esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Coated Bare Metal Samples  
In Figure 4, the OCT thickness calculations for samples 1-6 are compared to the thickness measurements using the MI system. The mean value for a three-dimensional OCT data set and an error bar using the standard deviation were plotted. The value of the MI system is an average over 50 sampling points that were chosen manually, trying to be as close as possible to the center of the circle. These values and their standard deviation can also be seen for comparison in Table 1.  
Figure 3: OCT B-Scan of corroded metal (sample 8): **red** line is the top of the coating layer, **green** line determines the end of the coating and the beginning of the corrosion, **yellow** line determines the end of the scattering-induced layer. Axes are given as optical path length \(n\times d\).  
For thin coating layers, the results of both techniques are in very good agreement with each other. For thicker layers, the values differ. This is due to the fact that the layer had to be brushed or sprayed multiple times for thicker samples. This leads to much more pronounced height variations, as can be seen in Figure 5. As the MI measurements are performed with a hand-held probe, a larger area might be investigated. This might be the reason for a higher standard deviation. Another possibility for the measurement uncertainty could be a density change, leading to a refractive index change as it was previously observed for thicker coatings [13]. For sample S6, increased scattering could be observed, supporting this theory.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Concept** & **Sample 1** & **Sample 2** & **Sample 3** & **Sample 4** & **Sample 5** & **Sample 6 \({}^{*}\)** \\ \hline OCT & 6.86 & 22.97 & 42.27 & 48.91 & 97.00 & 80.24 \\ std. OCT & 0.65 & 1.61 & 2.01 & 2.52 & 2.74 & 1.85 \\ MI & 7.16 & 21.89 & 43.85 & 56.57 & 83.34 & 88.70 \\ std. MI & 1.14 & 1.5 & 3.8 & 9.81 & 9.47 & 2.5 \\ expected th. value & 11.27 & 27.44 & 53.70 & 64.10 & 94.30 & 86.26 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between optical coherence tomography measurement, magnetic inductive (MI) measurement, and the expected theoretical values. The sprayed sample is marked with an \({}^{*}\). All thickness values are given in \(\upmu\)m.  
Figure 4: Comparison between OCT and MI measurements and the calculated theoretical value.  
Figure 5: 3D heat map of the calculated layer thickness for the OCT measurement of sample 5.
# Faster Algorithms for Mining Shortest-Path Distances from Massive Time-Evolving Graphs
###### Abstract  
Computing shortest-path distances is a fundamental primitive in the context of graph data mining, since this kind of information is essential in a broad range of prominent applications, which include social network analysis, data routing, web search optimization, database design and route planning. Standard algorithms for shortest paths (e.g., [PERSON]'s) do not scale well with the graph size, as they take more than a second or huge memory overheads to answer a single _query on the distance_ for large-scale graph datasets. Hence, they are not suited to mine distances from big graphs, which are becoming the norm in most modern application contexts. Therefore, to achieve faster query answering, smarter and more scalable methods have been designed, the most effective of them based on precomputing and querying a compact representation of the transitive closure of the input graph, called the 2-hop-cover labeling. To use such approaches in realistic _time-evolving_ scenarios, when the managed graph undergoes topological modifications over time, specific _dynamic algorithms_, carefully updating the labeling as the graph evolves, have been introduced. In fact, recomputing from scratch the 2-hop-cover structure every time the graph changes is not an option, as it induces unsustainable time overheads. While the state-of-the-art dynamic algorithm to update a 2-hop-cover labeling against _incremental_ modifications (insertions of arcs/vertices, arc weights decreases) offers very fast update times, the only known solution for _decremental_ modifications (deletions of arcs/vertices, arc weights increases) is still far from being considered practical, as it requires up to tens of seconds of processing per update in several prominent classes of real-world inputs, as experimentation shows. In this paper, we introduce a new dynamic algorithm to update 2-hop-cover labelings against decremental changes. We prove its correctness, formally analyze its worst-case performance, and assess its effectiveness through an experimental evaluation employing both real-world and synthetic inputs. Our results show that it improves, by up to several orders of magnitude, upon average update times of the only existing decremental algorithm, thus representing a step forward towards real-time distance mining in general, massive time-evolving graphs.  
