esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State of the Art
### Overview of IoT Simulators
#### 2.1.4 DEUS, COOJA and NS3  
A Java-based general-purpose hybrid simulation platform was proposed by [PERSON] et al. [31]. To simulate interconnected IoT devices, they used the concept of IoT Nodes, which represent generic Smart Objects, characterized by a mobility model, possible multiple communication models and an energy model.  
The top layer describes the application to be tested, while the Adaption layer is tasked with the coordination between the IoT Nodes. Below, there is the layer containing the three aforementioned models and, thereunder, the Java-based general-purpose discrete event simulator DEUS is in charge of advancing the simulation. DEUS is directly used by the IoT Nodes' mobility model, which is implemented by OSMobility, a DEUS based simulation environment to simulate motion. The energy model and network models are realized with COOJA and NS3 simulators, respectively. According to the authors, DEUS they chosen mainly due to scalability and versatility reasons.  
To verify their expectations, they conducted six different experiments within an urban smart parking scenario. The exact details of the use case, as well as the exact hardware configuration for the experiments, can be seen in [31]. In each of the experiments, [PERSON] et al. increased the number of employed nodes, so that there were 4508, 9016, 22,540, 45,080, 112,700 and 225,400 nodes, respectively. Subsequently, they counted the number of events that arose during the simulation runs and obtained for the aforementioned number of nodes \(2.7\times 10^{6}\), \(5.6\times 10^{6}\), \(1.4\times 10^{7}\), \(2.7\times 10^{7}\), \(6.3\times 10^{7}\) and \(1.2\times 10^{8}\) events per run, respectively.
## 3 Result and Discussion  
### Reconstruction Thermal Efficiency with Operation Mode Factor (OMF)  
The linear regression solved Equation (21) for T-mode and Equation (22) for PVT-mode for the wind loss effect [13]. In the same way, it was also repeated for T-mode and PVT-mode for the without wind loss effect on Equations (23) and (24). Equation (20) calculated the RMS value. Table 1 summarizes the results. Equation (17) uses the required parameters in Table 1 to prove the simulation using the results in [13]. Those parameters completed the ratio between PVT-mode to T-mode, such as \(OMF_{prot}/OMF_{t}\), \(K_{SA,pet}/K_{SA,t}\), \(U_{L,pet}/U_{L,t}\).  
Wind loss effect (from actual measurement):  
\[\mathrm{T-mode:}\quad\quad\eta_{t}=49.5-1726.2\frac{(T_{i}-T_{a})}{I} \tag{21}\]  
\[\mathrm{PVT-mode:}\quad\eta_{t}=56.7-3028.1\frac{(T_{i}-T_{a})}{I} \tag{22}\]  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{2}{c}{**Items**} & \multicolumn{4}{c}{**Wind Loss Effect**} & \multicolumn{2}{c}{**Without Wind Loss Effect**} \\ \hline \hline
**Parameter** & **Unit** & **T-Mode** & **PVT-Mode** & **T-Mode** & **PVT-Mode** \\ \hline \(K_{SA}\) & \(\mathrm{W/m^{2}.^{\circ}C}\) & 44.38 & 176.11 & 46.68 & 242.95 \\ \(U_{L}\) & \(\mathrm{W/m^{2}.^{\circ}C}\) & 28.24 & 36.57 & 23.34 & 32.93 \\ \(\eta_{t}\rightarrow(T_{i}-T_{a})/I=0\) & \(\times 100\%\) & 0.495 & 0.567 & 0.540 & 0.603 \\ \((T_{i}-T_{a})/I\rightarrow\eta_{t}=0\) & \(\circ\mathrm{C.m^{2}/W}\) & 0.029 & 0.019 & 0.035 & 0.021 \\ \(\eta_{t}/[(T_{i}-T_{a})/I]\) & \(\%.m^{2}/^{\circ}C.W\) & 17.26 & 30.28 & 15.58 & 29.00 \\ \(RMS_{r-e}\) & \(\%\) & 16.3 & 11.3 & 14.0 & 10.2 \\ \(OMF\approx F_{R}\) & - & 0.61 & 0.83 & 0.67 & 0.88 \\ \(OMF_{pot}/OMF_{t}\) & - & & 1.36 & & 1.31 \\ \(K_{SA,pet}/K_{SA,t}\) & - & & 3.84 & & 9.82 \\ \(U_{L,pet}/U_{L,t}\) & - & & 1.27 & & 1.43 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of parameters constructed from the regression of experimental results.  
