esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Key Issues and Challenges
### Operating System and Programming Language  
Data processing platforms utilize different operating systems as well as programming languages to operate according to the system requirements. BeagleBone and Raspberry Pi support Linux as an operating system thereby making the module cost-efficient [140; 141]. However, Linux is not a user-friendly operating system. Arduino operates on an Arduino-based programming language but due to its limited library resources, high computational efficiency in advanced research is not achieved [142]. PLC on the other hand utilizes OS 9 and VxWorks as its operating system while it is programmed by Ladder Logic, Function Block Diagram (FBD), Structured Text (ST), Instruction List (IL) and Sequential Function Chart (SFC). These programming languages require an expert as the written program is hard to debug, it consists of difficult syntax, and is difficult to edit online [143].
## 7 Discussion
### On Statistical Significance and Scalability  
While the experimental results show consistent trends across different algorithm variants, the current study does not include formal statistical tests such as ANOVA or the Wilcoxon signed-rank test. However, all variants were executed independently ten times per problem size, and the observed performance--especially for GAAM-TS--remained stable across runs. The low standard deviation in both total distance and delivery time (below 2% in all cases) supports the conclusion that the differences in performance are systematic and likely statistically significant. Future work will include formal significance testing to further validate these conclusions.  
Regarding scalability, our results confirm that computational time grows non-linearly with problem size, particularly for the GAAM-TS variant. While the GA and GA-AM variants maintain relatively consistent runtime increases due to their simpler structures, GAAM-TS exhibits superlinear growth caused by the additional neighborhood search required by Tabu Search. To further analyze scalability, we plan to extend experiments to include larger problem instances (35-50 locations), where computational efficiency and memory management will become more critical. These experiments will also help identify the practical limits of each algorithm variant under real-world constraints.
## 2 Materials and Methods
### Parallel Factor Analysis and complexation Modeling  
The identification of DOM components using EEM-PARAFAC analysis was carried out with the DOM Fluor toolbox in MATLAB R2021a. After removing Rayleigh and Raman scattering, a three-dimensional factor matrix was constructed, and the matrix was estimated using the Alternating Least Squares (ALS) method. After the factorization, the generalization ability of the model was evaluated by cross-validation. Finally, the fluorescence intensities corresponding to different excitation and emission wavelengths, as well as the maximum fluorescence intensity (Fmax) of each component were obtained.  
The quenched solutions were subjected to synchronous fluorescence measurement (Edinburgh FLS1000, Edinburgh, UK), with E(x) = 200-300 nm and E(m) = 250-600 nm, constant offset \(\Delta\lambda\) = 60 nm. The fluorescence intensities at the wavelengths corresponding to the excitation and emission wavelengths obtained from the PARAFAC analysis of each component were identified in the synchronous fluorescence spectra. The Stern-Volmer linear equation model (Equation (6)) was used for fitting [26; 34]. This model assumes that in the fluorescence quenching reaction, the HM ions form a 1:1 complex with the fluorescent probe, which is more suitable for describing the actual complexation reaction.  
\[\frac{F_{0}}{F_{0}-F}=\frac{1}{f\cdot K_{M}\cdot C_{M}}+\frac{1}{f} \tag{6}\]  
In this model, \(F_{0}\) and \(F\) represent the fluorescence intensities of each component before and after HM titration, \(C_{M}\) is the content of each HM ion, \(f\) is the fraction of the fluorescent component bound to the HM ion, and \(K_{M}\) is the complexation constant, reflecting the binding capacity of each component with the HM. \(K_{M}\) and \(f\) can be obtained from the linear fitting between \(F_{0}/(F_{0}-F)\) and \(1/C_{M}\).
