esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Vibration and Acoustic Sensors in Non-Destructive Testing of Port Cranes  
Vibration sensors such as accelerometers and strain gauges are commonly used to monitor the dynamic responses of port cranes [75]. They can detect changes in the structural integrity caused by factors such as fatigue, corrosion, and impact. Acoustic sensors, including AE and ultrasonic sensors, can capture high-frequency acoustic signals generated  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technique** & **Detection Depth** & **Material Limitation** & **Strengths** & **Inspection Limitations** \\ \hline VI & Surface & None & Quick, low cost & Subjective, surface only \\ \hline UT & Subsurface & All metals &
\begin{tabular}{c} Accurate, deep flaw \\ detection \\ \end{tabular} & Needs coupling medium \\ \hline RT & Deep & Most metals & High detail imagery & Radiation hazard, costly \\ \hline MPT & Near surface & Ferromagnetic only & Quick for surface & Limited to surface and \\  & & & cracks & magnetic materials \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of common non-destructive testing techniques for port cranes.  
by crack propagation, delamination, and other damage mechanisms [76; 77]. These sensors can be strategically placed on port cranes to monitor their health continuously.
## 4 Optimal Number of ABSs and Default Parameters and Assumptions
### Default System Parameters and Assumptions  
Table 3 shows the default parameters and assumptions to evaluate the performance of all the realized spectrum sharing techniques. We evaluate the downlink performance of all techniques, namely dedicated access, DySP, StSR, LSA, and LAA as well as analyze the relative outperformance of one to another in view of MNO 1. For the evaluation, we consider that MNO 1, MNO 2, and the satellite service provider have their own licensed spectrum of 20 MHz each (10 MHz for the downlink and 10 MHz for the uplink). Hence, for dedicated access, the scheduler of MNO 1 allocates its licensed 10 MHz downlink spectrum to both macro UEs and small cell UEs according to the ABS based eICIC technique in each TTI. For DySP, there is a pool of 40 MHz spectra available at the CoRS to share between MNO 1 and MNO 2. We assume that there is a mutual agreement between MNO 1 and MNO 2 for StSR such that 20% of the licensed downlink spectrum of MNO 2 (i.e., 2 MHz downlink) is rented exclusively by MNO 1 to share only with its in-building small cells.  
Like DySP, for LSA, the whole satellite spectrum is shared with in-building small cells per building following the ABS based eICIC technique. However, for LAA, no eICIC technique is applied to in-building small cells such that the whole spectrum of 60 GHz unlicensed band is shared with small cells in all TTIs.  
Note that the transceiver 1 of in-building small cells operates only on its licensed dedicated spectrum of MNO 1 and the transceiver 2 operates on either licensed or unlicensed shared spectrum of different providers varies with the type of spectrum sharing technique employed on small cells. Moreover, the shared spectrum considered for the evaluation of each technique is arbitrary. Any values other than these aforementioned ones can be considered. However, choosing such different values will not alter the performance evaluation results concluded in this section.
## 4 Results and Discussion
### AM-HDGAT for Text Performance Evaluation
#### 4.3.3 Adversarial Robustness  
The adversarial training component significantly enhances model robustness. Under PGD attacks with perturbation budget \(\epsilon=0.1\), AM-HDGAT maintains 82.4% of its original performance, compared to 67.8% for standard GAT and 71.2% for BERT-base, highlighting the security benefits crucial for document management applications.
# Leveraging Retrieval-Augmented Generation for Automated Smart Home Orchestration
## 3 System Overview
### Service Bundle Generation Process  
Building on the devices that have already been installed via the DIP, the Service Bundle Generation Process (SBGP) focuses on translating user requirements into runnable, on-demand code (see Figure 3). The process can be summarised as follows:  
1. User Requirement Input: A user expresses a desired automation in natural language. For instance, \"Adjust the living-room lamp brightness to 58%, turn on the speaker, and set its volume to 30\".
2. Device Selection: Based on the request, the orchestrator queries its databases--both vector-based for semantic matching and relational for structured lookups--to identify devices capable of fulfilling the tasks (e.g., lights for brightness control, and speakers for audio management).
