esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Functional Modules
### Base Station Module Hardware and Software Design  
The base station module is the core component of the whole system, and consists of a CPU, a GPRS communication module, the 2.4G wireless transceiver module, and a LCD display (Figure 8). Transactions are managed by the base station module through the ZKOS operating system. The GPRS remote communication module is designed with the GPRS/GSM integrated package whose power consumption is only 4 Watts, making it more suitable for outdoor applications.  
Figure 7: Nodes flow chart.  
ZKOS contains a timer that periodically sends a command through a serial port to the 2.4G wireless transceiver module to transmit current temperature and humidity. After the command is sent, ZKOS is kept in the awaiting status. When it gets current temperature and humidity, the LCD display is refreshed. According to the specifications of the TCP/IP protocol of ZKOS, the data will be packed, processed, and sent to the GRPS module after a predefined interval so that the specified host can obtain the data through the China Mobile network. ZKOS also supports the reverse operation, which means the packet can be unpacked and sent to the WEB distribution module. The operation can be configured by the keyboard in the base station, such as manually setting sampling intervals, GPRS transmission intervals, ID of GPRS, threshold of alarm values for temperature and humidity, mobile phone number which is used to receive alarm messages and the status of LCD screen, _etc_. (Figure 9).  
ZKOS is embedded in the CPU and controls the related hardware to accomplish related operations; all transactions can be classified into four tasks:  
Task 1. Schedule the procedure of A/D conversion for all analog channels (\(>\)10 us, may be manually specified), and convert the digital value to the real value.  
Task 2. Schedule the communication procedure for all data channels (\(>\)0.1 ms, may be manually specified), and obtain the sample data from the sensors.  
Task 3. Schedule the data packing procedure (\(>\)1 min, may be manually specified), and send the packet to remote INTERNET server through the GPRS module.  
Task 4. Reset the operation status of system, or the relevant execution according to the parsed command received by the GSM module.  
Figure 8: The architecture of base station module.
## 3 Results and Discussion
### Evaluation of Bread Quality  
The results regarding the analysis of the textural characteristics are presented in Table 2.  
Determining the texture of white bread when supplemented with oil demonstrated that a decrease in hardness occurred when the amount of oil added amounted to 10%. When an oil percentage of 15% was added to the dough, it increased the hardness value for all resulting bread; both white and black flour samples [39; 40]. This increase in hardness comes about during the process of changing the structure of the dough. The absorption of water in the starch granule is reduced, the proteins in the flour are covered with oil, and the gluten network is inhibited by the 15% addition of oil. The proteins' glutenin and gliadin require water to combine and form gluten strands, which gives the dough its strength, stretchability, and ability to trap fermentation gasses, preventing them from rising. This increase in hardness when adding 15% oil also has a negative effect on the elasticity of the crumb. This value decreases proportionally with the increase in the percentage of incorporated oil. The fermentation of the dough is slower with the addition of high percentages of oil, which leads to an increase in the fermentation time, a decrease in the retention capacity of the fermentation gases, and finally, a reduction in the volume of the bread [41; 42]. The gumminess of the crumb is correlated with the addition of oil to the bread samples. This characteristic is associated with chewability. Chewing properties (chewing time or number of chews) and the sensory perception of fat are attributes that the consumer does not like [43]. Comparable findings have been described by [PERSON] et al. (2020), and [PERSON], et al. (2016), correlating the perceived sensations to the appreciation of the textural characteristics of bread by panelists [44; 45]. The recommendation for consumers is that the bread samples with high gumminess can be toasted before consumption. The characteristics of the structure and texture of the bread were analyzed (Figure 6) to demonstrate the overall effect of the distribution of quality attributes. Two components accounted for 81% of the total variation in data; 29% and 52% of the variations found in main components 1 and 2, respectively. It is evident that the 650 and 550 flour bread samples correlate with the cohesiveness and extensibility properties of the dough. In the right quadrant are grouped bread samples obtained from 1350 flour, the control sample, and the sample with 15% oil that demonstrated increased hardness.
