esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Garlic Landraces from Foggia Province (Puglia Region; Italy)
## 3 Results and Discussion
### Volatile Compounds  
The volatile compound concentration in the studied garlic genotypes is reported in Table 4.  
The precursors of the volatile organic S-compounds in garlic are \(\gamma\)-glutamyl cysteine and cysteine sulfoxides, including deoxyallin and mainly allin (S-allyl-cysteine-S-oxide) [4], an odourless derivative of cysteine. This latter is enzymatically hydrolyzed (allinase or allin lyase, E.C.4.4.1.4) into a mixture of both volatile and non-volatile S-containing compounds, after the breakage of the tissue caused by cutting, mastication, and cooking [31]. The volatile S-containing compounds include thiosulfphinates, which are very unstable and are transformed into compounds belonging to Sulphides-di-allyl-sulphide (DAS), di-allyl-disulphide (DADS), di-allyl-trisulphide (DATS) or into compounds belonging to 'Vinyldithiins' (cyclic sulfur-containing compounds) or into others [31]. All of them are responsible for the typical flavour of garlic and the protective effects against cardiovascular diseases [1; 2; 32]. In the current study, the main fraction of volatile substances was the S-compounds (94% for landraces; 79% for the CG), with the non-sulphur compounds (including aldehydes, and 'others') representing only a minor fraction (6% for landraces; 21% for the CG). Among the examined genotypes, a total of 28 compounds were identified in the volatile fraction, 16 of them were S-compounds, while the other compounds were hydrocarbons, alcohols, and ethers (Figure S2). The total volatile concentration in CG bulbs was distinctively higher than those of landraces (15.0 vs. 2.2 mg kg\({}^{-1}\) dw, on average), mainly due to the amount of S-compounds (37.8 vs. 2.1 mg kg\({}^{-1}\) dw, on average). Among the non-sulphur volatile compounds, a lower concentration of total aldehydes (0.04 mg kg\({}^{-1}\) dw, on average) and 'other' compounds (0.10 mg kg\({}^{-1}\) dw, on average) was found in the examined landraces. In particular, the landraces were completely devoid of two out of four of the determined aldehydes--'2-butenal-2-methyl' and '4-heptenal'. Di-methyl-sulphide, trans-propenyl-methyl-di-sulphide, mercaptoacetic acid, 3-vinyl-4H-1, 2-dithin, and Allicin (S-oxo-di-allyl-di-sulphide) (among the S-compounds), and 4-heptenal-enantic aldehyde (among aldehydes), were detected in very low amounts (<0.01 unit). Except for the 1-propanethiol S-compound, the genotype distinctively affected the concentration and the profile of volatile compounds.
## 5 Results and Discussion
### Transmission Rate  
The quantity of information that may be sent through a data terminal or a communication medium within a certain amount of time is referred to as the data transmission rate. Linear and nonlinear distortions impact the transmission rate in UVLC systems. A communication indication is a visual or audible cue that conveys a person's status or level of communication readiness. It enables others to ascertain whether anyone has access to communication or if they are unavailable or otherwise engaged. In order to enhance successful communication and prevent needless disruptions, communication indicators are frequently employed in a variety of situations, including workplaces, businesses, and internet platforms. The transmission rate may be increased by removing the distortion. Table 4 shows the comparison of the transmission rate (%). Figure 8 displays the transmission rates of the proposed and existing methods. It demonstrates that the suggested approach has a 99% higher transmission rate. The transmission rate can be calculated as  
\[\textit{Transmission Rate}=\textit{Channel Capacity}\;*\;(1-Error\;Rate) \tag{16}\]  
The data rate, which may be transmitted at its highest rate via a given communication channel, is known as channel capacity. It is dependent on the channel's \(SNR\), bandwidth, and other characteristics. The probability of a transmission error is known as the error rate and is normally expressed as a decimal value ranging from zero to one.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Source** & **Computational Complexity (\%)** \\ \hline APNN [33] & 89 \\ TFCNN [34] & 65 \\ DSANet [35] & 83 \\ CE-LSTM [36] & 75 \\ AFL-DLE [Proposed] & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of computational complexity (%).  
Figure 7: The computational complexity of the proposed and existing techniques, namely APNN [33], TFCNN [34], DSANet [35] and CE-LSTM [36].
