esa-sceva/satcom-chunk-collection huggingface dataset:
# Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture
## 3 Architecture  
This section offers an overview of the architecture of video-on-demand streaming. Figure 2 depicts a conceptual architecture in consideration, which includes the platform's primary components.  
Users will interact with the application's Front-End directly. Requests will be sent to the gateway. The gateway is a device in charge of connecting to the local server. The local  
Figure 1: Methodology containing all stages of this work. The flowchart allows other researchers to replicate the study and to use the models for their demands.  
Figure 2: Evaluated architecture of a video on the demand service platform.  
server is a backend of many servers that conduct operations concurrently. The OCA server is in charge of distributing movies straight to clients more optimally and effectively. The data will be delivered to the users in response to the action they planned to take in the system, concluding the process of that request. All data created by users are delivered over a wireless network or Ethernet to carry out the communication. The data is then forwarded to the Back-End for processing. If the backend is overloaded, requests will be sent to a processing queue and processed on a first-come, first-served basis. Furthermore, if the Back-End queue is filled, data discard may occur. Following that, the server will respond to the user's request in the application, thereby finishing the cycle. The Back-End is the component that is in charge of processing requests; its capability varies. The Back-End is dispersed over various cloud providers in a true architecture for this sort of service. This architecture aims to process data streams emanating from user instructions issued during task execution in various application domains. Users will input information into the system by interacting with the Front-End of the application. Requests will be sent to a gateway device, which is responsible for connecting to the local OCA server system. The local OCA server is a Back-End system composed of multiple servers that perform operations concurrently. The OCA server system is responsible for efficiently distributing movies to clients. The data generated from user interactions will be transmitted over a wireless network or Ethernet connection to the Back-End for processing. If the Back-End is overwhelmed, requests will be placed in a processing queue and handled on a first-come, first-served basis. In the event that the Back-End queue becomes full, some data may be discarded. Once the Back-End has processed the request, it will send a response to the user through the application, completing the request cycle. The Back-End is a component that is specifically designed for processing requests, and its capabilities may vary. In order to efficiently handle data streams from user instructions in various application domains, the Back-End is distributed across multiple cloud providers in a distributed architecture.
# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
## 2 Materials and Methods
### Validation  
In this research, various validation methods were employed to assess the algorithm's effectiveness, aiming to evaluate its robustness and generalisability. Initially, we validated the model's performance using actual surface cover data for the year 2023. Subsequently, a validation approach involving a 70-30% split for training and testing was applied to all examined years, with the confusion matrix computed for each.  
Moving to 2015, a comparison was made with the Copernicus Global Land Service Land Cover data, available exclusively for the years 2015 to 2019. For 2020, our results were then compared with the WorldCover map generated by ESA, which is applicable for the years 2020 and 2021.
# Efficient and Secure Federated Learning for Financial Applications
## 3 Efficient and Secure Federated Learning Based on Gradient and Mask Sparsification
### Sparsify the Encryption Mask for Secure Aggregation
\right.\)
15\(G_{sparse}\gets encode((G_{t}^{k}+mask_{e})\odot mask_{t})\)
16\(G_{residual}\gets G_{t}^{k}\odot\
eg mask_{t}\)
17\(loss_{k}\gets loss_{0}\)
