esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Since the TRF antenna operates in P-band, the SRT front-end used for space debris monitoring is the P-band receiver, which is installed in the primary focus of the telescope. Compared to the early years when SRT was used for space debris measurement campaign [26], where the back-end was based on a spectrum analyzer [27] with its limitations (i.e., the slow frequency sweeps for low resolution bandwidths and the resulting lack of the real-time data storage function), the system was upgraded with a dedicated channel, that guarantees a more efficient data collection during the observations of space objects. The (then) new dedicated channel consisted of a back-end, with its acquisition chain, based on the Red Pitaya electronic board [23; 25; 27]. Whereas, the configuration with the spectrum analyzer remained available for maintenance and testing operations, where it was necessary to view immediately the echo radar in the instrument display, without a post-processing function. Both these back-ends performed only Doppler shift measurements, with an accuracy of few dozens of Hertz. However, a fundamental aspect for orbit determination processes is represented by also slant-range measurements. In fact, the combined use of Doppler shift and slant-range measurement would grant both an increase in the object state estimate accuracy and a reduction in the estimate uncertainty [25]. For this reason, and with the aim to align with the others European radars involved in operations of space surveillance and tracking, the BIRALET system has recently been improved with the development of a new ad hoc back-end based on the National Instrument USRP-2954R board [28], in order to measure both the slant-range and the Doppler shift, simultaneously. As concerns the receiving system of BIRALET, this new back-end replaced therefore the former based on the Red Pitaya board, that did not include, due to its limited performances [23], an ad-hoc software for processing slant-range measurements and therefore it performed only Doppler shift, with an accuracy of few tens of Hertz. The new back-end has been developed with the aim to maintain a distance error lesser than 100 m and a Doppler shift error up to 50 Hz, according to the EUSST program requirements. In this paper, the front-end and the new back-end of the Italian BIRALET radar are described. In particular, a detailed description of the new transmitting and the new receiving systems are presented, focusing on the architecture, implemented on the USRP-2954R board (NI, Austin, TX, USA), to perform Doppler shift and distance measurements. Finally, the results of several validation campaigns are presented, comparing the measured values with the estimated ones.
## 3 Experiments
### Remote Sensing Investigations of Large Rivers Deltas
#### 3.5.10 The Amazon River Delta  
The Amazon River is one of the major axial alluvial rivers on the planet, characterized by a massive volume of water and sediment seasonally exchanged through complex channel floodplain interactions [e.g., [469; 470; 471; 472]]. Understanding and predicting water storage changes is vital to evaluating the potential impacts of climate change on a continental and worldwide scale. Several remote sensing data studies have been conducted to model the river's hydrodynamics [473; 474; 475]. In particular, the work of [476] analyzed rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) satellite over the Tapajos River basin. They used the data as inputs to a large-scale hydrological model for the whole basin, demonstrating that satellite data are helpful to identify damaged or aberrant rain gauges on a basin-wide scale. The Amazon River floodplain's annual inundation has been comprehensively studied [see, for instance, [477; 478]]. The results show that many different water types can enter the floodplain and affect flooding events, including rainfall, flooding of local tributaries, groundwater, or exchange with the main channel [469]. Flooding is also influenced by the local topography, soil characteristics, and the vegetation of the floodplain. RS data to calculate the regional methane emission rates for the Amazonian wetlands are also a topic of great interest to this region and the world. Methane emissions in the atmosphere are generated by the anaerobic degradation of organic matter andcan significantly influence global climatic and environmental changes, land use, and river alterations [479; 480; 481; 482; 483; 484]. Amazonian wetlands' flooded forests, lakes, and floating macrophytes are globally significant tropospheric methane sources [483]. RS has made it possible to quantitatively analyze the inundation and vegetation cover in the wetlands of the Amazon basin [473; 477; 485; 486; 487; 488].
