esa-sceva/satcom-chunk-collection huggingface dataset:
# A Replica-Selection Algorithm Based on Transmission Completion Time Estimation in ICN
## 2 Related Works
Mobilityfirst uses a global name resolution system (GNRS) to obtain the locator of the replica, and then sends the content request to the nearest replica node according to the routing table. NetInf obtains the locators of all replica nodes, and selects the replica node according to factors such as network distance and delay. In a distributed system, the problem of replica node selection also exists. Algorithms can be divided into information-independent algorithms and information-aware algorithms. Information-independent algorithms mainly include fixed strategies, random strategies, and cyclic strategies. The advantage of this type of algorithm is that it does not require perceptual information, so the overhead is low, but with the static replica-selection strategy it is often difficult to achieve the optimal effect [29]. The information-aware algorithm actively or passively measures information such as RTT, bandwidth, and loads of different replica nodes, and selects replicas based on the measurement information. [PERSON] et al. [30] measure the available bandwidth and round-trip time (RTT) of different replica nodes, and estimate the time required for content download to guide the selection of replica nodes. However, since the algorithm needs to actively perform network detection, it will occupy additional network bandwidth. Ping-random [31] selects a set containing the five best-performing replica nodes based on ping times, and the client randomly selects a replica node in the set when making replica selection. The two random choices (2 RC) [32] algorithm randomly selects two replica nodes and chooses the less-loaded node to process the request. The approach of NBR-based ICN architecture is not suitable for NRS-based ICN architecture. MF's nearest-replica approach and ERS are suitable for the ICN architecture of NRS, but they do not consider the NDC download rate and NDC download delay that users are most concerned about. The approaches in traditional networks are often not directly applicable to ICN. This paper proposes a new replica-selection approach suitable for the ICN architecture of NRS. The main idea of TCTE is similar to the literature [30], which is based on the estimated value of the transmission completion time for replica selection. However, their replica node information maintenance and replica-selection steps are completely different.
# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
## 2 Materials and Methods
### Processing Chain
#### 2.3.5 Processing Chain Overview  
Figure 2 below shows a simplified overview of the processing chain. Flood detection begins by selecting satellite images covering the study area and applying a mask to identify flood-prone areas using HAND data. These selected images are then divided into preevent and post-event categories based on the flood event date. Subsequently, speckle filtering techniques are applied. Change detection is carried out by comparing each post-event image with multiple reference images acquired before the event, with a focus on selecting reference images that overlap with the post-event image. The results of the change detection process, involving multiple reference images, are integrated using a majority voting approach.  
The processing chain code is freely accessible and commented on Github: [[https://github.com/johrosa/ls-FloodMonitoring/blob/main/country_scale_flood_detection.ipynb](https://github.com/johrosa/ls-FloodMonitoring/blob/main/country_scale_flood_detection.ipynb)]([https://github.com/johrosa/ls-FloodMonitoring/blob/main/country_scale_flood_detection.ipynb](https://github.com/johrosa/ls-FloodMonitoring/blob/main/country_scale_flood_detection.ipynb)) (accessed on 30 September 2023).
## 3 Results and Discussion
### Effects of Particleboard Density
#### 3.2.2 Internal Bond Strength  
The presented results in Figure 10a demonstrate the effect of the board density on the internal bond property of the rice straw particleboard. The internal bond strength first increased and then decreased with increasing the particleboard density from 0.3-0.7 g/m\({}^{3}\). When the density increased from 0.3-0.5 g/m\({}^{3}\), the internal bond increased significantly from 0.003 to 0.020 MPa. On contrast, it decreased slightly from 0.020 to 0.008 MPa when the density increased from 0.5-0.7 g/m\({}^{3}\). This can be explained by the looser inner structure and a lower adhesive performance at low density. For a density of about 0.5 g/cm\({}^{3}\), the adhesive material was evenly distributed and effective, and could glue the rice straw completely. Hence, the internal bond strength reached the peak value. At a density higher than 0.5 g/cm\({}^{3}\) the internal structure is more compact, which causes a more difficult distribution of the adhesive material. These outcomes are similar to the results obtained by [10], where they reported that the flexural properties of cornstarch-based composites increased significantly with increasing of the density from 0.6-0.7 g/cm\({}^{3}\) and decreased slightly at density higher than 0.7 g/cm\({}^{3}\).
