esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 X-NETAD: The Idea
### The Irresponsible Forwarding (IF) Protocol
#### 3.3.2 Probability Assignment Function  
Let us consider a vehicle, at a generic distance, \(d\), from the source node (positioned at the origin of the horizontal axis), within the transmission range of the source. In Figure 2, \(N_{\rm z}\) denotes the number of nodes within the transmission range of the source, _i.e._, \(d\in\{d_{1},d_{2},\ldots,d_{z}\}\). According to the idea of the IF protocol, the vehicle should rebroadcast the packet only if the probability of finding another vehicle in the consecutive interval of length, \(z-d\), is low; otherwise, it should not. More specifically, when a vehicle receives a packet, it compares its position with that of the transmitter and computes its rebroadcast probability as follows:  
\[p = \exp\left\{-\frac{\rho_{\rm s}(z-d)}{c}\right\} \tag{1}\]  
where \(d\) is the distance between the vehicle and the transmitter and \(c\geq 1\) is a coefficient that can be selected to \"shape\" the probability of rebroadcasting. The higher the value of \(c\), the higher the probability of rebroadcasting at any position, \(d\). In the particular case with \(c=1\), the rebroadcast probability reduces to the probability that there is no vehicle in the interval of length, \(z-d\).  
In Figure 3, the rebroadcast probability is shown, as a function of the distance between the receiver and the transmitter, for various values of \(c\) and \(\rho_{\rm s}z\).  
This figure can be interpreted as follows. When \(d\) is small, the value of \(p\) is also small, because the receiving vehicle is very close to the transmitter, and therefore, it should let the other node take the responsibility of rebroadcasting. When \(d\) becomes larger, the value of \(p\) becomes larger and  
Figure 3: Rebroadcast probability, as a function of the distance, with \(z=200\) m, \(c\in\{1,3\}\) and \(\rho_{\rm s}z\in\{0.01,0.05\}\) veh/m.  
approaches one when the node is at the edge of the transmission range. Note that with the probability assignment in Equation (1), the node spatial density is also taken into account. When the network is sparse, the overall rebroadcast probability should be high (e.g., even if the receiving node is relatively close to the transmitter), in order to ensure complete reachability. As observed in Figure 3, when \(\rho_{\mathrm{s}}z\) decreases from 0.05 veh/m to 0.01 veh/m, the overall forwarding probability also increases. In addition, the coefficient, \(c\), is also effective at shaping the rebroadcast probability, as the overall rebroadcast probability can be increased by increasing the value of \(c\).
## 2 Materials and Methods
### Data Analyses
This test assesses whether the dichotomous data of two samples align with the null hypothesis (H0), i.e., whether the differences observed within the sample data vectors are simply random [53]. Two tests were performed depending on the \(p\)-value detected for each parameter examined: the T-test, when the \(p\)-value was greater than 0.05, and the non-parametric Mann-Whitney test for \(p\)-value values less than 0.05. Both statistical tests were used to compare the averages of the considered data sets, which were independent of each other, and to check whether they differed randomly [54; 55]. All the tests were performed using R software (vers. 4.0.3 for Windows), a free software environment for statistical computing and graphics. For statistical analyses, rstatix, stats, and car libraries were used (downloaded from [[https://cran.r-project.org/web/packages](https://cran.r-project.org/web/packages)]([https://cran.r-project.org/web/packages](https://cran.r-project.org/web/packages)), accessed on 4 November 2023). Analyses were carried out for heavy metals, the characterization of which provided values above the Limit of Quantification. Data was split into two vectors of six data each (for the San Giuliano reservoir) and eight and nine data each (for the Camastra reservoir), depending on the position of the sampling point, whether upstream or downstream of the reservoir. This data was subjected to statistical analysis, first through the Shapiro-Wilk test to assess the possible normal distribution of the data, and then using [PERSON]'s test to check whether the variances were homogeneous. By using the already mentioned tests, it was thus possible to define the type of analysis, parametric or non-parametric (T-test in the first case, Mann-Whitney test in the second), that best describes the dataset and allows the validity of the null hypothesis to be assessed. To define the possible influence of land use on the content of heavy metals and organic carbon [56], it was necessary to derive for both reservoirs, in a GIS environment, the sub-basins underlying each of the sampling stations, assumed to be the closing section. The land use characterization of all sub-basins was obtained based on the 2018 Corine Land Cover, prepared by the European Environment Agency, again through GIS analysis. The sub-basins are characterized by 23 different land use categories, grouped into six different classes, expressed as percentages: forest, urban, non-irrigated arable land, farm areas, water bodies, and others.
