esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Therefore, when the expression of the activated form of caspase-3 is increased, the expression of the inactive form (pro-caspase-3) is relatively decreased [18]. As shown in Figure 4, the activated form of caspase-3 was much more highly expressed. The upper band expressed the intact form of caspase-3 (inactive form) and the lower band showed the cleaved form (activated form) of caspase-3. In the control group, the upper band was more highly expressed. However, in the test groups which were treated with Pobt-La1 and La2, the lower band representing the expression of the activated caspase-3 was greatly intensified, meaning that the activated protein was expressed much more strongly in the test group. The expression patterns of caspase-8 are shown in Figure 4. In the case of caspase-8, unlike caspase-3, the cleaved forms of the expressed proteins were not observed. However, a higher expression of caspase-8 was detected in the control group, showing a higher expression in the upper band than the expression level of caspase-8 in the tested groups. For caspase-8 to be effective, the Fas protein must be trimmed and activated by FasL or the agonist anti-Fas antibody, and the FADD (Fas-associated protein with death domain) protein must bind to the intracellular DED (dead effector domain) site to form DISC (the death-inducing signaling complex) [19; 20]. The activation of caspase-8 is associated with extrinsic apoptosis. The results obtained from the current study combining RT-PCR and Western blot analysis confirmed that caspase-3 was activated by caspase-8, indicating that the proliferation of HT-29 cells was inhibited through extrinsic apoptosis. [PERSON] et al. reported that the heat-killed probiotics were effective on HT-29 cells. Intrinsic apoptosis occurred as indicated by box, caspase-3, and caspase-9 expression at the mRNA level [21]. [PERSON] et al. also confirmed the effect of heat-killed probiotics on HT-29 cells, showing that the expression of caspase-3 and caspase-8 was observed at the protein level. Extrinsic apoptosis with an increased expression of cleaved-caspase-3 and cleaved-caspase-8 was shown with the same patterns [22]. The proliferation inhibitory effects of the specific fraction prepared from _Lactobacillus_ sp. were confirmed on various carcinoma cell lines by several research groups [23; 24; 25]. In the present study regarding the use of the postbiotics Pobt-La1 and La2, the proliferation inhibitory effect against colon cancer cell was confirmed [26; 27]. From the results obtained in the current study, it is expected that the anticancer effect will appear in specific components of probiotics La1 and La2. The postbiotics compared for Pobt-La1 and La2 could provide crucial information for developing anticancer research. According to studies by [PERSON] et al., the activity that appears may vary depending on what kind of postbiotics are applied [28]. Therefore, if different types of postbiotics are extracted from the strains used in this study, the possibility of additional activity or better activity cannot be ruled out.
# A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images
## 2 Methodology
### Satellite Datasets: PlanetScope, Sentinel-1, and Sentinel-2
All bands were incorporated into the classification process, except for bands 1 and 9, which are primarily sensitive to aerosols and water vapor, respectively, and have a lower spatial resolution of 60 m.  
In line with Sentinel-1, the acquisition period for Sentinel-2 was also after the rainy season, spanning from April to September 2023. The queried data underwent preprocessing to remove cloud cover from the scenes. Only scenes with clear conditions proceeded to the next stages of processing.
## 2 Materials and Methods
### Raman Apparatus and Data Pre-Processing  
Here, we provide only a brief description of the Raman apparatus employed in this paper and of the pre-processing operations carried out on the resulting spectra. Further details are available in [6]. The Raman setup employed in this work was a Thermo Fisher Scientific DXR2 xi [25] confocal Raman microscope (Thermo Fisher Scientific Inc., Waltham, MA, USA), whose setup is schematically represented in Figure 1. The Raman setup was equipped with a 532 nm laser, whose power was set within the range 5-10 mW, representing a compromise between the need to avoid tissue damage and to achieve an adequate signal-to-noise ratio.  
