esa-sceva/satcom-chunk-collection huggingface dataset:
# Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring
## 3 Results and Analysis
### Accuracy Assessment Results of Image Harmonizing
Figure 5 shows that there are four GF-1 high spatial resolution images used for the time series images harmonizing, which are acquired on DOY 156, DOY 178, DOY 227 and DOY 252 respectively. There are only two image pairs that there are simultaneous GF-1 image, harmonized image and MODIS image on DOY 178 and DOY 227. In order to verify the accuracy of the improved Kalman filter, the image acquired on DOY 178 and DOY 227 are not used in time-series image harmonizing, which are used only for image harmonizing accuracy assessment in this study. If these two images are used for practical image harmonizing, the accuracy of which will be higher than the accuracy results listed in Table 1. As it can be seen from Table 1, the image harmonizing accuracy using the improved Kalman filter is higher than that using the ESTARFM model in DOY 178 and DOY 227. For the harmonized image on DOY 178, the coefficient of determination (R\({}^{2}\)) between the harmonized image using the improved Kalman filter and GF-1 image is 0.65, which is higher than the harmonized image using ESTARFM model (R\({}^{2}\) = 0.55). Moreover, the root mean square error (RMSE) of the improved Kalman filter (RMSE = 0.08) is lower than that of the ESTARFM model (RMSE = 0.09). Similarly, the harmonizing accuracy on the DOY 227 using the improved Kalman filter is higher (with R\({}^{2}\) = 0.84, RMSE = 0.07) than that using ESTARFM model (R\({}^{2}\) = 0.82, RMSE = 0.08).
## 1 Introduction
Results showed that the temperature of the gas film varied significantly from the inlet radius to the outlet radius of the seal, leading to a divergent deformation, a 17% increase in the leakage, and a 15% decrease in the opening force, with a decrease in sealing temperature from 300 K to 150 K. However, the thermal deformation and sealing performance of supercritical face seals are still unconsidered when seals operate under an ambient temperature gradient. In this manuscript, the effects of large ambient temperature gradients on the thermo-hydrodynamic behaviors of a supercritical helium spiral-grooved face seal are analyzed numerically under low-temperature conditions. Considering the real gas effect of helium in its supercritical state, a thermo-elasto-hydrodynamic lubrication model is established, and the thermal deformation characteristics of the face seal are discussed under different temperature gradient conditions. Additionally, the distributions of pressure, temperature, and film thickness of the gas film are assessed under a wide range of working conditions to explain the effects of temperature gradient further. Finally, the sealing performances of the face seal under different operating conditions are evaluated, including the sealing pressure, sealing temperature, rotational speed, and basic film thickness. The results obtained in this manuscript provide a theoretical basis for the rational design of gas face seals in supercritical helium precooled systems.
# Machine Learning-Based Adaptive Synthetic Sampling Technique for Intrusion Detection
## 3 Material & Methods
### Data Pre-Processing
#### 3.2.3 Normalization  
Normalization is a data pre-processing technique that narrows the scale of values for numerical features in a dataset without altering their correlations or fluctuations.  
The dataset contains X numerical features that do not fit a Gaussian distribution and have well-known bounds. For the following reasons, the numerical attributes have been normalized using the min-max method to the range [0, 1]:  
\[X_{processed}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{1}\]  
A numerical feature's maximum and minimum boundaries are defined by its boundaries, \(X_{min}\) and \(X_{max}\).
## 1 Introduction
Contributions dealing with the state observation problem for interconnected systems subjected to unknown interconnections have received less extensive treatment in the literature. In [28], a promising method to solve the state observation problem of nonlinear systems modeled by ODE-PDE series was proposed. A similar problem was also studied in [29], where the interconnected system was composed of a nonlinear system and a linear system. In this paper, the problem of state estimation for interconnected nonlinear dynamic systems is studied. An interconnected system consists of two nonlinear dynamic subsystems, and the interconnection point is unknown. Thus, one major difficulty is that state observation can only rely on the output of the terminal subsystem, making existing observers useless. Therefore, the problem considered here is that the output of the nonlinear system cannot be measured directly, while part of the state measurement of the second nonlinear system can be obtained. Two issues are highlighted here. Firstly, it is assumed that the measurement value used by the observer of the former subsystem is unmeasurable, and the solution is to replace it with the estimated value of the observer of the latter subsystem. Secondly, in the latter subsystem, the estimated interconnection provided for the former subsystem is regarded as an additional state to form a new, extended subsystem. The expression of the new state is obtained by calculating the derivative of the output equation of the former subsystem. The contribution of this paper mainly lies in its emphasis of the importance of the influences of local internal dynamics (actuator) on the global dynamics of a control system. A method is proposed to distinguish the influence of low subsystems in higher subsystems, even if the full measurement of local subsystems cannot be realized. Thus, the goal of the design methodology is to enable or simplify observer design for systems that are otherwise difficult to handle by allowing the designer to focus on a smaller, nonlinear subsystem. That is to say, we mainly focus on observing, for example, how the change of an internal parameter at the local level affects the global output at the global level. As a result, both local and global dynamics supervision is achieved, as well as analysis of the influences of local internal dynamics on the global dynamics. The rest of the paper is organized as follows. Problem formulation is introduced in Section 2, where the type of dynamic units of the interconnected system is explained, and the main objective is introduced. Section 3 contains all the results for the observer design, with respect to the interconnected systems. Some numerical simulation examples are given to illustrate the effectiveness of the proposed methods in Section 4. Finally, a conclusion is made in Section 5.
