esa-sceva/satcom-chunk-collection huggingface dataset:
# A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar
## 3 Two Layers LSTM Network for Wavelet Prediction
### Network Testing  
From the test set, we randomly selected two channels to connect into a sequence with 780 \(\times\) 2 = 1560 samples as the network input for the prediction of wavelet. For example, the 5 th, 32 nd, and 48 th channels of A-Scan data (corresponding to A, B, and C in Figure 4b, respectively) are used to evaluate the wavelet prediction ability of the trained network. Position A is far away from the target, and it contains less target signal. This channel of A-scan data is used as the ground truth of the wavelet. Position B is close to the target, and the corresponding A-scan echo contains some target signals. As shown in Figure 7, the predicted wavelet of green line is consistent with the ground truth of black dot line. The enlarged view in the upper right corner shows that the predicted wavelet amplitude is non-zero after the 510 th sampling point, due to the reflected signal of the underground layer.  
Figure 6: Wavelets predicted by different network layers.  
Figure 7: Comparison of prediction results with original A-Scan data.  
Another interesting observation is the wavelet prediction with the A-scan data at position C, which is just above the PEC target. The strong reflected signal of the metal is much larger than the wavelet of the background. At this time, the network cannot ignore the very strong target interference, resulting in the failure of wavelet prediction. Therefore, for the wavelet prediction of the actual measured data, it is necessary to remove the echo containing strong target signal.  
The effect of wavelet removal and deconvolution strongly depends on the quality of the wavelet. If the extracted wavelet is incorrect, the direct wave cannot be offset with the original data, and interference is also induced. The inaccurate wavelet tailing will affect the resolution of deep detection. In the following, the results of several methods of wavelet removal and deconvolution are compared to evaluate the accuracy of wavelet.  
Figure 8 shows the results of wavelet removal with different wavelet extraction methods. The above two figures still contain residual direct waves and the layered interference. Although the reference wave method removes the direct wave cleanly, the layered interference is still obvious. The removal of the wavelet predicted by the above LSTM network can effectively remove the direct wave and the layered interference at the same time and improve the signal-to-noise ratio and resolution.  
Figure 9 shows the deconvolution results of the wavelets. It can be seen that the deconvolution with the LSTM-predicted wavelet can effectively compress the wavelet tailing and the layered signal so that the signal of the deep target is highlighted.  
Figure 8: Wavelet removal extracted by four methods.
###### Abstract  
Machine learning has significantly advanced traffic surveillance and management, with YOLO (You Only Look Once) being a prominent Convolutional Neural Network (CNN) algorithm for vehicle detection. This study utilizes YOLO version 7 (YOLOv7) combined with the Kalman-based SORT (Simple Online and Real-time Tracking) algorithm as one of the models used in our experiments for real-time vehicle identification. We developed the \"ISTraffic\" dataset. We have also included an overview of existing datasets in the domain of vehicle detection, highlighting their shortcomings: existing vehicle detection datasets often have incomplete annotations and limited diversity, but our \"ISTraffic\" dataset addresses these issues with detailed and extensive annotations for higher accuracy and robustness. The ISTraffic dataset is meticulously annotated, ensuring high-quality labels for every visible object, including those that are truncated, obscured, or extremely small. With 36,841 annotated examples and an average of 32.7 annotations per image, it offers extensive coverage and dense annotations, making it highly valuable for various object detection and tracking applications. The detailed annotations enhance detection capabilities, enabling the development of more accurate and reliable models for complex environments. This comprehensive dataset is versatile, suitable for applications ranging from autonomous driving to surveillance, and has significantly improved object detection performance, resulting in higher accuracy and robustness in challenging scenarios. Using this dataset, our study achieved significant results with the YOLOv7 model. The model demonstrated high accuracy in detecting various vehicle types, even under challenging conditions. The results highlight the effectiveness of the dataset in training robust vehicle detection models and underscore its potential for future research and development in this field. Our comparative analysis evaluated YOLOv7 against its variants, YOLOv7x and YOLOv7-tiny, using both the \"ISTraffic\" dataset and the COCO (Common Objects in Context) benchmark. YOLOv7x outperformed others with a [EMAIL_ADDRESS] of 0.87, precision of 0.89, and recall of 0.84, showing a 35% performance improvement over COCO. Performance varied under different conditions, with daytime yielding higher accuracy compared to night-time and rainy weather, where vehicle headlights affected object contours. Despite effective vehicle detection and counting, tracking high-speed vehicles remains a challenge. Additionally, the algorithm's deep learning estimates of emissions (CO, NO, NO\({}_{2}\), NO\({}_{x}\), PM\({}_{2.5}\), and PM\({}_{10}\)) were 7.7% to 10.1% lower than ground-truth.  
