esa-sceva/satcom-chunk-collection huggingface dataset:
AI-driven maintenance management increases operational safety, optimizes maintenance plans based on real-time data, and enhances resource efficiency and cost control. The maritime industry is undergoing a digital transformation, with stakeholders--including ship designers, equipment manufacturers and shipping companies--collaborating to develop fully autonomous vessels. As part of this transformation, AI-based solutions play a key role in predictive maintenance, ensuring that technical systems operate at their full potential. This study aims to develop a tool that leverages AI and ML techniques for fault detection in naval auxiliary systems. By analyzing operational parameters, we aim to identify trends in system component failures and improve maintenance planning. The proposed solution will provide a diagnosis tool and decision support system, enabling more precise and timely maintenance actions. Ultimately, this approach will help prevent future incidents, increase equipment efficiency and improve the safety and reliability of maritime operations. The remainder of the paper is structured as follows: First, we discuss the foundational concepts and related work in the field. Subsequently, we provide an overview of naval equipment maintenance. Following this, the paper presents our methodology, proposes a fault detection algorithm and discusses the results of testing on a shipboard system. The research concludes with a summary, limitations and recommendations for future research directions.
## 6 Conclusions
This research was funded by the Major Program of the Natural Science Foundation of China (42090013), the General Program of the Natural Science Foundation of China (42471352), theOpen Fund of State Key Laboratory of Remote Sensing Science (No. OFSLRSS202310), and the Anhui Provincial Natural Science Foundation (No. 2308085 QD118). **Data Availability Statement:** Field-measured CI data can be found at [[https://gbov.acri.fr](https://gbov.acri.fr)]([https://gbov.acri.fr](https://gbov.acri.fr)), accessed on 1 February 2024. The MODIS-year land cover, land surface phenology and NDVI data with a spatial resolution of 500 m can be downloaded from [[https://lpdaac.usgs.gov/product_search/?view=cards&sort=title](https://lpdaac.usgs.gov/product_search/?view=cards&sort=title)]([https://lpdaac.usgs.gov/product_search/?view=cards&sort=title](https://lpdaac.usgs.gov/product_search/?view=cards&sort=title)), which was accessed on 1 January 2024. The GlobeLand30 dataset can be downloaded from [[https://www.webmap.cn/commres.do?method=globeIndex](https://www.webmap.cn/commres.do?method=globeIndex)]([https://www.webmap.cn/commres.do?method=globeIndex](https://www.webmap.cn/commres.do?method=globeIndex)) and was accessed on 15 January 2024. The MODIS CI data can be found at [[https://www.geodata.cn/data/datadetails.html?dataguid=214951029475816&docid=7534](https://www.geodata.cn/data/datadetails.html?dataguid=214951029475816&docid=7534)]([https://www.geodata.cn/data/datadetails.html?dataguid=214951029475816&docid=7534](https://www.geodata.cn/data/datadetails.html?dataguid=214951029475816&docid=7534)), accessed on 1 November 2023. **Acknowledgments:** MODIS data were obtained from the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC). We are grateful for the insightful criticism and suggestions from all the anonymous reviewers and the editor. **Conflicts of Interest:** The authors declare no conflicts of interest. **Appendix A** \begin{tabular}{c c c c c c c c} \hline \hline
**ID** & **Sites** & **Latitude** & **Longitude** & **IGBP** & **Year** & **Period** & **Field CI** & **MODIS CI** \\ \hline  
\hline \hline \end{tabular}
# Hybrid ARQ Scheme with Autonomous Retransmission for Multicasting in Wireless Sensor Networks
## 6 Simulation Results
However, since the number of autonomous transmissions also increases as \(q_{m,k}\) increases in the proposed HARQ, the required uplink feedback resources are maintained at almost the same level regardless of \(q_{m,k}\). Therefore, the proposed HARQ can save more uplink transmission power as \(q_{m,k}\) increases. For example, when \(q_{m,k}=0.1\), the proposed HARQ with \(T=3\) can save 27.8% of uplink transmission power for feedback as compared to the conventional HARQ, and when \(q_{m,k}=0.3\) the proposed HARQ can save 50.7% of uplink transmission power. Figure 10: Comparison of required uplink resources for ACK/NACK feedback and optimal \(N\) according to different value of \(q_{m,k}\) when \(K=10\). Figure 9: Comparison of spectral efficiency in the IEEE 802.16e downlink when MCS levels were dynamically changed over 4–11.
