esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed IGDT-Based OCL Strategy  
Figure 1 shows the single-line diagram of a conventional multi-chiller plant. In this system, the total electricity cost is minimized over the \(T\)-hour study horizon, as given by Equation (1), where \(\lambda_{t}\) and \(P_{i}^{t}\) are the electricity price and the power consumption of chiller \(i\) at operating time interval \(t\), which can be calculated from Equations (2)-(4), where, \(u_{i}^{t}\) is a binary decision variable that is equal to 1 if the \(i^{\text{th}}\) chiller is on at hour \(t\); otherwise, it will be 0. Based on Equation (3), the partial load ratio of chiller \(i\) is selected as another decision variable and represents the cooling capacity constraint. As is evident from Equation (4), when chiller \(i\) is on at hour \(t\), its electrical power consumption depends on the constant coefficients \(a_{i}\), \(b_{i}\), \(c_{i}\), and \(d_{i}\) and its cooling production; otherwise, it will be 0. Equation (5) demonstrates that the refrigeration capability of the chillers should be equal to or larger than the cooling demand at time interval \(t\). Note that \(CL_{t}\) and \(RT_{i}\) denote the cooling load at hour \(t\) and the refrigeration capacity of chiller \(i\), respectively [26].  
\[\text{Energy cost}=\sum_{t=1}^{T}\sum_{i=1}^{N}\bigl{(}\lambda_{t}\times P_{i }^{t}\bigr{)} \tag{1}\]  
\[u_{i}^{t}=\left\{\begin{array}{ll}0&\text{if chiller $i$ is off}\\ 1&\text{if chiller $i$ is on}\end{array}\right\} \tag{2}\]  
\[\textit{PLR}_{i}^{t}=\left\{\begin{array}{ll}0&\text{if chiller $i$ is off}\\ \frac{\text{Cooling load of chiller $i$ at time $t$}}{\text{Refrigeration capacity of chiller $i$}}=\text{rand}&\text{if chiller $i$ is on}\end{array}\right\} \tag{3}\]  
\[P_{i}^{t}=\left\{\begin{array}{ll}0&\text{if $u_{i}^{t}=0$}\\ a_{i}+b_{i}\textit{PLR}_{i}^{t}+c_{i}\bigl{(}\textit{PLR}_{i}^{t}\bigr{)}^{2}+d _{i}\bigl{(}\textit{PLR}_{i}^{t}\bigr{)}^{3}&\text{if $u_{i}^{t}=1$}\end{array}\right\} \tag{4}\]  
\[\textit{CL}_{t}\leq\sum_{i=1}^{N}\textit{PLR}_{i}^{t}\times\textit{RT}_{i} \tag{5}\]
# Formal Analysis of DTLS-SRTP Combined Protocol Based on Logic of Events
###### Abstract  
Security analysis of composite protocols is a critical issue in the field of network security. In complex network environments, the traditional approach of analyzing a single protocol becomes inadequate when dealing with scenarios involving multiple interactions and combinations of protocols. To address this challenge, this paper extends the Logic of Events Theory (LoET) and proposes a method for proving the security of composite protocols. Building upon the Logic of Events Theory, we introduce sequential composition rules, ordering rules, and relevant axioms. We incorporate the concept of invariants and formally abstract the DTLS-SRTP protocol, thereby verifying the mutual authentication and confidentiality of the two sub-protocols. In conclusion, our study demonstrates that the extended Logic of Events Theory offers an effective means of verifying the security of composite protocols.  
Keywords:formal methods; Logic of Events; protocol combination; mutual authentication +
Footnote †: journal: Applied sciences
# A Deep Learning Approach for Network Intrusion Detection Using a Small Features Vector
## 3 Background Theoretical and Conceptual Framework
### Model Evaluation  
The model's performance was evaluated to determine the quality of its output results. This step is important because models are designed to predict the class of future unlabeled data points. Evaluation is performed, which is based on the following metrics: accuracy, detection rate (DR), and false positive rate (FPR).  
The accuracy is a measure of how often the prediction of the model matches the labels, and the accuracy is calculated using:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}. \tag{8}\]  
The detection rate measures the ratio of normal packets, which the model correctly predicted as normal, to the actual number of normal packets. It is calculated using:  
\[Detection\ Rate=\frac{TP}{TP+FN}. \tag{9}\]  
The false positive rate measures the ratio of attack packets that the model incorrectly predicted as normal to the actual number of attack packets. It is calculated using:  
\[False\ Positive\ Rate=\frac{FP}{FP+TN}, \tag{10}\]  
where _TP_ is True Positive, _TN_ is True Negative, _FP_ is False Positive, and _FN_ is False Negative. These terms are defined as follows: TP indicates the model correctly predicted the positive class. TN indicates the model correctly predicted the negative class. FP indicates the model incorrectly predicted the positive class. FN indicates the model incorrectly predicted the negative class.
