esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Danger Theory (DT) Based Approach
### Second Level Detection
#### 4.2.2 Random Number Based  
As discussed in the literature review section, the XED (extremely efficient detection) [30] is built with the logic of exchanging the random numbers at various locations. The approach is based on the concept of remember and challenge. Accordingly, uncertainty did not encounter the correct number or the value were not the same (based on random number), which indicates the existence of a clone. By using this concept in the DT approach, replication attack can be reduced (replica meets the cluster head again at a different time interval, if frequency of node meeting with similar IDs and same private key (i.e., \(\lambda_{S}\))) occurrence in MWSNs. The CH transfers the information to the identified node (similar IDs with similar key features) to encounter random value. It would be a clone mobile if it encounters false information. If the CH encounters to the mobile node at t time interval \(\lambda_{S}\), \(r\) is random number value and \(S\) is IDs of identical mobile node. Accordingly, CH will send the message to the 'S' (identified same IDs) to encounter a random number. The check depends upon the binary numbers, if \(r=n\) indicates that the exchange number is wrong, and then the clone mobile node is identified. Conversely, if \(r=r\), an identified node is the original one. The method to check the value, if the right (i.e., \(r=r\to R\)) is provided, it would be the original node. Otherwise, it will be considered as a replicated node (i.e., \(r=n\to W\)) in MWSN. The password check procedure will only be initiated if the mobile node has similar IDs with similar keys. Furthermore, memory overhead has been calculated to show the minimum memory utilization in the proposed DT approach. Therefore, \(O(1)\) is the calculated value of the memory overhead, which permits the count to be constant and independent of the many mobile nodes meeting in the verification process of the replicated node. The memory is adequate to collect the data since a precise number of mobile nodes are executing this job. Thus, the CH maintains the constancy of the number check as it finds identical node identity with identical key by means of memory cost \(O(1)\). A distinct approach to check random number with identical identity mobile node has the ability to detect replica. This quick detection is the main feature of the proposed method which distinguishes itself from other existing clone detection approaches.  
\[s=\begin{cases}\text{Replicated node}&\quad\text{if }r=n\\ \text{Original node}&\quad\text{if }r=r\end{cases}\]
# Computer Vision as a Tool to Support Quality Control and Robotic Handling of Fruit: A Case Study
###### Abstract  
The food industry increasingly depends on technological assets to improve the efficiency and accuracy of fruit processing and quality control. This article enhances the application of computer vision with collaborative robotics to create a non-destructive system. The system can automate the detection and handling of fruits, particularly tomatoes, reducing the reliance on manual labor and minimizing damage during processing. This system was developed with a Raspberry Pi 5 to capture images of the fruit using a PiCamera module 3. After detecting the object, a command is sent to a Universal Robotics UR3e robotic arm via Ethernet cable, using Python code that integrates company functions and functions developed specifically for this application. Four object detection models were developed using the TensorFlow Object Detection API, converted to TensorFlow Lite, to detect two types of fruit (tomatoes) using deep learning techniques. Each fruit had two versions of the models. The models obtained 67.54% mAP for four classes and 64.66% mAP for two classes, A rectangular work area was created for the robotic arm and computer vision to work together. After 640 manipulation tests, a reliable area of 262 \(\times\) 250 mm was determined for operating the system. In fruit sorting facilities, this system can be employed to automatically classify fruits based on size, ripeness, and quality. This ensures consistent product standards and reduces waste by sorting fruits according to pre-defined criteria. The system's ability to detect multiple fruit types with high accuracy enables it to integrate into existing workflows, thereby increasing productivity and profitability for food processing companies. Additionally, the non-destructive nature of this technology allows for the inspection of fruits without causing any damage, ensuring that only the highest-quality produce is selected for further processing. This application can enhance the speed and precision of quality control processes, leading to improved product quality and customer satisfaction.  
robotics; computer vision; quality control; Raspberry Pi; UR3e +
Footnote †: journal:  
Article
# Image Super-Resolution Based on CNN Using Multilabel Gene Expression Programming
## 3 The Proposed Method
### Multifeature Representation of Images
#### 3.1.1 Color Feature  
The color feature is a global feature that describes the surface properties of the scene corresponding to the image or image area, and is also the most direct visual feature in the physical characteristics of the image. Compared with various other image features, the color feature has two obvious advantages: one is stability, low sensitivity to various changes in the image such as translation, scaling, rotation, etc., and strong robustness; the second is that its complicated calculation degree is low. The pixel values in the image are converted, and the corresponding numerical expression is used to obtain the image characteristics [16]. Because of the stability and simple calculation, the color feature has become a widely used image feature.  
