esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation
### Results and Discussion
#### 4.2.2 Path Stretch  
The path stretch metric represents the ratio of the actual forwarding path length, measured in terms of the number of hops, to the shortest possible path length between the consumer and the producer. The average hop count metric represents the average number of intermediate hops a Data packet travels from its source (producer) to its destination (consumer). Path stretch can directly impact the delay or latency experienced by Data packets as they traverse the network. When the stretch is close to 1 (i.e., the actual path is almost as short as the shortest path), Data packets reach their consumer faster, resulting in lower delay. However, if the path stretch is significantly higher, Data packets may take longer to reach their destination, leading to increased delay. An efficient mobility management strategy should ensure that the MP remains reachable using a path with minimal extension. Figure 9a,b, respectively, show the average path stretch and the hop count as a function of the producer's movement speed. As main observations, we notice that the slopes of the three schemes in the figures are not impacted much by the producer's mobility speed. We further notice that MAP-ME achieved the highest path stretch and average hop count among the three schemes; this is attributed to one of the drawbacks of the MAP-ME scheme, which tends to create a triangular path when the MP changes the PoA, as the producer sends an updated Inpt from the new PoA towards the old one to maintain a seamless producer mobility management. In contrast, the the DMMS achieves a minimal path stretch about 10% longer than the shortest possible paths, as shown in Figure 9a, and the average hop counts are 46% lower than those of the MAP-ME scheme, as shown in Figure 9b. This result is expected because of the strategic placement of Anchors  
Figure 8: Average Interest packet loss rates vs. mobile producer speed.  
within the network. By effectively positioning these Anchors at minimal distances from all nodes in the network, the additional distance traveled by packets through the Anchor remains relatively insignificant. Secondly, the location-forwarding strategy ensures that the Inputs are forwarded along the shortest path between the Anchor and the producer. It is worth noting that the multi-RV KITE scheme also has a lower path stretch due to its benefiting from rendezvous server placements, which reduces the path between the producer and consumers through the rendezvous servers.
# Scenario-Based Configuration Refinement for High-Load Cellular Networks: An Operator View
## 2 Analysis of High Traffic Load Cells
### Relationship between Access Success Rate and Equipment Capacity  
The success access ratio of the cellular network is directly related with the capacity of the network equipment, which is represented by the threshold of MNOU and the flow control supported by boards. We take the UMPT and LBBP produced by Huawei as examples. Moreover, we realized the MNOU is 400 for a single cellular network. The flow control, affecting user access, is triggered when the central processing unit (CPU) utilization ratio of UMPT and LBBP is larger than 85%. More detailed field data are reported in Tables 3 and 4. Table 3 summarizes the radio resource control (RRC) setup success rate and the unsuccessful times of physical uplink control channel (PUCCH) allocation under different numbers of online users when MNOU is larger than the predefined threshold. Table 4 studies the RRC setup success rate and the number of the dropped messages caused by flow control implemented on UMPT and LBBP, respectively.  
It is obvious that the RRC setup success rate significantly decreases when MNOU is larger than the predefined threshold with larger unsuccessful times of PUCCH allocation. Meanwhile, we observe that the number of dropped messages significantly increases when the maximum CPU utilization ratio is over 85%. According to field data reported in Tables 3 and 4, we have to leave a certain level of margin for MNOU and the CPU loads of UMPT and LBBP, respectively. It is worth noting that different venders provide the reference value of MNOU. For example, the waring threshold of MNOU is 180 out of 200 when using Bell equipment. When equipment manufactured by Huawei, ZTE, Ericsson, and DTT are used, the waring threshold is 300 and the corresponding MNOU is 400.
# Temperature Estimation Adaptive to Variables over Distance Using Infrared-LiDAR
## 4 Experimental Results
### Experimental Equipment and Datasets
#### 4.1.2 Datasets  
As for the data acquisition environment, as shown in Figure 13a, the digital count and distance of the blackbody were measured using the infrared camera and LiDAR together. Figure 13b shows the LiDAR map. The part marked by the red dot in the center is the location of LiDAR. The distance to the actual object is obtained by applying \(x,y,z\) points to Equation (12).  