3. Automatic Code Generation: Using the user's command and device details, the orchestrator formulates a structured prompt for an LLM, which generates Java code tailored to orchestrate the selected devices. This code adheres to OSGi conventions (e.g., Declarative Services annotations) so that it can be readily deployed as a service bundle.
4. Deployment and Execution: The new bundle is compiled and installed via the OSGi framework, becoming an active component in the system. Once running, it executes the specified automation logic. In more advanced scenarios, the orchestrator may decide whether to keep the bundle active or uninstall it after use, depending on resource management policies.  
Figure 2: Device installation sequence diagram showing the discovery, registration, and service bundle activation process for newly introduced smart home devices within the orchestrator.  
Through this process, user requests are swiftly transformed into service bundles that run in near real-time, illustrating the synergy between generative AI and dynamic, modular frameworks.
# Adaptive Feature Extraction of Motor Imagery EEG with Optimal Wavelet Packets and SE-Isomap
###### Abstract  
Motor imagery EEG (MI-EEG), which reflects one's active movement intention, has attracted increasing attention in rehabilitation therapy, and accurate and fast feature extraction is the key problem to successful applications. Based on wavelet packet decomposition (WPD) and SE-isomap, an adaptive feature extraction method is proposed in this paper. The MI-EEG is preprocessed to determine a more effective time interval through average power spectrum analysis. WPD is then applied to the selected segment of MI-EEG, and the subject-based optimal wavelet packets (OWPs) with top mean variance difference are obtained autonomously. The OWP coefficients are further used to calculate the time-frequency features statistically and acquire the nonlinear manifold structure features, as well as the explicit nonlinear mapping, through SE-isomap. The hybrid features are obtained in a serial fusion way and evaluated by a \(k\)-nearest neighbor (KNN) classifier. The extensive experiments are conducted on a publicly available dataset, and the experiment results of 10-fold cross-validation show that the proposed method yields relatively higher classification accuracy and computation efficiency simultaneously compared with the commonly-used linear and nonlinear approaches.  
Keywords:feature extraction; supervised explicit isomap (SE-isomap); motor imagery EEG (MI-EEG); wavelet packet decomposition (WPD); \(k\)-nearest neighbor (KNN) +
Footnote †: journal: Applied
## 4 Results  
### Recovered Energy  
The obtained recuperated energy results for each of the 25 simulated tracks are represented in Table 6.  
Recuperated or recovered energy is understood in this paper as the percentage of energy that is added to the accumulator compared to its original available energy value of 80% of the stored energy (=6800 Wh). Hence, when energy recuperation is 10%, this means that 10% of 6800 Wh (=680 Wh) are regenerated. It is worth mentioning that, in order to be used again, the recuperated energy will travel all the components until at the wheel once again, which will reduce the amount of energy that actually reaches the tarmac.  
Figure 10 shows a histogram with the results of recuperated energy for all 25 studied racetracks. An average value of 11.11% of energy was found to have been recuperated per track, with 17 of the results within the 10% and 12% recuperated energy range.  
\begin{table}
\begin{tabular}{c c c} \hline
**Racetrack** & **Location** & **Rec. En. [\%]** \\ \hline Circuit of the Americas & USA & \(11.57\) \\ \hline Brands Hatch Circuit & United Kingdom & \(11.27\) \\ \hline Hungaroring & Hungary & \(13.07\) \\ \hline Circuit de Barcelona-Catalunya & Spain & \(12.21\) \\ \hline Hockenheimring & Germany & \(10.89\) \\ \hline Norisring & Germany & \(11.28\) \\ \hline Albert Park Circuit & Australia & \(11.58\) \\ \hline Autodromo Hermanos Rodriguez & Mexico & \(11.74\) \\ \hline Circuit Gilles Villenueve & Canada & \(11.45\) \\ \hline Autodromo Nazionale Monza & Italy & \(7.88\) \\ \hline Moscow Raceway & Russia & \(14\) \\ \hline Nürburgring & Germany & \(11.43\) \\ \hline Motorsport Arena Oschersleben & Germany & \(12.26\) \\ \hline Bahrain International Circuit & Bahrain & \(11.05\) \\ \hline Autodromo Jose Carlos Pace & Brazil & \(10.04\) \\ \hline Sepang International Circuit & Malaysia & \(10.94\) \\ \hline Shanghai International Circuit & China & \(10.29\) \\ \hline Silverstone Circuit & United Kingdom & \(9.94\) \\ \hline Sochi Autodrom & Russia & \(11.46\) \\ \hline Spa-Francordamps & Italy & \(8.86\) \\ \hline Red Bull Ring-Spielberg & Austria & \(10.07\) \\ \hline Suzuka & Japan & \(9.5\) \\ \hline Yas Marina & United Arab Emirates & \(11.82\) \\ \hline Zandvoort & Netherlands & \(11.86\) \\ \hline Motorland Aragón & Spain & \(11.4\) \\ \hline \end{tabular}
\end{table}
Table 6: Simulated racetracks and respective recuperated energy values.
