esa-sceva/satcom-chunk-collection huggingface dataset:
# A Modular IoT Hardware Platform for Distributed and Secured Extreme Edge Computing
## 4 The Extreme-LT Routing Protocol
### Preliminary Tests
#### 4.1.2 Traffic Tests  
The next set of tests were designed to create heavy traffic conditions on the sink node and evaluate its capability to receive and process data from several nodes under such conditions. For this, the setup consisted on a receiver node connected to a terminal and 10 sending nodes deployed around it. Transmission power was set at \(-15\) dBm. After being connected, all sender nodes started sending messages to the sink with the following parameters: sending interval = 0.1 s; packet size per transmission = 95 B; minimum number of iterations per node = 1000 (which means that the test lasted until every sending node had sent at least 1000 packets). This sets a worst-case scenario to analyze the performance of the protocol under such conditions. The results obtained are shown in Table 5, computing a total amount of 11041 packets, with PDR (number of packets received at the destination divided by the packets sent by the source, expressed as a percentage) mean equal to 98,4 %. From these outcomes, where the worst PDR was more than 96%, it can be concluded that the sink node is able to endure heavy traffic conditions, being able to receive and process most of the messages sent to it (certainly very close to 100%).  
The results of these preliminary tests ensure the sink node will be able to support the incoming traffic and also determine the maximum transmission range of the nodes, and serve as the baseline for the following rounds of testing, in which the performance of the nodes under the routing protocol will be tested.
# Optimizing Time-Sensitive Traffic Scheduling in Low-Earth-Orbit Satellite Networks
## 3 TPC-CQF Improved from CQF
### Queue Priority Control of TPC-CQF
Use the waiting cycle and deadline to compute the dynamic priority, determining the assignment order. 3. Identify the set of feasible queues with sufficient capacity. From this set, sequentially select the queue with the highest total priority sum, the smallest capacity, and the fewest packets for packet assignment. Figure 5: Queue priority control of TPC-CQF. Check the priority or size differences between packet pairs, prioritizing the processing of packets with higher priority or larger size. If both are equal, randomly select one for processing. * Select the queue with the highest total priority sum to transmit its packets. After transmission, update the queue's capacity, total priority sum, and packet count. * Recalculate the waiting cycle and dynamic priority for packets in each queue. Update the queue's total priority sum to reflect the latest state. ```
Input: Packet set \(P\), queue set \(Q=\{Q1,Q2,Q3\}\), queue capacity \(K_{\max,Q}\), current time slot \(t\), fixed time slot duration T; Output: Updated queue assignments, total priority sum SQ, remaining capacity KQ;
1: Initialize: For all Q = {Q1, Q2, Q3}, set S\({}_{Q}\) = 0, KQ = \(K_{\max,Q}\), Packet_Count [Q] = 0, Packet_Assignments [p] = \(\O\);
2: for
3: For each packet p \(\in\) P arriving in time slot t: compute N\({}_{P}\) and \(\pi_{p}\) as well as FQ;
4: if \(F_{Q}=\varnothing\) do
5: Discard p;
6: Else
7: Select \(Q^{*}\) with the highest S\({}_{Q}\) from F\({}_{Q}\);
8: if SQ values are equal do
9: Select \(Q^{*}\) with the smallest K\({}_{Q}\);
10: if K\({}_{Q}\) values are equal do
11: Select min(PacketCount [Q]);
12: end if
13: Assign p to \(Q^{*}\), update: K\({}_{Q}\) [\(Q^{*}\)]\(-\)\(=\)\(s_{P}\), S\({}_{Q}\) [\(Q^{*}\)]\(+\)\(=\)\(\pi_{p}\), PacketCount [\(Q^{*}\)] \(+\)\(=\) 1, PacketAssignments [p] = \(Q^{*}\);
14: For each conflicting packet pair (p1, p2) \(\in\) P:
15: if \(\pi_{p_{1}}>\pi_{p_{2}}\) do
16: Prioritize processing p1 before p2, go to step 2-12;
17: else if \(\pi_{p_{1}}=\pi_{p_{2}}\) do
18: if \(s_{p1}>s_{p2}\) do
19: Prioritize processing p1 before p2, go to step 2-12;
20: else if \(s_{p1}=s_{p2}\) do
21: Randomly select one to process first, then the other;
22: end if
23: end if
24: Select argmax(\(S_{Q}\)) for transmission
25: endfor
26: Update queue S\({}_{Q}\), K\({}_{Q}\);
```  
**Algorithm 1**Queue Scheduling Algorithm Based on TPC-CQF  
The time complexity of Algorithm 1 is \(O(M)\), where M denotes the maximum number of packets arriving in a single time slot. When packet collisions occur, the worst-case scenario requires \(\frac{M(M-1)}{2}\) comparisons. Through priority pre-sorting optimization, the complexity is reduced to \(O(MlogM)\).
