esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel 3U Stand-Alone CubeSat Architecture for Autonomous Near Earth Asteroid Fly-By  
[PERSON]  
1 Space Systems Engineering, Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands, [EMAIL_ADDRESS] (A.C.); [EMAIL_ADDRESS] (E.  
on the particular target asteroid, and allowing mass production at low per-unit cost. Deployment around the Earth is considered. In Reference [13], an interesting 3U CubeSat architecture, aimed to fly-by a NEA, is explored. The mission concept is based on the deployment around Sun-Earth L1-L2 points (supposing a piggyback rideshare of a larger mission), and on a semi-autonomous navigation approach. However, these mission characteristics impose different constraints (especially on mass and volume budgets), which reflect in a different architecture approach. In fact, the architecture presented here requires a larger propulsion system, due to the different deployment, which lead to more rigid packing constraints for the other subsystems, both in terms of mass and volume.  
In Reference [14], an interesting mission concept involving 3U CubeSats is explored. The feasibility and utility of using CubeSats for asteroid exploration, as secondary payload of a larger spacecraft, is demonstrated. In particular, the proposed mission architecture is based on two 3U CubeSats, deployed in the close proximity of the target, which perform a detachment to achieve the final configuration of one 2U and four 1U CubeSats.  
This paper is then intended to show the feasibility of a stand-alone, compact (3U), and low-cost solution to explore NEAs, as the currently under development CubeSats for such missions are characterized by architectures larger than 6U. Moreover, this paper contributes to the literature by presenting proper COTS components performance analysis, and trade-off selection for each characterizing subsystem, reviewing, describing, and analyzing in depth the characteristics of the required components for such missions.  
This paper is organized as follows--first, typical NEA fly-by mission is described and characterized in order to derive mission requirements and characteristics. Afterwards, autonomous navigation phases are described and analyzed, focusing on a fully-autonomous strategy. Then, all the subsystems are presented, and their COTS options are evaluated and selected. Finally, after mass/volume/power budgets are defined, the final configuration is presented, highlighting pros and cons, and comparing it with two reference 3U architectures. Recommendations to make this configuration a good option for NEAs are reported.
## 3 Results
### Factors Influencing Changes in Vegetation NDVI
#### 3.2.3 Impact of Urbanization on NDVI  
Urbanization in Aksu has significantly influenced vegetation change. Analysis of nighttime light data from 2000 to 2020 reveals a marked increase in nighttime light intensity in urban development areas, reflecting the intensification of human activities. Nighttime light and NDVI displayed a significant positive correlation, with a correlation coefficient of 0.83 and R\({}^{2}\) = 0.6872, suggesting a strong association between urbanization and vegetation  
Figure 6: Correlation between NDVI and temperature in Aksu from 2000 to 2020.  
Figure 7: Correlation between NDVI and precipitation in Aksu from 2000 to 2020.  
change. Nighttime light data, as a proxy for human activity intensity, demonstrates its reliability and practicality in reflecting vegetation changes.  
Rapid urbanization in Aksu has brought dual impacts on the regional ecological environment: on the one hand, it has exacerbated the consumption of natural resources and increased ecological pressure; on the other hand, economic and agricultural developments have promoted improvements in vegetation coverage, particularly in areas surrounding urban centers. Therefore, studying the impact of urbanization on vegetation changes is crucial for balancing economic development and ecological conservation.  
The analysis of the spatial distribution and trend of the annual average nighttime light in the Aksu region reveals that the areas with the highest brightness are concentrated in Aksu City, Aral City, and the county towns of Shaya, Baicheng, Wushi, and Kuqa, reflecting significant levels of urbanization in these areas (Figure 8a). Notably, the desert oil field area in southern Shaya County has the highest nighttime brightness, reaching 61. The nighttime lights in urban areas such as Aral City, Aksu City, and Shaya County show an increasing trend, while the area at the border between Shaya County and Kuqa County exhibits a decreasing trend (Figure 8b).  
In addition, Figure 9a shows that the annual average nighttime light values in Aksu consistently increased over the study period, with an annual growth rate of 9.8%, particularly notable between 2016 and 2017. The correlation analysis between nighttime light and NDVI (Figure 9b) further confirms this observation. The positive correlation between nighttime light changes and NDVI (R\({}^{2}\) = 0.6872, correlation coefficient = 0.83) indicates that nighttime light effectively reflects the dynamic changes in vegetation coverage. The accelerated urbanization process in Aksu has facilitated the improvement of vegetation coverage, and the impact of human activities on vegetation far exceeds that of climatic factors.  
