esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Reputation Based on Security  
Based on the reputation model, we obtain the results provided in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Application** & **Category** & **Security Related Reviews** & **Functionality Related Reviews** & **Rating** \\ \hline Instagram & Social media & 100 & 2,288,402 & 4.5 \\ Facebook & Social media & 150 & 1,479,779 & 4.2 \\ CIC & Finance & 90 & 1715 & 4.7 \\ Messenger & Communication & 150 & 101,065 & 4.2 \\ SGC Connect & Finance & 6 & 10 & 4.2 \\ Whatapp & Communication & 150 & 2,824,936 & 4.3 \\ QR scanner & Tools & 80 & 11,026 & 4.7 \\ Ecobank & Finance & 150 & 643 & 3.5 \\ Clean Master & Security tools & 98 & 139,070 & 4.5 \\ Security Master & Security tools & 103 & 78,840 & 2.8 \\ Age Scanner Finger & Entertainment & 105 & 118 & 2.3 \\ Express Union & Finance & 13 & 13 & 3.6 \\ BICEC Mobile & Finance & 2 & 5 & 3.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dataset characteristics.  
Table 5 presents the polarity of the applications on a defined number of reviews related to security services. Then, it presents the security related reputation of these applications in general and by security service. Some of them (QR scanner, Messenger, Whatapp) have almost average reputation. It appears that none of those applications has a very good reputation. In particular, it reveals for example that \"Security Master\" has a bad reputation (42%) in terms of security design. Users feel not comfortable while using this application. Developers have missed to put effort on the security aspect in general. However, authentication measures are desirable within the application (67%). The other services are not respected. Despite the fact that Whatsapp has an average reputation, its developers put efforts on integrity (58%), authentication (68%) and availability (67%) to guarantee non alteration of exchanges, to guarantee the service and to verify identity of communicators. The \"Express Union\" application aiming to provide banking operations has a bad reputation (41%) meaning that users feel not safe when using this application to perform banking transactions.
## 3 State of Art: An Overview on Safety for AVs (Risks, Drawbacks, and Benefits)
\begin{table}
\begin{tabular}{c c} \hline \hline
**Strategy** & **Description** \\ \hline No-Response & No specific actions or dedicated regulations are implemented. An example might be the lack of dedicated national policies to manage AVs acceptance standards, traffic operations, and related consequences. Although this might seem the cheaper option, at first glance, AV-related risks left alone without management can be higher in the end, leading potentially to significant costs arising from the lack of risk management and making it not economically convenient in the long term. \\
**Prevention-oriented** & Risk is managed by avoidance, i.e., risk is reduced by removing the hazard, trying to prevent negative consequences by eliminating the source that would cause them. An example is the prohibition of AVs for certain times or areas of the road network. \\
**Control-oriented** & The hazard is here admitted and controlled by making predictions and regulations as an attempt to reduce the probability that consequences would happen. Examples of regulations could involve the mandatory requirements for AVs certified safety standards, dedicated road traffic policies, use of traffic prediction models, and so on. \\
**Toleration-oriented** & With this approach, governments tend to make sure to be in a strong and solid position when facing risks across diverse situations. An example is the publication of comprehensive lists to describe contingency plans or clarify insurance liabilities while seeking alternative solutions to mitigate risks. \\
**Adaptation-oriented** & Being an adaptation strategy, the approach consists of improving the reaction caused by an existing source, accepting the hazard and its uncertainties, reducing the impact of consequences, and improving system performances. The aim is to achieve a resilient system. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A brief description of several strategic approaches [17].
## 5 Conclusions  
In this paper we address the VNF-Provisioning problem in a 5G access network with an architecture based on Cloud/MEC NFV-enabled RAN. We propose a novel genetic algorithm, GASM, to tackle the aforementioned problem, reducing both the service blocking rate and CPU usage (and, therefore, the energy consumption) considering limitations in IT and optical network resources. We address the problem from two different points of view: Static planning (where the provisioning is designed on a static traffic estimation) or a reconfiguration scenario in which the VNFs are reconfigured online depending on the current number of user service requests (changing dynamically) to be served. Moreover, we also propose a new version of GASM for the reconfiguration scenario which is enhanced with a learning technique causing an improvement in the performance of the method when reconfiguration is allowed.  