## 5 Conclusions  
Tests on the use of the projection of ultra-cooled air for the removal of graffiti from railway vehicles have yielded significant results both for the degradation of the aerosols used in the graffiti and in the preservation of the vehicle's support materials. The preservation of these materials was confirmed by observing that they maintained their integrity without significant alterations in their adhesion, measuring variations of less than 5%, and in colourimetry, with \(\Delta\)Eab\({}^{*}\) results above 6 CIELab units (4.91), which are deemed acceptable according to industry standards [67].  
On the other hand, the degradation of the aerosols varied depending on the type of paint, showing noticeable changes in topography and adhesion. Bitumen-based paints, specifically MTN_T aerosol, stood out for their complete removal under specific conditions of pressure (3 bar), temperature (\(<\)\(-80\)\({}^{\circ}\)C), and increased exposure time, showing a 100% total loss of adhesion with prolonged treatment. In addition, significant changes were observed in the colourimetry and adhesion of the aerosols. The red colours, represented by the variants MTN_WB_R and MTN_94_R, experienced the largest colourimetric alterations, with differences of up to 6.00 and 4.59 in \(\Delta\)Eab\({}^{*}\), respectively.  
The adhesion of certain aerosols decreased markedly, with MTN_N2G_N being a prominent case, which, after 8 min of exposure to cold air, intensified its adhesion loss from a range of 5-15% to 15-35%. This demonstrates that ultra-cooled-air blasting is a promising and viable method for the removal of graffiti on rail vehicles, achieving effective removal of certain paints without compromising the integrity of the supporting materials. These results underline the potential of this technique as a sustainable and environmentally friendly solution for graffiti management in urban environments, paving the way towards the optimisation and practical application of this innovative cleaning method.  
Conceptualization, V.S.-C. and A.V.-B.; methodology, V.S.-C. and A.V.-B.; investigation, A.V.-B. and V.S.-C.; resources, A.V.-B. and V.S.-C.; writing--original draft preparation, A.V.-B., V.S.-C., J.A.L.-C. and V.D.-R; writing--review and editing, A.V.-B., V.S.-C., P.B.-R., J.A.L.-C., V.D.-R. and M.S.-P.; supervision, V.S.-C. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the support received for this research from the Vice-Rectorate for Research of the Polytechnic University of Valencia (PAID-11-22), grant number PID2022-139433 OB-I00, as well as the collaboration with Istobal S.A., facilitated by the ISTOBAL Chair of the Polytechnic University of Valencia (UPV). In addition, the authors would like to express their gratitude to CEICE-GVA and its grant Programme for Doctoral Studies (CIACIF/2021/404), funded by the European Union.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 4 Societal Benefits and Impact
### Proactive Health and Safety Monitoring  
The IoPVT-enabled HARISM framework aims to be a proactive health and safety monitoring paradigm, shifting from a crisis response to a prevention-based model, potentially stopping disasters before they happen. Traditionally, outdoor hazards, such as slippery walkways, compromised stairs, deteriorating air quality, and extreme weather events, have been reactively addressed, with solutions deployed only after accidents or noticeable degradation. With continuous sensing, HAR, and predictive analytics integrated, community leaders and individuals can anticipate challenges in advance and make timely, targeted decisions to deal with them.  
A critical part of this proactive approach is the ability to spot early indicators of risks and diffuse them before any real damage is done. For instance, environmental sensorsplaced along outdoor staircases in urban cities like Montreal can detect subtle changes in temperature and moisture that indicate ice formation, prompting maintenance crews or automated de-icing systems to intervene well before pedestrians face slipping hazards. In the same way, wearable biosensors and HAR solutions based on cameras recognize distress patterns, such as an elderly person climbing steps and falling over or a sudden change in gait that suggests a health emergency, which could trigger a cascade of alerts and responses from city officials, medical services, or neighbors [88,89].  
Proactive monitoring helps with long-term resilience and planning beyond short-term intervention. Historical data and predictive models can reveal patterns in infrastructure wear and tear, population movements, environmental factors, etc. These can influence the placement of sensors, the design of public spaces, the allocation of resources, and other aspects of urban planning [90,91]. By integrating these insights into policymaking and urban planning, cities can adapt more effectively to emerging challenges, such as shifting demographics, increased tourism, or the increasing impacts of climate change.  
