esa-sceva/satcom-chunk-collection huggingface dataset:
# Elemental Mercury Removal over CeO\({}_{2}\)/TiO\({}_{2}\) Catalyst Prepared by Sol-Gel Method
## 3 Results and Discussion  
### The Effect of Reaction Temperature on Catalytic Performance  
The Hg\({}^{0}\) removal efficiency of four CeO\({}_{2}\)/TiO\({}_{2}\) catalysts and one pure TiO\({}_{2}\) catalyst were measured at various reaction temperatures with the aim of obtaining the optimal reaction temperature and the optimal CeO\({}_{2}\) loading value. The reaction temperature ranged from 150 to 450 \({}^{\circ}\)C. The results are shown in Figure 3. It can be noted that the Hg\({}^{0}\) removal efficiency of pure the TiO\({}_{2}\) catalyst was much lower. The highest value was only about 25%, which is in consistent with the results of [PERSON] et al. [18]. In comparison, the Hg\({}^{0}\) removal efficiency of the catalysts increased dramatically after the addition of cerium oxides. The efficiencies of all CeO\({}_{2}\)/TiO\({}_{2}\) catalysts showed similar trends as the reaction temperature rose. From 150 to 350 \({}^{\circ}\)C, higher temperature led to better Hg\({}^{0}\) removal performance of the catalysts. However, the efficiency started to decrease when the temperature was higher than 350 \({}^{\circ}\)C, which agrees well with some literature [19; 12]. Nevertheless, the trend in Figure 3 seems to conflict with the result of our previous work, which is that the Hg\({}^{0}\) removal efficiency declined monotonically as the reaction temperature increased [8]. That is understandable because only Hg\({}^{0}\), O\({}_{2}\), and N\({}_{2}\) existed in the flue gas of reference [8], so physical adsorption was the dominant removal mechanism for elemental mercury. In comparison, the chemical reaction played a vital role in this study, since all flue gas components were considered in the simulated flue gas. As a result, the elemental mercury removal mechanism and the effect of reaction temperature on catalytic performance were changed. It also can be seen that different Ce loading values brought different performances. The Hg\({}^{0}\) removal efficiency increased remarkably from pure TiO\({}_{2}\) to Ce0.1 Ti. More specifically, the value rose from 25.1% to 86.4% when the reaction temperature was 350 \({}^{\circ}\)C. With a further increase of CeO\({}_{2}\) content, Ce0.2 Ti provided  
Figure 2: Schematic of the elemental mercury removal system.  
even better performance for elemental mercury removal. The value of \(\eta\) remained over 80% in the temperature zone from 300 to 400 \({}^{\circ}\)C and reached 94.0% at 350 \({}^{\circ}\)C. The excellent efficiency should be mainly attributed to the redox reaction between cerium oxides and elemental mercury [20]. If the Ce loading value continued to increase, the Hg\({}^{0}\) removal efficiency began to decrease. The efficiencies of Ce0.5 Ti and Ce1.0 Ti are 79.6% and 77.8%, respectively. The reason may be that excessive cerium oxides covered too much surface of the catalyst, which blocked part of the elemental mercury from entering the catalyst [21].
# Acoustic Emission and Modal Frequency Variation in Concrete Specimens under Four-Point Bending
###### Abstract  
The Acoustic Emission (AE) and Dynamic Identification (DI) techniques were applied simultaneously, in an original way, to examine the stress dependent damage progress in pre-notched concrete beams tested in four-point bending. The damage mechanisms were characterized by analyzing the AE signals registered during the tests, conducted by increasing the specimen's vertical deflection. In particular, the dominant fracture mode was identified, and correlations between dissipated and emitted energies were investigated. Moreover, variations in the natural bending frequencies, produced by the crack advancement under loading, were detected and put in relation with the cumulated AE energy. Two different types of piezoelectric (PZT) sensors, operating in well distinct frequency ranges, were used to measure AE and modal signals. This study may be of interest with an outlook on possible correlations between a multi-parameter structural monitoring and the solution of inverse problems by numerical models.  
acoustic emission; natural frequency; material damage; concrete specimen; four-point bending test; brittle failure; softening; emitted energy; dissipated energy; PZT sensor Article
## 3 Audio Characterization and Recognition Tests
### Data Analysis
#### 3.1.1 Continuous Time Stability  
To evaluate the stability of time domain and frequency domain features of a single audio file in continuous time, audio data with a duration of 10 s were selected for correlation analysis. The correlation coefficients of time domain amplitude and frequency domain amplitude of 10 frames were calculated using Equation (7).  
