esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Extensive research on transition metal perovskite oxides as electrodes in solid oxide cells (SOC) has highlighted the potential ability of Fe-based perovskite oxides to catalyze oxygen reduction/evolution reactions (ORR/OER). The layered perovskite-type system YSr\({}_{2}\)Cu\({}_{2}\)FeO\({}_{7+\delta}\) has been reported to possess attractive electrocatalytic properties. This work applies density functional theory (DFT) calculations to investigate oxygen ion diffusion in the YSr\({}_{2}\)Cu\({}_{2}\)FeO\({}_{7+\delta}\) system. For \(\delta=0.5\), it is found that in the most stable configuration, the oxygen vacancies in the FeO\({}_{1+\delta}\) plane are arranged to form Fe ions in tetrahedral, square pyramid, and octahedral coordination. _Ab initio_ molecular dynamics (AIMD) simulations for YSr\({}_{2}\)Cu\({}_{2}\)FeO\({}_{7.5}\) (\(\delta=0.5\)) yield an oxygen ion diffusion coefficient of 1.28 \(\times\) 10\({}^{-7}\) cm\({}^{2}\)/s at 500 \({}^{\circ}\)C (Ea = 0.37 eV). Complementary results for YSr\({}_{2}\)Cu\({}_{2}\)FeO\({}_{7.2}\) (\(\delta=0.2\)) and YSr\({}_{2}\)Cu\({}_{2}\)FeO\({}_{7.75}\) (\(\delta=0.75\)) indicate that the oxygen diffusion occurs in the FeO\({}_{1+\delta}\) plane, and depends on the oxygen vacancies distribution around the Fe centers.  
perovskites; cuprates; DFT; SCAN; AIMD; SOFC; OER +
Footnote †: journal: Applied Physics Letters  
Acticle Ab Initio Investigation of Oxygen Ion Diffusion in the Layered Perovskite System YSr\({}_{2}\)Cu\({}_{2}\)FeO\({}_{7+\delta}\) (0 \(<\delta<\) 1)
## 2 Materials and Method
### Materials
#### 2.1.2 Die and Other Components Materials  
Due to the processing temperature of 210 \({}^{\circ}\)C, materials capable of withstanding high temperatures were selected for the die, the die holders, and the other fixing apparatus. The chosen material for the die is AISI P20 Impax Supreme, which is characterized by its exceptional hardness, wear resistance, and stability at elevated temperatures. To achieve precision in the die's vortex zone, a five-axis CNC machine was employed for its manufacturing. Following the completion of the die, a hardening process was applied to elevate its hardness level to 56 HRC, ensuring enhanced durability during the SPD process. Additionally, AISI H13 (DIN 1.2344) steel, which offers excellent thermal resistance and mechanical strength, was used to fabricate the die holder, punch component, and all other fixing apparatus.
## 7 Case Studies
### Analysis of the Efficiency of the Proposed Distributed Cooperative Dispatch Method  
In order to verify the efficiency of the proposed distributed cooperative dispatch method, the independent dispatch method is used as a comparison. The independent dispatch method indicates that the optimal dispatch models for DHN and BESS are first solved to obtain the boundary power, and then the optimal dispatch model for DN is solved to obtain the dispatch scheme.  
Table 3 shows the comparison of the system costs of the two methods. Table 4 shows the comparison of curtailed energy for PV and WT of the two methods. Figure 4 shows the comparison of curtailed power for PV and WT of the two methods. Figure 5 shows the comparison of the power purchased from the superior grid by DN of the two methods.  
