esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Cooling Rate Index (CRI)  
The CRI corresponded to the slope of the temperature average variation during the first 10 min of cooling according to [42]. As the CRI assesses the cooling of the dry zones, the average temperature was recorded in the upper part of the samples (Figure 3). Only for the chalk where the capillary rise was very fast, the CRI was calculated during the first 5 min. Higher values of CRI meant fast cooling (Table 4).  
The Chalons Chalk showed the lowest cooling kinetics compared to all the stones. Despite its high porosity, the small pore sizes led to a very low thermal diffusivity of only 0.59 mm\({}^{2}\cdot\)s\({}^{-1}\)[58]. During the TT, the cooling rate increased up to double, which indicated a change of the surface properties. The variations through the TF also revealed an increase in the cooling velocity.  
The Fontainebleau sandstone showed an initial CRI of intermediate value between the stones. The main parameters involved in its kinetics are the low porosity and the high quartz content that increase diffusivity in relation to a standard sandstone (diffusivity between 1.13 and 1.67 mm\({}^{2}\cdot\)s\({}^{-1}\)) [58]. During the TT, there was an increase in the cooling kinetics up to double of the initial values indicating a variation in outer layers porosity. The temperature was too low to produce a mineralogical change. On the other hand, the TF revealed an increase in the cooling velocities that confirmed the microstructural change.  
The Gris Alba showed a low initial CRI, according to its low porosity and quartz content. A standard granite has a diffusivity of around 1.13 mm\({}^{2}\cdot\)s\({}^{-1}\) while a low porosity granite can have a lower one up to 0.77 mm\({}^{2}\cdot\)s\({}^{-1}\). At the end of the TT test, the CRI values doubled, reaching the highest ones at 120 \({}^{\circ}\)C. The fatigue at 200 \({}^{\circ}\)C produced a slight increase in the cooling rate, although enough to ensure that a microcracking was taking place, at least on the surface.  
The Golden Ski granite showed the highest CRI among the studied stones. That was probably due to its initial alteration with wide and long microcracks. A high permeability granite can reach diffusivity values of around 1.77 mm\({}^{2}\cdot\)s\({}^{-1}\). During the TT, the CRI decreased for all the heating cycles, with a percentage of around \(-30\%\) at the end of the test. The fatigue also led to a decrease in the cooling kinetics that implied an opposite behaviour than the rest of the rocks.
## 4 Discussion  
### Coupling Between Precipitation and Temperature with GPP and ET  
The spatial pattern of WUE in China's terrestrial ecosystem is influenced by temperature, precipitation, and the duration of the growing season [22]. WUE demonstrates considerable interannual variations, primarily attributed to the profound impact of climate change on GPP and ET. Temperature and precipitation also exhibit notable interannual fluctuations. Extensive research has established that these climatic factors are pivotal in driving GPP and ET on the QTP [20, 65]. The long-term average annual temperature of the QTP is 3.5\({}^{\circ}\)C, with annual precipitation averaging 333 mm/yr. Both temperature and precipitation exhibit a significant upward trend, and the spatial distribution of annual averages and interannual growth trends is highly heterogeneous, as further elaborated in the Supplementary Materials. WUE is determined by both GPP and ET. To explore the effects of climate change on ecosystem WUE, Pearson correlation analysis was conducted to assess the relationships between temperature, precipitation, GPP, and ET. GPP exhibits a strong positive correlation with both temperature (R = 0.897) and precipitation (R = 0.579). Similarly, ET is significantly positively correlated with temperature (R = 0.727) and precipitation (R = 0.420). Spatially, in the relatively humid eastern regions of the QTP, GPP shows significant positive correlations with both temperature and precipitation, while ET is positively correlated with temperature but negatively, albeit insignificantly, with precipitation. This suggests that in the eastern QTP, where both temperature and moisture are on the rise, GPP will rise, but ET may not necessarily follow suit. Conversely, in the drier western  
Figure 5: Spatial pattern of WUE and its interannual trend (**(a)** shows the spatial distribution of the annual average value of WUE, with the histogram in the upper left corner displaying the area proportion under different annual average values of WUE; **(b)** shows the spatial distribution of the standard deviation of the annual mean WUE; **(c)** shows the spatial distribution of the interannual change trend, with the upper left corner showing the significance of the growth trend at \(p<0.1\), where the green area indicates significant change and the red area indicates no significant change; **(d)** shows the scatter plot of WUE interannual change and the fitting curve of the interannual growth trend).  
