esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Physical plasma is the fourth state of matter and corresponds to a highly energized, partially or fully ionized gas [1]. Medical physical plasma, which only reaches temperatures slightly above human body temperature, is also used in therapy. The biomedical effects include antiseptic, anti-inflammatory, and antimicrobial activity on tissues, which has been used in dermatological treatments [2, 3]. A comparatively new field of application is plasma oncology. Here, devitalizing effects combined with simultaneous preservation of the surrounding tissue are used to treat solid tumors. This non-invasive physical plasma (NIPP) primarily exerts its activity via reactive oxygen and nitrogen species formed at the interface between NIPP and the ambient atmosphere. These reactive species exhibit antiproliferative, antimetastatic, and proapoptotic effects on cells and tissues [4; 5].  
Therefore, NIPP treatment provides potent antinocogenic efficacy and a new treatment strategy. The antioncogenic effects were observed in different in vitro models, including skeletal sarcoma, ovarian, prostate, pancreatic, and lung cancer cell models [6; 7; 8; 9; 10].  
The cytoplasmic membrane partitions intracellular and extracellular reaction spaces of eukaryotic cells and is the first to be affected by exogenous chemical and physical novae. The maintenance of osmotic and electrochemical gradients at the cytoplasmic membrane is essential for cell physiology and is maintained by the protein and lipid components of the membrane [11]. While small nonpolar molecules can pass through the cytoplasmic membrane almost unhindered, there are passive and active transport systems for larger and charged molecules, some of which are energy dependent. These are regulated in a complex manner and are crucial for cell physiology [12]. Due to the formation of reactive species, a NIPP impact on the biochemical integrity and functionality of the cytoplasmic membrane is reasonably likely. Recent studies in this field have indicated protein and lipid oxidations by NIPP [13; 14].  
This study aimed to characterize the permeability of the cytoplasmic membrane after NIPP treatment. For this purpose, the passability of low molecular weight compounds through the cytoplasmic membrane and the effects on cellular viability resulting from NIPP treatment were investigated. Regarding a potential NIPP application in plasma oncology, breast cancer was selected as a cellular model. Worldwide, breast cancer is one of the most common cancers and one of the leading causes of cancer-associated death in women [15]. Current treatment options include surgical resection, chemotherapy, endocrine therapy, immunotherapy, and radiation therapy. In many cases, a combination of treatment options is inevitable, leading to increased toxicity [16]. Therefore, NIPP treatment, which has minimal side effects, appears to be a promising alternative for improving therapeutic options in breast cancer treatment.
# Intelligent Traffic Management in Next-Generation Networks
## 3 DL/ML Applications in Software-Defined Networking
### Traffic Classification
#### 3.1.3 Discussion  
This subsection presents several ML-based solutions for traffic classification in SDN, as summarised in Table 8. It has been divided into two tasks: coarse-grained and fine-grained. Nowadays, as new applications emerge every day, it is not possible to have all the flows labeled in a real-time manner. Therefore, one of the most promising approaches is semi-supervised learning for fine-grained classification, which is closer to reality, as it profits through the use of labeled and unlabeled data. Moreover, DT-based models are the most frequently used for traffic classification where RF shows a good trade-off between performance and complexity. Furthermore, it requires less hyperparameter tuning [104]. Additionally, DL models, as with different domains, verify their effectiveness for network traffic classification. In addition, most of the researchers used a single classical approach for traffic classification. However, using ensemble learning by combining several classifiers achieves better accuracy than any single classifier. Nevertheless, multi-classifier approaches (i.e., ensemble learning) can increase the computational complexity of the model and the classification time. To solve this issue, we can reduce the number of captured packets per flow in order to reduce the classification time. For example, we can use a set of three packets instead of five packets as used in some works [62]. Additionally, based on the literature, each classifier is tested on its environment (i.e., data), and no paper explores all the classifiers with a deep comparison among them.
## 3 The Proposed CTHE Method
### Named Entity Recognition Module  
According to the aims of the named entity recognition module, the module implements two main functions. First, it accurately recognizes all named entities from the original sentence, including the entity name and the corresponding entity type. Second, it combines some simple rules to establish the entity pairs that may have a certain relation. Each entity pair includes five main parts: the head entity, the tail entity, their corresponding types, and the original sentence content. For the named entity recognition part, the BERT-based Bi-LSTM-CRF model is constructed. The framework of this model was shown in Figure 2.  
