esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Simulations and Results  
In this section, we presented a brief discussion about comparative schemes and simulation results. We evaluated the simulation under different performance metrics and simulation parameters. We compare our work in EPF with GIF and traditional flooding in NDN and evaluate the performance of our scheme. Selection of the best suitable threshold value for energy efficient strategy has also been presented in this section. We compared our scheme with an efficient forwarding scheme named Geographic Interest Forwarding (GIF) [19]. In GIF, the support for Push based traffic is provided for NDN-IoT environment. Forwarding strategy in GIF is designed to enhance the network performance in term of energy consumption and scalability of sensor devices in IoT environment. In this scheme, the sink node having more resources than the sensor nodes, but the maximum load on the sink node results in the resource consumption. Whenever a node sensed some data, after Producer table entry in sink node, the push based forwarding consume a lot of energy. Sensor nodes can die very early in GIF because of the regular passing of the interest and data packet. Path loss issue can be there because of the dead sensor nodes. If the sink node will die and consume its energy then there is no criteria to preserve recent entries in the sink node and define another sink node. This paper is the most relevant to our proposed EPF. There are papers that discuss controller-based forwarding, but there is a lack of papers in the IoT domain that handle SDN controller-based communication with NDN-enabled communication. Some papers present the architecture of SDN-enabled communication, but still do not describe a forwarding mechanism. Others provide forwarding schemes related to SDN-IoT but in VANETs related IoT, not for WSNs and IoT. For the comparative study and analysis, we selected the GIF scheme because it was the most relevant to our simulation parameters and provided us with a broader and clearer view of the results of our scheme.
## 2 The Resilient Recovery of the SN Based on Outer Synchronization
### Resilient Recovery Based on Outer Synchronization  
The actual SN after disruption acts as the response network, shown as Equation (4). Therein, \(A\
eq B\) (\(A=[a_{ij}]\), \(B=[b_{ij}]\)) as the structure has been changed, the status \(\psi_{i}\) of the actual SN is changed, so \(u_{i}\) is the control function. Here, \(\overline{\alpha}\) is the vector with the unknown parameter, which means that the original schedule is changed.  
\[\dot{y}_{i}(t) =F_{i}(y_{i}(t),\overline{\alpha},\psi_{i})+\sum\limits_{j\in N_{ i}}b_{ij}\Gamma y_{j}(t)+u_{i} \tag{5}\] \[=f_{1i}(y_{i}(t),\psi_{i})+f_{2i}(y_{i}(t),\psi_{i})\overline{ \alpha}+\sum\limits_{j\in N_{i}}b_{ij}\Gamma y_{j}(t)+u_{i}\]  
**Definition 1**.: _Outer synchronization is achieved between the actual network and scheduled network; for any initial values \(\varphi_{i}\) and \(\psi_{i}\), then parameters are \(\alpha\) and \(\overline{\alpha}\), such that_  
\[\lim_{t\rightarrow\infty}\lVert e_{i}\rVert=\lim_{t\rightarrow\infty}\lVert y _{i}(t,\overline{\alpha},\psi_{i})-x_{i}(t,\alpha,\varphi_{i})\rVert=0\]  
_where \(e_{i}\) is an synchronization error [38]._  
The outer synchronization between the actual network and the corresponding scheduled network is shown in Figure 2a. Particularly, if \(m=10\), \(r=28\), \(b=8/3\), the system is typically in a state of chaos, as shown in Figure 2b.  
Figure 2: Synchronization of the supply network (SN). (**a**) Outer synchronization, (**b**) The status of node.  
As shown in Figure 3, the status of node \(y_{i}\) synchronizes to the status of corresponding node \(x_{i}\). The status in the actual network fluctuates near the scheduled status and tends to the status in the scheduled network.  
**Definition 2.**_Resilient recovery is a process of outer synchronization between the actual network and scheduled network._
# Oxygen Self-Diffusion in Fluorite High Entropy Oxides
## 2 Methodology  
Molecular dynamics calculations were performed using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code [28; 29]. We employed classical molecular statics and dynamic simulations using the Buckingham empirical potentials [30; 31], the transferability of which has been tested in previous studies [32; 33]. Static transition state atomistic simulations were performed using the GULP code [34; 35]. Entropy-stabilized oxides posed challenges for the atomic scale simulation methods as the many different cations led to a great number of potential configurations, all of which may have influence over the overall bulk properties. Although classical potentials are of the order \(10^{4}\) times faster than the electronic structure calculations, automated methods for producing simulation data are still necessary.  
