esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Hierarchical Control Development
### MPPT Controller  
In specific periods of time during the day, particularly in sunny seasons of year, sun irradiance can increase; then, PV arrays harvest a surplus power. Under these conditions, it is possible to perform a better harness of this increased power collected by using a MPPT algorithm. There are many strategies to implement a MPPT algorithm; the most accepted of them are summarized in [40]. Nevertheless, the most popular algorithm and the one selected for this development is called perturb and observe \((P\&O)\); this is a sampling technique that uses two variables measurement, current and voltage, to calculate power.  
Figure 6: Feedback state controller structure.  
\(P\&O\) can be implemented in analog version or in digital version and does not require a tuning procedure.  
Voltage and current PV array output measurements are used to calculate a current-power data is calculated \(P_{1}\); then, a voltage little variation (\(\Delta V\)) or a duty cycle disturbance (\(\Delta D\)) in boost converter leads to a new power value calculation, \(P_{2}\). A comparison between \(P_{1}\) y \(P_{2}\) is performed. If \(P_{2}\) is greater than \(P_{1}\), disturbance moved in the right direction, else, disturbances must move in the opposite direction. In this way, it is possible to obtain the power peak value (\(P_{mpp}\)) and, consequently, voltage peak value (\(V_{mpp}\)). Algorithm 1 flowchart of the MPPt controller is shown in Figure 7.  
Figure 7: MPPT controller flowchart algorithm.  
Behavior of boost converters, the two versions, performing in both modes: voltage regulation mode, in closed loop, facing load changes and MPPT controller perfomance are presented and commented in next section.
# Low Current Ripple Parameter-Free MPCC of Grid-Connected Inverters for PV Systems
## 4 Results  
Aiming at validating the effectiveness of the proposed parameter-free MPCC for GCI, a 200 W experimental prototype is constructed as shown in Figure 9. The three-phase inverter adopts FF300R12 ME4 IGBT modules and the controller carries TMSS320F28335 as the digital signal processor. It should be noted that the DC power is the input of the control platform, instead of traditional PV panel input, boost circuit and DC-link capacitance. The control platform consists of DSP, inverter, voltage sensors, and current sensors. The inductance and resistance are viewed as the grid filter. Three-phase grid-connected current can be produced through the inverter powered by 3 kW DC power. The current clamp (ETA 5301A) and the voltage probe (ETA 5010) are used for the real-time measurement of grid-connect current and voltage, respectively. All the parameters of the experimental machine are listed in Table 3. The switching and sampling frequency of the system are both set at 10 kHz.
## 4 Scope of Work  
This work develops experimental measurement results for the following:  
* Performance of Wi-Fi6 in a test simulation mine developed with the intention of providing an easily accessible environment which closely resembled the conditions of a real mine with a stope. The experiments were conducted in a high multipath stope environment as well as a low multipath spacious tunnel environment within the mine to evaluate the impact of excessive multipath propagation on Wi-Fi6.
* The performance parameters of throughput, jitter, power consumption, and latency were considered. Different criteria such as the effects of channel bandwidths, transmission frequencies, mesh routing protocols, transport layer protocols, and multi-hop impact on the signal performance were investigated.
* The performance comparison of Wi-Fi6 and IEEE 802.15.4 (OpenThread) in terms of power consumption was performed. The experiment setup features the nRF7002 dk alongside the Zephyr operating system transmitting at a frequency of 2.4 GHz. The results indicated that even though Wi-Fi6 has a greater instantaneous power consumption, its overall power consumption is much lower than that of OpenThread due to the shorter transmission times. The idle power consumption of both protocols was noted as being very similar.
* The performance comparison of HWMP and B.A.T.M.A.N.-adv routing protocols in terms of reactive and proactive routing for stope environments. The experimental test bed featured the open-source OpenWrt OS installed in the Wi-Fi6 nodes which were configured using the IEEE 802.11s mesh standard. The results indicate that even with the increased overhead of HWMP, it still outperforms B.A.T.M.A.N.-adv for stope environments.
* The throughput, latency, and jitter performance of the protocols was surprisingly found to be better in a stope than a spacious tunnel. This was attributed to the Wi-Fi6 technology taking advantage of the waveguide nature of a stope and its ability to efficiently handle the multipath signals.
