esa-sceva/satcom-chunk-collection huggingface dataset:
# Ciphered BCH Codes for PAPR Reduction in the OFDM in Underwater Acoustic Channels
## 1 Introduction
An XOR operation is performed for the input message data with a predefined key of the same block length. The obtained ciphertext is then transmitted, whereby the receiver performs an XOR operation for the received data with the same key to get the original message. In this work, we use XOR ciphering on BCH codes to propose a low-complexity PAPR reduction scheme for an underwater acoustic OFDM. The proposed XOR-ciphered BCH codes not only have low PAPR, but they also provide a medium level of encryption of the transmitted data while improving the bit error rate (BER) at the same time. The main contributions of this paper are:  
* A low-complexity scheme based on XOR-ciphered BCH encoded symbols for PAPR reduction in UWA OFDM systems. * An offline vector identification technique to shortlist random key vectors that will result in the lowest possible PAPR of XOR-ciphered BCH codes. * Evaluation of the proposed technique using a shallow underwater channel model showing PAPR as well as BER reduction. The rest of the paper is structured as follows: Section 2 details the recent advances in the domain of PAPR reduction for underwater acoustic channels, coding techniques proposed for UWA OFDM and using coding for PAPR reduction in OFDM systems. Section 3 briefly describes the OFDM transceiver architecture, the UWA channel model and the coding technique utilized. Section 4 gives a detailed overview of the proposed PAPR reduction technique, followed by Section 5 which is devoted to the results and discussions. In the end, a short conclusion is provided in Section 6.
## 2 Materials and Methods
### Algorithm Design
#### 2.2.2 Algorithm Flow Design  
The particle swarm algorithm is an optimization method based on iteration. The system is initialized as a set of random solutions. Through iterative optimization, the particles follow the optimal particles to search in the solution space. In each iteration, the particle updates itself by tracking the optimal solution currently found by itself (individual extreme value) and the optimal solution currently found by the entire population (global extreme value). The principle is shown in Figure 4[28].  
Suppose that in a \(D\)-dimensional search space, a population composed of \(n\) particles \(X=(X_{1},X_{2},\ldots,X_{n})\), where the \(i\)-th particle represents the \(D\)-dimensional vector \(X_{i}=(X_{i1},X_{i2},\ldots,X_{in})^{T}\), represents the position of the \(i\)-th particle in the \(D\)-dimensional search space, and also represents a potential value of the problem. According to the objective function, the fitness value corresponding to each particle \(X_{i}\) can be calculated. \(V_{i}=(V_{i1},V_{i2},\ldots,V_{in})^{T}\), its individual extreme value, is \(P_{i}=(P_{i1},P_{i2},\ldots,P_{in})^{T}\), and the global extreme value of the population is \(P_{g}=\left(P_{g1},P_{g2},\ldots,P_{gn}\right)^{T}\).  
In each iteration process, the particle updates its own speed and position through individual extreme values and global extreme values. The mathematical description and iteration formula are as follows:  
\[V_{id}^{k+1}=\omega V_{id}^{k}+c_{1}rand(\cdot)\left(P_{id}^{k}-X_{id}^{k} \right)+c_{2}rand(\cdot)\left(P_{id}^{k}-X_{id}^{k}\right) \tag{19}\]  
\[X_{id}^{k+1}=X_{id}^{k}+V_{id}^{k+1} \tag{20}\]  
where \(d=1,2,\ldots,D\); \(i=1,2,\ldots,n\); \(\omega\) is the inertia weight; \(c_{1}\) and \(c_{2}\) are acceleration factors, and \(c_{1}=c_{2}\), ranging between 0 and 4; rand is \((0,\ 1)\) and indicates random numbers.  
The specific optimization steps of the laser are shown in Figure 5, and the optimization steps of the LED are similar.  
Figure 4: Schematic diagram of particle swarm algorithm.
# A Statistical Approach to Detect Jamming Attacks in Wireless Sensor Networks
## 5 EWMA Algorithm
During the detection of this attack, the main interest is to view the situation where the upper limit is exceeded. There are also instances where the observed features of the traffic falls below the lower control limit, which can also be as a result of network anomaly. In this work, we have developed an algorithm to detect jamming attacks using EWMA to observe the auto-correlated data, which is the packet IAT of the network traffic.
