esa-sceva/satcom-chunk-collection huggingface dataset:
# Addressing Text-Dependent Speaker Verification Using Singing Speech
## 3 Speaker Identity Discrimination in Different Feature Space  
The theoretical principle of speaker verification is that each person's voice has its unique characteristics. The unique property is determined primarily by two factors, the size of the acoustic cavity and the manner in which the vocal organ is manipulated [23]. In speaker verification, these properties are included in the acoustic feature space. In this section, we focus on exploring the speaker identity discrimination in two feature spaces, the pitch and the MFCC feature spaces. In these spaces, we examined the feature discrimination between normal speaking (reading) and singing speech of the same speaker, and the speaker discrimination under the same speech style between different speakers.
## 3 Results and Discussion
### Thermal Management Experiment  
The experiments consist of a heating run followed by a cooling run. The heating run lasted until \(T_{b}\) reached the maximum temperature in all cases with and without PCM referring to steady state. The maximum temperature happened to be different at each power input level as it reached steady state. The heating run is followed by a cooling run until \(T_{b}\) stabilizes below 25 \({}^{\circ}\)C reaching steady state with ambient indicating that PCM had completely solidified in case of HS with PCM.
## 5 Concluding Remarks  
From current analysis, we conclude that:  
* The axial velocity of third-grade fluid in the microchannel is enhanced due to an increase in electroosmotic parameter. * For higher values of Schmidt or Soret number, concentration profile decreases. * The magnitude of pressure rise decreases in the pumping region with the increase of electroosmotic parameter. * Pressure gradient is more for EOF of third order fluid in comparison to peristaltically flowing fluid. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**(a) \(\epsilon\)=0.385,\(\Theta\)=1.0,\(m_{e}\)=2,\(U_{hs}\)=\(-\)1.0. **} \\ \hline \(x\) & \(\Gamma\) & & \\ \hline  & 0.0 & 0.2 & 0.4 \\ \hline
0.1 & \(-\)2.4917 & \(-\)3.0742 & \(-\)0.8223 \\
0.2 & \(-\)2.3669 & \(-\)3.8790 & \(-\)2.2711 \\
0.3 & \(-\)2.6497 & \(-\)4.5689 & \(-\)4.3309 \\ \hline \multicolumn{4}{c}{**(b) \(\epsilon\)=0.385,\(\Theta\)=1.0,\(m_{e}\)=2,\(\Gamma\)=0.1. **} \\ \hline \(x\) & \(U_{hs}\) & & \\ \hline  & \(-\)1.0 & 0.0 & 1.0 \\ \hline
0.1 & \(-\)3.6869 & \(-\)1.1370 & 0.5727 \\
**0.2** & \(-\)3.7974 & \(-\)1.2903 & 0.4311 \\
0.3 & \(-\)3.8395 & \(-\)1.4188 & 0.3188 \\ \hline \multicolumn{4}{c}{**(c) \(\epsilon\)=0.385,\(\Theta\)=1.0,\(\Gamma\)=0.1,\(U_{hs}\)=\(-\)1.0**} \\ \hline \(x\) & \(m_{e}\) & & \\ \hline  & 2 & 4 & 6 \\ \hline
0.1 & \(-\)3.6869 & \(-\)10.778 & \(-\)6.3530 \\
0.2 & \(-\)3.7974 & \(-\)8.7149 & \(-\)4.7301 \\
0. * Axial distribution of shear stress enhances for increasing values of maximum electroosmotic velocity. * Volume of trapped bolus is strongly dependent upon the electroosmotic parameter whereas it is suppressed for Deborah number and maximum electroosmotic velocity. * Temperature distribution in microchannel is crucial for electroosmotic parameter, Deborah number and Brinkman number. Electro-osmotic exchange of energy and mass has a role in reservoir engineering. The outcomes of the present model may be used by engineers in chemical industry and in micro-fabrication technologies. In the future, this model accompanying porosity will help in understanding the hydrodynamics of rheological fluids in a typical channel employed in a LOC system. This course requires promotion, being of significance in factual life applications. **Author Contributions:** Formal analysis, S.N. ; Funding acquisition, A.H.; Investigation, S.W. ; Methodology, S.W. ; Resources, S.N. ; Supervision, S.N. ; Writing--original draft, S.W. ; Writing--review & editing, A.H.  
