esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Discussion
Published clinical studies, showed different opinions regarding surgical approaches to phyllodes tumors: Some recommend extensive tumor excisions, others recommend mastectomies, although it has been proven that there are no differences between the two, in terms of the recurrence rate [30; 31; 32]. A skin-reducing mastectomy is considered safe from the oncological point of view, as studies have shown no increase of local recurrence. Recurrence generally occurs 2 years after, the time being shorter in the case of malignant tumors compared to borderline and benign tumors [33]. Some studies indicate that it is necessary to perform the sentinel lymph node biopsy procedure or axillary lymphadenectomy [31]. For recurrent phyllodes tumors, the treatment is re-excision and radiotherapy. Radiotherapy is also indicated for cases with positive margins, studies showing that there are no major differences between the conservative surgery with clear margins, and positive margins tumorectomy and radiotherapy, in terms of the survival rate [34]. There are no prospective randomized data supporting the use of radiation treatment with phyllodes tumors. However, in the setting where additional recurrence would create significant morbidity (e.g., chest wall recurrence following mastectomy), radiotherapy may be considered following the same principles, which are applied to the treatment of soft tissue sarcoma [35; 36; 37]. In return, there is no protocol regarding the use of the adjuvant role of chemotherapy (doxorubicin/dacarbazine in combination with cisplatin or ifosfamiode) and hormonal therapy in phyllodes tumors, which is more correlated with the histological grade and mitotic activity of the cells, suggesting that these tumors should be treated as sarcomas, rather than as carcinomas [38; 39]. The 5-year survival rate for benign and borderline phyllodes tumors is 91-93.8%, and 70-82% for malignant phyllodes [19; 40]. In the case presented here, the excision with negative, but probably insufficient, tumor-free margins, has led to the local recurrence and transformation into malignant phyllodes. Initially, in 2016, the patient underwent surgery in an oncological surgery department, but they did not consider the re-excision of the tumor margins. The patient came to our medical unit 2 years after, when the size of the recurrent tumor was much larger, and with the issue of a much wider excision or mastectomy with immediate reconstruction. We consider mastectomy a radical operation, therefore, the rules of wide margins of safety cannot be applied, especially since the tumor was already subtegumentary. In the case of large recurrent tumors, in which safe excision margins cannot be ensured, the best option is mastectomy. We conclude that in the case presentation of our patient, with a large, recurrent multifocal tumor, in which a mastectomy with negative excision margins and a low histological grade was performed, radiotherapy and adjuvant chemotherapy were not recommended.
## 3 Results and Discussion
### Model Predictions for KOH and KCl Release  
In order to give an overview of the expected release behaviour of all three main K species, Figure 6 presents the concentrations of gaseous KOH, KCl, K(g), HCl and chloromethane (CH\({}_{3}\)Cl) for wheat straw (Figure 6a,b) and Salix (Figure 6c,d) as predicted by the numerical model. Panels (a) and (c) show the devolatilization phase in detail, while panels (b) and (d) display both devolatilization and char combustion.  
Evidently, for the conversion conditions used in this work, the simulations predict that atomic K is a minor compound compared to KCl and KOH, which are highly abundant. KCl dominates in the beginning of the pyrolysis, due to the high availability of chlorine gas, whereas in the char conversion phase, as the particle temperature increases and oxygen reaches the particle, KOH starts to be released (Figure 6a,b). A similar behaviour was also observed experimentally [12].  
As expected from the fuel composition, the concentrations of the K species and the total K release are much higher for wheat straw than for Salix. For example, \(>\)100 times more KCl is released from wheat straw compared to the Salix sample, due to the high Cl  
Figure 5: Comparison between TDLAS-measured and simulated atomic K concentrations at different laser beam heights (2, 4 and 8 mm) above wheat straw (**a**) and Salix (**b**) particles. Lines with markers represent the measured data; lines show the model prediction.  
Figure 6: Model-predicted concentrations of KOH, KCl, K(g), HCl and CH\({}_{3}\)Cl at HAP 4 mm for wheat straw (**a,b**) and Salix (**c,d**). Panels (**a,c**) focus on the devolatilization phase, while panels (**b,d**) also include the char combustion stage.  