By means of a simulation study, we show that our methods which outperform other proposals from the literature in both scenarios, achieving a favorable trade-off between the service blocking rate and the number of CPU cores actives. Results also show that reconfiguring the VNF-Provisioning reduces the service blocking rate and makes a more efficient use of the computation resources in the MEC and CO servers. Finally, our method enhancement incorporating the learning stage improves the service blocking rate performances by almost an order of magnitude respect to the other algorithms, producing the best balance between that metric and an efficient usage of IT resources.  
Conceptualization, all authors; Methodology, all authors; Software, L.R.; Investigation, L.R., R.J.D., I.M., J.-J.-P.-M., P.P.-M., and P.S.K.; Visualization, L.R.; Writing-Original Draft Preparation, L.R., R.J.D., and I.M.; Writing-Review and Editing, all authors; Supervision, R.J.D., P.P.-M., and P.S.K.; Funding Acquisition, R.J.D., I.M., P.S.K., P.P.-M.  
This work has been supported by Spanish Ministry of Economy and Competitiveness (TEC2014-53071-C3-2-P, TEC2017-84423-C3-1-P, TEC2015-71932-REDT), the fellowship program of the Spanish Ministry of Education, Culture and Sports (BES-2015-074514 and FPU14/04227) and European H2020-ICT-2016-2 METRO-HAUL (no. 761727).  
The authors declare no conflict of interest.
# Cost Analysis and Optimization of Modern Power System Operations
## 1 Introduction
In this study, the Mixed Integer Programming (MIP) approach is adopted for solving the UC problem due to its flexibility, high accuracy, and ability to model complex operational constraints. MIP is well-suited for large-scale problems and can produce globally optimal solutions. Nevertheless, it needs to be noted that computational complexity and tractability can become challenging for very large systems due to the combinatorial nature of mixed-integer formulations and specific features such as network topology, local operational policies, regional factors (e.g., market structure), and other elements that may require model customization. The optimization is carried out using the General Algebraic Modelling System (GAMS), which offers a wide range of solvers and efficient computational performance, significantly reducing solution time. The MIP framework also supports the potential future enhancement of system adaptability through collaborative scheduling of different resources (e.g., PEVs, building temperature control systems), as suggested in [26]. To improve fairness, the framework can be enhanced to use quantified contributions (e.g., energy traded, flexibility) as bargaining powers--following the Nash bargaining method. This allows profit allocation to better reflect each subsystem's actual value, beyond equal sharing [27]. The primary objective of this paper is to demonstrate the cost-saving potential of incorporating demand-side flexibility and to analyze how the integration of renewable energy sources and energy storage systems impacts overall operating costs.
# Rolling Bearing Fault Diagnosis Based on CEEMDAN and CNN-SVM
## 3 CEEMDAN and CNN-SVM Fault Diagnosis Models  
### CNN Network Structure Design  
In order to take advantage of the powerful feature extraction capability of CNN, a CNN structure is designed in this paper as shown in Figure 2. The convolutional layer is used to extract image features from the input image; the pooling layer serves to downscale and compress the features, remove redundant information and reduce the computational effort; the fully connected layer spreads the feature vectors into one-dimensional vectors; the dropout layer prevents overfitting by discarding the neurons; and finally, fault classification is achieved by the GWO-SVM classifier.  
