esa-sceva/satcom-chunk-collection huggingface dataset:
# Fake Base Station Detection and Link Routing Defense +
## 4 Fake Base Station Threat
### Fake Base Station Threat against Availability  
In our work, the attacker has the goal of depriving and disabling the connectivity availability of the user equipment. As described in Section 3.3, 5G AKA provides some resistance against fake base stations. The core network authentication verification using the USIM equipped within the user equipment device can serve to indicate the legitimacy of the base station; the base station legitimacy check fails if the authentication fails.  
Our threat occurs before the NAS and before AKA. In our threat, the fake base station injects the fake SIB1 message to establish the RRC and wireless communication channel with the user equipment. The fake base station increases the transmission signal power to transmit the SIB1 with the highest signal power on the user equipment, which causes the user equipment to connect to the fake base station as described in Section 3.2. The fake base station continues with the RRC setup as depicted in the yellow-shaded region in Figure 2a. Once the RRC setup is complete, when the user equipment continues with the NAS-layer process and sends the registration request, the fake base station refrains from sending it to the core network and stops the rest of the process. The fake base station does not comply with the protocol and ceases transmitting any transmissions following/after receiving the user equipment's registration request, as described in Figure 2b. In Figure 3, we show a proof-of-concept fake base station attack on an Android user equipment device connected to a real-world 4G LTE using our threat prototype described in Section 7. The fake base station establishes the RRC connection by sending system information messages with higher signal strength, and it maintains the connection with the user equipment as long as it wants. As a consequence, the user equipment is deprived of cellular services from legitimate base stations.  
The timing of the fake base station threat in stopping the RRC/NAS process of setting up connectivity is critical for impacting availability. If the fake base station stops the RRC/NAS before the registration request, then the user equipment gets disconnected as soon as the process stops. Furthermore, the fake base station cannot proceed with the authentication request because it does not hold the core network's private key (or, if using 4G, the symmetric key) and cannot generate the correct digital signature; the user equipment can check the legitimacy of the core network/base station and disconnect the NAS/RRC immediately. However, the user equipment is persistent with the registration request by 5G design because there can actually be accidental connectivity availability issues due to a failure in the base station and the farther away core network.
## 4 Experiments and Analyses
### Forward Verification  
#### 4.2.1 Project Overview  
The Lianhua Tunnel commences in the Xiangdong District of Pingxiang City and extends to Liushi Township in Lianhua County. Designed as a bifurcated tunnel, its left and right passages measure 3210 m and 3220 m in length, respectively. Surrounding rock classifications are divided into grades IV, V, and III. Specifically, grade IV rocks make up 47.6% of the tunnel's entire length, grade V rocks account for 33.7%, and grade III rocks account for 18.7%. The deepest point of the tunnel lies at 317 m below the surface. Characterized by its shallow depth, wide span, and exceptional length, this structure is a notable example of a large-scale highway tunnel.
## 3 Background
### Robustness  
Robustness can be defined as the performance of well-trained models facing adversarial examples [58]. Essentially, robustness considers how sensitive a model's output is to a change in the input. The robustness of a model is related to the generalization error of the model. There is a recognised trade-off between accuracy and robustness in machine learning. That is, highly accurate models are less robust to adversarial examples. Machine learning models in adversarial domains must be both highly accurate and robust. Therefore, improving the robustness of machine learning models enables safer deployment of ML systems across a wider range of domains.  
To critically evaluate and make fair comparisons, robustness metrics are necessary. Common metrics include precision (Equation (5)), recall (Equation (6)), and F1-score (Equation (7)).  
\[Precision=\frac{TruePositives}{TruePositives+FalsePositives} \tag{5}\]  
\[Recall=\frac{TruePositives}{TruePositives+FalseNegatives} \tag{6}\]  
\[F1 Score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{7}\]  
Other possible useful metrics to evaluate robustness include: the Lipschitzian property, which monitors the changes in the output with respect to small changes to inputs; and CLEVER (cross-Lipschitz extreme value for network robustness), which is an extreme value theory (EVT)-based robustness score for large-scale deep neural networks (DNNs). The proposed CLEVER score is attack-agnostic and computationally feasible for large neural networks improving on the Lipschitzian property metric [59]. Table 2 details some advantages and disadvantages of some robustness metrics.
