esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 New Electronic Control System of the Receiver for the Installation on SRT
### Mixer Biasing Block: The SIS Mixer Biasing Control Board  
One side of the SIS junction is connected to a properly designed interconnecting bias circuitry that acts as an interface with the outside. This circuitry is implemented by IRAM in the so-called Junction Bias (JB) board (see Figure 6a), which permits both bringing out the IF signal and to providing bias driving in voltage or current modes [31]. This board, equipped with an internal reference voltage, provides the input junction voltage or current using a DC motor with a potentiometer or a digital-to-analogue converter (DAC), respectively. To evaluate the optimal biasing of the SIS junction, the board is fitted with two analogical outputs that trace the I/V characteristics. Unfortunately, the IRAM JB board has no onboard intelligence and is unsuitable for a receiver that is installed on the SRT. For these reasons, a new electronic control board has been developed for the purpose of managing the IRAM JB board (see Figure 6b), remotely. The new control board is equipped with a power supply stage designed to provide different voltage supplies (i.e., 5 V for digital logic circuit and for the potentiometer calibration, \(\pm 15\) V for operational amplifiers, and 12 V for motor-potentiometer), a readout interface, and a control stage of the potentiometer motor. The readout stage is based on a high-resolution analogue-to-digital converter (ADC), model ADS1115, fitted on Adafruit modules from Texas Instruments [44] that allows the reading of the voltage and current values about the junction biasing, the potentiometer motor position, and the DAC of the IRAM JB board. The control stage of the potentiometer motor enables the tuning of the potentiometer using digital controls to set a specific current or voltage value for the SIS mixer junction. Furthermore, this stage also permits the setting of supplies for both the motor-potentiometer configuration and the DAC configuration of the IRAM JB board.  
A dedicated module of Tester100 GHz reads all ADC values and set the voltage values managing the potentiometer or the DAC.
## 5 Evaluation Protocol and Results
### Analysis of Features
#### 5.4.2 Relations between Features and EEG Signals  
Figure 7 shows the effect of each extracted feature on EEG signals. As shown, the first and third features are highly correlated with the signals of normal subjects, especially in the occipital brain region, but they are negatively correlated with the signals of schizophrenics. On the other hand, the second and fourth features are negatively correlated with normal signals, and they have a less negative correlation with signals of schizophrenia subjects, especially in the frontal and occipital brain regions. It is important to note that alterations in certain EEG frequency bands, particularly beta and gamma, have been previously reported in schizophrenia. These changes are believed to reflect underlying dysfunctions in cortical connectivity and neuronal synchronization, which are critical in the pathophysiology of schizophrenia.  
From Figure 7, we conclude that the second and fourth features are dominant in schizophrenia detection as compared to the first and third features. Also, the first and third features have a high correlation with the EEG signals of the normal class. However, there was a negative correlation between the second and fourth features with the EEG signals of the normal class.
# Internet of Things-Assisted Vehicle Route Optimization for Municipal Solid Waste Collection
###### Abstract  
The efficient collection of municipal solid waste poses a significant challenge for the prospective development of smart cities. Using Internet of Things (IoT) technology enables the detection of various kinds of waste-related information, facilitating the implementation of a comprehensive plan for efficient waste collection. In this paper, an innovative waste collection mechanism leveraging the IoT sensors and widely recognized Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a robust multi-criteria decision analysis method is introduced. Recognized for its ability to yield optimal solutions amidst multiple influencing parameters, TOPSIS proves particularly advantageous when confronted with diverse factors affecting decision making. A TOPSIS-based algorithm for optimal route selection is developed to collect waste using metrics such as toxicity, volume, and time duration of the waste while minimizing the route distance. To demonstrate the efficiency of our proposed algorithm, a comparative analysis with existing algorithms that are dependent on and optimized for single parameters is conducted. Through rigorous evaluation, it is shown that our proposed algorithm reduces the total distance traveled to collect the waste from all the bins by 14% by effectively considering multiple criteria and optimizing waste collection routes.  
