esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Example
### Constructing the Knowledge Graph
#### 4.1.2 Knowledge Extraction  
In our process of building the knowledge graph, structured data constitutes a significant proportion. The remaining semi-structured and unstructured data require further processing. The semi-structured corpus can be converted into structured data in batches by defining rules manually, and structured data can be directly mapped to triples to construct the knowledge graph. Unstructured texts, on the other hand, need to undergo manual text annotation and the training of knowledge extraction models to achieve automated knowledge extraction from the text corpus.  
A typical example of an unstructured corpus is shown in Figure 11. This corpus comes from the anomaly event report, which contains the anomaly event name, anomaly time,  
Figure 10: Ontology design of an event chain subgraph.  
Figure 9: Ontology design of a spacecraft abnormal event graph.  
spacecraft name, anomaly phenomenon, anomaly equipment, anomaly parameter, anomaly cause, and handling plan.  
Before employing the model to extract knowledge, it is necessary to annotate a sizeable amount of data to train the model. The interface of the text annotation software, doccano, used for this purpose is shown in Figure 12.  
In this study, a total of 50 anomaly records were selected for manual annotation. The models for both entity recognition and relationship extraction were divided into training and test sets at a ratio of 4:1 based on the annotated corpus. It is important to emphasize that this division is specifically intended to measure the performance of the model in the Named Entity Recognition (NER) phase, not to represent final performance. To evaluate the accuracy of the extraction results, the F1 score is used for assessment, and its calculation formula is:  
\[F_{1}=\frac{2\times P\times R}{P+R} \tag{4}\]  
In the formula, precision \(P\) is the ratio of the number of correctly recognized entities to the number of recognized entities and recall \(R\) is the ratio of the number of correctly recognized entities to the total number of entities. Table 1 presents the evaluation results of the entity recognition model on the test set, demonstrating that the constructed model can basically satisfy the knowledge extraction task of anomaly events.  
Figure 11: Unstructured corpus example (sensitive information removed).  
Figure 12: The interface of doccano for anomaly report text annotation (partial).  
Using the entity recognition and relationship extraction models built in this study, we conducted knowledge extraction on all unstructured text corpora in the library. A total of 1195 entities, including anomaly events, anomaly times, spacecraft, and anomaly equipment, were extracted, along with 1311 relationships corresponding to anomaly times, anomaly phenomena, and anomaly equipment. The extraction results are shown in Table 2.
# Deep Reinforcement Learning-Based Joint Scheduling of 5G and TSN in Industrial Networks
## 2 Related Work  
### Overview of TSN Scheduling Schemes  
Standardization within the IEEE 802.1 TSN task group (TG) enables Ethernet-based networks to be reliable real-time communication networks, which can satisfy the demands of multiple time-critical and non-time-critical applications simultaneously. TSN standards define mechanisms in the aspects of time synchronization, bounded low latency, reliability and resource management to provide deterministic services in local areas. TSN scheduling schemes play an important role in bounded latency, such as Time-Aware Shaping (TAS) defined in IEEE 802.1 Qbv, Cyclic Queueing and Forwarding (CQF) defined in IEEE 802.1 Qch, Frame Preemption (FP) defined in IEEE 802.1 Qbu, and Asynchronous Traffic Shaper (ATS) defined in IEEE 802.1 Qcr. Among these TSN scheduling schemes, TAS is widely used for industrial scenarios due to its technological maturity and \"no-waiting\" scheduling character. Therefore, the TSN scheduling method considered in this article is based on TAS. More details on the standardization efforts of IEEE 802.1 TSN TG can be found in [6,7].  
As shown in Figure 1, different applications are mapped into various priority queues in the egress of TSN switches; meanwhile, based on time synchronization achieved among devices, TAS adopts a time-triggered gate mechanism to enable (OPEN) or disable (CLOSE) transmission states of priority queues. Furthermore, GCL is used to set the gate state and accurate eligible transmission period of each priority queue, aiming to decide when and how many frames are selected for transmission.  
Figure 1: Time-aware shaping in TSN switch.  
