esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In recent years, 5G technology has emerged as the most significant communication service as the globe moves towards a new era of communication. This is due to the fact that 5G technology provides low latency over Multiple Input Multiple Output (MIMO) fading conditions with high channel capacity, channel aggregation and a higher number of users. On the other hand, new portable gadgets are becoming lighter and thinner, while requiring powerful processing capabilities. This work presented a four-port MIMO antenna system with high performance characteristics. The proposed antenna was shaped in a hybrid Olympic sign, hence it was named as Olympus antenna. The total unit size of the proposed antenna was 12 \(\times\) 14 \(\times\) 0.254 mm\({}^{3}\) while for four element MIMO assembly it was noted to be 48 \(\times\) 14 \(\times\) 0.254 mm\({}^{3}\) (4.48\(\lambda_{0}\)\(\times\) 1.12\(\lambda_{0}\)\(\times\) 0.02\(\lambda_{0}\)). The antenna was fabricated and tested using in house facilities and the simulated results were found to be in an excellent agreement with the simulations. The gain of the MIMO system at 28 and 38 GHz was noted to be 11 and 10.9 dBi, respectively while the minimum isolation among any two two radiating elements was found to be \(<\)20 dB. Through the achieved results, the proposed MIMO system can be considered as potential candidate for future mmwave applications.  
Conceptualization, M.E.M. and S.H.K.; methodology, H.S.S. and N.O.P.; software, M.E.M. and S.H.K.; validation, H.S.S. and N.O.P.; formal analysis, H.S.S. and N.O.P.; investigation, H.S.S. and N.O.P.; resources, J.K. and H.M.; data curation, H.S.S., J.K. and H.M.; writing--original draft preparation, M.E.M. and S.H.K.; writing--review and editing, S.H.K., H.S. and J.K.; visualization, M.E.M. and N.O.P.; supervision, M.M. and H.M.; project administration, M.M. and H.M.; funding acquisition, M.M. and H.M. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Ant.** & **Frequency** & **Bandwidth** & **Size** & **Gain** & **Isolation** & **ECC** \\  & & **(GHz)** & **MHz** & **mm\({}^{2}\)** & **(dBi)** & **(dB)** & **(dB)** \\ \hline
[7] & 4 & 23.45–30.79 & 734/402 & 13 \(\times\) 11.25 & 4.5 & NA & NA \\  & & /31.72–35.74 & /330 & & & & \\  & & /36.3–39.60 & & & & & \\
[8] & 4 & 26–31 & 5 & 48 \(\times\) 31 & 5.2 & 21 & 0.0015 \\
[9] & 4 & 26.5–29.5 & 3 & 30 \(\times\) 30 & 6 & \(>\)30 & 0.0005 \\
[10] & 4 & 27.5–28.5 & 1/1 & 24 \(\times\) 24 & 7 & \(>\)25 & 0.001 \\  & & /37.5–38.5 & & & & & \\
[11] & 4 & 26–30 & 4 & 30 \(\times\) 30 & 6,12 & \(>\)25 & 0.16 \\
[12] & 4 & 26–29.5 & 350 & 25 \(\times\) 15 & 7.8 & 17 & 0.00016 \\
[13] & 4 & 36.83–40.0 & 317 & 47.4 \(\times\) 32.5 & 6.5 & 45 & 0.01 \\
[14] & 4 & 26.64–28.55 & 191 & 35 \(\times\) 35 & 5.07 & 27 & 0.0005 \\ Prop. & 4 & 23–33/37.75–41 & 10/325 & 48 \(\times\) 12 & 5.7 & \(>\)20 & 0.00015 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the proposed antenna system with the literature.
## 3 Analysis and Reliability Verification of an Engineering Case
### Analysis of Theoretical Calculation Results
#### 3.2.3 Comparison between Uniform Expansion Model and Nonuniform Expansion Model  
Aiming at the nonuniform expansion model of grouting proposed by [PERSON] et al. [2], this study compared a nonuniform expansion model with the uniform expansion model. Figure 8 compares the compensated level of horizontal displacement of the tunnel calculated by the two expansion models. It is obvious that the distributions of horizontal displacement of the tunnel determined by the two models basically coincide. The maximum horizontal displacement in the center of the tunnel calculated by the nonuniform and uniform expansion models were 3.31 and 3.24 mm, respectively, or a difference of only 0.07 mm. Therefore, the results of the two models were basically equal. Considering the simplicity of model construction and calculation, it is more convenient and faster to select a uniform expansion model for the calculations.  
