esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The results of the algorithm can be tested under the background of different objectives and two quality indicators. The results reveal the influence of the attribute of the feature model, the implementation environment, and the number of objectives on the performance of the algorithm [23]. [PERSON] established a multi-objective water supply optimization model considering cost, reliability, and water quality for the mountain water distribution network (WDN). The NSGA-II algorithm was used to optimize the WDN design model in the complex terrain of the mountain area, which provided valuable information for the decision makers in the complex terrain WDN [24]. [PERSON] has developed a proxy-assisted stochastic optimization inversion algorithm called \"dam parameter identification\". This algorithm assesses the influence of randomly selected training and testing datasets on the modeling and prediction outcomes of artificial neural networks [25]. [PERSON] used the dataset collected from the Mekong River test project as an example to train and test a multi-objective dataset by evaluating the results of on-site load tests [26]. Taking the multi-objective optimization model of foundation pit dewatering as the main research objective, this paper carries out research through theoretical research, NSGA-II algorithm design, a multi-objective optimization model MATLAB solving Pareto solution set, and GMS numerical simulation verification, and develops a multi-objective optimization program and quantitative evaluation system based on the Analytic Hierarchy Process. The multi-objective dewatering optimization model solution and numerical simulation verification are carried out for the foundation pit dewatering project of the Xixiayuan canal head inverted siphon section at pile number XZ0+326 to XZ0+500, which provides the dewatering optimization decision scheme for the foundation pit dewatering design and construction. Establishment of Multi-Objective Optimization Model and Evaluation System for Foundation Pit Dewatering
## 3 Results and Discussion  
### Atomistic Models  
The atomistic model of pillared graphene represents two sheets of monolayer graphene with SWCNTs (9,9) of 1.2 nm in diameter between them. These tubes were chosen for two reasons: (1) The tubes of this diameter are being synthesized most often; (2) earlier it was shown that the pillared graphene with tubes (9,9) is the most energetically favorable [45]. Two models that differ in the height of the tubes were built (Figure 1). Figure 1a,b shows the supercells of the model I containing 2760 atoms, Figure 1c shows the supercell of model II with 2976 atoms. The translation vectors of the supercells were 49.3 A in the X direction and 42.6 A in the Y direction. During the building of the pillared graphene supercell the nanotube with open edges was chemically bonded to the graphene sheet that had the cut hole corresponding to the diameter of the attached CNT. The resulting structure matched with experimental data on pillared graphene [46]. The heat of formation of the pillared graphene supercells was 1.2-142 kcal-mol\({}^{-1}\)-atom regardless of the CNTs length. Six pairs of penta/heptagons, as well as three octagons, were formed in the graphene-SWCNT contact region.  
Further, the pores of the pillared graphene were filled with Si16 clusters. As was shown earlier, these clusters had the highest stability among silicon ones [47; 48]. Such anode samples have been already synthesized in the world. For example in [46] real samples of carbon-silicon composite on the base of monolayer pillared graphene were obtained by the method of single-stage chemical vapor deposition (CVD) with the incorporation of precursor gases mixture (H2, C2H4). The layer of amorphous silicon was added to already grown 3D-carbon nanostructure by deposition and evaporation.The pores of pillared graphene were filled with Si16 clusters randomly with the condition that the distance between the clusters and pillared graphene would be at least 3.5-3.6 A. The number of clusters varied from 5 to 59. After optimization of the supercell with silicon the distance between the carbon framework and silicon clusters was \(\sim\)3.3 A. Here, the clusters changed their position in the pores. The clusters are located homogeneously, partially \"stuck\" at the area of the junction between SWCNTs and graphene near non-hexagonal elements, while the rest was \"stuck\" at the walls of SWCNTs. There are no clusters inside the tubes. Encapsulation of silicon clusters inside tubes is energetically unfavorable; therefore, filling was observed only in the pores of pillared graphene. Figure 1d shows the supercell with five silicon clusters.
