esa-sceva/satcom-chunk-collection huggingface dataset:
# BA-YOLO for Object Detection in Satellite Remote Sensing Images
## 2 Related Work
### Multi-Head Self-Attention  
Currently, transformer models, as discussed in reference [33], dominate the fields of natural language processing and target recognition and detection. This is because they excel at learning complex dependencies between input sequences through self-attention mechanisms. When dealing with large-scale and intricate scene images, such as those captured by drones or satellite remote sensing, it is crucial to enhance semantic distinctiveness and reduce category confusion. Gathering and associating scene information from extensive neighborhoods contribute significantly to understanding relationships between objects. Traditional target detection models based on CNNs are constrained by the locality of convolution operations, limiting their ability to capture global contextual information. In contrast, transformers, with their multi-head self-attention mechanisms, are more proficient at preserving spatial information, allowing them to globally attend to the dependencies between image feature blocks. This, in turn, enhances the performance of target detection models.  
Additionally, the Swin transformer [34] introduces a novel attention mechanism known as Shifted Window (Swin) Attention, which enables the model to effectively process large-scale images and capture information at various scales. This attention mechanism achieves this by dividing the image into different blocks, with overlapping regions between each block to ensure the consistency of global information. The integration of Shifted Window Attention equips the model to handle large images better, making it suitable for computer vision tasks involving high-resolution images. Compared to traditional CNNs, visual transformers exhibit greater robustness when dealing with severe occlusion, disturbances, and domain shifts. Therefore, an intuitive approach to enhancing detection performance is to embed transformer layers into a pure convolutional backbone, providing more contextual information and learning more discriminative feature representations without significantly increasing the parameter count. To better integrate the advantages of multi-head attention and convolutional networks, the ACmix model was proposed by [PERSON] et al. [35]. The ACmix model integrates self-attention and convolution modules by sharing the same computationally intensive operations, thereby improving detection accuracy while maintaining efficiency. Therefore, to more effectively capture features, we added ACmix to the baseline model.
###### Abstract
In addition, the other challenges in OCC are also carried over from those in computer vision, such as the impact of lighting conditions (day vs. night, morning vs. evening and sunny vs. cloudy), weather conditions (fog, rain or snow) and mobility (variable perspectives between transmitter and receiver) on the quality of LED signal registered on the camera image. Considering the advances made in the computer vision and image analysis areas with the help of deep neural networks (DNN), in this paper, we hypothesize that DNN models can be helpful in addressing the OCC challenges. In this regard, we present a systematic empirical evaluation of existing state-of-the-art DNN models used for classification problems in computer vision towards demodulation of the LED signal registered on a camera image. The problem statement of the work in this paper is to \"classify\" whether the (single) LED state as registered on a set of camera image pixels corresponds (demodulated) to a particular quantization level of the symbol (we evaluate two (1 bit), three (1.59 bits) and four (2 bits) levels in this paper). We conduct the evaluations based on the classification accuracy of the signal levels using different state-of-the-art DNN models (with or without our customization). Next, we select the best performing DNN model towards evaluating OCC under a multiple-input (LED array) multiple-output (camera pixels) configuration presented as two case-studies: (CASE STUDY A) communicating one page of text multiplexing using a \(2\times 2\) RED color LED array to the camera and (CASE STUDY B) communicating using a diffused BLUE color five LED array to a camera in underwater setting and using diversity (all LEDs transmit the same bits). The overall goal of the research conducted in this work is to investigate the feasibility and robustness of DNN image object classification models on OCC LED signal demodulation. The rest of the paper is structured as follows: Section 2--Related Work; Section 3--DNN Based Demodulation Evaluation; Section 4--Case Study A: LED Array MIMO Multiplexing; Section 5--Case Study B: Underwater OCC under MIMO Civersity; Section 6--Conclusions.
# Parcel Classification and Positioning of Intelligent Parcel Storage System Based on YOLOv5
## 2 Related Work
### Overall Design of the Parcel Storage System  
The role of unmanned parcel storage is a system that serves as a connective element and safety measure between the delivery driver and the final consumer. The purpose of the delivery driver is to safely deliver the goods to the end consumer through parcel storage and to allow the final consumer to receive the stored goods without losing the goods or being exposed to other risk factors. Therefore, accurate parcel information recognition and safe storage are considered essential when designing a parcel storage system.  
