esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring Water Area Dynamics in Kashgar (2003-2023) Using Multi-Source Remote Sensing Data
## 2 Materials and Methods
### Methods  
#### 2.3.1 Water Body Extraction  
This study evaluates five classification methods--Mahalanobis distance [25], minimum distance [26], maximum likelihood [27], support vector machine [28], and random forest [29]--to classify land cover using Sentinel-2 and Landsat imagery. This comparative analysis aims to identify the optimal classification algorithm and remote sensing data combination for the Kashgar region. Land cover is categorized into five classes: water bodies, deserts, vegetation, ice and snow, and built-up areas. This study focuses on assessing the accuracy of water body extraction.  
This study focused on remote sensing imagery acquired from June to September, coinciding with the rainy season in Kashgar, a period conducive to the selection of suitable water body extraction methodologies. However, this period is characterized by vigorous vegetation growth, resulting in prevalent forest shadows and occasional cloud shadows within the imagery. These features exhibit spectral signatures similar to water bodies, posing a challenge for accurate classification. To enhance the discrimination between shadows and water bodies, this study incorporated auxiliary feature values, namely the Normalized Difference Water Index (_NDWI_) [30] and the Enhanced Vegetation Index (_EVI_) [31], into the extraction process, thereby mitigating classification errors. The NDWI and EVI are calculated as follows:  
\[NDWI=\frac{\rho_{Green}-\rho_{NIR}}{\rho_{Green}+\rho_{NIR}} \tag{1}\]  
\[EWI=\frac{\rho_{Green}-(\rho_{NIR}+\rho_{SWIR})}{\rho_{Green}+(\rho_{NIR}+ \rho_{SWIR})} \tag{2}\]  
where \(\rho_{Green}\), \(\rho_{NIR}\), and \(\rho_{SWIR}\) represent the reflectance values in the green, near-infrared, and short-wave infrared bands, respectively. For Landsat 5 imagery, \(\rho_{Green}\), \(\rho_{NIR}\), and \(\rho_{SWIR}\) correspond to bands 2, 4, and 5, respectively; for Landsat 8 imagery, they correspond to bands 3, 5, and 6, respectively.
## 4 Results and Discussion
### Generating the DCGAN Image  
The DCGAN method is used to generate a new dataset based on the original data by considering factors from the original image. There are two training parts used here:  
* Part 1: the discriminator is trained to maximize the probability of correctly classifying the given input as either real or fake.
* Part 2: the generator is trained by minimizing _log (1-D(G-(Z)))_ to generate a better fake image.  
The process starts by generating fake images based on the DCGAN approach. Initially, the picture looks like a blank canvas. In the 252 nd iteration, the shadow on chest X-ray images gradually appeared. Following the 504 th iteration, the chest portion became more precise. Once the 1000 th iteration was reached, the produced image presented a recognizable chest X-ray image. Afterwards, the picture gradually appeared throughout the iteration. Finally, in iterations > 2000, the generated image revealed an appealing result, with transparent portions of the lung area, rib bone, and even back spine bone observed, as shown in Figure 6.  
Figure 6: DCGAN iteration for image generation.  
In Figure 7, during the image generation process, the generator (\(G\)) loss rate is relatively high initially and then gradually decreases relative to the iterations. In comparison, the discriminator has fluctuated with respect to its value since the beginning of the iterations. However, when it reaches the 1000 th plus iteration, the discriminator loss is flat and reaches zero.
## 3 Full-Scale Testing
### Test Methodology  
The printed specimens have been tested in the Strength of Materials laboratory at LSBU. After taking measurements of cross-sectional dimensions and length, the specimen is placed in a Zwick/Roell 100 kN universal testing machine as shown in Figure 11a. Care has been taken to position the specimens so that the load is applied as concentrically as possible in order to minimize any additional bending stresses arising from load eccentricity. A displacement transducer is placed in contact with the upper loading platen in order to record the vertical displacement of the platen, which corresponds with the end shortening of the elliptical tube specimen. Strain gauges have been affixed around the midspan cross-sections of PLA specimens EHS01, EHS10 and EHS16 at the positions indicated in Figure 11b to assess the influence of cross-sectional aspect ratio on longitudinal strain. The specimens are loaded under displacement control at a rate of 0.5 mm/min until failure, equating to an average strain rate of 3.0 \(\times\) 10\({}^{-4}\) s\({}^{-1}\). Considering the viscoelastic response of polymers to strain rate, the Ree-Eyring [42] model adapted by [43] has been applied to assess for any significant changes to the mechanical properties of the 3D-printed PLA and ABS materials in compression. It is found that, when taking the difference in applied strain rates from the mesoscale material tests and full-scale stub column tests into account, there is little difference found in the mechanical properties.  
