esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Experiments on the NWPU VRH-10 Dataset  
NWPU VRH-10 [32] is a well annotated dataset that includes remote sensing target detection, which contains 2D bounding boxes annotated on 650 remote images for airplane (757 instances), ship (302 instances), storage tank (655 instances), ballpark (390 instances), tennis court (524 instances), basketball court (159 instances), ground track filed (163 instances), harbor (224 instances), bridge (124 instances), and vehicle (447 instances), with 10 categories in total. The split ratio of training and testing set is 6:4, and the Intersection over Union(IoU) threshold of the evaluated is 0.5 on the testing.  
As shown in Table 5, we evaluate our proposed method on the NWPU VRH-10 dataset to compare with the other popular architectures reviewed and re-implemented in [42]. Our CISPNet model achieved the highest mAP score (0.917) compared to others. Obviously, CISPNet is better than SSD in detecting small and medium targets such as aircraft, ship, storage tankers and so on. The detailed average precision of each category is listed in Table 5, and our proposed CISPNet performs well compared to most of them.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Faster R-CNN** & **YOLO** & **SSD** & **ESSD** & **CISPNet** \\ \hline Airplane & 0.899 & 0.874 & 0.956 & 0.973 & 0.975 \\ Ship & 0.679 & 0.847 & 0.937 & 0.882 & 0.936 \\ Storage tanker & 0.643 & 0.427 & 0.617 & 0.807 & 0.804 \\ Ballpark & 0.932 & 0.931 & 0.995 & 0.974 & 0.982 \\ Tennis court & 0.697 & 0.658 & 0.860 & 0.849 & 0.871 \\ Basketball court & 0.529 & 0.870 & 0.944 & 0.917 & 0.931 \\ Ground track filed & 0.946 & 0.975 & 0.987 & 0.978 & 0.980 \\ Harbor & 0.616 & 0.800 & 0.950 & 0.936 & 0.944 \\ Bridge & 0.218 & 0.903 & 0.966 & 0.937 & 0.941 \\ Vehicle & 0.542 & 0.704 & 0.745 & 0.806 & 0.810 \\
**mAP** & 0.669 & 0.799 & 0.894 & 0.907 & 0.917 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different detection methods based on NWPU VHR-10 benchmarks.  
Figure 9: Fuzzy targets detection examples on the remote sensing image test dataset with SSD and CISPNet models. (**a**) The results of the SSD. (**b**) The results of CISPNet.
# Natural Versus Anthropic Influence on North Adriatic Coast Detected by Geochemical Analyses
## 3 Results
### Geochemistry  
The chemical composition of sediments collected at the S1 and E1 sites is reported in Table 3 (complete geochemical dataset in Appendix A). High MgO content characterizes sediments from the S1 site, whereas higher CaO content is observed in sediments from E1. Regarding trace elements, sediments from the S1 area have higher concentrations of Cr, Cu, Ni, Pb and Zn than those from the E1 site.  
Geochemical analyses show that Pb and Zn average concentrations of the S1 sediment core are, respectively, 1.5 and 0.5 times higher than the background levels of the site (Table 2), while the E1 average concentrations and the background values are similar.  
