esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Millimetre-Wave Network Applications
### Terrestrial  
For next generation mobile networks, the application of mm-wave technologies can be considered, both for high bandwidth application specific scenarios (such as live high-definition video editing or AV data transfer) and wider-scale mobile service provisioning. Particularly in the latter case, mm-wave transmission and generation will be required to integrate seamlessly with radio-over-fibre (RoF) links, which transport data signals between a baseband unit (BBU), located at a central office (CO), and a remotely deployed remote radio head (RRH), from where the wireless signal is transmitted and received. These optical distribution links (known as fronthaul) are now critical radio access network (RAN) components, which must support future mobile network scaling, in terms of both data rate and the number of deployed RRHs. This network evolution will require a shift away from the currently deployed binary modulation based fronthaul, which uses the enhanced common public radio interface (eCPRI) protocol, to a more spectrally efficient and flexible scheme [12]. Within this context, many recent works have proposed the use of more advanced single and multi-carrier modulation formats (which can be broadly categorised as digital (D)-RoF and analog (A)-RoF, respectively) for fronthaul transmission [13; 14; 15].  
The potential of RoF and other photonic technologies to enable the deployment of high throughput mm-wave communications has been noted by the research community. This has led to a major focus on the development of hybrid photonic/mm-wave systems [16; 17; 18], including those leveraging the heterodyne process [19; 20; 21].
# PNER: Applying the Pipeline Method to Resolve Nested Issues in Named Entity Recognition
###### Abstract  
Named entity recognition (NER) in natural language processing encompasses three primary types: flat, nested, and discontinuous. While the flat type often garners attention from researchers, nested NER poses a significant challenge. Current approaches to addressing nested NER involve sequence labeling methods with merged label layers, cascaded models, and those rooted in reading comprehension. Among these, sequence labeling with merged label layers stands out for its simplicity and ease of implementation. Yet, highlighted issues persist within this method, prompting our aim to enhance its efficacy. In this study, we propose augmentations to the sequence labeling approach by employing a pipeline model bifurcated into sequence labeling and text classification tasks. Departing from annotating specific entity categories, we amalgamated types into main and sub-categories for a unified treatment. These categories were subsequently embedded as identifiers in the recognition text for the text categorization task. Our choice of resolution involved BERT+BiLSTM+CRF for sequence labeling and the BERT model for text classification. Experiments were conducted across three nested NER datasets: GENIA, CMeEE, and GermEval 2014, featuring annotations varying from four to two levels. Before model training, we conducted separate statistical analyses on nested entities within the medical dataset CMeEE and the everyday life dataset GermEval 2014. Our research unveiled a consistent dominance of a particular entity category within nested entities across both datasets. This observation suggests the potential utility of labeling primary and subsidiary entities for effective category recognition. Model performance was evaluated based on F1 scores, considering correct recognition only when both the complete entity name and category were identified. Results showcased substantial performance enhancement after our proposed modifications compared to the original method. Additionally, our improved model exhibited strong competitiveness against existing models. F1 scores on the GENIA, CMeEE, and GermEval 2014 datasets reached 79.21, 66.71, and 87.81, respectively. Our research highlights that, while preserving the original method's simplicity and implementation ease, our enhanced model achieves heightened performance and competitive prowness compared to other methodologies.  
