esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Isotropic Consolidation and Undrained Compression Testing  
The saturated sand specimens and four binary mixtures (1%, 5%, 10%, and 15% NS) were isotropically consolidated to four target mean effective stresses (\(p^{\prime}{}_{\text{c}}\)) of 100, 200, 300 and 400 kPa in the triaxial apparatus. The consolidation stage was followed with strain-controlled undrained compression employing an axial displacement rate of 1 mm/min, which was deemed suitable in minimizing creep deformations. A submersible load cell located inside the triaxial cell chamber monitored the deviatoric force imposed along the specimen axis. Peak and post-peak responses were captured in the form of stress path and stress-strain traces. The testing program is summarized in Table 4, listing the relative density, NS content, mean effective stress achieved at the end of the consolidation stage (i.e., \(p^{\prime}{}_{\text{c}}\)), and corresponding void ratio (\(e_{\text{c}}\)), for each test specimen.  
In Table 4, the Test ID gives the specimen NS content in brackets, followed by \(p^{\prime}{}_{\text{c}}\) value applied; for example, S(15):200 stands for an F161 sand specimen grouted with 15 wt.% NS that was isotropically consolidated to \(p^{\prime}{}_{\text{c}}=200\) kPa. The void ratio at the end of the consolidation stage was determined for each specimen from its measured consolidation volumetric strain and water content determined at the end of the compression stage, which, when it was undrained, was the same as that occurring at the end of the consolidation stage.  
Figure 3: Set up for preparation of grouted specimens and triaxial compression testing: (**a**) monotonic triaxial apparatus; (**b**) remolded specimen in double-split-type mold under small 15 kPa vacuum pressure applied at two points around perimeter; (**c**) NS-stabilized test specimen.
# Comparative Study of Harmony Search Algorithm and its Applications in China, Japan and Korea
## 3 Harmony Search Applications in China
### Engineering
#### 3.1.2 Mechanical  
[PERSON] et al. [15] recommended a revised a variant of the HS algorithm known as Adaptive Dynamic Harmony Search (ADHS) using the knowledge given by the HM for the optimization of aircraft panels. After optimizing, it was seen that ADHS should have a reliable architecture and that suitable local approaches for optimization problems with several local minimum structures could be minimized based on ADHS.  
[PERSON] et al. [16] recommended the multi-target Automated Guided Vehicle Scheduling Problem (AGVSP) for an Improved HS-based Material Transfer Scheduling Algorithm (IHSMTSA). Four changes to the standard HS algorithm were made to IHSMTSA's new version:  
1. A discrete coding strategy was developed to decrease the search range in the solution space;
2. The Opposition-Based Learning (OBL) approach was used for initializing the HM;
3. The value of HMCR was changed to adapt;
4. A local search method for enhancing the quality of the optimal solution was recommended.  
As noted in the simulation analysis, the proposed IHSMTSA had substantially higher success when solving the AGVSP compared with other recorded algorithms. Figure 1 contains a flowchart of the IHSMTSA.
## 3 Results and Discussion  
### Characterization of the Lime Sand Bricks  
The specific surface area of the brick determined using the BET method is 1.75 m\({}^{2}\)/g with an average pore size of 12.00 nm and the total pore volume on the brick was 0.0051 cm\({}^{3}\)/g. The surface area of bricks was a bit higher compared to chitosan of 1.05 m\({}^{2}\)/g [21] and perlite of 1.66 m\({}^{2}\)/g [22]. This property provides a larger surface area for the lime sand bricks, providing more active adsorption sites for adsorption. The surface topography of the brick was analyzed by SEM, as shown in Figure 2. It was observed that the surfaces of lime sand bricks were uneven with large numbers of pore spaces, which perhaps provided sites for adsorption of Cu(II) either physically or chemically [23]. Figure 3 shows the FTIR spectra of the lime sand brick before Cu(II) adsorption. The bands at 1444 cm\({}^{-1}\) before adsorption was assigned to the O-H bending vibration of lime sand bricks [24]. The peaks at 2520 cm\({}^{-1}\) for lime sand bricks before adsorption can be assigned to \(\sim\)C=OH-OH [25]. The bands in the range 729-877 cm\({}^{-1}\) may be assigned to the \(\sim\)C=CH\({}_{2}\) double bonds [26]. The bands in the range 1010-1092 cm\({}^{-1}\) can possibly be assigned to the \(\sim\)C=O-O- bond [27]. Studies have shown that carboxyl (-COOH), hydroxyl (-OH) and phenol (R-OH) can coordinate with heavy metal ions such as Cu\({}^{2+}\)[28]. Table 2 shows the elemental composition of the lime sand bricks, which mainly consisted of CaO, SiO\({}_{2}\), MgO, Al\({}_{2}\)O\({}_{3}\) and Fe\({}_{2}\)O\({}_{3}\). The content of CaO was the largest among all elements (42.16%). Calcium and magnesium are exchangeable cations allowing for its exchange with Cu(II), which could have a positive effect on Cu(II) adsorption.  