# Fast Coherent Video Style Transfer via Flow Errors Reduction
###### Abstract  
For video style transfer, naively applying still image techniques to process a video frame-by-frame independently often causes flickering artefacts. Some works adopt optical flow into the design of temporal constraint loss to secure temporal consistency. However, these works still suffer from incoherence (including ghosting artefacts) where large motions or occlusions occur, as optical flow fails to detect the boundaries of objects accurately. To address this problem, we propose a novel framework which consists of the following two stages: (1) creating new initialization images from proposed mask techniques, which are able to significantly reduce the flow errors; (2) process these initialized images iteratively with proposed losses to obtain stylized videos which are free of artefacts, which also increases the speed from over 3 min per frame to less than 2 s per frame for the gradient-based optimization methods. To be specific, we propose a multi-scale mask fusion scheme to reduce untraceable flow errors, and obtain an incremental mask to reduce ghosting artefacts. In addition, a multi-frame mask fusion scheme is designed to reduce traceable flow errors. In our proposed losses, the Sharpness Losses are used to deal with the potential image blurriness artefacts over long-range frames, and the Coherent Losses are performed to restrict the temporal consistency at both the multi-frame RGB level and Feature level. Overall, our approach produces stable video stylization outputs even in large motion or occlusion scenarios. The experiments demonstrate that the proposed method outperforms the state-of-the-art video style transfer methods qualitatively and quantitatively on the MPI Sintel dataset.  
style transfer; video stylization; video stabilization; deep networks +
Footnote †: journal:  
csciences
## 2 Proposed Dual-Electrode Actuator  
This section offers descriptions of the working principle of the newly proposed dual-electrode electrostatic resonant actuator and the design overview. The main components of the proposed actuator module include the inertial, grounded mass in the center of the module, and, on either side of the mass, a spacer and an electrode. Figure 2 shows an exploded view and working principle design of the ERA module. Unlike traditional electrostatic actuators, it incorporates a moving mass, which is grounded electrically, in the module to increase vibration intensity. This mass is suspended between an upper and lower electrode through radial beam springs. A small air gap separates the mass and either electrode, allowing the mass to displace up and down when an electrostatic force is generated upon the application of high voltage inputs. When an electrode is charged with a high voltage input, a capacitance is created between the grounded mass and the electrode. Because the electrodes are fixed, the mass is pulled into the housing where the electrodes are attached. When the mass gets displaced, the radial beam springs create a restoring force that pulls it back towards the equilibrium position. This rapid change of forces causes the mass to oscillate, making the actuator vibrate.  
The stainless-steel grounded mass in the module acts as a neutral conductive object while the electrodes act as electrically charged objects. When the electrodes are charged with a high voltage input, an electrical force attracts the mass to the electrodes. The equation for the electrostatic attraction force between the mass and a single (top or bottom) electrode is [16]:  
\[F_{e}(t)=\frac{\varepsilon_{r}\varepsilon_{0}A}{2}\Bigg{(}\frac{V}{d_{air}-t_ {Dielectric}\big{(}1-\frac{1}{K_{e}}\big{)}}\Bigg{)}^{2} \tag{1}\]  
where \(\varepsilon_{r}\) is the relative permittivity of air, \(\varepsilon_{0}\) is the vacuum permittivity, \(A\) is the area, \(V\) is the input voltage, \(d_{air}\) is air gap distance, \(t_{Dielectric}\) is the thickness of the dielectric film, and \(K_{e}\) is the relative permittivity of the dielectric film. The values for each parameter can be found in Table 1.  
Figure 2: Exploded view model and working principle design of the proposed electrostatic resonant actuator (ERA) module.
