esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Spaceborne Target Detection Based on Yolov5 and Image Compression
## 5 Simulation Results
### Compression Performance Analysis  
An image of the Shanghai area was used as the analysis set. In the compression, the small image size was 1536\(\times\)256 pixels, which was the smallest input unit for the ADV212 in our design. There were 9744 small images in total in the analysis set, and except for three pictures whose compression ratios were lower than 4:1, the rest were consistent with 4:1. When the compression ratio was 10:1, there were 4558 images with a fixed compression ratio of 9.931:1 and the remaining images were the same, with ratios of 4:1 and 2:1. The real average compression ratio was 11.46:1 when the ADV212 ratio was settled as 2:1 and 4:1 and the real average compression ratio was 12.64:1 when the ADV212 ratio was settled as 10:1.  
When the compression ratio of ADV212 was settled as 2:1, the maximum real compression ratio was 100.17:1. There were 17 images whose compression ratio was larger than 50:1 and 37 images whose compression ratio was larger than 40:1. The image with the maximum compression ratio was the sea surface at the estuary of the Yangtze River, and the actual compression ratio was 100:1. Most areas in the image were smooth with low contrast as shown in Figure 16.  
The image with the minimum compression ratio was the sea surface at the estuary of the Yangtze River, and the actual compression ratio was 3.59:1. Most areas of the image were rough with high contrast as shown in Figure 17.  
Table 3 shows the main indices of image compression, and we chose the buildings image in Figure 18 as an example to test the JPEG2000 performance with different compression rates. Note that the compression rate in ADV212 was not the final compression rate; ADV212 merely guaranteed that the SINR of the compressed image was larger than a particular threshold. This means that the compression rate set in ADV212 was the lower bound of the real compression rate. The least significant bit in the image was 12 bits. From Table 4, we can see that PSNR was larger than 50 dB when the compression ratios were 2:1 and 4:1, which means the compressed image was nearly the same as the original image. When the compression ratio was 10:1, the PSNR was reduced to 46.7 dB as the size was reduced. In addition, SSIM and MUSSIM, which are used to value the similarity of different images, were both larger than 0.99 with different ratios. Thus, ADV212 can deal with spaceborne image compression.  
Figure 16: The image with the maximum compression ratio (smooth Yangtze River).  
Figure 17: The image with the minimum compression ratio (dynamic Yangtze River).
# Object Detection Algorithm Based on Multiheaded Attention
## 3 Methods
### MANet Model
#### 3.2.4 Multiple Modules  
Feature maps of different resolutions contain different feature information. SSD [2] directly uses the features of the two-layer skeletal network (i.e., VGG16) and the four additional layers obtained by the convolution with stride 2 to construct the feature pyramid. So we suspect that each feature map in the feature gold tower contains insufficient feature information to cause poor detection performance.  
Figure 2: Structure of the fusion attention module.  
Therefore, we can make up the information of the current layer by merging the information from other scale feature maps. In this study, we constructed a multiheaded attention module that was used to (a) fuse information among different scales, and (b) enhance the feature expression ability of different global scales to improve the accuracy of target detection.  