The main design of the system can be divided into a display unit (object recognition unit), a control unit (central control + backup unit), a robot unit, and a storage unit. To acquire continuous frame images from the storage entrance and display part (object recognition part) and recognize objects with a high rate of accuracy, it is essential to develop a camera and apply related AI technology. To detect an object, it must be designed as an appropriate AI deep learning model to balance detection speed and accuracy [10]. The control part stores the information received from the storage entrance and display part in the central control and backup, and retransmits it to the storage entrance and display part. Based on the object information transmitted from the control part, the mobile robot, receiving the command from the central control part, uses the gripper to move the parcel and load it into the storage space. The design of the proposed parcel storage system should consider the information exchange  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Related Work** &
\begin{tabular}{c} **Research** \\ **Classification** \\ \end{tabular} & **Method** & **Accuracy** \\ \hline
[1] & Parcel tracking & GPS & - \\ \hline
[5] & Parcel tracking & RFID & - \\ \hline
[6] & Parcel tracking & Android 4.4.2 & - \\ \hline
[7] & Parcel Classification & EfficientNet & 83\% \\ \hline
[8] & Parcel Classification & MobileNet, ResNet50, VGG16 & 84.6\% \\ \hline
[9] & Parcel Classification & Improved MobileNet-v2 & 86.75\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of related works.  
and linkage between modules, data storage, real-time box recognition, and robot transfer technology. A diagram of the system is shown in Figure 1.
# Next-Generation Dual Transceiver FSO Communication System for High-Speed Trains in Neon Smart City
## 4 Channel Model
### Path Loss  
The attenuation of laser power through the atmosphere under the exponential Beer-Lambert law is given by [17]:  
\[h_{a}(z)=\exp(-\sigma z) \tag{4}\]  
where \(\sigma\) is the total extinction coefficient. The extinction coefficient is determined by the combined effects of laser photon absorption and scattering caused by different aerosols and gaseous molecules present in the atmosphere. These factors contribute to the overall degradation of the optical signal during transmission.
# Recent Developments of Solar Cells from PbS Colloidal Quantum Dots
## 2 Solar Cells with PbS Quantum Dots--The Principle Function
Finally, quantum dots can be applied as sensitizers in solar cells, similar to the dye molecules in DSSCs. Here, the aforementioned advantage is used so that the band gap can be tailored by geometrical or chemical modifications of the ligands, without changing the material of the quantum dot itself. Like in DSSCs, the excited electron of the quantum dot is inserted into the TiO\({}_{2}\) layer, leading to the oxidation of the quantum dot, and regeneration into the ground state is obtained by the use of an electron from the redox mediator [53]. Like in some other promising approaches, the reachable efficiencies are often lower than expected, e.g., due to surface states of a different nature or back electron transfer [54]. This is why we give here an overview on the most recent results of quantum dot-based solar cells, concentrating on colloidal PbS quantum dots, which can be tailored to start light absorption between 900 nm and 2000 nm, and are thus ideally suited for light harvesting in the near-infrared [55; 56]. Figure 2: Different quantum dot-based photovoltaic cells. From [41], reprinted with permission. It should be mentioned that sometimes the boundaries between different types of solar cell are blurred, in which cases the studies are sorted into the sections where they fit best.
# A Multi-Scale Rebar Detection Network with an Embedded Attention Mechanism
## 3 Methods
### SD_IoU Bounding Box Loss Function
\[\Gamma=\cos(2\times(\arcsin(\frac{c_{h}}{\sigma})-\frac{\pi}{4})) \tag{15}\]  
\[x=\frac{c_{h}}{\sigma}=\sin(\alpha) \tag{16}\]  
\[\sigma=\sqrt{\left(b_{c_{x}}^{gt}-b_{c_{x}}\right)^{2}+\left(b_{c_{y}}^{gt}- b_{c_{y}}\right)^{2}} \tag{17}\]  
