esa-sceva/satcom-chunk-collection huggingface dataset:
# Methods for Studying Bacterial-Fungal Interactions in the Microenvironments of Soil
## 1 Introduction  
Living organisms are always and constantly interacting with their biotic and abiotic environment, irrespective of habitat, trophic level, or biological function. The network of interacting organisms is a mosaic and entwined reality that, depending on the scale of analysis, may be described at different levels of complexity. Cumulative cell-to-cell interactions among organisms belonging to very different taxa levels and origins, such as animals, protists, fungi, bacteria, archaea, and viruses, determine the overall microbial community activity in a given habitat. These interactions have an effect not just on their surrounding environment (i.e., microenvironment), but also influence large-scale fluxes and, thus, impact global ecosystem processes [1]. However, especially for soil biota, there is still a marked gap between studies performed in laboratory conditions and the in vivo reality [2]. Although providing crucially relevant data, this reduction in complexity cannot resolve general principles of microbial interactions at the ecosystem level [3].  
In this review, we focus on bacterial and fungal interactions (BFI) in soil, and how the technologies available today can be applied for a better understanding of the behavior and phenotypic diversity of these organisms when closely linked to their micro-habitat. Information on the properties of soil and on how its spatial and chemical heterogeneity affects the dynamics of BFI will be described first, to justify the selection of strategies and methodologies reported in the later sections. This minireview is not meant to be exhaustive on either topic, as the arrays of approaches and studies available is extensive and beyond the means of this review. When possible, relevant examples on BFI are given but, in many cases, it was inevitable to refer to research on interactions within bacteria or other taxa.
# Dense Small Object Detection Based on an Improved YOLOv7 Model
## 4 Experiments and Analysis of Results
### Evaluation Metrics  
In order to test the performance of the proposed model, precision (\(P\)), recall (\(R\)), average precision (\(AP\)), mean average precision (\(mAP\)), and network parameter size were used for evaluation.  
False positive (\(FP\)) refers to the number of negative samples detected as positive samples, true positive (\(TP\)) refers to the number of negative samples detected as positive samples, false negative (\(FN\)) refers to the number of positive samples that were not detected as negative samples, and Intersection over Union (\(IoU\)) refers to the ratio of the area of overlap between the predicted bounding box and the ground truth box to the area of their union.  
Precision (\(P\)) refers to the ratio of correctly predicted positive observations to the total predicted positives. It is a measure of how many detected targets are relevant. The formula is as follows:  
\[P=\frac{TP}{TP+FP} \tag{2}\]  
Recall (\(R\)) refers to the ratio of correctly predicted positive observations to all observations in the actual class. It measures how many relevant targets are detected. The formula is as follows:  
\[R=\frac{TP}{TP+FN} \tag{3}\]  
Average precision (\(AP\)) refers to the area of the precision and recall curve for each category and the axis. The formula is as follows:  
\[AP=\int\limits_{0}^{1}P(R)dR \tag{4}\]  
Mean average recision (\(mAP\)) refers to the mean of the average precision scores for each class. It is calculated through Formula (5). It is widely used for evaluating object detectors and provides a single-value summary of the precision-recall curve. In this study, \(mAP\)@0.5 refers to the value of \(mAP\) when the \(IoU\) threshold is 0.5. And \(mAP\)@0.5 : 0.95 refers to the \(mAP\) under multiple \(IoU\) thresholds, within the interval [0.5, 0.95]. Ten \(IoU\) thresholds are taken in steps of 0.05, the \(mAP\) under each of these ten \(IoU\) thresholds is calculated, and then the average value is taken. In addition, \(mAP\)(s), \(mAP\)(m), and \(mAP\)(l) refer to the \(mAP\) values of small, medium, and large targets, respectively.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline image size & 640 \(\times\) 640 \\ epochs & 300 \\ warmup & 3 \\ batch size & 16 \\ optimizer & Adam \\ learning rate & 0.01 \\ momentum & 0.937 \\ weight decay & 0.0005 \\ \hline \end{tabular}
\end{table}
Table 1: Experimental settings.  
