esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Language Concepts and Extensions
#### 3.2.2 Compatibility Limitations
For instance, in real-time systems where it is necessary to balance memory usage with time, including collection latency, lower growth and threshold reduction factors will trigger more frequent collections with minimal delay due to fewer allocations. Conversely, in applications with larger and more intensive processing demands, such as batch processing and servers, less frequent collection with a higher threshold can improve throughput by reducing interruptions. Although this approach comes at the cost of slightly higher memory consumption, once load decreases, memory can be freed in a single run, preparing the system for further servicing [5].
## 6 Discussion and Summary
### CNN SHM Highlights  
* The optimization techniques for the design of CNN architectures for SHM systems should be considered. Some popular proposals include optimization algorithms such as PSO and HSA. The design of the CNN architecture in SHM applications is the focus of attention since there is no method to select the hyperparameters of the CNN architecture.
* Two main dimension variants are used in the proposed CNN-based SHM systems: 1D-CNN and 2D-CNN. In the proposals with 1D input in SHM CNN, marginal HHT spectra, raw response time series windows, mode curvature differences, first-order mode shapes and Fourier amplitude spectra (FAS) have been used as input vectors. For 2D input matrices in SHM CNN, time-frequency plots, time series data rearranged into square matrices, multi-sensor matrix series data, heat map matrices and discrete response histograms have been used.
* A large amount of training data are needed to train CNN architectures. FE-generated data can be used, but this generated data can be unreliable if the FE model has uncertainties. Data enrichment can be performed to make the data reliable for training purposes, as shown in [92]. Data augmentation techniques and GAN-generated data can also be used and it is also necessary to balance the training datasets.
* CNN shows reliable performance, especially on noisy datasets [94] using raw measurements, compared to SVM [2] and MLP models [99]. In addition, CNN pre-preserves dominant frequency signal features, eliminating high-frequency noise components [99].
# Tuberculosis Vaccines: An Update of Recent and Ongoing Clinical Trials
## 3 TB Novel Vaccines
### Inactivated Tuberculosis Vaccines
#### 3.4.2 RUTI  
Phase II clinical trials have also been conducted and are ongoing in the use of another inactivated TB vaccine. RUTI is a polyantigenic liposomal vaccine made of detoxified fragmented _M. tuberculosis_ cells, indicated for the prevention of active TB in subjects with latent TB infection [34]. Murine models demonstrated that RUTI could be given as an adjunctive intervention to already proven therapeutic agents [35]. The TB therapy with rifampicin and isoniaizd was markedly more effective when treatment was administered concurrently with a regime of RUTI administration in weeks 17, 19, and 21 post infections [35]. RUTI triggered a Th1/Th2 response, as demonstrated by the production of IgG1, IgG2a, and IgG3 antibodies against a wide range of peptides [35]. Previous clinical trials revealed that RUTI was reasonably well-tolerated, while triggering specific immunological responses against _M. tuberculosis_ in healthy subjects, compared to the placebo [35]. This was further confirmed in people with latent TB infection [34]. A phase II clinical trial, to test the safety of RUTI vaccination in those with multidrug-resistant TB after successful treatment is registered, however, its status is unknown (ClinicalTrials.gov: NCT02711735), while a further phase II clinical trial to evaluate the efficacy of RUTI vaccination in drug sensitive and multidrug-resistant patients is planned, but not yet recruiting (ClinicalTrials.gov: NCT04919239).
