esa-sceva/satcom-chunk-collection huggingface dataset:
# Ribosomal Hibernation-Associated Factors in _Escherichia coli_
## 2 100S Ribosome
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Mr** & **Gene Position** & **Main Function Related to 100S** \\  & & & **Ribosomes** \\ \hline \hline \multicolumn{4}{c}{**Factors directly involved in 100S ribosome formation**} \\ \hline RMF & 6.5 k & 1016137–1016304 & Dimerizing ribosomes \\ \hline HPF & 10.8 k & 3346028–3346315 & Stabilizing 100S ribosomes \\ \hline \hline \multicolumn{4}{c}{**Major factors indirectly involved in 100S ribosome formation**} \\ \hline RaiA & 12.8 k & 2735810–2736151 & Inhibiting ribosome dimerization \\ \hline RelA & 83.9 k & 2910073–2912307 & Synthesis of (p)ppGpp \\ \hline YqjD & 11.1 k & 3248031–3248336 & Localizing ribosomes to membrane \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Factors related to ribosome hibernation in _Escherichia coli_. is named the hibernation stage, and ribosomes in this stage are known as hibernation ribosomes [12]. Information on the detailed structure of the 100S ribosome in Gram-negative organisms is limited. The structure of the _E. coli_ 100S ribosome was revealed using cryo-electron microscopy in 2010 [25]. The 100S ribosome is formed by two 70S ribosomes that contact each other at the 30S subunit (see Figure 1). At the junction of the 30S subunits, the ribosomal protein S2 of one subunit fits into the pocket formed by the ribosomal proteins S3, S4, and S5 of the other subunit. This pocket is a pathway for mRNA; therefore, mRNA cannot bind to the 100S ribosome if this pocket is blocked by ribosome dimerization. In addition, an extra dense mass has been observed in the 30S subunit of the 100S ribosome, which was not present in the structure of the previously observed 70S ribosome. These structural features of the 100S ribosome have also been observed by cryo-electron tomography in situ [26]. Recent structures determined that using cryo-electron microscopy with an improved resolution visualized RMF and HPF on the _E. coli_ 100S ribosome, as shown in Figure 1[18]. This structure revealed that RMF and HPF mediate 70S dimerization indirectly by stabilizing the ribosomal proteins S1 and S2, in contrast to those observed in Gram-positive bacteria in which the long-form of HPF directly participates in the dimerization interface [16; 17; 19; 20; 34]. The structure also indicated the presence of deacylated E-site tRNA and ribosomal protein S1 on the 100S ribosome. This ribosomal protein S1 is thought  
Figure 1: Structure of the 100S ribosome in _Escherichia coli_ (PDB ID: 6H58). The 100S ribosome is formed by 30S subunits of the two 70S ribosomes (50S-30S-30S-50S). RMF, the key factor for ribosome dimerization, is shown in green. HPF, a factor that stabilizes the 100S ribosome, is shown in red. Ribosomal proteins S1 and S2, which play important roles in forming the 100S ribosome, are shown in yellow and blue, respectively. The tRNA of the E site bound to the 100S ribosome is shown in pink. to be the extra-dense mass mentioned above. Interestingly, S1 has an inactive structure for translation initiation and contacts the RMF [18].
## 3 Results and Discussion
### Measurement of the DPD Concentration in Various Environmental Samples  
To test whether the method developed in this study can be used to measure DPD concentrations in environmental samples, we selected environmental samples presumed to contain DPD: cell cultures, such as _E. coli_ K12, _Aerononas_ sp. YB-2, _Enterobacter_ sp. YB-3, and activated sludge in MBRs.  
As shown in Figure 4, the DPD concentrations were 0.38 (\(\pm\)0.05) uM for K12, 0.48 (\(\pm\)0.05) uM for YB-2, and 0.32 (\(\pm\)0.06) uM for YB-3, showing relatively narrow standard deviations and thus a good precision. After culturing the microorganisms (K12, YB-2, YB-3, and activated sludge) for approximately 12 h, considerable amounts of DPD were found to have accumulated in the supernatant. In particular, the DPD concentration was 0.28 (\(\pm\)0.16) uM for the activated sludge in this study. In previous reports, (i) the DPD concentrations determined by HPLC-FLD for _E. coli_ MG1655 and _V. harveyi_ BB120 were 3.7 uM (486.1 ng/mL) and 35.8 uM (4725.6) ng/mL, respectively [29], and (ii) the AI-2 levels in the activated sludge in MBR liquor determined by LC-MS ranged from 0.0038-0.014 uM (0.5-1.8 ng/mL) [23].  
