esa-sceva/satcom-chunk-collection huggingface dataset:
# Object Semantic Grid Mapping with 2D LiDAR and RGB-D Camera for Domestic Robot Navigation
## 6 Conclusions  
In this paper, we presented an object semantic grid mapping system for domestic robot navigation. We used the Cartographer to build an occupied grid map and Mask R-CNN to detect the objects, including beds, couches, sinks, ovens, microwaves, and toilets. These objects are always static and useful to compute room concepts. To generate the point cloud more accurately, we employed joint interpolation to refine the camera poses. We utilized the statistical filter to remove some outliers and the Euclidean cluster segmentation to get object instances. Furthermore, the maximum range of the depth image and geometric constraints, such as the minimum number of points of the object instances and the height of objects' points, were used to refine the object point clouds. We proposed a dominant direction detection method for rectangle layout environments to generate the object-oriented minimum bounding rectangles of the object instances. We built the object goal space to help the robot select goals conveniently and socially. We used the precision and recall of object instances as the evaluation standard and employed the Robot@Home dataset to verify the system. Experimental results show that the average precision of our system is 68.42%, and average recall is 65%. In our future work, we will improve the system and build semantic topological and concept maps to finally generate a multi-layer map that contains a semantic grid layer, a semantic topological layer, and a semantic concept layer for domestic robot navigation.  
Conceptualization, X.Q. and W.W.; methodology, X.Q.; software, X.Q., Z.L., X.Z., and D.Y.; validation, X.Q., Z.L., X.Z., and D.Y.; formal analysis, X.Q.; investigation, X.Q.; resources, W.W.; data curation, X.Q.; writing--original draft preparation, X.Q.; writing--review and editing, W.W., Z.L., X.Z., and D.Y.; visualization, X.Q.; supervision, W.W. and R.W.; project administration, X.Q.; and funding acquisition, W.W. and R.W. 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 (No. 91748101).  
We thank [PERSON] for reviewing and editing the English.  
The authors declare no conflict of interest.
## 1 Introduction
Innovative architecture: development of a new WSN architecture that expands network coverage through data mules communicating via DTN, while reducing energy consumption at terminal nodes by utilizing an optimized BLE protocol. 2. Real-world validation: practical implementation and extensive testing in a real-world scenario in the Amazon, demonstrating the viability and efficiency of the \"FloatingBlue\" architecture in adverse environments. 3. Energy consumption optimization: development of optimized firmware for BLE devices, resulting in significantly lower energy consumption than traditional approaches, which is crucial for extending the lifespan of sensors in remote areas. The remainder of this paper is organized as follows: In the next section, we provide an overview of related studies on this topic. Our proposed \"FloatingBlue\" architecture is presented in Section 3. Then, in Section 4, we provide implementation details, including hardware and software. Section 5 presents the different experimental results obtained regarding energy consumption, functional experiments and communication experiments, including discussion. Finally, Section 6 presents the main findings of our work, along with future research lines.
# Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images
## 2 Materials and Methods
### Index for the Quality Evaluation of Sharpened Images
#### 2.5.4 Spatial Correlation Coefficient (sCC)  
This study also used the sCC index, which evaluates the similarity of spatial information between panchromatic and sharpened images. The sCC applies the Laplacian filter to two images and calculates the correlation between the results. Values approaching one indicate greater similarity in the spatial information of the two images [29].
## 3 Results
### Geodetector of GPP Spatial Distribution
#### 3.3.1 Factor Detector  
Five natural and human factors, including land surface temperature, precipitation, downward surface shortwave radiation, nighttime light data, and land use type data, were calculated using Geodetectors. Taking 2020 as an example, the results in Table 2 show the explanatory power of each factor on the spatial differentiation of GPP, ranked as follows: land use type (0.336) > temperature (0.325) > night light data (0.236) > precipitation (0.092) > downward surface shortwave radiation (0.032). It can be seen that for the spatial distribution of GPP in the growing season, land use type has a great influence, and nighttime light data show that human activities also have great influence on the distribution of GPP. Among the natural factors, surface temperature is the dominant factor, and precipitation and downward surface shortwave radiation have less influence, which may be related to the uniform distribution of precipitation and downward surface shortwave radiation in the whole research area. Throughout the time period, human activity was more influential than nature in the study area.  
