esa-sceva/satcom-chunk-collection huggingface dataset:
# Underwater Highly Pressure-Sensitive Fabric Based on Electric-Induced Alignment of Graphene
## 3 Results and Discussion
This indicates that the conductive coating still has a stable output underwater. In addition, we conducted external vibration detection tests on the underwater sensors. As shown in Figure 4b, when the outer wall of the petri dish is tapped with tweezers, a regular resistance response can be observed, and the current response signal returns from peak to baseline level after each tap [12]. In addition, the sensor exhibits a slight change in resistance when the sample is placed on the bottom of the container with an ear-washing ball (Figure 4c). High sensitivity can extend the detection range of the sensor. For example, the sensor detects small pressure changes due to vocal cord vibrations (Figure S7). This may have implications for sensing vibrations and changes in liquid surfaces. Notably, the superhydrophobic sensor can also be used in underwater wearable devices. As shown in Figure 4d, we attached the fabric sensor to the fish to monitor the swimming and resting state of the small fish in real-time. During the swimming process of the simulation fish, we successfully detected resistance changes with the sensor. As shown in Figure 4e, we tested the electrochemical stability of the sensor in different underwater environments. The fabric sensor was placed on the bottom of the container, and solutions of different pH values were added to the container. The I\(-\)V curve of the sensor follows Ohm's law under acidic, alkaline and neutral conditions. This indicates that the fabric still exhibits stable electrochemical properties under extreme conditions [29]. Finally, we placed the fabric sensor in water at different temperatures (Figure 4f), and the fabric exhibited good electrochemical performance; from the I\(-\)V curves, the resistance of the fabric increased as the temperature increased. The drop occurred in a small range, indicating that the fabric sensor may also be used to detect changes in underwater temperature.
# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 5 Evaluation
### Simulation Results
#### 5.5.2 Effect of Maximum Number of Samples  
This evaluation is done to study how the proposed method reacts to a different maximum number of samples. The maximum sample number used in previous researches is 50. In this evaluation, maximum number of samples varied between 10 to 100 and the effects of these variations on MSL*, LCC and IACS are presented in Figure 7a-e. Generally, a higher maximum sample number has little effect on the localization accuracy and overall closeness value. However, it increases the computation, communication and energy consumption.  
Figure 6: (**a**) Total Average Estimation Error, (**b**) Total Average Closeness, (**c**) Total Average Computation Cost, (**d**) Total Average Communication Cost, and (**e**) Total Average Energy Consumption for Default Parameter Scenario.  
From the simulation, although the communication, computation, and energy costs increase, IACS consistently shows the least costs compared to MSL* and LCC. However, similar accuracy and closeness value are observed between the methods without obvious changes when the maximum number of samples is increased.
## 4 Experiments and Results
### Results and Discussion  
Several experiments have been conducted to evaluate the proposed models on SenWave and SenAIT datasets. Table 3 shows the results of the proposed models. We compare the performance of each model (Baseline, AraBERT, and MARBERT) using SenWave dataset. Then, we evaluate these models using the newly generated dataset, SenAIT. In addition, we show how emojis replacement and DWLF approaches can improve the performance of the proposed models. As shown in Table 3, the baseline model performed poorly compared with the transformer-based models, AraBERT and MARBERT, under all settings. The significant improvement of these models over the baseline model can be attributed to the contextual representation of the input tweets learned by AraBERT and MARBERT models from the large Arabic corpora used to train these models. Thus, the learned vocabulary would be more representative of Arabic morphemes and sub-word tokens, which would enable the model to learn a better contextual representation of Arabic words. In addition, we observed that the baseline model performs better for short tweets (length of 5-10 words), whereas AraBERT and MARBERT work well for longer tweets since these models are developed to capture contextual information of long texts. Although AraBERT and MARBERT models are using the same architecture as the BERT transformer, MARBERT outperformed AraBERT on both datasets. The improvement in F1-Micro with MARBERT is between 2.0% and 3.3% compared to AraBERT. This improvement can be attributed to the fact that MARBERT utilizes massive amounts of user generated content sourced from Twitter to train the language model. These texts represent social media text characteristics, including wrong spellings, irregular syntax, abbreviations, etc., which is similar to the nature of our training datasets. In addition, these texts represent
## 4 Clinical Methods  
A total of 20 patients (i.e., 40 sites) were selected and grouped into two categories: Control and test (split mouth design). The control group (20 left sites) was treated with SRP. The test group was treated by SRP plus ALADENT (20 right sites).  
