esa-sceva/satcom-chunk-collection huggingface dataset:
# Utilizing Energy Storage for Operational Adequacy of Wind-Integrated Bulk Power Systems
## 3 Operational Adequacy Evaluation Framework
### Optimal Power Flow  
The adequacy of a system state in satisfying the load of different load points is assessed through a linearized optimal power flow (OPF) developed based on References [26; 27], which sufficiently serves the purpose of this study.  
The objective function given by Equation (17) is formulated to minimize the operating costs associated with conventional generators, wind generators, and the ESS system and curtailment costs associated with load points for every time interval.  
\[Min.\quad\sum_{g=1}^{N_{C}+N_{w}+N_{L}}(\varepsilon_{g}*P_{g})+\sum_{l=1}^{N_ {L}}\alpha_{l}*\forall_{l} \tag{17}\]  
Subject to:  
\[P_{B}-(D\times A)\times\theta=0 \tag{18}\]  
\[P-B^{\prime}\theta=0 \tag{19}\]  
\[\left[\begin{array}{c}P_{g,min}\\ -P_{B,max}\\ -\pi\\ \alpha_{min}\end{array}\right]\leq\left[\begin{array}{c}P_{g}\\ P_{B}\\ \theta\\ \alpha\end{array}\right]\leq\left[\begin{array}{c}P_{g,max}\\ P_{B,max}\\ \pi\\ \alpha_{max}\end{array}\right] \tag{20}\]  
The constraints in Equations (18) and (10) represent the transmission line flows and bus injections, respectively. The constraint in Equation (20) represents the wind generator, ESS discharge capacity, conventional generator and transmission line capacity limits, bus voltage angle limits, and curtailment limits. Finally, linearized OPF is run for each \(n_{w}\) substates within \(m\) possible system states at each \(int\) interval within each \(n\) UCPs of the evaluation period and gives the solution vector as shown in Equation (21):  
\[x=\left[P_{g}\ P_{B}\ \theta\ \alpha\right]^{T} \tag{21}\]  
Aggregated loss of load probability (LOLP) and/or aggregated units per million (UPM) can be used as the operational adequacy index (OAI). LOLP has been considered as OAI in this work due to its preference in adequacy studies. The LOLP is evaluated by associating the curtailment value at each load point, \(\alpha_{u,k\ j^{\prime}}^{j}\) given by the linearized optimal power flow with a probability value, \(p_{k,l^{\prime}}^{j}\) given by the dynamic system state probability evaluation approach. The LOLP is first calculated for each interval. These values are aggregated to obtain LOLP for a unit commitment period. These are again aggregated to yield LOLP for an entire evaluation period. Once such evaluations are done considering all of the states of historical wind power distribution at the start of the evaluation period, aggregated LOLP for the \(l^{th}\) load point, \(LOLP_{l}\), can be calculated as shown in Equation (22), and for the overall system, \(LOLP_{sys}\), as shown in Equation (23), respectively.  
\[LOLP_{l}=\sum\
olimits_{u=1}^{n_{w}}(\sum_{k=1}^{n}\sum_{l=1}^{int}\sum_{k=1} ^{m}\sum_{u=1}^{n_{w}}\ \ \left(\Delta p_{k,l}^{j}\right)*\pi_{u,k}*\beta_{u,k,l}^{j})*\tau_{u} \tag{22}\]  
\[LOLP_{sys}=Max\Big{(}LOLP_{1},\ldots,LOLP_{l},..,LOLP_{N_{L}}\Big{)} \tag{23}\]
## 2 Model and Methods
### Retrieval Method  
The retrieval method aims to estimate the DSD parameters by using the attenuation measurements of both vertically and horizontally polarized signal links. For the LEO satellite communication systems, the elevation angle is constantly changing during an overpass. Assuming the raindrops have zero canting angles, we examine the extinction cross section for various drop sizes for 30 GHz signals at different elevation angles. Table 1 lists the values of \(Q_{H}(D)\) when the elevation angle is at 40, 55, 70 and 90 degrees for raindrop sizes from 0.5 to 6.0 mm in diameter. Table 2 lists the \(Q_{V}(D)\) to \(Q_{H}(D)\) ratio to show the difference between the two. As the elevation angle goes higher and as the drop size becomes smaller, the gap between \(Q_{V}(D)\) and \(Q_{H}(D)\) diminishes. This indicates that for better measurement of the DSD, we need to examine the horizontal and vertical polarization attenuations at lower elevation angles where their difference is more distinct.  
