esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Auxetic Materials for Sports Applications: Expanding Options in Comfort and Protection
## 3 Common Auxetic Materials and Structures
### Gradient Materials  
Gradient materials can be made from one material by varying its macro-structure to have different mechanical and structural properties in different regions (e.g., auxetic and conventional regions). Gradients can be discrete [22; 23; 272] or continuous [22; 272]. Composite sandwich structures employing discretely gradient honeycombs have a higher compressive modulus and are stronger than conventional sandwich structures [41]. The same sandwich structures exhibit a large increase in bending stiffness at the transition between conventional and NPR regions [42]. Opposing synclastic/anticlastic (domed/saddled)curvature associated with negative/positive Poisson's ratio respectively [9] cause increased shear modulus between regions and a localised stiffening effect. A similar stiffening effect can be seen in AM honeycombs with re-entrant inclusions during tensile tests [273]. Stiffening between discrete gradients of conventional and NPR could increase the bending stiffness (of the whole structure or specific regions) in sports equipment that contains honeycomb or fibre reinforced composites (e.g., skis, snowboards, tennis rackets and hockey sticks to name a few). Auxetic composite sandwich structures have not been tested for sports applications.  
Gradient foams have been fabricated, by applying variable compression gradients to different sample sizes (~2 \(\times\) 2 \(\times\) 2 cm to ~30 \(\times\) 30 \(\times\) 2 cm) using rods [22; 23] and/or selecting mismatched uncompressed foam and mould shapes [22; 23; 272] (e.g., uniform foam sample compressed in a tapered mould). These gradient auxetic foam samples can exhibit vastly different cell structure and mechanical properties in different regions, which can be explained using an analytical model [23]. Gradient structures could be employed to foams or other materials (i.e., fabrics) to allow the development of garments which will fit to the wearer and adapt to their shape as they move.
## 3 Feature Selection Model  
As shown in [8; 29; 33], the feature selection problem can be seen as a binary optimization model, and in this section, we introduce it in details. Specifically, the main purpose of feature selection is to reduce the data dimension by retaining the most valuable features through feature selection methods. Thus, there are two possibilities for each feature, i.e., to be selected and to be discarded. Therefore, the feature selection problem can be regarded as an optimization problem with a binary solution space.  
It is can be seen from Figure 1, the solution space of the considered feature selection problem is a binary. Each feature is represented by a binary number, and if that binary number is 1, it means that the feature is selected, and conversely, if that binary number is 0, it means that the feature is discarded. Thus, the feature selection of a dataset can be represented by a binary array as follow:  
\[(\text{Decision variables})\quad X=[x_{1},x_{2},x_{3},\ldots,x_{N_{dim}}], \tag{1}\]  
where \(N_{dim}\) is the number of features, in other words, the dimension number of the dataset. Under this model, there are two main objectives of the feature selection, i.e., to reduce the classification error rate of the obtained feature subsets, and to reduce the feature number of feature subsets. Thus, the feature selection problem is a multi-objective problem in which the first objective can be expressed as follows:  
\[(\text{Objective 1})\quad f_{1}=1-f_{acc}, \tag{2}\]  
where \(f_{acc}\) is the classification accuracy of the obtained feature subsets. Note that we introduce the KNN as a classifier to evaluate the feature subsets and the reasons are analyzed in following section. Moreover, the second objective of this work is to reduce the feature number of feature subsets, which can be expressed as follows:  
\[(\text{Objective 2})\quad f_{2}=\frac{N^{\prime}_{dim}}{N_{dim}}, \tag{3}\]  
where \(N^{\prime}_{dim}\) is the feature number of the selected feature subsets. To simultaneously the aforementioned objectives, we introduce the fitness function as follows:  
\[(\text{Fitness function})\quad f_{fit}=a\times f_{1}+b\times f_{2}, \tag{4}\]  
where \(a\in[0,1]\) and \(b=1-a\) are constants that denote the weights of the two objectives \(f_{1}\) and \(f_{2}\), respectively. Specifically, we can increase \(a\) to obtain a higher classification accuracy or increase \(b\) to obtain a smaller dimensional feature subset.
## 4 AI-Driven Concepts in MCNs
### Lightweight AI/ML Methods  
When the target predictive function is complex (e.g., multi-factor optimal routing prediction), usually large, dense and computationally intensive AI/ML models are required to ensure high accuracy. These models can be hosted for inference only in central clouds or distant locations, given that ships or UAVs are equipped with resource-constrained devices. Lightweight AI/ML methods allow complex models (\"teachers\") that are trained centrally (e.g., cloud-based) to transfer their knowledge to simpler models (\"students\") [74]. These lightweight models can operate efficiently on ships or buoys with limited computing power while ensuring adequate performance. Such techniques are known as Teacher-Student Learning and include methods such as Knowledge Distillation [74] (see Figure 7b), Model Pruning [75] (see Figure 7c), or Model Quantization [76] (see Figure 7d). Knowledge distillation involves training a compact \"student\" model to replicate the behavior of a larger \"teacher\" model. The Maritime sector may benefit from these methods in the following cases:  
* **Lightweight inference at the edge:** Knowledge distillation allows for on-ship diagnostics or onboard object detection by using distilled or pruned models for real-time tasks.
* **Hierarchical decision-making:** Complex teacher models can handle strategic planning, while small-scale student models can operate in real-time at the edge.
* **Model compression:** Using distillation to compress large predictive models (e.g., collision forecasting) for onboard inference without cloud dependence.  
The above-mentioned examples are crucial for low-power, bandwidth-limited, or autonomous edge maritime systems.  
The AI concepts introduced in this section form the basis of intelligent, adaptive operations in maritime environments. In the next section, we present concrete use cases and applications, ranging from autonomous vessel navigation to predictive maintenance, where these AI techniques are actively leveraged to meet the performance, scalability, and resilience demands of future maritime communication networks.  
Figure 7: (**a**) Generative Adversarial Network architecture; (**b**) Teacher-Student models for lightweight model creation; (**c**) Model pruning by cutting edges (left) or nodes (right); (**d**) Model quantization via weight rounding for memory-efficient model creation.
## 3 Results
### Correlation Between NDR Length and Exogenous Gene Expression Level  
To verify the relationship between NDR length and gene expression, three L-NDR and three S-NDR transcriptional regulatory sequences, along with the coding sequence of the GFP gene, were chemically synthesized, cloned into the pAUR101 vector, and introduced into _S. cerevisiae_ BY4741 cells for expression. The mRNA relative expression levels of the GFP gene under the regulation of each transcriptional regulatory sequence were detected by RT-qPCR. The results showed that the relative mRNA expression levels of GFP under the regulation of L-NDR sequences were all higher than those of S-NDR sequences (Supplementary File S3: Figure S2A). The average relative expression level of mRNA under the regulation of L-NDR was three times higher than that of S-NDR sequences (Figure 6A). Independent _t_-test results showed that the expression differences were significant between L-NDR and S-NDR groups (_p_ < 0.01).  
The protein expression was detected by ELISA and Western blotting. The results showed that the expression levels of the GFP protein under the regulation of L-NDR sequences were all higher than that of S-NDR sequences (Supplementary File S3: Figure S2B), and the average expression level of the GFP protein under the regulation of L-NDR sequences was seven times higher than that of S-NDR sequences (Figure 6B). Independent _t_-tests revealed that the green fluorescent protein expression differences between L-NDR and S-NDR groups were significant (_p_ < 0.001). Using Western blot analyses, 27 kDa products were detected in the recombinant _S. cerevisiae_ protein samples (Figure 6C). The green fluorescent protein expression levels followed the same trend as the relative mRNA expression levels.
## 7 Performance Evaluation of NDA-EVM-AM on an NLOS Scenario
### Accuracy of Channel Quality Metric  
We consider the root of mean-square errors (RMSEs) as a standard of measuring the accuracy of channel quality metric. We compare the estimation of NDA-EVM and traditional metrics DA-SNR (e.g., there is one reference signal (RS) for every seven data symbols in PUSCH for a normal cyclic prefix (CP) over the \(\eta-\mu\) channels). We transmit data over the \(\eta-\mu\) fading channels with different channel parameter \(\mu\) and \(\eta\). For every \(\eta-\mu\) fading channel, the estimated NDA-EVM (i.e., \(\xi_{E}\)) can be obtained using Equation (14). Meanwhile, we can measure the NDA-EVM (i.e., \(\xi_{M}\)) by the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Target SER & 0.001 \\ Outage probability \(P_{out}\) & 0.1 \\ Radio frequency carrier & 2.4 GHz \\ Transmitter power \(P_{0}\) & 1 W \\ Symbol period \(T_{sym}\) & 66.7 μs \\ Data-aided interval \(T_{DA}\) & 0.5 ms \\ Constellation size of MQAM & QAM/16 QAM/64 QAM/256 QAM \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters of wireless transmitter received symbols in the simulation as a real NDA-EVM (i.e., \(\zeta_{R}\)), which is employed by Monte Carlo approach (i.e., \(n=1000\) rounds in our simulation). The RMSE of NDA-EVM can be obtained as \(RMSE=\sqrt{\sum\left(\zeta_{R}-\zeta_{E}\right)^{2}/n}\). In a similar way, the RMSE of DA-SNR can also be obtained.  
The channel change frequency is a decreasing function of the channel parameter \(\mu\)[30]. This causes the RMSE to increase with decreasing \(\mu\), as shown in Figure 3. The data-aided estimation accuracy is more sensitive to variations in \(\mu\) because of the fixed time interval between RSs; by contrast, the NDA-EVM is more robust and has lower RMSEs at smaller \(\mu\) values. Interestingly, \(\eta\) has the opposite effect on NDA-EVM as that of \(\mu\). Accordingly, the NDA-EVM estimation yields lower RMSEs compared with the DA-SNR estimation as the parameter \(\eta\) increases. As seen in Figure 3, the NDA-EVM is an effective metric for evaluating the quality of wireless fading channels in NLOS scenario.
# Retinal Nerve Fiber Layer in Patients with Alcohol Use Disorder
## 1 Introduction  
Excessive alcohol consumption currently constitutes a huge, worldwide social health issue. The latest health report of the World Health Organization (WHO) published in 2018 [1] highlighted that 3.1 billion people worldwide had consumed alcohol in the last year, 2.1 billion of whom were regular consumers. In addition, in 2016 harmful alcohol use caused approximately 3 million deaths worldwide (5.3%), reaching even higher rates than deaths caused by diabetes, HIV/AIDS, and tuberculosis [1].  
There is evidence that alcohol consumption impairs the central nervous system (CNS) [2; 3]. However, no routine techniques are used to asses this impairment in patients with alcohol consumption. This is due to their complexity and high costs.  
The retina is an accessible area of the CNS for the purposes of clinical examination, and for this reason it is considered a \"brain window\" [4]. It has been proposed that optical coherence tomography (OCT) is a non-invasive technique for assessing retinal impairment caused by several substances, such as cocaine, and at the same time, it has been correlated with brain atrophy [5]. Some authors, such as [PERSON] et al. [6], have postulated that changes observed in the retina could reflect changes in the brain tissue, and in this way, it could constitute an accessible marker for evaluating CNS impairment. Several studies have been published in which OCT has been used to assess CNS impairment in neurological diseases such as Parkinson's disease or multiple sclerosis. However, there is little research regarding mental illnesses and the results are controversial [7].  
Due to the high level of alcohol consumption worldwide and the neurotoxicity caused by alcohol, we aimed to carry out a study on abstinent alcoholic patients who met the criteria for alcohol use disorders (AUD). Our first objective was to determine the harmful effects of alcohol consumption on the retina. Secondly, we aimed to assess whether this impairment could correlate with alcohol consumption and the associated cognitive impairment.
# A Matheuristic for Joint Optimal Power and Scheduling Assignment in DVB-T2 Networks +
## 4 A Matheuristic for DVB Design
#### 4.1.2 Fitness Function  
As a fitness function, it is natural to use the coverage that is provided by an individual as a function of the power vector/chromosome \(p\). We denote such function by \(COV(p)\) and it returns the sum of the population of TPs for which the SIR inequalities are satisfied and whose serving stations are identified following the steps illustrated earlier.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 6 Carbon-Based Nanomaterials
### Activated Carbons  
Activated carbons (AC) are widely used in photocatalysis as adsorbents for pollutants, owing to their high specific surface area, substantial pore volume, low cost, defect-rich morphology, and excellent adsorption capacity [153]. ACs exhibit a higher affinity for non-polar molecules, attributed to their carbon-rich conjugated surface structure, which has a non-polar nature. They demonstrate significant adsorption efficiency for volatile organic compounds (VOCs), even at low pollutant concentrations. However, their efficiency in removing high concentrations of VOCs is limited by factors such as desorption and flammability [154; 155].  
The adsorption force--the force required to release the adsorbate trapped in the pores--increases as pore dimensions decrease [156]. Initially, pollutant adsorption occurs in the micropores, where the adsorption force is strongest, before progressing to larger pores. The adsorption capacity of ACs increases rapidly at first but slows down as saturation approaches. Notably, the adsorption capacity does not necessarily correlate with specific surface area and pore volume; thus, these metrics alone do not determine adsorption effectiveness. For instance, a well-organized arrangement of micropores enhances the diffusion of adsorbate compounds [157], thereby increasing the overall adsorption capacity. Additionally, the size of the adsorbate affects the adsorption characteristics of ACs with varying pore structures. While an adsorbate can enter larger pores, excessively large pore sizes can weaken the adsorption potential. For example, with toluene as a target pollutant, mesoporous volume plays a crucial role in adsorption capacity, which increases as mesoporous volume rises [158].  
Figure 17: Number of studies about nanoadsorbents for pollutant removal during 2007–2017 [152].  
In photocatalytic applications, ACs are often coupled with semiconductors like TiO\({}_{2}\) to enhance photocatalytic efficiency by acting as both a support material and an adsorbent. Furthermore, activated carbons can be doped with metal oxides to improve their affinity and adsorption of both polar and non-polar VOCs by increasing the number of functional groups on their surfaces. However, desorption of pollutants from AC pores may occur due to competition from other compounds that exhibit stronger adsorption forces [159].
# Model Updating of Bridges Using Measured Influence Lines
## 2 Materials and Methods
### Measurements
#### 2.2.3 Ambient and Traffic-Induced Vibration Test  
Operational modal analysis (OMA) was performed from the 60 min long signal, captured after the last passage of the calibration vehicle, the second night at 3:00 AM. Only a few passenger cars crossed the bridge in that time window. ARTeMIS Modal Pro 7.2 software [22] with the enhanced frequency domain decomposition (EFDD) method was used to estimate the modal parameters. The 66% overlap and 4096-point data samples resulted in approximately 0.06 Hz frequency resolution. Figure 11 shows the singular spectral density values for the limited range from 0 to 10 Hz. The coloured markers denote the detected modes used in the FEMU study, and the nearby text denotes the frequency values.  
Figure 10: E_MG1 (**a**) and E_MG2 (**b**) strain ILs showing the speed (in)dependence.  
Figure 9: E_MG1 (**a**) and E_MG2 (**b**) strain ILs showing the (in)dependence of vehicle load and type.  
Eight modes were identified and considered in the FEMU. Modes are named by their shape (B for bending, T for torsion), direction (Y for movement in Y, Z for movement in vertical direction), and harmonics (1 for first harmonic, etc.). Mode name 'B-Y-1', for example, represents the first bending mode in the Y direction. Not all modes, represented by evident peaks, were considered. Their significance was decided based on their complexity, shape observation, modal assurance criterion (MAC) value, ability to distinguish them from the nearby modes, and whether they could be automatically detected and paired with measured modes. Table 2 shows natural frequencies and corresponding mode shapes from the ambient and traffic-induced vibration test. Red indicates the highest, and blue the lowest displacement magnitudes. Semi-transparent grey colour denotes the undeformed geometry.  
Table 2 and Figure 12 demonstrate a strong similarity between B-Y-1, B-Y-2, and B-Y-3 modes, between B-Z-2 and B-Z-3 modes, and the T-1 and T-2 modes. This is expected, since they represent the primary movement in a similar direction.
## 3 RNAi Traits for Rootworm Control
### Target Site Discovery
#### 3.2.1 Larvicidal RNAi
It should be noted that _sec23_ dsRNA T0 maize events were protected against insect damage in greenhouse experiments and larval offspring of female adults that had been exposed to sublethal concentrations of _sec23_ dsRNA in diet feeding assays displayed reduced weight and survival [126]. These examples illustrate that RNAi targets involved in essential processes can have larvicidal activity, translating to decreased root damage when hairpin RNAs are expressed. Although these genes are expressed throughout the larva, except for _DvSSJ1_, which is relegated to the midgut epithelia, it is unclear whether the larvicidal activity is primarily related to the loss of function of essential genes/gene products in the midgut/alimentary system cells compared to a more systemic effect on the larva.
