esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Discussion
### Results and Discussion  
The empirical results of this study confirm the utility and effectiveness of the TCM-ABC-LSTM model. The same dataset is used to evaluate the prediction performance of the designed models relative to traditional neural networks and various meta-heuristic trainers, including ARIMA, LSTM, and GA-LSTM. Many researchers have demonstrated the high accuracy of the compared models before and used these models to improve many problems. Moreover, these models are among the most widely used and effective techniques in the field of prediction [29; 46; 47; 48; 49]. The performance and precision of our proposed model are  
Figure 5: RMSE changes over time.  
evaluated in comparison to other prediction models, employing well-established metrics such as MAE, MSE, and RMSE. Among the diverse array of models compared in this study, our proposed model stands out, gaining the lowest mean absolute error, mean square error, and root mean square error, as shown in Tables 2 and 3, bold indicates the best result for each indicator. Slight differences in these evaluation indicators have a big influence on forecast accuracy because of data standardization. To provide a comprehensive insight into the performance gains facilitated by our proposed model, using the MAE prediction results as an example, Table 4 elucidates the marked enhancements in prediction accuracy following inverse normalization. From the table, it can be seen that our model has a significantly improved prediction accuracy, with improvements ranging from 26.79% to 82.16% compared to other models. This indicates that the data volatility has been well captured in the fitted model, thereby affirming its robustness and efficacy as a futures prediction model.  
Figures 6 and 7 illustrate how well the ARIMA, FNN, LSTM, Transformer, GA-LSTM, and TCM-ABC-LSTM models predict the prices of gold and natural gas, respectively. It is evident from the graph that the TCM-ABC-LSTM-predicted futures commodity prices closely match the actual data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ARIMA** & **FNN** & **LSTM** & **Transformer** & **GA-LSTM** \\ \hline MAE (decline) & \(57.18\%\) & \(81.24\%\) & \(56.59\%\) & \(82.16\%\) & \(26.79\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model MAE performance improvement.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **MAE** & **MSE** & **RMSE** \\ \hline ARIMA & \(0.01188\) & \(3.0\times 10^{-4}\) & \(0.0174\) \\ FNN & \(0.01902\) & \(4.0\times 10^{-4}\) & \(0.0216\) \\ LSTM & \(0.00906\) & \(1.2\times 10^{-4}\) & \(0.0110\) \\ Transformer & \(0.03253\) & \(4.2\times 10^{-3}\) & \(0.6531\) \\ GA-LSTM & \(0.00755\) & \(9.2\times 10^{-5}\) & \(0.0095\) \\ TCM-ABC-LSTM & \(\mathbf{0.00619}\) & \(\mathbf{6.7\times 10^{-5}}\) & \(\mathbf{0.0077}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of gold price prediction models.  
Figure 7: Prediction results of natural gas prices for various models.
###### Abstract  
Tool wear monitoring is crucial for optimizing cutting performance, reducing costs, and improving production efficiency. Existing tool wear prediction models usually design integrated models based on a convolutional neural network (CNN) and recurrent neural network (RNN) to extract spatial and temporal features separately. However, the topological structures between multi-sensor networks are ignored, and the ability to extract spatial features is limited. To overcome these limitations, a novel spatial-temporal adaptive graph convolutional network (STAGCN) is proposed to capture spatial-temporal dependencies with multi-sensor signals. First, a simple linear model is used to capture temporal patterns in individual time-series data. Second, a spatial-temporal layer composed of a bidirectional Mambo and an adaptive graph convolution is established to extract degradation features and reflect the dynamic degradation trend using an adaptive graph. Third, multi-scale triple linear attention (MTLA) is used to fuse the extracted multi-scale features across spatial, temporal, and channel dimensions, which can assign different weights adaptively to retain important information and weaken the influence of redundant features. Finally, the fused features are fed into a linear regression layer to estimate the tool wear. Experimental results conducted on the PHM2010 dataset demonstrate the effectiveness of the proposed STAGCN model, achieving a mean absolute error (MAE) of 3.40 \(\upmu\)m and a root mean square error (RMSE) of 4.32 \(\upmu\)m in the average results across three datasets.  
