esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 4 Evaluation and Analysis
### Scenarios  
While the results from this work may be more generally applicable to mobile DTN applications, we focus our evaluation on scenarios inspired by the flying fox tracking project. We perform simulations for two animal species that have different daily activity patterns, to explore the performance of our proposed algorithms under different scenarios.  
Specifically, we assume the tracked animals periodically visit foraging areas where they look for food and rest at roosting camps between the feeding periods. Some of the rest areas are equipped with base stations that download data from animals and forward it to a server. In addition to the suite of sensors that track animal locations and activities, each node has a solar panel for energy replenishment.  
There are five resting areas and five foraging areas on the simulated map. Two rest areas are equipped with base stations. We simulate twenty animal nodes with GPS tracking sensors, and one-half of the animals periodically come back within the transmission range of a base station each day.  
Each scenario runs for one week (7 days and 7 nights). The flying and foraging time is 3 h, and during this time GPS position is recorded every 4-6 min. Each GPS reading generates a 100-byte packet, and each node generates approximately 3.5 KB per day. If a node runs out of energy, it will stop taking any more GPS readings or participating in communication with other nodes.
###### Abstract  
This paper demonstrates the ability of a functional food based on probiotics and selenium nanoparticles (SeNPs) to annihilate the toxic effect of cadmium on the kidneys. SeNPs were obtained by eco-friendly method used _Lactobacillus casei_. The morphological features and size of SeNPs were characterized by Atomic Force Microscopy (AFM) and Dynamic Light Scattering (DLS). Two kind of SeNPs were used, purified and Lacto-SeNPs (LSenNPs), administered by gavage at three concentrations (0.1, 0.2, and 0.4 mg/Kg b.w.) for 30 days in a mouse model of cadmium renal toxicity. The blood marker of renal injury (creatinine) significantly decreased in groups where the mice were treated with both form of SeNPs. The antioxidant capacity of plasma was evaluated by Trolox Equivalent Antioxidant Capacity (TEAC) assay and revealed that SeNPs in co-treatment with Cd, promotes maintaining antioxidant activity at the control level. Histopathological analysis of kidneys demonstrated morphological alteration in the group that received only cadmium and restored after administration of SeNPs or LSeNPs. In addition, immunohistochemical analysis revealed anti-apoptotic effects through reduction of pro-apoptotic _bx_ and increasing of anti-apoptotic _Bcl-2_ protein expressions. Moreover, co-administration of Cd with SeNPs significantly decreased gene expression of kidneys inflammatory markers (TNF-\(\alpha\), IL-6, NF-kb) in a dose dependent manner, with the best results for LSeNPs at highest dose (0.4 mg/kg). Therefore, the _L. casei_ strain is a potential SeNPs-enriched probiotic for application as functional food in the future to annihilate cadmium-induced kidneys toxicity.  
