esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Laboratory medicine plays a fundamental and decisive role in every physician's decision-making process by providing essential test results that guide diagnosis and treatment [1, 2, 3]. However, laboratory reports often present raw numerical data with little to no contextual interpretation, leaving clinicians with the task of interpreting these results [4]. Moreover, patients frequently receive their laboratory results with limited explanations, beyond indicating whether values are within or outside the reference range.  
Laboratory reporting comments are a crucial communication tool between the laboratory, the report holder, and the requesting physician. These comments, formulated in various ways, are generally aimed at clarifying interpretative aspects and suggesting possible diagnostic implications. In most of these applications, the reporting comment represents a kind of diagnostic complement necessary to increase the meaning and clinical use of a single result or, more frequently, of multiple results that are linked by pathophysiological interrelationships.  
Without adequate guidance, patients frequently turn to online resources, such as search engines or AI-based tools like ChatGPT v.3.5, to interpret their results [5; 6]. This often creates more confusion and leads to incorrect information [7]. Many recent studies have integrated clinical data into post-analytical interpretations [8; 9], leading to inaccurate conclusions (or hallucinations) due to the complexity of clinical information [10; 11]. In contrast, our approach focuses on interpreting laboratory test results in isolation, ensuring that the conversational chatbot remains focused on explaining the data presented in the report without attempting to diagnose or evaluate specific clinical conditions.  
This study aims to bridge the gap between patients and primary care physicians by offering AI-guided clear explanations of laboratory test results [12; 13], improving patient understanding without straying into diagnostic territory [14; 15]. We have developed, trained, and validated a virtual conversational chatbot capable of interpreting a wide range of blood chemistry parameters, making information more accessible and meaningful for patients while avoiding the common pitfalls associated with integrating complex clinical data.  
The decision to focus on non-diagnostic interpretation reflects both regulatory requirements and clinical best practices. Diagnostic decisions require integration of laboratory data with clinical history, physical examination findings, and other diagnostic modalities--elements that fall outside the scope of laboratory reporting systems. Our approach represents a deliberate focus on enhancing the interpretative aspect of laboratory medicine while preserving the essential role of clinicians in diagnostic synthesis.
###### Abstract  
Recent convolutional neural network (CNN) models in image processing can be used as feature-extraction methods to achieve high accuracy as well as automatic processing in bearing fault diagnosis. The combination of deep learning methods with appropriate signal representation techniques has proven its efficiency compared with traditional algorithms. Vital electrical machines require a strict monitoring system, and the accuracy of these machines' monitoring systems takes precedence over any other factors. In this paper, we propose a new method for diagnosing bearing faults under variable shaft speeds using acoustic emission (AE) signals. Our proposed method predicts not only bearing fault types but also the degradation level of bearings. In the proposed technique, AE signals acquired from bearings are represented by spectrograms to obtain as much information as possible in the time-frequency domain. Feature extraction and classification processes are performed by deep learning using EfficientNet and a stochastic line-search optimizer. According to our various experiments, the proposed method can provide high accuracy and robustness under noisy environments compared with existing AE-based bearing fault diagnosis methods.  
fault diagnosis; bearing fault; machine health monitoring; acoustic emission signals; convolutional neural network +
Footnote †: journal: Applied Sciences
# Communication Architecture for Grid Integration of Cyber Physical Wind Energy Systems
## 4 Simulation Results
### Substation Automation Results  
The communication network for the offshore WF substation is shown in Figure 3. The architecture is based on IEC 61850 with three levels: a process level, bay level, and station level [33]. We assumed that all MU IEDs and CB IEDs were sending metering values (76,800 bytes/s) and the status of the breaker (16 bytes/s) to the central P&C unit at the station level on the offshore platform. Three scenarios were configured with different link capacities of 10, 100 and 1000 Mbps. To validate the results, we compared the generated amount of traffic from different IEDs with the received traffic from the server. The server FTP traffic received was about 416 bytes/s (26 CB-IEDs \(\times\) 16 bytes/s) and 1,920,000 (25 MU-IEDs \(\times\) 76,800 bytes/s) for the CB-IEDs and MU-IEDs, respectively. Figure 16 shows the traffic received, which agrees with our calculations.  
