esa-sceva/satcom-chunk-collection huggingface dataset:
# CQFormer: Cyclic Quasi-Dynamic Transformers for Hourly Origin-Destination Estimation
## 5 Experiments
### Comparison Model  
We select four benchmark methods for comparison, two advanced OD estimation methods and two learning-based methods. The c-SPSA and EKF are the most typical models in the two major categories of methods estimating OD matrix from traffic count (Gradient-based Estimation, Iterative State Estimation). Since this study explores OD estimation under multiple demand structures and implements diversified traffic assignment modes, it is hard to deploy the Constrained Optimization method here, which is more theoretical. As far as the author knows, MLP is the earliest and most widely used model that applies neural networks to the field of OD estimation. Since CycleGAN is one of the earliest works to apply cyclic consistency in neural networks and inspires the design of the proposed model, it is also used here as a comparative experiment.  
1. Cluster-SPSA(c-SPSA), an effective way to cope with multiple magnitudes of OD flows. Referring to [43], the number of clustering kernels is set to 3.
2. Extended Kalman Filter (EKF), a non-linear extension of Kalman Filter, shows promising performance in arterial network [17].
3. Multi-Layer Perceptron (MLP), a widely used neural network block, was applied to the OD estimation in [19; 48]. Here, we use the encoder-decoder framework including six linear transformation layers with dimensions of \(1024,512,256,256,512,1024\), respectively, and the LeakyReLU is used as the activation function between layers. The encoder-decoder framework can realize effective feature compression and extraction through dimensionality reduction.
4. Cycle Generative Adversarial Networks (CycleGAN), one of the first works to introduce the concept of cyclic consistency, with notable success in the field of stylized image generation [51]. The proposed model is inspired by and incorporates the concept of cyclic consistency, thus we use this model as a comparison model.  
Figure 5: **The number of trips in different hours.** The y-axis displays the normalized proportion of the current traffic demand to the daily average traffic demand. The box-plot represents the traffic demand’s distributional property within the generated 600-day data. The circles are outliers of the box-plot.
## 4 Results and Discussion
### Challenges and Limitations  
Despite the high accuracy of the LSTM autoencoder model in detecting stuck pipe events, several challenges and limitations must be acknowledged. A key limitation is the model's reliance on a well-balanced dataset; any imbalance in classes can affect its predictive ability, leading to a higher false positive or false negative rate. Additionally, real-world drilling operations involve dynamic and unpredictable environments, and unseen operational differences can negatively affect model performance. Another challenge is the sensitivity of the reconstruction error threshold; fine-tuning is required in order to achieve optimal detection accuracy and minimize misclassification. Lastly, the computational cost of deep learning model training, particularly in resource-constrained industrial environments, is a limiting factor for real-time deployment. Future work should focus on adaptive thresholding techniques and real-time calibration to enhance model robustness.
# Architectural Design for Digital Twin Networks
## 3 Digital Twin Network
### Core Definition  
DTNs are designed using four essential components that enable network management. These components, inspired by digital twins in other industries, allow DTNs to analyze, diagnose, emulate, and control real networks. Table 1 summarizes these core elements and their roles in DTN functionality.  
These four core components, Data, Models, Interfaces, and Mapping, form the foundation of DTNs, enabling them to emulate networks and provide essential insights for analysis, diagnosis, and control across the network's lifecycle. Together with optimization algorithms, management strategies, and expert knowledge, these components ensure the support of both repeatability and reproducibility.  
Proper DTN implementations also drive efficient resource allocation and configuration, enhancing overall performance. They streamline troubleshooting and maintenance by enabling effective issue diagnosis. Moreover, DTNs are invaluable for planning and deployment, allowing for the simulation of new designs and configurations to evaluate their impact before implementation.
## 4 Agricultural and Research Applications
### Pollinator Research  
At the opposite end of the spectrum are basic science studies using these new methodologies. For example, [PERSON], USDA-ARS, Iowa, and his team looked at the sugar content of nectar, mass spectrometry chromatography of volatile compounds, and PERs assays [106] to examine new varieties of perennial soybeans. Only a few proved to be attractive to honey bees.  