As shown in Table 3, compared to the independent dispatch method, the operation cost of DN is reduced by 61.2%, the operation cost of DHN is increased by 22.5%, the profitability of BESS is reduced by 65.8%, and the total cost of the system is reduced by 34.5% of the proposed method. This is because the proposed method achieves efficient synergy among DN, DHN, and BESS, which cannot be achieved by an independent dispatch method. As shown in Table 4, compared with the independent dispatch method, the curtailed energy for PV of the proposed method is reduced by 7393.2 kWh. This is because in 7:00-18:00, the curtailed power for PV and WT of the proposed method is significantly smaller than that of the independent dispatch method, as shown in Figure 4. Further, the penalty cost  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Curtailed Energy for PV** & **Curtailed Energy for WT** \\  & **(kWh)** & **(kWh)** \\ \hline Proposed method & 1185.3 & 0 \\ Independent dispatch method & 8578.5 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of curtailed energy for PV and WT of the proposed method and independent dispatch method.  
Figure 4: Comparison of curtailed power for PV and WT of the proposed method and independent dispatch method.  
for curtailment for PV and WT of the proposed method is reduced by 86.2% compared with the independent dispatch method. Due to the higher capacity of PV and WT power consumption of the proposed method, less power is purchased from the superior grid compared to the independent dispatch method, as shown in Figure 5. As a result, the cost of purchased electricity from the superior grid of the proposed method is reduced by 7.0% compared to the independent dispatch method. Ultimately, the total system cost of the proposed method is reduced by 34.5% compared to the independent dispatch method.  
In summary, compared with the independent dispatch method, the proposed distributed cooperative dispatch method realizes the efficient synergy between DN and the two types of flexible resources, DHN and BESS, which can effectively improve the absorption capacity of DN for PV and WT and then reduce the operation cost.
# Facial Privacy Protection with Dynamic Multi-User Access Control for Online Photo Platforms
## 5 Experiments
### Experiment Design
#### 5.1.2 Deep-Learning Model Configuration  
For face detection, we employed the same model as in our previous work [17], based on the YOLOv8 architecture. The model was trained on the WIDER Face dataset [34] to detect facial regions in images. Specifically, we utilized the YOLOv8m pre-trained model [35] and fine-tuned it on the WIDER Face dataset to adapt it to our use case. YOLOv8m is a medium-sized model in the YOLOv8 family, achieving a 50.2 mAP on the COCO validation set [36]. With 25.9 million parameters and 78.9 billion FLOPs, the model achieves a balance between accuracy and computational efficiency. YOLOv8m is more complex than YOLOv8m and YOLOv8s but less resource-intensive than YOLOv8l and YOLOv8x. It processes images in 234.7 ms on a CPU (ONNN) and 1.83 ms on an NVIDIA A100 GPU (TensorRT), making it suitable for real-time applications [37].  
Our fine-tuned model was trained using Python version 3.10 with PyTorch version 2.2.0 [38] and the Ultralytics Python library version 8.1.9 [18]. Training was performed on a system with an NVIDIA RTX 3070 GPU for 300 epochs, achieving a precision of 0.875 and a recall of 0.651 in the final epoch.  
As shown in Figure 7, our model achieved high accuracy on both the validation and test sets of the WIDER Face dataset. In the test set, it obtained Average Precision (AP) scores of 0.924 for easy, 0.907 for medium, and 0.809 for hard levels, demonstrating its effectiveness in detecting facial regions in previously unseen images, even in challenging conditions.
## 3 Proposed TFR Approach
### Operations
#### 3.2.2 Re-establishing the Synchronization Hierarchy  
The second step in the TFR approach is to identify the live MCs to the slave nodes in order to elect the best master between them and to reestablish the master-slave synchronization hierarchy, depending on the MC and the active ports of the slave nodes. As a result of the previous step, each MC in the network segment will broadcast an Announce message toward the SCs after receiving the Link-Alive message. This Announce message contains the local clock timing of the MC; depending on this particular message, the SC identifies the accuracy of each MC in the network. Subsequently, the slave ports receive the Announce message and start by performing a Best-Master election operation that selects the best MC between all the clocks that transmitted the Announce message toward the slave ports.  
