esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### AR Effectiveness  
[PERSON] and [PERSON] [23] found that from a human-machine (HCI) perspective, user-centric designs in AR accounted for 14.3% (38 papers out of 266), while AR experiments for general users accounted for only 7.9% (21 papers out of 266). [PERSON] [24] also pointed out that only 10% of the 161 studies conducted AR experiments performed by users. The insufficient research on AR user experience has led to insufficient training on how to evaluate AR experience, how to design experiments, how to choose and apply correct methods, and how to analyze experimental results. Therefore, finding effective experiments to analyze AR user experience will help improve AR usability. At present, AR effectiveness is mainly analyzed using questionnaires, observation, semi-structured interviews, and video analysis [5; 25]. The System Usability Scale (SUS) developed by [PERSON] [26] to measure usability is a widely used standardized questionnaire-based method for determining subjective usability [27; 28]. [PERSON], [PERSON], and [PERSON] [29] developed usability principles for smartphone AR applications by analyzing existing research about heuristic evaluation methods and developed an AR application prototype which conducted usability testing by observation of and the questionnaire from the participant to verify the effectiveness of usability principles. However, questionnaires and observation collect data from the user perspective, whereas data on the actual behaviors of users in a venue, such as distance travelled and trajectory, usually requires additional equipmentto collect. Trajectory data can be collected by analyzing videos [30; 31], or placed external sensors into the pockets [32] or shoes [33] of participants to determine the direction, trajectory, and distance travelled of walking. However, it is limited with regard to collecting large quantities of behavioral data in the future. Thus, to understand whether AR actually helps users in a space, we developed an AR space behavior analysis system navAR [34] to serve as a research tool without any additional sensors or cameras.
###### Abstract  
We evaluated the energy consumption of a 3T MRI using a central monitoring system, focusing on hospital energy costs during peak winter months from 2021 to 2023. We analyzed consumption during non-productive phases like end-of-day standby and assessed their impact. For active use, we compared standard and AI-enhanced protocols on phantoms, scheduling high-demand protocols during off-peak hours to benefit from lower energy prices. Standard protocols consumed 3.4 to 15 kWh, while optimized protocols used 2.3 to 10.6 kWh, reducing consumption by 32% on average. Savings per scan ranged from EUR 0.03 to EUR 3.7. The electrical consumption of a brain MRI protocol is equivalent to that of 3-4 knee protocols or 2-3 lumbar spine protocols. Using AI-optimized protocols and management, 41 protocols can be completed in 12 h, up from 30, reducing daily costs by EUR 2.38 to EUR 29.18. Annually, AI-optimized protocols could save 7900 to 8800 kWh per MRI unit, totaling 10,500 to 11,600 MWh across France's MRI fleet, equivalent to the yearly consumption of about 4700 to 5300 people. Optimizing MRI resource use can expand patient access while significantly reducing the associated energy footprint. These findings support the implementation of more sustainable practices in medical imaging without compromising care quality.  
MRI; AI; energy consumption; green radiology; cost reduction 2025  
2024
## 3 Results
### Model Usability, Performance, and Transferability
#### 3.3.1 Model Usability  
In the following section, we provide an assessment of model usability based on four criteria.  
1. _Ease of installation_ While LANDIS-II NECN requires the installation of two Windows programs, depending on the options desired, PPA-SiBGC is contained in a single R script and requires only a working R installation.
2. _Ease of parameterization_ While both models can be difficult to parameterize for regions with little to no observational data, the simple biogeochemistry in PPA-SiBGC requires an order of magnitude fewer parameters than LANDIS-II NECN. In addition, PPA-SiBGC uses commonly available forest inventory data while NECN requires a number of parameters that may be difficult to locate.
