esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model and QoT Estimation
### Link Weight Calculation Method
#### 3.2.2 Backup Path Link Selection Method  
To minimize the redundancy of a network resource, we used the SBPP scheme [10] to allocate backup resource for requests. In addition, spectrum fragmentation is further considered in the backup path link selection process. Equations (3) and (4) calculate the spectrum fragmentation of the backup path.  
\[F_{P,backup}=\sum_{l_{backup}\in P_{backup}}F_{l,backup}, \tag{3}\]  
\[F_{l,backup}=(\sum_{i=1}^{n}f_{i})\cdot\sqrt{\sum_{i=1}^{n}f_{i}}\cdot\frac {\sum\limits_{i=1}^{n}f_{i}\cdot\sqrt{f_{i}}}{n}+(\sum_{p=1}^{m}s_{p})\cdot \sqrt{\sum_{p=1}^{m}s_{p}\cdot\frac{\sum\limits_{p=1}^{m}s_{p}\cdot\sqrt{s_{p }}}{m}}, \tag{4}\]  
where \(f_{i}\) denotes the number of free FSs in the \(i\)th available block, \(s_{p}\) stands for the number of shareable FSs in the \(p\)th shareable spectrum block for \(r\), \(m\) represents the total count of available shareable blocks, \(l_{backup}\) represents the link in backup path, \(F_{l,backup}\) quantifies the spectrum fragmentation on the backup path link \(l\), and \(F_{P,backup}\) measures the fragmentation level of the selected backup path. A larger \(s_{p}\) means that the number of frequency slots contained in the shareable frequency blocks is greater, and a smaller \(m\) means that the shareable frequency blocks are more densely distributed. Therefore, the higher the \(F_{l,backup}\) value, the more free and shareable spectrum resources are available to the link and the less free and shareable fragmentation there are for the current service.  
Figure 1 illustrates the process of selecting the primary and backup paths for the request. As shown in Figure 1, we assumed that the FSs of request was 2, with nodes A and D serving as the source and destination, respectively. From Equations (1) and (2), we calculated that the primary path weights of P1, P2, and P3 are 16, 52.9, and 125, respectively. Therefore, path P3 was selected as the primary path of request. According to the principle of link disjointness, P1 and P2 can be selected as alternative backup paths; then, according to Equations (3) and (4), the backup path weights of P1 and P2 were 126 and 107, respectively. Therefore, path P1 was selected as the request alternate path.
###### Abstract  
Recently, various unicast routing protocols have been proposed to deliver measured data from the sensor node to the sink node within the predetermined deadline in wireless sensor networks. In parallel with their approaches, some applications demand the specific service, which is based on broadcast to all nodes within the deadline, the feasible real-time traffic model and improvements in energy efficiency. However, current protocols based on either flooding or one-to-one unicast cannot meet the above requirements entirely. Moreover, as far as the authors know, there is no study for the real-time broadcast protocol to support the application-specific traffic model in WSN yet. Based on the above analysis, in this paper, we propose a new (\(m\), \(k\))-firm-based Real-time Broadcast Protocol (FRBP) by constructing a broadcast tree to satisfy the (\(m\), \(k\))-firm, which is applicable to the real-time model in resource-constrained WSNs. The broadcast tree in FRBP is constructed by the distance-based priority scheme, whereas energy efficiency is improved by selecting as few as nodes on a tree possible. To overcome the unstable network environment, the recovery scheme invokes rapid partial tree reconstruction in order to designate another node as the parent on a tree according to the measured (\(m\), \(k\))-firm real-time condition and local states monitoring. Finally, simulation results are given to demonstrate the superiority of FRBP compared to the existing schemes in terms of average deadline missing ratio, average throughput and energy consumption.  
