esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
This study considers the load balancing problem as an optimization issue to reduce power consumption, reduce energy consumption, assign tasks to appropriate nodes, decrease the imbalance degree, and improve utilization. Mostly, some objects do the same tasks. As a result, a task group may be described as a collection of objects that execute comparable and interchangeable activities. Most IoT applications will have many task groups. Numerous items with varying abilities may be found in the network. Virtual objects are items that have been chosen from each task group to be displayed. They divide tasks among members of their group and assign the task of processing requests to these physical objects. Some workgroups may be overloaded when assigned. So, tasks are removed from that workgroup and assigned to another node of a suitable workgroup based on energy consumption and execution time using the chemical reaction optimization algorithm. Load balancing, as the main issue for reducing the power consumption, is known as an NP-complete problem. Therefore, this paper proposes an artificial chemical reaction optimization algorithm for effective and efficient load balancing in IoT. The artificial chemical reaction optimization algorithm is a novel computational method inspired by chemical reactions. The artificial chemical reaction optimization algorithm is applied successfully to solve optimization problems and classification rules. The artificial chemical reaction optimization algorithm is an adaptive technique based on the process of chemical reactions [15]. Hence, this article employs this algorithm for load balancing and increasing energy usage via a fitness function in each workgroup. Briefly, the contributions of this research are as follows:  
* Applying an artificial chemical reaction optimization for the load balancing of IoT-based smart recycling machines;
* Improving the imbalance degree improvement in IoT-based smart recycling machines;
* Reducing the energy consumption and delay time in IoT-based smart recycling machines. The remainder of the article focuses on the sections below. Section 2 discusses the associated work. The system model and definitions of problems are presented in Section 3. In Section 4, the proposed method is described. The findings of the tests conducted to assess the efficacy of the proposed method are demonstrated in Section 5. Eventually, in Section 6, the conclusions and future works are presented.
## 2 Working Conditions and Mathematical Modelling of Masts
### Limitations  
#### 2.4.1 Selection of Hazardous Points for Mast Calibration Section  
By calculating the internal force of the mast, the section with the maximum shear force, axial force and bending moment is selected as the dangerous section. According to the stress distribution law of the box section of the mast, points 1, 2, 3 and 4 shown in Figure 3 are selected as the dangerous points for calculation.
# Optimal User Association Strategy for Large-Scale IoT Sensor Networks with Mobility on Cloud RANs
###### Abstract  
In networking systems such as cloud radio access networks (C-RAN) where users receive the connection and data service from short-range, light-weight base stations (BSs), users' mobility has a significant impact on their association with BSs. Although communicating with the closest BS may yield the most desirable channel conditions, such strategy can lead to certain BSs being over-populated while leaving remaining BSs under-utilized. In addition, mobile users may encounter frequent handovers, which imposes a non-negligible burden on BSs and users. To reduce the handover overhead while balancing the traffic loads between BSs, we propose an optimal user association strategy for a large-scale mobile Internet of Things (IoT) network operating on C-RAN. We begin with formulating an optimal user association scheme focusing only on the task of load balancing. Thereafter, we revise the formulation such that the number of handovers is minimized while keeping BSs well-balanced in terms of the traffic load. To evaluate the performance of the proposed scheme, we implement a discrete-time network simulator. The evaluation results show that the proposed optimal user association strategy can significantly reduce the number of handovers, while outperforming conventional association schemes in terms of load balancing.  
