esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Setting Time of FA-Based Geopolymer Repair Material  
Figure 4 displays the setting times of FA-based geopolymer paste produced. It can be seen from the same figure that the initial and the final setting times range from 40 to 22 min and 53 to 30 min with respect to NaOH concentration of 8 M to 14 M, respectively. Both setting times are negatively proportional to the NaOH concentration, in which the setting time of FA-based geopolymer paste material decreases with the increase of NaOH molarity in the activator solution. The authors believed that a high concentration of NaOH solution plays a significant role in dissolving various mineral oxides of the fly ash, in particular, aluminium, silicate and iron oxides in which their content is majoritarian in the FA. In other words, using a high concentration of alkaline solution accelerates what is called geopolymerization process, which in turn occurred due to a faster dissolution of Si\({}^{4+}\) and Al\({}^{3+}\) then leading to the formation of sodium alumina-silicate hydrate (N-A-S-H) and calcium alumina-silicate hydrate (C-A-S-H) gels. The physical properties of the FA has also an important effect of both workability and setting times of geopolymers [23]. Therefore, the considerable amount of calcium oxide (CaO) (18.4%) in the prime material is believed to have a substantial effect on the setting time of the final geopolymer product [1]. The selectivity and the high reactivity of CaO in the presence of a quasi-strong alkaline solution permit  
Figure 3: The bonding zone (BZ) between the FA-based geopolymer specimens and OPC substrate at: (a) 8 M, (b) 10 M, (c) 12 M and (d) 14 M.  
to earlier formation of chemical cross-links between the Ca++ cations and other different cations in the geopolymeric system, in which strong bonds of the C-A-S-H gel are built.  
It has been reported that during the mixing process, a quantity of heat is generated due to an exothermic reaction occurred at an early stage of geopolymerization. The quantity of heat generated is believed to be a result of the dissolution of various element oxides in the raw material by the alkaline activator, where a rupture of bonds occurred, and at the same time allowed the resulted cations and anions to build new bonds in a gel forms. This provides additional benefits in terms of improving the strength at early stages due to the faster formation of gels, hence decreasing the setting time of the mixture [24; 25].
# A Hybrid DNN Multilayered LSTM Model for Energy Consumption Prediction
## 3 Proposed Model
### The DNN Multilayered LSTM Model
#### 3.2.3 Dense Layer  
The dense layer is a deeply connected layer connected to the multilayered LSTM layer, in which the layer neurons are linked to every neuron in the multilayered LSTM layer. The main attributes of the dense layer are the weight matrix, the activation function, and the bias vector, which are all used to obtain the desired output. The dense layer is expressed as follows: \(\text{Output}=\text{activation}(.(\text{input},\text{kernel})+\text{bias})\).  
The architecture of the proposed model is shown below in Figure 3.  
The hybrid forecasting model combines the DNN and LSTM algorithms. The reason for developing the hybrid model is to add a layer that is capable of learning and another layer that is capable of making relationships based on nonlinear data. The proposed model's inputs are divided into sequential time-based information (e.g., past energy consumption) and regular data (e.g., days of the week). Dividing the inputs is important, as the LSTM algorithm depends on time series value sequences as its input. However, considering every possible regressor in energy consumption prediction, some datasets do not have such properties. Generally, energy consumption and differentiation between the data types can be achieved by accounting for the interval data exemplified by the dataset. When  
Figure 2: LSTM cells: previous cell, current cell state, and next cell.  
the dataset shows an assortment of historical energy consumption measurements, it can usually be demonstrated as sequential time data points and uses the LSTM as regressors. In comparison, when energy data characterize an explicit element related to future data, they cannot be developed as a time series in the sequence [39].
# Comparing Link Budget Requirements for Future Space-Based Interferometers
## 3 Simulations of Phase Tracking Performance  
For all simulations, the results were compared to the analytical model presented in Equation (1) but with \(H(f)\) substituted for a full frequency domain model of the phasemeter used. The simulation was built on the same hardware-in-loop architecture as discussed in [12] but with additional functionality to simulate GRACE-FO-like gravity signals and the arm response of the retro-reflector interferometer. The numerical simulation was implemented on a National Instruments 7966-R FPGA (Austin, TX, USA), enabling real-time generation of noise and simulation of phasemeter tracking.  