large graph mining; algorithm engineering; experimental algorithmics; time-evolving data; big data processing +
Footnote †: journal:  
0000-0001-4000-0001-4000][PERSON]
# A New Hybrid Prime Code for OCDMA Network Multimedia Applications
## 3 Code Construction
### Code Construction Procedure
 & \multicolumn{2}{c}{**Resultant Code Sequences**} \\ \hline \multirow{3}{*}{12} & \multirow{3}{*}{\(C_{12}\)} & \multirow{3}{*}{\(X_{31}X_{12}X_{01}X_{42}\)} & \(C_{120}\) & \(X_{31}\) & \(X_{12}\) & \(X_{01}\) \\  & & & \(C_{121}\) & \(X_{42}\) & \(X_{31}\) & \(X_{12}\) & \(X_{01}\) \\
12 & & & & \(C_{122}\) & \(X_{01}\) & \(X_{42}\) & \(X_{31}\) & \(X_{12}\) \\  & & & & \(C_{123}\) & \(X_{12}\) & \(X_{01}\) & \(X_{42}\) & \(X_{31}\) \\  & & & & \(C_{123}\) & \(X_{12}\) & \(X_{01}\) & \(X_{42}\) & \(X_{31}\) \\ \hline \multirow{3}{*}{13} & \multirow{3}{*}{\(C_{13}\)} & \multirow{3}{*}{\(X_{41}X_{01}\)} & \multirow{3}{*}{\(X_{41}X_{22}X_{01}X_{52}\)} & \(C_{130}\) & \(X_{41}\) & \(X_{22}\) & \(X_{01}\) \\  & & & & \(C_{131}\) & \(X_{52}\) & \(X_{41}\) & \(X_{22}\) & \(X_{01}\) \\
13 & & & & \(C_{132}\) & \(X_{01}\) & \(X_{52}\)Table 4 shows that the number of sequences provided by the OOC, MPC, and HPC are 2, 25, and 160, respectively. Moreover, the code lengths of the OOC, MPC, and HPC are 32, 25, and 24, respectively when \(P_{2}=7\).
## 3 Proposed Protocol Architecture
### Protocol Architecture
#### 3.1.1 Border Router  
The border router acts as a topological anchor point for the entire IP-WSN domain. The main role of the border router is to maintain accessibility to the sensor node while the node moves within or outside the IP-WSN domain. The border router includes a binding cache entry for each sensor node, encapsulation and decapsulation sections and a SMAG information table. The binding cache entry at the border router is used for holding the information of the mobile sensor node. It includes different information such as a sensor node's address, the sensor node's home network prefix, and a flag bit indicating sensor proxy registration.  
The border router has sufficient memory to hold all of these necessary records. It has sufficient power supply and processing capability. It also acts as the interfacing device between IP-WSN domain and the Internet domain. As an interfacing entity it facilitates the communication of IP-WSN with the Internet world. In this scheme, authentication, authorization, and accounting (AAA) service have been integrated within border router. We name this integrated service a Sensor network-based Aauthentication, Authorization, and Accounting (SAAA) scheme. The SAAA scheme helps the SMAG and sensor nodes achieve secured mobility in the IP-WSN domain. It also facilitates authentication services for each fully functional sensor node.
## 5 Multiple DC Modulation Coordination Strategies  
The coordinated optimization strategy of multi-DC modulation is shown in Figure 3, and its specific steps are as follows:  
1. For the leading oscillation mode \(\lambda_{k}\), the maximum acceleration power deviation of each unit is taken as the reference value, and the energy participation weight \(\eta_{\lambda_{k}}\) of each unit in oscillation mode \(\lambda_{k}\) is calculated;
2. Calculate the transient energy change \(\Delta V_{t_{k}}\) of oscillation mode \(\lambda_{k}\);
3. Apply the same power disturbance to the DC of the system \(l\) to excite the oscillation of the system;
4. Prony algorithm is used to identify the system;
5. Determine the modulation sensitivity \(\varphi_{m}\) of each DC, and sort \(l\) DC to form a DC modulation sequence table;