IoT; waste collection; vehicle route optimization; smart city +
Footnote †: journal: Applied sciences
## 4 Discussion
### Data Distribution (Statistical Data Heterogeneity) Challenge
#### 4.3.2 Non-IID Mitigation Methods
\[w_{t+1}^{g}\leftarrow\sum_{k=1}^{K}\frac{1}{K}\times w_{t+1}^{k}\] (7) [PERSON] et al. [19] proposed that the weighting coefficient is \(\alpha_{k}=\frac{m_{k}^{c}}{\sum_{c=1}^{C}m_{k}^{c}}\), as shown in Equation (8), where \(m_{k}^{c}\) and \(\sum_{c=1}^{C}m_{k}^{c}\) are denoted as the clusters size in medical node \(k\) and the total number of clusters in community-based federated learning, respectively. In their method, the algorithm considers the weighted average from the cluster patient community,  
\[w_{t+1}^{g}\leftarrow\sum_{k=1}^{K}\frac{m_{k}^{c}}{\sum_{c=1}^{C}m_{k}^{c}} \times w_{t+1}^{k} \tag{8}\]  
Finally, [PERSON] et al. [45] proposed that the weighting coefficient is \(\alpha_{k}=\frac{n_{k}}{n}\times\left(\frac{\varepsilon}{2}\right)^{-(t-timestamp^{k})}\), as shown in Equation (9), where \(e\) is the natural logarithm number to denote the time effect and \(timestamp^{k}\) is the round in the newest updated local model. Their proposed weighting coefficient considers not only the data samples held by node \(k\) shown by the portion of data \(\frac{n_{k}}{n}\) but also the time required to update the global model in the local node. \[w_{t+1}^{g}\leftarrow\sum_{k=1}^{K}\frac{n_{k}}{n}\times\left(\frac{e}{2} \right)^{-(t-timestamp^{k})}\times w_{t+1}^{k}\] (9) 2. In addition, the adaptive loss function has the ability to change conditions based on the loss score function. The loss function was used to measure the model performance. The lower the loss score, the better a model was trained. Specifically, [PERSON] et al. [46] proposed the LoAdaBoost method based on loss function in the FL environment for patient mortality prediction. In their proposed method, the adaptive loss function boosts the training process adaptively from the weak learners node. On each training step, the local node will send both the local model and training loss. If the training loss score is more than the loss threshold, it will be retraining again. Otherwise, it will send to the aggregate server. **Domain Adaptation Method**. Domain adaptation (DA) is a subset of transfer learning in which a model developed in one or more \"source domains\" is applied to a new (butrelated) \"target domain.\" DA is used when the source and target domains share the same feature space but different data representations and distribution [59]. In comparison, transfer learning is used when the target domain's feature is different from the source domain's feature. The goal of DA is to minimize discrepancies in data distributions. [PERSON] et al. [18] incorporated domain adaptation in their FL algorithm. The fundamental assumption is that DA approaches can increase the overall performance of multiple nodes in the FL environment with non-IID. Specifically, the author implemented a mixture of expert (MoE) and adversarial domain adaptation methods. The MoE implements adaptation near the model output layer, whereas the adversarial domain alignment implements adaptation on the data knowledge representation level.
## 5 Analysis and Results
### Comparison between OLSR:Self-organized and AODV:Self-organized
Finally, as analysis of comparisons among the protocols OLSR: Self-organized with the AODV: Self-organized, a better use of the AODV: Self-organized protocol is observed in atmospheres in that the WMN alternates among the scales average and large ones.
# Autonomous Target Tracking of UAV Using High-Speed Visual Feedback
## 4 Vision-Based State Estimator
### Relative Pose Estimation-- Perspective \(n\) Points (PnP)  
Once we have tracked features \(\mathbf{p}_{1}\), \(\mathbf{p}_{2}\), \(\cdots\), \(\mathbf{p}_{n}\in\mathbf{R}^{2}\) on the image, with a known camera matrix and a set of 3D positions \({}^{O}\mathbf{X}_{1}\), \({}^{O}\mathbf{X}_{2}\), \(\cdots\), \({}^{O}\mathbf{X}_{n}\in\mathbf{R}^{3}\), we can derive the camera pose \({}^{O}\mathbf{T}\) by employing the Perspective-n-Point (PnP) algorithm [45]. The general formulation of PnP is to find the homogeneous transformation matrix \({}^{O}\mathbf{T}\) that minimizes the image reprojection error, as follows:  
\[\arg\,\min\sum_{k}\left\|\mathbf{p}_{n}-\hat{\mathbf{p}}_{n}\right\|^{2} \tag{4}\]  
where \(\hat{\mathbf{p}}_{n}\) is the reprojection of the 3D point \({}^{O}\mathbf{X}_{n}\) into the image according to the transformation \({}^{O}\mathbf{T}\).  