cadmium; selenium; _Lactobacillus casei_; kidneys; histology; apoptosis; inflammation; functional food; probiotics
## 3 Processing Methods
### Filter-Based Real-Time LEO Satellite Clock Determination
Figure 3: Processing timelines of the two methods for real-time LEO satellite clocks determination based on the kinematic model and using the Kalman filter. where the observation data precede the availability of GNSS real-time products. In other words, GNSS real-time products may not be available due to their latency to determine the LEO satellite clocks at the final epoch of the observation period. In this study, we indicate this situation as \"end-loss\". When employing the arc-processing method, as shown in the right panel of Figure 2, it is necessary to consider the discontinuity of the GNSS real-time products in the near-real-time processing arc (the black line in the lower part of Figure 3), which is labeled as \"mid-gap\". In this method, attention needs to be paid to the place and duration of the appeared gaps, which will influence the subsequent LEO clock predictions. As explained above, the latency of the GNSS products leads to the problem of \"end-loss\", and discontinuity of the GNSS products leads to the problem of \"mid-gap\". When performing filter-based real-time LEO satellite clock determination, the GNSS product loss can be handled as follows:  
* In real-time processing mode, when \"end-loss\" occurs due to the latency of the GNSS products. The orbits are tested for two scenarios: (1) Orbit extrapolation based on the available real-time orbital information using the 9 th-order Nevil model [31]; (2) using the ultra-rapid orbits employed for estimating the clocks, i.e., only the clocks are considered delayed (see Scenarios C and E in Table 2). For the delayed real-time GNSS satellite clocks, two scenarios are tested as well: (1) Employing the last-epoch clock that is available in the real-time products. For example, the software RTKlib Version 2.3.4 [31] utilizes this strategy; (2) predicting the clocks based on linear extrapolation of the last \(N\) epochs, and \(N\) is the value of the \"end-loss\" duration divided by the GNSS clock sample. * In near-real-time processing mode or post-processing mode, when \"mid-gap\" occurs during the LEO satellite clocks determination, linear interpolation of GNSS satellite clocks is performed using the last epoch before the clock product interruption and the first epoch after the interruption. The interpolation method is performed as follows:  
\[dt^{g}=\frac{dt_{2}^{g}\times\Delta t_{1}+dt_{1}^{g}\times\Delta t_{2}}{\Delta t _{2}-\Delta t_{1}} \tag{7}\]  
with  
\[\Delta t_{2}=t_{2}-t \tag{8}\]  
\[\Delta t_{1}=t-t_{1} \tag{9}\]  
where \(t_{1}\),\(t_{2}\) is the last epoch before the clock product interruption and the first epoch after the interruption, respectively; \(t\) is the epoch to be interpolated at; \(dt_{1}^{g}\) and \(dt_{2}^{g}\) is the GNSS clocks at \(t_{1}\),\(t_{2}\); \(dt^{g}\) is the GNSS clock at \(t\) to be interpolated.
## 2 Materials and Methods
### Tunnel Point Cloud Displacement Detection  
The methodology for monitoring tunnel deformation using Mobile Laser Scanning technology involves computational steps to align, match, and analyze point cloud data obtained at different time steps in the same region [35]. The aim is to assess the structural behavior and identify any significant changes in surface and shape. The algorithm designed for displacement analysis is executed through Python 11.0 programming under the Jupyter editor.  
The alignment algorithm proposed in this research used the ICP algorithm to align two sets of point cloud data collected at different periods (\(P_{0}\) and \(P_{i}\)). Since environmental changes and sensor misalignments make it difficult to establish direct correspondences between data points, ICP finds the closest corresponding points by minimizing the Euclidean distance between them. It iteratively refines a transformation matrix (rotation and translation) to align the two point clouds. Once aligned, displacement is detected by measuring the distance between corresponding points in the two datasets, identifying areas where significant deviations indicate potential structural changes in the tunnel.  
Surface matching is a crucial technique in tunnel deformation analysis, particularly when working with point cloud data. It involves aligning two surfaces to determine their relative displacement or deformation over time. In the context of shield tunnel deformation monitoring, surface matching facilitates the comparison of the tunnel's current surface state with earlier scans to detect shifts.
## 3 The Proposed Method
### Image Sharing and Identifying Information Embedding  
In this study, the algorithm opts for \(q=2_{8}\) to establish a finite field for executing secret sharing of the image within the domain. The image is divided into three parts: part A contains pixels with data \(\mathbf{WL}\), part B holds the remaining edge information, and part C encompasses the other pixels. For part B, a \((r,n)\)-threshold secret sharing scheme is exclusively employed. Initially, a sequence of coefficient matrices \(\mathbf{Xs}=\mathbf{X}_{1},\mathbf{X}_{2},\ldots,\mathbf{X}_{LB}\) is generated based on the key \(K_{p}\), where \(LB\) represents the number of pixels in part B. For segmenting pixel values \(\mathbf{IR}(i,j)\) and embedding identifying information, \(8(r-1)\) bits of data are extracted from the identifying information \(\mathbf{md}=0,1^{N}\). After encryption with the identifying information hiding key \(K_{d}\), every 8 bits are converted into decimal numbers, resulting in the sequence \(\mathbf{ms}=ms_{1},ms_{2},\ldots,ms_{r-1}\).  
