esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
The aim of this study was to derive the MTSA-related ARR from routes near the assessed project sites when undergoing MTSA evaluation. The key procedures of this study are listed below and summarized in Figure 2:  
* Data collection included an assessment of projected site data, marine accident data, and coastal passenger ship operation data, followed by data pre-processing.
* The classification of maritime routes for coastal passenger ships related to the assessed projected sites and those unrelated through GIS analysis.
* The classification of MTSA-related accidents among marine accidents occurring on these routes using GIS analysis.
* Performing oversampling on the group of MTSA-related accidents with a relatively low occurrence compared to the coastal passenger ship operation information.
* The calculation of feature importance for deriving accident occurrence probabilities using the ML algorithm based on coastal passenger ship operation information and MTSA-related accident information on routes unrelated to the assessed projected sites.
* The prediction of MTSA-related accident occurrences assuming no MTSA implementation using the trained ML model on the operation information of routes associated with the assessed projected sites.
* Validation and evaluation of the modeling results using both sampling and original data.
* The derivation of MTSA-related ARR by comparing the predicted number of MTSA-related accident occurrences based on the MTSA implementation status derived from the modeling results.  
To carry out these processes, it is necessary to examine important methods such as the concept of MTSA-related accident scope, data sampling, marine accident reduction, and ML techniques. The results of this analysis are discussed in the following subsections.
# Comparative Analysis of 5G Mobile Communication Network Architectures
## 1 Introduction
This is due to the fact that the computing resources allocated to an application server for that particular region may not be sufficient to handle the congested data traffic in the allocated time. Although MEC-based networks can significantly decrease delay time through computer resource distribution and forward deployment of application servers, they are vulnerable to regional data traffic spikes, if insufficient computing resources are allocated in that region. In summary, the CCC and MEC architectures have their advantages and disadvantages. The CCC architecture can utilize centralized computing resources for all users' traffic, thus being more robust to regional data traffic spike. However, the transmission delay in the CCC architecture is directly proportional to the physical distance between feature elements, resulting in greater time delay for data traffic generated far away from the central cloud. Conversely, the MEC architecture, by locating feature elements in close proximity, can reduce transmission delay. However, it is vulnerable to regional data traffic spikes due to the limited computing resources allocated to each application server. In this study, we propose a new hybrid network architecture, appropriately named Hybrid Cloud Computing (HCC) architecture, which combines the advantages of both CCC and MEC network architectures while minimizing their risks. This newly proposed architecture, along with the standard CCC and MEC architectures, is analyzed in terms of the data traffic processing capability using a discrete event-based simulation model. The comparative analysis results for the three network architectures are presented in this paper. The following are the contributions of this study: (1) Establishing a simulation model of a communication network architecture that incorporates an offloading decision algorithm considering the queuing status in the system, which is a major factor influencing the latency in actual network environment, and (2) Assessing the feasibility of various communication network architectures using this model, including the newly proposed HCC architecture, with actual field data to reflect real-life situations.
###### Abstract  
Stormwater control measures such as dry detention basins and wetlands are often used to reduce the discharge of urban runoff and nutrients to streams, but differences in nutrient treatment may vary between practices. The goal of this study was to compare the nitrogen treatment efficiency of a dry detention basin before and after it was converted into a stormwater wetland. Inflow and outflow from a detention basin in Greenville, North Carolina was sampled during 13 storms and the stormwater wetland was sampled during 10 storms. Total dissolved nitrogen (TDN), NO\({}_{3}\)\({}^{-}\), NH\({}_{4}\)\({}^{+}\), chloride, dissolved organic carbon (DOC), and physicochemical properties were evaluated. Inflow and outflow from the detention basin had identical median concentrations of TDN (0.47 mg L\({}^{-1}\)). The median TDN concentration for wetland outflow (0.18 mg L\({}^{-1}\)) was 63% lower relative to inflow (0.49 mg L\({}^{-1}\)). The hydraulic residence time of stormwater in the wetland was more than 10 times greater relative to the dry basin. There was a significant (\(p<0.001\)) reduction in dissolved oxygen and oxidation reduction potential and an increase in median DOC concentrations in wetland outflow relative to inflow. Most of the reduction in TDN within the wetland was attributed to loss of NO\({}_{3}\)\({}^{-}\) (80% reduction), possibly due to denitrification. Conversion of dry detention basins to wetlands may provide significant benefits with regards to reducing TDN transport associated with urban runoff.  