# Analysing and Computing the Impact of Geometric Asymmetric Coils on Transformer Stray Losses
## 5 Impact of Shell-Type Configuration on Additional Losses  
Transformer manufacturers widely recognise the shell-type configuration as an effective method to mitigate the stray of the magnetic field and, consequently, reduce additional losses. Despite its significance, the quantification of these benefits has not received widespread promotion. In Figure 10, the model of the shell-type configuration and its analysis path for a pad-mounted-type distribution transformer are depicted. The design and manufacturer parameters for the analysed 1500 kVA shell-pad-mounted transformer are detailed in Table 6. Noteworthy characteristics include a Delta connection at 22.5 kV in the primary winding, a 220/127 V square core cross section, an average magnetic field of 1.6 T in the core and an efficiency of 99% for the shell type compared to 98.8% for the core type. Figure 11 shows the magnetic field distribution in the active elements (core and coils) as well as on the tank walls of the shell-type transformer. In this representation, the average magnetic field in the core is approximately 1.6 T, while on the tank walls, the average field is around 300 mT, with darker spots reaching 350 mT in areas where the active elements are close to the walls.  
Figure 12 shows a comparison of the magnetic field distribution along an analysis path for a 1500 kVA core-type pad mounted l transformer (represented by the solid blue line) and a shell-type transformer (represented by the dotted green line). Notably, the core-type transformer exhibits a shorter analysis path due to its shorter front and rear walls in comparison to the shell type. Furthermore, the magnetic field values for the core type are higher on the tank walls, reaching approximately 410 mT. In contrast, the shell type registers its highest magnetic field value around 350 mT, signifying a 14% reduction in the stray magnetic field reaching the magnetic tank. While this reduction would ideally correspond to a proportional decrease in additional losses, it is essential to consider that the tank for the shell-type transformer is longer to accommodate the active elements. As a result, the anticipated reduction in losses is more modest, estimated to be in the range of 6% to 8%. Table 7 provides results obtained through the finite element method (FEM) and the proposed model (9), facilitating a comparison and confirming the effectiveness of reducing stray losses in the transformer tank with the adoption of a shell-type configuration. It must be clarified that although it is observed that stray losses are lower in the shell-type  
Figure 9: Comparison of stray magnetic field absorption: (**a**) stray magnetic flux density at magnetic shunt 1 mm; (**b**) characterization of 1 B.
## 2 Related Work  
With the use of VLANs, the study in [5] improved the functionality of already-existing computer networks, specifically regular LAN networks. The findings demonstrated that a VLAN network is more reliable than a LAN in terms of throughput, delay, jitter, and packet loss and the VLAN network is more dependable than LAN when there is a lot of traffic. [PERSON] analyzed how implementing VLANs affects network performance and demonstrates how to satisfy security requirements in any field. They concluded that when the LAN grows and new network devices are added, creating VLANs is essential for data and traffic control in order to maintain the broadcast frequency's validity [3]. [PERSON] et al. evaluated the effectiveness of LAN and VLAN networks in many circumstances by measuring important performance factors such as throughput, average latency, and traffic sent and received. OPNET 17.5 Student Version was used to run the simulation. The simulation findings showed that without VLAN technology, there is more existing traffic and the average queuing delay is cut in half [4]. [PERSON] et al. explains the precise definition the operation of VLAN memberships in a switched network. The use of virtual local area networks can simplify network management and improve the security of networks [6]. [PERSON] et al. improve the performance of a business network by implementing VLAN through the simulation of an existing LAN. They utilized a virtual local area network to address the issues with the current business network to model and contrast the performance of the new (VLAN) and old (NO VLAN) enterprise networks. Additionally, standard deviation (SD) was utilized to determine the consistency of the responses and satisfaction levels. Through the use of a VLAN, network virtualization provided further advantages [7].  
All the previous studies examined the impact of VLANs in a local area network in terms of performance and security, but in this study, the impact of VLANs on the management of the wireless networks and performance improvement is examined and an investigation into routing protocols to improve the performance of the wireless network is carried out.
# FTUC: A Flooding Tree Uneven Clustering Protocol for a Wireless Sensor Network
## 6 Simulation and Analysis
### Energy Efficiency  
Figure 12 shows the evolution of LiveN(t), the alive node numbers in FTUC, UCR, and LEACH during the network's lifetime. In the same operating conditions, FTUC shows its ability to increase the lifetime of the network by evenly distributing the CHs and by creating unequal clusters.  
We extracted the LT-1 and LT-2 evaluation parameters from these experiments as resumed in Table 3. It shows that FTUC has an increased lifetime compared to LEACH and UCR (both according to LT-1 and LT-2 indicators). It shows up to 16% improvement over UCR in LT-1, because the tree-type network decreases the energy consumption required to obtain the global distance information, and no long-distance communications are needed. Furthermore, the network lifetime of FTUC is up to 24.7%, which is more than that of UCR in LT-2, due to a better distribution of CHs among the network, this in turn providing a better balance in the energy consumption of nodes. The improvements over LEACH confirm that creating unequal clustering is an efficient way to improve energy efficiency. Furthermore, LEACH cannot avoid long distance communications between all the nodes and the BS.  