3. _Ease of program operation_ Both models use a command-line interface and are thus equally easy to operate. Yet, PPA-SiBGC is cross-platform and uses comma-separated-value (CSV) files for input tables, which are easier to work with than multiple tables nested within an unstructured text files. This additionally allows for simplification in designing model application programming interfaces (APIs), or model wrappers, a layer of abstraction above the models. These abstractions are important for simplifying model operation and reproducibility, and enable a number of research applications.
4. _Ease of parsing outputs_ All PPA-SiBGC outputs are provided in CSV files in a single folder while LANDIS-II NECN generates outputs in multiple formats in multiple folders. While the PPA-SiBGC format is simpler and easier to parse, the image output formats used by LANDIS-II carry considerable benefit for spatial applications. Both models may benefit by transitioning spatiotemporal data to the NetCDF scientific file format used by most general circulation and terrestrial biosphere models.
# A Novel Expectation-Maximization-Based Blind Receiver for Low-Complexity Uplink STLC-NOMA Systems
## 4 Blind Energy Estimation (BEE)
### Initialization (Lines 5 to 6)  
The AP divides observations \(\mathbf{z}\) into \(M\) sets in descending order, each with the same number of elements, and calculates the means and variance by taking each set as a cluster. As aforementioned, we consider the common variance for each cluster, which is obtained by taking an average over the variances for all clusters. The weights are initialed by \(\pi_{k}=1/M\) for all clusters since all constellation points of the STA tend to be uniformly drawn due to purely random bit generations. Then, the log-likelihood function is calculated from the initial GMM parameters and observations.
# Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review
## 2 Materials and Methods
### Data Collection  
The review was undertaken by carrying out a bibliographic search on \"Web of Science\" Database (1990-October 2019) for a combination of keywords: \"Landslide\"\", \"Himalaya\", and \"India\". For the current analysis, only peer-reviewed journal articles written in English were considered, as peer-reviewed journals are considered as having the best quality articles and because the English language ensures that these contributions could be fully understandable for the whole international scientific community. The filtering of the articles was carried out by the number of citations (a) articles published before 2008 with 10 or more citations; (b) articles from 2008 to 2010 with 5 or more citations; and (c) all articles thereafter independent of the number of citations received (following the approach adopted from [PERSON] et al. (2012). After performing a screening of the relevant studies, the number of selected articles was narrowed down to 226 (Figure 3).  
Figure 2: Geology of: (**a**) South Asia highlighting the Indian Himalayan region; and (**b**) the study region (Source: USGS World Geologic Map).  
The articles were published across 75 different journals, with nearly 60% of the works published in 11 journals (Figure 4). The articles were initially majorly published in Engineering Geology and Geomorphology. The focus has shifted towards other journals such as Natural Hazards, Landslides, and Geomatics, Natural Hazards and Risk. Generally, after a major landslide event, the preliminary report is published in Current Science. However, extensive analysis of landslide study misses in such journals and is mostly focused on post-landslide studies.  
Figure 4: Distribution of the number of articles across the top 11 journals (out of 75). Horizontal bars depict the number of articles and its thickness denotes the average number of citations per article.  
Figure 3: Analysis of the literature database during 1990–October 2019 from Web of Science. The left ordinate axis represents the number of articles per year and the right ordinate axis depicts the cumulative number of articles during the entire analysis period.
# Double-Constraint Fuzzy Clustering Algorithm
## 4 Experiment
### Eight Real Datasets from UCI  
Eight actual datasets from UCI [26] were used to assess the clustering accuracy and the mandatory constraint on the volume of data in each cluster. These datasets were selected due to their representativeness of different clustering structures and characteristics. Other datasets from UCI have similar features to these eight datasets. The correct clustering labels  
Figure 1: Synthetic datasets with different features.  
and the volume of data in each cluster are known a priori. These labels remain separate from the clustering process and are only used to evaluate the accuracy of various clustering results. Table 1 shows the number of clusters, the volume of data in each cluster, and the dimensionality of each dataset.  
