esa-sceva/satcom-chunk-collection huggingface dataset:
# TRC-Based High-Precision Spot Position Detection in Inter-Satellite Laser Communication
## 7 Conclusions  
In inter-satellite optical communication terminals without independent beacon light, the energy of the beacon light allocated by the ATP system is very small, which makes the output SNR of QD often lower than \(-17\) dB. Thus, the QD-based spot position detection accuracy seriously degrades. Therefore, we propose the TRC algorithm with window function to suppress strong noise and enhance the line spectrum of the weak photocurrent signal to accurately detect the photocurrent amplitude.  
This method uses a cosine signal to modulate the intensity of the light beam, so that each photocurrent signal output by the QD becomes a line spectrum signal. Then, the TRC operation concentrates the energy of the broadband noise to the time 0 point, and the energy of the valid signal is distributed to the entire envelope of the output result. Therefore, the noise can be effectively removed by the window function, and then the FFT operation is used to obtain the amplitude value of the line spectrum signal. The TRC method with window function can use a small amount of data to obtain a high SNR gain, thereby improving the spot position detection accuracy under extremely low SNR conditions. In the experiment, when the output SNR of the QD is as low as \(-17.86\) dB, the proposed method can still detect the spot position, and the max error is limited within \(0.0238\) mrad. Through simulation and experimental comparison, the detection accuracy of the proposed method under the condition of extremely low SNR is obviously better than that of direct FFT method and Kalman filter method.  
The SNR gain of the TRC method depends on the data length used in the calculation, but the real-time detection of the system must also be considered in practical applications. Therefore, considering the two factors of the SNR gain and the real-time performance, the data length used in the experiment is 8000 points, and relatively good results have been obtained. Furthermore, the TRC algorithm does not need to consider signal synchronization. These advantages are very important in inter-satellite laser communication that requires high real-time performance.  
Conceptualization, Q.L. and S.X.; methodology, Q.L. and S.X.; software, H.G. and D.H.; validation, H.G.; formal analysis, Q.L.; investigation, Y.H. and Z.P.; resources, Q.L. and Y.X.; data curation, H.G and Y.X.; writing--original draft preparation, Q.L. and S.X.; writing--review and editing, Q.W.; visualization, Y.H. and Z.P.; supervision, Y.H.; project administration, Q.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (NSFC) (61775030, 61571096), National Key R&D Program of China (2017 YFB1103002) and Sichuan Science and Technology Program (2019 YJ0167).  
The authors declare no conflict of interest.
## 4 Decentralized Data Valuation
### Block Generation Workflow  
In our DPoS blockchain, servers that ascend to the top \(M_{D}\) votes join the consensus group and become delegated servers. Servers with the same number of votes are sorted by ascending hash values of public keys. To attract enough votes, servers need to build strong and positive reputations by following data regulations in DML.  
For round \(r\), a leading server \(m^{*}_{r}\), \(m^{*}_{r}\in\mathcal{M}_{D}\) = {1, 2, \(\cdots\), \(M_{D}\)}, from the delegated servers is selected by  
\[m^{*}_{r}\equiv r+L\pmod{M_{D}}. \tag{10}\]  
Then, delegated server \(m^{*}_{r}\) collects transactions, aggregates local weights and generates a new block. Note that \(L\) equals the number of output digits from \(w_{0}\).  
Figure 4: Proposed blockchain for DML considering data valuation.  
As an additional clarification of Figure 4, we first show genesis block generation at the start of DML: _Genesis Block Generation_: (round 0): (1) Blockchain stakeholders deploy initial weights \(\mathbf{w}_{0}\) with total round number \(R\) and number of delegated servers \(M_{D}\); (2) verified servers and smartphones register in DML by submitting their _public_ keys; (3) smartphones submit votes \(\mathcal{V}_{0}\) to select \(M_{\text{D}}\) servers; (4) a server is selected by (10) to create a genesis block.  
