esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Method
### Approach of Citrus Tracking and Counting Based on YOLOv7-Tiny and DeepSORT  
Citrus fruit counting is generally accompanied by the bagging of citrus. At this time, citrus has entered the coloring period and there is a clear difference in color with the background, which is conducive to the counting and estimation of yield. The citrus tracking and counting method based on YOLOv7-tiny and DeepSORT proposed in this paper mainly consists of the following steps: (1) target detection: use YOLOv7-tiny network to detect the citrus in the video, and get the target detection frame and the corresponding features; (2) target prediction: use Kalman filtering algorithm to predict the position and state of the citrus in the next frame of the video, and (3) target matching: use the Hungarian algorithm to cascade match and IOU match the predicted tracks and detections in the current frame, which are updated by Kalman filtering and output as tracking results. Among them, the track that fails in target matching is initialized as a new tracking track; in order to reduce the fruit loss and repeated matching errors caused by the new track, the two stages life filter is additionally added to the algorithm in this paper.  
The two stages life filter (TTSLF) and three consecutive frames detection are applied in new track. The filtered track enters the target matching session after successful detection and the track is deleted after failure. The overall flow of the tracking and counting method is shown in Figure 3.  
In the target detection part, the YOLO algorithm has the advantage of high target detection accuracy, while agricultural robots have high requirements for easy scalability and high accuracy. Therefore, we focus on agricultural robot harvesting research based on algorithm comparison. The YOLOv7-tiny uses fewer convolutional layers and channels than YOLOv7, YOLOX, and other models, the inference speed is faster, suitable for real-time detection of citrus with high real-time requirements, and the model is small in size, occupies less storage space and video memory, and is suitable to be deployed in resource-limited devices. It is also suitable for deployment on resource-limited devices, which is favorable for the development of agricultural robots in citrus orchards. Therefore, YOLOv7-tiny is chosen as the target detection algorithm for citrus.  
In the target tracking part, the DeepSORT algorithm [18] added Re-ID appearance features, reducing ID switches by 45% after applying the deep association metric to targets. By considering both motion and appearance in the matching strategy, it achieved good performance at high rates. So, we added a two-stage life filter to the DeepSORT algorithm, which significantly reduced matching failures and improved fruit counting accuracy.  
Figure 2: Causes and effects of data augmentation.
## 6 Results and Evaluation
### Cooja Simulated Scenario  
To provide RPL performance in large density networks, we proposed a random scenario of 1000 nodes using Cooja simulator to measure the RPL performance while using OF0 and ETX. The results of Cooja simulations are listed as follows.  
Control Traffic Overhead  
In Figure 15, total control traffic is represented under OF0 and ETX while running a random simulation of the RPL process during 1300 s simulation time. Routes calculated using OF0 need more control messages than ETX as in dense networks. The number of hops is increased while the network size increased. Control messages are used to update the routing table in case of OF0; routing entries to the destination is increased with the network size.  
Figure 14: The average power consumption in terms of radio on time for OF0 and ETX with uniform and.  
Figure 13: The average power consumption for OF0 and ETX with uniform and random topologies.  
Figure 15: The total control traffic in Cooja simulator for OF0 and ETX with random RPL topology.  
Packet Delivery Ratio (PDR)  
The higher the value of PDR, the lower the value of retransmission which leads to less resource waste. Figure 16 shows the variation of PDR value with respect to the network size using OF0 and ETX. It shows that as the network size increases. The PDR decrease and ETX outperform OF0 in route calculation. Hop count OF delivers from 65-35% as the number of nodes increases to reach 1000 while ETX delivers 80-55%.  