Figure 17 shows the average ETE delay for all protection and control information considering link capacities of 100 Mbps and 1 Gbps. Compared with the timing requirement of the IEEE 1646 standard, the link capacity of 100 Mbps cannot satisfy the delay requirement owing to a high delay of about 8.5 ms. The link capacity of 1 Gbps can satisfy the protection requirement with a delay less than 1 ms, as shown in Figure 17.  
Figure 16: Traffic received at the measurement central P&C unit (channel capacity 100 Mbps, 1 Gbps).  
(a) CB-IEDs (b) MU-IEDs.  
Figure 17: Average ETE delay for P&C information in the WF (channel capacity 100 Mbps, 1 Gbps).  
It was observed that using 10 Mbps for channel capacity was not sufficient, as the amount of generated traffic was much higher than the link capacity. Figures 18 and 19 show the worst case scenario in case of 10 Mbps where data loss and higher delays occur. Table 12 shows the ETE delay of the MU-IEDs and CB-IEDs for the feeders (F1, F2, and F3), bus, and transformer with link bandwidth of 100 Mbps and 1 Gbps. Figure 20 shows the ETE delay of the MU-IED and CB-IED at the collector bus.
## 2 Control Strategies
### Linear Quadratic Lateral Tracking Control
#### 2.1.1 Controller Structure  
The controller architecture, shown in Figure 1, comprises a reference model, an LQR controller, and a brake pressure split logic. According to the RT conditions, the control quantities have been discretized with a sample time of 0.001 s. The reference model has the task of providing the desired values of the side-slip angle and yaw rate. It receives as inputs the driver demands: steering wheel angle and desired vehicle longitudinal speed. Modeling the car through the formulations shown in **Target Vehicle Kinematics** section, the controller allows one to track the ideal vehicle behavior, leading the side-slip angle and yaw rate errors to decrease. This ensures improved lateral stability performances. To ensure that the vehicle achieves this condition, a logic that allocates braking pressures to each wheel's actuators is implemented.
# A Multi-Scale Feature Extraction Algorithm for Chinese Herbal Medicine Image Classification
## 5 Experiments and Results Analysis  
### Evaluation Metrics and Experimental Setup  
Following previous work, we chose accuracy, precision, recall, and F1 score as evaluation metrics. These metrics are widely used in classification tasks. Accuracy represents the proportion of correctly predicted samples, reflecting overall classification capability; Precision measures the proportion of actual positives among predicted positives, indicating prediction exactness; Recall calculates the proportion of actual positives correctly identified, demonstrating model coverage; F1-Score is the harmonic mean of Precision and Recall, providing a balanced evaluation for imbalanced datasets. The formulas are defined as:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{10}\]  
\[Precision=\frac{TP}{TP+FP} \tag{11}\]  
\[Recall=\frac{TP}{TP+FN} \tag{12}\]  
\[F1\_score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{13}\]  
where \(TP\) denotes True Positives, \(TN\) denotes True Negatives, \(FP\) denotes False Positives, and \(FN\) denotes False Negatives.  
We divided the dataset into training, validation, and test sets in a ratio of 7:2:1. The training set contains 3140 images, the validation set contains 897 images, and the test set contains 448 images, ensuring a balanced sample distribution across each category.  
To enhance the model's generalization ability, we performed data augmentation on the dataset. First, each image was rotated by 90, 180, and 270 degrees, expanding the sample size to four times the original. Then, the training set images were uniformly resized to 256 \(\times\) 256, followed by random horizontal or vertical flipping, and finally randomly cropped to 224 \(\times\) 224. The validation and test set images were directly randomly cropped to 224 \(\times\) 224.  
To avoid certain features having an excessive impact on model training and to speed up the training process, we perform normalization on the input image tensor. Specifically, for each channel of the image, we subtract the mean and divide it by the standard deviation, ensuring that the mean of each channel is 0 and the variance is 1. The formula is as follows:  
\[p_{n}=\frac{p_{0}-\mu}{\sigma} \tag{14}\]where \(p_{0}\) represents the pixel value in the original image, \(p_{n}\) represents the normalized result, \(\mu\) denotes the mean of the pixel values in the dataset, and \(\sigma\) denotes the standard deviation of the pixel values in the dataset.  
The main training parameter settings are as follows: the number of training epochs is 200, the batch size per epoch is 32, and the optimizer used is AdamW.
