esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Beam-Column Joints of Composite Frames Under Seismic Loads
### Beam-Column Joints of Composite Frames Under Seismic Loads: State-of-the-Art
(2019) [27] reviewed experimental research on composite special moment frame connections and presented a database of 165 tests conducted on these connections. They summarized connection behavior, evaluated them based on standard requirements, and recommended different connection types for specific frame configurations for seismic applications. Moreover, [PERSON] et al. (2019) [28] examined the mechanical behavior of connections between composite steel-concrete beams and concrete-filled square steel tubular columns. Tests on six specimens under cyclic loading were conducted to evaluate the damage patterns, hysteretic behavior, joint ductility, and energy dissipation capacity. The result of joint geometries, connection details, and loading path on connection behavior were also analyzed. [PERSON] et al. (2020) [29] analyzed the dynamic behavior of three modular composite joints with various beam-column connections through quasi-static tests. Factors, such as failure mode, strain distribution, hysteresis curve, strength degradation, and energy consumption, were considered. Furthermore, [PERSON] et al. (2020) [30] examined the seismic behavior of steel beams in large-diameter composite column connections. They found that there are no unified design provisions for these connections in high-rise buildings, while the pertinent Chinese code GB 50936-2013 is inadequate for these connections. To investigate the seismic behavior and validate the code-specified calculation method, they tested five connection specimens, examining their energy dissipation stiffness, failure mode, deformation capacity, and ductility. [PERSON] et al. (2017) [31] discussed the effects of slab isolation on the structural performance of joints. Properly isolating the slab from the column, even with a small gap, prevents any over-strengthening outcome on the behavior of the joint, improving control of the seismic response of the braced frame. They also found that under lateral loads, the joint acts as a hinge and transfers loads to the bracing system, while under gravitational loads, the continuity of longitudinal rebar leads to a mostly clamped joint performance. Additionally, recent research on composite steel-concrete beam-to-column joints was discussed by [PERSON] and [PERSON] (2019) [32]. They examined various loading conditions, such as elevated temperatures, combined bending and axial loads, sagging bending moments, and cyclic loading. They also proposed methods to characterize the \"concrete slab in compression\" component for sagging moments and presented an improved analytical procedure for M-N loading. Figure 2: Detailed joint system configuration for a reinforced concrete-filled steel tube (RCFST) column connected to reinforced concrete beams, highlighting interface characteristics (adapted from [PERSON] et al. (2019) [26]).
## 4 Digital Watermarking System for JPEGs
### Feature Extraction Mechanism  
Feature extraction ensures watermark retrieval after image transformation. The system employs ORB (Oriented FAST and Rotated BRIEF) features that were selected over alternatives such as SIFT or SURF due to their superior computational efficiency combined with robust rotation invariance [64]. Unlike SIFT and SURF, the ORB features offer patent-free implementation and comparable descriptor performance [64; 65].  
The system extracts \(n\) ORB features per image and realigns the images through the comparison of ORB features. This feature matching is essential during watermark extraction after potential attacks or transformations. This helps realign potentially distorted images with the original to facilitate accurate watermark extraction.
