esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Text Mining Process  
IRAMUTEQ (Interface de R pour les Analyses Multidimensionnelles de Textes et de Questionnaires) is an open-source software package designed for multidimensional text analysis, supporting various text types including articles, surveys, and legal documents [28]. Unlike commercial alternatives like NVivo[29] or ATLAS.ti[30], IRAMUTEQ specializes in automated lexicometric analysis through natural language processing (NLP) and statistical methods, with particular strengths in [PERSON]'s descending hierarchical classification (DHC) for thematic identification [31; 32]. This approach differs from the manual coding emphasis of MAXQDA[33] or the keyword-focused analysis of WordStat[34], offering researchers a unique balance between automated processing and statistical rigor. The software's integration with R provides flexibility for advanced analyses [35; 36], although this dependency presents steeper initial setup requirements compared to web-based tools like Voyant[37] or standalone packages like KH Coder[38].  
A critical phase in IRAMUTEQ's workflow--and indeed in most text mining processes--involves careful corpus preparation, where UTF-8 formatted texts undergo cleaning and segmentation into Elementary Context Units (ECUs) [39; 40; 41]. This preprocessing stage shares similarities with tools like Leximancer[42] and QDA Miner[43], though IRAMUTEQ's automatic text cleaning features provide distinct efficiency advantages for large datasets [44]. The software's analytical capabilities span frequency analysis, specificity examination, and similarity mapping through graph theory [45], which is comparable to functions found in Gensim[46] or MonkeyLearn[47] but with a stronger emphasis on qualitative interpretation. Notably, IRAMUTEQ's DHC implementation offers particular value for uncovering latent thematic structures in extensive text collections [48], which is a feature that complements rather than replaces the manual coding approaches favored in grounded theory research [49].  
When evaluating IRAMUTEQ against competing solutions, several tradeoffs emerge. The package excels in automated text processing and statistical integration through R[50],making it ideal for researchers requiring reproducible, quantitative text analysis. However, its visualization capabilities remain less interactive than NVivo's dynamic graphs [51], and the requirement for R proficiency creates barriers compared to more accessible web tools [52]. These characteristics position IRAMUTEQ as particularly suitable for mixed-methods researchers who value statistical rigor and thematic discovery, while other tools may better serve projects emphasizing qualitative depth [53] or rapid exploratory analysis [54]. The choice between these alternatives ultimately depends on the research objectives, technical capacity, and the specific balance required between automated processing and researcher interpretation in textual analysis [55].
# Evolution of Residual Stress Based on Curvature Coupling in Multi-Roll Levelling
## 7 Discussions  
The results show that the multi-roll levelling could reduce or eliminate the initial residual stress and also cause new rolling residual stresses. The portion of the initial residual stress in the elastic region was not changed and the portion in the plastic region was completely eliminated. The large plastic deformation caused by the large intermesh of the work rolls at the entry was beneficial to the complete elimination of the initial residual stress, but the rolling residual stress increased at the same time. Therefore, if the main purpose of the multi-roll levelling is to eliminate the initial residual stress of the sheet, the intermesh of the work rolls at the entry should be appropriately increased, and if the main purpose is to eliminate the longitudinal bow of the sheet, the intermesh should be appropriately reduced to avoid excessive residual curvature sensitivity.  
Besides, the proposed method reveals the evolution mechanism of the residual stress in multi-roll levelling. If combined with the data acquired from different smart sensors, it may help to achieve real-time adjustment of levelling parameters aimed at minimising residual stress. This is one of the steps to fulfilling the intelligent control of the multi-roll levelling lines, which is also a typical application of Industry 4.0 and Intelligent Manufacturing.  
The limitation of this method is that the sheet should be an isotropic single plastic material. If it involves allotropic or multiphase materials, the material properties may vary along the thickness of the sheet and stratified cumulative calculation may be needed to solve this problem, in addition, the situation is more complicated if the material is anisotropy, which still needs to be investigated.