machine learning; vehicle detection; YOLOv7; SORT; emission
# Biosynthesis of Edible Terpenoids: Hosts and Applications
## 2 Microbial Chassis as Cell Factories for Edible Terpenoids
### Prokaryotic Microorganisms
#### 2.1.3 Bacillus subtilis  
_B. subtilis_, a model strain of gram-positive bacteria, possesses clear genetic information. It can be easily manipulated genetically and shows a strong ability to utilize inexpensive substrates. Since it has a fast growth rate and robust expression and secretion capabilities, it is extensively used in the production of chemicals and protein products. In addition, it has an endogenous MEP synthesis pathway and is considered GRAS [46]. Thus, it is a potential chassis for edible terpenoids.  
The introduction of heterologous MVA pathways in bacteria presents an effective strategy for enhancing IPP supply. However, the static regulatory strategies may potentially impact the titer and yield of the cell factories because of the accumulation of toxic intermediates and metabolic imbalances. To address this, researchers have devised a dynamic regulation strategy aimed at balancing the metabolic flux between the MEP and MVA pathways in _B. subtilis_. In this research, a series of genetic circuits activated by pyruvate and inhibited by malonyl-CoA were designed to respond to intracellular levels of pyruvate and malonyl-CoA, and an AND gate was used to balance the flux of the MEP and MVA pathways to reshape the IPP synthesis pathway. In the strain, BS17-P20 synthesizing menaquinone-7 (MK-7, Vitamin K2-7) with dynamically regulated central metabolic pathways, IPP synthesis pathways, and terpenoid synthesis pathways, the level of IPP was increased by 4-fold, and the MK-7 titer reached 467.2 mg/L. This dynamic cascade regulation system is also applicable to the biosynthesis of \(\beta\)-carotene; the titer of carotenoids and lycopene in the engineered bacteria BS17-P13 increased by 1.9 and 10 times, respectively [36]. In another case, all the genes in the MEP pathway were integrated on a plasmid to increase the flux of the MEP pathway. The precursor FPP supply was increased by enhancing the expression of IspA. Then, GFP was fused to the N-terminus of the amorphadiene synthase (ADS) to improve its translation efficiency. These metabolic strategies combined with medium optimization enabled _Bacillus subtilis_ to synthesize amorphadiene with a yield of 416 mg/L [37].
## 2 Methodology
### Influence of Image Overlap  
The influence of image overlap on image acquisition time is shown Figure 7a, and post processing time is discussed in next section. A quadratic growth was observed in acquisition time and post processing time when image overlap was increased (Figure 7b).  
Figure 5: Accuracy of chemical at different altitude of sensors.  
Figure 6: Mapping accuracy for different size of chemical samples.  
Following are the results about determining the speed of the sensor for different altitude. According to the manufacture of the sensor the suggested speed is based upon the percentage of image overlap user wants. The SpecGrabber, which is the software used for controlling the sensor, and calculates the suggested speed based on the altitude, field of view, swath and consider image overlap as 70%. Speed is calculated for different altitude and different overlap percentage, as shown in Table 3, and in Table 4 the suggested speed for different altitude is shown. The vehicle operator can select the speed from the Table 3 based on their requirement of overlap. Figure 8 shows the minimum, maximum and suggested speed. Table 5 discusses the flight time and number of raw images the sensor will store when scanning an area of 10 m\({}^{2}\) at a speed of 10.5 cm/s at different altitude.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Altitude (cm)** & **Suggested Speed (cm/s)** \\ \hline
40 & 4.7 \\
60 & 7.0 \\
90 & 10.5 \\
120 & 14.0 \\
150 & 17.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Altitude and suggested speed for sensors altitude.  
Figure 7: (**a**) Influence of image overlap on image acquisition time; (**b**) influence of altitude on image acquisition time.
