esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
They proposed an optimized support scheme composed of H-G anchor cable, high-strength anchor bolt, W-shaped steel belt, net, and shotcrete, which achieved good results. [PERSON] et al. [22], aiming at the floor heave problem caused by high horizontal stress, proposed a solution of combining concrete-filled steel tube corner piles with floor grouting through theoretical derivation. Simulations and tests demonstrated that this solution effectively improves the performance of the floor. [PERSON] et al. [23] utilized comprehensive research methods such as mechanical experiments, in situ stress measurements, and numerical simulations to systematically study the deformation and failure characteristics of deep surrounding rock in the Wanbei mining area. They proposed a coordinated support technology integrating reinforcement support and grouting, which has achieved good results in on-site applications. Therefore, this work takes Ronghua No.1 Mine as the research background and adopts research methods of on-site measurement, numerical simulation, and on-site application. By conducting in situ stress tests in the mining area, the distribution characteristics of the in situ stress around the roadway are obtained. Meanwhile, the stability of the surrounding rock of the roadway is explored in combination with the angle between the direction of the maximum horizontal principal stress and the roadway strike, as well as the lateral pressure coefficient. According to the research results, the principle of controlling the stability of the surrounding rock in the high horizontal stress roadway is proposed, and an optimized support scheme is formulated. The roadway support is improved by comparing the numerical simulation effects before and after optimization. Subsequently, the optimized support scheme is applied on-site, and data monitoring is carried out for a period of 6 months. By comparing the deformation amounts of the roof, two sides, and the floor, the feasibility of the obtained support scheme is judged, providing reliable technical support and practical guidance for the roadway support in Ronghua No.1 Mine.
## 3 Results and Discussion
### Single-Notch Filter with Infinite Rejection  
Stopband rejection can be greatly improved by fine-tuning \(\lambda\) according to Equation (21). For parameters in Table 1, the value of \(\lambda\) required for infinite rejection is 0.79, well within range for the coupling tuning mechanism. Figure 7b compares the simulated and measured scattering parameters obtained in this case. A performance improvement of over 50 dB is achieved compared with that in Figure 7a, with 70 dB rejection at port 2. This improvement is at the expense of reduced isolation of port 3 with a minimum attenuation of 18 dB at resonance between ports 1 and 3. However, losses in \(S_{21}\) are still extremely low.
## 5 Feasibility Study Through System Simulations
### NN Structure for Each Pattern-Matching Method and Model Training Details
#### 5.8.2 NN Structure for Pattern-Matching Method 3-MultFingIn-PosOut  
In pattern-matching method 3-MultFingIn-PosOut, the mapping from the fingerprints to the position coordinates is solved as a regression problem. The NN structure consists of an FFNN with \(N_{ST}\times N_{F}\) inputs, with one for each fingerprint from each sensor in the 2D sensor array, and two outputs for the estimated position coordinates. The pattern-matching method is explained in Section 4.4.3 and summarized in Table 6 (Section 4.4).  
Pattern-matching method 3-MultFingIn-PosOut implements the FFNN shown in Table 12. The model is trained with the RMSprop optimizer, with a MAE cost function. The RMSprop optimizer performs the optimization in the training of the model with an adaptive learning rate.
# Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
## 6 Discussion  
This study proposed a DTM for bridges that integrates spatial, temporal, and expert knowledge models. This model, jointly driven by data and expert knowledge, enables precise monitoring and prediction of bridge structural health. In comparison with traditional DTM, this study's proposed model offers enhanced proactive state assessment capabilities, thereby advancing the development of smart O&M systems.  
In contrast to prior studies, this paper modeled data separately in spatial and temporal dimensions. The spatial model recorded real-time bridge state data, while the temporal model forecasted trends in bridge state data. Furthermore, integrating an expert knowledge model enhanced the DTM's logical reasoning capabilities, allowing for the assessment and prediction of bridge health conditions. Through the establishment of a data-driven module and an expert knowledge evaluation module, the model's proactive capability in bridge health monitoring was significantly enhanced.  