dry detention basin; nitrogen; stormwater; urban runoff; wetlands +
Footnote †: journal: Applied Sciences
# Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling
## 2 Related Work
### Knowledge-Based Navigation Frameworks  
Recently, there is a growing interest in integrating the knowledge-based approaches into robotic systems, which use ontologies for knowledge representation or semantic web technologies for linking local robotic knowledge with web resources [15]. Therefore, the focus of robotic research community has been shifted to use these approaches for developing efficient robot navigation systems. [PERSON] et al. [16] proposed Ontology-based Multi-layered Robot Knowledge Framework (OMRKF). It aimed at improving the robot's intelligence with its four levels of knowledge related to the model, context, perception, and activity. Its knowledge representation helped the robots to recognize objects with incomplete information and execute a task by only determining high-level service. It modeled the objects, actions, and concepts using Prolog [17]. Later, [PERSON] et al. extended OMRKF and built OUR-K [12] knowledge framework for robot navigation, object recognition, action selection, manipulation, and planning tasks by applying ontology. It consisted of knowledge description for low-level data integration with high-level information and knowledge association in conjunction with bidirectional and unidirectional rules to enable the robot with reasoning capabilities when only partial information about an object was available.  
With the rise of cloud-robotics, the web-scale approaches for knowledge processing are used by robotic frameworks. Few researchers suggest reusing and sharing abstract representations of high-level knowledge across different platforms. However, the problem is that most data depends on hardware-specific configurations. Besides, current database systems usually store only one type of data in isolation. These constraints limit the data reuse efficiently. [PERSON] et al. [13] addressed this problem in the RoboEarth, in which data collection, storage, and sharing were independent of specific robot hardware. It allowed the robots to access and share real-world environment and object models. In its web-based knowledge base, the robot's skills, surrounding environment, and objects were defined as generic action recipes. It generated the semantic maps for robot localization and navigation by integrating the geometrically precise visual SLAM methods with semantically enriched models of recognized objects that could be downloaded from the database. It relied on reference ontology and provided the semantic representation of actions along with the support for reasoning capabilities and rule-based learning [18]. Similarly, the KnowRob [19] knowledge processing system was a semantic framework for performing manipulation tasks that were previously vaguely described. It reduced the gap between detailed description and vague task description. It modeled the environment while integrated the common sense and encyclopedic knowledge with task and object description, along with robot capabilities. It also supported reasoning capabilities and used inference methods to query the knowledge base.
## 3 Results  
### Temperature Measuring System  
Figure 6a shows the averages of the temperatures by zones for the groups with the standard deviation (SD). The results of the temperature averages in diabetic patients are higher, although they are not significant between the same zone for diabetics and healthy subjects, since the SDs are very large.  
Figure 6b shows the results obtained from calculating the mean absolute difference of each region for all patients. In this case, zone 2 was significantly different, its \(p<0.05\) (2-way ANOVA, post-test: Bonferroni); these results can be expected because it  
Figure 5: Diagram of the hub of information on diabetic foot. This shows the synchronous process that the systems follow to achieve the results. First, the data acquisition through USB, which operates at a maximum speed of 480 Mbps to each of the systems, then saves the information in.xls format to use it later on. The device reads through the file using Python language; then, the system finds the graphical results [26].  
Figure 6: (a) Average by zone of the temperature difference of the right foot with respect to the left foot (subtraction of the temperature of the right and left feet), bars = SD. (b) Average by zone of the absolute difference in temperature of the right foot with respect to the left foot (absolute value of the subtraction of the temperature of the right and left feet), bars = SD, significant differences (*).  
is the zone with the highest incidence of diabetic foot ulcers. However, all regions have significantly different means between the two groups. The fact that the other regions do not reach statistically significant differences may be due to the small sample size. From the results of the point temperature analysis, we can say that this physical property of plantar tissues can be used as a predictor of diabetic foot. We found that the averages of zone 2 temperature are significantly different in diabetic patients. Our results allow us to find significant differences between the data measured in different zones of the foot of patients with diabetes and healthy subjects. However, due to the size of the sample and not having a diagnosed diabetic foot, it is not possible at this time to know the medical reasons for these differences.