We further clarify the workflow of one round as follows: _Regular Block Generation_: (round \(r,r\in\{1,2,\ \cdots,R\}\)): (1) Any smartphone \(n\), \(n\in\mathcal{N}\), collects health data to train a set of local weights \(\mathbf{w}_{n,r}\); (2) after exchanging \(\mathbf{w}_{n,r}\) in a peer-to-peer network, each smartphone \(n\) calculates and broadcasts other smartphones' data contributions and determines \(d_{n,r}\) based on received local weights and its local health data, respectively; (3) any smartphone \(n\) submits \(\mathbf{w}_{n,r}\), \(V_{n,r}\) and \(d_{n,r}\) to servers by launching a blockchain transaction signed by its _private_ key; (4) each server receives and verifies \(N\) transactions by the _public_ key set recorded on the genesis block, and then, a server is selected by DPoS to aggregate model weights; (5) the selected delegated server signs \(\mathbf{w}_{r}\) with its _private_ key and generates a new block, and then, the block is propagated to all servers for verification; (6) once \(\mathbf{w}_{r}\) is recorded on the blockchain, permitted smartphones can download \(\mathbf{w}_{r}\); (7) any smartphone can verify \(\mathbf{w}_{r}\) by the server's _public_ key.
## 4 Performance Analysis of an FSO RIS-ACs Link over F-S \(\mathcal{F}\) TI fading Channels
### Channel Capacity (\(C_{\gamma}\))  
The ergodic channel capacity (\(C_{\gamma}\)) of end-to-end SNR for the considered FSO RISACs system that operates under the IM-DD modulation technique is given by ([24], Equation (31)):  
\[C_{\gamma}=\frac{1}{\ln{(2)}}\int_{0}^{\infty}\ln{(1+\frac{e}{2\pi}\gamma)}p_ {\gamma}(\gamma)d\gamma \tag{31}\]  
By substituting (18) in (31) and by applying ([41], Equation (07.34.03.0456.01)), ([41], Equation (07.23.26.0007.01)) and ([41], Equation (07.34.21.0013.01)) in (31), respectively, the C-F C\({}_{\gamma}\) was calculated and given as:\[C_{\gamma}=\frac{1}{(2\pi)^{2}}\frac{2^{a_{1}+b_{1}+a_{2}+b_{2}-1}}{ \ln{(2)(\frac{\varepsilon}{2\pi})^{a_{2}/2}}}\frac{{a_{1}}^{a_{1}}{a_{2}}^{a_{2} }}{2\beta(a_{1},b_{1})\beta(a_{2},b_{2})(b_{1}-1)^{b_{1}}(b_{2}-1)^{b_{2}}\gamma ^{a_{2}}}\] \[\times\beta(a_{2}+b_{1},a_{1}+b_{2})\frac{\Gamma(a_{1}+b_{1}+a_{2 }+b_{2})}{\Gamma(a_{2}+b_{2})\Gamma(b_{1}+a_{2})\Gamma(a_{1}+b_{1})\Gamma(a_{1 }+b_{2})} \tag{32}\] \[\times G_{6,6}^{6,5}\left(\frac{(a_{1}a_{2})^{2}}{((b_{1}-1)(b_{2 }-1)\hat{\gamma}(\frac{\varepsilon}{2\pi})^{1/2})^{2}}\right|\begin{array}{c} \frac{1-a_{2}-b_{2}}{2},\,\frac{2-a_{2}-b_{2}}{2},\,\frac{1-b_{1}-a_{2}}{2}, \,\frac{2-b_{1}-a_{2}}{2},\,-\frac{a_{2}}{2},1-\frac{a_{2}}{2}\\ 0,\,\frac{1}{2},\,\frac{a_{1}-a_{2}}{2},\,\frac{a_{1}-a_{2}+1}{2},\,-\frac{a_{ 2}}{2},\,-\frac{a_{2}}{2}\end{array}\right)\]  
The numerical analysis of \(C_{\gamma}\) in terms of different TI fading propagation conditions is provided in the Numerical Results.  