Depth profiles of cores from the S1 site show an increasing trend of the MgO contents upwards, relatively stationary concentrations of Cr and Ni and gradual decreasing upward trend of Pb (from 39 to 33 mg kg\({}^{-1}\)), whereas Zn shows a slight increase between \(-16\) and \(-8\) cm (from 132 to 138 mg kg\({}^{-1}\)) and then a decrease to 114 mg kg\({}^{-1}\) Zn at the top (Figure 5). In cores from E1 site, MgO and Ni contents are relatively uniform, Cr reaches a peak at \(\sim\)5 cm of 146 mg kg\({}^{-1}\) and Zn and Pb contents show a  
Figure 4: Shepard diagram of the sediment composition in S1 and E1 (cruise ANOC-16) sediment cores.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Major Elements** (dw \%)} \\ \hline  & \multicolumn{2}{c}{**S1**} & \multicolumn{2}{c}{**E1**} \\ \hline  & Min–max & Mean & Min–max & Mean \\
**Al\({}_{2}\)O\({}_{3}\)** & 13.36–13.94 & 13.64 \(\pm\) 0.17 & 11.90–14.81 & 12.93 \(\pm\) 0.89 \\
**MgO** & 3.98–4.75 & 4.45 \(\pm\) 0.20 & 3.49–3.85 & 3.72 \(\pm\) 0.13 \\
**CaO** & 8.77–9.79 & 9.49 \(\pm\) 0.30 & 9.67–12.73 & 11.34 \(\pm\) 0.90 \\ \hline  & \multicolumn{3}{c}{**Trace elements** (mg kg\({}^{-1}\))} \\ \hline  & \multicolumn{2}{c}{**S1**} & \multicolumn{2}{c}{**E1**} \\ \hline  & Min–max & Mean & Min–max & Mean \\
**Cr** & 176–190 & 183 \(\pm\) 4 & 132–146 & 139 \(\pm\) 4 \\
**Cu** & 28–40 & 34 \(\pm\) 3 & 15–23 & 18 \(\pm\) 3 \\
**Ni** & 120–128 & 128 \(\pm\) 3 & 71–95 & 80 \(\pm\) 7 \\
**Pb** & 32–39 & 36 \(\pm\) 3 & 18–29 & 22 \(\pm\) 4 \\
**Zn** & 114–138 & 129 \(\pm\) 8 & 71–92 & 79 \(\pm\) 7 \\ \hline \hline \end{tabular} Major elements are reported in percent dry weight. Trace elements are reported in mg kg\({}^{-1}\). Min, max, mean and standard deviation values of all sediment layers.  
\end{table}
Table 3: Geochemical analyses of S1 and E1 sediment cores (ANOC-16).  
moderate decreasing trend between 16 and 7 cm, a sharp increase between 7 and 2 cm and peaks at \(\
eg\)3 cm reaching 91 and 29 mg kg\({}^{-1}\), respectively.
# Comparison of Prediction Methods for Axial Strength of Ground Connections with Shear Keys
## 1 Introduction
[12] obtained the correlation of these parameters through a great deal of parameter analysis and provided suggestions for ensuring the design reliability of bearing performance. In addition, they [13] also analyzed the impact of abrasion of the steel and grouting contact surface on the strength of grouting materials through experiments. [PERSON] [14] and [PERSON] et al. [15] studied the axial bearing performance of grouted connections with different parameters, and established a regression equation between the shear stress and the radial stiffness of the grouting pipe. Through continuous exploration by scholars all over the world, the strength calculation formula of the grouted connection has been constantly improved; still, it is worth conducting further in-depth research given the complexity of its bearing mechanism. Based on the previous research, prediction methods for the axial strength of grouted connections were proposed in the European and American codes. It is difficult for engineers to choose suitable codes. As with most problems in marine engineering, the prediction methods for axial strength of grouted connections is a concern of engineers. Due to the different sources of prediction methods, different characteristics are revealed in the methods. There is currently no analysis and comparison of different prediction methods using a large amount of experimental data. In this paper, the prediction methods for axial strength of grouted connections proposed in different codes are compared and the applicability and safety of different methods are analyzed. Some advice for marine structural engineers is provided. A reasonable design method is suggested to design grouted connections in marine engineering. Above all, the application scope and prediction methods of different codes are compared and analyzed. Then, taking a practical grouted connection between steel piles and the jacket foundation of a 4 MW wind turbine as a prototype, a comparison of the axial strength obtained from different codes is performed through parameter conversion. In addition, axial compression tests are conducted on seven specimens. Using the detailed test data, finite element model parameter fitting work is carried out, and reliable finite element model parameters are obtained. A wide range of experimental results are collected to set up a database, which is also supplemented with finite element analysis, and finally the standard calculation results are compared with the experimental results. Through experiments, finite element numerical simulation, and data analysis, this paper reveals that the results of axial strength obtained by different calculation methods are different. The applicable conditions of different codes are analyzed, with an evaluation of the associated economy and safety. The conclusions can provide a reference for the subsequent design of strong grouted connections.
