esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Free Radical Scavenging Activity upon the Addition of Five Peptides in Chi-Aroma Baijiu
#### 3.5.2 Changes of VOC Characteristics  
In order to identify the characteristic substances that allow peptides to exert a significant influence on the VOCs in Baijiu, we used partial least squares analysis (PLS-DA) to assess the influence of different peptides on Baijiu's VOCs [44], presented in Figure 6A,B. The results demonstrated clear distinctions among the Baijiu samples, indicating variations in volatile components resulting from different peptide additions. Baijiu samples containing peptides DF, PL, PV, and PA were positioned closer to each other, suggesting that these four peptides exert a similar effect on Baijiu's VOCs. In contrast, peptides GSH and GL were distinctly separated. Hierarchical cluster analysis (HCA) was also conducted. The HCA results were similar to those of the PLS-DA; all samples were separated into two groups (Figure 6C). The above results indicate that the Baijiu presented different volatile flavor compounds after supplementing peptides.  
Additionally, based on the analysis model, we assessed the importance of each VOC using VIP values [44], identifying 15 VOCs with VIP \(>\) 1. Spearman correlation analysis yielded correlation coefficients between the addition of different peptides and volatile compounds, selecting 19 VOCs with \(p\) < 0.05 (Supplementary Table S2), of which 14 compounds exhibited a positive correlation, while five exhibited a negative correlation. Three VOCs with positive correlations, VIP values \(>\) 1, and \(p\) < 0.05 were screened out: ethyl nonanoate, diethyl acetal, and octanal. These three substances were identified as the characteristic substances of the influence of peptides on volatile components. [PERSON] [45] used the criteria of VIP \(>\) 1 and \(p\) < 0.05 to identify the components that significantly contribute to distinguishing the three varieties of Hehong tea. [PERSON]'s [46] research revealed that 26 volatile compounds, which were analyzed using PLS-DA, had a substantial contribution to the flavor of marinated tofu. These chemicals were determined to have a statistical significance (\(p<\) 0.05) and VIP \(>\) 1.  
Figure 5: The changes of VOCs in Chi-aroma Baijiu after adding peptides. (**A**) Heat map of VOCs, (**B**) Concentration of VOCs. Different letters represent statistically different values at \(p<0.05\).
## 6 Conclusions
**Nomenclature**  
_Parameters_  
\(R\) & Resistance of Grid Interfaced Converter  
\(L\) & Inductance of Grid Interfaced Converter  
\(C\) & DC-Link Capacitor  
\(J\) & Virtual Inertia  
\(k_{pp}\delta k_{pq}\) & Proportional Coefficients of Control Components  
\(k_{ip}\delta k_{iq}\) & Integral Coefficients of Control Components  
\(\omega_{12}\) & Angular Frequencies of Low Pass Filter  
\(\alpha_{12}\) & The Control Factor of Active Power  
\(\beta_{12}\) & The Control Factor of Reactive Power  
_Abbreviations_  
_RESs_ & Renewable Energy Sources  
_DSC_ & Double Synchronous Controller  
PMSG & Permanent Magnet Synchronous Generator  
_VMPE_ & Virtual Mechanical Power Error  
_VI_ & Virtual Inertia  
_LPF_ & Low Pass Filter  
_PCC_ & Point of Common Coupling  
_Variables_  
\(i_{dq}\) & Interfaced Converter Currents  
\(v_{dq}\) & The Voltages of PCC  
\(v_{dc}\) & DC Link Voltage of Interfaced Converter  
\(u_{dq}\) & Switching Functions of Interfaced Converter  
\(i_{dc}\) & DC Link Current of Interfaced Converter  
\(P\) & Active Power of Interfaced Converter  
\(Q\) & Reactive Power of Interfaced Converter  
\(P_{c1}\) & Power of DC link current and d Component Voltage  
\(P_{c2}\) & Power of DC link current and q Component Voltage  
\(P_{pd}\) & Power due to d Component Voltage  
\(P_{pdq}\) & Power due to d and q Component Voltage  
\(P_{m}\) & Virtual Mechanical Power  
\(\omega\) & Angular Frequency  
\(\Delta P\) & Active Power Error  
\(\Delta Q\) & Reactive Power Error  
\(\Delta P_{m}\) & Virtual Mechanical Power Error  
\(\Delta\omega\) & Angular Frequency Error  
\(P*\) & The Reference Value of Active Power  
\(Q*\) & The Reference Value of Reactive Power  
\(P_{m}*\) & The Reference Value of VMP  
\(\omega*\) & The Reference Value of Angular Frequency
