esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Computational Modeling
### Simulation Setup  
As previously mentioned, the calculations are performed in ANSYS CFX 14.0. Impeller 1 and Impeller 2 are set to operate at a rotation speed of n = 2920 min\({}^{-1}\). The working fluid is water with density \(\rho\) = 997 kg/m\({}^{3}\). Radial diffuser 1, Radial diffuser 2, Annular seal and Outlet are stationary (fixed). The movement of the impellers relative to the fixed elements is simulated by applying Rotating Frames of Reference. The spatial discretization of the governing equations is based on the finite volume method. For the numerical solution of the RANS and URANS equation systems, the turbulent model k - \(\omega\) SST, including the application of the Automatic Wall Function, is used. Simulation calculations are performed  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mesh** & **Numbers of Nodes** & **Numbers of Elements** \\ \hline Mesh1.4 & 2,356,423 & 6,106,712 \\ \hline Mesh1.3 & 2,591,368 & 6,460,859 \\ \hline Mesh1.2 & 2,990,093 & 7,496,622 \\ \hline Mesh1.0 & 4,246,084 & 10,775,711 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information about generated meshes.  
under the Steady State condition and Transient Blade Row using Profile Transformation. The time step is 0.000171233 s, which corresponds to an angle of impeller rotation of 3\({}^{\circ}\). High resolution is selected as the advection scheme. The transient scheme is Second Order Backward Euler, and thus, the turbulence numeric is high resolution. For convergence criteria, the Max Residual \(5\times 10^{-4}\) is considered. The maximum number of iteration steps in each time step is set to 30.  
Some specific (typical) boundary conditions are presented in Figure 2.  
At the inlet of Impeller 1, the inlet boundary condition is applied. Mass Flow Rate and Zero Gradient option for turbulence are set. The Outlet section of the Outlet element is set to the Opening condition, with a relative pressure of \(8\times 10^{5}\) Pa and the Zero Gradient option for turbulence. As parts of the impellers and diffusers are being modeled, a Rotational Periodicity Interface Model was used for some of the surfaces presented in Figure 2 (colored in yellow). To simulate the pump shaft rotation, the cover disk of Impeller 2 and the cylindrical surfaces at the Outlet of the impellers have conditions set for rotation (at speed \(\mathrm{n}=2920\) min\({}^{-1}\)), as well as part of the surfaces of Radial diffuser 1 and Radial diffuser 2, whose surfaces are colored black in Figure 2. When applying the Steady State condition, the interface between the rotating impeller and the volute is set as stage. The interface model of Radial diffuser 1-Annular seal, Annular seal-Radial diffuser 2 and Radial diffuser 2-Outlet is General Connection without Frame Change/Mixing Model.
## 6 Conclusions  
When WRIM is used to invert DLS data, IDS of WRIM has a great effect on the inversion accuracy. If the choice of IDS is inappropriate, the inversion results of WRIM may not be optimal. In order to guide the use of WRIM, the scale effect of WRIM is carried on further studies in this paper. Through the studies, the following conclusions are drawn: (1) The relative errors of the inversion PSD and the true PSD vary on the different IDSs. In order to improve the inversion accuracy of PSD, the IDS with minimum relative error should be chosen as the optimal IDS. (2) In WRIM, the optimal IDS is related to the noise level of DLS data. When uniformly distributed noises are added, in the same inversion range, the optimal IDS usually shows a downward trend with the increase of noise level. Most importantly, considering the practical measurement, the true PSD is unknown. Minimum relative error as the criterion determining optimal IDS is infeasible. A practical application criterion determining the optimal IDS is put forward. This criterion obtains the optimal IDS through the minimization of the sub Tikhonov regularization problem on scale 1. According to this criterion, an improved WRIM is proposed in this paper. By means of the improved algorithm, high accuracy inversion PSD is obtained from DLS data.  
Conceptualization, Y.W. and J.S.; Formal analysis Z.D.; Funding acquisition, Y.W.; Investigation, X.Y., W.L., and Y.W.; Software, X.Y., Z.D., and S.M.; Supervision, Y.W.; Writing--original draft, Y.W.; Writing--review & editing, Y.W.  
This research was funded by the Natural Science Foundation of Shandong Province grant numbers ZR2016E116, ZR2018 MF032, ZR2018 FP014, ZR2017 MF009, ZR2017 LF026, and the Key Research and Development Program of Shandong Province grant number 2017 GGX10125.  
