esa-sceva/satcom-chunk-collection huggingface dataset:
# Object-Level Semantic Map Construction for Dynamic Scenes
## 5 Discussion  
### Object Motion Detection  
Our object motion detection system is based on the assumption that moving objects generally undergo continuous motion. Using the object database maintained by the last frame data, the dynamic objects in the current frame are removed and the rough camera pose is obtained. This pose can serve as a good initial value of the camera pose for object motion detection, enabling the motion status of each object in the object database to be obtained in current frame. The motion status of each object is updated in real time according to the object 3D tracking results. Note that we use the current frame raw data to perform object 3D tracking. Our method only performs motion detection on large objects with a large number of data stored. Since small objects (such as tea cups and pencils) do not have sufficient data constraints for object 3D tracking, unreliable tracking results may be produced. In addition, small moving objects affect the camera pose estimation less than large objects. Therefore, in order to reduce the memory consumption of the system, smaller objects are ignored in the motion detection and our system only reconstructs and tracks the objects occupying more than 0.5% of pixels in the image. The advantage of  
Figure 8: Static object models in the static object database for a (**a**) TV, (**b**) potted plant, and (**c**) chair. (**d**) Dynamic object model in the dynamic object database at different moments.  
Figure 10: Object TV model fuse construction with different 3D tracking results displayed: (**a**) Accurate 3D tracking result and object model fuse construction; (**b,c**) 3D tracking results with errors and object model fuse construction with ghosts.  
Figure 9: Scene map construction in surfel-based representation: (**a**) Static object model and background model representation; (**b**) overlay of dynamic pedestrian and static object models at a certain moment; (**c**) semantic label map representation of the scene.  
our method is that it performs 3D tracking for object motion detection in 3D space while fully utilizing the depth information and gray texture information stored in object. This motion detection strategy finds hidden moving objects, which move perpendicular to the viewpoint of the camera, more easily than methods performed in 2D space with only using epipolar constraints between the previous and current frames. In addition, the accuracy of object segmentation directly affects the object 3D tracking results.
## 4 Results and Discussion
### Regression Analysis Based on the Results of the Experiment  
To compare the real and theoretical results of the daily incident solar irradiation of the different mounting systems, the real data were adjusted, using a fitting equation. Figure 14 shows the scatter diagram of the daily incident solar irradiation on the \(PV\) field in the three mounting systems under study, together with the curve resulting from the fitting of the data using an exponential function of the following form:  
\[y=a\cdot e^{\left[-\left(\frac{x-c}{b}\right)^{2}\right]} \tag{28}\]  
where \(a,\,b,\,\) and \(c\,\) are parameters. The correlation coefficient (\(R^{2}\)) was also calculated for the fit equations. The results obtained were \(0.842\,\) for the fixed tilt angle system, \(0.825\,\) for the horizontal single-axis tracker (Scenario 1), and \(0.822\,\) for the horizontal single-axis tracker (Scenario 2):  
Figure 14: _Cont._  
Figure 13: Daily energy gain.  
The following observations can be deduced from Figure 14:  
1. Except in the fixed tilt angle system, the adjustment equations of the other two mounting systems provided very similar results.
2. The theoretical study obtained better results than the study under real operating conditions in all three mounting systems.
# Experimental Investigation of Thermal Contact Conductance in a Bundle of Flat Steel Bars
###### Abstract  
The phenomenon of thermal contact conduction in two-phase (fluid-solid) media determines many technological processes. An example of such a process is heat treatment of steel bars, when a heated charge has a form of a packed bundle. In order to determine the optimal heating curve it is necessary to have knowledge about the intensity of transfer through contact areas of the bars. This phenomenon is quantified by the thermal contact conductance \(h_{ct}\). The article describes the methodology of determining the \(h_{ct}\) coefficient for bundles of flat steel bars. The starting point for the analysis is the measurement of the effective thermal conductivity \(k_{\phi}\) performed for 5 \(\times\) 20 mm and 10 \(\times\) 20 mm bars. Individual samples of the same bars differed in arrangement. The analytical investigation used the concept of an elementary cell. This approach consisted in analysing resistances for individual heat transfer types: conduction, contact conduction and radiation. Based on the performed calculations it has been established that the value of the \(h_{ct}\) coefficient for the analysed samples is within the range 128-472 W/(m\({}^{2}\) K). Changes of the \(h_{ct}\) coefficient in the temperature range 25-700 \({}^{\circ}\)C can be described with a second degree polynomial. It has been established that \(h_{ct}\) assumes maximum values in the temperature range from 300 \({}^{\circ}\)C to 400 \({}^{\circ}\)C.  