Figure 7: Comparison of the compensated level of horizontal displacement of the tunnel calculated by the original method with that estimated by the simplified method.
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 1 Introduction  
Pervasive monitoring allows capturing and characterizing people's practices and patterns in various scenarios, such as learning, shopping or homecare. This monitoring requires the capture of data from different kinds of sensors, across the different situations and contexts that the people might encounter in their everyday lives. Although smartphones can be used to address this activity, these devices still have some limitations to perform pervasive monitoring; for instance: (1) their sensors are not very accurate; (2) the continuous sensing process involves a high energy cost [1], and their computing power is usually not enough to perform computationally intensive classifications and inference tasks (e.g., speech or image recognition) [2].  
These limitations have promoted the appearance of both collaborative sensing techniques that help improve data quality and save energy [3; 4; 5; 6], and also software platforms that help developers create monitoring applications for smartphones. Most these solutions involve the use of centralized components, or assume homogeneity of the devices' capabilities or stability of the communication links among the participants [7; 8; 9]. These assumptions do not represent a limitation for conductingsome monitoring activities (e.g., crowdsensing [10] or participatory sensing [11]); however, they do for monitoring collaboration scenarios where the smartphone users perform loosely-coupled mobile interactions [12]; for instance, firemen performing first response activities or students involved in mobile learning activities.  
In order to support the development of distributed pervasive monitoring applications for this particular niche (i.e., loosely-coupled mobile work), we present a framework named Mobile Autonomous Sensing Unit (MASU) that is able to collect and share data among devices involved in smartphone-based pervasive sensing. Although this framework was initially conceived to support pervasive monitoring in mobile learning contexts, it can also be used to perform pervasive monitoring in a variety of work scenarios.  
The MASU framework was evaluated to determine its performance and usage costs in terms of energy, computation and network traffic. The evaluation included simulations and also an empirical test. The preliminary results indicate that this infrastructure is not only useful for data gathering and sharing, but also for reducing the battery consumption of the devices involved in the sensing tasks.  
The next section presents and discusses the related work. Section 3 describes the design of the proposed pervasive sensing framework, including its architecture, services, interaction protocols, messages and data retrieval mechanism. Sections 4 and 5 describe the evaluation process using simulation and an empirical test, respectively, and discuss the obtained results. Section 6 presents a comparison between well-known sensing frameworks and the one presented in this paper, by showing similarities and differences. Finally, Section 7 presents our conclusions and possible future work.
The PMT Acquisition and Trigger Generation System of the HEPD-02 Calorimeter for the CSES-02 Satellite  
[PERSON]  
1 National Institute for Nuclear Physics, Sezione di Napoli, Via Cintia, I-80126 Naples, Italy;  
[EMAIL_ADDRESS] (A.A.); [EMAIL_ADDRESS] (A.B.); [EMAIL_ADDRESS] (V.M.);  
[EMAIL_ADDRESS] (G.O.); [EMAIL_ADDRESS] (F.P.); [EMAIL_ADDRESS] (B.P.); [EMAIL_ADDRESS] (V.S.);  
[EMAIL_ADDRESS] (A.V.);  
[PERSON]  
1 National Institute for Nuclear Physics, Sezione di Napoli, Via Cintia, I-80126 Naples, Italy;  
[EMAIL_ADDRESS] (A.A.); [EMAIL_ADDRESS] (A.B.); [EMAIL_ADDRESS] (V.M.);  
[EMAIL_ADDRESS] (G.O.); [EMAIL_ADDRESS] (F.P.); [EMAIL_ADDRESS] (B.P.); [EMAIL_ADDRESS] (V.S.);  
[EMAIL_ADDRESS] (A.V.);  
[EMAIL_ADDRESS] (A.  
From CSES-01 first scientific data, it was possible to obtain different results that include the monitoring of the G3 geomagnetic storm, which happened on 26 August 2018 [5], the study of the solar modulation of the cosmic rays [6] and the trapped proton fluxes inside the South Atlantic Anomaly (SAA) [7].  
Furthermore, a retrospective analysis of high-magnitude seismic events, such as the Palu earthquake on 28 September 2018 (\(M_{W}=7.5\)) and the Papua New Guinea earthquake on 14 May 2019 (\(M_{W}=7.6\)) [8], has been made using data collected by the CSES-01 detectors. From these studies, several ionospheric anomalies that could be related to the preparation phase of these seisms can be deduced [6; 7; 9; 10].  