nested entity; named entity recognition; NER; sequence labeling; text classification; merged label; BERT model +
Footnote †: journal: Applied Sciences
## 1 Introduction
Given the critical consequences of cable fires, exploring this research gap is of paramount importance. Moreover, when addressing data uncertainty in the risk assessment process, the cloud model (CM) [27] emerges as a potent tool. It has introduced an innovative bidirectional conversion method that bridges qualitative fuzzy concepts and quantitatively precise data. The cloud model adeptly considers both fuzziness and randomness within the evaluation process and has found extensive use in tackling problems involving uncertainty conversion [28; 29]. Accordingly, the application of the hybrid CM-enabled DBN method in cable fire dynamic risk assessments is warranted. With the identified research gaps highlighted above, only a limited number of approaches have previously attempted to tackle the aforementioned problem. Given the dynamic nature of cable fire risk assessment of uncertainty, the primary objective of this study is to present a dynamic risk assessment methodology and subsequently evaluate the probability of cable fires, taking into account both randomness and fuzziness within the assessment process. Notably, this study pioneers the utilization of dynamic Bayesian networks (DBNs) in determining the dynamic risk probability of cable fires. To enhance the precision of this assessment, the CM is applied to calculate membership degrees, characterizing the dynamic data with respect to various states of the root nodes. These membership degrees are then integrated into the DBN as prior probabilities for the root nodes. Additionally, the Birnbaum (BM) importance measure [30] and Fussell-Vesely (FV) importance measure [31] are introduced to offer valuable insights for dynamic risk prevention and control strategies. Ultimately, the viability and rationality of the proposed methodology are substantiated via a comprehensive case study involving a cable located in a low-voltage distribution room at the Chongqing Tobacco Logistics Distribution Center. The remainder of this paper is organized as follows. Section 2 presents the framework of cable fire risk assessment and the hybrid CM-enabled DBN method proposed in this study. Section 3 first describes the primary concerns of the case study and then provides the computational results obtained from the case study to validate the effectiveness of the proposed fire risk assessment method. The fire risk assessment results and a comparison between the proposed method and the traditional DBN method are presented in Section 4. Finally, Section 5 concludes the study by providing valuable managerial insights and future directions.
# Optimization of DG-LRG Water Extraction Algorithm Considering Polarization and Texture Information
## 4 Results and Discussion
### Examples of Flood Emergency Monitoring
#### 4.3.2 Monitoring of Flooded Transmission Towers in Fangshan District  
Based on the flood inundation area extracted above, monitoring was conducted on the submerged towers in Fangshan District. Affected by the spread of floods and the surrounding environment of the towers, a total of 88 affected towers in Fangshan District were determined based on a 10 m buffer zone in the flooded area. According to on-site survey statistics by inspectors, 83 towers were significantly affected by floods. Accordingly, the proposed method obtained a Precision rate of 94.32% and a FAR of 5.68%, proving that the method proposed in this paper can be effectively applied for the emergency monitoring of towers affected by flood disasters. Based on factors such as the distance between the tower and the flooded area, combined with on-site survey results, the risk level of the affected towers was divided. A total of 0 low-risk towers, 4 medium-risk towers, and  
Figure 8: Results of Water Expansion in Fangshan District on August 1 st. The blue region shows the original water body and the red region represent the results of Water Expansion on August 1 st. Points (**A**), (**B**), and (**C**) denote the most pronounced expansion zones along the Dashi River, Xiaoqing River, and Yongding River systems, respectively.  
79 high-risk towers were determined in Fangshan District. Figure 9 shows the distribution map of the affected towers in Fangshan District.
# Gait Phase Identification and Damping Control for Knee Orthosis Using Time Series Forest Classifier
## 4 Machine Learning Algorithm for Gait Phase Identification: Training, Evaluation, and Results
### Time Series Forest Classifier (TSFC)
#### 4.1.2 Number of Estimators  
It defines the number of decision trees comprising the forest. For this application, 195 estimators were used to balance computational cost and acceptable results through random search.
## Problem Description and Modeling
### Dynamic Disruption Rescheduling Model  
Some disruptions may occur in the uncertain scheduling environment after some activities have been completed as planned, which may cause a scheduling delay or prescheduling failure. Then, the dynamic rescheduling model is established. The objective function of Equation (13) is to minimize the sum of the earliness and tardiness values of all of the activities' completion time cost, comparing the recovery schedule with the baseline schedule.  
\[\delta=\min\sum_{i\in I}\sum_{j\in I^{*}}\Bigl{(}\left|D_{ij}^{*}-D_{ij}\right| \Bigr{)} \tag{13}\]  
\(D_{ij}^{*}\) represents the start time of the incomplete activity \(j\) of carrier aircraft \(i\) after disruption, and \(D_{ij}\) represents the start time of the incomplete activity \(j\) of carrier aircraft \(i\) before disruption.  
