esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Relational Extraction Model PTKRE
### Top-K Prediction Set Generation Layer  
The main purpose of this layer is to generate Top-K prediction sets for all samples. When provided with a vector representation H for a sentence, as generated by the input layer, we first extract the vectors associated with the \"
\" token and the vectors of the corresponding positions of the two entities in H. Since the sentences do not have fixed lengths for the head entity and the tail entity, performing separate average pooling on the representations of the head entity and the tail entity becomes necessary. The \"
\" token has a fixed length, requiring no additional processing. Then, the \"
\" token and the vector representations of the processed two entities are passed into the fully connected layer for processing, respectively, to obtain the vector representations \(s\), \(e_{1}\) and \(e_{2}\). Here, \(s\), \(e_{1}\) and \(e_{2}\)\(\in\)\(R^{d}\) and \(d\) are the size of the hidden layer vectors outputted by the pre-trained language model. For example, the RoBERTa-large model uses a dimension of 1024. The vector representations \(s\), \(e_{1}\), and \(e_{2}\) are then concatenated:  
\[r=FC([s\parallel e_{1}\parallel e_{2}]) \tag{1}\]  
The \(\parallel\) represents vector concatenation. \(FC\) is the fully connected layer. The representation \(r\) is input into the SoftMax and the loss function layers. The SoftMax layer calculates the probabilities of potential relationships between entities, and we select the Top-K most probable relationship categories by configuring the hyperparameter \(k\), resulting in the formation of the Top-K prediction set. The cross-entropy loss is used in the loss function layer to optimize the model, and this loss is denoted as \(Loss_{1}\).
## 2 Materials and Methods
### Model Improvements
#### 2.3.5 Optimization  
We introduced a new calibration scheme to provide an optimized parameterization, and quantify uncertainty related to the equations used in 3D-CMCC-PSM to simulate NEE, and choice of a subset of 45 species-specific physiological parameters (Table S1). Calibration was performed on each site independently, using a training dataset composed by 3 years of EC daily NEE time-series. We chose 2000-2002 for DEHai, 2001-2003 for FIHyy, ITCpz, and FRPue; 2001-2004 for ITCol; 2006-2008 for ITRen. Because of the strong autocorrelation characterizing time-series, we didn't randomly split our dataset, and decided to use sub-time series of contiguous years [58]. We preferred to use the beginning of each simulation for training, before the global recession following the Financial Crisis of 2007-2008. We decided to do so to (1) facilitate comparison among different sites' simulations; (2) have the calibration start when vegetation structure was known from literature; and (3) reduce the risk for parameters to be fitted over a changing environment instead of eco-physiological properties.  
To sample the parameterization space (the realistic values of each physiological parameter of the species simulated in each site), we randomly extracted 1000 parameter-set combinations from prior distributions. Prior distributions were assumed to be the same among individuals of the same species, different across species. We assumed each parameter to follow a truncated normal distribution, to avoid any possibility to have non-realistic negative values. Average and variance were estimated by using values found in literature, as in [37]. We used the same averaged value as in [37] for those parameters whose observations in literature were less than 3, because we didn't have enough information to calculate sample standard deviation. The optimization was performed by choosing the parameterization set maximizing the objective function QF through:  
\[QF=\left(\frac{\sum_{i}^{n}Y_{i}^{obs}\cdot Y_{i}^{sim}-\frac{\sum Y_{i}^{obs} \cdot\sum Y_{i}^{sim}}{n}}{\sqrt{\sum\left(Y_{i}^{obs}\right)^{2}-\frac{\left( \sum Y_{i}^{obs}\right)^{2}}{n}}\sqrt{\sum\left(Y_{i}^{sim}\right)^{2}-\frac{ \left(\sum Y_{i}^{sim}\right)^{2}}{n}}}\right)^{2}\times\left[1-\frac{\sum_{i} ^{n}\left(Y_{i}^{obs}-Y_{i}^{sim}\right)^{2}}{\sum_{i}^{n}\left(Y_{i}^{obs}- \overline{Y}^{obs}\right)^{2}}\right], \tag{14}\]  
where \(Y^{obs}\) represents EC daily NEE, \(Y^{sim}\) Modeled NEE for the same day, \(\overline{Y}^{obs}\) the average EC daily NEE over the train time series. The first part of the RHS of the equation represents the square of the Pearson Correlation coefficient (R), the second the Nash-Sutcliffe Efficiency index (NSE).