Network Latency (End-to-End delay)  
Figure 17 presents network latency while packets travel from client's nodes to the root node (MP2P) traffic using OF0 and ETX in Cooja simulated scenario. In this paper, we consider the time delay of the successful packet delivery, packets retransmission, and packets buffering. However, the network becomes denser and the number of hops increased, and still the rank values using OF0 provide low latency than ETX. As the number of hops is still fixed, OF0 can find the best path with minimum hops quickly rather than using ETX, which based on more retransmissions as the network size increased. ETX and OF0 nearly have the same behavior as the network size, still small, while OF0 outperforms ETX in large-scale networks.  
Figure 18 illustrates the radio on-time percentage using OF0 and ETX with Cooja random scenario. The radio on-time percentage is increased as the network density increased, which reflect the power consumption during the network for all client nodes. ETX has small radio on-time percentage than OF0, as choosing the best path using hop count in large density network consume a lot of energy to calculate the paths, update the routing tables and share the routing information and candidate parents.  
Figure 16: packet delivery in Cooja simulator using OF0 and ETX with random network topology.  
Figure 17: Latency comparison using OF0 and ETX with random Cooja simulator.
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 4 Constellation Navigation and Orbit Determination Technology
### Inter-Satellite Measurement Autonomous Navigation Method
[PERSON] studied the critical technologies for autonomous operation of navigation satellite constellations based on the actual needs of China's regional tracking network [91]. In general, to achieve long-term autonomous navigation of near-Earth/near-Moon satellite constellations based on inter-satellite measurements, additional information needs to be introduced to determine the overall direction of the constellation in space, which cannot yet be called truly autonomous navigation.
# Reconstruction and Measurement of Irregular Karst Caves Using BLST along the Shield Metro Line
## 3 Test Results
### Attenuation Law of Lasers with Different Wavelengths in the Same Suspended Media
#### 3.2.4 Fine Sand Suspended Medium  
For a suspended medium of fine sand, we compared the lasers with wavelengths of 405 nm, 450 nm, 532 nm, 650 nm, and 808 nm. When the laser wavelength is 450 nm, the increasing rate of the laser attenuation coefficient is the lowest with the increase of turbidity. When the turbidity is lower than 40 NTU, the 450 nm laser attenuation coefficients are all less than those of lasers with other wavelengths, as shown in Figure 6a. The cloud diagram of laser attenuation coefficients with different wavelengths and different turbidity grades in fine sand medium is shown in Figure 6b. In order to  
Figure 5: Attenuation coefficient—turbidity curve in clay dissolution medium: (**a**) Two dimension; (**b**) Three dimension  
compare the attenuation characteristics of 450 nm and 650 nm lasers in fine sand medium, we solved the following two simultaneous equations:  
\[Y =0.30205X-0.89683 \tag{13}\] \[Y =0.5807X+0.40815 \tag{14}\]  
When the turbidity is \(-4.7\) NTU, the attenuation coefficients of the two are equal. Since the water turbidity grades are all higher than 0, when the suspended medium is fine sand, the 450 nm laser is suggested.  
From the above results, it can be seen that the \"blue and green window\", which is widely used in marine water, is not suitable for the complex water-filled environment typical of karst caves. When a karst cave is filled with water in order to dissolve the particles of CaCO\({}_{3}\), silt, and clay, the attenuation coefficient of the blue light wavelength (450 nm) is the lowest when the turbidity is low, and the attenuation coefficient in the near infrared wavelength (650 nm) is the smallest when the turbidity is high. [PERSON] [26] also mentioned the existence of a \"near infrared window\" in water with high turbidity. When the water contains medium fine sand with large particles, the precipitation rate is fast. However, some fine sand particles will still be suspended over the period of time when the attenuation coefficient of the blue laser with a wavelength of 450 nm is the smallest.  
At present, the lasers are extensively applied in the detection of ocean water, which mainly includes a variety of ions, such as Na\({}^{+}\), Mg\({}^{2+}\), Ca\({}^{2+}\), and Cl\({}^{-}\). However, abundant suspended particles in the cave water and the particle size and mass can have great effects on the laser attenuation coefficient. The characteristics and weights of different suspended particles will lead to great differences in scattering intensity. Large wavelength lasers have greater penetration, resulting in different slopes of the fitted curve.
