esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this paper, an evolutionary multi-objective optimization algorithm named NSGA-II was used to determine the optimum radius for shape memory alloy (SMA) wires employed in conjunction with the lead rubber bearing (LRB), referred to as an SMA-LRB isolator. This algorithm simultaneously minimizes the mid-span displacement and the base shear force. Then, the optimized SMA-LRBs were implemented in a benchmark bridge to reduce excessive displacements. The results obtained from the nonlinear dynamic analysis show that the implemented approach could effectively optimize the SMA-LRBs. These improved smart isolators can noticeably reduce the maximum displacements and residual deformations of the structure; meanwhile, the base shear and deck acceleration remain less than those of the non-isolated benchmark bridge. This isolator can reduce the maximum mid-span displacement of the bridge by up to 61%, and the mid-span residual deformations by up to 100%, compared to an uncontrolled isolated bridge under different ground motions. This optimized passive system was compared with nonlinear dampers, passive SMA dampers, and a negative stiffness device. The results indicate that the optimized SMA-LRB isolators are generally more successful in reducing and recovering displacements than the other controllers.  
Keywords:seismic structural control; base isolation; shape memory alloys; iron-based shape memory alloys; benchmark highway bridge; multi-objective optimization; NSGA-II +
Footnote †: journal: Applied Sciences
# An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN
## 3 Proposed Model Description
### Model Identification  
Various membership functions including triangular, trapezoidal and sigmoidal were examined for the fuzzification; however, a normalized Gaussian membership function, with the general form of Equation (2), yielded the best result, where \(\sigma\) is the standard deviation and determines the spread of \(\mu\), and \(\tau\) is the mean, which determines the center of the \(\mu\).  
\[\mu_{c,\sigma}(x)\ =\ e^{-(x-\tau)^{2}/2\sigma^{2}} \tag{2}\]  
Two approaches were considered for the identification of the premise structure. The first approach was to define fuzzy if-then rules using all the possible permutations of all or some of the fuzzified inputs. For instance, using common sense knowledge of propagation one could define the following implication:  
* \"If \(d_{los}\) is short and \(s_{m}\) is high and \(c_{los}\) is clear then \(RSSI\) is good.\"  
This states that \"if the transmission was done over a short distance, with a high spreading factor, and the LoS was relatively clear of clutter, then reception should be good, regardless of other input parameters\". However, this approach can be prone to combinatorial explosion of rules, especially for complex systems. Considering that there was a total of seven inputs, each with two membership functions, then the total number of rules is \(2^{7}\).  
The second approach was to use a clustering method [36]. A subtractive clustering [37] was chosen for the identification of the rules, since subtractive clustering does not require an initial estimate of the center or the number of clusters [38]. Other clustering algorithms could be used, where eventually, each cluster center forms a fuzzy rule.  
A first-order T-S model was selected, as it provided a higher accuracy compared to zero-order T-S. Hence, output membership functions were of the form \(y\) in Equation (1), where the output linear functions (\(p_{k}\)) were identified by linear least-squares optimization.
# Development of a Simplified Human Body Model for Movement Simulations
## 2 Materials and Methods
### The Built Experimental Numerical Model  
The developed simplified human body model is built in Adams software as a 3D numerical dynamic model (Figure 2a,b). This is in order to perform simulations and verify the developed model. Assuming the height (H = 187 cm) and total mass of the human body (M = 70 kg weight), the dimensions and masses of individual segments are determined according to the formulas in Tables A1 and A2, where the results of these calculations are also provided. The numbers describing the individual segments (yellow solids) visible in Figure 2a correspond to those in Figure 1a. Joints are additionally indicated by inserting red spheres with negligible mass. As for the human motion standard--the developed model follows the International Society of Biomechanics (ISB) recommendations for anatomical coordinate systems and joint definitions, similar to the case presented in [4]. In order to facilitate the observation of some segments, characteristic elements (negligibly changing the mass and its distribution) were added to help determine the orientation of these solids: the head has nose and hollows symbolizing the eyes, the torso has a hole symbolizing the naively, and the hands have small protruding rods on the inside.  
Figure 2: The 3D numerical model built in Adams developed for the human body: (**a**) view in frontal plane (numbers indicate segments); (**b**) view in isometric projection (red spheres indicate joints).  
As described earlier, with the known mass and volume of each segment, the solids in the model were assigned appropriate densities. For this reason, the positions of COM and the mass moments of inertia for all the segments were simplified and followed the [PERSON] calculated values of these parameters.  