Shown in Figure 2, the color histogram is widely used in many image retrieval systems. It describes the proportion of different colors in the entire image, and does not pay attention to the spatial location of each color; that is, it cannot describe the objects or objects in the image.  
The color histogram can be defined as the joint probability density function of the three color channels (RGB) in the image:  
\[h_{R,G,B}(a,b,c)=N\bullet P(R=a,G=b,B=c) \tag{1}\]  
where \(R\), \(G\), and \(B\) represent the RGB color channels of the image, \(N\) indicates the number of image pixels, \(P\) represents the probability density function, and \(h\) represents a histogram function, defined as a four-dimensional eigenvector \(H(H_{\rm R},H_{\rm G},H_{\rm B},\mu)\). The first three dimensions \(H_{\rm R}\), \(H_{\rm G}\), and \(H_{\rm B}\) correspond to the three color channels, and the last dimension \(\mu\) indicates the proportion of the color in the entire image.
## 4 Discussion
Equivalently, it can be reasoned that RG centers fill at a faster pace with larger DPPs, allowing a greater collection of electron-hole pairs without recombination. Conversely, lower DPPs result in a higher proportion of RG centers yet to be filled, thereby reducing the relative sensitivity [53]. Studies show that the DPP dependency observed with the Octavius 1000 SRS (PTW Freiburg GmbH, Freiburg, Germany) decreases at a high dose rate [43; 44]. A similar trend was observed with NN edgeless diodes and reported with the n-type EDGE diodes of the SRS MapCHECK [42]. These results from the literature, and this work, indicate that pre-irradiated lower-resistivity p-type substrates are the best option to achieve minimal DPP dependency. The angular dependence of all 25 diodes in the edgeless array was conserved within 2% for angles smaller than 80\({}^{\circ}\) or larger than 120\({}^{\circ}\); however, there is a measurable effect of attenuation of neighbor diodes. The most notable reduction in signal occurs at angles between 80\({}^{\circ}\) and 120\({}^{\circ}\), displaying up to \(-4\)% variation comparing the edgeless-to-TPS dose ratio at 0\({}^{\circ}\). The diodes exhibit less than 1% discrepancies to face-up and face-down irradiation, demonstrating the excellent performance of the 3D junction and negligible response variation due to attenuation by the substrate. This enables complete charge collection even when irradiation comes from the rear side, which may be unattainable with other planar semiconductor technologies. Considering that single p-type edgeless diodes exhibited a consistent angular response within 4% across 180\({}^{\circ}\) of irradiation angles in the transverse plane [31], it is concluded that the array structure has not significantly altered the intrinsic angular independence of the diodes even though the measurements were compared to the TPS dose, which depends on its dose algorithm. This result is promising for the development of larger arrays or higher densities for improved spatial resolution. To ensure precise absolute dosimetry during SRS treatment, QA correcting for the angular dependency of up to 4% is necessary. Yet, when compared with other 2D arrays, it is possible that correcting the edgeless array during QA might diminish its directional response for lateral irradiation, which has not yet been attained in commercial detectors. Additionally, the angular response of single edgeless diodes remains largely unaffected by the 2D 5 \(\times\) 5 array structure, confirming the expected angular independence of the edgeless array compared to existing QA dosimeters.
## 4 Experimental Datasets and Results
### Experimental Results on Indian Pines Dataset  
Indian Pines dataset was collected over northwestern Indiana's test site, Indian Pines, by AVIRIS sensor and comprises of 145 \(\times\) 145 pixels and 224 bands in the wavelength range 0.4-2.5 \(\times\) 10\({}^{-6}\) m. This dataset consists of two-thirds agriculture area and one-third forest or other naturally evergreen vegetation. A railway line, two dual-lane highways, low-density building and housing and small roads are also part of this dataset. Furthermore, some corps in the early stages of their growth is also present with approximately less than 5% of total coverage. The ground truth is comprised of a total of  
Figure 1: Computational Time for MLR-LORSAL Classifier for all Query Functions for three different datasets.  
