esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
At intermediate levels (around 250 m), the average values of velocity (see Figure 6a) and temperature (see Figure 6b) are closer to the overall average, and the pollutant concentration (see Figure 6c) decreases due to vertical mixing. At \(z\) = 0 m, the lowest average velocity is observed (see Figure 6a) of about 1.7 m/s, with maximum velocity values at individual points reaching \(\approx\)4.0 m/s. The temperature field (see Figure 6b) is the highest average value (16.8 \({}^{\circ}\)C), with maximum temperatures being close to 19 \({}^{\circ}\)C. The concentration of pollution (see Figure 6c) is high, with maximum values reaching \(\approx\)100 \(\upmu\)g/m\({}^{3}\). At \(z\) = 250 m, the parameters approach the general average value: the average speed is about 2.1 m/s, the temperature is about 16.1 \({}^{\circ}\)C, and the pollution concentration is already somewhat lower, an average of about 40-45 \(\upmu\)g/m\({}^{3}\). At \(z=500\) m, the average velocity can reach 2.5-3 m/s with local maxima higher, and the temperature is somewhat lower, for example, 15.5 \({}^{\circ}\)C, which corresponds to a typical temperature inversion in the upper layers of the lower atmosphere. The concentration of pollutants is minimal compared to surface values. A gradual increase in mean velocity is observed with altitude from the lower levels (approximately 1.7 m/s) to the upper levels (approximately 2.5 m/s). Temperature decreases with altitude (from 16.8 \({}^{\circ}\)C to 15.5 \({}^{\circ}\)C), and pollutant concentrations show an exponential decrease, which is typical of atmospheric mixing models (see Figure 6d). The vortex structure of the airflow leads to a local decrease in speed in the center and an increase at the periphery. The vertical stratification of temperature and pollution confirms traditional models of distribution in the atmosphere: the hotter and more polluted surface environment mixes with the colder and cleaner upper environment. The central region is characterized by a higher level of pollution and temperatures, while the peripheral regions demonstrate higher air flow velocities and lower pollutant content.
# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 3 Background
### Signal Classification
#### 3.4.1 Automatic Modulation Classification  
Automatic modulation classification (AMC) classifies radio signals by identifying modulation types, schemes, and types of wireless transmission [20; 59; 60]. AMC can extract digital baseband information even under limited prior knowledge [59]. Moreover, AMC is important for many applications, including signal detection, spectrum monitoring, software-defined elastic optical networks, and cognitive radio [61; 62]. To achieve efficient transmission, transmitted signals are generally modulated using different modulation techniques to encode data on multiple carriers frequencies. The most well-known modulation methods are 4 FSK, 16 QAM, BPSK, QPSK, and OFDM. The AMC method can be categorized as likelihood-based and feature-based approaches [21; 22; 63], where likelihood-based classifiers need parameter estimation. On the other hand, feature-based approaches do not require parameter estimates and have recently increased interest. In contrast, feature-based approaches consist of two steps: feature extraction and classification, which can make decisions based on a specific criterion.
# A Lightweight Small Target Detection Algorithm for UAV Platforms
## 3 The Proposed RSG-YOLO Algorithms
### Neck Improvements  
The small target and complex background in the UAV viewpoint make it difficult to capture distinct features, and with the different levels of feature extraction and high downsampling in the backbone, it is found that the deeper level contains less small target semantic information and even integrates into the background noise, which results in the degradation of detection performance. Therefore, a feature layer with rich small target semantic information is added at the neck end, which is linked to the 160 \(\times\) 160 shallow feature residuals in the backbone. The rich feature information contained in the shallow  
Figure 4: The CAMSPD operation details.  
layer is fused with the deeper semantic information, expanding the resolution of the model and further enhancing the ability to perceive small target features.  
Due to the use of a decoupled head structure, the model parameters of the YOLOv8 detector head are mainly concentrated in three different scales: 80 \(\times\) 80, 40 \(\times\) 40, and 20 \(\times\) 20. In UAV aerial photography application scenarios, the targets are usually mainly small or even tiny targets, and the frequency of large targets is low. Taking the VisDrone2019 dataset as an example, it is found that up to 44.7% of the targets in its training set are small targets, with pixel areas less than 32 \(\times\) 32 pixels, and with 12.05% of the small targets having a pixel size less than 16 \(\times\) 16 pixels [23]. Based on the above analysis, it was decided that the deepest 20 \(\times\) 20-pixel large target detection layer in the feature pyramid at the neck end of the original network should be removed, and the target detection head connected to it at the head end is also removed to reduce redundancy in the network and achieve a lightweight network status.  