# AI Chatots for Mental Health: A Scoping Review of Effectiveness, Feasibility, and Applications
## 3 Results
### Supporting Emotional Well-Being in Specific Health Conditions  
The potential of AI chatbots to support individuals with specific health conditions, such as depression, Parkinson's disease, and migraines, has been explored in several studies. [PERSON] et al. (2019) focused on the role of an AI chatbot in enhancing completion rates for internet-based cognitive-behavioral therapy (iCBT) among workers with subthreshold depression. In this case, the AI chatbot program was not a standalone intervention as it was offered as an adjunct to other therapy, specifically to iCBT. The addition of a chatbot that sent personalized messages encouraged program adherence, resulting in significantly higher iCBT completion rates compared to the control group. However, both groups demonstrated similar improvements in depression and anxiety symptoms, suggesting that while the chatbot improved engagement, the intensive nature of the program may have limited its impact on clinical outcomes.  
In the study by [PERSON] et al. (2019), the impact of an AI chatbot on emotional well-being in Parkinson's disease patients was investigated. The unique aspect of this research was the exploration of facial expressions and speech patterns as indicators of emotional changes. The intervention group interacted daily with an AI chatbot and had weekly video visits with a neurologist, while the control group received only the video visits. The AI chatbot was used as a component of a broader telemedicine approach, complementing the weekly video conferencing sessions with neurologists. It was not used as a standalone therapy but as an additional tool to enhance patient monitoring and engagement between the weekly neurologist consultations. Although clinical rating scales did not show significant differences, the chatbot group exhibited increased smile parameters and reduced filler words in speech, suggesting improved facial expressivity and fluency, respectively. The correlation between smile features and cognitive and motor ratings, along with the accuracyof machine learning models in predicting these aspects, highlights the potential for remote symptom monitoring in [PERSON]'s disease.  
The BalanceUP app, developed by [PERSON] et al. [62], specifically targets mental health support for individuals suffering from migraines. This app utilizes a chat-based interface with predefined and free-text input options, guiding users through personalized psycho-cational content and behavioral tasks rooted in Cognitive-Behavioral Therapy for Migraine Management (MIMA). The BalanceUP chatbot program appears to have been designed and evaluated as a standalone intervention, rather than as a component or adjunct to other therapy. The app also includes various engagement strategies and tailors content based on user-specific needs. In a randomized controlled trial, the BalanceUP app significantly improved mental well-being, demonstrating its effectiveness as a digital intervention for individuals with migraines.
## 2 Architecture and Operational Analysis
### Maximum Queue Length  
The vehicle queue length, developed using the periodic offset characteristics, varies according to the offset variation. Due to the correlation between the difference in the upstream and downstream intersections' cycle lengths and the offset, changes in the queue length will impact the overall model trend. Therefore, controlling the variables can help establish fixed values and investigate their relationship with a single variable. Hence, the following were assumed: a high traffic volume condition for merging traffic, a transition section length of 800 m, and a range of values for the difference in cycle lengths between the upstream and downstream intersections between 10 s and 40 s, with an adjustment increment of 10 s. According to Equation (6), vehicle speed, stopping wave speed, and starting wave speed can be determined by varying the volume of merging traffic for through, left-turn, and right-turn movements at the upstream intersection. These values can be calculated based on the Greenshields relationships. The outcomes of the two transition types are shown in Figure 5.  
The changes in the difference in cycle lengths between upstream and downstream intersections for the two transition types will result in alterations in the periodicity of queue length variations and the range of numerical changes. For an L-S cycle transition, as the difference in cycle lengths increases, the numerical values of the queue length become more evenly distributed across various operational cycles. The peak value gradually decreases, indicating an overall declining trend. Conversely, for an S-L cycle transition, as the difference in cycle lengths increases, the numerical values of the queue length exhibit more pronounced variations across different operational cycles. The minimum value gradually increases, indicating an overall upward trend.