Figure 8: Distribution and change trend of the average annual night light in the Aksu region from 2000 to 2020: (**a**) light distribution and (**b**) lighting trend.  
Figure 9: Correlation analysis between nighttime light and NDVI in Aksu from 2000 to 2020: (**a**) annual average nighttime light change and (**b**) correlation between nighttime light and NDVI.
## 3 Centroid Error Analysis
### Fade Probability  
Fade probability is a significant metric when evaluating the reliability of a tracking channel. It is defined as the probability when the SNR falls below a certain threshold over the tracking channels. It is related to the detector's sensitivity limit, and can also be described in terms of received photon counts. Thus, the fade probability of a CMOS detector, over the log-normal tracking channels, is expressed as  
\[\begin{split} P_{fCMOS}=P(\gamma_{CMOS}\leq\gamma_{thCMOS})=P(K_ {s}\leq K_{thCMOS})\\ =\int_{0}^{K_{thCMOS}}f(K_{s})dK_{s}=\frac{1}{2}erfc\left(-\frac{ \left(\ln K_{thCMOS}-m_{s}\right)}{\sqrt{2}\sigma_{k}}\right)\end{split} \tag{24}\]  
where \(\gamma_{thCMOS}\) and \(K_{thCMOS}\) are the thresholds in terms of SNR and received photoelectron counts, respectively, and \(erfc\left(\cdot\right)\) is the complementary error function.  
Applying the same conditions as Equation (24), the fade probability of a QAPD detector is also expressed as  
\[P_{fQAPD}=\int_{0}^{K_{thQAPD}}f(K_{s})dK_{s}=\frac{1}{2}erfc\Bigg{(}-\frac{ \left(\ln K_{thQAPD}-m_{k}\right)}{\sqrt{2}\sigma_{k}}\Bigg{)}, \tag{25}\]  
where \(K_{thQAPD}\) is the threshold of received photoelectron counts. Given that the SNR threshold is 1 (i.e., \(\gamma_{thCMOS}=1\) and \(\gamma_{thQAPD}=1\)), we can compute two thresholds for the CMOS and QAPD detectors from Equations (13) and (15): \(K_{thCMOS}=\left(1+\sqrt{1+4K_{nC}}\right)/2\) and \(K_{thQAPD}=\left(F+\sqrt{F^{2}+4K_{nQ}}\right)/2\).
## 4 Data Descriptions
### Traffic Accident Data Set  
All reported accident data in Korea can be obtained from the Traffic Accident Analysis System. A sample of 518 subjects was acquired for accidents on the Naebu Expressway for the nine-year period from 2007 to 2015. The data contained information on accident location (longitude and latitude), date, time, driver characteristics (age and gender), and road surface conditions (dry and wet).
# Analysing the Performance of a Trust-Based AODV in the Presence of a Flooding Attack
## 7 Simulation and Evaluation
### Performance Based on Number of RREQs Varying the Number of Malicious Nodes  
This simulation was run varying the number of malicious nodes from 5 to 25 with a constant number of 200 nodes and a flooding rate of 50 packets per second.  
Figure 2 shows that increasing the number of malicious from 5 to 25 increases the number of RREQs from 85,000 to 380,000 in the AODV case, whereas in TAODV, the highest  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Simulator & NS-3.33 & - \\ Packet size & 512 & byte \\ Simulation time & 100 & s \\ Simulation area & 1200 \(\times\) 1200 & m \\ Number of nodes & 50, 80,..., 200 & - \\ Node speed & 1 & m/s \\ MAC protocol & 802.11b & - \\ Transmission range & 250 & m \\ Total of simulation runs & 10 & - \\ Malicious nodes & 5 & - \\ Attack type & flooding & - \\ Flooding rate & 50 & packet/s \\ Routing protocol & AODV, TAODV & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used to evaluate performance of the TAODV protocol under flooding attack.  
Figure 1: Number of RREQs in TAODV vs. AODV (95% CI).  
number of RREQs is fewer than 190,000 when the number of malicious nodes reaches the highest value of 25.