Figure 11: (**a**) Specimen EHS01 positioned for testing in Zwick/Roell 100 kN universal testing machine; (**b**) locations of strain gauges A–D, with point of initiation of buckling noted.
## 6 Simulation Results
### SQ-Based CSI Feedback and RVQ-Based DI Feedback  
The sum-rate curves of IA with perfect CSI and mixed CSI/DI feedback in \((2\times 2,1)3\), \((3\times 2,1)4\) and \((3\times 3,1)5\) are compared in Figures 11-13, respectively, and the corresponding approximation errors are calculated in Figure 14. The theoretical sum-rate curves are based on (27). In each snapshot, the channel matrices and decoders were separately quantized using SQ and RVQ. The quantization bit number for CSI was fixed at \(B=8\), and the quantization bits for DI increased from 10 to 16 in \((2\times 2,1)3\) and \((3\times 2,1)3\) and increased from 16 to 21 in \((3\times 3,1)5\). In all the SNR regimes and different quantization bit configurations, the normalized approximation errors are all less than 5%, which makes the theoretical sum-rate expressions applicable to general IA scenarios.  
As shown in Figures 11 and 12, the rate loss resulting from decoder quantization with \(B_{u}=16\) is minor even for an SNR of 40 dB. Meanwhile, in Figure 13, considerable rate loss results from using RVQ with 21 quantization bits when the SNR is higher than 20 dB. However, since a 21-bit RVQ codebook hardly seems practical, the dominant area for the VQ-based quantization strategy may be limited to scenarios with fewer receiving antennas and low SNR regimes.  
Figure 10: Normalized error of the theoretical IA sum-rate with SQ-based CSI feedback in three cases.  
Figure 11: Performance comparison of theoretical and simulation results with SQ-based CSI feedback and RVQ-based DI feedback with different quantization bits in \((2\times 2,1)3\).  
Figure 12: Performance comparison of theoretical and simulation results with SQ-based CSI feedback and RVQ-based DI feedback with different quantization bits in \((3\times 2,1)4\).  
Figure 14: Normalized error of the theoretical IA sum-rate with SQ-based CSI feedback and RVQ-based DI feedback in three cases.  
Figure 13: Performance comparison of theoretical and simulation results with SQ-based CSI feedback and RVQ-based DI feedback with different quantization bits in \((3\times 3,1)5\).
## 2 Materials and Methods  
LUT techniques have been widely used in SFDI to determine optical properties without performing time-consuming fitting procedures [18]. However, LUT techniques are rarely used to quantify tissue perfusion properties in conjunction with SFDI imaging. In this study, we developed a highly efficient two-stage LUT technique consisting of two connected LUTs. The first and second stage LUTs rapidly quantify tissue optical and perfusion properties, respectively. Figure 1 summarizes the overall workflow of the two-stage LUT technique.  
The stage-1 LUT was generated by inverse modeling diffuse reflectance obtained by Monte Carlo simulation in the spatial frequency domain, and the stage-2 LUT was generated by inverse modeling of an analytical model of tissue absorption. Quantification of tissue perfusion properties was based on SFDI imaging at 660 and 850 nm. These two wavelengths located on the opposite side of the isosbestic wavelength, around 800 nm, have been previously demonstrated to effectively quantify blood perfusion properties [16]. With  
Figure 1: Overview of two-stage LUT. Monte Carlo simulation of reflectance and an analytical model of blood absorption are inverted to generate LUTs for optical property mapping (stage-1) and perfusion mapping (stage-2), respectively. SFDI measurements at 660 and 850 nm are used as inputs to quantify absorption and reduced scattering coefficients. The absorption coefficients are later used as search indices to quantify oxygen saturation and total hemoglobin concentration.  
the two-stage LUT, reflectance and modulation measurements at both 660 and 850 nm were first used as search indices to determine absorption and reduced scattering coefficients using the stage-1 LUT. The absorption coefficients at 660 and 850 nm were subsequently used as searching indices to efficiently determine oxygen saturation and blood concentration using the stage-2 LUT. In the rest of this section, detailed procedures for generating and evaluating the performance of two-stage LUT for quantifying perfusion properties are presented.