Let us define \(S_{ij}\) as the time domain amplitude correlation coefficient of frame \(y_{i}\) and frame \(y_{j}\), and define \(F_{ij}\) as the frequency domain amplitude correlation coefficient of frame \(y_{i}\) and frame \(y_{j}\), where \(1\leq i\leq j\leq 10\). The maximum and minimum values of the correlation coefficients were taken for analysis, and the results are shown in Table 2.  
The correlation coefficients of the 10 frames' time domain amplitude were lower than 0.313 for any two frames of the background noise and PD, and the adjacent frames showed no correlation, while for the mechanical vibration, the correlation coefficients of the adjacent frames reached 0.956, but the non-adjacent frames showed no correlation. This shows that the waveforms of background noise and PD were not similar, and the amplitude changes were irregular, while the waveforms and amplitude of mechanical vibration were more similar in continuous time.  
The correlation coefficients of the 10 frames' frequency domain amplitude were higher than 0.94 for normal vibration, 0.732 for abnormal vibration, 0.462-0.884 for PD, and 0.102-0.857 for background noise. This shows that the frequency domain amplitude distribution of mechanical vibration and PD were similar in continuous time, while the frequency domain amplitude distribution of background noise was less similar.  
In terms of feature stability in continuous time, the time domain features of mechanical vibration were more stable, the frequency domain features of mechanical vibration and PD were more stable, whereas the background noise was less stable in both time and frequency domain features.  
Figure 2: SVM classification principle.
## 5 Methods for Estimation of HFR Video Characteristics
### Detection Model and Evaluation
The WkNN classification model includes the so-called Mahalanobis distance calculations in the feature domain, their sorting, taking k-nearest neighbors, assigning weights and voting. For the WkNN model, training and testing are performed in order to obtain results, where 1 (Yes/True) denotes that TRR exists and 0 (No/False) denotes that the tested video is original. An improved version of kNN is the WkNN [81; 82], where feature space is corrected. Namely, features are assigned according to position between the points. If weights \(W_{i}\) are calculated using squared inverse method based on the distances \(d\), the voting can be written as  
\[\hat{y}_{q}=\underset{j}{\text{argmax}}\underset{i}{\sum}W_{i}I(y_{i}=j)=\frac{ \underset{i}{\sum}W_{i}y_{i}}{\underset{i}{\sum}W_{i}},\ W_{i}=\frac{1}{d\big{(} x_{q},x_{i}\big{)}^{2}}. \tag{12}\]  
There are various metrics [83] that can be applied for finding distance between two feature vectors \(x=(x_{1},x_{2},\ldots,x_{m})\) and \(y=(y_{1},y_{2},\ldots,y_{m})\) for classifying the point into one of the groups. For example, Euclidean, Cityblock and Mahalanobis are common choices, expressed by  
\[d_{Euclid}^{2}=\sum_{i=1}^{m}(x_{i}-y_{i})^{2}, \tag{13}\]  
\[d_{Cityblock}=\sum_{i=1}^{m}|x_{i}-y_{i}|, \tag{14}\]  
\[d_{Mahalan}^{2}=(x-y)^{T}C^{-1}(x-y), \tag{15}\]  
respectively, where \(C\) is the corresponding covariance matrix. Euclidean distance assumes that features are somewhat independent with a spherical distribution, where Mahalonobis is similar to Euclidean but seems to be a good alternative in such cases by taking into account the distance between a point and a distribution [82]. The temporal recovery detection model through trace sequences is presented in Figure 5b. The binary classification between the temporal recovered and original data are performed similarly to [82; 84] with 5-fold cross-validation. Video content that was included in the training process for each crf was not part of the testing. The classification performance is measured using accuracy (_Acc_):  
\[Acc=(TP+TN)/(TP+TN+FP+FN), \tag{16}\]  
\[w_{rel}=\frac{w_{r}}{w}=\frac{\alpha_{\max}-\alpha_{0}}{\alpha_{\max}-\alpha_{ \min}}, \tag{8}\]0 **(a)** The concept of k-nearest neighbors (kNN) binary classification and **(b)** the proposed model for temporal resolution recovery (TRR) detection. And true positive rate (\(TPR\)) or recall, as well as positive predictive value (\(PPV\)) or precision, are calculated as  
\[w_{rel}=\frac{w_{r}}{w}=\frac{\alpha_{\max}-\alpha_{0}}{\alpha_{\max}-\alpha_{ \min}}, \tag{8}\]1  
Using true positives (\(TP\)), true negatives (\(TN\)), false positives (\(FP\)) and false negatives (\(FN\)) in TRR detection.