Specifically, this operation performs the standard BMC algorithm to identify the best MC in the network segment based on the clock accuracy of the MC. After electing the best MC, the nodes perform the synchronization hierarchy rebalancing operation by establishing the synchronization tree based on the active ports of the slave nodes that exclusively received the Announce message. Afterwards, the SCs perform the ports pruning operation to prevent a loop inside the PTP network by changing the state of these ports to a passive state. Furthermore, this process excludes the main ports of the synchronization from the ports pruning operation by maintaining the state of these ports as active ports.  
Eventually, the result of this step is that the separated slave ports in the network perfectly recover the fault occurrence in the network by using alternative synchronization paths without affecting network traffic performance, and by using multiple paths without a loop-free structure. Furthermore, the SCs eliminate the faulty paths from the synchronization flow between the MC and the slave ports, and they elect the BMC in the network to fulfill accurate synchronization for the SCs in PTP networks.
# Underground Disposal of Coal Gauge Backfill in China
## 1 Introduction
Coal-producing countries keep developing and deepening the utilization of coal gangue on the ground, as shown in Figure 1.  
Australia, Russia, Germany, Poland, the Ukraine, and other countries have also carried out backfill mining research on coal mining with pneumatic stowing [30], backfilling in highwall mining [31], partial backfilling [32], cemented paste backfilling [33; 34; 35; 36] and other innovations in backfill mining [37; 38; 39; 40]. The utilization rate of coal gangue in China has reached 73% [3]. Some major progress has been made in the industrialization of coal gangue recycling. However, due to the intrinsic features of coal gangue, technical limitations, and other external factors, the number of gangue recycling programs that can generate high economic yield and are practically feasi  
Figure 1: The utilization of coal gangue on the ground. ble is limited [41]. More than 35% of coal gangue still exists in the form of waste heaps [42]. Given the huge annual production of coal gangue in China, the coal gangue recycling on the ground alone cannot meet the growing demands for gangue disposal. China encourages gangue backfill mining to control ground subsidence, farmland destruction, and coal gangue discharge on the ground. In particular, China has formulated favorable policies to push forward the disposal of coal gangue. According to the environmental protection tax law of China, the entities conducting coal gangue recycling in accordance with the national and local environmental protection standards are exempt from the environmental protection taxes (CNY 5 per ton of coal gangue). The resource tax will be cut by 50% for backfill mining. At present, the commonly used gangue backfilling technologies are mechanical backfilling, fully mechanized mining with solid backfilling [43], mixed workforce using a gangue backfilling and caving method, longwall-roadway cemented backfilling, and paste backfilling [44; 45]. These backfilling technologies are mainly intended for coal mining under buildings (structures), railways, and water bodies and are considered the key technologies for green coal mining [46]. The development of the underground disposal of coal gangue, especially gangue backfilling, is not only directly envisaged by the national policies, but meets the practical needs of backfill disposal. The remaining part of this paper contains a review of existing gangue backfilling technologies. Their principles, advantages and deficiencies, efficiencies, and application conditions are discussed, and the latest progress in the high-efficiency, large-scale underground gangue backfilling technology is summarized. The key equipment for the underground gangue backfilling technology is also analyzed. This paper provides theoretical support for large-scale gangue backfilling in China.
# A Lightweight Slice-Based Quality of Service Manager for IoT
## 5 Validation, Evaluation and Obtained Results
### Per-Flow QoS: IntServ  
The slice-based QoS policing is actuated at an interface level, dropping excess traffic that crosses into it and not taking advantage of QoS queues to do so. On the other hand, traffic shaping is applied on an OvS port, allowing multiple queues' creation and flow control on its OvS switch side. Hence, it enables the use of a set of IntServ rules in our proposed solution.  
As our goal is to address two use cases, it is vital to guarantee the stability of the implemented scenarios having two network slices in mind. Hence, all clients are required to possess an IntServ classification (flow entry) that would forward them based on their destination IP port. This feature results in additional traffic in the OpenFlow switch table. The details of the implemented slice-based QoS queues are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Slice ID** & **Queue ID (Name)** & **Defined Maximum Bandwidth** & **OpenFlow Match: Destination Port** \\ \hline
1 & 1 (A) & 20 Mbit/s & Odd \\ \hline
1 & 2 (B) & 10 Mbit/s & Even \\ \hline
2 & 1 (A) & 15 Mbit/s & Odd \\ \hline
2 & 2 (B) & 5 Mbit/s & Even \\ \hline \hline \end{tabular}
\end{table}
Table 1: Two slices’ IntServimplementation details.  
