esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Morphological Analysis
As the reaction continues, the crosslink density continuously increases, forming a highly crosslinked polymer with a 3D network structure, as shown in Figure 8f. From a molecular chemistry perspective, the epoxy asphalt curing process is essentially the polymerization of epoxy monomers into single chains that are repolymerized into a crosslinked network, which gradually densifies [28; 29]. During the curing reaction of epoxy asphalt, the epoxy monomers are polymerized into single chains, and the single chains are repolymerized into crosslinked network. The molecular weight of the epoxy resin continuously increases, and the compatibility between the epoxy resin and the thermoplastic asphalt decreases, which promotes phased development. The dispersed asphalt particles are surrounded by a crosslinked network of epoxy resin, forming a two-phase 3D network with each other. A schematic of this process is shown in Figure 9. The results of the micromorphological changes that are shown in Figures 7 and 8 are consistent with this process. In summary, an analysis of the test data shows that the main mechanism of epoxy asphalt strength formation is the curing of epoxy asphalt into a stable 3D network. The curing process can be divided into three stages: an initial stage, an intermediate stage, and a late stage. In the initial stage, \(G_{\alpha}\) is less than 27%, no network exists, the reactivity is high, and the material strength increases rapidly. In the intermediate stage, \(G_{\alpha}\) is between 27% and 65%, a 3D network starts to form, and the network skeleton is relatively soft and easy to deform. Thus, the epoxy asphalt should not be disturbed to avoid affecting the increase in the epoxy resin strength. In the late stage, \(G_{\alpha}\) is greater than 65%, and the skeleton gradually densifies into a stable two-phase 3D crosslinked epoxy resin network containing asphalt particles. This dense uniform 3D network in the cured epoxy asphalt increases the deformation of the epoxy resin. The asphalt fills in and protects the epoxy resin against aging, and the crosslinked network formed by the polymerization of the epoxy resin changes the thermoplasticity of the asphalt, resulting in a high strength and good thermosetting properties.
## 1 Introduction
### Main Contributions  
This paper aims at developing an onboard battery model considering partial electrochemical properties and an accurate and robust SOC estimator in the entire SOC area. The unique contributions brought about in this paper are the following.  
1. Four typical ECMs and four improved ECMs considering partial electrochemical properties are compared under the new European Driving Cycle (NEDC) and the dynamic stress test (DST) working conditions to obtain a more suitable battery model for the entire SOC area.
2. A subarea parameter-identification method based on PSO is proposed to improve the global model accuracy in the entire SOC area.
3. A SOC estimator based on extended kalman filter (EKF) for our proposed model is developed, and its accuracy and robustness are verified by experiments.
# Single-Stage Fractionation of Vine Shoots Using Microwave Heating
## 3 Results and Discussion
### Refining of OS
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Composition (g/L)** & **145 \({}^{\circ}\)C** & **160 \({}^{\circ}\)C** & **175 \({}^{\circ}\)C** & **190 \({}^{\circ}\)C** \\ \hline
