esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
We will describe some of the functional challenges the system needs to overcome to satisfy the fundamental pillars of modern software development. * System design: the system design must be capable of delivering the needed functionality showcasing the required building blocks, the reasoning behind the choice, the individual block role and behavior, and the connections to the other parts of the system. We will outline the building process of the system architecture, beginning with a simple foundational structure and progressively adding complexity as technical challenges are introduced. * Technology background: having an end goal, the used technology stack shares the same level of complexity. We will investigate the used technologies for each building block, the general infrastructure, and the technology principles used in the design and development of the collaborative system. * System development: we will detail the actual development of the application, covering topics such as conflict resolution, storage handling, application code, services configuration, tooling usage, and microservices interactions [15]. * Architecture validation: we will validate the proposed application architecture in terms of scalability, availability, throughput, and resource consumption [16]. Individual components of the system were subject to a series of challenges, disaster scenarios, and tests, to showcase their contribution towards a modern available and scalable application. To further emphasize the validity of the work, performance metrics, and test results were provided. This paper brings the following contributions in areas such as system design and trustworthy and dependable systems:  
* It provides a general and detailed overview of how a collaborative software system can be designed to work in a recoverable and scalable way to respect the modern software development pillars. The development process started as a very basic and crude system and further on involved analysis of the pain points, understanding of the technological limitations, and in the end, planning and implementation of a scalable solution
* It describes the process of selecting the appropriate technology stack for a given task. It is natural to have a considerable amount of technologies backing the actual functionality. Tech stack selection provides valuable resources for future system designs because it addresses possible common issues encountered in designing high-scale systems. * It provides a valuable overview of how distributed resources can be maintained by multiple peers while maintaining an overall simple and scalable architecture. This was achieved by using the right class of Conflict-Free replication algorithms, stateless microservices development, and event-driven architecture. * It provides proof of concept on implementing a highly flexible and scalable multi-client side connection-dependent system. The number of client-side connections is a physical constraint that can be overcome with software techniques.
## 5 Conclusions  
The presented method's clinical application can broaden the scope of applying a 3D ST scanner for scoliosis detection and 3D imaging to improve case analysis. The proposed method allows for calculating the spinous processes based on a four-directional 3D ST point cloud and a single X-ray image. The method is intended for use in patients with spinal deformities, for whom it is difficult to determine the line of the spinous processes only on the point cloud. This method may benefit a patient who requires two orthogonal X-rays of the spine to detect the spinous processes. Combining data from an optical scanner with a frontal radiograph can reduce the need for a sagittal X-ray in scoliosis cases when information about the spine line's approximate shape is needed. The approximate shape of the spine line can be determined based on the frontal radiograph and the point cloud describing the shape of the examined person's upper body surface for scoliosis cases with Cobb angles that qualify for conservative treatment. The final effectiveness of the method in patients with greater curvatures was not validated. Obtaining a confirming result in the study of a single case of more extensive scoliosis qualifying for surgery suggests an optimistic premise that the method may be useful in cases where the use of optical methods has been limited thus far. Further research should be devoted to analyzing a larger group of scoliosis cases with a larger Cobb angle.  
Conceptualization, S.P. and W.G.; methodology, S.P. and W.G.; software, S.P.; validation, S.P. and W.G.; formal analysis, S.P. and W.G.; investigation, S.P. and W.G.; resources, S.P. and W.G.; data curation, S.P. and W.G.; writing--original draft preparation, S.P.; writing--review and editing, S.P. and W.G.; visualization, S.P. and W.G.; supervision, S.P. and W.G.; project administration, W.G.; funding acquisition, S.P. and W.G. All authors have read and agreed to the published version of the manuscript.  
This research was supported by project NR13-0109-10 /2011, funded by the National Center for Research and Development and partially by the statutory financial support of the Ministry of Scientific Research and Higher Education for the Institute of Micromechanics and Photonics, Warsaw University of Technology. The APC was funded by the Warsaw University of Technology.  
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the Medical University of Warsaw for the project entitled \"A three-dimensional computer system for designing the functional evaluation of orthopedic equipment in patients with disorders of statics and kinematics of the body in the course of civilization diseases of the musculoskeletal system\" (Protocol code KB 22/2012; date of approval Jan 17, 2012).  
