esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Adjustment of the UFPA Model through Genetic Algorithm (GA)
#### 3.3.1 UFPA Model Adjustment Objective Function  
To adjust the model, the Root Mean Squared Error (RMSE) was defined as the objective function, which calculates the difference between the predicted and measured values using (6), where \(n\) is the number of samples, \(Mv\) is the measured value, and \(Pv\) is the predicted value for the \(i\)-th sample.  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(M_{vi}-P_{vi})^{2}}{n}} \tag{6}\]
# Experimental Study on Dynamic Exhaust Law for Ventilation System of Gas Tunnel
###### Abstract  
The ventilation system is the only channel for gas exchange inside and outside the gas tunnel, which determines whether the high-risk gas in the tunnel can be exhausted in time after a gas leakage accident; thus, it is essential to study the dynamic ventilation characteristics. A fire-retarding division of the gas tunnel in Songjiang District of Shanghai is taken as the study object, and, based on the similarity theory, a similarity experiment platform is built. The simulation experiments of exhaust are carried out under different exhaust velocities, ventilation equivalent diameters and initial gas concentrations by using the control variable method. The changes in ventilation duration and gas concentration are analyzed in detail. The conclusions are as follows: (1) Time-concentration curves at all positions in the gas tunnel exhibit an \"asymptote\" distribution. (2) Average gas concentration has a linear relationship with time at the beginning and becomes an exponential relationship after a certain time. (3) When the exhaust velocity is 5 m/s, the initial gas concentration is 15%VOL; when the equivalent diameter of the vent in the model is 0.2 m, the ventilation duration is 100.2 s. A calculation model of ventilation duration for the standard gas tunnel is established, and the application limits of the model are \(1\leq v\leq 5\), \(0.5\leq d\leq 1.5\), and \(5\leq\varphi_{0}\leq 25\). (4) In practical engineering, an exhaust velocity of 5 m/s and an equivalent ventilation diameter of 1 m are relatively optimal ventilation parameters.  
gas tunnel; ventilation and exhaust; similarity experiment; ventilation duration; concentration variation +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Action Strategies against Coastal Flooding  
In the face of the socio-economic impact that a coastal flooding event can cause, such as in the case of Ria of Arosa, it is crucial to develop and implement various strategies of action. These strategies can help minimize the damage caused by flooding and the associated repair costs. Currently, it is proposed that the most appropriate action is to carry out a combination of structural and non-structural measures, which are based on coastal territorial planning [48; 49]. The preparation of these maps is above all useful to correctly establish territorial planning measures on the coast. In this work, different strategies focused on action against coastal flooding are presented.  
One of the most commonly employed and potentially effective strategies in addressing the risk of coastal flooding involves coastal infrastructure, which can be divided into two types of defenses.  
The first is hard engineering, which involves the construction and fortification of coastal structures (such as levees, breakwaters, groynes, and retaining walls). These measures can reduce the impact of the risk by mitigating wave intensity and coastal erosion, thus protecting populated areas and critical infrastructure. However, it is important to note that these measures may not be sustainable in the long term, as they can disrupt the natural physical environment, potentially altering the hydrodynamic regime.  
The second is soft engineering, primarily focused on beach replenishment to protect areas experiencing sediment loss due to erosion. This approach aims to safeguard against extreme weather events, such as storms.  
Another method that can be utilized is the restoration and management of coastal ecosystems like mangroves, wetlands, or marshes. These natural features can act as natural barriers against hazards, absorbing and retaining water [50; 51].  
Land use planning and urban development constitute another strategy against flood risk that can help reduce both damage and costs associated with natural events. This involves identifying high-risk zones and avoiding development in flood-prone areas, establishing buffer zones, and imposing construction restrictions in vulnerable areas. Promoting resilient construction practices in coastal regions is also crucial [51; 52].  
Passive strategies for mitigating flood risk include hazard response systems, early warning alerts, and community education and awareness. Hazard response and early warning systems entail monitoring and predicting sea levels, and disseminating clear information about flooding conditions and safety measures. This aids in predicting imminent flooding and the swift evacuation of populations in high-risk areas. Community education and awareness efforts involve providing information and promoting best safety practices, hazard response, and evacuation plans, as well as raising awareness about the importance of coastal systems [51; 53].
# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 4 Location and Installation of the Measuring Devices
### Bespoke Network
#### 4.2.2 Communication Devices  
The process starts with an initial design for the distribution of the communication devices, taking into account high places, to maximize reach and coverage in the city (which is set at between 300 and 600 m above sea level), and also that they have an electrical power supply. Then, the RF propagation is simulated [95] considering specific parameters, estimated for the city: average height (22 m, as the most unfavorable value, taking into account that the buildings can act as a screen), built-up density (75%) and electromagnetic conditions of the surrounding area; and of the devices: height (5 m for AWSs and 3 m for the communication device), transmitter power (23 dBm), sensitivity of the receivers (\(-139\) dBm), antenna gain (2 dB emitters, 10 dB receivers), internal loss in the devices, reception threshold (\(-139\) dB), and radiation patterns.  
The locations are validated when the simulated theoretical coverage between the communication devices and the AWS is acceptable (a value of 50 is equivalent to an SNR of 0 dB; each unit represents 1 dB in the SNR) (Figure 5). An error margin of 10 dB is established, considering that the simulator provides values above the real ones due to the lack of measurements from the electromagnetic background noise (from buildings, trees, and other signal barriers). It can be seen that, of the 25 AWSs, 16 are covered by the three antennas and nine are covered by two, which backs up the effectiveness of the data dispatch, ensuring reception.  
Then, an in situ verification is carried out, using an emitter device, with AWS and a pole to simulate the projected height, as well as a receiver with a LoRa antenna and gateway connected to the internet via a router or SIM and powered by a transformer connected to a vehicle. The ChirpStack is accessed, configured in the local network, and the AWS plots are captured in real time. This allows the signal levels to be evaluated until an SNR higher than 3 dB is found. Thus, the final placing of the 25 AWSs and the communication devices in small perimeter chains around the city are decided: A, fixed to a lamppost (at an altitude of 454 m above sea level on the 'La Montana' road), and B and C in potable water treatment stations (at 490 m above sea level in 'Sierrilla' and 519 m above sea level in the 'Cerro de los
# Test Bed of Semantic Interaction of Smart Objects in the Web of Things
## 3 Architecture Deployed
### Application Layer  
This layer hosts the applications that will allow the user to interact with the information to define the interaction services desired by the user. The applications and interfaces used are listed:  
* Clipio: a mobile application that manages the objects of the test bed. It communicates its requests through the broker server, in this way it can create and eliminate interaction services, modify the states (turn on and off) of the basic services and interaction, and consult metadata about the objects. Clipio is developed under Android Lillipop (5.0 or higher); jdom (handling of XML), Paho (management of client MQTT) and kSOAP 2 (handling client SOAP) libraries were used. The application was installed on a LG Nexus 5 mobile device.
* NFC tag: corresponding to an entity or smart object. When aimed at an entity, the tag contains the name of the entity and its global position (longitude and latitude). Mifare Ultralight adhesive tags were used. The tag identified the room of the house and the coordinates in which the other nearby smart objects were displayed.
* User panel: presents information about the test bed such as instantaneous states of each resource of the objects, general information about the operation of each object, real-time information of the interaction activities between the objects. This information is stored and subsequently used in the evaluation of the test bed. It is developed using Html, Javascript, Php, and Ccs. For the database MySQL is used.
* Initialize object: a program in Python that initializes the smart object for the first time, retrieving the metadata from the object of the indexing service and requesting the user for missing data. It synchronizes the physical representation with the digital representation of the object (SOO). This interface interacts with the user when the object is registering in the system. It is supported in the Python \"SmartObject.py\" directory.
# Halophyte Plants and Their Residues as Feedstock for Biogas Production--Chances and Challenges
## 3 The Potential Use of Halophytes
### What Makes Halophytes Interesting as a Biomass Resource for Biogas Production?  
Anaerobic digestion (AD) of plant material is generally regarded as a sustainable method to exploit the energetic value of organic matter with simultaneous nutrient recycle when using the digestate as fertilizer. Especially when treating agricultural residues and livestock waste in biogas plants, the reduction of CO\({}_{2}\) emissions is two-fold by: (1) producing renewable energy to replace fossil-based energy resources and (2) reducing greenhouse gas (GHG) emissions that are released without AD treatment.  
