esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Non-Targeted LC-QTOF-MS Based Metabolomics Data Analysis
#### 4.2.4 Terpenes  
Terpenes are hydrocarbon compounds made up of 5C units called isoprenes. They are classified according to these units' size. Their biosynthesis is mediated by the cytosolic mevalonate (MVA) pathway, which provides farnesyl diphosphate (FPP) for sesquiterpenoids (C15) and squalene as precursors for triterpenoids (C30) and sterols. Alternatively, they might come from the patricidal DOXP/MEP pathway, which provides GPP to form monoterpenoids [8] (Figure 6). Almost 30% of the data obtained via LC-QTOF-MS are related to various terpen and terpenoids. These compounds have shown multiple therapeutic benefits, including suppressing the immune system response against COVID-19, and inhibition in many species of bacteria and fungi [11]. Additionally, they have been reported to exhibit antimicrobial, repellant, antiallergy, anticancer, antifungal, antibacterial, antioxidant, anti-inflammatory, antidepressant, sedative, anticonvulsant, analgesic, gastoprotective, and antispasmoic properties [11].  
The main precursors of the metabolites identified from LC-QTOF-MS metabolomics data were described and used for integration in the metabolic reconstruction (Table 4). The integrated data are mainly primary precursors for metabolic modules of interest: anthocyanin biosynthetic pathway which is an extension of flavonoid pathway; fatty acid biosynthesis, degradation, and elongation; phenylalanine, tyrosine, and tryptophan biosynthesis; and terpenoid backbone biosynthesis. After data integration, the reconstruction increased by 297 active reactions and 118 metabolites.
# Ultra-Reliable Link Adaptation for Downlink MISO Transmission in 5G Cellular Networks
## 1 Introduction
### Contributions  
In this paper, the idea is to enable robust link adaptation, so that user data rates can be supported with the desired reliability. In order to decode transmissions successfully within delay constraints, a transmission rate has to be selected such that a codeword can be decoded with high reliability. Without loss of generality, we focus on achieving a reliability target of 99.999%, and leave out the availability considerations.  
We investigate SINR variations caused by the mobility related changes in the wanted and interfering channels, as well as interferer precoders. It is assumed that the channels are Rayleigh fading with Jakes' Doppler spectrum [18], and single stream precoding from \(N_{T}\) transmit antennas is used. Link adaptation is based on perfect knowledge of the instantaneous channel state at the time of measurement, channel statistics, and the correlation between measured and realized channels. Using this information we devise robust link adaptation, which can support URC targets. A transmission rate is selected so that the channel capacity at the time of transmission supports the selected rate with high probability. We find that fading of the link between transmitter and receiver, even with moderate mobility, dominates over fading of interference, and interference variability. Moreover, knowledge of channel and interference statistics, as well as the instantaneous channel and interference realization, is invaluable for robust link adaptation for URC.
# Fault-Tolerant Optimal Consensus Control for Heterogeneous Multi-Agent Systems
## 2 Preliminaries
### UGV Dynamics Model  
According to Reference [27], a single UGV model be simplified as Equation (1):  
\[\begin{cases}\dot{p}_{gi}=v_{gi}\\ \dot{v}_{gi}=u_{gi}\end{cases} \tag{1}\]  
where \(p_{gi}=\left[p_{gi^{\prime}}^{x},p_{gi}^{y}\right]^{T}\) denotes the position in \(XY\) space, \(v_{gi}=\left[v_{gi^{\prime}}^{x},v_{gi}^{y}\right]^{T}\) denotes velocity, which corresponds to the speed of the UGV moving along the direction of \(p_{gi}\), and \(u_{gi}=\left[u_{gi}^{x},u_{gi}^{y}\right]^{T}\) signifies the input of the agent \(i\). For \(m\) UGVs, the formula is transformed into the following states:  
\[\dot{x}_{Gi}=A_{G}x_{Gi}+B_{G}u_{Gi} \tag{2}\]  
where \(x_{Gi}=\left(p_{i},v_{i}\right)^{T}\), \(p_{i}=\left(p_{i}^{x},p_{i}^{y}\right)\), \(v_{i}=\left(v_{i}^{x},v_{i}^{y}\right)\), \(u_{Gi}=\left(u_{i}^{x},u_{i}^{y}\right)\), \(i=1,2,\ldots,m\).  
