esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Photonics for IoT, RoF, and Distributed Fog-Cloud Computing: A Comprehensive Review
## 6 Key Enablers of High-Speed Networks Technology
### Wavelength Division Multiplexing (WDM)
The 3D view (right) shows the structural layout, highlighting the doping and metal layers. Red indicates p\({}^{+}\)-doped silicon, orange is p-doped silicon, yellow is n-doped silicon, dark yellow is n\({}^{+}\)-doped silicon, gray regions correspond to metal electrodes, and light blue represents the buried oxide (BOX) layer. Figure 7: WDM transmitter and receiver demonstrations based on silicon photonic integrated circuits. (**A**) Layout of a silicon PIC with integrated arrayed waveguide grating (AWG) and photodetectors for a 40-channel WDM receiver. The inset shows a 40 Gb/s optical eye diagram. (**B**) Layout of a DWDM transmitter PIC integrating 10 x 25 Gb/s modulators with an AWG. The inset displays the eye diagrams of all ten output channels. Figure taken from [75]. which is strategically designed to optimize the overlap between carrier depletion regions and the optical mode. Panels (c) and (d) display simulations of the transverse electric (TE) mode and electric field distribution within the waveguide, respectively, demonstrating effective modulation under reverse-bias conditions. Finally, panel (e) provides a finite-difference time-domain (FDTD) simulation depicting the coupling region of the microring modulator to a bus waveguide for monitoring. Figure 10 presents a schematic illustration of a micro-ring resonator (MRR)-based transceiver array employed in co-packaged optics (CPO). The depicted transceiver integrates drivers, micro-ring modulators (MRMs), and receivers with an MRR-based demultiplexer (DEMUX) and thermal tuning mechanisms. This architecture addresses key performance constraints such as bandwidth, energy-efficiency, and compactness, that are essential for future data center optical interconnects. Hybrid and monolithic integration methods have demonstrated data rates of up to 112 Gb/s per channel, achieving high energy-efficiency despite the challenges posed by parasitic capacitances and integration complexities. Thermal tuning is critical to stabilize resonant wavelengths against variations induced by manufacturing processes and operating conditions [58].
# Optimal Deployment of Vector Sensor Nodes in Underwater Acoustic Sensor Networks
## 2 Communication Performance Based on Single-Vector Sensors  
### Summary of Previous Research  
Recently, [PERSON] and [PERSON] [13] suggested a methodology to maximize communication coverage while maintaining connectivity among the sensor nodes for a given number of nodes. It was assumed in most previous studies that the communication ranges of all underwater sensor nodes were identical. In contrast, the algorithm proposed by [PERSON] and [PERSON] finds the best locations of the sensor nodes based on a communication PS, which represents the spatial distribution of the relative performances in an underwater communication system. The algorithm is composed of four subcategories: (1) underwater acoustic channel modeling, (2) communication performance estimation, (3) the communication PS construction, and (4) optimal nodal placement. In their study, the targeted area in the East Sea of Korea was 22 km \(\times\) 22 km and was divided into 100 grid points. The underwater acoustic channel impulse responses, as a function of the distance between the source and receiver, were simulated using the BELLHOP ray-tracing model with eight azimuthal angles for each grid point in the targeted area. The acoustic parameters for the targeted underwater area (such as bathymetry, water sound speed profile, and geoacoustic parameters for surficial sediments) are required to produce accurate BELLHOP model outputs.  
We assumed a SIMO system consisting of sensor nodes in which each receiver was a 3-channel vertical line array. The communication signals received at every sensor array were simulated through convolution of the original binary phase-shift keying (BPSK) communication sequence with the estimated channel impulse responses. Then, the signals were decoded using multichannel combining and adaptive decision feedback equalization (DFE), and then the bit error rate (BER) was estimated. The communication range was defined as the range corresponding to a BER of 2%, which was used as a tolerance criterion for the sensor network. The communication ranges were averaged for the eight azimuthal angles and then interpolated for all grid points to create the communication PS. Lastly, based on the PS map, the VFPSO method [25] was applied to obtain the optimal deployment scheme of the underwater sensor nodes. The details of the optimal deployment procedure are given in Reference [13].