tool wear prediction; spatial-temporal graph neural network; attention mechanism; multi-sensor fusion Article
## 1 Introduction
A recently published review article analyzing feeding studies on AsA biosynthesis via the three alternative pathways, namely D-galacturonate, L-glucose, and Myo-inositol, showed over a 100% increase in AsA content due to the overexpression of their key encoding genes [14]. Apart from the mentioned pathways, the regeneration of AsA occurs through the Foyer-Halliwell-Asada cycle and contributes to AsA level maintenance in the cell [29]. Moreover, metabolic pathways, including de novo biosynthesis, degradation, and recycling of AsA, collectively contribute to AsA biosynthesis in plants [30]. In addition, AsA concentration is also controlled by transcription factors (TFs), particularly under stress conditions, e.g., the leucine zipper domain (bZIP) transcription factor (TF) triggers AsA content in kiwifruits exposed to cold stress [16]. In Arabidopsis, the binding of ABA INSENSITIVE 4 (ABI4) TF to the promoter region of the VITAMIN C DEFECTIVE 2 (VTC2) gene inhibits AsA biosynthesis and, as a result, negatively regulates salt tolerance [31]. In summary, the relative contribution of AsA biosynthesis pathways and their specific role needs further elucidation; the overall AsA content varies depending on the plant species [32; 33; 34; 35], cultivar, and genotype [36], tissue type [37], developmental and fruit ripening stage [38], and environmental conditions [24]. The current study identified genes associated with the four AsA biosynthesis pathways and analyzed their expression levels across tissues, developmental stages, and under stress conditions. Subsequently, we determined the AsA levels in the dried mass of selected tissues of _G. max_ during development and stress conditions to validate the relevance of these pathways in soybeans. The identified genes and pathways might be targeted to increase AsA synthesis, with consequences for both nutritional plant quality and stress resilience in soybean crops, and they may have implications for agricultural practices in maximizing nutrient content under fluctuating environmental conditions.
# Experimental Demonstration of Single-Channel EEG Signal Using 32 x 32 Pixel OLED Screen and Camera
## 5 Experiments and Results
### Extraction of EEG Signal from EEGtoolbox  
This section illustrates EEG signal extraction from EEGLAB Open Source Matlab Toolbox followed by pre-signal processing.  
Initially, the EEG signal was obtained from the EEGLAB Open Source Matlab Toolbox known as EEGlab. The EEGlab is a software designed by researchers in the US to extract the EEG signals using MATLAB and to use these EEG signals for laboratory research work [51]. The signal obtained from EEGlab using MATLAB is shown in Figure 4a. The EEG signal had an amplitude of millivolts and the number of samples taken in our experimental work were 1000 for transmission. The optical channel transmits the optical intensity, which cannot be negative, hence the EEG signal was normalized in the range of 0 to 1, as shown in Figure 4b. Thereafter, the signal obtained in digital form was up-sampled for optical transmission to aid in reconstruction of the original signal transmitted at the receiver (later stage). Then the useful bits obtained were transferred to the microcontroller using a USB cable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(N\) & Total number of data bits which are transmitted through a matrix of rows and columns on the transmitter OLED screen \\ \(D\) & Size of pixel \\ \(R_{1}\) & Rows per frame as per value of D \\ \(C_{1}\) & Columns per frame as per value of D \\ \(b_{n}\) & Transmitted bits before S/P \\ \(s^{(t)}\) & dimensional signal formed because of rows and columns of \\ \(y^{(t)}\) & OLED screen, transmitted after S/P in t (the time frame) \\ Additive white noise, noise realization & Received dimensional signal from camera \\ Signal represented at optical channel & \\ S/P and P/S & Serial to parallel conversion and parallel to serial conversion, \\ Fps & respectively \\ \multicolumn{2}{c}{Frames per second} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nomenclature.