# Noise Impact Analysis of School Environments Based on the Deployment of IoT Sensor Nodes
## 2 Methods
### Architecture of the IoT Infrastructure
The presentation and monitoring of sensor data were performed both in the Node-Red dashboard and Grafana platforms. Figure 2 delineates the aforementioned described architecture and the telecommunication infrastructure of the buildings. Figure 3 illustrates the location of the data collection stations in buildings 1, 2, and 3. In particular, Wi-Fi-based IoT sensor nodes (noise, humidity, and temperature measures) were installed in buildings 1 and 2 (one per floor). These nodes collect data and send it to the Node-Red platform (cloud-based) via the school's Wi-Fi network. The LoRa-based IoT sensor node was installed in Building 3. The received measurements were transmitted via the LoRa network to building 1, where one of the Wi-Fi IoT with LoRa reception modules forwarded them to the cloud platform. The measuring stations were named according to their position. The number convention used consists of two digits (Figure 4). The first number corresponds to the building (1, 2, 3), while the second one corresponds to the floor (0: ground floor, 1: 1 st floor), as follows:  
* Station 10 (Wi-Fi): Building 1, floor 0;
* Station 11 (Wi-Fi): Building 1, floor 1;
* Station 20 (Wi-Fi): Building 2, floor 0;
* Station 21 (Wi-Fi): Building 2, floor 1;
* Station 30 (LoRa): Building 3, floor 0. To this end, 5 IoT sensor nodes (stations) were installed in the 3 buildings. Additionally, there is a LoRa receiver (gateway LoRa to Wi-Fi) in the 1 st building that receives the data sent by Station 30. Consequently, 6 stations have been incorporated in total. Moreover, the stations were placed in the corridors of the buildings about the middle of their length, which is 54 m in total. This was suggested by the teachers that have noticed an increased activity (entrance--office exit, dense passage of students--teachers), as shown in Figure 5. Therefore, drawing from both the input provided by the teachers and findings from similar studies in Greece [4; 5; 6], we placed the sensors in specific locations and at certain heights in the corridors outside the classrooms and the teachers' offices, at an optimal height (not  
Figure 2: Telecommunication topology and architecture of the IoT infrastructure. The arrows show the direction of the data communication. reachable by the pupils) for monitoring the sound levels and the environmental conditions. This placement was intended to ensure accurate data collection.
# Satellite Image Compression Guided by Regions of Interest
## 2 Materials and Methods
This methodology allows modeling the baseline algorithms considering a high level of abstraction, focusing on functionality. ForSyDe envisions automated generation of optimized codes for the target platform in the near future, and a trade-off analysis of the demands and resources savings as part of the design space exploration tool in ForSyDe [14]. In this paper, Matlab and Python code was created manually based on the ForSyDe models to perform the analyses in Section 3. The baseline algorithms are detailed in the following sections.
# Measures and Methods for the Evaluation of ATO Algorithms
## 3 Approach for ATO Testing
### Extraction of Challenging Scenarios for Performance Evaluation  
The third step to evaluate the performance of an ATO algorithm is the extraction of challenging scenarios. This would mean generating a set of situations that could reveal the limits of the algorithm under test or confirm the satisfaction of the given requirements.  
Other transportation sectors use scenarios with small time scope in the context of safety assessment. Scenarios for the performance evaluation of ATO algorithms need to be much longer than just a critical situation to allow the identification of performance changes. The envisaged scenario-based performance test approach evaluates comprehensive driving situations, e.g., a train route between two or more stations.  
Two ways to generate or define scenarios exist: data-driven and knowledge-based. The data-driven approach requires the existence of a sufficiently large database consisting of recorded traffic situations and drives under a wide range of conditions. Since such comprehensive datasets are not available in the rail sector to the author's knowledge, the data-driven scenario definition is not explored further, but maybe an interesting research direction.  
The proposed approach is therefore employing the knowledge-based concept. Several scenario types were developed by [PERSON] et al. and described in [27]. They are utilized within this approach as well and consist of the following steps:  
* Identification of functional scenarios,
* Extracting corresponding logical scenarios,
* Further development of logical scenarios into concrete scenarios with little effort.
## 5 Conclusions  
The paper proposes an unsupervised learning approach that utilizes continuous clustering to assess the QoE level of video services the TV operator delivers to the customers' households. The estimation is based on the logs that signalize the various aspects of STB's behavior. Data related to the bitstream level, stall occurrences, viewership, and the number of active devices have been reforged into SCI, SCTI, STCSI, and VSBCT features used during the clustering process. The ACR scale was used to mark the QoE level on the STBs. Grade 1 and 2 determined the worst video quality while the 4 or 5 pointed mainly the sightless degradations. Gathered assessments were applied to search the localization with the highest impairment level. The proposed approach might be effectively used by the TV operators to proactively eliminate the weak points in the network to overcome the increased degradation and, as a result, increase the customers' contentment.  
