esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Testing of the Photovoltaic Solar Cells  
To investigate the effects of graphene fillers on the performance of TIMs with the concentrated photovoltaic solar cells, we applied graphene-enhanced TIMs between the solar cell (acting as a heat source) and an aluminum heat sink (see Figure 1). The TIM layer enhances the thermal contact between the two surfaces by replacing the air gaps with thermally conductive paste. The pressure between the two surfaces was fixed at 100 kPa in all measurements. The TIM function is reducing the thermal resistance, \(R_{\text{TIM}}\), between two adjoining surfaces. This resistance is affected by several factors, including TIM thermal conductivity, \(K_{\text{TIM}}\), thermal contact resistances, \(R_{\text{C}}\), between the TIM layer and the two surfaces, and the distance between the two surfaces or \(BLT\). These factors are related to \(R_{\text{TIM}}\) by the equation  
\[R_{\text{TIM}}=\frac{BLT}{K_{\text{TIM}}}+R_{\text{C1}}+R_{\text{C2}} \tag{1}\]  
The graphene enhanced TIMs and reference TIMs without graphene were carefully spread on the back surface of the solar cell panels to ensure uniform and complete coverage, and to avoid excessive amount of material, which would negatively affect \(R_{\text{TIM}}\) via large \(BLT\) (see Equation (1)). Figure 3 shows the back side of the solar cell with TIM before and after the attachment of two surfaces.  
The photovoltaic solar cell with different TIMs has been tested by placing it under a solar simulator (Sol1A Class, Newport Corporation, in Irvine, CA, USA) as shown in Figure 4. The solar simulator uses a xenon lamp that produces a 6 \(\times\) 6\({}^{\prime\prime}\) size beam that emits the 5800 K blackbody-like spectrum. The simulator uses a special power supply to maintain a strictly constant level of illumination. The light produced by the xenon lamp is reflected by a mirror to pass through a spectral correction filter that simulate the AM1.5 G solar spectra. The light beam then passes through a collimating lens that emulate the 0.5\({}^{\circ}\) angle that the sun light has when it reaches the earth. The open circuit voltage is recorded every second using a voltage logger and the temperature of the solar cell is measured using a multi-channel temperature logger. A large convex lens is placed between the solar simulator and solar cell. By varying the distance between the lens and the solar cell, one increases or decreases the light concentration on the solar cell (simulating 500-2000 suns). In high concentration experiments we employed two lenses: a large convex lens and a small lens placed on the solar cell. All experiments were taken at a room temperature of 25 \({}^{\circ}\)C (\(\pm\)2 \({}^{\circ}\)C).  
Figure 3: Image of the actual photovoltaic cells with the dispersed thermal interface material.
## 4 Simulation and Performances  
### Simulation Conditions  
The radio model used in this paper is a two-path propagation model, consisting of two types of reflecting wave and a LOS (line of sight) wave. All sensor nodes were assumed to transmit a certain amount of homogeneous data after converting the measured data into digital signals using an internal A/D converter, and we made a general assumption about the wireless sensor field. That is, the wireless sensor network consists of homogeneous sensor nodes, the distance can be measured according to radio signal strength, and once deployed, the node does not move. Additionally, all sensor nodes have the same initial energy, and the base station is located in the centre or outside of whole the sensor field. Experimental parameters are defined as shown in Table 4 and parameters and meanings for the radio model are summarized in Table 5.  
We used MATLAB to simulate the proposed ELEACH-DFL and compare it with the LEACH protocol in terms of energy consumption. We evaluate and compare FND (first node dead), HND (half node dead) and LND (last node dead) values, which are normally used to evaluate energy performance.