**Funding:** This research received no external funding. The APC was funded by Ton Duc Thang University, Ho Chi Minh City, Vietnam. However, no grant number is available from source. **Acknowledgments:** The third author would like to thank Ton Duc Thang University, Ho Chi Minh City, Vietnam for the financial support. **Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Conclusions  
Tunable DFB lasers are a key feature in a wide variety of different applications ranging from process control to medical diagnostics. Moreover, many of the concepts shown here have left the prototype stage behind and are already implemented in various high impact applications. Nevertheless, there are still a lot of areas where product development has just started, promising many interesting new applications to appear in the next few years. In particular, widely tunable monolithic DFB lasers enabling reduced costs and smaller measurement systems as well as the commercialization of DFB Interband Cascade Lasers are subjects of current development efforts, which will be published in the near future.
# Resilience in the Internet of Medical Things: A Review and Case Study
## 6 A Case Study: Remote Patient Monitoring of a Heart Patient in the IoMT Networks
### Proposed Methodology  
Based on the above-mentioned scenarios of a general and a single point of failure, there is a requirement for a network with distributed functionalities and resilience [17; 100].  
The proposed methodology should consider a number of possible faults and work both proactively and reactively, as per the situation demands. Figure 6 represents an  
Figure 5: A single-point-of-failure issue in an IoMT network.  
expected paradigm of IoMT infrastructure that should be in operation during a failure with the distributed functionalities. It represents a large IoMT infrastructure that interconnects a large group of patients from different regions with an IoMT-equipped health center at a specific location. These multiple group of patients are connected through different ISPs and all these ISPs are connected to the central public networks. These patients are monitored in real-time by medical professionals at the health center. The critical health status of the patient is transmitted through this existing network.  
For controlling and monitoring the data traffic, a controller (C) with all existing functionalities is connected at its designated location. The entire remote patient monitoring is dependent on this existing network infrastructure. This controller is useful for continuous monitoring, security, traffic, and resource management in normal situations. However, a link failure with this controller would bring about a single point of failure.  
This expected framework of IoMT networks works well in this situation and distributes the basic functionalities of M, C, D, and R over different nodes as a backup strategy at the time of controller failure, as shown in the figure. Moreover, resilience is provided in this paradigm during any link failure as a backup with sufficient redundancy of each functionality is available on different nodes. This expected framework of IoMT networks can be realized by integrating the three technologies, machine Learning, software-defined networking, and microservices, into a single framework.  
In the following subsections, we elaborate on how different combinations of these three technologies can fulfill all the requirements of resilience in the expected framework of IoMT networks.
## 4 NFV and SFC Frameworks Taxonomy
### Resource Allocation and Service Orchestration Frameworks
#### 4.1.1. Eden Framework  
The Eden framework was proposed by [PERSON] et al. [70] as a framework suitable for virtual network function provisioning on end-user devices. Eden leverages the concept of data-plane programmability by implementing NFs on end-user devices written in the F# language, which is a high-level programming language. Eden comprises three functional components that work together to handle packet processing tasks: the centralised logical controller, which is used for service orchestration and for providing a global view of the infrastructure. The second component is the _Stage_, which is simply a name for kernel modules, libraries, or applications on hosts that are used for the classification of packets before being sent to the Enclave (the third component in Eden).  
The Enclave is responsible for handling the functionalities of a programmable data plane, which can be implemented on NICs, FPGAs, hypervisors, or operating systems. Eden maintains match-action rules in the Enclave, with traffic association carried out on the host device by the stage component of the framework. The Enclave is also responsible for interpreting the bytecode, which is obtained from compiling action functions. Eden still leaves the open question about where to best deploy the network functions, that is, either on the user OS or on the programmable NIC. In Section 5, we shed some light on this open challenge by arguing for a hybrid implementation framework for fast packet processing and efficient service chain creation in next-generation networks.
## 4 Design and Development
### Architecture Overview
#### 4.1.2 Layered System Breakdown  
1. Application Layer: This layer consists of user-facing components and the ABAC Policy, which handles attribute-based authorization, access simulations, and client requests.
