esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this research work, we have proposed a high-speed implementation of the well-known AES-128 algorithm properly developed for a custom, very short-range and high-frequency communication system, called Wireless Connector; specifically, this last supports high-throughput data transmission on a frequency range around 60 GHz between two mobile stations located at short-range (1-10 m). The core of the communication system is constituted by a Xilinx ZCU102 FPGA platform, which manages all the base-band operations, including the encryption and decryption of the data packets; the prototype of the Wireless Connector was realized, demonstrating its proper operation. In particular, a pipelined approach has been applied to the round-based elaboration typical of the AES algorithm, allowing simultaneous processing of multiple successive plaintext packets each clock period and thus reaching higher data throughput values; furthermore, a 32-bit \(16\times 16\) Sbox matrix was employed to speed up the Substitute Byte step compared to the classic 8-bit implementation.  
Encryption and decryption VHDL blocks have been developed on the Xilinx ZCU102 FPGA platform, carrying out multiple elaborations of the incoming data packets to comply with the 3 Gbit/s data rate, constraint required by the Wireless Connector application. The developed encryption system can operate at a 220 MHz maximum clock frequency, supporting an encryption time of just 10 clock periods. Thanks to the pipelined elaboration, the proposed implementation is able to process and provide the encrypted packets each clock period (namely, \(4.54\) ns \(=\frac{1}{220\) MHz), reaching a maximum data throughput higher than 28 Gbit/s (i.e., \(\frac{128\) bit/\(packet}{4.54\) ns \(=28.16\) Gbit/s). Similarly, the decrypting system employs just 10 clock period for obtaining the plaintext data packets.  
Furthermore, developed AES-128 encryption implementation is featured by higher efficiency (8.63 Mbps/slice) compared to similar solutions operating on the same frequency range, requiring just 1631 CLBs, 13043 LUTs and 3877 FFs. However, the decryption implementation requires higher resource utilization compared to the encryption one (3464 CLBs, 27713 LUTs, 3912 FFs and 1 BUFG), due to the four matrices derived from Inverse SubBytes, Inverse Shift Rows and Inverse Mix Columns operations, each containing 32-bit elements; the greater resource utilization is associated with the Inverse Mix Columns operation, given the multiplicative constants involved in its matrix representation and its LUT-based implementation inside the FPGA, as detailed in the Section 3.2.  
editing, P.V. and R.V.; visualization, R.V.; supervision, R.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# Research of Calibration Method for Industrial Robot Based on Error Model of Position
## 2 Robot Calibration Modeling
### Robot Flange Coordinate Calibration Model  
The key step of robot calibration is to obtain the actual pose error of robot end by measuring. However, the FCS of robot end is usually \"hidden\", that is, it cannot be directly measured by measuring equipment [22]. There is a certain offset of the actual measured position with respect to the FCS (as expressed in Figure 2).  
Let \({}_{\theta}^{D}T\) denote the transformation from robot FCS to DCP coordinate system. If the pose is neglected, it can be expressed as follows:  
\[\begin{array}{c}{}_{0}^{D}T=\left(\begin{array}{cccc}1&0&0&P_{x}{}^{L}\\ 0&1&0&P_{y}{}^{L}\\ 0&0&1&P_{z}{}^{L}\\ 0&0&0&1\end{array}\right).\end{array} \tag{3}\]  
Assuming that the FCS position of the robot is O in a certain state, when the position of the robot's end remains unchanged, the position coordinates of O point can be obtained by fitting the center of the sphere through only changing the pose of the robot. The spherical center coordinate can be obtained by fitting more than four non coplanar points in space. Suppose the spherical center coordinates are \((a,b,c)\), radius is R, and the coordinates of the four measuring points are \((x_{1},y_{1},z_{1}),(x_{2},y_{2},z_{2}),(x_{3},y_{3},z_{3}),(x_{4},y_{4},z_{4})\), respectively. The following equations can be obtained from the function expression of the sphere:  
\[\left\{\begin{array}{c}(x_{1}-a)^{2}+(y_{1}-b)^{2}+(z_{1}-c)^{2}=R^{2}\\ (x_{2}-a)^{2}+(y_{2}-b)^{2}+(z_{2}-c)^{2}=R^{2}\\ (x_{3}-a)^{2}+(y_{3}-b)^{2}+(z_{3}-c)^{2}=R^{2}\\ (x_{4}-a)^{2}+(y_{4}-b)^{2}+(z_{4}-c)^{2}=R^{2}\end{array}\right.. \tag{4}\]  
By solving Equation (4), the origin position of FCS can be obtained, and then the pose of FCS needs to be obtained. As long as the robot controller can control the robot to move along the axes \(x\), \(y\), \(z\) of the FCS in its TCP coordinate system, more than two points are taken to determine the direction of the coordinate axis. Finally, the vector \(\overrightarrow{n}\), \(\overrightarrow{o}\), \(\overrightarrow{a}\) of the three axes \(x\), \(y\), \(z\) in the FCS can be obtained. On the basis, the pose matrix of the FCS at the end of the robot in the MICS can be obtained:  
\[\begin{array}{c}{}_{C}^{6}T=\left(\begin{array}{cc}6R&\frac{6}{C}P\\ 0&1\end{array}\right).\end{array} \tag{5}\]  
Figure 2: Actual measuring point and flange coordinate system (FCS).  