## 2 Materials and Methods
### Precision Farming as a Motivating Scenario  
The precise management of agricultural land has been made possible due to the availability of new technologies such as GNSS, geographic information systems (GIS), sensors, automated agricultural machinery, and high-resolution imaging. Precision in the application of agricultural chemicals can improve crop quality, increase human safety, and reduce production costs. In the RHEA project, sustainable crop management is achieved via fleet of heterogeneous (_i.e._, land-based and aerial), intelligent vehicles equipped with advanced sensors and actuators. Precise application can only be achieved using advanced localization equipment and techniques. Because this study focuses on the navigation in an agriculture area, coordinate height components were not considered; however, this component is crucial in geodetic applications.  
In the RHEA scenario, the human operator controls an autonomous vehicle using a user portable device (UPD) that has a built-in low-cost localization module. The low-cost GNSS localization modules are sufficient for localizing the UPD in a clear field, similar to the RHEA scenario, with a direct line of sight (LOS) and a reduced multipath effect. However, most agricultural work is performed in fields either near forests or in forests, where shadowing due to dense foliage attenuates the direct signal. The positioning capabilities of the UPD can be enhanced by providing an external GNSS positioning system, in which the user must carry a larger system containing a special antenna, an external receiver and a battery, however, the system could be difficult to handle. Therefore, the proposed hybrid solution could be utilized in GNSS inaccessible environments, such as dense forests, where agricultural machines are used for tree harvesting or similar work.
## 3 Experimental Setup
### Materials
#### 3.1.1 Data Set  
The ALL-IDB comprises images belonging to healthy individuals and patients affected by ALL. The PBSs were collected at the M. Tettamanti Research Centre for Childhood Leukaemia and Haematological Diseases, Monza, Italy. The images were digitised with a single light laboratory microscope but coupled with two different digital cameras: an Olympus Optical C2500L and a Canon PowerShot G5. This acquisition leads to different variations in colour and brightness. Furthermore, different magnification levels, ranging from 300 to 500, were used during the acquisition process, introducing further challenges due to differences in scale and cell sizes. The data set consists of two versions named ALL-IDB1 and ALL-IDB2. The former contains the original images in JPG format with 24-bit colour depth. It is also the most challenging version since the contained images present a variable number of cells and different clusters. The number of PBS images in this version is 108, labelled by experts as 59 healthy and 49 ALL cases. ALL-IDB2 version instead is a collection of single WBC extracted from the previous version. It contains 260 images, and each has a single centred leukocyte, 50% of which are lymphoblasts. Since we were interested in emphasising direct image-based approaches for leukaemia classification, in this work, we used the ALL-IDB1 version for the CNN training and testing process. In contrast, we used the ALL-IDB2 version to create synthetic images that reproduced the cell number and distribution of leukemic PBS but without lymphoblasts. For this purpose, we exploited the pixel-wise ground truth in the form of a binary mask for ALL-IDB2 provided here (ALL-IDB2 masks, accessed on 13 November 2021), in order to extract the foreground that contained the WBC only.
# Secure Cloud Infrastructure: A Survey on Issues, Current Solutions, and Open Challenges
## 4 Security Issues in Cloud Computing Infrastructure
### Network Level Issues
#### 4.3.2 Confidentiality Issues  
Issues regarding confidentiality are rising due to the growing number of cloud users working in a multi-tenant environment, where compromising one system could lead to a chain of subsequent compromises in other systems. Sniffing attack is the most prominent issue. This occurs in a cloud environment when unencrypted packets of data are transferred between two entities in the cloud. These packets can, therefore, be captured, leading to the exploitation of confidential information. In a cloud environment, the existence of an entity with a promiscuous mode in the network node highly suggests that data in the node are being monitored by an attacker [44]. In addition, the reused IP addresses lead to the compromising of confidentiality, if not handled properly when reassigned to another user [44].
## 5 Evaluation
### Experimental Setup  
For our experiments, we used the dataset generated by the DeepMIMO [38]. The dataset is a generative dataset based on ray-tracing measurements, which is semi-customizable by user specifications of the system's parameters [38]. Our experiments were fully conducted considering the O1 (Outdoor 1) scenario of the DeepMIMO, as shown in Figure 13. Concretely, we are interested in a modern wireless communication network containing both 4G and 5G. Hence, among all available operating frequency choices in the DeepMIMO, we considered 3.4 GHz, 3.5 GHz (4G LTE Band 42 [93]), and 28 GHz (5G NR FR2 Band n257 [94]). We activated six BSs in the DeepMIMO simulation tool, where the B55 plays the role of RIS using the same method discussed in [73]. We specified the BSs, RIS, and DOI, all equipped with MIMO antennas. Detailed settings are listed in Table 1.  