Then, the sharing vector \(\mathbf{a}_{ij}=[\mathbf{IR}(i,j),\,a_{1}(i,j),\,\ldots,\,a_{r-1}(i,j)]^{T}\) is constructed, and the mapping \(e_{ij}:(i,j)\to f_{k}(i,j)\) is generated by the key \(K_{p}\), where \(f_{k}(i,j)\) is the sub-secret of the previous pixel of pixel \(\mathbf{IR}(i,j)\), \(k\) is the position index of the sub-secret in the vector \(\mathbf{f}\), \(k\in 1,2,\ldots,n\). When \(\mathbf{IR}(i,j)\) is the first pixel in part B, \(f_{k}(i,j)\) is a random value. Calculate \(a_{1}(i,j)\) with \(a_{2}(i,j),a_{3}(i,j),\ldots,a_{r-1}(i,j)\), \(h\in 1,2,\ldots,r-1\) from the following equation:  
\[\left\{\begin{array}{ll}a_{1}(i,j)=f_{k}(i,j)+ms_{1}\\ a_{h}(i,j)=ms_{h}\end{array}\right. \tag{10}\]  
Determine \(\mathbf{X}_{k}\), \(k\in 1,2,\ldots,LB\) in the generated sequence of \(\mathbf{Xs}\) from the positional ordinal number of pixel \(\mathbf{IR}(i,j)\) in the pixel of part B. Finally, we perform secret sharing according to the equation, and embed \(\mathbf{ms}\):  
\[\left(\begin{array}{c}f_{1}(i,j)\\ f_{2}(i,j)\\ \vdots\\ f_{n}(i,j)\end{array}\right)=\left(\begin{array}{cccc}x_{11}&x_{12}&\ldots &x_{1r}\\ x_{21}&x_{22}&\ldots&x_{2r}\\ \vdots&\vdots&\ddots&\vdots\\ x_{n1}&x_{n2}&\ldots&x_{nr}\end{array}\right)\times\left(\begin{array}{c} \mathbf{IR}(i,j)\\ a_{1}(i,j)\\ \vdots\\ a_{r-1}(i,j)\end{array}\right)\bmod q \tag{11}\]  
Once all pixel values in part B have been shared, \(f_{k}(i,j)\) of the same identity \(k\) are aggregated to obtain \(n\) sub-secrets \(\text{B}_{k}\) of part B. These are then combined with parts A and C to generate \(n\) encrypted images \(\mathbf{IE}_{k}\), which are distributed by the image owner to \(n\) data hiders.