\[c_{h}=\max(b_{c_{y}}^{gt},b_{c_{y}})-\min(b_{c_{y}}^{gt},b_{c_{y}}) \tag{18}\]  
In the above equation, \(\sigma\) is the distance between the center point of the prediction box and the true box; \(c_{h}\) is the height difference between the center point of the prediction box and the true box; \(b_{c_{x}}^{gt}\) and \(b_{c_{y}}^{gt}\) are the center coordinates of the true box; and \(b_{c_{x}}\) and \(b_{c_{y}}\) are the center coordinates of the prediction box. Figure 8 shows the schematic diagram of SloU. In Figure 8, \(B\) and \(B^{GT}\) are the prediction box and the true box, respectively, and \(\alpha\) is the angle between the line connecting the center point of the prediction box and the true box and the rectangle formed by the center point of the two boxes as the vertex on one side. When \(\alpha\) is less than 45 degrees, \(\alpha\) is minimized; otherwise, \(\beta\) is minimized. When \(\alpha\) is minimized to 0, \(\Gamma\) is equal to 0, and at this time, the contribution of the angle loss \(\Gamma\) is zero. In order to avoid the angle loss disappearing to zero, we also consider the distance  
Figure 8: SloU diagram. factor between the prediction box and the true box. Therefore, we design a new distance loss function, calculated as shown in Equations (19)-(22). \[\Delta=2-e^{-\gamma\rho_{x}}-e^{-\gamma\rho y} \tag{19}\]  
\[\rho_{x}={\left(\frac{b_{cx}^{\delta t}-b_{cz}}{d_{w}}\right)}^{2} \tag{20}\]  
Figure 7:0  
Figure 7:1  
In the above equations, \(d_{w}\) and \(d_{h}\) are the width and height of the smallest outer rectangle of the predicted and true boxes, respectively; \(\rho_{x}\) and \(\rho_{y}\) in Equations (20) and (21) are assigned to the distance loss \(\Delta\) using powers of two, thus indicating that the distance loss is larger than the angle loss for the overall SD_IoU Loss contribution. In this paper, by designing SD_IoU Loss, the angular deviation and distance relationship between the predicted box and the true box are taken into consideration, which greatly helps the convergence process and effect of training by improving the accuracy of the detected box. To achieve the purpose of optimizing and improving the accuracy of the model, we use the SD_IoU Loss bounding box function with better performance. Figure 9 shows the comparison of the bounding box loss curves before and after using SD_IoU Loss, where the horizontal coordinates are the number of iterations and the vertical coordinates are the loss values. It can be seen that the algorithm converges faster using SD_IoU Loss, and the loss value is smaller than the original YOLOv5s; therefore, it proves that the SD_IoU Loss designed in this paper has better performance.
## 3 Numeric Examples
### Numerical Examples for the EFM
#### 3.2.2 Implementation of the EFM in the 3-RRR Manipulator  
Following the steps for the 3-RRR planar manipulator, one can find the determinants q and \(\
u\) as:  
\[\begin{array}{l}\text{q}=-1.35\times 10^{7}\lambda^{4}+1.2\times 10^{-8} \lambda^{3}+1.35\times 10^{7}\lambda^{2}-2.54\times 10^{-9}\lambda+2.49 \times 10^{6}\\ \text{v}=-4.75\times 10^{7}\lambda^{4}+8.84\times 10^{7}\lambda^{3}-8.252\times 1 0^{7}\lambda^{2}+4.39\times 10^{7}\lambda-9.30\times 10^{6}\end{array}\]  
As seen in Figure 4, the value of \(\lambda\) has to be between 0.47-0.69.  
By changing the \(\lambda\) value in that interval and finding two roots of \(\mu\) and \(\
u\) values, the resulting figure is given in Figure 6. The error function in Equation (37) is plotted for four variants.  
\begin{table}
\begin{tabular}{c c c c} \hline
1-) & \(\lambda=0.660\) & \(\mu=0.931\) & \(\
u=0.609\) \\
2-) & \(\lambda=0.718\) & \(\mu=1.334\) & \(\
u=0.741\) \\ \hline \end{tabular}
\end{table}
Table 10: Physically meaningful solutions (the EFM).  
Figure 5: The graphics of \(\epsilon\) versus \(\lambda\).  
As seen in Figure 6, the error function has two minima. The \(\lambda\) values belonging to these minimum points are used as the initial values for the non-linear equation solver used. It is observed that this method also gives the same solutions as those shown in Table 9.
