esa-sceva/satcom-chunk-collection huggingface dataset:
# SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks
## 2 Related Work
### CC Algorithm Evaluation Index  
The congestion control algorithm should be evaluated from five aspects: bandwidth utilization, fairness, packet loss rate, convergence speed, and link queue delay. Bandwidth utilization can be expressed as the ratio of the average aggregation speed of a network adapter to the bandwidth value of a network adapter in a period:  
\[\overline{y_{1}}=\lim_{t\to\infty}\frac{1}{T}\int_{0}^{T}y_{1(t)}*dt\approx C \tag{3}\]  
where \(y_{1(t)}\) is the aggregation speed (the sum of the throughputs of all data flows) of a network adapter at a certain moment, \(\overline{y_{1}}\) is the average speed, \(C\) is the bandwidth of the network adapter, and \(\overline{\zeta}\) is the bandwidth utilization.  
We use the fairness index to evaluate the fairness of the CC algorithm:  
\[y(x_{1},x_{2},\dots,x_{n})=\frac{(\sum_{i=1}^{n}x_{i})^{2}}{n*\sum_{i=1}^{n}x _{i}^{2}} \tag{4}\]  
where \(x_{i}\) is the bandwidth share of a stream, and the final calculation result ranges from \(1/n\) to \(1\), with \(1\) representing the best fairness and \(1/n\) representing the worst fairness.  
Packet loss rate is an important indicator to test the quality of congestion control because packet loss not only means that part of the bandwidth is wasted but it will also cause the HOL (head of line) problem, and subsequent TCP packets must be cached to wait for the retransmission of lost packets. This adds additional latency. Routers usually implement a packet loss policy when the buffer overflows. We calculate the packet loss rate by dividing the number of lost packets over a period by the throughput on the router.  
The convergence rate of the CC algorithm refers to the time required for the system to reach the target state from the initial state, and the less time required for convergence, the faster the convergence rate. Convergence speed includes the convergence to the target link utilization and convergence to the fairness state. Convergence to the target link utilization refers to the time required for the congestion control mechanism to occupy the idle bandwidth when the link has idle bandwidth. Convergence to fairness refers to the time required for the newly added flow to obtain a fair throughput rate with other flows when the link utilization is close to 100%.  
The essential reason for the increase in queue delay is that many packets are piled up in the network cache, resulting in a buffer queue of a certain length. The queue delay can be calculated by measuring the RTT when the network is congested and subtracting the RTT when the network is idle, which is one of the important indicators to evaluate the quality of the congestion control algorithm.
# _Thuja occidentalis_ L. (Cupressaceae): Ethnobotany, Phytochemistry and Biological Activity
## 4 Biological Activities
### Antioxidant Activity  
The antioxidant capability is the best described property of phenolic compounds toward free radicals which are produced by cells metabolism [88].  
In our previous studies, we showed a high antioxidant activity of a mother tincture of _Thuja occidentalis_ by 2,2-diphenyl-1-picrylhydrazyl radical (DPPH) containing (3.9 mg GAE/g d.w.) polyphenols, as well as its in vitro antioxidant capacity on Caco-2 cells exposed to oxidative stress induced by H\({}_{2}\)O\({}_{2}\), by MDA- and GSH-level assessments [37].  
[PERSON] et al. (2016) showed that the antioxidant potential of the _Thuja occidentalis_ methanolic extract was assumed to high DPPH radical scavenging activities, ABTS, NO and lipid peroxidation assays [36].  
In their study, [PERSON] et al. (2010) examined the antioxidant and antimicrobial activity of four essential oil types, whose major constituents were \(\beta\)-thujone, \(\alpha\)-thujone, \(\alpha\)-thujone/\(\beta\)-thujone, and 1,8-cineole/cambor/\(\alpha\)-thujone/\(\beta\)-thujone, and the composition was investigated by using capillary GC and GC/MS technique. The results of the study showed that all examined oils had a great antimicrobial potential, and, in addition, they assessed the antioxidant capacity by different in vitrotests [89]. Moreover, in a study, [PERSON] et al. (2019) assessed the antioxidant potential, antiglycation, and the total phenolics content of essential oils extracted from 19 medicinal plants [90].  