One clinician (D.L.) performed the diagnosis of chronic periodontitis of the enrolled sample. The diagnosis was assessed following Multi-State Markov Models proposed by [PERSON] et al. in 2014 [52], which associate the state of chronic periodontitis to clinical pocket depth (PD)/clinical attachment loss (CAL) values higher than 4 mm with or without BOP. For this reason, prior to treatment, PD, bleeding on probing (BOP) and CAL were detected in 6 different points for each selected site: Disto-lingual/palatal, mesio-lingual/palatal mesio-buccal, disto-buccal, mid-lingual/palatal, and mid-buccal. BOP was calculated using the Papillary Bleeding Index (PBI) introduced by [PERSON] in 1975 [48], identifying four different scores, depending on the gravity of gingival papillae bleeding. Microbial analysis was also conducted: The isolation of the sites was obtained by using cotton rolls, subgingival samples were collected with sterile absorbable paper points (size 60) in the deepest part of the periodontal pocket and were immediately processed in a microbiological lab. Total bacterial loadings (TBL) were then evaluated.  
The same clinician ([PERSON]) performed SRP treatment in all the selected sites at the baseline measurement, using ultra sonic scalers. After SRP, PDT with ALADENT was performed in the test sites by the same operator. The product was inserted in each pocket with the dedicated capillary tip. The incubation of the product lasted 60 min. Each site was illuminated with a 630 nm led source (TL-01 Alpha Strumenti) for 7 min, with a total light dose of 120 J/cmq (Figure 1). The tip of the illuminator was kept very close to the site, and it was positioned perpendicular to the surface of the gingival mucosa. TBL and PD/CAL/BOP were recorded again from both sites in each patient after 15 days and after 6 months, respectively. After treatment, all the included patients were asked to observe adequate oral hygiene maneuvers (both in case and control sites): Teeth brushing three times a day, daily use of dental floss, application of periodontal gel with 1% chlorhexidine. Principal outcomes were reported using means and percentages.  
Total bacterial loading was analyzed by performing Real-Time Polymerase Chain Reaction [53].
# Recent Improvements in the Development of Soft Grippers
## 3 Dexterous Manipulation
### Framework for Characterizing Dexterous Soft Grippers and Their Performance
#### 3.4.1 Soft Gripper Characterization  
We propose a selection of physical attributes to characterize a dexterous soft gripper. First, the gripper's architecture is usually a function of the targeted applications. Next, the number of degrees of freedom plays a significant role in dexterous performance, as reported in various works. We then precise the type of contact as it determines the grasp  
Figure 6: A review of dexterous soft grippers with their dexterity level. **(I-a)** Soft gripper actuated by FEAs, which have embedded ionogel sensors for somatosensitive manipulations [17]; **(I-b)** soft gripper actuated by servo motors and FEAs, with convertible grasping modes [16]; **(I-c)** underactuated soft gripper with tendons and motors for efficient finger-gaiting-based dexterous manipulations [20]; **(I-d)** soft robotic hand (RBO Hand 2) actuated by FEAs for dexterous grasping [13]; **(II-a)** foam hand driven by tendons for dexterous manipulations [12]; **(II-b)** soft robotic hand (BCL-13) actuated by FEAs for dexterous grasping and in-hand manipulations [15]; **(II-c)** the Jamband actuated by servo motors and particle jamming for dexterous manipulations with minimal actuation [22]; **(II-d)** soft gripper with active palm and reconfigurable fingers actuated by SPAs and servo motors for dexterous in-hand manipulations [88]; **(II-e)** soft robotic hand (BCL-26) actuated by FEAs for dexterous in-hand manipulations [29]; **(II-f)** soft gripper (Edgy-2) actuated by SPAs for robust dexterous grasping [24]; **(II-g)** dexterous soft robotic hand actuated by FEAs for delicate in-hand manipulations [15]. The image shows the type of actuation of the grippers and their architecture.  
type, and finally, we highlight the physical achievements of the gripper if assessed by the authors.  
Our proposed gripper characterization can be made for any individual soft dexterous gripper, or for a number of soft dexterous grippers in order to compare them as in Table 2 where we selected a number of articles in which we could identify all or nearly all our chosen physical attributes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
# Limitations and Performance Analysis of Spherical Sector Harmonics for Sound Field Processing
## 4 Limitations on Radial Extrapolation
### Exterior Case  
We test the validity of (18) using three unit strength point sources with Cartesian coordinates \((0.5,0,0)\) m, \((0,0.5,0)\) m, and \((0,0,0.5)\) m. First, we investigate the extrapolation  
Figure 2: Spherical sector sound field radial extrapolation setups for (**a**) exterior case and (**b**) interior case. Illustration of the sound source region \(\mathbb{S}\), the measurement region \(\mathbb{M}\), and the extrapolation region \(\mathbb{T}\).  
performance within a small sector region defined by \(\theta_{M}=120^{\circ}\), \(R_{M}=1\) m, \(R_{T}=R_{M}\), and \(R_{T}=5R_{M}\). We then estimate the sound field using (18) and (19).  
