esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 State of the Art
### Proposed Coexistence and Coordination Techniques  
#### LTE LAA Approach  
Towards a coexistence technique that respects the regional regulations, 3 GPP announced the LTE LAA standards in Release 13, including the description of a Listen Before Talk (LBT) procedure (also known as CCA) [15]. Initially, LTE LAA is scheduled to operate within the 5-GHz unlicensed spectrum and for DL traffic only, but in a later phase, it is expected to be extended to the 2.4-GHz unlicensed band, as well as for both DL and UL traffic. Initially, an eNB will be able to activate and deactivate a secondary cell operating in the unlicensed spectrum. Through this cell, only data traffic (via the PDSCH) can be sent, while the LTE control signals and the UL traffic (PUSCH) will be transmitted via the licensed anchor. The eNB must perform the LBT procedure and sense the channel  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Frequency/Region** & & **2.4 GHz** & **5150-5250 MHz** & **5250-5350 MHz** & **5470-5725 MHz** & **5725-5850 MHz** \\ \hline \multirow{3}{*}{EU} & Coexistence & \multicolumn{5}{c}{Listen Before Talk, Maximum Transmit (TX) power, Emission mask} \\ \cline{2-7}  & Protect incumbent & - & Indoor & Indoor/Outdoor & Indoor/Outdoor & \multirow{3}{*}{-} \\  & & & & DFS/TPC & DFS/TPC & \\ \hline \multirow{3}{*}{USA} & Coexistence & \multicolumn{5}{c}{FCC Part 15.247, 15.401-407, Maximum TX power, Emission mask} \\ \cline{2-7}  & Protect incumbent & - & Indoor & Indoor/Outdoor & Indoor/Outdoor & \multirow{3}{*}{-} \\  & & & & DFS/TPC & DFS/TPC & \\ \hline \multirow{3}{*}{China} & Coexistence & \multicolumn{5}{c}{Maximum TX power, Emission mask} \\ \cline{2-7}  & Protect incumbent & - & Indoor & Indoor/DFS/TPC & - & Indoor/Outdoor \\ \hline \multirow{3}{*}{Japan} & Coexistence & \multicolumn{5}{c}{Listen Before Talk, Maximum burst length (4 ms), Maximum TX power,} \\  & & & & Maximum antenna gain, Emission mask & \\ \cline{2-7}  & Protect incumbent & - & Indoor & Indoor DFS/TPC & Indoor/Outdoor & \multirow{3}{*}{-} \\  & & & & DFS/TPC & \\ \hline \multirow{3}{*}{Korea} & Coexistence & \multicolumn{5}{c}{Maximum TX power, Maximum antenna gain, Emission mask} \\ \cline{2-7}  & Protect incumbent & - & Indoor & Indoor/Outdoor & Indoor/Outdoor & \multirow{3}{*}{-} \\ \cline{2-7}  & Protect incumbent & - & Indoor & DFS/TPC & DFS/TPC (5470-5650) & \\ \hline \end{tabular}
\end{table}
Table 1: Regional requirements for the unlicensed spectrum.  
prior to a transmission in the unlicensed spectrum. When the channel is sensed as busy, the eNB must defer its transmission by performing an exponential backoff. If the channel is sensed to be idle, it performs a transmission burst with a duration from 2-10 ms, depending on the channel access priority class. The authors in [16] analytically describe the LTE LAA procedure. They provide an overview of the LAA mechanism including the motivation and use cases where it can be applied. Additionally, they present a coexistence evaluation methodology and results, which have been contributed by 3 GPP. Figure 5 shows the LTE LAA and Wi-Fi coexistence in the same channel in the unlicensed spectrum.
## 4 System Model
### Protocol's Assumptions  
It is imperative to go over the underlying assumptions that the proposed protocol is based upon before delving into the specifics of the proposed protocol. The assumptions below are developed with the understanding that every IoT network functions in a unique context.  
1. Every network area size is customized to reflect a particular IoT application.
2. There is an abundance of IoT SNs distributed throughout a rectangular geographic area positioned at random, with one the sink node within the IoT network.