## 3 Problem Definition
### Definition of Terms
#### 3.2.2 Buffer Timeout  
The buffer timeout is the maximum wait time allocated to the buffers to receive data before its transfer between machines occurs, even if the buffer was not full, and while benefiting throughput, it can cause latency issues. An example of this is if the buffer timeout is set to a value of 500 ms in an attempt to increase the throughput of the system, while that of the default value of it is 100 ms as shown in Figure 5. This longer timeout results in a potentially slower performance, so to counteract this, there are two extreme cases where we can maximize throughput at the cost of latency without severely affecting the system: either by setting the Buffer Timeout value to anywhere close to zero (a value of zero can cause a severe performance degradation), or by setting it to \"\(-1\)\", effectively removing the timeout and simply waiting for the buffers to fill.  
Figure 4: Generating watermark based on the maximum out of orderness of six seconds for the data stream.  
Figure 3: Distribution of operators in a distributed stream processing framework (adapted from [12]).
## 3 Results and Discussion
### Test Problems  
To study the effectiveness of the MSAMT, fourteen generally used test functions with different characteristics were adopted, and all of them considered five dimensions, namely 10D, 20D, 30D, 50D, and 100D. Table 2 provides the relevant details about these functions.
# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 4 Advances in Speech Recognition for Uyghur, Kazakh, and Kyrgyz
### Technical Development
#### 4.2.2 Kazakh  
Compared to Uyghur, research on Kazakh speech recognition started much later. In 2015, [PERSON] et al. [140] studied the Kazakh-Russian bilingual speech recognition system. They built a Kazakh-Russian bilingual keyword recognition system based on the DNN-HMM structure with 147 h of telephone data. In 2016, [PERSON] et al. [141] constructed a continuous speech recognition system based on the GMM-HMM. Since 2019, the Institute of Information and Computational Technology and al-Farabi Kazakh National University published several papers on Kazakh speech recognition and verified the DNN-HMM system [142; 143], BLSTM-CTC end-to-end system [144; 145], and Transformer CTC/attention system [146] with their private data. At the same time, [PERSON] et al. [147] constructed an LSTM-CTC end-to-end Kazakh ASR system based on transfer learning. Since these research works were based on private data, their value is limited.  
Like [PERSON], researchers have gradually become aware of the importance of open databases for Kazakh ASR. In 2021, [PERSON] et al. [148] reported their crowdsourcing platform and claimed that 50 h of data had been collected through this platform. However, they did not publish the platform, nor the data. In 2021 also, researchers from Nazarbayev  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Models** & **CER (\%)** \\ \hline DNN-HMM & 24.3 \\ \hline Chain-TDNN & 17.6 \\ \hline BLSTM-CTC/Attention & 31.5 \\ \hline Transformer-CTC & 21.4 \\ \hline Conformer-CTC & 11.6 \\ \hline Conformer-CTC-MTL & 7.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: SOTA results tested on THUYG-20 [127].  
University released the first large-scale Kazakh database, KSC [114], offering the first open benchmark for Kazakh speech recognition research. Since then, research on Kazakh has been in the fast lane. For example, [PERSON] et al. [149] conducted a multilingual study of Kazakh, Russian, and English based on KSC. In 2022, KSC was further expanded and reached 1128 h [117]. Table 5 presents the performance of different models tested on KSC, from which we can see the roadmap of technical development. In this paper, the DNN-HMM is the hybrid system, and the others are two end-to-end systems with LSTM and Transformer as the encoder blocks, respectively. Valid and Test represent the validation set and test set, respectively.
