esa-sceva/satcom-chunk-collection huggingface dataset:
# Protocol for Energy-Efficiency in Networked Control Systems Based on WSN
## 2 General Background
Additionally, in order to support the reconstruction process, robust codes at the sender side may be included. That is the case of the interleaving techniques studied in [33]. 2. **ARQ-based full-reliable protocols. ** In situations where the reception of the complete set of transmitted packets must be ensured, Automatic Repeat reQuest (ARQ) protocols can be adopted. ARQ follows these simple steps: (1) after receiving a packet, the receiver sends back to the sender an acknowledgment packet (ACK); (2) the sender waits, for a certain period of time, for the ACK signal and (3) if the corresponding ACK is not received, the sender retransmits the data packet. Many state-of-the-art protocols and standards use ARQ as a basis, including Transmission Control Protocol(TCP), Carrier sense multiple access with collision avoidance Media Access Control (CSMA/CA MAC) protocols [34]. 3. **FEC and redundancy. ** For enhancing reliability of a communication system, Forward Error Correction (FEC) or redundancy techniques may be adopted. FEC is applied in unreliable channels (with no ARQ protocol), and relies on adding redundant bits for allowing the decoder to retrieve the original data. Redundant packets can also be transmitted to avoid packet loss between source and sink, because the chance of a given data, among several copies, to reach the decoder increases with the number of copies sent. 4. **Semi-reliable protocols. ** In WSN, semi-reliable communication of prioritized data packets can be adopted to achieve some energy savings requirements. Some transforms (e.g., Wavelet, etc.) may allow to separate different components of an input signal. Generally, the transmission of a small set of components is enough to retrieve the original signal. Thus, the components are transmitted with different levels of reliability, according to their importance on the reconstruction of the final signal and other decision information (which is called a semi-reliable protocol). In [35; 36], this concept is adopted in a communication protocol specialized in transmitting data associated to pictures in a semi-reliable manner, aiming to save energy resources. In both works, the original image is split in different coefficient levels by a 2-Dimensional Discretized Wavelet Transform (2D-DWT). The resulting coefficients are grouped by wavelet level, with decreasing priorities starting with the approximated image by a low-pass filter (the highest priority). Then, a semi-reliable protocol is executed by the intermediate nodes, which are able to either (reliably) forward or not an incoming packet considering its associated priority level and the corresponding batteries state-of-charge of the nodes. This protocol was extended in [37], allowing nodes to decide the retransmission of packets based not only on their own batteries status, but also on the following nodes on the path towards the sink. This particular mechanism has inspired the proposal of this paper, as commented in Sections 4 and 5.
## 6 Conclusions and Future Work  
Over the past decade, space debris has caused an increased congestion of the orbital domain. Fragments of past space endeavours are trapped in orbit around Earth, posing a threat to the sustainability of the space sector. As space debris accumulates over time, it poses an increasingly higher risk to the satellites that are currently operational and to future space missions. This paper proposes an autonomous navigation (AN) and tracking framework for separation assurance (SA) in the low earth orbit (LEO) region. A suitable tracking algorithm was tested in both ground-based and space-based scenarios to accomplish AN/manoeuvring for avoidance of collisions with debris and other resident space objects (RSO). Particle swarm optimization (PSO) algorithms were developed to implement autonomous on-board trajectory generation and manoeuvre planning for RSO avoidance. Simulation case studies were performed in the presence of both ground-based surveillance and space-based space surveillance (SBSS), demonstrating the validity of the proposed techniques and the complementarity of ground-based and space-based tracking techniques to support DSS AN for RSO collision avoidance. Current research is addressing the optimal fusion of SBSS and ground-based sensors (using both conventional and AI-based techniques) to enhance the tracking accuracy, coverage, and timeliness of AN tasks in complex mission scenarios. By incorporating suitable AI techniques, trusted autonomous satellite operations (TASO) can be achieved using optimal combinations of SBSS and ground-based tracking data.  
Conceptualisation, K.F.H. and R.S.; methodology, K.F.H. and R.S.; software, K.F.H. and K.T.; validation, K.F.H., K.T., A.G. and R.S.; writing--original draft preparation, K.F.H. and K.T.; writing--review and editing, A.G. and R.S.; visualisation, K.F.H., K.T. and R.S.; supervision, A.G. and R.S. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the SmartSat Cooperative Research Centre (CRC) for their partial support through the collaborative research project No. 2.13s.  
