esa-sceva/satcom-chunk-collection huggingface dataset:
# CrossDeFi: A Novel Cross-Chain Communication Protocol
## 5 Proposed Work Overview  
The primary goal of this work is to introduce CrossDeFi, a novel DeFi Cross-chain communication protocol designed to enhance interoperability and ensure finality across various DeFi systems. Inspired by existing solutions such as Cosmos and Polkadot, CrossDeFi differentiates itself through unique miner and bridge selection methods, enhanced cross-chain interoperability, and a robust finality guarantee mechanism. The block diagram of the proposed CrossDeFi is shown in Figure 2.  
Firstly, the proposed protocol integrates an advanced bridge hub that supports various types of bridges, enhancing cross-chain interoperability in terms of speed and reducing transaction fees. Additionally, CrossDeFi introduces novel cryptographic techniques in asset-transfer confirmation to ensure a high level of transaction finality, thus preventing financial losses and improving user experience.  
The CrossDeFi achieves seamless interoperability and ensures a higher level of finality guarantees by integrating two mechanisms: miner and bridge selection (MBS) and improved transfer confirmation (ITC). MBS selects optimal miners and efficient bridges based on multi-criteria information, enhancing cross-chain adaptability across different DeFi applications. ITC introduces a novel bi-directional interaction strategy that ensures asset transfer across multiple DeFi systems with minimal cost and high security.
# Numerical Investigation of the Hydrodynamic Characteristics of 3-Fin Surfboard Configurations
## 3 Results and Discussion
### Optimisation Results  
Figure 12 demonstrates the optimised fin design successfully delays the major lift loss to a later AOA. For the optimised set, the lift loss between 30 to 40\({}^{\circ}\) is similar to the baseline template's drop between 20 and 30\({}^{\circ}\). The overall maximum \(C_{L}\) is lower for the optimised design, but the lift drop has been reduced from the already improved 16.7% for the large-rake template to only 9.4% across 20 to 30\({}^{\circ}\). As with the \(C_{L}\), the optimised set has pushed the \(C_{D}\) trend breakpoint to the later 30\({}^{\circ}\) mark.  
As the optimised design includes these large-rake side fins, the wake turbulence from the side fins in the optimised set in Figure 13c is closely comparable to the full set (b). However, at the interaction between the centre fin and the side fin, the turbulence from the optimised design has a lower intensity compared to the large-rake and baseline designs. The turbulence from the baseline and large-rake designs also indicates the centre fin has fully separated flow across the low-pressure face of the fin. However, the optimised centre fin exhibits on-body flow at the same point.  
Figure 10: Turbulent kinetic energy in the wake region of rake fin designs (y = 50 mm). (**a**) Small-rake at 20\({}^{\circ}\); (**b**) large-rake at 20\({}^{\circ}\); (**c**) small-rake at 30\({}^{\circ}\); (**d**) large-rake at 30\({}^{\circ}\).  
As shown in the template results, a turbulence reduction would decrease the lift loss and, therefore, improve the hold and control of the fins. The large-rake template produced the most gradual lift loss after the stall point, making it the clear choice of template for this goal. However, the interaction between the side fin turbulence and the centre fin seemed to cause issues with this design. To improve the performance of the centre fin, the trailing edge curvature was decreased. This was done as the large-base results indicated this template had much lower areas of separation on the centre fin near the board connection (where the area was at maximum) but similar separation towards the tip. Another change to the centre fin was slightly lowered depth. This aimed to keep the centre fin out of the intense  
Figure 11: Baseline vs. large-depth vs. small-depth. (**a**) Lift and drag coefficients; (**b**) lift and drag forces.  
upstream tip vortices produced by the side fins. The success of the design can be seen in the results for both coefficient plots and turbulence contours as the fins successfully reduced the turbulence (Figure 13) and had a significantly reduced lift loss after the stall point (Figure 12). However, the drag of the fins has also been increased, meaning there could be greater losses in velocity should the surfer remain in these angle ranges for longer periods.