Figure 3: (**a**) Chromatograms of the AI-2 derivative (1-(3-methyl-benzo[g]quinoxaline-2-yl)-ethane-1,2-diol), a reaction product of DPD and DAN, as a function of DPD concentration (HPLC with a UV detector at 268 nm). (**b**) Plot of peak area versus DPD concentration.
## 4 Discussion
### Control of Support Pressure Based on the Surface Displacement  
In the complex urban environment, especially in areas with high requirements for ground deformation, it is not enough to control the support pressure only by the pressure value of the limit state. Generally, during the construction of shield tunneling, the deformation caused by the support pressure of the excavation face accounts for 10% to 20% of the total deformation [1,2]. Therefore, reasonable determination of the support pressure can effectively control the ground deformation in front of the excavation face.  
According to the results obtained in this paper, when the pressure increases to about 1 to 4 times the initial pressure, there exists a slight heave on the ground surface, and when the pressure continues to increase to more than 5 times, the surface deformation begins to increase significantly. However, at the same time, it will also bring about the surface settlement directly above the excavation face. Furthermore, when the burial depth is large (\(\geq\)1.5D), the ground deformation caused by the excess support pressure is difficult to extend to the surface, but it will cause great disturbance in the ground, which may affect the structures in the ground. Therefore, the effect of reducing the initial settlement by increasing the support pressure is limited, and may even further cause greater surface settlement directly above the excavation surface. In the case of insufficient support pressure, the ground deformation is more sensitive compared with the excess support pressure. The shallower the buried depth, the more sensitive the ground deformation as well as the greater the range of ground deformation disturbance. In addition, when the buried depth of the tunnel is large, the ground deformation does not extend to the surface once the support pressure reaches the limit value, but as the excavation surface displacement continues to increase, the ground deformation begins to increase sharply. Hence, when the buried depth is shallow, the support pressure needs to be strictly controlled to avoid being insufficient. When the burial depth is large (\(\geq\)1.5D), if the supporting pressure is insufficient, it is necessary to pay attention to the discharge amount of soil to avoid further accelerating the ground deformation disturbance.  
In practical engineering, the limit of maximum ground surface displacement induced by supporting pressure can be determined by the standard applied in the project. The supporting pressure control coefficient m can be derivated through the data of the test section and then the determined relationship between the support pressure and the maximum surface deformation can provide theoretical guidance for construction. However, it needs improvement and greater applicability through a lot of engineering practices.
# A Survey of Vehicular VLC Methodologies
## 2 Visible Light Communication
### VLC Channel  
In a VLC system, the connection between the transmitting device and the receiving device occurs through the optical carrier medium in free space. It is important to note that light primarily functions as an electromagnetic wave, and its strength diminishes with the square of the distance, similar to other wireless communication systems. When designing VLC applications, several crucial factors need to be taken into account, particularly the disparities between indoor and outdoor environments, with interferences having a more pronounced impact on indoor applications [46; 47]. Interferences in indoor settings can be caused by reflections from walls or objects within the space, leading to symbol interference, or by neighboring users, resulting in co-channel interference. On the other hand, outdoor applications are influenced by various surrounding environmental factors, which necessitate consideration [29]. These factors include the noise generated by sunlight and artificial light sources, as well as attenuation caused by different weather conditions in the atmosphere, user movement or traffic, and the density of users.  
