esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight Integrity Preserving Scheme for Secure Data Exchange in Cloud-Based IoT Systems
###### Abstract  
The information obtained from external sources within the cloud and the resulting computations are not always reliable. This is attributed to the absence of tangible regulations and information management on the part of the information owners. Although numerous techniques for safeguarding and securing external information have been developed, security hazards in the cloud are still problematic. This could potentially pose a significant challenge to the effective adoption and utilization of cloud technology. In terms of performance, many of the existing solutions are affected by high computation costs, particularly in terms of auditing. In order to reduce the auditing expenses, this paper proposes a well-organised, lightweight system for safeguarding information through enhanced integrity checking. The proposed technique implements a cryptographic hash function with low-cost mathematic operations. In addition, this paper explores the role of a semi-trusted server with regard to smart device users. This facilitates the formal management of information prior to distribution through the IoT-cloud system. Essentially, this facilitates the validation of the information stored and exchanged in this environment. The results obtained show that the proposed system is lightweight and offers features such as a safeguarding capability, key management, privacy, decreased costs, sufficient security for smart device users, one-time key provision, and high degree of accuracy. In addition, the proposed method exhibits lower computation complexity and storage expenses compared with those of other techniques such as bilinear map-based systems.  
data integrity; dynamic integrity checking; lightweight; semi-trust server; one time key; smart device user
# Model Predictive Control of Grid Forming Converters with Enhanced Power Quality
## 3 Frequency Control  
The FCS-MPC algorithm decides the optimal states to minimize the voltage error, that is given in every sample, but it leads to random commutations, resulting in no control over the switching frequency for any time span. This may lead to introduce some losses, electromagnetic interference, resonances, and unbalanced stresses on the switches. In order to control the switching frequency through a single CF using the FCS-MPC, the algorithm should comply with some requirements such as reference tracking, computation time, simplicity, and flexibility. In the same time, all objectives should be regulated without hindering each other. In the case of initiating frequency control, it is important tohave controllable objectives, which could be equal or unequal, in term of importance, to the proposed derivative term in order to achieve good power quality. That can be done by designing the weighting factors of each objective in the whole CF. Thus, in the discussed FCS-MPC strategy here, the CF should regulate the switching frequency, not at the expense of the other objectives or at least with a minimum impact. Thereby, if a control objective overwhelms the others, the controller will only care about minimizing the considered objective, fading the other objectives or making them less important. In this context, a detailed explanation for the improved CFs using different frequency control techniques of simple penalization (SP), Notch (N) and Periodic (P) will be given in the next subsections.  
In this paper, the improvement is based on tracking the capacitor current, and including it in the main CF as a secondary objective in order to control its importance by a factor \(\lambda_{d}\). Accordingly, the predictive schemes in Equations (27)-(29), should guarantee the optimum operation without introducing any conflicts between the other objectives which is leading to a degradation in the controller performance. The controllers damping ability, derivative term, has been tested at 2 kHz, which is the concentrated switching frequency, and that is relatively higher than the resonance frequency. The idea is to test all kind of studied CFs at the same conditions. Furthermore, the physical filter values LC, as introduced in Table 1, were available in the laboratory for experimental validations.
# Astronomical Investigation to Verify the Calendar Theory of the Nasca Lines
###### Abstract  
As in many regions of the world, astronomy also played a major role in the ancient Peruvian cultures. However, the discussion of the astronomical relevance of the Nasca geoglyphs is very controversial. A really precise and extensive investigation of astronomical phenomena has not yet taken place; the necessary data were simply missing. In the Nasca project Dresden, these data have been recorded in recent years and stored in an Oracle database. In the very first step, all geoglyphs with an astronomical orientation documented by [PERSON] were checked and verified. Subsequently, all lines of the entity \"straight line\" were systematically examined with regard to the celestial bodies of the Sun and bright stars. For this purpose, on the one hand, the ellipsoidal azimuths of all straight lines were calculated and, on the other hand, the elevation angles in relation to the horizon with the help of digital terrain models (DTM) were determined. Corrections for refraction, the curvature of the Earth, visibility and atmospheric disturbances were largely considered. The azimuths of the celestial bodies during the Nasca period were calculated with software developed in-house (theses by students) and compared with those of the lines. As a result, it was possible to establish that there are individual straight lines that are aligned with the Sun and the seven randomly selected bright stars. However, the number of hits found does not justify the theory that the Nasca Pampas are an astronomical calendar system.  