To reduce the number of calculations, we have eliminated two small scales (\(1\times 1\), \(3\times 3\)) from the multiheaded module, and we directly input these in the final prediction module. As shown in Figure 3, we used the \(5\times 5\) feature map as an example, and we designed three different multihead fusion models to fuse the features of the various scales. To facilitate the calculation, we integrated the features of the different scales. First, the sizes of the feature maps of the different scales were unified to the same scale. To reduce the computation, the unified scale operation was directly executed using bilinear interpolation. No parameters were introduced, the number of calculations was small, and the channel was then unified to the same dimension based on \(1\times 1\) convolution. The details of the three multihead models are as follows:  
1. Model a performs the concatenation operation on the integrated feature tensor, and then directly inputs it into the fusion attention module to obtain the final fusion result. Equation (3) was used, where \(f_{\text{all}}\) denotes the new feature outcome after the application of the concat operation on the three features, and \(FA\) is the fusion attention module. \[\begin{array}{c}f_{\text{all}}\ =\ concat(f_{38\times 38},f_{19\times 19},f_{10 \times 10})\\ output_{\text{a}}\ =\ FA(f_{5\times 5},f_{\text{all}})\end{array}\] (3) Model b inputs the integrated feature tensors into the fusion attention module, and then concatenates the output results to obtain the final outcome according to Equation (4), where \(f_{1},f_{2},f_{3}\), indicate that the features of different scales are merged with the current feature scales by the fusion attention module to obtain new features, and \(f_{1},f_{2},f_{3}\), are concatenated to obtain the final result. \[\begin{array}{c}f_{1}=\ FA(f_{5\times 5},f_{38\times 38})\\ f_{2}\ =\ FA(f_{5\times 5},f_{19\times 19})\\ f_{3}\ =\ FA(f_{5\times 5},f_{1
# A Power Processing Circuit for Indoor Wi-Fi Energy Harvesting for Ultra-Low Power Wireless Sensors
###### Abstract  
This article proposes a complete power processing circuit for an indoor 2.45 GHz Wi-Fi energy harvesting system. The proposed power processing circuit works by using power harvested from indoor Wi-Fi transmitters. The overall system of this work is simplified as an equivalent circuit and analyzed mathematically. A two-port network is analyzed in formulating the relevant equations of the equivalent circuit. The importance of matching the impedance of a harvesting antenna to the rectifier circuit is highlighted by using simulation analysis, and it is shown that the impedance matching for both components has satisfied the conditions for a high sensitivity circuit and radio frequency-to-direct current (RF-to-DC) power conversion. Actual experiments showed that the proposed power processing circuit could operate with an incident power as low as \(-\)50 dBm. It has been found that the proposed harvesting system stored 0.11 j in a 200 mF supercapacitor as the storage device in 20 hours of the experimentation periods. Moreover, actual results for the overall energy harvesting system is compared with previous research, and it has been found that the proposed system has advantages over the listed works.  
alternating current-direct current (AC-DC) converter; power conversion; energy storage; radio frequency; Wi-Fi; rectenna +
Footnote †: journal: Applied  
_Article_  
A Power Processing Circuit for Indoor Wi-Fi Energy Harvesting for Ultra-Low Power Wireless Sensors
# Person Search via Deep Integrated Networks
## 4 Experimental Results
### Experimental Setup and Dataset  
In the pedestrian detection network, when one image frame with an arbitrary size is input, it would be resized to \(563\times 1000\) pixels. The detailed parameters of sharing convolutional layers were set as follows: 96 kernels with a kernel size of \(7\times 7\) pixels, two pixels of the stride size, and three pixels of padding were used in the first convolutional layer. Hence, feature maps with a size of \(282\times 500\times 96\) could be obtained. The feature map size was unchanged after the LRN layer and downsampled to \(142\times 251\times 96\) after max pooling layer with a kernel size of \(3\times 3\) pixels, two pixels of the stride size and one pixel of padding. The size of the feature maps output by the fifth convolutional layer was 36 \(\times 64\times 256\) pixels. In the ROI pooling layer, the parameters of pool_w and pool_h were set to 6, and spatial_scale was \(6\times 6\) for all the proposals. Besides this, in the identification network, the input size was \(227\times 227\times 3\). The output sizes of FC6, FC7 and FC8 were 4096, 4096 and 2048, respectively.  
In order to evaluate the system performance, several benchmarks have been used for person re-identification. Among them, the dataset PRW [70] is a challenging dataset that includes images captured by the six cameras with different scene views (Figure 5); hence, the poses of the pedestrian images varied. The image resolution captured by the sixth camera was \(720\times 576\) pixels and \(1920\times 1080\) pixels for the other five cameras. The PRW comprised 11,816 RGB images, and 43,110 bounding boxes were manually labelled. Among them, 34,304 bounding boxes were assigned with 932 person IDs, and the remaining boxes for which the models were unsure about a person's ID were assigned an ID of \(-2\). Each bounding box contained five pieces of information [i.e., \(x\), \(y\), \(w\), \(h\) and \(s\)], where (\(x\), \(y\)) is the left-corner coordinate of the bounding box, (\(w\), \(h\)) denotes the width and height of the bounding box, and \(s\) is the person ID.  
Figure 5: Example images captured by six cameras in the PRW dataset [70].