Figure 2: The scanning electron microscopy (SEM) image of lime sand bricks observed at different scales. (**a**) 2.00 \(\upmu\)m; (**b**) 5.00 \(\upmu\)m; (**c**) 10.0 \(\upmu\)m; (**d**) 20.0 \(\upmu\)m.
# Mechanical Structure Design and Experimental Study of Gamma-ray Monitor for Small Satellite Payload
## 3 Modeling
### Structural Finite Element Dynamic Analysis Equations
#### 3.2.2 Sinusoidal Vibration Analysis Equations  
Sinusoidal vibration analysis is for the purposes of studying the dynamic response of a structure under a series of periodic loads in the form of sine or cosine, and is mainly used to characterize the amplification effect of the structure on the external loads at different frequencies. For the dynamic response characteristics of the system, the modal superposition method is usually used to solve the external excitation. The solution formula is given by [26]:  
\[y(t)=\Phi q(t)=\sum_{j=1}^{n}q_{j}\Phi_{j} \tag{6}\]  
where \(y(t)\) is the system displacement as a function of time, \(\Phi_{j}\) is the first order damped self-oscillation, and \(q_{j}\) is the oscillation coordinate, representing the weighting factor of the oscillation. The generalized motion equation is obtained by multiplying the above equation with \(\Phi^{T}\):  
\[M^{*}\ddot{q}(t)+C^{*}\dot{q}(t)+K^{*}q(t)=F^{*} \tag{7}\]  
where \(q(t)\) is the nodal displacement as a function of time, \(M^{*}\) is the mass matrix, \(C^{*}\) is the damping matrix, \(K^{*}\) is the stiffness matrix, and \(F^{*}\) is the external load.  
Equation (7) is decomposed into \(n\) independent single degree of freedom equations of motion:  
\[\ddot{q}_{j}+2\zeta_{j}\omega_{j}\dot{q}_{j}+\omega_{j}^{2}q_{j}=\frac{F_{j}^ {*}}{m_{j}^{*}} \tag{8}\]  
The solution of the above equation represents the displacement, velocity and acceleration, so as to measure the dynamic response of the system. For simple harmonic vibration, the dynamic equation of the system is:  
\[M\ddot{y}(t)+C\dot{y}(t)+Ky(t)=-MEx_{0} \tag{9}\]  
where \(E\) is the elasticity modulus of the material, and \(x_{0}\) is the excitation displacement vector. The generalized equation can be obtained as:  
\[M^{*}\ddot{q}(t)+C^{*}\dot{q}(t)+K^{*}q(t)=-\gamma x_{0}e^{i\omega t} \tag{10}\]  
where \(\gamma\) represents the equation feature vector.  
At this time, the frequency response matrix of the system is:  
\[H(\omega)=\left(K-\omega^{2}M+i\omega C\right)^{-1} \tag{11}\]  
thus, the steady-state solutions of the equation are obtained as:  
\[y(t)=\sum_{j=1}^{n}q_{j}\Phi_{j}=\sum_{j=1}^{n}-\gamma_{j}H_{j}\Phi_{j}x_{j}e^ {i\omega t} \tag{12}\]  
\[\ddot{y}(t)=-\omega^{2}\sum_{j=1}^{n}-\gamma_{j}H_{j}\Phi_{j}x_{j}e^{i\omega t} \tag{13}\]  
In Equation (13), \(\ddot{y}(t)\) is the response acceleration of some point to external excitation.
