esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Equipping satellites with a series of high-precision frequency references is essential; however, even advanced active hydrogen masers can often be too heavy and expensive for the current satellite payload constraints. Moreover, in geostationary Earth-orbit communication satellites lacking atomic clocks, onboard oscillators can degrade the performance of time-frequency transmission methods. To address these challenges, this study proposes a novel phase-locked transponder that leverages [PERSON]'s synchronization theory and real-time carrier-phase compensation to improve the transmission performance of satellite frequency transfer systems while mitigating the noise from onboard satellite oscillators. Notably, this requires only simple modifications to the existing transponder structure. By replicating the high-precision atomic frequency standards from ground stations to satellites, the proposed system achieves enhanced frequency synchronization without additional onboard clocks. The feasibility of the satellite-to-ground link was validated through both a theoretical analysis and an experimental verification. Specifically, ground experiments demonstrated a reproducibility of 6.33 ps (1\(\sigma\)) over a 24 h period, with a long-term frequency stability of 3.36 \(\times\) 10\({}^{-16}\) at an average time of 10,000 s under dynamic conditions, showcasing the potential of this approach for advanced frequency synchronization. This paper presents a cost-effective and scalable solution for enhancing frequency synchronization in geostationary satellites, improving communication reliability, supporting advanced scientific and navigational applications, and enabling the development of high-precision, space-air-ground integrated time-frequency synchronization networks.  
carrier phase; [PERSON] synchronization; frequency transfer; geostationary satellite; satellite transponder [[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).
## 5 Conclusions  
This paper analyzed the relationships of the spatial-temporal variation characteristics of multiple factors with NDVI in the Loess Plateau during S1, S2, and S. Some interesting results were obtained: (i) the growth rate of vegetation coverage increased in the Loess Plateau over the past 15 years due to reforestation, which is approximately 4.5 times higher than that from 1982 to 2000 and 29.5 times than that in the whole of China over the past 34 years; (ii) anthropogenic activities, such as accelerated urbanization and excessive mining easily caused vegetation degradation, especially in area I of Loess Plateau; (iii) The increase of vegetation in the Loess Plateau is associated with the change of extreme weather events and was embodied by a slowing of the decrease rate of extreme temperature cold indices as well as the slowing of increase rate of extreme temperature warm indices; (iv) the regional climate change shifted toward warm and dry over Loess Plateau; and (v) atmospheric water vapor or PWV was strongly correlated with NDVI over Loess Plateau, as confirmed by Pearson correlation coefficients reaching 0.94.  
Although some novel findings were discovered in this paper, some results involve qualitative data, and quantitative results could not be obtained because of some limitations. For example, vegetation growth revealed an inhibitory effect on the change in extreme climatic indices, but the specific contribution rate of vegetation growth to the inhibitory effects of extreme climatic indices was not determined. In future research, such problems may be resolved by establishing the corresponding quantitative models.  
[PERSON]; [PERSON] participated in the design of this study, and they both performed the statistical analysis. [PERSON], [PERSON], and [PERSON] carried out the study and collected important background information. [PERSON]; [PERSON] processed the corresponding data. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (41904036).  
We thank [PERSON] and [PERSON] of NASA GSFC for making available to the GIMMS NDVI3g dataset. China Meteorological Administration (CMA) and ECMWF are also thanked for providing the corresponding meteorological and PWV data.  
The authors declare no conflicts of interest.
###### Abstract  
Currently, with the continuous advancement of network and communication technology, the amount of data carried by the optical network is very huge. The security of high-speed and large-capacity information in optical networks has attracted more and more attention. Quantum key distribution (QKD) provides information-theoretic security based on the laws of quantum mechanics. Introducing QKD into an optical network can greatly improve the security of the optical network. In order to reduce the cost of deployment on QKD infrastructure, quantum signals in QKD and classical signals in optical networks are multiplexed in the same fiber by wavelength-division manner. Moreover, due to the limited wavelength resources in an optical fiber, time-division technology is adopted to construct different kinds of channels in QKD system for efficient utilization of wavelength resources. Under such situation, how to satisfy the security requirements of service requests and complete the efficient scheduling of multi-dimensional resources, i.e., wavelengths and timeslots, is a challenging problem. This paper addresses this problem by considering multi-dimensional routing, wavelength, and timeslot allocation (RWTA) in short-distance quantum key distribution optical networks (QKD-ON), in which any two nodes can directly establish a quantum channel, and the maximum distance between any two nodes is less than the distance that can carry out point-to-point quantum key distribution process. While accommodating services with security requirements in QKD optical networks, to avoid the wavelength time-slot fragmentation caused by the constraints of wavelength consistency and time-slot continuity, we propose a time-window-based security orchestration strategy as well as relative-loss of time continuous compactness based RWTA strategy. We conducted the simulations under various scenarios, e.g., different key updating periods and different distributions on wavelength resources, etc., and the results show that the proposed strategy can achieve better performance compared with the baselines in terms of key success rate, key-updating delay, and blocking probability.  