Thanks to the reviewers and editor for manuscript improvements. This work was supported by the Natural Science Foundation of Shandong Province grant numbers ZR2016 EL16, ZR2018 MF032, ZR2018 PF014, ZR2017 MF009, ZR2017 LF026, and the Key Research and Development Program of Shandong Province grant number 2017 GGX10125.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**300 nm Unimodal Particles**} & \multicolumn{2}{c}{**100 nm/450 nm Bimodal Particles**} \\ \cline{2-5}  & **Peak Value (nm)** & **Peak Value Error \%** & **Peak Value (nm)** & **Peak Value Error \%** \\ \hline IDS 1 & — & — & — & — \\ CONTIN & 316.23 & 5.41 & 94.06,376.27 & 5.94, 16.38 \\ Improved WRIM & 385.56 & 4.81 & 109.06, 455.52 & 9.06, 1.23 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Inversion data of three methods.  
Figure 11: Inversion PSDs of the experimental particles under the different methods. (**a**) 300 nm unimodal particles and (**b**) 100 nm/450 nm bimodal particles.
# COALA: A Protocol for the Avoidance and Alleviation of Congestion in Wireless Sensor Networks
## 3 Proposed Protocol Description
### Preliminary Considerations and Terms  
First, a densely deployed WSN where homogeneous nodes are positioned in a uniformly random way, such as the one illustrated in Figure 1a, is considered to be the reference point for the protocol description. It is assumed that there are is one sink. It is also supposed that every individual node knows both its location and the location of the sink.  
Additionally, it is assumed that incidents referred here as _events_, are created and all nodes that are located within the range of each event, send related data to the sink. Thus, the number of data sources is variable. Also, all nodes are considered to have the same transmission and sensing range. Moreover, it is supposed that multihop routing through direct neighboring nodes is utilized, whenever the sink is not placed within the transmission range of the node that relays data to the sink. Furthermore, CSMA is considered to be used as the medium access control (MAC) protocol.  
For every individual node \(i\):  
* the area where the signals sent by node \(i\) can reach, is defined as _Transmission Range TR(\(i\))_
* the minimum number of hops for node \(i\) in order to reach the sink, is defined as _level L(\(i\))_
* every node \(j\) that is placed within \(tr(i)\) and has \(L(j)=L(i)\pm 1\), is defined as _Neighboring Node NN(\(i\))_
* the ratio of the total number of \(NN(i)\) whose level \(L\) is greater than \(L(i)\) to the total number of \(NN(i)\), is defined as _Vicinity Index VI(\(i\))_
* the ratio of the accumulative participation of a node in data flows over time is referred as _Popularity Index PI(\(i\))_
* the so called _Availability Index AI(\(i\))_ expresses either the ability (when having value 1) of the node to relay data or the unavailability (when having value 0) of the node to do so, because the node has either limited buffer space, or limited energy or because its neighboring nodes that have lesser level are blocked
## 4 Discussion
### Analysis of Method Performance Superiority
Firstly, we performed split tests on the three modules within the WRPM, with the results detailed in Table 7. The findings indicate that the integration of FFA-Net and LS-UNet via FIM yields optimal performance. Secondly, we evaluated the performance of SCP-Net, as presented in Table 8. The results demonstrate that SCP-Net significantly enhances positioning accuracy. Finally, we examined various loss functions employed in SCP-Net, with the outcomes reported in Table 9. The experimental data reveal that our proposed composite loss function outperforms all alternatives tested.