# Compressive Sensing-Based IoT Applications: A Review
## 3 Compressive Sensing Overview
### Reconstruction Algorithms
#### 3.3.1 Convex Optimization  
A straightforward approach to recover a sparse vector \(\mathbf{x}\) is to solve the \(\ell 0\) minimization problem:  
\[\hat{\mathbf{x}}=arg\min\|\mathbf{x}\|_{0}\quad subject\quad to\quad\mathbf{y}=\mathbf{\Phi} \mathbf{x} \tag{2}\]  
However, the \(\ell 0\) minimization problem is considered as an NP-hard for large scale matrix [39], i.e., solving Equation (2) for any large matrix \(\mathbf{\Phi}\) is necessarily computationally intractable. Therefore, convex relaxation to Equation (2) has been considered to overcome the drawback of \(\ell 0\), leading to \(\ell 1\)-minimization.  
The \(\ell 1\)-minimization approach, known as basis pursuit (BP) [40], considers the following solution:  
\[\hat{\mathbf{x}}=arg\min\|\mathbf{x}\|_{1}\quad subject\quad to\quad\mathbf{y}=\mathbf{\Phi} \mathbf{x} \tag{3}\]A noisy acquisition model adapts itself by considering the basis pursuit denoising (BPDN) solution [41]:  
\[\hat{\mathbf{x}}=arg\min\|\mathbf{x}\|_{1}\quad\textit{subject}\quad\textit{to}\quad \|\mathbf{y}-\mathbf{\Phi}\mathbf{x}\|_{2}\leq\epsilon \tag{4}\]  
where \(\epsilon\) represents the upper bound on the noise level.  
Further, if there is no knowledge about the noise level, least absolute shrinkage and selection operator (LASSO) approach [42] can be explored:  
\[\min_{\hat{\mathbf{x}}}\frac{1}{2}\|\mathbf{\Phi}\hat{\mathbf{x}}-\mathbf{y}\|_{2}+\gamma\|\hat {\mathbf{x}}\|_{1} \tag{5}\]  
where \(\gamma>0\) represents a tuning parameter for different levels of sparsity.
# Removal of Crotamiton from Reverse Osmosis Concentrate by a TiO\({}_{2}\)/Zeolite Composite Sheet
###### Abstract  
Reverse osmosis (RO) concentrate from wastewater reuse facilities contains concentrated emerging pollutants, such as pharmaceuticals. In this research, a paper-like composite sheet consisting of titanium dioxide (TiO\({}_{2}\)) and zeolite was synthesized, and removal of the antipruritic agent croatmiton from RO concentrate was studied using the TiO\({}_{2}\)/zeolite composite sheet. The RO concentrate was obtained from a pilot-scale municipal secondary effluent reclamation plant. Effective immobilization of the two powders in the sheet made it easy to handle and to separate the photocatalyst and adsorbent from purified water. The TiO\({}_{2}\)/zeolite composite sheet showed excellent performance for croatmiton adsorption without obvious inhibition by other components in the RO concentrate. With ultraviolet irradiation, croatmiton was simultaneously removed through adsorption and photocatalysis. The photocatalytic decomposition of croatmiton in the RO concentrate was significantly inhibited by the water matrix at high initial croatmiton concentrations, whereas rapid decomposition was achieved at low initial croatmiton concentrations. The major degradation intermediates were also adsorbed by the composite sheet. This result provides a promising method of mitigating secondary pollution caused by the harmful intermediates produced during advanced oxidation processes. The cyclic use of the HSZ-385/P25 composite sheet indicated the feasibility of continuously removing croatmiton from RO concentrate.  