## 4 High Capacity 2 \(\times\) 2 OPS Node with Contention Resolution  
In this section, we characterize a 2 \(\times\) 2 OPS node, shown in Figure 11a, with two strategies to deal with packet contention: optical buffering with FDLs and packet dropping. In the control plane, the label processor consists of multiple optical decoders [42; 43] that recognize an input label and send triggering signals to a buffer manager implemented on a field programmable gate array (FPGA) [44; 45]. The label generation process is described on Section 3.1. The buffer manager is able to electronically control the optical switch matrix to perform packet forwarding, packet dropping or packet buffering. The optically transparent data plane uses one 1 \(\times\) 8 PZLT optical switch on each input port. This 2 \(\times\) 2 OPS node will be the key subsystem in the packet contention resolution experiment of this section and in the packet transmission plus a switching experiment of Section 5.
## 6 Performance Evaluation and Discussion
### Simulation Results  
The optimization of the transmit power allocation process to the MTCDs is the primary objective of the proposed power control scheme to prolong network and battery life. The power control problem is complex to solve and susceptible to complex interference patterns due to massive number of devices and the lack of an external power source. The proposed scheme is analyzed in different device densities and available battery energy levels to test its robustness and the obtained results are presented along with brief discussions.
## 1 Introduction
### The Need for Novel Probes and Sensors  
For each of these objectives, specialized equipment is needed to empirical obtain data of the radio wave propagation and the resulting radio channel. Duplicating our previous measurement system is not practical because of its size and the costs involved. System designs could not be adopted from other groups either: [PERSON] et al. [29] and [PERSON] [30] did not measure polarization. [PERSON] et al. used large fixed antennas and did not achieve sufficient cross-polarization [24]. We therefore decided to design a novel system of probes and sensors that is portable (<20 kg), low-power (<5 \(W\)), quickly installed (<2 h) and autonomous. The sampling speed will be increased from 0.5/s to 3000/s. For this design we were able to build on the experience gained in our previous experiments. This publication covers the system design, practical realization and deployment criteria.  
The article is structured as follows: The experiment with one central sensor and multiple probes is described in Section 2. Frequency selection for the experiment is described in Section 3. The design of the central sensor is detailed in Section 4. The sensor specifications and link budget calculations are provided in Section 5 are then used to specify and design the probes in Section 6. Section 7 deals with deployment and Section 8 validates the design in a field deployment. The article concludes with a discussion and suggestions for future work.
# PPA-SAM: Plug-and-Play Adversarial Segment Anything Model for 3D Tooth Segmentation
## 2 Related Work
### Medical Image Segmentation  
Medical image segmentation holds significant importance in clinical diagnosis, treatment planning, and disease research [31]. Accurate segmentation results can assist doctors in precisely locating and quantifying lesion areas, providing crucial diagnostic information, and supporting disease diagnosis and treatment. For instance, MRI brain image segmentation is employed to localize and quantify brain structures such as the cerebral cortex, hippocampus, and basal ganglia. This is crucial for studying the impact of brain disorders like epilepsy, Alzheimer's disease, and brain tumors, as well as for planning surgical procedures. In CT lung imaging, medical image segmentation assists in locating and quantifying pulmonary structures, thereby supporting the diagnosis and treatment of diseases such as lung cancer and chronic obstructive pulmonary disease (COPD). In dermatology, image segmentation techniques aid doctors in identifying and analyzing the boundaries of skin lesions, facilitating diagnosis and treatment planning, especially in the early detection of skin cancer.  
However, medical images often exhibit complex structures and diversity. Magnetic Resonance Imaging (MRI) utilizes magnetic fields and harmless radio waves to generate high-contrast images with detailed anatomical information. Medical image segmentation in MRI faces several challenges, including the integration of multi-channel information, handling high-resolution complex structures, coping with intense contrast variations, addressing the need for local and global consistency, and sensitivity to noise and artifacts. Computed Tomography (CT) images, obtained through X-ray imaging, provide detailed cross-sectional images of internal body structures. Automatically segmenting medical images in CT also faces several challenges, including artifact interference, radiation dose noise, voxel resolution differences, and a wide dynamic range. These issues make segmenting complex structures and small features from the images more challenging.  