\[A_{G}=\begin{bmatrix}0&1\\ 0&0\end{bmatrix}\otimes I_{2},B_{G}=\begin{bmatrix}0\\ 1\end{bmatrix}\otimes I_{2}.\]  
\(A_{G}\), \(B_{G}\), respectively, represent the state-space matrix and input-state matrix of the UGVs. \(\otimes\) denotes the Kronecker product, indicating that the multi-UGV system is a linear system. Considering the failure of actuators in UGV, \(u_{Gi}^{f}=(u_{ix}^{f},u_{iy}^{f})\) represents the input after the actuator failure. The specific expression \(u_{Gi}^{f}\) is as follows:  
\[u_{Gi}^{f}=(I_{2}-\mathcal{P}_{Gi})u_{Gi},\]where the equation \(\mathcal{P}_{Gi}=diag(\rho_{ix},\rho_{iy})\in R^{2}\) is central to our analysis. This paper primarily examines partial actuator failures within a multi-following uncrewed ground vehicle system, in which \(\rho_{ix},\rho_{iy}\) does not uniformly equal one across all instances. The dynamic model of each of the \(m\) UGVs in the system can be represented as follows:  
\[\dot{x}_{Gi}=A_{G}x_{i}+B_{G}u_{Gi}^{f},\ i=1,2,\ldots,m \tag{4}\]  
For the \(m\) UGVs actuator failure model described by Equation (4), when the multi-UGV system achieves consensus, it means that all the UGV's states satisfy the following equation:  
\[\lim_{t\rightarrow\infty}\lVert\mathrm{P}_{\mathrm{i}}(t)-\mathrm{P}_{0}(t) \rVert=0,\ \lim_{t\rightarrow\infty}\lVert\mathrm{v}_{\mathrm{i}}(t)-\mathrm{v}_{0}(t)\rVert=0 \tag{5}\]
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 5 Different VANET Technologies Based on Transmission Range
### Long-Range Communications Technology
#### 5.3.4 Field Testing  
Numerous pilots and feasibility studies have been carried out during the previous decade or so to evaluate the practical performance and suitability of AVs. A comprehensive yet non-exhaustive list of different trials and testing facilities of AVs can be found in [179] for the UK and [180] in Europe and elsewhere. The V2X technologies and communication interfaces are currently in active testing. The US Department of Transport (DoT) is running US's Safety Pilot Model Deployment [181] as well as NYCDOT [182], THEA DOT [183] and WY DOT [184] pilots that explore DSRC based communication. HarborNet [185] is a test-bed that allows 4G backhauling and Wi-Fi connectivity along with DSRC. The testbed allows cloud-based code deployment, remote network control and distributed data collection from moving container trucks, cranes, tow boats, patrol vessels and roadside units. Google's automotive driving and testing facility [186] has conducted extensive road operation testing over the last few years. Tesla, Tencent and Baidu have used simulators and road-testing methods together to conduct extensive testing in demonstration zones [187]. 5 GIC test bed in Surrey, UK and Espoo and Ispra trials sites in Italy are being utilised for 5G-NR V2X testing [188].  
Finally, the wireless technologies which have already been implemented in AVs are discussed in Table 8.  
\begin{table}
\begin{tabular}{c l c} \hline \hline
**Technology** & **Task** & **References** \\ \hline BLE & Wirelessly connect sensors having relaxed latency to the ECU/OBU & [138; 139] \\  & Indoor Positioning and Localisation (On-demand content for the passenger) & [140; 189; 190] \\ \hline ZigBee & Forward Collision Warning System & [41] \\  & Prevent Collision Warning System & [191] \\  & Wirelessly connect non-safety and non-security critical sensors to the ECU/OBU & [148] \\  & Advanced Driver Assistance Systems & [149] \\ \hline UWB &
\begin{tabular}{l} RSU are connected with UWB fixed nodes to improve the position accuracy \\ where the coverage of GNSS is poor. Performance is further improved to 10 cm. \\ \end{tabular} & [155] \\ \hline \hline \end{tabular}
\end{table}
Table 8: Wireless technologies in AVs.
# A Design of CGK-Based Granular Model Using Hierarchical Structure
## 5 Experiment and Results Analysis  
To examine the validity of the CGK-based Granular Model (CGK-GM), which is the method proposed in this study, as well as the CGK granular model with an aggregated structure (AGM), the experiments were performed through a concrete compressive strength database [57] which are benchmarking databases used in the field of forecasting. For convenience in the experiments and the results analysis, the two proposed granular models are labeled CGK-GM, AGM. The databases used in the experiments are presented as follows. The concrete compressive strength database was collected by Tsinghua University in Taiwan, and it comprises 1030 instances and 9 variables. The input variables include the cement, fly ash, blast furnace slag, water, superplasticizer, coarse aggregate, fine aggregate, and time. The output variable is the concrete's compressive strength.