3. The sink node is positioned outside the physical boundaries of the network and possesses boundless energy. Furthermore, by broadcasting, the sink node can distribute messages to every SN in the network sensory area.
4. Each IoT SN and the sink node have unique IDs and are stationary.
5. All SNs have finite energy capacity and are isomorphic. This means that they have the same amount of beginning energy and the same processing capacity.
6. Every SN is equipped with GPS and knows not only the coordinates of the sink node but also those of other SNs.  
7. SNs die when their energy runs out completely.
8. SNs may regulate their power at which they transmit data according to how close they are to the receiver.
9. SNs are allotted specific transmission slots for data fusion purposes, and within these TSs, SNs always have data to communicate, since our protocol is data-driven (i.e., having continuous data to send), not event-driven.
10. The distance between the source and the receiver can be estimated based on the received signal strength.
# Enhanced Path Planning by Repositioning the Starting Point +
## 5 Conclusions
### Future Research Trajectories  
Several promising research directions emerge from these findings. Future investigations should incorporate environmental variables including wind patterns, temperature fluctuations, and atmospheric conditions into the optimization framework to enhance real-world applicability. The development of adaptive algorithms capable of adjusting launch pad positioning and route assignments in real time based on changing mission requirements and environmental conditions presents another valuable research avenue.  
Extending the current distance-focused approach to encompass multiple objectives such as flight duration, energy consumption, and mission completion probability would provide more comprehensive optimization capabilities. Investigation of optimization strategies for heterogeneous drone fleets with varying capabilities, battery capacities, and operational constraints could significantly expand the methodology's practical utility. Comprehensive field testing across diverse operational scenarios and geographical conditions remains essential for validating laboratory findings and refining optimization algorithms.
## 2 Materials and Methods
### Performance Evaluation  
The gasification temperature \(T\), oxygen equivalence ratio \(R_{\rm o}\), mole of CO\({}_{2}\) to carbon ratio, \(R_{\rm c}\) and the MSW blending ratio \(R_{\rm M}\) are four significant parameters in this co-gasification process; they can be expressed as follows,  
\[R_{\rm o}=\frac{m_{\rm actO_{2}}/m_{\rm fuel}}{m_{\rm stO_{2}}/m_{\rm fuel}} \tag{3}\]  
\[R_{\rm c}=\frac{m_{\rm CO_{2}}/44}{y_{\rm c}m_{\rm fuel}/12} \tag{4}\]  
\[R_{\rm M}=\frac{m_{\rm MSW}}{m_{\rm MSW}+m_{\rm coal}} \tag{5}\]  
where \(m_{\rm actO2}\) and \(m_{\rm stO2}\) represent the actual and stoichiometric oxygen mass in gasifying agents, respectively; \(y_{\rm c}\) is the carbon content in the fed fuel; \(m_{\rm CO2}\) is the recycled input CO\({}_{2}\) mass; and \(m_{\rm MSW}\) and \(m_{\rm coal}\) are the mass flows of MSW and coal, respectively.  
The lower heating value (LHV) of the product syngas is an important index to evaluate the quality of the product gas, which is defined as Equation (6) [41]:  
\[LHV_{gas}=282993X_{\rm CO}+241827X_{H_{2}}+802303X_{CH_{4}} \tag{6}\]  
where, \(X_{\rm CO}\), \(X_{\rm H2}\), and \(X_{\rm CH4}\) denote to the molar fraction of species CO, H\({}_{2}\), and CH\({}_{4}\), respectively. In addition, the cold gas efficiency (CGE) and the overall energy efficiency (OEE) of this system are calculated by the Equations (7) and (8) [42].  
