esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy
## 3 Quantification of Performance Indexes  
### Cost Function for Longitudinal Car-Following Performance  
It is accepted that the longitudinal car-following performance is usually evaluated via tracking errors: \(\Delta d\) and \(\Delta v\). However, the strong convergence of tracking errors will result in a frequent acceleration or deceleration, which might weaken the lateral stability and also reduce the longitudinal ride comfort or fuel economy, while the poor convergence will also cause a great amount of problemssuch as frequent cut-in, unsatisfied driver response, or even rear-end collision. Thus, the longitudinal performance indexes are designed to achieve:  
* During the steady car-following scenario, the tracking errors should converge to tiny values;
* If the preceding vehicle accelerates, the tracking errors should be in a driver-permissible tracking error range to reduce frequent cut-in or driver intervention, and while it decelerates, rear-end collision must be avoided;
* As the fuel consumption increases with the absolute increase of the vehicle acceleration [7], the fuel economy can be improved indirectly via penalizing acceleration;
* To promote longitudinal ride comfort, the longitudinal jerk should be decreased as much as possible [5];
* The level of longitudinal acceleration should be restrained to some extent to guarantee sufficient lateral stability [12].  
Accordingly, employing a 2-norm for tracking errors, actual acceleration, jerk and desired acceleration, the longitudinal cost function is built as  
\[J_{longi}=w_{\Delta d}\Delta d^{2}+w_{\Delta\rho}\Delta v^{2}+w_{a_{x}}a_{x}^{2 }+w_{j}{}^{2}+w_{a_{x,des}}a_{x,des}^{2} \tag{19}\]  
where \(w_{\Delta d}\), \(w_{\Delta\sigma}\), \(w_{a_{x}}\), \(w_{j}\) and \(w_{a_{x,des}}\) are the corresponding weight coefficients.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 4 Localization Techniques
### Device-Based Localization
#### 4.1.2 Wi-Fi-Based Localization  
By detecting the Wi-Fi network, the device gets located. The device's location linked with the network is estimated by using identified positions of specific Wi-Fi networks. The framework of the Wi-Fi-based localization technique is shown in Figure 9. In this type of localization, accuracy depends upon the Wi-Fi access point capacity.  
Wi-Fi fingerprint-based localization [45] uses the strongest access point for the maximum received signal strength (RSS) value. In the next phase, duplication of the access  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Measurement Technique** & **Type of Algorithm** & **Accuracy** & **Cost** &
\begin{tabular}{c} **Energy** \\ **Efficiency** \\ \end{tabular} & **Scalability** \\ \hline \multirow{4}{*}{Range-based} & AoA & Medium & High & High & Complex \\  & ToA & High & High & High & Complex \\  & TDoA & High & High & High & Complex \\  & RSSI & High & Low & High & Complex \\ \hline \multirow{4}{*}{Range-Free} & Hop Count & Low & Low & High & Simple \\  & Centroid & Low & Low & Low & Simple \\  & APIT & Medium & Low & High & Simple \\  & Analytical Geometry & Low & Low & High & Simple \\  & Mobile Anchor Node & High & High & Low & Simple \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of Measurement Techniques.  
Figure 9: Wi-Fi based localization.  
points of the fingerprints and the distance generates accurate approximation irrespective of the building structure and data dispersal of the access point. The framework of the proposed approach is as shown in Figure 10.  
Triangulation ensures that broadcast constraints and AP localizations will be accessible in the initial stage itself [46]. Though complete data is offered, this real-time localization is not often suitable for environmental fluctuations. By merging RSSI received from the Wi-Fi and the inertial sensor dimensions of the smartphone to determine the location [47], it attains insistent location accuracy compared to the other methods.
