esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Step-by-Step Calculation of QSA on a Five-City
### One QSA Iteration (\(t=0\to 1\))  
Parameter values:  
\[T_{\text{max}}=1.0,\,\sigma_{\text{init}}=0.10,\,\alpha_{\text{thermal}}=\alpha _{\sigma}=0.9,\,\delta=0.5,\,\gamma=1.2,\,\beta_{\text{tunnel}}=1.2.\]  
Agent 1 calculations:  
Thermal step (Equation (15)):  
\[\mathbf{thermal}^{(1)}=(-0.30,\,0.12).\]  
Pull-to-best is zero because Agent 1 is currently the best. Collision vector (Equation (17)) computed from its two nearest neighbors is \(\mathbf{collision}^{(1)}=(+0.14,\,-0.09)\). Quantum tunnelling (Equation (16)) is \(\mathbf{0}\) since \(\mathbf{pos}^{(1)}=\mathbf{pos}^{(\text{best})}\).  
Position update (Equation (18)):  
\[\mathbf{pos}^{(1)}_{\text{new}}=(0,0.5)+(-0.30,0.12)-(0.14,-0.09)=(-0.44,\,0.7 1).\]  
Discrete re-mapping:  
Cities are sorted by increasing distance to \((-0.44,0.71)\), yielding the permutation  
\[x_{\text{candidate}}^{(1)}=(B,A,E,C,D).\]  
Local search (2-Opt):  
Reversing the segment \((A,E)\) gives \(x_{\text{improved}}^{(1)}=(B,E,A,C,D)\) with cost \(F=4.000<4.414\), hence it is accepted (Equation (22)).  
Population summary after Iteration 1:  
Repeating the above for Agents 2\(-\)5 produces new costs {4.00, 4.24, 4.41, 4.59}. The global best is updated to  
\[x_{1}^{(\text{best})}=(B,E,A,C,D),\qquad F_{\text{min,1}}=4.000.\]
## 10 Discussion
### Impact on Blockchain Technology  
Our findings may have wide implications for the overall blockchain ecosystem beyond Ethereum because the mathematical framework developed will provide insightsthat can aid in the design and improvement of state management systems on various blockchain platforms.  
First, our findings show that the relationship between network size and proof size is fundamentally logarithmic. That is, even under exponential growth in user adoption, the state verification overhead will only grow relatively slowly. The base of this logarithm, however, is a very sensitive parameter in Ethereum's case, and it seriously affects absolute proof sizes. Future blockchain designs may hence consider alternative branching factors, which optimally balance the trade-off at hand.  
Second, the exact characterization of path length distributions enables more accurate capacity planning for blockchain networks. Infrastructure providers can estimate their storage requirements and network bandwidths better based on projected user growth. Our model provides that for a network growing from N to kN users, the average proof size will grow by approximately 0.36 ln(k) nodes, providing a concrete basis for scalability planning.  
Third, our work proves that probabilistic analysis and corresponding structural optimization of tries lead to significant efficiency improvements. The potential 70% proof size reduction we have found may be of great benefit for Layer 2 solutions and other scaling solutions where state proofs are used. In turn, this indicates that in blockchain scalability, mathematical data structure optimization may play at least an equally important role to that of protocol optimizations. The methodology we devised to analyze trie structures can easily be extended to other tree-based data structures in blockchain systems, such as account trees in UTXO-based cryptocurrencies or state channels. Combining analytical work with large-scale empirical validation provides a template for the rigorous evaluation of blockchain data structures.
# Video over DSL with LDGM Codes for Interactive Applications
## 4 Findings
### Impact upon Video Distortion from LDGM  
In experiments in this Section, ADSL was again assumed with small packet sizes of 50 B and 100 B for each of two sets of tests respectively. The ADSL configuration was the same as in the Section 4.1. However, the results from testing with a larger 100 B packet size are also included in this Section, bringing the packet size closer to that of an MPEG2-TS packet. For 100 B packets a downstream bitrate of 10 Mbps was configured, with a per-packet link latency of around 100 ms. For 50 B packets and two-way communication a 1 Mbps effective datarate was assumed, with a per-packet link latency of around 10 ms. As previously, the PER was set 1% [8], though with burst lengths of 8 and 10 packets for the 50 B and 100 B packets respectively. Thus, the channel conditions were more benign than in the previous Section's experiment.  
