esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.2 Land Suitability Extraction Module  
In this paper, the multi-LUCC suitability extraction problem is transformed into several binary classification problems. The various types of land expansion were separated to extract the suitability for conversion to 6 types of land. The specific steps are as follows: (1) taking other spatial driving factor data and neighborhood efficiency as independent variables, the expansion data of six land types between LUCC from 2015 to 2020 are extracted as dependent variables; (2) stratified sampling 10% of the data input into the RF model for training; and (3) put all the driving factor data into the trained RF model for prediction, and obtain the expansion suitability data of 6 types of land. Based on the idea of integrated learning, the random forest model combines multiple decision trees into a forest and uses the majority voting rule to calculate the probability \(P_{i,k}\) of converting the land use type of cell i to k. The formula for calculating \(P_{i,k}\) is as follows:  
\[P_{i,k}=\frac{\sum\limits_{m=1}^{n}I(h_{m}(x)=Y_{k})}{n} \tag{7}\]  
where the value of \(Y_{k}\) is 0 or 1, the value of 1 indicates that other land use types are converted to land use types k, and the value of 0 indicates other conversions; \(x\) is a vector composed of multiple driving factors; \(h_{m}(x)\) represents the prediction result of the mth decision tree model according to \(x\); \(I()\) is the classification result index function; \(n\) is the total number of decision trees.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 2 Related Work
\begin{table}
\begin{tabular}{p{42.7 pt} p{34.1 pt} p{34.1 pt}} \hline \hline
**Reference** & **Year** & **Research Focus** \\ \hline
[20] & 2015 & This study surveys major collision avoidance mechanisms discussed in different publications. These mechanisms are based on sensing, detection and collision avoidance. Authors briefly explained various characteristics, benefits and drawbacks. \\ \hline
[21] & 2016 & This survey addresses the characteristics of UAVs for civil applications over the period 2000 to 2015 considering networking and communication aspects. Authors surveyed the data requirements, network mission parameters and quality of service requirements. Moreover, they elaborated common networking requirements including scalability, security, privacy, safety, adaptability and connectivity. \\ \hline
[22] & 2017 & This study discusses open-source flight controllers, which are being used for academic research. It also introduces UAV along with required components. This study fully addresses software and hardware open-source flight controller platforms. \\ \hline
[23] & 2018 & The study focuses on UAV cellular communication and bridges the gap between 3 GPP standardization status quo and the future research. Specifically, it addresses downlink command and control (C\&C) channel for aerial users. \\ \hline
[24] & 2019 & This study presents a comprehensive survey of UAV developments and its integration into cellular networks. It highlights consumer UAVs, interferences challenges and mitigative solutions, UAV prototype and testbed activities, regulations, challenges and security issues of UAV-aided cellular communications. \\ \hline
[25] & 2019 & This study provides a comprehensive tutorial on applications and advantages of UAVs in wireless communication. It investigates potential challenges and important trade-offs in UAV-assisted wireless networks. It highlights key factors including energy efficiency, channel modeling, performance analysis and 3D deployment. It also describes several mathematical tools and analytical frameworks including game theory, transport theory, stochastic geometry, machine learning, and optimization theory. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Surveys of UAVs issues and challenges.
## 4 Instrument Design
### Detailed Designs
#### 4.2.2 Design of Sensor System  
Semiconductor sensors can receive charged particles incident into the probe. In terms of the linear response range, energy resolution, and spatial applicability, ion-implanted silicon semiconductor sensors are currently the optimal performing semiconductor sensors.  
The sensor systems in the high-energy proton and high-energy electron probes of this scheme both adopted the semiconductor detector telescope method [25; 26]. The high-energy proton probe used five ion-implanted silicon sensors in combination with a collimator to form a detection field of view with an opening angle of 40\({}^{\circ}\). The high-energy electron probe used six ion-implanted silicon sensors in combination with a collimator to form a detection field of view with an opening angle of 30\({}^{\circ}\). The function of the sensor system is to measure the energy loss of charged particles in the semiconductor detector and convert the energy into electrical signals and provide them to subsequent electronic circuits for analysis. Through the signals of the sensor and by combining with the amplitude analysis method and the appropriate logical working mode, the energy spectrum and flux of the particles can be measured. Ion-implanted semiconductor sensors have excellent energy resolution and are the mainstream sensors currently used.  
