esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Fire Consequence Prediction Model
## 3 Configuration of the Prediction Model
### Determination of the Fire Size  
This study analyzed the fire consequence of the maximum thermal radiation and damage distance. In this case, the fire size must be determined based on the maximum thermal radiation and damage distance. The effect of thermal radiation was obtained using the Korea Occupational Safety and Health Agency (KOSHA) accident damage prediction method in Table 5, which presents the damage effect according to the intensity of thermal radiation [17; 18].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Set**} & \multicolumn{2}{c}{**Error**} \\ \cline{2-3}  & **Distance** & **Power** \\ \hline Training set & 8.62\% & 6.25\% \\ Test set & 12.46\% & 6.95\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Errors of consequence prediction data.  
Figure 10: MSE of consequence prediction data.  
A value of 12.5 kW/m\({}^{2}\) is the minimum thermal radiation that can cause injury to a person within a short time. Thermal radiation higher than 25 kW/m\({}^{2}\) can cause injury to people and damage steel structures. The damage distance classifies the distances of damage from a flame in the event of a fire as 10 m, 50 m, and 100 m.  
The fire consequences are grouped into three levels, C1, C2, and C3, according to the maximum thermal radiation and damage distance, as presented in Table 6. The size of the fire is classified into three levels of consequence. C1, C2, and C3 are the terms used in the matrix to consider the variation in consequences due to fire radiation with changing distance.  
The final size of the fire was determined by comparing fire consequences C1, C2, and C3, which were classified by referring to Table 6, with the small, medium, and large hole sizes predicted through monitoring. The hole size is an important factor when determining fire risk because if the hole size is large, the size of the fire increases instantly.  
To set the size of the fire, the hole size and severity were indicated by levels 1, 2, and 3 by applying the concept of the conventional risk matrix. It is assumed that the size of the fire is large if the value obtained by multiplying each level is high. The size of the fire was determined to be small, medium, and large if it corresponded to (1) or (2), (3) or (4), and (6) or (9) in Table 7, respectively.
## 6 Security Analysis and Discussion
### Security Discussion and Countermeasures against Possible Attacks  
In this authentication structure, the server only stores the list of original challenges and the information of trained CNN. Therefore, the responses are not stored in the server  
Figure 8: The values for false acceptance rate (FAR) and false rejection rate (FRR) for deep PUF.  
storage. This property improves the security against the insider attack that can be executed by a malicious entity with authorized server data access.  
Snooping-based and modeling attacks take place when multiple CRPs related to the same memory block are accessed and learned by the adversary [34]. These attacks can be prevented by exploiting various and separate blocks of the DRAM during the deep PUF configuration. Since, as the implementation results indicate, different memory blocks entail various features, it is difficult to model all blocks of a DRAM chip using the limited leaked CRPs.  
PUF re-use attacks are theoretically possible in the deep PUF-based authentication mechanism, as the server may send identical challenge to a PUF device. In such a way, if the adversary can intercept in the process, the authentication system can suffer from re-use attacks. One alternative way to avoid such attacks is to utilize a simple encryption algorithm to enhance the security of transmitted data [18]. One-time-use protocol is another way, which can be employed to prevent re-use attacks considering the application requirements [35]. Another solution is to include erasability and certifiability as two additional features in PUF application [36]. The property of certifiabilty provides an offline certification to check and verify the expected features from the PUF responses. Erasability can be conducted using reconfiguration methods, which are compatible with DRAM PUF organization. We leave the implementation and validation of these techniques to future work.
## 4 Evaluation and Discussion
### Evaluation Index of the Quality of the Generated Data
#### 4.1.2 Cumulative Distribution Function (CDF)  
The CDF, also known as the cumulative density function of a real-valued random variable \(Z\), which is assessed at z, is the probability \(Z\) will have a value that is either less than or equal to z [1]. Under the scenario of continuous distribution, it provides the area under the probability density function ranging from minus infinity to z. The CDF of the random variable \(Z\) can be expressed as the integral of its probability density function (f\({}_{\text{Xz}}\)), as will be shown in Equation (11):  
\[\text{F}_{\text{z}}(\text{z})=\text{P}(\text{Z}\leq\text{z})=\int_{-\infty}^{ \text{z}}\text{f}_{\text{z}}(\text{t})\text{dt} \tag{11}\]  
The CDF compares the statistical similarity between the original samples and samples made with the GAN-CSVR model by looking at the probability distribution of solar radiation data indirectly.  
