esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging IGOOSE-XGBoost for the Early Detection of Subclinical Mastitis in Dairy Cows
## 5 Prediction Model for Subclinical Mastitis in Dairy Cows Based on IGOOSE-XGboost
### Building the IGOOSE-XGBoost Classification Model  
#### 5.2.1 Evaluation Metrics for Classification Models  
To predict whether a cow had subclinical mastitisti, we used four different metrics [30] to evaluate the model: accuracy (ACC), precision (PRE), F1 score (F1), and recall (REC). Their expressions are as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{10}\]  
\[Precision=\frac{TP}{TP+FP} \tag{11}\]  
\[Recall=\frac{TP}{TP+FN} \tag{12}\]  
\[F_{1}=\frac{2 PR}{P+R} \tag{13}\]
## 5 Conclusions  
This study aimed to identify young people's attitudes towards the use of AI technology for emotion regulation and to analyse the factors that influence their decision to use or not use such technologies. The findings revealed that personal, technological and environmental factors jointly shape young people's behaviours, with perceived usefulness, trust, privacy and convenience emerging as key motivators, while concerns about emotional connection, value and complexity acted as barriers. These insights provided initial guidance for the design of AI technology for emotion regulation that is inclusive, accessible and aligned with the needs of young users. Future research with larger and more diverse samples is needed to validate and extend the theoretical model proposed in this study.  
J.W., conceptualisation, formal analysis, investigation, methodology and writing--original draft; H.T., conceptualisation, data curation, formal analysis, investigation, method ology and writing--original draft; S.-S.M., conceptualisation, funding acquisition, methodology, writing--original draft and writing--review and editing; Y.C., conceptualisation, investigation, methodology, resources and writing--original draft; S.Z., conceptualisation, investigation, methodology, project administration, resources, supervision, validation, writing--original draft and writing--review and editing; H.-S.C., conceptualisation, project administration, resources, supervision and writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (72301110), Philosophy and Social Science Planning Project of Guangdong Province of China (GD25 YYS35), the Guangzhou Municipal Science and Technology Bureau (2024A04J2279) and the Fundamental Research Funds for the Central Universities (QNMS202418).  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the South China University of Technology (code: 20230015, 5 September 2023).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:
## 2 Context and Research Objectives
### Aerospace Manufacturing Use-Case  
AIRBUS Defence and Space is the Division of Airbus Group in charge of defence, security, communication, intelligence and space applications. Within Airbus Defence and Space, the Military Aircraft business unit manufactures Military Transportation and Fighter Aircraft, including A400M, C295, CN235, A330 MRTT and Eurofighter Typhoon.  
The presented use case aims at integrating Industrial IoT for flexible management and optimization of cyber physical systems across Tablada, San Pablo Sur and CBC factories (Figure 2) in Spain. An industrial IoT deployment into these AIRBUS D&S factories will be carried out in the future. After that, factory assets such as rivet shaving machine (Roboshave), industrial oven (Autoclave) and advanced smart tools control system (Gap Gun) will be integrated within this IIoT Network, enabling near real time remote process monitoring, optimization, control and resilience.  
This IIoT system is to be deployed over three sites and will cover three types of manufacturing assets. To support secure design, deployment and operation of this IIoT network, a complete virtual model of the system will be realized. It shall enable simulation-based architecture decision making, security analysis, testing and validation, operation and training. The use of smart wearable and environment sensors will also enable to model behavior of human operators, in particular with consideration for handling of gap gun which is a hand tool involved in quality measurement of aeronautical parts. User behavior modeling can also be performed on the IT layer, by analyzing network traffic and kernel data, as well as by using non-intrusive sensors to support User and Entity Behavior Analytics (UEBA).  
Figure 2: Architecture Aerospace Manufacturing Use-Case.
# Situationally Sensitive Path Planning
## 4 Methodology
### Global Environment Layer
#### 4.1.3 Site and Situation Factors  
Recall that in our implementation we consider four broad classes of site and situation properties: segment length, footpath condition, PLOS, and risk. These are open to design levers, but as an example, here we represent these properties in terms of four factors: distance (segment length, _dist_), cleanliness/dirtiness (footpath quality, _dirt_), safety (the inverse of risk, _safe_), and crowd density (PLOS, _density_). In addition, we consider a static fifth _base_ cost to each segment that allows the system designer to dictate a consistent cost to each segment of their choice (see the \"base cost graph\" illustration in Figure 3). Both segments and individual agents internally maintain scalar weight values that correspond to each factor and are considered during [PERSON]'s distance cost calculations.  