The authors declare no conflict of interest.
## 2 DQL Method
### Linear Step  
#### 2.4.1 Framework of Linear Step  
If the quadratic step fails to find an improvement, then the method performs the linear step. The idea of the linear step is to find evaluation candidate(s) in a desired direction \(d\in\mathbb{R}^{n}\) at some step length(s) \(\alpha^{j}\in\mathbb{R}^{n}\). The search candidates can be obtained as  
\[x_{l}^{j}=x_{0}^{k}+\alpha^{j}d, \tag{21}\]  
and we denote the set of all candidates as \(\mathbb{L}^{k}\). The idea of the linear step is to perform a quick search in the direction that is likely to be a descent direction. The pseudocode of the linear step in the DQL method is shown in Algorithm 4. Note that in order to perform Line 2 of Algorithm 4, there are two components we need to determine: the desired direction and the step length(s). We discuss this in the next section.  
```
1:Determine the search direction \(d\in\mathbb{R}^{n}\)
2:Determine the step lengths \(\{\alpha^{j}\in\mathbb{R}\}\)
3:The linear search candidates \(\mathbb{L}^{k}=\{x_{0}^{k}+\alpha^{j}d^{j}\}\)
4:improvement_check(\(\mathbb{L}^{k}\), \(x_{0}^{k}\), \(\delta^{k}\))
5:Update Linear_Flag;
```  
**Algorithm 4**Linear_Step (\(x_{0}^{k}\), \(\delta^{k}\))The linear step is an efficient and quick method to quickly search for an improvement. Not only does it not require as many function calls as the direct step, but it also does not require as much computational power to determine the candidate as the quadratic step. However, it is not as robust as the direct step or as precise as the quadratic step. If a linear step fails, then it indicates that we are either converging to a solution, or the method to determine the desired direction is not performing well for the current problem. In either case, the result from the linear step can provide some crucial information for future iterations, which is discussed in Section 4.  
Linear Step Strategies  
We propose two methods to find the linear search directions. The first method is to use the centred-simplex gradient from the direct step. In particular, \(d=-\
abla_{c}f(x_{0}^{k};\delta^{k}D^{k})\) is the approximated steepest descent direction.  
The second method is to use the last descent direction as the desired direction. If the method was able to find an improvement in this direction, then it is likely that an improvement can be found again in this direction. This direction can be calculated as \(d=x_{0}^{k}-x_{0}^{s}\), where the index \(s\) is the most recent successful iteration before \(x_{0}^{k}\).  
To determine the step length, the simplest way is to use \(\delta_{k}\) as the search step length, that is \(\mathbb{L}^{k}=\{x_{0}^{k}+\delta^{k}d\}\). The other method is to consider (approximately) solving the following problem  
\[\min_{\alpha}\{F(\alpha):\alpha\geq 0\}, \tag{22}\]  
where \(F(\alpha)=f(x_{0}^{k}+\alpha d)\). To solve this problem, we utilize the safeguarded bracketing line search method [21]. Combining the two ways of determining the search directions and the two ways of determining the search step, we obtain four linear search strategies, as shown in Table 1.
