esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
## 4 Results
### Statistical Validation
#### 4.1.2 Total Number of Stops  
Minimizing the number of stops is a key objective of the platoon-based signal-optimization algorithm, as reducing unnecessary stopping improves traffic flow and overall intersection efficiency. The analysis compared the performance of the platoon-based approach against both actuated signal control and the individual vehicle-based algorithm across different penetration levels of connected vehicles (CVs).  
The findings demonstrate that the platoon-based algorithm effectively decreases the frequency of stops in both under-saturated and over-saturated traffic scenarios, as shown in Table 3. Under under-saturated demand conditions, the number of stops decreased by 55% compared to actuated signal control (\(p<0.001\)) and by 40% when compared to the vehicle-based algorithm (\(p<0.01\)). Under over-saturated demand conditions, the reduction in the number of stops was 35% relative to actuated signal control (\(p<0.01\)), while the improvement over the vehicle-based method was 17% (\(p<0.05\)). These reductions highlight the ability of the platoon-based approach to optimize vehicle movements and minimize unnecessary stopping.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Condition** & **Platoon-Based vs. Actuated (\%)** & **\(p\)-Value** & **Platoon-Based vs. Vehicle-Based (\%)** \\ \hline Under-Saturated & 50\% & \(<\)0.001 & 30\% \\ Over-Saturated & 30\% & \(<\)0.05 & 18\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reduction in HCV delay across CV penetration levels.  
The reduction in the number of stops is most pronounced under under-saturated conditions, where smoother platoon coordination allows vehicles to pass through intersections with minimal disruption. By prioritizing platoon movements and synchronizing their arrivals with green signal phases, the algorithm significantly reduces stop occurrences, leading to improved traffic progression.  
In over-saturated conditions, although the reduction in stops is lower compared to under-saturated scenarios, the platoon-based approach still provides statistically significant improvements. The efficiency of the algorithm under these conditions suggests its robustness in handling high traffic volumes while minimizing disruptions caused by frequent stopping.  
Overall, these findings reinforce the effectiveness of the platoon-based optimization strategy in reducing stop occurrences, particularly at high CV penetration levels. The ability to limit stop frequency contributes to smoother traffic flow and enhances intersection efficiency, making this approach a viable alternative to conventional signal-control methods.
## 2 Priority-based Coverage-aware Congestion Control (PCC)
### Link Quality Measurement  
As we discussed in Section 2.1., collisions in MAC layer and link failures in PHY layer influence the probability that a packet is successfully received by the sink. Therefore, when the MAC layer is ready to send the packets in the queue, we also need to update \(P_{i}\) to record the link quality information. A number of parameters can provide the link quality information, such as the number of interfering neighbors and Signal-to-Interference-and-Noise Ratio (_SINR_). However, due to limited resources in a WSN, we prefer to find an efficient parameter which can also be easily obtained through link measurement. In our system, we choose the ratio of the number of successful transmissions (\(M_{S}\)) to the total number of transmission attempts (\(M_{T}\)) as the metrics to indicate link quality. First, \(M_{T}\) and \(M_{S}\) represent the influence from both collisions in the MAC layer and transmission failures in the PHY. Second, each node can easily maintain this information by counting the transmissions in the MAC layer.  
Note that wireless link quality in a WSN is usually time-variant. Therefore, recent measurement results are more important than those that are older; the new measurements can more accurately represent the current link quality. In other words, if we time \(0\) to \(t\) to be slotted into small intervals, \(\frac{M_{S}(t_{2})}{M_{T}(t_{2})}\) is more valuable than \(\frac{M_{S}(t_{1})}{M_{T}(t_{1})}\) as long as \(0\leq t_{1}\leq t_{2}\leq t\). On the other hand, we also do not want the instantaneous perturbation of link quality to destroy the accuracy of estimation of \(P_{i}\), so we cannot simply abandon the information from \(\frac{M_{S}(t_{1})}{M_{T}(t_{1})}\). Therefore, we follow the basic idea of machine learning [12]. In each time interval \(t_{j}\), we independently collect the statistic information of \(\frac{M_{S}(t_{j})}{M_{T}(t_{j})}\), and the link quality in time interval \(t_{j}\), denoted by \(L(t_{j})\) to be given by  
\[L(t_{j})=(1-\alpha)\times L(t_{j-1})+\alpha\times\frac{M_{S}(t_{j})}{M_{T}(t_ {j})} \tag{13}\]  
Then in Part 2.2 we update \(P_{i}=P_{i}\times L(t_{j})\) where \(t_{j}\) is the current time. The parameter \(\alpha\) will depend on the wireless network is and its value may be decided by the network administrator. If the link changes quickly, for example, as in an underwater WSN, \(\alpha\) will be set close to \(1\) so as to incorporate more recent  
Figure 5: Candidate \(F_{N}(N)\) and \(d_{E}(N)\) functions.  