[PERSON], one of our graduate students at UM, is conducting his thesis research on determining what effect, if any, native plant diversity may have on honey bee foraging at the landscape scale. Individual bees are not tracked but rather the foraging activity of the honey bees within an entire area is mapped using LIDAR. To accomplish this, 154 one-meter plots were planted with plants native to the Rocky Mountain Region of Montana. Each plot contained 20 individual plants but varied in diversity between 1 and 5 plant species. In May of 2014, all the plant species within the plots were in bloom and LIDAR scans were run from 2 p.m. to 4 p.m. three times a week.  
Honey bees that foraged within the diversity plot area preferred the diverse plots over the monoculture plots (Chi square \(P=0.016\)). Forty percent of the bees that foraged within the diversity plots were mapped over an area that encompassed only 23% of the diversity plot. Within this 23% plot area that the honey bees elected to forage, the bees were most frequently found within the areas that contained the three species plots, suggesting that the bees preferred the plots with three-species diversity (Figure 13). Similar studies have been conducted, but most involved groups of students standing in a field with click counters. The downsides of this manual approach are: (1) the presence of observers in the field is likely to alter bee foraging; and (2) observer bias that causes each observer to have different ability to focus on the task, to see, and to count rapidly moving small insects. LIDAR removes all of these variables and provides decimeter locations of each observed and counted bee.  
Working with researchers and seed producers in the US and overseas we have been able to improve seed set and pollination efficacy by conditioning bees to the crop of interest. This is especially useful for crops that bees are not naturally attracted to, such as onions. For countries which have limited numbers of bee colonies for pollination, and who do not wish to import bees from other areas due to concerns about introducing new bee diseases and pests, the ability to improve pollinator fidelity and to increase the numbers of bees on blooms using locally available bees is a preferred option. Though we know that others working for DARPA have managed to condition moths and wasps to respond to scents in a laboratory or tent, we have never seen either of these insect groups entraining at our conditioners in the field. Wasps and moths sometimes discover the conditioning trays, but they never discover the \"target\" of interest. However, we know that we can successfully condition both honey bees and bumble bees for scent-mediated search.
# Fault Analysis of a Small PV/Wind Farm Hybrid System Connected to the Grid
## 3 PV Array with Grid Connection Model
### Control of VSC  
The VSC needs to maintain power factor at one and helps to regulate the DC bus voltage. As depicted in the image of Figure 2, two control loops are used by the control system: \(I_{d}\) and \(I_{q}\) network currents (active and reactive current components) are controlled by an internal control loop, while DC link voltage is controlled by an external control loop. The output of the external DC voltage controller is the \(I_{d}\) current reference. As a way to preserve a power factor of one, zero is entered as the \(I_{q}\) current reference. The voltage  
Figure 6: P and O algorithm flowchart.  
outputs \(V_{d}\) and \(V_{q}\) of the current controller are transformed into the three signals with modulation \(U_{ref-abc}\) that the PWM three level pulse generator uses. The PI controller is utilized to create both the internal and external control loops [15; 16; 17; 18; 19; 20; 21; 22; 23]. The PLL is used to obtain the phase angle necessary for the \(abc\) to \(dq\) transformation module.  
Inside a forward-thinking loop, the grid voltage vector is employed to account for the grid harmonics [24]. Below is a representation of the inverter's voltage and power equations in \(d-q\) synchronous reference frame:  
\[\begin{cases}V_{d}^{*}=RI_{d}+V_{d}-\omega L_{f}I_{q}+L_{f}\frac{d_{L}}{dt}\\ V_{q}^{*}=RI_{q}+V_{q}-\omega L_{f}I_{d}+L_{f}\frac{d_{L}}{dt}\end{cases} \tag{7}\]  
\[\begin{cases}P=V_{d}I_{d}+V_{q}I_{q}\\ Q=-V_{d}I_{q}+V_{q}I_{d}\end{cases} \tag{8}\]  
In the \(d-q\) reference, the voltages shown by Equation (7) are connected. The method for compensating for the coupling of the axes \(d\) and \(q\) is shown in Figure 7.  