## 5 Conclusions  
This paper proposes a systematic control-scheduling co-design methodology for selecting the task scheduling period for clutch engagement control. The co-design has two steps. In the first step, stability analysis is conducted for the slipping phase based on a linearized system model enveloping the driving and driven part of the clutch, feed-forward and feedback control loop together with a zero-order signal holder. The critical period is determined according to pole locations. The evolution of the critical period with respect to five key parameters--the natural frequency, damping coefficient, proportional gain, integral gain, and time delay--is illustrated. In the second step, hardware-in-the-loop experiments are performed in a dSPACE real-time environment to inspect the dynamic response concerning the friction state transition. The results show that the critical period identified in the first step can induce intensive slip-stick motion and it represents a turning point in terms of clutch engagement performance. Specifically, the vehicle jerk and clutch frictional loss generally increase with the increase in the scheduling period. When the scheduling period is shorter than the critical period, the rate of increase is mild. However, once the scheduling period exceeds the critical period, the rate of increase becomes very  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(T\) **< 0.01 s** & **0.01 s \(\leq T\leq\) 0.30 s** & **\(T\) > 0.30 s** \\ \hline Average increasing rate of vehicle jerk (m/s\({}^{4}\)) & 7.1 \(\times\) 10\({}^{2}\) & 1.6 \(\times\) 10\({}^{5}\) & 2.3 \(\times\) 10\({}^{12}\) \\ Average increasing rate of frictional loss (J/s) & 1.2 \(\times\) 10\({}^{3}\) & 2.7 \(\times\) 10\({}^{5}\) & 1.9 \(\times\) 10\({}^{20}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the influence of the task scheduling period on the engagement performance.  
Figure 9: Evolution of the engagement performance with respect to the task scheduling period \(T\).  
high. Furthermore, a sub-boundary of the scheduling period is found to guarantee the control performance to satisfy the engineering requirements.  
The proposed methodology provides a useful reference for the design of clutch control systems, especially for the case of the advanced optimal control algorithm that requires a predefined task scheduling period. The methodology can also be extended to other industrial applications that employ electronic controllers.  
Data curation, Z.D. and X.C.; Formal analysis, J.C.; Project administration, L.C.; Supervision, C.Y.; Writing--original draft, Z.D.; Writing--review & editing, L.C. All authors have read and agreed to the published version of the manuscript.  
This project is supported by the National Natural Science Foundation of China (Grant No. 51475284).  
Not applicable.  
All data are reported in the manuscript.  
The authors declare no conflict of interest.
# RingFFL: A Ring-Architecture-Based Fair Federated Learning Framework
## 4. Penalty Mechanism in RingFFL  
In FL, each distributed client trains a local model according to its data set locally. Then, all the local model parameters can be safely aggregated into a global model to be used for more accurate prediction. However, each client may be corrupted and does not share their local model with other clients. In this case, they can synthesize a more accurate model to improve their prediction accuracy, while other clients cannot, although they share their local models.  
To deal with this, we put forward RingFFL to ensure that if a dishonest client refuses to send their local model to honest clients after receiving others' local models, the honest ones who do not obtain the results will obtain a certain amount of digital currency compensation provided by the dishonest ones. The proposed mechanism is applicable to FL with ring architecture, and clients that can form the ring topology can participate in FL, such as mobile devices, IoT terminals, and self-driving vehicles. In the paper, we deal only with the static adversaries and assume that all clients will honestly train their local models. The main notations used are shown in Table 1.  
Figure 2. The work flow of RingFFL.  
To gain a better understanding of the mechanism, motivated by [38; 39], we introduce the process of digital currency changing on the blockchain. Firstly, sender \(S\) of the deposits broadcasts a transaction to the network. The transaction involves the number of deposits \(coins(b)\), a boolean circuit \(C(\cdot)\), and the time limit \(t\). Secondly, the recipient \(R\) of the deposits provides evidence \(e\) in time \(t\). Finally, if \(e\) satisfies \(C(e)=1\), the transaction will be recorded on the blockchain, and \(coins(b)\) will be sent to \(R\)'s wallet. If \(R\) cannot provide \(e\) or \(C(e)=0\) in time \(t\), then the transaction will not be recorded, and \(coins(b)\) will be returned to \(S\)'s wallet. The penalty mechanism is detailed below.