(and K) content in the straw-based biomass. As presented in Table 1, chlorine accounts for 7% of the ash in wheat straw, while this value is only 0.7% in the case of Salix. Thus, chlorine facilitates the release of potassium during devolatilization through evaporation of KCl. Apart from the K species, small amounts of HCl and CH3 Cl are also released during pyrolysis. However, the mass fractions of these species are orders of magnitude lower than those of KCl. Future studies comparing the particle model to experimental data should include measurements of KOH and KCl. The model can be improved by including the gas-phase reactions of the trace elements (K/Cl/S) in the vicinity of the particle.
# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 5 Analysis
#### 5.1.1 Machine Learning Based PLA
**(S20)** developed an intelligent decision method based on the AdaBoost learning algorithm to detect spoofing attacks. AdaBoost is an acronym of 'adaptive boosting' developed by [PERSON] and [PERSON] [98] and is the most extensively used form of boosting algorithm. AdaBoost is a powerful technique that can be used in conjunction with base classifiers to create a committee form that outperforms other base classifiers. The AdaBoost principle states that it improves its performance through an adaptive iterative process in prospective weak classifiers, known as learners, that are altered to enhance cases misclassified by prior classifiers. Due to the limited computing ability and high real-time requirements of IoT devices, the identification algorithm must meet the computational requirements. Therefore, in **(S4)**, a method was proposed for recognizing individual industrial internet of things (IIoT)devices, preventing and detecting MITM attacks in the IIoT environment by using a k-nearest neighbours (KNN) classifier. In addition to the above, the researchers in **(S21)** suggested two models for spoofing detection in dynamic wireless networks by leveraging the channel states of radio packets, which establish the test threshold based on reinforcement learning without knowing all channel parameters. The first model is based on Q-learning as a simple reinforcement learning technique; Q-learning allows each classifier to learn its optimal strategy in dynamic environments. For example, in spoofing detection with Q-learning, the receiver builds the hypothesis test to determine the sender for each packet received in the time slot. The second model is based on Dyna-Q as an extension of Q-learning; Dyna-Q uses Dyna architecture to formulate a learned-world model that consists of the significant functions of the online planning receiver. Dyna-Q accelerates learning in a dynamic environment with unknown parameters by obtaining hypothetical experiences from the world model. As a result, spoofing detection with Dyna-Q improves performance over Q-learning.
# Backdoor Attacks to Deep Neural Network-Based System for COVID-19 Detection from Chest X-ray Images
## 2 Materials and Methods
### Backdoor Attacks  
The procedure for establishing a backdoor in the COVID-Net model was based on a previous study [34]. To obtain a contaminated training dataset, a backdoor trigger was applied to 698 (\(\sim\)10%) images (398 normal, 273 pneumonia, and 25 COVID-19) that were randomly selected from the training images in Dataset 1. The trigger was set to a square measuring 5 \(\times\) 5 pixels (\(\sim\)1% height and width of the images) and a pixel intensity of 250, and it was placed at the lower right corner [near pixel coordinated (398; 398)] of the images. For each image \(x\), image \(x_{t}\) (the trigger) was generated by applying the trigger to \(x\) using the matrix of a \(480\times 480\) image mask, \(m\), which assumed a value of 1 at the coordination where the trigger was located, and 0 otherwise: \(x_{t}=\tau(\mathbf{x})=\mathbf{x}\circ(1-\mathbf{m})+250m\), where \(\circ\) indicated the element-wise product and \(\mathbf{1}\) was the \(480\times 480\) matrix in which all elementswere 1. Figure 1 shows the examples of normal, pneumonia, and COVID-19 images, with and without the trigger. Furthermore, incorrect labels were assigned to the images with the trigger. For non-targeted attacks, we assigned pneumonia, COVID-19, and normal labels to normal images, pneumonia images, and COVID-19 images, respectively. For targeted attacks, a target label was assigned to all the images.  
Using the contaminated training dataset, we fine-tuned the COVID-Net model with batch sizes of 32 and 50 epochs. The other settings (e.g., learning rate and optimizer) were the same as those used for training the original COVID-Net model.