# Evaluation of Resilient Modulus and Rutting for Warm Asphalt Mixtures: A Local Study in Iraq
## 3 Results and Discussions  
### Mechanical Properties  
The Marshall technique is used to compute the optimum amount of the content of asphalt binder for the two mixtures HMA and WMA with 4% air voids as the standard for establishing this number. Fifty-four Marshall cylindrical samples were prepared and divided into eighteen samples of asphalt mixture, three for each asphalt content, to take the mean value of the air voids. The relationship between the air spaces and the asphalt binder contents is shown in Figure 3.  
Figure 3: Optimum asphalt binder content for 4% air voids.  
Figure 2: Typical load-time cycles with rest periods during M\({}_{\rm r}\) test.  
Figure 3 demonstrates that the optimum amount of the asphalt binder content for HMA and WAM with 0.3% and 0.5% zeolite is 4.7%, 4.3%, and 4.5%, respectively. The WMA that contains 0.3% zeolite has the lowest value of asphalt binder content.  
Investigations of the mechanical properties of the WMA and HMA were conducted by using the test of Marshall Stability and flow using ASTM D6927-10 specification. To determine the average value for Marshall Stability and Flow, three of the nine cylinder-shaped Marshall specimens were constructed for each type of asphalt mixture. Figure 4 presents the Marshall Stability for both hot and warm mixtures. The figure shows that both mixtures HMA and WMA have a significant value of stability which is higher than 8 kN, the minimum required value for the wearing asphalt layer according to Iraqi specifications [28], which is a good indication for the WMA. The highest value of stability is for the HMA, followed by the 0.3% zeolite WMA and the lowest value was for the 0.5% zeolite WMA because the Marshall Stability is highly affected by the mixing and compaction temperatures. This result complies with [PERSON] et al. [25] and [PERSON] [38]. From the error bars presented in the figure, the results are reliable and no overlaps between the error bars for all results that's means there is a significant difference between the Marshall Stability of the two types of mixtures (WMA and HMA).  
Figure 5 presents the Marshall flow, and the required range of the flow is (2-4) mm according to Iraqi specifications [13]. As presented in the figure, the flow values for all mix types are within the required range of the flow and are considered satisfactory. The figure shows that the HMA has the lowest Marshall Flow value, and the increase in zeolite additive concentration increases the flow values. The result complies with [PERSON] et al. [39]. Error bars demonstrate the validity of the data, and as there are no overlaps between the bars, the WMA and HMA flow mixtures differ significantly from one another.  
Figure 4: Results of Marshall Stability.
# Innovative Learning in a Digital Forensics Laboratory: Tools and Techniques for Data Recovery
## 2 Related Work
### Data Extraction from NAND Flash Memories  
A wide variety of methods are available for data acquisition, depending on the degree of alteration of the original evidence. Methods can be classified according to cost, required time, or complexity, or according to the level of destructiveness, as depicted in Figure 1, which shows destructiveness ranked by levels from 1 to 5. For instance, if a device is neither damaged nor locked, it is preferable to employ a level 1 non\(-\)invasive technique to ensure the integrity of the original data [11]. The five levels are explained as follows:  
* Level 1. Manual extraction. This involves a low\(-\)complexity, non\(-\)destructive, and thorough search for evidence. A locked device is its primary limitation. Additionally, manually navigating through evidence sources may affect data integrity. Tools such as ZRT Screen Capture [12] and Eclipse 3 Pro\(-\)Kit [13] can be used at this level.
* Level 2. Logical extraction. This is performed by connecting the device to a forensic workstation for data extraction using Oxygen Forensic Detective (OFD), Cellebrite, or OpenText EnCase Forensic (EnCase) software [14]. As a limitation, not all log files and data from analyzed devices are accessible.  
* Level 3. JTAG and HEX dump. This involves testing the circuits of memory cards, USB drives, SSDs, or eMMC devices and requires tools to disassemble devices and access their JTAG ports. The method is semi-destructive as it uses TAPs located on motherboards for functionality testing. Figure 2 shows various available commercial models. Although JTAG is primarily used in smartphone manufacture, it can also be employed to bypass OS-imposed access restrictions.