## 2 Materials and Methods
### Electronic Nose Analysis  
All 167 truffle samples were analysed by electronic nose. A quantity of 2 g of each sample gleba was grated into a 20 mL glass vial, sealed with a screw cap equipped with a Teflon/silicone septum to examine the odour fraction of the headspace. A portable Electronic Nose PEN2 (WMA Airsense Analytics GmbH, Schwerin, Germany), equipped with 10 metal oxide semiconductor (MOS) sensors, was used.  
Prior to E-nose acquisition, the vials were placed in a thermostatic bath at 53 \({}^{\circ}\)C for 5 min to allow for equilibrium of volatile compounds in the headspace of the sample [17].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Sample ID** & **Truffle Species** &
\begin{tabular}{c} **No. Fruiting** \\ **Bodies** \\ \end{tabular} & **Harvesting Site** & **Harvesting Date** & **Altitude (m)** & **Host Tree** \\ \hline Caserta hill & _Tuber bordhi_ & 6 & Caserta & February 2023 & 370 & Oak \\ Caserta coast & _Tuber bordhi_ & 23 & Caserta & March 2023 & 0 & Aleppo and Stone pine \\ Salerno hill & _Tuber bordhi_ & 12 & Salerno & February, March, and April 2023 & 800 & Oak \\ Salerno coast & _Tuber bordhi_ & 19 & Salerno & February and March & 2023 & 0 & Aleppo and Stone pine \\ Alburni & _Tuber mesentercium_ & 25 & Alburni-Vallo di & February, March, and April 2023 & 1200–1300 & Beech \\ Eremita-Marzano & _Tuber mesentercium_ & 18 & Eremita-Marzano & January, February, and March 2023 & 1000 & Beech \\ Partenio-Taburno & _Tuber mesentercium_ & 39 & Partenio-Taburno & January, February, March, and April 2023 & 900–1100 & Beech \\ Picentini & _Tuber mesentercium_ & 25 & Picentini & February, March, and April 2023 & 800–1500 & Beech \\ \hline \end{tabular}
\end{table}
Table 1: Sample information for _Tuber bordhi_ and _Tuber mesentercium_ truffles, including collection sites, harvesting period, altitude, and associated trees.  
The odour sampling system in the headspace consisted of an aspiration needle that extracted and transferred the volatile fraction to the E-nose sensors at a constant flow of 400 mL/min. Data were recorded every second by a computer, and the experiment lasted for 60 s. The recovery time for sensor cleaning with reference air was 240 s [24]. The E-nose was used at 25 \({}^{\circ}\)C \(\pm\) 1. The average G/G\({}_{0}\) values of the response of the ten MOS sensors were calculated from measurements in the range time of 45-55 s (stability of the response of the sensors) using Winnuster v.1.6 software (Airsense Analytics GmbH, Schwerin, Germany).