In Equation (5), the pose of the FCS in the MICS is represented by \({}_{C}^{6}R\), and the position of the center of the FCS in the MICS is represented by \({}_{C}^{6}P\).  
Assuming that DCP measured by the measuring instrument under the above robot pose is \({}_{C}^{D}T\), and the coordinate transformation from the robot FCS to the measuring point is \({}_{6}^{D}T\), and the following transformation relations exist:  
\[{}_{C}^{D}T={}_{C}^{6}T\cdot{}_{6}^{D}T. \tag{6}\]  
The pose transformation equation \({}_{6}^{D}T\) of the robot's end FCS to DCP point can be obtained:  
\[{}_{6}^{D}T={}_{C}^{6}T^{-1}\cdot{}_{C}^{D}T. \tag{7}\]
## 20 Existing Fault Classification and Location Methods  
There are various methods for estimation of faults in MT-HVdc transmission system besides the methods of traveling and non-traveling waves. Impedance method is found applicable for permanent faults and is very practical approach to locate and classify faults in long distance HVdc transmission lines. However, an accuracy is compromised with the increase in the length of transmission lines. Therefore, voltage and current measurements-based methods are developed to address issues of accuracy and speed. Further, signal processing and time frequency analysis are incorporated to enhance performance of voltage and current measurement-based methods. There is a significant improvement in the speed and accuracy of detection with the embedding of knowledge-based methods. However, there are some limitations like inaccuracy in case of incomplete information of faults and time inference in case of bulk power systems. Table 3 gives detailed information about fault classification and location methods, and their limitations.
# A Low-Cost Soft Robotic Hand Exoskeleton for Use in Therapy of Limited Hand-Motor Function
## 2 Materials and Methods
### Control System
#### 2.3.1 Electrical Design  
A custom Arduino shield was designed to provide an interface to connect the Arduino to the various hardware components in the actuation mechanism. Arduino shields have become popular in the consumer market as they provide a simple plug-and-play interface between the microcontroller and the external system by integrating all necessary electronics and connectivity on a single printed-circuit  
Figure 5: (**a**) Mechanical integration and (**b**) fully assembled robotic hand exoskeleton connected to the microcontroller via a wire harness (black—soft neoprene glove, blue—3D-printed ergonomic base, green—tendon connection points to linkages).  
board (PCB) that mirrors the Arduino form factor. Our Arduino shield featured three integrated TB6612 motor drivers with appropriate connectivity to facilitate the motor velocity and direction control. Additional headers provided an interface for the appropriate connections to be made between the potentiometers and the analog-to-digital converter (ADC), 5 V and ground pins on the Arduino. Three tactile pushbuttons were integrated into the shield to provide a rudimentary user interface for the calibration process and controls to manually operate the actuators, if needed.  
Our custom shield could be manufactured by the end user using a PCB or CNC milling machine, or custom ordered (fully or partially assembled) from PCB fabrication and assembly houses for a very low price (estimates fit well within our required budget). While the form factor of a manufactured shield is preferred in the final constructions process, we have designed the mechanical enclosure with ample space to allow users to manually assemble the electrical components consisting of only wiring and motor drivers.  