The antioxidant potential of _T. occidentalis_ L. cones was assessed in raw chicken ground meat during refrigerated storage in a study made by [PERSON] et al. (2014). DPPH free-radical scavenging activity method was used to estimate the antioxidant activity of thuja cones (_T. occidentalis_) and peach seeds (_Prunus persica_). They also estimated the total phenolics, flavonoids, and reducing power in these extracts. Thuja cones extract and peach seeds extract had a total phenolics of 7.80 \(\pm\) 0.04 and 1.92 \(\pm\) 0.04 mg TAE/g d.w., respectively [39].  
Moreover, there has been a remarkable DPPH radical scavenging activity shown by both extracts (25.52 \(\pm\) 1.92% and 24.99 \(\pm\) 0.32%). However, thuja cones extract showed a better reducing power, as compared to peach seeds extract (3.32 \(\pm\) 0.01 and 0.49 \(\pm\) 0.01) [39].
# A GPU-Accelerated Method for 3D Nonlinear Kelvin Ship Wake Patterns Simulation
## 4 GPU Parallel Computing Framework
### GPU Solver Implementation
#### 4.2.3 Inverting Preconditioner Matrix  
Comparing the Math Kernel Library which is famous for the computation of sparse linear algebra, the cuSolverSP library is generally faster for solving sparse linear systems [24]. In this paper, the cuSolverSP library is adopted to invert the preconditioner matrix. The present sparse linear system is special; the right-hand side of the system \(\mathbf{v}\) changes continuously in the iteration, whereas the left-hand side does not. The characteristic of the sparse linear system suggests using QR factorization to calculate \(\mathbf{P}^{-1}\mathbf{v}\)[21]. By QR factorization, the sparse matrix is decomposed into an orthogonal matrix and an upper triangular matrix, which are saved in GPU memory and are directly used to solve linear equations in each iteration. Finally, the preconditioner-vector products \(\mathbf{P}^{-1}\mathbf{v}\) can be obtained.  
* Using CSR data format to save the preconditioner matrix with an appropriate bandwidth;
* In the analysis stage, cusolverSpXcsqrAnalysis() function is used to analyze the sparsity of orthogonal matrix and upper triangular matrix in QR decomposition. This process may consume a large amount of memory. If the memory is insufficient to complete the analysis, the program will stop running and return the corresponding error message;
* In the preparation stage, cusolverSpXcsqrAnalysis() function is used to select the appropriate computing space to prepare for QR decomposition. Here, two memory blocks are prepared in the GPU: one to store the orthogonal matrix and the upper triangular matrix, and the other to perform QR decomposition;
* The cusolverSpDcsrqrSetup() function is called to allocate storage space for the orthogonal and upper triangular matrices based on the results of the preparation stage. Then, cusolverSpDcsrqrFactor() function is used to complete the QR decomposition of coefficient matrix outside the cycle;
* Using the cusolverSpDcsrqrZeroPivot() function checks the singularity of the decomposition results, if the nearly singular the program terminates operation and error is given, return to step 1 to choose the bandwidth again;
* In the loop body, the cusolverSpDcsrqrSolve() function is repeatedly called, and the solution of linear equations can be obtained directly by using the decomposition results stored in the GPU;  
The main CUDA functions are shown in Table 1.
## 2 Experimental Investigation
### Experimental Methods
#### 2.5.3 SEM Observation  
A SEM is a device that scans an electron beam on the surface of a sample to generate secondary electrons to observe the morphology of the microscopic sample surface. In this study, SEM images were taken to determine the fiber morphology and distribution within the cement matrix under repetitive electrical heating. The analysis equipment used TESCAN's MIRA3-LMH high-resolution field emission scanning electron microscope (FE-SEM, model name). Following the heating performance test, the cured specimens were pulverized and the obtained fiber was dried, coated with platinum in a vacuum state and then observed through a SEM.
