esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Increasing Model Efficiency  
To demonstrate the efficiency improvements achieved by our model, we compared its performance and runtime when processing the entire dataset versus only sentences with high Sexism Scores. Table 9 provides a detailed comparison of these scenarios.  
Figure 12: Threshold vs. number of selected tokens.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Bin** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline \((-0.332,\,-0.0644]\bigcup(0.734,\,0.984]\) & **0.79** & **0.78** & **0.79** & **0.78** \\ \((-0.0644,\,0.202]\bigcup(0.202,\,0.468]\) & 0.78 & 0.76 & 0.78 & 0.77 \\ (0.468, 0.734] & 0.77 & 0.75 & 0.77 & 0.76 \\ All data (Task A) & **0.79** & 0.77 & **0.79** & 0.76 \\ \hline \((-0.332,\,-0.0644]\bigcup(0.734,\,0.984]\) & **0.72** & **0.70** & **0.72** & **0.71** \\ \((-0.0644,\,0.202]\bigcup(0.202,\,0.468]\) & 0.71 & 0.69 & 0.71 & 0.70 \\ (0.468, 0.734] & 0.70 & 0.68 & 0.70 & 0.69 \\ All data (Task B) & 0.71 & 0.69 & 0.71 & 0.68 \\ \hline (-0.332,\,-0.0644]\bigcup(0.734,\,0.984]\) & **0.68** & **0.66** & **0.68** & **0.67** \\ (-0.0644, 0.202]\bigcup(0.202,\,0.468]\) & 0.67 & 0.65 & 0.67 & 0.66 \\ (0.468, 0.734] & 0.66 & 0.64 & 0.66 & 0.65 \\ All data (Task C) & 0.67 & 0.65 & 0.67 & 0.64 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance metrics for the test dataset in each bin across Tasks A, B, and C. The best performance result per task is **bolded.**As shown, by focusing on sentences with higher Sexism Scores, the runtime is reduced by 13% while maintaining comparable performance metrics. This significant reduction in processing time demonstrates that prioritizing sentences with higher Sexism Scores can enhance model efficiency without substantially compromising accuracy, precision, recall, or F1 score.  
This approach leverages the insights gained from the explainability analysis, where tokens with higher SHAP values were identified as more impactful in the model's decision-making process. By concentrating computational resources on these high-impact tokens, we achieve a more efficient processing pipeline. Details of the computing environment used for all experiments can be found in the Appendix A.
## 1 Introduction  
Football is the most popular sport in the world, and there is a massive amount of related data, such as news articles, match statistics, social media posts, and transfer histories. The data can be used to prepare scouting reports, improve match performances, and provide insight into the game. However, it also presents significant challenges for processing and extracting meaningful information from fragmented text. Recent researchers have proposed to apply named entity recognition (NER) into automatically identifying and extracting important entities in the context of football. Specifically, named entities such as football players, clubs, leagues, terminologies, and other related entities could be extracted via such an approach. Finally, it helps football coaches, players, fans, and researchers to efficiently access essential information in a game, apply analyses to the trends, and summarize shortcomings in the competitions.  
NER is also called \"named entity recognition\", \"named entity extraction\", \"named entity segmentation\", and so on [1], one of the significant topics in Natural Language Processing (NLP). NER is the basis of other NLP researches, such as relation extraction [2], knowledge graph construction [3], etc. Research on NER is spread globally across the fields of engineering [1], biotechnology-applied microbiology [2], medical informatics [3], biochemistry molecular biology [4], and other fields. However, little research has been performed in sports or football. In natural language processing for football-related tasks, there are significant challenges, due to a lack of standardized and easily accessible corpora for identifying named entities. In addition, Chinese NER in football faces some unique challenges due to the complexities and specificity of the Chinese language and the football context. The Chinese language has a vast and complex character set without evident segments, which makes it more challenging to identify named entities than other languages. As a result, we proposed a novel approach for NER in football to recognize information, transforming the unstructured data into structured triples, which, in certain extents, enriches the sports knowledge base and provides a football knowledge foundation for football domain experts and football fans. Football NER has significant importance in various areas related to football, from knowledge network construction, information retrieval, and analysis to fan engagement and business applications.  
Next, we will introduce several key techniques of NER and its research in the Chinese language field and in football.