Due to the peculiarity of the software, the joint connections assumed as kinematic pairs of class III and II (elbows) were modeled respectively as 3 or 2 rotational pairs (class I). They have a common center of rotation but different (perpendicular) axes of rotation. More precisely, in the joints between the segments, 2 spheres (Figure 2) with negligible mass were placed and then connected with rotational pairs to each other and to the appropriate body segments. Accordingly, in the elbow joints, it was enough to use one sphere. As a result, the assumed mobility of the model's individual parts is obtained. Furthermore, by applying a rotational drive to each kinematic pair, it is possible to simulate the movement of the human body in the model.  
The presented numerical model, as well as further experimental/simulation results, refer to a human volunteer with a height H = 187 cm and body mass M = 70 kg. The experimental measurements were carried out with the volunteer using the IMU sensors system.
## 3 Detection Approaches Review
### Sentiment Analysis on Audio Data  
Sentiment analysis has been successfully performed using only audio data. In Reference [19] an Automatic Speech Recognition (ASR) system is used to convert youtube videos to transcribed text, and then a Part Of Speech (POS) tagger based feature extraction technique to identify useful sentiment features in the text, to later be classified into positive or negative sentiment polarity by a Maximum Entropy (ME) based sentiment classification model; a method is proposed in Reference [20] to perform speech emotion recognition. The proposed method performs emotion recognition by employing Vowel-Like Regions (VLRs) and Non-Vowel-Like Regions (non-VLRs), and by choosing the features of either VLRs or non-VLRs for each emotion; a sparse autoencoder based feature transfer learning method is proposed in Reference [21] that uses a single-layer autoencoder to find a common structure in small target data and then applies it to reconstruct source data for performing knowledge transfer from source data into target task. The authors used the reconstructed data to build a speech emotion recognition engine for a real-life task and performed experiments with six publicly available corpora. The experiments showed that the proposed algorithm enhances emotion classification accuracy of the speech emotion recognition engine significantly; sentiment analysis on short spoken reviews was performed in Reference [22], where authors collected manually a set of user reviews and extracted acoustic features from the spoken reviews using the openEAR/openSMILE toolkit. Various algorithms were used, which were logistic regression, AdaBoost, a C4.5 decision tree, and a Support Vector Machine (SVM) classifier with a radial basis function (RBF) kernel. The best performing algorithm was AdaBoost with speaker-dependent features after applying manual feature selection, reaching an accuracy of 72.9%. The four works reviewed in this section give an overview of the different possibilities for emotion recognition on audio, having a classic ME model applied to POS features from transcripts, a work using different features and selecting concrete features for each emotion, a deep learning approach that leverages classic model detection, and a work using different algorithms. Therefore, we followed the inclusion/exclusion criteria in this section. The reported results shown a better accuracy reached by the work in Reference [20], using different features for each emotion, although the best overall results of accuracy are achieved by the work performed in Reference [22], where different algorithms are used.
## 1 Introduction
Thus, understanding the mechanisms of action will enable better use of MMW technology for therapeutic applications. Given the rising challenges of biochemical drug resistance and the adverse effects from ionizing electromagnetic spectra limiting the use of conventional radiation therapies [3], molecular mechanisms of non-ionizing radiation procedures can suggest novel approaches to finding effective solutions. While non-ionizing radiation has mostly been used in thermal ablation procedures, we explored the effect of MMW (85-105 GHz) irradiation on the _Saccharomyces cerevisiae_ yeast model of eukaryotic cell division in this article within the non-thermal safety standards set by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) maintained for the stringency of analyses to unravel novel mechanisms. The MMW were propagated using a standard pyramidal horn antenna in one case and a standard wave guide in another. Radiated power density and power distribution across the aperture was analyzed and the influence of MMW exposure on yeast cells manifesting in the retarded cell growth effect studied. Irradiation of cells with deletion of the RAD52 gene (_Arad52_), responsible for DNA damage repair was examined for radiation related genomic perturbations. Temperature rise measurements were conducted for analysis and maintenance of non-thermal exposure conditions. Non-ionizing MMW radiation absorption by cellular water content was investigated. Dosage dependent effects to achieve complete termination of yeast cell division were studied and compared using a waveguide delivering higher energy, which presented novel avenues for microbial infection control. Subsequently, the effect of different durations of irradiation (5 h vs. 6 h) and power densities (5 dBm vs. 6 dBm) was examined using constant frequency of 105 GHz and constant energy. Using the same number of cells, the experiment demonstrates that the inhibited cell division effect was MMW dependent (Figure 1b); being absent in the control (sham). We did not observe difference in the delay of the proliferation rate between 5 h (with 6 dBm power) and 6 h (with 5 dBm power) of irradiation. We conjecture that this is so because both exposure regimes involved the same amount of energy (i.e., product of time and power) (see Figure 1b). Single frequency at 105 GHz was used as it affects WT yeast cell growth to the same extent as for all the other examined MMW frequencies (see Figure 1a); but provides a lower bound for power densities (see Table 1) to elicit the targeted outcome. The agar layer thickness was kept constant throughout this and other repeated experiments under similar conditions. Power densities emitted by the horn antenna aperture are presented in Table 1.