For the materials considered here, the following method was adopted to provide an estimate of the mean squared displacement at an average temperature \(T\):  
1. An \(8\times 8\times 8\) supercell of the fluorite structure is created with a random assignment of cations according to a specified stoichiometry. Oxygen ion vacancies are introduced onto the oxygen ion sublattice to provide charge balance.
2. An equilibration series of simulations which consist of the following: 1. Annealing is at a set temperature of 1500 K to equilibrate the distribution of oxygen vacancies. 2. A 2 ns simulation at constant (zero) pressure a set temperature of \(T_{\text{set}}\) is used to establish the equilibrium cell parameters at temperature. 3. The cell is then set to the average value of the lattice parameters, and a further 50 ps of simulation time is run to allow the vacancy distribution to adapt to the altered cell volume.
3. The statistics run is calculated in a constant volume, a constant energy ensemble (i.e., without the thermostat) for a simulation time of 10 ns or the time required for the mean squared displacement of the oxygen ions to reach 10 A\({}^{2}\). The latter condition allows for efficient early termination of compositions with clear values of diffusivity. The simulation time and displacement of the ions \(R(t)\) is recorded in this last step and used to calculate the average temperature \(T\) (\(\sim\)\(T_{\text{set}}\)) and mean squared displacement (\(\langle R^{2}(t)\rangle\)) of the oxygen ions.  
The oxygen ion diffusivity, \(D(T\),conf.) of each configuration is then estimated by a least squares fit of the Einstein relation to the long timescale behavior of the displacement \(R(t)\) at time \(t\) as follows:  
\[\left\langle R^{2}(t)\right\rangle=6D(T,\text{conf.})t\]  
From these data, we can now characterize each composition by fitting an Arrhenius relationship to each set of \(T\), configuration data point to obtain a value for the activation energy \(E_{a}\) and preexponential \(D_{0}\) for a given composition assuming the following:  
\[D=D_{0}\text{exp}\left(\frac{-E_{a}}{k_{B}T}\right)\]
## 3 Probabilistic Capability Model of Wall Piers  
### Wall Pier Damage Indexes  
As a part of the performance-based structural seismic design, performance level definition also plays an important role in seismic vulnerability analysis. A structural performance level can be defined as the maximum degree of expected structure damage for a given earthquake intensity. According to previous studies [17], a five-performance-level classification is adopted for the wall pier girder bridges in this study. That is intact, minor damage, medium damage, severe damage, and complete destruction. The depiction of the pier damage states is shown as Figure 3, plotted by displacements on the horizontal and normalized lateral load on the vertical.  
Commonly used damage indices for pier components include material strain, ductility coefficient, two-parameter damage model, displacement drift ratio, etc. The displacement drift ratio is selected as the damage index of wall piers in this study considering the peculiarity of wall piers and the simplicity of the analysis. The displacement drift ratios when the components are cracked, yielded, and the structural bearing capacity reaches the peak and limit states are taken as the boundary point of each damage state.  
The out-of-plane performances of the wall piers are similar to that of the common pier columns. Therefore, the limit values of each out-of-plane damage state directly refer to previous research results [18] of the common pier columns, as shown in Table 3, where MDR denotes the displacement drift ratio, which is defined as follows:  
\[MDR=D/H \tag{1}\]  
where D is the displacement at the top of the pier and H is the net height of the pier. Considering that the in-plane performance of wall piers is quite different to that of common pier columns, numerical simulation is used to obtain the values of each in-plane performance point. By adopting the Bayesian stepwise regression method, the formulas of different performance points are obtained considering the impact of some factors.  
Figure 3: Dipiction of the pier damage states.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Level** & **Degree of Damage** & **Seismic Performance Index** \\ \hline level 1 & Intact & MDR \(\leq 0.11\%\) \\ level 2 & Minor damage & \(0.11\%<\text{MDR}\leq 0.38\%\) \\ level 3 & Medium damage & \(0.38\%<\text{MDR}\leq 0.84\%\) \\ level 4 & Serious damage & \(0.84\%<\text{MDR}\leq 2.23\%\) \\ level 5 & Complete destruction & MDR \(>2.23\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Division of each damage state to the wall pier in out of plane.
