esa-sceva/satcom-chunk-collection huggingface dataset:
# WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction
## 5 Experiment Results
### Result of Comparison
#### 5.4.2 Comparison to State-of-the-Art of Other Studies on Water Quality Detection  
Table 10 presents a comparative analysis of our water quality classification results in relation to the findings of previous researchers. Earlier investigations have emphasized several key observations. First, previous research predominantly focused on binary classification tasks. Notably, the NuSVC machine learning model achieved an accuracy of 75%, while the XGBoost machine learning model delivered accuracies ranging from 78% [4]. Furthermore, the Catboost machine learning model exhibited an accuracy level of approximately 79% [4]. In past studies, the Random Forest machine learning model achieved an accuracy rate of about 81% [3].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Research Work** & **Context** & **Method** & **Accuracy** \\ \hline [PERSON] et al. [1] & Water quality prediction using & & \\  & machine learning & Random Forest & 68\% \\ [PERSON] et al. [2] & Smart prediction of water & & \\  & quality system & & \\ [PERSON] et al. [5] & Supervised machine learning & & \\  & to predict WQI and WQC & & \\  & Water quality index using & & \\ [PERSON] et al. [6] & modified Random Forest & & 77.68\% \\  & technique & & \\ [PERSON] and [PERSON]. [3] & Water probability prediction & XGBoost & 78\% \\ [PERSON]. [PERSON] and [PERSON]. [3] & Model & & \\  & Multimodal-based for water & & \\  & quality classification & & \\
**Proposed Model** & **Water quality prediction** & & **83\%** \\  & **system** & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparative analysis with previous studies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-score** \\ \hline XGBoost & 82\% & 82\% & 82\% & 82\% \\ CatBoost & 82\% & 83\% & 82\% & 83\% \\
**Random Forest** & **83\%** & 82\% & 83\% & 82\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of the overall results of each model after tuning.  
In our comparative analysis with previous research, we noted significant differences in water quality classification and remarkable enhancements in accuracy. These improvements included our study's departure from binary classification methods in favor of a multiclass approach, the introduction of a novel data balancing technique ROS, and the observation of an increased accuracy rate of approximately 83% for the Random Forest machine learning model. Additionally, the XGBoost machine learning model demonstrated improved accuracy within the range of 81%, and the Catboost machine learning model showed a substantial accuracy boost, reaching nearly 81%. These findings underscore the efficacy of our approach in advancing water quality classification accuracy and establishing a more intricate multiclass classification framework.
# Low-Power, High Data-Rate Digital Capsule Endoscopy Using Human Body Communication
## 4 CEHBC System Measurement and Performance
### Performance of the CEHBC System
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **2007**[3] & **2013**[2] & **2015**[10] & **This** \\ \hline Application & \multicolumn{4}{c}{Capsule Endoscopy} \\ \hline data rate before image compression & 1 Mbps & 3–20 Mbps & 8 Mbps & 6 Mbps \\ \hline image compression & X & (lossy) & (lossless/lossy) & X \\ \hline compression ratio & – & 93\% (1:15) & \textgreater{}75\% (1:4) & – \\ \hline data rate after image compression & 1 Mbps & 1.34 Mbps & 2 Mbps & 6 Mbps \\ \hline communication & RF & RF & RF & HBC \\ \hline carrier frequency & 2.4 GHz & 920/925 MHz & 2.4 GHz & 32 MHz \\ \hline modulation & Amplitude Shift & Quadrature Phase Shift (ASK) & Gaussian Minimum Shift & Frequency Selective Digital Transmission (GMSK) & Frequency Distribution (FSDT) \\ \hline technology & CMOS & CMOS 180 nm & – & 130 nm (AGLN125V5) \\ \hline power consumption & 7.925 mW & 2.5 mW & – & 3.7 mW \\ \hline energy consumption & 7.925 nJ/bit & 1.865 nJ/bit & – & 0.616 nJ/bit \\ \hline resolution & – & 640 \(\times\) 480 & 320 \(\times\) 240 160 \(\times\) 120 128 \(\times\) 96 & 480\(\times\)480 \\ \hline frame rate & – & 3 f/s & 55 f/s & 3.13 f/s \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of wireless capsule endoscopy (WCE) transmitters.