## 4 Implementation
### BER Estimation for the Optical on-off Keying Modulation  
At the receiver side of the communication system, the electrical intensity of the signal is given as follows:  
\[r(t)=I(t)+\sum_{i=-\infty}^{+\infty}I(t)a_{i}g(t-iT_{symbol})+n(t) \tag{4}\]  
where \(a_{i}\) is the level of the \(i\)th OOK symbol and \(a_{i}\)\(\in\) [0,1]. We assumed that the probabilities of bits 0 and 1 were \(P_{0}\) and \(P_{1}\), while \(g(t)\) is the rectangular function, and \(T_{symbol}\) represents the symbol duration. Since only AWGN is present in the optic channel model, the BER can be displayed [21] in Equation (5) as shown below.  
\[P_{e}=\frac{1}{2}\text{erfc}(\sqrt{\frac{E_{b}}{2\sigma_{n}^{2}}}) \tag{5}\]  
Figure 10: The SNR measurements.  
Owing to the presence of only the AWGN channel, the OOK signal for bits 0 and 1 is presented as follows:  
\[r(t)=\left\{\begin{array}{l}n(t),a_{i}=0\\ 2I(t)+n(t),a_{i}=1\end{array}\right. \tag{6}\]  
Figure 11 shows the bit error probability of the O-OOK modulation versus pixel energy per bit to noise density ratio in the AWGN channel. The figure shows that the optical OOK schemes require a pixel Eb/N0 of at least 11 dB to achieve a BER of \(10^{-4}\). By properly controlling the camera's exposure period, the proposed system was able to obtain a BER of \(10^{-4}\) at a distance of 20 m as shown in Figure 10. As shown below, we can improve the SNR by increasing the exposure time; however, it reduces the bandwidth of OCC system. As a result, the exposure time setting must be carefully evaluated in order to achieve better results and a suitable modulation scheme [26].
## 3 Calculation of Seepage Field
### Numerical Model and Analysis of Calculation Results  
Steady-state seepage analysis was conducted using finite difference software, as shown in Figure 2. Given the homogeneous formation and circular tunnel cross-section, the three-dimensional model is horizontally symmetrical, allowing for half-model calculations. To avoid boundary effects, the model's range was set to 5\(D\)\(\times\) 6\(D\)\(\times\) 10\(D\), with a tunnel excavation length of 2\(D\), based on relevant studies [26; 27; 28]. As the water level increases, the burial depth in the model also increases accordingly to ensure it remains above the water level. The element size was approximately 2 m with a refined element size of about 0.5 m in the \(D\)-length area in front of the tunnel [22]. The formation's permeability coefficient was 10\({}^{-6}\) m/s. Boundary conditions were set as follows: the symmetrical plane and tunnel lining were impermeable, a constant water pressure was applied to the tunnel face, and the water pressure at the water level was kept constant. Figure 3 shows the contour plot of pore water pressure distribution in the surrounding rock near the shield tunnel face when \(h_{w}\) = 15 m.  
Figure 1: Diagram of seepage at the tunnel face.  
Figure 4: Numerical calculation results of water pressure distribution near the tunnel face.  
Figure 3: Contour plot of pore water pressure distribution induced by shield tunnel excavation.  
Figure 2: Numerical model for seepage analysis in shield tunnel excavation.  
For ease of analysis, the normalized water head \(h/h_{0}\) is defined, and its variation near the tunnel face is plotted, as shown in Figure 4. The analysis shows that within the tunnel face range (\(x/D<0.5\)), the water pressure variation along the tunnel cross-section direction (\(x\)-direction) is very small. Therefore, considering that the ultimate upper-bound failure mechanism only involves the front and upper local areas of the tunnel face, the variation in the \(x\)-direction can be neglected in subsequent water pressure distribution calculations. This reduces the Laplace governing equation from three dimensions to two dimensions. Further observation of the water pressure variations in the \(y\) (vertical) and \(z\) (tunnel axial) directions reveals that at \(3.5D\) in the \(y\)-direction from the horizontal position and in the \(z\)-direction from the tunnel face, the water pressure has essentially returned to its initial state with minimal influence from seepage.
# A Synthetic Nervous System Controls
## 2 Methods
### Neural Network as Control System
#### 2.4.3 Inter-Leg Coordination  
The legs of both insects and legged robots must communicate when they are in swing phase to ensure that the body always stays supported. Behavioral experiments have revealed correlations between the positions of different legs in arthropods [29; 45], but the underlying networks remain largely unknown. Work in locusts suggests that some direct neural connections between the legs may contribute to coordination during walking [3; 46]. Therefore, in SimRoach2, the levator-depressor (CTr) CPGs between legs are connected and neural pathways were engineered to produce the observed behavior.  