###### Abstract  
Background/Objectives: Open-source artificial intelligence models (OSAIMs), such as BiomedCLIP, hold great potential for medical image analysis. While OSAIMs are increasingly utilized for general image interpretation, their adaptation for specialized medical tasks, such as evaluating scoliosis on posturographic X-ray images, is still developing. This study aims to evaluate the effectiveness of BiomedCLIP in detecting and classifying scoliosis types (single-curve and double-curve) and in assessing scoliosis severity. Methods: The study was conducted using a dataset of 262 anonymized posturographic X-ray images from pediatric patients (ages 2-17) with diagnosed scoliosis. The images were collected between January 2021 and July 2024. Two neurosurgical experts manually analyzed the Cobb angles and scoliosis stages (mild, moderate, severe). BiomedCLIP's performance in detecting scoliosis and its type was evaluated using metrics such as accuracy, sensitivity, specificity, and AUC (Area Under the Curve). Statistical analyses, including Pearson correlation and ROC curve analysis, were applied to assess the model's performance. Results: BiomedCLIP demonstrated moderate sensitivity in detecting scoliosis, with stronger performance in severe cases (AUC = 0.87). However, its predictive accuracy was lower for mild and moderate stages (AUC = 0.75 and 0.74, respectively). The model struggled with correctly identifying single-curve scoliosis (sensitivity = 0.35, AUC = 0.53), while it performed better in recognizing double-curve cases (sensitivity = 0.78, AUC = 0.53). Overall, the model's predictions correlated moderately with observed Cobb angles (r = 0.37, \(p\) < 0.001). Conclusions: BiomedCLIP shows promise in identifying advanced scoliosis, but its performance is limited in early-stage detection and in distinguishing between scoliosis types, particularly single-curve scoliosis. Further model refinement and broader training datasets are essential to enhance its clinical applicability in scoliosis assessment.  
BiomedCLIP; scoliosis detection; medical image analysis; machine learning in healthcare; artificial intelligence +
Footnote †: journal: Applied sciences  
Brief Report
## 13 Discussion  
The impact of mining on land cover and vegetation dynamics has been of significant concern in South Africa; although mining contributes significantly to the country's gross domestic product (GDP). During mining operations, significant land-use-land-cover change (LULC) occurs as a result of infrastructure that is needed to support the mining operations, such as buildings, tailing dams, and ponds, etc. [39]. To investigate land-use change over the years, this study used Landsat data to examine land-cover patterns and processes over three decades in Emalahleni, South Africa. Using time series data to assess the changes from 1990 to 2020, the results revealed significant alterations to the land cover and vegetation dynamics.  
The extent of the change that has occurred in the study area included changes from vegetation land cover to settlement, water, bare terrain, and mining areas. The majority of the study area was found to be rocky or bare ground, accounting for approximately 60% of the total area. In Figure 6, it was also deduced that 64.56% of the study area experienced changes that are not attributed to mining and vegetation land use types (Figure 6).  
However, 13.40% of the total area has been used for mining, with the highest gain observed from 1990 to 2000, and the least gain from 2000 to 2010. This supports the findings of previous studies, indicating a continuous increase in mining activities in South Africa and the subsequent impact on the environment. A study conducted by [PERSON] et al. [40], indicates that an increase in mining activities in South Africa has resulted in high acid rain drainage into streams/rivers, which threatens the country's scarce water resources.  
In contrast, positive gains serve to make the land greener and contribute to urbanization. Figure 5 provides a picture of some gains in vegetation over the years. Additionally, 11.82% of the existing mining areas have been revegetated, while 7.66% of the area has seen changes from other land use types to vegetation, possibly due to reclamation practices. Although this is good, serious challenges remain as rehabilitated lands are not always returned to capabilities equivalent to their pre-mining state [41].
## 4 Federated Learning for Privacy-Preserving LLM Training in IoT
### Initialization and Data Considerations  
Effective federated LLM training depends significantly on model initialization and data handling:  
Model Initialization: Starting FL from a well-pre-trained LLM, rather than random initialization, significantly improves convergence speed, final model performance, and robustness to Non-IID data [100]. It allows FL to focus on adaptation rather than learning foundational knowledge from scratch [101].  
Data Processing: Handling massive, distributed datasets requires scalable tools. Libraries like Dataset Grouper aim to facilitate partitioning large datasets for FL simulation [102].  
Synthetic Data Generation: When local data is scarce or highly skewed, generating synthetic data can augment training [10]. LLMs show promise for generating high-quality synthetic data that reflects complex real-world distributions, potentially overcoming limitations of earlier generative models used in FL [103]. Frameworks like GPT-FL explore using LLM-generated data to aid FL. Selecting relevant public data using distribution matching techniques can also enhance privacy-preserving training via knowledge distillation [104].
