esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### The Concentrations of Cu, Mn, and Zn in Rats Serum and Liver Tissues  
The results in Table 8 revealed a significant increase in all of the investigated groups for the plasma concentrations of Cu, Mn, and Zn. The highest increase was observed in G2 and G3 compared with G1. However, treatment with wheat and barley significantly decreased those concentrations in the studied groups, other than G2 and G3. Treatment with 500 mg wheat and 500 mg barely grasses demonstrated the highest decline in these metals in the investigated rats. The data in Table 7 also demonstrate that the concentrations of Cu, Mn, and Zn in the liver tissues increase significantly after exposure to tap water or mixed metals compared with the negative control group. In addition, it is observed that the metal concentrations decreased significantly in all of the considered groups compared with those drinking tap water or the mixed HM groups. According to [PERSON] [73], Zn homeostasis is influenced by Cu. Cu is found in many proteins, including albumin. Because free Cu is extremely toxic, it is bound to proteins and one of the most essential Cu pools is the liver. Increased Zn levels typically result in lower Cu concentrations and vice versa. This may by why lower concentrations of Zn were found in our study compared with Cu in the serum and liver tissues, even though the exposure to Zn was higher. [PERSON] [74] stated that barley grass supports metabolic processes and helps to strengthen and detoxify the liver. In addition, [PERSON] et al. [75] stated that wheatgrass contains Ca, P, K, and Mn, as well as vitamins A, B, and C. It is a strong detoxifier that balances toxins with enzymes and cleases the body of HMs and other toxins that accumulate in tissues and organs.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameters Groups** & **T.P (g dl\({}^{-1}\))** & **ALB (g dl\({}^{-1}\))** & **G (g dl\({}^{-1}\))** & **A/G** \\ \hline
**Control\({}^{-}\)** & 7.08 \(\pm\) 0.15 abc & 3.52 \(\pm\) 0.10 ab & 3.56 \(\pm\) 0.25 ab & 0.99 \(\pm\) 0.10 b \\
**Control\({}^{+}\)** & 7.20 \(\pm\) 0.07 a & 3.46 \(\pm\) 0.12 ab & 3.73 \(\pm\) 0.20 a & 0.93 \(\pm\) 0.08 bc \\
**G3-1** & 6.99 \(\pm\) 0.11 bc & 3.65 \(\pm\) 0.11 a & 3.34 \(\pm\) 0.19 b & 1.09 \(\pm\) 0.08 a \\
**G3-2** & 7.15 \(\pm\) 0.06 ab & 3.61 \(\pm\) 0.12 ab & 3.53 \(\pm\) 0.16 ab & 1.02 \(\pm\) 0.07 ab \\
**G3-3** & 6.95 \(\pm\) 0.05 cd & 3.49 \(\pm\) 0.06 ab & 3.46 \(\pm\) 0.06 ab & 1.01 \(\pm\) 0.02 ab \\
**G3-4** & 7.22 \(\pm\) 0.04 a & 3.62 \(\pm\) 0.04 ab & 3.59 \(\pm\) 0.07 ab & 1.01 \(\pm\) 0.03 ab \\
**G3-5** & 6.79 \(\pm\) 0.14 d & 3.43 \(\pm\) 0.15 b & 3.36 \(\pm\) 0.28 b & 1.03 \(\pm\) 0.13 ab \\ \hline \end{tabular}
\end{table}
Table 7: Effect of wheat and barley grassed on changes in the total protein, albumin, and globulin of rats exposed to toxic metals (mean \(\pm\) SD).
