esa-sceva/satcom-chunk-collection huggingface dataset:
# Retrieval-Augmented Generation to Generate Knowledge Assets and Creation of Action Drivers
## 3 Materials and Methods  
### Retrieval Augmented Generation  
Retrieval-Augmented Generation [4] (RAG), a complex hybrid model architecture, enhances natural language generation (NLG) by incorporating external retrieval techniques to expand the model's knowledge base. Traditional large language models (LLMs), such as GPT-3 and BERT, which have been pretrained on massive corpora, are vulnerable to issues such as hallucinations, where the models generate believable but incorrect information due to the employment on only their internal representations of knowledge. Due to their inability to efficiently update their knowledge bases without retraining, these models are less successful for dynamic, knowledge-intensive tasks such as fact verification and addressing open-domain questions [16]. The authors in [4] introduced the RAG architecture, which grounds the generated text on factual knowledge by retrieving relevant current external materials.  
Retrieval-Augmented Generation (RAG) has been shown to be an effective and successful way to leverage external knowledge in large language models (LLMs). RAG retrieves relevant data based on the query and then instructs an LLM to provide a response within the parameters of the retrieved data. This approach significantly expands LLM's low-cost access to a vast amount of data.  
RAG has been used to improve the output of LLMs and to reduce hallucinations caused by them. LLMs can generate more consistent and factual results by providing the appropriate and relevant context.
## 6 Bode Plot Based Tuning of PII\({}^{1/2}\)DD\({}^{1/2}\) Control
As shown in Figure 4, the asymptotic gain of the P\(\Pi^{1/2}\)DD\({}^{1/2}\) controller with tuning \(C_{H}\) is higher than the one of the PID in two ranges of frequencies (\(\omega^{{}^{\prime}}{{}_{c1}}<\omega<\omega^{{}^{\prime}}{{}_{c2}}\) and \(\omega^{{}^{\prime}}{{}_{c3}}<\omega<\omega^{{}^{\prime}}{{}_{c4}}\)) and this results in a higher minimum magnitude \(mit_{PIID}\) of the exact gain; thus, a second conceivable tuning criterium (\(C_{L}\)) is to lower the P\(\Pi^{1/2}\)DD\({}^{1/2}\) plot obtained by the tuning \(C_{H}\) by multiplying all the control gains by the ratio \(min_{PIID}/min_{PIID}\); the resulting P\(\Pi^{1/2}\)DD\({}^{1/2}\) controller has the same minimum magnitude of the PID controller, as shown in Figure 4.  
Therefore, the procedure for the proposed tuning methods of the P\(\Pi^{1/2}\)DD\({}^{1/2}\) controller can be summarized as follows:  
* tune the PID gains starting from the given plant to obtain a closed-loop behaviour with adequate bandwidth and phase margin;
* obtain the two PID corner frequencies \(\omega_{c1}\) and \(\omega_{c2}\) by equation (23);
* select \(\rho\), with \(1<\rho<\rho_{max}=(\omega_{c2}/\omega_{c1})^{1/2}\), and obtain the four P\(\Pi^{1/2}\)DD\({}^{1/2}\) corner frequencies by equation (24);
* set the P\(\Pi^{1/2}\)DD\({}^{1/2}\) integral gain \(K_{i}\) to the same value of the PID integral gain tuned at step 1)
* obtain the remaining gains \(K_{p}\), \(K_{hi}\), \(K_{d}\), \(K_{hd}\) by Equations (17)-(20);
* if (tuning criterium = C\({}_{\rm H}\)) tuning is complete, else multiply all the five P\(\Pi^{1/2}\)DD\({}^{1/2}\) gains (K\({}_{\rm P}\), K\({}_{i}\), K\({}_{\rm hi}\), K\({}_{\rm d}\), K\({}_{\rm hd}\)) by the ratio \(\min_{\rm PID}\)/\(\min_{\rm PIID}\) to obtain the gains with tuning C\({}_{\rm L}\). In the rest of the paper, the PID and the P\(\Pi^{1/2}\)DD\({}^{1/2}\) with tuning \(C_{H}\) and \(C_{L}\) will be compared considering the closed-loop system of Figure 1 in terms of their frequency and step responses (Section 7); then the control performance will be tested on a real mechatronic system (Section 8).
