esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Research Directions
### Data Privacy  
An IoT service provider can utilize third-party services, such as Big Data analysis services, to analyze sensor information. Based on the analysis results, the service provider can provide various analytics to the users. For instance, the manufacturers of wearable medical sensors and fitness trackers can share users' health data with hospitals and pharmaceutical companies to learn users' daily activities and suggest prescriptions to the users accordingly. Similarly, the service provider of a smart home can share data collected from smart appliances, such as thermostats, refrigerators, washing machines, cameras, and so forth, with a third-party service to better understand the context of the smart home. However, sharing information with third-party services can be a threat to the privacy and confidentiality of user and sensor information. Therefore, the owners and users of the smart devices should be allowed to verify that the confidentiality of their data is not compromised during data collection, sharing, and collaboration phases.  
A data transparency service (DTS) can be implemented in IoT systems to protect data privacy. As shown in Figure 24, a DTS will allow the owners to know who has access to their data. DTS will also enable the owners to decide the consumers of the data. Moreover, DTS will let owners validate data genuineness, such as the origin of the data and data freshness.  
Figure 23: An adversary tracks the identifiers of target devices for disrupting communications. T2T = Things to Things, T2C = Things to Clouds.
## 1 Introduction  
Scrap in manufacturing processes often results from process instabilities, which may arise due to factors such as tool wear, raw material variation, environmental changes, or improper process parameter settings [1]. These instabilities can be quantified using quality data from manufactured parts, typically measured by coordinate measuring machines (CMMs). To compensate for such deviations, quality control loops are commonly implemented to adjust process parameters based on observed quality metrics [2]. Effective monitoring and control of manufacturing processes require real-time feedback to support timely corrective actions under dynamic conditions. Consequently, a central requirement of quality control loops is the ability to identify optimal process parameter settings in real time [3]. Prior research in this area has largely focused on single-objective optimization using reinforcement learning (RL), validated through theoretical modeling or industrial case studies.  
This study advances the field by exploring an RL-based strategy for multi-objective optimization of manufacturing process parameters. In particular, it aims to determine optimal settings for the production of pinion gears, with simultaneous consideration of two key quality attributes: flange radius and flange thickness. Pinion gears are essential components in a wide range of mechanical systems, prized for their ability to convert rotational motion into linear displacement. Common applications include steering assemblies, conveyor systems, gearboxes, bicycle drivetrains, and electric motors, highlighting their widespread industrial significance. This work focuses exclusively on addressing the challenges of multi-objective optimization through RL; single-objective methods are out of the scope of this investigation.
# Urban Rural Differences in Breast Cancer in New Zealand
## 1 Introduction  
Many rural communities have poor access to health services due to a combination of distance from specialist services and a relative shortage of general practitioners. These disadvantages can lead to differences in health outcomes. Breast cancer is the most commonly diagnosed cancer in New Zealand (NZ) women with 3000 new cases each year. It is also an important cause of death with 617 deaths per year [1]. Urban rural differences in breast cancer are well known. A comprehensive systematic review of the international literature suggested rural women were 19% more likely to be diagnosed with advanced stage at diagnosis [2]. Differences in mortality have also been shown between urban and rural women with breast cancer in Scotland [3], South Australia [4] and Germany [5]. It is difficult to generalise the findings from international studies as the definition of rurality can vary. For instance rural and remote communities in Australia and Canada will involve much greater distances from an urban medical centre than would be the case in Europe where population densities are greater.  
In New Zealand, differences have been shown in the presentation and outcomes of breast cancer for Maori and Pacific women compared with NZ Europeans [6; 7]. A prior study using NZ cancer registry data did not show differences in breast cancer mortality or stage of diagnosis for urban versus rural women after adjustment for age, ethnicity and socioeconomic status [8]. However the NZ cancer registry data has missing data and limited information on other characteristics that may influence outcomes, and the missing data may be differential by urban/rural status. For example, it may be that those living in more rural areas are more likely to have data missing. There is also variation in the definition of what is considered a rural population. This study is based on substantially more detailed clinical data available (including accurate staging, screening history and presence of biological markers) from two clinical breast cancer registers in the Waikato and Auckland regions [9]. Waikato District Health Board has a large rural community with 60% of patients living outside the main centre of Hamilton, while Auckland on the other hand is mainly an urban community. Our aims were (1) to compare the differences and characteristics of rural women particularly stage of diagnosis and tumour size as these are important measures of differences in diagnosis; (2) to assess breast cancer-specific and all-cause survival for urban and rural women with breast cancer; and (3) to assess whether the impact of rurality is different for Maori and NZ European women in New Zealand.
