esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficient Policies for Data Transmission in Disruption Tolerant Heterogeneous IoT Networks
## 6 Conclusions and Future Plans  
In a large number of IoT applications end nodes suffer intermittent connectivity due to mobility and sparse network coverage. Heterogeneous networks and DTNs have emerged as potential solutions to this problem. In this work we formulated the problem of finding energy efficient packet transmission policies, under the constraint of finite data storage, as a dynamic programming problem and utilized the framework of Markov Decision Processes to derive approximately optimal and suboptimal data transmission policies. Finally, we evaluated numerically the derived policies and showed that significant gains can be achieved by utilizing the derived approximately optimal and suboptimal policies.  
Future research activities include a revision of the proposed scheme to transform it to become more dynamic and adaptive, able to consider also the temporal variation of the network availability map. Additionally, future plans include the design of a variation of the proposed scheme to work with cognitive radio inspired IoT nodes (extending our previous works described in [36; 37] that have the ability to sense the spectrum and extract the network availability map without having to know the map beforehand. Finally, we are also working towards exploiting deep reinforcement learning to solve the problem at hand aiming to learn what is the optimal transmission policy for each scenario.  
Conceptualization: G.S., and E.Z.T.; Methodology: G.S. and A.T.; Analysis and Results: G.S.; Writing: G.S. and E.Z.T.; Review & Editing: E.Z.T. and A.T.; Supervision: A.T. and E.Z.T.; Funding Acquisition: E.Z.T.  
This work has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no 609094 and from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642743.  
The authors declare no conflict of interest.  
Figure 4: Mean percentage error increment in energy consumption for the Empty Backlog, Myopic and Rollout, with one and two policy improvement steps, policies, compared to the OPI policy.
## 3 Results and Discussion
### Alkaloid Amount of Extracts  
Table 2 displays the amount of extract (in mg) derived from a 1 g plant and the concentration of mg alkaloid per 1 g extract. Based on the solvents used, the sequence of alkaloid content in 1 g extract is as follows: methanol > chloroform > water. In a prior study, gas chromatography-mass spectrometry (GC-MS) was utilized to analyze the profiles and percentage compositions of alkaloids extracted with chloroform, methanol, and water. The extracts predominantly contained alkaloids, with non-alkaloid compounds only found in minor amounts. The alkaloid concentrations were ranked as water (98.6%) > methanol (100%) > chloroform (96.1%). All three solvents extracted the same alkaloid components, which include allocryptopine (making up 46.4-91.5% of the content), tetrahydropalmatine (1.5-31.8%), and tetrahydroberberine N-oxide (trans-cannadine-N-oxide) (3.1-21.9%) (Figure 1). The proportions of these alkaloids varied depending on the solvent used [12]. This variation can be attributed to the solvent properties. Organic solvents such as chloroform and methanol, which have lower polarity compared to water, are more efficient at dissolving lipophilic alkaloids [52; 53]. This implies that the solubility of alkaloids in solvents can vary depending on their lipophilicity, which is determined by their octanol-water partitioning coefficient [54]. A study by [PERSON] et al. [55] identified 17 isoquinoline alkaloids in _G. grandiform_ grown in Bulgaria and Algeria. They found allocryptopine as the major alkaloid in both plant sources and highlighted that the production of these alkaloids in _G. grandiform_ might be influenced by the plant's growth environment. They also suggested that these alkaloids could serve as chemotaxonomic markers for pinpointing the origin of the plants. In our research, allocryptopine emerged as the dominant alkaloid. Its concentration in the extracts was highest in chloroform (91.5%), followed by water (84.0%), and then methanol (46.4%). When considering the actual amount of allocryptopine, the order is chloroform (497.5 g/mg) > methanol (248.4 g/mg) > water (236.6 g/mg). For the alkaloid extracts concentrations of 100, 250, and 500 g/mL, the respective allocryptopine concentrations in chloroform, methanol, and water were 50; 25; 24, 124; 62; 59, and 248; 124; 118 g/mL [12].