paper-like composite sheet; zeolite; photocatalysis; reverse osmosis concentrate; pharmaceutical; inhibitory effect; intermediate
# Numerical Study of the Toughness of Complex Metal Matrix Composite Topologies
###### Abstract  
Fracture toughness tests (compact tension) of a dual material composed of a structured Metal Matrix Composite (MMC) (martensitic steel and titanium carbides, named MS-TiC) surrounded by martensitic steel (MS) are simulated with a Discrete Elements Model (DEM) developed with the GranOO Workbench. The MMC structures are micro-lattices such as gyroid, octet-truss and Face and Body-Centered Cubic with Z-truss (FBCCZ). The volume fraction of these MMC inserts and their cell size are fixed, the influence of the cell orientation is studied. The aim of the study is to determine the configuration of topology (shape and cell orientation) which absorbs the most energy and is the most crack resistant. From experimental tests, the Young's moduli and the failure stresses of the MMC material and the metal are estimated, and thanks to beam network discretization, a local stiffness and a failure criterion are evaluated to finally obtain a crack propagation path. To verify the suitability of the DEM model, a Compact Tension (CT) experimental test on MMC specimens is performed and a stress intensity factor is computed. A good agreement with an error less than 10% is obtained between experimental and simulated \(K_{lc}\) with values respectively equal to 35 and 37 MPa\(\sqrt{\text{m}}\). From DEM simulations based on the CT tests, the FBCCZ cell absorbs the most energy at the crack propagation compared to other structures and the steel. The crack propagation length depends on the shape of the topology. The originality of the study lies in the modeling, with granular properties using DEM, of the mechanical and elastic fracture behavior of these topological structures classically solved by Finite Elements Method (FEM): the microscopic constitutive relations have been validated macroscopically by experimental tests on homogeneous MMC materials.  
f 2020; 10, 6250; doi:10.3390/app10186250
## 4 Discussion
This subsequently delayed fibrosis in diabetic kidney disease and glomerular injury in lupus nephritis [20], and the clinical relevance for the treatment of lupus nephritis has been under investigation using dapagliflozin [24]. Regarding the use of SGLT2 inhibitors to treat acute nephropathy, dapagliflozin, canagliflozin, and empagliflozin have been reported to ameliorate renal fibrosis in hyperuricemic nephropathy and cisplatin-and methotrexate-induced acute nephropathy [21; 22; 35]. The nephoprotective effects of enavogliflozin have not yet been unveiled, but they need to be further investigated based on enavogliflozin's effects of reducing reactive oxygen species and upregulating endothelial nitric oxide synthase expression, along with improving vascular function [28]. The highest but nonlinear kidney distribution of enavogliflozin in this study should be considered to support pharmacokinetic-response relationships of enavogliflozin in relation to nephoprotective effect [36]. Recent studies have reported these cardioprotective effects regardless of the presence of diabetes [20; 28]. The use of SGLT2 inhibitors in T2 DM patients with cardiovascular disease has been reported to decrease major cardiovascular events [37]. The FDA expanded the indication of dapagliflozin to include heart failure with reduced ejection fraction (HF-rEF) or heart failure with preserved ejection fraction (HF-pEF) [38]. Patients who started using empagliflozin experienced reduced cardiovascular mortality or hospitalization [39]. Enavogliflozin is also undergoing a clinical trial to investigate the composite of cardiovascular death or hospitalization for HF-pEF (NCT06027307). Enavogliflozin has been reported to improve cardiovascular function of pig coronary artery endothelial cells (PCAECs) by reducing the production of reactive oxygen species and upregulating the expression of endothelial nitric oxide synthase [28]. Establishing a pharmacokinetic-pharmacodynamic correlation between enavogliflozin pharmacokinetics and biomarkers of cardiovascular function and allometry combined in silico physiologically based approach awaits further investigation to extend the in vivo therapeutic efficacy of enavogliflozin regarding the improvement of cardiovascular function [36]. In this context, the study of the pharmacokinetics of enavogliflozin in the fed state and under repeated doses, tissue distribution information, and mode of action may be important.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 4 Proposed Scheme-I (Key Recovery System Using Signcryption)
### Session Key Exchange and Encryption Phase  
In this phase, the key recovery system uses signcryption to ensure integrity and non-repudiation and performs encryption of the session key simultaneously as shown in Figure 5.  
* **Step 1:**\(Dev_{A}\) selects \(a\in\mathbb{Z}_{p}^{*}\) and calculate partial session key \(PSK_{A}=g^{a}\). \(Dev_{B}\) also selects \(b\in\mathbb{Z}_{p}^{*}\) and calculates partial session key \(PSK_{B}=g^{b}\). After that, \(Dev_{A}\) and \(Dev_{B}\) exchange \(PSK_{A}\) and \(PSK_{B}\) with each other.
* **Step 2:**\(Dev_{A}\) and \(Dev_{B}\) calculate the session key \(SK=(PSK_{B})^{a}=(PSK_{A})^{b}\) using the exchanged values \(PSK_{A}\) and \(PSK_{B}\).
