esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Crop Row Recognition and Navigation Line Extraction
#### 2.2.2 Determination of the Crop Row Target Region  
To effectively extract the crop row region, the initial horizontal target area was defined based on the plant spacing characteristics of soybean and maize. The width  
W of the target area was set to 3.5 m, and the length \(\Delta L\) was set to 0.20 m. This initial area served as the analysis range for point cloud projection, providing support for estimating the row-wise crop width.  
Within the initial target region, a K-means clustering algorithm was applied to the projected point cloud data to estimate the crop width. Based on the clustering results, the distribution width of each cluster was statistically analyzed, and the maximum width was selected as the basis for subsequent region delineation, as illustrated in Figure 5.  
After the crop row point cloud data were framed, as shown in Figure 6, a Euclidean clustering algorithm was employed to further segment and refine the soybean and maize row structures. By calculating the centroid coordinates of each cluster and incorporating the inter-row spacing characteristics, the algorithm effectively distinguished between soybean and maize rows.  
Figure 5: Initial target region and crop width delineation.  
Euclidean distance is a fundamental spatial metric used to measure the straight-line distance between two points in three-dimensional space. The calculation formula is as follows:  
\[d=\sqrt{(x_{2}-x_{1})^{2}+(y_{2}-y_{1})^{2}+(z_{2}-z_{1})^{2}} \tag{8}\]  
In the equation, \((x_{1},y_{1},z_{1})\) and \((x_{2},y_{2},z_{2})\) represent the 3D coordinates of two points within the feature crop row point cloud data.  
To control the clustering merging process, a distance threshold, thresh Dthresh, was defined. This threshold was determined based on the maximum crop width extracted from the initial target region. During the clustering procedure, a union-find (disjoint-set) structure was employed to determine whether two clusters should be merged: if the Euclidean distance between two clusters was less than or equal to thresh Dthresh, they were merged into the same category. This strategy enabled further refinement and accurate separation of the soybean and maize row regions, as illustrated in Figure 7.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 5 Publicly Available Datasets for Medical Video Analysis
### Kent Integrated Dataset  
The Kent Integrated Dataset (KID) [28] is an open-source and nonprofit dataset that contains 2500 endoscopy images and three videos. The dataset includes various types of diseased and normal organs, e.g., colon, stomach, and esophagus, and the disease conditions include bleeding, polyps, ulcer, and stenosis. Most of these data were captured using a microcam wireless capsule endoscope.
## 4 Experimental Results
In the implementation of the proposed method, the processing of pixel points can be computed in parallel. In addition, in disocclusions and OFRs filling, the calculations of the priority of pixel on the hole boundary and the matching cost between the patches can also be performed in parallel. Therefore, under the condition of applying algorithm and hardware parallel acceleration, the time for synthesizing the whole sequence does not increase too much. On the other hand, we mainly explore the contribution of artifacts handling method for single view rendering. during the whole process, we try to make the synthesized image closer to the ground truth. Furthermore, the temporal correlation of adjacent frames and the complementary relationship of multiple reference views are not exploited in this paper. The visual quality of our method for view interpolation may not be as good as the state-of-the-art multi-view rendering method due to the lack of valid pixels. For more complex scenes, such as alternating multiple foreground objects, there are only a few background pixels around \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test Sequence**} & \multicolumn{6}{c}{**PSNR (dB)**} \\ \cline{2-7}  & [14] & [15] & [12] & [16] & [17] & **Proposed** \\ \hline BA54 & 26.16 & 29.13 & 29.60 & 28.04 & 28.56 & 30.25 \\ BA52 & 23.95 & 24.82 & 24.33 & 23.31 & 23.97 & 25.93 \\ BA56 & 25.85 & 27.54 & 27.95 & 27.31 & 27.79 & 28.25 \\ BR43 & 28.27 & 28.81 & 28.99 & 29.26 & 29.68 & 29.96 \\ BR41 & 25.67 & 26.40 & 26.41 & 25.88 & 26.43 & 26.95 \\ BR45 & 28.19 & 29.55 & 29.63 & 29.58 & 29.74 & 30.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average peak signal-noise ratio (PSNR) comparison of the proposed and previous methods. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test Sequence**} & \multicolumn{6}{c}{**SSIM**} \\ \cline{2-7}  & [14] & [15] & [12] & [16] & [17] & **Proposed** \\ \hline BA54 & 0.7850 & 0.7937 & 0.8015 & 0.7898 & 0.7912 & 0.8102 \\ BA52 & 0.7306 & 0.7415 & 0.7372 & 0.7311 & 0.7341 & 0.7484 \\ BA56 & 0.7855 & 0.7913 & 0.7922 & 0.7876 & 0.7894 & 0.7933 \\ BR43 & 0.7828 & 0.7840 & 0.7842 the hole, and the proposed method may not be able to perfectly identify them and fill the holes. After all, our algorithm is based on traditional image processing algorithms. If deep learning techniques are introduced, the results may become better.
