esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Quality of Service  
Quality of Service (QoS) refers to \"_the collective effect of service performance which determine the degree of satisfaction of a user of the service_\" [30]. More technically, QoS can be defined as the \"_well-defined and controllable behavior of a system with respect to quantitative parameters_\" [31]. Interesting parameters to monitor for computer networks can be delay, jitter, throughput, fairness, and packet losses for example. However, the requirements of industrial automation networks are different from usual computer networks. The harsh industrial operation environment may degrade the wireless communication performance due to path loss and shadowing, multi-path propagation, and interference [12]. Moreover, the traffic is mainly composed of short packets containing sensor measurements or actuator commands that need to be delivered timely, instead of large multimedia streams or interactive traffic that prevails in computer networks. We next identify the specific QoS requirements and evaluate the aforementioned selected specifications with respect to their compliance to these requirements. Open issues are identified and discussed based on the results of this evaluation.
# Derivation and Verification of Laminated Composite T-Beam Theory
## 5 Conclusions  
The finite element analysis verified the assumptions made in deriving the theoretical model, and the applicable range of the proposed model could be determined. The following conclusions were drawn based on the results obtained from the theoretical analysis and finite element analysis:  
1. The composite laminated T-beams were analyzed using 1D beam theory, and the centroid positions of the symmetric and non-symmetric T-beams were deduced. The 1D theoretical analysis was based on the Kirchoff-Love deformation hypothesis. Therefore, only the axial strain was considered. Consequently, the errors for deflection and stress were higher than 15%.
2. The deflection and stress of the composite laminated T-beam were analyzed using 2D beam theory. The results suggested improvement in the accuracy of the 2D analytical solution when the aspect ratio was higher than 12. However, the accuracy was less than 2%. Therefore, the derived formula is considered applicable to slender beams.
3. The effective stiffness of the laminated T-beam was 14.67% higher than that of the I-beam when the same material and lamination sequence was used in both structures. The higher effective stiffness is related to the higher bending moment owing to the location of the centroid of the T-beam closer to the flange. The addition of a 0 lamination to the web for a laminated T-beam increased the stiffness and reduced the flange compressive stress by 35.64%.
4. The analytical solution for the laminated composite T-beam derived in this study is a fast and accurate theoretical method to calculate the deflection and bending stress of the composite beam.  
This study investigated the accuracy and applicability of the derived bending stress, deflection, and effective stiffness of laminated composite T-beams. Among various engineering components, especially structures with anisotropic and geometric asymmetry, several issues have been discussed in the literature. Further, the bonding ability and life of each laminate are worth discussing in an environment of high temperature, thermal cycling, and torsion. This could be an important research area to explore in the future.  
Conceptualization: Y.-C.C.; Data curation: Y.-T.L.; Methodology: T.-P.H. and A.-D.L.; Software: Y.-T.L.; Validation: T.-P.H. and Y.-C.C.; Experimental work: H.-A.T. and A.-D.L.; Writing--review and editing: T.-P.H. and H.A.; Supervision: H.A. and A.-D.L.; Funding acquisition: H.-A.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Metal Industries Research and Development Centre (MIRDC), grant number 11B5218335.  
The authors declare no conflict of interest.
