esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration
## 3 Materials and Methods
### Validation and Evaluation  
Fieldwork was conducted to collect samples and survey the identified regions of interest. Both rock and soil samples were collected during the field campaign; however, this study focuses exclusively on the soil samples. Aerial reconnaissance was performed using a high-resolution drone, DJI Mavic 3 Pro (sensor 4/3 CMOS; Effective pixels: 20 MP), that captured RGB images and videos of the study area. The samples were oven-dried at 50 \({}^{\circ}\)C and sieved (<200 \(\upmu\)m). After sieving and proper drying, five spectra were collected from each sample to ensure the representativeness of the data. The procedure for spectral analysis was the same as described in Section 3.4.
## 4 Experimental Results and Analysis
### Relationship between Surface Hydroxylation and Photothermal Weak Absorption Effects  
The absorption loss of high-energy optics has become a very important parameter to evaluate their performance. As a non-destructive detection technology, the photothermal weak absorption detection technology can detect the absorption loss accurately, quickly, and in real-time [21]. If there are damaged precursors on the surface, the photothermal absorption peak of the corresponding intensity will appear on the detection chart, and the smaller the photothermal absorption signal obtained, the weaker the absorption loss. In this paper, the photothermal weak absorption detection technology is used to detect and evaluate the absorption characteristics of the IBS surface. The changes in photothermal weak absorption intensity of three marking points on fused silica surfaces before and after immersion were tested, and the size of each area was 3 mm \(\times\) 3 mm. Figure 3 shows the change curves of photothermal weak absorption intensity corresponding to the three marked areas on the sample surface.  
Figure 3: Changes in weak absorption intensity during different surface treatments.  
Figure 2: Infrared spectra with different treatment processes.  
It is obvious from Figure 3 that the photothermal weak absorption intensity of the IBS surface is significantly enhanced after deionized water immersion, indicating that a chemical reaction has occurred after the sputtered surface is immersed in deionized water, resulting in a change in the surface material properties.  
Previous studies have shown that when the IBS removal depth is greater than 1000 nm, ion sputtering damage will occur in the shallow layer of fused silica [9]. Although the overall photothermal weak absorption intensity of the HF acid-etched surface can be reduced by IBS, the structure damage on the shallow surface caused by sputtering accelerates the change in material characteristics, which further improves the photothermal weak absorption intensity of the corresponding area after immersion.
## 1 Introduction
### Proposition
Such an information system can be used to manage hundreds of unique process instances across multiple business units and can assign work items to specific resources, based on the logic encoded into the process models. Furthermore, a BPMS can utilise general-purpose technology integration approaches (e.g., middleware) to interact with a variety of enterprise information systems. These information systems are versatile and mature, prompting the proposal that they might benefit manufacturing. Firstly, this article presents the HORSE System, a reference architecture for a manufacturing operations management system (MOMS). At the core of this system architecture is a BPMS supplemented with several other cyber physical systems and technologies, including a sophisticated multi-actor control system and augmented reality functionality. Secondly, the reference architecture is also realised as a prototype and presented as demonstration in this article, giving evidence that the developed system indeed helps to overcome some of the obstacles toward increased flexibility and smart manufacturing. The prototype systems are deployed and verified in real production environments, placing the HORSE System at technology readiness level 6 [56; 57]. To be clear, this research proposes the addition of BPMton manufacturing, rather than replacing current techniques and technologies. Manufacturing processes are typically performed according to a strict schedule and in adherence to policies and procedures. BPM techniques and technologies can be used to define and enact the manufacturing processes, in accordance to the schedule, policies and procedures. Thus, the BPMS supports execution of the manufacturing operations as planned in the schedule. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Problem** & **Solution** \\ \hline Process management in manufacturing is fragmented, with different techniques applied to different parts of the enterprise. & BPM is often deployed to improve enterprise integration [52]. \\ Smart technologies are under-utilized, because the rigid process design and management does not encourage rapid reconfiguration and reassignment of resources. & BPM is often used for dynamic processes [53; 54]. \\ The number of hardware and software systems continue to increase, based on different control regimes from different vendors. These systems are difficult to integrate and utilise together in the same process. & BPM can orchestrate the activities of different resources, because it is technology agnostic [55]. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Indication of how business process management (BPM) may help to alleviate some of the problems identified in smart manufacturing.
# A Multi-Objective Hybrid BESSA Optimization Scheme for Parameter Extraction from PV Modules
## 9 Discussion
The combined objective function was also found to be lower for the Hybrid BESSA-based multi-objective parameter extraction technique as compared to that of the BES. The PV modeling output obtained using the multi-objective parameter extraction scheme based on the Hybrid BESSA and the BES was also tested on a hardware platform using ARDUINO boards, as depicted in Figure 13. It is evident from Figure 15 that the PV curve simulated using the parameters extracted via the multi-objective parameter extraction scheme based on the Hybrid BESSA was a better fit to the hardware-obtained results as compared to those of the BES. The PV Module AS-M3607-S was the basis for both the simulation and hardware results.