16 classes but they all are not mutually exclusive. The number of spectral bands is reduced to 200 from 224, by removing the water absorption bands.  
Further details about Indian Pines dataset can be found at [44]. Table 2 and Figure 2 presents the accuracies for different Classifiers as well as with different sample selection methods. From comparative results, one can see that fuzziness together with MLR-LORSAL classifier works better as compared to the other sample selection and classification methods. We also presents the Indiana Pines predicted geographical maps (classification maps) in Figure 3. These geographical locations of each predicted class label validate the superiority of our proposed pipeline. Figure 3 shows the complete performance assessment on experimental results with profound improvement. As shown in the figure, the classification maps generated by adopting the proposed pipeline are less noisy and more accurate.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **MI** & **BT** & **MBT** & **Fuzziness** \\ \hline
**Overall Accuracy** & 0.80 \(\pm\) 0.17 & 0.81 \(\pm\) 0.17 & 0.77 \(\pm\) 0.14 & **0.81 \(\pm\) 0.14** \\ \(\kappa\) & 0.78 \(\pm\) 0.19 & 0.79 \(\pm\) 0.19 & 0.74 \(\pm\) 0.16 & **0.80 \(\pm\) 0.16** \\
**Recall** & 0.92 \(\pm\) 0.01 & 0.81 \(\pm\) 0.02 & 0.69 \(\pm\) 0.03 & 0.93 \(\pm\) 0.02 \\
**Precision** & 0.95 \(\pm\) 0.01 & 0.80 \(\pm\) 0.02 & 0.63 \(\pm\) 0.03 & 0.92 \(\pm\) 0.02 \\
**F1-Score** & 0.94 \(\pm\) 0.01 & 0.79 \(\pm\) 0.01 & 0.66 \(\pm\) 0.03 & 0.92 \(\pm\) 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: five-fold cross-validation based statistical and accuracies analysis for MLR-LORSAL Classifier with different Sample Selection methods. The highest values are in bold face.  
Figure 2: Comparative Results for Several Classifiers with respect to the \(\kappa\) accuracy for different number of training samples ([50:100:2500]) selected in each iteration.  
Figure 3: Geographical Locations for Predicted Test Labels of Indian Pines Dataset with respect to the number of training samples, ranging from (**a**) 50 samples to (**z**) 2500 samples. The total number of samples in the data are 21,025.
# A Survey of Using Swarm Intelligence Algorithms in IoT
## 3 Applications of Swarm Intelligence Algorithms in WSN
### CH Selection Algorithms Based on Si
#### 3.1.2 CH Selection Algorithms Based on PSO  
Literature [19] proposed a hybrid algorithm of PSO and Tabu named Tabu-PSO to select the CH with the lowest energy consumption in the cluster and to improve the ability to select CH in the WSN. Clusters are formed by the distance from the node to the BS and the energy level of the node. Tabu search was used to improve the racial diversity of PSO to avoid local minimum problems by increasing the number of clusters and improving the survival rate of nodes. Compared with LEACH and PSO, their proposed algorithm can effectively reduce the average packet loss rate and average end-to-end delay.  
Reference [20] proposed a PSO based WSN routing algorithm named EPMS, which combines virtual cluster and mobile reception technology. Compared with Tabu-PSO, this algorithm considers not only residual energy but also node position (distance from a node to the center of gravity) for selecting CH. After the mobile sink visits the entire cluster heads, selecting the node whose residual energy is larger than the average residual energy of all nodes in the cluster as a new CH. The results demonstrated the improvement in prolonging the network life and reducing the average delivery delay.  
Reference [21] proposed a PSO-based CH selection algorithm and a cluster formation based on weight function. Different from the general cluster formation method that only considers the distance from non-CH to CH, this method uses the weight function (e.g., parameters include the distance to CH, the residual energy of CH, the distance from CH to BS) to form the cluster. Although the algorithm takes into account the WSN capacity balance and fault tolerance, it only applies to isomorphic networks.  
