esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This work demonstrates the possibility of producing pure single-phase HEOs with spinel structure (HESOs) under milder conditions than the standard solid-state techniques (i.e., with reduced environmental impact). Both XRD and Raman analyses highlight that the presence of copper or titanium in the equimolar combinations of the five metals considered favors the formation of secondary phase(s). SEM analysis shows that in ES syntheses, greater selectivity towards the NF formation is obtained using PAN as the polymer. Pure single-phase HESOs are evaluated as active anode materials in lithium-ion batteries. They exhibit great potential (e.g., very high initial specific capacities) but poor rate capability and long-term stability. Hence, two possible strategies are proposed and successfully implemented to improve their electrochemical performance, as shown for two representative samples. The common strategy of incorporating carbon is demonstrated in the case of electrospun (Cr,Mn,Fe,Co,Zn) HESO, while the incorporation of lithium  
Figure 7: Electrochemical performance of samples (**a,c,e**) 5 MF-b, (**b,d,f**) C5 MF-b in the Li-ion half cells. (**a,b**) CV curves (scanning rate of 0.1 mV s\({}^{-1}\)), (**c,d**) galvanostatic charge–discharge curves measured at 20 mA g\({}^{-1}\) and (**e,f**) galvanostatic charge–discharge curves measured at different current density. (**g**) Cycling stability at a current density of 500 mA g\({}^{-1}\) and (**h**) results of EIS measurements. in the HESO lattice is applied to the case of (Cr,Mn,Fe,Co,Zn) HESO produced via the sol-gel method. The addition of Li enhances entropy and generates oxygen vacancies, thus improving stability and ionic transport. After 300 cycles at 0.5 A g\({}^{-1}\), Li\({}_{1.8}\)(Cr\({}_{0.2}\)Mn\({}_{0.2}\)Fe\({}_{0.2}\)Co\({}_{0.2}\)Zn\({}_{0.2}\))\({}_{3}\)O\({}_{4}\) NPs deliver a much higher capacity than (Cr\({}_{0.2}\)Mn\({}_{0.2}\)Fe\({}_{0.2}\)Co\({}_{0.2}\)Zn\({}_{0.2}\))\({}_{3}\)O\({}_{4}\) NPs (484 against 150 mAh g\({}^{-1}\)). The addition of C improves the electronic transport and mechanical stability. At 2 A g\({}^{-1}\) rate, in composite C/(Cr\({}_{0.2}\)Mn\({}_{0.2}\)Fe\({}_{0.2}\)Co\({}_{0.2}\)Zn\({}_{0.2}\))\({}_{3}\)O\({}_{4}\) NFs, the measured delithiation capacity increases (from 15) to 345 mAh g\({}^{-1}\). Furthermore, after 400 cycles at a rate of 0.5 A g\({}^{-1}\), they still deliver 545 mAh g\({}^{-1}\) against 265 mAh g\({}^{-1}\) of pure (Cr\({}_{0.2}\)Mn\({}_{0.2}\)Fe\({}_{0.2}\)Co\({}_{0.2}\)Zn\({}_{0.2}\))\({}_{3}\)O\({}_{4}\) NFs. Despite the presented results indicating the need to better tune the preparation conditions to improve the rate capability and cyclability, this preliminary study provides a further contribution to the understanding of HEOs. This is of great importance since these materials can potentially be utilized for the development of all components of recharge-able Li\({}^{+}\) and Na\({}^{+}\) ion batteries. Moreover, thanks to their generality, the approaches shown can be extensively applied to the optimization of HEO properties, even aimed at different applications.
# Privacy-Preserving Overgrid: Secure Data Collection for the Smart Grid
## 4 Privacy-Preserving Overgrid
### Convergence Time  
Thanks to the scalability of Overgrid, creating virtual subnodes does not increase the convergence time. Instead, it slightly improves it, although it also increases the variability of intermediate results before convergence. To verify this, we simulate PP-Overgrid on a network of 100 nodes without sub-nodes (\(n=1\), i.e., no privacy) and with 5 or 10 sub-nodes in each node. The topology of the network is a random graph, with a fixed node degree \(D\) between 5 and 20, i.e., \(D\) is the minimum number of connections per node. In this experiment, we assume that all nodes exchange messages and execute a step of the Flow Updating scheme at regular time intervals, called cycles. Figure 5 shows the average power consumption \(p\) estimated over time for all the nodes of the grid, as well as the global network average (think green line). In the experiment, we start from a stationary state, and then the power demand of each node is suddenly changed to a new value at simulation cycles 50 and 100.  
