esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
Until this point, this work synthesized useful information to provide a complete understanding of the energy and communication challenges for IoT. The solutions and technologies have been divided into passive, semi-passive, and active to provide adequate organization. However, in the real world, this distinction is not so clear, and each case should be evaluated as a particular situation.  
Applications where communications occur at short distances can be handled by passive solutions, combining backscatter communication with WPT or EH. However, there are certain applications that, although require short-distance communications, have high power demands. In these cases, and if WPT or individual EH is sufficient, a hybrid EH solution may be considered.  
When possible, the use of passive solutions is always recommended due to its battery-less structure. However, if it is not an option due to high power demand, semi-passive systems can be considered. Using a battery allows independence from the receiving or harvesting system, and it ensures stable and uninterrupted flow of energy. To enhance its energy efficiency, the final solution must be designed to operate in active modes for the shortest time possible. One interesting method to ensure maximum energy efficiency is to implement WUR solutions. These techniques have architectures as simple as an RF-DC.  
To avoid battery replacement, recharging, and also to ensure a bi-directional communication system, SWIPT techniques can also be implemented. It is important to note that SWIPT requires demodulating information on the tag, which is possible in semi-passive solutions due to its stable energy flow and high computing capabilities. If possible, the use of amplitude modulation, more concretely OOK, can simplify the whole process. The demodulation can be built with a peak detector connected to a digital or analog pin.  
Due to the inconvenience of having a specific reader, some solutions for short-range communication rely on active devices. The most straightforward technology is BLE, which has sleep and active modes with considerably low power consumption, making it possible to be powered by a small coin battery. The lifetime of these solutions highly depends on the frequency of communications. In applications that only require a few communication per hour, coin batteries can last for some years.  
On the other hand, some applications need to communicate at long distances. Up to this point, LoRa and SigFox have proven to be very interesting technologies to do that; both have ranges of a few kilometers with relatively low power consumption. Like BLE solutions, an optimized tag based in LoRa or SigFox can operate with a coin battery.
## 4 Case Study
### IEEE RTS-24 Node Case Study System  
#### 4.2.1 System Parameter and Case Setting  
This section of the case study uses the IEEE RTS-24 node system. The transmission line data, generator data, maximum node loads, and parameters of the selected compressed air type energy storage are based on the data in [31]. A wind power plant is connected to node 23, and a photovoltaic power station is connected to node 4. The total planning period is divided into three stages, assuming that the investment behavior of each planning stage occurs in the first year of that planning stage. The three stages are 2, 3, and 5 years, corresponding to wind power grid connection capacities of 1000 MW, 1500 MW, and 3000 MW, and photovoltaic power plant grid connection capacities of 1000 MW, 1500 MW, and 3000 MW. The IEEE RTS-24 node system has a total of 17 load nodes, with the maximum load demand of each load node increasing at a rate of 20 MW per stage. Considering the development of energy storage technology, the cost of energy storage will also gradually decrease. The unit power cost of energy storage in each stage is 4000 yuan/kW, 3500 yuan/kW, and 3000 yuan/kW, respectively. The unit energy cost of energy storage in each stage is 200 yuan/kWh, 180 yuan/kWh, and 150 yuan/kWh, respectively. The annual discount rate of energy storage devices is 8%, and the annual discount rate of transmission lines is 10%. The economic service life of transmission lines is 25 years, and the unit cost of lines is 1 million yuan/km. \(c_{wind}\) is set to 0.1 yuan/MWh, and \(c_{solar}\) is set to 0.05 yuan/MWh. The number of newly built transmission lines is a variable, and the capacity of each new transmission line is the same as the capacity of the original single transmission line.  
To visually compare the differences between the method proposed in this paper and traditional planning methods, and to analyze the impact of coordinated energy storage and transmission grid planning on the development of the power system, three research scenarios are set in this paper:  
Scenario 1 employs a single-step static planning method, where static planning is conducted for the final target year's load and renewable energy integration requirements, using the method described in [21].  
Scenario 2 utilizes a single transmission grid planning method as described in [14; 15], without considering energy storage planning.  
Scenario 3 presents the multi-stage coordinated planning of energy storage and transmission networks proposed in this paper, characterized as dynamic planning.  
These three scenarios range from static planning to dynamic planning, extending from traditional methods to the method proposed in this paper, effectively comparing the strengths and weaknesses of different approaches.
