esa-sceva/satcom-chunk-collection huggingface dataset:
# Current Status, Issues and Future Prospects of Personalized Medicine for Each Disease
## 2 Rheumatid Arthritis
### Diagnosis and Treatment  
For the diagnosis of RA, the European League of Rheumatology (EULAR) updated their RA management recommendations in 2019 [18]. Under the criteria, when inflammation with swelling (synovitis) is observed in at least one or more joints and no disease other than RA is found to be the cause of the inflammation, the following four items are evaluated: (1) number of symptomatic joints; (2) rheumatoid factor (RF) or anti-cyclic citrullinated peptide antibody (ACPA); (3) C-reactive protein (CRP) or erythrocyte sedimentation rate (ESR); and (4) duration of symptoms. If the total score for each of these four items is six or more, RA is diagnosed and treatment with anti-rheumatic drugs is started. However, since diseases other than RA may cause the total score to be six or more, it is necessary to thoroughly examine whether other diseases are present before assigning a score.  
Accurate disease activity assessment that strictly controls disease activity is essential for treatment. Currently, the most reliable evaluation method is the use of a comprehensive disease activity index [19] such as the Disease Activity Score (DAS), Simplified Disease Activity Index (SDAI), and Clinical Disease Activity Index (CDAI), which is a combination of joint findings, the Visual Analogue Scale (VAS), and CRP and ESR as inflammatory markers.  
The first goal of treatment in RA is clinical remission, which is recognized as \"treatment to target (T2T)\" to prevent the progression of joint destruction and to maximize physical function over the long term. Methotrexate (MTX) is the mainstay drug used as first-line therapy in the absence of contraindications [20]). In patients with poor prognosis and high disease activity, a biologic TNF inhibitor may be considered in combination with MTX [21]. Other biologic agents are also emerging, such as anti-CD20 mAbs, IL-1 inhibitors, JAK inhibitors, and anti-IL-6 mAbs [22], but not all patients respond equally to these diversified agents [23], and they have many side effects. In addition, they are expensive, which imposes a heavy financial burden [24]. Therefore, personalized medicine is indispensable for prognosis, with such concerns as how to appropriately select therapeutic agents and whether it is possible to discontinue them.
# GAN Training Acceleration Using Frechet Descriptor-Based Coreset
## 1 Introduction
Recently, Coreset has attracted considerable attention on its applications including GAN. For example, an Inception embedding-based Coreset approach was proposed in [16] to accelerate GAN training. While this approach can be applied to the BlobGAN [8], with an Inception classifier being employed first to derive Inception embedding, we argue that additional computational cost occurs, which is against the goal of this study. In addition, the Inception embedding may be problematic to calculate the distance metric due to the fact medical images are known to be noisy. Motivated by object-focused image translation approaches, we introduce Frechet Descriptor Distance (FDD) derived from object-related statistics to accelerate GAN's training. To demonstrate this idea, we focus on one specific type of imaging problem: blob images. This type of imaging has been utilized in a number of medical applications such as nuclei in 2D microscopy images [17] and glomeruli in 3D kidney cationic ferritin-enhanced MRI [18; 19]. These images have some common characteristics: the number of blobs is large, and the shape of these blobs roughly follows Gaussian distribution. In this research, we introduce a new blob-based FDD to measure the distances between the image pair based on object (blob) statistics. The Coreset is then constructed using the new FDD metric on the image pairs, then the Coreset selection step can be taken out of the GAN training loop. To validate the performance of an out of loop FDD-Coreset for GAN training, we conduct two experiments. The first experiment is to identify the blobs in a 3D simulated blob image dataset where the locations of blobs are known. We choose the naive random sampling method and the Inception and Euclidean Distance-based Coreset (IED-Coreset) [16] for comparison. Other than computation time, eight performance metrics are used including Peak Signal-to-Noise ratio (\(PSNR\)), Detection Error Rate (\(DER\)), Precision, Recall, F-score, Dice, Intersection over Union (\(IoU\)), and Blobness (\(PB\)). In the second experiment, we implement the FDD-Coreset on 3D human kidney images obtained from MRI. Since there is no ground truth available,we compare against the above two methods using stereology results. Both experiments support the conclusion that FDD-Coreset can significantly accelerate the GAN training with comparable performance. The remainder of the paper is organized as follows: Section 2 provides a review on related works. Section 3 describes our proposed FDD-Coreset in detail. Section 4 demonstrates the comparative results on the 3D synthetic images and 3D human kidney images. Finally, the conclusions are presented in Section 5.