Informed Consent StatementInformed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author.  
Authors are thankful to Professor [PERSON] for his remarks after reading the manuscript.  
The authors declare no conflict of interest.  
References
## 2 Mathematical Preliminaries
Then, the coefficients, \(a_{j}\), of the FPS representation are given by \(a_{j}=\frac{1}{\Gamma(j)+1}D_{\tau_{0}^{+}}^{j\alpha}\omega(\tau_{0})\)._  
The FPS (4) about \(t=\tau_{0}\) can be rewritten as \(\omega(t)=\omega_{n}(t)+\mathcal{R}_{n}(t)\), where \(\omega_{n}(t)\) represent the \(n\)th approximate series of \(\omega(t)\) and \(\mathcal{R}_{n}(t)\) the tail of FPS, which can be given, respectively, by  
\[\omega_{n}(t)=\sum_{j=0}^{n}a_{j}(t-\tau_{0})^{j\alpha}, \tag{5}\]  
and \(\mathcal{R}_{n}(t)=I_{\tau_{0}^{+}}^{(n-1)\alpha}D_{\tau_{0}^{+}}^{(n-1)\alpha }\omega(t)=\omega(t)-\sum_{j=0}^{n}\frac{D_{\tau_{0}^{+}}^{j\alpha}\omega(\tau _{0})}{\Gamma(j+1)}(t-\tau_{0})^{j\alpha}\). Anyhow, the FPS is convergent to the exact solution whenever \(\lim\limits_{n\to\infty}\mathcal{R}_{n}(t)=0\). **Corollary 1**. : _Let \(\alpha\in(m-1,m]\), \(D_{\tau_{0}^{+}}^{j\alpha}\omega(t)\) exist for \(j=0,1,2,\ldots,n+1\), and \(\omega(t)\) has the FPS representation (4) such that \(\left|D_{\tau_{0}^{+}}^{(n+1)\alpha}\omega(t)\right|\leq M\), \(M>0\) for some \(n\in\mathbb{N}\). Then, for all \(\big{(}\tau_{0},\tau_{0}+R^{1/\alpha}\big{)}\), the reminder \(\mathcal{R}_{n}(t)\) of the FPS \(\omega(t)\) satisfies_  
\[\left|\mathcal{R}_{n}(t)\right|=\frac{M}{\Gamma((n+1)\alpha+1)}(t-\tau_{0})^{ (n+1)\alpha}. \tag{6}\]  
**Proof. ** From the assumption \(\left|D_{\tau_{0}^{+}}^{(n+1)\alpha}\omega(t)\right|\leq M\), we have  
\[-M\leq D_{\tau_{0}^{+}}^{(n+1)\alpha}\omega(t)\leq M. \tag{7}\]  
Thus, by applying the operator \(I_{\tau_{0}^{+}}^{(n+1)\alpha}\) on both sides of (7), one can get that \(\frac{-M}{\Gamma((n+1)\alpha+1)}(t-\tau_{0})^{(n+1)\alpha}\leq I_{\tau_{0}^{+ }}^{(n+1)\alpha}D_{\tau_{0}^{+}}^{(n+1)\alpha}\omega(t)\leq\frac{M}{\Gamma((n+ 1)\alpha+1)}(t-\tau_{0})^{(n+1)\alpha}\). Hence, \(\left|\mathcal{R}_{n}(t)\right|=\frac{M}{\Gamma((m+1)\alpha+1)}(t-\tau_{0})^{ (n+1)\alpha}\). \(\Box\)
## 3 Experiments
### Experimental Setup  
The dataset for each of the seven centers is first split into train and test sets with a ratio of 70:30, respectively, for a fair comparison. In all experiments, the model is first pre-trained using the six centers' training data and then fine-tuned on the seventh center's training data. We evaluate the performance of the model on (1) the seventh center's test set (new center) and (2) on the six centers' test set (old centers). We compare both metrics for the following fine-tuning techniques as shown in Figure 4.  
_No fine-tuning:_ In this, the pre-trained model is directly used to infer the test samples without any fine-tuning.  
