esa-sceva/satcom-chunk-collection huggingface dataset:
# Revolutionizing Urban Pest Management with Sensor Fusion and Precision Fumigation Robotics
###### Abstract  
Effective pest management in urban areas is critically challenged by the rapid proliferation of mosquito breeding sites. Traditional fumigation methods expose human operators to harmful chemicals, posing significant health risks ranging from respiratory problems to long-term chronic conditions. To address these issues, a novel fumigation robot equipped with sensor fusion technology for optimal pest control in urban landscapes is proposed. The proposed robot utilizes light detection and ranging data, depth camera inputs processed through the You Only Look Once version 8 (YOLOv8) algorithm for precise object recognition, and inertial measurement unit data. These technologies allow the robot to accurately identify and localize mosquito breeding hotspots using YOLOv8, achieving a precision of 0.81 and a mean average precision of 0.74. The integration of these advanced sensor technologies allows for detailed and reliable mapping, enhancing the robot's navigation through complex urban terrains and ensuring precise targeting of fumigation efforts. In a test case, the robot demonstrated a 62.5% increase in efficiency by significantly reducing chemical usage through targeted hotspot fumigation. By automating the detection and treatment of breeding sites, the proposed method boosts the efficiency and effectiveness of pest management operations and significantly diminishes the health risks associated with chemical exposure for human workers. This approach, featuring real-time object recognition and dynamic adaptation to environmental changes, represents a substantial advancement in urban pest management, offering a safer and more effective solution to a persistent public health issue.  
depth camera; fumigation; LiDAR; robot; sensor fusion; YOLO +
Footnote †: journal: Applied sciences
# A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data
## 1 Introduction
For aerosol properties retrieval, the classifications of the MERSI-II operational cloud mask (MERSI-CLM) cannot meet the requirements, therefore, it is urgent to carry out research on the multi-elemental identification of cloudy, hazy and clear skies based on the MERSI load observation data at present. It has always been a difficult problem to identify haze and clouds using satellite data because haze and clouds have similar optical and micro-physical properties, and it is difficult to distinguish them by using traditional threshold methods. Considering the effectiveness of the business products, the improved algorithm, preferably, does not rely on external data. Therefore, based on the difference of the spectral characteristics of cloud, haze and clear sky pixels, this paper proposes a parameter that is suitable for clear sky pixels on a bright surface according to the characteristics of the MERSI payload instrument. The spectral differences of many wavelengths were combined to construct a fusion identification method. Section 2 introduces the data and the algorithm principle adopted by the algorithm, and the recognition effect and accuracy evaluation of the algorithm is introduced in Section 3.
###### Abstract  
A large number of routing-related surveys are published so far for Wireless Sensor Networks (WSNs) that exhibit either complete or partial emphasis on routing in WSNs. These surveys classify and discuss the relevant routing protocols published mainly in the fields of classical, energy efficient, secure, hierarchical, geographic, intelligent, Quality of Service (QoS)-based and multipath WSNs. However, to the best of our knowledge, no study is presented so far which may clearly categorize the routing-related survey literature for WSNs.To fill this gap, an effort is made in this paper for presenting an _in-depth_ review of already published routing-related survey literature in WSNs. Our review initially proposes a generalized survey design model and afterwards analyzes the routing-related survey literature in the light of the devised _General Survey Design Framework (GSDF)_. Such an analysis describes the design soundness of the published routing-related surveys. Therefore, our review puts forth an original classification based on the _frequency-of-survey-publication_ and taxonomizes the corresponding routing-related fields into high, medium and low focused areas of survey publication in WSNs. Furthermore, the surveys belonging to each main category are sub-categorized into various sub-classes and briefly discussed according to their design characteristics. On the one hand, this review is useful for beginners who may easily explore the already published routing-related survey literature in WSNs in a single document and investigate it by spending less effort. On the other hand, it is useful for expert researchers who may explore the trends and frequency of writing surveys in different areas of routing in WSNs. The experts may explore those areas of routing which are either neglected or least focused or lack in design soundness as per general survey design framework. In the end, insights and future research directions are outlined and a reasonable conclusion is put forth which may outline guiding principles for routing-related survey research in future.  
