esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Model Selection and Classification Scheme
Firstly, these algorithms possess a unique capability to discern and rank significant factors, aiding in the identification of crucial influencers for accurate SSM development [34]. Additionally, their explainability through techniques like SHAP enhances transparency in understanding the decision-making process [76; 77]. Secondly, ensemble algorithms exhibit robustness against dataset noise, effectively mitigating overfitting and ensuring the generalizability of the SSM across diverse scenarios. This approach capitalizes on individual algorithm strengths while compensating for weaknesses, contributing to a more comprehensive and accurate sinkhole susceptibility assessment [78]. Thirdly, ensemble algorithms enhance model stability by aggregating predictions from multiple base models, particularly advantageous in the context of sinkhole susceptibility mapping where uncertainties and data variations exist. This mitigates the impact of individual model variability, resulting in more reliable predictions [79]. The split of the training-test dataset, and evaluation of model performances were executed utilizing the Python-based _scikit-learn_ library. The RF model was trained using _scikit-learn_, while other models were trained using the _XGBoost_ and _Microsoft LightGBM_ libraries. The hyperparameters of all classifiers were optimized using the Optuna framework, a Bayesian optimization tool that implements the Tree-structured Parzen Estimator algorithm. Optuna addresses inefficiencies in conventional hyperparameter tuning methods like grid search and random search by improving computational performance and minimizing the potential for suboptimal parameter selection. This approach ensures more effective exploration of hyperparameter spaces while avoiding convergence to local optima [80].
# Wireless Remote Control for Underwater Vehicles
## 4 Scenario Description and Simulation Setup
### Multimodal Layer  
The level of flexibility to be supported by the multimodal system required the implementation of several features in the DESERT Underwater framework, such as the possibility of using different PHY technologies to convey traffics with different QoS requirements and a dynamic management of the network stack. For this reason, our transmission control module contains several queues, one for each type of traffic to be supported, that potentially presents a different behavior. Furthermore, the module is able to manage the queues dynamically to the correct combination of network protocols, MAC layer protocols, and PHY technologies.  
For this reason, the multi_traffic_control layer presented in [122] was substantially extended to provide more functionalities, such as the possibility of setting the behavior of each queue used to buffer the outgoing data. For instance, a circular queue should be employed for video transmissions in order to avoid a long buffering time at the price of the loss of small segments of the video, while images need to be sent through a queue that is large enough to avoid discarding packets because even  
Figure 7: Instantaneous bit rate of the high-quality and grey-scale video streams (**a**) as well as of the very low-quality video transmitted via acoustic HF (**b**), calculated on a time window of 1 s.  
the loss of a small part of the image data will compromise the reception of the whole image. Moreover, this queuing management can be also used for traffic shaping. Specifically, while the default behavior of the multi_traffic_control layer lets the underlying physical layers transmit at their full capacity, this multimodal layer can also limit the transmission of a certain traffic type by forcing the packets stored in the corresponding transmission queue to be sent with a minimum inter-packet transmission time between each other, avoiding traffic without latency constraints saturating the physical layer. This is the case, for instance, of the image traffic: a whole image of size 2.5 Megabytes is indeed generated at one precise moment. If all packets required to carry this image are issued to be sent simultaneously, the optical physical layer will be completely devoted to the image transmission for almost 6 seconds, preventing all other traffic from being transmitted in the same time lapse. By setting an inter-packet transmission time bigger than 0 for the image traffic, instead the image packet transmissions can be limited, preventing, for instance, the video stream for being interrupted.  
In the multi_traffic_control layer, each traffic type T had a list of lower layers through which it was possible to forward packets, either directly (in the case of long range links) or after channel probing (in the case of short range links). Specifically, in this work, all traffic packets intended to be transmitted via optical or short range acoustic required channel probing while packets sent through long-range acoustic were transmitted directly. The complete stack used in our simulations is depicted in Figure 8.
## 3 Results
### Clinical and Radiographic Outcomes Following NSRME and SARME
#### 3.3.9 Buccal Bone Width (BBW)  
A total of seven studies measured changes in the buccal bone width (BBW) [18; 30; 32; 33; 38; 39; 42]. Three of these studies included patients treated with NSRME [30; 32; 33], and the other four utilized SARME [18; 38; 39; 42]. Methods of measuring the BBW varied between studies, which could contribute to the variability in results. While all studies used CBCT, four studies [18; 32; 33; 38] recorded the BBW on axial sections while three studies collected these measurements on coronal sections [30; 39; 42]. Additionally, some studiesmeasured the BBW at the apico-coronal height of the first molar function [18; 32; 38], two millimeters below the first molar truncation [42], at the most vestibular point of cortical bone [39], and at the height where buccal bone deflected [30]. One study failed to describe the specific point of measurement [33]. It is important to note the apico-coronal height at which the BBW was measured since alveolar bone will undergo a remodeling process at various heights and at different degrees of extension associated with the tipping caused by tooth- and bone-borne devices.  