regions of the QTP, both GPP and ET demonstrate significant positive correlations with temperature and precipitation, indicating that under the trend of warming and increased precipitation, both GPP and ET are expected to increase. The spatial pattern of correlation between the QTP's annual average temperature and precipitation with GPP and ET is illustrated in Figure 6.
## 2 Methods
### Methodology  
#### 2.2.1 Network Architecture  
The architecture of the network is displayed in Figure 1, integrating attention mechanisms to enhance the segmentation capability. The network is constructed based on the TransUNet framework, serving as its backbone. It comprises the encoding phase and the decoding phase. During the encoding phase, the image undergoes an improved coordinate attention module, which effectively extracts comprehensive contextual semantic features following a 3 \(\times\) 3 convolutional layer. The original TransUNet feature extraction uses the ResNet50 network, which is divided into three blocks, and features are extracted after each block. In this work, an improved coordinate attention module (ICAM) is placed after extracting features from each CNN block to enhance feature expression. Specifically, the extracting features from the last block of the ResNet50 architecture are divided into \(N\) pixel blocks and added with positional information before being sent to the improved vision transformer (IViT) for global information extraction. The sequence output by an improved vision transformer module is then convolved and integrated into 3D image features to prepare for upsampling. In the decoder phase, the CARAFE++ technique is employed for upsampling the feature map output by the encoder to obtain high-resolution features instead of traditional bilinear interpolation. Then, it jumps and connects with the features extracted from the corresponding blocks in the encoder to achieve a fusion of high- and low-resolution feature information. To reduce parameter calculations, depthwise separable convolutions are used instead of ordinary convolutions to gradually restore the fused features to the original image size. With a cascade approach, features with multiscale fusion are obtained with two jumps.
# Estimating Natural Frequencies of Cartesian 3D Printer Based on Kinematic Scheme
## 2 Vibration Analysis of 3D Printers
### Euler-Bernoulli First Normal Modes  
The natural frequency of the beam using the Euler Bernoulli theory is calculated by the formula [45]:  
\[\omega=a^{2}\sqrt{\frac{EI}{\rho AL}}, \tag{2}\]  
where \(E\) is elastic modulus, \(I\) is the second moment of the beam's cross-section area, \(\rho\) is beam material density, \(A\) is the cross-sectional area, and \(a\) is a wavenumber of Euler-Bernoulli mode.  
Let us calculate the natural frequency of a 20 \(\times\) 20 mm aluminum profile fixed at one end. The properties of the beam are:  
\[L=0.38\;\text{m},A=1.6\times 10^{-4}\;\text{m}^{2},I=0.7\times 10^{-8}\;\text{m}^{4},\]  
\[\rho=2700\;\text{kg}\;\text{m}^{-3},E=7.1\times 10^{10}\;\text{N}\;\text{m}^{-1}.\]  
Wavenumber \(a\) for a beam fixed at one end and corresponding to the first mode equals 1.875 [45].  
\[f=\frac{\omega}{2\pi}=\frac{1.875^{2}}{2\pi}\sqrt{\frac{7.1\times 10^{10}\times 0. 7\times 10^{-8}}{2700\times 1.6\times 10^{-4}\times 0.38}}=135.86\;\text{Hz}.\]  
In our previous study, we found the experimental natural frequency of the aluminum profile beam to test the theoretical results (see Figure 2). The beam was attached to a fixed base using hidden aluminum profile corner brackets.  
We investigated two options for fastening the beam to a fixed base: using one or two hidden brackets. The first mode frequency for the case with one hidden bracket was 22.1 Hz, and for the case with two hidden brackets, it was 45.2 Hz. As a result, the beam's natural frequency value calculated with the Euler-Bernoulli approach was significantly higher than the values obtained experimentally (the theoretical value is 135.86 Hz).  