###### Abstract
To address the limitations, in this work, we propose a novel approach to asynchronous PS-based distributed training, the **S**cale and **H**eterogeneity aware **A**synchronous distributed **T**training algorithm (**SHAT**) that takes into account (1) the scale of distributed training and (2) the heterogeneity among workers in updating the local model of each worker, for effectively reducing the difference among the local models of workers in asynchronous distributed training. We demonstrate that (1) the model trained by SHAT converges to the higher accuracy up to 5.22% than the existing state-of-the-art algorithms,  
Figure 1: The architecture of a distributed training based on the parameter server framework. and (2) the model convergence of SHAT is robust under various heterogeneous environments including the cluster with an extremely (\(\times\)100) slower worker. To the best of our knowledge, this is the first work to consider the scale of distributed training and the heterogeneity among workers at the same time for fast model convergence in asynchronous training. SHAT can achieve fast model convergence regardless of the scale of distributed training and the heterogeneity among workers. The main contributions of this work are as follows:  
* Identifying the limitations of existing asynchronous distributed training in updating local models of workers--i.e., not considering both the scale of distributed training and the heterogeneity among workers. * Proposing a novel asynchronous PS-based training algorithm, named as SHAT, successfully addressing the limitations of the existing asynchronous distributed training. * Comprehensive evaluation verifying the effectiveness of SHAT in terms of the convergence rate and robustness under heterogeneous environments. **Organization. ** The rest of this paper is organized as follow. We describe data-parallel distributed training in detail and introduce literature related to data-parallel distributed training in Section 2. Then, we describe the limitations of existing asynchronous distributed training algorithms and present a novel algorithm for effectively addressing the limitations in Section 3. We empirically evaluate SHAT in Section 4. Finally, we conclude this paper in Section 5.
# Impact of Mega Constellations on Geospace Safety
## 2 Models and Assumptions
### Starlink Constellation Configuration and Selection of Target Satellites  
#### 2.3.1 Starlink Constellation Configuration  
According to the FCC report SAT-MOD-20200417-00037 [8], the summary of the NGSO (Non-Geostationary-Satellite Orbit) constellation after the star chain modification can be obtained as Table 2. As shown in the table, it can be seen that a total of 4408 satellites will be deployed in the vicinity of 550 km orbit. To simplify the calculation, it is assumed that the satellites' orbits do not change during the calculated time-period, and their positions are designated according to the table below. 'Orbital planes' means the number of orbital planes, and 'Satellites per plane' refers to the number of satellites in each plane.
## 3 Materials and Methods
### Data Collection and Analysis  
To understand the workflow, interviews were carried out with OPC staff. In addition, patient and department data were obtained from the inhouse software. To simulate the OPC workflow that depicts real-time scenario data such as patient demand, arrival times, patient in-time and patient out-time, resources in-time and out-time, and resource schedule were collected. The waiting times, cycle times, processing times and utilization were extracted from the collected data [2]. The Easy-Fit tool was used to determine the probability distributions of service times and patient arrival times. A bimodal Poisson distribution [28; 29] was used to generate patient arrival times. The workflow in the OPC is as shown in Figure 1. There are two types of patients, new and review. The new patients are serviced through registration, vision, preliminary test, refraction, tension, dilatation and final examination. The processing time differs from patient to patient. Review patients follow the same clinical pathway, except the initial vision test. A few sequences in the pathway can be realigned.  