# Subsampling Algorithms for Irregularly Spaced Autoregressive Models
## 4 Subsampling Methods for the IS-AR(1) Model
### Sequential Thinning on Streaming Data
\begin{split}\tilde{\mathbf{\theta}}^{\text{(thin)}}=& \Big{(}r_{0}\tilde{\textbf{M}}_{r_{0}}^{\text{(ptl)}}(\tilde{\mathbf{ \theta}}^{\text{(thin)}})+r\tilde{\textbf{M}}_{r}^{\text{(thin)}}(\tilde{\mathbf{ \theta}}^{\text{(thin)}})\Big{)}^{-1}\\ &\times\Big{(}r_{0}\tilde{\textbf{M}}_{r_{0}}^{\text{(ptl)}}( \tilde{\mathbf{\theta}}^{\text{(thin)}})\times\mathbf{\theta}^{\text{(ptl)}}+r\tilde{ \textbf{M}}_{r}^{\text{(thin)}}(\tilde{\mathbf{\theta}}^{\text{(thin)}})\times \mathbf{\theta}^{\text{(thin)}}\Big{)},\end{split} \] (14) where \(\tilde{\textbf{M}}_{r_{0}}^{\text{(ptl)}}(\mathbf{\theta})\) is defined in (8) and \[\tilde{\textbf{M}}_{r}^{\text{(thin)}}(\tilde{\mathbf{\theta}})=\frac{1}{r}\sum_{j=1 }^{m}\delta_{j}^{\text{(thin)}}\tilde{\mathbf{\ell}}(\tilde{\theta};y_{j},y_{j-1},d_{j}).\]  
A detailed algorithm implementing **thin** is detailed in Algorithm 4. * **Set Parameters:** 1. Set \(r_{0}\geq 2\), \(q\in(1/2,1)\), \(\gamma\in(0,q-1/2)\). 2. \(\alpha\gets r/\left(m-r_{0}\right)\) 3. Set \(\epsilon_{1}>0\) such that \(\epsilon_{1}\ll\alpha\)
* **Step 1--Pilot Estimates and Preparations:** 1. \(\theta^{\rm(plt)}\leftarrow\arg\max_{\theta}\sum_{i=1}^{r_{0}}\ell(\theta;y_{j},y_{j-1},d_{j})\) 2. \(r_{*}\gets 0\) 3. \(\delta_{1},\ldots,\delta_{r_{0}}\gets 0\) 4. **for \(j=1\) to \(r_{0}\)** \(\left|I_{j}\leftarrow\mathbf{I}_{j}(\theta^{\rm(plt)}|y_{j-1},d_{j})\right.\) **end** 5. \(\mathcal{I}_{*}\gets r_{0}^{-1}\sum_{j=1}^{r_{0}}I_{j}\) 6. **for \(j=1\) to \(r_{0}\)** \(\left|\zeta_{j}\leftarrow\operatorname{tr}(\mathcal{I}_{*}{}^{-1}I_{j})-2\right.\) **end** 7. \(\zeta_{(1)},\ldots,\zeta_{(r_{0})}\leftarrow\operatorname{sort}(\zeta_{1}, \ldots,\zeta_{r_{0}})\) where \(\zeta_{(1)}\leq\cdots\leq\zeta_{(r_{0})}\) 8. \(r_{0}^{+}\leftarrow\lceil(1-\alpha/2)r_{0}\rceil,r_{0}^{-}\leftarrow\lfloor(1 -3\alpha/2)r_{0}\rfloor\lor 1\) 9. \(\hat{\mathcal{C}}_{*}\leftarrow\zeta_{(\lceil(1-\alpha)r_{0}\rceil)}\) 10. \(\beta_{0}\gets r_{0}/(r_{0}^{+}-r_{0}^{-})\) 11. \(h+\zeta_{(r_{0}^{+})}-\zeta_{(r_{0}^{-})}\) 12. \(h_{*}\gets h/r_{0}^{\gamma}\) 13. \(\hat{f}_{*}\leftarrow\left[\sum_{j=1}^{r_{0}}\mathbf{1}_{|\zeta_{j}-\hat{ \mathcal{C}}_{*}|\leq h_{*}}\right]/(2r_{0}h_{*})\)
* **Step 2--Sequential Thinning:** for \(k=r_{0}+1\) to \(m\) 1. **if \(r-r_{*}>m-k\)do** \(\delta_{j}\gets 1\)**for \(j=k,\ldots,m\) break
* **else if \(r_{*}\geq r\)do** \(\delta_{j}\gets 0\)**for \(j=k,\ldots,m\) break
* **end** 2. \(l_{*}\leftarrow\mathbf{I}_{j}(\theta^{\rm(plt)}|y_{j-1},d_{j})\) 3. \(\zeta_{*}\leftarrow\operatorname{tr}(\mathcal{I}_{*}^{-1}I_{*})\) 4. **if \((r_{*}+r_{0})/k\leq\epsilon_{1}\) or \(\zeta_{*}\geq\hat{\mathcal{C}}_{*}\)do** \(\delta_{k}\gets 1\) **else** \(\delta_{k}\gets 0\) **end** 5. \(\hat{\mathcal{C}}_{0}\leftarrow\hat{\mathcal{C}}_{*}\) 6. \(\hat{\mathcal{C}}_{*}\leftarrow\hat{\mathcal{C}}_{*}+\frac{(\hat{f}_{*})^{-1} \wedge\hat{\rho}_{0}(k-1)^{\gamma}}{k! }(\mathbf{1}_{\zeta_{*}\geq\hat{ \mathcal{C}}_{*}}-\alpha)\)