## 2 DQL Method
### Direct Step
#### 2.2.1 Framework of the Direct Step
The rotation matrix \(D^{k}\) for an undesired direction \(r^{k}\) is calculated as follows,  
\[D^{k}=L(\arccos{(r^{k}}^{\top}\mathbf{\hat{1}}),r^{k},\mathbf{\hat{1}}). \tag{15}\]  
After the search directions \(\bar{D}^{k}\) are built, the search candidates from the direct step at iteration \(k\) can be determined as  
\[\mathbb{D}^{k}=\{x_{0}^{k}+\delta^{k}d^{k}:d^{k}\in\bar{D}^{k}\}. \tag{16}\]  
We then check if any of the candidates yield improvement by  
Improvement_Check(\(\mathbb{D}^{k}\), \(x_{\text{best}}^{k}\), \(\delta^{k}\)). If an improvement is found, then the Direct_Flag is set to be true. Otherwise, the Direct_Flag is set to be false. The pseudocode of the direct step in the DQL method is shown in Algorithm 2. The direct step is always initiated at every iteration, and it produces \(\left|\mathbb{D}^{k}\right|=2n\) candidates, so it requires \(2n\) function evaluations to perform. Since these candidates are independent from each other, the function evaluations proceed in parallel. Although this step is computationally expensive, it is necessary to prove convergence of our method as shown in Theorem 4. The freedom of choosing the rotation direction is essential to the development of the Smart DQL method. This allows us to develop a variety of rotation strategies that are discussed in the next section. ```
1:Determine the rotation direction \(r^{k}\in\mathbb{R}^{n}\)
2:if\(r^{k}\) is a desired direction then
3: Align \(D^{k}\) towards \(r^{k}\)
4:else
5: Align \(D^{k}\) away from \(r^{k}\)
6:endif
7: The direct search candidates \(\mathbb{D}^{k}=\{x_{0}^{k}+\delta^{k}d^{k}:d^{k}\in\bar{D}^{k}\}\)
8:Improvement_check(\(\mathbb{D}^{k},x_{0}^{k}\), \(\delta^{k}\))
9: Update Direct_Flag accordingly
```  
**Algorithm 2**Direct_Step(\(x_{0}^{k}\), \(\delta^{k}\))Direct Step Strategy  
The first direct step strategy is inspired by the direct step in the RQLIF method [15]. The rotation directions alternate between two options:  
* A coordinate direction being the desired direction;
* A random direction being the desired direction. At odd iterations, the method searches on the positive and negative coordinate directions. At even iterations, the method searches on the random rotations of the coordinate directions. We denote this strategy as direct step strategy 1. In Section 4, when developing the Smart DQL method, we introduce new rotation strategies.
## 3 Results and Discussion
### Electronic Properties of Cu (II) Complex  
Electronic spectra (Figure 3a) of the ligand (L1) showed two absorption peaks in chloroform. The broad peak around 220-246 might be assigned to the \(\pi\)-\(\pi^{*}\) transition with a sharp peak at 302 nm to the n-\(\pi^{*}\) electronic transitions. For the metal complex, three distinct absorption peaks were obtained in the chloroform solution. The high-intensity peak at 246 nm and a sharp peak at 300 nm corresponded to \(\pi\)-\(\pi^{*}\) transitions and n-\(\pi^{*}\) transition peaks of the organic ligand, which show a slight decrease in wavelength after metal complex formation. Additionally, the peak at 350 nm has been observed for the ligand to metal charge transfer (LMCT) transitions while the broad peak at 420 nm might be assigned to charge transfer transition (\({}^{2}\)E\({}_{\text{g}}\)\(\rightarrow\)\({}^{2}\)T\({}_{\text{2g}}\)) due to the Jahn-Teller distortion of the geometry of the Cu (II) complex [31]. The obtained metal complex has shown strong solution stability in DMSO and DMF under the UV-Vis bands for a week. The electronic spectrum of the complex has shown a sharp peak at 217 nm and charge transfer (CT) peak at 293 nm in methanol. The intensity of the CT peak increased upon irradiation by UV light at 25 \({}^{\circ}\)C without any other changes in the visible spectra of the Cu (II) complex [32]. The effects of various alcoholic solvents (Figure 3b) on the electronic spectra of the synthesized Cu (II) complex were also studied at the room temperature. Upon increasing the alkyl chain of the corresponding alcohols (ethanol to butanol), the slight blue shift in the peaks (211 and 207, respectively) and red shift in the peaks (295 and 298, respectively) were observed. However, the electronic spectra in isopropanol solution shows a peak at 209 nm and a distinct sharp peak at 297 nm [33].
## 3 Results and Discussion
### Simulation of ADRC of Hot-Compression Molding Temperature
#### 3.3.1 Simulation Results without Signal Interference  
Drawing from the simulation structure diagrams presented in Tables 3 and 4 and Figure 5, the solution-derived simulation results are depicted in Figure 6. As evident from the figure, the traditional PID temperature control system for the heavy-duty multi-layer hot pressing of bamboo-based fiber composites exhibited significant overshoot above 140 \({}^{\circ}\)C, followed by slow cooling, ultimately returning to 140 \({}^{\circ}\)C. In contrast, the proposed ADRC (active disturbance rejection control) control strategy tailored specifically for the hot-pressing process of bamboo-based fiber composites commenced with an initial acceleration of the rate of temperature increase, enabling an earlier entry into the stabilization period while maintaining a constant 140 \({}^{\circ}\)C, thus achieving precise temperature regulation. Extensive testing of the bamboo-based fiber composite hot-pressing molding process revealed that the hot-pressing temperature was critical to ensuring product quality. When the temperature exceeded the set value, the phenolic resin adhesive curing accelerated, leading to premature curing of the surface layer. This, in turn, hindered the smooth release of internal steam, resulting in a \"boiler effect\" within the composite and the likelihood of a \"burst board\" phenomenon. Moreover, the pressure-holding process required a stable temperature of 140 \({}^{\circ}\)C. Significant temperature fluctuations during this phase could compromise heat transfer and reduce the adhesive curing speed, leaving internal reactions  
Figure 5: Structural diagram of temperature control system for hot-compression molding of bamboo-based fiber composites.  