Figure 7: The spatial distribution of all factors in 2020. ((**a**) is land surface temperature, (**b**) is precipitation, (**c**) is downward surface shortwave radiation, (**d**) is land use and land cover, (**e**) is night light data).  
Figure 8 shows that land surface temperature and land use type have always been the leading factors of spatial differentiation of GPP in BTH in the past 20 years. Human activities, such as urbanization processes reflected by night light intensity, represent the secondary economic and social factors affecting GPP change, and their influence keeps rising with the development of society and the economy. Precipitation and downward surface shortwave radiation have little influence on GPP. On the whole, the influence of human activities continues to rise, while the explanatory power of natural factors remains basically stable.
## 5 Three-Dimensional Path Planning Algorithms
### Machine Learning-Based 3D Path Planner  
Training an on-board computer to quickly identify objects and avoid collision with them in an environment in which UAV is set to navigate can be taken as a paradigm shift as it inherits the mechanism that a human being takes to avoid collision. The computational intelligence of a human brain is the degree that it is trained to, as is the artificial intelligence of the computer onboard a UAV. This is why intensive training of on-board computer is compulsory.  
Apart from the capabilities of ensuring the presence of objects and their relative locations from the UAV, the companion computer may be required to know the type of objects it detected. The YOLO object detection algorithm installed on the companion computer has such a capability. Strategies to avoid collision with an object may depend on the type of the object. For instance, the avoidance mechanism for a window (open obstacle) is different from the mechanism for a tree (closed obstacle). Our 3D path planner includes those capabilities, as explained below.
## 3 MFCS Model
### Thermal Subsystem  
Components of the MFCS thermal subsystem include a coolant tank, coolant circulation pump, diverter valve, intercooler, mixer, deionized, thermostat, radiator, and bypass valve; the purpose of the intercooler is to service the air supply subsystem. The thermal subsystem structure of the MFCS is shown in Figure 3, which ensures that the PEM fuel cell operates at a reasonable temperature. With a structure different from that of the SFCS thermal subsystem, the MFCS thermal subsystem established in this study is equipped with a diverter valve and a mixer so that the MFCS thermal subsystem achieves the thermal management of multiple stacks using a piece of the coolant supply and circulation system.  
The purpose of the MFCS thermal subsystem is to discharge the excess heat generated during the electrochemical reaction through cooling circulation in order to ensure the constant operating temperature of the PEM fuel cell. The main reason for the change of the PEM fuel cell stack temperature is the change of heat in the stack, which can be expressed as  
\[\dot{Q}_{\text{st}}=\dot{Q}_{\text{total}}-\dot{Q}_{\text{conv\_nat}}-\dot{Q}_ {\text{rad}}-\dot{Q}_{\text{conv\_force}}, \tag{7}\]  
where \(\dot{Q}_{\text{st}}\) denotes heat change of the stack, \(\dot{Q}_{\text{total}}\) denotes total heat production of the stack, \(\dot{Q}_{\text{conv\_nat}}\) denotes natural convective heat change, \(\dot{Q}_{\text{rad}}\) denotes radiant heat change, and \(\dot{Q}_{\text{conv\_force}}\) denotes forced convective heat change.  
The change in heat causes a change in the temperature of the PEM fuel cell stack,  
\[\frac{dT_{\text{st}}}{dt}=\frac{\dot{Q}_{\text{st}}}{c_{\text{st}}m_{\text{st} }}, \tag{8}\]  
where \(c_{\text{st}}\) denotes the average specific heat capacity of the PEM fuel cell stack; \(m_{\text{st}}\) denotes the mass of the PEM fuel cells (29, 100, and 172 kg for 20 kW, 70 kW, and 120 kW stacks, respectively).  
Heat generated by the electrochemical reaction of the PEM fuel cell is carried out by forced convective heat change of the coolant. The radiator is required to dissipate the heat of the circulating coolant. For the modelling of the MFCS thermal subsystem, an air-cooled radiator is selected, and the MAP of the radiator's calibration relationship under standard temperature conditions is shown in Figure 5.  
