esa-sceva/satcom-chunk-collection huggingface dataset:
# Shallow Water Waves and Conservation Laws with Dispersion Triplet
## 3 Dual-Nonlinearity (Gardner's Equation)
### Power-Law Nonlinearity  
Here, \(F(q)=a_{1}q^{n}+a_{2}q^{2n}\) for any real-valued constant \(a\). From (1), this leads to  
\[q_{t}+\Big{(}a_{1}q^{n}+a_{2}q^{2n}\Big{)}q_{x}+b_{1}q_{xxx}+b_{2}q_{xxt}+b_{3}q_ {xtt}=0, \tag{63}\]  
which means that (5) transforms into  
\[\begin{array}{c}(2n+1)(n+2)(n+1)vg^{2}-\{2a_{1}(2n+1)g^{n}+(n+2)a_{2}g^{2n}\} g^{2}\\ -2(n+1)^{2}(n+2)(b_{1}-b_{2}v+b_{3}v^{2})\big{(}g^{\prime}\big{)}^{2}=0.\end{array} \tag{64}\]  
Finally, separating variables and integrating yields a solitary wave solution as follows:  
\[q(x,t)=\frac{A}{\{D+\cos\mathbb{h}[B(x-vt)]\}^{\frac{1}{n}}}. \tag{65}\]  
Here, the inverse width is \(B\) with an external parameter \(D\), while \(A\) is the amplitude of the wave. The amplitude is introduced below as  
\[A=\frac{(2n+1)(n+1)(n+2)v}{\sqrt{a_{1}^{2}(2n+1)^{2}+(2n+1)(n+1)(n+2)^{2}a_{2} v}}, \tag{66}\]  
while the inverse width is  
\[B=n\sqrt{\frac{v}{b_{1}-b_{2}v+b_{3}v^{2}}}, \tag{67}\]  
which poses the same constraint as in (11). The parameter \(D\) is given as  
\[D=\frac{(2n+1)a_{1}}{\sqrt{\big{(}2n+1\big{)}^{2}a_{1}^{2}+(n+1)(n+2)^{2}(2n+1 )a_{2}v}}. \tag{68}\]  
The inverse width \(B\) and the parameter \(D\) must maintain the condition  
\[(2n+1)a_{1}^{2}+(n+1)(n+2)^{2}a_{2}v>0, \tag{69}\]  
for the solitary waves to exist. Figure 5 displays the surface plot of a solitary wave (65) for the power-law Gardner's equation with triple dispersion terms with \(n=1/2\). The parameter values chosen are \(a_{1}=1\), \(a_{2}=1\), \(v=1\), \(b_{1}=1\), \(b_{2}=1\), and \(b_{3}=1\).  
Figure 5: Profile of a solitary wave for the power–law Gardner’s equation, without radiation.  
Conservation Laws  
Here, the multipliers, densities, and fluxes are  
Case 1:  
\[Q=1\text{ and }T^{t}=-\frac{2}{3}b_{3}q_{xt}-\frac{1}{3}b_{2}q_{xx}-q. \tag{70}\]  
Case 2:  
\[Q=q\text{ and }T^{t}=-\frac{2}{3}qb_{3}q_{xt}-\frac{1}{3}b_{2}q_{xx}q+\frac{1}{3 }b_{3}q_{xt}q_{t}+\frac{1}{6}b_{2}q_{x}{}^{2}-\frac{1}{2}\ q^{2}. \tag{71}\]  
Case 3:  
\[Q=\frac{(2n+1)a_{1}q^{n+1}+(n+1)a_{2}q^{2n+1}+\big{(}2n^{2}+3n+1\big{)}(b_{1} \mu+b_{2}K+b_{3}v)}{(2n^{2}+3n+1)b_{2}}. \tag{72}\]  
The corresponding conserved density and flux are too cumbersome to construct. The first two conservation laws are  
\[M=\int_{-\infty}^{\infty}T^{t}dx=\int_{-\infty}^{\infty}qdx=\frac{2A}{2^{\frac {1}{2}}B}F\bigg{(}\frac{1}{n},\frac{1}{n};\frac{1}{n}+\frac{1}{2};\frac{1-D}{2 }\bigg{)}\frac{\Gamma\Big{(}\frac{1}{n}\Big{)}\Gamma\Big{(}\frac{1}{2}\Big{)} }{\Gamma\Big{(}\frac{1}{n}+\frac{1}{2}\Big{)}}, \tag{73}\]  
and  
\[P=\int_{-\infty}^{\infty}T^{t}dx=\frac{1}{6}\int_{-\infty}^{\infty}\{q^{2}-( b_{2}-6 vb_{3})q_{x}^{2}\}dx\]  
\[=\frac{A^{2}}{3.2^{\frac{1}{2}}n(n+4)B}\Bigg{[}\begin{array}{c}F\Big{(}\frac {2}{n},\frac{2}{n};\frac{2}{n}+\frac{1}{2};\frac{1-D}{2}\Big{)}\\ -(b_{2}-6 vb_{3})B^{2}F\Big{(}\frac{2}{n}+2,\frac{2}{n};\frac{2}{n}+\frac{3}{2}; \frac{1-D}{2}\Big{)}\end{array}\Bigg{]}\begin{array}{c}\frac{\Gamma\big{(} \frac{1}{n}\big{)}}{\Gamma\big{(}\frac{2}{n}+\frac{1}{2}\big{)}}.\end{array} \tag{74}\]