# Consistency Models of NoSQL Databases
## 2 Related Work
This benchmark fulfills the need for performance comparisons between NoSQL databases and their tradeoffs, such as read performance versus write performance, latency versus durability, and synchronous versus asynchronous replication. The benchmark tiers proposed in this paper include the Tier 1 - Performance and the Tier 2 - Scaling. However, YCSB benchmark lacks tiers, such as Availability, Replication, and Consistency. Although the first two tiers are proposed for future work. [PERSON] and [PERSON] present a critical comparison between NoSQL systems using multiple criteria [11]. The authors start to introduce multiple taxonomies to classify many NoSQL databases groups, even though there is not an official classification system on this type of databases. They define the following criteria to be used on the theoretical comparison: Persistence, replication, high availability, transactions, rack-locality awareness, implementation, influencers/sponsors, and license type. [PERSON] and [PERSON] concentrate this theoretical comparison into one single table. Afterward, the authors make an empirical performance comparison, between Cassandra, HBase, Sherpa, and MySQL, using YCSB [10]. This article lacks other empirical metrics besides performance, such as consistency. [PERSON] et al., in Reference [12], present a benchmarking effort on the replication and consistency strategies used in two databases: HBase and Cassandra. [PERSON] et al. motivation are to evaluate tradeoffs, such as latency, consistency, and data replication. The authors conclude that in the latency of read/write operations is hardly improved by adding more replicas to the database. Higher levels of consistency dramatically increase write latency and are not suitable for reading the latest version of data and heavy writes in Cassandra. This paper lacks a more in-depth comparison of how consistency is affected in different configurations. Instead, this work is more focused on studying how consistency levels influence other properties in HBase and Cassandra databases. Our study is different from all these works by purposing a comparative theoretical analysis of the five of the most popular NoSQL databases in the industry, Redis, MongoDB, Cassandra, Neo4j, and OrientDB, and evaluate how they implement consistency.
# Research on CNN-BiLSTM Fall Detection Algorithm Based on Improved Attention Mechanism
###### Abstract
The method based on wearable devices usually uses sensors, such as accelerometers, gyroscopes and inertial measurement units (IMU), to automatically detect falls and send help-needed information to medical staff through communication devices such as WIFI, mobile networks and Bluetooth [17; 18; 19]. With the development of sensor technology and the posture algorithm, IMU with a smaller size, higher accuracy and stronger performance have been applied in motion analysis. At present, the accuracy of the motion capture technology of the inertial sensor with the highest accuracy is close to that of video motion capture technology based on multiple cameras. Additionally, the gait analysis system based on wearable sensors has become a feasible means by which to promote a continuous fall risk assessment in non-hospital environments. With the development of inertial sensors and the constant increase in their detection accuracy, the method based on wearable devices may detect human body falls anytime and anywhere, and it will not infringe upon the privacy of users. Considering its low equipment cost and good user experience, this method is more advantageous than the other two methods. In this paper, we propose a CBAM-IAM-CNN-BiLSTM model and incorporate the improved convolutional attention block module (CBAM-IAM) into the network structure, so that the one-dimensional convolution layer replaces the dense layer to aggregate the information from the channels. Because of the parameter-sharing property of the convolution operation, the introduction of one-dimensional convolution can reduce the parameters of the channel attention module and improve the overall running efficiency of the model. Through experiments comparing SE, ECA, the CBAM and the CBAM-IAM, it is proved that the improved attention mechanism can effectively improve the performance of the model. At the same time, we designed the experimental scheme, collected fall and daily activity data and used these data to train and test the model, which proved that our proposed model has good reliability and practicability.
## 1 Introduction
In addition to BDAC, innovation capabilities (IC) are of essential importance in enhancing SCA, where IC indicates a firm's ability to introduce and define innovative ideas and deploy them in designing new products or enhancing the current products [42]. [PERSON] et al. [42] affirmed that innovation is a key factor in securing competitive advantage. Moreover, many firms consider big data to be a valuable resource that promotes innovation [37; 42] which, in turn, leads to competitive advantage [43; 44]. Recent literature has posited that creating sustainable new capabilities, like BDA, is necessary for the innovation process because doing so has the potential to support the positive impact of big data on innovation sustainability [41]. Many studies have investigated the role of innovation in firms' performance and competitive advantage [24; 42]. Nonetheless, until now, the literature has lacked sufficient evidence clarifying the relationship between BDA, innovative performance and firm performance as an index for its competitive advantage [42]. Most existing BDA studies have focused on its technological aspects; however, there is a lack of studies on BDA in strategic management [21]. Indeed, the gap in the BDA area is multifaceted since the topic is recent and the first publication referring to BDA appeared in 2012 [21]. BDA studies lack multidisciplinary synthesis in exploring the potential value of BDA in the context of strategic management [45]. The importance and role of BDA in an organization's performance lacks sufficient study and the link requires further exploration [19]. In addition, theoretical and empirical research on BDA's effect on firms' performance, considering sustainability, is still lacking [39; 46]. Moreover, there is insufficient empirical research on the value extracted from big data for sustainable innovation and firm improvement [41]. Drawing on these identified research gaps, this study aims to propose a strategic conceptual model that considers the effect of DA on BDA and the effect of BDA and IC on SCA. This study also attempts to benefit practitioners by providing an explanation and statistical validation for the proposed model. This study makes many contributions to the field. Firstly, it considers different topics and investigates their roles in attaining SCA; secondly, it provides empirical evidence supporting the extant literature; and thirdly, it considers three diverse aspects of sustainability in one model. Consequently, this study aims to address the following research questions: (1) How does DA affect BDAC? (2) How does BDAC contribute to a firm's IC? (3) How does BDAC enhance a firm's SCA? (4) What is the relationship between IC and a firm's SCA?
