esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Evaluation Metrics  
The evaluation metric followed the regulation \"Evaluation Methods and Procedures for 2010 I2B2/VA Challenge\" [51]. We used the exact F1, exact precision, and exact recall score to evaluate the performance of our model as most works using this dataset did. \"Exact\" means that the concept  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Records** & **Sentences** & **Tokens** & **Tokens Per Sentence** \\ \hline Training set & 170 & 16,315 & 149,666 & 9.17 \\ Testing set & 256 & 27,626 & 267,758 & 9.69 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptions of training set and testing set in the 2010 Informatics for Integrating Biology & the Bedside/ Veterans Affairs (i2b2/VA) dataset.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Hyperparameters** & **Value** \\ \hline Dimension of Embeddings from Language Models (ELMo) & 1024 \\ Bidirectional Long Short-term Memory (biLSTM) hidden size & 256 \\ Number of biLSTM layers & 2 \\ Number of attention heads & 2 \\ Dropout rate & 0.5 \\ Learning rate & 0.001 \\ Batch size & 64 \\ Epochs & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters chosen in our work.  
entities extracted must match the ground-truth entities exactly both in terms of boundaries and concept type. The definitions of precision, recall, and F1 are shown below.  
\[Precision_{c}=\frac{TP_{c}}{TP_{c}+FP_{c}}, \tag{18}\]  
\[Recall_{c}=\frac{TP_{c}}{TP_{c}+FN_{c}}, \tag{19}\]  
\[\text{F1}_{c}=\frac{2*(Recall_{c}*Precision_{c})}{Recall_{c}+Precision_{c}}, \tag{20}\]  
where c is the tag of an entity; \(TP_{c}\) stands for the true positives of entity c, which means that the actual tag of this entity is c, and the predicted tag is also c; \(FP_{c}\) stands for the false positives, which means that the actual tag of this entity is not c, but the predicted tag is c; and \(FN_{c}\) stands for false negatives, which means that the actual tag of this entity is c, but the predicted tag is not c. Specifically, as we used the exact metrics, we treated a predicted entity as a true positive only if it matched both the boundary and tag type of the corresponding actual entity. Micro F1 was used to integrate all F1\({}_{c}\).
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 2 Background
### Communication Technologies for Resilience
#### 2.1.3 Transport Layer Multi-Homing  
Multi-homing connects a host to more than one network, meaning that a multi-homed host requires multiple interfaces with multiple IP addresses. Depending on the destination, it is possible to route data simultaneously through more than one network to increase the connection performance. If a single link fails, packets can still be routed through the remaining links to increase network reliability. Standardized protocols for the TCP/IP model transport layer proposed for multi-homing include Stream Control Transmission Protocol (SCTP) and Multipath Transmission Control Protocol (MPTCP).  
SCTP supports data transfer over a network on a single IP or multiple IPs using multi-homing, not for load balancing but for redundancy, thereby enabling transparent failover between redundant network paths [15]. SCTP is a solution that is capable of noticeably improving network robustness to concurrent failures, while achieving delay behavior comparable to traditional TCP, even without modifications to the default protocol settings [16].  
MPTCP offers a way to establish communication between hosts rather than between interfaces as in TCP. This protocol allows for redundancy and enables inverse multiplexing of resources, thus increasing TCP throughput through the sum of data traffic from available link-level connections called sub-flows [17]. We can implement load balancing with MPTCP to distribute traffic within networks, maximize reliability and throughput, minimize response time and avoid overloading of the systems [18].