Figure 13: The O1 scenario in the DeepMIMO [38].  
Considering the positions of BSs and obstacles, we specified the test areas of DOI containing both LoS and NLoS regions for BS18, as shown in Figure 14. Taking the testing point (ROW 1268, COL 91) as the center, we could equidistantly expand the boundaries to obtain test areas, where half is LoS and the other half is NLoS for BS18. We tested the localization performances in the testing area.
# Signal Detection for GSTFIM Systems with Carrier Frequency Offset +
## 3 Proposed MMSE Detectors for GSTFIM Systems
### BC-MMSE Detector
By contrast, in the proposed SC-MMSE detector, the posterior probability \(P(\mathbf{\hat{X}}_{l}|\mathbf{X}_{l}=\beta_{i})\) is first employed to decide the possible GSTSK symbol for all the subcarriers, and then the activated subcarrier is obtained by comparing posterior probabilities of the possible GSTSK symbols. However, in the proposed BC-MMSE detector, the posterior probability of block carriers is divided into two parts, namely the posterior probability of the GSTSK (activated) symbol and the posterior probability of a zero matrix, which are obtained from the posterior probability \(P(\mathbf{\hat{X}}_{l}|\mathbf{X}_{l}=\beta_{i})\). Then, the posterior probability of block carriers is obtained as the sum of the above two posterior probabilities. Meanwhile, compared to our former work in [24], the original MMSE concept is divided to two detailed implementation styles, i.e., SC- and BC-aided MMSE, for flexible configuration. However, in the next section, we will further propose a class of new MP detectors with better performance.
## 2 Materials and Methods
### Measurements, Sampling Procedure, and Analytical Methods
#### 2.2.7 Colour Evaluation  
The colour analysis of mozzarella cheese was performed by implementing a Computer Vision System (CVS), following the method by [PERSON] et al. [PERSON] et al. (2016); [PERSON] et al. (2016), on the same batch of samples used for the sensory analysis (QDA) before tasting and for each cheese-making session. Briefly, each mozzarella slice was cut in two; one half was used in order to evaluate the inner colour, and the other half was used to evaluate the outer colour of the sample. The images were captured with a digital camera (CANON EOS 450D, 12.2 Megapixel; Canon Inc., Tokyo, Japan) at a distance of 30 cm, in a black wooden box, under four fluorescent lamps at 50 cm from the sample and angled at 45\({}^{\circ}\) to ensure uniform light intensity on the sample. The camera was connected to an NEC MultySync LCD monitor with sRGB colour space (standard RGB) and a resolution of 1600 \(\times\) 1200 pixels. The monitor was calibrated, and the ICC monitor profile was created using the Eye-One Match 3.2 software. The rendering intent used was of perceptive type. Colour evaluation was performed using Adobe Photoshop CS3 software. RGB images were transformed into L\({}^{*}\), a\({}^{*}\), and b\({}^{*}\) values after acquisition via RAW photographs. Particularly, three areas of the digital image of each sample were spotted by the cursor, and the colorimetric characteristics were measured.
## 3 Results and Discussion
### Cytotoxicity Assay of AuNPs
The synthesized active compound affects the monolayer sheets of the normal cells at high concentrations (i.e., 1000 ug mL\({}^{-1}\)), whereas normal shape is recovered by lowering the dose to or below 500 ug mL\({}^{-1}\). On the other hand, the morphological shape of cancer cell lines is highly affected by lower nanogold concentrations. The most significant morphological changes concern the integrity of the monolayer sheets of epithelial cells that are damaged. After treating the cell suspension with different concentrations of AuNPs, their aspect changes with the formation of shrunk, buoyant, grainy, and spherical cells. These morphological changes could be attributed to the production of ROS due to the entrance of NPs into mammalian cells through endocytosis. ROS can damage the mitochondrial membrane and enhance the apoptotic pathways, ultimately leading to cell death or cell morphological changes [111; 112]. [PERSON] [57] also reported the possibility of AuNPs affecting the nuclei of cells (in the case of a human cervical cancer cell line, HeLa); by damaging nuclei, the NPs affect the DNA and cause S-phase arrest (affecting apoptosis through an increase in Sub-G1). Figure 7: In vitro cytotoxic efficacy of phytosynthesized AuNPs against a normal cell line (OEC) and cancer cell lines (HepG2 and MCF7) evaluated by MTT assay method. Data represent average values \(\pm\) SD (n = 3). Different letters on the bars at the same concentration indicated that the mean values are significantly different (at \(p\leq\) 0.05); ns, the mean values are non-significantly different. Figure 8: Morphological changes of normal cell lines (OEC) and cancer cell lines (HepG2 and MCF7) after treatment with various concentrations of phytosynthesized AuNPs.