_Partial fine-tuning:_ This technique involves fine-tuning the pre-trained model's last decoder block using the seventh center's training set.  
_Full fine-tuning:_ This technique involves fine-tuning the entire pre-trained model using the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Center** & **City, Country** & **PET/CT scanner** & **Number of samples** \\ \hline HGJ & Montreal, Canada & Discovery ST, GE & 55 \\  & & Healthcare & 53 \\ CHUS & Sherbrooke, Canada & GeminiGXL 16, & 72 \\  & & Philips & \\ HMR & Montreal, Canada & Discovery STE, GE & 18 \\  & & Healthcare & 56 \\  & & Discovery STE, GE & 53 \\  & & GE Healthcare & 53 \\  & & Biograph mCT 40 ToF, & 72 \\  & & Discovery HR, RX, ST, & \\ MDA & Texas, USA & and STE (GE & 197 \\  & & Healthcare) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset origin and distribution.  
Figure 3: A sample of images from the dataset [23]. **(a,b)** depict the original CT and PET scans, respectively. **(c,d)** show the cropped CT and PET scans, and **(e)** shows the cropped ground truth mask.  
seventh center's training set.  
_Shallow prompt fine-tuning:_ This is a variant of prompt-based fine-tuning, where the prompts are introduced only in the input space. Only the prompts and the final convolutional layer are fine-tuned using the seventh center's training set, while the rest of the model is frozen.  
_Deep prompt fine-tuning:_ This technique is similar to shallow prompt fine-tuning; prompts at each level of the transformer layer are introduced. Thus, at each level, there are new trainable prompts to refine. The prompts and the final convolutional layer are fine-tuned using the seventh center's training set.
## 2 Test Sites and Data Preparation
### Stereoscopic Processing  
The stereo images were processed using the Agisoft Photoscan software package (version 1.2.2, build 2294, 64 bit). The processing algorithm was procedures of structure from motion (SfM). The three-dimensional modeling equations used in SfM only need relative positions and orientations of  
Figure 2: Generation of forest aboveground biomass (AGB) reference using Lidar data. (a) Model development (32 plots); (b) model validation (31 plots); (c) forest AGB map with a resolution of 15 m from lidar data.  
the camera, which can be calculated using identified matching points. The point cloud in local relative coordinate systems can be determined based on the numerous overlapping stereo images [39]. The real three-dimensional model was produced by transforming the relative point cloud into geo-referenced coordinates using GCP. Correspondingly, there were three basic steps involved in the stereo processing: (a) Image alignments, i.e., the relative positions and orientations of images were estimated using the key points extracted from images through analyses of image textures; (b) dense point cloud generation, i.e. based on the estimated relative positions and orientations of images--the depth information for each camera was calculated and then combined into a single dense point cloud. Five levels of quality of point densification are provided in the software, including lowest, low, medium, high, or ultra-high. The ultra-high level was used in this study to get the densest point cloud. (c) Coordinates transformation, i.e., the geo-referenced coordinates of point cloud was calculated from the relative coordinates using GCP. Three options were provided in step (b) to determine the filtering degree, including \"mild,\" \"aggressive,\" and \"moderate.\" For forested areas, the \"mild\" was recommended because the sparse single trees were always filtered out as noise by the other two settings. The default values of other parameters were used in this study.  
The field measurement of GCP using GPS with differential correction was time-consuming in field work. The longitude, latitude, and elevation of GCPs used in this study were directly collected on the very height resolution (VHR) images in Google Earth(tm). The same GCPs were used for the stereo imagery of both leaf-on and leaf-off to minimize their divergence. The spatial distributions and the positioning errors of GCPs were shown in the results section.
