esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Enabling Technologies for FSO Communication  
In the rapidly evolving field of FSO communication, various enabling technologies are pivotal in enhancing system performance and reliability. This section delves into key advancement such as AO, which corrects wavefront distortions; advanced modulation  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & & & \multicolumn{6}{c}{**Conditions**} \\ \hline
**Distribution Model** & \(\rho\) & \(\mathbf{Var}[|\mathbf{U_{L}}|]\mathbf{Var}[\mathbf{G}]\) & \(X\) & \(\Omega^{\prime}\) & \(\mathbf{Var}[\mathbf{X}]\) & \(\gamma\) & \(\Omega\) & \(\alpha\) & \(\beta\) \\ \hline Rice-Nakagami & 0 & 0 & & & & & & & \\ \hline Gamma & 0 & & & & & & & & \\ \hline Homodyned K & 0 & 0 & \(\gamma\) & & & & & & \\ \hline IT & 1 & & & 1 & & 0 & & & \\ \hline Shadowed-Rician & & & & & & 0 & & & \\ \hline Log-normal & 0 & 0 & & & & \(\to\) 0 & & \\ \hline K & 0 & & & & & 0 & & \\ \hline Exponential & 0 & 0 & & & & 0 & \(\rightarrow\infty\) & \\ \hline Gamma-Rician & & & & & & & & \(\rightarrow\infty\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Unified \(\mathcal{M}\)-distribution framework for various FSO models (adapted from [99]).  
schemes that improve data throughput; and aperture averaging to reduce signal fading. Additionally, spatial diversity and cooperative relaying techniques are explored for their roles in mitigating adverse atmospheric effects. Adaptive transmission methods and robust ECC are also discussed, highlighting their contributions to maintaining signal integrity and optimizing communication efficiency in FSO systems. Methods for mitigating atmospheric turbulence are shown in Figure 8.
# Relationship between [PERSON]'s Apparent Power and Instantaneous Power in Three-Phase Systems
## 1 Introduction
This methodology can be applied to determine Buchholz's apparent power and its components, not only for a part of the power system, but also for the entire system, thereby allowing new quantities to be obtained to measure phenomena in power systems which cannot be identified using other approaches. Thus, Section 4 describes the formulation of the power effects of the phenomena [19; 20] manifested by the neutral conductor in three-phase, wye-configured systems as an application of our proposed methodology. This novel power, referred to as neutral-displacement power, is not the neutral apparent power already defined in the literature [19; 20]; rather, it defines the amount of apparent power or impact at the sources and loads caused by neutral path operation (real or virtual) in power systems. The neutral-displacement power in three-phase, wye-configured systems depends on the zero-sequence fundamental and non-fundamental voltages at a source or load, but is independent of the neutral current. Therefore, our expression for neutral-displacement power can be applied to power systems with no neutral conductor. Section 5 analyzes a real three-phase four-wire urban installation according to the power expressions obtained from our proposed methodology in order to determine the effects of the deterioration of the neutral conductor using an Excel simulation. Finally, conclusions in Section 6 present our main contributions in this paper, namely  
1. Verification that instantaneous and Buchholz's apparent powers are formally related quantities, as only [PERSON] could demonstrate for single-phase sinusoidal systems, 120 years ago;
2. Proposal of an original methodology to fast and directly obtain the expressions of Buchholz's apparent power and its components in any three-phase system, sinusoidal or non-sinusoidal, balanced or unbalanced, star- or delta-configured systems, although the procedure is applied only to star-configured systems in this paper;
3. The methodology has been applied to obtain a novel quantity that we referred to as neutral-displacement power, which is able to determine the effects of the neutral path (real or virtual) in three-phase power systems.
## 3 Model Development
### PSO-BPNN Model
If the initial weights and thresholds are not properly assigned, the neural network tends to fall into local minima or even fail to converge. As a result, the PSO algorithm was employed to globally search the initial thresholds and weights for the BPNN. It can avoid falling into local optimal solutions by performing a global search over the entire space. Specifically, the PSO algorithm constantly updates the position and velocity of the particles while searching for the global optimal solution and selects the optimal solution as the initial parameter value of the neural network. Therefore, the PSO-optimized BPNN is capable of achieving faster convergence and higher accuracy. Moreover, cross-validation is used to assess the generalizability of the obtained neural network.