## 3 Results and Discussion
### Comparison of the Test Results  
From calculations in Equations (15) and (16), it can be concluded that one day for the sliced 10 mm thick specimen corresponded to 25 days for the 50 mm thick original sample. Figure 8 shows the graphs derived from the scaling factor applied slice-accelerated test and the long-term ageing test. When the slice test results were converted into thermal resistance for the original sample thickness during the specific period, graphs moved in both X-axis and Y-axis directions, as seen in Figure 9. This indicates that the thermal performance of material changes over time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multicolumn{5}{c}{**Thermal Resistance [m\({}^{2}\cdot\)K/W]**} \\ \cline{2-6}  & **Initial (R\({}_{\text{i}}\))** & **After 25 Years (R\({}_{\text{t}}\))** & **Change \% (1 - \(\frac{\text{R}_{\text{i}}}{\text{R}_{\text{t}}}\))** & **25 Years in** & **Change \% (1 - \(\frac{\text{R}_{\text{i}}}{\text{R}_{\text{t}}}\))** \\ \hline \#3 & 0.486 & 0.309 & \(\approx\)37\% & 0.341 & \(\approx\)30\% \\ \#4 & 0.486 & 0.288 & \(\approx\)41\% & 0.300 & \(\approx\)38\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Slice test results.  
Figure 8: Comparison of the long-term and scaled accelerated test results.  
The transition point was observed to occur after approximately 1000 and 5000 days for the long-term test and accelerated test results, respectively, thereby indicating a significant time difference between the two results. The difference between the two results was settled within 10% at around the time after 3600 days.
# Privacy and Security in Federated Learning: A Survey
## 4 Privacy in Federated Learning
### Privacy-Preserving Defense Strategies
#### 4.2.4 Secure Multi-Party Computation (SMC)  
Initially, the secure multi-party computation aims to jointly compute a function on different parties over their personal data. One of the benefits of this approach is the possibility of keeping the inputs private thanks to cryptography. According to [47], SMC is currently used in FL but in a different version, which only needs to encrypt the parameters instead of the large volume of data inputs. Although this approach prevents leaks from a malicious central server, the encryption is expensive to use and may have an impact on a larger scale. Thus, the main cost of this solution is efficiency loss due to encryption.
## 1 Introduction
For testing our approach, we applied OpenAI's Generative Pretrained Transformer (GPT) language model in a setting with a social robot in dialog with a human, where the robot used the GPT model to generate speech, processed the content of the dialog, and recognized image content from its front camera [23]. However, the proposed methods can also be used beyond a HRI context. As a cognitive architecture, we used the standalone application of ACT-R 7, for which we created a cognitive model in LISP [24]. The connection between the cognitive model and our robot application, which also contained the Application Programming Interface (API) to the LLM, was realized via a TCP/IP connection of the dispatcher, which acts as a server for client applications to ACT-R [25]. To incorporate different sensory abilities from the robot, we used the visual capabilities of the GPT model in one test method and text generation in another. In principle, the procedure remained the same with regard to the use of the cognitive model and its memory function, except that the LLM was used once to describe image content and once to describe textual content. In the vision method, the language model was instructed to analyze the content of images from the front camera on the robot's head and describe the main content of each image in three keywords or key phrases. In the other case, the utterances of a person in dialog with the robot were processed by an LLM so that the core content of the human question or problem was also expressed in three keywords. The number of keywords used for our experiments was arbitrarily set to three in order to ensure a certain variability for the association possibilities via the keywords, and at the same time, to keep the number of required productions of the cognitive model manageable. The principle outlined here is not dependent on the number of keywords. These keywords or phrases were passed as chunks to an ACT-R cognitive model, where they were processed with productions from procedural memory to search the declarative memory for existing memory content indexed by chunks in the same or similar form. The memory content contained an additional chunk that represented the actual recollection and could, for example, represent a fact worth remembering in the form of a short sentence. If there was a positive correlation between keyword chunks from the LLM and memory chunks, this recollection was passed to the robot application and thus to the LLM for prompt augmentation, which generated a response based on this knowledge. In principle, it is possible to accumulate factual knowledge a priori by creating corresponding chunks in declarative memory. In the following, we show a technical implementation of such an architecture as a combination of LLM/VLM integration, a humanoid social robot, and cognitive architecture and give examples of applications.