The OPC is situated on the ground floor. The floor layout of the OPC under study has one location/room for new registration, one for review registration, one for vision, five for refraction, one for tension and three for preliminary and final examinations. The physical dimensions of the OPC were collected, and the distances between locations were measured through the number of human steps (walking) which was later translated to time during the simulation. The time taken to walk a particular distance varied, and the number of steps also varied based on the age and height of the patient, respectively. Therefore, average numbers of steps was considered for simulation. The walking style or the pattern of the disabled patients was excluded as they contributed less than 1% of the patient volume.  
The in-house software provided the information about wait time, patient mix and service time. Process data of around 20,000 patients were obtained, and 2700 patient data among them were excluded as the recorded cycle time of these patients was less than 15 min. Either the patient left the workflow in-between or the patient visited only preliminary examinations. The remaining 17,300 patient data were used in building the model. The  
Figure 1: Outpatient clinic workflow of Aravind Eye OPC (created based on observations).  
service time was considered as variable, and it was derived from the empirical distribution. Service times were uniformly distributed between the minimum and maximum processing times from empirical data for each department and were randomly generated. As this study considers only layout optimization, the scheduling methods used by the OPC were not modified.
# A Multi-Agent Optimization Approach for Multimodal Collaboration in Marine Terminals
## 2 Problem Description and Modeling Approach
### Problem Description
#### 2.1.2 Berth Allocation for Inbound Vessels  
The Berth Allocation Problem (BAP) is a well-established topic in operations research. It involves optimizing the assignment of berthing locations and time slots to arriving vessels at the quay, taking into account relevant constraints [14]. A crucial objective is often the minimization of vessel waiting and exit times through optimal berth assignments. Approaches to BAPs can be classified as proactive, reactive, or hybrid [15]. Our approach is classified as proactive, as it is fully embedded in the planning phase. For the purposes of this study, the vessel arrival schedule is assumed to be deterministic and fixed and is examined over a full week in a tactical planning context, following a similar setup to [16]. The occurrence of delays and any other disruptions is not studied. In contrast to previous studies on VDTWs [17], a bidirectional impact between VDTWs and berth planning is assumed, indicating mutual influence between the two. This suggests that immediate access to the terminal from the seaside is not guaranteed, as congestion may occur due to vessels occupying berths with active VDTWs, which can make mooring impossible during specific time periods and incur waiting times. Immediate access may also be hindered if it leads to high deviation costs for logistics companies. Berth size and vessel compatibility are also considered, implying that not all vessels can use all berths. Finally, vessel exit times depend on factors such as berth productivity, which is influenced by the number of quay cranes, available internal vehicles, and the drayage time costs within the terminal. To model the intricacies of cargo handling by the terminal and its effect on vessel departure, a macroscopic approach is used to determine productivity rates, as previously proposed by [18]. This approach computes the processing time for loading and unloading vessels based on terminal resources and cargo levels, while also measuring the conditions of import and export areas and the demand for internal vehicles. This study does not incorporate vessel-specific weights or priority levels to differentiate between vessel types such as motherships and feeders. Additionally, transshipment activities and multimodal transport connections, which can significantly influence vessel turnaround times, are not explicitly modeled. These simplifications were made to focus on the core coordination mechanisms under controlled assumptions. Extending the problem to account for vessel heterogeneity and broader port operations, however, does not contrast with the selected approach to model the problem.