# Fast and High Precise Spatial Documentation of Traffic Accident Site Using Only Low-Cost RPAS
## 3 Results  
The whole experiment design (including flight path, reference object positions, image processing, etc.) was designed to produce the most accurate ground plan of arranged traffic accidents (in the local coordinate system). This model was supposed to be very similar to the traffic police's plan, which makes it possible to replace the traffic police's plan. Regardless of the need for a 2D model, which is usually produced by the traffic police, we created a 3D georeferenced model to be used and treated as a ground plan, with very precise measurements (Figure 3).  
A detailed view of the final model is depicted in Figure 4. Every detail (skids, cars parts, reference objects, debris, etc.) is very well visible, the same as in Figure 3.  
In addition, this model could be used as a 3D model with the possibility of measurement too (Figure 5). The parallel areas to the camera view axis do not have the same overall graphical quality as the perpendicular areas to the camera view axis, which can be observed on imprecise textures (vehicles' doors) in Figure 5. If this would be important for traffic accident documentation, a few oblique images should be required and added to image processing.  
Figure 3: Perpendicular view of the final 3D model, with an example of measurement.  
The graphic quality of the final model was assessed. Accuracy evaluation was determined from the data in Table 3. Regarding the object dimensions, the differences between the reality measurements and the model are 1% or less, except reference object no. 8, which reaches almost 2% in relative terms. Reference object no. 8 is round and lies in the corner of an arranged traffic accident. We assessed the area of reference objects (rectangular or round) and the measurements between points (reality vs. model).  
The final model meets the requirements for traffic accident documentation and enhances the accuracy and amount of graphic content. Moreover, using this method extremely shortens the time needed for documentation in situ, and the following processing into  
Figure 4: Detailed perpendicular view of the final model.  
Figure 5: Oblique view to final 3D model (license plates were blurred).  
the final plan in the office. The time necessary for pre-flight preparations, flight, and subsequent image processing was approx. 21 min.
# Optical-OFDM VLC System: Peak-to-Average Power Ratio Enhancement and Performance Evaluation
## 3 Assessment Criteria Employed to Judge the Effectiveness of MCM Approaches
### Bit Error Rate (BER)  
BER is an essential metric for evaluating the performance of a data transmission system. It gauges the occurrence rate of errors during data transmission, providing valuable insights into the probability of bit errors within a specific data stream. The exact definition of BER is the ratio of incorrect bits to total bits transmitted. This ratio is a fundamental indicator of the quality and reliability of a communication link [2]. A lower BER reflects greater accuracy and dependability in data transmission, underscoring the strength and reliability of the communication channel. The formula for calculating the BER is straightforward and can be expressed as follows:  
\[BER=\frac{Number\ of\ Error\ Bits}{Total\ Number\ of\ Transmitted\ Bits} \tag{32}\]  
While BER remains a critical parameter, communication transmission systems often incorporate additional metrics, including SNR and energy per bit to noise power spectral density ratio \(E_{b}/N_{O}\). SNR measures the power of the desired signal in relation to the background noise, while \(E_{b}/N_{O}\) represents the ratio of energy per bit to the noise power spectral density. These parameters are closely tied to the performance and quality of the transmitted signal and are commonly used alongside BER to perform a thorough evaluation of the system. It is important to acknowledge that each distinct modulation scheme possesses its unique error characteristic. This disparity arises from the varying performance of modulation types when exposed to noise. Specifically, higher modulation schemes (such as 64-QAM and others) capable of transmitting higher data rates tend to be less resilient in noisy environments. Conversely, lower modulation formats (like BPSK and QPSK) provide lower data rates but exhibit higher resistance to the effects of noise. The subsequent simulation results section outlines the BER performance of the previously mentioned systems.
