esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For instance, the span lengths of railway bridges are typically very short (below 20 m), and the trains cross the bridge at high speed, which implies that the duration of the vehicle traveling on the bridge is very short and the sensor signals recorded from the crossing vehicle are often not long enough for the application of a reliable signal processing method to extract the bridge's natural frequencies with the desired accuracy. Hence, to obtain the best accuracy for estimating the bridge's natural frequencies, most of the studies consider the crossing vehicles at low speed, as pointed out by [PERSON] and [PERSON] [11]. However, [PERSON] et al. [38] presented a new method to identify the bridge frequency from vehicles moving at high speed by combining the responses of multiple vehicles. It is shown that by combining the acceleration of several vehicles to extend the overall duration of the signal, the frequency resolution can be improved, and the bridge frequency can be successfully identified using vehicles traveling at high speed. However, currently, no regulations and/or standards exist for the application of the iSHM method, and currently, it is noted that almost all articles on the application of the iSHM method are based on theoretical studies and numerical simulations of the traveling train and bridge, respectively. Results of in situ measurements with instrumented trains and railway bridges and their comparison with theoretical and numerical simulation results are often missing part in scientific studies. Hence, the objective of this paper is to present the application of the vehicle-based iSHM method to extract the first bending frequency of railway bridges and to validate the results of numerical simulations by in-situ measurements of both the train and bridge vibrations during the train crossing events. In the first part of the paper, the theoretical train and bridge model is presented based on the vehicle-bridge interaction model composed of a two-dimensional multi-body system of eleven coaches interconnected through Jacobs bogies and a simply-supported Euler-Bernoulli beam. In the second part, besides numerical simulations of train crossings with different train speeds and analyzing the computed vibration responses in the frequency domain, in situ tests are conducted to validate the results of the numerical simulation. Both the considered steel deck bridge and the crossing train are, therefore, instrumented with several accelerometers, and the vibration responses are measured during train crossing. The results of numerical simulations and in situ tests are presented and compared. Finally, some conclusions will be given based on the conducted theoretical and experimental research work. Figure 1 illustrates the entire process applied in our research to extract natural frequencies of railway bridges--after first determination through forced vibration testing--from train recorded data only.
## 3 Material and Methods
### Decision Tree  
In addition to the radar chart, the authors have developed a decision tree that classifies the type of event based on the values measured by the indicators following the statements shown in Figure 4. The following specific terms are used in the decision tree (these terms are more fully explained in the Results section):  
* Transitional values: values acquired by Sk, K, FOS and CF in signal transition cycles, different of nominal values (NV in Figure 4). These thresholds were empirically defined and depend on the location or site where the measurements are developed.
* Differentiating value: value acquired by SOS that allows differentiating sags from interruptions.  
The methodology for developing the system is structured as follows in Figure 5:  
1. Calibration with synthetic signals: initially, signals are extracted from Database #1. Maximum, minimum, and nominal values for each indicator are studied. These values have defined the limits for radar chart vertices and boxplot normalization.
2. Observation of indicators' behavior patterns under different events from Database #1: indicators' behavior is reflected per cycle in the radar chart, obtaining specific patterns for each type of event. This behavior is also applied for the development of decision tree statements.
3. Signal processing and comparative analysis: each signal is analyzed in three ways: (a) generating radar charts per cycle, illustrating the signal's evolution, (b) drawing a boxplot per signal that compiles all values per indicator, and (c) decision tree results per signal. These analyses are compared, and subsequently, the correspondence in behavior of the same event extracted from two different databases is also compared.
4. Determination of tool accuracy. Once all the signals available in both databases have been processed, the percentage of events satisfactorily located by each tool, radar chart with decision tree and boxplot, is obtained. The tool accuracy will be explained in Section 4.3.  
Figure 4: Decision tree scheme method for power quality event classification.
# Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection
## 2 Review of Medical Imaging Techniques for BC Detection
### Medical Imaging Techniques for BC Diagnosis  
#### 2.1.1 Mammography  
Considering its sensitivity to breast calcifications, mammography, one of the most accurate screening procedures, reached superior results in identifying micro-calcifications or agglomerations of calcifications. The detection of suspicious micro-calcifications using mammography represents one of the earliest signs of a malignant breast tumor. A mammogram is a low-dose X-ray technique that is able to detect the subtle changes, including structural deformations and bi-lateral asymmetry together with other anomalies (e.g., calcifications, nodules, densities) [4].  
The mammography procedure is considered an essential tool for screening and for diagnostics. A screening mammogram comprises two visualizations of each breast, and it is highly recommended for women aged 40 years old or over. If suspicious regions are detected in the mammogram, additional mammograms need to be considered. A screening mammogram is performed in order to assess the breast tissue for any abnormalities and to establish a foundation for future investigations.  
