esa-sceva/satcom-chunk-collection huggingface dataset:
# The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather
## 5 Solving the Optimal Dispatch Model Based on MILP
### Model Simplification  
The objectives in this paper are all about costs that could be added up to form a total cost; the linear weighted sum method [28] is adopted to simplify the calculation in the early study.  
\[\begin{split}& F=\min\limits_{X}\sum\limits_{r=1}^{4}\beta_{r} \cdot f_{r}(X),X=[P_{Gi,t},I_{Gi,t},P_{VPPk,t}]\\ & s.t.\left\{\begin{array}{l}H_{p}(X)=0,(p=1,2,...,MT+T)\Leftrightarrow C _{01},C_{1}\\ G_{q}(X)\leqslant 0,(q=1,2,...,Q)\Leftrightarrow C_{02},C_{2},C_{3},C_{4},C_{5}\\ Q=MT+2(M+n)T+T+2M(T-1)+M\end{array}\right.\end{split} \tag{26}\]  
where \(X\) and \(F\) are the decision variable set and the comprehensive objective function; \(\beta_{r}\) is the weight of \(f_{r}\); \(C_{0}\)-\(C_{5}\) are constraints; \(H_{p}\) (\(p\) = 1,2,... \(\mathcal{M}\)+\(T\)) are equality constraints; \(G_{q}\) (\(q\) = 1,2,... \(\mathcal{Q}\)) are inequality constraints; and \(Q\) is the number of inequality constraints.  
Usually, the selections of the weights depend on the choices of the decision maker or field practical experience. Besides, Formula (27) is adopted to calculate weights, as well.  
\[\beta_{r}=\frac{\frac{1}{f_{r}(X^{*})}}{\sum\limits_{r=1}^{4}\frac{1}{f_{r}(X^ {*})}} \tag{27}\]  
where \(f_{r}(X^{*})\) is the optimal value of the single objective problem of \(f_{r}(X)\).
## 4 Experiments and Discussions
### Synthetic Data Experiments
#### 4.1.1 Experiments with the Noise Energy Ratio  
The energy ratio of each noise in the mixed signals affects the extraction of the MECG and identification of the FECG. Since the method in this paper is an adaptive method based on R peak detection, if the FECG is completely contaminated by noise, it will be difficult to identify the R peaks, which may lead to FECG extraction failure. Therefore, in order to verify the effectiveness of the methodin this paper, the FECG to noise energy ratio (FNR) was taken as the evaluation parameter for the experiments. The FECG to noise energy ratio (dB) is given by Formula (24):  
\[FNR=10\log_{10}\left(P_{s_{f}}/P_{n}\right) \tag{24}\]  
where \(P_{n}=\frac{1}{N}\sum\limits_{n=1}^{N}n^{2}\) is the energy of the noise.  
The 5 FECG signals and 21 MECG signals in Table 1 were combined to obtain 105 synthetic signals. All the synthetic signals maintained FECG energy at 1 (0 dB) and FMAR at 0.3. The FMHRR was determined according to the signal combination. The noise energy was changed according to \(FNR\). Figure 7 shows the SNR improvement and correlation coefficient results of adding different energy noises to the 105 synthetic signals using the proposed method. As can be seen, the maximum noise energy is 7.94 times the FECG energy when \(FNR=-9\). The minimum noise energy is 1/10 of the FECG energy when \(FNR=10\). The red line represents the maximum value of the experimental results of the 105 synthetic signals, while the blue line represents the minimum value. The black line represents the average value. The results show that in the \(FNR\) range of \(-8\) dB to 10 dB, the SNR improvement \(SNR_{improve}\) increases with increasing \(FNR\), but the change is not significant. This is because the increase of \(FNR\) indicates a decrease of noise energy, which is conducive to coherent extraction by MECG and FECG. Since the method in this paper does not specifically denoise random noise, the main contribution to SNR improvement comes from MECG elimination. Therefore, in the range of \(FNR\) changes, there is no great change in SNR improvement. However, the SNR is improved to a minimum of 12.4 dB, and the average reaches from 17.9 dB to 19.3 dB, indicating that it has a significant effect for MECG elimination. The correlation coefficient \(C_{R}\) increases with increasing \(FNR\). Within the \(FNR\) range of \(-8\) dB to 10 dB, the minimum \(C_{R}\) is 0.69, the maximum \(C_{R}\) is 0.96, and the average \(C_{R}\) is 0.8-0.9. This shows that the FECG extracted successfully using the method in this paper has good similarity with the original FECG.