## 4 Comparisons of Test Results and Code Predictions  
### Effective Moment of Inertia of Beam Section  
The deflection of RC beams with a cracked section can be calculated using the effective moment of inertia (\(I_{e}\)) along the beam length. The ACI 318-14 [5] considers that the \(I_{e}\) values vary with the ratios between the flexural cracking moment capacity (\(M_{cr}\)) of the beam section and applied moment (\(M_{a}\)), indicating that \(I_{e}\) significantly decreases with the propagation of the flexural crack. Meanwhile, the ACI 440.1R-15 [6] introduces a reduction factor (\(\lambda\)) to explain the tension stiffening of GFRP bars for the calculation of \(I_{e}\). The values of \(\lambda\) increase with the increase in \(M_{a}\). The values of \(I_{e}\) in both code provisions range between the moment of inertias calculated from the gross section (\(I_{g}\)) and cracked section (\(I_{cr}\)). Figure 10 shows the comparisons of \(I_{e}\) values calculated at each loading cycle up to the  
Figure 9: Work damage indicator at each loading cycle in the beam element.  
yielding of longitudinal reinforcement and predictions using design equations of code provisions. Note that the predictions of \(I_{e}\) were calculated using the ACI 318-14 equations for the beams with conventional steel bars and the ACI 440.1R-15 equations for the beams with GFRP bars. The figure also includes the details of the design equations. The ACI 318-14 equations tended to overestimate the variation in \(I_{e}\) values of the high-strength concrete beams with longitudinal steel bars, indicating that the overestimation was mostly independent of the magnitude of \(M_{a}\). This trend was not considerably affected by the yield strength of the longitudinal steel bars and/or \(f_{c}^{\prime}\). The ACI 440.1R-15 equations also overestimated the variation in \(I_{e}\) values of the beams with GFRP bars, whereas the overestimation decreased with the increase in \(M_{a}\) irrespective of \(f_{c}^{\prime}\). In particular, the beam element of the specimens with GFRP bars exhibited considerably lower \(I_{e}\) values than the predictions at the initial flexural crack. It is also found that the ACI 440.1R-15 equations give lower \(I_{e}\) values than the ACI 318-14 equations.
# Grid Anchor Lane Detection Based on Attribute Correlation
## 2 Related Work
Subsequently, confidence scores or non-maximum suppression (NMS) [34] are used to select appropriate anchors as prediction results. Line-CNN [6] and LaneATT [8] predefine a set of long straight lines with various starting points and angles along the image boundaries as anchor boxes. LaneATT incorporates an attention mechanism to aggregate global information and achieves higher efficiency compared to Line-CNN. CurveLaneNAS [9] employs neural architecture search (NAS) to determine the distances of lane lines relative to vertical anchor points. Ufldv2 [7] uses elongated anchors placed both horizontally and vertically to detect lane lines with varying angles but does not explicitly describe the relationship between anchor placement and lane angles. CLRNet [10] uses predefined anchors similar to Line-CNN and LaneATT, but introduces learnable starting points and angles for the anchors. Additionally, CLRNet proposes an IoU loss function specifically designed for lane lines. Since the shapes of predefined anchors are fixed, predicting lane lines with complex curvatures often requires features far from the anchor regions. As a result, this approach suffers from significant localization errors or fails to detect lane lines accurately in curved road scenarios. Table 1 provides a detailed comparison that illustrates the relative advantages of the proposed method over four deep learning methods. Section 3.1 offers a thorough explanation of the reasons for these advantages.
## 1 Introduction  
The interaction between dislocations is a well-known topic, discussed in scientific literature since the middle of last century. High-quality overviews on this topic can be found in reference books concerning dislocations (see for instance [1; 2; 3]). The aim of the present work is to investigate a particular aspect observed for non-mobile, isolated dislocations, in a platelet-shaped single crystal of 4H-SiC with low dislocation density (\(\sim\)10\({}^{3}\) cm/cm\({}^{3}\)).  
The dislocations in 4H-SiC cannot move easily at room temperature, and we can consider their distance \(D\) as constant. When this distance \(D\) is in the range 50-250 \(\upmu\)m, we observe a weak (\(\upmu\)radian) distortion of the region lying between parallel dislocations with opposite Burgers vectors. Dislocations in 4H-SiC crystals have been extensively studied through X-ray diffraction Rocking Curve Imaging (see for instance [4; 5; 6; 7; 8; 9]). The faint distortion we observe had not been investigated previously because there was no available experimental technique allowing its visualisation. We took advantage of the capabilities of Rocking Curve Imaging (RCI) [10] to perform this investigation. RCI is a Bragg diffraction imaging technique that has been developed and pushed to the limits at the European Synchrotron Radiation Facility (ESRF) over the last twenty years (see, as examples, [11; 12; 13]). Nowadays, it is able to provide maps of a crystal with an angular resolution in the \(\upmu\)radian range.  
