esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Segmentation and Feature Selection for Object-Oriented RF Model  
Image segmentation parameters include scale, compactness, and shape [55], which has great influence on the classification accuracy. The scale parameter determines the maximum size of the created objects, and the unit of scale parameter is indicated by pixel [27]. When the scale of segmentation is too small, the number of objects and the amount of computation increase greatly. On the contrary, if the segmentation scale is too large, the number of objects will decrease, which easily causes different ground objects to be merged into the same object and reduces the classification accuracy [49]. In addition, users apply weights range 0-1 for the compactness and the shape parameter to adjust the smoothness and the spectral homogeneity of an object [47]. By testing several different segmentation parameters, we found that setting the scale parameter to 30 with the compactness and shape parameter set to 0.1 and 0.6 can achieve better segmentation performance. [PERSON] et al. [27] also point out that, for forest objects, the scale of 30 and the shape with less weight are conducive to classification. In this study, eCognition Developer 8.64, an image-classification software, was used to perform segmentation.  
In this specific feature extraction process, minimum noise fraction (MNF) transformation was implemented on hyperspectral images. The previous three principal components are attained, of which signal to noise ratio (SNR) is 87.69%, 75.65%, and 20.47%, respectively. Then, according to the spectral features of tree species [1], we chose different bands for vegetation indices calculation [1,56]. The vegetation index we selected included normalized difference vegetation index (NDVI) [33,50,56], ratio vegetation index (RVI) [33,51], and enhanced vegetation index (EVI) [27]. Finally, 38 features were selected and extracted as shown in Table 2.  
Figure 4: (a) Impact of kernel size on accuracy, and note that the \(y\)-axis scale of the six figures is different; (b) impact of layer on classification accuracy and efficiency  
The extracted features were used to calculate the contribution rate based on the sample data (Figure 5). As shown in Figure 5, the first 10 features alone can achieve good accuracy. Therefore, we selected the first 10 features for training the random forest model: EVI, Band 24, Band 26, Band 27, NDVI, Band 30, Band 29, Band 16, and MNF1. We input the first 10 features into the random forest classifier for training and classification. Although the previous three principal components of minimum fraction are attained, only the first PC was selected to participate in the final classification.
## 3 Threat Model and Privacy Protection Framework  
### Threat Model  
Figure 1 shows the basic model of the SC system. There are three participants: requesters, workers, and the SC-server. Firstly, the worker submits their location to the SC-server, which collects and updates a dataset of worker locations. Then, requesters publish their tasks on the SC-server. Next, the SC-server queries the worker locations dataset to assign tasks as they are received from requesters. Finally, workers travel to the designated locations to execute the tasks.  
In SC, whether a worker is willing to accept a task or not may depend on the distance between the worker and task. Therefore, the SC-server must obtain the location of workers when assigning a task. However, locations are very important private information for the workers. From the workers' locations, attackers may infer the workers' home or work places, political views, religious inclinations, etc. A worker may not be willing to send their locations to the SC-server with a guarantee of location privacy.  
Intuitively, the SC-server cannot be fully trusted. An unpredictable loss may occur to workers when the SC-server leaks workers' locations due to system security vulnerabilities. Additionally, the SC-server may use these locations illegitimately because the location contains commercial value.