## 3 Results and Discussion
### Betulinic Acid Content  
All the analyzed composite plants were used for isolation and measurements of betulinic acid, a compound exhibiting antibacterial, antiviral and anticancer properties. The activity of betulinic acid against _Escherichia coli_ and _Staphylococcus aureus_[39] as well as its anti-HIV and even antimalarial properties have been described [40]. It should also be mentioned that this compound exhibits a cytotoxic effect against tumor cells (melanoma) [40; 41]. Five tested transgenic lines exhibited higher betulinic acid content in leaves than the control _Sarracenia_ plants (Figure 5). The best results were obtained for lines 7#1, 7#2 and 4#4, for which the highest amount of betulinic acid was measured. The levels of betulinic acid observed in these lines were 70%, 62% and 66% higher, respectively, than in the wild-type control plants. The transformed roots did not exactly show the same tendency, and for two lines, 4#2 and 4#4, an elevated amount of betulinic acid was recorded, with line 4#4 presenting the highest amount, which was 34% higher than in the untransformed roots. The obtained results indicate that _A. rhizogenes_-mediated transformation caused an increase in the betulinic acid amount, which is a compound of medicinal significance.  
Figure 3: Total polyphenol content measured in hairy roots and composite plants of _S. purpurea_. The levels of polyphenols were analyzed as described in Section 2. The data (\(\pm\)SD) were obtained from three samples per line.
## 3 Results and Discussion
### Interaction Effects on the RO16 Dye Removal  
Three-dimensional (3D) surface and two-dimensional (2D) contour plots were used to evaluate the effects of the investigated factors and to find the statistical relationship between the tested parameters and the removal of MB dye. Figure 4 presents 3D and 2D plots showing the significant effects of the interactions between the BCA-Mag/AB dosage and contact time, pH and BCA-Mag/AB dosage, and pH and contact time on the MB dye removal performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df** & **Mean Square** & **F-Value** & **_p-Value_** & \\ \hline Model & 12,849.29 & 9 & 1427.70 & 303.30 & \textless{}0.0001 & significant \\ A—Adsoorbent dose & 3362.00 & 1 & 3362.00 & 714.23 & \textless{}0.0001 & \\ B—Contact time & 4560.13 & 1 & 4560.13 & 968.77 & \textless{}0.0001 & \\ C—pH & 2556.13 & 1 & 2556.13 & 543.03 & \textless{}0.0001 & \\ AB & 56.25 & 1 & 56.25 & 11.95 & 0.0106 & \\ AC & 90.25 & 1 & 90.25 & 19.17 & 0.0032 & \\ BC & 289.00 & 1 & 289.00 & 61.40 & 0.0001 & \\ A\({}^{2}\) & 233.69 & 1 & 233.69 & 49.65 & 0.0002 & \\ B\({}^{2}\) & 482.06 & 1 & 482.06 & 102.41 & \textless{}0.0001 & \\ C\({}^{2}\) & 1037.85 & 1 & 1037.85 & 220.48 & \textless{}0.0001 & \\ Residual & 32.95 & 7 & 4.71 & & & \\ Lack of fit & 11.75 & 3 & 3.92 & 0.7390 & 0.5815 & not significant \\ Pure error & 21.20 & 4 & 5.30 & & & \\ Cor total & 12,882.24 & 16 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Analysis of variance (ANOVA) for MB removal by BCA-Mag/AB.  
The critical interaction between BCA-Mag/AB dosage and pH depicted in Figure 4 shows that gradually increasing the MAG dosage from 20 mg to 100 mg enhances the  
Figure 4: Three-dimensional (3D) surface and two-dimensional (2D) contour plots.  
adsorption performance of the MB dyes. The increased adsorption rate of the MB dyes is attributed to the increased number of binding sites on the BCA-Mag/AB surface. Additionally, gradually increasing the initial pH from 2 to 10 has strengthened the adsorption efficiency of the MB dyes. The pronounced effect of pH on MB adsorption is particularly linked to the changes in surface charge of the BCA-Mag/AB at different pH values. It is also shown that as the adsorption time extends from 20 min to 180 min, the dye removal (%) increases. This phenomenon can be explained by the facilitation of the accumulation of MB dye molecules in the pores of the BCA-Mag/AB over a longer duration.
## 4 Discussion  
### CLQ Evaluation Method Integrating Multi-Source Remote Sensing  
An efficient evaluation method is the basis for revealing the spatiotemporal patterns of CLQ. However, traditional evaluation methods need to acquire a large number of ground-measured soil data, which is time-consuming and laborious, and the evaluation results depend on a spatial interpolation method. Different spatial interpolation methods will affect the evaluation results of CLQ [10], and thus, the spatial information of the CLQ cannot be reflected objectively. Satellite remote sensing data have the advantages of wide coverage, abundant historical data, and objective spatial information, which are suitable for the spatiotemporal evaluation of the CLQ.  