(\(m\), \(k\))-firm real-time; broadcast; wireless sensor networks +
Footnote †: journal: Sensors
# Analysis of Life Cycle Environmental Impact of Recycled Aggregate
###### Abstract  
This study assessed the influence of matter discharged during the production (dry/wet) of recycled aggregate on global warming potential (GWP) and acidification potential (AP), eutrophication potential (EP), ozone depletion potential (ODP), biotic resource depletion potential (ADP), photochemical ozone creation potential (POCP) using the ISO 14044 (LCA) standard. The LCIA of dry recycled aggregate was 2.94 \(\times\) 10\({}^{-2}\) kg-CO\({}_{2\text{eq}}\)/kg, 2.93 \(\times\) 10\({}^{-5}\) kg-SO\({}_{2\text{eq}}\)/kg, 5.44 \(\times\) 10\({}^{-6}\) kg-PO\({}_{4}^{3}\)\({}_{\text{eq}}\)/kg, 4.70 \(\times\) 10\({}^{-10}\) kg-CFC\({}_{1\text{eq}}\)/kg, 1.25 \(\times\) 10\({}^{-5}\) kg-C\({}_{2}\)H\({}_{\text{eq}}\)/kg, and 1.60 \(\times\) 10\({}^{-5}\) kg-Antimony\({}_{\text{eq}}\)/kg, respectively. The environmental impact of recycled aggregate (wet) was up to 16\(\sim\)40% higher compared with recycled aggregate (dry); the amount of energy used by impact crushers while producing wet recycled aggregate was the main cause for this result. The environmental impact of using recycled aggregate was found to be up to twice as high as that of using natural aggregate, largely due to the greater simplicity of production of natural aggregate requiring less energy. However, ADP was approximately 20 times higher in the use of natural aggregate because doing so depletes natural resources, whereas recycled aggregate is recycled from existing construction waste. Among the life cycle impacts assessment of recycled aggregate, GWP was lower than for artificial light-weight aggregate but greater than for slag aggregate.  
recycled aggregate; concrete; life cycle assessment; environmental impact +
Footnote †: journal: Applied Soft. 2019, 9, 1021; doi:10.3390/app9051021
# ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems
## 7 Conclusions  
In this paper, a distributed and autonomous evacuation process based on the key principles of reinforcement learning and game theory was proposed in order to support distributed and efficient evacuation planning in Public Safety Systems. Specifically, two decision-making layers were identified and implemented for evacuees toward selecting the most appropriate and effective evacuation route among available ones. At the first layer, a reinforcement-learning technique was introduced where evacuees act as stochastic learning automata and select the evacuation route to which they want to go. Consequently, based on evacuees' decisions, a cluster of evacuees is created per evacuation route, where humans compete with each other toward each determining their final evacuation path. Evacuees' competitive behavior is captured via a non-co-operative minority game among evacuees per each evacuation route. The existence and uniqueness of a Pure Nash Equilibrium of the minority game is shown, where evacuees decide if they will finally evacuate he disaster area or not via the initially selected evacuation route. The overall framework is captured in a distributed and low-complexity algorithm, namely, ESCAPE.  
The operation and performance of our proposed framework were extensively evaluated through modeling and simulation, while the presented numerical results demonstrated its superior performance in an evacuation process. Our current and future work contains the consideration of evacuees' mobility during the evacuation process by capturing evacuee speed along a chosen escape route, as well as the formulation of the examined problem under the principles of the Tragedy of the Commons, where evacuation routes act as a common pool of resources that can fail to serve evacuees due to overexploitation. Moreover, we plan to consider evacuees' speed toward reaching and going through an evacuation route as a factor to update the probabilities to evacuate or not through the most convenient evacuation route.  
All authors contributed extensively to the work presented in this paper. [PERSON] developed the code of the overall framework, executed the evaluation experiment, and contributed to the discussions and analysis of the comparative evaluation results. [PERSON] had the original idea on which we based our current work and overall co-ordinated the writing of the article. [PERSON] was responsible for the overall orchestration of the performance evaluation work and contributed to the quantitative and qualitative evaluation of the ESCAPE framework.  
This research received no external funding.  
The research of [PERSON] was conducted as part of the UNM Research Allocation Committee award and the UNM Women in STEM Faculty Development Fund. The authors would like to thank [PERSON] for his initial involvement in the implementation of this research project as a minor high-school student performing his summer internship.  
The authors declare no conflict of interest.