## 2 Modeling of Hybrid-Propellant Launch Vehicle
### Aerodynamic Model  
Aerodynamic coefficients are critical parameters in the simulation model. These coefficients are utilized to understand the interaction and aerodynamic behavior of an object in the airflow. Used in simulation models, these coefficients play a crucial role in assessing and optimizing the performance of a launch vehicle. They assist engineers in improving their designs and optimizing aerodynamic performance. Therefore, aerodynamic coefficients have been incorporated into an aerodynamic model and integrated into the simulation tool. DATCOM, widely used semi-empirical aerodynamic prediction code, is utilized to calculate aerodynamic coefficients and integrate them into the flight mechanics subsystem [37]. DATCOM predicts aerodynamic forces, moments, and stability derivatives as a function of the \(\alpha\) (angle of attack) and M (Mach number) for the desired flight condition based on the altitude and Reynolds number (Re). The static coefficients and dynamic derivatives of the aerodynamic coefficients are obtained as an output file. In-flight mechanics, the static coefficients, and the dynamic derivatives are combined to calculate aerodynamic forces and moments. This study considers drag, lift, and side forces, as well as rolling, pitching, and yawing moments. The aerodynamic coefficients are dependent on various parameters (precisely \(\alpha\), Re, and Mach); however, they are modeled to be dependent on three variables: \(\alpha\), sideslip angle (\(\beta\)), and M. The coefficients are obtained within the range of \(-20\) to 20 degrees for \(\alpha\) and \(\beta\), and from 0 to 50 for M, ensuring they do not exceed the flight range. The study was conducted on a four-stage launch vehicle, and an optimization study was carried out by replacing the final stage with a hybrid-propellant motor. As the size of the final stage changes, the overall length of the launch vehicle is consequently altered. Therefore, adjusting the aerodynamic coefficients of the entire launch vehicle and each stage is necessary when each stage is separated. Aerodynamic coefficients are integrated into the simulation model and updated during the optimization process based on changes in the geometry of the launch vehicle. The geometry of the launch vehicle was modeled using MATLAB 2017b and integrated with the input file of the DATCOM program. Within this scope, during optimization, the stage length obtained from the propulsion model isused to update the DATCOM model through MATLAB 2017b, allowing the launch vehicle to be remodeled for the new geometry and obtaining updated aerodynamic coefficients without requiring additional inputs. Thus, by modeling the current state of the launch vehicle's aerodynamic performance and its effects on the flight, the challenge of treating them as constant values has been overcome.
# Research on Rapid Recognition of Moving Small Targets by Robotic Arms Based on Attention Mechanisms
## 4 Experimental Results and Discussion
### Experimental Setup for Reliability  
To ensure the reliability of the robotic arm operations, fruits were randomly placed at different heights and positions. Experimental results were referenced by the coordinates of the fruit targets. We conducted 100 sets of experiments under indoor lighting conditions, where the robotic arm performed identification and grasping tests. From these, we randomly selected 10 sets of fruit at different positions to gather data on detected coordinates and errors, as shown in Table 3.  
The data from Table 1 indicate that each set of experiments showed varying degrees of error between the system-calculated values and the actual measured values. The largest error in a group was 3.26%, and the smallest was 1.32%. Due to the distance between the target and the camera, this might lead to failures in the robotic arm's grasping attempts. However, overall, the experimental data showed a successful grasping rate of 96%. The overall errors met the precision requirements of the experiment and aligned with the expected objectives. This demonstrates that the system's performance was robust and reliable for practical applications, even considering the potential inaccuracies due to positional variances and operational conditions.  
The technical specifications of D435i are outlined in Table 4. From the table, it is evident that this camera features compact size, a wide field of view, high resolution, and easy installation. Equipped with Intel's latest depth-sensing hardware and software, the camera boasts high integration. Intel's official website offers the cross-platform development software Intel RealSense SDK 2.0, which provides a rich set of interfaces for secondary development.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Group ID**} & \multicolumn{2}{c}{**Camera 3D**} & \multicolumn{2}{c}{**Actual**} & \multicolumn{2}{c}{**Error**} \\  & **Coordinate Values** & **Measured Value** & \(\frac{1Z-Zx1}{Zx}\)/\% & **Grab Results** \\  & **(X, Y, Z)/mm** & **Zr/mm** & **Zr/mm** & **Zr/mm** \\ \hline
1 & (193.1, 66.2, 50.2) & 49.5 & 1.55 & Yes \\
2 & (169.0, 82.6, 57.9) & 57.2 & 1.32 & Yes \\
3 & (175.7, 67.6, 86.3) & 83.6 & 3.26 & No \\
4 & (210.5, 78.5, 69.5) & 67.9 & 2.36 & Yes \\
5 & (203.2, 53.2, 57.2) & 56.3 & 1.63 & Yes \\
6 & (206.6, 77.9, 84.5) & 82.1 & 3.01 & Yes \\
7 & (221.3, 86.2, 73.0) & 71.0 & 2.85 & Yes \\
8 & (190.5, 59.6, 77.7) & 76.2 & 2.06 & Yes \\
9 & (186.9, 65.4, 83.3) & 60.9 & 1.98 & Yes \\
10 & (200.1, 86.8, 62.1) & 59.6 & 1.88 & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Group system reliability testing.