## 2 Related Work
In [20; 21], a novel Underwater localization approach based on Directional Beacons (UDB) is proposed for UWSNs, where a mobile AUV moves according to a predefined route navigated by compass and sends directional beacons to localize unknown nodes. Compared with stationary localization algorithms, mobile ones are more suitable for dynamic UWSNs. In above mentioned typical underwater localization algorithms, there are four problems: (1) Many localization algorithms require time synchronization. However, few of them research on the time synchronization problem; (2) Most existing algorithms ignore the influence of ocean mobility; (3) Some localization algorithms use mobile anchor nodes, but how to reasonably and effectively design the path planning of mobile anchor nodes is not resolved; (4) The problems of malicious attacks and the related security mechanisms are not considered in the localization process. In [22], [PERSON] _et al._ firstly propose a Joint time Synchronization and Localization (JSL) algorithm. In JSL, the effect of depth information on the speed of sound and time synchronization is considered. However, in practical applications, the underwater acoustic velocity is not a constant, which varies with underwater environmental information, e.g., depth, temperature and salinity. In [23], [PERSON] _et al._ propose a new joint time synchronization and localization algorithm based on the uncertainty of acoustic velocity. However, the algorithm assumes that each sensor node is equipped with a directional navigation system, which is difficult to meet in practice. And in 2014, [PERSON] _et al._ further study the ranging and positioning problem in visual line of sight (LOS) and non line of sight (NLOS) environments [24]. Considering the mobility of underwater environment, [PERSON] et al. firstly propose a novel Localization for Active-restricted UWSNs (LAR) [25]. In [26], [PERSON] _et al._ propose a time Synchronization and Localization algorithm using Seawater Movement Pattern (SLSMP) to improve localization accuracy based on the unique characteristics of UWSNs, e.g., inevitable node movement and variable propagation delay. For the third problem, many path planning schemes of mobile anchor nodes have been proposed. For example, in [27], [PERSON] _et al._ firstly propose a path planning scheme based on triangle for a mobile anchor node to minimize the average localization error of unknown nodes. In [28], a cooperative path planning method is proposed for mobile UWSNs, which can be used to locate and navigate underwater mobile AUV; however this method is only suitable for tracking one AUV at a time. For the forth problem, the research of malicious attacks and security mechanisms in the process of localization is still at the initial stage. Few study has been worked on the secure localization problem and a lot of research problems need to be solved. That is why we study the secure localization problem in this paper. In the next section, we will present our proposed localization scheme CSLT in detail.
# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 1 Introduction
We also present the challenges in implementing DL-based Visual SLAM and VOE with input data obtained from RGB-D sensors. * We collected and published the TQU-SLAM benchmark dataset, including devices and equipment for data collection, data collection environment, data collection process, data synchronization/data correction and data labeling, and annotation/ground truth (GT) data preparation, and fine-tuned a VOE model with the MLF-VO framework for a comparative study. The VOE results are presented in detail and visually, and analysis and discussion are also presented. The structure of the paper is organized as follows. Section 1 introduces the studies of Visual SLAM and VOE, the previous surveys on Visual SLAM and VOE, and the advantages and disadvantages of deep learning-based methods for Visual SLAM and VOE. Related  
Figure 2: The taxonomy of DL is based on Visual SLAM and VOE surveys from data obtained by image sensors. The upper flow is the conventional Visual SLAM system, and the lower flow is the conventional VOE system. The studies on the two systems were performed based on (2) using DL to supplement the modules of Visual SLAM and VOE systems and (3) using end-to-end DL to build Visual SLAM and VOE systems. studies on previous surveys of Visual SLAM and VOE are presented in Section 2. Our surveys on deep learning based on the Visual SLAM and VOE are presented in Section 3. Section 4 presents the discussions and challenges of the Visual SLAM and VOE based on deep learning. Section 5 presents a comparative study of VOE on the TQU-SLAM benchmark dataset. We finally conclude and give some ideas for future works presented in Section 6.