optical network; RWTA; quantum key distribution +
Footnote †: journal: Applied Sciences
## 4 Taxonomy of Cooperation in UASNs
### Game Theoretic-Based Cooperation
#### 4.3.3 Evolutionary Game  
In [71] an Evolutionary Game-Theoretic (EGT) approach is proposed, where they study trust decision and its dynamics to illustrate the evolutionary process of sensor nodes (SNs) in selecting their action and approaches that can lead the SNs to choose the action trust as their final behavior. The authors in [51; 83; 84] proposed an intension recognition-based method that employs a repeated dilemma special context and evolutionary game, where the internal dynamic trust of intention recognizer and opponents were assessed. Moreover, they use direct interaction method of the past to predict their opponents' next move, and as a result they are able to prevail on the famous cooperative strategy of the repeated dilemmas. A model of adaptive trust on the basis of Request as well as Authentications (ATRAM) was proposed in [50; 85], where a game theory and the information history of interaction within peers is employed. The proposed scheme guarantees secure data transmissions in a mobile peer-to-peer network regardless of whether each of the peer node can have access to the trust information about the peer and risk information about the other peer or not. The scheme has been further categorized into four types of models that are called game-based, trust-based, risk-based and role-based. In [86; 87], an analysis of game theory which defines the factors of adaptive and cooperation development using EGT is proposed. It utilizes the assumption for zero-state information and non-zero state information between peers (i.e., trust value and risk). The designed model ensures that the priority is given to requested peers for a connection with a peer of resource. However, due to trade-off in terms of performance and security aspects, this model does not adequately address reliability of data packet among nodes. A comparative table of equations applied in evolutionary games is presented in Appendix D. Moreover, in this class of games rational behavior of a player can be possibly combined with pure strategy for enhancing the characteristics of their population (authority or group) and since players are not rational players this method is not suitable for UANSs cooperation.
## 3 Risk Assessment-Based Dynamic Access Model
### Implementation of The Proposed Model on XACML  
Based on eXtensible Access Control Markup Language (XACML), this paper implements the proposed dynamic access control model. As shown in Figure 3, the model is based on Policy Information Point (PIP), Policy Decision Point (PDP), and Policy Enforcement Point (PEP). PIP is responsible for encapsulating the attribute information of the request _req_, and PDP is used for decision-making. The specific process is as follows:  
1. The user makes an access request \(req\{V,X,Y\}\), where \(V,X,Y\) represent the subject, the action, and the resource, respectively;
2. The PEP submits the user request \(req\) to the PIP through the XACML context syntax;
3. PIP queries the relevant attribute engine to obtain the context attribute information of the current _req_ (IP address, MAC address, time, etc.);
4. The attribute engine returns the property value of _req_;
5. PIP adds the context attribute value to the _req_ request and passes it to the PEP through the XACML context;
6. PEP submits the reconstructed \(req\{V,X,Y,Z\}\) to the policy rule detection module and risk monitoring module of the PDP;
7. The policy rule detection module performs static rule judgment on the request according to the policy rule and obtains the judgment result \(M(z_{j})\). The risk assessment module adopts the method proposed in Section 3.1 to evaluate the risk of the access request and obtains the risk assessment result \(R(req_{i})\);
8. The risk assessment detection result and the policy rule detection result are submitted to the comprehensive assessment module, which performs a comprehensive assessment of the risk assessment result and the rule assessment result, and gives the comprehensive assessment result \(H(req_{i})\);
9. The decision module determines whether the decision for the current request \(req\) is _allow_ or _deny_ according to the value of \(H(req_{i})\).  
Under the above framework, when a cloud user makes an access request to a resource, it needs to go through the above 9 steps. While the request is judged by the policy rules, the environment of the requesting subject must pass the risk assessment. Moreover, the risk assessment algorithm in this model adopts the detection strategy of fuzzy AHP, which has less resource overhead and more accurate assessment results. Therefore, compared with the traditional access control model and other security models with risk detection, this framework can more accurately assess risks and protect the security of cloud resources.