# Identifying Growth Patterns in Arid-Zone Onion Crops (Alium Cepa) Using Digital Image Processing
## 1 Introduction
The authors of [18] presented a Mutual Subspace Method as a classifier in different farm fields and orchards, achieving 75.1% accuracy in high-altitude image-acquisition systems. By analyzing large amounts of data, such as weather patterns, soil quality, and crop yields, farmers and researchers can gain insight into the factors that influence crop growth and use this information to improve agricultural practices. [PERSON] et al. estimated tomato yield based on climate data, irrigation amount, and water content in the soil profile as input variables for Recurrent Neural Networks. The results showed that the Bidirectional Long Short-Term Memory model achieved an R\({}^{2}\) of 0.97 [19]. The remarkable work provides alternative solutions for crop monitoring and preventing disease factors. However, the conditions of an area with an arid climate can affect the quality and quantity of the food harvest, which makes it a challenge to propose alternatives that can improve and make agricultural processes more efficient without compromising land resources [20]. In this context, this study focuses on the state of Zacatecas, Mexico, a significant agricultural region known for producing corn, beans, and chili peppers. This study proposes a solution to increase the production percentage and minimize losses by analyzing the patterns that most impact crop growth during its phenological stage. Crops suffer losses during their development cycle, and it is necessary to increase production each time. Therefore, this work aims to analyze which patterns most affect crop growth during its phenological stage and identify these patterns at an early stage. In this way, a proposed solution to increase the production percentage of the crop is offered, and a determination is made of which patterns most influence crop growth throughout its phenological stage in the state of Zacatecas, Mexico. It was concluded that humidity, cloudy weather, and leaf blight greatly impacted crop development and overall agricultural production. This fungal disease can damage the foliage and ultimately lead to crop loss.
## 2 Scott Transformer Model for Real-Time Simulation
### Scott Transformer Model for the Real-Time Simulator  
Figure 3 shows the Scott transformer provided in the Library of RSCAD. As shown in Figure 1, a Scott connection converts a three-phase structure into two single phases. However, this model cannot handle the actual parameters of Scott transformers such as the leakage reactance of two phases. Although M- and T-phases have different impedance characteristics, only one reactance element is set into this model. Therefore, it is believed that the Library model cannot fully contain the attributes of the Scott transformer.  
To investigate the problem in detail, we performed an impedance voltage test and compared the simulation results with a Scott transformer's certified performance. The impedance voltage indicates the voltage drop only due to the impedance of the transformer. We can quantify the impedance voltage when one side of the transformer is configured as a short circuit as shown in Figure 4. The voltage when the rated current flows across both sides is called the impedance voltage. Therefore, the impedance voltage (%) is ideally equal to the percentage impedance of a transformer.  
In the simulation experiment using RSCAD, the percentage of impedance of the Library Scott transformer model was set to 12.5%, and the short circuit was configured as shown in Figure 4. We then generated the input voltage and changed its magnitude to quantify the impedance voltage (i.e., the voltage when the rated current flows between both ends).  
The measured impedance voltages are presented in Table 1. As the transformer's percentage impedance was set to 12.5%, the measured impedance voltage should be 12.5%  
Figure 4: Circuit configuration for impedance voltage test of the Library Scott transformer model.  
Figure 3: Scott transformer model in the Library of RSCAD.  
in the normal condition. However, the Library Scott transformer's impedance voltages (8.49% and 11.36%) differed significantly compared to the theoretical value as represented in Table 1. Furthermore, when the library model is used, both sides of the transformer are expected to maintain the balanced condition following Equations (2) and (3). However, Figure 4(a) shows that an unbalanced current flowed in the three phases. Although the currents flowing in M- and T-phases had a normal phase difference (90\({}^{\circ}\)), the two currents differed in magnitude, as shown in Figure 4(b). To sum up, impedance voltage tests show that the Library model cannot accurately simulate the Scott transformer.
