esa-sceva/satcom-chunk-collection huggingface dataset:
# Coordinated Control Strategy of CU-MTDC under Abnormal Conditions Considering Power Supply Security
## 4 Case Study
### Simulation under Shared Control Mode  
The initial simulation set is similar to 4.1. When \(t\) = 0.2 s, the output of the offshore wind farm is changed to zero. At \(t\) = 0.5 s, converter 1 is blocked due to a temporary fault and unblocked at \(t\) = 0.8 s. Simulation results are shown in Figure 10.  
As shown in Figure 10a,b, when \(t\) = 0.2 s, the maximum DC unbalanced power is changed to 460 MW since the active power of the offshore wind farm is decreased to zero.  
At \(t\) = 0.5 s, the master converter is blocked because of a temporary fault, and the active power is reduced to zero. The DC unbalanced power occurs, and the DC voltage reduces rapidly. Since the unbalanced power cannot be absorbed by one converter independently, the operation mode turns into a shared control mode. Similar to case 4.1, \(U_{ddcl2}\) and \(U_{ddcl4}\) are 391 kV and 390 kV, respectively. \(U_{dd2}\) and \(U_{dd4}\) are 379 kV and 383 kV, according to (12), (13), (14), and (16). Under the shared control mode, the DC unbalanced power is absorbed by both VSC\({}_{2}\) and VSC\({}_{4}\), and the active power is \(-176.3\) MW and \(-67.1\) MW. During the  
Figure 10: Simulation under shared control mode. (**a**) Active power of converters. (**b**) DC voltage of converters. (**c**) Active power of critical AC lines.  
whole process, the DC voltage of all the converters is within the security range. The fault is cleared after 0.3 s, and converters restore to the initial operation mode immediately.  
As shown in Figure 10c, the active power of \(L_{5-7}\), \(L_{13-15}\) and \(L_{13-14}\) are 237.4 MW, 347 MW and 308.4 MW under the shared control mode. The security of all the critical lines is guaranteed, and the load ratio of \(L_{5-7}\) and \(L_{13-15}\) + \(L_{13-14}\) is 0.9309 and 0.9363.  
According to the simulation results above, the DC unbalanced power can be well equilibrated by both the two converters under shared mode. The control objectives of power supply security and equal load ratio of critical lines are also realized.
# Forecasting Taxi Demands Using Generative Adversarial Networks with Multi-Source Data
## 4 Methodology
### Generative Adversarial Network (GAN) Model
#### 4.4.2 GAN_LSTM_CNN Model  
In the proposed model, and due to their stability, the generator component adopted LSTM networks and CNN for the discriminator network. Figure 15 illustrates the architecture of the GAN_LSTM_CNN Model.  
* The generator  
In our GAN model, we set the LSTM network as the generator according to its stability. The dataset contains 12 features (for 24 h of 91 days) as listed in Table 4. For building up a robust generator network with good performance, we use four layers of LSTM; the numbers of the neuron are 1024, 512, 256, and 128, followed by three fully connected layers. The activation function used in LSTMs is ReLU, and the dropout neurons are 20 percentage, and the neuron number of the latest layer will be the same as the output step we are going to predict.  
* The discriminator  
The discriminator in the proposed GAN model is a Convolutional Neural Network that aimed to distinguish whether the input data of the discriminator is real or fake. The input for the discriminator will be from the real data or the fake data from the generator. The discriminator network contains four Convolution layers with 32, 64, 128, and 256 neurons separately. Besides, a flatten layer is combined to flatten the output of the convolutional layers to generate a single feature vector for classification, which is performed by the following four fully connected layers with 220, 220,220, and a neuron, respectively. The Leaky Rectified Linear Unit (ReLU) has been set as the activation function on all layers, except the output layer (adopted Sigmoid function). The sigmoid function will produce a single scalar output, 0 or 1, which means real or fake as the final result.
## 3 Materials and Methods
### Calibration and Data Management Processes  
The starting point for the development work was to assess the current practices and processes of the industry partners to observe the general requirements. Although there can be slight variations in the processes of different organizations, e.g., due to their size and the number of calibrated instruments, the main principles remain the same. Based on the processes of the industry partners, generalized overviews of the processes and workflows were then formed and utilized to define the requirements for the DCC-based processes and the POC platform implementation.  