# Plant Electrical Signal Classification Based on Waveform Similarity
## 1 Introduction
Thus, there are significant differences among these signals, whose amplitude and duration have high dynamic range, as well as storage type of raw data. Therefore, there are some issues and challenges in efficient data management, fast data classification, and signal retrieval, etc. Indeed, both AP and VP are important plant bioelectrical signals. Compared to the APs, VPs induced by damaging stimuli do not follow all-or-nothing phenomenon and varied in diverse waveforms. For this reason, this paper focused on the relatively defined signals (APs) to simplify problem at the current research stage. Hence, we proposed an efficient plant electrical signal classification method which will be helpful for recognizing, classifying, searching APs in real time among large scale datasets stored in our plant electrical signal data sharing and analyzing prototype system based on distributed file system--Hadoop--and cluster computing architecture--Spark. It is meaningful to standardize plant electrical signal data and recognize important signals automatically and quickly for plant electrical signal research. Furthermore, similar to EEG or ECG which can reflect an animal's healthy state, it is foreseeable that plant electrical signal will be employed to interpret the higher plant growth and nutrition state, and applied into interactive greenhouse environment control, nutrition diagnosis, balanced fertilization, growth regulation, intelligent irrigation control, etc. In this paper, we used a low pass filter to decrease the impact of noise and artifacts produced by stimulation in action potential in cucumber, and designed an algorithm which can extract all possible waveforms like AP because APs have an obvious difference under different types of stimulation, even the stimulation artifacts could influence the recognition of AP. Consequently, we provided a template matching algorithm based on waveform similarity to improve the identification accuracy of AP. This algorithm considered time domain, frequency domain, statistics, and nonlinear analysis and extracted 19 characteristic parameters of AP waveform. Moreover, principal component analysis was employed to reduce feature dimension. We also used BP-ANNs, SVM, and deep learning to classify and detect the standard AP waveforms, then found that the accuracy was 84.8%, 78.2%, and 77.4% respectively. However, the accuracy of the template matching algorithm based on waveform similarity could reach to 96.0% under reasonable threshold settings. In the future, we will build standard AP template library to promote the development of new identification algorithms for different applications.
# Phase Alignment of an Array Optical Telescope System Using Balanced Detection
## 3 Simulation Experiment Process
### Receiver  
The receiving end combines digital signal demodulation and phase difference detection in the same receiving module, and uses the output value of the detector as a feedback factor to compensate for the phase difference.  
In order to simulate the phase difference caused by the tilt of the optical axis of the factor aperture, we add a phase error element in one arm. The phase shifter in the OptiSystem component library can only provide one input port and one output port. However, the phase shifter with single port input cannot be directly used. It needs to rely on MATLAB to complete the joint simulation, and MATLAB expands the electric input port for feedback adjustment [28]. Set phase error to generate phase error; set phase correct to correct the phase error. The binary sequence and NRZ pulse waveform are used as the DC signal generator, and the amplitude is set to one. The feedback signal is directly received from the low-pass filter at the receiving end. The parameter values are displayed in Table 4.  
As shown in Table 4, the delay time of one bit is set in the time delay module, which is equivalent to 0.1 ns in this system. It is shown in Equation (10)  
\[Delay=\frac{1}{Bit\ rate}=0.1\ \mathrm{ns} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Parameter** & **Value** \\ \hline \multirow{3}{*}{Optical Fiber} & Reference wavelength & 1550 nm \\  & Length & 0.01 km \\  & Attenuation & 0.2 dB/km \\ Phase Shift & Phase & \(-\pi/2\) rad \\ Time Delay & Delay & 0.1 ns \\ X Couper & Coupling coefficient & 0.5 \\  & Additional loss & 1 dB \\ PIN & Responsivity & 1 A/W \\ TIA & Magnification & \(10^{4}\) \\ Low-Pass Bessel Filter & Cutoff frequency & 8 GHz \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter design of the receiver.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline PRBS & 1 & 1 & 0 & 0 & 1 & 0 & 1 & 0 & \\ \hline A not gate & 0 & 0 & 1 & 1 & 0 & 1 & 0 & 1 & \\ A delay & & 0 & 0 & 1 & 1 & 0 & 1 & 0 & 1 \\ gate & & 0 & 1 & 0 & 1 & 1 & 1 & 1 & \\ \hline \end{tabular}
\end{table}
Table 3: Modulation pattern conversion process of DPSK signal.  