Without wind loss effect (ideal condition):  
\[\text{T}-\text{mode}:\quad\quad\eta_{t}=54.0-1556.7\frac{(T_{i}-T_{a})}{I} \tag{23}\]  
\[\text{PVT}-\text{mode}:\;\eta_{t}=60.3-2900.3\frac{(T_{i}-T_{a})}{I} \tag{24}\]  
The electrical and thermal efficiency complemented the correlation analysis. At steady state and MPPT [13], electrical efficiency as a function of collector surface temperature \(T_{S}\):  
\[\eta_{e}=14.21-0.0547T_{S} \tag{25}\]  
with the R\({}^{2}\) = 0.811. \(\eta_{e}\) theorizes as  
\[\eta_{e}=\eta_{r}(1-\beta(T_{S}-T_{r})) \tag{26}\]  
where \(\beta\) is the efficiency temperature coefficient. For a crystalline PV module, \(\beta\approx 0.0045\). \(\eta_{r}\) is the PV reference efficiency at the reference temperature \(T_{r}\)= 25 \({}^{\circ}\)C.
## 3 Results and Discussion
### Statistical Analysis  
To investigate the relationships between major elements and to identify their origin in the Mornag groundwater, principal component analysis (PCA) was conducted on a data set of 14 samples and 9 physicochemical parameters, i.e., pH, TDS, Na\({}^{+}\), Ca\({}^{2+}\), Mg\({}^{2+}\), K\({}^{+}\), SO\({}_{4}\)\({}^{2-}\), Cl\({}^{-}\), and HCO\({}_{3}\)\({}^{-}\) (Table 1). Each factor is defined by a number of variables that control particular mechanisms of water mineralization [38; 40]. The two preserved factors account for the maximum total variance of the samples. F1 and F2 were subsequently calculated for the individual samples and used for determining the position of the intensity of each of the factors controlling the hydrochemistry in each position. The first factor, recording about 55%, 70%, and 57% of the total variance in 2013, 2016, and 2019, respectively, is, in large part, correlated with salinity, Na, Mg, Ca, SO\({}_{4}\), and Cl. The second factor, accounting for 16%, 15%, and 23% of the total variance in 2013, 2016, and 2019 is highly associated with HCO\({}_{3}\), K, and pH (Figure 5).
# Emergence of Nano-Dentistry as a Reality of Contemporary Dentistry
## 4 Discussion
### Preventive Nano-Dentistry  
The goal of modern dentistry is to prevent rather than treat biofilm-dependent oral diseases, i.e., dental caries, endodontic and periodontal diseases. Nanotechnology offers new approaches for preventive measures in oral diseases, particularly dental caries and periodontal diseases [94].  
Prevention of caries is one of the main methods that reduces the prevalence of this disease. Caries prevention methods are constantly improving, but still the most affordable tool is the use of therapeutic and prophylactic toothpastes. With the development of nanotechnology in dentistry, now even daily brushing can ensure hygiene and protection of the oral cavity at the nano level.  
Researchers have developed a nano-toothbrush. Including colloidal particles of nanogold or nanosilver between the bristles of a toothbrush [95; 96] can lead to a significant reduction in periodontal disease.  
Oral hygiene products, such as toothpastes and mouthwashes, have also been nanomodified according to recent reports [97].  
For example, nanoclaim fluoride, which is part of mouthwashes, reduces the activity of caries, reduces dentin permeability, and increases the labile concentration of fluoride in oral fluid [98]. Toothpastes containing calcium carbonate nanoparticles and 3% nanosized sodium trimetaphosphate promote remineralization of early carious lesions compared to conventional toothpaste without nano-additives [99]. According to the results of an in vitro study, toothpastes containing nano hydroxyapatite (NHA) crystals significantly increased the value of microhardness in human enamel after erosive influence, compared to the same toothpaste without NHA [100].  
The higher reparative capacity of nanomaterials compared to the same material on a micro or macro-level may be due to the fact that inorganic building blocks in enamel have a size of 20-40 nm, which makes it logical to assume a higher affinity for nanosized particles. Remineralizing toothpaste with peptide complex and nano-hydroxyapatite Vivax Dent strengthens tooth enamel, prevents leaching of calcium and phosphorus from bone tissue, fights bacteria and plaque, and prevents caries and odor. It is suitable for the treatment of stomatitis, periodontitis, and other inflammatory processes of the oral cavity, as well as for the prevention of caries.  
Unlike traditional oral hygiene products, which act only superficially, the active components of innovative products, due to nanoparticles and effective carriers, penetrate deeply through natural barriers and have a positive effect on all tissues of teeth and gums [101].