Figure 13 shows the energy variance of nodes depending on the routing algorithm. As can be seen, FTUC's variance is lower than that of LEACH and UCR. This means that the FTUC algorithm decreases the energy consumption and balances the network energy consumption more efficiently.
# A Pilot Experience with Software Programming Environments as a Service for Teaching Activities
## 3 State of the Art in Cloud Orchestration  
To successfully apply ACTaaS in a real class environment, we need a tool to mitigate the teacher overload and produce the deployment and configuration of the environment in the cloud. The concept **cloud orchestration** refers to the process to automate the entire life-cycle of an application in a cloud infrastructure. It involves the deployment of all the computing resources, the installation and configuration of the different components of the application and its correct interconnection. To describe cloud applications, the OASIS consortium defined the Open Standard of the Topology and Orchestration Specification for Cloud Applications (from its acronym in English, TOSCA) [21], thus allowing portability and automated management between cloud providers, regardless the underlying platform or infrastructure.  
There are several open source orchestration tools and services on the market, as reflected in this review [22]. As an example, we can cite tools such as Cloudify [23] Starcluster [24], Elasticluster [25] and Apache ARIA [26], and projects such as OpenTOSCA [27], CELAR [28] and CompatibleOne [29]. However, most of them have limitations regarding the support of the wide range of providers existing in cloud technology and not all of them support the TOSCA standard.  
Our previous work in the area, the Infrastructure Manager (IM) [30] is a tool that facilitates the deployment and configuration of complex infrastructures on cloud platforms. The IM is compatible with most cloud providers, both public (i.e., Amazon Web Services (AWS) or Microsoft Azure), and private (i.e., Openstack or Opennebula), among others, and provides its own description language for users to build recipes in order to describe the infrastructure and configuration required. IM supports TOSCA for the definition of the topology, and integrates Ansible [31], to achieve the installation and configuration of all applications required by the user, providing a fully functional infrastructure. Because of its support of a wide range of cloud providers, the simplicity of its usage, and the compilation with the standards, IM is the tool we adopted for ACTaaS to deploy the required infrastructure in the cloud.
## 3 Results and Discussions
### Radiation Efficiency and Gain  
In Table 1, the simulated radiation pattern and gain of the antenna are listed. The simulated gain and radiation efficiency are extracted from the far field simulated results at the frequencies of 3.5 GHz, 5.7 GHz, 7 GHz and 9 GHz. From Table 1, it is shown that antennae show higher radiation efficiency at 3.5 GHz, 5.7 GHz and 9 GHz, as compared to 7 GHz, which is expected due the notch at this frequency. As compared to higher frequency band, this antenna shows better efficiency at lower frequencies. A maximum of 84.3% radiation efficiency is observed at 5.7 GHz for this antenna. The antenna expressed good gain at 3.5 GHz, 5.7 GHz and 9 GHz. The gain is very good at 7 GHz, which is obvious because of not band here.  
In this study, this antenna shows maximum 2.86 dBi gain at 9 GHz. From the study, it is noticed that higher frequency has higher gain in comparison with the lower frequency band. The lowest gain is noticed at 7 GHz.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency (GHz)** & **3.5 GHz** & **5.7 GHz** & **7 GHz** & **9 GHz** \\ \hline Gain (dBi) & 1.643 & 1.76 & -0.20 & 2.86 \\ Radiation Efficiency (\%) & 83.29 & 84.30 & 32.00 & 79.79 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Gain and radiation efficiency at various frequencies of the antenna.  
Figure 2: Return loss curve of the UWB notch antenna.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 3 Research Gaps Addressed by This Study
### Application of Neural Networks to QE Data  
Recent research has shown the potential of machine learning, particularly neural networks and hybrid models, in sports performance analysis [9; 11]. However, the application of neural networks to combine QE metrics and biomechanical data for predicting shot outcomes in badminton remains underexplored. Our study fills this gap by developing a neural network model that processes complex, multidimensional data, including both cognitive (QE) and physical (biomechanical) inputs, to predict shot outcomes with high accuracy.
# Research on the Performance of an End-to-End Intelligent Receiver with Reduced Transmitter Data
## 2 System Model  
This section will briefly describe the MIMO communication system composed of an intelligent receiver, which includes a MIMO transmitter, wireless channel and intelligent receiver model.
# IAACaaS: IoT Application-Scoped Access Control as a Service +
## 4 IoT Application-Scoped Access Control as a Service
### Sample Authorization Flow
#### 4.6.1 Initial Configuration  
The first step consists of the registration and configuration of the _Service_ and the _Device_. Figure 2 shows a schema of the components interaction. Every operation below is triggered by the _Service Admin_:  
1. The _Service_ is registered in the _IdP_, to obtain both the OAuth 2.0 credentials for its application (i.e., the client_id and client_secret) and those associated with its corresponding _PEP_ (i.e., the pep_username and pep_password).