# A Deep Learning Approach for Automatic Hate Speech Detection in the Saudi Twittersphere
## 4 Experiment Setup
### Neural Network Models
#### 4.2.2 CNN Architecture  
The first model we evaluated was a CNN model inspired from [PERSON]'s work [39] for text classification. Our CNN architecture, as illustrated in Figure 1, contains five layers: an input layer (embedding layer); a convolution layer, which basically consists of 3 parallel convolution layers with different kernel sizes (2, 3, and 4); a pooling layer; a hidden dense layer; and finally the output layer. As we mentioned before, all tweets are mapped into 300-dimensional real valued vectors by the embedding layer. Let \(m\) be length of longest tweet, the embedding layer then passes an input feature matrix with a shape of \(m\times 300\) to a dropout layer with a rate of 0.2. The main purpose of dropout layer is to reduce the problem of overfitting. Then, the output is received by the three parallel convolution layers. Each layer has 100 filters with kernel sizes of 2, 3, and 4 and a rectified linear unit function for activation. As part of convolution operation, each layer applies the 100 filters and produces 100 new feature maps with a size of (\(m-(kernel\ size-1)\times 100\)), passed into a dropout layer with a rate of 0.2. After that, these convolution features are fed as input to a max pooling layer (global) for down sampling, producing 3 (\(1\times 100\)) vector outputs. These 3 vectors are then concatenated and passed as input toa fully connected dense layer containing 50 neurons followed by a dropout layer with a rate of 0.2. The output is then fed to the output layer with sigmoid activation to produce the final predictions.
# An Aspect-Oriented Approach to Time-Constrained Strategies in Smart City IoT Applications
## 2 Existing Approaches
### Time-Constrained Strategies  
A time-constrained strategy can be defined as some algorithm, implemented as executable instructions, which operates on data in order to make decisions in an IoT system, where parts of the algorithm have relevance and acceptability constraints according to some WoV.  
Just as there are various IoT smart city applications in which the data are time-constrained, we propose that there are, similarly, various applications where the decision-making strategy itself is time-constrained and may not be applicable to the problem domain outside some WoV.  
In our considerations of future research directions in [16], we suggested one such example in the smart agriculture context, where certain IoT devices measure various crop parameters, with a decision-making strategy implemented on other devices (such as fog nodes) to control the irrigation of crops. In our example, while the data that drive the decision-making strategy (e.g., meteorological parameters, soil measurements, etc.) are time-constrained, the decision-making strategy itself may also change in accordance with externalities such as seasonal changes and even policy changes (e.g., the water authority may make a substantial change to the water quota, necessitating a new strategy for water allocation).  
Supposing that the irrigation strategy is implemented in a fog node physically located in each field, some mechanism must be incorporated in the software engineering design of the fog node to enable it to manage the time validity of the components of its decision-making process. It must recognize when its local strategy is outside its WoV and resolve this either through interaction with the high-level cloud infrastructure or with other fog-level agents in the MAS.  
Another potential practical application of time-constrained strategies could be to improve energy efficiency in the smart city infrastructure. For example, a smart city could potentially have thousands of streetlights which activate according to a lighting strategy. Rather than simply having the lights turn on and off at particular times of the day, the lighting strategy could be updated regularly, taking into account the dawn and dusk times, moon phase, individual light locations/conditions, and expected weather conditions. Thus, the lights could be activated optimally, providing better safety for pedestrians and motor vehicles while also reducing unnecessary activation time, thereby improving energy usage and cost at scale.  
The software architectures required to support such applications would make use of the orchestration capabilities of the cloud to disseminate up-to-date strategies to the fog nodes [24]. In the context of IoT software architecture, it is desirable that any such mechanism be easily implementable in the resource-constrained environments typical of IoT devices and fog computing [16].
## 4 Data Silos and Fragmentation:  
_Specific Statistics:_ A study published in the Journal of the American Medical Informatics Association (JAMIA) reported that 80% of healthcare data are unstructured, residing in documents, notes, and scanned images, making it difficult to extract, analyze, and share valuable clinical insights.  