The second satellite of the constellation, named CSES-02, will be launched in early 2024 and will be placed in an orbit with a 180\({}^{\circ}\) phase shift with respect to CSES-01 to increase the temporal resolution. Thanks to the improvements made on the HEPD-02 detector, the CSES-02 will operate also in polar regions and the SAA, while CSES-01 was designed to be operative only for latitude between \(\pm 65^{\circ}\) and outside the South Atlantic Anomaly (SAA).  
The instruments that will be onboard the CSES-02 satellite, are listed in Table 1 with a brief description of their observation targets [11].  
The responsibility of the realization of the High-Energy Particle Detector (HEPD-02) and the Electric Field Detector (EFD-02), for the second satellite, has been assigned to the Italian collaboration.  
The HEPD-02 will measure the flux of particles and their energies and will also be able to detect Gamma Ray Bursts in the range from MeV to tens of MeV [12] and will be described in detail in the next sections.  
The EFD-02 will measure the components of the electric field of the Earth, but its description is outside the scope of this article.
## 2 Background and Related Works
### Aspect-Based Sentiment Analysis
#### 2.2.2 Aspect Polarity Detection  
The goal of aspect polarity detection is the identification of the polarity towards each aspect extracted from a given customer review about a target entity. The aim is to obtain a set of \(\{a,p\}\) tuples that represents the user's opinions. The item \(a\) is extracted in the aspect extraction step; \(p\) can be positive, negative, neutral or conflict. The task can be regarded as a classification problem where the inputs are a sentence, \(s\), and an aspect, \(a\), and the output is the polarity, \(p\). A single sentence may contain opinions about different aspects, with different polarities. The key to solve this problem is to design a set of useful features. Such features need to be able to model the relations between the sentences and the aspects.  
In this context, many studies have proposed efficient solutions. For example, the Sentiue team [27] developed a three class polarity classifier by extracting features from the text and the aspect category, including Bag of Words (BoW), lemmas, bigrams, punctuation and polarised words. The ECNU team [28] found that each aspect is related to specific fragments in the corresponding sentence. Based on that idea, they extracted from a given sentence what they called pending words by applying two steps. First, each sentence was divided into several fragments. Then, one or more fragments from the sentence were associated to each aspect. The words in the selected fragments were the pending words. After that, they used four types of features extracted from the pending words to build logistic regression classifiers: linguistic, sentiment lexicon, topic-modelling and word-embedding. The NRC-Can-2014 team [24] trained one multi-class SVM classifier for all aspect categories. The feature set incorporated the information about a certain aspect category. Following these approaches, we have designed our aspect category polarity detection model, as explained in Section 3.4.
# Efficient Top-\(k\) Spatial Dataset Search Processing
## 5 The Baseline Search Scheme  
In this section, we propose a baseline top-\(k\) spatial dataset search scheme (KSDS), which utilizes the proposed similarity-calculation model GDSS to measure the similarity between spatial datasets. The baseline search scheme involves comparing an exemplar dataset with a collection of spatial datasets using the GDSS model to calculate their similarities. Consequently, a set of \(k\) ranked datasets are returned as the final search results, which are most similar to the exemplar dataset.  
In the baseline search scheme, when a top-\(k\) spatial dataset search is initiated by the data user, the data owner performs the search processing as follows: each spatial dataset in the data repository is traversed, and the similarity between the spatial dataset and the input exemplar dataset is calculated. The \(k\) spatial datasets with the highest similarity values to the exemplar dataset are selected as the search result and returned to the data user. The details of the baseline search scheme are outlined in Algorithm 1.  
In Algorithm 1, the grid distribution representation of the input exemplar spatial dataset is first generated. A priority queue is then initialized to store each spatial dataset along with its similarity to the exemplar spatial dataset. Next, for each spatial dataset in the data repository, the similarity between the spatial dataset and the exemplar spatial dataset is calculated based on Definition 4. Afterward, the spatial dataset and its similarity are considered for insertion into the priority queue. A new similarity-dataset pair is inserted, and the queue is updated only if the number of pairs in the priority queue is less than \(k\), or if the minimum similarity in the queue is lower than the newly calculated similarity. Finally, the spatial datasets in the queue are the search results.  