# Experimental Study on Vibration Control of
## 1 Introduction
wide availability in different shapes and sizes [38; 39], and low cost, piezoceramic transducers are commonly used to sense structural vibrations [40; 41]. Compared to passive devices, active and semi-active controls are more effective at suppressing vibrations; however, the control devices require power sources, and components are normally more complex and more likely to fail [42]. In general, passive damping devices have the advantages of simple structure, easy maintenance, and high reliability, and have been widely implemented in mechanical, civil, and aerospace structures [43; 44]. Commonly used passive vibration dampers include TMDs (tuned mass damper) [45; 46], viscoelastic dampers, impact dampers [47; 48], particle dampers [49; 50], oil dampers, friction dampers [51; 52], shape memory alloy (SMA) dampers [53; 54; 55], eddy current dampers [56; 57; 58], among others [59; 60; 61; 62]. In addition, bounding tuned mass damper (PTMD) is a new type of passive control damper [63] that has received extensive attention in recent years [64; 65; 66; 67]. It combines the advantages of the effective vibration control of a tuned mass damper (TMD), the quick energy dissipation of an impact/pounding damper, and the high energy dissipation of a viscous damper. Theoretical analysis and experimental results have demonstrated the effectiveness and efficiency of PTMD on vibration control [69; 70; 71], including traffic signal poles [72; 73], subsea jumpers [74], and power transmission towers [75], among others. However, it has never been applied to the vibration control of a suspended piping system. Therefore, in this paper, bounding tuned mass dampers (PTMDs) were designed to mitigate the vibration response of the suspended piping system. In the PTMD experiments, viscoelastic (VE) material was used for energy dissipation. Both TMD and PTMD were tested in free vibration and forced vibration respectively, to compare their performance regarding vibration control. The results showed that the PTMD system had good vibration control performance in both cases. The significance and originality of this work is the application of the emerging PTMD technology to suspended piping systems in an effort to increase the resilience of buildings that are prone to seismic activities and also improve the safety of the occupants therein.
## 4 Findings
### Smart Environments
#### 4.3.5 Smart Agriculture  
A blockchain-based application is implemented to store malicious information to prevent future attacks for a smart-farm security monitoring framework [130]. The application consists of three layers: the smart farm layer, the cloud layer, and the blockchain layer. The smart farm layer consists of different sensors to collect data, and the cloud layer is responsible for processing sensors' events and retrieving the required information. The blockchain layer comprises the Ethereum blockchain with smart contracts to check environmental conditions and store farming data. Ethereum nodes perform the mining process; an entity or individual controls these nodes. The cloud layer consists of an AWS cloud, Anomaly Lambda Function, and Infura Ethereum API that runs smart contracts and connects the middle layer with blockchain layer nodes. This framework is implemented to work with only one consensus algorithm: Ethereum proof-of-work (POW). Based on performance evaluations, this prototype resulted in nominal network latency.
# Friction Factor Correlation for Regenerator Working
## 3 Experimental Setup
### Data Collection and Reduction
The volume of the compliance, \(V_{c}\), and its cross-sectional area, \(S\), are measured manually, while pressure, \(P_{4}\), is measured by the pressure transducer located at the compliance. \(p_{m}\) is the mean pressure set for the experiment. Four differential pressure transducers (PCB#112A21), connected to a signal conditioner (PCB Piezotronics 480B21, Depew, NY, USA), are used to measure the amplitude of the oscillating pressure at locations P1, P2, P3, P4 and P5 (refer to Figure 2). Data is collected using a PC-based data acquisition board. The phase angle is monitored for each test condition to ensure that a near travelling-wave phasing is achieved in every test. The experimental results are used to calculate the friction factor using Equation (1). The velocity used for calculating the friction correlation of Equation (1) is an averaged value calculated as:  
\[u_{m}=\frac{u_{1}+u_{5}}{2\phi}, \tag{17}\]  
where \(u_{1}\) and \(u_{5}\) are the amplitudes of velocities at locations LD and P5, respectively. Strictly speaking, velocity \(u_{2}\) should be included in Equation (17), but this cannot be measured directly. Fortunately, there is only a short distance of empty resonator between locations 1 and 2, while the wave is travelling type. According to early design estimates \(u_{2}\approx u_{1}\) to within 1%, while \(u_{1}\) is easy to measure directly with the laser displacement sensor. The velocity, \(u_{m}\), represents the actual velocity amplitude in the regenerator. All the regenerator samples are tested at a frequency of 30 Hz and a mean pressure of 25 bar.
# A Full-Spectrum Registration Method for Zhuhai-1 Satellite Hyperspectral Imagery
## 3 Methods
### Absolute Registration
#### 3.2.2 Affine Transformation Relationship Transfer Strategy
The coordinates of matching points in Band 1 and Band 2 can be calculated for Band N, which allows for the registration of Band N.  