## 6 Specific Applications of Wastewater Bioprocessing with Simultaneous Biofuel Production
### Waste from the Munitions and Explosives Industry  
In the study of [PERSON] et al. [50], it was proposed to use microalgae cultures to recover nutrients from untreated waste from the munitions and explosives industry. This method permits the production of renewable energy sources, such as biodiesel or biogas, while also lowering the concentration of nutrients. The production of explosives, or active compounds, for the armaments industry, such as 3-nitro-1,2,4-triazoleol-5-one (NTO) and 1,3,5-tritroperohydro-1,3,5-triazine (RDX), generates large amounts of waste that contain organic pollutants that are rich in nutrients, primarily nitrogen. This wastewater requires physicochemical and/or biological treatment before being released into water bodies. Five species of laboratory microalgae (two freshwater and three seawater), and _Scenedesmus obliquus_ (ATCC\({}^{\circled}\) 11477), were selected. Ten munitions production plant sites were sampled for untreated waste, with varying levels of organic and inorganic carbon and nitrogen content. Only three of the waste streams needed chemical treatment because of their high concentrations of growth inhibitors, according to the results, while the majority of the streams could be used as microalgae culture nutrients without pre-treatment. In particular, _Scenedesmus obliquus_ (ATCC\({}^{\circled}\) 11477) had similar growth in a mixture of untreated wastes with added CO\({}_{2}\) (atmospheric or combustible), relative to the reference sample (synthetic substrate). In conclusion, the study demonstrates that by applying microalgae cultures, bioremediation of the burdened waste of this industry can be achieved, while reducing its energy footprint in a sustainable and efficient way [50].
###### Abstract  
Crop growth stages are integral components of plant phenology and are of significant ecological and agricultural importance. While the use of remote sensing methods for phenology identification in cropland ecosystems has been extensively explored in previous studies, the focus has often been on land surface phenology, primarily related to the start and end of the growing season. In contrast, the monitoring of crop growth within an agronomic framework has been limited, particularly in the context of recently developed solar-induced chlorophyll fluorescence (SIF) data. Additionally, some critical growth stages have not received adequate attention or evaluation. This study aims to assess the utility of SIF data, collected from both ground and satellite measurements, for identifying critical crop growth stages within the realm of remote sensing phenological estimation. A comparative analysis was conducted using enhanced vegetation index (EVI) data at the Shangqiu site in the North China Plain from 2018 to 2022. Both SIF and EVI time-series data, obtained from ground and satellite sources, undergo a comprehensive phenological estimation framework encompassing pre-processing, modeling, and transition characterization. This approach involves reconciling time-series phenological patterns with crop growth stages, revealing the necessity of redefining the mapping relationship between these two fundamental concepts. After preprocessing the time-series data, the framework incorporates the phenological modeling process employing two double logistic models and a spline model for comparison. Additionally, it includes phenological transition characterization using four different methods. Consequently, each input dataset undergoes an assessment, resulting in 12 sets of estimations, which are compared to select the ideal estimation portfolio for identifying the growth stages of maize and winter wheat. Our findings highlight the efficacy of SIF data in accurately identifying the growth stages of maize and winter wheat, achieving remarkable results with an R-square exceeding 0.9 and an RMSE of less than 1 week for key growth stages (KGSs). Notably, SIF data demonstrate superior accuracy, robustness, and sensitivity to phenological events when compared to EVI data. This study establishes an estimation portfolio utilizing SIF data, involving the Gu model, a double logistic model, as the preferred phenological modelling method together with various compositing methods and transition characterization methods, suitable for most KGSs. These findings create opportunities for future research aimed at enhancing and standardizing crop growth stage identification using remote sensing data for a wide range of KGSs.  