## 2 MIP Application
### Biomedical Diagnostic  
In biomedical applications, MIP is known as an artificial receptor that has basic capabilities like natural receptors, one of which is the ability to recognize cells [37]. Compared to natural receptors, MIP has advantages such as high affinity and selectivity, more temperature stability, rapid preparation, and low costs [38]. MIPs are designed to have specific recognition sites for target molecules such as antibodies and enzymes [38; 39]. In the immune system, antibodies must be specific in recognizing certain antigens. MIP as an alternative to antibodies is often used as an analytical method for diagnostic purposes [40], such as breast cancer diagnostics [41], cardiovascular disease [42], and dengue fever [43]. Therefore, MIP acts as a biomarker [44] because it is able to show certain physical characteristics or measurable biologically generated changes in the body associated with certain diseases or health conditions [45].
## 4 Results and Discussion
### Survey Findings
#### 4.1.3 Choice of Faculty  
At university, nearly half of the students are enrolled in a discipline that was not their first choice. This rate varies greatly depending on how students pursue their education after high school, with 47.2% conducting internet research, 25% asking advice from friends \begin{table}
\begin{tabular}{c c c c} \hline
**Variables** & **Value** & **N** & **\%** \\ \hline \multirow{4}{*}{What grade did you get on your high school diploma?} & Passable & 14 & 11.7\% \\ \cline{2-4}  & Fairly good & 32 & 26.7\% \\ \cline{2-4}  & Good & 48 & 40.0\% \\ \cline{2-4}  & Very good & 26 & 21.7\% \\ \cline{2-4}  & Yes & 5 & 4.2\% \\ \cline{2-4}  & No & 115 & 95.8\% \\ \hline \end{tabular}
\end{table} 
Table 4: Academic background. \begin{table}
\begin{tabular}{c c c} \hline
**Variables** & **Value** & **N** & **\%** \\ \hline \multirow{4}{*}{During this academic year, where are you living?} & At university residence & 52 & 43.3\% \\ \cline{2-4}  & At your parent’s house & 43 & 35.8\% \\ \cline{2-4}  & Rented accommodation & 11 & 9.2\% \\ \cline{2-4}  & In shared accommodation & 10 & 8.3\% \\ \cline{2-4}  & At another family member’s houses & 3 & 2.5\% \\ \cline{2-4}  & My apartment & 1 & 0.8\% \\ \hline \multirow{4}{*}{Do you have a university scholarship?} & Yes & 64 & 53.3\% \\ \cline{2-4}  & No & 56 & 46.7\% \\ \cline{2-4}  & Yes & 17 & 14.2\% \\ \cline{2-4}  & No & 103 & 85.8\% \\ \hline \multirow{4}{*}{If yes, how many hours do you work per week?} & Less than 5 h & 2 & 9.5\% \\ \cline{2-4}  & 5 to 14 h & 4 & 19.0\% \\ \cline{2-4}  & 15 to 24 h & 9 & 42.9\% \\ \cline{2-4}  & 25 to 34 h & 3 & 14.3\% \\ \cline{2-4}  & 35 h or more & 3 & 14.3\% \\ \hline \multirow{4}{*}{What is your main source of monthly income?} & Parents & 91 & 75.8\% \\ \cline{2-4}  & Scholarship & 19 & 15.8\% \\ \cline{1-1} \cline{2-4}  & Work & 10 & 8.3\% \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._and family, 9.7% not seeking information, 6% enrolling in open university registration, and 8% asking advice from high school teachers. When asking students what inspired them to enroll in university, a significant number of respondents (34.2%) chose university due to family encouragement, while another 34.2% enrolled by default after not being accepted into a selective field of study. Proximity to home was also a key factor for 20% of students, and 11.7% were influenced by the presence of friends attending the same institution. For their orientation experience, a majority (72.5%) expressed satisfaction, indicating that the existing orientation program met their requirements. However, 27.5% believe that a different sort of orientation would have been better (see Table 5). In addition, students stated that they chose their field of study based on career goals as well as their own abilities and interests. It is also noted that there were some students who chose one discipline by default, because they did not know what they wanted (see Table 6). These results show the considerable importance of external influences in university enrollment choices.
This paper presents a novel contribution to the literature in the field of antenna design and wireless communication. By integrating ring resonators as frequency-controlling elements, the proposed antenna design introduces a novel approach that enhances adaptability and overall performance of the reconfigurable antenna. Unlike conventional designs that often focus on individual components, the proposed antenna utilizes the unique properties of ring resonators to achieve reconfigurability, enabling precise frequency control. The incorporation of RF switches further amplifies the adaptability of the antenna, allowing for dynamic switching between resonance frequencies and bandwidths, thereby making it versatile in wireless communication applications. This study addresses a significant gap in the existing literature by performing a comparative analysis of two switching methods, PIN diodes and SPDT switches, for frequency-reconfigurable antennas in the sub-6 GHz range. Moreover, the antenna's ability to exhibit similar operating bands in different states enhances the reliability of the system. This characteristic is of paramount importance in wireless communication systems, ensuring stable connectivity even in the presence of interference or signal degradation. The omni-directional radiation pattern characteristic of the proposed antenna further emphasizes the significance of the design. By combining these features into an antenna design, this paper introduces an innovative approach to current research and paves the way for advancements in wireless communication technology. The capabilities of the proposed design such as improved frequency adaptability, reliability, and broad coverage, make a significant contribution to the existing literature for advancements in antenna engineering and its practical applications.
