esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Simulation Studies of QR-Code Tracking
Figure 14:7 BER performance over the distance between the vehicles. The BER performance in the presence motion blurriness is evaluated with and without the particle filter as in Figure 14:8 It is observed that the algorithm fails to detect the finder pattern of the QR code beyond the blur ratio of 0.025. This leads to a loss of communication and increases the BER to one. However, the use of the particle filter can be compromised for this mission detection. It can predict the likelihood of these finder patterns using their past or initial conditions. This enables reliable communications with minimal BER. As the blur increases, the intensity of LEA degrades, leading to a gradual increase in BER. Though the particle can predict the LEA location, degradation in the LEA intensity at higher blurriness leads to communication loss. The proposed QR-LEA pattern is fabricated and installed in the rear side of an actual car. The images are acquired from the trailing vehicle in the presence of the translation, rotation, and scaling variance. The range of translation is confined to the FOV of the camera, and the rotational angle is limited to 15-degrees. To ensure continuous detection, the position of the camera is adjusted to set the scaling factor above 0.5. The performance of the proposed tracking algorithm is evaluated in a daylight condition with geometric noise introduced by the ambient outdoor environment. The proposed technique detects all the possible locations of the finder pattern that satisfies the finder pattern ratio. Unlike the synthetic image, it is observed that false finder patterns are observed (ref Figure 23b). However, the geometric constraint of the QR code enables reordering of the finder pattern eliminating the false detection and capable of accurately tracking the QR code. Accurate detection of the finder pattern enables easy identification of the alignment pattern, which can be used to retranslate the QR-LEA. This makes the proposed algorithm efficient to track the QR-LEA admits camera non-linearity and color mixing. Figure 14:9 Blurred image. (**a**) Finder pattern, (**b**) data pattern of one, (**c**) data pattern of zero. Figure 12:0 BER performance in the presence of motion blurriness.
## 2 Related Work
### Model Merging  
Model merging is an emerging technique designed to integrate the parameters of multiple models, each fine-tuned for distinct tasks or data distributions, into a single high-performance model without requiring a complete retraining cycle [14; 15]. Unlike conventional ensemble methods [33], which combine the outputs of multiple models during inference, leading to increased computational costs and memory usage, model merging consolidates the learned representations into one model. This consolidation offers significant advantages in terms of efficiency, inference speed, and storage.  
One straightforward and commonly used model merging approach is parameter averaging [34; 35]. However, more advanced techniques, such as weighted averaging, assign different weights to models based on their relative contribution or performance regarding the target task [36; 37]. The success of these techniques typically stems from observations indicating that fine-tuned models originating from the same pretrained initialization tend to occupy similar regions within the loss landscape, thereby facilitating effective linear interpolation [38].  
A critical concept in this domain is the task vector [39], defined as the difference between the parameters of a fine-tuned model and those of the original pretrained model. Task vectors are considered to encapsulate the specific knowledge acquired during fine-tuning. By performing arithmetic operations on these vectors (e.g., addition or subtraction), the specialized skills of different models can be effectively manipulated or combined. Despite its promise, task vector manipulation is susceptible to interference when merging multiple vectors, and recent studies have proposed strategies to mitigate these effects [40; 41].  
Beyond simple parameter averaging, some studies have investigated layer-wise merging techniques that aim to independently align and integrate model parameters at each layer [42; 43]. These methods offer more granular control over the merging process, potentially better preserving the specialized knowledge of individual models. However, most existing research on model merging evaluates the integrated model in a single merging round rather than a continuous update framework.  
Our study addresses this gap by adopting a continual pretraining strategy in which the base model undergoes cycles of fine-tuning and merging [16]. This iterative approach provides insights into the robustness and stability of model merging over time and highlights the challenges of maintaining consistent performance across multiple update cycles. In particular, we examine the sensitivity of merging outcomes to hyperparameter settings and propose dynamic control mechanisms to mitigate the risk of performance degradation over successive rounds.
# COALA: A Protocol for the Avoidance and Alleviation of Congestion in Wireless Sensor Networks
## 4 Proposed Protocol Evaluation
### Simulation Process  
The simulation environment creates a number of user defined nodes, randomly positioned in a user defined geographical area of square shape. The node that is created first plays the role of the unique sink. The user defines the minimum node transmission range that enables two nodes have direct communication and thus be considered as neighboring nodes. The software environment assures that for every individual network node there is at least one routing path from this node towards the sink.  
