esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.5 SHAP Explanatory Model  
Shapley Additive exPlanations (SHAP) is a game theory-based interpretability method that quantifies each feature's contribution to a model's predicted outcome [50]. SHAP addresses the \"black-box\" problem of machine learning by calculating the marginal contribution of each feature to the prediction using concepts from cooperative game theory [51]. The method is grounded in three key axioms: local accuracy (ensuring exact prediction explanations at the individual sample level), missingness (assigning zero impact to absent features), and consistency (guaranteeing stable feature attributions across different models) [52]. In this study, SHAP is used to interpret the XGBoost model predictions of irrigated cropland CWUE by quantifying both the direction and magnitude of each driver's nonlinear influence on irrigated cropland CWUE. The formula is defined as follows:  
\[x_{i}=\sum_{s\in S_{i}}\frac{(n-|s|)!(|s|-1)!}{n!}[v(s)-v(s/i)] \tag{7}\]  
where \(x_{i}\) denotes the set containing all subsets of member \(i\); \(s\) indicates any subset of \(S_{i}\); \(|s|\) corresponds to the cardinality (size) of the subset \(s\); \(n\) denotes the total number of players; \(v(s)\) represents the gain of the full set \(s\); and \(v(s/i)\) is the set gain excluding member \(i\).  
The Shapley value provides a robust measure to quantify the individual contributions of each driver to the model's final predicted outcome.  
\[y_{i}=y_{base}+f(x_{i1})+f(x_{i2})+\ldots+f\big{(}x_{i,j})+\ldots+f(x_{i,k}) \tag{8}\]  
where \(y_{i}\) denotes the forecast result of the \(i\)th subset, \(y_{base}\) stands for the mean predicted value of every subset in the dataset, \(x_{i}\) denotes the \(i\)th subset, \(f\big{(}x_{i,j}\big{)}\) indicates the SHAP value of the \(i\)th sample and \(j\)th feature, and \(k\) is the quantity of input features.
# Application of Broadband Microwave Near-Field Sensors for Glucose Monitoring in Biological Media
## 4 Conclusions  
The interaction of the near-field coaxial conic probe with phantoms of biological tissues was investigated both theoretically and experimentally. The probe essentially was a piece of coaxial line with a variable cross section that supported the fundamental wave. This ensured the possibility of carrying out investigations in the wide frequency band, namely, from 10 MHz to 10 GHz. Blood, skin, muscles, and bones with known dielectric permittivities were used as phantoms of biological tissues. As the wavelength increased, there was a deeper penetration of radiation into biological tissue. This can lead to rather significant reflections of radiation from dense bone tissue and, consequently, to a deterioration in the signal-to-noise ratio. In the range we used, the radiation wavelength was short and could not penetrate through the skin layer into the biological tissue. At the same time, it provided the necessary near-field resolution of small blood vessels inside the human body.  
The main goal of the theoretical study was the primary substantiation of the possibility of using the proposed design of the probe for the purposes of near-field diagnostics of blood glucose in a wide frequency range. The experimental study was aimed at real confirmation of this possibility.  
Results of this experimental research demonstrated that the dielectric permittivities of these phantoms increase with increasing dextrose concentration in the sodium chloride solution. In experimental studies, we changed the glucose concentration in the range of 0-25 mmol/L (0-450 mg/dL). At the same time, in the frequency range 1.4-1.7 GHz, the possibility of monitoring the glucose concentration with a resolution of 1 mmol/L (18 mg/dL)  
Figure 6: Modulus of the complex reflection coefficient for the indicated dextrose concentrations in the frequency range 1.4-1.7 GHz.  
was noted. The measured signal-to-noise ratio in the experiments was about 30 dB. This fact was also confirmed by the independent research of other scientific groups [9]. Thus, the possibility of near-field electromagnetic sensing of phantoms of biological tissues for the determination of the dextrose concentration in them was justified.  
We believe that the procedure described above of the sensing of biological tissues can be embodied in an effective sensor for non-invasive glucose monitoring.  
Conceptualization, V.P.B. and V.P.Y.; Data curation, K.V.Z.; Formal analysis, A.S.Z., V.P.B., A.V.G., A.S.M. and A.V.K.; Funding acquisition, K.V.Z.; Investigation, A.S.M. and A.V.G.; Methodology, A.S.Z., V.P.B., V.P.Y. and A.V.K.; Software, A.V.G. and A.S.M.; Writing--original draft, A.S.Z., V.P.B. and A.V.K.; Writing--review & editing, A.S.Z., A.V.K. and A.S.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Russian Science Foundation grant number 18-75-10101.  