# Efficient and HRA Secure Universal Conditional Proxy Re-Encryption for Cloud-Based Data Sharing
## 3 Definition of Universal Conditional Proxy Re-Encryption
### Unidirectional and Multihop UCPRE
Moreover, the challenger needs to set_ **Cor**_:=_**Cor**_ \(\cup\) _i and_ **numkeys**_:=_**numkeys**_+1._
* _Oracle Query Phase_ **2**_: In this phase,_ \(\mathcal{A}\) _makes the following queries._
* _Encryption Oracle_ \(\mathcal{O}_{\text{Enc}}\)_: For a query on_ \((i,\textbf{m})\)_, where_ \(i\leq\text{numKeys}\)_, the challenger_ \(\mathcal{C}\) _computes the ciphertext_ \(\text{ct}\leftarrow\Pi_{i}.\text{Enc}(\text{pk},\textbf{m})\) _and sets_ **numC**_:=_**numC**_**_+1. Then,_ \(\mathcal{C}\) _stores the value_ \(c\) _in_ **Ct*
* _with key_ \((i,\text{numCt})\) _and return_ \((\text{numCt},\text{ct})\)_._
* _Re-encryption Key Generation Oracle_ \(\mathcal{O}_{\text{ReKeyGen}}\)_: For a query on_ \((i,j)\)_, where_ \(i,j\leq\text{numKeys}\)_,_ \(\mathcal{C}\) _outputs_ \(\bot\)_, if_ \(i=j\) _or_ \(i\in\text{Hon}\) _and_ \(j\in\text{Cor}\)_. Otherwise, the challenger_ \(\mathcal{C}\) _returns the re-encryption key_ \(\text{rk}_{i\to j}\leftarrow\text{ReKeyGen}\Big{(}1^{\lambda},\Pi_{i},\Pi_{j}, \text{sk}_{i},\text{pk}_{j}\Big{)}\)_._
* _Challenge Oracle: For a query on_ \((i,\textbf{m}_{0},\textbf{m}_{1})\)_, where_ \(i\in\text{Hon}\) _and_ \(\textbf{m}_{0},\textbf{m}_{1}\in\mathcal{M}\)_, the challenger_ \(\mathcal{C}\) _randomly chooses a bit_ \(b\) _from_ \(\{0,1\}\) _and sets the challenge ciphertext_ \(\text{ct}^{*}\leftarrow\Pi_{i}.\text{Enc}(\text{pk}_{i},\textbf{m}_{b})\)_. Meanwhile,_ \(\mathcal{C}\) _sets_ **numC**_:=_**numC**_+1 and adds_ **numC*
* _to the set_ **Deriv**_, and stores the value_ \(\text{ct}^{*}\) _in_ **Ct*
* _with key_ \((i,\text{numCt})\) _and return_ \((\text{numCt},\text{ct}^{*})\)_. We just do challenge query once._
* _Decision Phase_ **3**_: In this phase,_ \(A\) _outputs a bit_ \(b^{\prime}\in\{0,1\}\)_, return win if_ \(b=b^{\prime}\)_. The advantage of_ \(\mathcal{A}\) _is defined as_ \[\text{Adv}_{\text{hra}}^{\mathcal{A}}(\lambda)=\Pr[\text{ win }],\] _We say that our UCPRE is HRA secure if for any PPT adversary, it holds that_ \[\text{Adv}_{\text{hra}}^{\mathcal{A}}(\lambda)<\frac{1}{2}+\text{negl}(\lambda).\]
## 2 Materials and Methods
### Evolutionary Programming  
To establish the proportionality values of the LOS and NLOS measurement groups between PEM and BBM that best fit the generated exposure maps, a classical evolutionary algorithm has been programmed. A population of 50 individuals (pairs of k1 and k2 values representing the BBM-PEM proportionality for LOS and NLOS respectively) and a selection by tournament procedure are tested, so that the best 25 become parents of the next generation. Offspring are generated from the best 25 parents via the Gaussian mutation procedure, with no crossover (exchange of k1 and k2 values between individuals) allowed. The variance of the Gaussian mutation is reduced over the 50-generation run [29; 30; 31].