###### Abstract  
The detection and localization of radiation sources in urban areas present significant challenges in electromagnetic spectrum operations, particularly with the proliferation of small UAVs. To address these challenges, we propose the Multi-UAV Reconnaissance Proximal Policy Optimization (MURPPO) algorithm based on a distributed reinforcement learning framework, which utilizes an independent decision making mechanism and collaborative positioning method with multiple UAVs to achieve high-precision detection and localization of radiation sources. We adopt a dual-branch actor structure for independent decisions in UAV control, which reduces decision complexity and improves learning efficiency. The algorithm incorporates task-specific knowledge into the reward function design to guide UAVs in exploring abnormal radiation sources. Furthermore, we employ a geometry-based three-point localization algorithm that leverages multiple UAVs' spatial distribution for precise abnormal radiation source positioning. Simulations in urban environments demonstrate the effectiveness of the MURPPO algorithm, with the proportion of successfully localized target radiation sources converging to 56.5% in the later stages of training, approaching a 38.5% improvement over a traditional multi-agent proximal policy optimization algorithm. The results indicate that MURPPO effectively addresses the challenges of the intelligent sensing and localization of UAVs in complex urban electromagnetic spectrum operations.  
deep reinforcement learning; electromagnetic spectrum; localization; multi-UAV
###### Abstract  
Undesirable vibrations occurring in undersea pipeline structures due to ocean currents may shorten the lifecycle of pipeline structures and even lead to their failure. Therefore, it is desirable to find a feasible and effective device to suppress the subsea vibration. Eddy current tuned mass damper (ECTMD), which employs the damping force generated by the relative movement of a non-magnetic conductive metal (such as copper or aluminum) through a magnetic field, is demonstrated to be an efficient way in structural vibration control. However, the feasibility and effectiveness of ECTMD in a seawater environment has not been reported on before. In this paper, an experiment is conducted to validate the feasibility of an eddy current damper in a seawater environment. A submerged pipeline is used as the controlled structure to experimentally study the effectiveness of ECTMD. The dynamic properties of the submerged pipeline are obtained from dynamic tests and the finite element method (FEM). The optimum design of TMD with a linear spring-damper element for a damped primary structure is carried out through numerical optimization procedures and is used to determine the optimal frequency tuning ratio and damping ratio of ECTMD. In addition, the performance of ECTMD to control the submerged pipeline model is respectively studied in free vibration case and forced vibration case. The results show that the damping provided by eddy current in a seawater environment is only slightly varied compared to that in an air environment. With the optimal ECTMD control, vibration response of the submerged pipeline is significantly decreased.  
eddy current damping; tuned mass damper; submerged pipeline model; vibration control; seawater environment +
Footnote †: journal: Applied Soft. 2017
## 2 Background
### Botnet Components
#### 2.1.4 C&C Communication Channels  
It is common for botnets not to develop new network protocols for their own communication. Rather, they typically utilize pre-existing established communication protocols as follows:  
* Internet relay chat (IRC)-based C&C channels: These utilize a push-based model, where the botmaster issues new commands to the botnet, which then responds promptly to these commands.
* HTTP-based C&C channels: These adopt a pull-based model, where bots are set up to check in with the C&C server periodically and retrieve any new commands.
* Peer-to-peer (P2P)-based C&C channels: These make use of peer-to-peer communication to either relay commands or to locate a C&C server [7].
* Domain name system (DNS)-based C&C channels: These use DNS tunneling, which is a technique that allows the encapsulation of non-DNS traffic within DNS packets [8].  