Data supporting reported results can be made available on demand.  
The authors declare no conflict of interest.
# Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR
## 2 Materials and Methods
### Thermal Imagery
#### 2.2.2 The Temperature of Ground Features within Plots  
According to the actual sample investigation, the ground features were divided into five types, namely, bare land (BL), falling dead trees (FDT), dead trees with red crowns (RDT), living trees (LT), and shrubbery (SHR). The shapes and locations of the five types were extracted from visible UAV-based images with 0.02 m resolution that were shot on the same LiDAR data flight. The average temperatures of features at the plots were extracted from TIR images.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 2 Evolution of C-RAN Architecture, and Its Types, Advantages, and Challenges
### Cloud Radio Access Network  
In a C-RAN, the BBUs are relocated from the individual cell sites to a centralized and virtualized BBU pool. A C-RAN is composed of three main components: BBU pool, RRH, and fronthaul link (Figure 2b). C-RAN architectures proposed by both industries and academia are described in [27]. This subsection presents a brief overview of the components of a C-RAN.
# Collaborative Web Service Discovery and Recommendation Based on Social Link
## 4 Discovery and Recommendation Mechanism Based on Social Link
### Dclpl  
Given a user request, DCLPL performs search starting from the user's Preference Link to his/her Clustering Link, to narrow the search scope gradually, while matching the latent candidates preferentially.  
```
Input: user \(u_{m}\)'s request \(r\) Output: candidate service list \(L_{1}\)
1 initialize \(u_{m}\)'s Social Link \(SL_{m}\);
2 set \(L_{0}=\phi\) and \(L_{1}=\phi\) ;
3 foreach\(uv_{i}\in\)\(PI_{m}\)do//query the service collection in the preference link
4 calculate the similarity \(RSim_{i}\) between \(us_{i}\) and \(r\);//calculate and compare the matching degree
5 if\(RSim_{i}>RSim_{i}\)do
6 add \(uv_{5}\) to \(L_{1}\)//add the service into the candidate list
7 else
8 add \(uv_{5}\) and each \(uv_{i}\in\)\(SCL_{i}\) to \(L_{0}\)//add the service and its service collection in the clustering link into the unfit list
9 endif
10 for each \(uv_{5}\)\(\in\)\(SCL_{i}\)/\(uv_{5}\)\(\
otin\)\(L_{0}\)do//query other services in the clustering link of current service and not into the unfit list
11 repeat from step 4 to step 9
12 endfor
13 endfor
14 for each \(u_{n}\)\(\in\)\(UCL_{m}\)do//query the user collection in the current user's clustering link
15 for each \(uv_{5}\)\(\in\)\(PI_{m}\)/\(uv_{5}\)\(\
otin\)\(L_{0}\)do
16 repeat from step 4 to step 12
17 endfor
18 endfor
19 for each remained \(uv_{5}\)\(\
otin\)\(L_{0}\)do//query remained services
20 repeat from step 4 to step 6
21 endfor
22 return\(L_{1}\)
```  
**Algorithm** DCLPL  
Figure 5: Instance of a formalized social link.  
In the above algorithm, we calculate \(RSim_{i}\) by Equation (11):  
\[RSim_{i}=\omega_{1}\times NSim(ws_{i}.sn,r.sn)+\omega_{2}\times FSim(ws_{i}.o,r.o) \tag{11}\]  
Similar to Equation (3), \(r.sn\) is the user's demand for service name, and \(r.o\) is the user's demand for service functions. Additionally, the value setting of \(\omega_{1}\) and \(\omega_{2}\) is same with Equation (3).
# Three-Dimensional Obstacle Avoidance Strategy for Fixed-Wing UAVs Based on Quaternion Method
## 1 Introduction
### Contributions  
(1) Inspired by the work [19], we incorporate second-order kinodynamic constraints into the velocity by limiting the set of feasible velocities to those that can be achieved with position error below a predefined value. A reference trajectory resulting from the 3D-VO approach can be retained as formulated in [20]. We derive the control input of the velocity loop as well as the attitude loop to enable the UAV to converge to the reference trajectory, such that the motion planning problem can be transformed into a trajectory tracking problem, which has been extensively studied [23; 24]. Since the derivation of the reference trajectory does not directly depend on the kinodynamic model of the UAV, complex derivation of control obstacles (CO) [20] or acceleration-velocity obstacles (AVO) [19] can be avoided.  