astronomy; Nasca geoglyphs; Sun solstice; equinox; pre-Columbian cultures +
Footnote †: journal: Physics Letters B
# Optimal Trajectories of Diffractive Sail to Highly Inclined Heliocentric Orbits
###### Abstract  
Recent literature indicates that the diffractive sail concept is an interesting alternative to the more conventional reflective solar sail, which converts solar radiation pressure into a (deep space) thrust using a thin, lightweight highly reflective membrane, usually metalized. In particular, a diffractive sail, which uses a metamaterial-based membrane to diffract incoming solar rays, is able to generate a steerable thrust vector even when the sail nominal plane is perpendicular to the Sun-spacecraft line. This paper analyzes the optimal transfer performance of a diffractive-sail-based spacecraft in a challenging heliocentric scenario that is consistent with the proposed Solar Polar Imager mission concept. In this case, the spacecraft must reach a near-circular (helicoentric) orbit with a high orbital inclination with respect to the Ecliptic in order to observe and monitor the Sun's polar regions. Such a specific heliocentric scenario, because of the high velocity change it requires, is a mission application particularly suited for a propellantless propulsion system such as the classical solar sail. However, as shown in this work, the same transfer can be accomplished using a diffractive sail as the primary propulsion system. The main contribution of this paper is the analysis of the spacecraft transfer trajectory using a near-optimal strategy by dividing the entire flight into an approach phase to a circular orbit of the same radius as the desired final orbit but with a smaller inclination, and a subsequent cranking phase until the desired (orbital) inclination is reached. The numerical simulations show that the proposed strategy is sufficiently simple to implement and can provide solutions that differ by only a few percentage points from the optimal results obtainable with a classical indirect approach.  
diffractive sail; interplanetary trajectories; sun's polar observation; trajectory optimization +
Footnote †: journal: Applied Sciences
## 3 Experiment on Low-Temperature Battery Characteristics
### Low-Temperature Charging Experiment of Lead-Acid Battery  
Low ambient temperatures affect the charging capacity of a lead-acid battery. Similar to the experiment to calibrate battery capacity, in the charging experiment, we put the self-developed lead-acid battery into a low-temperature test chamber, and a two-stage charging mode was adopted. In the first stage, we charged the lead-acid battery with a constant current, and at the second stage with constant voltage charging. The temperature range for the experiment was \(-\)50-0 \({}^{\circ}\)C. The voltage for constant voltage charging was selected to be 14.6 V. The currents of constant current charging were selected as 1, 2, and 3 A, respectively, performed at each temperature. The interval of the experimental temperature change was 10 \({}^{\circ}\)C. Each process of charging was repeated, and the voltage-time curves were obtained at the different temperatures. Three experimental sets of curves are shown in Figure 8.  
Figure 7: Discharge curves of the lead–acid battery at low temperatures.  
As shown in Figure 8, we found that, at the same temperature, the capacity of the battery differed due to different charging currents. The battery-capacity calculation results of the charging experiment are shown in Table 4.  
