esa-sceva/satcom-chunk-collection huggingface dataset:
# Conscious Mobility for Urban Spaces: Case Studies Review and Indicator Framework Design
## 4 Case Studies
### Monterery: Quick, Safe & Sustainable Urban-Campus Community Mobility System
#### 4.5.4 Normativity, Economy and Politics  
As part of the current State administration (2021-2027), the Master's plan of Mobility contemplates a budget of almost 4900 million USD for the investment in infrastructure and mobility in four axes: dignified transport, smart mobility, complete streets, and regional connectivity [72]. Within these axes, several actions to advance sustainable and smarter mobility goals are described, focusing on the relocation of public transport routes, construction and maintenance of main roads, procurement of 1490 low emission and 110 electric units with an investment of 300 million USD, modernization of the ISMT and the center of mobility management, mobility sensor installation, and public spaces enhancement, with an investment of 41 million USD [72].  
There is a strong political will toward sustainable mobility; proof of this is stated in the Sustainable Urban Mobility Program [34], a legal framework that aims to include the vision of urban mobility that the state of Nuevo Leon wishes to achieve in the next 10 years. The framework is integrated by the Sustainable Mobility and Accessibility law for the state of Nuevo Leon, the law of Climate Change of the State of Nuevo Leon, the law of Construction and Rehabilitation of Pavements of the state of Nuevo Leon, the law of Road Signs of the state of Nuevo Leon, and the state technical standard of sidewalks of Nuevo Leon.  
At the municipality level, according to [73], several statements for the mobility initiatives of the DistritoTec are:  
1. Maximize interconnection between routes to facilitate accessibility through walking.
2. Plan for equal access distribution of public space for different users.
3. Technologies adoption to enhance efficient management and reduce negative externalities.
4. Increase transport mode offers that fosters a reduction in private vehicle use.
5. Prevent accidents by, enhancing infrastructure and urban mobility.  
Figure 7: Complete streets and traffic monitoring system located within DistritoTec.  
6. Foster inclusion, in particular women's accessibility to mobility to minimize gender violence and enhance public safety.
7. Encourage new financial mechanisms and business models for the operation of public transport.
8. Include carpool systems, private-public transport, and fostering bicycle use.  
In addition, DistritoTec, under the urban development plan of Monterrey's municipality 2013-2025 (PDUMM, spanish acronym), is eligible to fulfill a series of actions for urban reordering, where urban mobility, intermodal transport, and public infrastructure play an important role. This plan led to holding several meetings between the public and private sectors, as well as the residents affected by the proposed projects designs. Even though inhabitants' perspectives were considered in the improvement of the projects, in some cases, these do not fulfill the necessities of the surrounding local community [74].
## Materials and Methods
### Transcriptome Analysis  
The raw sequencing reads were filtered to obtain the clean reads of each sample. Three criteria were used to filter these reads: adaptors, more than 10% \"N\" bases, low-quality reads more than 50% of bases having a qualtiy value \(\leq\) 10. All the sequences obtained from the transcriptome were submitted to NCBI (Accession Number: SRR7427745-SRR7427748). _De novo_ assembly from the clean reads of all the samples was conducted using Trinity with default parameters (The completeness of our assembly was evaluate by BUSCO and test result show the complete BUSCO were 88.7%.) (Supplementary Table S6) [37; 38]. Unigenes were aligned through BLASTX with the best similar hit of an E-value < 1 \(\times\) 10\({}^{-5}\), including NCBI non-redundant (Nr) protein ([[ftp://ftp.ncbi.nih.gov/blast/db/FASTA](ftp://ftp.ncbi.nih.gov/blast/db/FASTA)]([ftp://ftp.ncbi.nih.gov/blast/db/FASTA](ftp://ftp.ncbi.nih.gov/blast/db/FASTA))), NCBI ([[ftp://ftp.ncbi.nih.gov/blast/db](ftp://ftp.ncbi.nih.gov/blast/db)]([ftp://ftp.ncbi.nih.gov/blast/db](ftp://ftp.ncbi.nih.gov/blast/db))), and SWISS-PROT ([[http://www.uniprot.org/](http://www.uniprot.org/)]([http://www.uniprot.org/](http://www.uniprot.org/))). Homology similarity was searched in the databases, including NCBI ([[ftp://ftp.ncbi.nih.gov/blast/db](ftp://ftp.ncbi.nih.gov/blast/db)]([ftp://ftp.ncbi.nih.gov/blast/db](ftp://ftp.ncbi.nih.gov/blast/db))) and SWISS-PROT ([[http://www.uniprot.org/](http://www.uniprot.org/)]([http://www.uniprot.org/](http://www.uniprot.org/))), through BLASTX by using the best similar hit of an E-value < 1\(\times\) 10\({}^{-5}\). The classification of protein function was searched in the Gene Ontology (GO) database ([[http://www.geneontology.org/](http://www.geneontology.org/)]([http://www.geneontology.org/](http://www.geneontology.org/))) and the Clusters of Orthologous Groups (COG) database (www.ncbi.nlm.nih.gov/COG). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database ([[http://www.genome.jp/kegg/keggl/.html](http://www.genome.jp/kegg/keggl/.html)]([http://www.genome.jp/kegg/keggl/.html](http://www.genome.jp/kegg/keggl/.html))) was used to determine the biological pathways of the unigenes.  