The constructed Delaunay triangulation will differ due to the different positions and number of feature points that are extracted for each band. The position of the calculated matching points in Band 1 can be outside the Delaunay triangulation in Band M (e.g., the red dotted circles in Figure 8). In this case, the center of gravity of each triangle was calculated to find the triangle closest to the matching point. This is represented as the red dotted triangle in Figure 8. The affine transformation relationship that is established by the closest triangle is used for resampling pixels. To reduce registration errors which might be caused by mixed pixels, a bilinear resampling operation was performed when applying the affine transformation.
# Recent Advances in Additive Manufacturing of Fibre-Reinforced Materials: A Comprehensive Review
## 3 Evaluation
### Quasi-Static Mechanical Testing  
Quasi-static tensile and bending tests are the most commonly used methods in the reviewed literature to describe the mechanical properties of a part. However, these tests alone cannot fully predict the product's behaviour for reinforced parts with a fibre core, such as carbon fibres. Most literature considers the orientation of the reinforcing material relative to the object's axis, as fibres primarily handle tensile loads. Therefore, different angles of the reinforcing material will be considered in the presentation of the results. The analysis must consider the volumetric ratio (Vr) between the reinforcing and matrix materials, as a higher reinforcing volume generally increases the stiffness of the object [43; 44].
# A Spectral Clustering Algorithm for Non-Linear Graph Embedding in Information Networks
## 4 Experiment
### Experimental Data  
To evaluate model performance, in this study we tested the model on various real-world datasets obtained from the UCI Machine Learning Repository and social information network datasets. A brief description of the datasets used can be found below as follows:  
* Wine: The Wine dataset, sourced from the UCI Machine Learning Repository ([PERSON] and [PERSON], 2007), comprises 178 instances and 13 features. These data are the results of chemical analyses of mature wine grapes. All samples are labeled with three categories of wine. In our study, we established a similarity graph.
* Pendigit: This dataset is a handwritten digit dataset consisting of 3498 samples and 16 features created by 44 writers.
* Minist: This dataset consists of handwritten digits and is available on [PERSON]'s webpage. Each image is represented as a 784-dimensional vector and there are 4000 images in total.
* Blogdata: This dataset is a social relationship information network provided by blog authors. This information network contains 10,321 nodes, 333,983 edges, and 39 different cluster labels. These labels represent the inferred and provided blog interests of the blog authors.
* 20 newsgroups: This dataset is a news social network containing 18,846 newsgroup documents and 26,214 features divided into 20 different groups.
* Seimic: This dataset is used to classify different types of mobile vehicles, selecting 98,000 samples, 50 features, and 3 classifications.
* Covtype: This dataset is a forest cover-type dataset containing 581,012 data, 54 features, and 2 classes. This dataset contains tree observations from four areas of the Roosevelt National Forest in Colorado, USA. Detailed information on all of these semantic networks is summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Node Count** & **Feature Count** & **Cluster Count** \\ \hline Wine & 178 & 13 & 3 \\ Pendigit & 3498 & 16 & 10 \\ Mnist & 4000 & 784 & 10 \\ Blogdata & 10,312 & 1 & 39 \\
20 newsgroups & 18,846 & 26,214 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(SEG\_SC experimental data information.
# A Review of Adsorbents for Heavy Metal Decontamination: Growing Approach to Wastewater Treatment
## 6 Factors Affecting the Adsorption Process  
### Effect of pH on Adsorption  
The pH of the mixture plays a significant role in sorption. It affects the metal ion's adsorption on the adsorbent. The pH effect of the solution may be demonstrated by altering the bio sorbents surface loads and the ionising degree in an aqueous solution of the metal ions. Therefore, it is essential to evaluate the effect of pH on adsorption. Furthermore, the presence of a significant number of H\({}^{+}\) and H\({}_{3}\)O\({}^{+}\) ions in water competes with metal ions for adsorption. At a moderate pH, the rate of adsorption rises because electrostatic repulsions are reduced due to deprotonation of the active sites [94]. Several other investigations have found that adsorption of heavy metal ions is better at moderate pH levels than at lower pH levels. For example, adsorption of Pb (II) and Cu (II) on the surface of chitosan/TiO\({}_{2}\) nano-fibers was maximum at pH 6.0 and lowest at pH 2.0 to 4.0 [95]. A comparable study found that increasing the pH from 4.0 to 6.0 increased the percentage of Cd (II) adsorption, which then remained constant (97%) at pH 9.0. After that, lowering the pH to 11.0 lowered the adsorption proportion to 80% [96]. According to the preceding explanation, a moderate pH is suitable for heavy metal ion removal because the sorbent surface is deprotonated at this pH, resulting in an increase in negatively charged sites. This results in higher electrostatic attractions between the adsorbent surface and metal cations, which increases adsorption capacity. However, at lower pH, there is an increase in positively charged adsorption sites, which leads to an increase in repulsive interactions between the adsorbent surface and positively charged metal ions, lowering metal ion adsorption [97].  