information. On the contrary, if the link is stable, \(\alpha\) should be smaller so as to accept more history information to avoid instantaneous variation of the link.
## 3 Results and Discussion  
To investigate the effectiveness and corrective capability of the predicted model, we printed different samples as Section 2.3.1 discusses. To obtain successive samples, there was some preparation we needed to do, as follows:  
1. Suppression bubbles of printing materials--the continuity of the extrusion line is very important for the accuracy and forming effects of the scaffold [22]. In the preparation of SA-GEL composite material, bubbles will be introduced due to mixing and other factors. The bubbles in the material will affect the continuity of extrusion, and the break point will affect the mechanical properties of scaffolds. Accordingly, bubbles in the barrel solution should be cleaned. In this paper, a static suppression method was used to solve the problem--the cylinder filled with composite solution was heated upside down until the bubbles in the material could not be observed by naked eyes.
2. Selection of printing platforms--unlike Fused Deposition Molding (FDM) method, the SA-GEL composite materials used in this paper had a large amount of water, so it was indispensable to select appropriate deposition platforms [23]. Hydrophobicity is the main index for selecting deposition platforms. The platform made of hydrophobic materials, such as plastic containers, shrinks the complex gel deposited on the hydrophobic platform to form a globule, which seriously affects the forming of scaffolds. When the gel is deposited on the platform made of hydrophilic materials, problem of excessive deposition will occur. Therefore, after many experiments, PS film was chosen as the deposition platform [24]. Due to the differences between hydrophilic and hydrophobic materials, the deposition and diffusion of hydrogels on PS film are not obvious. In addition, adhesion of the gel on the PS film can overcome surface tension, so that the shrinkage does not occur.
3. Calcium chloride assisted stereotyping--in the process of printing, the calcium chloride solution was used to semi-crosslink atomically with the deposited composite SA-GEL. As a result, not only the curing process, but also the shaping effects of scaffolds could be improved [25].
4. Selection of the diameter of conical needle--as the diameter of conical needle increased, the required air pressure decreased, and the needle speed needed to be accelerated. At the same  
Figure 6: The prediction relationship curves of different fiber spacings with porosity.  
time, when the pressure was low, cells in the composite materials received less damage due to extrusion. The acceleration of the needle movement reduced the printing time of the scaffold and improves preparation efficiency. With the increase of conical needle diameter, the porosity of the printing scaffold decreased gradually [26]. Additionally, considering that the diameter of tissue cells varies from more than ten microns to tens of microns, the outlet diameter was 0.4 mm to print the scaffold.  
From the prediction model of fiber width and porosity of scaffold, printing pressure, needle moving speed and spacing are key factors affecting scaffolds forming.
## 3 Results and Discussion
Therefore, the selected TMW has the highest frequency of occurrence. In the case event that this frequency of occurrence is equal, then the typical weather week would be the one with the lowest value of \(\left(\Delta Nsol_{q}^{w}\right)\). For example, the Nsol on the fourth day of the week in the middle of the spring season in the Colmenarejo Este location is 12.96 h.  