Figure 2: Phase amplitude spectral densities of the components contributing to Equation (1). Three cases of laser frequency noise align with those expected for each mission architecture from Figure 1: transponder is a free-running laser (\(1/f^{2}\) in phase), dual stabilized is a stabilized optical source (\(1/f\) in phase) and retro-reflector is a stabilized optical source with 200 km arm response (white in phase until arm response corner frequency). The GRACE-FO gravity signal and shot-noise-induced phase noise for a 200 femtowatts signal are also presented.
## 2 Results and Discussion
### Pls-Da  
After HCA and PCA analysis of Zhejiang SD and Guizhou SD, partial least squares discrimination analysis (PLS-DA) was applied to visualize the variations among SD samples. According to the PLS-DA model as shown in Figure 5, the cross-validated predictive capability (Q2), variance (R2Y), and variance (R2X) of the model are 0.988, 0.995, and 0.921, respectively, indicating that the model was stable. Based on the PLS-DA, a loadings plot was drawn to exhibit the contribution of each variable to the discrimination [40]. Variable important plot (VIP) score is an indicator of the correlation of variables. When the volatile oil has a VIP value greater than 1, it indicates that they have an above-average effect [41] and can be used to distinguish _S. doederleinii_ in Zhejiang and Guizhou, namely anethod(19), zingerone(49), 2,4-di-tert-butylphenol(40), ledene(42), hexyl hexanoate(25), \(\alpha\)-cadinol(52), phytone(58), hinesol(48), decanal(13), octadecene(56), cedren(30), 7-tetradecene(26), copaene(28), beta-humulene(37), 2-butyl-2-ocrenal(24), tetradecane(27), cedrol(47), calacorene(43), 6-dodecanone(23), \(\beta\)-caryophyllene(31), 4-oxoisophorone(9), \(\gamma\)-nonanolactone(22), 2-pentylfuran(5), 1,2-epoxyhexadecane(55), carvacrol(21), n-pentadecane(38), disobutyl phthalate, (53) farnesene(29), n-heptadecane(54), linalool(8), 1-octen-3-ol(2), phytane(57), and \(\beta\)-asarone(44). Therefore, these substances were important markers for the identification and quality control of _S. doederleinii_. Considering that the volatile oil of _S. doederleinii_ exhibited antioxygenic and antitumor effects, it is worth studying whether the above compounds are responsible for these activities.  
Based on the above chemometric analysis, the content of the volatile compounds showed great differences between different habitats of SD. It is deduced that the difference on SD might be related to the harvest season of the plant, the geographic regions, the processing technology of the herbal medicines, etc. [42].  
Figure 4: Hierarchical cluster analysis (HCA) of 24 samples of _Selaginella doederleinii_ from two districts using Ward’s method based on the Euclidean distance.
# On the Correlation of Symbols' Vibrational Behavior and Manufacturing Processes
## 4 Results  
### Stability Calculations  
The information derived from the CFDAC, and subsequently the SCI, is strongly related to frequency resolution [26]. In the present study, the effect of the number of measurement points, which determines the number of rows of the CFDAC matrix, is discussed. The SCI was calculated for various numbers of measurement points, namely 14, 28, 42, 56, 70, 84, 98, 112, 124, and 144. For the calculations, the measurement data of the B8-0, B8-1, B8-2, B8-3, B8-4, and B8-5 symbols were used. The SCI was derived using the CFDAC pairs between B8-0 and the other symbols (e.g., \(\text{CFDAC}_{h(88\cdot 0)_{h}(88\cdot 0)}\) and \(\text{CFDAC}_{h(88\cdot 0)_{h}(88\cdot 1)}\)). Each measurement number corresponds to an FRF. The FRFs for this analysis were randomly selected. For each point value (e.g., 14), the random selection was performed five times, providing five different sets of FRFs to be considered. Each set of FRFs was used for the calculations between the B8-0 symbols and one of the remaining symbols (e.g., the first set of FRFs was used for the \(\text{CFDAC}_{h(88\cdot 0)_{h}(88\cdot 0)}\) and \(\text{CFDAC}_{l_{\text{(B8\cdot 0)_{h}(88\cdot 1)}}}\) comparison). Figure 3 shows the SCI values calculated using both the real and imaginary values of the CFDACs.  
Figure 3 shows the convergence of the SCI values when the 144 measurement points are considered, since small variations are noticed even if a grid of 126 points is used, prescribing the use of all measurement points for the subsequent calculations presented in the paper. It should be noted that the SCI values based on the imaginary parts of the CFDACs (\(\text{SCI}_{\text{Im}}\)) are greater than the values based on the real parts (\(\text{SCI}_{\text{Re}}\)), in agreement with the findings originally presented and described in [26]. It is assumed that the correlation of the resonance frequencies at the centers of the lobes contribute to the higher values of the \(\text{SCI}_{\text{Im}}\).
