esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Results of Calculating Directionality
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Graphic Number** & **S1** & **S2** & **S3** & **S4** & **S5** \\ \hline \hline Oval rotation angle (\({}^{\circ}\)) & \(53.60\) & \(-31.93\) & \(-3.21\) & \(43.76\) & \(14.68\) \\ Long axis (mm) & \(3.11\) & \(1.27\) & \(1.84\) & \(1.908\) & \(1.32\) \\ Short axis (mm) & \(1.11\) & \(1.08\) & \(1.26\) & \(1.700\) & \(0.98\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Oval rotation angles in Figure 6.  
\(C_{an}\) represents the roughness correction coefficient tensor matrix for the off-diagonal array, while the array of diagonal roughness correction coefficients is referred to as \(C_{dig}\). Additionally, \(R\) denotes the rotation matrix in this equation. Since the ellipses studied are mainly two-dimensional with polar coordinates, we express them in the two-dimensional form \(C_{\text{dig}}= \begin{pmatrix}c_{\text{a}}&0\\ 0&c_{\text{b}}\end{pmatrix} \). Here, the longer and shorter axes of the ellipse are represented by a and b, respectively. Furthermore, \(C_{an}\) can be used, with \(C_{an}= \begin{pmatrix}c_{11}&c_{12}\\ c_{21}&c_{22}\end{pmatrix} \), and we use \(C_{ij}\) as a simple notation for the coefficient tensor matrix \(C_{an}\), denoted as \(C_{an}=C_{ij}\). Analyzing Figures 5 and 6 indicates that increased ellipse sizes improve the alignment. To apply the formula, the angles of the fitted ellipses in Table 8 should be put in order based on their maximum size and substituted into the formula above. We observe the following data in Table 9:  
1. The absolute value of \(C_{ij}\) falls between 0 and 3, which mostly correlates with the calculated results from Table 8 (1.0-3.0); that is, the results calculated for the roughness coefficient matrix are similar to the original results. 2. The roughness correction coefficient matrix and the roughness coefficient matrix for the granite sample both exhibit symmetric patterns on the secondary diagonal, which suggests stable surface roughness in all directions. In summary, we can conclude that the roughness correction coefficient is both anisotropic and symmetrical and can be expressed in tensor format. As a parameter reflecting fractured roughness, it is apparent that roughness is also subject to orientation and symmetry. Additionally, it has been observed that the surface roughness of rocks depends on both the dimensions of the sample and the scale of observation [27]. However, these results require further interpretation.
## 1 Introduction
### Background Development of SEM  
The idea of using electron microscopy dates back to the previous century when [PERSON] and [PERSON] conducted their experiments in 1932 [35; 36]. This instrument was named the transmission electron microscope (TEM), based on its working principle and application, in which electrons were transmitted through thin specimens to magnify beyond the levels of optical microscopes of that time. In 1938, a scanning coil was added to TEM by [PERSON], introducing the era of scanning transmission electron microscopy (STEM) [37; 38]. It provided a magnification of 8000\(\times\) with a resolution of 50-100 nm at 23 keV. [PERSON] developed a laboratory instrument with various features, which became the standard for developing and inventing new SEM systems [39]. A new explanation of SEM for analyzing thick samples was presented by [PERSON], [PERSON], and [PERSON] in 1942. It was found that the emission of secondary electrons can be used for topographic contrast. [PERSON] and [PERSON] developed the electrostatic lens for SEM in 1952. [PERSON] understood the role of signal processing in improving SEM micrographs and laid the foundation for nonlinear signal amplification. Another contribution from [PERSON] was the production of double deflection scanning for upgrading the scanning system [39]. [PERSON] designed a new stereoscopic pair for investigating the third dimension in SEM micrographs in 1953. The work of [PERSON] indicated the development of a secondary detector, which served as a tool for improving the signal-to-noise ratio and overall increasing the collected signals. [PERSON] used three magnetic lenses in building the SEM V system, which is considered the first commercial SEM instrument available, under the name \"Stereoscan\" Cambridge Scientific Instruments Mark 1 in 1965 [40]. Since then, several advancements have been made to improve SEM analysis, such as upgrading electron source for better electron emission, resulting in efficient and clear SEM resolution. Another beneficial advancement in SEM development pertains to the invention of an energy-dispersive spectrometer (EDS). The system has been used in conjunction with SEM since 1968 and makes use of solid-state detectors for measuring X-rays [39]. SEM has been developing with the advent of modern equipment. [PERSON] studied the effect of the environment on analyzed samples during 1991-1993, which led to the development of an environmental scanning electron microscope (ESEM) for examining the surface of a specimen, whether it is dry or wet [41; 42]. Among these advancements in SEM, the most recent is the generation of digital images, which are then displayed on computers for analysis. At present, the majority of SEM instruments have modern software for analyzing the obtained data and EDS systems, which make use of computer programming for evaluating the composition of various elements present in the sample [39]. The use of modern software provides improved quantitative analysis and converts the X-ray intensity into the chemical composition of the sample in a relatively shorter period of time.