two-phase heat transfer; thermal contact conductance; effective thermal conductivity; heat treatment; steel bars +
Footnote †: journal:  
Acticle  
Acticle  
Acticle  
Acticle Experimental Investigation of Thermal Contact Conductance in a Bundle of Flat Steel Bars.  
factors make the heat transfer which occurs within the bundle a complex phenomenon. One of the mechanisms which occurs here is contact conduction between the adjacent bars. The issue of contact conduction influence on the heat transfer intensity in a bundle of flat steel bars was analysed in the paper [21]. It has been shown that the heating time of such bundles can be lowered by 5-40% as a result of a decrease in the thermal contact resistance and depends on many factors such as: the bar size and bundle arrangement. Due to the importance of this problem for industrial practice, there is a justified need for a more in-depth study of thermal contact conduction in this type of porous charge. This phenomenon is quantified with the use of thermal contact conductance \(h_{ct}\) (this coefficient is the inverse of the thermal contact resistance), which corresponds to the convection heat transfer coefficient [22]. The article describes the research devoted to determination of the thermal contact conductance for bundles of flat steel bars.
# Investigation of the Influence of Sublayer Thickness on Pairing of Metallic MEMS Shutter Blades
## 4 Experimental Results and Discussion
### 3D Self-Assembled Paired Shutter Blades (Yin-Yang shapes)
The stiction length of shutter A (\(l_{s}\)) is continuously increasing, starting with no stiction (0 \(\upmu\)m), which was observed for the paired shutters in scenario (I), and ending at up to 90 \(\upmu\)m (III), meaning that 50 \(\upmu\)m of shutter A is not sticking to the substrate in scenario (III). To expand, at low RoCs, more pronounced curling for shutters is observed. When shutter A is pulled towards shutter B by the liquid surface tension, shutter A finds shutter B after a longer time than that which is observed at high RoCs, and hence, sticking to the substrate with not enough restoring forces it to roll back up, which can be seen clearly in Figure 9 (scenarios IIIa and IIIb). The results provide more insights into the geometry, its formation process, and how to introduce changes to obtain the desired geometries. However, we face various limitations, such as, the stiction of shutter A to the substrate and the turbulences of the isopropanol vapour during the drying process.
## 5 Wireless Han
### Not Based on IEEE Standards  
There are several protocols that are not based on any kind of standardized PHY or MAC layers. These are SimpliciTI, Z-Wave, EnOcean, and Insteon, among others. For comparison purposes, they are shown in detail in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Specifi-Protocols** & **SimpliciTI** & **Z-Wave** & **Insteon** & **EnOcean** & **Wavenis** & **WM-Bus** \\ \hline
**ISM Bands** & 2.4 GHz and Sub 1 GHz & 2.4 GHz & 315 MHz (USA) & 433 MHz (EU) & 169 MHz \\  & 2.4 GHz and 908.4 MHz (USA) & 915 MHz (USA) & 902.875 (USA) & 868 MHz (EU) & 433 MHz \\  & 2.4 GHz & 169 MHz (USA) & 169 MHz (USA) & 169 MHz \\ \hline
**Number of RF Channels** & Set by the application & 2 & 34 & 1 & 1 & 12 \\ \hline
**Network** & Star and & Mesh & Dual-mesh (RF and powerline) & Star, peer-to-peer and mesh & Star, peer-to-peer and mesh & Star, peer-to-peer \\ \hline
**MAC Scheme** & LBT (Listen-before-talk) & CSMA/CA & CSMA/CA & CSMA/CA & CSMA/CDMA & \\  & 2.4 GHz & & & & (otherwise) & \\ \hline
**Modulation** & MSK & FSK, GSK, narrowband & BPSK, FSK (in ISM Band) & ASK & GFSK & FSK, GSK, OOK, and ASK \\ \hline
**Nominal Rate** & Up to 250 kbps & 9.6 kbps (868 MHz) & 38.4 kbps & 120 kbps & From 4.8 kbps to 100 kbps. Usually 19.2 kbps & 2.4 kbps to 100 kbps \\ \hline
**Power Saving** & Supported & Supported & Supported & Supported & Supported & Supported \\
**Decryption** & Depends