# Monitoring Forest Diversity under Moso Bamboo Invasion: A Random Forest Approach
## 2 Materials and Methods
### Reference Data
#### 2.3.2 Diversity Measurement  
In field surveys, forest tree species are identified at the individual level, taking into account the richness and homogeneity of tree species. However, a single biodiversity index cannot accurately describe the forest diversity. The Margalef index serves as an easy species diversity indicator because it highlights the importance of species richness. In contrast, the Shannon index is predominantly employed to gauge the extent of species diversity within a community, while the Simpson index, often referred to as the dominance index, quantifies concentration. Finally, the Pielou index is utilized to assess the evenness of species distribution within a community. Therefore, in this study, we calculated the Margalef [42], Shannon [43], [PERSON] [44], and Pielou [45] biodiversity indices for a sample of 100 plots using the following formulas:  
\[D_{M}=\frac{S-1}{\ln N} \tag{1}\]  
\[H^{\prime}=-\sum_{i=1}^{s}(p_{i}\ln p_{i}) \tag{2}\]  
\[D_{s}=1-\sum_{i=1}^{s}p_{i}^{2} \tag{3}\]  
\[E=\frac{H^{\prime}}{\ln S} \tag{4}\]  
where \(D_{M}\) is the Margalef index, \(H^{\prime}\) is the [PERSON] index, \(D_{s}\) is the Simpson index, \(E\) is the [PERSON] index, \(S\) is the number of tree species in field, and \(p_{i}\) is the ratio of individuals of species \(i\) divided by all individual trees \(N\) of all species.
# A New Self-Balancing Assembly Line Based on Collaborative Ant Behavior
## 5 Simulation Experiments and Results
### Experimental Analysis with Stochastic Task Times  
As discussed previously, in a real-world manufacturing environment, it is common for the effective assembly times to be stochastic. Reference [37] classified variability levels in manufacturing systems into low, moderate, and high, on the basis of the magnitude of the CV values: low variation for CVs less than 0.75, moderate variation for CVs between 0.75 and 1.33, and HV high variation for CVs greater than 1.33. In this study, the performance of different assembly systems was examined with four \(CV\) values: 0.0, 0.5, 1.0, and 1.5. It was assumed in the ABB assembly cell that the assembly tasks assigned to the workers were the same as in Figure 5 and that no intermediate buffers existed. (The effect of in-between buffers will be discussed in the next subsection).  
Figure 7 compares the performance of the different assembly systems. Assembly jobs were transferred to downstream operators when all the workers were ready to start new assembly jobs in the GEN cell or when the immediate downstream worker was ready to take a new job in the NBB cell and ABB cell. When there was no variability (i.e., deterministic task time with CV = 0), not much performance difference could be seen among the three cell control methods. As the variabilities of the task times increased, the performance became worse in every assembly system. The higher variabilities of task times aggravated workload imbalance in the GEN cell because the line balancing efficiency changed due to stochastic task times different from fixed standard time assumed in the line design stage. The variability of task times also affected the performance of the NBB cell and the ABB cell because the hand-off and blocking delays were increased with higher task time variabilities (see Equation (3)).  
Figure 6: Performance of assembly methods under deterministic task time.  
The figure shows that the effect of assembly time variabilities on the performance was less in the NBB cell and the ABB cell compared to the GEN cell. Among NBB and ABB cells, the ABB cell provided consistently better performance than the NBB cell under a stochastic task time environment.  
The better performance of the ABB cell over NBB cell was due to reduced hand-off times and blocking times expected in the ABB cell. Figure 8 shows the hand-off times and blocking times in the NBB and ABB cells. It is seen that hand-off times and blocking times were consistently less in the ABB cell than in the NBB cell, and the difference became larger with higher CV values.
# Cold-Adapted Fungi: Goldmine of Biomolecules Applicable in Industry
## 4 Biotechnological Application of Psychrophilic Fungi in Food and Agriculture
### Pigments
Amount of PUFAs is shown in percentage of fatty acids. Table 3 clearly shows that there is a great variety of pigment-producing fungi. It is indisputable that carotenoids are the most widely produced pigments by microorganisms in the world [113; 131]. Carotenoids are classified into two main groups: carotenes, which are hydrocarbons (such as torulene, lycopene, and \(\alpha\)- to \(\epsilon\)-carotenes), and xanthophylls, which possess keto, hydroxyl, and/or carboxyl groups (such as lutein, zeaxanthin, torulandudin, and and astaxanthin). They range in color from bright yellow to deep red. The total value of carotenoids according to the Global Carotenoid Market reached 2.5 billion dollars in 2024, and it is estimated to be worth USD 3.6 billion by 2029, growing at a CAGR of 6.3% during this forecast period [132]. It is of particular significance to highlight the production of carotenoids, particularly \(\beta\)-carotene, by psychrophilic fungi, mainly yeasts. Carotenoids have been used for decades in the food industry as coloring agents, for example, to color soft drinks, cooked sausages, and baked goods [124]. Furthermore, they are employed in the manufacture of margarine and butter, fruit juices and beverages, soups, and dairy products, as well as in the fish, desserts, sugar, salad dressing, meat, pasta, egg, and mayonnaise industries [133]. They are also used as nutritional supplements, maintaining the aroma and vitamins in food. The addition of these compounds serves to preserve the color of foods while simultaneously enhancing their quality due to their UV-protective properties [113]. The antimicrobial activity of carotenoids derived from cold-adapted fungi has been demonstrated, representing a significant additional benefit and potential avenue for use in food preservation [116; 124]. In terms of agricultural applications, carotenoids can be incorporated into feeds as precursors to vitamins and
## 4 Characteristics of Different Time Distribution Systems
### Network Time Protocol
#### 4.1.2 Accuracy  
Each level in the NTP hierarchy is referred to as a Stratum. At the highest level, Stratum 1 servers synchronize directly with precise external time sources, such as atomic clocks or GPS devices (Stratum 0) [19]. NTP supports up to 15 strata, with synchronization accuracy decreasing as the stratum number increases. This means that the further a client is from a Stratum 0 time source, the lower the achievable synchronization precision.  