# Applying Real-Time Travel Times to Estimate Fire Service Coverage Rate for High-Rise Buildings
## 2 Methods
### Fire Apparatus Requirement
Therefore, in this study, the fire service coverage rate was estimated based on the objective value of a 4 min travel time, which is equivalent to the objective value of a 5 min respond time. Due to the economic constraints, it is impossible to build many fire stations and equip with many fire apparatuses for achieving the goal that all the fire apparatuses required by high-rise buildings can reach the fire scene within 4 min. Water fire trucks and aerial fire trucks can play an important role in fire-fighting and the rescue of high-rise buildings, and they have been widely deployed in fire stations. Therefore, we proposed that the minimum fire apparatus requirement for high-rise buildings is \"at least one water fire truck and one aerial fire truck should arrive at the fire scene of high-rise building within 4 min (240 s)\". If the minimum fire apparatus requirement for a high-rise building can be met, it is considered that the high-rise building can be effectively covered by fire services.
## 2 Autonomous Driving in Adverse Weather Conditions
### Autonomous Driving's Dataset Analysis for Adverse Weather Challenges
#### 2.5.1 Datasets for Adverse Weather Conditions  
To evaluate and enhance the performance of autonomous driving systems under different weather conditions, researchers have developed several specialized datasets:  
_IDD-AW (Indian Driving Dataset-Adverse Weather)_: This dataset captures unstructured driving conditions across urban, rural, and hilly areas in India, focusing on scenarios likerain, fog, snow, and low light. It provides over 5000 pairs of high-resolution RGB and Near-Infrared (NIR) images, designed to test model robustness with a metric called Safe mean Intersection over Union (Safe-mIoU), which prioritizes safety in model predictions [51]. The dataset's emphasis on real-world diversity makes it ideal for evaluating autonomous vehicle performance in complex weather conditions. SID (Stereo Image Dataset): SID is tailored for depth estimation and 3D perception in challenging environments such as rain, snow, and nighttime scenarios. It includes over 178,000 stereo image pairs, offering a robust foundation for training models that require precise depth information under adverse conditions [52]. DAWN (Vehicle Detection in Adverse Weather Nature): The DAWN dataset emphasizes vehicle detection under diverse conditions like fog, rain, and snow. With around 1000 annotated images, it is instrumental in evaluating object detection models in environments where visibility is greatly reduced [53]. WEDGE (Weather-Enhanced Driving Environment): Using generative models, WEDGE provides synthetic weather conditions, such as heavy rain and dust storms. This dataset consists of 3360 images, allowing researchers to evaluate model robustness and generalization across synthetic and real-world data, offering valuable insights into performance variability under different weather conditions [54]. To evaluate and enhance autonomous driving performance in adverse weather, researchers have developed specialized datasets summarized in Table 2. IDD-AW captures diverse driving conditions in India, such as rain, fog, and snow, with over 5000 pairs of high-resolution images focusing on safety metrics [51]. SID, with 178,000 stereo image pairs, supports depth estimation in challenging scenarios like rain and nighttime [52]. DAWN provides 1000 images for vehicle detection in low-visibility conditions such as fog and snow [53]. WEDGE, with 3360 synthetic images, allows testing model robustness across simulated weather scenarios like heavy rain and dust storms [54]. These datasets collectively support the development of resilient autonomous driving models for complex weather conditions.
# Prediction of Flight Delays at _Beijing_ Capital International Airport Based on Ensemble Methods
## 3 Methodology
### Explanatory Variables
#### 3.2.5 Cruise Pressure  
Cruise refers to the flight status after the flight completes the take-off phase and enters the predetermined route. We defined the cruise pressure to reflect the air route conditions. When the density of aircraft on an air route is too high or the weather is bad, the air traffic control department limits the traffic of certain routes, causing delays for some flights. However, it is difficult to characterize all the factors that contribute to flight delays on an entire route, such as bad weather. Therefore, we used the average delay of flights on the same route and similar routes to estimate the forecast flight delays, which we refer to as cruise pressure:  
\[CP_{i}=\sum_{j\in F}\frac{sc_{j}\times tc_{j}}{\sum\limits_{j\in F}sc_{j} \times tc_{j}}\times d_{j} \tag{5}\]  
\[tc_{j}=\frac{1}{2}\frac{\frac{\tau^{a}-\tau^{a}-\tau}{\tau}}{\tau} \tag{6}\]  
where \(CP_{i}\) is the cruise pressure of the flight \(i\), and \(F\) is the set of flights that share the same or similar air routes with flight \(i\). Because we need to predict the flight \(i\) delays \(\tau\) hours in advance, the range of actual arrival or departure time of flight \(j\) (\(t_{j}^{a}\)) is [\(t_{i}^{s}-\tau-6h\), \(t_{i}^{s}-\tau\)], where \(t_{i}^{s}\) indicates the scheduled arrival or departure time of flight \(i\). Route conditions obtained too early are not of great reference value, considering that conditions are always  
Figure 1: The circle of hours of the day.  