From the figure, it is clear that, without sub-nodes (top three figures), the convergence of PP-Overgrid is slower while, with a higher number of sub-nodes, the convergence is faster. The reason relies on the fact that increasing the number of sub-nodes significantly reduces the degree of the PP-Overgrid network (the external connections are distributed among the different sub-nodes). Indeed, as shown in [15], the convergence time of the Flow Updating algorithm will improve if the network is loosely connected (and this is what happens when creating the sub-nodes). Additionally, note that the global average (thick green line) converges quicker with more sub-nodes, but, taking a closer look, the overall network convergence speed is slower: indeed, the single nodes (local average) need more iterations to get closer to the global network average. Nevertheless, even in the presence of 10 sub-nodes (i.e., increasing the number of nodes by an order of magnitude), the increase in the convergence time is negligible, proving the feasibility of PP-Overgrid.
## 3 Results and Discussion
### Degree of Checking
#### 3.1.1 Board Cross-Section
These internal checks were on average considerably larger than checks found at the board's surfaces. Severe internal checking after weathering of TM spruce has not been reported by others [49; 55], but is known to occur after the modification process especially when pith is enclosed within the cross-section [50]. Checks across growth rings and internal checks are shown as examples in Figure 4a,b, respectively. Checks that had developed along growth rings were most often recorded on the pith side of TM boards (Table 3). This finding is in line with assessments made on TM spruce panels after 4 years of use [55]. These checks appeared also in control boards after weathering because of longitudinal separation of wood fibres in the tangential direction, but had then propagated only a few mm into the board. In detail, checks along growth rings in TM boards were on average 2-3 times deeper than for control boards (Table 3). Seven percent (7%) of the TM boards had checks along growth rings that developed over more than half the thickness of the timber, whereas this was 0% for control boards. Depending on their length, such deep checks are grade determining for untreated structural timber, specifically in load-cases where they may have a significant effect on strength such as shear strength of a beam [62]. [PERSON] et al. [49] did not find more checks along growth rings in TM than unmodified spruce and pine after 3 wetting cycles, and suggested that this could be explained by the absence of ultraviolet (UV) irradiation. However, Table 3 shows that number and maximum depth of checks along growth rings were statistically equivalent between the different groups for both flat sides of TM boards and the pith side of control boards, and thus did not depend on orientation or the presence of UV irradiation. Checks along growth rings on flat sides of a TM board are shown as an example in Figure 4c. Checks along growth rings were mainly found at the annual ring border on the pith side (radial surface) of boards (Table 3 and Figure 4, detail c). This is consistent with the location of checks found on the radial surface of unmodified wood of spruce and pine that was weathered for a period of 33 months [48]. However, no mention was made of checks that had developed between growth rings in previous studies on outdoor above-ground exposure of TMW for both natural and accelerated weathering tests [51; 52; 53; 54]. The number of checks across rings was more or less similar between sample sets and flat sides, but in general higher for exposed surfaces (Table 3). On the other hand, the number of checks along growth rings was higher on the pith side than the bark side, higher for TM than control boards, and seemed not to depend on board orientation. Maximum depth of checks did not depend on board orientation, with exception of the maximum depth of checks across rings on the bark side of control boards, which was greater for exposed surfaces (Table 3).