In this model, a five-level BIOSE tagging system is used to complete the tagging tasks for all corpus. The embedding layer of the model implements the conversion of character vector by the Chinese-BERT-Base. This is then followed by a Bi-LSTM network layer, a hidden layer, and a CRF layer (for the detailed calculation process of these layers, please see reference [9]). Finally, the sequence labeled results are output. According to the results of the entity list, we can quickly obtain all entity pairs. First, these entities are sorted based on the position in which they first appeared in the original sentence. Then, from the beginning to end, they are combined to establish the entity pair with one-to-one matching. The entity that appears at the front is determined as the head entity, and the entity that appears last is the tail entity. For example, we input an original sentence \"[PERSON] graduated from Harvard University\". The named entity recognition model output the entity list as [[PERSON], Harvard University-ORG], and we can establish the entity pair as ([PERSON], PER, Harvard University, ORG, [PERSON] graduated from Harvard University). Finally, all reasonable entity pairs are selected based on the filtering rules of the entity pair type. For example, if the type of the head entity and the tail entity is DATE-to-DATE, there may be no meaningful relation between the two entities, and it should be removed from the entity pairs list. However, if the type of entity pair is PER-to-ORG, it will be retained. Thus, we can identify all potential entity pairs.
## 3 Preliminaries
### Coloring of PFGs Based on Strong and Weak Adjacencies between Vertices  
In this part, we discuss a coloring of PFGs based on strong and weak adjacencies between vertices.  
Definition 11 ([32]): Given PFG \(\tilde{G}=(\tilde{V},\tilde{E})\) where \(\tilde{V}\) is a PFS on \(V=\{v_{1},v_{2},\cdots,v_{n}\}\), i.e., \(\tilde{V}=\{(v_{i},\mu_{1}(v_{i}),\eta_{1}(v_{i}),\
u_{1}(v_{i}))|\;v_{i}\in V\}\). Whereas, \(\tilde{E}=\{(v_{i}\
u_{i},\mu_{2}(v_{i}v_{i}),\eta_{2}(v_{i}v_{i}),\
u_{2}(v_ {i}v_{i}))\)\(|\;i\
eq j\}\) is a PFS on \(E\subseteq V\times V\). Let \(\Gamma=\{\gamma_{1},\gamma_{2},\cdots,\gamma_{k}\}\) be a family of picture fuzzy (PF) subsets of \(\tilde{V}\) where  
\[\gamma_{i}=\{(v_{j},\mu_{\gamma_{i}}(v_{j}),\eta_{\gamma_{i}}(v_{j}),\
u_{\gamma _{i}}(v_{j}))\}\]for \(1\leq i\leq k\), and \(1\leq j\leq n\). The family \(\Gamma\) is called as a \(k\)-vertex coloring of \(\tilde{G}\) if 1. \(\bigcup_{i=1}^{k}\gamma_{i}=\tilde{V}\), i.e., \[\max\{\mu_{\gamma_{i}}(v_{j})\}=\mu_{1}(v_{j}),\max\{\eta_{\gamma_{i}}(v_{j})\} =\eta_{1}(v_{j}),\min\{\
u_{\gamma_{i}}(v_{j})\}=v_{1}(v_{j}),\] for \(1\leq i\leq k;1\leq j\leq n\). 2. \(\gamma_{i}\cap\gamma_{j}=\mathcal{O}_{pfs}\forall i\
eq j\), i.e., \[\min\{\mu_{\gamma_{i}}(v_{1}),\mu_{\gamma_{j}}(v_{1})\}=0,\min\{\eta_{\gamma_{ i}}(v_{1}),\eta_{\gamma_{j}}(v_{1})\}=0,\max\{\
u_{\gamma_{i}}(v_{1}),\
u_{ \gamma_{j}}(v_{1})\}=1,\] for \(1\leq i,j\leq k;1\leq l\leq n\). 3. For every pair of strongly adjacent vertices \(u,v\in V\): \[\min\{\mu_{\gamma_{i}}(u),\mu_{\gamma_{i}}(v)\}=0,\min\{\eta_{\gamma_{i}}(u), \eta_{\gamma_{i}}(v)\}=0,\max\{\
u_{\gamma_{i}}(u),\
u_{\gamma_{i}}(v)\}=1,\] for \(1\leq i\leq k\). In other words, every pair of strongly adjacent vertices belongs to different PF subsets. The minimum value \(k\) for which \(\tilde{G}\) has \(k\)-vertex coloring is referred as the chromatic number of \(\tilde{G}\), denoted by \(\chi^{f}(\tilde{G})\).  
We describe the coloring of a PFG in Example 1 to provide a better understanding of the concept.  