# Design and Stiffness Optimization of Bionic Docking Mechanism for Space Target Acquisition
## 7 Conclusions  
(1) A bionic docking mechanism for space target acquisition, imitating wrist movement, is proposed to realize the buffering and unloading of six-dimensional spatial collisions through flexible rotating and linear components.  
(2) A dynamic model of the bionic docking mechanism in space docking using the Kane method is proposed, and the results of dynamic analysis of the optimization strategy show that the optimization of the stiffness of the bionic docking mechanism can avoid the instability of the base.  
Figure 21: Vibration displacements of each joint of the bionic docking mechanism; (**a**) Vibration displacements of under Damping 1; (**b**) Vibration displacements of under Damping 2.  
(3) Particle swarm optimization (PSO) was used to realize the stiffness optimization of the bionic docking mechanism, that is, the optimal stiffness coefficients are \(30\) N \(\cdot\) mm/ deg in Gyro-X joint, \(193\) N \(\cdot\) mm/ deg in Gyro-Y joint, \(43\) N \(\cdot\) mm/ deg in Gyro-Z joint, and \(0.85\) N/mm in Slide-Z joint. And the simulation results indicate that the soft contact machine-processed proposed can be verified.  
(4) Through the docking collision experiment of the spaceborne capture mechanism with the bionic docking mechanism, the simulation results indicate that the proposed bionic docking mechanism can prolong the collision time from \(0.01\) s to \(0.18\) s to win time for space acquisition, avoid velocity mutation of the free-floating base, and stabilize the bionic docking mechanism by damping vibration absorption; that is, the effectiveness of the bionic docking mechanism can be verified in buffering and loading the spatially six-dimensional collision caused by space target docking. The stabilization control of the spaceborne capture mechanism by damping optimization with the bionic docking mechanism could be a direction for future research.  
Conceptualization, S.X. and M.C.; methodology, S.X.; software, S.X.; validation, S.X., M.C. and H.S.; formal analysis, S.X.; investigation, M.C.; resources, S.X.; data curation, S.X.; writing--original draft preparation, S.X.; writing--review and editing, M.C.; visualization, H.S.; supervision, H.S.; project administration, M.C.; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the National Natural Science Foundation of China (Grant No. 51875046) and Natural Science Foundation of Beijing Municipality (Grant No. 3202021). The authors would like to thank anonymous reviewers for their valuable comments to improve the quality of this paper.  
Not applicable.  
The authors declare no conflict of interest.
# On Training Data Selection in Condition Monitoring Applications--Case Azimuth Thrusters
## 3 Results
### Computation Time in System Identification  
Figure 11 shows the computation time used in algorithm training as the average of two tests. The computations were performed with a standard office computer (2.20 GHz, Intel(r) Core(tm) i7-8750H CPU, 16.0 GB RAM) and MATLAB(r) R2020b. On the plot on top, the algorithm was trained with all its steps (see Figure 1) using the 'basic training subsets' approach. The plots below show the computation time without the quality control of samples, which means that each signal passed through the QC process without any processing.  
Figure 11: Time used in algorithm training. Plot on top shows entire computation time when ‘basic training subsets’ approach was used. Plots below show corresponding computation time without quality control using ‘basic training subsets’ (left) and ‘adaptive training subsets’ (right). Note that vertical axes have different scales.  
The results show that the size of the sample set and computation time are strongly correlated. The larger the sample set was, the more time was needed. The same goes for the dimensionality of inputs. The algorithm with the bearing model required more computation than the algorithm with the gear model because the number of features was higher then. Comparing the plot on top with the plot below on the left, it becomes clear that QC consumed the most time in algorithm training. In QC, the most time was used by the check of moving mean range. The process was affected especially by the signal length, the window size and the motion of the window, which were 16,384; 100; and 1 value(s) here, respectively. To clarify further, the time used in quality control was independent of the approach to training subset selection (see Figure 1).  