###### Abstract  
In this study, a dispersive solid-phase extraction (DSPE) pretreatment procedure using zeolite H-Beta as a sorbent was exploited for the determination of eight neonicotinoids in bottled water and honey products based on ultra-high-performance liquid chromatography-tandem mass spectrometry analysis. The zeolite H-Beta was demonstrated to be a suitable sorbent for neonicotinoid insecticides, even after 10 necycles of reuse. The method performance was evaluated by the linearity (\(R^{2}\geq 0.998\)), recovery (71-108%), precision (0.1-7.8%), limit of detection (0.05-0.1 ng/mL) and limit of quantification (0.1-0.2 ng/mL), which suggested excellent stability and high sensitivity with the use of the DSPE procedure. The method was further successfully applied in the test of neonicotinoid insecticides in 34 samples. Zeolite H-Beta shows promise as an efficient and practical material for monitoring neonicotinoid insecticides in bottled water and multiplex honey matrices.  
neonicotinoid; zeolite H-Beta; bottled water and honey products; liquid chromatography--tandem mass spectrometry; dispersive solid-phase extraction +
Footnote †: journal: Applied Sciences
# A Survey on Recent Advanced Research of CPS Security
## 4 Discussion
### MADC Types
#### 4.3.4 Control  
With the development of computation and communication capabilities, CPS has become a complex, extensive system. The control of system security is no longer based on traditional performance detection but is combined with various control theories and technologies. Studies about control have mainly been performed at the application layer and include device pairing, cloud service management, access control, and trusted computing. The other works mainly address key management [19; 82] and encrypted communication schemes [83; 106].  
Device pairing is a significant control task that is responsible for coordination between multiple devices. Researchers have designed pairing protocols for devices with different sensor types [114] and augmented reality (AR) headsets [115]. They also tried to develop a noisy vibration scheme against eavesdropping attacks [96]. Moreover, cloud computing scenes attract attention in security research, and the main studies are about autonomous collaborative networks [24], decentralized trigger-action platforms [63], and secure message distribution in clouds [62]. Access control and trusted computing are analyzed above, so we do not address them here.
## 3 Results
### Radiation Spatial Distribution Variation  
It is worth noting that during the interaction with the laser pulse, the electron also exhibits changes in the shape of its radiation spatial distribution. The shape of the radiation distribution transitions from a central peak to a vortex and then to a ring, which is similar to normal models. Notably, the change in the radiation shape occurs between approximately \(tspan=40\) and \(60\), while the electron's motion states change at \(tspan=58,74\), and \(96\). Therefore, the shape change of the radiation spatial distribution only occurs in the first phase. At the same time, during the fourth stage, as the interaction between the electron and the laser pulse diminishes, a reverse change in the radiation spatial distribution shape similar to that in the first stage will also occur. In the second and third phases, the radiation spatial distribution remains ring-shaped, with only the radiation peak gradually increasing. This indicates that the two reversals in the electron's motion during the process affect the radiation shape, but the change in motion direction only alters the primary radiation direction without affecting the shape of the radiation distribution.  
Figure 6 shows the changes in the radiation spatial distribution shape during the electron's motion process, with \(tspan\) taken from 40 to 60. Since we are only focused on the change in the radiation shape during the interaction process, the maximum value of the radiation is normalized for easier observation of the change in the electron's radiation shape. From the figure, it can be seen that during the first phase of the electron's motion, the radiation shape changes from a peak to a vortex and then to a ring. This is due to the increasing amplitude of the laser pulse and the decreasing beam waist radius over time, which enhances the laser pulse's confinement effect on the electron, making the radiation distribution more compact. The thickness of the high-radiation ring decreases, and the radius of the radiation ring increases as the laser pulse amplitude increases, resulting in changes in the observed shape distribution of the radiation.
## 4 Analysis of Experimental Results  
### Simulation Scene Setting  
This study investigates the effectiveness of using MDF-clustering for initializing maneuvering target trajectories in complex environments. The proposed method is compared with the nearest neighbor method [23], the Hough transform method [24; 25], the sliding window method [26], and the intuitive method [27].  
