esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
###### Abstract  
Electronic health records (EHRs) are a critical tool in healthcare and capture a wide array of patient information that can inform clinical decision-making. However, the sheer volume and complexity of EHR data present challenges for healthcare providers, particularly in fast-peaced environments such as intensive care units (ICUs). To address this problem, the automatic summarization of the main problems of patients from daily progress notes can be extremely helpful. Furthermore, by accurately predicting ICU patients' lengths of stay (LOSs), resource allocation and management can be optimized, allowing for a more efficient flow of patients within the healthcare system. This work proposes a hybrid method to summarize EHR notes and studies the potential of these summaries together with structured data for the prediction of LOSs of ICU patients. Our investigation demonstrates the effectiveness of combining extractive and abstractive summarization techniques with a concept-based method combined with a text-to-text transfer transformer (T5), which shows the most promising results. By integrating the generated summaries and diagnoses with other features, our study contributes to the accurate prediction of LOSs, with a support vector machine emerging as our best-performing classifier with an accuracy of 77.5%, surpassing existing systems and highlighting the potential for optimal allocation of resources within ICUs.  
natural language processing (NLP); text summarization; electronic health records (EHR); intensive care unit (ICU); length of stay (LOS); MIMIC-III; classification
# Fiber-Based Sensors and Energy Systems for Wearable Electronics
## 4 Fiber-Based Energy-Storage Systems
### Fiber-Based Battery  
Lithium-ion batteries have made remarkable advances in human life over the past 20 years. Many portable devices, for instance, mobile phones, laptop computers, smart-watches, wireless earphones, and even vehicles, contain rechargeable batteries based on  
Figure 5: Temperature sensing fiber. (**a**) Schematic of an RGO-based fiber knitted into the fabric and worn on the arm. (**b**) The fiber showed fast response time (7 s) and a great recovery time of (20 s). Reproduced with permission from [38], Copyright (2018), Wiley. (**c**) Cross-sectional micrograph image of the chalcogenide-based fiber. (**d**) The \(8\times 8\) fiber array detected heating locally with the touch of a finger and cooling with a piece of ice. Reproduced with permission from [25], Copyright (2006), Wiley. lithium ion. Recently, many studies have fabricated flexible LIBs, especially fiber-based lithium-ion batteries (FBLIBs) [136; 137; 138]. For ideal FBLIBs, safety and electrochemical and mechanical properties should be considered. In order to maintain stable electrochemical performances even with repeated external forces, all components, including cathode, anode, separator, and electrolyte of FBLIBs, should have sufficient flexibility and fatigue resistance. In many cases, MWCNT is used as an active material (anode) or conductive framework combined with other active material particles (cathode), due to its remarkable mechanical and electrical properties. For example, the FBLIB was fabricated by twisting an aligned MWCNT/MnO\({}_{2}\) fiber and a lithium wire as cathode and anode, respectively (Figure 6a) [139]. However, FBLIBs with MWCNT had unsatisfactory scalability and specific/volumetric capacities. The intrinsically low capacities originate from the dead volume as MWCNT does not store energy when it is used in the cathode. To store more energy, [PERSON] et al. proposed a silicon (Si)-based FBLIB with a theoretical capacity of Si is \(\sim\)4200 mAh/g (MWCNT: \(\sim\)300 mAh/g) (Figure 6b) [48]. In the study, they coated a thin layer of Si on the aligned MWCNT by CVD to form a core-sheath structured fiber. However, this fiber battery was a half cell, and most of all, the capacity retention needed to be significantly improved for wide applications.
# Electron Beam Characterization of REBCO-Coated Conductors at Cryogenic Conditions
## 2 Material and Methods
### Sample Analysis
#### 2.2.1 Surface Morphology Imaging via SEM  
Figure 2 presents the SEM images of the two different REBCO tape samples, PLD-deposited (left) and RCE-deposited (right). The apparent surface texture and morphology differences can be attributed to the different deposition methods. The high contrast of nanocrystals against the Gd-Ba-Cu-O substrate in the background is likely due to a combination of relatively higher atomic mass (higher Ba and Gd content) and a localized charging effect due to the dielectric nature of the oxide nanocrystals.  