general survey design framework; big data analysis & issues; WSNs; hierarchical; energy-efficient; multipath; computationally-intelligent; secure; QoS-based and geographic +
Footnote †: journal: Sensors  
_Sensors_
## 5 Discussion of the Integration Approach
### Generalizability of the Overall Approach
In summary, the data types for earth observation, DUL and the SSN ontology can be directly reused in all adoptions of the integration approach. Beyond that, all additionally required data types (mainly for representing system components as observing agents) can be constructed by following a pattern that is similar to the specific forest fire constructs introduced above (Section 4.2).The same holds for the instantiations of the DUL and SSN reference frame. In this sense, the proposed solution is highly re-usable and work required for adoption is kept to a minimum. Further examples could be added following the same pattern. For additional evaluations, extra cases might be integrated from more diverse fields, such as urban planning or 3D cadastre.
# Trace Elements and Mineralogy of Upper Permian (Zechstein)
## 6 Results  
### Mineral Composition of Potash-Bearing Deposits  
The mineral composition of the studied potash-bearing Zechstein deposits was defined using data of XRD analyses (55 rock samples) and the results of petrographic thin section studies (37 new rock samples and data from archive six thin sections) supplemented with SEM microprobe analyses of sulphates in selected samples (four analyses [31]). Thirty distinguished individual minerals and three mineral groups (chlorites, carbonates, and clay minerals) and their occurrence frequency (number/percent of detections of each mineral in the whole number of studied petrographic, XRD, and SEM analyses) are listed in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mineral Chemical Formula**} & \multicolumn{3}{c}{**Number (Percent) of Analyses with Detected Mineral**} & \multirow{2}{*}{**Sum (Percent) of Analyses with Detected Mineral**} \\ \cline{2-2}  & \multicolumn{2}{c}{**Cyclothem and Lithostratigraphic Unit**} & & \multicolumn{1}{c}{**Analyses with Detected Mineral**} \\ \cline{2-2}  & \multicolumn{2}{c}{**PZ1,**} & **PZ2,** & **PZ3,** & **Detected Mineral** \\  & \multicolumn{2}{c}{**Polyhalites within A1d**} & \multicolumn{1}{c}{**Na2+K2 and K2**} & \multicolumn{1}{c}{**Na3+K3 and K3**} & \\ \hline \begin{tabular}{c} **\({}^{**}\)** anhydride \\ CaSO\({}_{4}\) \\ \end{tabular} & 9 (42.85) & 28 (10.85) & 38 (14.96) & 75 (14.07) \\ \hline \begin{tabular}{c} \({}^{*}\) berlinite \\ AlPO\({}_{4}\) \\ \end{tabular} & - & - & 1 (0.39) & 1 (0.18) \\ \hline \begin{tabular}{c} **\({}^{**}\)** bischofite \\ MgCl\({}_{2}\)6H\({}_{2}\)O \\ \end{tabular} & - & 3 (1.16) & 3 (1.18) & 6 (1.12) \\ \hline \begin{tabular}{c} **\({}^{**}\)** bloedite \\ Na\({}_{2}\)SO\({}_{4}\)MgSO\({}_{4}\)5H\({}_{2}\)O \\ \end{tabular} & - & 3 (1.16) & - & 3 (0.56) \\ \hline \begin{tabular}{c} **\({}^{**}\)** calcite \\ CaCO\({}_{3}\) \\ \end{tabular} & - & 2 (0.77) & 4 (1.57) & 6 (1.12) \\ \hline \begin{tabular}{c} **\({}^{**}\)** carnalite \\ KCl\({}_{2}\)6H\({}_{2}\)O \\ \end{tabular} & - & 7 (2.71) & 26 (10.23) & 33 (6.19) \\ \hline \begin{tabular}{c} \({}^{*}\)** crossedite \\ Fe\({}^{2+}\)2\({}_{2}\)Fe\({}^{3+}\)((Si,Fe\({}^{3+}\))\({}_{2}\)O\({}_{5}\))(OH)\({}_{4}\) \\ \end{tabular} & - & 5 (1.93) & - & 5 (0.93) \\ \hline \begin{tabular}{c} **\({}^{**}\) gypsum \\ CaSO\({}_{4}\)2H\({}_{2}\)O \\ \end{tabular} & 1 (4.76) & 3 (1.16) & - & 4 (0.75) \\ \hline \
\begin{tabular}{c} **\({}^{**}\) halite \\ NaCl \\ \end{tabular} & - & 47 (18.21) & 49 (19.29) & 96 (18.01) \\ \hline
\begin{tabular}{c} **\({}^{**}\) hexahydrite (sakite) \\ MgSO\({}_{4}\)6H\({}_{2}\)O \\ \end{tabular} & - & - & 1 (0.39) & 1 (0.18) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Frequency of detected main minerals in the Upper Permian (Zechstein) potash-bearing deposits of Poland (PZ1, PZ2 and PZ3 cyclothems).
# Use of Bilberry and Blackcurrant Pomace Powders as Functional Ingredients in Cookies
## 2 Materials and Methods
### Preparation of Cookies  
The recipe of the control cookies (labeled CC) contained 500 g white wheat flour, 187 g sugar, 218 g vegetable fat, 4 g baking powder, 1 g sodium chloride, and 156 mL water. In the supplemented cookies, 2.5%, 5%, and 10% of the wheat flour was replaced with bilberry (labeled as CBI2.5, CBI5.0, and CBI10.0) and blackcurrant (labeled as CBC2.5, CBC5.0, and CBC10.0) pomace powder, respectively, while keeping all the other ingredients constant. The replacement levels were chosen based on previous works [19; 27] and preliminary tests. Cookie dough was manufactured in a planetary mixer (Rohnson RS86, 700 W, Praha, Czech Republic). Fat and sugar were thoroughly creamed with the flat beater for 12 min at medium speed; then, water containing the sodium chloride and baking powder was added and mixed for 6 min at high speed to obtain a homogenous cream. Finally, the wheat flour was gradually added to the cream mass under continuous mixing and further kneaded for 20 min. The dough was sheeted to a thickness of 6.13 mm by using an automatic dough sheeter ([PERSON], Waico Group, Isola Vicentina, Italy). Round-shaped cookies were cut from the dough sheet using a cookie cutter of 5 cm in diameter. The cookies were transferred to a baking tray and baked at 200 \({}^{\circ}\)C for 10 min in an electrically heated deck oven (MIWE Condo, Arnstein, Germany). After baking, the cookies were left to cool on racks to room temperature (20 \({}^{\circ}\)C) and packed in airtight Ziploc bags for further analysis. Cookies from each formulation were manufactured in two independent batches. The determinations of the color parameters, proximate composition, and physical, textural, and sensory properties of the cookies were carried out 24 h after baking.  
Proximate Composition and Physicochemical Properties of Bilberry (BIPP) and Blackcurrant (BCPP) Pomace Powders  
The proximate composition of BIPP and BCPP (dry matter, protein, fat, fiber, and ash content) was determined following standard methods [60].  