It means that almost all energy received by the beam from the impact hammer was turned into vibration caused by the joint flexibility rather than the beam elasticity. Furthermore, various beam mounting options affect the obtained values of natural frequencies, which verifies this assumption.  
The flexible mounting option is not considered in the Euler-Bernoulli theory; however, it can be extended to such a case. Meanwhile, theoretical calculation confirms that the condition (1) is satisfied. Experimental results show that vibration on Euler-Bernoulli's first normal mode of the beam is almost indistinguishable compared to the vibration caused by a finite joint stiffness \(k\). So we can conclude that the condition (1) allows considering the aluminum profile beams used in the 3D printer construction as a rigid body.
# Long-Range Multispectral Thermal Imager for Simultaneous Measurement of Distance and Temperature
## 3 Experiments  
The field environment of the experiment is shown in Figure 2a. The multispectral thermal imager is mounted on a rotating platform to facilitate the flexible adjustment of its observation angle and position. By utilizing the rotational capability of the platform, the imager can swiftly and accurately change its viewing direction, enabling it to adapt to various measurement scenarios and acquire the desired perspective and data. The true value is obtained by a laser rangefinder. The laser rangefinder measures the distance by modulating the phase, and its basic principle is to emit a laser beam modulated by sine signal or pulse signal; the laser beam is reflected by the target object and returned. By comparing the phase difference between the emitted laser signal and the returned signal, the flight time of light propagation can be determined. Since the speed of light is constant, the distance between the target and the rangefinder can be precisely calculated based on the phase difference. The laser rangefinder selected in this paper is self-developed, and the corresponding wavelength is 0.5 \(\upmu\)m and 0.8 \(\upmu\)m. These two wavelengths do not overlap with the wavelength of the multispectral thermal imager, so it will not affect the radiation of the measured target.  
First, the distance calibration of the remote multispectral thermal imager is performed to determine its imaging characteristics at various test distances. Then, through this calibration process, the homography matrices corresponding to different test distances are obtained, enabling accurate image correction and temperature measurement conversion under varying distance conditions. As shown in Figure 2b, the calibration rod and the optical axis of the multispectral thermal imager were located on the same horizontal plane.  
The calibration rod with a length of 2 m was used in the calibration process, and the calibration rod was sprayed with high reflectivity and low reflectivity materials at 20 cm.  
The relationship between \(\Delta x\) and distance \(z\) can be obtained by calibration. In order to facilitate calculation, the calibration formula is as follows:  
\[\Delta x=A+\frac{B}{z}+\frac{C}{z^{2}} \tag{15}\]  
Among them, \(A\), \(B\), and \(C\) are substitution parameters, and the six groups of calibration results and calibration curves obtained are shown in Figure 3.
## 2 Materials and Methods  
### The Geophysical Investigations  
Electrical resistivity tomography (ERT) and Electromagnetic (EM) induction were implemented to achieve the objectives of this study. The DC resistively method of geophysical exploration is popular, has proved to be successful, and has many implications in  
Figure 2: The main drainage system of Al-Ahsa [23].  
agriculture, environmental studies, and hydrogeology. Moreover, the conductivity meter is an inexpensive, rapid, and effective means for mapping soil salinity. In this study, 2-D electrical resistivity measurements were carried out using a SuperSting R8/IP 8 channel multielectrode resistivity (Advanced Geosciences (AGI), Inc. Geoscience Dr., Austin Texas, TX, USA) and IP imaging system with 112 electrodes manufactured by Advanced Geosciences, Inc., (AGI), (Figure 3A), while the EM38-MK2 m (Geonics Limited, Meyerside Dr., Mississauga, Ontario, Canada) was used in vertical mode (Figure 3B) to measure the electrical conductivity (EC) along the study area.  