## 3 Data and Empirical Results
### Empirical Results
#### 3.2.1 CD-Vine COPAR Models
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**SEZ Economic** & \multicolumn{2}{c}{**Manufacturing**} & \multicolumn{2}{c}{**Agriculture**} & \multicolumn{2}{c}{**Service**} \\ \cline{2-7}
**Performance** & **C–Vine COPAR** & **D–Vine COPAR** & **C–Vine COPAR** & **D–Vine COPAR** & **C–Vine COPAR** & **D–Vine COPAR** & **D–Vine COPAR** \\ \hline \multirow{4}{*}{**pair–copula**1**} & 5.470 & 4.682 & \(-\)0.010 & \(-\)0.042 & 0.510 & \(-\)0.411 \\  & (12 \(=\) [PERSON]) & (12 \(=\) [PERSON]) & (12 \(=\) rotated [PERSON]) & (12 \(=\) rotated [PERSON]) & (12 \(=\) [PERSON]) & (12 \(=\) [PERSON]) \\ \cline{2-7}  & 1.221 & \(-\)2.303 & 1.581 & \(-\)1.046 & 0.486 & \(-\)1.024 \\  & (13 \(=\) rotated [PERSON]) & (23 \(=\) [PERSON]) & (13 \(=\) rotated [PERSON]) & (23 \(=\) rotated [PERSON]) & (13 \(=\) [PERSON]) & (23 \(=\) [PERSON]) \\ \cline{2-7}  & \(-\)4.583 & 1.276 & \(-\)16.812 & \(-\)0.164 & \(-\)0.954 & \(-\)0.388 \\  & (231 \(=\) [PERSON]) & (132 \(=\) [PERSON]) & (231 \(=\) [PERSON]) & (132 \(=\) [PERSON]) & (231 \(=\) [PERSON]) & (132 \(=\) [PERSON]) \\ \hline \multirow{4}{*}{**[PERSON]’s tau**} & 0.700 & 0.660 & \(-\)2.516 & \(-\)0.020 & 0.340 & 0.221 \\ \cline{2-7}  & 0.380 & \(-\)0.243 & 0.245 & \(-\)0.044 & 0.054 & \(-\)0.046 \\ \cline{2-7}  & \(-\)0.430 & 0.389 & \(-\)0.823 & \(-\)0.105 & \(-\)0.410 & \(-\)0.267 \\ \cline{2-7}  & [0.0,865] & [0.0,840] & [0,0] & [0,0] & [0,0] & [0,0] \\ \cline{2-7}
**tail dependence**2 & [0.0,567] & [0,0] & [0.450,0] & [0,0] & [0,0] & [0,0] \\ \cline{2-7}  & [0,0] & [0.581,0] & [0,0] & [0,0] & [0,0] & [0,0] \\ \hline
**AIC** & 76.254 & 59.125 & 100.422 & 135.718 & 111.162 & 131.362 \\ \hline
**Vine–COPAR(\(p\)) 3** & \multicolumn{2}{c}{D–Vine COPAR (\(3\))} & \multicolumn{2}{c}{C–Vine COPAR (\(3\))} & \multicolumn{2}{c}{C–Vine COPAR (\(2\))} \\ \hline \end{tabular} Note: ”pair-copula”: CFP (1), FP (2), TRADE (3): 1 full dependencies: [lower, upper: 3] selection for appropriate lags for manufacturing: AIC (\(-525\times 10^{3}\)), HQ (\(-535\times 10^{3}\)), SC (\(-521\times 10^{3}\)), and FP (\(74.5\times 10^{3}\)); for agriculture AIC (\(-524\times 10^{3}\)), HQ (\(53.1\times 10^{3}\)), SC (\(-526\times 10^{3}\)), and FPE (\(7.24\times 10^{3}\)); for service: AIC (\(-515\times 10^{3}\)), HQ (\(54.1\times 10^{3}\)), SC (\(-527\times 10^{3}\)), and FPE (\(7.18\times 10^{3}\)).  
\end{table} 
Table 1: CD-Vine COPAR models, [PERSON]’s correlation, and upper-lower tail dependence.