The water solubility index (WSI) of the pomace powders was determined according to the method provided by AACC [61] and was calculated as follows:  
WSI (%) = (Weight of dry solids in supernatant/Weight of dry sample) \(\times\) 100  
The water absorption capacity (WAC) was determined following the method described by [PERSON] et al. [2]. Briefly, the mixture of 1 g of the sample and 10 mL of distilled water was kept for 30 min at ambient temperature (20 \({}^{\circ}\)C) and were then centrifuged for 30 min at 3000 rpm. The water absorption capacity was calculated as follows:  
WAC (%) = (weight of residual/weight of sample) \(\times\) 100
## 3 Results and Discussion  
### Isolation and Identification of a Suitable Urease Active Bacterium  
Although a total of more than 100 bacterial strains were isolated from different soil samples, urease producing bacteria were encountered in only 6 locations. Apparently, the proportion of ureolytic bacteria in each case was smaller than that of non-ureolytic bacteria except for location L2 (Figure 2). As per these results, it is evident that ureolytic bacteria are not ubiquitous in the dynamic coastal regions as much as in the inland environments. Based on the simple urease activity test and the corresponding increase in pH in reaction mixture during first 24 h (data not included), 6 isolates were considered for further analysis. The phylogenetic groupings of the chosen isolates are shown in Table 3.  
Figure 2: Relative abundance of ureolytic bacteria in different sampling locations.  
In terms of OD\({}_{600}\), all the isolates showed a stable growth at 30 \({}^{\circ}\)C. However, the strain belonging to _Sporosarcina_ sp. preferably witnessed a more stable growth over the entire test period (see Supplementary Materials).  
The test tube precipitation test results are a good relative indication of the potential of each strain for sand solidification by MICP [32] as the strength improvement is proportional to the amount of bio-precipitate formed. The results indicated that the strain belonging to _Sporosarcina_ sp. shows the highest performance for microbial induced carbonate precipitation and the weight of precipitate is approximately twice as that from others (Figure 3). The strains belonging to _Sporosarcina_ sp. strain are gram stain positive, rod shaped, motile, aerobic nonpathogenic bacterium which produces active intracellular urease in large quantities [13; 33; 34; 35; 36; 37].  
On the other hand, strains belonging to _Sporosarcina_ sp., specially _Sporosarcina pasteurii_, has been extensively studied for a vast variety of MICP applications and has shown successful results [13; 15; 16; 33; 35; 38; 39; 40; 41; 42]. More interestingly, this specific strain has the ability to tolerate extreme conditions, for example pH levels as high as 9 [43], making it a good candidate for the target application. Considering all these facts, the _Sporosarcina_ sp. strain was identified as the most suitable bacterium, and remaining laboratory investigations were carried out employing this strain.
# Search of the Shortest Path in a Communication Network with Fuzzy Cost Functions
###### Abstract  
A communication network management system takes the measurements of its state variables at specific instants of time, considering them constant in the interval between two consecutive measurements. Nevertheless, this assumption is not true, since these variables evolve in real time. Therefore, uncertainty is inherent in the processing of the measurements during the intervals so that they cannot be efficiently managed using crisp variables. In this paper, we face this problem by modeling the communications network as a type-V fuzzy graph, where both the nodes and the links are described with precision, but the cost of each link is modeled as a triangular fuzzy number. Different fuzzy cost allocation functions and fuzzy optimization strategies are described and applied to the search for the shortest path between two nodes. An experimental study has been conducted using two representative networks: the backbone network of Nippon Telegraph and Telephone Corporation (NTT) and the National Science Foundation's Network (NFSNET). In these networks, our fuzzy cost functions and strategies have been compared with the well-known crisp equivalents. The optimal search strategies are based on the proposed Fuzzy Dijkstra Algorithm (FDA), which is described deeply. The simulation results demonstrate that in all cases the fuzzy alternatives surpass or equal the crisp equivalents with statistically significant values. Specifically, the so-called Strategy 8 presents the best throughput, as it significantly exceeds the performance of all those evaluated, achieving a Global Mean Delivery Rate (GMDR) close to 1.  
