esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 4 Experiments
### Implementation Details
#### 4.1.3 Optimizer  
The AdamW optimizer [57] is adopted. AdamW proposed weight decay to solve the ineffectiveness of adaptive learning rate optimizer with L2 regularization. In the experiment, the weight decay is 0.0005, and the learning rate is 0.00001. In Table 1, the influence of the learning rate is illustrated.
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 4 Proposed Approach
### Cluster Head Election Strategy
#### 4.2.3 Euclidean Distance from the Node to the Cluster Head  
The Euclidean distance between the node \(i\) and its cluster head is considered. Shortening the distance can increase the probability that the node \(i\) becomes the next cluster head, which is conducive to maintaining the reliability of the entire network structure. We use the symbol \(l_{i}\) to represent the distance index of this node. \(l_{i}\) can be expressed as  
\[l_{i}=\sqrt{\left(x_{i}-x_{H}\right)^{2}+\left(y_{i}-y_{H}\right)^{2}+\left(z _{i}-z_{H}\right)^{2}} \tag{17}\]where \(x_{i}\) and \(y_{i}\) are the horizontal and vertical coordinates of the node \(i\); and \(x_{H}\) and \(y_{H}\) are the horizontal and vertical coordinates of the cluster head. The distance is normalized by  
\[L_{i}=\log\left(\frac{l_{i}}{r}+1\right) \tag{18}\]
# Design of Three-Dimensional Electrical Impedance Tomography System for Rock Samples
## 2 Principle and Methods
\[\Phi=(\Delta\overset{\rightarrow}{d}-\overset{\rightarrow}{jm})^{T}(\Delta \overset{\rightarrow}{d}-\overset{\rightarrow}{jm})+\lambda(R\overset{ \rightarrow}{m})^{T}(R\overset{\rightarrow}{m}) \tag{1}\]  
where \(\Delta\overset{\rightarrow}{d}\) is the difference between observation data and forward modeling data; \(\overset{\rightarrow}{m}\) is the parameter vector of the model at individual nodes; \(\overset{\rightarrow}{J}\) is the Jacobian coefficient matrix; and \(\lambda\) is the Lagrange factor, which can be used to control the smoothness of the model. A larger value of \(\lambda\) can prevent abrupt changes in the model's parameters. Initially, a relatively large value is assigned to \(\lambda\) during the inversion process, and as the number of iterations increases, \(\lambda\) gradually decreases; \(R\) is the roughness matrix and it is expressed as  
\[R= \begin{bmatrix}0&\cdots&\cdots&\cdots&\cdots&0\\ -1&1&0&\cdots&\cdots&\vdots\\ 0&-1&1&0&\cdots&\vdots\\ \vdots&&\ddots&\ddots&\ddots&\vdots\\ \vdots&&&\ddots&\ddots&0\\ 0&\cdots&\cdots&0&-1&1\end{bmatrix}  \tag{2}\]  
The model parameters can be modified using Equation (3):  
\[\Delta\overset{\rightarrow}{m}=(\overset{\rightarrow}{J}\overset{\rightarrow }{J}+\lambda R^{T})^{-1}\lambda\overset{\rightarrow}{d} \tag{3}\]  
Figure 1: Schematic diagram depicting the operation of the instrument. We utilize the root mean square error as a metric to evaluate the similarity between the inversion model and the measured data. The \(RMS\) is expressed as Equation (4):  
\[RMS=\sqrt{\frac{1}{N}\sum_{i=1}^{N}{(\frac{d_{i}-f(\overrightarrow{m})_{i}}{d_{ i}})}^{2}}\times 100 \tag{4}\]  
where \(N\) is the number of observed data points; \({}_{i}\) represents the \(i\)-th observed data value; and \(RMS\) denotes the root mean square error. The inversion process stops when the rate of change of \(RMS\) becomes sufficiently small, satisfying the termination condition.