## 4 Results  
### Fire Risk Mapping and Integration Equation  
Results are shown to have a consistent coefficient, CR = CI/RI = 0.062 \(<\) 0.1, ensuring the reliability in computation [31], where CR is a consistency ratio, CI is the random coincidence index, and the RI is the random number determined in n (number of factors). With the weighting as determined in Table 4, the fire risk function equation incorporating all nine parameters is as follows:  
\[\begin{split}\text{CR}=\text{FC}*0.24+\text{HM}*0.20+\text{EL}*0. 16+\text{DT}*0.12+\text{SL}*0.09+\text{SD}*0.07\\ +\text{SI}*0.06+\text{UF}*0.04+\text{RA}*0.02\end{split} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Indicator** & **FC** & **HM** & **EL** & **DT** & **SL** & **SD** & **SI** & **UF** & **RA** & **Weight** \\ \hline
**FC** & 0.26 & 0.23 & 0.28 & 0.34 & 0.26 & 0.23 & 0.20 & 0.18 & 0.16 & 0.24 \\
**HM** & 0.26 & 0.23 & 0.28 & 0.23 & 0.20 & 0.18 & 0.16 & 0.14 & 0.16 & 0.20 \\
**EL** & 0.13 & 0.12 & 0.14 & 0.11 & 0.26 & 0.23 & 0.20 & 0.11 & 0.14 & 0.16 \\
**DT** & 0.09 & 0.12 & 0.14 & 0.11 & 0.13 & 0.12 & 0.12 & 0.14 & 0.11 & 0.12 \\
**SL** & 0.06 & 0.08 & 0.03 & 0.06 & 0.07 & 0.12 & 0.16 & 0.14 & 0.14 & 0.09 \\
**SD** & 0.06 & 0.08 & 0.03 & 0.06 & 0.03 & 0.06 & 0.08 & 0.11 & 0.11 & 0.07 \\
**SI** & 0.05 & 0.06 & 0.03 & 0.04 & 0.02 & 0.03 & 0.04 & 0.14 & 0.11 & 0.06 \\
**UF** & 0.05 & 0.06 & 0.05 & 0.03 & 0.02 & 0.02 & 0.01 & 0.04 & 0.05 & 0.04 \\
**RA** & 0.04 & 0.04 & 0.03 & 0.03 & 0.01 & 0.01 & 0.01 & 0.02 & 0.03 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison matrix and weighted value of indicators based on the analytical hierarchy process (AHP) method.  
Figure 6: Evaluative layers for establishing a forest fire risk map in Son La Province: (**a**) slope; (**b**) elevation; (**c**) slope direction; (**d**) forest cover; (**e**) distance to roads; (**f**) distance to residential areas; (**g**) humidity; (**h**) distance to rivers; and (**i**) distance to fields.  
Results are classified into five levels, corresponding to low, medium, high, dangerous and extremely high fire risk (Figure 7,b).
# Technological Advancement in Tower-Based Canopy Reflectance Monitoring: The AMSPEC-III System
## 3 Results  
A comparison of the measured green, red and NIR radiance and irradiance is in Figure 3, with respective scatterplots are in Figure 4.  
Figure 3: Green (**a**,**b**), red (**c**,**d**) and NIR (**e**,**f**) normalized irradiance and radiance measured with UDC (dashed black) and JC (dotted red). Since both instruments provide raw measurements in arbitrary units, the data shown here were linearly rescaled between minimum and maximum values (0–1). Time is GMT (hh:mm).  
Figure 5 shows an example of UDC and JC spectra acquired on 1 September, 2013 over the observation cycle in Figure 2. The variability in reflectance is largely due to the sun-observer geometry (_cf_: [7,8]) as the sensor observes the same location around the flux tower every 30 min.  
One of the main applications of previous AMSPEC installations was the determination of photosynthetic light use efficiency (LUE), which describes how efficiently a plant converts the  
Figure 4: Scatterplots of green (**a**,**b**), red (**c**,**d**) and NIR (**e**,**f**) irradiance and radiance measured with UDC and JC. The linear regression lines are in solid.  