* Level 4. Chip-off. This complex destructive technique involves physically extracting the device memory chip for reading using specialized equipment, e.g., RT809H, sourced from Shenzhen Sintech Electronic Co., Shenzhen, China. It is crucial not to damage the chip during extraction, as this could cause irreversible bit or even byte failures; this approach is therefore only recommended when less invasive methods cannot be used.
* Level 5. Microreading. This involves the physical observation of chips using scanning electron microscopy, e.g., JEOL Ltd., Tokyo, Japan. It is typically reserved for high-priority cases as it requires more resources, time, and expertise than the other extraction methods [6; 15; 16].
## 1 Introduction
Based on measuring the changes in the thickness of the slurry discharge pipelines at a slurry shield tunneling project in Singapore, the effect of the abrasiveness of the Bukit Timah granite on the wear of slurry discharge pipelines was investigated, the average wear rates of slurry discharge pipes used for Bukit Timah granite were determined, and the correlation between the slurry discharge velocity and the wear rate of the slurry discharge pipes was derived [17; 18; 19; 20]. These studies focused on the wear rate of the pipeline under different lithological conditions and carried out a large number of fits. The results showed that the wear rates of weathering grades G(I) to G(IV) of Bukit Timah granite were highly similar to each other, whereas the wear rates of the G(V) grade and mixed ground were 1.55 times higher than those of the G(I) to G(IV) grades. Another study examined slurry discharge pipeline wear during slurry shield tunneling in China. [PERSON] [21] analyzed the reasons for wear and vibration in slurry pipelines and proposed vibration and wear reduction measures such as thickening eccentric designs, increasing wear rings, and adding vibration damping pipes at pipeline connections. [PERSON] [22] conducted an analysis of the blockage of pebbles in a slurry discharge pipe. [PERSON] et al. [23] investigated the effects of pipe material, flow rate, slurry density, and solid particle size on the wear rate. [PERSON] et al. [24] proposed adopting flexible piping to solve the severe wear at the elbow position of a pipeline. In these reports, the issues of pipeline wear and damage were raised, the wear was measured, and measures to combat pipeline damage and wear were introduced. However, the types of slurry pipelines involved in these studies were relatively homogeneous; a comprehensive survey of slurry discharge pipeline damage and wear does not exist, and the failure of slurry discharge pipe joints, the damage to slurry pumps, and how to lay pipe in an existing tunnel have not been addressed in previous studies. In this study, data from a slurry shield tunneling machine used to dig a tunnel for the Beijing South-to-North Water Diversion Auxiliary Project were introduced. The tunnel, 6 m in outer diameter and 5.4 m in inner diameter, ran through a pebble layer and rock ground. During machine driving, natural rock particles from the pebble layer and rock fragments due to rock cutting when tunneling through the rock ground were transported in the slurry discharge pipelines of the slurry shield machine. Based on field-measured and observed data, the mechanisms and causes resulting in the observed large, uneven damages and wear were identified and assessed. Countermeasures to address issues such as adjusting pipeline layout, welding reinforcement plates in advance, and preparing additional pipelines were highlighted. The wear rates of different types of pipelines, the effectiveness of new pipeline fixing methods, and the probability of pipeline leaks in different strata were discussed in detail.
## 8 Energy Optimization Techniques in UAV-Based Cellular Networks
### Energy Optimization of UAV-Assisted Cellular Networks  
In the previous subsection, we reviewed the cases where only the UAV power consumption was considered irrespective of whether the UAV was deployed as a standalone network or UAV-assisted cellular network. However, in this subsection, we consider various techniques, based on conventional and ML algorithms, that have been employed for the optimization of the total energy consumption of UAV-assisted cellular networks where the energy consumption of both terrestrial BSs and UAV-BSs are jointly optimized. Specifically, we focus on the literature regarding energy optimization in UAV-assisted heterogeneous networks (HetNets).
