esa-sceva/satcom-chunk-collection huggingface dataset:
# The Cytotoxic Activity of Diiron Bis-Cyclopentadienyl Complexes with Bridging C\({}_{3}\)-Ligands
## 2 Results and Discussion
With reference to the biological action, although the conversion of **2a** into **3a** does not occur in water, it cannot be ruled out that the fragmentation of complexes of type **2** leading to **3** takes place in a physiological environment to some extent not exclusively through a redox mechanism (see Introduction) [20; 38]. In order to outline a possible correlation between the activity of **2a** and that of **3a**, we investigated the influence of time on the IC\({}_{50}\) values on A2780 and A2780 cisR cells. The ratio between the IC\({}_{50}\) values after 24 and 72 h of incubation, respectively, are 11 (A2780) and 21 (A2780) and 2.3 (A2780) and 3.3 (A2780 cisR) for **3a**. This set of data highlights that the action of the diiron **2a** is much slower than that of the monoiron **3a** which is, however, less cytotoxic; it may be concluded that **2a** is better internalized than **3a** inside cells, and the kinetic of its antiproliferative activity is correlated to fragmentation routes (including that leading to **3a**). Relevant to the cytotoxicity trials, the stability of **4a-c** was monitored at variable times in DMSO-d\({}_{6}\)/D\({}_{2}\)O mixtures, maintained at 37 \({}^{\circ}\)C (see Experimental and Table 2 for details). According to 1H NMR spectroscopy, the degradation rate of the complexes follows the trend **4a < 4c < 4b**, however extensive degradation occurs after 72 h in all cases (Table 2). The same trend was observed in analogous DMSO-d\({}_{6}\)/deuterated cell culture medium (DMEM-d) mixtures after 24 h, albeit with a slower process concerning especially **4c** and **4b**. The diiron complexes **4a-c** exhibit a moderate antiproliferative activity after 72 h incubation against the A2780 cells (Table 1); it is noteworthy that such activity is similar to that detected for the same compounds on the cisplatin resistant A2780 cisR cell line. On the other hand, the cationic vinyliminium complexes **2** are much less cytotoxic towards the A2780 cisR cells with respect to the A2780 ones. Interestingly, the selenophenes **5a-b** are significantly less cytotoxic than the corresponding precursors **4a-b** (Table 1); this fact outlines that the capability of the investigated di-organoiron complexes to kill cancer cells may be mainly ascribable to the interference with the cell redox balance promoted by the iron centers, with a possible secondary contribution offered by the bridging organic moiety. In addition, carbon monoxide might play some adjuvant role, and investigations are in due course to give insight into this feature. It should be mentioned that a range of transition metal carbonyl complexes have been widely studied for the therapeutic properties associated to the controlled release of the carbonyl ligand(s) [39].
## 3 Orthogonal Simplex Chebyshev-Laguerre Cubature Kalman Filter
### High Order Chebyshev-Laguerre Quadrature Rule  
Due to the quadratic term \(r^{2}/2\) in the exponential function, it is difficult to deal with the radial integral (15) directly. Hence, we make an equivalent transformation that \(t=r^{2}/2\), and then Equation (15) can be rewritten as follows:  
\[G_{s}=\frac{1}{2}\pi^{-\frac{n_{x}}{2}}\int_{0}^{\infty}S\Big{(}\sqrt{2t} \Big{)}t^{\frac{n_{x}}{2}-1}e^{-t}dt\mathrm{v}, \tag{22}\]  
The integral on the right-hand side is in the form of the generalized Gauss-Laguerre integral. Numerical integration is generally utilized because seeking an optimal or analytical solution to Equation (22) is typically intractable. Here, we evaluate it using quadrature points \(t_{k}\) and associated weights \(w_{i}^{\varepsilon}\):  
\[G_{s}=\sum_{k=1}^{n_{c}}w_{i}^{\varepsilon}S\Big{(}\sqrt{2t}\Big{)}, \tag{23}\]  
which are determined by the Chebyshev-Laguerre polynomials [29]:  
\[L_{n_{c}}^{(a)}(t)=(-1)^{n_{c}}t^{-a}e^{t}\frac{d^{n_{c}}}{dt^{n_{c}}}\big{(}t ^{+n_{c}}e^{-t}\big{)}, \tag{24}\]  
where \(a=n_{x}/2-1\) and \(n_{c}\) is the order of the Chebyshev-Laguerre polynomials.  