Compared to 2D images, 3D images can provide more spatial information, better representing the three-dimensional structures and morphological features of organs and lesions. Some researchers have proposed segmentation networks based on 3D convolutions [32; 33; 34; 35], applying convolution operations directly to 3D data. With the emergence of SAM models based on large datasets, continuous improvements and adaptations of segmentation models for medical image tasks have been proposed [14; 15; 16].
## 3 Methodology
### Quantifying the Resilience of Sparse Road Networks  
#### 3.3.1 Average Travel Delay  
Traffic flow indicators provide a more integrated analysis of the residual functionality of various network components following a geological disaster, reflecting the decrease in network performance due to travel delays on road segments [43; 44].  
Sparse road networks are typically bidirectional and consist of two lanes. Therefore, the topology of such networks can be simplified into an undirected graph model using graph theory, represented as \(G=(V,E)\), where \(V=\{v_{1},v_{1},\cdots,v_{n}\}\) denotes the set of network nodes, including bridges, tunnels, and intersections, and \(E=\{e_{1},e_{2},\cdots e_{n}\}\) represents the set of all travel paths. The performance indicators of a sparse road network, AvTD, can be expressed using Equations (1) and (2):  
\[AvTD(t)=\sum_{i=1}^{m}\omega_{i}\frac{T_{i}^{pre}}{T_{i}(t)} \tag{1}\]  
\[\omega_{i}=\frac{l_{i}\cdot C_{i}^{0}}{\sum_{i=1}^{m}l_{i}\cdot C_{i}^{0}} \tag{2}\]  
In the formula, m represents the total number of segments in the sparse road network. \(T_{i}(t)\) denotes the travel time required for segment \(e_{i}\) at time t during the recovery process. \(T_{i}^{pre}\) indicates the normal travel time for segment \(e_{i}\). The weight \(\omega_{i}\), which is a function of segment length \(l_{i}\) and initial flow \(C_{i}^{0}\), reflects the importance of segment \(e_{i}\) within the network, with the condition \(\sum_{i=1}^{m}\omega_{i}=1\). According to the formula, when AvTD = 1, all components in the network have been repaired.  
The \(C_{i}^{0}\) represents the baseline traffic volume of a road under normal operating conditions before a disaster event. It is derived from historical traffic data recorded at traffic flow survey stations prior to the disaster. The calculation formula is given in Equation (3).  
\[C_{i}^{0}=\frac{\sum_{k=1}^{n}F_{k}}{n} \tag{3}\]  
where \(F_{k}\) denotes the observed traffic flow on day \(k\), and \(n\) represents the total number of observation days.
