esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Healthcare Network Management: A Comprehensive Review
## 6 Architecture Implementation Plan
### Low-Power Low-Range Wireless Networks  
WBAN elements are best configured with IEEE 802.15.6, while other IoT devices can be configured with IEEE 801.15.4. IEEE 801.15.4 provides a more extended range compared with IEEE 802.15.6. Implanted sensors can only be configured as relayed devices that should not relay any packets from other sensors [12]. Wearable sensors might be configured as relayed or relaying devices. The PDA (or hub, as defined by IEEE 802.15.6) should be positioned in a place that supports star topology.  
Given the anticipated large number of monitored and managed smart healthcare users, the Personal Digital Assistant (PDA) is optimally configured as a software-defined networking (SDN) device, which is programmed by an SDN controller hosted by a third-party provider. Traffic flows within smart healthcare systems can be categorized into three main types: physiological data, environmental data, and sensor health data.  
Physiological or vital sign data can be further divided into periodic physiological data and emergency physiological data [105]. Environmental data, on the other hand, may be collected from indoor or outdoor settings. The PDA is responsible for classifying and directing the traffic flows generated by various sensors to their designated destinations. Figure 8 illustrates the traffic flow from sensors to the PDA and onward to the respective end systems.  
Figure 7: Smart health system architecture implementation plan supported by software-defined networking and deep learning.
## 3 Results and Discussion
### Electrical Conductivity of the Fabricated CFRP  
The out-of-plane electrical conductivity on both through thickness and volume direction (Figure 14) was calculated utilizing Equation (6), as described at Section 2.7. The calculated conductivity values are quoted in Table 3 for both reference and PCF\({}_{4.25\,\mathrm{W}}\) laminates. There was an increase of about 43% on the out of plane volume electrical conductivity of the irradiated sample, exhibiting a value of \(4.9\times 10^{-2}\) S/cm when compared to the reference sample, which had a conductivity of \(3.4\times 10^{-2}\) S/cm. When comparing the out-of-plane thickness conductivity of the laminates, an enhancement of 18% was observed on PCF\({}_{4.25\,\mathrm{W}}\) sample. The improvement on the electrical conductivity on both thickness and volume directions can be attributed to the improved conduction paths afforded by the enhanced surface graphitic structure on the surface of the fibers and interlaminar region of the composite structure. It should be noted that the results are in good agreement with the enhancement in the electrical conductivity of the TCF and PCF CFs (Figure 10, Table 2)  
Figure 13: Fractography analysis of the reference (**a,b**) and PCF\({}_{4.25\,\mathrm{W}}\) (**c,d**) samples after Mode I test.
## Appendix A Derivation of the Hamiltonian Formalism
### Hamiltonian Formalism  
The Hamiltonian formalism allows representing the relative dynamics of satellites in close proximity in terms of the Hamiltonian variables \(\mathbf{q}=\boldsymbol{\rho}\) and \(\mathbf{p}=\frac{\partial\mathcal{L}_{r}}{\partial\mathbf{v}^{2}}\), explained by Equations (4). The Hamiltonian function is obtained by applying the Legendre transformation to the Lagrangian  
\[H=\dot{\boldsymbol{\rho}}^{T}\mathbf{p}-\mathcal{L}_{r}\] (A22)  
leading to  
\[H=H^{(0)}(\mathbf{q},\mathbf{p})+H^{(k)}\Big{(}\mathbf{q},\mathbf{p},J_{n}^{k }\Big{)}\] (A23)  
where  
\[H^{(0)}=\frac{1}{2}(p_{x}+ny)^{2}+\frac{1}{2}\big{(}p_{y}-\bar{a}n-nx\big{)}^{2 }+\frac{p_{z}^{2}}{2}-\frac{3}{2}n^{2}x^{2}+n^{2}\frac{z^{2}}{2}\] (A24)  
\[H^{(k)}=-\mathbf{v}^{(1)}\cdot\mathbf{q}+f\Big{(}\mathbf{q},J_{n}^{k}\Big{)}\] (A25)where, as for Equation (A21), the terms depending on the zonal harmonic coefficients are collected in \(f\Big{(}\mathbf{q},f_{n}^{k}\Big{)}\).  
