esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The Internet of Things (IoT) is an emerging technology in underwater communication because of its potential to monitor underwater activities. IoT devices enable a variety of applications such as submarine and navy defense systems, pre-disaster prevention, and gas/oil exploration in deep and shallow water. The IoT devices have limited power due to their size. Many routing protocols have been proposed in applications, as mentioned above, in different aspects, but timely action and energy make these a challenging task for marine research. Therefore, this research presents a routing technique with three sub-sections, Tri-Angular Nearest Vector-Based Energy Efficient Routing (TANVEER): Layer-Based Adjustment (LBA-TANVEER), Data Packet Delivery (DPD-TANVEER), and Binary Inter Nodes (BIN-TANVEER). In TANVEER, the path is selected between the source node and sonobucys by computing the angle three times with horizontal, vertical, and diagonal directions by using the nearest vector-based approach to avoid the empty nodes/region. In order to deploy the nodes, the LBA-TANVEER is used. Furthermore, for successful data delivery, the DPD-TANVEER is responsible for bypassing any empty nodes/region occurrence. BIN-TANVEER works with new watchman nodes that play an essential role in the path/data shifting mechanism. Moreover, achievable empty regions are also calculated by linear programming to minimize energy consumption and throughput maximization. Different evaluation parameters perform extensive simulation, and the coverage area of the proposed scheme is also presented. The simulated results show that the proposed technique outperforms the compared baseline scheme layer-by-layer angle-based flooding (L2-ABF) in terms of energy, throughput, Packet Delivery Ratio (PDR) and a fraction of empty regions.  
TANVEER; acoustic; energy consumption; empty-region; achievable region; angle-based flooding; watchman nodes; binary inter nodes IEEEexample:TANVEER IEEEexample:TANVEER Transactions on Information and Systems (ISIS)  
2021  
TANVEER: Tri-Angular Nearest Vector-Based Energy Efficient Routing Routing for IoT-Enabled Acoustic Sensor and Actor Networks (I-ASANs)
# Indoor Air Quality Assessment in Grocery Stores
## 3 Results and Discussions
### Comfort Parameters  
#### 3.1.1 Temperature  
Figure 2 represents the mean levels of temperature assessed in the studied GSs during the working period, where the mean temperature level was 20.4 \(\pm\) 1.2 \({}^{\circ}\)C, ranging from a maximum value of 23.8 \(\pm\) 0.8 \({}^{\circ}\)C (GS M5, Location A) and a minimum of 18.4 \(\pm\) 1.1 \({}^{\circ}\)C (GS M11, Location B).  
Figure 2: Temperature levels assessed in all locations of the studied GSs. The green area represents the acceptable range (18-25 \({}^{\circ}\)C) for workplaces defined by Portuguese Legislation [31].  
The guideline ISO 7730 establishes the optimal temperature range for indoor environments and, in winter, this range is between 20 and 24 \({}^{\circ}\)C [32]. Considering this range, 62% of the GSs presented mean levels within this acceptable range, while the remaining 38% showed mean temperature levels below the minimum threshold of 20 \({}^{\circ}\)C (namely, M4, M10, M11, and M12), which highlights the lack of heating during the cold period when the monitoring occurred.  
Considering the Portuguese Decree-Law nr 243/86 of 20 August--Article 11 th [31], which states that workplaces, as well as common facilities, must offer good temperature and humidity conditions, with the workplace temperature being between 18 \({}^{\circ}\)C and 22 \({}^{\circ}\)C (except for specific conditions that may reach 25 \({}^{\circ}\)C), all the studied GSs had mean temperature levels within the permitted range.  
The type of facilities of the GSs showed to have a significant impact on temperature levels (ANOVA: F(2,22) = 3.665, \(p\)-value = 0.042), however, when performing the post hoc test to understand the differences between the influence of the different type of facilities on temperature levels, no significant difference was found (\(p\)-value for all the cases was above \(p\)-value > 0.050).