The fragments per kilobase of exon model per million mapped reads (FPKM) were calculated to quantify the expression level among the six tissues of _P. notoginseng_. The false discovery rate (FDR) was used to compute the differences in the significance of transcript abundance. log\({}^{2}\) ratio \(\geq\) 2 and FDR \(\leq\) 0.05 were considered to identify differentially expressed genes (DEGs) (Differential expression analysis were carried out at the gene level.). Heatmaps were constructed using \(R\) programming language and software (R 3.4.2, R packages).
## 3 Proposed Architecture
### Network Domain Controller  
The network domain controller (SDN Controller) is in charge of network elements (network domain). It has standard southbound interfaces to communicate with the network elements. The Domain controller SBI relies on using the Network configuration protocol (NETCONF) to interact with the underlying technology's network elements. The SDN controller also has a northbound interface to communicate with the SDTN controller or the OSS/BSS Systems using RESTCONF.
## 4 Results and Discussion
### Time History Analysis
#### 4.2.4 Maximum Tensile Stress  
On the Tank Wall  
Figure 11 demonstrates that the tensile stress values are considerably higher in soft soil than in other soil types, given the same structure and seismic forces. In soft and normal soils (types 1 and 2), the critical condition arises in the empty tank and the thicker part of the wall. This is attributed to maximum tensile stress in the empty tank wall at a height ranging between 0 and 1 m from the foundation, which can be traced back to the thicker wall footing that enhances stiffness. Consequently, heightened stiffness augments the tanks' capacity to bear greater loads.  
The peak tensile stress is observed when the soil is hard in the water-filled tank. However, the maximum values are approximately one-third of those in soft soil. The rationale behind the high tensile stresses above the wall in all analyses is incorporating the floor in the model. In a general sense, the presence of the slab restricts the wall from functioning as a complete cantilever.  
In addition, the change in soil material from sand to clay significantly increased the tensile stress in the tank wall. The main reason for this behavior was the hardening of the tank wall relative to the soil, making the tank wall capable of withstanding more stress.  
Figure 11: Maximum tensile stress at the tank wall according to the different types of soil and the water level in the tank.  
Figure 10: Soil pressure at the tank wall for different soil types according to the water level in the tank.
## 2 Finite Element Modeling
### Parametric Finite Element Study
#### 2.6.2 Parametric Study: Results of Minor Axis  
The effect of different reinforcement spacings for the uprights under minor axis loading is discussed in this section. Minor axis models are provided with the same spacing and the same thickness as the major axis models. Figures 17-20 indicate the normalized load-displacement curves for different models and thicknesses under minor axis loading. According to the figures, thickness and length play important roles in flexural strength. Additionally, more reinforcement leads to more ultimate strength. Figures 17-20 show that using reinforcement with shorter spacing increases the strength of the sections. Sections with a shorter length and thicker cross-sections already have higher flexural strength, but this strength is improved noticeably by employing reinforcement. Due to the restrained buckling and better distribution of force along the section's length, specimens with dense reinforcements indicate better distortional buckling behavior. Generally, the distortional buckling behavior is enhanced, and section failure is changed from general global buckling to local buckling, mostly due to partial closing of the upright section.  
Figure 16: Percentage of increased ultimate load of major axis analysis with different reinforcement spacing in length order.  