In every single simulation test, several different topologies are created. For every topology, a number of simulation runs is applied. During simulation tests, events are created in arbitrary positions and random time instances and each event range, i.e., a cyclical area that surrounds the event position, was supposed to be varying. Each node, located within an event range, is supposed to sense the occurrence of this event and has to transmit corresponding event notifications to the sink. Since multiple nodes may serve the same event, the sink may receive multiple notifications for the same event. Each event notification is accompanied by a corresponding event message header. This event header contains a time stamp that denotes the time of its generation.  
As the event range increases from an initial value to a maximum value, the number of the nodes that are located within the area where the specific event takes place increases too. Subsequently the number of the notification data sent to the sink also arises.  
The initial energy of every individual node is considered to range between a high and a low limit value, which are user defined. This condition has been set in order to conform to the fact in real WSNs applications the network nodes have different energy reserves. Additionally, the dissimilarity in the energy levels of the sensor nodes allows simulation tests to demonstrate better the ability of COALA protocol to achieve energy efficient performance.  
The configuration parameters along with their values are summarized in Table 2.
## 3 Results and Discussion
### Gold Adsorption from Chloride Media
#### 3.3.3 Influence of the Carbon Nanotubes Dosage  
A series of experiments were performed using aqueous solutions containing 0.005 g L\({}^{-1}\) gold(III) and 0.1 M HCl, whereas the carbon nanotubes dosage was varied from 0.05 to 0.5 g L\({}^{-1}\), in order to investigate the influence of this variation on gold(III) adsorption. Table 6 shows the variation of the percentage of gold adsorption and metal loading for different carbon nanotubes dosages. This shows that the increase of the amount of carbon nanotubes added to the solution increased the percentage of metal adsorbed onto the nanotubes.
## 3 Results
### Temporal Effect of Accuracy Correction  
The temporal accuracy of the estimations is illustrated in Figure 9 for Area 20,667.  
Here, the estimation and validation results of each quarter-hour time window for each test week were saved, and the average relative errors were calculated accordingly. Figure 9 illustrates the average estimation error of the network with the developed algorithm and correction. This correction may increase the average error for the entire supply areas but keeps it below 1% for all four cases, thus eliminating very erroneous estimations and reducing the deviation of the absolute value of errors per bus. More detailed examination of the correction method, especially the proper identification of the bus, which is to be used for the correction, will be carried out by the authors.
# A History of Channel Coding in Aeronautical Mobile Telemetry and Deep-Space Telemetry
## 1 Introduction  
Telemetry refers to the process of obtaining physical measurements and transmitting those measurements to another place for recording, display, and analysis. In aeronautical mobile telemetry, the measurements are obtained on an airborne test article and transmitted to the ground using an RF link. The data are used by flight test engineers to assess the performance of the airborne test article. In deep-space telemetry, the measurements are obtained on a space probe and include internal engineering measurements and scientific measurements. The measurements are transmitted to Earth using an RF link over enormous distances. The engineering data are used by engineers to monitor to health of the space probe; the science data are used by scientists to explore a variety of planetary and cosmic phenomena.  
Aeronautical mobile telemetry emerged first. Deep-space telemetry had to wait until technological advances made it possible to send a probe into interplanetary space. The two telemetry systems were initially very similar. Over time, differences have emerged as the constraints placed on the two telemerties pushed them in different directions. Despite the fact that deep-space telemetry arrived on the scene after aeronautical mobile telemetry, deep-space telemetry adopted channel coding decades before aeronautical mobile telemetry.  
This paper tells the story of channel codes in both telemetry applications. To limit the page count, the scope of this article is restricted to the use of channel codes in the RF links. Important contributions of coding to recording using magnetic tape and solid-state memory are not described in this article. In addition, the use of channel coding in Earth satellites is also not described. These histories will have to wait for another article to be written.  
This article is organized as follows. After reviewing the early history and definitions in Section 2, the history of channel coding in deep-space telemetry is told in Section 3. The history of channel coding in aeronautical mobile telemetry follows in Section 4.  
In the description of the channel codes, the following notation is used: An \((n,k)\) block code maps \(k\) information bits to \(n\) coded bits where \(k<n\). The block length of the code is \(n\), the dimension of the block code is \(k\), and the code rate is \(k/n\). A rate-\(r\) constraint length \(K\) convolutional code is denoted by the \((K,r)\) convolutional code.
