esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Bayesian Network Model
#### 2.4.1 Learning Structure  
In our approach, we learned the BN from data [29; 30]. We utilised a bootstrap approach [31] to generate 1000 samples of the BN using a constraint-based algorithm, named Incremental Association Markov Blanket (IAMB) [32]. The IAMB algorithm effectively constructs Bayesian networks by incrementally including variables based on mutual information [33], the latter of which quantifies the statistical relationship between variables. This approach helps to identify relevant variables and construct a network that precisely represents the relationships found in the data.  
A bootstrap approach ensures that the results are robust and helps minimise false patterns. This method outputs a summary table, illustrated in Table 1, that shows pairs of variables (called \"features\") and their relationships. The first two columns, \"From\" and \"To\", show the features associated with the outcome. The last two columns, \"Strength\" and \"Direction\", indicate the likelihood of a connection between the pairs and the direction of this connection. This process helps us understand which variables are related to the outcome, how strong their relationships are, and the direction of these relationships [34].  
We only selected feature pairs where our outcome was present in either the \"From\" column or the \"To\" column. This selection was made because we are specifically interested in understanding the relationship between the outcome and the features at baseline. Additionally, we filtered only those directions with a probability greater than 50% and then ordered the summary table in descending order based on strength.  
Next, we began constructing the first BN using only the first row of the table. As depicted in Table 1, this initial BN would consist of two nodes, with the \"feature 1\" node having an arrow pointing towards the \"outcome\" node (Figure 2A). We then proceeded to build another BN using the first two rows. In this case, our outcome node receives arrows from both the \"feature 1\" and \"feature 2\" nodes, as illustrated in Figure 2B. We continued this process until all the connections from the bootstrap output had been used.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Index** & **From** & **To** & **Strength** & **Direction** \\ \hline
1 & Feature 1 & Outcome & 0.93 & 0.86 \\
2 & Feature 2 & Outcome & 0.84 & 0.74 \\
3 & Outcome & Feature 3 & 0.75 & 0.55 \\
4 & Feature 4 & Outcome & 0.41 & 0.66 \\
5 & Feature 5 & outcome & 0.21 & 0.77 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bootstrap output example.
# Performance Analysis of Pile Group Installation in Saturated Clay
## 3 Two-Pile Group Analyses
### Displacement of Adjacent Pile  
#### 3.1.1 Lateral Displacement  
In the process of subsequent pile installation, the lateral displacement of the adjacent pile varies. Figure 8 illustrates the lateral displacement distribution of the adjacent pile along the pile shaft with different penetration depths (\(h\)) of the subsequent pile. During the initial stage of subsequent pile penetration, i.e., \(h<0.4\ L\), the lateral displacement of the adjacent pile decreases with pile depth. As the penetration depth of the subsequent pile increases, the location of the peak lateral displacement moves downwards. Once the  
Figure 6: Schematic diagram of the two-pile group installation.  
Figure 7: Schematic diagram of the separate auxiliary tube method.  
penetration of the subsequent pile is complete (i.e., \(h=L\)), the peak lateral displacement occurs at the pile tip.  
In practice, the pile center distance (\(S\)) is usually set as 4-6 times the pile diameter. Figure 8(a) compares the lateral displacement distribution of the adjacent pile with different spaces, i.e., \(S=4\)\(\mathrm{D_{e}}\), \(4.5\)\(\mathrm{D_{e}}\), \(5\)\(\mathrm{D_{e}}\), \(5.5\)\(\mathrm{D_{e}}\), \(6\)\(\mathrm{D_{e}}\). The results indicate that lateral displacement initially increases and then decreases with increasing depth from the pile top, with the peak lateral displacement occurring in the lower-middle part of the pile, approximately 0.7 \(L\) from the pile top. The peak lateral displacement of the adjacent pile is negatively correlated with \(S\). As the center distance increases, the peak lateral displacement of the adjacent pile decreases. Figure 8(b) shows that with small \(L/\mathrm{D_{e}}\), the lateral displacement decreases with depth, whereas the peak lateral displacement occurs at the pile top. For large length-to-diameter ratios (\(L/\mathrm{D_{e}}>15\)), the peak lateral displacement occurs approximately 0.7 \(L\) from the pile top. In addition, the peak lateral displacement of the adjacent pile increases with higher \(L/\mathrm{D_{e}}\), which is mainly due to the fact that the lateral bending stiffness of the pile reduces with the increase in \(L/\mathrm{D_{e}}\).