The GCL of each export in the switch is usually pre-configured and operated circularly, so the TAS can transmit frames based on a precise time. Furthermore, the eligible transmission period defined by GCL determines the amount of network resources that can be used for each queue, which regulates the transmission rate of each queue. With GCL settings, the higher priority applications, such as industrial control or real-time multimedia, are separated from other non-time-critical applications with lower priority and can achieve dedicated time slots for transmission. Therefore, TAS can provide a deterministic behavior that enables predicable and reliable communication over Ethernet for time-critical applications as well as meeting QoS requirements for other non-real-time applications.
## 3 Materials and Methods
### Data Analyses
#### 3.3.5 Validation of Crop Yield and Growing Season Rainfall Trends at the Sub-National Scale  
To validate the results at the sub-national scale and to ensure that the models used here to assess vulnerability adequately reproduce the variations in the yields of the concerned crops, a validation model was employed. For the sub-national spatial variations in crop yield and precipitation, this work used yield data for the period 1919-2020 for the CANESM model to validate the yield and precipitation observations. The yield model was first run for the RCP 4.5 scenario for wheat and barley, for which data were available for the period 1991-2020. Later, the RCP 8.5 scenario was used to evaluate wheat and barley. This was also conducted for precipitation under the RCP 4.5 and RCP 8.5 scenarios. The spatial yield and precipitation models were run within the MOSAICC simulator interface available at [60] ([[http://196.200.148.123/mosaic/](http://196.200.148.123/mosaic/)]([http://196.200.148.123/mosaic/](http://196.200.148.123/mosaic/)) accessed on 3 November 2021), within which all model parameters were selected and specified, and the model was run. The models used here are considered reliable as they reproduce most of the spatial variations in yield and the error terms of the regression equations are considered valid.
## 4 Experiments
### Visualization of Experimental Results  
To compare the prediction error more intuitively and further validate the robustness of the human keypoint detection module, the visualization results of the actual skeleton points with and without occlusion are shown in Figure 13, where only part of the region containing the human body target is intercepted due to the large size of the original image. The green skeleton points in the figure indicate the actual values, and the red skeleton points indicate the predicted values. It can be seen by comparing the experimental results that the human keypoint detection model can still accurately detect the position of each human keypoint when it is used for skeleton point recognition in the scene with occlusions. As shown in Figure 14, comparing Figure 14b,c, it can be seen that the improvement of YOLOX-s reduces missed detection due to occlusions.  
Six abnormal behavior localization experiments were conducted, involving both single-person and multi-person scenes. Figure 15 presents the experimental results for one group of single and multi-person instances at specific time nodes in the time series. The time series is divided into frames, and the blue circle in the figure represents normal behavior exhibited by the corresponding target in a specific frame. The red circle denotes the period when abnormal behavior occurs in the video sequence.  
To visually represent the detection effect after joint inference of the models on the visual graph, the visualization includes human body bounding boxes, tracked identity IDs, human body skeleton maps, and the classification results of the temporal behaviors.  
The temporal action detection method employed in this chapter uses a sliding window with a length of 30 frames. Behavioral classification is not performed when the number of  
Figure 13: Label and prediction results display plot with and without occlusions.  
image frames containing the target is less than 30 frames. The results of each experiment shown here start from the 30 th frame onward.  
Taking the example of a single person, in the first frame of the experiment, the timing behavior category of person 1 is outputted. In the 80 th frame, the person is detected as climbing, which is an abnormal behavior. The abnormal behavior continues until the 243 rd frame, when the behavior returns to normal. In the 404 th frame, the person is detected as touching the paddle, which is another abnormal behavior. The abnormal behavior persists until the 439 th frame, when the behavior returns to normal.  
Figure 14: Label and prediction results display plot with and without occlusions. (**a**) Location of key points manually labeled. (**b**) Location of key points for model predictions. (**c**) Manual labeling and model prediction of key point locations are simultaneously restored in the original drawing.
