esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation Study on an ICT-Based Maritime Management and Safety Framework for Movable Bridges
###### Abstract  
Maritime management is a crucial concern for movable bridge safety. Irregular management of water vehicles near movable bridges may lead to collision among ships and bridge infrastructures, causing massive losses of life and property. The paper presents a theoretical framework and simulation of an intelligent water vehicle management system for movable bridges corresponding to vehicle traffic responses. The water regime around the bridge is considered in virtually separated domains to estimate the desired safety actions based on the position of the approaching ships. An emergency clash avoidance control system is represented to prevent ship-infrastructure collision and ensure transportation safety. In addition, a simulation platform is developed specifically adaptable for movable bridge maritime and dynamic traffic management. The proposed theory is experimented using the simulation platform for different ship speeds and bridge-vehicle traffic volumes. Based on analyzing the velocity profile of approaching ships at different incidents, the bridge is found incapable of evacuating vehicles and unable to open promptly in case of speeding ships and high traffic density of vehicles on the bridge. Computational results show that the emergency control system is effective in reducing ship speed and prevent certain collisions. Lastly, the transportation policy for the newly proposed maritime management system is validated by real-world implementation in movable bridges across the world.  
movable bridge; maritime management; emergency control; simulation; information communication +
Footnote †: journal: Applied Sciences
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 3 SatScope Model Framework
### Network Simulator
#### 3.3.4 Load Balancing  
To ensure long-term stable operation in the complex environment of outer space, satellites need to accomplish their payload missions while minimizing power consumption. To achieve this, SatScope reduces the frequency of using inter-satellite laser links (ISLs) for routing paths. After calculating the geographical locations of ground stations and end-users, SatScope categorizes routing tasks into two types: when both the ground station and the end-user are within the coverage of the same satellite, it employs the Bent Pipe (BP) routing method to transmit data directly through that satellite; if the ground station and the end-user are not within the coverage of the same satellite, ISLs are activated to assist in completing the routing. Bent-pipe routing tasks preferentially utilize V1.0 satellites.  
SatScope implements a default load-balancing strategy based on node load monitoring, as outlined in Algorithm 2. This strategy combines path selection with continuous node load assessment to achieve effective load distribution. The algorithm dynamically selects paths with lower loads and moderate distances using a priority queue. By calculating path scores, it evaluates the overall load situation in real-time, prioritizing routes with the lowestscores. This approach effectively mitigates network congestion and better accommodates dynamic changes in the network topology.  
```
0: Graph \(G(V,E)\), Starting node \(s\), Target node \(t\)
0: Path from \(s\) to \(t\), if exists
1: Initialize a priority queue \(Q\) with a tuple \((s,[],0)\)
2: Initialize an empty set \(V\leftarrow\emptyset\)
3:while\(Q\) is not empty do
4: Dequeque the tuple \((u,P,\text{load})\) from \(Q\)
5:if\(u\in V\)then
6:continue
7:endif
8:if\(u=t\)then
9: return\(P\)
10:endif
11: Add \(u\) to \(V\)
12: Retrieve neighbors of \(u\)
13:for each neighbor \(v\) of \(u\)do
14: Calculate the distance \(\text{dist}(v,t)\) from \(v\) to \(t\)
15: Retrieve the current load \(L(v)\) of node \(v\)
16: Compute the path score: \(\text{score}=\alpha\times\text{dist}(v,t)+\beta\times L(v)\)
17:if\(v\
otin V\)then
18: Enqueue \((v,P+[v],\text{load}+L(v))\) into \(Q\) with priority score
19:endif
20:endfor
21:endwhile
22:return an empty list
```  
**Algorithm 2** Load Balanced Routing Algorithm
## 3 RS Applications in Ocean
### Ocean Tide (OT)
#### 3.5.6 Summary and Future Direction  
The importance of areas affected by OT expands to food production, coastal storm protection, trapping pollution, and drowning prevention. Waterline extraction has widely been explored using optical, SAR, and LiDAR data to delineate tidal flats. These data have improved the monitoring of tidal flats and have facilitated shoreline stabilization programs compared to conventional techniques. Additionally, GNSS, UAV, and altimetry datasets help in determining spatial distribution of tidal channels where drowning fatalities happen. Moreover, tidal wetlands have been mapped using various types of RS data. SAR-based methods have also facilitated OTL and tidal currents studies. Finally, altimetry datasets have been employed for mapping the global distribution of tidal currents.  