6. Calculate \(\Delta V_{dc_{k}}\) and determine \(p\) DC that participates in DC modulation;
7. Using the DDPG algorithm, the FLC parameters of \(p\) DC were optimized and the optimization results were output.
# Power-Efficient Random Access Design for Machine Type Communication
## 4 Link Budget Analysis and Simulation Results
### Support for Larger Cells  
So far in the discussion, a CP size of 35 us (corresponding to typical cell sizes of less than 5 km) is used for the random access channel to absorb the propagation delay. Due to the low duty cycle of the random access (e.g., 10%), the overall overhead of the random access channel CP is rather small even with a much larger CP. Therefore, very large cells can be supported with the current design. For instance, a CP of 235 us (for a cell size of 35 km) can be supported corresponding to an overall system overhead of less than 4%.
## 2 Materials and Methods
### Integration of the Haptic Device and the VE  
A haptic device was developed and used to interact with the VE; it consists of three Degrees of Freedom (DoF), with one encoder on each joint for monitoring its angular position. Because the system has been designed for active rehabilitation, for the arm end-effector (third link), a 3D printed pencil was designed and attached employing a passive universal joint (without orientation sensors), which allows pencil rotation when the user moves their wrist and arm. The pencil internally has a mini DC motor that generates vibrotactile haptic feedback and a button that allows interaction between the user and the VE (see Figure 4). It is worth mentioning that the VE only shows the end-effector of the haptic device before the universal joint and the pencil, which do not affect the interaction with each virtual scenario.  
Furthermore, the low- and high-level haptic device controls were implemented in the NUCLEO-STM32F767 ZI embedded system board using the Simulink libraries for STM32 microcontrollers [43]. The low-level control is in charge of monitoring the encoders, user button, and the home button used to reset the encoders' position and calibrate the haptic interface when it is in the home position; additionally, the variation of the pulse width modulation to control the vibration of the mini DC motor. In the high-level control, the forward kinematics and the end-effector velocity are programmed and estimated. It may be noted that the control loop frequency runs at 1 kHz.  
The communication protocol is implemented using a high-speed USB UM232H module configured at 3 Mb/s to allow communication between the board and the host PC in real time. Finally, in the host PC, a GUI Simulink(r) model, which runs in background mode, manages the haptic interface and VE data (end-effector position and velocity, the collision presented) that are exchanged with Unreal Engine 4 via UDP (at 100 Hz).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multicolumn{4}{c}{**Scenario**} \\ \cline{2-5}  & **Shapes** & **Supermarket** & **Fruit** & **Building** \\ \hline \multirow{3}{*}{Vibrotactile haptic feedback} & \multirow{3}{*}{Collisions with shapes} & Collisions with products, shelves and arrows & Collisions with rocks, fruits and worms & Collisions with the guide where the blocks \\ \cline{1-1} \multirow{3}{*}{Clues} & \multirow{3}{*}{When a wrong figure is chosen a sound is played} & Each product is marked with its name & A virtual guide indicates the place where the fruit will fall & A virtual guide indicates where to put the block \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 4: Haptic device and its components.  
For the VE display, the system can use two different devices, the first is a monitor (laptop or PC), and the second is the HTC Vive Pro with a base station. In Figure 5 the components of the rehabilitation system are shown.
# Three Steps towards Better Forecasting for Streamflow Deep Learning
## 2 Methodology
### Study Data  
The SMART catchment has an area of 160 km\({}^{2}\) equipped with a rain gauge and doppler current meter at 28 hydrological stations. The sensors collect rainfall and flow data and transmit the data to the control center using telemetry. Within the 28 hydrological stations, data from 11 telemetry stations are used for modeling. The rest of the stations are meant for observation only. This study collects historical data of 30 min interval rainfall at the 11 telemetry stations and the flow at the confluence of the Klang River and Ampang River from January 2008 to August 2021. Seventy percent of the historical data from January 2008 to August 2019 are used for training, while the rest are used for testing. Normal flow at the confluence of Ampang and Klang Rivers is generally within the range of 5 to 10 m\({}^{3}\)/s. However, this flow can increase tremendously above 150 m\({}^{3}\)/s depending on the intensity of the precipitation.