\[CGE=\frac{m_{sys}\cdot LHV_{gas}}{m_{fuel}\cdot LHV_{fuel}} \tag{7}\]  
\[OEE=\frac{m_{sym}\cdot LHV_{gas}+Q_{5}-Q_{1}-Q_{2}}{m_{fuel}\cdot LHV_{fuel}+ W_{ASU}+Q_{CA}} \tag{8}\]  
where \(m_{\rm syn}\) and \(m_{\rm fuel}\) are mass flow of product syngas and fed fuel, respectively; \(Q_{1}\) and \(Q_{2}\) denote to the heats required for MSW and BC drying, respectively; \(Q_{5}\) is the released heat from syngas cooling; \(W_{\rm ASU}\) is the energy consumption for O\({}_{2}\) production; and \(Q_{\rm CA}\) refers to the heat amount for CO\({}_{2}\) absorption unit.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Description** \\ \hline ASU & - \\ Oxygen content in oxidant & 95\% \\ Temperature/Pressure & 25 \({}^{\circ}\)C/0.12 MPa \\ Power consumption & 0.324 kWh/Nm\({}^{3}\) \\ Co-gasification process & - \\ Total fuel flow rate & 100 kg/h \\ MSW blending ratio (\(R_{\rm M}\)) & 0–1 \\ Gasifier temperature/Pressure & 750–1100 \({}^{\circ}\)C/ 0.101 MPa \\ Oxygen to carbon ratio (\(R_{\rm o}\)) & 0.1–1 \\ CO\({}_{2}\) to carbon ratio (\(R_{\rm c}\)) & 0.1–1 \\ CO\({}_{2}\) absorption unit & - \\ CO\({}_{2}\) capture efficiency & 90\% \\ CO\({}_{2}\) absorption energy consumption & 3 MJ/kg \\ Compressor isentropic efficiency & 0.85 \\ Compressor mechanical efficiency & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The operating parameters of co-gasification system of MSW and coal in CO\({}_{2}\)/O\({}_{2}\) atmosphere.
# Empirical Trials on Unmanned Agriculture in Open-Field Farming: Ridge Forming
## 3 Results and Discussion  
### Operation Data Analysis  
During the operation, the PTO revolution speed, PTO torque, and ridge-forming depth were analyzed using both the conventional method and the developed robot. The PTO revolution speed was consistently maintained at 450 RPM, and the vehicle's velocity was 1.5 kmh\({}^{-1}\). However, the torque data fluctuated more with the conventional method compared to the developed robot. The average torque for the ridge-forming operation was 5.89 \(\pm\) 1.40 and 5.40 \(\pm\) 2.19 kg\(\cdot\)m for the conventional method and robot, respectively. The conventional method showed higher deviation due to the driver's intuitive adjustments to the implement depth, which resulted in a deeper ridge than the target depth. Although this might seem like better performance, it leads to unnecessary wear and tear on the machinery, reducing its maintenance efficiency. In contrast, the robot initially formed a shallower ridge but soon achieved a consistent ridge depth of 17.87 \(\pm\) 0.88 cm between 2 and 9 m of travel. The ridge depth at the end of the travel was 15.6 cm (Figure 4). This demonstrates the robot's ability to maintain a more consistent depth, minimizing unnecessary damage to the equipment.  
Figure 4: The mechanical data during ridge forming: (**a**) conventional method, (**b**) robot.
# Inferring Emotion Tags from Object Images Using Convolutional Neural Network
## 3 Results and Discussion
### Comparison with the State-of-the-Art Study  
This section provides a comparative analysis of the proposed scheme for inferring emotion tags with state-of-the-art techniques. A comparison of the proposed method with the well-known existing approaches is presented in Table 3. It can be noted from Table 3 that all the previous studies are based on the self-developed datasets, such as images obtained from Flicker, to accomplish a desirable inferring emotion tag performance. Moreover, these studies are based on visual features, such as context and content-wise analyses of visual stimuli. Consequently, these schemes perform poorly in real-world scenarios. Moreover, it is also hard to opt for the human behavioral analysis based on such schemes as most of the time, the visual information like color and texture of an image is not enough to perceive a specific emotion tag. However, the proposed scheme considers an image object category as a visual feature to perceive an emotion tag. Thus, it is capable of efficiently infer nine emotion classes based on three types of analysis, i.e., combined, female, and male emotion responses. The proposed scheme effectiveness can be demonstrated by comparing the output results for emotion inferring with the existing state-of-the-arts. It can be observed from Table 3 that the accuracy rates achieved for the proposed scheme outperform the reported results of the existing emotion recognition studies. The apparent advantages of our proposed scheme offer effective decision making over the existing schemes, which are crucial to opt for real-time applications.  