Regarding the two datasets, the CDF between the average original solar radiation data values and curves and the average generated solar radiation data values and curves by the GAN-CSVR model are compared. The average error value of the CDF of solar radiation data between the original and augmented datasets by the GAN-CSVR model is 7.443981, 4.968554, and 1.495882 for Los Angeles, San Diego, and San Francisco, respectively. The visualization curves for the CDF of solar radiation data between the original dataset and the augmented dataset by the GAN-CSVR model for San Francisco are quite near each other, followed by San Diego compared to Los Angeles. This difference in visualization curves is due to the diverse topography of Los Angeles, as well as quick weather variations and the periodic cloudiness cycle.  
These findings support the statistical similarity features of new data generated by the GAN-CSVR model, depicted in Figure 5 (i.e., one curve is quite near another). It is important to note that there are very close curves in proximity between the two CDFs in all three locations. These demonstrate and validate that the GAN-CSVR model can produce new data with a similar distribution to the original dataset for the three locations. In conclusion, the GAN-CSVR model could better mimic the original data distribution and support the statistical similarity.  
Figure 5: _Cont._  
In this study, the probability density functions (PDF) of the original and augmented data are continuous, which are represented by \(\mathrm{P_{g}(t)}\) and \(\mathrm{P_{data}(t)}\), respectively. As a result, the associated CDF is shown in Equations (12) and (13):  
\[\mathrm{F_{data}(z)}=\mathrm{P(Z\leq z)}=\int_{-\infty}^{z}\mathrm{P_{data}(t)dt} \tag{12}\]  
\[\mathrm{F_{z}(z)}=\mathrm{P(Z\leq z)}=\int_{-\infty}^{z}\mathrm{P_{g}(t)dt} \tag{13}\]
## 7 Performance Evaluation
### Simulation Model  
The network comprises 400 nodes randomly deployed in a 2,000 m \(\times\) 2,000 m area. We use the same energy model in _ns-2.1b8a_[21] that requires 0.66W, 0.359W and 0.035W for transmitting, receiving and idling, respectively. We set the power consumption rates of RC5 according to [22][23] for encryption, MAC computation, and random number generation are 0.65W, 0.48W, and 0.36W, respectively. As analyzed in [19][24], we set the time consumption for encrypting 64 bits with RC5 0.26 ms, generating 64 pseudorandom bits takes 0.26 ms, and computing a 4- byte MAC requires 0.13 ms. The simulation uses MAC 802.11 _Distributed Coordination Function_ (DCF) and nominal transmission range of each node is 250m. _Two-ray ground_[25] is used as the radio propagation model.  
Each data packet has 64 bytes, query packets and the others are 36 bytes long. Additional bytes for MACs and _nonce_ values are also put into each message. The default number of sinks is 8 moving with speed 10m/s according to _random way-point_ model. Two sources generate different packets at an average interval of 1 second. Summary of parameters and defined values are shown in Table 3.
## 1 Introduction
### Contribution of the Paper  
This paper further develops our research on the utilization of spectrum [32; 33]. In Ref. [32; 33], we formulated and analyzed the utilization of spectrum for the SUs and PU, i.e., sum utilization of spectrum. In Ref. [33], we considered different SU and PU co-existing scenarios, and investigated the sum utilization of spectrum, with retransmission capability for the SU, to ensure the reliable packet delivery. We characterized the gain in the sum utilization of spectrum with the SU retransmission capability, as SU was allowed to retransmit its packet during the PU mean inactive duration, and SU packet length, in order to improve the link reliability. In our previous studies, sum utilization of spectrum was characterized while considering the single channel. By contrast, in this study, we formulate and analyze the utilization of spectrum policy for multi-channel scenarios, with the aid of sensing and handoff capabilities. We investigate the utilization of spectrum for PU and SU, with SU opportunistically operating on various PU channels. We derive closed-form expressions for sum utilization of spectrum, evaluating the impact of spectrum handoff under different network topologies. Our performance results show that the spectrum handoff approach, based on sensing outcomes and PU interruptions, enhances the sum utilization of spectrum.
# Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth
## 2 Materials and Methods
### Cropped Land Modeling  
Of the several ML algorithms that are available on the GEE platform [39], we elected to use the random forest (RF) model. RF is an ensemble learning method that uses the most common output of a large number of independent decision trees as its prediction. RF models have been widely used in interpreting remote sensing images because they produce accurate results, are computationally efficient, and can handle high data dimensionality without overfitting [40]. The model was parameterized using training data consisting of four S2 features and three S1 features for each of the labeled pixels. The S2  
Figure 2: Examples of 10 \(\times\) 10-m sections of UAV images representing selected categories: (**a**) Maize, (**b**) Beans, (**c**) Cassava, (**d**) Bananas, (**e**) OtherVeg, (**f**) Trees, (**g**) NonVeg.  
imagery from December 2018 through February 2019 was evaluated for cloud cover, and two images from late January were identified based on minimal cloud cover for the entire country (Table 3). The four S2 features were signal values in B2, B3, B4, and B8, extracted from a composite S2 image that was generated by filling in clouded pixels in the 2019-01-28 scene with unclouded pixels from the 2019-01-23 scene when available. The three S1 features were median VV values for the months of November 2018, December 2018, and January 2019. The training/testing datasets were generated from the ground-truth data using an 80/20 split.
# Advances in Random Fiber Lasers and Their Sensing Application
## 5 Conclusions  
In the last decade, researchers have proposed different structures of RFLs. RFLs based on the AOF or the special optical fiber are gradually explored, not only commercial SMF. This means gain mechanisms are not only including passive gain, but also including active gain and hybrid gain. Moreover, more properties of the RFL have attracted much attention from researchers, such as polarization, dispersion, and nonlinear effects.  
Through reviewing the progress of the RFL's output emission in the past ten years, it is found that hot areas of the RFLs involve output power, wavelength, and line-width. Approaches of improving output characteristics have been developed greatly, including the aspect of the pump, gain, and feedback, which would drive the further development of core technology in related areas.  
The practical application is an important orientation for the RFL, such as optical fiber sensing. In the RFL system, long-distance sensing can be used to achieve the purpose of safety monitoring, regardless of whether the optical fiber plays the role of light transmission or sensing.  
Figure 19: Experimental setup of 175 km BOTDA. EOM, electronic-optical modulator; AOM, acoustic optical modulator; FUT, fiber under test; BPD, balanced photo-detector; DAQ, data acquisition.  
Figure 20: BFS difference versus distance. (**a**) Along the whole fiber; (**b**) around the heated section [127].  
However, the RFL still requires further theoretical and experimental exploration. It could be worth exploring other pump sources, except for ASE or narrow line-width lasers, as they could be utilized to optimize the output characteristics of the RFL. The random feedback of most RFLs is provided by Rayleigh scattering, which is quite weak in the scheme, and a few investigations on improving the strength of random feedback have been finished. The physical properties of the output beam have yet to be completed. Furthermore, the potential application of RFLs could be explored in optical fiber sensing.  
[PERSON], [PERSON], and [PERSON], proposed the ideas, wrote, reviewed, and edited the manuscript. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] revised the manuscript. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number: 61527819&61875146; Key Research and Development Program (High-Tech Field) of Shanxi Province, grant number: 201903D121177&201803D121064; Sanjin Scholar Distinguished Professors (expert) Support Program of Shanxi Province, grant number: 201617; and Science and Technology Innovation Project of Colleges and Universities in Shanxi Province, grant number: 2019L0290.  
The authors declare no conflict of interest.
# A Perspective on Terahertz Next-Generation Wireless Communications
## 5 Forward View  
In this review, we have only scratched the surface of the numerous research efforts underway toward terahertz wireless communications. While many such efforts have been helpful in advancing the state-of-the-art, relatively few include the implementation and testing of complete terahertz communication systems (see also [114] for a list of 17 such systems). This appears to be an increasingly important direction forward for several reasons. As mentioned earlier, channel sounding is still immature in the terahertz, but requires realistic signals and situations (e.g., mobile receivers, full-speed modulators) to quantify. Terahertz devices for wireless communications can be improved in isolation, but a holistic design approach may yield much more dramatic improvements overall. Furthermore, complete, space-based terahertz systems are already being researched, or implemented, offering opportunities to borrow and translate technology to terrestrial systems. As 5G fully deploys, research  
Figure 4: Dynamic interference in different low-noise amplifiers generated by pulse-laser induced SEE experiment. For the core cascode stages, either forward (F) or inverse (I) mode SiGe HBTs are used. Used with permission from [109].  
into terahertz communication will increasingly demand fully operational communication systems. These are still quite rare, and well-developed ones are still non-existent. Nevertheless, the number of applications that would benefit from high-speed terahertz wireless continues to increase [114]. This provides ample motivation to someday make terahertz the next generation (6G?) of wireless communication technology on the verge of full deployment.  
