esa-sceva/satcom-chunk-collection huggingface dataset:
# New RFI Model for Behavioral Audience Segmentation in Wi-Fi Advertising System
## 2 Related Works
### Behavioral Analysis
Hence, investigations have been conducted in this study to evaluate the influence of the newly proposed RFI model in segmenting the engagement behaviors of the audience in the Wi-Fi advertising industry. The new RFI model is designed to discover the pattern of the audience's behavior towards the advertisement. The RFI model is replacing the monetary value with the interest of the audience towards the advertisement, which is represented as the I. Although the recency component remains in this proposed model, the definition of recency is different. In the RFI model, the recency is defined as the duration of when the audience is being impressed by the advertisement. A shorter interval of time results in a stronger impression of the advertisement remaining in the audience's mind. The frequency is defined as how many times the audience is being impressed by the advertisement. The higher the frequency, the stronger impression implanted in the subconscious mind of the audience, whereas the interest level of the audience is used for indicating the impression and attraction of the advertisement to the audience [20]. In the study [21], the interest and engagement of users towards social media posts are determined by the multiple types of attributes, such as the actions of clicking, viewing, liking, and commenting, as well as sharing the advertisement post. These attributes are considered the impressions of the social media users towards the advertisement posts. In the Wi-Fi advertising system, the available advertising attributes are limited to viewing, clicking for accessing the advertisement details, or skipping the advertisement. The advertisement video will continuously play if there is no action either to view the advertisement details or skip the advertisement. Therefore, the view duration of the audience towards the advertisement is a basic advertising attribute to evaluate the interest level of the audience towards the advertisement. The view duration of the audience is recorded from the initial moment when they begin watching the advertisement until they skip it or access the advertisement details. According to [22], the click attribute is an important indicator for evaluating the effectiveness of an advertisement to attract and engage its viewers. In the study [23], the click motivations are divided into four categories: literal interpretation (an evaluative signal of liking particular content), acknowledgement of viewing (demonstrating that they have seen and recognized a specific post), social support and grooming (with a goal of maintaining or deepening social relationships), and utilitarian (as a personal archive of content curated for future reference). The audience will only click on advertisement details wherever they are well engaged and attracted by the advertisement. This is a crucial component that can determine the interest level of the target audience in the advertisement.
## 2 Materials and Methods
### Positions of Measurement and Apparatus Used for Measuring Technical Seismisity
#### 2.3.2 Svantek 958 A--Class 1  
The monitoring point was placed about 630 m from the blasting works, which were carried out in the quarry. A special instrument by SVANTEK-SVAN 958A was used to measure noise and vibration. SVAN 958A Class 1 Four-Channel Sound and Vibration Analyzer are designed for all applications requiring simultaneous Class 1 audio and vibration assessment. Each of the four input channels can be independently configured to detect sound or vibration with different filters and RMS detector time constants that provide a great flexibility in measurement. The main parameters of the device are as follows: SV 84 triaxial high sensitivity accelerometer for ground or building vibration measurements; (1 V/g) SV 38 triaxial accelerometers for whole-body measurements (1 V/g MEMS type); 2 vibration profiles for simultaneous measurement of PPV and VDV; simultaneous FFT analysis; simultaneous WAVE recording, according to DIN 4150-3 standard; frequency range: 0.5 Hz \(\div\) 20 kHz. The real advantage of the SVAN 958A is its ability to perform advanced analysis simultaneously with a level meter. In practice, this allows us to obtain broadband results [51,52].  
The measurement positions S1 and S2 were located inside the residential building No. 91 and at the entrance to the cells of the object under assessment. At the S1 position, a (Minimate Pro 6) vibrograph was placed at the entrance to the residential building on the foundations. Vibrograph Svantek 958 (position S2) was placed in the corridor of the entrance to the kitchen inside the residential building (Figure 5). The vibrographs provide a digital record of all three components of the particle velocity, dominant frequency, peak acceleration and peak displacement of the environment in the horizontal longitudinal-v\({}_{\rm x}\), horizontal transverse-v\({}_{\rm y}\) and vertical-v\({}_{\rm z}\) directions, respectively. The vibrographs  
Figure 4: Placement of the vibrographs Minimate Pro 6-Position S1 (source: author).  
operate autonomously, automatically performing a channel test without any intervention, and the operators influence the measured and registered vibration characteristics.