## 3 Results and Discussion
### Cytotoxicity and Anticancer Properties
This effect has also been seen in other cancer cells treated with curcumin, including prostate ([PERSON] et al., 2015), papillary thyroid ([PERSON] et al., 2015), pancreatic ([PERSON] et al., 2015), ovarian ([PERSON] et al., 2015), and colon cancer cells ([PERSON] et al., 2015). Other effects on cancer cells have been observed, such as nuclear shrinkage and chromatin condensation in cutaneous T-cell lymphoma cells ([PERSON] et al., 2015), and induction of cell cycle arrest in prostate cancer cells ([PERSON] et al., 2015). Recent studies with colon cancer cells revealed that curcumin induced reactive oxygen species production, which ultimately caused DNA fragmentation (the hallmark of apoptosis), cell cycle arrest, and induction of apoptosis through the mitochondrial pathway ([PERSON] et al., 2015). Another study by [PERSON] et al. (2014) revealed that curcumin suppressed NF-kB (NFxB-dependent inflammation mediator) activation, and downregulated all of its target genes (involved in the regulation of apoptosis and proliferation) ([PERSON] et al., 2015). However, in vitro studies on lymphocytes, hepatocytes, HDF, and rat dermal fibroblasts have demonstrated that curcumin has no effect on cell proliferation of non-cancerous cells ([PERSON] et al., 2015). In healthy cells, p53 induces G1 arrest to allow the cells to repair before progressing the cell cycle ([PERSON] et al., 2015). This occurs in association with cyclin-dependent pathways. Curcumin causes cell cycle arrest at the G0 by downregulating cyclin D1, while simultaneously upregulating Cip1. This results in cell cycle arrest at the G0 phase without apoptosis. However, in cancer cells, levels of cyclin D1 level are so high that despite curcumin-induced upregulation of Cip1; it is insufficient to cause cell cycle arrest. This allows normal cells to be immune to curcumin-induced apoptosis at G2 phase, in which curcumin makes a significant induction of p53, accelerating apoptotic pathway ([PERSON] et al., 2015; [PERSON] et al., 2015). Figure 9: Cell viabilities of HaCat, HDF, and A375 cells after seeding on BCC0.5 and BCC1.0 films for 24 h and 48 h. The values were expressed as mean \(\pm\) SD (\(n\) = 3). Statistically significant differences in cell viabilities of HaCat, HDF and A375 were compared with the same treatment: a \(p\) < 0.05 versus HaCat, b \(p\) < 0.05 versus HDF and c \(p\) < 0.05 versus A375.
## 3 Results and Discussion  
### Total Phenols, Ortho-Diphenyls, Flavonoids Content and Antioxidant Capacity  
As evident from the data presented in Table 2, each variety within each sub-product exhibits distinct values. This variability can be attributed to a myriad of factors, encompassing the genetic and physiological traits inherent to each variety, the specific geographic location of cultivation, soil composition, fertilization practices, and the prevailing environmental conditions [8; 16; 21]. Notably, it is important to acknowledge that all the varieties under investigation were collected from the same geographic location. Given this common origin, the observed differences can be attributed primarily to the physiological characteristics unique to each variety. Upon closer examination of the results, particularly regarding pruning firewood, the total phenol contents demonstrated a range from 18.40 \(\pm\) 0.43 to 24.01 \(\pm\) 1.34 mg GA g\({}^{-1}\) DW, with the Borrado das Moscas and Encruzado varieties exhibiting the respective endpoints of this spectrum. The ortho-diphenyl content ranged from 15.90 \(\pm\) 0.59 mg GA g\({}^{-1}\) DW for Borrado das Moscas to 23.73 \(\pm\) 1.31 mg GA g\({}^{-1}\) DW for the Jaen variety. In terms of flavonoids, the values varied from 14.40 \(\pm\) 0.62 mg CAT g\({}^{-1}\) DW for Borrado das Moscas to 21.98 \(\pm\) 0.85 mg CAT g\({}^{-1}\) DW for the Jaen variety. These findings underscore the importance of considering the inherent characteristics of each grape variety when interpreting the results. While external factors do contribute to variability, the distinctive attributes of each variety play a significant role in shaping the observed differences in phenolic content within the pruning firewood sub-product. The data reveals a consistent trend: Jaen consistently demonstrates the highest values across the studied phytochemical parameters, standing out as significantly different from the other varieties. Conversely, Borrado das Moscas consistently exhibits the lowest values, also significantly distinct from the other varieties. This trend persists when examining the stem samples. The Jaen variety consistently displays the highest values for all assessed phytochemical parameters, indicating significant differences when compared to the other varieties. Conversely, Borrado das Moscas consistently exhibits the lowest values, often showing significant differences when compared to the other studied varieties. Shifting focus to the values obtained for the lees, a noteworthy finding emerges. Among the by-products, red wine lees showcase the highest concentration of phenolic compounds, including total phenols, ortho-diphenyl, and flavonoids, as shown in Table 3. This observation highlights the considerable richness of these compounds within red wine lees, showcasing their significant potential.