# Deep Unsupervised Embedding for Remote Sensing Image Retrieval Using Textual Cues
## 2 Proposed Methodology
### Image Representation Using (BiT) Models  
The backbone of image-embedding module is based on BiT models (i.e., BiT-S, BiT-M, and BiT-L) [32]. These BiT models are trained on three upstream datasets with different scales: ImageNet-1k [33] (BiT-S), ImageNet-21k (BiT-M), and JFT-300 M [34] (BiT-L). While ImageNet-1k is a dataset designed for ILSVRC image classification task, which is composed of more than 1.28 M images and 1k classes. On the other hand, ImageNet-21k is a large-scale few-shot dataset that contains 14 M images and 21k classes; it is also called the full ImageNet. The JFT-300 M dataset is a subsequent version of the dataset introduced in [35,36]. It has 300 M real-world images and 18k classes with each image approximately having 1.26 labels, resulting in a total of 375 M labels. Note that these BiT models yield state-of-the-art performances on several benchmark datasets for transfer learning with 928 M parameters.  
BiT models adopt a standard ResNet-v2 [37] architecture with different sizes (i.e., a ResNet-50 (R50 \(\times\) 1), a ResNet-50 that is three times wider (R50 \(\times\) 3), a ResNet-101 \(\times\) 1, a ResNet-101 that is three times wider (R101 \(\times\) 3), and a ResNet-152 that is four times wider (R152 \(\times\) 4)), and some updates. Unlike the standard ResNet architecture, BiT models are based on the idea that two new layers, called group normalization (GN) and weight normalization (WN), supersede batch normalization (BN) in all convolutional layers (Figure 3). Note that there are other normalization methods, such as layer norm (LN) and instance norm (IN), which can be considered as extreme cases of GN. Figure 4 illustrates the various normalization techniques and the relations between BN, LN, IN, and GN.  
Figure 2: Flowchart of the proposed method.  
GN has proven to be effective in many applications, such as detection and segmentation [38] and video classification [39], which makes it a better alternative for BN. It has been proposed with a layer that divides the channels into groups, and then the normalized features in each group according to the mean and the variance of the group. It has been shown that GN is more stable than BN with respect to batch size. This is because in GN, calculations of the batch statistics are inherently avoided. Furthermore, the batch dimension is not exploited in GN and it can be transferred to fine-tuning from pre-training irrespective of batch size changes.  
In this work, we use these BiT models for image representation. In particular, we feed the image as input to the network and extract the corresponding feature representation before the classification layer. Then these feature are further projected and normalized into a low dimensional feature space using a dense layer as shown in Figure 2.
## 3 Results and Discussion
### Design of Experiment (DOE)
#### 3.2.1 Pareto Chart  
The Pareto chart is a useful tool to identify the most important factors that influence the response. For the feed phase (Figure 6), the most influential factor was the interaction between the Aliquat-336 concentration and the temperature, followed by the interaction between the Cr(VI) concentration, the Aliquat-336 concentration, and the temperature, and, finally, the Cr(VI) concentration. [PERSON] et al. [43] reported in their study that 100% of Cr(VI) can be achieved at pH 2 and 2 g\(\cdot\)L\({}^{-1}\) of powdered activated carbon. Their analysis of the Pareto chart shows that quantity-activated carbon was the most influencing factor of the system. Similar outcomes were obtained by [PERSON] et al. [44] using biosorbents based on orange peels, which could be explained by the increase in the number of active sites on the surface.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Run** & **Center** & **[Cr(VI)]** & **[Aliquat-336]** & **T** & \
\begin{tabular}{c} **Yield S** \\ **Experimental (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Yield S** \\ **Predicted (\%)** \\ \end{tabular} & **Residuals** \\
**Order** & **Pt** & **[Cr(VI)]** & **[Aliquat-336]** & **T** &
\begin{tabular}{c} **Yield S** \\ **Experimental (\%)** \\ \end{tabular} & **Predicted (\%)** & **Residuals** \\
1 & 0 & 0 & 0 & 0 & 88.38 & 87.03 & 1.34 \\
2 & 1 & \(-\)1 & \(-\)1 & 1 & 75.08 & 75.64 & \(-\)0.55 \\
3 & 1 & 1 & 1 & 1 & 89.02 & 89.58 &  
From Figure 7, the most important factors influencing the back-extraction efficiency of Cr(VI) in the receiving phase were the NaCl concentration, the temperature, the [NaCl] * [Aliquat-336] interaction, and, finally, the [NaCl] * T interaction. However, [Aliquat-336], the [Aliquat-336] * T interaction, and the [NaCl] * [Aliquat-336] * T interaction had a weak effect on the Cr(VI) back-extraction efficiency.  