## 2 Test Materials and Test Methods
### Sample Preparation  
For the CS-SS samples, the properties of the cemented soil were controlled by the water content of the in situ soft soil, cement content, and water-cement ratio. The preparation of the CS-SS samples must strictly ensure that the properties of the cemented soil in the same test group remain unchanged. The soft soil was dried and crushed and passed through a 0.075 mm sieve. When preparing the samples, according to the test scheme, the corresponding masses of the dried soft soil particles, sand particles, cement, and water were weighed. The dry cement powder and sand particles were mixed into the dried soft soil particles first for thorough mixing, and they are placed into the cement mortar mixer. The appropriate amount of water was mixed with them thoroughly to ensure the sand particles were evenly distributed in the cemented soil to form the mixed slurry of the CS-SS samples. A clean three-flap mold was used and a thin layer of petroleum jelly was smeared on its inner walls. The mixed CS-SS slurry was evenly loaded into the mold three times. Each time after loading the mixed slurry, the mold was placed on the shaking table for 2 min vibrating to remove the air bubbles in the mixed slurry. Once the mixed slurry loading procedure was completed, it was covered with a plastic top cap, wiped with a damp cloth, and pasted with a test group label. After a 1 d curing period, the mold was removed and the CS-SS sample was formed. The prepared samples were sealed with plastic film and placed in a curing box with the temperature fixed at 20 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C and relative humidity greater than 95% until the design curing age. The dimensions of the sample were 39.1 mm in diameter and 80 mm in height.
# The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
## 7 Challenges and Limitations of AI Chatbots in Infectious Disease Outbreaks
### Ethical and Accountability Concerns  
The deployment of AI chatbots in healthcare during crises raises ethical and accountability concerns, as users may over-rely on them for medical advice, potentially leading to self-diagnosis and the avoidance of professional consultation [129]. When chatbots provide incorrect or misleading information, determining liability is complex, as the advice comes from an AI system rather than a human. Current frameworks do not sufficiently address these issues, leaving gaps in legal and ethical responsibility [130].  
While AI chatbots offer scalable solutions for disseminating health information, they also pose risks such as misdiagnosis and data privacy breaches. Users tend to trust these systems, which increases the likelihood of over-reliance on chatbot advice [131]. Balancing the benefits of widespread access to health information with the potential harms requires a comprehensive approach that includes technical and socio-technical measures.  
On the technical side, ensuring transparency in chatbot decision making is fundamental for building trust. Chatbots should provide clear explanations for their recommendations, and safety protocols should be in place to alert users to seek professional help when necessary [132]. Protecting user privacy through compliance with regulations like GDPR is also essential to maintain confidentiality and secure sensitive data [133].  
Socio-technical interventions are equally important. Ethical guidelines tailored to AI in healthcare must be integrated into chatbot design to ensure patient safety and fairness [134]. Educating users about the limitations of AI chatbots can prevent over-reliance and reinforce the importance of consulting healthcare professionals when needed [135]. Clear accountability frameworks should define the responsibilities of AI developers and healthcare providers to ensure ethical compliance and legal clarity [136].  
Research is needed to develop frameworks for determining accountability when AI chatbots cause harm and to explore how users' over-reliance on AI affects their health outcomes [137]. Additionally, protecting user privacy while enabling personalized healthcare remains a critical challenge. Research into advanced encryption and anonymization techniques is essential for ensuring data security while providing personalized health solutions [138].
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 6 Applications: XAI's Impact across Diverse Domains
### Healthcare
#### 6.1.1 Example 1: Enhanced Breast Cancer Prognostication  
An illustrative case highlighting the pivotal role of XAI in healthcare is the work of [PERSON] et al. [72]. They developed an ML approach utilizing biochemically-inspired algorithms to predict outcomes related to hormone and chemotherapy for breast cancer patients. Their predictions are rooted in extensive data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) study. Through this XAI-based model, healthcare providers gain a deeper understanding of the factors contributing to these predictions, ultimately improving the precision and personalization of breast cancer treatment.
## 3 Modelling of CRPDC
### Modelling of Capacitive Reservoir Circuit
#### 3.1.2 Discharging Modelling of CRPDC  
The equivalent resistance and inductance of the proportional valve coil (\(R_{4}\), \(L_{1}\)), the energy-storage capacitance (\(C_{1}\)), and the current acquisition resistance (\(R_{5}\)) make up the equivalent discharging model of CRPDC, as shown in Figure 3d. Based on Kirchhoff's Voltage Law (KVL), the voltage relationship of the equivalent discharging circuit can be written as:  
\[u_{R4}+u_{R5}+u_{L}=u_{C} \tag{12}\]  
where \(u_{R4}\) is the voltage of resistance (\(R_{4}\)), \(u_{R5}\) is the voltage of resistance (\(R_{5}\)), \(u_{L}\) is the voltage of inductance (\(L_{1}\)), and \(u_{C}\) is the voltage of capacitance (\(C_{1}\)).  
