esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum Compressive Sensing: Mathematical Machinery, Quantum Algorithms, and Quantum Circuitry
## 5 Projection
### Quantum Imaginary Time Evolution  
Define a \"Gaussian\" operator \(\hat{\Pi}_{i}\) as:  
\[\hat{\Pi}_{i}=\exp\left[-\frac{(\hat{N}_{i}-x_{i})^{2}}{2\sigma^{2}}\right], \tag{12}\]  
where the measurement-vector operator \(\hat{N}_{i}\) is defined as in Equation (11). Because \(\hat{\Pi}_{i}\) is diagonal, its action on a computational basis state \(|z\rangle\) can be written as  
\[\hat{\Pi}_{i}|z\rangle=\exp\left[-\frac{(\langle z|\hat{N}_{i}|z\rangle-x_{i}) ^{2}}{2\sigma^{2}}\right]|z\rangle. \tag{13}\]  
Therefore, applying \(\hat{\Pi}_{i}\) on our Born machine \(\Psi\) exponentially suppresses any basis state whose expectation value \(\langle\hat{N}_{i}\rangle\) differs from \(x_{i}\) considerably. Repeating this procedure for each \(\hat{N}_{i}\), we measure all qubits, and obtain a probabilistic estimate of the basis vector \(y\) most consistent with all measurement outcomes \(x_{i}\). Because \(\langle z|\hat{N}_{i}|z\rangle\) can only take discrete values, we require a finite variance \(\sigma^{2}\) to accommodate all measurement outcomes; \(\sigma=0.5\) is a sensible choice, although it may be tuned to balance accuracy and generalizability.  
This approach is especially attractive because there is no syndrome measurement--success is \"guaranteed\". However, the Gaussian operator \(\Pi_{i}\) is neither unitary nor even idempotent--there is no way to implement it on an arbitrary quantum state. Instead, we may use the methods of Quantum Imaginary Time Evolution (QITE) to implement a unitary operator which _effectively_ realizes the same operation (after re-normalization), for our _particular_ input state. There are two major technical difficulties in the formulation of QITE used by [PERSON] et al. [33]. First, quantum state tomography over \(|\Psi\rangle\) is required to construct an effective Hamiltonian \(\hat{H}\), and second, the time-evolution \(e^{-i\hat{H}\delta\tau}\) must then be implemented over many small time-steps via Suzuki-Trotter decomposition. Because our operator \(\Pi_{i}\) is diagonal, it may have a straightforward implementation. However, because it entangles all qubits, this implementation still scales exponentially. In this work, we steer clear of these difficulties via implementing \(\Pi_{i}\) only by direct classical simulation (i.e., calculating \(\langle z|\Pi_{i}|z\rangle\) for all \(|z\rangle\)). Nevertheless, QITE is an active area of research and we judge this method to be the most likely to yield favorable scaling in the long-term.
## 3 Distance-Aware Replica Adaptive Data Delivery Scheme
### Node Delivery Probability Calculation  
In DRADG, each node calculates its delivery probability in accordance to its frequency of meeting with the sink node (_i.e._, the frequency that the node moves into the communication range of the sink node and can directly communicate with it), and forwards data messages to nodes with high delivery probability in order to further enhance the data gathering performance. The delivery probability of a node indicates the possibility that the node can directly deliver data messages to the sink node. Let \(P_{i}\) denote the delivery probability of \(n_{i}\). Due to the node mobility feature, the delivery probability varies with the change of the node's activities, so each node needs to calculate the delivery probability periodically and broadcast the value to its neighbors through hello messages.  
Figure 1: Illustration of community-based mobility model.  
