esa-sceva/satcom-chunk-collection huggingface dataset:
# Order-Preserving Multiple Pattern Matching in Parallel
## 3 Parallel Algorithms for the OPMPM Problem
### Parallel OPMPM Algorithm Using the Fingerprint Table  
The algorithm that solves the OPMPM problem using a fingerprint table in parallel is as follows: In the preprocessing phase, position table \(POS_{P_{a}}\) and fingerprint table \(FP_{a}\) are created in parallel for each pattern of \(P_{a}(1\leq a\leq k)\). The \(POS_{P_{a}}\) calculation is performed in parallel using \(|P_{a}|\) threads. Each thread \(t(1\leq t\leq|P_{a}|)\) linearly searches \(P_{a}\) to calculate the order \(r\) of \(P_{a}[t]\). By the definition of \(POS_{P_{a}}\), it satisfies \(POS_{P_{a}}[r]=t\), and the position tables for all patterns can be calculated in parallel in \(O(\overline{m})\) time using \(O(M)\) threads. \(FP_{a}\) is created in parallel using \(q\) threads (Algorithm 3). Each thread \(t(1\leq t\leq q)\) calculates the fingerprint of \(\widetilde{P}_{a}[m-q+1..m]\) in parallel. Here, atomic operations are used to prevent multiple threads from accessing \(FP_{a}\) concurrently (Line 8 in Algorithm 3). Thus, the fingerprint table can be calculated in \(O(q)\) time using \(O(kq)\) threads, and the preprocessing phase is conducted in \(O(\overline{m})\) time using \(O(M)\) threads.  
In the search phase, all substrings of \(T\) are checked in parallel using \(n-m+1\) threads (Algorithm 4). Each thread \(i(1\leq i\leq n-m+1)\) first calculates \(f(T[i+m-q..i+m-1])\). \(f(T[i+m-q..i+m-1])\) and each of \(FP_{a}(1\leq a\leq k)\) are sequentially compared, and if they are the same, it is verified whether \(P_{a}\) and \(T[i..i+|P_{a}|-1]\) are order-isomorphic using \(POS_{P_{a}}\) (from Lines 2 to 5 in Algorithm 4) [13]. If \(P_{a}\approx T[i..i+|P_{a}|-1]\), then \((i,a)\) is printed. In the worst case, it verifies whether all substrings of \(T\) are order-isomorphic to all patterns. Thus, the search phase can be performed in \(O(M)\) time using \(O(n)\) threads. This parallel algorithm can be solved as an OPMPM problem in \(O(\overline{m}+M)\) time using \(O(max(M,n))\) threads.  
Figure 2: Search phase of the parallel OPMPM algorithm using the Aho–Corasick automaton.  
```
Input: A set of strings \(\{P_{1},P_{2},\ldots,P_{k}\}_{n}\), int \(m\), int \(q\) Output: \(FP_{1},FP_{2},\ldots,FP_{k}\) \(1\) parallel for\(a\gets 1\) to \(k\)do
2 parallel for\(t\gets 1\) to \(q\)do
3\(FP_{a}\gets 1\) \(c\gets 0\) for\(i\gets 1\) to \(t-1\)do
4 if\(P_{a}[m-q+i]<P_{a}[m-q+t]\)then
5\(c\gets c+1\) \(\textbf{atomicAdd}(FP_{a}\gets FP_{a}+c\times(t-1)!)\)
```  
**Algorithm 3** Parallel calculation of the fingerprint table. \(FP\)
## Materials
### Moderate Resolution Imaging Spectroradiometer (MODIS) LAI Product  
We used the latest eight-day version of the MODIS LAI product (MCD15A2H Collection 6), which temporal phase is consistent with GLAS product, as the comparative dataset. Here, five MODIS tiles (i.e., H25V03, H25V04, H26V03, H26V04, H27V04) were used to mosaic the LAI map for Heilongjiang Province. Compared with the previous version (MCD15A2H Collection 5), the Collection 6 product was generated by using the improved reflectance and land cover product, which made it more accurate than the Collection 5 product [43]. Moreover, the Collection 6 product was generated at a native resolution of 500 m rather than the 1000 m resolution of Collection 5.