This rescheduling model with disruptions differs from static scheduling. The dynamic scheduling problem is incorporated into the formulation in Equation (13) through optimizing the makespan objective function firstly, and then minimizing the sum of the earliness and tardiness values of all of the activities' completion time cost. The additional objective function is added to optimize the stability of the rescheduling activities, and obtain a more optimal new scheduling for the disruptions.
## 6 Performance Evaluation
### Evaluation for the Analytical Model
#### 6.2.6 One-Hop Timer Delay for the Two-Lane Scenario  
Figure 10 illustrates the influence of different vehicular traffic density on the one-hop timer delay in the two-lane scenario. In this figure, we use the case of both lanes are of identical vehicular traffic density, which can refers to cases with non-identical \(\rho\). Again, the results from the analysis accord with those from the simulations, which verifies that our analytical model works also in the two-lanes scenario. With the increase of the vehicular density, we can also observe that the distribution shape tends to be more compact.
## 3 Model Development  
### Groundwater Flow Model  
#### 3.1.1 Model and Spatial Discretization  
The numerical models were developed using the finite difference code Modflow [27]. The modelled area was equal to 32.8 km2. Two hydrogeological units were represented in the numerical flow model (Figure 3): (1) the chalk aquifer that covered the whole modelled area, reaching a thickness of 300 m in the southern part of the model, and (2) a shallower aquifer located in the southern area and overlying the chalk aquifer. This shallower aquifer is made up of marine and fluvial-alluvial sediments from the Cenozoic age. They consist of sand, silt, and clay, structured in alternating permeable and low-permeable formations.  
The modelled area was discretized using irregular rectangular cells. The dimensions of the cells were 10 \(\times\) 10 m in the quarry and increased by a factor of 1.05 in the direction of the boundaries. The mesh was divided vertically in three layers of cells representing the hydrogeological units. The first layer represented the shallower aquifer, made of marine and fluvial-alluvial sediments, whilst the two lower layers represented the chalk aquifer. The central layer was characterized by a constant thickness of 40 m, corresponding to the thickness of the quarry. The Obourg quarry, which was used for PSH, as well as the other quarries, were therefore contained in this layer. This configuration allowed the expected variations of the water table in the quarry and in the aquifer to be contained in the central layer. This enabled us to avoid numerical problems related with the saturation and desaturation of the cells. The lower layer was characterized by a variable thickness, ranging from 10 m in the north to 300 m in the south, forming a beveled layer.
## 7 Conclusions  
A hybrid network design strategy for remote sensing classification is proposed to address the lack of a widely accepted remote sensing deep neural network model caused by the inability to establish a standard remote sensing sample dataset from imagery developed using different remote sensing data formats and researchers' different classification requirements. Remote sensing imagery features are enhanced and data redundancy can be reduced using a convolutional auto-encoder. Then, those feature maps which reflect the essential information of the remote sensing data can be used as inputs into a convolutional neural network. This study demonstrates that the strategy not only improves image classification efficiency and accuracy, but also greatly reduces the number of labelled samples  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **CNN (CAE\_CNN)** & **Best\_CNN** \\ \hline Convolution layer & 1242 & 2895 \\ Fully-connected layer & 3005 & 2885 \\ Total & 4247 & 5780 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The number of parameters of the models.  
Figure 9: Classification accuracy with decreasing training set size.  
required. The strategy proposed has produced a viable and effective deep neural network design method for object-oriented remote sensing classification. In a future study, the authors will apply the proposed model to a broader range of data to establish a more universal model and find a more accurate and efficient way to determine the channel and scale of the output feature maps of the CAE.  
The following are available online at www.mdpi.com/1999-4893/11/1/9/s1.  
This work was supported by the Wuhan Science and Technology Plan Program under Grant 2016010101010023, the CRSRI Open Research Program under Grant CKWV2015242/KY, the National Natural Science Foundation of China under Grant 41571514, and the National Key R&D Program of China under Grant 2017 YFB0503700.  
[PERSON] and [PERSON] conceived the original idea for the study, analyzed the experiment results, and revised the manuscript; [PERSON] performed the experiments and wrote the manuscript; [PERSON] analyzed the data. All authors read and approved the submitted manuscript.  