on the & AES128 & No & No & 3 DES & DES \\  & radio MAC & AES128 & No & No & AES128 & AES128 \\ \hline
**Data** & Depends on the & 8-bit node LD & 24 bit pre-assigned & 8/32-bit & 48-bit MAC & \\
**Authentication** & Radio MAC & 32-bit home LD & module LD & & & addresses \\ \hline
**Data Integrity** & Depends on the & Assigned by primary & CRC16 & CRC8 & BCH (32,21) & CRC16 \\  & radio MAC & controller & & & & \\ \hline
**Autonomy** & Depends on & Depends on Battery & Depends on Battery & No batteries & Depends on Battery & Depends on Battery \\ (Days) & Detectors & Specifications & Specifications & Specifications & electromagnetic) & Specifications \\ \hline
**Range (meters)** & 10 & 30 & 45 (outdoors) & 30 & 200 (indoors) & Up to 1000 \\ \hline
**Application** & Distributed & Remote control & Energy savings, & Building & Industrial & Automation, AMI, \\  & alarm and & lighting and & irrigation control, & Automation, Smart & AMR, Smart & Smart Meters \\  & security devices, & automation, & Occupancy sensing, & Homes, Logistics, & Access control, cold- & (Electricity, \\  & energy meters & in residential and home & Remote control & industry and & chain monitoring, & Gas, Water, \\  & automation & commercial buildings & heating and air & transportation & active RFID & and Heat) \\  & automation & & conditioning & & & applications \\ \hline
**Advantages** & Small code size & Controllers and slaves & Reliability, low cost, & Ultra-low Power, & & \\  & and low & network, flexible & scalability and & no batteries, Easy & & \\  & software & network configuration & flexibility & to install and time & & \\  & complexity & & & & \\ \end{tabular}
\end{table}
Table 2: Wireless Network Standards Not Based on IEEE Standards.
# Data Quality Analysis and Improvement: A Case Study of a Bus Transportation System
## 6 Experimental Results and Analysis
### Results of Cleaning Redundant, Conflicting, and Erroneous Data  
#### 6.1.1 Results of Cleaning Redundant Data  
According to the statistics, it can be seen that there are some redundant data in the \"QR_code\", \"Swipe\", \"IAO_station\", \"Bluetooth\", \"GPS\", and \"Wi-Fi\" tables. Among them, the \"Bluetooth\" table has the highest redundancy rate. Specifically, the number of redundant records is 2.98 million, which accounts for 3.8% of the total records. The \"QR_code\" table has the second highest redundancy rate with a value of 2.8%, which contains 20,828 redundant records. The \"Swipe\" table has the lowest redundancy rate with a value of 0.17%, which contains 5413 duplicate records. We used the method described in Section 2 to clean the redundant data. The method firstly utilizes Spark to load the raw dataset from HDFS into the memory and transforms it as a _dataframe(RDD)_ for subsequent processing. It then calls the _distinct()_ function to remove the redundant records from the dataframe, creating a filtered _dataframe_. Finally, it saves the filtered _dataframe_ to HDFS.
# Biophotonics in Dentistry
## 3 Applications in Dentistry
### Lasers
#### 3.4.4 Regenerative Laser Periodontal Therapy  
Regenerative periodontal procedures, demonstrating remarkable alveolar bone growth, revealed a layer of epithelium instead of cementum along the surface of the root. Histological examination revealed that an essential parameter to promote healing is the need to form a junctional epithelium with CTA (connective tissue attachment) [133,134]. Removal of crystal epithelium inhibits the movement of crestal cells into the pocket [135,136]. This forms the foundation for regeneration, which is then initiated when new fibres are introduced to the root surface. The frameworks of other treatment modalities, such as the modified Widman flap procedure, subgingival curettage and the \"excisional new attachment procedure\", rely on the exclusion of pocket epithelia and the subsequent creation of an environment for the formation of new CTA [120].  