## 3 Results
### Impact of Land Use and Land Cover Change and SWC Techniques on Soil Loss  
#### 3.4.1 Gross Soil Loss  
To analyze the spatiotemporal fluctuations in the yearly soil erosion rate during the timeframe spanning from 1980 to 2020, the factors of the RUSLE were multiplied for different dates (Figure 10). The most substantial rates of soil loss are situated in the northwestern, northwestern, and southwestern regions of the catchment. Those zones are characterized by steep slopes and higher rainfall erosivity.  
Figure 8: Erosion control practice maps for the period from 1980 to 2020.  
Figure 10: Gross soil loss maps for the period from 1980 to 2020.  
Figure 9: Sediment delivery ratio maps for the period from 1980 to 2020.  
The areas characterized by slight erosion severity increased from 46.6% of the watershed area in 1980 to 56.6% in 2000 and to 57.6% in 2020. Areas with moderate GSL decreased during the period from around 21 to around 14%. Areas with high to very high GSL showed a slight decrease from around 11 to 8%, whereas those with extreme severity remained rather stable at around 11% (Figure 11).  
Expectedly, the steepest areas exhibited the highest values of the GSL, while the gentle slopes showed the lowest values. The decrease in the surface area with GSL values above the tolerable soil loss rate (10 t/ha/year), from 32.7% in 1980 to 27.6% in 2020, was mainly observed in the SWC implementation areas. Overall, the mean annual GSL decreased from 18.05 t/ha/year in 1980 to 14.9 t/ha/year in 2000, slightly increasing again to 16 t/ha/year in 2020 (Table 2).  
The results suggest that soil erosion is likely to continue if there is inadequate management of land use and land cover changes, along with declining efficiency of facilities due to an increase in structural breaches and a lack of maintenance. To investigate the impact of spatiotemporal LULC changes on soil erosion in the study area, the P factors were held constant and set equal to 1. The GSL/P is the gross erosion occurring in the absence of any SWC techniques in the catchment area. The GSL/P rate was increased throughout the 1980-2020 period compared to the GSL. The SWC implementation reduced gross erosion by about 15% in 1990 and about 25% in the period 2000 and 2020.
# An Initial Load-Based Green Software Defined Network +
## 3 GreSDN: The Green SDN System  
The design of the green SDN system (GreSDN) is shown in Figure 1. In the control plane, three modules are designed for the green scheme. First is the topology management module. Different from the common topology management module, the topology management module in the green scheme manages two topologies of the network. One is the real topology of network which includes all the switches and links, called the physical topology. The other one consists of the links in working state and the switches connected to them, called the virtual topology. The routing algorithm calculates paths for the traffic demand based on the two topologies. Detection and Device management are two new modules added for the green scheme.  
The control process is divided into two parts, awaking and sleeping. The awaking process is marked with (a) at the left part in Figure 1. When new traffic flows arrive, the routing algorithm calculates paths for them based on the physical topology and virtual topology. Then the set of the links that need to be waken up can be acquired by comparing the calculated paths with the virtual topology. According to this link set, the device management module sends a wake command to the corresponding switches to wake up the corresponding links. After waking up the links, the switches return ACK messages to the control plane to confirm that the links have been awoken. Then, the control plane sends flowtables for the new communications demands to the switches.  
The sleeping process is marked with (b) at the right part in Figure 1. The detection module detects the statuses of all the links periodically. The set of idle links can be acquired according to the detection results. Then the device management module sends the sleeping command to the switches to turn these links into sleep mode, and the topology management module updates the virtual topology based on this set at the same time. After turning the links into sleep mode, the switches return ACK messages to the control plane.  
Obviously, in GreSDN, the sleeping links may be awoken to serve the traffic demands during each detection period of the network. Thus, for energy saving the additional energy consumption of the transmission paths added to the current virtual topology should be as small as possible in each detection period. In the next section, the problem of finding the optimal paths will be formulated.