# Efficacy of Four In Vitro Fungicides for Control of Wilting of Strawberry Crops in Puebla-Mexico
## 2 Materials and Methods
### DNA Extraction, PCR Amplification, and Sequencing  
DNA was extracted from conidia, conidiophores, and mycelium of isolates obtained with morphological characteristics associated with _F. solani_. This procedure was performed with 2% cetyl trimethylammonium bromide (CTAB) [31]. Genomic DNA was suspended in 100 \(\mu\)L of sterile Milli-Q water and quantified by spectrophotometry in a Nano Drop 2000c (Thermo Scientific, Waltham, MA, USA). To determine the DNA quality, absorbance values between 1.8 and 2.2 at A\({}_{280}\)/\({}_{280}\) and A\({}_{230}\)/\({}_{260}\) nm were considered acceptable. Finally, the DNA was diluted to 20 ng \(\mu\)L\({}^{-1}\) and then stored at \(-20\)\({}^{\circ}\)C for until further processing.  
From the most representative colonies present in diseased strawberry plants, isolate 20 from plot 1 was chosen and named strain MA-FC120. Molecular identification of strain MA-FC120 was carried out based on the analysis of internal transcribed spacer (ITS) region sequences using primer pairs ITS5/ITS4 [32], and by sequencing a part of translation elongation factor-1 alpha (EF-1\(\alpha\)) using primers EF-1 and EF-2 [16]. Amplified PCR products were verified by electrophoresis on a 1.5% agarose gel (Seakem, CA, USA). They were then purified and ligated into pGEM T-Easy Vector (Promega) and bidirectionally sequenced by Macrogen, Seoul, Korea. Sequences were assembled and edited using SeqMan (DNASAT, Madison, WI, USA) and compared to established sequences in GenBank(tm) using the Blast algorithm.  
Phylogenetic analyses were performed with concatenated sequence alignment of genes ITS/TEF-1\(\alpha\) using MEGA X software [33]. The analysis involved 11 nucleotide sequences obtained from the GenBank(tm) database (Table 1). Sequences from the ITS and TEF-1\(\alpha\) regions of _Fusarium oxysporum_ were used as an outgroup. Evolutionary history was inferred using the maximum likelihood method and the Tamura-Nei model [34], applying the Neighbor-Join and BioNJ algorithms to a distance matrix using the all-sites option. There was a total of 17,513 pb in the final dataset per 1000 bootstrap replicates to assess relative branch stability.
## 1 Introduction
This implies that it has some of the highest carbon density regions on the planet compared to other tropical regions. As an important area of tropical vegetation, few studies have focused on the drought impacts on vegetation in Southeast Asia in comparison to a number of studies in the Amazon area [15; 34] and African tropical ecosystems [16; 35]. Furthermore, agriculture constitutes the largest sector in the economy of Southeast Asia, accounting for 40 percent GDP. However, agriculture in this region highly depends upon rainfall which occurs during the wet season. Since plant growth and carbon uptake are constrained to a large extent by drought [3], it is important and urgent to monitor and investigate the impacts of the widespread drought induced by the 2015/2016 El Nino event on land vegetation for agricultural planning and carbon budgets in this region. From Oceanic Nino Index (ONI), the 2015/2016 El Nino started at the end of 2014, peaked in late 2015, and ended in May 2016. It lasted 19 months and was 6 months longer than the 1997-1998 El Nino, the strongest El Nino on record [36]. However, direct evidence of the drought impacts during the 2015/2016 El Nino event on Southeast Asia vegetation is currently lacking. In this study, we evaluated the degree of this drought over Southeast Asia by using multiple satellite-based dataset-conventional greenness-based VIs, newly available retrievals of SIF and meteorological data. In particular, we examined whether SIF can be an early indicator for large-scale vegetation health status under water stress in the tropical region.
# Deep Convolutional Neural Network with KNN Regression for Automatic Image Annotation
## 3 Our Proposal  
Conventional AIA algorithms consider the image as holistic by analyzing images globally rather than dealing with each present object. In real cases, however, few concepts may describe the image holistically, such as 'joy' or 'wild', but most concepts concern some specific regions (areas) of the image, such as 'football', 'human', or 'cloud'. As a result, for an AIA system to produce good annotation results, it must account for visual distinctions across regions as well as semantic interconnections between labels. Given that a concept-region co-occurrence matrix is derived from an annotated training image subset, our proposed solution investigates the similarity among characteristics of a candidate region and the training subset using this concept-region co-occurrence matrix. By doing so, we ensure that visual correlations among areas are taken into consideration. Thereafter,we employ a k-nearest neighbors regression (KNN-r) algorithm to annotate new regions. Figure 1 depicts a general scheme of the proposed approach.  
