esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Environment
### Cloud Environment for Bee Monitoring  
The main data center for collecting data, geo-wide monitoring of many apiaries, and notifying beekeepers is located in the Amazon Web Services (AWS) cloud. Communication between the monitoring IoT device and the data center occurs mainly when the varroosis is detected and performed using the MQTT protocol. The data center acquires MQTT messages from IoT devices, saves the information in the database, and finally informs the end user (a beekeeper) with an email message that a bee infected with varroa virus was detected.  
To perform these tasks, the cloud-based data center consists of several modules. First, the MQTT messages from monitoring IoT devices are received by the AWS IoT Core. The AW IoT Core plays the role of the Cloud gateway. It enables the management of the information sent in the MQTT messages. The received data is stored in the AWS DynamoDB database. This is a key-value store that provides flexibility to changes in the data structure. It stores the data such as the timestamp of the message, the health state of the bee (healthy or sick), and the photo of a bee in the _Base64_ binary format. Finally, if an infected bee is detected, the serverless AWS Lambda service launches a code responsible for sending an email message to the user with the AWS Simple Notification Service (AWS SNS). The whole process is presented in Figure 3.  
Importantly, the detection of varroosis is performed at the Edge, at the TPU-accelerated IoT device. The device-to-cloud data transmission is initiated only in case of detecting _Varroa destructor_ mites on bees. In such a way, the data transmission is limited to necessary cases--when there is a necessity to notify the beekeeper. The image data are transmitted together with the alert message to be stored in the global repository to constitute a reservoir of images of infected bees. These images supply the training phase and updates in the machine learning models that detect _Varroa destructor_ infections. We train the ML models using the Google AI of the Google Cloud Platform, which is compatible with the Google  
Figure 2: A software organisation for the _Varroa destructor_ detection performed on the Edge IoT device.  
Coral Accelerator we use for the detection. The accelerator integrates well with its native Cloud environment.  
The trained ML model for varroosis detection is then deployed to the Google Coral Accelerator. This is performed periodically when the detection model is updated to include new cases.
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### Similarity and Distance Measure
#### 3.4.2 Jaccard Coefficient  
The Tanimoto coefficient or Jaccard coefficient is a common statistical coefficient found in NLP [144]. The Jaccard coefficient is a measurement unit that determines how similar two items are by dividing the intersection of the objects by their union. The Jaccard coefficient is applied to the text document to compare the sum of the weight of terms found in either of the two documents and the total weight of shared words, but they must not be shared terms. Equation (27) is a presentation of the mathematically correct definition of the Jaccard coefficient:  
\[\textit{Jaccard}\left(\overrightarrow{t_{a}},\overrightarrow{t_{b}}\right)= \frac{\overrightarrow{t_{a}}\overrightarrow{t_{b}}}{\left| \overrightarrow{t_{a}}\right|^{2}+\left|\overrightarrow{t_{b}}\right|^{2}- \overrightarrow{t_{a}}\overrightarrow{t_{b}}} \tag{27}\]  
The Jaccard coefficient is a measure of similarity with a range of [0, 1]; when \(\overrightarrow{t_{a}}\) and \(\overrightarrow{t_{b}}\) are mutually exclusive, the coefficient is 0, and when they are equivalent, it is 1.
## 4 Protocol Design
### Discovery and Forwarding Phases  
Discovery phase is necessary for proper decision of the next forwarder of data. Network discovery is initialized by the sink node, which periodically sends the information about its temperature \(\phi_{SINK}\), which is the maximum temperature and it is always constant. All neighboring sensor nodes calculate (based on the previously described Algorithm 1) own temperature and send this information to their direct neighbors. By this periodical one hop beacon exchange, the overall network is kept in the actual state. Every sensor node keeps small routing table of its neighbors with their temperature values.  
Forwarding phase of temperature transfer based network is very trivial. From the previous phase, where every node calculated own temperature and created table of the neighbors, sensor node knows which neighbor is having the highest temperature. That neighbor is used for data forwarding. If the node at the first place in the routing table is disconnected, the table of neighbors is updated, the own temperature is recalculated and the warmest neighbor in the refreshed table is used for forwarding.  
However, in order to avoid loops, only nodes with higher temperatures than is the temperature of the actual are used for forwarding.