All of the authors contributed equally to the conceptualization, investigation, original draft preparation, review, and editing of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 4 Challenges in Indoor Localization Systems
### Privacy and Security Concerns  
In healthcare environments, tracking systems must comply with regulations to protect patient privacy. The continuous tracking of patients, staff, and assets raises concerns about data breaches and unauthorized access [23]. Encryption protocols, access controls, and anonymization techniques are necessary to ensure data security. Similarly, in military applications, the risk of adversaries intercepting localization data necessitates robust encryption and tamper-proof hardware [30].
## 2 Finite Element Modeling
### Modeling Details and Load Application  
The frame was modeled in OpenSees (3.3.0). Beam and column elements adopted the distributed plastic hinge element dispBeamColumn for its accuracy and convergence. Beam-column joints were simulated using the joint2D element. The material behavior of concrete and steel was modeled with Concrete02 and Steel02, respectively. Existing studies [18; 19; 20] have demonstrated that the selected element types in OpenSees can effectively capture the mechanical behavior of concrete structures. Accordingly, the same elements and material models are adopted in this study for simulation. To capture composite action accurately, both concrete and steel sections were divided into ten fibers; an example of column section discretization is shown in Figures 7-9, illustrating the finite element model, where the y-direction represents the five-span frame width and the z-direction of the frame height. All parameters match those of the prototype structure. Grain-induced lateral and vertical pressures were simulated by applying horizontal line loads to columns and vertical line loads to beams at each floor level, with corresponding nodal masses adjusted to reflect load conditions; the specific calculation method is as follows: according to Equation (1) of the Code for Design of Grain Storehouses (GB 50320-2014) [21], the lateral pressure at the wall base is calculated and used to determine the total lateral force on the wall, which is then divided by the column height to obtain the equivalent uniform line load on the column; beam loads are computed using Equation (2) and likewise converted into equivalent uniform line loads for application in the model.  
\[P_{\rm hk}=k_{\gamma}s \tag{1}\]  
In the equation, \(k\) is the lateral pressure coefficient for the grain, taken in accordance with the code; \(s\) is the distance from the grain's surface to the section under consideration; and \(\gamma\) is the unit weight (weight density) of the grain.  
The vertical pressure of piled bulk grain on the ground can be calculated by the following equation.  
\[P_{\rm vk}=\gamma h \tag{2}\]  
In the equation, \(h\) is the grain fill height.  
Eight loading conditions were designed, as listed in Table 5, to evaluate the seismic performance of the CSSC grain warehouse under varying grain-fill scenarios. The analysis was conducted at frequent, design-level, and rare seismic intensities, with emphasis on differences in structural response, pushover curves, and performance points.  
Figure 6: Plan layout of frame structure. Note: 1–4 and A–D are axis labels used for layout reference.  
Figure 8: Openeses model diagram.  
Figure 7: Section fiber discretization of the model.  
Figure 9: Loading schematic for 3-span illustrative model (EFE).
# Prediction of the Moisture Content in Corn Straw Compost Based on Their Dielectric Properties
## 4 Discussion
The energy consumed by friction and collision between polar molecules increases, which revealed an increasing trend of the loss factor \(\varepsilon^{\prime\prime}\). A similar trend was observed in wheat seeds and wheat flour [50]. The analysis revealed that the moisture content of the compost could be expressed in terms of the dielectric parameters of the compost. Therefore, the coupled PCA-SVR algorithm with parameter optimization was used in this experiment, and the results revealed that the full variables and the characteristic variables selected by PCA did not differ considerably, which indicated that both \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) can predict the analysis of compost moisture content to some extent. However, moisture conditions of the compost at various decomposition stages differed considerably. Furthermore, the dielectric parameters were affected considerably by material species, bulk weight, and temperature. In this study, only the moisture profile of the composted sheep manure straw after decomposition was discussed based on dielectric properties. Although the model can predict the compost moisture content satisfactorily, some limitations still exist. Therefore, for improving the accuracy of the dielectric method for detecting the moisture content of compost materials in the composting process, further studies are critical for different stages and species of compost.
# Weak and Dynamic GNSS Signal Tracking Strategies for Flight Missions in the Space Service Volume
## 5 Simulation and Experiment Results  
Under normal conditions, the comparison between loop measurement error and its threshold is used to judge whether the loop is out of lock. Considering the balance of both measurement accuracy and rapid response capability, a threshold of 15\({}^{\circ}\) is regarded as a judgment indicator about whether the loop is out of lock or not in this section. Hereafter, a testing scenario is built up at first, then the experimental system and initial settings are introduced, and the Doppler frequency estimation error is used to weigh the performance of our tracking strategies.