esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 DSP Algorithm
### DSP Description  
The main purpose of the proposed DSP algorithm is to provide fault-tolerant communication for wired and wireless networks by sending unicast frames from a source to a destination through redundant paths. Unlike other path redundancy-based techniques that discover redundant paths by exchanging control messages, the DSP algorithm finds paths based on the network topology information; namely, the link table. The paths are node-disjoint paths, thus providing seamless communication with zero switchover delay in the case of network failures. In addition, the proposed algorithm establishes two paths, called dual paths, between the source and the destination, instead of all possible paths; this results in the improvement of network traffic performance.  
The DSP algorithm finds the dual separate paths between the source node and destination node in a network. The dual paths are chosen based on the following criteria:  
* _Optimal paths_: The dual paths have the best distance possible between the source and destination nodes.
* _Separate paths_: The dual paths are node-disjoint paths.  
The DSP algorithm works on any network topology that does not contain any single point of failure, particularly mesh and ring topologies.  
The DSP algorithm consists of the following three phases:  
1. _Phase 1_: Finding all possible paths between the source and destination nodes.
2. _Phase 2_: Sorting the possible paths in ascending order of path distance.
3. _Phase 3_: Selecting dual separate paths based on the sorted path list.
## 4 Discussion
### Methodology and Limitations  
Four muscle synergies were generally required to adequately reconstruct the original data with sufficiently high VAF values. Despite a few exceptional cases where five muscle synergies were needed, the number of muscle synergies was set at four to avoid noise being explained by additional muscle synergies [15]. Four muscle synergies were reported to be sufficient by the majority of similar studies [1; 2; 14; 16; 17].  
Similarity between two muscle synergies was assessed by Pearson's correlation coefficient \(r_{p}\). A pair of muscle synergies was considered as similar for \(r_{p}\geq 0.623\), as in multiple other studies [1; 3]. An additional analysis could have been performed to verify similarity. Muscle synergies of one data set could be used to reconstruct another data set in which muscle synergies were considered to be similar. [PERSON] et al. [15] extracted functional muscle synergies from non-stepping data, and used them to reconstruct stepping data. The muscle synergies extracted from non-stepping data accounted for \(85\pm 3\%\) of the overall variability for the stepping data, while the variance-accounted-for was \(93\pm 1\%\) for the entire data set consisting of stepping and non-stepping data. Since muscle synergies for non-stepping data explain the stepping data sufficiently, the corresponding muscle synergies could be considered as similar. Such an approach would presumably not cause the present results to deviate, as it is expected that muscle synergies for perturbed and unperturbed walking do not differ more from each other than muscle synergies for stepping and non-stepping.  
The muscle synergies and weighting curves correspond highly to those in the study by [PERSON] et al. [14], in which four bilateral muscle synergies were also composed of combinations of unilateral muscle synergies. In general, the same muscles were included, and comparable activation patterns were shown. However, some main contributing muscles to the synergies found by [PERSON] were not included in the present study. For instance, the vastus medialis was excluded in the present study, but contributed largely to [PERSON]'s early stance synergy. Similarly, the tensor fasciae latae was a main contributor to [PERSON]'s initial swing synergy. It is expected that including missing muscles would only add additional components to the existing synergies, while overall synergy structures would remain intact.  
Applying perturbing pushes at the level of the pelvis during walking represents an ecologically valid perturbation modality that is related to real-life perturbations resulting from people bumping into each other in a crowd. Other researchers have used perturbing pushes at the pelvis extensively [6; 18; 19]. However, people also face other types of perturbations during walking, such as slips and trips. Although the balance responses to these perturbations may show some resemblance in terms of kinematics and kinetics to those in response to push perturbations [19], the underlying muscle synergies may differ. Therefore, the results of our study may not be generalizable across other types of perturbations.
## 1 Introduction
### Limitations in OAM-Based OWC Systems  
Despite the numerous advantages of OAM in OWC systems, there are some predominant limitations such as (1) mode crosstalk in OAM beams due to smaller differences in 1 between different modes, (2) lower power efficiency of the receiver due to very high order LG modes in OAM, (3) mode leakage due to pointing errors, and (4) phase modulation under atmospheric turbulences. As per the author's best knowledge, OAM beams are not investigated in OSC systems for ultra-high capacity and long transmission.  