# Two-Level-Composite-Hashing Facilitating Highly Efficient Anonymous IoT and D2D Authentication
## 5 Security Analysis and Performance Evaluation
### The Formal Protocol Verification in AVSIPA and Security Analysis
#### 5.1.2 Security Properties Analysis
\begin{table}
\begin{tabular}{l l} \hline \hline
**Schemes** & **Security Properties** \\ \hline \multirow{4}{*}{D2S authentication} & Mutual authentication between a device and a server; \\  & anonymity of devices; unlinkability of devices’ successful connections; resistance to desynchronization-based DoS \\  & attacks; session key forward secrecy. \\ \hline \multirow{4}{*}{S-assist-always D2S2D authentication} & Mutual authentication between the two communicating devices; Authentication of a server to devices; anonymity of devices; unlinkability of devices’ successful connections; resistance to desynchronization-based DoS \\  & devices; unlinkability of devices’ successful connections; resistance to desynchronization-based DoS attacks; session key forward secrecy. \\ \hline \multirow{4}{*}{Direct D2D authentication} & Mutual authentication between the two communicating devices; \\  & anonymity of devices; unlinkability of devices’ successful connections; resistance to desynchronization-based DoS \\ \cline{1-1}  & attacks; session key forward secrecy. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the security properties. any successful connection; therefore, each pseudonym could only result in at most one successful authentication. This ensures the property of \"unlinkability among devices' successful connections\". Resistance to desynchronization-based DoS attacks: In our designs, each device (say \(D_{i}\)) keeps its active level-1/level-2 pseudonyms and the past \(w1\) level-1 pseudonyms/the past \(w2\) level-2 pseudonyms while the server or a responding device (say \(D_{i}\)) keeping its recorded \(D_{i}\)'s active pseudonyms and the future \(w1\) level-1 (and \(w2\) level-2) pseudonyms. This arrangement facilitates the two parties to cope with the situations that \(D_{i}\)'s active pseudonym at the two communicating entities are not synchronized, due to possible attacks or unreliable connections. The robustness of desynchronization-based-DoS-resistance depends on the window size (\(w1\) and \(w2\)); the larger the size, the stronger robustness (also the larger storage space). The choice of the size should consider the threat severity, the desirable robustness, and the storage space. Perfect forward secrecy of the session keys: Each session key calculation involves the secret key and a random-and-fresh Diffie-Hellman value \(xyP\), which ensures the forward secrecy of the session keys.