## 1 Introduction
In comparison, this present paper proposes passive measures providing the results for the whole building stock, not only for a building. [PERSON] et al. have studied the potential renovation rate [13] providing also an estimation of the potential savings in energy consumptions and CO\({}_{2}\) emissions occurring in case of deep renovation, but using a repository of Deep Renovation Packages Based on Industrialized Solutions [14]. In this study, the repository of deep renovation packages is based on solutions with specific technologies, among all industrialised elements, whose implementation requires significant investments. The present paper addresses technologies adapted to the European Mediterranean area where industrialised solutions are not usual yet. In the Mediterranean area, [PERSON] et al. have focused on studying the Urban Heat Island (UHI) effect, as climate change and UHI scenarios foresee a fast growth of energy consumption for next years, due to the widespread of air conditioning systems and the increase of cooling demand. This study investigates the intensity of UHI in Barcelona (Spain), a Mediterranean coastal city, and its impact on the cooling demand of residential buildings [15]. [PERSON] et al. [16] applied the cost-optimal methodology in two school buildings located in the North-East of Italy by defining different measures of energy retrofitting in both the building envelope and the heating system. The results showed that the methodology is suitable to assess cost-optimality and energy efficiency in the renovation of school buildings. In comparison, the present paper applies the cost-optimal methodology in a residential building stock. [PERSON] et al. [17] also focused on school buildings in the city of Valencia when applying the cost-optimal methodology to carry out studies for the energy renovation of their thermal envelope. Again, the difference with the present paper is the type of buildings per use, i.e., the residential buildings. Nevertheless, none of these studies has evaluated the potential of energy savings of a building stock (at regional level in this case) by using the MedZEB approach [18], i.e., looking for beyond deep renovation by using optimal renovation strategies through life-cycle analysis adapted to Mediterranean Climate. After analysing the current research context, we can determine the main novelties proposed in this paper:  
* The renovation measures used to evaluate the potential energy savings are adapted to the Mediterranean Area [18]. * The renovation measures have been optimized following the LCC methodology proposed by the European commission in the recast of the directive of energy efficiency of buildings [19]. * The impact of implementing the renovation measures in a big scale context (the whole EVha's social housing stock management in Comunitat Valenciana which represent 4319 blocks containing 35,571 dwellings) has been assessed.
# Capability Analysis of Earth Observation Data for Integrated Emergency Management
## 4 R2-D2 Case Study Results
### Pilot Studies Investigating Satellite Imagery in Support of Emergency Management
#### 4.3.2 IWS Fire  
Based on the methods described by [PERSON] et al. [73] and [PERSON] [74], this case study utilised Google Earth Engine (GEE) and Sentinel-1 imagery to detect changes in the height of a known waste site. We used four Sentinel-1 images (dated 24 April 2015, 6 May 2015, 18 May 2015 and 30 May 2015) of the former Alex Smiles Limited recycling facility in Deptford Terrace, Sunderland, chosen as it was the location of rapidly changing waste site heights in the weeks before its eventual closure in May 2015.  
The analysis is based upon a sequential omnibus change algorithm ([PERSON] et al. [75]), comprising independent likelihood ratio tests that determine changes at each pixel location. For each pixel position, the quotient of the F probability distribution for the two images is used as a likelihood ratio test statistic [76] to decide whether a change has occurred between the two acquisition dates at that position [77]. The image pyramid level for calculations depends on the zoom factor, which is maintained at 10 m to ensure consistent output. In addition, the algorithm has a \"median\" variable, which is ignored as the changes the algorithm detects are small. If the variable were set to true, the p-values for each likelihood ratio statistic would be passed through a median filter before being compared to the significance threshold, reducing the'salt and pepper' effect in no-change regions caused by the uniform distribution of p-values.  