## Materials and Methods
### Characterization  
The morphology and elemental composition of the adsorbents were analyzed via SEM (GeminiSEM500, Carl Zeiss AG, Jena, Germany) and EDS (X-max ExtremeX, Oxford Instruments, Abingdon, UK). The specific surface area and pore size of the adsorbents were determined based on the Barrett Joyner Halenda (BJH) adsorption model and the t-plot micropore model using a specific surface area and porosity analyzer (BET, ASAP2020, Micromertics Instruments, Norcross, GA, USA). XRD (Rigaku, Rigaku Corporation, Tokyo, Japan) was used to analyze the crystal structure and material composition of the adsorbents in the 20 range of 2-80\({}^{\circ}\) using CuK\(\alpha\) radiation. The functional groups of nZVI@ATP were analyzed at 4000-400 cm\({}^{-1}\) via FTIR spectroscopy (Vertex70; Bruker, Ettlingen, Germany; 1 mg sample and 110 mg of KBr). The zeta potential of the samples was determined using a zeta potential tester (Nano-ZS90, Malvern Panalytical, Malvern, UK). XPS (Kratos AXIS Ultra DLD, Kratos, Manchester, UK) was performed to analyze the valence states of the major elements in the adsorbents after calibration based on the C1s peaks. The Ni\({}^{2+}\) concentration in aqueous solutions was determined via FAAS (220 FS, Varian, Palo Alto, CA, USA).
## 4 Experimental Results and Analysis
### Comparison of TCP-Based and UDP-Based Transmission on the Network-Side  
To conduct real-time analysis of Modbus UDP and Modbus TCP, data transmission/reception tests were performed for both protocols under ideal conditions (0% packet loss rate and 0% out-of-order rate) and weak network conditions (30% \(P_{loss}\) rate and 10% out-of-order rate simulated by the weak network simulation tool Clumsy). Latency is measured through Python and Wireshark packet capture: while Python sends requests and  
Figure 18: Photograph of the designed gateway prototype used for experimental testing.  
receives responses, Wireshark captures the packets; then the Modbus packets captured by Wireshark are filtered out and exported in 'csv' format; and finally the exported 'csv' file is imported into Python to calculate the latency.  
Figure 19a shows the Request-Response Latency comparison between 100 Modbus UDP and Modbus TCP requests sent to the slave device under ideal conditions. Figure 19b,c display the packet capture results of 100 Modbus TCP and Modbus UDP requests sent to the gateway under weak network conditions, while Figure 19d compares their Request-Response Latency.  
Figure 19a reveals that under ideal conditions, the Request-Response Latency of TCP-based and UDP-based transmissions is nearly identical, with a difference below 1 ms. Figure 19b-d demonstrate that under weak network conditions, UDP-based transmission exhibits smaller latency fluctuations, whereas TCP-based transmission suffers from frequent congestion control mechanism triggers. This results in the Request-Response Latency of Modbus UDP transmissions remaining stable at approximately 5 ms, while the latency for Modbus TCP packet transmissions exhibits significant instability.  
Figure 19: (**a**) Comparison of Request–Response Latency between UDP-based and TCP-based transmissions under 0% packet loss and 0% out-of-order conditions. (**b**) Packet capture diagram of UDP-based transmission under weak network conditions. (**c**) Packet capture diagram of TCP-based transmission under weak network conditions. (**d**) Comparison of Request–Response Latency between UDP-based and TCP-based transmissions under weak network conditions.  
Therefore, Modbus UDP demonstrates significant advantages over Modbus TCP in real-time performance under weak network conditions.
# Strategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing
## 6 Portfolio Optimization Implementation
### Qibo Model
To calculate the Sharpe ratio of a strategy, we test the strategy on different assets and calculate the Sharpe ratio in accordance with the results of the backtest. We can now formulate the first part of our QUBO formulation. Suppose there are \(l\) strategies available. The following equation determines which fraction of the budget should be spent on which strategy, based on the Sharpe ratio of the respective strategy:  
\[-\sum_{i=0}^{7l-1}2^{i\text{ }mod\,7}SR_{floor(i/7)}x_{i} \tag{21}\]  
As explained, we dedicate 7 bits to each strategy in order to enable the approach to dedicate only a certain fraction of the whole capital to a strategy within a portfolio. Notice that the bits \(x_{i\text{ }mod\,7},x_{(i+1)mod\sigma},\ldots,x_{(i+6)mod\sigma}\) for \(i=7s,s\in\{\,0,\ldots,l-1\,\}\) constitute for one of the \(b_{i}\) as introduced in Equation (20). Thus, in the above equation the Sharpe ratio \(SR\) of a single strategy is used 7 times. Hence, \(SR_{k}=SR_{k+1}=\ldots=SR_{k+6}=SR_{floor(k/7)}\) with \(k=7s,s\in\{\,0,\ldots,l-1\,\}\). The Sharpe ratio here is the value calculated by the backtesting of the preprocessing. As diversification is an important measure to reduce risks involved in investing, we add a second term that uses the covariance between different strategies, analogously to [PERSON] et al. [5], which fosters the creation of well diversified portfolios. \[\sum_{i=0}^{7l-1}\sum_{j=i+1}^{7l-1}cov(s_{floor(i/7)}s_{floor(j/7)})x_{i}x_{j} \tag{22}\]To complete our approach, we add a final term, which penalizes both overspending and underspending of the available budget:  
\[(127-\sum_{i=0}^{7l-1}2^{i\ mod\ 7}x_{i})^{2} \tag{23}\]  
As this was the last step of our approach, the final QUBO formulation of our approach is given as  
\[-\theta_{1}\sum_{i=0}^{7l-1}2^{i\ mod\ 7}SR_{floor(i/7)}x_{i} \tag{24}\] \[+\theta_{2}\sum_{i=0}^{7l-1}\sum_{j=i+1}^{7l-1}cov(s_{floor(i/7) }s_{floor(j/7)})x_{i}x_{j}\] \[+\theta_{3}(127-\sum_{i=0}^{7l-1}2^{i\ mod\ 7}x_{i})^{2}\]  
Remember, \(l\) denotes the number of available strategies, while parameters \(\theta_{1},\theta_{2},\theta_{3}\) are weights to give more importance to different parts of the QUBO model. For example, to emphasize minimizing the risk, \(\theta_{2}\) should be increased. As mentioned earlier, if the investor needs a finer breakdown of the capital, all 7s in Equation (24) needs to be replaced by the number of bits the investor needs. Additionally, in Equation (24), the value 127 needs to be replaced by the maximum number representable by the chosen number of bits.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 6 Open Issues
### Link Conditions  
Satellite links are hampered not just by the large propagation delay but also by the complicated link circumstances. The satellite communication link must pass through the atmosphere in order to reach the ground because satellites are propelled through space at speeds of hundreds to thousands of kilometers per hour. As a result, weather variables, including rain, clouds, water vapor, and fog, can affect satellite communication systems. Rain attenuation is the most important factor for spectrum over 10 GHz [51].  
Different from terrestrial links, satellite links may experience severe channel fading due to poor weather, resulting in a breakdown in communication. Considering the long propagation delay of satellite links, obtaining timely and accurate channel state information is more difficult compared with terrestrial networks. MEO/LEO satellites travel at a rapid rate in relation to the earth. The satellite-e-ground linkages thus undergo a greater Doppler shift, a greater phase shift, and a faster time variation.  
An HSTN relay network architecture can be used to improve transmission when the communication links between the satellite and users are unstable due to unfavorable weather circumstances or high channel dynamics because of the complex characteristics of satellite link conditions. Nevertheless, the link conditions are seriously unbalanced in a relay network for an HSTN, and this imbalance must be addressed, as the relaying performance is determined by the \"weakest link\".