The following decoupled voltages equation is obtained from the inverter's voltage equation in a \(d-q\) synchronous reference frame [25]:  
\[\begin{cases}V_{d}^{*}=RI_{d}+V_{d}-\omega L_{f}I_{q}+K_{p}\big{(}I_{d}^{*}-I_ {d}\big{)}+K_{i}\int\big{(}I_{d}^{*}-I_{d}\big{)}dt\\ V_{q}^{*}=RI_{q}+V_{q}-\omega L_{f}I_{q}+K_{p}\big{(}I_{q}^{*}-I_{q}\big{)}+K_ {i}\int\big{(}I_{q}^{*}-I_{q}\big{)}dt\end{cases} \tag{9}\]  
where \(K_{p}\) and \(K_{i}\) refer to the regulator's proportional and integral gains, respectively. Figure 8 shows the Simulink model for the various VSC building blocks:  
Figure 7: Compensation of the axis’ cross coupling.
# Simulation and Design of Three 5G Antennas
## 3 Design of 5G Microstrip Array Antennas
### Design and Simulation of Antenna Units
#### 3.1.1 Calculation and Optimization of Antenna Unit Parameters  
As illustrated in Figure 3, we assume a resonant frequency \(f_{0}\) 5 GHz for a rectangular microstrip patch antenna with a dielectric substrate of thickness h, width W, length L, and a relative dielectric constant \(\text{e}_{r}\). Next, optimization and determination will be carried out for the selection of the substrate thickness h, width W, and length L of the rectangular microstrip patch.
# Secure and Dynamic Memory Management Architecture for Virtualization Technologies in IoT Devices
## 3 Virtualization on Embedded Devices
One zone is considered as a secure zone, dedicated for running OEMapplications, and the other zone is a non-secure zone, intended for running user applications. The main drawback of ARM-TrustZone technology is that it is restricted to a single secure zone. That means only one secure zone is available at a time. According to the architecture developers in MIPS Technologies, Inc., the ARM-TrustZone model security approach is complex to implement [29]. MIPS architecture developers used a more scalable solution named MIPS multi-domain security [29]. The MIPS multi-domain technology provides multiple secure zones to each system function through the separation of memory spaces. MIPS multi-domain security is achieved through hardware-assisted full virtualization. Section 4 explains the full virtualization in MIPS. In an embedded hypervisor architecture as shown in Figure 2, the VCPU is directly assigned to a specific CPU and a secure execution environment is achieved by securely partitioning the memory [17; 26]. Embedded systems are usually based on microcontrollers (i.e., memory-integrated CPU), and hence are smaller in memory size. Running multiple applications on a single platform requires more memory than a single application running on the same platform. Therefore, the dynamic allocation of memory in an embedded hypervisor would be an added advantage towards the performance of virtual machines. It is understood from the above observations that memory isolation and dynamic memory allocation are essential requirements of an embedded hypervisor for security and performance enhancements. Architecture support for memory isolation (ASMI) is a proposal for improving the security and performance of virtual machines in normal hypervisors [30]. The main features of the ASMI architecture are  
* Dynamic memory allocation to virtual machines: Allocating memory to virtual machines on demand. * Memory isolation among virtual machines (VMs): Separating the memory of each VM from others and also from the hypervisor for security and performance enhancements in VMs. This indicates that a memory region should be assigned to a single VM at a time. As the features mentioned above of ASMI are the essential requirements of embedded hypervisor for security and performance enhancements in virtual machines, we propose ASMI on an embedded platform for performance and security enhancements. We decided to analyze the feasibility of ASMI on MIPS embedded architecture as the memory isolation feature of MIPS Multi-domain Security architecture matches with the feature of ASMI. The next section explains the MIPS architecture. Figure 2: Architecture of embedded hypervisor.