## 5 Conclusions  
We designed a log-based abnormal detection model: LTAnomaly. The semantic information of log sequences and component-valued parameters in logs were utilized to represent logs. We applied Transformer with LSTM to implement anomaly detection. The model offers a more accurate representation of logs and various anomaly detection methods to improve detection efficiency. The experimental results demonstrated that wider applications increased detection accuracy. The model's ability to accurately represent logs using semantic information in log sequences and component-valued parameters in logs can enable wider applications and improve detection efficiency. One potential scenario for the use of this model is in detecting anomalies in large-scale distributed systems, such as cloud computing infrastructures. The LTAnomaly model can automate the process of detecting anomalies, thereby reducing the time required to identify and resolve issues. Furthermore, the LTAnomaly model can help detect anomalous activities within network logs and alert security personnel to take immediate action to prevent a security breach.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Techniques** & **Precision** & **Recall** & **F1-Measure** \\ \hline \multirow{4}{*}{BGL} & LSTM & 0.92 & 0.86 & 0.889 \\  & Transformer & 0.95 & 0.91 & 0.929 \\  & LTAnomaly & **0.97** & **0.98** & **0.975** \\ \hline \multirow{4}{*}{HDFS} & LSTM & 0.96 & 0.98 & 0.970 \\  & Transformer & **0.99** & 0.97 & 0.979 \\  & LTAnomaly & 0.98 & **0.99** & **0.985** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance of different anomaly detection technology combinations in datasets.  
However, a limitation of our work is that variable parameters in log messages cannot be well handled. In the future, we aim to represent logs in a simpler and more comprehensive manner and detect anomalies more accurately and quickly. A direction of our future work is to be able to forecast anomalies and take effective measures to protect against them, hence reducing their associated damages.  
Conceptualization, M.L.; Funding acquisition, D.H.; Methodology, M.S.; Supervision, M.L. and Q.C.; Validation, D.H; Writing--original draft, M.S.; Writing--review and editing, M.S.; Data curation, Q.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (2022 YFB4004401) and the Pilot Project for Integrated Innovation of Science, Education, and Industry of Qilu University of Technology (Shandong Academy of Sciences) (2022 JBZ01-01).  
Not applicable.  
Datasets can be accessed upon request by the corresponding author.  
The authors appreciate all reviewers for their insightful comments and constructive suggestions.  
The authors declare no conflict of interest.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 2 Related Work
### Future Directions and Research Gaps  
While significant progress has been made, several research gaps remain. The integration of physiological data, such as heart rate variability and neural activity, into QE research is still in its early stages. For instance, recent work has used Convolutional Neural Networks (CNNs) to classify eye movements, providing a basis for combining gaze behavior and physiological metrics, such as heart rate variability, to gain deeper insights into cognitive and physical states during high-level performance [16]. This multimodal approach, as shown in studies on elite table tennis players [17], highlights the potential of combining physiological and visual attention data to explore how stress impacts performance.  
Machine learning has made significant progress in prediction, but interpretability remains a black box. As neural networks become more complex, it is important to understand their inner workings to apply their insights effectively in coaching and performance optimization. Future research should focus on making these models less of a black box and more transparent to ensure they can be used practically in sports training.
# Low-Cost Biochar Adsorbents for Water Purification Including Microplastics Removal
## 4 Discussion
### Adsorption Capacities of Chemical Compounds  
The pine bark ACs showed excellent adsorption capacities for phenol. The obtained maximum adsorption capacity for pine bark AC (169 mg/g, Table 5) is comparable to capacities found in the literature for much higher surface area ACs (Table 7). These results along with selected previous work show that efficient phenol removal is not solely dependent on an ultrahigh surface area (Table 7). Phenol adsorption occurs in the micropores, but sufficient mesoporosity is also needed to create channels into the micropores. Steric effects inhibit diffusion of the phenol molecule deeper within the carbon pores [38]. Another parameter influencing results is the solution pH. At pH less than the pKa value of phenol (9.89 at 298 K), phenol occurs in its non-dissociated form, which is the most active adsorbing form. Above pH 9, phenol dissociates into the phenolate anion and has to compete with other negatively charged ions (e.g., hydroxyl ions) for adsorption sites. Lower pH is, therefore, more favorable for phenol adsorption and respectively, in some cases removal of ash using an acid wash may be necessary [18; 39]. The results of this experiment were affected as the high ash content of the ACs increased the solution pH above 9 for most of the AC samples, particularly the spruce ACs.  