## 1 Introduction  
The phenomenon of freezing and thawing, especially in regions exposed to floods, can significantly influence the properties of the building envelope. Walls, especially brick masonry ones, which can be saturated with water due to flooding in the rainy autumn period and exposed to cycles of freezing and thawing (labeled F/T in the rest of the text) due to the cold winter that follows the rainy season, may experience damage and a consequent reduction in mechanical properties. Freezing occurs in porous and permeable materials in the presence of water at temperatures below 0 \({}^{\circ}\)C. Frost resistance represents the ability of moistened material to resist F/T cycles without damage. It depends on the climatic conditions, thermal conductivity, water absorption, and porosity [1]. The phenomenon and the mechanisms of deterioration of materials due to F/T are dealt with by three main theories: the hydraulic pressure theory, closed container theory, and icence growth theory, which are described in [2, 3, 4]. Many contributions in the literature deal with the influence of F/T on mortar, brick, and plaster as the constituent components of the wall [5, 6, 7, 8]. Considerably less authors have studied the influence of F/T on the mechanical properties of the wall as the composite [9] as well as the mathematical models that describe the dependence of mechanical properties and level of damage depending on F/T action [10]. We are often faced with complex and expensive experiments, in which it is hard to modify and adjust all variables that occur in the observed phenomena. Therefore, mechanical properties are generally not determined from experimental measurements, but by using suitable physical models, which are then experimentally verified. In the case of complex phenomena, such as cyclic F/T of brick masonry, ordinary physical models are unreliable and inaccurate due to uncertainty in the material, execution, and complex dynamic processes that take place in the experiment and nature. We attempt to achieve satisfactory description with optimally designed experiments where key variables are changed, and then the phenomenon is described by statistical tools using engineering logic and the integration of knowledge of individual physical models. The latest approach, which was also used in this paper, corresponds to a so-called problem of *small data*,which is about finding the causation, the reason why, in contrast to the problem of \(\cdot\)big data\(\ast\), which is about finding correlations in enormous amounts of data [11] (which are typically not available in structural engineering). The purpose of this paper is to develop a mathematical model based on the experimental results, which will be able to predict key mechanical characteristics and the damage index for a given number of F/T cycles by different qualities/types of mortar, taking into account uncertainties in the data and results. The article presents results obtained on brick wallets built with pure lime mortar as well as wallets built with lime mortar with the addition of ground granulated blastfurnace slag.
## 2 Materials and Methods
### Life Cycle Inventory
#### 2.2.1.2 Battery Cell Production  
The Argonne National Laboratory adopts a prismatic pouch cell structure in Everbatt. The pouch is made of a tri-layer polymer/Aluminium material. Aluminium and Copper foils serve as the current collectors at the cathode and anode, respectively [42].  
The anode is typically composed of graphite and a PVDF binder [22]. To ensure the absence of any oxygen impurity in the graphite, it is baked at 1100 \({}^{\circ}\)C in an inert or reducing  
\begin{table}
\begin{tabular}{c c c c} \hline  & **NMC111** & **NMC622** & **NMC811** \\ \hline Cell materials & kg & kg & kg \\ \hline Active Cathode Material & 0.287 & 0.263 & 0.253 \\ Graphite & 0.160 & 0.171 & 0.168 \\ Carbon black & 0.020 & 0.018 & 0.014 \\ Binder (PVDF) & 0.025 & 0.024 & 0.029 \\ Copper & 0.134 & 0.134 & 0.131 \\ Aluminium & 0.069 & 0.069 & 0.068 \\ Electrolyte: LiPF6 & 0.018 & 0.018 & 0.021 \\ Electrolyte: Ethylene Carbonate & 0.050 & 0.050 & 0.057 \\ Electrolyte: Dimethyl Carbonate & 0.050 & 0.050 & 0.057 \\ Plastic: Polypropylene & 0.012 & 0.012 & 0.011 \\ Plastic: Polyethylene & 0.003 & 0.003 & 0.003 \\ \hline Non-cell materials & & & \\ \hline Copper & 0.003 & 0.002 & 0.003 \\ Aluminium & 0.184 & 0.186 & 0.187 \\ Steel & 0.007 & 0.004 & 0.006 \\ PET & 0.005 & 0.004 & 0.005 \\ Electronics & 0.037 & 0.037 & 0.038 \\ \hline \end{tabular}
\end{table}
Table 2: Bills of materials (BOMs) of three types of NMC batteries per kg of battery pack.  
atmosphere [22]. The polymeric binder material holds the active material particles together, and a porous membrane separates the two electrodes [42].  