2. ABAC Policy Engine: Core functions include access policy parsing, rule matching, and simulation orchestration. It bridges client interactions with blockchain smart contracts, a design pattern supported by recent work on dynamic policy enforcement in blockchain-based systems [13].
3. Blockchain Network: Powered by Hyperledger Fabric for decentralized identity, chaincode execution, and immutable logging.
4. Smart Contracts: Encapsulate logic for user registration, access policy creation, and access evaluation.
5. Data Layer: Comprises users, resources, and structured datasets.
# Internet of Drones: Routing Algorithms, Techniques and Challenges
## 4 Routing Algorithms
### Bee Optimisation Algorithm  
Another algorithm used for IoD routing is the bee optimisation algorithm. The bee algorithm is based on how bees employ foraging in their search for food. The algorithm uses the best solution to an optimisation problem. The best solution is the path the UAV has to take. Bee optimisation algorithm has three contributors:  
* Worker
* Supervisor
* Scout  
The possible routes for data transfer are considered as \"food\" (analogous to \"chromosomes\" in GA). The workers are equal in number to the supervisors, and the number of food sources is set equal to the number of workers. A new candidate that is a possible route solution serves as food. Selection is based on the previously occurring neighbours of food sources. The best solutions are compared with the previous sources and a quality check (analogous to fitness function in GA) is made. The worker bees return to the hive to share the food source information with the supervisor bees, and the supervisor bees select food sources according to their fitness. Food is termed as possible routing solutions. Figure 7 illustrates the step-by-step organisation of the bee optimisation algorithm.  
Figure 6: The genetic optimisation algorithm.  
After a random initialisation, the bees are subjected to a \"waggle dance\", and the \"food\" search is divided into global search and local search. When the neighbourhood shrinks, the bees return or some of them abandon the site. All the information is treated as a population update that is a reduced number of paths. Finally, the quality check determines which paths the bees should take. The UAV would take the path in a similar manner using the bee optimisation algorithm.
# E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol
## 4 Implementation
As this class uses the TCP socket to receive any MQTT packet, the underlying TCP protocol guarantees the confirmation of packet reception. When the _CMMqttEventHandler_ class receives an MQTT or E-MQTT packet, it calls the _processEvent()_ method, which manages subsequent tasks. This class method distinguishes the packet types, prompting the invocation of the other internal processing methods that correspond to each packet type. In the specific case of PUBLISH packets, the processing method evaluates the associated QoS level. Subsequently, based on the QoS level, an appropriate response packet is transmitted. To elucidate, for QoS 0, no response is issued; for QoS 1, a PUBACK packet is issued; and for QoS 2 and 3, a PUBREC packet is transmitted. When receiving PUBREC, PUBREL, and PUBCOMP packets, the _CMMqttEventHandler_ class handles different procedures according to the QoS level, specifically QoS levels 2 and 3. Especially in E-MQTT, when the _CMMqttEventHandler_ of the publisher receives a PUBREC packet that is registered in the synchronization object, it recognizes that E-MQTT is operating in the synchronous mode. The _CMMqttEventHandler_ then wakes up the _CMMqttManager_ class by calling the _notify()_ method of the synchronization object so that the _CMMqttManager_ class can conduct the next task. The _CMMqttInfo_ class fulfills the role of maintaining all pertinent information necessary for the operation of E-MQTT. Acting as a vital intermediary, the _CMMqttEvent_ class serves as a wrapper encompassing an MQTT packet, facilitating its exchange between various CM nodes. As it is an abstract class, _CMMqttEvent_ is inherited by the wrapper classes of 14 MQTT packets such as _CMMqttEventPUBLISH_, _CMMqttEventPUIBREC_, and so on.
