esa-sceva/satcom-chunk-collection huggingface dataset:
# The Domain Mismatch Problem in the Broadcast Speaker Attribution Task
## 2 The Speaker Attribution Problem
Regarding the first block, the division of the audio is done by means of the Speaker Change Point Detection (SCPD) techniques. Some considered technologies for this subtask are metric-based (Bayesian Information Criterion (BIC) [21], \(\Delta\)BIC [22], etc.) or models such as Hidden Markov Models (HMMs) [23] and Deep Neural Networks (DNNs) [24]. With respect to the clustering stage, multiple alternatives have been proposed, such as Agglomerative Hierarchical Clustering (AHC) [25; 26], mean-shift [27; 28; 29], K-means [30; 31] and Variational Bayes [32], or statistical approaches, such as i-vectors [33] or PLDA [34; 35; 36; 37; 38]. Finally, the identity assignment block is exclusive from speaker attribution and responsible for assigning each estimated cluster to the corresponding enrolled speaker. In order to make this assignment, clustering techniques are usually applied, such as the Ward method based on the Hottleing t-square statistic [39], the symmetrized Kullback-Leibler divergence [5; 8] or the Cross-Likelihood Ratio (CLR) [7]. Other alternatives, i.e., the ones presented in [10], consider the assignment as a speaker recognition task. Alternatively, other contributions to speaker attribution present a totally new approach, integrating clustering and identity assignment blocks and simultaneously performing diarization and speaker attribution. This approximation as a dual task is solved by graph-based techniques [11]. Unfortunately, despite the great amount of developed techniques, the speaker attribution problem is still far from being solved for all domains. Broadcast data can be considered a wide collection of shows and genres with particular characteristics and, therefore, huge variability. This variability is due to the different acoustic environments in the data but also to other reasons related to the speakers (different number of speakers, unbalanced amount of speech among speakers, etc.). Thus, a general system may suffer from degradation to each particular scenario, and the same large amount of shows and genres discourages having multiple particular systems. Besides, in the broadcast domain, it is usually required to deal with unseen subdomains (e.g., a new show). While certain works have mitigated the domain mismatch between training and evaluation scenarios [37], in speaker attribution, we must also bear in mind the potential extra domains in the enrollment audios. This case is very common in real life, especially concerning public figures whose data can be collected in multiple conditions. Finally, other important factors for the speaker attribution performance are the benefits and limitations of open-set and closed-set scenarios. The former scenario presents the unconstrained version of the speaker attribution task, whereas the latter condition simplifies the attribution problem by not considering the unknown identity, potentially boosting the performance. This consideration is important in the broadcast domain where some shows potentially fit into the closed-set condition, such as debate programs.
# Flexible Wearable Heart Rate Monitoring System and Low-Power Design: A Review
## 5 Design of the Flexible Wearable Heart Rate Signals Computation Module  
The heart rate signal computation module performs operations such as feature point detection, heart rate abnormality detection, and heart rate variability analysis on the preprocessed heart rate signals.  