* **Step 3:**\(Dev_{A}\) generates random number \(x\in\mathbb{Z}_{p}^{*}\) and \(k=pk_{KRC}^{x}\ mod\ p\), which is then divided in half into \(k_{1}\) and \(k_{2}\). \[k=(k_{1}\parallel k_{2})\] (5)
* **Step 4:**\(Dev_{A}\) generates \(c,r\) and \(s\) using \(k_{1},k_{2},sk_{A},pk_{A}\) and \(SK\).  
\[c=E_{k_{1}}(SK) \tag{6}\] \[r=H_{k_{2}}(g^{x}\ mod\ p,SK)\] (7) \[s=x/(r+sk_{A})\ mod\ q \tag{8}\]
* **Step 5:**\(Dev_{A}\) divides \(c,r\) and \(s\) to \(c_{i},r_{i}\) and \(s_{i}\). \[c=c_{1}\oplus c_{2}\oplus\ldots\oplus c_{n}\] (9) \[r=r_{1}\oplus r_{2}\oplus\ldots\oplus r_{n}\] (10) \[s=s_{1}\oplus s_{2}\oplus\ldots\oplus s_{n}\] (11) where \(n\) is the number of KRA and \(c_{i}=\{c_{1},\cdots,c_{n}\},r_{i}=\{r_{1},\cdots,r_{n}\},s_{i}=\{s_{1}, \cdots,s_{n}\}\).
###### Abstract  
This study reports data from three clinical studies using the time-resolved diffuse optical spectroscopy (TRS) system among breast cancer patients. The parameters of oxy-hemoglobin (O\({}_{2}\)Hb), deoxy-hemoglobin (HHb), total hemoglobin (tHb), and oxygen saturation (SO\({}_{2}\)) were evaluated using TRS, and its efficacy was tested in three trials. In trial 1, we recruited 118 patients with primary breast cancer to estimate the tumor detection rate. The cumulative detection rate was 62.7%, while that in T stage 0 was 31.3% and in T stage 1 was 44.7%. These were lower than those of T stage 2 (78.9%) and T stage 3 (100%). Next, we used TRS to monitor tumor hemodynamic response to neoadjuvant chemotherapy (\(n=100\)) and found that pathological complete response (pCR) tumors had significantly lower tumor tHb than non-pCR tumors; a similar result was observed in estrogen receptor (ER)-negative tumors, but not in ER-positive tumors. The third trial monitored hemodynamic response to antiangiogenic therapy, bevacizumab (\(n=28\)), and we demonstrated that sequential optical measurement of tumor SO\({}_{2}\) might be useful for detecting acute hypoxia 1-3 days after bevacizumab initiation. Next, response monitoring of neoadjuvant endocrine therapy (\(n=30\)) suggested that changes in tumor tHb during treatment can predict and distinguish between responsive and non-responsive tumors early in letrozole therapy. In conclusion, our results show that hemodynamic monitoring of tumors by TRS could pair the unique features of tumor physiology to drug therapy and contribute to patient-tailored medicine. We recently established a platform for performing TRS in patients with breast cancer.  
breast cancer; diffuse optical spectroscopy; chemotherapy Article
# Bar-Wave Calibration of Acoustic Emission Sensors
## 3 Results and Discussion
### AE Sensor Response
#### 3.2.6 Pico Sensor  
This sensor is a nominally 3.2 mm element diameter, 500 kHz design for broadband applications. Results of bar-wave calibration tests are given in Figure 14. The blue curve is from the present displacement sensitivity test, while the red curve was reanalyzed from the previous test data for Figure 13d in reference [8]. The dashed green curve is the normally incident wave displacement sensitivity (Figure 10b in reference [4]). The dotted blue curve is for the bar-wave velocity calibration. Again, the red curve from the previous study showed a decrease below 120 kHz due to radial resonance effect.  
In this case, a spectral dip may exist at 1.05 MHz, but this seems to be from a scatter in the data. Because the sensor radius is small, aperture null effects are absent. In this Pico sensor, a distinction between the blue and dashed green curves or between the guided-wave response and normally incident wave sensitivity is less pronounced.  
Figure 14: Receiving sensitivity of PAC Pico (3804). The displacement receiving sensitivities to bar waves are shown in solid blue curve for the present work and in red curve from the old data [8] (reproduced with permission from [8]. Copyright AE Group, 2010). Green curve is the displacement receiving sensitivity to normally incident waves [4]. The velocity sensitivity corresponding to the solid blue curve is given in dotted blue curve.  
