esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study analyzes charging characteristics of the TMED parallel hybrid electric vehicle during the regenerative braking period and proposes an algorithm for improving accuracy of SOC determination. Conclusions obtained by this study can be summarized as follows:  
1. In the low-speed Section A, the error of the charge amount due to the regenerative braking was less influenced by initial SOC. The amount of recovered electric energy in the high-speed Section B is large, influencing increase of the charge amount error.
2. The error slope between Zone A and Zone B is approximately 3.67 times at SOC 35% and 2.75 times at 65%. SOC error increased because charging is performed more frequently in low-SOC condition.
3. To reduce error due to current integration, we proposed an SOC estimation algorithm that measures and integrates the amount of charge current of the secondary battery by SOC at each braking deceleration section (low speed, medium speed, high speed).
4. Also, it is expected that power performance of an electric vehicle through an energy supply device such as an ultracapacitor can be optimized, and improvement of power prediction accuracy of a more complicated system will be required.  
[PERSON] and [PERSON] conducted an HEV vehicle experiment to acquire data; [PERSON] supported the setting of the experimental equipment of the HEV vehicle and conducted preliminary experiments; [PERSON] and [PERSON] arranged and analyzed the acquired data; [PERSON] and [PERSON] analyzed the data and wrote the paper; [PERSON] supervised and advised all parts in this paper.  
This research was funded by **MOTIE** (Ministry of Trade, Industry and Energy) in Republic of Korea.  
This research was supported by the R&D project on Industrial Core Technology (2018) of **MOTIE** (Ministry of Trade, Industry and Energy) in Republic of Korea.  
The authors declare no conflicts of interest.  
**Nomenclature**  
ASM 2525  
BMS  
Battery management system  
CVS 75  
Constant volume sampler mode 75  
DTC  
Diagnostic trouble codes  
ESR  
Equivalent series resistance  
HESS  
Hybrid energy storage system  
IM 240  
Inspection & maintenance driving cycle 240  
NEDC  
New European driving cycle  
OBD  
On-board diagnostics  
OEM  
Original equipment manufacturer  
PST  
Potter SOC tracker  
SC  
Super capacitor  
SOC  
State of charge  
TMED  
Transmission-mounted electrical device  
**Greek Symbols**  
\(\Delta\) Delta  
**Subscripts**  
real  
Calculated current value  
reference, calculated SOC value  
n  
Numerate value and integration of SOC from calculated current value
## 3 Experimental Results  
### The Experiment Data  
In this study, RADARSAT-2 quad-pol SAR data acquired during the 2011 Norwegian oil-on-water experiment (59\({}^{\circ}\)59\({}^{\prime}\) N, 2\({}^{\circ}\)27\({}^{\prime}\) E) were used for analysis. The data was received at 17:27 of 8 June 2011 UTC in fine-quad polarimetric mode, with the spatial resolution of \(4.7\times 4.8\) m in range and azimuth directions. The incident angle of the image is 34.5-36.1\({}^{\circ}\) and the local wind speed is 1.6-3.3 m/s. For the convenience of processing and display, a data sample with \(2000\times 2000\) pixels was picked from the single look complex (SLC) data. The pseudo RGB image of the RADARSAT-2 data on the Pauli basis are provided in Figure 4. In the scene, three verified slicks were present; from left to right, they were: biogenic film, emulsions and mineral oil [33]. The biogenic film was simulated  
Figure 3: The demonstration of stacked autoencoder.  
by Radiagreen plant oil. Emulsions were made of Oseberg blend crude oil mixed with 5% IFO380 (Intermediate Fuel Oil), released 5 h before the radar acquisition. Additionally, the Balder crude oil was released 9 h before the radar acquisition [34].