Compared to screening, a diagnostic mammogram is applied when there is a need for an accurate diagnosis. In this case, when there is any evidence of a lump or small tumor, a more focused investigation is conducted. A diagnostic mammogram is also performed when a specific region of the breast is being followed over a period of time [5]. Diagnostic mammograms provide much more information based on the acquired additional images that also include spot compression or spot compression with magnification [6] and can also offer a broader insight into the detected malformations during the screening phase.
# Improved Dragonfly Optimization Algorithm for Detecting IoT Outlier Sensors
###### Abstract
Sensors can be used in small spaces due to their small size and limited memory, processing power, and battery. Since there is no base station in wireless sensor networks and their radio frequency is low, the dispersion of nodes should be such that the nodes are in each other's radio coverage and deliver the sensed data to the destination from the nearest route while thoroughly covering the area. Usually, due to the geographical location of these types of networks in dangerous environments and the remote locations of some nodes, it is impossible to recharge or use them properly [16]. Therefore, choosing the correct location of sensors and removing extraneous sensors increase a system's efficiency and cause it to consume less energy. This paper has tried to propose an approach to detect outlier sensors of the Internet of things by using a genetic algorithm. In this article, after reviewing the Internet of things and its challenges, a modified design of the dragonfly optimization algorithm is introduced, and the suggested technique is described. In the following, after introducing the simulation environment, simulation parameters, and evaluation criteria, the research findings are presented. The main contributions of this paper can be highlighted as follows:  
* Recommends a modified dragonfly optimization algorithm structure for better area coverage and reduced energy use. * Utilizes the minimal number of nodes in the coverage area, the lifetime of the network, which includes the period from the commencement of the first node to the time of the first node's shutdown, and the network power to assess efficiency. * Makes comparisons with some other approaches that have been published. The structure of the paper is as follows: In Section 2, a literature review concerning the method is explained. Section 3 describes the method of designing for providing the improved version of the dragon fly optimizer. In Section 4, the optimal coverage methodology for the problem based on the improved dragon fly optimization algorithm is explained. Section 5 defines the simulation results, and the paper is concluded in Section 6.
## 2 Materials and Methods
### Testing Protocol
#### 2.5.3 Dry-Land Test Protocols  
Static Balance Test Protocols  
Static balance was assessed pre- and post-intervention using the BTS P-Walk device on a stable surface with athletes barefoot.  
* Bipedal test: participants stood with their feet on designated markers, hands on hips, eyes closed, maintaining balance for 30 s.
* Unipedal test: Participants stood on one leg for 15 s, with the free foot resting at the ankle. This protocol mimics unilateral weight distribution demands of skiing.  
Counter-Movement Jump (CMJ) Protocol  
The Counter-Movement Jump (CMJ) test was used to evaluate explosive force, with jump height measured via the G-walk system. The test involved an initial eccentric phase, where the leg extensor muscles were stretched through a preparatory contraction, followed by a concentric phase, characterized by an explosive extension in the opposite direction.  
Participants performed 15 consecutive CMJs with hands on hips to eliminate the influence of arm swing, and the G-walk system calculated the average jump height. We selected the Counter-Movement Jump (CMJ) to measure explosive force because it is a well-established, valid indicator of lower-limb power, requiring minimal technical skill and demonstrating high reliability in adolescent populations [45]. This aligns directly with this study's goal of evaluating improvements in lower-limb power for alpine skiing, where rapid force generation is critical for propulsion and quick directional changes.
# Simulation of a Solar-Assisted Air-Conditioning System Applied to a Remote School
## 2 System Description
chilled water circuit allows controlling the number of cooling coils used for air conditioning, allowing a saving of thermal energy when air conditioning is not required in all classrooms. Figure 3: Schematic diagram of the equipment and circuits of the solar thermal cooling system. Figure 2: Components of the thermosolar absorption cooling system at Puertecitos school, where we can appreciate the **(a)** thermal energy storage tank, **(b)** cooling tower with freshwater and seawater storage tanks, **(c)** auxiliary heater and chiller inside the machine room, and **(d)** titanium coil inside the cooling tower. Depending on the mode of operation in the cooling tower mentioned above, the cooling water circuit is modified with manual valves for the use of seawater and/or freshwater only. When seawater is required as cooling fluid in the tower, the seawater reposition tank fills the cooling tower tank while the P1 pump recirculates the fluid in a closed circuit between the V1 and V3 valves, cooling the seawater by evaporation. During this process, the P2 pump takes water from the freshwater tank and supplies it to the chiller to remove the heat generated. Then, it transfers it to the seawater by means of a coil heat exchanger submerged in the cooling tower reservoir (Figure 2d) and returns it to the freshwater tank, all in the closed circuit between V2 and V4. This configuration avoids the problem of freshwater evaporation in the cooling tower by using seawater, an easier resource to acquire in the community. On the other hand, when freshwater is used as a direct cooling medium, the P2 pump takes water from the cooling tower reservoir through the V3-V4 circuit, it removes the heat from the chiller, and, through the V2-V1 circuit, it cools the freshwater through evaporation in the tower. The freshwater tank is positioned in a way that when the water level in the tower tank decreases, the water is replaced by gravity and controlled by a float. In the latter configuration, the P1 pump does not work.