## 2 Materials and Methods
### Quality Control  
The quality of the analytical method was validated by internal control. A procedural blank sample was prepared by acidifying distilled water with nitric acid to obtain a 1% acidified solution. The same reagent (nitric acid) was used to acidify the water samples. The blank allowed zeroing the device and was analyzed after each batch of 10 samples during the analysis in order to determine possible contaminations and eliminate the quantization errors. Moreover, the repeatability and accuracy of the results were checked using the average values of standard solutions and triplicate samples that were regularly analyzed (each 10-sample batch). The calculated coefficients of variation for the analyzed trace elements of the triplicates were <5% and ranged between 0.75% for Ni to 4.98% for Mn. The recovery rates of the 8 trace element concentrations in standard solutions ranged between 94. 88% for Cd and 103.15% for Ni.
## 4 Corrosion Protection Measures
### Internal Anti-Corrosion Technique  
4.3.1. Corrosion Inhibitor  
Usually, corrosion inhibitors are used in the interior of pipelines to slow down the corrosion of the pipelines. The corrosion inhibitor forms a single or multi-layer dense protective film on the surface of the substrate. The inhibitors have the advantages of high corrosion inhibition efficiency, long-lasting corrosion inhibition, and no environmental pollution [118]; thus, they can mitigate the pipeline's corrosion.
## 4 Results and Discussion  
In order to demonstrate the effectiveness of the proposed control functionalities described in Section 3, a test system was considered, comprising an hybrid AC/DC distribution network (Figures 7 and 8), the ST from Figure 2 and the equivalent representation of the upstream AC power system (Figure 10). Moreover, the performance of the proposed control functionalities was evaluated in four operational scenarios for the hybrid distribution network, namely:  
* Over frequency event in the upstream AC grid and net load scenario in the hybrid distribution network;
* Over frequency event in the upstream AC grid and a net generation scenario in the hybrid distribution network;
* Under frequency event in the upstream AC grid and a net load scenario in the hybrid distribution network;
* Under frequency event in the upstream AC grid and a net generation scenario in the hybrid distribution network.  
The main data and model parameters used in the test system are presented in Section 4.1. The obtained simulation results are presented and discussed in Section 4.2.
## 2 Materials and Methods
### Remote Sensing Data  
MOD09 GA is the daily MODIS surface reflectance product, which was atmospherically corrected [35]. The band reflectance data of MOD09 GA can be obtained from Google Earth Engine ([[https://earthengine.google.com/](https://earthengine.google.com/)]([https://earthengine.google.com/](https://earthengine.google.com/))) and its spatial resolution and temporal resolution are 250 m and daily, respectively [36]. The \(3\times 3\) pixels around the AR station were extracted from MOD09 GA data (Figure 3). Daily NDVI and EVI were calculated from the reflectance data using the following formulas:  
\[\mathrm{NDVI}=\frac{\rho_{\mathrm{NIR}}-\rho_{\mathrm{RED}}}{\rho_{\mathrm{NIR }}+\rho_{\mathrm{RED}}} \tag{1}\]  
\[\mathrm{EVI}=2.5\times\frac{\rho_{\mathrm{NIR}}-\rho_{\mathrm{RED}}}{\rho_{ \mathrm{NIR}}+6\times\rho_{\mathrm{RED}}-7.5\times\rho_{\mathrm{BLUE}}+C} \tag{2}\]  
where \(\rho_{\mathrm{RED}}\), \(\rho_{\mathrm{NIR}}\), and \(\rho_{\mathrm{BLUE}}\) are the band reflectance of the red, near-infrared, and blue band, respectively. \(C\) is the adjustment parameter, and the value of \(C\) is set to 1.