With SNRs at multiple ground receivers estimated for both horizontally and vertically polarized links elevated at 40\({}^{\circ}\) (\(\rho_{H}(40)\) and \(\rho_{V}(40)\)), the specific attenuations \(a_{H}(40)\) and \(a_{V}(40)\) for a targeted area can be retrieved through a tomographic algorithm. This retrieval process involves dividing the area of interest into voxels, solving the specific attenuations of each voxel using algorithms such as least-squares [39] and iteratively updating the noise figures of the sky noises (see [19] (p. 5439) for details).  
Figure 1: An oblate raindrop has different extinction cross sections and causes different attenuations for vertically and horizontally polarized signals.  
With the two specific attenuations ready, from Equations (1) and (3), we obtain  
\[\frac{a_{V}}{a_{H}}=\frac{\int_{0}^{\infty}D^{\mu}e^{-\Lambda D}Q_{V}(D)dD}{\int_ {0}^{\infty}D^{\mu}e^{-\Lambda D}Q_{H}(D)dD}\, \tag{5}\]  
in which parameter \(N_{0}\) is eliminated. By assuming that the shape parameter \(\mu\) is a fixed known value, Equation (5) provides a one-to-one mapping between \(a_{V}/a_{H}\) and \(\Lambda\). When \(\Lambda\) is successfully estimated, \(N_{0}\) can then be solved using Equations (1) and (3).
## 5 Conclusions  
In this paper, the response surface methodology, Taguchi method, and ANSYS parametric modeling language were combined to optimize the electrostatic ring of a 500 kV power transformer. The APDL extracted the maximum electric field intensity automatically and the mutual calls between MATLAB and ANSYS solved the problem of repeated manual modeling and meshing in traditional methods. To avoid the increased calculating workload of developing experiments aroused by irrelevant variables, the Taguchi method was utilized to filter the variables before making response surface experiments, by which the optimization process was effectively simplified. The experimental points were constructed by the experimental design method of CCD, and the response surface model was then obtained. In order to ensure the accuracy and predictability of the model, the response surface model was analyzed by ANOVA, diagnostic analysis, and the significance test of regression, and the final response surface equation was determined. Comparing the optimization results with the traditional finite element method, on the premise of ensuring accuracy, the proposed method in this paper has a higher optimization efficiency.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Before and After** & \multicolumn{4}{c}{**Variables**} & \(E_{\text{max}}\) & **Decline** & **Experimental** \\ \cline{2-7}
**Optimization** & \(R_{1}\)**(mm)** & \(h\)**(mm)** & \(s\)**(mm)** & \(w\)**(mm)** & **(V/mm)** & **Rate (\%)** & **Time(s)** \\ \hline
**Before** & **18** & **15** & 4 & 48 & 9303.30 & - & \\
**Optimization** & FEM & 22 & 20 & 5 & 40 & 8219.83 & 11.6 & 2400 \\
**RSM** & 22 & 20 & 5 & 40 & 8192.64 & 12.1 & 61.8 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Optimization results.  
Figure 9: Comparison of optimization results: (**a**) FEM; (**b**) RSM.  
In general, the structure of the electrostatic ring is optimized more systematically and completely through the Taguchi method and response surface experiments. The feasibility and efficiency of this method were verified, which provides a new and more systematic, fast, and efficient way for the optimization of the transformer insulation structure.  
Conceptualization, G.L.; methodology, G.L., D.H., and D.Y.; software, G.L.; validation, X.Z.; formal analysis, Y.S.; investigation, X.Z.; resources, L.L.; data curation, D.Y.; writing--original draft preparation, D.H.; writing--review and editing, G.L.; visualization, D.H. and D.Y.; supervision and project administration, L.L.; funding acquisition, X.Z.  
This work is supported in part by the National Key Research and Development Program of China (Grant No. 2017 YFB0902703), in part by the National Natural Science Foundation of China under Grant 51407075, in part by Fundamental Research Funds for the Central Universities of China under Grant 2019 MS076.  
The authors declare no conflict of interest.
## 6 Simulation Environment and Results  
Various simulators are used in the maritime field to train personnel in navigation, ship handling and the use of ship bridge equipment. Simulators typically consist of real equipment, real consoles, and instrumentation while the ship and its environment are virtual [52; 53]. Autonomous navigation systems (ANS), which can make navigational decisions and command the ship's propulsion systems, represent a central part of autonomous or remote-controlled vessels.