## 2 Materials and Methods
### Gas Chromatography-Mass Spectrometry (GC-MS) Analysis  
The volatile components of TEO and AEO were analysed using GC-MS. The same instrument was used for all analyses: Agilent GC-7890B system equipped with autosampler Agilent 7693 7890B GC and with Agilent 5977 MSD mass spectrometer (Santa Clara, CA 95051, USA) occupied by column HP-5 MS 5% Phenyl methyl Siloxane with dimensions of 30 m length, 250 \(\upmu\)m inner diameter, and 0.25 \(\upmu\)m film thickness (Agilent, Santa Clara, CA 95051, USA). The temperature of the inlet was set to 250 \({}^{\circ}\)C. Helium was utilised as the carrier gas, maintaining a consistent flow rate of 1 mL/min.  
For SPME analysis, the splitless mode was used, and Agilent inlet liner, Ultra Inert, splitless, straight, 0.75 mm id (Agilent, Santa Clara, CA, USA). The temperature of the GC oven was increased in a controlled manner. It started at 45 \({}^{\circ}\)C and remained at this temperature for 5 min. Then, it was raised to 250 \({}^{\circ}\)C at 10 \({}^{\circ}\)C per minute. Once it reached 250 \({}^{\circ}\)C, it was further increased to 280 \({}^{\circ}\)C at a faster rate of 20 \({}^{\circ}\)C per minute. The temperature was then held at 280 \({}^{\circ}\)C for 5 min. The entire analysis process took a total of 32 min. Following each injection, the fibre was cleaned at a temperature of 250 \({}^{\circ}\)C for 10 min to guarantee its impeccable condition.  
The constituents' identification was based on comparing their retention indices, retention times, spectra, and match and reverse match factors given in the National Institute of Standards and Technology Library ver. 2.0.f (NIST, Gaithersburg, MA, USA), as well as with authentic standards and literature [23]. The RI was calculated for compounds separated using HP5-MS column using the retention times of \(n\)-alkanes series ranging from C8 to C40 for each EO analysed. The values of the match and reverse match factors were higher for all identified compounds than 890, which is considered a good match according to the NIST library guidelines [24]. The relative percentages of the components were calculated based on the gas chromatography peak areas. The analyses were performed in triplicate.
## 7 Regulons and Couplons  
As already mentioned above, the abundant NAPs bind the chromosomal DNA sites in a quasi-continuous mode with affinities differing by three orders of magnitude, affecting the supercoil dynamics of the chromosome and thus contributing to analog control of gene expression. However, numerous target genes have been identified that are regulated by NAPs directly via specific, high-affinity DNA binding sites and therefore, the NAPs are featuring as hubs in the electronically compiled digital TRN [8]. Together, such directly regulated gene classes constitute the TRN regulons, attributed to a particular NAP or a global transcription factor, such as e.g., Catabolite Repressor Protein (CRP) or Fumarate and Nitrate Reduction Regulator (Fnr). Likewise, the various sigma factors of RNA polymerase, having distinct DNA binding specificities and directing the holoenzyme to disparate gene promoters, also establish their cognate regulons [85; 113]. The overlaps between the regulons of a particular NAP and a particular sigma factor make it possible to reveal sets of genes under the control of both regulators and thus, assess the coordinated impacts of the transcription machinery of particular composition (i.e., RNAP associated with a certain sigma factor) and a particular NAP (or a global TF, such as e.g., CRP and Fnr) on genomic transcription. Groups of genes regulated conjointly by a particular couple of RNAP holoenzyme (sigma factor) and NAP (or a global TF), are dubbed couplons [3].  
Couplons were found to harbor functionally related genes that could be activated or repressed relatively independent of parent regulons, thus featuring as genuine entities reflecting the modular organization of the genetic regulation system. There are couplons with distinct functional properties as well as couplons sharing functional properties, such that combinations of couplons with different sets of genes can support similar functions. However, the vast majority of couplons inherit distinct local functions [3; 39]. Notably, while both the NAPs and sigma factors are produced over a range of concentrations and represent analog components of the genetic regulation system, the conversion of analog information into the digital (punctuation, as it were) is achieved by intersections of their concentration gradients, revealing unique genes under the control of a particular couple of NAP (or TF) and sigma factor.