Changes of ambient temperature during actual MFCS operation would affect the heat dissipation, so real-time correction of heat dissipation is required. The actual heat dissipation by the radiator can be expressed as follows:  
\[\dot{Q}^{\prime}_{\text{rad}}=\dot{Q}_{\text{rad}}(\frac{T_{\text{coolant\_out }}-T_{\text{amb}}}{60}), \tag{9}\]  
Figure 4: PEM fuel cell hydrogen cycle diagram.  
where \(\dot{Q}_{\text{rad}}^{\prime}\) denotes the actual heat dissipation, \(T_{\text{coolant\_out}}\) denotes the coolant temperature at the outlet from the mixer, and \(T_{\text{amb}}\) denotes the ambient temperature.
# Experimental Investigation of Clearance Influences on Cage Motion and Wear in Ball Bearings
## 2 Experimental Approach
### Description of Test Rig  
The specially established bearing test rig is shown in Figure 2, which consists of motor, coupling, supports, shaft, and bearing box. The tested bearing of 7013 AC with redesigned cage was installed on the end of the cantilever shaft, while the inner ring was rotating with the shaft. The bearing was tested under combined loads, where a constant radial force (along the \(z\) direction) and axial force (along the \(x\) direction) were applied on the bearing house and the corresponding values were measured by force transducers as shown in Figure 2b. The outer ring was cut specially to make the eddy transducer probes closer to the cage surface by a processing method of wire electrode discharge machining (WEDM). Two probes \((y_{c},z_{c})\) were installed in a bearing house 90 degrees apart and obtained with a data acquisition system to measure the radial cage motion. Additionally, the measured cage motion was analyzed by fast Fourier transform (FFT) and trajectories. More details for the test rig can be found in Ref. [17].
# Design and Analysis on Decoupling Techniques for MIMO Wireless Systems in 5G Applications
## 4 The Methods of Decoupling
### Internal Decoupling
#### 4.2.1 Orthogonal Polarization Technique  
Constructing a dual orthogonal polarization antenna module is a general approach to internal decoupling. The four-antenna MIMO suitable for 5 G smartphones was designed [58]. The proposed antenna pair was composed of a split on the metallic bezel and a slot on the ground. Additionally, the currents around the slot were in-phase to produce y-axis polarization while the currents besides the split are out-phase, producing x-axis polarization. Therefore, two orthogonal modes within the same antenna area were excited, and more than dB isolation was obtained. Furthermore, the author modified the above MIMO system [59]. A T-shaped monopole was inserted in the slot on the long ground edge to constitute the CPW-fed dual antenna pair. Feeding from two ports, the two orthogonally polarized radiation can be generated by monopole and slot, respectively. Good isolation of better than 20 dB can be obtained. The 8 \(\times\) 8 MIMO for a metal-rimmed smartphone with two sets of orthogonal characteristic modes was designed in [60]. The antenna element with polarized diversity consisted of T-shaped monopole and coupling-fed dipole, slot and split. In consequence, even though dual-antenna pairs shared a common radiator, the isolation in the lower and higher bands can be better than 12 dB. Based on conventional dual-port feeding patch antenna, a modified square loop antenna with two coupled feeding strips was proposed as shown in Figure 6[61]. Due to this structure, the feature that two orthogonal modes share one common radiation element can be kept and the bandwidth was further improved. The isolation of more than 11.4 dB can be obtained in its eight-port MIMO system.  
Further, the triple orthogonal polarization modes are also applied in the decoupling scheme of the MIMO system. The twelve-port MIMO in [PERSON] et al. [62] provided tri-polarized modes for 5 G smartphones. The orthogonal polarization blade was formed by two slot radiation elements and a quarter mode substrate integrated waveguide antenna whose  
Figure 6: MIMO antenna with orthogonal polarization: (**a**) geometry of proposed MIMO; and (**b**) antenna element with dual-polarized modes (Results from [61]).  
radiation apertures were mutually orthogonal, so we were able to attain tri-polarized modes and good isolation of more than 12.5 dB.  
A MIMO system with tri-polarized antennas applied in the vehicle to everything (V2X) communications was designed [63]. The tri-polarization element consisted of two-directional loop dipoles and an omnidirectional monopole, which the two dipoles generated \(\pm 45^{\circ}\) dual polarization waves and monopoles in the second radiating element generated vertical line polarization waves. The isolation was better than 48 dB.
# Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?
## 6 Learning Analysis  
In this section, we present the main results obtained by using the Machine Learning algorithms identified in Section 5 to predict the congestion level over the different scenarios. The evaluation has been done for both binary and multi-level congestion classification, and unsupervised approaches have been compared with the performance yielded by the supervised solution based on SVC, which is thus used as a benchmark. In particular, for binary classification we have compared _k-means_ and SVC, while in the multi-level case SVC is compared with the hierarchical solution, HMM and RNN. Before running the different algorithms, the dataset was scaled, ensuring that both metrics, delay and moving IaT standard deviation, are comparable. Each feature was scaled to be in the \([0,1]\) range. Although different weights can be given to each parameter, in this work both features are given the same relative weight.  
Figure 8: Hierarchical clustering dendrogram for a scenario with a data rate of 50 fbps and 50 m.
# On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset
## 3 Proposed System Architecture
### Neural Network Architectures
#### 3.2.3 MobileViT  
MobileViT [35] was built by incorporating MobileNet CNN blocks, followed by lightweight vision transformer blocks that capture global features. This architecture allows for a balance between performance and efficiency. MobileViT_V2 [31] was then introduced to tackle the computational bottleneck present in the multi-headed self-attention layer in MobileViTs with quadratic complexity. By replacing the multi-headed self-attention layer with a separable self-attention layer with linear complexity, MobileViT_V2 proves to be a great choice for deployment in devices with constrained hardware resources. Out of the various versions of MobileViT_V2 available in the Keras package [33], MobileViT_V2_050, MobileViT_V2_100, and MobileViT_V2_175 were chosen for this experiment.
###### Abstract  
Hydrological modeling for water management in large watersheds requires accurate spatially-distributed rainfall time series. In case of low coverage density of ground-based measurements, gridded precipitation products (GPPs) from merged satellite-/gauge-/model-based rainfall products constitute an attractive alternative. The quality of which must, nevertheless, be verified. The objective of this study was to evaluate, at different time scales, the reliability of 6 GPPs against a 2-year record from a network of 14 rainfall gauges located in the Ankavia catchment (Madagascar). The GPPs considered in this study are the African Rainfall Estimate Climatology (ARC2), the Climate Hazards Group Infrared Precipitation with Station data (CHIRFS), the European Centre Medium-Range Weather Forecasts ECMWF Reanalysis on global land surface (ERA5-Land), the Integrated Multi-satellite Retrievals for Global Precipitation Measurement V06 Final (IMERG), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS), and the African Rainfall Estimation (RFEv2) products. The results suggest that IMERG (R\({}^{2}\) = 0.63, slope of linear regression \(a\) = 0.96, root mean square error RMSE = 12 mm/day, mean absolute error MAE = 5.5 mm/day) outperforms other GPPs at the daily scale, followed by RFEv2 (R\({}^{2}\) = 0.41, \(a\) = 0.94, RMSE = 15 mm/day, MAE = 6 mm/day) and ARC2 (R\({}^{2}\) = 0.30, \(a\) = 0.88, RMSE = 16 mm/day, MAE = 6.7 mm/day). All GPPs, with the exception of the ERA5, overestimate the 'no rain' class (0-0.2 mm/day). ARC2, IMERG, PERSIANN, and RFEv2 all underestimate rainfall occurrence in the 0.2-150 mm/day rainfall range, whilst CHIRPS and ERA5 overestimate it. Only CHIRPS and PERSIANN could estimate extreme rainfall (>150 mm/day) satisfactorily. According to the Critical Success Index (CSI) categorical statistical measure, IMERG performs quite well in detecting rain events in the range of 2-100 mm/day, whereas PERSIANN outperforms IMERG for rain events larger than 150 mm/day. Because it performs best at daily scale, only IMERG was evaluated for time scales other than daily. At the yearly and monthly time scales, the performance is good with R\({}^{2}\) = 0.97 and 0.87, respectively. At the event time scale, the probability distribution function PDF of rain gauge values and IMERG data show good agreement. However, at an hourly time scale, the correlation between ground-based measurements and IMERG data becomes poor (R\({}^{2}\) = 0.20). Overall, the IMERG product can be regarded as the most reliable gridded precipitation source at monthly, daily, and event time scales for hydrological applications in the study area, but the poor agreement at hourly time scale and the inability to detect extreme rainfall >100 mm/day may, nevertheless, restrict its use.  