# Low-Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm
## 3 Proposed Fingerprint Algorithm
### Probability Map Generation and Positioning
\[o=\langle(gw_{1},rssi_{1}),(gw_{2},rssi_{2}),\ldots,(gw_{k},rssi_{k})\rangle. \tag{2}\]  
We adopted a Maximum Likelihood Estimation approach to estimate the locations of the end-device [11; 12; 22]. We can define a posterior probability \(Pr(l_{i}|o^{*})\) and likelihood probability function \(Pr(o^{*}|l_{i})\) as given by Equation (3). \(Pr(l_{i})\) is the prior probability, and we set \(Pr(l_{i})\) as a uniform distribution for experiments. We could ignore the marginal probability term \(\sum_{j=1}^{n}Pr(o^{*}|l_{j})Pr(l_{j})\) and the prior during the calculation of the maximum likelihood. We considered only one sequence; hence, updating the prior was not necessary. \[Pr(l_{i}|o^{*})=\frac{Pr(o^{*}|l_{i})Pr(l_{i})}{\sum_{j=1}^{n}Pr(o^{*}|l_{j}) \Pr(l_{j})}. \tag{3}\]  
Figure 12: Signal-strength distribution at a fixed location. [22]. Figure 9:0 Vector space RSSI data collection. The estimated location \(l^{*}\) in the Equation (4) was defined as the location where the likelihood value becomes the maximum. In given the interpolated fingerprint maps in advance, we could calculate the probability \(Pr(o^{*}|l_{i})\) for all locations \(l_{i}\) in the experimental area. Equation (5) represents the likelihood at each location. Figure 9:1  
Figure 9:2  
\(F(o^{*},l_{i})\) is the function of likelihood, and the denominator \(\sum_{j=1}^{n}F\big{(}o^{*},l_{j}\big{)}\) makes Equation (5) to the form of probability distribution. The denominator \(\sum_{j=1}^{n}F\big{(}o^{*},l_{j}\big{)}\) can be ignored during the estimation of the location \(l^{*}\). The function of \(F(o^{*},l_{i})\) can be derived by summation of the probabilities for all gateways as given by Equation (6). We constructed the conditional probability \(Pr\Big{(}rssi_{j}^{*}\Big{|}gw_{j},l_{i}\Big{)}\), that is, the probability that gateway \(gw_{j}\) has RSSI measurement \(rssi_{j}^{*}\) at location \(l_{i}\). This probability can be calculated by comparing the \(o^{*}\) with \(o\) on the interpolated fingerprint maps. Figure 9:3  
We conducted experiments with three different weighting methods. First, we assigned the same weighting factors to all gateways. Second, we assigned the weighting factors normalized with the number of cells in each candidate region. The gap in its values with distance increases when the signal strength is strong according to the characteristics of LoRa RSSI. However, the gap decreases when the signal strength is weak, as shown in Figure 9:4 The candidate region occupies a large area in the RSSI matching map for a weak RSSI signal (e.g., one of \(-120\) dBm or less). Third, we formulated the general equation given in Equation (7) by using an exponential function to assign higher weights for stronger RSSI values. We estimated the coefficients using Equation (8) through brute force trials. In the trials, we increment variable \(a\) by 0.1 (\(1\leq a\leq 9\)), \(b\) by 0.0001 (\(0\leq b\leq 1\)), and \(c\) by 0.1 (\(10\leq c\leq 150\)). Figure 9:5  
Figure 9:6
# Visual and Phonological Feature Enhanced Siamese BERT for Chinese Spelling Error Correction
## 2 Related Work  
### Unsupervised Methods  
The CSC task attracts much attention from the community in recent years. Earlier works in CSC mainly focused on unsupervised methods [1; 13]. Character-level n-gram language models were used to detect potential misspelled characters with low probabilities below some pre-defined threshold. To be specific, [1] performs a four-stages procedure to handle spelling check, including character-level n-gram language model, pronunciation and shape-based candidate set generation, candidate corrections filter, and find the best candidate. In [13], the spelling correction process includes two cascaded components: spelling error detection and spelling error correction. In spelling error detection phase, characters with a low score under the language model are regarded as potential error characters. Independent characters after automatic word segmentation are also collected as potential error characters. In the spelling error correction phase, a candidate set will be generated for each error character. The candidate set generation is based on similar pronunciation or shape dictionary (confusion set). The generated candidate set will be filtered by testing if the candidate character can construct a legal word with neighbor characters. After filtering, the character with the highest score calculated by the language model will be selected as the best candidate. However, such unsupervised methods cannot model the semantic context, which is important information in CSC tasks.
# Substitute Seed Nodes Mining Algorithms for Influence Maximization in Multi-Social Networks
## 2 Influence Maximization in Multi-Social Networks
### Multi-Social Networks Aggregation Algorithm based on Bridge Users  
In order to solve the problem of multi-social networks influence maximization, this section proposes a multi-social networks aggregation algorithm based on a _Bridge User_, to aggregate multiple social network graphs into one graph, and then we can apply some widely explored algorithms (such as Greedy [6], CELF [20], CELF++ [21] and so on) to obtain the initial seed node set \(S\). The algorithm is described as Algorithm 1.  