# An Instrument Error Correlation Model for Global Navigation Satellite System Reflectometry
## 4 Discussion
### Limitations  
The correlated error model \(R_{mod}\) exhibits complex dynamics that are generally plausible but difficult to validate outside of the large-scale statistical estimation discussed in Section 3.1. The fact that the bulk statistical behavior between the estimated autocorrelation model averaged over all tracks is consistent with the observed double-difference autocorrelation (cf. Figure 3) is reassuring, but the disparity between the individual track model estimated autocorrelations and the single-track double-difference estimations (c.f. Figure 4) suggests that either the model is insufficient at capturing the dynamics track-to-track or that the correlated error may not be as stationary as assumed.  
With only about a day of data during the overlap period, there remains the possibility that the validation dataset is unrepresentative of the overall statistical behavior of the observation. \(R_{mod}\) is designed only to account for errors at short spatial and temporal scales relevant for data assimilation purposes; therefore, this model may not necessarily account for changes of the underlying statistical distributions at larger scales due to seasonality or orbital precession.  
The error model assumes that errors are generally isotropic and that \(R_{mod}(i,j)=R_{mod}(j,i)\). This assumption has important practical utility for simplifying the implementation of \(R_{mod}\) and the conditioning of the required matrix inversion. Anisotropies may exist in any of the components of the error model but are assumed to be small compared to the modeled behavior.  
Further, this model generally assumes no spatial dependence. Spatial dependence of the correlated error is primarily driven by the errors in the retrieval and not the instrumentation, which orbits Earth every 95 min. The'spatial' dependence of error from CYGNSS transiting its orbit has largely to do with the dynamics of the thermal loading of the receiver as lighting conditions evolve in orbit. This is accounted for in the assumptions of the component models \(R_{PS}\) and \(R_{PS}\) as discussed in Appendices B and C, respectively. The correlated error resulting from the evolution of the observing geometries as both GPS and CYGNSS propagate in their orbits is captured in the correlated structure of the antenna gain maps and the zenith-specular ratio explored in Appendices D and E.
# Technique Variables Associated with Fast Bowling Performance: A Systematic-Narrative Review
## 4 Discussion  
The review highlights the breadth of recent research exploring biomechanical technique parameters associated with fast bowling performance. It further provides a comprehensive, critical and objective analysis of the nature and methodology of the variables studied, and their association with fast bowling performance metrics. The majority of fast bowling performance research considered BRS to be the primary indicator of performance and hence technique parameters associated with BRS were typically studied. Some variables were consistently found to be linked to fast bowling performance across all studies, while a few variables were reported to have an inconsistent relationship to BRS.  
Varied methods were adopted to study the major contributors known in the literature to be associated with fast bowling performance. With a sample size of 18 and above across most studies, the studies provide a foundational understanding of fast bowling techniques associated with BRS. Although studies with limited sample size may provide valuable initial insights, the generalizability of the findings to a broader population should be carefully considered [12; 19]. Relationships between run-up speed [2; 20; 21], front knee angle at BR [2; 10; 11; 17; 19; 20; 21], rear knee angle at BFC [3; 10], trunk kinematics from FFC to BR [2; 6; 12; 20], position of the bowling arm at BR [2; 8; 11] and BRS have been widely studied.
## 5 Results
### Lidars' Performance in Presence of the Motion
#### 5.2.3 Rotational Motion: Roll (Rotation along E-W)  
The statistical analysis of lidar data during roll motion (tests 12, 22, 23, and 51) can be seen in the fourth row of Figure 6. Test cases 12, 22, and 23 have motion frequencies of 0.2 Hz and amplitudes of \(15^{\circ}\), \(10^{\circ}\), and \(5^{\circ}\). In test 51, where \(f_{0}=0.1\) Hz and \(A_{0}=15^{\circ}\), the moving WindCube V1 exhibits a slightly lower average wind speed across all tests when compared to the reference WindCube V1. However, the moving lidar has higher TI, indicating increased measurement fluctuations due to lidar motion. For the ZephIR 300 lidars, moving lidar also has a lower average wind estimation, yet the TI estimations are less different when compared to the WindCube V1 estimates except for test 51. The test cases 12, 22, 23 only differ in motion amplitude and have higher frequency than test 51. Interestingly, the TI in these test cases is lower than test 51.  
The APE for WindCube V1 is less than ZephIR 300 at 85 m and this value for both lidars remains less than 2.5%, except for test 51 in which WindCube V1 shows an APE higher than 5%. While the APE for ZephIR 300 remains at around 2.5% at 145 m, this value for WindCube V1 reaches 7% (Figure 7 Roll: a, b). The correlation between the lidarsin Figure 7 (Roll: c, d) indicate that the ZephIR 300 lidars are reasonably correlated, even with the presence of the motion except for test 51, while the correlation of WindCube V1 is affected more and decreases at 145 m.