# Adaptive Quick Reduct for Feature Drift Detection
## 1 Introduction
### Windowing  
As streaming data are discrete but by definition infinite, it is common to use a window model to analyze them, as a compromise between real-time and full batch processing. Memory and time constraints obviously limit the width of the window, for which four common models exist: sliding, damped, landmark and tilted (please see [1] for a review).  
* Sliding windows are the classic model where the most recent \(d\) observations are considered, with a fixed overlap of \(o\) observations between a window and the next;
* Damped windows are a model where data receive a decreasing weight through a decay function (usually exponential) as they age, reducing progressively their relevance;
* Landmark windows are consecutive windows separated by special observation called landmarks, with no overlap between a window and the next;* Tilted windows are windows whose width increases with the age of the observations, following the principle that newer observations require a finer level of detail.
## 3 Wake-Up Radio Communication Protocols
### Analysis of Reviewed Mac Protocols
The ID-based signal is essential in periodic or query based data collection applications such as humidity or temperature level monitoring, where a single sink node aggregates the data of multiple sensor nodes. The data collector can request the data from a particular node by simply sending the wake-up signal with the target node ID without waking up the other nodes. However, the protocol in [79] which is for event-driven applications where events take place infrequently, such as forest fire detection or surveillance system whose duty is intrusion detection, the wake-up call is sent as a signal only without ID. The sensor devices wake-up asynchronously to exchange theirdata whenever there is an event to report. The protocol in [114; 108] encapsulates and send the signal together with the preamble signal. The preamble sequence is used for time synchronization by the wake-up radio. For instance, in [114], the preamble sequence indicates the wake-up signal arrival time. However, repeated preamble needs to be received before determining the exact wake-up signal arrival time, which may result in misdetection and as a result add extra delay. Therefore, the preamble-based signal method will not be suitable for applications that require high reliability and low latency, such as medical and security applications. Some other protocols send the wake-up signal along with the beacon frame. Furthermore, the protocols in [99; 100; 101; 102] require acknowledgment of the wake-up signal, indicating that the main radio of the receiver node is actively listening, before the transmitting node begins to send its messages. This further reduces energy consumption and increase reliability. The test-bed implementation of wake-up radio MAC protocols used in [79] is more realistic, making it more valid and accurate than the simulation. However, evaluation of the other ten of eleven protocols in Table 2 is implemented by computer simulation. A major setback faced in the passive wake-up radio MAC protocol design in [101; 102; 110] is the delay arising from the time needed to harvest enough energy to wake-up. This increased the wake-up latency, and hence the overall network delay.
## 3 Problem Formulation
### Timing Exchange Protocol  
Users \(A\) and \(B\) engage in a cooperative timing exchange in which they alternate transmitting and receiving a waveform that contains both communications feedback and navigation reference sequences. These exchanges are depicted in Figure 2. The transmit timestamps are deterministic and shared, while the receive timestamps are estimated using a variety of ToA estimation techniques. These timestamps are denoted by \(t^{(.)}_{(.),(.)}\); the first subscript indicates the node at which the event occurs, the second subscript indicates whether it was a transmit or receive event, and the superscript indicates in which frame the event occurred. Two successive frames comprise a cycle that is \(L\) seconds long and is denoted \(\{(n-1),(n)\}\) where the successive frames \((n-1)\) and \((n)\) are \(l\) seconds apart. Despite rigorous scheduling, the nodes disagree on time, so these cycle and frame lengths are variable and different at each node. These definitions are summarized in the Table 1.  
In each cycle, user \(A\) transmits a message to \(B\) in the frame \((n-1)\), B waits for an agreed frame separation \(l\), and transmits a response to \(A\) during the next frame \((n)\). Each message contains the transmit and receive timestamps \(t_{(.),Tx}\), \(t_{(.),Rx}\) from the previous  
Figure 2: Interactions between two users \(A\) and \(B\). Successive transmissions are separated by \(l_{A}\), and successive cycles are separated by \(L_{A}\). These users exchange timing information each frame to drive the localization algorithm.  
frame. This exchange enables estimation of the clock offset \(T\) and relative ToF \(\tau\) between the two users for each cycle.  