**Example 1**.: _Let us consider PFG \(\tilde{G}=(\tilde{V},\tilde{E})\) in Figure 1. The set \(\tilde{V}\) is a PFVS on universal set \(V=\ \{A,B,C,D\}\)._  
1. _The pairs of strongly adjacent vertices are_ \(\{A,B\},\{A,D\},\{B,C\},\{B,D\}\)_, and_ \(\{C,D\}\)_. Meanwhile,_ \(\{A,C\}\) _is the pair of weakly adjacent vertices._
2. _Therefore, we obtain the PF-subset_ \(\gamma_{1}=\{(A,0.1,0.3,0.1),(C,0,0.2,0.1)\}\)_. Since_ \(B\) _and_ \(D\) _are strongly adjacent,_ \(\gamma_{2}=\ \{(B,0.2,0.1,0.5)\}\)_, and_ \(\gamma_{3}=\{(D,0.2,0.3,0.5)\}\)_. We obtain the family_ \(\Gamma=\ \{\gamma_{1},\gamma_{2},\gamma_{3}\}\)_._
3. _Thus, the chromatic number of_ \(\tilde{G}\) _is_ \(\chi^{f}(\tilde{G})=3\)_._
# On Cross-Layer Interactions for Congestion Control in the Internet
## 5 Performance Evaluation
### Access Link, Common TCP
Another problem that appears in this scenario is much lower fairness in uncongested and mildly congested networks, which results from varied RTTs. TCP flows with longer RTT occupy a much smaller part of bandwidth than TCP flows with shorter RTT. Deploying AQM already introduces randomization to which flow experiences loss. To improve this approach, both AQMs could adopt a simple mechanism to approximate the flow rate, e.g., borrowed from CHOKe [34]. CHOKe verifies if the upcoming packet belongs to the same flow as a randomly selected packet in the queue. If both packets belong to the same flow, it decides to drop the packet. **Summary:**  
* Varied RTT significantly worsens the achievable fairness among competing flows for any combination of TCP congestion control mechanism and buffer management strategy. * DT leads to a very high drop ratio, unfairness, and persistently full buffer. Applying PIE or CoDel improves the experienced delay and delay variations and, in most cases, fairness provided by the TCP congestion control mechanism. * TCP Cubic benefits the most from replacing DT with AQM. * In combination with DT, C-TCP provides a very high Jain's index, acceptable average queue size; however, high queue size variations and only in an uncongested network scenario. * **The following combinations provide the best results in terms of satisfactory fairness, delay, and drop ratio at the same time: PIE with C-TCP (for uncongested \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **DT** & **PIE** & **CoDel** \\ \hline \multirow{3}{*}{UC} & New Reno & 0.06 & 0.03 & 0.02 \\ \cline{2-5}  & Cubic & 0.26 & 0.12 & 0.07 \\ \cline{2-5}  & C-TCP & 0.01 & 0.01 & 0.01 \\ \hline \multirow{3}{*}{MC} & New Reno & 0.75 & 0.85 & 0.71 \\ \cline{2-5}  & Cubic & 0.89 & 1.31 & 1.15 \\ \cline{2-5}  & C-TCP & 0.32 & 0.48 & 0.40 \\ \hline \multirow{3}{*}{HC} & New Reno & 11.03 & 15.82 & 11.44 \\ \cline{2-5}  & Cubic & 13.68 & 15.54 & 12.38 \\ \cline{1-1} \cline{2-5}  & C-TCP & 10.92 & 15.89 & 11.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Packet drop ratio [%] in the Access Link scenario with common TCPs, UC—uncongested, MC—mildly congested, HC—heavily congested network. network), PIE with Cubic (for mildly congested network), and PIE with any TCP (during heavy congestion).
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications
## 2 Design Methodology
### Phase 3: Design of Dual Band-Notched SWB Antenna (Antenna 3)  
To deal with undesirable possible intervening of the narrowband regime with the SWB antenna, another intervention band in extension with the AWS band was created at 6 GHz, which was capable of covering the bandwidth of 4-7.2 GHz (C band). A split elliptical-shaped slot was engraved at the lower edge of the patch to stop the C band. Figure 4 represents the antenna structure with two band-notches and its VSWR result. From the VSWR graph, we can deduce that the second band notch introduced in the antenna structure did not affect the efficacy of the single band notch (Antenna 2).  
Figure 2: Phase 1: VSWR result of the SWB antenna.
