esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Future Opportunities and Research Directions
### Large-Scale Deployment and Commercialization Prospects  
Although tremendous progress has been made in RFEH at the laboratory scale, transitioning to widespread commercial deployment remains a major challenge. One of the primary hurdles is standardizing the performance metrics and testing protocols for RF energy harvesters across different frequency bands and application scenarios. Collaborative efforts between academia, industry stakeholders, and regulatory bodies are essential to develop global standards that facilitate interoperability and commercialization [130]. Emerging market sectors such as smart homes, smart cities, healthcare monitoring, and environmental sensing represent the initial targets for the large-scale deployment of RF-powered devices. Implementation roadmaps are increasingly focusing on phased pilot deployments in smart city environments, leveraging existing municipal IoT infrastructures to validate RF harvesting technologies under real-world conditions. Testbeds integrating RF harvesters with common IoT platforms such as LoRaWAN and NB-IoT are being proposed to evaluate system interoperability and scalability [128; 131].  
Innovative business models will also be needed, such as energy-as-a-service platforms where energy harvested from ambient RF sources is monetized or shared across IoT ecosystems [131; 132]. Furthermore, hybrid energy-harvesting solutions that integrate RF with solar, vibration, and thermal energy harvesting are expected to gain traction, ensuring a reliable and continuous power supply, even with fluctuating RF availability. Future commercialization strategies include developing modular RF harvesting units with plug-and-play capabilities, enabling easy integration into consumer electronics and industrial sensors. Additionally, industry consortia are exploring certification programmes to guarantee device performance and compatibility, thus building consumer and enterprise trust in RF-powered products [68]. Future research must address cost optimization, miniaturization, and seamless integration into consumer products to unlock the full commercial potential of RFEH.
# The Impact of Optogenetics on Regenerative Medicine
## 3 Biomedical Applications of Optogenetics
### Applications in Post-Stroke Recovery  
Optogenetics allows us to selectively stimulate or inhibit specific populations of neurons and specific brain areas for targeted functional studies or possible rehabilitative therapeutic solutions. The main issues in cell manipulation are related to stimulating specific cells without impacting those surroundings [39]. Optogenetics can ensure high precision, as only those neurons that express ChR2 are selectively activated by blue light. In the post-stroke period, a stimulation on primary neurons of the motor cortex ipsilesional (iM1) can promote functional recovery. Experiments on mice with induced stroke condition, showed improvement in cerebral blood flow, after repeated light stimulation. Good results were also observed in the expression of neurotrophins in the contralesional cortex. Optogenetic stimulation also showed a significant increase in a protein associated with the plasticity growth marker. In conclusion, the future application of the technique to stimulate hemisphere neurons attacked by stroke may be a good solution to promote neurofunctional recovery [40].
## 5 Computational Experiments and Numerical Results
### Numerical Results
#### 5.2.3 Evaluation of Allowed Waiting (Buffer) Time  
The allowed waiting time for each task is referred to as delay tolerance. The delay tolerance length is related to the type of applications requested by tasks, such as web browsing, file transfer, or video streaming. The correlation between the delay and objective value is considered in this case. In general, the short tolerance delay for a task implies less flexibility for task assignment and scheduling. It results in a higher blocking probability than in the case with a longer tolerance delay. However, the results obtained by analyzing in Figure 10 reveal that different waiting times and bursty traffic loads are designed in combination with tolerance length. _SP_, _OI_, and _UP_ exhibit increasing trends in the saturation region in the allowed buffer time from 20 to 80.  
Figure 10: Objective value versus different allowed waiting time.  
Figure 9: Objective value versus different number of tasks in bursty traffic load.  
The reasons for the increasing trend are: (i) The tasks are assigned to servers at a long time slot; (ii) the burst traffics arrive after a long buffer waiting time for aggregation within the long time available for task migration. The longer the waiting time, the higher is the revenue incurred. The delay tolerance creates long time-space by manipulating or scheduling tasks. In summary, an operator should set an appropriate QoS metric for SLA embedded into an orchestrator to recommend user applications with the maximum acceptable delay tolerance.