## 5 Discussion
### Performance Analysis
The aggregation and subsequent rounding of decimal values for display purposes may cause some rows to total slightly more or less than 100%. In the WISDM dataset results (Figure 9(b)), 99% of standing activities are accurately classified, whereas roughly 2% are erroneously classified as sitting. This apparent discrepancy (99% + 2% = 101%) is a byproduct of the rounding process applied to the normalized aggregated values from all five folds, where the actual decimal values total precisely 100%. The consistently low standard deviations in our performance metrics (approximately \(\pm\)0.2% for UCI-HAR and \(\pm\)0.25% for WISDM) across multiple runs indicate the model's reliability and stability. This robust performance is due to several key architectural components: the SE mechanism's effective feature recalibration, the residual connections' optimization of gradient flow, and the bidirectional GRU's ability to capture temporal patterns.
# Parallel Accelerated Fifth-Order WENO Scheme-Based Pipeline Transient Flow Solution Model
## 5 Model Validation and Case Analysis
### Model Application and GPU Acceleration Performance Evaluation
#### 5.2.2 Analysis of Model Computation Efficiency and GPU Acceleration Performance
This model employs the NVIDIA GeForce GTX 1660 Ti graphics card, while the acceleration effect can be more than 13 times compared with a single CPU program by using GPU parallel computing technology with a grid number of 500,000 and a grid accuracy of 0.4 m. With the GPU-WENO scheme, which has a high application prospect in solving practical engineering problems, the computation speed can easily reach more than tens of times that of the commonly used MOC or differential scheme. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Number of Grid Grids**} & \multirow{3}{*}{**Grid Accuracy/(m)**} & \multirow{3}{*}{**CPU-WENO (t/s)**} & \multicolumn{6}{c}{**GPU-WENO (t/s)**} & \multicolumn{6}{c}{**Acceleration Ratio/(Times)**} \\ \cline{3-10}  & & & & **Number of Threads** & & & **Number of Threads** & \\ \cline{3-10}  & & & **8** & **16** & **32** & **64** & **8** & **16** & **32** & **64** \\ \hline
600 & 400 & 4.44 & 5.22 & 5.21 & 5.19 & 5.22 & 0.85 & 0.85 & 0.86 & 0.85 \\
6000 & 40 & 48.16 & 11.55 & 6.33 & 5.20 & 5.23 & 4.17 & 7.61 & 9.26 & 9.21 \\ \(6\times 10^{4}\) & 4 & 479.24 & 91.28 & 57.88 & 47.27 & 46.96 & 5.25 & 8.28 & 10.14 & 10.21 \\ \(6\times 10^{5}\) & 0.4 & 4731.08 & 793.81 & 502.24 & 358.96 & 354.39 & 5.96 & 9.42 & 13.18 & 13.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effect analysis table of GPU acceleration. Figure 11: Comparison of calculation speed of different simulation methods. Figure 12: Accelerated rendering with different threads and grid numbers in GPU.
# Robust Optimization for Household Load Scheduling with Uncertain Parameters
###### Abstract  
Home energy management systems (HEMS) face many challenges of uncertainty, which have a great impact on the scheduling of home appliances. To handle the uncertain parameters in the household load scheduling problem, this paper uses a robust optimization method to rebuild the household load scheduling model for home energy management. The model proposed in this paper can provide the complete robust schedules for customers while considering the disturbance of uncertain parameters. The complete robust schedules can not only guarantee the customers' comfort constraints but also cooperatively schedule the electric devices for cost minimization and load shifting. Moreover, it is available for customers to obtain multiple schedules through setting different robust levels while considering the trade-off between the comfort and economy.  