## 3 The KLSVM Model
### Kernel Function
#### 3.9.1 Kernelizing Linear SVM  
LSVM has a simplified perceptron of a linear weight \(w\) on the input features resulting in a linear decision boundary \(f(x)=0\) developed by a Lagrangian optimization in the form:  
\[\max_{0\leq\lambda\leq R}\sum_{i}\lambda_{i}-\frac{1}{2}\sum_{j}\lambda_{i} \lambda_{j}y^{(i)}y^{(j)}(x^{(i)}\cdot x^{(j)}) \tag{23}\]  
subject to \(\sum_{i}\lambda_{i}y^{(i)}=0\). That leads to an equivalent dual formation process in which the objective function depends on the matrix \(K_{ij}=x^{(i)}\cdot x(j)\) on pairwise dot product called the Gram Matrix.  
If our data are not linearly separable on the original feature \(x_{1}\), we can add quadratic deterministic features, for example, moving from one feature \(x_{1}\) to two features \(x_{2}=(x_{1}^{2})\) so that the data lie on a curve. In our new higher dimension space (see Figure 5), our data are more likely to be linearly separable. This affects the dual form of the sum using a feature transform Phi(x) \(\hat{y}(x)=sign[w\cdot\Phi(x)+b]\). We transform \(x^{(i)}\) and \(x^{(j)}\) to find the new feature vectors, and then the dual form involves the dot products between these transformed vectors, defined as  
\[\max_{0\leq\lambda\leq R}\sum_{i}\lambda_{i}-\frac{1}{2}\sum_{j}\lambda_{i} \lambda_{j}y^{(i)}y^{(j)}\Phi(x^{(i)})\Phi(x^{(j)})^{T} \tag{24}\]  
subject to \(\sum_{i}\lambda_{i}y^{(i)}=0\).  
Let us consider the polynomial features defined as  
\[\Phi(x)=(1\sqrt{2x_{1}},\sqrt{2x_{2}}...x_{1}^{2}x_{2}^{2}...\sqrt{2x_{1}x_{2}} \sqrt{2x_{1}x_{3}}...)\] (25a) For the dual form, we need to compute inner product of two expanded feature vectors \[\Phi(x^{(i)})\Phi(x^{(j)})^{T}\] by denoting \[x^{(i)}\] and \[x^{(j)}\] to \[a\] and \[b\] respectively. Listing our features for both points, we compute them as \[\Phi(a)=(1\sqrt{2a_{1}}\sqrt{2a_{2}}...a_{1}^{2}a_{2}^{2}...\sqrt{2a_{1}a_{2} }\sqrt{2a_{1}a_{3}}...) \tag{25b}\]  
Figure 5: KLSVM: A modeled correlation-based high dimensional multi-category classification. In addition, the results show one detected failed link and four captured outliers, with extremely inconsistent behavior.  
\[\Phi(b)=(1\sqrt{2b_{1}}\sqrt{2b_{2}}...b_{1}^{2}b_{2}^{2}...\sqrt{2b_{1}b_{2}} \sqrt{2b_{1}b_{3}}...) \tag{25c}\]  
We find that the dot product is the sum, defined as  
\[\Phi(a)^{T}\Phi(b)=1+\sum_{j}2a_{j}b_{j}+\sum_{j}a_{j}^{2}b_{j}^{2}+\sum_{j}\sum _{K>j}2a_{j}a_{k}b_{j}b_{k}+... \tag{25d}\]  
If we manipulate the sum algebraically, we find that it is equivalent to a much simpler computation  
\[\Phi(a)^{T}\Phi(b)=(1+\sum_{j}a_{j}b_{j})^{2}=K(a,b) \tag{25e}\]  
denoting the value \(K(a,b)\) instead of actually linking our higher dimensional feature transform and then computing the inner product of those vectors, we could instead take the very simple non-linear function similarity (the kernel) and compute on the linear and the original number of features our expansion fee has; for example, quadratic features \(\Phi(a)\) and \(\Phi(b)\) have all squared features and computing their dot product in only \(0(n)\) computations.