## 3 IEMG Objective Function and Solution Strategy
### Synchronous ADMM Distributed Solution  
Firstly, establish the optimization problem for IEMG\(i\) and microgrid \(j\). The Lagrangian function corresponding to its objective function is denoted by \(L_{i}\big{(}x_{i},X_{ki}^{t},\lambda_{i}^{t}\big{)}\) and \(L_{i}\big{(}x_{j},X_{kj}^{t},\lambda_{j}^{t}\big{)}\), and through appropriate transformations, introduce the dual variable \(X_{i}\) for \(X_{ki}\), and the dual variable \(X_{j}\) for \(X_{kj}\), then convert it to the following:  
\[L_{i}\big{(}x_{i},X_{ki}^{t},\lambda_{i}^{t}\big{)} =f_{i}(x_{i})+\frac{\rho}{2}\big{\|}X_{i}-X_{ki}^{t}+\lambda_{i}^{t }\big{\|}_{2}^{2} \tag{36}\] \[L_{j}\Big{(}x_{j},X_{kj}^{t},\lambda_{j}^{t}\Big{)} =f_{j}\big{(}x_{j}\big{)}+\frac{\rho}{2}\big{\|}X_{j}-X_{kj}^{t}+ \lambda_{j}^{t}\big{\|}_{2}^{2} \tag{37}\]  
Let IEMG\(i\) be coupled with IEMG\(j\), \(k\), IEMG\(j\) be coupled with IEMG\(i\) and \(k\), \(X_{ki}^{t}\) and \(X_{kj}^{t}\) be, respectively, IEMG\(i\) and the fixed reference values of the \(j\) and \(t+1\) iterations; then, the mean value of the mutually connected interaction coupling variables IEMG\(i\), \(j\) can be obtained after the \(t\) iteration, as shown in the following equation:  
\[X_{ki}^{t}=X_{kj}^{t}=\frac{\left(X_{i}^{t}+X_{j}^{t}\right)}{2} \tag{38}\]  
Once the optimization problems for each IEMG are defined, a distributed solution is implemented. The specific steps are as follows:  
1. In the \(t+1-\) th iteration, IEMG\(i\) and IEMG\(j\) use parallel computing methods to calculate functions \(L_{i}\big{(}x_{i},X_{Ki}^{t},\lambda_{i}^{t}\big{)}\) and \(L_{i}\big{(}x_{j},X_{kj}^{t},\lambda_{j}^{t}\big{)}\), finding the minimum decision variables contained within this area. Simultaneously, they can obtain the interaction coupling variables \(X_{i}^{t+1}\) and \(X_{j}^{t+1}\) for each IEMG system. \[x_{i}^{t+1} =\text{argmin}L_{i}\big{(}x_{i},X_{ki}^{t},\lambda_{i}^{t}\big{)}\] (39) \[x_{j}^{t+1} =\text{argmin}L_{j}\Big{(}x_{j},X_{kj}^{t},\lambda_{j}^{t}\Big{)}\] (40)2. Determined by the coupling relationship between IEMG\(i\) and IEMG\(j\), \(X_{Ki}^{t}\) and \(X_{Kj}^{t}\) are concluded and implemented as parameters in the reference values for the subsequent iteration. 3. Update the dual variables within the IEMG: \[\lambda_{i}^{t+1} =\lambda_{i}^{t}+\left(X_{i}^{t+1}-X_{Ki}^{t+1}\right)\] (41) \[\lambda_{j}^{t+1} =\lambda_{j}^{t}+\left(X_{j}^{t+1}-X_{Kj}^{t+1}\right)\] (42) 4. When the following conditions are fulfilled, the ADMM algorithm terminates the iterations, indicating its convergence. If the conditions are not met, it continues with the assessment, looping the iteration. \[\left\|X_{i}^{t}+X_{j}^{t}\right\|_{2}^{2}\leq\delta\] (43)
# Tensegrity Applications to Architecture, Engineering and Robotics: A Review
## 3 Space Applications
### Rovers  
Another potential application of tensegrity structures is the fabrication of rovers for space exploration. One of the most paradigmatic examples is the Super Ball project [71]. However, there are many other examples that will be elaborated in the next section, focused on electromechanical engineering and robots. Most of those robots have locomotion properties that could be applied to rovers for space exploration.
## 4 Conclusions  
Overall, the lab-scale formulations showed improved results compared to the previous formulations studied. These formulations showed high resistance during the in vitro digestion, particularly the formulations with avocado oil. The recovery percentage of oleic acids increased 2 times for AOY and 1.3 times for ABY. The use of alginate in the bigel preparation did not affect the coconut digestion resistance and maintained the high percentages of lauric acid recovery. The use of avocado oil in yogurt production allowed an improvement in the HH and HPI ratios, which are related to health benefits for human health, particularly in the management of normal cholesterol levels. The only drawback of the proposed fortified yogurts was the apparent decrease in their nutritional value, specifically in the case of coconut oil yogurts due to the high amount of saturated fatty acids; however, this may be  
Figure 5: Modulation of inflammatory response in RAW 264.7 cells ((**A**) IL-6 secretion; (**B**) TNF-\(\alpha\) secretion). The left part of all graphs corresponds to the non-stimulated cell’s response, and the right is related to the anti-inflammatory effect. Different letters mean significant differences as determined by a one-way ANOVA test (\(p<0.05\)) between samples. Control—is the basal activity, Control + is the basal activity in LPS-stimulated cells. CT—control yogurt, COY—coconut oil yogurt; CBY—coconut bigel yogurt; AOY—avocado oil yogurt; ABY—avocado bigel yogurt.  