## 5 Conclusions  
The new control strategy to maximize the generated power through the maximum power point estimator is developed and discussed. Initially, the power mapping characteristics with the load circuit and the power electronic circuit is modeled to develop the estimator. The estimator curve evolved out of this design is used as an estimator by the controller in order to maximize the power generation. The modeled estimator is tested with the laboratory system using the generator and the electronic drive controller with dynamic loads. It is observed through the experimental design that the controller tracks the maximum power point as the load is fluctuated. This helps the generator to maximize the power generated. The experimental results confirm that with the new estimator control strategy, the average power generation capability is increased by 12% and the peak value is increased by 22%. The developed strategy can be implemented with other generator systems; however, the tuning needs to be done based on the application.  
The authors would like to acknowledge the support of University Putra Malaysia for providing space for the experimental setup.  
[PERSON] conceived and designed the experiments, [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed for the analysis tools; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
## 3 Trust Establishment and Trust-Based Monitor Selection
### Trust Establishment and Link Duration Estimation
#### 3.1.3 Indirect (Recommendation-Based) Trust  
Indirect trust is calculated based on recommendations coming from trusted UAVs about other unknown/known UAVs. Most of the existing solutions suggest creating a new message type called _recommendation_, and they choose either a cluster-based technique or an aggregation method to reduce the additional overhead. To avoid affecting the communications bandwidth and UAVs energy, we propose modifying the format of the exchanged data messages by adding only two fields: (i) the neighbor identity and (ii) the opinion of the sender about that neighbor. For example: if a UAV \(i\) considers that a UAV \(j\) is untrusted, it will put the UAV \(j\)'s identity within the data message along with an opinion which can be \(<\)0.5 (untrusted node) or \(>\)0.5 (trusted node). This opinion correspond to the global trust evaluation of the recommender about the recommended node \(Trust(i,j)\). Figure 4 illustrates the new messages format.  
To avoid the negative influence of dishonest UAVs' opinions on the indirect trust, a UAV \(i\) computes the trade-off between the trust and opinions of different recommend  
Figure 4: Piggybacking of Recommendation Contents.  
is the level of trust of a neighbor, the more is its opinion taken into account. Equation (5) shows how the indirect trust is computed by a UAV \(i\) about another UAV \(j\).  
\[\begin{split}\forall k\in\{trusted\ direct\ neighbors\ of\ i\}\\ IT(i,j)=\Big{[}\prod_{N}\big{(}DT(i,k)\cdot Opin(k,j)\big{)}^{ \frac{1}{N}}\end{split} \tag{5}\]  
In this equation, \(N\) refers to the number of recommenders, \(IT(i,j)\) is a combination of the recommenders' (\(k\)) direct trust \(DT\) and their opinions about the UAV \(j\). In addition, we consider a neighbor UAV as a trusted UAV if its trust \(Trust(i,j)\) is higher than a predefined threshold which can be adapted to the security requirements and the traffic type.
# Transformer Self-Attention Change Detection Network with Frozen Parameters
## 4 Experiment
### Comparative Experiment
#### 4.3.2 Comparative Experiments on the LEVIR-CD Dataset  
Additionally, we conducted comparative experiments on the LEVIR-CD dataset, and the specific experimental results are presented in Table 4. In Table 4, our network achieved the best accuracy, specifically outperforming the other networks by at least 0.58% in the _IOU_ and 0.36% in the \(F1\) score. This indicates that our network exhibits good generalization performance. In addition, we also visualized the performance of all the networks on the LEVIR-CD dataset. The specific visualization results are shown below.  