Figure 6: Feed phase Pareto chart of standardized effect.  
Figure 7: Receiving phase Pareto chart of standardized effect.
# Track Modulus Assessment of Engineered Interspersed Concrete Sleepers in Ballasted Track
## 2 Background and Theory
A model developed by [PERSON] [28], which is based on [PERSON]'s method and employs [PERSON]'s foundation model, has an advantage over other models because in the upper spring layer, no concentrated reactions or infinite reaction pressure can appear [28]. For this study's comparative analysis of the effect of inserting EIC sleepers in continuous-welded rail timber-sleeper track, a beam on elastic foundation (BOEF) model developed by [PERSON] [29] was employed. This method, which is well-established in the international railway community, is known to be reasonably representative, and allows for determination of the track modulus (i.e., the stiffness of the spring \(k\) per unit length of track) [30]. In this model, a concrete sleeper is represented by a single spring with a constant \(\kappa\), while the timber sleepers are represented by a Winkler base with a track modulus \(k\) (Figure 3) [31]. The adjacent wheels are not considered due to their negligible effect on the overall behavior of the system. The corresponding governing equation for this model is:  
\[Elw^{IV}\ +\ kW(x)\ =\ q_{0} \tag{1}\]  
where \(w\) is the vertical deflection of the rail axis at \(x\); \(El\) is the vertical flexural stiffness of one rail; \(k\) is the track modulus (for one rail), and uniformly distributed load; \(q_{0}\) is the general solution for \(w\) is as shown in Equation (2). Based on the above model, the corresponding force that the rail exerts on the concrete sleeper is:  
\[F_{c-t}=\kappa w(0)=\kappa\frac{q_{0}}{k}\bigg{(}1-\frac{\frac{\kappa}{2 El}}{ 4\beta^{3}+\frac{\kappa}{2 El}}\bigg{)}=\kappa\frac{q_{0}}{k}\bigg{(}\frac{4 \beta^{3}}{4\beta^{3}+\frac{\kappa}{2 El}}\bigg{)}, \tag{2}\]  
whereas the corresponding force that the rail exerts on the timber sleeper is:  
\[F_{q_{0}}=q_{0}\times a \tag{3}\]  
Therefore, the effect of the inserted concrete sleeper is:  
\[\frac{F_{c-t}}{F_{q_{0}}}=\ \frac{\kappa}{ak}\bigg{(}\frac{1}{1+\beta\kappa/(2k)} \bigg{)} \tag{4}\]  
Figure 3: The analytical model of the interspersed concrete sleeper in a continuous welded rail track [29]. The EIC sleeper used in this study attempts to reduce the differential stiffness between sleeper material types through an optimized structural design that reduces bending stiffness, and an innovative engineered rail seat plate and pad system.