The existing abundant remote sensing data can effectively support the comprehensive evaluation of the CLQ. However, previous studies have only focused on a single attribute of the CLQ, such as soil fertility or soil moisture [35; 37]. Few studies have integrated  
Figure 8: The obstacle degrees of the evaluation indicators in Guangzhou (GZ) and its 10 districts from 2010 to 2018. CH: Conghua district; ZC: Zengcheng district; HP: Huangpu district; TH: Tianhe district; NS: Nansha district; HD: Huadu district; BY: Baiyun district; HZ: Haizhu district; PY: Panyu district; LW: Liwan district.  
multi-source remote sensing data to comprehensively evaluate the CLQ. In this study, a comprehensive indicator system for evaluating the CLQ was developed by integrating Landsat, Sentinel-2, and MODIS NPP data products. These remote sensing data and related products satisfy the spatial scale and temporal continuity requirements to reveal the spatiotemporal changes in the CLQ.  
The verification for the accuracy of the results obtained in this study showed that the results of CLQ evaluation calculated by integrating multi-source remote sensing data were reliable. Our results on the spatial distribution of CLQ were also consistent with previous studies [49]. This indicated that the CLQ evaluation method integrating remote sensing data is reasonable and feasible.
## 5 Evaluation of the Use of Assessment Templates
### Guided Assessment Plan
Additionally, the assessment includes examining the refresh rate at which the system updates its position measurements, with a rate exceeding 1 Hz being the general target for the geofencing application to ensure timeliness. Maintaining confidence in the measurements is vital. The system should be capable of quantifying the uncertainty associated with position measurements. This quantification aids in assessing the reliability and robustness of the geofence solution. Incorporating a second observer for plausibility checks and using dual-frequency receivers are evaluated to determine how they contribute to the overall integrity of the geofence system. The accuracy and reliability of GNSS-based geofence solutions can be assessed by evaluating these criteria. In applying the cybersecurity attributes delineated in Section 4.3, the emphasis is placed on preventative measures. Specifically, the objective is to fortify the vehicle against unauthorised control by employing authenticated and encrypted communication protocols. Detection mechanisms are essential to identify malicious activity during remote assistance, and in the case of failure or an attack, a fail-safe reaction strategy should be employed. This transition ensures the safe operation with reduced functionality and may involve over-the-air updates for security patches. To evaluate the presence and appropriateness of cybersecurity measures, although not a direct component of the ODD, including cybersecurity criteria improves the evaluation process. The enhancement entails a direct association between threat agents, their underlying motives, and potential attack surfaces within the ODD. This refined linkage provides more precise guidance for implementing measures against prospective attacks. By integrating cybersecurity measures, the assessment plan comprehensively evaluates the system's safety and resilience, aligning with future type-approval requirements. The attributes derived in Section 5 have enhanced assessment planning and analyses of use cases. They emphasise the importance of maintaining 5G communication coverage with a QoS bandwidth priority to ensure a consistent bandwidth, whether due to natural factors or malicious actions. Further assessments of these attributes' suitability for proving ground testing are pending. Utilising the prototype-assessment templates in collaboration with specific functional scenarios--such as minimal risk manoeuvres, activation, and deactivation--facilitates the evaluation of 19 distinct test scenarios. These indicators are especially relevant to 5G communication and geofencing conditions. They have been categorised into different domains, including activation conditions, minimal risk manoeuvres, and external and internal HMI considerations. The distribution of these conditions within each category is not uniform; for instance, at least four conditions are explicitly pertinent to 5G communication, while six conditions are geared towards geofence considerations.
# Interaction Strength Analysis to Model Retweet Cascade Graphs
## 3 The Interaction Strength-Based Network (ISN) Approach to Generating Retweet Cascade Graphs
### Twitter User's Interaction Strength  
Next, we describe the procedure that exploits the interactions to generate the cascade graphwhich is also depicted in Figure 1. For a user \(u\), its timeline is first downloaded by means of the Twitter API. Then, its absolute interaction strength \(AbsoluteIS_{u,w}\) is found with respect to any node \(w\) that is retweeted before \(u\).  