# Adaptive Modem Based on LSTM-AutoEncoder with Vector Quantization
## 5 Results  
### Training Settings  
The structure of the VQ-LATM-AE model itself has a lightweight effect, so a large amount of computing resources is not required during training. When performing end-to-end joint training, the training set used is composed of 8000 random integers ranging from 0 to N - 1, and the test set is a random number of 100,000 symbols. The \(E_{b}/N_{0}\) during training cannot be too high, so that the model deliberately ignores the impact of noise, nor can it be set too low, to cause slow convergence. The other tuned hyperparameter settings are shown in Table 1. T is the time step length, z is the hidden layer size, and the minimum setting can be four. When it is greater than eight, the effect is relatively stable. There are D quantization centers in the codebook, and \(\beta\) is the weight hyperparameter in the loss function that affects the learning speed of the codebook.  
Figure 5: Framework of VQ-LSTM-AE-based modulation.
## 6 Simulation Evaluation
### Comparison and Analysis of Simulation Results
#### 6.2.3 Energy Consumption of Communication and Movement  
Figure 11a,b show the comparison of the each node's average energy consumption of communication and movement varying with the number of nodes on NDACS and VBDA, respectively, where the value of initial communication radius is 60 m in the simulation scenario (default settings for other parameters). As shown in Figure 11a, for VBDA, its rate of increase of average communication energy consumption is relatively fast as the number of nodes increases, and its value is always greater than that of the NDACS algorithm. This is because VBDA needs more communication energy, and all nodes are in an active state and must broadcast messages to their surrounding neighbor nodes. However, since the NDACS algorithm only needs some nodes in the active state, their states can be transformed flexibly through the spanning tree strategy. Therefore, VBDA needs more communication energy when the number of nodes increases. As shown in Figure 11b, the average movement energy consumption of VBDA presents a decreasing trend with the increase of the number of nodes. However, the average movement energy consumption of the NDACS algorithm surpasses that of VBDA, and it initially presents an increasing then decreasing trend. With this finding, the NDACS only executes depth-adjustment strategy on the set of non-intersecting neighbor nodes \(\textit{Nei}(\textit{f}_{j}^{i})-\textit{Nei}\textit{Inter}_{i}^{i+1}\) for a long period of time. However, when \(\textit{Nei}(\textit{f}_{j}^{i})-\textit{Nei}\textit{Inter}_{i}^{i+1}\) is null set, the NDACS algorithm may require the set \(\textit{Nei}\textit{Inter}_{i}^{i+1}\) to execute depth adjustment and deploy them to a deeper location such that they will consume more movement energy. Overall, as shown in Figure 10a,b, the total average energy consumption of VBDA is greater than that of the NDACS.  
Figure 10: Comparison of network connectivity rate between NDACS and VBDA: (**a**) comparison of network connectivity rate between NDACS and VBDA varying with number of nodes, and the initial communication radius is 60 m; (**b**) comparison of network connectivity rate between NDACS and VBDA varying with communication radius, and the default number of nodes is 200; (**c**) network connectivity rate of VBDA varying with number of nodes on different values of \(\alpha\).
# Light-Controlled Microbots in Biomedical Application: A Review
## 5 Imaging
### Magnetic Imaging  
Magnetic imaging is used primarily in the diagnosis of diseases to look inside the human body. The imaging is done with highly intense fluctuating magnetic fields. The widely used magnetic imaging system is known as Magnetic Resonance Imaging or MRI. In MRI, the static and gradient magnetic field orients and excites the hydrogen nuclei inside the body to construct a detailed image of the body with high accuracy. A powerful magnetic field is utilized to align the magnetic nuclear spins, and then a radio frequency (RF) pulse perturbs the alignment. Nuclear spins experience resonant absorption and then return to the original state, emitting energy that can be detected and processed to generate an NMR spectrum, when the frequency of the electromagnetic pulse is appropriately tuned (depending on the intensity of the magnetic field) [217]. MRI is mainly used in neuroimaging, cardiovascular imaging, musculoskeletal imaging, gastrointestinal imaging, etc. MRI can sometimes be used to control microbots that are designed to operate magnetically [218]. One of the major advantages of magnetic imaging is that it can generate three-dimensional (3D) images in real-time, which can be very useful in the maneuverability of the microbots. In general, MRI is a very safe imaging system, but it can be devastating if the patient is wearing pacemakers, heart rings, cochlear implants, or any other metallic implants inside the body. The use of magnetic particle imaging (MPI) and magnetic maneuverability using a magnetic field at the same time has been demonstrated by [PERSON] et al. [219]. Magnetic imaging can be troublesome for pure light-actuated systems if ferromagnetic materials are used in the fabrication of the microbots. The imaging system might interfere with the proper actuation of the robots.