The _Iris_ dataset contains 150 data points, each characterized by four attributes and distributed across three clusters. Each cluster contains 50 data vectors. Two clusters overlap, while the third cluster is linearly separable from those two clusters. In the past decades, this dataset has frequently been used to assess the clustering results of different clustering algorithms. The _Tea_, and _Breast_ datasets exhibit characteristics similar to those of _Iris_. The _Wisconsin_ dataset is high-dimensional, containing 683 instances after removing 16 instances due to missing values. Each instance has nine attributes. This dataset contains two clusters: 444 samples are categorized as \"Benign\" and 239 as \"Malignant\". These two clusters are mainly in two different hyperplanes that respond to different components (attributes). The _Cancer_, _Appendicitis_, and _Wisconsin_ datasets have similar characteristics.
# Robust Deep Speaker Recognition: Learning Latent Representation with Joint Angular Margin Loss
## 2 Related Work  
### Speaker Recognition  
For SR tasks, the i-vector [1] is used as a de facto feature-extraction method. Extracted features are then classified using PLDA [2] and heavy-tailed PLDA [3]. Though these methods achieve considerable results, there is still scope for improvement [22]. Recently, the use of low-level speech was investigated by researchers [23]. They considered under degraded conditions for speaker recognition, feature-extraction methods, and short-term features. Deep-learning methods demonstrated major advances in feature extraction and pattern recognition [24; 25; 26; 27; 28; 29; 30; 31]. For example, in [25], the authors proposed a CNN-based method where the recording of a speaker was treated as an image. In [26], the authors proposed a CNN-based deep neural network for the speaker's short speech embedding. They used 5 s of speech for both sides of verification. A DNN was directly applied to time-frequency speech representation. In [29], the authors proposed to add a filter-bank layer within a CNN as an extra layer that learned jointly with the rest of the network to optimize cross-entropy loss. A filter-bank layer with multiple hidden layers has been proposed by the authors in [30] for spoofing detection. They showed that the filter bank produced cepstral coefficient features that distinguished between natural and synthetic speech more precisely than naive DNN features can, and manually designed cepstral coefficients. Before the output layer of the DNN, the authors of [32] proposed to add an L2 normalization layer followed by a scale layer, which normalizes the learned embeddings in an end-to-end fashion. The authors showed from the experiment that performance could be significantly improved by setting a proper value of scale parameter \(\alpha\). In [33], the authors proposed robust speaker embedding where embeddings are extracted without a nonlinear activation function. Towards deep speaker embedding, the authors from [34] proposed attentive statistical pooling for extracting features. The pooling layer calculated weighted standard deviation and weighted means over frame-level features that only focused on important frames. A recent trend is to learn directly from raw waveforms and completely avoid the feature-extraction step; this showed good results, including in speech-emotion recognition [35], speaker verification [36], and in spoofing detection with raw waveforms [37].
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 4 Expt. 2: Spectral Reallocation Preferences for Band-Limited Speech
### Results
#### 4.3.3 Adjustment Time  
Listeners took longer to make their choices as the noise level increased, in all conditions, and for those conditions with lower overall scores, the time taken was significantly longer. Across all conditions and noise levels, a very clear inverse relationship between adjustment time and intelligibility is evident (\(r=-0.98\), \(N=16\), \(p<0.001\)). As in Expt. 1, there is no significant correlation between an individual listener's adjustment time and intelligibility (\(r=0.22\), \(N=37\), \(p=0.20\)). A linear mixed-effects model analysis (again omitting the CF condition) indicated significant effects of SNR (\(\chi^{2}(3)=161\), \(p<0.001\)) and feature (\(\chi^{2}(2)=33.5\), \(p<0.001\)) together with a significant interaction (\(\chi^{2}(6)=16.2\), \(p<0.05\)). As in Expt. 1, listeners spent longer on adjustments in the \(-6\) dB condition than in quiet for the three features (all \(p<0.001\)).