More precisely, to derive the \(AbsoluteIS_{u,w}\) the following information is required:  
* The number of quotes \(Q_{u,w}\) that user \(u\) expressed to the node \(w\);
* The number of replies \(RP_{u,w}\) that user \(u\) did to \(w\);
* The number of retweets \(RT_{u,w}\) that user \(u\) did for \(w\).  
The absolute interaction strength between \(u\) and \(w\) is defined as:  
\[AbsoluteIS_{u,w}=Q_{u,w}+RP_{u,w}+RT_{u,w}.\]  
The \(AbsoluteIS\) is found only with respect to nodes that were retweeted before \(u\), and we also discarded interactions that happened after the root tweet creation (\(root_{time}\)) and those that are too old to represent an ongoing trust relationship (trust relations tend to change over the years). In fact,  
Figure 1: Toy example of the proposed approach.  
as reported in the toy example in Figure 1, the dashed edge between [PERSON] and [PERSON] is not considered for IS since [PERSON] retweeted before [PERSON], and thus the interactions are not needed to build the cascade graph. Moreover, we discarded interactions that occurred more than two years before the \(root_{time}\). We relied on \(root_{time}\) as the threshold date for all the users' relations involved in the cascade \(C\) since, in general, the tweet information cascade lifetime is short and concentrated in proximity of the tweet creation date. Figure 2 reports the retweet distribution given the time delay between the retweet action date and the original tweet posting time for all the 16,304 cascades in the sample. Figure 2 depicts the temporal dynamics of the retweets after the respective \(root_{time}\), showing a decreasing trend, as the highest number of interactions occurred soon after \(root_{time}\) (the original tweet creation date).  
After a weighted IS value \(WeightedIS_{u,w}\) is found, by assigning different weights to quotes (\(w_{q}\)), replies (\(w_{r}\)p) and retweets (\(w_{r}\)t):  
\[WeightedIS_{u,w}=w_{q}*Q_{u,w}+w_{rp}*RP_{u,w}+w_{rt}*RT_{u,w}.\]  
Finally, the \(IS_{u,w}\) value is found by dividing \(WeightedIS_{u,w}\) by the sum of all the weighted IS values for the user \(u\). In this way, we obtained \(IS\) values for \(u\) that range within \([0,1]\), and are thus easier to evaluate and compare among retweets cascades. For instance, if \(IS_{u,w}\) is 1, this means that \(w\) is the only user that retweeted before \(u\) and with whom \(u\) has interacted, as represented in Figure 1 in the edge between [PERSON] and [PERSON]. The next section describes the grid search procedure adopted to derive the optimal weights \((w_{q},w_{rp},w_{rt})\).
## 2 Materials and Methods  
### Study Area  
In this study, Southern China refers to the southern part of the eastern monsoon zone of China (Figure 1). This region has a complex human and geographical environment, covering about 25% of China's land area and 60% of China's population [53]. The region is dominated by subtropical and tropical monsoon climate types, with winter average temperatures exceeding 0 \({}^{\circ}\)C and annual precipitation above 800 mm. We applied high-quality land cover data produced by [PERSON] and [PERSON] [54], which encompassed Landsat and Moderate-resolution Imaging Spectroradiometer (MODIS) data, along with the random forest algorithm. We used annual land cover with a resolution of 30 m from 2001 to 2020 ([[https://zenodo.org/records/4417810](https://zenodo.org/records/4417810)]([https://zenodo.org/records/4417810](https://zenodo.org/records/4417810)), accessed on 5 July 2024), including nine types: forest, grassland, shrub, cropland, water, snow/ice, barren, impervious, and wetland. A cropland type was defined as cropland on the same grid that did not change in at least 11 years during 2001-2020 (Figure 1). In this way, we roughly divided southern China into four major agricultural regions based on a grid of highly agglomerated cropland: the upper Yangtze River region (UYR) (i.e., the Sichuan Basin), the middle Yangtze River region (MYR) (mainly including Hubei and Hunan provinces), the lower Yangtze River region (LYR) (mainly including Jiangsu, Anhui, and Jiangxi provinces), and the South
## 2 Model-Based Analysis of MSK Function
### Forward Simulations  
An alternate method to the inverse approach detailed above are forward simulation methods which use muscle activations and the resulting muscle force to drive MSK models and subsequently estimate the resulting kinematics and dynamics. As such these models allow users to establish a causal relationship between the forces acting on the human body and the resulting movement, thereby allowing an analysis of 'what if' scenarios. An extension of these forward simulations are predictive simulations, which can be employed to generate novel movements without relying on experimental data. Predictive simulations are typically used to identify muscle excitations and therefore movement patterns that optimise a user-defined cost function (e.g., minimise joint contact forces, muscle fatigue, etc.) subject to constraints describing, among others, the dynamics of the MSK system. These methods exist stand-alone [46] but have also been integrated into existing software packages [47, 48].  