Cloud RAN; user association; handover; load balance; IoT Sensor; mobility; wireless networks; optimization +
Footnote †: journal: Accepted: 10 October 2019; Published: 12 October 2019
## 4 Results and Discussion  
### Experimental Datasets  
The datasets from a total of 147 Multi-GNSS Experiment (MGEX) stations, at which BDS-3 and BDS-2 signals can be both tracked, are used for the performance evaluation of BDS-3/BDS-2 real-time PPP. The geographical distribution of stations used in our analysis is shown in Figure 2. The cut-off elevation is set to 7\({}^{\circ}\). The sampling interval of the BDS-3/BDS-2 observations is 30 s. It should be noted that the positioning performance of real-time PPP can only be substantially assessed with the use of an observation sampling rate of 30 s. When the sampling interval of observations is reduced, the convergence time of real-time PPP is expected to be shortened [37]. The real-time stream CLK93 released by CNES is employed to acquire the precise corrections for the satellite clocks and orbits in the real-time scenario, on the basis of broadcast ephemeris. The data rate of the CLK93 stream is 5 s. The analysis period covers 70 days from DOY 124 to 193 of 2020 for the assessment of real-time precise product quality, while a time span of a week from DOY 140 to 146 of 2020 is employed for the availability analysis of satellite constellations and the evaluation of real-time PPP performance.
## 2 Materials and Methods  
### Reagents  
Grape pomace was obtained from Biocal (Luzzi, CS, Italy). The grapes used for the production of the pomace were grown in Calabria (Italy) and the pomace was collected after the winemaking process.  
The following reagents were purchased from Sigma-Aldrich s.r.l. (Milan, Italy): dextran from _Leuconstoc_ spp. (DEX), hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), ascorbic acid (AA), 2,2-diphenyl-1-picrylhydrazyl radical (DPPH), Folin-Ciocalteu reagent, gallic acid monohydrate--98+%, A.C.S. reagent, sodium carbonate, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT), paraformaldehyde, Neutral Red (NR), acetic acid, nickel sulfate, propidium iodide, Triton X-100, and \(\gamma\)-globulin. FACS buffer is composed by Dulbecco's Buffered Saline (PBS), 0.5-1% Bovine Albumin Serum (BSA), 0.1% sodium azide all purchased from Sigma-Aldrich s.r.l. (Milan, Italy).  
Strat-M(r) membranes (25 mm discs) were purchased from Merck Millipore (Darmstadt, Germany).  
4\({}^{\prime}\),6-diamidino-2-phenylindole (DAPI), goat pAb IgG (TRITC), goat pAb IgG (FITC) were purchased from Abcam (Cambridge, UK).  
Collagen III Monoclonal Antibody and Elastin Polyclonal Antibody, Human IL-12 ELISA Kit, and Human MMP9 ELISA were purchased from Invitrogen (Carlsbad, CA, USA).  
The following kits were purchased from Abcam (Cambridge, UK): Human TNF-alpha ELISA Kit, Human IL-6 ELISA kit, Kit Beta Galactosidase Assay Kit, CPD ELISA Kit (cyclobutane pyrimidine dimers, DNA samples) and Mitochondrial DNA Isolation Kit.  
Intracellular Total ROS Activity Assay Kit was purchased from Abnova (Heidelberg, Germany).  
Human Collagen Type III ELISA Kit and Human Collagen Type I ELISA Kit were purchased from Cloude Clone (Wuhan, China).  
8-hydroxy-2\({}^{\prime}\)-deoxyguanosine (8-OHdG) ELISA Kit, Human IL-1 alpha, and Human Hyaluronic Acid ELISA Kit were purchased from Abokine (Heidelberg, Germany).  
Human Elastin ELISA Kit was purchased from Elabscience (Houston, TX, USA).  
The DNeasy Blood and Tissue Kit was purchased from Qiagen (Venlo, Netherlands).  
The MATTEK EPI-212-SIT and the full thickness skin tissues EFT-412 3D Human Epidermis Full Thickness Micro-Tissues were purchased from MatTek Life Sciences (Ashland, MA, USA).  
