esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Tests  
### Frequency Domain Performance Evaluation  
In a similar manner to the simulation study, the performance of the five proposed control strategies (A1-A5) was evaluated from the acceleration transmissibility of the active seat with respect to the MAST using experimental tests over the frequency range (0.5-18 Hz), as shown in Figure 12. It can  
Figure 11: Schematic diagram of the active seat suspension. Reproduced with permission from [14], Elsevier, 2015.  
Figure 10: General experimental setup and HIL simulation of the proposed controllers. Thick Blue (active control force); Black (feedforward states); Red (Feedback states).  
be observed from these results that for all five controllers the vibration attenuation of the active seat suspension is superior to the passive seat. Interestingly, the simplest controller A1 demonstrates the best performance over the low frequency range (3.5-4.5 Hz), although its performance deteriorates at higher frequency where the dummy dynamics are more significant. This may be due to the open loop nature of this controller that uses only feedforward signals, which do not respond to the dummy and the seat dynamics. This phenomenon is also observed in the experimental performance of the feedforward controller A2, although it is not shown in the simulation, as the linear and lumped mass nature of the dynamic system neglects this behaviour.  
Controller A3 combines the feedforward suspension states with the vertical acceleration of the seat in order to generate the control force. This provides superior vibration attenuation when compared with the A1 and A2 controllers, especially over the human body sensitive frequency range (4-8 Hz). This indicates the importance of feedback states in the suppression of broadband vibration.  
The controller A4 performs less well over a low frequency range (<5 Hz) although its performance is superior above 5 Hz. Likewise, in simulation, the controller A5 shows the best performance across a wide frequency range although this was not backed up by the experimental results, especially at a low frequency range (4-5.8 Hz). Moreover, it amplifies the vibration around the fundamental natural frequency of the seat (\(f_{n}\) = 3.33 Hz). This is attributed to the effects of unmodelled higher order dynamics and system nonlinearities (particularly friction), and signal noise. Such effects will compromise the feedback signals and result in an incorrect control force that introduces a disturbance to the seat instead of attenuating the vibration. Overall, controller A4 delivers the best performance in vibration attenuation over the whole frequency range of interest, with a minimum and maximum reduction level of 10 dB and 19.5 dB over the human body sensitivity frequency range.
###### Abstract  
Low-cost and high-speed single-mode semiconductor lasers are increasingly required as wide-band access fiber communication expands in recent years. Here, a high-speed laterally coupled distributed feedback (LC-DFB) laser array is achieved based on a SiO\({}_{2}\) planarization process. The device exhibits low thresholds of about 12 mA and high slope efficiencies over 0.26 W/A. Stable single mode operation and high-speed performance are realized with side mode suppression ratios (SMSR) over 45 dB, and 3-dBe bandwidths exceed 14 GHz for all four channels. Such a high-speed and process simple LC-DFB laser array shows great potential to the low-cost fiber communication networks.  
laterally coupled gratings; high-speed modulation; SiO\({}_{2}\) Planarization; 1.3 \(\mu\)m; distributed feedback laser +
Footnote †: journal: Applied Sciences
# Rate of Penetration Estimation with Parameter Correction
## 2 Well Depth Correction
### Static Elongation  
Traditional methods of measuring well depth typically involve using the nominal length of the drill string as a proxy for true depth. However, this approach overlooks several important dynamic factors that can lead to significant errors, particularly in deep wells. These factors include tension elongation, hydraulic effects, thermal expansion, and dynamic drilling conditions, all of which must be accurately accounted for to improve the reliability of depth measurements. In [6], some of critical elongations are listed, see the details below.  