## 4 XAI Methods in Medicine
### Intrinsically Interpretable Models
#### 4.2.1 Classical Medical Knowledge-Based Systems  
Some knowledge-based systems, commonly known as \"expert systems\", are some of the classical AI models that were first developed at the end of the 1960s. Explanations were sometimes introduced as a feature of these first rule-based expert systems by design, as they were needed not only by users, but also by developers, to troubleshoot their code during the design of these models. Thus, the importance of AI explainability has been discussed since the 1970s [44; 45].  
In medicine, many of these systems were developed aiming to be an aid for clinicians during the diagnosis of patients and treatment assignment [46]. We must reiterate that explanations for patient cases are not easily made, in many cases, by human medical professionals. The most widely known of these classical models was MYCIN [47], but many more based on causal, taxonomic, and other networks of semantic relations, such as CASNET, INTERNIST, the Present Illness Program, and others, were designed to support rules by models of underlying knowledge that explained the rules and drove the inferences in clinical decision-making [37; 48; 49]. Subsequently, the modelling of explanations was pursued explicitly for the MYCIN type of rule-based models [50]. The role of the explanation was frequently recognised as a major aspect of expert systems [51; 52; 53].  
As shown in Figure 4, expert systems consist of a knowledge base containing the expertise captured from human experts in the field, usually in the form of rules, including both declarative or terminological knowledge and procedural knowledge of the domain [45]. This knowledge base is consulted by an inference algorithm when the user interacts with the system, and an explanation facility interacts with both the inference system and the knowledge base to construct the corresponding explaining statements [54].  
The explanations provided were pre-modeled [55] and usually consisted of tracing the rules used by the inference mechanism to arrive at the final decision and present them in an intelligible way to the user. Such an approach aimed to explain how and why the system produced a diagnosis [53] and, in some more sophisticated cases, even the causal evolution of the patient's clinical status [56].  
However, these explanations were limited by the knowledge base constructed by the system's designers; all the justifications for knowledge had to be explicitly captured to produce specific explanations [53]. Knowledge acquisition (KA) and updating is a challenging task (see Section 6.2) and this was not efficiently resolved, leading KA to become the bottleneck that resulted in the decline of interest in knowledge-based AI systems (as a main example of symbolic AI) in favour of data-based AI and ML systems (which we can call subsymbol AI).  
Figure 4: Basic diagram of a medical knowledge-based expert system.
## 6 Thematic Taxonomy of UWSNs
### Deployment  
Underwater wireless sensor networks comprise some nodes that are deployed underwater and some deployed on the surface of the water and accomplish their respective functions in the specific areas. Underwater sensor nodes that cover a sparse region are essential to be optimally deployed to use the limited battery life. Node deployment is considered to be a fundamental procedure in underwater sensor networks due to its capability to support many essential tasks, such as routing protocol, localization, and network topology, which contribute to significant impact to network performance. In [28], the node deployment in UWSNs is categorized into three groups: static or fixed, self-adjusted or limited mobility, and free mobility or movement-assisted deployment. In the static implementation, all sensor nodes are fixed in a particular region of interest and attached to surface buoys or anchored on the sea floor. This algorithm has higher coverage and connectivity compared to self-adjustment and mobile deployment but requires a more significant number of sensor nodes to cover a particular monitored area. For self-adjustment deployment, the movement of sensor nodes is generally in a vertical form with limited mobility. However, it is different from mobile deployment, where sensor nodes such as in an autonomous underwater vehicle (AUV) can move freely in all directions. Most of the mobile deployment algorithms in UWSNs have focused on reducing energy consumption as well as improving coverage and connectivity.  
An underwater mobile sensor can change an initial node placement based on the newly obtained information of the target in real time. According to [29], a rearrangement in sensor node position is the strategy to accomplish a final anticipated configuration. Reorganization or redeployment is essential after some changes have occurred to the networks, which are typically caused by sensor failure (malfunction, energy reservation) or due to target/event detection. Based on previous studies ([30; 31; 32]), the authors expected that the mobile sensor nodes would be able to change the current position dynamically to provide maximum connectivity and maintain the network coverage. Furthermore, refs. [33; 34] also emphasized that mobile sensor nodes can improve the detection rates compared to static and hybrid sensors. Table 3 shows the current algorithms that are garneringincreased interest among researchers. Overall, most of the mobile deployment algorithms consider coverage, connectivity, and energy efficiency as the objectives.