## 3 Results and Discussion
### Correlation of Carbons with Chemical Species in PM\({}_{2.5}\)  
Based on the assumption that airborne carbons are formed by anthropogenic activities, the correlation of ions generated from anthropogenic sources was compared with OC and EC illustrated in Figure 4. Data in Figure 4 enclose whole data from all the classrooms which may indicate general tendency. OC and EC clearly showed different relationship between ion species. While EC has a certain relation with ion amount approximately from the slope of 0.1 to 1.0, OC does not indicate any consistent dependency on ion mass ratios, showing less than 0.03. Besides overall correlation, the linearity of individual school classrooms was not so high despite an obvious proportional relationship.  
The ion concentrations of sulfate and nitrate behave in a similar manner more to inorganic carbon (EC) aerosols in indoor PM\({}_{2.5}\) than the organic carbon (OC). In particular, nitrate ion shows a relatively consistent relationship with both carbons (0.16 for OC and 1.0 for EC), probably because it inflows mainly from outside. Potassium and chlorine ions showed a low gradient (approximately 0.11 for both), but increased obviously with increasing EC.  
Based on a simple statistical analysis with the probability value, less than 0.01, indoor PM\({}_{2.5}\) showed the correlation coefficients with OC and EC, 0.37 and 0.26, respectively. As summarized in Table 4, OC correlated with EC at 0.6, which was lower than the outdoor correlation, 0.87. The correlation with ion species was higher in EC than OC except chlorine ion. Chlorine ion showed lower correlations with PM\({}_{2.5}\), OC and EC than other ions, but relatively higher with OC (0.29) than EC and PM\({}_{2.5}\). Other ions are generated in outdoors, and flow into the classrooms with PM\({}_{2.5}\). In particular, ammonium ion is one of the key species capable of forming PM\({}_{2.5}\) in urban atmosphere. Although potassium ion is known to overlap OC with its emission sources, such as biomass burning and incineration, the present study showed that they are more closely related to EC in classroom PM\({}_{2.5}\). In conclusion, this result strongly suggests the presence of other internal OC sources independent of combustion, such as polymer compounds composed of carbon and chlorine.
## 5 Results  
### Topology 1: Results  
The satellite node h23 is connected to the ground station node h25 using ISLs and satellite gateways h26, h29, h30, and h27, as shown in Figure 2a. The configured delay, rate limiting, and packet loss under different test scenarios are depicted in Table 1.
## 3 Results and Discussion
### Extraction with ED
The results indicate that the combined hydrometallurgical-electrodialytic cell allows the extraction of Li and Co from the particles and the separation of the metals in a catholyte solution. Furthermore, it would be possible to selectively separate the metals, as cobalt metal can be obtained via electrodeposition at the cathode surface. The experiments indicated that the dissolution of the solid particles limits the time efficiency of the technique. Future work is planned to evaluate the performance of this kind of set-up in combination with reducing agents and optimizing the applied electrical current to the dissolution of the solid particles.