The pores of both this separator and the active materials are filled with an electrolyte, modelled as LiPF6 (Lithium hexafluorophosphate). The NMC solvent N-methyl-2-pyrrolidone (NMP) was added to the mixture and then evaporated after the paste had been applied to the substrate [22].  
The complete inventory of this process is shown in Table S2.
## 3 Results
### The Detection of PCRRf Patterns  
The _uSL[s-\(\Delta t\)]_ signal was used to find the variation of first rotor rotation frequency in time [21] (with a generic sample_fP1i_, defined with the rotation period \(T_{p1i}\) as_fP1i_ = 1/_Tp1i_) during a first experiment with no load test in stationary regime (with a duration of 20 s and sampling time \(\Delta t\) = 10 us) at the theoretical synchronous speed of 750 rpm, as shown in Figure 13.  
Figure 12: A zoomed-in detail in region D of Figure 11.  
Figure 13: The variation of the rotor rotation frequency over time (low-pass filtered with a BMAF, four samples in the average) during a no-load stationary regime at the theoretical synchronous speed of 750 rpm.  
There is a small peak-to-peak variation of 0.045 Hz around an average of \(f_{p1}=12.486463437276486\) Hz) for an average period of \(T_{p1}=1/\!\!f_{p1}=0.080086727921266\) s (useful to find by FASS the PCRRf1 pattern of the first rotation speed of the rotor). The frequency variation from Figure 13 is caused by the relative vibrations between the laser sensor and the rotor and the torsional vibrations of the rotor. The average first rotor's rotation speed is \(n_{p1}=60f_{p1}=749.187\) rpm.  
It is interesting to note that a similar study can be carried out in relation to the frequency \(f_{IV1i}\) of the IV (based on a similar approach of the signal \(u_{VT}[s\cdot\Delta t]\)). Figure 14 shows the variation of this frequency over time during the same experiment.  
There is a very small peak-to-peak variation of 0.034 Hz (due entirely to the behaviour of the electrical network supply) around an average value of \(f_{IV1}=49.9826\) Hz. This average frequency defines the exact value of the first synchronous motor's speed \(n_{s1}\), as \(n_{s1}=60f_{IV1}/p_{1}\) (with \(p_{1}=4\) the number of pole pairs), so \(n_{s1}=749.739\) rpm. This average frequency \(f_{IV1}\) should be considered related to the average period \(T_{IV1}=1/\!\!f_{IV1}\). This average period is used to define the number of samples \(h=2001\) (from \(h=[\,T_{IV1}/\Delta t]\)) in the BMAF used in Equation (1) to define the IAEPf.  
With these values of \(n_{p1}\) (also known as the first mechanical speed of the rotor) and \(n_{s1}\) (also known as the first electrical speed of the stator), it is possible to define an important motor characteristic, the no-load slip, as \(s_{1}=100\cdot(n_{s1}-n_{p1})/\!\!n_{s1}=0.07362\%\). This slip value (normally less than \(5\%\)) describes the torque provided by the motor (it increases as the torque increases).  
A similar approach was performed for a second no-load test at a stationary regime for the second theoretical synchronous speed (1500 rpm, \(p_{2}=2\)). This time, these values were obtained: \(f_{p2}=24.979842608525143\) Hz), \(T_{p2}=0.040032277851852\)\(s\) (useful to find the PCRRf2 pattern of the second rotation speed of the rotor), \(f_{IV2}=49.9955\) Hz, \(n_{p2}=1498.790\) rpm, \(n_{s2}=1499.865\) rpm, \(h=2000\) and \(s_{2}=0.07167\%\).