The computation time with the 'adaptive training subsets' approach appears low enough to be used in practice (with limited computing resources), which can be concluded from the results in the lower right plot in Figure 11. In addition, the plot reveals that the computation time was at a fixed level when N was 2200-3000 but the reason for that is unclear. However, the shape of the distribution is relatively stabilized with those datasets, as shown in Figure 5.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 7 Open Challenges
### Data Processing and Management  
The substantial volume of IoMT data demands effective pattern analysis for informed decision-making. While traditional methods rely on manual observation via self-reporting tools such as questionnaires and interviews, big data platforms provide automated frameworks such as MapReduce and Hadoop for parallel and distributed analysis. Additionally, tools such as Cascading, Pig, and Hive can aid in handling interrelated data groups [55]. However, selecting the most suitable framework poses a challenge, as some cater to continuous streaming data while others handle batch-type data.
# Stomatal Response of Maize (_Zea mays_ L.) to Crude Oil Contamination in Soils
## 3 Results
### Concentrations of K and Na in Maize Leaf  
As the major mineral elements in plant tissues, K and Na play important roles in maintaining the physiological functions, especially in regulating the opening and closure of stomata. It is necessary to investigate the effect of crude oil contaminated soil on the K and Na assimilation of maize. As shown in Figure 4, both K and Na concentrations in maize leaf significantly decreased with increasing TPH levels of soils, indicating the crude oil contaminated soil had a significant effect on the assimilation of K and Na in maize plant. Additionally, K and Na concentrations in maize leaf at high levels of contaminated soil (above 6500 mg/kg) were much lower than those of the control.
## 4 Tests and Results
### Parameters of the Bio-Inspired Model
#### 4.1.3 SNN Results  
Although the SNN operates under a concept of pulses as illustrated in Figure 14, it should be remembered that it operates under the same structure as the CNN presented above. Therefore, similar results to the CNN are highly expected.  
After analyzing the results, a processing time of around 50 milliseconds is observed, with a percentage of accuracy slightly lower than that of the CNN.  
For the case of Figure 10, where the license plate corresponds to \"BJZC72\", the SNN obtains a 33% error, equivalent to 2 incorrect characters, and after applying the bio-inspired retina model, the error decreases to 16%. In the case of Figure 11 (\"JD GC 57\"), the SNN model leaves 2 undetected characters, meaning a 33% error, which is reduced to 0% after applying the bio-inspired retina model. In the case of Figure 12, corresponding to \"JF FP 14\", a 33% error is also obtained, but when applying the retina, it is reduced to 0%. Finally, for the license plate \"CTCB99\", corresponding to Figure 13, although the error is 0% with the SNN model, after applying the retina model, the error increases to 33%. In this way, and in the cases presented, the SNN has a response capability similar to that of the CNN, as expected, but stands out with a shorter data processing time. The overall test results are presented below.
## 5 Experiments and Performance Evaluation
### Effect of the Number of Nodes Deployed at Each Tower  
Figure 6 shows the effect of the number of nodes at each tower on the network reliability per unit cost. We consider a simulation of an 80-tower network in normal, bad and extreme weather. The price of 1000 m communication-range node is $50. The cost increases approximately linearly with respect to the number of nodes deployed at each tower, but the reliability has a slower incensement. For any given network and a certain type of sensor nodes, this graph can be used to find the most cost effective arrangement. In this case, the optimal number of nodes deployed at each tower is one, two and three for normal, bad and extreme weather. Generally, we consider three nodes in each tower for conservation design.  