In the real world, the activities of mobile objects (e.g., people, vehicles or animals) usually show remarkable regularity and predictability [19], so we can predict the meeting probability of a mobile node within a future period of time based on the historic meeting records of the node. As to any node, e.g., \(n_{i}\), it maintains a timer with a timeout interval of \(\tau\), records its meeting times with the sink node in the latest interval of \(\tau\), denoted as \(Num_{i}\). Then the meeting frequency of \(n_{i}\) in the most recent interval of \(\tau\), denoted as \(freq_{i}\), can be computed by Equation 2 as follows:  
\[freq_{i}=\begin{cases}\dfrac{Num_{i}}{Num_{TH}},&Num_{i}<Num_{TH}\\ 1,&Num_{i}\geq Num_{TH}\end{cases} \tag{2}\]  
where \(Num_{TH}\) is the threshold value that should be defined based on the application. Therefore, for \(n_{i}\), its delivery probability \(P_{i}\) can be calculated as follows:  
\[P_{i}=\alpha\times freq_{i}+(1-\alpha)\times P_{i} \tag{3}\]  
In Equation 3, the exponentially weighted moving average places an emphasis proportional to \(\alpha\) on the most recent interval \(\tau\).
# A Review of Reactor Designs for Hydrogen Storage in Clathrate Hydrates
## 2 Hydrate-Formation Technologies
### Unidirectional Growth  
The unidirectional growth technique is known as a suitable set up for interfacial investigation in crystal-growth applications. [PERSON] et al. [53] studied the formation of tetrahydrofuran (THF) hydrate from kinetic hydrate inhibitors aqueous solution using the unidirectional-growth technique. A conceptual drawing of a unidirectional-growth apparatus is presented in Figure 8. The reactor cell is made of two parallel stainless-steel plates separated by a silicon or metal spacer. Thermocouples are located horizontally along the length of reactor. Two connected tubes to the starting and the end of the chamber, are designed to provide the gas and cold water, respectively. The unidirectional growth apparatus will be provided by two heat exchanger blocks, the temperature of the first block is set to the values lower than the equilibrium temperature (T\({}_{\rm eq}\)) and the other block is heated to a temperature higher than T\({}_{\rm eq}\). The reactor chamber is placed between two sets  
Figure 7: Hydrate-growth comparison between the stirred tank, silica gel, silica sand, and polyurethane foam.  
of cold and hot blocks. The applied heating gradient due to the designed arrangement of the blocks can be measured by the array of the thermocouples mounted in the body of the reactor. Hydrate crystals can nucleate in the cold side of the reactor where the cold water is added into the chamber through the designed capillary. To observe hydrate growth, a step motor was used to pull the chamber towards the cold block at a constant velocity.
# Impedance-Based Stability Analysis of Grid-Connected Inverters under the Unbalanced Grid Condition
## 3 The Simplified Stability Analysis Method Based on the GNC and Verification
### Stability Analysis Method of Inverters under the Balanced Grid Condition  
First, the stability analysis of the inverter under the balanced condition is carried out. The block diagram of the DSOGI-PLL-based grid-connected inverter under the balanced grid condition is drawn as shown in Figure 5.  
For the stability analysis, we should obtain the transfer functions of the forward path and feedback path. From Figure 5, the transfer function of the forward path is the admittance model of the inverter under the balanced condition, which can be expressed by the inverse matrix of impedance matrix in Equation (15) as  
\[\mathbf{Q}_{\mathrm{b}}=\begin{bmatrix}Y_{11}&Y_{12}\\ Y_{21}&Y_{22}\end{bmatrix} \tag{17}\]  
where \(\mathbf{Q}_{\mathrm{b}}\) is the admittance matrix.  
From Figure 5, the transfer function of the feedback path is the grid impedance model. According to the frequency relationship of perturbation and coupled voltage, we can obtain the transfer function of the feedback path as  
\[\mathbf{F}_{\mathrm{b}}=\begin{bmatrix}Z_{\mathrm{gp}}&0\\ 0&Z_{\mathrm{gc}}\end{bmatrix} \tag{18}\]  
where \(Z_{\mathrm{gp}}\) and \(Z_{\mathrm{gc}}\) represent the grid impedance at the perturbation frequency and coupled frequency, respectively.  
For such a MIMO system, the closed-loop stability of the grid-connected inverter system can be carried out by analyzing eigenvalues of the return-ratio matrix. Based on the derived transfer functions, the return-ratio matrix can be expressed as  
\[\mathbf{M}_{\mathrm{r}}=\mathbf{Q}_{\mathrm{b}}\mathbf{F}_{\mathrm{b}}= \begin{bmatrix}Y_{11}Z_{\mathrm{gp}}&Y_{12}Z_{\mathrm{gc}}\\ Y_{21}Z_{\mathrm{gp}}&Y_{22}Z_{\mathrm{gc}}\end{bmatrix} \tag{19}\]  
where \(\mathbf{M}_{\mathrm{r}}\) represents the return-ratio matrix.  