The model hyperparameters were set as follows: the initial learning rate was set to 0.001; the ReLU function was used as the activation function of the network; the cross-entropy function was used as the loss function; the batch size in the training was set to 64; the number of iterations was set to 150; the deactivation rate of dropout was set to 0.2; and the Adam optimiser was used to update the network parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Layer** & **Parameter Setting** & **Pacemaker** &
\begin{tabular}{c} **Network Layer** \\ **Output** \\ \hline Convolutional layer 1 & 8@3 \(\times\) 3 & 1 & 8@32 \(\times\) 32 \\ Pooling layer 1 & 2 \(\times\) 2 & 2 & 8@16 \(\times\) 16 \\ Convolutional layer 2 & 16@3 \(\times\) 3 & 1 & 16@16 \(\times\) 16 \\ Pooling layer 2 & 2 \(\times\) 2 & 2 & 16@8 \(\times\) 8 \\ Convolutional layer 3 & 32@3 \(\times\) 3 & 1 & 32@8 \(\times\) 8 \\ Pooling layer 3 & 2 \(\times\) 2 & 2 & 32@4 \(\times\) 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model parameters.  
Figure 2: CNN structure diagram.
# Throughput Enhancement in Downlink MU-MIMO Using Multiple Dimensions
###### Abstract  
This paper focuses on the throughput performance enhancement in the single cell multi-user MIMO (MU-MIMO) downlink system model. For better quality of service, this paper proposes the scheme that increases system throughput and improves the spectral efficiency. Specifically, the signal transmission and detection schemes are proposed by using multiple dimensions. At the transmitter side, two dimensions (power and space) are adopted at the same time. To achieve multiple access (MA), the space domain is exploited by using a block diagonalization (BD) precoding technique, and the power domain is exploited to transmit more data symbols. At the receiver, the signal detection structure corresponding to a transmitter is also proposed. In the simulation results, comparisons of throughput performance are presented in various aspects. As a result, the proposed scheme outperforms the conventional schemes using only one dimension in terms of throughput. This paper shows strong performance in MU-MIMO scenarios by adopting multiple dimensions.  
multi-user MIMO; space division multiple access (SDMA); block diagonalization (BD); non-orthogonal multiple access (NOMA); broadcast channel +
Footnote †: journal: Electronics
## 2 Contribution of This Work
### Closed Loop Congestion Mitigation
#### Congestion Mitigation Phase  
The activation of the control center in response to the notification signals the completion of the closed loop process for congestion mitigation in IoT networks. The implementation of appropriate control measures to mitigate network congestion is the responsibility of the control center. In this phase, network traffic management considers the bandwidth of the network. Numerous strategies are used, including network resource optimization, traffic reduction, queue length adjustment, and packet and node scheduling.
## 3 Results  
### Heating Process in a Test Specim  
By measuring at four points on and in the concrete, the heating process can be described over the test duration for the specimen. The diagram in Figure 4 illustrates the course of the heating curves as an example for one test. The figure shows a sample of the UHPC 1 tested at a test frequency of 10 Hz and a related maximum stress level of 70%. The four curves display the temperature differences reached at the four measuring points via the test. The influences of the surrounding temperature  
Figure 3: Testing machine during an experiment.  
and the temperature induced by the testing machine have already been eliminated here and in the following temperature curves. Only the warming of the concrete due to fatigue stress is shown.  
The diagram shows an increase in temperature at all measuring points plotted over the number of load cycles. The maximum value was measured with 47 K inside the sample. It was reached immediately before failure after 72.588 load cycles. The same characteristic displays the curve on the surface in the middle of the height of the specimen (red line). However, the temperatures reached at this point were lower. This resulted in a temperature difference in the cross-section between the center and the surface. This difference increased over the entire duration of the test until failure. It reached a maximum value of 13 K at a radius of 30 mm. The increases at the bottom and at the upper end of the specimen were significantly lower and very similar to each other. From this it can be seen that the warming up during fatigue loading is generated most strongly in the middle of the specimens. The difference between the internal measuring point and the surface is caused by the transfer of the temperature to the environment, which is hampered inside. The influence of the used testing machine on the temperature development was negligible low.  
For UHPC 2, curves with the same characteristics were recorded at the same configuration, whereby the achieved number of load cycles and temperature values varied.