Our experiment shows that the region lying between parallel dislocations with opposite Burgers vectors exhibits an angular misorientation with respect to the crystal matrix that differs from the simple sum of the misorientation of the individual dislocations. The distortion field is modified and appears to extend the distortion associated with the neighbourhood of the core of the dislocations. In the present work, we characterise this misorientation and discuss its origin.
## 2 Materials and Methods
### Method  
#### 2.3.1 NPP Calculation  
The CASA model was used to calculate annual \(NPP\), and temperature, precipitation, solar radiation, \(NDVI\), and land cover were selected as variables. \(NPP\) was estimated as the product of absorbed photosynthetically active radiation (\(APAR\)). We defined an annual value for \(NPP\), having the unit of grams of carbon per square meter, as follows:  
\[NPP(n,m)=APAR(n,m)\times\varepsilon(n,m) \tag{1}\]  
where \(m\) is area and \(n\) is time; \(NPP\) (\(m,n\)) is net primary productivity fixed by vegetation photosynthesis (gC-m\({}^{-2}\)); \(APAR\) (\(m\), \(n\)) is photosynthetically active radiation over area \(m\) during time t(gC-m\({}^{-2}\)); \(\varepsilon\) (\(m\), \(n\)) is obtained light-use efficiency over area \(m\) during time \(n\) (g C M)\({}^{-1}\)). \(APAR\) (\(m\), \(n\)) and \(\varepsilon\) (\(m\), \(n\)) are given by Equations (2) and (3):  
\[APAR(n,m)=SOL(n,m)\times FPAR(n,m)\times 0.5 \tag{2}\]  
where \(SOL\) (\(m\), \(n\)) is total solar radiation (gC-M)\({}^{-1}\)), and \(FPAR\) (\(m\), \(n\)) is photosynthetically active radiation derived from \(NDVI\)[35].  
\[\varepsilon(n,m=T_{\varepsilon 1}(n,m)\times T_{\varepsilon 2}(n,m)\times W_{ \varepsilon 1}(n,m)\times\varepsilon_{max} \tag{3}\]  
where \(T_{\varepsilon 1}\) (\(m\), \(n\)) and \(T_{\varepsilon 2}\) (\(m\), \(n\)) are temperature stresses; \(W_{\varepsilon 1}\) (\(m\), \(n\)) is water stress, which represents the reduction in light-use efficiency and \(\varepsilon_{max}\) (gC-M)\({}^{-1}\)) is the maximum light-use efficiency under ideal conditions.
## 5 Active Transmitting Wearable Body Area Network with a Dual Polarized Antenna  
Figure 3a presents a basic transmitter block diagram with an active antenna. A printed compact dual polarized transmitting antenna is presented in Figure 3b. The antenna volume is 50 mm \(\times\) 50 mm \(\times\) 0.005 mm. In Figure 3b, the high-power amplifier, HPA, is an integral part of the antenna. The HPA, a MMIC GaAs MESFET, is connected to the transmitting antenna. The active transmitting dual polarized antenna layout is shown in Figure 3b. The amplifier pin description is shown in Figure 3b. The transmitting active antenna block diagram is presented in Figure 3c. The radiating element is connected to the HPA via an output HPA matching network. The HPA input matching network connects the amplifier port to the transmitter. The amplifier specifications are given in Table 3. The high-power amplifier complex S parameters are listed in Table 4. The active transmitting dual polarized antenna S11 parameters, computed and measured, is better than 3:1 in the frequency range from 410 to 450 MHz. The active transmitting dual polarized antenna S21 parameter, gain, on human body is presented in Figure 3d. The active dual polarized antenna gain, computed and measured, is 14 \(\pm\) 3 dB for frequencies ranging from 380 to 600 MHz. The active transmitting dual polarized antenna output power is around 18 dBm.  
Figure 3: _Cont._  
Figure 2: (**a**) Printed compact dual polarized wearable antenna; (**b**) Active printed compact dual polarized receiving antenna layout; (**c**) Active dual polarize antenna S21 parameter, gain, on human body; (**d**) Active dual polarize receiving antenna noise figure.  