# 5G V2X System-Level Architecture of 5 GCAR Project
## 5 Conclusions  
The 5G system layer architecture will play a fundamental role in the delivery of C-V2X communications capable of providing and sustaining QoS tailored to advanced vehicular applications. In particular, an ensemble of notable feature is highlighted in 5 GCAR as critical for V2X communications. In this paper, we provided an overview of the 5 GCAR architecture design, along with deeper insights on selected components, which we argue are important for the deployment of V2X.  
V2X service negotiation and location-aware scheduling provide the network with insights about the mobility pattern and application requirements of the vehicular UEs, enabling the network to optimize the delivery of services in space and time, with positive repercussion on the resource utilization efficiency, and therefore of the availability and scalability of automotive applications.  
Evolution of infrastructure-based communication for localized V2X traffic deals with the existence of safety-critical traffic to be treated in the shortest possible time, reducing to a minimum the communication latency by introducing routing paths through the infrastructure that run as close as possible to the vehicular UEs.  
Multi-link multi-RAT joint exploitation is a key feature to support and facilitate vehicular communications in multiple different ways, providing enhanced reliability jointly exploiting the PC5 and Uu links, increased data rates, and enabling the network to selectively offload different traffic flows on the appropriate links based on the application needs and on the network load.  
Multi-operator support is necessary to enable seamless and low latency communication between vehicles and road users in general, regardless of the MNO each single actor is subscribed to. In 5 GCAR, approaches and related procedures have been proposed to address the issue.  
The integration of the architectural modules developed in 5 GCAR, and notably of those presented in this paper are the key for 5G networks to deliver V2X communications capable of satisfying the stringent requirements of safety-critical applications and to provide extra value to the whole automotive vertical by means of advanced cooperative traffic optimization.  
Conceptualization, M.C., L.G., L.M., A.K., P.S., M.M., T.M.; formal analysis, M.C., L.G., L.M., A.K., P.S., M.M., T.M.; investigation, M.C., L.G., L.M., A.K., P.S., M.M., T.M.; writing M.C., L.G., L.M., A.K., P.S., M.M., T.M.  
This work has been performed in the framework of the H2020 project 5 GCAR co-funded by the EU. The views expressed are those of the authors and do not necessarily represent the project. The consortium is not liable for any use that may be made of any of the information contained therein.  
The authors declare no conflict of interest.
## 1 Introduction
### Power Demands and Wastewater Generation in the Arab Gulf States  
The increasing power demands and wastewater generation by the Arab Gulf, more specifically the Gulf Co-operation Council (GCC) states highlight the importance of sustainable use of energy resources. According to the International Energy Agency, power generation units and water desalination plants within the GCC region consume approximately 15-27% of all energy available for domestic use [2]. The electricity and desalination plants are powered by crude/gas oils, heavy fluid oil and natural gas.  
Electricity consumption in the GCC region grew at an annual rate of 4% between 2014 to 2018, where Saudi Arabia and the UAE accounted for 52% and 19.8% of the total electricity consumption, respectively. Additionally, the volume of municipal wastewater has been steadily increasing at an annual rate of 5% (Figure 1). As of 2019, the total wastewater generation within the GCC region alone was approximately 4763 \(\times\) 10\({}^{6}\) m\({}^{3}\), where Saudi Arabia and the UAE accounted for 65% and 16% of the total wastewater volume, respectively. Wastewater sludge management for the energy recovery creates an opportunity for sector integration for both wastewater treatment plants and renewable energy production.  