changing. Thus, the upper limit of the interval is 6 h earlier than \(t_{i}^{s}-\tau\). \(sc_{j}\), \(tc_{j}\), and \(d_{j}\) are the similarity coefficient, time coefficient, and delay of flight \(j\), respectively.  
The similarity coefficient \(sc_{j}\) is set by the similarity degree of air routes between specific flights [20]. As the difference between the arrival or departure times of the two flights increases, the time coefficient \(tc_{j}\) reduces. The symbol \(\lfloor\cdot\rfloor\) is the floor function, calculating the largest integer value that is not greater than the given value.
## 2 Related Works
### Indirect Privacy Protection
The true recipient can confirm the transaction's validity using the corresponding keys, thereby obfuscating the association between the transacting parties. Many research efforts have focused on improving stealth address schemes. [PERSON] et al. [25] proposed a scheme that reduces the storage cost for users by only requiring them to store a key pair during initialization. In the original protocol, the recipient needs to continuously scan transaction data and compute the target address, which poses limitations in resource-constrained environments like the Internet of Things (IoT). To address this, [PERSON] et al. [26] introduced DKSAP-IoT, a double-key stealth address protocol based on blockchain, which achieves faster communication between parties and reduces the size of transactions. Although stealth address schemes have their merits, they are not without flaws. Each anonymous transaction requires the inclusion of a transaction's temporary public key, making the privacy of such transactions susceptible to identification, resulting in the loss of certain private information. Additionally, there are efficiency issues, such as the need for the recipient to repeatedly perform elliptic curve scalar multiplication operations. These challenges need to be addressed, especially in scenarios like data transactions that prioritize both privacy and service performance.
## 5 Analysis of Training Results
### Analysis of Visual Segmentation Results  
To visually assess the semantic segmentation performance of the models on leakage regions, this study compared the prediction results of SE-TransUNet with those of TransUNet, UNet, DeepLabV3 plus, SegNet, and FPN using real annotated masks as the ground truth, as shown in Figure 9. Deviations between the predicted outputs and ground truth masks were highlighted with red bounding boxes to illustrate the differences among models.  
The results show that in Case 1, where the lining surface contains shadowed indentations, SE-TransUNet, TransUNet, and UNet all demonstrated relatively accurate segmentation, whereas SegNet exhibited noticeable edge blurring. In Case 2, characterized by surface contamination such as white spots and color patches, only SE-TransUNet and TransUNet maintained effective segmentation, while the other models exhibited false negatives and false positives. In Case 5, where the water stain was faint and poorly contrasted with the background, TransUNet produced blurred edges, while SE-TransUNet achieved more precise segmentation. For Case 6, involving linear water leakage, SE-TransUNet produced segmentation results that better preserved fine details and more closely aligned with the ground truth than those of TransUNet. In Case 8, which featured partially occluded  
Figure 9: Segmentation results of various models for water leakage images.  
block-shaped leakage areas, both SE-TransUNet and TransUNet presented varying degrees of deviation but were able to correctly identify the occluded regions.  
Overall, across all cases, the segmentation outputs of SE-TransUNet were more closely aligned with the annotated masks compared to those of the other models evaluated.
_Article_  
**Smart Solutions for Sustainable Cities--The Re-Coding Experience for**  
**Harnessing the Potential of Urban Rooftops**  
**[PERSON] 1\(\Box\), [PERSON] 2,*\(\Box\), [PERSON] 3, [PERSON] 4 and [PERSON] 3**  
1 Future Urban Legacy Lab--FULL, Department of Energy, Politecnico di Torino, 10129 Turin, Italy; [EMAIL_ADDRESS]
2 Responsible Risk Resilience Centre--R3C, Department of Energy, Politecnico di Torino, 10129 Turin, Italy
3 Future Urban Legacy Lab--FULL, Department of Architecture and Design, Politecnico di Torino, 10129 Turin, Italy; [EMAIL_ADDRESS] (L.B.); [EMAIL_ADDRESS] (M.R.)