## 4 Conclusions  
This study proposed a comprehensive fire station location model based on risk assessment and the traditional MCLP model. The developed model was utilized and evaluated by taking Shanghai as an example. The spatial location, land attributes, population density, building volume ratio, and incident density of Shanghai were collected to build a four-level risk classification system. Risk classification parameters were added as tuning parameters to optimize the objectives of the MCLP model. Baidu Map API and Gurobi 9.1.2 were used to calculate coverage areas and site layout.  
Based on the four-level risk classifications system, 5000 candidate fire station points were determined and 50, 100, 150, 200, 250, and 300 stations were then selected to assess the possibility of constructing fire stations. The results show that increasing the number of stations from 0 to 150 will lead to a decrease in average travel times. However, when the number of stations exceeds 150, the rate of decline slows down and essentially remains unchanged. The overall response coverage rate is already close to 90% when the number of stations reaches 100. The re-planning of fire station siting in Shanghai should be around 150. With the re-planning of existing fire stations, there were no coverage areas with excessively small areas, resulting in a more balanced distribution of fire resources. The objective model with risk assessment will provide guiding principles to maximize the coverage of response areas for various needs and scenarios.  
Conceptualization, Q.H.; methodology, Q.H., L.X., Y.Y. and P.D.; software, Q.H., M.L., and P.D.; validation, Q.H., L.X., Y.Y. and P.D.; formal analysis, Q.H., L.X., Y.Y. and P.D.; investigation, Q.H., P.D. and M.L.; resources, L.X. and Y.Y.; data curation, M.L.; writing--original draft preparation, Q.H.; writing--review and editing, P.D.; visualization, Q.H. and P.D.; supervision, Q.H. and P.D.; project administration, Q.H., L.X., Y.Y. and P.D.; funding acquisition, Q.H. and P.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Fire and Rescue Administration Technology Plan Project (2020 XFLR40, 2022 XFLR32) and the Shanghai Sailing Program (22 YF1452400).  
Not applicable.  
Our self-built dataset will be made publicly available after publication.  
We are thankful for the data support from the Command Center of Shanghai Fire and Rescue Department.
## 3 Generic Routing Solutions
### Link-State Routing Protocols (LSRPs)
In contrast, routing based on hop count only, without considering the link quality, may result in choosing unreliable paths with low bandwidth and PDRs. Therefore, TBRPF provides the option of selecting paths based on link metrics, e.g., signal strength, and selecting along paths with higher quality over minimum hop paths. In general, the main advantage of LSRPs is the complete knowledge of the network topology, which allows nodes to find the shortest path to the destination quickly and efficiently. As well as recalculate the paths immediately in case of topology changes or link failures. In contrast, the two main disadvantages of LSRPs are (i) lack of scalability and (ii) excessive consumption of resources. The lack of scalability means that the routing protocol performance decreases with the increase in network size. This lack of scalability appears mainly as the number of nodes in the network increases, leading to an increase in advertisements and topology updates. Even though most LSRPs use triggered updates or partial updates, they often have high traffic loads as topology changes must propagate globally, which floods the network periodically with unnecessary packets. This flood tends to create excessive control overhead during path establishment. The more topology changes occur, the more advertisements are resent; thus, the higher control overhead and bandwidth consumption. Consequently, this type of routing is deemed unsuitable for (i) large networks or (ii) unstable networks with rapid topology changes. Moreover, using an LSRP may impose high costs on the nodes. Each node has to (i) process packets, (ii) generate advertisement responses, and (iii) store massive amounts of topology and routing information. This results in excessive consumption of resources in terms of memory size, CPU processing, and power usage. Therefore, using an LSRP in a constrained network, such as LLN, that (i) often has nodes with very limited resources and (ii) suffers from rapid topology changes due to its Lossy channels, which may result in unstable performance with low PDR and high power consumption.