## 1 Introduction
Both studies employed MODIS data with a low resolution and focused on regional and global areas. The combination of Landsat and MODIS data provides a new perspective on a local scale because Landsat, with its medium resolution, is capable of providing more detailed information about specific locations. On the other hand, there is a limited number of studies that have mapped the temperature distribution during ENSO events using different remote sensing sensors [5; 6; 13]. The combination of sensors is important to fill gaps in data due to the inability of thermal data to penetrate clouds [6]. It is crucial to provide spatial information, such as that obtained using remote sensing techniques, to classify affected areas during these incidents. Understanding the Oceanic Nino Index (ONI) as an indicator of ENSO strength and its impact on urban temperature is particularly important. In tropical and subtropical climates, research using remote sensing approaches to understand the long-term effects of ENSO on urban temperature is still limited. Knowledge and spatial information, including the location of affected areas during ENSO incidents, are crucial. Understanding the varying impact of ENSO strength on urban temperature is essential for predicting local weather and climate patterns in the future. By leveraging remote sensing (RS) technology and Geographic Information Systems (GISs), our understanding of ENSO effects and their spatial distribution can be improved, contributing to effective mitigation strategies and informed decision making. This information is valuable for various responsible entities, including disaster management departments, national councils, charities, local governments, and the Ministry of Health. Accurate spatial data on areas with temperatures above 30 \({}^{\circ}\)C, which can make humans uncomfortable [5] (hotspot areas), can aid city planners and local governments in predicting and implementing infrastructure measures, such as green and blue areas, to mitigate the impact of El Nino on surface temperatures and urban climates. The findings of this study can serve as a reference for policymakers and agencies involved in disaster management and healthcare to develop targeted measures during ENSO events. RS and GIS capabilities in mapping hotter areas and cold spots in urban areas provide valuable information for policymakers and agencies to provide assistance. Enhancing our knowledge on the extent of ENSO's impact on temperature enables us to better plan land development and conduct research on climate change [5; 6].
# Cryptanalysis of a New Color Image Encryption Using Combination of the 1D Chaotic Map
## 2 Review of the Original Scheme  
Before encryption divides the color image with size \(M\times N\), this needs to be encrypted, into three grayscale images according to trichromatic theory. Then, link the grayscale images into another grayscale image with size \(M\times 3N\). The sequence \(S=\{s_{1},\ s_{2},\ \ldots s_{M\times 3N}\}\) is produced by reshaping the last gray image, and the length of which is \(M\times 3N\).  
The flow diagram of the 1D chaotic encryption scheme is shown in Figure 1. In permutation phase, \(X\), which is a chaotic sequence, is acquired by iterating the new chaotic system \(M\times 3\)\(N\)+\(N_{0}\) times and discarding the former \(N_{0}\) elements. The new chaotic map in [37] is defined by the following equation:  
\[x_{n+1}=F_{chos}(u,x_{n})\times 2^{k}-floor\Big{(}F_{chos}(u,x_{n})\times 2^{k} \Big{)} \tag{1}\]  
where \(F_{chos}(u,x_{n})\) is one of 1D chaotic maps as logistic, sine and Chebyshev maps. \(x_{n}\) is the output chaotic sequence. \(u\in(0,10]\) and \(k\in[8,20]\) are the initial values. The parameters of \(u\), \(k\) and \(N_{0}\) are utilized as the security key. The permutation position vector \(X\prime=\{x\
u_{1},x\
u_{2},\...\ x\
u_{M\times 3N}\}\) is acquired by sorting vector \(S\) in ascending order. The image pixel vector \(P\), which is permuted, is acquired by the equation of \(P(i)=S(X\prime(i))\).  
In the diffusion phase, the diffused image pixel sequence \(C\) is acquired by the following equation:  
\[C(i)=\text{mod}(P(i)+D(i),256)\oplus C(i-1) \tag{2}\]  
where \(C(0)\) is \(P(M\times 3N)\) and \(D\) is the diffusion sequence which is obtained by Equation (3):  
\[D(i)=\text{mod}(floor(X(i)\times 10^{14}),256) \tag{3}\]  
Figure 1: The flow diagram of the 1D chaotic image encryption.  
The new cipher sequence of \(C\)' is acquired after \(C\) is rotated to the left by the quantity of \(lp\) which is used as the security key, too. The eventual cipher image is obtained by reshaping \(C\)' into RGB images.