## 5 Conclusions  
For the characteristics of light weight, high integration, and high gain for LEO satellite communications, this paper proposes a Ku-band dual-polarized shared-aperture multilayer patch phased array based on PCB and CNC technology. The optimal non-uniform subarray scheme reduces the T/R modules and suppresses the sidelobe levels. In addition, the differential-fed SICL and MS architectures are responsible for the low cross-polarization  
Figure 18: Simulated and measured results of the 32 \(\times\) 24 phased array: (**a**) Reflection coefficients of the eight Tx ports. (**b**) Reflection coefficients of the eight Rx ports. (**c**) Isolation between the Tx and Rx ports. (**d**) Realized gains in the normal direction.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref.** & **Structure** & **Element** & \(f_{0}\) & **BW** & **Gain** & **SLL** & **CPL** & **Isolation** \\  & **Number** & **(GHz)** & & **(dBi)** & **(dB)** & **(dB)** & **(dB)** & **(dB)** \\ \hline
[13] &
\begin{tabular}{c} Air-filled \\ waveguide \\ \end{tabular} & \(8\times 8\) & 33 & 26.3\% & 28.5 & \(-13\) & \(-30\) & 36 \\ \hline
[14] &
\begin{tabular}{c} Air-filled \\ waveguide \\ \end{tabular} & \(4\times 4\) & \(12.5\) & \(8.9\%\) & \(26.51\) & \(-13\) & \(-32.9\) & \(47.3\) \\
[16] &
\begin{tabular}{c} Transmitarray \\ \end{tabular} & \(788\times 777\) & \(12.5\) & \(4.2\%\) & \(30.2\) & \(-23\) & \(-20\) & \(>\)39.5 \\
[19]* &
\begin{tabular}{c} patch \\ metal \\ \end{tabular} & \(16\times 16\) & 30 & \(13.3\%\) & 28 & \(-13\) & \(-22\) & n.a. \\ \hline
[26] & patch & \(4\times 4\) & 28.7 & \(19\%\) & \(12\) & \(-8\) & \(-22\) & 15 \\
[27] & patch  
and high gain features. Octagonal elements with Via-loaded rings are adopted for dual polarizations. To validate the beam-scanning characteristic, 32 \(\times\) 24 phased arrays with T/R phase-shifting modules are fabricated. Measured flexible beam scanning, low pointing error, and stable radiation performances indicate the proposed antenna array is a good validation for phased array SATCOM.  
Conceptualization, Y.H.; methodology, X.L.; software, J.Z.; validation, Z.Q., F.L. and H.J.; formal analysis, Z.Q.; investigation, Y.H.; resources, X.L.; data curation, J.Z.; writing--original draft preparation, Y.H.; writing--review and editing, H.Z., X.X. and X.G.; visualization, Z.Q.; supervision, X.L.; project administration, X.L.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the project of 62201035 from the National Natural Science Foundation of China (NSFC).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy considerations.  
The authors would like to thank the Beijing Engineering Research Center of EMC and Antenna Test Technology for support with the tests in the anechoic chamber.  
The authors declare no conflicts of interest.
# Secure Control of Linear Controllers Using Fully Homomorphic Encryption
## 4 Control System with Encrypted Controller
### Comparison of Four Schemes  
In terms of safety and efficiency, we contrast the scheme proposed in this study with a number of traditional schemes that have been previously offered. For their specific processing time, we refer to the literature [16], and the average processing time in this paper will be given in the next section.  
