esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Traffic situation awareness and alerting assisted by adverse weather conditions contributes to improve traffic safety, disaster coping mechanisms, and route planning for government agencies, business sectors, and individual travelers. However, at the city level, the physical sensor-generated data are partly held by different transportation and meteorological departments, which causes problems of \"isolated information\" for data fusion. Furthermore, it makes traffic situation awareness and estimation challenging and ineffective. In this paper, we leverage the power of crowdsourcing knowledge in social media and propose a novel way to forecast and generate alerts for city-level traffic incidents based on a social approach rather than traditional physical approaches. Specifically, we first collect adverse weather topics and reports of traffic incidents from social media. Then, we extract temporal, spatial, and meteorological features as well as labeled traffic reaction values corresponding to the social media \"heat\" for each city. Afterwards, the regression and alerting model is proposed to estimate the city-level traffic situation and give the suggestion of warning levels. The experiments show that the proposed model equipped with gcForest achieves the best root mean square error (RMSE) and mean absolute percentage error (MAPE) score on the social traffic incidents test dataset. Moreover, we consider the news report as an objective measurement to flexibly validate the feasibility of proposed model from social cyberspace to physical space. Finally, a prototype system was deployed and applied to government agencies to provide an intuitive visualization solution as well as decision support assistance.  
city-level traffic alerting; adverse weather; social transportation; crowdsourcing knowledge; intelligent transportation system +
Footnote †: journal: Applied Soft 2018
# Software-as-a-Service Security Challenges and Best Practices: A Multivocal Literature Review
## 5 Results
### FL Analysis
#### 5.1.3. SaaS cloud Security Issues and Challenges  
We selected 58 scientific studies as primary studies after applying the I&E criteria. SaaS security issues and challenges were identified after a detailed screening of these primary studies. According to the studies' results, data loss/leakage is the key SaaS security challenge with a frequency of 41. Identity and access management and a lack of user control are the second most important challenges with a frequency of 39 each, after which is logical storage segregation and multi-tenancy/data locality with a frequency of 36. The remaining issues and challenges along with their frequency of occurrence and reference of scientific studies are detailed in Table 2.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Challenges** & **Freq** & **Occurrence in FL** \\ \hline \multirow{5}{*}{Data security/data loss or leakage} & \multirow{5}{*}{41} & S1, S3, S4, S6, S7, S9, S11, S12, S13, S14, S15, S16, S17, S19, \\  & & S22, S24, S25, S26, S27, S28, S30, S31, S32, S33, S37, S38, \\  & & S39, S40, S41, S43, S45, S46, S47, S48, S50, S52, S53, S54, \\  & & S55, S56, S57 \\ \hline \multirow{5}{*}{Identity and access management issues} & \multirow{5}{*}{39} & S3, S4, S5, S6,S7, S9, S10, S11,S12,S13, S14, S15, S16, S18, \\  & & S22, S25, S26, S27, S28, S29, S30, S33, S34, S36, S37, S38, \\  & & S41, S43, S44, S46, S48, S49, S50, S51, S52, S53, S55, S56, S57 \\ \hline \multirow{5}{*}{Lack of user control/visibility} & \multirow{5}{*}{39} & S1, S2, S4, S6, S9, S11, S12, S13, S14, S16, S17, S18, S19, S23, \\  & & S24, S25, S26, S27, S28, S29, S31, S32, S35, S36, S38, S42, \\ \cline{1-1}  & & S43, S44, S45, S48, S49, S50, S51, S52, S53, S54, S55, S56, S58 \\ \hline \hline \end{tabular}
\end{table}
Table 2: SaaS security issues and challenges identified from FL.  
Figure 8: Retrieved studies frequency w.r.t. year of publication.  
The issues or challenges with a frequency of one are combined under the heading 'others', including not incorporating security into SDLC, documentation, and different service delivery/receiving models.  