## 2 Background and Related Works
### Related Works
#### 2.2.4 Other XRL Methods  
In addition to the above methods, there are several other XRL methods which use the causal model [27], and distill the trained agent into a transparent model [28; 29]. [PERSON] et al. [27] proposed a method that uses a causal model to explain the cause and counterfactual explanation of the behavior of the RL agent. This method requires that the causal model be prepared manually, which can inhibit implementation, especially when an expert is not available to build or verify it. [PERSON] et al. [28] proposed an RL method in which they initially trained a DRL agent and derived transparent programmatic policies. [PERSON] et al. [29] approximated a DRL Q-function using linear model U-trees where the leaf node of the tree is a linear model. Our method is different from the above because, instead of distilling the RL policy into a transparent model, it exposes the reward components and their proportion to make the second-last layer of the NN transparent, producing explanations at a finer level of granularity. With the existing methods [4; 5; 6; 8; 11; 12], the quantity of the generated explanation will be the sum of the number of input features (\(N_{I}\)) and the number of reward components (\(N_{C}\)), while our method can produce \(N_{I}\times N_{C}\) explanations.
## 3 Modeling
### Model for Order Splitting Level  
In the demand plan for medical infusion device orders, multiple orders are typically involved. Each order generally comprises products with various specifications, and each product consists of multiple parts. The primary purpose of the order splitting level is to decompose the demand plan into several parts orders under certain constraints. The objectives of the order splitting level mainly include achieving optimal splitting granularity and minimizing splitting costs. 1. Splitting granularity. Splitting granularity refers to the production quantity or scale of each sub-order and its distribution when an overall order is divided into multiple sub-orders. A reasonable splitting granularity requires that each sub-order is neither excessively large nor excessively small, which is manifested in three aspects: economic efficiency, flexibility in production scheduling, and coordination and balance. The mathematical expression is as follows: \[P_{1}=\frac{\sum\limits_{i\in I}y_{i}}{I}\] (1) \[P_{2}=\frac{\sum\limits_{i\in I}\left(\sum\limits_{j,p}x_{ijp}\right)^{2}}{ \sum\
olimits_{j,p}1}\] (2) \[P_{3}=\frac{\sum\
olimits_{i,j,p}\left(q_{i,j,p}-D_{j,p}\right)^{2}}{\sum \
olimits_{j,p}D_{j,p}2}\] (3) where \(P_{1}\), \(P_{2}\), and \(P_{3}\) represent the penalty functions for economic efficiency, flexibility in production scheduling, and coordination and balance, respectively, and F denotes the splitting granularity function. The model aims to minimize the splitting granularity penalty. 2. Split costs. The order splitting cost can be regarded as the additional cost incurred during the process of dividing an overall order into multiple parts orders. These costs primarily manifest as equipment preparation and changeover costs, material scheduling and logistics costs, and planning and management costs. Its mathematical expression is shown as follows: \[C_{equ}=\sum\limits_{i\in I}\sum\limits_{p\in P}\sum\limits_{c\in C}\gamma^{ equ}\cdot\left(n_{i,p,c}-1\right)\] (4) \[C_{mat}=\sum\limits_{i\in I}\sum\limits_{j\in P}\sum\limits_{c\in C}\left( \gamma^{mat}_{0}+\gamma^{mat}_{1}\cdot Q_{i,j,p,c}\right)x_{i,j,p,c}\] (5) \[C_{\text{plan}}=\sum\limits_{i\in I}\sum\limits_{p\in P}\sum\limits_{c\in C }\gamma^{\text{plan}}\cdot\left(n_{i,p,c}-1\right)\] (6) where \(C_{equ}\), \(C_{mat}\), and \(C_{plan}\) represent equipment changeover costs, material scheduling costs, and planning costs, respectively. After completing the construction of the splitting granularity and splitting cost models, the objective function of the mathematical model for the order splitting level can be expressed as Equations (7) and (8). \[min\ F=\frac{3}{\frac{\lambda_{1}}{P_{1}+\epsilon}+\frac{\lambda_{2}}{P_{2}+ \epsilon}+\frac{\lambda_{3}}{P_{3}+\epsilon}} \tag{7}\] \[min\ C=C_{equ}+C_{mat}+C_{plan} \tag{8}\]where Equation (7) aims to minimize the splitting granularity and Equation (8) aims to minimize the whole split costs. Additionally, the constraints are divided into splitting granularity constraints and splitting cost constraints, and the related constraints are expressed by Equations (9)-(22).