By adding a 160 \(\times\) 160-scale shallow target detection layer and removing the redundant 20 \(\times\) 20-scale deep target detection layer, the shallow spatial information of small targets is better utilized and combined with the deep semantic information, which enhances the perception of weak and small targets while achieving lightness of the model. This improvement allows the model to perform better in the target detection task with regard to the UAV aerial photography perspective, especially in detecting small and tiny targets, which improves the detection speed and accuracy.
# Positioning Methods and the Use of Location and Activity Data in Forests
## 4 Location-Based Services, the Internet of Things, Wearable Technology, and Big Data
### Wearable Technology  
Wearable technology is a general term that refers to miniaturized sensors such as accelerometers, gyroscopes, and magnetometers built into watches, clothing, or other accessories that can be worn comfortably on the body to enable real-time, unobtrusive monitoring of physical metrics [189; 190; 191]. The first wearable technology that received significant media attention was the Wearable Motherboard, a smart shirt designed to monitor body vital signs in combat situations [189]. Wearable technology has since been deployed in a variety of health and fitness applications, predominately related to the continual, long-term observation of patient health status remotely [190]. Individuals with chronic conditions such as Parkinson's disease can wear unobtrusive sensors in the home so that health care providers can more accurately assess physical or physiological changes and recommend treatment or medication [190]. Wearable technology can also facilitate disease diagnosis and preventative care [189; 190]. Since physiological data can be collected and processed in real-time in networked environments, wearable technology can initiate medical response in the case of sudden emergencies like cardiac arrest or epileptic seizures.  
Wearable technology can also refer more generally to any computerized device worn on the body. For instance, eye-wearable devices such as Google Glass(tm) deliver visual information to a user via a small screen. Unlike many other wearable device applications that focus on collecting data, eye-wearable technology is typically employed as a conduit for conveying data to a user, such as hands-free instruction in medical training or vehicle maintenance [191; 192].  
Figure 12: Example use of a geofence to separate grazing from newly planted conifer seedlings. The virtual fence is defined by a series of GNSS coordinates. When a GNSS collar worn by livestock approaches the boundary, an alerting sound or sensation is triggered.  
[PERSON] et al. monitored logger work activities and sleep patterns with fitness and sleep bands in order to evaluate worker health and identify hazardous work conditions [193]. The [PERSON] et al. study highlighted several considerations for deploying wearable sensors in forestry applications, including long battery life, little to no operation requirements by users, and low-cost due to the potential for devices being dislodged and lost during work activities [193]. In another study of loggers, [PERSON] et al. collected heart rate and step count data using Fitbits(tm) in conjunction with in-situ reaction time measurements in an attempt to predict worker fatigue [194]. The 2017 study illustrates the variability of physiological data among individuals and the importance of context for understanding and interpreting field data. Additionally, due to the personal nature of information collected with wearable technology, privacy and the ramifications of sharing such data should be carefully considered [194].
# TEDL: A Text Encryption Method Based on Deep Learning
## 10 Experiments and Performance Analysis  
This section presents the experiments and corresponding analysis of TEDL, showing that it achieves a balance between security and efficiency, which make it suitable for transmission of a large amount of data. All the experiments are performed on an identical platform with system configuration of 17 processor @ 2.50 GHz and 8 GB Ram, and evaluated on two datasets in different languages, one is Chinese Wikipedia corpus (about 1.3 GB) ([[https://dumps.wikimedia.org/zhwiki/](https://dumps.wikimedia.org/zhwiki/)]([https://dumps.wikimedia.org/zhwiki/](https://dumps.wikimedia.org/zhwiki/))) and the other is a subset of the English Wikipedia corpus (about 600 MB) ([[https://dumps.wikimedia.org/enwiki/](https://dumps.wikimedia.org/enwiki/)]([https://dumps.wikimedia.org/enwiki/](https://dumps.wikimedia.org/enwiki/))). Relative codes are available on GitHub ([[https://github.com/AmbitionXiang/TEDL](https://github.com/AmbitionXiang/TEDL)]([https://github.com/AmbitionXiang/TEDL](https://github.com/AmbitionXiang/TEDL))).  