To counter error bursts, the packet block size was set to \(k=300\), 400. The number of redundant packets, \(n-k\), was somewhat reduced to 9% of the whole. The latency budget remains well below the previously mentioned 1000 packets length of large block coding schemes.  
Table 1 shows five sample runs each with a different seed and the resulting mean PSNR. (The code seed was set to 40, 50, 70, 80, and 90 for 1-5 respectively.) The mean gain after application of LDGM was between 1 and 2 dB with the video PSNR approaching a level suitable for broadcast. Interestingly from the point of view of latency, increasing the block size does not necessarily lead to a reduction in video distortion.  
For the larger packet size and the greater bandwidth of Table 2, the video distortion reduction is more consistent and is 3-4 dB. The consistency is due to a constant code seed of 50 throughout. Notice that in view of the larger 100 B packet size the block sizes are decreased. Again a larger block size appears not to lead to an advantage. This effect may be linked to the pattern of packet burst erasures. Comparing the 100 B PSNR gain to that of 50 B packets, for the latter the FEC gain appears to have saturated, suggesting a reduced FEC rate is possible.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Before FEC** & **1** & **2** & **3** & **4** & **5** & **Mean PSNR (dB)** \\ \hline \(k=300\) & 35.53 & 35.08 & 38.63 & 36.14 & 37.37 & 36.55 \\ \(k=400\) & 34.46 & 37.66 & 38.54 & 36.40 & 39.00 & 37.21 \\ \hline
**After FEC** & **1** & **2** & **3** & **4** & **5** & **Mean PSNR (dB)** \\ \hline \(k=300\) & 37.33 & 39.00 & 39.00 & 37.57 & 37.84 & 38.15 \\ \(k=400\) & 36.91 & 37.76 & 39.00 & 37.64 & 39.00 & 38.06 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Objective video PSNR for 50 B packets before and after FEC.  
Figure 6: Percentage of recovered packets with Packet Error Rate (PER) \(=1\%\) and increasing packet burst lengths (\(Ln\)) for three channel codes.
## 3 Results
### MLF Assay and Basic Chemical Parameters
#### 3.4.4. Aroma Components Analysis before and after MLF  
A total of 27 volatile aroma compounds were detected in non-MLF control wine, and the total amount of volatile aroma compounds was 200 mg/L (Table S1). After MLF, 47 different volatile aroma compounds were detected in different fermented wines, including 29 esters, 8 alcohols, 4 organic acids, 1 terpene, 5 aldehydes, and ketones. The types and contents of volatile aroma compounds in wines increased significantly after MLF.  
Compared with _O. oeni_ 31-DH, A7, A50, and B28 added nine, eight, and seven kinds of volatile aroma compounds, respectively. Compared with _L. plantarum_ BV-S2, A7, A50, and B28 added ten, nine, and seven kinds of volatile aroma compounds, respectively. These volatile aroma compounds were mainly esters. A7 had the most volatile aroma compounds, and the total amount could reach 314.07 mg/L. The volatile aroma compounds of A50 and B28 can reach 244.39 mg/L and 244.12 mg/L (Table S1). A7 increased the content of esters and terpen. These volatile aroma compounds together constitute the complex aroma of wine (Figure 9).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Strains** & **Tryptamine** & **\(\beta\)-Phenylethylamine** & **1,4-Butanediamine** & **Cadaverine** & **Tyramine** & **Histamine** \\  & **(mg/L)** & **(mg/L)** & **(mg/L)** & **(mg/L)** & **(mg/L)** \\ \hline Before & 0.64 \(\pm\) 0.01 ab & 1.04 \(\pm\) 0.08 a & 1.46 \(\pm\) 0.22 a & 0.36 \(\pm\) 0.02 a & 0.30 \(\pm\) 0.02 ab & 0.35 \(\pm\) 0.02 a \\ A7 & 0.58 \(\pm\) 0.04 ab & 0.46 \(\pm\) 0.00 bcd & 0.12 \(\pm\) 0.00 c & 1.11 \(\pm\) 0.09 a & 0.58 \(\pm\) 0.08 a & 0.79 \(\pm\) 0.055 c \\ A50 & 0.42 \(\pm\) 0.01 b & 0.18 \(\pm\) 0.18 d & 0.76 \(\pm\) 0.05 b & 0.39 \(\pm\) 0.17 b & 0.14 \(\pm\) 0.02 b & 0.68 \(\pm\) 0.004 b \\ B28 & 0.48 \(\pm\) 0.04 ab & 0.34 \(\pm\) 0.07 c & 0.86 \(\pm\) 0.04 b & 0.49 \(\pm\) 0.08 ab & 0.11 \(\pm\) 0.01 b & 0.38 \(\pm\) 0.053 b \\
31-DH & 0.72 \(\pm\) 0.18 a & 0.61 \(\pm\) 0.04 ab & 1.22 \(\pm\) 0.28 ab & 0.62 \(\pm\) 0.14 ab & 0.11 \(\pm\) 0.00 b & 0.38 \(\pm\) 0.08 ab \\ BV-S2 & 0.56 \(\pm\) 0.02 ab & 0.86 \(\pm\) 0.10 ab & 1.26 \(\pm\) 0.02 ab & 0.31 \(\pm\) 0.00 b & 0.11 \(\pm\) 0.00 b & 0.27 \(\pm\) 0.00 ab \\ \hline \hline \end{tabular} Note.: a, b, c, and d indicate significance analysis.  