(2) The additional tasks add to the overall flexibility of the UAV. Inspired by the concept of behavior-based control [2; 25], we establish a guidance framework, where multi-tasks are arranged according to a hierarchy plan and conducted in a desired priority. The subtasks can be defined as velocity vectors as presented in [25]. In line with the conventional behavior-based approaches and similar to the approach proposed in [2], we decompose the overall mission into several rotations, which are parameterized by quaternions.
## 3 Results and Discussion
For each emulsion containing the other fat phases (T-2, T-3, T-4, T-5), values were recorded that were not statistically significantly different from each other when the differences in the amount of HPMC introduced into these systems were equal to 0.2% \(w/w\) (i.e., 0.6 and 0.8% \(w/w\) or 0.8 and 1.0% \(w/w\)). The type of fat phase used in the systems also showed an effect on adhesion strength, although there was no apparent correlation between the ratio of fats in the fat phase (vegetable oil to animal fat) and the values obtained for this parameter. The determined viscosity values of the E1-E15 systems 48 h after their manufacture are shown in Figure 8. The type of fat phase used affected the viscosity of the emulsion systems tested, although it was not correlated with the ratio of mutton tallow and hemp seed oil in the modified mixtures constituting the fat phases. The effect of the HPMC concentration on the viscosity of the emulsion systems was also observed. Regardless of the fatty phase used, the viscosity of the systems for each spindle speed used reached higher values as the concentration of the texture modifier increased (E1 < E2 < E3, E4 < E5 < E6, etc.). Larger differences in viscosity were recorded at the lowest spindle speed analyzed (2 rpm). On the other hand, at the highest speed (15 rpm), differences in viscosity values due to HPMC concentration were much smaller. All analyzed systems containing hydroxypropylmethylcellulose as a texture modifier exhibited the characteristics of shear-thinning (pseudoplastic) non-niqutonic liquids [32]. Similar results were obtained  
Figure 2:0 Hardness and adhesion strength of E1–E15 systems determined 48 h after their manufacture. a–k—different letters indicate statistically significant differences between the averages for individual hardness parameters (\(p\leq 0.05\)). in their study by [PERSON] et al. (2020) [33], who analyzed emulsion cocoa creams stabilized with HPMC.
## 2 Materials and Methods
### Analytical Methods and Calculations  
The voltage and current were both set and measured using Gamry 1000B potentiostat and the Gamry Framework v7.06 (Gamry Instruments, Warminster, PA, USA) respectively. Chemical oxygen demand and alkalinity of wastewater samples were analysed using COD and Acid capacity Cell Test kits, respectively (Merck, Darmstadt, Germany). Biogas was analysed using the 8610C gas chromatograph (SRI Instruments, Torrance, CA, USA) equipped with a thermal conductivity detector and with a Haysep-D (HD) and MoleSieve (MS13X) column which separates oxygen, nitrogen, methane and carbon dioxide. The carrier gas was Helium at 4 bar pressure and the oven temperature was kept constant at 80 \({}^{\circ}\)C.  
The electrochemical methane yield (EMY) was calculated according to Equation (4). Eight electrons are required to reduce 1 mole CO\({}_{2}\) to produce 1 mole of CH\({}_{4}\). The heterotrophic methane yield (HMY) was calculated according to Equation (5). One mole of acetic acid requires 64 g of oxygen for complete oxidation (COD) and 1 mole of COD produces 1 mole of methane gas.  
\[EMY=\frac{\left[\frac{X\ (mL)}{24,450\ mL}\right]}{\left[\frac{n_{e}\ (C)}{8 *F\ (C)}\right]}\times 100 \tag{4}\]  
\[HMY=\frac{\left[\frac{X\ (mL)}{24,450\ mL}\right]}{\left[\frac{\left(COD_{ frad}-COD_{effluent}\right)*volume\ of\ feed}{64}\right]}\times 100 \tag{5}\]  
where \(X\) is the volume of methane produced, and one mole gas at 25 \({}^{\circ}\)C at 1 atm occupies a volume of 24,450 mL. \(n_{e}\) is the number of electrons consumed, which can be calculated as an integral of current over time (obtained from Gamry Echem Analyst v7.06) and \(F\) is Faradaic constant (96,485 C/mol e\({}^{-}\)).
