esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Device-Free Counting Method Based on Channel Status Information
## 4 Experiment Results and Analysis  
### Experiment Setup  
To evaluate the performance of proposed approach, a real experiment platform is built at the elevator. We utilize the AP-link wireless router with two-antennas as transmitter and the PC with three-antennas as receiver, which form monitoring area. As illustrated in Figure 7a,b, all volunteers randomly walk in the monitoring area. In addition, considering the influence of different moving speed on counting accuracy, as such, volunteers have the same speed motion in this paper.  
Since CSI information is highly sensitive to environmental factors. If too much people closely walk with each other in the monitoring area, it inevitably appears signal occlusion. Therefore, in this paper, the sensing range is 25 m\({}^{2}\) of the elevator mouth, both AP and PC are placed at the height of 1.5 m, the distance between them is 5 m. Different volunteers scattered move within the sensing range, the transmitter continuously sends packets to the receiver, and the CSI information will be detected. It's worth mentioning that these people try not to hide other people while walking. The 4 min data is collected at each sample point and 400 stable data packets are selected in the intermediate state, which will be experimental samples.  
Figure 6: Antenna diversity: (**a**) antenna diversity of amplitude features; (**b**) antenna diversity of phase features.
# Sustainable Microalgae and Cyanobacteria Biotechnology
## 4 Other Important Applications of Microalgal Biotechnology
### Phycoremediation  
The phycoremediation of wastewater using microalgae is a highly efficient and economically viable process that allows the removal of chemical pollutants and nutrients by using them as energy sources, as well as the use of CO2 to produce biomass with multiple applications using biotechnology and bioprocesses such as biofuel production, feed additive extraction, and so on [116]. To achieve maximum efficiency in the phycoremediation process, it is critical to use the most efficient metabolic growth mode, which can be classified into three types that correspond to the use of a single source of carbohydrates, though in waste management or phytoremediation, these processes proceed with a mixture of carbohydrates or a non-common carbohydrate, respectively: (1) autotrophic, (2) heterotrophic, and (3) mixotrophic [117]. Photo-autotrophic microalgae require light, water, atmospheric CO2, and nutrients to grow, resulting in high lipid, carbohydrate, and protein production. In the dark, heterotrophic microalgae can produce carbohydrates and lipids by using organic and inorganic CO2. This type of microalgae has the advantage of simple maintenance and operation, as well as low production costs; however, it has the disadvantages of a limited number of microalgal species that can be used, the addition of nutrients, contamination with opportunistic bacteria, and the inability to produce sunlight-induced metabolites [118]. Mixotrophic microalgae, on the other hand, combine the capabilities of autotrophic and heterotrophic microalgae at the same time and use a variety of organic carbon sources such as glycerol, carbohydrates (hexoses and pentoses), amino acids, organic acids, and low-cost substrates such as agro-industrial residues [117], making them the most interesting for use in industrial processes by reducing cultivation costs and achieving maximum production, as proven by species namely _Cryphecodimium colniti_, _Chlorella vulgaris_, and _Chlorella sorokiniana_[87, 118, 119].