\end{table}
Table 6: Biogenic amine in cabernet Sauvignon wines.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Strains** & \
\begin{tabular}{c} **Ethanol** \\ **(\%)** \\ \end{tabular} & **pH** &
\begin{tabular}{c} **Total** \\ **Sugar** \\ **(g/L)** \\ \end{tabular} & **Tirtable** & **Volatile** & **L-Lactic** & **Acid** & **Chromicity** & **Hue** & **Gycerol** \\  & & & **(g/L)** & **(g/L)** & **(g/L)** & **(g/L)** & & & \\ \hline Before & 14.75 \(\pm\) 0.03 ab & 3.52 \(\pm\) 0.01 d & 3.63 \(\pm\) 0.05 a & 8.19 \(\pm\) 0.25 a & 0.28 \(\pm\) 0.08 b & 0.08 \(\pm\) 0.01 f & 17.89 \(\pm\) 2.07 a & 0.8
## 2 Related Works
They also developed mathematical models and provided SDN network-related cost metrics, such as migration and reconfiguration costs. While the study covered SDN network optimization in great detail, it limited its scope by focusing the analysis on the Iridium constellation, effectively excluding optimization aspects regarding the constellation itself. The work conducted in [16] analyzed the handover performance in a Low Earth Orbit-based Non-Terrestrial Network (LEO-NTN) via system-level simulations, with a focus on the ground-segment optimization. The work was separated into two phases. First, it conducted a performance analysis of a conventional Fifth-Generation of Cellular Networks (SG) new radio handover algorithm in LEO-NTN scenarios in order to find optimal parameters with respect to chosen key performance indicators. Secondly, there was a comparison in several terrestrial scenarios based on urban macro-scenarios with high-speed trains.The results in [16] showed that the mobility results were dominated by handovers happening too late, which were causing failure cases. However, due to the assumptions made within the simulations, the study in [16] focused solely on the ground segment, and it did not take into account independent or joint optimizations aspects of the space and user segments. The authors in [17] considered a Speech Emotion Recognition (SER) application for autonomous vehicles, for which a 5G-enabled Space-Air-Ground Integrated Network (SAGIN) was designed. As such, the work explored ground, space, and user segments for the application of interest, but it only provided the design and architecture of the network, without explaining the related optimization processes. However, the user-segment optimization related to SER was covered in great detail, as they explained the Artificial Intelligence (AI) model used for acoustic data modeling. The authors in [18] discussed LEO communication architectures to support high-speed UAV, and, in particular, the resource allocation in uplink connectivity. A convex optimization problem in terms of throughputs and energy-efficiency metrics was formulated in [18] and solved via Matlab CVX software (a software for disciplined convex programming). Table 1 summarizes the related work and what we bring with respect to existing studies and surveys.