The BJ (human fibroblast), Balb/3T3 Clone A31 (murine fibroblast), THP-1 (human monocyte), and HaCaT (human keratinocyte) cell lines were obtained from the American Type Culture Collection (ATCC) located in Manassas, VA, USA. The BJ and HaCat cells were cultured in MEM containing 10% FBS and 1% Penicillin/Streptomycin and 1% sodium pyruvate. The Balb/3T3 clone cells were cultured in DMEM containing 10% BCS and 1% Penicillin/Streptomycin. The THP-1 cells were maintained in RPMI 1640 containing 10% FBS, 1% Penicillin/Streptomycin, and 0.05% \(\beta\)-Mercapto-ethanol. All cell lines were maintained at a temperature of 37 \({}^{\circ}\)C in a humidified atmosphere of 5% CO\({}_{2}\).
## 2 Problem Formulation  
For the bus arrival time prediction problem, the departure time, when the bus departs from the originating station, can be regarded as a known variable. As long as the travel time between any two adjacent stations of the entire line is accurately predicted, we can accumulate them and add the sum to the departure time to get the arrival time of any station. Therefore, the arrival time prediction can be converted into a travel time prediction.  
Suppose there are \(N\) sites on the route of a certain bus. Then the route can be divided into \(N-1\) road segments. The arrival time of the \(i\)-th station is \(t_{i}\) (i = 1, 2... \(N\)). The travel time of the \(i\)-th road segment is \(y_{i}\). The relationship between travel time \(y\) and arrival time \(t\) is:  
\[y_{i}=t_{i+1}-t_{i},\ i=1,2,\ldots N-1. \tag{1}\]  
We divide the factors affecting bus arrival time into static and dynamic factors. Static factors can be expressed as:  
\[x_{Si}=(d_{i},c_{i},l_{i},b_{i}) \tag{2}\]  
where \(d_{i}\) is the length of the \(i\)-th road segment, \(c_{i}\) is the number of intersections, \(l_{i}\) is the number of lanes, and \(b_{i}\) indicates whether a bus lane exists (\(b_{i}\) can be \"1\" or \"0\"; \"1\" means \"yes\", \"0\" means \"no\").  
Dynamic factors can be expressed as:  
\[x_{Di}=(td_{i},n_{i},E_{i}) \tag{3}\]  
where \(td_{i}\) is the dwell time of the \(i\)-th station, \(n_{i}\) is the passenger number of the \(i\)-th road segment, \(E_{i}\) is bus driving efficiency of the \(i\)-th road segment. In previous research [19], we proposed a method to measure the efficiency of bus driving. The calculation method of \(E_{i}\) is:  
\[E_{i}=\frac{n_{i}}{y_{i}(1+\lambda(\frac{n_{i}}{n_{\max}})^{\omega})}. \tag{4}\]  
The travel time \(y_{i}\) is predicted by the data of the last \(T\) road segments. The prediction formula for the travel time is:  
\[\hat{y}_{i}=F_{1}(x_{S(i-T+1)},\ldots,x_{Si},x_{D(i-T+1)},\ldots,x_{Di},y_{(i- T+1)},\ldots,y_{(i-1)}), \tag{5}\]  
\[\hat{y}_{(i+1)}=F_{2}(x_{S(i-T+2)},\ldots,x_{Si},x_{S(i+1)},y_{(i-T+2)},\ldots,y_{(i-1)},\hat{y}_{i}), \tag{6}\]  
\[\ldots\]  
\[\hat{y}_{(i+k)}=F_{2}(x_{S(i-T+1+k)},\ldots,x_{S(i+k)},y_{(i-T+1+k)},\ldots, \hat{y}_{(i+k-2)},\hat{y}_{(i+k-1)}), \tag{7}\]  
\[\ldots\]  
\[\hat{y}_{(N-1)}=F_{2}(x_{S(N-T)},\ldots,x_{S(N-1)},y_{(N-T)},\ldots,\hat{y}_{(N -3)},\hat{y}_{(N-2)}). \tag{8}\]  
\(\hat{y}_{i}\) is the prediction of the current road segment based on both static and dynamic factors. The values from \(\hat{y}_{(i+1)}\) to \(\hat{y}_{(N-1)}\) are the prediction of the remaining road segments, and dynamic factors are not available in these cases. \(F_{1}\) and \(F_{2}\) represent two prediction models which have different inputs and structures. When \(i<T\), historical data will be entered into the prediction model to ensure that the length of the input data sequence is \(T\).  