By using iterative Levenberg-Marquardt optimization [46], we can find such a pose that minimizes the reprojection error of these four points on the target. The pose represents the relative position \({}^{O}\mathbf{t}\) and attitude \({}^{O}\mathbf{R}\) between the target's coordinate system and the camera's coordinate system.  
Figure 4: Image processing for selecting four inliers for pose estimation.  
Finally, since we use the relative position for the UAV's position control directly, we applied a smoothing filter to the estimated position \({}^{O}\mathbf{t}^{\prime}\) as follows:  
\[{}^{O}\mathbf{t}^{\prime}(i)=\frac{1}{m+1}\sum_{j=-m}^{0}{}^{O}\mathbf{t}(i+j), \tag{5}\]  
where \(i\) is the time step, and \(m\) is the order of smoothness. In the experiment, we set \(m\) to 7. On the other hand, the estimated orientation will be transformed to a quaternion and integrated with the IMU using the extended Kalman filter (EKF) algorithm for the attitude controller. Figure 5 shows the principle of PnP algorithm.
## 4 Experiments
### Performance Comparison
#### 4.2.2 Performance Comparison of the Knowledge Graph Recommendation Model
The experimental results of path-based PER are poor because PER needs to find the meta-path between users and items to be recommended, but it is difficult to obtain the best scheme for the design of the meta-path. The experimental results based on the embedded CKE model are poor because TransE may not fully use KG information. Compared with the above two methods, RippleNet, KGCN, KGAT, HAGERec, and CKAN show satisfactory performance. RippleNet independently and iteratively expands the user's potential interests along the path in the knowledge graph but may not observe the graph information of recommended items. KGCN obtains the item representations through the graph convolution network, but KGCN does not effectively use the user's historical items to mine the user's potential interests. HAGERec and CKAN are superior to KGAT because HAGERec can automatically capture the representations of users and items through a two-way information dissemination strategy and filter out some noise by relying on an aggregation strategy with good interpretability. CKAN processes collaborative filtering information and knowledge graph information separately, which can effectively solve the data noise in KG and achieve satisfactory results. The ATKGRM comprehensively analyzes \(\mathbf{u}_{long}\) and \(\mathbf{u}_{short}\) and combines them to obtain the features of users. The model introduces the idea of adversarial training into the recommendation model to dynamically and adaptively adjust the aggregation weight of the knowledge graph so that it can learn the user feature and the item feature more reasonably.
# Development of Fog Detection Algorithm Using GK2A/AMI and Ground Data
## 1 Introduction
The Advanced Meteorological Imager (AMI) of GK2A and Advanced Himawari Imager (AHI) of Himawari-8 contain 16 channels and high spatial resolutions (0.5, 1, and 2 km at 0.64 \(\upmu\)m, other visible (VIS), and infrared channels, respectively). GK2A is the second geostationary satellite of South Korea, launched in December 2018. Remarkable improvement in the observing capability of GK2A, compared to COMS, in terms of the number of channels (5 to 16), spatial resolution (1-4 to 0.5-2 km), and observing frequency (15 to 10 min), provide an opportunity to detect local and variable fog [26; 27]. To develop an algorithm for real-time fog detection using GK2A/AMI data, we utilized a decision tree method. As the availability of satellite data differed between day and nighttime, the fog detection algorithm was developed separately, according to the SZA. In addition, as the geographic background and characteristics of fog over the Korean Peninsula differed, according to geographic location, the algorithms were developed differently based on land, sea, and coast. Additionally, we used numerical model data as it was difficult to distinguish the fog from the low clouds using only the satellite data. Details about the fog detection methods and data used are described in Section 2. In Section 3, the qualitative and quantitative results of fog detection are presented. Finally, discussions on the performance of fog detection algorithms and conclusions are presented in Sections 4 and 5, respectively.