A 7.4V, 2200 mAh lithium polymer (LiPO) battery was chosen to power the system as LiPO batteries have the most stable discharge characteristics providing a steady current supply. The average current draw of one DC motor was measured to be approximately 350 mA while under load. Battery run time was calculated using the following equation.  
\[\text{T}_{\text{run}}=\text{Capacity}/\text{I}_{\text{load}}=(2200\text{ mAh})/(350\text{ mA}\times 5\text{ motors})\approx 1.25\text{ h} \tag{1}\]  
The estimated run time accounts for a worst-case scenario where all the motors are constantly under load, which is rarely the case. A typical estimated load time per motor is in the order of ten times less than our worst-case scenario estimate. The runtime satisfies a typical physical therapy session of one hour with ample capacity to account for degradation of battery life, ensuring that the exoskeleton is operational well within the typical LiPo battery 500 discharge cycles lifetime and accounting for typical degradation of LiPo performance by 25% after the first 200 recharge cycles.
## Objective and Experimental Plan  
This paper is aimed at observing the effects of W-oil on the performance of aged asphalt and analyzing the potential of W-oil as an asphalt rejuvenator. It can not only provide an environment-friendly way to treat W-oil, it can also become a substitute for non-renewable petroleum-based asphalt rejuvenator, which will promote the development of sustainable pavement construction.  
The test plan is shown in Figure 1. First, aged asphalt is obtained by two steps: virgin asphalt is aged by rolling thin film oven test (RTFOT), then put the aged residue of RTFOT into a pressurized aging vessel (PAV) for further aging to get aged asphalt. Second, 5%, 10%, 15%, and 20% W-oil is added into the aged asphalt to prepare rejuvenated asphalt. Third, the safety property, aging property and pavement performance of virgin asphalt, aged asphalt, and rejuvenated asphalt is analyzed. Pavement performance is characterized by two indexes, namely, the traditional indexes and rheological indexes. The flash point test is to characterize the asphalt's safety, and the RTFOT test is to characterize its aging property. In the traditional indexes, the penetration test, soften point test, and ductility test are used to characterize asphalt pavement performance. Corresponding to the traditional indexes for measuring asphalt pavement performance, superpave proposed the test methods based on rheology theory, so that the rheological index can be directly correlated to the field pavement performance. The viscosity test is used to characterize the high-temperature workability, the multiple stress creep recovery (MSCR) test is used to characterize the rutting property, the time sweep (TS) test is used to characterize the medium-temperature fatigue property, and the bending beam rheometer (BBR) test is to characterize low temperature property.
## 4 Discussion  
### The Deformation Comparison between the Linear and Non-Linear Thermal Buckling Response  
In thermal buckling analysis, the first-order buckling mode is the critical state, in which buckling mode distribution can best reflect the initial state of buckling, becoming the key basis to judge the thermal instability of structures. In the paper, for the linear and non-linear thermal buckling, their first-order modal deformations along the blade are compared in Figure 14 [1].  
In Figure 14a, for linear thermal buckling, the critical buckling deformation of the blade mainly occurred in the range of 15\(\sim\)30 mm away from the blade root and about 20 mm from the blade tip. In addition, at the leaf roots, the buckling deformation shows a peak shape. The deformation at 15 mm from the blade root will suddenly increase and reach the maximum value of 1.327 mm at 20 mm from the blade root, and then gradually decrease along radial direction from 20 mm to 50 mm. Meanwhile, the buckling deformation of the tip is an upturned shape with the maximum deformation of 0.819 mm. In nonlinear buckling, the critical buckling deformation of the blade is the warpage of the overall structure, and the deformation becomes larger gradually along the radial direction. The maximum deformation appears at the blade tip with 1.424 mm, which was 7.3% larger than maximum deformation of linear thermal buckling.  
It is clearly found in Figure 14b that, along the axial direction, the range of 5\(\sim\)15 mm from the blade edge is the main occurrence area for critical linear thermal buckling deformation. Meanwhile, the maximum deformation is at the same position by comparison of the maximum deformation along the radial direction of Figure 14a. This also points out that the linear critical thermal buckling deformation can happen only on one side of the blade. However, critical nonlinear thermal buckling deformation is approximately symmetrical along the axial direction, and both blade tips are bent to the middle part.