It can be seen from Table 1 that in the previous partially homomorphic encryption schemes, the security of data transmitted in the channel during the process from sensor to actuator and data in the controller cannot be ensured at the same time. This suggests that security is not ideal. Subsequent BGN encryption schemes can well avoid this problem. The BFV encrypted controller scheme proposed in this paper and BGN encrypted controller can ensure that the data inside the controller and in the transmission channel are ciphertext. In terms of efficiency, the two partially homomorphic encryption schemes are efficient, and BGN is relatively inefficient. But this time can also be suitable for the control system's sampling cycle. However, by accelerating precomputation, the scheme using the BFV technique suggested in this study achieves an efficiency that is almost identical to partially homomorphic encryption. On the other hand, considering the homomorphic operation inside the controller, the homomorphic multiplication of BGN can only be performed once, but partially homomorphic encryption and the BFV scheme proposed in this paper do not have this defect.  
Through the above analysis, the BFV scheme is excellent in both security and efficiency.
## 2 Hydraulic Servo Shaking Table
### Dynamic Model  
The power mechanism of the hydraulic servo shaking table includes servo valve, hydraulic cylinder, and load, which can be simplified as symmetric hydraulic cylinder controlled by four-way valve. The schematic diagram of the power mechanism is shown in Figure 2, where \(p_{s}\) and \(p_{b}\) are the supply/return pressure, \(q_{i}\) and \(q_{o}\) are the input/output oil flow of the cylinder, \(p_{i}\) and \(p_{o}\) are the input/output pressure, \(V_{i}\) and \(V_{o}\) are the input/output oil volume, \(m\) is the mass, \(x_{v}\) is the valve position, \(B\) is the piston's viscous damping coefficient, \(y\) is the load position, \(A\) is the effective area of the cylinder. The physical features of shaking table's hydraulic system can be described by the following three equations.  
The linearized flow equation of the servo valve can be expressed as  
\[q_{L}=K_{q}x_{v}-K_{c}p_{L} \tag{1}\]  
where \(q_{L}=(q_{i}+q_{o})/2\) is load flow, \(K_{q}=\partial q_{L}/\partial x_{v}\) is flow gain, \(p_{L}=p_{i}-p_{o}\) is pressure burden, \(K_{c}=\partial q_{L}/\partial p_{L}\) is flow-pressure coefficient.  
The flow continuity equation of the cylinder is  
\[q_{L}=A\frac{dy}{dt}+C_{tp}p_{L}+\frac{V_{t}}{4\beta_{e}}.\frac{dp_{L}}{dt} \tag{2}\]  
where \(C_{tp}\) is leakage coefficient, \(V_{t}=V_{i}+V_{o}\) is the total volume of the actuator's two chambers, \(\beta_{e}\) is the effective bulk modulus.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Parameter** \\ \hline Piston diameter & 40 mm \\ Rod diameter & 35 mm \\ Stroke & 25 mm \\ Supply pressure & 8 Mpa \\ Frequency range & 0–50 Hz \\ Maximum velocity & 1.4 m/s \\ Maximum acceleration & 10 m/s\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main parameters of shaking table.  
Figure 2: Schematic diagram of the hydraulic system.  
The force balance equation is  
\[F_{g}=A(p_{i}-p_{o})=Ap_{L}=m\frac{d^{2}y}{dt^{2}}+B\frac{dy}{dt}+F_{L} \tag{3}\]  
Performing Laplace transform from Equation (1) to Equation (3), the transfer function \(G_{p}(s)\) from the valve displacement \(x_{v}\) to the hydraulic cylinder displacement \(y\) can be expressed as  
\[G_{p}(s)=\frac{Y}{X_{v}}=\frac{K_{q}/A}{s\left(\frac{s^{2}}{\omega_{h}^{2}}+ \frac{2\zeta_{h}}{\omega_{h}}s+1\right)} \tag{4}\]  
where \(\omega_{h}=\sqrt{\frac{4\xi_{p}A^{2}}{mV_{r}}}\) is load inherent resonant frequency, \(\zeta_{h}=\frac{K_{c}}{A}\sqrt{\frac{\xi_{p}m}{V_{r}}}+\frac{B_{c}}{4A}\sqrt{ \frac{V_{r}}{\xi_{p}m}}\) is load damping ratio.