# GNSS Spoofing Identification and Smoothing Localization Method for GNSS/Visual SLAM System
## 2 System Model
### Visual SLAM System Modeling and Error Analysis
The objective function for optimizing the depth of feature points in the visual processing sliding window and the rotation and translation matrix \((\mathbf{R}_{k},\mathbf{t}_{k})\) of the camera is  
\[\min\mathbf{\xi}_{i}^{\rm V}=\min\sum_{i=1}^{n}\sum_{k=1}^{m}\left(\left\|f(\mathbf{ \hat{x}}_{k}^{\beta_{i}},\eta)\right\|^{2}\right) \tag{5}\]  
Through the Ceres solver [21], a stable solution and the optimized \((\mathbf{R}_{k},\mathbf{t}_{k})\) of the camera in each frame can be obtained. In the back-end of the system, we can use the optimized \((\mathbf{R}_{k},\mathbf{t}_{k})\) of the camera to perform mutual verification with the results of the GNSS position calculation. Let the ENU coordinate vector of the visual SLAM system at time \(i\) be defined as \(\mathbf{\chi}_{i}^{\rm V}\). Then, the formula for calculating the motion position of visual SLAM from time \(i-1\) to time \(i\) is  
\[\mathbf{\chi}_{i}^{\rm V}=\mathbf{R}_{i}\mathbf{\chi}_{i-1}^{\rm V}+\mathbf{t}_{i} \tag{6}\]
# Compact Spatial Pyramid Pooling Deep Convolutional Neural Network Based Hand Gestures Decoder
## 3 Basic Theory for Optimizing DCNN
### Spatial Pyramid Pooling Layer (SPP)  
The use of a spatial pyramid pooling (SPP) layer eliminates the input image dimension constraints of DCNNs. SPPs are based on original work more commonly referred to as spatial pyramid matching (SPM). SPM is an extension of the bag-of-words (BoW) model that was proposed by [PERSON] et al. [33], a classical computer vision algorithm that divides input image feature vectors into finer-to-coarser forms or sections. Later, the algorithm aggregates feature maps in the sections. SPP not only helps to produce representations for processing from uniformly scaled images/windows, but also helps to feed the DCNN images of different sizes or scales during image preparation. Training with images of variable size improves the scale-invariance and eliminates the over-fitting of a DCNN. [PERSON] et al. demonstrated that integration of SPP into a DCNN design improves accuracy in traditional DCNN architectures, such as Lenet\(\ast\)5, Alexnet with the Imagenet dataset, etc.  
Figure 4 shows the main workings of the SPP layer in the DCNN. In the block diagram, (A) is the input image with any arbitrary input. The image is put through the convolutional feature pooling layer in (B). The features pulled by the convolutional layer are passed to the SPP layer. SPP generates a fixed-length output regardless of the size of the input, whereas the common DCNN sliding window pooling used in normal deep networks cannot perform this operation (C). The operation improves upon BoW models, such that the network retains spatial information by performing max-pooling in local spatial bins. The sizes of these spatial bins are proportional to the image size, so the number of bins is fixed regardless of the image size. This is in contrast to the sliding window pooling used in classical DCNN approaches, in which each spatial bin reflects the max-pooling responses of each filter. The outputs of SPP are presented as \(kM\)-dimensional vectors, where \(M\) denotes the number of bins and \(k\) is the number of filters in the last convolutional layer. Figure 4D shows this process. These vectors are then transferred to the fully connected layers for classification (Figure 4E) and display (Figure 4F).  
Figure 3: Image resizing cause distortion of spatial information, resulting mis-classification and restricting deep learning convolutional neural network (DCNN) input dimensions. Here, (**A**) is the original image, resizing it to feed into DCNN cause distortion shown in (**B**,**C**).
# Monocular Depth Estimation Using Res-UNet with an Attention Model
## 3 Proposed Method
### Proposed Model  
UNets comprise an encoder-decoder model which is symmetric on both sides. The encoder uses a combination of convolutional layers and takes the spatial characteristics out of the original input image. From the extracted spatial features, the decoder creates depth maps. The decoder is made to up-sample and creates the necessary output masks or depth maps. In estimating depth maps, the model suffers from extracting boundaries and edges during training. Hence, we rebuilt the traditional UNet with residual blocks and added a spatial attention model at the decoder. Attention in a model mainly focuses on the small features, as well as edges and boundaries, to develop a very detailed depth map. The respective layers in our model are explained below.
## 4 Discussion  
The VOC-Net is further examined using the Grad-CAM method [29], which produces a heatmap superimposed on the raw spectral input to visualize and elucidate the model's decision making process. The gradients of each target molecule class from the inputs to the last convolutional layer (in our case the second convolution layer) are averaged. Then the gradient multipliers are summed with the corresponding feature maps to generate the  
Figure 14: VOC-Net classification of experimental spectra (25–36). The corresponding softmax scores for each classification are given with each spectra.  
Figure 15: ROC curve for the classification of 36 experimental spectra.  
heatmap. Localized spectral peaks prioritized by the model for classification are easily visualized and illustrate the molecule-discriminating regions of the spectra. The class activation maps for six experimental spectra correctly classified by VOC-Net are shown in Figure 16. The class activation map for the single misclassification of the methanol spectra at 1 Torr, which VOC-Net incorrectly predicted to be formic acid, is shown in Figure 17. Figures 16 and 17 depicts the softmax scores corresponding to each spectrum next to the class activation maps.  