# Current Biomedical Applications of 3D Printing and Additive Manufacturing
## 4 Healthcare Applications
### Drug Delivery
#### 4.2.1 Tablets  
Solid-form tablets are manufactured using compression of a powder mixture containing the active pharmaceutical ingredient (API) into molds [136]. This industrial process is efficient for producing large quantities of simple tablets; however, it is not well suited for the creation of tablets with complex structures and variable dosing. AM provides an opportunity in this field by allowing the fabrication of specialized tablets that perform complex and unique functions customizable for different patients. Fused deposition modeling (FDM) is a commonly used 3D printing technique due to its low cost and speed for creating ready-to-use tablets [137; 138]. Other printing techniques outlined in this review are also utilized, however, mainly for research purposes [139; 140]. A study by [PERSON] et al. [137] showcased how 3D printing a tablet with holes >0.6 mm throughout allowed for release mechanics that qualified for an \"immediate release\" designation as outlined by United States Pharmacopeia (USP) as compared with same-dimension solid tablets that did not meet the designation. Their tablets were printed from hydrochlorothiazzide-impregnated thermoplastics and they attributed the improved release characteristics to an increased surface-to-volume ratio achieved using a 3D-printed design [137]. Another study by [PERSON] et al. [141] utilized FDM and a hydroalcoholic gel-based paste to print a three-compartment tablet capable of releasing three different therapeutics (captopril, nifedipine, glipizide). Interestingly, through modification of the paste, they demonstrated that a single tablet was able to release Captopril with zero-order kinetics while releasing the other two drugs with first-order or other kinetics [141]. It is challenging for conventional tablet manufacturing techniques to offer a wide range of tablet dosing; hence, only certain established doses of drugs exist. However, given the vast variability in the physiology of every patient, a standard dose of a drug can have a variable side-effect profile between different patients [142; 143]. Therefore, AM provides an economic solution for producing customizable tablets with functions tailored to each patient. Recent studies have also shown the potential of AM for on-demand tablet printing for use in emergencies and for printing tablets containing perishable APIs [5; 144].
# The Effect of Hot Water Extract of Tilapia on Exercise Capacity in Mice
###### Abstract  
Tilapia (_Orechronnis mossambicus_) has become one of the main aquatic products of Taiwan. The aim of this study was to evaluate the efficacy of a hot water extract of tilapia (HWET) in relieving fatigue and enhancing exercise performance in mice in a swimming endurance test. Male ICR mice were randomly divided into four groups (n = 10 per group) and treated with either a vehicle (control group) or different doses of HWET, which were designated as HWET-L (800 mg/kg/day), HWET-M (1600 mg/kg/day), and HWET-H (4000 mg/kg/day). The results of the swimming endurance test showed that HWET treatment significantly improved exercise-induced fatigue as the swimming time of the mice increased (\(p<0.05\)). One hour after the test, blood samples were collected from each mouse and serum biochemical parameters were measured. The serum levels of lactate, creatine kinase (CK), and blood urea nitrogen (BUN) were lower in mice treated with HWET compared to the control group. Moreover, HWET treatment increased serum glucose levels and glycogen content in the liver. Enhanced glutathione (GSH) content in the liver and muscle was also found in the HWET-M and HWET-H groups. Western blot results showed that the expression of tumor necrosis factor-\(\alpha\) (TNF-\(\alpha\)) in the liver tissue was downregulated by HWET treatment. Taken together, our results demonstrate that HWET supplementation could enhance exercise performance and alleviate fatigue via biochemical profile improvements. This suggests that HWET has the potential for future development into functional foods or nutritional supplements to relieve fatigue.  
Keywords:tilapia (_Orechronnis mossambicus_); anti-fatigue; exercise capacity; glycogen; glutathione +
Footnote †: journal: Applied
###### Abstract  
Maritime accidents remain a significant concern for the shipping industry, despite recent technological developments. In the Republic of Korea, the leading cause of maritime accidents is navigator error, particularly in collisions and groundings; this cause has led to 79% of maritime accidents, according to a recent assessment. The reduction of navigator error is crucial for accident prevention; however, the lack of objective measures to monitor navigator error remains a challenge. The purpose of this study was to develop an objective classification of navigation behaviors in a simulated environment. The statistical model of classification of lookout activity was developed by collecting participants' lookout behavior using a Kinect sensor within a given scenario. This classification model was validated in non-scenario experiments. The results showed that seven standard lookout activities during a lookout routine were accurately classified in both the model development and validation phases. The proposed model classification of lookout activity using an optical sensor is expected to provide a better understanding of how navigators behave to help prevent maritime accidents in practice.  
