esa-sceva/satcom-chunk-collection huggingface dataset:
# An Easy Partition Approach for Joint Entity and Relation Extraction
## 4 Experiments
### Comparison with Previous Work
#### 4.2.3 Detailed Results  
Detailed results on overlapping patterns and triple numbers. We compare the ability of our model to extract sentences with different numbers of triples and triples with different overlapping patterns in the WebNLG dataset. We refer to the previous work ([13; 36]) and divide the sentences into three classes according to three overlapping patterns: normal, SEO (single entity overlap), and EPO (entity pair overlap). The sentences are divided into five subcategories according to the number of triples: 1, 2, 3, 4, and \(\geq 5\). The results are shown in Figures 2 and 3. We observe that in both cases, the improvement of the F1 score of our model comes from the most challenging subclass (EPO and \(\geq 5\)). The best results are obtained under the conditions of EPO and N \(\geq 5\). In these sentences, entities and relations become more complex. This proves that our model shows better advantages than other baselines in dealing with complex scenarios.  
\begin{table}
\begin{tabular}{c c} \hline
**Model** & **Computing Time** \\ \hline CasRel [36] & 76.8 \\ TPLinker [13] & 25.6 \\ PFN [15] & 23.8 \\ EPRE & 10.2 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of computational efficiency. Computing time represents the model’s average time (ms) to process a sample.  
Figure 2: Partial matching F1 score of relational triples extracted from sentences with three different overlapping patterns in the WebNLG dataset.  
Detailed results on entity density. We compare the ability of EPRE in terms of extracting entities under different entity densities. Note that the WebNLG dataset adopts the evaluation method of partial matching; thus, for a fair comparison, we use the ratio of the number of entities to the number of words contained in the text as the calculation formula for entity density. For example, in the sentence \"The current CEO of Apple is [PERSON]\", we can observe that there are three entities (CEO, Apple, [PERSON]) and the number of words in the sentence is 7, so the density of the sentence is 3/7. The sentences in datasets are divided into three categories: dense \(\geq\) 10%, 10% > dense \(\geq\) 5%, and dense \(<\) 5%. Our results are shown in Figure 4. We observe that the effect is best when the density of the entity is the highest. The lower the density of entities in the sentence, the worse the model's performance.
## 4 Experiments
### Dataset Descriptions
#### 4.1.1 Guangzhou City Dataset  
This dataset is composed of a bitemporal multispectral image pair with the spatial resolution of 2.5 m, captured by the Systeme Probatoire d'Observation de la Terre 5 (SPOT-5) satellite. It depicts the land cover change over urban areas of Guangzhou City between October 2006 and October 2007, respectively, as shown in Figure 2. The bitemporal images are the size of \(877\times 738\) pixels, including red, green, and near-infrared bands. Its annotation focuses on vegetation change.  
Figure 2: Guangzhou City dataset: **(a)**\(T_{1}\)-time image, **(b)**\(T_{2}\)-time image, **(c)** ground truth image.
## 2 Materials and Methods
### Kinetic Models  
The growth rate, r\({}_{\text{x}}\), can be expressed in terms of first order kinetics with respect to the biomass concentration according to Equation (4) [24]:  
\[\text{r}_{\text{x}}=\frac{\text{d}\text{X}}{\text{dt}}=\mu\,X, \tag{4}\]  
where \(\mu\) is the specific growth rate (h\({}^{-1}\)).  
By applying the separation of variables in Equation (6) and the integration with the initial condition (X = X\({}_{0}\) at t = t\({}_{0}\)), it is possible to obtain Equation (5) [24]:  
\[\mu=\frac{\ln\left(\frac{X}{X_{0}}\right)}{\text{t }-\text{t}_{0}}, \tag{5}\]  
where t\({}_{0}\) is the initial time of the exponential growth phase and X\({}_{0}\) is the corresponding biomass concentration.  
Unsegregated and unstructured kinetic models were proposed to express the specific growth rate as a function of the limiting substrate concentration, as in the Monod model [21] in Equation (6); the cell density, as in the logistic model [25] in Equation (7); and both the cell and substrate concentrations, as in the Contois model [26] in Equation (8).  
\[\mu\,(\text{S})\,=\,\mu_{\text{max}}\,\frac{\text{S}}{\text{S }+\text{ K}_{\text{s}}}, \tag{6}\]\[\mu\left(X\right)\ =\ \mu_{\max}\ (1\ -\ \frac{X}{X_{\rm m}}), \tag{7}\]  
\[\mu\left(X,\ S\right)\ =\ \mu_{\max}\ \frac{S}{S\ +\ K_{\rm s}X}, \tag{8}\]  
where \(\mu_{\max}\), \(S\), \(K_{\rm s}\), \(X\), and \(X_{\rm m}\) are the maximum specific growth rate (h\({}^{-1}\)), limiting substrate concentration (g/L), semi-saturation constant (g/L), biomass concentration (g/L), and maximum biomass concentration (g/L), respectively. In the Contois model, \(K_{\rm s}\) (g/g) is the substrate to biomass ratio corresponding to half of the maximum specific growth rate.  