###### Abstract  
The mineral composition in coalbed methane (CBM) reservoirs significantly influences fracture morphology, making the description of reservoir heterogeneity challenging. This study develops a fracture propagation model for CBM reservoirs that incorporates the varying mineral properties within the reservoir's lithology. Dynamic logging data are considered to characterize rock mechanical properties, which form the basis for in situ stress estimation. Using an adjusted critical circumferential stress calculation for coal rock, the model considers the impact of complex lithology on fracture propagation. A comprehensive fractal index is introduced to capture the influence of different minerals on fracture morphology and propagation randomness. Models representing clay, quartz, and pyrite with varied compositions were constructed to explore the effects of each mineral on fracture characteristics. In single-component models, clay-rich reservoirs exhibited the highest induced fracture density, with quartz and pyrite showing approximately 65% and 20% of the fracture density observed in clay, respectively. Fractures primarily propagated toward quartz-rich regions, while pyrite significantly inhibited fracture growth. In mixed-mineral models, increasing the quartz proportion by 40% resulted in a 20 m increase in fracture length and a 30% reduction in fracture density. Fractures predominantly propagated around pyrite boundaries, demonstrating pyrite's resistance to fracture penetration. Clay and quartz promote fracture development, whereas pyrite hinders fracture formation. The fracture inversion model presented here effectively captures the influence of complex mineral distributions on fracture morphology, offering valuable insights for optimizing fracturing production strategies.  
coalbed methane reservoir; mineral composition; hydraulic fractures; fracture propagation; comprehensive fractal index +
Footnote †: journal: applied sciences  
Article  
Influence of Complex Lithology Distribution on Fracture Propagation Morphology in Coalbed Methane Reservoir  
Weiping Ouyang  
1 National Engineering Laboratory for Exploration and Development of Low-Permeability Oil and Gas Fields, Xi'an710018, China; [EMAIL_ADDRESS] (N.O.); [EMAIL_ADDRESS] (M.Z.)  
2 Changing Downable Technology Company, CNPC Chuanqing Drilling Engineering Company Limited, Xi'an710018, China  
3 School of Petroleum Engineering, Yangtze University, Wuhan 430100, China; [EMAIL_ADDRESS] (Correspondence: [EMAIL_ADDRESS] (L.H.); [EMAIL_ADDRESS] (J.L.)
# 3D DNA Nanostructures: The Nanoscale Architect
## 5 Applications
### DNA Nanomechanics
#### 5.3.1 Tunable Plasmonics  
As plasmonic signals are dependent on the geometry of their metallic components, dynamic DNA nanostructures can switch between different modes of plasmonic signals. This enables optical readout of targeted events and also provides a single device that could generate an optical profile of multiple targets by responsively changing their geometry in response to their tagged target. [PERSON] et al. demonstrated this principle using a DNA origami tripod structure, where each leg was decorated with a gold nanorod [152] (Figure 8C). Struts, which were comprised of two DNA duplexes, spanned each pair of legs. The tripod legs could be adjusted between angles of 30\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\), which changed the plasmonic response of the structure. [PERSON] et al. designed a system using two straight DNA origami bundles arranged in a four-arm junction that rotate with respect to a center point. By attaching gold nanorods to each bundle, a circular dichroism signal could be generated. ssDNA along each bundle could be selectively hybridized when provided corresponding oligonucleotide input strands, which would then set the respective angle of the rods to modulate changes in the signal [148].  