4 Future Urban Legacy Lab--FULL, Department of Management and Production Engineering, Politecnico di Torino, 10129 Turin, Italy; [EMAIL_ADDRESS]
* Correspondence: [EMAIL_ADDRESS]; Tel.: +39-011-090-4528 check for updates  
Received: 16 September 2020; Accepted: 9 October 2020; Published: 13 October 2020  
**Featured Application: This work presents the analysis of the element of the 'roof' as a methodological approach to assess the renovation opportunities of existing rooftops, as a stimulus to mitigate the urban phenomena of heat island mitigation by focusing on the role of codes, policies, and regulations in cities.**  
**Abstract: Urban rooftops are a potential source of water, energy, and food that contribute to make cities more resilient and sustainable. The use of smart technologies such as solar panels or cool roofs helps to reach energy and climate targets. This work presents a flexible methodology based on the use of geographical information systems that allow evaluating the potential use of roofs in a densely built-up context, estimating the roof areas that can be renovated or used to produce renewable energy. The methodology was applied to the case study of the city of Turin in Italy, a 3D roof model was designed, some scenarios were investigated, and priorities of interventions were established, taking into account the conditions of the urban landscape. The applicability of smart solutions was conducted as a support to the review of the Building Annex Energy Code of Turin, within the project 'Re-Coding', which aimed to update the current building code of the city. In addition, environmental, economic, and social impacts were assessed to identify the more effective energy efficiency measures. In the Turin context, using an insulated green roof, there was energy saving in consumption for heating up to 88 kWh/m\({}^{2}\)/year and for cooling of 10 kWh/m\({}^{2}\)/year, with a reduction in greenhouse gas emissions of 193 tCO\({}_{\text{2 eq}}\)/MWh/year and 14 tCO\({}_{\text{2 eq}}\)/MWh/year, respectively. This approach could be a significant support in the identification and promotion of energy efficiency solutions to exploit also renewable energy resources with low greenhouse gas emissions.**  
**Keywords: energy efficiency; smart rooftop technologies; green roof; solar energy; urban heat island; building codes; energy policies; 3D roof city model; urban landscapes**
## 3 Results  
### TCE Inhibited Platelet Aggregation Induced by Platelet Agonists Without Damaging Platelets  
To evaluate the effects of TCE on platelet aggregation, various agonists were used, including collagen (2.5 ug/mL), ADP (20 \(\upmu\)M), thrombin (0.05 U/mL), and TPA (10 \(\upmu\)M). Stimulation with these agonists led to significant platelet aggregation, which was markedly suppressed by TCE treatment in a concentration-dependent manner (Figure 1A).  
At 100 ug/mL, TCE nearly completely inhibited aggregation induced by collagen, ADP, and thrombin. The half-maximal inhibitory concentrations (IC\({}_{50}\)) of TCE were determined to be 25.1, 42.3, and 79.1 ug/mL for collagen-, ADP-, and thrombin-induced  
Figure 1: Effects of TCE on platelet aggregation and cytotoxicity. (**A**) Human platelets were pretreated with TCE for 2 min and subsequently stimulated with the following agonists for 5 min: (**a**) collagen (2.5 ug/mL), (**b**) thrombin (0.05 U/mL), (**c**) ADP (20 \(\upmu\)M), or (**d**) TPA (10 \(\upmu\)M). (**B**) Cytotoxicity analysis following TCE treatment. The data are expressed as mean \(\pm\) SD (\(n\) = 3). Statistical significance is indicated as * \(p\) < 0.05 versus agonist-treated platelets; NS denotes no significant difference compared to untreated platelets.  
aggregation, respectively. However, TPA-induced platelet aggregation was not affected by TCE treatment.  
Additionally, cytotoxicity analysis demonstrated that TCE did not induce platelet damage, as assessed by LDH release assays (Figure 1B). These findings suggest that the anti-platelet effects of TCE result from its functional inhibition of platelet activation rather than cytotoxic effects on platelets.