compensated by the positive impacts upon the target cellular metabolism. In fact, all yogurt samples were capable of exerting positive modulation over various obesity-related key processes, such as reduction of lipid accumulation both in hepatocytes and adipocytes and leptin secretion, and they also showed anti-inflammatory potential as they reduced IL-6 secretion in LPS-stimulated macrophages. Comparing all yogurts, functionalized yogurts stand out positively in relation to the increase in adiponectin secretion and in the reduction of IL-6 in LPS-stimulated cells. In conclusion, the developed functional yogurt prototypes despite showing high potential as future obesity-modulating foodstuffs still require some optimization to improve their nutritional value, mainly the improvement of fatty acids stability during digestion.  
Methodology: M.M., formal analysis and investigation: M.M., writing--original draft preparation: M.M.; writing--review and editing: L.M.R.-A., A.M.G. and M.P.; funding acquisition: M.P.; resources: M.P.; supervision: A.M.G. and M.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Funds from FCT--Fundacao para a Ciencia e Tecnologia through project UIDB/50016/2020 and through author [PERSON]. [PERSON]'s FCT doctoral grant (ref.: SFRH/BD/136701/2018).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Biosynthesis and Function of Glandular Trichome-Produced Compounds
### Defensive Proteins  
Apart from secondary metabolites, trichomes are also able to produce significant amounts of proteins with defensive functions, such as proteinase inhibitors (PIs) [134], polyphenol oxidases (PPOs) [135] and phylloplanins [37]. PIs can be either constitutively expressed (e.g., in flowers) or induced upon wounding or herbivory in leaves and their trichomes [53] and induced PIs slow down the growth of herbivores upon ingestion [136, 137] probably via inhibition of digestive proteinases in the herbivore gut. PPOs constitute a class of enzymes that utilize molecular oxygen for the oxidation of mono- and \(O\)-diphenylols to \(O\)-dihydroxyquinones [138]. Significant amounts of PPOs can accumulate in trichomes. For instance, in glandular trichomes of the wild potato, PPO can constitute up to 70% of the total protein content [139]. In the cultivated tomato, there is evidence that some isoforms of the PPO family are expressed in specific trichome types and not in others [140]. For example, PPO-A and C are expressed in type I and IV trichomes, as well as PPO-E and F while, in contrast, type VI trichomes express PPO-D, E and F, but not A and C. PPOs and their substrates are compartmentalized probably to prevent spontaneous reactions. In the head cells of tomato type VI trichomes, PPOs are stored in leucoplasts whereas their phenolic substrates are present in the vacuoles [140]. When the tissue is damaged, for instance by walking herbivores, the PPOs will mix with vacuolar content of the head cell and rapidly oxidize \(o\)-dihydroxyphenolics to the corresponding \(O\)-quinones [141]. These quinones, in turn, are highly reactive molecules that covalently bind to nucleophilic -NH2 and -SH groups of molecules such as amino acids and proteins, thereby reducing the availability of essential amino acids to the herbivores and/or the digestibility of proteins [141, 142], or perhaps interfering directly with enzymes. Apart from reducing the nutritive quality of leaves to herbivores [141], trichome-PPOs have also been implicated in resistance to plant pathogenic bacteria. Overexpression of a PPO from potato (_Solanum tuberosum_) in cultivated tomato yielded transgenic plants that were much more resistant to the bacterial pathogen _Pseudomonas syringae_[143, 144], and in dandelion (_Taraxacum officinal_) suppression of _PPO-2_ via silencing increased plant susceptibility to _P. syringae_[145]. Glandular trichomes may also actively secrete proteins, as shown in cultivated tobacco, where proteins can be deposited on the leaf surface through pores that are present in the cuticle of short glandular trichomes [37, 146] which are reminiscent of tomato type VII trichomes [147]. These secreted proteins, termed tobacco phylloplanins, inhibited spore germination and leaf infection by the oomycete pathogen _Peronospora tabacina_[37]. It has been suggested that these proteins, possibly in interaction with other secreted trichome-produced compounds, are broadly distributed over the leaf surface of tobacco plants, thereby providing constitutive resistance against diseases [148].