## 1 Introduction
The architecture proposed in this work enables the management of RINA nodes and the management of SDN switches. The DAF management enables a DIF layer to establish management connections between the RINA nodes and the SDN controller. A RINA SBI driver is proposed to translate the SDN data models into RINA data models. The RINA SBI driver is a Ryu framework [10] component with a well-defined API to enable the development of control and management applications. Finally, the RINA L2 VPN provides a transparent connection between applications that cannot use the RINA API to allocate flows. These developments have been accomplished within the context of the TERMINET [11] project. In summary, the main contributions and novelties of the proposed work are the following:  
* A RINA-based DAF to manage RINA nodes along the edge-fog-cloud continuum. A detailed description of the Management DAF can be found in Section 3.1; moreover, a validation can be found in Section 5.1.1. * The SBI driver SDN to enable the hybrid centralized management of SDN switches and RINA nodes. A detailed description of its design and implementation be found in Section 3.2; moreover, a performance evaluation can be found in Section 5.2.1. * The RINA L2 VPN to enable seamless communication between edge nodes and the cloud by using RINA to facilitate the data exchange between Network Functions (NFs). A detailed description of RINA L2 VPN's design and implementation can be found in Section 3.3; moreover, a performance evaluation can be found in Section 5.2.2. The remainder of this document is organized as follows: Section 2 reviews the current SDN and NFV technologies with a special focus on IoT environments, and presents the RINA concept and its current developments. Section 3 describes the proposed architecture to integrate SDN technologies with RINA. Section 4 presents the smart building use case implementation. Section 5 provides information on the obtained results and, lastly, Section 6 compiles the lessons learned and presents the future work.
# Molecular Dynamics Simulation Reveal the Structure-Activity Relationships of Kainoid Synthases
###### Abstract  
Kainoid synthases are key enzymes in the biosynthesis of kainoids. Kainoids, as represented by DA and KA, are a class of naturally occurring non-protein amino acids with strong neurotransmitter activity in the mammalian central nervous system. Marine algae kainoid synthases include PnDabC from diatoms, which synthesizes domoic acid (DA), and DsKabC and GfKabC from red algae, which synthesize kainic acid (KA). Elucidation of the catalytic mechanism of kainoid synthases is of great significance for the rational design of better biocatalysts to promote the industrial production of kainoids for use in new drugs. Through modeling, molecular docking, and molecular dynamics simulations, we investigated the conformational dynamics of kainoid synthases. We found that the kainoid synthase complexes showed different stability in the simulation, and the binding and catalytic processes showed significant conformational transformations of kainoid synthase. The residues involved in specific interactions with the substrate contributed to the binding energy throughout the simulation process. Binding energy, the relaxed active pocket, electrostatic potential energy of the active pocket, the number and rotation of aromatic residues interacting with substrates during catalysis, and the number and frequency of hydrogen bonds between the individual functional groups revealed the structure-activity relationships and affected the degree of promiscuity of kainoid synthases. Our research enriches the understanding of the conformational dynamics of kainoid synthases and has potential guiding significance for their rational design.  
kainoid synthases; structure-activity relationships; molecular dynamics simulation; promiscuity
## 1 Introduction
**The main contributions of this research are as follows:**  
* An end-to-end system is proposed for detecting Bengali sign characters and generating meaningful sentences. * A quantization technique for the YoloV4-Tiny model is proposed for detecting hand signs and predicting the characters and to make it implementable on edge devices. * A sentence-generation model based on LSTM is proposed that takes input from the predicted characters of the detection model and generates meaningful sentences. * The proposed system achieves a mAP of 99.7% for the detection phase, as well as an accuracy of 99.12%, which is obtained by the sentence-generation model. * A comprehensive performance analysis of some detection models such as Yolov4, Yolov4-Tiny, and Yolov7 is also provided. The rest of the paper is organized in the following manner: Section 2 describes the literature review. Section 3 represents the dataset description. The proposed methodology is described in Section 4. Section 5 illustrates the experimental results analysis. Lastly, Section 6 concludes the paper.