# Effects of Drying Process on the Volatile and Non-Volatile Flavor Compounds of _Lentinual edodes_
## 3 Results and Discussion  
### Electronic Nose Analysis of L. edodes Samples  
The electronic nose obtains comprehensive flavor information within a short time by mimicking the human olfactory system [20]. It was used for classifying and monitoring the drying process of _L. edodes_[14; 21]. Principal component analysis (PCA) is a multivariate chemometric method that can be used to identify the correlation patterns of constituent variables involved in the distinction between samples [16]. PCA also can provide a better visualization and highlight the differences in volatile profiles. As shown in Figure 1, the accumulative variance contribution rate of the first two PCs was 93% (PC1 accounted for 81.67% and PC2 accounted for 16.29%), which indicated the feasibility of PCA and the two main components contained most of the information about volatile compounds [22]. Despite sensor instability among replications, volatile profiles from different samples were located into four separated areas. FD samples located in the positive axis of PC1, while both ND and HAD samples were in the negative axis of PC1, which indicated that the volatile compounds of ND and HAD samples were significantly different from those of FD samples. These results also showed that the drying processes altered shitiake mushroom aroma profiles.
## 3 Methodology and Overall Framework
### Adaptive Process Scheduling  
Predictive maintenance provides a fast and sensitive method for detecting the potential faults or unexpected behaviors in the system. However, detecting such anomalies is not enough if further actions are not taken to protect the device/machine/process from a possible damage. We provide a methodology that incorporates PM results as feedback in mitigating the anomalies autonomously in order to keep the process running as efficient as possible, while protecting the system from damage. This approach also saves time for further inspection and maintenance planning. In general, our PM-based adaptive scheduling mechanism analyzes a predicted abnormal condition to calculate an error that is defined as the deviation of the current system behavior from an expected behavior. We calculate a PID error for this purpose, which is commonly used in industrial control to calculate the deviation between a desired state and a measured state.  
Our adaptive scheduling process is shown in Figure 5. First, we calculate the input power error of the conveyor system, which is a direct indicator of abnormal loads or behaviors, for example, glitches on the motor, extra friction on the conveyor belt or extra load on the conveyor system. The PID error (\(Err_{op}\)) is calculated between the real-time power reading (\(P_{real}\)) and the nominal power expected to be drained by the system (\(P_{nom}\)) under a requested condition. This calculation is done by the digital twin using the requested operational speed (\(v_{req}\)) and a carried load (\(\tau_{cur}\)) as covered in Section 3.2. If \(Err_{op}\) is larger than a pre-defined threshold, a new scheduling for the operation mode is initiated. The threshold value can be optimized according to the operational requirements, for example, a cautious operation would require a bigger threshold value to act quick to even small deviations. Hence, we set it proportional to the allowed operational limits defined in the selected operation mode to allow rescheduling as soon as the mode is obsolete for the environmental conditions (see the operational limits and their analysis in Section 4.5.2). Finally, an optimal operation mode is adaptively scheduled (according to the error), which provides allowed speed and power intervals for the conveyor belt to run without damaging the system (detailed in Section 3.4.1).
## 6 Numerical Experiments  
### Data Description  
Three routes of a liner shipping company are applied to study the CSAECRP in an online booking mode. As shown in Figure 4, the fixed-capacity container ships, which belong to the liner company, serve three routes. The container ships provide transportation services to shippers according to a fixed schedule (weekly) and a regular call order. The call order of the three routes is as follows:  
Route1: Shanghai (SH)-Ningbo (NB)-Xiamen (XM)-Yantian (YT)-Singapore (SP)-Felixstowe (FT)-Rotterdam (RD)-Gdansk (GD)-Wilhelmshaven (WS)-Felixstowe (FT)-Port Ke-lang (PK)-Yantian (YT)  
Route2: Tianjin (TJ)-Dalian (DL)-Qingdao (QD)-Shanghai (SH)-Ningbo (NB)-Singapore (SP)-Piraeus (PR)-Rotterdam (RD)-Hamburg (HB)-Antwerp (AT)-Rotterdam (RD)-Shanghai (SH)  
Route3: Qingdao (QD)-Shanghai (SH)-Ningbo (NB)-Kaohsiung (KS)-Hong Kong (HK)-Yantian (YT)-Singapore (SP)-Piraeus (PR)-Colombo (CL)-Singapore (SP)-Hong Kong (HK)-Shanghai (SH)  
The range of parameters is shown in Table 2, and the source of parameters is adjusted according to [17] and the field investigation by the authors. The capacity of container ships on the three routes is 12,000 TEU, 10,000 TEU, and 10,000 TEU, respectively. The transportation cost of a container is positively correlated with transportation distance. It is well known that the container freight rate is strongly associated with transportation costs. Therefore, the unit transportation freight rate and unit transportation cost of the container in this paper are set according to the navigation distance. In addition, the service provided for TS cargo is more attentive than that for ordinary cargo, and TS cargo does not bear the risk of delayed delivery. Consequently, it is reasonable to set the unit freight rate of TS cargo higher than that of ordinary cargo. The value is set at 1.25, based on the average data from the field investigation, and it can be adjusted in other cases to better reflect associated scenarios. The unit leasing cost of empty containers obeys the uniform random distribution U (150, 300), and the unit storage cost of containers obeys the uniform random distribution U (650, 750).  