The following sections will detail specific examples of the use of MSK modelling and simulations and detail pathways for integration into clinical practice. A majority of the detailed applications are focused on lower-limb MSK loading due to the extensive available literature and its relevance to a range of MSK conditions rather than a specific pathology. However, these methods can and have been extending to other segments including the spine [49], upper limbs [50], and head-neck [51].  
To conclude, parameters and analyses made available through the use of MSK models and modelling provide valuable information that can be used for not only research studies on clinical populations, but also enhancing clinical treatment pathways, as described below. Indeed, MSK models combined with the tools and methods described above allow detailed analyses of muscle and joint contact forces, in addition to joint angle and moment analysis that most clinical practice software (e.g., Vicon Plug-in-gait (Vicon Motion Systems, Oxford, UK)) provide. Facilitating the adoption of these models are a number of software packages, both commercial (e.g., AnyBody [52]) and open source (e.g., OpenSim [53, 54]). Both modelling and simulation platforms are typically released with different generic models and are supported by a combination of dedicated developers and their respective research community. The provision of predictive simulation techniques was just recently facilitated through the extension of commonly used software (i.e., OpenSim) to include these predictive simulations as a standard tool [47, 48].
# A Generative Adversarial Network Structure for Learning with Small Numerical Data Sets
## 4 Experimental Study
### Experiment Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Seeds**} & \multicolumn{2}{c}{**SVM\_Poly**} & \multicolumn{2}{c}{**SVM\_Rbf**} & \multicolumn{2}{c}{**Decision\_Tree**} & \multicolumn{2}{c}{**Naive\_Bayes**} \\ \cline{3-10}  & & **SDS** & **PM** & **SDS** & **PM** & **SDS** & **PM** & **SDS** & **PM** \\ \hline \multirow{2}{*}{10 with 100 virtual samples} & average & 70.593\% & 75.391\% & 84.771\% & 84.953\% & 73.184\% & 76.453\% & 70.462\% & 72.682\% \\ \cline{2-10}  & Stddev & 8.443\% & 9.056\% & 8.625\% & 5.982\% & 9.110\% & 8.107\% & 10.769\% & 9.997\% \\ \cline{2-10}  & \(p\)-value & * & & ns & & * & * & * \\ \hline \multirow{2}{*}{15 with 100 virtual samples} & average & 74.963\% & 81.224\% & 86.921\% & 88.761\% & 80.253\% & 81.741\% & 79.937\% & 81.592\% \\ \cline{2-10}  & Stddev & 8.102\% & 9.857\% & 3.960\% & 3.773\% & 7.032\% & 6.351\% & 7.043\% & 6.980\% \\ \cline{2-10}  & \(p\)-value & *** & & ns & & * & * & \\ \hline \multirow{2}{*}{20 with 100 virtual samples} & average & 75.971\% & 82.891\% & 87.267\% & 88.938\% & 83.535\% & 86.171\% & 80.922\% & 82.782\% \\ \cline{2-10}  & Stddev & 8.421\% & 7.453\% & 4.005\% & 3.361\% & 4.899\% & 4.367\% & 4.98\% & 6.754\% \\ \cline{2-10}  & \(p\)-value & ** & & ns & & ns & & ns & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The result of generating 100 virtual samples by WGAN_MTD for the Cervical Cancer dataset. The results shown in Tables 3-6 indicate that the process of adopting WGAN_MTD can improve the learning accuracy when facing small datasets. We also compared the performance of WGAN_MTD with that of MTD and WGAN. As mentioned, MTD can output the generated virtual samples while WGAN produces a dataset similar to that of the input. By applying them to small datasets, as the input, MTD generates virtual data and produces the augmented dataset, including generated data and original data. In addition, WGAN produced a dataset that is more analogous to the original small dataset. We conducted the comparison based on the learning accuracy of the SVM whose inputs came from MTD, WGAN, and WGAN_MTD. For MTD and WGAN_MTD, we considered the case of 20 original data and 100 virtual data. Table 6 shows that WGAN_MTD can perform well in both of three datasets because it not only generates virtual samples, in the same manner are MTD, but also ensures the generated data are more similar to the original data.