The authors declare no conflict of interest.
## 3 Methodology
### Instance Feature Extraction Step
#### 3.1.1 Problem Instances  
In our experiments, we use the well-known CVRPTW Homberger set of 300 instances [21]. The instances consider a number of clients ranging from 200 to 1000, a number of vehicles proportional to the corresponding number of clients, and varying distributions of client locations, vehicle capacities, and time windows. The instances can be grouped into six subsets, as shown in Table 1. The main difference between the instances suffixed 1 and 2 in these subsets is their scheduling horizon. Lower vehicle capacities in C1, R1, and RC1 force a short scheduling horizon, reducing the number of clients that can be served by a single vehicle.
# Silica-Coated Magnetic Nanocomposites for Pb\({}^{2+}\) Removal from Aqueous Solution
## 2 Materials and Methods
### Adsorption Capacity
#### 2.3.1 Effect of the pH on the Adsorption Capacity  
We suspended 0.1 g of the adsorbent in 25 mL solution of 10 mg\(\cdot\)L\({}^{-1}\) Pb\({}^{2+}\) solution, for 1 h at 298 K. To adjust the pH of Pb\({}^{2+}\) solutions in the 1-6 range, NaOH aqueous solution was used. Studies have not been conducted at pH \(>\) 6 because there is a risk of lead precipitation. The residual concentration was determined.
## 2 Materials and Methods  
### Shingled Module Circuit Modeling  
A recently highlighted method for manufacturing PV modules is the shingled technology to enhance the output power of the module by fabricating a shingled structure of solar cells called a shingled string. This method involves cutting solar cells with a laser, connecting the divided cells in series using electrically conductive adhesives (ECA) to make a shingled string, then arranging strings to fabricate a PV module. The advantage of this method is that it allows an increase in the number of cells and the active area of the module by removal of spaces between solar cells, resulting in a higher output. In addition, it provides a more attractive exterior because there is no visible circuitry on it, and strings can be manufactured in various sizes depending on the installation location, so, it can serve as an aesthetically pleasing building cladding material [24; 25; 26]. Because of these characteristics, PV modules can be effective in generating power in areas with limited installation space, especially for the BIPV [27].  
Before fabricating the shingled BIPV module, PSpice simulation was carried out first to predict the change in output value based on the spacing between strings through a simulation model. Figure 1 shows the schematic of the double diode model and a divided cell circuit diagram for the PSpice simulation.  
For the simulation, divided cells were measured with a solar simulator (WXA-155S-L2, WACOM, Kazo, Japan) and modeled using a double diode model. Figure 2 shows the schematic illustration of a shingled string and module modeling.  
Figure 1: A double diode model and a divided cell circuit diagram.  
Seventeen divided solar cells were interconnected in series to make one shingled string, and a total of four strings were used to fabricate a module. Also, the resistance component was applied to each string to reflect the series resistance from the ECA for interconnecting the 17 divided solar cells.
## 3 Results and Discussion
### DOM and HM Bonding Capability and Its Characteristics
#### 3.3.2 Binding Capability of Humic Acid-like Substances to HM  
The improved Stern-Volmer model was used to analyze the fluorescence intensity, enabling a deeper investigation into the binding capabilities of various DOM components with different HM. The relevant fitting parameters were outlined in Table 2. Based on the fitting parameters of DOM and BC + H-DOM with Mn, the Log \(K_{M}\) values range from 2.01 to 3.68. Among them, BC-C3 in BC-DOM exhibited the strongest binding affinity for Mn, with the corresponding \(f\) value being the largest. In BC + H-DOM, BC + H-C2 showed the strongest binding ability to Mn, followed by BC + H-C3. Notably, BC + H-C1 had the highest \(f\) value, indicating that although the binding affinities of these three components are comparable, their binding sites and processes differ. BC + H-C1 may bind to weaker binding sites but maintains a higher Mn-complexing effect, while BC + H-C3 showed the opposite trend. For Cu, the Log \(K_{M}\) of BC-DOM/BC + H-DOM binding Cu ranged from 0.71 to 1.30, and the \(f\) ranged from 0.39 to 0.69, which was in agreement with the results of a previous study [18], with the Log \(K_{M}\) value of BC + H-DOM being lower than that of BC-DOM, suggesting that compared to BC + H-C1, C2, and C3, BC-C1, C2, and C3 exhibit stronger complexing ability with Cu.  
