esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results

Table 6: Performance results of bLR (after applying oversampling techniques). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Metric/Sampling** & **SMOTE** & **SVM SMOTE** & **Borderline SMOTE** & **ADASYN** \\ \hline Accuracy & 98.41 & 98.41 & 98.41 & 98.41 \\ Ba Accuracy & 92.57 & 92.57 & 92.57 & 98.41 \\ Precision (Mac\(\backslash\)Wght) & 95.58;98.37 & 95.58;98.37 & 95.58/98.37 & 95.58/98.37 \\ Recall (Mac\(\backslash\)Wght) & 92.57;98.41 & 92.57;98.41 & 92.57/98.41 & 92.57/98.41 \\ MajClass & 99.43 & 99.43 & 99.43 & 99.43 \\ MinClass & 85.71 & 85.71 & **85.71** & **85.71** \\ F1 Score (Mac\(\backslash\)Wght) & 94.01;98.38 & 94.01;98.38 & 94.01;98.38 & 94.01;98.38 \\ ROC\_AUC & 0.99 & 0.99 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance results of RF (after applying oversampling techniques). \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Metric/Sampling** & **CNN** & **TL** & **All-KNN** & **RENN** & **ENN** & **NCR** & **NM1** & **IHT** \\ \hline
**Accuracy** & 98.94 & 95.76 & 95.23 & 97.35 & 96.29 & 96.29 & 95.76 & 76.19 \\
**Ba Accuracy** & 96.14 & 71.42 & 67.85 & 82.14 & 75.00 & 75.00 & 97.71 & 83.85 \\
**Precision (Mac\(\backslash\)Wght)** & 96.14;98.94 & 97.81;95.95 & 96.14;98.94 & 98.07;96.43 & 98.07;96.43 & 98.07;96.43 & 81.81/97.30 & 61.02/93.58 \\
**Recall (Mac\(\backslash\)Wght)** & 96.14;98.94 & 71.42;95.76 & 67.85/95.23 & 82.14/97.35 & 75.00/96.29 & 75.00/96.29 & 97.71/96.43 & 83.85/96.19 \\
**MajClass** & 99.43 & 100 & 100 & 100 & 100 & 100 & 95.43 & 74.86 \\ MinClass & 92.86 & 42.86 & 64.29 & 64.29 & 50 & 50 & **100** & **92.86** \\
**F1 Score (Mac\(\backslash\)Wght)** & 96.14;98.94 & 78.88;94.96 & 75.06;94.17 & 82.35/95.71 & 82.35/95.71 & 82.35/95.71 & 87.71/96.18 & 60.98/81.73 \\ ROC\_AUC & 0.99 & 0.99 & 1.0 & 1.0 & 0.99 & 0.99 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance results of RF (after applying undersampling techniques).
###### Abstract  
The India-France SARAL/AltiKa mission is the first Ka-band altimetric mission dedicated primarily to oceanography. The mission objectives were firstly the observation of the oceanic mesoscales but also global and regional sea level monitoring, including the coastal zone, data assimilation, and operational oceanography. SARAL/AltiKa proved also to be a great opportunity for inland waters applications, for observing ice sheet or icebergs, as well as for geodetic investigations. The mission ended its nominal phase after three years in orbit and began a new phase (drifting orbit) in July 2016. The objective of this paper is to highlight some of the most remarkable achievements of the SARAL/AltiKa mission in terms of scientific applications. Compared to the standard Ku-band altimetry measurements, the Ka-band provides substantial improvements in terms of spatial resolution and data accuracy. We show here that this leads to remarkable advances in terms of observation of the mesoscale and coastal ocean, waves, river water levels, ice sheets, icebergs, fine scale bathymetry features as well as for the many related applications.  