## 2 Materials and Methods
### Study Design and Procedures
For serum samples, fasting and 2-h blood samples were collected in a plain tube (with no clotting factors) and this was centrifuged at 2500 RPM for 15 min using a Beckman General Purpose centrifuge (Beckman Coulter Inc., CA, USA) to obtain serum and for urine samples, the urine was collected in specimen containers supplied by the laboratory and all samples were transported daily in an ice-box for processing at pathology practice (PathCare Laboratory, Cape Town, South Africa). The level of kidney function was measured using estimated glomerular filtration rate (eGFR) which was calculated using the 4-variable Modification of Diet in Renal Disease (MDRD) (eGFR = 175 \(\times\) (SCr)\({}^{-1.154}\)\(\times\) (age)\({}^{-0.203}\)\(\times\) 0.742 [if female]) [29], and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations (eGFR = 141 \(\times\) min(SCr/\(\kappa\), 1)\({}^{\alpha}\)\(\times\) max(SCr /\(\kappa\), 1)\({}^{-1.209}\)\(\times\) 0.993 Age \(\times\) 1.018 [if female], where SCr was defined as serum creatinine, \(\kappa\) was 0.7 (females) or 0.9 (males), \(\alpha\) was \(-0.329\) (females) or \(-0.411\) (males), min indicated the minimum of SCr/\(\kappa\) or 1 and max indicated the maximum of SCr/\(\kappa\) or 1. The correction factor for African American ethnicity was not included as recommended for our study population [30] Only the MDRD data are shown, as similar results were obtained for both equations. CKD was defined as an eGFR of <90 mL/min/1.73 m\({}^{2}\) and/or albumin-to-creatinine ratio (ACR) >3 mg/mmol as recommended by the current Kidney Disease: Improving Global Outcomes (KDIGO) guidelines [29]. Participants were classified a \"current smoker\", if serum cotinine levels were >15 ng/mL [31] and a \"drinker\" if they self-reported the consumption of alcohol.
## 3 Measurement Procedures of Various Methods
### Mpmd  
The procedure of MPMD is introduced in detail as follows:  
1. The incident ray \(\overset{\rightarrow}{P}\) is calibrated using the system calibration. The pose of the screen relative to the camera (\(R_{m}\), \(T_{m}\)) can be obtained through the pre-calibration procedure;
2. The initial coefficient matrix \(\mathbf{c}\) and Zernike polynomial are used to represent the surface shape of the SUT;
3. The incident ray \(\overset{\rightarrow}{P}\) is reflected from the SUT to the screen. The reflected ray \(\overset{\rightarrow}{r}\) can be calculated with Equation (5), and the intersection \(S^{\tau}\) on the screen is obtained;
4. The coefficient vector \(\mathbf{c}\) and pose of the screen (\(R_{m}\), \(T_{m}\)) are optimized by minimizing the differences between the re-projection \(S^{\tau}\) and the measurement S of the screen among the N pairs of correspondence in the least squares sense;
5. Output the shape coefficient and reconstruct the surface shape. The procedure of MPMD is illustrated in Figure 4b.  
Figure 4: Flow charts: (**a**) PM; (**b**) MPMD.
## 3 Results and Discussion
### Determining Optimum Pretreatment Conditions  
In this study, the quality of gelatin was defined as the relative amount of un-hydrolyzed, complete form of gelatin. Therefore, intensities of \(\alpha\)-, \(\beta\)-, and \(\gamma\)-bands as well as low molecular-weight bands (smaller than 100 k) in SDS-PAGE are the good indicators of the quality of gelatin.  
For type A gelatin, intensities of \(\alpha\)-, \(\beta\)-, and \(\gamma\)-bands of gelatin were highest in the 1 h pretreated group and decreased with the pretreatment time (Figure 1a and Figure S1a). Similarly, the highest yield, 6.81% (dry gelatin weight/wet tissue weight), was recorded in the 1 h treatment group (Table 1). No gelatin bands were observed in the supernatant of pretreated solution in the 1 h treatment group(Figure S1a), suggesting no gelatins were lost by the pretreatment in this condition. A similar trend was obtained for type B gelatin, where the sample pretreated with 0.1 M NaOH for 1 h had the strongest gelatin bands (Figure 1b and Figure S1b) with maximum yield (10.56%, Table 1). Again, no loss of gelatin was observed in this condition (Figure S1b). The intensity of low molecular-weight bands in SDS-PAGE increased with the pretreatment time for both types of gelatins (Figure 1a,b and Figure S1a,b). The intensity of low molecular-weight bands was higher in type B gelatin than in type A (Figure S1a,b); thus, acid pretreatment produces higher quality but lower yields of gelatin than does the alkali pretreatment.  