## 4 Results and Discussion
### Finding the Optimum Number of Clusters
#### 4.1.2 Ten Clusters Evaluation Criteria  
Similarly, the selected 55 audiograms are classified into 10 clusters. The Silhouette plot indicates that four audiograms are wrongly assigned. The first two stages are conducted to remove the wrongly assigned audiograms, as shown in Figure 4. In the second stage, the plot shows that two audiograms are on the border between two clusters (very small negative Silhouette coefficients of \(-\)0.004755, \(-\)0.0007911). The third stage shows that all audiograms are with positive Silhouette coefficients. Other clustering evaluation criteria values are listed in Table 4. As seen from the results the best clustering performance is for stage 3, where SI and CHI are the highest and DBI is the lowest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criterion** & **Stage 1** & **Stage 2** & **Stage 3** \\ \hline No. of audiograms & 55 & 48 & 47 \\ Silhouette Criterion Values & 0.3907 & 0.4640 & 0.4814 \\ Calinski-Harabasz Criterion Values & 36.7956 & 38.5503 & 38.5476 \\ Davies-Bouldin Criterion Values & 1.0427 & 0.9670 & 0.9426 \\  & 0 & 0 & 0 \\  & 0.0502 & 0.0364 & 0.0357 \\  & 0.1313 & 0.0842 & 0.0795 \\ Eigenvalues & 0.2077 & 0.1636 & 0.1548 \\  & 0.2749 & 0.2667 & 0.2727 \\  & 0.3700 & 0.2978 & 0.3163 \\  & 0.4163 & 0.3735 & 0.3914 \\  & 0.5062 & 0.4122 & 0.4137 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Different clustering evaluation criteria for eight clusters.  
Figure 3: Silhouette plots for eight clusters.
## 2 Survey for Outdoor Agricultural UAV Applications
### Weed Detection  
Weed detection is also among precision agriculture applications. Weeds in a field adversely affect the growth of other main crops and cause losses in crop yield and growth. To prevent this, weeds should be detected, and their growth should be prevented [42]. However, as the distribution of weeds is not regular (heterogeneous), precise detection is required. In this case, deep learning-based methods have been very actively studied recently [43]. After successful detection, weed control is achieved using herbicides. Traditional methods overuse herbicides, resulting in reduced crop yields. Instead, spraying a sufficient amount of herbicides on weeds detected by UAV is much more beneficial in terms of cost, environmental pollution and yield. The marking of weedy areas on UAV images (mapping) is required for precise spraying of the herbicide [3; 44]. In a sample study, [PERSON] et al. [45] proposed a method that uses UAV images and applies deep learning to detect weeds in spinach and bean fields. The images of the detected weeds are shown in Figure 4.  
Figure 3: UAV adaptive CWSI map using thermal infrared images [41].
# Industry 4.0 Technologies for Manufacturing Sustainability:
## 6 Manufacturing Sustainability in Industry 4.0
### Environmental Sustainability  
Due to strict government regulations and customer awareness, industries are now focusing more on improving standards of living and environment-related issues. Environmental sustainability can be considered one of the major pillars which contribute to the sustainability in business practices [14]. The development in innovations and technology are growing at a faster rate to improve the quality of life [8; 10]. These developments are not very sustainable because of a lack of proper knowledge about Industry 4.0 technologies and their potentials [4]. Most of the developments in the environmental sustainability are focused on the resource efficiency and climate issues. Industry 4.0 technologies consist of cyber physical systems, intelligent sensors and smart logistics which have the potential to reduce the energy, water and resource consumption in industry [39]. Additive manufacturing technologies work on a pull strategy which has the potential to reduce the inventory levels, lead times as well as complete the mass customized demands [44]. Industry 4.0 technologies require higher energy consumption as these technologies are based on sensors, data centers and smart equipment which have a negative impact on the environment during manufacturing operations [147]. Hazardous waste disposal and greenhouse gases emissions are the major concern in the environmental sustainability [115]. During manufacturing, activities many scraps and other waste is generated which affect both environmental and economic sustainability. There is a requirement for sustainable processing and scheduling plans which can minimize the emissions and waste at the machine level. However, few studies have addressed these research issues but they have limitations and subjective to some constraints [47; 148]. Hazardous chemical disposal may lead to accidents which is the major concern in developing nations due to lack of standardized technologies for Industry 4.0. Therefore, there is a need to develop new tools and methods for detection and control of these type of issues in industries. The main influence of Industry 4.0 on environmental sustainability is presented in Table 4.  
RQ3: What are main research gaps in Industry 4.0 technologies for manufacturing sustainability and what are the research issues that need to be addressed in future?