# Addressing Gaps in Small-Scale Fisheries: A Low-Cost Tracking System +
## 3 Results
### Processing and Analysis  
A total of 28 trips were identified in November 2021, starting at the moment when the fishing vessel leaves the port and ending on arrival in port. These are reported in Table 2 along with related statistics.  
Maps in Figure 4 highlight the positions where the retrieving of the gear occurs, as revealed by the hauling activity.  
Figure 4: Identified port-to-port trips and sensor activity (red points). The geofence area is represented by the gray solid line.  
The 28 trips were often of few hours (taking a maximum of 4/5 h) and were short hauling operations, characterizing the fishing activity of these vessels with two trips per day. Accordingly, in some trips no hauling activity was detected (Table 2, Figure 4), as the gear was only deployed, and its retrieving was postponed to the following trip. Examples of this include trips 3-4, trips 7-8, and trips 14-19. In other trips due to a mixed behavior of the vessel retrieving, a new setting of the gear can occur in sequence in the same place or in other place, resulting in a more confused spatial pattern of the hauler signals (e.g., trips 10, 11, 12, 20, 21, and 22).  
Checking when the vessel leaves and when it enters the geofence, most of the identified trips reported a single enter and exit event. However, in a few trips (e.g., trips 1, 2, 10, 12, and 17), the vessel entered and left the geofence twice to visit fishing grounds set on opposite sides of the designated polygon. It generated false enter/exit events and suggested how the geofence should be reshaped.  
Hailing activity (i.e., _sensor_ on; Figure 5a) indicates that gear was mostly retrieved at night and/or early in the morning (01:00-06:00) and occasionally in the evening.  
Speed profiles were strongly affected by hauling activity (Figure 5b), as speed is very low and rarely exceeds 2 km/h while retrieving the gear (i.e., _sensor_ on).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Trip** & **tripStart** & **tripEnd** & **Duration (h)** & **SA * (kmh)** & **Distance** (km)** & **Hauler (h)** & **Entry** & **Exit** \\ \hline
1 & “2021-11-01 01:40:10\({}^{\prime}\) & “2021-11-01 06:50:47\({}^{\prime\prime}\) & 5.18 & 4.27 & 47.27 & 3.03 & 2 & 2 \\ \hline
2 & “2021-11-03 07:48:34\({}^{\prime}\) &
## 4 Performance Analysis of Optimized Model
### Comparison of Optimized Model Performance  
After multi-objective optimization of the rotor structure of proposed model two, performance analysis was conducted on the optimized model. The torque of the optimized model exhibits a significant increase compared to that of proposed model two in Figure 15.  
Figure 14: Multi-objective optimization scatter plot.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Item** & **Unit** & **Value** \\ \hline PM1 length & LM1 & mm & 5.5 \\ PM2 length & LM2 & mm & 2.5 \\ PM3 length & LM3 & mm & 5 \\ PM4 length & LM4 & mm & 7.9 \\ PM5 length & LM5 & mm & 4.4 \\ PM1 width & DM1 & mm & 1.4 \\ PM2 width & DM2 & mm & 1.4 \\ PM3 width & DM3 & mm & 2  
The magnetic flux lines within the stator of the optimized model exhibit a significant increase in Figure 16a, indicating an enhanced excitation effect. The magnetic density in the internal magnetic circuit of the optimized rotor is more saturated in Figure 16b. We can see the specific performance data of the optimized model in Table 6. The optimized model has an increase of 43.85% in the RMS value of the no-load back EMF compared to proposed model two. The RMS value of no-load air gap magnetic density increased by 41.99%, the average torque increased by 18.14%, the torque ripple decreased by 5.22%, the total loss decreased by 1.35%, and the efficiency improved by 1.05%. The optimized model has improved in all aspects compared to proposed model two, indicating that the optimized model has good motor performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Unit** & **Value** \\ \hline Back EMF (RMS) & V & 256.926 \\ Air gap flux density (RMS) & T & 0.585 \\ Average torque & N-m & 56.854 \\ Torque ripple & \% & 29.932 \\ Power & W & 9473.31 \\ Total loss & W & 508.87 \\ Efficiency & \% & 94.63 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance parameters of the optimized model.  