## 4 Application Design and Implementation  
This section proposes an overview on the design and implementation of the X-NETAD system. At first, we explain how the system operates, discussing also some challenges faced during the implementation process. Next, we describe the message exchange in the system, together with a description of the architecture blocks of X-NETAD nodes, focusing on the structural and logical differences between PUs and SUs.
## 5 Buffeting Fatigue Damage Analysis of CFRP Stay Cables
### Fatigue and Damage Analysis Theory  
Fatigue failure occurs under cyclic stress loading, where the relationship between the stress amplitude and the number of cycles is defined by the _S-N_ curve as Equation (13). Serving as a critical indicator of the fatigue performance of components, this curve characterizes the entire temporal progression from stress initiation and crack formation to functional failure.  
\[S^{m}\times N=C \tag{13}\]  
where \(m\) and \(C\) are constants related to the material properties, stress ratio, and loading mode, typically determined experimentally; \(S\) denotes the stress amplitude, and \(N\) represents the number of cycles.  
Fatigue failure under constant-amplitude loading can be estimated using the material's _S-N_ curve to predict the number of cycles to structural failure. However, for real-world cable-stayed bridges subjected to stochastic wind loads, direct application of the _S-N_ curve is inadequate. This necessitates the use of fatigue cumulative damage theory, which accounts for variable-amplitude stress histories. The [PERSON] rule postulates a linear relationship between fatigue damage and applied load cycles, assuming independence between stress-level effects. Total damage is calculated as the linear summation of individual cycle contributions, expressed as follows:  
\[D(t)=\sum\frac{n_{i}}{N_{i}} \tag{14}\]  
where \(n_{i}\) is number of cycles at stress level \(S_{i}\); \(N_{i}\) is cycles to failure at \(S_{i}\), which is obtained from the _S-N_ curve.  
Given the complexity of fatigue phenomena and the current limitations in cumulative damage theory, alternative models to the [PERSON]'s linear cumulative damage rule often involve excessive computational complexity, hindering their practical engineering applications. Therefore, this study employed the Miner's rule to investigate the fatigue damage of CFRP stay cables under turbulent wind loads. Furthermore, based on reference [29,30], the _S-N_ curves for CFRP and steel stay cables were, respectively, defined as follows:  
\[S=-20.512\lg(N)+541.33 \tag{15}\]\[\lg(N)=\left\{\begin{array}{l}14.36-3.5\lg(S)\;S>200\;\text{MPa}\\ 37.187-13.423\lg(S)\;S<200\;\text{MPa}\end{array}\right. \tag{16}\]  
where \(S\) denotes the stress amplitude, and \(N\) represents the number of cycles to material failure under the corresponding stress amplitude.
# Exploring Decentralized Warehouse Management Using Large Language Models: A Proof of Concept
## 3 System Architecture
### Protocol Layer
#### 3.4.1 Agent Design  
In the decentralized warehousing system, package and warehouse agents make autonomous decisions based on a combination of perceptual data, derived insights, a decision process, and a defined action space. Perceptual data include real-time information such as storage availability and pricing, while derived insights are based on historical trends and performance metrics, offering a broader context for decision-making. Each agent uses a decision process to evaluate these data types and optimize its objective function. The action space outlines the available actions--such as sending, accepting, or forwarding offers--that agents execute to achieve their goals within the system. The agents balance real-time operations with strategic objectives through these components, ensuring efficient and optimized performance.  
Package Agent Design  
A package agent operates autonomously to ensure continuous storage for its package while minimizing the total cost associated with storage services. The agent relies on perceptual data (inputs) to make informed decisions and has a set of actions (outputs) available to achieve its objectives.  
**Perceptual data**  
The perceptual data \(D_{\text{perceptual}_{i}}\) for package agent \(i\) include both the current occupied slot ID and the list of scheduled services, as shown in Equation (32):  
\[D_{\text{perceptual\,i}}=(\text{Slot}_{i},\ S_{i}) \tag{32}\]  
where \(\text{Slot}_{i}\) represents the abstracted identifier of the warehouse slot occupied by package agent \(i\) (see Equation (23)), and \(S_{i}\) is the list of scheduled services \(S_{i}=[s_{i1},\ s_{i2},\ \dots,\ s_{in}]\). Each service \(s_{ij}\) in \(S_{i}\) is defined as per Equation (25) and includes details such as the start time \(t_{\text{start}_{ij}}\), end time \(t_{\text{end}_{ij}}\), and price \(p_{ij}\).