Second-Order (S-O) Performance Analysis of FSO RIS-ACs Link over F-S \(\mathcal{F}\) TI Fading Channels  
The important S-O performance metrics of the considered SISO FSO RIS-ACs system over F-S \(\mathcal{F}\) TI fading channels based on average SNR assuming the IM/DD technique such as the level crossing rate (LCR) and average fade duration (AFD) were further examined. Moreover, S-O statistics can be useful for error control codes design, interleaver design, a throughput and burst error rate analysis in wireless communications [23; 43; 44].
# Risk-Based Access Control Model: A Systematic Literature Review
## 2 An Overview of Access Control
### Dynamic Access Control Models  
The core principle of dynamic access control models is that they consider not only access policies but also dynamic and contextual features that are collected at the time of the access request to make access decisions [15]. This provides more flexibility and can adjust to various situations and circumstances while making the access decision. The need to adopt dynamic access control approaches should be one of the essential priorities to provide efficient and flexible access control model. However, most existing access methods are relying on static and rigid access policies and manual processes. These approaches are unable to provide a roadmap to improve automation significantly. This absence of automation results in a heavy involvement of human analysis that is error-prone and susceptible to various types of attacks based on social engineering. Additionally, current classical approaches have issues with resolving risks and threats in real time, especially when handling a previously unidentified threat. This is because these approaches make their access decision based on a set of policies built by a security analyst, who cannot resolve different access control situations in real time but can deal only with problems that were recognized before [16]. Instead of static policies, dynamic access methods use dynamic and real-time features to provide access decisions. These dynamic features can include trust, context, history, risk and operational need. Besides, these dynamic methods can adapt to different situations and circumstances at the time of deciding access decisions [5; 17]. This dynamic access control approach can be valuable for several applications such as healthcare and the military, where considering exceptional access requests to provide the access can literally save thousands of lives. Table 1 provides a comparison between traditional and dynamic access control approaches. The security risk is one of the dynamic features that is used to build a risk-based access control model. The next section provides an overview of the risk-based access control model.
## 3 Results
### Kinetics of Sr\({}^{2+}\), Mn\({}^{2+}\), Pb\({}^{2+}\) and As\({}^{5+}\) Adsorption  
Figure 1 illustrates the effect of contact time on the uptake of each metal ion from the aqueous solution by almond shell biochar during the kinetic experiments. As clearly shown, the adsorption rate was very fast for Sr\({}^{2+}\) and Mn\({}^{2+}\), removing 92.2% of Sr\({}^{2+}\) and 95.7% of Mn\({}^{2+}\) within the first 15 min. On the other hand, for Pb\({}^{2+}\) the adsorption rate was lower at the same time interval, removing 37.3% of Pb\({}^{2+}\), while it was very low in the case of As\({}^{5+}\), removing only 4% of As \({}^{5+}\). Equilibrium was achieved in 3 h, 2 h, 7 h and 12 h for Sr\({}^{2+}\), Mn\({}^{2+}\), Pb\({}^{2+}\) and As\({}^{5+}\), respectively, with maximum removal efficiency at 10 mg/L initial metal concentration 98.9%, 96.9%, 99.2% and 29.4%. A wide variation of equilibrium contact times has been reported in previous investigations for these metals, between 30 min and 60 h [7; 16; 24], implying a strong dependence of adsorption kinetics on the physical and chemical properties of biochars.  