The aim of identifying these SaaS security challenges and issues is to address our research question one (RQ1). This will help SaaS customers and service providers to gain an in-depth overview of SaaS security issues. Addressing these challenges will help to improve the security of the SaaS cloud.
## 2 Materials and Methods
### Power Saving Strategies
#### 2.4.2 Power Reduction in Hardware  
Improving power efficiency can also be attained through hardware optimization and efficient operation. Numerous approaches in this regard have been proposed and extensively examined, as detailed below.  
Reducing the Operating Voltage  
All microcontrollers (MCUs) and peripheral devices housed within the sensor node function within specific voltage ranges. The system's efficiency varies across these voltage levels. Our research aims to explore the potential for power consumption reduction by operating the system at the lowest feasible voltage level. Additionally, we conduct an extensive examination of the trade-off between the operating voltage level and overall system efficiency to determine the optimal and most efficient operating configuration.  
External Timer Use and I/O Modifications  
In this work, we utilize multiple MCU boards, each with distinct capabilities. Notably, the TI-CC2650-Launchpad is the only variant, which includes an active internal timer, enabling it to wake the system up from deep sleep mode. This feature sets it apart from the Adafruit Feather and Atmega328 boards, which lack this internal timer functionality. To compensate for this disparity, we propose incorporating the TPL5110 as an external timer to manage sleep cycles and facilitate wake-up from deep sleep mode. This timer boosts impressive ultra-low-power performance, drawing a mere 35 nA during sleep mode. The research further delves into various configurations employing this timer, providing valuable insights. Additionally, when dealing with the Atmega328p chip, it is advisable to configure the unused I/O to optimize system efficiency.
###### Abstract  
Self-reconfiguration in electrical power grids is a significant tool for their planning and operation during both normal and abnormal conditions. The increasing in employment of Intelligent Electronic Devices (IEDs), as well as the rapid growth of the new communication technologies have increased the application of Feeder Automation (FA) in Distribution Networks (DNs). In a Smart Grid (SG), automation equipment, such as a Smart Breaker (SB), is used. Using either a wired or a wireless network or even a combination of both, communication between the Control Center (CC) and SBs can be made. Nowadays, wireless technology is widely used in the communication of DNs. This may cause several security vulnerabilities in the power system, such as remote attacks, with the goal of cutting off the electrical power provided to significant consumers. Therefore, to preserve the cybersecurity of the system, there is a need for a secure scheme. The available literature investments proposed a heavyweight level in security schemes, while the overhead was not considered. To overcome this drawback, this paper presents an efficient lightweight authentication mechanism with the necessary steps to ensure real-time automatic reconfiguration during a fault. As a first stage, authentication will be made between CC and SB, SB then sends the information about its status. To ensure the integrity of the authentication exchange, a hash function is used, while the symmetric algorithm is used to ensure privacy. The applicability of the suggested scheme has been proved by conducting security performance and analysis. The proposed scheme will be injected on ABB medium voltage breaker with the REF 542_plus_ controller. Therefore, the probable benefit of the suggested scheme is the contribution to provide more flexibility for electrical utilities in terms of reducing the overall computational overhead and withstanding to various types of attacks, while also opening new prospects in FA of SGs.  
smart grid; control center; smart breaker; authentication scheme; distribution networks; Feeder Automation 1 (2018) 8 (2018) 10.3390/app8091508
## 2 Research Aims, Materials, and Method  
### Research Questions  
The present study is underpinned by the following research questions:  
1. What are the types of hazards to cultural heritage that have been studied using satellite imagery so far?
2. Are all the types of hazards equally addressed?
3. Is there a correlation between a specific type of damage and satellite-based technology?  
The outcomes consist of a comprehensive reconstruction of the current trends and limits in the application of RS and EO techniques by Europe-based researchers over the last 20 years to document human- and nature-induced damage to archaeological cultural heritage. This will be useful to (1) indicate the best tools and methodologies for each specific type of hazard and (2) suggest recommendations and future trajectories for properly reframing the discrepancy between those types of damage that received the greatest attention and the most impactful ones in terms of the number of sites damaged, so as to deepen the preliminary findings discussed by [10].