The Nearest Neighbor (NN) method is a classical approach that has been extensively applied in multi-target tracking due to its computational simplicity and efficiency in environments with low clutter. However, its performance deteriorates significantly in high-noise conditions, as it lacks the inherent ability to discriminate between targets and environmental clutter. Similarly, the Hough Transform is recognized for its robustness in detecting straight-line trajectories, yet it encounters difficulties when tracking targets with complex motion dynamics, such as curvilinear or highly maneuverable behaviors. The Sliding Window method, though effective in handling short-term fluctuations in data, fails to sustain accuracy when tracking targets over longer durations, particularly in scenarios with trajectory crossovers or dense clutter. These methods were chosen as baselines in this study because they represent well-established, standard approaches in the field of target tracking, offering a comprehensive framework for evaluating the performance of the proposed MDF-clustering algorithm. By comparing against these traditional methods, the enhancements of the MDF-clustering approach in terms of accuracy, robustness, and adaptability in cluttered and dynamic environments are effectively demonstrated.  
The scene construction for space-based infrared sensor observation and tracking scenarios of airborne targets is conducted using STK 12.1 simulation software. The sensor platform's flight altitude is set at 1000 km, and three targets are placed within the scene based on aerial target motion characteristics [28] and scene characteristics [29]. The initial speeds of the targets in different simulation groups are all 300 m/s, exhibiting various motion patterns such as uniform linear motion, uniform curvilinear motion, uniformly accelerated linear motion, variable-speed linear motion, and variable-speed curvilinear motion. The infrared sensor's sampling frequency is 0.1 s, with a spatial resolution of 25 m. Three scenarios are simulated: target existence with missed alarms, target trajectory existence with crossover, and target presence in cloud clutter background. Poisson distribution [30] is used to add clutter to the sensor output, with data ranging from 3% to 11% based on the false alarm rate. Each scenario comprises 30 datasets per false alarm rate.
## 1 Introduction
Optimal network utilization mechanisms could significantly reduce packet loss and retransmission rate due to switch congestion and link congestion, reduce FCT and improve network throughput. The contributions of this paper are as follows:  
* We introduce a software-defined synergetic sampling and detection approach for long flow and analyze its performance in a data center network environment. * We introduce an optimal network utilization mechanism for long flow scheduling, considering not only link status but also switch status. * We propose an heuristic long flow scheduling algorithm for SonumExperiment results show that Sonum can detect long flows and schedule long flows more efficiently than existing methods. Compared to ECMP and Hedera [12], Sonum performs better in terms of flow completion time and throughput. The remainder of this paper is organized as follows. Section 2 describes the background of this research and Section 3 reviews the related work. Section 4 details the system design of Sonum from two parts: Synergetic long flow detection approach and long flow scheduling mechanism, and then Section 5 presents the simulation experimental results and discussion. At last, Section 6 concludes the whole paper and points out future work.
## 4 Working Principle  
The closely spaced antennas in an MIMO system radiate electromagnetic (EM) waves in free space and on its ground. This causes an increase in mutual coupling. In order to reduce this effect, the FSS unit cell acts as a stopband filter, showing a wide stopband of 33.5 GHz.  
The combination of the FSS stopband filter blocks the unwanted EM waves from the neighboring mounted antenna element in the MIMO system, which leads to isolation improvement in both designs. However, there is more room for improvement. Therefore, a metallic wall beneath the FSS unit cells and the slot on the shared ground plane is implemented. The metallic strip helps in the prevention of air coupling, whereas the ground slot aids in the separation of the elements from each other. This combination further  
Figure 16: Transmission coefficients of Design 2: no wall, FSS wall, FSSs and metallic wall, FSSs, metallic wall, and ground slot.  
Figure 17: Surface current distributions: Design 1 without (**a**) and with (**b**) the proposed mutual coupling suppression structure; Design 2 without (**c**) and with (**d**) the mutual coupling suppression structure.  
enhances the isolation in the MIMO system design. The actual gain in Design 1 before adding the proposed isolation structure is 4.55 dBi, and, after adding the aforementioned structure, it rises to 6.32 dBi. Similarly, the actual gain in Design 2 before adding the proposed structure is 4.99 dBi, and, after adding the FSS wall, it becomes 6.47 dBi.
## 4 Conclusions  
In the current study, a magnetized zeolite showing a high Cs removal efficiency was synthesized from anthracite, and the microstructure, textural properties, and crystalline structure were determined. The Cs adsorption and zeolite recovery properties were determined with a preliminary study of the magnetic separation technology for adsorption of simulated Cs+-polluted soil, and the conclusions are as follows:  
1. The synthesis of the highly efficient magnetized zeolite adsorbent from anthracite is industrially feasible, and the yields ranged from 18 to 53%. The zeolite demonstrated high surface porosity and crystallinity, with tunable particle sizes.  