The proposed DTM improves the bridge maintenance process in several key ways. First, by leveraging real-time data acquisition, the model continuously monitors bridge health, allowing early detection of potential issues. The temporal model further analyzes data trends to predict future changes in bridge deterioration at both surface and internal levels. Additionally, integrating an expert knowledge model enhances logical reasoning, enabling more efficient monitoring and prediction of bridge status. Leveraging bridge state predictions, the model optimizes O&M workflows to reduce unnecessary inspections, improving resource allocation. This proactive approach helps prevent sudden damage and minimize long-term degradation, ultimately extending the bridge's lifespan.  
Despite advancements in applying DT technology in this study, certain limitations and challenges persist. Data accuracy critically influences the overall performance of the DTM, especially in large-scale bridges, where data volumes are extensive. The integration speed of multi-source heterogeneous data affects the model's real-time performance. Additionally, managing non-deterministic and specialized bridge expert knowledge during expert knowledge base construction poses challenges. The robustness of machine learning algorithms in the model largely depends on training data quality and diversity, potentially impacting the model's generalizability across bridge types and environmental conditions.  
Figure 9: Experimental process for the application of the DK-DTM framework to the Hedong Bridge.
# Speaker Diarization: A Review of Objectives and Methods
## 9 Ways to Evaluate Speaker Diarization  
Measuring success for SD is more complicated than for other speech applications. For example, speech coders and synthesizers seek high intelligibility and naturalness. ASR and SV seek minimal word and speaker identity errors, respectively (all, of course, wish to minimize cost and latency). However, SD involves both timing estimation and speaker recognition; it is not immediately clear which is more important. A common measure of SD success is the _Diarization Error Rate_ (DER). DER treats three error types with equal weight: _false alarm_ (FA: section of audio labelled incorrectly as speech), _miss_ (duration of speech not so labelled), and _confusion_ (portion of speech with mislabelled speakers). Thus, DER = (FAs + Misses + Confusions)/Total Duration of audio.  
DER does not explicitly judge whether time stamps are appropriate, i.e., penalize errors in locating times where a speaker stops/starts; it examines the average of labelling accuracy over all frames. Indeed, some SD challenges allowed error deviations in time stamps via a _forgiveness collar_, which meant ignoring small timing errors (e.g., under 250 ms). This \"score collar\" around every boundary of reference segments was intended to mitigate effects of inconsistency in reference transcriptions.  
Other SD measures include WDER, which is a version of the DER measure based on words, rather than audio durations. The _Jaccard error rate_ (JER), as in the DIHARD II set, is based on the Jaccard index, a similarity measure used to evaluate the output of image segmentation systems. JER = (FAs + Misses)/Total; so, it is DER, not counting confusions. DER values typically exceed 10% on most standard datasets (see next section) and can range much higher on some.
# Machine Learning-Based Model for Emergency Department Disposition at a Public Hospital
###### Abstract  
With the increasing global demand for artificial intelligence solutions, their role in medicine is also expected to grow as a result of their advantage of easy access to clinical data. Machine learning models, with their ability to process large amounts of data, can help solve clinical issues. The aim of this study was to construct seven machine learning models to predict the outcomes of emergency department patients and compare their prediction performance. Data from 75,803 visits to the emergency department of a public hospital between January 2022 to December 2023 were retrospectively collected. The final dataset incorporated 34 predictors, including two sociodemographic factors, 23 laboratory variables, five initial vital signs, and four emergency department-related variables. They were used to predict the outcomes (mortality, referral, discharge, and hospitalization). During the study period, 316 (0.4%) visits ended in mortality, 5285 (7%) in referral, 13,317 (17%) in hospitalization, and 56,885 (75%) in discharge. The disposition accuracy (sensitivity and specificity) was evaluated using 34 variables for seven machine learning tools according to the area under the curve (AUC). The AUC scores were 0.768, 0.694, 0.829, 0.879, 0.892, 0.923, and 0.958 for Adaboost, logistic regression, K-nearest neighbor, LightGBM, CatBoost, XGBoost, and Random Forest (RF) models, respectively. The machine learning models, especially the discrimination ability of the RF model, were much more reliable in predicting the clinical outcomes in the emergency department. XGBoost and CatBoost ranked second and third, respectively, following RF modeling.  
emergency department; machine learning; modeling; artificial intelligence; prediction Article  
202515628  
1
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 3 GCS-Routing Design
### GCS-Routing Overview  
The operations in GCS-routing were divided into two major phases: initialization and maintenance. In the initialization phase, GCS generates routing table for each drone in a centralized manner based on the distance between drones. Since LoS environment is usually guaranteed in FANET, it is reasonable to estimate the link cost based on distance. In the maintenance phase, GCS updates and sends routing tables if the network topology changes due to the drones' mobility. Since GCS knows future drone locations, future topology can also be predicted.  