As the scheme in Figure 1 shows, our model takes a set \(D\) of images \(D=\{I_{1},\,\ldots\,,\,I_{N}\}\), some of which are labeled (for training) and the rest of which are not. It should be mentioned that each training image \(I_{n}\) is labeled with \(I_{cn}\) concepts: \(I_{cn}\in C/C=\{C_{1},\,\ldots\,,\,C_{M}\}\). All images are passed through a preprocessing step in which they are segmented, using JSEG algorithm, into visually homogeneous regions. An aggregation approach is subse  
Figure 1: The different phases that constitute our proposed AIA approach. Black solid arrows correspond to training images, whereas the blue ones correspond to test images. All images pass through a segmentation phase using JSEG algorithm, the segmented regions are fed to a CNN for feature extraction, features are encoded, a codebook is generated, and then KNN regression is employed to link blobs with labels and assign new labels.  
quently used to decrease the large number of areas by codifying comparable areas into blobs (codebook), with each blob corresponding to one label. Using the generated codebook and the annotation from the training subset, our model generates a co-occurrence matrix that codifies the appearance frequency of each blob-concept. Finally, we engage KNN regression to predict annotations corresponding to blobs extracted from unannotated images. Each of these steps will be further discussed hereafter.
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 3 The Causes of Thermal Hazards Associated with Lithium-Ion Batteries
### Manufacturing Defect and Aging  
Apart from the external factors, battery internal defect due to poor manufacturing, such as a low-quality separator, material contaminant, and improperly arranged constituents, can also cause battery failure and result in thermally hazardous conditions [66]. As stated in Section 2, the main cause of incidents in the Samsung Note 7 was a battery fault, in other words, Samsung adopted an excessively thin separator to increase the energy density of battery which raised the possibility of battery short circuit [67]. [PERSON] et al. [68] undertook a systematic investigation on several plausible cathode defects such as agglomeration, pinholes, metal particle contamination and non-uniformity. The findings showed that cathode agglomeration aggravated the cycle efficiency and resulted in faster capacity fading. Electrode pinholes showed substantially lower discharge capacities compared to the baseline of electrodes. Metal particle contaminants had an extremely negative effect on performance, and the electrodes with severe non-uniform coatings showed poor cycle life. In summary, cathode material defects decrease battery performance and increase the risk of thermal hazard. In addition, a low-quality separator will reduce the efficiency of Li\({}^{+}\) passing through the separator and result in serious Li plating, which will further penetrate the separator and induce the internal short circuit [69]. The improper arrangement of constituents is also harmful to the operation of battery, deteriorates the heat generation and heat release, therefore decreases battery safety.  
Finally, due to deterioration associated with battery aging, its thermal hazards will increase correspondingly. The aged battery will lose quantities of lithium and active materials [70,71]. Partially inactivated Li\({}^{+}\) will transfer to Li metal and deposit onto the surface of electrodes and even the separator to form Li plating. Upon the progression of battery aging, the degree of Li plating will increase gradually and subsequently, dendrite is generated. The dendrite may penetrate the separator and form a bridge between the electrodes, which will cause micro-shorting inside the battery and eventually lead to failure of the battery. At the same time, the thickness of the solid electrolyte interface (SEI) layer will gradually increase with the aging of the battery, which will lead to increases in the battery resistance, heat generation and thermal hazards.  
To better understand the thermal hazards of LIBs, some aspects regarding typical battery components, a single battery and a battery pack will be reviewed. In addition, the influence of certain parameters such as low pressure and cathode chemistry are also considered.
