esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed PMSM-SCS Based on DTC Strategy
### PMSM-SCS Based on DTC Strategy Using Constant Flux
#### 2.1.1 Fractional-Order Speed Controllers for the PMSM-SCS Based on DTC Strategy
* FO-Lead-Lag Speed Controller for PMSM-SCS Based on DTC Strategy  
The general form of the transfer function for a FO-Lead-Lag controller is expressed as follows [15]:  
\[G_{c}(s)=K_{c}\left(\frac{s+\frac{1}{\lambda}}{s+\frac{1}{x\lambda}}\right)^{ \alpha}=K_{c}x^{\alpha}\left(\frac{\lambda s+1}{x\lambda s+1}\right)^{\alpha},\ 0<x<1 \tag{12}\]  
where the fractional order of the FO-Lead-Lag controller is denoted by \(\lambda\). Note that for \(\alpha>0\), the FO-Lead-Lag controller has a lead effect, while for \(\alpha<0\) the FO-Lead-Lag controller has a lag effect. In Equation (12), for \(k^{\prime}=K_{c}x^{\alpha}\), the usual form of the FO-Lead-Lag speed controller is obtained [15]:  
\[G_{c}(s)=k^{\prime}\left(\frac{\lambda s+1}{x\lambda s+1}\right)^{\alpha} \tag{13}\]  
Also, if \(k^{\prime}=\alpha=1\), \(\lambda=K_{p}/K_{i}\) and x has a high value (e.g., \(x>10\),000), the transfer function of the FO-Lead-Lag controller is transformed in the transfer function of the FO-PI controller. This allows greater flexibility in the use of the FO-Lead-Lag controller in a control loop and the possibility of obtaining higher performance from the PMSM-SCS, based on the DTC strategy.
# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## 3 Materials and Methods
### Optimization Algorithm Based on PSO-GA Improvement  
The problem of determining the optimal weights of a combinatorial model belongs to the optimization problem of multi-peak functions, i.e., the need to find the optimal value of the function among a number of extremes, which makes determining the optimal combination of weights for a combinatorial model in a wide search domain quite tricky. It has been pointed out that the serial hybrid approach using PSO followed by the GA is suitable for solving optimization problems with multi-peak functions, which can jump out of the local optimal solution and determine the global optimal solution efficiently [50]. Therefore, this study proposes a novel PSO-GA weight optimization algorithm to determine the weights in each combination prediction on this basis. In order to assess the superiority of the weight assignment method in this study, we compare the traditional statistical weight assignment methods, such as the mean and median methods, with the current optimization method. The mean is considered as a benchmark, assigning the same weight to each base model. The median method (Median) selects the middle value of the prediction results of each model as the prediction result of the current combination. In the same combination model, the optimal weight combination for the current combination is determined by calculating the optimal fitness values for different weight combinations under the specified fitness function (see Definition 2).  
**Definition 2**.: _Thus, the optimization problem of computing reasonable weights for each model is defined as follows:_  
\[\mathrm{Min}\;\mathrm{G}(\mathbf{y}-\mathbf{\hat{y}_{combined}}),\;\;\mathrm{s.t.}\sum _{i=1}^{\mathrm{m}}\mathbf{w}_{i}=1. \tag{8}\]  
_where \(G\) is a predetermined function that can be used as the sum of squares (SSE), mean square (MSE), absolute sum (SAE), mean absolute value (MAE), etc., and \(\mathbf{y}\) is the observed value. The fitness function chosen for this study is the mean square (MSE). \(\mathbf{w}_{i}\) denotes the weights of the base models within a combination, \(m\) denotes the number of base models within a portfolio, and the sum of the weights of all base models within the same portfolio is required to be one._
* Adaptive weighted similarity measurement for appearance variation: To enhance detection accuracy, a weighted similarity score is generated in the Similarity Measurement Module to distinguish positive and negative pairs based on a patch-by-patch matrix. * Comprehensive multi-dataset validation: MetricNet achieves appealing performance on three typical datasets with drastic illumination and seasonal changes. It also delivers reliable results in scenes with significant viewpoint variations and performs well in localization applications. The rest of this paper is organized as follows: A brief introduction of related works is provided in Section 2. The theoretical derivation and implementation of the proposed method are detailed in Section 3. Experimental results are presented in Section 4, and the conclusion is drawn in Section 5.