## 3 Description of the State of Knowledge
### Combined Action of AgNPs and Known Antifungal Drugs  
So far, studies have shown that nanoparticles improve the activity of known anti-fungal drugs. The synergism between AgNPs and fluconazole was confirmed in several papers [27; 33; 43]. In turn, other studies tested the antifungal activity of AgNPs in combination with ketoconazole against Malassezia furfur. It is a representative of yeast, which naturally inhabits the human skin microbiota and is associated with several skin diseases, e.g., folliculitis, seborrheic dermatitis, and dandruff versicolor. The observations showed no antagonistic effect, and synergism was observed in 17.08% of the 40 tested M. furfur isolates [44]. Synergism between AgNPs and amphoteric B against C. _albicans_ and _C. tropicalis_ was demonstrated as well. An aqueous extract of Maytenus royleanus was used for the synthesis of these nanoparticles. The conjugation of amphotericin B with silver nanoparticles enhanced the antifungal activity of the systemic drug with an MIC value of 1.5 and 5 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, respectively [45]. The study by [PERSON] and [PERSON] attempted to identify the mechanisms of synergistic action between AgNPs and fluconazole. The results of this study showed that AgNPs can promote fluconazole accumulation in resistant _C. albicans_. Increased ROS production was identified as the probable cause of the synergy between AgNPs and fluconazole [33].
## 2 Overview of Related Supporting Projects  
The Lianhuashan Tunnel is a two-way, 6-lane, separated highway tunnel located north of Liuzhou City, Guangxi Province. It is a neighborhood tunnel (the clear distance ranges from 15.12 m to 22.12 m). The drilling and blasting methods are adopted in the tunnel construction. The excavation section is more than 150 m\({}^{2}\) and the length is approximately 1.87 km. The lithology of the tunnel site is mainly limestone and red clay. The surrounding rock grade is mainly IV and V, and the geological longitudinal section is shown in Figure 1. A supplementary survey of the left line mileage K2 + 920-K3 + 060 section and the right line mileage YK2 + 920-YK3 + 060 section of the Lianhuashan Tunnel shows that 16 of the 21 geological exploration boreholes reveal karst phenomena such as dissolution grooves, caves, and cavities. The rate of encountering caves is 84%, the rate of line karst is 16.7%, and karst is strongly developed. Karsat caves are mostly filled with clay, block, and stones, etc. Some of the karst caves invade the interior of this tunnel, some are distributed outside the contour line of the tunnel, and some are beaded by multiple karst caves. Shallowly buried karst caves or karst funnels are mostly connected to the surface. In the dry season, there is generally no groundwater in the tunnel area. In the rainy season, the infiltration of surface water will form unevenly distributed fissure water, and groundwater may infiltrate the tunnel in a point or linear manner.  
The original design methods for the Lianhuashan Tunnel are the double-side heading method and the middle partition method. Among them, the double-side heading method is suitable for poor sections of V- and IV-grade surrounding rock, and the middle partition method is suitable for general sections of IV-grade surrounding rock. Due to the need to shorten the construction period by half a year for the opening of the whole line of the project, according to engineering experience, the Lianhuashan Tunnel was changed and optimized at the initial stage of construction. The double-layer advanced small pipe, step method, and reserved core soil method were adopted for the V-grade surrounding rock section, and the single-layer advanced small pipe and step method were adopted for the IV-grade surrounding rock section. However, disasters such as collapse, large deformation, and karst cave inrush often occur during construction.
## 4 Numerical Applications
### Sensitive Analysis  
#### 4.2.1 The Effect of \(E_{max}\) of Energy Storage  
The effect of \(E_{max}\) of the energy storage system installed at different positions to system reliability is shown in Figure 8. To maintain the same reliability, the changing trends of \(P_{dchmax}\) against the \(E_{max}\) of energy storage system are the same. With the increasing of \(E_{max}\) of the energy storage, the installed \(P_{dchmax}\) can be reduced, until the \(E_{max}\) of energy storage increases to 1000 kWh, when the \(P_{dchmax}\) reduction is less pronounced. The increase of \(E_{max}\) and \(P_{dchmax}\) may improve the available capacity of energy storage under the same reliability index. Therefore, to maintain the same system reliability, \(E_{max}\) and \(P_{dchmax}\) are both positively associated with system reliability. Thus, the two factors are inversely correlated when the system reliability is determined. However, with regard to the same \(E_{max}\), a different \(P_{dchmax}\) is necessary for the different installed sites of energy storage. Furhtermore, Figure 8 shows that for a given \(E_{max}\), the desired \(P_{dchmax}\) increases when the installed locations are LP30, LP40, LP45 and DG, respectively. The desired \(P_{dchmax}\) is close when the energy storage is installed at LP40 and LP45, and is higher than that installed at LP30. The relation between the installed locations and \(P_{dchmax}\) is irrelevant to \(E_{max}\) of the energy storage, but the effect decreases with an increasing \(E_{max}\).