# Multi-Layer Feature Fusion-Based Community Evolution Prediction
## 1 Introduction
The algorithm first constructs the global and local feature matrices of nodes in the same community. Then MCEP performs non-negative matrix factorization (NMF) on these two matrices to obtain the role of the node. At the community layer, because the community is a mesoscopic structure, we call the features of the mutual influence between communities the community mesoscopic features. To obtain the community mesoscopic features, we regard the community as a hypernode and use the hypernode to construct the hypergraph. Then, we perform NMF on the adjacency matrix of the hypergraph to obtain the community mesoscopic features. After all the features are obtained, we merge the obtained features. The merged feature is the final feature representation of the community. In addition, to incorporate time information into the training set, we integrate the features of the community into multiple snapshots. Finally, MCEP constructs a training set according to the obtained features and then trains a classifier as the final community evolution prediction model. Experimental results show that the algorithm proposed in this paper is better than other algorithms. The main contributions of this paper can be summarized as follows:  
* We propose to obtain features from the node layer and the community layer, respectively. In this way, the features of nodes and the influence of inter-community relationships on community evolution can be better captured. * We propose the global and local-based community role-extraction algorithm. The algorithm first obtains the features of the node from the global scope and the local scope. Then, global and local feature matrices are constructed based on the obtained node features. Finally, we obtain the role attribution matrix of the node through NMF. Through the role attribution matrix, we can assign community roles to each node. * We propose the obtaining of the community mesoscopic features through a community hypergraph. To obtain the community mesoscopic features, we first construct the hypergraph according to the similarity between the communities. Then we perform NMF on the adjacency matrix corresponding to the hypergraph to obtain the mutual influence between the communities. The rest of the paper is organized as follows: Section 2 introduces the overall process of community evolution prediction and related work. Section 3 introduces the MCEP algorithm proposed in this paper in detail. Section 4 shows the results of the comparative experiment and other related experiments. Section 5 summarizes the full paper.
###### Abstract  
The aim of this study is to investigate the variations of depth dose enhancement (DDE) on different nanoparticle (NP) variables, when using the flattening-filter-free (FFF) photon beam in nanoparticle-enhanced radiotherapy. Monte Carlo simulation under a macroscopic approach was used to determine the DDE ratio (DDER) with variables of NP material (gold (Au) and iron (III) oxide (Fe\({}_{2}\)O\({}_{3}\))), NP concentration (3-40 mg/mL) and photon beam (10 MV flattening-filter (FF) and 10 MV FFF). It is found that Au NPs had a higher DDER than Fe\({}_{2}\)O\({}_{3}\) NPs, when the depths were shallower than 6 and 8 cm for the 10 MV FF and 10 MV FFF photon beams, respectively. However, in a deeper depth range of 10-20 cm, DDER for the Au NPs was lower than Fe\({}_{2}\)O\({}_{3}\) NPs mainly due to the beam attenuation and photon energy distribution. It is concluded that DDER for the Au NPs and Fe\({}_{2}\)O\({}_{3}\) NPs decreased with an increase of depth in the range of 10-20 cm, with rate of decrease depending on the NP material, NP concentration and the use of FF in the photon beam.  
nanomaterial; gold nanoparticles; iron oxide nanoparticles; cancer therapy; depth dose enhancement; Monte Carlo simulation; nanoparticle-enhanced radiotherapy
# TACRP: Traffic-Aware Clustering-Based Routing Protocol for Vehicular Ad-Hoc Networks
#### Contribution of the Research  
* The contribution of the research is to improve the traffic management of dynamically varying VANET topology and also to reduce energy consumption and communication overhead.
* In the cluster-based VANETs, in a maximum of the cases energy loss happens during the time a vehicle leaves a cluster and joins a new cluster. This process increases the congestion in the network, and this is reflected in an increase of communication overhead.
* For this reason, a Traffic Management Unit (TMU), that helps to monitor the congestion, and a clustering model, which works to reduce the consumption of energy during the process of leaving and joining clusters is proposed in the Traffic-Aware Clustering-based Routing Protocol (TACRP).
