esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
A smart university can enforce the use of certain technologies and specific architectures, while a smart city in general will have to deal with a greater heterogeneity in such areas, which usually require complex solutions to integrate the numerous previously existing computational systems. Due to the previously mentioned differences, it is essential to study specifically the most appropriate architectures and technologies for developing smart campuses and universities. In contrast to previous reviews and surveys on smart campuses/universities, which are presented as systematic literature reviews [8; 9], are focused on defining certain generic concepts/applications [10], or are centered on specific technologies [11; 12; 13; 14; 15; 16], this article provides a holistic review that analyzes the application of the latest key technologies and architectures for the development of smart campuses and universities, including the following contributions, which have not been found together in the previous literature. * After defining the essential characteristics of a smart campus and a smart university, the most common communications architectures are detailed together with their evolution: from traditional cloud-based to the latest ones based on edge computing. * The application of blockchain to such architectures is studied as a tool to create a distributed immutable log that provides transparency and cybersecurity to higher education and smart campus applications. * The characteristics of the most relevant smart campus deployments and initiatives are analyzed. * The latest smart campus deployments are enumerated and multiple examples of their applications are described. * The most recent communications technologies for outdoor and indoor smart campus applications are studied. * The main challenges that will be faced by university planners, IoT vendors, and developers are listed. The rest of this paper is structured as follows. Section 2 defines the concept of smart campus and its main features. Section 3 details the latest communications architectures for smart campuses and smart universities. Section 4 analyzes the potential applications of blockchain for deploying higher education and smart campus applications. Section 5 describes the most relevant smart campuses that have been already deployed, emphasizing their main applications and communications technologies. Finally, Section 6 indicates the main challenges for university planners, IoT vendors, and developers, and Section 7 is devoted to the conclusions.
## 4 Results and Discussion
### Evaluation of the Graph-Based Method  
Since the EAHX operating conditions were identical to those described in Case 1 of Section 2.4, monitoring data for periods with outdoor temperature approaching or exceeding ASHRAE's 0.4% annual dry bulb design temperature (37 \({}^{\circ}\)C, see Table 1) are used to evaluate the graph-based design method.  
The analysis of the contour lines in Figure 9 (measured data) reveals that when outdoor air temperatures exceed 35 \({}^{\circ}\)C, temperature differences (between maxima of the red and blue lines) lay in the 6.5 to 9.2 K range. Using Figure 3 from the graph-based method, a fair estimation is achieved with temperature differences in the 8 to 10 K range when \(z=5.5\) m, \(35\leq T_{a0}\leq 40\)\({}^{\circ}\)C and considering Case 1 fraction \(x/L^{*}=0.75\).  
As regards load extraction results, using the analytical expressions supporting the graph-based method, Figure 11 includes light blue lines \(\dot{Q}^{\text{analyst}}_{\text{rm}}\) and \(\dot{Q}^{\text{analyst}}_{\text{oa}}\) for room and outdoor air load removal when the ventilation rate is 8000 m\({}^{3}\)/h. For outdoor temperatures above 35 \({}^{\circ}\)C (and below 39 \({}^{\circ}\)C), the analytical results provide a good fit to monitored outdoor air and to upper room load removal.  
Table 2 highlights the results depicted in Figure 11 for the _design_ outdoor air temperature \(T_{a0}=37\)\({}^{\circ}\)C.  
Table 2 shows that the EAHX removes \(\sim\)9 kW of room air loads (\(\dot{Q}^{\text{meas}}_{\text{rm}}\)), an average value between 7 and 11 kW (points \(\underline{\text{n}}\)4) and \(\underline{\text{n}}\)5) corresponding to a cooling capacity per room floor area between 15.5 and 24.4 W/m\({}^{2}\). Outdoor air loads (\(\dot{Q}^{\text{meas}}_{\text{oa}}\)) removed are approximately 13.75 kW (point \(\underline{\text{n}}\)6), corresponding to 30.5 kW/m\({}^{2}\) of cooling capacity.  