In general, calibrations are based on bilateral relationships between a service provider and an instrument owner, i.e., the customer. The process begins with a calibration order or request issued by the instrument owner. If the calibration is to be performed in an external laboratory, the instrument needs to be delivered as well. Upon receipt of the request and instrument, the calibration is performed and results are analyzed by the service provider based on the requirements defined by the customer. If an instrument does not perform within its tolerance, it will need to be adjusted or repaired, after which it must  
Figure 3: An illustration of the POC implementation. VTT MIKES is represented in dark blue, Vaisala in blue, Aalto University in turquoise, Beamex in dark green, and Orion in light green.  
be recalibrated. The calibrations before and after any significant changes made to the calibrated instrument are commonly referred as \"as-found\" and \"as-left\" calibrations, respectively [11]. To report the results of the calibration and a statement on the conformity of the instrument as required by the customer, the calibration vendor issues a calibration certificate, sends it to the instrument owner, and returns the instrument. At the receiving end, the calibration certificate is inspected to make sure it is correct and that it meets the requirements that have been defined for the calibration process. Once the certificate is deemed to fulfill the requirements, the data are imported into a calibration management system (CMS).  
The calibration process should be considered as a closed-loop process between the instrument owner, i.e., calibration customer and calibration service provider. Thus, fully standardizing the communication and ensuring interoperability in wider industrial use requires also a format for the digital calibration request (DCR) in addition to the DCC. However, in the POC, the implementation concentrated only on the use of the DCC as its development has progressed further. Figure 4 shows a simple illustration of the communication between the instrument owner and calibration provider.  
It is currently common that several different systems are used for the exchange and handling of the information in different parts of the process. An example of the systems used in the process is illustrated in Figure 5.
## 6 Conclusions  
This paper has discussed a reliable and seamless mobility support scheme for IoT sensor nodes, which are extremely energy and resource constrained devices in nature, _i.e._, less CPU processing, low memory, and power supply capabilities than Internet devices. A variety of IoT services have been attempted, e.g., healthcare monitoring services, public transport vehicle service, V2I automatic vehicle networks, home networks, automotive networks, automatic systems, industrial networks, interactive toys, and remote meters. Specifically in the healthcare service, the reliable data transmission of vital sensing data is very important while the mobile sensor node moves into different wireless network domain. To guarantee the reliable data transmission, the reliable mobility management protocol is required while considering the characteristics of the constrained device. We proposed a mobility  
Figure 16: Packet loss as a function of packet arrival rate.  
Figure 17: Impact of wireless link delay on packet loss.  
management protocol named CoMP, which can make the sensing data of sensor nodes to be retrieved effectively, while IoT sensor nodes are moving. The salient feature of CoMP is that it makes use of the IETF CoAP protocol at application layer for mobility management, instead of using Mobile IP at network layer. It can eliminate additional signaling overhead of Mobile IP, providing the reliable mobility management, and preventing the packet loss. We have designed the architecture, message formats and detailed signaling procedures of CoMP. More specifically, the IETF CoAP message formats are extended for supporting the registration, discovery, binding and holding operation. Finally, by both mathematical analysis and simulation, we have conducted comparative performance evaluation between CoMP and MIPv6/HMIPv6 in terms of handover latency and packet loss. The results show that the proposed CoMP is superior to previous mobility management protocols. Further work may be required to be done on the security issues related to CoMP.  
This study was supported by the BK21 Plus project funded by the Ministry of Education, Korea (21A20131600011).