_Illustrative Example:_ Inefficient data extraction and analysis due to unstructured information can limit healthcare researchers' ability to identify patterns and trends across large patient populations. This impedes medical advancements and the development of personalized treatment approaches. By incorporating specific statistics and illustrative examples, the problem statement concerning traditional EMR data sharing and analysis gains depth and context. The challenges related to data interoperability, privacy and security, legal and regulatory hurdles, and data silos become more tangible, emphasizing the urgent need for innovative solutions. Addressing these challenges is crucial for unlocking the full potential of EMRs and promoting data-driven healthcare practices that lead to better patient outcomes and advances in medical research. Collaborative efforts from healthcare stakeholders, policymakers, and technology experts are essential in overcoming these hurdles and creating a more efficient and secure data sharing ecosystem in the healthcare industry. Addressing these challenges is crucial to unlock the full potential of EMRs for medical research and collaborative learning. A solution is needed that ensures the privacy, security, and integrity of EMR data while fostering trust, facilitating seamless collaboration, and enabling efficient access to diverse datasets. The integration of blockchain technology with federated learning techniques holds promise in overcoming these obstacles, providing a decentralized and transparent framework that empowers stakeholders, protects patient privacy, and facilitates the discovery of valuable medical insights [9].
# Algorithms Utilized for Creep Analysis in Torque Transducers for Wind Turbines
## 13 Conclusions
By performing data-driven analyses, researchers can uncover correlations between various environmental conditions, operational parameters, and creep behavior in torque transducers. Determining the disparity in the magnitude of the upper and lower signals, along with precisely estimating the associated uncertainty, plays a critical role in establishing the accurate and typically linear attributes of the transducer, alongside its corridor of uncertainty. This knowledge can help optimize the design and implementation of torque transducers in wind energy systems. For instance, the analysis may reveal that specific wind speeds or temperature ranges significantly influence creep, leading to adjustments in the turbine's operating parameters or torque transducer materials and designs. Algorithms and AI methods, such as the Kalman filter and artificial neural networks, offer powerful tools for analyzing and compensating for creep effects in torque measurements of wind turbines, enabling the accurate estimation of true torque values and facilitating proactive maintenance interventions and the early detection of potential failures. Conceptualization, J.D.F., J.G.P. and P.F. ; methodology, J.G.P. and J.D.F. ; software, J.G.P. ; validation, P.F. and J.D.F. ; formal analysis, P.F. and J.D.F. ; investigation, J.D.F. ; resources, J.D.F. ; data curation, P.F. ; writing--original draft preparation, J.G.P. ; writing--review and editing, J.D.F., P.F. and J.G.P. ; visualization, J.G.P. and J.D.F. ; supervision, J.D.F. ; project administration, J.D.F. ; funding acquisition, J.D.F. All authors have read and agreed to the published version of the manuscript. This research was funded in part by the European Metrology Innovation and Research Program (EMPIR), Project Number: 19 ENG08, Short Name: WindEFCY: traceable mechanical and electrical power measurement for efficiency determination of wind turbines. All data are contained within the article. We would like to acknowledge [PERSON] (Central Office of Measures) for help with the torque transducer investigations and [PERSON] for providing a photo of the torque transducer. The authors declare no conflicts of interest.