The sorption of Pb\({}^{2+}\) and Hg\({}^{+}\) ions by the modified polyglycidyl methacrylates were investigated under a non-competitive environment at various pH values and times [98]. It was found that Hg\({}^{2+}\) and Pb\({}^{2+}\) ions extraction by the resins increases as the solution pH values are increased, particularly in the pH range of 1-5.7 for Hg and for lead 1-4.8 higher pH promotes electrostatic attraction between positively charged metal ions and negatively charged biosorbent surfaces, increasing adsorption efficiency. This slow adsorption may be explained by the presence of electrostatic repulsion between these positively charged species on the adsorption surface and metal ions [99].
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 2 Related Works
### Hop-by-Hop Congestion Control  
Using the hop-by-hop congestion control scheme, each router detects congestion by monitoring its outgoing queues and then shapes the forwarding rate of the Interest. [PERSON] et al. proposed the Hop-By-Hop Interest Shaping mechanism (HoBHIS) [29], which predicts congestion and decreases the shaping rate before the receiver can detect congestion via timer expiration, calculating the shaping rate based on Data queuing occupancy. [PERSON] et al. proposed a method called Hop-By-Hop congestion control (HBH) [30], where each intermediate router monitors the occupancy of outgoing queues to identify congestion. If the queue size reaches its minimum or maximum specified threshold, an explicit notification is sent to downstream routers and consumers, specifying the increase or decrease rate for sending Interests. Moreover, they proposed another mechanism called Hop-By-Hop Interest Rate Notification and Adjustment (IRNA) [31; 32]. IRNA requires each router to monitor the occupancy of the outgoing Interest queue in order to detect and issue congestion signals in advance.  
This type of method alleviates congestion by adjusting the forwarding rate of Interests on a hop-by-hop basis. However, these methods are essentially local optimal solutions. When the network hosts complex applications, the above solutions may fail to ensure global optimization of the network performance.
## 7 Evaluation and Results
### Precision  
For evaluation in more than one criteria, we also used the precision metric, which represents the degree of how many particular accuracy values are reached [3; 8]. Five localization error values are considered as follow, \(<\)1.5 m, \(<\)3 m, \(<\)4.5 m, \(<\)6 m and \(<\)7.5 m. The ratio of how many values were attained is calculated with each of the previous localization error values in the 50 simulation runs. Figure 16 shows the precision evaluation results when \(d_{max}=70\) and \(\sigma=3\).  
In Figure 16a and when WCL is used, the number of the localized nodes with localization error within 1.5 m is very high with over 0.8 precision in FLPPL. Again, the reason behind it is the consideration of employing both the RSSI values and the distance metrics in the decision movement which therefore leads to a better localization. In fact, all localization error values were kept in control under 3 m in FLPPL. NLA_MB also provided a high precision when compared to the other static models with more than 0.6 within less than 1.5 m and more than 0.9 precision within less than 3 m of errors. The static models of SCAN, LMAT and Z-Curve acted very weakly only few values reached within 6 m of the real locations. This precision increased slightly with 0.46 in Z-Curve, 0.7 in SCAN and 0.74 in LMAT when a localization error of 7.5 m is considered which means the rest of the localized nodes in each model were achieved in more than 7.5 m or error rate.  