Land and water resources are limited, thereby creating frictions and competition for these limited resources, and raising concerns about food security. Biofuel production, except when based on agricultural residues and waste, requires land; therefore, it competes with other economic and agriculture activities, urbanization and with some environmental objectives, especially the protection of biodiversity and carbon sequestration [67]. Currently, the amount of land devoted for growing crops for energy purposes is only 0.19% of the world's total land area and only 0.5-1.7% of global agricultural land [68]. With increasing production of energy crops, however, the amount of available land for food crops and the supplied quantity of grains, as is the case of maize, will decrease; therefore, the quantity demanded for basic grains rises, combined with an increase in prices [67]. From a sustainability perspective, the use of crops such as maize for biogas production requires land, and thereby, may be in competition for food and feed production and might lead to indirect land use changes (ILUC) and distortion of food prices, as stated before. Consequently, crop-based biogas production that was established earlier in countries such as Germany for example [68] is no longer supported, and biogas plant operators are looking for new feedstocks with a high biogas yield of which the use is environmentally sound; halophyte plants and their residues could fill this gap.  
Based on this, biomass from energy crops is one important factor for biogas production. As mentioned above, land and water are limited resources; consequently, the biomass obtained from energy crops depends in part on land and water availability. Therefore, new cultivation techniques and the use of other potential crops to exploit areas that are not suitable for traditional crops (coastal areas, saline soils) are necessary. Furthermore, due to the perennial life span of many halophytes, it allows harvesting for several years without reseeding while maintaining high biomass production with adequate lignocellulosic properties [1,69]. As biogas production is a tailored process to exploit the energetic value of halophytes plants grown on saline soils on the one hand, the range of feedstocks for biogas plants would be broadened.
## Materials and Methods
### Data Analysis  
Quantitative data were analyzed to determine the correlation of the questionnaire items and their factors: (i) display of responses to the item; (ii) reliability analysis; (iii) analysis of partial correlations between items; (iv) SEM with the variable practice opportunities as a predictor of opportunities to promote curiosity, motivation, perseverance, and regulation of self-learning. SEM allowed estimating the strength of the correlation and the degree to which the model fits the observed data [81]. Additionally, it allowed accounting for measurement error, gaining information on the variability explained by the latent construct and what cannot be explained by it. To fit and adjust the model, we used the total information maximum likelihood estimation to manage missing data [82].  
Qualitative data analysis was performed using CCM since it is helpful to identify underlying and subjacent themes in interviews [83]. It was carried out deductively and inductively. The previous categories were derived from the definitions found in the literature regarding practice opportunities and lifelong learning tendencies. Other categories were deduced from empirical data collected throughout the interviews [84]. Therefore, categories were built partly theoretically (from the definition of each of the variables) and partly with empirical data (emerging categories). Using ATLAS.ti9, the texts were fragmented into smaller parts associated with codes. Each new segment was compared with previous codes, so similar segments were associated with the same category [85]. After this, the co-occurrences among codes were explored, and each was revisited, revising the original texts.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **QUAN** & **Qual** \\  & & **n = 231** & **n = 8** \\ \hline \multirow{3}{*}{Gender} & Woman & 223 & 7 \\  & Man & 7 & 1 \\  & Prefer not to answer & 1 & - \\ \hline \multirow{3}{*}{Age (years)} & \textless{}20 & 14 & 2 \\  & 20–22 & 95 & 2 \\  & 23–25 & 90 & 2 \\  & \textgreater{}25 & 32 & 2 \\ \hline \multirow{3}{*}{Program} & Early childhood & 114 & 3 \\  & Elementary & 91 & 2 \\  & Secondary & 26 & 3 \\ \hline \multirow{3}{*}{Years in program} & 1 & 43 & 2 \\  & 2 & 41 & 2 \\ \cline{1-1}  & 3 & 55 & 2 \\ \cline{1-1}  & 4 & 67 & 2 \\ \cline{1-1}  & Five or more & 25 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Socio-demographic characteristics of the participants.  