To improve the numerical processing, Equations (A23)-(A25) are converted to a dimensionless form  
\[\begin{cases}\mathbf{\dot{q}}=\frac{\mathbf{q}}{DU}\\ \mathbf{\dot{p}}=\mathbf{p}\frac{TU}{DU}\end{cases}\] (A26)  
where \(DU=\tilde{a}\) and \(TU=1/n\) are the units of distance and time.  
\[H^{(0)}=\frac{1}{2}(\hat{p}_{x}+\hat{y})^{2}+\frac{1}{2}\big{(}\hat{p}_{y}-1- \hat{x}\big{)}^{2}+\frac{\hat{p}_{z}^{2}}{2}-\frac{3}{2}\hat{x}^{2}+\frac{\hat{ x}^{2}}{2}\] (A27)  
\[H^{(k)}=-\mathbf{\dot{\phi}}^{(1)}\cdot\mathbf{\dot{q}}+\tilde{f}\Big{(} \mathbf{\dot{q}},f_{n}^{k}\Big{)}\] (A28)  
Finally, considering the results of the scale analysis in Appendix A.2, the expression of \(H^{(k)}\) can be rearranged as the sum of two terms, \(H^{(1)}\) and \(H^{(2)}\), collecting, respectively, the zonal coefficients \(J_{n}\) and \(J_{n}^{2}\). The complete expression of \(H\) in dimensionless form is given by Equations (5)-(8), where the _hat_ sign was discarded to ease the notation.
# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 3 Game Theory-Based Routing Protocols in WSNs
### Routing Protocols Based on Noncooperative Games
#### 3.2.1 Resource Allocation Strategy for Cluster-based WSNs
Technical strengths:  
* Development of an energy consumption model with resource allocation scheme;
* The coalition formation game model can be integrated with the traditional protocols like AODV and DSR for a better performance. Technical weaknesses:
* The energy consumption model is incomplete. It was based on the data transmission of the CMs. The energy consumed by the sink node and CHs were ignored. Applications:
* Application cannot be specified owing to the incomplete protocol. Probable future improvements:
* The data transmission phase between the CHs and sink node should be included;
* The energy consumption for data reception can be included in the energy consumption model;
* A performance comparison with the existing state-of-art protocols can be added.
# ME-YOLO: Improved YOLOv5 for Detecting Medical Personal Protective Equipment
## 4 Experiments and Results
### Comparison of Mainstream Object Detection Models  
To verify the performance of this algorithm for medical personal protective equipment detection, nine other network structures--SSD, RetinaNet, CenterNet, YOLOv3, YOLOv4, YOLOv4-tiny, YOLOv5s, YOLOv5m, and YOLOv5l--were selected for comparative experiments. In this experiment, the mAP, F1-score, parameters, and FPS (frames per second) were used as the evaluation metrics for the above detection algorithm. The comparative results for performance metrics achieved by the ME-YOLO algorithm and its counterparts are shown in Table 4 and Figure 11.  