## 4 Open Issues
### QoS-Aware Power Management  
Energy conservation is a major concern in both WSNs and WSANs. The lifetime of untethered sensor/actuator nodes is tightly restricted by the available battery energy. Since wireless communication is much more energy-expensive than sensing and computation, the transmission power of nodes has to be properly managed in a way that the energy consumption is minimized in order to prolong the lifetime of the whole network. Due to the increasingly heavy computational burdens, a significant amount of energy will be consumed by the computations in actuator nodes. Therefore, the CPU energy consumption of actuators should also be minimized, e.g. by exploiting the dynamic voltage scaling technology.  
However, minimizing energy consumption and maximizing QoS are in most cases two conflicting requirements. For instance, reliability can be improved by increasing the number of maximum allowable retransmissions or using higher transmission power levels; however, more energy will be expended in both cases. Therefore, tradeoffs must be made between energy conservation and QoSoptimization. The problem then becomes how to make these tradeoffs at runtime. Is it possible to find an integrated performance metric that accounts for both energy efficiency and QoS, and then optimize it, either online or offline?  
Depending on the network topology and the QoS requirements, the power management mechanisms for actuator nodes may be different from those used in sensor nodes. Thus the QoS can be maximized through exploiting the different capabilities of sensors and actuators. In like manner, different transmission power levels may be assigned to the same node with respect to different types of traffic. In-network computation can be exploited to reduce the energy consumption of both sensor and actuator nodes since it reduces traffic load at the cost of slightly increased computation in each involved node. Still, the inherently non-deterministic and open nature of wireless channels poses great challenges for QoS-aware power management.
# A Comprehensive Analysis of Various Tokenizers for Arabic Large Language Models
## 4 Experiments
### Datasets
#### 4.3.6 Question Answering (QA)  
In the QA task, the model is expected to extract the correct answer to a given question from input text passages. For this task, we employed four datasets that contain only MSA text. Descriptions of these datasets are provided below:  
* Arabic Reading Comprehension Dataset (ARCD): ARCD is an Arabic QA dataset that was built to solve the issue of open domain factual Arabic QA [70]. It contains 1395 unique question-answer pairs posed by crowdworkers on Wikipedia articles and a machine translation of another Arabic QA dataset. The articles were originally collected from Wikipedia articles written in Arabic.
* Cross-lingual Question Answering Dataset (XQuAD): XQuAD is a benchmark dataset designed to evaluate the preference of crosslingual question answering [71]. It consists of 240 paragraphs and 1190 question-answer pairs developed from SQuAD v1.1 [72]. It covers 11 diverse languages. The dataset has been translated into ten languages: Arabic, Russian, Greek, Turkish, Vietnamese, etc. In this study, we only included the Arabic subset of XQuAD benchmark.
* A Benchmark for Information-Seeking Question Answering (TyDiQA): TyDiQA [73] is a multilingual QA benchmark for 11 different languages, including Arabic. Originally, the dataset consisted of 204 K question-answer pairs. However, we extracted the Arabic subset only, which comprises 15,726 question-answer pairs.
* Evaluating Cross-lingual Extractive Question Answering (MLQA): Similar to XQuAD, MLQA is a benchmark dataset to evaluate the preference of crosslingual question answering [74]. It consists of between 5000 and 6000 question-answering instances in each language, thus presented in SQuAD format in seven languages: Arabic, German, Vietnamese, English, etc. The Arabic subset contains 5852 question-answer pairs.
# Role of Irisin in Endocrine and Metabolic Disorders--Possible New Therapeutic Agent?
## 6 Osteoporosis  
Irisin displays anabolic actions on the skeleton. In athletes, the irisin level has been positively correlated with bone mineral density (BMD) and bone strength. Compared to adipose tissue, bone tissue is more sensitive to the action of irisin, where the effect of irisin depends on the administered dose. Recombinant irisin (r-irisin), given at a low dose (100 ug kg/week) in young mice increased cortical bone mineral density and positively modified bone geometry [2].  
Irisin exerts its effect on osteoblasts by enhancing the differentiation and activity of bone-forming cells, through increasing activity of transcription factor 4 expression. Low-dose r-irisin also increased osteepontin and decreased sclerostin synthesis. These findings offer an explanation for the positive outcome on the skeleton by skeletal muscle during physical activity [33].  