The quadrature points \(t_{k}\) are the roots of the Chebyshev-Laguerre polynomials, and associated weights can be obtained as follows [30]:  
\[w_{k}^{\varepsilon}=-\pi^{-\frac{n_{x}}{2}}\cdot\frac{n_{c}\Gamma(a+n_{c}+1)} {2\dot{L}_{n_{c}}^{(a)}(t_{k})L_{n_{c}+1}^{(a)}(t_{k})}, \tag{25}\]  
where \(k=1,\cdots,n_{c}\). From the theory of Laguerre polynomials, the relation between \(L_{n_{c}}^{(a)}(t_{k})\) and \(L_{n_{c}+1}^{(a)}(t_{k})\) is described as follows:  
\[L_{n_{c}+1}^{(a)}(t_{k})=-t_{k}\dot{L}_{n_{c}}^{(a)}(t_{k}), \tag{26}\]  
and then we obtain the following:  
\[w_{k}^{\varepsilon}=\pi^{-\frac{n_{x}}{2}}\cdot\frac{n_{c}!\Gamma(a+n_{c}+1)} {2t_{k}\bigg{[}\dot{L}_{n_{c}}^{(a)}(t_{k})\bigg{]}^{2}}. \tag{27}\]  
Obviously, the precision of the quadrature formula can be readily improved with the increase of the polynomials' order, which is linear to the number of quadrature points.
# Optimization of Inspection Period in Natural Stone Claddings
## Materials and Methods
### Natural Stone Cladding
#### 2.1.2 Inspection Period  
Regarding the adequate inspection period in NSC, the information in the literature is limited. Although not directly applied to claddings, [PERSON] and [PERSON] [50] pointed out that current inspection should occur at the 15-month interval to detect anomalies of rapid evolution, to monitor anomalies detected in previous inspections, and to check all aspects that are influenced by the seasons and, every 5 years, current inspection should be replaced by detailed inspections in order to determine the extent, severity, and respective causes of the anomalies. Later, in a study about the durability of marble claddings, where the causes and mechanisms for the loss of integrity of these elements are discussed, [PERSON] et al. [43] referred that the inspections must be carried out at intervals between 2 and 5 years. In Portugal, the RGEU/Decree-Law No. 177/2001 (New Juridical Regime of Urbanization and Edification) [51] suggests that after 8 years, the buildings should be subjected to maintenance actions (without specifying the type of actions that should be carried out). In this sense, since NSC is a durable cladding material, it seems reasonable to consider that periodic inspections should be performed every 8 years, even to evaluate the degradation condition of the buildings and to confirm whether the maintenance actions are really necessary. More recently, [PERSON] et al. [23] proposed that inspections should be carried out every 5 years and that specialized equipment should be used to detect anomalies related to the presence of water and loss of adhesion. Based on these works, the time interval between inspections varies between 2 and 8 years, 5-year being the most common value. Furthermore, in the literature, these inspections only serve to assess the degradation condition of the NSC, without any action being carried out to improve the condition of the claddings.  
Figure 1: Illustrative examples of NSC (natural stone claddings) in different degradation conditions. None of the case studies within the sample belongs to condition E: (**a**) condition A; (**b**) condition B; (**c**) condition C; (**d**) condition D.
# A Total Crop-Diagnosis Platform Based on Deep Learning Models in a Natural Nutrient Environment
###### Abstract  
This paper proposes a total crop-diagnosis platform (TCP) based on deep learning models in a natural nutrient environment, which collects the weather information based on a farm's location information, diagnoses the collected weather information and the crop soil sensor data with a deep learning technique, and notifies a farm manager of the diagnosed result. The proposed TCP is composed of 1 gateway and 2 modules as follows. First, the optimized farm sensor gateway (OFSG) collects data by internetworking sensor nodes which use Zigbee, Wi-Fi and Bluetooth protocol and reduces the number of sensor data fragmentation times through the compression of a fragment header. Second, the data storage module (DSM) stores the collected farm data and weather data in a farm central server. Third, the crop self-diagnosis module (CSM) works in the cloud server and diagnoses by deep learning whether or not the status of a farm is in good condition for growing crops according to current weather and soil information. The TCP performance shows that the data processing rate of the OFSG is increased by about 7% compared with existing sensor gateways. The learning time of the CSM is shorter than that of the long short-term memory models (LSTM) by 0.43 s, and the success rate of the CSM is higher than that of the LSTM by about 7%. Therefore, the TCP based on deep learning interconnects the communication protocols of various sensors, solves the maximum data size that sensor can transfer, predicts in advance crop disease occurrence in an external environment, and helps to make an optimized environment in which to grow crops.  