## 3 Results
### Statistical Analysis Results--Main Hypothesis
As for linear regression results, due to the high level of correlation (66%) between the dietary fiber ratio and the raw fruits and vegetables ratio, only the latter was included among the independent variables. For WCP, we could not build a meaningful model due to the low level of explained variability. For the WL slope, the best model included the age, adherence rate and the raw fruits and vegetables ratio as independent variables with an adjusted explained variance R\({}^{2}\) = 41%. The coefficient of the age was 0.34 (\(p\) < 0.001, relative importance 64%), while the coefficient of the raw fruits and vegetables was \(-\)0.20 (\(p\) = 0.016, relative importance 24%). The effect of the adherence ratio was not significant and the gender was not selected into the model. These results prove a strong negative effect of the patients' age on weight loss success, a higher age being linked with a less steep WL slope (due to the positive coefficient), and a weaker, but substantial positive effect of fruits and vegetables consumption on the rate of weight loss. We note that the WL slope model used only the data of the 45 patients with a significant WL trend, and an extreme outlier was also omitted from the model to conform to the assumptions of the regression test, resulting in \(n\) = 44. However, repeated tests also including the outlier confirmed the results.
# A Two-Stage Routing Protocol for Partitioned Underwater Wireless Sensor Networks
## 4 Results and Discussion
### Network without Partitions
#### 4.1.4 Energy Consumption  
We assume that all nodes are equipped with Evo Logic's S2 CR 48/47 acoustic modem [30].  
Figure 14 shows the overall average energy consumption. The higher communication overhead, in the form of \"denial of relay service\" notifications, localization requests and corresponding beacons in case of partition, results in higher energy consumption. Another very important reason for the gap between the energy consumption of the two schemes is absence of transmission activity by the partitioned nodes due to the lack of partition handling capability in case of NPH. As there is no partition handling mechanism in NPH, if a node gets partitioned, it will not be able to receive any beacon and, thus, will not indulge in any transmission activity. This will keep its energy intact thus widening the gap between the two curves. Moreover, the elimination of partitioned nodes also reduces contention (and therefore reduces energy consumption) in the main body of the network as fewer nodes contend for the medium. However, being unable to connect the partitioned nodes, NPH achieves lower packet delivery ratio. As partitioned nodes cannot send their sensed data, it results in a reduction of the number of deployed nodes for the sensing mission. The disconnected nodes are useless as their data cannot be delivered.
# Simulation Tool for Winter Navigation Decision Support in the Baltic Sea
## 4 Model Data
Figure 2 shows the plots of these polynomial expressions of HV curves. On the x-axis is the ice thickness, varying from 0 m (open water) to 1 m (Figure 2A) and 0.4 m (Figure 2B). On the y-axis is the speed achieved by the vessel in knots. As observed in both plots, the open water speed is 15 kn. Figure 2B shows that the speed rapidly decreases with an increase in ice thickness. The speed reaches near 0 kn around 0.4 m. Figure 2A shows that speeds of up to 9 kn can be achieved for ice thicknesses between 0.4-1 m, when the vessel is assisted by an icebreaker at a distance. The ice data from FMI are used in two formats: SIGRID-3 and NetCDF. There are multiple fields of information for which the World Meteorological Organization (WMO) has defined standard nomenclatures and abbreviations [19]. Ice fields can change every day, affecting the routing. The hv curves are determined with respect to level ice thickness. In the case studies, real ice data have been used to calculate equivalent ice thickness as an average of the ice concentration in a square grid. The ice data are updated every week. This updating frequency can be increased as required. Equivalent ice thickness can be defined differently depending on what ice data are available. In this article, the equivalent ice thickness is calculated using the NetCDF data, as a product of concentration and thickness, therefore maintaining volume equivalence [20]. The equations are similar to those used in [14; 15]. For more information, readers are pointed to [21].