crop growth stage; SIF; EVI; phenology extraction; time series 2023155689. [[https://doi.org/10.3390/rs15245689](https://doi.org/10.3390/rs15245689)]([https://doi.org/10.3390/rs15245689](https://doi.org/10.3390/rs15245689))
## 4 Anti-Cancer Activities of Tanshinones
### Anti-Proliferation and Pro-Apoptosis  
#### 4.1.1 Tanshinone I (Ti)  
TI could induce growth inhibition in various cancer cells including colon [124], stomach [125], breast [126,127], liver [128], prostate [129], lung [130], and leukemia [131] through inhibiting proliferation (induction of cell cycle arrest) and promoting apoptosis, in a broad concentration range from sub-\(\upmu\)M to high \(\upmu\)M. Up-regulation of p53 and p21 was found to be in association with the induction of cell cycle arrest and apoptosis [124,125]. Increase of Bax/Bcl-2 ratio [124,125,126,128,131], isruption of mitochondrial membrane potential (MMP) [131] and activation of caspase-3 [124,126,131] might contribute to TI-induced apoptosis, suggesting an involvement of the intrinsic apoptosis pathway. [PERSON] _et al._[131] reported that TI-induced apoptosis was associated with inhibition of PI3K/AKT kinases in K562 and HL-60 leukemia cells, and was mimicked by PI3K inhibitor (LY294002), indicating a possible involvement of PI3K/AKT pathway in TI-induced apoptosis. Moreover, [PERSON] _et al._[127,129,130] reported that TI inhibited DU 145 prostate cancer, MCF-7 and MDA-MB-231 breast cancer and H1299 lung cancer cell growth in a concentration-dependent manner, in association with down-regulation of Aurora A (a member of the aurora oncogenic family of mitotic serine/threonine kinases over-expressed in cancers). Knock-down of Aurora A significantly attenuated TI-induced cell cycle arrest and apoptosis, suggesting Aurora A as a possible mediator of TI-induced inhibition in these cells. Mechanically, TI decreased Aurora A through epigenetically suppressing acetylation of the histone H3 associated with Aurora A [127]. _In vivo_ administration of TI (150 mg/kg and 200 mg/kg of TI in corn oil via oral gavage) retarded the growth of prostate and lung xenograft tumors in immunodeficient mice with minimal side effect [129,130].
# Feasibility of Developing Sustainable Concrete Using Environmentally Friendly Coarse Aggregate
## 4 Results and Discussion
### Microstructure  
The tensile strength of concrete is strongly influenced by the cement gel-aggregate bond strength [3]. The interfacial transition zone (ITZ) has a different microstructure to the other regions in the cement matrix and is identified as the weakest interface for the generation of early micro-cracking. The ITZ in concrete is produced by the disorder of packing the anhydrous cement grains in the transition zone [68]. Figure 7 shows that RCA had a strong bond with C-S-H gel matrix especially in the ITZ. Both combined RCA concretes had a small number of microcracks in ITZ region at 7 days, however, these cracks filled with C-S-H gel due to ongoing hydration and formed a densified zone at 28 and 90 days. This further increased the bond between coarse aggregates and the cement matrix. Hence, higher flexural and splitting tensile strengths were observed for both combined RCA concretes exceeding the values predicted based on AS3600 at 28 days.  
The ultrasonic pulse velocity of combined RCA-25 and RCA-40 concretes at 7, 28, and 90 days are 3.25 km/s, 3.75 km/s, 3.95 km/s and 3.30 km/s, 3.85 km/s, 4.15 km/s. [PERSON] and [PERSON] [69] reported that if ultrasonic pulse velocity of PC concrete falls between 3.5 and 4.5 km/s, that can be classified as good quality concrete free from large cracks and voids, which affect the structural reliability and durability in the long term. Due to the low density and high water absorption capacity of recycled  
Figure 6: (**a**) Water absorption and (**b**) apparent volume of permeable void (AVPV) of combined RCA concrete.  
aggregates, RCA concretes are expected to have slightly lower UPV values. Except at early age (7 days), the ultrasonic pulse velocity obtained for combined RCA concretes is in the good quality concrete range by 28 days, correlating with the less porous densified microstructure identified by the microscopy. This result is also in alignment with those reported in other RCA concrete literature [40; 70].  