## 5 Discussion and Practical Evaluation
The seasonality of the data collected (e.g., wind speed forecasting [54]), the number of target variables, as well as a multitude of underlying causes and features (e.g., forecasting the number of COVID-19 deaths [50]) can shape the nature of a forecasting application. In our opinion, the systematic characterization of applications and datasets would be of great value to modeling applications in order to fully exploit the capabilities of big data and data-driven networks. The ARIMA also exhibits better predictive performance than its machine learning counterparts, in cases where the available dataset is characterized by a limited range of values or a limited time-span ([6, 32, 45]). This observation can be attributed to the fact that machine learning, and especially deep learning models, require a large amount of data to train effectively. As a result, their performance can be inferior to statistical approaches for small datasets or for short-term forecasting. The algorithmic nature of the ARIMA models provides some insight into their implementation value in the problem of time series forecasting. In comparison to complex machine learning models, ARIMA is a relatively explainable and intuitive approach that is widely used due to its flexibility and reliability. However, its focus on linear time dependencies in the data as well as its univariate modeling approach render it unsuitable for standalone application in forecasting complex real-world problems. Apart from the details of the time series data, which can be a determining factor in the choice between ARIMA and machine learning models, an important aspect of the problem regards the computational and time complexity of the two approaches. In the case of machine learning forecasting models, a wider range of resources is needed, regarding the storage of the network architecture and weights, the training time, and the network optimization procedure, all of which are not necessary in the application of classical statistical algorithms. Furthermore, as the forecasting problem at hand becomes more complex and acquires longer time dependencies, so does its artificial intelligence modeling approach. In Table 6, we present some aggregated results and observations based on the practical evaluation of the ARIMA versus the machine learning approach to the problem of time series forecasting.
## 4 Design
### Optimization and Security Analysis in the Consensus Stage
#### 4.5.2 Security  
When the primary node is a Byzantine node, it may broadcast inconsistent pre-prepare messages to all consensus nodes or not respond to client requests. In such abnormal situations, the consensus cannot be reached in the prepare phase, triggering a RAFT election to replace the primary node. Our solution aims to reduce trust in the malicious primary node. In subsequent operations to update the set of consensus nodes, the malicious primary node is moved to the lowest level of the hierarchical network. This ensures the trustworthiness of the nodes involved in the consensus. The specific operations have been described in Section 4.2.  
When the number of Byzantine nodes in a certain layer is f, and the total number of nodes in that layer is greater than or equal to \(3\)f + 1, any node can receive consistent messages from at least 2f nodes during the prepare and response phases. Therefore, in this layer, Byzantine nodes do not affect the consensus process. In summary, the improvedconsensus algorithm in this paper can successfully pass verification in each round, and the results are trustworthy. By utilizing the ECC elliptic curve encryption algorithm, compared to the RSA encryption algorithm used in the original HBFT algorithm, the ciphertext in the consensus phase of this paper is more secure and reliable, and the signature verification phase is more time-efficient than in the original HBFT algorithm. This will be further discussed in detail in the experimental analysis section in Section 5.
## 4 Numerical Simulation Results  
### Smooth Road Surface (Cases 1-3)  
With a smooth road surface (\(\Delta\) = 0), three events of a 30-ton vehicle running through Renyihe Bridge at different speeds were simulated (Cases 1-3). The representative vertical acceleration time-histories for Cases 1-3, which were calculated at the mid-span of the 3 rd main span, are compared in Figure 9. As shown, the vehicle-induced structural response of Case 3 is markedly greater than that of Case 2, and the structural response of Case 2 is greater than that of Case 1. These suggest that with the increase in vehicle speed, a greater structural response ought to be induced. To reveal the causation behind this observation, the acceleration time-histories were transformed into amplitude spectra and shown in Figure 10. According to Figure 10, for the amplitude spectra of Cases 1 and 2, the energies are accumulated in the bridge's low-order natural frequencies (the frequency range around 1 Hz), and the background responses are not significant; however, for the spectrum of Case 3, the resonant response in the low-frequency range and the background response in the high-frequency range are comparable, and both of them are significant. These suggest that with increasing vehicle speed, the corresponding increase in structural response should mainly be attributed to the stronger force of vehicle excitation in the high-frequency range. The resonant response is also amplified for the high-vehicle-speed case, but the amplification is not so large as to account for a significant portion of the total response increment.  