The lactic acid production rate, \(\rm r_{\rm p}\), was expressed by the Luedeking\(-\)Piret model [27; 28], as shown in Equation (9):  
\[\rm r_{\rm p}=\frac{dP}{dt}\ =\ \alpha\frac{dX}{dt}+\ \beta\ X, \tag{9}\]  
where P (g/L) is the lactic acid concentration and \(\alpha\) (g/g) and \(\beta\) (g/g h) are the growth-linked and non-growth-linked constants.
## 5 Methodology  
In natural language processing pipeline, information extraction, parsing and neural machine translation employs the POS tags as an additional linguistic feature. Recent research on the topic of deep learning methodologies such as CNN or RNN models has accorded to the possible application of similar techniques through different NLP tasks. That is, instead of using the output of a model as an input for another model, a single one unified model to do translation tasks from Arabic Dialect (AD) to Modern Standard Arabic (MSA) and from MSA to English (ENG) as well as performing Dialectal Arabic POS tagging task can be developed. The Multitask Learning (MTL) model automatically learns and shares relevant and necessary information across the tasks. MTL as an approach induces transfers that improve the generalization by using the domain information contained in the training signals of related tasks as an inductive bias. MTL utilizes the correlation and the shared representation between related translation tasks, such as AD-MSA and MSA-ENG and exploiting the Arabic dialect linguistic features from the POS tagging task to improve translation quality by learning the tasks in parallel. The information learnt for each task has the possibilities of aiding other tasks to effectuate effective learning. The primary purpose of the inductive transfer is to leverage additional sources of information to improve the performance of learning on the current task. Inductive transfer can be used to enhance generalization accuracy, the speed of learning and the intelligibility of learned models. A learner that learns many related tasks at the same time can use these tasks as inductive bias for each other and thus better learn the domain's regularities. This can make learning more precise and allow translation tasks with a small amount of training data to be learned better. The architecture of the designed model is a Recurrent Neural Network (RNN) based-encoder decoder architecture with two translation tasks and one part of speech (POS) tagging task. The central point of the proposed multitask NMT model for Arabic dialects is, the sharing scheme in latent feature space. In deep learning models, the latent features can be considered as the hidden states of the Bi-LSTM at the end of the sentence. Here, the study has incorporated a shared-private scheme with multitasking NMT model. All the translations tasks use a separate translation decoder across all the various language pairs such as DA-MSA and MSA-ENG and a separate encoder encodes each source language due to the peculiarities the language. All the translation tasks share a private layer and invariant layer and the Arabic Dialect segment level POS tagging task shares the same encoder with AD machine translation task. Sharing more information across the tasks is preferred and the model details are described in the following section.
# A Critical Review of Flood Risk Management and the Selection of Suitable Measures
## 1 Introduction  
Floods can be devastating, and they are frequently compared to all other natural disasters worldwide, because they have the highest number of casualties and the highest economic losses. The worldwide average direct annual cost of natural disasters between 2000 and 2012 was roughly $100 billion [1]. The following facts are presented based on flood-associated studies [2]. One-third of natural catastrophes can be considered to be floods, one of every ten fatalities due to natural disasters are flood-related, and flooding is responsible for one-third of overall economic loss ($250 billion worldwide over the last fifteen years alone). A stark 95-97% of fatalities from natural disasters are caused by floods in developing countries. At the same time, 90% of natural disasters include flooding, causing a $6 billion annual loss in the world economy. The impact of floods can vary throughout theworld due to geographical, agricultural, and economic reasons. The Nile, the Mekong, and the lower Indus flood plains are used for regular agricultural needs, while the risk of floods is largely overlooked by the growing population and industrial activities of both developed and developing countries [3; 4]. However, some research studies support the severity of floods, and in recent years, the frequency of floods has increased [5]. Flood situations tend to worsen with climate change, land subsidence, urbanization, and population growth.  