The extrapolation results are presented in Figure 3. Notably, when \(R_{T}=R_{M}\), the sound field estimation is accurate and matches the ground truth. However, when \(R_{T}>R_{M}\), the extrapolated sound field deviates from the actual sound field, which means we cannot conduct an accurate extrapolation with (18) in this example.  
Subsequently, we investigate the extrapolation performance under various conditions, including different frequencies (\(f=300\), \(500\), \(700\), and \(900\) Hz), sizes of \(\mathbb{T}\) (\(\theta_{M}~{}=~{}45^{\circ},75^{\circ},105^{\circ}\), and \(135^{\circ}\)), and distances between \(R_{T}\) and \(R_{M}\) (ranging from \(0\) to \(19\lambda\), where \(\lambda\) is the wavelength). We keep \(R_{M}=1\) m constant, and the distance between \(R_{T}\) and \(R_{M}\) is represented in units of wavelength (\(\lambda\)). Additionally, we ensure an adequate number of microphones (\(Q=\left\lceil 1.3(N_{s}+1)^{2}\right\rceil\)) for the simulations.  
The following observations can be made from the results shown in Figure 4: (i) the extrapolation error (\(\epsilon\)) increases rapidly within a distance of \(2\lambda\) and gradually saturates as the distance grows; (ii) lower frequencies yield faster convergence of \(\epsilon\) to smaller values; and (iii) smaller sector regions tend to result in larger \(\epsilon\) values.  
Based on the simulation results, we find that (18) alone cannot accurately extrapolate the sector sound field under the exterior case. However, under certain conditions, such as low frequencies, large sector regions, and small distances from the measurement region, (18) can still provide acceptable estimations of the extrapolated sound field.  
Figure 4: _Cont._  
Figure 3: Spherical sector sound field radial extrapolation for the exterior case, with \(\theta_{M}=120^{\circ}\), \(R_{M}=1\) m, \(f=300\) Hz, \(Q=100\): (**a**) sector sound field with \(R_{T}=1\) m; (**b**) estimated sound field with \(R_{T}=1\) m; (**c**) sector sound field with \(R_{T}=5\) m; (**d**) estimated sound field with \(R_{T}=5\) m.
# Task Offloading Based on Lyapunov Optimization for MEC-Assisted Vehicular Platooning Networks
## 5 Problem Solution
### Offloading Decision  
The offloading decision vector at time \(t\) is expressed as  
\[a(t)=[a_{m}(t)|m\in\{1,2,...M\},a_{m}(t)\in\{0,1,...M\}], \tag{8}\]where \(a_{m}(t)=k\) means that PM \(m\) offloads the task to computation node \(k\) for execution, and we can also express it as \(a_{m,k}(t)=1\). In particular, when \(m=k\), it means that the task is executed locally. Therefore, the offloading decision of PM \(m\) can be expressed as  
\[\begin{cases}a_{m,k}(t)=1,\\ \sum_{k=0}^{M}a_{m,k}(t)=1,\end{cases} \tag{9}\]  
where the below equation is to ensure that a task can only be processed at one computing node.  
\(b_{k}(t)\) represents the computation size of the task that reaches the \(k\)-th queue at time \(t\), which is related to the offloading decision. Then,  
\[b_{k}(t)=\sum_{m=1}^{M}a_{m,k}(t)\cdot A_{m}(t). \tag{10}\]  
\(T(a(t))=\{T_{1}(t),T_{2}(t),...,T_{M}(t)\}\) is the set of execution length for all PMs at time \(t\), and it can be expressed by  
\[T(a(t))=\{\sum_{k=0}^{M}a_{1,k}(t)\cdot T_{1}^{k}(t),\sum_{k=0}^{M}a_{2,k}(t) \cdot T_{2}^{k}(t),...,\sum_{k=0}^{M}a_{M,k}(t)\cdot T_{M}^{k}(t)\}, \tag{11}\]  
where \(T_{m}^{k}(t)\) is obtained by Equation (4).  