Combined pretreatment produced lower intensities of gelatin bands with higher intensities of low molecular-weight bands in SDS-PAGE and lower yields (5.38%), when compared with HCl and NaOH pretreatments (Figure 1c and Figure S1c and Table 1). Lower yields might have been caused by over hydrolysis and loss of collagen by the combined pretreatment (NaOH \(\rightarrow\) HCl). The control samples without pretreatment had the lowest gelatin bands (Figure 1d and Figure S1d) and the lowest yield (3.97%, Table 1). Thus, optimized acid and alkaline pretreatments increase the efficiency of gelatin extraction.  
In summary, the optimum pretreatment period was determined as 1 h in both acid and alkaline pretreatments. Combined pretreatment was not recommended. The optimum pretreatment period does not lead a loss of gelatin, but over-pretreatment evokes loss of gelatin and a decrease in yield in both acid and alkaline pretreatments.
# Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data
## 1 Introduction
### Background
However, changes in macro trends such as globalization of markets, rapid adoption of modern Information and Communication Technologies (ICT) for e-commerce [13], and a shift from the 'contractual-relationship dominant' paradigm [14] in the B2B domain have caused an increase in efforts to adapt to the new environment [15] and apply knowledge and good practices demonstrated to yield tangible results in identifying customers at risk of leaving. Most notably, the feasibility of approaches to customer relationship analytics commonly leveraged in the B2C domain (which has received significantly more attention when predictive churn modeling is in question [16]) have been explored [17], indicating that some could be effectively used in B2B context as well. Such efforts are gaining increased interest from both academia and industry, but there is still a notable lack of studies where the results of field experiments with real-world data are reported.
## 1 Introduction
As mentioned above, the seismic and structural uncertainties should be considered for performance-based earthquake engineering [27], both of which have particular effects on the seismic behavior of the container crane. However, it has been discovered that the influence of seismic uncertainty on structural performance is much larger than that of structural uncertainty [28; 29; 30]. Given that fact, this paper only considers seismic uncertainty to simplify the fragility analysis of the  
Figure 1: Collapsed cranes after the Kobe Earthquake [10]. container crane. Therefore, the structural material and geometric parameters in the finite element (FE) model are deterministic and equal to their respective mean values. Compared to bridges and steel moment frames, the structure of the gantry crane is relatively simple, but the payload of the gantry crane is relatively large with complicated working conditions. There mainly exist the following obvious differences when it comes to the seismic analysis of the gantry crane in this paper: (1) The seismic demand measures (DMs) are specified based on the structural characteristics of the gantry crane. As will be explained later, a new DM called the local displacement ratio (LDR) is proposed as the optimal DM to describe the seismic performance of the gantry crane more precisely. (2) The seismic operating capacity of the gantry crane is also investigated to assess the ability of the gantry crane to protect the crane operator and the lifted goods. (3) This research focuses on the fragility analysis of the gantry crane subjected to near-field ground motions. This paper is structured as: The original and FE models of a typical gantry crane are presented in Section 2. The probabilistic seismic demand model (PSDM) and five groups of limit states are defined for the gantry crane in Section 3. In Section 4, twenty near-field ground motions are selected for the fragility analysis. Fragility curves for the gantry crane subjected to ground motions under three different working conditions are developed in Section 5, and the influence of the seismic incident direction on the structural susceptibility is also investigated. Besides, seismic valuation for the regular operation of the gantry crane is also investigated in this section. Finally, the significant conclusions and discussions of this research are presented in Section 6.