## 4 Model Architecture Design
### Key Components
#### 4.2.2 Multi-Head Self-Attention  
As shown in Figure 9, the self-attention mechanism is the core of the Transformer architecture, allowing the model to consider contextual information from all positions while processing the input sequence [40]. The SelfAttention class implements this mechanism by linearly transforming the input into query, key, and value components, and then calculating attention weights. Multi-head attention further extends this process to multiple parallel attention heads, increasing the model's parallelism and its ability to attend to different positional relationships. Specifically, the introduction of masking operations ensures that only historical information is utilized to predict the future during the autoregressive process, maintaining the causality of the sequence.  
Figure 9: Model training results.  
We designed a multi-head self-attention module that implements parallelized attention computation, enhancing the model's ability to handle long-distance dependencies [40]. By dividing the input into multiple \"heads\" and performing attention computation independently, the model can capture various association patterns at different positions. Meanwhile, the masking mechanism ensures the autoregressive property, meaning that predictions rely only on historical information.
# Comparative Transcriptomics for Pepper (_Capsicum annuum_ L.) under Cold Stress and after Rewarming
## 4 Discussion
### Plant Hormones Reflect Cold and Rewarming Condition of Pepper  
Plants can quickly sense and respond to stress through hormone-mediated signal transduction pathways. Plant hormone signal transduction plays a central role in response to cold stress, especially the role of ABA in sensing and receiving cold signals, which triggers the corresponding responses of plants to adapt to abiotic stress [54]. Under cold stress, ABA content in plants increased, and ABA entered the central hydrophobic site of PYL and induced the closure of related structures of PYL, creating conditions for the binding of _PP2 Cs_. Then, the tryptophan residues in _PP2C_ were inserted into the ABA structure and tightly locked ABA in the appropriate position [55; 56]. In the PYL-ABA-PP2C complex, the binding of ABA-PYL with PP2 Cs blocked the binding of SnRK2 with PP2 Cs to prevent their interaction. The released SnRK2s were self-activated through phosphorylation effect proteins including TF (_ABF_, etc.) and downstream stress response genes, thus improving the tolerance of plants to the low-temperature environment [57]. It has been reported that _SnRK2.6_ (OST1) activated by cold stress phosphorylates _ICE1_ to activate CBF-COR gene expression cascade and cold tolerance [58]. In this study, _PYL_ (Capana00g003406 and Capana03g002049), _ABF_ (Capana04g000551), and _SnRK2_ (Capana02g003135) were all upregulated in T4 treatments and the expression levels of these genes in CT were significantly higher than those in CS. This suggests that the high expression of these genes may contribute to the recovery of pepper seedlings after cold stress.  
_JAZ_ also interacts with ICE1 to inhibit the ICE-CBF signaling pathway. Cold induces the expression of JA synthesis-related genes and the synthesis of bioactive JA-Ile, which activates JA receptor COI1 to bind to JAZ1, resulting in the degradation of JAZ1 through 26S proteome after ubiquitination. Then, the ICE-CBF transcriptional regulation cascade signaling pathway is activated, and the expressions of cold-regulated genes are induced to improve plant cold tolerance [59]. The downregulation of _JAZ_ can also reduce the binding with _ICE_ to regulate cold stress. In this study, under T3 and T4 groups, the expression level of _JAZ_ in CS was significantly higher than that in CT. This may be an important reason why cold-resistant cultivars recover more easily than cold-sensitive cultivars. Further, there are increasing evidences that _SAUR_ genes, a family of auxin-responsive genes, are involved in stress/defense responses [60]. Under cold treatment, 8 _SAUR_ genes were upregulated, which indicated they may play an important role when the poplar is subjected to cold stimulation [61]. In this study, three _SAUR_ genes (Capana03g001923, Capana09g001771 and Capana01g003581) were upregulated in T4 group, this suggested that _SAUR_ may play an important regulatory role in recovery stage after cold stress in peppers.