Figure 5: Spectra for the observation cycle in Figure 2 (\(340^{\circ}\leq\phi_{v}\leq 145^{\circ}\)) from (**a**) UDC and (**b**) JC.  
absorbed solar radiation into biomass [39, 40]. Figure 6 shows the comparison between half hourly EC-measured LUE and the PRI observed by UDC and JC at SOBS on 1 September, 2013.  
Figure 7 illustrates the bi-directional reflectance distribution measured using off-nadir observations from both spectrometers, using a semi-empirical kernel approach [41, 42]. Kernel based BRDF models are one of the most commonly used methods to describe BRDF effects. These functions represent angular reflectance distribution as linear combination of basic BRDF shapes describing volumetric and geometric scattering effects [42]. Their simple character allows acquisition of model parameters from mathematical inversion of relatively few reflectance observations, thereby facilitating applications over a wide range of spatial scales. Examples of BRDF models for UDC and JC recorded on 1 September, 2013 at SOBS are shown in Figure 7.  
Figure 6: Box-and-whisker plot of 30 minutes EC-measured LUE and PRI observed by (**a**) UDC and (**b**) JC, with box width proportional to the number of observations. (**c**) UDC and JC measured PRI linear regression with regression line in solid.  
Figure 7: BRDF models for UDC and JC.
## 1 Introduction
The purpose of the present work is then to fill this gap by summarizing the important published findings on their thermophysical properties and heat transfer enhancement. The present paper is organized as follows: Section 2 focuses on the thermophysical properties of ethylene- and propylene-glycol-based nanofluids, while Section 3 reports on the heat transfer enhancement due to these nanofluids in some current applications. Finally, Section 4 proposes some research perspectives for nanofluids to penetrate the industrial sector more broadly. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline Ethylene Glycol Solution (\% by Volume) & 0 & 10 & 20 & 30 & 40 & 50 & 60 \\ Temperature (\({}^{\circ}\)C) & 100 & 101.1 & 102.2 & 104.4 & 104.4 & 107.2 & 111.1 \\ \hline Propylene-glycol solution (\% by volume) & 0 & 10 & 20 & 30 & 40 & 50 & 60 \\ Temperature (\({}^{\circ}\)C) & 100 & 100 & 100.5 & 102.2 & 103.8 & 105.5 & 107.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Boiling points of ethylene-glycol- and propylene-glycol-based water solutions at various temperatures [2]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Properties** & \(\rho\) **(kg\(\cdot\)m\({}^{-3}\))** & \(C_{p}\) **(J\(\cdot\)kg\({}^{-1}\)\(\cdot\)K\({}^{-1}\))** & \(k\) **(W\(\cdot\)m\({}^{-1}\)\(\cdot\)K\({}^{-1}\))** & \(\mu\) **(Pa-s)** \\ \hline Ethylene-glycol & 1126 & 2354 & 0.256 & \(21\times 10^{-3}\) \\ Propylene-glycol & 1035.3 & 2479 & 0.1962 & \(57.571\times 10^{-3}\) \\ Water & 999 & 4185 & 0.6 & \(1.002\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Thermophysical properties of ethylene- and propylene-glycol compared to those of water at 20 \({}^{\circ}\)C.
## 2 Materials and Methods
### HPLC Analyses  
The quantification of arbutin within the IAP extract was performed utilizing an Agilent HPLC-1200 series instrument, which was equipped with a variable wavelength detector (Agilent Technologies, Inc., Santa Clara, CA, USA). This analysis was conducted employing a Capcell Pak C18 UG120 column (4.6 mm \(\times\) 250 mm, 5 mm; Osaka Soda Co. Ltd., Osaka, Japan). The IAP extract, alongside standard arbutin (L14945; Alfa Aesar, Ward Hill, MA, USA), was dissolved within a mobile phase solvent. The solvent comprised a mixture of 10 mM monopotassium phosphate (KH\({}_{2}\)PO\({}_{4}\)) and acetonitrile at a ratio of 98.9:1.1, respectively. The HPLC samples were filtered through 0.45 \(\upmu\)m membrane filters. During the course of analysis, the column was maintained at a consistent temperature of 30 \({}^{\circ}\)C and the sample assessment was performed at 280 nm. A sample volume of 10 \(\upmu\)L was injected at a flow rate of 0.8 mL/min. Subsequently, the resulting data were quantified by comparison with the established standard arbutin.
