esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Scalable Video Coding (SVC)  
Sending a raw quality video over the Internet consumes enormous network resources. Various techniques of video compression were considered compressing raw video to a smaller size without compromising quality. In 2003, AVC was introduced, which compressed/encoded the video with a specific quality (i.e., resolution, frame rate and signal-to-noise ratio). In contrast, SVC is an AVC extension that can encode the video in separate layers. If combined, these layers can improve the overall quality of the video. SVC comprises two layer types: base layer (BL) and enhancement layer(s) (EL). The SVC media has one base layer and multiple enhancement layers. Figure 2 shows such segmented layers of the \"Representation 2\" in Figure 1. The base layer provides the basic quality of the video. Adding enhancement layers to the base layer can increase the quality of the video. All lower quality level enhancement layers are needed to decode a higher level EL. SVC enables graceful video quality degradation compared to AVC by coping with congestion in real time by adding or removing layers to meet the network capacities of all devices. It also provides the opportunity to serve many users with heterogeneous devices with an improved QoE [15]. AVC generates multiple versions with different qualities of the same media that are transferred simultaneously over a network. This is also called simulcasting, as illustrated in Figure 3. Simulcast forwards several versions of media with higher qualities to higher bandwidth subscribers and lower qualities to lower bandwidth ones. Using AVC with DASH (AVC-DASH) to stream multimedia to heterogeneous devices, each segment will be encoded with different qualities. The multiple storage of these media segments increase the requirements for storage and network resources use. In AVC-DASH takes decisions are taken at the end of the segment. Since SVC media comprises layers, it has only one version of each segment, relying storage strain on servers. DASH coupled with SVC (SVC-DASH) enables flexible segment selections as compared to DASH only [16] The granularity of SVC-DASH quality control extends horizontally over time and vertically over layers.  
Figure 3: Simulcasting.  
Figure 2: Segmented Layers of Scalable Video Coding (SVC).
## 4 Multiple Objective Motion Optimization Problem
### Preventive Control to Avoid Future Collisions
#### 4.3.1 Example of Encountering a Collision  
Figure 5 shows an example when the greedy motion in one step leads to a situation where collision is hard to avoid in the next interval. The left sub-figure in Figure 5 shows the situation at time \(t\). If the MASS chooses to update its velocity to \(v_{1}\), the situation at \(t+1\) is shown in the right-top sub-figure in Figure 5. Since the MASS is very close to another ship \(B_{1}\), the VO of \(B_{1}\) covers the feasible velocity region of the MASS. Therefore, it becomes hard for the MASS to find a feasible acceleration to avoid a collision with \(B_{1}\). The right-bottom sub-figure shows the case when the MASS selects \(v_{2}\) at \(t\). The MASS arrives at \(X_{t+1}\) at \(t+1\), which makes it easy to plan its next motion step.  
Obstacles are hard to avoid when the MASS is very close to them. In such a case, its feasible velocity region is highly covered by the VOs of the surrounding ships, and the MASS cannot find a feasible way out to avoid collisions with some ships. In this case, collisions will happen in the future step. An intuitive way for the MASS to avoid such cases is to evaluate the risk of sailing into a region that is closely surrounded by other ships.
# An Adaptive Hybrid Automatic Repeat Request (A-HARQ) Scheme Based on Reinforcement Learning
## 4 The Proposed Adaptive HARQ Q-Learning Model
### Model Description
#### 4.1.1 5G NR System  
In this study, the \"NR TDD Symbol-Based Scheduling Performance Evaluation\" module of the 5G Toolbox of MATLAB R2021a is used as the main system simulation environment. Moreover, the visualization function of the BLER in the \"NR Cell Performance Evaluation with Physical Layer Integration\" module was invoked to form the simulation environment of this study [14; 19; 20; 21; 22]. The former example models a symbol-based scheduling scheme in the TDD mode and evaluates the network performance. Symbol-based resources schedule shorter transmission durations that span only a few symbols within a timeslot. In the TDD mode, physical uplink shared channel (PUSCH) and physical downlink shared channel PDSCH (PDSCH) transmissions are scheduled in the same frequency band with separation in the time domain. The latter example demonstrates the integration of a high-fidelity 5G Toolbox(tm) physical layer in a 5G NR node and models a 5G NR cell consisting of a set of user equipment (UE) connected to a 5G Base Station (gNB). The NR stack on the nodes includes radio-link control (RLC), medium-access control (MAC), and physical (PHY) layers.  