In future works, the outcoming clusters should be challenged with the customer feelings to find a better threshold between the positive and negative clusters concerning TV service. The adjustment of the clustering algorithm might be a subject of future work due to the rapid increase of data stream instances related to TV system development. The infrastructure expansion leads to utilized device expansion. The QoE measurements may be a key indicator in assessing the network performance regards the video services.  
To the best of our knowledge, this is the first study that utilizes continuous clustering with reclustering methods to assess the QoE level of devices connected to production television systems based on adaptive streaming technology.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12168288/s1](https://www.mdpi.com/article/10.3390/app12168288/s1)]([https://www.mdpi.com/article/10.3390/app12168288/s1](https://www.mdpi.com/article/10.3390/app12168288/s1)).  
Conceptualization, P.A., K.K. and P.Z.; methodology, P.A., K.K. and P.Z.; software, P.A.; validation, P.A., K.K. and P.Z.; formal analysis, K.K. and P.Z.; investigation, P.A., K.K. and P.Z.; resources, P.A.; data curation, P.A.; writing--original draft preparation, P.A.; writing--review and editing, P.A., K.K. and P.Z.; visualization, P.A.; supervision, K.K. and P.Z.; project administration, K.K. and P.Z.; funding acquisition, P.A. and P.Z. All authors have read and agreed to the published version of the manuscript.  
The authors thank the Polish Ministry of Education and Science for financial support (Applied Doctorate Program, No. DWD/4/24/2020). This research was funded in part by the Polish Ministry of Science and Higher Education (No. 0313/SBAD/1307).  
Not applicable.  
The dataset was provided by Fiberhost S.A.  
The authors declare no conflict of interest.
# Mechanical Properties of Marble Under Triaxial and Cyclic Loading Based on Discrete Elements
## 1 Introduction
[18] employed the finite element numerical method, coupled with the Drucker-Prager yield criterion and subleading surface theory, to develop a dynamic constitutive model for the deformation and failure characteristics of rock materials under cyclic loading. This model was validated against the results of indoor uniaxial cyclic loading tests. [PERSON] et al. [19] performed indoor triaxial cyclic loading tests on granite to investigate the fatigue characteristics of rock specimens. Concurrently, a constitutive model for describing rock fatigue damage was developed based on the test results and in conjunction with the FEM, and the hydrodynamic effects of underground crude oil storage measures subjected to cyclic load during operation were simulated and analyzed. [PERSON] et al. [20] established an intrinsic model based on the concept of the boundary surface and simulated three different cyclic loading tests of rock materials in combination with the finite element method to study the variation rule of the volumetric strain of rock with the number of cycles. Significant scientific research has been conducted both domestically and internationally on the mechanical properties of rocks under indoor cyclic loading tests. Scholars have systematically examined the influence mechanisms of circumferential pressures, the cycle count, and other factors on the mechanical characteristics of rocks. However, most existing studies focus on soft rock with low strength and focus on uniaxial cyclic loading tests, while the circumferential pressures settings in triaxial cyclic loading tests are generally low, and there is a lack of understanding of the differences in rock fracture processes between conventional triaxial testing and cyclic loading, making it difficult to truly simulate the complex stress path adjustment environment during different excavation methods for deep rock mass. Additionally, the application of the FEM based on the continuum hypothesis is restricted by the presence of common discontinuous structural features in natural rock masses. In contrast, the DEM has become an effective tool for studying the micromechanical behavior of rock and soil materials due to its unique advantages in characterizing the progressive failure process of discontinuous media. Based on this, the discrete element software PFC\({}^{\text{2D}}\) is used to establish a discrete element numerical model on the basis of marble laboratory tests, and conventional triaxial and cyclic loading numerical tests are carried out. The evolution of the mechanical parameters, the number and angle of the microcracks and the distribution of the microscopic force field of marble under high circumferential pressures are compared and studied. The research findings offer an experimental foundation for the analysis of the stability of underground caverns, disaster prediction, and the development of support measures for surrounding rock.