maritime accidents; lookout behavior classification; optical sensor; machine learning model; simulation environment +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Experiment 1. Feature Selection  
Results of this experiment will yield the features that best describe the chromosomes and the related accuracy for each tested feature set. For a first training round, the set of algorithms \(a_{1}\) and the group of features \(c_{1}\) were used, along with a multi-class architecture, \(q_{1}\), where the chromosomes were classified in classes from 1 to 24. In this experiment, the highest accuracy was 60.80%, obtained by the Random Forest (RF) algorithm. The rest of the tested classification algorithms achieved a classification accuracy under 57%. The experiment was repeated with the normalized version of \(c_{1}\) features (set \(c_{2}\)). Results show that the Multilayer Perceptron (MLP) artificial neural network (ANN) obtained an accuracy of 60.72%. During the first round of this experiment, the accuracy of the RFalgorithm was 0.08% greater than that of the MLP algorithm. In the second round, when using \(c_{2}\) set (normalized data), all the classification algorithms obtained better results, except the RF. In Figure 5, the accuracy for \(c_{1}\) and \(c_{2}\) feature sets are compared.  
This experiment did not yield the optimal feature set to describe the chromosomes, although its results shown that the feature vector must be normalized in order improve the accuracy in 98% of the tested algorithms. In the other hand, though this experiment, the training time for each classifier could be measured. The training time of the 51 classifiers, using 32 features on the selected hardware, took at least 24 h for each classifier, and thus it was proposed to reduce the number of algorithms to be tested, in order to work with the algorithm that obtained the best results in the shortest time.  
In this experiment, accuracy results from the models generated with reduced feature sets are lower (60%), compared to the models where the full set of features is used. This indicates that feature selection and reduction would not improve the obtained results when the full set of features was used.
## 3 Results and Discussion  
In total, 40 independent observations were collected for each method. Data were aggregated and analyzed without considering the order in which the methods were applied in the apiaries because of the limited number of observations. Summary results about the duration of the inspection and the number of detected SHBs are reported in Table 5.  
We also divided the number of detected SHBs according to five class intervals in order to evaluate the trend of SHB population in each hive and the detection efficacy of each method (Figure 2).  
The data obtained did not follow a normal distribution (Shapiro test; 1.779 \(\times\) 10\({}^{-7}\) for the Mobile divider and 8.574 \(\times\) 10\({}^{-5}\) for the Official method). Data reported in Figure 2 show that, regardless of the number of SHB present at the time of inspection, both methods were able to detect the beetles with almost identical frequency without denoting a clear relationship or pattern.  
We also applied the non-parametric Wilcoxon-test to compare the results from both methods, but no statistically significant differences were detected (\(p\)-value 0.1079). Apparently, only small variations concerning the number of detected beetles as well as the time needed to inspect the hives were registered where the mean number of beetles was slightly higher (2.95 vs. 2.05) and the mean inspection time for the mobile divider method was faster by 25 s.  
Further to these considerations, it should be noted that the mobile divider method is applicable throughout the year during the regular visits that beekeepers carry out in their hives. On the contrary, the \"official method\" appears as a \"method dedicated to the search of SHB only\" precluding a thorough examination of the hive directed at evaluating its correct management, i.e., stocks, health status, brood development, presence of pollen, organization of the hive, etc.  
\begin{table}
\begin{tabular}{c c c} \hline
**Apiary 2** & **Day 1** & **Day 2** \\ \hline
**ID Hive** & **OFFICAL METHOD** & **MOBILE DIVIDER** \\ \hline
**14** & **3** & **3** \\ \hline
**15** & **2** & **2** \\ \hline
**16** & **1** & **1** \\ \hline
**17** & **7** & **11** \\ \hline
**18** & **2** & **1** \\ \hline
**19** & **8** & **5** \\ \hline
**20** & **1** & **2** \\ \hline
**Total** & **57** & **40** \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**N. of SHB**} & \multicolumn{2}{c}{**Inspection Time (in minutes)**} \\ \cline{2-5}  & **Mobile Divider** & **Official Method** & **Mobile Divider** & **Official Method** \\ \hline min & 0 & 0 & 00:03:00 & 00:02:50 \\ Median & 2 & 1 & 00:05:12 & 00:06:00 \\ Mean & 2.95 & 2.05 & 00:05:37 & 00:06:02 \\ Max & 20 & 11 & 00:10:21 & 00:09:55 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary results of the number of _A. tunida_ detected and inspection time required.