## 4 Experimental Results and Analysis
### Strain Analysis
This can be explained by the fact that the stirrups of the specimen SC1 contributed directly to the shear resistance while those of specimen SC2 were open-loop stirrups, which were non-structural reinforcements. The efficiency of the additional plates is also seen in the crack pattern (Figures 7 and 8). Indeed, the number of cracks and the crack openings were more important at the joint panel in specimen SC1. One can conclude that the steel plates can replace the shear resisting stirrups in a joint panel. The order of the yielding at the location of strain gauges D2 and D3 indicates that the use of the embedded steel allowed for the force from the beam to the column to spread. The yielding at D3's location took place before the one at D2's location proves that the embedded steel profile changed the distribution of the column bending moment, as well as the way the force was transferred from the steel beam to the column. Indeed, the yielding at D2 after the one at D3 means that the force acting from the longitudinal rebar on concrete via the strut-and-tie mechanism decreased in the joint region. Thus, the risk of concrete failure in the joint region was less in specimen SC2 than specimen SC1. Looking at the location of strain gauge T2, the yielding occurred early in specimen SC1 (at 1.52% drift) but very late in specimen SC2 (at 3.92%). This is to say that the force transmission from the embedded profile to the RC column was not the same in the two specimens. In specimen SC1, the point R1 located in the web of the embedded profile was subjected to a significant tensile force (compared to specimen SC2) from the upper flange of the steel beam. As a result, the yielding at R1 took place earlier, which caused the additional tensile force in the stirrups. Furthermore, there were more diagonal cracks around the location of T2 in specimen SC1 than specimen SC2. On the other hand, in specimen SC2, because of the two additional plates, the stiffness was locally reinforced at the joint region; therefore, although the transmitted force of the beam was larger, the embedded steel part in specimen SC2 could spread the force in a distributed way to the surrounding concrete. This explains why the yielding at the locations of T2 and T3 of the specimen SC2 occurred almost at the same applied load. Meanwhile, the yielding at the location of T3 of specimen SC1 took place later, namely at 4.35% drift, because the embedded steel part was not stiff enough to spread the force in a distributed way to the surrounding concrete.
## 3 Experimental Verification
### Experimental Testing  
#### 3.2.1 Post-Tensioning and Installed Strain Gauges  
A typical experimental setup for applying the post-tensioning force is shown in Figure 5. A hydraulic jack of 30 tons was used to generate the tension force of the external steel tendons. The details of the post-tensioning procedure are described by [PERSON] et al. [22]. They are therefore only briefly described here. The post-tensioning procedure consisted of six steps. In the first and second steps, two steel tendons were subsequently pressurized to 10% of the yield stress force of the tendon, which aims to close the gap and eliminate the slack between the segments. Then, two tendons were alternately post-tensioned to 50% and 100% of the designed post-tensioning force. The designed effective prestress of the steel tendons was equal to 60% of the tendons' nominal tensile strength, which is 110 kN. Then, two load cells were attached to the steel tendons to record the stress of the steel tendons during the post-tensioning process. Next, four Kyowa PL-60-11 strain gauges were attached to the web of the second and third segments of the beam in the experimental tests, as shown in Figure 6, to measure the strain responses during the post-tensioning process. These strain measurements are used to determine the neutral axis location and quantify the prestress force in the precast prestressed segmental beam in the study.  
The segment of the beam is denoted by the symbol S, for example, S2 and S3 denote the second and third segments of the tested beam, respectively. Figure 6 shows that the top strain gauges attached to S2 and S3 are denoted as II_t and III_t, respectively. The strain gauges attached at the bottom location are denoted as II_b and III_b, respectively. II_t and II_b were attached near the mid-span of S2, while III_t and III_b were bonded near the joint between S2 and S3. The strain values measured during the post-tensioning process are used to quantify the prestressing force in the tendons, which are compared with the values measured by the load cells installed at the end of the tendons.  
Figure 5: Typical setup for applying the post-tensioning force.  
Figure 6: Locations of the strain gauges attached on the segmental beams.
## 3 The Motion State Estimation Method
### Moving Object Segmentation
#### 3.3.2 CNN Segmentation Model  
When the 5D range image is generated and used for moving object segmentation, a CNN segmentation model is used. In the CNN segmentation model, the meta-kernel convolution technique [16] is used to effectively incorporate the 3D feature information into the model after the context extraction module, and the backbone network uses the classical range image semantic segmentation model SalsaNext [15] to estimate the motion object points and generate the binarized motion segmentation labels. Finally the range image segmentation results are projected into 3D space; the segmentation results are refined using kNN as [14; 16].  
(1)Context Extraction Module  
The context extraction module utilizes the residual structure as shown in Figure 4. The 3D information of the original point is fused using the meta-kernel convolution after the residual structure. The meta-kernel convolution is demonstrated in Figure 5, and the operation details can be found in [16]. Its output is further fed to the SalsaNext model for motion segmentation.  
