esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Closed-Looped Control Strategy  
In general, the application rate control was realized using the strategy described in Figure 1. It was essentially an open-looped method because there was no feedback data for updating parameters \(k\) and \(b\). \(k\) and \(b\) were the parameters of the control model expressed as Equation (3). The value of the parameters (\(k\) and \(b\)) varied with the increase of usage time or changes in working conditions. If there was no effective device to sense the change of the model parameters, the fluted-roller rotation speed calculated from Equation (3) deviated the actual control target and caused a control error. Therefore, in order to maintain excellent control performance, effective methods were needed to sense the change of \(k\) and \(b\) and dynamically adjust the control model parameters.  
A closed-looped control system for controlling the application rate was constructed, which is shown in Figure 2. The parameters of the self-calibrating system and application rate measuring system were introduced as the feedback channel to establish a closed-looped control system. The new relationship model between the mass flow rate and rotation speed was linearly fitted by recording the dynamic cumulative application rate \(Q\)(t) and the dynamic cumulative rotation cycles \(N(t)\) in a certain period. The parameters of the new relationship model were compared to the original model parameters, and then the control model parameters were corrected and updated according to the preset conditions. The \(k\) and \(b\) measured by the calibrating system replaced \(k_{0}\) and \(b_{0}\) in the original calibration model to maintain the control accuracy of the application rate.
# Analysis of Electromagnetic Field Characteristics of Wave Glider
## 1 Introduction
Reference [16] used a wave glider equipped with temperature and salinity depth sensors for observing surface water temperature and wave height in coastal locations with a flow velocity of approximately 0.5-1 m/s and a water depth of approximately 30-40 m.  
It can be seen that underwater gliders equipped with sensors for ocean target detection have become a research hotspot in various countries. Underwater electromagnetic fields have the advantages of being less affected by hydrological and meteorological conditions, stable detection performance, and good concealment [17; 18]. They have also been widely used in underwater target detection, recognition, and positioning [19; 20; 21]. The use of electricity and magnetic fields to detect underwater target signals needs to be based on accurate measurements. In terms of ocean electric field measurement, carbon fiber electrodes and Ag/AgCl electrodes are mainly used as electric field sensors, with Ag/AgCl electrodes being the most commonly used. Flux gate sensors are usually used to measure weak magnetic signals underwater. Electromagnetic sensors are sensitive to shaking due to their structural characteristics. Currently, there are no electromagnetic sensors installed on wave gliders. For underwater unstable platforms such as wave gliders, studying the electromagnetic field characteristics of wave gliders has practical engineering significance for the next step of underwater electromagnetic field detection. The manuscript analyzes the structural characteristics and motion features of wave gliders, combines the practical application requirements of wave gliders equipped with electric and magnetic field sensors for underwater electromagnetic field detection, designs the wave glider platform installed with electric and magnetic field sensors, and carries out sea tests in a sea area of Qingdao, and tests and analyzes the electromagnetic field characteristics of the wave glider platform. Based on the sea test data, the electromagnetic self-noise of the platform is obtained, the noise characteristics of the platform are provided, and an engineering proposal for equipping underwater electromagnetic field sensors is put forward.
# Amphishea: A Novel Persistent Buffer Management Strategy to Improve SMR Disk Performance
## 5 Amphisbaena Design
### Locality-Aware Buffer Space Management
#### 5.2.3 Reallocation of the Persistent Buffer  
Real-world workloads may present different locality characteristics in each period. To ensure the stability of SMR disk performance, a key issue is how to adaptively adjust the ratio of HFB and LFB according to the workload locality characteristics. For this purpose, a heuristic-based dynamic reallocation policy for the persistent buffer is designed. During system initialization, HFB and LFB are allocated in a default ratio. Subsequently, the space allocator reviews the historical access records of the HFB and LFB in each predefined sampling period to decide whether their capacity needs to be adjusted. In this way, the space allocator allocates buffer space to the HFB and LFB in the optimal ratio for the persistent buffer, allowing Amphishea to better adapt to the current access patterns of the workload. Figure 8b,c show the process of the data classifier reallocating space to the HFB and LFB.  
To reduce the complexity and overhead of space reallocation, the chunk containing contiguous LBA space is used as the base unit for space reallocation. In addition, the size of the HFB is limited to between 25% and 75% of the persistent buffer capacity to avoid performance jitter caused by the overallocation or deallocation of space. Specifically, if the ghost blocks in the resident list are updated again within a sample period and their number exceeds the size of a chunk (i.e., the number of blocks it can hold), it means that the current capacity of the HFB is insufficient to accommodate more frequently updated blocks. In this case, the space allocator reclaims a certain number of chunks from the LFB to allocate more space to the HFB. The number of chunks deallocated from LFB is calculated by dividing the number of ghost blocks by the size of the chunks. For example, after a sampling period, the space allocator reviews the history and finds that 60 ghost blocks write hit, meaning that these blocks are frequently updated and have a long update period, so the capacity of the HFB must be reallocated to accommodate these blocks. If a chunk can hold 16 blocks, then the number of reallocated chunks is 60 divided by 16, rounded to 4. Conversely, if any block in a reallocated chunk obtained from the LFB is not updated again within a sample period, the reallocated chunk is returned to the LFB. The guidelines for data migration in the HFB and LFB can be found in Section 5.3.  