# Confocal Laser Endomicroscopy for Bladder Cancer Detection: Where Do We Stand?
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Author, Year of Publication [Ref]** & **Type of Study** & \begin{tabular}{c} **Cellvizio Probe** \\ and Penetration \\ Depth \\ \end{tabular} & \begin{tabular}{c} **Control Delivery** \\ **Method (n)** \\ \end{tabular} & \begin{tabular}{c} **Enrolled** \\ **Patients (n)** \\ \end{tabular} & \begin{tabular}{c} **Median Age** \\ **(Range)** \\ \end{tabular} & \begin{tabular}{c} **Overall** \\ **Lesions (n)** \\ \end{tabular} & \begin{tabular}{c} **Inter-Observer** \\ **Agreement CLE** \\ **Images** \\ \end{tabular} & \begin{tabular}{c} **Histology-CLE** \\ **Correspondence** \\ \end{tabular} \\ \hline [PERSON], 2009 [15] & Feasibility & 2.6 mm-60 μm & \begin{tabular}{c} EV (10) \\ BI (5) \\ Boch (12) \\ \end{tabular} & 27 & 73 (range 47-90) & NR & NR & NR & NR \\ \hline [PERSON], 2013 [16] & Feasibility & NR & NR & NR & NR & 31 & \begin{tabular}{c} -Experenced CLE \\ autogus 90\% \\ -Novice CLE autogus 77\% \\ -Pathologies 81\% \\ \end{tabular} & NR \\ \hline [PERSON], 2019 [17] & Prospective & 2.6 & BI & 53 & NR & 72 & \begin{tabular}{c} Software-based \\ interpretation \\ \end{tabular} & \
\begin{tabular}{c} PPV: 74\% \\ NP: 88\% \\ \end{tabular} \\ \hline [PERSON], 2019 [18] & Prospective & 2.6 mm & BI & 21 & 61 (32-81) & 21 & NR & 81\% \\ \hline [PERSON], 2019 [19] & Prospective & 2.5 mm & BI & 75 & 68.32 (\(\pm\)9.45 SD) & 119 & NR & 87\%:68\% \\ \hline [PERSON], 2020 [20] & Prospective & 2.6 mm-65 μm & BI & 53 & 70 (62-79) & 66 & 76\% & 70\% \\ \hline [PERSON], 2020 [21] & Prospective- pilot study & 2.6 mm & EV & 12 & 74 (52-94) & 34 & 73.5\% &
\begin{tabular}{c} PPV: 54.6\% \\ NP: 82.3\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics of included studies. CLE: confocal laser endomicroscopy; EV: intravenous contrast administration; BI: bladder contrast instillation; PVV: positive predictive value; NPV: negative predictive value; SD: standard deviation; NR: not reported.
# Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals
## 3 Preliminary
### Synchronous and Asynchronous Transmissions  
Figure 1a,b illustrate the examples of synchronous and asynchronous scenarios, respectively. For these examples, a transmitted signal is generated using OFDM, which is the most common modulation adopted in LTE and Wi-Fi [30; 31]. Transmitted signals consist of several OFDM symbols, each of which is composed of a cyclic prefix (CP) part and a data part. In Figure 1a, all OFDM symbols are well aligned at the start of the cyclic prefix parts, representing the scenario of synchronous transmission. In contrast, Figure 1b illustrates the asynchronous transmission situation where all mobile devices start their transmissions randomly.  
In general, if the CPs of two transmitted signals overlap with each other, it can be said that the two transmissions are synchronized. Figure 1c shows an example of overlapping CPs. In Figure 1c, when a receiver starts to extract time-domain samples from a received signal in the red region indicated by '(i)' where two CPs overlap, two signals in the extracted samples are synchronized due to the property of the CP. Therefore, the extracted samples in the blue region marked with '(ii)' have synchronized signals. On the other hand, as shown in Figure 1d, if there is no overlap of the CPs, this is called asynchronous transmission and synchronized signals cannot be extracted.  
Figure 1e presents the case where synchronous and asynchronous transmission is mixed in a received signal. The extracted samples of Device 1 and Device 2 are synchronized, but those of Device 1 and Device 3 (or Device 4) are not synchronized. This means that the transmitted signals in a received signal can or cannot be synchronized with other transmitted signals. Therefore, the extracted samples from a received signal can be classified into three cases: (1) samples of synchronous transmission, (2) samples of asynchronous transmission, and (3) samples of a mixture of synchronous and asynchronous transmission.  
In summary, perfectly synchronized signals cannot be obtained at a receiver if not all of the CPs of transmitted signals overlap. Furthermore, even if there is a region where all CPs overlap, synchronized signals cannot be obtained if a receiver does not start sampling within the overlapping region. Therefore, the input of our proposed network can be a mix of synchronous and asynchronous transmission, as well as synchronous transmission.