Figure 4: _Cont._
## 5 Simulation
### Experimental Results and Analysis
#### 5.2.4 Round-Time Cost Analysis  
The round-time cost for our framework and HierFAVG is compared in Figure 6. The proposed asynchronous framework demonstrates a lower average round time of 9.8 s per round, compared to 12.3 s for HierFAVG. This significant reduction in round time is achieved through the asynchronous aggregation mechanism, which allows global model updates to proceed independently of slower devices, thus mitigating the impact of network latency and computational delays.  
Figure 5: Global model accuracy across 50 rounds. (**a**) Global accuracy with our framework. (**b**) Global accuracy with HierFAVG.  
The asynchronous framework's ability to tolerate outliers is particularly evident in scenarios where individual devices experience temporary spikes in communication time. For example, IoT 2 occasionally exhibits communication delays of up to 14 s. Despite such fluctuations, the overall system time remains stable due to the asynchronous handling of updates, which prioritizes aggregate progress over strict synchronization.  
In contrast, the HierFAVG approach requires synchronous updates from all devices, resulting in higher round times as slower devices delay the aggregation process. While this ensures consistent contributions from all devices, it also highlights a scalability limitation in heterogeneous environments, where devices with varying computational capabilities and network conditions are common. The results emphasize the scalability and efficiency of the asynchronous aggregation mechanism in reducing the round-time costs while maintaining robust system performance. This capability is particularly advantageous in large-scale IoT deployments, where minimizing latency and maximizing throughput are critical for effective federated learning.
## Materials and Methods
### Quantitative Determination of Dyes  
The quantitative determination of dyes concentration was operated by using an UV-Vis spectrophotometer (model UV-3000, Shanghai MAPADA Instruments Co., Ltd., Shanghai, China). The samples in the top phase (aqueous phase) of MAMTPS were withdrawn and diluted for analysis of the content of dyes. Another tube with the same components but without dyes was used as the blank. The dyes were extracted and adsorbed in the bottom phase (surfactant rich phase). The absorbance of each dye was determined, and then the mass of dyes in the top phase was calculated. The mass of dyes in the bottom phase was obtained by the subtraction method from the total mass of dyes added to the tube. The extraction efficiency of dyes in the bottom phase was defined in the Equation (1):  
\[\text{Extraction efficiency}\left(\%\right)=\frac{\text{M}_{\text{b}}}{ \text{M}_{0}}=\frac{\text{M}_{0}-\text{C}_{\text{t}}\text{V}_{\text{t}}}{ \text{M}_{0}}\times 100 \tag{1}\]  
where \(\text{M}_{\text{b}}\) and \(\text{M}_{0}\) represented the mass of dyes in the top phase and the original mass added to the tube, respectively. \(\text{C}_{\text{t}}\) and \(\text{V}_{\text{t}}\) represented the concentration of dyes and volume of top phase, respectively. The detection conditions and standard curves for analysis of the three dyes are shown in Table 1. The standard curves were constructed by plotting the absorbance (\(\gamma\)) as a function of the pure dyes concentration (\(X\)).
# Evaluating the Effect of Financing Costs on PV Grid Parity by Applying a Probabilistic Methodology
## 3 Results
### 500 kW Case Study
#### 3.3.1. LCOE--Calculation Method Analysis for 500 kW and 1100 kWh/kWp Case  
Introducing all the previous parameters and fixing the regulated tariff at 17.4 \(\upxi\)/kWh, including taxes and with a fixed interest rate of 10%, produces the following results (Table 12), considering an energy production of 660,000 kWh/yr, OM annual costs of 11,490\(\upxi\) and a lifetime for the installation of 25 years. The recovery factor value is 0.1102.  
With the results obtained for the LCOE, it can be concluded that Spain would reach grid parity under all conditions represented in this paper. However, the IRR calculated at 25 years with a standard loan of 10% reaches positive values between 1 \(\upxi\)/Wp and 0.8 \(\upxi\)/Wp. It can be concluded that there is a floor of 8.02% of IRR for the current panel cost scenario (0.4 \(\upxi\)/Wp) and the worst-case scenario of yield and interest rate.  
Figure 14 illustrates a clear presentation of the point at which the retail costs and generation costs reach an equilibrium. As can been seen, there is a considerable deviation impacting the grid parity forecast.  