## 3 Results and Discussion
### Construction of the Proposed H2O2 Probe
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Detection Limit** & **Linear Range** & **Reference** \\ \hline Enzymatic colorimetric detection & 2.5 uM & 0.05–0.50 mM & [36] \\ Hybrid microflower enzymatic amperometric detection & 50 uM & 100 uM–100 mM & [19] \\ Small-molecule fluorescence detection & 5.3 uM & & [37] \\ Small-molecule fluorescence detection & 0.07 uM & 0.5–200 uM & [18] \\ Small-molecule fluorescence detection & 21 nM & 3–500 uM & [38] \\ Small-molecule fluorescence detection & 25 nM & 1–60 uM & [39] \\ Small-molecule fluorescence detection & 160 nM & 5–20 uM & [40] \\ Nanocomposite colorimetric detection & 14 nM & 0.01–30 uM & [17] \\ Nanocomposite ratiometric fluorescence Detection & 168 nM & 0.5–10 uM & [22] \\ Nanocomposite nonenzymatic colorimetric detection & 3.87 nM & 10 nM-10 mM & [41] \\ Nanocomposite nonenzymatic fluorescence detection & 22 nM & 30–300 nM & [42] \\ Nanocomposite nonenzymatic ratiometric fluorescence detection & 11 nM & 30–110 nM & \\ Nanocomposite nonenzymatic ratiometric fluorescence detection & 10 nM & 0.025–50 uM & [43] \\ Nanocomposite nonenzymatic amperometric detection & 1.6 uM & Up to 14 mM & \\ Nanocomposite nonenzymatic amperometric detection & 2.2 uM & Up to 15 mM & [44] \\ Nanocomposite nonenzymatic amperometric detection & 0.1 uM & Up to 20 mM & \\ Nanocomposite nonenzymatic amperometric detection & 0.5 uM & 0.002–10 mM & [45] \\ Nanocomposite nonenzymatic amperometric detection & 1 uM & 10 uM–15 mM & [46] \\ Nanocomposite nonenzymatic amperometric detection & 2 mM & 12.64 nM–2104 uM & [47] \\ Nanocomposite nonenzymatic photoelectrochemical detection & 1.2 uM & 5–250 uM & [48] \\ CFP-Prx/mTrx-YFP ratiometric fluorescence Detection & 4 nM & 10–320 nM & This study \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the performance of various H\({}_{2}\)O\({}_{2}\) sensors.
## 3 Results and Discussion
### Identification of Aroma Compounds in Peach Spirits
However, the contribution of esters to the peach spirits produced by different separation methods should be researched deeply. There were more lactones in the pervaporation produced peach spirit than in the distilled peach spirit. In the distilled peach spirit, only \(\gamma\)-butylrolactone was detected and contributed a fruity note. While \(\gamma\)-butylrolactone, \(\gamma\)-hexalactone (tobacco), \(\gamma\)-octalactone (apricot and peach), \(\gamma\)-decalactone (apricot and peach), and \(\gamma\)-nonalactone (milky notes) were all identified in the pervaporation produced peach spirit. Among them, \(\gamma\)-decalactone and \(\gamma\)-nonalactone showed high FD factors. \(\gamma\)-Decalactone has been reported in the peach brandy [22]. Lactones played an important role in the overall aroma of brandy [32]. In particular, those with 4 to 8 alkyl chains (\(\gamma\)-lactones; \(\gamma\)-C8 to \(\gamma\)-C12) or 3-7 alkyl chains (\(\delta\)-lactones; \(\delta\)-C8 to \(\delta\)-C12) mainly contributed pleasant sensory properties, such as apricot, peach, and coconut, which was similar to the results in this study [33]. Terpenoids have been identified as important contributors to the aroma profile of spirit [34; 35]. Contrary to the case of lactones, terpenoids were identified more in distilled peach spirit than in pervaporation produced peach spirit. And citronello1, dihydro-\(\beta\)-ionol, and (E)-\(\beta\)-damascenone exhibit higher FD values in distilled peach spirit compared to pervaporation produced peach spirit. In addition, trans-nerolidol, trans-\(\beta\)-ionone, and (E,E)-farnesol were identified as aroma-active compounds only in the distilled peach spirit.
# Visual and Haptic Guidance for Enhancing Target Search Performance in Dual-Task Settings
## 1 Introduction
When a target appeared in the secondary task behind the users, a visual arrow or haptic vibration feedback would simultaneously appear, pointing in the direction of the target to prompt the user to search for it behind them. Our research methodology used user studies to evaluate the effectiveness of various design factors of visual and haptic feedback. The first study evaluated the effectiveness of dynamic visual arrows compared to static arrows, considering factors such as guidance continuity and target location. The second study introduced haptic feedback as an additional modality and compared its effectiveness to visual guidance. Our findings demonstrate the following:  
* Dynamic visual arrows, as opposed to static ones, significantly improve both reaction time and selection time in target search tasks. * Haptic feedback is introduced as a more salient guidance technique compared to visual cues. It is shown to facilitate quicker response times, enabling users to maintain visual attention on primary tasks while managing secondary tasks through haptic cues. This research enriches the existing body of knowledge on visual-haptic interactions by offering empirical evidence that underscores the efficacy of these modalities in improving target search performance. Additionally, it provides insights for the design of user-friendly interfaces and systems, which are crucial for supporting multitasking in complex environments.