# Unbiased 3D Semantic Scene Graph Prediction in Point Cloud Using Deep Learning
## 4 Experimental Section
### Ablation Experiments
#### 4.5.2 Point Cloud Data Dimension  
The validation model additionally uses the normal vectors of the point cloud as features, and the experimental results are shown in Table 5. However, the experimental performance in the scene graph classification (SGCls) task is slightly better in the R@K and ngR@K indexes than the model using only the point cloud coordinates. However, all other indexes, especially in the mean Recall@K, are descending, and the complexity and running time of the model are substantially increased.  
Therefore, only using the point cloud coordinate information to extract the features in the scene graph classification task is recommended, which is the most cost-effective.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & **w/o** & \multirow{2}{*}{**R@20**} & \multirow{2}{*}{**R@50**} & \multirow{2}{*}{**R@100**} & \multirow{2}{*}{**ngR@20**} & \multirow{2}{*}{**ngR@50**} & \multicolumn{2}{c}{**ngR@**} & \multirow{2}{*}{**mR@50**} & \multicolumn{2}{c}{**mR@@**} \\  & & & & & & & & & **100** \\ \hline \multirow{6}{*}{SGCls} & -ENA & 34.6 & 35.7 & 35.9 & 36.3 & 40.8 & 43.7 & 28.3 & 31.1 & 31.4 \\  & -MP & 32.7 & 34.1 & 34.2 & 34.6 & 39.0 & 41.6 & 27.9 & 30.2 & 30.4 \\  & -UME & 34.3 & 35.6 & 35.8 & 36.5 & 41.2 & 44.1 & 26.8 & 29.3 & 29.8 \\  & -GW & 34.8 & 36.6 & 36.7 & 36.7 & 41.0 & 43.8 & 25.5 & 28.7 & 29.3 \\  & Ours & **35.8** & **37.1** & **37.2** & **38.2** & **42.6** & **45.4** & **31.1** & **33.8** & **33.9** \\ \hline \multirow{6}{*}{PredCls} & -ENA & 63.2 & 66.1 & 66.2 & 68.2 & 83.2 & 91.7 & 59.7 & 63.9 & 64.0 \\  & -MP & 63.1 & 66.5 & 66.7 & 68.3 & 81.0 & 89.8 & 59.4 & 63.8 & 63.9 \\ \cline{1-1}  & -UME & 59.2 & 62.8 & 62.9 & 63.8 & 77.7 & 88.7 & 45.7 & 49.4 & 49.5 \\ \cline{1-1}  & -GW & **65.0** & **68.7** & **68.9** & **69.4** & **83.2** & 91.0 & 56.2 & 60.3 & 60.4 \\ \cline{1-1}  & Ours & 63.8 & 67.2 & 67.3 & 68.6 & 83.0 & **91.9** & **60.9** & **64.7** & **65.0** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of ablation experiments of SGCl-PredCLs (each module is removed separately, and the experimental results are observed using percentages % of results, bolded for best performance and underlined for secondary performance).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dimension** & **R@20** & **R@50** & **R@100** & **mR@20** & **mR@50** & **mR@20** \\ \hline xyz + normal & **36.2** & **38.0** & **38.1** & 27.0 & 30.4 & 31.2 \\ xyz & 35.8 & 37.1 & 37.2 & **31.1** & **33.8** & **33.9** \\ xyz + normal & 63.6 & 66.5 & 66.6 & 59.6 & 63.9 & 64.1 \\ xyz & **63.8** & **67.2** & **67.3** & **59.9** & **64.7** & **65.0** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of each prediction using the normal vector of the point cloud in the extraction stage of point cloud features (in % and bolded for best performance).
## 2 Resources and Methodology
### Methodology for Obtaining the Robot Digital Twin
#### 2.2.1 Robot Modeling and Identification  
Once the ECU-RBP is configured to communicate bidirectionally with the P3-DX robot (see Figure 1), a series of open-loop tests are conducted. A bilateral UDP wireless link enables reliable data exchange, supporting model identification and validation. The nonlinear components (dead-zone and saturation) and the linear ones (state-space linear model) of the P3-DX robotic unit are obtained, where \(u^{\prime}_{k}\) is the input vector including linear (\(V^{\prime}_{i,k}\)) and angular (\(W^{\prime}_{i,k}\)) velocities, and \(y_{k}\) is the output vector with velocities (\(V_{o,k}\), \(W_{o,k}\)) obtained from odometry following the study presented in [43]. These elements are used to complete the Simulink model depicted in Figure 3. The identified model parameters, determined using a sampling time of 25 ms, are detailed in Appendix A.1. Additionally, remote access to the robot from the ECU-RBP via the wireless network introduces a delay of q samples, which directly influences the dimensionality of the state vector in the discrete-time linear model.  