# Adaptive Parameters for LoRa-Based Networks Physical-Layer
## 3 Proposal
### SlidingChange: A Proposal of a Sliding Window for Automatic Parameter Changing
(2018) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] An energy-efficient solution for multi-hop communications in low power wide area networks. In Proceedings of the 2018 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), Valencia, Spain, 6-8 June 2018; IEEE: New York, NY, USA, 2018; pp. 1-5. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Extending ADR mechanism for LoRa enabled mobile end-devices. _Simul. Mod. Pract. Theory_**2021**, _113_, 102388. [CrossRef]
* [PERSON] and [PERSON] (2013) [PERSON]; [PERSON] _Next Generation Wireless LANs: 802.11 n and 802.11 ac_; Cambridge University Press: Cambridge, UK, 2013. * Semtech (2015) Semtech. _LoRa@ Modulation Basics. Semtech Corporation_, 2 nd ed. ; ANI200.22; 2015. * Alliance (2020) Alliance, L. _RP002-1.0. 1 LoRaWAN Regional Parameters_; 2020. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]. A Theoretical and Experimental Evaluation on the Performance of LoRa Technology. _IEEE Sens. J._**2020**, _20_, 9480-9489. [CrossRef]
* [PERSON] et al. (2015) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] SINR, RSRP, RSSI and RSRQ measurements in long term evolution networks. _Int. [PERSON] Wirel. Mob. Netw._**2015**, \(7\), 113-123. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Survey on Internet of Things Enabled Smart Campus Applications. 2017, 1-7. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] Internet of Things Market Analysis Forecasts, 2020-2030. In Proceedings of the 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), London, UK, 27-28 July 2020; pp. 449-453. [CrossRef]

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## 3 Results and Discussion
### OAV Analysis
Previous research has shown that limonene, cineole, sabinene and linalool were the main aroma components of green huajiao samples from Sichuan [39]. Other studies reported differentiated key aroma compounds in huajiao, which include limonene, linalyl acetate, \(\beta\)-pinene, hinokia and linalool [33; 40]. It is said that the aroma compounds with higher OAV in huajiao oils from Hancheng, Shaanxi and Hanyuan, Sichuan, contain compounds such as \(\beta\)-laurenes, \(\beta\)-raisins, limonene and linalool, which can be used as key aroma compounds to distinguish different samples [37]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Attributes**} & \multicolumn{8}{c}{**Sum of Ranks**} \\ \cline{3-8}  & & **G1** & **G2** & **G3** & **G4** & **G5** & **G6** & **G7** \\ \hline L1 & \(\alpha\)-pinene & 5372 & 3398 & 2779 & 3474 & 2720 & 3164 & 1249 \\ L2 & sabinene & 2719 & 1295 & 1426 & 3839 & 1934 & 1760 & 601 \\ L3 & \(\beta\)-pinene & 963 & 647 & 570 & 1062 & 586 & 592 & 292 \\ L4 & myrcene & 54,548 & 53,862 & 28,974 & 76,058 & 44,258 & 35,467 & 13,581 \\ L5 & phellandrene & 551 & 592 & 325 & 539 & 314 & 870 & 286 \\ L6 & terpinolene & 167 & 157 & 169 & 201 & 124 & 185 & 137 \\ L7 & p-cymene & 694 & 406 & 333 & 320 & 229 & 306 & 134 \\ L8 & (+)-limonene & 31,118 & 18,530 & 13,236 & 36,373 & 20,031 & 24,753 & 7904 \\ L9 & 1,8-cineole & 7858 & 5216 & 5216 & 2125 & 2125 & 3232 & 3232 \\ L10 & ocimene & 2301 & 2645 & 2118 & 3364 & 1385 & 2874 & 1153 \\ L11 & \(\gamma\)-terpinene & 83 & 72 & 74 & 108 & 51 & 98 & 60 \\ L12 & trans-linalool oxide & 2036 & 890 & 975 & 910 & 4005 & 578 & 2016 \\ L13 & \(\alpha\)-terpinene & 3305 & 1320 & 1532 & 1398 & 2027 & 971 & 3122 \\ L14 & linalujunelool & 51,508 & 49,364 & 58,776 & 46,232 & 63,237 & 47,766 & 64,740 \\ L15 & thujone & 1618 & 1447 & 1605 & 1646 & 2156 & 1566 & 537 \\ L16 & (+)-citronellal & 1431 & 1447 & 1493 & 1500 & 1797 & 1788 & 1948 \\ L17 & terpinen-4-ol & 252 & 157 & 81 & 176 & 107 & 133 & 110 \\ L18 & \(\alpha\)-terpineol & 287 & 203 & 267 & 207 & 233 & 320 & 256 \\ L19 & carvool & 29 & 25 & 21 & 28 & 35 & 43 & 22 \\ L20 & cuminaldehyde & 127 & 67 & 64 & 70 & 136 & 50 & 118 \\ L21 & carvone & 4851 & 2706 & 1276 & 1880 & 8684 & 728 & 6174 \\ L22 & linalyl acetate & 1790 & 2826 & 2250 & 2625 & 2640 & 1884 & 782 \\ L23 & (-)-\(\alpha\)-copaene & 49 & 47 & 50 & 47 & 0 & 108 & 145 \\ L24 & tetradecane & 136 & 105 & 243 & 155 & 188 & 90 & 159 \\ L25 & trans-caryophyllene & 913 & 852 & 1119 & 943 & 753 & 1733 & 1757 \\ L26 & trans-nerolidol & 156 & 126 & 31 & 82 & 32 & 6771 & 65 \\ L27 & caryophyllene oxide & 23 & 9 & 12 & 12 & 38 & 11 & 56 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: OAV of key aroma substance components of green huajiao.
## 4 Active Distribution Network Scheduling Model
### Objective Functions
#### 4.1.2 Maximize Utilization Rate of Renewable Energy  
The output of WTs and PV cells depends on wind and solar energy during this period. By rationally scheduling the controllable units, the maximum consumption of renewable energy can be realized. Taking a 24-hour day as a complete scheduling cycle, this paper measures the utilization of renewable energy by the proportion of renewable energy generated as Equation (14):  
\[\text{max}f_{2}=\frac{\sum\limits_{t=1}^{T}\left(P_{PV,t}+P_{WT,t}\right)}{ \sum\limits_{t=1}^{T}\left(P_{PV,t}+P_{WT,t}+P_{MT,t}\right)} \tag{14}\]
## 6 Performance Evaluation
### Simulation Results Analysis
#### 6.2.1 The Change of Trust Value of the Normal Node and MN  
The network topology in the experiment is shown in Figure 13. In Figure 13, nine nodes are deployed in the water. Most notably, node 10 is an MN with the selective forwarding attack mode. The trust values of the MN and a normal node, node 7, during 20 transmission rounds are recorded and they are shown in Figure 14.  
From Figure 14, it is seen that as the number of transmission rounds increases, the trust value of the normal node gradually increases. When the number of transmission rounds exceeds six, the trust value of the normal node reaches 0.7 and then remains constant. In contrast, when the number of transmission rounds exceeds two, the trust value of the MN decreases to 0.52 and then remains constant. As mentioned earlier, the trust value is related to the residual energy of the node as well as the data packet forwarding behavior and the HELLO packet forwarding behavior. The trust value of the MN increases in the second transmission round because the MN forwards HELLO packets normally. However, after the second round, the trust value of the MN begins to decrease because the MN does not forward the received DATA packets. Since the nodes with low trust values have a small probability of being selected as the best next-hop forwarding node, the trust value of the MN tends to remain constant as the number of transmission rounds increases.  