# The Development and Evaluation of a Training Monitoring System for Amateur Rugby Union
## 5 Conclusions
### What Are the Key Findings?  
* Both the strength and conditioning (S&C) coaches and players alike value an online training monitoring system (TMS) but the greatest barriers to its effectiveness are lack of player compliance, data inconsistency, gathering match data, and external confounders outside of the sport.
* Muscle soreness, fatigue, sleep duration and readiness to train (RTT) were highly rated as effective pre-session measures and are particularly useful at signalling red flags that prompt conversations between coaches and players.
* Session rating of perceived exertion (sRPE) was a highly rated metric by S&C coaches and players for aiding training prescription and mitigating injury risk.
# Fast Airfoil Selection Methodology for Small Unmanned Aerial Vehicles
## 3 Main Airfoil Selection Procedure Description
### Lift Coefficients Calculation  
The two main computational parameters the designer has to know at this point are the average weight \((W_{avg})\), of the aircraft and the main wing's area \((S)\). These values are assumed to be already computed during preliminary design. The average weight can be determined using the simple Equation (1):  
\[W_{avg}=\frac{1}{2}\ \Big{(}W_{i}+W_{f}\Big{)} \tag{1}\]  
where \(W_{i}\) represents the initial weight of the aircraft before cruise and \(W_{f}\) represents the final weight of the aircraft after the cruise. If the UAV is electric powered, which is the most possible situation depending on the Reynolds numbers the article refers to, then \(W_{avg}=W_{i}=W_{f}\).  
All of the required parameters regarding the various lift coefficients, which are used in the procedure, can be calculated by applying the equations of the following Table 2.
## 3 REDSwitch
### Ethernet Parser Supporting Redundancy  
The REDSwitch, when working in PRP mode, can face two different scenarios: receiving a packet coming from the onboard CPU port or receiving a packet from an outside LAN. The scenario where it receives a packet coming from an outside LAN can be separated into two sub-scenarios: a packet coming from a DAN or from a SAN, since both can be connected to a PRP LAN. The tasks performed by the Ethernet parser in these two different scenarios, as well as a third scenario, where the REDSwitch works as a regular switch, can be observed in the state diagram presented in Figure 4, where the \"Shift Words\" state has three output arcs to three different branches of the diagram.  
The packets that come from the onboard CPU (SRC port = 0) will need to have the PRP fields (LAN, length of the link service data unit (LSDU), sequence number, and PRP ID) at the end of the packet. These fields must be added before sending it to both LANs. This task is performed in the \"Add PRP Tailer\" state of the left branch of the state diagram presented in Figure 4.  
When a packet comes from one of the LAN networks (SRC port!= 0), it is necessary to receive the entire packet to check the PRP ID field value at the end. This is required to understand if it was sent by an SAN or DAN. When parsing the packet (in the \"Read PRP\" state in the right branch of the state diagram presented in Figure 4), the PRP tailer will be stored on a temporary register until the PRP ID is received and verified in the \"Check PRP\"  
Figure 4: Ethernet parser state diagram.  
state. If the PRP ID confirms that the packet comes from a DAN, the information stored on the temporary register will be sent out by the Ethernet parser. If the PRP ID field does not match, it means that the packet was sent by an SAN, which means that the parser must discard the information stored on the temporary register and calculate and add the PRP tailer, as in the scenario where a packet comes from the onboard CPU.  
In any of the presented scenarios, the REDSwich needs a cyclic redundancy check (CRC) calculation tool. This tool will progressively calculate the packet CRC by calculating the CRC of each word and updating the CRC value after every new word. In the REDSwitch implementation, we integrated a CRC calculation tool developed by easics [23].  