In this work, an ultra-high capacity OSC system is presented incorporating the OAM multiplexing technique over 22,000 km, and a detailed investigation of the OSC system is performed at different input parameters such as OSC link length, pointing errors, and receiver telescope antenna diameter in terms of log BER, signal to noise ratio (SNR), and EVM%.  
The rest of the paper is structured as follows: Section 2 discusses the principle of OAM and OSC. The simulation of the proposed OAM-OSC system is elaborated in Section 3. The investigation and results of the proposed system are given in Section 4, followed by a state-of-the-art comparison with reported OSC systems and with final concluding remarks in Section 5, along with the future scope.
# Oceania's 5G Multi-Tier Fixed Wireless Access Link's Long-Term Resilience and Feasibility Analysis
## 4 Discussion  
A carefully articulated approach is needed to face the challenges of the continued development of 5G innovations, creating new opportunities and added value for businesses and the environment in Oceania, ensuring sustainability and competitiveness. There has been much discussion recently on 5G's possible contribution to the fight against climate change. Although information and communication technologies (ICT) are thought to be responsible for 2.1-3.9% of greenhouse gas emissions, their contribution to smart resource management could help society reduce its carbon footprint significantly by optimizing usage and better comprehending environmental implications. Therefore, the role of 5G  
Figure 15: 5G communication long-term resiliency.  
in Oceania's green transformation requires careful balancing of the possible impacts and contributions of the technology. Significant changes are required for the 5G rollout plan in Oceania, including changes to network infrastructure, systems, and applications, as well as to data ethics, privacy, and workforce consequences. These and other difficulties, including those related to social inclusion, point to the significance of 5G as well as the requirement to increase collaboration and synergies across Oceania's countries' telecommunications operators and service providers. It is projected that 6G will become commercially accessible in 2030. It is a technology that is being developed in response to the growing popularity of distributed RAN and the need to leverage the terahertz spectrum (THz) to increase capacity and reduce latency. It is expected that many of the purported problems with millimeter-wave radio deployment for 5G radio will be resolved in time for network designers to take on the challenge of developing a 6G service. Up to one terabyte per second (Tb/s) speed is anticipated in 6G networks, which will significantly enhance current 5G capacity and latency. This will also broaden the capabilities available to support new and cutting-edge tools for wireless cognition, detection, and imaging. Additionally, it is anticipated that 6G wireless sensing solutions will use multiple frequencies selectively to detect absorption and modify frequencies as necessary to increase signal penetration and transmission ranges.
# Composite Kernel Method for PolSAR Image Classification Based on Polarimetric-Spatial Information
## 2 Related Work
### Polarimetric and Spatial Features of PolSAR Image
#### 2.1.2 Spatial Features
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{1}{c}{**Polarimetric Signatures**} & \multicolumn{1}{c}{**Expression**} \\ \hline Amplitude of upper triangle matrix elements of C & \(|c_{ij}|,1\leq i\leq 3,i\leq j\leq 3\) \\ \hline Amplitude of upper triangle matrix elements of T & \(|t_{ij}|,1\leq i\leq 3,i\leq j\leq 3\) \\ \hline Ratio between HV and HH backscattering coefficient & \(\langle S_{hv}S_{hv}^{*}\rangle/\langle S_{hh}S_{hh}^{*}\rangle\) \\ \hline Ratio between VV and HH backscattering coefficient & \(\langle S_{vv}S_{hv}^{*}\rangle/\langle S_{hh}S_{hh}^{*}\rangle\) \\ \hline Ratio between HV and VV backscattering coefficient & \(\langle S_{hv}S_{hv}^{*}\rangle/\langle S_{vv}S_{vv}^{*}\rangle\) \\ \hline Depolarization ratio & \(\langle S_{hv}S_{hv}^{*}\rangle/\langle(S_{hh}S_{hh}^{*})+\langle S_{vv}S_{vv} ^{*}\rangle)\) \\ \hline Phase difference HH-VV & \(\arg(\langle S_{hh}S_{vv}^{*}\rangle)\) \\ \hline Entropy, alpha angle, anisotropy and eigenvalues in Cloude & \(H,\alpha,A\) \\ Decomposition & \(\lambda_{1},\lambda_{2},\lambda_{3}\) \\ \hline nine parameters of the Huynen Decomposition & \(A_{0},B_{0}+B,B_{0}-B\), \\  & \(C,D,E,F,G,H\) \\ \hline Power of surface, double-bounce, volume and helix scatter components & \(P_{s},P_{d},P_{v},P_{h}\) \\ in Yamaguchi Decomposition & \(f_{v},f_{d},f_{s}\) \\ \hline Coefficient for the volume, double bounce and surface components in & \(f_{v},f_{d},f_{s}\) \\ Van Zyl Decomposition & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summarization of polarimetric signatures considered in this paper. morphological transformations to a signal channel image separately may cause loss or corruption of information [29]. Therefore, we use the _SPAN_ image to build the MP for PolSAR image classification.