## 3 Results
### DNA-Based Analysis of CMJ Performance
#### 3.2.3. Concentric Mean Force
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Symbol** & **Gene** & **Polymorphism** & **dbSNP** & **Genotype** & **Responder** & **Non-Responder** & \(p\) **Value** \\ \hline \multirow{2}{*}{_ACE_} & \multirow{2}{*}{Angiotensin-converting enzyme} & \multirow{2}{*}{I/D} & \multirow{2}{*}{rs4646994} & DD & \(6\) (60.0\%) & \(17\) (63.0\%) & \\  & & & & ID & \(3\) (30.0\%) & \(3\) (33.3\%) & \\  & & & II & \(1\) (10.0\%) & \(1\) (3.7\%) & \\ \hline \multirow{3}{*}{_ACTN3_} & \multirow{3}{*}{Alpha-actinin-3} & \multirow{3}{*}{c.1729C\(>\)T} & \multirow{3}{*}{rs1815739} & CC & \(4\) (40.0\%) & \(6\) (22.2\%) & \\  & & & & CT & \(4\) (40.0\%) & \(4\) (14.8\%) & 0.042 \\  & & & & TT & \(2\) (20.0\%) \(\downarrow\) & \(17\) (63.0\%) \(\uparrow\) & \\ \hline \multirow{3}{*}{_AMPD1_} & \multirow{3}{*}{Adenoise monophosphate deaminase 1} & \multirow{3}{*}{c.34C\(>\)T} & \multirow{3}{*}{rs17602729} & CC & \(4\) (40.0\%) \(\downarrow\) & \(22\) (81.5\%) \(\uparrow\) & \\  & & & CT & \(6\) (60.0\%) \(\uparrow\) & \(4\) (14.8\%) \(\downarrow\) & 0.022 \\  & & & TT & \(6\) (0.0\%) & \(1\) (3.7\%) & \\ \hline \multirow{3}{*}{_CKM_} & \multirow{3}{*}{Muscle-specific creatine kinase} & \multirow{3}{*}{c.*800A\(>\)G} & \multirow{3}{*}{rs8111989} & GC & \(1\) (10.0\%) & \(2\) (7.4\%) & \\  & & & GA & \(3\) (30.0\%) & \(9\) (33.3\%) & \\ \cline{1-1}  & & & AA & \(6\) (60.0\%) & \(16\) (59.6\%) & \\ \hline \multirow{3}{*}{_MLCK_} & \multirow{3}{*}{Myosin light chain kinase} & \multirow{3}{*}{c.49C\(>\)T} & \multirow{3}{*}{rs2700352} & CC & \(7\) (70.0\%) & \(18\) (66.7\%) & \\ \cline{1-1}  & & & CT & \(3\) (30.0\%) & \(9\) (33.3\%) & \(0.847\) \\ \cline{1-1} \cline{2-7}  & & & & TT & \(0\) (0.0\%) & \(0\) (0.0\%) & \\ \cline{1-1} \cline{2-7}  & & & & CC & \(0\) (0.0\%) & \(0\) (0.0\%) & \\ \cline{1-1} \cline{2-7}  & & & & CA & \(3\) (21.4\%) \(\uparrow\) & \(1\) (4.3\%) \(\downarrow\) & 0.022 \\ \cline{1-1} \cline{2-7}  & & & AA & \(11\) (78.6\%) \(\downarrow\) & \(22\) (91.3\%) \(\uparrow\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Genotype distribution in responder and non-responder football players in concentric mean force for muscle performance polygenic profile.