Figure 8: Best-case confusion matrices obtained for inferring emotion tags based on bottom-level object categories using K-NN classifier with (**a**) male responses, (**b**) female responses, (**c**) combined responses. The columns represent the predicted values, while the rows represent the ground truths. Here, e1, e2, e3,..., e9 represent Amusement, Anger, Awe, Boredom, Contentment, Disgust, Excitement, Fear, and Sad emotion, respectively.  
Despite efficient recognition performance as compared to the existing studies, our proposed system entails certain limitations. For example, it only takes into consideration the gender information for emotion perception and analysis. However, emotions do vary for different human beings with respect to their regions, traditions, and other demographics. To be more precise, as we presented in Section 2.2.3, for example, the object \"dog\" is category classified under \"contentment\" emotion tag, which is highly diverse between individuals belonging to different region, age, and profession. We just use the majority voting in this case for assigning the emotion tags. Thus, the classification of emotion tags from images based on the proposed scheme suffers from the problem of subjectivity. Therefore, for accurately modeling human emotions based on object interactions, it is required to incorporate the detailed user demographics in the proposed model as well to generalize a large diverse population.
# Quantifying Privacy Risks for Continuous Trait Data
###### Abstract
In this paper, we provide the first detailed quantitative derivation for the probability of the privacy risks of continuous trait data, which can be used to measure the vulnerability, the probability that the data will be unable to resist the privacy-breaching attack [24]. Our contributions can be divided into three major areas. (i) We formalize the genome privacy breaching attack as a series of single-feature three-classification problems. This formalization also allows us to construct synthetic data with configurable parameters, which is needed because our quantitative analysis requires parameters that can be set. Moreover, there are few real-world data for testing our approaches. (ii) We present a Bayesian method that matches continuous trait profiles to the genotypes. This allows us to draw deeper insights, such as: the factor affecting the matching accuracy does not depend on the _accuracy_ of classification but depends on the _sensitivity_ (see Section 6.3.1) and the number of continuous traits; both the prior probabilities of genotypes and the number of continuous traits have significant impacts on distinguishing true positive and false positive matching; the larger the matching dataset, the lower the privacy risk. (iii) Our analysis of the real dataset confirms that with _sensitivity_, the number of traits, and the prior probabilities of genotypes, we can accurately estimate the privacy implications of a trait dataset. **Organization**. We introduce the background knowledge about traits and genotypes in Section 2, review the relevant previous work in Section 3, present our adversarial model in Section 4, and then describe the principle of synthetic data generation in Section 5. In Section 6, we analyze the main factors affecting data privacy risk through simulation experiments and mathematical derivation and verified our analysis results with real data. We also discuss the application scenarios of our findings and what needs to be improved in future work in Section 7, before concluding in Section 8.
## 2 Methods for Determination of Lipophilicity
### Direct Experimental Determination of Lipophilicity
#### 2.2.1 Shake-Flask Method  
The shake-flask method uses \(n\)-octanol (hydrophobic) and water (hydrophilic) as the biphasic liquid system, and it is the standard method recommended by Organization for Economic Co-operation and Development (OECD) [32]. This classical procedure consists of dissolving the sample, shaking until equilibrium has been reached, and measuring the compound concentration in each phase of the biphasic system [32].  
The shake-flask method is a direct measurement of the partition coefficient, and that is its main advantage [33]. However, this procedure has several drawbacks. It requires control over a large number of experimental parameters (different experimental conditions usually produce different values of log P for the same analyte) [34]. It is a highly time-consuming method as the partition needs to reach equilibrium. The time needed to reach equilibrium concentrations varies hugely (from 1 to 24 h), and partitioning rates may be fast or slow depending on the log P of the solute and the degree of shaking [35]. It consumes large amounts of solvent per analyte [35], and it is not suitable for degradable compounds and surface-active materials [27]. Finally, it is not appropriate for poorly soluble compounds [35].  