## 3 Adaptive Thresholds for Robust Fault Detection
### Residual Evaluation  
The goal of the methodologies presented in the subsection is to identify the normality of data in the system and to optimally determine a pair of adaptive thresholds for identifying anomalous data and transient faults. In the proposed detection system, our residual evaluation is divided into two parts: anomaly filtering and fault detection. After the first filtering identifies anomalous data for the generated residuals, the second is to detect the transient fault from the identified anomalous data. To perform each of the anomaly filtering and fault detection, the two thresholds are required. The first threshold provides a certain boundary that is able to distinguish a region with anomalous data from a region with normal data. The second dynamic threshold indicates a certain boundary that is able to distinguish transient faults from the anomalous data.  
Before performing residual evaluation using two thresholds, an adaptive threshold pool needs to be preconfigured in the proposed architecture. It has one pair of two thresholds for each operation of the Jackal UGV, which is generated by using a normality model and an optimization tool. Many possibilities for the anomalous regions defined by the normality model are used to provide for optimal threshold generation. Non-linear programming is applied for one optimal pair for each operation.
## 4 System Level Perspectives of JCRS RF Hardware
### Beyond the Hardware  
As mentioned in the previous sub-sections, the intended waveforms and related signal processing play an important role in the use of the hardware. Waveforms are by far the most investigated topic in this field, considering different high energy efficiency and high spectral efficiency options [89; 90]. A reconfigurable and flexible physical layer will definitely help to balance out any over-design requirements. Ref. [91] proposed a multifunctional receiver to facilitate the spectral convergence. In the medium access protocol (MAC) layer, new algorithms, specifically machine learning/artificial intelligence-aided algorithms need to be integrated. The networking layer needs to account for the additional latency and security requirements in critical applications. The security aspects while using beamforming will be of paramount importance [92]. Latency will be important for RaaS as well. New networking concepts for 6G integrating IoT devices need to be considered [93]. The possibilities will also be determined by the standardization activities. The overall software and operating systems supporting the devices using JCRS need to account for additional privacy or security requirements of data collection and usage.
# Optimization Strategy for Electric Vehicle Routing under Traffic Impedance Guidance
## 2 Establishment of the Electric-Power-Traffic Network Model
### Dynamic Traffic Network Model
#### 2.2.1 Speed-Flow Model  
For road travel speed, existing research mostly simplifies its impact by setting it as a constant value. However, in reality, vehicle travel speed is limited by both the road traffic volume and the maximum capacity of the road. This paper considers the influence of real-time traffic volume and establishes a speed-flow model to analyze the real-time travel speed of the road [23], as shown in Equation (4):  
\[\left\{\begin{array}{l}v_{ij}(t)=\frac{v_{ij0}}{1+\left(\frac{q_{ij}(t)}{M_{ ij}}\right)^{n}}\\ h=a+b\cdot\left(\frac{q_{ij}(t)}{M_{ij}}\right)^{c}\end{array}\right. \tag{4}\]  
The equation consists of the following variables: v\({}_{ij}\) represents the driving speed of an EV on road segment (i,j) at time t; v\({}_{ij0}\) represents the zero-flow speed of segment (i,j), which is the speed limit for vehicles under free-flow conditions, set as 60 km/h in this paper; M\({}_{ij}\) denotes the maximum capacity of road (i,j), which varies depending on different road grades; q\({}_{ij}\) represents the real-time traffic flow between nodes i and j at time t; a, b, and c are specific parameters of the model, with values set as a = 1.726, b = 3.15, and c = 3 in this paper. Compared to previous research that assumes a constant driving speed for EVs or determines the driving speed based on a normal distribution, this model calculates the EV driving speed based on the real-time traffic flow on each road segment. As the power consumption of EVs is closely related to driving speed, this model allows for a more accurate estimation of power consumption that varies with road conditions, providing a more precise characterization of energy consumption in EV trips.
# An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders
## 2 Multi-Objective Optimization Applied to Underwater Glider Path-Planning  
With this work, authors want to address the problem of multi-objective glider path planning using the evolutionary algorithm NSGA-II. This algorithm has been widely used for MOOP since its creation [3], becoming a standard. This algorithm is still considered a competitive candidate in many optimization challenges, and has never been used for the kind of glider path planning problems proposed in this paper.  