In this section, we will explore how the heart rate signal computation module achieves low-power design while ensuring its functionality. In hardware design, low-power and  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Innovation** & **Practicality** &
\begin{tabular}{c} **Power Optimization** \\ **Method** \\ \end{tabular} & **Power Consumption** & **Power Comparison** \\ \hline
[44] & \begin{tabular}{c} Band-pass filter based on tunable \\ pseudo-resistor. \\ \end{tabular} & \
\begin{tabular}{c} Suitable for heart rate \\ signal preprocessing. \\ \end{tabular} & Hardware & 460 \(\mu\)W &
\begin{tabular}{c} Compared with \\ traditional filters, the \\ power consumption is \\ reduced by 30\%. \\ \end{tabular} \\ \hline
[69] & \begin{tabular}{c} Optimizing the step \\ size and filter order to \\ enhance denoising \\ performance. \\ \end{tabular} & \
\begin{tabular}{c} Enhance the quality of \\ ECG signals and \\ provides support for \\ heart rate monitoring. \\ \end{tabular} & Algorithm & N/A &
\begin{tabular}{c} Compared with \\ traditional filters, the \\ power consumption is \\ reduced by 30\%. \\ \end{tabular} \\ \hline
[99] & \begin{tabular}{c} A neural \\ network-based heart \\ rate tracking method \\ for motion scenarios. \\ \end{tabular} & \
\begin{tabular}{c} Reduce computational \\ complexity and is \\ suitable for flexible \\ wearable devices. \\ \end{tabular} & Algorithm & N/A &
\begin{tabular}{c} Compared with the \\ traditional filtering \\ algorithms, it can more \\ accurately suppress \\ motion interference. \\ \end{tabular} \\ \hline
[106] & \begin{tabular}{c} A method of using \\ feature normalization \\ preprocessing is \\ proposed to improve \\ recognition accuracy. \\ \end{tabular} & \
\begin{tabular}{c} Suitable for mental \\ health monitoring and \\ human-computer \\ interaction scenarios. \\ \end{tabular} & Hardware–Algorithm & 14.4 mW &
\begin{tabular}{c} Compared with the \\ scenario where no \\ normalization method \\ is used, the \\ preprocessing power \\ consumption can be \\ reduced by 25\%. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._high-computational-efficiency microprocessors are selected, and the circuit layout is optimized to minimize unnecessary energy consumption. In terms of algorithm design, complex neural network algorithms are employed to ensure the accuracy of signal processing while reducing computational load and thus power consumption. Additionally, hardware-algorithm co-design should give full play to the advantages of both, coordinating their operation to achieve low-power performance. The following sections will elaborate on these key design points in detail.
# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 4 Experiments and Results
### Datasets
#### 4.2.3 Kvasir-SEG Dataset  
The Kvasir-SEG dataset [25] consists of 1000 annotated images of colon polyps, featuring a range of resolutions from \(332\times 487\) to \(1920\times 1072\) pixels. The dataset was partitioned such that eightly percent was randomly allocated as the training set while the remaining twenty percent was evenly divided between the validation and test sets. These subsets facilitated the evaluation of the model's performance.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 6 Directions for Future Research
### Emergency Procedures  
As we have discussed, fire risk is a particular concern when flying UAVs over forest environments, owing to the flammability of batteries and fuels used in their propulsion. In line with international aviation legislation, any BVLOS operation will have to demonstrate that the safety of people and property that are being flown over are being taken into consideration and risks are mitigated accordingly. The mitigation of these risks has been largely covered within this manuscript. However, forest environments can be extremely large and inaccessible, and if these mitigations do fail, emergency management is a critical factor that needs to be considered in how to access and manage an incident. Further research in this area would be a great benefit to the unmanned aviation community, and a particularly interesting area would be to examine what practitioners are currently doing for emergency management, as well as the commonalities for the development of future practice.
## 1 Introduction  
Isotope analysis of organism tissues, particularly nitrogen and carbon isotopes found in collagen and carbonate materials, is a well-known method for gathering information about an organism's diet. This analysis can differentiate between carnivorous and plant-based diets and reveal the mobility patterns of various organisms, including mammals, individual humans, and populations. Additionally, isotopic ratios enable us to determine the isotopic composition of plants and mammals as potential food sources. The associated fractionations reveal the different agricultural and livestock practices. Furthermore, conducting studies using multiple isotopes (carbon, nitrogen, and sulfur) can provide a more accurate understanding of local subsistence practices. This can be achieved by integrating the recorded sulfur isotopes found in the tissues of various organisms (such as bone and hair) with carbon and nitrogen isotopes, as well as their proteins (including collagen and keratin).  
This study measures carbon, nitrogen, and sulfur isotopes of C3 and C4 plants, as well as fauna and flora samples, from different areas of Greece. Additionally, mammal bones and fish bones were analyzed. The stable isotope ratios of carbon (\(\delta^{13}\)C), nitrogen (\(\delta^{15}\)N), and sulfur (\(\delta^{34}\)S) are influenced by parameters such as climate, rainfall, physiological processes, distance from the sea, altitude, latitude, soil properties, and cultivation practices [1; 2; 3; 4; 5]. Therefore, stable isotope analysis can be a powerful tool for discriminating plants based on their biological, geographical origin, and cultivation practices [6].  