## 4 Biomedical Applications of ML-Assisted SWIR Techniques
### Assistance in Diagnosis
#### 4.1.1 Cardiovascular Diseases (CVDs)
ML models have been utilized to enhance the quantification of edema; a key manifestation of HF [76]. Edema arises when an excessive volume of fluid accumulates in organs due to reduced blood pumping. Molecular chemical imaging in SWIR, as a hyperspectral technique, has been performed on patients. Spectral data obtained from these analyses were processed using Partial Least Squares (PLS) ML models to: (1) differentiate spectra from healthy and HF cases and (2) quantify the degree of edema.
## 2 Review of Sampling Methods
### Sample Size Determination through Cluster Sampling  
Cluster sampling involves classifying a population into several small groups (clusters), randomly selecting a predetermined number of small groups from the clusters, and sampling from the selected groups through simple random sampling. Because cluster sampling does not sample all clusters but only a subset of them, there is a risk of selecting a biased sample if the clusters are internally homogeneous. In cluster sampling, for this reason, clusters must be heterogeneous within and homogeneous across clusters, unlike stratified sampling ([PERSON] et al., 2016; [PERSON] et al., 2018; [PERSON] et al., 2018).  
When organizing clusters, the size of each cluster should be as similar as possible to ensure that the size of the cluster is set at an appropriate level for control, and consideration should be given to reducing the on-site survey time and cost. This can be achieved by selecting a small number of clusters and taking many samples from each or selecting a large number of clusters and taking a few samples from each. The choice between these two depends on which method is more efficient in terms of the survey time and cost. Cluster sampling divides the population into multiple clusters and extracts the required number of clusters through simple random sampling. These selected clusters are then used as a procedure to construct a sample from the elements within the extracted clusters.  
[PERSON] (2022) conducted a study responding to the heightened demand for the analysis of sensor multivariate data in manufacturing settings driven by advancements in sensor technology. The research involved performing hierarchical clustering analysis based on distance correlation coefficients using process data that included binary outcomes, such as pass/fail decisions. The primary objective was to investigate the potential independence among independent variables by grouping sensors exhibiting high correlation into a unified entity ([PERSON] et al., 2021). [PERSON] et al. (2021) delved into the challenges of estimating population parameters for various ecological indicators, such as forest area, productivity, and biodiversity, within forest ecosystems. To enhance the precision of these estimations, the study explored the utility of generalized systematic adaptive cluster sampling as a remedy to the limitations associated with systematic adaptive cluster sampling. The research aimed to improve the accuracy of estimating population parameters for diverse ecological metrics in forest ecosystems ([PERSON] et al., 2021).
# A Data Processing and Distribution System Based on Apache NiFi
## 3 State of the Art
The precise goal of Apache NiFi is to fetch the input TTI traces (in the form of an archive file), retrieve proper files from the archive, and upload them to the cloud. Finally, the author of the master's thesis in [21] proposed a generic architecture that aggregated monitoring data from heterogeneous sources in a 5G infrastructure. The author implemented the architecture by using two different approaches, i.e., NGSI-LD Context Broker and Apache Kafka. In both cases, Apache NiFi was used as a data producer (collecting data from the original source and preprocessing them) and consumer (with receiving notifications being the final output of the system). One must note that all of these works focused on 5G infrastructure, but were not devoted to the usage of optical networks. Additionally, the mentioned approaches that did not utilize Apache NiFi had to use separate tools to collect and preprocess the data. Finally, the usage of Apache NiFi in the context of optical infrastructure for a 5G network has been considered, but has not been carefully investigated or validated in any research papers. Thus, to the best of our knowledge, this is the first research paper to simultaneously propose, optimize, and experimentally validate an application of Apache NiFi for the purpose of monitoring an optical network in a 5G infrastructure.