# A Review of In-Situ and Remote Sensing Technologies to Monitor Water and Sanitation Interventions
## 2 Sensing WASH
### Handwashing  
Sensors can also provide an objective and nonobtrusive characterization of handwashing behavior. For example, organizations in the water and sanitation sector have used accelerometers and motion sensors to monitor household water consumption, community adoption of latrines, and the use of handwashing stations [49].  
SmartSoap, developed by Unilever, is an ordinary looking bar of soap with an embedded accelerometer that measures motion on three axes, allowing the detection of use. On its own, SmartSoap can provide an accurate count of the number of times the soap bar is used each day. Through combining SmartSoap data with data from a motion sensor placed on the vessel holding water for anal cleansing, [PERSON] et al. [50] were able to detect handwashing events after defecation. Although overall soap use increased, they found that there was no increase in the number of soap uses following defecation.  
Similarly, Mercy Corps used motion-detector-based latrine sensors combined with water-flow sensors to monitor the prevalence of handwashing after latrine use. They found that water use after latrine use was very low (<10%) in all but one district, which registered almost 40% use of water after latrine use. They also found that self-reported use of a latrine and handwashing after using a latrine were much greater (up to 4 times and 25 times, respectively) than that detected by the sensors [45].
## 3 Approach and Analysis: Architecture and Methodology
### Methodology: Machine Learning-Based Approach
#### 3.3.7 Agents  
Agents were trained on different dog sounds with their corresponding sound actions. Using the sound features extracted, prediction policy was used for determining the sound patterns of the dogs. The prediction policy process is to determine the sounds scenario acquired to make predictions. It also includes past predictions and information from the learning layer of the sounds trained. With this technique, predictions were achieved in an efficient manner, and further changes in the sounds of the dog learning were updated for the improved performance.
# A Multi-Loop Vehicle-Counting Method under Gray Mode and RGB Mode
## 3 Methods
### Evaluation Index  
To evaluate the counting result, four metrics were used in this paper, that is, \(Recall\), \(F\)-\(measure\), and \(Accuracy\).  
Recall is a measure of the success in detecting relevant objects, that is, the percentage of vehicles correctly counted in all vehicles that should be counted, while \(Precision\) is the percentage of vehicles correctly counted in all counted objects. These two metrics could be expressed as follows [33]:  
\[Recall=\frac{TP}{TP+FN} \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \(SLoop^{1}\) & \(PLLoop^{1}\) & \(SLoop^{2}\) & \(PLLoop^{2}\) & \(SLoop^{3}\) & \(PLLoop^{4}\) & \(PLLoop^{4}\) & \(SLoop^{5}\) & \(Occupancy of \(Lane^{1}\) \\ \hline Frame 533 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & Not occupied \\ Frame 534 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & Occupated \\ Frame 544 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & Occupied \\ Frame 545 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & Not occupied \\ \hline \hline \end{tabular}
\end{table}
Table 6: The state of loops at the four key frames of the left black vehicle in Figure 2a.  