For accurate and confident classification (high softmax scores), the Grad-CAM results illustrate that distinguishing transitions, characterized by strong absorption peaks, should be activated, where activation refers to the features that the model prioritizes in its classification decisions. Furthermore, weak transitions and noise fluctuations should have low activation. The model misclassifies when large numbers of weak absorption features within the noise floor causes activation. Both methanol and formic acid have a large number of absorption peaks in the considered frequency range. In the activation map for the misclassified methanol spectrum (Figure 17, misclassified as formic acid), we observe that the major transitions near 8.4 cm\({}^{-1}\) and 9.8 cm\({}^{-1}\) are not well activated. The class activation maps also provide valuable insight regarding the identification of the necessary or preferred features for spectral classification. Thus, in terms of selecting spectroscopic transitions or frequency ranges for sensor design, the Grad-CAM method may act as a guiding tool.
# The SXFEL Upgrade: From Test Facility to User Facility
## 2 Main Parts of the SXFEL User Facility
### Linac  
The linac of SXFEL-UF includes a photocathode injector to generate a high-quality electron beam, a laser heater (LH) to suppress the microbunching instability, three main boosting sections (L1, L2 and L3) to accelerate the beam to 1.5 GeV, two-stage chicanes (BC1 and BC2) to compress the beam and the beam transport line for beam matching and transportation. Upgradations are performed including LH, BC2, several C-band accelerating sections and the beam transport line.  
In the injector, a temporal Gaussian-like drive laser, instead of the pulse stacking scheme, is adopted to suppress the microbunching instability [34], coordinated with the laser heater system. The 125 MeV electron beam, generated in the S-band injector with a charge of 0.5 nC and pulse duration of about 10 ps, is boosted to 250 MeV in the L1 S-band accelerating section. An X-band linearizer is used to compensate the quadratic term of the energy chirp [35]. The electron beam is then compressed to about 700 A in the downstream bunch compressor chicane (BC1) and accelerated to about 1.5 GeV in C-band L2 and L3, as shown in Figure 2. The second bunch compressor (BC2) is usually not used, aiming to suppress the microbunching instability.  
The 35-mm-aperture transport line delivers the beam to the switchyard section. The longitudinal wakefield of the accelerating structures and the resistive wall wakefield of the transport line lead to the \"banana\"-like longitudinal phase space of the electron beam. The phase space evolution of the electron beam, simulated by ELEGANT [36], are given in Figure 3. The designed parameters are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Values** & **Unit** \\ \hline \multicolumn{3}{c}{Seeding line} \\ \hline Central wavelength & \(\sim\)3 & nm \\ Output peak power & \(\geq\)100 & MW \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Section** & \begin{tabular}{c} **Energy** \\ **(MeV)** \\ \end{tabular} & \begin{tabular}{c} **Pulse Duration** \\ **(ps, FWHM)** \\ \end{tabular} & \
\begin{tabular}{c} **Gradient** \\ **(MV/m)** \\ \end{tabular} &
\begin{tabular}{c} **RF Phase/Bend** \\ **Angle (deg)** & **R56 (mm)** \\ \hline L1 & 250 & 10 & 20 & \(-\)30.5 & - \\ X & 235 & 10 & 16 & 180 & - \\ BC1 & - & 0.8 & & 3.968 & \(-\)48 \\ L2 & 640 & 0.8 & 38 & 5 & - \\ BC2 & - & 0.8 & & 0 & 0 \\ L3 & 1500 & 0.8 & 38 & 5 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Designed parameters of the linac.  
Figure 2: Schematic layout of the main linac of the SXFEL-UF.
## 2 Results and Discussion
### Material Characterisation

Table 1: Change in void and window size upon addition of graphene oxide nanoparticles (GONPs). PolyHIPE: polymeric high internal phase emulsion  
Figure 1: Field emission scanning electron microscope (FESEM) images showing **(a)** the unmodified polystyrene-divinylbenzene high internal phase emulsion (PS-\(co\)-DVB polyHIPE) and **(b)** the GONP-modified PS-\(co\)-DVB polyHIPE polymerised within fused silica capillary housings. Magnification of 10,000\(\times\) and scale bar of 5 \(\upmu\)m.  