Figure 17: Normalized moment-deflection curves for 1800 mm models about minor axis.  
Figure 18: Normalized moment-deflection curves for 2400 mm models about minor axis.  
Figure 19: Normalized moment-deflection curves for 3000 mm models about minor axis.  
Figure 20: Normalized moment-deflection curves for 3600 mm models about minor axis.  
Figure 21 indicates the normalized ultimate flexural strength with respect to the reinforcement spacing for models under minor axis loading. It can be observed that length has an inverse relation with flexural strength, where both spacing and thickness have a direct relation with the ultimate load. Hence, shorter models with higher thickness and shorter reinforcement spacing indicate higher flexural strength. For example, the model with 1800 mm length and 3 mm thickness (1800-3.0 mm) with 50 mm reinforcement spacing shows the greatest ultimate load.  
Figure 22 illustrates a comparison between different increased ultimate loads with respect to reinforcement spacing with length order. Spacing and thickness have the same contribution in increasing the flexural strength.  
Figure 21: The normalized ultimate moment of 1800, 2400, 3000, 3600 mm models about minor axis with respect to reinforcement spacing.
## 4 Results
### Qualitative Analysis of Model Interpretability  
#### 4.2.1 Interpretability of Models on the PSD Dataset  
In the power spectral density theory, different modulation formats significantly influence the PSD of signals due to their distinct frequency domain distribution characteristics. Typically, the PSD is concentrated at the baseband with a main lobe and multiple side lobes. As energy becomes more concentrated, the main lobe width narrows, and the main lobe widths of different modulation formats, from largest to smallest, are QPSK, 8 PSK, and 16 QAM. Therefore, the main feature of the power spectral density is the location of its main lobe, situated in the center of the spectrum. In the model, positions with higher weights indicate a greater contribution of that feature in the model's decision-making process. When high-weight features in the model align with theoretical expectations (i.e., the main features are in the main lobe region), it indicates that the model has good interpretability.  
In Figure 6, we intuitively observed the interpretability results of three modulation formats under different techniques. For QPSK, both the AMI-CNN model and Grad-CAM  
Figure 5: Computation time of AMI-CNN, LeNet, ResNet18, and VGG19 on the PSD dataset and the constellation phase histogram dataset.  
highlight features concentrated around the main lobe position, consistent with the power spectral density theory. However, Grad-CAM also marks some low-weight features on the sides, which is less understandable to humans. The goal of interpretability is for the model to highlight features that are consistent with the power spectral density theory; thus, the AMI-CNN model has an advantage here by only marking the most prominent features. In contrast, the features highlighted by LIME and SHAP tend to be more scattered, without emphasizing the most important feature. LIME focuses on the importance of local features, leading to a more dispersed focus, while SHAP, considering the contributions of all feature combinations, is influenced by the approximation algorithm in more complex data, resulting in scattered marked regions. The interpretability results for 8 PSK are essentially consistent with those for QPSK. In the interpretable results for 16 QAM, only the AMI-CNN model focuses on the main lobe features, whereas the attention features of Grad-CAM, LIME, and SHAP are scattered on both sides without clear and understandable characteristics.  
In summary, it can be concluded that the attention features of the AMI-CNN model are consistent with the theoretical basis and most align with human understanding, providing optimal interpretability.
# Quality-of-Service-Linked Privileged Content-Caching Mechanism for Named Data Networks
## 5 Results and Discussion
### Content Store Hit Rate and Percentage of QoS Nodes in the Network  
This evaluation answers the question \"what percentage of overall NDN nodes should be designated as QoS nodes?\" For \(|M|\) = 500, 50 endpoint nodes are content producers. The network generates \(\delta=50\) interest packets per second towards content producers, and the QoS nodes vary between 5% and 50% in number. For \(|M|\) = 1000, 100 endpoint nodes are content producers, and the network generates \(\delta=100\) interest packets per second towards content producers; the QoS nodes in the network vary between 5% and 50% in number. Figure 4 depicts the percentage of the content store hit rate, corresponding to the percentage of QoS nodes in the network. It can be observed that the content store hit rate increases till (0.35) \(|M|\), i.e., 35% of the network nodes are QoS nodes, and begins to decrease because although QoS traffic is given a higher priority, regular traffic is allocated less content score space. It is important to designate the optimum number of NDN nodes as QoS nodes to obtain the desired results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute** & **Value** \\ \hline Link delay & 15 milliseconds \\ Bandwidth & 1 mbps \\ Routing algorithm & NDN-adaptive routing \\ Packet payload size & 1024 bytes \\ Duration of simulation & 4000 simulation seconds \\ Interest packet rate per second & \(\delta=50\) \& \(\delta=100\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Network simulation settings.