As the drill string is subjected to axial forces due to its weight and the WOB, it experiences tension elongation. The corresponding elongation due to tension can be expressed as  
\[\Delta L_{\text{tension}}=\int_{0}^{L}\frac{F_{\text{r}}(s)}{E\cdot A(s)}\,ds, \tag{11}\]  
where \(E\) is the Young's modulus of the material and \(A(s)\) is the cross-sectional area of the drill string. When the drill string moves along the wellbore, frictional forces come into play, which affect the tension distribution and elongation. The drag-induced elongation can be approximated by  
\[\Delta L_{\text{drag}}=\int_{0}^{L}\frac{\mu_{a}\cdot F_{c}(s)}{E\cdot A(s)}\,ds, \tag{12}\]  
where \(\mu_{a}\) is the axial friction coefficient and \(F_{c}(s)\) is the total normal contact force along the string. The drill string is also subject to hydraulic effects due to the pressures exerted by drilling fluids inside and outside the string. These effects can be significant, especially in high-pressure environments. The elongation due to pressure ballooning is given by  
\[\Delta L_{\text{ballooning}}=\int_{0}^{L}\frac{F_{\text{hydraulic}}}{E\cdot A(s)}\,ds. \tag{13}\]  
The temperature variations along the wellbore cause the drill string to expand or contract, impacting the well depth measurement. The correction for thermal expansion can be written as  
\[\Delta L_{\text{thermal}}=\beta\cdot L\cdot\Delta T, \tag{14}\]  
where \(\beta\) is the linear thermal expansion coefficient of the drill string material and \(\Delta T\) is the temperature difference along the string. The combined effects of tension, friction, hydraulic  
Figure 1: DWOB correction diagram.  
forces, and thermal expansion must be considered to accurately correct the well depth. The total elongation of the drill string is given by  
\[\Delta L_{static}=\Delta L_{\text{tension}}+\Delta L_{\text{drag}}+\Delta L_{ \text{ballooning}}+\Delta L_{\text{thermal}}. \tag{15}\]
# SDToW: A Slowloris Detecting Tool for WMNs
## 6 Discussion  
Although SDToW blocks the attacker using its MAC address, it is possible to change the blocking module to perform the block using the source's IP address. Blocking an attacker by its IP address permits Web server protection against attacks that come from the Internet. During a Slowloris attack coming from the Internet, it is not appropriate to block the attacker by its MAC address. This limitation occurs because the source MAC address is not the attacker's address but is the MAC address from the gateway or is the MAC address from the one-hop device directly connected to the blocking module. Therefore, if the blocking module operates in gateway devices, it should be configured to block the attacker by its IP address.  
Figure 13: False positive errors comparison between Snort and SDToW.  
Another question that arises regards IP or MAC spoofing. SDToW is not able to detect IP or MAC spoofing. However, it is possible to combine SDToW with a firewall or IPs, thereby protecting from MAC spoofing and IP spoofing.  
SDToW can work with load balancing and a reverse proxy. However, it creates two possible changes to SDToW architecture. The first one is to install the collection module (CM) on the server that provides the reverse proxy or the server that provides load balancing. This change would permit SDToW the ability to capture the traffic dedicated to the Web servers. The second possibility is to use a promiscuous switch port that grants full access to the same network that hosts the Web servers. Therefore, the collection module could be installed on a different node and connected with the promiscuous switch port. Consequently, the collection module would be able to collect the Web traffic.  
Slowloris can be mitigated or prevented using restrictions on the Web server access. It is possible to limit the number of parallel connections per user and discard additional connections from the same user. Additionally, it is possible to restrain the amount of low-rate traffic on the Web server, thereby finishing connections that take a long time to have their requests completed. The drawback of these restrictions relies on the difficulty to distinguish legitimate Web traffic from the attacker traffic. Therefore, restrictions created to mitigate or prevent Slowloris attacks can also lead to user access problems. SDToW does not create any traffic restrictions and it blocks the attacker near its first AP. Hence, it mitigates the attack with a lower network impact.
# Laser Absorption Sensing Systems: Challenges, Modeling, and Design Optimization
## 4 Strategies for Noise Reduction and Design Optimization
### Sources of Noise
#### 4.1.2 Quantization Noise and ADC Resolution  
The demands for accuracy and precision in trace species detection present great challenges for DAS. Therefore, high-speed and high-resolution ADC is required to recover the analogue detector signal with low quantization noise and to reduce the laser relative intensity noise (RIN, same as excess noise) through fast sampling. The frequency filtering feature of WMS has been widely adopted as it could improve the SNR by modulating and demodulating the absorption signal at an elevated frequency band. On the other hand, [PERSON] et al. pointed out that as long as RIN is the dominating noise, the requirements for ADC resolution in \(2\)/\(\mathrm{WMS}\) and DAS setups are in fact similar [72]. As suggested by the simulation results, when the RIN is low and sufficient ADC resolution is provided, DAS can provide higher SNR than WMS. In this case, with the advances of modern electronics and digital signal processing, DAS can have more advantages over WMS [73].