# Structural Optimization of Vented Brake Rotors with a Fully Parameterized Model
## 3 Results and Discussion
### Case 1: Frequency Separation Optimization
After optimization, a very significant reduction in thermal distortion was obtained, with the objective function values reduced from 0.0611 mm to 0.0037 mm, a great improvement of 94%. As an added bonus, the total mass of the optimized, complete model (before slicing) also decreased by 6.9%, compared with that of the baseline model. Figure 16 shows the temperature distribution (\({}^{\circ}\)C) and deformation in the y-direction (meters) of the optimized model at 5.5 s for Case 2. Notably, the thickness of the outboard disc, \(T_{F1}\), reduced from 6.00 mm to 4.81 mm, and the inboard disc, \(T_{F2}\), increased to 7.20 mm, resulting in a pattern of deformation distribution after optimization, shown in Figure 16b, that was very different from that of the baseline model at 5.5 s, shown in Figure 15. Comparing Figures 14 and 16a reveals that at time = 5.5 s, the temperature distribution was more uniform between the outboard and inboard discs for the baseline model, while the inboard disc assumed a much lower temperature than the outboard disc for the optimized model. This uneven temperature distribution in both discs might have offset, to some degree, the coning effects of abrupt temperature variation near the intersection of the hub and outboard disc. Figure 16: Optimization results for Case 2 at time = 5.5 s: (**a**) temperature distribution; (**b**) deformation in y-direction. \begin{table}
\begin{tabular}{c c c} \hline \hline
**DVs and Conditions** & **Baseline Model** & **Optimized Model** \\ \hline DV: \(Dl_{1}\) (mm) & 123.00 & 127.68 \\ DV: \(T_{F1}\) (mm) & 6.00 & 4.81 \\ DV: \(T_{F2}\) (mm) & 6.00 & 7.20 \\ DV: \(T_{RIB}\) (mm) & 5.00 & 5.86 \\ \(U_{
## 8 Experimental Results
### Case 2  
We consider in this second example the case where the object can leave the field of view during its displacement from the initial position to the final position. The problem is described in Figure 17. The numerical values of the parameters of the studied process are practically the same as those in Case 1, with the exception of the model of the trajectory \(y^{*}(x^{*})\), which must satisfy the four following conditions:  
\[y^{*}(x_{i}^{*})=y_{i}^{*},\;y^{*}\Big{(}x_{f}^{*}\Big{)}=y_{f}^{*},\;y^{*} \Bigg{(}\frac{x_{f}^{*}+x_{i}^{*}}{2}\Bigg{)}=4y_{f}^{*}-y_{i}^{*}\;\text{and} \;\frac{dy^{*}}{dx^{*}}\Bigg{(}\frac{x_{f}^{*}+x_{i}^{*}}{2}\Bigg{)}=0\]  
Figure 16: Case 1: Evolution of the control law \(u_{2}\).  
Figure 14: Case 1: Evolution of the trajectory of the robot in the Cartesian coordinate system.  
Figure 15: Case 1: Evolution of the control law \(u_{1}\).  
Figure 18 represents the evolution of the trajectory of the robot in the image frame. It is clear that the object leaves the field of view during the enslavement. Figure 19 represents the evolution of the trajectory of the robot in the Cartesian frame. Compared with Figure 14, we can easily see how the robot corrects its trajectory after missing the target and succeeds in reaching its desired trajectory; this is obtained thanks to an open-loop control based on flatness control. Figures 20 and 21 respectively represent the variations of the two commands applied to the robot. The absence of the target followed by its recovery by the camera installed on the robot are visible in the fluctuations in the control law \(\mathrm{u}_{2}\) between the time frames 5 s and 8 s. It should be noted that the switching between the two control approaches (in closed loop and in open loop) is made between the instants \(t=5.5\) s and \(t=7.5\) s. It should also be noted that the switchover does not cause any sudden changes in the operating states of the actuators, and consequently, no oscillation in the responses of the system is noticed.  