###### Abstract  
Text summarisation plays a pivotal role in efficiently processing large volumes of textual data, making it an indispensable tool across diverse domains such as healthcare, legal, education, and journalism. It addresses the challenge of information overload by condensing or generating concise, meaningful summaries that improve decision-making, enhance accessibility, and save valuable time. Advances in artificial intelligence continue to propel the growth of text summarisation research, particularly with the evolution from traditional extractive approaches to cutting-edge abstractive models like BERT and GPT, as well as emerging innovations in multimodal and multilingual summarisation. To trace the development of this field, this study integrates bibliometric analysis and an in-depth survey, leveraging data from the Web of Science database to explore citation trends, uncover influential contributors, and highlight emerging research areas. Furthermore, bibliometric and critical evaluations are employed to outline strategic pathways and propose future directions for the continued advancement of the field. By incorporating sophisticated visualisation tools such as VOSviewer and RawGraphs, the analysis provides an enriched understanding of the field's trajectory, identifying significant methodologies, landmark contributions, and existing gaps. This comprehensive exploration not only underscores the progress achieved in text summarisation but also serves as an invaluable resource for shaping forthcoming research endeavours and inspiring innovation in this dynamic area of study.  
bibliometric analysis; WoS; text summarisation; NLP; LLM Article
## 3 Materials and Methods
### The Principle of LSTM-Based Risk Estimation Methodology
#### 3.3.3 Data Flow across the LSTM-Based Deep Learning Network Layers  
To more effectively elucidate the training procedure of the LSTM-based deep learning networks, the data flow for hazardous situation descriptions processed by each network layer is shown in Figure 7. During the training process, a specific number (BatchNorm_size in this paper) of sequences associated with hazard descriptions were extracted from the training dataset each time to create a subset, which is utilized for computing network parameters and updating them only once, a cycle of A-B-C-D-E, as shown in Figure 7. Upon employing all the training data, the average value was used for fine-tuning the entire network's parameters. For each training subset, the changes in data flow across the network layers are as follows:  
(A) The input of _wordEmbeddingLayer_ is a two-dimensional matrix, the size of which is (_Batch_size_, _Sequence_length_), wherein _Batch_size_ is the size of training subset, _Sequence_length_ is the length of hazardous situation description. Each row of the two-dimensional matrix represents the sequence of a hazardous situation description.  
(B) After passing through the _wordEmbeddingLayer_, the data are a three-dimensional matrix, the size of which is (_Batch_size_, _Embedding_dimension_, _Sequence_length_). The index of each word in the dictionary is transformed into an embedding vector (the size of the vector is _Embedding_dimension_) after word-embedding processing. For example, the index of the word \"robot\" in the dictionary is 2, which is changed into [\(-0.1481\), \(0.0303\), \(0.0881\), \(\ldots\), \(0.1650\), \(0.0865\)] after word embedding.  
(C) The data are sequentially input into the _lstmLayer_ according to the length of the _Sequence_length_ step. The data size for each step is (_Batch_size_, _Embedding_dimension_, 1), indicating that the data of each step are processed by a LSTM cell unit. Given that the output mode of LSTM is set to \"last\", the output data have a size of (_Batch_size_, _Number_hidden_units_). Thus, each hazardous situation description will correspond to a vector of size _Number_hidden_units_, which encapsulates the features of the risk indices.  
(D) After being processed by _fullyConnectedLayer_ and _softmaxLayer_, the data will be transformed into a size of (_Batch_size_, _Number_risk_index_, 1). The risk index corresponding to each hazardous situation is a six-element probability vector. Each element of this vector signifies the likelihood of the hazardous situation being associated with one of the six risk indices, 1 to 6, respectively.  
(E) After processing by the _classificationLayer_, the risk index of each hazardous situation description in the training subset is computed.  
Figure 7: Sketch of data flow across the LSTM-based deep learning network layers.
# Computationally Efficient PM Power Loss Mapping for
###### Abstract
Therefore, it is necessary to analyze PM power loss of the motor in depth and establish an efficient calculation method for PM power loss of a vehicle motor with different topologies in the whole working field. [PERSON] et al. proposed to use two-dimensional (2-D) finite element analysis (FEA) simulations, which are much faster but unable to capture end effects. The importance of 3-D effects may not be negligible, especially if axial magnet segmentation is used due to the 3-D path of eddy currents inside each magnet segment. To easily incorporate 3-D effects into 2-D simulations, a correction factor is proposed in [14] to be applied to magnet resistivity. A formula proposed in [14] makes it possible to map permanent magnet losses, for a given machine design, as a function of d-axis current Id and q-axis current Iq as well as of the rotor speed \(\omega\), as follows:  
\[P_{PM}=\left({al_{q}}^{2}+{b_{I}}_{d}^{2}+c{l_{d}}+d\right)\left(\frac{\omega} {\omega_{n}}\right)^{2} \tag{1}\]  
where \(\omega_{n}\) is the rated speed and the parameters \(a\), \(b\), \(c\), and \(d\) are identified through dedicated time-step FEA's performed at the rated speed, \(\omega_{n}\). However, in [14], a study was carried out on sinusoidal current input. This paper continued this method and further proposed a calculation method for the PM loss under PWM input, which is much more practical.