Figure 14: Trust values of a normal node and the MN.  
Figure 13: Experimental topology.
# Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models
## 2 Materials and Methods  
### Experimental Area and Treatments Evaluated  
The study area is located in the municipality of Pereira Barreto in the State of Sao Paulo at the mouth of the Tiete River. According to the Koppen classification, the region climate is tropical rain forest with summer rainfall and winter drought (Aw) [14]. Average temperatures are between 26.8 and 21.2 \({}^{\circ}\)C, average annual rainfall is 1128 mm, and the average altitude of the region is 347 m.  
Aiming to set water availability parameters so that there is no influence of water deficit on the VIs, in view of the differentiation only of soybean cultivars and not of other external factors, data from irrigated areas were used, in which irrigation management allowed the supply of water necessary for the proper crop development over the cycle (Figure 1).  
Figure 1: Location of areas cultivated with soybean cultivars in central pivot systems in southeastern Brazil. Each point represents the repetitions used (pixel by pixel).  
Data were collected from five irrigated central pivots and with the same sowing date in the 2015/2016 crop season. Each pivot was grown with a different cultivar, which consisted of the following materials: CV1--P98y12 RR, CV2--Desafio RR, CV3--M6410 IPRO, CV4--M7110 IPRO, and CV5--NA5909 RR (Figure 1).
## 5 Results
### Locating Results with Fixed Parameter  
In order to verify the self-adaptability of the AECTF method, the impact signal was filtered by a 100-200 kHz band. Then the signal arrival time was obtained by using the fixed threshold amplification factor and the fixed threshold respectively. The distribution of absolute errors using the fixed parameters is shown in Figure 17. It can be seen from the figure that the locating results had large error points, whether based on fixed threshold amplification factor or fixed threshold.  
The comparison of locating errors with different processing methods is shown in Table 5. It can be seen that the locating results have higher accuracy and stability when located by AECTF method.
# Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem
## 4 Experiments
### Performance Analysis
#### 4.3.1 Comparison with Construction Heuristics  
For test data, we generate 1000 instances with 40, 60, 80 and 100 tasks, and experimental results are shown in Table 8. GDNN-PPO outperforms all the competitors in all task-scale instances with the highest Wilcoxon \(p\)-value of 0. Specifically, the PSP of GDNN-DQN reaches 11.6%, and the PSPs of the other four algorithms are all over 45%. Therefore, GDNN-PPO can obtain high-quality solutions for solving the AEOSSP. Regarding AST, GDNN-PPO has the longest solution time in all instances. However, even for instances with 100 tasks, the AST of GDNN-PPO is within 2 s, which is acceptable for its high solution quality.
# SARAC4N: Socially and Resource-Aware Caching in Clustered Content-Centric Networks
## 1 Introduction
These relationships are modeled using social metrics such as degree, betweenness, and closeness centrality. Figure 3: CS table. Unlike prior socially aware caching frameworks that focus narrowly on node popularity or mobility patterns (e.g., vehicular networks), SARAC4N integrates both resource profiling and social context within a cluster-based caching structure. It is unique in incorporating backup cluster heads, a dynamic workload distribution, and context-aware caching using centrality-driven selection criteria. This work is motivated by the central question: How can Content-Centric Networking be improved using socially aware and resource-optimized clustering and caching mechanisms to support scalable, low-latency data access in dynamic and resource-constrained environments such as IoT and multimedia-rich networks? This paper is organized as follows: Section 1 contains the Introduction; Section 2 describes related work in the same field; Section 3 explains the methodology and working mechanics of SARAC4N; Section 4 describes the performance metrics; and Section 5 presents the conclusion and future work. To enhance clarity, Table 1 provides definitions for all major acronyms used throughout the manuscript. This will help readers follow the proposed framework and comparative methods without ambiguity.
## 2 DPCC with Delay Compensation
### Parameter Mismatch Error Analysis  
In the practical running of a motor, there will be phenomena such as temperature increases and flux linkage saturation [9] which will lead to the controller parameters being different from the actual parameters of the motor, and this will then generate a steady-state error.  