## 3 Frequency Response Calculation of the CLD Plate Considering Two Kinds of Damping
### Derivation of Dynamic Equation of the CLD Plate Under Base Excitation  
Figure 2a shows the cantilever thin plate attached with CLD on one side, and the partial cross section of the composite plate is shown in Figure 2b. Symbols \(H_{\mathrm{v}}\), \(H_{\mathrm{b}}\), and \(H_{\mathrm{c}}\) represent the thickness of the viscoelastic core layer, metal substrate, and constraining layer, respectively. Subscript v, b, and c denote the viscoelastic core layer, metal substrate, and constraining layer, respectively.  
For the viscoelastic composite structure, the equation of the motion in the frequency domain can be written as:  
\[\Big{(}-\omega^{2}\mathbf{M}+\mathrm{i}\omega\mathbf{C}_{1}+\mathrm{i}\mathbf{C}_{2}( \omega)+\mathbf{K}(\omega)\Big{)}\mathbf{X}=\mathbf{F} \tag{3}\]  
where, \(\mathbf{M}\) is the mass matrix of the composite structure, \(\mathbf{C}_{1}\) is the remaining equivalent viscous damping matrix, \(\mathbf{C}_{2}(\omega)\) is the material damping matrix, \(\mathbf{K}(\omega)\) is the stiffness matrix, \(\mathbf{X}\) is the response vector, and \(\mathbf{F}\) is the excitation force vector. Two kinds of damping are contained in Equation (3), while, for most researches about the modeling of the viscoelastic composite structure, only the material damping was considered.  
Traditionally, the stiffness matrix \(\mathbf{K}(\omega)\) and the material damping matrix \(\mathbf{C}_{2}(\omega)\) can form the complex stiffness matrix, that is:  
\[\mathbf{K}^{*}(\omega)=\mathbf{K}(\omega)+\mathrm{i}\mathbf{C}_{2}(\omega) \tag{4}\]  
The base excitation vector can be further expressed as:  
\[\mathbf{F}=-\mathbf{M}\mathbf{G}\tilde{\mathbf{U}} \tag{5}\]  
Figure 2: Cantilever thin plate attached with CLD. (**a**) Viscoelastic composite plate; (**b**) Cross section.  
where, \(\vec{U}\) is the acceleration of base excitation, \(G\) is the influence coefficient vector, and the dimension of the influence coefficient vector is consistent with the degree of freedom of the vibration system. For the degree of freedom which is in the direction of base excitation, the corresponding element in the influence coefficient vector is 1, and the other elements are 0. Equation (5) indicates that the vibration of the viscoelastic composite structure is produced by the inertia force of the base excitation, and the level of the force is related to the mass of the composite structure and the level of the base excitation.
# User Identity Linkage across Social Networks by Heterogeneous Graph Attention Network Modeling
## 4 Methods
### User Identity Linkage  
In this subsection, we use a multi-layer perceptron to predict user identity linkage. Give two social networks \(\mathcal{G}_{s},\mathcal{G}_{t}\) and two user nodes \(v_{i}\in\mathcal{G}_{s},v_{j}\in\mathcal{G}_{t}\). Let \(h_{i},h_{j}\) denote the user-level attention layer aggregated vectors of \(v_{i},v_{j}\), respectively. First of all, to improve the training speed and avoid the gradient disappearing, the batch normalization [28] is applied to normalize \(h_{i},h_{j}\) respectively:  
\[h^{\prime}_{i}=\text{BN}(h_{i})\text{ and } \tag{17}\]  
\[h^{\prime}_{j}=\text{BN}(h_{j}), \tag{18}\]  
where BN is the batch normalization layer. The input of the multi-layer perceptron is \(x=[h^{\prime}_{i}\|h^{\prime}_{j}]\) and \(\|\) is the concatenate function. The \(l\)-th layer of the neural network is defined as:  
\[y^{l}(x)=\text{ReLU}(W^{l}\cdot y^{l-1}(x)+b^{l}), \tag{19}\]  
where \(W^{l},b^{l}\) are the \(l\)-th layer parameters, \(\text{ReLU}(a)=\max{(0,a)}\) is the linear rectification function, and \(y^{0}(x)=x\). Finally, the 1-dimension vector \(\hat{y}\in[0,1]\) is the output by the neural network as the predicted value of user identity linkage:  
\[\hat{y}_{ij}=\sigma(W\cdot y^{l}(x)+b), \tag{20}\]  
where \(\sigma\) is the sigmoid function, \(W\in\mathbb{R}^{d\times 1}\) is the weight matrix, \(d\) is the dimension of \(y^{l}(x)\), and \(b\in\mathbb{R}^{1}\) is the bias vector. Then the loss of the user identity linkage is:  
\[L_{\text{UIL}}=-\sum_{v_{i}\in\mathcal{G}_{s},v_{j}\in\mathcal{G}_{t}}y_{ij} \log{(\hat{y}_{ij})}+(1-y_{ij})\log{(1-\hat{y}_{ij})}, \tag{21}\]  
where \(y_{ij}\) is the ground truth of user identity linkage pair \((v_{i},v_{j})\).