# Cooperative Buffering Schemes for Time-Shifted Live Streaming of Distributed Appliances
## 4 Experimental Results
### Startup Delay  
Figures 12-14 show the startup delay of the _R_RC_-based scheme and the _D_DP_-based scheme with \(w\) values of 0.1, 0.5, and 0.9 and a buffer size of 300 s when using _distribution 1_, 2, and 3, respectively. Similar to the experimental results on the playback continuity, the _R_RC_-based scheme outperforms the _D_DP_-based scheme in all cases because the _R_RC_-based scheme is capable of evaluating the number of actual requests more accurately. The average startup delay of the _R_RC_-based scheme for the first 10 time-shifting hours in _distribution 1_ is 6.6 s, while those of the _D_DP_-based scheme with \(w\) values of 0.1, 0.5, and 0.9 are 7.1, 9.0, and 12.3 s, respectively.  
We can also see that the differences in the startup delay among the _R_RC_-based scheme and _D_DP_-based schemes with three \(w\) values become longer as the playback  
Figure 14: Comparison of startup delay between _D_DP_ and _R_RP_ in _distribution 3_ with a 300 s buffer.  
Figure 12: Comparison of startup delay between _D_DP_ and _R_RP_ in _distribution 1_ with 300 s buffer.  
Figure 13: Comparison of startup delay between _D_DP_ and _R_RP_ in _distribution 2_ with 300 s buffer.  
requests are more concentrated around the live broadcast time as in _distribution 1_. The largest differences in the average startup delay of _distribution 1_, 2, and 3 among them are 5.7, 3.7, and 0.6 s, respectively. This is because, as the playback requests are more uniformly distributed over time as in _distribution 3_, the proximity degree to reflect the concentration degree of playback requests around live broadcast time becomes a relatively less important factor for determining the target time range.  
As expected, the startup delay of all the schemes increases as the playback positions go further from the live broadcast time, for example, when the startup delays of the \(R\_RC\)-based scheme for 2 and 16 h away from the live broadcast time are 5.2 and 11.9 s, respectively. The reason is that the chunks required for initial playback can be received more quickly around the live broadcast time than at distant positions since much more PVRs are likely to play back around the time.
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 4 Acoustic Emission Source Localization Techniques for Three-Dimensional (3D) Structures
### Improvement Algorithm  
[PERSON] et al. [59] proposed a new acoustic source localization method, FastWay, which used the fastest rather than the shortest propagation path between the source and each sensor to localize the AE source. This algorithm performs sound source localization by improving the traditional Dijkstra algorithm (shortest path algorithm). The method was more accurate and reliable than the conventional acoustic source localization methods. This method requires 12 sensors and is suitable for anisotropic materials.  
Figure 13: Schematic of three-dimensional location of cube monitoring network: (**a**) AE sensors at A, B, C, D, E; (**b**) AE sensors at A, B, C, D, H; (**c**) AE sensors at A, B, C, D, G; (**d**) AE sensors at A, B, C, D, F.  
[PERSON] et al. [92] proposed a three-dimensional positioning method for reinforced concrete beams called time reverse modeling (TRM), and confirmed the accuracy of the results by obtaining three-dimensional crack distribution through X-ray computed tomography.  
[PERSON] et al. [93] proposed a three-dimensional AE tomography algorithm for locating three-dimensional structures. The method was verified by numerical investigations.  
[PERSON] et al. [94] proposed a new algorithm for three-dimensional sound source localization. According to sensor coordinates, the arrival time of acoustic waves, the velocities of acoustic waves in two kinds of media, the space-time relation equations of the AE source point and the measuring point were established by the precise coordinates of the AE source based on [PERSON]'s law.
## 4 Experiments and Results
### Results and Analysis
#### 4.4.3 Discussion  
In addition to the comparison with the A3C and PPO algorithms, we also compared the MADDPG algorithm using mutual information. Figure 6 shows the comparison results of A3C-based AEN and MADDPG. In _Cleanup_, the AEN embodies the advantages of the ultimate collective gain. In Harvest, although the collective returns are not much different, the convergence speed in the early stage is still improved. This demonstrates the effectiveness of the AEN structure for enhancing collective reward and promoting cooperation in the SSDs environment.  
Table 1 shows the maximum collective reward of our proposed algorithm in the two environments. We can see that the PPO algorithm using SR and AEN is very significant in the _Cleanup_ environment. Compared to the baseline, there is also a performance improvement. The application of the two algorithms in the two environments also reflects the general applicability of the method we proposed. The method we propose can be applied  
Figure 5: Experiment results in _Harvest_. The top two figures are the results of our method applied to A3C. The bottom two figures are the experiment results based on PPO.  
to a variety of basic algorithms. The intrinsic reward can solve the problem of conflict of interest between individuals and collectives in a multi-agent system.