## 2 Literature Review
[21] have presented Grammatical Evolution techniques to improve energy efficiency by predicting the dynamic power of the CPU and memory subsystems using the hardware counters of each application. The model helps predict the power consumption of two different tasks co-allocated in the same server. This work is performed using hardware counters of individual applications without running the co-allocated application. Another work [22] focuses on optimizing the energy efficiency through dynamic prediction of CPU idle intervals. The idea is to optimize the sleep intervals of the CPUs to improve the energy efficiency. The energy efficiency problem in data centers has been tackled as a traffic engineering problem using a time-aware model in [23], proving that it is a Non-deterministic Polynomial-time (NP) hard problem. The authors then assign VMs to servers to reduce the traffic and generate favorable conditions using traffic engineering. This work focuses on optimizing the routing to conserve energy. Another work [24] also focuses on assignment of VM resources for energy optimization. A similar approach is proposed in [25] for an energy efficient initial VM placement. Another energy-efficient routing framework for cloud data centers is proposed in [26]. This work considers the problem under network-as-a-service paradigm and extends the energy-efficient routing from single-resource to multi-resource. A greedy routing algorithm is proposed, which selects the traffic flows gradually to exhaust capacities of active nodes. The use of artificial intelligence techniques has also been seen in some of the latest works on energy optimization for data centers. A cooperative multi-agent solution to manage servers for VM consolidation is proposed in [27] by [PERSON] et al. Their work focuses on managing the physical servers to avoid overloading and underutilization by assigning VMs. However, the results of this work are highly dependent on the quality of the decision making process. Swarm intelligence is used by [PERSON] et al. [28] for consolidation and migration of VMs in the system under changing network states. They ensure that the Quality of Service (QoS) requirements are guaranteed before any energy optimization is done. Similarly in [29] [PERSON] et al. use the reinforcement learning techniques for VM consolidation and placement. [PERSON] et al. [30] propose a resource management scheme which combines dynamic voltage/frequency scaling and server consolidation to achieve energy efficiency. They ensure that the required service level agreements in cloud data centers are met. Their scheme integrates timing analysis, queuing theory, integer programming, and control theory techniques to improve energy efficiency of the system.
## 3 Numerical Simulations
### Parameter Identification under Gaussian and Non-Gaussian Noises
#### 3.3.1 Parameter Identification under Gaussian Noises  
In numerical analysis, based on the previous analysis and discussion, the fourth-order Runge-Kutta integration is selected, and the integration time step is adopted as 0.02 s. The identified results at different Gaussian noise levels (low noise level conditions) are shown in Figure 8. Notably, the identified curves of the stiffness and damping are almost in accordance with the actual values even when the noise level reaches 10%, which indicates that the parameters of the four-DOF system can be identified precisely by the EKF under Gaussian noises.  
Figure 7: The distribution curves of Gaussian and non-Gaussian noises: (**a**) 10% root mean square RMS for Gaussian noise; (**b**) 10% root mean square for non-Gaussian noise.
## 4 Design of the Platform
### Architecture Design with Microkernel Style  
The architecture of a software system defines the stable and schematic layout of the system, where various components are deployed. The architecture of software platforms reveals a higher degree of variability than the architecture of a single application. This is because there are various types of differences, i.e., variation points, among application systems that are built upon the platform [33].  
This becomes especially evident with the medical analytics platform proposed in this paper, where variability exists for the target disease types, diagnosis processes, machine learning models deployed, and the policy for determining disease occurrences and their stages.  
To handle the variability, we applied the microkernel architecture style to design the platform architecture, as shown in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Diagnosis Tasks** & **Medical Objects** \\ \hline \multirow{3}{*}{Segmentation} & Organ \\  & Lesion \\  & Tumor \\ \hline \multirow{3}{*}{Classification} & Medical Features of Tumors \\  & Temporal Change in Tumors \\ \cline{1-1}  & Occurrence of Diseases \\ \cline{1-1}  & Stage of Diseases \\ \hline \hline \end{tabular}
\end{table}
Table 1: Diagnosis tasks performed by machine learning models.  
The microkernel architecture style is characterized by the division of system responsibilities between the core control layer and the plugin control layer, which binds externally devised plugin components on-the-fly. The core control layer provides functionality that is common among various diagnosis systems and is designed with closed-design schemes.  
The plugin control layer defines a set of required interfaces in UML that are implemented/realized in plugin components. Externally implemented plugin components are then bound to the plugin control layer to fulfill the variation points.  