# Constraint Preserving Mixers for the Quantum Approximate Optimization Algorithm
## 3 Construction of Constraint Preserving Mixers
### Conditions on the Mixer Hamiltonian
The connection between \(H_{M}\) and \(U_{M}\) is general knowledge from linear algebra. An example illustrating Corollary 3 is provided by the transition matrix \(T\in\mathbb{R}^{4\times 4}\) with zero diagonal and all other entries equal to one. A unit eigenvector of T, which fulfills Theorem 1, is \(v=1/2(1,1,1,1)^{T}\). For any \(B=\{|z_{1}\rangle,|z_{2}\rangle,|z_{3}\rangle,|z_{4}\rangle\}\), the uniform superpositions of these states is an eigenvector, since  
\[\frac{1}{\|v\|_{2}}Ev=\frac{1}{2}(|z_{1}\rangle,|z_{2}\rangle,|z_{3}\rangle,| z_{4}\rangle)(1,1,1,1)^{T}=\frac{1}{2}(|z_{1}\rangle+|z_{2}\rangle+|z_{3} \rangle+|z_{4}\rangle).\]  
This result holds irrespective of what the states are and which dimension they have. **Theorem 2** (Products of mixers for subspaces). : _Given the same setting as in Theorem 1. For any decomposition of \(T\) into a sum of \(Q\) symmetric matrices \(T_{q}\), in the following sense_  
\[T=\sum_{q=1}^{Q}T_{q},\quad(T_{q})_{i,j}=(T_{q})_{j,i}= \begin{cases}\text{ either}&(T)_{ij},\\ \text{or}&0,\end{cases}  \tag{18}\]  
_we construct the mixing operator via_  
\[U_{M}(\beta)=\prod_{ \begin{subarray}{c}n=1\\ qn\in\{1,2\cdots Q\}\end{subarray} }^{N}e^{-i\beta T_{q_{m}}}. \tag{19}\]  
_If all entries of \(T\) are positive, then \(U_{M}\) provides transitions between all pairs of feasible states, i.e., condition (6) is fulfilled, if for all \(1\leq j,k\leq|J|\) there exist \(r_{m}\in\mathbb{N}\cup\{0\}\) (possibly depending on the pair) such that_  
\[\big{(}\prod_{ \begin{subarray}{c}m=1\\ qn\in\mathbb{Q}\end{subarray} }^{M}T_{q_{m}}^{r_{m}}\big{)}_{j,k}\
eq 0. \tag{20}\]  
Proof. : Combining Equations (15) and (16), we have  
\[ \begin{split}\big{\langle}x_{j}\big{|}U_{M}(\beta)|x_{k}\big{\rangle} &=\sum_{j_{1}=0,j_{2}=0,\cdots,j_{M}=0}^{\infty}\frac{(-it)^{j_{1}+ j_{2}+\cdots+j_{m}}(T_{q_{1}}^{j_{1}}T_{q_{2}}^{j_{2}}\cdots T_{q_{M}}^{j_{M}})_{j,k}}{j_{1 }!j_{2}!\cdots j_{m}!}\\ &=\sum_{j=1}^{\infty}\frac{(-it)^{j}}{j!}\sum_{j_{1},\cdots,j_{M} \text{s.t.}\,\left(\sum_{m=1}^{M}j_{m}\right)=j}(T_{q_{1}}^{j_{1}}T_{q_{2}}^{ j_{2}}\cdots T_{q_{M}}^{j_{M}})_{j,k}.\end{split}  \tag{21}\]  
Using that \(T\) only has positive entries and the condition in Equation (20), the same argument as in Theorem 1 can be used to show that \(U_{M}(\beta)\) is not the zero function, and therefore, we have transitions between all pairs of feasible states. As Theorem 1 leaves a lot of freedom for choosing valid transition matrices, we will continue by describing important examples for \(T\).
# Data-Driven Analyses of Low Salinity Waterflooding in Carbonates
## 2 Methodology
### Data Collection and Cleaning
Different units for parameters (such as the composition of brines, total salinities, temperature, and pressure drop) were reported in the literature. Hence, all data were converted to a unified unit system in order to make comparative analyses. Different dimensionless numbers, such as dimensionless sulphate concentration (DS), cations concentration (DC), salinity (DTDS), acidity of oil (AB), and recovery factor (DRF), were developed to scale the controlling parameters while preserving their physical significance, as shown in Equations (1)-(5). \[\text{DS}=\frac{\text{SO}_{4}{}^{2-}(\text{HS})-\text{SO}_{4}{}^{2-}(\text{LS} )}{\text{TDS}(\text{HS})} \tag{1}\]  
\[\text{DC}=\frac{\text{Cations}(\text{HS})-\text{Cations}(\text{LS})}{\text{TDS} (\text{HS})} \tag{2}\]  
\[\text{DTDS}=\frac{\text{TDS}(\text{HS})-\text{TDS}(\text{LS})}{\text{TDS}( \text{HS})} \tag{3}\]  
\[\text{AB}=\frac{\text{AN}}{\text{BN}} \tag{4}\]  
\[\text{DRF}=\frac{\text{RF}_{3}-\text{RF}_{2}}{100-\text{S}_{\text{wi}}-\text {RF}_{2}} \tag{5}\]  
where HS and LS show the high salinity and low salinity water conditions, respectively. \(\text{SO}_{4}^{2-}\) is the concentration of sulphate (ppm), TDS is the total dissolved salts (ppm), Cations is the concentration of cations, AN is the acid number of crude oil (mgKOH/g), BN is the base number of crude oil (mgKOH/g), RF\({}_{3}\) is the recovery factor after tertiary flooding (% OOIP), RF\({}_{2}\) is the recovery factor after secondary flooding (%OOIP), and S\({}_{\text{wi}}\) is the initial water saturation (%). The active mechanisms that explain the positive effect of LSW injection on oil recovery enhancement are difficult to establish. To study the relationships between the proposed mechanisms and the conditions required for LSW to work, data collected from available coreflooding tests were statistically analyzed. Mechanisms such as MIE, rock dissolution, IFT reduction, EDL expansion, and micro-dispersions were evaluated. By statistical analysis of the controlling parameters (including effluent PDI concentration, wettability, pressure drop, IFT, pH of effluent brine, and zeta potential), the occurrence of these mechanisms was investigated. Figure 1: Probability of incremental recovery factor achieved by tertiary stage injection, %OOIP.