Figure 17: Two slices’ policing implementation total bandwidth.  
The comparison of both slices' achieved bandwidth is depicted in Figure 18, showing the efficiency of our solution. Clients were behaving in the same way as in the traffic policing evaluation, using TCP and injecting 1 Mbit/s each. With that in mind, Slice 1 (ID = 1) is expected not to go beyond 30 Mbit/s (ID 1 = 20 Mbit/s and ID 2 = 10 Mbit/s), while Slice 2 (ID = 2) should stabilise around 20 Mbit/s (ID 1 = 15 Mbit/s and ID 2 = 5 Mbit/s).
# Explore Long-Range Context Features for Speaker Verification
## 7 Results and Analysis
### Comparison of Long-Range Feature Extraction Visually for Three Methods  
Class activation mapping is used to demonstrate the impact of the three methods and combinations on speaker verification visually. We randomly select one utterance  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Vox1-O**} & \multicolumn{2}{c}{**Vox1-E**} & \multicolumn{2}{c}{**Vox1-H**} \\  & **EER** & **minDCF** & **EER** & **minDCF** & **EER** & **minDCF** \\ \hline ResNet50 & 2.03 & 0.196 & 1.86 & 0.209 & 3.28 & 0.319 \\ HS-Net50 & 1.46 & 0.136 & 1.34 & 0.163 & 2.58 & 0.243 \\ ResNet50+cross & 1.57 & 0.150 & 1.43 & 0.176 & 2.61 & 0.254 \\ ResNet50+DSSA & 1.76 & 0.157 & 1.55 & 0.173 & 2.84 & 0.264 \\ HS-Net50+cross & 1.26 & 0.104 & 1.22 & 0.144 & 2.39 & 0.226 \\ HS-Net50+DSSA & 1.27 & 0.101 & 1.19 & 0.147 & 2.29 & 0.219 \\ HS-Net50+cross+DSSA & 1.10 & 0.099 & 1.02 & 0.113 & 2.10 & 0.202 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of three methods and their combination on the Vox1-O/E/H.  
Figure 5: DET curves for all systems.  
in the VoxCeleb sets and perform the visualization analysis by Grad-CAM [39], which derives the attention weight distribution of the model on the feature map from the gradient information. The CAM heatmaps of three methods are shown in Figure 6. With these three approaches incorporated, the model tends to model long-time dependencies and extracts long-range features, which further demonstrates the effectiveness of our methods in extracting long-time features.