## 3 Data and Methods
### Features and Clustering
The stability \(S(C)\) of a cluster is calculated using the sum of the differences between the maximum core distance \(d^{max}\) and the minimum core distance \(d^{min}\) of each cluster at different levels, as shown in the following Equation (2):  
\[S(C)=\sum_{i}(d^{max}_{i}-d^{min}_{i}) \tag{2}\] \begin{table}
\begin{tabular}{l l} \hline \hline
**Human Visual** & **HDBSCAN** \\ \hline \multirow{4}{*}{Ship at anchor and moving faster than 3 knots} & cluster 1 heading NE \\  & cluster 2 heading NWN and turning to KN \\  & cluster 4 heading SW terminating at TC \\  & cluster 7 heading SW from NE or from TC \\ \multirow{2}{*}{East–west between TC-KM} & cluster 5 heading W from TC \\  & cluster 6 coming out from TC \\ \multirow{2}{*}{Near mainland China} & cluster 0 heading NE \\  & cluster 3 heading SW \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Expected results comparison for the 2 methods. where \(S\) is the stability score, \(C\) represents the cluster, and \(d\) is the core distance. Clusters that represent noise points are marked as \(-1\), indicating that these points do not belong to any cluster. Finally, each data point \(x\) is labeled as belonging to a cluster \(C\) or as noise, represented as Equation (3):  
\[label(x_{i})= \begin{cases}C_{j}&if\ x_{i}\ belongs\ to\ cluster\ C_{j}\\ noise&otherwise\end{cases}  \tag{3}\]  
Based on the steps above, the foundation of the HDBSCAN algorithm is constructed, enabling it to hierarchically and effectively handle clustering analysis related to big data.
## 1 Introduction
### Related Works
#### 1.3.2 BIM in Terms of Standardization  
There are many definitions of the meaning of BIM. Most of them show different explanations depending on the purpose of use. With regard to the international standardization process, the most important definition is the one according to the technical standard EN ISO 19650 [16], which reads as follows:  
\"Building Information Modelling = the use of a shared digital representation of a built asset (building) to facilitate the design, construction and operational processes to create a reliable basis for decision making\". BIM encompasses not only the actual creation and editing of data, but also the processes required in these actions. It is necessary to determine how the data is handled, for what purpose it is created, who creates the data, and who reads and processes it and when.  
In the sense of the above definitions, it is necessary to distinguish between BIM as a model or information model of the building, i.e., as a form of information database, and BIM as a modelling process that uses the information model of the building for the exchange and sharing of information. These exchanges and sharing must take place throughout the life cycle of the building and between all participants. Thus, the BIM method requires the establishment of rules of collaboration and their adherence.  
In the document Concept for the introduction of the BIM method in the Czech Republic [1], three basic conditions were identified as the closest objectives for the determination of contracts using the BIM method:  
* digital information model of the building (in 3D),
* use of CDE and linked systems (e.g., Computer Aided Facility Management--CAFM) for data and information storage,
* creation of special contractual arrangements, the so-called BIM-protocol, including the BIM execution plan (BEP).  
The simplified principle of BIM processes is shown in Figure 2. A new drawing or document is created and processed in the Developed phase, then through review and approval it moves to the Shared phase where it is visible to other professions for coordination. After coordination with other project participants, it goes to the Published phase, which means sharing with the investor.
## 3 Results
### Desorption of Cr (III) Loaded in OP  
The result of the desorption process (Figure 5) suggests that the removal rate of Cr (III) on the surface of OP depends on the pH and the treatment of the bioadsorbent, which suggests that a portion of the ions on the surface are easily removed, especially with pH less than 1, which has a desorption percentage greater than 50% for both MTOP (51.8%) and WTOP (53.0%). This causes an increase in the number of sites available for the reabsorption of the ions present in the solution, which agrees with what was reported in other studies [20; 22]; however, with a pH greater than 1, there are removal percentages that remain practically constant for WTOP and MTOP. This result indicates that the process is reversible and provides an idea of the desorption capacity; however, it is necessary to carry out an extensive investigation that evaluates different operating conditions and with which the regeneration capacity of the treated orange peel can be optimized. For OPTmW, it was observed that the desorption of Cr (III) depends on the concentration of the present adsorbent (Cads), having a maximum removal percentage of 27%, which indicates that a greater energy would be needed to try to remove the metal from the surface of the adsorbent, compared to that used with the other two treatments. Similar  
Figure 4: Analysis of external mass transport at different pH, (**a**) WTOP, (**b**) MTOP, and (**c**) OPTmA.  
results in the Cr (VI) removal process have been reported using the same concentration of NaOH [10; 22; 40].