```  
**Algorithm 2**Secure Aggregation with Mask Sparsification on node k.  
The participants utilize the DH protocol key as a random seed to produce a uniformly distributed mask matrix \(mask_{r}\in[p,p+q)\). If two mask matrices correspond to the same participants and hold the same key, they are considered equal. For each participant involved in the training, the dynamic threshold can be ascertained by taking into account the total number of model gradient updates \(N\) and the sparsity rate \(R\). \[R=(\alpha+\beta-\frac{t}{T})\cdot R \tag{2}\]  
where \(\alpha\) is the constant attenuation factor and \(\beta\) is the loss change rate of the participant. The greater the loss change, the more severe the change. \(t\) is the number of iterations and \(T\) is the specified number of training rounds. The higher the number of iterations, the smaller the change. \(R\) is the gradient sparsity rate, where the upper limit is 1 and the lower limit is the specified minimum sparsity ratio \(R_{min}\). According to the threshold, we can filter out the mask matrix of the Top-k gradient update. If \(G\) is the original local gradient update, there is:  
\[\forall g_{i}\in G,mask_{top}=|g_{i}|\geq\sigma \tag{3}\]  
\(\sigma\) is the random encryption mask filtering threshold:  
\[\sigma=p+\frac{k}{x}\cdot q \tag{4}\]  
where \(x\) is the number of participants and \(k\) is the random mask ratio. \(mask_{e}\) is the random uniform distribution mask matrix filtered by \(\sigma\) and \(mask_{t}\) is the mask matrix for sparse transmission. Therefore, in order to ensure the safety of the original gradient after sparsification, the gradient after sparsification is updated as:  
\[G_{sparse}=encode((G+mask_{e})\odot mask_{t}) \tag{5}\]  
To make the encryption mask matrix sparse, a technique is used that involves setting a certain proportion of the matrix elements to zero. This sparseness helps to optimize communication by reducing communication costs. In the original secure aggregation framework, there was no deliberate effort to create a mask matrix with zero local values. However, such a matrix is significant in optimizing the gradient sparseness of the security aggregation framework. When certain positions in the mask matrix are set to zero, the corresponding gradient updates do not need to be transmitted in that round of communication. As a result, during the aggregation of the encrypted gradient updates at the server, there is no problem with eliminating the mask. Furthermore, since the gradient update values for these positions are not transmitted, the amount of communication data is reduced and communication efficiency improves.
# A Swarming Approach to Optimize the One-Hop Delay in Smart Driving Inter-Platoon Communications
## 4 Swarming Approach Description  
In this section, we propose a swarming approach to optimize the one-hop delay for inter-platoon communications in two steps, i.e., Sections 4.1 and 4.2. In Section 4.1, a swarming approach is proposed  
Figure 2: Inter-platoon communications model.  
to find a minimum average one-hop delay of inter-platoon communications by adjusting the minimum contention window of each backbone vehicle iteratively. In Section 4.2, the proposed swarming approach is used again to optimize the one-hop delay of each backbone vehicle to get close to the minimum average one-hop delay found in Section 4.1. After the second step, we can obtain the optimal minimum contention window sizes, i.e., the adjusted minimum contention window sizes that enable the one-hop delay of each backbone vehicle to get close to the minimum average one-hop delay.
## 2 Dataset and Methodology  
### Site Properties  
Gaziantep, in the southeast Anatolia region is one of Turkey's prominent cities in terms of solar energy potential. With an area of 6222 km\({}^{2}\), it covers approximately 1% of Turkey's total land area. The elevation of the city center is 850 m above sea level. The Amanos Mountains have elevations rising up to 1527 m in the southwest. The region lies at the transition point between Mediterranean and continental climates. The average monthly temperatures range from 3.9 \({}^{\circ}\)C to 28.7 \({}^{\circ}\)C, with an annual average temperature of 22.3 \({}^{\circ}\)C. The annual total average precipitation is 601.6 mm [33; 34]. Its geographical structure, characterized by extensive flat terrains, dry climatic conditions, and long sunshine durations, makes the city an attractive location for PV farms. The average sunshine duration in Gaziantep is 8.15 h, and the total solar radiation ranges between 1550 and 1800 kWh/m\({}^{2}\) annually. These factors highlight Gaziantep's suitability for expanding solar energy investments and harnessing renewable energy sources efficiently [31; 35]. Gaziantep's annual average sunshine duration is significantly higher than the national average in Turkey. The lowest daily sunshine duration occurred in December, with 4.38 h, while the highest was recorded in July, reaching 11.74 h. Similarly, daily average solar radiation values in Gaziantep exceed the national average. For instance, in June, the solar radiation reaches a level of 6.78 kWh/m\({}^{2}\)/day, highlighting the region's exceptional potential for solar energy generation. These metrics underline Gaziantep's capacity to support efficient solar energy projects and its strategic importance in renewable energy development [31]. Gaziantep's solar energy potential, supported by its favorable climatic and geographic characteristics, positions it as a critical area for renewable energy development in Turkey. The largely flat topography of Gaziantep facilitates the installation and efficiency of PV farms [31; 33; 36]. By leveraging this potential,Gaziantep can play a pivotal role in advancing the nation's transition to sustainable energy systems. Figure 1 represents the Gaziantep location based on the AHP dataset.