In NTP, only the smallest observed round-trip times are considered to minimize the impact of network queuing delays. However, path delay asymmetry is not accounted for. Additionally, NTP networks experience extra latency because they rely on software-based processing, requiring timestamp requests to wait for the local operating system. In real-world environments, NTP is subject to various factors that cause asymmetrical runtimes in the round trip, leading to errors in time synchronization [76].  
Variable network latency causes the precision of NTP to degrade, from a few microseconds at the top nodes, as achieved by NTPv4 in a local area network (LAN), to tens of milliseconds in the lower hierarchies, such as in wide area networks (WANs) [19].
# Quantitative Microbiological Analysis of Artisanal Stretched Cheese Manufacture
## 3 Results
### Coagulation  
In the coagulation step, the microbial population partitioning between the curd and whey was examined. The results are presented in Figure 3 as the whey/curd index (W/C in %), which represents the average percentage value of individual viable counts determined in whey (in CFU/mL) from the counts in curd (in CFU/g). Generally, it seems that approximately less than half of the population was removed from curd to whey within the syneresis. In detail, for example, 70 \(\pm\) 16% of the TVC population remained in the curd. Expressed as log values, this means that on average, the counts determined in whey were lower by 0.6 (TVC) to 1.0 (EC) log CFU/mL as compared to the log counts in curd. Testing paired data of indicators determined in whey and curd via ANOVA showed statistically significant differences between the datasets in all cases of microbial indicators at the level of \(p\) = 0.05.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Microbial Group** & **Raw Milk** & **Fresh Curd** & **Whey** &
\begin{tabular}{c} **Fermented Cheese Curd** \\ **(Before Stretching)** \\ \end{tabular} & **Stretched Cheese** \\ \cline{2-6}  & **[log CFU/mL]** & **[log CFU/g]** & **[log CFU/mL]** & **[log CFU/g]** \\ \hline Total viable count & 4.88 (\(\pm\)0.50) & 7.18 (\(\pm\)0.69) & 6.58 (\(\pm\)0.38) & 9.36 (\(\pm\)0.64) & 7.44 (\(\pm\)0.98) \\ Presumptive lactococci 2 & 4.55 (\(\pm\)0.25) & 6.89 (\(\pm\)0.66) & 6.29 (\(\pm\)0.92) & 8.74 (\(\pm\)0.44) & 5.50 (\(\pm\)0.56) \\ Lactobacillus sp. 2 & 4.06 (\(\pm\)0.75) & 6.68 (\(\pm\)0.41) & 6.16 (\(\pm\)0.38) & 8.81 (\(\pm\)0.24) & 4.34 (\(\pm\)0.41) \\ Coliforms & 2.57 (\(\pm\)0.20) & 2.91 (\(\pm\)0.45) & 2.27 (\(\pm\)0.37) & 4.99 (\(\pm\)0.24) & 1.02 (\(\pm\)0.50) \\ _E. coli_ & 1.49 (\(\pm\)0.54) & 1.82 (\(\pm\)0.50) & 0.73 (\(\pm\)0.49) & 3.01 (\(\pm\)0.53) & 0.64 (\(\pm\)0.81) \\ Presumptive \(\&\)_a. aureus_ & 2.97 (\(\pm\)0.79) & 3.19 (\(\pm\)0.39) & 2.45 (\(\pm\)0.37) & 3.97 (\(\pm\)0.14) & 2.46 (\(\pm\)1.27) \\ Yeasts and molds & 3.90 (\(\pm\)0.64) & 4.43 (\(\pm\)0.69) & 3.70 (\(\pm\)0.69) & 5.43 (\(\pm\)0.71) & 2.81 (\(\pm\)0.64) \\ \hline \hline \end{tabular}  
* These results of 20 replicate cheese-making trials (\(n\) = 20) are expressed as the average value \(\pm\) standard deviation. 2 Average of three replicates.  