# Routing Selection Algorithm for Mobile Ad Hoc Networks Based on Neighbor Node Density
## 1 Introduction
### Related Work
Moreover, it introduces a local route repair scheme based on maintenance nodes to enhance the reliability of the communication process. Both of these adaptive algorithms seek the best routes based on different factors but do not consider the issue of excessive network resource consumption that may occur in densely populated nodes. In [15], a novel routing metric called Enhanced-Ant-AODV is proposed. It utilizes a combination of parameters such as node density, signal strength, distance, and remaining node energy as the routing metrics. The ant colony optimization algorithm is employed to find an optimal route based on these parameters. To ensure the selection of an optimal route that avoids nodes with low energy levels and significant signal strength differences, the routing decision process avoids forwarding routing control information through such nodes. This helps reduce network overhead to some extent. The consideration of multiple factors in the routing metric allows it to adapt well to different environments and find routes that effectively meet the transmission requirements. However, in environments with high node density, this approach may still experience significant network overhead. Further research is needed to address this limitation and develop more efficient strategies for routing control in dense node environments. The remainder of this paper is organized as follows: Section 3 reviews the cyber storm problem and related solutions. The following sections outline the key points covered. In Section 4, the detailed process and the existing problems of ND-AODV are introduced, and then a new scheme CND-AODV is proposed. Section 5 presents a simulation experiment on the NS3 platform to evaluate the performance of the proposed CND-AODV. Finally, Section 6 summarizes the whole paper and gives the direction of future work.
## 8 Use Case Output
### Use Case Layer 3: Performance Index per Technology  
Different network technologies provide different network experiences. It is, therefore, essential to perform device analytics based on technology and distributed by the used operating systems.  
In Figure 7, the analytics result is based on the throughput. We can see that, for 4G technology, Android-based devices present high throughput in video streaming applications, followed by iOS devices. However, any combination of KPIs can be used.  
In Figure 8, the analytics is based on packet loss to determine which technology is susceptible to packet loss. High packet loss is registered on the uplink on both technologies. In the graph, we can see that there is a high rate of packet loss on the uplink than on the downlink. M2M applications use more uplink stream than downlink--hence the need to optimize uplink transmission.  
Another aspect of device analytics is to help the operators in expanding the competitive force into the market. The adoption of specific or individual devices is presented in Figure 9, which displays the number of subscribers possessing devices of a certain manufacturer. The top manufacturer retains many more subscribers than any other.  
Samsung has more subscribers as shown in Figure 9, followed by Huawei, Nokia, and Apple, respectively. Adoption by device type is illustrated in Figure 10. The most frequently used device type is deduced from the dataset. The most frequently used communicating devices are smartphones, followed by tablets, dongles, and legacy 2G-capable devices.  
Figure 8: The Layer 3 use case result based on packet loss.  
Figure 7: The Layer 3 use case result.
## 3 Inherent Flow Characteristics and Numerical Analysis  
### Inherent Flow Characteristics  
The characteristics that indicate the unique flow rate performance of valves according to the opening rates are referred to as the inherent flow coefficients [11]. The flow characteristics of valves vary according to the inherent flow coefficients, which are significantly affected by valve type. Flow characteristics are an important factor in selecting a control  
Figure 1: Configuration of the proposed cylinder-in-ball valve: (**a**) 3D model of the cylinder-in-ball valve; (**b**) perforated holes in the ball and cylinder; (**c**) coaxial counter-rotating motion of the cylinder and ball at an opening rate of 20%.  
value, and these are divided into three types: equal percentage, linear, and quick opening. Figure 2 shows the curves regarding the opening rates of inherent flow characteristics for various types of valves. The equal percentage characteristic describes a flow rate that increases exponentially with increases in the valve opening rate, and this is the most common characteristic of control valves. The linear characteristic refers to a flow rate that constantly increases with increases in the valve opening rate. The quick opening characteristic allows for a significant amount of flow to pass through the initial opening of the valve. Among the three characteristics, flow control valves that use the equal percentage characteristic make up 80% of the control valves used in industry because it shows higher efficiency in controlling major factors such as flow rate and pressure, and this feature provides sophisticated flow control even when a small opening rate is used.
## 3 Methodology
### Second Phase
#### 3.2.3 Specific Geomechanical Study  
For this experimentation phase, the geomechanical rock stations, previously defined, were used for a specific analysis. A set of geomechanical parameters was used to characterise rock quality, such as the rock quality designation (RQD), the geological strength index (GSI), the degree of weathering and the lithostructural groups. These geomechanical parameters made it possible to characterise the conditions of landslide and detachment susceptibility that currently exist in \"Las Cabras\" hill.  