To support packet duplication and discarding, the Ethernet parser will have to store the packets that were already sent and received. These packets will be stored in a hash table, which allows the verification of previously received packets and duplicate identification.  
When the parser is in the \"Packet Complete\" state (Figure 4), it checks if it is in PRP mode. If it is not in PRP mode, it returns to the initial state; otherwise, it moves to the \"Table Operation\" state. In this state, the Ethernet parser will search the hash table, using the combination of the source MAC and sequence number, to verify whether the packet is a duplicate or not, inserting it if it is new.
## 6 Numerical Results and Analysis  
Extensive simulations have been conducted to compare the transmission reliability and efficiency, resource consumption, and latency performance in the elastic transmission mechanism [35; 36] with a resource redundancy protection mechanism and backup path protection mechanism for validation of its effectiveness. We defined four reliability and efficiency performance indicators that we measured by continuing to increase the network traffic and link failure probability. We built a simulation framework in the Windows-environment-based C++language, with the network topology depicted in Figure 6.  
Figure 5: Illustration for grouping algorithm.  
Figure 7 shows the data transfer arrow signs of three methods, in which the source nodes S1 and S2 need to transmit the packets b1 and b2 to the destination node \(T\), respectively.  
In the process of simulation, it is assumed that the probability of link failure is independent at each hop. The source node sends a steady stream of traffic to the destination node. When each generation of packets is received by destination nodes, they record packet information including packet size, time  
Figure 6: Network topology for simulation experiments.  
Figure 7: Packet transmission for three fault-tolerance schemes.  
stamp, encoding operation, decoding operation, and other statistics. The sensor and sink nodes also calculate the transmitting times of packets.  
In our simulation configuration, we adopt the following method: the traffic flow is established between each pair of source and destination nodes. This traffic flow would generate new packets according to the assumed statistical distribution (e.g., Poisson distribution), meanwhile, it would specify the size of packet and the average rate of generating packets. In this way, we can configure the dynamic packets. The main simulation parameter values are listed in Table 4 below.
## 3 Root-Cause Detection and Identification Algorithm
### Implementation and Training Details  
All experiments were conducted using Python 3.9 with TensorFlow 2.11 and Keras APIs 2.11 ([[https://keras.io](https://keras.io)]([https://keras.io](https://keras.io)); accessed on 8 June 2025).  
Feature selection via Relieff was implemented using the \"skrebate\" package [27], while the Genetic Algorithm (GA) optimization followed a custom wrapper built upon \"DEAP\" and \"Optuna\" libraries. MinMax scaling and preprocessing were handled with scikit-learn.  
The Genetic Algorithm executed 50 generations with a population size of 20, optimizing the number of hidden layers, neuron counts, and dropout rates. This setup achieved better validation performance than manual tuning and aligns with recent developments in Neural Architecture Search (NAS) [28].  
Training used categorical cross-entropy loss and the Adam optimizer. Early stopping was applied with a patience of 10 epochs to prevent overfitting [29]. All results reported were validated using a fixed 80/20 training-validation split.  
Figure 7: Correlation matrix of the selected features, highlighting the interdependence between sensor variables such as cell temperature, irradiance, and current.  
The methodology is designed for reproducibility and scalability. Scripts for data generation, feature ranking, and model training are available upon request to facilitate replication or further study.  
Although the synthetic dataset was constructed by independently varying irradiance, temperature, and degradation parameters, we acknowledge that the resulting features may exhibit stronger correlations than observed in real-world scenarios. This is due to the deterministic nature of simulation outputs and the absence of sensor noise or fabrication variances. These factors can lead to idealized patterns that exaggerate feature dependencies.
# Analyzing the Economic Performance of a TLS-Based Structural Safety Diagnosis Process
## 4 Discussion  
The integrated adoption of TLS-based 3D scanning reverse modeling for structural safety assessment provides several key implications.  