## 4 Discussion
Depending on the number of monitored patients some network design decisions that can dramatically improve the overall system performance:  
* In \"Controlled Area\" systems with simultaneous monitoring of multiple patients, such as LOBIN [44], MASN [38] and MEDISN [46], typically a single wireless traffic source is used to monitor each patient in order to minimize the physical medium occupancy. In case multiple sensors are used to monitor each patient, it is common to use a \"concentrator of measurements\" (through a BAN) and send them all at once. These projects often introduce an AN network segment (instead of a PAN) in the communications architecture in order to properly distribute the traffic and avoid bottlenecks, providing scalability to the WHMS. In such AN segment of the studied projects different topologies can be found, namely a network of relay points as in MASN [38], a multiple gateway network as in LOBIN [44], or a mesh network as in MEDISN [46]. * In \"Controlled Area\" systems in which the network is not shared by several patients as [PERSON] _et al._[42], or in \"Wide Area\" and \"Self-Monitoring\" systems in which the patients have their own networks (as in AMON [19] and HealthGear [28] respectively), there are no problems of scalability and distribution of traffic.
# Biological Importance of Cotton By-Products Relative to Chemical Constituents of the Cotton Plant
## 4 Chemical Compounds in Cotton
### Fatty Acids, Carbohydrates and Proteins  
Fatty acids are carboxylic acids with long aliphatic chains that are synthesized in the cytosol of plant cells from malonyl-CoA, which in turn is derived from acetyl-CoA. Palmitic acid (Figure 16) is a base fatty acid from which other fatty acids are formed by 2-carbon increments or reduction. The synthesis of palmitic acid (Figure 17) from the precursor malonyl-CoA follows a five step repeating cycle of acylation, condensation, reduction, dehydration, and reduction, which is catalyzed by the fatty acid synthase complex [149; 150].  
Figure 16: Palmitic acid, a base fatty acid from which other fatty acids are formed.  
Figure 17: Fatty acid biosynthetic pathway in plants.  
Saturated fatty acids which occur in the cotton plant include myristic acid (tetradcanoic acid), melissic acid (triacontanoic acid), palmitic acid (hexadecanoic acid), stearic acid (octadecanoic acid), and palmitoleic acid (9-hexadecanoic acid) [18; 19; 151]. Unsaturated fatty acids identified in cotton include eicosadienoic acid, linoleic acid (octadecadienoic acid), linoleic (octadecatrienoic acid), and elaidic acid (octadecenoic acid) [18; 19]. Most fatty acids identified in cotton are free fatty acids (not linked to any molecules) and play functional roles as a source of energy for plant growth [19].  
Cotton, like all plants, is comprised of cellulose and hemicelluloses, proportions of which vary between different parts of the plant. Cotton fibre itself is comprised mainly of cellulose at levels greater than 94% by weight [25]. Raffinose is a unique minor sugar found in cotton plants predominantly in the seed [117; 118].  
Alkali and water soluble proteins are also found in cotton [152], including water soluble globular proteins vicilin and legumin (Table 1) present in the seeds of cotton [20]. Proline-rich protein H6 is involved in the development of the cell wall structure of cotton fibre [153; 154].
## Sound Power Change Calculation Model and Plan of the Experiment  
### Sound Power Change Calculation Model  
The period of repetition of turning and impact forces of a wheel defect on the rail [10; 12] is:  
\[T=\frac{L_{riad}-L_{F}}{v} \tag{1}\]  
where \(L_{riad}\) is the length of the wheel rolling surface, \(L_{F}\) is the defect length and \(v-\) is the wagon speed.  