Figure 14: Four key frames of the left black vehicle in Figure 2a. (**a**) Frame 533 (\(Lane^{1}\) is not occupied). (**b**) Frame 534 (\(Lane^{1}\) is occupied). (**c**) Frame 544 (\(Lane^{1}\) is occupied). (**d**) Frame 545 (\(Lane^{1}\) is not occupied).  
\[Precision=\frac{TP}{TP+FP} \tag{15}\]  
where \(TP\) is the number of vehicles correctly counted, \(FN\) is the number of vehicles that should be counted but not, and \(FP\) is the number of objects that should be not counted. In this case, the number of vehicles that should be counted (\(True\)) and the number of counted objects (\(Counted\)) could be calculated as follows:  
\[True=TP+FN,\;Count=TP+FP \tag{16}\]  
where \(F\)-_measure_ is the weighted harmonic average of \(Recall\) and \(Precision\), which combines the results of \(Recall\) and \(Precision\), and we make the weights equal to each other in this paper. In this case, \(F\)-_measure_ could be expressed as follows [33]:  
\[F\text{-}measure=2\times\frac{Recall\times Precision}{Recall+Precision} \tag{17}\]  
In contrast, for \(Accuracy\), it is used to evaluate the difference between the true value and the counted value, which is defined as follows [34]:  
\[Accuracy=1-\frac{|True-Counted|}{True} \tag{18}\]  
Using Equation (16) to replace \(True\) and \(Counted\) with \(TP\), \(FN\), and \(FP\), Equation (18) could also be expressed as follows:  
\[Accuracy=1-\frac{|FN-FP|}{TP+FN} \tag{19}\]  
Let us analyze these metrics briefly. Compared with the \(F\)-_measure_ introduced above, \(Accuracy\) evaluates the overall difference between counted values and true values, ignoring the details of errors in treating noise as vehicle or errors with missing vehicles. As a result, it is a more direct metric of the overall outcome. Therefore, a comprehensive analysis of \(F\)-_measure_ and \(Accuracy\) is more scientific.
## 4 Experiment Setup and Result Analysis
### Result Analysis
#### 4.2.2 Experimental Test  
In cities, GNSS signal occlusion is a common phenomenon due to the blocked factors such as high buildings, tree shades, and tunnels. Occlusion can lead to signal loss, and then the output of the corresponding receiving channel is noise. If the fault channel cannot be identified and isolated from the system in time and accurately, the fault information will inevitably contaminate the navigation solution. The experimental system shown in Figure 6 is placed in the occluded situation to verify the robustness of deep integration. The test procedure is to place the trolley in an open area to complete the processes of searching for stars, positioning and INS alignment. After the system output is stable, the trolley is pushed along a high building. Then, it is pushed back to the starting point. The test lasts about 10 minutes for three round trips.  
Figure 11: Code phase errors of eight satellite signals corresponding to abrupt fault: (**a**) before using a robust algorithm; (**b**) after using a robust algorithm.  
Figure 12: The graph of fault identification statistics corresponding to abrupt fault: (**a**) before using a robust algorithm, (**b**) after using a robust algorithm.  
The data collected at one of the trips is utilized for analysis. Figure 13 shows the changing process of C/No of GNSS signals. As shown in the figure, the C/No of all GNSS signals was normal from the beginning to 50 s. Then, during the period of 51 s to 68 s, the signals of satellite 22, 24 and 31 were lost successively due to the occlusion of high building, and the corresponding C/No decreased rapidly. After passing the high building, the signals of the three satellites recovered one by one. Next, in the process of returning along the original route, the occlusion was experienced again. From 189 s to 208 s, the C/No showed a rapid decline, and the signals of satellite 22, satellite 24 and satellite 31 were lost for the second time.  
In Figure 14a, the positioning results deviate greatly in the period of 51 s to 68 s and 189 s to 208 s. These two periods correspond to the time of two satellite signals loss. When three satellite signals are occluded, the system makes use of the receiving data of the remaining five satellites to maintain the output of positioning results and predict the code phase and carrier frequency of each satellite signal. Once the occluded GNSS signals recover, the system quickly captures these signals based on the predicted loop parameters, then the positioning results return to normal rapidly. After using the proposed robust algorithm, the positioning results are shown in Figure 14b. Although the position errors fluctuate slightly during the period of signal occlusion, the maximum deviation does not exceed 10 m, which is much smaller than the deviations in Figure 14a.  