# Correlation of Gravity and Magnetic Field Changes Preceding Strong Earthquakes in Yunnan Province
## 4 Discussion
Based on the performance of the annual change in the gravity field and the total geomagnetism in the seismogenic process, and combined with the observation data from 2011 to 2021, the two different field sources of the gravity and magnetic field pass through the change value. After normalization, we jointly study the correlation between the two and the characteristics of the gestation process of strong earthquakes as an exploration of the fusion of two field sources to study the characteristics of strong earthquakes. This article has two shortcomings: First, the time of obtaining gravity and geomagnetic data cannot be guaranteed to be consistent. The time of gravity observation and geomagnetic observation in Yunnan Province is not synchronized, and the difference is 1-2 months, so the calculated correlation coefficient is small. Secondly, there are 25 border counties in Yunnan which are, respectively, joined by Myanmar, Laos, and Vietnam. The national border is 4060 km long, but there are no gravity and geomagnetic data in areas beyond the border, resulting in mutation data in the calculation of correlation coefficient between the two. Multiple field sources are combined to analyze the mechanism of earthquake preparation, and the characteristics of earthquake occurrence are quantitatively analyzed based on indicators such as correlation coefficients. As an exploration of multifield source fusion to analyze the characteristics of strong earthquakes, this study serves for mid-term prediction of strong earthquakes. In this study, the determination of the epicenter location is not introduced. In the future, we will try to jointly study this problem through ground observation technologies such as the Global Navigation Satellite System and InSAR.
## 4 Emerging Biogas Upgrading Technologies
### Separation via Phase Addition
#### 4.1.1 Inorganic Solvent Scrubbing
\[2\text{NaOH}+\text{CO}_{2}\rightarrow\text{Na}_{2}\text{CO}_{3}+\text{H}_{2} \text{O} \tag{1}\]  
\[2\text{KOH}+\text{CO}_{2}\rightarrow\text{K}_{2}\text{CO}_{3}+\text{H}_{2} \text{O} \tag{2}\]  
\[\text{Ca}(\text{OH})_{2}+\text{CO}_{2}\rightarrow\text{Ca}\text{CO}_{3}+\text{ H}_{2}\text{O} \tag{3}\]  
\[\text{NH}_{4}\text{OH}+\text{CO}_{2}\rightarrow(\text{NH}_{4})\text{ HCO}_{3} \tag{4}\]  
Table 4 summarizes the results from experiments conducted by different researchers (except for case 5, for which the data are theoretical concerning the concentration of the upgraded biogas but experimental for the other parameters). Although the obtained results indicate that alkali compounds could be effectively used for biogas upgrading or as a pretreatment for CO\({}_{2}\) removal, the critical point for their implementation is their poor regeneration [86]. The identified procedures for the regeneration of the described alkaline compounds are a thermic process, lime treating, and regeneration or carbon storage with waste material [68]. The thermic process consists of applying high temperatures to decompose the formed carbonate. During this process, CO\({}_{2}\) is released, and the sorbent is regenerated. For NaOH regeneration, the reaction that takes place is the inverse of Equation (1). The working temperature of this process is around 800 \({}^{\circ}\)C [87]. Ca(OH)\({}_{2}\) regeneration is described in Equations (6) and (7), and it is also the process used for solvent regeneration via lime treatment. NH\({}_{4}\)OH regeneration can also be performed via heat addition with the advantage that it has lower energy requirements. In a CO\({}_{2}\) absorption process from combustion gases, [PERSON] et al. (2005) found a reduction of 62% in energy demand for ammonia regeneration compared to MEA [88]. Figure 5: Classification of emerging biogas upgrading technologies. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Solvent and** **Concentation** & **Apparatus** & **Operating** & **Biogas Composition** & **Product Purity** & **Loading Capacity** & & **Case/** \\
**Concentation** & **Apparatus** & **Operating** & **Conditions** & **(C\% Vol)** & **(Highest CH\({}_{4}\) Lowest CO\({}_{2}\))** & **(Mg CO\({}_{3}\)-Ag Solvent Solution)** & **Comments** & **Reference** \\ \hline
1.5 M NaOH & & & & 96.23\% CH\({}_{4}\) & 0.29 & & \\  & & & & 3.76\% CO\({}_{2}\) & & \\ \hline
1.5 M KOH & Glass cylinder & & & 92.1\% CH\({}_{4}\) & 0.18 & & \\  & (H = 54 cm; D = 5 cm) & Ambient T & 34.2\% CO\({}_{2}\) & & 7.89\% CO\({}_{2}\) & & \\ \hline
0.5 M Ca(OH)\({}_{2}\) & & & & 85.99\% CH\({}_{4}\) & & 12 min & \\ \hline NH\({}_{4}\)OH & & & & & \\ (0\% aqueous sol.)