Considering the analytical results for Case 1, with 8000 m\({}^{3}\)/h and \(x/L^{*}=0.75\) (blue line in Figure 11 and \(T_{a0}=37\)\({}^{\circ}\)C), the outdoor air load removal is (as expected) in excellent agreement with measured data; room load extraction, 11 kW (point \(\underline{\text{n}}\)5), is a fair fit to the averaged monitoring values of room load extraction (9 kW).  
The agreement observed between measured data and analytical results and diminishing variability in data for higher outdoor air temperatures--for design temperatures--supports the applicability of the graph-based method for EAHX design in hot and dry climates.
## 4 Conclusions  
This paper presents an extensive optimization analysis of the impacts of diesel and methane masses, diesel injection duration, and engine rotational speed, which were optimized numerically by employing the NSGAII algorithm and CONVERGE 3.0 CFD via mode-eFRONTIER 2024R1.1 optimization software to obtain optimum cases regarding decreasing PFP, ISFC, and (CO, HC, NOx) emissions. The study ended with the main conclusions, summarized as follows.  
* A well-justified selection of parameters, meshing strategies, and numerical methods ensures that simulations are both computationally feasible and practically useful.
* The highest indicative thermal efficiency of the methane/diesel RCCI engine was 51%, with an efficiency enhancement of 10% compared with the diesel engine.
* High power was achieved by RCCI engines, but with a tradeoff of high NOx emissions, which were out of standards, so the highest power achieved with an acceptable range of emissions and without being used after treatment was 7.03 kW, which is in the range of 0.89 from the maximum allowable load of CDE.  
Figure 12: Contour plot of in-cylinder temperature, equivalent ratio, and NOx emissions for case A.  
Figure 13: Contour plot of in-cylinder temperature, equivalent ratio, and NOx emissions for case B.  
* All feasible results were in the range of the access air ratio of 2.22 to 2.37, MES in the range of 0.82 to 0.87, methane mass of 9.45 to 9.87 mg, diesel mass range of 1.71 to 2.44 mg, duration of 1.99 to 3.36 CA, SOI of \(-\)34 to 35 aTDC, and rotational speed range of 2020 to 3010 rpm.
* The minimum outcomes of (CO, HC) emissions and ISFC achieved were (0.00543, 0.0187) g/kWh and 146 g/kWh, respectively, which are lower than the CDE emissions by 99%, 72%, and 28.7%, respectively.
* Minimum NOx emissions were gained when the high range of cylinder temperature was the lowest and stayed for a short period. This was within the EURO IV standards of 0.4 g/kWh with a reduction of 97.5% compared with CDE.
* Minimum soot emissions were obtained when the SOC advanced and the in-cylinder temperature in the high range was higher than in other selected optimum cases.
* The improvement in IFSC along with reduced emissions suggests that utilizing methane-diesel in internal combustion engines is a cost-effective and promising approach.
## 2 Materials and Methods  
### Chemicals and Reagents  
All chromatographic reagents were of HPLC grade. n-Hexane, methanol, acetonitrile, and formic acid were bought from J.T. Baker (Phillipsburg, NJ, USA). Oleic acid, linoleic acid, and \(\alpha\)-linolenic acid were purchased from Sigma-Aldrich (St. Louis, MO, USA). The standards (purity \(>\) 95%) used for the identification and quantification of glycerol, ethanol, glucose, organic acids, fatty acids (including dodecanoic acid, tetradecanoic acid, hexadecanoic acid, 9-hexadecenoic acid, octadecanoic acid, oleic acid, linoleic acid, \(\alpha\)-linolenic acid, eicosanoic acid, and docosanoic acid), and volatile compounds (including 1-hexanol, (E)-3-hexen-1-ol, (Z)-3-hexen-1-ol, 1-propanol, isobutyl alcohol, isoamyl alcohol, 2,3-butanediol, 1-octanol, 1-octanol, 1-octyl-3-ol, 1-decanol, benzyl alcohol, 2-phenylethylhanol, ethyl acetate, isoamyl acetate, hexyl acetate, phenethyl acetate, hexanoic acid, octanoic acid, decanoic acid, isobutyric acid, ethyl butanoate, ethyl hexanoate, ethyl decanoate, ethyl decanoate, ethyl hexadecanoate, methyl octanoate, isoamyl hexanoate, propyl octanoate, citronello, 3-(methylthio)-1-propanol),and \(\beta\)-damascenone) were also obtained from Sigma-Aldrich. Yeast extract, peptone, and analytical glucose were purchased from Sangon Biotech (Shanghai, China). Analytical reagent ethanol, tartaric acid, sodium hydroxide, hydrochloric acid, and trichloromethane were obtained from Sinopharm Chemical Reagent Beijing Co. Ltd. (Beijing, China).