Modification of the shake-flask method has been proposed that enables the determination using different alternatives to the \(n\)-octanol/water system aimed to imitate different physiological cell barriers (e.g.: chloroform/water, alkane/water, or propylene glycol dipelargonate/water) [36].
## 1 Introduction
Guided by the application requirements of emergency observation, this paper focuses on the key technologies of the multi-satellite dynamic scheduling problem and employs the proposed mathematic model and algorithm in an automatic mission control system for constellations of satellites. The overall objective of this study is to design a mission-oriented satellite scheduling architecture under emergency conditions based on the following goals: (1) to design priority evaluation criteria for emergency missions. The seven impact factors that influence the mission priority, including imaging mission level, type of observation images, visibility of target to satellite, execution urgency degree, type of mission, mission conflict degree and revenue of imaging mission, are taken into account to obtain the priority level of mission, which is then used as heuristic information for solving constraint scheduling problem; (2) to analyse the main constraints of satellite scheduling and the objective function in detail. Each term of the objective functions is maximizing the mission priority, maximizing the mission revenue and minimizing waiting time for missions that require an urgent execution time. Then, the hybrid genetic tabu search algorithm is used to obtain the initial satellite scheduling plan, so as to avoid useless iterations when close to the best solution in the late stage of algorithm implementation; and (3) to develop a dynamic scheduling algorithm based on mission priority to solve the scheduling problem in case of newly arrived missions. In this approach, newly arrived missions are added into the initial scheduling plan to obtain a new scheduling plan. The validity of the algorithm is verified through experimental simulations, and the feasibility of the emergency observation mission scheduling model is verified based on a model performance evaluation function.
# Enhanced Dual Reversible Data Hiding Using Combined Approaches
## 2 Preliminaries  
### Hamming Codes  
We describe the fundamental concepts of Hamming code (HC) [41; 42] as applied to cover coding. HC(\(n,k\)) is a linear, perfect, single-error-correcting code characterized by a minimum Hamming distance of 3. For an integer \(m\geq 2\), the codeword length is given by \(n=2^{m}-1\), while the message length is \(k=2^{m}-m-1\). However, HC cannot guarantee error correction if multiple errors occur within a single codeword. The parity check matrix \(\mathcal{H}\) for HC is defined in Equation (1). Let \(\mathcal{H}\) be a \(k\times n\) matrix such that \(G\cdot\mathcal{H}^{T}=[0]_{(n-k)\times k}\), where \(G\) is the generator matrix:  
\[G=\begin{bmatrix}1&0&0&0&1&1&0\\ 0&1&0&0&1&0&1\\ 0&0&1&0&0&1&1\\ 0&0&0&1&1&1&1\end{bmatrix}.\]  
\[\mathcal{H}=\begin{bmatrix}0&0&0&1&1&1&1\\ 0&1&1&0&0&1&1\\ 1&0&1&0&1&0&1\end{bmatrix} \tag{1}\]  
Let \(y\in\mathbb{F}_{2}^{n}\)\((n=2^{k}-1)\) represent a codeword generated from an information word \(x\in\mathbb{F}_{2}^{n-k}\) using a \((n-k)\times n\) generator matrix \(G\), such that \(y=x\cdot G\). If a single-bit error occurs in the codeword, the error pattern can be determined to correctly decode the received data. Assuming the received codeword is \(y^{\prime}\) and the error pattern is \(e=(y\oplus y^{\prime})\), the position of the error can be identified using Equation (2).  
\[y^{\prime}\cdot\mathcal{H}^{T}=(e\oplus y)\cdot\mathcal{H}^{T}=e\cdot \mathcal{H}^{T}+y\cdot\mathcal{H}^{T} \tag{2}\]  
where \(y\cdot\mathcal{H}^{T}=0\) by definition. Therefore, the equation simplifies to \(y^{\prime}\cdot\mathcal{H}^{T}=e\cdot\mathcal{H}^{T}\). For instance, if an error occurs in the 6 th bit of the codeword \(\hat{y}\), the error pattern is \(e=(e_{1},e_{2},\ldots,e_{7})=(0\ 0\ 0\ 0\ 0\ 1\ 0)\). In this case, the computed syndrome \(\eta=(110)\) precisely identifies the error. Thus, the error location within the codeword can be accurately determined using the syndrome.  