# Improvement of Buildings' Air Quality and Energy Consumption Using Air Purifying Paints
## 1 Introduction
The experimental study of the current work, in terms of air quality (AQ), was carried out using EN16980:2021 as a reference for the lab-scale experiments. The real-scale experiments were performed in the Demo Houses, a unique European facility located in the premises of FORTH in Crete, based on the comparison between the concentration level of a pollutant in a \"reference\" and a \"green\" room, respectively. The elimination in energy consumption in the Demo Houses was calculated based on the monitoring data and on the DesignBuilder Pro, V5 2017 simulation tool. DesignBuilder is advanced interface simulation software, which provides to users access to the most commonly required simulation capabilities covering buildings' design and use like thermal mass, glazing, shading, renewables, HVAC, mechanical ventilation, energy efficiency, CO emissions, and financial analysis. To the best of our knowledge, the current study which is part of the LIFEVISIONS project (www.lifevisions.gr accessed on 14 March 2024), is the first attempt to qualitatively and quantitatively study a close-to-market innovative photocatalytic paint, under both lab- and real-scale application, for its efficiency to reduce air pollutants and energy consumption.
# A Game Theoretic Approach for Energy Sharing in the Italian Renewable Energy Communities
## 3 Game Theoretic Approach for Value Redistribution
### Energy Community as a Coalitional Game
#### 3.3.2 Stability and Fairness  
The study of the stability of the coalition, that is the confirmation that everybody wants to form the gran coalition instead of smaller ones, is trivial. The reason is that the rules of the game state that the participation to the energy community has to be open and voluntary. This means that, even if some coalition could be preferred from a subset of players, they are not allowed to exclude others from the coalition. From their side, all the users want to take part to the community, since they can only gain from this participation. From the producers side, since the optimal portfolio for the community as a whole has already been evaluated in the optimization phase, the community will constrain its generation portfolio to that one. In other words, it is not the single investor that decides which generator is more profitable, but is the community that defines the set of generators that have to be installed. The choice of the producer is to invest or not in that specific portfolio. Given this consideration, the study of the game is reduced to the research of a fair way of dividing the grand coalition's payment amongst its members.  
When dealing with fairness, two issues are of particular importance. The first concerns the division of the value produced between consumers and producers. It is important for those who invest in community facilities to know that they are getting a fair return for their financial intervention. At the same time, value should be distributed among consumers according to their contribution to value generation, because not everyone contributes in the same way. The contribution of a passive user will be zero if his energy demand occurs at a time when the total load already exceeds the current production. On the contrary, a user will produce a greater value if he consumes the energy produced by the facilities of the community when there are no other users who require it. The calculation of the Shapley value can solve both issues.  
For the application into this field, we propose a two-level payoff distribution. In the first level, the payoff is distributed with the Shapley value between producers and groups of consumers. The distribution based on group of consumers instead of each single consumer introduces an approximation, since the marginal contribution of the group of costumers can be different from the sum of marginal contributions of each single user. Nevertheless, in the planning phase, this can be accepted in exchange of a computational feasibility. The second level of distribution concerns the division amongst each single consumer within each group with a proportional distribution.
# Real-Time Guitar Amplifier Emulation with Deep Learning +
Footnote †: This article investigates the use of deep neural networks for black-box modelling of audio distortion circuits, such as guitar amplifiers and distortion pedals. Both a feedforward network, based on the WaveNet model, and a recurrent neural network model are compared. To determine a suitable hyperparameter configuration for the WaveNet, models of three popular audio distortion pedals were created: the Ibanez Tube Streamer, the Boss DS-1, and the Electro-Harmonix Big Muff Pi. It is also shown that three minutes of audio data is sufficient for training the neural network models. Real-time implementations of the neural networks were used to measure their computational load. To further validate the results, models of two valve amplifiers, the Blackstar HT-5 Metal and the Mesa Boogie 5:50 Plus, were created, and subjective tests were conducted. The listening test results show that the models of the first amplifier could be identified as different from the reference, but the sound quality of the best models was judged to be excellent. In the case of the second guitar amplifier, many listeners were unable to hear the difference between the reference signal and the signals produced with the two largest neural network models. This study demonstrates that the neural network models can convincingly emulate highly nonlinear audio distortion circuits, whilst running in real-time, with some models requiring only a relatively small amount of processing power to run on a modern desktop computer.
## 4 Power Converter Topologies
### Charger Stage (Bidirectional DC-DC Converters)  
The main purpose of a DC-DC converter is to regulate the output voltage and current on the common DC bus to suit the current required to charge the EV [13]. This paper is focused on bidirectional topologies; however, if the V2G operation is not required, this capability is not necessary [13]. Several topologies are discussed on the literature [13; 22; 34; 38]. These topologies are classified into isolated and non-isolated DC-DC converters. However, isolated converters are more preferred than non-isolated types because of the high gain and galvanic isolation [13].