To further verify the suitability of this algorithm for edge computing devices, we deployed YOLOv3, YOLOv4, YOLOv5s, YOLOv5m, YOLOv5l, and ME-YOLO on the Nvidia Jetson TX2 platform, using FPS as the evaluation metric; the results are shown in Table 5. It can be observed that the ME-YOLO algorithm achieves 42 FPS, meaning that it can be used for actual medical personal protective equipment detection tasks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Backbone** & **mAP (\%)** & **F1** & **Parameters (M)** & **FPS (Frame-s 1)** \\ \hline SSD & VGG16 & 80.1 & 75 & 90.6 & 30 \\ RetinaNet & ResNet50 & 76.0 & 58 & 138.9 & 27 \\ CenterNet & ResNet50 & 77.4 & 67 & 124.0 & 71 \\ YOLOv3 & Darknet-53 & 90.5 & 86 & 234.7 & 25 \\ YOLOv4 & CSPDarknet53 & 92.1 & 88 & 244.0 & 22 \\ YOLOv4-tiny & CSPDarknet53 & 87.8 & 83 & 22.6 & 67 \\ YOLOv5s & CSPDarknet53 & 94.2 & 93 & 7.0 & 56 \\ YOLOv5m & CSPDarknet53 & 95.4 & 94 & 21.2 & 47 \\ YOLOv5l & CSPDarknet53 & 96.1 & 94 & 46.5 & 32 \\ ME-YOLO & - & 97.2 & 96 & 7.5 & 53 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of mainstream object detection models.  
Figure 11: AP for each model.  
Figure 12 visualizes the detection results of YOLOv5s and ME-YOLO. For dense objects, small-scale objects, and occluded objects, ME-YOLO is superior to the YOLOv5s algorithm. As can be observed from the first column of images, both ME-YOLO and YOLOv5s could detect all objects. As can be observed from the second column of images, ME-YOLO could detect all objects, but YOLOv5s mistakenly detected the scarf as a glove. As can be observed from the third column of images, ME-YOLO could detect all objects, but YOLOv5s neglected a glove. In the last column of images, YOLOv5s produced missed and false detections when the objects were severely obscured and overlapped, while ME-YOLO produced accurate detection.
# Organic Crystals for THz Photonics
## 6 Conclusions and Outlook  
Organic NLO crystals have attracted a considerable interest within the fast-growing field of THz photonics. In this paper, we reviewed their unique properties that allow on the one hand for achieving state-of-the-art highly-intense THz fields and on the other hand can cover an ultrabroad THz frequency range of beyond 20 THz. These achievements give organic NLO sources an important advantage for the exciting fundamental and applied studies of THz light-matter interactions. We presented the basic material properties and requirements for optimized organic NLO crystals for THz photonics. These materials are composed of highly asymmetric electron-rich molecules with an ultra-fast response to the applied field, packed in a non-centrosymmetric crystalline lattice. Organic NLO crystals feature high long-term temporal as well as the required photochemical stability for high-optical-power applications. Few organic NLO crystals with record-high optical nonlinearities can be successfully grown and can be prepared with sufficient size, orientation, and optical quality for THz photonics applications. The development of novel materials is however being continued and is highly motivated by the wish of high THz power over a broad THz range without any gaps, which still present a challenge due to intrinsic phonon-mode resonances of NLO generation materials themselves. There is also a wish to increase the variety of organic NLO crystals optimized for certain available pump laser sources or compact high-power laser sources being currently developed, considering the optimal pump wavelength range and the desired THz frequency range. In parallel to the development of novel organic NLO crystals, there has been a significant progress in record high-power and ultra-broadband THz sources reported during the last few years. Organic crystalline THz sources became important in the development of the recently emerged field of nonlinear THz photonics, studying new fundamental aspects of matter enabled by intense THz waves. We also presented several examples of conventional THz spectroscopy and THz imaging based on organic crystalline THz sources, which show great potential for various scientific and industrial applications. We can therefore expect exciting further progress in research and applications of organic NLO crystals for THz photonics in the near future.  
Coordination, M.J.; Writing and proofreading, M.J., U.P., A.A. and A.Z.  
We acknowledge the support by the Swiss National Science Foundation (SNSF) Grant No. IZKSZ2_162129, InnoSuisse Grant No. 19281.2 PFNM-NM and the Slovenian Research Agency Grant P2-0348.  
The authors declare no conflict of interest.