Simulink Coder [42] enables the deployment of the Simulink-based robot model, depicted in Figure 4, onto the DT-RBP platform. This allows the virtual robot to be controlled, running on the electronic board, ready to work as a DT of the real process.  
Figure 3: Linear and nonlinear components of the identified model of the P3-DX robot (**a**). Variable relationship and Simulink model (**b**).  
Figure 2: Development of methodology for DTs, from testing the real process to validate the virtual one.
# Novel Techniques for Microbiological Safety in Meat and Fish Industries
## 2 Factors Affecting Microbial spoilage of Meat, Fish and Derived Products
### Water Activity  
One of the main requirements of growth and metabolic activity for microorganisms is the availability of water. As meat and fish are perishable foods with water activity above 0.85, the optimum ranges for the microbial growth is 0.995 to 0.980. Microbial flora is based on nutrient availability and the available moisture content of foods. Psychotropic bacteria usually cause meat spoilage through various microbial activities. According to a USDA (2005) report, some of the common spoilage microorganisms of meat and meat products grow when the specific required \(\mathrm{a_{w}}\) is present in a particular product. Some examples of microorganisms include: _Campylobacter_, _Pseudomonas_, _Salmonella_, _Clostridium botulinum_, _Listeria monocytogenes_ and _Staphylococcus aureus_, which have water activity 0.98, 0.97, 0.94, 0.96, 0.92 and 0.86, respectively [12]. Some molds also cause spoilage even at a low water activity level, such as Xeromyces, which even survives at a low water activity of \(\sim\)0.6, and _E. coli_ at 0.95. The spread of microbial growth can be controlled by lowering the water activity through various preservation methods, such as salting, syruping, drying and curing, that increase product shelf life [13].
## 4 Noise Reduction Analysis of the Noise Wall
### Influence of the Noise Wall Height
The maximum insertion loss under the 6 m noise wall condition is about 12 dB located at the area 15-20 m behind the noise wall. For the condition of an 8 m noise wall, the insertion loss decreases with the increase in distance from the noise wall, and the maximum insertion loss is about 18 dB. The SPLs outside the noise reduction area are generally increased small, within 8 dB. At the frequency of 71 Hz, the maximum insertion loss under the 4 m noise wall condition is located at the area about 1 m behind the wall with a maximum of 25 dB, while the noise reduction area under 6 m and 8 m noise wall conditions emerges zonal distributions, and the maximum values are 16 dB and 55 dB, respectively, but the noise of the area in front of the noise wall increases significantly with maximum increases of 21 dB and 25 dB, respectively, as shown in Figure 12d-f. It can be seen from Figure 12g-i that the noise reduction and increase areas are approximately divided by the noise wall plane at 82 Hz. The maximum noise reduction and increase values are located at different positions under different noise wall height conditions, but the maximum values are close, attaining 46 dB and 26 dB, respectively. For the scenario analyzed above, the ground reflection is considered, and the noise at each point in the sound field is mainly composed of the direct radiation part from the sound source and the superposition part of the ground-reflected sound waves. After setting the noise wall, there are three transmission paths for the radiated noise at the noise wall interface: reflection, diffraction, and transmission. The reflection path mainly affects the noise in the area below the bridge, causing increases in SPLs. The diffraction and transmission paths mainly affect the area behind the noise wall, and the sound waves are attenuated after transmitting the noise wall, resulting in a great noise reduction effect of the sound shadow zone. The diffracted and transmitted sound waves further influence the noise superposition results in other areas through ground reflection, causing changes in the SPL distribution outside the sound shadow zone.