The main current \(i\) can be expressed as:  
\[i=-C_{1}\frac{du_{C}}{dt} \tag{13}\]  
With the main current in Equation (13), the voltages of resistances (\(R_{4}\), \(R_{5}\)) and inductance (\(L_{1}\)) can be derived as:  
\[u_{R4}=iR_{4}=-R_{4}C_{1}\frac{du_{C}}{dt} \tag{14}\]  
\[u_{R5}=iR_{5}=-R_{5}C_{1}\frac{du_{C}}{dt} \tag{15}\]  
\[u_{L}=L_{1}\frac{di}{dt}=-L_{1}C_{1}\frac{d^{2}u_{C}}{dt^{2}} \tag{16}\]  
When the voltage Equation (14) of resistance (\(R_{4}\)), voltage Equation (15) of resistance (\(R_{5}\)), and voltage Equation (16) of inductance (\(L_{1}\)) are substituted into Equation (12), the differential equation for capacitance voltage \(u_{C}\) can be formulated as:  
\[L_{1}C_{1}\frac{d^{2}u_{C}}{dt^{2}}+(R_{4}+R_{5})C_{1}\frac{du_{C}}{dt}+u_{C}=0 \tag{17}\]  
Based on Equation (17), the capacitance voltage \(u_{C}\) can be deduced as:  
\[u_{C}=Ae^{-at}\sin\left(\omega_{d}t+\varphi\right) \tag{18}\]  
where  
\[\alpha=\frac{R_{4}+R_{5}}{2L_{1}} \tag{19}\]  
\[\omega_{0}=\frac{1}{\sqrt{L_{1}C_{1}}} \tag{20}\]  
\[\omega_{d}=\sqrt{\omega_{0}^{2}-\alpha^{2}} \tag{21}\]  
When the capacitance voltage Equation (18) is substituted into Equation (13), the main current \(i\) can be rewritten as:  
\[i=C_{1}Ae^{-at}[\alpha sin(\omega_{d}t+\varphi)-\omega_{d}cos(\omega_{d}t+ \varphi)] \tag{22}\]By substituting the initial voltage \(u_{\text{C0}}\) of capacitance (\(C_{1}\)) and the initial current \(i_{L0}\) of inductance (\(L_{1}\)) into the capacitance voltage Equation (18) and the inductance current Equation (22), respectively, the phase \(\varphi\) and the coefficient \(A\) can be obtained as:  
\[\varphi=cot^{-1}\bigg{(}\frac{\alpha}{\omega_{d}}-\frac{i_{L0}}{C_{1}u_{\text{C 0}}\omega_{d}}\bigg{)} \tag{23}\]  
\[A=\frac{u_{\text{C0}}}{sin\varphi} \tag{24}\]  
Therefore, while the CRPDC is switched to be in discharging circuit mode, the phase \(\varphi\) and the coefficient \(A\) can be determined by Equations (23) and (24), respectively. The following states of capacitance voltage \(u_{\text{C}}\) and main current \(i\) can be calculated by Equations (18) and (22), respectively.
# A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification
## 2 Materials and Methods
### Methods
#### 2.2.3 Modifications in Network Architecture  
All pre-trained models employed in this research were from the Tensorflow v2.10.0 library [56] which were pre-trained using the ImageNet dataset [49] on Google servers. The top layer of each of these previously trained models was removed while the underlying layers were frozen, and no training was performed on these layers. Then, we added a few new, trainable layers on top of the frozen layers. By training only these new layers, the models learned to turn the old features into predictions on the EuroSAT dataset. Table 1 highlights new layers added on top of the previously trained models' frozen layers. The first layer after the frozen layers is Global Average Pooling 2D, which involved the global average pooling calculation for spatial data [57]. The next layer, Flatten, squeezes the input data to a single dimension rather than two dimensions [56]. After this layer, there is the first Dense layer which is a fully connected layer [56] with ReLU activation [58]. The size of the dense layer before the output dense layer was chosen at 128, 256, 1024, and 4096. We tested all of these sizes on each model and kept the best one only in each case. The final layer is another Dense layer which is used in conjunction with softmax activation [56; 59] for the output of the classification.
## 5 Conclusions
### Limitations  
It should be noted that the presented methodology has a few limitations. First, it is more suitable for SCRaMs that have smooth continuous bodies to facilitate the ACTR's motion. Additionally, the presented design employs an external actuator between the manipulator's tip and the ACTR, which represents the tendon's second attachment point. This arrangement may not be suitable for applications requiring interaction with the manipulator's body. Finally, it should be noted that the current method may reduce the dexterity of multi-segment SCRaMs; hence, it is more suitable for applications requiring a volumetric workspace with a low dexterity.