Datasets set the temperature of the blackbody at \(T_{min}=25\,^{\circ}\)C and \(T_{max}=45\,^{\circ}\)C, and measured \(T_{min}\) and \(T_{max}\) in 1 m increments, excluding 0.3 m from about 0.3 m-10 m distance. Since the proposed method uses interpolation, the proposed method can be used if there are data about \(T_{min}\) and \(T_{max}\) at appropriate distances. Figure 14 is the thermal image of the blackbody by distance obtained through the infrared camera. Furthermore, to measure the performance of the proposed method, a digital count by distance was obtained for the 5 \({}^{\circ}\)C interval temperature between \(T_{min}\) and \(T_{max}\). In addition, additional data on the blackbody at distances of about 3.5 m and 7.5 m were collected. In addition, digital counts for each distance were obtained for humans to ensure that the same temperature estimates were made at each distance for body temperature. Thus, the total acquisition data set was acquired at 5 temperatures at 5 \({}^{\circ}\)C intervals from 25 \({}^{\circ}\)C to 45 \({}^{\circ}\)C and 11 points at 1 m intervals of approximately 0.3 m to 10 m. It was also acquired at two points, 3.5 m and 7.5 m. Thus, 65 data for black bodies and 11 data for humans were obtained.  
\begin{table}
\begin{tabular}{l l} \hline
**Velodyne VLP-16 Specifications** \\ \hline Channel & 16 \\ Measurement range & \(\sim\)100 m \\ Accuracy & \(\pm\)3 cm \\ Field of view (vertical) & 30\({}^{\circ}\) (+15\({}^{\circ}\) to \(-\)15\({}^{\circ}\)) \\ Field of view (horizontal/azimuth) & 360\({}^{\circ}\) \\ Angular resolution (vertical) & 2\({}^{\circ}\) \\ Angular resolution (horizontal/azimuth) & 0.1\({}^{\circ}\)–0.4\({}^{\circ}\) \\ Rotation rate & 5–20 Hz \\ \hline \end{tabular}
\end{table}
Table 2: LiDAR specifications.  
\begin{table}
\begin{tabular}{l l} \hline
**Blackbody Specifications** \\ \hline Temperature range & +5 \({}^{\circ}\)C– 50 \({}^{\circ}\)C \\ Black target surface diameter & 80 \(\times\) 80 mm \\ Effective emissivity & 0.96 \(\pm\) 0.02 \\ Temperature resolution & 0.01 \({}^{\circ}\)C \\ Temperature measurement accuracy & \(\pm\)(0.15 + 0.003 \(|\)\(|\)\(|\)\(|\)\(|\)\({}^{\circ}\)C \\ Size/Weight & 110 \(\times\) 110 \(\times\) 190 mm/1.7 kg \\ \hline \end{tabular}
\end{table}
Table 3: Blackbody specifications.  
Figure 14: Blackbody thermal image acquired through an infrared camera (about 1–9 m).  
Figure 13: Experiment environment: (**a**) Picture (**b**) LiDAR map.
## 3 Results and Discussion
### Fatty Acid Composition  
Fish oil is widely regarded as an important source of long-chain polyunsaturated fatty acids (PUFA), particularly eicosapentaenoic acid (EPA, 20:5n-3) and docosahexaenoicenoic acid (DHA, 22:6n-3). Eating fish has many health benefits that have been linked to n-3 fatty acids. For example, clinical trials have shown that fish oils prevent arterial hypertension, type 2 diabetes, and memory impairment in older people [49]. Indeed, the European Food Safety Authority (EFSA) has approved several health claims related to EPA and DHA, listed in [PERSON] et al. [50]. The fatty acid compositions of the commercial protein concentrates are shown in Table 5. The fatty acid compositions of HYDR1, HYDR2, and COLL1 could not be determined due to analytical problems. However, the total amount of lipids in these samples were very low (0.4%, 0.1%, and 0.1%, respectively), making the composition of the fatty acids an unimportant factor.  
Figure 2: Size exclusion chromatograms (SEC) of commercial fish protein concentrates, hydrolyses, and hydrolyzed collagen. (**A**) CONC1, (**B**) CONC2, (**C**) CONC3, (**D**) HYDR1, (**E**) HYDR2, and (**F**) COLL1. Fish protein concentrates = CONC1–3, fish protein hydrolyses = HYDR1–2, hydrolyzed fish collagen = COLL1.  
Figure 3: Proportion (%) of molecular weight (MW) distributions of commercial fish protein concentrates, hydrolysates, and hydrolyzed fish protein. Fish protein concentrates = CONC1–3, fish protein hydrolysates = HYDR1–2, hydrolyzed fish collagen = COLL1. Different letters within the same molecular weight grade indicate a statistically significant difference with ANOVA post hoc pair comparison (\(p<0.05\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fatty Acid** & **CONC1** & **CONC2** & **CONC3** \\ \hline C14:0 & 3.1 \(\pm\) 0.0 & 2.6 \(\pm\) 0.0 & 1.5 \(\pm\) 0.0 \\ C16:0 & 15.4 \(\pm\) 0.1 & 17.8 \(\pm\) 0.0 & 17.8  
All protein concentrates were good sources of n-3 fatty acids as in CONC1, CONC2, and CONC3 the proportion of n-3 fatty acids was 29.2%, 34.2%, and 40.9% of the total amount of fatty acids, respectively. No statistically significant differences were found between the samples. Interestingly, the amount of DHA seemed to be inversely proportional to the oxidation of the concentrates. According to [PERSON] et al. [51], the DHA content of cod, saithe, and haddock by-products was of the same order of magnitude (except for saithe caught in winter it was lower). Since CONC1 is made from the by-products of cod, saithe, and haddock, and CONC2 and CONC3 from the by-products of cod filting and backbone, respectively, the DHA concentrations of the samples could be expected to be quite similar. Therefore, the lower DHA content of CONC1 may be due to the degradation of DHA caused by oxidation.
