esa-sceva/satcom-chunk-collection huggingface dataset:
# A Mobile-Based System for Detecting Ginger Leaf Disorders Using Deep Learning
## 4 Results and Discussion
### Performance Analysis of the Detection Model  
In this section, we present the detection results obtained from the YOLOv5 deployment. After training and validation, the model's loss function values were computed and plotted in Figures 19 and 20. These curves indicate the bounding box detection loss, object detection loss, and classification loss. The bounding box detection loss is used to show whether the model is capable of identifying the center point of the target object and whether the target object is correctly covered by the predicted bounding box. Similarly, the object loss is used to show the model detection capability in ROI. The classification loss accurately indicates the model's capability of detecting the class. The smaller values of these curves denote the good performance of the model. Thus, it is evident from the obtained results that the loss functions had a downward trend during the training and validation processes. At each iteration, the loss functions were rapidly changed while the model accuracy increased.  
Figure 17: Testing accuracies of the deficiency nutrients with variant batch sizes.  
Figure 18: Testing accuracies of the soft rot with variant batch sizes.  
After a few iterations, the loss functions slowly decreased towards the minima. Finally, the model stabilized and the best optimal training weights were computed.  
The model's visualization capability is derived in Figures 21 and 22, where different ginger defects are shown. It is noticeable that the model accurately identified the correct classes of each input image. Here, the pest pattern, nutrient deficiency, and soft rot disease are detected and localized by the deployed YOLOv5 model.  
Figure 19: Model evaluation during training on pest pattern and nutrient deficiency classes.  
Figure 20: Model evaluation during training on soft rot disease class.  
The model is also statistically evaluated in terms of precision, recall, and mAP scores. The obtained results against different ginger defect classes are reported in Table 6. It can be noted from the Table that the algorithm has achieved a precision rate of 0.86743, 0.80899, and 0.80569, a recall rate of 0.79167, 0.79028, and 0.78028, and a mAP rate of 0.79004, 0.80931, and 0.80151 on soft rot, pest pattern, and nutrient deficiency, respectively. In short, the deployed YOLOv5 model outperformed the developed ginger dataset and showed its effectiveness and applicability for ginger defect detection.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Category** & **Precision** & **Recall** & **mAP** \\ \hline Soft rot disease & 0.86743 & 0.79167 & 0.79004 \\ \hline Pest pattern & 0.80899 & 0.79028 & 0.80931 \\ \hline Nutrient deficiency & 0.80569 & 0.78028 & 0.80151 \\ \hline \hline \end{tabular}
\end{table}
Table 6: YOLOv5 results on different ginger defects classes.  
Figure 21: Set 1 of visual examples showcasing the qualitative evaluation of the YOLOv5 model on ginger dataset.
## 4 Numerical Examples
### The Case \(p=10\)  
In Figure 1a-c, we consider the behavior of the average of \(100\) independently sampled function values with a fixed \(p=10\). We observed that for all values \(r\), all methods tended to converge to the saddle values.  
According to Figure 2, which shows the behaviors of the relative errors of the function value for each problem size, we noticed that the IDSM with an appropriate delay bound selection, particularly for a fixed delay bound of \(\tau=10\), provided the most accurate results with the lowest relative error across all sizes. Additionally, for fixed delay bounds of \(\tau=5\) and \(\tau=0\), it still exhibited lower relative error values when compared with the AN-23 and NO-09 methods.  
Figure 1: Behaviors of the average of \(100\) independent values \(F(\widetilde{\mathbf{u}}[N],\widehat{\mathbf{v}}[N])\) performed by NO-09, AN-23, and the IDSM for the cases \(p=10\) and \(r=10,100\), and \(1000\).
## 3 Principals of Routing Protocols and Their Breakdowns
### Energy Aware Multi-Path Routing Protocols
#### 3.1.4 Pa-Eps-Case  
In [27], the author proposed a proactive routing approach with energy-efficient path selection (PA-EPS-Case). It uses [PERSON]'s algorithm to find the shortest path for both dense and partially dense networks. For sparse networks, they form a cluster and choose a CH based on residual energy. The CH collects the data from the cluster and forwards it to the next CH, and this process continues till the destination node.  