## 3 Results
### Electrical Properties  
Figure 15 shows the electrical properties of ITO-nanorod treated with oxygen plasma at gas contents 20, 40, 60, 80, and 100 sccm, compared to ASD film. From the electrical properties test results, it was found that when the oxygen gas flow rate increased, the sheet resistance value gradually decreased. It was found that the oxygen gas flow rate of 100 sccm showed the lowest sheet resistance value. On the contrary, the ASD film gave the highest sheet resistance value. In this, the decrease in sheet resistance is because of the bombardment of oxygen ions with the film surface with the highest oxygen concentration. Thus, it has a higher ability to attack the ITO-nanorod film surface, which exhibits the lowest sheet resistance. With this low resistance, ITO-nanorod films will greatly benefit DSSCs, as they will enable efficient charge transfer and reduce energy loss in the cells.
# A Vision of 6 th Generation of Fixed Networks (F6G): Challenges and Proposed Directions
## 5 Optical Switching: Key Challenges and Proposed Directions
### Ultra-High Capacity Switching
#### 5.2.1 On the Road towards Realizing a WBSS  
Our vision in this direction for not-too-distant future networks is based on switches routing entire communication bands (S, C, or L). Multi-band transmission alone will not provide sufficient capacity, and links will incorporate SDM solutions as well, as already mentioned in the previous section. Under this upcoming scenario, a new switching paradigm allows one to switch entire bands and can offer high output port counts. A new switching architecture needs to be designed and implemented on a chip that combines adaptive filtering for band selection and adaptation as well as crossbar switches for scalable output port counts. The switch needs to be implemented on a PIC platform, on account of its low losses and thermal stability to utilize fast phase modulators that can operate  
Figure 38: (**a**) Route and select topology implemented with WBSS between ingress and egress fiber ports. (**b**) Multi-granular optical node enabled with interconnecting OXC for access to emerging full-band add and drop transceivers, as well as WDM level granularity with existing WSS technology in support of legacy transmission.  
under 1 \(\upmu\)s, draw very little power per switching event, and be honed to operate with low polarization dependencies.  
Moreover, the envisaged hierarchical network node aims to offer fast, route-and-select architecture at the band level implemented at the top tier, with a secondary route-and-select architecture implemented with today's per-band WSS for backward compatibility with legacy transmission schemes. An OXC disposed in between the two tiers offers C/D/C access to add/drop channels. The network node can be assembled and chassis-integrated, sharing a common control plane and communication port, with enhancements for faster network reconfiguration times. Regarding the performance characterization of mixed optical circuits flowing through the node, some bands rapidly switched at the top tier and others flowed through the bottom tier (mimicking legacy DWDM traffic), targeting low crosstalk (\(\leq\)\(-\)20 dB) and spurious transient strength (\(\leq\)1 dB) and duration (\(\leq\)1 \(\upmu\)s) on quasi-static, bottom tier flows. These are important technical metrics that can enable the seamless operation of F6G services during their entire life-cycle.
## 4 Numerical Simulations and Discussion
### Comparison with Other AOA Methods
#### 4.3.3 Correlation  
Correlation or similarity between the arriving signals impacts negatively on the performance of direction estimation systems. A simulation is next run for two correlated received signals with correlation coefficient (CC = 0.95) incident on a linear array with \(M=8\) elements and \(d=0.5\lambda\). The other simulation parameters are number of samples, \(N=10\), and SNR = 1 dB. In order to ensure a fair and comprehensive comparison, a thousand sets with pairs of AOAs are randomly generated, each set being applied to all algorithms. The RMSE is computed for each trial and then plotted as a cumulative distribution function (CDF). As can be seen from Figure 9, the PDDA presents the best accuracy among the AOA methods and this demonstrates that the proposed method has good robustness and less sensitivity to correlation of source signals. It has been also observed that OGSBI and Root-SBL, based on Bayesian compressed sensing and sparse signal reconstruction principles, have high sensitivity to correlated signals. MUSIC still gives a good performance, whereas Min Norm, ESPRIT and MVDR algorithms perform poorly under such correlation conditions.  
Figure 8: ARMSE vs. SNR, with \(M=8\), \(D=2\), \(N=3\), 1000 Monto Carlo simulations for each SNR.