Then, 1 dB noise is added to the second coupler, and the amplitude is found to be 0.161 v with OptiSystem. The final received differential mode signal is electrically filtered, and the cutoff frequency of the low-pass filter is set to 8 Gbps. It is shown in Equation (11).  
\[Cutoff\ frequency=0.8*Bit\ rate=8\mathrm{Gbps} \tag{11}\]  
The signal output by the low-pass filter is input to the third port of the bit error analyzer as the signal wave at the receiving end. A pseudo-random binary sequence (PRBS) of the first port receiving end of the bit error analyzer; the second port of the bit error analyzer receives the NRZ pulse sequence at the sending end.
# A Study of Cislunar-Based Small Satellite Constellations with Sustainable Autonomy
## 4 Cislunar Dynamics (Constellation Scenario)
### Orbital Formation  
Apart from the Space Situational Awareness, the formation of each orbit remains an important part of this research. Though the major results on this subject will be published separately, the dynamics used in simulating the proposed constellation and autonomy design of this paper emphasize the importance of formation in each orbit. The referred article that followed for this part of formation assurance is demonstrated in [35], which is for a challenging starling mission for experimenting with an optical system.  
Interestingly, it addresses the verification process of the optics alignment and formation conformity supporting the Monte Carlo trials which fit best for the constellation scenarios as well as if each orbit is considered to be a formation of a set of small satellites. For this paper, a similar relative and absolute spacecraft/orbit approach is taken into consideration. The equations governing this Monte Carlo simulation are detailed in [35] thoroughly. These formations also have to be autonomous due to various factors of environment and congestion in these NewSpace times. There needs to be self-navigating capabilities in orbit and to make certain and specific decisions to avoid sudden decay or misalignments in the constellation orientation. Reference [36] is one such article on this formation flying autonomy, which gives a detailed review, reasons, literature, dynamics, and requirements in the NewSpace that are elaborated extensively. The most important section presented is on collision-free formation flying. This will remain crucial for the Cislunar space with small satellite constellations with severe space environments and its relevance to the proposed methodology in this paper. The safety limit, defined as \(d_{\min}\), depends on the separation among the satellites Along and Cross. This is critical when it is for a constellation, even though it is well-spaced with other missions and satellites among the same mission. Onedefined condition for the distances among the satellites is for the permitted and prohibited constellations depicted as an inequality as:  
\[\begin{array}{l}\delta r_{m}^{\min}\geq d_{\min};safe\\ \delta r_{m}^{min}<d_{\min};unsafe\end{array} \tag{20}\]  
And \(J_{2}\) is inclusive of these operations and expressed as a dependent factor as below:  
\[\wp=\frac{l_{2}}{2}\bigg{(}\frac{R_{E}}{a}\bigg{)}^{2}\frac{1}{\eta^{4}} \tag{21}\]  
where \(\eta\) is eccentricity dependent factor, \(a\) is semi-major axis, and \(R_{E}\) is the radius of the Earth. Hence, the role of \(J_{2}\) is critical in most of the operations in the Cislunar space as the missions from Earth move to the Moon from lower orbits. For the technique proposed, it forms one of the baselines for the space environment and its significance.
## 6 Future Research Directions  
While the current study demonstrates significant advancements in the integration of photonic radar technology with MIMO and IIR filtering for sustainable transportation, several future research directions can be pursued to further enhance this field:  
1. Enhanced Atmospheric Compensation: Future research could explore advanced atmospheric compensation techniques to further mitigate the effects of adverse weather  
Figure 6: Range resolution (**a**) without IIR and (**b**) with IIR for target 1 and target 2.  
conditions. This could involve the development of adaptive filtering algorithms and machine learning approaches to dynamically adjust to varying atmospheric disturbances.
* Integration with Other Sensing Technologies: Combining photonic radar with other sensing technologies, such as LiDAR, ultrasonic sensors, and thermal imaging, could provide a more comprehensive and robust detection system. Research could focus on the fusion of data from multiple sensors to improve the overall accuracy and reliability of autonomous vehicle systems.
* Extended Range and Resolution: Research efforts could aim at extending the range and improving the resolution of photonic radar systems. This could involve the use of higher frequency bands, advanced modulation techniques, and innovative signal processing methods.
* Real-World Testing and Implementation: Conducting extensive real-world testing and pilot implementations of the proposed radar system in various urban and rural environments will be essential to validate its performance and identify practical challenges. Collaborative efforts with industry partners and transportation authorities could facilitate this process.