## 2 Solar PV Modeling and Characteristics
### Solar PV System under Partial Shading  
The performance of the PV array is also affected by the change in irradiance from panel to panel due to shadows of passing clouds, trees, nearby buildings, changes in climate, disposition of dust, the aging effect of the panel, etc [84,85]. Therefore, partial shading conditions (PSC) can occur in the PV system due to any of the above reasons. When the PSC takes place in the PV system, the voltage-power (V-P) curve produces the only peak or multiple peaks [86, 87, 88, 89, 90]. This single peak and multiple peaks on V-P curves depend on sunlight irradiances on the panels of the PV system. If all the PV panels receive the same irradiance, then there is only a peak on the P-V curve. If the PV panels (one or two or all) face the shadow effect, then there are multiple peaks on the P-V curve of the PV system. Figure 5 shows the P-V curves of the PV system under various shading conditions. As a result, the operating point that meets the MPP criterion also changes depending on the ambient conditions. For maximizing the power output of PV systems, an MPPT mechanism, which is a control algorithm that can constantly track the MPP during operation, is required [91].  
Figure 3: The I-V and P-V curves.
## 4 IoT Adoption Benefits
### Direct Benefits
#### 4.1.3 Operation Process Efficiency  
IoT device interconnections improve operations and communications management [22], increase agility [82], and enhance performance by combining systems that run the business with controllers, sensors, and data storage to support business operations [73]. This connectivity helps to increase efficiency [63], performance [28], and optimization in the organization's day-to-day operations and assists in improving quality with high-end safety [3; 24]. For example, Mercedes-Benz has improved the quality and increased auto production's operational efficiency and flexibility by integrating IoT technology in assembly lines and materials handling in their factories [88].  
In addition, IoT sensors are used to perform remote monitoring [52] and automate workflows/processes without human intervention [58; 68], thereby reducing human error [31; 39; 51]. Such a process can enhance autonomy and manage various interruptions [1]. Furthermore, wireless connectivity, low-cost sensors, and big data techniques may offer valuable data to examine the equipment's status and performance [11]. Historical and real-time data can be modeled, correlated, analyzed, and visualized to investigate the performance status of the equipment, allowing predictive maintenance and cost reduction [3; 34], unplanned downtime [35; 36], and unanticipated interruptions [43]. This process promotes early diagnostics and part replacement according to the prediction without compromising the company's profit because of extended shutdowns [76]. As a result, IoT can reduce overall operational costs by enhancing predictive maintenance [41], minimizing equipment failure [33], reducing waste [59], and improving energy efficiency [15; 31].
## 5 Impact of Color Conflicts Caused by Late Arrivals, Node Mobility or Topology Changes
### Dysfunction Detected at the MAC Layer
#### 5.3.1 Detection by Listening  
If during the slot of color \(k\), \(N_{i}\) receives a data frame which was not sent by the neighboring node of this color (\(N_{niy}\) having the color \(C_{niy}\) equal to \(k\)), but was sent by a source \(N_{x}\), it means that a new link \(x->i\) exists and has just been discovered by \(N_{i}\), SERENA does not know the existence of this link yet. \(k\) is the color of the node \(N_{x}\). Till now, this new link was not taken into account at the local level, but the case does not induce any particular problem. It is mainly a warning pointing out a possible evolution of the network topology. It can affect the performance of the MAC layer when TDMA/CA is in the CA mode because channel sensing (CCA) will return a busy channel state.  
The situation is practically identical if during the slot of color \(k\), \(N_{i}\) receives an acknowledgement which was not sent by the neighboring node representing this color, but which was sent by a source \(N_{x}\), it means that a new link \(x->i\) exists. In the protocol implementation of TDMA/CA we use acknowledgements which contain the source and destination addresses. Thus node \(N_{i}\) can deduce the existence of a new link \(x->i\).
## 3 Research Findings and Discussion  
### Tensile Test Results  
#### 3.1.1. AZ91-AZ91; Bonded, Bolted and Bolted-Bonded Single Lap Plates  
In this section, there are the results obtained from experimental studies of AZ91-AZ91 single lap plates, which are arranged with geometric parameters bonding, bolted and bolted-bonded.  