## 2 Working Models
### VLC Multipath Channel Model  
The model implemented consists of nine LED spot lights uniformly mounted on the ceiling of a \(5\times 5\times 3\) m\({}^{3}\) (width, breadth, height) room as pictured in Figure 7. The LEDs are intensity modulated with the same chaotic carrier signal V\({}_{\text{BE\_TX}}\)(t). The Rxs are located 1 m above the floor level (simulating typical desk height) as it can be seen in Figure 8. Also shown in Figure 8 are both the LOS and non-LOS paths between LED and the Rx. Here only the first reflection (i.e., the most significant) is considered, which is ~7 dB (electrical) lower than the LOS component, and all subsequent reflections with little or no effect on the channel impulse response are ignored [31; 32]. Note that the bandwidth of the white phosphorous LEDs, the most commonly deployed, is in the low MHz region (e.g., <5 MHz), and has a first order low-pass filter response [33], whereas the channel bandwidth in an indoor non-LOS transmission link is also limited by multipath propagation, resulting in inter-symbol interference (ISI). Indeed, the multipath induced fading, ISI, and resulting delay spread are one of the main challenges in chaos synchronization in VLC systems, and VLC systems in general. Here, we have assumed that the LED impulse response h\({}_{\text{LED}}\)(t) is ideal and there is no noise, to allow the study to focus on the multipath effect alone. This model replicates a common VLC experimental testbed employed for research purposes. This can be built using standard electronic components and driver boards at a relatively low cost. The chaotic signals used can be generated using digital signal processing means, facilitating its incorporation to already available VLC technology.  
We investigate a MISO communication scheme where the Tx output is employed to drive a chaotic Rxs placed in three different floor positions, namely (0, 0), (2, 2), and (2.5, 2.5) considering the geometrical center to be the origin. The various positions of the Rxs determine the multipath delay induced, as we outline in the following.  
Figure 7: Typical room projections with Tx LED spotlights ceiling (**a**) and Rx positions (**b**).  
The channel impulse response in this model is given by:  
\[h(t)=h_{LOS}(t)+h_{NLOS}(t), \tag{4}\]  
where h\({}_{\text{LOS}}\)(t) and h\({}_{\text{NLOS}}\)(t) are the LOS and non-LOS impulse responses, respectively as defined in references [31; 34]. Figure 9 shows the predicted impulse responses for three different Rx's positions of (0, 0), (2, 2), and (2.5, 2.5).  
Figure 8: VLC with both LOS and non-LOS paths.  
Figure 9: VLC with both LOS and non-LOS paths.  
Based on the root mean square (RMS) delay spread analysis reported in [18], the average channel bandwidth is 100 MHz throughout the room. At the center of the room (i.e., position \((0,0)\)) there are 3 peaks for three different LOS paths (shortest to the longest), whereas at the position \((2,2)\), we observe responses from both LOS and non-LOS paths. Finally, with the Rx positioned at the corner of the room, lights arrive mostly from LOS paths with very little contributions from non-LOS paths.
## 2 Materials and Methods
### Ultraviolet (UV) and Vacuum Ultraviolet (VUV) Irradiation Profile Test  
For the analysis of the plasma effective area caused by UV or VUV irradiation on a sample surface, a detection based on fluorescent excitation with distinct wavelength sensitivity was established. For UV irradiation detection, a 2 mm quartz substrate was sedimented without binder with a fluorescent dye (BaMgA1\({}_{0}\)O17:Eu\({}^{2+}\) or short BAM:Eu). A further quartz plate shielded the fluorescent dye from the mechanical gas flow. The spectral sensitivity of the dye in combination with the covering quartz plate was in the range of 200 nm to 370 nm, hence allowing the OH (at approximately 310 nm) and N\({}_{2}\) (spectral range from 300 nm up to 410 nm) irradiation to pass through [4; 28]. For VUV irradiation detection, a glass plate was sedimented without binder with a different fluorescent dye (Zn\({}_{2}\)SiO\({}_{4}\):Mn). A MgF\({}_{2}\) window was placed on top of the fluorescent dye to shield the dye from the gas flow and allow for VUV light transmission. In this combination, a VUV sensitivity in the range of 115 nm to 275 nm could be achieved. Hence, the emission of the 2 nd continuum of the argon excimer (115 nm to 140 nm), atomic oxygen (130 nm) and atomic nitrogen (120 nm, 150 nm and 175 nm) in the VUV could be detected [28], as well as the third argon excimer continuum from 170 nm to 220 nm. The samples were photographed (Canon EOS 7D, Tokyo, Japan; lens-object distance: 47.3 cm, exposure time for UV: 3 s and for VUV: 3 s, aperture: 9.5, ISO 125 setting without white balance; 50 pixels = 1 mm) and analyzed by Matlab (Matlab R2012a, MathWorks(r), Natick, MA, USA). In sum, approximately 1.5 million ignitions were accumulated in one acquisition.