## 5 Application of Marine Accident Occurrence Prediction Model and Results
### Reduction in MTSA-Related Accidents upon Implementation of MTSA  
#### 5.5.1 Annual Number of Cases of Accidents before Application of MTSA (BNA)  
The collection of all possible events, represented as a set, is called the sample space. If the sample space is denoted by \(\Omega\), and assuming an experiment A is conducted under exactly the same conditions repeatedly n times, with the occurrence of A denoted as n(A), then the probability (P) of event A is represented as P(A) and is defined as follows, according to Equation (3):  
\[P(A)=\underset{n\rightarrow\infty}{lim}\frac{n(A)}{n}. \tag{3}\]  
Considering A as an MTSA-related accident and n as the number of data groups for passenger ship operations, the derived probability (P) P(A) can be interpreted as the expected number of MTSA-related accidents per data group in the 2022 MTSA-related route  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Acc** & **Pr** & **Rc** & **F1** \\ \hline
0.0 & & 0.71 & 1.00 & 0.83 \\ \hline
1.0 & & 1.00 & 0.60 & 0.75 \\ \hline Macro avg & 0.80 & 0.86 & 0.80 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of evaluation on the original Data U set for ML RF model.  
Figure 11: Illustration of probability distribution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Acc** & **Pr** & **Rc** & **F1** \\ \hline
0.0 & & 0.98 & 0.97 & 0.98 \\ \hline
1.0 & & 0.97 & 0.98 & 0.98 \\ \hline Macro avg & 0.98 & 0.98 & 0.98 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of validation on the SMOTE Data U set for ML RF model.  
information. Following this logic, by summing the individual real-numbered MTSA-related accident prediction probability values for the I data groups of all routes near the MTSA-assessed project sites and the ships navigating these routes, it is possible to obtain the predicted number of annual related accidents for coastal passenger ships when not subject to MTSA. However, for an accurate assessment, the accuracy value from the evaluation of the original Data U set is set as the correction factor (C) for the trained model, which is multiplied to correct the prediction.  
\[BNA_{i}=\sum_{i=1}^{N}P_{i}\times C. \tag{4}\]  
The results calculated using this formula are presented in Table 11 below.
## 4 Results
### Comparative Analysis  
To evaluate the effectiveness of the proposed FS methodology, a comparison was performed in this section between the hybrid FS mechanism and the six well known FS techniques (the ones that are contained within the selection mechanism of the proposed methodology). The comparison was performed on dataset D that gave the overall best prediction performance. SVM was finally used to evaluate the prediction capacity of all the FS techniques considered here.  
We performed and validated all six FS techniques separately, each one resulting into a different feature subset. SVM was finally trained on the resulted feature spaces of increasing dimensionality and the optimum feature subset was identified per case. As indicated in Table 10, the majority of the competing FS techniques provided lower testing performances compared to the proposed FS methodology. The wrapper technique based on LR was the only one that achieved an equal testing performance (%) with the proposed FS methodology. Specifically, the wrapper FS achieved its maximum accuracy at 70 features, while the proposed FS methodology achieved the same accuracy score using a smaller feature subset (55 features).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Data Used in the Training**} & **Best Testing** & \multirow{2}{*}{**Best Model**} & \multirow{2}{*}{**Num. of Selected Features**} \\ \cline{2-2} \cline{6-6}  & \multirow{2}{*}{**Baseline**} & **M12 Progress** & **M24 Progress** & & **Performance** \\  & & **Wrt Baseline** & **Wrt Baseline** & **(\%) Achieved** & & **Features** \\ \hline A & \(\bullet\) & & & 71.71 & Logistic Regression & 50 \\ B & & \(\bullet\) & & 63.98 & Logistic Regression & 25 \\ C & & & \(\bullet\) & 66.67 & Decision Tree & 225 \\ D & \(\bullet\) & \(\bullet\) & & 74.07 & SVM & 55 \\ E & \(\bullet\) & & \(\bullet\) & 71.81 & SVM & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Summary of all reported results.  