# Factors Influencing the Effects of Triticale on Laying Hens' Performance: A Meta-Analysis
## 3 Results
### Impact of Triticale Percentages on Performance of Laying Hens  
The meta-regression test (Tables 4 and 5) was conducted according to the tritticale inclusion percentages in groups of tritticale and laying hens' strains with significant heterogeneity levels (I\({}^{2}>50\%\)) to find out if it could influence the performance of laying hens. The meta-regression regarding the impact of tritticale percentages in tritticale strains suggested that the regression models were not significant for the EP, FI, and FCR (Table 4). \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables 1**} & \multicolumn{4}{c}{**Random-Effect Model 2**} & \multicolumn{4}{c}{**Heterogeneity 3**} & \multirow{2}{*}{_p-Value_} \\ \cline{2-3} \cline{5-10}  & & \multicolumn{4}{c}{**95\% CI**} & & & \\ \cline{2-3} \cline{5-10}  & **k** & **SMD** & **Lower** & **Upper** & & & & & \\ \hline \hline
**EYC** & & & & & & & & & \\ _Leghorn_ & \(7\) & \(-9.3294\) & \(-12.4773\) & \(-6.1814\) & \(91.0\) & \(16.5335\) & \(4.0661\) & \(334.41\) & \(3\) & \(<\)0.0001 \\ _Hy-Line_ & & & & & & & & & \\ _Brown_ & \(4\) & \(-4.8350\) & \(-7.4031\) & \(-2.2668\) & \(97.9\) & \(6.7380\) & \(2.5958\) & & \\ _Dekalb XL_ & \(1\) & \(-15.4948\) & \(-17.0853\) & \(-13.9044\) & \(-\) & \(-\) & \(-\) & & \\ _Lohmann_ & \(1\) & \(-0.6231\) & \(-1.0968\) & \(-0.1495\) & \(-\) & \(-\) & \(-\) & & \\ \hline \hline \end{tabular}  
* **EP**: Egg production; **EW**: Egg weight **FI**: Feed intake; **EYC**: Egg yolk color. 2 k: Study number; SMD: Standard mean difference. 3 12 F: Higgins statistic; Q: \(\chi^{2}\) statistics; \(\pi^{2}\): Heterogeneity variance of the true effect sizes; \(\pi\): Standard deviation of the true effect sizes; df: Degree of freedom.  
\end{table} 
Table 3: _Cont._On the other hand, a significant effect of triticale percentages was noticed on EW in the group of Juanilo strain (Estimate = 0.1292, QM = 4.7301, R\({}^{2}\) = 39.40, \(p\) = 0.0296). In addition, the meta-regression showed a significant effect of triticale percentages on EYC in the groups of Juanilo (Estimate = \(-\)0.1695, QM = 24.7806, R\({}^{2}\) = 89.16, \(p\) < 0.0001) and Joesong (Estimate = \(-\)0.3741, QM = 48.4956, R\({}^{2}\) = 77.84, \(p\) = 0.0011) strains. For the meta-regression on the effect of triticale percentages in laying hens' strains, the results showed that only the EYC was influenced (\(p\) < 0.05) by the triticale inclusion percentages (Table 5). Indeed, EYC was linearly reduced by the constant augmentation of triticale inclusions in diets on Leghorn and Hy-Line Brown strains.
# Thermal Characteristics and Parametric Analysis of an Improved Solar Wall
###### Abstract  
Solar air collectors installed on buildings can significantly reduce conventional energy consumption in winter and summer. However, some problems arise in the utilization process, such as overheating, inconvenient operation control and low energy efficiency, etc. This work is a parametric analysis focusing on the automatic control and thermal efficiency improvement of the solar wall. An improved color-changing solar wall integrated with automatic control components, such as a photoelectric fan and temperature-controlled damper, was proposed in this paper. Based on the experimental data, the average daily heat output of the color-changing solar wall is 1.08 MJ per unit floor area on clear days in winter and the average thermal efficiency is 56.8%. Meanwhile, a quantitative analysis was carried out based on monitoring experiments for evaluating the thermal characteristic of automatic control components. Furthermore, in order to improve the thermal performance of the solar wall, parametric analysis was performed by numerical simulation. Results from this paper can provide a theoretical basis for the application of solar air collectors in modern buildings.  