altimetry; Ka-band; oceanography; hydrology; ice; geodesy 29 November 2017; Accepted: 19 January 2018; Published: 24 January 2018  
The India-France SARAL/AltiKa mission is the first Ka-band altimetric mission dedicated primarily to oceanography. The mission objectives were firstly the observation of the oceanic mesoscales but also global and regional sea level monitoring, including the coastal zone, data assimilation, and operational oceanography. SARAL/AltiKa proved also to be a great opportunity for inland waters applications, for observing ice sheet or icebergs, as well as for geodetic investigations. The mission ended its nominal phase after three years in orbit and began a new phase (drifting orbit) in July 2016. The objective of this paper is to highlight some of the most remarkable achievements of the SARAL/AltiKa mission in terms of scientific applications. Compared to the standard Ku-band altimetry measurements, the Ka-band provides substantial improvements in terms of spatial resolution and data accuracy. We show here that this leads to remarkable advances in terms of observation of the mesoscale and coastal ocean, waves, river water levels, ice sheets, icebergs, fine scale bathymetry features as well as for the many related applications.  
The Benefits of the Ka-Band as Evidenced from the SARAL/AltiKa Altimetric Mission: Scientific Applications  
1
# High-Throughput Line Buffer Microarchitecture for Arbitrary Sized Streaming Image Processing
###### Abstract  
Parallel hardware designed for image processing promotes vision-guided intelligent applications. With the advantages of high-throughput and low-latency, streaming architecture on FPGA is especially attractive to real-time image processing. Notably, many real-world applications, such as region of interest (ROI) detection, demand the ability to process images continuously at different sizes and resolutions in hardware without interruptions. FPGA is especially suitable for implementation of such flexible streaming architecture, but most existing solutions require run-time reconfiguration, and hence cannot achieve seamless image size-switching. In this paper, we propose a dynamically-programmable buffer architecture (D-SWIM) based on the Stream-Windowing Interleaved Memory (SWIM) architecture to realize image processing on FPGA for image streams at arbitrary sizes defined at run time. D-SWIM redefines the way that on-chip memory is organized and controlled, and the hardware adapts to arbitrary image size with sub-100 ns delay that ensures minimum interruptions to the image processing at a high frame rate. Compared to the prior SWIM buffer for high-throughput scenarios, D-SWIM achieved dynamic programmability with only a slight overhead on logic resource usage, but saved up to 56% of the BRAM resource. The D-SWIM buffer achieves a max operating frequency of 329.5 MHz and reduction in power consumption by 45.7% comparing with the SWIM scheme. Real-world image processing applications, such as 2D-Convolution and the Harris Corner Detector, have also been used to evaluate D-SWIM's performance, where a pixel throughput of 4.5 Giga Pixel/s and 4.2 Giga Pixel/s were achieved respectively in each case. Compared to the implementation with prior streaming frameworks, the D-SWIM-based design not only realizes seamless image size-switching, but also improves hardware efficiency up to 30\(\times\).  
streaming architecture; low-latency; high-throughput; FPGA; D-SWIM; line buffer +
Footnote †: journal: Imaging
## 6 Discussion
Consequently, this shows that the adoption and introduction of the Fog computing layer to the network architecture of the smart building will optimise latency levels across all the smart IoT devices, thereby increasing response times for applications that require real-time and time-sensitive responses as a core function, such as healthcare applications in hospitals and care homes. The network bandwidth usage review of the comparison between Cloud and Fog computing also demonstrates that an increased number of devices results in an increase in the data generated, which leads to more data being transmitted to the centralised (Cloud) or decentralised (Fog) environments for processing. Consequently, this leads to increased network bandwidth usage in the smart building. Furthermore, the network bandwidth usage in Cloud computing is slightly higher than in the Fog computing layer, highlighting the benefits of Fog computing. This difference also demonstrates that Fog computing optimises network bandwidth usage efficiency, thereby addressing the weaknesses associated with Cloud computing in network architecture and improving data management performance and the efficiency of IoT smart devices within a smart building environment. However, simulation results based on real-world data can vary depending on the type of smart IoT device, the volume and size of the data generated, and the frequency of data generation and transmission, including the infrastructure parameter values used in the simulation experiment, and could lead to results different to those of this study. In addition, the results obtained from this study highlight the benefits of integrating the Fog computing layer into the traditional Cloud computing architecture within a smart office building environment. The analysis and interpretation of these results can assist and influence critical business decisions based on the design and operations, focusing on the benefits of Fog computing such as reduced energy consumption, improved latency, and fewer network bandwidth usage bottlenecks within smart building network architectures. Although latency and network bandwidth usage significantly improve energy savings, CCTV energy savings show limited results with poorer performance. This is due to the relatively low number of CCTV cameras in the smart office floor plan. However, in the scalability analysis section, energy savings for CCTV were achieved and showed significant improvements due to the increased number of CCTV devices. Additionally, these interpretations can optimise healthcare monitoring and management in hospitals and care homes using smart IoT device applications that operate in real-time. These Fog computing benefits address the need to meet the increasing demand for real-time responses by real-time applications. However, there could also be a need to combine both Cloud and Fog computing to achieve the best benefits for critical applications; this depends on the design of smart IoT applications and the operational criteria required for core functionality.