crop-diagnosis; deep leaning; farm cloud server; farm sensor gateway +
Footnote †: journal: Applied Sciences
# Noise Cancellation Method Based on TVF-EMD with Bayesian Parameter Optimization
## 4 Results and Discussion
### Analysis of the Simulated Signal
#### 4.2.2 The Effects of TVF-EMD Parameters  
In addition to \(CC\) and \(Kur\), other indices, including mutual information (MI) [33], energy entropy (EE) [34], and energy entropy mutual information (EEMI) [28], are commonly used to evaluate the degree of optimization in signal processing. As shown in Figure 7, the selection of optical parameters \(\xi\) and \(n\) did not significantly change the MI, EE, and EEMI indices of IMF1 to IMF7, indicating that these indices were insensitive to the optical parameters. In contrast, \(CC\), \(Kur\), and \(CCKur\) were sensitive to optical parameters.  
Different parameters affected the decomposition results to different degrees. The decomposition of the CO\({}_{2}\) concentration signal \(x(t)\) and the effect of TVF-EMD parameters on the decomposition results are shown in Figure 8. The bandwidth threshold \(\xi\) reached 0.95 in the decomposition results, indicating that the selection of the optimization index should be sensitive to the change in \(\xi\). Therefore, we analyzed the results of CC, Kur, MI, EE, EEMI, and \(CCKur\) under varying \(\xi\). As shown in Figure 9, when \(\xi\) assumed values of 0.1, 0.2, and 0.31 (the optimal parameter), eight, nine, and seven IMFs were obtained by decomposition, respectively. When \(\xi\) was at least four, two IMFs were obtained, indicating that mode aliasing and under-decomposition occurred. When \(\xi\) increased from 0.1 to 0.31, the MI, EE, and EEMI indices of the IMFs did not change significantly, suggesting that these indices were insensitive to the variation in \(\xi\). The CC, Kur, and \(CCKur\) indices were sensitive to \(\xi\); hence, they can be selected as indices for the best parameters. When \(\xi=0.31\), the sum of the \(CCKur\) indices of IMF1 to IMF9 was the largest, which indicates that \(CCKur\) can be regarded as the best optimization model. The results showed that (1) \(\xi\) had to be at least 4 for mode aliasing and under-decomposition to occur; (2) CC, \(Kur\), and \(CCKur\) indices were sensitive to \(\xi\), whereas MI, EE, and EEMI indices were not; (3) the proposed \(CCKur\) index and objective function \(\max\limits_{\tau\in\{\xi,n\}}\{CCKur\}\) were effective; and (4) the value of 0.31 for \(\xi\) was reasonable and correct.  
Figure 6: Comparison of signal noise cancellation and the simulated CO\({}_{2}\)-concentration signal.  
Figure 8: Hyperparameter importance in the decomposition result.  
Figure 7: Relationship between IMF and the optimization index.
# Hybrid Forecasting Models Based on the Neural Networks for the Volatility of Bitcoin
## 4 Concluding Remarks  
We develop the models based on the neural networks for forecasting volatility of Bitcoin price in this paper. Specifically, we propose several hybrid models to improve the forecasting and conduct more than 10,000 experiments to find the optimized model. We investigate as follows. Firstly, we construct the ANN-GARCH models with 1-day lagged volatility, Google Trends, VIX and outputs of GARCH models based on the previous works. Secondly, we propose the new hybrid models which incorporate the outputs of GARCH models as input to HONN model. HONN model, which use the linear combinations of the variables as the input variables, is efficient and performs generally better than the classic ANN mode when the number of good input variables for the ANN model is small. In fact, most of the proposed hybrid models show good performances with no statistical difference, but we focus on finding the best forecasting model for Bitcoin's volatility.  