Therefore, the total energy consumption also can be expressed as  
\[E(a(t))=\sum_{m=1}^{M}\sum_{k=0}^{M}a_{m,k}(t)\cdot E_{m}^{k}(t), \tag{12}\]  
where \(E_{m}^{k}(t)\) is obtained by Equation (6).
# Transit Traffic Filtration Algorithm from Cleaned Matched License Plate Data
## 3 Materials and Methods
### Transit Traffic Filtration Algorithm
The main loop iteration ends with the creation of a subset \(T_{h}^{\prime\prime}\) on line 17 that retains only the travel times of \(T_{h}^{\prime}\), where the absolute value of \(\Delta\tau mse\) is less than 0.05. This final filtering step ensures that only travel times that closely conform to the expected linear pattern are considered valid, effectively removing outliers caused by non-transit traffic. The highest value of travel time within \(T_{h}^{\prime\prime}\) is identified as the cutoff travel time \(c_{val}\) on line 18. This particular value is crucial as it establishes a threshold beyond which travel times are likely to represent vehicles that have stopped, thus addressing the limitations of previous methods that failed to accurately differentiate between transit and non-transit travel times. The cutoff travel time \(c_{val}\) is then used (line 19) to filter the initial set \(D_{h}\), consisting of the travel times associated with the assessed time interval. In this way, we achieve the set \(D_{h}^{\prime}\), which consists of travel times identified by the algorithm as valid. To initiate and complete the algorithm, two appropriate modifications are applied to construct \(T_{1}\) and \(T_{k}\). In \(T_{1}\), the first interval \(D_{h}\) is evaluated with two consecutive intervals \(D_{h+1}\) and \(D_{h+2}\), due to the absence of prior travel time information. Similarly, in \(T_{k}\), the last interval \(D_{h}\) is evaluated with the two preceding intervals \(D_{h-1}\) and \(D_{h-2}\).
## 2 Materials and Methods
### Model Development and Evaluation  
In this study, we refer to the detailed of the model information of previous studies, partial least squares regression (PLSR), support vector machine regression (SVMR), random forest (RF), and extremely randomized trees regression (ETR) models were used to analyze the soil sample data. Good results have been obtained in the past based on the PSLR model [20]. SVMR has been proven to be effective in predicting the TAs concentration in soil in many studies [43]. There are many methods for tuning the hyper-parameters of SVMR, grid search being the most frequently used. In this study, use as grid search computes performance at all pairs of e and C to get the performance surface [44]. RF has been reported as performing better than PLSR and SVMR [23; 45].  
In addition, we also considered the ETR model developed in recent years on the basis of the RF model. The ETR model has been reported as having a higher prediction accuracy than the RF model for soil elements, and has been used in soil spectral prediction models in recent years [46]. ETR was developed as an extension of another tree-based ensemble method (random forest) to be a more computationally efficient algorithm. It consists of three factors: \(K\) is the number of randomly selected variables for splitting a node, \(n_{min}\) represents the minimum number of samples required for splitting an internal node, and M, the number of trees formed in the ensemble model [47].  
According to the results of laboratory measurement samples, the 59 soil samples were divided using the 10-fold cross validation method into calibration set and validation set [48].  
The main steps of the work were shown in Figure 3. After hyper spectral image acquisition, correction and reflectance preprocessing, and the ROI spectrum was extractwed. Through the pretreatment methods of FD, SNV and MSC, combined with the [PERSON]'s rank correlation coefficients, the characteristic bands were selected, and the PLSR, SVMR, RF, and ETR models were compared. The best model was used to estimate the soil TAs content and generate the soil TAs distribution map.  
Figure 2: Average spectra each of the samples (59 samples).  
The parameters of the determination coefficients (\(R^{2}\)), root-mean-square error (RMSE), and relative error (RE%) were used to measure the accuracy of the models [39; 49]. The closer \(R^{2}\) is to 1, the better the stability of the model and the higher the degree of fit. RMSE and RE were used to test the predictive ability of the models. The smaller the RMSE and RE, the better the predictive ability.  
\[R^{2}=1-\frac{\sum\limits_{n-1}^{n}{{{(y_{i}-\hat{y}_{i})}^{2}}}}{\sum\limits_{ n-1}^{n}{{{(y_{i}-\overline{y}_{i})}^{2}}}} \tag{2}\]  
\[RMSE=\sqrt{\frac{\sum\limits_{n-1}^{n}{{{(y_{i}-\hat{y}_{i})}^{2}}}}{n}} \tag{3}\]  
\[RE=100\times\frac{RMSE}{\overline{y}} \tag{4}\]  
where \(n\) is the number of samples, \(y_{i}\) is the measured value, \(\hat{y}_{i}\) is the predicted value, and \(\overline{y}\) is the average of the measured values.