# Beam Grouping Based RS Resource Reuse and De-Contamination in Large Scale MIMO Systems
###### Abstract  
It is well known that large scale multiple-input multiple-output (LS-MIMO) systems are very attractive technology to increase both spectral efficiency (SE) and energy efficiency (EE). However, one of big obstacles to the realization of the LS-MIMO system is the overhead of reference signals (RSs), since the number of RS increases as the number of transmitter (TX) antennas increases. In this paper, the RS overhead problem is analyzed, and we propose an RS overhead reduction scheme based on beam grouping, which is called beam grouping based resource reuse (BGRR). The proposed scheme divides one cell into several sectors and reuses the RS resources for the different sectors. The resource conflict is reduced using beam separability. According to the analysis and the simulation results, the proposed scheme can remarkably reduce the RS overhead and improve the SE performance significantly.  
reference signal; large scale MIMO system; resource reuse +
Footnote †: journal: Applied Sciences
## 2 Literature Review
### Importance-Performance Analysis (IPA)  
[PERSON] and [PERSON] [42] introduced the importance performance analysis (IPA). An IPA is typically presented as an importance-performance matrix, which shows the relationship between the level of importance that customers place on various aspects of an institution's services and their level of satisfaction with those aspects. In other words, this concept is used to measure or assess consumers' acceptance of a product for a variety of characteristics. It is an easy-to-use analytical method for evaluating or measuring the performance and importance of a product that can lead to a breakthrough in effective marketing. By analyzing customer perceptions of importance, the most dominant variable can be identified and associated with the reality of the customer experience. For instance, if price is deemed a crucial factor by customers but they perceive the price to be high, then reducing the price could improve the company's performance. This can be represented in an importance-performance matrix that combines customer satisfaction levels with an institution's services [43].  
An IPA has been widely used in many other industries. [PERSON] et al. [44] applied an IPA in the tourist industry to investigate the tourists' satisfaction levels with tourism destination attributes in Ciamis Regency, Indonesia. By using IPA, they knew which attributes needed to be a priority to improve and which attributes should be maintained to ensure tourist satisfaction. Moreover, [PERSON] and [PERSON] [45] utilized an IPA to evaluate the importance and satisfaction levels of public green infrastructure and urban nature spaces in Perth, Western Australia. By applying the IPA technique, the researchers were able to collect valuable data that could be used to inform evidence-based approaches for managing and allocating resources to PGI and UN spaces. [PERSON] and [PERSON] [46] utilized an IPA to evaluate the satisfaction of local communities with ecosystem services in Chatra Weltand, India. An IPA has also been used by colleges for measuring student satisfaction levels to increase competitive advantages. [PERSON] et al. [7] implemented an IPA to evaluate services provided to students so the college could improve the quality of service based on students' perspectives to increase student satisfaction in the future.  