This case is analysed by introducing a sensibility analysis based on different interest rates, determining which loan terms and conditions allow the system to achieve the regulated tariff target for grid parity for a different module cost (\(\upxi\)/Wp), as shown in Table 13. The positions where the LCOE is lower than the retail cost of energy have been highlighted.  
Figure 14. Analysis of the LCOE against the retail electricity cost at a fixed interest rate of 10% (500,000 W inverter, deterministic method).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PV Module Cost (\(\upxi\)/Wp)** & **System Cost (\(\upxi\))** & **LCOE (\(\upxi\)/kWh)** & **IRR (\%)** \\ \hline
1 & 1,022,45 & 0.1585 & \(-\)0.316 \\
0.8 & 877,250 & 0.1385 & 1.67 \\
0.6 & 732,050 & 0.1184 & 4.36 \\
0.4 & 586,850 & 0.0984 & 8.02 \\
0.2 & 441,650 & 0.07834 & 14.52 \\ \hline \hline \end{tabular}
\end{table}
Table 12. Comparison of LCOE and IRR based on a recovery factor at 10% interest, for the 500 kW case.
## 3 Methods
### Measuring Sea Ice Motion and Deformation
#### 3.1.4 Measuring Sea Ice Deformation  
Sea ice deformation provides information on individual floe size as a separated area by high shear, especially in the application of high-resolution remote sensing, and sea ice kinematics composed of opening from brittle failure and closing forcing ice into pressure ridges [22]. The combination of the opening connected to new ice production from the freezing of the exposed ocean and the closing causing sea ice thickness changes is known to adjust the overall area and volume of sea ice in polar regions.  
Strain rate of each cell defined from the sea ice motion grid varies in velocity components, u and v. Invariants of the spatial gradient of the velocity component are calculated using strain rate components with approximations of the line integral around the boundary of each cell [17] using Equations (2) and (3).  
\[\text{{divergence}}=\frac{\partial u}{\partial x}+\frac{\partial v}{\partial y} \tag{2}\]  
\[\text{{shear}}=\left[\left(\frac{\partial u}{\partial x}-\frac{\partial v}{ \partial y}\right)^{2}+\left(\frac{\partial u}{\partial y}+\frac{\partial v}{ \partial x}\right)^{2}\right]^{1/2} \tag{3}\]  
To evaluate the applicability of the multi-sensor high-resolution optical satellite images for measuring kinematic characteristics and to examine the effects from different spatial resolution, the divergence and shear were delineated from the image pairs of both spatial resolutions, and spatial patterns and distribution of values were investigated for comparison between the datasets.
# Security Issues in IoT-Based Wireless Sensor Networks: Classifications and Solutions
## 3 Security Protection Schemes
### Integrating Blockchain Technology Schemes  
Blockchain plays an important role in improving the security of wireless sensor networks (WSNs). Due to its distributed and immutable characteristics, blockchain helps to protect data during transmission, to minimize the risk of attacks, and to detect malicious nodes in such networks. In [89], traditional methods in WSNs rely on centralized decisions, which makes it difficult to trace and reproduce data and unfair to detect malicious nodes. Integrating blockchain into WSNs helps to store data securely and to increase the ability to detect and to eliminate bad nodes through smart contracts and distributed ledgers. The authors of [90] proposed a WSN architecture that uses blockchain to protect transmitted data. Each node in the network can store data in the form of blockchain blocks containing hash values, which helps prevent tampering and ensure data integrity. At the same time, the system also uses algorithms such as Merkel-tree to optimize the encryption and storage process. The authors of [91] proposed a blockchain-based trust management model to eliminate malicious nodes and to ensure network reliability and stability. By using blockchain to record the trust value of each node, this model helps to improve the attack detection performance and to maintain the accuracy of wireless sensor networks.  
Blockchain stores data in an immutable way to reduce the risk of data tampering attacks, suitable for WSN-IoT applications that collect sensitive data (health information, location data, etc.) or applications that require transparency (IoT device status management, environmental warnings, etc.). Integrating blockchain will increase the reliability of data because many nodes can agree and verify data. In addition, this method supports strong authentication through digital signatures and hash functions. The distribution of control helps overcome the disadvantages of traditional WSNs (with the sink node being a single point for storing and processing data, and also being the target of attacks). However, this method requires high computing and storage costs and consensus mechanisms (PoW, PBFT...) and blockchain storage requires large computing resources, memory, and energy.