###### Abstract  
Granite residual soil exhibits a tendency to collapse and disintegrate upon exposure to water, displaying highly unstable mechanical properties. This makes it susceptible to landslides, mudslides, and other geological hazards. In this study, three common biopolymers, i.e., xanthan gum (XG), locust bean gum (LBG), and guar gum (GG), are employed to improve the strength and stability of granite residual soil. A series of experiments were conducted on biopolymer-modified granite residual soil, varying the types of biopolymers, their concentrations, and curing times, to examine their effects on the soil's strength properties and failure characteristics. The microscopic structure and interaction mechanisms between the soil and biopolymers were analyzed using scanning electron microscopy and X-ray diffraction. The results indicate that guar gum-treated granite residual soil exhibited the highest unconfined compressive strength and shear strength. After adding 2.0% guar gum, the unconfined compressive strength and shear strength of the modified soil are 1.6 times and 1.58 times that of the untreated granite residual soil, respectively. Optimal strength improvements were observed when the biopolymer concentration ranged from 1.5% to 2%, with a curing time of 14 days. After treatment with xanthan gum, locust bean gum, and guar gum, the cohesion of the soil is 1.36 times, 1.34 times, and 1.55 times that of the untreated soil, respectively. The biopolymers enhanced soil bonding through cross-linking, thereby improving the soil's mechanical properties. The gel-like substances formed by the reaction of biopolymers with water adhered to encapsulated soil particles, significantly altering the soil's deformation behavior, toughness, and failure modes. Furthermore, interactions between soil minerals and functional groups of the biopolymers contributed to further enhancement of the soil's mechanical properties. This study demonstrates the feasibility of using biopolymers to improve granite residual soil, offering theoretical insights into the underlying microscopic mechanisms that govern this improvement.  
granite residual soil; biopolymers; soil strength and stability; soil deformation behavior and failure modes; microscopic structure analysis +
Footnote †: journal: Applied Sciences
## 4 Experiments
### Experimental Details
#### 4.2.3 Parameter Settings  
The dataset samples are divided into training sets, validation sets, and test sets in the ratio of 7:2:1, and the same computing platform is used for all comparison experiments.  
A multi-scale non-cooperative object localization module uses a training batch size of 16 and is trained for 200 epochs. The training employs a stochastic gradient descent (SGD) optimizer with an initial learning rate of 1 \(\times\) 10\({}^{-2}\). The learning rate is attenuated using the cosine annealing method with a decay coefficient of 5 \(\times\) 10\({}^{-4}\).  
The human keypoint detection module applies image enhancement techniques such as mirror flipping and scale scaling to the samples during training. The pre-trained weights from the COCO dataset are utilized. The training iteration is set to 200 epochs.  
In the behavioral classification module, each video is sampled with 30 frames of frame-by-frame nudged temporal skeleton point data. This frame-by-frame nudged sampling serves as a form of dataset augmentation. The training process utilizes the Adam optimization strategy with a learning rate of 10 \(\times\) 10\({}^{-3}\) and a batch size of 16. The training is performed for 30 epochs. These parameters are the optimal parameters that we have obtained after many epochs of experiments and tests. When this framework is applied in different scenarios, one can use this parameter to complete the pre-training and then further adjust the parameters according to their own evaluation results.