The parameters calculated by application of the two dynamic models previously described by Equations (1) and (2) are displayed in Table 2. These results show that for all metals the pseudo-second order model fitted the data accurately. According to this model, the rate-limiting step between adsorbent and adsorbate was chemisorption, which has been verified in many studies [5; 7; 14; 16; 17; 24].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Volatile Matter** & **Fixed Carbon** & **Ash** & **C** & **H** & **N** & **O** & **S** & **Specific Surface Area** & **Micropore Volume \(\times\) 10\({}^{2}\) (cm\({}^{3}\)/g)** \\  & **
## 2 Overview of Random Linear Network Coding
### Sparse RLNC  
Standard RLNC described above is limited by the decoding complexity of the GE decoder that scales as \(O(K^{3})\) with the source block size. To some extent, and under low erasure rates, systematic RLNC approach may alleviate the complexity issue by removing received systematic packets from the decoding process. More flexible solutions resort to sparse RLNC (S-RLNC), where sparse random linear combinations are used to generate coded packets. This is typically done by changing the random sampling process of coding coefficients by promoting zero-valued coefficients:  
\[\mathbb{P}(g_{i,j}=v)=\left\{\begin{array}{ll}t&\text{if }v=0,\\ \frac{1-t}{q-1}&\text{if }v\in\mathbb{F}_{q}\setminus\{0\}.\end{array}\right. \tag{3}\]  
Similarly as in RLNC, S-RLNC schemes are also investigated for decoding probability, outage probability and average decoding delays. However, even for \(P_{d}(N)\), exact expressions are unknown but only approximated by upper/lower bounds [19; 20].  
Adaptive extension to S-RLNC is proposed in the form of Tunable Sparse RLNC (TS-RLNC) [21]. In TS-RLNC, at the beginning of a session, sparse linear combinations are used, while as the session continues, the coding density is increased, which may be tuned for desired trade-off between decoding complexity and average decoding delay.
# MViDO: A High Performance Monocular Vision-Based System for Docking A Hovering AUV
## 1 Introduction
### Related Works
#### 1.1.2 Vision-Based Related Works  
For purposes other than docking, works based on artificial vision were found. In [9] an approach is proposed for estimating the relative pose between AUVs in tight formation. For a pose estimation based on visual information, they use active light markers as optical markers placed on the follower AUV. The pose estimation method in [9] is then based on the minimization of a non-linear cost function comprising error terms from light marker projection, plus range and attitude measurements when available. The use of a camera to estimate the relative position of an underwater vehicle also serves other purposes, such as the work in [10], where it is intended that an AUV performs an autonomous home into a subsea panel in order to perform an intervention. For the subsea panel detection and subsequent relative position estimation, the authors in [10] propose a method in which the images gathered by a single camera on-board AUV are compared with a priori known template of the subsea panel. There are other works whose approach is based on stereo vision, such as the work in [11], where the authors use dual-eye cameras and an active 3D target. The real-time pose estimation is made by means of 3D model-based recognition and multi-step genetic algorithm. Although we recognize the advantage in using stereo vision, the energy and space requirements available in our vehicle dictated a solution based only on a single camera. Vision-based homing has been widely implemented in the ground and aerial robotics [12] and its concepts have been translated to underwater robotics in some works such as [13].
# Flying Real-Time Network to Coordinate Disaster Relief Activities in Urban Areas +
## 5 Real-Time Analysis  
As mentioned previously, the network system proposed in this article is based on an FRTN, which is built with TWUs (and eventually mules) that link the devices used by members of SAR teams and the IC. From an operating point of view, the FRTN is an opportunistic network in which there is no stable path between any pair of nodes in the system. Particularly, it is a special type of FANET [13], where real-time messages must be delivered before their deadlines in order to be useful. For this to occur, it is necessary to have bounded end-to-end transmission delays, and therefore the worst-case can be compared with the message deadline to verify the feasibility of the real-time operation of the network. The challenge in this case is that the network delay cannot be determined beforehand, as the topology is variable and there are also variable physical constraints. Determining these operation conditions involves performing a typical schedulability analysis of real-time systems [26]. In this sense, a contribution of this paper is to extend this analysis to consider opportunistic mixed networks composed of mobile nodes, semi-stationary, and stationary nodes (i.e., mules, TWUs, and the devices of the IC (located at the command post), respectively).  