Figure 11: The schematic diagram for the role transformation of data blocks.
## 2 Related Work
### Related PUF Designs  
A first approach for analog-based PUFs was presented by [PERSON] et al. in [22]. In their work, a cell array of identical small-gate transistors sequentially addresses a resistive load that generates an output bit-sequence depending on the device's output voltage levels.  
In another work, [PERSON] et al. [23] presented the Threshold Voltage PUF (TV-PUF), using a cascading method to increase small differences of threshold voltage variations in pass-transistors in order to exploit its cumulated impact on the output voltage.  
A hardware realization of transmission gate (TG) based analog PUFs, the Transmission Gate PUF (TG-PUF), incorporating and measuring voltage drops across transmission-gate transistors, was introduced in [24].  
Another inverter-based PUF has been proposed in [25], where the main focus relies on exploiting the inverter's nonlinear output characteristics and cascading multiple blocks in series, making the PUF more resilient to machine learning attacks in comparison to delay-based PUFs.  
In [26], an analog-based PUF structure uses a CMOS architecture with biasing at the point of maximum DC-gain, exploiting complementary inverter threshold voltage \(V_{th}\) variations.  
Development boards with respect to hardware security can be deployed for digital PUFs such as the HECTOR development board [27] or the SASEBO board [28].  
To the best of our knowledge, almost all of the published works so far have either been simulated, implemented on FPGAs, or fabricated as stand-alone chips and hence have not been characterized to show their inherent properties such as voltage at critical nodes for the functionality of the PUF design. Usually, when voltage levels at critical nodes are not known, instabilities in the response need to be computed solely based on the binary bit values of the response. The usage of analog-to-digital converters (ADCs) on these critical nodes allows for investigation of the voltages that lead to bit generation. Based on the gained knowledge, thresholding techniques for robust bit generation can be explored and deployed. To conduct further application-based research on any of the existing analog PUFs, the designs have to be reconstructed and the prototype chip further investigated.  
The presented work in this paper provides a building block for researching a scalable analog-based PUF design, enabling security evaluation and optimization. In general, the proposed discrete PUF design tackles typical shortcomings of chip-based PUFs which are higher initial fabrication costs, longer production times, and less accessibility as needed in a research environment.
# Recent Advances in Efficient Dynamic Graph Processing
## 1 Introduction
General analysisDynamic graph algorithms have made significant progress in efficiently handling dynamic networks, leveraging incremental updates (e.g., localized re-computation in \(k\)-core maintenance or betweenness centrality) and parallel/distributed processing (e.g., parallel betweenness updates). These techniques drastically reduce re-computation overhead compared to static approaches. Many algorithms, such as those for dynamic DFS and shortest paths, achieve linear or near-optimal update times by exploiting structural properties or incremental label propagation. However, when handling highly dynamic and large-scale graphs, some algorithms (e.g., exact clique enumeration) remain impractical due to NP-hardness or excessive memory usage. Efficiency is heavily influenced by factors such as update rate and frequency (e.g., the number of updates), graph size and structure (e.g., arboricity in coloring or core-truss hierarchies). While recent work has improved scalability via parallelism and approximation, challenges remain in streaming graph support, tight theoretical bounds, and handling complex graph types. In summary, our main contributions are the following:  
1. First, we provide a systematic classification of existing research on dynamic graph processing. For each class of works, we review the State-of-the-Art and representative works. 2. Second, we summarize three computational complexity models, including boundedness, semi-boundedness and relative boundedness. Specifically, we theoretically analyze the efficiency of algorithms among different research topics on dynamic graphs. 3. Third, we make discussions on the potential directions of future works, which will promote the development of dynamic graph processing. The organization of this survey is shown in Figure 1. Section 2 introduces the concepts and common terminology used in this paper. Section 3 extensively discusses the computational complexity model for evaluating the computational complexity of algorithms. Section 4 reviews and analyzes recent existing works for different categories of algorithms on dynamic graphs. Section 5 presents recent system works aiming at managing dynamic graphs. Section 6 provides a discussion on future research directions. Section 7 concludes this survey.