## 4 Energy Management Systems
### Rule-based Energy Management Systems
#### 4.1.8 Maximum Efficiency Point Tracking (MEPT)  
The objective of this strategy is to track the maximum efficiency of FCs [85], which is a technique similar to maximum power point tracking (MPPT) used for PV systems and wind systems. This approach is founded on a comprehensive understanding of the FC efficiency area [86], which enables the identification of the most efficient conditions for the FC, as shown in Figure 18. The ultimate goal of the EMS is to minimize the cost-effective consumption of hydrogen over a specified time period.
## 2 Materials and Methods
### Related Background  
#### 2.1.1 Discrete Fourier Transformation for Two-Dimensional Image  
In the field of signal processing, there is a fundamental principle that every signal can be decomposed into the sum of several sine waves, a concept known as the Fourier series. In the field of image processing, a common approach involves treating data as two-dimensional discrete signals, referred to as the spatial domain signals. Consider an object image with dimensions denoted by \(H\) and \(W\) for its height and width, respectively. The pixels can be organized into a matrix \(\textbf{Pix}\in\mathbb{R}^{H\times W}\). The frequency domain signal **Fre** of the **Pix** can be obtained using the two-dimensional Discrete Fourier Transform (2D DFT):  
\[\textbf{Fre}(u,v)=\sum_{m=0}^{H-1}\sum_{n=0}^{W-1}\textbf{Pix}(m,n)e^{-j2\pi( \frac{m}{H}+\frac{m}{W})}, \tag{1}\]  
where \((u,v)\) and \((m,n)\) denote the coordinates in frequency domain and original domain, respectively. Equation (1) can be further expand into a matrix multiplication form, written as:
## 5 Approaches of Optimal State Estimation Utilizing Filtering
### Cubature Kalman Filter (CKF)  
Cubature Kalman Filter (CKF) is a spherical-radial cubature rule that makes it possible to numerically compute each moment of a new state, which is encountered in the non-linear Bayesian filter [114].  
[PERSON] et al. have derived an algorithm from a third-degree spherical-radial cubature rule, which provides a position of cubature points scaling linearly with the state-vector dimension. Because of that, the authors proposed a CKF that can provide a systematic solution for high-dimensional non-linear filtering problems. Experimentally, two non-linear state estimation problems were tested. In the first problem, to calculate the second-order statistics of a non-linearly transformed Gaussian random variable, the proposed cubature rule was employed. In the second problem, the CKF was utilized to track aircraft maneuvering. Eventually, they concluded that both experiments prove the enhanced performance of the CKF compared to conventional non-linear Kalman filters [115].  
In [116], high degree non-linear CKF was considered to estimate the position and velocity of speedy UAV. From Figure 23, it can be noted that the proposed filter (CKF) achieved much better performance trajectory tracking compared with other Gaussian filters.  
In 2009, [PERSON] et al. considered the localization and target tracking problem using the Constrained Kalman Filter (CKF) based on measurements of TDOA and DOA. The study assumed that measurement noises are to be independent and identically distributed (i.i.d.). Moreover, minimum mean square error (MMSE) has been estimated by utilizing a pseudo measurement model that imposes a quadratic constraint on the state vector associated with the Ground Moving Target (GMT) dynamics. A solution to a similar quadratically constrained MMSE estimation problem was the first derivation by the authors. They recommended compensating for the hard constraint by its expectation in order to randomize the state vector for the GMT process. CKF was developed for those problems involving quadratic constraints and is appropriate for localization and tracking of GMTs and UAVs  
Figure 22: Performance comparison of considered filters for UAV tracking. Authors’ own elaboration.  
based on TDOA and DOA measurements. In this scenario, two UAVs were considered as sensors to geolocate and track the Ground Moving Targets (GMT). CKF based on TDOA and DOA measurements was proposed to achieve high accuracy of target estimation and compare the performance with conventional KF. The simulation result of the proposed CKF appears better than the conventional KF [25].