Therefore, for inverters under the balanced grid condition, the stability analysis can be carried out by analyzing the eigenvalues of this second-order matrix \(\mathbf{M}_{\mathrm{r}}\).
## 2 Related Works
### Cascade Convolutional Neural Network  
The previously mentioned two-stage DA methods only use the features from the last layer of the feature extractor. In order to learn a better classifier, a cascade structure is used to obtain multilevel features and discard easy samples in early stages. Before CNNs, [PERSON] et al. [47] built cascade classifiers from part-based deformable models to abandon sliding windows without any object, enhancing the detection efficiency. For CNN-based methods, it has been shown that convolutional features from different layers often contain different information. High-level features contain abstract semantic information, while lower-level features reflect detailed information of a sample [48]. Thus, CNNs with a cascade structure are used to utilize multilevel features and learn a more effective classifier. [PERSON] et al. [49] proposed a general classification framework for learning boosting cascade and applied it to face detection. In object detection, [PERSON] et al. [50] employed the cascade structure in both proposal generation and classification to provide better localized object proposals and reduce false-positive detection (mainly between ambiguous categories). To realize airport detection in remote sensing images, [PERSON] et al. [51] established two standard RPNs in tandem to reduce redundancy by performing nonmaximum suppression (NMS) [52] repeatedly. Furthermore, they conducted hard example mining for all the candidate regions after RoI pooling by alternating training. The multiple classifiers of the aforementioned methods were not trained jointly. [PERSON] et al. [53] proposed a method to jointly train cascade CNNs. In addition to image classification and object detection, [PERSON] et al. [54] integrated cascade RPNs with a Siamese network for visual tracking, making the classifier sequentially more discriminative. [PERSON] et al. [55] reranked the region proposals generated by the cascade structure with a formulating selection strategy to get the localization and scale of the object more precisely during tracking. To sum up, existing works on cascade CNNs are mainly dedicated to mining hard samples or fine-tuning the bounding box in multiple stages. However, they pay little attention to the application of cascaded structures in DA object detection. Moreover, the above methods either break the end-to-end training of the CNN or focus on face detection, where more prior knowledge can be utilized than general object detection, and there is no sufficiently effective design and analysis method for the data inadequacy and imbalance in tree-crown detection.
## 3 Results and Discussion
### Three-Dimensionality of Main Flow in Stalled and Unstalled Passages  
It is well known that the boundary layer flow is greatly determined by the main flow outside of the boundary layer. In this section, we present and analyze the three-dimensionality of the main flow in the stalled and unstalled passages. The circumferential distribution of normalized radial velocity U\({}_{r}\)/U\({}_{1}\) on different radial positions of the \(Z\)/\(b_{2}\) = 0.50 cross section is given in Figure 14, in which U\({}_{1}\) is the averaged inflow velocity. In the figure, the radial position is represented by the normalized value \(r^{*}=(r-r_{1})\)/\((r_{2}-r_{1})\), where \(r_{1}\) and \(r_{2}\) are the radii of the inlet and outlet of the blade, and ps1/ss1 and ps2/ss2 represent the pressure/suction surfaces of the stalled and unstalled passages, respectively. Since the separation of boundary layer flow results in large-scale vortices, the flow in the unstalled passage II will not be greatly affected by the main flow while the flow in the stalled passage I will be significantly influenced.  
In Figure 14a, at the inlet of the impeller \(r^{\star}=0.00\), the flow in passage II exhibits the same primary characteristics as the flow at a designed flow rate [4], with the addition of weak reversed flow near the pressure surface. The reversed flow on the pressure side is weak, and the flow is relatively stable from \(r^{\star}=0.00\) to \(r^{\star}=0.75\). At the radial position \(r^{\star}=0.95\) close to the outlet of the impeller, the radial velocity on the pressure side of passage I presents a fluctuating state under the influence of wake flow, and the flow is very unstable. Also, at the radial position \(r^{\star}=0.00\), the forward flow in passage I covers one-third of the passage, the reversed flow covers the remaining two-thirds, and the maximum reversal velocity reaches 31% of the maximum magnitude. At the position \(r^{\star}=0.25\), the positive peak velocity exists in the center of the passage, providing energy for recirculation of the separated vortex. At \(r^{\star}=0.50\), the peak velocity moves to the suction surface due to enhancement of the vortices, and the velocity is reduced to 0.52. At the outer radial position \(r^{\star}=0.95\), the area covered by the forward flow of passage I is less than one-third of the flow passage, indicating that the large area of separated vortex near the outlet results in the lowest capacity of through-flow. In the other subfigures, the flow development trends at different radial positions are very similar.