## 8 Experimental Results and Analysis
### Slant Distance Correction
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Azimuth** \\ **Angle** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Ray Tracing Method** \\ **Depth (m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **True Slant** \\ **Distance** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Traditional Method** \\ **Ray Tracing Method** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Constant Sound Speed** \\ **Ray Tracing Method** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Combined Ray Tracing** \\ **Method** \\ \end{tabular} } \\ \cline{5-10}  & & & & **Slant** & **Calculation** & **Slant** & **Calculation** & **Slant** & **Calculation** \\  & & & **Distance** & **Iterations** & **Distance** & **Iterations** \\ \hline \multirow{6}{*}{30\({}^{\circ}\)} & 10 & 10.4 & 11.5 & None & 11.5 & 6 & 10.8 & 1 \\  & 50 & 56.4 & 57.7 & None & 57.7 & 24 & 56.7 & 22 \\  & 100 & 113.2 & 115.5 & None & 115.3 & 47 & 114.8 & 45 \\  & 150 & 170 & 173.2 & None & 172.8 & 69 & 171.9 & 67 \\  & 200 & 228.3 & 230.9 & None & 230.3 & 92 & 229.2 & 90 \\ \hline \multirow{6}{*}{40\({}^{\circ}\)} & 10 & 10.8 & 13.1 & None & 13.1 & 6 & 11.6 & 1 \\  & 50 & 62.8 & 65.3 & None & 65.2 & 24 & 64.5 & 22 \\  & 100 & 125 & 130.5 & None & 130.0 & 47 & 128.4 & 45 \\  & 150 & 192.1 & 195.8 & None & 194.8 & 69 & 193.1 & 67 \\  & 200 & 256.1 & 261.1 & None & 259.6 & 92 & 257.6 & 90 \\ \hline \multirow{6}{*}{45\({}^{\circ}\)} & 10 & 11.7 & 14.1 & None & 14.1 & 6 & 12.2 & 1 \\  & 50 & 65.9 & 70.7 & None & 70.6 & 24 & 67.0 & 22 \\ \cline{1-1}  & 100 & 137.2 & 141.4 & None & 140.7 & 47 & 139.4 & 45 \\ \cline{1-1}  & 150 & 205.2 & 212.1 & None & 210.7 & 69 & 208.4 & 67 \\ \cline{1-1}  & 200 & 275.9 & 282.8 & None & 280.6 & 92 & 277.9 & 90 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Slant distance and calculation iterations of various methods. Figure 15: Ray tracing with an incident angle of 45 degrees. As it can be seen in Table 2, the combined ray correction (method M2) exhibits an error that is always smaller than the constant sound speed ray tracing method (method M1) and using a fixed sound speed (traditional method). The worst error observed for M2 (7.4% for 40\({}^{\circ}\), 10 m) is approximately one-third of the error obtained by M1 and the traditional method. Calculating the ratio error(M2)/error(M1), we can objectively find the reduction of the relative error when using M2 instead of M1. From Table 3 we can see that the reduction of the relative error reduced at least 20% when using the combined ray tracing method, and the maximum reduction is about 70%. From the simulation results, we can find that the combined ray tracing method reduces the relative error of the slant distance in USBL without increasing the amount of calculation iterations, which has research significance and engineering application value.
