esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2009**, \(9\), 1433-1453; doi:10.3390/s90301433  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Sink-oriented Dynamic Location Service Protocol for Mobile Sinks with an Energy Efficient Grid-Based Approach**  
**[PERSON] 1, [PERSON] 2, [PERSON] 1 and [PERSON] 1** 1**School of Information and Communication Engineering, Sungkyunkwan University, 300 Cheoncheon-dong, Suwon Gyeonggi-do 440-746, South Korea; E-Mails: [EMAIL_ADDRESS]; [EMAIL_ADDRESS]; [EMAIL_ADDRESS]
2 **Division of Electrical Electronic and Information Engineering, Wonkwang University, 344-2 Shinyong-dong, Iksan Chonbuk 570-749, South Korea**  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-31-290-7145; Fax: +82-31-299-4134  
_Received: 26 November 2008; in revised form: 25 February 2009 / Accepted:26 February 2009 / Published: 3 March 2009_  
**Abstract:** Sensor nodes transmit the sensed information to the sink through wireless sensor networks (WSNs). They have limited power, computational capacities and memory. Portable wireless devices are increasing in popularity. Mechanisms that allow information to be efficiently obtained through mobile WSNs are of significant interest. However, a mobile sink introduces many challenges to data dissemination in large WSNs. For example, it is important to efficiently identify the locations of mobile sinks and disseminate information from multi-source nodes to the multi-mobile sinks. In particular, a stationary dissemination path may no longer be effective in mobile sink applications, due to sink mobility. In this paper, we propose a Sink-oriented Dynamic Location Service (SDLS) approach to handle sink mobility. In SDLS, we propose an Eight-Direction Anchor (EDA) system that acts as a location service server. EDA prevents intensive energy consumption at the border sensor nodes and thus provides energy balancing to all the sensor nodes. Then we propose a Location-based Shortest Relay (LSR) that efficiently forwards (or relays) data from a source node to a sink with minimal delay path. Our results demonstrate that SDLS not only provides an efficient and scalable location service, but also reduces the average data communication overhead in scenarios with multiple and moving sinks and sources.  
keywords: Wireless Sensor Networks; Data Dissemination Protocol; Location-based Routing; Mobile Sink; Energy Efficiency; Lifetime.
## 4 Methodologies and Implementation
### Comparison with Other Segmentation Methods
#### 4.2.6 Robustness against Limited Annotated Data  
Deep learning models often require large amounts of annotated data for training. Limited data availability, especially in medical imaging, can pose challenges for achieving robust segmentation performance [89; 90].  
DDPM, with its probabilistic modeling, introduces a level of robustness against limited annotated data. The algorithm's ability to capture uncertainties in predictions can contribute to more stable performance when training data are sparse [53; 91].
# Patient-Tailored Dementia Diagnosis with CNN-Based Brain MRI Classification
## 4 Discussion
However, LIME tends to struggle with stability and consistency when analyzing high-dimensional inputs such as medical imaging data, where subtle variations can lead to entirely different diagnoses [44,47]. SHAP takes a game-theoretic approach, assigning contribution scores to input features based on Shapley values. It offers consistency and theoretical robustness. However, its application to image data is hindered by high computational cost and the assumption of feature independence, which is not true in pixel-wise correlated MRI scans [45,48]. Shap-CAM, developed as a SHAP-based application for CNNs, attempts to account for spatial dependencies and produces saliency maps through marginal contribution analysis. While promising, it remains computationally intensive and conceptually complex [46]. In contrast, Grad-CAM is designed explicitly for convolutional architectures, making it particularly well-suited for medical imaging applications. By using the gradients flowing into the final convolutional layers, Grad-CAM highlights class-discriminative regions in a visually intuitive manner [43]. It preserves the spatial hierarchy of CNNs and provides clinically meaningful heatmaps that localize the brain regions that contribute most to the model's decision. In dementia diagnosis, where identifying subtle structural abnormalities is crucial, Grad-CAM offers practical and focused interpretability balancing clarity, computational efficiency, and diagnostic relevance more effectively than other attribution methods. Nevertheless, combining Grad-CAM with techniques such as LIME or SHAP could further enrich model transparency by revealing different aspects of model behavior. Integrating multiple interpretability tools in future studies may enhance clinicians' trust in the model and guide its development by combining insights across spatial, feature-level, and local decision boundaries.