Although RS provides many advantages for OT studies, there are several challenges that need to be addressed in the future. For example, the temporal analysis of OTs and their effects on shorelines is limited due to the complex temporal nature of tides and the lack of suitable multitemporal RS datasets. Moreover, although a large volume of RS datasets is progressively produced, time dependency, low coverage, and complex processing steps still restrict the efficiency of RS techniques for OT monitoring. It is expected that the recent advances in ML and DL methods can fill some of these gaps. For example, DL architectures, such as CNN, Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) can facilitate temporal analysis of OT.
# Using ChatGPT in Software Requirements Engineering: A Comprehensive Review
## 6 Limitations and Risks of Using ChatGPT
### Information Hallucination  
ChatGPT can occasionally produce incorrect results, known as \"hallucinations\", which can lead to the generation of false-positive results [16]. Adopting human supervision mechanisms in the implementation of requirements generated by ChatGPT can help mitigate these hallucinations of the model and ensure a correct interpretation of the generated output [3].  
When ChatGPT generates inaccurate and purely fictional information or hallucinations, these conditions occur when the model lacks sufficient knowledge or information and must guess and attempt to fill in the missing piece using training examples as reference points. Incorrect outputs can manifest as hallucinations, causing problems in applications where accuracy is critical [5].  
Research is currently focused on understanding what makes hallucinations possible in language models. Recent studies have shown that this phenomenon may be multifaceted and related to factors such as model training techniques, data quality, and architectural design. Language models may also be biased to produce more \"interesting\" or well-written output, increasing the likelihood of hallucinations. Several methods have been proposed to address this issue, one of which is to use a large dataset that can help reduce the number of incorrect assumptions made by the model. This method exposes the model to different contexts and information during training, reducing the risk of hallucination.  
Understanding the phenomenon of information hallucination through ChatGPT, where fictitious data can be generated, helps to establish protocols for fact-checking, which is essential for the reliability of requirements specifications and, by extension, the robustness of the resulting software.  
The problem of the lack of explainability and transparency is raised in the literature, where its development is attributed to the lack of verifiability of GenAI systems, which will be discussed next.
## 4 Conclusions  
To address difficulties in the fast telemetry and communication under large dynamic Doppler of hypersonic vehicles, a novel fast telemetry and communication method based on Doppler diversity reception for hypersonic vehicles is proposed in this study. Different from the acquisition and tracking ideas under traditional large dynamic Doppler, the proposed method does not use the complicated phase-locked tracking but Doppler diversity to processes demodulating signals directly. Moreover, an additional Doppler coarse acquisition is involved before diversity reception due to the limitation of diversity reception. In addition, the proposed scheme lowers requirements on acquisition accuracy and neglects the accurate tracking, thus enabling the shortening of acquisition time significantly. According to simulation results, the proposed method can achieve the best performance when the residual Doppler is no higher than 1 kHz, normalized Doppler frequency offset is 0.01 and normalized artificial shift frequency is 0.4.  
In other words, the proposed scheme can accomplish fast telemetry and communication of hypersonic vehicles by controlling the residual Doppler within 1 kHz other than below 100 Hz in the traditional receiving scheme, which is easily accomplished by coarse acquisition module, and thus it has potential to realize fast telemetry and communication simply and effectively.  
Conceptualization, L.S. and L.Z.; methodology, L.S.; validation, C.Z.; formal analysis, B.Y.; writing, L.S.  
This work was supported in part by the National Natural Science Foundation of China under Grants 61871302, 61627901, 61771370, and 61873210, Shaanxi National Natural Science Foundation under Grant 2019 JZ-15, the National Basic Research Program of China under Grant 2014 CB34020.  
The authors declare no conflict of interest.
# Blind Channel Estimation Method Using CNN-Based Resource Grouping
## 3 Proposed Method
### Resource Grouping Pattern
Another case is the pattern where \(t=1\) and \(f=3\), in which a single group consists of one OFDM symbol in the time domain and three subcarriers in the frequency domain, resulting in a total of 560 groups for the entire pattern. Since the REs within each group of this pattern are four or fewer, it is excluded. Additionally, the minimum RE count condition is equally applied to the remainder groups. For example, in the grouping pattern where \(t=6\) and \(f=1\), a resource group consists of six consecutive OFDM symbols in the time domain and a single subcarrier in the frequency domain. In this case, the 13 th and 14 th OFDM symbols in the time domain combined with a single subcarrier in the frequency domain form the remainder group, which contains only two REs and, thus, fails to meet the minimum RE count condition. Consequently, this resource grouping pattern is also excluded. Therefore,  
Figure 2: Resource grouping pattern for \(t=7\), \(f=60\). considering \(t\) and \(f\), 212 patterns can be configured out of the 224 resource group patterns, excluding those that do not meet the minimum resource count condition.