## 5 Conclusions  
For the first time, the deep echo state network (DeepESN) was implemented in an optical wireless communication system to improve the system's performance for PAM and QAM-OFDM modulations. The optical signal was transmitted and received through a 40.5 m underwater channel in the experiment. The results confirm the feasibility of applying DeepESN to UWOC systems. DeepESN processing significantly improved the system's ability to reconstruct signals transmitted using both PAM and QAM-OFDM modulations, enabling power savings for the UWOC system to achieve the error-free limit. In most cases, particularly for higher-data-rate signals, DeepESN-processed signals exhibited lower BER compared to RLS-processed signals. This finding also validated DeepESN's properties in state dynamics of layered recurrent neural architectures in handling temporal data in optical communications.  
This paper also investigated the training and equalization performance of DeepESN with different configurations. In line with the conclusion that the number of reservoir units determines the network's training performance from previous work, this paper emphasized the importance of selecting the appropriate number of reservoir units to avoid insufficient training and overfitting. It is recommended to set the number of reservoir units between one and three times the symbol's oversampling factors. In terms of reservoir layers, DeepESN outperforms the ESN, which only contains a single reservoir layer. Signal quality can be improved with deeper DeepESN, but increasing reservoir layers inevitably raises the calculation time. To achieve efficient and effective DSP in a UWOC system, a trade-off between equalization performance and training efficiency is essential. A two- or three-layer network is practical for general UWOC systems.  
Future studies for DeepESN's application in UWOC systems could include addressing the slow time-varying distortions, such as turbulence and bubbles, which significantly impact the performance of UWOC systems. DeepESN could be a potential candidate to mitigate the effects of these distortions. In addition, other ESN configurations, such as parallel ESN, reported in Ref. [42], could also be applied for signal processing in UWOC.  
**Author Contributions:** Conceptualization, X.W., Y.P. and M.D.; methodology, X.W. and K.W.; software, K.W. and Y.G.; validation, K.W. and Y.G.; writing and original draft preparation, K.W. and Y.G.; writing--review and editing, X.W., Y.P. and M.D.; supervision, X.W.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Engineering and Physical Sciences Research Council (EP/R026173/1, EP/T001011/1).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data may be obtained from the authors upon request.  
**Acknowledgments:** K.W. would like to thank [PERSON] and [PERSON] for their support on the experiment and data processing.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Low Overhead Mapping Scheme for Exploration and Representation in the Unknown Area
## 3 Experiments
### The Memory Efficiency of Rmap  
In this section, the memory efficiency of the Rmap compared with the grid map is presented. For comparison, the simulated environment map of Figure 8a generated by the Player/Stage was used. The environment has an area of 22.4 m \(\times\) 24.1 m. Figure 8b is the grid map from the DP-SLAM algorithm for the environment of Figure 8a. The grid map consists of 215,936 cells with a 5 cm resolution.  
Figure 9 shows the results of the Rmap representation for the simulated environment of Figure 8a. Moreover, Table 1 enumerates the detailed experiment results. The Rmap achieved a 92% similarity compared to the grid map with only 19 nodes. The similarity means the percentage of the occupied area of the Rmap compared with the area of the grid map. Figure 9a through to (d) show the accuracy of the map increases as the number n of the rectangles increases. As shown in Figure 9d and Table 1d, the map shows 99% similarity with the grid map in 522 rectangles. Compared with the 215,936 cells of the grid map, this is a dramatically reduced number of nodes for showing the same quality of the map.
## 4 Experiments
### Model Performance Comparison
#### 4.2.2 Exploration on Seq2 Seq Structure of PESO  
At the same time, we explored several experiments on composition structure of the encoder and decoder in PESO to obtain the best performance, including PESO-BiLSTM-LSTM, PESO-GRU-GRU, PESO-BiGRU-GRU, and PESO. As -Table 6 shows, the smallest values of four metrics, RMSE, MAE, ADE, and FDE, are obtained from the structure of PESO, which consists of a Seq2 Seq structure of LSTM-LSTM.