In cultured osteoblasts, irisin enhanced proliferation, differentiation, alkaline phosphatase activity, and mineralization through the activation of p38 mitogen-activated protein kinase and extracellular signal-regulated kinase [34]. Irisin also suppressed osteoclast formation through the inhibition of receptor activator of NF-kB ligand (RANKL)/nuclear factor of activated T-cells, cytoplasmic 1 (NFATc1) in RAW264.7 cells [7].  
Significant increases in cortical bone mass and strength have been observed after the administration of r-irisin in mice; in particular, in cortical mineral density and periosteal circumference. This anabolic action was possible through the stimulation of bone formation and reduction in osteoclast number. The trabecular compartments of the same bones were spared [33].  
Except for the direct effect of irisin on osteoblasts [34; 35], this myokine can also downregulate the expression of the parathormone (PTH) receptor in osteoblast. In a recent study, an inverse association between PTH and irisin concentration was observed after 12 weeks of orthodox fasting among overweight metabolic healthy individuals, indicating a reverse musculoskeletal biological interaction. It could be postulated that additional parameters--including dietary ones--might be implicated in the association between irisin and bone metabolism [36].  
Irisin might be a useful biomarker for the assessment of muscle and bone health, due to the fact that, in humans, its synthesis is increased by physical activity [7].  
Due to the fact that irisin is diminished in patients with previous osteopororotic fractures and in post-menopausal women [37], it has been speculated that irisin plays a protective role in bone health. It is not known whether this association is independent or whether it is due to low muscle mass [38].
## 2 Materials and Methods
### Analysis  
After getting familiarised with children's comments [64], a qualitative thematic analysis was used to identify patterns [65] among participants' open-ended responses. Once an initial framework of themes and sub-themes [64,65] was created, the table with participants' characteristics and comments was imported into NVivo 12 (QSR International Pty Ltd. Version 12, 2018). All descriptive characteristics (e.g., school, gender, commuting mode, travel with, stop) were imported as closed responses, while children's likes and dislikes were imported as open-ended responses and coded into nodes, under the main nodes of 'likes' and 'dislikes'. The thematic analysis was both deductive, using themes already reported in the existing literature, and inductive, with themes emerging from children's answers [66] (Figure 3). However, as the scope of this paper is to explore children's experiences according to behaviour change frameworks, themes and sub-themes were then grouped into typologies (wider themes) [65], based on TDF (Figure 3), in order to explore them through the COM-B model [55,67] (Figure 4). Finally, the nodes were visualised as charts, coding by 'attribute value' NVivo analysis option, to explore any associations between themes and schools, between themes and genders and between themes and means of travelling to school [43].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & **Walking** & **Cycling** & **Car** &
\begin{tabular}{c} **Park and** \\ **Stride** \\ \end{tabular} & **Bus** & **School Bus** & **NA 2** & **Total** \\ \hline \multirow{2}{*}{**School**} & **Total** & **72** & **7** & **43** & **5** & **4** & **7** & **7** & **145** \\ \hline \multirow{2}{*}{**School**} & A & 31 & 0 & 19 & 0 & 1 & 0 & 6 & 57 \\  & B & 11 & 0 & 6 & 5 & 0 & 0 & 0 & 22 \\  & C & 9 & 0 & 3 & 0 & 1 & 1 & 0 & 14 \\  & D & 19 & 3 & 8 & 0 & 1 & 0 & 1 & 25 \\  & P 1 & 9 & 4 & 7 & 0 & 1 & 6 & 0 & 27 \\ \hline \multirow{2}{*}{**Gender**} & Girl & 39 & 2 & 25 & 3 & 2 & 2 & 6 & 79 \\  & Boy & 32 & 5 & 18 & 2 & 2 & 5 & 1 & 65 \\  & NA 2 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\ \hline \multirow{2}{*}{**Accompanied**} & Yes & 51 & 5 & 36 & 4 & 4 & 1 & 6 & 107 \\  & Sometimes & 5 & 0 & 0 & 0 & 0 & 0 & 0 & 5 \\  & No & 14 & 1 & 0 & 0 & 0 & 2 & 1 & 18 \\  & NA 2 & 2 & 1 & 7 & 1 & 0 & 4 & 0 & 15 \\ \hline \multirow{2}{*}{**Travel with**} & Adult(s) & 32 & 2 & 17 & 2 & 2 & 1 & 4 & 60 \\  & Adult(s) & & & & & & & & \\  & and & 18 & 1 & 15 & 1 & 1 & 0 & 1 & 37 \\  & Child(ren) & & & & & & & & \\  & Child(ren) & 6 & 0 & 0 & 0 & 0 & 1 & 1 & 8 \\  & Alone & 6 & 1 & 0 & 0 & 0 & 0 & 0 & 7 \\  & NA 2 & 10 & 3 & 11 & 2 & 1 & 5 & 1 & 33 \\ \hline \multirow{2}{*}{**Stop**} & Yes & 25 & 1 & 21 & 1 & 0 & 1 & 2 & 53 \\  & Sometimes & 5 & 0 & 0 & 0 & 0 & 0 & 0 & 5 \\  & No & 24 & 1 & 8 & 4 & 1 & 0 & 1 & 39 \\  & NA 2 & 9 & 1 & 7 & 0 & 2 & 0 & 1 & 20 \\  & Not & 9 & 4 & 7 & 0 & 1 & 6 & 0 & 27 \\  & Applicable & & & & & & & & \\ \hline \hline \end{tabular} \({}^{1}\) Pilot study school; 2 no answer provided.  