## 3 Methods  
### System Architecture  
The F-ALBERT Distillation System performs the process of distilling ALBERT into F-ALBERT. The system carries out a two-step distillation process. The first distillation process is the LR-ALBERT Distillation, which distills the ALBERT model into LR-ALBERT. This process consists of two components: Student LR-ALBERT Creator and LR-Distillation. The Student LR-ALBERT Creator generates and initializes the Student LR-ALBERT using ALBERT. LR-Distillation distills the output results of ALBERT for all dataset samples into the Student LR-ALBERT. LR-ALBERT is the result of the first distillation process. LR-ALBERT is structurally identical to ALBERT, but it is a model with fewer layers reused. The second distillation process, IP-ALBERT Distillation, distills LR-ALBERT, which results from the first distillation process, into Student IP-ALBERT. This process consists of two components: Student IP-ALBERT Creator and IP-Distillation. The Student IP-ALBERT Creator creates and initializes the Student IP-ALBERT using LR-ALBERT. IP-Distillation distills the output results of LR-ALBERT for all given data samples into the Student IP-ALBERT. F-ALBERT is the final Student IP-ALBERT model after it goes through the distillation process. Figure 1 provides a summary of the entire process.  
Figure 1: The F-ALBERT Distillation System distills ALBERT into F-ALBERT through two distillation processes (LR-ALBERT Distillation, IP-ALBERT Distillation).
## 3 Results and Discussion
### Electrical Parameter Testing and Drug Detection
By placing textile materials of different conductivity, we actually manufactured a variable resistor in the microfluidic world, and numerous fluids inside the component could additionally contribute to the alteration of the total electrical resistance on the two terminals. It is important to note that the fabricated microfluidic platform can be easily mounted onto wearables or applied directly to the skin. The complete electronic device was developed for processing and displaying measured data from the proposed microfluidic platform. A block diagram of the main steps is depicted in Figure 6a. The electronic read-out system was incorporated into a hand-held plastic package (Figure 6b) where it was possible to select the type of the fabric (as well as the frequency point) on the display, and the real-time measured concentration of CPA was presented on the display. The electronic device functions on the principle of an auto-range ohmmeter, meaning that the device constantly performs monitoring under the microfluidic sensing platform. The Arduino Nano system with an ATmega328P microcontroller represents the main part of the device. The system is capable of calculating the concentration of applied drug in real time on the basis of the measured resistance. The refreshment of the presented values on the Organic Light Emitting Diode (OLED) display is performed each 500 ms. The display has resolution of 128 \(\times\) 64 pixels and the possibility to present data in a combination of blue and yellow colors. The electronic system is supplied with a voltage of 5 V. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Microfluidic Platform Type**} & \multicolumn{2}{c}{**Sensitivity [S]**} & \multirow{2}{*}{**R\({}^{2}\)**} \\  & **(\(\Omega\)/\(w\)/\(\%\) of CPA)** & & \\ \hline CO-PANI/TiO\({}_{2}\)-1 & 888.33 & 0.8515 \\ CO-PANI/TiO\({}_{2}\)-2 & 55.00 & 0.9199 \\ PET-PANI/TiO\({}_{2}\)-1 & 141.66 & 0.9539 \\ PET-PANI/TiO\({}_{2}\)-2 & 17.00 & 0.9383 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sensitivity and linearity of the analyzed structures. Figure 6: **(a)** A block diagram of the electronic circuit for data acquisition from the microfluidic platform, processing and displaying measured data (ADC - analog-to-digital converter); **(b)** The electronic device for displaying the measured concentration of CPA. It is also possible to collect sweat in vivo using the described microfluidic platform, and with the assistance of our portable electronic system or with a mobile phone, different parameters or biomarker concentration changes in sweat can be monitored.