On the other hand, the light intensity profile is an essential factor through which the channel is described in VLC systems because it mainly affects the interactions of environmental elements with the emitted rays during their path until they reach the PD. The ideal Lambertian model usually describes indoor lighting, because it has uniform radiation. However, this does not apply to all LED light sources with wildly irregular light patterns (asymmetric patterns), which are usually the patterns in outdoor lights. However, photodiode surface areas play an essential role in the VLC channel because they mainly affect the total number of captured beams and thus affect the real power collected. Therefore, the channel modeling method for VLC systems is essential to evaluate the system's performance. The modeling method is applied on the basis that the response is a non-negative pulse h(t) > 0 and at a Linear Time-Invariant (LTI), as shown in Figure 3 and expressed as follows [26]:  
\[\text{Y(t)}=\text{RX(t)}\times\text{h(t)}+\text{N(t)} \tag{1}\]  
where Y(t) represents the electrical signal that has been received, R represents the responsivity of a photodiode, X(t) denotes the input light power, h(t) represents the channel impulse response, (*) represents convolution, and N(t) indicates the noise of the receiver (additive white Gaussian noise (AWGN)).  
Moreover, light rays can be refracted or reflected through the channel or scattered in sub-rays. Therefore, the VLC signal arriving at the receiving device is the sum of two essential components: the first component is a line-of-sight (LoS), where the light signal reaches the receiver directly, and the second component is non-LoS (NLoS), where the light signal reaches the receiver after reflections and scattering due to obstacles [26].
## 5 Results and Discussion
### Classification Accuracy with Regard to Data Sources and Features
The rest of the object-based features used in this branch (_i.e._, GNDVI in November, mean NIR in September and Normalized Differential Senescent Vegetation Index (NDSVI) in May) were aimed at discriminating tomato, cucumber and aubergine crops. The last box in Figure 6 still contains 419 objects classified as tomato in the DT with nine terminal nodes. The whole DT, presenting 34 terminal nodes, continues from that point using the other 23 features extracted along different periods, such as mean reflectance values (NIR, Blue, SWIR2 and SWIR1), standard deviations (SWIR2, blue, green and NIR) and VIs (NDSVI, Normalized Difference Tillage Index (NDTI), GVI and GNDVI). In view of the above, the identification of under-greenhouse pepper crops can be carried out with high success rates, mainly based on the crop management techniques applied during the growing cycle for each agricultural region. In our test field, an OA of 92.7% and a kappa coefficient of 0.81 were achieved considering only two classes (pepper and others) by using Landsat 8 time series with no GLCM strategy. In addition, a very simple DT (similar to the top part of Figure 6) with only six terminal nodes and only five features (_i.e._, AGO11 mean SWIR2, JUL26 mean SWIR1, JUN24 mean GREEN, SEP12 SD BLUE and JUL26 GVI) were required for the achievement of the aforementioned results.
# Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories
## 2 Materials and Methods  
### Research Site and Tomato Cultivation  
The experiment in this study was conducted in the sunlight-type plant factory of the National Agriculture and Food Research Organization (36.025399\({}^{\circ}\) N, 140.101125\({}^{\circ}\) E), Tsukuba, Ibaraki, Japan. The eave height of the plant factory was 5.1 m, and the total area was 2551 m\({}^{2}\) (63 m \(\times\) 40.5 m). The whole building was covered with an ethylene tetrafluoroethylene film (F-clean GR diffused type, AGC Greentech, Tokyo, Japan), including the roof and the glass on the walls. The plant factory was divided into eight small compartments to accommodate several hydroponic systems and crops [23]. One small compartment with a growing area of 162 m\({}^{2}\) (9 m \(\times\) 18 m) was employed for cultivating the tomato plants used in this experiment. The Ubiquitous Environment Control System was adopted to control the environment in the growing house. The temperature target was set between 14 \({}^{\circ}\)C and 25 \({}^{\circ}\)C. An automatic shade curtain (LS screen, XLS16, Seiwa Co., Ltd., Tochigi, Japan) was used to shield the light when the outdoor solar radiation was greater than 1.2 kW\(\cdot\)m\({}^{-2}\).  