## 6 Results and Discussion
### Use Case 1: Only Monolingual (Urdu) Data Available
As can be seen from Figure 6, the performance of the student models generally tends to improve as the threshold values increase. We evaluated the statistical significance of the improvements using bootstrap resampling [45]. For this, we used the standard MT evaluation tool _compare-mt_[46] ([[https://github.com/neulab/compare-mt/tree/master](https://github.com/neulab/compare-mt/tree/master)]([https://github.com/neulab/compare-mt/tree/master](https://github.com/neulab/compare-mt/tree/master)); accessed on 1 July 2025). We found that the difference in BLEU scores between SKD and CEKD were not statistically significant.
## Discussion and Limitations  
### The Problem/Challenge  
Regional events are happening in parallel at different geospatial locations. The tendency of similarity between these events is very high.  
1. Which surface is suitable for seismic wave detection?
2. Which sensors are best for ground motion frequency, magnitude, and angle measurement?
3. How to align the crust or surface with sensors for ground motion detection?
4. How to reduce the computation by minimizing mathematical complexity?
5. How to detect if the disaster threshold is exceeded for the early warning?
6. How does the data need to be filtered for feature extraction and pattern recognition?
7. How to relate the curves that they can be decisive evidence of the seismic event?
8. What is the probability of an earthquake?
9. Which data structure is more suitable for the real-time accuracy of prediction?
10. What are the recommended specifications of the sensor resolution ideal for SWEDA?
11. What should be the communication configuration of urban-scale seismic sensor nodes for an efficient algorithmic response?
# Optically Pumped Magnetometer Measuring Fatigue-Induced Damage in Steel
###### Abstract  
Uniaxial fatigue testing of micro-mechanical metallic specimens can provide valuable insight into damage formation. Magnetic and piezomagnetic testing are commonly used for qualitative characterization of damage in ferromagnetic specimens. Sensitive and accurate measurements with magnetic sensors is a key part of such a characterization. This work presents an experimental setup to induce structural defects in a micro-mechanical fatigue test. Simultaneously, the resulting piezomagnetic signals are measured during the complete lifetime of the tested specimen. The key component is a highly sensitive optically pumped magnetometer (OPM) used to measure the piezomagnetic hysteresis of a small specimen whose structural defects can be analyzed on a small scale by other metallographic characterization methods as well. This setup aims to quantify the magnetic signatures of damage during the fatigue process, which could enable non-destructive mechanical testing of materials. This paper reports the initial results obtained from this novel micro-magneto-mechanical test setup for a ferritic steel specimen.  
m pacs: **Keywords:** magnetic material testing; cyclic mechanical loading; fatigue damage; hysteresis; optically pumped magnetometers (OPM); quantum sensing; ferritic steel; mesoscale specimen; magnetomechanical effects +
Footnote †: journal: Applied
# Gender Differences in Visual Information Perception Ability: A Signal Detection Theory Approach
## 4 Results  
### Perception Error According to Gender  
As shown in Figure 4, the frequency of errors in correctly perceiving visual signal stimuli was 3.48 times (SD = 3.78) for males and 3.72 times (SD = 3.70) for females, indicating that males made fewer errors than females. However, this difference was not statistically significant at the 0.05 significance level (F = 0.089, \(p\) = 0.766).  
Applying signal detection theory to the perception of visually presented signal stimuli allows human perception errors to be classified into two types: miss errors (failing to detect a signal) and false alarm errors (incorrectly identifying noise as a signal). As shown in Figure 4, the average frequency of false alarm errors (incorrectly perceiving non-signal as signal) was 1.58 times (SD = 1.57) for males and 1.95 times (SD = 2.32) for females, indicating a slightly lower error rate for males. However, this difference was not statistically significant at the 0.05 level (F = 0.718, \(p\) = 0.399), suggesting no meaningful difference in signal perception ability between genders.  
Similarly, the frequency of miss errors (failing to recognize the presence of a signal) was 1.65 times (SD = 2.49) for males and 1.95 times (SD = 1.72) for females, also indicating a slightly lower error rate for males. Again, this difference was not statistically significant at the 0.05 level (F = 0.393, \(p\) = 0.532).  
In summary, while these findings are not statistically significant, they suggest a tendency for males to exhibit fewer perception errors in visual information tasks than females.