esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
## 4 Results and Discussion
### Decoding of Data at Fog End  
When data reaches the fog node, Figure 6 shows how it is decoded. The decryption key is used to decode the data at the fog node, where it will be quickly processed before being sent to the cloud server. As can be seen, the original data values are the same even though the curves are displaced as a result of mixing the decryption key. The original content is the same as was sent by the source.
# Parallel Load-Bearing and Damping System Design and Test for Satellite Vibration Suppression
## 8 Conclusions  
In the present work, a parallel damping vibration suppression scheme was adopted to improve the spacecraft interface's complex vibration environment, which could simultaneously maintain the structural stiffness. The number and coefficient of dampers were designed by comparison of the frequency response analysis results. As a result, the PAF was mounted with eight dampers. Based on the new model, the transverse bending frequency and longitudinal frequency were calculated, and the FEA results show that structure's natural frequencies were almost unchanged. The strength of the structure was verified through two critical load cases. The lightweight supports of the dampers were designed and optimized. The final weight increment meets the engineering requirements.  
Based on the design results, the damping system prototype was manufactured and installed into the PAF, in which the vibration test was implemented. Compared with the original state, the damping state PAF had lower transmissibility on the resonance peaks. Among them, the axial acceleration peak transmissibility decreased by 36%, and the lateral acceleration peak transmissibility decreased by 35% and 12%, while the transmissibility of other frequency ranges remained unchanged. The fundamental frequency of the structure changed by less than 5%.  
The test results coincide well with the theoretical analysis results. The effectiveness and feasibility of the parallel vibration isolation scheme were verified by the experiments.  
Conceptualization, S.C.; Data curation, Y.L.; Formal analysis, M.Y., Y.Z. and Y.L.; Funding acquisition, S.C. and Z.P.; Project administration, S.C. and Z.P.; Supervision, S.C.; Validation, M.Y. and Z.P.; Visualization, M.Y., Y.Z. and Y.L.; Writing--original draft, Z.Y. and M.Y.; Writing--review & editing, S.C. and Z.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China; Grant Number: 11672016.  
The authors declare no conflict of interest.
# Study on Asymmetric Support of Anchor Cable with C-Shaped Tube in Inclined Coal Seam Roadway
## 2 Research on the Mechanical Characteristics of Anchor Cable with C-Shaped Tube
### Analysis of the Outcomes  
Figure 4 depicts the typical \(ACC\) shear load/axial force-shear displacement curve.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Number** & **Material Type** & **Design Prestress (kN)** & **Actual Prestress (kN)** \\ \hline S1 & \(ACC\) & 100 & 100 \\ S2 & Anchor cable & 100 & 100 \\ S3 & \(ACC\) & 200 & 223 \\ S4 & Anchor cable & 200 & 211 \\ \hline \end{tabular}
\end{table}
Table 1: Test scheme.  
Figure 3: Test material assembly and test loading process.  
The curve of \"shear load-shear displacement\" of _ACCs_ may be separated into three phases, as depicted in Figure 4: rapid-rising stage, slow-rising stage, and stepped-falling stage. There are three phases to the \"axial force-shear displacement\" curve as well: gentle stage, ascending stage, and stepped-falling stage. Before the test, prestress was applied to the _ACC_ passing through the concrete test blocks, which squeezed each other, and there was a large friction force between the two joint planes. When the test first began, the shear load quickly overcame the friction and rose, and the _ACC_ axial force was in the gentle stage. Then, the shear load rose slowly, the _ACC_ gradually underwent elastic and plastic deformation, and the axial force reached the rising stage. When the shear load and axial force reached their maximums at the same time, the steel strands in the _ACC_ were broken one by one, and then the shear load and axial force entered the step decline stage.  
Figure 5 displays the shear load-shear displacement changes of the _ACC_ and anchor cable underneath various prestresses.  
Figure 5 illustrates that in the shear process, before the shear load reaches its maximum value, when the displacements of the _ACC_ and the anchor cable under the same level of prestress are identical, the shear load borne by the _ACC_ is higher than that borne by the anchor cable. The peak shear capacities of the _ACC_ with 100 kN of prestress were 19.4% higher than those of the anchor cable with 100 kN of prestress, and with 223 kN of prestress, the _ACC_'s peak shear capacities were 4.1% higher than those of the anchor cable with 211 kN of prestress. The conclusion is that, under the same circumstances, the _ACC_ has a much stronger shear load resistance capability than the anchor cable.  