# ReZNS: Energy and Performance-Optimal Mapping Mechanism for ZNS SSD
## 4 Evaluation
### Experimental Setup  
For evaluation, we used a 10-core, Intel(r) Xeon(tm) Gold 5215 @ 2.5 GHz server and 40 GB memory, running Ubuntu 20.04.6 LTS (64-bit) with Linux 5.16 kernel. We built ReZNS on NVMeVirt [28], which is a software-defined NVMe SSD device and basically supports the ZNS interface like a real ZNS SSD. Note that NVMeVirt runs based on RAM that has significantly better performance parameters than an SSD, but it never affects the experimental results because the performance metrics are measured using pre-defined latencies, such as read, write, and erase. Thus, the closer the pre-defined latencies are to those of the real SSD, the more accurate the results will be. In the configuration for ZNS SSDs, we set the size of each zone to 64 MiB and erase latency at zone granularity instead of block granularity. We also configure some metrics about latency and energy consumption to NVMeVirt [28; 51]. The detailed configurations in NVMeVirt are listed in Table 1. Finally, we mounted F2 FS [50] as a default file system on NVMeVirt [28]. To confirm the effectiveness of ReZNS, we compared it with the baseline, which is a default mapping mechanism for ZNS SSDs. For a comprehensive comparison, we used FIO [52], Filebench [53], and YCSB [29] to evaluate the performance impact and energy efficiency. We believe that the benchmarks are sufficient to cover all scenarios in datacenters, including write-heavy and read-heavy workloads based on multi-threading.  
Figure 2: An example of ReZNS.
# Implementation of Robots Integration in Scaled Laboratory Environment for Factory Automation
## 3 Mobile Robot and Industrial Robot Design
### Industrial Robot and Environment Setup  
The industrial robot system consists of the ABB IRB 140 robotic arm [36], dedicated end effector and a corresponding IRC5 controller. The external PC with ROS application is used to instruct the robot arm movements and to integrate the robot arm with the rest of the system. In order for the robot to know where each crate should be placed, an RFID sensor is used to identify each industrial crate. The setup of the industrial robotic arm and its environment was initially designed and simulated in the RobotStudio programming environment [37]. The developed software for simulation was afterwards used to control the real robot.  
Within the experiment setup, pallets with crates that the industrial robotic arm needs to sort are placed around the robot (Figure 6). There are six pallets placed around the robot to perform the palletizing task. Four out of six pallets are \"output\" pallets that should be transferred out after sorting is complete. The \"input\" pallet with unsorted industrial crates, brought by the mobile robot, is located in front of the robot arm and can contain up to 24 crates. In addition to the \"input\" pallet, the transitory pallet is set up to temporarily accommodate crates that do not currently have a defined location, as well as crates that have not been successfully identified.  
Figure 4: Experimental setup with Vicon motion capture and reflective markers for evaluation. RTAB-SLAM localization in the indoor environment.  
Figure 5: Comparison of RTAB-SLAM localization against trajectory obtained with motion capture system.  
After the spatial arrangement of the station elements was established and the algorithm was confirmed in the simulation environment, the system was created in the real world. The physical setup of the system in the laboratory is shown in Figure 7.  
For the task of manipulating industrial crates, a specific gripper was designed and prototyped. It is a two-finger electro-pneumatic gripper. The end effector consists of two claw-like fingers. They are designed in such a manner as to successfully reach within the openings on the sides of industrial crates. If the crates are not accurately positioned on the  
Figure 6: System setup and the layout in the RobotStudio software environment.  
Figure 7: Setup of an industrial robot station with surrounding EURO pallets in the laboratory.  
pallet, to compensate for this misalignment, there is an elastic link between the fingers and the base of the gripper.
# Shuffle Model of Differential Privacy: Numerical Composition for Federated Learning
## 7 Applications to Federated Learning
### Building Deep Learning Models  
Deep learning models, such as neural networks, are powerful tools for learning complex patterns from data. In federated settings, where data are distributed across multiple clients with privacy constraints, training deep learning models requires a collaborative approach that avoids sharing raw data. Federated learning enables this by allowing each client to train a local model on their data and then share model updates (e.g., gradients) with a central server. The server aggregates these updates to improve a global model, iteratively refining it over multiple communication rounds.  
To preserve privacy and ensure robustness, the model building process can be transformed into multiple rounds of gradient aggregation with clipping and randomization. After computing local gradients, each client applies gradient clipping to bound the influence of any single update: \(\hat{g}^{(k)}=\text{Clip}(g^{(k)},C)\), where \(g^{(k)}\) is the local gradient and \(C\) is the clipping threshold. Clients then randomize and contribute their clipped gradients using the shuffle model of DP. The central server observes privatized gradients from the shuffler, and then aggregates them to update the global model: \(g=\frac{1}{K}\sum_{k=1}^{K}g^{(k)}\). By iterating this process, the federated system collaboratively trains a deep learning model while protecting individual client data.  
For each gradient aggregation query, one can adopt the SOTA shuffle protocol for numerical vector summation from [19]. In their protocol, each message is computed from \(s\) uniform-randomly selected bits from \(d\) dimensions, and each bit is flipped with probability \(1-r\). The corresponding \(p\), \(\beta\) and \(q,n\) parameters can be computed using Theorem 1.