\[mAP=\frac{1}{k}\sum_{i=1}^{k}AP_{i} \tag{5}\]
## 3 Results and Discussions
Figure 12: Power in Texas (USA). previous peaks, generated a high activity in the electrolyzer observed in the purple area. Similarly, Figure 13 shows that the contribution of solar energy made at the peak of solar radiation highlighted in Figure 6 is not as high as the contribution made by the wind speed when it reached more than 10 m/s in the following days. These generation peaks occur due to increased wind and solar radiation causing an overshoot of the load demanded by the profile. In Table 6, a sum of the total energy generated monthly by the solar panel, the wind turbine, and the PEM cell for load profile 1 is presented. In addition, a comparison is made with the load demanded for the same period in each of the areas studied. This comparison allows us to observe that Bremen is the location with the highest excess energy generated due to the peaks of wind speed and solar radiation in the whole simulated period, reaching 9801 W, well above the 1389 W in Texas, equivalent to 14% of the excess energy generated in Bremen. Likewise, 3549 W was generated in excess in Beijing to reach 36% of what was generated in Bremen. Finally, in Puerto Bolivar, 4828 W was generated, reaching 49% of what was generated in Bremen. In spite of this, Bremen is the location with the second-highest number of months with a generation equal to the demand due to the load profile reaching 5 months, only surpassed by Texas that reached 6 months without excess generation, which shows that the high wind and solar activity presented in the first 3 months was decisive to achieve the 9801 W of excess generation in Bremen. On the other hand, Table 7 presents for load profile 2 the total sum of the energy generated monthly for each of the actors in the hybrid generation system. It shows Bremen as the location with the greatest excess of energy generated, reaching 7071 W, quite far from the excess generated in Texas, which only reached 204 W, meaning only 3.41% of the excess generated in Bremen. Likewise, in Beijing, 1600 W of excess energy was generated, representing 22% of the excess generated in Bremen. On the other hand, Puerto Bolivar was the closest location to Bremen, with an excess generation of 2646 W, being 37% of what Bremen generated. Besides this, Puerto Bolivar was the location that presented an excess of generation in more months, with 10 months exceeding the demand presented by the load profile 2. Only in September and December was this demand not exceeded. The opposite was the case with Texas, where the demand for the profile was exceeded in only three months and the PEM cell had to cover the demand imposed by the load profile. Figure 13: Power in Bremen (Germany).
## 3 Inherent Challenges in S2I Translation Processes  
S2I translation shares a common challenge with other cross-modal tasks, which is finding the semantic correlation between the two modalities. In our case, the system must connect the acoustic events present in the audio stream to the semantically correlated elements in the visual modality. While it is an easy and intuitive process for humans to learn semantic correlation between images and sounds, it becomes a challenging task for machines, largely due to the disparity between the audio waveform domain and the image RGB color domain [36]. For instance, there exists a common conceptual or semantic entity between the sound of a foreground dog bark in a background acoustic environment and an image featuring a prominently positioned dog. However, the heterogeneity of the representation of the dog entity across these two domains hinders the successful execution of S2I translation.
## 1 Introduction  
Dams have played a pivotal role in national life over the past few decades because they have secured water supply, reduced flood hazards, and maintained healthy and stable social development [1]. However, dam materials can degrade over time, and dam structures can experience a sudden increased load, both of which can lead to catastrophic failure [2]. Dam safety monitoring (DSM) is recognized as a valuable method for detecting critical responses to structures, with the aim of providing warning of anomalies or accidents at an early stage [3; 4].  
With the evolution of sensing and automation technology [5], it is now possible to easily collect massive amounts of high-quality data. However, after several years of operation, the amount of information in the raw and preprocessed data (data, images, etc.) can reach hundreds of gigabytes, which can lead to data mismatches and quite time-consuming access [6]. In addition, the advantages of hybrid models combining structural simulation and statistics continue to emerge [7; 8; 9; 10; 11]. However, it has become a great challenge to design and implement an automated framework that includes data storage, transmission, visualization, manipulation, and analysis.  
This paper highlights and implements an automated monitoring framework. The initial step is the introduction of the PAD algorithm to detect and generate an alarm regarding the outliers in the monitoring data to provide reliable data for the dynamic inversion and the LSTM model. A hybrid model consisting of dynamic inversion and the LSTM algorithm is proposed to improve monitoring. When the monitored value of the water level fluctuates frequently, the material parameters are obtained via dynamic inversion, and then the FE simulation of the mesh is performed to correct the predicted values of the LSTM. Data acquisition, hybrid model analysis, visualization, and anomaly alarm are integrated to establish the automated monitoring framework. Finally, the proposed framework is validated with an example of safety monitoring on an engineering project, confirming that the FE simulation corrects the data to an accuracy of 95%. The application shows that the system improves the efficiency of operation and enables engineers to have a real-time, clear, and accurate picture of the dam's operational status.
## 2 Approach and Improvements
### Overview  
Deployed at the border of a service platform, the system should decide whether messages are valid or invalid (_i.e._, suspicious and indicate an attack). Figure 1 shows on the right hand side how the system takes decisions on real messages received from the network, which represents the execution phase. Since the system processes each message independently of other messages and does rely on traffic characteristics, it does not need to keep transaction state. This enables parallelization and makes the system insusceptible to denial-of-service attacks.  