# Wave Planning for Cart Picking in a Randomized Storage Warehouse
## 1 Introduction
### The Importance of Order Picking for a Warehouse and the Type of Order Picking
For control and management purposes, the most important attributes in customer orders are what SKU (or item), how many quantities, and where (location) to deliver for each customer order (illustrated as (I Q L) in Figure 1), and in pre-packaged SKUs what are the SKUs and how many quantities are for pre-packaging (illustrated as (I Q) in Figure 1). A master order schedule (MOS) is another form recording what SKU and how many quantities are going to process for delivering. MOS is more like a filter that filters what customer orders and/or pre-packaged SKU are going to process based on similarity of delivery routing [3] or similarity of in-coming customer orders' time windows [3; 4]. The important attributes in MOS are what SKU, how many quantities, and when to load into what routing trucks (illustrated as (I Q R) in Figure 1). By considering SKU storage locations, customer orders in MOS are further batched or split [5; 6] based on similarity of their SKUs' storage locations. The major concerns of batching or splitting are what SKU, how many quantities, and where storage (illustrated as (I Q S) in Figure 1). Batched or split order picking calls for managers to assess current orders for popularity and place them into batches by their SKU. From there, pickers are in the best location of the warehouse to get orders fulfilled as quickly as possible. Batch picking works especially well for e-commerce businesses with somewhat predictable ordering trends, like those specializing in food and apparel. There are lots of order picking policies proposed at this research field, discrete picking, zone picking, and wave picking [5; 7]. Discrete pickingpolicy is when a picker picks items of multiple orders by batching during a single trip. Zone picking policy divides the picking area into a few subzones and the dedicated picker will pick items by splitting in that subzone only. The main research topic is the number of subzones and pickers. Wave picking is used if orders by splitting and/or batching for a common destination or common truck loading time are released simultaneously for picking in multiple warehouse areas. We summarized advantages, disadvantages, and applications of those different types of order picking policies or strategies as Table 1 below. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Types** & **Advantages** & **Disadvantages** & **Applications** \\ \hline \multirow{8}{*}{Discrete picking} & \(\bullet\) & Simplest for implement & & & \\  & \(\bullet\) & One of the most accurate & \(\bullet\) & May take more time in the pick area & \(\bullet\) & Warehouse has a small staff \\  & \(\bullet\) & Minimizes the number of touches to ship an order & & in the pick area & \(\bullet\) & Orders are for multiple items \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summaries of types of order picking policies or strategies. Figure 1: Forms and workflow of a warehouse management system (WMS).
# A Survey of of Side-Channel Attacks and Mitigation for Processor Interconnects
## 6 Layout-Based Interconnect Side-Channel
### Thermal-Channel Attack  
When processor cores operate under high-load condit ions, their temperatures significantly increase due to resistive heating. This excess heat radiates and spreads to spatially adjacent cores, where it can be detected by dedicated temperature sensors, as shown in Figure 6. Covert and side channels constructed based on this characteristic are referred to as thermal channels. Unlike previously discussed side-channel attacks, these channels do not exploit on-chip interconnects but rather leverage another critical property of the NoC: the planar layout of processor cores on the chip.  
It is easy to imagine that even if programs on different cores can only access temperature information from their respective cores, the presence of thermal radiation enables attackers to easily construct cross-core covert channels. The sender and receiver can pre-arrange a fixed-length time slice and the positions of their cores in the layout. As long as the sender and receiver cores are sufficiently close in the layout, the sender can execute computational operations at a high intensity within the fixed time slice to increase the temperature of its own core and the surrounding cores. The receiver continuously monitors the temperature changes in its own core, and when the average temperature within a time slice is significantly higher than usual, it means the sender has transmitted a bit \"1\"; otherwise, it sends a bit \"0\".  
It is worth mentioning that, unlike time-based covert-channel attacks, thermal channels can not only bypass spatial isolation of program resources but also temporal isolation. This is because the core performing high-load computations does not immediately return to normal temperature levels after the program ends, due to the residual heat effect. Consequently, processes following this computation in the execution schedule will have the opportunity to obtain information about that computation.  
Although thermal channels for covert communication have been extensively studied, it is difficult to relate the temperature changes in victim cores to specific secrets to construct side-channel attacks due to their coarse granularity in time. Nevertheless, thermal channels have been demonstrated to leak information about the computational nature of victim programs to processes on other cores, which can be used to complement other attack techniques, such as determining the timing of execution of attack code.
## Materials and Methods
### Machine Learning  
The research involved a machine learning process using the Python environment (Figure 1). Currently, Python has become the most popular and also the most effective tool for designing and executing machine learning models. In this study, Multilayer Perceptron (MLP) networks were designed and performed in a regression problem using Pandas and scikit-learn libraries. The machine learning process proceeded according to predetermined stages.  