## 1 Introduction
Several review papers [16; 25; 31; 32; 33; 34; 35; 36; 37; 38] have previously examined various issues related to big data, network security, and the potential of IoT in smart cities. However, there is a scarcity of resources that comprehensively cover the combination of IoT technologies and the contribution of AI to the smart city concept. This review article discusses the concept of smart cities and the role of IoT in developing them, explaining how IoT enables devices to communicate and become essential components of smart cities. It also highlights how integrating AI and IoT in smart cities can facilitate the advancement of development processes, offering novel opportunities and features while reducing human interaction. The article delves into the potential of AI in analyzing the vast amount of data produced by IoT sensors in smart cities. It highlights how such technology can enhance city administration and ultimately lead to better living standards for residents. The article is structured as follows: Section 1 is going to provide an introduction to the topic of smart cities, highlighting their significance and potential benefits. Section 2 is going to explain the methodology of searching the relevant article. Section 3 is going to define the concept of smart cities and discuss their essential characteristics and components. Figure 2: Available communication technologies in various applications for smart cities. Section 4 is going to delve into the IoT-based technologies necessary for creating smart cities, including sensors, networks, and data analytics. Section 5 is going to explore the various AI algorithms suitable for smart cities and discuss their potential impact on urban life. Section 6 is going to examine the possible future trends of smart cities, such as the integration of 5G communication technology and the impact of AI on various applications. Finally, in Section 7, we are going to provide conclusions and prospects for the future of smart cities. Overall, this review article aims to provide a comprehensive overview of the current state and future directions of smart city development, highlighting the potential benefits of this emerging field for urban residents, governments, and businesses alike.
# Efficient Route Management Method for Mobile Nodes in 6 TiSCH Network
## 3 Efficient Route Management Method for Mobile Nodes in 6 TiSCH Network
### Fast Parent Selection Technique for Mobile Nodes
In the first slot frame, at slot offset 7, the moving node determines that if it fails to transfer data to its current preferred parent node, E, the preferred parent node, the link to the preferred parent node is released and broadcasts a DIS message in a shared cell with slot offset 0 of the next slot frame. To receive unicast control messages from neighboring nodes, turn on and wait for the radio of the unicast control-only Rx cell. A node that receives a DIS message from a shared cell sends a DIO message from a Tx cell dedicated to the unicast control of that slot frame to a moving node and checks the ACK. The moving node sends an ACK when it receives a DIO message from a pre-set unicast control-only Rx cell. The neighboring nodes of the preferred parent node that received the ACK discontinue the task of sending control messages from shared cells. The moving node selects the node  
Figure 5: Simplified schedule of nodes that exist at a distance that can be communicated with the mobile node. Figure 6: Mobile node changes preferred parent node. with the largest value as the preferred parent node by calculating the incoming signal strength contained in the DIO message received from the unicast control-only Rx sent by each neighbor node. At this point, if the control message is not received from the unicast control-only cell, the back-off technique allows a continuous message transmission to be received. When networking is formed or maintained by the movement of moving nodes, it minimizes DIO message conflicts in shared cells and allows data transfer to be initiated through a fast cell allocation process for moving nodes.
## 5 Sensitivity Analysis and Design Optimization of Parameters  
Based on the factors and levels in Table 6, an orthogonal test is adopted to arrange numerical simulation tests for analyzing the influence of parameters on test indices, including maximum stress, the stress variation coefficient, and the torsion vibration fundamental frequency. The orthogonal test is carried out utilizing an orthogonal array, \(L_{9}(3^{4})\), and test results are listed in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Level**} & \multicolumn{4}{c}{**Factor**} \\ \cline{2-5}  & **Ramp Radius** & **Diaphragm** & **Box Girder** & **Supporting** \\  & \(R\) **(m)** & **Thickness \(D\) (m)** & **Height \(H\) (m)** & **Condition \(U\)** \\ \hline
1 & 30 & 1.2 & 1.6 & 1 \\
2 & 40 & 1.6 & 1.8 & 2 \\
3 & 50 & 2.0 & 2.0 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Factors and levels for the orthogonal test.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Support Position**} & \multicolumn{4}{c}{**Support Condition**} \\ \cline{2-4}  & \multirow{2}{*}{1} & \multirow{2}{*}{2} & \multirow{2}{*}{3} \\ \hline C1 & Double movable support & Double movable support & Single vertical support \\ C2 & Double fixed support & Double fixed support & Double fixed support \\ C3 & Single vertical support & Single vertical support & Single vertical support \\ C4 & Double movable support & Double movable support & Single vertical support \\ C5 & Single vertical support & Vertical eccentric support & Single vertical support \\ C6 & Double movable support & Double movable support & Single vertical support \\ \hline \hline \end{tabular} Note: the eccentric distance of vertical eccentric support in 2 is \(\varepsilon=0.4\) m.  