# New Achievements for the Treatment of Triple-Negative Breast Cancer
## 3 Novel Approaches to Therapy in TNBC
### Synthetic Compounds with Antiproliferative Activity in Preclinical Studies
#### 3.2.2 Indole Derivatives  
Recent literature data reviewed the encouraging anti-breast cancer potential of indole derivatives, which have multiple mechanisms of action, such as aromatase and microtubule inhibition, DNA-binding mechanism, induction of apoptosis or inhibition of PI3K/AKT/NFkB/mTOR (Table 3) [103]. In a recent study by [PERSON] et al. [104], benzopyran and indole pharmacophores were combined using molecular hybridization, obtaining a new library of 1,3,4,9-tetrahydropyrano[3,4-b]indoles. Among them, compound **14** exerted the most effective activity against MDA-MB-231 cells, with an IC\({}_{50}\) value of 2.29 \(\upmu\)M. This compound reduced cell growth, blocking the cell cycle at the G0/G1 phase. It disrupted the mitochondrial membrane potential (MMP), caused accumulation of reactive oxygen species (ROS) and reduced glutathione, triggering cell apoptosis through caspase pathway activation. This cell death mechanism is linked to the ability of compound **14** to inhibit the regulators of PI3K/AKT/mTOR pathway in MDA-MB-231 cells. [PERSON] and colleagues in 2021 designed 2-(substituted arylmethyl)-1-oxo-1,2,3,4-tetrahydropyrazino[1,2-4]indole-3-carboxamide analogues. Some of them showed potent anticancer activity against two BC cell lines together with selectivity against the MDA-MB-468 cell line. In some cases, this activity was higher than the anticancer effect of gefitinib, an EGFR- tyrosine kinase inhibitor. Particularly, compounds **15** and **16** inhibited Akt T308 phosphorylation, selectively inhibiting a kinase involved in its phosphorylation, phosphoinositide 3-kinase \(\beta\) (PI3K\(\beta\)) [105]. In another recent work, two libraries of indole derivatives were designed and synthesized. Four of the derivatives (**17-20**) were extensively evaluated for their pharmacological effect on BC [106]. These four compounds showed anti TNBC activity comparable or better than that of paclitaxel, causing autophagy in MDA-MB-231 cells and triggering cell cycle arrest and apoptosis by ROS generation, mitochondrial membrane disruption and caspase activation. Moreover, they stabilized microtubules, similar to paclitaxel, and some of them were safe and well-tolerated up to a 300 mg/kg oral dose in Swiss albino mice. Finally, [PERSON] and co-authors [107] described good anticancer activity against MDA-MB-231 by four new carbohydrate-based benzo[f]indole-4,9-dione derivatives (**21-24**). These compounds increased ROS, producing DNA damage and G2/M cell cycle arrest. Additionally, the four derivatives cleaved caspases 3 and 9 plus activate the Bax/Bcl-2 pathway in vitro, thereby triggering the intrinsic apoptotic pathway.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Structure** & **Compound** & **Ref** \\ \hline
**N** & **N** & **N** \\
**Br** & **10** & **10** \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._
# Cras: Curriculum Regularization and Adaptive Semi-Supervised Learning with Noisy Labels
###### Abstract
This approach avoids overfitting to noisy labels and improves the generalization performance. However, the accuracy of dividing the training data into clean and noisy labeled sets in DivideMix is highly sensitive to the performance of the network. If the dataset contains a large proportion of noisy labels, the divided training data are biased toward a set of unlabeled samples, and the number of labeled samples is small. As a result, the network overfits to the labels, and the generalization performance is poor. In this paper, we propose the curriculum regularization and adaptive SSL (CRAS) method, which is designed to address the challenges associated with LNL. CRAS incorporates two key components: (1) a warm-up phase using curriculum regularization (CR), which trains the network with robust regularization before the training data are divided, and (2) adaptive weighted loss (AWL) within SSL, which controls the strength of regularization by adaptive loss weights assigned to data bias that vary with the split at each training epoch. The method sets targets similarly to ELR+ and applies thresholds to ensure regularization using only reliable targets, with the thresholds automatically determined according to the learning difficulty of each class. CRAS offers a generally applicable solution that can be integrated with various methods that combine sample selection with SSL; it has demonstrated a promising performance on standard benchmarks and real-world datasets. The contributions of this paper are organized as follows:  