The RCE coating process produces sharp uniformly sized and spaced nanocrystals, while the PLD coating process gives a wide range of oval-shaped nanocrystal sizes spaced at irregular intervals. These nanocrystals uniformly cover the entire surfaces of both samples.
## 1 Introduction
DEM, an efficient method to study the particle-packed structure in recent years, can construct the real-packed and compressed processes of particles [29]. The DEM-CFD coupling method, which has been widely used in biomass drying [30], chemical mass transfer [31], and nuclear reaction [32], can accurately simulate airflow and heat transfer processes in the packed bed. In this research, an apparatus was designed to measure the airflow resistance through soybeans under vertical compression. Soybean pile units at different locations in grain silos were used as the research objects. The effects of the pressure drop characteristics of soybeans at 50, 150, and 250 kPa vertical pressures were studied through vertical compression and aeration tests. The DEM was used to simulate the random packing and compression process of soybeans, and the airflow velocity and pressure drop of soybeans at different vertical pressures were simulated and analyzed by the DEM-CFD method. The airflow path distribution and pressure drop equation coefficients were predicted based on the PathFinder code. The simulated and equation-calculated pressure drops were compared with experiments to verify their reliability.
## 2 Temperature Variation in Particle Flow Modeling Based on Energy Analyses
### Heat Conduction Effect  
Heat conduction will occur when there is a temperature difference between the contacted particles; therefore, the heat conduction effect cannot be ignored when computing a change in temperature. The heat conduction effect does not cause a change in total heat, but only transfers heat from one particle to another.  
In the particle flow modeling, the thermal material is represented as a network of heat reservoirs (associated with each particle) and thermal contacts (associated with mechanical contacts). Heat flow occurs by means of the conduction in the active thermal contacts connecting reservoirs. Each particle represents a heat reservoir, and each reservoir has a series of properties including temperature, mass, volume, specific heat capacity, and coefficient of linear thermal expansion. The thermal contact connects two heat sources, and the heat flow occurs only through the thermal contact [51].  
Although temperature change may occur due to strain change, it should be noted that this temperature change can be assumed to be negligible for quasi-static mechanical problems. The heat conduction effect can be described with the heat conduction equation for a continuous medium [51]:  
\[-\frac{\partial q_{i}}{\partial x_{i}}+q_{\rm v}=\rho C_{\rm v}\frac{\partial T}{ \partial t} \tag{11}\]  
where \(q_{i}\) is the heat flux vector, \(x_{i}\) is the location, \(q_{\rm v}\) is the volume heat source intensity or power density, \(\rho\) is the mass density, \(C_{\rm v}\) is the specific heat capacity at constant volume, \(T\) is the temperature, and \(t\) is the time.  
Note that for nearly all solids and liquids, the specific heat capacities at constant pressure and at constant volume are essentially equal. Consequently, \(C_{\rm p}\) and \(C_{\rm v}\) can be used interchangeably [51].