## 3 Experimental Section
### Gene Functional Annotation  
To assign putative gene function, unigenes were searched against the NR (NCBI non-redundant protein sequences), NT (NCBI nucleotide sequences), TAIR10, PFAM (Protein family; Available online: [[http://pfam.sanger.ac.uk/](http://pfam.sanger.ac.uk/)]([http://pfam.sanger.ac.uk/](http://pfam.sanger.ac.uk/))), and Swiss-Prot (A manually annotated and reviewed protein sequence database; Available online: [[http://www.ebi.ac.uk/uniprot/](http://www.ebi.ac.uk/uniprot/)]([http://www.ebi.ac.uk/uniprot/](http://www.ebi.ac.uk/uniprot/))) databases using BLAST software with an _E_-value cutoff of \(10^{-5}\)[68]. Himmerscan was adopted for PFAM annotation, and Blast2 GO was used for GO annotation (Gene Ontology; Available online: [[http://www.geneontology.org/](http://www.geneontology.org/)]([http://www.geneontology.org/](http://www.geneontology.org/))) [69] with the same _E_-value. To evaluate the completeness of the library and the efficacy of the annotation process, the annotated sequences were searched for the possible functions involved in KOG/COG (Available online: [[http://www.ncbi.nlm.nih.gov/COG/](http://www.ncbi.nlm.nih.gov/COG/)]([http://www.ncbi.nlm.nih.gov/COG/](http://www.ncbi.nlm.nih.gov/COG/))) classifications. To determine which pathways are active in leaves and roots, the annotated sequences were mapped to the reference pathways in KOG/COG, KO (KEGG Ortholog database; Available online: [[http://www.genome.jp/kegg/](http://www.genome.jp/kegg/)]([http://www.genome.jp/kegg/](http://www.genome.jp/kegg/))).
## 5 Discussion  
### Analysis of Sensitivity Law on Navigation Receiver  
Although the working frequency point of the navigation receiver measured by the proposed sensitivity criterion and measurement method deviates from its standard working frequency point, the deviation is not significant, which is consistent with the results of sensitivity studies of other equipment involved in the previous relevant literature [20,21]. The obtained critical interference threshold curve is not completely consistent with the sensitivity law of the general tested equipment. In most of the sensitivity studies, there is only one sensitive frequency band. However, the tested navigation receiver is an exception. The sensitivity curves of the tested navigation receiver in this study present two U-shaped sensitive frequency bands: a working frequency band which is extremely sensitive to electromagnetic radiation, namely, the first sensitive frequency band, and another wider sensitive frequency band including the working frequency band, namely, the second sensitive frequency band. The bandwidth of the second sensitive frequency band is about  
Figure 4: Sensitivity curves of \(-90\) dBm and \(-110\) dBm working power.  
Figure 5: Sensitivity curves of in band.  
10 times that of the first sensitive frequency band. Except to the first sensitive frequency band, the second sensitive frequency band on each of the sensitive curves shows two steps in general, where the sensitive threshold on the right side of the second sensitive frequency band is higher than that on the left side. This is a unique sensitivity law of the tested navigation receiver in this study. Multiple sensitive frequency bands also appeared in a previous relevant study [9], which is considered to be the influence of the internal mixing and crystal space radiation on the receiver. Meanwhile, its multiple sensitive frequency bands are all narrow and they appear symmetrically on the left and right sides of the working frequency point. However, in this study, the sensitive frequency band is wider than the result in previous study and mainly appears on the positive frequency offset side. The special sensitive phenomenon may be due to the pass-band design of the RF front-end filter. Yet, this design is harmful for anti-jamming because of the wide sensitive frequency band. If we realize that it intends to provide a backup of the working band, there is no transition band here. The internal chips of the tested navigation receiver are highly integrated. Although the sensitive bandwidth design intention of the tested navigation receiver cannot be explained more reasonably in this study, the unique sensitive law of the tested navigation receiver found in this study is of great significance to its subsequent research, especially for effect prediction research.
# The Effect of the Improvement Technology on the Quality of Midu Pork Roll
## 3 Results and Discussion  
### The Influence of Process Optimization on the Physicochemical Analysis  
The acidity of MPR has a great impact on its overall flavor. A lower pH value will reduce the sensory score of the product [22; 23]. As is shown in Table 2, the pH of the T1 and T2 were 5.84 and 5.79, respectively, and for C, processed by traditional technology formula, it was 4.26, which was due to the long fermentation during the traditional process. The sour taste mainly came from lactic acid bacteria, which was influenced by the fermentation temperature and time [24]. For the traditional MPR, it is the long fermentation time that causes low pH, and many consumers can hardly accept it. Therefore, in this study, the fermentation time and temperature of test groups were adjusted to change the pH of MPR.  