Figure 16: (**a**) Magnetic flux line of the optimized model under no-load conditions. (**b**) Magnetic flux density of the optimized model under load conditions.  
Figure 15: Comparison of torque between proposed model 2 and optimized model.
# Design and Application of a Smart Lighting System Based on Distributed Wireless Sensor Networks
## 4 Results  
In this section, the energy-saving performance of the proposed smart lighting system in real-life conditions is evaluated. Two testing scenarios were used, metro station and office room. The first test was conducted in the metro station to check the functionality of the proposed system, and the second test was conducted to evaluate the reaction of the proposed system to environmental conditions and schedules.
# Quality of Experience Experimentation Prediction Framework Through Programmable Network Management
## 1 Introduction
The main contributions and findings of this work outline,  
* A hybrid simulation environment to run both P4 [2] and Openflow [3], along with Python 2 and 3 instances, DASH and Mininet. With all essential packages installed, an error-free test environment for running this experiment, and applying previous and similar work to compare and contrast results. Our open-source test bed configuration is shown here [4]
* A P4 SDN test bed over Mininet with the ability to control DASH initial buffering, stalling, switching, monitoring, bit rate adaptation, and bandwidth limitation over selected ports. The test bed provides the capacity for comprehensive user experiments and data collections, which lead to our insights and analysis of congestion for congestion-related experiments along with full re-configurablity over the data plane and everything mentioned above. Our open-source test bed setup is shown here [4]
* A proposed experimentation framework structure through programmable network management for the generation of ML training-ready data and MOS/QoE prediction. * Human experiment with QoE MOS-based feedback to benchmark the accuracy of the predicted QoE and network features. * Analysis of state-of-the-art machine learning algorithms, along with the creation of an experimentation framework for feature evaluation in network experiments. Our open-source analyses are shown here [4]
# Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
## 3 Materials and Methods
### The Proposed Approach  
The aim of this study is to predict ICU patients' LOSs using as input their main diagnoses and problems along with structured demographic and clinical data such as age, gender, vital signs, etc. These diagnoses are extracted from the patients' EHR daily progress notes through summarization.  
In pursuit of this objective, and as shown in Figure 1, our approach begins with summarizing EHR progress notes using NLP methods, focusing on extracting main problems and diagnoses; this method was submitted to the \"BioNLP Workshop 2023: Problem List Summarization\" challenge [10]. This task involves identifying and generating patient problems and diagnoses from the progress notes that are taken daily during the patient's stay in the ICU by doctors, nurses, etc, and is known as problem list summarization.  
The proposed summarization method consists of a hybrid technique wherein the extractive summarization step aims to extract the most important sentences from the EHR daily progress notes using several tools and techniques that will be introduced in this section, and a pre-trained T5 model that has been fine-tuned using concept masking performs the abstractive summarization.  
Following completion of the summary of the problem list, we proceed with the extraction of additional features from the dataset: the demographic data and the LOSs of the ICU patients are mapped to the EHR daily progress notes dataset. These features, such as gender, discharge location, and other relevant factors, contribute to a comprehensive understanding of the medical profile of each patient.  
Finally, the generated diagnoses and the extracted variables are used as features to predict the patients' LOSs in the ICU. Our method represents a novel approach by integrating vital signs and laboratory results into the first hybrid summarization technique proposed in this context and aimed at predicting ICU LOSs.
# Experimental Analysis of a Novel GFRP Stiffened Pipe
## 3 Stiffness Investigation of the GFRP Pipe  
### Flatwise Compression Tests of the Proposed GFRP  
Stiffness is one of the most important properties of buried GFRP pipe structures, and represents the deformation resistance and stability of the pipe [9]. The following section presents the investigations into the ring stiffness of the newly proposed pipe structure under a flatwise compressive loading condition. Because the calculation method of determining ring stiffness is not sufficiently accurate, the ISO standard stipulates that ring stiffness should be calculated by results after testing [40]. According to the ISO7685-2019 test method, the specified pipe samples were vertically compressed between two parallel plates according to the specified conditions. The initial ring stiffness \(s_{0}\) can be calculated using the following formula:  
\[s_{0}=f\times F/(L\times y), \tag{1}\]  
where \(F\) is the applied load, expressed in newtons; \(y\) is the deflection, expressed in meters; \(L\) is the length of the specimen; and \(f\) is the deflection coefficient, given by the formula:  
\[f=1860+(2500\times y/D_{m}), \tag{2}\]  
in which \(D_{m}\) is the mean diameter, expressed in meters.  