# Iterative MLFMA-MADBT Technique for Analysis of Antenna Mounted on Large Platforms
## 1 Introduction
the BT region can be modeled with an extremely coarse mesh and scarcely affect the accuracy. The remainder of the paper is organized as follows. The outline of the iterative MLFMA-MADBT method is introduced in Section 2. The key points and the details of the proposed method are described in Section 3. The numerical results of the proposed method and two conventional methods are compared in Section 4, while conclusions are drawn in Section 5.
## 9 Conclusions  
This work introduces and validates a robust, scalable, and non-intrusive framework for predicting belt degradation in induction motor-driven systems by combining motor current signature analysis (MCSA) with advanced deep learning models. By employing LSTM networks, particularly the LSTM-ReLU variant with spectral analysis, the framework effectively captures the evolving patterns of hidden faults over time with both accuracy and interpretability.  
Extensive testing on a long-term industrial dataset demonstrates that this approach consistently outperforms traditional methods such as ARIMA, while also providing a prescriptive layer that supports maintenance decisions with clear and actionable insights. This ability to convert electrical signals into reliable prognostic information promotes proactive maintenance, helping to avoid unexpected failures and reduce downtime.  
That said, analysis using higher-resolution data and multi-band spectral features could potentially reveal additional insights and influence model performance. However, since this study was developed in close collaboration with an industrial partner, practical considerations require balancing the maximum achievable resolution with computational efficiency and user interpretability. The current focus on a well-defined fault frequency has yielded strong predictive results, suggesting that while incorporating multi-band analysis and finer resolution might enhance the model, these improvements are unlikely to deliver significant additional value for the main end users, maintenance teams, and asset managers. In other words, the current approach optimizes the trade-off between detailed data analysis and operational usefulness, ensuring that the framework remains both effective and accessible. Overall, this research offers not only a highly accurate fault prediction model but also a practical decision support tool aligned with Industry 4.0 objectives, enhancing operational reliability, extending asset lifespan, and maximizing the benefits of data-driven maintenance strategies. Furthermore, the proposed methodology can be readily integrated with other Industry 4.0 technologies to create more comprehensive and intelligent maintenance systems. For example, it can leverage IoT sensors to collect diverse real-time data streams from multiple assets, enhancing the granularity and the scope of monitoring. Cloud computing and edge processing can support scalable data storage and rapid model inference, enabling near real-time fault detection and response. In addition, combining this framework with advanced analytics and digital twin technologies can facilitate predictive simulations and scenario planning, further optimizing maintenance scheduling and asset management. This integration ensures that the methodology is not only effective in isolation but is also adaptable to evolving smart factory environments, supporting more automated, flexible, and efficient industrial operations.
## 3 APE-GAN
#### 7.1.2 Preprocessing  
Carefully planned preprocessing processes were also developed to limit the influence of adversarial perturbations. In this regard, a study [83] presented feature squeezing by spatial smoothing or pixel color bit depth reduction. In addition, picture modifications, such as total variance reduction and image quilting, were revealed to assist in removing adversarial perturbations, according to [84]. Additionally, in [85], the authors recommended that adversarial samples be denoised before being fed into a classifier using a GAN.  
Furthermore, we have also included a list of defensive tactics that fall under the preprocessing umbrella as follows:  
1. ME-Net  
The research in [86] presented a defense technique named matrix estimation (ME-Net) to deal with the adversarial samples in deep neural networks (DNNs). This was achieved by taking incomplete or damaged images and eliminating the noise from these images to eliminate the adversarial examples from the original pictures affecting the classification performance. Thus, there were two stages for the image before it was processed: first, arbitrary pixels were discarded from the picture, and then the picture was rebuilt using ME. According to the authors, the results showed that the ME-Net had made the deep neural networks more robust against adversarial attacks than other methods.  