The results in Table 4 demonstrate that the battery capacity at \(-10\)\({}^{\circ}\)C was 40.0 Ah, 45.4 Ah, and 47.1 Ah with charging currents of 1 A, 2 A, and 3 A, respectively. The battery capacity increased with the charging currents. However, when the experimental temperature was \(-20\)\({}^{\circ}\)C, the relationship between the battery capacity and charging current was different from that at \(-10\)\({}^{\circ}\)C. The battery capacity at \(-20\)\({}^{\circ}\)C was 30.0 Ah, 27.4 Ah, and 25 Ah with charging currents of 1 A, 2 A, and 3 A, respectively. Hence, the battery capacity decreased with the increase of the charging currents from 1 A to 3 A. This phenomenon occurred mainly because the drop in ambient temperature to a certain level decreased the activity of the active material on the two-electrode plates of the lead-acid battery decreased, resulting in a slower chemical reaction rate. The battery capacity at \(-30\)\({}^{\circ}\)C was 16.7 Ah, 16.5 Ah, and 16.2 Ah with charging currents of 1 A, 2 A, and 3 A, respectively. The battery capacity under different charging currents changed little, because the activity of the active material on the two-electrode plates was further reduced.
# A Framework for Pedestrian Attribute Recognition Using Deep Learning
## 3 Proposed Approach
### Transfer Learning Approach  
The concept of transfer learning arrived from the scarcity of training data. A CNN architecture, trained for a specific task on a particular dataset, can gain the knowledge of detecting edges and other low-level features. The kernels of the CNN architecture are updated during the training. These kernels are responsible for extracting features. Thus, when there is limited data for a particular task, and the goal is to better perform with this limited data, the transfer learning approach is used in this scenario. This technique uses the previously learned knowledge and applies it to a new task. Previous works [4; 5; 6] included transfer learning technique to obtain satisfactory results. We also applied the transfer learning technique on several powerful CNN architectures in our proposed framework, i.e., Inception ResNet v2, Xception, ResNet 152 v2, ResNet 101 v2. These architectures are trained on the IMAGENET dataset, which has over 14M images and 1000 classes. The best performing architecture, i.e., ResNet 152 v2, is obtained by tuning the layers of the neural network. The ResNet 152 v2 architecture has approximately 60 M parameters according to [42]. The performance comparison among the architectures is illustrated in the next section. Then, fine-tuning the ResNet 152 v2 architecture by freezing all layers except the last 4, 8, 12, 14, 20, all, and no layers is performed. The purpose is to increase the efficiency of the architecture. The details of the experiment are discussed in the next section. The architecture of the proposed ResNet 152 v2 is shown in Figure 5. A brief explanation of each of the blocks is given below:  
* **Conv1:** It is the first block of ResNet 152 architecture. The kernel size is \(7\times 7\) which reduces the feature map. The purpose of this block is to reduce image size.
* **Max Pool:** To extract the dominant features, initially, a max pool operation is performed. As a result, the feature map becomes smaller.  
Figure 5: Overview of the ResNet 152 v2 architecture.  
**Conv R:** Several blocks, i.e., Conv2 R, Conv3 R, Conv4 R, Conv5 R with residual connections are shown in Figure 5. Each block has a series of convolution layers with an increasing number of channels. The convolution layer has a residual connection among them. This connection aims to make the network learn from its early layers so that the knowledge is not forgotten. The increasing number of channels indicates that the feature map gradually decreases and captures the dominant spatial features.
# Intra-Plot Variable N Fertilization in Winter Wheat through Machine Learning and Farmer Knowledge
## 2 Materials and Methods
### Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM)  
The NDVI was selected as the reference index due to its prior use in many wheat studies ([PERSON] et al., 2019), and because its relationship with wheat yield was previously confirmed by [PERSON] et al. (2019) for the study region. The NDVI was calculated as (Equation (1)):  
\[\text{NDVI}=(\text{NIR}-\text{RED})/(\text{RED}+\text{NIR}) \tag{1}\]  
where NIR is the near infrared wavelength and RED is the red wavelength. The spatial resolution of both bands is 10 m. While the RED band is centered at 665 nm and its width is 30 nm, the NIR band is centered at 842 nm and its width is 105 nm.  