## 3 Results
### Combinations of the Reference and Repurposed Drugs
#### 3.5.3 Synergistic Effect of Combination  
Using the CompuSyn program, the synergistic effect was measured for the combined drugs. The combination index (CI) was calculated using the Chou-Talalay method [28]. The results of the effects of combinations are represented in Table 2. A combination of paclitaxel + benztropine demonstrated synergism (CI \(<\) 1) in both combinations tested (Figure 19). The lowest and highest doses of benztropine produced the following effects:  
Figure 17: The effects of paclitaxel and benztropine (alone and in combination) on the cell viability (% of control) of MCF-7 cells at 48 h. Results are expressed as mean \(\pm\) standard error of the mean (SEM) and represent four independent experiments (\(n=4\)). ***** _p_\(<\) 0.0001.  
0.7434 and 0.915, respectively. The combination of paclitaxel and thioridazine resulted in a synergistic (CI \(<\) 1) and antagonistic effect (CI \(>\) 1). The combination with the lowest thioridazine concentration, corresponding to its IC50, resulted in a synergistic effect. Using thioridazine at a 100 uM concentration resulted in an antagonistic effect. By applying these different concentration values for thioridazine (6.87 and 100 uM), we generated similar effects, 0.7851 and 0.8681.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dose of Paclitaxel** & **Dose of Benztropine** & **Dose of Thioridazine** & **Effect** & **CI** \\ \hline
8.5 nM & 15.84 uM & & 0.7434 & 0.73380 \\ \hline
8.5 nM & 100 uM & & 0.915 & 0.86588 \\ \hline
8.5 nM & & 6.87 uM & 0.7851 & 0.56480
## 3 Method
### FP-Growth Algorithm
#### 3.2.1 Basic Concept  
The fundamental concepts utilized in the FP-Growth algorithm are defined as follows:  
1. Itemset \(Q\) is a collection of items \(Q_{i}\), which is defined as \[Q=Q_{F}\cup Q_{R}=\{Q_{1},\;Q_{2},\;\cdots,\;Q_{m}\}\] (4) where \(Q_{F}\) is the front itemset and \(Q_{R}\) is the rear itemset, with \(Q_{F}\
eq\varnothing\) and \(Q_{R}\
eq\varnothing\). There is no intersection between \(Q_{F}\) and \(Q_{R}\), namely, \(Q_{F}\cap Q_{R}=\varnothing\). For the landslide deformation response analysis, \(Q_{F}\) and \(Q_{R}\) represent the triggering factors and the deformation events, respectively.  
2. Transaction database \(P\) consists of task-related transaction items, with \(P=\{P_{1}\), \(P_{2}\),..., \(P_{n}\}\), \(P_{k}\subseteq Q(1\leq k\leq n)\). Each transaction \(P_{k}\) is a nonempty itemset, containing at least one front item and one rear item. 3. The antecedent item \(X\) is the nonempty proper subsets of \(Q_{F}\), and the consequent item \(Y\) is the nonempty proper subsets of \(Q_{R}\). The association rule (AR) represents a correlation between items, like AR \(X\Rightarrow Y\). \(X\) is regarded as the condition of AR, while \(Y\) is regarded as the conclusion of AR. It means that if item \(X\) occurs, item \(Y\) will also occur with a certain probability. 4. Support refers to the probability of items \(X\) and \(Y\) appearing simultaneously in the collection of transaction database \(P\), namely, the proportion of transactions containing both \(X\) and \(Y\) to the total transactions in the database, which is defined as follows: \[Supp(X\Rightarrow Y)=\frac{P(X\cap Y)}{P}\] (5) 5. Confidence indicates the occurrence probability of \(Y\), assuming that \(X\) has already occurred, which is defined as follows: \[Conf(X\Rightarrow Y)=\frac{P(X\cap Y)}{P(X)}\] (6) 6. Lift denotes the degree to which the \(Y\) is affected by the appearance of \(X\) in the collection of transaction database \(P\). The appearance of \(X\) promotes the Y when the value of lift is greater than one. The expression is as follows: \[Lift(X\Rightarrow Y)=\frac{P(X\cup Y)}{P(X)}/\frac{P(Y)}{P}\] (7)  
The corresponding support, confidence, and lift are required for each association rule, which is utilized to determine the validation of the association rules. If the candidate association rule CAR \(X\Rightarrow Y\) satisfies the threshold of minimum support, indicating that CAR \(X\Rightarrow Y\) is a frequent pattern. The CAR \(X\Rightarrow Y\) is referred to as the strong association rule (SAR) if it satisfies the threshold of minimum support and the threshold of minimum confidence simultaneously, and the lift should be greater than one.
# Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset
## 4 Conclusions  
Creating object detectors of a particular class in digital images requires the development of an appropriate learning dataset. The application of a limited learning dataset is possible for image sequences in which there is a moderate, highly predictable variability of imaging conditions (lighting conditions, background variability, scene composition, etc.) in which the visual material is recorded. In the studied cases, a diagnostic flight along high-voltage lines in non-urbanized areas was implemented, and known deep neural network architectures such as You Only Look Once (YOLO), Single- Shot MultiBox Detector (SSD), Faster R-CNN, and R-FCN were used to develop the object detector.  
This article presented the comparison results for the detection of an object of the \"power insulator\" class while maintaining a limited training dataset consisting of five, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, and 60 frames of visual material. The research aimed to assess the influence of the size of training dataset on the achieved efficiency for various deep neural network architectures. For the evaluation of efficiency, three IoU threshold values (at the level of 0.25, 0.5, and 0.75) were used to assess the quality of the obtained detections. The best results were reached for two networks, Faster R-CNN and R-FCN. Faster R-CNN received the highest AP at the level of 0.8 for 60 training frames. During the study, it was demonstrated that the number of training data directly translated into the results obtained by the trained detector. The R-FCN model got a poorer AP result; however, it can be observed that the relationship between the number of input samples and the obtained results had a much smaller impact than in the case of the other networks, which, in the authors' opinion, is a desirable feature in the case of a limited input dataset.  
The main contribution of the work is the evidence that a limited training set (in our case, just 60 training frames) could be used for object detection, assuming an outdoor scenario with well-defined conditions. The decision of which network will generate the best result for such a limited training set is not a trivial task. In contrast to a previous study carried out [24], in our case, the Faster R-CNN network obtained a better AP result for the same limited training sample, which suggests that deep neural networks will achieve different levels of effectiveness depending on the amount of training data. As part of the in-depth study, additional network tuning was conducted, considering the results gained after the first stage of training. For the two analyzed networks (Faster R-CNN and R-FCN), in each of the three cases (15, 30, and 60 frames), the precision parameter improved while the recall parameter deteriorated.
## 2 Description of Chemicals, Brine, and Crude Oil
### Add-On Chemical: Nonionic Surfactant Versus Heavy Alcohol  
This paper compared the effects of the addition of a nonionic surfactant in ASP flooding to those of adding heavy alcohol. We selected polyoxyethylene polyoxypropylene alkyl ether (C\({}_{12}\) 60 PO:40 EO) as the nonionic surfactant, since it is neutral and does not result in significant adsorption. This nonionic surfactant, referred to as LB-95, is an (EO-PO) alkyl ether type in which a PO chain has been introduced between the EO chains. In addition, the alkyl chain of the EO type surfactant has been used as a solubilizer because it displayed excellent emulsification and solubilization for various oily substances with different polarities. As the reference material, a heavy alcohol (alcohol with larger than three carbon atoms [35]) was chosen. Iso-butanol (IBA; C\({}_{4}\)H\({}_{10}\)O) is an aliphatic alcohol that is used as a solvent in chemical reactions and as a starting material for organic synthesis. Table 2 summarizes the chemicals used in the experiments, and Figure 1 describes their chemical structures. The base mixture consisted of 0.25 wt% 1:4 (weight ratio; wt/wt) of LAS and DOSS anionic surfactant pair, 0.2 wt% Na\({}_{2}\)CO\({}_{3}\) as the alkaline, and 0.15 wt% HPAM as the polymer. The proposed formulation added 0.2 wt% of nonionic surfactant (LB-95), while the comparison added the same amount of IBA instead of LB-95.