Figure 4: Typical _ACC_ shear load/axial force–shear displacement curve.  
Figure 5: Shear load–shear displacement curves of _ACC_ and anchor cable.
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 3 Results and Experiments
### Performance Evaluation Under Challenging Environmental Conditions  
In addition to the core evaluation of the Smart Farm Surveillance System (SFSS) using conventional datasets, analyzing the system's ability to adjust to varying environmental conditions, such as changes in weather and lighting, is essential. In order to achieve this, an additional dataset of 80 photos was gathered from multiple Kaggle datasets, containing difficult situations like hazy photos, different weather (from rainy to sunny and cloudy), and midnight photos. This dataset was created especially to assess the YOLOv8 model's resilience in practical settings where environmental influences might have a big influence on image quality.  
Testing the YOLOv8 model on this difficult dataset showed that its intrusion detection accuracy was around 50%. Considering that there were not any images of these challengingsituations in the training and testing datasets, this performance is remarkable. The YOLOv8 model's performance under these difficult circumstances is shown in Figure 22, which also demonstrates the model's detection ability across a range of image quality levels.  
The absence of training data containing difficult conditions, like low-resolution, fuzzy, or poorly lit photos, is the reason for this decreased accuracy. These circumstances may cause misclassifications or missed detections by obscuring important characteristics needed for precise object detection. For example, low-contrast and inadequate illumination are common problems with nighttime photos, and cloudy or wet weather can bring noise and distortions that make it more difficult for the model to correctly identify things.  
The YOLOv8 architecture's intrinsic durability and adaptability are suggested by the model's remarkable 50% accuracy under such conditions. The results show that although the model performs at a respectable level, there is still much room for improvement. In order to increase the model's accuracy and dependability in practical applications, future research could concentrate on supplementing the training dataset with pictures that depict these difficult circumstances. Also, investigating possible mitigation strategies, like using image preprocessing methods to improve clarity and resolution, could strengthen the system's functionality in unfavorable environmental conditions.
# Performance Analysis of Post-Quantum Cryptography Algorithms for Digital Signature
## 4 Implementation
### Libos Library  
The _libqs_ library is a set of post-quantum algorithms prepared as a part of the Open Quantum Safe project [20]. The library has been designed to function as a comprehensive testing and evaluation toolkit for researchers and developers, providing them with the opportunity to experiment with a range of cryptographic schemes that are believed to be resistant to potential attacks from quantum computers.  
The library is written in C, providing a performance-optimized foundation for cryptographic operations. To ensure broader accessibility and integration into various software stacks, liboqs provides language wrappers, including a Python wrapper. This wrapper enables the use of post-quantum algorithms in Python applications and has been included in the implementation. In order to use _liboqs-python_, it is first necessary to build and install the _liboqs_ library and, then, to properly configure the Python virtual environment.
# Application of Thermal Image Data to Detect Rebar Corrosion in Concrete Structures
## 4 Conclusions  
The cross-sectional area of the rebar decreases as the rebar corrosion progresses, and the resistance of the conductor is proportional to its length and inversely proportional to its area. Therefore, the following hypothesis was established: When a fixed amount of current is applied to the rebar, the distribution of temperature curve that appears on the concrete's surface varies owing to the differences in heat generation caused by the electrical resistance.  
To examine the impact of the cover thickness, the experiment variables were set at cover thicknesses of 10, 20, and 30 mm and rebar corrosion ratios of 0%, 1%, 3%, 5%, 7%, and 10%. Each variable was tested and a total of 60 specimens were created (54 specimens and 6 preliminary specimens). In this study, corrosion was applied using an electrochemical corrosion method that employs [PERSON]'s law, i.e., the law of conservation of electric charge.  