## 2 Blockchain-Based Data Storage Methods: On-Chain, Off-Chain, and Hybrid Approaches
### On-Chain Data Storage Methods
Besides the mentioned advantages and disadvantages of storing data on the blockchain, on-chain methods are implemented with various mechanisms that increase the performance, security and scalability of the blockchain network. Table 1 provides a detailed classification and examples of these mechanisms. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Data Storage** **Environment** & **Storage Mechanism** & **Description** & **Advantages** & **Disadvantages** & **Examples** \\ \hline \multirow{8}{*}{Blockchain Database Structures} & Merkle Tree [21] & Hash-based tree structure used for data verification in Blockchain. & Data integrity and ease of verification. & Scalability is limited. & Bitcoin, Ethereum is limited. & Bitcoin, Ethereum is limited. \\ \cline{2-6}  & \multirow{4}{*}{Sharding Structures} & Sharding [17; 18; 22] & A scalability method where data are distributed across different nodes & High performance and network capacity & Technical complexity & Ethereum 2.0, Zilliqa, Polkadot, Harmony, Elrend, Solana, Near Protocol \\ \hline \multirow{8}{*}{Smart Contract Based Storage Storage Storage Storage Storage Storage Storage Storage Storage Storage Storage Storage Storage on Ethereum on Ethere
# Distinction of Scrambled Linear Block Codes Based on Extraction of Correlation Features
## 3 Analysis of Correlation Characteristics of Scrambled Linear Block Code  
### Cross-Correlation Characteristics of Symbols  
It can be seen from Equation (2) that the \(k\)-th symbol (\(y_{k}\)) of the self-synchronous scrambling sequence correlates with the previous \(L\) symbol \((y_{k-i},\,i=1,2,\ldots,L)\) regardless of the state of the LFSR.  
It can be seen from Equation (3) that the \(k\)-th symbol (\(y_{k}\)) of the synchronous scrambling sequence correlates with the first \(L\) states \((s_{k-i},\,\,i=1,2,\ldots,L)\) of the LFSR. The relationship between the \(k\)th symbol and the state of the LFSR is  
\[y_{k-1}=x_{k-1}\oplus\sum_{i=1}^{L}\oplus c_{i}s_{k-i-1}. \tag{6}\]  
Furthermore,  
\[s_{k-1}=\sum_{i=1}^{L}\oplus c_{i}s_{k-i-1}=y_{k-1}\oplus x_{k-1}. \tag{7}\]  
The state of the LFSR at each moment satisfies the following relationship [14]:  
\[s_{k}=\sum_{i=1}^{L}c_{i}s_{k-i}. \tag{8}\]  
Combining Equations (2) and (6)-(8), it can be deduced that the following correlation exists between the \(k\)-th symbol and the first \(L\) symbol:  
\[\begin{array}{ll}y_{k}&=x_{k}\oplus s_{k}=x_{k}\oplus c_{1}s_{k-1}\oplus c _{2}s_{k-2}\oplus\cdots\oplus c_{L}s_{k-L}\\ &=x_{k}\oplus c_{1}\cdot(x_{k-1}\oplus y_{k-1})\oplus c_{2}(x_{k-2}\oplus y_ {k-2})\oplus\cdots\oplus c_{L}(x_{k-L}\oplus y_{k-L})\end{array} \tag{9}\]  
However, when the linear block code is not scrambled, the preceding and following symbols have no correlation with the LFSR, and they are only related to the generator  
Figure 2: Signal model studied in the present paper.  
matrix of the linear block code. In summary, the linear block code scrambling correlation distribution is shown in Figure 3.  
It can be seen in Figure 3 that there is no correlation between the symbols of the linear block code and the LSFR; there is a correlation between the two symbols of the linear block code self-synchronous scrambling, and the symbols have nothing to do with the state of the LFSR; there is a correlation between the symbol and states of the first L LFSRs at the moment of the synchronous scrambling of the symbols of the linear block code, and, at the same time, this leads to a correlation between the symbols. It can be seen from the above analysis that scrambling causes a correlation between the preceding and following symbols. This correlation is related to the state of the LFSR (synchronous scrambling) or is independent (self-synchronous scrambling), so the intercepted symbol sequence can be used as a correlation feature that is used for linear block code scrambling classification.
## 2 Materials
### Data Acquisition and Preprocessing
#### 2.2.2 UAV-LiDAR Data  
In this study, airborne LiDAR data collection was conducted in July 2024 at three locations: Horqin Right Wing Middle Banner, Songshan District, and Linxi County. A total of 30 sets of high-precision UAV LIDAR point cloud data were collected in this survey, covering an area of about 370 hectares. The main tree species collected were poplar, camphor pine, and oil pine, of which poplar comprised about 215 hectares, Mongolian Scots pine 50 hectares, and Chinese red pine 105 hectares. All data were acquired using the Zenith L1 sensor mounted on DJI's (Guangzhou, China) Longitude M300 RTK drone. The size of the plot areas and the specific characteristics of the UAV, particularly its payload capacity and resulting flight time, were carefully considered to determine the coverage area for each flight and the number of flights required to cover the entire region. The flights were carried out at an altitude of 60/80 m, with a side overlap of no less than 60% and a forward overlap of 85%. A triple echo pattern was employed, resulting in a point cloud density of approximately 450 points per square meter.  