## 2 Problem Formulation
### Multiple-Vehicle Bike Sharing System Routing Problem
Using these four variables, the integer linear programming formulation for the mBSSRP is described as follows:  
minimize \[\sum_{i\in V}\sum_{j\in V}\sum_{k\in K}t_{ij}x_{ijk}\] (1) subject to \[\sum_{j\in V\setminus\{0\}}x_{0 jk}\leq m \forall k\in K\] (2)  
\[\sum_{h\in V\setminus\{0\}}x_{h0k}\leq m \forall k\in K \tag{3}\] \[\sum_{h\in V}x_{hik}=y_{ik} \forall i\in V\setminus\{0\},\;\forall k\in K\] (4) \[\sum_{j\in V}x_{ijk}=y_{ik} \forall i\in V\setminus\{0\},\;\forall k\in K\] (5) \[\sum_{k\in K}y_{ik}=1 \forall i\in V\setminus\{0\}\] (6) \[z_{ijk}\geq 0 \forall i,\;\forall j\in V,\;\forall k\in K\] (7) \[z_{ijk}\leq qx_{ijk} \forall i,\;\forall j\in V,\;\forall k\in K\] (8) \[\sum_{j\in V}z_{ijk}-\sum_{h\in V}z_{hik}=b_{i}y_{ik} \forall i\in V,\;\forall k\in K\] (9) \[\sum_{i\in V}\sum_{j\in V}t_{ij}x_{ijk}+\tau\sum_{i\in V}|b_{i}|y_ {ik}\leq T \forall k\in K\] (10) \[\sum_{j\in V\setminus\{0\}}x_{0 jk}\leq m \forall k\in K\]0 (11) \[\sum_{j\in V\setminus\{0\}}x_{0 jk}\leq m \forall k\in K\]1 (12) \[\sum_{j\in V\setminus\{0\}}x_{0 jk}\leq m \forall k\in K\]2 (13) \[\sum_{j\in V\setminus\{0\}}x_{0 jk}\leq m \forall k\in K\]3 (14) \[\sum_{j\in V\setminus\{0\}}x_{0 jk}\leq m \forall k\in K\]4 (15) \[\sum_{j\in V\setminus\{0\}}x_{0 jk}\leq m \forall k\in K\]5 (16) \[\sum_{j\in V\setminus\{0\}}x_{0 jk}\leq m \forall k\in K\]6  
Equation (1) is the objective function of the mBSSRP used to minimize the total travel time of all vehicles. Equations (2) and (3) describe that bicycles can be relocated using up to \(m\) vehicles, which leave from and return to the depot. Equations (4)-(6) state that every port should be visited exactly once by a vehicle. Equation (7) states that when the vehicle moves from port \(i\) to port \(j\), the number of bicycles loaded onto the vehicle is larger than 0, and Equation (8) states that when the vehicle moves from port \(i\) to port \(j\), the number of bicycles loaded onto the vehicle is less than the capacity of the vehicle throughout the tour (capacity and loading constraints). Equation (9) states that after visiting port \(i\), the number of bicycles on the vehicle can increase or decrease (supply constraint). Equation (10) describes that all vehicles must return to the depot within the time limit \(T\). Equations (11)-(15) are sub-tour elimination constraints obtained using flow formulation. Finally, Equations (16) and (17) define the variables as binary values. Our formulation of mBSSRP has a time limit constraint and a requirement that all ports are visited, which are not considered in the other BSS study formulations. Thus, all variables are needed.
## 4 Reynolds Number Effect on VIV Characteristics and the Underlying Mechanism  
### VIV Response  
In the four tests with different spring stiffnesses corresponding to four different Reynolds numbers, the variation in the dimensionless VIV amplitude with the dimensionless wind speed is shown in Figure 7. Here, the dimensionless amplitude is defined as \(A/H\), where \(A\) is the vertical displacement of the main beam and \(H\) is the height of the main beam. The dimensionless wind speed is defined as \(U\!/\!f_{\rm h}B\), where \(f_{\rm h}\) is the vertical natural frequency and B is the width of the main beam.  
Figure 7 shows that in all four tests, the main beam model experienced VIV. In Tests 1 and 2, VIV was more significant, whereas in Tests 3 and 4, VIV was less pronounced. Despite these differences, the VIV process across all tests followed a similar progression, including the pre-VIV stage, the lock-in ascent stage, the extreme amplitude point, the lock-in descent stage, and the post-VIV stage, as shown in the curve for Test 1. Specifically, in these four tests, the onset wind speed, maximum amplitude, wind speed  
Figure 7: VIV amplitude versus the wind speed.  