Figure 13: C/No of GNSS signals.  
Figure 15: Code phase errors of eight satellite signals during occlusion: (**a**) before using a robust algorithm; (**b**) after using a robust algorithm.
# Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues
## 1 Introduction
The main contributions of this article are as follows:  
* The motivation behind using FL in maritime environments and differences with applying FL in other industrial settings is discussed. * Maritime transportation, security and privacy, fault detection, and underwater applications are discussed together with the current state of the art in conventional ML- and FL-aided solutions;
* An illustrative use case aiming at ship fuel consumption reduction using FL with real datasets is investigated, highlighting a high accuracy and a reduced complexity over centralized ML approaches;
* Several open issues are discussed for stimulating further research in FL-assisted maritime environments. It must be emphasized that since FL can handle non-independent and identical distributed datasets (non-i.i.d.s.) [9], it can be applied in a plethora of maritime scenarios; for example, supporting fault diagnosis, predictive maintenance, just-in-time arrival, weather forecasting, and wind pattern prediction, as long as appropriate FL objective functions are defined. The rest of this article is organized as follows. Section 2 provides an architectural description of FL and the motivation behind its use in maritime settings. In Section 3, FL-aided maritime applications are discussed while Section 4 presents our experimental results with multiple vessels participating in the FL process. Then, Section 5 includes relevant open issues, while conclusions are given in Section 6.
## 3 Materials and Methods
### Method
#### 3.3.3 Identifying Key Underlying Factors  
Between 2009 and 2018, cultivated land increased by 14.53% in the groundnut basin of Senegal [8]. Expanding agricultural land (1) can increase the vulnerability of forested areas [41]. In addition, prior work has highlighted that precipitation, such as rainfall (2), determines the potential distribution of terrestrial vegetation and constitutes the principal factor in the genesis and evolution of soil. The importance of the socio-economic development of the African continent is on the rise in the context of climate change, incorporating elements such as rising temperatures (3) and pressure on land [55], particularly in forested areas. Climate, topography, flora, soils, and other natural resources are all considered as parts of the concept of land, specifically the use of forested areas. As a result, essential factors related to land evolution must be considered when assessing forested area changes.  
The nexus between elevation (4), vegetation index (5), and forested areas is closely linked. A comprehensive scientific analysis of natural factors such as elevation and road distance (6) may be essential in implementing effective policy strategies for forested areas. Elevation and topography influence cultivated land ridge orientation [39]. In the study area, the topography is relatively high in the west. The maximum elevation is located at the center at 141 m. Or, in the south, the low value reached \(-13\) (Figure 3). This situation may negatively impact the conservation of forested areas.  
For this reason, these variables are chosen as indicators. Also, erosion reduces soil quality and induces the degradation of forested areas. From then on, the slope degree (7) can cause potential soil erosion that can reduce the soil quality and induce the degradation of forested areas. From the point of view of socio-economic development, increasing population density (8) and manufacturing added value (9) lead to the extension of road and highway networks and worsening pressure on forested areas. Finally, agricultural added value (10) is integrated into the variables to understand the effect of agricultural land use on forested areas. These integrated data may help to comprehensively assess the factors influencing forested areas in the Thies region. This study selected the forested area's dominant morphology, namely, PD, LPI, AI, and LSI, as dependent variables.  
A mixed linear regression model was built to explore the potential influencing factors on forested areas in the Thies region. The multiple regression model was formulated as follows:  
\[Y=a+\beta_{1}\ X_{1}+\beta_{2}X_{3}+\cdots+\beta_{n}X_{n} \tag{8}\]  
Here, \(Y\) represents the dependent variable, and \(X1\), \(\ldots\), and \(Xn\) are the \(n\) independent variables. In calculating the weights \(a\), \(b1\), \(\ldots\), \(bn\), a regression analysis ensures the maximal prediction of the dependent variable from the set of independent variables and is performed by least squares estimation [56].