* Comparison: Both PA-EPS-CASE and SPRINT use vertical layer and opportunistic routing, respectively, do not require localization, and both use Hello packet. PA-EPS-CASE has multiple sinks, whereas SPRINT has a single sink, and the simulation was performed in MATLAB, it uses RSS for selecting forwarding node selection, and Dept is used for multi-hop ARQ. In Table 3, we compare the proactive routing protocols.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Protocol** & **Hello Packet** & **Localization** & **Simulation Tool** & **No. of Sink** & **System Model** & **Forwarding Node Selection** \\ \hline CTP-SEEC & Not used & Not needed & MATLAB & Single & Layer based & Depth \\ \((U-ACH)^{2}\) & Used & Needed & Not given & Multiple & Clustering & Depth \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of reactive routing protocols.  
* Hybrid Routing protocols Hybrid routing protocols combine proactive and reactive routing protocols and produce better performance than both separately. The protocols divide the network into two different zones, using a separate mechanism to communicate within the zone and other zones. The zone routing protocol (ZRP), for example, uses proactive routing to communicate within the zones while using reactive routing to communicate with the other zones. Hybrid routing protocols are preferred when the extensive network can be divided into zones without wasting energy. Some of the hybrid routing protocols for UWSN are discussed below.
## 2 Related Works
### CSI Data Preparation Methods
However, the process of generating spectrograms is computationally expensive,which particularly poses challenges for real-time analysis in resource-constrained Wi-Fi devices. Additionally, it requires setting multiple parameters, which increases the likelihood of inaccurate results due to parameter setting errors. Even though high-dimensional CSI data can help to improve the accuracy of Wi-Fi sensing, it can also lead to increased computational costs and model over-fitting issues. Therefore, methods for generating low-dimensional features have been proposed. They extract only important information from the measured CSI data by using PCA (Principle Component Analysis) or autoencoder [32; 33]. These techniques can maintain the main components of the measured CSI data while reducing noise. However, some useful information may be lost in the process of reducing dimensions, and the complexity of calculations may make real-time application difficult.
## 3 Methodology
### Crossover and Mutation in GA  
When using the GA, a population of individuals is created that contains the variables that need to be optimized, which are known as chromosomes. A population is defined in (1) with the row vector \([x_{1,1}x_{1,2}x_{1,3}\ldots x_{1,m}]\) representing the solution vector in a single iteration. \(X\) is a matrix of all individuals that take part in the optimization process. The total population throughout the run is defined in (2), where \(n\) represents the \(n\)-th iteration, and \(m\) represents the number of agents or chromosomes:  
\[X=\begin{bmatrix}x_{1,1}&x_{1,2}&x_{1,3}&\ldots&x_{1,m}\\ x_{2,1}&x_{2,2}&x_{2,3}&\ldots&x_{2,m}\\ x_{3,1}&x_{3,2}&x_{3,3}&\ldots&x_{3,m}\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ x_{n,1}&x_{n,2}&x_{n,3}&\ldots&x_{n,m}\end{bmatrix} \tag{1}\]  
\[pop=\begin{bmatrix}pop_{1}\\ pop_{2}\\ pop_{3}\\ pop_{n}\end{bmatrix} \tag{2}\]  
Furthermore, let \(P\) represent the percentage of the best fitness values to be chosen as the number of parents in the population; thus, assuming \(P=30\%\), the top \(30\%\) of the population in terms of fitness will be chosen for recombination/crossover. If \(x_{i}^{n}\) represents the \(i\)-th individual in the \(n\)-th generation, and this individual's fitness is in the top \(30\%\) of the population, it will be chosen as a parent; otherwise, it will not be chosen. This is represented in (3) as follows:  
\[x_{iselect}^{n}(t)=\begin{cases}[x_{iselect}^{n}(t-1),x_{i}^{n}]&if\ fitness(x_{i}^{n})\; \epsilon\;(P\times x^{n})\\ \\ x_{iselect}^{n}(t-1)&if\ otherwise\end{cases} \tag{3}\]  
where \(x^{n}\) is the whole population, \(t\) is the iteration count, \(x_{iselect}^{n}(t)\) is the parent array at the end of given iteration of the _for_ loop in line 6 of Algorithm 1, \(x_{iselect}^{n}(t-1)\) is the parent array at the end of the previous iteration, the expression \([x_{iselect}^{n}(t-1),x^{n}]_{i}\) means that \(x_{i}^{n}\), which is the \(n\)th individual is appended to the parent array \(x_{iselect}^{n}(t-1)\) from the previous iteration. The first condition in (3) implies that an \(i\)-th individual will be appended to the parent array if its fitness value is in the best \(P\%\) of the whole population, whereas the parent array remains the same if otherwise.  