All but one study [18] found a decrease in the BBW at every site ranging between 0.2 mm and -1.24 mm after maxillary expansion. Notably, [PERSON] and colleagues reported no clear differences emerged between the effects of NSRME and SARME techniques on the change in BBW [32].
## 3 Results
### Applications in Patient Education  
Educational and motivational methods play an important role in informing individuals about their oral health status, including oral pathologies that affect oral tissues, thus helping to enhance their compliance with oral care at home. In particular, oral health education is effective in improving patient's attitude and practice of oral hygiene, providing useful devices and techniques for dental plaque control, which may promote gingival health and decrease caries occurrence [52; 53].  
AR and VR can be considered important tools to educate old and young patients. The expected outcome in using VR and AR for oral home care is to provide a digital instrument to improve oral hygiene practice in children and adults, motivating the prevention of oral diseases and making educational practices attractive. Moreover, by using games or educational interfaces, VR and AR may become important accessories in the process of teaching and learning. Although few articles reported the application of VR in patient education, it seems to be an engaging learning tool, and patients who participated in the studies perceived it to be beneficial in understanding their health status [54; 55]. Mainly in children, the advantage in using a game consists on: whenever the player executes the brushing technique incorrectly, the system provides a visual response which allows the patient to correct the brushing technique. However, further efforts to investigate the role of VR in education and oral health care should be explored in the future.
# Outage Performance of Wireless Powered Decode-and-Forward Relaying Networks in Rician Fading
## 1 Introduction  
### Background  
The rapid development of mobile communication technology has greatly changed the way of people's life. High speed, low delay, mass device connection, and low power consumption have become the prominent features of the 5 th generation mobile networks (5G). The popularity of the 5G network not only provides reliable communication for unmanned driving, telemedicine, virtual reality, smart home, and other applications, but also brings great improvement to the wireless network device, wireless technology, and derivative industry chains.  
Meanwhile, 5G commercial makes it possible for realizing the Internet of Everything (IoE). It is estimated that tens of billions of wireless network devices will be connected to 5G networks in various fields by 2025. Thus, the power supply for large numbers of battery-powered wireless devices will become a thorny problem, which may also lead to a series of environmental problems caused by waste batteries. More importantly, replacing the battery for mass wireless network devices will cost too much. For example, when the wireless network devices are deployed in the mountain top, forest, or chemical plant with harmful gas, it is difficult and dangerous to replace the batteries manually.  
Since wireless power transfer (WPT) is able to transmit power from an energy source to energy-limited devices wirelessly [1], it is capable of making network devices get rid of the shackles of wires and providing stable energy without frequent battery replacement. So, it provides an efficient solution to the problem of power supply for the low-power wireless device in Internet of things (IoT). There are two main branches of the research ofwireless power transfer. One is the simultaneous wireless information and power transfer (SWIPT) [2], i.e., the simultaneous interpreting of energy and information transmitted by sending signals to the wireless network devices, which was proposed in [3]. Later, [PERSON]. [4] proposed two receiver architectures: time switching receiver (TS) and power splitting receiver (PS) [5]. Another branch is the wireless powered communication network (WPCN) [6; 7], where energy and information are transmitted separately over orthogonal channel, which was proposed in [8].  
In addition to the limited energy, another big problem in IoT is the long-distance transmission of information. When the distance between the sender and the receiver is relatively far, the signal will suffer from serious fading, which leads to the degradation of communication quality. To release this issue, cooperative relay technology [9] was presented to shorten the data transmission distance, expand the network coverage, improve the transmission reliability, and provide a reliable guarantee for the long-distance transmission of information. One popular relaying protocol is the decode-and-forward (DF) protocol, with which the relay first decodes the information from the signal transmitted from the source, and then forwards the information to the destination node.