routing: type-V fuzzy graph; communication network; Dijkstra algorithm; triangular fuzzy number; shortest path +
Footnote †: journal: Computer Physics Communications  
15 June 2021  
1534. [[https://doi.org/10.3390/sym13081534](https://doi.org/10.3390/sym13081534)]([https://doi.org/10.3390/sym13081534](https://doi.org/10.3390/sym13081534))
## 3 Results  
### Verification of the Implementation of the Homogenization Algorithm  
The homogenization technique used in the study was implemented from scratch according to [PERSON] and [PERSON]'s paper [10] in the Python programming language. The mathematical details of the method were briefly introduced in Section 2.3. Here, the validation analysis was performed based on the numerical example from [10]. The example was computing the \(\mathbf{ABDR}\) matrix for the RVE of corrugated board with saw tooth geometry. The cardboard considered was a single-wall profile with three layers of paper. The axial spacing between the liners (outer layers of the cardboard) was 3.51 mm. The total thickness of the board was 3.8 mm. In the FE input model, the four-node quadrilateral elements with full integration scheme were used; this kind of element is labelled as S4 in Abaqus FEA [25]. These elements are recommended due to their versatility and effectiveness in structural analysis; they accurately model complex geometries and handle stress concentrations. The comparison between the results obtained from this study's implementation and ref. [10] is presented in Table 2. The effective stiffness considered, the value computed by the implemented Python algorithm, the value from [10] and the value of error are presented in the columns of the table, respectively. The error was computed according to the formula presented in Section 2.5.  
\begin{table}
\begin{tabular}{c|c c c} \hline \hline \multirow{2}{*}{**Effective Stiffness**} & \multicolumn{2}{c}{**Implementation of**} & \multirow{2}{*}{**Published in [10]**} & \multirow{2}{*}{**Error**} \\  & **homogenization [10]** & & \\ \hline \(A_{11}\) [kPa \(\cdot\) m] & 2139.7 & 2140.0 & 0.01\% \\ \(A_{22}\) [kPa \(\cdot\) m] & 1664.5 & 1665.0 & 0.03\% \\ \(A_{12}\) [kPa \(\cdot\) m] & 382.9 & 382.9 & 0.00\% \({}^{*}\) \\ \(A_{33}\) [kPa \(\cdot\) m] & 662.5 & 662.5 & 0.00\% \({}^{*}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The stiffnesses of representative volume element of corrugated board with saw tooth geometry computed by numerical homogenization method from Python implementation confronted with data from [PERSON] and Gajewski [10].
## 5 Conclusions  
This paper introduces an upgraded YOLOv8_Adv model aimed at overcoming the difficulties in detecting and recognizing electrical equipment in infrared images. Building on an in-depth analysis of the YOLOv8 model, comprehensive optimizations were applied to its structure by incorporating the FasterNet module with PConv into the backbone, integrating the GSConv and VoVGSCSP modules in the neck, and adding the EMA attention mechanism. Furthermore, a specific detection layer designed for small objects was integrated into the head to boost the model's efficiency in identifying electrical equipment in infrared images. These improvements significantly enhance the model's lightweight nature and detection accuracy. The findings indicate that the detection precision across seven categories of electrical equipment rose from 94.8% to 98.7%, accompanied by an 18.5% reduction in GFLOPs, effectively fulfilling the demands of substation equipment monitoring using infrared imagery. Future research will focus on processing infrared video data of electrical equipment and deploying the improved model on edge computing platforms for drones, enabling practical applications on mobile devices. However, challenges persist in scenarios such as defect detection, continuous 24 h monitoring, and temperature-based detection using infrared temperature data. Future work will focus on the processing of infrared image data of electrical equipment and deploying the enhanced model on edge computing platforms for drones, facilitating its practical application on mobile devices.  