The UAV data were post-processed using the 3D reconstruction module of DJI Terra to handle the LiDAR point cloud data. The point cloud fusion parameter was set to \"high density\" (100% sampling rate), ensuring that all point cloud data were utilized in processing. This setting produces the highest quality results but requires the longest processing time. Additionally, point cloud accuracy optimization was enabled, which refines the point cloud data collected at different time intervals, thereby enhancing the overall accuracy of the final dataset. Subsequently, point cloud data were pre-processed using LiDAR 360 software (Version 5.2), which included steps such as denoising, classification, and normalization. During the classification process, the improved progressive TIN densification (IPTD) filtering algorithm [25] was employed to categorize denoised data into ground points, vegetation points, and building points.
## 3 Results and Discussion
### Cytotoxicity Testing of bHNTs
We envision our construct as a potential modular platform for use in anti-cancer therapies. The HNT interior can be doped with anti-cancer drugs (or other chemotherapeutics) and serve as a \"death cargo\" designed to kill cancer cells and provide imaging data on the drug's effects. The HNT surface can also be modified with gold, silver or iron nanoparticles. The metal-coated bHNTs could then be used in photothermal therapy by converting light to heat inside tumors. Iron-coated bHNTs aimed for anti-cancer therapies based on magnetic  
Figure 4: Live/Dead Assay. CT-26 cells were culture in the presence of bHNTs at 0, 25, 50, 100 and 150 \(\upmu\)g/mL. From left to right, brightfield, green filter (live cells) and red filter (dead cell) images. Bar = 100 microns. hyperthermia is another possibility. Our bi-functionalized HNT drug delivery system can target cancer cells without injuring normal cells providing localized and targeted therapies that limit or reduce side effects, reduce patient costs and length of hospital stays, and improve the quality of life. However, several questions regarding this anti-cancer nanoparticle remain to be answered and are the basis for current and future studies. Is the observed increase in cell death with exposure to concentrations greater than 150 \(\upmu\)g/mL to a cytotoxic response or due to disruption of cell function? Do the bHNTs accumulate or are they degraded in the cell? Is their accumulation toxic to cells? What is the mechanism of cell death, necrosis or apoptosis? Furthermore, the mechanism by which bHNTs enter the cells has important implications not only for their ultimate fate but also for adding a \"death cargo\", a drug or additional surface modifications (i.e., gold nanoparticles) that will kill the tumor directly. At present, our continuing research efforts are directed towards testing the HNT-DAS-FA/FITC in osteosarcoma cell culture as a means of validating and assessing the utility of the system as a targeted drug delivery vehicle. Additionally, other bioactive molecules are being explored for attachment to the system in hopes of building a modular drug delivery system.
# UAV Charging Station Placement in Opportunistic Networks
## 1 Introduction
Thus, implementing a suitable routing algorithm remains as challenging problem in opportunistic network research. In this paper, we investigate the problem of UAV charging station placement as batteries limit the flight time of the UAVs. The contributions of this work are summarized as follows:  
* We define an application scenario where commercially available UAVs are leveraged in an opportunistic environment. * We introduce a cluster-based solution for UAVs' limited-lifetime battery problem. Through clustering, load balancing is achieved, which, in turn, has an impact on UAV throughput. * For our application scenario, we have tested three methods (random placement, with K-means clustering and DBSCAN) to decide the position of the charging stations. We also examined Epidemic, Spray and Wait, SCR packet routing strategies for each charging station placement method. The charging station placement techniques are evaluated by performance metrics of different routing strategies on the application scenario. The novelty of the paper is about examining the challenge and providing a systematic solution to the problem of the allocation of the charging stations. Although K-means and  
Figure 1: UAV-aided opportunistic network architecture. DBSCAN are well-established clustering algorithms, they were not previously applied in the context of charging station placement in opportunistic networks to the best of our knowledge. We believe that the simulation study results are extensive and more realistic in nature as we used two real-world datasets. The rest of the paper is organized as follows. Related work is given in Section 2. A mathematical representation of the model is explained in Section 3. Section 4 gives a detailed description of the application scenario. Our proposed approach is presented in Section 5. Section 6 explains the clustering approaches while the routing protocols are explained in Section 7. The simulation study along with the performance evaluation results are discussed in Section 8. Finally, our conclusions are given in Section 9.