M odagascar; GIRE SAVA; Ankavia; gridded precipitation products; IMERG
## 1 Introduction
### Interpersonal Communication Media  
The most popular forms of communication today are no longer what they used to be, changing from traditional gatherings, written letters in sealed envelopes, or even a long telephone call to a friend to chatting over social networking or short messaging services. Technology has rapidly advanced and is now a well-established part of our lives. Young adults in particular have grown up in a world where it has always existed, and they have become dependent on social media as means of communication [9]. Technology has, by and large, become a substitute for mediated social interactions [2]. Online social network sites like Facebook and Twitter have taken place of some direct exchanges just as short-messaging services, also known as text messaging, have somewhat replaced written letters and phone conversations. Mobile phones also allow for access to social networking, so they may be used to complete tasks that were once limited. Taken together, digital media have undoubtedly influenced our social interactions and even begun to shape our identities [1]. The real world and virtual world have become highly intertwined.  
Examining the use of socially interactive technologies, a lead consumer is the United States. Over 80% of Americans have mobile phones with about over two-thirds of owners using short messaging services (_i.e._, text messaging) [10]. However, other countries are using such digital media at increasing rates. For instance, estimates suggest that slightly less than 80% of Poles own mobile phones and, of those owners, over three-quarters send and receive short messages [10]. Estimates also suggest that over three-quarters of the American population use the Internet [11] and about two-thirds of the Polishpopulation do [12]. This reveals similarities in general use of socially interactive technology, which has allowed us to communicate with others without having to be physically present in the same area. Therefore, comparable digital use exists in these countries.  
While social media has had many positive things to offer such as the ability to connect, meet, and exchange information, we have not really given much thought to the downside of it, especially when it comes to how it may influence our personal, intimate relationships. Research indicates that technology can change real life behavior [13], and individuals have used it as a means by which they can transgress [5, 6, 7, 14]. Thus, a transferability of negative behaviors may be taking place from the real to the virtual world; an old act in a new guise. Numerous stories have surfaced about online harassment and peer bullying, highlighting the consequences of such victimization, yet socially interactive violence among the adult population has received little attention, and intimate relationships have received even less. This age group is important to study because its members have the highest risk of being victimized by intimates [15, 16]. Exploring this may yield noteworthy insights and offer new discourse in relationship research.
## 3 Causes and Characteristics of Traffic Interruptions in Highways to Tibet
### Characteristics of Traffic Interruptions
#### 3.2.3 Traffic Interruption Severity
Although the mean interruption duration of the Sichuan-Tibet Highway is lower than that of the Qinghai-Tibet  
Figure 8: Boxplot of traffic interruption severity for different highways to Tibet. Highway and Xinjiang-Tibet Highway, its mean interruption mileage is considerable. This phenomenon correlates with its traffic volume. The main causes of traffic interruptions along the Yunnan-Tibet Highway and Xinjiang-Tibet Highway are geological and meteorological disasters, with accumulated snow being a significant factor. The Yunnan-Tibet Highway traverses mountains and gorges with steep cross-slopes, severely weathered geology, and a high susceptibility to collapses. Debris flows tend to occur in rainy seasons. However, significant temperature differences between day and night at high altitudes facilitate winter snow accumulation to freeze, which makes snow the predominant cause of traffic interruptions on this route. The Xinjiang-Tibet Highway is mostly paved with asphalt and meets second-level technical standards. It traverses depopulated zones with low temperatures and oxygen depletion at high altitudes and stretches through vast Gobi deserts characterized by extreme cold. Snow disasters in winter are the main cause of traffic interruptions on the Xinjiang-Tibet Highway. The Xinjiang-Tibet Highway exhibits a longer mean interruption duration compared to the Yunnan-Tibet Highway, from the perspective of traffic interruption characteristics. However, the traffic interruption mileage for both highways is relatively low due to their small traffic volumes.