## 2 Types of Integrated Microbial Fuel Cell System
### Integrated MFC-Capacitive Deionization (MFC-CDI) System  
Several technologies were integrated with MFC to attain maximum benefit in the form of improved wastewater effluent and energy production. Capacitive deionization (CDI) was one of the recent technologies assimilated with MFC through diverse assemblies.  
The integrated MFC-CDI system was tested to associate deionization through energy production and sewage water treatment [24]. CDI is a potential deionization alternative that can be used to remove salt from an aqueous solution to increase freshwater sources [23].  
This process depends on the electrosorption that accompanies the charge separation for accumulation and releases considerable quantities of ions with the help of porous carbon electrodes. The mechanism starts with the electrostatic segregation of the ions from the water. This is followed by the adsorption of the ions in the electrode-solution interface. The initiation of the procedure requires a potential gradient (usually less than 1.2 V) to be applied among the electrodes. As the potential difference required is very low, continuous flow MFCs were employed to improve the function of the CDI for the elimination of electrolytes. This integration of MFC-CDI can be used in the post-treatment of the effluent containing ionic pollutants such as PO4\({}^{3-}\) and NO3\({}^{-}\). Here, the MFC is used as a secondary treatment for NH\({}_{4}^{+}\) and the removal of organic carbon, along with electricity generation, whereas CDI is used to desalinate and decontaminate wastewater effluent [25], as illustrated in Figure 2.
# Impact of Peri-Implant Inflammation on Metabolic Syndrome Factors: A Systematic Review
## 4 Discussion
identified significantly higher total cholesterol and LDL cholesterol levels in peri-implantitis patients compared with peri-implant health group, whereas in [PERSON]'s study, significant differences were found in triglycerides instead of total cholesterol and LDL. Notably, the two studies both had limited sample sizes, and both did not exclude periodontitis or smoking habits as confounding factors. Thus, further research with a larger sample size and greater exclusion of potential confounding factors is required to fully confirm the relationship between peri-implantitis and dyslipidemia. Due to their cross-sectional design, the results of the two studies were also influenced by inter-individual differences. To overcome this shortage, cohort studies are needed to track the prognosis of peri-implantitis and meanwhile monitor the changes in the lipid profile. In [PERSON]'s study [49], BMI was recorded in the clinical and demographic characteristics of the patients, and it was not significantly different among peri-implantitis, peri-implant mucositis, and peri-implant health groups. It was the same in the study conducted by [PERSON] [50]. One possible reason is that the severity of obesity is affected by various factors. The indirect effects from the elevated inflammatory burden caused by peri-implantitis were covered by other more direct effects including genetic or dietary factors. This shortage can also be overcome by cohort design, where individuals are studied case-wise. Another limitation of this systematic review is that the diagnostic criteria for peri-implantitis followed by the included studies were not consistent. According to the 2017 World Workshop on the Classification of Periodontal and Peri-implant Diseases [61], radiographic bone loss of \(\geq\)3 mm in combination with BOP and probing depth \(\geq\)6 mm is indicative of peri-implantitis. Two of the included studies [12; 45] followed this criterion while the others [44; 46; 47] had different definitions on the presence of peri-implantitis. This might hamper the generalizability of our findings.
## 9 Future Directions and Research Opportunities
### Enhancing Scalability and Flexibility  
The wider adoption of autonomous maritime systems depends on the scalability and flexibility of their AI architectures. To serve a global and heterogeneous fleet, AI systems must adapt to vessels of various sizes and types, from container ships to offshore support vessels [158]. This requires generalizable models that can tailor their behavior to different propulsion systems, sensor configurations, and mission profiles.  
Figure 7: Future directions and research opportunities in autonomous maritime AI (own elaboration).  
Scalability also extends to operational contexts. Autonomous systems must function effectively under diverse maritime conditions, including changes in weather, visibility, sea state, and traffic density. AI agents need adaptive capabilities to modify their decision-making strategies in real time, maintaining safe and efficient performance under fluctuating scenarios [158].  
Distributed AI architectures offer a solution by enabling knowledge-sharing across multiple vessels. Such collaborative learning allows fleets to benefit from each other's experiences, improving situational awareness and operational consistency. At the same time, modular AI components can be integrated or upgraded independently, allowing vessels to evolve without requiring complete system overhauls. This modularity is essential for long-term sustainability and technological responsiveness.