# Construction of Two-Derivative Runge-Kutta Methods of Order Six
## 5 Discussion and Conclusions  
In this work, for the first time, sixth-order conditions for an explicit TDRK method of the general case has been derived. Also, a new method with five stages of algebraic order 6, phase-lag order 10, and amplification order 9 has been constructed. In order to demonstrate the efficiency of the new method, we have chosen four well-known RK methods of order 6 and 8 with 7, 8, and 13 stages (Butcher, Verner, Dormand-Prince, and Fehlberg) and a special two-derivative Runge-Kutta method of order 6 with five stages. Generally, the new method performs better than all RK methods tested, including the special TDRK method. The plot of the maximum absolute error with the CPU time are given for all problems studied. For Problem 1 of CPU time less than 0.4 s, the new method gives an error less than \(10^{-10}\), whereas \(CT\) and \(DP\) give errors less than \(10^{-8}\) in 0.5 s and the other methods reach an error of \(10^{-6}\) in 0.6 s. Similar results are produced for Problems 2 and 3. Then, we have used two stiff problems to test the efficiency of the method derived in this work concerning the famous Prothero-Robinson problem (\(k=-200\)) and the Van der Pol oscillator (\(\delta=5\)). For the first stiff problem (Problem 4), the TDRK methods have a superior performance compared to the RK methods. The TDRK methods give an error less than \(10^{-9}\) in 0.6 s, while the RK methods need 1.6 s to give an error of \(10^{-8}\). For the second stiff problem (Problem 5), the new method clearly has a superior performance of \(10^{-11}\) in 0.3 s, while methods \(CT\) and \(DP\) give errors of \(10^{-7}\) at the same time.  
The advantage of the TDRK methods is that the number of stages needed for a specific order is much fewer than those for the RK methods. Method \(CT\) in [10] has order 7 and five stages, and the method constructed in this study has order 6 and five stages. Concerning sixth-order methods _Butcher_ and _Verner_ use seven and eight stages, respectively, while the eighth-order methods \(DP\) and _Fehlberg_ use 13 stages.  
In this work, we have given the framework of constructing methods of the general type of order 6. In order to illustrate the construction of such methods, we give an example with a reduced phase-lag and amplification error. Certainly, methods can be derived with other characteristics. In future work, we will focus on TDRK methods of the general type and construct embedded methods to be used for variable step size. Also, we shall consider symmetric methods and methods with frequency-dependent coefficients.  
The authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.
## 2 Design and Implementation
### RF Power Harvesting Design-II
The chip measurement is done using Agilent power meter where output powers are measured with respect to frequency bands ranging from 3 GHz to 5.5 GHz, 5.5 GHz to 7.5 GHz and 7.5 GHz to 10.5 GHz, respectively. Therefore, the corresponding output powers of 4 mW to 7.5 mW, 7 mW to 0.1 mW, and 0 mW to 2.7 W are achieved when input power is considered as 0 dBm. The measured and simulated results of output powers are shown in  
Figure 5: Proposed RF power harvesting circuit with its (**a**) schematic and (**b**) die microchip photograph (including on-chip antenna).
# Crossband Filtering for Weighted Prediction Error-Based Speech Dereverberation
## 4 Experimental Results
### Crossband Filtering--Length Extension  
To thoroughly explore the impact of different crossband filter lengths (\(L_{\text{cb}}\)) in conjunction with various choices of \(L_{\text{bb}}\), we systematically varied \(L_{\text{cb}}\) within the range of 0 to \(L_{\text{bb}}\), while keeping \(L_{\text{bb}}\) fixed for each specific experiment. Results show that early crossband samples indeed improve the dereverberation performance, and, in all cases (i.e., for each measure and each choice of \(L_{\text{bb}}\)), the optimal performance is achieved for \(L_{\text{bb}}>0\). To our surprise, introducing late samples from the crossband components leads to a decrease in  
Figure 1: Performance of conventional WPE for different filter lengths: (a) FWSegSNR—optimal length is 15. (b) CD—optimal length is 14. (c) PESQ—optimal length is 20.  
performance, even when the length of the band-to-band component remains fixed. This intriguing finding strongly suggests that late samples of the crossband components may have a detrimental effect on the overall performance, even compared to the conventional WPE. Drawing from this observation and the outcomes presented in Figure 1, we propose that both the traditional and proposed methods of WPE attain optimal performance at a specific length choice. Surprisingly, beyond this optimal value, the performance deteriorates, despite the availability of additional information for dereverberation.