Figure 2: Fourier transform infrared (FTIR) absorption spectrum of an unmodified polyHIPE where no OH absorption region is observed. Interestingly, when the GONP-modified polyHIPEs (#1, orange and #2, purple) were analysed using FTIR as shown in Figure 3, the 250-nm GONP polyHIPEs had an absorption profile where two distinct absorption peaks resulted in the OH region. The two peaks are typically observed when OH groups are present on a molecule surface and no hydrogen bonding can occur [43]. It was also noted that carbonyl absorption was observed in the FTIR results. This was expected, as the polyHIPE containing GONPs was expected to absorb in the region of 1720 cm\({}^{-1}\) as graphene oxide contains multiple C=O groups; however, this absorption peak could also be attributable to the starting compound Span(r) 80. As shown in Figure S4, the C=O peak for Span(r) 80 was evident at 1740 cm\({}^{-1}\). FTIR spectra were also obtained on washing of Soxhlet extraction solvents methanol and water (shown in Figure S5), where there is absorption in the region of 1740 cm\({}^{-1}\) in the MeOH Soxhlet extraction solvent. Therefore, the peak in Figure 2 at approx. 1740 cm\({}^{-1}\) is likely attributable to Span(r) 80, and the equivalent peak in Figure 3 could mask a GONP C=O peak in this area. As both the starting material Span(r) 80 and the GONPs have C=O stretches, this compromises the assignment of any peaks observed in this region. For this reason, the presence of OH stretches in the region of 3200-3600 cm\({}^{-1}\) more conclusively supports GONP incorporation, as these peaks are not observed in Figure 2, only Figure 3. There was no absorption peak observed at 1720 cm\({}^{-1}\) for either of the GONP-containing polyHIPEs, which may also suggest that the GO may have been at least partially embedded within the polymeric material. That the GONPs did not protrude fully from the pore surface as expected, but instead partially embedded within the material, likely contributed to the lack of expected surface area increase. Partitioning is known to occur between emulsion phases [44] and is dependent on the surfactant layer at the interface [14]. Span(r) 80 is associated with insufficient packing in the interfacial film owing to the cis conformation of the oleic tail, which can lead to partitioning and GONPs embedding in the pore walls [45]. A similar effect has been reported for iron oxide NPs in dicyclopentadiene polyHIPEs, where the amount of NPs embedded in the polymer was found to be directly attributable to the volume of surfactant [46].
# Real-Time Multi-Label Upper Gastrointestinal Anatomy Recognition from Gastroscope Videos
###### Abstract  
Esophagostroduodenoscopy (EGD) is a critical step in the diagnosis of upper gastrointestinal disorders. However, due to inexperience or high workload, there is a wide variation in EGD performance by endoscopists. Variations in performance may result in exams that do not completely cover all anatomical locations of the stomach, leading to a potential risk of missed diagnosis of gastric diseases. Numerous guidelines or expert consensus have been proposed to assess and optimize the quality of endoscopy. However, there is a lack of mature and robust methods to accurately apply to real clinical real-time video environments. In this paper, we innovatively define the problem of recognizing anatomical locations in videos as a multi-label recognition task. This can be more consistent with the model learning of image-to-label mapping relationships. We propose a combined structure of a deep learning model (GL-Net) that combines a graph convolutional network (GCN) with long short-term memory (LSTM) networks to both extract label features and correlate temporal dependencies for accurate real-time anatomical locations identification in gastroscopy videos. Our methodological evaluation dataset is based on complete videos of real clinical examinations. A total of 29,269 images from 49 videos were collected as a dataset for model training and validation. Another 1736 clinical videos were retrospectively analyzed and evaluated for the application of the proposed model. Our method achieves 97.1% mean accuracy (mAP), 95.5% mean per-class accuracy and 93.7% average overall accuracy in a multi-label classification task, and is able to process these videos in real-time at 29.9 FPS. In addition, based on our approach, we designed a system to monitor routine EGD videos in detail and perform statistical analysis of the operating habits of endoscopists, which can be a useful tool to improve the quality of clinical endoscopy.  