## 3 Results and Discussion
### M. sylvestris Effect on TE Extractable Fraction and Mobility in the Sediment
It should be noted that the percentage of Pb mobility (Supplementary Materials Table S2) was already extremely low before cultivation (\(<\)0.012% of the total Pb, Supplementary Materials Table S2). For Cu, a slight plant effect was observed (\(p<0.05\)), leading to an increase in the extractable concentration in Area 3 at harvest. However, this effect was not present when Cu mobility was studied (Supplementary Materials Table S2, \(p>0.05\)). _M. sylvestris_ cultivation alone did not reach the phytostabilization objectives of TE mobility reduction in our sediment conditions. In order to reduce the Cd mobility and to enhance stabilization, combining an amendment with the cultivation of _M. sylvestris_ could be an option. As already discussed in our previous study on the same site [18], a biochar amendment could reach this objective by interacting with Cd\({}^{2+}\) and CdOH\({}^{+}\) to form a stable complex without altering the pH. The study of a biological amendment such as mycorrhiza fungi might also be an option.
## 3 Results and Discussion  
### Flowability and Fresh Density  
Flowability serves as a crucial parameter in the practical application of soil-based CLSMs. For the purpose of comparing the influence of PCE, T1 and T2 (refer to Table 3) were designed. T1 and T2 kept the identical cement content and initial water content, specifically 8% and 38.8%, respectively. As shown in Figure 3, the flow values for all mixtures except T1 exceeded 200 mm, satisfying the criteria for backfilling underground pipes [31]. Furthermore, the fresh density of all mixtures ranged from 1590 to 1970 kg/m\({}^{3}\), meeting the target density of 1360 to 2000 kg/m\({}^{3}\) for fresh CLSMs containing industrial by-products [32]. The fresh density exhibited a slight rise with the increasing cement content in Figure 3a, and a notable decline with the increasing initial water content in Figure 3b. Compared to T1, the introduction of PCE into the soil-based CLSMs significantly increased the flow value. This enhancement can be attributed to the PCE's side chain adsorbing onto the surfaces of the cement particles and hydrate particles, with its long side chain present in the liquid mixture [33]. Additionally, PCE adsorbed onto the surfaces and interlayers of the clay minerals, promoting their dispersion. Due to steric hindrance, electrostatic repulsion, adsorption, and depletion effects, PCE dispersed the cement particles and clay minerals, thereby enhancing the mixture's flowability [34]. Moreover, the adsorption behavior between the PCE and cement particles facilitated the release of weakly bound water [35]. Consequently, the incorporation of PCE in the soil-based CLSMs proved effective in reducing water consumption and ensuring optimal flowability.
## 5 LoW Mob Mobity Support Scheme
### Proposed Message Formats
#### 5.3.1 Location Update Message Format  
Figure 6 shows the format of an LU message that a SN sends to the GW when the MN associates with it.  
The SN sends an LU message with a unique dispatch. The dispatch indicates that the message is an LU message that has to be forwarded to the GW. The message also contains the 16 bits short address of the source SN. The intermediate nodes relay the LU packet to the GW by checking the dispatch value. When the GW receives the packet it identifies the packet as LU message and updates the entry of the MN by replacing the previous SN's short address with the next SN's address. The entry for the MN remains in the binding table of the GW, as long as the GW keeps receiving LU messages on behalf of MNs. When the GW stops receiving the LU messages of MNs, it deletes its entry in the binding table after the timeout of which the exact value is implementation specific.