This study facilitated bidirectional control between the power supply and the computer, as well as the individual output of the two power output ports. A feature was added to turn the output off via program control when the desired cumulative voltage and current values were attained. In this study, the optimal IRT conditions that clearly distinguish the amount of corrosion was established prior to conducting the preliminary experiments. Based on the results of the experiments, the surface temperature indicates higher when the corrosion rate of rebar is high. In particular, the variations of the temperature distribution were great when the specimen was left 480 s after the 300 s heating was stopped.  
The test results of height, width, and area of temperature distribution curve were analyzed, and the height of temperature distribution curve was increased as the heating time was grown. In addition, the area of temperature distribution varied dependent upon the corrosion rate and cover thickness. In particular, the height of temperature distribution curve indicated similar variations even though the heating of rebars were stopped. Based on these results, the height of the temperature distribution curve would be useful to construct the database of corrosion rate of rebars.  
I.P. administrated the overall of this study and calculated the quantities of building materials. S.N. carried out the structural analysis and the assessment of the carbon dioxide emissions from three slab systems.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government Ministry of Education (No. NRF-2017R1D1A1B03033452).  
The authors declare no conflict of interest.  
Figure 11: Comparison of areas of temperature distribution curves (cover thickness of 10 mm).
# Video Multi-Scale-Based End-to-End Rate Control in Deep Contextual Video Compression
## 4 Experimental Results  
The proposed algorithm is embedded into DCVC software. DCVC software is implemented using the official default configuration and model. The structural similarity (SSIM), PSNR, and BD-rate index are used to evaluate coding quality. The experimental platform is shown in Table 1.
## 2 Model Tests
### Test Apparatus  
The apparatuses used in the tests include a soil container (internal diameter, 500 mm; height, 800 mm; wall thickness, 20 mm), 5 load cells, 2 linear variable differential transformers (LVDTs), and strain gauges. The load cells and LVDTs were used to measure the load carried by each pile and the displacement on the raft at each loading stage, respectively. Strain gauges were attached to evaluate the load transfer as shown in Figure 2a. Prestressed concrete piles (PCP) and micropiles (MP) were used as the existing piles and underpinning piles, respectively. To avoid the effect of a pile group, the spacing of the micropile to the prestressed concrete pile was set as 3 times the diameter of the PCP as shown in Figure 2b [33; 34].  
The effects of the piles' form and length, and consequently their axial stiffness, were evaluated by comparing two types of conventional micropile (CMP) and two types of waveform micropile (WMP) in foundation underpinning tests. The WMP (Figure 2a), with shear keys along the pile shaft, demonstrated a higher stiffness and load bearing capacity than the CMP [35; 36; 37]. Although the limitation of the soil stress effect in small-scale tests is difficult to overcome, to establish a reliable relation between the small-scale model and the prototype model requires adopting a suitable scale and satisfying the scaling law [38; 39]. Excluding the account of soil effect, the model piles and the geometrical properties of the model piles listed in Figure 3 and Table 1 were determined according to the 1 g scaling laws with a scaling factor of 20 [39], as follows:  
\[\frac{E_{p}A_{p}}{E_{m}A_{m}}=\frac{N^{2}}{\sqrt{N}} \tag{1}\]  
\[\frac{L_{p}}{L_{m}}=N \tag{2}\]  
where E and A are respectively [PERSON]'s modulus and the cross-sectional area of the prototype (p) and model (m) pile. The piles' lengths, L, are related by the scale factor N.  
The soil used in this study was Jumunjin sand. It is classified as SP in the Unified Soil Classification System (USCS) with \(\gamma_{min}=1.351\ g/cm^{3}\), \(\gamma_{max}=1.596\ g/cm^{3}\), and \(C_{u}=1.402\)[40]. Where \(\gamma_{min}\) is the minimum unit weight, \(\gamma_{max}\) is the maximum unit weight, and \(C_{u}\) is the coefficient of uniformity. The first step consisted of filling and compacting the lower, dense (relative density 70%), sand layer to a height of 400 mm as a bearing stratum (Figure 2a). Then, piles of length, 400 mm and 500 mm were penetrated into this layer to depths of 50 mm (3D) and 150 mm (9D), respectively. The piles were then fixed in place with a holder and the upper, loose (relative density 35%), soil layer was poured into the container to a thickness of 250 mm.  
Figure 2: Schematic of experimental set-up. (**a**) Elevation view; (**b**) top view.