To avoid any additional delays caused by Q-learning models in real networks, we adopt an asynchronous framework that can separate the training process from the decision-making process of Q-learning. The framework includes two stages: offline training and online decision, and the detailed design is given in Section 4.2.
## 2 Study Area  
The Lithuanian coast is divided by the 1.1 km wide Klaipeda Strait, where Klaipeda Port is located into two parts: the Curonian Spit (hereinafter--the spit) sea coast (51 km) and the mainland coast (39 km) (Figure 1). Beyond 1991, when the Curonian Spit National and Seaside Regional Parks were established, the major part of the Baltic Sea coast in Lithuania (about 70 km long) has acquired the status of a protected area. Several coastal sectors with different characters can be distinguished: a technogenic coast that predominates near to Klaipeda and Sventoji Ports, i.e., areas of waste water disposal from the Butinge oil terminal, and the Mazeikia oil processing plant, and protected areas such as the Curonian Spit National Park, the Seaside Regional Park, the Baltic Sea Talasological Reserve, and the Butinge Geomorphological Reserve (Figure 1).  
Figure 1: Study area. (**1**) surface sand sampling site, (**2**) main settlements, (**3**) D6 oil platform (Ru), (**4**) Bütíngé oil terminal, (**5**) conservation areas, (**6**) borders, (**7**) offshore and nearshore dumping area, (**8**) beach nourishment site in Palanga, and (**9**) storage site of dredged bottom sediments.  
The Holocene elastic deposits occur along the entire length of the Lithuanian coastal zone. The Lithuanian coastal sediments are composed of quartz, K-feldspar (orthoclase, microcline), plagioclase (albite, anorthite), carbonates (dolomite, calcite), mica (biotite, muscovite), and clay minerals (illite, chlorite, kaolinite, montmorillonite, glauconite, and vermiculite) with admixture of heavy minerals [21; 50].  
On the mainland coast, the average 20-100 m wide beaches are backed by forendunes 4-12 m in height or by moraine cliffs 5-24 m in height. The southern part of the mainland coast structure between the 25 th and 31 st km (distance from Latvia-Lithuania border) is characterised by glacial (moraine) deposits formed during the Late Pleistocene and that, in most cases, occur on the abraded cliff coast. The first moraine cliff is located between the 25 th and 27 th km on the mainland coast near Saipiai (Figure 1). The cliff is 2.1 km long and 5-8 m in height. The second moraine cliff (Olando kepure) is 950 m long and 24 m in height at its highest point [56]. The highest cliffs are between the 30 th and 31 st km at Karkle. On the Curonian spit sea coast, the average width of the beaches varies from 32 to 75 m, and the relative height of the foredune changes from 5 to 16 m [12].  
The Baltic Sea is nontidal (amplitudes reach 3.5-4.0 cm); therefore, a wind-wave regime dominates. The cold (autumn-winter) season is marked by most days with strong winds. The annual mean wind speed is 4.7 m s\({}^{-1}\) and the wave height is 0.65 m. During the strongest storms, the wave height varies from 5 to 6 m [56]. The prevailing westerly (SW, W, NW) winds and waves are dominant in the coastal zone and generate the alongshore sediment transport from the Sambian Peninsula to the end of the Curonian Spit and along the mainland coast [24; 27; 57]. Currently, the alongshore sediment transport is disrupted by the Klaipeda port gate [58].
## 1 Introduction
### Identified Research Needs and Opportunities
In other situations, technical reliability becomes critical, and during environmental crises, minimizing the environmental impact associated with energy consumption takes precedence. Therefore, it is essential to develop a strategy that delivers optimal performance regarding economic, technical, and environmental aspects, allowing for intelligent and efficient energy management that is aligned with the real-world operational requirements of MGs in both grid-connected and islanded modes.