The benefits of adopting the microkernel architecture style when building this platform are as follows:  
* **Separation of Concerns**: Microkernels separate the invariant components from the components with variability, allowing for a clear separation of concerns.
* **Customizability**: The functionality of components with variability can be customized for specific diseases, diagnosis methods, and ML models applied. Updating or replacing plugin objects does not require modifying the kernel, making the system adaptable and extensible.
* **Platform Independence**: The components in the core control layer are stable and platform-specific elements, making it easier to port to different medical image analytics applications.
## 2 Materials and Methods
### Fluixcom Gpp  
Based on machine learning, the CO\({}_{2}\) flux measurements from the FLUXNET eddy covariance towers, merged with remote sensing and meteorological data, have successfully been used to produce global gridded carbon flux data (FLUXCOM GPP) [61]. The FLUXCOM GPP includes two datasets: (1) one at 0.0833\({}^{\circ}\) resolution, using Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data (abbreviated as RS); and (2) another at 0.5\({}^{\circ}\) resolution, using remote sensing and meteorological data (abbreviated as RS + METEO) [62; 63]. The dataset is available at [[ftp://ftp.bgc-jena.mpg.de/pub/outgoing/FluxCom](ftp://ftp.bgc-jena.mpg.de/pub/outgoing/FluxCom)]([ftp://ftp.bgc-jena.mpg.de/pub/outgoing/FluxCom](ftp://ftp.bgc-jena.mpg.de/pub/outgoing/FluxCom)) (assessed on 1 December 2021). In this study, we used the FLUXCOM GPP global product (RS_V006) at a temporal resolution of 8 days and a spatial resolution of 0.0833\({}^{\circ}\), the good performance of which for carbon fluxes has been revealed by cross-validation [64]. To match the SIF dataset, we obtained the FLUXCOM (RS_V006) GPP from February 2018 to December 2020 and resampled it to 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) (Figure 1a).
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 8 User Study and Real-World Validation
### Results and Findings  
The study results, summarized in Table 4, indicate that the MR-assisted system significantly improved identification accuracy while reducing task completion time.  
The results demonstrate that the MR-assisted system outperforms traditional methods in terms of both accuracy and efficiency. Participants completed tasks approximately 60 percent faster than with manual identification and 40 percent faster than with barcode-based methods. Additionally, the error rate was reduced by 57.3 percent compared to manual identification, ensuring safer and more reliable medication handling.
# Surface Subsidence Monitoring in Kunming City with Time-Series InSAR and GNSS
## 3 Dataset and Processing  
### Study Area  
Kumming city in central Yunnan province (24\({}^{\circ}\)23\({}^{\prime}\)-26\({}^{\circ}\)33\({}^{\prime}\) N, 102\({}^{\circ}\)10\({}^{\prime}\)-103\({}^{\circ}\)40\({}^{\prime}\) E) is selected as the research area. The specific research area is located in the yellow rectangle covered area in Figure 2. The triangle represents 8 CORS sites. It is located in the Yunnan-Guizhou Plateau. The overall terrain gradually decreases from north to south, the central uplift, the east and west sides are lower, and the average elevation is 1900 m. The Kunming Basin is located in the Late Cenozoic fault basin of Kunming. The Quaternary loose sediments with thickness of 100-400 m are widely developed. The main composition structure is loose, and there are a large amount of clay rich in pore water, large void and high compression ratio [35]. The groundwater in the land part is rich, mainly karst water. The geological structure in the region is complex, the crustal movement is active, and many active faults are developed, such as Pudu River West Hill fault, Heilongtan-Guandu fault, etc. The study area is dominated by lacustrine landform, and the soil types are mainly silty soil and soft clay of lacustrine facies, which are easy to be eroded by running water. In recent years, with the acceleration of urbanization and the concentration of population and economy in large and medium-sized cities, the rapid increase of industrial water consumption has led to the rapid reduction of confined water level and the frequent occurrence of serious geological disasters such as surface subsidence.  
Figure 1: Data processing flow chart.