# Research Status and Development Trend of Wastewater Treatment Technology and Its Low Carbonization
## 2 Conventional Wastewater Treatment Technology
### Sequential-Batch Activated Sludge Process  
The sequential-batch activated sludge process is also known as the SBR process because of the intermittent form of operation, so each reaction tank is a batch of sewage treatment; hence the name. Because of the high flexibility of the SBR operation, it can replace the continuous activated sludge process in most situations to achieve the same or similar results. By changing the operation mode of the SBR, you can simulate the operation mode of the full hybrid and push-flow processes. In the reaction stage, the organic matter in the reaction tank is degraded by microorganisms, and the wastewater concentration becomes lower and lower, which is very similar to the steady-state push flow, except that it is a kind of temporal push flow. If the influent period is long and the accumulation of organic matter in the wastewater of the reaction tank is very small during this period, then the situation is close to a complete mixture. The technological process is shown in Figure 5. When the reference values of the various design parameters used in the treatment of urban sewage are the same as 2.2, the removal rate of BOD, COD and TSS can reach 90% [33; 34; 35; 36; 37].  
Figure 4: Process flow chart of pure-oxygen-aerated activated sludge method.  
The process improves on the traditional push-flow activated sludge method, and can simulate a complete mixture in certain cases, which can better make up for some problems, such as long aeration time and easy sludge swelling caused by sludge disposal. However, it still fails to solve the fundamental problems of aeration energy consumption and sludge resources, there are still problems of high operation, management and maintenance costs, and the process has relatively high requirements for drainage equipment. During the whole treatment process, when the sewage is biochemically treated by an activated sludge system, a large amount of external energy is consumed for the oxygen supply, and a large amount of greenhouse gases such as CO\({}_{2}\), CH\({}_{4}\) and N\({}_{2}\)O are released during the treatment process. However, there are many ways to dispose the residual sludge. Simple treatment such as random accumulation and landfill will also result in the emission of CO\({}_{2}\), CH\({}_{4}\) and N\({}_{2}\)O due to anaerobic fermentation and other factors.
## 2 Data and Methods  
### Study Area  
Centrally located in the hinterland of China, Wuhan City is situated in the eastern part of Jianghan Plain in Hubei Province, precisely at the intersection of the Yangtze River and Han River, spanning longitudes 113 deg41' to 115 deg05' E and latitudes 29 deg58' to 31 deg22' N, and nestled between the undulating hills of the Dabie Mountains in northeastern Hubei and the Mufu hills in southeastern Hubei. As the urban core, the Wuhan's MDA comprises a total area of 3400 km2 and is primarily characterized by a rolling terrain alternating between hills and plains (Figure 1).  
The study area, located at the convergence zone between the Qinling-Dabie orogenic belt and the Yangtze Platform, has experienced a series of geological cycles and multi-stage evolutionary processes. Specifically, the Late Triassic Indo-China orogenic movement significantly shaped the east-west fold framework of the region. Subsequently, the Yanshanian Pacific Rim activities superimposed the northeast-trending fault system, faulted volcanic basins, and intrusive magmatic events. Moreover, the Himalayan intracontinental evolution contributed to the deposition of faults and depression basins, ultimately resulting in the intricate geological structure framework observed in the surveyed region (as illustrated in Figure 2). Importantly, the neotectonic movement further accentuated the geological diversity within the study area, characterized by substantial variations in the types and thicknesses of Quaternary sediments. This notable variability has given rise to the co-existence of numerous terrace types and landform morphologies in the present-daylandscape. Continuous and multiple-phase tectonic movements have resulted in a highly complex geological structure, characterized by extensive folds and faults (Figure 1e). The widespread distribution of carbonate rocks has led to severe karst collapse issues in Wuhan. Therefore, understanding the spatial distribution characteristics of Wuhan's geological structures and stratigraphic structures is crucial for the study of comprehensive environmental geological issues, as well as for the planning, site selection, and construction of engineering infrastructures in the city.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 3 Road Extraction Based on Different Data Sources
### Road Extraction Based on High-Spatial Resolution Images
#### 3.1.2 Status and Prospects  
Each algorithm has its advantages and disadvantages. For example, [PERSON] et al. [71] compared the heuristic and data-driven road extraction methods based on four public datasets. Their results indicated that data-driven methods could achieve more than 10% accuracy compared to that achieved using heuristic methods. Similar conclusions were reported in [34]. However, data-driven methods have limitations such as the requirement of large training samples, long processing time and high-speed computing (most algorithms require graphics processing units). In addition, the training parameters used in one dataset may not be able to achieve high accuracy when used in another dataset. In contrast, some heuristic methods require less time and can meet real-time demands in some applications.  