* Due to the accountable traffic management system, the delay and routing overhead in the network can be greatly reduced, which leads to improved network throughput and packet delivery ratio.
* By using the concept of stable clusters, energy consumption is reduced, which leads to improved energy efficiency of the network. Likewise, the proposed method helps to improve the overall performance of the dynamically varying VANET network.  
The organization of the paper is as follows. In Section 2, the earlier related works of traffic and cluster-based VANET networks, and their merits and demerits, are discussed. In Section 3, the AODV routing protocol is elaborated on. In Section 4, the proposed Traffic-Aware Clustering-based Routing Protocol (TACRP) is discussed. In Section 5, the performance evaluation and results are discussed. In Section 6, the conclusion is given, with the future direction of the research.
# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
## 2 Literature Review
### Sandia Voltage Shift:  
This uses positive feedback to prevent islanding. A positive feedback of voltage and the DG inverter reduces its current and thus its output power. At normal operation, there is no effect when the power is reduced. However, during the absence of the utility, there is a reduction in the voltage. This reduction results in the reduced DG inverter current. These reductions can be calculated to find the islanding condition. The weakness of this method is the inverter's decreased efficiency and a decrease in the output power quality [50].
## 3 Materials and Methods
### Feature Encoding  
The feature encoding can be explained simply as the transformations needed for the data to be translated in a numerical way that a machine learning algorithm can use as an input.  
In this subsection, only the set of methods important for this study will be presented:  
* Standard Scaler: Divides the value by a _normalization constant_. This does not change the shape of each feature distribution but normalizes the values to be understood by the model ([20]);
* Dummy Codding: This is one of the methods to encode categorical variables (qualitative variables). It transforms labels into a new feature with 0 or 1 and it guarantees an advantage when compared to the one-hot encoder (another method to encode categorical variables)--it removes one degree of freedom and it uses one less feature in the representation ([20]);
* Label Encoder: It is also used to encode categorical variables, with multiple labels. It associates with these labels one different and progressive integer number ([21]).
## 5 Experiments
### Experiment Results
#### 5.4.2 Horizon Analysis  
As can be seen from the Figure 6 and Table 4, as the horizon increased, the prediction error increased steadily. The model had higher prediction accuracy for the near-term and lower prediction accuracy than for the long-term. Different evaluation indicators were not completely consistent with the direction of model validation. For example, the model achieved the minimum value of the MAPE on the PEMS07 dataset, while the MAE and RMSE were always at the highest values.
# Network Tower Sharing Analysis in Greece: A Structure-Conduct-Performance Approach
## 3 Results
### Market Structure  
The current section aims to describe the structure of the mobile communications market in Greece, in order to provide evidence about significant aspects related to operators' behavior. In 2022, the number of mobile telephony connections came up to 13.8 million, recording a marginal increase of 0.1% versus 2021, while active connections decreased by 1.2% to approximately 11.4 million. The term \"connections\", instead of \"subscribers\", is preferred since a unique subscriber (individual or legal entity) can hold more than one connection. On the other hand, \"Active connections\" indicate that retail or wholesale revenues were generated during the last quarter (Table 1). The vast majority of the above-mentioned number of total connections concerns residential connections, reaching 12.5 million connections at the end of 2022, which decreased by 0.36% versus 2021. On the other hand, business users' connections increased by 4.3% from 2021 to 2022, reaching 1.3 million connections (Figure 2, Table 1). Regarding the penetration of mobile telephony on Greek population, it increased at the end of 2022 by 2.3%, reaching 132% (from 129% in 2021) in terms of total connections, and 109% (from 108% in 2021) in terms of active mobile telephony connections. In 2022, Cosmote's market share in terms of total connections performed an increase of 47.8% versus 46% in 2021. On the contrary, the WIND market share showed a decrease in 2022 to 21.9% (from 23.8% in 2021), whereas Vodafone's market share remained stable at 30.2%. In regard to active connections, Cosmote's share is in the range of [45-55%], followed by Vodafone in the range of [25-35%] and WIND in the range of [15-25%] (Table 1). In addition, the penetration rate of active mobile telephony connections on Greece's population at the end of 2022 reached 109%, which was an increase of approximately 1 percentage point compared to 2021 (penetration 108%). In addition, the penetration rate in terms of total connections was 132% in 2022, versus 129% in 2021. However, focusing on the evolution of the above-described values over time, the situation seems stable for at least the last six years, evolving in a way that is not unusual. Looking at the big picture in terms of MNO's market shares in total connections, the incumbent (Cosmote) has a leading position with a market share of around 47% on average, while the other two alternative MNOs (Vodafone and WIND) share the rest of the market, following with approximately 30% and 23% on average, respectively. In particular, Vodafone reached a maximum market share at the end of 2016 (35.3%), declining since then,