Our experiments focus on following issues:  
* _Recovery_. It verifies that the ciphertext is decrypted successfully, even if the ciphertext is tampered with in an insecure channel.
* _Consumed time for brute force_. It measures the ability of encryption methods to resist brute force attacks.
* _Frequency analysis_. It is about the frequency distribution of cipher symbols, characterizes the confusion.
* _Correlation_. It refers to the correlation analysis between encrypted data and original data.
* _Sensitivity analysis_. It also measures the strength of encryption methods against cracking and hacking threats. For plaintext sensitivity or key sensitivity, it is high when changing a small number of bits in plaintext or key results in a large variance in ciphertext. As for ciphertext sensitivity, it is embodied when a natural error or intentional tampering in the ciphertext is remarkable [52].
* _Efficiency analysis_. It measures encryption speed.
* _Generality analysis_. It studies whether a method is suitable for multiple models.
## 3 Results and Discussion
### Substrate Temperature Effects
A larger-than-expected (>60 atomic %) oxygen content measured in all films indicated some experimental errors originating from the oxygen atoms being either present within the substrate pores or trapped within the film pores. For the amorphous-phase garnet-type layer, deposited from a metal-oxide mixed sputtering target of nominal composition Bi\({}_{2.1}\)Dy\({}_{0.9}\)Fe\({}_{3.9}\)Ga\({}_{1.1}\)O\({}_{12}\), only the volume-averaged composition of films could be derived (Table 1) from the obtained EDS data. The experimental results summarized in this section can be used as a guide for depositing new garnet-type thin-film materials with high MO performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Samples/Parameters** & **Sr\({}_{\text{I}}\)** & **Sr\({}_{\text{I2}}\)** & **Sr\({}_{\text{I3}}\)** & **Sr\({}_{\text{I4}}\)** \\ \hline Substrate temperature & RT (-22) \({}^{\circ}\)C & 150 \({}^{\circ}\)C & 300 \({}^{\circ}\)C & 600 \({}^{\circ}\)C \\ Substrate stage rotation rate (rpm) & 15.5–16 & 15.5–16 & 15.5–16 & 15.5–16 \\ Substrate type & Quartz & Quartz & Quartz & Quartz \\ Argon (Ar) pressure & 2 mTorr & 2 mTorr & 2 mTorr & 2 mTorr \\ RF power densities at target & 3.8 W/cm\({}^{2}\) & 3.8 W/cm\({}^{2}\) & 3.8 W/cm\({}^{2}\) & 3.8 W/cm\({}^{2}\) \\ Deposition run time & 62 min & 62 min & 62 min & 62 min \\ Garnet layer thicknesses deposited & 340 nm & 350 nm & 345 nm & 235 nm \\ Annealing process parameters & 670 \({}^{\circ}\)C for l h & 670 \({}^{\circ}\)C for l h & 670 \({}^{\circ}\)C for l h & 670 \({}^{\circ}\)C for l h \\ \hline Best MO & Sp. Faraday rotation (deg/\(\mu\)m) & 7.5 & 6.84 & 7.15 & 5.02 \\ performance & MO figure of merit (degrees) & 15.2 & 13.23 & 13.21 & 8.7 \\ measured in & Coercivity (H\({}_{\text{d}}\)) (Oe) & –300 & –150 & –100 & –220 \\
532 nm & Saturation magnetization (H\({}_{\text{sat}}\)) (Oe) & –500 & –300 & –500 & –480 \\ \hline \multicolumn{5}{c}{Average crystallite size (nm)} & 33.23 & 36.30 & 35.95 & 30.01 \\ \hline \hline \end{tabular} MO: magneto-optical; RF: radio frequency; rpm: revolutions per minute; RT: room temperature; Sp.: Specific.  
\end{table} 
Table 1: Summary of the process parameters and the obtained materials’ properties for Bi\({}_{21}\)Dy\({}_{0.9}\)Fe\({}_{39}\)Ga\({}_{1.1}\)O\({}_{12}\) garnet thin films fabricated at different substrate temperatures. Figure 2: _Cont._Figure 2: Measured transmission and fitted absorption spectra of annealed garnet layers deposited at different substrate temperatures (**a**,**b**); X-ray diffraction datasets obtained from annealed Bi\({}_{2.1}\)Dy\({}_{0.9}\)Fe\({}_{3.9}\)Ga\({}_{1.1}\)O\({}_{12}\) layers deposited at different substrate temperatures onto quartz substrates (**c**); measured concentrations (atomic %) of the five elements found to be present in the sputtered garnet (as-deposited) layers (**d**).