In order to find the best model among the proposed models, we carry out many experiments changing the activation functions and the number of nodes. We also adopt three performance measures to compare the forecasting accuracy of the proposed models. Consequently, the hybrid models based on the HONN model which can capture higher-order correlations in input variables show the improved performance for forecasting of Bitcoin volatility. Compared to the best GARCH model, the best GT-VIX-GARCH-H model improves by 11%, 2.2% and 30% for MAE, RMSE and MAPE, respectively. In addition, compared to the best ANN-GARCH model, the best GT-VIX-GARCH-H model improves by 2.2%, 2.5% and 3.9% for MAE, RMSE and MAPE, respectively. In other words, these results show that the hybrid models based on the HONN model provide more accurate forecasting results and are appropriate for forecasting of volatility in the Bitcoin market.  
G.K. designed the experiments; M.S. collected and analyzed the data; M.S. and G.K. contributed analysis tools; M.S. and G.K. wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Research Foundation of Korea grant funded by the Korea government (No. NRF-2017R1E1A1A03070886).  
The authors declare no conflict of interest.
## 1 Introduction
[17,18] investigated the flow and heat transfer characteristics of ball bearings with oil jet lubrication using CFD. They concluded that the bearing temperature was lower in areas with more oil. Currently, CFD-based methods are widely used to predict the oil distribution in a gearbox/bearing. However, the existing research has limitations, such as the consideration of only oil injection lubrication, or the consideration of only a pair of gears or a single bearing, rather than the entire gearbox. Furthermore, due to the complex structure of an intermediate gearbox, time-consuming CFD calculations, and the uncertain convection heat transfer law associated with the intermittent behavior of lubricating oil, analyses on the temperature characteristics of an intermediate gearbox with splash lubrication are few. Therefore, detailed studies are required to improve our understanding. In this study, we establish a CFD thermal-fluid coupling model of a helicopter intermediate gearbox with splash lubrication. Subsequently, a multiple reference frame (MRF) model is used to describe the rotational characteristics of the gears and bearings. The lubricating oil temperature and the lubrication and temperature characteristics of an intermediate gearbox with splash lubrication are obtained and discussed in detail.
## 4 Results and Discussion
### Intelligent Detection of UAV Images  
After the process of radiometric calibration, a quantitative relationship was established between the digital quantization values of the unmanned aerial vehicle (UAV) remote sensing imagery and the corresponding radiance values of the field of view. Additionally, since these images were captured by a lightweight UAV at an approximate altitude of 80 m above the ground under clear weather conditions, they are almost unaffected by atmospheric influences. The multi-temporal UAV multispectral images of the mining area are illustrated in Figure 3.
# Building Scalable and Secure Multicast Delivery Infrastructure in a Local Area Network
## 2 The Motivation
### Brief Overview of the PrECast Protocol  
Broadcasting is one of the essential features in the IPv4 protocol. Several IPv4 services/applications like ARP, Dynamic Host Configuration Protocol (DHCP), etc. depend on IP broadcasting. Attackers from both inside and outside networks exploit this feature and launch several attacks against a network. Most of the research works available in the literature attempt to solve a specific problem that results from IP broadcasting (like man-in-the-middle, ARP poisoning, etc). In addition, the available solutions use either a computationally-intensive cryptographic scheme, a priori relation between the hosts and the network or a modified protocol stack at every host. There was no holistic light-weight solution available in the literature to solve IPv4 broadcasting related security issues.  
In our earlier paper [1], we provided an elegant crypto-free solution called PrECast to solve several attacks that are due to IPv4 broadcasting. This is by effectively eliminating broadcasting in an IPv4 network, without modifying the protocol specification. Our solution does not require any modification at the client-side. In addition, our solution does not use any strong cryptographic techniques that consume CPU (central processing unit) cycles and memory. PrECast assumes the existence of a secure multicast tree in a corporate LAN.  
The heart of the PrECast protocol is the secure multicast tree that consists of switches in the network to which hosts are connected. Whenever a host initiates an IPv4 broadcast-based service (like ARP, DHCP-request, or Bootstrap Protocol (BOOTP)), the switch to which the host is connected uses its host-cache information to convert the broadcast message to a unicast message and sent over the multicast tree. Whenever the host switch has no information about the intended destination, it multicasts the service over the tree. The switch to which the intended destination host is connected uses its ARP cache to forward the multicast message as a unicast message to the destination host. By doing this way, we efficiently eliminated IPv4 broadcasts in a network. Thus, PrECast also solves a number of security-related problems that are due to IPv4 broadcasting.  