## 4 Overview of the Type of Classifiers Used for MR
### Knn  
KNN is a simple classifier and is defined as a nonparametric approach that does not require information about data distributions. The fundamental principle of KNN is the distance between samples; the first sample is called the trained sample, whereas the second sample is the test sample.  
A KNN classifier calculates the distances of the test sample with respect to the class in the training sample [90]. In [102], eight digitally modulated signals, namely, 2 FSK, 4 FSK, MSK, BPSK, QPSK, 8 PSK, 16 QAM, and 64 QAM, were classified using KNN. KNN has various disadvantages, such as nonparametric, lazy learner, cannot determine parameter k, and computationally greedy algorithm [12]. GP was used in conjunction with KNN in [54; 103]. In [103], BPSK, QPSK, QAM16 and QAM64, which are included in the IEEE 802.11a standard, were considered. The classification process was divided into two stages to improve the classification accuracy. Two trees were created in each stage for the classification of BPSK, QPSK, QAM16, and QAM64 modulation schemes. A single tree was created at the first stage to classify BPSK, QPSK, and QAM16/QAM64. Another tree was created at the second stage to classify QAM16 and QAM64. In [90], the performance of KNN was compared with that of another classifier that uses cyclostationarity features. The results showed that the performance and computational complexity of KNN were better than those of SVMs at low SNR.
###### Abstract  
The present study aimed to investigate the deformation behavior of three different clear aligner systems, CA(r) Pro+ Clear Aligner (Scheu Dental, Iserlohn, Germany), Taglus Premium (Taglus Company, Mumbai, India), and Spark Trugen (Ormco Corp., Orange, CA, USA), under compression testing, using the digital image correlation (DIC) technique. **Materials and Methods**: A total of 15 patients were treated with each of the three aligner systems, resulting in 45 sets of aligners. Each aligner set was fixed on the 3D-printed dental arches and then in an articulator. Then, the samples were subjected to occlusal forces using a purpose-built test stand to allow for controlled force application and precise displacement determination. The DIC technique was used for capturing the deformation behavior, providing detailed strain and displacement fields. Statistical analysis was performed using one-way ANOVA and Bonferroni post hoc tests with a significance of 0.05. **Results**: The results indicate that the Spark system exhibited the most substantial rigid displacement. Furthermore, the elastic deformation values of the Spark and Taglus systems were significantly higher than those of the CA Pro+ system (\(p>0.05\)). **Conclusions**: The Spark Trugen clear aligner system demonstrated a lower stability to rigid displacement and elastic deformation under compression testing compared to the Scheu CA(r) Pro+ Clear Aligner and Taglus Premium. All three tested clear aligner systems showed an increased resistance to elastic displacement and rigid deformation in the mandibular arch.  