2. Point Reassignment  
Although the motion segmentation based on the range image has a faster inference speed, the blurring of the range image boundaries cause the dynamic points and static point to be prone to segmentation errors in the junction area. As can be observed from Figure 6, some of the locations with better segmentation results on the 2D range image are still all estimated as motion points in the 3D point cloud despite the large differences in the locations of the foreground and background points.  
Therefore, to solve this problem, a \(k\)-neighborhood (KNN) search is used to reassign ambiguous points that are at the boundaries [4; 14]. For each estimated moving point, the \(k\) closest points are selected for the consistent voting, and the label with the most number of \(k\) points is selected as the label of that point after reassignment. The number of neighboring points \(k\) is empirically set to be the same as [4; 14].  
Figure 4: The context module.  
Figure 5: The meta-kernel convolution [16].  
Figure 6: The comparison of the motion segmentation result on the range image and 3D coordinate.
# A Feasibility Study for a Hand-Held Acoustic Imaging Camera
## 7 Miniaturized Array Optimization: Results and Discussion
### Thirty-Two-Microphones, [2; 6.4] kHz, Array 21 \(\times\) 21 cm\({}^{2}\)  
Increasing the number of iterations in the optimization algorithm alone does not fully suppress grating lobes at higher frequencies, even in an optimized field of view. We hypothesize that enlarging the dimensions of the main acoustic lobe provides additional degrees of freedom for the algorithm to minimize secondary grating lobes. A wider main lobe increases the target spatial region for overall sidelobe suppression. However, expanding the filter tap length can sometimes yield unstable and non-convergent solutions. We experimentally evaluate these trade-offs between main lobe width, number of taps (FIR length), and iterations. The following experiments systematically vary lobe parameters and tap length to assess their impact on grating lobe artefacts and algorithm stability. We optimize over an expanded parameter space to determine configurations that maximize grating lobe mitigation while maintaining convergence and solution integrity. This exploration provides practical insights into the interaction between beam pattern specifications, filter design constraints, and robust algorithm convergence for optimal array performance. We report the better results of the following simulation (Figures 15 and 16) in comparison to the previous case:  
* \(L\) = 21 cm;
* \(N^{\text{o}}\) of microphones = 32 mic;
* \(K\) = 31 (FIR length);
* \(u\in[-1.5;1.5]\) ; \(v\in[-1.41;1.41]\);
* \(N^{\text{o}}\) of iterations \(\approx 10^{5}\);
* \(uMainLobe_{low}=-0.2\) ; \(uMainLobe_{high}=0.2\);
* \(vMainLobe_{low}=-0.2\) ; \(vMainLobe_{high}=0.2\);
* Bandwidth = [2000, 6400] Hz.  
Figure 14: Simulation array 25 \(\times\) 25 cm\({}^{2}\). Beam patterns within the optimization band and the aperiodic microphone localization exhibit low sidelobes and main lobe focusing (Figure 4). (**a**) 2 kHz, (**b**) \(\approx\)4 kHz, (**c**) \(\approx\)6 kHz.  
The 0.21 m array in the bandwidth [2000, 6400] Hz achieves better directivity than the 0.21 m array in the full range [500, 6400] Hz below 4 kHz, confirming substantial miniaturization optimized in a sub-range of frequencies is viable. A sub-range of \(u\) and \(v\) now optimizes the beampatterns avoiding grating lobes at higher frequencies (Figures 17 and 18), even if this action reduces of course the FOV of _Dual Cam_ 2.0.  
Figure 16: (**a**) Directivity and (**b**) white noise gain metrics confirm reasonable performance across the [2,6.4] kHz band even from the reduced 0.21 m array.  
Figure 17: Simulation results for [2, 6.4] kHz optimization of a 32\(-\)element 0.21 m acoustic array. Beampattern comparison: BP (**left**) vs EBPP (**right**) at 2 kHz.  
Figure 15: Simulation results for [2, 6.4] kHz optimization of a 32\(-\)element 0.21 m acoustic array: (**a**) cost function convergence over \(\approx 10^{5}\) iterations, (**b**) optimized 32\(-\)microphone 0.21 m array layout.