**NVC** & & & & \\ \hline Glucose & 0.07 \(\pm\) 0.00 & 0.10 \(\pm\) 0.01 & 0.23 \(\pm\) 0.01 & 0.38 \(\pm\) 0.02 \\ Xylose & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 & 0.21 \(\pm\) 0.01 & 3.56 \(\pm\) 0.21 \\ Galactose & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 & 0.30 \(\pm\) 0.02 & 0.00 \(\pm\) 0.00 \\ Arabinose & 0.18 \(\pm\) 0.02 & 0.25 \(\pm\) 0.02 & 0.25 \(\pm\) 0.01 & 0.00 \(\pm\) 0.00 \\ Mannose & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 \\ GOS & 4.32 \(\pm\) 0.22 & 4.42 \(\pm\) 0.25 & 1.44 \(\pm\) 0.10 & 1.79 \(\pm\) 0.08 \\ XOS & 0.89 \(\pm\) 0.06 & 7.96 \(\pm\) 0.09 & 13.1 \(\pm\) 0.11 & 2.14 \(\pm\) 0.20 \\ GaOS & 0.97 \(\pm\) 0.10 & 1.46 \(\pm\) 0.04 & 1.03 \(\pm\) 0.04 & 0.00 \(\pm\) 0.00 \\ AOS & 0.81 \(\pm\) 0.05 & 0.59 \(\pm\) 0.03 & 0.25 \(\pm\) 0.02 & 0.00 \(\pm\) 0.00 \\ MOS & 0.17 \(\pm\) 0.02 & 0.10 \(\pm\) 0.00 & 0.50 \(\pm\) 0.03 & 0.00 \(\pm\) 0.00 \\ AG & 0.40 \(\pm\) 0.02 & 2.20 \(\pm\) 0.12 & 2.35 \(\pm\) 0.03 & 1.66 \(\pm\) 0.08 \\ ONVC & 3.66 \(\pm\) 0.23 & 5.53 \(\pm\)0.15 & 6.78 \(\pm\) 0.22 & 9.57 \(\pm\) 0.45 \\ \hline
**Volatile compounds** & & & & \\ \hline Acetic acid & 0.20 \(\pm\) 0.02 & 0.71 \(\pm\) 0.02 & 1.41 \(\pm\) 0.04 & 2.21 \(\pm\) 0.09 \\ HMF & 0.00 \(\pm\) 0.00 & 0.03 \(\pm\) 0.00 & 0.15 \(\pm\) 0.02 & 0.28 \(\pm\) 0.02 \\ F & 0.00 \(\pm\) 0.00 & 0.04 \(\pm\) 0.00 & 0.08 \(\pm\) 0.01 & 0.75 \(\pm\) 0.05 \\ \hline \hline \end{tabular}  
* NVC: non-volatile compounds; COS: glucose units in oligosaccharides (OS); XOS: xylose units in OS; GaOS: galactose units in OS; AOS: arabin units in OS; MOS: mannose units in OS; AG, acetyl groups linked to OS; ONVC: other non-volatile compounds; HMF: hydroxymethylfurfurural; F: furfural.  
\end{table} 
Table 1: Effects of fractionation treatments on the concentrations of the reaction products.
## 3 Spread-Spectrum Technology  
Spread-spectrum technology refers to the extension of narrowband data to a wider band and its transmission. The receiver obtains the spread-spectrum gain and recovers the data through correlated de-spreading. During this process, as the noise in the channel only experiences one spread spectrum, the energy is extended to a wider band. Thus, the in-band noise energy is reduced and the signal-to-noise ratio of the received signal is enhanced. With a better autocorrelation performance of the spread-spectrum sequence, any multipath energy beyond the duration of a code slice can be treated as noise. As the spread-spectrum symbol duration generally exceeds the multipath delay of the shallow water channel, ISI does not affect too many subsequent symbols, so the spread-spectrum technology itself has a strong anti-multipath performance.  
Figure 1: Tanner chart.
## 3 Related Technologies
### Node Remote Reprogramming Mechanisms
#### 3.4.3 Size Reduction Mechanisms  
While the dissemination protocol aims to minimize the overhead related to delivering updates, reducing the size of transmitted software is used to decrease the size of the updates. Three main techniques are used to achieve this objective: compression, differential patching and high-level instructions. Several reducing size mechanisms were proposed, such as: Reijers [51], Rsync [52] and Remote Incremental Linking [53]. Reijers reduces the image size generation differential script. The script is downloaded as a series of packets where each packet contains the new instruction code and its address. The script is applied against the currently running program stored in the external memory, usually an EEPROM, gradually building a new image. When this process is completed, the boot loader loads the new image into the running memory. The scripting language is cpu-specific, which is the Reijers algorithm major flaw. Rsync also provides incremental software updates but its algorithm is CPU independent. The Rsync script can either specify unmodified block to be copied for a new address or can contain modified code. The updater node asks the node being updated for the checksum of its current blocks and, according to the response, only sends changed code. Remote Incremental Linking was designed for mica2 more hardware platform and supports dynamic and static updates. An image program is composed by several functions and at the end of the functions some space is reserved to allow future expansion. New versions of the functions can be written at the same start address memory, as long as they fit, without the need to update references to the functions elsewhere in the program. Otherwise, either adjacent functions or the new functions are moved, which minimizes page writes.