## 5 Discussion
### Error Analysis and Interpretation of Results  
The quantitative evaluation highlights that Transformer-based models (HF BETO) substantially outperformed traditional baseline methods (LogReg, TextCatBOW) in identifying racist and sexist comments. Particularly noteworthy is the contextualized BETO variant's balanced performance, underscoring the critical role that preceding dialogue context plays in accurately detecting nuanced expressions of bias. Conversely, externally-trained Hugging Face models like piuba-bigdata/beto-contextualized-hat-speech and unitary/multilingual-toxic-xlm-roberta displayed significant deficiencies in recall, revealing a strong reliance on lexical cues rather than contextually nuanced features.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Variant** & **Positives w/ Slur (\%)** & **Negatives w/ Slur (\%)** \\ \hline \hline \multicolumn{4}{c}{Sexism} \\ \hline BETO (SpACy) & ST & 89.83\% (53/59) & 69.29\% (97/140) \\ BETO (SpACy) & CTX & 100.00\% (55/55) & 97.92\% (141/144) \\ TextCatBOW & ST & 92.31\% (12/13) & 74.19\% (138/186) \\ TextCatBOW & CTX & 100.00\% (39/39) & 98.12\% (157/160) \\ \hline \multicolumn{4}{c}{Racism} \\ \hline BETO (SpACy) & ST & 96.36\% (53/55) & 66.20\% (94/142) \\ BETO (SpACy) & CTX & 100.00\% (55/55) & 96.48\% (137/142) \\ TextCatBOW & ST & 96.88\% (31/32) & 70.30\% (116/165) \\ TextCatBOW & CTX & 100.00\% (37/37) & 96.88\% (155/160) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Lexical overlap analysis: proportion of predicted positive and negative examples containing at least one slur from the HurtLex- [80] and the BigScience Roots-based [60] dictionary.  
Qualitative error analysis indicated that most classification errors arose from implicit forms of sexism or racism, especially in cases involving sarcasm or multi-turn ironic exchanges. Models struggled notably in differentiating genuine expressions of subtle bias from sarcastic or satirical comments, emphasizing the complexity of pragmatic phenomena and pointing to the necessity for advanced pragmatic modeling in future research.
# Enhancing BER Performance Limit of BCH and RS Codes Using Multipath Diversity +
## 1 Introduction  
The past decade or so has witnessed remarkable growth in the demand for providing reliable communication links and high transmission rates. A reliable digital communication system involves the sending and receiving of data with vanishingly small error rates [1, 2]. Any wireless communication system is prone to a certain level of noise, reflection, diffraction, shadowing, and fading. Furthermore, the signal that is transmitted through a wireless channel arrives at the receiver via a number of different paths, referred to as multipath transmission, and this leads to fading (signal distortion and burst errors) [3]. Therefore, transmission reliability is very challenging on wireless channels. One of the most widely used techniques to provide reliable communication is forward error correction (FEC). The investment of FEC requires either increasing channel bandwidth or decreasing the rate of the transmission [4]. Therefore, the high transmission rate and transmission reliability need high bandwidth, but the bandwidth is a substantial issue for communication which means thatincreasing the bandwidth is not a wise decision [5]. In contrast, the multipath phenomenon can be utilised to improve communication reliability and increase the transmission rate without increasing bandwidth. In this paper, the effectiveness of the multipath phenomenon to improve the error correction capability (transmission reliability) with as little redundancy as possible is considered. In this article, Reed-Solomon (RS) and Bose, Ray-Chaudhuri, and Hocquenghem (BCH) codes with different parameters are used to provide high data rate transmission and analysis of the communication performance with and without multipath propagation.  
The paper is organised as follows: Related work is given in Section 2. A brief overview of FEC is demonstrated in Section 3. The multipath phenomenon is described in Section 4. The methodology is explained in Section 5. In Section 6 the proposed combiner is described. Simulation parameters, and results, are presented in Section 7 with the conclusions being reported in Section 8.
## 4 Arsenic Removal Processes
### Precipitative Processes  
This category of arsenic removal processes includes coagulation and filtration [87], coagulation-assisted microfiltration [102], enhanced coagulation [103] and lime softening [104]. These are very popular processes that require the addition of either aluminum sulfate, ferric chloride or ferric sulfate as a coagulant to change either the chemical or physical properties of dissolved colloidal or suspended matter [105]. To promote rapid settling out of the particles by gravity, the enhancement of agglomeration is used in some cases, otherwise filtration is used to remove the particles [105]. Agglomeration is usually accomplished by changing the surface charge properties of solids with coagulants to enable formation of a flocculated precipitate. The resulting products of this process are larger particles or flocs that filter or settle out more easily under gravity [106].  