When these networks support communication in particular application domains (e.g., emergency responses), they must consider the way in which such a scenario operates. For instance, the international search-and-rescue protocols establish that the communication between the SAR teams and the IC (or command post) should be performed hierarchically, as shown in Figure 9. Therefore, the IC typically delivers orders to SAR teams, and the latter return responses and awareness information to the IC. In this exchange, the sender transmits information to the closest TWU, which in turn acts as a gateway to mules or meta-TWUs that allow communication with the receiver.  
Figure 8: Historical evolution of the best deployment of TWUs when genetic algorithms (GAs) (**left**) and particle swarm optimization (PSO) (**right**) were used.  
Figure 9: Hierarchical topology of a Flying Real-Time Network (FRTN) that supports first responses after a disaster.
# Enhanced Collaborative Filtering for Personalized E-Government Recommendation
## 3 Proposed Negative Mixing CF
### Optimization
#### 3.5.2 Training  
We use Adam [25] to optimize prediction loss \(\mathcal{L}_{ours}(\theta)\). Adam algorithm performs one-step optimization for the stochastic objective function. The method computes individual adaptive learning rates for different parameters from the estimates of first and second moments of the gradients. The training process is aimed to find out the local minimum of \(\mathcal{L}_{ours}(\theta)\), by using Adam to update the \(\mathcal{L}_{ours}(\theta)\) w.r.t. parameters. The gradient of \(\mathcal{L}_{ours}(\theta)\) concerning the parameters is present in Equation (15). The process of training in our work is performed in Algorithm 2. We empirically find out that the parameters have converged within 30 epochs.  
\[\frac{\partial\mathcal{L}_{ours}\Big{(}x_{j}^{(u)},x_{k}^{(i)},x_{j}^{(u)},x_{ k}^{(i)}\,|\theta\Big{)}}{\partial\theta}=\sum_{(u,i)\in\mathrm{Q}^{T}\cup\mathrm{Q}^{-}} (y_{ui}-\hat{y}_{ui})\Big{(}\theta^{T}Z\Big{)}^{\prime}+\lambda\|\theta\| \tag{12}\]  
\[\Big{(}\theta^{T}Z\Big{)}^{\prime}=\left\{\begin{array}{cc}\hat{y}_{yi}^{1}, &\theta=w_{1}\\ \hat{y}_{ui_{mix}}^{2},&\theta=w_{2}\\ \mathrm{w}_{1}(\sum\tilde{w}_{i})+\mathrm{w}_{2}(\sum\tilde{w}_{i}+(\sum w^{ \prime}_{u})\tilde{w}_{ii}),\theta=(w_{hu})_{m\times|U|}\\ \mathrm{w}_{1}(\sum w_{u})+\mathrm{w}_{2}(\sum w_{ui}\tilde{w}_{i}^{\prime}(1 +\sum w^{\prime}_{u})),\theta=(\tilde{w}_{ii})_{m\times|I|}\\ \mathrm{w}_{2}(\sum w_{u}\tilde{w}_{i}(\sum\tilde{w}_{i})),\theta=(w^{\prime} _{hu})_{m\times|U|}\\ \mathrm{w}_{2}(\sum w_{u}(\sum w^{\prime}_{u}\tilde{w}_{i})),\theta=(\tilde{w }^{\prime}_{hi})_{m\times|I|}\\ \end{array}\right.\]  
where \(w_{u},w^{\prime}_{u}\in R^{m}\) describe the column vectors of the user \(u\) and selected user embedding weight matrix, respectively. Additionally, \(w_{hu}\)and \(w^{\prime}_{hu}\) indicate their elements located in \(h^{th}\); Likewise, \(\tilde{w}_{i}\), \(\tilde{w}^{\prime}_{i}\) are the column of embedding weight matrix of the item \(i\) and selected negative item and their elements \(\tilde{w}_{ii}\), \(\tilde{w}^{\prime}_{hi}\) are in \(h^{th}\).  