## 2 Materials and Methods
### Proposed Method
#### 2.3.1 Problem Formulation  
We formulate pFedKD-WCL as a bi-level optimization problem. At the client level, each client \(i\) optimizes a personalized model \(\theta_{i}\in\mathbb{R}^{d}\) using a KD-based loss with the global model \(w\) as the teacher:  
\[\min_{\theta_{i}\in\mathbb{R}^{d}}\big{\{}F_{i}(\theta_{i},w):=(1-\gamma) \cdot f_{i}(\theta_{i})+\gamma\cdot\text{KL}(p_{w},p_{\theta_{i}})\big{\}}, \tag{3}\]  
where \(f_{i}(\theta_{i})=\mathbb{E}_{\mathcal{D}_{i}\sim P_{i}}[\hat{f}_{i}(\theta_{ i};\mathcal{D}_{i})]\) is the local CE loss, and \(\text{KL}(p_{w},p_{\theta_{i}})\) measures the divergence between the global model's predictions \(p_{w}\) and the local model's predictions \(p_{\theta_{i}}\) on \(\mathcal{D}_{i}\), with standard softmax outputs (\(T=1\)). The parameter \(\gamma\in[0,1]\) balances the two loss terms, with a low \(\gamma\) allowing each client's model to stick with its unique data patterns, while a high \(\gamma\) prioritizes alignment with the global teacher's predictions via KL divergence, enabling flexible personalization in non-IID settings.  
At the server level, the global model \(w\) is optimized to minimize the average divergence from local models:  
\[\min_{w\in\mathbb{R}^{d}}\Bigg{\{}F(w):=\frac{1}{N}\sum_{i=1}^{N}\text{KL}(p_ {w},p_{\hat{\theta}_{i}(w)})\Bigg{\}}, \tag{4}\]  
where \(\hat{\theta}_{i}(w)=\arg\min_{\theta_{i}}F_{i}(\theta_{i},w)\) is the optimal local model for client \(i\) given \(w\). This formulation encourages the global model to align with local predictions, enhancing robustness to heterogeneity. KL divergence is preferred over MSE in knowledge distillation loss because it aligns probability distributions, effectively capturing the teacher's class relationships. Unlike MSE, which compares raw logits and may over-penalize differences irrelevant to probabilities, KL preserves the distributional structure, ensuring the student mimics the teacher's predictive behavior. In federated learning, KL's focus on distributions enhances robustness to non-IID data, reducing client drift.
# Seismic Performance Evaluation of Highway Bridges under Scour and Chloride Ion Corrosion
## 3 Effect of Cl\({}^{-}\) Corrosion on the Seismic Performance of Bridge Column  
### Specimen Design and Quasi-Static Test  
Column specimens were designed with different heights according to the following ideas. (1) The same type of concrete (C40) and reinforcement (HRB400 and HRB335) as the prototype were adopted in specimens; (2) hoops in the specimen were configured in accordance with the principle of equal ratio of reinforcement volume to the prototype; (3) the axial pressure was set in the specimen on the basis of the principle of the same axial pressure ratio as the prototype; (4) the scale ratios for section and height dimensions of both low and middle columns were 1:6.25, 1:8.33, respectively. The quasi-static test and the column specimen are shown in Figures 19 and 20, respectively. The specific design parameters are shown in Table 4. The specimens of high, medium, and low longitudinal reinforcement ratio with a difference of approximately 2% were designed to investigate the difference in seismic performance of bridge columns subjected to Cl\({}^{-}\) corrosion with variable reinforcement ratios [47; 48; 49; 5  
Electrochemical Corrosion of Bridge Columns  
Electrochemical corrosion tests on maintained reinforced concrete columns positioned in NaCl solution with a concentration of 5% were carried out under dry and wet cycles. After 12 h immersion, the solution was drained and the evaporation of water in the test column was accelerated by means of fan air supply to achieve 12 h drying, taking a total of 24 h to complete a wet and dry cycle [51,52].  
In the test, the axial compression ratio consistent with the prototype bridge project was applied for vertical loading. The mixed control of variable amplitude and constant amplitude displacement was applied for horizontal loading, each level of which was loaded cyclically 3 times. When the lateral force dropped to 85% of the peak lateral force, the specimen was considered damaged and the loading was stopped.
## 2 About the Chosen Digital Signature Algorithms
### Sphincs+  
Sphincs+ serves as a cutting-edge post-quantum digital signature algorithm engineered to withstand quantum-computing attacks. Grounded in hash-based cryptography, the scheme's security is straightforward to evaluate and hinges solely on the characteristics of the hash function in use. Sphincs+ essentially builds on the foundational architecture of Sphincs but enhances it through parameter optimization and the incorporation of novel methods aimed at boosting both the algorithm's speed and its security profile [25].