C&C communication channels are utilized by botnets to facilitate communication between the botmaster and bot clients, as well as between bot clients themselves. In the first generation of botnets, internet relay chat (IRC) was commonly used for this purpose. The bots would connect to the IRC channels created by the botmaster on the C&C server to await commands to perform malicious activity. However, this approach had the significant drawback of relying on a single point of failure. If IRC servers were taken down or identified, the entire army of bot clients would become ineffective. To address this issue, the second generation of botnets transitioned to peer-to-peer (P2P) protocols [7, 9]. This mechanism eliminated the vulnerability of a single point of failure. However, managing and controlling P2P-based botnets proved to be a challenge. As a result, botmasters have now shifted to utilizing HTTP for implementing C&C communication [9, 10, 11]. Furthermore, a new type of botnet has emerged, combining both P2P and HTTP protocols, known as hybrid botnets [6].
## 1 Introduction
In this work, PAT imaging is combined with this novel full ring UST system. The PA imaging modality can be easily combined with UST since both modalities share the same acquisition hardware. For this reason, the addition of the PAT to the UST is straightforward and will provide valuable functional information about a given tissue and is expected to improve the diagnostic capability of breast US for physicians. Figure 1: The three methods of illumination for photoacoustic tomography (PAT) imaging that are compared in this study, with the definitions of vertical and cross-sectional imaging depths. The design of our combined UST/PAT imaging system has been previously presented [39; 40; 41; 42]. This setup uses a ring illumination in conjunction with a ring US transducer for combined UST/PAT imaging. The ring-shaped beam in this system is generated by using a cone mirror and a parabolic reflector. This work specifically compares three different illumination methodologies for PAT imaging: full-ring, diffuse, and point illuminations. Using new findings from the three methods, it aims to show that full-ring illumination is the most effective method for creating PAT images due to its inherent cross-sectional fluence uniformity across vertical imaging depths (Figure 1). This is especially important for breast cancer screening when imaging close to the chest wall proves difficult. The three illumination methods are compared by imaging a three-layer polyvinyl chloride (PVC) tissue-mimicking phantom to gauge the advantages and disadvantages of common PAT imaging techniques. The experiments presented in this paper also all use the same data acquisition system and settings. Comparisons are made between PAT amplitudes for each cross-section and illumination methodology. Furthermore, the optimum position of the ring beam with respect to the targeted cross-section is examined.
# Laser Transmission Characteristics of Seawater for Underwater Wireless Optical Communication
## 3 Results and Discussion  
### Average AOA  
To study the laser divergence characteristics, the average AOA was obtained from two sources: (a) the AOA distribution on the whole receiving plane (perpendicular to the incident laser) for analyzing the overall divergence characteristics; (b) the AOA distribution at different offsets relative to the beam center on the receiving plane for analyzing the laser divergence characteristics at specific offsets.
# Review of Advanced Medical Telerobots
## 1 Introduction
### History of Telerobotics in Medicine
It was then replaced by the successful da Vinci Surgical System (from Intuitive Surgical Inc., Mountain View, CA, USA) [46], which added the 3D stereoscopic vision (enabling depth perception for the surgeons, which was detrimental in previous telerobotic systems), and enhancing maneuverability of MIS instruments. This technology was initially aimed at minimally invasive abdominal surgery [47], but it rapidly opened to other clinical applications [48]. It can be mentioned that da Vinci Surgical System was a complete revolution in the field of medical robotics. More details are provided in the subsequent sections of this paper. After the invention of the da Vinci Surgical System, other industries and academic centers proposed new robotic systems for specialized surgeries [49; 50]. For instance, in 2009, the German Aerospace Center (DLR) designed the MiroSurge telerobot for experimental research purposes. This robot introduced force feedback: a major missing function in commercial telerobotic systems. Haptic feedback is still an open research question, and several techniques and technologies have been proposed in research centers and industries with the hope of addressing the corresponding challenges (which mainly associate with the cost, instrumentation, transparency, and stability); see Section 2. We can also mention the Flex (Medrobotics Inc., Raynham, MA, USA), specifically designed for use in Trans-Oral Robotic Surgery, the MAZOR SpineAssist/Renaissance(r) and the ROSA(r) (Zimmer Biomet-Medtech, Montpellier, France), both dedicated to spinal surgery, and the BrightMatter(tm) suite (Synaptive Medical Inc., Toronto, ON, Canada) robots [51]. Among the more recent introductions on the international market [52; 53], the following products and companies can be mentioned: the Senhance (TransEnterix, Morrisville, NC, USA, approved by FDA for gynecologic and colorectal procedures in 2017), the Mako (from Stryker Corp., Kalamazoo, MI, USA, for total knee and hip replacement), and the Versius Robotic System (Cambridge Medical Robotics, CMR Surgical, Cambridge, UK, approved for use in Europe and currently studied by the American FDA).