## 3 Results and Discussion
Moreover, the microstructure of the formed thin films suggests that a repeated surface nucleation may happen due to chosen technological parameters. The deposition temperature also contributes to the enhancement in crystal growth [35]. In addition, the chosen formation method leads to different properties of the formed thin films, for example, pulsed laser deposition results in nanocolumnar growth and high crystalline nanostructures' formation due to the high energetic plasma that reaches the substrates [46; 47; 48; 49; 50; 51; 52], whereas the ionization region in the e-beam does not reach the substrate [52]. When the constant deposition parameters are kept, the crystallite size depends on the compound, lattice strains, structural imperfections, additional phases, etc. The lattice parameters and crystallite sizes were calculated for each material and summarized in Table 3. It is seen that the yttrium dopant influences the lattice parameters and crystallite sizes. Crystallite size values for barium cerate decreased with the content of yttrium from 106.2 nm to 10.3 nm. A similar trend was observed in barium zirconates; the crystallite size values for barium zirconate decreased with yttrium content from 12.0 nm to 8.6 nm. The lattice parameter increases with the addition of yttrium dopant for cubic phase with values of 4.2436 and 4.2446 nm for BZO and BZY20, respectively. Those values are slightly higher than the reported [53; 54; 55]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Thin Films** & \begin{tabular}{c} **Referential** \\ **PDF Card** \\ \end{tabular} & \begin{tabular}{c} **Crystallographic** \\ **Phase** \\ \end{tabular} & \multicolumn{4}{c}{**Texture Coefficients for the Expressed Crystallographic Orientations**} \\ \hline BCO & 01-078-4102 & _Pnma_ & \begin{tabular}{c} (002) \\ **2.89** \\ \end{tabular} & \begin{tabular}{c} (202) \\ 0.05 \\ \end{tabular} & \begin{tabular}{c} (240) (321) \\ 0.73 \\ \end{tabular} & \begin{tabular}{c} (242) (400) \\ 1.20 \\ \end{tabular} & \begin{tabular}{c} (323) (402) \\ 0.11 \\ \end{tabular} \\ \hline BCY10 & 01-083-5709 & _Pbnm_ & \begin{tabular}{c} (020) \\ 0.55 \\ \end{tabular} & \begin{tabular}{c} (220) \\ 0.38 \\ \end{tabular} & \begin{tabular}{c} (024) (132) \\ **3.06** \\ \end{tabular} & \begin{tabular}{c} (040) (224) \\ 0.48 \\ \end{tabular} & \begin{tabular}{c} (240) (332) \\ 0.50 \\ \end{tabular} \\ \hline BCY20 & 01-084-7856 & _Pm-3m_ & \begin{tabular}{c} (110) \\ 0.89 \\ \end{tabular} & \
\begin{tabular}{c} (200) \\ 0.15 \\ \end{tabular} &
\begin{tabular}{c} (211) \\ **1.95** \\ \end{tabular} & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Texture coefficients of the formed thin films.
# Energy-Optimized Content Refreshing of Age-of-Information-Aware Edge Caches in IoT Systems
## 2 Related Work
quantify the data freshness using the AoI metrics and formulate an update optimization problem for the cache to minimize a cost that considers the users' AoI and the sensor's energy consumption. Instead, other works design cache refreshing schemes to balance AoI and latency. For example, in [24], [PERSON] et al. proposed two cache-refreshing schemes: in the first one, the cached items are updated in a round-robin manner; in the second one, the cached items are updated upon requests with a certain probability. However, both the proposed schemes may lead to unnecessary cache refreshing because they do not take into account the current state of the cached data. In [25], [PERSON] et al. proposed a cache-assisted lazy update and delivery (CALUD) scheme to balance content freshness and service latency in vehicular networks. In [26], [PERSON] et al. proposed a cache-refreshing scheme where the cached items are refreshed upon user requests if their AoI exceeds a given threshold called refreshing window; then, the value of the refreshing window is set solving an optimization problem that minimizes the average delay under the average AoI constraint of all sources. However, a requirement about the average AoI does not give any guarantee about how AoI values are distributed, so it is not possible to express a requirement in terms of the percentile of the distribution. Moreover, it is not possible to take into account the energy consumption due to the sampling operations, as they consider a device that generates updates on demand, i.e., the AoI at the device is always zero. It is thus not possible to apply this solution for devices having a periodic sampling behaviour. In this work, we propose a model-driven cache-management scheme to configure the parameters of a cache deployed on an edge-based IoT proxy. More in detail, we define and solve an optimization problem that aims at minimizing the energy consumption on the device and consider the freshness of the data, expressed in terms of AoI, as a constraint. The cache system could also be managed using data-driven schemes [27]; however, we aim to propose a simple cache-management scheme so that it can easily scale in case of massive IoT deployments.
## 4 Conclusions  
An anomaly detection model based on iForest is proposed to assist in the quality monitoring of MRSW. The work is based on a dataset with the class imbalance problem. In terms of the dynamic resistance and heat input, the change during the electrode life is analyzed, and features are extracted from them for anomaly detection. The following conclusions can be obtained.  
1. Class imbalance and overlap exist in the quality estimation of MRSW production and require proper anomaly detection algorithms for quality monitoring.
2. The similarity of dynamic resistance profile and heat input compared with the previous ten welds are valid features for detecting incomplete fusion welds.
3. For the classification of incomplete fusion welds and normal welds, the iForest model is a good candidate with a high AUC score of 0.9525 and high efficiency.  