## 4 Material and Methods
### Data Acquisition  
P- and S-wave refraction data were collected along 17 acquisition lines in front of the Osirion site, forming a consistent grid, where 9 lines were oriented NW-SE and 8 lines were oriented perpendicular to them (Figure 3). The total length of each line was 200 m, and each was composed of two overlapped spreads. The length of each spread was 115 m, with an overlapping distance of 30 m. The total number of geophones in each line was 41 geophones, with an interval of 5 m. To acquire the P-wave and S-wave data, 14 Hz vertical and horizontal geophones were used, respectively. Five shots were employed for each spread at distances of \(-\)2.5, 27.5, 57.5, 87.5, and 117.5 m. A sledgehammer with a mass of 15 kg was used as a seismic source. For the P-wave refraction, the sledgehammer was struck vertically on a steel plate, while it was struck horizontally on a wooden truck for the S-wave refraction data [33]. For both acquisitions, the shots were stacked 3 to 5 times. The shots were recorded on a 24-channel Geomtrics(r) GEODE, with a 0.25 ms sampling interval and a recording length of 200 ms. No acquisition filters were applied.
# Effect of Whole Body Vibration Conditions on Lower Limb Muscles during Sling Exercise
## 4 Conclusions  
In this study, the effect of BSS-sling with the vibration exercise on lower limb muscle activation according to frequency was investigated for 20 healthy male subjects. The findings are summarized as follows.  
In all muscles, compared to the no-vibration condition, in the condition with vibration, muscle activation was increased. The highest activation was observed under the condition of applying vibration at 30 Hz.  
Therefore, it was found that during BSS, the unstable environment and WBV were factors with a positive impact on the activation of lower limb muscles. In particular, WBV at 30 Hz vibration frequency had the most positive effect on muscle activation of the lower limb muscles, and accordingly, the optimal vibration frequency was confirmed at 30 Hz. These results can be used as scientific basic data for sports scientists and athletes who need to improve the function of lower limb muscles, and thus improve athletic performance.  
Conceptualization, H.-J.W., M.Y. and T.-K.K.; data curation, H.-J.W.; formal analysis, H.-J.W. and M.Y.; investigation, H.-J.W., M.Y. and T.-K.K.; methodology, H.-J.W., M.Y. and T.-K.K.; supervision, M.Y. and T.-K.K.; visualization, H.-J.W.; writing--original draft, H.-J.W.; writing--review and editing, M.Y. and T.-K.K. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea government (MOTIE) (project no.: P0004793, project title: Establishment of the Open Lab Ecosystem for Solving Social Problems Based on Senile Disorders.) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1F1A1076549).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 8: Results of muscle activation according to 30 Hz vibration frequency conditions.
# Smooth 3D Dubins Curves Based Mobile Data Gathering in Sparse Underwater Sensor Networks
## 5 Conclusions  
This study investigated the smooth-path-planning-based mobile data collection problem for 3D USNs. The 3D Dubins curves based trajectory planning algorithm for MDG uses \(G^{1}\) continuous Dubins curves in the \(X\)-\(Y\) plane and Bezier interpolation in the \(Z\)-axis to construct 3D smooth curves, so as to guarantee \(3D-G^{1}\) continuity and therefore overcome AUVs' nonholonomic constraints in 3D regions. Simulation results demonstrate that the proposed algorithm can support AUVs' kinematic constraints and satisfy 3D smooth mobile data collection requirements with only a little extra cost. In our future works, sensor nodes' passive drift by irregular sea currents will be considered. Moreover, obstacle avoidance will be considered in the process of mobile data collection path planning.  
[PERSON] proposed the main idea. [PERSON] and [PERSON] generated the simulation results and drafted the manuscript. They also responded to the reviewers' comments. These authors contributed equally to this work.  
This research was partially supported by the Natural Science Foundation of Zhejiang Province (Grant Nos. LY18F030006 and LY16F030004) and the National Natural Science Foundation of China (Grant Nos. 6187010532 and 6180010671).  
The authors would like to thank the anonymous reviewers for their helpful and constructive comments that greatly contributed to improving the final version of the paper.  
The authors declare no conflict of interest.
## 5 Experiment and Results
### Experiments and Discussion
#### 5.4.6 Inference Speed Results  
As this model is designed for deployment on resource-constrained devices, its inference speed is evaluated on the mobile graphics card MX350 and the Jetson TX1 development kit to verify its efficiency on mobile devices.  
Table 7 compares the inference speed performance of various models for meter reading recognition. Please note that networks with lower accuracy and slower inference speeds were not evaluated on the Jetson TX1. Our model surpasses YOLOv8s in accuracy, with a slight compromise in inference speed. This trade-off results in a model that occupies less memory than YOLOv8n and exhibits appropriate real-time inference speed while maintaining robust recognition capabilities.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **mAP@50:95/\%** & **Parameters/M** \\ \hline YOLOv8n & 87.92 & 3.15 \\ YOLOv8n+MDA & 88.46 & 3.15 \\ \hline YOLOv8n-MobileNetV3 & 87.58 & 2.25 \\ YOLOv8n-MobileNetV3+MDA & 87.64 & 2.25 \\ \hline DA-YOLO & 88.60 & 3.46 \\ DA-YOLO+MDA & 88.77 & 3.46 \\ \hline DAP-YOLO & 88.34 & 2.40 \\ DAP-YOLO+MDA & 88.82 & 2.40 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the meter data augmentation results for the meter reading recognition dataset.  