## 2 The Principle of DS Positioning with LEO Communication Satellites
### Pdop  
The estimated value of DS can be computed at the forecasted position \(\mathrm{P}_{0}\). Based on Equation (7), we derive the following conclusion:  
\[\Delta\mathbf{f}_{d}=\mathbf{f}_{d}-\overset{\star}{\mathbf{f}}_{d}=\mathbf{A} \Delta\mathbf{r}+\epsilon, \tag{9}\]  
where \(\Delta\mathbf{r}=\left(\mathbf{P}-\mathbf{P}_{0}\right)^{T}\) is the difference between the actual value of the receiver position and the predicted value. Ignoring the higher-order error vector \(\epsilon\), from the least squares method, it can be inferred that  
\[\Delta\mathbf{r}=\left(\mathbf{A}^{T}\mathbf{A}\right)^{-1}\mathbf{A}^{T} \Delta\mathbf{f}_{d}, \tag{10}\]  
Referring to the definition of PDOP in GNSS positioning, according to the above Equation (10), the PDOP in the doppler shift positioning of communication satellite SOPs can be defined as follows:  
\[\mathrm{PDOP}=\sqrt{tr\left(\mathbf{A}^{T}\mathbf{A}\right)^{-1}}, \tag{11}\]  
According to the existing studies, we can see that, the smaller the PDOP, the better the positioning accuracy.
## 6 Results and Discussion  
### Testing the Accuracy of the WSO-WOA Algorithm  
The WSO-WOA algorithm was utilized to fine-tune the parameters of two control schemes--PI-PI and FOPI-FOPI--with the goal of enhancing the performance of the DSTAT-COM, thereby improving the overall functionality of the MG system depicted in Figure 1.  
The system data are provided in Table 2. The performance of the WSO-WOA optimizer was compared against several other optimizers, including WSO, WOA, the Sine Cosine Algorithm (SCA) [36], and the Grey Wolf Optimizer (GWO) [37]. The five optimizers were tested under the scenarios presented in Table 3. The population size used in the optimization run was 20, and the maximum number of iterations was 50.
## Dry Distillation of Radioiodine from TeO\({}_{2}\) Targets  
**[PERSON] *** and [PERSON]**  
Copenhagen University Hospital Herlev, Herlev Ringvej 75, DK-2730 Herlev, Denmark; E-Mail: [EMAIL_ADDRESS]  
*** Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +45-2299-4402.  
_Received: 26 July 2013; in revised form: 8 October 2013 / Accepted: 21 October 2013 / Published: 28 October 2013_  
**Abstract:** As medical cyclotrons are becoming more abundant, \({}^{123}\)I and \({}^{124}\)I are getting more attention as alternatives to \({}^{131}\)I for diagnostics of thyroid disease. Both \({}^{123}\)I and \({}^{124}\)I provide better diagnostics, deliver less dose to patients and both reduce the risk of thyroid stunning, facilitating subsequent therapy. Dry distillation of radioiodine from tellurium dioxide targets has become the standard approach to producing these radioiodines. Setting up such a production of radioiodine is associated with a lengthy optimization of the process and for this purpose natural tellurium is often used for economical reasons. In this paper, the distillation parameters are scrutinized to ensure optimal distillation temperature, in order to minimize time spent and prevent loss of expensive target material. It is further demonstrated how the individual iodine isotopes, produced from proton bombardment of \({}^{nat}\)Te, will diffuse out of the target in a time dependent ratio. We believe the effect is due to the isotopes having their maximum cross section at different energies. The individual isotopes produced will thus have their highest concentration at different depths in the target. This results in individual mean diffusion lengths and diffusion times for the different isotopes.  
**Keywords:** cyclotron; radioiodine; production; distillation; iodine-124; tellurium dioxide
# Orthogonal Chirp Division Multiplexing for Underwater Acoustic Communication
## 5 Conclusions  
The aim of this paper is to evaluate the feasibility of OCDM with a basic principle originating from the Fresnel transform that uses a set of orthogonal chirp waveforms to transmit information data instead of sine waves as OFDM. The transmission and reception structures of OCDM are relatively close to traditional OFDM ones, except for the decoding stage, which requires an FDE algorithm. By evaluating the MSE, BER and EDR performances, OCDM is demonstrated to achieve good spectral efficiencies, while providing high robustness against the UWAC channel by efficiently exploiting the time and frequency diversities of the channel. Performance comparisons against conventional OFDM and SC systems over different UWAC channels prove that in the fully-loaded system, the enhancement of OCDM is not obvious. However, by modulating only a subset \(N_{u}\) of the \(N\) orthogonal waveforms, OCDM provides both spreading and diversity gains, leading to quasi-error-free transmission at low SNR. In such underloaded configurations, the OCDM system is demonstrated to be clearly superior to traditional OFDM and also outperforms the SC system with iterative FDE, making OCDM an interesting technique for robust UWAC applications.  