## 2 Experimental Principles and Method  
### Quartz and Sample Preparation  
In order to simulate different porous media of various formations, quartz sand samples were used as formation simulation material. Fine quartz sand samples were selected in order to ensure differentiation of grain size [29; 30]. This experiment used custom-made screens with different apertures to screen the quartz sand samples, which makes this type of research more suitable for use in field engineering. The quartz sand samples were divided into 8 grain sizes of 0.1-0.2 mm, 0.2-0.5 mm, 0.5-1 mm, 1-2 mm, 2-3 mm, 3-4 mm, 4-6 mm and 6-8 mm.  
Sieved quartz sand samples could still have some impurities, and in order to avoid clay or other impurities having a significant impact on the induced polarization effect, the sieved sand samples were cleaned, dried and then had water poured over their top until saturation before testing. Water samples from a tunnel project were utilized to simulate the actual engineering conditions. The quartz sand samples, after screening and cleaning, are shown in Figure 1. At the same time, in order to reduce the influence of temperature on the induced polarization effect, the measurements were to be carried out in an environment with small temperature changes.
# Experimental PIV Radial Splitting Study on Expansive Soil during the Drying Process
## 1 Introduction
[27; 28] developed a foundation-bearing capacity model test system based on PIV technology and measured non-interference relative displacement on a test model constructed using transparent soil. [PERSON] et al. [29] conducted triaxial compression, consolidation, and permeability tests on prepared transparent soil. [PERSON] et al. [30] analyzed tunnel-induced ground motion using a transparent soil model and PIV technology. [PERSON] et al. [31] used PIV technology and a model test to analyze the influence of the slope hydraulic stress state on gravity creep deformation. [PERSON] et al. [32] used PIV technology to track the hydrodynamic behavior of fine-grained sediments with different hydraulic characteristics. [PERSON] et al. [33] derived the displacement field using PIV technology to study soil deformation during the process of casing jacking and measured the soil deformation caused by the belled pile. [PERSON] et al. [34] investigated the penetration characteristics of piles in sandy soils based on the PIV technique. [PERSON] et al. [35] used a PIV device to divide the radial splitting experiment process into four stages; the feasibility of the test device was demonstrated, but the object of study was remolded expansive soil, which did not involve undisturbed expansive soil. Accordingly, in this study, Nanyang expansive soil was selected as the research object. We conducted a series of radial splitting tests on undisturbed and remolded expansive soil specimens during the drying process using the PIV test system and a self-developed splitting test apparatus to further examine the tensile strength change pattern and the crack development pattern of expansive soils during the drying process and their intrinsic mechanism. Our goal was to provide a scientific basis for the construction of geotechnical engineering related to expansive soil.
## 3 Results
### Needle Pose Estimation Accuracy  
This test involved detecting two needles sized 120 mm and 160 mm standing vertically in front of the camera and rotated 15\({}^{\circ}\) around needle axis at four different distances from the headset. Subsequently, the displacement of both the base point at the grabber and the tip of the needle from their algorithmically determined positions was measured using a caliper. The results are presented in Tables 2 and 3. It is noticeable that the offset from the physical needle tip increases significantly in the case with the needle oriented frontly, as the needle is too close or too far from the camera. When rotating the needle horizontally, the accuracy increases at shorter ranges but decreases at greater distances. A higher needle tip error in comparison to the base error results when determining its position based on the grabber part of the tool, which is a needle's length away.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Distance from**} & \multicolumn{2}{c}{**Needle 120 mm**} & \multicolumn{2}{c}{**Needle 160 mm**} \\ \cline{2-5}  & **Needle Base** & **Needle Tip** & **Needle Base** & **Needle Tip** \\ \multicolumn{5}{c}{**HMD [cm]**} \\ \multicolumn{5}{c}{**Error [mm]**} \\ \hline
30 & 1.53 \(\pm\) 0.70 & 3.25 \(\pm\) 1.59 & 0.89 \(\pm\) 0.49 & 3.25 \(\pm\) 1.43 \\
40 & 1.47 \(\pm\) 0.50 & 1.61 \(\pm\) 0.95 & 1.19 \(\pm\) 0.46 & 2.52 \(\pm\) 1.04 \\
50 & 1.16 \(\pm\) 1.00 & 2.24 \(\pm\) 1.06 & 0.87 \(\pm\) 0.23 & 2.60 \(\pm\) 1.38 \\
60 & 2.68 \(\pm\) 1.18 & 2.36 \(\pm\) 1.37 & 2.80 \(\pm\) 0.43 & 3.20 \(\pm\) 1.63 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the needle pose estimation test when the tool is placed in front of the camera. The error is provided by the mean value \(\pm\) standard deviation of ten measurements.  