## 2 Study Area and Data Sources
### In Situ Data  
#### 2.2.1 Irrigation and Meteorological Data  
An automatic weather station is installed near the fields of study. The weather station measures continuously the air temperature, solar radiation, relative humidity, wind speed and precipitation amounts with a time step of 30 min.  
For the flooded irrigation fields, water is brought to the fields by a network of concrete channels with different sizes and flow rates. The amount of irrigation over each plot is determined from the flow rate of the canal knowing the opening duration of the channel through surveys in the field. For the drip-irrigated fields, irrigation amounts are measured by flowmeters. The owner of D1 and D2 uses the FAO-56 [40] approach to determine the irrigation amount and timing. A flowmeter was installed downstream of a valve that controls the distribution of water in the drip pipes [43]. On average, irrigation lasts about 105 min corresponding to an amount of 12.5 mm per event. At the beginning of the season, irrigation was applied every day during the first week of December (about 9 days after sowing just before emergence of wheat) with a total of 182.74 mm over D2 and 140.76 mm over D1. Such large amounts are generally applied by the farmers in the region in order to store water in the soil deep layers, to facilitate crop emergence and to avoid soil crusting [9; 35]. Afterwards, events are programmed every two days (each period of irrigation) with amounts ranging from 7 to 22 mm for 2016-2017, and from 1.2 to 22 mm in 2017-2018. Irrigation was stopped around mid-April during 2016-2017 while no irrigation was applied after the beginning of February and March over D1 and D2, respectively, for the 2017-2018 season because of the wetter conditions of this season compared to 2016-2017. Concerning the flooded fields, four irrigation events were recorded over F1 with an amount of 60 mm each while over F2, 8 events of 64 mm are repartitioned on the period between the end of December and the end of April. For F3, the flowmeter was installed on January 21 st while wheat already emerged at this date. 8 events were recorded form last January to the beginning of May with different amounts ranging from 23 to 50 mm. General information on irrigation practices over the irrigated fields (D1, D2 and F1-F3) are summarized in Table 1 in the Appendix A.
## 2 Materials and Methods
### Laboratory Measurements
#### 2.2.3 Data Processing  
In order to determine the amplitude reflection of the thin bed, measured datasets were processed in ReflexW software, version 9.5.6, to obtain a single stack trace from each record. Time calibration and bandpass filters were applied to the data before stacking. After stacking, all the data were merged so that all the results could be visualized and compared on a single plot. Figure 5 illustrates the final results of the mentioned processing steps applied on datasets that were measured to compare the air-filled fractures (traces 1-3) with the resin-filled fractures (traces 4-5). Figure 6 shows the results for measurements on the air-filled fractures (traces 1-22) and the PVC-filled fractures (traces no. 23-44). Figures 5 and 6 were produced by applying a global normalization so that an equal gain is applied to each trace. Thus, the reflection amplitudes generated by the thin bed can be visually compared to obtain a preliminary qualitative result.  
From the preview of the laboratory data, we can notice that GPR has observed the thin layer, although weakly, even with the smallest thickness. As the fracture aperture increases, the amplitude increases, and we can also appreciate slight amplitude differences at equal thickness suggesting that air-filled fractures are more reflective than resin-filled or PVC-filled fractures. These are encouraging observations, but the more substantial issue is whether these findings demonstrate that GPR can distinguish between the air-filled thin layers and the resin-filled or the PVC-filled thin layers with the sensitivity that is needed to apply the method to real blocks with unpredictable fracture geometries and thickness values. To prepare the theoretical background for interpreting the data from the laboratory test, in the next section we apply the thin bed analytical model to predict the expected amplitudes of the reflections.