As shown in Figure 6d and Table 3, according to the tensile test results, the maximum tensile stress (max shear) is 34.68 MPa, the maximum tensile elongation is 2.93 mm, the maximum tensile force is 5202.26 N, and the maximum tensile elongation is 11.74 mm value. In Figure 6a, partial breaks seen in the adhesive on both surfaces in bonded samples indicate that the adhesive has good adhesion. In bolted samples (Figure 6b), a crush occurred in the hole in the lower and upper part. The bolt, on the other hand, has breaking damage. In the upper and lower part, where partial ruptures occur in bolted-bonded samples (Figure 6c), it can be seen that the adhesive on the plate has good adhesion. While crushing occurred in the holes in the lower and upper parts, fracture damage occurred in the bolt.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **AZ91-AZ91-** & **Az91-AZ91-** & **Az91-** & **C-CF-** & **CF-CF-** & **C-CF-** & **Calbed-** & **Az91-CF-** & **Az91-CF-** & **Calbed-** \\  & **Bonded** & **Bolded** & **Bonded** & **Bonded** & **Bonded** & **Bonded** & **Bolded** & **Az91-CF-** & **Calbed-** \\ \hline Maximum Stress (MPa) & T & T & T & T & T & T & T & T & T \\ \hline Maximum Force (N) & T/B & T/B & T/B & T/B & T/B & T/B & T/B & T/B & T/B \\ \hline Maximum Elongtion (\%) & T & T & T & T & T & T & T & T \\ \hline Maximum Elongtion (mm) & T & T & T & T & T & T & T & T \\ \hline Damage Type & T/B & T/B & T/B & T/B & T/B & T/B & T/B & T/B \\ \hline Displacement (mm) & B & B & B & B & B & B & B & B \\ \hline Standard Deviation & T/B & T/B & T/B & T/B & T/B & T/B & T/B & T/B \\ \hline \end{tabular}
\end{table}
Table 2: All joint points were performed and tested in the study (T = tensile test B = four-point bending test).  
Figure 6: _Cont._
# A Lightweight Barcode Detection Algorithm Based on Deep Learning
## 5 Conclusions  
For barcode detection tasks in real-world scenarios, this paper proposes a barcode detection algorithm based on an improved YOLOv8. In the model's backbone, the EfficientViT Block is utilized to enhance the accuracy of feature extraction. In the model's neck, improved C2f modules and ADown modules are employed, optimizing the efficiency of feature fusion at the convolutional level. Additionally, the model's detection head is reconstructed, significantly improving training quality and detection performance with lower parameter and computational costs. Experimental results indicate that, compared to the original algorithm, the improved model achieves a 1.8-percentage-point increase in recall rate, a 1.9-percentage-point improvement in mAP50:95, a 20 frames-per-second (FPS) increase, a 74.2% reduction in model parameters, and a 79.6% decrease in FLOPs. The algorithm strikes a good balance between recognition efficiency, recognition accuracy, algorithm versatility, and deployment costs.  
Currently, there is still room for optimization of this model. Researchers will conduct more experiments to further enhance the model's feature extraction capabilities, improve  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Decoding Success Rate (\%)** & **Number of Missed Tests** \\ \hline Baseline & 93.7 & 48 \\ YOLOv5s & 92.3 & 58 \\ YOLOv7-tiny & 93.4 & 50 \\ YOLOv9s & 93.2 & 52 \\ YOLOv10s & 92.8 & 55 \\ RT-DETR & 91.4 & 65 \\ Ours & **95.5** & **34** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of detection and decoding results for barcodes using different detection models.  
Figure 10: Comparison of detection results for barcodes in real-world scenarios using different models. The bar code detection model designed in this paper has the best performance.  
the efficiency of feature fusion, and refine the model's network structure, aiming to design a universal barcode detection model suitable for real-world scenarios.  