at the maximum amplitude, and lock-in range of VIV varied. In Test 1, the onset wind speed was 14.35 m/s, the stop wind speed was 20.32 m/s, the maximum amplitude was 0.076, and the corresponding wind speed was 18.58 m/s. In Test 2, the VIV was slightly weaker than that in Test 1, with an onset wind speed of 14.94 m/s, a stop wind speed of 18.87 m/s, a maximum amplitude of 0.057, and a corresponding wind speed of 17.81 m/s. In Tests 3 and 4, the maximum amplitudes were 0.006 and 0.004, respectively, which were 0.070 and 0.072 lower than those in Test 1. Overall, in the four tests, the Reynolds numbers corresponding to the maximum VIV amplitudes were 3.2 \(\times\) 10\({}^{4}\), 4.7 \(\times\) 10\({}^{4}\), 7.1 \(\times\) 10\({}^{4}\), and 8.3 \(\times\) 10\({}^{4}\), respectively. As the Reynolds number increased, the onset wind speed of VIV increased from 14.35 m/s to 16.03 m/s, the maximum amplitude decreased from 0.076 to 0.004, when \(Re\) > 4.7 \(\times\) 10\({}^{4}\), the VIV amplitude sharply decreased, and the lock-in range decreased from 14.35-20.32 m/s to 16.03-16.93 m/s. In conclusion, the VIV response of the main beam exhibited a significant Reynolds number effect, with stronger VIV responses at lower Reynolds numbers and weaker responses at higher Reynolds numbers.  
Figure 8 shows the vibration amplitude spectra at the maximum amplitude point for the four test cases.  
Figure 8 shows that the vibration amplitude spectra exhibit distinct dominant frequencies of 1.93 Hz, 2.98 Hz, 4.57 Hz, and 5.31 Hz, which are close to or equal to the corresponding system's vertical natural frequencies. The spectra display typical narrow-band characteristics, confirming that vertical VIV occurred in the four test cases [45; 46; 47].
# Fuzzy-Based Dynamic Time Slot Allocation for Wireless Body Area Networks
## 5 Fuzzy-Based Dynamic Time Slot Allocation  
Once the traffic is generated, the initial equal slot assignment may fail due to the dynamic conditions in the networks such as traffic flow, buffer availability, energy consumption by each node, and so on. Therefore, each of these parameters in the network is highly unpredictable. The solution proposed to this situation is the dynamic time slot allocation technique (DTS), where the slots are allocated to nodes depending on the packet interval, buffer availability, and the remaining energy of each node. In order to improve the reliability and efficiency of packet transmission, fuzzy-based dynamic slot allocation has been proposed. Fuzzy logic [41] can give an appropriate solution or can integrate many factors to solve an evaluation problem. In this method, it is used to find a dynamic time slot to each node based on the mentioned factors.
###### Abstract  
Water treadmill (WT) exercise is gaining popularity among equine athletes as it allows for increased workload through the resistance posed by water. However, the effect of an artificial river (AR), which further increases this resistance, on equine fitness indicators has not yet been investigated. This study aimed to determine whether WT exercise with varying water depths and the presence of an AR influences physiological response indicators. Fifteen school horses (\(n=15\)) underwent five treadmill exercise sessions: on a dry treadmill (DT), in fetlock-depth water with and without AR, and in carpal-depth water with and without AR. Physiological responses were assessed pre-exercise, during the highest workload, and at 30 min and 24 h post-exercise by measuring heart rate (HR), blood lactate concentration (LAC), red blood cell count (RBC), hemoglobin concentration (HGB), and the activity of three serum enzymes: creatine phosphokinase (CK), aspartate aminotransferase (AST), and lactate dehydrogenase (LDH). HR and LAC increased significantly (\(p<0.0001\)) from the pre-exercise resting state to the highest workload time point during treadmill exercise, regardless of session type. During the carpal-depth WT+AR session, horses achieved a fitness level characterized by the highest HR (\(p<0.0001\)), LAC (\(p<0.0001\)), and LDH activity (\(p=0.001\)). Thus, horses' physiological responses to walking on a WT with AR differ from those observed on a DT and a WT without AR. WT exercise with AR represents a low-to-moderate intensity aerobic workload for horses, which increases with water depth from fetlock to carpal levels. However, further research is required to evaluate its inclusion in training or rehabilitation programs for athletic horses and its potential beneficial effects.  
equise; hydrotherapy; water counterflow; water resistance; conditioning Article