clear aligner; elastic deformation; rigid displacement; digital image correlation +
Footnote †: journal: _Sci_. **2024**, 14, 7496. [[https://doi.org/10.3390/app14177496](https://doi.org/10.3390/app14177496)]([https://doi.org/10.3390/app14177496](https://doi.org/10.3390/app14177496))
## 8 Performance Evaluation
### Handoff Overhead
#### 8.2.1 Location Update Cost  
Let LU\({}_{\text{LoWMob}}\) be the LU cost, in bits, incurred due to MN's K movement inside a PAN for LoWMob:  
\[LU_{LoWMob}=\pi_{0}\times S_{LU}+(\mu(\text{K}_{1})-1)\times S _{LU}+(\mu(\text{K}_{1})-1)\times S_{NW}\times 2\ (1)\] \[S_{LU}=b\times L_{1}\ (2)\] \[\pi_{0}=\frac{1}{2}\ (3)\]  
Similarly for DLoWMob, the LU cost can be given by:  
\[\text{LU}_{\text{DLoWMob}}=\pi_{0}\times S_{1}+(\mu(K_{2})-1)\times S _{2}+(\mu(K_{1})-1)\times S_{NW}\times 2\ +\ (\mu(K_{1})-1)\times S_{3}\ (4)\] \[S_{1}=b\times L_{1}\ (number\ of\ hops\ from\ SN\ to\ GW)\ (5)\] \[S_{2}=b\times L_{2}\ (number\ of\ hops\ from\ MSP\ to\ GW)\ (6)\] \[S_{3}=b\times L_{3}\ (number\ of\ hops\ from\ SN\ to\ MSP)\ (7)\]  
Equation 1 includes both the LU cost to the GW and the cost to send _new_node_ message. Also \(\pi_{\ast}\) is the equilibrium probability of the state \(0\) that represents the probability of MN staying outside a given PAN. In this case, we assume that the probability of [PERSON] staying outside the PAN is same as staying  
\begin{table}
\begin{tabular}{|l|l|l|} \hline
**Name** & **Description** & **Value** \\ \hline b & LU message cost for one hop & 144 bits \\ t & Tunneling cost in one hop & 16 bits \\ S\({}_{\text{NW}}\) & new\_node message cost in one hop & 160 bits \\ N\({}_{\text{SN}}\) & Number of SNs & 36 \\ N\({}_{\text{MSP}}\) & PAN size in terms of number of MSPs & 4 \\ S\({}_{\text{AR}}\) & Area of the Association Range & 900 m\({}^{2}\) \\ SD & Area covered by an MSP & 8,100 m\({}^{2}\) \\ v & Average speed of MN & 10 m/s \\ \(\mu(\text{K}_{1})\) & Average number of SNs a MN associates with in a PAN & 2.8 \\ \(\mu(\text{K}_{2})\) & Average number of MSP a MN associates with in a PAN & 0.999969 \\ L\({}_{1}\) & Average number of link hops between GW and SN & 10 \\ L\({}_{2}\) & Average number of link hops between GW and MSP & 5 \\ L\({}_{3}\) & Average number of link hops between MSP and SN & 1 \\ \hline \end{tabular}
\end{table}
Table 2: The default parameter values for calculation of LU and tunneling cost.  
inside. For the LoWMod scheme \(\mu\)(\(K_{J}\)) is calculated as 2.8, from the equations derived in [33]. Also it can be seen from [33] that \(\mu\)_(\(K_{J}\))_ is not affected by the speed. For DLoWMod, \(\mu\)_(\(K_{2}\))_ is calculated as 0.999969 and is less than \(\mu\)_(\(K_{J}\))_ as MSPs area of coverage is larger than a single SN. Thus the MN stays within an MSP's region for a longer amount of time; this reduces the number of LUs to the GW. Also the average number of hops between a MSP and GW is less as compared to the number of hops between SN and GW.
## 3 Channel Modeling of the Ground Electrode Current Field in a Tunnel Environment  
### Analysis of Strong Penetration Technology Based on Ground Electrode Current Injection  
The strong penetration technology based on ground electrode current injection uses extremely low-frequency electrical signals, which are applied to the two electrodes driven into the soil layer or into the collapsed body of the tunnel, thereby forming a current field in the rock layer or soil layer. The signal detection of the terminal achieves the transmission of strong wireless penetration information, as is shown in Figure 1[19].  
Figure 1: Strong penetration technology based on ground electrode current injection.  
In the landslide formed by the tunnel construction collapse accident, the penetration environment includes different media, such as rock, sand, reinforced concrete, air cavities, etc., showing the characteristics of non-uniformity. During the transmission of electromagnetic waves in the earth channel, they will be affected by the inhomogeneous media. The interaction between electromagnetic waves and the medium produces various electromagnetic effects, such as polarization, magnetization, and conduction [20]. The influence of these electromagnetic effects on the transmission of electromagnetic waves is mainly reflected in the reflection, refraction, and attenuation of electromagnetic wave signals. The inhomogeneity of the earth medium, the influence of topographic features, and multipath transmission will all cause signal distortion, fading, or changes in the direction of electromagnetic wave propagation.  
The non-uniform earth medium is a semiconductor dielectric material. Since the characteristics of the electrostatic field in the semi-conductive medium are similar to the characteristics of the constant current field in the conductive medium, when studying the propagation characteristics of the conduction current field in the conductive medium, it can be used at a certain time and is observed as a quasi-electrostatic field. Therefore, when studying the information transmission characteristics based on the ground electrode current field, the relevant knowledge of the electrostatic field can be used for analysis [21].