# Collaborative Mobile Robotics for Semantic Mapping: A Survey
## 2 Single-Robot Semantic Mapping
### Semantic-Map Representation
#### 2.2.1 Visual Geometric-Semantic Representations  
This simply consists of attaching semantic information directly to the associated physical features in the geometric map. For example, [PERSON] et al. [29] use ORBSLAM2 to create a point cloud of the environment, and the points associated with each object are modeled by a different color. In [4], a semantic occupancy map is created by adding minimum bounding rectangles (MBRs), which represent objects' occupied spaces, to their topological areas in the map. The different object categories are represented by different colors. [PERSON] et al. [3] also used ROS's Gmapping module to create an occupancy map of the environment. During the process, as the robot receives semantic information, it combines it with its position and adds a new semantic node to the map.  
These methods provide maps that enable direct interpretation of semantic information in positions. Therefore, they can be easily deployed in mobile robot navigation systems to perform semantic navigation tasks. However, such simple representations are insufficient to allow robots to perform complex tasks such as mission reasoning.
## 4 Conclusions  
A dissimilar joint of the V-4 Cr-4 Ti alloy/pure Ti was successfully prepared by using the hot forging technique in the temperature range between 1150 \({}^{\circ}\)C and 950 \({}^{\circ}\)C. The microstructure and mechanical properties of the joint were studied. Based on the results, conclusions are shown below.  
1. A sound diffusion-bonded interface was obtained. The interface was about 100 \(\upmu\)m in width and can be divided into two different zones in which zone I is the Widmanstatten structure consisting of an \(\alpha\)-Ti phase and \(\beta\)-Ti phase and zone II--adjacent to V-4 Cr-4 Ti substrate--is the single \(\beta\)-Ti phase band structure.
2. A maximum micro-hardness value of 332 HV was measured at the transitional region between the Ti substrate and the V-4 Cr-4 Ti/Ti joint while the minimum level of hardness reaching only 182 HV was located in a region in the V-4 Cr-4 Ti substrate adjacent to the joint. The average hardness of the Ti substrate and the V-4 Cr-4 Ti alloy are 190 HV and 258 HV, respectively.
3. The bonding strength of the V-4 Cr-4 Ti/Ti joint was higher than 165 MPa, according to the results of push-out tests. It was demonstrated that the fracture of the joint took place in the region with the highest hardness, which is probably ascribed to the stress concentration caused by the phase transformation of the Ti substrate during the air cooling process.  
This research is supported by the National Magnetic Confinement Fusion Science Program of China (No. 2014 GB120000), the Beijing Natural Science Foundation (No. 2182043), and the Fundamental Research Funds for the Central Universities (No. FRF-TP-16-029A1).  
[PERSON] conceived and designed the experiments. [PERSON] performed the experiments and contributed with reagents/materials/analysis tools. [PERSON], [PERSON], [PERSON], and [PERSON] analyzed the data. [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
Figure 6: Scanning electron microscope (SEM) micrographs and chemical analysis of the fracture surface on the V–4 Cr–4 Ti alloy side: **(a-1,b-1)** surface appearance at 1000\(\times\) magnification; **(a-2,b-2)** surface appearance at 10,000\(\times\) magnification; **(a-3,b-3)** chemical analysis of the selected areas in **(a-1,b-1)**, respectively.
## 2 Materials and Methods
### Experimental Set-Up and Testing Procedures
#### 2.6.3 Testing Procedure for the Transmitter and Receiver Block Located in the Same PCB  
The validation of the transmitter and receiver blocks on the same board involved the transmission of a 4 MHz sine signal, generated by the FPGA. Subsequently, the outputs from the ADC and the comparator were observed using the ILA, as shown in Figure 32.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Bio-Inspired Energy-Efficient Routing Protocols (Bio-Inspired E2 RPs)
#### 5.1.3 Comparison of Bio-Inspired Energy-Efficient Routing Protocols  
The properties of the physiologically affected optimum routing protocols are compared in Table 8 and are covered in this section. The construction of the best, most appropriate algorithms can benefit from biological insect modification to meet the many self-configuration and self-organizing problems in UWSNs. Furthermore, in terms of energy efficiency, network throughput, and end-to-end latency of the network, bio-inspired routing algorithms are thought to be more effective routing protocols for large-scale UWSNs. Consequently, the vast majority of bio-inspired routing protocols are quite effective. Therefore, these routing methods can boost network performance immediately. However, in bio-inspired routing protocols, acquiring a great network performance is a crucial and difficult challenge. The fact that no specific energy-efficient model was emphasized. In order to receive and transmit data packets, FFRP employs the basic energy-efficient model.  