# Effect of Chitosan Nanoemulsion on Enhancing the
## 3 Results
### Antioxidant Activity
#### 3.9.2 FRAP Method  
The results of the analysis of variance of the antioxidant activity data using the FRAP method showed that there was a statistically significant difference between the samples treated with chitosan nanoemulsion and the control samples at a 1% level. In other words, there was a significant difference between chitosan nanoemulsion levels and the control samples (\(p<0.01\)). However, there was no statistically significant difference between the sixth and the ninth day in any of the treatments (chitosan nanoemulsion of 2.5 and 5 g L\({}^{-1}\) and the control). Furthermore, the antioxidant activity level varied from 0.11275 mol/L to 0.03238 mol/L (Figure 4b).  
Figure 4: Effect of chitosan nano-emulsion coatings (at 0 g L\({}^{-1}\), 2.5 g L\({}^{-1}\), and 5 g L\({}^{-1}\)) on antioxidant activity (a) 2,2-diphenyl-1-picrylhydrazyl-hydrate (DPPH) and (b) Ferric reducing antioxidant power (FRAP) of raspberries stored for 9 d (at \(4\pm 1\)\({}^{\circ}\)C with 90–95% RH). Control refers to untreated raspberries. The data shown are the mean \(\pm\) standard error of four replicates. Different letters indicate statistical significance (\(p<0.01\)).
## Blind Demodulation of Chaotic Direct Sequence Spread Spectrum Signals Based on Particle Filters  
**[PERSON] *, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]**  
College of Mechatronics Engineering and Automation, National University of Defense Technology,  
Deya Road, Changsha 410073, China; E-Mails: [EMAIL_ADDRESS] (D.Z.);  
[EMAIL_ADDRESS] (Z.H.); [PERSON]DRESS] (C.L.); [PERSON]MAIL_ADDRESS] (S.S.);  
[EMAIL_ADDRESS] (Y.Z.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-0731-8457-6387; Fax: +86-0731-8457-6387.  
_Received: 9 July 2013; in revised form: 22 August 2013 / Accepted: 10 September 2013 / Published: 13 September 2013_  
**Abstract:** Applying the particle filter (PF) technique, this paper proposes a PF-based algorithm to blindly demodulate the chaotic direct sequence spread spectrum (CDS-SS) signals under the colored or non-Gaussian noises condition. To implement this algorithm, the PFs are modified by (i) the colored or non-Gaussian noises are formulated by autoregressive moving average (ARMA) models, and then the parameters that model the noises are included in the state vector; (ii) the range-differentiating factor is imported into the intruder's chaotic system equation. Since the range-differentiating factor is able to make the inevitable chaos fitting error advantageous based on the chaos fitting method, thus the CDS-SS signals can be demodulated according to the range of the estimated message. Simulations show that the proposed PF-based algorithm can obtain a good bit-error rate performance when extracting the original binary message from the CDS-SS signals without any knowledge of the transmitter's chaotic map, or initial value, even when colored or non-Gaussian noises exist.  
**Keywords:** blind demodulation; chaotic direct sequence spread spectrum; particle filter; colored non-Gaussian noise  
**PACS Codes:** 05.45.Vx
## 2 Study Area and Data
### Research Data and Processing
#### 2.2.4 DEM Data  
Digital Elevation Models (DEM) play a crucial role in InSAR processing and the identification of geological disaster points [10; 23; 24]. Geological disasters such as landslides and debris flows are strongly influenced by terrain features like elevation and slope. Many studies consider topographic features as primary factors in the susceptibility of geological disasters. The Shuttle Radar Topography Mission (SRTM) data provided by the USGS Earth Explorer platform ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) (accessed on 6 December 2024)) have a spatial resolution of 30 m. Figure 3 below shows the DEM data of the study area, which contains the region's topographic information.