1. DIPDefend  
A research work in [87] presented a defense technique named \"deep image prior driven defense\" (DIPDefend) to remove adversarial examples from the image before passing the image into the classifier. Furthermore, this method was distinct for its adaptability to different types of attacks. Thus, it examined the internal prior of the image and then divided it into two steps: robust feature learning and non-robust feature learning. Additionally, it reconstructed the image by beginning with robust features and then non-robust features to make them stronger against adversarial attacks. According to the authors, the DIPDefend strategy yielded better visual results by eliminating adversarial disturbance while preserving picture information. It is worth noting that the DIPDefend technique can be applied without pretraining, making it useful in various situations.  
1. Stochastic Transformation-based Defenses  
The authors in [88] proposed an improved method based on transformation to extract the features of the clean images. Moreover, they employed two transformation-based approaches that are already in use: pixel deflection (PD) [89] and the image random resize and pad (RRP) [90]. Furthermore, they investigated the impact of random image alterations on clean pictures to understand better how accuracy deteriorates. They trained a unique classifier to identify distinguishing characteristics in the distributions of softmax outputs ofconverted clean pictures and predict the class label. Additionally, untargeted assaults on CNN have been studied, and it would be interesting to compare their distribution classifier approach with targeted attacks.
## 1 Introduction
ANNs are particularly effective for complex tasks for which traditional models fall short, such as pattern recognition, classification, and nonlinear problem-solving [40,41]. This artificial intelligence modeling technique is highly suitable for digitalization in the context of Industry 4.0 [42,43,44]. ANNs have been successfully used to model and predict the performance of AWJ processes (cutting and milling) [45], including surface quality, accuracy, and cutting efficiency [34,37,38,39]. Unlike traditional regression models, ANNs can handle many variables and complex interactions without requiring explicit equations to describe the underlying processes [45,46]. [PERSON] et al. successfully developed an ANN model capable of predicting the vibration and surface roughness in AWJC [47]. [PERSON] et al. used a machine learning approach to predict surface roughness in the abrasive waterjet milling of alumina ceramic. Their study investigated the individual and interactive effects of process parameters on surface roughness [48]. [PERSON] et al. used ANN to predict the surface roughness in the AWJ cutting of stainless steel. The ANN model, trained on data from varying traverse speeds, depths of cut, and abrasive flow rates, was validated through cross-validation, showing that it could reliably predict surface roughness, thus reducing the need for extensive experimental trials and optimizing the cutting process [38]. ANN is used to predict the geometric characteristics (width, depth, and taper) of a microchannel fabricated through AWJ milling. This prediction considers process parameters such as abrasive size, traverse speed, abrasive flow rate, and standoff distance [49]. [PERSON] et al. developed ANN models to predict performance metrics like material removal rate and surface roughness in rock cutting using AWJ. By training the models with experimental data on parameters such as water pressure, abrasive flow rate, and traverse speed, they demonstrated that ANNs could accurately forecast these outcomes, aiding in process optimization [39]. Although many ANN models have been used in the literature to model AWJ cutting and milling, an ANN model for optimizing the AWJ piercing process has yet to be developed. The present paper proposes an artificial neural network (ANN) model to optimize process parameters for eliminating delamination in carbon fiber-reinforced polymer (CFRP) materials during abrasive water jet (AWJ) piercing. By preventing delamination, the model helps avoid costly rework and the replacement of large expensive components, particularly in the aerospace and automotive industries. This study demonstrates how ANNs can improve AWJ machining processes, reducing material waste, downtime, and overall production costs, making it a valuable tool for enhancing both the quality and economic efficiency of CFRP manufacturing.
# Laplacian Controllability and Observability of Multi-Agent Systems: Recent Advances in Tree Graphs
## 6 An Illustrative Example  
In this Section, we practice with the overall algorithm through an illustrative example, which also puts in evidence the effectiveness of the proposed approach when dealing with large graphs and hence in the framework of complex networks.  