The back-scattered photons were collected with a 100\(\times\) objective and the out-of-focus components of the detected light were suppressed through an aperture (pinhole) of diameter 25 \(\upmu\)m. A 1200 g/mm diffraction grating allowed the separation of the spectral components from the detected light. We show a schematic representation of the Raman setup in Figure 1. Each spectrum was collected with 10 accumulations and an acquisition time of 0.2 s for each accumulation. The resulting Raman spectra had a spectral resolution of \(\sim\)2 cm\({}^{-1}\) and they were defined within the spectral interval between 400 and 1800 cm\({}^{-1}\). In Figure 2, we show images of H&E-stained sections associated with the four types of tissue examined in this work.  
The spectra were collected in grids of pixel resolution 4 \(\upmu\)m \(\times\) 4 \(\upmu\)m, within the portions of unstained samples. The final dataset included \(N_{s}=400\) spectra, 80 spectra for EC, 80 for G1, 84 for G2, and 93 for G3. We applied a baseline subtraction algorithm to the raw Raman spectra through the tool Omnisci, integrated into the application controlling the CRM. We carried out this operation by subtracting a 5 th-order polynomial. Then, we referred the spectra to the minimum signal. We did not normalize the spectra, to exploit the additional information of the absolute signal intensity for the following ML and DL classification routines.  
Figure 1: Schematic representation of a confocal Raman microscope equipped with a Rayleigh rejection filter, a pinhole, a high-resolution diffraction grating, and an electron-multiplied CDD camera as a detector. The sample is illuminated with a laser beam. The resulting scattered light is collected by an objective lens, filtered by a Rayleigh rejection filter and by a pinhole, in order to remove the out-of-focus components of the collected signal. Finally, a diffraction grating and a CCD camera allow a spectral analysis of the measured signal to be performed.
# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
## 4 Discussion
### Hardware Failure of Information System and Its Causes
#### 4.1.3 Module Failure and Its Causes  
In the specific arrangement of information system communication technology, the problem of hardware failure is also reflected in the specific operation of the module. Although the probability of such module failure is relatively small, the threat is relatively large. Once there is a threat of loss in this area, it will easily affect its overall improper operation, bring serious economic losses, and even cause information leakage [26]. Based on the occurrence of this kind of module fault, the causes are also various, such as voltage instability of the whole system, or a problem in the switch, or even the destruction of human factors, which may affect the operation effect of the module, and the resulting threat is more prominent.
# A Construction Method for the Random Factor-Based G Function
## 2 Mechanisms of the G Function  
Compared to the traditional frequency-hopping communication method that uses a pseudo-random code sequence to create the frequency-hopping sequence, the differential frequency-hopping signal utilizes the G function to generate the sequence ([PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2020). This sequence dictates the order in which the signal jumps between frequencies, directly affecting the system's ability to resist interference and the efficiency of data transmission. The current hopping frequency \(\mathrm{F_{n}}\) is determined by the previous hopping frequency \(\mathrm{F_{n-1}}\) and the information data \(\mathrm{X_{n}}\) being transmitted ([PERSON] et al., 2019). This relationship can be expressed as follows:  
\[F_{n}=G(F_{n-1},X_{n}) \tag{1}\]  
Figure 1 illustrates the transfer relationship between the various frequency points within the frequency set. Each node represents a frequency hopping point. The number of data bits (BPH) carried by the carrier in each hop allows each node to derive two BPH frequency point branches, which is called the fan-out coefficient ([PERSON] et al., 2019). With BPH = 2, there are four possible mapping routes available for each frequency hopping point. The current hopping carrier frequency point is determined by selecting one of the mapping routes based on the transmitted data information on each branch.  
The receiver is responsible for identifying the frequency of each differential frequency-hopping signal and for demodulating the transmitted information data \(X_{n}\). This is achieved by utilizing the inverse G function to demodulate the adjacent hopping frequencies \(F_{n}\) and \(F_{n-1}\). The relationship expression is as follows:  
\[X_{n}=G^{-1}(F_{n},F_{n-1}) \tag{2}\]  
The G function in a differential frequency hopping system determines the frequency-hopping rules and the performance of the frequency-hopping sequence, which in turn affects the communication capability of the communication system [24; 25]. Therefore, a good design of the G function is the key to achieving high-performance communication.