# Potentiometric Biosensor Based on Artificial Antibodies for an Alzheimer Biomarker Detection
## 3 Results and Discussion  
### Design of the Plastic Antibodies  
Herein, a plastic antibody synthesized on the oxidized SWCNT surface was successfully used for A\(\beta\)-42 detection with potentiometric transduction. The SWCNTs were selected for this propose due to their large surface area, which promotes an increase in the number of binding sites of the MIP material. The overall process for preparing the MIP involved the attachment of A\(\beta\)-42 to the surface of the SWCNTs (Figure 1), filling the vacant places around the A\(\beta\)-42 with an appropriate rigid structure and removing the peptide from the polymer. The polymerization steps were carried out under mild conditions in order to guarantee that 3D-structure and electrostatic environment of A\(\beta\)-42 remained stable.  
First, the peptide was covalently bound to the previously oxidized SWCNT. The carboxyl groups were activated by an EDAC and NHS reaction. In the first step, a highly reactive \(O\)-acylurea intermediate is formed [59], which reacts rapidly with NHS to form a more stable ester (succinimidyl intermediate). This ester performs a nucleophilic substitution with any accessible amine group on A\(\beta\)-42, leading to the formation of an amide bond between the SWCNTs and A\(\beta\)-42.  
The formation of stable complexes between the monomers and the target molecule and their preservation in solution are crucial for achieving a suitable selectivity of the imprinting polymer. In this work, electrostatic interactions were established between the target and the monomers. For this purpose, 2-AEMA was used as the functional monomer, forming hydrogen bonds with A\(\beta\)-42 [63]. Polymerization then starts with the addition of the monomer AAM, the crosslinker NNMBA and the initiators APS and TEMED after peroxide (OO) bond cleavage by the initiator. Crosslinking between the NNMBA chains was ensured by two terminal vinyl groups. The modified SWCNTs were washed three times with the MOPS buffer [64]. The A\(\beta\)-42 was then removed from the polymer by proteinase K treatment. Several washing steps were then performed to remove the free peptide fractions from the surface of the biomimetic material.  
Figure 1: Synthesis of the plastic antibody.
## 3 Results and Discussion
### Fermentation Kinetics
Fermentable sugars decreased as a consequence of the LAB and the yeasts biosynthetic demands during their growth. Sugar consumption was not completed, leaving around 14.45 g/L of residual sugars in the  
Figure 1: The evolution of principal microorganisms, sugar, and ethanol concentration during the fermentation of sotol (_Dasylirion_ spp.) must. (**A**) Viability of filamentous fungi (\(\blacksquare\)), total yeasts (\(\bullet\)), and lactic acid bacteria (LAB) (\(\Circinus\)). (**B**) Profiles of sugar (\(\bullet\)) and ethanol production (\(\Circinus\)). culture medium at day five of the fermentation. The value of ethanol at the end of the fermentation was 25.53 g/L, and based on sugar consumption, the fermentation efficiency was 42.3%. Previous studies of artisanal fermentation of _Agave_ spp. for mezcal and tequila report alcoholic fermentation efficiency values of 34.9-39% in mezcal [23] and 33-34% in tequila [24]. Similarly, in these works, there was an activity of mixed strains of _Saccharomyces_ and non-_Saccharomyces_ strains, and the consumption of sugars was not complete, leaving sugars in the fermenting media. Yeast are capable of producing ethanol with high yields when optimum parameters are established; unfortunately, aerial or external contamination is a factor that disturbs the fermentation system [17]. This factor makes spontaneous fermentations performed in open tanks susceptible to microbial contamination, such as in Lambic breweries where the presence of enterobacteria in the initial stage has been attributed to contamination from the air [19]. LAB contamination can raise organic acid values, altering ethanol production, and a concentration of lactic acid around 0.9% (_w/v_) prior to fermentation can decrease yeast growth and affect ethanol production [25]. Thus, this is an issue that should be further studied, as we detected a significant growth of LAB by microbial culture. Thus, bacteria strains can be identified by non-culture-based techniques in subsequent works. Moreover, other reasons that can induce a low fermentation yield in beverages produced with agave juice are nitrogen, vitamins, or oxygen deficiencies, as the thermal processing of agave reduces the assimilable nitrogen content, affects vitamin values, and can render insignificant levels of oxygen [26]. Nitrogen levels in the stool medium should be further studied, as the presence of amino acids in the medium increases the ability of rapid synthesis of degraded proteins as glucose transporters, which allow the yeasts to achieve complete fermentation in the particular case of non-_Saccharomyces_ strains [27]. However, _Saccharomyces cerevisiae_, which is considered as glucosophilic due to its sugar transport mechanisms, exhibits greater fermentation capabilities attributed to its ability to metabolize both inorganic and organic nitrogen sources from the fermenting media [28].