# Synchrotron X-ray Microprobes: An Application on Ancient Ceramics
## 2 Materials and Methods
The XRF signal was acquired by a SGX Sirius SD silicon drift detector installed at 90\({}^{\circ}\) from the incident beam. A visible light microscope located perpendicularly to the sample surface allowed micrometric visualization and navigation of the sample. During XRF analyses, the samples were scanned at 18 keV with a step size of 10 \(\upmu\)m using 1 s acquisition time per pixel. Samples were scanned in two geometries, namely with the gloss facing the beam and in cross-section. On these latter samples, after XRF mapping, specific points on the surface of the gloss were selected to perform XANES measurements across the Fe K and Zn K absorption edges. The XANES spectra were collected in fluorescence mode with 1 s/point acquisition time. For Fe an energy resolution of 2 eV was chosen in the pre-edge range from 7.03 to 7.08 keV and in the post-edge zone from 7.2 to 7.33 keV. The area around the edge from 7.08 to 7.2 keV was scanned with higher resolution in steps of 0.5 eV. For Zn an energy resolution of 2 eV was used in the pre-edge range from 9.37 to 9.4 keV and in the post-edge range from 9.47 to 9.71 keV, while close to the edge from 9.4 to 9.47 KeV an energy resolution of 0.5 eV/step. Reference standards of Fe\({}^{2+}\) and Fe\({}^{3+}\) were used for calibration and to help Fe-speciation identification, while ZnO and ZnFe\({}_{2}\)O\({}_{4}\) (Gahnite) were used for calibration and help with Zn-speciation. Collected spectra were compared both to the measured reference standards and to reference spectra available in the literature. For a pre-selection of the samples to be mapped in cross-section, XANES maps were acquired at 4 different energies across Fe and Zn K absorption edges, 7330 eV, 7136 eV, 7132 eV, 7127 eV for Fe and 9872 eV, 9688 eV, 9669 eV and 9665 eV for Zn, respectively. Some of these energies were used to acquire insights on Fe (7127 eV, 7132 eV) and Zn (9688 eV, 9669 eV and 9665 eV) speciation distribution in 2D, in addition to the single point spectra previously acquired. XANES maps were acquired over areas of 150 \(\upmu\)m \(\times\) 150 \(\upmu\)m, with 5 \(\upmu\)m step size and 1 s acquisition time per point, centering the area as 50 \(\upmu\)m above surface and 100 \(\upmu\)m below surface (that is, inside the sample paste). For comparison, on the same analysis area scanned at PUMA, traditional microchemical investigation was performed by SEM-EDS at IPANEMA facility labs [42] by using a ZEISS Supra55 VP SEM-EDS with a Schottky Field Emission Gun (FEG) equipped with a Bruker EDS system. Measurements were carried out without any metal coating on the sample surface to assure a non-invasive and non-destructive testing; nevertheless, to obtain better images and microanalysis, studied samples were wrapped in aluminum foil leaving out a small window in the region of interest to analyze.
# Evaluation of Glottal Inverse Filtering Techniques on
## 2 Analysis Methodology and GIF Methods
### Analysis Methodology  
The designed analysis methodology, as depicted in Figure 1, can be divided into two main phases: parameter tuning and error computation. The same speech database is considered as input for both steps. The procedure is repeated for each utterance in the database. The output of the parameter tuning stage is the best parameter configuration for the considered GIF technique given an error metric, whereas a set of typical glottal error measures are obtained as results of the second stage. In both cases, the estimated glottal flow signal, obtained from the GIF method, is evaluated with respect to the glottal flow ground truth, which is included in the dataset's speech audio files. Notice that the methodology includes a block devoted to computing GCIs from the speech signal, which is necessary for those GIF methods that rely on this information as an anchor point. Finally, the glottal error measures, subsequently used for conducting statistical analyses, are computed at the pulse level (based on GCIs) to compare the different GIF methods.