# Experimental Investigation on a Throttleable Pintle-Centrifugal Injector
## 4 Conclusions  
This study presents and experimentally evaluates a novel throttleable pintle-centrifugal injector system for hybrid rocket engines, focusing on enhancing atomization and achieving precise throttling control. Cold-flow tests with deionized water demonstrated the injector's ability to regulate injection pressure drop and optimize atomization across a wide flow rate range.  
The results highlight the pintle mechanism's deep throttling capability, which is a key advantage over conventional swirl or impinging injectors, by precisely modulating flow path geometry and pressure drop. Complementing this, the centrifugal injection design induces a swirl effect that significantly enhances atomization by imparting rotational momentum to the oxidizer, independent of pintle stroke adjustments. This synergy not  
Figure 12: Flow field diagram for different mass flow rates (Injector 1, \(t=1.50\) s).  
only improves spray cone angle and droplet size distribution, especially at lower flow rates, but also ensures stable and efficient atomization across varying operational conditions.  
The main contribution of this work lies in combining the strengths of both mechanisms: the pintle's throttling capability and the swirl's atomization enhancement. This dual approach optimizes combustion dynamics, supporting the advantages of hybrid rocket engines such as efficiency, safety and controllability. These findings provide valuable insights into the future development of hybrid propulsion systems.  
Future work will involve hot-fire testing of the pintle-centrifugal injector in operational hybrid rocket engines, exploring its long-term performance, combustion efficiency, and integration potential with various oxidizer and fuel combinations.  
Conceptualization, Z.Z.; Methodology, T.L.; Investigation, T.L., Z.Z. and Y.Z.; Data curation, T.L.; Writing--original draft, T.L.; Writing--review & editing, N.Y.; Visualization, T.L.; Project administration, N.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets presented in this article are not readily available because they are undergoing ongoing validation for subsequent research. Requests to access the datasets should be directed to corresponding author on reasonable request.  
The authors declare no conflict of interest.
## 4 Losses Analysis
### Core Loss  
Core loss accounts for a substantial proportion of total power loss and is much higher than other losses in HSPMSMs. The core loss of soft magnetic material in electrical machines is generally estimated according to loss separation theory, including hysteresis loss \(P_{h}\), eddy current loss \(P_{e}\) and anomalous loss \(P_{a}\)[19, 20],  
\[P_{c}\ =\ P_{h}+P_{e}+P_{a}\ =\ k_{h}fB_{m}^{2}+k_{e}f^{2}B_{m}^{2}+k_{a}f^{1.5}B_{ m}^{1.5}, \tag{1}\]  
where \(k_{h}\), \(k_{e}\), and \(k_{a}\) are the coefficients of hysteresis loss, eddy current loss, and anomalous loss, respectively; \(f\) is operating frequency; and \(B_{m}\) is flux density magnitude. In this paper, a high-silicon sheet was selected for developing the HSPMSM. On the basis of the experimental data, the loss coefficient could be obtained by the fitting method, and the obtained coefficients were \(k_{h}\) = 133.47, \(k_{e}\) = 0.014, and \(k_{a}\) = 1.26.  
Figure 7: Campbell diagram of HSPMSMSs with different rotors. (**a**) M1; (**b**) M2; (**c**) M3; (**d**) M4.
# Threat Detection Model for WLAN of Simulated Data Using Deep Convolutional Neural Network
## 2 Materials and Methods
### RF Impairments and Channel Configuration  
This stage includes passing every frame throughout two channels: (1) AWGN and Rayleigh multipath fading, and (2) Radio impairments, which may contain DC offset; frequency offset, and phase noise.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline No. of Known Routers & 4, and 7 & Channel Number & 153 (WLAN channel number) \\ No. of Unknown Routers & 10, and 3 & Channel Band & 5 (GHz) \\ No. of Total Routers & No. Known Routers + No. of Unknown Routers & Frame Length & 160 (L-LTF sequence length in samples) \\ SNR & 20 dB & No. of total Frames Per Route & 5000 \\ No. of training Frames Per Router & No. of Total Frames Per Router \(\times\) 0.8 & No. of test Frames Per Router & No. of Total Frames Per Router \(\times\) 0.1; \\ No. of Validation Frames Per Router & No. of Total Frames Per Router \(\times\) 0.1 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The setting values of the model parameters.  
Figure 3: The effect of a router intrusion is that an impersonator router (evil twin) tries to transmit beacon frames by replicating the MAC address of a known router.