# A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning
## 2 Thermal Consideration and FEM Stimulation
### Temperature and Thermal Contrast Curves
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Row\#** & **Depth (Left; Middle; Right)** & **Shape** & **Defect Size (Left; Middle; Right) (mm)** \\ \hline \multirow{3}{*}{A} & 1 & Depth = 0.5; 0.8; 1.1 & Quadrangle & Size = 3 ; 16; 13 \\  & 2 & Depth = 0.6; 09; 1.2 & Round & Diameter = 8; 3; 16 \\  & 3 & Depth = 0.7; 1.0; 1.3 & Quadrangle & Size = 13; 8; 16 \\ \hline \multirow{3}{*}{B} & 1 & Depth = 1.4; 17; 2.0 & Quadrangle & Size = 8; 3; 16 \\  & 2 & Depth = 1.5; 18; 2.1 & Round & Diameter = 13; 8; 3 \\  & 3 & Depth = 1.6; 19; 2.2 & Quadrangle & Size = 16; 13; 8 \\ \hline \multirow{3}{*}{C} & 1 & Depth = 0.5; 0.8; 1.1 & Round & Size = 4; 17; 14 \\  & 2 & Depth = 0.6; 09; 1.2 & Quadrangle & Diameter = 9; 4; 17 \\  & 3 & Depth = 0.7; 1.0; 1.3 & Round & Size = 14; 9; 4 \\ \hline \multirow{3}{*}{D} & 1 & Depth = 1.4; 17; 2.0 & Round & Size = 9; 4; 17 \\  & 2 & Depth = 1.5; 18; 2.1 & Quadrangle & Diameter = 14;9;4 \\ \cline{1-1}  & 3 & Depth = 1.6; 1.9; 2.2 & Round & Size = 17; 14; 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Defect characteristics of testing samples. Figure 4: Temperature contrast evaluation for defects located at different depths with the same size (0.5 mm, 1.0 mm, 1.5 mm) in training samples (the framerate: 158 frame/second). Figure 5: Data distribution of temperature contrast evaluation for different depths in training samples (**a**) 0.5 mm; (**b**) 1.0 mm; (**c**) 1.5 mm (the framerate: 158 frame/second); (**d**) five defective point with corresponding reference point on a defect region. Figure 3: Representative CFRP training sample 1 configuration  
Notice that each training sequence to be processed in this work are extracted from the three quarters frames in the total frames of the one thermal sequence which already include the information of peak thermal contrast \(\Delta Tmax\) and corresponding \(t_{x}\). As a result, the last quarter frames of thermal curves were not extracted in this work which would show a dramatic decrease of the thermal contrasts in the graphs. To be notice that, all the thermal sequence stimulated from the COMSOL software based on the parameters as indicated on Table 2. All the extraction work was implemented in a program in MATLAB software. This partial extraction in thermal data could save the computational expense for the training in GRU to some extent. A corresponding synthetic thermogram frame for training sample 2 in t = 3 s generated from COMSOL is indicated at in Figure 5(a); nine artificial flat bottom Figure 5(b) hole defects were embedded with different depths in the shapes of either circles or squares.
###### Abstract  
Atmosperic profiles derived from Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation (RO) measurements make up for the lack of operational radiosonde soundings with a high spatiotemporal distribution, and their performance over China is assessed in this paper. COSMIC-retrieved atmospheric wet profiles from 2014 to 2015 are compared to the contemporaneous radiosonde profiles from 120 stations, and the vertical mean differences are used. The results show that the vertical mean biases of temperature, pressure and vapor pressure are \(-0.10\) K, \(0.69\) hPa and \(-0.01\) hPa, respectively, and that for refractivity is \(0.17\) N. Moreover, the temperature differences are positively correlated with station altitude, yet both the pressure and vapor pressure differences are negatively correlated with station latitude, as is the refractivity difference. The large temperature difference arising from the Qinghai-Tibet Plateau (QTP) region may be associated with the complex topography of the area and the limitations in the background model used in the COSMIC profile retrieval. Furthermore, negative refractivity bias between COSMIC and radiosonde data occurs below 5 km and is large in wet southern China, with a value of less than 1%. This result may be related to more humid conditions and super-refraction.  
atmospheric profile; COSMIC; radio occultation; radiosonde +
Footnote †: journal: Atmosphere
## 4 Testbed Design
### Monitoring Stack  
Our testing environment incorporates a transparent monitoring stack (Figure 4) capable of extracting various utilization metrics from the system under test. These metrics encompass CPU, memory, network utilization, and FL-level metrics, including model accuracy/loss and training time per round (Table 1). To achieve this, we deploy a containerized monitoring agent on each node that follows a probe-based multi-threaded paradigm [38]. The agent, namely cAdvisor [39], collects performance metrics by initiating different probes for each sub-component, such as the group probe and OS probe. It then exposes an http endpoint for Prometheus [40] to periodically retrieve and store the metrics in its embedded timeseries databases. Prometheus is integrated with Grafana [41] to provide real-time inspection of our experiments through its dashboard-as-a-service software.  