\end{table}
Table 5: Support conditions of an irregularly-shaped bridge.  
In order to determine the influence order of factors and optimal levels, orthogonal analysis is conducted for the test indices. The analysis results are listed in Table 8. \(k_{i}\) (\(i=1\), 2, 3) represents the corresponding index value at each parameter level and \(R\) denotes the difference between the largest and the smallest values of \(k_{i}\). It is apparent that the parameter with the largest \(R\) value has the most significant influence on the test index.
# Large Language Models in Computer Science Classrooms: Ethical Challenges and Strategic Solutions
## 5 Tools and Strategies for Ensuring Academic Integrity
### Practical Methods to Encourage Original Work
The mere knowledge that students will be required to present their complete work process significantly enhances the likelihood of independent effort, discouraging them from simply obtaining pre-made work from an artificial intelligence-based programmer. Even if they do acquire a completed work, the expectation to showcase their own work process will compel them to strive for a deeper understanding in order to replicate such a process. **Strategic Approach:** Teachers can incorporate challenging elements into the programming task, which might be too advanced for students. The aim is to determine whether students can tackle a task that appears beyond their capabilities. If students provide answers that significantly exceed their expected skill level, this will suggest that they might have copied it or sought assistance from sources such as AI tools. **Oral Presentation:** Incorporating oral presentations or defenses is an effective method to combat plagiarism, as has been mentioned by [48; 62; 65]. Requiring students to verbally explain their code not only ensures their genuine understanding [62] but also helps educators to assess their comprehension of programming concepts. As has been suggested by [29], students should be prepared to defend their ideas and address questions or modifications proposed by instructors in real-time. Pairing oral examinations with code submissions is favored by educators. Examinations can be administered to all students, a random selection, or those arousing suspicion. During the oral defense, students may be asked, for example, to discuss the roles of specific variables or parameters in the code, explore the impact of altering a parameter on the outcome, elaborate on the implications of a recursive call from one function to another, describe the significance of a particular function and the consequences of modifying it, and predict the program's output after a parameter's value or type is changed. **Hand-Written Exams:** An alternative approach with a similar outcome is to periodically gather students for in-person assessments of their programming exercises, specifically when they lack internet access [65]. As part of these assignments, students will be required to write code without the ability to run it. To summarize, this section presents various practical strategies that educators can use to motivate students to produce original work and deter reliance on AI tools for programming assignments. These include designing unique assignments, incorporating multi-media elements, emphasizing logical questions, focusing on the entire learning and programming process, requiring students to give oral presentations, and conducting handwritten exams. These methods aim to ensure that students genuinely understand their work and discourage plagiarism.
## 3 Materials and Methods
### Clustering Models for Lithology Prediction  
Spatial grouping, known as Clustering, includes methods for unsupervised machine learning. This means that patterns and connections are sought within the data set that are not clearly visible, i.e., the patterns are not known in advance and it is left to the algorithms to recognize them. Data are grouped into clusters based on their relationship with the surrounding data (Link in Supplementary Materials: Unsupervised learning). In the end, groups are made up of elements with similar or the same characteristics. The principle of grouping non-hierarchical models can be based on the density of data in a certain area, on the distribution of data, or data added to centroids. For the clustering of eastern and western Drava Basin datasets, a few of the clustering algorithms have been tested, such as K-Means on standardized and Principal Component Analysis (PCA) reduced data, the Gaussian Mixture Method, Spectral Clustering, Agglomerative Clustering, and Mean Shift (Figure 5). Well log data have been divided into an optimal number of clusters based on specific curve patterns (Figure 5). The dataset on which the clustering was performed is the same dataset used for training supervised prediction models. The difference is that for the clusters to be generated, models do not need to be presented with the output lithologies. However, to verify results, the lithologies in terms of sandstone bodies and shale deposits have been conventionally interpreted using the well logs, and nine different sandstone bodies have been recognized throughout all the wells (Figure 6).  
Figure 5: Clusters being recognized by the Clustering algorithms in the selected well WH; this well has all the input logs measured from the start of the well, which is why Miocene deposits can be distinguished from Pliocene deposits on the left side; the dashed lines represent conventionally interpreted sandstone bodies for results verification; the number of clusters is five (statistically optimal by the silhouette method).  
As an additional feature of comparison, the volume of shale has been calculated using the following formula:  
\[Vsh_{calculated}=\frac{GR_{measured}-GR_{minimum}}{GR_{maximum}-GR_{minimum}} \tag{5}\]  
Intervals for calculating the volume of shale (\(Vsh_{calculated}\)) have been restricted to intervals used for machine learning training data and those are the intervals with measured gamma ray responses (\(GR_{measured}\)) (Link in Supplementary Materials: Petrophysical calculations). The lowest gamma ray log values are associated with the lowest shale concentrations, and vice versa.