# EmuCD: An Emulator for Content Dissemination Protocols in Vehicular Networks
## 6 Conclusions  
In this article we presented EmuCD, an emulator for supporting the development and testing of content dissemination protocols for VANETs. EmuCD models the VANET mobile nodes in containers, configuring the network by customizing the interfaces of the container's operating system.  
This strategy to emulate a VANET guarantees independence between the protocol and the communication layer, allowing the development of new protocols that can be tested on the emulated network and used in real equipment (hardware for vehicular communication) without any change in its code. Besides, EmuCD is versatile and can emulate VANETs using real datatraces harvested directly in a production network such as Oporto's or generated through simulators such as SUMO. Finally, we demonstrate the facilities of EmuCD through the evaluation of three content dissemination protocols, IPFS, BitTorrent and Sprinkler.  
When comparing EmuCD with a real VANET, results have shown that if connectivity/mobility data is accurate, EmuCD can successfully emulate content dissemination transfers in a VANET, being able to work as a platform for the development of content dissemination protocols in VANETs.  
As future work, we aim to expand PIPE's facilities by adding a procedure to automate the creation of containers and manage the execution of simulations. Finally, we will evaluate the inclusion of a real time dashboard to show all information about the execution of the simulation, and integrate new content distribution protocols.  
All authors designed the solution, analyzed the results and wrote the paper. [PERSON] implemented the solution, prepared the evaluation scenarios and the results. [PERSON], [PERSON] and [PERSON] supervised the entire research process. [PERSON], [PERSON] and [PERSON] supervised this work on a company environment. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the European Regional Development Fund (FEDER), through the Competitiveness and Internationalization Operational Programme (COMPETE 2020) of the Portugal 2020 framework, and Public and National Financial Support (FCT)(OE) through project MobiWise (POCI-01-0145-FEDER-016426).  
Figure 15: Comparison between IPFS (blue), BitTorrent (green) and Sprinkler (orange) with a 50 MB file being distributed through 200 nodes in Oporto from 15 h to 15:20 h.
## 2 Results
### CpGPPS.SSU1 and CpGPPS.SSU2 Interaction with GGPPS  
To validate if the CpGPPS.SSU1 and CpGPPS.SSU2 can interact with the GGPPS/GPPS.LSU to form a heterodimer, we performed the yeast 2 hybrid (Y2H) assay. The result revealed that yeast cells carrying the plasmid pairs BD-CpGPPS.SSU1 + AD-CpGGPPS2, BD-CpGPPS.SSU2 + AD-CpGGPPS2, BD-CpGPPS.SSU2 + AD-AtGGPPS1 and the positive control plasmid pair grew and turned blue  
Figure 4: Subcellular localization of CpGPPS.SSU1, CpGPPS.SSU2, CpGPPS, CpGGPPS1, and CpGGPPS2 in the _N. benthamiana_ leaves. GFP signals from the adaxial leaf surface were observed by using a confocal laser scanning microscope. GFP, green fluorescence image; Autofluorescence, chlorophyll autofluorescence image; Merge, overlay of GFP and autofluorescence.  
on the QDO/X medium, indicating that the testing plasmid pair could interact and activate the reporter genes in the GAL4 system. On the other hand, yeast cells harboring the plasmid pairs, namely BD-_Cp_GPPS.SSU1 + AD-_Cp_GGPPS1, BD-_Cp_GPPS.SSU2 + AD-_Cp_GGPPS1, BD-_Cp_GPPS.SSU1 + AD-_At_GGPPS1, BD-_Cp_GPPS.SSU1 + AD-empty, and BD-_Cp_GPPS.SSU2 + AD-empty plasmid pairs and negative control could not grow and did not show any interaction on the QDO/X medium (Figure 5).