An IPA has the advantage of assessing consumer acceptance in marketing programs. It is a low-cost assessment, has easy-to-understand assessment techniques, and provides important in-depth information in terms of the marketing mix. It is very important in defining the marketing mix of an organization because it allows management to analyze what factors are important to improve and develop and to effectively manage the company's resources or budgets.
## 8 Conclusions  
This study introduced Multi-Element Contextual Hypergraph Extractive Summarizer (MCHES), a novel framework designed to tackle the challenges of extractive text summarization. Our comprehensive experiments and analyses, conducted across diverse datasets, demonstrate the superiority of MCHES over existing baseline models. The key contributions of our research can be summarized as follows:  
* The MCHES framework significantly outperforms baseline models in terms of ROUGE scores and BERTScore, affirming its effectiveness in capturing salient information and maintaining the semantic integrity of the original texts.
* The ablation study revealed the importance of each component in the MCHES framework, illustrating their synergistic effect on the model's overall performance.
* Despite the complexity of the summarization task, particularly within specialized domains such as the biomedical literature, MCHES maintained a consistent level of high performance, underscoring its robustness and versatility.  
The findings from this work not only set a new benchmark for extractive summarization tasks but also open up new avenues for future research. Potential directions include exploring the adaptability of MCHES to other NLP tasks, further refinement of its components to enhance efficiency, and extending the model to accommodate multilingual summarization. In conclusion, MCHES represents a significant stride forward in the domain of NLP, providing a powerful tool for the automatic generation of accurate and coherent summaries. Its development is a testament to the potential of integrating advanced neural network architectures and natural language understanding to create models that can truly grasp the essence of human-written texts.  
Conceptualization, A.O. and H.A.; methodology, A.O.; software, H.A.; validation, A.O. and H.A.; formal analysis, A.O.; writing--original draft preparation, A.O.; writing--review and editing, H.A.; visualization, A.O.; supervision, H.A. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Taif University Researchers Supporting Project number TURSP-HC2024/13, Taif University, Saudi Arabia.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.
# Perspectives and Challenges in Robotic Neurorehabilitation
## 3 Current Trends of Robot-Aided Neurorehabilitation
### Multimodal Assessment of Recovery
#### 3.2.3 Neural and Muscular Correlates of the Sensorimotor Performance
So far, multi-domain evaluations (behavioral, muscular, and neural correlates) have not performed continuously, and in our opinion, an elegant robotic solution should be accompanied by validation studies of how the rehabilitation robot impacts the patients' life, by assessing, at the same time, the neural and behavioral correlates' changes and their interplay [167; 220]. Hence, combining these assessments might increase the sensitivity to better understand the recovery of the sensorimotor functions, to individuate potential maladaptive compensatory strategies, to plan customized therapeutic intervention, and to monitor disease progression more accurately. These studies could potentially open new insights about brain function and about the effectiveness of a particular rehabilitation program and device in facing the neurological disease under examination. As an example, in the field of gait rehabilitation for subjects affected by multiple sclerosis, the effectiveness of robot-assisted training for locomotor function recovery is still debated [221]. However, with the multimodal framework showed here, further understanding might be achieved. Finally, as recently reported [25], the neurophysiological and clinical factors that influence the recovery of sensorimotor function should drive the robotics rehabilitation process. In this context, the combination of these multi-modal approaches seems an encouraging scenario to exploit and better understand these crucial factors. For example, electrophysiological recordings from human muscles might serve as control signals for robotic rehabilitation devices, thus assigning EMG the double role of shaping the amount of rehabilitation intervention and of biofeedback of the performed movement [217].