As for the performance and FL monitoring metrics, the challenging part is to expose fine-grained metrics from the running FL workloads. To do that, we enhance the existing codebase of Flower to record accuracy, loss, and duration data into well-organized files, which are stored locally on each client. To capture performance metrics, we employ timers that intersect with the existing methods to extract the duration of training time. As for accuracy and loss, we extract these metrics from the FL master. At the end of the experiment, these files are automatically transferred into a central repository and combined with the performance metrics collected by Prometheus.  
Figure 4: Monitoring stack for repeatable FL experimentation.
## 7 Conclusions  
Aiming at the autonomous dynamic collision avoidance problem for a USV, a collision avoidance control method based on an improved CS algorithm was proposed. Firstly, according to the different aspect ratios of obstacle contours, these are represented by circle and ellipse collision avoidance models. In the process of collision avoidance, the constraints of collision avoidance rules are integrated to ensure the safety of the USV during a voyage. Secondly, the CS algorithm was improved by using the adaptive step-size control factor, so as to dynamically adjust the flight step-size and improve the search ability; by introducing a mutation and crossover strategy, the diversity of the population and local optimization ability were strengthened. The improved CS algorithm, standard CS algorithm and PSO algorithm were simulated and verified. By comparison, the improved CS algorithm shows great improvement in the convergence speed and optimization accuracy, and it can quickly solve the collision avoidance strategy in the process of collision avoidance.  
Figure 11: Input and output results for the controller. (**a**) The input of the controller; (**b**) The final control output of USV.  
Through experiments for electronic chart scene simulation, it was verified that the algorithm designed can make the USV complete autonomous dynamic collision avoidance safely and quickly in multi-dynamic obstacle scenes, which provides a feasible and effective engineering application method for autonomous dynamic collision avoidance of a USV. In future research, it will be necessary to further combine the relevant contents of COLREGs with a collision avoidance controller to ensure the sailing safety of the USV.  
Conceptualization, Y.F.; methodology, X.S.; software, X.S.; validation, X.S.; formal analysis, Y.F.; resources, G.W.; data curation, X.S.; writing--original draft preparation, X.S.; writing--review and editing, X.S.; visualization, D.M.; supervision, G.W.; project administration, G.W.; funding acquisition, Y.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 51609033, the Natural Science Foundation of Liaoning Province of China under Grant Number 20180520005, the Key Development Guidance Program of Liaoning Province of China under Grant Number 2019 JHS/10100100, the Soft Science Research Program of Dalian City of China under Grant Number 2019J11 CY014 and the Fundamental Research Funds for the Central Universities under Grant numbers 3132019005 and 3132019311.  
Not applicable.  
The authors declare no conflict of interest.
## 2 The Proposed Method
### Quantitative Evaluation
In order to make a quantitative comparison in this case, we selected six segments with different lengths in the closed-loop scene, which are marked as segments A to F, as shown in Figure 9, and the estimated results with three methods are shown in Table 2.  
We can see the accuracy is improved by the proposed method in Table 2. This trend will be more obvious with longer segments, as for the segments B and E. We also found that the lengths of the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Real Length of** & **Length Obtained** & **Relative** & **Length Obtained** & **Relative** & **Length Obtained** & **Relative** \\
**the Corridor** & **by LiDAR-Only** & **Error** & **by LiDAR \& IMU** & **Error** & **by the Proposed** & **Error** \\
**(m)** & **Method** & **Fusion Method** & **Fusion Method** & **Fusion Method** & **Fusion Method** & **Fusion Method** \\ \hline \hline \(28.9\) & \(26.5\) & \(-8.3\%\) & \(27.1\) & \(-6.2\%\) & \(27.8\) & \(-3.8\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Error Comparison in Open Loop. Figure 9: The selected segments in closed-loop scene. segments estimated by Cartographer with the LiDAR-only method are often shorter than the actual lengths in both open-loop and closed-loop scenes, which is caused by the similarity of two adjacent frames in the low-texture environment. That means that because there are not many differences between two frames in a low-texture environment, for the optimization process, the LiDAR-only method does the scan matching around the previous pose, and it tends to converge earlier to the error tolerance. However, the fusion-based method does the scan matching with the initial pose estimated by the fusion of IMU and the previous pose, and the result is closer to the actual value. The gap between the two kinds of methods (with IMU or not) will be greater if a low-frequency output of LiDAR is used.