For a transmission from \(A\) to \(B\) during frame \((n-1)\), node \(B\) will receive the signal at time \(t_{B,Rx}^{(n-1)}\), which is a function of the transmit time \(t_{A,Tx}^{(n-1)}\), the time delay \(\tau^{(n-1)}\), and the time offset \(T^{(n-1)}\), given by:  
\[t_{B,Rx}^{(n-1)}=t_{A,Tx}^{(n-1)}+\tau^{(n-1)}-T^{(n-1)}. \tag{1}\]  
Similarly, for a transmission from \(B\) to \(A\) during frame \((n)\), node \(A\) will receive the signal at time \(t_{A,Rx}^{(n)}\), given by:  
\[t_{A,Rx}^{(n)}=t_{B,Tx}^{(n)}+\tau^{(n)}+T^{(n)}. \tag{2}\]  
For convenience, we define the transmit timestamp \(t_{B,Tx}^{(n)}\) as perceived by node A as \(\tilde{t}_{A,Tx}^{(n)}\), which includes the clock offset depicted in Figure 2, as:  
\[\tilde{t}_{A,Tx}^{(n)}=t_{B,Tx}^{(n)}+T^{(n)}. \tag{3}\]  
At user \(A\), the frame length \(l\) is perceived as \(l_{A}\), and the cycle separation \(L\) is perceived as \(L_{A}\), which for the current cycle becomes:  
\[l_{A}^{(n-1)}=t_{A,Tx}^{(n)}-t_{A,Tx}^{(n-1)}, \tag{4}\] \[L_{A}^{(n-1)}=t_{A,Tx}^{(n-1)}-t_{A,Tx}^{(n-3)}. \tag{5}\]  
For conciseness, we derive the estimators and tracking algorithms for \(T\) and \(\tau\) at user \(A\). The results are symmetrical and can be applied at user \(B\) with trivial substitutions but are excluded for brevity.
## 2 System Overview
### Electrical System Schematic with Fuel Cell Stack and Supercapacitor
(ii) Also, the supercapacitor is pre-charged to 5 V before flight, and if it loses any charge during the purge cycle of the fuel cell, the fuel cell can re-charge the supercapacitor as it supplies the APM. Because the current required by the APM is low (of the order of a few hundred mA), a 5 F supercapacitor at 5 V can store energy to supply power to the flight computer in case of emergencies where the drone has to make a sudden landing, e.g., if the batteries powering the motor run low. Having the supercapacitor as a last auxiliary power supply when both the fuel stick and battery are exhausted has the potential to increase the amount of time the APM and the communication equipment stays powered on, which would enable the drone to keep sending its position to its home base regularly, and thus, improve the chances of its recovery. Also, note from Figure 4 that the BEC output voltage is slightly above 5 V; this does not cause issues with the supply to the Arducopter APM flight controller because although a 5 V supply is preferred, the absolute maximum that the flight controller can tolerate is around 6 V. \begin{table}
\begin{tabular}{c c} \hline
**Item** & **Description** \\ \hline Capacity & 10 L \\ Storage material & AB5 metal hydride \\ Cylinder material & Aluminium \\ Cartridge size & 22 mm (diameter) \(\times\) 88 mm (height) \\ Weight & Approximately 105 g \\ Rated charging pressure & 3 MPa \\ Typical discharging performance & 0.3–0.5 L/min \\ Working temperature & 0–55 °C \\ Service life & 10 years \\ \hline \end{tabular}
\end{table} 
Table 2: Hydrostick Pro metal hydride fuel stick specifications. Figure 4: A snapshot of battery eliminator circuit (BEC) output voltage with/without the supercapacitor in parallel across it when the BEC is supplied by the fuel cell stack.
# Experimental Validation of a GNSS Receiver Antenna Absolute Field Calibration System
## 2 Materials and Methods
### Validation Methodology
#### 2.3.3 GNSS/GPS Network Validation  
The third experimental validation was conducted at a GNSS/GPS network solution level, i.e., based on the adjustment results of a GNSS/GPS network. Four GNSS receiver antennas (I/L: Z1, Z2, Z3, Z4) were utilized for the validation test, forming a network of four stations: CB1 (antenna Z1), CB2 (antenna Z2), BRSK (antenna Z3), and MDVG (antenna Z4). The GNSS/GPS network is depicted in Figure 8.  