The LEVIR-CD dataset contains densely populated clusters of small buildings. On the one hand, the model may have difficulty in distinguishing the boundaries of change regions, resulting in two separate change regions being detected as a single unified change region. On the other hand, the dense arrangement of buildings can result in missed detections and false detections. In Figure 9, we illustrate three different scenarios of building clusters. Our network achieved the best results in these three scenarios, successfully avoiding the two aforementioned issues.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & _PR (\%)_ & _RC (\%)_ & _IOU (\%)_ & _F1 (\%)_ \\ \hline FC-EF & 87.78 & 81.09 & 73.24 & 84.42 \\ FC-Siam-Conc & 87.59 & 84.48 & 76.03 & 86.52 \\ FC-Siam-Diff & 89.91 & 83.10 & 76.89 & 86.44 \\ TCDNet & 88.86 & 87.99 & 79.25 & 88.42 \\ SNUNet & 89.80 & 87.73 & 78.60 & 88.75 \\ MFGAN & 90.07 & 88.84 & 81.08 & 90.04 \\ STANet & 89.26 & 87.10 & 79.20 & 88.13 \\ DASNet & 90.84 & 86.09 & 78.67 & 88.79 \\ TFI-GR & 90.89 & **91.11** & **83.49** & **91.00** \\ ChangeNet & 89.44 & 89.49 & 80.32 & 89.49 \\ BIT & **91.70** & 89.44 & 82.74 & 90.55 \\ ChangeFormer & 90.49 & 89.02 & 81.41 & 89.75 \\ \hline ZAQNet (our) & **92.03** & **90.67** & **84.07** & **91.36** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative experiments on the LEVIR -CD dataset. The best and second best data are shown in bold.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & _PR (\%)_ & _RC (\%)_ & _IOU (\%)_ & _F1 (\%)_ \\ \hline FC-EF & 74.24 & 45.96 & 39.64 & 56.78 \\ FC-Siam-Conc & 78.15 & 47.34 & 41.92 & 59.01 \\ FC-Siam-Diff & 84.86 & 44.87 & 41.54 & 58.70 \\ TCDNet & 88.34 & 74.09 & 67.53 & 80.46 \\ SNUNet & 88.59 & 75.14 & 68.56 & 81.34 \\ STANet & 88.53 & 75.27 & 69.25 & 81.82 \\ DASNet & 88.67 & 75.55 & 69.52 & 82.05 \\ TFI-GR & **88.81** & 75.73 & 69.76 & 82.29 \\ ChangeNet & 88.63 & 77.15 & 70.19 & 82.46 \\ MFGAN & 87.95 & 78.11 & 71.07 & **82.70** \\ BIT & 87.99 & 75.52 & 69.03 & 80.23 \\ ChangeFormer & 88.72 & **78.26** & **71.45** & 82.53 \\ \hline ZAQNet (our) & **88.82** & **80.49** & **73.08** & **84.45** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative experiments on the BTRS-CD dataset. The best and second best data are shown in bold.
# Moment-Based Parameter Estimation for the \(\Gamma\)-Parameterized TWDP Model
###### Abstract  
Two-wave with diffuse power (TWDP) is one of the most promising models for description of a small-scale fading effects in the emerging wireless networks. However, its conventional parameterization based on parameters K and \(\Delta\) is not in line with model's underlying physical mechanisms. Accordingly, in this paper, we first identified anomalies related to usage of conventional TWDP parameterization in moment-based estimation, showing that the existing \(\Delta\)-based estimators are unable to provide meaningful estimates in some channel conditions. Then, we derived moment-based estimators of recently introduced physically justified TWDP parameters K and \(\Gamma\) and analyzed their performance through asymptotic variance (AsV) and Cramer-Rao bound (CRB) metrics. Performed analysis has shown that \(\Gamma\)-based estimators managed to overcome all anomalies observed for \(\Delta\)-based estimators, simultaneously improving the overall moment-based estimation accuracy.  
TWDP fading channel; moment-based estimation; Cramer-Rao bound; asymptotic variance
## 2 The Elementary Principle and Design of the Method
### Quantification Method
#### 2.2.1 Scenario Modeling  
RL is widely used in various areas, such as robotics, neuroscience, computer science, and automatic control. It introduces a way of programming agents by reward and punishment, without needing to specify how the task is to be achieved. Markov decision processes (MDPs) [41] are modeling basics of RL problems that usually include the following elements: S, A, \(\mathcal{R}\), \(\mathcal{T}\), and \(\pi\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Severity Class**} & \multirow{2}{*}{**Exposure Class**} & \multicolumn{4}{c}{**Controllability Class**} \\ \cline{3-5}  & & **C1** & **C2** & **C3** \\ \hline \multirow{4}{*}{S1} & E1 & QM & QM & QM \\  & E2 & QM & QM & QM \\  & E3 & QM & QM & A \\  & E4 & QM & A & B \\ \hline \multirow{4}{*}{S2} & E1 & QM & QM & QM \\  & E2 & QM & QM & A \\  & E3 & QM & A & B \\  & E4 & A & B & C \\ \hline \multirow{4}{*}{S3} & E1 & QM & QM & A \\  & E2 & QM & A & B \\ \cline{1-1}  & E3 & A & B & C \\ \cline{1-1}  & E4 & B & C & D \\ \hline \end{tabular}
\end{table}
Table 1: ASIL determination.  
* S is a set of states s of every agent.
* A is a discrete set of available agent actions that can be selected at a certain state.
* \(\mathcal{R}\) is the reward function that represents the reward gained from taking a certain action at a specific state.