# Context-Aware Wireless Sensor Networks for Smart Building Energy Management System
## 4 Proposed EMS for SEEB
### WSN Network Packet Delivery Evaluation
#### 4.5.3 Signal Power Test and Distance Measurement
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{\(\text{RSSI}\)} & \multirow{2}{*}{\(\text{RSSI}\)} & \multirow{2}{*}{\(\text{RSSI}\)} & \multirow{2}{*}{\(\text{RSSI}\)} & \multirow{2}{*}{\(\text{RSSI}\)} & \multirow{2}{*}{\(\text{RSSI}\)} & \multirow{2}{*}{\(\text{RSSI}\)} & \multirow{2}{*}{\(\text{RSSI}\)} \\ \cline{5-6} Room 002 & & & & & & & & & & \\ \cline{5-6}  & & 8.50 & 31.73 & 36.43 & 36.43 & 29.61 & 27.63 & 19.30 & 17.87 & 39.04 \\ \hline \multirow{2}{*}{Room 002} & \multirow{2}{*}{\(\text{3.20}\)} & 3.20 & 28.85 & 27.9 & 30.63 & 23.10 & 35.63 & 29.13 & 12.76 & 30.99 \\ \cline{5-6}  & & & 36.43 & 39.04 & 41.84 & 34.00 & 24.06 & 15.08 & 15.08 & 36.43 \\ \cline{5-6} Room 003 & & & 27.60 & 28.08 & 31.14 & 26.08 & 33.09 & 25.30 & 12.33 & 29.15 \\ \cline{5-6}  & & & 13.84 & 19.55 & 17.03 & 11.24 & 4.77 & 9.21 & 4.09 \\ \hline \multirow{2}{*}{Room 010} & \multirow{2}{*}{\(\text{13.28}\)} & \multirow{2}{*}{\(\text{18.11}\)} & \multirow{2}{*}{\(\text{13.28}\)} & \multirow{2}{*}{\(\text{10.60}\)} & \multirow{2}{*}{\(\text{7.50}\)} & \multirow{2}{*}{\(\text{16.53}\)} & \multirow{2}{*}{\(\text{5.02}\)} \\ \cline{5-6}  & & & 13.28 & 18.11 & 13.28 & 10.60 & 7.50 & 16.53 & 5.02 \\ \cline{5-6}  & & & & 7.42 & 8.50 & 6.03 & 15.08 & 22.45 & 11.24 \\ \cline{5-6} Room 014 & & & & 3.60 & 4.08 & 3.30 & 17.20 & 20.91 & 12.30 \\ \hline \multirow{2}{*}{Room 015} & \multirow{2}{*}{\(\text{15}\)} & \multirow{2}{*}{\(\text{15}\)} & \multirow{2}{*}{\(\text{17.78}\)} & \multirow{2}{*}{\(\text{27.63}\)} & \multirow{2}{*}{\(\text{22.45}\)} & \multirow{2}{*}{\(\text{6.00}\)} & \multirow{2}{*}{\(\text{6.90}\)} & \multirow{2}{*}{\(\text{21.06}\)} \\ \cline{5-6} Room 016 & & & & & & & & & \\ \cline{5-6}  & & & & & & & & & \\ \hline \multirow{2}{*}{Room 016} & \multirow{2}{*}{\(\text{1}\)} & \multirow{2}{*}{\(\text{1}\)} & \multirow{2}{*}{\(\text{1}\)} & \multirow{2}{*}{\(\text{1}\)} & \multirow{2}{*}{\(\text{1}\)} & \multirow{2}{*}{\(\text{1}\)} & \multirow{2}{*}{\(\text{1}\)} & \multirow{2}{*}{\(\text{9}\)} \\ \cline{5-6}  & & & & & & & & & & \\ \cline{5-6}  & & & & & & & & & & \\ \hline \multirow{2}{*}{Router 2} & \multirow{2}{*}{\(\text{Real distance (m)}\)} & \multirow{2}{*}{\(\text{19.30}\)} & \multirow{2}{*}{\(\text{19.43}\)} & \multirow{2}{*}{\(\text{1}\)} & \multirow{2}{*}{\(\text{1}\)} & \multirow{2}{*}{\(\text{1}\)} & \multirow{2}{*}{\(\text{1}\)} & \multirow{2}{*}{\(\text{1}\)} \\ \cline{5-6}  & & & & & & & & & & \\ \hline \multirow{2}{*}{Router 3} & \multirow{2}{*}{\(\text{1}\)} & \multirow{2}{*}{\(\text{1}\)} & \multirow{2}{*}{\(\text{1}\)} & \multirow{2}{*}{\(\text{1}\)} & \multirow{2}{*}{\(\text{1}\)} & \multirow{2}{*}{\(\text{1}\)} & \multirow{2}{*}{\(\text{1}\)} & \multirow{2}{*}{\(\text{1}\)} & \multirow{2}{*}{\(\text{1}\)} \\ \cline{5-6}  & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Distance based on RSSI versus actual distance in meters.