## 3 Results  
The FSU was subjected to six moments of pure moments of 1 Nm working in flexion, extension, axial rotation, and lateral bending. Throughout the application of the loads, the range of motion (ROM) was monitored using a reference point (RP) located on the center of the previously created rigid surface. All simulations were performed using \"Dynamic Implicit\" analysis.  
Figure 3: (**a**) Boundary conditions applied in the FSU model and (**b**) moments applied in the FEM.  
The accuracy of the developed model was validated by comparing output predictions with previously published experimental data. The results for flexion and extension were compared with the studies from [PERSON] et al. [35] and [PERSON] et al. [34]; the results for axial rotation and lateral bending were compared only with the study from [PERSON] et al. [34].  
Figure 4 shows that the results of the simulations fell within the experimental corridor of the selected in vitro studies. During flexion, the model registered an ROM of 2.03\({}^{\circ}\), where the literature shows values of 3.7\({}^{\circ}\)\(\pm\) 2.2 [35] and 3.7\({}^{\circ}\)\(\pm\) 2.1 [34]. For extension, 2.53\({}^{\circ}\) was recorded, while the in vitro tests reported 3.7\({}^{\circ}\)\(\pm\) 2.2 [35] and 3.4\({}^{\circ}\)\(\pm\) 1.9 [34]. These results are consistent with the literature, indicating that the FEM of the C6-C7 segment was validated in terms of both flexion and extension. The FSU showed 1.95\({}^{\circ}\) in left rotation and 1.75\({}^{\circ}\) in right rotation, for a total of 3.70\({}^{\circ}\), while [PERSON] et al. [34] reported 2.9\({}^{\circ}\)\(\pm\) 0.8. As such, although the simulation values are near the upper bound of the range provided by the experimental study, they are nonetheless consistent with them, validating the axial rotation. Lastly, for lateral bending the registered ROM was 2.46\({}^{\circ}\) (1.14\({}^{\circ}\) in right bending and 1.32\({}^{\circ}\) in left bending). This is the only test that shows results outside of the experimental range; the study by [PERSON] et al. [34] reported that the C6-C7 segment had a lateral bending rotation of 5.4\({}^{\circ}\)\(\pm\) 1.5. As such, the FSU results are somewhat lower than the expected values.
# Bounce: A High Performance Satellite-Based Blockchain System
## Appendix A Response Time from Broadcast Ground Stations with Different Network Bandwidths  
Figure 14 presents results from experiments conducted under varying network speeds with a configuration of 20 Broadcast Ground Stations. Compression is worthwhile when network speeds are limited, but response time suffers. For these tests, we applied the zstd library with a compression level of \(-22\), yielding a compression ratio of approximately 1.4. The associated compression and decompression times were 500 ms and 370 ms, respectively. Even in a low-speed network environment with a 1 Gbps interconnect between Broadcast Ground Stations, the Sign Merkle Tree request involving one million transactions could  
still be completed within 30 s using this compression approach. We consider this too slow for credit card style transactions.
## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### Patient Rights and Informed Consent in AI Healthcare
#### 5.5.5 Navigating Intellectual Property and Transparency in AI Healthcare Consent  
Moreover, the transparency of AI systems is affected by issues surrounding intellectual property (IP) rights, which can impact the level of insight patients have into the AI systems involved in their healthcare. Patients should be aware of how IP considerations could potentially obscure the workings of AI systems, affecting the care they receive. The dynamic nature of AI algorithms necessitates that informed consent be treated as an ongoing process, with patients allowed to regularly review and renew their consent in line with the evolution of AI applications in healthcare [38].  
Engagement and education are critical to informed consent. Both healthcare professionals and patients should be actively involved in the development and application of AI systems. Education about the capabilities and limitations of AI is essential to ensuring that consent is genuinely informed. Integrating AI into healthcare demands a dynamic, transparent, and informed approach to patient consent. This approach must tackle AI biases, protect privacy, enhance transparency and explainability, preserve the clinician-patient relationship, adapt consent processes, and establish robust regulatory and accountability frameworks [2,38].