The tomato plant samples were selected from five tomato cultivars for investigation in this experiment. They included \"CF Momotarou\", \"Rinnka 409\", and \"DR03-103\", which are Japanese tomato cultivars that are produced for consumption as raw food; and \"Tomimaru mucyo\" and \"Endeavour\", which are tomato cultivars from the Netherlands that are produced for processing and cooking. All of the samples were seeded on July 13, 2016. After three days of processing under a dark condition at 28 \({}^{\circ}\)C, with hastening by sprouting equipment, all the tomato plants were moved to a \"Nae Terrace\" to raise the seedlings for 21 days. They were then transplanted in sunlight in a plant-factory growing house on August 12 after secondary raising of the seedlings in the grow room. A rock wall slab and high wire (3.4 m) were adopted in this experiment. The nutrient solution was initially set to 1.0 dS/m\({}^{-1}\) and then gradually increased to 2.8 dS/m\({}^{-1}\). The fruit setting was increased by using 4-chlorophenoxyacy acetic acid (4-CPA), a plant growth hormone (0.15%, Ishihara), until the third fruit setting. Then the pollination was conducted by bees.
# Borophene Is a Promising 2D Allotropic Material for Biomedical Devices
## 2 Main Properties of Borophene
### Borophene in Sensors for Medical Devices  
Borophene could be applied for the production of microelectronic sensors and biosensors with high sensibility in signal transmission and detection.  
Recent studies have investigated the crystalline structures that characterize B\({}_{36}\) borophene, as it can be highly interesting in translational applications related to physics, chemistry, and other fields of microelectronics [25]. Specifically, the encouraging results obtained from B\({}_{36}\) borophene in the realization of new biosensors for gas detection have been the first step for further studies promoted within the scientific community. Formaldehyde (HCOH) is known to be highly toxic; however, it is commonly used in many industrial and medical fields because of its high thermal stability and good chemical reactivity. The potential use of B\({}_{36}\) borophene as a HCOH biosensor was evaluated by analyzing its consolidated hexagonal hollow structure; specifically, the energy absorption of different HCOH was evaluated by plotting the profiles of LUMO (lowest unoccupied molecular orbital) B\({}_{36}\) borophene and HOMO (highest occupied molecular orbital) B\({}_{36}\) borophene. In this context, B\({}_{36}\) borophene can reliably play the role of a high-conductivity semiconductor, thus generating an electrical signal in the presence of HCOH (Figure 4).  
Finally, the use of borophene for the improvement of 2D nanometric devices has been recently reported [26], in which the hexagonal structure of B\({}_{36}\) borophene showed excellent properties, making it ideal for 2D nanostructures to be integrated into biometric sensors.  
Figure 4: Borophene can reliably play the role of a high-conductivity conductor, thus generating electrical signals in the presence of samples to analyze.
# A Binaural MFCC-CNN Sound Quality Model of High-Speed Train
## 3 Methods
### Bitaural MFCC-CNN Sound Quality Model  
The proposed binaural MFCC-CNN sound quality model was built on MATLAB to study the relationship between the subjective evaluation and HST interior noise. The  
Figure 10: MFCC feature extraction process.  
Figure 9: Mel-scale filter banks.  
architecture is displayed in Figure 11. The MFCC of the sound signal was used as input to the model.  
In order to train a CNN model better, original sound data from each measuring point were divided into multiple one-second segments to generate more training data. Each recorded interior noise signal was split into 30 noise samples, and a total of 660 noise samples are obtained. HST interior noise can be regarded as stationary signals at the same speed, so it was reasonable to assign the same subjective evaluation score to the noise samples from the same measuring points [19].  
The signal was framed into 25 ms each frame with an overlap length of 15 ms, so there were 98 frames in each sample. Twenty-six filter banks were applied to each frame, so 26 MFCC coefficients were extracted. Thus, the input data size was 98 \(\times\) 26 for each input channel.  
The size of convolutional layers C1-C4 was 3 \(\times\) 3 with a stride of 1 and no padding. The number of filters in C1 and C3 was 16. The number of filters in C2 and C4 was 32. The ReLU activation function was applied after each convolutional layer. The method in pooling layers P1-P4 was max pooling. The size was 3 \(\times\) 3 with a stride of 2 and no padding. The number of neurons in Fc1 and Fc2 was 50. An additional layer was implemented after Fc1 and Fc2 to simulate the summation of the intensity of sound. It adds inputs from the previous layer. The size of Fc3 was set to 5 for classification because there are 5 annoyance levels. The output layer contains the softmax layer and classification layer.  