# Collaborative Web Service Discovery and Recommendation Based on Social Link
## 1 Introduction  
Web service can realize interoperable interactions between different machines via standard interfaces and communication protocols without the aid of additional third-party software or hardware. As an important innovation in service computing, more and more Web services are developed and published to the Internet. Therefore, service discovery is becoming a critical problem in service application. According to a survey [1], although there are a huge number of services advertised on the Web, about 75% of them have not been used, and only 16% of these services have been discovered or invoked. The main reasons are: most existing discovery methods only consider services as isolated functional islands with no links to related services, and they usually deal with each user and each request in isolation instead of mining and utilizing the latent social relationship among them [2].  
To address these problems, in this paper, we propose a new method that differs from existing approaches in that: (1) we pay more attention to the potential social relations between users and services in the whole process of Web service discovery; (2) we redefine and formalize the social link through mining and defining typical link factors; and (3) we present a collaborative mechanism with high flexibility and satisfied performance. In this way, our proposal tackles the opportunity of exploiting social links to improve the performance of service discovery. Experimental results show that compared with similar methods, the proposed method has higher efficiency and precision.  
The rest of this paper is organized as follows. Section 2 discusses the application and significance of social information in service discovery. Section 3 details our definitions about link factors and the formalization of social link. A collaborative discovery and recommendation mechanism based on social link is described in Section 4 before conducting the experimental evaluations in Section 5. The conclusion is given in Section 6.
## 4 Results and Discussions
### Crash Analysis of Oromia Region
#### 4.3.3. Crashes in the Day of Week and Time of Day  
RTCs occur more frequently on Thursday and then on Sunday in a week and from 12:00 to 13:00, 18:00 to 19:00 and 09:00 to 10:00 in a day. We recommend that the Oromia region's traffic management, transportation authority and traffic police bureau intervene and monitor traffic flows during peak crash days and times when a higher number of crashes are likely to occur. The details are demonstrated in Figure 6. Thursday is more likely associated with a market day in the Oromia region, when a large number of people may travel from place to place and Sunday is an off-day, but most of the society and road users may prefer to consume alcohol, resulting in more traffic crashes. Crash rates were higher from 12:00 to 13:00 and 09:00 to 10:00 during the day and these are the times when higher traffic flows are observed (peak hours), so the risk of RTC could be increased. Another critical time revealed was 18:00 to 19:00. This was most often associated with overnight collisions when there would be insufficient visibility for vision.
## 6 Conclusions  
Accurately estimating the WSAW frequency is one of the main problems in cross-media communication. To overcome this problem, this paper presents an improved RELAX algorithm for WSAS detection, which modifies the traditional RELAX signal model to improve estimation accuracy and applicability. A critical process of the improved algorithm is to perform nonlinear filtering on the data in advance, compensating the filtered gain, thus reducing the influence of side lobe signals. The other critical process is to combine the GIP with the RELAX algorithm to process the non-stationary signals. Finally, the signals are divided into strength signals based on the micro-amplitude signal characteristics, with different signal intervals obtained to achieve normal decoding. The results demonstrate that the proposed method can accurately estimate the micro-amplitude frequency information even if the number of sampling points reduces or the frequency interval is small. Hence, the developed algorithm can improve the communication system's performance indicators.  
Conceptualization, J.L.; methodology, J.L.; formal analysis, J.L.; investigation, X.B.; resources, X.L.; data curation, X.B.; validation, J.L., X.B. and Q.G.; writing--original draft preparation, J.L.; writing--review and editing, J.L., X.B., Q.G., T.Z. and J.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors thank all colleagues who participated in the cross-media system design and the acquisition of measured data. The authors would like to express their gratitude to the anonymous reviewers and the editor for their constructive comments on the paper.
# Modelling and Assessment of a New Triple-Frequency IF1213 PPP with BDS/GPS
## 3 Results and Discussion
### BDS/GPS PPP Performance  
This section describes the BDS/GPS dual-system experiment with the same data sources as described in Section 3.1. The PPP models are the DF, TF-F, TF-C and TF-CF models. In the TF-CF model, the GPS is the first system, and the BDS is the second system. The other data-processing strategies are consistent with those given in Table 3.