## 7 Future Directions
### Cooperative Handover  
Cooperative HO is a technique that has gained significant attention from researchers and industry experts for enhancing HO and mobility performance in 5G and B5G HetNets. Much research has been done to enhance the mobility and HO performance in 5G HetNets. Cooperative HO can provide several benefits, including reduced HO delay, improved reliability, and enhanced network performance. By sharing information about network conditions and traffic load, source and target BSs can make informed decisions about the HO process, leading to a seamless and efficient HO. Additionally, by exchanging information about neighboring cells and their interference patterns, the source and target BSs can select an HO target with minimum interference, improving the reliability and performance of the network. Overall, cooperative HO can enhance HO performance in 5G and B5G HetNets by facilitating a smooth and efficient HO process through sharing of information and collaboration between the source and target BSs.
## 4 Confidence Determination of Multi-Sensor Data
### Credibility Assignment  
After the confidence distance was determined for the ZigBee-GPS/BDS positioning data, the multi-sensor positioning data was divided into corresponding confidence intervals. In order to improve the filtering accuracy of the data fusion algorithm, the confidence factor was given according to the confidence distance of the positioning data, and the confidence factor was associated with the hierarchical sampling of the proposed TCPF algorithm. The higher the confidence of the positioning data, the higher the corresponding fusion bias, which helps to improve the filtering accuracy of the TCPF algorithm for the location information of the unmanned ship. The specific steps are as follows:  
Step 1 define \(\beta_{i}\) as the comprehensive support degree of the \(i\)th positioning data, which is composed of several confidence weight coefficients [22].  
\[\beta_{i}=y_{1}\omega_{1}^{\prime}+y_{2}\omega_{2}^{\prime}+\ldots+y_{n}\omega_ {n}^{\prime} \tag{25}\]  
\[\omega^{\prime}{}_{i}=\frac{1}{n}-\frac{1}{2a}+\frac{1}{na}\underset{i=1}{ \overset{n}{\sum}}d_{i} \tag{26}\]  
\[\underset{i=1}{\overset{n}{\sum}}\beta_{i}=1 \tag{27}\]  
where \(\omega^{\prime}{}_{i}\) is the confidence weight coefficient of the \(i\)th positioning data, \(n\) is the numerical amount of information collected by multiple sensors, and \(a\) is the adjustment parameter and can be expressed as \(a=(n-1)/2\). \(y_{1},y_{2},\ldots,y_{n}\) are a set of non-negative numbers.  
Step 2 according to the comprehensive support degree of the defined positioning data, the norm equation of the dynamic support factor \(\beta_{i}^{o}(k)\) for multi-sensor information and the probability model \(p_{r}(x_{i}^{\text{ZGB}}(k))\) for the positioning data is constructed.  
\[\beta_{i}^{o}(k)=\frac{\left\|p_{r}(x_{i}^{\text{ZGB}}(k))\right\|_{F}}{ \underset{i=1}{\overset{n}{\sum}}\left\|p_{r}(x_{i}^{\text{ZGB}}(k))\right\|_{F}} \tag{28}\]  
where \(\left|\left|\left|\left|\left|\left|\left|\left|\left|\left|\left|\left|\left| \left|\left|\left|\left|\left|\left|\left|\left|\left|\left|\left|\left|\left| \left|\left|\left|\left|\left|\left|\left|\left|\left|\left|\left|\left|\right| \right|\right\|\right|\right|\right|\right|\right|\right|\right|\right|\right|\right| \right|\right|\right|\right|\right|\right|\right|\right|\right|\right|\)}\) is the Frobenius norm, \(k=1,2,\ldots,T,i=1,2,\ldots,n\).  
Step 3 Calculate the multi-sensor measurement error \(w_{i}^{\text{ZGB}}\) according to the dynamic support factor \(\beta_{i}^{o}(k)\):  
\[w_{i}^{\text{ZGB}}(k)=x_{i}^{\text{ZGB}}(k)-A\beta_{i}^{o}(k) \tag{29}\]  
where \(A\) is the state transition matrix.  