# An Adjusted Frequency-Domain Algorithm for Arc Array Bistatic SAR Data with One-Moving Transmitter
## 3 Imaging Processing of AA-BiSAR with One-Moving Transmitter
### Azimuth Pulse Compression  
It is necessary to perform azimuth inverse Fourier transform on the echo signal of Equation (22), and the matched filtering of azimuth needs to be performed in the range doppler domain. The azimuth matched filtering can be realized by the fast convolution, mainly to copy the pulse perform Discrete Fourier Transform (DFT) after zero padding. Then, taking the complex conjugate of the result, the convolution kernel can be expressed as:  
\[h_{a}(\varphi)=\exp\bigg{[}j2\pi\frac{f_{c}}{c}R_{r}\cos\beta_{r}\cos(\varphi -\theta_{n})\bigg{]} \tag{23}\]  
So, the matched filtering function of azimuth can be calculated as:  
\[H_{\rm az}(f_{\varphi})=\{{\rm FFT}[h_{a}(\varphi)]\}^{*} \tag{24}\]where \(\left[\ \right]^{*}\) stands for the complex conjugate operation. After azimuth matched filtering, the echo signal is finally focused as:  
\[\begin{array}{ll}s_{r,\text{ka}}(t_{r},\varphi)&=\text{IFFT}\left\{\left(\text{ FFT}(s_{r,k}(t_{r},\varphi))\right)\times H_{\text{az}}(f_{\varphi})\right\}\\ &=A_{0}\phi_{a}(\varphi-\theta_{n})\phi_{r}\Big{(}t_{r}-\frac{d_{0}+\rho}{c} \Big{)}\cdot\exp\Bigl{[}-j2\pi\frac{f_{z}}{c}(d_{t0}+\rho)\Bigr{]}\end{array} \tag{25}\]  
where \(\phi_{a}(\varphi-\theta_{n})\) denotes the envelope of azimuth direction.  
The complete imaging flowchart of AA-BiSAR with one-moving transmitter is shown in Figure 4. After the residual phase caused by the moving transmitter is compensated in the range frequency-azimuth time domain, the echo signal is no longer related to the velocity of the transmitter. Then, using keystone transform, the coupling between the range frequency and azimuth angle is successfully eliminated, and the focused images are output after azimuth matched filtering.
# Energy Optimisation of Industrial Limestone Grinding Using ANN
## 7 Optimisation of the Grinding Process Using Artificial Neural Networks
### Design of a Hybrid Ensemble for Evaluation Indicators
#### 7.2.2 Data Preparation  
A historical log is first filtered for steady-state intervals. Records are split chronically into 80% training and 20% testing segments to mimic forward prediction. Continuous inputs are standardised, while the target undergoes the log-transform \(\hat{y}=\log(1+y)\), yielding near-Gaussian residuals and stabilised variance; the inverse map \(\exp(\cdot)-1\) is applied before reporting errors in physical units.
# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 4 Results
### XAI Methods  
As shown in Figure 5, the majority of recent XAI papers used local explanations (53%), or a combination of global and local explanations (29%).  
SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) are the most commonly used local XAI methods (Figure 6). While LIME is fully model-agnostic, meaning it is independent of the prediction model and can be used on top of any linear or non-linear model, the SHAP toolbox includes both model-agnostic XAI tools (such as the SHAP Kernel Explainer) and model-specific XAI tools (such as the TreeExplainer, which has been optimized for tree-based models [239]). However, LIME has faced criticism for its instability, meaning the same inputs do not always result in the same outputs [32], and its local approximation lacks a stable connection to the global level of the model. In contrast, SHAP boasts four desirable properties: efficiency, symmetry, dummy, and additivity [240], providing mathematical guarantees to address the local-to-global limitation. These guarantees may explain SHAP's higher popularity in recent XAI application papers (Figure 6). Another local model-agnostic method used in recent XAI application papers is Anchors, which belongs to the same XAI group as SHAP and LIME but is much less popular in recent XAI application papers (e.g., [167; 188; 190; 229; 241]).  
While perturbation-based techniques, such as LIME (e.g., [65; 175; 242; 243]) and SHAP (e.g., [65; 175; 186; 244; 245]), are often the choices in recent XAI studies for tabular data, studies involving images or other more complex data frequently use gradient-based techniques such as Grad-CAM (e.g., [89; 94; 164; 178; 243]), Grad-CAM++ (e.g., [41; 94; 246; 247; 248]), SmoothGrad (e.g., [249; 250; 251; 252]), Integrated Gradients (e.g, [50; 179; 182; 253; 254]), or Layer-Wise Relevance Propagation (LRP), such as those in [175; 179; 241; 255; 256]. Figure 4 shows eight examples of saliency maps from image data of diverse recent XAI applications from various domains.  