_Int. J. Mol. Sci._**2011**, _12_, 9514-9532; doi:10.3390/ijms12129514  
International Journal of  
Molecular Sciences  
ISSN 1422-0067  
www.mdpi.com/journal/ijms  
_Review_  
**Volatile Compounds in Honey: A Review on Their Involvement in Aroma, Botanical Origin Determination and Potential Biomedical Activities**  
**[PERSON] 1, [PERSON] 1,2 and [PERSON] 1,***  
1 Microbial Pathogenicity and Molecular Epidemiology Research Group, Department of Biochemistry and Microbiology, Faculty of Science and Agriculture, University of Fort Hare, P/Bag X1314, Alice 5700, South Africa; E-Mails: [EMAIL_ADDRESS] (C.E.M.-L.); [EMAIL_ADDRESS] (R.N.N.)  
2 Laboratory for Emerging Infectious Diseases, Department of Biochemistry and Microbiology, Faculty of Science, University of Buea, Box 63, Buea, Cameroon  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS] or [EMAIL_ADDRESS]; Tel.: +27-836-516-892; Fax: +27-46-622-5468.  
_Received: 2 November 2011; in revised form: 29 November 2011 / Accepted: 12 December 2011 / Published: 20 December 2011_  
**Abstract:** Volatile organic compounds (VOCs) in honey are obtained from diverse biosynthetic pathways and extracted by using various methods associated with varying degrees of selectivity and effectiveness. These compounds are grouped into chemical categories such as aldehyde, ketone, acid, alcohol, hydrocarbon, norisoprenoids, terpenes and benzene compounds and their derivatives, furan and pyran derivatives. They represent a fingerprint of a specific honey and therefore could be used to differentiate between monofloral honeys from different floral sources, thus providing valuable information concerning the honey's botanical and geographical origin. However, only plant derived compounds and their metabolites (terpenes, norisoprenoids and benzene compounds and their derivatives) must be employed to discriminate among floral origins of honey. Notwithstanding, many authors have reported different floral markers for honey of the same floral origin, consequently sensory analysis, in conjunction with analysis of VOCs could help to clear this ambiguity. Furthermore, VOCs influence honey's aroma described as sweet, citrus, floral, almond, rancid, _etc_. Clearly, the contribution of a volatile compound to honey aroma is determined by its odor activity value. Elucidation of the aroma compounds along with floral origins of a particular honey can help to standardize its quality and avoid fraudulent labeling of the product. Although only present in low concentrations, VOCS could contribute to biomedical activities of honey, especially the antioxidant effect due to their natural radical scavenging potential.  
**Keywords**: honey; natural product; VOCs; aroma; floral origin; biomedical activities
## 3 Mechanical Properties Analysis
### Deformation and Strength Properties  
According to the results of the triaxial compression test, the elastic modulus and peak differential stress of granite under different confining pressures are shown in Figures 6 and 7. The elastic modulus of granite under the maximum free water absorption rate when the confining pressure is in the range of 0-20 MPa is between 31.36 and 44.18 GPa and the axial stress is between 146.0 and 307.6 MPa.  
Figure 5: Typical granite differential stress-axial strain curves under different confining pressures.  
The elastic modulus E and confining pressure \(\sigma_{3}\) are approximately linearly correlated as shown in Figure 6, which can be expressed by the fitting function (Equation (2)), using a correlation factor of 0.844.  
\[E=33.352+0.54\sigma_{3}\ \left(R^{2}=0.844\right) \tag{2}\]  
The axial stress and confining pressure also increase approximately linearly (Figure 7), as shown in the fitting function (Equation (3)), using a correlation factor of 0.993.  
\[\sigma_{1}=151.6+8.1\sigma_{3}\ \left(R^{2}=0.993\right) \tag{3}\]  
According to the Mohr-Coulomb strength criterion and the fitting of Equation (3) (m = 8.1, b = 151.6), the internal friction angle (\(\varphi\) = 51\({}^{\circ}\)) and cohesion (c = 26.84 MPa) were calculated.  