In [22], a method based on fuzzy clustering and PSO was proposed to reduce the number of sensors that cannot connect to the CH and the number of CHs that cannot connect to the BS in 3D-WSN. In their method, a function of the total number of unconnected sensors in all clusters is designed as the objective function of the PSO algorithm to find CH, and then the optimal CH is used in the improved FCM (fuzzy c-means) algorithm.  
Reference [23] proposed an algorithm using BFO to overcome the premature convergence of PSO. The algorithm minimizes the distance between CH and cluster members compared with the LEACH algorithm, their proposed algorithm can improve the network life by reducing the total energy consumption.  
The literature [24] proposed a clustering method using PSO (EC-PSO) in a square heterogeneous network. It is to avoid the generation of energy hole and to select CHs by searching the energy center. Their method assumes that the nodes near CH also require a lot of energy, so CH should be located in the energy center. The algorithm adopts a mobile data collector to avoid hot spot problem, but it may result in packet losses. Also, the method has good performance in energy consumption and network life, but the data transfer needs to be improved.
## 2 Test Scheme Design and Implementation
### Loading Scheme
The theoretical values of critical loads at different stages of failure for the P1, P2, and P3 test beams are calculated and presented in \[M_{0}=\sigma_{0}\omega_{0}\]1  
The critical loads for different failure stages were determined through theoretical calculations. To achieve the experimental objectives, two loading methods, hierarchical loading and trapezoidal cyclic loading, were designed. Before the actual loading test, a lead break test was conducted to determine the propagation velocity of sound waves in the beam. Subsequently, a pre-loading procedure was conducted. The loading scheme is as follows. The loading mode diagram is shown in \[M_{0}=\sigma_{0}\omega_{0}\]2  
(1) The P1 beam is a non-prestressed reinforced concrete beam. Due to the relatively small theoretical value of the failure load, a hierarchical loading approach is employed, with each level of loading increased by 5 kN until failure occurs. (2) The P2 beam is a prestressed reinforced concrete beam with a relatively large theoretical value of the failure load. Therefore, a trapezoidal cyclic loading method is employed. Each level of loading increases by 20 kN. Initially, the load increases from 0 to 20 kN, then decreases to 10 kN, followed by an increase to 40 kN, and then a decrease to 10 kN, and so on. Once 60 kN is reached, the load increment per level is reduced to 10 kN. This loading pattern continues until the test beam fails. (3) Similarly, the P3 beam is a prestressed reinforced concrete beam with a relatively large theoretical value of the failure load. However, unlike P2, the P3 beam uses a hierarchical loading approach. Initially, each level of loading increases by 10 kN. Once the load reaches 50 kN, the load increment per level is reduced to 5 kN. This pattern continues until the test beam fails.
## 4 Experimental Results and Analysis
### Experimental Results of Applying to Top 10 Weighted Stocks in Twse  
To verify the effectiveness and scalability of the proposed framework, we retrained the proposed model and calculates the accuracy for TWSE Top 10 weighted stocks: Taiwan Semiconductor Manufacturing Co., Ltd. (TPE:2330), MediaTek Inc. (TPE: 2454), Hon Hai Precision Industry Co., Ltd. (TPE:2317), Formosa Petrochemical Corporation (TPE:6505), Chunghwa Telecom (TPE:2412), Fubon Financial Holding Co., Ltd. (TPE:2881), Cathay Financial Holding Co., Ltd. (TPE:2882), United Microelectronics Corporation (TPE:2303), Evergreen Marine Corporation (TPE:2603), and Delta Electronics, Inc. (TPE:2308). Except for TPE:2330, other models were trained with basic volume and price attributes to compare the differences with and without technical indicators. The results are shown in Table 4. The experimental results show that the accuracy of the model can still reach nearly 70% even without the use of technical indicators. Among all the forecast results, the recall/precision with a flat forecast is the highest, and there are individual differences in other categories.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Trend** & \begin{tabular}{c} **GRU-** \\ **Att\_B** \\ \end{tabular} & \