When WCWCL is applied, the precision was improved as shown in Figure 16b. This is because WCWCL modified the weight calculation and gave more effect of the distance and nearby anchors. FLPPL obtained about 0.9 of precision where localization error was kept less than 1.5 m. All FLPPL  
Figure 16: Precision of all mobility models versus the localization error in (**a**) WCL, and (**b**) WCWCL, (\(d_{max}=70,\sigma=3\)).  
localization errors were under 3 m of error. Even in NLA_MB, the precision increased from 0.66 when WCL was used to 0.7 in WCWCL when less than 1.5 m was considered. The impact of WCWCL is clear on the static models precision as well. The precision values improved gradually from 4.5 m to less than 7.5 m.
# Multiple Mobile Sinks for Quality of Service Improvement in Large-Scale Wireless Sensor Networks
## 4 Results and Discussion
### Throughput Evaluation  
When deploying WSN applications with service quality constraints, the amount of collected data by the sink becomes important to consider.  
To address this, in the second phase of the assessment, we analyzed the packets received by the sink (throughput) of the network with multiple mobile and static sinks.  
In this simulation scenario, we compared the amount of data collected (Throughput) by varying the number of mobile and static sinks monitoring the network to study the impact of multiple mobile sinks on the throughput in the LS-WSNs.  
The simulation results illustrated in Figure 4a show that the use of a single mobile sink, whatever the model, increases the average flow rate compared to a fixed sink by \(+8.6\%\) for RWP up to \(+14\%\) for RD, and the best result obtained with the RD model and the EA-CRP routing protocol with a throughput gain of \(+15.5\%\).  
The simulation results illustrated in the other figures show that the use of multiple mobile sinks increases the average flow compared to that of a single static sink, as illustrated in Figure 4a respectively:  
* Using two mobile sinks (Figure 4b) from \(+25\%\) for RWP up to \(+31\%\) for RD, the best result is obtained with the RD model and the EA-CRP routing protocol \(+38\%\).
* Using four mobile sinks (Figure 4c) from \(+49\%\) for RWP to \(+57\%\) for RD, the best result is obtained with the RD model and the EA-CRP routing protocol \(+73\%\).
* By using eight mobile sinks (Figure 4d) from \(60.5\%\) for RWP to \(68.5\%\) for RD, the best result is obtained with the RD model and the EA-CRP routing protocol \(+90.2\%\).  
It can be deduced from the previous results that the best mobility model is RD, which offers almost double the throughput compared to a single fixed, using eight mobile sinks.  
More precisely, the RD moves in the network by realistically approaching the clusters of SN to reduce the distance between the sink and the CH, consequently reducing the phenomena of collisions and packet retransmission and increasing the throughput.  
On the other hand, by comparing the flow scenarios, we note that the use of four mobile sinks gives results very close to that of eight mobile sinks. So, we can conclude that in terms of profitability, the use of four mobile sinks establishes a good compromise between the flow rate and the investment cost, and we can obtain a better throughput of 73% more with only four mobile sinks by using RD mobility model and EA-CRP routing protocol.
# End-to-End Mispronunciation Detection and Diagnosis Using Transfer Learning
###### Abstract  
As an indispensable module of computer-aided pronunciation training (CAPT) systems, mispronunciation detection and diagnosis (MDD) techniques have attracted a lot of attention from academia and industry over the past decade. To train robust MDD models, this technique requires massive human-annotated speech recordings which are usually expensive and even hard to acquire. In this study, we propose to use transfer learning to tackle the problem of data scarcity from two aspects. First, from audio modality, we explore the use of the pretrained model wav2 vec2.0 for MDD tasks by learning robust general acoustic representation. Second, from text modality, we explore transferring prior texts into MDD by learning associations between acoustic and textual modalities. We propose textual modulation gates that assign more importance to the relevant text information while suppressing irrelevant text information. Moreover, given the transcriptions, we propose an extra contrastive loss to reduce the difference of learning objectives between the phoneme recognition and MDD tasks. Conducting experiments on the L2-Arctic dataset showed that our wav2 vec2.0 based models outperformed the conventional methods. The proposed textual modulation gate and contrastive loss further improved the F1-score by more than 2.88% and our best model achieved an F1-score of 61.75%.  
mispronunciation detection and diagnosis (MDD); computer-aided pronunciation training (CAPT); transfer learning; pretrained model; text modulation gate +
Footnote †: journal: Applied Sciences