# Enhanced Graph Autoencoder for Graph Anomaly Detection Using Subgraph Information
## 4 Experiments
### Datasets  
To evaluate the proposed method, this study employed six of the most widely used real-world attributed networks in prior research: Cora [45], CiteSeer [45], Pubmed [45], BlogCatalog [46], Flickr [46], and ACM [47]. Cora, CiteSeer, Pubmed, and ACM are four publicly available citation graphs consisting of literature publications. In these networks, nodes represent published papers and edges indicate the citation relationships among them. The titles and abstracts of papers are used to create attributes for nodes. BlogCatalog is a social blog sharing network in which nodes represent users and links represent follower-followee connections between users. Flickr is an online image sharing and hosting network with a network structure similar to that of BolgCatalog. In both BlogCatalog and Flickr, a list of tags that describe users are used to generate their node attributes. Since these datasets contain no ground-truth anomalies, synthetic attribute anomalies and structural anomalies are injected into the datasets according to the commonly applied perturbation scheme described in [24; 26]. To inject structure anomalies into the attributed network, small cliques consisting of initially unconnected nodes in the graph are constructed. To form each clique, \(m\) nodes are selected at random from the set of nodes V and structured in a fully connected manner. This procedure is iteratively executed \(n\) times, thereby generating \(m\times n\) structure anomalies in the graph. For all six datasets--Cora, CiteSeer, Pubmed, BlogCatalog, Flickr, and ACM--the clique size \(m\) is set to 15, while the number of cliques \(n\) varies as 5, 5, 20, 10, 15, and 20, respectively. An equivalent number of \(m\times n\) nodes are randomly chosen and perturbed as attribute anomalies within the attributed network. For each selected target node \(v_{i}\), \(k\) additional nodes are randomly selected as candidate nodes. These candidate nodes are evaluated based on their Euclidean distance to the candidate node \(v_{i}\). The node \(v_{j}\) that exhibits maximum deviation from \(v_{i}\) in terms of the Euclidean distance among the candidate nodes is chosen. The attribute of node \(v_{j}\) then becomes the perturbed attribute for node \(v_{i}\). This process is consistently applied across all six datasets with \(k\) set to 50. Detailed statistics of these six datasets are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **Cora** & **CiteSeer** & **Pubmed** & **BlogCatalog** & **Flickr** & **ACM** \\ \hline \#Nodes & 2708 & 3327 & 19,717 & 5196 & 7575 & 16,484 \\ \hline \#Edges & 5429 & 4732 & 44,338 & 171,743 & 239,738 & 71,980 \\ \hline \#Attributes & 1433 & 3703 & 500 & 8189 & 12,074 & 8337 \\ \hline \#Anomalies & 150 & 150 & 600 & 300 & 450 & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of datasets.
## 4 Applications of Methods and Tools for Additive Manufacturing  
AM, intended to integrate design software and 3D printing machines to complete the product's manufacturing [18], guarantees multiple benefits in Industry 4.0. The most significant are: customisation, design and development, prototyping, virtual inventory, reduced wastages, speed, flexibility, risk reduction, customer satisfaction, accuracy, productivity, improved profitability, improved supply chain performance, and cost reduction [18].  
The variety of materials employed in AM processes (i.e., metals and alloys, polymers, composites, ceramics, and concrete) makes this technology applicable to several industrial sectors. The most trending could be summarised as follows [19]:  
* _Biomedical and Biofabrication_: the biomedical market represents around 16% of the total AM market share ([PERSON] report). AM can guarantee the realisation of high-complex shapes [4, 20] for innovative biomedical implants, organs, and controlled drug delivery systems. Open-source design methodologies can be used to quickly share 3D AM CAD files among researchers [3].
* _Aerospace_: this sector represents around 18% of the total AM market share. AM may allow the production of complex geometries resulting from parts consolidation, fluidodynamics simulation (to maximise efficiency [2]), and topology optimisation (to maximise the performance to weight ratio). Furthermore, functional electronics (e.g., resistive sensors) can be easily 3D printed [5].
* _Buildings, architecture, and cultural heritage_: this is an infancy sector expected to grow. AM can reduce construction time, increase geometry complexity, and reduce the consumption of heart resources. Reconstruction, documentation, and preservation are the typical AM purposes for cultural heritage [6].
* _Transport and automotive_: AM can produce lightweight components in this sector, which are increasingly requested to comply with energy consumption regulations. AM can also speed up the product development phase. For example, AM is currently adopted to realise soft assembly tools to increase productivity and manage customised parts without increasing production complexity and cost [21].
# Interval Uncertainty Optimization Method for Electromagnetic Orbital Launcher
## 2 Description of the Optimization Object  
An electromagnetic orbital launcher is a new type of kinetic energy weapon produced based on electromagnetic launch technology. It has the advantages of high firing efficiency, a fast response, and good controllability, which have resulted in its large application prospects in military and civilian fields. The setting of the model size, parameters, and structures is a key step in the study of electromagnetic orbital launchers. Different shapes of the armature structures have different pivot rail contact states, firing efficiency, and performance, but the objective laws of their responses are consistent. In this paper, a U-shaped electromagnetic orbital launcher is used as the research object, and the established electromagnetic orbital launcher model is shown in Figure 1.  