Figure 6: Fluorescence intensity of quenching of different HM by Biochar-DOM and Biochar + Human-DOM components.
# Analysis of a Collision-Energy-Based Method for the Prediction of Ice Loading on Ships
## 4 Results  
### Analysis of Data Set A  
Based on the description of the prevailing operating conditions of data set A, we calculate line ice loads for each of the collision scenarios defined in Table 2. An excerpt of the results is presented in Figure 5, comparing measured and calculated line ice loads for two different contact geometry assumptions, namely round and angular. In Figure 6, we compare loads calculated for each collision scenario with the corresponding measurements in terms of 10-min mean, standard deviation (SD) and maximum.  
Table 3 presents the percentage-deviations between the measured and the calculated 10-min mean \(\Delta_{mean}\) and extreme \(\Delta_{max}\) values. As per Table 3, we find that a relatively good agreement is obtained by assuming angular contact geometry in combination with either ship-ice floe impact having a load length of 0.4 m, or a ship-ice field impact with a load length of either 0.4 m or 0.8 m. All other combinations of assumptions result in \(\Delta_{max}\) and or \(\Delta_{mean}\) exceeding \(\pm\) 40%, indicating that  
Figure 5: Excerpt of the measured and calculated line ice loads for data set A.  
Figure 6: Comparison between loads calculated for different collision scenario assumptions with corresponding full-scale measurements in terms of 10-min mean, standard deviation (SD) and maximum. Load length 0.4 \(m\) corresponds to loads on one frame spacing (frame 134), whereas load length 0.8 \(m\) corresponds to loads on two frame spacing (frame 134 + 134.5).  
their descriptions of the ship-ice interaction significantly deviate from the actual ship-ice interactions behind data set A.
## 4 Case Example
### Step 1. Parametrize Models  
At first, the simulations models need to be parametrized. For the grid model, the following parameters have been selected, so an almost infinite power grid is implemented (Table 1).  
The battery model can be parametrized following the methodology defined in [29; 32; 33]. A commercial lithium polymer battery cell from Kokam, model SLPB100255255 HR2, has been selected for this case study, which is a standard lithium-ion pouch cell [40]. The experimental data for calculating the ageing model parameters have been obtained from [40] and [33], where the complete theoretical and experimental validation for the selected battery cell has been performed. The battery ageing model performs with an error below 5%, as detailed in [33]. The selected battery cell main parameters are shown in Table 2.  
The load needs a power consumption profile as input. For this case example, two 24-h profile on a 1-second basis have been selected, corresponding to a typical day in summer and a typical day in winter in an industrial building. The selected building is the CEDEX facility in Madrid, located at Julian Camarillo 30, 28037 Madrid (Spain). For the analysis performed in this paper, the load corresponds to a three-phase load with no unbalance between phases, whose active power profiles are shown in Figure 7 for both summer and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Reference voltage & 400 V \\ Short-circuit ratio & 25 \\ X/R ratio & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Grid model parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Value** & **Remarks** & \\ \hline Reference capacity & 55 Ah & 0.2 C, 23 °C & \\ Impedance & Max. 0.60 m\(\Omega\) & AC 1 kHz & \\ Average voltage & 3.7 V & & \\ Lower limited voltage & 2.7 V & & \\ Upper limited voltage & 4.2 V & & \\ \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ a & \(1.3961\times 10^{-7}\) & b & \(-7.7116\times 10^{-5}\) \\ c & 0.0107 & d & \(-1.2959\times 10^{-3}\) \\ e & 0.4404 & f & 164.7155 \\ g & 0.6898 & h & \(-4.2126\times 10^{3}\) \\ z & 0.5 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Battery model data and parameters [33; 40].  
winter. The cumulative energy consumed by the load for each typical day is also shown in Figure 8.