According to the different weeks presented in Table 4, the frequency of occurrence in week 641 is the same as in week 642. This coincidence in the number of occurrences can be explained by the fact that these two weeks differ by one day: one starts on 8 April 1998 and the other on 9 April 1998. Therefore, the final choice of the representative week of spring meteorological conditions in Colmenarejo Este is given by the week with the lowest \(\left(\Delta Nsol_{q}^{w}\right)\) value. In the case of Colmenarejo Este, week 642 is the typical week that represents the spring weather conditions for the period 1991 to 2005. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number of Week** & **Frequency** & **Year** & **Month** & **Day** & \(\rm{Nsol_{q}^{w}}\) & \(\Delta Nsol_{q}^{w}\) \\ \hline
235 & 24 & 1993 & 5 & 2 & 13.91 & 0.78 \\
378 & 3 & 1995 & 4 & 3 & 12.64 & 0.49 \\
496 & 5 & 1996 & 5 & 5 & 14.05 & 0  
The Madrid region is not very large, and consequently, a small difference of the order of magnitude for both PAR and temperature is observed in a given season when moving from one locality to another (Figures 2 and A1-A3). This can also be seen in Tables 2 and A1 (Appendix A), in which the statistics of long-term meteorological data are given for four locations. The daily mean values of the TMW parameters for each season were obtained, and the variability of some parameters was plotted, which is illustrated in Figure 2, showing the daily variation of the mean values of PAR and temperature indices for the different seasons. Figures 2 and A1-A3 indicate that, for both meteorological parameters, there is inter-seasonal variability. The highest PAR and temperature values are observed during summer, and the lowest values are observed during winter. On the one hand, thermal oscillation is greater between summer and winter. Since Madrid is located in the central peninsular area, a possible explanation may come from the disappearance of the moderating effect of the sea, which decreases as one moves away from the coast. Furthermore, Figures 2 and A1-A3 show that the temperature range is narrower in winter. The spring and autumn seasons have approximately similar average daily temperatures. However, the daily average PAR is lower in autumn compared to spring, and its variability is sometimes similar to that of winter in certain localities. This can be explained by the predominance of cloudy and probably rainy skies at this time of year. In fact, cloudiness reduces insolation by obstructing solar radiation.
###### Abstract  
Ginkgo biloba L. (Ginkgoacea) contains an abundance of beneficial compounds and has demonstrated positive clinical effects in the management of metabolic syndrome. Recent studies have emphasized its efficacy against type 2 diabetes mellitus (T2 DM), including improvements in diabetic nephropathy and retinopathy. Particularly noteworthy are ginkgolic acid analogs, which have shown potential in combating T2 DM by inhibiting protein tyrosine phosphatases (PTPs), facilitating glucose uptake, and influencing signaling pathways. In this study, we isolated six derivatives of ginkgolic acid from the MeOH extract of _G. biloba_ leaves with the guidance of liquid chromatography-mass spectrometry (LC/MS). We determined the chemical structures of these isolated compounds as 2-hydroxy-6-(10\({}^{4}\)-hydroxopentadec-11\({}^{\prime}\)(\(E\))-en-1-yl) benzoic acid (**1**), 2-hydroxy-6-(11\({}^{\prime}\)-hydroxopentadec-9\({}^{\prime}\)(\(E\))-en-1-yl) benzoic acid (**2**), 2-hydroxy-6-tetadecylbenzoic acid (**3**), 2-hydroxy-6-pentadecylbenzoic acid (**4**), 2-hydroxy-6-(12\({}^{\prime}\)-hydroxyheptadec-13\({}^{\prime}\)(\(E\))-en-1-yl) benzoic acid (**5**), and 2-hydroxy-6-(11-hydroxyundocy!) benzoic acid (**6**) using NMR spectroscopic data and LC/MS analysis. To assess their potential for addressing T2 DM, we subjected the isolated compounds (**1**-**6**) to tests measuring their inhibitory activity against six PTPs: PTPTN1, PTPN2, PTP1B, DUSP9, PTPRS, and PTPN9. Among these compounds, compounds **3**-**5** displayed enzyme inhibition exceeding 90% against all six PTPs. In conclusion, ginkgolic acid derivatives, acting as PTP inhibitors relevant to insulin resistance, hold promise as potential therapeutic candidates for the prevention and treatment of T2 DM.  