Triangulation between quantitative and qualitative data was done to confirm and add depth to the relationship found.
## 3 Results and Analysis
### Prediction of Various Dependent Variables
This was not expected to correlate strongly with the variables used as the other inputs. The F1 score--which represents the harmonic average of precision and recall--measured most output scores considerably lower than their accuracy scores. This is because the data were concentrated on certain elements when predicting elements lower than the output variable; furthermore, because of the nature of the algorithm, the prediction method may have been more concentrated on variables with considerable data. However, in the case of severity, the accuracy and F1 score values were very similar; the injured part was predicted with six elements but showed a tendency to decrease slightly compared to other variables. Although it \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Output** & **Method** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{3}{*}{Year} & Ensemble & 34.50\% & 41.93\% & 26.63\% & 32.57\% \\ \cline{2-6}  & SVM & 26.69\% & 16.89\% & 21.08\% & 18.75\% \\ \hline \multirow{3}{*}{Type of work} & Ensemble & 66.04\% & 30.28\% & 21.79\% & 25.34\% \\ \cline{2-6}  & SVM & 64.46\% & 23.06\% & 16.54\% & 19.26\% \\ \hline \multirow{3}{*}{Type of accident} & Ensemble & 50.34\% & 32.05\% & 37.12\% & 34.40\% \\ \cline{2-6}  & SVM & 37.24\% & 28.72\% & 19.44\% & 23.19\% \\ \hline \multirow{3}{*}{Injured part} & Ensemble & 48.86\% & 29.31\% & 27.10\% & 28.16\% \\ \cline{2-6}  & SVM & 45.03\% & 36.26\% & 22.71\% & 27.93\% \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Assailing materials \\ \end{tabular} } & Ensemble & 53.27\% & 28.63\% & 33.86\% & 31.03\% \\ \cline{2-6}  & SVM & 48.34\% & 27.25\% & 15.15\% & 19.47\% \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Cause of accident \\ \end{tabular} } & Ensemble & 69.89\% & 31.47\% & 24.95\% & 27.84\% \\ \cline{2-6}  & SVM & 66.24\% & 33.51\% & 21.08\% & 25.88\% \\ \hline \multirow{3}{*}{Severity} & Ensemble & 67.29\% & 68.60\% & 65.14\% & 66.82\% \\ \cline{2-6}  & SVM & 55.45\% & 52.59\% & 54.40\% & 53.48\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Output performance in terms of accuracy, precision, recall, and F1 score. did not achieve high accuracy in predicting various outputs, predictions were possible to some extent, and because separate correlations may exist between variables and elements, a detailed analysis of each output was conducted.
# An Efficient Causal Group Communication Protocol for Free Scale Peer-to-Peer Networks
## 2 Materials and Methods
### System Model  
A free scale peer-to-peer network consists of peers that are connected only to super peers and peers that have a direct connection with other peers. Based on this, a free scale peer-to-peer network can be divided into two groups (see Figure 1):  
1. Internal group (internal peers that are connected only a super peer); and
2. External group (interconnected external peers).  
Peers that form an internal group are called internal peers. Peers in an external group are called external peers (see Figure 1). Peers in a free scale network can only belong to one group (internal or external). A super peer is a member of both groups at the same time. An internal group can have only one super peer, while an external group can consist of several.  
A super peer is a special node with major processing and bandwidth capacities in comparison to peers. Peers in an internal group are considered to have lower processing power or bandwidths compared to external group peers. Peers in an internal group can be represented by mobile phones and tablets, among others, connected by a cellular network to the Internet. A super peer in an external group can be seen as a meta-process [15] representing all of the events in an internal group and as a meta-process representing all of the events in the external group for peers in the internal group. With this representation, peers in the internal group do not require an extensive knowledge about peers in an external group and vice versa.  
The communication channels are considered to be reliable with random but finite delays. Thus, every message will eventually arrive to its destination process. In addition, the channels are considered to be non FIFO (First In, First Out) channels. This implies that messages can be reordered by a communication channel.