Figure 7: Needle tracking interface on HMD.
# Bench Test and Analysis of Cleaning Parameter Optimization of 4 L-2.5 Wheat Combine Harvester
## 3 Results
### Optimization and Verification of Cleaning Parameters  
#### 3.3.1 Optimization of Cleaning Parameters  
It is desirable that the cleaning loss and impurity rates are as low as possible and the operation speed is maximal to achieve the optimum cleaning operation level and the maximum feed quantity. According to the response effect analysis of each parameter relative to cleaning loss rate, it can be seen that to ensure the minimum cleaning loss rate, it is necessary to ensure the maximum opening of the chaffer and the minimum throttle opening, fan speed, operation speed, and crank speed of a shale shaker. To minimize the impurity rate, it is necessary use the minimum operation speed and opening of chaffer and the maximum throttle opening, fan speed and crank speed. To ensure the collection efficiency and undertake an effective cleaning operation of a wheat machine, the feed quantity and the operation speed are required to be at the maximum values. To predict the optimum combination of the cleaning parameters when the cleaning loss and impurity rates were the smallest and the feed quantity was the largest, it was necessary to perform an optimization analysis of five cleaning parameters. To this end, the following model was established:  
\[\left\{\begin{array}{l}\text{maxA}\\ \text{minS}\\ \text{minZ}\\ -1\leq\text{A},\text{B},\text{C},\text{D},\text{E}\leq 1\end{array}\right. \tag{9}\]  
Figure 6: Response surface analysis of each parameter on impurity rate.  
By using the Design Expert software to optimize the cleaning parameters, it was concluded that the optimum combination of parameters was as follows: operation speed--2.2 m/s; opening of chaffer--26 mm; throttle opening--20\({}^{\circ}\), fan speed--1099.8 r/min; and crank speed of shale shaker--350 r/min. By applying these settings, a cleaning loss rate of 1.5% and an impurity rate of 1.9% were obtained.
# The Effect of Land Cover on the Nectar Collection by Honeybee Colonies in Urban and Rural Areas
## 2 Materials and Methods
### Statistical Methods  
The following variables were analysed to ascertain the differences between location types (urban vs. rural): annual hive weight gain, annual hive weight loss, and the number of forage days. Student's t-test was utilised for this purpose. Seasonal variation in nectar collection (i.e., monthly hive weight gains) was analysed using the non-parametric Kruskal-Wallis test separately for urban and rural apiaries. The medians were then compared across April, May, June, July, and August, where the Kruskal-Wallis test indicated significant differences; post hoc pairwise comparisons were conducted using the Mann-Whitney U test with Bonferroni correction. The Mann-Whitney U test was also used to assess differences in the percentage share of individual land cover classes within a 3 km radius around urban and rural apiaries. The proportion of each land cover class in the areas around the hives (1.5 km vs. 3 km) was compared using a chi-squared test (\(\chi^{2}\)) based on contingency tables.  
To explore the relationship between landscape structure and nectar foraging efficiency, a multivariate and rank-based approach was applied. [PERSON]'s tau coefficient (\(\tau\)) was calculated between annual hive weight gain and the percentage cover of each land cover type within the surrounding landscape. Additionally, pairwise correlations were computed between all landscape variables to assess co-occurrence patterns.  