From a practical point of view, Aw measures the amount of water available for microbial metabolism, which controls the pathogen and spoilage microorganism growth and reproduction [25]. [PERSON] et al. [26] reported that products have a longer shelf life and a higher quality with lower Aw. As Table 2 shows that the improved process and formulas had no effect on the ratio of free water and combined water of MPR. The study reported that the appropriate range of Aw along the fermentation and ripening processes is considered fundamental to achieving product stability [27]. The Aw values of the final MPR were slightly higher than those described by other authors [28; 29]. However, after sterilization and modified atmosphere packaging, it is expected that a slightly higher Aw will not affect the shelf life of MPR.  
The differences in water content, cooking loss rate and yield were used to compare the quality differences of MPR in three groups. Water content, cooking loss rate and the yield values of the three samples were significantly different (\(p<0.05\)). The differences were mainly attributed to the addition of sodium hexametaphosphate and sodium alginate, water retaining agents, which played an important role in the improved process. The addition of the water-retaining agent improves the water holding capacity of MPR, reduces the loss of product nutrients and increases product quality. The results are consistent with those from [PERSON] et al. [30], who reported that adding phosphate to cured meat products could increase water binding, thereby improving the product yield and reducing the cooking loss.  
Color, as a key parameter for understanding images and describing objects, was essential in areas such as food quality assessment and real-time monitoring of food processing [31]. The color of meat is associated with several factors, such as pH and the iron state of pigment [32; 33]. In this study, the \(L^{*}\) value of T1 and T2 were significantly lower than C (\(p<0.05\)), whereas the \(a^{*}\) and \(b^{*}\) values of T1 and T2 were higher than C (\(p<0.05\)). Raw meat was kneaded for 0.5 h to make the red yeast rice pigment and meat fully mixed, which would have a certain effect on the bright red color of MPR.
# A Systematic Experimental and Computational Analysis of Commercially Available Aliphatic Polyesters
## 2 Materials and Methods
### Molecular Weight Determination  
Molecular weight distributions were determined by means of gel permeation chromatography (GPC) using a HPLC 6000 (Waters) with RI detector 1260 (Agilent) equipped with a column PLgel 5 \(\upmu\)m MiniMIX-C 250 \(\times\) 4.6 mm and a pre-column PLgel 5 \(\upmu\)m MiniMIX-C 50 \(\times\) 4.6 mm (Agilent). Chloroform was employed as eluent at a flow rate of 0.3 mL min\({}^{-1}\), sample concentration of 5 mg mL\({}^{-1}\), and an injection volume of 10 \(\upmu\)L. The calibration curve was obtained using a polystyrene standards kit (Agilent) in the Mw range of 0.5-3000 kDa.
# Implementation of Tools for Lessening the Influence of Artifacts in EEG Signal Analysis
## 4 Implementation
### Complete Removal of Independent Components: fa_ica_removed_components_method  
This function completely removes the ICs of EEG signals with a high correlation with EOG signals. To this end, the correlation coefficients between the ICs of EEG signals and EOG data are obtained, sorted, and the ones with the largest amplitude are removed to obtain the clean data.  
The usage of this function is shown next:  
_cleaned_data = fa_ICA_removed_components_method(data_eeg.fg)_: this function removes ICs from the input EEG data based on correlation and draws (if _fg_ = 1) a figure with the correlation values between the EEG and EOG signals, before and after the process.  
This function makes use of the following:  
* _ft_selectdata_: Fieltrip function to select EOG data.
* _ft_componentanalysis_: Fieldtrip function that obtains ICs from EEG data for all channels.
* _fta_corroeff_: Ad hoc function that calculates correlation coefficients (see Section 4.2).
* _fta_select_comp_from_coeff_: _correlation_: Ad hoc function to select ICs with a high correlation between EOG signals and ICs (see Section 4.3).
* _fta_channels_from_ica_reject_components_: Ad hoc function to reject components previously selected, e.g., using _fta_select_comp_from_coeff_: _correlation_ (see Section 4.4).  