## 6 Hardware Architecture and Interfaces
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Specifications/Quantity** \\ \hline Output resolution & Side by side 2\(\times\) (2208 \(\times\) 1242) @ 15 fps \\  & 2\(\times\) (1920 \(\times\) 1080) @ 30 fps \\  & 2\(\times\) (1280 \(\times\) 720) @ fps \\  & 2\(\times\) (640 \(\times\) 480) @ 100 fps \\ \hline Output format & YUV 4:2:2 \\ \hline Field of view & Max. 110\({}^{\circ}\) (D) \\ \hline Baseline & 120 mm \\ \hline Interface & USB 3.0 \\ \hline Sensor type & 1/2.7\({}^{\prime\prime}\) \\ \hline Active array size & 4 M pixels per sensor \\ \hline Focal length & 2.8 mm (0.11\({}^{\prime\prime}\))—f/2.0 \\ \hline Shutter & Electronic synchronized rolling shutter \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Stereo camera specifications. Figure 6: Platform control unit and low-level controller. The electric vehicle employs a 3 kW BLDC motor with 3000 r/min that generates the needed torque. The rotational force is transferred to wheels via a gearbox and vehicle differential. The EPS communicates with the low-level controller through the CAN Bus. It is responsible for changing the steering angle. The brake motor driver also receives commands via the CAN Bus. The power supply box is equipped with several DC-to-DC converters that provide the proper voltage and current for each device. Figure 8 depicts the designed autonomous electric vehicle prototype.
## 4 Discussion
### Limitations of the Study
The CNN-based angle estimation proposed in this study will enable objective and quantitative angle determination, which is expected to lead to improved diagnostic accuracy. It is also expected to contribute to the reduction of patient radiation exposure by reducing the need for re-imaging. However, further verification and improvements are needed to introduce the system proposed in this study into actual clinical practice. In particular, evaluation using actual X-ray images and comparative studies with radiologists are important. In addition, issues such as the impact on the work of radiologists and costs associated with the introduction of the system should also be considered. The main limitations of this study include the aforementioned problems with the dataset and the imprecise basis for model decisions. We used a ResNet pre-trained on ImageNet, but we must also consider using a model pre-trained on medical images [36] to learn our dataset in this study. The other several limitations of this study should be acknowledged. First, it is unclear whether the regression and classification models created look at the vertebrae and determine their angles in the same manner as humans. Both pseudo-created and actual X-ray images contain various information besides vertebral bodies, so it is uncertain which parts the models used to estimate angles. While this study used pseudo-generated lumbar spine X-ray images for model creation, future studies should validate the accuracy of the model using real-world X-ray images to confirm its applicability in clinical settings. Additionally, the study focused only on lumbar oblique images and did not examine the applicability to other sites or imaging methods. Furthermore, the study concentrated solely on the accuracy of angle estimation, but in actual clinical practice, factors other than angle, such as the patient's body shape, pathological condition, and characteristics of the equipment used, may also influence the optimization of imaging. A more comprehensive study that takes these factors into account is needed.
# MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning
## 3 Proposed Method  
### Overview of the Proposed Model's Architecture  
Based on the above analysis of unknown bearing fault diagnosis problems and the research into the existing method, we propose a method of unknown type bearing fault diagnosis based on transfer learning: the Multi-scale Convolution-Convolution Reconstruction Network. The overall structure of the method is shown in Figure 4, including data acquisition and preprocessing, feature extraction, loss function, and diagnostic result display.  
Specifically, firstly, the data collected by the sensor are preprocessed, and then the feature extraction network is used to extract the fault features and reconstruct the feature map, and the training process is optimized based on the improved loss function. This section is the core of the model, comprising a multi-scale feature extraction module and a convolutional reconstruction module. In the multi-scale feature extraction module, based on the characteristics of bearing vibration signals under different operating conditions, we selected four convolutional modules with scales ranging from 1 to 7 to extract features at various scales. This enables our method to accurately distinguish different fault locations and fault diameters, facilitating the diagnosis of unknown fault types.  
In the convolutional reconstruction module, we reconstruct convolutions in both spatial and channel dimensions to reduce the extraction of redundant features and prevent features generated by changes in load and rotational speed from being mistakenly extracted.  
Figure 3: Open Set Domain Adaptation by Backpropagation. (**a**) source domain and target domain contain the same fault type; (**b**) target domain contains unknown fault class; (**c**) target domain contains an unknown fault size; (**d**) target domain contains both an unknown fault class and fault size.  