Step 4 The credibility of the positioning data of the unmanned ship positioning system is assigned using \(w_{i}^{\text{ZGB}}\):  
\[\varsigma_{i}^{\text{ZGB}}(k)=\frac{\left(w_{i}^{\text{ZGB}}(k)\right)^{-1} \omega^{\prime}{}_{i}}{\sum\limits_{i=1}^{n}w_{i}^{\text{ZGB}}(k)} \tag{30}\]  
where \(\varsigma_{i}^{\text{ZGB}}(k)\) is the confidence factor of the ZigBee-GPS/BDS multi-sensor positioning data.
## 3 Results
### Statistical Analysis  
To validate the findings, the Ra and Rz parameters obtained during milling and brushing were subjected to statistical analysis. First, the said parameters were tested for normality with the Shapiro-Wilk test, and subsequently, the equality of variances and mean values were verified at the standard level of significance of \(\alpha=0.05\).  
The results of the statistical analysis performed for the Ra and Rz parameters obtained on the end face surfaces of the sample during milling with a trochoid step of str = 5% and str = 30% are presented in Table 5 and in Figures 22 and 23.  
Figure 21: Comparison of the experimental and numerical results of the surface roughness parameters, Rz, depending on the cutting speed, vc, for the trochoidal step, str = 15%, for the AZ91D alloy after milling and brushing.  
Figure 22: Results from Shapiro–Wilk test of normality conducted for the distribution of the Ra parameter results after trochoidal milling: Ra, AZ31 str = 30%.  
From the statistical analysis presented above, it can be seen that the hypothesis of equality of variances was confirmed in all cases. The obtained mean values of Ra after milling and brushing reveal that in surfaces machined at a higher trochoidal step, Ra shows a statistically significant increase along with the increase in \(\mathrm{s_{tr}}\). No differences were found for the Rz parameter measured on the end face of the AZ31 workpieces.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 1 Introduction
* Lastly, we propose the use of sectorized beam-forming for UAV antenna tracking which is capable of providing 0-360\({}^{\circ}\) coverage. This work is limited to UAV ground and air communication link leaving behind the UAV satellite link. The rest of the paper is organized as follows. Section 2 presents an overview of a UAV antenna tracking system. Section 3, provides discussions on the various types of antenna suitable to be deployed for a UAV antenna tracking system. Section 4, performs a state-of-the-art on a UAV antenna tracking system, analyzing the recent trends in the field. Section 5 is dedicated to MIMO technology as a suitable replacement to the mechanical UAV antenna tracking. The paper is concluded in Section 6.
## 1 Introduction
### Cassava Mosaic Disease  
Cassava mosaic disease caused by the whitefly-transmitted Geminivirus species is one of the important biotic constraints of cassava [16; 19; 20; 21; 22]. It is widespread in Africa and species that cause this disease include African cassava mosaic virus, East African cassava mosaic virus and the South African cassava mosaic virus. The disease was first identified in the Democratic Republic of Congo (DRC) and virus transmission was done by a whitefly of the genus _Bemisia_[23; 24]. Since it was first reported, it has become a threat to cassava production in Africa. Whiteflies use their stylets to pierce the plant tissues and inject toxic saliva during sucking of the phloem elements into the plant. Symptoms of infection by CMD on cassava plants include chlorotic mosaic symptoms, leaf deformation and stunting. Infection with CMD can cause between 20% and 60% of storage yield reduction and sometimes total crop failure. Farmers use insecticides to control the spread of whiteflies from transmitting the virus to other plants [19]. However, due to their ability to resist infection by the virus, the use of resistant varieties is the control measure that is being given more attention. Any diseased plants that are found in the field are also removed as a control measure.
## 3 Principles of Operation
### Fuel Cells  
The actual output voltage of the PEM fuel cell, \(E^{cell}\), is lower than its equilibrium potential due to irreversible losses. The losses include the polarization or overpotential originating primarily from activation loss, ohmic loss and concentration loss. \(E^{cell}\) is a function of the output current density, temperature, partial pressure of reactant.  
\[E^{cell}=E^{0}-\eta^{act}-\eta^{phmic}-\eta^{con} \tag{9}\]  
where \(E^{0}\) is the open-circuit potential or thermodynamic equilibrium potential, \(\eta^{act}\), \(\eta^{phmic}\) and \(\eta^{con}\) are over-potentials with respect to activation, ohmic and concentration losses, respectively.  
The power density during the PEM fuel cell operation is given by  
\[P=i\cdot E^{cell} \tag{10}\]  
where \(i\) is the current density.