In some cases, LoS may not be guaranteed. To deal with this problem, each drone monitors link failures between itself and its neighbors by counting the number of received beacons from the neighboring drones' AP interface (not their ad-hoc interface). Broadcasting beacons does not affect routing performance in ad-hoc networks because periodic beacon broadcasts by AP interfaces are enabled by default. When a drone detects an obstacle or a link failure, it reports the failures to GCS. The GCS then updates the link cost and transmits the new routing table to the corresponding drones.  
Figure 2 shows the proposed GCS-routing architecture composed of two main components: GCS and drones. The GCS consists fleet management and route management modules. The fleet management module plans and generates flight commands to each drone, tracks drone position and movement schedules, and hands this information to the route management module. The route management module constructs routing tables and updates link cost in a centralized manner. The GCS then sends routing tables to each corresponding drone through the ad-hoc interface.  
Drones include communication and flight management modules. The communication management module updates its routing table using route information sent from GCS,and continuously monitors link failures between itself and neighbors. If link failure is detected, error reports are sent to GCS through the ad-hoc interface. The flight management module relays flight commands from GCS to the drone and reports sensor information to GCS.
# Review of Magnetoelectric Effects on Coaxial Fibers of Ferrites and Ferroelectrics
## 4 Spinel Ferrite-Ferroelectric Coaxial Fibers
### NiFe\({}_{2}\)O\({}_{4}\)-PZT Core-Shell Nanofibers
The strength of DME in NFO-PZT was also measured by the magneto-dielectric effect, i.e., H-induced variation in the real part of relative permittivity \(\epsilon_{\rm r}\)\({}^{{}^{\prime}}\) of a film of the composites assembled in a magnetic field gradient produced by a permanent magnet [39]. Figure 8a shows an SEM image of such a film of sample B. Results of the variation of \(\epsilon_{\rm r}\)\({}^{{}^{\prime}}\) vs. f for a series of H at 20-22 GHz, well above FMR frequencies for the fibers, are shown in Figure 8b. The procedure  
Figure 6: (**a**) XRD pattern of NFO-PZT core–shell nanofibers. SEM images of fibers with (**b**) NFO shell–PZT core and (**c**) PZT shell–NFO core. [39] (**d**) Scanning microwave microscope (SMM) image of capacitance at 5.4 GHz for a fiber with an NFO core and a PZT shell [68]. Figure 7: (**a**) SEM image showing the alignment of fibers in disc of fibers of sample B pressed in a magnetic field of 3 kOe. The arrow represents the direction of H. (**b**) P vs. E data for the disc in an applied H up to 7 kOe. (**c**) Fractional change in the remnant polarization P\({}_{\rm r}\) as a function of H for the disc for increasing and decreasing H [39]. for the measurements is described in [39]. The fractional changes in \(\epsilon_{\mathrm{r}}^{{}^{\prime}}\) under H at 21 and 22 GHz are shown in Figure 8c. A decrease in \(\epsilon_{\mathrm{r}}^{{}^{\prime}}\) under H is evident and is much higher than the reported values for single phase multiferroics [1,2]. The converse ME (CME) effects on composites were investigated by measuring the magnetization as a function of H under an electric field or the shift in the FMR resonance field or frequency under E [4]. [PERSON] et al. [68] employed an SMM for FMR in an isolated fiber of an NFO core and a PZT shell, as shown in Figure 9a. The amplitude and phase of the reflected microwave power (scattering matrix S\({}_{11}\)) from the fiber at 5.4 GHz was recorded as the static magnetic field H applied to the fiber was scanned from 0 to 2 kOe. The FMR at the resonance field H\({}_{\mathrm{r}}\) manifested as a dip in the amplitude and an 180\({}^{0}\) phase change in the S\({}_{11}\) vs. H profile, as seen in Figure 9b. The FMR profiles were then recorded as a function of applied DC voltage V, and the dependence of H\({}_{\mathrm{r}}\) on V is shown in Figure 9c. One observes a small increase in H\({}_{\mathrm{r}}\) as V is increased from 0 to 2 V and is followed by a linear decrease in H\({}_{\mathrm{r}}\) with increasing V. The voltage-CME coefficient A\({}_{\mathrm{v}}\) = dH\({}_{\mathrm{r}}\)/dV= \(-\)24 Oe/V was determined from the slope of the linear regime, as shown in Figure 8c [68], and is either comparable or higher than the reported A\({}_{\mathrm{v}}\) for several ferromagnetic-ferroelectric composites [50].