```
Input:\(G_{1}(V_{1},E_{1}),\)\(G_{2}(V_{2},E_{2}),\)\(G_{3}(V_{3},E_{3}),\)\(\ldots,\)\(G_{n}(V_{n},E_{n}),\)\(U=\left\{u^{1},u^{2},u^{3},\ldots,u^{m}\right\}\), \(BU,\ P_{\textit{max}}\); Output: Aggregation graph \(G^{*}(V^{*},E^{*})\); (1) for \(i=1\) to \(n\) // Traversing n social network graphs (2) for \(j=1\) to \(|V_{i}|\) // Traversing all nodes of \(G_{i}\) (3) if \(u^{j}_{i}\in BU\) (4) if \(u^{j}_{0}\in V^{*}\) then (5) Create edge \((u^{j}_{i},u^{j}_{0}),\)\((u^{j}_{0},u^{j}_{i})\); (6) Add activated probability of edge \(p_{u^{j}_{i},u^{j}_{0}}=1,\)\(p_{u^{j}_{0},u^{j}_{i}}=P_{ij}\); (7) Add edge \((u^{j}_{i},u^{j}_{0})\) and \((u^{j}_{0},u^{j}_{i})\) to \(E^{*}\); (8) else (9) Create node \(u^{j}_{0}\); (10) Create edge \((u^{j}_{i},u^{j}_{0}),\)\((u^{j}_{0},u^{j}_{i})\); (11) Add activated probability of edge \(p_{u^{j}_{i},u^{j}_{0}}=1,\)\(p_{u^{j}_{0},u^{j}_{0}}=P_{ij}\); (12) Add edge \((u^{j}_{i},u^{j}_{0})\) and \((u^{j}_{0},u^{j}_{i})\) to \(E^{*}\), add node \(u^{j}_{0}\) to \(V^{*}\); (13) end if (14) end if (15) end for (16) end for (17) Output \(G^{*}(V^{*},E^{*})\);
```  
**Algorithm 1** Multi-social networks aggregation algorithm based on Bridge Users.
# Improved Anti-Collision Algorithm for the Application on Intelligent Warehouse
## 5 Simulation and Comparison  
In this section, we compare GMQT algorithm to QT [1], 8-ary QT, IACA [5], BAT [10], MQT [11] from system efficiency and communication complex. We consider an ideal transmission channel, without capture-effect and path-loss affects. The RFID system, running on a MATLAB 2016a simulation platform, consists of a single reader and numerous tags, and the number of tags is increased from 5 to 1005. Each tag has a unique ID, and the length of tag ID is 16 bits.  
1. System efficiency  
Figure 3 shows the system efficiency with a different number of tags for GMQT, QT, 8-ary-QT, BAT, IACA, and MQT (m = 2). The system efficiency of GMQT is significantly better than other algorithms because GMQT reduces collision slots by grouping query, and it uses accurate prediction of group number collision bits by mapping rules to avoid idle time slots. Moreover, the system efficiency of GMQT doesn't decrease with the increase of the number of tags, and remained stable between 0.6 and 0.65. The second is MQT, whose output is between 0.55 and 0.6, and the output of BAT is about 0.5-0.55, and others are not more than 0.5. The mapping mechanism helps the GMQT algorithm avoid idle queries and the method of group query effectively reduce collisions, so when the number of tags increases, the efficiency of the algorithm will remain at a stable level.  
2. Communication complexity  
Figure 4 shows the communication complexity with the different number of tags for GMQT, QT, 8-ary-QT, BAT, IACA, MQT (m = 2). The number of bits transmitted by GMQT is the least among the six algorithms. QT algorithm uses bit-by-bit identification to query tags, which consumes more slots, and for each slot the reader broadcasts the query prefix, the tag matching the prefix sends its ID to the reader completely. Therefore, using the QT algorithm, the number of bits transmitted in the communication channel is the largest and the communication complexity is the highest. The 8-ary-QT algorithm is similar to QT, and tags respond complete IDs to the reader. Compared with QT, 8-ary-QT reduces collision slots effectively, even though there are more idle slots, and there are only query instructions and prefixes transmitted by the reader in the idle slots, and no tags transmit data. Therefore, the communication  
Figure 3: System efficiency with different protocols.  
complexity of the 8-ary QT algorithm is better than that of the QT algorithm. Although MQT can accurately predict the collision bits to avoid idle slots, the mapping code returned by the tags which causes the transmission burden of the system. GMQT only maps the group number, which reduces the number of mappings. At the same time, the characteristics of the group also reduce the number of response tags. Therefore, GMQT can effectively reduce the communication complexity.