# A Novel Mobile Communications Authentication Scheme with Roaming Service and User Anonymity
## 2 Preliminaries
### [PERSON]'s Scheme
#### 2.1.3 Roaming Phase  
**L1.** MU\(\rightarrow\)FA: m\({}_{\rm LR01}\){m\({}_{i}\), mac}  
The MU calculates m\({}_{i}\) = (TCert\({}_{\rm MU}\) \(|\) ski\({}_{i+1}\) \(|\) Other)\({}_{\rm ski}\) and mac = H(TCert\({}_{\rm MU}\) \(|\) ski\({}_{i+1}\) \(|\) Other) and sends the roaming message {m\({}_{i}\), mac} to the FA, where sk\({}_{i+1}\) is a session key for the next communication.  
**L2.** FA: m\({}_{i}\), mac  
When received the roaming message from the MU, the FA calculates the session key and decrypts m\({}_{i}\). The FA then checks the validity of mac. If it is valid, the FA updates the session key sk\({}_{i}\) with sk\({}_{i+1}\) for the next communication.
# Fragile Watermarking for Image Authentication Using the Characteristic of SVD
## 4 Experiments and Performance Analysis
### Performance under Unintentional Attacks  
In addition to the malicious attacks, the host images also suffer from some unintentional attacks in the image processing, such as salt and pepper noise and brightness adjustment. To test the fragility of the proposed watermarking algorithm, three unintentional attacks are performed on the watermarked images, and their corresponding detection results are shown in Figure 9. From the detection results in Figures 8 and 9, we can draw the conclusion that the proposed method is sensitive to any manipulation including malicious attacks and unintentional attacks.  
Figure 8: The corresponding tamper localization maps: (**a**) Image cropping; (**b**) Copy–paste attack; (**c**) Text addition; (**d**) Collage attack; (**e**) Content-based attack; (**f**) Constant-average attack.  
Figure 9: Tamper detection under unintentional attacks: (**a**) Salt and pepper noise (0.005); (**b**) Tamper detection result; (**c**) Brightness adjustment (+4); (**d**) Tamper detection result; (**e**) JPEG compression (Q = 99); (**f**) Tamper detection result.
### Multiple Sclerosis, Neuropathology, Treatment Avenues and Outcomes  
MS is a degenerative neuroinflammatory disease affecting the central nervous system (CNS). It is characterised by significant demyelinating plaques in both grey and white matter that can be identified by the loss of myelin and the associated oligodendrocytes presumably of an autoimmune aetiology [8; 9]. The degree of permanent clinical deficits observed is more closely related to the degree of axonal atrophy and loss compared to the extent of demyelination or the number of lesions present [10]. These inflammatory lesions are composed mainly of T-lymphocytes and lesser amounts of B-lymphocytes, plasma, microglia, and macrophages. These types of cells seem to be actively involved in the demyelinating process. The presence of histocompatibility antigens, cell adhesion molecules and cytokines expressed in the lesions also suggests a T-cell mediated immuno-response is responsible for the neuroinflammation in MS [11].  
MS can be thought of as a disorder with several subtypes and variants. Three main types of MS have been identified; (1) relapsing-remitting MS, which consists of episodes of worsening or new symptoms. (2) primary progressive MS, which consists of a linear worsening of symptoms over several years, but there are still periods where symptoms improve and (3) secondary progressive MS, where symptoms worsen and there are no longer signs of remission. The presentation of symptoms and the symptom severity depend on where the demyelinating plaques are located in the CNS [12].  
As is commonly known, there is currently no cure for MS. Despite this, there are two main pharmacotherapeutic avenues: the first of them includes disease-altering drugs which can modify the course of the disease, such as recombinant interferon \(\beta\)-1a (e.g., Avonex), recombinant interferon \(\beta\)-1b (e.g., Betaferon) and glatiramer acetate [13], which appear to not only help prevent relapse but also to improve neuropsychological outcomes in pwMS [14]. They do this by blocking gamma interferon and inducing the release of anti-inflammatory cells [15]. The second pharmacotherapeutic avenue includes symptom-altering drugs which can improve the patient's quality of life by improving pain and spasticity and mood issues associated with MS, such as the GABA-B receptor agonist baclofen and the \(\alpha\)2 adrenergic receptor agonist tizanidine [16].