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 5 Case Study: Economic Benefits of 5G-Enabled Healthcare Solutions
### Economic Impacts of AI  
It is expected that in the future, AI will have a big impact on the healthcare industry with the AI health market growth expected to reach $6.6 billion by 2021 [76]. The interest in AI-based healthcare systems can be gauged from the fact that Babylon Health (London, UK) raised close to $60 million in April 2017 for its platform that uses AI and smartphone-based chatbots [77]. A research work conducted by Accenture projected that the use of AI in healthcare could significantly enhance (nearly double) the annual economic growth of the twelve developed economies studied by 2035 [78]. According to the report by Accenture, 10 key AI-based healthcare applications can potentially create $150 billion in annual savings for the U.S. economy by 2026. The Accenture report also estimated that the shortage of physicians will likely double over the next nine years. AI and 5G communication have the power to alleviate this burden on clinicians by providing remote care settings directly to the patients. In another study conducted by Frost & Sullivan, it was projected that AI has the potential of improving health outcomes by 30-40% while reducing the costs of treatment by 50% [79]. The estimated impact of AI applications on the health sector and the national economies is shown in Figure 10).
# Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data
## 4 Discussion
### Future Work  
Satellite derived GPP and NPP are difficult to validate, due to the differences in spatial scale between the ground and satellite data. First, the height of the sensors at FLUXNET sites, which is related to the representativeness of the observed data, depends on the type of the vegetation, the extent of fetch, the range of wind velocity and the frequency response of the instrument [51]. Second, the land surface at the site should ideally be flat, with an extensive fetch of uniform vegetation. But in the real environment, many flux tower sites are on undulating or gently sloping terrain, as this is where native vegetation exists [52]. Moreover, some sites consist of mono-specific vegetation (such as cropland and grassland), and others contain a mixture of species. Spatial heterogeneity may lead to some mixed pixels in remote sensing data, therefore, leading to some bias in GPP and NPP estimation and validation. In the future, we may study the spatial representativeness of ground observation data through footprint analysis [53; 54; 55] and validate the remotely sensed vegetation productivity through the upscaling of ground observation and fine spatial resolution remote sensing data.  
Producing accurate estimates of predictions of LUE is another issue that urgently needs to be addressed when estimating vegetation productivity. In this study, the biome-specific potential LUE \(\varepsilon_{max}\) was set at a certain value for each vegetation type. In addition, in this algorithm, actual LUE was calculated based on the assumption that LUE was influenced only by low or high temperatures and water shortages. However, in the real environment, LUE could also be influenced by many other factors, such as the eco-physiological processes in the vegetation, light intensity, and concentrations of CO\({}_{2}\) and O\({}_{3}\) in the atmosphere [56; 57]. In addition, LUE shows obvious spatial heterogeneity and temporal variation under the combined influence of vegetation distribution and climate features [58; 59; 60]. Therefore, accurate predictions of LUE are an important issue should be solved. In the future, we may obtain the spatial distribution of LUE by regression or machine learning methods, such as random forest and support vector machine. Parameter optimization based on process models (such as the Biome-BGC) may be another way to estimate the spatial patterns of LUE. Moreover, relationships between LUE and the fraction of PAR absorbed by chlorophyll (FPARchl) [61] could also be used in LUE estimation.
## 6 Conclusions  
This study analyzed the structure of the UAV-UGV formation motion control system and the relationship between the subsystems. A UAV-UGV formation-maintenance controller was designed based on the optimal control theory. In addition, simulations were performed for the UAV-UGV formation-maintenance control system, including a UGV with a given motion state, a UAV with a first-order stability-control system, and an optimal maintenance controller. The following conclusions were drawn.  
Figure 2: Simulation results of case 2. (**a**) Three-dimensional motion trajectory of UGV and UAV; (**b**) velocity of UAV; (**c**) deflection angle of UAV; (**d**) motion trajectory in \(X_{r}\)-axis direction; (**e**) motion trajectory in \(Y_{r}\)-axis direction; (**f**) motion trajectory in \(Z_{r}\)-axis direction.  
1. The physical significance of the UAV-UGV relative-motion model, based directly on the UAV motion relationship in the relative coordinate system, was clear.
2. The optimal UAV-UGV formation-maintenance controller designed in this study had quadratic optimal properties for the UAV-UGV relative-motion state, as well as the formation-control energy. The controller could overcome the constant perturbation of the UAV-UGV relative motion caused by the velocity of the UGV. The optimal UAV-UGV formation-maintenance controller could overcome the given motion state of the UGV as an input perturbation, while the UGV performed a prolonged motion.