Site and situation weight factors for segments and agents can either remain statically set or dynamically alter with respect to environmental changes. For example, if a segment accumulates an increasing number of agents over time, its _density_ weight will naturally increase, resulting in an increased cost value for that segment. Similarly, agents can be modified to have dynamic personalized weight values that conceptually correspond to different emotional moods or cognitive states. More broadly, agents can be spawned with randomized combinations of different preferences to elicit dynamic perspectives among pedestrians.  
In implementation, it is expected that each region segment \(s\) tracks the following:  
* base cost set by the system designer (\(s_{base}\));
* Its Euclidean length between its end waypoints (\(s_{dist}\));
* Its spatial area with respect to the scale of the virtual environment (\(s_{area}\));
* The number of active agents moving along it (\(s_{pop}\));
* Any contextual virtual artifacts that thematically impact a segment's quality, such as trash items, animals, etc. (\(s_{dirt}\), \(s_{Safe}\)).  
Each agent \(a\) also maintains a similar set of personality weights (i.e., \(a_{dist}\), \(a_{density}\), \(a_{dirt}\), \(a_{safe}\)) with an exception to the _base_ cost (which is a segment-specific property).
## 2 Background  
### Smart City  
It is expected that 66% of the world's population will live in urban areas by 2050. Providing this population with adequate resources, including sufficient energy, safe food, and clean water, with economic and social guarantees and environmental sustainability is a real challenge. Many cities today hope to become the smart cities of tomorrow. In order to achieve this goal, a complex plan should be developed, including participation from public and private sectors, product vendors, and providers of information technology services [3; 4]. Most of the smart city sub-systems are shown in Figure 2[5].  
To implement these applications and operations, many advanced technologies are used, and they are summarized as follows [6]:  
1. **Wireless Sensor Network (WSN):** An essential component in smart cities that can be used to provide remote control and real-time monitoring for smart city resources and infrastructure conditions.
2. **Internet of Things (IoT):** The technology that facilitates the lifestyle of humans through connecting physical things with sensory devices and allowing them to interact between each other and with people.
3. **Cyber-Physical System (CPS):** Used to provide the connection between computation, networking, and physical processes; in other words, it is the umbrella that includes the interaction between the virtual and physical worlds.  
Figure 2: Smart city subsystems.  
4. **Robotics and Unmanned Aerial Vehicles (UAVs):** Support smart cities with useful automated services, such as environmental monitoring, traffic monitoring, telecommunication services, security and safety control, and enhanced delivery of services.
5. **Fog computing:** This technology aims to support low latency, location awareness, better mobility, synchronization, coordination, data streaming, and real-time services for smart city applications when there is a large distance between the cloud platform and smart city sensors and devices, as well as when there is a large number of heterogeneous sensors and devices distributed in large areas. This makes it difficult for cloud computing to manage and deal with this situation. So, in this case, fog computing is preferred.
6. **Cloud computing:** This technology represents an important element of any smart city system, since it provides scalable processing power, as well as cost-effective, large, and scalable data storage and updated software services that support, manage, and control different smart city applications.
7. **Big Data:** The collected sensory data will be analyzed using this technology, in order to support optimized decision-making for smart city applications.
## 5 System Architecture for the Generation and Utilization of the 3D Indoor Routing Service
### Automated Generation of an 3D Indoor Model for XML3D  
Aiming at an easy and fast generation of 3D indoor models for different buildings from _IndoorOSM_ information, an automatic generation is required. Therefore, information from OSM about the desired building (which is mapped according to the _IndoorOSM_ mapping proposal) needs to be gathered. Thereby, the real world coordinates of OSM with latitude and longitude information must be transformed into some kind of local reference system (e.g., based on the building bounding box), because XML3D is currently not able to handle double precision coordinate values.  
The algorithm is depicted as pseudo-code in Figure 5 and a human-readable explanation is given in the following: The algorithm sequentially retrieves all the different levels (floors) of the building. For each room on the corresponding floor, a XML3D geometry is created in that way that all corresponding walls are created as well as the floor and ceiling geometry. These faces are then triangulated, because XML3D is currently only capable of dealing with triangle geometries. The respective room elevation is based on the current level elevation, which can be calculated from all the other levels. For example,  
Figure 4: Route graph with x, y, z coordinates of the source and target nodes with connectivity and oneway information (**a**) and a complete 3D route graph for a sample building (**b**).  
when computing the level elevation of floor 2, the elevations of level 0 and level 1 are accumulated (based on the OSM key _height_). The elevation of the ceiling of a room is the level elevation plus the height of the corresponding room. After computing all the room geometries, they are added to the XML3D indoor model. Being able to hide different levels in the web application (cf. also next chapter), all rooms are grouped, according to their level, in the DOM of the web application. In doing so, it is possible to set the visibility of the different groups in the DOM via JavaScript and CSS on-demand during runtime.