# Spreading-Based Voice Encryption by Means of OVSF Codes
## 4 Experimental Results and Analysis
### Results and Analysis
#### 4.3.3 Computational Cost  
As discussed above, the tests performed used a 10 s signal, and a 160 s signal was obtained. Figure 4 shows the computational times for both the encryption process and recovery process. In either case, the execution times were shorter than the duration of the original signal, which is important for real-time systems. These tests were performed on a PC with Windows 10 Pro 64-bit operating system, an Intel Core i7-4790S processor at 3.2 GHz, 8 GB of RAM using Matlab R2019b.
# High-Resolution, Broad-Range Detection Setup for Polarimetric Optical Fiber Sensors
## 2 Materials and Methods
The quarter-wave plate was rotated by the action of the micrometer actuator in the rotation mount. The rotation of the waveplate was 776 \(\upmu\)rad (2.4 arc min) per graduation, corresponding to the retardation change of 1.55 mrad (4.8 arc min), while the full travel of the micrometer head yielded 175 mrad (\(10^{\circ}\)) of retardation change. Additionally, a Soleil-Babinet compensator (i.e., SBC-VIS from Thorlabs or RKQ 10 from B. Halle Nachfl. GmbH, Berlin, Germany) was used in place of the rotated quarter-wave plate. A side view of two configurations of the measurement setup is presented in Figure 4.  
Figure 4: Side view of the measurement setup (**a**) with a laser diode and a quarter-wave plate and (**b**) with superluminescent diode source and a Soleil-Babinet compensator. LD—laser diode; SLD—superluminescent diode; L—lens; P—polarizer; \(\lambda/4\)—quarter-wave plate; SBC—Soleil-Babinet compensator; WP—Wollaston prism; A—analyzer; C—CMOS camera.
## 2 Materials and Methods
### ROS Production  
Intracellular formation of ROS was determined via the oxidation-sensitive fluorogenic probe 2\({}^{\prime}\),7\({}^{\prime}\)-dichlorohydrofluorescein-diacetate (DCFH-DA). RAW264.7 cells were plated in 96-well plates (1 \(\times\) 10\({}^{5}\) cells/mL, 200 \(\upmu\)L/well) in DMEM supplemented with 10% FBS for 24 h and BWSPHs and SGID BWSPHs were added to the wells for a further 24 h at a final concentration of 0.5% (\(w/v\) dw). Cells were then washed with phosphate buffer saline (PBS) and exposed to DCFH-DA prepared in DMEM (20 \(\upmu\)M, 200 \(\upmu\)L/well) for 40 min at 37 \({}^{\circ}\)C in the dark. Intracellular esterases hydrolyse DCFH-DA to nonfluorescent 2\({}^{\prime}\),7\({}^{\prime}\)-dichlorohydrofluorescein (DCFH), which is then oxidised to fluorescent 2\({}^{\prime}\),7\({}^{\prime}\)-dichlororescein (DCF) upon reaction with ROS. DCFH-DA was aspirated, and cells were washed with PBS. The cells, except the negative control, were exposed to H\({}_{2}\)O\({}_{2}\) (1 mM, 200 \(\upmu\)L/well) for 60 min. The positive control was cells treated with H\({}_{2}\)O\({}_{2}\). The negative control was cells exposed to DMEM only. Fluorescence of cells was measured at a wavelength of 485 nm followed by excitation at 530 nm every 5 min over the 60 min exposure period (VarioskanFlash, Thermo Scientific). Results were expressed as % of the positive control.