## 4 Promising Neuropeptides Inhibitors for the Development of Effective Cancer Therapy
### Emerging Neuropeptides Inhibitors in Colorectal Cancer
#### 4.1.4 Substance P  
Substance P (SP) supports the proliferation and development of cancer cells in CRC, mainly due to the activation of NK1R with the help of various proinflammatory cytokines. Application of Spantide 1, an antagonist of NK1R, resulted in a decreased expression of cancer cells in CRC [94]. Aprepetitant, another NK1R antagonist, also significantly downregulated cellular growth and proliferation of two colon cancer cell lines, i.e., LiM6 and DLD1 [90]. Inactivation of the SP/NK1R signaling pathways led to aberrant inhibition of the Wnt signaling pathway, thus resulting in the downregulation of various cellular processes [90; 91]. However, SP/NK2R signaling in CRC has not been explored well yet [129]. The influential work of [PERSON] and colleagues gave rise to a renewed interest in understanding the higher expression of NK2R, which was demonstrated to be associated with tumorigenesis, metastasis, and poor survival of CRC patients. The higher expression of NK2R in colon cancer was observed due to interferon (IFN\(\alpha/\beta\)) stimulation and polyinosinic-polycidylic acid (poly IC) administration in vitro and in vivo, respectively. However, the use of a potent and selective antagonist (GR 159897) against the NK2R, inhibited the tumor cell proliferation in vitro and the tumor formation in cancer-bearing mice, suggesting it is an optimistic target in patients with colon cancer [130].
# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 2 Motion and Distortion Induced Artefacts
### Optical Distortions  
Optical distortions in OCT imaging result from light rays that deviate from the optimal path. This may happen due to aberrations in the optics interface between the scanner and the retina, as well as misalignment or other limitations of the optical setup [17; 18; 19; 20]. These distortions may be caused by non-telecentric scan geometries, refraction of the probing light in the sample or, in the case of time domain systems, nonlinearities in the reference arm scan. To correct these artefacts, a few approaches have been proposed. [PERSON] et al. [17] presented a methodology for correcting non-telecentric scan patterns, as well as an approach for refraction correction in layered media based on [PERSON]'s principle.  
[PERSON] et al. [18] quantified the distortions introduced by scanning and eye motion using two quantities, a lateral and an axial distortion. These values represent deviations of the image points from the points where the user imagines or expects the points to be. More recently, [PERSON] et al. [19] suggested to correct the optical distortions by incorporating the geometry of the scanning beam as part of a custom volume rendering software.  
Although a few software-based approaches have been suggested to correct optical distortions induced by the setup (OCT device), artefacts caused by retinal motion or varying subject alignment in respect to the OCT system remain and will influence the outcome of longitudinal studies. Thus, intra- and inter-subject OCT data will be affected, hampering subsequent interpretation and analysis.
# Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism
## 2 Background
### Cold Boot Attack Model  
Based on our previous discussion on cold boot attacks, we assume an adversary has knowledge of the defined structures for the storage of the target private key in memory and has possession of the corresponding public parameters without any noise. Moreover, we assume the attacker procures a noisy version of the target private key via a cold boot attack. However, we do not tackle the issue of locating the memory region where the bits of the private key are stored. Such issue would be of great importance to deal with when carrying out this attack in practice and may be tackled via applying several key finding algorithms [10]. As a result of this assumption, the adversary's main objective is to reconstruct the original private key.  
We denote \(\alpha=P(0\to 1)\) as the probability of a 0 to 1 bit-flipping, i.e., that a 0 bit in the private key changes to a 1 bit in its noisy version. Moreover, we denote \(\beta=P(1\to 0)\) as the probability of a 1 to 0 bit-flipping, i.e., that a 1 bit in the private key changes to a 0 bit in its noisy version. According to our previous discussion on cold boot attacks, one of these values normally may be very small (approximately 0.001) and not liable to variation over time, while the other values do increase over time. Moreover, the adversary may estimate both \(\alpha\) and \(\beta\) and they remain unchanged across the memory region where the bits of the private key are stored. As stated by our previous discussion on cold boot attacks, these suppositions are plausible, since an adversary may estimate the error probabilities by comparing original content with its corresponding noisy version (e.g., using the public key), and the memory regions are normally large.