# Application of Fiber Bragg Grating Sensing Technology for Bol Force Status Monitoring in Roadways
## 2 Structure and Principle of FBG Bolt Force Sensor  
### Structural Design  
The FBG bolt force sensor mainly consists of a cylinder, tubing, a pressure gauge, and an FBG pressure sensor. The cylinder is connected to the pressure gauge and the FBG pressure sensor through the tubing and tee joint, respectively. This sensor provides the function of real-time online monitoring and on-site reading, as shown in Figure 1a.  
(1) There is hydraulic oil in the cylinder that is sealed with an O-ring, a piston, and an end cover, as shown in Figure 1b. Hydraulic oil as a sensor force transfer medium is a path for transferring an external force to the sensor. Its role is to transfer an external load to the FBG pressure sensor and a measured value is indicated on the pressure gauge in real time.  
(2) The FBG pressure sensor as a core component of the sensor mainly includes a planar diaphragm, a cantilever, a dowel bar, a shell, and two FBGs, as shown in Figure 1c. The diaphragm as a dynamometric elastic element is a force component of the sensor. The two FBGs as sensing elements of the sensor are bonded in symmetric positions on the top and bottom surfaces of the cantilever and are led out from one end of the sensor after being connected to the coupler through an optical fiber.
## 2 Simplified Mechanical Model
### Boundary Conditions and Coefficients  
The equation for the deflection of buried pipelines in the present study has a symmetry; thus, in the calculated interval of \(-l\) to \(l\), the maximum bending moment and deflection occur at the position of \(x=0\). It can also be concluded that the shear force and rotation angle at this location are zero, as  
\[\left\{\begin{array}{ll}x=0,&\theta_{0}=0\\ x=0,&Q_{0}=0\end{array}\right.\]  
From this, the deflection equation of buried pipelines can be simplified as  
\[w(x)=f(x)+w_{0}\phi_{1}(\beta x)-M_{0}\frac{1}{EI\beta^{2}}\phi_{3}(\beta x) \tag{7}\]  
By deriving Equation (7), it can be obtained that the rotation angle equation of buried pipelines is  
\[\theta(x)=f^{\prime}(x)-4\beta w_{0}\phi_{4}(\beta x)-M_{0}\frac{1}{EI\beta} \phi_{2}(\beta x) \tag{8}\]  
In this present study, the displacement of buried pipelines occurs only in subsidence areas, and there is no deformation in the non-subsidence area. So, at the connection of subsidence areas and non-subsidence areas, the boundary condition can be regarded as a fixed end restraint. It means that the deflection and rotation angle of pipelines are zero at the position of \(x=l\), and the boundary condition is as follows:  
\[\left\{\begin{array}{ll}x=l,&w_{l}=0\\ x=l,&\theta_{l}=0\end{array}\right.\]  
Thus, by substituting the boundary condition into Equations (7) and (8), \(w_{0}\) and \(M_{0}\) can be derived:  
\[\begin{array}{ll}w_{0}=\frac{kD\delta}{EI\beta^{2}}\frac{1}{\left(1+\frac{ \pi^{4}}{164^{4}}\frac{1}{464^{4}}\right)}\left[\frac{\frac{\pi^{2}}{4^{2}} \frac{1}{464^{2}}\phi_{3}(\beta l)-\frac{1}{46}\phi_{1}(\beta l)}{\phi_{2}( \beta l)}+\frac{\frac{\pi^{2}}{42}\frac{1}{462^{2}}\phi_{2}(\beta l)+\phi_{4} (\beta l)-\frac{\pi}{2l}\frac{1}{464^{2}}\frac{1}{464^{2}}\phi_{3}(\beta l)- \frac{\pi}{2l}\frac{1}{464^{2}}\frac{1}{464^{2}}\phi_{3}(\beta l)-\frac{\pi}{ 2l}\frac{1}{464^{2}}\frac{1}{464^{2}}\phi_{4}(\beta l)}\right]\\ \left./\frac{1}{EI\beta^{2}}\left[\frac{\phi_{3}(\beta l)}{\phi_{3}(\beta l)}+ \frac{\phi_{2}(\beta l)}{4\phi_{4}(\beta l)}\right]\end{array}\right.\]  
Substitute the initial parameters of \(w_{0}\) and \(M_{0}\) into Equation (7), and the specific form of the pipeline deflection equation can be obtained. It is obvious that an explicit solution to the settlement of buried pipelines using the above method is obtained, which can be solved by simple programming.