In the initial stage, numerical data were prepared. The dataset contained information on input and output variables. The input (quantitative) variable in the set determined the diameter of liposomes (liposome size is the average of three independent measurement repetitions of size distribution by intensity) in acid and phenolic compounds. The output (quantitative) variables informed the probability of solution concentration at a given liposome size. The probabilities of occurrence/non-occurrence of acid and phenolic compound concentrations were determined numerically via binary, that is, as a value of 1 and 0 for each output variable specifying the concentration in solution: 0, 5, 10, 20, 50, 100. As a result of machine learning data preparation, five learning sets were created. The next step required determining the topology of the artificial neural network. Considering the research problem specified, a regression model was used. In the Python environment, the responsible regression models are the Linear Regression model and the Multilayer perceptron model (MLP Regression). Initial work was done using linear regression. Unfortunately, adequate models were not determined using the Linear Regression algorithm because they did not obtain the optimal coefficient of determination indicating a correct fit to the model. The second algorithm, i.e., the Multilayer perceptron model, was selected, which satisfied the model fit criterion. The structures of each neural network was characterized with one neuron in the input layer, three hidden layers (200 neurons in the first layer, 100 neurons in the second layer and 50 neurons in the third layer) and six neurons in the output layer. As part of the machine learning stage, the adam algorithm was used [14; 24; 25]. In this research stage, an optimal MLP model by regression was obtained, which was able to effectively identify oleanolic acid and phenolic compounds based on the size of liposomes.
# PV-Powered Charging Station with Energy Cost Optimization via V2G Services
Yet, their optimization problems are mostly based on day-ahead prediction of EV profiles modeled with a probability distribution function, whereas the proposed optimization algorithm in this paper is actualized at every arrival of a new EV, considering the impact of the uncertainties in real-time simulation. This paper is an extension of [42], where V2G is realized with a rule-based control scheme, whereas in this extended version, the focus is on the energy cost optimization problem with V2G implementation in the PVCS. In this paper, a PVCS with five chargers that can support three charging modes, slow, average, and fast charging, is presented. The PVCS combines PV sources, a stationary storage system, a public grid connection, and EVs as a flexible load that can operate in V2G mode. The human-machine interface (HMI) allows the EV users to interact with the PVCS and choose their preferences, such as charging mode, desired state of charge SOC at departure, and willingness to participate in V2G service. Additionally, EV users arrive arbitrarily at the PVCS, and their arrivals are unpredictable. In [43], the authors presented a control mechanism aimed at minimizing the discomfort of EV users for a charging station equipped with PV sources and connected to a public grid, but without a stationary storage system. However, their study differs from ours in several ways. Firstly, they only allow for one charging mode, namely the slow mode, whereas we offer three different charging modes. Secondly, their focus is on maximizing social welfare and minimizing the discomfort of EV users, while the objective in this study is to minimize the total energy cost of the PVCS, with optimization being updated for each EV arrival. Furthermore, the energy injected into the grid from EVs and the energy distribution for each EV charging from each power source are being analyzed to provide a more comprehensive understanding of the system's energy dynamics. To sum up, the main contributions of this paper are:  
1. Proposing an energy cost optimization problem in a PVCS with V2G service, taking into consideration the uncertainty of the arrival time of EVs in a real-time simulation;
2. Actualizing the optimization problem formulated via MILP at every arrival of a new EV; the arrival of EVs is not modeled based on day-ahead prediction; instead it is randomly generated as unpredicted events in MATLAB;
3. Assessing the energy consumption of every EV from each power source and the energy participation among the power sources (PV, energy storage, and grid). The paper is organized as follows: Section 2 introduces the PVCS with V2G energy cost optimization. Section 3 develops the energy cost optimization problem. Section 4 describes the different simulation cases with V2G service. Section 5 analyzes the energy cost results. Section 6 concludes the paper.
## 4 Simulation Analysis
### Control message overhead  
The simulation results of the control message overhead are presented in Figures 8(a,b). At a node speed of 5 m/s, the proposed scheme shows results that are better than AODV-DFR and ZRP and similar to AODV, whereas at a node speed of 20 m/s, the proposed scheme shows results that are better than those of AODV, AODV-DFR and ZRP. The proposed protocol uses slightly more control messages than LAR due to its use of an additive one-hop beacon message from the main route nodes.
## 4 Remote Sensing Platforms Used in Peatland Research
### UAV-Based Remote Sensing  
Remote sensing technologies, such as unmanned aerial vehicles (UAVs), have revolutionised environmental monitoring and management. In recent years, UAV-based remote sensing has gained prominence in peatland research due to its ability to provide high-resolution data for mapping and monitoring purposes (Table 5).