## 2 FPAPr Design and Fabrication
### The Custom Linear FPAPr Design  
Two types of FPAPs were designed, as shown in Figure 5, for an effective application of the PAUT on irregular curvature surfaces or slopes found in field pipelines. The strategic points for the designs were as follows: First, in FPAPr, the element was placed at the probe-end to minimize limited access during weld zone inspection. Second, a transmission-reception (T/R) type element arrayed in a split-type structure was additionally fabricated to allow the application to thin pipes, in contrast to the pulse-echo (P/E) type. When the pulse duration of each piezoelectric element becomes smaller, the grass echo is lowered due to the broadband characteristics, thus improving the SNR [27; 28; 29]. Third, it was designed to allow stable contact of the PA probe with the curvature surface by adding a flexibility function. The bending property of the probe was thus maintained in different surface curvature shapes [30; 31; 32; 33; 34; 35]. Lastly, the PA probe design considered the generation of side lobes. The PA probe element was arrayed with a constant pitch. Ultrasound elements propagate rectilinearly, wherein the radiation beam varies according to the pitch, size, gap, and aperture of the element. The side lobe refers to the beam radiated in directions other than the main beam. Particularly, the side lobe generated from an adjacent element could interfere with the main beam, which degrades the ultrasound detection sensitivity or changes the direction of radiation. Such a phenomenon could result in a false signal assessment from the PAUT. In the PA probe design, the pitch must be equal to or lower than half the wavelength (\(\lambda\)) to prevent the generation of side lobes [36; 37]. Therefore, two types of FPAPr were designed considering the above factors, and their specifications are summarized in Table 1.
## 2 Related Work
### Satellite-Based QKD  
A detailed review can be found in [17; 18; 19]. Due to the transmission loss scales exponentially over the optical fiber, free-space QKD becomes an effective scheme in the context of long distances. The free-space QKD relying on the satellite is also called the satellite-based QKD. Since the first quantum satellite Micius was launched in China, many research studies have been conducted on satellite-based QKD [20; 21]. The feasibility of the satellite-based QKD in daylight was proven in [22]. In [23], a time-delayed single quantum repeater node was presented for global QKD with a single satellite. However, most of the researchers focused on the QKD scheme with a single satellite, which cannot fulfill the increasing requirements of the global QKD.
## 1 Introduction
The CTGAN algorithm is a mixed model of the conditional--GAN algorithm and the tabular--GAN algorithm, and several problems related to the existing GAN model for tabular data have been improved [30]. Specifically, a Variational Gaussian Mixture (VGM) was used to estimate the mode value of the distribution for each variable [30; 31]. In this way, it is possible to perform optimized learning on the distribution directly, rather than on the Gaussian distribution of the standardized data [31]. The data generation methods such as SMOTE have (1) issues of overlapping classes or unnecessary noise generation, and (2) issues that may be inefficient and inappropriate for high-dimensional data [32]. In the case of GAN-based data generation, (1) the issue of non-convergence probability, (2) the issue that the generator and discriminator may be biasedly mis-learned, and (3) the mathematical basis for judging overfitting when evaluating performance may be insufficient [33]. In addition to these limitations and issues, in the case of data with small daily variability and periodicity, such as body weight data, errors may occur in the learned and augmented data. To solve these problems, we propose a new outliers/noise detection algorithm, and a outliers/noise generation algorithm to resolve data imbalance. First, we observe the imbalance of outlier-related classes. Since there are typically less outliers than normal values, class imbalance [34] occurs. If the class imbalance is severe, the performance of the model may be limited [35; 36]. Thus, the class imbalance is corrected first and then the model is trained. Second, we analyze body weight data with an anomaly detection model that reflects temporal information. Unlike other lifelog data such as steps and sleep, there may be cyclical variations in body weight [37; 38; 39; 40; 41]. In summary, (1) the outlier values of the collected body weight data might not always be significantly different from the normal values, and (2) the body weight data may have a periodicity. To resolve these problems, we propose a new outlier generation algorithm that reflects time information. The proposed method is as follows:  
1. Propose an anomaly detection algorithm reflecting the characteristics of data with small daily variability and periodicity;
2. Propose an anomaly detection algorithm that utilizes weekly data transformation to reflect outliers with small differences from normal values;3. Suggest a way to improve performance through outlier generation to solve the problem of outlier class imbalance. This paper is structured as follows. Section 2 describes materials and methods, Section 3 describes the results: the data set, experimental design, and experimental results, and Section 4 presents the discussion, and Section 5 shows the conclusion and future works.