###### Abstract  
The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled to be in orbit in 2019 onboard the GEO-KOMPSAT 2B satellite and will continuously monitor air quality over Asia. The GEMS will make measurements in the UV spectrum (300-500 nm) with 0.6 nm resolution. In this study, an algorithm is developed to retrieve aerosol optical properties from UV-visible measurements for the future satellite instrument and is tested using 3 years of existing OMI L1B data. This algorithm provides aerosol optical depth (AOD), single scattering albedo (SSA) and aerosol layer height (ALH) using an optimized estimation method. The retrieved AOD shows good correlation with Aerosol Robotic Network (AERONET) AOD with correlation coefficients of 0.83, 0.73 and 0.80 for heavy-absorbing fine (HAF) particles, dust and non-absorbing (NA) particles, respectively. However, regression tests indicate underestimation and overestimation of HAF and NA AOD, respectively. In comparison with AOD from the OMI/Aura Near-UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13 km \(\times\) 24 km V003 (OMAERUV) algorithm, the retrieved AOD has a correlation coefficient of 0.86 and linear regression equation, \(\text{AOD}_{\text{GEMS}}=1.18\text{AOD}_{\text{OMAERUV}}+0.09\). An uncertainty test based on a reference method, which estimates retrieval error by applying the algorithm to simulated radiance data, revealed that assumptions in the spectral dependency of aerosol absorptivity in the UV cause significant errors in aerosol property retrieval, particularly the SSA retrieval. Consequently, retrieved SSAs did not show good correlation with AERONET values. The ALH results were qualitatively compared with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products and were found to be well correlated for highly absorbing aerosols. The difference between the attenuated-backscatter-weighted height from CALIOP and retrieved ALH were mostly closed to zero when the retrieved AOD is higher than 0.8 and SSA is lower than 0.93. Although retrieval accuracy was not significantly improved, the simultaneous consistent retrieval of AOD, SSA and ALH alone demonstrates the value of this stand-alone algorithm, given their nature for error using other methods. The use of these properties as input parameters for the air mass factor calculation is expected to improve the retrieval of other trace gases over Asia.  
remote sensing; aerosol optical depth; single scattering albedo; aerosol layer height; optimal estimation 201810 162 (0.3390/rs10020162) 10.
## 3 Results and Discussion
### Clinical Sample Analysis  
The analytical performance of the fabricated electrochemical aptasensor was evaluated with respect to GA detection in clinical samples by using spiked normal human serum. Prior to performing analysis in spiked serum, the optimal dilution of serum was investigated. Serum was diluted from 1:1 to 1:1000 with PBS buffer (pH 7.4). Sigmoidal increasing current changes were observed with decreasing dilution from 1:1000 to undiluted serum (Figure S3). A 1:1000 dilution was chosen because there was little interference from other serum proteins. Different concentrations of GA protein were spiked into the diluted serum. The concentrations of spiked GA were 49.5 and 233 \(\upmu\)g mL\({}^{-1}\). The recovery rates were 98.7% and 92.2%, respectively (see Table 2), which were within the acceptable range. RSD values were 2.1% and 2.6%. The recover assay in complex samples renders the high selectivity of the aptasensor toward GA detection supporting the data provided in Section 3.4. The accuracy and selectivity of the aptasensor signified the utility and reliability of the aptasensor. Therefore, the developed aptasensor can be applied as a potential tool for GA detection in real clinical samples.  
For the practicality of the aptasensor for diagnostic purposes, GA concentrations in all serum samples were determined using our developed aptasensor. The GA concentrations in diabetic sera were significantly higher than that in non-diabetic sera: the values ranged from 8.9-37.5 mg mL\({}^{-1}\) and 2.1-11.4 mg mL\({}^{-1}\) in diabetic and non-diabetic serum, respectively (Table 3). The data were shown to be normally distributed using normal quantile plots and excess kurtosis calculations. The data were analyzed by using an independent two-sample t-test, resulting in a statistically significant difference between the two groups at a 95% confidence interval (\(p<0.001\)) (Figure 7). The normal GA concentrations reported by this study was comparable with reference values reported by the enzymatic method [40]. Moreover, the concentrations of GA detected in both diabetic and non-diabetic sera were significantly correlated with other glycemic markers such as  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**GA Concentration Spiked in Serum** & **Measured Concentration** & **Recovery (\%)** & **RSD (\%) (\(n=5\))** \\ (\(\upmu\)g mL\({}^{-1}\)) & **(\(\upmu\)g mL\({}^{-1}\))** & **Recovery (\%)** & **RSD (\%) (\(n=5\))** \\ \hline
49.50 & 48.90 \(\pm\) 0.01 & 98.70 & 2.1 \\ \hline
233.00 & 214.80 \(\pm\) 0.02 & 92.20 & 2.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Recovery assay of GA protein in human serum by the developed aptasensor.  