# Deep Reinforcement Learning Based Resource Management in UAV-Assisted IoT Networks
## 4 Methods and Materials
### Experimental Setup  
The data collection area (i.e., National Technology University of Technology (NTUT)) is 500 and 700 m as a target of two clusters. The bandwidth and carrier frequency used is 3 MHz and 900 MHz, respectively. First, in a random form of the RM method, the agent randomly selects a sub-band for transmission at each time slot. The users are grouped by their similarities, and sub-bands are allocated iteratively to the user in each grid point. The data were obtained from a horizontal distance of 2 \(\times\) 2 m with 35 users per grid of 10 second intervals. As shown in Figure 3a, a UAV-Bs were placed on top of buildings numbered 15 (fifteen academic buildings) and 3 (complex building). The buildings, device users, trees, and other shadowing effects are present in the working environment. The UAV-Bs reached the specified clusters and assumed the central placement for the UAV. An entire raw record of 46,000 values was collected. After preprocessing is carried out, we had a total of 45,500 structured data points. Then, 75% (34,125) and 25% (11,375) of the dataset were selected for the training and testing phases, respectively.  
In Figure 3b for the sideways of Rx at each grid level, the machine reads and stores the OFDM signals via the Universal Software Radio Peripheral (USRP) unit. The initial code and design of the GNU Radio Companion (GRC) was compiled with C language. Then, the Ubuntu platform was used for the implementation of a software-oriented signal. The average bandwidth for UAV-BS is 3 MHz. Once the altitude of the UAV-BS increased and the LOS remained constant, the frequency of the signal quality obtained decreased at each grid level. Nevertheless, if there are buildings and trees, the UAV-BS elevation could be high. Therefore, Tx could transfer better-improved signal values than at low-level elevation.
# Construction of Geosynthetic-Reinforced Pavements and Evaluation of Their Impacts
## 2 Field Construction
### Geosynthetics Installation
#### 2.2.1 Geogrid Composite on Subgrade  
The geogrid composite was placed at the interface between the base and subgrade (Figure 2); from laboratory testing, the subgrade had a California Bearing Ratio (CBR) of 2%. The geogrid composite is made of biaxial polypropylene geogrid manufactured using a punching and drawing process, heat-bonded to a continuous filament non-woven polyester geotextile. The reinforcement is offered by the geogrid element while the mechanically and chemically stable geotextile acts as a filter/separator preventing the movement of fine soils in saturated soil [31].  
As per the installation guide [32], the subgrade was prepared by removing debris, remnants, or any plants before installation. The surface was levelled and the geogrid composite was unrolled over the surface as shown in Figure 2. As each section is only 15 m, the manual installation of the geogrid composite material was challenging. As it is usually stored in rolls, the geogrid composite may not be completely flat when placed in the field and at least four workers were needed to hold each corner of the sheet during placement to reduce wrinkles. After rolling out the product, several shovels of fill were placed on the edges and corners to hold down the geogrid composite, as depicted in Figure 2. When necessary, adjacent geogrid composite rolls were overlapped by at least 500 mm [32].  
It is not recommended for heavy equipment to directly drive on top of geogrid composite, especially when the subgrade is relatively soft. In this case, the granular  
Figure 2: Installation of geogrid composite on the subgrade.  
base material was first dumped and gradually pushed over the geogrid composite, with the dozer blade raising slowly to spread the fill material. After levelling the fill, more loads were dumped over the ground until it reached the design thickness as demonstrated in Figure 2. Finally, the base layer was compacted and graded to be ready for the asphalt placement. Using this construction procedure, it is recommended to sample granular materials after levelling the base surface and verify that no aggregate segregation occurs with gradation analysis. In addition, the lightweight deflectometer (LWD) testing was conducted; LWD results showed that the modulus for the base on top of the geogrid composite was consistent after installation.
## 2 Proposed Method
### Road Extraction
The second term is the Laplacian regularization term, which considers effect of each segment's vicinity in the objective function and attains the smoothness of \(\mathbf{Q}\) with respect to matrix \(\mathbf{L}\). For the final learning step, we manually select \(n_{sf}\) pixels from road regions and \(n_{sf}\) pixels from non-road regions of two training images of the geo-referenced color imageset. We then extract \(n_{f}\) kinds of features (\(\mathbf{Y}^{w}\in R^{d_{w}\times n_{sf}},w=1,...,n_{f}\)) for the region around each of these manually selected pixels. These feature types will be explained in Section 3.1. We next use Equations (2) and (3) to obtain respective feature representations, \(\mathbf{Q}\)s, for the \(n_{sf}\) road and \(n_{sf}\) non-road pixels. The two vectorized \(\mathbf{Q}\)s are used to form the dictionaries, namely, \(\mathbf{D}_{r}\in R^{d_{f}\times n_{sf}}\) (road dictionary) and \(\mathbf{D}_{nr}\in R^{d_{f}\times n_{sf}}\) (non-road dictionary). Here, \(d_{f}\) is the dimension of the vectorized feature representation \(\mathbf{Q}\). Finally, the feature vector (\(\mathbf{x}\in R^{d_{f}\times 1}\)) of a candidate pixel is represented by \(\mathbf{D}_{r}\) and \(\mathbf{D}_{nr}\) in two similar quadratic optimization problems with equality constraints, which can be solved analytically by writing the KKT conditions [14]. The candidate pixel, \(\mathbf{x}\), is classified as road if the reconstruction error with respect to dictionary \(\mathbf{D}_{r}\) is less than the reconstruction error with respect to dictionary \(\mathbf{D}_{nr}\). Otherwise, it is classified as non-road.
## 5 Experimental Setup
### Training Stage  
The Jetson Nano has been equipped with a range of SDKs and toolkits, including JetPack 4.6.1, TensorRT 8.2.1, CUDA Toolkit 10.2, cuDNN 8.2.1, and PyTorch 1.1.0. We chose PyTorch due to its simplicity and growing popularity in recent years. The \"jetson-inference\" library was used to train, optimize, and run the model on the Jetson hardware, eliminating the necessity for a robust computer with a GPU for tasks such as model training, conversion, and optimization. This approach not only conserves time but also ensures the efficient utilization of the Jetson Nano's hardware capabilities when running within the TensorRT framework. We used Python codes from the \"jetson-inference\" library [60] for training, evaluation, optimization, and inference of the \"mo-bilenet-v1-ssd-mp-0_675.pth\" model on both the test images and the live camera stream using the detectNet module. This architecture processes 300 \(\times\) 300 images as input.  
We implemented two learning schedules for model training. The initial approach adopted a stepwise reduction of the learning rate (Step Decay/Multi-Step Learning Rate), starting at 10-3 and decreasing it by a factor of 10 every 50 steps (as per Formula (2)). The second method involved testing a technique wherein the learning rate continuously diminished following the cosine function (Formula (3)). The training process spanned 120 epochs in both instances.  
\[LR_{i}=\begin{cases}\begin{array}{c}10^{-3},\ \ T_{i}<50\\ 10^{-4},\ \ 50\ \leq T_{i}<100\\ 10^{-5},\ 100\ \leq T_{i}\leq 120\end{array}\end{cases}, \tag{2}\]  
* LR\({}_{i}\) current learning rate;
* T\({}_{i}\): current epoch.  
\[LR_{i}=\frac{1}{2}*\ LR_{max}*\bigg{(}1+(\cos\frac{T_{i}}{T_{n}}*\ \pi\bigg{)} \bigg{)}, \tag{3}\]
* LR\({}_{\text{max}}\): initial learning rate (0.01);
* T\({}_{\text{n}}\): all epochs (120).
## 2 Network Compression
### Network Compression Based on Pre-Configuration and Existing Measurements  
When the distribution network has \(n\) buses, the PMU has \(n\) candidate locations. Based on the rules of observability, the bus which needs to be placed with PMU and the buses which do not need to be placed with PMU can be determined. Through pre-configuration, the bus which does not need to be placed with PMU is merged with its neighbor to reduce PMU candidate locations. The specific methods are as follows:  
(1) Place a PMU on the neighbor of the leaf bus, and merge the leaf bus with its neighbor into a new bus.  
The leaf bus is a bus which is only connected to one bus. The distribution network is radial, so it always has many leaf buses. When a PMU is installed on a leaf bus, the number of new observable buses is only two: leaf bus and its neighbor; when a PMU is placed on the bus which is connected to a leaf bus, more than two buses become observable buses.  
Therefore, PMU is not placed on the leaf bus, PMU is placed on the bus which is connected to the leaf bus instead. Therefore, the leaf bus becomes an observable bus, which does not need to be considered in the process of OPP. The leaf bus can be merged with its neighbor. Network compression based on pre-configuration can reduce the number of buses in the network and improve network measurement redundancy when there are many leaf buses in the distribution network.  
(2) Merge the ZIB with one of its neighbors while only two buses are connected to the ZIB.  
For the ZIB which has only two neighbors: (a) if a PMU is placed on any one of the neighbors, both the ZIB and another neighbor become observable; (b) if all two neighbors of the ZIB are observable buses, the voltage phasor of the ZIB can be calculated by KCLs, so this ZIB is identified as an observable bus. Therefore, if the ZIB has only two neighbors, this ZIB does not need to be placed with a PMU and can be merged with one of the neighbors.  
In summary, the network compression method provides a compressed network for the OPP model based on the pre-configuration and existing measurements.
## 2 Dynamic Modeling of the Proposed QVIP
### Deformation of the QVIP  
The simplified mechanical model of the QVIP is shown in Figure 1c. \(M\) is the mass, \(L_{i}\) is the length of inclined rods, \(L_{h}\) is the length of horizontal rods, \(\theta\) is the initial angle between inclined rods and vertical direction, \(k_{h}\) is the stiffness of horizontal springs, \(k_{p}\) is the stiffness of compression inclined springs, \(k_{n}\) is the stiffness of stretch inclined spring, \(c\) is the damping coefficient of the horizontal damper, \(\Delta y\) is the total vertical displacement, \(\Delta x\) is the total horizontal displacement, and \(\varphi\) is the rotation angle of the inclined rod. All the parameters used in the modelling are listed in Table 1 in Appendix A.  
Nondimensionalization is performed for \(L_{i}\), \(L_{h}\), \(k_{h}\), \(k_{p}\), and \(k_{n}\). The definitions of dimensionless parameters are \(L_{i}=\varepsilon\cdot L\), \(L_{h}=\lambda\cdot L\), \(k_{h}=\xi\cdot k\), \(k_{p}=\mu\cdot k\), \(k_{n}=\
u\cdot k\). During compression, the parallelogram mechanism maintains vertical motion. The relationships between motions in different directions are expressed by Equations (1)-(3). The rotation  
Figure 1: Modelling and mechanism of the QVIP: (**a**) 3D design of the QVIP, (**b**) mechanism of QZS, and (**c**) simplified mechanical model and the geometric deformation of the QVIP.  
angle \(\varphi\) and total horizontal displacement \(\Delta x\) are nonlinearly correlated with the total vertical displacement \(\Delta y\), indicating a nonlinear relationship between horizontal and vertical motions.  
Rotation angle of the inclined rod is written as  
\[\varphi=\theta-\mathrm{arc}\cos(\cos\theta+\frac{\Delta y}{2L_{i}}) \tag{1}\]  
Total horizontal displacement is obtained as  
\[\Delta x=2L_{i}(\sin(\theta-\varphi)-\sin\theta) \tag{2}\]  
Total vertical displacement is obtained as  
\[\Delta y=2L_{i}(\cos\left(\theta-\varphi\right)-\cos\theta) \tag{3}\]  
Initial length of compressed inclined spring is obtained as  
\[l_{p0}=\sqrt{{L_{h}}^{2}+{L_{i}}^{2}-2L_{h}L_{i}\mathrm{sin}\theta} \tag{4}\]  
Length of compressed inclined spring during motion is obtained as  
\[l_{p1}=\sqrt{{L_{h}}^{2}+{L_{i}}^{2}-2L_{h}L_{i}\mathrm{sin}(\theta-\varphi)} \tag{5}\]  
Deformation of compressed inclined spring is obtained as  
\[\Delta l_{p}=l_{p1}-l_{p0} \tag{6}\]  
Initial length of stretch inclined spring is obtained as  
\[l_{n0}=\sqrt{{L_{h}}^{2}+{L_{i}}^{2}+2L_{h}L_{i}sin\theta} \tag{7}\]  
Length of stretch inclined spring during motion is obtained as  
\[l_{n1}=\sqrt{{L_{h}}^{2}+{L_{i}}^{2}+2L_{h}L_{i}sin(\theta-\varphi)} \tag{8}\]  
Deformation of stretch inclined spring is obtained as  
\[\Delta l_{n}=l_{n1}-l_{n0} \tag{9}\]
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 2 Mission
### Scientific and Technical Goals and Payloads
#### 2.4.6 Experiment for Precise Orbit Determination (EPISODE)
A potential solution is a scaled reflector to CubeSat dimensions, similar to the Champ-Grace LRR, developed by German Research Centre for Geosciences (GFZ), consisting of four prisms shown in Figure 7.  
Figure 6: Steps that are necessary to achieve a navigation solution and tasks performed by an FPGA. Figure 7: left: Cube Laser Ranging Reflector Experiment (LRR) exemplarily positioned on CubeSat top side; right: 3D printed CubeLRR reduced by approx. factor 4 with respect to the original sized (approx. 10 cm \(\times\) 10 cm \(\times\) 10 cm) Champ reflector holder.