Figure 19: The inference results for the Jetson TX1 intelligent car for meter reading recognition.
## 3 Materials and Methods  
### LoRa System Signal Coverage-Characterization  
#### 3.1.1 Materials  
Figure 1 describes the implemented communication system, using several sensors for temperature (BMP280, DFRobot), humidity (BME280, Adafruit), noise/sound (Analog Sound Sensor v2.2, DFRobot), and NO\({}_{2}\) concentration (DGS-NO2 968-043, Spec Sens) wired to a microcontroller board (LoRa32u4 II, BSFrance) (Figure 1a). The board features a LoRa node chip microcontroller (SX1276, AtmegaQR 32u4 3.3V @ 8 MHz MCU), with transmitter power +20 dBm, receiver sensitivity between \(-136\) dBm @ LoRa 125 Khz SF12 293 bps and \(-118\) dBm @ LoRa 125 Khz SF6 9380 bps, and an operating frequency of 868 MHz and transmission current-consumption of 128 mA. A 5 V portable power bank drives the micro-controller circuit. The LoRa gateway (LPS8, Dragino), with a transmitter power of +20 dBm, was placed indoors (University of Vigo office), while the micro-controller LoRa-transceiver was taken outside for signal coverage measurements.
## 4 Gs-Eera
### The Next Hop Selection  
To obtain a suitable relay node, we fully consider the residual energy, energy harvesting rate, distance and data transmission time to define a metric and adopt the greedy strategy to select the optimum next hop from the candidate node(s). The metric is defined as follow:  
\[M_{ij}=\alpha\widehat{E}_{r}^{j}+\beta\widehat{E}\widehat{H}_{j}+\gamma\left( 1-\widehat{D}_{j}\right)+\delta\left(1-\widehat{T}_{j}\right) \tag{27}\]  
where \(\alpha\), \(\beta\), \(\gamma\) and \(\delta\) are the weighting coefficients. \(E_{r}\), \(EH\), \(D\) and \(T\) denote the residual energy, energy harvesting rate, distance and transmission time, respectively. \(\widehat{E}\), \(\widehat{E}\widehat{H}\), \(\widehat{D}\) and \(\widehat{T}\) represent the normalized functions, which meet the following rules:  
\[\left\{\begin{array}{c}\alpha+\beta+\gamma+\delta=1\\ \\ \widehat{E}_{r}^{j}=\frac{E_{r}^{j}}{E_{m}};\ \ \widehat{E}\widehat{H}_{j}=\frac{ EH_{j}}{EH_{max}}\\ \\ \widehat{D}_{j}=\frac{D_{il}}{D_{il}};\ \ \widehat{T}_{j}=\frac{T_{j}}{\sum_{a\in CN (i)}T_{a}}\end{array}\right. \tag{28}\]  
where \(E_{m}\) and \(EH_{max}\) indicate the maximum value of residual energy and energy harvesting rate, respectively.  
Therefore, the probability that node \(i\) chooses node \(j\) as the next hop can be calculated as:  
\[\mathbb{P}_{\bar{\bar{\bar{\bar{\bar{\bar{\bar{\bar{\bar{\bar{\bar{\bar{ \bar{\bar{\bar{\bar{\bar{\bar{\baras shown in Figure 8, \(S\) is the source node, and \(A\), \(B\) and \(C\) are the neighbor nodes of \(S\). Firstly, the data are forwarded to node \(A\) through the greedy forwarding method. Since node \(A\) is a void node, it adopts the perimeter forwarding strategy to relay the data bypass the routing void area along \(A\to B\to C\to D\). Obviously, if node \(S\) forwards data along \(S\to B\to C\to D\) or \(S\to C\to D\), the total energy overhead and the data transmission time cost may be relatively less.  
In this paper, we propose a method that combines void recovery and void avoidance to deal with the routing hole problem. If the candidate nodes set of a node is empty, the \(VFlag\) is adjusted to 1 (indicating this is a void node and cannot become a candidate node of others), the \(RFlag\) of the packet to be processed at this time is adjusted to 1 (i.e., the node should forward packet through the perimeter forwarding strategy) and selects the optimal next hop from its neighbor nodes with \(Flag=1\). If a non-void node receives a packet with \(RFlag=1\), then \(RFlag\) is adjusted to 0 (i.e., stopping the perimeter forwarding process and starting the greedy strategy-based packets forwarding).