This approach facilitates a culture of preparedness and shared responsibility for health and safety. As communities increasingly trust that their surroundings are being actively observed and cared for, public confidence in civic technology grows. This trust, in turn, fosters greater collaboration between stakeholders, from city planners and public health experts to residents and entrepreneurs, who can use shared data and insights to drive innovative safety interventions, build health infrastructure, and optimize quality of life. Instead of relying on a reactive status quo, proactive monitoring is a forward-thinking mindset that embraces, instead of settling for, the sustainability of culture for us and future generations.
# PRISMA on Machine Learning Techniques in Smart City Development
## 3 Results
### Transport Systems & Traffic Flow  
The first identified theme revolves around the transformative role of smart mobility solutions within the context of smart cities. This theme includes a range of elements, including sustainable transportation systems, active transportation modes, efficient public transportation, pedestrian-friendly infrastructure, network traffic management, and the integration of low-emission vehicles [5; 54].  
The paradigm shift from traditional reactive approaches to traffic management toward proactive measures is emphasized by the infusion of machine learning algorithms and data analytics into urban planning frameworks [26]. This transition not only emphasizes thesignificance of predictive models in accurately forecasting traffic flow but also provides valuable insights for decision-makers to enhance road accident management within the smart city context.  
Moreover, these proactive measures extend beyond traffic optimization; they contribute to broader environmental goals. Studies by [PERSON] et al. [39] and [PERSON] et al. [5] highlight the potential of machine learning techniques in reducing fuel consumption, carbon dioxide emissions, and the overall carbon footprint. The findings also suggest interventions such as early alarms, speed governors, and traffic calming measures as effective means to mitigate the impact of influential factors on crash occurrences [29]. This integrated approach not only improves road safety but also aligns with sustainability objectives within smart city planning.  
The integration of advanced technologies, particularly Graph Neural Networks (GNNs), also plays a crucial role in real-time traffic-speed estimation for smart cities. GNNs leverage the inherent graph structure of urban road networks to capture spatial and temporal dependencies. A notable example is the Spatio-Temporal Graph Generative Adversarial Network (STGGAN), which stands out for its integration of spatio-temporal data, graph structures, edge features, and a residual structure. This results in highly accurate real-time traffic-flow prediction [31; 37; 43].  
Transport Systems and Traffic Flow is a major theme that encompasses the crucial role of smart mobility solutions within the context of smart cities. This theme has been further divided into three subthemes: vehicle speeds, traffic congestion, and improvement in existing traffic forecasting studies.
## 3 The PPBR Key Management Scheme
### Key Updating and Revocation  
When the residual energy of sensor nodes is less than a certain threshold, they will automatically report to the MS node, and send disengaging requests automatically. The MS removes the disengaging node's information from the \(ID\) table, and deletes all key information associated with the departing node after sending the MS reply confirmation. When sensor nodes are captured by an adversary, the network will find the compromised nodes by using the intrusion detection mechanism, and then the MS will delete all the information related to the captured nodes.  
After the MS sets up a session key with sensor nodes, it can communicate with sensor nodes safely with the session key by using a symmetric encryption algorithm. Symmetric encryption algorithms have the advantage of lower energy consumption, but the session key is easy to crack when the same session key is used for a long time, so the session key should be updated regularly. The MS launches a key update at interval of time \(T\), generates a random number \(r\) which is encrypted by session key: message = \(E_{KMS-Si}(r)\), and then broadcasts the message to the sensor nodes which already had established a session key with the MS. Sensor nodes will receive the message, decrypt the random number \(r\) with the corresponding session key, and meanwhile they will complete the key update with the \(H\) function: \(K^{\prime}_{MS-Si}=hash(K_{MS-Si}\mid\mid r)\). After the updating, the random number \(r\) will be deleted.