Figure 10: Abnormal welds and the previous 10 welds: (**a**) incomplete fusion weld; (**b**) abnormal wire weld; for incomplete fusion weld: (**c**) \(R_{t}\) profile; (**d**) \(Q_{E}\); for abnormal wire weld: (**e**) \(R_{t}\) profile; (**f**) \(Q_{E}\).  
**Author Contributions:** Conceptualization, B.C.; methodology, B.C.; formal analysis, J.Z. and R.T.; investigation, J.Z. and R.T.; resources, B.C.; writing--original draft preparation, J.Z.; writing--review and editing, B.C.; supervision, B.C.; project administration, B.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors would like to thank [PERSON] for technical support of data acquisition system.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Spatial and Temporal Sampling Properties of a Large GNSS-R Satellite Constellation
## 4 Examples
### Sampling of a Landfalling Hurricane  
To illustrate the scientific implications of the sampling properties of an optimized constellation of GNSS-R satellites, a specific example is considered. Hurricane Ida made landfall along the U.S. Louisiana coast on 29 August 2021 at 1655 UTC. The measurements that would have been made on that day by two different GNSS-R constellations have been simulated and overlaid with GOES imagery of the storm. One constellation consists of 8 satellites arranged in a common orbit plane at an inclination of 30\({}^{\circ}\), similar to the CYGNSS configuration. The other constellation consists of 8 satellites in each of 3 orbit planes with RAANs of 0\({}^{\circ}\), 120\({}^{\circ}\) and 240\({}^{\circ}\) and a common 30\({}^{\circ}\) inclination. The global coverage with this configuration was shown in Figure 2. All spacecraft in both constellations are assumed to be capable of 16 simultaneous specular reflection measurements. Results of the simulation are shown in Figure 10. In the figure, four 6-hourly intervals of time are considered, consistent with the initialization time increment between operational numerical hurricane forecasts. All samples made during each 6 h interval are shown, together with the GOES image of the storm taken at the center of the four time intervals, namely at 0600, 1200, 1800 and 2400 UTC on 29 August.  
In Figure 10, the right column corresponds to measurements made by the 24 spacecraft distributed over 3 orbit planes. In each 6 h interval, the storm is well sampled both in its inner core region and across the surrounding wind field in all four principle quadrants. Inner core measurements support the determination of maximum sustained winds and hurricane intensity, while coverage of the wider wind field supports the determination of storm size and duration during landfall. The left-hand column in Figure 10 corresponds to measurements made by the 8 spacecraft constellation. The storm is quite well sampled throughout the inner core and surrounding wind field during only one of the four 6 h intervals (1500-2100 UTC). In two of the other intervals, samples are made over only two of the four principle quadrants of the storm, with little of the inner core sampled in either case. The fourth 6 h interval has no samples whatsoever of the storm.  
Figure 10: _Cont._
# A C-Based Framework for Low-Cost Real-Time Embedded Systems
## 8 Performance Evaluation and Analysis  
This part of the paper studies the overhead of RTFw in terms of execution performance and memory footprint. The testbed used for the experimental measurements consists of two boards equipped with an NXP LPC1768 ARM Cortex-M3 microcontroller [54] running at 100 MHz, one acting as controller card (with RTFw) and the other as remote I/O card (without RTFw). The two cards are connected via a 1 Mb/s CAN bus and use Modbus-CAN to communicate. Since the default resolution of the FreeRTOS tick timer is only 1 ms and improving it would cause additional overhead, a dedicated 32-bit hardware counter that runs at the same frequency as the CPU clock and has negligible access overhead has been used to collect timestamps. Timestamps are stored in a dedicated, on-chip 32  KB bank of static RAM (SRAM).  
During the experiments, which were conducted in a lab setting, no communication errors were detected. The effect of communication errors on RTFw performance has not been evaluated because RTFw works above ISO communication layer 7, and hence, completely relies on the underlying network protocol layers for error detection and recovery. For instance, Modbus-CAN and CANopen rely on the automatic layer-2 retransmission of CAN, Modbus-RTU on RS-485 implements layer-7 retransmission, Modbus-TCP relies on layer-4 TCP segment retransmission, and the various Modbus-UDP proposals implement some form of layer-4 UDP datagram retransmission. As a consequence, any communication error affects RTFw exactly as it would affect any other framework also working above layer 7 and using the same protocols. Nevertheless, a thorough evaluation of how communication errors affect the timeliness of the framework, especially when using event-driven programming, is crucial and could be the subject of future work.  
Memory footprint evaluation has been performed statically, instead, on RTFw object code, by means of the size toolchain command. This command apportions the footprint to three standard categories: text and read-only (RO) data, read-write (RW) initialized data, and read-write uninitialized data (BSS). These categories are important from a practical standpoint because, in an embedded system, they likely correspond to different kinds of memory. For instance, text and RO data can be stored in Flash memory (if available on the target) rather than RAM.