# AIR-YOLOv3: Aerial Infrared Pedestrian Detection via an Improved YOLOv3 with Network Pruning
## 3 Air-YOLOv3
### YOLOv3
#### 3.1.3 Multi-Scale Detection  
The anchor boxes design still adopts the clustering algorithm, which is divided into three scales according to the labeled ground truth boxes. YOLOv3 utilizes the upsample and concat to achieve multi-scale feature fusion, and takes the three scales of feature map, which are \(13\times 13\), \(26\times 26\), and \(52\times 52\), respectively, to predict different scale objects.  
The network structure of YOLOv3 is shown in Figure 1c. It contains the backbone network DarkNet53 and three detection headers. Furthermore, the composition and functions of the main modules are as follows:  
* **DBL** (Darknetconv2D-BN-Leaky) modules are the main components of the network. They contain a convolutional layer, a batch normalization layer, and an activatinglayer. The convolutional layer and activating layer are connected by the BN layer in the whole network except the last convolutional layer in front of the detection header.
* **Res-unit module** is composed of two DBL modules and a residuals structure, which can effectively increase the network depth.
* **ResN module** is made up of \(N\) Res-units and a DBL module.
* **Concat** is tensor splicing. Unlike adding residual, it links the middle layer of darknet and the later upsample layer, which will change the output feature map's dimension.
## 5 Results
### Results of the Proof of Concept
#### 5.1.2 Sensitivity to the Amount of Cloud Cover  
As a next step, the loss of quality in the filled images was examined with a changing amount of cloud cover (CC) (_cf._ Figure 7). The percentage of cloud cover was varied from 10% to 50% to evaluate the stability of the restoration result depending on the amount of available information for the fill algorithm. The DN of all filled images slightly tend to indicate artificial spectral innovation since they are negatively biased (Figure 8). The negative values of the DV indicate a loss of image content (Figure 9). The similarity between the original image and the filled image decreases with the increase of the percentage of cloud cover, indicated by an increase of the STD-DI (Figure 10). The Correlation Coefficient has only a small deviation but also shows a decreasing trend with increasing cloud cover (Figure 11). Also SAM only slightly increases but follows the major trend of all statistical measures (Table 3). The magnitude of the deviations compared with the particular optimum value of the statistical measure is very small, which indicates a very good image restoration result. The fill algorithm is very stable in terms of the number of available pixels for the derivation of the CFV.  
Figure 7 gives a visual overview of the results for a small image subset. The changes are very small and the restoration of the original DN evinces a very high performance. The findings of the image restoration by means of a perfect data source can be outlined as follows:  
1. The integration of an inverse distance weighting function causes a loss of image restoration quality. IDW is therefore neglected in further image restoration steps.
2. The cloud removal algorithm is capable to compensate for a high percentage of cloud cover, provided that the quality of the data source is sufficient.  
3. The set of statistical measures is not sufficient to completely describe the image quality. Therefore a visual comparison is mandatory.  
Figure 7: Visual comparison of the original image and a filled image by means of a perfect data source (_i.e._, the image itself) as a function of the amount of cloud cover (CC). (**a**) CC 10%; (**b**) Original Image; (**c**) Filled Image; (**d**) CC 20%; (**e**) Original Image; (**f**) Filled Image; (**g**) CC 30%; (**h**) Original Image; (**i**) Filled Image; (**j**) CC 40%; (**k**) Original Image; (**l**) Filled Image; (**m**) CC 50%; (**n**) Original Image; (**o**) Filled Image.  
Figure 8: Mean Bias depending on the amount of cloud cover.  
Figure 9: Difference of Variances depending on the amount of cloud cover.  
Figure 11: Correlation Coefficient depending on the amount of cloud cover.  