* Proposal of CR: A robust warm-up method for handling noisy labels, which uses only reliable targets for regularization. The proposed CR builds on ELR+ and functions as a potent warm-up technique specifically tailored to noisy labels. In comparison with ELR+, this method offers an enhanced confidence during the warm-up phase for the hard samples, which in turn improves the model's overall performance. In CRAS, the model trained by CR is used for sample selection. This differs from existing methods, which use the model trained by cross-entropy. * Development of adaptive SSL: AWL replaces the weights of the unsupervised loss used in existing combined sample selection and semi-supervised learning methodswith the weights that we designed. This approach achieves SSL, which is highly robust to noisy labels by monitoring the bias of training data at each epoch, which has not previously been considered, and by adjusting the weights of unlabeled losses with an AWL. * State-of-the-art performance: CRAS has demonstrated exceptional results on image classification using standard benchmark image classification datasets, including CIFAR-10 and CIFAR-100 [26], and the real-world datasets mini-WebVision [25] and Clothing1M [2]. The remainder of this paper is organized as follows. Section 2 provides a review of related work on deep learning with noisy labels. Section 3 introduces our proposed CRAS method. Experimental results are discussed in Section 4, followed by the conclusions in Section 5.
# Advances in Riblets Design
## 5 Computational Efforts
In the design conditions, the authors achieved a 5% reduction in drag. However, the reduction in off-design conditions was less pronounced. When considering the primary performance parameters for UAV missions, the results demonstrated a 7% improvement in endurance or a 5% enhancement in payload capacity. A summary of numerical simulations is tabulated in the Table 4. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference/Year** & **Geometry** & **Method** & **Solver** & **Result and Discussion** \\ \hline [PERSON] et al. [78] & Flat plate, triangular & DNS & In-house code & Re = 4200, Different triangular geometries \(s^{+}\approx\) 20 drag reduction: 5–6\% \(s^{+}\approx\) 40 drag increased & Re = 4200, Different triangular geometries \(s^{+}\approx\) 20 drag reduction: 5–6\% \(s^{+}\approx\) 40 drag increased & Re = 4200, Different triangular geometries \(s^{+}\approx\) 20 drag reduction: 5–6\% \(s^{+}\approx\) 40 drag increased & Re = 3500 \\ [PERSON] et al. [128] & Flat plate, triangular, semi-circular, bladed & RANS & Two-equation eddy viscosity model & 10,000 \(<\) Re \(<\) 15,000 Drag reduction achieved for \(h^{+}\sim\) 10 Little drag reduction in \(h^{+}>\) 15 \\ [PERSON] et al. [79] & Flat plate, triangular & DNS & In-house code incompressible Newtonian fluid governed by NS & Re = 3500 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Numerical studies of riblets in aviation. Figure 9: NLF wing body, computed optimal riblet height, (**a**) upper side and (**b**) lower side [69]. Conducting comprehensive numerical simulations to determine optimal riblet configurations prior to their implementation is essential. These simulations encompass integrating aerodynamic properties over the aircraft surface. Notably, the optimal shapes and sizes of riblets might differ between commercial aircraft and UAVs due to the distinct operational requirements and aerodynamic characteristics of each aircraft type. This approach ensures that riblet designs are tailored to specific aircraft categories, maximizing their performance benefits and adaptability across diverse applications [96].