Motivated through three different challenges mentioned in this section to build a secure multicast delivery system, in this paper, we propose a core-based tree multicast tree construction algorithm. The crypto-algorithm used for solving various security challenges in our multicast delivery mechanism is the pseudo-identity based encryption. Section 3 deals with the overview of the pseudo-identity based encryption and how it is useful in securing our multicast delivery mechanism.
###### Abstract  
This study was conducted in Sawena district, located in the Eastern Bale Zone of Ethiopia, with the aim of analyzing climate variability and identifying adaptation strategies. Secondary data covering the period from 1984 to 2023 were utilized, along with structured and unstructured questionnaires. Primary data were gathered from 350 pastoralist households across six kebeles through a household survey. This study used the Mann-Kendall test, Sen's slope estimator, the coefficient of variation, descriptive statistics, and a multivariate probit model to analyze climate variability and adaptation strategies. The Mann-Kendall test, Sen's slope estimator, and coefficient of variation analysis results showed significant rainfall increases in September, October, and November, with high winter variability and an upward autumn trend. Temperature analysis revealed consistent warming, with the greatest increases in September (0.049 \({}^{\circ}\)C/year) and summer (0.038 \({}^{\circ}\)C/year), and an annual mean rise of 0.034 \({}^{\circ}\)C per year, indicating climate shifts affecting pastoralist and agro-pastoral livelihood strategies and water resources that lead the area toward vulnerability. The descriptive results indicated that pastoralist households have adopted various adaptation strategies: 45.1% participate in seasonal livestock migration, 26.3% rely on productive safety net programs, 19% pursue livelihood diversification, and 9.7% engage in agroforestry. Multivariate analysis indicates that education, age, credit access, livestock ownership, asset value, and media exposure influence these strategies. The findings highlight the importance of policies to enhance climate resilience through diversification, sustainable land management, and improved access to resources like credit and markets, alongside strengthened education and targeted extension services.  
climate; variability; trend; adaptation strategies; pastoralism Article
Therefore, it is necessary to develop a positioning and tracking algorithm. To realize an accurate system state estimation using a classical kalman filter, the prerequisite is that the dynamic model of the system is accurate and that the noise is an uncorrelated white noise. Also, to ensure system stability, appropriate initial value and variance matrix must be selected [20; 21; 22]. However, in a complex indoor environment, it is obviously difficult to guarantee. Aiming to solve this problem, many adaptive filtering algorithms based on the estimation algorithm of statistical characteristics of time-varying noise have been proposed. When the covariance matrix \(Q\) of the system noise and the covariance matrix \(R\) of the measurement noise are uncertain, the adaptive filtering uses the information on the observed data to estimate and correct the statistical characteristics of the noise continuously so as to reduce the state estimation error [23; 24]. Amongand \(R\) algorithms for uncertainty, the Sage-Husa algorithm is most representative [25; 26]. Simulation and practical results show that the accuracy of the adaptive kalman filtering algorithm is much higher than that of the conventional kalman filtering algorithm, but the tracking ability of the adaptive kalman filtering algorithm decreases when the observed value anomaly and system equation disturbance occur. Thus, the adaptive kalman filter does not have the ability to deal with the system mutations. To ensure filter reliability, in [27], it has been suggested that it is possible to sacrifice certain accuracy for achieving better filter stability, and a strong tracking filter algorithm [27] named the strong tracking filter (STF) was proposed. This method is to multiply the covariance matrix P of the predicted state by a weighted coefficient, so the strong tracking kalman filter has strong robustness to the changes in system parameters and a strong ability to deal with the system mutations. Although the robustness of the strong tracking kalman filter is significantly improved compared with the adaptive kalman filter, the optimal condition of the filter is destroyed, and the accuracy of the filter is reduced compared with the adaptive kalman filter [28; 29; 30; 31]. In [31], a fuzzy logic adaptive system was used to adjust the scaling factor of an r-array when the p-array scaling factor of the strong tracking kalman filter was obtained [31]. This method has the disadvantage of estimating the r-matrix independently, and the control logic of the FLAS is complex. This paper proposes a Bluetooth array positioning system for a complex indoor environment, including shelter and multipath. The results of the positioning method of mutation status tracking ability of a strong tracking kalman filtering algorithm for target tracking, singular value decomposition (SVD) [32; 33; 34]. On the basis of further improvement of the strong tracking kalman filter robustness, an adaptive factor is introduced. Finally, the feasibility of the algorithm is verified by the measured data.