## 3 Results  
The mean, SD, and \(p\)-Value of the 3D deviation of the measurements were shown in Table 2. For the angular deviation, the mean deviation was not statistically significant among all technologies. Considering the 3D deviation at the entry point, PolyJet achieved a lower mean of the deviation than that of the other groups. Lastly, the lowest deviation of the 3D offset tip was produced by PolyJet. There was a statistically significant difference between PolyJet and DLP (\(p\) = 0.007), but no significant difference between PolyJet and SLA was found.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **DLP** & **PolyJet** & **SLA** & \\  & **(n = 10)** & **(n = 10)** & **(n = 10)** & \\ \hline
**Measurement** & & **Mean \(\pm\) SD * & & \(p\)-Value** \\ \hline Angular Deviation (degree) & 2.47 \(\pm\) 0.72 & 2.54 \(\pm\) 0.70 & 2.3 \(\pm\) 0.61 & 0.72 \\ Deviation at entry point (mm) & 1.87 \(\pm\) 0.25 a & 1.48 \(\pm\) 0.07 b & 1.66 \(\pm\) 0.15 a & \textless{}0.001 ** \\ Deviation at apex (mm) & 2.03 \(\pm\) 0.26 a & 1.72 \(\pm\) 0.12 b & 1.86 \(\pm\) 0.22 a, b & 0.01 ** \\ \hline \hline \end{tabular}  
* Different superscript between 2 groups within the same measurement means that the difference is statistically significant. (\(p\) < 0.05). ** \(p\)-Value \(<\) 0.05.  
\end{table}
Table 2: The comparison of Mean \(\pm\) SD of the 3D deviation among DLP, PolyJet, and SLA 3D printers.  
Figure 5: Boxplots of mean 3D deviation: (**a**) Angular deviation; (**b**) 3D deviation at entry point; and (**c**) 3D deviation at apex. The asterisk represents whether the difference of a measurement between any 2 groups is statistically significant (\(p\) < 0.05).  
In terms of manufacturing speed, DLP clearly demonstrated the fastest printing time of 22 min, followed by Polylet and SLA with the printing time of 248 and 337 min, respectively. When accounting for the post-processing time, the overall processing time rankings remained unchanged (Table 3).
# Performance Evaluation of Direct-Link Backhaul for UAV-Aided Emergency Networks
## 3 The Architecture of Backhaul for UABS in Emergency Networks  
### Backhaul Architecture  
We proposed a general architecture for UAV-aided networks as illustrated in Figure 1. It is based on the architectures described in [7; 10; 12; 15; 21]. The access network, indicated in orange, uses the 2.6 GHz frequency and connects the users to the UABSs through 3G LTE femtocell base station technology. This provides essential communication like voice, text and limited data in case of a disaster. A detailed explanation of the access network in this architecture could be found in [5]. Understanding the capacity requirements of the access network, as well as the restrictions in emergencies, is vital to determine the appropriate backhaul network. We propose a backhaul link configuration called direct link. In this backhaul configuration, the UABSs have a direct wireless connection with the CN, as shown by the blue arrow. This configuration is similar for the BSs of the terrestrial network, where microwave links are typically used [27] and are therefore considered as out-of-band backhaul links having the advantage of minimal or none interference with the access network. The direct link uses long term evolution (LTE) release 11, which can manage carrier aggregation. The usage of a known technology like this, aids in the management of resource allocation parameters used in this architecture, defining the UAV like a flying user in an LTE network. We proposed to use two different frequency bands namely the 3.5 GHz and 60 GHz bands that will be described in the next subsection. The radio frequency parameters are also described in Section 4.2.  
Our architecture used LTE-advanced time division duplexing (TDD) in the backhaul network. This configuration aids in the usage of TDD time synchronization that will minimize intra interference in the proposed architecture [28]. Moreover, because our study assumes that all the land-based cellular network is offline; further inter-cell interference is ruled out too. Hence, no interference is considered in our architecture.  
Figure 1: Direct backhaul scenario for an emergency network using unmanned aerial base stations (UABSs).
# Second-Order Text Matching Algorithm for Agricultural Text
## 5 Experiments
### Experimental Setup
#### 5.1.3 Experimental Metrics  
In the field of text matching, the retrieval accuracy and efficiency of an algorithm or model are usually considered as very important attributes. We evaluate the retrieval accuracy using the Precision@K (\(P@K\)) metric. \(P@K\) is an evaluation metric that measures the ratio of the number of correct predictions considering only the first \(K\) items in a given list of actual and predicted categories to the first \(K\) items of each predicted category. Equation (2) gives the formula for \(P@K\).  
\[P@K=\frac{TP@K}{TP@K+FP@K} \tag{2}\]  
where \(TP@K\) is the number of truly correct results in the first \(K\) results of the prediction and \(FP@K\) is the number of incorrect results in the first \(K\) results of the prediction.  