## 3 Prism Pair CEP Shifter
### Comparison between Different CEP Shifters  
As different CEP shifters based on various physical phenomena can be found in the literature, it is interesting to compare their performances with the EO CEP shifters. In order to do so, we have restricted ourselves to five systems. These are the AOPDF [4, 15], the grating compressor [11, 12], the glass wedges system [9, 10], the \(4\mathrm{f}+\mathrm{LCD}\) system [16] and the lens/rotating grating system [27]. Mainly, we chose to compare the induced group delay, \(\Delta\tau_{g}\), and the induced GDD of each system and added, when significant, specific data, such as the mechanical displacement for the grating compressor and the electric field for the EO devices. Table 1 gives these parameters, when required, for all the systems, for a CEP phase shift of \(\pi\) radians at a wavelength of 800 nm. In the case of the EO CEP shifters, the single pass configuration and the double pass two-wedged crystal isochronous configuration are selected for comparison and a single pass propagation length, \(L=40\)mm, chosen in RTP for the central wavelength rays. This corresponds to RTP crystal lengths, which are commercially available and that lead to relevant CEP shift at moderate values of the electric field. 1,200 grooves/mm gratings were considered at 37\({}^{\circ}\) incidence for the grating compressor. Concerning the glass wedge system, the basic configuration described in [10] is considered, with a displacement of two silica wedges perpendicular to the beam axis to vary the thickness of silica. We also give an estimate of the correction bandwidth, defined as the inverse of the time needed to change from one CEP value to another.  
It is to be noted that the above bandwidths concerning the grating compressor, the glass wedges and the 4f\(\vdash\)LCD systems are only to be considered as \"typical values\". In practice, the effective bandwidth depends on the specific configuration, and the main result to be kept in mind is that their response time is considerably lower than those of the Dazzler or the EO CEP shifter, for example.
## 4 Experimental Setup and Evaluation
### Data Visualization and Collection  
The data required for simulation, such as the location of existing 4G sites and cost price based on the global tender in Nepal, are collected from real data from one of Nepal's telecommunication service providers. The assumptions are made for simulation suitable for the real case study field.
# Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition
###### Abstract  
Multimodal signal analysis based on sophisticated sensors, efficient communication systems and fast parallel processing methods has a rapidly increasing range of multidisciplinary applications. The present paper is devoted to pattern recognition, machine learning, and the analysis of sleep stages in the detection of sleep disorders using polysomnography (PSG) data, including electroencephalography (EEG), breathing (Flow), and electro-oculogram (EOG) signals. The proposed method is based on the classification of selected features by a neural network system with sigmoidal and softmax transfer functions using Bayesian methods for the evaluation of the probabilities of the separate classes. The application is devoted to the analysis of the sleep stages of 184 individuals with different diagnoses, using EEG and further PSG signals. Data analysis points to an average increase of the length of the Wake stage by 2.7% per 10 years and a decrease of the length of the Rapid Eye Movement (REM) stages by 0.8% per 10 years. The mean classification accuracy for given sets of records and single EEG and multimodal features is 88.7% ( standard deviation, STD: 2.1) and 89.6% (STD:1.9), respectively. The proposed methods enable the use of adaptive learning processes for the detection and classification of health disorders based on prior specialist experience and man-machine interaction.  
multimodal signal analysis; pattern recognition; machine learning; computational intelligence; polysomnography; sleep stage classification Article Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition