esa-sceva/satcom-chunk-collection huggingface dataset:
# An FPGA Design with High Memory Efficiency and Decoding Performance for 5G LDPC Decoder
## 1 Introduction
However, the design produces an increase in the hardware usage of registers. Another hardware-friendly structure was presented in [22]. Based on the 5G LDPC basis matrix's orthogonality property [9], a layer-merging and a split storage methods were utilized to improve efficiency and reduce the check-to-variable messages memory cost, respectively. The message reordering and selective-shift structure approach are applied to optimize the interconnection blocks. However, the space of memory that holds AP messages is slightly expanded due to the selective-shift structure. Recently, the hardware architecture of the QC-LDPC decoder with additional architectural transformations for parallel processing and lower routing complexity was presented in [23]. The decoder kernel was based on the Simplified Offset Min-Sum (SOMS) algorithm. In that work, they suggested a new message grouping technique that reduced the computational complexity of the QC-LDPC decoder. However, the error correction performance was reported to be lower than that of the conventional OMS decoder. Inspired by the pros and cons of the decoders above, this paper presents an FPGA hardware implementation for the 5G LDPC decoder architecture using the HOMS algorithm [19] as the decoding kernel. This decoder leads to a reduction in hardware cost and memory size, as well as a better decoding performance. In order to achieve this, it is necessary to exploit several techniques, such as pipelining, layered scheduling, appropriate quantization bits, partially parallel structure, and parallel processing datapath units. In particular, unlike the conventional MS decoder, the proposed decoder does not need to determine the first two minimum values of all variable-to-check message inputs during check node processing; instead, it only needs to compute the first minimum value. As a consequence, it is possible to minimize hardware resources and memory at the same time. The rest of this paper is structured as follows. Section 2 provides some definitions and fundamental information for 5G QC-LDPC codes. The proposed hardware design is described in Section 3 and the results and associated discussions are presented in Section 4. Finally, Section 5 concludes the paper.
# Biomechanical, Healing and Therapeutic Effects of Stretching: A Comprehensive Review
## 2 Topics and Results  
### Biomechanical Parameters, Healing, and Therapeutic Effects of Stretching  
Interesting results arose from numerous recent investigations and various stretching programs applied in medical practice and sports. The biomechanical parameters and therapeutic effectiveness of stretching applications could modify joint, tendon, and muscle flexibility. For this purpose, different variables, such as collagen and elastin syntheses, fiber elongation and elasticity, energy absorption, etc., could be used as mechanobiological, cellular, and molecular biomarkers.  
Many retrospective and prospective studies have been performed and stratified on the acute and chronic effects of stretching, both under physiological conditions and in pathological states. Progressive static stretching is effective during the prophylaxis of injuries in sports and exercise training [8; 9; 10]. The healing properties of stretching are of importance in the prophylaxis and treatment of joint injuries when also combined with other rehabilitation procedures (massage, heat/cold, warming up, etc.) [7; 11; 12; 13; 14]. Recent studies have reported a high effectiveness of stretching in the treatment and prevention of contractures and fascitis, as well as useful methods for application in routine orthopedic and traumatological practice [5; 15; 16; 17; 18; 19].  
As a rehabilitation method, stretching has been applied to improve the biomechanical parameters of muscles, tendons, ligaments, fascia, and joints [4; 6; 7; 9; 20; 21].  
The viscoelastic responses of muscles, tendons, ligaments, fascia, and joints to slow stretching exercises could result in less passive tension, compared to faster procedures [22; 23]. The faster the stretch, the higher the muscle stiffness will be [6]. Most stretching techniques (static, dynamic, ballistic, etc.) have been successfully implemented in clinical practice [2; 4; 24; 25].  
The effects of stretching on muscle and joint flexibility are closely related to the joint range of motion (ROM), whereby the increased range of motion induces the analgesic effects of stretching.  
Various stretching techniques have been compared. Unfortunately, the current results of the chronic effects of static stretching (SS) exercises on the muscle strength, flexibility, joint ROM, and muscle power are still controversial [5; 18; 26].
# Cybersecurity and Medical Imaging: A Simulation-Based Approach to DICOM Communication
## 2 Background  
DICOM defines a set of entities representing different aspects of medical imaging and patient information [40]:  
* Patients (\(P\)) encapsulates individual patient data, including attributes such as name, ID, and birthdate. A patient \(p\) can be associated with one or more studies (\(P\rightarrow[S_{1},S_{2},\ldots]\)).
* Study and Study Series (\(S\) and \(Se\)) includes related medical images and metadata, while Series groups images within a study based on common characteristics. A study \(s\) can include one or more series (\(Se=[S_{1},S_{2},\ldots]\)).
* Image (\(I\)) refers to a set of DICOM images \(I\); Series \(Se\) groups related images (\(Se\rightarrow[I_{1},I_{2},\ldots]\)).
* Physician (\(Ph\)) represents healthcare professionals; images \(I\) are interpreted by a physician \(Ph\) (\(Ph\rightarrow[I_{1},I_{2},\ldots]\)).
* Modalities (\(M\)) refers to imaging-related equipment or healthcare modalities, including imaging devices, specifying manufacturer details and software version. Images \(I\) are generated by a specific modality \(M\) (\(M\rightarrow[I_{1},I_{2},\ldots]\)). DICOM is designed for the management of medical images and associated data within a healthcare environment.
# Chloride Resistance of Assembled Bridge Piers Reinforced with Epoxy-Coated Steel Bars
## 3 Results and Discussion
### Polarization Resistance (\(R_{p}\))  
As shown in Figure 11, the polarization resistance of all steel bars exhibits a decreasing trend with increasing electromigration time. This decrease is attributed to the gradual breakdown of the passive film on the steel bars due to the increasing chloride ion content in the concrete, which results in a reduction in polarization resistance, indicating a decline in the corrosion resistance of the steel bars. In both Joint 1 and Joint 2, the polarization resistance of the various steel bars follows this order: untreated > knocked > bent > scratched. This suggests that the corrosion resistance of the steel bars decreases as the damage rate to the epoxy coating increases.  
Figure 10: Corrosion current density.  
The polarization resistance of steel bars in Joint 2 is lower than that in Joint 1 at the same electromigration time, suggesting more pronounced defects at the interface of Joint 2. The faster arrival of chloride ions at the surface of the steel bars and their gradual accumulation accelerated the destruction of the passive film on the steel bars, consistent with the conclusions drawn from self-corrosion potential and corrosion current density measurements previously mentioned. It is considered that when polarization resistance falls below 25,000 \(\Omega\cdot\)cm\({}^{2}\)[23, 24], the passive film on the surface of the steel bars is completely compromised, initiating a stable corrosion process. Based on this criterion, scratched steel bars in Joint 1 began to corrode after 10 days of electromigration, while those bent started corroding after 46 days of electromigration. Steel bars were subjected to knocking, and those untreated did not begin to corrode until the final period of electromigration, approximately between the 77 th and 84 th days. At Joint 2, the corrosion started for scratched steel bars on the 8 th day, for bent steel bars on the 18 th day, for knocked steel bars on the 30 th day, and for untreated steel bars on the 45 th day. The polarization resistance curves of Joint 2 decrease rapidly before the 35 th day and then stabilize after the 49 th day, following the order of the damaged area ratio. This suggests that during the early phase of electromigration, the passive film on the steel bars gradually dissolved but did not completely disappear, resulting in a noticeable decline in polarization resistance. In the later phase of electromigration, after the complete destruction of the passive film, the corrosion resistance of the steel bars (reflected in polarization resistance) was primarily determined by the integrity of the epoxy coating, which was not degraded by chloride ions. Consequently, the stabilization of polarization resistance in the later stages of electromigration aligns with the degree of coating damage.
## 4 Proposed Localization Method
### Performance Analysis
Using the conditions C1 and C2, we obtain, after some involved algebraic manipulations, that  
\[\mathbf{G}_{3}=\frac{\partial\mathbf{r}^{\mathrm{o}}}{\partial\mathbf{u}^{ \mathrm{o}}},\mathbf{G}_{4}=-\frac{\partial\mathbf{r}^{\mathrm{o}}}{\partial \mathbf{s}^{\mathrm{o}}},\mathbf{G}_{0}=-\left(\frac{\partial\mathbf{r}_{ \mathrm{c}}^{\mathrm{o}}}{\partial\mathbf{s}^{\mathrm{o}}}\right),\mathbf{G}_{ \mathrm{c}}=-\left(\frac{\partial\mathbf{r}_{\mathrm{c}}^{\mathrm{o}}}{ \partial\mathbf{c}^{\mathrm{o}}}\right) \tag{85}\]  
By this point, we can draw the conclusion that  
\[\mathrm{cov}(\mathbf{u})^{-1}=\mathbf{CRLB}_{\mathrm{c}}(\mathbf{u}^{\mathrm{o }})^{-1} \tag{86}\]  
That is, the proposed solution accomplishes the CRLB accuracy if the two conditions C1 and C2 are satisfied. In reality, localization scenarios, which satisfy the conditions C1 and C2, are not rare. These two conditions can be satisfied if the unknown target and the calibration targets are far from the transmitters and receivers, if not these conditions can still be satisfied if the BR measurement noise and the transmitter/receiver/calibration target position errors are sufficiently small.
# A New Algorithm for Computing the Distance and the Diameter in Circulant Graphs
## 4 Diameter Formulas for Circulant Graphs
### Diameter of \(C_{n}(1,s)\) When \(n\) Is Even and \(s\) Is Odd
Therefore, \(\gamma\geq 4\), \(\lambda\geq 6\), \(s\geq 7\) (when \(s\leq 5\), \(R_{3}=\emptyset\)), and \(d=\frac{\lambda}{2}+\frac{s+1}{2}-\frac{s-\gamma+1}{2}\). Let \(i=(\frac{\lambda}{2}-\frac{s-\gamma+1}{2}+1)s+\frac{s-1}{2}\) be a vertex \(C_{n}(1,s)\). By a simple calculation of the lengths \(\ell^{1}(i)\) to \(\ell^{4J}(i)\) represented in Lemma 3, and by applying Lemma 4, we obtain \(d(i)=\ell^{1}(i)=d\). Thus, the following theorem follows from Lemmas 6 and 7. **Theorem 3**. : _When \(n\) is even and \(s\) is odd,_  
\[diam(C_{n}(1,s))=\lceil\frac{\lambda}{2}\rceil+\frac{s+1}{2}-\min(\lceil\frac{ \gamma}{2}\rceil,\lceil\frac{s-\gamma+1}{2}\rceil).\]
## 4 Discussion  
### Intrinsic and Extrinsic Influences on Model Performance  
Past research indicated that traditional CT models developed for one time period or image date could be inadequate when applied directly to other dates [20,22]. This is because the classification accuracy of CT models can be influenced substantially by intrinsic factors, such as aquatic vegetation conditions, and extrinsic factors, such as environmental or physical conditions. The intrinsic factors affecting the performance of a CT model can be related to changes in plant structural characteristics and foliar pigment content associated with phenological development. The extrinsic factors affecting the performance of a CT model are related to atmospheric conditions, water transparency, sun-view angle and other factors that vary with time and influence the remote sensing signal in ways independent of the condition of the aquatic vegetation [32,33]. Atmospheric conditions, which have a direct impact on remotely sensed reflectance, exhibits significant differences over time, especially in the wet season when atmospheric water vapor remains high [34]. The study by [PERSON] _et al._[20] indicated also that 71.1% and 28.9% of the instability of traditional CT models originated from extrinsic and intrinsic factors, respectively, when applied to different time periods. Overall, there are many uncertainties and errors involved in applying the same CT model to identify aquatic vegetation types for different time periods.  
We were unable to find any previous research studying whether each pixel is affected by the same intrinsic and extrinsic factors from one image date to another. If the effects of intrinsic and extrinsic factors on each pixel were the same or similar, there would be a good linear relationship between the SFs of two images dates, and the thresholds of SFs in a CT model generated from one image date could be linearly adjusted to fit another date (hypothesis I). However, our analysis showed that this assumption could not always be established (see Figure 6A,C,E) and we conclude: (1) that such an approach should be considered only when there is strong correlation between the SFs of two date images for a vegetation class of interest and (2) there are differences in the effects of intrinsic and extrinsic factors on each pixel over time that challenge the use of a modified model with an approach based on hypothesis I.
# Design of Parabolic Off-Axis Reflector Optical System for Large Aperture Single Star Simulators
Although the optimized wave aberration of the system is slight, the system distortion value is not small enough, which makes the imaging shape of the system not accurate enough, and the aperture of the system is not large enough to realize the ground calibration and testing of the star sensitizer with an aperture of more than 250 mm. The primary and secondary mirrors are aspherical and challenging to process and adjust. Comprehensively, the existing single star simulator optical systems with larger apertures do not have good enough imaging quality, and the single star simulator optical systems with better wave imaging quality have smaller apertures. Therefore, to design a large aperture single star simulator optical system with high imaging quality, this paper proposes a design scheme for a large aperture single star simulator with a parabolic off-axis reflective optical system. To achieve this goal, we determined the design parameters of the optical system of the single star simulator according to the technical specifications of the star sensitizer and the current development of the optical system of the single star simulator; then, we analyzed the parameters of the optical system to select the structure of the optical system; next, we calculated the initial structural parameters of the optical system; and then we used Zemax19.4 software to optimize the initial structure of the optical system and obtain the optical system that meets the requirements of the specifications. Then, the Zemax software was used to optimize the simulation of the initial structure of the optical system to obtain the optical system that meets the requirements of the index, and the tolerance analysis of the optical system was carried out. The wave aberration, parallelism of the emitted light, and focal length of the actual optical system meet the system's requirements,and the optical system's imaging quality is good, making the simulated star point image of the single star simulator more realistic and accurate. After the optical system of the single star simulator and other structural parts of the single star simulator is built, it can realize the high-precision calibration and test of the technical indexes of the star sensitizer and provide ideas for the development of the optical system of the large aperture single star simulator in the future.
# Impact of Aerodynamic Interactions on Aeroelastic Stability of Wing-Propeller Systems +
## 3 Dynamic Model Setup
### Steady Aerodynamic Interaction  
Since the aerodynamic interaction effect is the main focus of this study, a time-domain simulation of the clean wing and the wing-propeller system is performed in DUST to highlight the impact of aerodynamic interference on load distribution in a steady-state condition. The angle of attack is set to \(5^{\circ}\) with the propeller operating in the thrusting condition. Symmetry at the half-wing root is assumed, and the sectional loads are post-processed. For both simulations, the spanwise \(c_{L}\) distribution of the wing is shown in Figure 6. The expected elliptical distribution is seen for the clean wing (dashed line). For the wing-propeller system (solid line), local peaks in sectional \(c_{L}\) arise due to the propeller wake. The peak's center corresponds to the hub span position of the propeller.  
The behavior is similar to those found in the literature [33; 34], with the low \(c_{L}\) values for the up-moving half of the propeller disk and positive \(c_{L}\) on the down-moving half. Figure 7 depicts the flow field of the coupled wing-propeller model through the iso-surfaces of the Q-criterion. The pressure coefficient contours are shown for both the propeller and the wing. The interactions of the propeller wake with the wing are clearly visible. High  
Figure 5: Stability map for standalone propeller in SDBox (\(V_{\infty}=133\) m s\({}^{-1}\), \(rpm=2500\), and collective pitch \(\beta=38.2^{\circ}\)).  
Figure 6: The \(c_{L}\) distribution of clean wing and wing with propeller attached at 50% span in thrusting condition at an angle of attack of \(5^{\circ}\).  
vortex areas are observed for the helical system generated by the propeller system and for the tip vortices of the wing.  
Aerodynamic interference acts on the propeller load distribution as well. Indeed, due to the presence of the wing, propeller performances are slightly altered. The next section, in particular, will show how the collective pitch has been corrected to match the windmilling and the thrusting conditions selected for the aeroelastic analyses, due to the upwash of the wing interfering with the propeller.
## 4 Results  
### Characteristics and Interpretation of Deep Seismic Reflection Profiles  
The deep seismic reflection profile A-A' is situated in the southern part of the Linfen Basin (Figure 3), oriented NW-SE, with a length of approximately 55 km, spanning the length of the basin [22].  
The profile reveals that the Linfen Basin is a typical 'half-grapen' basin (Figure 5b). From west to east, the main faults include the Luoyunshan (F3), Fenxi (F6), Quting (F7), Dayang West (F9), Dayang East (F10), West Zuo (FX), and Fushan (F2). [PERSON] et al. interpreted the deep seismic reflection profile by applying sediment thickness constraints and normal fault-related folding principles [23]. The results show that the Luoyunshan and Fushan faults are the boundary faults in the southern part of the basin. The Luoyunshan Fault is a 'listic' normal fault with shallow steep and deep gentle angles. It is nearly vertical at the surface, with a dip angle that flattens to approximately 37\({}^{\circ}\) at a depth of approximately 3.5 s two-way travel time (TWT) in the upper crust and continues to flatten at greater depths. Other faults intersect near-surface sediments in a normal faulting manner, controlling graben formation in the basin. These faults converge downward into the Luoyunshan Fault at the base of the upper crust (Figure 5b).  
At TWT 9-9.5 s, the wave group with strong reflected energy (blue dashed line) is interpreted as the boundary between the middle and lower crust. At TWT 12.5-14.0 s, it is horizontally continuous, then vertically extends for approximately 1.0 to 1.2 s, and an upward-arching reflection band is visible on the profile. This is referred to as the crust-mantle transition zone, which has a thickness of approximately 3.0 to 3.6 km. The bottom boundary of the crust-mantle transition zone (pink band) corresponds to the Moho surface (Figure 5b). The vertical undulations of the Moho surface are significantly greater than those of the boundary between the middle and lower crust. This suggests that the tectonic dynamics and crustal deformation in the Linfen Basin primarily originate from deeper sources.
# The Active Segmentation Platform for Microscopic Image Classification and Segmentation
## 4 Materials and Methods
### Segmentation Metrics  
Evaluating results of segmentation is a non-trivial problem, which very much depends on the objective of the analysis. If the objective is correct segmentation of blobs then small errors of the boundary detection will not affect the desired result. In contrast, if pixel-wise error metric is used this situation may result in a large error bond. This has induced some authors to propose non-local or regional metrics. A systematic empirical study of this problem was performed in [7] where the authors recommended specially normalized versions of the \"Rand error\" \(V^{rand}\) and \"Variation of Information\" \(V^{info}\) which best matched expert judgments of segmentation quality for EM structure segmentation.  
\[V^{rand}:=2\frac{\sum_{i,j}p_{ij}^{2}}{\sum_{j}s_{j}^{2}+\sum_{i}t_{j}^{2}} \tag{10}\]  
where \(p_{i,j}\) is the probability that a randomly chosen pixel belongs to segment \(i\) of the boundary in \(S\) (segmented class) and segment \(j\) of the boundary in \(T\) (ground truth class). \(s_{i}=\sum_{j}p_{i,j}\) is the marginal distribution density and denotes the probability of that a randomly chosen pixel belongs to segment \(i\) in \(S\). \(t_{j}=\sum_{i}p_{ij}\) is the co-marginal distribution density and denotes the probability that a randomly chosen pixel belongs to segment \(j\) in \(T\).  
\[V^{info}:=2\frac{I(S|T)}{H(S)+H(T)} \tag{11}\]  
where  
\[I(S|T)=\sum_{i,j}p_{ij}\log p_{ij}-\sum_{i}s_{i}\log s_{i}-\sum_{j}t_{j}\log t _{j} \tag{12}\]  
is the mutual information and \(H=-\sum p\log p\) is the corresponding entropy.
# FRAMUX-EV: A Framework for Evaluating User Experience in Agile Software Development
## 7 Discussions
### Limitations  
This study presents different limitations that must be acknowledged. First, the validation survey was conducted with only 34 practitioners, limiting the generalizability of the results. A larger sample size would offer more robust insights and improve the reliability of the findings. Additionally, since this is the first version of the framework, only one validation has been conducted, and it has not yet been applied in real-world agile software development projects. As a result, its effectiveness and practicality in actual settings remain to be evaluated, leaving a gap between theoretical development and real-world applicability.  
Furthermore, the UX events proposed in the framework are specifically designed for use within Scrum, and their applicability to other agile methodologies, such as Kanban or Extreme Programming (XP), has not yet been addressed. However, this limitation primarily applies to UX events, as UX artifacts and UX evaluation methods could potentially be used in other agile approaches. Finally, the study lacks a detailed specification of each proposed event and artifact, including aspects such as descriptions, objectives, life cycles, frequency, duration, and key considerations. This absence may pose challenges for teams attempting to implement the framework, as it corresponds to the first version of FRAMUX-EV. However, these details will be included in the next version of the proposal after refinements and additional experiments.