Road extraction using the currently available high-spatial-resolution images has some challenges. The key step in road extraction from high-resolution images is to describe the road features. Describing the linear or narrow bright band of a road, which provides good detection results, is the main focus of most existing methods. However, with the improvement in image resolution, we can obtain more noise interference (shadows, buildings and road obstructions) and more detailed road features; therefore, the road objects can also be described more precisely in this case [34; 173]. Furthermore, road objects include many complex phenomena such as occlusion or shadows, discontinuities, sharp bends and near-parallel boundaries with constant widths. Incorporating all these factors and modelling them into a single model is almost impossible. Therefore, it is essential to establish a multimode approach to extract roads from images with high spatial resolutions.
# Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation
## 3 Evaluation Model of Foundation Cushion Filling Effect Based on Elastic Wave Monitoring
### Elastic Wave Monitoring: BP Neural Network
#### 3.2.3 Test for BP Neural Network  
To test the accuracy of the model, 100 groups of data with clear states and not involved in the training were randomly selected. The testing results are shown in Figure 11. Among the 100 groups of test samples, the state was successfully evaluated for 93 groups, and seven groups were misjudged. For four groups, the unfilled state was misjudged as full; for three, the full was misjudged as not full. The overall inspection accuracy of this model for the test set was 93%. This indicates that the trained network model effectively evaluated the sand-filling state.  
Figure 10: Error change of the model.
## 8 Conclusions  
In this work, a solution proposal for smart parking is given, followed by the previous analyses and results of an experiment involving a test of the functionality of NB-IoT parking sensors in real time, which supports the hypothesis of the effectiveness of this solution. NB-IoT devices send the full information about the status of each parking spot (e.g., the time and date) that is available, meaning the parking controllers and drivers can check which parking spaces are available and when, all in real time. A parking space can be reserved via a smartphone application, which will helps drivers to find and reserve spots, park their vehicle, and pay, as well as helping the parking managers to manage the whole parking area and the reservations. This smart parking system will save the drivers time, effort, and cost. The real-time information on parking space usage can be utilized to reduce parking search traffic, optimize the usage of the parking, reduce emissions, and improve parking revenue.  
The embedded sensor's collected data travels via NB-IoT and is sent to the server. After reporting the status, the node can go into sleep mode. The test results show the expected sensor and network functionality regarding the real-time responses. The datasets obtained from the parking locations and the development and improvement of the proposed solution represent implementation challenges for future practical applications. Regarding the IoT solutions, the specific implementation requirements should be considered systematically, which will involve the selection of devices and technologies, individually and also in terms of interoperability, regarding the sensors, the connectivity, the IoT platform, the applications, as well as the future operation and user perceptions.  
The authors of this paper focused on certain aspects, consciously providing future researchers with various domains of knowledge (e.g., radio, core, and application information) and a complete overview with development guidelines for end-to-end functional solutions such as in smart parking. Future research may focus on applicative solutions, including artificial intelligence elements.  
Conceptualization, E.K.; investigation, E.K., N.Z., C.R. and N.H.; methodology, E.K., N.Z., C.R. and N.H.; supervision, C.R.; visualization, E.K. and N.H.; writing--original draft, E.K. and N.H.; writing--review and editing, E.K. and N.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors E.K., N.Z. and N.H. would like to thank [PERSON] from the University of Siegen for supporting this work. Special thanks also go to the BH Telecom d.d. Sarajevo and QSS companies for providing the experimental test with network resources (LTE-Advanced, NB-IoT) and test pieces of equipment.  
The authors declare no conflict of interest.
# Blended Filter-Based Detection for Thruster Valve Failure and Control Recovery Evaluation for RLV
## 4 Results
### Thruster Control Recovery Evaluation
steady-state error was adjusted, the number of thrusters was reduced according to the principle of energy consumption optimization. In the steady-state stage, as shown in Figure 13f, at 44 s, the two thrusters were in operation, then were closed after a command was given, but in the period of 45-48 s, the thruster was not fully closed due to a type C fault, hence, only one thruster closed. At this stage, the steady-state error increased, and a new demand for operating the thruster was given. After adjustment, the steady-state error stabilized, and the performance requirements were met, as shown in Figure 13e. In the above cases, after the failure of a thruster valve, the remaining fault-free thrusters can satisfy the recovery control performance.