## 6 Evaluation
### Evaluating the Objective Function
In our present work, apart from these properties, we also included hierarchical properties, like the hierarchical tension, and also the voice leading. Table 1 shows a comparison of the statistics resulting from organizing the subjective ratings obtained in the listening test as a function of distance \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **M** & **Lerdahl’s Model** & **ChordAIS** \\ \hline \multirow{2}{*}{Curve A} & \(p\)-value & **2.5\(\times\) 10\({}^{-4}\)** & 3.8\(\times\) 10\({}^{-3}\) & 2.7\(\times\) 10\({}^{-3}\) \\ \cline{2-5}  & \(\rho\) & **0.8128** & 0.7345 & \(-\)0.6177 \\ \hline \multirow{2}{*}{Curve B} & \(p\)-value & **4.9\(\times\) 10\({}^{-6}\)** & 6.1\(\times\) 10\({}^{-6}\) & 3.2\(\times\) 10\({}^{-5}\) \\ \cline{2-5}  & \(\rho\) & **0.8999** & 0.8547 & \(-\)0.7490 \\ \hline \multirow{2}{*}{Curve C} & \(p\)-value & **9.2\(\times\) 10\({}^{-6}\)** & 1.7\(\times\) 10\({}^{-5}\) & 8.7\(\times\) 10\({}^{-4}\) \\ \cline{2-5}  & \(\rho\) & **0.9346** & \(-\)0.9190 & \(-\)0.7812 \\ \hline \multirow{2}{*}{Curve D} & \(p\)-value & **3.1\(\times\) 10\({}^{-5}\)** & 2.2\(\times\) 10\({}^{-5}\) & 9.2\(\times\) 10\({}^{-4}\) \\ \cline{2-5}  & \(\rho\) & **0.8727** & 0.8326 & \(-\)0.6906 \\ \hline \multirow{2}{*}{Curve E} & \(p\)-value & **1.2\(\times\) 10\({}^{-4}\)** & 3.9\(\times\) 10\({}^{-3}\) & 1.8\(\times\) 10\({}^{-2}\) \\ \cline{2-5}  & \(\rho\) & **0.8727** & 0.8156 & \(-\)0.6357 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the statistics resulting from organizing the subjective ratings as a function of measures from other tonal tension models. measures from other chord representations. To clarify the statistical analysis, we only calculated the statistical analysis with the curve that correlates best with the subjective values, ignoring the other possibilities, that resulted in lower correlation values in all the cases, independently of the model applied. \(M\) is the objective measure from Equation (5). For the other columns, \(L\) stands for the distance according to the [PERSON]'s model of tonal tension, and \(C\) stands for the ChordAIS model, in which only linear properties of the music are considered. The statistical analysis shows that \(M\) captures the tonal tension profile of the chords better than \(L\) and \(D\). The high \(\rho\) values obtained indicate the linear regression of the subjective ratings as a function of the objective values and the \(p\)-values are all below the 1% threshold for the null hypothesis, which suggests that the listeners' ratings correlate with the fitness measure well. Note that [PERSON]'s measure \(L\) also correlates with the subjective ratings of the listening test, but obtaining slightly lower values for \(\rho\) and \(p\)-value. Likewise, the \(\rho\) and \(p\)-value in \(D\) are correlated, but below the rates obtained for \(M\).
# Modeling the 2013 Zika Outbreak in French Polynesia: Intervention Strategies
## Methodology
### Transit Network  
French Polynesia is divided into five administrative divisions. Transmission of Zika in all islands with population greater than 500 people was studied to make the simulation as complete as possible. Table 3 shows the population and administrative divisions for all the islands that were part of the simulation. The census 2012 data was used to get the population count for the islands [32].  