## 3 Q2: What Are the Challenges of the Current UIoT System
### Security Challenges of UIoT
#### 3.3.1 Complex Environment  
As discussed in Section 3.2.3, the UIoT is complex and insecure. For most of the applications, the sensor nodes are sparsely deployed and not well managed. This makes way for attackers to inject malicious nodes inside the UIoT networks. Furthermore, as discussed in Section 3.2.7, the underwater nodes can be physically broken due to the natural behavior of deep-sea and other living organisms. Therefore, monitoring and protecting nodes in a complex environment is an important discussion for the developers.
## 2 Related Works
adopted an unsupervised Gaussian mixture model to learn the statistical features and identify outliers in the data by adaptive thresholding based on interquartile spacing. Experimental results on the dataset showed that this model could accurately detect unknown attacks [13]. [PERSON] et al. transformed the original features by logging the marginal density ratio to obtain new features with better feature expressiveness, and then the new features were used to build Support Vector Machine (SVM) based intrusion detection system [14]. [PERSON] et al. proposed an intrusion detection framework based on SVM and Naive Bayes model (NBM) by first using the NBM. The new data were then used to train the classifier [15]. [PERSON] et al. investigated the role of feature selection in intrusion detection systems by using different feature selection methods in different classification models for testing. The experimental results showed that methods could significantly improve the performance of IDS [16]. [PERSON] et al. constructed an intrusion detection system based on the LongShort-Term Memory (LSTM) DL model and used the Principal Component Analysis (PCA) technique to analyze the data. The PCA technique usedmutual information as a feature selection technique. Experiments on the KDD-99 dataset showed that the model achieved the best training and testing accuracy in both binary and multi-classification [17]. In summary, existing attack graph techniques generally build attack prediction models for static networks, which cannot adapt to changes in the network state. It is difficult to achieve an active update of the attack graph and the purpose of real-time prediction of attackers' attack behavior. Therefore, the study proposed an IL-based intrusion detection method that enabled the model to learn the features of new data and ensured the prediction capability of the model for new data. The method was then combined with Bayesian attack graphs to achieve real-time prediction of attacker attacks.
## 4 Test Results and Discussion  
### Failure Modes  
Pictures of the anchorages installed in the experimental beams, before and after the flexural test, are shown in Figure 4. The control beam, which was a non-prestressed beam, failed at the mid-span when the top of the concrete was crushed after the tension bar yielded. This failure is typical of a reinforced concrete beam and, thus, it was expected, because the control beam had a steel ratio that was less than the balanced steel ratio. One of the beams (LHS), was prestressed with a conventional anchorage and a prestressing tendon, exhibited considerable cracking around the anchorage as the load was increased after the yielding of the tension bar, while the epoxy used to fill the space between the anchorage and the concrete was expelled (Figure 4a). On the other hand, the two remaining beams that used the conventional anchorages Type II and Type III (LHT beam and LHU beam) exhibited considerable cracking around the anchorage after the yielding of the tension bar. The pull-out of a few stud bolts led to local failure of the anchorage and, thus, failure of the beam (Figure 4b,c). In the beams that were prestressed with a prestressing tendon and fixed using the proposed anchorage (JBM series), only the epoxy was expelled, and neither cracking around the anchorage nor pull-out of the stud bolts was observed (Figure 4d).
# Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning
## 1 Introduction
The following research questions guided this study:  
(R1) How can FL be leveraged for efficient, data-privacy-preserving collaboration in the eye healthcare space? (R2) How can FL maintain high accuracy and minimize performance loss compared to CL? (R3) In FL, how can the computational power of training models be effectively distributed to the clients without sacrificing the training time/speed required for an effective and accurate global model? This research hopes to comprehensively analyze FL's potential within the eye healthcare space by answering these research questions, allowing for security/privacy and effective collaboration. While the architecture developed in this study has potential application to any healthcare domain (where data privacy is of utmost concern), it specifically focused on applying FL for ocular health.
## 5 AM Processes
### Selective Laser Melting (SLM)  
Selective Laser Melting (SLM), introduced in 1995, emerged as an alternative technology to SLA [36]. It is categorized as a powder-based AM technique. As shown in Figure 6c, the SLM system includes an XY scanning mirror, a laser source, a powder supply piston, a powder build piston, and a re-coater. A thin layer of powder is applied to the building platform by the re-coater. The powder is then scanned by a laser, which follows a specific trajectory that corresponds to the 3D design's cross-section. When the laser moves, it melts the powder, causing it to fuse. Once a layer is complete, the powder build piston lowers slightly, and a fresh layer of powder is added. The part is constructed by fusing each new layer with the one before it. The completed 3D part is then revealed once the unused powder is removed. In order to minimize oxidation, the printing area is frequently flushed with argon or nitrogen gas during the SLM process. Several kinds of metals can be 3D printed with the SLM technique, including titanium, steel, aluminum, and nickel alloys [37].  