Figure 8 shows a map of the study area with changes in pixels relating to the height of the waste visualised. Epoch 1, changes between the first (24 April 2015) and second (6 May 2015) images, shows many definite positive changes located around different areas of the Deptford Terrace site. Epoch 2, changes between the second (6 May 2015) and third image (18 May 2015), shows many definite negative changes. The third epoch, changes between the third (18 May 2015) and fourth image (30 May 2015), shows both definite positive and negative changes. Though more rigorous validation of the results is necessary, the analysis demonstrates the potential for change detection using Sentinel-1 SAR images to monitor waste site heights over time. Such analysis could be extended to aid in the identification of IWSs, particularly if prior knowledge of potential sites exists.  
Figure 8: Maps of Deptford Terrace, Sunderland, showing changes in the height of the IWS for three epochs in May 2015. Red pixels are positive definite changes, blue pixels are negative definite changes, and yellow pixels are indefinite changes.
## Appendix A  
### Anchor Boxes  
Anchor boxes are predefined bounding boxes, which are characterized by width and height. In the case of Faster R-CNN, the anchor boxes are used on a sliding window to find candidate bounding boxes, while in YOLOv3 they are placed in each grid cell, with the center coordinates of the bounding box being aligned with the center of the grid cell or sliding window. A candidate bounding box is said to be found if it overlaps with a bounding box with an IoU greater than a threshold. Anchors are usually fine-tuned to a certain dataset, as the objects in that dataset can have variable sizes and the anchor boxes must be able to generalize. YOLOv3 uses k-means to find the anchor boxes that best cover all ground-truth targets in the dataset, so they do not need to be hand-crafted. Starting from an anchor box, a bounding box is computed via offsetting the center of the anchor box and scaling it to fit the object of interest. An example of how they work in a single-stage object detector can be seen in Figure A2.
# Mechanical Properties and Energy Evolution Law of Fractured Coal under Low Confining Pressure
## 5 Evolution Characteristics of Triaxial Strain Energy  
### Principle of Energy Analysis  
In the process of deformation and failure of the rock under load, there is typically the input, accumulation, dissipation and release of energy. In this process, the strain energy can be divided into three types: total strain energy \(U\), elastic strain energy \(U_{e}\) and dissipation energy \(U_{d}\). The dissipative energy can generate surface energy and plastic energy of the fracture surface, resulting in rock damage and strength reduction. The instantaneous release of the elastic strain energy is an important factor causing sudden failure of the rock.  
\[U=U_{e}+U_{d} \tag{2}\]  
Figure 12 shows the relationship between the internal dissipation energy and the elastic strain energy of the rock during loading test. In the figure, \(U_{i}^{d}\) is the dissipated energy, which represents the energy consumed in the process of rock damage and failure, mainly including plastic deformation, friction and other activities. The shadow area of \(U_{i}^{e}\) represents the released strain energy stored in the rock. \(E_{i}\) is the unloading elastic modulus.  
Figure 11: Relationship between fractal dimension and crack number.  
Figure 12: Relationship between internal dissipation energy and elastic strain energy of rock mass.  
Generally, the elastic energy is calculated with the loading-unloading stress-strain curve, and the unloading modulus is replaced by the initial elastic modulus to simplify the calculation of elastic energy \(U_{e}\). The calculation formula of elastic strain energy in the loading process is [40]:  
\[U_{e}=\frac{1}{2E_{0}}\Big{[}\sigma_{1}^{2}+2\sigma_{3}^{2}-2v\Big{(}\sigma_{3} ^{2}+2\sigma_{1}\sigma_{3}\Big{)}\Big{]} \tag{3}\]  
where \(E_{0}\) is the initial elastic modulus and \(v\) is Poisson's ratio.  