## 1 Introduction
Current developments in the eddy covariance methodology for investigating carbon fluxes at the ecosystem level make it possible, today, to understand the overall carbon budget and its separate components, namely photosynthesis and respiration, in relation to seasonal climate and crop phenology [10]. Most eddy covariance studies in Russia focused on forest ecosystems and Central Russia is still one of the less GHG-investigated European areas [11], especially in the case of agroecosystem monitoring. Thus, the most complete study of the terrestrial carbon budget of Russia is based, among other things, on 14 eddy covariance stations, none of which represent active agroecosystems, and Central Russia being represented by a tower located in the spruce forest near Tver (about 100 km north of Moscow) [12]. In this study, we set up a pair of EC flux towers to measure net ecosystem exchange (NEE) at two representative agroecosystems of Central Russia belonging to different climate and soil zones, and technological conditions, but both with the same barley crop. The objective of this study was to analyze the carbon balance determinants and their diurnal and seasonal variations in two barley fields located within two contrasting agroclimatic zones in Central Russia, in the Moscow and Kursk regions, which span a latitudinal gradient of about 600 km from North to South. At the same time, we wanted to analyze the interplay of climate conditions and agronomic practices, since the two sites also represent different Russian agronomic models: a highly intensive and environmental impact system in Moscow, and a lower input and more sustainable one in Kursk--often referred to in Russia as \"semiorganic farming\". Our investigation attempted to elucidate the trade-offs between favorable climatic conditions in low intensity cultivation (Kursk) and the less favorable climate with better crop technologies (Moscow), in the light of sustainable production.
## 4 Correlation between the Structural Monitoring and the Seismic Activity
Similar considerations can be proposed in the case of AEs. Furthermore, the possibility to evaluate a close correlation between the regional seismic activity and the AE monitoring results was recently presented [18]. First of all, the AE data series were acquired for the two periods considered above, and the verification, if the couples of events previously recognized were in the AE monitoring intervals, was done. At this point, we proceeded to remove from the AE data series all signals used to determine the sources of damage through the AE localization. In this way, all the AE data directly related to the structural damage of the masonry and the wood structures were excluded from the AE time series used to evaluate the correlation between AE and seismic activity. In order to verify the original hypotheses, the historical time series of the AE data were reported for two 25-day intervals contained in the two time windows presented. In Figure 13, the seismic time series are represented together with the variations of the AE average \(b\)-values detected on the structures of the tower. In this case, the variations trends resulted particularly evident for both the two considered periods. The average values of the AE \(b\)-value before the anticipant events are 1.05 and 1.42 for the two anticipating intervals, respectively (see Figure 13c,d). After the two anticipating events, average AE \(b\)-values of 0.79 and 1.18 were observed for the two intervals before \(m_{L}*=2.3\) and \(m_{L}*=1.9\), respectively (Figure 13c,d). These two events were both anticipated by the previously mentioned series where the average \(b\)-values encountered reductions of 24% and 25%. After the occurrence of the two events recognized as main events, the \(b\)-values trends returned to grow significantly--the average \(b\)-values were 0.90 for the first time window and even 2.08 for the second (Figure 13c,d). The evidence here reported are very important and denounced the possibility of using the AE average \(b\)-values to offer a discrimination of the foreshock and aftershock period in a region affected by seismic activity.
# Analyzing Zone-Based Registration under 2-Step Paging in Mobile Communication Network
###### Abstract  
In this study, we have considered zone-based registration (ZBR), which is the most widely adopted type in mobile communication networks. Based on a performance comparison between one-zone-based registration (1 ZR) and two-zone-based registration (2 ZR), 2 ZR is known to be superior to 1 ZR in most cases. However, the existing studies on the comparison of 1 ZR and 2 ZR have a critical problem: The basic assumption in 1 ZR is that the entire zone is paged simultaneously when a call arrives, whereas, in 2 ZR, the zone is paged by two steps. With these different paging schemes, a proper comparison cannot be made. Therefore, in this study, we analyzed the performance of 1 ZR by adopting 2-step paging (2 SP) such as 2 ZR for a proper performance comparison under equivalent conditions. This study also presents an analytical model of assigning paging areas under 2 SP in 1 ZR when the zone consists of multiple cells. Considering the mobility characteristics assumed in the previous studies on 2 ZR, we have presented the mobility model for movement among cells in a zone, obtained the steady-state probability of each cell by using the Markov chain model to calculate the paging cost under 2 SP, and ultimately calculated the total signaling cost. Through various numerical results, it was observed that, when 1 ZR also adopts 2 SP such as 2 ZR, 1 ZR can be superior to 2 ZR in many cases. In conclusion, by adopting 2 SP in both 1 ZR and 2 ZR, it would be possible to reduce the total signaling cost by selecting the better 1 ZR and 2 ZR while considering traffic changes.  