## 5 Conclusions  
We have successfully developed a new approach of deep learning-based seismic inversion by incorporating seamlessly the rock physics model to generate a vast amount of synthetic pseudo rock properties and their seismic responses. The rock physics library plays a significant role as a comprehensive synthetic dataset input for network training, validation, and testing. Meanwhile, the complex deep learning network architecture, which includes a weakly supervised network, has proven to be useful to enhance computational work efficiency and prediction accuracy while handling data non-linearity and the non-uniqueness of the solutions.  
Application of the proposed method on the elastic fluvial-dominated region in the Malay basin reveals the applicability of the method for accurate rock properties prediction. Comparison with the conventional method demonstrated the advantage of the proposed deep learning-based inversion in identifying the reservoir occurrence and distribution. The conventional method exhibited the presence of scattered amplitude noises and prominent seismic imprints masking the reservoir, whereas the proposed method showed more stable and less noises inversion results, yet with faster turn-around time. There are substantial improvements of up to 31% in correlation accuracy achieved upon implementing proposed method for elastic properties prediction compared to conventional method. The trained network model can apprehend the behavior between seismic amplitude characteristics concerning various rock physics combination possibilities and is competent to recognize the unique characteristics and relationships between actual references seismic and well data during the weak supervision transfer learning process. This implies that the proposed method can provide good elastic properties prediction framework and is able to address data limitation and sparsity issues in typical deep learning-based inversion.  
Conceptualization, M.I.A.F., M.S.J. and M.H.; methodology, M.I.A.F. and M.S.J.; software, M.I.A.F.; validation, M.I.A.F., M.S.J. and M.H.; formal analysis, M.I.A.F. and M.A.I.; investigation, M.I.A.F.; resources, M.I.A.F.; data curation, M.I.A.F. and M.S.J.; writing--original draft preparation, M.I.A.F.; writing--review and editing, M.I.A.F., M.S.J., M.A.I. and M.H.; visualization, M.I.A.F.; supervision, M.S.J. and M.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by UTP industrial research grant with grant number 015 MD0-077.  
**Institutional Review Board Statement:** Industrial research grant.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable.  
**Acknowledgments:** The authors would like to thank PETRONAS Research and Centre for Subsurface Imaging UTP for giving permission to use the data for this study and granting the opportunity to publish the result.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Analytical Processes  
The degradation of Nile Blue dye was examined using a UV-Vis spectrophotometer. Absorbance readings were recorded at the dye's specific maximum wavelength, \(\lambda_{\text{max}}\) = 636 nm, to track the reaction's progress. The kinetic parameters were evaluated as follows [26]:  
\[k=-\frac{In(Ct/Co)}{t}\]  
\[t_{1/2}=\frac{ln2}{k}\]  
The starting concentration of Nile Blue is referred to as C\({}_{0}\), and Ct indicates the concentration at a specific time, t. The pseudo-first-order rate constant (k) was determined from the slope of the linear graph that plots ln(Ct/C\({}_{0}\)) against time. This method facilitated a straightforward evaluation of the dye degradation kinetics over time.  
Regarding the mineralization of the dye, the COD values were measured. The Chemical Oxygen Demand (COD) quantifies the oxygen required to oxidize organic matter into CO\({}_{2}\) and water, as well as certain inorganic compounds such as nitrite and ammonia [27].  
In this study, the COD values of the dye solutions were determined by a heated digestion process, which facilitated the oxidation of the organic matter in the sample within a predetermined time period of two hours. During digestion, hexavalent chromium in the reagent was reduced to trivalent chromium. The amount of reduced chromium was then measured colorimetrically, with the intensity of color being determined by a compatible photometer and the COD concentration displayed in mg L\({}^{-1}\) O\({}_{2}\).  