# Seismic Responses of a Cable-Stayed Bridge with Consideration of Uniform Temperature Load
## 6 Conclusions  
The main purpose of this article is to analyze the effects of uniform temperature on the seismic responses of a cable-stayed bridge. A three-dimensional finite element model was constructed for nonlinear dynamic time history analysis of the cable-stayed bridge. The nonlinear behaviors, such as geometry nonlinear, material nonlinear, and \(p-\Delta\) effect, were considered in the finite element model. A case study was performed to investigate the effects of the temperature changes on the seismic responses of the bridge. Based on the numerical research, the main results are as follows:  
(1) The seismic responses of the bridge are significantly increased with the consideration of temperature load. They almost linearly increase with the temperature changes of \(\pm\)20 \({}^{\circ}\)C and \(\pm\)35 \({}^{\circ}\)C. The result indicates that the effects of temperature load on the seismic responses of the bridge should be considered during the design phase of cable-stayed bridges.  
(2) The connections between the deck and the pylons have significant effects on the seismic responses of the bridge with and without temperature load. It is also found that the responses of the critical components in Connection 3 (deck to pylon is restrained in longitudinal direction) are much larger than those in other connections. Therefore, Connection 1 (deck to pylon is connected by bearing) or Connection 2 (deck to pylon is not connected) can be selected in practice with the consideration of both earthquake and temperature loads.  
This paper only investigates the seismic responses of a prototype bridge under thermal effects with a small number of numerical analysis. More analysis will be conducted to generalize the effects of uniform temperature on the seismic responses of bridges in the future.  
**Acknowledgments:** The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by State Key Laboratory of Disaster Reduction in Civil Engineering under Grant No. SLDRCE14-B-14; the National Natural Science Foundation of China under Grant Nos. 51478339, 51278376, 51608161, and 91315301; the National Key Technology Research and Development of the Ministry of Science and Technology of China under Grant No. 2015 BAK17B04; and the Science Technology Plan of JiangXi Province under Grant No. 20151 BBG70064. All support is gratefully acknowledged.  
**Author Contributions:** [PERSON] mainly proposed the problem that the effects of the uniform temperature on the seismic responses should be considered during design phases of cable-stayed bridges. [PERSON], [PERSON], and [PERSON] established the finite element model of the prototype bridge. All of the authors did the simulation analysis, and discussed the results, as well as contributed to the paper writing work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Color Point Cloud Registration Algorithm Based on [PERSON]
## 3 Algorithm Implementation  
We designed a color point cloud registration algorithm that is suitable for different lighting conditions. The algorithm process is shown in Figure 3. The input of the algorithm is two point clouds, the target point cloud P and the point cloud Q. The initial part of the algorithm consists of three steps: color space conversion from RGB to hue for P and Q, color information continuation, and depth information continuation only for point cloud P. The main content of the algorithm is a loop. Firstly, apply the current transformation to Q and get a new point cloud Q'. Then, the corresponding point pairs of P and Q' are determined using the nearest point principle [7]. Then, calculate the error function in the current iteration and if the error function is smaller than the preset threshold, the iteration stops, and the current transformation is output. Otherwise, the current transformation is updated with gradient descent and the iteration continues.  
Figure 2: The SSD of gray and hue varying with different lighting conditions.
# Swarm Robotics: A Perspective on the Latest Reviewed Concepts and Applications
## 6 Applications Using Swarm Robotics
### Farming  
Another relevant area for the application of swarm robotics is farming [142; 151]. These applications can be used for various tasks, such as inspection and mapping [152; 153]. These techniques also apply to seeding [154], cereal harvesting [155], plant care [156], weeding [157], and so on. [PERSON] et al. [152] developed a roadmap to implement a swarm robotic solution for weed detection and control in the field. They propose the usage of UAVs and computer vision to monitor and identify weeding within a farm environment. They performed bench tests that indicate the feasibility of their proposal. [PERSON] et al. [153] study the usage of UAVs for crop inspections in farming. They overview a set of features, displaying advantages and disadvantages of different UAV models in these tasks performance. [PERSON] et al. [154] present a platform for seeding tasks in fields. This robot has the typical swarm robotics features, with a minimal approach on sensors and computational power to remain operative. The task's intelligence comes from a centralized entity, which also connects the robots to cloud services. They performed tests both on simulated environments and with real-world prototypes. [PERSON] et al. [155] approach a novel swarm robotics system for autonomous cereal harvesting. They proposed an architecture with decentralized control to reach higher flexibility, scalability, and overall robustness. Their testing was performed in a simulated environment to probe the effect of various parameter changes in the system's performance. [PERSON] et al. [156] propose a novel robot-basedsystem for plant care. Through their process, they evaluate such appliance constraints, raising the steps of the robot creation process. Finally, they developed a modular robot prototype for testing the concepts in a fertilization task from winter wheat.