Figure 10: Amplitude spectra of vertical acceleration time-histories calculated at the mid-span of the 3 rd main span in Cases 1–3.  
Figure 9: Vertical acceleration time-histories calculated at the mid-span of the 3 rd main span in Cases 1–3.  
The maximum tensile stress distribution on Renyihe Bridge for Case 1 is shown in Figure 11. In Figure 11, the high tensile stresses can be observed at the mid-spans of each span, which agrees with the basic rules of physics. The largest tensile stress for Case 1 is 0.72 MPa, which is much smaller than the tensile strength of C50 concrete (1.89 MPa). Similar situations hold true for Cases 2 and 3, which suggest that without sufficient surface roughness, structural damages are not likely to be induced by the usual vehicles running at various speeds.
## 2 Methodology and Experimental Setup  
The methodology of testing wired communication via RF transceivers running at 868 MHz and 2.4 GHz for DC PLC involves two stages: preliminary circuit measurements and simulation, experimental testing and data recording for various payload sizes at maximum data rate allowed by the IEEE 802.15.4 standard for those frequencies. Since it is common knowledge that at low data rates the data signal is more noise resilient and therefore it offers a better communication stability, the DC PLC experiment is set to investigate the 150 kbps for 868 MHz and 250 kbps for 2.4 GHz to determine if these high data transfers may be exploited on a common power line connected to four 18650 Li-ion cells arranged in parallel and then in series, whereas at 100% SOC. The metric for a stable connection is provided by the RSSI and the transmission errors BER and PER. It is expected that for a stable connection, the RSSI is under \(-50\) dBm, while BER and PER is 0 since variable loads or significant noises are not included in this proof-of-concept experimental stage.
# Performance Impact of Optimization Methods on MySQL Document-Based and Relational Databases
## 5 Performance Tests--Results and Analysis
### Delete Operation
#### 5.4.2 Soft Delete  
Marking the clients of several businesses as soft deleted was conducted, as presented in Table 14.  
To mark one or more elements as deleted, the same operation is used as in the case of the update; in fact, this is an update of the deleted field. Using the keyword \"in\" followed  
\begin{table}
\begin{tabular}{r} \hline \hline \multicolumn{1}{c}{**Relational MySQL: Hard Delete Operation**} \\ \hline @Query(value = “delete c.* from user u inner join client c on u.id = c.user\_id \\ where u.business\_id = \text{businessId}\", nativeQuery = true) \\ @Modifying(clearAutomatically = true) \\ void deleteAllClientByBusinessId(@Param(“businessId”) long businessId); \\ \hline \multicolumn{1}{c}{**Document-based MySQL: Hard delete operation**} \\ \hline collection.remove(“id = 10 and user.client.name is not null”).execute(); \\ \hline \hline \end{tabular}
\end{table}
Table 13: Hard delete operation.  
Figure 13: Performance time (ms) for the hard delete operation.  
by a list, all customers of these businesses are marked as deleted, and in both cases, the orders are similar to the update. As we deleted certain elements or marked them as deleted, we are not interested in excluding those already deleted because this would involve an additional filtering that can take longer; therefore, all elements that meet the specified conditions are included.  
The performance improvement can be seen in the case of both databases after we applied the optimization methods, where document-based MySQL is faster before and after optimizations, as shown in Figure 14.  
The soft delete operation is an update in which several elements and a single field are modified. The duration of the query is due to the joins between the tables, with the update itself being fast. The differences in duration are quite significant between the two databases before optimization, especially when the number of elements increases due to the join and the lack of an index on the deleted field.