* \(\mathcal{T}\) is the state transition probability matrix, which specifies the probability of taking action for a state. This is consistent with Markov Property, where the state transfer effect of taking action is only related to the current state, irrespective of the historical state.
* \(\pi\) is the policy: S\(\rightarrow\)A, where the goal of RL is to learn the optimal policy \(\pi*\) to obtain the optimal expected reward.  
In the following, we designate the vehicles we want to test as the host vehicles (HVs), and the other vehicles as the background vehicles (BVs). Based on [10, 11, 12], scenario S can be represented as a sequence of states, where subscripts indicate moments:  
\[\mathbb{S}=(s_{0},s_{1},s_{2},\cdots,s_{k}),\forall s\in S. \tag{1}\]  
Due to the Markov Property:\((s_{t},a_{t})\to s_{t+1}\)[46], where \(s_{t}\) is a current state and \(s_{t+1}\) is the next state, a scenario S can also be represented as a sequence of initial state and actions in the following moments:  
\[\mathbb{S}=(s_{0},a_{0},a_{1},\cdots,a_{k-1}),\forall s\in S,\forall a\in A. \tag{2}\]  
This is an adequate simplification. It not only takes into account vehicle-driving characteristics but also reduces the dimension of the scenarios, which helps to solve the bottleneck problem, known as the \"dimension explosion\", in testing scenario generation.
## 3 Results
### Accuracy Assessment
#### 3.4.2 Comparison with Rice Paddy Acquired from IRRI  
After comparison with field plot data, the performance of the proposed rice classification method was then assessed by comparing the classified rice paddy map to the rice paddy products from IRRI derived using MODIS data. The IRRI rice products showed a consistent spatial pattern with our rice map (Figure 8b,d), though the area of the IRRI rice products was 36,386.69 km\({}^{2}\), which was much higher than the identified rice area of 32,066.81 km\({}^{2}\) in this study. The comparison results show that our proposed approach can identify smaller rice paddies effectively with its high spatial resolution, and at a higher accuracy, while the MODIS-based rice results overestimated the rice cover in the perimeter areas of rice fields, with an overall accuracy of 74.76% (Table 3). Despite the various spatiotemporal resolutions among the rice paddy data, the intercomparison verified the effectiveness of our method with Sentinel-1 SAR data to obtain a rice paddy map.
## 4 Simulation and Experimental Results  
The performance of the proposed controller in this work was investigated via numerical simulations and experimental implementation according to the scheme presented in Figure 2.  
The considered PMSM parameters are described in Table 1.  
In this regard, it is important to mention that, in particular for the experimental results, the presented evaluation establishes by itself an evidence of the robustness properties of the proposed scheme since it is well known in practice that the parameters listed in Table 1 are only one approximation to their real value. Thus, if the controller is implemented by considering them, parametric uncertainty is involved in the experiments.  
Figure 1: (**a**) Control scheme for tracking speed trajectories. (**b**) Control scheme for tracking position trajectories. (**c**) Control scheme for tracking torque trajectories.  
Figure 2: Control scheme.  
On the other hand, it is necessary to justify the profiles considered for the evaluation. In this sense, they were chosen in such a way that the effort demanded to the control scheme corresponds to a real practical behavior. In this sense, these profiles exhibit a smooth behaviour that includes sudden changes in both operational senses that implicitly forces the motor to achieve a positive value to later on change the direction of the rotational variables.  
Finally, another practical consideration concerns the implementation of the derivative operator involved in the mathematical expressions. As is widely recognized, implementation of pure derivatives induces noise amplification. To avoid this undesirable situation, the evaluation of the controller the derivative operators were implemented using the third-order numerical differentiator proposed in [27] described by  
\[\begin{split}\dot{z}_{1}&=z_{2},\\ \dot{z}_{2}&=z_{3},\\ \dot{z}_{3}&=-{\lambda_{1}}^{3}z_{1}-3{\lambda_{1}}^ {2}z_{2}-3{\lambda_{1}}z_{3}+{\lambda_{1}}^{3}\theta+3{\lambda_{1}}^{2}z_{4}+3 {\lambda_{1}}z_{5}-{\lambda_{2}}^{2}z_{4}-2{\lambda_{2}}z_{5}+{\lambda_{2}}^{2 }\omega_{d},\\ \dot{z}_{4}&=z_{5},\\ \dot{z}_{5}&=-{\lambda_{2}}^{2}z_{4}-2{\lambda_{2}} z_{5}+{\lambda_{2}}^{2}\omega_{d},\end{split} \tag{62}\]  
where \(\theta\) is the measured position, \(z_{1}\) is the filtered position, \(z_{2}=\omega\) is the rotor speed, \(z_{3}=\dot{\omega}\) is the acceleration, \(z_{4}=\omega_{d}\) is the desired speed, \(z_{5}=\dot{\omega}_{d}\) is the desired acceleration. The stability of (62) is achieved for all \(\lambda_{1}>0\) y \(\lambda_{2}>0\).