Some basic preliminary results were obtained in the conference paper [55], as an initial exploratory proof of concept. Here the idea is extended, formally defined, and the experimental design fully developed in order to get insight into the algorithm behaviour, and obtain statistically significant conclusions.  
This application problem is of special interest from the optimization point of view, due to the special vehicle operation characteristics and the complex environment uncertainties. On the other hand, the results could have many practical applications, in the form of decision support tool for multi-objective glider missions, since they are always defined as multi-purpose.  
Regarding the related work previously presented, the closer approaches are [43; 53]. The first one also analyses NSGA-II parameters influence in a MOPP problem, but the robot behaviour (it is not a glider) is much simpler there, and the scenario is 2D and does not include temporal variation, so building a path is not conditioned by the environment like in our case. The second work, similarly considers the use of gliders, but no simulation details are provided, and the navigation problem seems to be simplified, discarding temporal and 3D factors; additionally, the use of intermediate and target waypoints, instead of headings, introduce side effects depending on the precision ratio defined.  
The main contribution of this work is two-fold: on one side, provide results in a new application problem for the NSGA-II algorithm; on the other side, contribute to the building of navigation assistance systems for glider pilots, or even autonomous navigation system, in complex missions.
# Mapping Gaps in Sugarcane Fields in Unmanned Aerial Vehicle Imagery Using YOLOv5 and ImageJ
###### Abstract  
Sugarcane plays a pivotal role in the Brazilian economy as a primary crop. This semi-perennial crop allows for multiple harvests throughout its life cycle. Given its longevity, farmers need to be mindful of avoiding gaps in sugarcane fields, as these interruptions in planting lines negatively impact overall crop productivity over the years. Recognizing and mapping planting failures becomes essential for replanting operations and productivity estimation. Due to the scale of sugarcane cultivation, manual identification and mapping prove impractical. Consequently, solutions utilizing drone imagery and computer vision have been developed to cover extensive areas, showing satisfactory effectiveness in identifying gaps. However, recognizing small gaps poses significant challenges, often rendering them unidentifiable. This study addresses this issue by identifying and mapping gaps of any size while allowing users to determine the gap size. Preliminary tests using YOLOv5 and ImageJ 1.53k demonstrated a high success rate, with a 96.1% accuracy in identifying gaps of 50 cm or larger. These results are favorable, especially when compared to previously published works.  
planting failure; productivity estimation; semi-perennial
## 3 Instrument Characteristics and Tests
### Characteristics of Radiometer Sub-Systems
#### 3.1.2 Front-End Loss  
The total loss \(L_{\mathrm{front}}\) of the front-end determines the residual noise \(T_{\mathrm{RM\,0}}\) of the radiometer. Based on the specifications (Appendix) of the front-end components (SW, ISO1, BP1, and three semi-rigid coaxial cables with SMA connectors) the total loss at the center frequency 1,314.5 MHz was estimated as \(L_{\mathrm{front}}\approx\) 1.42 dB, yielding \(T_{\mathrm{RM\,0}}\approx\) 125 K for the system temperature \(T_{\mathrm{0}}\) = 313 K (see Section 2.1.1). Figure 5b shows the measured frequency transfer function of the front-end. For the different switch inputs the measurements were within 0.015 dB, and the repeatability of consecutive measurements was better than the sensitivity of the measurements (\(\approx\)0.005 dB).  
The frequency response of the front-end is dominated by the characteristics of the BP1 with the specified \(-\)3 dB bandwidth of 22 MHz at the radiometer center frequency 1,314.5 MHz. At this frequency, the overall front-end loss measured is \(L_{\mathrm{front}}\approx\) 1.09 dB, which is well below the value expected from the specifications of the front-end components. Accordingly, the residual noise estimated from the measured \(L_{\mathrm{front}}\) is \(T_{\mathrm{RM\,0}}\approx\) 108 K, which is smaller than \(T_{\mathrm{RM\,0}}\approx\) 125 K estimated using the component specifications.