# Review of Monitoring and Control Systems Based on Internet of Things
## 3 Monitoring and Control Systems Based on IoT
### Monitoring and Control Systems for Smart Cities Based on IoT
Experimental results show that the proposed system effectively monitors the level and weight of waste in the bin, which allows for effective waste collection and disposal management. Additionally, using the Twitter platform to share updates on the status of the bin helps to increase public awareness of waste management and environmental protection. The article's conclusions indicate that intelligent waste monitoring systems can improve city cleanliness, environmental hygiene, and intelligentwaste management, which is an important step towards sustainable development and environmental protection. [PERSON] et al. in [60] noticed that the IoT plays a crucial role in intelligent parking systems. Sensors installed in parking lots collect data on the availability of parking spaces, which are then processed and analyzed to manage parking applications effectively. Traditional parking management systems often need help accurately detect available spaces, especially in large, free parking lots. To overcome them, the paper proposes using infrared sensors to precisely detect parking spaces, eliminating problems related to accuracy, lighting conditions, and weather. The system architecture includes several phases, including the development of an Android application for parking reservations, identification of vacant spaces using infrared sensors, user authentication using RFID tags, classification of parking spaces based on vehicle size, and navigation to assigned spaces using GPS. In particular, the system is based on Arduino Uno microcontrollers, which are microcontrollers based on the ATmega328P system. They have 14 digital I/O pins, six analog pins, a USB interface, and many other features such as Arduino IDE software programs and interfaces with sensors, Wi-Fi, and RFID modules. Additionally, the system provides visualization tools for parking center owners to monitor reservation details, space availability, and billing information. Implementation details highlight using Arduino microcontrollers, RFID readers, and Wi-Fi modules to enable communication between sensors, mobile applications, and backend systems. The paper also includes experimental results that demonstrate the effectiveness of the proposed system in optimizing parking space utilization and improving user experience. In summary, the IoT-based smart parking system presented in this paper offers a promising solution for efficient parking management in urban areas. Although the prototype focuses on single-story parking lots, the model can be scaled to multi-story parking facilities, thus contributing to the overall development of innovative city initiatives.
## 4 Performance Analysis
### HIL Test Implementation  
In order to ensure the effectiveness of the proposed strategy, hardware-in-the-loop testing was conducted in addition to software simulation. The HIL platform constructed in this study included ECU rapid prototyping based on D2P and a NI real-time simulation cabinet, as shown in Figure 20. The rapid prototyping based on MotoHawk included the modeling and code generation of the controller, and code flashing and debugging based on MotoTune. Information interaction was achieved through CAN communication with the NI real-time simulation cabinet. The hardware contained in the NI real-time simulation cabinet  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Controller** & **PID** & **SMC** & **MPC1** & **MPC2** & **AMPC** \\ \hline Q\({}_{\text{loss}}\)(\(\%\)co) & 1.9921 & 1.8604 & 1.8418 & 1.7978 & 1.7940 \\ SOC & 0.7431 & 0.7470 & 0.7545 & 0.7562 & 0.7566 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Capacity loss value and SOC of the battery under each controller after the NEDC (SOHini = 1).  
Figure 19: Under 10 NEDCs, the battery parameters under the action of each controller: when SOH = 1, (**a**) battery capacity loss (**c**) SOC. When SOH = 0.9, (**b**) battery capacity loss (**d**) SOC.  
Figure 18: Under NEDC working conditions, the battery (SOHini = 1) parameters under the action of each controller: (**a**) battery capacity loss (**b**) SOC.
# Revisiting Label Smoothing Regularization with Knowledge Distillation
## 4 Method
### Component 1: Output Smoothing  
Reference [13] showed that a soft temperature larger than one is critical for the effectiveness of KD, so we extended the KL-loss to a generalized form and put forward the LSR-OS component:  
\[L_{LSR-OS}(p,q)=(1-\alpha)\times L_{CE}(p(1),q)+\alpha\times\tau^{2}\times L_{ KL}(u||p(\tau)). \tag{10}\]The temperature \(\tau\) is a critical factor in knowledge distillation methods. Increasing the temperature \(\tau\) for the model output in Equation (8) generates a smoother probability distribution. An example of a model prediction is shown in Figure 1(a); the predicted class is C8, which has the largest probability portion. The prediction \(p_{m}(\tau)\) corresponding to the \(m_{th}\) class is calculated by Equation (1). With \(\tau\) increasing, the predicted class value is decreased, and the remaining classes obtain a greater share. The smoothed model output distribution reduces the large confidence values and reveals more details of the smaller ones. It is reasonable to minimize the KL-loss between the uniform distribution teacher and the model output smoothed with a flexibly adjusted temperature than a hard output (\(\tau=1\)) in the original LSR method.  
Figure 2: Comparison of the soft model output of LSR-OS at different temperatures \(\tau\). C8 is the predicted class. (**a**) Soft model distribution comparison; (**b**) corresponding KL-loss between different temperature outputs and the uniform distribution; (**c**) KL-loss multiplied by a factor \(\tau^{2}\).