Regarding the crossover probability, for example, if the probability of crossover \(P_{c}\) is set to \(60\%\), then two randomly selected individuals will have a \(0.6\) probability of performing a crossover operation on each other. The same rule applies to the mutation operation as it does to the other operations. The number of parents, the probability of crossover, and the probability of mutation are typically provided as parameters to the GA algorithm before the optimization run is carried out. Notably, the individuals upon which these crossover andmutation operations are conducted are those whom are selected according to the criteria stated in (3).
## 4 Results
### Use Case #3: Sampling Error Detection Results  
Figure 6 shows the frequency histogram of a voltage waveform recorded by a DFR with faulty Digital Signal Processor (DSP) firmware. Only the sixty-minute interval histogram is displayed because the other four exhibit the same behavior. Figure 6 shows the PMF residing at the edges of the histogram limits and outside the \(\pm\)0.05 Hz operating tolerance. It is unknown when utility personnel detected this particular DFR sampling error; however, this specific DFR-imposed measurement error can be detected whenever most of a PMF's total area resides outside the \(\pm\)0.05 Hz tolerance.  
Figure 6: _Use Case #3: DFR Sampling Error: Frequency histogram generated from data recorded by a DFR with a faulty DSP chip firmware._
## 4 Accident Control Strategy
### The Movement toward Accident Countermeasures
#### 4.3.1 Level 1 Accident  
A Level 1 accident refers to the failure of some mechanical equipment. The strategy for dealing with a Level 1 accident is as follows:  
1. There are several pumps running in the system, and all the pumps have a backup. If an ongoing pump is broken, the movement is to switch to the backup one. Then, the system enters the normal shutdown procedure, because every pump in the system is indispensable. For the sake of guaranteeing safety and preventing potential damage, the system should not run without a backup pump.
2. If the motor of the three-phase separator (9) is broken, the solid phase in the effluent cannot be separated with the liquid phase anymore. To handle this accident, the effluent from the liquid-phase outlet of the three-phase separator (9) is transported to the underground tank (16) for storing, and the system is enters the normal shutdown procedure.
# A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution Networks
## 2 Fundamental Principles of Deep Transfer Learning
### Definition and Principles of Transfer Learning  
Transfer learning (TL) can realize the transfer of information between two distinct but potentially related domains, denoted as the source domain \(D_{s}\) and target domain \(D_{t}\)[23]. The objective of TL is to apply the rich knowledge in \(D_{s}\) to the knowledge-deficient but similar \(D_{t}\). Generally, \(D_{s}\) possesses comprehensive knowledge, while \(D_{t}\) has only partial knowledge [24].  
The tasks for source and target domains are expressed as follows:  
\[\left\{\begin{array}{l}T_{s}=\left\{Y_{s},f_{s}\right\}\\ T_{t}=\left\{Y_{t},f_{t}\right\}\end{array}\right. \tag{1}\]  
where \(T\) represents task, and \(Y\) denotes label spaces. \(f\) indicates the mapping function. The subscript \"\(s\)\" and \"\(t\)\" represent source and target domain, respectively.  
In this paper, the high-observable distribution networks are considered as the source domain, and the low-observable distribution networks are treated as the target domain. The proposed TL method is based on convolutional neural networks (CNN). The approach involves first performing feature learning to extract domain-invariant features (DIF) from \(D_{s}\) in the feature space [25]. Then, using incomplete data in \(D_{t}\), DIF are extracted and transferred to \(D_{t}\) for SE. Accordingly, the state estimation model obtained in the source domain can be applied to state estimation in distribution networks with low observability in the target domain.
## 5 Conclusions and Future Work  
Ubiquitous healthcare solutions on Android(tm) mobile devices are believed to have a significant impact in bringing heart rate management and ECG monitoring to individuals and patients in everyday life. The development of technology has greatly increased our diagnostic power. These developments have been widely reported, creating a widespread acceptance in both society and patients. This has increased the public expectations not only for high technology healthcare but also for rapid and unrestricted high quality healthcare services. Chronic diseases can be effectively controlled if they are regularly monitored with proper medication cares and guides.  
WSNs are expected to fulfill the unrestricted conditions of healthcare applications, hoping to reduce the mortality rate caused by chronic diseases. Wirelessly transmitting the ECG signal in a WSN can reduce the hassle of traditional wired ECG machines, provide a clean and stable ECG signal for real time heart rhythm analysis and achieve self monitoring, mobility and flexibility. Other than moving healthcare from clinical-centric to patient-centric, this idea would also move the healthcare from treatment to prevention. The early detection of diseases might give a recovery chance to the patient. The rise in global expenditures, shortage of medical staffs and equipment problems and growing incidences of chronic illness can be solved as well.  