To validate the applied theory and gain an in-depth insight into the failure mechanisms of the newly developed pipe structure, flatwise compression tests were carried out. The pipe specimens were cut from the prepared pipes. As shown in Figure 6, a universal testing machine (CMTS: SHK-A309) was employed to perform the flatwise compression tests following the standard ISO7685-2019 [40]. The pipe specimens were placed between two loading plates and a concentrated force was applied on the top loading plate to uniformly distribute the load over the circular cross-section of the specimen (as shown in Figure 6). In each of the GRFP pipe specimens, four strain gauge rosettes were attached in the middle cross-section of the specimen with angles of \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\) and \(180^{\circ}\) to the vertical diameter direction, respectively, as illustrated in Figure 7. Each rosette consisted of three unidirectional strain gauges of axial, oblique and hoop, respectively, where the axial direction was aligned in the longitudinal direction of the pipe.  
Before applying displacement loads, 100 N was applied to the test specimens. Then, the corresponding deflections and loads were tared. Next, specimens were continuously loaded with a speed of 5 mm/min until final failure.  
Figure 6: Flatwise compression tests of GRFP pipe specimens: (**a**) Plain type; (**b**) outer stiffened type; (**c**) inner stiffened type.
# Coupled Numerical Method for Modeling Propped Fracture Behavior
## Materials and Methods
### Computational Fluid Dynamics  
After the fracture was filled and the deformation of proppant particles and formation was examined, fluid flow through the deformed proppend fracture was investigated using computational fluid dynamics (CFD) software. In physics and engineering, fluid dynamics describes the flow of fluids--liquids and gases. According to [PERSON]. [PERSON] [46], three approaches exist in the history of fluid dynamics. The first approach is experimental fluid dynamics, which were laid in the 18 th century in England and France. There was a gradual development of theoretical fluid dynamics in the 18 th and 19 th centuries, primarily in Europe. In the 1990s, the appearance of high-speed computers and the development of accurate numerical algorithms have revolutionized fluid dynamics. The third approach has been introduced as computational fluid dynamics. The CFD solvers are based on the fundamental governing equations of fluid dynamics. They use the continuity, momentum, and energy equations to describe the flow of fluids.  
The essence of the continuity equations is that mass is neither created nor destroyed in the control volume. The control volume is a closed volume within the finite region of the flow. The integral form of the continuity equations can be written as:  
\[\frac{\partial}{\partial t}\iiint_{\text{V}}\rho dV=-g\rho u\cdot dS, \tag{9}\]  
where \(\rho\) is the fluid density [kg/m\({}^{3}\)], u is the flow velocity [m/s] and t is time [s]. On the left side rate of change of mass can be found, while the term on the right side means the net inflow of mass.  
The momentum equations are based on [PERSON]'s second law of motion. The point is the equilibrium of the fluid's rate of change of momentum to the external forces acting on the fluid within the control volume. The integral form of momentum equations:  
\[\frac{\partial}{\partial t}\iiint_{\text{V}}\rho udV=-_{\text{S}}(\rho u\cdot dS )u-_{\text{S}}p\ dS+\iiint_{\text{V}}\rho f_{body}dV+F_{surf}. \tag{10}\]  
The term on the left side of the equation is the change of the momentum within the control volume, while on the right side, the terms represent the net inflow of momentum, total pressure, total viscous force, and total body force [46].  
The third type of equation is the energy equations, which states that the energy can convert one form to another, and the total energy in a closed system remains constant. The energy equation can be written in the following form:  
\[\rho\frac{De}{Dt}=-\
abla\cdotp-p(\
abla\cdot\mathrm{V})+\hat{Q} \tag{11}\]  
where \(e\) is the internal energy [J], \(p\) is the vector field of the heat flux by thermal conduction, and \(\hat{Q}\) [J/s] is the rate of internal heat generation by the effects such as viscous friction or radiation [47].  
For numerical calculations of the flows through the proppant particles, ANSYS CFX software was utilized.