## 2 Methodology
### Optimization Approach  
The optimization system uses the Gurobi Optimizer software solution, which is renowned for its efficiency and scalability in managing large-scale programming tasks. The approach employs a Python integer stochastic optimization technique to integrate integer decision variables pertinent to investment and operational decisions. To make calculations more efficient and ensure we reach the solution quickly in this study, the solver settings have been adjusted precisely with a time limit set at 10 min to prevent computation times. Moreover, four CPU threads are used to speed up the solving process through computations. These configurations balance solutions' efficiency and accuracy, ensuring the optimization process is fast and dependable. Scalability is vital for handling the model's stage and scenario-dependent nature. The Gurobi Optimizer uses algorithms like branch and bound and cutting plane techniques to navigate the solution space even as the scenarios and periods increase in number. This adaptability guarantees that the model retains its efficiency and usefulness as portfolio complexity and unpredictability rise.
###### Abstract  
To address the vulnerability of the widely used block permutation and co-XOR (BPCX) encryption algorithm in reversible data-hiding in the encrypted domain (RDH-ED), which is susceptible to known-plaintext attacks (KPAs), and to enhance embedding capacity, we propose a novel technique of reversible data-hiding in encrypted images (RDH-El). This method incorporates adaptive quadtree partitioning and most significant bit (MSB) prediction. To counteract KPAs, we introduce pixel modulation specifically targeting pixels within blocks of the same level during the encryption phase. During data embedding, we utilize tagging bits to indicate the state of the pixel blocks, capitalizing on pixel redundancy within those blocks to augment embedding capacity. Our experimental results demonstrate that our method not only achieves reversibility and separability but also significantly boosts embedding capacity and method security. Notably, the average embedding rate across the 10,000 images tested stands at 2.4731, surpassing previous methods by 0.2106 and 0.037, respectively.  
adaptive quadtree partitioning; adaptive most significant bit prediction; image encryption; known-plaintext attack; reversible data-hiding in encrypted images +
Footnote †: journal: Applied sciences  
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Footnote †: journal: Applied sciences  
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Footnote 0: _Appl. Sci._**2024**, 14, 6376. [[https://doi.org/10.3390/app1416376](https://doi.org/10.3390/app1416376)]([https://doi.org/10.3390/app1416376](https://doi.org/10.3390/app1416376))
## 3 Materials and Methods
### Weight Selection Strategies  
DLWP loss is a cost-sensitive learning-based method that utilizes logit weight vectors to rebalance the relative loss across classes and help mitigate the effects from the imbalanced target class distribution. Together with some of the techniques used in baselines LDAM [47] and Class Balanced loss [23], we outline the strategies applied in this paper. First, the following strategies are used to generate the class balancing weights applied to the loss function:  
1. Empirical Risk Minimization (ERM): minimizes the empirical expectation of losses obtained by applying a prescribed loss function over some labeled training set. All training samples from all the classes are weighted equally.
2. Inverse Class Reweighting [23]: class balanced-loss for each training sample is obtained by reweighting the prescribed loss function by the inverse class frequency for its class.
3. Effective Number Reweighting [23]: class balanced-loss for each training sample is obtained by reweighting the prescribed loss function by the inverse of the effective number of samples for its class.
4. Delayed Effective Number Reweighting [47]: the class balanced-loss is obtained by applying the standard ERM until the last learning rate decay when the inverse effective number of sample-based reweighting is applied.  
Secondly, for the logit weight vectors, we use the strategies highlighted in Section 3.4, i.e., the relative likelihood approach or the effective number of samples approach. In conjunction with some of the weighting strategies listed above, we propose the following variants:  
1. Effective Number Probability (ENPr): inverse of the effective number of samples per class is converted to logit weight class probabilities and applied to the DLWP loss method as the _ideal logit weights_ probability distribution.
2. Delayed Effective Number Probability (DENPr): standard ERM until the last learning rate decay when the inverse of the effective number of samples per class is converted to class probabilities and applied to the DLWP loss method as the _ideal logit weights_ probability distribution.
3. Relative Likelihood Probability (RLPr): relative likelihood method is used to generate the class probabilities applied to the DLWP loss method as the _ideal logit weights_ probability distribution.