\begin{tabular}{c} **GRU-** \\ **Att\_R** \\ \end{tabular} & **[27]** & **[28]** & **KNN** & **SVM** &
\begin{tabular}{c} **Random** \\ **Forest** \\ \end{tabular} \\ \hline \multirow{3}{*}{Precision} & Up & 60.3\% & 59.2\% & 62.13\% & 54.37\% & 53.17\% & 47.08\% & 45.40\% \\  & Flat & 91.3\% & 87.2\% & 83.75\% & 81.48\% & 80.46\% & 74.70\% & 73.62\% \\  & Down & 49.4\% & 41.6\% & 52.47\% & 47.77\% & 49.59\% & 37.04\% & 32.95\% \\ \hline \multirow{3}{*}{Recall} & Up & 64.2\% & 61.0\% & 45.07\% & 38.07\% & 39.63\% & 26.98\% & 24.16\% \\  & Flat & 83.2\% & 81.4\% & 69.36\% & 66.94\% & 70.42\% & 62.25\% & 59.64\% \\  & Down & 67.6\% & 56.6\% & 38.46\% & 32.47\% & 26.41\% & 23.90\% & 22.91\% \\ \hline \multirow{3}{*}{F1 Measure} & Up & 62.2\% & 60.1\% & 52.24\% & 44.78\% & 45.41\% & 34.30\% & 31.53\% \\  & Flat & 87.1\% & 84.2\% & 75.88\% & 73.50\% & 75.11\% & 67.91\% & 65.90\% \\  & Down & 57.1\% & 48.0\% & 44.39\% & 38.66\% & 34.46\% & 29.06\% & 27.03\% \\ \hline Accuracy & & 79.2\% & 75.6\% & 71.58\% & 67.82\% & 68.63\% & 60.38\% & 58.01\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the Precision, Recall, F1 Measure and Accuracy. GRU-Att_B and GRU-Att_R represent the BIAS and RSI TIs, respectively.
# Optimal Control of an Ultraviolet Water Disinfection System
## Appendix C Details concerning the implementation and use of algorithm HTPSO_UV1  
The initialization phase of algorithm HTPSO_UV1 defines the following constants and variables:  
* _N_: number of the swarm's particles (_N_ = 30, in our implementation)
* _n_: the solution dimension;
* _vmax_: the maximum speed of a particle in any direction;
* \(C_{1}min\), \(C_{1}max\), \(C_{2}min\), \(C_{2}max\), \(C_{3}min\), \(C_{3}max\), \(wmin\), \(wmax\)_: the minimum and respectively maximum values of the coefficients that appear in Equation (21);
* _xmin_, _xmax_: vectors, with \(n\) elements, having the minimum and respectively maximum values of the particle's position for each direction;
* _T_: the estimated or maximum number of steps until convergence;
* _NF_: vector of \(N\) integer numbers, where _NF_(_i_) is associated with particle #_i_ and represents the number of steps without improving the best personal experience;
* _MINNF_: the minimum value of _NF_ elements used to assert the algorithm convergence (see line #35, where _found_ = 1 means convergence).  
NB: The folder \"UV_DS\" contains all the other folders mentioned below.  
The initialization of other constants and variables is shown in script \"HTPSO_UV1.m\", inside the folder \"HTPSO_UV1_Study1\". The objective function is implemented by the function \"Jfunction1.m\".  
Generation of Figure 5: The two drawings of Figure 5 are generated by the script \"DRAW_FIG5.m\" that calls the script \"HTPSO_UV1_evolution.m\".  
Generation of Table 4: The script that helps to construct Table 4 is \"Analyse.m\". It calls 30 times the script \"HTPSO_UV1_converg.m\", yielding the final values of the following variables: steps' number until convergence (\(t_{conv}\)), \(I_{0}\), \(Q_{0}\), performance index (GBEST), and the total number of calls until convergence.  
Number of steps until convergence (example):  
152 154 154 151 147 152 147 144 147 154 155 144 150 147 142 157 147 154 150 153 147 144 145 148 142 145 154 147.  
Generation of Figure 6: The folder \"HTPSO_UV1_study1\" contains the script \"Study1.m\" that calls repeatedly the script \"HTPSO_UV1.m\" for \(A_{0}\) = 2.5, 2.6,..., 4.1. At the end, it calls the script that draws the Figure 6.