solar energy; an improved color-changing solar wall; automatic control components; field measurement; parametric analysis +
Footnote †: journal: Applied Sciences
## 4 Experiment and Results  
### Experimental Setup  
In this section, we validate the effectiveness of the proposed deep metric learning-based airport delay prediction method using real-world data. The experiments are conducted with one year (2019) of WAF and air traffic data from Guangzhou Baiyun Airport (ZGGG). Each sample represents an MTWSI constructed from one-hour segments of WAF and air traffic data, along with delay level labels derived according to the classification rules described in Section 3.2. In the following experiments, the dataset is divided into training and test sets in a 7:3 ratio. Additionally, two sets of comparison experiments are conducted. The first compares the prediction accuracy of the proposed method with that of a traditional numerical feature processing approach. The second evaluates how different separation rates in multi-channel images affect prediction accuracy.  
The experiments were performed on a computer equipped with an AMD EPYC 7642 48-core processor, 24 GB RAM, and an NVIDIA RTX A5000 GPU. The Python programming language, version 3.7.9, was used. Deep learning frameworks TensorFlow 2.4.1 and Keras 2.4.3 were used for model construction and training. Data were processed and analyzed using the NumPy 1.19.5 and pandas 1.2.1 libraries. Machine learning tasks were implemented using the scikit-learn 0.24.1 library. The results were visualized using Matplotlib 3.3.4 and Seaborn 0.11.1 libraries. The experiments were conducted on Ubuntu 20.04.  
The predictive model is evaluated using accuracy, precision, recall, F1 score, and Matthews correlation coefficient.  
The raw dataset is highly unbalanced and significantly affects the performance of the classifier. To overcome this problem, this study will use sampling techniques to improve the category distribution and avoid overlap. Sampling techniques include under-sampling, oversampling, and hybrid methods. Currently, popular sampling techniques include the following:  
(1) Under-sampling (US): Random Under-Sampling (RUS), Edited Nearest Neighbors (ENN), and Tomek Links.  
(2) Oversampling (OS) [29; 30]: Random Over-Sampling (ROS), Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive Synthetic (ADASYN).  
(3) Combination (UOS) [29; 30]: SMOTETomek and SMOTEENN  
Figure 9 illustrates the original training dataset and the sampled training dataset generated by various sampling techniques to generate the sampled training dataset. The sampled data are used to train the estimation method, and the original test set is used to validate the performance.  
Figure 8: CNN Architecture.
# Connectivity Maintenance Based on Multiple Relay UAVs Selection Scheme in Cooperative Surveillance
## 2 Problem Definition
### UAV Dynamic Model  
Suppose that each UAV has a reliable flight control system, which effectively controls the aerodynamic surfaces to accurately track velocity and turn rate commands. Considering no wind disturbance, all UAVs fly at a constant altitude to perform tasks. Correspondingly, the UAV kinematic model in two dimensions can be expressed as follow:  
\[\left[\begin{array}{c}\dot{x}\\ \dot{y}\\ \dot{\psi}\\ \dot{v}\\ \dot{\omega}\end{array}\right]=f(\mathbf{x},\mathbf{u})=\left(\begin{array}{c}v\cos \psi\\ v\sin\psi\\ \omega\\ -\frac{1}{\tau_{\omega}}\varphi+\frac{1}{\tau_{\omega}}u_{\varphi}\\ -\frac{1}{\tau_{\omega}}\omega+\frac{1}{\tau_{\omega}}u_{\omega}\end{array}\right) \tag{1}\]  
where \(\mathbf{x}=\left(x,y,\psi,v,\omega\right)^{T}\) is UAV state vector; \(\left(x,y\right)\) denote the inertial coordinates of UAVs; and \(\psi\), \(v\), and \(\omega\) are the heading angle, velocity, and turn rate of UAV, respectively. \(\mathbf{u}=\left(u_{\varphi},u_{\omega}\right)^{T}\) is velocity  
Figure 1: Multiple unmanned aerial vehicles (UAVs) cooperative surveillance with ground station.  
and turn rate control command constrained by fixed-wing UAV dynamic model [26] in Equation (2). \((\tau_{v},\tau_{\omega})\) represent the time constants of actuator delay.  
\[|u_{v}-v_{0}|\leq v_{\max}\ |u_{\omega}|\leq\omega_{\max} \tag{2}\]  
where \(v_{0}\) is UAV cruise velocity. Respectively, \(v_{\max}\) and \(\omega_{\max}\) are UAV velocity and turn rate maximal variation ranges.