Validation at a network solution level is based on the evaluation of the developed LMMT antenna calibration system in the spatial domain and at the final product level. Experimental validation fundamentally relies on the analysis of the internal, or relative, network accuracy. Because only dual-frequency GPS antenna calibration results (L1 and L2) are validated, the network was processed in a dual-frequency GPS-only mode. The GPS data registered on all four network stations were processed two times using identical input data and processing parameters, different only in the applied antenna PCC models: firstly, with type-mean antenna PCC models by IGS and, secondly, with individual antenna PCC models by LMMT. All other processing parameters and input data were not changed. Therefore, a difference in the network solution and estimated station position accuracy is a direct consequence of the antenna patterns applied. As a scalar measure of local network accuracy, the standard deviation of station position is used. Furthermore, the global accuracy of the GNSS/GPS network is characterized by the standard deviation of point position \(s_{\text{M}}\), as defined by [PERSON] [42]:  
Figure 8: GNSS/GPS network established in this study for experimental validation of the LMMT receiver antenna calibration system. Station CB1—TRM57971.00 NONE antenna (I/L: Z1); station CB2—TRM57971.00 NONE antenna (I/L: Z2); station BRSK—TRM57971.00 NONE antenna (I/L: Z3); station MDVG—TRM55971.00 NONE antenna (I/L: Z4).  
\[s_{\text{M}}=\overline{s}_{0}\sqrt{\frac{\text{tr}(\textbf{Q}_{\text{XX}})}{u}}, \tag{5}\]  
where \(\textbf{Q}_{\text{XX}}\) is the network's cofactor matrix, \(\overline{s}_{0}\) is the _a posteriori_ standard deviation of unit weight, and \(u\) is the number of network unknowns.  
The main goals of this validation test are to determine how the application of different antenna PCC models transfers to the spatial domain and reflects on a GNSS network solution, and to evaluate if individual antenna calibration at the LMMT calibration facility results in higher local and global GNSS network accuracy when compared to type-mean antenna models provided by IGS.
# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration
## 3 Materials and Methods
### Relative Comparison (Ratio Between BRs)  
The BR results from each satellite were analyzed to better understand their similarities concerning PS results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sensor** & **BR Tested** \\ \hline PlanetScope PSB.SD & (B6/B4) masked values \textgreater{} 0.83 \\ Sentinel 2 MSI & (B4/B3) masked values \textgreater{} 0.41 \\ ASTER & (B2/B1) masked values \textgreater{} 0.87 \\ Landsat 9 OLI & (B4/B3) masked values \textgreater{} 0.34 \\ \hline \hline \end{tabular}
\end{table}
Table 2: BR tested and its masked values.  
A cross-sensor band ratio comparison was applied to examine the relationship between values detected by different satellites. The results from each satellite (represented in color slices) were used to compute the ratios. This process involves calculating the ratio between the BRs of two satellites for each pixel, always using the PS BR as the dividend. To interpret these ratios, specific thresholds were established to classify the similarity between the results of PS and the other satellites:  
* Positive results are all those between \(\geq\)0.5 and \(\leq\)1.99.
* Between 0.5 and 1, the PS identified fewer iron oxides than the other satellite, but there was no significant difference in the results.
* Between 1 and 1.99, the PS identified more iron oxides, but again, without any abnormality in the results.
* Values \(\geq\) 2, based on the logic of the ratio, imply a significant discrepancy between the results.
* On the other hand, values > 0 and \(\leq\)0.5 indicate that the PS identified far fewer iron oxides than the other satellite, which is, therefore, not an ideal result.  
This approach has advantages, such as allowing an understanding of the proportion or relationship between the values detected by the satellites instead of just absolute differences. It enables a direct interpretation of the values, providing a clear and objective analysis of the results, which can be assessed both qualitatively and quantitatively. It also allows for analyzing the sensor's spectral sensitivity in detecting the target mineral (in this case, iron oxides).  
However, the method also has its limitations. Due to the nature of ratios, division by zero is undefined. Therefore, when ASTER, Sentinel-2, or Landsat 9 have a value of 0, the ratio result will be NoData. If both satellites have a value of 0, the result will also be NoData. On the other hand, if PS has a value of 0, the ratio result will be 0.  
Although it is possible to apply filters or replace the NoData value with a real number, it was decided to keep it to preserve the integrity of the original data and avoid distortions in the results. Additionally, NoData values indicate areas with invalid or missing data, ensuring transparency in the analysis and facilitating its replication.