###### Abstract
By estimating the disturbance through the disturbance observer and using the estimated disturbance to decouple the system disturbance, the influence of the disturbance on the system can be reduced. In [19; 20], DOBC is used for performance improvement in robotic manipulators and motion control systems, not grid-connected inverter systems. In the case of ADRC, an extended state observer (ESO) is used to decouple the estimated disturbance to the controller input to make the control system robust. However, model uncertainty and sensor noise are typically regarded as a disturbance to a system in ADRC applications rather than the grid voltage at grid fault situations [21; 22]. In this paper, a stationary reference frame PI current control algorithm in a three-phase GCI system is developed and implemented for improvement of command tracking performance and resilience to disturbance. The CFFC is applied to a stationary reference frame PI current control to compensate drawbacks of AC signal control and improve command tracking performance for a GCI current control system. Additionally, disturbance rejection control (DRC) for a GCI current control system is developed and implemented by using a grid current observer to improve disturbance rejection ability of the PI controller and to reduce peak value of a grid current at grid fault and distorted grid voltage conditions. The command tracking and resiliency performance of the proposed grid current control algorithm for a GCI system is verified through simulation and experimentation.
## 3 Results and Discussion
### Dissolution/Release Test  
AGL-28 derives from an aqueous extraction process; therefore, it has a good water affinity. As shown by the dissolution graph (Figure 6), the extract dissolves for about 90% in 20 min. Its dissolution profile shows a not particularly linear dissolution behavior over time, probably due to extract solid flakes (Figure 5a) which interact differently with the solvent. At the same time, the particle system MPA dissolves almost completely reaching a 96% dissolution rate with a more linear profile than extract raw. As previously shown by microscopic results, the combination of the polymeric matrix and spray-drying leads to obtain a microparticulate powder form in an amorphous state. The amorphous material has no organization in the solid-state, it is random. This property confers important characteristics to the obtained powder such as enhancement of water interaction and dissolution rate. The microparticles structure offers a greater specific surface area exposed which can favor the interaction with the solvent during dissolution in water by increasing the powder dissolution rate.
# Design Optimization of Truss Structures Using a Graph Neural Network-Based Surrogate Model
## 3 Materials and Methods  
### Truss Size Optimization Problem  
The objective of truss size optimization is to minimize the weight of truss structures by attaining the optimal cross-sectional areas that can safely withstand the effects of external loads. Thus, a general truss size optimization problem can mathematically be written as:  
\[\begin{array}{ccc}\text{Minimize}&&W(\mathbf{A})=\gamma\sum\limits_{i=1}^{ ne}L_{i}A_{i}\\ \mathbf{A}&&\\ \text{Subject to:}&&|\boldsymbol{\sigma}|\leq\boldsymbol{\sigma}_{a}\\ &&|\boldsymbol{\delta}_{node}|\leq\boldsymbol{\delta}_{a}\\ &&\mathbf{A}^{L}\leq\mathbf{A}\leq\mathbf{A}^{U}\end{array} \tag{1}\]  
where the objective function \(W\) is the total weight of the truss; \(\gamma\) is the material unit weight; \(L_{i}\) and \(A_{i}\) are, respectively, the length and the cross-sectional area of the \(i\)th truss element; and \(ne\) is the total number of truss elements. Therefore, the size variables \(\mathbf{A}=\{A_{1},A_{2},A_{3},\cdots,A_{ne}\}\) are determined by minimizing the total weight.  
To ensure safety, stability, and functionality of the truss, specific stress constraints and displacement constraints must be satisfied. The stress constraints are specified to ensure that the stress levels in the truss elements remain below a predefined threshold to maintain the structural integrity and prevent failure. On the other hand, the displacement constraints aim to limit the magnitude or direction of the displacements at different truss joints to prevent excessive movements that could compromise the stability and overall performance of the structure. These constraints stipulate that the absolute stress in the truss members must not exceed the maximum allowable stress, \(\boldsymbol{\sigma}_{a}\), and the nodal displacements should stay within the specified maximum allowable displacement, \(\boldsymbol{\delta}_{a}\). The allowable stresses and displacements are influenced by factors such as the material type, truss geometry, and stiffness, and are typically defined by the relevant design codes and criteria. The truss size optimization also involves lower and upper bounds, \(\mathbf{A}^{L}\) and \(\mathbf{A}^{U}\), for the cross-sectional areas of the truss members.