incomplete upon reaching the cooling stage. This led to an excessive internal moisture content and suboptimal physical and mechanical properties of the board. The simulation results demonstrated that the ADRC algorithm effectively mitigated these issues, ensuring stable and precise temperature control throughout the hot-pressing process.  
The state estimation provided by the ESO is illustrated in Figure 7. Evidently, the expanded state observer, constructed utilizing the fal function, demonstrated the capability of smoothly and precisely estimating the state while simultaneously compensating for the control law, thereby fulfilling the necessary process requirements.
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 2 Kalman Filtering GNSS/INS Fusion Navigation Basic Principles  
### Basic Principles of Kalman Filtering  
The Kalman filtering algorithm [20] minimizes mean square error as its core estimation principle, cleverly combining current observation data with the previous moment's predicted value to achieve optimal estimation at the current moment. The primary formula of the Kalman filter is as follows:  
Prediction Equation:  
\[\left\{\begin{array}{l}X_{t}^{-}=FX_{t-1}^{+}+BU_{t}\\ P_{t}^{-}=FP_{t-1}^{-}F^{T}+Q\end{array}\right. \tag{1}\]  
State Update Equation:  
\[\left\{\begin{array}{l}\Delta Z_{t}=Z_{t}-HX_{t}^{-}\\ S_{t}=HP_{t-1}^{-}H^{T}+R\\ K_{t}=P_{t-1}^{-}H^{T}S_{t}^{-}\\ X_{t}^{+}=X_{t}^{-}+K_{t}\Delta Z_{t}\\ P_{t}^{+}=(I-K_{t}H)P_{t-1}^{-}\end{array}\right. \tag{2}\]  
In this context, \(X\) represents the initial state, \(P\) denotes the covariance matrix indicating uncertainty, \(B\) is the control matrix, \(U\) is the control vector, \(F\) stands for the system transition matrix representing the system's recursive process, \(Q\) represents process noise, \(H\) is the sensor transformation matrix, \(Z\) is the sensor measurement vector, \(R\) denotes sensor noise, and \(K\) signifies the Kalman gain.  
The Kalman filter algorithm is typically categorized into two processing modes: loosely coupled and tightly coupled [21].
# Doped Zinc Oxide Nanoparticles: Synthesis, Characterization and Potential Use in Nanomedicine
## 2 Materials for Doping ZnO Nanoparticles (NPs)  
Zinc oxide (ZnO) is an inorganic material that has been successfully applied in numerous fields, ranging from gas sensing [100] and energy harvesting [101; 102; 103] to biomedicine [70; 104], to name only a few. In addition to the properties mentioned above, it may be useful to add further functionalities to ZnO rather than to improve the pre-existing ones, with the final aim to design a multifunctional system.  
For this purpose, doping represents a valuable approach to tune various ZnO properties. Doping consists of the insertion of a specific ion into a crystal lattice, not originally present in the starting material. It can be particularly useful to modulate the energy band-gap, with a direct consequence on the ZnO photocatalytic properties and related antimicrobial activity. Moreover, the insertion of selected elements in the ZnO lattice allows for the generation of a weak ferromagnetic behavior in the doped particle [105; 106; 107], the tuning of the degradation properties in aqueous environments [58; 108], or even the modulation of the electromechanical response [98; 99]. All these aspects, summarized schematically in Figure 1, may be exploited in nanomedicine to design a multifunctional theranostic nanoplatform able to perform both diagnoses (for example, through magnetic resonance imaging) and therapy (such as photodynamic therapy or enhanced reactive oxygen species generation) at the same time.  