Figure 4: Diagram of complex shipping route network.  
On the basis of historical digital data, the mean and standard deviation for the demand and the on-time arrival rate are presumed in this paper. The demand for TS and ordinary cargo obeys the normal distribution N (200, 22), and the demand for empty containers submits to the normal distribution N (40, 22) [52]. The BC algorithm described in Section 5 was accomplished in C++ by applying CPLEX 12.6.3. The calculation result of the numerical experiment was obtained using a computer, operating with Microsoft Windows 11 and an Intel(r) CoreTM i9-12900F CPU at 2.40 GHz and 32.0 GB of RAM. Calculations are shown in Table 3, and the obtained data are the mean values of the three experiments.
# Attention-Based Mechanism and Adversarial Autoencoder for Underwater Image Enhancement
## 2 Materials and Methods
### Autoencoder Module  
The structure of Color-AE is depicted in Figure 4, comprising two modules: the multi-color encoder and decoder. The multi-color encoder extracts features from both the underwater image and its reverse medium transmittance map. These features are then fused through the feature fusion module. The fused features are directed to the decoder network, which generates the reconstructed output. The underwater image undergoes a color space transformation, leading to the formation of three encoding paths: the HSV path, the RGB path, and the Lab path. In each path, the color space features undergo enhancement through three consecutive residual enhancement modules, resulting in three levels of feature representations. Additionally, a 2\(\times\) downsampling operation is employed during this process. Furthermore, the features of the RGB path are combined with the corresponding features of the HSV path and the Lab path through dense connections to enhance the RGB path. Subsequently, the same level features of these three parallel paths are combined to form three sets of multicolor space encoder features. These three sets of features are then provided to their respective attention mechanism modules, which effectively capture rich and discriminative image features from multiple color spaces.  
As the Reverse Medium Transmission (RMT) map can partially reflect the physical principles of underwater imaging [20], regions with higher pixel values in the RMT map correspond to more severe degradation in the corresponding underwater image regions. Consequently, the network assigns a larger weight response to the degraded regions of the image, acknowledging their significance in the enhancement process. The refinement of the RMT map is achieved through 1 \(\times\) 1 convolutional layers with a step size of 2. Each convolutional layer is linked to the batch normalization layer and the Leaky ReLU activation function. Subsequently, a maximum pooling downsampling layer is employed to eliminate redundant repetitive information. The output of the feature fusion module is then transmitted to the corresponding residual enhancement module. Following three consecutive serial residual enhancement modules and two 2\(\times\) upsampling operations, the decoder features are forwarded to the convolutional layers, leading to the reconstruction of the final result.