# Review on Computer Aided Weld Defect Detection from Radiography Images
## 4 Defect Segmentation
The approach first detected the important edges by applying the Canny operator (a computational approach to edge detection) [26] with an appropriate threshold value. In order to obtain a closed contour, morphological image processing approach was used which dilated few similar boundaries and eroded some irrelevant boundaries [27]. [PERSON] applied Artificial Neural Networks (ANN) in the edge detection of X-ray images containing defects of welding replacing the application of filtering techniques. The results show that the directly contours of welding defects in radiograms is successfully delivered. It is also proved that the proposed neural segmentation technique is robust to the noise and variable luminance. However, in this type of application, the powerful and rapid computers are needed due to the slow execution speed of this technique [1]. [PERSON] implemented the segmentation process using a suitable threshold to convert the image in the specified window to binary image for separating the defects from the surrounding areas. Then they applied the eight-neighborhoods boundary chain code algorithm to identify the contours of defects. As a result, the coordinates of the boundary edges of all defects can be extracted and stored. The boundary edge codes marked can be used to calculate area, perimeter, width and height of the defects. This useful information could be used to assessment the defect and to classify the welding defects in the future [2]. Figure 3: Summary of the proposed segmentation process: (**a**) image-altering the application of the median filter. (**b**) Application of the Bottom-Hat filter. (**c**) Application of binary thresholding. (**d**) Application sectioning process. (**e**) Modification of minima. (**f**) Application of the Watershed transformation [21]. [PERSON] used graph-based segmentation algorithms [28] to extract segments that correspond to the defects or false positives. The method observed the global image characteristics other than local variation. Thus it can identify the distinct regions, even in the case that there is large variability in their interior [29].
## 5 Discussion  
### Time Sequence of Disaster Roads in Key Areas  
#### 5.1.1 Time Series Analysis of Disaster-Hit Roads in Guancheng  
Guancheng District is a recent river alluvial plain area. Its terrain is generally high in the southwest and low in the northeast, with a sloping slope of about 2% and an altitude between 100 and 150 m. The main rivers in the area are the Xionger River, Qili River, and Chao River, with a total watershed area of 435.9 square kilometers, all of which belong to the Huai River system.  
Guancheng District was more seriously affected by this storm disaster, and this section selects the district as the characteristic area for the time-series analysis of the flooded roads in the storm disaster. Figure 10 shows the time-series changes of the water body area in Guancheng District on 15 July, 20 July, and 22 July, and the specific water body area of that day is indicated in Figure 10. From the water body area, Guancheng District increased rapidly on the 20 th. A comparison of the time-series change map shows that the spatial distribution of the location of the increase in water body area on the 22 nd is generally more dispersed. However, in the northeast of Guancheng District, Wu Zhuang Village (Figure 10 in the red circle area) shows an obvious aggregation-type increase. Wu Zhuang village is adjacent to the Dongfeng Canal to the north, the main function of which is to drain water for urban sewage. It was reported that the roads in the city were seriously waterlogged on 20 July, and the Dongfeng canal continued to operate at high water levels as an urban drainage channel. Dongfeng Canal is the main flood relief river during heavy rainfall in Zhengzhou. Xiong'er River, Qili River, and other major urban rivers converge here, and their precipitation sinks into the Jialu River, flowing from northwest to southeast to achieve the drainage effect. Hence, the water flow of the canal is large. In the vicinity of Wuzhuang Village, two rivers converge, and the place is located at the bend of the Dongfeng Canal, where the rapid flow of water can easily form on land and cause flooding.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Type** & **Erai** & **Jinshui** & **Zhongyuan** & **Huiji** & **Guancheng** & **Total** \\ \hline Length of affected road network/km & 167.94 & 327.13 & 349.17 & 189.66 & 290.73 & 1324.63 \\ \hline \end{tabular}
\end{table}
Table 2: Statistics of the flooded length of the road network in each district.
## 6 Experimental Results
### Comparative Analysis
#### 6.2.2 Analysis of Packet Delivery Ratio  
The Packet Delivery Ratio (PDR) is defined as the number of packets sent in total to the destination from the source without loss. The formulation of PDR is defined as follows:  
\[\text{PDR}=\frac{\text{rec}^{\text{pkts}}}{\text{Tot}^{\text{tx}-\text{pkts}}} \times 100 \tag{40}\]  
where \(\text{rec}^{\text{pkts}}\) is the received packets at the destination, and \(\text{Tot}^{\text{tx}-\text{pkts}}\) is the total number of packets transmitted from the source.  