All cloud-free S2 images with 2A processing level (bottom of the atmosphere) ([PERSON] et al., 2019) from 8 February and 28 June 2019 were downloaded from the Copernicus Open Access Hub platform ([[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)) (accessed on 22 September 2022)). Thus, 14 sequential images were used in the analysis, encompassing the period of the crop cycle that has active vegetation and therefore covering the most relevant wheat development phases (GS21, GS30, GS39, and GS85, according to the Zadoks scale). The images between 8 February and 20 March correspond to the tillering growth phase, those between 30 March and 14 May to the stem elongation growth phase, and those between 15 May and 18 June coincide with the heading growth phase. Finally, the images from June 9 to June 28 represent the ripening growth phase. Each experimental plot used in this study was within the S2 30 TWN tile. As the images used were at processing level 2A, the step to correct 1C images (atmospheric effect correction) was not necessary. A 15 m buffer was applied to each plot to ensure that all S2 pixels were completely within the plot. Table 1 shows the number of pixels within each parcel.  
A DEM, derived from a LiDAR flight made in 2016, was used to extract the elevation of each pixel (Table 1). This information is available at Geoeuskadi, the official spatial information repository of the Basque Country ([[https://www.geo.euskadi.eus/s69-bisorea/es/x72](https://www.geo.euskadi.eus/s69-bisorea/es/x72) aGeoeuskadiWAR/index.jsp]([https://www.geo.euskadi.eus/s69-bisorea/es/x72](https://www.geo.euskadi.eus/s69-bisorea/es/x72) aGeoeuskadiWAR/index.jsp) (accessed on 22 September 2022)). The final dataset was composed of 4869 instances.
## 5 Analysis
### Performance
We can observe that the number of intermediary vehicles participating in message forwarding is increased as the more vehicles are distributed on the road. It is obvious that the authentication delay gets longer as the number of hops are increased. However, it should be noted that the more vehicles participate in message forwarding the shorter message delay occurs, as shown in Figure 7, because the carry delay depending on the moving speed of vehicles is much longer than the communication delay. Therefore, the authentication delay is insignificant and has little effect on the total message delay. We also evaluated the successful message delivery ratio for 500 s of message lifetime and Figure 8 shows the results. As aforementioned, if more vehicles are distributed on the VANET and move with high speed, vehicles can have more chance to meet other vehicles which results in higher possibility of message carrying-and-forwarding as well as shorter message delay. From our simulation results, we can see that almost all of the messages can be successfully delivered to the destination vehicles within 350 s when we put more than 135 vehicles moving with higher than 50 km/h speed. Figure 5: Average number of hops for forwarding messages to the destination vehicles. Figure 8: Successful message delivery ratio for the destination vehicles. Figure 6: Average forwarding delay (\(D_{auth}\)) to the number of hops burdened by authentication process. Figure 7: Average message delivery delay (\(D\)) from contact point RSUs to the destination vehicles.
## 1 Introduction
The primary contributions of this paper can be summarized as follows:  
* Identify the limitations of current RL-based routing schemes. * Propose an intelligent multi-objective routing protocol named DOS-RL for SDWSN-IoT. * Present distinctive features of the proposed routing scheme. * Implement and evaluate the performance of the proposed solution in comparison to the OSPF and SDN-Q routing schemes in terms of energy efficiency and other related parameters. The rest of this chapter is organized as follows. Section 2 provides a comprehensive review of recent studies on routing schemes for SDWSN, with a particular focus on RL-based routing schemes and architectures in the context of an intelligent SDWSN. Drawing from the insights gained in this review, we identify the limitations of existing studies and propose our intelligent multi-objective energy- efficient routing scheme for the SDWSN. Section 3 delves into the design and implementation details of our proposed scheme, offering a thorough examination of its various components. Furthermore, it provides an in-depth analysis of the unique characteristics that distinguish our routing scheme, including the introduction of shaped rewards during learning and the Dynamic Objective Selection algorithm. Section 4 showcases the experimental results and conducts a comprehensive performance evaluation of our proposed framework. Lastly, in Section 5, we conclude the paper and discuss potential future works.