# Deep Q-Learning for Two-Hop Communications of Drone Base Stations
## 4 Background
### Deep Q-Learning  
It can be seen that in large or continuous state and action spaces, the size of a Q-table would grow exponentially. Therefore, the Q-table is not practical anymore and a multi-layer neural network (NN) is used to learn and approximate the Q-values. In Deep Q-learning (DQN) the Q-function is approximated to the optimal Q-value by updating the parameter \(\theta\) or the weights. For a policy \(\pi\) we will have:  
\[Q^{\pi}(s,a;\theta)\cong Q^{*}(s,a). \tag{9}\]  
The NN is able to learn efficiently from a few samples and to generalize the model to the whole problem states.  
Although DQN is more flexible and practical than Q-learning, there are situations where the network weights can oscillate or diverge, making DQN unstable. One technique to remove correlation and non-stability of DQN in the training process, is \"experiencereplay\". In experience replay, the agent's experiences are gradually stored in the form of \(e=(s_{i};a_{t};r_{t};s_{t+1})\) in a fixed data-set size. During the training, a uniform batch of random experiences are then chosen from this data-set and new experiences are replaced the old ones.  
Additionally, two different neural networks to train are used in DQN, which are called \"target\" and \"local\" networks. They both start with the same parameters; however, the local network is updated at each time step, while the target network is only updated at scheduled intervals by the parameters of the local network.  
Figure 2 represents the Deep Q-learning model to solve a problem. The input is the state information, and the output is a set of estimated Q-values, where our goal is to pick its maximum one. The number of outputs represents the size of action space. One or more hidden layers can exist in a deep Q-learning model. Decaying Epsilon Greedy is another technique that can be employed in DQN to obtain better performance.
# A Family of ACO Routing Protocols for Mobile Ad Hoc Networks
## 3 AntOR-Based Protocol
### Pheromone Diffusion
This Hello message also contains a value \(v_{i}^{d}\) corresponding to the regular pheromone. In addition, AntOR applies the following premise:  
If node \(j\), which has a route to the destination \(d\), already has the regular pheromone and it gets the virtual pheromone contained in the Hello message during the pheromone diffusion process, then the virtual value is not updated at node \(j\), since it cannot simultaneously have non-zero values in both pheromones. The value of the final virtual pheromone is, therefore, zero. In addition, it should be noted that the ants from pheromone diffusion also serve to detect broken links. Exploration consists of a process to discover new routes that serve as alternatives for the sending of data packets. The diffusion process discussed earlier is essential for the correct functioning of the exploration. Initially, it generates the corresponding proactive forward ant PFA for subsequent sending. These ants are never sent in broadcast mode, since they only go by paths that have marked the route, either by the regular pheromone or by the virtual pheromone. In AntHocNet [12], the equation of route exploration is as follows:  
\[P_{in}^{d}=\frac{[max(\tau_{in^{\prime}}^{d},\omega_{in}^{d})]^{\beta_{3}}}{ \sum_{j\in N_{i}^{d}}[max(\tau_{ij}^{d},\omega_{ij}^{d})]^{\beta_{3}}}\qquad \qquad\beta_{3}\geq 1 \tag{11}\]  
In AntOR, the equation of exploration is as follows:  
\[P_{in}^{d}=\frac{(\psi_{in}^{d})^{\beta_{3}}}{\sum_{j\in N_{i}^{d}}(\psi_{ij} ^{d})^{\beta_{3}}}\qquad\qquad\psi\in\left\{ \begin{array}{ll}\omega&virtual \\ \tau&regular\end{array} \right. \tag{12}\]  
where \(\psi\) is a regular or virtual pheromone value and \(\beta_{3}\) a setting parameter relative to the influence of the pheromone concentration (with similar functionality to \(\beta_{1}\) and \(\beta_{2}\)). It is worth mentioning that AntOR uses the distance metric, a circumstance that does not occur in AntHocNet [12]. Thus, the number of hops from the best routes found is considered. In this way, a proactive ant is controlled and cannot go to more nodes than those set by the so-called hop limit, which is set according to the best routes (those with less distance in number of hops) calculated above. The reason for choosing this metric (and not others, such as the delay, for example) is that it is considered stable, since it does not influence the interference caused by other devices.