###### Abstract  
Inventory prediction is concerned with the forecasting of future demand for products in order to optimize inventory levels and supply chain management. The challenges include demand volatility, data quality, multi-dimensional interactions, lead time variability, seasonal trends, and dynamic pricing. Nevertheless, these models suffer from numerous shortcomings, and in this research, we propose a new model, MMCW-BiLSTM (modified multi-dimensional collaboratively wrapped BiLSTM), for inventory prediction. The MMCW-BiLSTM model reflects a considerable leap in inventory forecasting by combining a number of components in order to consider intricate temporal dependencies and incorporate feature interactions. The MMCW-BiLSTM makes use of BiLSTM layers, collaborative attention mechanisms, and a multi-dimensional attention approach to learn from augmented datasets consisting of the original features and the extracted time series data. Moreover, adding a Taylor series transformation allows for a more precise description of the features in the model, thus improving the prediction precision. The results show that the models make the least mistakes when they use the AV demand forecasting dataset, with MAE values of 1.75, MAPE values of 2.89, MSE values of 6.76, and RMSE values of 2.6. Similarly, when utilizing the product demand dataset, the model also achieves the lowest error values for these metrics at 1.97, 3.91, 8.76, and 2.96. Likewise, when utilizing the dairy goods sales dataset, the model also achieves the lowest error values for these metrics at 2.54, 3.69, 10.39, and 3.22.  
n  
n
# Ship's Digital Twin--A Review of Modelling Challenges and Applications
## 5 The Review Analysis of SDTs  
The analysis of the models, methods, applications, and steps of DT formulation processes developed so far and presented in the reviewed literature is listed in Table 1. The analysis was performed in accordance to the four-step iterative process for the SDT development proposed in this paper.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper** & **Model** & **Steps** & **Methods** & **Application** \\ \hline [PERSON], 2008, [18] & Ship electric power system & I, III, II, IV & Mathematical modelling & Various \\ \hline [PERSON] et al., 2018, [25] & Ship electric power system & I, II, III & MBD, HIL & Maintenance and condition monitoring \\ \hline [PERSON] [PERSON], 2018, [9] & Electric propulsion system & I, II, III, IV & ANN & Maintenance and condition monitoring \\ \hline [PERSON], 2018, [13] & Behavioural model of a ship & I, II, III & DDM, Statistics & Maintenance and condition monitoring \\ \hline [PERSON] et al., 2019, [8] & Propulsion system and ship hydrodynamics & I, II, III, IV & ANN, DDM & Maintenance and condition monitoring \\ \hline [PERSON] et al., 2019, [23] & Hull structure and machinery & I, III, II & MBSE & Decision-making support \\ \hline [PERSON] et al., 2019, [16] & Ship structure (balcony opening) & I, II, III & FEM & Maintenance and condition monitoring \\ \hline [PERSON] et al., 2019, [20] & Ship drivetrain & I, II, III, IV & FEM & Maintenance and condition monitoring \\ \hline [PERSON] et al., 2020, [15] & Diesel engine & I, III, II, IV &
\begin{tabular}{c} Mathematical \\ modelling, HIL, CMV \\ \end{tabular} & Various \\ \hline \hline \end{tabular}
\end{table}
Table 1: The review analysis of DT models, formulation steps, methods, and applications.  
The most common ship system being modelled is an electric power system or one of its components, as found in 23% of the reviewed case-studies, followed by the propulsion system and ship hull structure modelled in 18% of the reviewed publications, as shown in Figure 6. SDTs have been found mostly used for maintenance and condition monitoring, as proposed in 42% of the reviewed literature and shown in Figure 7.  
Figure 4: The use of the proposed steps in reviewed literature.  
Figure 5: Modelling methods review.  
Figure 6: Percentage of the ship’s systems modelled in reviewed papers.
## 4 Discussion
### Relation to Faults and Subsurface Characteristics  
The northern sector of PGPF is affected by an array of en echelon normal faults. The faults exhibit various degrees of overlapping, forming conjugate, convergent and divergent overlapping relay zones, depending on whether the faults dip toward the same or opposite directions, respectively ([PERSON] et al., 2011). Moreover, the area is affected by smaller faults, synthetic and antithetic to the above ones, that produce smaller topographic changes and smaller throws. The Agia Triada fault (AT.F) delimits the field to the north, although few small pockmarks have been observed further north. This dense fault system and the associated weakness zones reinforced the formation and development of many pockmarks, recording the highest spatial density of pockmarks (80/km\({}^{2}\)) in the northern sector and particularly at the proximity of the AT.F (100/km\({}^{2}\)). Collinear clusters of pockmarks have also been observed, trending in the WSW-ENE direction, which coincides well with the direction of the faults. The good coherence in orientation between collinear pockmarks and faults further supports the close relationship between the regional structural setting and the local fluid escaped patterns, which, in turn, control pockmark configuration and spatial distribution. At similar sites where the structural setting dominates, a pockmark field configuration has been reported, such as in the Barents Sea (Barents Sea, Ireland, 2011) and the Nasha Region of the South China Sea ([PERSON] et al., 2012).  