## 4 Results of Probabilistic Stability and Performance Assessment
### The Grid SCR Impact on System Stability and Performance
#### 4.1.4 Critical Mode Statistical Representation  
In addition to the previous results, probabilistic assessment could also provide a wealth of statistical information on the desired indices. In Figure 7, PDFs and cumulative density functions (CDFs) of the maximum damping factor and minimum damping ratio for different grid conditions were calculated and plotted. As can be seen, under a lower grid SCR, the PDFs of the damping factor became wider and moved to the RHP, which indicated a higher sensitivity to grid inductance variations and lower stability margins. The same conclusions as the previous subsections could be drawn for the damping factor and ratio changes under different grid SCRs. It could also be concluded that the maximum damping factor was generally related to the slower dynamics of the outer control loops. In contrast, the minimum damping ratio was highly related to faster internal control-loop dynamics, as could be concluded from participation factor analysis and Table 3.  
Furthermore, CDFs can also provide valuable information on the probability of system stability or the occurrence of desired conditions. They can relate stability analysis to the risk and reliability assessment. In this regard, the risk index of small-signal stability (RIS) is defined as the probability of a damping factor greater than zero \(P(\sigma\geq 0)\). From Figure 7c, \(P(\sigma<0)\) is 100%, meaning RIS is zero under all grid SCRs and the system was always stable. In addition, as another example, the likelihood of the desired damping factor \((\sigma<-5)\) for SCR = 1.6 was 68%, and for others it was 100%.  
Therefore, the probabilistic assessment could provide the likelihood of a specific condition. In contrast, the deterministic evaluation could not determine how stable a system was or the possibility of system instability. Deterministic methods are treated as binary, i.e., a particular situation is either stable or unstable.
# Synchronous Remote Calibration for Electricity Meters: Application and Optimization
## 4 Results and Discussions
### Discussions  
Based on the preceding experiments, it is clear that the proposed algorithm outperforms others across various scenarios. To gain deeper insights, additional analysis is presented in this paper.  
Remote synchronization tasks are executed synchronously between the client and server, with key parameters such as client deployment, server deployment, and timeline arrangement. Traditional algorithms, including Longest Task First (LTF) and Shortest Task First (STF), can achieve favorable results in specific one-dimensional deployment task scenarios. However, when applying these methods to synchronous task scheduling, challenges arise regarding whether to prioritize server-side or client-side deployment, as well as resolving conflicts that may emerge after one side's deployment is completed. Furthermore, these algorithms fail to adequately account for the interdependence between the client and server.This limitation can compromise efficiency, especially in scenarios where task synchronization between the client and server is critical.  
Recent research has proposed the Dynamic Synchronous Scheduling (DSS) algorithm [19], which employs a greedy three-tier strategy and a dynamic priority adjustment mechanism to synchronize remote calibration tasks between the client and the server. This algorithm prioritizes tasks with the least flexibility and the largest data volume from the pool of schedulable tasks, assigning them to the least busy server and client. While this approach improves synchronization efficiency between the server and client, certain limitations remain, such as the lack of timeline arrangement and the absence of rollback adjustment strategies. However, in practical remote calibration scenarios where tasks are scheduled one day in advance through instructions, the arrangement of each task has a substantial impact on overall efficiency.  
In contrast, this paper decomposes the dual-container synchronization scheduling problem into two steps: first, the collaborative deployment problem for the client and server, and second, the collaborative optimization problem for synchronized tasks. This method optimizes scheduling efficiency while ensuring task effectiveness by considering both task attributes and timeline arrangements. It provides a more systematic and scalable solution to task scheduling, addressing the limitations of existing methods and contributing to enhanced synchronization efficiency and reliability in remote calibration systems.