Greater than 90% As(V) removal has been successfully achieved with coagulation processes [107]. The use of coagulation with alum, ferric chloride and ferric sulfate for removal of As(III) is far less efficient than the removal of As(V) [107]. To ensure efficient arsenic removal, As(III) is usually oxidized to As(V) prior to coagulation. Effective As(V) removal is usually accomplished at pH 7.6 or less with iron and aluminum coagulants. However, in terms of stability, iron coagulants offer more advantage than aluminum coagulants when operating within a pH range of 5.5 to 8.5 [9]. The key factors that influence the choice of the optimum coagulant dose are the water quality and the arsenic concentration in the treated water. The overall cost of the water treatment process can be significantly increased if further adjustment of the pH is necessary to achieve a more effective removal of arsenic [106].  
Some field studies have demonstrated the effectiveness of coagulation/filtration in reducing the arsenic level to below 5 \(\mu\)g/L [106]. However, under optimum operating conditions, the achievement of a residual arsenic level of less than 3 \(\mu\)g/L is possible [107].
## 3 Results and Discussion
### Results of the Analysis of the Window Type with GRU-Based Training  
The results based on three different window types gave the highest accuracy values of 97.25% for the Hamming windows, 97.04% for the Hanning windows, and 97.60% for the rectangular windows for male speakers, and 95.10% for the Hamming windows, 95.63% for the Hanning windows, and 95.30% for the rectangular windows for female speakers. According to the results, the effect of the window type on the accuracy was insignificant.
# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 4 Clinical Dataset
### Balancing Data with SMOTE
#### 4.4.1 What Is SMOTE  
SMOTE stands for Synthetic Minority Oversampling Technique. It is a popular method used to address class imbalance in machine learning datasets, particularly in classification tasks. Class imbalance occurs when one class (the minority class) is significantly underrepresented compared to the others (the majority classes).  
The main goal of SMOTE is to balance the class distribution by generating synthetic samples for the minority class. In this study, SMOTE was applied to address the imbalance between positive and negative cases of Acute Aortic Syndrome (AAS). With only 129 positive cases among 148,578 negative cases, the dataset faced an imbalance challenge. By generating synthetic samples of the minority class, SMOTE ensured that the predictive model is not biased toward the majority class. This improved the model's ability to accurately identify both positive and negative cases of AAS, thereby enhancing its predictive performance.
## 1 Introduction
[31] found that the presence of channel-scale circulation at a confluence transforms slow mixing into rapid mixing. Rapid mixing occurs when a tributary flow flows into a main channel with a large junction angle. In such cases, a difference in concentration between the main and tributary channel creates a mixing interface in which the vertical gradient is increased, leading to the stimulation of vertical mixing and rapid completion of transverse mixing. In this context, [PERSON] et al. [14] stated that the momentum flux ratio and junction angle are important hydraulic factors that influence the location of the mixing interface. Furthermore, weak density contrast at the confluence increases the mixing area by distorting the mixing interface, leading to an increase in transverse mixing [32]. The effect of density contrast on mixing is more dominant farther downstream from the confluence than differences in the velocity of the main and tributary channel [33]. [PERSON] et al. [16] demonstrated that a tributary flow with strong momentum flux induces helical motion, which moves the mixing interface toward the outer bank, causing transverse mixing to increase. The  
Figure 1: Conceptual diagram of flow and mixing characteristics near channel confluence. strong momentum flux affects not only the helical motion but also the flow separation, which causes solute storage. [PERSON] et al. [26] demonstrated that strong Kelvin-Helmholtz instabilities induce vortex pairing in the mixing layer in the recirculation zone, which causes sediment to be actively mixed in the recirculation zone. Despite several studies related to mixing in confluences, it is still difficult to define the effects of storage zones induced by the recirculating flow in confluences on transverse dispersion. The goal of this study was to analyze the effects of recirculation zones in channel confluences on solute storage. This research focused on the influence of the confluence angle on solute mixing and associated storage effects. The flow structure at a confluence and its effects on solute mixing were simulated using the quasi-three-dimensional model Environmental Fluid Dynamics Code (EFDC). The EFDC model has been used frequently in many riverine, estuary, flow, and solute mixing simulations, and it has been proven to be reasonably accurate [34; 35; 36; 37]. The EFDC was applied to various junction angles to determine their effect on the size of the recirculation zone. An analysis of solute mixing through helical motion and recirculation zones was also conducted. Additionally, the retention time changes of solutes caused by junction angle changes were investigated based on the results of mixing characteristics in confluences.