(3) Instead of using point forecasting, which is often utilized in mid-term ELF research, we conducted a multistep-ahead time series forecasting study. This method employs historical data to forecast a sequence of future values and is used for predicting trends for crop yield, stock prices, traffic volume, and electrical load. Multi-step ahead load forecasting has been proposed due to its significant impact on power system planning and operation risk management. LSTM-based forecasting models excel among forecasting methods due to their capacity to closely track raw trends with a notable \"lag\" characteristic in multi-step forward predictions [35]. Thus, these models were implemented in the study. Given that loads up to 30 days out needed to be forecasted, all prediction models underwent training with time lags of all features up to 30 days prior. The study examined the impact of the selected features and the determined time lag values on the models. (4) Including the lag features of all input variables can lead to increased computational complexity, resulting in a larger feature candidate pool. Therefore, we conducted a feature engineering study and created a hybrid framework combining filter (PC) and embedded (RFR and DTR) methods. This approach helped us determine the correlation and importance of each feature. The study analyzed the impact of chosen features and their time lag values on three specific model subsets. During the feature extraction process, recursive feature elimination cross-validation (RFECV) was employed to eliminate redundant features. XAI methods have the ability to explain black-box models in two different ways. The first of these is locally generated explanations, in which the behavior of the model is attempted to be predicted within the framework of an input sample. In global explanation, which is another form of explanation, the contribution amount of each input feature is defined and the general prediction tendency of the model is interpreted together with all input features. Since the aim of the study carried out here is to evaluate the individual impact of each input feature on the decision-making process of the mid-term electricity load forecasting model, the LIME method, which allows making local explanations, was used. This technique has not yet been applied in mid-term ELF, to the authors' knowledge. The purpose of this study is to explain the findings to field professionals with limited expertise in data science. The remaining sections of this paper are organized as follows. Section 2 provides a theoretical background on the models and techniques utilized to conduct this study. Section 3 presents a detailed methodology that includes enhanced model architectures, assigned parameters, and experimental setups. Section 4 outlines the experimental results, categorized according to three datasets, along with visually-supported comparison tables generated by the models. Section 5 provides a summary of the study, compares the overall results using relevant tables, arrives at a specific conclusion, and suggests possible future directions for continued research in this area.
# Color Mutations Alter the Biochemical Composition in the San Marzano Tomato Fruit
## 3 Results
### Targeted Analysis of Volatile Compounds
Furthermore, \(r\), \(y\) and their double mutant had higher levels of many fatty acid derivatives, such as (\(E\))-2-pentenal (Figure 3d) and 1-penten-3-one (Figure 3e). Lastly, \(y\) was enriched in phenylalanine derivatives, such as 2-phenylethanol (Figure 3f). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Metabolomics** & **Metabolic Class** & **Abbreviation** & \multicolumn{3}{c}{**No. of Compounds**} & \multicolumn{3}{c}{**No. of Differentially Accumulated Compounds Over SM**} \\ \cline{4-10}
**Fraction** & & & & **Compounds** & \multicolumn{1}{c}{\(g\)} & \(r\) & \(y\) & \(r\_gf\) & \(y\_gf\) & \(y\_r\) \\ \hline Volatile & Benzenoids & B & 8 & 2 & 2 & 3 & 2 & & 3 \\  & Branched-chain amino & BCAA & 10 & 3 & 3 & 2 & 4 & & 1 \\  & acid derivatives & & C & 9 & 3 & 5 & 3 & 6 & 3 & 5 \\  & Esters & E & 2 & & & & & & & \\  & Fatty acid derivatives & L & 24 & 3 & 4 & 6 & 1 & & 4 \\  & Others & & Phe, S, T, & 15 & 6 & 4 & 8 & 4 & & 6 \\  & & No ID \({}^{\star}\) & 15 & 2 & 5 & 3 & 7 & 5 & 7 \\ \hline
**Total** & & **VOCs** & 68 & 2 & 1 & 1 & 2 & 3 & 19 \\ \hline Non-polar & Carotenoids & CAR & 15 & 2 & 5 & 3 & 7 & 5 & 7 \\  & Chlorophylls & CHL & 8 & 2 & 1 & & 2 & 3 & \\  & Fatty acids & FA & 14 & 1 & & & & 1 & \\  & Phospholipids & PHO & 1 & & & 1 & 1 & & \\  & Quinones & QUI & 6 & 1 & 2 & & 2 & & 3 \\  & Tocopherols & TOC & 5 & 1 & 1 & 1 & & 1 & 2 \\  & Others & STE, & No ID \({}^{\star}\) & 5 & 1 & 1 & & 1 & & \\ \hline
**Total** & & **NP** & 54 & 8 & 10 & 5 & 13 & 10 & 12 \\ \hline Polar & Amino acids & AA & 19 & 2 & 6 & 3 & 4 & 3 & 6 \\  & Acids & AC & 17 & 5 & 5 & 6 & 5 & 4 & 4 \\  & Amines & AM & 4 & & & 1 & 1 & & 1 \\  & Sugars and polyols & SAP & 15 & 3 & 3 & 3 & 2 & 4 & 2 \\  & Alkaloids & ALK & 11 & 1 & 4 & 2 & 3 & 1 & 2 \\  & Phenylpropanoids & PHE & 55 & 6 & 13 & 24 & 18 & 18 & 16 \\  & Vitamins & VIT & 3 & 1 & 2 & & & & 1 \\  & Others & A, NU, LI, & 5 & & & 1 & & 2 & 1 \\ \hline
**Total** & & **P** & 129 & 18 & 33 & 40 & 33 & 32 & 33 \\ \hline
**Gran total** & & & 251 & 43 & 61 & 67 & 63 & 45 & 64 \\ \hline \hline \end{tabular}  
* Undefined compounds.  
\end{table} 
Table 2: Number of compounds in the different categories and classes of metabolites that are significantly different from San Marzano (SM) in each of the six lines under study.
## 5 Conclusions  
This paper considered a problem involving content placement and user association in UDNs where proactive caching and CoMP are enabled. To alleviate the backhaul load and improve network performance, the CPCE-UACE algorithm was proposed to solve the problem. Simulation results demonstrated that the proposed algorithm was capable of decreasing the necessary backhaul traffic and improving network throughput simultaneously. Simulation results showed that the proposed cross-entropy based content placement scheme significantly outperformed the conventional random and MPC placement schemes, with a 50% and 20% backhaul load decrease respectively. Furthermore, the proposed cross-entropy based user association scheme could achieve 30% and 23% throughput gain, compared with the conventional \(N\)-best, No-CoMP, and Threshold based user association schemes.  
Conceptualization, Y.W.; Methodology, J.Y.; Project administration, S.G.; Supervision, Q.Z.; Writing--original draft, S.C.; Writing-review & editing, J.Y. and Y.Z.  
This research received no external funding.  
This work was supported in part by the National Natural Sciences Foundation of China (NSFC) under Grant 61701136; Shenzhen Basic Research Program under Grant JCYJ20170811154233370; and Shenzhen Science and Technology Projection under Grant JCYJ2016060815123996.  
The authors declare no conflict of interest.
## 2 Positioning in Cellular Networks  
In this section, we present the most relevant cellular positioning technologies, discuss the applicability of ML technologies to improve their accuracy, and highlight the advances of 5G networks. Accurate and real-time positioning is highly demanded by location-based services and can be beneficial for radio resource management in 5G networks, which are deployed to achieve significant performance improvements over existing cellular networks. Many new technologies, e.g., massive MIMO, mmWave communications, Ultra-Dense Network (UDN), and Device-to-Device (D2D) communications, are being introduced in 5G networks to not only improve communication performance, but also provide the ability to significantly increase positioning accuracy [18]. It is envisioned that 5G networks will be able to locate User Equipment (UE) with sub-meter accuracy and provide high network utility [26], such as seamless coverage, low delay, and high throughput, placing higher requirements for cellular communication systems.  
In cellular-based localization, the downlink transmissions from the Base Station (BS) to the mobile device and uplink transmissions from the mobile device to BS can be utilized to determine the position of the UE [18]. The cellular-positioning methods can be classified into two main categories depending on the entity that computes the position: (1) mobile-based, where the UE itself calculates its location, and (2) network-based, where the network location server computes the position of the UE. Most cellular-based positioning solutions are network-based due to its centralized nature that allows full control of the location service by the network operator, as well as its support to legacy devices. As shown in Figure 1, the 5G positioning architecture mainly consists of two parts: a Radio Access Network (RAN), which includes a multi-Radio Access Technology (RAT) network, edge cloud, and control cloud, where the location of the UE is determined based on the measurements of the UE and BSs [18]. Every reviewed solution is network-based in this section.  