# A Survey of Structure of Atmospheric Turbulence in Atmosphere and Related Turbulent Effects
## 2 Structure of Atmospheric Turbulence in Earth's Atmosphere
### Theoretical Studies on Atmospheric Turbulence
[PERSON] and [PERSON] thought that the deviations from Kolmogorov theory were not random. On the other hand, it is currently known that, in addition to the classical Kolmogorov spectrum, other types of spectra also appear at different scales depending on external conditions. They constructed a unified physical method to develop a generalized model of electromagnetic radiation propagation in a random atmosphere by taking into account the behavior characteristics of PSF under different conditions at different scales [4]. [PERSON] and [PERSON] investigated helical scaling in turbulence. When helicity was considered, an \(E\propto\kappa^{-7/3}\) energy spectrum was obtained in the asymptotic limit [5]. [PERSON] et al. studied the isotropic and anisotropic spectra of passive scalar fluctuations in turbulent flow, and it was shown that the anisotropic \(\kappa^{-3}\) spectrum of passive scalar fluctuations was universal and the derived general spectra recovered the known spectra of passive scalar fluctuations \(\propto\kappa^{-5/3}\) and \(\propto\kappa^{-17/3}\) in the particular case of the Kolmogorov spectrum of turbulent velocity field [6]. [PERSON] et al. investigated helicity cascades in fully developed isotropic turbulence, and it was concluded that the existence of simultaneous energy and helicity cascades was envisaged based on total helicity conservation in viscid incompressible flows, and the helicity spectrum was developed for the above case [7]. [PERSON] et al. explored mechanisms of turbulent mixing by numerical simulations of one-dimensional and two-dimensional mixing with Prandtl-number \(Pr<1\)[8]. [PERSON] et al. presented evidence for a saturated spectrum of atmospheric gravity waves [9]. To summarize, the further development of the theory of passive conservative flux transport has shown that although Kolmogorov spectrum is important, it is only part of the more general behavior of turbulent passive conservative flux transport. When the atmosphere is in a relatively stable state, the behavior is inconsistent with Kolmogorov turbulence statistics. [PERSON] defined this type of turbulence as non-Kolmogorov turbulence [10]. The existence of non-Kolmogorov turbulence was proved theoretically and above all, other theoretical studies have further confirmed this conclusion.
## 7 Evaluation Study: Comparison between the Two Models
Furthermore, as method 1 moves the estimated curves according to pre-defined coefficients (based on the typical scatter), the presence of an outlier has a minor effect as it affects only the estimated means. Indeed, both the original (i.e., the black one) and modified curves (i.e., the pink one) remain close to each other. On the other hand, in method 2, it is possible to note how the identification of outliers is a crucial aspect. The two estimated curves are significantly different in both the limited-life and long-life regions, especially in the latter. Here, if the outlier, whose lifetime is the highest at the load level, is removed, the new gear SN curve indicates a higher resistance. This counterintuitive behavior is related to the estimation of \(s_{\log{(\sigma_{F0})}}\). As a unique standard deviation is estimated by the model (i.e., \(s_{\log{(\sigma_{F0})}}\)), the presence of outliers implies an overestimation of \(s_{\log{(\sigma_{F0})}}\). Such overestimation leads to a higher variance associated with the long-life region, with a detrimental effect on the gear SN curve. This aspect turns out to be the most crucial one. Furthermore, it is also possible to note how, as the model considers all the experimental points as a unique dataset, high load level points influence the long-life region (and vice versa). Nevertheless, once the outlier has been removed, both methods 1 and method 2 give, once again, similar results.
# Condenser Design for On-Board ORC Recovery System
## 3 Thermodynamic Model of the Condenser  
The condenser is usually the power plant's largest component. Consequently, it is important to control its size and the pressure drop [19; 20; 21; 22]. The chosen condenser has a particular configuration: the working fluid passes into the pipes and the cooling water embeds the shell. Cooling water is always in circulation and a recirculation pump is used. As an initial configuration, this pump circulates water from the radiator to the condenser.  