Figure 11: The Cs adsorption (**a**) and recovery ratio (**b**) under room temperature with simulated Cs-contaminated soil for 24 h of the magnetized zeolites; the Cs removal efficiency compared to the literature [36; 37; 38; 39; 40; 41; 42; 43; 44] (**c**); the schematic figure illustrates the adsorption and recovery of magnetized zeolite (**d**).  
2. The magnetized zeolite had saturation magnetic strengths of 12.055, 5.8257, and 10.722 emu g\({}^{-1}\), which decreased with increasing particle sizes, and the magnetite content was 12% to 15%. The zeolite exhibited robust thermal stability at 75 \({}^{\circ}\)C.
3. The Cs+ removal rate from the simulated soil was as high as 92.82%, with a waste reduction ratio of 1.823 and a product recovery rate of 96.36%; there was no secondary waste generated, and the decontamination cost was low. Overall, the cost-effective magnetized zeolite derived from anthracite demonstrates robust Cs adsorption and good adsorbent recovery efficiency, which shows promise for use in industrial applications in place of state-of-the-art Cs removal strategies.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app13221292/s1](https://www.mdpi.com/article/10.3390/app13221292/s1)]([https://www.mdpi.com/article/10.3390/app13221292/s1](https://www.mdpi.com/article/10.3390/app13221292/s1)), Table S1: XRF measures fly ash composition after alkaline washing (wt%); Table S2: The Cs+ removal ratio and magnetized zeolite recovery ratio; Table S3: The Cs+ removal efficiency compare to literatures.  
Conceptualization, Y.Z. and S.Z.; methodology, X.Z. and Y.G.; investigation; writing--original draft preparation, Y.Z. All authors have read and agreed to the published version of the manuscript.  
The current study was supported by the Governor's Fund of China Institute of Atomic Energy (grant number YZ-163220).  
Not applicable.  
The data will be available upon request. Please contact the corresponding author for further information.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
## 3 Experiments and Evaluation  
This chapter explores and validates the application of the temporal-spatial attention mechanism-based Long Short-Term Memory (HTSA-LSTM) model for vehicle trajectory prediction. Using the NGSIM US101 trajectory dataset for analysis and model training, we demonstrate the superiority of the HTSA-LSTM in handling complex traffic scenarios. Comparisons with benchmark models further validate its advantages in prediction accuracy and generalization capability. Additionally, the model's effectiveness in real-world driving conditions was confirmed using actual vehicle driving data.  
During the dataset segmentation and model training phase, the data were split into training, validation, and test sets with a 7:1:2 ratio. The model inputs consisted of time-series segments, enabling precise future trajectory predictions. The configuration and optimization of model parameters, along with the experimental environment setup, laid a strong foundation for the research. This chapter also provides a detailed list of the model's hyperparameters, serving as a reference for future experiments and comparisons.  
Theoretically, the floating-point operations (FLOPs) for a single forward pass through the HTSA-LSTM model, considering both its spatial-temporal attention mechanism and LSTM parameters, are estimated to be approximately 820,000. Combined with hardware performance characteristics, the theoretical latency is projected to be 4.1 ms. Experimental measurements, which incorporate data preprocessing and communication overhead, reveal an estimated end-to-end latency of 5.6 ms. This result achieves a 10 Hz real-time processing rate, satisfying the system's real-time performance requirements. All experiments were  
Figure 4: HTSA-LSTM model diagram.  
conducted using Python 3.8, CUDA 11.6.2, a 12-core Intel Xeon Platinum 8255C @ 2.50 GHz CPU, an Nvidia RTX 3080 (10 GB) GPU, and 128 GB of RAM.  
In order to prevent overfitting, we adopted early stopping and regularization strategies during training to enhance the model's generalization capabilities. Future work will further investigate the model's generalization performance in larger-scale and more diverse data scenarios, as well as the impact of training data selection on performance.