Figure 10: Standard Deviation of the Difference Image depending on the amount of cloud cover.
## 4 Results and Analysis
### LAI Retrieval in Saihanba National Forest Park
#### 4.2.1 GLAS LAI vs. TRAC LAI
The above two reasons caused the illusion that the observation footprint had dense canopy (i.e., large LAI); however, the reality was that the observation footprint had a sparse forest (i.e., small LAI), as shown in Figure 8a. The ground validation in this footprint was very poor; the difference between the GLAS LAI and the TRAC LAI was equal to 1.84, which was the largest of all validation footprints. Figure 9 shows an example of a comparison of GLAS observations with low and high SNR, respectively (i.e., SNR = 21 vs. 119). Low SNR means poor data quality, and it usually increases the difficulty in preprocessing. From Figure 9, we can note that the GLAS observation with low SNR has difficulty recognizing useful information from the received waveform compared to the GLAS observation with a high SNR value. Therefore, uncertainty may be introduced during the preprocessing process, such as determining the ground position, thereby affecting the accuracy of LAI derived from our method. The validation based on the TRAC measurement also proves the impact of SNR on our method, that is, the GLAS observation with high SNR value performed much better than the one with low SNR value; the difference between the GLAS LAI and TRAC LAI was 0.17 vs. 1.26. Figure 7: (**a**) The slope distribution of GLAS footprints over Saihanba National Forest Park. (**b**) The SNR (i.e., signal-to-noise ratio = i_maxRecAmp/i_sDevNsoDl) distribution of the GLAS data over Saihanba National Forest Park. (**c**) Comparison between the recalculated GLAS LAI (i.e., an integration of LAD from the canopy top to the layer 1 m above the ground) and TRAC-measured effective LAI after removing the outliers caused by a large slope and low SNR.
# Joint Embedding of Semantic and Statistical Features for Effective Code Search
## 4 Experiment and Analysis
### Research Questions  
In this work, we aim to answer the following research questions:  
* RQ1: Is JessCS effective?
* RQ2: How do influencing factors impact the effectiveness of JessCS?  
We evaluate the effectiveness of JessCS in the answer of RQ1 from two perspectives: we firstly check the accuracy of code search model, then we conduct an evaluation on the searching results from 30 real code search attempts. We also investigate how the selection of code features and dimension of embedding vectors impact the effectiveness of JessCS in RQ2.
## 5 Prediction Process
The KNN was the second-best, achieving 62.1% accuracy. These results are shown in Figure 14.  
The same prediction was in a second instance applied to Dataset 2, with the expectation that the additional variables could have some influence in the prediction performance. The Random  
Figure 14: Algorithm accuracy comparison for Dataset 1 with four independent variables with high correlation. Figure 12: The graphic that represents the best number of neighbors to choose for k-nearest neighbor (KNN), where k represents the number of neighbors. Figure 13: The random forest accuracy (identified as score), based on the estimators’ numbers. Forest algorithm was again the best performing one, having 62.42% accuracy, almost achieving the previous result. The KNN showed a decrease in accuracy performance from 62.1% to 50.8%. The second-best algorithm for Dataset 2 is the Logistic Regression, with 59.63% accuracy, a large gap from the Random Forest. With the second dataset, the prediction values are slightly lower; nevertheless, they are more meaningful, since, by replacing these variables with values, we can predict 1 out of 4 incidents with 62.42% certainty, as shown in Figure 15.  
The prediction accuracy was not as high as desirable (only around 60%). This might be the result of some specificities: (1) limited correlation levels between variables; (2) the dependent variable can take four possible values (fire (building fires), infrastructure (floods and falls), industrial-technological (gas leaks and suspicious situations), and accident (with equipment or with elevators)), which is more demanding in terms of prediction than if it was a binary variable; and (3) limited volume of data (slightly above 6300 entries).
## 2 GEO Grid ASTER EVI Products  
The developed GEO Grid ASTER EVI product generation algorithm consists of three modules: The radiometric calibration, atmospheric correction, and vegetation index modules. We describe the first two modules in Section 2.1, and then the vegetation index module, including the equations and theoretical backgrounds of the EVI, in Section 2.2.  