\end{table}
Table 1: The average counts of microorganisms determined in the cheeses produced from raw milk at different processing steps 1.  
Figure 2: Fitted distributions for microbiological indicators in raw milk: (**a**) total viable count (TVC); (**b**) yeasts and molds (Y&M); (**c**) coliforms (CFM); (**d**) _Escherichia coli_ (EC); (**e**) presumptive _Staphylococcus aureus_ (pSTA).
# A Cross-Level Iterative Subtraction Network for Camouflaged Object Detection
## 2 Related Work
### Multi-Level Feature Integration  
Multi-level feature integration is a common approach in object detection and segmentation. Low-level features capture detailed local information, while high-level features capture global semantic context. Combining features from different levels enables the incorporation of local and global contextual information, which can enhance the model's ability to handle appearance variations in COD tasks. Previous works have proposed some approaches for multi-level feature integration. For example, C2 FNet [15] is designed with an attention-induced cross-level fusion module. It integrates the cross-level features by utilizing a Multi-Scale Channel Attention (MSCA) to obtain both local contexts and global contexts, which makes it exploit multi-scale information to alleviate scale changes. However, it is noteworthy that this method incurs significant computational overhead. MINet [31] uses elemental addition to fuse adjacent two-level or three-level features to integrate contextual information from neighboring resolutions and enhance the representation of features at different resolutions. This straightforward feature fusion not only makes it difficult to utilize effective information, but also leads to redundancy. In order to maintain the consistency and correlations of features and enrich semantic context, FSNet [32] proposed a cross-connection strategy to transfer information between low-resolution features and high-resolution features. Although this method enables sufficient interaction across different feature levels, it may not fully exploit their inherent characteristics.  
Unlike these methods, our approach takes into account the difference in features between cross-level features. To effectively capture the subtle distinctions within the object, we propose to integrate strip convolution with element-wise subtraction. This approach is specifically designed to extract information regarding the difference between the high-level features and their corresponding lower-level features, thereby achieving feature complementarity. Subsequently, we fuse the extracted difference features from various convolutional layers using element-wise addition. In this way, the fused features we obtain are not only rich in discriminative information but also free of redundancy.
## 2 Materials and Methods
### Proposed Methodology
#### 2.2.2 Detection Algorithm  
The adversarial image detection model, denoted as \(Adv-aware(x)\), is addressed in Algorithm 2. We first pass a test image (\(x\)) to the primary DNN model, which is DNN(x) with \(Top\_1\), and to the secondary model, which is the RF model with \(Top\_k\) donated, as denoted by RF(x, k). Then, we have two outputs: a single-class prediction from the primary DNN model (\(y\)) and \(k\) class predictions from the secondary model (\(Top\_k\)) as a list of \(k\) classes. We check whether \(y\) predicted classes exist in the Top_k prediction list. If \(y\) exists in the Top_k, then it returns a boolean \"false\" value for forged status with the DNN(x) label (\(y\)). Otherwise, it returns \"true\" without a label or none, which detects a possible adversarial sample.  
For instance, as shown in Figure 1, we use a \(STOP\) road sign as an input sample to our model. It passes to the primary model and the secondary model concurrently. In an adversarial attack scenario where the \(STOP\) sign image is a manipulated image, the predicted class from the primary model is \(SPEED\)_LIMIT 70_, whereas the second model provides a Top_3 list of predictions, for example, \(STOP\), \(Roundabout\), and \(No\)_entry_. Our model detects the input image as a forged \"true\", since the predicted class from the primary model does not exist in the list of the secondary model. In the case of clean detection, the predictions have to be found in both model predictions. During our evaluation, we excluded misclassification samples in this section, which are tackled in Section 2.3.  
```
1[forged,label]=Adv-aware(x) Input: image x. Output: Whether the image is forged by adversarial attack or a clean image; classification label if x is a clean image. \(y\leftarrow\) DNN(x) # DNN model classification label for the image x Top_k \(\leftarrow\) RF(x, k) # The top k group of labels generated by the RF classification model ify \(\in\)Top_k then  forged = false; label = y else  forged = true; label = None endif return[forged, label]
```  
**Algorithm 2** Adversarial-Aware Deep Learning System.