# A Time Bank System Design on the Basis of Hyperledger Fabric Blockchain
## 3 System Overview  
### Overview of the Application Scenarios  
The time bank system presented in this work includes several different entities. The first one is the service receiver (SR), who upon needing help sends a service demand (or request) to the blockchain network. The second one is the service provider (SP) who is capable of providing some services within some specific time periods. SPs post when they are available and what kinds of services they can supply (provide) to the Blockchain. SRs and SPs are the majority members of a time bank, and there are a handful of special entities called grading managers. Grading managers are responsible for managing secret keys and using those keys to reveal some members' grades which were encrypted on the ledger.  
When we say a service-exchange process is complete, it means that the fulfillment of a matched demand-and-supply service pair is accomplished. To complete a service-exchange, the entities of a time bank system have to follow the processing steps shown in Figure 1.  
* Step 1: SRs and SPs post their service supply-and-demand proposals on the ledger. After a proposal is verified by the blockchain, the system tries to match it up with all other not-yet-matched service proposals already recorded on the ledger.
* Step 2: If a provider's supply matches a receiver's demand, that specific service supply-and-demand pair is said to be settled (or the service-exchange is paired for short). In other words, the deal of a service-exchange pair between an SP (say [PERSON]) and an SR (say [PERSON]) is made.
* Step 3: After their service-exchange is paired, [PERSON] pays [PERSON] some amount of pre-negotiated time credits (or time coins). The balance records in their on-ledge wallets will be updated accordingly.
* Step 4: In the end, both SP and SR grade each other. That is, [PERSON] grades [PERSON] according to the provided service quality; likewise, [PERSON] grades [PERSON] according to [PERSON]'s attitude while receiving the service. All the grades will be encrypted and recorded on the ledger.
# SambotII: A New Self-Assembly Modular Robot Platform Based on Sambot
## 2 Mechanical Structure of SambotII
### Autonomous Mobile Body  
The autonomous mobile body of SambotII is a cube with four passive docking surfaces (except for the top and the bottom surfaces). Each passive docking surface contains four RGB LED lights and a pair of grooves. The hooks on the active docking surface of a SambotII robot can stick into the grooves of a passive docking surface of another SambotII robot to form stable mechanical connection between them. The LED lights are used to guide the active docking robot during the self-assembly process. Also, they are used to identify different passive docking surfaces by multiple combinations of colors. In addition, two wheels on the bottom surface of the main body provide mobility for SambotII.
## 4 Experiments
### Quantitative Evaluation
Some combinations without considering scale variation such as PB and AU were detrimental to improving the tracking performance, and the proposed method combining all components performed better than any other combination. The OTB-100 contains sequences with attributes such as illumination variation (IV), scale variation (SV), occlusion (OCC), deformation (DEF), motion blur (MB), fast motion (FM), in-plane rotation (IPR), out-of-plane rotation (OPR), out-of-view (OV), background clutter (BC) and low-resolution (LR). Figure 7 shows the average success rate of trackers for each attribute and demonstrates that the proposed method outperformed the existing trackers for all attributes. To consider the real-time tracker, we measured the frames-per-second (FPS) on the correlation filter-based trackers. KCF [21] and DSST [22] averaged 203 FPS and 43 FPS, respectively. The proposed method using the non-sparse updating version processed 35 frames per second. However, the sparse updating version processed 46 FPS on average for 100 sequences. As a result, the proposed method was slower than the KCF, but could be considered as a real-time tracker. Moreover, the proposed method outperformed the baseline KCF and DSST in terms of the AUC by 14.45% and 7.25%, respectively. Figure 8 shows the tracking results of the experimental sequences in OTB-100 database. From the top-left to bottom-right, the sequences in Figure 8 are Panda, Liquor, Freeman3, Walking2, Car1, Car24, Human8, Lemming, Box, Dog1, Coke, Vase, Skating1, CarScale, Singer1 and KiteSurf, respectively. The images contained various attributes, and the results of the proposed method were identified by a red bounding box. As intended, the proposed method was responsive to partial occlusion and scale variation. Figure 7: The average success rate of the proposed tracker and existing trackers with all attribute sequences in OTB-100. The number indicates the number of sequences including attributes; illumination variation (IV), scale variation (SV), occlusion (OCC), deformation (DEF), motion blur (MB), fast motion (FM), in-plane rotation (IPR), out-of-plane rotation (OPR), out-of-view (OV), background clutter (BC) and low-resolution (LR).