Conceptualization, Methodology, Software by [PERSON] and [PERSON]; Writing--Original Draft Preparation by [PERSON]; Supervision, Writing--Review & Editing by [PERSON]. [PERSON] was sponsored by the China Scholarship Council to study abroad at L@bisen Yncea-Ouest.  
The authors would like to sincerely thank [PERSON] (L@bisen Yncea-Ouest) for proofreading this document in depth.  
The authors declare no conflict of interest.  
Figure 8: System performance comparison of OCDM, OFDM and SC-FDE over the Watermark NOF1 channel.
# Influence of Crack Geometry on Dynamic Damage of Cracked Rock: Crack Number and Filling Material
## 4 Conclusions  
In this study, dynamic compressive tests on sandstone specimens with different numbers of cracks and different filling materials were conducted using the split Hopkinson pressure bar (SHPB) apparatus. The energy absorption in this process was analyzed and the damage variable was obtained. The main conclusions are as follows:  
1. The SHPB test results demonstrate that the dynamic damage of cracked rock is obviously influenced by the geometric characteristics of cracks. With different spatial geometry, the energy absorption and the damage variable of the cracked rock is different during the impact process.
2. The energy absorption and damage variable increased with the increasing number of cracks, which suggests that cracked rock with more joints usually holds a lower strength.
3. The different strengths or cohesion of the filling material resulted in different strengths of cracked rock. The filling material that has great strength and high cohesion can contribute more strength. As a result, the corresponding energy absorption and dynamic variable decrease accordingly. The cracked rock filled with material with great strength and good cohesion always has a higher strength.  
F.W., writing--original draft preparation. S.W. and Z.X., writing--review and editing. Z.X. and F.W., testing investigation and theoretical calculation. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Curves of damage variable versus different filling materials.  
Funding: This research was supported by National Natural Science Foundation of China (Grant Nos. 51474050 and U1602232); Liaoning Science and Technology Project (2019 JH2/10100035) and the China Scholarship Council (CSC) (201806080103). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. Conflicts of Interest: The authors declare that they have no conflict of interest. References
# Optimal Intrinsic Mode Function Based Detection of Motor Bearing Damages
## 2 Hilbert-Huang Transform
### Hilbert Transform (HT)  
Hilbert transform 31 mends the previous problems on nonlinear and non-stationary signal analysis. The conventional HT of a continuous signal \(c_{i}(t)\) is computed, as shown in Equation (2).  
\[H[c_{i}(t)]=\frac{1}{\pi}\int_{-\infty}^{\infty}\frac{c_{i}(t^{\prime})}{t-t^{ \prime}}dt^{\prime} \tag{2}\]  
Coupling the complex conjugate pair \(H[c_{i}(t)]\) and \(c_{i}(t)\), we can get the analytic signal \(z_{i}(t)\), as shown in Equation (3).  
\[z_{i}(t)=c_{i}(t)+jH[c_{i}(t)]=a_{i}(t)e^{j\phi(t)} \tag{3}\]  
where \(a_{i}(t)\) and \(\phi(t)\) are the instantaneous amplitude and instantaneous phase respectively, as shown in Equations (4) and (5).  
\[a_{i}(t)=\sqrt{c_{i}^{2}(t)+H^{2}[c_{i}(t)]} \tag{4}\]  
\[\phi_{i}(t)=\arctan\frac{H[c_{i}(t)]}{c_{i}(t)} \tag{5}\]  
Figure 2: Flowchart of empirical mode decomposition (EMD) decomposition.  
The instantaneous frequency is defined as the time derivative of the instantaneous phase, as shown in Equation (6).  
\[\omega_{i}(t)=\frac{d\phi_{i}(t)}{dt} \tag{6}\]  
**3. Greedy Algorithm Based on Empirical Mode Decomposition Selector**  
This study proposes the GEMD model to select the optimal IMFs and to make HHT spectrum easier in the detection system.