The distinctive features of laser in periodontal therapy can be utilized for epithelial removal. A carbon dioxide laser (10.6 \(\upmu\)m wavelength) has the ability to delay epithelialization in wounds. Studies have demonstrated the potential of CO2 laser to delay epithelial growth for 14 days following a periodontal surgery. CO2 laser can therefore be utilized to eliminate the epithelium without resulting in any damage to the underlying connective tissue layer [137]. For a detailed conceptualization of laser light for the epithelial retardation procedure, it is essential to understand the mechanism of wound healing. Pulsed CO2 has the potential to form a distinctive wound in the gingiva. This is a unique vaporization mechanism of intracellular fluid that subsequently results in degradation of cell structure [138]. Laser wounding results in retardation of re-epithelialization due to parameters such as diminished wound contraction and reduced inflammatory response. Figure 12 illustrates a few advantages for the use of lasers in periodontal therapy in contrast to conventional periodontal surgeries [120].  
A brief summary of uses of laser in dentistry is presented in Table 2; the applications are vast for both soft tissue and hard dental tissue. Other hard tissue applications include bleaching for cosmetic purposes, diagnostics, treatment of hypersensitivity, and restorative removal and curing. Additional soft tissue applications include photodynamic therapy for malignancies, gingivectomy (removal of hyperplastic tissue), frenectomy, crown lengthening and periodontitis [85]. The reader is directed to a review of laser for restorative dentistry by [PERSON] et al. for further reading on the applications of lasers within dentistry.
## 3 Results and Discussion
### Diversified Analysis  
To focus on the differences in the characteristic flavor of various dried oysters, separate PLS-DA models were developed based on active taste substances and active odor substances (Figure 8). PLS-DA, a statistical method of supervised discriminant analysis, which is different from PCA, could actually interpret the observed values and accomplish the prediction of the corresponding variables [18; 20]. The characteristics of the taste and aroma of the five samples can be distinguished separately and effectively, with the accumulative contribution of the first two main elements components reaching 81.6% and 72.5%, respectively. In PLS-DA models, the reliability and predictive power of the model can be evaluated by R\({}^{2}\) and Q\({}^{2}\), with R\({}^{2}\) and Q\({}^{2}\) above 0.5 indicating an acceptable model fit and values closer to 1 indicating a stronger predictive power. Both PLS-DA models in this study met the requirement (R\({}^{2}\)(X) = 0.994, R\({}^{2}\)(Y) = 0.996, Q\({}^{2}\) = 0.993; R\({}^{2}\)(X) = 0.991, R\({}^{2}\)(Y) = 0.995, Q\({}^{2}\) = 0.988). Moreover, since 200 permutation tests, the intersection of the Q\({}^{2}\) regression line with the vertical axis was less than zero, showing the model was not overfitted and the model validation was effective. Therefore, these results could be used to analyze the signature substances of different dried oysters. The aroma compounds of VIP \(>\) 1 were regarded markers that can be used to distinguish among the samples. Additionally, four non-volatile flavors and eight aromas were screened as markers to differentiate among five samples: glutamic acid, glycine, betaine, IMP, pentanal, ethyl heptanoate, (E, Z)-2,4-nonadienal, 1-octen-3-one, 2-hexenal, 2-octenal, hexanal and decanal.  
Figure 7: Radar plot of the characteristic flavor in dried oysters under different drying conditions. CK: blanched oysters; VFD: vacuum freeze drying; VD: vacuum drying; NSD: sun drying; HAD: hot air drying.
## 3 Results  
The following section presents the main findings of the reviewed articles on the potential of EO for mapping small-scale agriculture and cropping systems in West Africa:  
* First, the distribution of publications in different journal categories over time is shown in Section 3.1.
* In Section 3.2., the publications are subdivided spatially, both with regard to the affiliation of the first author, the origin of the funding and with regard to the study area.
* The analysis of the sensor name and sensor type, as well as their carrier system, is presented in Section 3.3.
* In Section 3.4, the spatial and temporal resolutions, as well as the different study periods, are analyzed in detail.