To hide message bits, the cover image is divided into \(N/n\) subsets, each containing \(n\) pixels, where \(n\) is the length of the codeword. Errors may occur at any of the \(n\) positions within each codeword. The HC decoding process is outlined as follows:  
**Step 1:**: Calculate the syndrome \(\eta(y^{\prime})=b(y^{\prime})\cdot\mathcal{H}^{T}\), where \(b(y^{\prime}_{i})=(y^{\prime}_{i}\bmod 2)\) and \(y^{\prime}\) is the received codeword.
**Step 2:**: Identify the error pattern \(e\) from the computed syndrome \(\eta(y^{\prime})\).
**Step 3:**: Correct the error and decode: modify the cover object so that \(y=(y^{\prime}\oplus e)\), and then recover the original message \(x=y\cdot G^{-1}\).  
This structured approach ensures accurate decoding and error correction, even in the presence of single-bit errors.
## 2 Distribution Network Flexibility Resources and Flexibility Evaluation Indicators
### Distribution Network Flexibility Resources
#### 2.2.1 Networked Flexibility Resource  
SOP can achieve dynamic active and reactive current control between feeders, balancing load currents and optimizing system voltage distribution, improving the controllability of distribution network currents. At the same time, SOP can be used as a transmission bridge for flexibility, coordinate and deploy flexibility resources, and improve the management efficiency and responsiveness of node-type flexibility resources. This paper presents an illustration of the fundamental configuration of SOP, exemplified by a back-to-back voltage source type converter, as depicted in Figure 2.  
Figure 1: Distribution network flexibility supply and demand.  
When performing active distribution network operation optimization, the variables of SOP are generally selected for their output active and reactive power, and their operating boundaries are shown in Figure 3.  
The two ends of the SOP are connected to ADN at nodes \(i\) and \(j\). The constraints that should be satisfied for its operation are as follows:  
\[P_{t,j}^{\text{SOP}}+P_{t,j}^{\text{SOP}}+P_{t,j}^{\text{SOP}}=0 \tag{1}\]  
\[P_{t,ij}^{\text{SOP}}=A_{i,\text{SOP}}\Big{|}P_{t,i}^{\text{SOP}}\Big{|}+A_{j, \text{SOP}}\Big{|}P_{t,j}^{\text{SOP}}\Big{|} \tag{2}\]  
\[\sqrt{\Big{(}P_{t,j}^{\text{SOP}}\Big{)}^{2}+\Big{(}Q_{t,j}^{\text{SOP}}\Big{)} ^{2}}\leq S_{i}^{\text{SOP}} \tag{3}\]  
\[\sqrt{\Big{(}P_{t,j}^{\text{SOP}}\Big{)}^{2}+\Big{(}Q_{t,j}^{\text{SOP}}\Big{)} ^{2}}\leq S_{j}^{\text{SOP}} \tag{4}\]  
where \(i\) and \(j\) are nodes of ADN; \(t\) is time period; \(P_{t,i}^{\text{SOP}}\) and \(Q_{t,i}^{\text{SOP}}\) are active and reactive power injected by SOP; \(P_{t,ij}^{\text{SOP}}\) is the active loss of converter at both ends of SOP; \(A_{i,\text{SOP}}\) and \(A_{j,\text{SOP}}\) are the corresponding loss coefficients; \(S_{i}^{\text{SOP}}\) and \(S_{j}^{\text{SOP}}\) are the installed capacities of SOP.