The PDR comparison of proposed MTS-SDWSN and existing works SEMRP-WSN and EDASS are shown with respect to node speed in Figure 7. When the speed of nodes gets increased, the PDR gets decreased due to channel interference and packet loss. Our proposed MTS-SDWSN model gains better PDR than the existing works. The reason for such better PDR is that we perform multi-tier scheduling and MTD-based secure two-way routing. The multi-tier scheduling is performed among WSN-CLs, CLs-LBSs, and LBSs-SN using the NCFT algorithm. The utilized multi-tier scheduling model overcomes the complexity issue. The MTD-based two-way routing is performed in both infrastructure and switch layers in which the best optimal routes are selected using the SORP method that amalgamates AFT and MCDM. The utilized SORP lessens the PDR to the outer level, whereas the existing models SEMRP-WSN and EDASS lack utilizing such better routing protocol and also lack scheduling, which leads to higher packet and thereby less packet delivery ratio than the proposed work.  
The quantitative analysis from the graph shows that the proposed MTS-SDWSN gains higher PDR of 50% when the speed of SDWSN nodes reaches 10 m/h, whereas for the same speed of nodes the PDR rate of SEMRP-WSN and EDASS gains with 28% and 15%, respectively, which is 22-35% less than our proposed model.  
Figure 6: Number of SDWSN nodes vs. energy consumption.
# A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions
## 2 Related Work
### Other Applications  
Occupancy, death, or anomaly detection applications have been related to HEMS and also explored in literature. In [71], the authors proposed a method to tackle the problem of detection indoor office occupancy based on statistical approaches and Machine Learning techniques. The metering system was installed at the circuit breaker level in an office, thus contributing to building context awareness energy-efficient buildings. The authors of [72] proposed deep learning (DL) based approach to recognize daily activities performed in a smart home. The model separates the normal from the anomalous activities. In addition, they identify the anomalous days based on the number of activities performed in a day. On the other hand, in [73], the authors proposed a model to detect abnormal inactivities  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Year** & **Method** & **Type** \\ \hline
[15] & 2020 & Policy gradients (DDPGs)-based energy management algorithm. & RL-based \\
[1] & 2020 & Two-level distributed Deep RL (DRL) model. & RL-based \\
[63] & 2020 & Optimization based on user preference. & Rule-based \\
[14] & 2020 & Single/Multiple objective optimization. & RL-based \\
[64] & 2020 & Indoor and domestic hot water tank temperature control. & Rule-based \\
[65] & 2020 & Multi-objective optimization using discomfort index. & Rule-based \\
[60] & 2020 & Human comfort-based model. & Rule-based \\
[66] & 2021 & Fuzzy logic systems coupled with genetic algorithms. & RL-based \\
[67] & 2021 & Optimization model for cost reduction. & Rule-based \\
[35] & 2021 & Q-learning for offline optimization. & RL-based \\
[68] & 2021 & Appliance Scheduling-based Residential Energy Management System (AS-REMS). & RL-based \\
[69] & 2021 & Nonlinear models and adjustable parameters. & Rule-based \\
[62] & 2022 & Mixed integer linear programming (MILP) model. & Rule-based \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different methods to solve Scheduling and Control in HEMS.  
that included immobilizing medical conditions or sudden deaths of elderly or disabled occupants who live alone, which aroused the interest of the healthcare community.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 2 Principles
### Fraunhofer Diffraction Model  
The Fraunhofer diffraction theory [57] can be used to describe the far-field interference of OPAs, in which scalar field \(e\) in the \(x_{0}y_{0}\)-plane diffracts to the \(x_{1}y_{1}\)-plane at a large distance \(z_{0}\)[13], following the rule below:  
\[e(x_{1},y_{1})=\frac{je^{-jkz_{0}}}{\lambda z_{0}}e^{-\frac{jk}{2z_{0}}(x_{1}^ {2}+y_{1}^{2})}\iint e(x_{0},y_{0})e
## 2 System Architecture and Design Principles
### Data Preprocessing  
In order to ensure the consistency of the data structure during data transmission, data preprocessing is employed in the system design with the following workflow: First, the overall data are extracted from the DDR using the AXI protocol and then packetized using a specific packet architecture, as illustrated in Figure 2. To clearly define the boundaries of the data packet, a start marker (header) is inserted at the start of the data, while an end marker (end of frame) is appended at the end. Then, the overall data are segmented based on the state machine design, with each set consisting of 239 bytes of data to be encoded.  
Figure 1: Overall structure diagram of the UWOC system.  
The frame header \"A5\" is added before each frame as a start flag, which is detected by the encoding module to start the encoding program. The \"96\" indicates the end of the encoding process for a data set. The \"_RS_code_\" in Figure 2 represents a data segment after RS encoding, which has a complete structure facilitating subsequent transmission and decoding processes.