The trapezoidal excitation current is a widely used model loading technique in engineering practice. Figure 2 illustrates the parameters of the excitation current. The excitation process can be divided into three phases [27]:  
Figure 1: Finite element model of electromagnetic orbital launcher.  
1. The increasing phase lasts from 0 to 0.5 ms, during which the current rises from 0 to 0.32 MA;
2. The constant phase extends from 0.5 ms to 2.0 ms, with the current maintaining a steady value of 0.32 MA;
3. The decreasing phase occurs from 2.0 ms to 2.5 ms, where the current gradually decays from 0.32 MA to 0.1 MA.  
In the finite element model, the excitation current enters through the lower face of the upper track, passes through the armature, and then exits from the lower face of the lower track.  
Based on the finite element simulation results of the armature electromagnetic thrust and the maximum current density at the contact surface of the armature rail at the moment of 1.0 ms during the current constant phase, an interval multi-objective optimal design of the armature with uncertain parameter variables is obtained for the electromagnetic orbital launcher. The armature tail width \(x_{1}\), armature length \(x_{2}\), armature tail length \(x_{3}\), and armature tail height \(x_{4}\) are selected as design variables \(X\), and the armature electromagnetic thrust \(f_{1}\) and the maximum current density at the pivot rail contact surface \(f_{2}\) are selected as optimization objectives. The data before the optimization of the electromagnetic orbital launcher are shown in Table 1, and the ranges of its design parameters are taken as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Variable** & \(x_{1}\)**/mm** & \(x_{2}\)**/mm** & \(x_{3}\)**/mm** & \(x_{4}\)**/mm** & \(f_{1}\)**/mm** & \(f_{1}\)**/(\(\times\)10\({}^{10}\)A/m)\({}^{2}\) \\ \hline Initial & 7 & 10 & 6 & 7.5 & 2.93 & 1.46 \\ \end{tabular}
\end{table}
Table 1: Data before optimization of electromagnetic orbital launcher.  
Figure 2: Schematic diagram of excitation current.
Due to the limitation of current systems and propagation losses in the mmWave band, the increased cost, complexity and power consumption resulting from increasing the number of transmit antennas with RF chains, an IRS is expected to provide the solution needed by offering better energy and spectrum efficiency to meet the desired throughput. The aim of this survey is to discuss the importance of an IRS in 5G wireless networks and highlight the potential of its use in future 6G and beyond wireless networks. Furthermore, this survey highlights key research opportunities/gaps covering a wide spectrum of areas of IRS technologys, i.e., channel estimation, beamforming design, practical protocol design, IRS-assisted heterogeneous networks (HetNets), IRS-aided unmanned aerial vehicles (UAVs), localization, device-to-device communication and future research direction to address those gaps. This paper builds on existing surveys by discussing the system architecture, providing a detailed overview of the internal architecture, performance limits and deployment strategy and also outlines potential scenarios of achieving a smart radio environment with an IRS. The outline of this survey is shown in Figure 1, and is in the following order. In Section 2, a literature review is presented. In Section 3, system architectures of an IRS are presented. In Section 4, IRS-aided wireless network performance and deployment strategy is presented. In Section 5, a discussion on smart radio environment with an IRS is carried out. Finally, Section 6 provides a detailed discussion on the key research gaps/opportunities and the possible future research directions to address those gaps and also provides the conclusions.
# Evaluation of Coastal Sea Level Offshore Hong Kong from Jason-2 Altimetry
## 3 Data Sets  
The Jason-2 mission started operating in July 2008, while the high-quality tide gauge data were accessible until 2014 only. Besides, the ALES data were available for cycle 1 to 252 (July 2008 to May 2015). These constraints define the study time period: from July 2008 to December 2014, corresponding to cycles 1-238 of Jason-2 satellite, i.e., a time span of 6.5 years.