The problem and its solutions are nothing new, but the demand for further efficient and effective flood management is quite obvious with the ever-growing environmental awareness, higher living standards, and technological expertise. Moreover, management of floods is an obtuse, complex phenomena that should be periodically revised roughly every 30-50 years [6]. A variety of new strategies, concepts, and initiatives have consequently been proposed, namely: flood protection, evacuation, environmentally-friendly solutions, the denaturalization of rivers, foolproof structural measures, non-structural measures, soft measures, hard measures, room for the river, flood fighting, resilient approach, sustainable flood management, integrated approach, risk-based flood management, living with floods, nature-based solutions, economical solutions, spatial planning modifications, flood risk zoning, coping with floods, floodplain restoration, non-adverse impact approaches, catchment management, community cooperation, compensation, dykes, floodwalls, warning system, encroachment control, flood insurance, floodproofing, flow diversion, groundwater recharge, public awareness, relief efforts, river improvement, storage and retention, and sustainable drainage systems, just to name a few.
## 4 Conclusions  
An improved neural network-based equalization method was proposed and experimentally demonstrated. The up-to-137 Gb/s transmission of PAM-4 signals with 25 G class 850 nm optical devices was achieved over an in-house fabricated 40 cm optical backplane. An in-depth investigation was conducted regarding the impact of delayed taps and spans on equalization performance. The performance comparison of the proposed method with the traditional MLSE and DFE was also undertaken. Furthermore, the specific BER performances for varied maximum achievable bit rates under different RoPs that applied MLSE and the proposed method were also analyzed. This provides an important potential solution to realize the future data centers.  
Conceptualization, Q.Z. and H.Z.; Data curation, Q.Z., Y.J., C.D., S.D., Z.W., Y.L., Y.S., J.C. and J.Z.; Formal analysis, Q.Z., Y.J., H.Z., S.D., Z.W., Y.L. and Y.S.; Investigation, Q.Z., Y.J., J.Z. and M.W.; Methodology, Q.Z., Y.J., C.D., S.D., Z.W., Y.L., Y.S. and J.C.; Project administration, H.Z., S.D. and Z.W.; Resources, Y.L., Y.S., J.C. and J.Z.; Software, H.Z., Z.W. and Y.L.; Supervision, J.C., J.Z. and M.W.; Visualization, C.D. and S.D.; Writing--review & editing, C.D., Y.W., T.W. and M.W.  
This research was funded by National Natural Science Foundation of China (Project No. 61601279, 61420106011, 61601277, 61635006); Shanghai Science and Technology Development Funds (Project No. 17010500400, 16511104100).  
The authors declare no conflict of interest.
# A Method for Autonomous Generation of High-Precision Time Scales for Navigation Constellations
## 3 Time Scale Algorithm Principle
### Genetic Algorithm  
Genetic Algorithm (GA) was first proposed by [PERSON] in the United States in the 1970s [10]. This algorithm is designed and proposed according to the evolution law of organisms in nature. It is a computational model of the biological evolution process that simulates natural selection and genetics mechanisms, and a method of searching for optimal solutions by simulating the natural evolution process. GA expresses the decision variables in the problem space as an individual in the genetic space through a certain coding method, which is a genotype string structure data. At the same time, the value calculated by the fitness function is used to evaluate the quality of the individual and serve as the basis for genetic operations. Genetic operations include the following three basic operators (genetic operator): selection; crossover; mutation. The basic process of genetic algorithm is shown in the Figure 2:Through the investigation of the literature, it is found that each time scale algorithm either adopts an equal weight method for the clock groups composed of the same type of clocks, or uses the Equation (11) to determine the weight [5; 8; 26]. When the constellation runs autonomously for a long time, the performance of the atomic clock will change with time, so the method of equal weight is not applicable to the actual situation. When Equation (11) is used to determine the weight \(\omega_{i}\), the weight changes with the change of the sampling interval, and the final generated time scale also changes with the change of the sampling interval. In this paper, the genetic algorithm is used to optimize \(\tau\) in Equation (11); the problem space of the GA represents the sampling interval \(\tau\) in Equation (11). Then, the calculation model of the genetic algorithm combined with KPW time scale algorithm is:  
\[\left\{\begin{array}{l}TA(P)=\sum\limits_{i=1}^{N}{\left[\sum\limits_{m=1}^{ N}{\frac{1}{{{\tau_{m}(P)}}}}\right]^{-1}{\left[\frac{1}{{{\tau_{i}(P)}}} \right]}({{\tau_{ij}(P)}})-({{x_{ik}}}(t-\Delta t)+\Delta t{{y_{ik}}}(t-\Delta t )+1/2D(t-\Delta t)^{2})}\\ \\ f(P)=\sigma_{TA(P)}(\tau)\end{array}\right. \tag{13}\]  
where \(TA(P)\) is the time scale generated by calculating the problem space using the KPW time scale algorithm in Section 3.1, \(\sigma_{TA(P)}(\tau)\) is the Hadamard variance of this time scale, and \(\tau\) is the sampling interval corresponding to the Hadamard variance. The initial parameters of the GA in this paper are respectively set as follows: the problem space P is coded in decimal, the length L of the coded string of each variable is 4, the population size M is 50, and the termination algebra T is 50. The genetic operator adopts the crossover operator and mutation operator, respectively, and the crossover probability and mutation probability are set to 0.9 and 0.2, respectively.