Ginkgo biloba; Ginkgoacea; ginkgolic acid derivatives; type 2 diabetes mellitus; protein tyrosine phosphatases +
Footnote †: journal: Applied Sciences
## 2 Background and Related Work
### Time-Aware Recommender Systems
#### 2.2.1 Evolution of User Preferences
The proposal is validated with datasets from several domains, music being one of them. The authors report that their method is based on the assumption that two users have similar tastes if they give the same tags to the same items. They also consider that the closer in time the tag assignments are, the greater the degree of similarity between users. In this work, several decay functions are examined, but in the experimental study, the similarity is computed from a binary implicit rating (the user bookmarked the item or not) without considering tag content. The application of this method can be affected by the sparsity drawback, since only a small portion of users gives tags to items. In addition, not all systems have tagging functions. Due to their successful application in many domains, the growing popularity of deep learning algorithms has meant that these techniques are now also being used in the area of recommender systems, especially when the process involves image processing. This is the case with the work of [PERSON] and [PERSON] [54], where visual features are integrated with other kinds of attributes in a recommendation method especially indicated for large-scale systems and application areas, such as fashion, where the visual characteristics of the items are critical. The method addresses the quick changes in preferences in this field through a complex procedure that involves a time-dependent function to adapt to the dynamic changes of visual features, which are extracted from images by means of a convolutional neural network (CNN). Periodic context-based models can be considered as context-aware recommender schemas where time is the context. Thus, recommendations are given for a specific context formed by one or more dimensions corresponding to contextual variables (time, location, weather, companion, mood, etc.). The next section is devoted to these approaches. Dealing with time dynamics is a problem that is present in many application domains and therefore multiple methods are proposed to address it. In addition to the approaches described before, evolutionary and nature-inspired algorithms [55; 56] have also been used for this purpose in fields such as electrical energy distribution, renewable energy sources or biomedical signaling and image processing, among others. These methods and other advanced algorithms [57] are generally aimed at solving more complex problems than the one presented in this study. Thus, they are beyond the scope of this paper.
# Concentration Influence of Complexing Agent on Electrodeposited Zn-Ni Alloy
###### Abstract  
Zinc (Zn) coatings, which are widely used to protect metals from corrosion, can be further improved by alloying with nickel (Ni). Increasing the Ni content enhances the corrosion-resistant properties of the Zn coating. This study investigated the effect of tetraethylenediamine (TEPA) concentration on the Ni content and the properties of the Zn-Ni alloy coating. Zn-Ni alloy coatings were electrodeposited via the Hull cell test with TEPA concentrations of 0, 0.035, 0.07, and 0.1 M. We found that increasing the TEPA concentration improved the brightness of the coating at low current density and influenced the crystal orientation and morphology. When the TEPA concentration was increased to 0.7 M, the Ni content of the Zn-Ni alloy coating significantly increased before leveling off. However, the thickness of the coatings decreased with increasing TEPA concentration. The electrochemical behavior of the Zn-Ni alloy electrodeposition was validated via partial polarization curves of the Zn and Ni depositions.  
Zn-Ni alloy; electrodeposition; complexing agent; Ni content; gloss Article
## 4 Blending Ensemble Learning-Based Landslide Susceptibility Mapping and Risk Assessment
### Evaluation of Model Accuracy  
Over the past decades, the ROC curve has evolved from its roots in the radiology community into a specialized tool used for predictive purposes to assess the discriminative capabilities of various methods and test results. The ROC curve illustrates the sensitivity (true positive [TP] rate) and 1-specificity (false positive rate) function [38]. Four possible indices were used for measuring ACC, namely TP, True Negative (TN), FP, and False Negative (FN), where TP and FP denote correctly determined landslide and non-landslide pixels, and TN and FN denote incorrectly determined landslide and non-landslide pixels [2]. The calculation formulas for the aforementioned four indicators are shown as follows:  
\[\text{Accuracy }(\text{ACC})=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+ \text{FP}+\text{FN}} \tag{4}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{5}\]  
Precision, recall, and F-measure were used to validate model performance.  