According to the index design of the instrument, the specific indexes of the high-energy proton and high-energy electron probe sensors are as follows. The thickness of the first sensor is 500\(\upmu\)m, and the thickness of the other sensors is 1 mm. The area and diameter of the sensor affect the geometric factor of the instrument, that is, the ability of the instrument to accept particles. Hence, the diameter of the sensor in the high-energy particle probe of this instrument is 12 mm.
# Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
## 4 Volumetrics
### Technological Solutions
#### 4.7.4 Depth Camera and Vision System  
The study presented by [PERSON] et al. [PERSON] et al. (2019) proposes an automated system for volumetric measurement of rectangular parcel boxes using a single LiDAR depth camera. It addresses the need for efficient and accurate dimensioning of boxes in the logistics industry, particularly in warehouse management. The system utilises a range of image processing techniques, including the flood fill algorithm, to detect the top plane of boxes, extract their dimensions, and measure their height, while the study does not directly delve into solving the 3D-BPP, the Pallet Loading Problem, or the CLP, it does briefly mention the integration of the box dimensions into a 3D bin packing application. Since the system proposes the integration of automated dimensioning with a 3D bin packing application, it supports principles of automation, data-driven decision making, efficiency, real-time tracking, and sustainability, all of which are central to Industry 4.0's goals for smart manufacturing and logistics.  
The authors of another study [35] employ a Realsense depth camera as part of their experimental setup to capture data in the real-world environment. This vision system is crucial for perception in the context of the Online 3D-BPP problem. Specifically, it is used to simulate the online packing and palletising scene. The target object, represented by a cuboid wooden block, is captured by the depth camera shooting vertically downward during the process. The authors present fitting results of three target objects segmented by Euclidean clustering, with the geometric centre points identified. The actual dimensions of the target objects are compared to the final average fitting results after 300 tests. The reported error of the final fitting results is within 1%, indicating a high level of accuracy.
## 6 Simulation Results
### Simulation Results of 2 WF-1 SST Topology
#### 6.2.2 Permanent dc Line-Line Fault on Line L\({}_{56}\)  
In the second test, dc line-line fault is developed on line L\({}_{56}\), which results in the operation of HVdc circuit breakers CB\({}_{56}\). This situation results in the disconnection of GS-VSC\({}_{3}\) from WF-VSC\({}_{5}\) and WF-VSC\({}_{6}\). Consequently, power from WF\({}_{5}\) starts flowing through WF-VSC\({}_{4}\) while \(P_{\text{WF6}}\) is lost, not accomplishing the GBSQSS standard--a drawback. The flow of power and the dc-link voltage under this scenario are similar to test-1 (disconnection of GS-VSC\({}_{1}\)) as shown in Figure 12.  
It is clear from the conducted tests that the 2 WF-ISST is not conforming to the GBSQSS and is hence not viable during GS-VSC disconnection and dc-link fault within remote/far-end/the last unit (unit-2 or unit-3 in Figure 5). As a result, a more feasible configuration is required to accommodate shortcomings of literature's best two topologies i.e., WFRT and 2 WF-ISST.
## 4 Evaluation Results and Discussion
### Experimental Setup
#### 4.1.1 Simulation Environment  
The adopted experimental setup is developed in the ALgorithms for Embedding of VIrtual Networks (ALEVIN) simulation tool, due to its extensive modularity [82]. It provides a wide spectrum of physical and virtual elements to facilitate any possible scenario. New algorithms and metrics can also be integrated on a regular basis due to its modular orientation. ALEVIN's scalability and modularity enabled the deployment of the designed simulation environment on a mid-range processing platform.
## 1 Introduction
The relationship between the propagation speed of the slow deformation wave in the rock and the loading rate has not been specifically studied. Since slow deformation waves mainly propagate in the Earth's crust, typically through rocks as the media, it is important to study their propagation and evolution in rocks. In the present study, indoor uniaxial loading tests were performed for a group of red sandstone samples under the loading rates of 0.1, 0.5, and 1 mm/min, respectively. In the process of deformation and failure of the rock samples, the speckle photography technique combined with the digital image correlation (DIC) technique were used to quantify the slow deformation waves. The evolution of the strain component \(\varepsilon_{xx}\) under the three loading rates was studied, and the propagation rates of the deformation waves under different loading rates were calculated. The relationships between the wave propagation velocity and loading rate were further analyzed. The analysis of obtained nephograms indicated that the Luders deformation occurred predominantly in the flow channels formed at the stage of macro-elastic deformation. It was related to the extreme values of the elastic standing waves.