In the GEO Grid system, this algorithm has been incorporated into the ASTER Grid system and can be executed via the ASTER Grid portal, a web-based interface for searching and ordering ASTER products on the GEO Grid system. Details of the ASTER Grid system and portal can be found in [4].
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 2 Materials
### Reference Flood Data/Maps  
To validate our flood detection methodology, we collected reference flood data from the Copernicus Emergency Management Service [34]. The reference data consisted of various types of information, including ground data on floods in TIF format and maps generated using ESRI World Imagery, COSMO-SkyMed, Radarsat-2, and other satellite images, including Sentinel ones. The flood maps in the TIF file provided precise geographic boundaries of the flooded areas, allowing for an accurate comparison and evaluation of the flood detection results. These shapefiles contain detailed information on the extent and location of the flood-affected areas. Where this information is not available, flood maps produced using other satellite images provided a visual representation of the events. These maps have often been produced by studying Sentinel 1 images, and where possible, we considered the same data to compare results.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 7 Hydrological Application
### Flash Flood Warning  
Accurate and real-time rainfall measurement plays an important role in flash flood warning [112; 113]. A flash flood early warning system using rainfall intensity data, which are retrieved from terrestrial CMLs and the geostationary satellite in Kenya, Africa, is designed by [114], and it has been proven to be an effective measure to strengthen the resilience to climate change of developing countries. CMLs are sparsely deployed in mountainous areas and the link length is relatively long: rainfall measured by CML is the line average of the whole path, and high local rainfall intensities are smoothed, i.e., the peak rainfall intensities can easily be ignored. To resolve this problem, [PERSON] et al. [115] proposed a new method to identify the potential conditions of flash floods using only a single long microwave link integrated with weather radar. Radar measurement is first used to identify rainfall changes along the link path, and then a CML is used to quantify the rain rate. The results show that there is a close relationship between the proposed inverse-kurtosis-rain-rate relation and the flash flood response, and thus flash flood warning systems can possibly be improved.
# Real-Time Monitoring Method for Traffic Surveillance Scenarios Based on Enhanced YOLOv7
###### Abstract  
Due to the impact of scale variation of vehicle targets and changes in traffic environments in large-scale traffic monitoring systems, vehicle target detection methods often face challenges. To improve the adaptability of detection methods to these variations, we proposed an enhanced YOLOv7 for traffic systems (ETS-YOLOv7). To mitigate the effects of complex environments, we introduced the convolutional block attention module (CBAM) into the YOLOv7 framework, which filters important features in both channel and spatial dimensions, thereby enhancing the model's capability to recognize traffic object features. To address the influence of aspect ratio variations in vehicle targets, we replaced the original complete intersection over union (CIoU) with wise intersection over union v3 (WIoUv3), eliminating the aspect ratio consistency loss and enhancing the model's ability to generalize and its overall performance. Additionally, we employed the compact layer aggregation networks (CLAN) module to replace the efficient layer aggregation networks (ELAN) module, reducing redundant computations and improving computational efficiency without compromising model accuracy. The proposed method was validated on the large-scale traffic monitoring dataset UA-DETARC, achieving a mean average precision (mAP0.5-0.95) of 90.2%, which is a 3% improvement over the original YOLOv7. The frames per second (FPS) reached 149, demonstrating that the proposed model is highly competitive in terms of detection efficiency and vehicle detection accuracy compared to other advanced object detection methods.  
traffic detection; surveillance scenarios; attention mechanism; roadside perception +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Real-Time Monitoring Method for Traffic Surveillance Scenarios Based on Enhanced YOLOv7. _Appl. Sci._**2024,** _14_, 7383. [[https://doi.org/10.3390/app14167383](https://doi.org/10.3390/app14167383)]([https://doi.org/10.3390/app14167383](https://doi.org/10.3390/app14167383))