We use two metrics, average time and coefficient of variation, to measure the efficiency. Average time, is the mean value of the time spent on each query. In this paper, seconds (s) are used as the unit of measurement. We also use average time as the expected time for the algorithm or model to retrieve a problem. Coefficient of variation, also known as dispersion coefficient, is a relative statistical measure of the degree of variation in data. It is used to compare the degree of variation of two or more sample data with different mean values. Typically, the smaller the coefficient of variation, the smaller the degree of variation in the data and vice versa. Equation (3) shows the formula for coefficient of variation.  
\[C.V=(\frac{\sigma}{\mu})\times 100\% \tag{3}\]  
where \(C.V\) is the coefficient of variation, \(\sigma\) is the standard deviation, \(\mu\) is the mean. Standard deviation \(\sigma\) is used to measure the dispersion of the data. The larger the standard deviation, the more scattered the data, i.e., the greater the difference between the data and vice versa. Mean \(\mu\) is used to measure the central tendency of the data. Equation (4) gives the formula for calculating standard deviation \(\sigma\).  
\[\sigma=\sqrt{\frac{\sum\left(x_{i}-\mu\right)^{2}}{n}} \tag{4}\]  
where \(x_{i}\) is the value of each element in the data set, \(\mu\) is the mean of the data set and \(n\) is the size of the data set.
# Carousel Greedy Algorithms for Feature Selection in Linear Regression
## 3 Algorithm Description and Preliminary Experiments  
### Basic Algorithm and Default Settings  
In contrast to the application of CG to linear regression in [1], shown in Algorithm 1, we present a general CG approach to linear regression with a cardinality constraint in Algorithm 2.  
Here, the inputs are:  
\(I\): the set of explanatory variables,  
\(k\): the number of variables we want in the model,  
\(S\): the initial set of variables with \(|S|=k\),  
\(\beta\): the percentage of variables we remove initially,  
\(\alpha\): the number of carousel loops where we have \((1-\beta)|S|\) carousel steps in each loop, and  
\(\gamma\): the number of variables we remove/add in each carousel step.  
The starting point of Algorithm 2 is a feasible variable set \(S\) with order. We drop a fraction of \(\beta\) from \(S\). Then, we start our \(\alpha\) carousel loops of removing and adding variables. In each carousel step, we remove \(\gamma\) variables from the tail of \(R\) and add \(\gamma\) variables to the head of \(R\) one by one according to forward selection. The illustration of head and tail is shown in Figure 1. Each time we finish a carousel step, the best set of variables in terms of RSS is recorded. When carousel loops are finished, we add variables to the best recorded set according to the forward selection criterion one by one until a feasible set of \(k\) variables is selected.  
For a specific linear regression problem, \(I\) and \(k\) are fixed. \(S,\beta,\alpha,\gamma\) are the parameters which must be set by the user. In general, the best values may be difficult to find and they may vary widely from one problem/dataset to another.  
As a result, we start with a default set of parameter values and run numerous experiments. These parameter values work reasonably well across many problems. We present the pseudo-code and flowchart for this simple implementation of a CG approach in Algorithm 3 and Figure 2.  
Figure 1: An illustration of head and tail for \(k=5\), \(\beta=0\).  
```
1:\(I,k\)
2:\(R\)
3:\(S\leftarrow\) the solution produced by forward stepwise regression \(\triangleright\) In the order of selection
4:\(R\gets S\)
5:\(LastImpro=0\)
6:\(RECRSS=\) RSS of \(R\)
7:while\(LastImpro<k\)do
8:\(LastImpro=LastImprove+1\)
9: Remove 1 variable from the tail of \(R\)
10: Add 1 variable to the head of \(R\) according to forward selection
11:if RSS of \(R<RECRSS\)then
12:\(LastImpro=0\)
13:end if
14:end while
15:return \(R\)
```  
**Algorithm 3** Pseudo-code of the default version of carousel greedy we recommend for linear regression with a cardinality constraint.  
In other words, the default parameters are:  
\(S=\) the result of stepwise regression with \(k\) variables,  
\(\beta=0\),  
\(\alpha\) is set in an implicit way such that we have \(k\) consecutive carousel steps without improvement of RSS, and  
\(\gamma=1\).