2. The _Device_ is registered in the scope of the _Service_, to obtain the _Device_ credentials (i.e., the device_username and device_password).
3. The desired roles and policies are created in the _PAP_, and the relationships between them are equally defined. The _PAP_ will then store those entities and their relationships in the _Policies DB_.
4. The desired roles are granted to the _Device_ (in the scope of the _Service_) using the _IdP_. To display the available roles, the _IdP_ must send a request to the _PAP_. As has been explained above, roles can be assigned to devices or to groups of devices, so the _Service Admin_ may optionally manage groups of devices and assign roles to them (again in the scope of the _Service_).
## 1 Introduction  
Due to industrialization and significant consumerism in the world, often not meeting the assumptions consistent with the concept of green chemistry, it is reasonable to improve the wastewater purification technologies. It allows the assessment of the quality of the environment in order to protect human health. Various methods are used to remove organic and inorganic pollutants, such as precipitation, extraction, osmosis, filtration and oxidation, for example. Sorption is one of the most popular and widely used techniques. This process consists of concentrating the dissolved contaminant by transferring it to the sorbent phase. The efficiency of the process is closely related to the physicochemical properties of the sorbent (it depends, inter alia, on the type and amount of active centers/functional groups, structure, surface area and the energy of particle interaction). The universality of the method is related to its simplicity and it is used primarily in the development of effective procedures for removing various pollutants, which in the times of heavy industrialization constitute a serious problem for the environment. The use of the sorption process in environmental protection is usually carried out using the dynamic method, i.e., columns packed with an adsorbent.  
Industrial application is preceded by laboratory tests in the batch system. In addition to the already known adsorbents, more effective and appropriate materials are being sought. The popular sorbents such as activated carbons [1], zeolites [2; 3], low-cost adsorbents [4; 5; 6], ion exchange resins [7; 8], have found application in dye removal technologies. Dyes are widely used in the textile, chemical and cellulose industries, for the production of plastics, paints and varnishes. Over 10,000 different dyes and more than 700,000 tons of dyes are produced every year, and 2-50% are discharged into the aquatic environment [9; 10]. Many dyes, especially those containing azo groups or aromatic rings, e.g., acid type dyes, discharged into water as a waste can causes serious danger as they are difficult to degrade [9]. Moreover, some of them are mutagenic and carcinogenic [9].  
This study presents synthesis and application of the functionalized polymeric resin DVB-_co_-GMA-TETA for the removal of three textile dyes (C.I. Acid Violet 1, Acid Green 16, C.I. Acid Red 18) from aqueous solutions. Physicochemical characteristics of DVB-_co_-GMA-TETA were determined using different techniques, e.g., Fourier transform infrared spectroscopy (FT-IR) with attenuated total reflectance mode (ATR), nitrogen adsorption/desorption analysis, elemental analysis and optical microscopy. The impacts of parameters such as dyes-microspheres phase contact time, dye initial concentration and auxiliaries (Na\({}_{2}\)SO\({}_{4}\), CH\({}_{3}\)COOH, anionic surfactant sodium dodecyl sulfate (SDS)) concentration were evaluated on dye sorption effectiveness on the modified polymeric resin. Desorption of dyes from the resin was also studied.
## 1 Introduction
In room acoustic scale measurements, the problem of air attenuation is very significant, and different solutions have been applied to compensate for the very pronounced air attenuation at the measurement frequencies, which are ten-times higher than normal in a 1:10 scale model. It has been suggested to make corrections using the pure-tone attenuation at the octave band center frequency [16] or using the pure-tone attenuation at the lower limiting frequency of the octave band [12]. Both methods neglect the non-linear behavior of the air attenuation in octave bands. Another method is to perform the corrections in the frequency domain instead of the time domain [17]. This requires Fourier transforms of time samples of the impulse response, and inverse Fourier transforms back to the time domain after the correction for air attenuation. In the present article, the summation method for calculating the octave-band energy attenuation coefficient as a function of the time of propagation is presented in Section 2. It is demonstrated that the attenuation is a non-linear function of time of propagation, and an approximation using a linearization is suggested in Section 3. To overcome the problem of the temperature-dependent speed of sound, the method uses a transformation from the time domain to the distance domain. Section 4 explains how a measured impulse response can be normalized to a standard atmosphere. As an alternative, a simple method for normalizing reverberation time measurements is also suggested. During the present work, the summation method was implemented in the ODEON room acoustics software version 18.11, which was used for postprocessing the measurements in Section 5. The purpose of the present article is to fill the gap between the well-established theoretical calculation model for pure-tone air attenuation and a practical approach to estimate the octave-band air attenuation in measured impulse responses.