After setting the bus on the \(i\)-th road segment, the arrival time of the \(k\)-th station is \(t_{k}\):  
\[t_{k}=t_{i-1}+\sum_{n=i}^{k}\hat{y}_{n},i\leq k\leq N. \tag{9}\]
## 2 Theory and Formulation  
A GPLRC cylindrical panel in the thermal environment is shown in Figure 1. As drawn in Figure 1, for modeling the GPLRC materials as reinforcing of the system, four different distributions are considered. Functions of the volume fraction for each diagram along thickness direction are stated as [69]:  
\[\begin{array}{ll}V_{GPL}(k)=V^{*}_{GPL}&GPL-UD,&Pattern1\\ V_{GPL}(k)=\frac{2V^{*}_{GPL}[2k-N_{L}-1]}{N_{L}}&GPL-X,&Pattern2\\ V_{GPL}(k)=\frac{2V^{*}_{GPL}[1-(2k-N_{L}-1])}{N_{L}}&GPL-O,&Pattern3\\ V_{GPL}(k)=\frac{2V^{*}_{GPL}[2k-1]}{N_{L}}&GPL-A,&Pattern4\end{array} \tag{1}\]The parameters that are used in Equation (1) are given in Ref [69] in detail. The relation between weight fraction \(g_{GPL}\) and its \(V^{\star}_{GPL}\) are acquired as follows:  
\[V^{\star}_{GPL}=\frac{g_{GPL}}{g_{GPL}+(\rho_{GPL}/\rho_{m})(1-g_{GPL})} \tag{2}\]  
where \(\rho_{m}\) and \(\rho_{GPL}\) are the mass densities of the polymer matrix and the GPL, respectively. Also, the effective elasticity modulus according to Halpin-Tsai model can be computed as [70]:  
\[\overline{E}=\left(\frac{3}{8}\bigg{(}\frac{1+\xi_{L}\eta_{L}V_{GPL}}{1-\eta_ {L}V_{GPL}}\bigg{)}+\frac{5}{8}\bigg{(}\frac{1+\xi_{W}\eta_{W}V_{GPL}}{1-\eta_ {W}V_{GPL}}\bigg{)}\right)\times\,E_{M} \tag{3}\]  
Here \(\xi_{L}=2\frac{L_{GPL}}{L_{GPL}}\)\(\xi_{W}=2\frac{\varpi_{GPL}}{L_{GPL}}\), \(\eta_{L}=\frac{\left(\frac{E_{GPL}}{L_{M}}\right)-1}{\left(\frac{E_{GPL}}{L_{ M}}\right)+\xi_{L}}\) and \(\eta_{W}=\frac{\left(\frac{E_{GPL}}{L_{M}}\right)-1}{\left(\frac{E_{GPL}}{L_{ M}}\right)+\xi_{W}}\). By utilizing the rule of mixture, other material properties are stated as:  
\[\begin{array}{l}\overline{v}=v_{GPL}V_{GPL}+v_{M}V_{M},\\ \overline{\rho}=\rho_{GPL}V_{GPL}+\rho_{M}V_{M},\\ \overline{\alpha}=\alpha_{GPL}V_{GPL}+\alpha_{M}V_{M}\end{array} \tag{4}\]  
In the above equations, E\({}_{\text{M}}\), \(v_{M}\), \(\alpha_{M}\) are the elasticity modulus, [PERSON]'s ratio, and thermal expansion of the matrix, respectively. Also, \(v_{GPL}\), and \(\alpha_{GPL}\) are Poisson's ratio, and thermal expansion of the GPL, respectively.  