* This is followed by an in-depth examination of the crops of interest in Section 3.5, including the comparison of the crops represented in the studies versus their contribution to the overall agricultural economic value and their proportion of agricultural land.  
Figure 2: (**a**) Workflow chart outlining the literature search process used to identify n = 163 relevant scientific articles about the potential of RS for mapping small-scale agriculture and cropping systems in West Africa. (**b**) Outline of the Web of Science “topic” search.  
Subsequently, in Section 3.6, an in-depth analysis of the thematic foci of the respective studies is presented in order to identify conclusive research gaps.
# Study on Gearbox Fault Warning Based on the Improved M-IALO-GRU Model
## 3 Method, Principle, and Improvement  
### The GRU Network Model  
A GRU (gated cycle unit) is a recursive neural network (RNN) variant proposed by [PERSON] et al. [30] in 2014 to process sequence data. The GRU [31; 32] is designed to solve the gradient disappearance problem often encountered by traditional RNNs with long sequence data and to improve the model memory of time steps by introducing a gating mechanism. Compared to an LSTM (long and short-term memory network), a GRU simplifies the structure while maintaining similar performance and, therefore, is more computationally efficient and comparable to LSTM in many tasks. The core idea of GRU is to use gating mechanisms to regulate the flow of information between time steps to dynamically determine which information needs to be retained and which needs to be discarded. This mechanism enables the GRU to efficiently address long-term dependency issues. Compared to the traditional RNN, the GRU has two main gatings: the update door and the reset gate. The update gate combines the forgetting gate and the input gate of the LSTM to regulate the retention of information, while the reset gate controls the discarding of information. With this structure, a GRU can effectively capture dependencies in temporal data while reducing training time and maintaining high prediction accuracy. The GRU memory unit structure is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Type** & **Correlation** & **Parameter Type** & **Correlation** \\ \hline Environmental wind speed & 0.794 & Wind direction & \(-\)0.302 \\ Environmental temperature & 0.842 & Controller wheel hub temperature & 0.204 \\ Impeller speed & 0.753 & Leaf Angle & \(-\)0.508 \\ Generator speed & 0.726 & Generator slip ring temperature & 0.761 \\ Engine room temperature & 0.748 & Generator bearing A temperature & 0.842 \\ Gearbox bearing temperature & 0.945 & Generator bearing B temperature & 0.841 \\ Hydraulic tank temperature & 0.461 & Converter voltage & 0.588 \\ Active power & 0.776 & Reactive power & \(-\)0.803 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Correlation coefficient for the partial parameters.  
For the input time series, the GRU units separately conduct the following calculations:  
\[r_{t}=\sigma(W_{r}x_{t}+U_{r}h_{t-1}) \tag{4}\]  
\[z_{t}=\sigma(W_{z}x_{t}+U_{z}h_{t-1}) \tag{5}\]  
\[\widetilde{h}_{t}=\tanh(W_{h}x_{t}+U_{h}(r_{t}\odot h_{t-1})) \tag{6}\]  
\[h_{t}=(1-z_{t})\odot h_{t-1}+z_{t}\odot\widetilde{h}_{t} \tag{7}\]  
where \(\sigma\) is the Sigmoid activation function; \(W_{r}\), \(W_{z}\), \(W_{h}\), \(U_{r}\), \(U_{h}\), and \(U_{z}\) are the weight matrix; \(x_{t}\) is the input of time \(t\); \(h_{t-1}\) is the memory information of time \(t-1\); \(\widetilde{h}_{t}\) is the new memory information; and \(h_{t}\) is the output vector.
## 4 Experimental Analysis
### Collision Detection Efficacy
#### 4.4.2 Performance of SVM Collision Detection
The precision-recall curve is illustrated in Figure 10.  
The average precision-recall score was 0.88. Initially, the precision was very high. As the recall rate increased, the precision decreased but remained relatively high overall. This demonstrated the model's strong capability in recognizing positive samples (traffic accidents). It is important to note that within the limited test set, our model achieved a perfect recall of 1.0, ensuring that all positive samples were correctly identified. Although there is potential for improving precision, this approach remains effective as a screening tool, ensuring that no collision incidents are overlooked. Additionally, the simplicity and low computational resource requirements make this method practical for real-world applications.