paging; two-step paging; location registration; mobility management; zone-based registration 2022
## 2 Experimental Procedures
### Methods  
The determination of major and minor chemical components (Na\({}_{2}\)O, MgO, Al\({}_{2}\)O\({}_{3}\), SiO\({}_{2}\), P\({}_{2}\)O\({}_{5}\), K\({}_{2}\)O, CaO, TiO\({}_{2}\), MnO, Fe\({}_{2}\)O\({}_{3}\)) was performed by X-ray fluorescence analysis, according to the procedure suggested by [PERSON] et al. [54], and considering the measurement accuracies as reported in [PERSON] et al. [55]. Loss on ignition (LOI) was determined as mass loss in the temperature range 20-950 \({}^{\circ}\)C.  
The qualitative mineralogical compositions of the samples were obtained by X-ray powder-diffraction (XRPD), using a Bragg-Brentano geometry and Ni-filtered CuK\({}_{\alpha}\) radiation, obtained at 40 kV and 20 mA.  
Simultaneous Thermogravimetric/Differential scanning calorimetry analysis (TG/DSC) was used to measure the total volatile components in the marble-powder samples. TG/DSC was obtained on about 25 mg of sample, using a thermal analyzer Netzsch 449 C Jupiter in the following experimental conditions: open alumina crucibles, heating rate of 10 \({}^{\circ}\)C/min, and 30 mL/min nitrogen gas flow.  
The particle size distributions of selected materials were determined by dry sieve analysis, according to EN 933-1 [56] and EN 933-2 [57].  
The water absorption coefficient by capillarity and the amount of water absorbed by total immersion were determined after 28 days of curing on 40 mm \(\times\) 40 mm \(\times\) 80 mm parallelepiped specimens, following the procedures reported in the EN 15801 [58] and EN 13755 [59], respectively. The apparent density was determined in accordance with the EN 1015-10 [60] after 28 days of curing. The volume of each specimen was measured by using a hydrostatic balance, and the accuracy of volume determinations was better than 0.02% by vol. for specimens larger than 30 cm\({}^{3}\)[61].  
Uniaxial compressive strength tests were performed using a Tecnostest KL 300/CE press following the EN 1015-11 [62]. Tests were carried out at 2, 7 and 28 days of curing. Determination of the propagation speed of ultrasonic pulses was carried out on samples at 28 days of curing. The ultrasonic pulses velocity was measured by a Proceq Pundit PL-200 instrument, using the transmission technique, in accordance with EN 12504-4 [63].
## 5 Experimental Results and Discussion
### Performance Evaluation and Analysis
#### 5.3.2 Decoding-Complexity Controlling Performance  
The experimental results summarized in Table 2 show the percentage error in the decoding-complexity controlling function of the proposed encoding algorithm (achieving a specified decoding-complexity is not possible for any of the state-of-the-art algorithms). The proposed algorithm shows on average an \(\approx\)1.78% decoding-complexity controlling error for both complexity levels considered (30% and 40% reductions over HM 16.0). The results suggest that the proposed algorithm is capable of generating a bit stream that adheres to a given bit rate and a decoding-complexity level. Furthermore, the frame-wise decoding-complexity error illustrated in the Figure 3 also reveals that the proposed encoding algorithm is capable of maintaining a very low error despite the dynamic nature of the video content. In summary, numerical and graphical results for the simultaneous rate and decoding-complexity control capability of the proposed method indicate that the proposed method is content-adaptive and capable of achieving specified bit and decoding-complexity targets.  
Figure 2: **(Top row)**: An illustration of the frame-wise percentage error between the allocated bits and actual bits for the HM 16.0, proposed, and other state-of-the-art algorithms for three HD test sequences. **(Bottom row)**: A further illustration of the percentage frame-wise rate control performance of the proposed algorithm.