HEMS; household load scheduling; robust optimization; uncertain parameters +
Footnote †: journal: Applied Sciences
# Analysis of Multilayered Ground Amplification Characteristics by Scaled-Down Model Tests
## 2 Materials and Methods
### Soil Properties and Experimental Equipment
The natural periods of the empty box and fill box are 0.04-0.05 s and 0.1 s. Figure 2 shows the empty Laminar shear box and soil sample set up in the box. When using a 1 g shaking table for dynamic testing, it is very important to select accelerometers and a data logger suitable for test purposes. Figure 3 shows the data logger and accelerometer used in this study. The prediction range of response acceleration measured in this test is no more than 20 m/s\({}^{2}\), the frequency component of response data is 40 Hz, and the temperature is room temperature. Therefore, an ARF-20A acceleration transducer, which can be measured up to 20 m/s\({}^{2}\), was selected for the accelerometer. The mass of the accelerometer should be less than or equal to the ground density so as not to affect the inertia of the accelerometer. The weight of the test accelerometer is 13 g. The data logger has 24 channels, compatibles with the ARF-20A, the data heap itself features L.P filter, and the data storage interval is up to 0.005 s. The main parameters of accelerometers and data logger are shown in Table 3.  
Figure 2: Laminar shear box: (**a**) Empty Laminar shear box in this study; (**b**) Soil sample set up in the LSB; (**c**) Accelerometer set up for a dense layer; (**d**) Accelerometer set up for a loose layer.
# Wind Speed Measurement via Visual Recognition of Wind-Induced Waving Light Stick Target
###### Abstract  
Wind measurement in confined spaces is a challenge due to the influence of the dimensions of anemometers in intrusive flow-field measurements where the anemometer probes directly contact and influence the near-probe flow field. In this work, a new wind speed detection methodology is proposed based on wind-induced motion of a stick via vision-based recognition. The target's displacement in pixel coordinates is mapped to its angular displacement in world coordinates to derive wind speed and direction information by applying the calibration coefficients. Simulation experiments were carried out to validate the model, the error of which was within an angular displacement of 4.0\({}^{\circ}\) and 3.0\({}^{\circ}\) for wind speed and direction detections, respectively. When applied to the measurement of wind speed in the inner equipment cabin of a stationary high-speed train, the error was within \(\pm\)1.1 m/s in terms of average RMSE. Thus, the proposed method provides an accurate and economic option for monitoring 2D wind in a confined space.  
wind speed measurement; visual detection; stick recognition; rail transit 2023
## 4 Materials and Methods
### Field from Annular Dipole Antenna Array and Optimization Procedure
Moreover, this reduces the necessary computing power and shortens the calculation time. The presented approach (see Equations (5-18)) allows for a multi-objective optimization problem for the SAR ratio of two selected volumes of interest and optimal heating of only one of them. In the analyzed case of the breast model affected by cancer, the purpose was to heat the tumor region most effectively, while limiting the heating of other female breast tissues. This goal can be achieved by fulfillment of the following objective function [17; 35]:  
\[\mathbf{E}_{n}(\mathbf{r})=\sum_{i}^{x,y,z}E_{n,i}(\mathbf{r})\exp[i\Phi_{n,i} (\mathbf{r})]\mathbf{e}_{i} \tag{5}\]7  
where \(w\) denotes the relevant weighting factors. In our optimization procedure, it was assumed that all \(w\) parameters have unit initial values. In the next step, the optimal input powers and phase values for individual dipoles that meet the Equation (19) were specified. The Sim4 Life in-built optimization procedure assumes that all dipole antennas in the array are supplied independently, and that there is no coupling between their feeding cables, and thus it is not possible to determine their mutual transmission coefficients \(S_{ij}\) (\(i\
eq j\)), as defined in [7]. Additionally, the given optimization procedure assumes that the phases of the dipole sources \(\phi_{i}\) range from \(-180^{\circ}\) to \(180^{\circ}\) and the total MW power of the entire dipole antenna array is equal to the sum \(P_{i}\) = 8 W, with the initial values for all the applicators as follows: \(\phi_{0}\) = 0 and \(P_{0}\) = 1 W. All the amplitudes for dipole sources have unit values of \(A_{i}\) = 1 V. A similar procedure for a two-spherical object has been briefly described in [17]. The inverse form of such an objective function (19) based on a desired frequency bandwidth has been shown in [71]. It is important to note that recently, some novel energy/SAR-focusing optimization techniques have been investigated in the field of hyperthermia treatment, including \(E\)-field focusing via constrained power optimization (FOCO) [34], the \(E\)-field focusing [21], the time reversal focusing method [23], and others [19]. As demonstrated in paper [34], the FOCO approach gives slightly better results compared to the clinical benchmark using particle swarm-based optimization (PSO), especially for larger volumes of treated regions. A similar study [18] proposes a multi-frequency-based phased array with 8 monopole antennas for targeted hyperthermia treatment of deep-seated and multi-locus carcinomas in the head and neck. The conclusion was that multi-frequency excitation improves SAR accumulation.