## 5 Conclusions  
In this paper, a DL-based ontology with SWRL rules for knowledge representation in process planning for LPBF is developed. Firstly, a set of top-level DL concepts, DL relations, DL instances, and DL axioms are created to represent the knowledge in general process planning activity. Then, specific DL concepts, DL relations, DL instances, and SWRL rules are respectively developed to describe the knowledge in part orientation, support generation, model slicing, and path planning. After that, the application of the developed ontology is demonstrated via process planning on an LPBF part. Finally, the benefits of the ontology are outlined and illustrated using examples. The illustration results show that the ontology has rigorous computer-interpretable semantics, which would lay solid basis for development of an LPBF process planning tool with autonomous decision-making capability.  
Each of existing LPBF process planning tool has its specific knowledge representation method. Although ontology-based method has advantages in automatic consistency checking, knowledge reasoning, and semantic query and is gaining importance and popularity in AM knowledge representation, it should not be considered as a complete replacement of the methods used in existing tools, but more as an alternative method to improve them in some aspects. At present, existing ontologies for knowledge representation in LPBF are not yet mature. More powerful ontologies and related tools that can realise autonomous decision-making need to be further investigated.  
Future work will aim especially at improving the developed DL-based ontology to overcome a main limitation: knowledge representation in process planning for multi-part production is not considered. An important feature of LPBF is that it enables rapid fabrication of multiple different parts in a mixed batch [60]. The developed ontology focuses on knowledge representation in process planning for production of one single part, it cannot be applied to the situation where multiple parts in the same build need to be planned simultaneously. From the perspective of application, it is of necessity and importance to improve the ontology to handle this situation. Further, the conventional LPBF process planning pipeline is somewhat complex and a simpler pipeline has been presented in [82]. It would be interesting to develop a DL-based ontology for this simpler pipeline.  
Conceptualisation, Z.L. and Y.Q.; methodology, Z.L. and Y.Q.; software, Z.L. and Y.Z.; validation, Z.L. and Y.Z.; writing--original draft, Z.L.; writing--review and editing, Z.L., M.H., Y.Z. and Y.Q.; supervision and funding acquisition, Y.Q. and M.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 52105511 and No. 51765012).  
Figure 15: Example of a DL query from the ontology.
# A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System
## 4 Experiments and Results
### Deep Feature Learning from a Single Modality of an Individual for Personality Prediction
#### 4.1.2 Personality Trait Analysis Based on Speech Features  
For the speech feature, we used CNN as the model for acoustic feature extraction. In developing the CNN architecture, we constructed four pairs of convolutional and pooling layers regarding the above feature extraction model structure and used two fully connected layers successively. In the first convolutional layer, we chose 32 filters with a kernel size of 3 \(\times\) 3, used a padding method to fix the input size dimension and output size, and used ReLU as the activation function. For the next pooling layer, we set the pooling window size to 2 \(\times\) 2 by the maximum pooling method, 64 filters for the second and third layers, 128 filters for the fourth layer, and the same parameters as in the facial feature model. Again, before the final output score, we added a flatten layer in front of the fully connected layer, with 128 neurons in the first fully connected layer. We set the neurons to one in the last layer for the score prediction task and used the linear activation function for the prediction. Table 4 demonstrates the validation of the performance of the model using MAE, MSE, and other evaluation methods to verify the performance of the model. Figure 6 demonstrates that although the initial loss value is higher, the loss value of the model tends to decrease gradually as the epoch increases.
# Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking
## 3 Data Collection and Processing
### Data Observation
#### 3.4.5 58 Predictors  
From eight available time-domain data, 58 predictors were created with average and variance using the tachometer, microphone, and all accelerometer data in the three axes, the number of zero-crossing and six different power bands from the accelerometer data. Average and variance information from tachometer data should not be needed for machine learning because the rotating frequency from the tachometer should be similar or the same. However, these two predictors were not removed so we could confirm their importance when finding the most important predictors for the study.
# Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya
## 2 Study Area  
The study area is in Machakos County, about 100 km south-east of Nairobi in Kenya (Figure 1). The study area lies between the latitudes 1\({}^{\circ}\)17\({}^{\prime}\)53.71\" S and 1\({}^{\circ}\)31\({}^{\prime}\)8.54\" S and between the longitudes of 37\({}^{\circ}\)28\({}^{\prime}\)15.79\" E and 37\({}^{\circ}\)40\({}^{\prime}\)33.43\" E. The total study area covers about 677 km\({}^{2}\) with elevation ranging from 400 m to 2100 m above mean sea level (MAMSL). The climate is semi-arid with a highly variable rainfall regime distributed over two rainy seasons, hence two cropping seasons namely the short rain season and the long rain season. Short rains occur from November to January, and long rains from March to June with an average rainfall ranging from 500 to 2000 mm (mean annual precipitation) and a mean annual maximum temperature of 28 \({}^{\circ}\)C [23].  
The most widespread vegetation type in Machakos is semi-arid deciduous thicket and bush land, dominated by _Acacia_ spp. (Fabaceae) and _Commiphora_ spp. (Burseraceae). In drier locations below the elevation of 900 m, thorn bush grades into semi-desert vegetation. Moreover, arable land covers about 64% of the total landmass of the study area [24], with mixed cropping regularly practiced in this region [11]. The most prevalent crops in the region are maize, bean, pigeon pea and cowpea. Maize and bean in most cases are mixed in the long rainy season, while cowpea is mainly mixed with maize and bean in the short rainy season. Recent uncertainties in rainfall patterns have encouraged mixed cropping with the majority of farmers mixing maize with bean [25]. In addition, irrigated farming is also practiced in locations neighboring the Athi River to facilitate small-scale cultivation of vegetables, tomatoes and chili peppers.  
Figure 1: Locations of the study area in Machakos County, Kenya, with the dark blue polygon showing the study area. Grey lines illustrate sub-county boundaries.
# Experimental Study on Horizontal Pipeline Transportation Characteristics of Coarse Particle
## 4 Results and Discussion  
### Velocity Distribution Characteristics of Different Coarse Particle Group Flow Patterns  
Section 2.2 describes the five flow regimes in detail; however, during the experimental process, the flow patterns primarily manifested as the sedimentation bed type with jumping rolling particles, displacement motion, and heterogeneous flow suspension motion state. The sedimentation bed type and pseudo-homogeneous flow suspension motion state were found to be extreme and less representative. Therefore, the following analysis focuses on summarizing the flow patterns of the coarse particle group in the three intermediate states mentioned above.  
Sedimentary bed type with jumping and rolling particles  
Figure 9a-c present schematic diagrams illustrating the velocity distribution extraction process for three different particle sizes, with the velocity distribution diagrams for the three types of particles displayed in the lower right corner. Under this flow pattern, a sedimentary bed of coarse particles forms within the pipeline. Particles near the centerline of the pipeline exhibit flipping, jumping, and rolling motions. The coarse particles do not fully occupy the entire pipeline, and for the majority of the time, there is no particle movement in the upper section of the pipeline. The lower part of the pipeline consists of areas where coarse particles are deposited, with particle velocities approaching zero. The maximum particle velocity is observed near and just below the centerline of the pipeline.
# Internet of Bikes: A DTN Protocol with Data Aggregation for Urban Data Collection
## 2 Related Work
### Communication Based on DTN Paradigm with the Internet of Things  
In previous years, several researchers have focused on the application of the Delay Tolerant Networking paradigm to the IoT field [27]. [PERSON] et al. [28] discussed the concept of \"Challenged IoT\" in which Internet disconnection between mobile users and smart devices is established. They proposed DISCO (Direct Interaction with Smart Challenged Objects) system enabling local and autonomous communications of smart objects. In [29], [PERSON] et al. introduced an optimized delay-tolerant approach for Integrated RFID-Sensor Networks called DIRSN. It represents a new framework for integrated RSNs in the IoT field for data routing. The authors in [30] investigated the case of data collection with low energy consumption where the connectivity is frequently intermittent. They proposed an architecture to associate an M2M platform to Delay Tolerant Networks to gather data from mobile devices in an energy efficiency manner. Thanks to the wake-up system of sensors, their proposal reduces the energy consumption four times less.  
Many researchers have focused on investigating DTN with IoT in the domain of delay-tolerant WSN that focus on routing algorithms [31]. Most of the proposal works do not use standard protocols, while they propose approaches dedicated to targeted applications or sensors such as studies on underwater sensor networks.