## 3 Iterative Learning Observer-Based Thruster Fault Reconstruction Approach
### Design of the Learning Output-Feedback Fault-Tolerant Controller  
An anticipated nominal formation configuration without any thruster control and space perturbations was assumed as follows,  
\[\left\{\begin{array}{l}\dot{\mathbf{q}}_{0}=\mathbf{A}\mathbf{q}_{0}\\ \mathbf{y}_{0}=\mathbf{C}\mathbf{q}_{0}\end{array}\right. \tag{28}\]  
where \(\mathbf{q}_{0}\) is the system state of the nominal configuration. By subtracting (3) from (28), a formation configuration that tracked error dynamics could be established as  
\[\left\{\begin{array}{l}\dot{\mathbf{e}}_{\mathbf{q}}=\mathbf{A}\mathbf{e}_{\mathbf{q}}-\mathbf{B}\mathbf{ u}-\mathbf{E}\mathbf{d}(t)-\mathbf{H}\mathbf{F}(t)\\ \mathbf{e}_{\mathbf{y}}=\mathbf{C}\mathbf{e}_{\mathbf{q}}\end{array}\right. \tag{29}\]  
where \(\mathbf{e}_{\mathbf{q}}=\mathbf{q}_{0}(t)-\mathbf{q}(t)\) and \(\mathbf{e}_{y}(t)=\mathbf{y}_{0}(t)-\mathbf{y}(t)\) are the state tracking error and output tracking error, respectively.  
In order to track the desired state of the nominal system and maintain the formation configuration in the presence of thruster faults and space perturbation forces, based on fault-reconstructing signals obtained using the aforementioned ILO (5), the learning output-feedback fault-tolerant tracking controller was established as follows,  
\[\mathbf{u}_{f}=\pi(t)\mathbf{K}\mathbf{e}_{\mathbf{y}}-\mathbf{B}^{+}\mathbf{E}\mathbf{v}(t)-\mathbf{B}^{+} \mathbf{H}\mathbf{\dot{F}}(t) \tag{30}\]  
where \(v(t)\) is a jointed compensation term of both space perturbation forces and actuator fault reconstruction errors; \(\pi(t)\) is a learning parameter, which was designed to solve the potential uncertainties; \(\mathbf{B}^{+}\mathbf{H}\mathbf{F}(t)\) represents the fault compensation term; \(\mathbf{B}^{+}\) is a generalized inverse of matrix \(\mathbf{B}\); \(\mathbf{K}\in\mathbb{R}^{3\times 6}\) is the gain matrix that is designed later. In the controller (30), \(\pi(t)\) is updated by the following learning algorithm [27],  
\[\pi(t)=G_{1}\mathbf{\pi}(t-\tau)+G_{2}\mathbf{e}_{q}^{T}(\mathbf{K}\mathbf{C})^{T}(\mathbf{K}\mathbf{C })\mathbf{e}_{\mathbf{q}} \tag{31}\]  
where \(G_{1}\) and \(G_{2}\) are scalars that are designed later. Learning parameter \(v(t)\) was updated by the following learning algorithm,  
\[v(t)=\mathbf{G}_{3}v(t-\tau)+G_{4}\mathbf{K}\mathbf{C}\mathbf{e}_{\mathbf{q}}(t) \tag{32}\]  
where \(\mathbf{G}_{3}=diag\{g_{1},\ g_{2},\ g_{3}\},\ g_{i}>0\) and \(G_{4}\) is a constant.  
By substituting (30) into (29), a closed-loop spacecraft formation tracking error system could be expressed as:  
\[\left\{\begin{array}{l}\dot{\mathbf{e}}_{\mathbf{q}}=(\mathbf{A}-\mathbf{B}\pi\mathbf{K}\mathbf{C}) \mathbf{e}_{\mathbf{q}}(t)-\mathbf{E}\mathbf{e}_{\mathbf{h}}(t)\\ \mathbf{e}_{\mathbf{y}}=\mathbf{C}\mathbf{e}_{\mathbf{q}}\end{array}\right. \tag{33}\]  
where \(\mathbf{e}_{\mathbf{h}}(t)=\mathbf{h}(t)-v(t)\) and \(\mathbf{h}(t)=\mathbf{d}(t)+\mathbf{e}_{F}(t)\) denote synthesized disturbance forces.
## 3 Results and Discussion  
### Comparison with Numerical Solution  
The solution obtained from the analytical equation was compared with the solution from the numerically calculated method. The exact same properties of reservoir and fracture were used to compare the results. The plots were shown in Figures 7 and 8.  
Figure 8: Results Comparison between Analytical Model and Numerical Calculation.  
Figure 7: Results Comparison between Analytical Model and Numerical Calculation in Log-Log scale.