## 4 Proposed Protocol
### MST and Chain Construction Period
(2018) [PERSON]; [PERSON]; [PERSON] [PERSON] Cluster-Head Restricted Energy Efficient Protocol (CREEP) for Routing in Heterogeneous Wireless Sensor Networks. _Wirel. Pers. Commun._**2018**, _100_, 1477-1497. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Cluster-Based Routing Protocol with Static Hub (CRPSH) for WSN-Assisted IoT Networks. _Sustainability_**2022**, _14_, 7304. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Robust Cluster-Based Routing Protocol for IoT-Assisted Smart Devices in WSN. _Comput. Mater. Contin._**2021**, _67_, 3505-3521. * [PERSON] and [PERSON] (2011) [PERSON]; [PERSON] Ant Colony Optimization. In _Encyclopedia of Machine Learning_; Springer: Boston, MA, USA, 2011; pp. 36-39. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON] An improved ant colony optimization algorithm based on context for tourism route planning. _PLoS ONE_**2021**, _16_, e0257317. [CrossRef] [PubMed]
* VINT Project (1997) VINT Project. The Network Simulator-NS2. 1997. Available online: [[http://www.isi.edu/nsnam/ns](http://www.isi.edu/nsnam/ns)]([http://www.isi.edu/nsnam/ns](http://www.isi.edu/nsnam/ns)) (accessed on 20 November 2022). * [PERSON] et al. (2004) [PERSON]; [PERSON]; [PERSON] MIT uAMPS LEACH ns Extensions. 2004. Available online: [[http://www-mtl.mit.edu/research/iscystems/uamps/leach](http://www-mtl.mit.edu/research/iscystems/uamps/leach)]([http://www-mtl.mit.edu/research/iscystems/uamps/leach](http://www-mtl.mit.edu/research/iscystems/uamps/leach)) (accessed on 20 November 2022). * [PERSON] and [PERSON] (2020) [PERSON]; [PERSON]; [PERSON] An information entropy based-clustering algorithm for heterogeneous wireless sensor networks. _Wirel. Netw._**2020**, _26_, 1869-1886. [CrossRef]

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# Comparative Study of Energy Savings for Various Control Strategies in the Tunnel Lighting System
## 2 Tunnel Lighting Controls: Strategies and Technologies
### Daylight-Adaptation Control Strategy  
For the daylight-adaptation control strategy, lamps' outputs in the tunnel interior are dimmed following the ambient luminance to meet the luminance requirements for drivers' eye adaptation. A luminance meter deployed outside the tunnel entrance is used to monitor the ambient illuminance level, which varies with the tunnel orientations, climatic conditions, and geographical locations. The strategy enables the controller to use a simple control algorithm, such as producing luminous levels in a fixed proportion to the estimated daylight level, to dim lamps. This control scheme is illustrated in Figure 2.
## 3 Results and Discussion
The statistical comparison of generated and measured cell values is shown in Figure 10.  
The obtained results show that the kriging technique adequately maps the geotechnical parameters, and these parameters are similar to original measurements. From a geotechnical engineering point of view, the interpolated values were acceptable for the feasibility and planning stages of infrastructure  
Figure 9: (a) Statistical Evaluation of AASHTO Map; (b) Statistical Evaluation of Allowable Bearing Capacity Map; (c) Statistical Evaluation of Engineering Soil Classification Map. development projects. In addition, these maps supplement each other and further strengthen the derived conclusions from individual maps. The capability of the ArcMap software to house a 1-TB-file database can considerably reduce the hazardous management of conventional printed data boxes. The database can be further converted into meaningful and ready-to-use technical information. Upon the closure of megaprojects, storage of files and data boxes remains troublesome; typically, these data, which are worth millions of rupees, are destroyed owing to the inability to physically manage them. Without the ability to manage data electronically, the quality of works suffers, and hundreds of millions of rupees worth of data is lost forever. Millions of rupees can be saved if the geotechnical data were properly managed and represented. This study is an effort to create an electronic database and generate ready-to-use maps in three spheres of geotechnical engineering. After extensive field exploration, a regional geotechnical map was developed using the kriging analysis technique, which is available under spatial analyst tools of the ArcGIS software. The distribution of field survey sites in a grid pattern is also an innovative approach, which provides uniformly spaced samples for subsequent evaluations.