP) and _dorsal pathway_ (DP) [15]. The stream (V1\(\rightarrow\) V2\(\rightarrow\) V4) to the ventral pathway is critical for object discrimination, and the stream (V1\(\rightarrow\) V2\(\rightarrow\) MT) to the dorsal pathway is functionally related to visual motion [14]. This strongly indicates that these areas compute generic scene representations of visual information [16].  
In this study, in order to assess the video quality in a manner consistent with human perception, the assessment of visual information is inspired by the hierarchical processing in the primate visual system. LGN is modeled as band-pass filtering, and the areas are modeled with hierarchical gradient measures. Furthermore, visual attention similarity evaluated by a new efficient measure is involved successively. As shown in Figure 2, the reference video and the distorted video are denoised respectively. Each video is divided into two parts: a prediction part (denoted as \(P_{r}\) and \(P_{t}\) respectively)  
Figure 1: Simplified hierarchical structure of the primate visual cortex and approximate area locations [14]. MT denotes the middle temporal area, and LGN denotes the lateral geniculate nucleus.  
Figure 2: Flowchart of the proposed hierarchical video assessment model. BL: block level gradient similarity; FL: frame level gradient similarity; MSE: mean square error. PL: pixel level gradient similarity.  
and a noise part (denoted as \(N_{r}\) and \(N_{t}\) respectively). MSE between the noise parts is evaluated, and the hierarchical gradient measures are performed on the prediction parts. The hierarchical gradient measures consist of pixel level (PL) gradient similarity, block level (BL) gradient similarity and frame level (FL) gradient similarity. It should be noted that the proposed metric reacts in the same way to different types of distortion.
# An Optimized Graphene-Based Surface Plasmon Resonance Biosensor for Detecting SARS-CoV-2
###### Abstract  
Graphene-enhanced surface plasmon resonance (SPR) biosensors offer promising advancements in viral detection, particularly for SARS-CoV-2. This study presents the design and optimization of a multilayer SPR biosensor incorporating silver, silicon nitride, single-layer graphene, and thiol-tethered ssDNA to achieve high sensitivity and specificity. Key metrics, including SPR angle shift (\(\Delta\theta\)), sensitivity (S), detection accuracy (DA), and figure of merit (FoM), were assessed across SARS-CoV-2 concentrations from 150 to 525 mM. The optimized biosensor achieved a sensitivity of 315.91\({}^{\circ}\)/R/IU at 275 mM and a maximum \(\Delta\theta\) of 4.2\({}^{\circ}\) at 400 mM, demonstrating strong responsiveness to virus binding. The sensor maintained optimal accuracy and figure of merit at lower concentrations, with a linear sensitivity response up to 400 mM, after which surface saturation limited further responsiveness. These results highlight the suitability of the optimized biosensor for real-time, point-of-care SARS-CoV-2 detection, particularly at low viral loads, supporting its potential in early diagnostics and epidemiological monitoring.  
surface plasmon resonance; graphene; silicon nitrite; SARS-CoV-2; biosensor Article
# Bell-X, An Opportunistic Time Synchronization Mechanism for Scheduled Wireless Sensor Networks
## 4 Problem Statement and Proposed Advertising Method
### Reset Triggered by Higher Layers of the Protocol Stack  
A procedure by which it is possible to reset the status of the current bell has been included in order to allow for the mechanism to act against events that may occur in the network. In this way, it is possible to reset the value of the beacon period when there is a change of RPL parent or even through a user-level request. The mechanism can be restarted when it receives any request from the upper layers of the protocol stack. Figure 6 shows the evolution of the beacon transmission period in time, highlighting the moment in which the normal process of the bell is interrupted by a restart that is triggered by the upper levels of the stack.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline Interval min (Imin) & This parameter corresponds to the minimum value of the beacon period, which coincides with the bell valley area. \\ \hline Doublings (D) & It is the parameter that indicates how many times the timer is doubled before reaching its maximum value. \\ \hline Interval max (Imax) & This parameter corresponds to the maximum value of the beacon period. This is a non-configurable parameter and corresponds to the result of doubling the value of the Interval min as many times as the Doublings parameter indicates which equation is \\ \(\left(I_{max}=I_{min}.2^{doublings}\right)\). \\ \hline Valley Factor \((V_{F})\) & This value allows configuring the time that a node remains with the minimum beacon period. The duration of the timer in the valley zone (Valley Period) can be obtained by multiplying the value of the EB transmission period in that zone by the valley factor. In this way as many beacons can be sent as indicated in this parameter during the valley area. \\ \hline Step Factor \((S_{F})\) & This value allows configuring the time that a node remains in each of the steps between the minimum and the maximum beacon period. The duration of the timer in each of the step zones (Step Period) can be obtained by multiplying the value of the EB transmission period in that zone by the step factor. In this way as many beacons can be sent as indicated in this parameter during each of the step periods. \\ \hline Peak Factor \((P_{F})\) & This value allows configuring the time that a node remains with the maximum beacon period. The duration of the timer in the peak zone (Peak Period) can be obtained by multiplying the value of the EB transmission period in that zone by the value of the peak factor. In this way as many beacons can be sent as indicated in this parameter during the peak area. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration parameters for the proposed dynamic method.
# Grey-Box Fuzzing Based on Reinforcement Learning for XSS Vulnerabilities
## 2 Related Work  
### XSS Vulnerability Detection  
According to analysis methods, XSS vulnerability detection is divided into static, dynamic, and hybrid analysis [14]. Static analysis detects vulnerabilities by analyzing the source code of web applications. However, this method could lead to false positives, and the source code is sometimes unavailable. The dynamic analysis method detects vulnerabilities by injecting artificially generated payloads into websites and observing whether abnormalities are triggered. Due to the inability to cover all input points, this method has a high false negative rate. Hybrid analysis methods combine static and dynamic features to detect vulnerabilities.  
[PERSON] et al. [15] proposed the CSSXC framework, which can be deployed as a cloud service to detect XSS vulnerabilities. When a user requests, the web server extracts information such as parameters and links to detect injection points. This information is sent to a malicious JavaScript detection server. If there is a malicious script at the input point, CSSXC will delete the malicious script in the request and return the result to the web application server. Experimental results show that the method detects XSS payloads with fewer false positives and less resource consumption.  
[PERSON] et al. [16] proposed a method based on second-order crawl scans to detect XSS vulnerabilities. This method first crawls all uniform resource locators (URLs) of the website and tries to inject strings with malicious scripts. Then, the scanner crawls URLs containing special strings again to detect whether there are vulnerabilities. Experimental results show that this method reduces the time overhead of XSS vulnerability detection while ensuring high accuracy.  
[PERSON] et al. [17] proposed a machine learning-based DOM-based XSS vulnerability detection method. They collected more than 18 billion JavaScript functions and used the data to train a deep neural network to predict whether it was vulnerable to DOM-based XSS attacks. By testing a series of hyperparameters, the authors trained a well-performing classifier, which can be used as a pre-filter for taint tracking, while reducing the resource cost of independent taint tracking.  
[PERSON] et al. [18] proposed a black-box hybrid XSS detection (HXD) tool. HXD extracts URLs from weblogs and optimizes them to correct input URLs, then uses static analysis and dynamic browsers to detect XSS vulnerabilities. Since HXD uses PhantomJS, a headless browser, to execute JavaScript code, it can detect vulnerabilities in JavaScript frameworks. Experiments show that HXD has a low false positive rate and can detect XSS vulnerabilities that other black-box scanners miss.