## 3 Experiments and Results
### Ablation Analysis  
In this part, to explore the availability of MSACN, we explore whether the difference in the heads of soft attention modules in the backbone affects the overall model accuracy from two aspects: the difference in the backbone and the number of attention modules. As can be seen from Table 4, the backbone uses ResNet50 and the number of head soft attention modules is set to 8, which works best. So, the results indicate a nuanced trade-off between the complexity of the model structure, as defined by the multiple heads, and its overall performance. While a higher number of heads can enhance accuracy, it might incur an additional computational burden. At the same time, the ResNet50 backbone, especially with eight heads, emerges as particularly effective, achieving the highest quantitative evaluation indexes like accuracy, IoU, Precision, Recall, and F1 among the configurations tested.
## 2 Materials and Methods
### Antimicrobial Assays  
#### 2.5.1 Well-Diffusion Assay  
As an initial screening of the different extract's capacity to inhibit the growth of the selected microorganisms, a well-diffusion assay was carried out as previously described [18]. To that end, Petri dishes containing 20 mL of Muller-Hinton Agar (MHA; Biokar Diagnostics, Allonne, France) for bacteria or Sabouraud Dextrose Agar (SDA; Biokar Diagnostics, Allonne, France) for _C. albicans_ were inoculated using microorganism suspensions with a turbidity of 0.5 in the McFarland Scale. Afterwards, 4 mm wells were punctured into the inoculated agar and filled, in triplicate, with 50 \(\upmu\)L of an aqueous saturated solution of each of the freeze-dried extracts (sterile filtered using 0.22 \(\upmu\)m syringe filters). MHA plates were incubated for 24 h at 37 \({}^{\circ}\)C, while SDA plates were incubated at 30 \({}^{\circ}\)C for 48 h.
# From Service Composition to Mashup Editor: A Multiperspective Taxonomy
## 3 Service Composition
### Service Composition's Research Landscape
#### 3.1.2 The Publication and Discovery of Services  
Service publication and discovery are two important operations for the composition process and particularly for the selection of the most relevant service. The publishing operation essentially raises issues of data and information engineering summarized in database technologies and access means to populate those databases with services description. Service discovery, based on those database technologies, has to provide not only access means but should also select the service descriptions that best fit the selection criteria (the request). This actually constitutes an optimization problem. From the perspective of SOA specifications, UDDI technology with its variants is the reference. Semantic technologies are also an alternative for optimizing the discovery and selection services in the composition process.
## 3 Distribution of FLD Output \(C_{2\varphi}\)
### C\({}_{2\varphi}\) Distribution under Frequency Unlock  
Under low C/N\({}_{0}\) condition, frequency loop may be unlocked. After simplification of Equation (7), \(\varphi\) is uniformly distributed in \((-\pi,\pi)\), and its probability distribution function is shown as:  
\[f(\varphi)\approx\frac{1}{2\pi}\quad|\varphi|\leq\pi \tag{20}\]When \(M=1\), the probability distribution functions of \(\Delta\varphi\) and \(f_{\mathrm{C_{2\varphi}}}(c_{2\varphi})\) are shown follows, respectively:  
\[f_{\Delta\varphi}(\Delta\varphi)=\left\{\begin{array}{ll}\frac{1}{2\pi}+ \frac{\Delta\varphi}{4\pi^{2}}&(-2\pi,0)\\ \frac{1}{2\pi}-\frac{\Delta\varphi}{4\pi^{2}}&(0,2\pi)\end{array}\right. \tag{21}\]  
\[f_{\mathrm{C_{2\varphi}}}(c_{2\varphi})=\left\{\frac{1}{\pi\sqrt{(1+c_{2 \varphi})(1-c_{2\varphi})}}\quad|c_{2\varphi}|\leq 1\right. \tag{22}\]  
According to (22), the expectation and variance of \(C_{2\varphi}\) are provided:  
\[E\{C_{2\varphi}\}=\int\limits_{-\infty}^{+\infty}\cos(2\Delta\varphi)f(\Delta \varphi)d\Delta\varphi=0 \tag{23}\]  
\[VAR\{C_{2\varphi}\}=E\{C_{2\varphi}^{2}\}-E^{2}\{C_{2\varphi}\}=\int\limits_{ -\infty}^{+\infty}\cos^{2}(2\Delta\varphi)f(\Delta\varphi)d\Delta\varphi=0.5 \tag{24}\]  
From the above section, the distribution of FLD output in unlock situation is completely different from that in lock situation.