## 3 Proposed Channel-Quality Aware Query Tree Algorithm
### Channel Error Rate Estimation  
Since PER is required to evaluate the efficiency of asking the unreadable tag to retransmit the message or to split the collided tags, CAQT estimates PER through sampling the slot in which only one tag transmits. The following shows the details:  
First, the tree-based RFID anti-collision operation is utilized to distinguish whether the unreadable slot is due to collision or channel error. That is, if the reader only identifies one tag in the split slots after detecting an unreadable slot, this unreadable slot can be deducted as the slot interfered by noise rather than the collided slot, as shown in Figure 6.  
Second, the reader estimates the PER by calculating the number of the readable slots between the two adjacent slots in which only one tag transmits and suffers channel error, named \(U_{s}\) herein. That is, as shown in Figure 7, the slots between two adjacent \(U_{s}\), which means the slots starts after \(U_{s}^{i-1}\) and ends to (includes) \(U_{s}^{i}\), can be categorized into three groups including readable slots, idle slots, and unreadable slots. The readable slots mean that only one tag transmits in these slots including \(U_{s}^{i}\), the unreadable slot indicates there are more tags transmitted in one slot, and these tags therefore collide; the idle slot is the slot that no tag transmits in it. The PER for the readable slots between \(U_{s}^{i-1}\) and \(U_{s}^{i}\) can be calculated by \(\frac{1}{N_{r}^{i}}\), where \(N_{r}^{i}\) is the number of readable slots between \(U_{s}^{i-1}\) and \(U_{s}^{i}\). Thus, to assume that the channel error is uniform distributed in each slot, the PER for the slots between \(U_{s}^{i-1}\) and \(U_{s}^{i}\), named \(\widetilde{PER^{i}}\), can be estimated by  
\[\widetilde{PER^{i}}\approx\frac{1}{N_{r}^{i}}, \tag{1}\]  
Finally, the exponential smoothing method is applied to forecast the PER in the following slots, which is,  
\[\widetilde{PER}=\alpha\times\widetilde{PER^{i}}+(1-\alpha)\times\widetilde{ PER_{old}}, \tag{2}\]  
where \(\widetilde{PER}\) is the estimated PER in the following slots and \(\widetilde{PER_{old}}\) is the previous estimated PER. In addition, the coefficient \(\alpha\) is used to revise \(\widetilde{PER}\).  
Figure 6: An example of unreadable slots.
## 3 Results and Discussion
### Aggregate Properties
#### 3.1.3 Phase Compositions  
The XRD analysis results of type A limestone, type B limestone, diabase, granite, and basalt are shown in Figure 7. The analysis results show that the type A limestone and type B limestone are mainly composed of two minerals, quartz and dolomite. This indicates that the mineral composition of the two types of limestone is similar, and the mineral element composition is relatively simple. The other three types of rocks are magmatic rocks with more mineral phases, among which basalt contains the most complex mineral phases.  
In the system of evaluating the acidity and alkalinity of magmatic rock aggregates, the SiO\({}_{2}\) content is generally considered to be one of the important influencing factors. The main component of mineral phase quartz is SiO\({}_{2}\). The proportion of mineral phase quartz in the five rocks from high to low is granite, type B limestone, type A limestone, diabase,  
Figure 7: Mineral phase content of different aggregates. (**a**) Type A limestone; (**b**) type B limestone; (**c**) diabase; (**d**) granite; (**e**) basalt.  
and basalt. The quartz content in diabase, granite, and basalt is 11.51%, 30.1%, and 5.38%, respectively.  
By combining XRF and XRD tests on the five types of aggregates, it was found that the SiO\({}_{2}\) content varied. The SiO\({}_{2}\) content of limestone was around 26%, where the element Si was present in the quartz mineral phase and belonged to alkaline dolomitic limestone. The SiO\({}_{2}\) content of diabase and basalt is 55.94% and 54.40%, respectively, where the element Si exists in the ores of silica, feldspar, pyroxene, and hornblende, which are neutral magmatic rocks. The SiO\({}_{2}\) content of granite is 67.23%, and the Si element exists in four minerals, namely chlorite, silica, plagioclase, and black mica, which are acidic deep magmatic rocks.