## 3 Experiment and Discussion
### Results and Analysis
This local-global collaborative feature learning mechanism can more comprehensively learn the hierarchical semantic information of clinical notes compared to the single-granularity modeling method, thereby significantly improving the prediction performance. Experimental results show that the multi-granularity modeling strategy that considers both entity details and contextual relationships is more in line with the cognitive process of clinical diagnosis. Thanks to the designed sentence-level self-attention mechanism for effectively learning the joint expression of features, the performance of DESAM-cp is second only to the model in this paper, which not only proves the importance of learning global feature interdependence, but also provides a theoretical basis for the design of our model. However, the performance of DESAM-cp did not surpass the model in this paper. We speculate that this is because the model in this paper constructs an \"entity-sentence\" multi-granularity feature learning mechanism, which not only inherits the sentence-level modeling advantages of DESAM-cp, but also introduces an entity-level attention mechanism to more completely simulate the cognitive process of clinical diagnosis. Secondly, compared with DESAM-cp, this paper also introduces a dynamic memory component, which enables the model to simultaneously model the patient's current clinical manifestations and historical disease evolution characteristics. This temporal perception ability is more in line with the real clinical decision-making process. In addition, due to the heterogeneity of multi-modal data and the complexity of its parameter space, it is difficult to effectively improve the efficiency of feature fusion through simple heterogeneous concatenating. This paper designs the MacLN to learn the expression of heterogeneous inputs, which effectively alleviates the challenges of multi-modal data fusion. At the same time, MacLN realizes the continuous tracking and integration of historical features through a dynamic memory mechanism, and encodes the temporal dependency into the feature representation, simulating the doctor's progressive diagnostic thinking and verifying the rationality of MacLN. In addition, the DGM model designed in this paper uses a feature selection mechanism based on attention weights, which enables the model to ignore noise information, automatically focus on diagnosis-related features, and adaptively update memory information. The designed models work together to enable the model to fully understand the patient's complete medical history, accurately integrate heterogeneous features, and capture deep cross-modal correlations, thereby improving the accuracy of clinical predictions.
## 3 Experimental Result Analysis
### Failure Characteristics  
Figure 2 shows the damage characteristics. Under each strain rate, due to the end effect, the secondary cracks inside the rock sample first crack in the central region. With the increase in the loading stress, secondary cracks extend along the internal weak surface of the rock sample to the upper end face. According to this study [23], under high stress conditions, secondary cracks accelerate their propagation and penetration to form shear main cracks. Under uniaxial compression, the propagation path of the main crack shows nonlinear characteristics, and there is a significant correlation between its geometric morphology and the strain rate. When the stress reaches the peak stress, the elastic strain accumulated inside the rock sample can be released sharply, and the rock sample undergoes shear slip failure along the main crack path, as shown in Figure 2. The particle size distribution of the destroyed rock blocks varies under different strain rates. Rock samples with low strain rates mainly consist of more large-sized complete rock blocks, while rock samples with high strain rates retain more small-sized fragmented rock blocks. The difference in this failure mode stems from the variation of secondary crack propagation characteristics. The main reason for this is as follows. When the strain rate is low, the propagation rate of secondary cracks exceeds the threshold of the internal defect response of the rock sample. The main cracks rapidly penetrate along a single main path but fail to promote the formation of bifurcated secondary cracks. On the contrary, high-rate loading provides sufficient elastic strain energy for the bifurcation evolution of secondary cracks, which is conducive to the formation of complex reticular secondary cracks and promotes the rupture of rock samples along multi-path crack surfaces.  
Figure 1: Strength and deformation characteristics: (**a**) stress-strain curve; (**b**) relationship between peak stress, elastic modulus and peak strain and loading rate.  
Figure 2: _Cont._
## 3 On-Body Communication and Connectivity
### Transceiver Implementations and Architecture Considerations
More recently, the authors of [35] designed and implemented an IEEE 802.15.6-compliant transceiver building upon the HBC physical layer (as also done in [32]). The analog front-end consists of a transmitter (transmit filter and output electrode) and a receiver (gain stage with automatic gain control and a hard decision detector). The digital part is implemented on a Xilinx Virtex 5 FPGA. The design reaches 763 kbps (bit error rate of 0.21). The dynamic power consumption of the design is 4.5 nJ/bit with a spreading factor of 8 (it varies approximately linearly with the spreading factor). The essential properties of these implementations are summarized in Table 2. As can be seen, implementations have been proposed for either NB or HBC PHY; typically those that support NB PHY also support BT-LE and/or Zigbee The achieved data-rates are either spot-on with the standard, or slightly below or above. However, it can be noted that the IEEE 802.15.6-compliant chipsets are yet to be commercially widely available. This section focuses on on-body communication. In what follows, we carry on the discussion with the next communication tier, i.e., body-to-body communication.