# Seismic Performance of Frame Structure with Hysteretic Intermediate Discontinuity
## 2 The Mechanical Model
### Gain Coefficients  
The results of the MS are compared with those of the AS to verify if the introduction of the intermediate discontinuity reduces the displacements and drifts of the structure. The comparison between the displacements and drifts of the two models is made through three coefficients that are named gain coefficients and are defined as  
\[\alpha_{1}=\frac{\max[u_{1}(t)]}{\max[u_{1}^{A}(t)]};\ \ \alpha_{2}=\frac{\max[u_{2}(t)-u_{d}(t)]}{\max[u_{2}^{A}(t)-u_{1}^{A}(t)]};\ \ \alpha_{3}=\frac{\max[u_{d}(t)-u_{1}(t)]}{u_{0}}, \tag{8}\]  
where the displacements \(u_{1}\), \(u_{2}\), and \(u_{d}\) refer to the MS (Figure 2a), \(u_{1}^{A}\) and \(u_{2}^{A}\) refer to the AS (Figure 2b), and \(u_{0}\) is the threshold value of the acceptable displacements. The proposed gain coefficients represent a relative evaluation of the effectiveness of the hysteretic intermediate discontinuity. They evaluate the performances of the system with the discontinuity with respect to a reference system, represented by the corresponding linear elastic system without intermediate discontinuity.  
The gain coefficient \(\alpha_{1}\) refers to the substructure and is the ratio between the displacements of the substructures of the MS and AS. Such a coefficient evaluates the performance of the superstructure as TMD for the substructure. The gain coefficient \(\alpha_{2}\) refers to the superstructure and is the ratio between the drifts of the superstructure of the MS and AS. In this case, \(\alpha_{2}\) evaluates the behaviour of the intermediate discontinuity as base isolation for the superstructure. Finally, \(\alpha_{3}\) is the ratio between the displacement of the intermediate discontinuity and the maximum threshold value \(u_{0}=15\) cm. Such value was chosen in order to limit excessive displacements on the discontinuity layer that may jeopardize the safety of the structure. Each gain coefficient evaluates a different capability of the intermediate discontinuity. If a gain coefficient is less than unity, the introduction of the discontinuity is beneficial for a specific part of the structure. Therefore, the improvement of the dynamical behaviour of the frame means obtaining gain coefficients lower than unity (i.e., a reduction of displacements and drifts). A simultaneous control on these coefficients leads to a unitary approach to the dynamic enhancement of the structure following from the introduction of the discontinuity.  
Two additional gain coefficients are introduced with the purpose to evaluate how the intermediate discontinuity affects the accelerations of the substructure and superstructure. They read  
\[\alpha_{1}^{A}=\frac{\max[\bar{u}_{1}(t)]}{\max[\bar{u}_{1}^{A}(t)]};\ \ \alpha_{2}^{A}=\frac{\max[\bar{u}_{2}(t)-\bar{u}_{d}(t)]}{\max[\bar{u}_{2}^{A} (t)-\bar{u}_{1}^{A}(t)]}, \tag{9}\]  
where \(\bar{u}_{1}(t)\), \(\bar{u}_{2}(t)\), and \(\bar{u}_{d}(t)\) are the accelerations of the MS. Similarly, \(\bar{u}_{2}^{A}(t)\) and \(\bar{u}_{1}^{A}(t)\) are the accelerations of the AS.