A principal component analysis (PCA) was performed to reduce multicollinearity among landscape variables. The analysis was based on a data matrix consisting of 10 apiaries \(\times\) 8 variables (i.e., the percentage share of each of the 8 land cover classes). Principal components were then extracted and interpreted based on their variable loadings. The coordinates of the apiaries on the first two components (PC1 and PC2) were then utilised for-apiary site clustering. Two clustering methods were evaluated: non-hierarchical k-means clustering and DBSCAN (Density-Based Spatial Clustering), with the latter adapted for small sample sizes. The objective of this analysis was to ascertain whether apiaries naturally grouped according to similar landscape compositions. To this end, differences in nectar yield between the identified clusters were subjected to one-way ANOVA. All analyses and graphical interpretations were performed using Statistica 13 (Informer Technologies Inc., Los Angeles, CA, USA) [41].
# Additive Manufacturing of Variable Density Lenses for Radio Frequency Communications in X-Band
## 2 Dielectric Permittivity Measurements
### Materials
#### 2.2.3 Stochastic and Regular Meshing  
Once the solid polymers were characterized, the complex permittivity of the same materials but with different densities was measured. The objective was to have graphs for different materials and frequencies relating density to dielectric properties. Since these properties seem to be quite constant in the frequency range, only the measurements for 2.45 and 24.5 GHz were performed. Furthermore, not all the materials were measured at this stage; since Dental and Though 200 seem to have similar properties, only Dental was used. Composite X and Rigid 10K presented different dielectric constants, so both were measured. Similarly, the measurements were also performed for PA11 and UPP.  
The samples (cylinder and cuboid) were manufactured using porous lattices. These structures are widely used in various fields, including materials science and engineering. They can be broadly categorized into two types, both used in this work: regular lattices (gyroid mesh) and stochastic lattices (Voronoi mesh). The printing densities were 25, 50 and 75% (100% is the solid sample), as it is shown in Figure 5. To design the gyroid mesh, the Flattpack v.2025 software was used, which is a freely distributed library implemented in Matlab, developed within the framework of the University of Nottingham, and allows for the processing of geometrical objects by directly uploading the models to the program and processing them to obtain porous structures. To design the Voronoi mesh, the Grasshopper 3D tool from Rhinoceros 3D version 8 commercial CAD software was used.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**f = 2.45 GHz**} & \multicolumn{3}{c}{**f = 6.25 GHz**} & \multicolumn{3}{c}{**f = 24.5 GHz**} \\ \cline{3-8}
**Resin** & **Dens. (g/cm\({}^{3}\))** & \(\mathbf{e_{r}}\) & \(\mathbf{\tan(\delta)}\) & \(\mathbf{e_{r}}\) & \(\mathbf{\tan(\delta)}\) & \(\mathbf{e_{r}}\) & \(\mathbf{\tan(\delta)}\) \\ \hline CX & 1.52 & 3.54 & 0.15131 & 3.67 & 0.19420 & 3.47 & 0.02900 \\ Dental & 1.30 & 2.84 & 0.05549 & 2.83 & 0.05006 & 2.94 & 0.06000 \\ Tough & 1.11 & 2.89 & 0.03341 & 2.95 & 0.03745 & - & - \\ Ultrasint PP & 0.89 & 2.19 & 0.03320 & 2.19 & 0.03584 & 2.11 & 0.02570 \\ Rigid 10K & 1.63 & 3.23 & 0.03479 & 3.32 & 0.03489 & 3.17 & 0.02000 \\ PA11 & 1.01 & 2.72 & 0.05020 & - & - & 2.63 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Solid sample measurements.  
Figure 5: Cylindrical Rigid 10K samples: (**a**) gyroid mesh; (**b**) Voronoi mesh.
## 3 Results and Discussion
### Thermal Control Property
From the figure, the NC and CC have higher emissivity values than some forms of PEO and anodizing coatings applied to aluminum alloys [14,43], ranging from 0.863 to 0.870 and 0.875 to 0.883, respectively, at different temperatures. According to [PERSON]'s law, for an opaque object under thermal equilibrium conditions: \(\varepsilon=\alpha=1-\rho\) (\(\alpha\) and \(\rho\) are the absorption rate and reflectance of the sample with respect to the projected energy, respectively) [44]. Therefore, the emissivity of the NC is reduced due to its high-reflectance property. In addition, due to the excellent thermal stability of alumina, the plasma-sprayed alumina coating's emissivity is less affected by thermal effects, and the variation is less than 0.01 in the range of 300-393 K.