Advanced imaging techniques, such as positron emission tomography-computed tomography (PET-CT) and magnetic resonance imaging (MRI), offer higher sensitivity, but are expensive, not always available in routine clinical settings, and may be contraindicated in certain patient populations [10]. Recent meta-analyses suggest that combining plain radiography with simple diagnostic tools, such as the probe-to-bone (PTB) test, can enhance the diagnostic accuracy [1]. Considering these diagnostic limitations, artificial intelligence (AI) and deep learning (DL) have emerged as promising tools for medical image analysis. They aim to enhance diagnostic accuracy and reduce interobserver variability [11; 12; 13]. Deep convolutional neural networks (CNNs) such as ResNet-50 have shown a strong performance in medical imaging tasks, including the detection of fractures, tumors, glaucoma, and vascular changes [14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. While AI has been widely studied in medical imaging, its application to diabetic foot complications remains limited. Most existing studies have focused on ulcer detection, segmentation, and risk prediction using clinical data and photographic images [24; 25]. Only a few have addressed the diagnosis of DFO, and those that have typically rely on advanced imaging techniques such as MRI [26]. The use of AI for DFO detection based on plain radiographs is therefore still underexplored. Our study addresses this gap by validating a ResNet-50-based DL model using histopathological confirmation as the gold standard, offering a novel and accessible diagnostic tool, particularly valuable in resource-limited settings. AI models can analyze large volumes of imaging data, detect subtle radiographic patterns that may elude the human eye, and ensure consistent, objective evaluations. This study aims to develop and validate a ResNet-50-based DL model for the automatic detection of DFO in plain radiographs of patients with DFU. This could potentially provide a robust and scalable tool for enhancing clinical decision making. To our knowledge, this is the first study to validate a ResNet-50-based deep learning model for the detection of DFO using plain radiographs, with histopathological confirmation. This represents a novel approach within the context of diabetic foot diagnostics, where AI applications remain largely underexplored.
# Semantic Features Based N-Best Rescoring Methods for Automatic Speech Recognition
## 5 Experiments
### LDA-Based Rescoring
For a topic similarity score with 40-dimension LDA, the fallibility weight reduces WER at 0.16% and 0.21% absolutely for the two testsets respectively from the same feature without fallibility weight. In addition, with fallibility, the rescoring results are less sensitive to the hyperparameter of a number of topics. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Score-Type** & **Dev93-c** & **Dev93-s** & **Dev93-i** & **Eval92-c** & **Eval92-s** & **Eval92-i** \\ \hline word probability & \(-15.16\) & \(-15.20\) & \(-16.08\) & \(-14.68\) & \(-14.68\) & \(-15.68\) \\ topic similarity & \(-0.35\) & \(-0.40\) & \(-0.36\) & \(-0.34\) & \(-0.40\) & \(-0.36\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The average scores for words with “c”, “s” and “i” tags for Dev93 and Eval92 testsets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Score-Type** & **\#Topics** & **Fallibility** & **Dev93 WER** & **Eval92 WER** \\ \hline baseline & - & - & 9.58 & 6.98 \\
2 gram & - & - & 9.57 & 6.88 \\ smallRNN & - & - & 9.44 & 6.57 \\ LDAprob & 10 & no & 9.44 & 6.59 \\ LDAprob & 10 & yes & 9.35 & 6.52 \\ LDAprob & 20 & no & 9.38 & 6.65 \\ LDAprob & 20 & yes & 9.35 & 6.52 \\ LDAprob & 30 & no & 9.39 & 6.66 \\ LDAprob & 30 & yes & 9.38 & 6.52 \\ LDAprob & 40 & no & 9.44 & 6.66 \\ LDAprob & 40 & yes & 9.36 & 6.54 \\ LDAtopsim & 10 & no & 9.53 & 6.70 \\ LDAtopsim & 10 & yes & 9.40 & 6.54 \\ LDAtopsim & 20 & no & 9.45 & 6.73 \\ LDAtopsim & 20 & yes & 9.38 & 6.61 \\ LDAtopsim & 30 & no & 9.47 & 6.75 \\ LDAtopsim & 30 & yes & 9.38 & 6.57 \\ LDAtopsim & 40 & no & 9.50 & 6.75 \\ LDAtopsim & 40 & yes & 9.34 & 6.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Word error rate (WER) results of rescoring by LDA sentence probability score.