# Multiple Instances QoS Routing in RPL: Application to Smart Grids +
## 5 Performance Evaluation  
In this section, we evaluate our proposition \(OFQS\) in comparison with \(MRHOF/OF0\) in terms of four performance metrics: End-to-end delay, network lifetime, load balancing and packet delivery ratio. It is important to mention that our approach is not specific to SGs but it is mostly suitable to any context with different applications on the same physical topology with different characteristics/QoS. SGs are only an example of such applications. We note that in addition to the preliminary results obtained by simulation and available at [41], these experimentation results provide a large scale evaluation of our metric in real environment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline OS & Contiki master version \\ \hline Testbed & FIT IOT-LAB \\ \hline Communication protocols & CSMA, RDC contikimac, IEEE 802.15.4, ContikiRPL, IPv6 \\ \hline OF & 1-OFQS with 2 instances \\  & 2-MRHOF (ETX) \& OF0 (HC) \\ \hline Number of nodes & 67 clients and 1 server \\ \hline Sensors & M3 \\ \hline Microcontroller Unit & ARM Cortex M3, 32-bits, 72 MHz, 64 kB RAM \\ \hline Maximum packet size & 30 kb \\ \hline Sending interval & 1 packet every 1 to 60 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the experimentation.
## 4 Network Interference Probability Analysis  
In this section, we will analyze the probability of interference-optimality by adjusting the transmission range and explore the properties of this probability in detail. Due to the different node deployment scenarios (which are shown in Figure 2), there are two different probabilities: the Homogenous probability and the Heterogeneous probability.  
As discussed in Section 3, the interference of edge (\(u\), \(v\)) is defined as the number of nodes that node \(u\) and node \(v\) covered. When the transmission ranges are reduced, node \(u\) and node \(v\) cannot communicate with each other directly; therefore, node \(n\) will be chosen as the relay node. The node \(n\) can communicate with node \(u\) and node \(v\), respectively (see Figure 4). So the interference of link (\(u\), \(n\), \(v\)) will be:  
\[\text{Int}(u,n,v)=\text{int}(u,r_{1})\cup\text{int}(n,r_{2})\cup\text{int}(v,r _{3}) \tag{2}\]  
where \(r_{1}\), \(r_{2}\), and \(r_{3}\) are the transmission ranges of \(u\), \(n\), and \(v\), respectively.  
In [26], the authors have proved that for the uniformly distributed sensor network, the number of nodes that fall into the coverage area of a node (the coverage area of node is \(\pi\pi^{2}\), \(r\) is the transmission range) follows the Poisson distribution. Therefore, the probability that a node has \(N\) neighbor nodes is:  
\[P(N)=\frac{\left(\rho\pi r^{2}\right)^{N}}{N!}e^{-\rho\pi r^{2}} \tag{3}\]  
where \(\rho\) is the node density and \(\rho=\frac{n}{A}\); \(n\) is the node number in the network and \(A\) is the area in which the nodes are deployed. Because the number of nodes that a node covered is stochastic and decided by both transmission range \(r\) and node density \(\rho\), in this paper, we consider the mean value of the node number. Since the nodes are uniformly distributed, the mathematical expectation of the node number that a node covered is:  
\[E(N)=\rho\pi\
u^{2} \tag{4}\]  
The Equation (4) indicates that in WSNs, the expectation of interference is \(\rho\pi r^{2}\). Therefore, when the node density is constant, the interference will be decided by the node's coverage area. Based on this conclusion, the issue of interference can be abstracted as the issue of the node's coverage area.  
\begin{table}
\begin{tabular}{c|c} \hline
**Parameters** & **Meaning of the Parameters** \\ \hline \(r\) & the original transmission range of node \\ \(r_{1}\) & the transmission range of node \\ \(d\) & the distance between node \(u\) and node \(v\) \\ \(d_{1}\) & the distance between node \(u\) and node \\ \(s\) & the coverage area of nodes \\ \(p\) & the probability of interference-optimality under different original transmission range \(r\) \\ \(p_{s}\) & the probability of interference-optimality under different transmission range \(r_{1}\) \\ \(P_{uw1}\) & the energy needed before reducing the transmission range \\ \(P_{uw2}\) & the energy needed after reducing the transmission range \\ \(\text{Int}(u,v)\) & the interference of edge (\(u\), \(v\)) \\ \hline \end{tabular}
\end{table}
Table 1: The notations used in this paper.