## 6 Model Construction
### Indicators for Model Evaluation  
To evaluate the performance of the model, commonly used metrics, such as accuracy, precision, recall, and F1 score, were used as they apply to multicategorical problems. Accuracy, as the most intuitive global metric, measures the overall correctness of the model's prediction for all categories, but it may be biased in data with unbalanced categories. To compensate for this limitation, this study further introduced precision and recall for in-depth analysis in two key dimensions, namely, prediction reliability and category coverage. Precision focuses on the percentage of true positive categories in the samples predicted by the model to be positive, whereas recall reveals the model's ability to recognize true positive categories. The F1 score, a reconciling mean metric, seeks an optimal balance between the two and is especially suitable for scenarios with uneven class distributions. The samples were categorized into true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) by comparing the predicted and actual results. The accuracy, precision, recall, and F1 score formulas are as follows:  
\[\text{accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Model** & **Hyperparameter** & **Search Space** & **Parameter Type** \\ \hline \multirow{8}{*}{LightGBM} & num\_boost\_round & [200, 1000] & int \\  & num\_leaves & [20, 100] & int \\  & learning\_rate & [0.01, 0.3] & float \\  & feature\_fraction & [0.6, 1.0] & float \\  & bagging\_fraction & [0.6, 1.0] & float \\  & bagging\_freq & [1, 10] & int \\  & min\_child\_samples & [5, 100] & int \\  & reg\_alpha & [0, 1] & float \\  & reg\_lambda & [0, 1] & float \\ \hline \multirow{8}{*}{Catboost} & iterations & [200, 1000] & int \\  & depth & [2, 12] & int \\  & learning\_rate & [0.01, 0.3] & float \\  & l2\_leaf\_reg & [1, 12] & float \\  & border\_count & [32, 256] & int \\ \hline \multirow{8}{*}{RF} & n\_estimators & [200, 1000] & int \\  & max\_depth & [10, 50] & int \\  & min\_samples\_split & [2, 10] & int \\  & min\_samples\_leaf & [1, 10] & int \\  & max\_features & [’sqrt’, ’log’2, ’None] & categorical \\ \hline \multirow{8}{*}{SVM} & C & [0.01, 10.0] & float \\  & kernel & [’linear’, ’rbf’, ’poly’] & categorical \\  & gamma & [’scale’, ’auto’] & categorical \\  & degree & [2, 5] & int \\ \hline \multirow{8}{*}{MLP} & epochs & [200, 1000] & int \\  & units\_layer1 & [32, 128] & int \\  & units\_layer2 & [16, 64] & int \\ \cline{1-1}  & activation & [’relu’, ’tanh’] & categorical \\ \cline{1-1}  & learning\_rate & [\(1\times 10^{-4}\), \(1\times 10^{-3}\)] & float \\ \cline{1-1}  & batch\_size & [16, 32] & int \\ \cline{1-1}  & dropout\_rate & [0.2, 0.5] & float \\ \hline \end{tabular}
\end{table}
Table 5: _Cont._\[\text{precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{5}\] \[\text{recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\] (6) \[\text{F1}=2\times\frac{\text{precision}\times\text{recall}}{\text{ precision}+\text{recall}} \tag{7}\]
# A Blockchain Solution for Remote Sensing Data Management Model
## 2 Problem Description
### Characteristics of Remote Sensing Data
In the current remote sensing data production process, it is almost impossible to produce multi-source remote sensing products under the premise of ensuring the privacy of non-public data. At present, multi-data centers use a data aggregation mode for data sharing and trading among units, and the data sharing process requires intermediate institutions for aggregation, which poses problems, such as complex business processes, data leakage, and non-traceability. In addition, the willingness of remote sensing production workers to share product data is low, and the driving force is insufficient at present; the lack of auditing and of effective evaluation of the shared data makes it difficult to guarantee the quality of the shared data; the problem of data validation after sharing, and the inability to validate the rights, will affect the motivation of data sharing. These are other important challenges faced regarding remote sensing products. In summary, when facing remote sensing data, a blockchain still has certain problems in the data sharing and supervision mechanism, trusted data sharing mode, and data access control. In order to achieve the safe and efficient sharing of remote sensing data, the data-sharing business has the following needs: (1) the sharing of remote sensing data requires the administrator to authenticate and manage the members involved in data sharing, to prevent malicious members from participating in data sharing; (2) due to the sensitivity of remote sensing data, and the characteristics of big data, it is necessary to adopt a storage method with high security, to guarantee the safety of remote sensing data storage; (3) the adoption of a trusted data sharing mode to get rid of the complicated process and inefficiency caused by the need for intermediate organizations to summarize the traditional sharing mode; (4) data resources and data sharing records are traceable, to increase the possibility of recourse for data misuse.