Before execution, the system must be fed with training data (lefthand side in Figure 1), which enables the classifier to take decisions later. The starting point in the training phase is a set of messages, where for each message it is known whether it is valid or not. To be able to use mathematical classification algorithms, numerical dimensions are extracted from the messages in a feature extraction step. The dimension of the feature space is not defined a priori, therefore it creates a vector of features and corresponding values of non-predefined length.  
The computational complexity during classification depends on the formulation of the classification model. The complexity is influenced by the number of support vectors needed to describe the model and the number of attributes. Since not all features provide relevant input to the classification model, limiting the amount of features to a minimum set improves performance not only in the training but also in the execution phase. Consequently, in the attribute reduction step, we remove all features (also called attributes) with little or no significance to the decision between valid and invalid messages. Since feature extraction and attribute reduction are the key aspects of our work, we provide more details on them in the following two subsections.
# Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes
###### Abstract  
Assuming the safety of automated vehicles is essential for their timely introduction and acceptance by policymakers and the public. To assess their safe design and robust decision making in response to all possible scenarios, new methods that use a scenario-based testing approach are needed, as testing on public roads in normal traffic would require driving millions of kilometres. We make use of the scenario-based testing approach and propose a method to model simulated scenarios using Gaussian Process based models to predict untested scenario outcomes. This enables us to efficiently determine the performance boundary, where the safe and unsafe scenarios can be evidently distinguished from each other. We present an iterative method that optimises the parameter space of a logical scenario towards the most critical scenarios on this performance boundary. Additionally, we conduct a novel probabilistic sensitivity analysis by efficiently computing several variance-based sensitivity indices using the Gaussian Process models and evaluate the relative importance of the scenario input parameters on the scenario outcome. We critically evaluate and investigate the usefulness of the proposed Gaussian Process based approach as a very efficient surrogate model, which can model the logical scenarios effectively in the presence of uncertainty. The proposed approach is applied on an exemplary logical scenario and shows viability in finding concrete critical scenarios. The reported results, derived from the proposed approach, could pave the way to more efficient testing of automated vehicles and instruct further physical tests on the determined critical scenarios.  
Keywords:gaussian process; probabilistic sensitivity analysis; machine learning; safe automated vehicles; scenario-based testing; logical scenario; critical scenarios; corner cases; CAV +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Microhardness of Coatings  
Figure 5 indicates the microhardness distribution of the 316L coating and the composite coating along the building direction. The results show that the microhardness of the 316L coating and the composite coating follows the same trend from the bottom to the top of the coating. It gradually decreases along the direction of coating growth and eventually tends to a stable value. The average microhardness of the fabricated 316 coatings is 181.14 \(\pm\) 2.77 HV, which is consistent with existing research results [29]. When 10 wt% TiC and 10 wt% NbC ceramic particles are added, the microhardness of the composite coating is 232.12 \(\pm\) 2.91 HV, which is 1.28 times that of the 316L coating. The increase in hardness of the composite coating is the result of grain refining and solution strengthening. As shown in Figure 5, fine hard particles are distributed near indentation 4. These high hardness TiC (32 GPa) and NbC (24.1 GPa) act as a frame, weakening the grain dislocation and inhibiting the deformation of the composite coating. In addition, the microstructure analysis shows that TiC and NbC can refine the grains of the composite coating, resulting in the improvement of plastic deformation resistance. Interestingly, the microhardness of the 316L and composite coatings decreases with the deposition height. As the number of deposited layers increases, heat accumulation results in a gradual increase in the maximum temperature of the melt pool. As a result, the previous deposition layer was subjected to a tempering heat treatment, which led to a gradual decrease in microhardness [30]. However, the high density of NbC particles (7.56 g/cm\({}^{3}\)) tends to be more distributed at the bottom of the single track under the effect of strong Marangoni convection, resulting in the high microhardness at the bottom of the coating [16]. The material on top of the coating would be removed during subsequent machining, so the characteristics of the microhardness distribution are beneficial to improve the practical application of the coating. Therefore, it should be noted that the maximum microhardness in the heat-affected zone is 315 HV. According to the microstructure near Indentation 2, the acicular martensite is generated due to rapid cooling, which effectively improves the microhardness. With the increase in the test depth, the cooling rate of the substrate decreases gradually. Therefore, the acicular martensite was gradually transformed into lamellar martensite. The microhardness of the substrate decreased gradually, and finally approached the microhardness value of 45 steel 190.08 \(\pm\) 2.11 HV.