# Experimental and Mathematical Study of Flexible-Rigid Rail Vehicle Riding Comfort and Safety
## 5 Results and Discussion
### Running Comfort  
To examine vehicle response considering car body flexibility and to compare it with a rigid car body model, vehicle simulation speeds are considered from 30 km/h to 180 km/h. For the simulation of the running comfort, the Sperling Ride Index method is used for the vehicle system, and the track spectrum of the dedicated Mathura to Palwal line is used. The variations in ride quality at the car body center and end for bending frequency for different car body damping ratio values are shown in Figures 7 and 8, respectively. Figure 7 indicates that for car body bending frequencies below 7.5 Hz, the car body vibrations are very high, and the resonance is observed at a frequency of nearly 6.4 Hz. With car body damping factors \(\zeta\) = 0, 0.02, 0.03 and 0.05, the ride index at resonating frequency at the center is observed to be 2.84, 2.52, 2.42, 2.2, respectively, which indicates that the damping factor has isolation effects on the resonance peak. With car body damping factors \(\zeta\) = 0, 0.02, 0.03 and 0.05, the ride index at resonating frequency at the end is observed to be 3.27, 3.03, 2.87, and 2.65, respectively (Figure 8). Figures 7 and 8 also suggest that when the damping factor is increased from 2% to 5%, the ride index is reduced to nearly 12.7%. Field test reports have indicated that the car body damping factor of a loaded car body in India is nearly 2.5%, and a further increase in this value requires an advanced car body design.  
Figure 6: Acceleration response near the vehicle body’s mass center (**a**) 60 km/h. (**b**)120 km/h. (**c**) 150 km/h. (**d**) 180 km/h.  
Figure 7: The Sperling Ride Index at the rigid and flexible car body center.  
The car body damping factor is limited to 2.5%. Therefore, the other substitute for the isolating car body flexural vibrations raises its first bending frequency. Figures 6 and 7 also indicate that when the frequency in the first bending mode is more than 10 Hz, the ride index for the end of the flexible and the rigid car body is identical. At the center, the ride index value of the flexible car body approaches that of the rigid car body, irrespective of the value of the damping factor. At 7 Hz and a damping factor of 2%, the ride index is nearly 2.4, which is acceptable.  
The simulation results using the Sperling Ride Index are compared with the experimental results and shown in Table 2. It can be seen from the table that the ride comfort for the considered speed varies from 2.54 to 3.38 for the RF simulation and 2.2 to 3.3 for the MR simulation. Field test reports have indicated that the ride comfort varies from 2.2 to 3. Hence, when comparing the experimental results, the % error for RF varies from 4 to 14 %, and for MR, it varies from 0.4 to 6%.
# Face Swapping Consistency Transfer with Neural Identity Carrier
## 3 Methods
### Consistency Transfer
#### 3.2.2 Detail Consistency Transfer  
Prior face-swapping methods rely on heavy training on input data to synthesize realistic and abundant details, such as wrinkles and moles. But the excessive training will cause the carrier's degradation that the _U-Net_ will no longer learn the noise-free contents but noises themselves. Thus, the over-trained _U-Net_'s results are inevitably direct to the flickers, and visual artifacts appear. On the contrary, the basic facial information can not be preserved well if we train the model insufficiently. To address the problem, we propose to introduce a novel 3D representation manner to help enhance the detail information of \(X_{o}\) without suffering the issues brought by the excessive or insufficient training process.  
According to the observation, one individual will show different details when taking different expressions and poses. The detail information of a subject is not all static. To address this issue, we suppose that the detail information should be separated into two parts, dynamic detail, which represents expression-related detail information, and static detail, which represents resident detail information. In this paper, we utilize a detail UV displacement map \(D\) to represent the details (both dynamic and static). By extracting the static detail information from identity images, NICs can learn fine-grained facial structure.  
Figure 2: The pipeline of our proposed framework. In the initial face-swapping stage, the face-swapping proxy \(X_{ref}\) is obtained by swapping the identity face \(X_{id}\) to the target face \(X_{t}\). We utilize the NICe to extract the face-swapping proxy’s information and train the NICe under 3D supervision in the consistency transfer stage. We can directly input a target image/video for inference. This framework is efficient in producing coherent and realistic swapped results.
## 4 Illustrative Example
### Analysis of the Influence of the Number of Measuring Points  
The more measuring points, the more data will be obtained. However, the more measuring points, the more sensors need to be arranged, resulting in a waste of resources. In order to give the suggested value of a reasonable number of measuring points, by the same token, one can change the number of measuring points in conditions 1-2 in Table 2 and study the recognition effect of the algorithm.  