## 4 Results and Discussion
### Environmental Considerations  
As shown in Figure S3 of the Supplementary Material, more than 50% of the total samples show significantly increased Cd, Pb and Zn concentrations. In detail, more than 50% of the samples showed considerable to very high enrichments in Pb and Zn, while for Cd, more than 50% of the samples could be considered very highly enriched. The samples showed only moderate enrichment in the remaining analyzed elements. However, when the estuary and coastal areas are compared within this index, notable differences can be found, as is shown in Figure S3 (Supplementary Material). Except for As, the enrichment of which was higher in the coastal area than in the estuary, the rest of the elements presented greater enrichment in the estuary area than in the coastal area. Specifically, Cd and Cu showed moderate increases in their concentrations inside the estuary, compared with the coastal area, supporting the hypothesis of the existence of sources inside the estuary. In the cases of Pb and Zn, both elements showed moderate to extreme enrichment in the estuary and coastal areas, but this was much more significant inside the estuary, where Pb and Zn showed Cf values of 58 and 135, respectively. For this reason, both elements are widely dispersed throughout the whole area, but the main sources seem to be located inside the estuary.  
Regarding the pollution load index (PLI) values (Figure 5), most of the sediment samples from the internal estuary showed a high degree of pollution, with a clear decreasing trend moving towards the coastal environment, where most of the samples presented an unpolluted to moderately polluted status. This corroborates the thesis that the estuary acts as a pollution sink, and does not spread the pollution beyond it. Notable in this aspect are the samples located at the front of the Xago beach, which do not present contamination, despite the fact that the general dynamics of coastal sediment transport operate in the W-E direction [48], supporting the previous statement.  
Finally, for a complete evaluation of the study area, it is necessary to develop an environmental risk assessment of the sediments. In this case, the ERI index was selected, as it takes into consideration the toxicity of the elements [34; 49] and is thus the best option to provide a reasonably reliable evaluation. Based on this index, as was the case with the PLI, the most risky sediments were in the middle-internal area of the estuary, as a consequence of the significant contaminant load. In the rest of the areas, most of the sediments pose a low-to-moderate environmental risk, with some isolated samples showing a higher environmental risk, but without any apparent spatial continuity. It should be noted that the Zeluan area, despite being within the estuary, generally presents a low risk of transference due to the predominance of sandy sediments with a low metallic load.
## 9 Local Search by Swapping Ties
### Experimental Evaluation
We then considered the average number of steps needed by the algorithms to finish their execution. As can be expected, the number of steps increases as the incompleteness, \(p_{1}\), rises. This happens for all algorithms and all problems sizes, and it is more clear as \(n\) increases. This can be seen, for example, in Figure 9(a), which shows the average number of steps for LSTt on problems of a size of 10 and in Figure 9(b), which shows the results for \(n\) = 30. Figure 9(c) shows that the number of steps needed by LSTt for \(p_{1}\)=0.8 decreases as \(n\) increases. Moreover, it decreases as the amount of ties (\(p_{2}\)) increases. This behavior is the same for the other algorithms and is due to the increased probability of finding a perfect matching on larger problems. For instance, Figure 9(d) shows how steps vary considering problems of a size of \(n\) = 30 and \(n\) = 90. We also measured the execution time of our algorithms. The execution time is mainly influenced by the size and nature of the neighborhood that has to be explored at each search step. The neighborhood used by LTIU depends on blocking pairs, and so, it is larger in problems with few ties. On the other hand, the neighborhoods defined for LSTt and LSTk are bigger as the number of ties rises. For these reasons,  
Figure 9: Average number of steps for LSTt, LSTk and LTIU. (**a**) Average number of steps for LSTt for \(n\) = 10; (**b**) average number of steps for LSTt for \(n\) = 30; (**c**) average number of steps for LSTt varying \(n\) and fixing \(p_{1}\) = 0.8; (**d**) average number of steps for LSTk, LSTt and LTIU on problems of a size of 30 and 90. Fixing \(p_{1}\) = 0.8, \(k=50\%\) and \(t=0.5\). the execution time of LTIU tends to slightly decrease as \(p_{2}\) increases, no matter the size of the problem for fixed values of \(p_{1}\) (see Figure 10(a)). Figures 10(b) and 10(c) show, respectively, the execution time of LSTk and LSTt. In both cases, the execution is longer as \(p_{2}\) becomes larger. The difference is that the size of the neighborhood in LSTt varies dynamically according to the weights of the ties and the threshold, \(t\). This speeds up the algorithm drastically, and as we can see, the execution time of LSTt is about half the execution time of LSTk. Summarizing, both LSTk and LSTt are effective in terms of the size of the returned marriages, but when we also take into account the execution time, LSTt has to be preferred.