The CEC measures an adsorbent's capability to sorb positively charged ions and is, therefore, directly related to adsorbents' applicability for water treatment. The cation sorption capacity of biochars and ACs is dependent on various parameters such as surface charge density, chemical properties (atom/molecule radius, solubility, pK\({}_{\text{a}}\)), and solution ion density [42]. It has also been found that ion exchange is the dominating mode by which biochars from fast pyrolysis adsorb metal ions [43]. Higher CEC estimates for biochars can be found in literature (e.g., [44]) compared to current work. Comparison is, however, difficult because of the high variability in the used CEC and carbonization methodology and feedstock materials [45]. Both bark ACs had acceptable CECs for application in water treatment, with pine bark ACs performing slightly better because of their higher surface area. The quantities of potentially environmentally harmful elements, such as aluminum and phosphorus, were low. The alkalinity caused by the high ash contents of both bark ACs favors metal adsorption.
## 4 Behavior of Coupled Holder-Pile-Ground System during Earthquake
### Effect of Duration on Pile Support  
Next, the results of the short-duration seismic motion (B) analysis conducted in the previous study [13] were republished and compared with seismic motion (A) to evaluate how differences in the input seismic motion affect the amount of displacement of the holder ground and the pile support function. Figure 21 shows the changes in the mean effective stress over time, Figure 22 shows the settlement behavior during and after the earthquake at point a in Figure 7, and Figure 23 shows the progress of the pile damage. The holder foundation settled approximately 0.1 m during the earthquake and ultimately 0.3 m after the earthquake owing to long-term consolidation settlement. However, the settlement amount was small compared to that from the seismic motion (A). The mean effective stress was almost zero in the surface sandy soil layer (B layer/Yus layer) approximately 5 s after the occurrence of the earthquake, resulting in liquefaction. The mean effective stress of the deep clay soil layer also slightly decreased with time, but the seismic motion duration was short; thus, this decrease was not as prominent as that for seismic motion (A). Focusing on the extent of pile damage, almost all areas were damaged in the five-second period from 4.0-9.0 s after the earthquake, and unlike that with seismic motion (A), damage occurred within a very short period of time. Figure 22 also shows the settlement behavior during and after the earthquake in the without-piles model. The settlement amount of the holder foundation was approximately 0.1 m during the earthquake and ultimately approximately 0.3 m, which was almost equal to that of the with-piles model. In other words, unlike the case of seismic motion (A), the settlement suppression effect due to pile presence could not be observed.  
Figure 20: Shear strain distribution (maximum value until the end of an earthquake) of (**a**) with-piles and (**b**) without-piles models during seismic motion (A).  
the bottom foundation (ground reaction force) was lost immediately after the earthquake occurrence owing to the settlement of the B layer, and the axial force of the pile increased. However, unlike the case of seismic motion (A), no progressive increases in the axial force were observed during the earthquake, and all parts of the pile were damaged. In other words, the support function of the pile was immediately lost without any sign of increased resistance of the pile due to decreased ground rigidity, as was the case in seismic motion (A); therefore, the settlement suppression effect could not be obtained. Figure 25 shows the shear strain distribution of the with-piles and without-piles models. Unlike seismic motion (A), there were no differences between the presence or absence of pile modeling, and the shear strain was predominant only near the ground surface. It can be observed from this as well that the piles were damaged without resisting seismic motion and that no support functions were observed.
# An Efficient Two-Level-Partitioning-Based Double Array and Its Parallelization
## 2 Preliminaries
### Double Array  
For a string \(s\) in a dataset \(D\), the character \(c\) is called a separated character if c is a sufficient character to distinguish \(s\) from all other strings in D. The substring from the beginning of \(s\) to its separate character \(c\) is called the reduced substring of \(s\). Consequently, a trie built on reduced substrings of strings in \(D\) is a reduced trie.  
DA uses two one-dimensional arrays called BASE and CHECK to store the reduced trie for a dataset \(D\). For a node \(x\), which is also a position \(x\) in the two arrays, BASE[\(x\)] stores the base value used for all children of \(x\), whereas CHECK[\(x\)] stores the parent node index of \(x\). An additional array, TAIL, is used to store the remaining suffixes.  