3. Within the flight envelope of the UAV-UGV formation, the optimal UAV-UGV formation-maintenance controller was able to overcome the errors introduced by the linearization of a nonlinear model.  
The authors of the authors are the following: conceptualization: all authors; methodology: H.Z. and X.Y.; software: T.X.; writing: J.Z.; investigation: T.X. All authors have read and agreed to the published version of the manuscript.  
The authors appreciate the financial support from the National Natural Science Foundation of China (NSFC) (62173274), Natural Science Foundation of Shaanxi Province (2020)Q-219), and Aviation Fund (20200001053005).  
Not applicable.  
All data used during the study appear in the submitted article.  
All of the authors declare that they have no known competing financial interests or personal relationships that could appear to influence the work reported in this paper.
# Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
## 3 Big Data Tools
### Existing Tools
#### 3.1.7 Apache Storm  
Apache Storm [94], initially released in September 2011, is a distributed stream processing computation framework originally created by [PERSON] and the team at Back-Type. Storm was later acquired and open-sourced by Twitter and became a standard for distributed real-time processing systems in a short amount of time. Storm is written predominantly in the Clojure programming language. It uses custom created \"spouts\" and \"bolts\" to define the data manipulation process to allow batch, distributed processing of streaming data. \"Spouts\" would take in data and distribute to \"bolts\", and the functions and codes users write would be processed in bolts. The whole storm application composed of \"spouts\" and \"bolts\" could be represented by a directed acyclic graph (DAG) and form a \"topology\".  
Data flow through edges on the graph are named streams. Together, the topology acts as a data transformation pipeline. The general topology structure is similar to a MapReduce job but the data are processed in real time instead of in individual batches. Additionally, Storm topologies run indefinitely until being shut down or encounter failure, while a MapReduce job DAG must eventually end.
## 5 Performance Evaluation
### Parameter Tuning of G-TIAG
#### 5.3.2 Epochs  
In the attack identification work, we investigate the selection of four epochs separately and explore the change of three indicators under different epochs to spend as little time as possible to achieve a high classification performance, as shown in Figure 7a. We find that G-TIAG achieves precision, recall, and F1-score of about 0.85 in only 100 epochs and about 0.90 in 200 epochs. With the increase in the training epochs, the classification ability generally improves. When the epochs are more extensive than 200, the increase in three indicators from about 0.90 to 0.93, however, is negligible since the training time increases  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameters** & **Adaptive Interval** & **Final** \\ \hline Training Epochs & [50, 100, 200, 300] & 200 \\ Learning Rate & [0.01, 0.005, 0.001, 0.0005] & 0.001 \\ Batch Size & [32, 64, 128, 256] & 64 \\ Dropout & [0.1, 0.2, 0.4, 0.5] & 0.5 \\ GRU Hidden Units & [64, 128, 256, 512] & 256 \\ Time Interval of Graph Construction & [100, 200, 300, 400] & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Hyperparameters selections for G-TIAG.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Benign** & **Dos** & **DDoS** & **PortScan** & **Bot** & **Web Attack** \\ \hline Number of Flows & 103,403 & 24,742 & 21,623 & 24,420 & 1966 & 2180 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Datasets description.  
exponentially. Therefore, we choose 200 epochs to balance classification performance and time cost.
# Protein Dynamics in F-like Bacterial Conjugation
## 5 The Structures of Conjugative DNA Transfer Proteins
### TraY and IHF  
The relaxosome accessory protein TraY is a 131 aa cytoplasmic protein and has the least structural information available of the proteins in this section. The relaxosome complex usually contains between 2 and 3 accessory proteins including, TraY, TraM, and the integration host factor (IHF), a genome encoded heterodimeric protein [3]. The origin of transfer (_oriT_) presents two binding sites for each protein, IHFA, IHFB for IHF and _sbyA_ and _sbyC_ for TraY [3; 123]. It is presumed that TraY and IHF cooperatively stabilize the conformation of DNA that surrounds the nic site to allow TraI to bind and cleave the DNA, producing single strands [124; 125; 126]. Both proteins can induce a bend in bound DNA of 160\({}^{\circ}\) and 50-55\({}^{\circ}\) by IHF and TraY respectively [3]. TraY is in a class of homologous DNA-binding proteins like P22 phage repressors Arc and Mnt, which express a ribbon-helix-helix (RHH) motif in which two homodimers bind DNA [37; 64]. However, TraY has been shown to bind DNA as two monomers [64; 127]. The assembly of both TraY and IHF at _oriT_ is required prior to TraI binding for the initiation of DNA transfer [128].