Figure 12: Features selected in datasets A to E in (**a**–**e**), respectively. Axis y (selection criterion) denotes how many times a feature has been selected (6 declares that a specific feature has been selected by all six FS techniques and so on). Features have been ranked based on the selection criterion \(V_{j}\) and are visualised with different colors each one representing a specific feature category.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{6}{c}{**FS Criteria**} \\ \hline \multicolumn{1}{c}{} & \multicolumn{1}{c}{**Filter Algorithms**} & \multicolumn{2}{c}{**Wrapper**} & \multicolumn{2}{c}{**Embedded**} & \multicolumn{2}{c}{**FOS Criterion**} \\ \hline \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Chi-2**} & \multirow{2}{*}{**Pearson**} & **Logistic Regression (L2)** & **Logistic Regression (L2)** & **Random Forest
# Managing Emergency Situations in VANET Through Heterogeneous Technologies Cooperation
## 3 Architecture and Communication Protocol
### Proposed Architecture
#### 3.1.2 Edge Layer  
This layer is composed of several devices that have the main goal to accomplish local tasks such as communicate potentially dangerous events to vehicle, filtering collected data to transfer on cloud. Moreover, it provides a solution for local traffic distribution in case of emergency in order to avoid congestion on the involved area. In this way, the system is more flexible and it can react in a shorter time to events, dynamically rearrange traffic by sending ad-hoc messages to smart traffic lights and road signs. The LRTM is commonly an embedded device based on ARM architecture. It must be able to instantiate an embedded Linux distribution and to elaborate data coming from VANET layer. It has to accomplish several tasks related to the management of partial map areas. They can be placed with some RSU to extend their functionalities. It can communicate with several RSUs by exploiting dedicated interface called _Inter-Low-Layer Interface_ (ILLI). It is depicted in Figure 2.
# Does Below-Above Canopy Air Mass Decoupling Impact Temperate Floodplain Forest CO2 Exchange?
## 2 Materials and Methods  
### Site Description and Characteristics  
The studied lowland floodplain forest is situated 6.5 km north of the confluence of Morava and Thaya rivers (48.6815483 N, 16.9463317 E), on a flat surface area at elevation 150 m a.s.l. in the Czech Republic (Figure 1).  
The long-term average annual precipitation is 517 mm and the mean annual temperature is 9.5 \({}^{\circ}\)C. The average groundwater level is \(-\)2.7 m. The last flood was in 2013 and only in the lowest parts of the studied site above the soil surface. The experimental site consisting of a 122 (in the year 2022) years-old forest is composed of typical hardwood species. The main tree species are English oak (_Quercus robur_ L.), narrow-leaved ash (_Fravimus angustirolia_ [PERSON]) and hornbeam (_Carpinus betlulus_ L.) with average diameter at breast height (DBH \(\pm\) standard deviation) of 51.8 (\(\pm\)12.3) cm, 56.4 (\(\pm\)9.2) cm and 27.7 (\(\pm\)11.2) cm [9]. The average stand height is 27 m and the average height of the three main species separately is 31.0 (\(\pm\)3.6) m for oak, 35.9 (\(\pm\)3.4) m for ash and 23.4 (\(\pm\)6.9) m for hornbeam. The predominant soil types are Eutric Humic Fluivisol, Haplic Fluivisol and Eutric Fluivisol (according FAO 2014 Classification) with minimal soil depth of 60 cm [36]. Rising air temperatures and decreasing water level depths can be observed in the study region which are likely caused by climate change. Additionally, hydrological management via building of artificial dams in the study region amplified the drying of the study site and yielded that natural flooding very rarely occurs nowadays. Consequently, the water regime of the site changed over the years and nowadays represents relatively dry conditions for a floodplain forest.
## 2 Study Area and Data Source  
### Study Area  
Xiamen, located in the southeastern coastal region of Fujian Province in East China, features an undulating terrain primarily characterized by hilly topography. It is one of China's five major Special Economic Zones. As the leading city in the economic development of Fujian Province, Xiamen has attracted a significant convergence of people, goods, and commercial activities. It boasts beautiful natural surroundings, a pleasant climate, a rich cultural atmosphere, and a diverse and distinctive culture. It is acclaimed as one of the best places to live and the best tourist city in China. In theory, the characteristics of high population density and urban scale result in a significant demand for taxi services, leading to a substantial volume of taxi operations and travel activities, thus generating a wealth of taxi trajectory data. The complexity of the transportation network and the unique terrain also contribute to the diversity of taxi trajectory data in Xiamen. In practice, urban management and traffic planning in Xiamen have improved the convenience and efficiency of urban transportation by optimizing road layouts and enhancing transportation infrastructure, which has benefited taxis' operation and travel activities. Furthermore, technological application advancements and data collection techniques have made acquiring taxi trajectory data more efficient. Therefore, selecting Xiamen as the study area holds significant theoretical and practical value.  