Materials_**2014**, \(7\), 4524-4535; doi:10.3390/ma7064524  
_Materials_  
**ISSN 1996-1944**  
www.mdpi.com/journal/materials  
Improving the Pass-Band Return Loss in Liquid Crystal Dual-Mode Bandpass Filters by Microstrip Patch Reshaping  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 3 and [PERSON] 3**  
Departamento de Tecnologia Electronica, Universidad Carlos III de Madrid, C.\Butarque n\({}^{\circ}\) 15, Leganes 28911, Spain; E-Mails: [EMAIL_ADDRESS] (V.U.); [EMAIL_ADDRESS] (J.M.S.-P.)  
Institute of Applied Physics, Military University of Technology, Kaliskiego 2, Warsaw 00-908, Poland; E-Mail: [EMAIL_ADDRESS]  
Departamento de Teoria de la Senal y Comunicaciones, Universidad Carlos III de Madrid, C.\Butarque n\({}^{\circ}\) 15, Leganes 28911, Spain; E-Mails: [EMAIL_ADDRESS] (A.G.); [EMAIL_ADDRESS] (D.S.)  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-91-624-8397; Fax: +34-91-624-9430.  
_Received: 28 February 2014; in revised form: 1 April 2014 / Accepted: 3 June 2014 / Published: 13 June 2014_  
**Abstract:** In this paper, the design and experimental characterization of a tunable microstrip bandpass filter based on liquid crystal technology are presented. A reshaped microstrip dual-mode filter structure has been used in order to improve the device performance. Specifically, the aim is to increase the pass-band return loss of the filter by narrowing the filter bandwidth. Simulations confirm the improvement of using this new structure, achieving a pass-band return loss increase of 1.5 dB at least. Because of the anisotropic properties of LC molecules, a filter central frequency shift from 4.688 GHz to 5.045 GHz, which means a relative tuning range of 7.3%, is measured when an external AC voltage from 0 V\({}_{\rm rms}\) to 15 V\({}_{\rm rms}\) is applied to the device.  
**Keywords:** liquid crystals; tunable devices; dual-mode filters; inverted-microstrip structure; microwave frequencies
## 3 Methods
### The Spatial Distribution Characteristics Identification and Trend Detection  
The spatial distribution of GPP was explored. First, the pixel-wise multiyear averaged monthly GPP was calculated to check the GPP spatial distribution and the phenology patterns in different months. Second, the annual-maximum and annual-averaged GPP were identified to detect the distribution of GPP in the Arctic.  
A pixel-based simple linear regression, in which time is the independent variable and GPP is the dependent variable, was applied to detect the trend of GPP. In addition, the significance of the interannual trend was evaluated utilizing the Mann-Kendall (MK) test [44], and the significant trends (\(p<0.025\)) of the Arctic were retained.  
In this study, both MOD17A2H data and the linear regression function were provided by Google Earth Engine (GEE), which is a cloud-based computing platform for planetary-scale data analysis, mapping, and modeling, providing free access to numerous global datasets and advanced computational capabilities [45]. GEE was employed for the following reasons: it provides easy access to the MOD17A2H datasets and other related datasets such as land cover types and elevation; it enables rapid exploration of long time series datasets without downloading them; and it provides a library of functions such as linear regression function, which are applied for data analysis and result display.
# Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning
## 2 Data Collection and Preprocessing
### Data Augmentation  
Data augmentation is a familiar ML strategy, and we use it to increase data quantity [39]. We enhanced the data by modifying the existing source data, we augmented about 20 samples of each alphabet from the existing dataset. In audio data augmentation, we used a pitch variation factor to retain the originality of the audio dataset and have minimal effect on the pronunciation. We found this technique suitable for this work after cross-checking audio files audibly. It was also the only technique that did not have any negative impact on the Arabic audio dataset. We obtained 6 modified samples from each alphabet by varying pitch between levels \(-0.3\), and \(0.3\). Figure 3(a) shows time-frequency representation without augmentation and Figure 3(b) shows time-frequency representation with augmentation. This augmented dataset was cross-checked by an expert to ensure the pronunciation of the alphabet is not compromised during augmentation.