## 5 Results and Discussion
### Interpretability: Unpacking Proficiency Dynamics  
A core advantage of the MHMM over black-box models such as LSTMs is its interpretability. The model's learned parameters are not abstract weights but tangible probabilities that offer direct insights into proficiency dynamics. Figure 7 visualizes the dynamic transition matrices under different simulated contexts.  
The heatmaps show how the probabilities of transitioning between proficiency states change from the Baseline condition in response to high complexity and collaboration intensity (Collab) and fatigue and decay.  
These matrices provide quantifiable, actionable metrics:  
* Baseline Learning Rates: Under standard conditions, the probability of a Novice transitioning to Intermediate, as defined by the transition matrix in Equation (2), is 15%, implying an expected dwell time of approximately six to seven procedures in that state.
* Contextual Adjustments: The model quantifies how context impacts proficiency. High task complexity reduces the Novice-to-Intermediate transition probability from 15% to 9%. The onset of fatigue and skill decay increases the probability of an Expert regressing to Intermediate from a mere 1% to 12%.  
Additionally, in Figure 8, visually represents the most commonly observed state-to-state transitions for a sample participant. This bar chart effectively highlights the most frequent pathways of proficiency change (e.g., \"Novice \(\rightarrow\) Novice\" or \"Intermediate \(\rightarrow\) Expert\"), offering an intuitive overview of the predominant flow of proficiency states within the simulated trajectory.  
Figure 8: Top most frequent state transitions (Sample Participant).  
Figure 7: Dynamic transition matrices of the MHMM (Expert System) under different contexts.  
This level of transparency allows trainers and system designers to understand why a model makes a certain prediction. It enables data-driven interventions (e.g., scheduling rest breaks when the regression risk is high) and fosters trust in the AI system, a critical factor for adoption in high-stakes environments.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Elements** & **PCI** & & **CF** & **EF (Mean \(\pm\) SD)** & & **Ige (Mean \(\pm\) SD)** \\ \hline Na & 0.24 & low contamination & **0.08** & low contamination & 0.10 \(\pm\) 0.05 & no enrichment & \(-4.38\pm 0.63\) & uncontaminated \\ \hline Mg & 1.40 & reasonable contamination & 0.55 & low contamination & 0.68 \(\pm\) 0.27 & no enrichment & \(-1.55\pm 0.54\) & uncontaminated \\ \hline Al & 1.01 & reasonable contamination & 0.80 & low contamination & 1.00 \(\pm\) 0.05 & slight enrichment & \(-0.90\pm 0.18\) & uncontaminated \\ \hline Si & 0.84 & low contamination & 0.68 & low contamination & 0.85 \(\pm\) 0.18 & no enrichment & \(-1.17\pm 0.20\) & uncontaminated \\ \hline P & 4.11 & severe or very severe contamination & 1.41 & reasonable contamination & 1.84 \(\pm\) 1.11 & slight enrichment & \(-0.24\pm 0.76\) & uncontaminated \\ \hline S & 2.46 & reasonable contamination & 0.89 & low contamination & 1.15 \(\pm\) 0.67 & slight enrichment & \(-0.91\pm 0.73\) & uncontaminated \\ \hline K & 1.36 & reasonable contamination & 0.89 & low contamination & 1.12 \(\pm\) 0.23 & slight enrichment & \(-0.78\pm 0.37\) & uncontaminated \\ \hline Ca & 2.34 & reasonable contamination & 0.34 & low contamination & 0.42 \(\pm\) 0.51 & no enrichment & \(-2.71\pm 1.14\) & uncontaminated \\ \hline Cr & 8.34 & severe contamination & 5.34 & considerable contamination & 6.55 \(\pm\) 1.49 & reasonably high & 0.39 \(\pm\) 0.41 & tends to moderately \\ \hline Mn & 14.83 & very severe contamination & 2.17 & reasonable contamination & 2.66 \(\pm\) 2.85 & slight enrichment & 1.76 \(\pm\) 0.34 & moderately contaminated \\ \hline Fe & 4.09 & severe contamination & 1.75 & reasonable contamination & 2.15 \(\pm\) 0.73 & slight enrichment & 0.12 \(\pm\) 0.92 & tends to moderately \\ \hline Ni & 8.55 & severe contamination & 5.68 & considerable contamination & 6.95 \(\pm\) 1.93 & reasonably high & 0.11 \(\pm\) 0.61 & tends to moderately \\ \hline Cu & 16.60 & very severe contamination & 6.39 & high or very high contamination & 7.82 \(\pm\) 2.50 & reasonably high & 1.86 \(\pm\) 0.38 & moderately contaminated \\ \hline Zn & 5.59 & severe contamination & 2.37 & reasonable contamination & 2.91 \(\pm\) 0.89 & slight enrichment & 2.00 \(\pm\) 0.38 & tends to highly \\ \hline Rb & 42.95 & very severe contamination & 6.56 & high or very high contamination & 8.20 \(\pm\) 6.95 & reasonably high & 0.57 \(\pm\) 0.45 & tends to moderately \\ \hline Sr & 1.48 & reasonable contamination & 0.76 & low contamination & 0.10 \(\pm\) 0.05 & no enrichment & 1.82 \(\pm\) 0.96 & moderately contaminated \\ \hline \end{tabular}
\end{table} 
Table 4: Background concentration of individual elements and calculation of eco-environmental indices.
# Employing a Chatbot for News Dissemination during Crisis: Design, Implementation and Evaluation
## 1 Introduction  
A \"bot\" (a shortening of \"robot\") is any automated software that responds to incoming information or data, using predetermined rules and/or artificial intelligence to select a response [1]. Chatbots refer to any software application that engages in a dialog with a human, using natural language. The term is most often used in connection with applications that converse via written language. However, with advances in speech recognition [2], they can be programmed to respond differently to certain keywords or employ machine-learning techniques to adapt their responses based on words included in queries [3; 4; 5].  
For several years, social, conversational bots have been used to provide benefits to companies, who use them to reduce time-to-response and provide enhanced customer service, increasing satisfaction and engagement [6]. Chatbots consist of a specific AI-based software category developed by companies, to automate communications and management of transactions with their customers [7; 8]. Use of this technology in news media platforms, although relatively recent, offers many advantages to journalists and media professionals and, at the same time, facilitates users' interaction with useful and timely information. Initially, when newsbots were interacting with users on social media platforms, such as Twitter and Facebook, they tended to mainly function as re-broadcasters of traditional news content to social media [9], namely to alert, aggregate and monitor content for their users. However, news chatbots have moved a long way forward and are now able to offer a variety of informational services to their users, from the dissemination of information to data categorization and taxonomy.  
This study aims to show the usability of a news chatbot during a crisis situation and uses the 2020 pandemic crisis of COVID-19 as a case study. Drawing from [PERSON] et al. (2010), [PERSON] and [PERSON] (2011), and [PERSON] and [PERSON]'s (2017) ([PERSON], 2017) studies regarding the use of chatbots in crisis situations, the basic targets of this research are to design and implement a chatbot in a news media platform with a two-fold aim in regard to evaluation: first, the technical effort of creating a functional and robust news chatbot in a crisis situation, both from the AI perspective and interoperability with other platforms, which constitutes the novelty of this approach; and, second, users' perception regarding the appropriation of this news chatbot as an alternative means of accessing existing information during a crisis situation. The news chatbot designed was evaluated in terms of effectively fulfilling the social-responsibility function of crisis reporting to deliver timely and accurate information regarding the COVID-19 pandemic to a wide audience. In this light, this study shows the advantages of implementing chatbots in news platforms during a crisis situation, when the audience's needs for timely and accurate information rapidly increase.