The most commonly used global model-agnostic techniques are Partial Dependence Plots (PDP), such as those in [65; 74; 102; 257; 258], Accumulated Local Effects (ALE), as seen in [136; 157; 258; 259; 260], and Permutation Importance (e.g., [74; 136; 137; 156; 180]). Conversely, the most commonly used global intrinsically explainable methods are decision trees (e.g., [88; 91; 183; 191; 261]) and logistic regression (e.g., [50; 53; 61; 191; 211]). It should be noted that the latter two are used in countless other papers, but, given their inherent interpretability, they are often not explicitly listed as XAI methods [31].  
Figure 5: Number of papers in our corpus that used global versus local explanations.
# Damping Estimation of an Eight-Story Steel Building Equipped with Oil Dampers
## 4 Application to the Investigated Building
The equation of motion of the building in each of the two translational directions may be expressed by  
\[\mathbf{M}\ddot{\mathbf{x}}+\mathbf{C}\dot{\mathbf{x}}+\mathbf{K}\mathbf{x}+ \mathbf{T}_{D}^{\mathrm{T}}\mathbf{f}_{D}\Big{(}\mathbf{x},\dot{\mathbf{x}}, \mathbf{c}_{d},\mathbf{k}_{d}\Big{)}=\mathbf{M}\ddot{\mathbf{x}}_{g} \tag{32}\]  
where \(\mathbf{M},\mathbf{K}\), and \(\mathbf{C}\) are, respectively, the real mass, stiffness, and inherent damping matrices, \(\mathbf{x}\) represents the vector of floor displacement relative to the structure base, \(\ddot{\mathbf{x}}_{g}\) is the acceleration input, corresponding to the acceleration of the ground floor of the building, \(\mathbf{I}\) is the vector of earthquake influence coefficients, \(\mathbf{f}_{D}(\cdot)\) denotes the vector of damper forces, \(\mathbf{T}_{D}\) is the transformation matrix, relating movement across the ends of each damper to \(\mathbf{x}\), and \(\mathbf{c}_{d}\), and \(\mathbf{k}_{d}\) are the vectors of damping coefficients and stiffness  
Figure 4: Finite element model of the building as developed in SAP2000 environment. parameters of linear Maxwell models, respectively, composed of the respective characteristics of the oil dampers for each floor. The damper force, for example, for the \(i\)th floor damper is expressed as  
\[\frac{\dot{f}_{d,i}}{k_{d,i}}+\frac{f_{d,i}}{c_{d,i}}=\dot{x}_{d,i} \tag{33}\]  
where \(f_{d,i}\) and \(x_{d,i}\) are, respectively, the damper force and the damper deformation between its two ends. For the investigated building, the supporting brace stiffness is estimated based on their design information and is much larger than the identified damper stiffness. Since these two components are connected in series, the damper deformation is taken as the corresponding inter-story drift. Besides, note that a representation of the form of Equation (32) holds separately for each of the horizontal directions, though for notational simplicity, this distinction is not explicitly noted herein.
## 4 Test System Description and Experimental Setup for Algorithmic Benchmarking
### Input Parameters and Control Variables  
The master optimization stage (PMVO) requires the following input data, as summarized in Table 2:  
* **Active and reactive power demand profiles** at each node, represented by \(P_{j}^{d}(t)\) and \(Q_{j}^{d}(t)\), where \(j\in 1,\ldots,33\) and \(t\in 1,\ldots,24\);
* **Photovoltaic generation profiles**\(P_{j}^{PV}(t)\) at nodes \(j=12,25,30\), derived from local solar irradiance measurements;
* **Technical specifications of BESS units**, including rated energy capacity \(E_{\text{max}}\) and round-trip efficiency \(\eta\);
* **State of charge (SoC) constraints**, defined by \(\text{SoC}_{j}^{\text{min}}\leq\text{SoC}_{j}(t)\leq\text{SoC}_{j}^{\text{ max}}\);
* **Initial SoC values** for each node equipped with a BESS.  