## 4 Experiments
### Experimental and Performance Analysis of BiResNeRF
#### 4.3.5 Stability Analysis  
The statistical data from Table 5 indicate that the average number of iterations in the first phase is 9.3 k, with an average time consumption of 160.85 s. The training time ranges from 1 min 21 s to 5 min 12 s, which shows that our method can adaptively adjust the number of iterations based on the scene's complexity. In the NeRF that utilizes a joint optimization pose framework, stability can be assessed through the loss value due to the absence of accurate pose information. As the training advances, the evident improvement in image quality and the decrease in loss values attest to the overall stability enhancement, indicating the gradual refinement of pose and three-dimensional structure. Consequently, the loss value not only corroborates the rendering improvement but also acts as an effective measure of pose stability.  
We compare the smooth warm-up learning rate scheduling strategy with the cosine annealing learning rate scheduling strategy. By observing the magnitude of pose error changes in Figure 8a,b, it can be seen that the smooth warm-up learning rate scheduling strategy is noticeably more stable. Figure 8c shows the PSNR values of rendered pixels. Under the smooth warm-up learning rate scheduling strategy, the image accuracy improves more rapidly, while there is a cliff-like drop under the non-smooth strategy. This validates the effectiveness of the smooth warm-up learning rate scheduling strategy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{\(p\)**-Value**} \\ \cline{2-7}  & **Rotation** & **Translation** & **PSNR** & **SSIM** & **LPIPS** & **Training Time** \\ \hline BAA-NGP & 0.71 & 0.666 & 0.421 & 0.593 & 0.562 & 2.98 \(\times\) 10\({}^{-6}\) \\ BARF & 0.942 & 0.713 & 0.055 & 0.095 & 0.225 & 1.98 \(\times\) 10\({}^{-4}\) \\ NeRFAcc & 0.412 & 0.322 & 0.096 & 0.238 & 0.663 & 1.37 \(\times\) 10\({}^{-10}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Significance test results for different methods.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Scene** & **Lego** & **Chair** & **Drums** & **Ficus** & **Hotdog** & **Materials** & **Mic** & **Ship** & **Mean** \\ \hline Position(k) & 9 & 5.6 & 9.6 & 7.6 & 5 & 13.2 & 14.6 & 5.2 & 9.3 \\ Time & 2 m 49 s & 1 m 26 s & 3 m 2 s & 2 m 18 s & 1 m 16 s & 4 m 12 s & 5 m 3 s & 1 m 21 s & 2 m 41 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Signal triggering positions and times for stage transitions in different scenes.
## 4 Conclusions  
The only GNSS method consistently yielding sub-decimeter position accuracy in typical alpine skiing conditions was a differential method using GPS and GLONASS satellite systems and the satellite signal frequencies L1 and L2. In conditions with minimal satellite signal obstruction, valid results were also found if either the satellite system GLONASS or frequency L2 was dropped from the best configuration. All other methods failed to meet the accuracy requirements for alpine skiing even in conditions favorable for GNSS measurements. The methods with good positioning accuracy had also the shortest times to compute differential solutions.
## 3 Materials and Methods
### Evaluation Metrics
#### 3.5.2 Mean Squared Error (MSE)  
MSE calculates the average squared difference between actual and predicted values. MSE penalizes significant errors more heavily, making it useful for detecting significant deviations in predictions.  
Mean Squared Error, is given by [37]:  
\[MSE=\ \frac{1}{n}\ \sum_{i=1}^{n}(F_{i}\ -A_{i}\ )^{2}\]  
where n is the number of observations, F\({}_{i}\) is the forecasted value for observation i, and A\({}_{i}\) is the actual value for observation i.