As shown in Figure 19, there is a positive correlation between \(N_{\rm S}\) and \(IR_{\rm Z}\), and a better recognition effect can be achieved by setting five measuring points in the whole bridge. When it is fewer than five, it will drop rapidly. This phenomenon happens because the fewer measuring points there are, the fewer data sets are used for analysis, and the interference of noise is relatively strong, thus reducing the recognition rate of damage.
## 3 Results  
### Spatio-Temporal Patterns of the TWSA  
The period is partitioned into two phases: the initial phase, spanning 2000 to 2009, is deemed to be the period of natural conditions, while the latter phase, covering 2010 to 2019, is regarded as the reservoir regulation phase. Figure 3 shows the spatial patterns of the linear trend of GRACE-derived TWSA. All TWSA products indicate a significant decreasing trend in the northern region of the study area from 2002 to 2009 (Figure 3a-c). The JPL mascon shows the most severe drying trend, with a range of values from \(-2\) to \(-10\) cm/yr. However, since 2010, with the impounding of the Xiaowan and Nuozhadu reservoirs, a marked increase trend in the TWSA has been observed. Similarly, the JPL mascon shows more significant trends (2 to 10 cm/yr) compared to CSR mascon and averaged spherical harmonics data (Figure 3d-f). Precipitation in the northern region of the study area showed a decreasing trend from 2002 to 2009, with the fastest decreasing rate of decline exceeding 20 cm/yr, while in the southern region, it increased. From 2010 to 2019, precipitation showed a slightly decreasing trend, dominating the study region.  
The TWSA time series in the study region, as well as the Lancang River basin and Mekong River basin, derived from five GRACE solutions (Figure 4), are in agreement with the results shown in Figure 3. During the period from 2002 to 2009, the TWSA in the study region showed a slight upward trend, while exhibiting a particularly pronounced decline (\(p<0.05\)) in the Lancang (\(-2.21\) cm/yr) and Mekong River basins (\(-8.65\) cm/yr). From 2010 to 2019, the Mekong River basin's TWSA decreased (\(-2.48\) cm/yr), while both the study region (\(8.96\) cm/yr) and the Lancang River basin (\(1.85\) cm/yr) showed an upward  
Figure 3: Spatial trends (first row: 2002–2009; second row: 2010–2019) of the total water storage anomaly (TWSA) derived from (**a–d**) JPL mascon, (**b–e**) CSR mascon, and (**c–f**) average spherical harmonics in the study region. Spatial information of precipitation (averaged from IMERG and CMAGrid) displayed as trends (**g**) 2002–2009, (**h**) 2010–2019, and annual average value (**i**) 2002–2019.  
trend. Over the entire 2002-2019 period, the study region showed a significant upward trend (2.95 cm/yr), while both the Lancang (\(-\)1.40 cm/yr) and Mekong (\(-\)3.36 cm/yr) River basins showed significant downward trends (Table 2).
## 6 Performance Evaluation
### Experimental Framework  
In our simulation, 80 sensor nodes are randomly deployed in a 5000 m \(\times\) 5000 m \(\times\) 1500 m three-dimensional space. These sensor nodes are identical in every feature, such as the initial energy, communication power, transmission range and so on. Five sink nodes are randomly deployed on the water surface. As it is convenient to maintain these sink nodes, we assume that these sink nodes have infinite energy and can communicate with each other by radio. Thus, packets can be sent to any sink node. We place a source node at the bottom layer of the network. The transmission speed is 1500 m/s. At the source node, packets are generated and prepared to send to the sink node following an independent Poisson process with a rate \(\lambda\) (packets/s). The initial energy, energy warning threshold are set to 150 J and 75 J. Because \(\beta_{1}\) and \(\beta_{2}\) are in equal positions, they are set to the same value: \(\beta_{1}=\beta_{2}\). The constant cost \(\beta_{0}=1\) and \(\varphi_{ij}=1\). We use \(\varphi_{0}\) to restrict the detour routing. The underlying MAC protocol is the underwater power control (UPC) protocol [24]. Moreover, the preamble signal length should be considered in the end-to-end delay as it is long in UWSNs. For example, the preamble of the Aqua-sent OFDM is 0.49 s, significantly increasing the end-to-end delay. In accordance with the hardware specifications of underwater OFDM modems [25], the simulation parameters are listed in Table 2.