# YOLOv7 Optimization Model Based on Attention Mechanism Applied in Dense Scenes
## 5 Application
### Summary  
The above two comparative experiments demonstrate the accuracy of the proposed YOLOv7B-CBAM model for small object recognition in dense scenes, and it has fast detection speed and low computational cost while ensuring accuracy. Comparing the model proposed in this paper with other models that are currently more popular, SSD needs to process feature maps of different scales, which may be affected in dense scenes. In contrast, Fast-RCNN first performs region extraction on the image, and then classifies and localizes the extracted regions. In this case, Fast-RCNN may perform better in terms of accuracy, but is slower and cannot reach the practical application requirements of real-time detection. In addition, the model uses a special Anchor-Free technology, which does not require a preset anchor and can be more flexible than YOLOv5 to adapt to objects of different sizes and shapes. This makes the model promising for a wide range of applications in many related fields.  
For normative mask-wearing recognition, the YOLOv7B-CBAM model can be applied to face mask-wearing detection to help relevant departments monitor the wearing of masks by the crowd. This application scenario can be used in public places, transportation, etc., to improve public health and epidemic prevention and control. In addition, the model has a wide range of applications in plant- or fruit-related fields. For example, it can be applied to the identification and classification of fruits, vegetables, and grains to help farmers better manage their crops and improve the efficiency and yield of agricultural production. Also, the model can be used for plant disease detection to help farmers take timely measures to reduce the spread and loss of diseases. It should be noted that although the model has high accuracy in recognizing normative mask-wearing and tomato fruit recognition, adjustments and optimizations need to be made based on specific scenarios and data in practical applications to improve the model's accuracy and stability.
## 3 ReSL0 Algorithm  
The ReSL0 algorithm is an improved algorithm on the basis of the SL0 algorithm. The advantage of the ReSL0 algorithm over the SL0 algorithm is that it can reconstruct the algorithm in the presence of noise. That is to say, Equation (4) will be studied and analyzed. The ReSL0 algorithm only changes the projection part of the SL0 algorithm. The regularization factor and error term are added to adjust the sparse solution and error term, and then a compromise value between the two terms is taken to realize signal reconstruction in the case of noise. The overall structure of the ReSL0 algorithm is the same as that of the SL0 algorithm, which consists of the inner and outer loops. The inner loop is composed of the steepest descent optimization method, as \(\hat{x}\gets x+\mu\sigma^{2}\
abla F_{\sigma}(x)\). The outer loop features the following improvements:  
\[\min_{x_{p}}\lVert x_{p}-\hat{x}\rVert_{2}\quad s.t.\quad\ \ \ \ \ \lVert Ax_{p}-y\rVert_{2}\leq\delta \tag{5}\]  
The Lagrangian form of the above formula is as follows:  
\[\hat{x}_{p}=\arg\ \min_{x_{p}}\lVert x_{p}-\hat{x}\rVert_{2}^{2}\ \ +\ \ \lambda\ \ \lVert Ax_{p}-y\rVert_{2}^{2}, \tag{6}\]  
where \(\lambda\) is a positive regularization factor, which is used to compromise the sparse solution and reconstruction residual term. Then, Equation (6) can be obtained by the weighted least squares method as follows:  
\[\hat{x}_{p}=\big{(}I_{n}+\lambda A^{H}A\big{)}^{-1}(\hat{x}+\lambda A^{H}y), \tag{7}\]where \(I_{n}\) is the unit matrix of \(m*m\) and \(A^{H}\) is the conjugated transposed matrix of \(A\). We can see from [18] that  
\[\left(I_{n}+AB\right)^{-1}=I-A\left(I_{n}+BA\right)^{-1}B \tag{8}\]  
Then, the following solution is obtained:  
\[\hat{x}_{p}=\hat{x}-A^{H}\left(AA^{H}I_{n}+\lambda^{-1}I_{m}\right)^{-1}\left(A \hat{x}-y\right) \tag{9}\]  
Next, the pseudocode of the ReSL0 algorithm can be summarized as Algorithm 1.  