# Development of a Stock Price Prediction Framework for Intelligent Media and Technical Analysis
## 3 The Developed Model
### Results from Fundamental and Technical Analysis Experiments
#### 3.3.1 Fundamental Analysis: Experiment 1  
The main objective for Experiment 1 was to predict whether Vodacom's stock would go up or down for 14 days by analysing and extracting sentiments from news headlines and tweets linked to Vodacom. The experiment used a comparison of four machine learning models in its quest to predict the stock movement. To evaluate the performance of all three experiments, the experiment used accuracy, precision, recall, the f1-score, and a confusion matrix.  
Table 1 shows the results from Experiment 1.1; this experiment yielded the lowest accuracy in the three sub experiments. It can also be seen that the random forest model was the best performing model on the news headlines dataset in Experiment 1.1. Experiment 1.2 introduced the same objectives as Experiment 1.1, and a sentiment feature and Vodacom's stock financial data were also introduced to the dataset. Experiment 1.2 returns a far better accuracy in all the models due to the abovementioned features. Linear discriminant analysis was the best performing model, with an accuracy of 94%, as seen in Table 2. The last experiment followed the same objective as the first two; in this experiment, tweets related to Vodacom were added onto the news headlines, the sentiment feature from this text, and Vodacom's stock financial data. The experiment outperformed both experiments, and the linear discriminant analysis was also the best performing model, with an accuracy of 96%, as seen in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Accuracy** \\ \hline Support vector machine & 44\% \\ Decision tree & 46\% \\ Random forest & 49\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment 1.1 results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Accuracy** \\ \hline Support vector machine & 54\% \\ Decision tree & 75\% \\ Random forest & 66\% \\ Linear discriminant analysis & 94\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment 1.2 results.
## 3 Results
### Cost Analysis  
Table 4 shows the list and description of projects used to design and implement irrigation control and automatic spectral acquisition equipment. The cost analysis is expressed in U.S. dollars (USD). Due to the fluctuation of the exchange rate in the global market, these costs will also change accordingly. The costs shown here do not include transportation costs and labor costs. It can be seen from Table 4 that the cost of the IICS is $35,111.8 USD, and the cost of the automatic spectral monitoring system is $19,429 USD, totaling $54,540.8 USD. We think this set of equipment is expensive and not suitable for farmers to use directly. The most expensive of this set of equipment is the soil moisture sensors and spectrometer, with the prices of $33,472.8 USD and $17,433.75 USD, respectively. As we are conducting basic scientific research, considering the accuracy requirements, we use the instruments with the highest accuracy produced in the United States, which directly increases the cost of investment. Nowadays, some domestic equipment can meet  
Figure 12: The seasonal changes of (**a**) SIF, (**b**) NDVI, (**c**) NDRE and (**d**) OSAVI under different water stresses. The red, yellow, blue and green solid circles represent well-watered, mild stress, moderate stress and severe stress, respectively. All values are averaged from 9:00 to 16:00. DAP means days after planting.  
the requirements of agricultural production. Most importantly, domestic equipment has greatly reduced the cost and made it affordable for farmers.
## 2 Instrument Design
### Critical Components
#### 2.2.4 Amplifiers  
Low-noise amplifiers AMP1/2/5 are selected to minimize the noises \(T_{\rm AMP1/2/5}\) of the amplifiers in the low-signal parts of the radiometer (MA and ACS). Their noise figure is rated to \(N\!F<0.5\) dB over the protected band, corresponding to \(T_{\rm AMP1/2/5}\approx 34\) K at \(T_{0}=313\) K. The instrumentation amplifiers AMP3a/b are selected to have a low input noise level, which contributes approximately 0.4 mV to the total uncertainty \(\sigma U_{\rm RM}\) of a single measurement \(U_{\rm RM}\) performed with the shortest possible recording time \(\tau_{\rm rec}=2.5\) ms (see Section 3.2). Furthermore, the capability to easily set the gain with a single resistor was also considered in the selection.