SLM is ideal for complicated parts because it provides high-quality printing and precise resolution. A significant advantage is that the powder acts as a support structure, eliminating challenges in removing additional support material. However, SLM has its drawbacks, including high costs and slower processing times compared to other AM techniques [16].
# Ultrasound-Assisted Extraction of \(\beta\)-Asarone from Sweet Flag (_Acorus calamus_) [PERSON]
## 2 Materials and Methods
### Characterization  
The NMR spectra data were obtained from a 400 MHz Bruker AVANCE III spectrometer with chemical shifts (8) expressed in ppm and TMS as an internal standard in CDCl\({}_{3}\). The coupling constants (_J_) are reported in Hz. The UV measurement was carried out using ethanol solvent with UV-Vis spectrophotometer ([PERSON], UV-1800, Shimadzu Corporation, Kyoto, Japan). The IR measurement was carried out on the Perkin-Elmer RX1 FT-IR (Perkin Elmer, Waltham, MA, USA) spectrophotometer using the thin film method with NaCl cell. HRMS data were obtained from an Agilent 6530 Q-TOF (Agilent Technologies, Santa Clara, CA, USA) mass-spectrometer coupled with an Agilent 1200 series Rapid Resolution LC system (Agilent Technologies, Santa Clara, CA, USA). All characterization was performed with the extracted \(\beta\)-asarone.  
The physical structures of the rhizomes before and after being treated with both ultrasound-assisted and conventional extraction were observed under a field emission scanning electron microscope (FESEM, Hitachi SU8220, Hitachi High-Tech Corporation, Tokyo, Japan). Samples were uncoated and observed under 1 kV.
# Trigger-Based Dexterous Operation with Multimodal Sensors for Soft Robotic Hand
## 4 Experiment and Results
### TDO Validation  
The validation experiment of TDO is shown in Figure 7, with three kinds of action being operated. The grasp action is used to grasp the plastic frame when it touches the palm and the palm touch force is triggered. The pinch action is used to grasp the roller ball pen and screwdriver when the touch force of the index fingertip is triggered. The handshake action is executed when the touch force of multiple fingertips is triggered. The experimental results show that the actions are operated exactly.  
Figure 6: The sensor character of (**a**) CPCS and (**b**) RPCS.  
The results of trigger state detection are shown in Figure 8.  
Figure 8: The trigger state detection of TDO. (**a**) Palm touch force trigger detection: plastic frame. (**b**) Index fingertip touch force trigger detection: roller ball pen. (**c**) Index fingertip touch force trigger detection: screwdriver. (**d**) Multiple fingertip touch force trigger detection: handshake.  
Figure 7: The validation experiment of TDO.  
As shown in Figure 8a, the palm touch force trigger is detected when the plastic frame touches the lowest row of the RPCS, and when the voltage values of the RPCS cross the threshold values (TV) at the trigger point (TP), the trigger occurs. The RPCS includes nine of the same sensors, and the palm touch force trigger detection was judged only by the signals from the three bottom sensors, as displayed in Figure 8a. As shown in Figure 8b, the index fingertip touch force trigger is detected when the roller ball pen touches the index CPCS; when the voltage value of the index CPCS crosses the threshold value 0.57 V, the trigger occurs. As shown in Figure 8c, as the screwdriver touches the index CPCS, the voltage value of the index CPCS crosses the threshold value, and the trigger occurs. As shown in Figure 8d, the multiple fingertip touch force trigger is detected. The corresponding threshold values are index fingertip with 0.57 V, middle fingertip with 0.53 V, ring fingertip with 0.58 V, and little fingertip with 0.55 V. When a human hand touches the index, middle, ring, and little fingertips, the corresponding voltage values cross the threshold values, and the SRH executes a handshake with the human hand. The subfigures in Figure 8b-d show the voltage transformation of different fingers.