First, employing scanning technology throughout the entire project significantly reduced both waiting and service times compared to visual inspections. This improvement can be attributed to transitioning from a highly variable production system (the visual inspection method) to one characterized by deterministic service times (M/D/1). Such findings highlight the substantial efficiency gains achievable by introducing manufacturing principles, such as automation and standardization, into the construction industry.  
Second, at the individual project level, initial investments, such as the purchase of scanning equipment, may seem financially burdensome. However, cumulative benefits increase considerably when performing multiple projects over an extended period. This is clearly demonstrated by the combined ROI of Projects A, B, and C, confirming that the investment is highly beneficial at the company level.  
Third, other construction industry processes traditionally hindered by labor-intensive approaches (e.g., design, construction, and maintenance) can similarly benefit from applying queueing theory-based analyses. Future integration of additional digital technologies (e.g., UAVs, IoT, AI-based image processing) and comprehensive evaluation of investment costs versus benefits could provide more objective grounds for strategic decision-making in construction companies.
## 2 Data and Method
### CMOD7 and CDOP  
Developed for global applications and implemented on the C-band advanced scatterometer (ASCAT), the empirical GMF known as CMOD7 has surpassed its predecessor CMOD5.N with higher wind retrieval accuracy [22]. This model covers a valid incidence angle range from 18\({}^{\circ}\) to 60\({}^{\circ}\), well consistent with the incidences of S-1 IW images. CMOD7 establishes a relationship between NRCS, incidence angle, wind speed, wind direction (relative to radar line of sight), and polarization under neutral atmospheric stability, as defined by the following equation:  
\[\sigma^{0}=B_{0}[1+B_{1}\text{cos}(\phi)+B_{2}\text{cos}(2\phi)] \tag{1}\]where \(\phi\) represents the wind direction concerning the antenna look angle. \(B_{0}\) determines the NRCS level for a specific wind speed, while \(B_{1}\) and \(B_{2}\) describe the upwind/downwind and upwind/crosswind asymmetry of NRCS, respectively. All these three terms can be expressed as functions of incidence angle, wind speed, and wind direction [22].  
Assuming corrected DCA information after a series of steps, the geophysical DCA can be still considered as the combination of sea state and sea surface current [13]. As such, the sea-state contribution should be removed to obtain a clean current velocity. The C-band Doppler model (CDOP) is designed to achieve such a purpose by building the relationship of DCA with respect to the radar configuration and sea surface wind field [12]. This model takes the form of  
\[f^{DA}=CDOP(U_{10},\varphi_{w},\theta,pol) \tag{2}\]  
where \(U_{10}\) and \(\varphi_{w}\) are the wind speed and direction (relative to the radar looking direction) at a height of \(10\)m, respectively, \(\theta\) is the incidence angle, and \(pol\) is the polarization mode of radar. The function form and values of each coefficient are detailed in [12].
## 5 Experimentation and Results
### Discussion of Results
This, together with the lack of the need for deep learning or black box algorithms to obtain results above 90% accuracy, means that the level of interpretability of MIL models remains high and can be used in real-world tools to identify potential problems in distance learning courses with large numbers of failures, as well as to identify specific students at risk of failing. On the other side, the approach of this work may also have limitations that should be taken into account. The authors can identify two main problems that can be addressed in future works. First, this work has been carried out by analyzing the dropout and failure profiles together. Although both profiles correspond to students who do not pass the course, this could be due to different causes, so it is worth considering the possibility of carrying out a separate analysis to identify each type of student at risk. Second, the study has been made considering all the activities of the course, i.e., it is required to have reached an advanced point of the course to have all the information of the assignments that have been submitted. This limits the possibilities of action to prevent an at-risk student from failing the course. It would be desirable to tweak the approach to only use the information from the assignments up to a certain point in the course, in order to have enough time before the final exam to be able to adequately guide the at-risk student to avoid failure.