## 1 Introduction  
Various symmetrical and unsymmetrical multidentate organic ligands have played a crucial part in designing metal complexes via coordination with different metal ions [1]. The versatile design of various metal complexes has been observed due to their diverse oxidation states and coordination properties [2, 3]. The metal complexes have been widely used as drugs and diagnostic agents because they have shown easy interactions with different biomolecules and hence contributed to the development of new therapeutic or diagnostic agents [4, 5]. In various biological systems, copper shows many spectral and chemical properties due to the presence of different organic ligands in the coordination sphere [6]. The novel oxindole derivatives have been extensively explored and investigated for various biological applications [7, 8]. The cyclization reaction of various oxindole-derivatives is applied to the construction of C-C bonds [9; 10; 11; 12]. The functionalization of various C-H bonds of oxindole-derivatives has been reported as oxidative coupling of C-H bonds, Heck reaction, etc. for economical and elegant applications [13; 14; 15; 16]. Various transition metal complexes have been explored in several applications as optoelectronics [17; 18], semiconductors [19], biochemistry [20] and sensors for polymer membranes [21; 22; 23]. However, the nitrogen-containing macrocycles exhibit high affinity towards metal ions for coordination [24].  
In this study, the synthesis and various spectroscopic studies were performed for a novel mixed Cu (II) complex: the as-synthesized organic ligand named 1\(H\),1\({}^{\prime}\),1\({}^{\prime\prime}\)-H-[2,3\({}^{\prime}\):2\({}^{\prime}\)3\({}^{\prime\prime}\)-terbenzo[\(b\)]pyrrol]-2\({}^{\prime\prime}\)(3\({}^{\prime\prime}\)H)-one (L1) and the well-known diethyldithiocarbamate (DTC) ligand (L2) mixed with copper (II) chloride dihydrate (2:1:1 ratio) in methanol solvent to produce the target Cu (II) complex. The electrochemical investigation of the novel Cu (II) complex was studied by cyclic voltammetry (CV) in a three-electrode system by using tetrabutylammonium hexafluorophosphate (TBAPF\({}_{6}\)) as a supporting electrolyte. The prepared Cu (II) complex was utilized as electrode materials for the electrochemical detection of chloride ions (Cl\({}^{-}\)) using NaCl (10 \(\mu\)L, 0.1 M) in a standard buffer solution (pH = 7) with different scan rates.
# On-the-Fly Machine Learning for Improving Image Resolution in Tomography
## 2 Background and Notation
### Tomography
The number of angles from which the projection data has been acquired also influences the minimal voxel size, but, when a  
Figure 1: A schematic overview of the cone-beam geometry. The object is mounted on a rotation stage at distance \(SOD\) from the source. The object is supported on \(\Omega\), and the voxel grid \(\mathbf{x}\) is supported on \(\Omega_{\mathbf{x}}\). The detector is at distance \(SDD\) from the source and is divided into \(N_{u}\times N_{v}\) pixels of size \(r\times r\). We use the coordinates \((u,v)\) to denote a location on the detector. sufficiently large number of angles has been used, the voxel size as determined in Equation (4) is not further limited [22]. In addition to the detector pixel size, multiple optical phenomena influence the formation of projection images. Many of these optical effects introduce a blurring effect on the projection images. For instance, pixel cross-talk causes an element of the photo-multiplier array to measure some fraction of the incoming photons of its neighboring pixels [23]. Cross-talk can occur both in the scintillator, where the high-energy photons are converted into the visible spectrum, and in the photo-sensor itself. Blurring can also be introduced when the focal spot, the region where the X-rays originate from, is a larger disc-like region rather than a point source [7], Chapter 9. These optical effects can be modeled by a _point spread function_ (PSF), e.g., a 2D Gaussian filter, describing the projection on the detector of a point-like object. A consequence of these optical effects is that the effective resolution on the detector is lower than the pixel resolution, thereby increasing the effective voxel size [7]. Even though detector resolution is limited, Equation (4) shows that the voxel resolution can be increased by modifying the acquisition geometry. The projection of a feature located on the rotation axis has a _magnification factor_ of  
\[\alpha=\frac{\text{SDD}}{\text{SOD}}. \tag{5}\]  
By moving the object closer to the source, this magnification factor is increased, which, thereby, as a consequence of Equation (4), decreases the minimal voxel size. As the object is moved closer to the source, the projections of the object may become _truncated_. Consequently, only a part of the object, the _region of interest (ROI)_, is consistently projected onto the detector at every angle. This causes _truncation artifacts_ in the reconstruction. Various techniques exist to minimize these artifacts [24; 25; 26; 27]. Nonetheless, these methods only improve resolution in the region of interest, hence new methods are required to reconstruct the entire volume at high resolution.