###### Abstract  
Hydrolytic activity for chlorogenic acid (CGA) has been recognized as an important side activity of some types of ferulic acid esterases. The purpose of this work was to enhance the efficient expression of ferulic acid esterase (FAE) and to explore its application in the processing of sunflower seed. Two novel FAEs from _Aspergillus aculeatus_ (_A_e_b14) were expressed in genetically engineered _E. coli_ BL21 (DE3), and their properties, including temperature, pH, metal ions and substrate specificity, were characterized after purification. Competitive CGA hydrolysis activity was observed in these recombined ferulic acid esterases (reFAEs) with reFAE1 of 246.37 U/g and reFAE2 of 340.95 U/g, which were 56.6 and 78.4 times higher than that of the wild strain (4.35 U/g), respectively. Meanwhile, the fermentation cycle was greatly shortened to 2.0 d. These reFAEs were recognized as type C FAE through substrate specificity assays. Treatment of sunflower seed protein (SSP) using reFAE2 resulted in a remarkable color change, from green to milk-white, confirming the activity of CGA biodegradation. Therefore, it shows certain potential in the processing of sunflower seed and other related foodstuffs.  
Aspergillus aculeatus; chlorogenic acid; ferulic acid esterase; enzyme properties; sunflower seed Article  
Heterologous Expression and Characterization of a Ferulic Acid Esterase from _Aspergillus aculeatus_ with Potential Use in Sunflower Seed Processing.
## 2 Materials and Methods
### Assessment of Video Quality and Reliability  
The video information and quality index (VIQI) was used to analyze the overall educational quality and content of videos [16; 17; 18; 21; 24]. The VIQI scale, consisting of the contents of the Global Quality Scale (GQS), uses a 5-point Likert scale ranging from 1 (poor quality, poor flow, most information missing, not useful) to 5 (high quality, excellent flow, very useful) to evaluate the 4 following video features: the flow of information, information accuracy, quality (1 point each for the presence of still images, animations, interviews with individuals in the community, video captions, and a report summary), and precision (level of coherence between the video title and content). The total score ranges from 4 to 20 points [21; 25; 26]. Reliability was assessed using a modified 5-point DISCERN tool [27] and the _Journal of American Medical Association_ (JAMA) benchmark criteria [28; 29]. The DISCERN system was created by a specialized group at Oxford University (UK) as a tool to assess the quality of written consumer health information on treatment choices. It includes 16 questions ranging from 1 to 5, and the total score is between 16 and 80 points [30]. In this study, we used the short form of the DISCERN that was adapted by [PERSON] et al. The modified-DISCERN (m-DISCERN) questioning tool consists of 5 items about clarity, reliability of the source of information, lack of bias, reference supplementation and mention of uncertainty. According to m-DISCERN, video scores > 3 points indicate good reliability, a score of 3 points shows moderate reliability, and scores < 3 points refer to poor reliability [27]. The JAMA criteria evaluate the reliability of online health information, assessing authorship, attribution, disclosure and currency of the video with the total score ranging from 0 to 4 [28; 29].