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Dataset/Provenance and Compliance Tags
#### 6.15.2 Datasets  
The datasets in Table A17 demonstrate real-world applications of provenance tracking and drift detection frameworks from the two papers, spanning healthcare (Hungarian chickenpox), environmental science (Global land temperature), and business process management (Helpdesk Log3). Publicly available, these datasets highlight metadata-driven approaches for tracing concept/data drift and align with transparency goals. For example, event logs (CPEE) test edge-tagging methods, while time-series data (Sales Prediction) validate versioning schemas, where hash mismatches (\(\Delta\
eq 0\)) or schema diffs signal model behavior shifts. Regulatory-focused datasets (Ozone levels) emphasize compliance tagging, as schema changes (e.g., updated sensor protocols in global temperature) link metadata updates to drift. However, limitations persist: most datasets are static snapshots, contrasting with real-world streaming systems (Dublin footfall 2022) requiring continuous metadata logging. Additionally, live environments demand automated drift detection, unlike pre-annotated public repositories.
## 4 Results
### Selection of Spatial Econometric Model  
Based on the spatial correlation test, it is proved that there is spatial effect in China's regional S&T innovation ability. Therefore, the next step is to choose a spatial econometric model to analyze the influencing factors and explore the spatial effect. Before analyzing the spatial panel data, this paper carries out the Lagrange multiplier (LM) test, the likelihood ratio (LR) test and the Hausman test, respectively, to select the spatial econometric model.  
(1) Firstly, the possibility of the presence of spatial interaction effects in the model is examined. This is performed through a hypothesis test to check the possibility of the presence of spatial error or spatial lag in the model. The LM test is conducted to judge the spatial lag term and spatial error term. Results are shown in Table 4. According to the LM test results, the test values in spatial error and spatial lag are both significant at the level of 5%, rejecting the assumption of no spatial lag term and no spatial error term. Therefore, this paper selects the spatial Durbin model (SDM) which includes both spatial error term and spatial lag term.  
(2) For the sake of rigor, the LR test is carried out to further investigate whether the SDM will degenerate to the SEM or SLM. Results of the LR test are shown in Table 5, rejecting the hypothesis that the SDM can degenerate into the SEM or SLM. Results of the LR test verify the rationality of applying the SDM in this paper.  
(3) Finally, the Hausman test is used to determine the individual fixed-effect model or individual random-effect model. In the current research, the statistical quantity in the Hausman test is 72.32, rejecting the null hypothesis of random effect at the significance level of 1%. Therefore, the fixed-effect model is selected. Specifically, we have controlled both spatial fixed effect and time fixed effect.  
Based on the above analysis, the current research finally adopts SDM with individual fixed effect to carry out the regression analysis of the influencing factors of regional S&T innovation ability.
## 3 Materials and Methods  
### Materials Used  
Commercial TiO\({}_{2}\) P25, produced by Evonik (formerly Degussa; Essen, Germany) and supplied by EIGENMANN & VERONELLI S.p.A. (Rho, Italy), was used as a benchmark. Carbon dioxide (CO\({}_{2}\)), methane (CH\({}_{4}\)), oxygen (O\({}_{2}\)) and helium (He), all with a purity >99.99%, were purchased from AirLiquide (Milano, Italy). Ammonium metatungstate hydrate (AMT) (Sigma Aldrich, St. Louis, MO, USA, >99.99% purity) and titanium tetraisopropoxide (TTIP) (Sigma Aldrich, >97% purity) were used as precursors for the preparation of the metal oxides through flame spray pyrolysis. Dimetilformamide (DMF) (Sigma Aldrich, >99.9% purity) was used as a solvent for the preparation of the precursor solution. Sodium sulfite (Na\({}_{2}\)SO\({}_{3}\), Sigma Aldrich, >98% purity) was used as a hole scavenger (HS) during the photocatalytic test. NaOH (Sigma Aldrich, 98% purity) was used to achieve a basic pH (pH = 14) during the experiments. The operating conditions, including the use of a basic pH, were optimized in previous work to maximize the catalyst productivity [40].