This enhances the model's generalization capability. Additionally, we have improved the loss function to increase the training efficiency under conditions such as class imbalance and optimized the adversarial target generation process during backpropagation. Finally, the collected bearing vibration data are detected to realize the fault identification.
# A Wideband Circularly Polarized Antenna with a Multiple-Circular-Sector Dielectric Resonator
###### Abstract  
This paper presents the design of a wideband circularly polarized antenna using a multiple-circular-sector dielectric resonator (DR). The DR is composed of twelve circular-sector DRs with identical central angles of 30\({}^{\circ}\) but with different radii. A genetic algorithm is utilized to optimize the radii of the twelve circular-sector DRs to realize wideband circular polarization. The proposed antenna is excited using an aperture-coupled feeding technique through a narrow rectangular slot etched onto the ground plane. An antenna prototype is experimentally verified. The measured \(-10\) dB reflection and 3 dB axial ratio (AR) bandwidths are 31.39% (1.88-2.58 GHz) and 19.30% (2.06-2.50 GHz), respectively, covering the operating bands of the following systems: UMTS-2100 (2.145 GHz), WiMAX (2.3 GHz), and Wi-Fi (2.445 GHz). A measured peak gain of 7.65 dBic at 2.225 GHz and gain variation of less than 2.70 dBic within the measured 3 dB AR bandwidth are achieved. In addition, the radiation patterns of the proposed antenna are presented and discussed.  
a 2016161849; doi:10.3390/s16111849
## 4 Experiments and Analysis of Results
### Comparison Experiments
#### 4.5.3 Comparison of Different Detector Models
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Performance** & **50 Epochs** & **100 Epochs** & **150 Epochs** & **200 Epochs** & **250 Epochs** & **300 Epochs** \\ \hline Loss & 0.731 & 0.634 & 0.563 & 0.502 & 0.448 & 1.087 \\ Precision/\% & 91.01 & 94.14 & 95.45 & 96.33 & 96.44 & 96.09 \\ Recall/\% & 80.24 & 85.51 & 87.07 & 87.41 & 88.44 & 89.69 \\ mAP50/\% & 86.17 & 90.76 & 92.09 & 92.62 & 93.28 & 93.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of YOLOv11n during the training process. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{6}{c}{**APS0\%**} & \multirow{2}{*}{\ \begin{tabular}{c} **mAP50** \\ **office** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Params** \\ **office** \\ \end{tabular}  } \\  & & **Mouse** & **Open** & **Circuit** & **Short** & **Spur** & **Spor** & **P/\%** & **R/\%** & **mAP50-95\%/\%** \\  & & **Bite** & **Circuit** & **Copper** & & **Copper** & & **P/\%** & **mAP50-95\%/\%** \\ \hline FR-CNN & 93.3 & 81.3 & 82.8 & 91.7 & 80.2 & 85.1 & 88.4 & 82.6 & 85.7 & 47.3 & 136.5 \\ SSD & 85.5 & 71.2 & 75.4 & 8  
To verify the performance of the PCES-YOLO model across various defect detection tasks, the confusion matrices for YOLOv11n and PCES-YOLO were examined, as illustrated in Figure 8. PCES-YOLO demonstrates higher correct detection rates and lower missed detection rates for all defect categories compared to YOLOv11n. However, the probability of misclassifying background regions as spur and spurious_copper shows a slight increase relative to YOLOv11n. This occurs because the SCDown lightweight downsampling module adopted in the neck network, while reducing parameter count, may compromise some spatial information, leading to boundary ambiguity between background features and the textures characteristic of spur and spurious_copper defects. In contrast, the probability of misdetection for other defect types decreases due to EFAN's enhanced preservation of detailed features. The experiment shows that the overall performance of the model is good, mainly manifested in the improvement in detection accuracy for all defect categories. To further assess detection performance, comparative testing was conducted on randomly selected defective images from six categories, with results in Figure 9. Both models  
Figure 8: Confusion matrix of YOLOv11n and PCES-YOLO. (**a**) YOLOv11n; (**b**) PCES-YOLO. Figure 7: Performance curves: before and after improvement. (**a**) loss curve; (**b**) precision curve; (**c**) recall curve; (**d**) mAP50 curve. avoid missed and false detections, but PCES-YOLO demonstrates higher detection accuracy across all defect types compared to the baseline YOLOv11n. In summary, PCES-YOLO outperforms mainstream detection algorithms, including Faster R-CNN, SSD, and YOLOv8n, in overall performance metrics. Figure 9: Comparison results of visualization experiments on self-enhanced dataset.