Cellular-based localization technologies can be divided into four main categories: cell-identity-based, angle-based, range-based, and fingerprinting-based [18].
# Terminal Distributed Cooperative Guidance Law for Multiple UAVs Based on Consistency Theory
## 5 Numerical Simulation
### Verification of Upper Level Distributed Coordination Strategy  
Let us assume that three UAVs form a network formation to attack the same fixed target, and all UAVs are required to attack the target at the same time. The proportional guidance coefficient of each UAV is 3, and the maximum acceleration is no more than 5 g. The other simulation parameters are shown in Table 2.  
Figure 7: The simulation results of cooperative attack on maneuvering targets with NSMG.  
Suppose that the network communication matrix of the UAV formation has the following three forms.  
\[S_{1}=\left[\begin{array}{ccc}1&1&1\\ 1&1&1\\ 1&1&1\end{array}\right]\qquad S_{2}=\left[\begin{array}{ccc}1&1&0\\ 1&1&1\\ 0&1&1\end{array}\right]\qquad S_{3}=\left[\begin{array}{ccc}1&1&1\\ 1&1&0\\ 1&0&1\end{array}\right] \tag{51}\]  
The corresponding network topologies of the three communication matrices are shown in Figure 8.  
As can be seen from the figure, in the first network topology, the three UAVs can exchange information with each other, which can be called the ring network topology. However, in the second and third network topologies, all interconnections cannot be realized. These two forms can be called chained network topologies. The following is the simulation verification for different network topologies.
## 5 Proposed Method
### Method Using Mpr  
OLSR's MPR selects the minimum number of adjacent nodes that connect to all nodes 2-hops away and eliminates redundant relay nodes, thereby suppressing wasted messages in flooding. We consider MPR as a reference for constructing an efficient and reliable relay network for UAVs. Therefore, we propose a method of selecting relay nodes using the MPR concept (hereafter, \"using MPR\").  
**(1),(2)**: are the same as \"more adjacent nodes\".
**(3)**: The node receiving a HELLO message knows which node is two hops away (2-hop neighbors). \(N\) is the subset of neighbors in all nodes. \(N2\) is the subset of 2-hop neighbors. \(N2N(i)\) is the neighbors of node \(i\). \(D(i)\) is defined as the number of neighbors of node i, \(M\) is the MPR set.
**(4)**: As shown in Algorithm 2 and Figure 8, the node selects the MPR set with the smallest number of neighbors connecting to all nodes two hops away.
**(5)**: One of the nodes with the lowest number of neighbors is selected as a relay node; then, its MPR set is selected as a relay node, and so on; all relay nodes are selected through sequential propagation.  
```
1:while\(N2\)! = \(\emptyset\)do
2:\(M=M+\{i\}\) such that \(D(i)\) is the maximum for all \(i\) in \((N-M)\)
3:for each \(i\) in \((N-M)\)do
4:\(N2=N2-N2N(i)\)
5:endfor
6:endwhile
```  
**Algorithm 2** Algorithm for method using MPR.
# Unmanned Aerial Traffic Management System Architecture for U-Space In-Flight Services
## 4 UTM System Architecture
### Discussion
Although this service is supposed to provide deconfliction information to the UAS operators through the USM, in our scheme this role is played by the EM module. This is because the automated decision-making capability is implemented in the EM module, which uses the TD module to get support generating possible alternative plans. Then, the EM is the one in charge of deciding the best option for real-time deconfliction.
###### Abstract  
Object detection in aerial images can provide great support in traffic planning, national defense reconnaissance, hydrographic surveys, infrastructure construction, and other fields. Objects in aerial images are characterized by small pixel-area ratios, dense arrangements between objects, and arbitrary inclination angles. In response to these characteristics and problems, we improved the feature extraction network Inception-ResNet using the Fast Architecture Search (FAS) module and proposed a one-stage anchor-free rotation object detector. The structure of the object detector is simple and only consists of convolution layers, which reduces the number of model parameters. At the same time, the label sampling strategy in the training process is optimized to resolve the problem of insufficient sampling. Finally, a decoupled object detection head is used to separate the bounding box regression task from the object classification task. The experimental results show that the proposed method achieves mean average precision (mAP) of 82.6%, 79.5%, and 89.1% on the DOTA1.0, DOTA1.5, and HRSC2016 datasets, respectively, and the detection speed reaches 24.4 FPS, which can meet the needs of real-time detection.  
dense image recognition; decoupled object detection; Fast Architecture Search; ellipse center sampling; anchor-free; deep learning Article