It should be mentioned that sometimes planners tend to value the network reliability above cost. Especially in this case, three-node arrangement in extreme weather is almost as cost effective as four-node arrangement, but the network reliability in four-node arrangement (0.9398) is much higher than three-node arrangement (0.7206). Therefore, four-node arrangement is more acceptable. This analysis can be a decision aid in obtaining the optimal number of nodes in network planning.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Monte Carlo Experiments**} & \multicolumn{3}{c}{**Exhaustive Simulations**} \\ \cline{2-7} \(N\) & \multicolumn{2}{c}{\(\varepsilon\) = 1\%} & \multicolumn{2}{c}{\(\varepsilon\) = 0.1\%} & \multirow{2}{*}{**Network Reliability**} & \multirow{2}{*}{**Time (s)**} \\ \cline{2-2} \cline{5-7}  & **Number of Inputs** & **Time (s)** & **Number of Inputs** & **Time (s)** & \\ \hline
10 & \(1.3\times 10^{3}\) & 0.23096 & \(9.3\times 10^{3}\) & 1.29338 & 0.83373 & 0.09567 \\
15 & \(5.8\times 10^{3}\) & 0.80959 & \(2.1\times 10^{4}\) & 2.93454 & 0.74736 & 2.35184 \\
20 & \(5.2\times 10^{3}\) & 0.76325 & \(1.8\times 10^{4}\) & 2.75917 & 0.66994 & 76.6096 \\
25 & \(6.2\times 10^{3}\) & 1.75463 & \(2.9\times 10^{4}\) & 4.31000 & 0.60054 & 2569.68 \\
30 & \(7.5\times 10^{3}\) & 2.22855 & \(3.1\times 10^{4}\) & 4.59022 & 0.53833 & 85,312.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between Monte Carlo and an exhaustive experiment.
###### Abstract  
In this study, we investigated geometrical characteristics of the inkjet-printed lines with non-zero receding contact angle (CA) on plasma-treated substrates in terms of various printing variables and analyzed the fluidic behavior and hydrodynamic instability involved in the line formation process. The printing variables included surface energy, droplet overlap ratio, printing frequency, a number of ink droplets, substrate temperature and printing procedures. For the study, a colloidal suspension containing 56 wt % silver nanoparticles in tetradecane solvent was used as a printing ink. It has electrical resistivity of 4.7 \(\upmu\)C-cm. The substrates were obtained by performing a plasma enhanced chemical vapor deposition (PECVD) process with C4F8 and O2 under various treatment conditions. As results of the experiments, the surface shape and pattern of the inkjet-printed Ag lines were dominantly influenced by the surface energy of the substrates, among the printing variables. Accordingly even when the receding CA was non-zero, bulging instability of the lines occurred forming separate circular patterns or regular bulges connected by ridges. It is a new finding of this study, which is completely different with the bulging instability of inkjet lines with zero receding CA specified by previous researches. The bulging instability decreased by increasing surface temperature of the substrates or employing interlacing procedure instead of continuous procedure for printing. The interlacing procedure also was advantageous to fabricate thick and narrow Ag lines with well-defined shape through overprinting on a hydrophobic substrate. These results will contribute greatly to not only the production of various printed electronics containing high-aspect-ratio structures but also the improvement of working performance of the devices.  
Keywords: inkjet printing; plasma-treated substrates; nanosilver line; non-zero receding contact angle; bulging instability; printing variables +
Footnote †: journal: Applied Soft. 2018, 8, 280; doi:10.3390/app8020280
# Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing
## 2 Related Work
A scheme for joint multitask offloading and resource allocation in satellite is proposed, significantly reducing the offloading cost [27]. Due to the uncertainty of the air environment, [PERSON] et al. presents an integrated network architecture between the air and ground and designs an adaptive joint deep reinforcement learning offloading scheme to select the most suitable LEO or task offload UAVs based on energy and computational capability, which improves the energy and computational efficiency. Under the condition of satisfying energy dynamics and considering the UAV on-board computing resources and energy constraints, it is proposed in [28] that IoT could locally handle and transfer it to servers, improving the success rate of the task. From the above analysis, few past works have focused on UAV-assisted LEO edge computing for task offloading in emergency scenarios, where tasks from ground-based IoT devices are offloaded to LEO satellites for processing with the assistance of UAVs. In addition, tasks in emergency scenarios are computationally intensive and latency sensitive. Therefore, multiple satellites are considered for collaborative task processing to reduce energy consumption and task processing delays by optimising resource allocation and offloading strategies.