Figure 8: Applications of DNA nanomechanics. (**A**) A rotating DNA origami nanostructure with a fluorophore attached on the tip to track the activity of DNA rotation from genome-processing motor proteins (reprinted with permission from Springer Nature [171]); (**B**) A DNA tetrahedron that undergoes a physical change to generate a FRET-based signal in response to detecting an mRNA biomarker (reprinted with permission from AAAS [172]); (**C**) A tripod DNA nanostructure that can reconfigure the angles of its legs by swapping out ssDNA modules within the struts that span adjacent legs (reprinted with permission from American Chemical Society [152]).
## 4 Application  
### Case Study  
The proposed design procedure has been applied to a case study of a deep excavation of metro station in Fuzhou City, China. The excavation is carried out by \"bottom-up\" construction method. The length and width of the excavation is 200 m and 19.7 m, respectively. The underground enclosures are composed of 80 cm thick diaphragm walls. The geology at site is mainly composed of miscellaneous fill, silt, silt with sand, fine-medium sand with silt, silty clay, medium-coarse sand, silt with sand and gravel. The typical geology profile is presented in Figure 7. Table 1 lists the physico-mechanical properties of soils from the laboratory test.  
The hydrogeology at the site is a multi-aquifer-aquitard system (MAAS). The groundwater system is mainly composed of a phreatic aquifer and two confined aquifers (I and II). The confined aquifer I consists of fine-medium sand with silt, which has a piezometric head of 4.0-5.0 m below he ground surface (BGS). The confined aquifer II consists of medium-coarse sand and gravel, and the piezometric head of confined aquifer II is 3.0-4.0 m BGS. The aquitards (e.g., silt with sand, silty clay)  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Soil Layer**} & **Average** & \multicolumn{2}{c}{**Hydraulic Conductivity**} & **Void Ratio** & **Unit Weight** & \multicolumn{1}{c}{**Compression**} \\ \cline{3-6}  & **(m)** & \(k_{h}\) **(m/d)** & \(k_{w}\) **(m/d)** & \(e\) & \(\gamma\) **(kN/m\({}^{\ast}\))** & **Modulus** \\ \hline miscellaneous fill & 2.8 & \(8.64\times 10^{-2}\) & - & 18.5 & 4.0 \\ silt & 4.2 & 0.00085 & 0.00046 & 1.71 & 15.6 & 1.6 \\ silt with sand & 4.3 & & 0.13 & 1.60 & 15.9 & 4.0 \\ fine-medium sand & 10.0 & & 8.5 & - & 18.5 & 6.0 \\ with silt & 3.0 & 0.004 & 0.003 & 0.71 & 19.2 & 7.0 \\ silty clay & 11.0 & & 15 & - & 19.0 & 13.0 \\ medium-coarse sand & 2 & & 0.13 & 1.1 & 17.4 & 3.5 \\ silt with sand & 8.0 & & 40 & - & 18.0 & 40.0 \\ \hline \end{tabular}
\end{table}
Table 1: Physico-mechanical properties of soil at the site.  
are discontinuous at the excavation site. Due to the partial presence of skylights of the upper confining bed in confined aquifers at the central area of the excavation site, there is a very close hydraulic relation between confined aquifer I and II, resulting in a great challenge for groundwater control during excavation. Massive jet-grorted bottom sealing barriers are applied for the excavation to lower the water lever inside the excavation and simultaneously minimize the effect of pumping on adjoining buildings and utilities outside the excavation.
# Systematic Literature Review of Predictive Analysis Tools in Higher Education +
## 3 Results
### Predictors
This predictor was tested with a population of 584 first-year students enrolled in mathematics and liberal-arts classes at a university in California (USA). The experiment was initially performed during the first semester and then repeated in the following one. After SEM confirmed that there was a correlation between stress overload and low performance. Path analysis revealed that stress scores predicted semester GPA better than most other traditional predictors (\(p<0.0001\)). This held true for both of the studied semesters. However, dropout could only be effectively predicted using grade data.
# Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus
## 5 Performance Evaluation
### Experimental Results
#### 5.3.1 Effect of Hyperparameters
Based on the analysis conducted above, the method yields outstanding results when the parameter values are set to K = 80 and N = 100 in the car-hacking dataset. The \(\rho\) determines the range of normal vehicle data. While the three-sigma rule suggests that \(\rho\) is optimal when set to three, we acknowledge that variations in the vehicle's environment can lead to fluctuations in the car's normal data. Hence, it becomes necessary to evaluate the impact of \(\rho\). In Figure 9, we search for the optimal of \(\rho\). Based on the figure, \(p\) significantly influences the accuracy of the method, particularly when \(\rho\) is set to two. This observation can be attributed to the fact that a smaller \(\rho\) may not encompass most of the normal data, leading to an increased number of false positives. Additionally, it is worth noting that an increase in \(\rho\) can also result in a decrease in recall. For instance, in Figure 9b, we observe a gradual decrease in the recall rate as the value of \(\rho\) increases. This phenomenon occurs because, as the threshold range expands, attacks that deviate only slightly from the normal state may remain undetected. Based on the observations, we can conclude that in this particular environment, the method achieved its optimal overall performance when the \(\rho\) was set to three. We apply the same method to analyze the Xiaoyu dataset and observe that the majority of ID intervals in this dataset also exhibit a pattern of being multiples of 20, similar to car hacking. Therefore, we decide to conduct experiments on the Xiaoyu dataset using \(N\) = 100, \(K\) = 80, and \(\rho\) = 3. Our method requires a pre-analysis of vehicle data to select the values of \(N\) and \(K\). Automobile developers have enough time and conditions to complete this task because the specific data are set by themselves. Figure 8: The influence of hyperparameters \(K\) and \(N\) on the experimental results under fuzzy attack. Figure 7: The influence of hyperparameters \(K\) and \(N\) on the experimental results under DoS attack.
###### Abstract  
The extraction of velocity information from radar data by means of the Doppler effect is the driving factor for the investigations presented in this paper. A method for the quantification of the Doppler tolerance in continuous emission (CE) noise radar is introduced, addressing a current lack in literature within the frame of CE noise radars. It is shown that noise radar is highly sensitive to the Doppler effect, an issue that often results in a low Doppler tolerance especially for long coherent integration intervals. In general, the Doppler sensitivity is considered as a drawback but, in this paper, along with the absence of range-Doppler coupling in noise radar, it is turned into an advantage allowing for a very precise Doppler estimation. This new signal processing approach for Doppler extraction is detailed and its feasibility is proven on the basis of experimental data. The presented method requires much less data, i.e., target illumination time, than conventional Doppler analyses and, therefore, is beneficial in terms of radar resource management.  
noise radar; Doppler tolerance; radar signal processing; velocity measurement; Doppler measurement +
Footnote †: journal: Applied Optics and Computational Harmonic Analysis  
1
Footnote 1: [[https://www.mdpi.com/journal/signals](https://www.mdpi.com/journal/signals)]([https://www.mdpi.com/journal/signals](https://www.mdpi.com/journal/signals))
# MT-VTON: Multilevel Transformation-Based Virtual Try-On for Enhancing Realism of Clothing
## 3 Background  
### Appearance Flow  
Appearance flow [24] was initially introduced for synthesizing novel views by warping an image using a flow field. The flow field refers to vectors that map the spatial coordinates from one view to another. The flow fields are often learned through machine learning techniques, allowing for more-accurate and -flexible view generation.  
Let \(x_{1}\) and \(x_{2}\) be different views of one object and \(W_{\mathcal{F}}\) be a warping operator using a flow field \(\mathcal{F}\). To estimate \(\mathcal{F}_{x_{1}\gets x_{2}}\), which maps from \(x_{2}\) to \(x_{1}\), we can minimize the distance between the target view \(x_{2}\) and the warped view \(W_{\mathcal{F}_{x_{1}\gets x_{2}}}\) as follows:  
\[\arg\min_{\mathcal{F}_{x_{1}\gets x_{2}}}||x_{2}-W_{\mathcal{F}_{x_{1} \gets x_{2}}}(x_{1})||^{2} \tag{1}\]  
Virtual try-on can be considered a specific application of novel view synthesis, where the goal is to generate new perspectives of a cloth image. For this reason, many studies [4; 5; 6; 7; 8] have utilized appearance flow to warp a clothing item to align it with the target pose. However, in virtual try-on, additional factors such as cloth folds or human pose should be taken into account, making the problem more challenging.