The angular speed of the wheel \(\omega\) can be calculated:  
\[\omega=\frac{v}{R_{w}} \tag{2}\]  
where \(R_{w}\) is the wheel radius.  
The effect of the wheel defect on the rail is:  
\[f=\frac{1}{T}. \tag{3}\]  
The sound power level \(L_{w}\) is calculated from the total energy of the sound source \(W\) (radiated in all directions over a period of time) [13]:  
\[L_{w}=10\log_{10}\left(\frac{W}{W_{ref}}\right) \tag{4}\]  
where \(W_{ref}\) is the reference (background) sound level energy, which is usually \(10^{-12}\) W.  
Vibration theory allows for substituting the sound source energy \(W\) (radiated in all directions during the period of time) with other parameters that have linear dependency on energy.  
Assuming that \(W=W(F)\), \(W=a\cdot F\) and \(\theta\)\(W_{ref}=a\cdot F_{ref}\), then  
\[L_{w}=10\log_{10}\left(\frac{F}{F_{ref}}\right). \tag{5}\]  
where \(W\) is the full sound source energy (radiated in all directions during the period of time), \(W_{ref}\) is the reference (background) sound level energy, \(a\) is the linear coefficient of the energy dependency on the force, \(F\) is the impact force of a wheel with a defect into a rail and \(F_{ref}\) is the reference constant wheel force acting on the rail.  
Because the sound power change \(L_{w}\) in the source is already known, and we can physically measure the sound pressure change \(L_{p}\) or sound pressure \(P\) at some distance from the source \(r\). They can be calculated using the following equations:  
\[L_{p}=L_{w}-\left|10\log\left(\frac{Q}{4\pi r^{2}}\right)\right|. \tag{6}\]  
where \(Q\) is the coefficient that depends on the number of reflecting surfaces in the environment (Figure 1) and  
\[L_{p}=10\log_{10}\left(\frac{P}{P_{ref}}\right). \tag{7}\]  
Using Equations (6) and (7), we can calculate the sound power change \(L_{w}\) in the source, if we know the physically measured sound pressure change \(L_{p}\).
# Steganalysis of Context-Aware Image Steganography Techniques Using Convolutional Neural Network
## 3 Experimental Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**S. No.**} & \multirow{2}{*}{**Steganography Technique/**} & \multicolumn{3}{c}{**HILL**} & \multicolumn{3}{c}{**S-UNIWARD**} & \multicolumn{3}{c}{**WOW**} \\ \cline{2-10}  & & **0.2** & **0.3** & **0.4** & **0.2** & **0.3** & **0.4** & **0.2** & **0.3** & **0.4** \\ \hline
1 & Softmax Classifier & 62.49 & 68.59 & 72.26 & 70.00 & 78.68 & 84.11 & 76.02 & 83.36 & 87.74 \\ \hline
2 & SVM classifier & 63.25 & 69.91 & 73.07 & 69.54 & 79.88 & 84.79 & 77.02 & 83.95 & 87.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance analysis of the softmax and SVM classifier. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Steganography Technique/**} & \multicolumn{3}{c}{**HILL**} & \multicolumn{3}{c}{**S-UNIWARD**} & \multicolumn{3}{c}{**WOW**} \\ \cline{2-10}  & **0.2** & **0.3** & **0.4** & **0.2** & **0.3** & **0.4** & **0.2** & **0.3** & **0.4** \\ \hline SRNet & 60.85 & 67.10 & 68.79 & 67.17 & 76.18 & 79.06 & 77.40 & 83.97 & 88.49 \\ \hline Ye-Net & 53.79 & 61.60 & 65.61 & 62.32 & 70.60 & 74.98 & 70.42 & 78.81 & 82.37 \\ \hline Yedroudj-Net & 57.28 & 64.14 & 66.91 & 63.51 & 71.28 & 74.15 & 73.49 & 81.93 & 85.46 \\ \hline Zhu-Net & 63.81 & 69.85 & 77.27 & 73.40 & 79.89 & 83.40 & 79.51 & 87.55 & 92.66 \\ \hline Proposed Method & 64.49 & 74.10 & 77.13 & 75.06 & 83.64 & 86.85 & 81.50 & 88.81 & 91.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance assessment while considering Case II. Data augmentation was used to increase the size of the available images of the two datasets BOSSBase and BOWS2, as discussed in Case III. The improvement in the detection accuracy is evident in Table 6. Except for one scenario, in all scenarios, the result of the proposed technique was better. Augmented data enhanced the detection accuracy by 7.75%, 2.82%, and 5.15% for HILL with payloads of 0.2, 0.3, and 0.4 bpp, respectively. For S-UNIWARD with 0.2, 0.3, and 0.4 bpp payloads, there was a benefit of 7.10%, 3.59%, and 4.52%, respectively. WOW with the 0.2, 0.3 and 0.4 bpp payloads showed an increment in the detection accuracy by 1.82%, 0.46%, and 1.38%, respectively. Only in one scenario did Zhu-Net give a 1.10% better result for WOW \(\rho=0.4\) bpp, otherwise the proposed technique superseded the other techniques.