The PGPF also shows an affection for the Holocene sedimentary deposits (SS I), of which the thickness varies from 2 to 34 m (Figure 8). The strong correlation between the pockmark depth and the thickness of the Holocene sequence (SS I) suggests that the largest pockmarks have been formed in thick sediments (Figure 4d), like pockmarks in the Belfast and Passamaquoddy Bays (Bays, 2011, 2011).  
Throughout the study area, many examples of upward gas migration have been detected in the marine sediments. Gas-related acoustic patterns mainly include Acoustic Turbid Zones (ATZs), Enhanced Reflectors (ER) and, to a lesser extent, Intrasedimentary Gas Plumes (IGP). All of them were recorded solely within the Holocene sequence (SS I) and constitute evidence for an upward gas migration from the Pleistocene/Holocene boundary, which can be considered a gas accumulation horizon. ATZs are a widely spread acoustic character along the PGPF except for two limited areas (A and B) at the southern end of the northern sector and at the central part of the southern one (Figure 11b). These areas exhibit an interesting difference in pockmarks appearance; area A is characterised by a cluster of pockmarks, and area B is characterised by an almost lack of pockmarks--apart from a very small one. This difference is probably attributed to a different upward-migrating gas status: (i) in area A, continuous upward migration of gas toward the floor of the pockmarks and total escape of gas and (ii) in area B, near absence of upward migration of gas except the formation of the small pockmark.
## 4 Defense Strategies Against Deliberate Attacks on IECPS
### Process Response
#### 4.2.2 System Fault Isolation
To facing this challenge, researchers have adopted data-driven methods for fault diagnosis and isolation. In [157], a data mining technique was developed to aggregate and cluster system information, providing algorithmic support for system fault diagnosis by extracting energy efficiency data from integrated energy systems. In [158], a gradient boosting regression algorithm was used to predict the operational states of coupled components in integrated energy systems, enabling fault identification and localization. In [159], nine types of faults and three sub-faults in integrated energy systems were analyzed, and a two-level fault isolation scheme was developed using convolutional neural networks, consisting of a high-level classifier for system faults and a low-level classifier for sub-faults, achieving accurate isolation of predetermined faults. In [160], an entropy-based causal learning framework was proposed to learn Bayesian network structures. This framework identifies causal relationships between fault states and fault manifestations, thereby establishing a Bayesian network model that includesmultiple cross-system fault outcomes. It is evident that data-driven approaches do not require domain expert knowledge, thereby facilitating model development and expansion easier, and enabling the handling of more unknown system faults. Therefore, they are more suitable for addressing unknown deliberate attacks.
# Energy Efficient Multipath Routing Algorithm for Wireless Multimedia Sensor Network
## 1 Introduction
Therefore, without appropriate and careful routing of multimedia data in WMSNs, the performance can be affected and the lifetime of WMSNs can also be shortened. In this paper, we propose an energy efficient multipath algorithm with dynamic clustering using genetic algorithm(GA) meta-heuristic optimization. Since finding optimal routes in a network is known to be an NP-complete problem [18], efficient heuristic search algorithms based on reduced-complexity are necessary. The algorithm uses two different methods, namely, clustering and multipath routing algorithms, to reduce energy consumption without unnecessarily impacting on the performance of the network. In the proposed approach, the sensor nodes in the network are first dynamically partitioned into clusters using a novel dynamic clustering algorithm. Then the cluster head for each cluster is selected using an efficient cluster head selection algorithm. Finally, an inter-cluster multipath routing is developed using the GA optimization algorithm to find the best path for the transmission of the sensed data. Since the proposed routing algorithm uses GA-based meta-heuristic optimization, it can select the best possible routing and has great ability to effectively deal with the dynamic topologies of WMSN. Our contributions in this paper can be summarized as follows:  
* We propose a novel algorithm for dividing the network into clusters each with a cluster head (CH) to save more energy and to improve network lifetime. * We proposed energy efficient multipath algorithm with dynamic clustering using Genetic Algorithm (GA) based meta-heuristic optimization technique. * We performed an extensive performance analysis of the proposed energy efficient multipath algorithm and compared it with several states of the art routing algorithms for WMSN  
The rest of the paper is organized as follows. In Section 2, the related works are reviewed. In Section 3, the system model used in this study is presented. In Section 4, the proposed routing algorithm is presented. The experiment and the analysis are discussed in Section 5. The conclusion and future work are presented in Section 6.