The physical and chemical properties of doped ZnO are strongly dependent on the doping element. Actually, several aspects, such as the ions' dimensions, the electronegativity, the coordination state etc., all contribute to determine the final properties of the doped material [109]. For these reasons, the use of _ad-hoc_ dopants must be foreseen on the basis of the expected new functionality. In the following, the most used ions reported in literature are briefly presented, and their effects, due to their inclusion in the wurtzite ZnO crystal lattice, are summarized.
## 2 Materials and Methods
### XUV Optimization Studies
#### 2.2.5 XUV Radiation Coherence  
The overall coherence of the generated XUV radiation was initially tested by recording diffraction images resulting from the illumination of a copper mesh with 70 lines per inch and 5 um-thick wires. The copper mesh was placed at the entrance of the chamber of Stage C, and the diffraction images were recorded by the XUV CCD camera located at the end of the six-way cross of Stage D. A picture of the copper mesh is shown in Figure 6a, while in Figure 6b,c, the corresponding pictures of the diffraction image and the reconstructed image are shown, respectively. Since the diffraction image was obtained using the full XUV spectrum without any spectral filtering, there is no safe way to accurately calculate the characteristic dimension of the mesh from the reconstructed image. However, considering the optical geometry layout and using an average XUV wavelength of 50 nm, the characteristic dimension of the mesh was calculated close to the value given by the manufacturer.
# SubvectorS_Geo: A Neural-Network-Based IPv6 Geolocation Algorithm
## 3 The SubvectorS_Geo Algorithm
### Encoder
#### 3.3.1 Dataset Construction  
Landmark: The landmark is the collated measurement data. M probes are sent to N landmarks to construct the M*N measurement path dataset \(E_{ld}\) and the delay dataset \(T_{ld}\) corresponding to the hop value, combined with the Rapid7 query to build the landmark measurement path hostname \(H_{ld}\). The hostname information is set to zero if not queried, and the landmark router path set \(E_{ld}\) is analyzed to collect the identity IP set \(R_{i}\).  
\[E_{ld}=(p_{m1},p_{m2},p_{m3},...,p_{mn}) \tag{1}\]  
\[T_{ld}=(t_{m1},t_{n2},t_{m3},...,t_{mn}) \tag{2}\]  
\[H_{ld}=(h_{m1},h_{m2},h_{m3},...,h_{mn}) \tag{3}\]  
\[R_{i}=(r_{1i},r_{2i},r_{3i},...,r_{zi}) \tag{4}\]  
Figure 2: Overview of the proposed IP geolocation framework.  
\(p_{mn},t_{mn}\), and \(h_{mn}\) record a complete path from the \(m\)th probe to the \(n\)th landmark router information, delay information, and hostname information. It is worth noting that the data here are stored in the order of path hop values to facilitate subsequent data filtering work. \(r_{zi}\) is the identification IP collected for the experimental region according to [PERSON]'s method [13].  
Target IP: Data probing with M probes for X target IPs to be located. The obtained raw measurement data are cleaned to obtain the target IP path set \(E_{ld}\) and the delay dataset \(T_{td}\). Build the landmark measurement path hostname dataset \(H_{td}\) in combination with the Rapid7 query, and set to zero if the hostname information is not queried.  
\[E_{td}=(p_{m1},p_{m2},p_{m3},...,p_{mx}) \tag{5}\]  
\[T_{td}=(t_{m1},t_{n2},t_{m3},...,t_{mx}) \tag{6}\]  
\[H_{td}=(h_{m1},h_{m2},h_{m3},...,h_{mx}) \tag{7}\]  
\(p_{mx},t_{mx}\), and \(h_{mx}\) record a complete path from the \(m\)th probe to the \(x\)th target router information, delay information, and hostname information.
## 3 Experimental Design and Setup
### Listening Test Software and the Annoyance Scale  
Figure 2 shows the experimental software's graphical user interface (GUI). The software guided the subjects through the test and recorded their annoyance ratings. The subjects listened to a few orienting and a few training sounds before starting the main listening test. The stimuli were presented in a random order for each subject. They were played back once only, with a 1.5-s break between stimuli after each complete playback.  
Figure 2: Experimental software’s graphical user interface (GUI).  
As shown in Figure 2, short-term annoyance was rated on the ICBEN 11-point numerical scale [33]. Thereby, the following (modified) question was asked for every stimulus: \"when you imagine that this is the sound situation in your garden, what number from 0 to 10 represents best how much you would be bothered, disturbed or annoyed by it?\"