When density of concrete increases, the elastic modulus of concrete increases as well. This is due to a linear relationship between pore volume and the elastic modulus of concrete [71]. The packing density and porosity in concrete further determine the modulus of elasticity in concrete. Hence, a higher packing density would provide higher elastic modulus in concrete [72]. The low water absorption, dense C-S-H gel matrix, lack of micro-cracks in the ITZ and higher ultrasonic pulse velocity values at 28 days all indicate that the combined RCA concrete is free of large voids and cracks. This is hypothesised as the reason for the comparable elastic modulus evident in combined RCA concrete and the quarried aggregate concrete.
# Adaptive and Blind Audio Watermarking Algorithm Based on Chaotic Encryption in Hybrid Domain
## 2 Watermarking Algorithm in Hybrid Domain
### Principle of Watermark Extracting  
The watermark extraction process is an inverse process of the watermark embedding process. Firstly, divide \(A^{\prime}\) into \(M\) audio segments \(A^{\prime}_{i}\) with \(N\) sample points, and then perform \(r\)-level DWT on \(A^{\prime}_{i}\) to get \(De^{\prime}(r,n)\). Secondly, separate \(De^{\prime}(r,n)\) into two packets, and then perform DCT to get \(C^{\prime}_{1}(r,j)\) and \(C^{\prime}_{2}(r,j)\). Finally, the average amplitudes are calculated according to Formulas (16) and (17), and the watermark hidden in the audio signal is extracted according to the extraction formula. According to the embedding rules as shown in Formulas (14) and (15), if \(w(q)=1\), the average amplitudes of the two packets are:  
\[M^{\prime}_{c1}=\frac{2^{r+1}}{N}\sum_{j=1}^{N/Z^{r+1}}|C^{\prime}_{1}(r,j)|=(1 +\lambda)M_{l} \tag{16}\]  
\[M^{\prime}_{c2}=\frac{2^{r+1}}{N}\sum_{j=1}^{N/Z^{r+1}}|C^{\prime}_{2}(r,j)|=(1 -\lambda)M_{l} \tag{17}\]  
The average amplitude of \(C^{\prime}_{l}(r,n)\) is:  
\[M^{\prime}_{l}=\frac{1}{2}[(1+\lambda)M_{l}+(1-\lambda)M_{l}]=M_{l} \tag{18}\]  
It can be seen that the overall average amplitude of each audio segment does not change after the DCT coefficients are modified. Replace \(M_{l}\) with \(M^{\prime}_{l}\) in Formulas (16) and (17) to get the average amplitudes of the modified packets.  
\[M^{\prime}_{c1}=(1+\lambda)M^{\prime}_{l} \tag{19}\]  
\[M^{\prime}_{c2}=(1-\lambda)M^{\prime}_{l} \tag{20}\]  
According to Formulas (19) and (20), when \(\lambda\geq 0\), \(M^{\prime}_{c1}\geq M^{\prime}_{c2}\). If \(w(q)=-1\), the average amplitudes of \(C^{\prime}_{1}(r,j)\) and \(C^{\prime}_{2}(r,j)\) are:  
\[M^{\prime}_{c1}=(1-\lambda)M^{\prime}_{l} \tag{21}\]  
\[M^{\prime}_{c2}=(1+\lambda)M^{\prime}_{l} \tag{22}\]  
According to Formulas (21) and (22), when \(\lambda>0\), \(M^{\prime}_{c1}<M^{\prime}_{c2}\). It can be known from the above analysis that the watermark can be extracted from \(A^{\prime}_{i}\) according to Formula (23).  
\[w^{\prime}_{2}(q)=\left\{\begin{array}{ll}1,&\quad\text{if }M^{\prime}_{c1} \geq M^{\prime}_{c2}\\ 0,&\quad\text{if }M^{\prime}_{c1}<M^{\prime}_{c2}\end{array}\right.,\;q=1,2, \ldots,L \tag{23}\]  
Using \(Ch(x_{1},\alpha,\delta)\) as the secret key to generate the binary chaotic sequence \(c(q)\), then obtain the decrypted picture \(w^{\prime}(q)\) according to the Formula (24).  
\[w^{\prime}(q)=w^{\prime}_{2}(q)\oplus c(q) \tag{24}\]