Figure 17: Case 2: Where the object leaves the field of view of the camera.  
Figure 18: Case 2: Evolution of the robot trajectory in the image plane.
# SmartFog: Training the Fog for the Energy-Saving Analytics of Smart-Meter Data
## 4 Implementing SDAE over the Computing Nodes of SmartFog  
To perform the per-layer pretraining over the available Fog and Cloud nodes of Figure 1, we propose a (novel) adaptive version of the elitist GA [9]. The goal is to minimize the total computing-plus-networking energy consumed by the resulting SmartFog platform of Figure 1. Specifically, in the implemented GA, the chromosome is the vector \(\mathbf{a}\in\mathbb{R}^{L\times 1}\) containing the \(L\) indexes of the involved computing nodes. The goodness of each chromosome \(\mathbf{a}\) is measured through an appropriate fitness function. In each generation, a set of elements is selected by comparing their fitness, the best element is retained, a fraction of the \"best\" elements of the current population is recombined through crossover, while the remaining \"worst\" elements are modified by randomly mutating each one over randomly selected positions. For this purpose, the implemented Crossover function generates a random pointer to the location index at which the crossover is performed and, then, carries out the corresponding swapping [4]. After generating a random vector of pointers to the locations to be mutated, the Mutation function returns the generated mutated values at the pointed locations of the vector \(\mathbf{a}\). The fitness function used by SmartFog is the _inverse_ of the total computing-plus-networking energy \(\mathcal{E}\) consumed by the SmartFog platform for the execution of these operations. It equates to [7]:  
\[\begin{split}\mathcal{E}&=\sum_{j=1}^{N}P_{j}^{(cmp)} \Delta t_{j}^{(cmp)}+\sum_{j=1}^{N}P_{j}^{(C-idle)}\Delta t_{j}^{(C-idle)}\\ &\quad+\sum_{j=1}^{N}P_{j}^{(net)}\Delta t_{j}^{(net)}+\sum_{j=1} ^{N}P_{j}^{(N-idle)}\Delta t_{j}^{(N-idle)}.\end{split} \tag{4}\]  
Figure 2: The structure of the considered SDAE. \(\mathbf{x}\), \(\widetilde{\mathbf{x}}\) and \(\widetilde{\mathbf{x}}\) are equal-size and real-valued vectors that represent the input, noise-corrupted and recovered data, respectively. \(\mathbf{W}_{k}^{*}=\mathbf{W}_{k}^{T}\) is the \(k\)-th tied weight matrix.  
According to the clone-based virtualized Fog architecture detailed in [7], in (4), we have that: (i) \(P_{j}^{(cmp)}\) is the power consumed by the virtual clone at the \(j\)-th Fog node for the computing; (ii) \(P_{j}^{(C-idle)}\) is the power consumed by the \(j\)-th Fog node in the idle state; (iii) \(P_{j}^{(net)}\) is the power consumed by the virtual clone at the \(j\)-th Fog node for the communication with the SM, the Cloud node and/or with other Fog nodes; and, (iv) \(P_{j}^{(N-idle)}\) is the power consumed by the \(j\)-th network card in the idle state, while the related \(\Delta t_{j}\) is the execution time of state \(j\). The power consumed by the computing nodes of Figure 1 for computing and networking is proportional to the squared values of the CPU frequencies and communication bandwidths [7]. Detailed analytical models for the evaluation of the computing/networking power present in (4) are developed in [7] and, for the sake of brevity, are not replicated here.