The heart of DA lies in the following two important formulas:  
\[\text{BASE}[x]+\text{CODE}[c]=y \tag{1}\]  
\[\text{CHECK}[y]=x \tag{2}\]  
From the above Formulas, child node \(y\) of node \(x\) can be easily obtained by executing BASE[\(x\)] + CODE[\(c\)], and parent node \(x\) of node \(y\) can be found in CHECK[\(y\)]. Thus, accessing parent and child node is extremely fast by simple, add operations. CODE[\(c\)] is the numerical code of a character \(c\). The codes corresponding to characters \"#\", \"a\", \"b\", \"c\"... \"z\" are \(1,2,3,\ldots,27\), respectively.  
Figure 1 shows the reduced trie and DA created on a dataset \(D=\) [\"abcd#\", \"abdf#\", \"abae#\"]. To distinguish strings like \"ab\" and \"abc\", a special character \"#\" is added to the end of each string.
## 1 Introduction
### Contributions  
In this paper, the authors designed, implemented, and tested a HS-GNSS receiver. The proposed receiver implements two operating modes: high-sensitivity mode and normal-sensitivity mode. When operating in high-sensitivity mode, the receiver is capable of acquiring and tracking Galileo E1b/c signals with a \(C/N_{0}\) down to 20 dB-Hz (equivalent \(C/N_{0}\) observed at the post-correlation level), enabling the derivation of navigation solutions. On the other side, when operating in normal-sensitivity mode, the receiver processes GPS L1 C/A, Galileo E1b/c, GPS L5, and Galileo E5a signals with an acquisition sensitivity of approximately 37 dB-Hz. While the ideal scenario involves acquiring and tracking both GPS and Galileo signals in high-sensitivity mode, the initial concept demonstrator presented in this paper is designed to showcase high-sensitivity mode only for Galileo E1b/c signals.  
To enhance the availability of satellite signals, the receiver is capable of processing Galileo E1 b/c signals in high-sensitivity mode, while simultaneously processing GPS L1 C/A, GPS L5, and Galileo E5a signals in normal-sensitivity mode. This approach implements a dual-band, multi-GNSS receiver centered at 1176.45 MHz and 1575.42 MHz.  
When operating in high-sensitivity mode, the receiver uses assistance to speed up the acquisition of weak Galileo E1b/c signals and ultimately to decrease the time-to-first-fix (TTFF).  
The proposed design is based on the SoC-FPGA receiver architecture introduced in [20,21]. In the presented work, the authors upgraded this architecture and developed a concept demonstrator implementing high sensitivity mode. This enhancement enables the processing of weak Galileo E1b/c signals, thereby obtaining navigation solutions from these signals.  
This paper focuses on the high-sensitivity capabilities of the proposed receiver. A brief description of the SoC-FPGA receiver architecture is also provided. A more detailed description of the SoC-FPGA receiver architecture and design methodology can be found in [21].
## 4 Results
### Threshold-Based--Experimental Results
#### 4.1.1 Sorting Results
The camera's maximum frame rate is 59.6 fps, which translates to one picture every 1/59.6 = 0.0168 s. Nevertheless, taking into account **tm4** from Table 1, each frame is processed in roughly 0.16 s, which is roughly ten times longer. As a result, several optimization techniques (in this case, minimization) were taken into consideration after this preliminary assessment of the system's response times. In practice, tests were repeated with the goal of minimizing the most important processing time, which is **tm1** as indicated in Table 1. As a result, the camera's ROI was set to the value that was used in the _incorop_ function; in other words, the _incorop_ function is no longer necessary because the camera now only gives photos of the ROI. Furthermore, the image distortion correction (the _undistorImage_ function) was removed, and the _videoinput_ (_Matlab_ function) object setting was switched to manual triggering, following the suggestions presented in [24] to minimize the execution time of the _getsnapshot_ function. Table 2 reports the resulting times with these adjustments. It is possible to observe a reduction in **tm1** by a factor of 1000 and a reduction in the total time **tm4** by a factor  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **tm1 [s]** & \multicolumn{2}{c}{**tm2 [s]**} & \multicolumn{2}{c}{**tm3 [s]**} & \multicolumn{2}{c}{**tm4 [s]**} \\ \hline  & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) \\ \hline Box A & 0.118 & 0.004 & 0.144 & 0.005 & 0.016 & 0.005 & 0.165 & 0.007 \\ \hline Box B & 0.117 & 0.002 & 0.143 & 0.002 & 0.015 & 0.006 & 0 of 10, which brings it closer to the optimal maximum values permitted by the camera's frame rate. Returning to the actual sorting for the different cases and package types, Figure 7 displays the trajectories for case CS1, and Figure 8 displays the trajectories for case CS2. More precisely, the _AprilTag_'s contour is indicated in red, and the locations and paths of the tag's center are indicated by circular markers and continuous green lines, respectively. Beginning with the first case CS1, it can be seen that Box A proceeds straight in Figure 7a, while Box B performs the sorting immediately in Figure 7b. On the other hand, for the second case, CS2, Figure 8a shows the sorting at the end of the line for Box A, while it happens at the beginning of the line for Box B, as in Figure 8b. The efficacy of this control for the examined tasks is demonstrated by these two images, which highlight agreement between the intended sorting results (Figure 6) and the actual ones.