## 6 Future Research Direction and Discussion
### Wavelet-Bi-LSTM (W-Bi-LSTM)
Water managers and strategists need long-term assessments to keep a balance between the supply and demand of groundwater resources, therefore, yearly simulation of GWL is critical. W-Bi-LSTM can utilize the advantages of both wavelet transform and Bi-LSTM networks to make year ahead predictions. As discussed above, collected data (both meteorological and hydrological) are vulnerable to varying missing values and noise because of certain factors including human error and data collection sensor failure. The wavelet transform has the capability to decompose large-scale noisy data and find hidden periodic trends, which Bi-LSTM then uses to learn underlying long-term dependencies between the input variables and make predictions. Considering the data span that is required to make yearly predictions, it is important to mention that standalone LSTM might not learn the data as well as Bi-LSTM can, since the latter processes data in two directions and maintains contextual information. To evaluate the performance of the said model, correlation coefficient (\(R^{2}\)) and root mean square error (RMSE) are good choices as both are widely used in groundwater level  
Figure 6: Graphical representation of Bi-LSTM. studies. I believe this proposed model can serve to predict yearly GWL with complex [109] input variables. This study sheds light on a review of the most used conventional numerical and machine learning (ML) and deep learning models for groundwater levels (GWL) simulation. Significant advancements have been made in terms of prediction efficiency over the last 2 decades. In addition, most of the time researchers' focus has remained on predicting GWL on a monthly, weekly, daily, and hourly basis. However, the water managers and strategists need multi-year GWL simulation to take effective steps towards the sustainable supply of groundwater. In this paper, a collection of state-of-the-art theories for developing and designing a novel methodology and improving modeling efficiency are also considered in the applicable field of evaluation. Examining modeling techniques used in all the reviewed studies, it was estimated that the machine learning and deep learning approaches are efficient enough for modeling GWL. Moreover, we also provide possible future research directions and recommendations to enhance the accuracy of the groundwater level prediction models and improve the relevant understanding.
## 4 Illustration: Technology Roadmapping in the Case of Underwater Vehicles
### Phase 1: Data Collection and Pre-Processing  
To explain the process of the proposed approach, this study chooses the field of underwater vehicles. Most consumers may ignore underwater vehicles because of the difficulty of understanding the specifics of the relevant technology embodied in the products, such as the systems and components. New product development is pushed not by consumers' needs but rather by advances in technology [2]. The suggested approach is based on technology because it includes the concepts and prototypes of products, and the hierarchical structure of complex underwater vehicle technology is identified to provide firms in the underwater vehicle field with a feasible and promising strategy.  
The sea is the second largest space for human development after the land among the four major strategic spaces: land, sea, air and sky. It is also the strategic base for the development of biology, energy, water and metal resources and the most realistic space with the greatest potential for future development [77]. In this study, 155 patents whose application dates are between 2000 and 2019 were retrieved from the USPTO database, and 150 product manuals were targeted for analysis from the website www.nauticexpo.cn. To make the analysis more accurate and effective, the collected documents were preprocessed in Python, a machine learning language. Word segmentation was implemented to divide the patent documents into single words and stop and function words were removed to prepare for the next phase.
# Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models
## 3 Queueing Models
### Probabilistic ON/OFF Model  
The reduction in queueing delays that may appear in the ON/OFF model can be addressed via the probabilistic ON/OFF model (Figure 2a).  
In this model, the arrival process remains Poisson, and messages wait to be served while the server is ON. However, when the server is OFF, messages may leave the system  
Figure 1: M/M/1 and ON/OFF queues.  
with probability \(1\) - \(\zeta\) or enter the queue with probability \(\zeta\). The probabilistic ON/OFF queue \(q^{prob}_{\text{on/off}}\) can be defined via the tuple:  
\[q^{prob}_{\text{on/off}}=(\lambda,\lambda^{\text{on/off}},\mu,\zeta,T_{\text{ ON}},T_{\text{OFF}}). \tag{5}\]  
where \(\lambda\) refers to the messages' arrival rate, \(\zeta\) refers to the probability of entering the queue, \(\lambda^{\text{on/off}}\) is the messages' output rate, and \(\mu\) is the service rate (during \(T_{\text{ON}}\)) for the message transmission. When \(\zeta=1\), we have the ON/OFF model of Section 3.2 (i.e., during OFF periods, all messages enter the queue).  
\(\Delta^{prob}_{\text{on/off}}\) is the mean response time (the time during which a message remains in the system) for the \(q^{prob}_{\text{on/off}}\) queue. In [41], we provided the following formula for the estimation of \(\Delta^{prob}_{\text{on/off}}\):  
\[\Delta^{prob}_{\text{on/off}}\ =\ \frac{E(n)_{\text{on/off}}}{\lambda_{eff}} \tag{6}\]  
where \(\lambda_{eff}\) is the rate of messages served in the probabilistic ON/OFF queue \(q^{prob}_{\text{on/off}}\).  
Such a probabilistic ON/OFF model aids system designers in improving or tuning delays of components that transmit messages during period \(T_{\text{ON}}\), but also may disconnect for a period \(T_{\text{OFF}}\). More specifically, such a component can represent an IoT sensor (which is resource-constrained as far as memory and energy are concerned) that can periodically be on sleep mode, while its probabilistic behavior can be defined via the application layer.