## 3 Results
### The Influence of Talbot Power Exponent on Permeability  
The effective stress calculated according to Equation (6), the relationship between the effective stress, the permeability \(k\) and the broken coal with different Talbot power exponents was plotted as shown in Figure 9.  
As \(n\) increases from 0.1 to 1.0, the permeability \(k\) of the broken coal samples generally follows a power exponential distribution with three stages, which are shown in the YZ projection surface of Figure 9. In the first stage, the permeability \(k\) increases slowly. This is due to the dominance of smaller coal particles within the coal sample during this stage. As a result, the total voids within the sample remain relatively small, resulting in minimal changes in permeability. In the second stage, the permeability \(k\) generally shows a linear increase. This is due to an increased proportion of larger particle sizes, which increases the voids within the coal sample. Consequently, the internal penetration rate increases, resulting in significant variations in flow rate and rapid changes in permeability \(k\). In the third stage, permeability shows exponential growth. This is due to the compression of smaller coal particles to less than 10%. Consequently, larger coal particles dominate within the coal sample. The gaps between the larger particles widen and interconnect to form an  
Figure 8: The relationship between seepage velocity and pressure gradient and Talbot power exponent.  
extensive network of seepage. This leads to an increase in overall porosity, resulting in a second sharp increase in permeability.  
Figure 9 shows that the permeability of coal samples increases with the increases in the Talbot power exponent. This observation is consistent with the permeability model for broken rock masses proposed by [PERSON] et al. [30]. Under conditions of maximum gradation particle size, the permeability \(k\) of the coal sample reaches its peak value. This phenomenon may be attributed to the initial state of gap development and larger particle sizes, where a coal sample with a higher Talbot power exponent forms a highly loose skeleton. The strong pore connectivity in this state increases the permeability of the coal sample.
# Tertiary Waves Measured during 2017 Pohang Earthquake Using an Underwater Glider
###### Abstract  
An underwater glider equipped with a hydrophone observed the acoustic sounds of an earthquake that occurred on 15 November 2017 05:29:32 (UTC) in the Pohang area. The underwater glider observed the earthquake sounds after 19 s (05:29:51) at approximately 140 km from the Pohang epicenter. In order to distinguish the earthquake sound from the glider's operation noise, the noise sources and Sound Pressure Level (SPL) of the underwater glider were analyzed and measured at laboratory tank and sea. The earthquake acoustic signal was distinguished from glider's self-noises of fin, pumped Conductivity-Temperature-Depth profiler (CTD) and altimeter which exist over 100 Hz. The dominant frequencies of the earthquake acoustic signals due to the earthquake were 10 Hz. Frequencies at which the spectra had dropped 60 dB were 50 Hz. By analysis of time correlation with seismic waves detected by five seismic land stations and the earthquake acoustic signal, it is clearly shown that the seismic waves converted to Tertiary waves and then detected by the underwater glider. The results allow constraining the acoustic sound level of the earthquake and suggest that the glider provides an effective platform for enhancing the earth seismic observation systems and monitoring natural and anthropogenic ocean sounds.  
underwater glider; Pohang earthquake; tertiary waves (T-Waves); acoustic signal +
Footnote †: journal: Applied Sciences
## 2 Upper-Level Controller
### Controller Design  
The NMPC controller aims to ensure that the vehicle can accurately follow the desired trajectory. Hence, the error between the predicted states and the reference path is as tiny as possible. In addition, smooth control also needs to be guaranteed, which means that  
Figure 2: Kinematic model of underground mining articulated vehicle.  
changes in the control inputs should be minimized. Consequently, the cost function of the NMPC controller can be expressed as  
\[J(\chi(t),\Delta\mathcal{U}_{t})=\sum\limits_{k=1}^{N_{\mathrm{p}}}\|\hat{\chi}(t +k|t)-\chi_{\mathrm{ref}}(t+k|t)\|_{\mathrm{Q}}^{2}+\sum\limits_{k=0}^{N_{ \mathrm{c}}}\|\Delta u(t+k|t)\|_{\mathrm{R}}^{2} \tag{5}\]  
where \(\hat{\chi}(t+k|t)\) represents the predicted state of the vehicle based on the current measured state, \(\chi_{\mathrm{ref}}(t+k|t)=\big{[}x_{\mathrm{ref}}(t+k|t)\quad y_{\mathrm{ref }}(t+k|t)\quad\theta_{\mathrm{ref}}(t+k|t)\quad 0\big{]}^{T}\) is the status information of the reference path, \(N_{p}\) is the predictive time domain, and \(N_{c}\) is the control time domain. \(Q\) is the weight matrix for tracking error, \(R\) is the weight matrix for control inputs' increment, and both are the positive define matrices.  