With additional personalized medicine care assistance in a healthcare system, a more comprehensive and affordable healthcare solution is provided to the patient, assisting the patient in the medication administration process, without the need of any extra hardware devices and costs. As a conclusion, the proposed solution is easy to be applied with only an ultra ECG wearable device embedded with a sensor node and an Android(tm) smart phone device.  
At this moment, this Android(tm) based ubiquitous healthcare system is only able to monitor ECG vital signs in real time. In the future, more health parameters such as blood pressure, blood glucose level, and body temperature are considered to be included in this system, to provide more health information and a more precise monitoring scheme. An alarm system can also be included in the system as well to activate an alarm sound sending warning messages wirelessly to a doctor's mobile phone when an event occurs. Using GPS capability, the location of the patient will be easily tracked then if rescue is needed.
## 4 Simulation Results
### Evaluation of CSIN Estimation Module  
In CSIN estimation, the number of \(\mathbf{h}_{1}[n]\) branches was 15 and the FIR filter length for each branch was 16. As a performance metric, we adopted the averaged \"error-to-signal power ratio\" (ESR), which is defined as follows:  
\[\text{ESR}_{\text{CSIN},\mathbf{h}_{1}}=10\log_{10}\frac{\sum_{n}\left|\eta[n]- \widehat{\eta}[n]\right|^{2}}{\sum_{n}\left|y[n]\right|^{2}} \tag{20}\]  
where \(y[n]\) is the output signal of the powerline channel without any additive noise. Assumed the frequency offset is less than \(\pm 4\%\) of the frequency of the mains voltage. Thus, we consider the cyclic frequency offset \(\Delta=\pm 4.8\) Hz in our simulation. As shown in Figure 8a, \(\Delta\) significantly degraded the performance of the CSIN estimation module. When the \(E_{b}/N_{0}\) was less than 24 dB, the FRESH filter \(\mathbf{h}_{1}[n]\) became counterproductive in terms of ESR, unless compensation for the cyclic frequency offset was provided. As shown in Figure 8b, the our proposed scheme succeeded in suppressing the value of \(\text{CFRE}_{\text{CSIN}}\) to within approximately \(\pm 0.65\%\). Note that the value of \(\text{CFRE}_{\text{CSIN}}\) is about 3.85% in the case of no compensation for the cyclic frequency offset, i.e., \(\widehat{\Delta}_{1}=0\) Hz. This implies our method suppresses \(\text{CFRE}_{\text{CSIN}}\) by at least 15.45 dB.
# Optimal Steady-State Range Prediction Filter for Tracking with LFM Waveforms
## 4 Performance Evaluation
### Theoretical Analysis  
RMSE\({}_{\text{pred}}\) values calculated using Equations (14)-(17) of the conventional and proposed methods are compared for various \(\Gamma_{\text{D}}\) and \(c_{\text{RD}}\). We assume that \(\sigma_{\text{w}}\) and \(T\) are normalized to 1.  
Figure 1 shows the relationship between \(\Gamma_{\text{D}}\) and RMSE\({}_{\text{pred}}\) for \(c_{\text{RD}}=\pm 0.25\) and \(\pm 0.5\). For all instances, RMSE\({}_{\text{pred}}\) of the proposed method is smaller than that of the conventional method. For \(c_{\text{RD}}>0\), although the performance of the proposed method is slightly better than that of the conventional method for relatively small \(\Gamma_{\text{D}}\), the performance difference becomes large for large \(\Gamma_{\text{D}}\). For \(c_{\text{RD}}<0\), the performance difference between the conventional and proposed methods for relatively small \(\Gamma_{\text{D}}\) is large compared with instances with \(c_{\text{RD}}>0\).  