The steps followed by this method are shown next:  
1. Obtain the vertical and horizontal electro-coulogram channels: VEOG and HEOG.
2. Obtain the ICs from all EEG channels using the Fieldtrip function _ft_componentanalysis_.
3. Calculate the correlation coefficients between the ICs and EOG signals using the function _fta_corroeff_ (see Section 4.2). A matrix of coefficients is returned, where each row corresponds to each EOG channel and each column to the ICs of the EEG signals.
4. The sum of all the modulus of correlation coefficients of the EOG channels with each of the independent components is obtained.
5. ICs to be removed are selected using the function _fta_select_comp_from_coeff_: _correlation_ (see Section 4.3). To do this, outliers are selected. In addition, the sum of the correlationcoefficients is ordered from highest to lowest to show these components can be selected using a different criterion. These components are the ones selected for elimination.
6. The selected ICs are removed using the function _fa_channels_from_ica_reject_components_ (see Section 4.4). This function reconstructs the EEG channels from ICA components, except those selected for removal. The inputs to this function are as follows: 1. _data_eeg_: Fieldtrip-structured data with EEG data to remove ocular artifacts. 2. _fig_: Integer (optional) scalar value (1 or 0) to plot, for illustration, the sum of the correlation coefficients before and after applying the method. The output of the function are as follows: 1. _cleaned_data_: Fieldtrip-structured data with less influence from ocular artifacts. In Section 5, the scripts of the Matlab code will be shown.
## 3 Results and Discussion
The satisfactory agreement with the experiments confirms the effectiveness of the pre-processing choices, which were based on reproducing the plate geometry using a validated forming procedure and evaluating the impact of various parameters through sensitivity analysis. The global percentage range error is within \(\pm 4.4\%\), with absolute differences below 1 kPa and experimental pressure drops up to 50 kPa; as reported in [13], the measurement uncertainty was 1%. Further investigation revealed another notable feature. Figure 12 shows a contour of the wall shear stress \(\tau_{w}\), which is generally defined as follows:  
\[\tau_{w}=\mu\left(\frac{\partial v}{\partial y}\right)_{y=0} \tag{6}\]  
where \(v\) is the velocity tangential to the wall and \(y\) the normal direction. Thus \(\tau_{w}\) is the force-per-unit area acting on the surface due to friction and serves as an indicator of a boundary-layer separation when it approaches zero. Figure 11: Pressure drops results for the entire channels. Figure 10: Comparison of total pressure trends along the S1 channel width, resulting from **(a)** the \(k-\varepsilon\) model and **(b)** the \(k-\omega\) model. Higher values of \(\tau_{\text{tr}}\) are observed where the flow accelerates, driven by both the reduction in passage area and the convergence of streamlines near the front surface of the brazed points. On the contrary, downstream, a distinct \"V\" shape in blue indicates potential flow separation; this occurs as particles encounter a sudden expansion due to the increasing channel height, which reaches its maximum for \(D=2h\). Understanding the distribution of \(\tau_{\text{tr}}\) can be useful for gaining a preliminary insight into the expected heat flux behavior, as the two are connected by the Reynolds analogy [32]. Future simulations incorporating heat transfer will be necessary to demonstrate the actual existence of the mentioned relationship.
## 5 Discussion  
### The Green-Up of Cropland and Natural Vegetation  
The herbaceous and barren land has greened up more than forests in this semi-arid region (Figure 5). This is in part because forests' NDVI are larger than those of non-forests--a small increase in NDVI for non-forests results in a large \"percentage\" increase in vegetation greenness. In general, in this semi-arid region, where forests and non-forests experienced similar climatic and environmental changes, the non-forest land greened up more significantly.  