# Fault Analysis of a Small PV/Wind Farm Hybrid System Connected to the Grid
## 3 PV Array with Grid Connection Model  
The three-phase grid-connected PV system is made up of a PV array, a boost converter, a DC link, a controlled inverter, a filter, a step-up transformer, and a grid. The boost converter control and the inverter control are the system's two controllers. We must sense the PV array current and voltage before sending them to the MPPT to produce reference voltage in order to execute the boost converter control. The created reference voltage is compared with the PV real voltage to produce an error, which is sent to the PI controller in order to produce the reference signal. To create the control signal for a boost converter, a PWM generator receives the created reference signal. We must perceive both the three-phase inverter current and the three-phase grid voltage in order to control the inverter. The outer voltage loop and the inner current loop are the two control loops for this inverter. The DC reference voltage is compared with the DC voltage in the voltage loop to produce an error, which is then sent to the PI controller to produce the reference currents \(I_{d}^{s}\) and \(I_{d}^{s}\) set to zero. When the generated reference current \(I_{d}^{s}\) and \(I_{d}^{s}\) in the current loop are compared with the inverter's real current, an error is created. The error is then sent to the PI controller, which uses it to obtain the reference voltage \(V_{d}^{s}\) and \(V_{d}^{s}\). The three-phase inverter's control signal is generated by the PWM generator using the reference voltages \(V_{d}^{s}\) and \(V_{d}^{s}\), which are converted to the \(abc\) reference voltage. Four phases are used to design the three-phase grid-connected PV solar. The PV solar array is designed first (the number of series and parallel modules we need to reach the required output power and voltage). The boost converter's second stage of design. The inverter's third stage of design is based on an \(RL\) filter concept. Lastly, the controller (MPPT and PI controller) is created. Figure 2 displays the main schematic model of the PV system that is linked to the utility grid [15,16].  
Figure 1: Architecture for hybrid systems.
## 2 Simulation of Bowden Cable-Fabric Force Transfer and Analysis of Lower Limb Motion
### In Vitro Simulation of Force Transfer with Bowden Cable-Fabric  
The model of the flexible lower limb knee joint assist device was designed as shown in Figure 1. The motor inside the electric control box drives the Bowden cable to pull the fabric at the knee joint, which connects the lower leg movement. In this process, there is friction inside the Bowden cable and friction between the fabric and the lower leg strap. At the same time, the movement of the lower leg and the deformation of the fabric will lead to the displacement lag of the force transmission.  
In order to investigate the friction loss and displacement hysteresis from the swing of the lower leg during walking, an in vitro experimental platform was built according to the transfer path of the Bowden cable and the fabric worn on the human body, as shown in Figure 2. In the experiment, the knee joint is simulated by a rotating cylinder mounted on the shaft of a DC brushless motor. The knee joint model is fixed together with the model of the lower leg. The knee joint model can rotate with the tension of the driving end. A DC brushless motor is used to drive the knee joint model and drive the lower leg model to simulate the position change in human lower limbs during walking. A servo motor that sets the desired force curve drives the Bowden cable-fabric to provide power-assistedcontrol for the lower leg. The start force sensor is used as the feedback of the control, and the end force sensor is mainly used to verify the accuracy of the output force value.
# A Secure Protocol against Selfish and Pollution Attacker Misbehavior in Clustered WSNs
## 6 Conclusions  
In this study, we designed a scheme to prevent misbehaving nodes influencing network availability and integrity, using the \(NC\) technique and data redundancy creation at source nodes. The approach integrates a module to detect these malicious nodes and executes a punishment mechanism to exclude them from the routing process. It also ensures confidential data routing in the presence of eavesdroppers.  
Our approach was implemented in a cluster-based network exposed to a \(DoS\) attack and pollution attack using _MATLAB_. These attacks were generated by a coalition of selected misbehaving \(CH\) nodes. They dropped the packets received from source nodes on their way to the \(BS\) when executing a \(DoS\) attack, and they incorrectly altered the packet in transit or injected a new fake one into the network when executing a pollution attack. The protocol that we have designed improves the percentage of correct messages received as compared to a defenseless \(SNCR\) protocol. The \(BS\) successfully rebuilds the original data from a source node despite selfish behavior that diminishes the number of packets received compared to the number of packets sent. In addition, it ensures a complete and correct reception of all original packets sent by source nodes with a number of malicious \(CH\) nodes initiating a pollution attack and varying in the range of a certain threshold \(K\). The \(BS\) successfully reconstructs the original data of a source node even when the adversary alters the packets in transit before being sent to the \(BS\). Moreover, the protocol favors scalability. It reduces the percentage of lost packets as the size of the network and so the number of malicious nodes increases.  
Conceptualization, H.R. and K.T.; methodology, H.R.; software, H.R.; validation, D.S., K.T., R.A. and S.G.; formal analysis, H.R. and D.S.; writing--original draft preparation, H.R.; writing--review and editing, D.S.; supervision, R.A. and S.G.; funding acquisition, D.S. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the Region Nouvelle-Aquitaine under the grant for project \"SVP-IoT\" and by the MIRES research federation under grants for projects \"CANIoT\" and \"SECIoT\".  
The authors declare no conflict of interest.  
Figure 9: The network clustered.