# A Lightweight Slice-Based Quality of Service Manager for IoT
## 2 Related Work
While our solution focuses on non-3 GPP unlicensed communication, it overlooks the QoS requirements of IoT applications. A network slicing solution [26] for enabling the coexistence of enhanced Mobile Broadband (eMBB) and IoT sharing the RANis very interesting in the 5G domain. They validated the solution in a 5G prototype using the Open Air Interface (OAI) and FlexRAN SDN controller. However, in our work, we focus on non-3 GPP unlicensed wireless communication technologies for IoT. [PERSON] et al. [27] demonstrated network slicing as an enabler for Industrial IoT (IIoT) for QoS requirements consisting of multiple sensor devices with WiFi/BLE communication modules. This work relates to ours regarding the implementations and tools used (Ryu controller, OvS). However, our proposed slice-based QoS manager is implemented in a full IoT software-defined and virtualised IoT gateway employing container concepts for edge computing. Network slicing has also been applied in LoRa networks [28, 29] to provide specific QoS guarantees. A dynamic and adaptive inter-slicing resource reservation was proposed and evaluated in an NS3 simulation environment. However, this work did not consider the real SDN-based network for validation as done in our proposed solution. Although the purpose of LoRaWAN networks is directed towards IoT applications, the means to carry out the integration procedures with 5G systems are not yet established as reference and technical standards in 3 GPP. Hence, [PERSON] et al. [30] presented a model for integrating LoRaWAN networks with LTE applied to the Evolved Packet Core (EPC) network. The approach does not need to change the structure of the core of the pre-existing network, and there is no need for protocols or signalling commands other than those already established. The LoRaWAN gateway acts as the User Equipment (UE). Four different approaches for integrating into the 4G structure were discussed [31]. The integration methods covered were (i) integration via the 3 GPP access network, (ii) non-trusted access by non-3 GPP, (iii) encapsulating components of the LoRaWAN structure as part of the eNodeB, and (iv) virtualizing components of the LoRaWAN network architecture as part of the core network LTE. However, those works were focused on the integration of LoRaWAN with LTE without QoS requirements. Our proposal differs from the aforementioned works as it focuses on the provision of specific QoS parameters. While we identified a few efforts in this regard, most of them were for specific application scenarios, with a strong focus on the IoT within the 5G domain. On the other hand, we are looking at non-3 GPP IoT solutions over unlicensed band wireless technologies and how to provide slice-based QoS for Industry 4.0 and digital farming deployments considering a lightweight concept of network slicing.
# Measuring Efficiency and Effectiveness of Highway Management in Sustainability
## 2 Theoretical Considerations
### Relation between Efficiency and Effectiveness  
There are some studies encompassing contents on the relation between efficiency and effectiveness ([PERSON], 2010; [PERSON], 2010; [PERSON], 2010). [PERSON] insisted that efficiency is an inevitable consequence of effectiveness ([PERSON], 2010). That is, effectiveness is prior to efficiency because the most important thing is to achieve what a government proposed to do rather than to work efficiently. [PERSON], [PERSON], and [PERSON] (2010) explain the relationship between the two values by using the process of input-output-outcome (see Figure 2). The authors state, \"effectiveness is more difficult to assess than efficiency, since the outcome is influenced political choice\" (p. 3) which implies the impact of environmental factors is more crucial on effectiveness than on efficiency.  
The 2 by 2 matrix of values depicted in Table 1 shows that some states achieve both effectiveness and efficiency together while some would have one of the two values or fail to attain either. For a government or an organization, the top priority is to achieve its goal, and the effectiveness is assessed by the extent to which a government's outputs reach the goal. At the point when a government produces outputs for their goals with the least expenses, it is called an efficient government. For comparison, the best practice happens when the effectiveness accompanies the efficiency; the second best practice indicates when an organization achieves goals effectively but is less efficient than others; the third best practice means less effective but more efficient results, and the worst practice occurs when a government fails both values.  
According to the theoretical literature and the conceptual framework, we assume efficiency and effectiveness are related. Prior studies present how to measure the two values, how the values are conceptualized in the process of input-output-outcome, and why effectiveness is prioritized over efficiency. However, few studies have empirically examined the relationship of the values. Thus, it is important and interesting to investigate whether and how efficiency and effectiveness are related.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Ineffective** & **Effective** \\ \hline Efficient & 3 rd best practice & Best practice \\ Inefficient & Worst practice & 2 nd best practice \\ \hline \hline \end{tabular}
\end{table}
Table 1: Matrix of Effectiveness and Efficiency.  
Figure 2: Framework of Efficiency and Effectiveness ([PERSON], 2010).