# Model-Based Fault Analysis and Diagnosis of PEM Fuel Cell Control System
## 2 PEMFC System Model and Verification
### PEMFC Model
#### 2.1.4 Fuel Cell Stack  
Hydrogen and air are supplied to the anode and cathode, respectively, and they react with the electrolyte to form ions. In the process of forming water from electrochemical reactions between the generated ions, electrons are generated at the anode and move to the cathode, eventually generating electricity. An electric potential is formed between the electrodes, and the electrode potential, \(E\), is determined by the Nernst equation, which is shown below.  
\[E=E^{0}+\frac{RT_{fc}}{2F}ln\frac{pH_{2}p_{O_{2}}^{1/2}}{pH_{2}O} \tag{13}\]A standard reversible cell potential \(E^{0}\) of 1.229 V was obtained. When the entropy change of the reaction is assumed to be constant, the equation can be written as:  
\[E=1.229-0.85\times 10^{-3}\ \Big{(}\ T_{fc}-298.15\ \Big{)}+4.3085\times 10^{-5}T_{fc }\Big{[}ln\big{(}p_{H_{2}}\big{)}+\frac{1}{2}ln\big{(}p_{C_{2}}\big{)}\Big{]} \tag{14}\]  
When the cell is in operation, it is followed by three losses: activation loss, ohmic loss, and concentration loss. Therefore, the actual voltage of the fuel cell is written as follows.  
\[v_{fc}=E-v_{act}-v_{ohm}-v_{conc} \tag{15}\]
# Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models
###### Abstract  
As the educational landscape evolves, understanding and fostering student adaptability has become increasingly critical. This study presents a comparative analysis of XAI techniques to interpret machine learning models aimed at classifying student adaptability levels. Leveraging a robust dataset of 1205 instances, we employed several machine learning algorithms with a particular focus on Random Forest, which demonstrated highest accuracy at 91%. The models' precision, recall and F1-score were also evaluated, with Random Forest achieving a precision of 0.93, a recall of 0.94, and an F1-score of 0.94. Our study utilizes SHAP, IIME, Anchors, ALE, and Counterfactual explanations to reveal the specific contributions of various features impacting adaptability predictions. SHAP values highlighted 'Class Duration' significance (mean SHAP value: 0.175); IIME explained socio-economic and institutional factors' intricate influence. Anchors provided high-confidence rule-based explanations (confidence: 97.32%), emphasizing demographic characteristics. ALE analysis underscored the importance of 'Financial Condition' with a positive slope, while Counterfactual scenarios highlighted the impact of slight feature variations of 0.5 change in 'Class Duration'. Consistently, 'Class Duration' and 'Financial Condition' emerge as key factors, while the study also underscores the subtle effects of 'Institution Type' and 'Load-shedding'. This multi-faceted interpretability approach bridges the gap between machine learning performance and educational relevance, presenting a model that not only predicts but also explains the dynamic factors influencing student adaptability. The synthesized insights advocate for educational policies accommodating socioeconomic factors, instructional time, and infrastructure stability to enhance student adaptability. The implications extend to informed and personalized educational interventions, fostering an adaptable learning environment. This methodical research contributes to responsible AI application in education, promoting predictive and interpretable models for equitable and effective educational strategies.  
compariative analysis; educational data mining; educational predictive modelling; XAI; feature importance; machine learning interpretability; model transparency; predictive analytics in education; student adaptability; AI in education policy 2024202024145141[[https://doi.org/10.3390/app1425141](https://doi.org/10.3390/app1425141)]([https://doi.org/10.3390/app1425141](https://doi.org/10.3390/app1425141))
# Handover Management for Drones in Future Mobile Networks--A Survey
## 7 Future Directions
### IoT and Drones  
Since IoT and drones can support low-cost platforms and services, their combination will certainly be a future component [84]. The increasing growth in IoT and the great need for higher data rates and low latency will likely necessitate the use of drones. Drones can contribute to significant solutions in several IoT use cases. Future networks should therefore incorporate the latest research and enhancements. However, the massive increase in these technologies will also increase the issues related to mobility. Higher handover probabilities may occur. Moreover, the need to balance load between the serving cells will increase. Thus, the implementation of drones in IoT use cases will need to be investigated further.