## Materials and Methods
### Methods
#### 2.2.4 Bootstrap Aggregate (Bagged) Regression Tree (BRT)  
Bagging (bootstrap aggregating) is one of the learning methods of the ensemble learning model [52]. In this method, the training data series is divided into \(N\) new training data series by the bootstrap sampling method, and a weak learner is used to train \(N\) datasets. In the bootstrap sampling method, random sampling is performed by replacement, which means that some of the training series data may be repeated, and some may be omitted. In the bagging regression tree (BRT) method, each of the \(N\) training data series is learned by a tree regression model. The final result is obtained by averaging the output of the \(N\) tree models (Figure 4). In the tree regression method, the results of each tree individually have high variance and low bias. Averaging the results of \(N\) trees reduces the variance of the model, increases the accuracy, and prevents overfitting of the model. The performance of the BRT method depends on the correct choice of the number of trees (\(N\)). To determine the optimal value of \(N\), out-of-bag (OOB) error estimation curves can be used. Usually, two-thirds of the data series are used in model training by bootstrapping. One-third of the remaining data that does not enter the training phase in each tree is called out-of-bag (OOB) observations. OOB observations are used to estimate the prediction error. The error value of the obtained OOB observations is a good criterion for model error validation. In the present study, the fit ensemble function in MATLAB software was used to build a bagged regression tree model.
###### Abstract  
Composite discoloration is considered one of the most significant disadvantages of dental restorations. The purpose of our study was to assess the color susceptibility of two esthetic composite filling materials, microhybrid composite (ValuxTM Plus) and nanohybrid composite (FiltekTM Z250 XT), to different solutions and compare the results among different measuring methods (Intraoral scanner, Easyshade spectrophotometer, and Vita classical shade guide). A total of 100 specimens were fabricated in this experimental study. Each sample was soaked in either Coca-Cola, orange juice, tea, coffee, or distilled water for 30 days. Color measurement was performed using the spectrophotometer, IOS 3 Shape Trios, and Vita classical shade guide. L*a*b* values were recorded to determine the color changing (\(\Delta\)E00). The significance level was \(p<0.05\). All samples showed clinically visible color changes after immersion in coffee, tea, orange juice, and Coca-Cola. The nanohybrid composite showed high color change when placed in tea (\(\Delta\)E00 = 12.5) measured by the Intraoral scanner, spectrophotometer (\(\Delta\)E00 = 23.21), and by Vita classical shade guide (\(\Delta\)E00 = 25). Less color discoloration was perceptible in the microhybrid composite when immersed in Coca-Cola (\(\Delta\)E00 = 1.78) measured by spectrophotometer, intraoral scanner (\(\Delta\)E00 = 2.85), and measured by Vita classical shade (\(\Delta\)E00 = 3.2). The results showed that measurements with the spectrophotometer and Vita classical shade guide were correlated. The results were analyzed using the chi-square and Wilcoxon signed-rank tests, and there was a significant difference (\(p<0.001\)) in both composite resins for all the solutions, with microhybrid being more color stable. Microhybrid composite had more color stability than nanohybrid composite, especially when immersed in tea and coffee. The spectrophotometer with Vita classical shade guide showed better interrater agreement than with the intraoral scanner.  
color stability; composite resins; dentistry; staining; solutions; method comparison Article
## 5 Key 6G Technologies
### Other Perspectives
#### 5.3.5 Energy-Neutral Devices and Backscattering Communication in 6G Networks  
Passive or energy-neutral devices draw power from the ambient environment, particularly from RF signals, to sustain their operation. These devices, often equipped with capacitors charged by incoming RF fields, use backscattering for communication. Through load-modulated backscattering antennas, these devices alter the load impedance to modify the backscattered field, facilitating communication with network nodes [210; 211; 212; 213; 214; 215].  
Energy-neutral devices open avenues for various applications, such as widespread sensor telemetry and tracking of products and people in factories, hospitals, and smart cities. The allure lies in the ability to deploy electronics on a massive scale without the need for batteries, eliminating concerns about charging, replacement, and the use of toxic battery materials.  
However, backscattering communication faces challenges, particularly in the link budget, where path gain scales as \(\beta^{2}\). At the network node or device side, directional antennas or antenna arrays might be required to combat high path loss. A key component of 5G and 6G networks, massive MIMO provides array gains proportional to the number of antennas, which makes it an essential tool for backscattering device communication. Projects like H2020-REINDEER are actively developing this technology [12]. Cost is another challenge, as deploying numerous devices at scale can amplify overall costs, necessitating cost-effective solutions [216; 217; 218].