# Improving Energy Efficiency on SDN Control-Plane Using Multi-Core Controllers
## 1 Introduction  
Data center networks are increasing in size, data processing capacity and throughput to attend to the high demand of computing services required by: the growing demand on the cloud [1], smart cities, the advent of the Internet of Things (IoT), big data applications and deep neural networks among other technologies. The demand from DC for energy and its related environmental implications are increasing accordingly. A study by [PERSON] et al. [2] shows that the energy consumption of Information and Communication Technology (ICT) equipment was responsible for 4.6% of all energy consumption by 2012. In addition, the network interconnection equipment is accountable for 15% up to 50% [3] of the energy used in these facilities. In a recent review, [PERSON] et al. [4] showed that total energy consumption of data centers worldwide has increased from 95 TWh in 2010 to 130 TWh in 2018, and it is expected to reach 155 TWh by 2023.  
The equipment present on a Data Center (DC) can be divided into three categories: infrastructure, servers and network. The energy-efficiency of a data center has been measured as the portion of overall energy consumption that is delivered to ICT equipment,i.e., how much of the energy measured on the power source of a DC is used by the ICT equipment.  
The most common metric for a DC energy's efficiency is the Power Usage Effectiveness (PUE), which is defined as the ratio between the facility's total power and the power consumed by the ICT equipment. The PUE is an important metric, but it lacks granularity on where the energy is consumed, observing the three aforementioned categories. Thus, [PERSON] et al. [5] proposed new metrics for each category, the Network Power Usage Effectiveness (_NPUE_):  
\[NPUE=\frac{\text{Total ICT power}}{\text{Power consumed by network equipment}}, \tag{1}\]  
which is the equivalent to the portion of network equipment on the PUE, and the Communication Network Energy Efficiency (\(CNEE\)), which may be defined as \"energy to deliver a single bit of information\" [5]:  
\[CNEE=\frac{\text{Power Consumed by Network Equipment}}{\text{Effective Network Throughput Capacity}} \tag{2}\]  
These are the ones that relate to network equipment and energy efficiency.
## 9 Discussion on Acoustic Findings  
The simulation results demonstrate significant improvements in both halls' acoustic performances. The reduction in reverberation time (T20) from 4.8 s to 1.7 s in Sisto V Hall and from 3.9 s to 1.9 s in Aula Magna, as shown in Figure 8, represents a meaningful compromise between speech intelligibility requirements and the preservation of some acoustic warmth appropriate for occasional musical performances [53]. This balance aligns with recommendations for multi-purpose spaces found in Barron [32] and Kuttruff [16].  
The spatial distribution of C50 values, as shown in Figure 9, shows improvement throughout both halls, though some areas (particularly in the rear sections) still present values slightly below the optimal range for speech intelligibility. This spatial variation is consistent with [PERSON]'s findings [49] regarding the challenges of achieving uniform clarity in historical spaces with pronounced longitudinal geometry.  
The proposed amplified audio system complements the passive acoustic treatments, with the simulated SPL distribution (90-93 dB) ensuring adequate sound pressure levels throughout the seating areas. The limited low-frequency coverage observed in the frequency response curves (Figures 10b and 11b) is less critical for speech applications but may require consideration for musical performances in Sisto V Hall.  
From a conservation perspective, the proposed interventions emphasize reversibility, using suspended elements and movable acoustic treatments that respect the historical integrity of these spaces [54]. The strategic placement of a limited number of acoustic elements yields substantial benefits while minimizing both cost and visual impact.  
The simulations for both occupied and unoccupied conditions suggest that these acoustic treatments would perform effectively across varying occupancy levels, addressing a practical consideration raised by [PERSON] [34; 55] regarding the consistency of acoustic performance in public spaces.