# Super Twisted Sliding Mode Observer for Enhancing Ventilation Drive Performance
## 2 System Modelling
### Super-Twisted Slide Mode Observer (ST-SMO) Speed Estimator
#### 2.2.4 Flux and Rotor Speed Observers  
By applying Equations (3), (4), (19), and (20), along with the previously stated assumptions, the rotor flux estimate can be expressed as  
\[p\hat{\Psi}_{r}^{\alpha}\ =\ -\bigg{(}\frac{1}{t_{r}}\hat{\Psi}_{r}^{\alpha}+ \hat{\omega}\hat{\Psi}_{r}^{\beta}-\frac{1}{t_{r}}L_{m}\hat{\tau}_{s}^{\alpha} \bigg{)}, \tag{35}\]  
\[p\hat{\Psi}_{r}^{\alpha}\ =\ -\bigg{(}\frac{1}{t_{r}}\hat{\Psi}_{r}^{\beta}+ \hat{\omega}\hat{\Psi}_{r}^{\alpha}-\frac{1}{t_{r}}L_{m}\hat{\tau}_{s}^{\beta }\bigg{)}. \tag{36}\]  
An alternative representation of the rotor flux estimation, derived from Equations (35) and (36), is given as  
\[\hat{\Psi}_{r}^{\alpha}\ =\ -a_{1}|S_{\alpha}|^{\frac{1}{2}}sign(S_{\alpha})-a_{2 }\bigg{\lceil}sign(S_{\alpha}), \tag{37}\]  
\[\hat{\Psi}_{r}^{\beta}\ =\ -a_{3}|S_{\beta}|^{\frac{1}{2}}sign(S_{\beta})-a_{4 }\bigg{\lceil}sign(S_{\beta}). \tag{38}\]  
Equations (37) and (38) indicate that flux estimation involves the integration of the SM function.  
\[\hat{\Psi}_{r}^{\alpha}\ =\ -\bigg{\lceil}\Psi_{\alpha}dt \tag{39}\]  
\[\hat{\Psi}_{r}^{\beta}\ =\ -\bigg{\lceil}\Psi_{\beta}dt \tag{40}\]  
Issues such as DC offset and variations in motor variables affect the pure integration of the SM function, complicating flux estimation. To address this issue, a pure integrator with a negligible negative response is employed as an alternative [29].  
In this study, using this integrator yields  
\[\hat{\Psi}_{r}^{\alpha\beta}\ =\ \frac{1}{s+\omega_{c}}\Psi^{\alpha\beta}+\frac{ \omega_{c}}{s+\omega_{c}}Z(L), \tag{41}\]  
where \(\omega_{c}\) represents the cutoff frequency, which has been empirically established to enhance performance throughout the complete speed spectrum. \(Z(L)\) denotes the output of the saturation block, which receives the integrator's output signal, with an amplitude limited to \(L\). The limiting level \(L\) must correspond with the actual flux amplitude to efficiently remove the DC element from the output.  
The estimated speed is calculated using the following expression:  
\[\hat{\omega} = \frac{\Psi_{r}^{\alpha}\hat{\Psi}_{r}^{\beta}-\Psi_{r}^{\beta}\hat{ \Psi}_{r}^{\alpha}-\frac{L_{m}\left(\frac{\hat{\Psi}_{r}^{\alpha}\hat{\Psi}_{r}^ {\beta}}{2}-\frac{\hat{\Psi}_{r}^{\beta}\hat{\Psi}_{r}^{\beta}}{\hat{\Psi}_{r}^ {\beta}}\right)}{\left(\hat{\Psi}_{r}^{\alpha}\right)^{2}-\left(\hat{\Psi}_{r}^ {\beta}\right)^{2}}. \tag{42}\]  
Figure 1 depicts the configuration of the comprehensive STSMO used for estimating stator current, rotor flux, and rotor speed.