Conceptualization, H.T. and Z.W.; methodology, H.T.; validation, A.P. and Z.W.; resources, Z.W.; visualization, A.P.; writing--original draft preparation, H.T. and A.P.; writing--review and editing, A.P. and Z.W.; supervision, Z.W.; project administration, Z.W.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the Key Foundation of Zhejiang Provincial Natural Science of China under Grant No. LZ22F010005.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Layers** & **Parameters** & **FPS** & **GFLOPs/G** \\ \hline YOLOv8n & 207 & 2,921,964 & 175.4 & 12.4 \\ YOLOv8\_FasterNet & 218 & 1,717,396 & 137.0 & 9.2 \\ YOLOv8\_Gsconv & 231 & 2,638,140 & 192.3 & 11.6 \\ YOLOv8\_fastC2f & 225 & 2,560,764 & 181.8 & 11.1 \\ YOLOv8\_Biformer & 215 & 2,932,140 & 172.4 & 12.7 \\ YOLOv8\_Adv & 344 & 2,341,644 & 163.9 & 10.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of computational efficiency across different models.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results and Discussion
### HLD Equation of the SDHS-AAE(6 EO4 PO) Systems  
The HLD equation of the binary anionic-nonionic mixture can be completed by the HLD equations of SDHS and AAE(6 EO4 PO). As noted, the mixed system of this study was conducted at reference temperature (25 \(\pm\) 2 \({}^{\circ}\)C) (T \(-\) T\({}_{\rm ref}\) = 0) with no cosurfactant addition (f(A) = \(\varnothing\)(A) = 0). The constants of the HLDSDHS were already known, as mentioned in the method part of this study (K\({}_{\rm i}\) = 0.13 and \(\sigma\) = \(-\)1.13). The constants of the HLD\({}_{\rm AAE(6 EO4 PO)}\) were also determined and are shown in Equation (8). The \(\frac{\rm{G_{FX}}}{\rm{RT}}\) values at the optimal condition were calculated by Equation (3) and Table 2. The \(\frac{\rm{G_{FX}}}{\rm{RT}}\) results were fitted with the polynomial trendlines (2 nd order) with R\({}^{2}\) = 0.71-0.92 as presented in Figure 6. All  
Figure 5: Optimal salt concentrations (S*) from the HLD prediction and the experiment of AAE(6 EO4 PO) through Excel Solver.  
the \(\frac{G_{\rm grav}}{\rm RT}\) values were in the positive region. Thus, the actual SDHS-AAE(6 EO4 PO) mixed system was more hydrophobic than the ideal mixture. The \(\frac{G_{\rm grav}}{\rm RT}\) values of all the model oils, except for the n-pentane, tended to increase when the mole fractions of the nonionic surfactant increased. At the same anionic-nonionic mixing ratio, the \(\frac{G_{\rm grav}}{\rm RT}\) value tended to increase with an increase in the hydrophobicity of the oil. However, for the n-pentane, the \(\frac{G_{\rm grav}}{\rm RT}\) value initially increased along with an increase of AAE(6 EO4 PO), and then suddenly decreased at the 5:5 ratio.  
According to the Margules equation (Equation (4)), the thermodynamic constants (A\({}_{1}\) and A\({}_{2}\)) could be determined. [PERSON] and [PERSON] (2009) mentioned that the A\({}_{1}\) value is zero for systems that have a controlled temperature of 25 \({}^{\circ}\)C [42]. Thus, the A\({}_{2}\) values could be calculated by \(\frac{G_{\rm grav}}{\rm RT}\) and the molar fraction (A\({}_{2}=\frac{G_{\rm grav}}{\rm RT}\)). The average A\({}_{2}\) value of all the model oils and all the anionic-nonionic ratios was 2.53 \(\pm\) 1.17. The HLD equation of SDHS-AAE(6 EO4 PO) at 25 \(\pm\) 2 \({}^{\circ}\)C, which, to our knowledge, is being proposed for the first time, would be as follows:  
\[\begin{split}&\rm HLD_{SDHS-AAE(6 EO4 PO)}\\ &=\ X_{\rm SDHS}\{\ln(S)-0.13(EACN)-1.13\}+\ X_{\rm AAE(6 EO4 PO)} \{(0.12)S-(0.15)(EACN)-0.46\}\\ &+2.53X_{\rm SDHS}^{2}X_{\rm AAE(6 EO4 PO)}\end{split} \tag{9}\]