Figure 1: Schematic of a graphene nanoplates-reinforced composite (GPLRC) cylindrical panel subjected to thermal loading.
# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 4 Methods
### Output and Audit Module
#### 4.4.3 Reputation Management  
Reputation management, a core function of the audit verification contract, ensures node parameters reflect true performance, guiding the node selection contract.  
* Reputation Score \(C_{i}\) Update: \(C_{i}\in[-1,1]\), initialized at 0, is updated based on task outcomes: Correct task: \(C_{i}^{\prime}=\min(C_{i}+0.1,1)\). Incorrect task: \(C_{i}^{\prime}=\max(C_{i}-0.3,-1)\). Severe malicious behavior (e.g., data tampering): \(C_{i}^{\prime}=-1\). Incentives for consistency: Three consecutive correct tasks: \(C_{i}^{\prime}=\min(C_{i}+0.2,1)\); two consecutive incorrect tasks: \(C_{i}^{\prime}=\max(C_{i}-0.2,-1)\).  
* Computing Power \(P_{i}\) Update: \(P_{i}\in[0,1]\), initialized at 0, is periodically updated via off-chain tests (e.g., benchmarks), increasing with hardware performance.
* Task Completion Rate \(R_{i}\) Update: \(R_{i}\in[0,1]\), initialized at 0, reflects historical reliability, calculated as follows: \[R_{i}=\frac{\text{Successful Tasks}}{\text{Total Tasks}}\] (16) Updated in real-time by the audit verification contract based on task logs.  
New nodes start with \(C_{i}=0\), \(P_{i}=0\), \(R_{i}=0\). The contract optimizes these parameters over time and shares them with the node selection contract. The latter computes a weighted score from \(C_{i}\), \(P_{i}\), and \(R_{i}\), adding a verifiable random offset \(\Delta_{i}\) to yield the final score \(S^{\prime}_{i}=S_{i}+\Delta_{i}\). This balances opportunities for new nodes while prioritizing high-reputation ones, ensuring dynamic and accurate node selection.
## 4 Discussion  
The primary aim of this study was to observe and investigate the transition frequency of postural control strategies by using a proposed sigmoid model based on CCF coefficient data distribution. This paper is the first to use a sigmoid function model in order to observe the postural strategy transition frequency. The finding shows that the subjects' COM and COP displacements were decreased during increasing frequency and increased during decreasing frequency. In this paper, the transition frequency could be obtained from the proposed sigmoid model based on CCF coefficient data. All subjects showed difference transition frequencies at both COM-COP and ankle-hip. The transition frequency of ankle-hip occurred earlier than that of COM-COP during both increasing and decreasing frequencies.  
Sigmoid Model Based on Cross-Correlation Coefficient Data for the Determination of Transition Frequency  
Postural strategy transition can be defined as the changes of postural states between two attractors that result from the consequence of changes between programs operating at the level of the central nervous system (CNS) or of the self-organized nature of the postural system [9]. To date, several studies have investigated the transition between postural strategies by using a relative phase, either using point estimate relative phase (PRP) or continuous relative phase (CRP) [10; 24; 25; 26]. However, in the present study, we proposed an analysis of the transition of postural strategy coordination by using a sigmoid model based on CCF coefficient data. The CCF coefficient is a statistical measure that calculates the strength of the relationship between the relative movements of two variables. Besides, since the CCF coefficient data distribution exhibits a similar pattern to the sigmoidal pattern, the sigmoid model was proposed to determine the transition frequency of the postural control strategies. This proposed model was shown to be suitable with any CCF coefficient data distribution even though the data were spread in a large distribution.  
Figure 7: The transition frequency of (a) COM–COP and (b) ankle–hip during increasing and decreasing frequency translation perturbation. The dark line represents the mean value of transition frequency.