## 1 Introduction
The results of the proposed network can be applied to lane-keeping, steering control, evading obstacles, collision prevention, speed control, and creating a driving path for autonomous driving and driving assistance. According to many recent studies, it is predicted that the development of autonomous driving technology and the spread of autonomous vehicles in the near future will lead to more advanced traffic safety standards, reduced environmental pollution, and improved social equality [22; 23]. It is also expected that the annual number of self-driving cars will increase by 50 percent by 2030, global trade volume by 70 percent, and that the current number of cars will double to 1.2 billion by 2050 [23]. Accordingly, the purpose of this research is to enable safer and more reliable autonomous driving. As input into the proposed network, a three-dimensional voxel-based point cloud obtained by a 3D LiDAR is first converted to a top view image of two-dimensional pixels. A pixel requires less storage space than a voxel and is more advantageous than a voxel in terms of the number of required operations [20]. The network for simultaneous segmentation and detection proposed in this study is trained using the point cloud top view image as input, and the training results can be used to obtain the segmentation and detection results. The network has a parallel structure of a segmentation branch and a detection branch. The segmentation branch transforms and expands the network (LoDNN) [24] for detecting the road, while the detection branch consists of a CNN that we designed. The segmentation and detection performance and the inference speed of the proposed network are investigated through training experiments. This paper is structured as follows. Section 1 introduces the purpose and method of the research. In Section 2, the input data used for training and the configuration method are proposed. In addition, then, we explain the structure of the proposed network, and Section 3 introduces the experiments used for verifying the performance of the proposed network and the relevant results. Finally, we provide conclusions in Section 4 of this paper.
## 3 Design of FAS Structures  
Due to the vast search space in automated search, each new structural connection in the generated subnetwork requires a considerable number of iterations, resulting in extremely high time complexity for the algorithm. For instance, training a NASNet (RL-based) model on the ImageNet dataset demands approximately 1800 PetaFLOPs of computational resources [20]. Given that a single RTX3090 GPU has a peak computational performance of 36 TFLOPs, this process would require around 600 GPUs operating for 4 days. Equivalently, a single GPU would take approximately 2400 days to identify the optimal architecture. Such an extensive training duration significantly hampers the reproducibility and deployment of this method, particularly in scenarios requiring rapid iteration and practical constraints. Therefore, practical automatic architecture search methods require manual restriction of the search space by defining a specific search space, allowing the machine to search all candidate networks within this space through bilevel optimization and weight sharing in a single pass. However, this poses challenges for memory optimization and is not suitable for small-scale single-GPU training. This study proposes the Fast Architecture Search (FAS) method, which introduces a one-stage anchor-free object detection network. Compared with anchor-based object detection networks, anchor-free object detection networks eliminate the anchor-matching step, thus significantly improving search efficiency and reducing memory consumption. Meanwhile, this study optimizes the NAS-FPN search space, which includes four feature fusion search methods: forward, bottom-up, top-down, and residual fusion search [21]. Nodes at the edge of the search space can either perform feature fusion with preceding nodes or employ residual connections to skip ahead and continue feature fusion.  
If tensor dimension mismatch occurs during the fusion process, it can be automatically resolved by \(1\times 1\) convolution and \(3\times 3\) convolution for dimension matching [22].  
Simultaneously, loss is calculated during each search to confirm the optimal feature fusion path. After optimization, a relatively superior feature fusion path can be found in just 3 GPU-days, making this method more easily applicable to practical projects.  
Figure 1 illustrates the Fast Architecture Search space based on the feature pyramid, where dashed arrows represent all candidate feature fusion connections within the search space. The feature maps extracted from Inception-ResNet-SPP first undergo a fixed top-down feature fusion before entering the search space.
## 5 Discussion
### Limitations and Future Work  
Our method and experiments had limitations, which encourages us to modify our design and conduct more user studies in the future. Firstly, we utilized the virtual reality method to perform the outdoor mixed-reality experiment. However, the virtual reality scene we implemented for the experiments was not mirrored and realistic enough to fully simulate the physical environment, which affected our results. Thus, we will implement a more realistic set for outdoor mixed-reality experiments and eventually conduct field experiments in the actual outdoor environment to improve the reliability of the results. Secondly, we did not consider classifying different features in the experiments, such as age, gender, and profession. Moreover, some participants also suggested that they expected to interact with the minimap during navigation more diversely, which inspired us to consider navigation interactions in mixed-reality devices to provide more interaction with digital information during navigation.