```
Input: training set \(\mathbf{u},\mathbf{l},\mathbf{u}^{\prime},\mathbf{l}^{\prime},\mathbf{y}_{\mathbf{u},i}\mathbf{u}^{\prime}, \mathbf{l}^{\prime}\) are the selected negative samples Output: parameters \(\mathbf{\theta}\)
1. Initialize \(\mathbf{\theta}\)
2. while not convergent do
3. for all\(\mathbf{u},\mathbf{l},\mathbf{u}^{\prime},\mathbf{l}^{\prime}\)do
4. calculate latent features \(\mathbf{e}_{\mathbf{w}}\,\mathbf{e}_{\mathbf{l}}\,\mathbf{e}_{\mathbf{u}^{-}}\,,\mathbf{e}_{\mathbf{l}^{-}}\) from embedding layer
5. calculate CF score \(\mathbf{s}\big{(}\mathbf{e}_{\mathbf{l}},\mathbf{e}_{\mathbf{u}^{-}}\big{)}\) according to Equation (6)
6. calculate predictor \(\tilde{\mathbf{y}}_{\mathbf{u},\mathbf{l}^{\prime}}\) according to Equations (7)-(9)
7. perform parameters updating via Adam algorithm
8. end for
9. end while
10. return\(\mathbf{\theta}\)
```  
**Algorithm 2**Negative item mixing collaborative filtering.
###### Abstract
Here the intra- and inter-satellite cooperation aspect could be utilized by offloading the computational effort of the lost controller node unto another node in the system. The re-assignment process of control-authority should have several desired properties:  
* It should keep the work load distribution in the intra- and inter- satellite node network optimal while still maintaining the required closed-loop performance of the original control process. * It needs to keep the computational burden of the distribution low and scale with the number of possible node choices. * In should allow multiple optimization objectives to take the heterogeneity of the nodes into account  
The main contribution of this work is to propose a solution to the controller node failure problem via a distributed self-organized control reassignment process while realizing the aforementioned desired properties. This is realized in a two step approach:  
1. Robust detection of controller loss/failure
2. Intelligent re-assignment of controller responsibilities by performing a distributed multi-objective optimization using a robust control task auctioning procedure  
As example control application formation control was chosen, building upon the networked formation control work in [27] combined with refined distribution optimization objectives from [25]. The proposed solution is not limited to formation control and can be applied to other control objectives and applications like attitude control as well. The rest of this work is structured as follows: First the employed test hardware, software and network architecture that is representative for a typical fractionated spacecraft system is introduced in Section 2.1. Followed by a description of the relative dynamics of the satellite formation as well as the Model Predictive Control (MPC) formulation. In Section 2.5 we discuss the required adaption rule of the MPC to the changing network characteristic. After the controller formulation we discuss the detection of controller failures in Section 2.6 followed by the description of the self-organizing controller failure recovery in Section 2.7. A first validation of our approach executed in a hardware testsetup is presented for pure network-loss in Section 3.2 and for additional controller failure in Section 3.3. Finally a conclusion and outlook is given in Section 4.
# Accident Risk Analysis of Gas Tankers in Maritime Transport Using an Integrated Fuzzy Approach
## 4 Findings  
### Fuzzy Delphi Method Findings  
The study used the Fuzzy Delphi method to identify 20 risk factors based on expert opinion. The risks were ranked from highest to lowest. Table 5 below shows the risk group and priority score for a total of 20 risks selected by the experts. These risk factors were identified through a two-round Fuzzy Delphi process involving eight experts with at least ten years of experience in gas tanker operations and management. The first set of factors was supplemented with insights from a panel of experts regarding the risks associated with existing gas tanker operations. The final selection of 20 risks reflects both the most frequently cited hazards in the literature and those most emphasised by practitioners during the Delphi process. Risk factors were categorised into six groups--Operational, Environmental, Security, Human Factors, Regulatory, and Technical--based on the primary source or nature of the risk. For example, \"Technical\" includes equipment-related failures such as corrosion and fire suppression malfunctions, while \"Environmental\" includes risks arising from weather or ecological interactions.