# Polarization Differential Visible Light Communication: Theory and Experimental Evaluation +
## 3 Measurement Setup
### Signal Processing  
In setup A and B, the LED transmitter switching voltages are controlled by a National Instruments USB-6215 Data Acquisition System (DAQ) and USB-6212, respectively in order to transmit data at 4 kbps. While the polarization differential modulation scheme allows for higher data rates, the data rate in this setup is mainly limited by the used hardware, namely the transient response of the power LEDs and corresponding drivers, and the DAQs used to sample the receivers. The data rate is thus deliberately chosen low as not to disfigure the measurement results and to assess the operating principle accurately. The same DAQ oversamples the photodetector voltages synchronously at 80 kHz. The differential signal is then calculated and DC-balanced to ensure the antipodality of the signal, taking into account any possible asymmetry between the LED transmitters and the receiver photodetectors. This results in a binary antipodal non-return-to-zero coded signal as described in Section 2.3. This signal is then demodulated by matched filtering and based on a decision threshold, the output is mapped back to data bits. As the signal can be inverted depending on the receiver  
Figure 6: Setup B: four quadrant receiver with aperture containing four linear polarizers subsequently rotated over 45\({}^{\circ}\), note the orientation of the linear polarizing film at each quadrant.  
orientation, the data bits can be inverted too. This is accounted for by using a known training sequence at the start of the data transfer to determine whether or not bits should be inverted. The output is then further examined to determine the \(SNR\) and compared to the transmitted data to check for bit errors. The key parameters of used photodetectors and DAQ settings for both setups are listed in Table 1. A block diagram illustrating the signal processing chain used for both setups is depicted in Figure 7.
## 3 Experimental Results
### Force-Displacement Curves  
Figure 6 shows the relationship curves of the axial force versus the axial displacement, which was obtained by recording the average value of four wire-type displacement meters on each side. Since the concrete was rapidly crushed, and the concrete inhibition effect was weakened, specimen HGW-1 was damaged at the ultimate load. However, since the vertical stiffening ribs on both sides of the steel plate constrain the development of a single wave outside of the steel plate, specimen HGW-2 continued to bear loads, even after the ultimate load. The curve of HGW-3 was gentler, mainly because the two skins of the steel plate were able to effectively constrain the transverse deformation of the inner concrete.  
Table 2 lists the load values and displacement values of the three test specimens at every stage. For specimen HGW-3, the crack load cannot be obtained, due to the in-filled concrete, and the failure load was assumed to be 15% of the ultimate load, according to the literature [10]. Compared to specimen HGW-1, the axial compressive bearing capacity of specimen HGW-2 increased by 13.59%, and the axial deformation increased by 2.32%, mainly because the stiffening ribs delayed the compressive strain development of the concrete, and improved the cooperative performance between the steel plate and concrete. In contrast to specimen HGW-1, the axial compressive bearing capacity of specimen HGW-3 increased by 28.21%, and the axial deformation increased by 112.25%, chiefly because the outer steel plate kept the concrete in a state of multi-axial compression, and the inner vertical separators played the role of a web. For specimen HGW-3, the corresponding displacement at the failure load was 1.76 times higher than that at the ultimate load, and 6.09 times higher than that at the yield load, which indicated that the composite shear wall with two skins of steel plate, demonstrated a good elastic-plastic deformation performance.
# Lightweight Integrity Preserving Scheme for Secure Data Exchange in Cloud-Based IoT Systems
## 1 Introduction
Here, cryptographic hash function with simple mathematic operations and the key chain method are utilized to minimize the duration of integrity checking. Consequently, this helps semi-trusted servers working as a representative of smart device users to manage information prior to the distribution of such information to the smart device user. In addition, this confirms the integrity of information while avoiding any potentially problematic effects on smart device users. Moreover, this system allows semi-trusted servers to conduct audits in the absence of a local replication of the information. Basically, the IoT-cloud system is unable to create an accurate reaction without the necessary information. A one-time key is implemented to protect against various security threats. The remainder of this paper is organized as follows. In Section 2, we provide an overview of Related Work, while in Section 3 the Problem Statement is presented. We discuss the Proposed Scheme in Section 4, while the Security Analysis of this scheme is presented in Section 5. The results and analyses are described in Section 6. Finally, Section 7 concludes the paper.