## 1 Introduction
However, the exact solutions of those complex KPs could not be easily found on large \(n\). Recently, the mathematical HyMKP [34] was proposed in O(\(mnC\)) for the 0/1-mKP with most optimal solutions (in \(\tau\) s) on \(n\leq 500\). For large \(n\) and massive \(C\), the existing meta-heuristic algorithms for the 0/1-KP can be applied to solve the 0/1-mKP in polynomial time (but requiring the proper knapsack orders). For high performance, the exact DP could find the optimal solution of the 0/1-KP in O(\(nC\)) and O(\(m!nC\)) for the 0/1-mKP (with \(m!\) orders, \(C=\max(C_{i})\)) for at least 99% optimal solutions (but for small \(m\), \(n\), and C). In this study, we are interested to solve the 0/1-mKP for large \(m\), \(n\), \(C\) with the proper orders in efficient time. Our hypothesis is \"For each of \(m\) knapsacks, apply unbiased filtering before using the exact DP on remaining \(n^{\prime}\) and \(C_{i}\)' can find most optimal solutions in efficient time\". Figure 1: Two different 0/1-knapsack problems (\(n\) objects): (**a**) the popular 0/1-KP (one knapsack with capacity \(C\)) and (**b**) the complex 0/1-mKP (\(m\) knapsacks with capacity \(C_{i}\), \(i=1,\,2,\,\ldots,\,m\)). In this research, we introduce a novel research track (a hybrid approach of time-space reduction) for solving the 0/1-mKP in efficient time with expected 99% optimal solutions. In our hybrid approach, we propose the integration of DP transformation (reducing \(C\) to \(C\)), exact-fit (best) knapsack-order (reducing \(m!\) to \(m^{2}\)), and 3. robust unbiased filtering (for polynomial time). First, we propose the DP transformation (DPT) algorithm to find the optimal solutions of the 0/1-KP (for each of \(m\) knapsacks) in O(\([n^{2},nC]\)), or O(\(n^{2}\)) in the best case and O(\(nC\)) in the worst case, before being applied for \(m\) knapsacks. Second, for the 0/1-mKP (\(m\) knapsacks), we propose the exact-fit (best) knapsack order (in our multi-DPT) in O(\(m^{2}[n^{2},nC]\)) for achieving the good solutions as \(m!\) orders (at least 99% optimal solutions). Third, robust unbiased filtering is incorporated to solve the 0/1-mKP in polynomial time (O(\(m^{2}n\))) while retaining 99% optimal solutions. The correctness and complexity of the DPT and multi-DPT algorithms are analyzed. In experiments, the original multi-DPT and the multi-DPT + robust unbiased filtering are evaluated on random and benchmark datasets (\(n\leq\) 10,000, \(m\leq 100\)). The main parts of this paper are organized as follows: Section 2 reviews the related work. Section 3 presents the DPT algorithm to find the optimal solutions of the 0/1-KP in O(\([n^{2},nC]\)). Section 4 proposes the multi-DPT algorithm with the exact-fit (best) knapsack order to solve the 0/1-mKP in O(\(m^{2}[n^{2},nC]\)) and reduced to O(\(m^{2}n\)) by our robust unbiased filtering. Section 5 provides the algorithm analysis (correctness and complexity). Section 6 performs the experiments to evaluate the performance of our multi-DPT algorithm and robust unbiased filtering. Section 7 concludes this study.