HbA1c, fructosamines and fasting blood sugar (Table S1). In this study, a relatively low sample volume (7 uL) was needed thus reducing the burden of sample collection for clinical use. These results imply that the GA detected by the aptasensor was trustworthy and the proposed electrochemical aptasensor is promising for the analytical determination of GA levels for glycemic monitoring.
# Hardware-in-the-Loop Test of a Prosthetic Foot
## 3 Experimental Setup and Parameters
### Modeling an Amputee Using the VPP Model
#### 3.1.3 Equations of Motion  
These modifications result in a new set of EoMs: While the dynamics of the left leg (index L) still contribute to the EoMs as in Equations (5)-(7), the contributions of the right leg (index R) change. In the VPP model, forces orthogonal to the leg axis solely result from the hip torque. In the HiL tests with the real prosthetic foot, however, there are forces acting at the CoP orthogonal to the leg axis. These forces are measured by the FTS, i.e., \(F_{\text{int},\perp}\
eq 0\,\text{N}\), which results in a hip torque \(F_{\text{int},\perp}l_{\text{R}}\). To satisfy the VPP concept, the GRFs need to be redirected to the VPP by the VPP controller. According to Equation (1), the total hip torque \(\tau_{\text{h},\text{R}}\) to redirect the forces along the leg axis \(F_{\text{int},\parallel}\) (corresponds to \(F_{\text{s,j}}\) in Equation (1)) into the VPP:  
\[\tau_{\text{h},\text{R}} =-F_{\text{int},\parallel}\tan(\gamma_{\text{VPP,R}})l_{\text{R}} \tag{10}\] \[=\tau_{\text{VPP,R}}-F_{\text{int},\perp}l_{\text{R}}. \tag{11}\]  
Equation (11) shows that the total hip torque comprises the contributions of the VPP controller (\(\tau_{\text{VPP,R}}\)) and the torque resulting from \(F_{\text{int},\perp}\). Solving Equation (11) for \(\tau_{\text{VPP,R}}\) and dividing by \(l_{\text{R}}\), the orthogonal forces applied at the foot point by the VPP controller \(F_{\text{r},\text{R}}=\frac{\tau_{\text{VPP,R}}}{l_{\text{R}}}\) are:  
\[F_{\text{r},\text{R}}=F_{\text{int},\perp}-F_{\text{int},\parallel}\tan( \gamma_{\text{VPP,R}}). \tag{12}\]  
Overall, the EoMs for the model of the amputee are written as:  
\[m_{\text{VPP}}\ddot{x} =GRF_{\text{L},\text{x}}+F_{\text{u},\text{x}}-F_{\text{int}, \perp,\text{x}}+F_{\text{r},\text{R},\text{x}} \tag{13}\] \[m_{\text{VPP}}\ddot{y} =GRF_{\text{L},\text{y}}-F_{\text{u},\text{y}}-F_{\text{int}, \perp,\text{y}}+F_{\text{r},\text{R},\text{y}}-m_{\text{VPP}}\dot{y}\] (14) \[-J_{\text{VPP}}\ddot{\phi}_{\text{VPP}} =r_{\text{VPP}}\big{(}\sin(\phi_{\text{VPP}})GRF_{\text{L},\text{ y}}-\cos(\phi_{\text{VPP}})GRF_{\text{L},\text{x}}\big{)}\] \[+r_{\text{h}}\big{(}\cos(\phi_{\text{VPP}})(F_{\text{u},\text{x} }-F_{\text{int},\perp,\text{x}}+F_{\text{r},\text{R},\text{x}})\big{)}\] (15) \[+r_{\text{h}}\Big{(}\sin(\phi_{\text{VPP}})(F_{\text{u},\text{y} }+F_{\text{int},\perp,\text{y}}-F_{\text{r},\text{R},\text{y}})\Big{)}+\tau_{ \text{h},\text{R}},\]  
In the principle of angular momentum, the first line in Equation (15) is the contribution of the left, intact leg and the second and third lines are the contributions of the right, amputated leg.