## 2 Physical Scenario
The values \((T-y)_{x_{\rm a},\rm j}\) are compared with those of the simulated model at \(x_{\rm a,I}=x_{\rm b,I}+x_{\rm o}\), \(x_{\rm o}=x_{\rm a}-x_{\rm b}\). The new functional is built whose value is \(\Psi_{\rm a,I}\). \[\Psi_{\rm a,j}=\sum_{1}^{N}\Bigl{[}(T-y)_{x_{\rm a},\rm j}-(T-y)_{x_{\rm a}, \rm j}\Bigr{]}^{2} \tag{8}\]  
Once the former steps related to the first iteration (j = I) are finalized, the partial results \(v_{\rm o,I}\), \(x_{\rm b,I}\),\(v_{\rm b,min,J}\), \(x_{\rm a,I}\) and \(\Psi_{\rm a,I}\) are retained. Step 4. \(v_{\rm o,I}\) is modified by decreasing it by a small value, \(\Delta v_{\rm o,I}\) (a small fraction of \(v_{\rm o,I}\), for example, is 0.05\(\cdot v_{\rm o,I}\)). The model is simulated again for the new velocity of \(v_{\rm o,II}=v_{\rm o,I}-\Delta v_{\rm o,I}\). Steps 2 and 3 are repeated for the new iteration (j = II) providing the new values, \(v_{\rm o,II}\), \(\Psi_{\rm b,II}\), \(\Psi_{\rm b,min,I}\), \(x_{\rm a,II}\) and \(\Psi_{\rm a,II}\). Step 5. If \(\Psi_{\rm b,min,II}+\Psi_{\rm a,II}\) is less than \(\Psi_{\rm b,min,J}+\Psi_{\rm a,I}\), go back to Step 4 (which takes one back to the application of steps 2 and 3, with j = III) to obtain the new values \(v_{\rm o,III}\), \(x_{\rm b,III}\), \(\Psi_{\rm b,min,III}\), \(x_{\rm a,III}\) and \(\Psi_{\rm a,III}\). Continue with new iterations until reaching one, j + 1, for which \(\Psi_{\rm b,min,j+1}+\Psi_{\rm a,j+1}\) is greater than \(\Psi_{\rm b,min,j}+\Psi_{\rm a,j}\). The estimated velocity is that corresponding to iteration j. Experimental profiles \((T-y)_{x_{\rm a},\rm j}\) and \((T-y)_{x_{\rm b},\rm j}\) are estimated to be at locations \(x_{\rm b,j}\) and \(x_{\rm a}\). The characteristic length can then be determined from its expression using Equation (6). Step 6. If \(\Psi_{\rm b,min,II}+\Psi_{\rm a,II}\) is greater than \(\Psi_{\rm b,min,I}+\Psi_{\rm a,I}\), go back to Step 4, but take a negative increment for the velocity, \(-\Delta v_{\rm o}\). Step 7. To estimate \(T_{\rm w}\), new successive values of this input temperature, \(T_{\rm w,2}=T_{\rm w,1}+\Delta T_{\rm w}\), \(T_{\rm w,3}=T_{\rm w,2}+\Delta T_{\rm w}\)..., with \(\Delta T_{\rm w}\), a fraction of \(T_{\rm w,1}\), and of each of these temperatures, steps 1 to 6 are repeated. The final estimates of \(v_{\rm o}\), \(x_{\rm b}\) and \(x_{\rm a}\) are those values of these quantities for which the functionals \(\Psi_{\rm b}\) and \(\Psi_{\rm a}\) are minimum. Figure 2 shows a block (flow) diagram of the protocol, which could be optimized for faster convergence through programming routines, selecting non-constant values (dependent on the results of each iteration) for \(\Delta v_{\rm o}\).
# Audio-Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model
## 2 Related Work
### Speech Recognition with a Hybrid CTC/Attention Architecture  
An alignment between an encoder and a decoder is one of the main issues in speech recognition. Although the attention algorithm is widely used to solve the alignment, this approach inherently has a problem of allowing non-sequential alignments. However, a CTC loss addresses this problem because it forces a monotonic alignment. A hybrid CTC/attention architecture using a CTC loss in an attention-based encoder-decoder model has been successfully applied to AVSR as well as audio speech recognition or lip reading (e.g., [19; 35; 36]).
# Characterization of CYGNSS Ocean Surface Wind Speed Products
## 3 Results
### Sampling Properties  
#### 3.3.1 Temporal Sampling of Global Winds  
Figure 15 shows the mean revisit time of the CYGNSS FDS wind speed product as a function of latitude, derived from measurements in 2022 but typical of its performance throughout the mission lifetime. Revisit time is defined here as the mean time between measurements within a 72 h time frame for each 0.25\({}^{\circ}\) lat, lon box between \(-\)35\({}^{\circ}\) and 35\({}^{\circ}\) latitude that has two or more measurements and 70% spatial coverage. The strong latitudinal dependence seen in the figure results from the 35\({}^{\circ}\) orbit inclination of the constellation. Near the equator, the mean revisit time has a maximum of \(\sim\)4 h. The revisit time decreases to a \(\sim\)3 h minimum at 35\({}^{\circ}\).  