\"Holk Cruse's rules\" describe inter-leg influences that control the timing of stepping [29]. Three of these influences have been shown to be the most important to coordinate walking, and were implemented into SimRoach2. Influence 1 inhibits the start of swing phase in the rostral leg while the caudal leg is in its swing phase. Influence 2 excites the start of swing phase in the rostral leg when the caudal leg enters its stance phase. Influence 3 excites the start of swing phase in the caudal leg when the rostral leg is in stance phase, increasing in strength over time as the caudal leg is in stance phase [29]. Influences 2 and 3 are also present between contralateral legs. Table 2 summarizes these three influences. These influences are redundant, such that any perturbation to a walking gait will result in nearly immediate re-coordination of legs. The specific neural networks that were designed to replicate these three influences are shown in Figure 6. Verification of each of these networks is presented in the Supplementary Materials (Section: Methods Explained).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Influence Number** & **Direction of Influence** & **Effect of Influence** \\ \hline Influence 1 & \multirow{2}{*}{Rostral} & Inhibits the start of swing phase when the posterior leg is in its swing phase \\ \hline Influence 2 & \multirow{2}{*}{Rostral and contralateral} & Excites the start of swing phase when the posterior leg enters its stance phase \\ \hline Influence 3 & \multirow{2}{*}{Caudal and contralateral} & Excites the start of swing phase while the anterior leg is in its stance phase, increasing in strength over time \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of inter-leg influences.  
Figure 5: Importance of the integral part in the joint control network. (**a**) Joint controller with integral part disabled. The proportional part alone is not enough to move the joint to the desired position. There is always a steady state error. (**b**) Joint controller with both proportional and integral parts. The integral part removes the steady state error.
# Exploring Metalloprotease from _Dunaliella_ sp.: Production, Regulation, and Structural Insight
## 2 Materials and Methods
### PCR Amplification, Sequencing, and Phylogenetic Analysis  
Genomic DNA extraction, amplification of the 18S rRNA gene, and purification and sequencing of DNA were performed as described by [PERSON] et al. [37]. In brief, genomic DNA was extracted using a NucleoSpin(r) Microbial DNA kit (MACHEREY-NAGEL, Duren, Germany) according to the manufacturer's recommendations. The primer set EukA(5\({}^{\prime}\)-AACCTGGTGATCCGCCAGT-3\({}^{\prime}\)) and EukB (5\({}^{\prime}\)-TGATCCTTCTGCAGGTTCACTAC-3\({}^{\prime}\)) was employed in the PCR targeting the amplification of the 18S rRNA gene. The PCR was conducted using a CFX96 thermal cycler (Bio-Rad Laboratories, Hercules, CA, USA) following a protocol comprising initial denaturation by heating at 95 \({}^{\circ}\)C for 2 min, followed by 35 cycles at 95 \({}^{\circ}\)C for 30 s, at 55 \({}^{\circ}\)C for 30 s, and at 72 \({}^{\circ}\)C for 2 min. A final extension step at 72 \({}^{\circ}\)C for 7 min concluded the PCR process. Following amplification, 10 \(\mu\)L of the PCR products underwent electrophoresis on a 1% agarose gel, which was subsequently stained with ethidium bromide and visualized under UV light. Amplicons obtained from positive samples were purified using the QIAquick Gel Extraction kit (Qiagen, Courtaboeuf, France). These purified DNA fragments were then sequenced through the Biotools sequencing service ([[http://biotools.eu/en/](http://biotools.eu/en/)]([http://biotools.eu/en/](http://biotools.eu/en/)) accessed on 12 July 2024). Taxonomic identification of the microalga was performed as described by [PERSON] et al. [41] and [PERSON] et al. [42]. This was done by comparing the obtained sequences with those available in the GenBank database ([[https://blast.ncbi.nlm.nih.gov/](https://blast.ncbi.nlm.nih.gov/)]([https://blast.ncbi.nlm.nih.gov/](https://blast.ncbi.nlm.nih.gov/)) accessed on 15 August 2024) using BLASTn analysis. Phylogenetic analysis was conducted via MEGA11 software through the Neighbor-Joining method. evolutionary distances were computed using the p-distance method and the associated taxa were clustered together in the bootstrap test with 500 replicates.