The COD results were considered a key parameter in evaluating the feasibility of the photochemical process for the mineralization of Nile Blue Chloride dye. The equation used for this purpose is as follows:  
\[\%\ mineralization=\frac{COD_{0}-COD}{COD_{0}}\times 100\]  
COD\({}_{0}\) = initial COD of the dye solution and COD = final COD of the dye solution [28].  
The COD of the dye solution was estimated before and after treatment. Samples were withdrawn at an interval of 60 min consistently for 3 h.  
The determination of the amount of iron leaching in the solution during the photocatalytic treatment with the MOF or goethite was performed by inductively coupled plasma mass spectroscopy (ICP-MS) (model Thermo Scientific, Fisher Scientific, Ottawa, Canada) with Qtegra(tm) Intelligent Scientific Data Solution(tm) Software version 2.7 (further analytical details can be found elsewhere [29]).
## 3 Results
### Impact of Different Preprocessing Methods on Data and Model Accuracy
#### 3.2.2 Impact of Different Preprocessing Methods on Model Accuracy
Therefore, for the soil spectral data in the experimental area, SG denoising is superior to N and DWT denoising methods. The comparison of the four mathematical transformations and four modeling methods after SG denoising is shown in Figure 7. Among these, 1 DR-BP achieved the best results in R\({}^{2}\), MAE, MARE, MBE, and RPIQ. Other 1 DR preprocessing results were also excellent, such as 1 DR-CNN, which performed best in RMSE, MAE, MARE, MBE, and RPIQ among the four mathematical transformations using CNN modeling, and 1 DR-SVM, which performed best in MARE, MBE, and RPIQ among the four mathematical transformations using SVM modeling. This indicates that 1 DR preprocessing effectively reflects spectral data characteristics. The experimental results on the impact of different preprocessing methods on data and model accuracy show that SG-1 DR preprocessing is the best preprocessing method. This conclusion is consistent with the findings of [PERSON] et al. [53] and [PERSON] et al. [25], as well as with [PERSON] et al. [54], who found the optimal preprocessing method for soils in the northwestern part of Yunnan Province, which is close to the experimental area. This effectively highlights the correlation between spectral data and SOM content and improves model generalization ability.
## 2 Algorithm
### Double Differenced Positioning Algorithm in Pseudolite System  
#### 2.1.1 Observation Model in Pseudolite System  
Due to the complex indoor environment and the poor stability of commercial pseudolite clocks, the pseudolite high-precision positioning algorithms are generally realized based on DD carrier-phase positioning mode. The error of clock synchronization and antenna phase can be eliminated or weakened by theDD model [32]. The DD observations do not include tropospheric and ionospheric delay because the signal from the pseudolite itself does not pass through the troposphere and ionosphere. Furthermore, since the pseudolite constellation is fixed, there are no ephemeris errors and relativistic effects. Therefore, the indoor single-frequency pseudolite DD observation equations can be formulated as [33; 34; 35],  
\[\begin{array}{c}\
abla\Delta P_{br}^{\rm ls}=\
abla\Delta p_{br}^{\rm ls}+ \
abla\Delta e_{P}+\
abla\Delta M_{P}\\ \
abla\Delta L_{br}^{\rm ls}=\lambda_{1}\
abla\Delta q_{br}^{\rm ss}=\
abla \Delta p_{br}^{\rm ls}+\
abla\Delta e_{L}+\
abla\Delta m_{L}+\lambda_{1}\
abla \Delta N_{br}^{\rm ks}\end{array} \tag{1}\]  
where \(\
abla\Delta\) is a DD operator; \(r\) and \(b\) represent the rover and base stations, respectively. \(k\) and \(s\) represent pseudolites. The pseudorange and carrier phase observations are represented by \(P\) and \(\varphi\). \(\rho\) represents the geometric distance between pseudolites and the receivers. \(e\) and \(\varepsilon\) denote the noise of the pseudorange and carrier phase observations, respectively. \(M\) and \(m\) represent the multipath effects of pseudorange and carrier phase observations. \(N\) indicates the integer ambiguities of carrier phase observations. \(\lambda\) indicates the wavelength.