Figure 1: GCC wastewater influent. Data source [12].  
As of 2020, the renewable energy share of the total-installed power capacity was 3271 MW, or 2% of the total capacity [13]. Solar power is the dominant form of renewable energy in the GCC region, accounting for 96% of the total renewable energy sources, followed by wind and bioenergy, respectively [13]. In addition, natural gas has become a valuable oil-substitute energy source for domestic power generation (Table 1). It is worth noting that Qatar is the only member state in the GCC region where bioenergy is integrated within its energy profile. Scientific literature on bioenergy production and utilization in the GCC region is still limited. Biogas production potential and feedstock analysis were addressed by multiple investigations [4; 14; 15; 16; 17; 18], together with similar studies on biofuels [19; 20; 21; 22] and the potential of municipal solid waste incineration and syngas production [23; 24].
## 1 Introduction  
### 1.1 Internal Waves  
Internal waves are a phenomenon arising from variations in temperature and salinity within the ocean, typically occurring in regions with density stratification [1]. These waves can exhibit significant amplitudes, exceeding 100 m, and travel distances spanning tens to hundreds of kilometers [2], rendering their detection challenging. Consequently, internal waves have emerged as a prominent focus of research within the field of oceanography.  
The generation and propagation of internal waves are pervasive phenomena in the ocean, playing a crucial role in the transport of mass, momentum, and energy within oceanic systems. Breaking oceanic internal waves induces turbulent mixing, which in turn facilitates the vertical transport of water, heat, and other crucial climatic tracers within the ocean. This process holds significant importance as it actively influences the circulation patterns and the distribution of heat and carbon in the climate system [3], contributing to global climate change [4]. As a result, they can exert substantial influence on the safety and efficiency of marine engineering, oceanic communications, and oil exploration [5], and contribute to broader environmental factors, including their role in global climate change. Therefore, the study of internal waves in the ocean, particularly through the automatic recognition of these waves in remote sensing imagery, holds immense academic and practical significance.  
With the continuous advancement of remote sensing technology, the exploration of internal waves in the ocean has shifted away from traditional field observations. Instead, researchers have embraced the use of remote sensing imagery, presenting a novel approach to this study. Among the various remote sensing instruments, the moderate resolution imaging spectroradiometer (MODIS) stands out as one of the most vital and distinctive tools currently available. It is integrated into platforms like Terra and Aqua and represents a state-of-the-art \"all-in-one\" optical remote sensing device in today's world.  
MODIS boasts an impressive array of data with 36 bands, offering varying spatial resolutions of 250 m, 500 m, and 1000 m. Its scanning capacity spans an impressive 2330 km [6]. On average, local data can be acquired on a daily basis, and these data are readily accessible, making them the premier data source for global monitoring purposes. Nonetheless, the data provided by MODIS images are not only extensive in quantity but also substantial in size. Given that internal waves within the ocean occupy only a small fraction of the image area, the initial challenge lies in identifying and isolating images containing internal waves from the vast MODIS dataset before embarking on the process of detection and characterization.
# FrameSum: Leveraging Framing Theory and Deep Learning for Enhanced News Text Summarization
## 2 Literature Review  
### Text Summarization  
#### 2.1.1 Extractive Summarization  
Broadly speaking, extractive summarization methods can be seen as addressing a binary classification problem, where each sentence is classified as either summary-worthy or not. In 1958, [PERSON] [14] first proposed the concept of automatic text summarization, and the mainstream approach in early work was based on statistics. Graph-based methods are represented by the TextRank algorithm, where [PERSON] et al. [15] consider words in the document as vertices of a graph and construct edges based on the co-occurrence relationship between words to calculate the importance of words and the value of corresponding sentences. The lexical chain-based method was proposed by [PERSON] et al. [16], which extracts summary sentences through three steps: text segmentation, lexical chain identification, and finding strong lexical chains. [PERSON] et al. [17] used classifier and selector architectures based on recurrent neural networks (RNNs) to capture sentence saliency and provided conditions under which each architecture can achieve optimal performance. [PERSON] et al. [18] combined recurrent neural networks with graph convolutional networks to compute the importance of each sentence. Existing extractive summarization methods have achieved high ROUGE scores, but they all suffer from low readability.