Precision and sensitivity (recall) are quantified using the following equations.  
\[\text{Precision }(\text{PRE})=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{6}\]  
\[\text{Recall }(\text{SST})=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{7}\]The F1-score integrates sensitivity and precision into a unified metric by calculating their harmonic mean, providing equitable importance to both aspects [39].  
\[\text{F}-1=2\times\frac{\text{PRE}\times\text{SST}}{\text{PRE}+\text{SST}} \tag{8}\]  
In landslide susceptibility analysis research, a higher F1-score typically indicates that the model has better predictive performance and can effectively identify high-risk landslide areas. Furthermore, the receiver operating characteristic (ROC) curve and the area under curve (AUC) value are important metrics for evaluating the performance of classification models, which have been widely applied in geological hazard susceptibility studies [40]. The ROC curve is formed by the variation curve of the True Positive (TP) and False Positive (FP) rates. The area under curve (AUC) value is used to measure the predictive accuracy of the model, with its numerical range between 0 and 1. The closer the AUC value is to 1, the better the predictive accuracy of the model.
# Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
###### Abstract  
Unmanned Aerial Vehicle (UAV) deployment and placement are largely dependent upon the available energy, feasible scenario, and secure network. The feasible placement of UAV nodes to cover the cellular networks need optimal altitude. The under or over-estimation of nodes' air timing leads to of resource waste or inefficiency of the mission. Multiple factors influence the estimation of air timing, but the majority of the literature concentrates only on flying time. Some other factors also degrade network performance, such as unauthorized access to UAV nodes. In this paper, the UAV coverage issue is considered, and a Coverage Area Decision Model for UAV-BS is proposed. The proposed solution is designed for cellular network coverage by using UAV nodes that are controlled and managed for reallocation, which will be able to change position per requirements. The proposed solution is evaluated and tested in simulation in terms of its performance. The proposed solution achieved better results in terms of placement in the network. The simulation results indicated high performance in terms of high packet delivery, less delay, less overhead, and better malicious node detection.  
U
## 1 Introduction
Given the AL characteristics and the narrow optical bandwidth of the FSO receiver, it is considered that a broadband light source with a flat power spectral density (PSD) in the passband can be used as AL for theoretical analysis and experimental verification. AL noise can be expressed in the same form as amplified spontaneous emission (ASE) [31; 32]. This study analyzes the reasons for the loss of signal-to-noise ratio (SNR) in coherent reception and non-coherent reception with a preamplifier when the OOK signal light contains AL and reports the single sideband PSD (SSPSD) of different noise. The SNR loss in dB represents the sensitivity deterioration. With coherent reception, and with an increase in the PSD of the AL noise, the main component of the noise changes from shot noise to AL-local oscillator (LO) beat noise. The theoretical calculation results are consistent with the experimental results. The sensitivity deterioration of the coherent and non-coherent receivers was measured experimentally in AL with different PSDs; curves in two conditions of sensitivity deterioration vs. AL PSD were obtained. It was demonstrated that a coherent OOK receiving system has better anti-AL performance than a non-coherent OOK receiving system. AL intensity in the experiment was evaluated in terms of W/Hz. The design flow of the FSO system and calculation of the link margin were presented; the direct sunlight coupling efficiency was calculated, and the sunlight power coupled to the receiver single-mode fiber (SMF) for the specified optical bandwidth was analyzed. With the sun shining directly on the OOK receiver, the receiver optical bandwidth was 2 nm; the receiver lens diameter was 20 cm; the signal light had the optimum coupling efficiency, and the optical power coupled to the SMF was 16.96 nW. With this optical power, the PSD of sunlight can be calculated as \(6.84\times 10^{-20}\) W/Hz. According to the curve of sensitivity deterioration vs. PSD of AL noise measured in the experiment, the sensitivity deterioration by direct sunlight in coherent reception and non-coherent reception was 1.15 dB and 2.60 dB, respectively. This indicates that the FSO OOK system can operate in direct sunlight and sunlight power entering the SMF does not increase with an increase in the receiver lens diameter within a certain range. Thus, AL noise can be controlled by increasing the receiver lens diameter, which is significant in FSO system research. The remainder of this study is organized as follows. In Section 2, the principle of OOK coherent/non-coherent reception is introduced, and the corresponding high-sensitivity OOK receiver is built to prepare for the study of sensitivity deterioration caused by the AL. Section 3 introduces the link margin of FSO system and the coupling efficiency of signal light and sunlight to the receiver SMF. Section 4 theoretically analyzes the influence of AL on OOK coherent/non-coherent reception. Section 5 introduces the experimental research on the influence of AL on coherent/non-coherent reception of OOK signal light. Section 6 presents the summary and conclusions.