In the triaxial tests, the external working force of the coal specimen includes the axial stress and the confining pressure of the testing machine. The axial stress works on the specimen and produces axial deformation. The confining pressure causes circumferential deformation [41]. Therefore, the total energy \(U\) absorbed by the specimen includes the axial strain energy \(U_{1}\) absorbed by the axial deformation and the circumferential strain energy \(U_{3}\) generated by the circumferential deformation:  
\[U=U_{1}+U_{3} \tag{4}\]  
For any time \(t\) during the loading test, the absorbed axial strain energy and the circumferential strain energy with confining pressure as negative work consumption can be obtained by integrating the stress-strain curve [42]:  
\[U_{1}=\int_{0}^{\varepsilon_{1(t)}}\sigma_{1}d\varepsilon_{1}=\sum_{t=0}^{t_{ 0}}\frac{1}{2}\Big{(}\sigma_{1(t+1)}+\sigma_{1(t)}\Big{)}\Big{(}\varepsilon_{ 1(t+1)}+\varepsilon_{1(t)}\Big{)} \tag{5}\]  
\[U_{3}=2\int_{0}^{\varepsilon_{3(t)}}\sigma_{3}d\varepsilon_{3}=\sum_{t=0}^{t_ {0}}\Big{(}\sigma_{3(t+1)}+\sigma_{3(t)}\Big{)}\Big{(}\varepsilon_{3(t+1)}+ \varepsilon_{3(t)}\Big{)} \tag{6}\]
# Evaluating the Effect of Intensity Standardisation
###### Abstract  
Atrophy quantification is fundamental for understanding brain development and diagnosing and monitoring brain diseases. FSL-SIENA is a well-known fully automated method that has been widely used in brain magnetic resonance imaging studies. However, intensity variations arising during image acquisition may compromise evaluation, analysis and even diagnosis. In this work, we studied whether intensity standardisation could improve longitudinal atrophy quantification using FSL-SIENA. We evaluated the effect of six intensity standardisation methods--z-score, fuzzy c-means, Gaussian mixture model, kernel density estimation, histogram matching and WhiteStripe--on atrophy detected by FSL-SIENA. First, we evaluated scan-rescan repeatability using scans taken during the same session from OAIS (\(n=122\)). Except for WhiteStripe, intensity standardisation did not compromise the scan-rescan repeatability of FSL-SIENA. Second, we compared the mean annual atrophy for Alzheimer's and control subjects from OASIS (\(n=122\)) and ADNI (\(n=147\)) yielded by FSL-SIENA with and without intensity standardisation, after adjusting for covariates. Our findings were threefold: First, the use of histogram matching was counterproductive, primarily as its assumption of equal tissue proportions does not necessarily hold in longitudinal studies. Second, standardising with z-score and WhiteStripe before registration affected the registration performance, thus leading to erroneous estimates. Third, z-score was the only method that consistently led to increased effect sizes compared to when omitted (no standardisation: 0.39 and 0.43 for OASIS and ADNI; z-score: 0.45 for both datasets). Overall, we found that incorporating z-score right after registration led to reduced inter-subject inter-scan intensity variability and benefited FSL-SIENA. Our work evinces the relevance of appropriate intensity standardisation in longitudinal cerebral atrophy assessments using FSL-SIENA.  
intensity standardisation; FSL-SIENA; longitudinal atrophy quantification; brain magnetic resonance imaging +
Footnote †: journal: Applied Sciences
# GA-Based Optimization Method for Mobile Crane Repositioning Route Planning
## 3 Mobile Crane Repositioning Route Optimization Method
### Site Data Modeling  
The mobile crane installation is limited owing to work safety issues and the physical attributes of the crane. The manager must determine whether the road surface is adequately strong to install outriggers to ensure the safety of mobile crane operation. Additionally, the manager must consider whether there are obstacles, such as high-voltage power lines, telephone poles, streetlights, and roadside trees. Furthermore, the working range (\(R_{W}\)) is defined by the physical attributes (boom length, vehicle size, etc.) of the mobile crane and the work safety regulations. There are multiple demand points (\(S_{L}\) = {5\({}^{1}\), \(S^{2}\),..., \(S^{i}\)}) and supply points (\(P_{L}\) = {\(P^{1}\), \(P^{2}\),..., \(P^{i}\)}) on the site, and their locations and lifting radius should be considered. The RPOS determines the feasible locations by considering (1) points already occupied by buildings, etc., (2) supply points, (3) points where the bearing capacity of soil has not been secured, (4) points where the safe work distance has not been secured, and (5) lifting radius of the mobile crane. Figure 1 shows the method and procedure of RPOS for identifying the feasible locations of the mobile crane in a sample field.  
Figure 1: Procedure of identifying the feasible locations of the mobile crane.  