# Optimizing Automated Detection of Cross-Recessed Screws in Laptops Using a Neural Network
## 4 Discussion
They were more prevalent in the perfect detection category (51.2%) compared to the poor detection category (21.7%), indicating that the neural network was better at detecting interior screws than exterior ones. Additionally, when examining screws with a taper, all 20 of them were categorized as deep screws. Furthermore, 19 out of these 20 screws were located either on the laptop's exterior or in corner positions that were already shown to be more difficult to detect. When analyzing the number of false detections over all trials, DELL1 and GATE2 had the highest false detection counts (207 and 173, respectively). When the data were limited to just trials at 10 and 15 mm/s, the two laptops with the highest false detection counts were DELL1 and FUJII with 81 and 69 false detections. Looking at these false detections in further detail reveals that, on the FUJII laptop, various surface marks and shapes comprised 28 false detections, and 19 false detections were categorized as stickers. In comparison, the DELL1 had 65 false detections that were categorized as stickers, specifically various circular shapes and images found on the stickers from third-party certifications. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reported Work** & **Types of Screws Detected** & **Reported Accuracy** & **Accuracy Metric Used** \\ \hline This work & Cross-recessed screws on the back of laptops & Average over all trial groups up to 73.4\%, Individual trials up to 95\% laptop- and parameter-dependent & \(TD-FD\) \\ \hline [PERSON] and [PERSON] [22] & Cross-recessed screws on the back of laptops & Up to 86.7\% dependent on laptop color and camera settings & \(TD\) \\ \hline [PERSON] et al. [45] & Crosshead screws on components of LCD monitors & Up to 78\%, depending on scenario. & \(TD\) \\ \hline [PERSON] et al. [46] & Hex screws and cross screws on mobile phone boards & \(>\)97\% in test cases & \(TD\) \\ \hline [PERSON] et al. [47] & Six screw heads—Pozdiriv, hex-socket, slot-drive, Phillips (cross-recessed), hex, and Torx on motors & 86\% small-motor and 88\% large-motor & \(TD\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Screw detection comparison with other work. \(TD=\) true detections, \(FD=\) false detections, and \(T=\) total number of screws.
## 2 Background and Challenges
### Indoor-Outdoor Detection  
Differentiating indoor and outdoor scenarios was the first application of the data analytic approach on GPS readings. In fact, earlier approaches to IO-detection [10; 11] criticize using simple data analytics on the number of visible satellites as being slow and energy inefficient. As a result, the authors applied various machine learning techniques to fuse non-GPS sensor readings, while [10] applied a naive decision tree, and [11] has attempted several semi-supervised learning techniques. Nevertheless, classical machine learning techniques are too sensitive to feature selection, which can be an obstacle when facing sophisticated data inputs. Recently, SatProbe [12] revisited the number of visible satellites for IO-detection, and proposed efficient algorithms to extract this feature. However, the access to raw GNSS measurements, introduced in Android 7, provides users with much more information (e.g., the spatial relation among visible satellites) than just this counter-based feature. Moreover, existing approaches all categorize urban scenarios into three classes: outdoor, semi-outdoor, and indoor--this, apparently, oversimplifies our real-life location context.