# Three-Dimensional Change of Lip after Two-Jaw Surgery in Facial Asymmetry Using Facial Scanner
## 3 Results
### Correlation between the Vertical Changes in Cheilions and the Changes in Other Variables  
In the transverse axis, there were no significant correlations between the vertical changes in the cheilions (\(\Delta\)ChZ-D/ND) and the changes in all the other landmarks (\(p\) > 0.05, Table 5). In the anteroposterior axis, \(\Delta\)ChZ-D/ND showed significant correlations with  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Landmarks**} & \multicolumn{3}{c}{**Ax (T2-T1)**} & \multicolumn{3}{c}{\(\Delta\)**y (T2-T1)**} & \multicolumn{3}{c}{\(\Delta\)**z (T2-T1)**} \\ \cline{2-10}  & **Mean** & **SD** & \(p\) **Value** & **Mean** & **SD** & \(p\) **Value** & **Mean** & **SD** & \(p\) **Value** \\ \hline Hard tissue & & & & & & & & \\ U3-D & \(-\)0.65 & 1.40 & 0.040 \({}^{*}\) & \(-\)1.82 & 2.11 & 0.001 \({}^{**}\) & 0.93 & 1.72 & 0.020 \({}^{*}\) \\ U3-ND & \(-\)0.55 & 1.67 & 0.170 & \(-\)1.4 & 2.23 & 0.008 \({}^{**}\) & 1.65 & 1.94 & 0.001 \({}^{**}\) \\ U6-D & \(-\)0.35 & 1.65 & 0.338 & \(-\)1.73 & 2.36 & 0.002 \({}^{**}\) & 2.31 & 1.37 & 0.000 \({}^{***}\) \\ U6-ND & \(-\)1.30 & 1.64 & 0.001 \({}^{**}\) & \(-\)1.03 & 2.30 & 0.048 \({}^{*}\) & 3.17 & 1.35 & 0.000 \({}^{***}\) \\ Mf-D & \(-\)3.19 & 2.76 & 0.000 \({}^{***}\) & \(-\)6.14 & 4.76 & 0.000 \({}^{***}\) & 3.21 & 2.90 & 0.000 \({}^{***}\) \\ Mf-ND & \(-\)3.28 & 2.74 & 0.000 \({}^{***}\) & \(-\)7.25 & 4.88 & 0.000 \({}^{***}\) & 4.67 & 2.36 & 0.000 \({}^{***}\) \\ Me & \(-\)3.69 & 3.14 & 0.000 \({}^{***}\) & \(-\)6.20 & 4.76 & 0.000 \({}^{***}\) & 4.30 & 3.09 & 0.000 \({}^{***}\) \\ Soft tissue & & & & & & & & & \\ Ls & \(-\)0.20 & 1.35 & 0.499 & \(-\)0.36 & 1.55 & 0.286 & \(-\)0.44 & 1.28 & 0.123 \\ Ch-D & \(-\)1.68 & 2.40 & 0.004 \({}^{**}\) & \(-\)2.27 & 2.73 & 0.001 \({}^{**}\) & \(-\)0.57 & 1.93 & 0.178 \\ Ch-ND & 0.16 & 1.75 & 0.678 & \(-\)1.69 & 2.72 & 0.008 \({}^{**}\) & 0.09 & 2.38 & 0.864 \\ Li & \(-\)0.72 & 1.48 & 0.032 \({}^{*}\) & \(-\)5.43 & 3.42 & 0.000 \({}^{***}\) & 0.25 & 2.13 & 0.590 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Changes in hard and soft tissue landmarks on 3D CT and FS images between the T1 and T2 stages.  
Figure 5: Schematic diagram of the upper and lower lip planes. (**A**) The coronal view of schematic diagram, (**B**) The axial view of schematic diagram. Abbreviations: MSP, midsagittal plane; FH, Frankfort horizontal plane; Ls, labrale superius; Ch-D/ND, cheilion on the deviated side/non-deviated side; Li, labrale inferius; b \(-\) a, transverse lip asymmetry; a + b, lip width; c, vertical lip asymmetry; and d, anteroposterior lip asymmetry.