# State of the Art and Trends Review of _Smart Metering_ in Electricity Grids
## 4 Development of _Smart Metering_ Worldwide
### America
#### 4.2.3 Latin America  
The deployment of _Smart Metering_ solutions in Latin America is still poor. Brazil and Mexico are currently the first potential markets and Argentina and Chile are also firm candidates for introducing _Smart Metering_ solutions in the coming years [45].  
The energy regulator in Brazil, ANEEL, replaced its ambitious goal of replacing all electricity meters with _SM_s in 2009 with a revised target of replacing 63 million by 2021 [74]. In 2012, ANEEL scaled down the _Smart Metering_ rollout, making _SM_s mandatory only for new customers starting in 2014 and optional for existing consumers [46]. According to Brazilian utility companies, 4.5 million _SM_s are expected to be installed by 2017. The approach of Brazilian _Smart Metering_ deployments is to help reduce fraud, electricity theft, and inefficiency, as occurs in other emerging countries, which cost the country close to $4 billion per year [47].  
Mexico is the second-largest potential market for _SM_s in Latin America after Brazil, and is expected to have 21 million _SM_s installed by 2020 [74]. Many _SG_ and _Smart Metering_ pilot programs in Mexico are being boosted in an effort to respond to high rates of electricity theft, power outages, and poor energy infrastructure [46].
# Precoding for RIS-Assisted Multi-User MIMO-DQSM Transmission Systems
## 5 Detection  
Assuming that the receiver has perfect knowledge of the channel gains, the maximum likelihood (ML) criterion compares the Euclidean distance between the received signal and all possible noiseless received signals in the system. The optimal ML detection criterion for the RIS-MU-DQSM-I system is defined as  
\[\mathbf{\hat{\hat{x}}}_{k}=\underset{\xi\in\mathcal{A}}{\text{argmin}}\parallel \mathbf{y}_{k}-\mathbf{H}_{k}\mathbf{W}_{k}\mathbf{D}\parallel_{F}^{2}\,. \tag{34}\]  
where the matrix \(\mathbf{D}\in\mathbb{C}^{Nr\times 2^{m}}\) accounts for the complete group of possible noiseless DQSM signals in the reception. For those users who are free of MUI, \(\mathbf{W}_{k}=\mathbf{I}\) in (34). For the RIS-MU-DQSM-II system, the ML detection criterion is defined as  
\[\mathbf{\hat{\hat{x}}}_{k}=\underset{\mathbf{\hat{x}}\in\mathcal{A}}{\text{ argmin}}\parallel\mathbf{y}_{k}-\mathbf{H}_{k}\mathbf{G}\mathbf{Z}_{k}^{Eq} \mathbf{D}\parallel_{F}^{2}\,. \tag{35}\]
###### Abstract  
A hybrid proportional double derivative and linear quadratic regulator (PD2-LQR) controller is designed for altitude (z) and attitude (roll, pitch, and yaw) control of a quadrotor vehicle. The derivation of a mathematical model of the quadrotor is formulated based on the Newton-Euler approach. An appropriate controller's parameter must be obtained to obtain a superior control performance. Therefore, we exploit the advantages of the nature-inspired optimization algorithm called Grey Wolf Optimizer (GWO) to search for those optimal values. Hence, an improved version of GWO called IGWO is proposed and used instead of the original one. A comparative study with the conventional controllers, namely proportional derivative (PD), proportional integral derivative (PID), linear quadratic regulator (LQR), proportional linear quadratic regulator (P-LQR), proportional derivative and linear quadratic regulator (PD-LQR), PD2-LQR, and original GWO-based PD2-LQR, was undertaken to show the effectiveness of the proposed approach. An investigation of 20 different quadcopter models using the proposed hybrid controller is presented. Simulation results prove that the IGWO-based PD2-LQR controller can better track the desired reference input with shorter rise time and settling time, lower percentage overshoot, and minimal steady-state error and root mean square error (RMSE).  
quadcopter; UAV controller; attitude motion; altitude motion; natural inspired algorithm +
Footnote †: journal: Applied  
16 February 2021  
Accepted: 15 March 2021  
Published: 17 March 2021