# Unmanned Surface Vehicle Using a Leader-Follower Swarm Control Algorithm
## 4 System Structure of the USV
### Configuration of the Operating System  
#### 4.3.1 Design of the Ground Operation Console  
The land operation console is a device that transmits mission commands to each USV. It is waterproof and has a wheeled carrier for marine exploration missions where the installation location often changes each time a mission is performed. An AC/DC converter, commercial PC parts, a USB serial expansion port, and an Ethernet expansion port for supplying power to communication equipment are installed inside the waterproof case. Considering that it is mainly used in a salty environment, a bracket, jig, storage space, and top plate for fixing internal parts were manufactured using an aluminum material that does not easily corrode. The operating console's operating system (OS) is a user-friendly Windows and is equipped with a graphical user interface (GUI) program and a remote access program that can be directly connected to the USV. Figure 13 shows the internal and external configuration of the land operation console manufactured in this study.  
Figure 11: Configuration diagram of the power system.  
Figure 12: Configuration diagram of the communication system.
# NOMA or OMA in Delay-QoS Limited Satellite Communications: Effective Capacity Analysis
## 4 Results
Figure 8: The sum capacity difference between NOMA and OMA versus \(\rho\) for various fading conditions, \(\theta_{c,t}\), and \(\alpha\) values. Figure 7: The impacts of various fading conditions and \(\alpha\) values on the effective capacity difference between NOMA and TDMA for User \(t\).
# A Miniaturized and Fast System for Thin Film Thickness Measurement
## 4 Simulation  
The following problems have been found in the actual measurement of various types of films. (1) The absorption and reflection coefficients varying with film materials, which results in different fringe contrast of the actual film interference signal; (2) the influence of external stray light causes local noise in the reflected spectrum signal, which have an impact on the film thickness calculation. Besides,  
Figure 4: Peak extraction and film thickness calculation.  
to clarify the effective range, inherent error, and the feasibility of this system, we conduct a simulation analysis based on the thin-film interference model. By adjusting the fringe contrast, the thickness, and the noise of the interference signal, we clarify the error influence of different measurement conditions, thus verify the robustness of the algorithm.
## 4 Result and Discussion
### PMP-MPC Framework  
The mission profiles of HEAGV in flight and on the ground under two driving cycles are shown in Figure 9. The first driving cycle A used in this paper consists of two flight phases and one ground phase, totaling 2360 s. To cross obstacles, the HEAGV switches to flight mode. In the first flight stage, HEAGV goes through the vertical takeoff stage, adjusts its attitude to start horizontal flight after reaching a certain altitude, and then completes vertical landing by hovering. In the second stage, the HEAGV switches to ground driving mode. At this stage, the battery needs to accumulate energy to meet the needs of the next flight, and the SOC curve generally shows an upward trend during this process. In the third stage, the HEAGV is used to cross obstacles, just as in the first stage. The second driving cycle B consists of two ground phases and one flight phase for a total of 1205 s. First HEAGV drives from the ground, then switches to flight mode, and finally switches to ground mode. The speed of HEAGV under two driving cycles A and B is shown in Figure 10.
# Amphishea: A Novel Persistent Buffer Management Strategy to Improve SMR Disk Performance
## 5 Amphisbaena Design
### Locality-Aware Buffer Space Management  
#### 5.2.1 Space Layout of the Persistent Buffer  
Figure 8a depicts the state of the persistent buffer at the initialization of the storage system. The space allocator divides the persistent buffer into two sub-buffers, HFB and LFB, at a default scale, and sets different granularities for their space allocation units to  
\begin{table}
\begin{tabular}{l p{227.6 pt}} \hline \hline
**Role Type** & **Description** \\ \hline LUP Block & The data blocks have not been updated, or their last update interval exceeded the short update period threshold. \\ SUP Block & The last update interval of the data block is within the short update period threshold. \\ Ghost Block & This role block is used to record the block number of the blocks that are evicted from the high-frequency buffer. \\ Shadow Block & This role block is used to record the block number of the blocks that are not admitted to the high-frequency buffer. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed descriptions of the different roles.  