To validate the binaural MFCC-CNN model, 80% of noise samples in each scale were put into the training data set, while the remaining 20% of noise samples were used to test the performance of the training model. Some training parameters need to be specified before training. The initial learning rate was 0.005, and it dropped to its half every 50 epochs. The maximum epoch was 1000 epochs. The mini-batch size was 32. The training used a single Nvidia RTX 3090 graphic card.
## 2 Conventional Direction Detection Technology
### Marker-Based Approaches  
A marker-based approach detects the direction of a ground station indirectly, wherein markers are placed on the ground stations. There are two methods for marker detection: one using contour information and the other using other methods.  
An AR marker is detected by acquiring contour information through image processing. AR markers cause lower error detection because of the use of contour information, and the position of a marker can be determined more precisely. For instance, an ArUco marker relies on the thresholding of image contours and polygon extraction to detect markers from input images. Conversely, if the contours are blurred due to low resolution or motion, the performance of marker detection is significantly degraded. In fact, it has been observed that the detection performance of normal ArUco markers degrades as motion blur increases in intensity [8]. A number of studies address this issue. For instance, a method for detecting markers that are not identified due to motion blur has been proposed using deep learning (DL) [9]. DL is a machine learning technique employed for object detection in the field of image processing. Another approach utilizes circular markers for the detection of unclear marker images in underwater environments [10].  
Given that AR markers are detected and tracked as objects within an image rather than as markers, the technique of object tracking, which is an image processing method for monitoring specific objects in a degraded image, can be employed. This work concerns the tracking of moving objects, such as cars, in an image. One example of object tracking is optical flow, which can estimate the vector of movement of an object, such as a marker. Indeed, [PERSON] et al. demonstrated that a camera attached to a two-axis gimbal can track a small UAV using optical flow [11].
# Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model
## 2 Materials and Methods
### Yolov8  
The YOLOv8 algorithm [24], the present day in the YOLO sequence [25], offers 5 vital models: YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. In comparison to different algorithms in the YOLO series, its detection concepts resemble these of YOLOv5 [26] and YOLOv7 [27], consisting of 4 major components: input, backbone, neck, and head. Figure 2 illustrates the shape of the YOLOv8 model.  
The input undergoes preprocessing, including data augmentation, before entering the backbone network for feature extraction. The neck module consolidates the extracted features to achieve elements of different sizes (large, medium, small), which are then forwarded to the detection head for the result output. YOLOv8's backbone network comprises convolutional layers, C2f, and SPPF (Spatial Pyramid Pooling-Fast) modules. The C2f module, derived from YOLOv5's C3 module and YOLOv7's ELAN module, preserves the original C3 module's transition layer shape while modifying the computation blocks. The SPPF module utilizes pyramid pooling to pool the input feature map, generating  
Figure 1: Illustration depicting the structure of the forest fire dataset.  
Figure 2: The architecture of YOLOv8.  
sub-feature maps for specific pooling sizes, which are combined to create a large feature map, capturing contextual information at distinct scales. The neck module in YOLOv8 adopts the PAN-FPN (Path Aggregation Network with Feature Pyramid Network) design, enhancing semantic features through characteristic pyramids and FPN cascading down, and propagating finding elements up through PAN. In comparison to YOLOv5, YOLOv8 replaces the PAN-FPN upsampling stage's C3 module with the C2f module, eliminating the CBS \(1\times 1\) convolutional structure. It directly integrates characteristics sourced from various levels of the backbone into the upsampling procedure, thereby amplifying the detection prowess for targets exhibiting diverse sizes and shapes. In the Head module, YOLOv8 adopts a decoupled approach, removing the object branch while preserving solely the decoupled classification and regression losses. Furthermore, YOLOv8 transitions from anchor-based to anchor-free, eliminating the need for predefined anchor boxes, making the regression of bounding box loss more challenging.  
Regarding training data augmentation, YOLOv8 incorporates the concept proposed by YOLOX [28], disabling mosaic augmentation in the final 10 training rounds. In addition to concat augmentation, it supports MixUp [29] and CopyPaste data augmentation. Mixed data augmentation generates new vectors and labels through random selection and linear interpolation, while CopyPaste data augmentation involves copying portions of an image and pasting them onto other images.