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ASP Element** & **Name** & \begin{tabular}{c} **Trade Name** \\ **(Abbreviation)** \\ \end{tabular} & **Concentration (wt\%)** \\ \hline Alkaline & \begin{tabular}{c} Sodium carbonate \\ (Na\({}_{2}\)CO\({}_{3}\)) \\ \end{tabular} & \begin{tabular}{c} Sodium carbonate \\ (Na\({}_{2}\)CO\({}_{3}\)) \\ \end{tabular} & 0.2 \\ \hline Polymer & \begin{tabular}{c} Hydrolyzed \\ Polyacrylamide \\ \end{tabular} & A-430 PB (HPAM) & 0.15 \\ \hline \begin{tabular}{c} Anionic surfactant pair \\ \end{tabular} & \begin{tabular}{c} Linear alkylbenzene \\ sulfonate (C\({}_{11}\)–C\({}_{13}\)) \\ \end{tabular} & ASCO96 (LAS) & 0.05 \\ \hline \begin{tabular}{c} Dioctyl sulfosuccinate \\ (C\({}_{8}\)–C\({}_{8}\)) \\ \end{tabular} & ASCODOSS (DOSS) & 0.2 \\ \hline \begin{tabular}{c} Nonionic surfactant \\ (control group) \\ \end{tabular} & \begin{tabular}{c} Polyoxyethylene \\ polyoxypropylene alkyl \\ ether (C\({}_{12}\) 60 PO:40 EO) \\ \end{tabular} & \begin{tabular}{c} Hannong Koremul-LB-95 \\ (LB-95) \\ \end{tabular} & 0.2 \\ \hline \
\begin{tabular}{c} Heavy alcohol \\ (comparison group) \\ \end{tabular} &
\begin{tabular}{c} Isobutyl alcohol \\ (C\({}_{4}\)H\({}_{10}\)O) \\ \end{tabular} & Iso-butanol (IBA) & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the chemical materials as the alkaline–surfactant–polymer (ASP) components used in this study. LAS: linear alkylbenzene sulfonate; DOSS: dioctyl sulfosuccinate; HPAM: hydrolyzed polyacrylamide.  
Figure 1: Chemical structures: (**a**) LAS; (**b**) DOSS; (**c**) LB-95; (**d**) IBA.
## 4 Discussion  
The results of various methodologies proposed in the literature that study the automatic classification of multiple faults that can occur in an electromechanical system using IRT are shown in Table 7. The methods used are described, as well as the precision that each methodology has for automatic classification [17, 18, 19, 20]. It has been observed that precision values above 90% are reported for the classification of multiple faults in electromechanical systems. But most of these methodologies only focus on studying a single element of the electromechanical system, such as the IM, focusing the study on failures, such as stator windings, damaged bearings, and broken rotor bars, among others, or on the kinematic chain for imbalance and mechanical misalignment. Nevertheless, they do not take into account another element of the electromechanical system, such as the GB; since it is an element that is subjected to mechanical stress, it can present wear on the gear teeth. In this sense, this research focuses on the study of multi-faults in an electromechanical system, analyzing both the IM and GB. Although the results presented in the different methodologies are notable, the proposal of new methodologies based on CNN is of great interest in solving this type of task. In this sense, the proposed methodology analyzed different conditions in the IM such as its healthy state, failures due to a broken bar, outer bearings, and mechanical misalignment, as well as the conditions in the GB, analyzing it in a healthy state and with the wear of the gear teeth at 50% and 75%, obtaining an accuracy of 98.5% for the classification of multi-faults on the electromechanical system. The most effective method for accurately identifying these faults is through the fusion of different variables, such as current and vibration signals or vibration and stray flux [7, 8, 9].