* Policy and Regulatory Frameworks: Research could also focus on the development of policy and regulatory frameworks to support the integration of advanced photonic radar systems in autonomous vehicles. This includes addressing safety standards, data privacy concerns, and the ethical implications of autonomous transportation technologies.  
Conceptualization, S.C. and A.S.; Formal analysis, S.C. and A.S.; Investigation, J.M.; Methodology, S.C. and A.S.; Software, A.S.; Supervision, J.M.; Visualization, Q.L. and Y.M.; Writing--original draft, A.S.; and Writing--review and editing, S.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were generated in this manuscript.  
The authors declare no conflicts of interest.
# A Review of Performance, Energy and Privacy of Intrusion Detection Systems for IoT
## 2 Existing Surveys of Intrusion Detection for IoT
As with the other studies discussed earlier, authors do not take into account the performance characteristics of intrusion detection systems and therefore do not address the gap highlighted in this paper. In summary, although existing surveys of intrusion detection for IoT highlight advancements and open challenges, these do not consider performance efficiency of IDS for IoT which is significant due to limited resources for these devices, such as CPU, memory, storage, bandwidth and battery. This paper is focused at addressing this gap and presents a thorough analysis of existing IDS efforts for IoT taking into consideration performance metrics, such as energy consumption, RAM, and CPU usage for these efforts. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Survey ID** & **Detection Approaches** & **Review Criteria** & \begin{tabular}{l} **Performance Efficiency** \\ **Considered** \\ \end{tabular} \\ \hline [PERSON] et al. (2017) & Rule and Anomaly-based & \begin{tabular}{l} Holistic Detection Intelligence \\ Security Threats and \\ Validation Strategies \\ \end{tabular} & No \\ [PERSON] et al. (2019) & Network-based IDS & \begin{tabular}{l} Threats, Placement, Validation \\ Strategies \\ \end{tabular} & No \\ [PERSON] et al. (2016) & Game Theory-based IDS & \begin{tabular}{l} TPR, FPR, FNR, TNR \\ Security and Performance \\ Metrics \\ \end{tabular} & No \\ This Study & \
\begin{tabular}{l} Anomaly, Signature, \\ Specification and Hybrid \\ \end{tabular} &
\begin{tabular}{l} Security and Performance \\ Metrics \\ \end{tabular} & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of this study with existing reviews of intrusion detection systems (IDSs) for Internet of Things (IoT).
# Concrete Silos: Failures, Design Issues and Repair/Strengthening Methods
## 2 Failure of Reinforced Concrete Silos
### Materials for Reinforcing Concrete
The impact of material-related factors on the quality of grains is more pronounced in traditional, small scale silos constructed by farmers. [PERSON] et al. (2019) note that cost factors have often informed the selection of materials such as termite mound clay [27] and reinforced clay, which is inappropriate for sustainable silo structures based on susceptibility to brittle fracture. In addition, clays are permeable to water-induced deformation and have limited structural integrity compared to reinforced concrete. The risk of moisture transfer is influenced by the chemical composition of the clays--Kaolinite and illite, Mn, and Fe oxides are the primary constituents [34]. A comparative review of reinforced concrete, steel, and termite mound clay and steel silos showed that the silos made of clay were highly susceptible to moisture transfer compared to the reinforced concrete structures. In particular, the moisture content increased by 2.6%, increasing the risk of moisture-induced degradation of the stored grains. Further experiments confirmed that grains stored in termite mound clay silos recorded the highest decline (39%-54%) in ash content, starch content, crude fiber content, oil, and protein content, as shown in Table 4[35]. In brief, reinforced concrete silos are superior, given the negative effect of termite mound clay silos on the quality of the stored grain. However, the selection of sustainable materials for concrete structures involves cost tradeoff--the cost of reinforced concrete structures is higher compared to termite mound clays. The risk of moisture accumulation is also predicted by the material properties of the bulk solids; the storage of rapeseeds in reinforced concrete silos has been associated with a higher risk of dampness [36] and structural failure of the hopper walls. In contrast, non-food bulk solids such as ground gypsum have a lower moisture content, and there is a minimal risk of dampening. On the downside, the low risk of dampening is offset by high bulk density (1174 kg/m\({}^{3}\)) [37], which is about 1.5 times the bulk density of wheat (761 kg/m\({}^{3}\)) [38]; this demonstrates that no solid bulk material is ideal--different bulk solids pose distinct risks on the structural integrity of the wall structures.