The decision variables optimized by the PMVO correspond to the hourly charging and discharging powers, \(P_{\text{bess},j}(t)\), for the BESS units installed at nodes \(j=6,14,31\), over a 24 h time horizon.  
Each candidate solution generated by the PMVO encodes the hourly state-of-charge values, \(\text{SoC}_{j}(t)\), which serve as the primary control variables of the optimization. The charging and discharging power profiles, as well as the resulting battery currents, are implicitly defined by the temporal evolution of SoC. To evaluate the feasibility of each solution under AC power flow constraints, the Successive Approximation (SA) method is applied to compute the corresponding nodal voltages.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Symbol** & **Source / Value** \\ \hline Active Demand & \(p^{d}(t)\) & IEEE 33-bus benchmark + load profile \\ Reactive Demand & \(Q^{d}(t)\) & IEEE 33-bus benchmark + PF assumptions \\ PV Generation & \(P^{PV}(t)\) & Scaled irradiance (Medell\(\underline{n}\), Colombia) \\ Initial SoC & \(\text{SoC}^{0}\) & 50\% of \(E_{\text{max}}\) \\ BESS SoC Limits & \(\text{SoC}^{\text{min}}\), \(\text{SoC}^{\text{max}}\) & 10–90\% of \(E_{\text{max}}\) \\ BESS Energy Capacity & \(E_{\text{max}}\) & 1000/1500/2000 kWh \\ Charging efficiency & \(\eta\) & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of input data and control variables for the proposed optimization model.  
The simulation covers a 24 h period with an hourly resolution. At each time step, the load and photovoltaic generation profiles are updated, and the optimization algorithm determines the charging or discharging strategy for the three BESS units. These decisions are subject to constraints on the state of charge, power limits, and network feasibility, with the latter verified through the SA method.
# Dense Small Object Detection Based on an Improved YOLOv7 Model
## 4 Experiments and Analysis of Results
### Experimental Analysis of Different Improvement Points  
#### 4.4.1 Improved Detection Head for YOLOv7  
To verify the detection performance of the YOLOv7 network model by adding a P2 detection header and removing the P5 detection layer, the following experiments were conducted based on the Visdrone2019 data set: For Experiment 1, the structure of the benchmark model (YOLOv7) is shown in Figure 8; for Experiment 2, the P2 detection head was added, whose structure is shown in Figure 9; for Experiment 3, the P5 detection head was removed on the basis of Experiment 2, but P5  
The experimental results are presented in Table 2; adding the P2 detection head (Experiment 2) improved the detection performance for small and medium-sized targets compared to the benchmark YOLOv7 model (Experiment 1). The mAP for small targets increased from 18.6% to 21.0%, and the mAP for medium-sized targets increased from 38.7% to 40.7%. Removing the P5 detection head but retaining P5 downsampling (Experiment 3) resulted in a slight improvement in the mAP for small targets (from 21.0% to 21.3%) compared to Experiment 2. However, the mAP for large targets decreased slightly from 47.8% to 47.2%. Completely removing the P5 layer (Experiment 4) resulted in the highest mAP for small targets at 21.6% and the highest overall [EMAIL_ADDRESS]% at 51.4%. This also led to a significant reduction in the number of parameters, from 36.53 M in the benchmark model to 11.31 M in Experiment 4, indicating a more efficient model with less computational complexity.  
The changes made in Experiments 2, 3, and 4 generally improved the detection performance for small and medium-sized targets without a significant loss in performance for large targets. Experiment 4, which completely removed the P5 layer, achieved the best balance between performance improvement and model efficiency. These conclusions highlight the trade-offs between detection performance and model complexity, demonstrating that modifications like adding a P2 detection head and removing the P5 layer can lead to better detection performance, particularly for small targets, while also reducing model complexity.
# Improving Route Selections in ZigBee Wireless Sensor Networks
## 5 Motivating Examples
### Example 1: Symmetric Topology  
Consider the topology shown in Figure 1, in which sensors at node 3 at (\(0,-80\)), node 4 at (\(-130\),0), and node 5 at (130,0) send sensor measurements to the concentrator 0 at (0,80); and node 1 at (\(-35\),0) and node 2 at (35,0) are available for routing packets. Using the propagation model adopted for the IEEE 802.15.4 physical layer in the ns-3 simulator, nodes 4 and 5 are hidden from nodes 0 and 3.  