# Geotechnical Study of Raspadalica Cliff Rockfall, Croatia
## 4 Results
### Kinematic Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Cliff Zone** & **Joint Set** & **Mean Dip** & **Discontinuity** & **Separation** & **Infilling *** & **Roughness** & **Weathering** & **Block** & **Volume (m) *** & **RMR *** \\  & **Direction/Dip *** & **Spacing (cm) * & **(mm) *** & **Infilling *** & **(JRC) *** & **Grade *** & **Volume (m) * & **RMR *** \\ \hline \multirow{3}{*}{Z1} & Joint set 1 & 10/85 & 300–500 & 5–100 & None & 8–10 & SW/MW & \multirow{3}{*}{1.2–10.0} & \multirow{3}{*}{58} \\  & Joint set 2 & 280/80 & 80–100 & -5 & None & 8–10 & SW/MW & & \\  & Bedding & 305/30 & 50–200 & 1–50 & Hard filling & 6–8 & SW/MW & & \\ \hline \multirow{3}{*}{Z2} & Joint set 1 & 190/85 & \textgreater{}100 & 1-5 & None & 8–10 & SW/MW & \multirow{3}{*}{0.5–15.0} & \multirow{3}{*}{60} \\  & Joint set 2 & 110/85 & 100-300 & 0.1–5 & None & 8–10 & SW/MW & & \\  & Bedding & 25/25 & 50–200 & -5 & Hard filling & 6–8 & SW/MW & & \\ \hline \multirow{3}{*}{Z3} & Joint set 1 & 205/75 & 50–300 & -5 & None & 4-6 & SW & \\  & Joint set 2 & 110/80 & 20–300 & -5 & None & 4-6 & SW & \\  & Bedding & 230/20 & 10-200 & 1-5 & Hard filling & 4-6 & SW/MW & & \\ \hline \multirow{3}{*}{Z4} & Joint set 1 & 10/85 & 50–300 & -5 & None & 6–8 & SW & \\  & Joint set 2 & 280/75 & 50–300 & -5 & None & 6–8 & SW & \\  & Bedding & 0/15 & 50–300 & -5 & Hard filling & 4-6 & SW/MW & & \\ \hline \multirow{3}{*}{Z5} & Joint set 1 & 5/75 & 50–300 & -5 & None & 6–8 & SW/MW & & \\  & Joint set 2 & 90/80 & 50–300 & -5 & None & 6–8 & SW/MW & 0.05–18.0 & 58 \\ \cline{1-1}  & Bedding & 10/20 & 20–200 & 1-5 & Hard filling & 6-8 & SW/MW & & \\ \hline \end{tabular}
\end{table} 
Table 4: The mean values of the joint set orientations and dip directions, discontinuity features, block volumes, and RMR values for each shift zone based on results of in situ traditional geological and geotechnical surveys, remote sensing survey, and data analysis. Superscript * indicates data based on in situ traditional geological and geotechnical surveys, * indicates data based on remote sensing survey, while * indicates data obtained by data analysis. Abbreviations: JRC = Joint Roughness Coefficient: Wathering Grades: SW = Slight Weather, MW = Moderate Weather. Figure 10: Kinematic analyses of the Raspadalica Cliff by cliff zones according to planar sliding, direct toppling and flexural toppling, conducted using Roccscience _Dips_ software [91]. The results are presented in rows for each zone (e.g., Zone Z1 is in the first row). The results of planar sliding are in the first column, direct toppling in the second column, and flexural toppling in the third column.
## 5 Data Imbalance
### Data Imbalance Handling Techniques Future Research Directions
#### 5.5.6 Transfer Learning  
Transfer learning typically involves using pre-trained models on large datasets and then transferring the feature extraction parts of these models to new, smaller datasets for fine-tuning and adapting to new tasks. This method is particularly suitable for datasets with a few categories, as pre-trained models may have already learned general feature representations that are very useful for identifying minority categories [129,130].