According to the procedures of the search processing, the time complexity of the baseline search algorithm (KSDS) is \(O(n(\alpha\beta+\log k))\), where \(n\) is the number of spatial datasets in the repository, \(k\) is the number of requested search results and \(\alpha\) and \(\beta\) are the average numbers of grids in MCG of the exemplar dataset and spatial datasets, respectively.
## 4 Simulation Results
### Experimental Results
#### 4.2.4 Stability  
The stability period is crucial for many applications where the feedback from the WSNs must be reliable.  
Figure 11 shows the network stability in the presence of the heterogeneity in energy level and transmission range. The proposed protocol provides best characteristics compared with EECDA and LEACH-Hetero in terms of lifetime, throughput, and stability. The reason is that EDACP-CS efficiently  
Figure 10: Throughput of the network.  
Figure 9: Transmission range _vs._ lifetime.  
compresses data, besides the excelent selection of a sensor node to become a CH. Therefore, EDACP-CS efficiently balance the energy consumption among sensor nodes and hence the stability period is enhanced which is the main requirement for the lifetime of the WSNs.
# Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions
###### Abstract  
With the rapid development of space exploration technology, the detection of extraterrestrial bodies has become increasingly important. Among these, path planning and target recognition and positioning technologies are particularly critical for applications in intelligent agents with low computational power operating in complex environments. This paper presents a novel approach to path planning on low-resolution lunar surface maps by introducing an improved A\({}^{\star}\) algorithm with an adaptive heuristic function. This innovation enhances robustness in environments with limited map accuracy and enables paths that maintain maximum distance from obstacles. Additionally, we innovatively propose the Dynamic Environment Target Identification and Localization (DETIL) algorithm, which identifies unknown obstacles and employs spatiotemporal clustering to locate points of interest. Our main contributions provide valuable references for the aerospace industry, particularly in lunar exploration missions. The simulation results demonstrate that the improved A\({}^{\star}\) algorithm reduces the maximum elevation difference by 55% and the maximum cumulative elevation difference by 68% compared to the traditional A\({}^{\star}\) algorithm. Furthermore, the DETIL algorithm's obstacle identification component successfully recognizes all the obstacles along the path, and its spatiotemporal clustering improves the average number of target discoveries by 152% over the conventional DBSCAN clustering approach.  
Keywords:extreme environments; multi-task intelligent agents; path planning; obstacle recognition; points of interest localization +
Footnote †: journal: Applied sciences
## 2 Failure Recovery in SFC and Challenges
### Software-Defined Failure Recovery Studies' Review
Contrary to the proactive backup path setup, a hybrid method proactively calculates the backup paths, but installs them only when the failure occurs [22]. Although this saves flow table resources, it increases the recovery delay.
## 1 Introduction
### Limitations of Static and Unimodal Proficiency Assessments  
Traditional proficiency assessments, such as retrospective self-reports (e.g., the NASA Task Load Index [NASA-TLX]), unimodal physiological indicators (e.g., heart rate variability [HRV]) or basic behavioral metrics (e.g., task completion time [TCT]), are inadequate for capturing the dynamic and complex demands of modern performance environments [6; 7; 8]. These methods exhibit several critical limitations:  
* Temporal Disconnect: Retrospective tools, such as NASA-TLX, fail to capture moment-to-moment fluctuations in workload or proficiency, limiting their ability to reflect real-time cognitive demands [6].
* Subjective Variability: Self-reported data are susceptible to recall bias, fatigue, and individual differences in perception, reducing reliability [6; 9].
* Incomplete Insight: Unimodal metrics, such as HRV or TCT, often overlook contextual or cognitive nuances essential for comprehensive performance assessment [10; 11; 12; 13].  
Even advanced tools such as the Surgery Task Load Index (SURG-TLX) inherit many of these limitations, including subjective bias and a lack of real-time granularity [14]. Similarly, unimodal models struggle to generalize across diverse settings due to noise and individual physiological or behavioral variability [12; 15; 16]. To address these shortcomings, a new assessment paradigm is required-one that integrates context-sensitive, multimodal data (e.g., combining physiological, behavioral, and contextual inputs) and operates in real time to provide a more accurate and holistic evaluation of proficiency [12; 17].  