The transit between the islands is taken into account in the simulation of the outbreak. The data on maximum passengers traveling from one island to another via flights and ferries per week were collected from [33]. A symmetric graph (Figure 3) with islands as nodes and number of passengers traveling from one node to another each day was constructed using these data. In the simulation, human agents were transported from one island to another as per the graph at the start of each day.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Island** & **Admin Division** & **Population** \\ \hline Tahiti\({}^{*}\) & Windward Islands & 183,480 \\ Moorea & Windward Islands & 16,899 \\ \hline Bora Bora & Leeward Islands & 9596 \\ Raitea\({}^{*}\) & Leeward Islands & 12,237 \\ Tahaa & Leeward Islands & 5220 \\ Huahine & Leeward Islands & 6303 \\ Maupiti & Leeward Islands & 1194 \\ \hline Nuku Hiva\({}^{*}\) & Marquesas Islands & 2967 \\ Ua Huka & Marquesas Islands & 621 \\ Ua Pou & Marquesas Islands & 2175 \\ Hiva Oa & Marquesas Islands & 2184 \\ \hline Arutua & Tuamotu Gambier Islands & 680 \\ Rangiroa\({}^{*}\) & Tuamotu Gambier Islands & 2567 \\ Manihi & Tuamotu Gambier Islands & 685 \\ Fakarava & Tuamotu Gambier Islands & 806 \\ Hao & Tuamotu Gambier Islands & 1066 \\ Makemo & Tuamotu Gambier Islands & 832 \\ Takaroa-Takapoto & Tuamotu Gambier Islands & 1262 \\ Gambier & Tuamotu Gambier Islands & 1239 \\ \hline Tupuai\({}^{*}\) & Austral Islands & 2170 \\ Rimatara & Austral Islands & 873 \\ Rurutu & Austral Islands & 2322 \\ Raivavae & Austral Islands & 940 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Population and administrative divisions of the islands (the capital islands are asterisk marked \({}^{*}\)). Source: Census 2012 [32].
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 5 Cryosphere Applications  
A significant collection of spaceborne GNSS-R data for analyzing cryosphere applications, such as sea ice, snow, ice sheets, and freeze/thaw investigations, is available through TDS-1's and SMAP's frequent measurements across the polar regions.
# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
## 5 Proposed Classification
### Classification of RL-Based Routing Protocols Based on Learning Algorithm  
Reinforcement learning algorithms can solve challenges and issues related to the routing process, which are mentioned in Section 4.3. These algorithms use intelligent flying nodes that observe and collect information from the network environment to make an optimal policy for deciding on the best routes in the network. In the proposed categorization, the routing methods are divided into two categories based on the learning algorithm: reinforcement learning-based routing and deep reinforcement learning-based routing. In the following, we explain the most important characteristics of the two groups. Moreover, a comparison between these two schemes is presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Routing Scheme**} & \multicolumn{2}{c}{**Convergence**} & \multicolumn{2}{c}{**Computational**} & \multicolumn{2}{c}{**Generalization**} & \multicolumn{2}{c}{**Learning Speed**} & \multicolumn{2}{c}{**Scalability**} & \multicolumn{2}{c}{**State and Action**} & \multicolumn{2}{c}{**Implementation**} & \multicolumn{1}{c}{**Fault-Tolerance**} \\ \hline \multirow{4}{*}{**RL-based**} & **Single-agent** & Low & Low & No & Low & Low & Small & Simple & No \\ \cline{2-10}  & **Multi-agent** & High & Very high & No & High & Medium & Medium & Complex & Yes \\ \cline{2-10}  & **Model-based** & High & Very high & No & Low & Low & Small & Complex & No \\ \cline{2-10}  & **Free-model** & Medium & Low & No & Medium & Medium & Small & Simple & No \\ \hline \multirow{4}{*}{**DRL-based**} & **Single-agent** & High & High & Yes & High & High & Large & Complex & No \\ \cline{2-10}  & **Multi-agent** & Very high & Very high & Yes & Very high & High & Large & Complex & Yes \\ \cline{2-10}  & **Model-based** & High & Very high & Yes & Medium & High & Large & Complex & No \\ \cline{2-10}  & **Free-model** & High & High & Yes & High & High & Large & Complex & No \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of RL-routing methods and DRL-routing schemes.
## 2 Hardware of Transient Luminous Event (TLE) Observation System  
### Background  
Under the constraints imposed by CubeSat Design Specification (CDS Rev.13), a CubeSat is limited in size, weight and power [28]. For such a CubeSat to observe a short-lived phenomenon, it requires developing compact and power-efficient hardware. Specifically, the hardware should include low-level logic to process signals from the detector and CMOS image sensor on the order of a few microseconds, as well as high-level processing unit capable of handling large amounts of data in a few seconds. Such a hardware will allow implementation of the TLE observation algorithm involving the detection of TLEs and the acquisition of digital images. By taking into account the tight design requirements of a CubeSat and the recent trend in embedded hardware design, the SoC architecture was chosen as the basis for hardware design. The SoC consists of field programmable gate array (FPGA) and embedded Nios-II softcore, so that it can implement not only low-level hardware logic for TLE detection but also high-level data handling process, efficiently.