## 3 Protocols Verifying the Authenticity of Digital Files in Distributed Networks
### Security Analysis in Distributed Networks
The doctor keeps the nonce, and the key needed to decrypt the file, while the cipher text and encrypted message of authenticity are kept by a storage node. To allow a second doctor to access the file, the doctor openly transmits the nonce, which will confirm the authenticity of the ciphertext, then secretly transmits the key deciphering the file. That way, the doctors are not required to store the files on their server to retrieve the information as needed. The suggested protocols are relatively well protected against attack vectors such as replays, man-in-the-middle, and side channel analysis. New nonces, key pairs Sk-Pk, and seeds are generated for each file and used only once. After a proof of authenticity cycle, the entire chain of information is openly distributed: the file, the ciphertext, nonce, seeds, public key, and message of authenticity. Even the knowledge of Sk, the private key, becomes irrelevant. This is a departure from many other DSA schemes used in cryptology in which the leak of the secret key is catastrophic. A remaining vulnerability of the protocol presented in the first two cases is the leak of nonce \(\omega\). Adversaries are then enabled to decrypt the files, which defeats the purpose of the protocol. A potential remedy is to use Multi-Factor Authentication (MFA). The nonce can be XORed with a password before storage by the client. During the verification cycle, the client shall XOR it again with the password to retrieve the nonce. Another option is to use the additional layer of security presented below in Section 4.
## 2 Materials and Methods
### Methods
#### 2.3.2 Trend and Change-Point Detection Methods  
This study used various methods to analyze the trends and change points in the LDD process in coastal areas. First, the nonparametric Mann-Kendall (M-K) test was applied to assess the trend of the LDDI index. The M-K test is widely used and effective for analyzingt trends in time series data, particularly in hydrology and meteorology [43; 44]. The M-K test is given as follows:  
\[Z=\left\{\begin{array}{ll}\frac{S-1}{\sqrt{V\pi(S)}}&S>0\\ 0&S=0\\ \frac{S+1}{\sqrt{V\pi(S)}}&S<0\end{array}\right. \tag{5}\]  
where statistic \(S\) can be calculated as:  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}\text{sgn}\big{(}x_{j}-x_{i}\big{)} \tag{6}\]  
where \(x_{i}\) and \(x_{j}\) are the observations at the \(i\)th and \(j\)th moments, respectively, and \(n\) is the length of the series. When \(x_{i}-x_{j}\) is greater than, equal to or less than 0, \(\text{sgn}(x_{j}-x_{i})\) equals 1, 0, or \(-1\), respectively. The statistic \(Z\) can measure the trend, with \(Z>0\) and \(Z<0\) indicating increasing and decreasing trends, respectively. A significance level of 0.05 was considered in this study, where \(Z>1.96\) and \(Z<-1.96\) indicate significant increasing and decreasing trends, respectively. Additionally, the Theil-Sen slope [45] was used to calculate the robust trend rate of the LDDI, which can eliminate the impact of missing data or anomalies on the trend test. The slope is estimated by:  
\[\beta=Median\left[\frac{(x_{j}-x_{i})}{(j-i)}\right],\forall j>i \tag{7}\]  
where \(\beta\) is the estimate of the slope of the trend; \(x_{i}\) and \(x_{j}\) are the observations at the \(i\)-th and \(j\)-th moments, respectively. Through the Sen median trend analysis and the \(Z\) statistic from the M-K test, the LDD process in coastal areas was divided into five categories (as seen in Table S1) [12; 46].  
In addition, this study used a Bayesian ensemble algorithm (BEAST) for change-point detection [47]. For a given time series, BEAST outputs a model capturing the seasonality of the data, the long-term trend, as well as the probability and location of breakpoints in the trend. Briefly, BEAST combines several models into an average model (Bayesian model average) instead of relying on a single model to detect changes in time series. It allows for estimating the probability of abrupt changes at any given time, unlike other change point algorithms that simply identify whether a change point is present. Compared with traditional change-point detection methods (e.g., sliding \(t\)-test, M-K test), BEAST can not only determine the time points of change points, but also calculates the likelihood of multiple change points occurring at multiple time points. [PERSON] et al. [47] provided the mathematical details of BEAST, which was run using the \"Rbeast\" package in R [48; 49].