Figure 2 shows the relationship between \(c_{\text{RD}}\) and RMSE\({}_{\text{pred}}\) for \(\Gamma_{\text{D}}=0.1\) and 1. For tracking with LFM waveforms, range estimation accuracy deteriorates considerably for small (negative) \(c_{\text{RD}}\)[10], which is also a feature of results from conventional methods. However, the proposed method achieves relatively small tracking accuracy for \(c_{\text{RD}}<0\). This is because it directly minimizes the range prediction errors without assuming the Kalman filter equations. The gain relation is limited to Equation (7) when we assume the random acceleration process noises in the Kalman filter equation of the conventional method. However, the optimal gains with respect to the range prediction calculated with the proposed method are not acquired from Equation (7). Moreover, Figure 2b shows that the tracking accuracy of the conventional method for \(\Gamma_{\text{D}}=1\) also deteriorates around \(c_{\text{RD}}=1\). The reason is that the optimal gains for the conventional methods are not effectively obtained as a consequence of the limited gain relation, Equation (7). These results theoretically verify that the proposed method achievesbetter tracking accuracy than the conventional RMSE-based method by the direct optimization of the range-tracking RMSE (the RMS-index) without using the Kalman filter equations.
# Model Predictive Control for Six-Phase Induction Machines with Insight into Past Current Errors
## 3 FCS-Mpc
### The Limits of the Standard Cost Function  
It must be noted that the expression described in (7) has the objective of minimizing the sum of four different errors, but could allow for an individual increase in some of the components as long as the other errors compensate the value of the sum. In order to better illustrate this fact, experimental tests were performed at a mechanical speed of 800 rpm and a sampling frequency of 20 kHz. Figure 4 displays the squared values of the errors for both the primary subspace (Figure 4a) and the secondary one (Figure 4b and the zoom in Figure 4c), in which the errors are multiplied by the weighting factor (\(K_{xy}\)) to more accurately describe the perception of the cost function. The value of \(K_{xy}\) has been set heuristically to reduce copper losses without compromising current tracking in the primary subspace. Nevertheless, it can be observed how the algorithm favors control actions that permit a notable increase in either \(e_{xs}^{2}\) or \(e_{ys}^{2}\) during many cycles (see the red stars in Figure 4b). This behavior leads to an excessive and undesirable accumulation of the error in one of the components, a fact that can be better appreciated in Figure 4c, which is a zoom of the section marked in grey in Figure 4b. Here, it is clear how the errors accumulate between sampling periods despite the efforts made by the cost function to keep the sum to a minimum. In other words, the tendency of individual errors to grow is not compensated because the cost function is actually blind to it. As it stands, the algorithm only focuses on the sum of the errors, not on their individual values or how they change through time.  
Figure 4: Experimental measurements of errors in a standard FCS-MPC scheme. (**a**) Squared errors in the primary plane, (**b**) squared errors in the secondary plane, and (**c**) zoom of the squared errors in the secondary plane.
# A Novel Intelligent Condition Monitoring Framework of Essential Service Water Pumps
## 3 Methodology  
### Test Bench and Data Description  
To obtain a condition monitoring framework suitable for ESWPs, we built a model pump test bench for an ESWP for data collection and validation; the main parameters of the model pump are shown in Table 2. Due to the power of the test bench pump being lower than 200 kW, vibration measurement points are set according to the Chinese standard GB/T 6075.7-2015 [31] and actual needs. The test bench and vibration measuring points are shown in Figure 1. Five channels of the water pump are measured, and the main parameters of the model pump are shown in Table 2.  
The vibration data collected from the test bench include data representing normal status, coupling misalignment, mass imbalance, casing ring wear, blade wear (one blade wear), and bench instability. Before each fault is prefabricated, 40 s of normal status data are collected before each fault, and 10 s data of each fault are collected, respectively. Each sampling includes three channels of vibration acceleration signals from the bearing (in the x, y, and z directions) and two channels of vibration acceleration signals from the motor (in the x and y directions), all with a sampling rate of 10.24 kHz. The designed speed of the test pump is 2900 rpm, and the sample length is suggested to satisfy the Nyquist Sampling  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(N\) & 2900 revolutions per minute (rpm) \\ \(H\) & 20 m \\ \(Q\) & 100 m\({}^{3}\)/h \\ P & 6.80 kW \\ \hline \hline \end{tabular} Where \(N\) represents the rated speed of the motor, \(H\) represents the pump head, \(Q\) represents the rated flow rate, and \(P\) represents the rated power of the driven motor.  
\end{table}
Table 2: Main design point parameters of the model pump.  
Theorem, set as 1250 points (0.122 s). This sample length is enough for the designed speed or a much lower operating speed in practice operating. The sample usage of UAD and FD is shown in Table 3. Moreover, 10 s of strong blade wear (2-blade wear) data are collected, and 10% of samples are randomly selected to test the proposed framework.