First, RPOS divides the site using the X and Y axes and sets the upper leftmost point of the site as the reference point (\(xI=1,yI=1\)). This is done to synchronize the site grid coordinate system and system matrix structure. The grid size (\(gs\)) affects the precision and computational speed of finding the optimal solution. In other words, as \(gs\) decreases, the practicality of the mathematical model increases, but the computational time for finding the optimal solution increases. The user considers this relation when determining \(gs\). If \(gs\) has been determined, the RPOS converts the site into an \(M\times N\) orthogonal coordinate system. Furthermore, a matrix \(M_{L}\) of \(M\times N\) size that has 0 as the attribute value is created: \(M_{L}\) = zeros (\(M,N\)). \(M_{L}\) is a matrix for storing points in the system memory, which are identified as locations where the mobile crane cannot be installed. Here, \(M\) and \(N\) are determined by dividing the vertical length (\(L_{V}\)) and horizontal length (\(L_{H}\)) of the site area, respectively, by \(gs\) (Equation (1)).  
\[M=\frac{L_{V}}{gs}+1,\;\;N=\frac{L_{H}}{gs}+1. \tag{1}\]  
Next, RPOS defines the coordinates of areas occupied by buildings. For this, the Industry Foundation Classes (IFC) file extracted from the BIM model is input into the system. The IFC file is a text file containing entity instances according to the IFC EXPRESS schema. The location information of the building components is managed by the IfcProduct entity. RPOS synchronizes the coordinate system used by IfcProduct and the on-site grid coordinate system by performing the origin coordinate correction process.
## Abbreviations  
ADCP Acoustic Doppler Current Profiler  
AIS Automatic Identification System  
API Application Programming Interface  
BAG Bathymetric Attributes Grid  
CSSA Common Shore-Based System Architecture  
ECDIS Electronic Chart Display and Information System  
ENC Electronic Navigational Charts  
GNSS Global Navigation Satellite System  
IALA The International Association of Marine Aids to Navigation and Lighthouse Authorities  
IMO International Maritime Organization  
IMU Inertial Measurement Unit  
J2 EE Java 2 Platform Enterprise Edition  
MEMS Micro Electro Mechanical Systems  
MSP Maritime Service Portfolio  
OSB Oracle Service Bus  
RAC Real Application Cluster  
S-100 The S-100 Universal Hydrological Data Model  
S-101 The S-101 Universal Hydrological Data Model  
S-102 The S-102 Universal Hydrological Data Model  
S-57 The S-57 Universal Hydrological Data Model  
SAR Synthetic Aperture Radar  
SOA Service-Oriented Architectures  
TCP Transmission Control Protocol  
USVs Unmanned Surface Vehicles  
VDES VHF Data Exchange System  
VTS Vessel traffic service  
WSDL Web Services Description Language  
XML Extensible Markup Language
## 2 Related Work
### Application Example
#### 3.6.1 Creation of _Envelope_, _Payload_, and _Message_ Components  
As a starting point for implementing the DCDM architecture, it is necessary to implement the classes detailed in Figure 5. In this regard, an abstract class was used to create the message envelope of the _Envelope_ composite element. This represents the basis of the properties and functions that were extended to the _Payload_ and _Message_ classes required to generate the core of the message. This served as a container for the group of transactions to be executed through the MAP structure (an abstract data structure that stores key-value pairs) and the properties of the entities. The basis for implementing these classes is the abstract class _Envelope_. Then, the _Payload_ component implementation extending their properties and functions is created. Finally, the _Message_ compound is also created, which is extended from the same component (class _Envelope_) at the same level of the class _Payload_, thus creating a tree with its leaves (nodes) ready to be loaded with the instructions to be executed in the algorithms managed by the _strategy_ pattern.
# Maximizing UAV Coverage in Maritime Wireless Networks: A Multiagent Reinforcement Learning Approach
## 3 System Model and Problem Statement  
In this study, we propose a multidrone path coverage planning method based on MDRL for data collection in unknown marine environments. In this section, we first introduce the various system models in detail, then formulate the optimization problem for maximization of the coverage rate. We summarize the parameters used in this section in Table 1.