## 3 Experimental Setup and Results
### Evaluation Metrics  
Eight evaluation metrics are used to evaluate the models because of data imbalance problems, namely: _accuracy_, _precision_, _recall_ (_sensitivity_), _specificity_ (or _selectivity_), _F1-score_, confusion matrix, ROC (receiver operating characteristic) curve, and the area under the curve (AUC).  
On a classification task, a confusion matrix provides a summary of the predicted and true labels.  
_Accuracy_, _precision_, _recall_, _specificity_, and _F1-score_ can be calculated by the following formulas:  
\[accuracy=\frac{TP+FN}{TP+FP+TN+FN} \tag{1}\]  
\[precision=\frac{TP}{TP+FP} \tag{2}\]  
\[recall\ (or\ sensitivity)=\frac{TP}{TP+FN} \tag{3}\]  
\[specificity\ (or\ selectivity)=\frac{TN}{TN+FP} \tag{4}\]  
\[F1-score=\frac{2\times precision\times recall}{precision+recall} \tag{5}\]  
ROC curve is a graphical representation of a binary classification model's performance. It is determined by comparing the true positive rate (_TPR_) to the false positive rate (_FPR_) at various discrimination thresholds, where _TPR_ is also known as sensitivity or recall and _FPR_ is known as false positive rate (1-_specificity_). By adjusting the model's discrimination threshold, the confusion matrix can be changed, and a point on the ROC curve can be plotted.
# An Integrated Model for the Harvest, Storage, and Distribution of Perishable Crops
## 2 Literature Review
Therefore, the authors propose an \(\epsilon\)-constraint-based two-phase iterative heuristic and a fuzzy logic method. The computational results, carried out on a case study and on a set of randomly generated instances, show the goodness of the proposed approach. [PERSON] et al. [32] propose an MILP model which aims to balance the following three P's in the food supply chain: profit, people, planet. In fact, multiple objectives are jointly taken into account: maximization of the average food quality, minimization of the amount of CO\({}_{2}\) emissions, minimization of the total weighted delivery time, minimization of the total expense of the system (i.e., fixed and variable production cost, total inventory cost, total routing cost). A particle swarm optimization algorithm is proposed to solve efficiently a real-life case, which refers to a meat supply chain. [PERSON] et al. [33] focus their attention on product quality, and on warehouse and vehicle temperature. The proposed production-inventory-routing problem is solved through a hybrid search algorithm, which combines the advantages of the variable neighborhood search and simulated annealing. The application to a real chicken-packing plant in Iran reveals the opportunity to reduce distribution and inventory costs, and minimizing food waste. A sensitivity analysis is carried out to determine the most suitable temperature for vehicles and warehouse. [PERSON] et al. [6] consider a multi-plant perishable-food production-routing problem. Since, in many real-life situations, the package can influence the quality decay rate of the food products, they integrate the package selection decision within a MILP model, which is solved by a hybrid matheuristic. The results of the computational experience show that integrating package selection into the production routing planning is profitable. Moreover, a study on different discount policies is carried out, in order to understand how they impact the profit. Some considerations can be drawn from the literature review. First of all, the most significant contributions about the production-inventory-routing problem in the case of perishable products are recent, then this topic is currently of great interest in the scientific landscape. Moreover, our review shows that, as regards the production activities, there is no focus on the harvesting of perishable agri-products. Then, to the best of our knowledge, this paper can represent one of the first attempts at an integrated harvesting-inventory-distribution model. As regards the concept of collaboration [34], traditionally more emphasis has been given to the vertical collaboration [35; 36; 37], while the horizontal collaboration [38; 39; 40; 41; 42] has been less explored over the years.