The condenser dimensions strictly depend on the overall heat coefficient U (maximum value adopted). Besides, the U coefficient depends on the exchange area and the heat transfer coefficients. Optimizing U is difficult because a compromise between multiple parameters has to be found. Condensation occurs in three different phases:  
1. The de-superheating, where the fluid's temperature almost reaches the condensation temperature.
2. The condensation phase, where the gaseous working fluid becomes liquid.
3. The sub-cooling phase is used to slightly decrease the temperature in order to make sure that all the fluid is liquid.  
The condenser has been theoretically divided into these phases; each phase has been evaluated and designed on the basis of the simulator's results. In each part, a turbulent stream is considered in order to maximize heat exchange. The cooling fluid is water. To maximize the heat exchange and reduce the component dimensions, copper elements  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**R245 fa Input Data**} \\ \hline
**mass flow rate (kg/s)** & 0.5 \\
**boiler inlet temperature (K)** & 333 \\
**boiler outlet temperature (K)** & 390 \\
**boiler inlet pressure (kPa)** & 1250 \\
**boiler outlet pressure (kPa)** & 1225 \\
**condenser inlet temperature (K)** & 370 \\
**condenser outlet temperature (K)** & 341 \\
**condenser inlet pressure (kPa)** & 610 \\
**inlet cooling water temperature (K)** & 313 \\
**outlet cooling water temperature (K)** & 334 \\
**power output (kW)** & 5 \\
**power absorbed by the pump (kW)** & 0.233 \\ \hline \hline \end{tabular}
\end{table}
Table 1: ORC plant simulation results.  
were used. This material provides excellent thermal performance and is compatible with refrigerants. The aim, as mentioned above, is to minimize the overall condenser dimensions and maximize the pressure drop. The following figure shows the condenser as it appears in the plan configuration.  
Considering Figure 3, the streams numbered 1 and 2 are the inlet and outlet of the working fluid, 3 is linked to a tank (that could fill the circuit when needed; it is not present in the plant shown), and 4 and 5 are the streams representing the inlet and outlet of the cooling fluid. In this study, we operated in the most critical condition by adopting a temperature of 40 \({}^{\circ}\)C (which would correspond to a typical summer situation). The geometry of the condenser will be square, so the figure is for reference only. The various possible methods for designing the condenser are briefly outlined and analyzed below.
## 3 Method
### The Multi-Agent Communication and Negotiation Mechanisms in the CoReaAgents Framework
#### 3.3.3 Static and Dynamic Task Planning Combination by the Reflect Agent  
The Reflect Agent performs subtask reflection and plan reflection, consulting with the Tool Agent and the Plan Agent. When the Tool Agent completes each subtask, the Reflect Agent performs subtask reflection on its result, as shown in Figure 5.  
* \(<\)_Re-Select\(>\)_ indicates that the tool invoked for this subtask needs to be reselected. The Tool Agent needs to reselect the tool and execute it based on the feedback.
* \(<\)_Re-Call\(>\)_ indicates that the tool call failed and necessitates a reconsideration of the tool parameters. The Tool Agent needs to analyze the parameters of the call again.
* \(<\)_Re-Sum\(>\)_ indicates that the Tool Agent needs to execute the subtask again and summarize it based on the feedback.  
The Reflect Agent performs plan reflection for the Plan Agent, thereby enabling continuous iterative updates to the task plan:  
* The Reflect Agent combines information about completed subtasks \(\{s_{1},s_{2},s_{3},\dots,s_{i}\}\) to plan the next task \(s^{\prime}_{i+1}\), drawing inspiration from ReAct [15].
* \(s^{\prime}_{i+1}\) is compared with the corresponding subtask \(s_{i+1}\) in the overall plan.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Basic Attribute** & **Notation** & **Attribute Description** \\ \hline Current subtask & \(s_{i}\) & Logical position in the collection of subtasks \\ Subtask target & \(t_{i}\) & Subtask target expressed through comments \\ Tool requirement & \(a_{i}\) & Tool to be invoked expressed through code \\ Parameter configuration & \(p_{i}\) & Parameters required to invoke the tool \\ Subtask result & \(r_{i}\) & Result of this subtask \\ \hline \hline \end{tabular}
\end{table}
Table 1: The details of subtasks’ basic attributes.  
* \(<\)_Re-Plan\(>\)_ indicates that their task target \(t_{i+1}\) or invoked tool \(a_{i+1}\) is inconsistent. the Plan Agent should replan the subsequent tasks.  
After completing the above steps, dynamic planning with flexible execution can be integrated into each step of the overall planning, realizing the organic combination of static and dynamic planning.