Rock Quality Designation (RQD)  
According to [PERSON] [52], the RQD is a modified core recovery percentage, in which all pieces of sound core over 100 mm (4 in.) long are summed and divided by the length of the core run. The RQD index is an index of rock quality \"in that problematic rock that is highly weathered, soft, fractures, sheared and jointed is counted against the rock mass\" [52] (Table 1).  
For field observations, we applied the methodology given by [PERSON] [51] (Equation (1) and Table 2), where the RQD parameter is estimated using a correlation with the volumetric joint count (Jv).  
\[J_{v}=\sum_{i=1}^{n}\frac{1}{S_{i}}, \tag{1}\]  
where Si is the average spacing for the joint sets  
Weathering Degree, Geological Resistance Index (GSI) and Lithostructural Group  
The study of the weathering degree, which implies reduced resistance, altered physical state and variation of the tension state on the slope or slopes, is necessary for the  
\begin{table}
\begin{tabular}{c c} \hline
**RQD\%** & **Quality** \\ \hline \(<\)25 & Very poor \\
25–50 & Poor \\
50–75 & Fair \\
75–90 & Good \\
90–100 & Excellent \\ \hline \end{tabular}
\end{table}
Table 1: RQD values according to [PERSON] [52].  
\begin{table}
\begin{tabular}{c c} \hline
**RQD\%** & **Condition** \\ \hline RQD = 115–3.3 Jv & Si Jv \(>\) 4.5 \\ RQD = 100 & Si Jv \(\leq\) iJ \\ \hline \end{tabular}
\end{table}
Table 2: Correlation between RQD and volumetric discontinuity according to [PERSON] [53].  
engineering-geological evaluation [54; 55; 56]. The weathering degree was determined by visual observation and by the method proposed by [PERSON] [57], based on the International Society of Rock Mechanics (ISRM) reference value [53; 58] (Table 3).  
GSI is a system for characterizing the geomechanical properties of the rock mass through easy identification by visual evaluation of geological properties in the field [57]. The rock mass was characterised with the GSI using the table given by [PERSON] [59; 60]. Finally, the different materials were classified according to the lithostructural groups established by [PERSON] and [PERSON] [61]: strong massive rock (I), strong discontinuous rock (II), composite rock (III), tectonically weakened rock (IV), weak granular rock (V), karst rock (VI), anisotropic rock and ground-like rock (VII).
# Coverage, Rate, and Last Hop Selection in Multi-Hop Communications in Highway Scenarios
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project  
5G  Fifth-Generation  
6G  Sixth-Generation  
BS  Base Station  
BQ  Best Quality  
CQI  Channel Quality Indicator  
EMBB  Enhanced Mobile Broadband  
FO  First Opportunity  
HPBW  Half-Power Bandwidth  
IAB  Integrated Access and Backhaul  
IEEE  Institute of Electrical and Electronics Engineers  
ISD  Inter-Site Distance  
IMG  Incremental Multicast Grouping  
LoS  Line-of-Sight  
MCS  Modulation and Coding Scheme  
mmWave  Millimeter Wave  
NLoS  Non Line-of-Sight  
NOMA  Non-Orthogonal Multiple Access  
NR  New Radio  
OMA  Orthogonal Multiple Access  
PPP  Poisson Point Process  
PQ  Prescribed Quality  
SINR  Signal-Interference + Noise Ratio  
V2X  Vehicle to Everything  
V2V  Vehicle to Vehicle  
\(\lambda\)  Vehicle density on a lane, unit/m  
\(H_{T}\)  Antenna deployment height, m  
\(H_{A}\)  mmW BS height, m  
\(H_{V}\)  Road clearance, m  
\(d\)  Distance between BSs, m  
\(d_{s}\)  Minimum separation distance between vehicles, m  
\(v\)  Vehicle speed, m/s  
\(l_{v}\)  Length of vehicle, m  
\(N_{l}\)  Number of lanes  
\(M\)  Number of lanes of the same direction  
\(w\)  Lane width, m  
\(w_{v}\)  Vehicle width, m  
\(P_{E}\)  Technology penetration rate  
\(L_{A}(f,d)\)  Absorption loss, dB  
\(\tau\)  transmittance of the medium  
\(K\)  absorption coefficient  
\(P_{T}\), \(P_{T_{W}}\)  BS emitted power, dB/W  
\(G_{A}\), \(G_{U}\)  Antenna array gain, dB
## 2 Methodology
### ANN Modeling
#### 2.3.4 Practical Application of Real-Time Conformity Monitoring
These deviations are defined as follows:  
(1) 24 h Fluctuation\({}_{prediction}-24\) h Fluctuation\({}_{measurement}\)  