In order to achieve higher tracking accuracy and improve the stability of the closed-loop system [27], a terminal cost is proposed based on the above NMPC controller as follows:  
\[\mathcal{Z}(\chi(t))=\|\hat{\chi}\big{(}t+N_{\mathrm{p}}\big{|}t\big{)}-\chi_ {\mathrm{ref}}\big{(}t+N_{\mathrm{p}}\big{|}t\big{)}\|_{\mathrm{P}}^{2} \tag{6}\]  
where \(P\) is the weight matrix for  
The first group of the solution is applied to the vehicle, which can be expressed as  
\[u(t)=\Delta u^{*}(t|t)+u(t-1) \tag{16}\]  
After applying the control inputs, the vehicle feeds back the current states again to correct states in the controller. NMPC performs a new round of predictive control based on the new reference path and the new states of the vehicle.
## 4 Dataset Description
### Scenarios and Environments
#### 4.1.1 Static Scenario (S1)  
The first scenario is a long static acquisition (45 min), performed in a near-perfect open-sky environment. We took a small light post as a visible landmark to perform the acquisition with each device separately. The devices were placed on a stable tripod as represented in Figure 3a, allowing long acquisition with minimal disturbances. The tripod landmark was surveyed using our reference receiver to obtain its precise location. While the light post is relatively wide, this setup was assessed to be suitable for the purpose of the survey, given the positioning accuracy of the devices. Moreover, the positioning of the antenna phase centre is unknown for all Android devices, so a highly precise benchmark is not required.  
The receiver taken as reference for the visibility analysis in this scenario is a base station located on top of the nearest building, about 100 m northeast. Figures and statistics of the survey are provided in Appendix B.
# A Key Management Scheme for Mobile Wireless Sensor Networks
## 4 Evaluation and Comparison
### Size of the Network and Storage Efficiency
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Prestorage** & **Working** \\ \hline PCAT & \((n-1)\cdot(m+l_{h})+m\cdot l_{a}+l_{pu}+l_{pr}\) & \((n-1)\cdot(m+l_{h})+m\cdot l_{a}+l_{pr}+v(l_{k}+l_{ID})\) \\ FPWK & \((n-1)\cdot l_{k}\) & \((n-1)\cdot l_{k}\) \\ EG & \(r\cdot(l_{k}-l_{kID})\) & \(r\cdot(l_{k}+l_{kID})+v\cdot(l_{ID}+l_{kID})\) \\
2C & \(r\cdot(l_{k}+l_{kID})\) & \(r\cdot(l_{k}+l_{kID})+v\cdot(l_{k}+l_{ID})\) \\
1–5C & \(r\cdot(l_{k}+l_{kID})+l_{k}\) & \(r\cdot(l_{k}+l_{kID})+v\cdot(5\cdot l_{kID}+l_{ID})+l_{k}\) \\ RSDTMK & \(r\cdot l_{s}+l_{k}+r\cdot l_{sID}\) & \(\varrho\cdot(l_{k}+l_{kID})+v\cdot(l_{ID}+l_{kID})\) \\ SSL & \(l_{pr}+2l_{pu}+l_{sign}\) & \(l_{pr}+2l_{pu}+l_{sign}\) \\ PGK & \(l_{k}\) & \(l_{k}\) \\ LEAP+ and FLEAP & \(2\cdot l_{k}\) & \(v\cdot(l_{k}+l_{ID})+l_{k}\) \\ SKKE & \(l_{k}\) & \(v\cdot(l_{k}+l_{ID})+l_{k}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Memory required by the protocols. PCAT: public-key cryptography with authentication table; FPWK: full pairwise keys; EG: Eschenauer and [PERSON] key predistribution; 2C: \(q\)-composite with \(q=2\); 1–5C: \(q\)-s-Composite with \(q=1\) and \(s=5\); RSDTMK: Random seed distribution with transitory master key; SSL: Secure Sockets Layer; PGK: plain global key; LEAP+: localized encryption and authentication protocol plus; FLEAP: fast LEAP; SKKE: symmetric-key key establishment. LEAP+, FLEAP and SKKE require a pairwise key per link. PGK has the lowest request, since only one key must be stored. Although SSL requires a large memory area, but fixed. The additional parameter \(l_{sign}\) represents the length of a signature with public-key cryptography. Let us consider the following case study: \(n=100,m=4,r=10,v=10,e=14,l_{k}=16\) bytes, \(l_{a}=1\) byte, \(l_{ID}=1\) byte, \(l_{sign}=64\) bytes, \(l_{slD}=2\) bytes, \(l_{h}=20\) bits, \(l_{pu}=l_{pr}=32\) bytes and \(l_{hD}=1\) byte (2 in RSDTMK). During the working phase, the memory used to store secret material in PCAT 535 bytes, in FPWK 1584 bytes, in EG 190 bytes, in 2C 340 bytes, in 1-5C is 246 bytes, in RSDTMK 282 bytes, in SSL 160 bytes, in PGK 16 bytes, in LEAP+, FLEAP and SKKE 186 bytes. These dimensions can be compared with the storage area of the MSP430 microcontroller on Tmote Sky, which features 10 kB of RAM and 48 kB of Flash memory.