The dynamic nature of human proficiency in Industry 5.0 environments, influenced by factors such as learning, fatigue, and task complexity, necessitates assessment tools capable of capturing these temporal fluctuations. Unlike traditional static evaluations that offer only a snapshot in time, a robust assessment framework must track skill acquisition (e.g., the transition from a 'Novice' to an 'Intermediate' state [18; 19]) and identify periods of skill decay or fatigue-induced regression [12]. This real-time understanding of an operator's evolving competence is crucial for developing human-centric AI systems that can provide adaptive support, personalize training interventions, and optimize human-AI collaboration [17; 20].
# Multi-Scale Aggregation Residual Channel Attention Fusion Network for Single Image Deraining
## 4 Experiment Results and Analysis
### Quantitative Analysis  
SSIM and PSNR [43] were used as metric functions for quantitative analysis. To maintain consistency with previous research, this study computed the SSIM and PSNR of the Y-channel (YCbCr color space). As summarized in Table 2, our test results were compared with those for the state-of-the-art methods RESCAN [7], PReNet [8], LPNet [9], MSPFN [10], MPRNet [11], VRGNet [12], PCNet [13], and RAiANet [14]. Our proposed method used the average of five cross-validations as the PSNR and SSIM results. This ensured that the module output was stable and accurate. Overall, MARCAFNet resulted in the most favorable PSNR and SSIM for the Rain100H and Rain100L datasets among [7; 8; 9; 10; 11; 12; 13; 14]. For the heavy rain synthetic dataset Rain100H, the proposed MARCAFNet improved a PSNR and SSIM of 2.2 dB and 0.01, respectively, outperforming the current state-of-the-art methods. For the light rain synthetic dataset Rain100L, the proposed MARCAFNet again outperformed the current state-of-the-art methods with a PSNR and SSIM of added 0.6 dB and 0.002, respectively. These results indicate that MARCAFNet can capture both light and heavy rain features in images and can complete the deraining task to obtain clean rain-free images. Notably, the proposed MARCAFNet, using a combination of receptive fields on various scales, can process images with various degrees of complexity, ensuring its robustness across datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & \multicolumn{3}{c}{**Rain100H**} & \multicolumn{3}{c}{**Rain100L**} \\ \cline{2-5}  & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline RESCAN [7] & 28.86 & 0.8646 & 33.12 & 0.9534 \\ \hline PReNet [8] & 26.83 & 0.8582 & 32.53 & 0.9501 \\ \hline LPNet [9] & 23.77 & 0.8226 & 25.63 & 0.8983 \\ \hline MSPFN [10] & 28.23 & 0.8496. & 32.13 & 0.9263 \\ \hline MPRNet [11] & 30.41 & 0.8895 & 36.40 & 0.9646 \\ \hline VRGNet [12] & 30.06 & 0.8855 & 36.87 & 0.9743 \\ \hline PCNet [13] & 28.28 & 0.8699 & 34.19 & 0.9526 \\ \hline RAiANet [14] & 30.42 & 0.8941 & 36.79 & 0.9673 \\ \hline MARCAFNet (our) & 32.62 & 0.9046 & 37.47 & 0.9766 \\ \hline \hline \end{tabular}
\end{table}
Table 2: PSNR and SSIM quantitative comparison of the proposed MARCAFNet with state-of-the-art methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Rain14000 [31]** & **Rain1800 [41]** & **Rain100h [41]** & **Rain1001 [41]** & **Rain800 [41]** & **Rain12 [41]** \\ \hline Train samples & 11,200 & 1800 & 0 & 0 & 700 & 12 \\ \hline Test samples & - & 0 & 100 & 100 & - & 0 \\ \hline Test rename & - & - & Rain100h & Rain100l & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of datasets used for synthesis rain images.
# Mechanical Performance Analysis and Experimental Study of Four-Star-Type Crank-Linkage Mechanism
## 4 Experimental Study of Four-Star-Type Crank Linkage
### Analysis of Experimental Data
#### 4.2.2 Effect of Rotational Speed on the Dynamic Response of the Mechanism  
Considering the existence of mounting clearance in the test bench itself, the installation state of the first-stage linkage fisheye bearing without wear is taken as a reference. The acceleration-velocity phase diagrams of the first-stage slider at different rotational speeds under 80 r/min, 160 r/min, and 240 r/min working conditions are shown in Figure 32. As can be seen from the figure, the phase diagram trajectory is relatively turbulent at low rotational speed. As the rotational speed increases, the phase trajectory becomes more and more concentrated. The increase of rotational speed increases the slider acceleration amplitude and reduces the chaos of the system.