## 3 Algorithm Implementation and Case Study
### Algorithm Applications  
The following arithmetic example randomly selected 10 coordinate points in Shanghai, as shown in Table 2, the selection principles of 10 coordinate points are as follows:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Field Type** & **Field Name** & **Field Meaning** \\ \hline Integer & NodeID & The ID of an expanded node \\ \hline \multirow{2}{*}{Bool} & bSameLine & Whether the node and the expanded node are in the same line? If yes, the value is true; otherwise, the value is false. \\ \multirow{2}{*}{Bool} & bStartPoint & Is it the starting point? If yes, the value is true; otherwise, the value is false. \\ \multirow{2}{*}{Bool} & bDestination & Is it the destination? If yes, the value is true; otherwise, the value is false. \\ \multirow{2}{*}{Bool} & bPR & Is it the P&R? If yes, the value is true; otherwise, the value is false. \\ \multirow{2}{*}{Short} & g & The value of \(G(N)\) from the starting node \(O\) to the expanded node \(N\). \\ \multirow{2}{*}{Short} & h & The value of \(H(N)\) from the expanded node \(N\) to the ending node \(D\). \\ \multirow{2}{*}{ExNode} & pLastNode & The last node of the expanded node. \\ \hline \hline \end{tabular}
\end{table}
Table 1: The data structure of the expanded node.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Point No.** & **Name** & **Coordinate X** & **Coordinate Y** \\ \hline
1 & The intersection of Boxue Road and Fengnian Road & \(-16\),337.7225 & \(13\),934.2415 \\
2 & Fashion Valley Creative Par & \(-28\),572.1326 & \(-22\),363.2356 \\
3 & The intersection of Zhenhua Road and Fuping Road & \(-5873.8770\) & \(3594.2072\) \\
4 & The intersection of Wuning Road and Zhongshan North Road & \(-5242.9461\) & \(988.5317\) \\
5 & Shanghai Railway Station & \(-1496.1732\) & \(1535.3135\) \\
6 & Intersection of Liayang Road and Dongbo Road & \(10\),788.9938 & \(5577.3211\) \\
7 & People’s Square & \(326.9187\) & \(79\),0569 \\
8 & No. 2 High School of East China Normal University & \(11\),500.3261 & \(-3554.2670\) \\
9 & Yongjingyuan of Xinjiangwan City & \(5180.1871\) & \(11\),049.6381 \\
10 & The intersection of Qingyu Road and Xinyu Road & \(-10\),740.1864 & \(1314.6496\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Starting and ending points of algorithm instances.  
travel for city residents living in the suburbs and corresponds to the low-carbon travel philosophy. The application and development space for P&R traffic modes is extensive [39; 40; 41]. For example, by the end of 2022, the number of P&R parking lots in Shanghai has grown to 20. A typical hybrid network transfer algorithm that mixes restricted and unrestricted networks is the optimal path-search algorithm for P&R travel. Private cars operate within an unrestricted network, whereas public transportation operates within a restricted network. The two requirements must be combined by constructing a topological relationship. Two representative examples of P&R travel in Table 3 are selected for detailed elaboration.
## 2 Preliminary Experiments  
### Reactor System Design Concepts  
During LOHC dehydrogenation, the reactor has to manage a large volume of hydrogen precipitation. For example, 1 mL of H18-DBT produces >650 mL of hydrogen gas under ambient conditions. The produced hydrogen displaces the LOHC liquid to reduce the contact area between the LOHC and catalyst. The effective thermal conductivity of the medium also decreases. After the dehydrogenation reaction, small hydrogen bubbles are trapped inside the pores between catalyst particles and cover part of the catalyst surface, reducing the reaction rate in stationary packed bed reactors. The centrifugal force created by the rotating catalyst bed develops a positive radial pressure gradient, facilitating detachment of the hydrogen bubbles from the catalyst surface and buoyancy-driven migration through the porous catalyst bed, so the macroscopic hydrogen production rate is faster than that of a stationary packed bed reactor.  
A schematic of the proposed triple-loop dehydrogenation system is displayed in Figure 1. The reservoir is the common part of the reaction, heating, and chilling loops. Through the reaction loop, which is represented by the green solid line box in Figure 1, LOHC liquid circulates between the reactor and the reservoir. The additional pipe (represented by the orange dotted line) delivers a mixture of hydrogen and LOHC vapor from the reactor to the reservoir. Through the heating loop, which is represented by the red dashed line, LOHC liquid circulates between the heater and the reservoir driven by a pump. The heater supplies the reaction enthalpy through a sufficiently large heat exchange area to maintain the heating surface temperature below the thermal degradation temperature. Moreover, the circulation pump provides sufficient flow rate to ensure a sufficiently large convective heat transfer coefficient. Through the chilling loop, which is represented by a blue dash-single dotted line, the mixture of hydrogen and LOHC vapor passes through the condenser. The dry hydrogen gas is then released through the mass flow meter (MFM), and the condensed LOHC is recovered to the reservoir.  
Commercially available LOHC material and catalyst pellets were selected. The preliminary experiments were conducted to acquire the knowledge required to design the reactor, reservoir, heater, and condenser.