Figure 13: Receiving sensitivity of PAC S9220. The displacement receiving sensitivities to bar waves are shown in solid blue curve for the present work and in red curve from the old data [8] (reproduced with permission from [8]. Copyright AE Group, 2010). Green curve is the displacement receiving sensitivity to normally incident waves [4]. The velocity sensitivity corresponding to the solid blue curve is given in dotted blue curve.
# Real-Time Maintenance Optimization with Industrial Internet of Things
## 2 Literature Review
Compared to IIoT integration, the novelty is in handling uncertainty through scheduled adjustments, but it lacks real-time data feedback and dynamic optimization provided by IIoT systems. [PERSON] et al. (2022) propose a maintenance optimization policy that involves spare parts ordering and defect prevention [13]. It uses Monte Carlo simulations to determine the best maintenance and spare parts strategies. This model focuses on minimizing costs through effective maintenance decisions, but unlike IIoT-enabled systems, it does not leverage continuous data monitoring or predictive analytics for real-time decision-making. [PERSON] et al. (2022) focus on joint optimization of production scheduling, inventory control, and group preventive maintenance. Using metaheuristics, the model reduces system costs by optimizing intervals and component groupings [14]. The novelty lies in integrating these three elements, but it still does not apply IIoT technologies. [PERSON] et al. (2022) introduce a joint production and maintenance optimization model that considers system reliability and quality-contingent demand [15]. The model aims to minimize expected costs while ensuring quality demands are met. The paper's innovation is in combining maintenance with quality control, but it does not utilize IIoT for real-time state information or predictive maintenance strategies. [PERSON] et al. (2022)integrate maintenance, quality control, and buffer stock management in a single-machine system, using a P control chart [16]. The model addresses machine failures, product quality, and inventory disruptions. The novelty is the integration of these factors into a single model, but it does not leverage IIoT for real-time data collection or optimization. [PERSON] et al. (2022) propose a bi-objective model for joint production planning and multi-level preventive maintenance scheduling, focusing on reliability and due-date satisfaction [17]. It uses metaheuristics to find optimal production and maintenance schedules. The model's strength lies in considering reliability-based decisions, but like the others, it does not incorporate IIoT technologies for adaptive and real-time maintenance. [PERSON] et al. (2022) address a service-oriented optimization strategy that integrates maintenance grouping and technician routing in multi-location systems [18]. This model optimizes technician movements and maintenance operations based on machine degradation. While innovative in integrating maintenance and service operations, it does not utilize IIoT for real-time performance data or predictive analytics. [PERSON] et al. (2021) focus on optimizing maintenance policies for complex systems with intermediate buffers [19]. They propose control limit properties and a stochastic branch-and-bound algorithm for maintenance scheduling. The novelty lies in the use of complex system structures and stochastic optimization, but the model does not use IIoT data for dynamic decision-making.
# Optoelectronic Devices Based on Atomically Thin Transition Metal Dichalcogenides
## 3 Photodetectors
Figure 4b shows \(I\)-\(V\) characteristics of a heterostack device at different back-gate voltages, which show a strong dependence on illumination. The Fermi levels in graphene can be positioned by asymmetric electrostatic or chemical doping of the top and bottom graphene sheets, so that a built-in electric field is produced even without bias (see Figure 4c). The field can further be enhanced by application of an external voltage. Upon illumination, photo-generated carriers are separated in the TMD layer and collected by the graphene electrodes. The photocurrent was mapped by scanning photocurrent measurements and it was verified that the photocurrent was indeed generated across the whole face of the heterojunction. Samples were also prepared on flexible substrates. The measured responsivities were of the order of \(\sim\)0.1 A/W, comparable to state-of-the-art bulk semiconductor flexible membranes [61]. Despite the low out-of-plane mobility in layered TMDs [62], picosecond photoresponse times were measured because of the short carrier travel distance in ultrathin heterostructures [63]. Figure 3: (**a**) Device structure of a hybrid quantum dot/MoS\({}_{2}\) photodetector. Reproduced with permission from [57], Copyright John Wiley and Sons, 2014. (**b**) Schematic drawing of the charge separation at the quantum dot/MoS\({}_{2}\) interface. Reproduced with permission from [57].