# Reliability Modeling and Analysis of Multi-Degradation of Momentum Wheel Based on Copula Function
## 5 Analysis of Examples  
### Reliability Function Based on Lubricant Residual Quantity  
In this paper, the data of the remaining lubricant in [10] are used to establish the degradation trajectory model. The remaining amount of lubricant data is simulated by the ground test to simulate satellite airborne conditions and is obtained after an interval of time by dissecting momentum wheels, infrared spectroscopy, and other methods. Table 2 shows the quality change data of the bearing lubricant system, including the initial value of lubricant quality, the measured value of the eighth month, and the measured value of the 11 th month.  
Dealing with test data, the data of the bearing lubrication system's degradation is input into Equation (12), and the estimated results of the corresponding parameters are then obtained.  
\[\left\{\begin{array}{l}\hat{u}=0.04225\\ \hat{\sigma}=0.46730\end{array}\right.\]  
Substituting the parameter estimates into the equation, the reliability of the mission time \(t\) is estimated by \(\hat{u}\), \(\hat{\sigma}^{2}\) as  
\[\begin{array}{ll}R(t)&=F(t)=1-F(t;\hat{\mu};\hat{\sigma}^{2})\\ &=\Phi\Big{(}\frac{l-\hat{\mu}t}{\hat{\sigma}\sqrt{t}}\Big{)}-\exp\Big{(} \frac{2\hat{\mu}l}{\hat{\sigma}^{2}}\Big{)}\Phi\Big{(}\frac{-l-\hat{\mu}t}{ \hat{\sigma}\sqrt{t}}\Big{)}\end{array} \tag{15}\]  
According to expert analysis, failures can be considered only when the weight of the oil supply system is close to dry weight. At this point, the failure threshold of the remaining lubricant is determined. By substituting the relevant parameters and the failure threshold estimated above into Equation (15), the reliability curve of the remaining amount of lubricant can be obtained and is shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Suttle**} & **Initial** & **The Weight in the First** & **The Weight in the Second** \\  & & **Weight** & **Anatonmic Analysis** & **Anatonmic Analysis** \\ \hline
1 & 36.334 & 41.560 & 41.521 & 41.497 \\
2 & 36.356 & 41.727 & 40.526 & 40.456 \\
3 & 36.350 & 41.730 & 41.681 & 41.306 \\
4 & 36.344 & 41.525 & 41.305 & 41.227 \\
5 & 36.345 & 41.344 & 41.179 & 41.076 \\ Mean & 36.346 & 41.577 & 41.242 & 41.112 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Change in weight of bearing lubricant system.  
Figure 1: Momentum wheel reliability curve based on lubricant residual amount.
## 2 Materials and Methods
### Satellite Remote Sensing Data  
The 8-day composite MOD09A1 (\(\sim\)500 m \(\times\) 500 m) and 16-day composite MOD13Q1 (\(\sim\)250 m \(\times\) 250 m) data for individual Moderate Resolution Imaging Spectroradiometer (MODIS) pixels that contained EC towers were obtained from the Oak Ridge National Laboratory's Distributed Active Archive Center. The 500 m MODIS pixels included some portions of neighboring pasture(s) in most cases, while the 250 m MODIS pixels were located within the pastures. We used both products in this study due to their different spatial and temporal resolutions. We calculated the enhanced vegetation index (EVI) from blue, red, and near-infrared bands [38] and the normalized difference vegetation index (NDVI) from red and near-infrared bands [39]. Image pixels affected by clouds were excluded from EVI and NDVI computations. Interannual variability in vegetation phenology was compared for six (from 2015-2016 to 2020-2021) dormant seasons in each pasture using EVI and NDVI of both spatial resolutions. Multi-year mean NDVI and EVI values were derived by averaging them for the same day of the year. The 8-day (500 m) NDVI and EVI values had some gaps (i.e., missing values), but the 16-day (250 m) NDVI and EVI values did not have any gaps. Thus, the 16-day composite NDVI and EVI values from the 250 m MODIS pixels were summed or averaged to derive cumulative or average NDVI and EVI values in this study. Standard deviations (SD) were also computed for average or cumulative NDVI and EVI values. We also used GPP fluxes, derived from EC measurements, to further explain discrepancies in vegetation phenology as GPP is a more direct reflection of vegetation greenness.