Cluster-based (C-b) routing methods are among the most effective routing protocols for UWSNs. Sensor Rx nodes are grouped when employing C-b E2 RPs, and each group has a cluster leader (CH). The CH gathers and mixes communicating data packets from the members of its cluster before transferring them from the Tx anchor node to the sensor Rx node. Since they manage most of the tasks and put in the greatest effort, however choosing a CH wisely is essential. UWSNs employs both clustering architectures, which are based on layer and grid configuration. (i) Using clustering-based protocols on layer, for which the seafloor is broken up into layers, with the sensor Rx nodes creating numerous clusters in each layer. The communicating data packets are gathered and combined by the cluster leader from its cluster members. The final communicating data packet is communicated to the next CH layer after communicating data packets from its cluster members when combined. Furthermore, in this sort of multi-hop mechanism used by UWSNs, communication data packets are sent from the Tx anchor node to the sensor Rx node. (ii) The concept of layer-based clustering routing protocols and grid-based clustering routing protocols are almost same. Layer-based clustering routing protocols split the seabed into various numbers of layers, whereas grid-based clustering routing methods divide the seafloor into various numbers of grids in UWSNs. This is the major distinction between the two types of clustering routing protocols. In this part, we reviewed E2 RPs, which serve as the framework for UWSN clusters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Protocol/** & **Objective** & **Need of** & **Implementation** & **Strategy of** & **Energy** & **Advantages** & **Disadvantages** \\
**Year** & **Objective** & **Localization** & **Efficiency** & **High** & **Increased** & **Computational** & **cost is high \\ \hline FFRP/ & Find reliable & & & & & & \\  & and stable & & & & & & \\  & routing & & & &
# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 3 Methods
### Model Fit Evaluation and Parameter Consideration  
In this study, model fit was assessed using the Log-Likelihood (LL), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). While all three metrics are likelihood-based, AIC and BIC differ in how they penalize model complexity. AIC imposes a linear penalty (2k), whereas BIC applies a logarithmic penalty (ln (n)-k), where k is the number of estimated parameters, and n is the number of observations. In the full estimation approach (Approach 2), the value of k includes the total number of estimated parameters: student abilities (\(\theta\)), item difficulties (b), and time sensitivity (\(\lambda\)) for time-aware models. In contrast, for the K-fold cross-validation approach (Approach 3), only \(\theta\) was re-estimated on the test data, while b and \(\lambda\) were kept fixed from the training phase. Therefore, when calculating AIC and BIC for the test set, k was set equal to the number of \(\theta\) parameters (i.e., number of students in the test fold). This distinction ensures that the complexity penalty reflects only the truly optimized components of the model during each evaluation context. By treating b and \(\lambda\) as constants in the cross-validation setup, we maintain consistency with the principles of information-theoretic model selection and ensure fair comparisons across different modeling approaches.
# Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks +
## 5 Prototype Implementation
In practice, more compact encodings, such as the use of binary representations for opcodes and other numerical values and the definition of fixed field lengths without separators, would allow for much smaller absolute message sizes. Similarly, compression methods could be used, such as run-length coding or DEFLATE-based methods [41]. However, as part of the evaluation of the clustering communication costs in Section 6.2.4, the focus was on examining the relative relationship between network size and the amount of data sent.
## 4 Simulation and Analysis
### System Performance of CPCE under Different Storage Capacity of BSs  
Figures 6 and 7 show the impact on network performance of the BSs' different storage capacity. It is clear that the more files that BSs can cache, the more possibility that BSs hit required files. We assume the total number of files is 20. When the storage capacity of BSs is half of total files, both backhaul load and time delay of the proposed CPCE is as a third as that of MPC. Even when storage capacity is very limited (for example, only 1 file can be cached), the backhaul load of CPCE is acceptable, as shown in Figure 6. Meanwhile, as shown in Figure 7, time delay decreases as the storage capacity increases, and the proposed CPCE outperforms the other two.  
The performance of Random and MPC comparing with the proposed CPCE in terms of time delay and backhaul load is listed in Table 2.  
Figure 4: Backhaul load under different numbers of UEs (\(\gamma=1\)).  
Figure 5: Normalized time delay under different numbers of UEs (\(\gamma=1\)).