# Algorithms in Low-Code-No-Code for Research Applications: A Practical Review
## 5 LCNC Used in Existing Research
### Applying AI/ML Algorithms  
The AI/ML algorithms supported by modern LCNC platforms include regression, clustering, deep learning decomposition analysis, and others.
# High-Resolution Spaceborne SAR Geolocation Accuracy Analysis and Error Correction
###### Abstract  
High-accuracy geolocation is crucial for high-resolution spaceborne SAR images. Most advanced SAR satellites have a theoretical geolocation accuracy better than 1 m, but this may be unrealizable with less accurate external data, such as atmospheric parameters and ground elevations. To investigate the actual SAR geolocation accuracy in common applications, we analyze the properties of different geolocation errors, propose a geolocation procedure, and conduct experiments on TerraSAR-X images and a pair of Tianhui-2 images. The results show that based on GNSS elevations, the geolocation accuracy is better than 1 m for TerraSAR-X and 2 m/4 m for the Tianhui-2 reference/secondary satellites. Based on the WorldDEM and the SRTM, additional geolocation errors of 2 m and 4 m are introduced, respectively. By comparing the effectiveness of different tropospheric correction methods, we find that the GACOS mapping method has advantages in terms of resolution and computational efficiency. We conclude that tropospheric errors and ground elevation errors are the primary factors influencing geolocation accuracy, and the key to improving accuracy is to use higher-accuracy DEMs. Additionally, we propose and validate a geolocation model for the Tianhui-2 secondary satellite.  
SAR geolocation; tropospheric correction; TerraSAR-X; Tianhui-2 Article
## 3 Computational Experiments Comparing MOMmCAA to Other Algorithms
### Benchmark Instances  
A total of \(27\) benchmark instances with complicated characteristics were used to compare the performance of the proposed MOMmCAA: DLTZ1-DLTZ7, ten quadratic problems, and ten CEC2020 test instances. These problems exhibit various characteristics, such as a convex, concave, mixed, disconnected, or degenerated PFs and a multimodal, biased, deceptive, and nonlinear variable PS.  
For each instance, the compared algorithms are ranked according to the performance metrics, with the ranks shown in square brackets. The mean rank (MR) for each algorithm for each instance is also presented in the tables. As a result of the Wilcoxon rank sum test at \(5\%\) significance level, a result labeled \(+\) denotes that the compared algorithm outperforms the MOMmCAA; in contrast, \(-\) means that the MOMmCAA has a better performance than the compared algorithm, while \(\approx\) means that there is no statistically significant difference between MOMmCAA and the compared algorithm. The data in orange in every table show the best mean metric values yielded by the algorithms for each instance over \(30\) independent runs.
# Performance of Cooperative Relay NOMA with Large Antenna Transmitters
## 4 Numerical Results and Analyses
### BER Performance  
For BER performance evaluation, we use (12) and (15) that are based on the channel coefficients between the TX, RS and UEs. The transmissions between TX and UEs through were further examined using (20) and (21). The BER of UE\({}_{1}\) and UE\({}_{2}\) was estimated using (32) and (35), respectively. Figures 7 and 8 show the BER for various SNR values in dB for AF and DF coding schemes. The number of TX antenna elements in the proposed 3D GBSM channel model is sixty-eight (68) for both URA and CA. The performance of the 3D model using CA at the transmitter achieves better at low SNR values as compared with the URA in Figures 7 and 8. In both cases of Figures 7 and 8, the performance of UE\({}_{1}\) is better than UE\({}_{2}\). This is because the received power at UE\({}_{1}\) is higher than that of UE\({}_{2}\) since it is closer to the RS.  
The reason is due to the high unpredictability of the GBSM parameters and high computational complexity, which causes a significant amount of noise signal in the channel and at the RS.  
Figure 6: Achievable rates of DF and AF schemes of the proposed 3D channel model with (**a**) URA and (**b**) CA.  
Figure 7: BER of AF and DF schemes of the proposed 3D channel model with (**a**) 3D URA and (**b**) 3D CA.