This significant change to non-forest land could have profound impacts on the carbon and water cycles, energy exchanges, and microclimate in semi-arid regions. [PERSON] et al. [24] suggested that the inter-annual variability of atmospheric CO\({}_{2}\) concentration is largely driven by the dynamics of semi-arid ecosystems. To be more specific, it is probably the non-forest vegetation of semi-arid climatic zones that plays a dominant role in driving the interannual variability of the global carbon cycle. [PERSON] et al. [26] suggested that semi-arid ecosystems in Australia became more prone to water stress from 1982 to 2010, and with the increase in atmospheric CO\({}_{2}\), semi-arid catchments in Australia not only greened-up but also consumed more water, leading to significant reductions in streamflow. We suspect that significant changes to non-forests in semi-arid regions of Northwest China could also have similar impacts on the carbon and water cycles and microclimate, such as reductions in streamflow, which needs further investigation.  
In this semi-arid region, the magnitudes of green-up for cropland and grasslands were very similar during the period from 2000 to 2019. Previous studies (e.g., [6; 7; 8]) suggested that agricultural practices, such as fertilization and irrigation, promote greening in agricultural land. This is not the case in the semi-arid region in Northwest China. This suggests that fertilization and irrigation practices have not changed much since 2000 in this semi-arid region, meaning that climate [14] and the CO\({}_{2}\) fertilization effect [1] play major roles in the greening of cropland, as well as in the greening of natural vegetation. The CO\({}_{2}\) fertilization effect causes plants to assimilate more carbon at the same level of stomata opening. The cropland greenness may further increase if more advanced agricultural management practices, such as drip irrigation, are adopted in this region [54].
## 3 Experimental Results and Error Analysis
### Experimental Conditions and Results
The accuracy estimate of each algorithm was based on the values provided in corresponding papers, therefore, different criteria were considered. Considering the alternative algorithms with similar average mAP, it can be seen that the proposed algorithm is approximately 7 times faster. Moreover, the proposed algorithm has approximately 1.71 times faster speed and 1.72 times higher accuracy than the random forest algorithm. The results of the proposed method are shown in Figure 5. If a tool identified in the previous frame (a) is not found in the current frame (b), the missing tool detection algorithm is applied. (c) is the result of missing tool detection. After the presence of the missing tool is recognized, a white O symbol is displayed in the upper left corner of the image (c, d). Taking Figure 5 as an example, we can describe in more detail that an irrigator is detected in the previous frame (a), however, in the current frame, no surgical tools were detected through YOLO9000 (b). Therefore, through the surgical tool mapping applied on the previous frame and the current frame, it is recognized that the missing tool exists. This is indicated by the white O symbol in the upper left corner of the image. Thereafter, the missing irrigator is detected through the motion vector predicting step, and the class of the detected tool is displayed under the white O symbol. Table 4 compares precision, recall, and F1 scores, according to whether the missing tool detection algorithm is applied or not. Application of missing tool detection allows the precision to be reduced by  
Figure 5: Results of the proposed method. (**a**) The result in the previous frame (t-1). (**b**) As a result of applying only YOLO9000 to the current frame (t), it is found that a missing tool exists. (**c**) YOLO9000 and the missing tool detection algorithm are executed in the current frame (t), and as a result, the missing surgical tool is detected. (**d**) An enlarged view of the upper left corner of image (**c**) (the symbol O in white indicates that the tool is missing, and the class name shown below indicates the class of the tool detected through missing tool detection). \begin{table}
\begin{tabular}{c|c|c|c|c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Bodenstedt** \\ [25] \\ \end{tabular} & \begin{tabular}{c} **EndoNet** \\ [19] \\ \end{tabular} & \
\begin{tabular}{c} **[PERSON]** \\ [24] \\ \end{tabular} &
\begin{tabular}{c} **Proposed** \\ **Algorithm** \\ \end{tabular} \\ \hline Accuracy (mAP) & - & 81.0 & 81.8 & 84.7 \\ \hline Accuracy (\%) & 86.0 & - & - & 95.5 \\ \hline Speed (FPS) & 22.2 & 5.0 & 5.0 & 38.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of performance of our method with and without missing tool detection. approximately 0.63%, the recall by 4.95%, and the F1 score by approximately 2.35%. The reason for the precision decrease is attributed to the erroneous detection of a tool as a missing tool in YOLO9000. Accordingly, an additional detection process is executed.