The goal of this example was to illustrate how the LS-based and the LQI-based procedures influence the route selection of node 3, which has two routes available to reach node 0: routes 3,1,0 and 3,2,0.  
Consider first a low traffic load scenario where node 3 generates an average of 0.5 of a packets and nodes 4 and 5 generate an average of 0.02 packets. Given the symmetry of the scenario, both routes have the same cost; i.e., \(c_{3,1,0}=c_{3,2,0}\). We separately simulated the performance of the LS-based and the LQI-based estimation procedures and observed the estimated cumulative costs measured at node 3. In this case, both estimation procedures produced cumulative cost estimates \(\hat{\epsilon}_{3,1,0}^{(LS)}(t)=\hat{\epsilon}_{3,2,0}^{(LS)}(t)=\hat{ \epsilon}_{3,1,0}^{(LQI)}(t)=\hat{\epsilon}_{3,2,0}^{(LQI)}(t)=2\) most of the times and with very little variance. The same results were obtained by simulating the system with nodes 3, 4, and 5 all generating 0.5 packets/s. This shows that, at least for symmetric topologies with low traffic load, either estimation procedure could be used and there would be no need for more elaborate procedures.  
Consider now a higher traffic load scenario in which nodes 3, 4, and 5 all generate 20 packets/s. The scenario is still symmetric and both routes still have the same cost; i.e., \(c_{3,1,0}=c_{3,2,0}\).  
Figure 1(a) shows the cost estimates produced over the course of a single simulation run considering the LS-based procedure; and Figure 1(c) shows the cost estimates produced by the LQI-based procedure. Both graphs show the cumulative cost at node 3 whenever it received a RREQ rebroadcasted from nodes 1 and 2.  
From Figure 1(a), we can observe that the LS-based estimated costs \(\hat{\epsilon}_{3,1,0}^{(LS)}(t)\) and \(\hat{\epsilon}_{3,2,0}^{(LS)}(t)\) varied significantly over the course of the simulation, even though \(c_{3,1,0}=c_{3,2,0}\).  
In contrast, from Figure 2c, we can observe that the LQI-based estimates were \(\hat{c}^{(LS)}_{3,1,0}(t)=\hat{c}^{(LS)}_{3,2,0}(t)=2\) most of the times and with very little variance.
Sensors_**2013**, _13_, 7472-7491; doi:10.3390/s130607472  
_Series of_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**The Localized Discovery and Recovery for Query Packet Losses in Wireless Sensor Networks with Distributed Detector Clusters**  
**[PERSON] \({}^{1,\bullet}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Wireless Network Research Institute, National Institute of Information and Communications  
Technology, Yokosuka 239-0847, Japan; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Center for Information and Neural Networks, National Institute of Information and Communications  
Technology, and Osaka University, Osaka 565-0871, Japan; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 24 April 2013; in revised form: 29 May 2013 / Accepted: 31 May 2013 / Published: 7 June 2013_  
**Abstract:** An essential application of wireless sensor networks is to successfully respond to user queries. Query packet losses occur in the query dissemination due to wireless communication problems such as interference, multipath fading, packet collisions, _etc_. The losses of query messages at sensor nodes result in the failure of sensor nodes reporting the requested data. Hence, the reliable and successful dissemination of query messages to sensor nodes is a non-trivial problem. The target of this paper is to enable highly successful query delivery to sensor nodes by localized and energy-efficient discovery, and recovery of query losses. We adopt local and collective cooperation among sensor nodes to increase the success rate of distributed discoveries and recoveries. To enable the scalability in the operations of discoveries and recoveries, we employ a distributed name resolution mechanism at each sensor node to allow sensor nodes to self-detect the correlated queries and query losses, and then efficiently locally respond to the query losses. We prove that the collective discovery of query losses has a high impact on the success of query dissemination and reveal that scalability can be achieved by using the proposed approach. We further study the novel features of the cooperation and competition in the collective recovery at PHY and MAC layers, and show that the appropriate number of detectors can achieve optimal successful recovery rate. We evaluate the proposed approach with both mathematical analyses and computer simulations. The proposed approach enables a high rate of successful delivery of query messages and it results in short route lengths to recover from query losses. The proposed approach is scalable and operates in a fully distributed manner.  
**Keywords:** sensor networks; query loss; immune systems; query loss discovery; collective recovery