The parameters in the predictive model of (5) are fixed and expressed as \(L_{0}\), \(R_{0}\), and \(\psi_{f0}\). The practical parameters of the motor under various working conditions are expressed as \(L_{s}\), \(R_{s}\), and \(\psi_{f}\). Therefore, (5) can be rewritten as follows:  
\[\left[\begin{array}{c}u_{d}(k)\\ u_{q}(k)\end{array}\right]={G_{0}}^{-1}\left\{\begin{array}{c}i_{d}^{*}(k+1) \\ i_{q}^{*}(k+1)\end{array}\right]-F_{0}(k)\left[\begin{array}{c}i_{d}(k)\\ i_{q}(k)\end{array}\right]-H_{0}(k)\right\} \tag{7}\]  
where \(G_{0}=\left[\begin{array}{cc}\frac{T_{k}}{L_{0}}&0\\ 0&\frac{T_{k}}{L_{0}}\end{array}\right]\), \(F_{0}(k)=\left[\begin{array}{cc}1-\frac{T_{k}R_{0}}{L_{0}}&T_{s}\omega_{e} (k)\\ -T_{s}\omega_{e}(k)&1-\frac{T_{s}R_{0}}{L_{0}}\end{array}\right]\), and \(H_{0}(k)=\left[\begin{array}{c}0\\ -\frac{T_{s}\psi_{f0}\omega_{e}(k)}{L_{0}}\end{array}\right]\).  
By combining (7) and (3), we obtain the following:  
\[\left\{\begin{array}{c}i_{d}(k+1)=\frac{L_{0}}{L_{s}}i_{d}^{*}(k+1)+\frac{( \Delta L-\Delta RT_{s})}{L_{s}}i_{d}(k)+\frac{\Delta L\omega_{e}(k)T_{s}}{L_{s }}i_{q}(k)\\ i_{q}(k+1)=\frac{L_{0}}{L_{s}}i_{q}^{*}(k+1)+\frac{(\Delta L-\Delta RT_{s})}{L_{ s}}i_{q}(k)-\frac{\Delta L\omega_{q}(k)T_{s}}{L_{s}}i_{d}(k)-\frac{\Delta t \varphi_{d}(k)T_{s}}{L_{s}}\end{array}\right. \tag{8}\]  
where \(\Delta L=L_{s}-L_{0}\), \(\Delta R=R_{s}-R_{0}\), and \(\Delta\psi_{f}=\psi_{f}-\psi_{f0}\) denote the errors between the practical motor parameters and the parameters in the predictive model. With the control method of \(i_{d}=0\), the \(d\)-axis reference current is consistently \(0\). Then, from (8), we obtain the following:  
\[\left\{\begin{array}{c}i_{d}(k+1)-i_{d}^{*}(k+1)=\frac{(\Delta L-\Delta RT_{s })}{L_{s}}i_{d}(k)+\frac{\Delta L\omega_{e}(k)T_{s}}{L_{s}}i_{q}(k)\\ i_{q}(k+1)-i_{q}^{*}(k+1)=-\frac{\Delta L}{L_{s}}i_{q}^{*}(k+1)+\frac{(\Delta L -\Delta RT_{s})}{L_{s}}i_{q}(k)-\frac{\Delta L\omega_{e}(k)T_{s}}{L_{s}}i_{d}(k )-\frac{\Delta t\varphi_{d}(k)T_{s}}{L_{s}}\end{array}\right. \tag{9}\]  
Figure 1: Diagram of delay compensation DPCC.  
According to (9), the error of the \(dq\)-axis current is associated with the inductance, the resistance errors, and the electrical angular velocity. In addition, the \(q\)-axis current error is also related to the flux linkage error.  
Therefore, it is necessary to compensate for the error to solve the DPCC current static error caused by the mismatched motor parameters.
## 3 Discussion of Hull Girder Load Adjustment and Load Distribution
### Distribution of Adjustment Load and Analysis Results  
The distribution of the load added for load adjustment may vary depending on the adjustment method, and the analysis results may vary accordingly. In the case of CSR, the shear force is distributed by calculating the shear flow of the cross-section, and the axial force is distributed using beam theory. The QP method considers only the node location and distributes it so that the sum of the load vector size is minimised. Since this method does not consider the area and arrangement direction of the elements, a load concentration phenomenon may appear in a fine mesh model. Even if the hull girder loads are adjusted to the same value, the analysis results may differ depending on the applied method.  