# Exploring the Potential of Yellow Mealworm (_Tenebrio molitor_) Oil as a Nutraceuticalal Ingredient
## 3 Results and Discussion
### Fatty Acids Composition and Nutritional Quality Index of Tenebrio molitor Larvae Oil
Results were even better than some vegetable margarines, especially the TI, which was 2-fold lower in TM oil. Similar values of AI were found in avocado oil [51]. The specific effects of fatty acids on cholesterol metabolism are evaluated by the hypocholesterolem-hypercholesterol (HH) index. In this case, high HH values are desired from a nutritional standpoint. Results showed a higher HH index in TM oil than in vegetable oils like avocado fruit (2.06) [48]. Health-promoting index (HPI) was proposed to assess the nutritional value of dietary fat, which focuses on the effect of FA composition on cardiovascular disease. Higher HPI values are considered to be beneficial to human health. Results indicate a 10-fold higher index in comparison with HPIs of other animal fats, like dairy products [42], but also higher than previously published in the literature about TM oil (1.38) [52]. Oxidizability value (COX) is a parameter that evaluates the oil's tendency to undergo autoxidation. The rapidity of oxidation depends on the degree of unsaturation, the presence of antioxidants, and the prior storage conditions. Compared with literature data, TM oil showed a better autoxidation tendency than vegetable edible oils rich in unsaturated FAs, such as sunflower oil (7.25), peanut oil (4.63), or certain seed oils, but lower than previously reported for extra virgin olive oil (2.38) [39; 53].
## 3 Results
### The Application of 4 HR Did Not Increase ER\(\alpha\) Expression and p-ERK  
The application of BPA (1 to 100 \(\upmu\)M) to MCF-7 cells increased the expression of ER\(\alpha\) and ER\(\beta\) (Figure 2a). However, the application of 4 HR (1 to 100 \(\upmu\)M) to MCF-7 cells neither increased nor decreased the expression of both ER\(\alpha\) and ER\(\beta\) (Figure 2b). The expression of ER\(\beta\) was much more increased compared to that of ER\(\alpha\) in the application of BPA. The application of BPA increased relative expression level of ER\(\alpha\) compared to that of 4 HR (Figure 2c). The application of BPA increased relative expression level of ER\(\alpha\) compared to that of 4 HR (Figure 2d).  
It has been known that xenoestrogen activates the ERK pathway in the presence of ER\(\alpha\)[10, 20]. The application of BPA (1 to 100 \(\upmu\)M) to MCF-7 cells increased p-ERK1/2 (Figure 3a). However, the application of 4 HR (1 to 100 \(\upmu\)M) to MCF-7 cells showed no change of p-ERK1/2 level, indicating no activation of the ERK pathway (Figure 3b). The application of BPA slightly increased relative expression level of ERK1/2 compared to that of 4 HR (Figure 3c). However, the difference between groups seemed to be not significant. The application of BPA increased relative expression level of p-ERK1/2 compared to that of 4 HR (Figure 3d).  
Figure 1: The results of MTT assay. (**a**) The application of BPA (1 to 100 \(\upmu\)M) to MCF-7 cells showed increased value compared to solvent only group at 48 and 72 h after administration. However, the application of 4 HR with the same range of concentration showed decreased value compared to the solvent only group at both 48 and 72 h after administration. (**b**) The same trends were observed in SK-BR-3 cells (* \(p<0.05\)).  
Figure 2: The expression of estrogen receptors after the administration of BPA or 4 HR. (**a**) The application of BPA (1 to 100 \(\upmu\)M) to MCF-7 cells increased the expression of ER\(\alpha\) and ER\(\beta\). (**b**) The application of 4 HR (1 to 100 \(\upmu\)M) to MCF-7 cells did neither increase nor decrease the expression of both ER\(\alpha\) and ER\(\beta\). (**c**) Relative protein expression of ER\(\alpha\) to \(\beta\)-actin. The application of BPA increased relative expression level of ER\(\alpha\) compared to that of 4 HR. (**d**) Relative protein expression of ER\(\beta\) to \(\beta\)-actin. The application of BPA increased relative expression level of ER\(\alpha\) compared to that of 4 HR. Full length blots were available in Supplementary Figures S1 and S2.