Xiamen is divided into a total of six administrative districts. Siming District is Xiamen's historical and cultural nucleus, serving as the epicenter of its political, commercial, and cultural activities. Huli District, located at the city's heart, boasts a reputation for its bustling commercial districts and convenient transportation network. The proximity of these two districts has fostered the generation of exceedingly bountiful travel data. JimeiDistrict holds the mantle of Xiamen's educational and research hub, while Haicang District, nestled in the southeastern part of the city, boasts abundant marine resources and natural splendor, making it an area with relatively dense travel activities. Despite their expansive territorial reach, Tong'an District and Xiang'an District experience disparate identities. The former thrives on agriculture, while the latter is a burgeoning developmental precinct. Consequently, these two districts register the lowest density of personnel travel. The kernel density analysis map of travel data in the study area, as shown in Figure 1, illustrates that the travel data of people in Xiamen City is extensively distributed among the six administrative districts. The areas of high and low travel density accurately reflect the functional attributes of each section.
## 3 Results
### Dense Image Matching (DIM)  
The dense image matching algorithms in Inpho (Match-T) and Metashape are entirely different. In Inpho, the \"3 px\" default parameter (3\(\times\) GSD) for the generation of dense point clouds/DSM is used [49]. On the other hand, the \"high\" level--2\(\times\) GSD--is the usually adopted option in Metashape. With these two approaches, densities of 69 pts/m\({}^{2}\) (Inpho) and 156 pts/m\({}^{2}\) (Metashape) are expected. To expand the DIM analysis, a 1 px (1\(\times\) GSD) point cloud density in Inpho and the \"medium\" density (4\(\times\) GSD) are also chosen. In each configuration, a dense point cloud is generated and compared with the available reference data (Section 2.1).
# Secure Utilization of Beacons and UAVs in Emergency Response Systems for Building Fire Hazard
## 6 Discussion
### Position Accuracy
This is partly responsible for the sometimes wide variance in proximity data that smart sensors receive from beacons. In our study, the goal of position recognition is to locate building occupants and inform them of appropriate evacuation routes in an emergency situation. For this purpose, we considered a small location range of up to 1 m acceptable. Accordingly, we set a tolerance threshold value of 50 cm on the data received from the smart sensor. That is, if the smart sensor calculates the position of the user as being 3 m away, we regard the user's correct position as a range from 2.5-3.5 m.  
Experimental results show that the accuracy of proximity data provided by systems that use beacons is significantly low (33.7%, 48.7% and 30%). This is because beacons broadcast inconsistent proximity data. Most of the data broadcasts may be correct, but on occasion, they may also broadcast a highly incorrect distance. For example, if a user is 3 m away from a smart sensor, the raw method may read an incorrect broadcast from a beacon and calculate that the user is at a distance of 12 m. Likewise, the inaccuracy of beacon broadcasts also adversely affects the data calculated using the  
Figure 11: The experimental setting used for localization accuracy evaluation. Participant walks in a (a) 3 m radius, (b) 4 m radius, (c) 5 m radius, (d) 3–5 m radius. average method. At distances of 3-4 m, the average method shows decreasing accuracy as time for collecting the proximity data increases; see Figure 11a,b. For these reasons, neither the raw method, nor the average method are suitable for the calculation of precise location using beacons. In contrast to those methods, the median method provided a significantly high level of accuracy. This accuracy was also sustained over long periods of collection time for the proximity data. At all distances, 3 m, 4 m and 5 m, the proximity data gathered in a 30-s period and calculated using the median method showed an accuracy of more than 90%. Thus, in our UAV-EMOR system, we program smart sensors to collect proximity data from a beacon for 30-s periods, then to calculate the median proximity value of that data and send it to the server as the user's location.