Using a distribution using beam theory, such as the CSR method, is more appropriate. However, it is inconvenient to calculate the shear flow. An alternative method using the beam theory is to use the stress field and effective area in each direction, as shown in Figures 20 and 21. Each arrow is an area and load vector with magnitude and direction. The length and colour of the arrows indicate the relative magnitude of the vectors. The distribution of axial stress and shear stress that changes linearly at all locations of the cross-section is called the stress field, and the load distribution can be generated by multiplying this by the effective area in each direction. VSF, HSF, and TM can be adjusted from the shear force distribution, and VBM, HBM, and AF can be adjusted from the axial force distribution.  
Figure 20: The distribution of shear force added for load adjustment. (**a**) Effective shear area. (**b**) Shear stress field. (**c**) Shear force distribution.
_Entropy_**2014**, _16_, 3121-3135; doi:10.3390/e16063121  
_Entropy_**ISSN 1099-4300**  
www.mdpi.com/journal/entropy  
_Article_  
**Quantum Flows for Secret Key Distribution in the Presence of the Photon Number Splitting Attack**  
**[PERSON] \({}^{1,*}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{2}\)**  
\({}^{1}\) Time and Frequency Division, Centro Nacional de Metrologia, Carr. a los Cues Km. 4.5,  
Municipio El Marques, Queretaro C.P. 76246, Mexico;  
E-Mails: [EMAIL_ADDRESS] (J.M.L.); [EMAIL_ADDRESS] (E.D.C.-L.)  
\({}^{2}\) Tecnologico de Monterrey, Campus Estado de Mexico, Carr. a Lago de Guadalupe Km. 3.5,  
Atizapan de Zaragoza, Estado de Mexico C.P. 52926, Mexico;  
E-Mail:[EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]ADDRESS];  
Tel.: +1-52-442-211-0543.  
_Received: 8 April 2014; in revised form: 23 May 2014 / Accepted: 29 May 2014 / Published: 5 June 2014_  
**Abstract:** Physical implementations of quantum key distribution (QKD) protocols, like the Bennett-Brassard (BB84), are forced to use attenuated coherent quantum states, because the sources of single photon states are not functional yet for QKD applications. However, when using attenuated coherent states, the relatively high rate of multi-photonic pulses introduces vulnerabilities that can be exploited by the photon number splitting (PNS) attack to brake the quantum key. Some QKD protocols have been developed to be resistant to the PNS attack, like the decoy method, but those define a single photonic gain in the quantum channel. To overcome this limitation, we have developed a new QKD protocol, called ack-QKD, which is resistant to the PNS attack. Even more, it uses attenuated quantum states, but defines two interleaved photonic quantum flows to detect the eavesdropper activity by means of the quantum photonic error gain (QPEG) or the quantum bit error rate (QBER). The physical implementation of the ack-QKD is similar to the well-known BB84 protocol.  
**Keywords:** QKD; PNS; ack-QKD; bi-qubit; quantum photonic error gain; \(\Delta\)Q
## 2 RF Features
### Channel System Information  
Compared to the mentioned RF features, Channel System Information (CSI) provide much richer information, such that all the other values can be extracted by analysing it. CSI provides information about the communication channel, e.g., fading, scattering, and power decay, and how the propagating signal is impacted at a specific carrier frequency at each path. The CSI amplitude and phase are dependent on the poses of the transmitter, receiver, and the surrounding environment with objects therein. Compared to RSS, TOA, TDOA, and RSS, which provide one value per measurement, CSI consists of a matrix, with each entry representing the Channel Frequency Response (CFR). Thus, there are much more data to deal with and analyse. Moreover, it is raw information that is not particularly intuitive compared to the other measurement. Hence, machine learning algorithms and fingerprinting are suitable candidates for CSI applications.