anatomy recognition; deep learning; endoscopy; multi-label; video analysis +
Footnote †: journal: Medical Image and Image Processing
## 6 Behavior of Composite Beam-Columns Under Cyclic Loads
### Behavior of Composite Beam-Columns Under Cyclic Loads: Key Findings  
Composite beam-columns have improved ductility, allowing for greater energy dissipation during seismic events. The composite action between steel and concrete results in higher strength and stiffness, improving overall structural performance. Concrete encasement provides confinement, enhancing compressive strength and ductility. The interaction between steel and concrete through shear connectors plays a crucial role in load transfer and overall structural behavior. Under repeated cyclic loading, there may be strength and stiffness degradation, and concrete encasement can help mitigate local buckling of steel sections. The bond between steel and concrete can deteriorate under cyclic loading, affecting overall performance. Composite beam-columns may accumulate residual deformationsafter severe seismic events, affecting post-earthquake serviceability. Understanding various failure modes (concrete crushing, steel yielding, and shear connector failure) is crucial for proper design. The performance of beam-column connections in composite structures is critical for seismic resistance and energy dissipation.
# A Transfer Learning and Optimized CNN Based Maritime Vessel Classification System
## 4 Dataset and Classification Results
### Experiment Results and Analysis
#### 4.4.2 Modified CNN Model  
As was discussed in the preceding section, the proposed EfficientNetB2 structure is selected to boost the network's performance. Three additional blocks are added, with each block consisting of a fully connected (FC) layer. Following this layer, a ReLU layer was added to learn the new properties of our training dataset. This layer was followed by dropout layers. The modified EfficientNetB2 architecture is illustrated in Figure 6. With the help of a publicly available ship dataset and a number of different permutations of the vectors of fully linked layers in the proposed classifying block, an efficient EfficientNetB2 model was investigated. The goals of this research were to make the network more stable and find the fully connected layers with the best feature extraction vector.  
However, in order to accomplish transfer learning, the features that were collected from earlier layers were used to propose the classification block, obtaining the ideal weight and distortion from the input dataset. The proposed classification method was trained and validated using the same learning and momentum parameters. The suggested network of two fully linked (connected) layers, with higher functional vectors, had a much higher precision compared to other fully connected layers, with lower functional vectors. The first layer, which was totally related to 1408 characteristics, was located beneath the classifying block. At the same time, the classifying network block received the introduction of the highly functioning, fully connected layers. The examination of our network at various  
Figure 5: Different depth layers accuracy performance for ship classification dataset.  
Figure 6: Modified EfficientNetB2 Architecture.  
depth layers came next. Table 4 displays the accuracy of each layer to demonstrate the network's performance.  
Several learning rates were acquired in order to build a classification network with a total accuracy of 97.75%. By using the modified EfficientNetB2 architecture, adding a classification block with three fully connected layers, followed by a ReLU layer and dropout layer, and testing it on a dataset from the Game of Deep Learning Ship datset, our suggested method outperformed earlier results. Table 4 lists the performance indicators.
## 3 Results
### Cytogenetic Studies  
As a preliminary approach for the cytogenetic studies, we studied the effects of the most common sealer (AH Plus) used in routine dental consultations. The cytogenetic study was performed on the HGF-1 cells exposed to material-conditioned medium with AH Plus sealer and to a normal culture medium as the control group. Forty GTG metaphases of cells exposed to a normal culture medium and a total of 90 GTG metaphases, with both 10 mg/mL and 100 mg/mL of AH Plus concentration, were analyzed. Cells exposed exclusively to normal culture medium exhibited certain chromosomal abnormalities, with the majority of the cells remaining normal. However, after the incubation with AH Plus we observed a severe decrease in mitotic index and a five-fold increase in chromosomal aberrations, most structural associated with breakage, involving chromosomes 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 16, 17, 19 and 22. Are examples of these chromosomal alterations several unbalanced translocations, marker chromosomes, and other complex rearrangements.  
Figure 3: Morphology study after 24 h of HGF-1 fibroblasts incubation with material-conditioned media with 100 mg/mL of each sealer (500\(\times\)). (**A**) Control group: cells organized in large number on the surface; (**B**) AH Plus group: the cells were distributed homogeneously on the glass substrate; (**C**) Bio MTA+ group: normal cell confluence was also observed; (**D**) Bio C group: presence of cell-free areas with a decrease in cell confluence.