# CD44 Depletion in Glioblastoma Cells Suppresses Growth and Stemness and Induces Senescence
## 2 Materials and Methods
### RNA-Sequencing and Gene Set Enrichment Analysis  
Parental or CD44 KO (clone #28) U251 MG cells were grown in low-attachment conditions for six days. Total RNA was extracted by employing ReliaPrep RNA Miniprep System kit (Promega, Madison, WI, USA). Sequencing libraries for six samples were prepared using a TruSeq stranded total RNA library preparation kit with RiboZero Gold treatment (Illumina, San Diego, CA, USA) following the manufacturer's instructions. Sequencing was carried out on NovaSeq 6000 platform utilizing a whole lane of SP flow cell (Illumina) by the SNP&SEQ Technology Platform at the Science for Life Laboratory (Uppsala, Sweden). Three biological triplicates of each group were subjected to whole transcriptome analysis. The generated reads were mapped against the annotated human reference genome (GRCh38-hg38) and the annotated genecode primary assembly (genecode v38) using STAR aligner (v2.7.2b). The mapped reads were then quantified using the FeatureCounts tool of the Subread package (v2.0.0), and normalized to calculate the counts per million (CPM) values of each sample, followed by differential expression analysis using the DESeq2 package (v1.30.1) in R (v4.0.5). The gene set enrichment analysis (GSEA) was performed by utilizing the molecular signature database (MSigDB) and GSEA tool (v4.1.0) from the Broad institute. Gene sets with a nominal \(p\)-value < 0.05 and a false discovery rate (FDR) \(q\)-value < 0.1 were considered significant. Complete RNA-seq analysis can be found in Tables S2-S4.
## 3 Modeling for Typical Task Uncertainty Evaluation
### Uncertainty Model for Form Error Measurement Task  
The form error is defined as the variation of the measured actual shape elements relative to the ideal shape elements [33]. Form measurement is not the specialty of the CMM measurement function unless there is a commitment in the measurement time. Compared with roundness meters, autocollimators, level meters and other special form tolerance instruments, CMM is slightly inferior in measurement accuracy, but its functional diversity can significantly improve the comprehensive efficiency of measurement. Therefore, CMM, when selected for form measurement, should be compatible with the competence of the measuring instrument, that is, the elements to be measured should be the task \"measurable\" by CMM when normal measurement conditions are satisfied, and the requirements for detection efficiency should be fully considered when sampling strategy is selected.  
Different from dimensional measurement tasks, the form measurement belongs to miniature measurement and is comparatively sensitive to \"overestimation\" in time of uncertainty evaluation. Dimensions are characterized by the measurement of absolute values, but form error are considering relative changes in a very small test space. In the analysis of indication errors, linear impacts can be ignored, just focusing on the bias of the form measurement indications caused by residual system errors of the measuring instrument. The CMM ability for form detection is represented by MPE\({}_{P}\) and is calibrated by the sphericity of the standard ball, which essentially reflects the comprehensive impact of the residual system errors on the form measurement results in different directions and at different positions. It is more reliable for using \(\text{MPE}_{P}\) to evaluate the uncertainty component caused by the indication errors of the CMM form error measurement; the formula for qualification is as follows:  
\[u_{\text{E}}=\frac{\text{MPE}_{P}}{\sqrt{3}} \tag{9}\]  
Similarly, the uncertainty components caused by repeatability and reproducibility can be obtained according to Formula (6) and Formula (7). According to formula (3), the universal model as follows can be achieved for evaluating the uncertainty of the CMM form measurement tasks according to Formula (3):  
\[uu_{\text{c}}\sqrt{\frac{\text{MPE}_{P}^{2}}{3}+\frac{1}{N(n-1)}\sum_{i=1}^{n} \left(y_{i}-\overline{y}\right)^{2}+\frac{1}{(m-1)}\sum_{j=1}^{m}\left( \overline{y}_{j}-\overline{y}\right)^{2}} \tag{10}\]