## 2 Materials and Methods
### Western Blotting  
HaCaT cells were homogenized using RIPA lysis buffer (containing 1% protease and phosphatase inhibitors) (WSE-7420, Atto, Tokyo, Japan) on ice. The obtained cell lysates were centrifuged at 8000\(\times\)\(g\) for 15 min and the supernatants were collected. The amounts of proteins were calculated using a Bradford assay reagent (#5000006, BioRad, Hercules, CA, USA). Next, 25-50 \(\upmu\)g of proteins were separated using 10% SDS-PAGE electrophoresis and then transferred onto PVDF membranes (Merck Millipore, Carrigtwohill, Ireland). All membranes were then incubated with a blocking buffer containing 5% skimmed milk in 1X PBS for 2 h at room temperature. After washing, all membranes were incubated with the primary antibodies at 4 \({}^{\circ}\)C overnight, followed by the horseradish peroxidase-conjugated anti-IgG secondary antibody. The primary antibodies for phosphorylated Akt (p-Akt, #9271), Akt (#9272), phosphorylated extracellular signal-regulated kinase (p-ERK, #4370), and ERK (#9102) were procured from Cell Signaling (Danvers, MA, USA). The antibodies against keratin 16 (K16, ab53117) and K17 (ab53707) were obtained from Abcam (Cambridge, MA, USA). The antibody for \(\upbeta\)-actin (A1978) was purchased from Sigma-Aldrich (St. Louis, MO, USA). HRP-conjugated secondary antibodies, including goat anti-rabbit IgG (#31460) and goat anti-mouse IgG (#32430), were purchased from Invitrogen Inc. (Carlsbad, CA, USA). The blots were detected using ECL solution (#1705061, BioRad, Hercules, CA, USA). Quantification of protein band intensities was conducted using GelPro V3.1 software (Media Cybernetics, Rockville, MD, USA).
# Explainable Machine Learning Model for Chronic Kidney Disease Prediction
## 3 Methodology
### Preprocessing
#### 3.2.2 Data Imputation  
Handling missing values is a critically important step, as missing values can introduce biases, make data analysis more difficult, and reduce the efficiency of the model [41]. Simply deleting missing data can result in biases and impair the generalizability of the results. Therefore, imputation of missing values is crucial [42]. Using imputation, which involves replacing missing data with suitable values, much of the dataset's information can be preserved [43]. Traditional techniques like mean or median imputation work well when there is a small amount of missing data [44]. However, in our case, we have a significant percentage of missing data, as shown in Figure 3. Thus, we use KNN imputation for numerical values and mode imputation for categorical values.  
Figure 3: The missing values.  
Figure 2: Data preprocessing steps, including data encoding, imputation, and scaling.  
KNN imputation fills in missing values by leveraging the values of the nearest neighbors, identified using a similarity measure such as euclidean distance. This multivariate technique captures the inherent relationships in the data, making it especially effective for numerical variables [45; 46].  
Algorithm 1 outlines the steps for imputing missing data. First, the dataset \(X\) is separated into numerical and categorical features. For each numerical feature \(j\), if a sample \(i\) has a missing value, the euclidean distance between sample \(i\) and all other samples is computed, identifying the 5 nearest neighbors (5-NN). The missing value is then imputed by averaging the values of these neighbors. For categorical features, missing values are filled with the mode. Finally, the imputed numerical and categorical features are combined to produce the complete dataset \(\hat{X}\).  
```
0: Dataset \(X\)
0: Imputed dataset \(\hat{X}\)
1: Separate numerical and categorical features
2:for each numerical features \(j\)do
3:for each sample \(i\) with missing value \(X_{i,j}\)do
4: Compute \(d_{im}=\sqrt{\sum_{l}(X_{i,l}-X_{m,l})^{2}}\) and identify 5-NN
5: Impute \(X_{i,j}\) with \(\hat{X}_{i,j}=\frac{1}{5}\sum_{m\in 5\text{-NN}}X_{m,j}\)
6:endfor
7:endfor
8:for each categorical features \(j\)do
9: Find \(\text{Mode}(X_{\cdot,j})=\text{most frequent}(X_{\cdot,j})\) and fill missing values \(\hat{X}_{i,j}=\text{Mode}(X_{\cdot,j})\)
10:endfor
11: Combine imputed numerical and categorical features
12: Return the imputed dataset \(\hat{X}\)
```  
**Algorithm 1** The pseudocode of missing values imputation