## 3 Integrated Navigation
### Aiding Methods  
This section briefly looks at the details of using GNSS-derived heading, as well as pseudo-observations of vehicle velocity and height to help improve the overall solution.
## 4 Result and Discussion
### Water Hammer Change Rule of End Valve Under Different Influencing Factors
Figure 11: Variations in maximum water hammer pressure with different branch pipe distances: **(a)** Unit D1; **(b)** Unit D2; **(c)** Unit X1; **(d)** Unit X2. Figure 12 illustrates the effect of the number of valve closures on the maximum water hammer pressure. The horizontal axis represents the number of valve closures (2, 3, and 4), and the left vertical axis represents the maximum water hammer pressure (in kPa). For 2, 3, and 4 valve closures, the maximum water hammer pressures are 3367, 3680, and 3836 kPa, respectively. The vertical axis on the right represents the percentage increase in the maximum water hammer pressure relative to the steady-state water pressure for each number of valve closures. For 2, 3, and 4 closures, the pressures increased by 8.02%, 10.97%, and 15.68%, respectively. The results showed that as the number of valve closures increased, the water hammer pressure increased significantly, exhibiting an increasing trend. When the number of valve closures was 2, the maximum water hammer pressure was approximately 3250 kPa. As the number of closures increased to 3 and 4, the water hammer pressures increased to approximately 3750 and 4000 kPa, respectively, corresponding to pressure increases of 10% and 16%, respectively. Based on the previous analysis, under different pipeline diameters and valve closure programs, increasing the number of valve closures leads to higher transient pressure peaks. However, considering that the transient water hammer pressure peaks in pressurized pipelines are influenced by factors such as the pipeline structure, operating conditions, and boundary conditions, and that this study does not account for more complex factors such as unsteady friction or fluid-structure interactions, the conclusion is only applicable to the specific conditions of the multi-branch pipeline and simultaneous closure of multiple valves at the Xialongkou Hydropower Station. This effect is likely due to the combined impact of fluid inertia and the simultaneous closure of multiple valves. Particularly in larger-diameter pipelines, the water hammer effect is more pronounced, thus increasing the transient pressure of the system. This figure provides quantitative support for optimizing the number of valve closures at the power station. It helps in designing a reasonable valve closure program, ensuring that the water hammer pressures meet the safety requirements and minimize the impact of transient pressure fluctuations on the pipeline system. To establish a universally applicable relationship between the number of valves and water hammer pressure, further validation with additional operational conditions and experimental data are required. Moreover, a broader range of influencing factors should be considered to provide a more comprehensive assessment of the mechanisms by which multi-valve closures affect the water hammer pressure. Figure 12: Variation of water hammer pressure with different numbers of shut-off valves.
## Priority-Based Protocol Extension Method
### Communication Flow Extension  
After the application extension frame is added, the startup process of the communication between the sub-station and the primary station needs to increase the total summoning session. All telesignalization and telemetry data of the sub-station are uploaded by the total summoning, and the real-time data transmission is started after the total summoning is completed, and the startup process ends. The communication startup process is shown in Figure 5. It should be noted that the main station and PDC/uMPMU, PDC, and uMPMU can all form the relationship between the main station and sub-station.  
After the startup process is over, if the main station needs to remotely control the sub-station, it needs to follow a certain sequence of steps. The remote control process includes remote control selection, remote control confirmation, and remote control execution. The main process is shown in Figure 6.  
Figure 5: Communication startup process.  
In addition to the aforementioned total summoning and remote control process, after the startup process, sub-station transmission-changed data includes digital quantities and analog quantities that do not require time synchronization. At the same time, the communication mechanisms, such as packet summmons and periodic transmission, can be added. The main station can communicate directly with \(\upmu\)MPMU, and the main station also communicates with \(\upmu\)MPMU through PDC. The above communication process is actually an interaction process of the three. When the method is implemented, attention needs to be paid to the mapping of each device. The PDC as the intermediate between the main station and \(\upmu\)MPMU should save the upper and lower point tables. After receiving the remote data of \(\upmu\)MPMU, the data point number is mapped to the point number saved by the main station, and the remote data is then uploaded. After receiving the remote command of the main station, the remote control point number is mapped to the \(\upmu\)MPMU point number, and the remote control command is then sent.