Consider the starlike tree graph depicted in Figure 10, which is made of 6 branches and 37 nodes, and it corresponds to \(\mathcal{S}(3,4,4,5,5,6,10)\). Nodes are labeled according to the convention adoptedalong the paper, namely the center is labeled as Node 1 and then the branches are numbered from the center to the leaves. They are ordered starting from the shortest to the longest one.  
In the following, we discuss the graphical result of the algorithm, as shown in Figure 11. Colored nodes are related to nonzero eigenvector components obtained by making all the possible reductions, namely for \(k=1,2,\ldots,6\), and for each applying Algorithms 1, 2, or 3. The color pink is associated to reductions in \(N_{3}\), light green to \(N_{4}\), and purple to \(N_{5}\). All other possible reductions do not match either (R1) or (R2), so the related eigenvalues are not eigenvalues of \(\mathcal{S}\).  
In the following, we report the multiplicity of the eigenvalues of \(N_{i}\), \(i=1,\ldots,6\), together with the set of control/observation nodes that are selected according to Algorithms 2 and 3.  
\[\lambda\in\Lambda_{N_{3}},m_{g}(\lambda)=1, \mathcal{V}_{c}^{N_{3}}=\{2,3,4,29,30,31,32,33,34,36,37,38\},\] \[\lambda\in\Lambda_{N_{4}},m_{g}(\lambda)=1, \mathcal{V}_{c}^{N_{4}}=\{5,6,7,8,9,10,11,12\}, \tag{16}\] \[\lambda\in\Lambda_{N_{5}},m_{g}(\lambda)=1, \mathcal{V}_{c}^{N_{5}}=\{13,14,15,16,17,18,19,20,21,22\},\]  
while all the other \(\lambda\in\Lambda_{N_{i}}\) for \(i\in\{1,2,6\}\) are not eigenvalues of \(L_{\mathcal{S}(3,4,5,5,6,10)}\).  
Figure 10: Sketch of the starlike graph considered in the illustrative example.  
Considering that the above sets \(\mathcal{V}_{c}^{N_{l}}\) are disjoint, a set of control/observation nodes to achieve controllability must contain one node for each of the above set, and an example of a minimal set is \(\mathcal{V}^{c}=\{2,5,13\}\).
## 5 New Taxonomy for Constructability Constraints and Features
### Validation of the New Taxonomy Through External Project Examples
#### 5.2.3 The Palazzo Italia in Milan [37; 77; 78]  
Designed by Nemesi & Partners for Expo 2015 in Milan, this project is notable for its innovative design featuring a unique geometric texture evoking the random interweaving of branches, creating a dynamic \"urban forest\" concept. Furthermore, advanced sustainable technologies were employed in this project to achieve near-zero energy consumption and reduce air pollutants.  
The pavilion's external \"branched\" envelope, composed of over 700 unique panels, necessitated advanced computational design approaches. These ensured each panel could be precisely fabricated to fit together seamlessly, despite their complex shapes. Computational design strategies transformed this ambitious architectural vision into a tangible structure, highlighting the critical role of digital tools in modern construction.  
According to [37; 77; 78], this project presented several geometric constructability challenges associated with its complex design. The prefabricated pieces required adherence to specific dimensions, ensuring their seamless integration into the overall structure. Geometry refinement was necessary to optimize the steel weight, reducing the material used while maintaining structural integrity. Additionally, the project incorporated adjustable connections to facilitate assembly and improve adaptability during construction. Another significant challenge involved reducing the variety of angular component types to simplify fabrication while preserving the design's intent. The building's unique branched facade pattern, evocative of a forest, added an additional layer of complexity to the geometric considerations.  
Apart from geometric concerns, the project faced challenges related to material innovation and project management. Innovative materials were employed to reduce air pollutants, aligning with the building's sustainability goals. The integration of photovoltaic glass into the roof design exemplified the intersection of aesthetic and environmental considerations. Furthermore, the project's complexity necessitated an integrated design process, fostering seamless collaboration among architects, engineers, and construction teams to address the intricate demands of the design and construction phases.