Figure 7: A schematic diagram of the organization of the role blocks.  
accommodate blocks with temporal locality characteristics and blocks with spatial locality characteristics, respectively. However, in some real-world workloads, write requests at different epochs exhibit either spatial locality or temporal locality. Fixed-size LFB and HFB make it difficult for persistent buffers to efficiently cope with write requests with different locality characteristics. Therefore, the sizes of LFB and HFB are periodically reallocated to match the current I/O access pattern of the workload. Figure 8b,c show the state of the HFB and LFB when they are reallocated.  
Specifically, HFB uses a block that is the same size as the physical sector of SMR disks (i.e., 4 KB) as the basic space allocation unit. The block-based allocation unit makes it easier for the space allocator to allocate space for different roles of blocks in a fully associative manner, facilitating more efficient space utilization in the HFB. Compared to the HFB, the LFB accommodates most of the new write traffic. Since coarse-grained allocation units feature stronger spatial locality, which contributes to improved I/O efficiency for large requests and cleanup activity, the chunk is used as the space allocation unit in the LFB. The space allocator dynamically allocates a chunk to any unmapped zone in a set-associative manner. As shown in Figure 9, Zone 0 consists of 256 chunks of size 1 MB, and each chunk maps 256 contiguous 4 KB block offsets within the zone. For example, chunk 0 is mapped to a contiguous address from LBA = 0 to LBA = 255 within zone 0. When the write request 1 arrives, the space allocator writes target blocks 0\(\sim\)2 to chunk 0 according to the block offset in zone 0.
## 4 Hardware Customization
### RF- and LoRa-Based Coordinator Node  
The RF and LoRa-based coordinator is designed and integrated into the architecture for improving the connectivity for maintaining stable communication between accessories detection, health mote, and gateway. It consists of two different wireless communication technologies i.e., 433 MHz-based long-range (LoRa) and IEEE 802.15.4-based Zigbee module, as shown in Figure 13a.  
Moreover, it monitors and supervises all of the detection modules of the individuals and sends the sensor information to the gateway. The RF- and LoRa-based coordinator customized board is shown in Figure 13b, it consists of a RF transceiver, Microcontroller, LoRa Module, and power supply unit. Technical specifications of all of the modules used are listed in Table 7.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Specifications** \\ \hline Microcontroller: & ATMega328P \\ Communication module: & nRF24L01+ Wireless module (2.4 GHz) \\ Voltage converter: & +5 V \\ Temperature and Humidity Sensor: & DHT11 \\ Smoke Sensor: & MQ-2 \\ Fire Sensor: & Fire Flame Sensor \\ Vibration Sensor: & Vibration detection module \\ Display: & 20 \(\times\) 4 Alphanumeric LCD \\ Power trails: & +5 V \\ Power jack: & +12 V External power supply \\ Programming pins: & FTDI and ICSP \\ \hline \hline \end{tabular}
\end{table}
Table 6: Technical Specifications of Site Health Monitoring Mote.  
Figure 13: (**a**) RF and LoRa Coordinator (**b**) Customized Hardware of RF and LoRa coordinator.
# Error Investigation on Wi-Fi RTT in Commercial Consumer Devices
## 3 Wi-Fi RTT Distance Error Measurements  
### Experiment Devices and Software  
Many new Android-powered smartphones support the Wi-Fi RTT feature. This study chose three smartphones from different manufacturers as the initiator. This includes Google Pixel 2 (from Google), Xiaomi Redmi K20 (from Xiaomi), and LG V50S (from LG). All of them were embedded with an Android 9+ system to support the Wi-Fi RTT feature. More details can be found in Table 1.  
However, as discussed in Section 2.2, Google Wi-Fi is the only standard with proven compatibility available to consumers at the moment. Therefore, we chose the latest Google Nest Wi-Fi in this study. The AP operated in simultaneous dual-band (2.4 GHz/5 GHz, only 5 GHz supports Wi-Fi RTT).  
To collect Wi-Fi RTT measurement samples, we installed the Android app named WiFiRTScan (developed by Google) on each smartphone to send ranging requests and receive ranging results.  
All the data processing, analysis, and algorithms were operated using Python 3.8 and Matlab on a desktop with an Intel i7-9700 CPU (3.00 GHz) and 32 GB memory.