Conceptualization, J.C. and N.D.; methodology, X.H.; software, J.C.; validation, J.C. and N.D.; formal analysis, X.H. and J.C.; investigation, J.C. and N.D.; resources, X.H. and Y.Y.; data curation, X.H. and J.C.; writing--original draft preparation, Y.Y.; writing--review and editing, X.H., N.D. and J.C.; visualization, J.C.; supervision, X.H. and J.C.; project administration, X.H. and J.C.; funding acquisition, X.H. and N.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The Science and Technology Program of Zhejiang Province, China (No. 2022C01202, No. 2022C01065), Zhejiang Provincial Department of Education Research Project (Y202455953) and the Zhejiang Sci-Tech University Research Start-up Fund, China (No. 23242083-Y).  
Not applicable.  
Data Availability StatementData will be made available on request.  
The authors declare no conflicts of interest.
## 5 Simulations and Discussions
### Discussions  
This subsection calculates the cost and the profit of the proposed systems and the conventional system that is based on the simulation results presented in Section 5.1. Figures 16 and 17 show the total cost except for \(C_{\text{WF}}^{\text{I}}\) (\(C_{\text{WF}}^{\text{I}}\) is the same in all scenarios as shown in Table 3). Figures 18 and 19 and Table 7 summarize the cost except for \(C_{\text{WF}}^{\text{I}}\), the profit and average of simulation results, respectively. It is clear that the proposed systems can increase the profit in the lifespan of WG (20 y). Especially, \(C_{\text{ESS}}^{\text{I}}\) in both proposed systems are lower than that in the conventional system. The profit of the proposed system with the MA KE controller and with the optimal KE controller does not differ much when the Li-ion battery is used. By contrast, the profit of the proposed system with the optimal KE controller is the highest in those systems when the NaS battery is used. Consequently, both proposed systems increase the profit by approximately 5 [100 million JPY] as
## 1 Introduction
To solve this issue, in this paper, we propose an efficient SSDML method called ISMLP; rather than building the probability model via the instance-instance distance parameterized by the learned Mahalanobis matrix, we propose to learn a set of proxy vectors and transform the instance-instance relationship as the instance-proxy relationship. We minimize the labeled instances and their corresponding proxy vectors to efficiently mine the information of the unlabeled data; inspired by the SERAPH, we incorporate entropy regularization. Importantly, the Mahalanobis matrix is constricted as a hierarchical form to further boost training efficiency. An Alternating Direction Method (ADM) technology is adopted to seek a feasible solution for ISMLP, and the sub-problem concerning the Mahalanobis matrix can be efficiently solved by an iterative method on the product space of two Riemannian manifold. The merits of using proxy vectors lie in two folds: on the one hand, the time complexity of ISMLP is linearly dependent on the number of instances, thus can be easily extended to large-scale datasets; on the other hand, the instance-instance distances may be corrupted because of the noise instances in the dataset. The proxy vectors can be considered as aggregating the class/local information of the dataset, therefore, it is more stable than SERAPH. * We propose a novel information-theoretic-based SSDML method called ISMLP, which simultaneously learns multiple proxy vectors as well as the Mahalanobis matrix. Specifically, we adopt the entropy regularization to mine the discriminant information of the unlabeled data. * The merits of the proposed ISMLP lie in two folds: on the one hand, compared to those manifold-based SSDML methods, ISMLP does not rely on manifold assumptions. Thus, it can be applied to border scenes; the time complexity of ISMLP is linear with respect to the number of training instances, and thus can be easily extended to large-scale datasets. Extensive experimental results on classification and retrieval experiments can validate the superiority performance and in the meantime can be trained more efficiently than those compared methods. The rest of this paper is organized as follows: In Section 2, we briefly introduce the SERAPH framework. Then, we introduce the construction process of the proposed method in Section 3, followed by the extensive numerical experiment in Section 5. Finally, in Section 6, we make a conclusion and provide a possible future direction of the proposed ISMLP.