```
Initialization: (1) Set \(L\), \(\mu>0\) and \(\hat{x}^{0}=A^{H}\left(AA^{H}\right)^{-1}y\). (2) Set \(0<\rho<1,\sigma_{1},\sigma_{j}\) and \(\sigma_{j}=\rho\sigma_{j-1}\) where \(j>1\).
```  
While \(\sigma_{j}>\sigma_{j}\)  
(1) Let \(\sigma=\sigma_{j}\). (2) Initialization: \(x=\hat{x}^{j-1}\). -for \(k=1,\ldots,L\) (a) \(x\gets x+\mu x\exp(-x^{2}/\left(2\sigma^{2}\right))\). (b) \(x=x-A^{H}\left(AA^{H}I_{n}+\lambda^{-1}I_{m}\right)^{-1}\left(Ax-y\right)\). (3) Set \(\hat{x}^{j}=x\). The estimated value is \(\hat{x}=\hat{x}^{j}\). ```
**Algorithm 1**The pseudo-code of the ReSL0 algorithm.
# Earthquake Detection Using Stacked Normalized Recurrent Neural Network (SNRNN)
## 3 Stacked Normalized Recurrent Neural Network (SNRNN) Architecture
### Layer 1: Input Data  
The dataset consists of fourteen attributes: event number, date and time, latitude, longitude, station number and name, magnitude, type, depth, source number, source description, reference, etc. We removed the irrelevant information and kept only the relevant information. As a result, we obtained five input attributes to feed into the proposed model: time, latitude, longitude, magnitude, magnitude type, and depth. To provide greater insight, the architecture of our model's inputs and outputs is depicted in Figure 3. Following feature selection, the dataset was normalized using a min-max scaler. This scaler transforms the features by transforming their values to the interval [0, 1]. In this way, the original distribution shape was maintained. We split the dataset into training (75%) and testing (25%) subsets to train and test the model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Papers** & **Models** & **Stations** & **Dataset** & **Normalization** & **Ensemble Type** \\ \hline
[29] & MagNet (CRNN) & Multiple & STEAD & – & Convolutional, recurrent \\
[12] & LSTM, GRU & Single & Morocco, Japan, and Turkey & Layer & Recurrent \\
[9] & EEWPEnsembleStack & Multiple & Japan & – & Machine learning \\
[14] & BNGCNNAtt & Multiple & Alaska, Japan & Batch, attention & Convolutional \\
[16] & Attention-based LSTM & – & Japan & Attention & – \\
[26] & FCDNet & Multiple & STEAD & Batch, attention & Convolutional \\
[21] & Transformer, LSTM, & Multiple & STEAD & – & Convolutional, recurrent \\
[19] & CNN & Multiple & Italy & – & – \\
[30] & LSTM & Multiple & Turkey & – & – \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: The five-layered architecture of the proposed stacked normalized recurrent neural network (SNRNN).
# Bushfire Disaster Monitoring System Using Low Power Wide Area Networks (LPWAN)
## 1 Introduction
This model directly relates the impact of various management strategies to changes in fire characteristics across the landscape [15], and we added a variant of wind to demonstrate the solution using PAN to be used by fire agencies, town planners and policymakers. The benefit of this proposal is that it does not require a significant modification or addition of a field to carry the LID within an existing signalling system. However, it may require minimal changes of the A number handling by a carrier network, whilst the actual A number is not required for accounting during the call setup, as it will be regarded as an emergency call. If it is normal to be charged, charging arrangements by the carrier may also be required when provisioning the service. To deliver the PAN, the existing calling line identification presentation (CLIP) feature can be used for data management by the DRA. For implementation of the calling party numbers in multiple networks, it will require collective co-operation among the network operators and service providers in a region or country. Currently, there is no known activity of standardising emergency call handling among countries, whilst there are rules on how to handle emergency numbers such as 000 and 911 in Australia, for instance. For developing countries, this proposal may be useful to monitor disaster status with minimal modifications in their carrier networks. Figure 1 shows the network topology of LPWAN and carrier networks.