Using the Hoek-Brown strength criterion, it was found that the rock sample used in this test was a complete rock block; therefore, s = 1 was assigned. According to the uniaxial compression test, \(\sigma_{c}\) = 146 MPa, and by fitting the triaxial compression test data, m = 21.42, \(\alpha\) = 0.499, the fitting formula is shown in Equation (4) and the fitting curve is shown in Figure 8. From the above analysis, the fitting effect of the Hoek-Brown criterion  
Figure 6: The curve of elastic modulus E changes with confining pressure.  
Figure 7: The curve of axial stress changes with confining pressure.  
was demonstrated to be superior to that of the Mohr-Coulomb criterion, indicating that the rock sample had slight nonlinear characteristics.  
\[\sigma_{1}-\sigma_{3}=(3127.32\sigma_{3}+21,316)^{0.499}\ \left(R^{2}=0.999\right) \tag{4}\]
# Adaptive Feature Fusion for Small Object Detection
## 3 Experiment
### Introduction to the Dataset VisDrone  
In order to verify the effectiveness of the proposed method for problems such as small object continuity and occlusion, we selected VisDrone [41], a dataset captured by drones that contains a large number of scale-variant objects, occlusions, and class imbalances with complex backgrounds and variable angles. It is collected by the AISKYYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China. It was shot using different types of drones and included various scenes in various cities such as low light, rainy weather, exposure, etc. Unlike conventional detection datasets, each image contains hundreds of objects to be detected, and the dataset contains a total of 2.6 million annotated boxes, and some objects that are very close to each other may also have overlapping bounding boxes. Because the dataset is captured by drones, the annotation frames of pedestrians and distant objects are very small, which poses a certain challenge to the ability of the model to generate an a priori frame. The object size distribution of the VisDrone dataset is shown in the Figure 7.  
The training pictures of different scenes and different climates in the VisDrone dataset are shown in Figure 8.  
Figure 6: Comparison between the coupling detector and decoupling detector.
# A Tweak-Cube Color Image Encryption Scheme Jointly Manipulated by Chaos and Hyper-Chaos  
[PERSON]  
1 College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China; [EMAIL_ADDRESS] (S.-M.W.); [EMAIL_ADDRESS] (J.-H.X.)1222212122122212  
of a color image at the same time and make these components affect each other [19]. Nonetheless, the Logistic map still has several defects: the chaos region is small, the output sequences are uneven, and the dynamic characteristics are not complicated enough. As a consequence, some researchers have ameliorated the Logistic map to design algorithms. [PERSON] et al. presented a cryptosystem with a two-dimensional (2D) Logistic map of complicated basin structures and attractors [20]. [PERSON] et al. combined the Logistic map with another two existing 1D maps to raise a novel chaotic system for image encryption [21], and [PERSON] et al. also introduced a new 2D Sine Logistic modulation map (2D-SLMM) to change the image pixel positions [22]. Even recently, some scholars yet have been hammering at encryptions with Logistic map transformation. [PERSON] et al. adopted a linear-nonlinear-linear theory to shuffle plain images [23]. [PERSON] et al. put forward a novel butterfly-like structure algorithm by using the improved Logistic map [24].  
Considering that the above techniques have insufficient finite parameter space to resist brute force attacks and are not conducive to real-time communication, the contributions we have made are summarized as follows. (1) We propose a novel 1D chaotic map with larger key space and better chaotic properties. Simultaneously, it is combined with a 4D hyperchaotic map to control the generation of the key streams, which makes the randomness more superior. (2) We exogitate a color image encryption scheme that mimics the Rubik's cube motion, which can be effectively used for real-time communication and authentication without compromising time cost and safety quality. Moreover, due to our scheme can scramble images by rotating and shifting them at the pixel-level and bit-level promptly, it is suitable as a secure transmission mode in telemedicine systems. The rest of this paper is organized as follows. Section 2 introduces the modified 1D chaotic map and hyperchaotic system. Section 3 presents our cryptosystem. Section 4 gives simulation results. Section 5 evaluates the security performance. And the final section draws a conclusion.