Figure 14: Scatterplot of matchups between the 34-knot wind radius derived from CYGNSS MRG wind fields and those measured by ASCAT. Different storm quadrants are denoted by symbol shapes. Different ranges of storm intensity are denoted by symbol color.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Triplet of Matchups**} & \multicolumn{4}{c}{**RMSE (km)**} \\ \cline{2-5}  & **BT** & **ASCAT-B** & **CYGNSS** & **SMAP** \\ \hline BT, ASCAT-B, CYGNSS & 46.85 & 36.82 & 58.33 & - \\ \hline BT, CYGNSS, SMAP & 45.57 & - & 49.47 & 53.64 \\ \hline BT, ASCAT-B, SMAP & 40.05 & 43.32 & - & 43.48 \\ \hline ASCAT-B, CYGNSS, SMAP & - & 34.73 & 55.23 & 48.80 \\ \hline Average RMSE over all triplets & 44.2 & 38.3 & 54.3 & 48.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Root Mean Square Error (RMSE) in 34-knot significant wind radius (R34) products produced by Best Track (BT), ASCAT-B, SMAP, and CYGNSS MRG. The RMSE in individual products is estimated for each triplet by the triple colocation analysis procedure.
# Remote Sensing of Sewing Work Levels Using a Power Monitoring System
## 3 Methodology  
The main purpose of this study is to remotely measure an operator's \(L_{S}\) and a task's \(L_{D}\) from among the factors affecting the productivity of sewing work. This section summarizes the factors affecting the productivity of garment production lines and describes how \(L_{S}\) and \(L_{D}\) were remotely measured using a power monitoring system.
## 2 Related Works
### RIS-Assisted NOMA  
This section details the RIS for wide coverage in the NOMA system. The authors of [10] investigated the performance of a pair of NOMA UE over Nakagami-\(m\) fading channels. It derives expressions for outage probabilities with both pSIC and iSIC schemes. The authors of [15] explored partial RIS selection (PRIS) to analyze outage probabilities and throughput for two UE systems. The findings show that PRIS can enhance user fairness and improve system performance compared to conventional NOMA systems without RISs.  
The authors of [11] suggested a novel ISAC system using NOMA and a dedicated RIS providing virtual line-of-sight (LoS) links for radar targets, addressing path loss and blockage. The study optimized the beamforming technique to maximize the beampattern gain. For a given PA, active beamforming, and RIS phase values, the authors of [25] developed a max-min problem to optimize the sensing beam pattern with SE constraints. The recommended low-complexity alternate optimization (AO) algorithm demonstrates improved beampattern gain. The authors of [26] investigated the performance of active RISs in NOMA networks considering HWDs.  
In [12], an RIS-assisted NOMA system based on space shift keying (SSK) is introduced to leverage RISs to enhance DL transmission by optimizing phase shifts, resulting in significant gains in BER performance. The suggested system outperforms conventional RIS-assisted OMA systems by \(\sim\)10 to \(\sim\)20 dB under similar conditions. The authors of [13] explored UE pairing strategies for DL RIS-assisted HNOMA. The study delves into optimizing UE pairing to enhance communication efficiency and reliability. The findings suggested that RIS-assisted HNOMA significantly enhances SSE in high-SNR regions.
# InfNets Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task
## 4 Classification Experiment
### Training And Validation  
We recruited 22 subjects; each subject completed 30 motor imagery trials (repeat 10 times for rock, paper and scissors, respectively). Therefore, we obtained a total of 660 trials as our dataset. The algorithm read all trial fNIRS data, took the task part and randomly shuffled them, then selected 80%, a total of 528 trials, as the training set, and the remaining 20%, a total of 132 trials, as the test set. We built the networks based on tensorflow1.14 and trained them using a training set on Nvidia RTX2080 Ti GPU, Intel i9 9900 K CPU and tested this on the completely independent test set. The parameters of ResNet we set were a batch size is 64, learning rate of 0.0001, training epochs of 600 and [PERSON] as the optimizer. The parameters of Inception were set as follows: batch size is 64, learning rate is 0.005, training epochs are 600 and optimizer is Adam.  
We inputted the HbO, HbR, and HbT matrices into the CNN-based TSC algorithm, respectively, evaluated and compared their performance. We also performed four data-preprocessing operations on the raw fNIRS data, including Min-Max Normalization, Z-normalization, FIR band-pass filter and Butterworth band-pass filter, and similarly evaluated and compared their performance. Traditional machine learning methods (such as SVM) were evaluated as well, and we compared their results with the CNN-based TSC methods.