Figure 7: Diagram of test room (5 m \(\times\) 3 m \(\times\) 3 m). (2) T/RH band\({}_{measurement}\in\) T/RH band\({}_{prediction}\) (the measurement bands are included in the prediction control ones). These deviations provide a critical region between safe and dangerous conditions for preventive conservation. A positive value indicates that the actual micro-environment is more stable and safer than the predicted condition. Conversely, a negative value indicates that there is no critical region, and the actual conditions fall outside the scenarios captured by the trained ANN predictor, which triggers a built-in alarm. In such conditions, retraining the ANN with new measurement data is necessary. The test was run continuously for four days to assess the robustness of these ANN predictors working in real-time. If any alarms are triggered during the test, that ANN model should be retrained with new data to improve its robustness. Conversely, if no alarm was triggered, the robustness could be considered acceptable.
## 3 Results and Discussion
### TGA Analysis  
The curves of NR and LNR samples in the air atmosphere were investigated by a thermogravimetric analysis (TGA) instrument, which described the decomposition behavior of NR and LNR at different number-average molecular weights, as shown in Figure 14. Referring to Figure 14a, thermogravimetric (TG) curves are weight loss against temperature at a heating rate of 40 \({}^{\circ}\)C/min. The decomposition of NR and LNR samples occurred one step in the temperature range of 50-850 \({}^{\circ}\)C. The thermal degradation temperatures of NR and LNR at different molecular weights are listed in Table 1. The initial decomposition temperature (T\({}_{\rm i}\)) and final decomposition temperature (T\({}_{\rm f}\)) using a bitangent method were obtained TGA curve, and temperature at maximum weight loss rate (T\({}_{\rm m}\)), which can be obtained from the peak of the DTG curves, as shown in Figure 14b. It can be seen that the thermal degradation temperature of LNR samples decreased with a decrease in the M\({}_{\rm n}\). NR had a T\({}_{\rm i}\), T\({}_{\rm m}\), and T\({}_{\rm f}\) of 392 \({}^{\circ}\)C, 429 \({}^{\circ}\)C, and 469 \({}^{\circ}\)C, respectively, while all LNRs exhibited lower degradation temperatures when compared with NR. The T\({}_{\rm m}\) of LNR decreased from 428 \({}^{\circ}\)C to 398 \({}^{\circ}\)C when the M\({}_{\rm n}\) decreased from 50,023 to 10,825 g/mol. The thermal  
Figure 13: Correlation between the predicted values obtained from Equation (5) and experimental data with R\({}^{2}\) = 0.9771.  
degradation curves of LNR, compared to NR, shifted to lower temperatures. This shows that the reduction in molecular weight led to thermal instability resulting in a reactive material, which can be more prone to reacting with lower energy usage.
# Q-Pandora Unboxed: Characterizing Resilience of Quantum Error Correction Codes Under Biased Noise
## 4 Examining Noise in Quantum Computing
### Modeling Noise for Quantum Computing  
We have employed three noise models, inspired by the previously described noise types, to characterize QECCs. These models, code-capacity, phenomenological, and circuit-level, provide a comprehensive framework for various error scenarios in quantum computing [42; 43] (Figure 3).  
1. Code-capacity model: Assumes flawless measurements. It integrates a depolarizing probability at each round's start and a random gate error after every Clifford operation. This model, capturing both pre- and post-operation phases, gauges the full resilience of the error-correcting code.
2. Phenomenological noise model: Assumes perfection in all but measurements. This model includes the probability of a qubit undergoing an X or Z flip pre-measurement and post-reset.
3. Circuit-level noise model: Acknowledges potential imperfections throughout the quantum system, covering quantum gates, initialization, and measurements. It is the most comprehensive model, accounting for various errors: depolarizing probability at each round's inception, gate errors post Clifford operations, and qubit flips both pre-measurement and post-reset. This thorough approach provides a granular insight into each quantum operation's potential error contribution.