## 3 Perception Sensors for the UGV Detection System  
The architecture of the UGV is based on two main subsystems: sensor perception and control planning [16]. The first module is responsible for acquiring and processing data from all installed sensors (cameras, LiDAR, and other scanners) to create a representation of the surrounding environment.  
Based on the perception data, the control system generates signals such as steering angle, speed, and degree of braking, which are subsequently sent to the actuators. In this work, we examined the performance of sensors in an operational environment using the unmanned TAERO platform.  
A critical component of the perception system in autonomous vehicles is a camera that plays a key role in safe and effective navigation. Cameras provide a visual representation of the environment to detect and track objects, follow roads, and avoid obstacles.  
There are several types of vision systems commonly used in unmanned vehicles [17]:  
Figure 3: View of the developed TAERO vehicle: manned (**a**) and unmanned (**b**) configuration.  
Figure 2: User interfaces: service (**a**) and operator (**b**).  
* Stereo cameras that use two cameras to capture images of the same scene from slightly different perspectives, generating a 3D map of the environment, including the location and shape of objects and obstacles;
* RGB cameras that capture color images of the environment. These images can be used for a variety of purposes, including object recognition, lane detection, and semantic segmentation;
* Thermal cameras that detect infrared radiation emitted by objects, even in complete darkness.  
The developed UGV was equipped (for evaluation purposes) with two types of vision systems: daylight-thermal and stereo camera.
# Virtual Flux Control Methods for Grid-Forming Converters: A Four-Method Comparison
## 3 Control Schemes
### Virtual Flux-Oriented Control  
As shown in Figure 2, the original control scheme consists of three control loops. The first one, located in the upper left part of Figure 2, is referred to as the Reactive Power Control (RPC). This block enables the decision of whether to regulate the reactive power (PQ mode) or the converter voltage (PV mode), with the second case being used for the tests performed in this article. A proportional controller, with \(n_{q}\) gain, determines the reference magnitude of the virtual flux to be imposed on the converter, \(\psi_{v}^{*}\).  
The terms \(\psi_{v0}\) and \(\Delta\psi_{v1}\) correspond to the nominal flux, set at 1 p.u., and the output of the reactive current limiter (RCL) block, respectively. The latter is responsible for limiting the reactive current supplied by the converter by adjusting the reference flux magnitude whenever the predefined limits are exceeded. Therefore, the calculation of the reference flux can be expressed as  
\[\psi_{v}^{*}=\psi_{v0}+n_{q}(v^{*}-v)+\Delta\psi_{v1} \tag{8}\]  
The second control loop, referred to as Active Power Synchronization (APS), is located in the lower left part of Figure 2. This loop determines the control angle \(\Theta\), allowing the converter to emulate the inertial response of a SG and damp power oscillations. The first stage of the loop calculates the term \(\omega^{\prime}\) which emulates the swing equation of a SG, where \(J\) represents the virtual moment of inertia, defined as twice the inertia constant \(H\), and the damping coefficient \(D\).  
Next, the terms \(\omega_{1}\) and \(\omega_{2}\) are introduced. The first, \(\omega_{1}\), corresponds to the output of the Power System Stabilizer (PSS), responsible for damping the active power response. The second, \(\omega_{2}\), is the output of the active current limiter (ACL), which operates similarly to the reactive current limiter but regulates the active current of the converter by adjusting the control angle. Thus, the control angle used for Park transformations is obtained through the following equation  
\[\Theta=\int w\,dt=\int\left(w^{\prime}-w_{1}+w_{2}\right)dt \tag{9}\]  
Finally, the virtual flux-oriented control (VFOC) loop is implemented. In this loop, the \(dq\) components of the virtual flux are regulated using two PI controllers along with the feedforward signals (\(\omega\psi_{dv}\), \(\omega\psi_{qv}\)) to compensate the cross-coupling terms. Once the reference voltages \(e_{d}\) and \(e_{q}\) are obtained, an inverse Park transformation is applied, followed by a PWM modulation scheme to generate the converter's switching signals \(S_{1\ldots 6}\).