## 1 Introduction
In order to minimize the time and human resources required for future maintenance of the urban tree cadastre and to integrate other planning and science-relevant features on a large scale, a transdisciplinary approach is being tested in Kaiserslautern (Rhineland-Palatinate, Germany). This approach operates on different databases and involves a wide range of stakeholders. The starting point is the city's existing tree cadastre, which is incomplete and contains few attributes but is a good starting point due to its precise GPS locations. A deep learning model can be trained to recognize not-yet-recorded trees in the tree cadastre using aerial imagery. It is planned to train another AI model, using panoramic imagery and LIDAR data from road surveys, to recognize tree genera. The results will be randomly evaluated by experts, creating a feedback loop that will gradually improve the models. A web application is being developed that will provide a user-friendly interface for researchers and interested citizens to display the available data, edit it, and add more features--more models could be trained on this data. Other attributes, such as tree height, can be automatically extracted from the LIDAR data. As part of the process, different databases will be used as training data, considering legal frameworks, costs, and availability, and statements will be made about the quality of the results. Another objective is to design the AI models to be open and transferable, allowing direct application in regions with similar tree populations. In addition, these models could be used to update the tree cadastre and monitor data quality as new information becomes available. There are already several initiatives in Germany working to enhance the urban tree cadastre through the use of technological innovations. In the Metropole Ruhr, for example,the location of trees is determined based on similar data and using AI [3]. As the models are intended to be released as open source, they could be integrated into the approach being pursued in this context. Furthermore, there are already deep learning models for urban tree detection, e.g., [PERSON] et al. [4] and [PERSON] et al. [5]. The first one was adapted and will be discussed in more detail in Section 3. A successful application of a well-maintained tree register is to engage the public in watering initiatives. In Berlin [6] and Leipzig [7], for example, platforms are used to coordinate and track the watering of urban trees. In Magdeburg [8], information on felled trees is also openly available online. In the following, we will first introduce the project context and address the role of urban trees in mitigating climate impacts. We then present our approach to improving the urban tree cadastre by using a combination of an adapted deep learning model for accurate tree detection and a citizen science approach. The different data sources that were available and the challenges of using them within our approach are also discussed. The paper concludes with a summary and an outlook.
# An Analysis of MPTCP Congestion Control
## 7 MPTCP Congestion Control
### Coupled CC
#### 7.2.1 Lia  
LIA was the first proposed coupled CC algorithm for MPTCP. In LIA, Slow Start (SS), fast retransmit and fast recovery algorithms are unchanged from TCP-Reno. During Congestion Avoidance (CA), the increase component of the Additive Increase Multiplicative Decrease (AIMD) algorithm is coupled among subflows. Upon arrival of an ACK on each subflow, _cvond_ is increased as a proportion of the total _cvond_. The LIA algorithm increases _cvond_ for subflow \(i\) as per (1) on receipt of an ACK, where \(\alpha\) is defined as per (2). Parameters _cvond\({}_{i}\)_ and _cvond\({}_{total}\)_ are the _cvond_ size on each subflow and the sum of total _cvond_ of all subflows, respectively. Parameter \(RTT_{i}\) refers to RTT on subflow \(i\). Here, we assume that _cvond_ is maintained in packets.  
LIA aims to ensure that an MPTCP subflow is no more aggressive than a regular TCP flow in the same scenario (Goal 2). The increase Formula (1) returns the minimum of the calculated increase for the MPTCP subflow (first argument) and the increase a TCP flow would get in the same circumstances (second argument).  
\[Min\;(\frac{\alpha}{cvnd_{total}},\;\frac{1}{cvnd_{i}}) \tag{1}\]  
Parameter \(\alpha\) in (2) describes the aggressiveness of MPTCP. The parameter is calculated in a way that the overall bandwidth of MPTCP is equal to the bandwidth a TCP flow would get on the best available path. To estimate the bandwidth of a regular TCP flow, MPTCP uses estimated loss rates and RTTs, and a computed target rate.  
\[\alpha=cvnd_{total}*\frac{Max(\frac{cvnd_{i}}{RTT_{i}})}{(\Sigma_{k}\frac{cvnd _{k}}{RTT_{k}})^{2}} \tag{2}\]  
When packet loss occurs on a subflow, _cvond_ is decreased as per (3). This formula is unchanged from TCP-Reno.  
\[cvnd_{i}/2 \tag{3}\]
# Optimization Design of the Spaceborne Connecting Structure for a Lightweight Space Camera
## 2 Connecting Structure Design Principle
### Materials of the Components  
In terms of materials, to ensure that the connecting structure can isolate external loads and protect the camera, the materials need to have the characteristics of high specific stiffness and a small linear expansion coefficient. The higher specific stiffness means the stronger ability to withstand external loads and a higher lightweight ratio. The smaller linear expansion coefficient means a smaller deformation under thermal loads. Titanium alloys have been widely used in space cameras because of its high elastic modulus and small linear expansion coefficient [15]. Considering the manufacturing process and characteristics of the connecting structure, titanium alloy is adopted as its material. The materials used for the camera backplane, the satellite platform, and the connecting structure are summarized in Table 1.