# RIP Sensing Matrices Construction for Sparsifying Dictionaries with Application to MRI Imaging
## 3 Sensing Matrix Construction
Moreover:  
\[HH^{\top} =A^{+}GDD^{\top}G^{\top}(A^{+})^{\top}+N_{A}N_{A}^{\top}\] \[=A^{+}AA^{\top}(A^{+})^{\top}+N_{A}N_{A}^{\top}=A^{+}A+N_{A}N_{A}^ {\top},\]  
which is the identity. Finally, note that (i) implies (iii). An alternative factorization can be derived from orthonormal bases for the ranges and nullspaces of prescribed equal rank \(A,D\in\mathbb{R}^{l\times n}\) as follows. Let \([U_{A}\ N_{A}],[U_{D}\ N_{D}]\) be orthonormal matrices, where the columns of \(U_{A},U_{D}\) span the ranges, and the columns of\(N_{A},N_{D}\) the nullspaces, of \(A,D\). Extend \(DU_{D}\), \(AU_{A}\) to invertible square matrices \(\widetilde{DU_{D}},\widetilde{AU_{A}}\), appending columns. Then:  
\[G:=\widetilde{DU_{D}}\widetilde{AU_{A}}^{-1}\quad\text{ and }\quad H:=U_{A}U_{D}^{ \top}+N_{A}N_{D}^{\top} \tag{4}\]  
are invertible, respectively, orthonormal, and \(GAH-D=(GAU_{A}-DU_{D})U_{D}^{\top}\) is zero since \(GAU_{A}=DU_{D}\). We next detail a factorization for the practically important case of a prescribed tight frame dictionary \(D\). We may then assume \(DD^{\top}\) to be the identity. **Theorem 3**. : _Suppose \(A,D\in\mathbb{R}^{l\times n}\) have full rank \(l\leq n\), and \(D\) is a tight frame. Then \(D=GAH\) with invertible, respectively orthonormal:_  
\[G:=O(AA^{\top})^{-1/2}\quad\text{ and }\quad H:=A^{\top}G^{\top}D+N_{A}N_{D}^{ \top},\]  
_where \(O\in\mathbb{R}^{l\times l}\) is any orthonormal matrix, and the columns of \(N_{A},N_{D}\in\mathbb{R}^{n\times n-l}\) are any orthonormal bases of the nullspaces of \(A\), respectively \(D\). In particular, if \(A\) is also a tight frame, then \(G\) is orthonormal._  
Proof. : Since \(GAA^{\top}G^{\top}\) is the identity and \(AN_{A}\) the zero operator, a direct calculation shows \(GAH=D\). While \(G\) is by definition invertible, \(DD^{\top}\) being the identity implies that \(HH^{\top}=A^{\top}(AA^{\top})^{-1}A+N_{A}N_{A}^{\top}\) is the identity.
Sensors_**2014**, _14_, 5860-5889; doi:10.3390/s140405860  
_Seinsoys_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Review_  
**Synthesis of Silver Nanostructures by Multistep Methods**  
**[PERSON] 1,2,*, [PERSON] 1,2,3, [PERSON] 1,2 and [PERSON] 1,2**  
1 School of Electronic Science and Engineering, Southeast University, and Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing 210096, China; E-Mails: [EMAIL_ADDRESS] (Y.-J.S.);  
[EMAIL_ADDRESS] (X.-Y.Z.); [EMAIL_ADDRESS] (J.-Y.W.)  
2 Suzhou Key Laboratory of Metal Nano-Optoelectronic Technology, Suzhou Research Institute of Southeast University, Suzhou 215123, China  
3 School of Chemistry and Chemical Engineering, Southeast University, Nanjing 211189, China  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-25-8379-2449 (ext. 833); Fax: +86-25-8336-3222.  
_Received: 13 January 2014; in revised form: 16 March 2014 / Accepted: 18 March 2014 / Published: 25 March 2014_  
**Abstract:** The shape of plasmonic nanostructures such as silver and gold is vital to their physical and chemical properties and potential applications. Recently, preparation of complex nanostructures with rich function by chemical multistep methods is the hotspot of research. In this review we introduce three typical multistep methods to prepare silver nanostructures with well-controlled shapes, including the double reductant method, etching technique and construction of core-shell nanostructures. The growth mechanism of double the reductant method is that different favorable facets of silver nanocrystals are produced in different reductants, which can be used to prepare complex nanostructures such as nanoflags with ultranarrow resonant band bandwidth or some silver nanostructures which are difficult to prepare using other methods. The etching technique can selectively remove nanoparticles to achieve the aim of shape control and is widely used for the synthesis of nanoflowers and hollow nanostructures. Construction of core-shell nanostructures is another tool to control shape and size. The three methods can not only prepare various silver nanostructures with well-controlled shapes, which exhibit unique optical properties, such as strong surface-enhanced Raman scattering (SERS) signal and localized surface plasmon resonance (LSPR) effect, but also have potential application in many areas.  
keywords: reductant; nanoparticle etching; composited nanostructures; shape control; plasmonic sensors +
Footnote †: journal: Journal of Applied Physics