# Ceramic Matrix Composites for Aero Engine Applications--A Review
## 7 Manufacturing Processes for CMCs
### Matrix Forming via Pyrolysis of C- & Si-Containing Polymers  
Polymer infiltration pyrolysis is a low-cost fabrication process for SiC (from silicon-derived polymer precursors) reinforcements and nanoparticles CMCs and plays a key role in the oxidation resistance of the resulting composite [215]. There are several steps that the reinforcement fiber and base ceramic material must undergo to create an efficient composite [216]. The first step is to impregnate the reinforcing fibers with a resin and apply partial curing. In continuous, the preparation is shaped into a mold. The mold is combined with a flexible upper mold, pressed against the prepreg with atmospheric pressure (vacuum bag) or high air pressure (gas pressure bag), and turned into an autoclave [217]. Then the infiltration of the pre-ceramic polymer occurs while the preform is added into it, vacuum or pressed assisted. Subsequently, the pyrolytic decomposition of the preceramic polymer takes place in an Argon atmosphere at high temperatures, manufacturing the final material composition. Different atmospheres can be introduced into the pyrolysis, resulting in different matrices. After the final matrix is fabricated, the infiltration pyrolysis is repeated up to ten more times to minimize the porosity of the ceramic matrix.  
The preceramic polymer field has been developed, producing the Si-C-N-O system, focusing exclusively on non-oxide ceramic fibers [218]. Further investigation on the fabrication of Grade Niclon SiC fiber and HI-Nicalon SiC fiber CMCs, with the PIP process, has been reported, indicating that these fiber-reinforced CMCs could be effectively used in high temperature (up to 1200 \({}^{\circ}\)C) structural applications for long periods [219]. Furthermore, it has been shown that pyrolysis can be performed between 1000-1200 \({}^{\circ}\)C under nitrogen flow. The effect of lower pyrolysis temperature, with higher ramp rates, by using SiC/SiC CMCs, has been researched. This allows for a cheaper industrial production cost; high residual porosity and microcracks occur due to the relative density and bending strength being lower. It was concluded that to achieve higher density, and the used preform must be evacuated before the polymer solution is added into the mix, which is not feasible to happen for the fabrication of large components [216].
## Abstract  
The aim of this study was to investigate the effect of preceded tumor therapeutic irradiation on the tensile bond strength of three modern universal adhesives applied in self-etch mode on dentin. Specimens prepared from 135 extracted human third molars were divided into three superior groups. These received either no radiation, 5 Gy, or a total dose of 60 Gy in vitro irradiation, fractionally applied. The samples of each group were further randomly assigned to six subgroups to test three adhesives (Futurabond(r) U, Voco; AdheSE(r) Universal Ivoclar Vivadent; Xeno(r) Select, Dentsply Sirona) in the self-etch application mode (\(n=15\)). Tensile bond strength was determined using a universal testing machine (1.0 mm/min). Data were analyzed with ANOVA (\(p<0.01\)) and Tukey's test (\(p<0.05\)). The influence of irradiation on the microdensile bond strength of the used dentin adhesives proved to be significant. For each material, a decrease in adhesion value was registered after irradiation. However, only for the material Xeno(r) Select were significantly reduced adhesion values determined after irradiation with 60 Gy compared to 0 Gy. Within the limitations of an in vitro study, some effects of tumor therapeutic irradiation of human dentin on the tensile bond strength of universal adhesives used in self-etch mode could be observed. Those decreases were only partly significant, depending on the material and the radiation dose. Whether the tensile bond strength on irradiated dentin depends on the particular application mode (etch-and-rinse vs. self-etch) of the universal adhesives remains to be investigated.