# Traffic-Predictive Routing Strategy for Satellite Networks
## 6 Simulation Analysis
### Simulation Scenario
#### 6.2.1 Scenario 1: Different Packet Forwarding Rates
Therefore, as the packet delivery rate increases, the packet loss rate curves of the four strategies all show a linear upward trend. When the packet loss rate is high, it indicates that the network has exceeded its processing capacity and cannot effectively handle all incoming packets. It can be seen that the G-AODV strategy in the figure has the lowest packet loss rate, which is due to its ability to predict the future load status of the node. When a node may be in a heavily loaded state, the node sends a congestion signal to the upstream node, and the upstream node directs traffic to a lighter loaded node. This ensures that in a network environment with a large packet delivery rate, the node can adjust its own load status so that the data packet can find a smooth route to the destination node. Even after the link is broken, the node can adopt a maintenance mechanism to repair the route and ensure the link connection. This strategy controls the increase in packet loss rate to a certain extent. The simulation results show that as the packet forwarding rate increases, the packet loss rate of G-AODV is significantly lower than the comparison routing strategy. Compared with E-AODV, DS-DSR, and NCMDSDV, the packet loss rate of G-AODV is reduced by 5%, 19%, and 22%, respectively. Figure 7: Average end-to-end delay under different packet delivery rate scenarios.
# Fat Graft in Surgical Treatment of Medication-Related Osteonecrosis of the Jaws (MRONJ)
## 2 Materials and Methods  
The authors performed a retrospective comparative study according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines of patients diagnosed with MRONJ attended to our Maxillofacial Surgery Unit since 2007. The following inclusion criteria were considered during patients' selection: confirmed MRONJ diagnosis, administered BMAs (i.e., zoledronic acid, pamidronic acid, or associations of both), unresponsiveness after 8 weeks of systemic antibiotic therapy and oral hygiene, performed surgical treatment, and available follow-up for at least 1 year after surgery. On the other hand, reasons for the exclusion were history of radiation therapy to the head and neck region, surgical treatment refused or not performed due to contraindications to local/general anesthesia or surgery, incomplete follow-up (Table 1).  
The selection included only those patients who were operated on by the same surgeon and met the inclusion and exclusion criteria. The following data were retrieved from the clinical database: age, gender, reason for BMAs assumption (i.e., oncological disease or osteoporosis), localization of the primary tumor (in case of oncological disease), MRONJ localization, MRONJ stage, type of assumed BMA, therapy duration, comorbidities, type of surgical treatment received (i.e., sequestrectomy alone or sequestrectomy and adipose graft), and pattern of disease evolution during the follow-up period.  
According with the performed surgical procedure, all patients that met the inclusion and exclusion criteria were divided in two groups:  
Group A (\(n=9\))--patients who received sequestrectomy and autologous fat graft;  
Group B (\(n=12\))--patients who received sequestrectomy alone.  
Bivariate appropriate tests (Fisher test) have been used to assess the associations between each of the independent characteristics and the performed surgical procedure. After performing the exploratory bivariate analyses, only those variables found to be associated at the \(p\)-value \(<0.25\) level were introduced into a multivariate regression model performed to assess the independent predictors of sequestrectomy surgical procedure followed by autologous fat graft. Analyses were performed using Stata 15 software (StataCorp, 2017. Stata Statistical Software: Release 15.1. College Station, TX, USA. StataCorp LLC).
# An Interactive Real-Time Locating System Based on Bluetooth Low-Energy Beacon Network +
## 2 Related Work
### Implementations of Bluetooth Mesh Networking  
In Section 1 we have mentioned that the beacon technology only provides unidirectional communication. Although Bluetooth supports bidirectional communication, at the time of our implementation the communication is limited to a star topology. In July 2017 the Bluetooth Special Interest Group (SIG) finally released the official Bluetooth mesh networking specifications [22]. Before the Bluetooth mesh networking specifications were released, chip vendors and researchers have devoted themselves to developing mesh protocols for BLE. In 2014 a proprietary flood-based mesh protocol for BLE called CSRmesh was released by CSR Inc. (acquired by Qualcomm in 2015) [23]. CSRmesh supports up to 65535 nodes per network, and the propagation delay between nodes is less than 15 ms. [PERSON] et al. studied the packet delivery ratio of different setups using CSRmesh protocol [24]. They found that although using the flood-based approach has the advantage of simplicity over route-based approaches, flooding does not work well in traffic-intensive application scenarios. nRF OpenMesh [25] is also a flood-based mesh protocol for BLE, which is developed by Nordic Semiconductor Inc. In nRF OpenMesh, the Trickle algorithm [26] is used to dynamically control the rebroadcasting behavior based on node density and value update frequency. An opportunistic routing (OR)-based mesh protocol called BLEmesh [27] was proposed by [PERSON] et al. In BLEmesh the source node broadcasts the packets and selects the next forwarding nodes among those who successfully received the packets in an opportunistic manner. Compared with conventional routing protocols for wireless mesh networks, using OR requires a smaller number of broadcast packets to finish the transmission, which also means less power is consumed.