## 6 Equivalent Circuit of the Proposed Unit Cell  
The equivalent circuit model of the proposed unit cell is made up of passive components, such as inductor (\(L\)) due to metallic resonator patch, and capacitor (\(C\)) due to split gap of the ring resonator. The split gap of the ring with metallic resonator creates LC resonance circuits that supply the resonance frequency at a certain frequency, which is changed by the variation in the split gap and metallic strip of the metamaterial resonator [35].  
\[f=\frac{1}{2\pi\sqrt{LC}} \tag{5}\]  
The overall inductance was computed according to the principle of the transmission line [36]:  
\[L=0.01\times\mu_{0}\left\{\frac{2(d+g+h)^{2}}{\left(2w+g+h\right)^{2}}+\frac{ \sqrt{\left(2w+g+h\right)^{2}+l^{2}}}{\left(d+g+h\right)}\right\}t \tag{6}\]  
Furthermore, the overall capacitance was calculated using:  
\[C=\epsilon_{0}\left[\frac{2w+g+h}{2\pi(d+h)^{2}}\ln\left\{\frac{2(d+g+h)}{ \left(a-l\right)}\right\}\right]t \tag{7}\]where \(\varepsilon_{0}=8.854\times 10^{-12}\) F/m, \(\mu_{0}=4\pi\times 10^{-7}\) H/m, \(w=\) metallic resonating patch width, \(h=\) substrate thickness, t = metallic resonating patch thickness, and \(l=\) length.  
The optimized equivalent circuit of the proposed metamaterial unit cell using the passive element (\(L\), C) responsible for generating the quadruple resonance frequency is shown in Figure 7a. The component values are adjusted using an ADS simulator. By adjusting the component values, the \(S_{21}\) results are computed so that the ADS results are very similar to the CST results. A comparison of \(S_{21}\) is shown in Figure 7b. It was observed that the equivalent circuit results were expressed identically to those observed in unit cell results using CST in case of first to third resonance frequency. The metamaterial resonator consists of a split ring resonator and two split based Coptic cross shape resonator. The split ring resonator can be represented by L1, C1, L2, and C2, which is responsible for the creation of resonance frequency of 2.02 and 6.985 GHz. The Coptic shape with two split rings contributes to the creation of two resonance frequencies at 9.985 and 14.425 GHz through the representative L3, C3, L4, and C4 component of proper tuning. In the equivalent circuit model, L5 and L6 boost the magnitude of resonances representing the equivalent of the four cross shaped resonators. C5 represents the coupling capacitor.
# A Nested Chinese Restaurant Topic Model for Short Texts with Document Embeddings
## 5 Experimental Results
### Experimental Results for Complete and Partial Document Embeddings  
In this section, we discuss the impact of noisy information of document embeddings in our model. We compared several DESTM models with different parameter settings on dataset News. Firstly we use the PTM model to represent the condition without any document embeddings. Settings of PTM follow experiments above. Secondly, we set parameters of DESTM as \(\alpha=0\) and \(\eta=-1\). The model with these settings means aggregating short texts according to the complete document embeddings and of cause including all noisy information. Thirdly, we set \(\alpha=15\) and \(\eta=-1\). The model with these settings means aggregating short texts according to the similarity distribution and distances of short texts without discarding any distances. Then we vary the threshold \(\eta\) to be \(-0.5\), \(0.0\), and \(0.5\). We get models incorporating partial document embeddings with different threshold values. Finally, we set \(\alpha=15\) and \(\eta=1\). The model with these parameters means aggregating short texts only according to the similarity distribution. For other parameters of DESTM, we set \(\beta=2\), \(\gamma=0.1\), and \(\delta=0.01\). The evaluation results of PMI score and classification accuracy are shown in Figure 4.  
In these results, the PTM model achieves the worst PMI score and the second-worst classification accuracy. Without any additional semantic information, the performances of the PTM model are very poor. The DESTM model with \(\alpha=0\) and \(\eta=-1\) incorporates the complete document embeddings without discarding noisy information. The model of these settings achieves the third-worst PMI score and the worst classification accuracy. Results show that aggregating short texts directly according to document embeddings will incorporate a lot of noisy information and leads to poor performances. For models with \(\alpha=15\) and \(\eta\) varies from -1 to 1, the number of distances between short texts that  
Figure 4: Comparison results between models with complete and partial document embeddings.  
are discarded varies from zero to all distances. The results show that the model with \(\eta=0.0\) achieves the best performance. The model with \(\eta=-0.5\) achieves the second-best performance. The model with \(\eta=-1\) is the third best and the model with \(\eta=0.5\) is the fourth one. Finally, the model with \(\eta=1\) achieves the second-worst PMI score and the third-worst classification accuracy. The results show that similarity distributions and distances of short texts are all beneficial to our model and both information should be retained. The threshold of distances should be carefully designed, discarding too many distances or retaining too many distances will all reduce the performances.