Ten profiles of 2-D (P-1 to P-10) electrical tomography using the SuperSting R8/IP 8 multielectrode channel resistivity and IP imaging system with 112 electrodes at one-meter electrode spacing were implemented. The locations of these profiles were chosen to cover different zones across the entire Al Hassa oasis. Moreover, 2240 measuring points using EM38-MK2 at vertical mode with the transmitter-receiver of 0.5 and one-meter spacing were measured with twenty electrical conductivity profiles. Figure 4 shows the base map of the ten conducted 2-D profiles along the study area. It is worth mentioning that the EM38-MK2 measured electrical conductivity was measured at the 10 exact locations of the measured 2-D profiles to correlate soil conductivity/resistivity at near-surface (These lines are Labeled Line One-1, Line Two-1, etc.).  
In contrast, the other ten EM38-MK2 electrical conductivities, each of them, were measured five meters away from the location of the 2-D transects. These EC lines are labeled Line One-2, Line Two-2, etc., to distinguish them on the data logger files from those measured at the exact locations of the 2-D transects. These ten EC lines (labeled Line One-2, Line Two-2, etc.) were measured for correlation with those measured at locations of the 2-D transects.  
For more details about the irrigation water quality refer to, for example, [PERSON] et al. [31]. For the soils of Al Hassa, refer to [PERSON] [21].  
Figure 3: Geophysical data acquisition using the SuperSting R8/IP 8 resistivity (**A**) and the EM38-MK2 system electrical conductivities (**B**) at the study area.
## 2 The Framework
### Rationale and Summary
#### 2.1.3 Technological
* Information access based on role, for example, based on Lightweight Directory Access Protocol (LDAP). * Routine backups (extent, intervals, storage concept, times, and durations). * Secure and complete removal. * Every element of the system can be aimed at by an attack; hence, weaknesses and protection analysis. * Measures are required (end-to-end security). * What are the accessible technologies aimed at ensuring encryption and key administration? * Methods to be used are supposed to be assured and encrypted securely. * Keys should be randomly generated with enough key length. * The exchange of keys should be secure and asynchronous. * The length of the keys should be short and their storage should be secure. * Destruction of keys, for example, using Security Assertion Markup Language (SAML). * Rigor verification of users in the cloud (two-factor authentication). * What are the accessible technologies to overcome the absence of standardization in CC? * The customer should make sure the service supplier utilizes standardized technology and interfaces; this should be stated in its early stage of contract. * Mix cloud methods to prevent compatibility issues between the cloud and IT systems in the customer's organization.
## 4 Conclusions  
This paper proposed new patching materials of polymer asphalt and dense-graded mixtures, which will ease the contradiction between the workability and the pavement performance in cold mixtures. It is based on the microcapsules' technique and polymer reinforcement of additive and dense gradation of aggregates. Laboratory tests and field surveys were performed to evaluate the performance when the cold mixtures were installed in low temperature and wet conditions. The main conclusions can be drawn as follows:  
The performance of polymer asphalt and dense-graded mixtures was compared with the commonly used dense-graded mixtures and open-graded mixtures in the laboratory. These tests measured the workability, storability, cohesion, and stability durability of mixtures. The polymer asphalt and dense-graded mixtures had satisfactory workability and storability for the microcapsules alleviating the cured reaction, which were similar to the open-graded mixtures. All three mixtures had adequate cohesion to resistance reveling. Stability of the polymer asphalt and dense-graded mixtures had a significant advantage in the initial term, low temperature and immersion, as well as stability forming speed, which is meaningful for pothole patches to withstand the traffic load in adverse weather. The polymer asphalt and dense-graded mixtures had strong durability for the insusceptibility of water with a high TSR of 81% and low dishing depth when accelerated loaded in immersion.  
Based on the above conclusions, it is believed that the polymer asphalt and dense-graded mixtures is practicable to apply for timely and durable pothole repairs in low temperature and wet conditions. Unfortunately, this paper only concentrated on the performance improvements of cold mixtures by microcapsules technique and polymer reinforcement in cold and wet conditions. Further studies involving properties of microcapsules in binder will be conducted at a later time.  
Figure 11: Accelerated loaded test results of DG mixtures, OG mixtures, and PADG mixtures in dry and immersed condition (at 30 \({}^{\circ}\)C).  
**Author Contributions:** Conceptualization, S.H., M.L. and W.S.; Data curation, M.L. and X.Q.; Formal analysis, S.H. and M.L.; Investigation, X.Q., Z.Z. and S.D.; Methodology, M.L.; Project administration, S.H. and W.S.; Writing--original draft, M.L.; Writing--review & editing, S.H.  
**Funding:** This research was funded by National Natural Science Foundation of China (No. 51578076), the Fundamental Research Funds for the Central Universities, CHD (No. 300102219207) and Science and Technology Project of Shaanxi Provincial Communication Group (No. 15-04K).  
**Conflicts of Interest:** The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
## 4 Prospective
### Application of Inverse Design in Optical Neural Networks
#### 4.2.2 \"Black-Box\" ONNs  
\"Black-box\" ONNs go beyond the paradigm of layered feed-forward networks. Through an inverse designed platform, \"black-box\" ONNs map input signal to desired output portsdirectly. For example, [PERSON] et al. utilized the nanophotonic neural medium (NNM) to realize handwritten digital recognition [130]. The handwritten digits are modulated to optical signals and fed to the input waveguides of NNM. After interactions with NNM, the modulated optical signals are transferred to ten output waveguides, which represent digits from 0 to 9. The output waveguide ports with highest light intensity suggests the input digit to be such value. Their \"black-box\" ONN is shown to be much more compact than the layered ONNs as no layers or neurons are needed in this case. However, the \"black-box\" ONN is not scalable. For more difficult tasks, the footprint of \"black-box\" ONN needs to be increased, which makes the inverse design of NNM too time-consuming to be realized.  
Integrating all optical neural networks on silicon platform is still an emerging and significant research topic. The silicon platform with large effective refractive index is compatible to CMOS technology, which makes the cheap and massive fabrication possible. With the assistance of inverse design, many compact components could be designed and integrated for complex ONNs, while energy consumption can be reduced comparing with electronic DNNs.
## 3 Methodology
### Time and Space Complexity
#### 3.4.1 Time Complexity  
The original YOLOv5 performs convolution and feature map processing across multiple detection layers. The main sources of time complexity arise from operations such as convolution, Feature Pyramid Networks (FPNs), and Non-Maximum Suppression (NMS). Assuming the number of convolution layers is L and the computational complexity of each layer's convolution is \(O\left(H\times W\times C_{in}\times C_{out}\times k^{2}\right)\), the overall time complexity is the sum of the complexities of each convolution layer along with the post-processing step of NMS.  
With the addition of BiFPN, each feature fusion operation requires the execution of element-wise weighted summation, convolution operations, and bidirectional processing of the feature pyramid, which increases the time complexity. However, BiFPN enhances detection accuracy and the efficiency of utilizing multi-scale features by optimizing the feature fusion strategy through learnable weighted fusion. The integration of CBAM introduces an attention module that generates importance weights for feature maps. Since the output of each convolution layer must pass through the attention mechanism, CBAM increases the computation time of the network.  
On the other hand, the C3 Ghost module reduces some of the computationally intensive convolution operations by utilizing Ghost modules that simulate convolution through linear transformations, thereby lowering the time complexity. Compared to the original YOLOv5 convolution layers, the complexity of C3 Ghost is decreased.
# Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron
## 6 Simulation Setup, Evaluation Criteria, Dataset, Results and Discussion
### Evaluation Criteria  
#### 6.2.1 Accuracy  
One of the evaluation criteria that determines the model's overall accuracy is accuracy. The percentage of all samples that the classifier successfully classifies is known as overall accuracy. Equation (4) can be used to calculate accuracy.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{4}\]  
When an observation is true positive (TP), it means that it is both predicted to be positive and is, in fact, positive. A true negative (TN) observation is one that is both predicted to be and really is negative. False positive (FP) observations are those that were expected to be positive, but turned out to be negative. False negative (FN) observations are those that are projected to be negative, but turn out to be positive.