# Portfolio Learning Based on Deep Learning
## 4 Experiments
### Experiment 1: Different Network Performance  
In Figure 2, we show the autoencoder used in our model. Various network architectures were compared to find the best encoder structure, while the decoder structure remained the same, which consists of four upsampling layers. In backtesting, total return, daily Sharpe ratio, max drawdown, daily mean return, monthly mean return, and yearly mean return were used to evaluate the performance of different encoders. For network architectures, we tried AlexNet, VGG, DenseNet, ResNet, and ShuffleNet. As can be seen in Figure 4, ResNet achieved the best result in total return. Additionally, the training speed of ResNet was fast. In Table 1, experimental results are shown. Except for the total return, ResNet also achieved the best results in daily Sharpe ratio, daily mean return, monthly mean return, and yearly mean return.  
The experimental results show that ResNet achieved the best results in most indicators and thus has the best overall performance. Thus, we choose ResNet as the network structure of the model encoder in the remaining experiments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **VGG** & **DeseNet** & **ResNet** & **AlexNet** & **ShuffleNet** \\ \hline Total Ret. & \(-9.95\%\) & \(-3.06\%\) & \(37.62\%\) & \(-4.77\%\) & \(0.07\%\) \\ Daily Sharpe & \(-0.15\) & \(0.02\) & \(0.73\) & \(0.04\) & \(0.11\) \\ Max Drawdown & \(40.66\%\) & \(33.33\%\) & \(38.33\%\) & \(31.26\%\) & \(30.78\%\) \\ Daily Mean Ret. & \(-3.08\%\) & \(0.47\%\) & \(20.62\%\) & \(-0.42\%\) & \(2.23\%\) \\ Monthly Mean Ret. & \(-1.84\%\) & \(1.22\%\) & \(22.30\%\) & \(0.08\%\) & \(2.55\%\) \\ Yearly Mean Ret. & \(-4.86\%\) & \(-1.76\%\) & \(18.55\%\) & \(-2.52\%\) & \(-0.06\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different networks’ performance.  
Figure 4: Comparison of different networks’ performance in total return. ResNet obtained the best result among all network structures. The abscissa is the time, ranging from the first trading day on 1 January 2017 to the last trading day on 31 December 2018. The ordinate is the rate of return.
## 3 Results  
### Lengths of the Source Array  
Figures 6 and 7 show the s-IGDT images according to the lengths of the source array and the differential images between the reference and obtained s-IGDT images, respectively. In comparison to the reference images, the s-IGDT images reconstructed with the truncated projections exhibited noticeable step band-shaped artifacts, and the phenomenon was clearly identified through the differential images. The artifacts still remained in the s-IGDT images restored by the PDC method, whereas the proposed method significantly mitigated the artifacts for all the lengths of the source array.  
Figures 8 and 9 present the voxel value profiles and the results of the quantitative analysis for the s-IGDT images obtained with the various lengths of the source array, respectively. As can be seen in Figure 8, the proposed out-painting method successfully restored the voxel values for truncated regions, and non-truncated regions in the s-IGDT image closely matched those in the reference image. The PDC and proposed methods increased the SNRs of the s-IGDT images obtained with the various lengths of the source array by an average of 23.35 and 79.62%, respectively. On average, the SNRs for the proposed method were 45.94% higher than those for the PDC method. In terms of the NRMSE, the proposed method outperformed both the conventional s-IGDT imaging and PDC methods, reducing the rates by 24.11-57.35 and 5.34-29.83%, respectively. Across all the lengths of the source array, the lowest NRMSEs were consistently observed with the proposed method. The PSNRs of the s-IGDT images were increased by both the PDC and proposed methods for all the lengths of the source array, and the increase ranges were 8.84-31.19 and 10.19-37.87% for the PDC and proposed methods, respectively. The proposed method yielded the maximum PSNRs of the s-IGDT images obtained with all the lengths of the source array, and the values were 1.24-5.10% higher than those for the  
Figure 6: Reconstructed s-IGDT images with **(a–d)** 660, **(e–h)** 990, and **(i–l)** 1320 mm X-ray source arrays. The first to second columns represent the images reconstructed with the GT (reference) and truncated projections, respectively. The third to fourth columns correspond to the restored images using the PDC and proposed out-painting methods, respectively. The voxel value profiles were plotted along the yellow line.  
PDC method. The s-IGDT images using the proposed method had higher SSIMs than those using the truncated projections and PDC method, with improvements ranging from 16.50 to 32.12 and 0.49-0.77%, respectively.