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 1 Introduction
Section 3 describes the main design decisions made for implementing UW-ASN, and we justify the decisions made about the medium access control, message routing, and fault-tolerance protocols. Section 4 presents the system model and describes its components; e.g., the physical propagation model, the slot allocation model and the real-time model. Section 5 discusses the scheduling aspects by means of an example. Section 6 presents a meta-heuristic approach to solving the NP-hard problem of the slot allocation. Moreover, it shows several network topologies that are used to compare the performance of the proposed slot allocation algorithm, versus an integer linear programming solution. Section 7 describes a case study based on several simulations, which shows the network performance under various initial conditions. Such a section introduces the simulation tool, the obtained results, and it also compares the network behavior using the proposed model, with the shortest-path-first routing approach. Finally, Section 8 presents the conclusions and the future work.
## 3 Results
### The 400 m Sprint Analysis Results
Data points are color-coded by subject: Subject A (blue circles) and Subject B (red squares). Overlapping data points appear darker due to transparency settings, visually indicating areas of higher data density. Method 2 demonstrated improved accuracy, with an _RMSE_ of 0.168 Hz versus 0.260 Hz for Method 1, along with smaller _bias_ (0.012 Hz versus 0.026 Hz) and stronger ICC values (0.858 versus 0.746). Figure 5A-D illustrates the relationship and agreement between the GNSS-based methods and IMU-derived _SF_, highlighting the superior performance of Method 2 through tighter clustering around the reference values and narrower LOAs in the Bland-Altman plots. Figure 4: Comparison of GNSS-based methods (Method 1 and Method 2) versus IMU-derived _SF_ in the 400 m sprint (left and right legs). (**A**,**B**) Scatter plots for Subject A (blue circles) and Subject B (red squares), with the _SF_ for the left leg (**top**) and right leg (**bottom**) compared with the IMU-derived _SF_. (**C**,**D**) Corresponding Bland–Altman plots, indicating _bias_ (solid line) and LOA (dashed lines). Overlapping data points appear darker due to transparency settings, visually indicating areas of higher data density. The running parameters over the entire 400 m sprint were analyzed using Method 2. Due to its superior accuracy and reliability in prior assessments, Method 2 was selected for comprehensive monitoring. Using the vertical velocity minima identified by Method 2, we calculated the _SF_, _SL_, _running velocity_, and elapsed time for each step. These metrics were plotted against the cumulative distance to visualize the running parameters over time (Figure 6). This per-step analysis captured the dynamic changes in _velocity_, _SF_, and _SL_ throughout the sprint, providing detailed insights beyond traditional average-based methods. Both subjects exhibited dynamic changes in _SF_ and _SL_ throughout the sprint. Subject A reached a peak _running velocity_ of 8.4 m s\({}^{-1}\) at the 50.2 m point (_SL_ of 2.10 m and _SF_ of 4.00 Hz at 7.13 s), while Subject B achieved a peak velocity of 6.6 m s\({}^{-1}\) at the 63.4 m point (_SL_ of 1.86 m and _SF_ of 3.57 Hz at 10.75 s). Subsequently, a gradual decline in _running velocity_ was observed for both subjects as the sprint progressed. For Subject A, periods  
Figure 5: Comparison of GNSS-based methods (Method 1 and Method 2) with IMU-derived _SF_ across curved and straight track sections. (**A,B**) Scatter plots for Subject A (blue circles) and Subject B (red squares), with data separated into curved (**top**) and straight (**bottom**) sections. (**C,D**) Bland–Altman plots indicating _bias_ (solid line) and LOA (dashed lines). Overlapping data points appear darker due to transparency settings, visually indicating areas of higher data density. during which the GNSS solution shifted from 'Fix' to 'Float' are highlighted with shaded areas in Figure 6A, indicating reduced positional accuracy.