# ARNS: Adaptive Relay-Node Selection Method for Message Broadcasting in the Internet of Vehicles
## 5 Results and Analysis
### Evaluations of ARNS in the Scenario with Obstacles  
In this subsection, we simulated ARNS and the complete relay-node selection method on the curved road [25], which do not consider obstacles, to show the advantages of considering obstacles in three aspects of broadcast coverage, partition delay, and contention delay. The simulation results of partition delay and contention delay indicate that the proposed method ARNS significantly reduces the delay of the relay-node selection.  
Figure 5: Maximum hops.  
Figure 6: Packet delivery ratio (PDR).  
As shown in Figure 7, the broadcast coverage of the curved road method decreases with the increase of vehicle density. It was because when vehicle density was low, the curved road method selects relay-nodes along the curved road to achieve broadcast coverage. When vehicle density increases, the curved road method gradually reduces the number of times to select relay-nodes along the curved road. However, the number of times to select relay-nodes across the curved road increases gradually (in Figure 1, for example, Node S\({}_{3}\) selects Node S\({}_{4}\) as a relay-node). Thus, the broadcast coverage of the curved road method gradually decreases.  
Choosing different optimal positions in the same scenario will cause different partition delays and contention delays. Thus, as shown in Figures 8 and 9, ARNS has obvious advantages in the partition delay and contention delay. As vehicle density increases, these advantages become more apparent. At a high density of 0.25 vehicle/meter, the partition delay of ARNS was reduced by 16.4% compared with the complete method, while the contention delay of ARNS was reduced by 52.2%. These results are reflected in the end-to-end delay as shown in Figure 10, and compared with the complete method, ARNS can reduce end-to-end delay on a curved road by up to 16.3%.  
Figure 8: Partition delay.  
Figure 7: Broadcast coverage.
# A Cloud Microservices Architecture for Data Integrity Verifiability Based on Blockchain
## 4 The Verifiability Blockchain Service Interface (VBSI)
### Audit: Consulting  
In order to consult information, Audit has another service, _consulting_, see Figure 3. Let us assume that the Event API-Gateway receives an event \(Ev(To,Hx,R)\) to consult a data that previously was stored within VBSI. There, _To_ is provided by the authorization service; and  
\[R=\big{\{}\!\!\{T0^{\prime},A_{tr},A_{sc}\}\!\} \tag{5}\]  
In this case, \(R\) is the receipt received when the storing action was carried out (it is the same as explained in Section 4.2); note that \(T0^{\prime}\) was the token id approved in the transaction \(A_{tr}\) for the smart contract address \(A_{sc}\).  
Consulting service involves the following off-chain operations:  
* Verify with the authorization service that _To_ has enough permissions to execute _Ev_; it is used with \(permit(To,Ev,R_{S})\) function, which is detailed in Equation (9).
* If \(b\) is true, then it calls to the following remote function: \[Rp=getReceipt(R)\] (6) There, \(Rp\) states for the receipt details consulted with the blockchain and \(Hx^{\prime}\in Rp\) is the hashed information previously stored within the blockchain.
* Then, it is compared \(Hx\) with \(Hx^{\prime}\), if they are the same, \(r\) variable is changed to \(true\).
* A tuple _Res_ is returned, where: \[Res=\{\!\!\{r,Rp\}\!\}\] (7) _Res_ can be one of two options according to the previous steps: (a) \(r=true\) and the detailed receipt \(Rp\) when integrity is noted; otherwise, (b) it returns \(r=false\) and an empty receipt.