## 3 Results and Discussion
### The Removal Effect of the Mixed Filter Material
#### 3.2.3 The TOC Removal Effect  
Figure 9 shows the TOC removal effect of single volcanic rock and coconut shell activated carbon filter media and their mixed filter media over 36 days. In the initial stage of operation, the mixed filter media with a mixing ratio of 1:3 (Reactor 5\({}^{\#}\)) had the best TOC removal rate compared with other mixed filter media (Reactors 4\({}^{\#}\) and 6\({}^{\#}\)), with a removal rate of 54.97 \(\pm\) 1.08%. In previous studies, it was found that among single filter media, Reactor 3\({}^{\#}\) had the best removal effect on TOC. Through comparison, it was found that the average removal rate of Reactor 5\({}^{\#}\) was 2.15 percentage points higher than that of Reactor 3\({}^{\#}\). By mixing volcanic rock with coconut shell activated carbon filter material, a multi-level pore structure is formed, which enhances the filtration and retention effect of the filter material and improves its removal effect. After 26 days of operation of the BSF reactor, the TOC removal rate reached a stable state. Reactor 5\({}^{\#}\) has the best removal effect on TOC, which is 0.96 percentage points higher than that of Reactor 3\({}^{\#}\). This difference is masked by the standard deviations of the two, indicating that it does not significantly exceed the performance boundary of a single filter material. This is related to the influence of the uniformity of the distribution of microbial communities.  
Figure 8: The removal effect of the mixed filter material on NH\({}_{3}\)-N.
# A Survey of Data Representation for Multi-Modality Event Detection and Evolution
## 9 Future Work  
With the rapid growth of multi-modality data, online data are now considered to be more complex with the characteristic of multi-modality, cross-platform, noisy and information redundancy. Data representation became an important step of event detection tasks. Although some significant achievements have been made in this field, multi-modality event detection remains a very difficult task and further efforts can be made in the following aspects in the future.  
1. Take advantage of the diversity of multi-modality data. Multi-modality data types are not limited to modality diversity, such as textual and visual information, but also include social links and user behavior information such as comments and repost. Therefore, we should consider how to use this information in future research;
2. Event evolution problems. Although there have been many topic evolution studies on multi-modality data, which have yielded valuable results, there are still some problems such as no standard model evaluation benchmark for multi-modality topic evolution models. Therefore, an evaluation method for multi-modality topic evolution models should be proposed for the performance evaluation;
3. Lack of public datasets. Although there are some public datasets for multi-modality event detection, such as SED 2011-SED 2014 and multi-modality social event datasets mentioned above, these datasets still lack diversity. Therefore, it is necessary to develop more diversified datasets that contains more attribute information and conduct more comprehensive multi-modality event detection in the future;
4. Improve the interpretability of event detection models. The current event detection models are mostly black-box models. The model and results lack reasonable explanation. Therefore, we should focus on how to improve the interpretability of event detection models in the future;
5. Improve the data representation for multi-modality data. In existing multi-modality topic detection methods, modality fusion is mostly based on simple feature concatenation. In addition, time synchronization in different modalities is not considered. What is more, existing representations also have high time complexity, which is what we need to research in the future.  
Investigation, K.X. and B.Q.; resources, K.X. and B.Q.; writing--original draft preparation, K.X. and Z.Q.; rewriting and editing, Z.Q. and B.Q.; supervision, Z.Q. and B.Q. All authors have read and agreed to the published version of the manuscript.  
This research is funded by Humanity and Social Science Youth Foundation of Ministry of Education of China under grant number 21 YJCZH117; National Natural Science Foundation of China under Grant number 61772534 and Open Project Program of National Engineering Laboratory for Agri-product Quality Traceability under grant number AQT-2018-YB4.  
Not applicable.
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 3 AI and Ophthalmology: View of the Ensemble
### AI and Posterior Segment Diseases
#### 3.3.6 AI and Retinal Detachment  
Japanese researchers utilized a DL network to identify rheematogenous retinal detachment in fundus images obtained from Optos ultrawide-field eyes. They showed a sensitivity of 97.6% and a specificity of 96.5% [55].