# Moving Ad Hoc Networks--A Comparative Study
## 5 Sea Ad-Hoc Networks (SANET)
If the destination node cannot be reached, there will still be several nodes receiving the request, and these nodes form another virtual node. * Step 3 is performed recursively until some of the nodes can deliver the message to the destination node, and these nodes form the last virtual node. At this time, the route between each virtual node has been determined. * At this point, data is sent from the source node to the first virtual node. Currently, all of the nodes in the first virtual node receive the message and distribute the message to each node in the second virtual node, recursively. This process is carried out until the last virtual node sends the message to the destination node. Based on the genetic algorithm routing method, not only can we obtain the feasible path from the source to the destination, but we can also find the optimal path according to the communication resources of the link, and the average resource of the obtained link is about 20% more than AODV. The protocol selection algorithm uses the weighted values of the signal quality, spectrum status, physical layer bandwidth, and resource allocation as the criteria to select the unit service transmission path with the smallest weight, which is regarded as the best route; when the above parameters change, the protocol interaction and route selection are repeated, The network route is automatically updated to ensure service transmission quality, system throughput, and end-to-end transmission delay. The performance advantages and disadvantages of various systems are obvious, as shown in Table 7. The most prominent problem is that no communication method can achieve stable full ocean coverage and unrestricted transmission distance, and ensure  
Figure 13: SANET routing protocols classification diagram. smooth broadband access, lower equipment costs and usage fees. Moreover, the existing various communication systems are isolated from each other, lacking a unified coordination and management mechanism, and cannot guarantee the reasonable and efficient operation of the entire marine communication system. Table 8 shows a summary of the existing surveys on SANET.
# A Computational Framework for 2D Crack Growth Based on the Adaptive Finite Element Method
## 2 Procedure of the Developed Program  
The geometrical dimensions, stresses, material characteristics, and other restrictions are first introduced into the 2D model of crack growth. Incremental stress analysis is performed using the FEM during the pre-processing stage. The SIFs are determined at each step of crack propagation using the displacement extrapolation technique (DET). The crack propagation trajectory is then estimated using the maximum circumferential stress theory (MCST). For the purpose of generating the mesh, the advancing-front method has been implemented. Using this procedure, it is necessary to define the domain boundaries, generate the elements, smooth the mesh, and renumber the nodes [39]. The error estimator provides an estimate of the particular scale of each element, and that estimate is utilized to control the mesh refinement process. The solution's components, such as stresses, displacements, and strains, among others, are transferred from the old mesh onto the new mesh once the new mesh has been generated. Figure 1 depicts the computational strategy for the crack propagation program.
## 3 Method
### LightGBM Algorithm  
LightGBM (Light Gradient Boosting Machine) represents a sophisticated advancement in machine learning, which is usually adept at navigating complex and nonlinear relationships. As a framework rooted in decision-tree-based gradient boosting, LightGBM distinguishes itself through its efficiency and capacity for handling extensive datasets. In comparison to conventional machine learning methodologies, LightGBM exhibits several distinctive features and benefits. Primarily, LightGBM integrates two novel technologies: Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB) [30]. GOSS selectively maintains instances with larger gradients while downsampling those with smaller gradients, effectively reducing data volume without compromising model accuracy. This approach supersedes traditional random subsampling methods by preserving crucial information about instances misclassified. Concurrently, EFB, by consolidating sparse features into denser formats, significantly optimizes computational efficiency, particularly with large-scale sparse datasets. Furthermore, LightGBM's parallel training capacities are notably efficient. It demonstrates substantial superiority over widely used machine learning techniques, such as Random Forest, in aspects of training speed, memory consumption, and predictive accuracy. These attributes render LightGBM particularly adept for processing large datasets while ensuring robust generalization capabilities and stability [31]. In this study, LightGBM is used to examine the nonlinear interactions between various vegetation indices (i.e., SIF, NDVI and kNDVI) and a range of environmental factors, including precipitation, soil moisture, VPD, solar radiation (Srad), and monthly average temperature.
## 4 Experimental Design  
### Speaker Identification System Construction Process  
The overall framework of the self-supervised speaker identification system based on the pre-trained model is shown in Figure 4, which can be roughly divided into the following steps.  
Figure 4: Flowchart of air-ground communication speaker identification.  
The air-ground communication speaker identification system is divided into a training phase and a testing phase, as shown in Figure 4. In the training phase, the model is pre-trained using approximately 1100 h of real annotated data to obtain a well-performing system model as the seed model. The loss function (Sub-Center Loss) is used to guide the network to update its weights, gradually reducing intra-class distances and increasing inter-class differences. The entire training process continues until the model converges, which means training stops when the loss function reaches its optimal solution.  
In the testing phase, audio samples used for enrollment (denoted as L) are first input into the pre-trained neural network to extract their deep representations. For multiple audio samples from the same speaker, the extracted deep representation vectors are averaged to obtain the speaker's central vector, known as the speaker prototype. These speaker prototypes together form the speaker database in the testing phase. Subsequently, the audio sample to be recognized (Speaker A) is also input into the neural network to extract the corresponding deep representation and map it to the category probabilities of each speaker prototype in the speaker database. Finally, the test sample is classified as the speaker prototype with the highest probability, completing the speaker identification task.