## 4 Implementation Details
### TCG Block  
The architecture of the TCG block is illustrated in Figure 4. For simplicity, the bias circuitry is omitted from the figure. To minimize parasitic capacitances, all switches are implemented with minimum length NMOS devices. It is important to note that while regular NMOS devices are used for the \(S_{R}\) switch, thick gate devices are chosen for the remaining switches connected to the \(V_{c}\) node to minimize leakage current. Additionally, both the \(S_{discharge}\) and \(S_{R}\) switches are designed with turn-on resistance significantly smaller than the \(R_{metal}\), ensuring that the time-constant measurement is dominated by the \(R_{metal}\), \(R_{poly}\), \(C_{metal}\), and \(C_{MIM}\).
# A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud-Fog Computing
## 7 Evaluation  
The case study conducted in this paper evaluates the efficacy of the cost-aware scheduling framework in serving heterogeneous job workloads. The cloud-fog computing environment is built in Java, in which queues are utilized to implement the cloud layer. Service demands, QoS requirements, and service cost of energy for each fog job are generated using the mathematical model proposed in this paper. The implementation of the framework is coded using a Workstation with 8 GB main memory in a Core i7-8550U CPU @ 1.80 GHz 1.99 GHz.
## 4 Results
### The Converter Set-Up  
The converter presented in Section 2 is based on three Semikron IGD-2-424 power stacks (Semikron, Nuremberg, Germany). The control is implemented into two TMS320F2809 DSP-based control boards, one dedicated to control the general operation state machine and the DC-DC interleaved converter and the other committed to controlling the inverter. Control strategies are executed at 8 kHz. The hardware and software relevant parameters for the DC-AC and the DC-DC converters are summarised in Tables 1 and 2. Figure 8 shows a picture of the full converter. Furthermore,  
Figure 7: Control schemes of the sinusoidal wave short-circuit proof algorithm.  
all short-circuit faults and recoveries are generated using a switch-line breaker (ABB OT200) and considering wires with less than 0.1 m\(\Omega\).
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 3 Methods and Materials
### Mathematical Preliminaries
#### 3.2.1 [PERSON]'s Cat Map  
[PERSON]'s cat map involves a chaotic transformation used in image encryption to scramble pixel positions [22].  
\[x_{n+1} =y_{n}d+x_{n}c\] \[y_{n+1} =ax_{n}+bx_{n}\]  
\(x_{n}\), \(y_{n}\) are the coordinates of the point at iteration in two-dimensional space.  
Where \(x_{n}=0.45\); \(y_{n}=0.54\); \(a\), \(b\), \(c=1\); and \(d=2\). This map has the following features:  
Ability to generate random-like, non-repeating sequences;  
Sensitivity to initial conditions [22].
# Least-Squares Estimation of Tangent Space for a Triangular Mesh
###### Abstract  
This paper presents a novel method for computing an orthonormal tangent space at every vertex of a triangular mesh provided with texture coordinates, which is essential in normal mapping. Our method employs least-squares estimation of rotation between two sets of corresponding points: one set from the positions of the 1-ring neighbors of a vertex in texture space and the other set from their corresponding positions in world space. This optimal rotation represents a local space transformation that defines the tangent space at the vertex. Consequently, our method ensures that the resulting tangent space is inherently orthonormal, eliminating the need for intricate operations such as averaging and orthonormalization, which are required in the previous methods. In addition, we introduce texture space alignment of triangles to address vertices on seams, which are inevitable in texture mapping to a closed surface mesh. Experimental results demonstrate the effectiveness of the proposed methods in various experiments.  
normal mapping; tangent space; least-squares estimation; optimal rotation +
Footnote †: journal: Applied
## 2 Materials and Methods
### Software Design for Terminal Control System
#### 2.3.2 Data Storage and Display  
The monitoring system acquisition software displays GPS location information, seeding depth, and vehicle operating parameters (traveling speed, seed-fertilizer box level status, etc.) on the interface, and the system software also saves all data for subsequent analysis and processing.