## 1 Introduction
This makes it difficult to implement machine learning predictive models in enterprises, since users want to understand why the model generates a particular forecast. To solve this problem, research is being conducted in the field of explainable artificial intelligence (XAI) [26; 27]. For complex models that are not interpretable, a posteriori explanation can be used, currently represented mainly by algorithms such as local interpretable model-agnostic explanations (LIMEs) [28] and Shapley additive explanations (SHAPs) [29]. The application of LIME and SHAP methods in the power industry is still at an early stage. For example, in article [30], both methods were used to forecast SPP generation. However, the authors only used one feature relating to cloudiness. All resulting interpretations were dominated by two a priori obvious features: direct solar radiation and the hour of the day. Thus, the explanation provides the user with little new information to understand the model operation. Our contributions to the state of the art are as follows:  
* An algorithm of processing cloud observations in natural language for machine learning applications in forecasting SPP generation is proposed;
* It is experimentally substantiated that the use of different features describing the cloudiness increases the accuracy of SPP generation forecasting;* A comparative analysis of various algorithms for constructing decision tree ensembles in SPP generation forecasting with many categorical features is carried out;
* The possibility of increasing the interpretability of SPP generation forecasts with a modified SHAP algorithm is investigated. The modification consists of combining a number of features that have close meaning for a user during visual interpretation. In addition, the article describes real data pre-processing in detail, taking into account such features as discrete time discrepancies, omissions, and various data storage formats. A description of data pre-processing may be of interest to researchers faced with similar problems when processing data obtained from real power industry enterprises with low levels of digitalisation when collecting, processing and storing data. The remainder of the paper is organized as follows. Section 2 presents descriptions of the object under study as well as the proposed data pre-processing algorithm, machine learning models and approach for results' interpretation. Section 3 shows the results of applying various machine learning models to a pre-processed dataset. In Section 4, the paper is concluded with the scope of future work.
# A Reliability Engineering Approach for Managing Risks in CubeSats
## 3 Materials and Methods
### Step 3. Perform the Criticality Analysis
#### 3.3.2 Defining the Severity Index  
A Severity (S) index has to be assigned to each specific fail (failure mode at part level) to identify its impact on the mission. The severity ranges in a scale of 1 to 5, where 1 represents a failure mode with minimum effects on the mission, and 5 is a catastrophic failure mode in terms of mission operations (see Table 4).
# Impact of Multiple Beams and Mobility-Based Beam Alignment Error on Millimeter Wave Communication +
## 1 Introduction  
To meet the capacity requirements of an evolving cellular network, channel measurements have demonstrated the potentials of an mmwave spectrum. The smaller wavelength at mmwave facilitates implementing highly directional steerable antennas with array gains that can mitigate the peculiarities at this band, such as increased path loss, sensitivity to blockages and other-cell interference. In order to achieve the maximum directivity gains, the base station (BS) and user equipment (UE) beams need to be perfectly aligned, but the increased directionality may incur beam misalignment errors [1; 2]. This experiment demonstrates that a system operating at a beam misalignment of 18\({}^{\circ}\) reduces the link budget by about 17 dB and throughput by up to 6 Gbps or may completely break the connectivity [3].  
Considerable research to analyze the performance of mmwave cellular networks exploiting directionality with perfect beam alignment can be found in the literature [4] and references therein, while limited work considers imperfect beam alignment. In practice, if the angle-of-arrival estimation of incoming signal is not accurate, the devices will not be able to steer their main beams accordingly, leading to imperfect beam alignment. Therefore, it is important to incorporate beamsteering errors into the analysis, and practical mmwave systems rely on beam management procedures [5; 6]. Several works that demonstrate the effect of beam misalignment on the coverage probability (CP) performance of downlink mmwave cellular networks are presented in [7; 8; 9]. Other works to determine optimal beamwidth in the presence of beam misalignment that maximizes system throughput and transmission capacity are reported in [10; 11]. In addition, mobility-based beam misalignment errors also reduce the received signal strength at UE, which may lie within the side lobe of the previous beam selected during synchronization signal burst (SSB) [6].  
In this work, we consider multi-beam transmission and incorporate beam alignment error due to UE mobility in the uplink mathematical model of mmwave cellular systems.  
Our results show that there exist an optimum number of BS beams that maximize coverage probability at different UE velocities and cell radii. Moreover, an increased number of beams at UE is found to give the best coverage performance due to increased beamforming gain.