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 9 BC and Cyberattack Prevention
### Performance Evaluation Metrics  
The performance of a BC security system used in a WSN depends on the effectiveness of its peer trust, node authentication, access control, smart contracts, consensus mechanisms, resources management, and big data processing and storage. There are multiple performance criteria of interest, including transaction throughput, response time, latency, storage overhead, and energy consumption, which are the most commonly used metrics for security analysis of BC-based WSNs, and can be defined as follows [117]:  
* Transaction Throughput: the maximum amount of transactions that are processed and committed by the BC in a specific period of time, usually represented by transactions per second (tps).
* Response time: the time required to handle and verify the transactions processed by peers. The response time increases with increasing batch size, such as when the number of transactions in the queue grows; this can result in system congestion, as peers are required to handle more transactions.
* Latency: the period of time between when the transaction is invoked by a node and the time the transaction is added to the ledger.
* Transaction size: the amount of data in the transaction to be added to the next block.
* Block size: the size of the block, that is, the number of transactions included in the block.
* Storage Overhead: the storage capacity required for BC operations, which may exceed the node's storage capabilities due to the large amount of data accumulated by security tasks [118].
* Residual energy: the remaining energy in the sensor nodes; this metric is important to consider for energy-related attacks which shorten the network lifetime by wasting nodes' energy by launching malicious activities [119].  
Other metrics include processing frequency, percentage of central processing unit (CPU) usage, computational complexity of encryption [120], and processing time of trust evaluation. In addition, the authors of [121] used the probability of attack success and strength of attack detection to evaluate secure mechanisms using BC. The probability of attack detection identifies how efficiently a secured mechanism can distinguish between legitimate and malicious entities targeting an IoT network, while attack strength is determined by an attacker's ability to compromise a certain node and force the network to behave maliciously.
## 3 Results and Discussion
### Comparison of the Three Pretreatments  
As a comparison of the results obtained with the three organosolvents used, Figure 1 exhibits that acetone treatment provides the highest cellulose composition (\(\approx\)99%) for the optimized post-treatment solid; the amount of lignin is minimal and no other additional components are visible.  
The reasons for the better results obtained when using acetone could be related to its physicochemical properties. Thus, the lower dielectric constant for acetone (20.7 at 25 \({}^{\circ}\)C), compared to the other two solvents, ethanol and GVL (24.5 and 36.47 at 25 \({}^{\circ}\)C, respectively) [63; 64], would involve lower polarity and therefore result in greater ease to solubilize lignin, considering that this biopolymer has a hydrophobic nature [65]. The hydrophobicity of lignin can be justified by the presence of aromatic rings in its structure that delocalize electrons and decrease lignin polarity, together with the complex cross-links among aromatic structures hindering water interaction with hydrophilic functional groups from lignin.  
In all cases, the hemicellulose fraction disappears and the percentage of lignin decreases appreciably. However, it is important to point out that although the objective is to obtain a material as rich as possible in cellulose, it should not be forgotten that the \(Y_{c}\) values cannot be very high as this would mean a significant mass loss. In this sense, when GVL was used, an excessive experimental value was obtained (36.1%), while with ethanol and acetone, the values of this parameter, although still high, were lower (\(\approx\)27%).  
To verify the effect of the three solvents used, tests were carried out under the optimum conditions determined for each of them, but without organosolvents. In these cases, cellulose, hemicellulose, and lignin concentrations (%) varied between 35.3 and 38.0, 5.1 and 8.3, and 25.9 and 33.2, respectively. Therefore, these values make it evident that the action of GVL, ethanol, or acetone is essential in order to obtain a cellulose-rich material, as a consequence of significant hemicellulose and lignin removal.