\end{table}
Table 1: Participants’ characteristics.
# New Satellite Selection Approach for GPS/BDS/GLONASS Kinematic Precise Point Positioning
## 4 Test and Analysis
### Positioning Accuracy of the NSS Model  
In order to test and validate the proposed NSS model, the performance of dual-frequency GPS/BDS/GLONASS undifferenced and uncombined kinematic PPP using this satellite selection model was evaluated based on MGEX daily observations and measured GNSS data. The MGEX data collected on 27 August 2017 at five MGEX stations (DARW, GMSD, KARR, MRO1, and XMIS) were used to simulate the kinematic environment in this study. The measured GNSS data was collected on 23 January 2018 using a Trimble netR9 receiver in Wuhan University of Technology of Wuhan, Hubei province. The sampling frequency of the static data is 1 Hz and continued for 7 h. To evaluate the positioning accuracy of kinematic PPP with the NSS model, the result of the original PPP without the satellite selection algorithm and kinematic PPP with the optimal DOP algorithm (number of selected satellites: 12) were also used for comparison. For the processing strategy, all the PPP experiments were performed based on the opensource GAMP software which can be freely accessed through the GPS Toolbox webpage (i.e., [[https://www.ngs.noaa.gov/gps-toolbox/GAMP.htm](https://www.ngs.noaa.gov/gps-toolbox/GAMP.htm)]([https://www.ngs.noaa.gov/gps-toolbox/GAMP.htm](https://www.ngs.noaa.gov/gps-toolbox/GAMP.htm))) [31]. The precise products from Wuhan University were used and we estimated a receiver clock offset for one system and the inter-system bias parameter [32]. The carrier phase ambiguities were kept floating and estimated as constant for each continuous satellite arc. Additionally, the inheritance of ambiguity method was adopted. The weighing between the three systems was 1:1:1 in the software. Due to the worse orbit and clock quality of BDS GEO satellites [33; 34], the weighing between the BDS GEO satellite and the other BDS satellite was 1:10 [35]. The related GDOP calculation also agrees with this weighing strategy.
# WDS-YOLO: A Marine Benthos Detection Model Fusing Wavelet Convolution and Deformable Attention
## 5 Discussion  
To address key challenges in marine environments including turbid backgrounds, optical refraction distortions, and size variations of marine bentnos objects, we introduced the WDS-YOLO algorithm as an effective solution for marine bentnos detection.  
Regarding detection accuracy, the proposed algorithm demonstrated superior performance in small object detection compared to conventional methods, as Table 3 demonstrates. Specifically, it achieved 85.6% mAP@50, outperforming YOLOv5s (+2.7%) and YOLOv7-tiny (+3.3%). Notably, the introduced approach also achieved superior performance on the RUOD dataset, confirming enhanced robustness in marine bentnos detection.  
Regarding practical deployment, the proposed model maintains operational feasibility while achieving competitive performance. WDS-YOLO maintains a compact parameter size of 3.2 M (74.2% reduction from 12.4 M) with moderate computational costs. This demonstrates an effective balance between detection performance and model efficiency. With a processing speed of 104.5 FPS (marginally below YOLOv8n's 114.1 FPS), the model maintains a real-time capability that fulfills practical requirements for marine bentnos detection under computational constraints.  
Despite these advancements, two limitations require consideration. First, we observed a modest performance decrease on the RUOD dataset with increased categories and background complexity. This suggests image quality and background complexity may affect detection accuracy, motivating future research to improve robustness through image data enhancements. Second, although the current 104.5 FPS satisfies real-time requirements, computational efficiency could be improved via channel pruning or quantization while preserving accuracy. These aspects require further exploration in future studies to enhance the algorithm's utility.