# Image Degradation Modeling for Real-World Super Resolution via Conditional Normalizing Flow
## 3 Materials and Methods
### Training Results  
We trained the proposed IDFlow with only the NLL loss as mentioned above for 60 k iterations. During each training batch, we sampled 16 RGB HR-LR pairs randomly cropped from the original training images in RealSR-Train for blur degradation modeling. The cropped HR patch size was set to 128 \(\times\) 128. For real-world noise modeling, we used 16 noise patches with a size of 128 \(\times\) 128 randomly cropped from noisy image patches in DPED noises. Data augmentation was performed during training, including random rotation by 90\({}^{\circ}\), as well as horizontal and vertical flipping. We utilized the Adam optimizer [28] to optimize IDFlow training with \(\beta_{1}=0.9,\ \beta_{2}=0.99,\ and\ \varepsilon=10^{-8}\). The learning rate was first set to \(2\times 10^{-4}\) and decreased to half after 30 k, 40 k, and 50 k training iterations.  
To illustrate that IDFlow can help promote the performance of existing SR methods in real-world scenarios, two known SR models RRDBNet [4] and ESRGAN [4] were used for \(\times\)4 super resolution in subsequent experiments. For the model RRDBNet, we used L1 loss to train RRDBNet for 60 k iterations. The learning rate was set to \(2\times 10^{-4}\) and decreased to \(1\times 10^{-4}\) after 50 k training iterations. Moreover, ten images were selected from RealSR-Test as the validation set to test the model SR performance after every 2 k iterations. The model with the best PSNR results was used for experiments. For the model ESRGAN, we trained it for 60 k iterations, and, based on the training code provided by [10], the losses for training included L1 loss, perceptual loss, and GAN loss [4]. The final trained model was used for experiments. The learning rate was initialized as \(1\times 10^{-4}\) and decreased to half after 5 k, 10 k, 20 k, and 30 k training iterations. Both RRDBNet and ESRGAN training were optimized with the Adam optimizer with the default setting. We implemented IDFlow as well as SR models by using the PyTorch framework [29] on a desktop workstation equipped with an NVIDIA 1080 Ti GPU.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 4 BackCom: Signal Processing and Detection
### BackCom Signal Processing Aspect
#### 4.1.3 BackCom Decoding  
It is a challenging task to obtain transmitted data from small power signals. However, there are various proposed decoding techniques for BackCom to extract information from weaker reflected signals. [PERSON] et al. designed a composite hypothesis testing decoding rule for non-coherent channel-coded BiS radio tags [73]. A multi-antenna decoding scheme that does not require channel estimation was proposed [32], and it could be implemented using standard analog components. This decoding type is referred to as \"\(\mu\)mo\" (MicroMo) decoding, as shown in Figure 14. The dual-antenna MicroMo receiver (Alice) could decode [PERSON]'s backscattered information. Moreover, backscattered signals can be separated from direct TV transmissions. The best part of this decoding scheme is that it accomplishes all tasks without any channel estimation or digital computation. Furthermore, the BiGroup decoding technique allows parallel decoding of commodity off-the-shelf (COTS) RFID tags. It can also address high tag collisions in BackCom networks [83]. Usually, the decoding of information depends on the detection of signals. Once signals are detected, they can be compared to a certain threshold level to decide the desired bit. Let us now discuss the signal detection schemes for BackCom.  
Figure 13: Interference in BackCom.