# Radiomics in Gynaecological Imaging: A State-of-the-Art Review
## 3 Endomet Cancer
### Nodal Involvement  
Due to the limitations of conventional imaging in assessing nodal status and its prognostic relevance, many studies investigated this topic. [PERSON] et al. reported that five texture features correlated with the presence of LNM, even if their accuracy and specificity were lower compared with conventional MRI features [40]. For the evaluation of nodal status, the AUC of a whole tumour radiomics model established by [PERSON] and colleagues yielded 0.73 and 0.72 in training and test cohorts, respectively [41]. [PERSON] and colleagues developed four different predictive models, one based on clinical features exclusively, one based solely on radiomic features, and two based on both radiomics and clinical features. Moreover, the authors evaluated the diagnostic performance of the MRI reading of two specialized radiologists. One of the models based on radiomic features, node size, and cancer antigen 125 (CA125) showed the best discrimination ability in the training cohort (AUC: 0.892) and test cohort (AUC: 0.883). On a sub-analysis based on node size, this model achieved the highest sensitivity (97%) and specificity (86%) in the subgroup of enlarged nodes (diameter > 8 mm), which was comparable with radiologists' diagnostic performance (accuracy: 0.970; sensitivity 81%). For the normal-sized nodes (diameter: 3-8 mm) and the small-sized nodes subgroup (diameter < 3 mm) analysis, this model displayed an accuracy of 0.846 and 0.849, respectively, and a sensitivity of 64.7% and 47.1%, respectively. In both cases, the model outperformed radiologists' reports [47]. A multicentre study established a radiomics model for assessing pelvic LNM on preoperative MRI of a large cohort of patients (\(n=662\)), divided into one training set and two validation sets. The authors compared the performance of the radiomics model with the performance of two experienced radiologists based on MRI findings alone and with the aid of the radiomics model. The AUC values of the model were 0.935 for the training set, and 0.909 and 0.885 for the validations sets. Moreover, with the support of the radiomics model, the authors demonstrated an improvement in radiologists' diagnostic performance in predicting LNM [48]. Finally, a recently published study reported mean AUCs of radiomics classifiers for the assessment of pelvic and para-aortic nodes metastasis of 0.72 and 0.82, respectively [46].
## 2 Materials and Methods  
### Materials  
Styrene (St) (99%), 4-vinylpyridine (4 VP) (95%), trimethylolpropane trimethacrylate (TRIM) (90%), divinylbenzene (DVB) (85%), 2,2'-azobis(2-methylpropionitrile) (AIBN) (98%), ibuprofen (IBU) (99%), ketoprofen (KETO) (99%), aspirin (ASP) (99%), and sodium lauryl sulfate (SDS) (99%) were obtained from Sigma-Aldrich. Toluene, methanol, ethanol, tetrahydrofuran (THF), acetone, and methanoic acid were from POCh (Gliwice, Poland). The 1,4-dimethoxyyloiloxybenzene (14 DMB) was obtained in our laboratory according to the earlier described procedure [21]. Polystyrene standards for inverse size exclusion chromatography (ISEC) experiments were obtained from Toyo Soda (Tokyo, Japan). THF, acetonitrile, and methanol of HPLC-grade used in chromatography experiments were from Merck Milipore. Monomers: 4 VP, TRIM, and styrene were purified of inhibitor by vacuum distillation and stored in a refrigerator until use. Other reagents were used as received without further purification.
# Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms
## 1 Introduction
This task allocation approach is tightly coupled with a search-based path planning approach, which plans the robot movements and estimates the expected costs [12]. Thanks to this coupling, HTAPF is able to dynamically adjust the robots' density in different areas, granting consistently good performance across different problem instances. The main contributions of this paper are the following: (i) introduction of a decentralized hierarchical approach to the task assignment problem; (ii) tight coupling of the task assignment and path finding processes based on a novel utility function; (iii) validation of the proposed approach in realistic settings; (iv) statistical analysis and comparison of the performances of the system against two different baselines. In the following, we first discuss the literature about coupled MRTA and MAPF problems (see Section 2). Next, we provide a formal description of the analyzed problem together with the proposed approach in all its constituent parts (see Section 3). Section 4.1 presents the results obtained from the experimental evaluation, and Section 5 concludes by discussing the benefits and limitations of the proposed approach.