Additionally, analyzing sulfur isotopes in bone collagen can provide information about an organism's ecology, diet, and mobility. Isotopic signatures of bone collagen in organisms (mammals and fish) reflect their protein intake and, therefore, mirror the environment where plants grow. Therefore, isotopic values of sulfur, carbon, and nitrogen indicate the diet and geographical origin of consumed foods.  
Using isotopic analysis of carbon, nitrogen, and sulfur, this paper aims to clarify terrestrial and aquatic ecosystems in Greece and to reconstruct the dietary habits and husbandry practices of domesticated herbivores. This study presents recent findings on sulfur isotopes from plant, mammal, and fish samples collected from both freshwater and marine environments in Greece. The results from modern samples are then compared to ancient samples. These isotopic data will provide essential baseline data for archaeological, ecological, and forensic studies.
# KRDroid: Ransomware-Oriented Detector for Mobile Devices Based on Behaviors
## 5 Evaluation
### Experiments  
In this work, we will answer the following three questions to evaluate the detection performance of KRDroid. For each question, we first describe an experiment and give the corresponding results. Then, we provide a brief insight to summarize. The training dataset of all the experiments is the same.  
We used 1526 ransomware in the period of 2014-2015 from reference [36], including _Koler_, _Locker_, _PronDroid_, _Simplocker_, _Sypeng_, and unlabeled ransomware applications as positive samples to train KRDroid. We used 400 malware and 1200 goodware applications in the period of 2014-2015 as negative samples to train KRDroid; in KRDroid, the issue is  
Figure 5: The composition of dataset.  
not to only distinguish ransomware from malware applications but rather to distinguish ransomware from goodware applications.  
In addition, we compared the MD5 of each sample in the test dataset with the training dataset before we started experiments to make sure that all the samples in the test dataset of the following experiments are different from samples used for training.  
**Q1**: What is the ransomware detection capability of KRDroid?  
**Q2**: Will KRDroid misjudge other malware applications as ransomware?  
**Q3**: Is the efficiency of KRDroid acceptable?
## 1 Introduction  
In recent decades, novel and significant approaches to the design of antennas for various microwave and millimeter-wave applications have been attempted. Although techniques such as antenna miniaturization, array configuration, gain and bandwidth enhancement have been extensively studied, they are still important issues in the practical applications of antennas. With that, in 2020 we prepared and published the Special Issue 'Antenna Design for Microwave and Millimeter Wave Applications: Latest Advances and Prospects' and introduced 14 papers describing the relevant latest issues for advanced antenna designs. However, with the advent of new applications, such as 5G/6G mobile and satellite communications, autonomous driving, and biomedical radars, more advanced techniques are required to develop antennas with compact sizes and better radiation characteristics. Therefore, we are extending the previous Special Issue to Volume 2 to include more recent and important developments. This Special Issue aims to collect relevant papers describing the latest advances and prospects in antenna design for microwave and millimeter-wave applications. The fields of interest for this Special Issue include, but are not limited to, design methods of antennas such as miniaturization, optimization, and array. In total, 24 papers were submitted and 20 of them were published in the Special Issue. The following section provides brief summaries of each published paper.
# Research on Passive Ranging Technology of Moving Ship Based on Vertical Hydrophone Array
## 2 Basic Theory
### Range Estimation by Sound Intensity of The Pressure Difference
#### 2.2.2 Range Estimation  
We consider one normal situation where the source moves from far to near with a constant moving speed \(v_{0}\). When the source reaches the point nearest to the receiving point, the range is denoted as \(r_{0}\) and the corresponding time is denoted as \(t_{0}\). At this time, the point is named as Closest Point of Approach (CPA). The trajectory of the moving source is shown in Figure 3.  
When \(r_{0}\) and \(t_{0}\) are known, the range \(r\) of any point can be expressed as follows according to the geometric relationship:  
\[r(t)=\sqrt{{r_{0}}^{2}+(t-t_{0})^{2}{v_{0}}^{2}} \tag{17}\]  
where \(t\) is the moving time.  
The time at any point on the trajectory is recorded as \(t\), and the time at a very close range is denoted as \(t-\delta t\). The range between this point and the receiving point is denoted as \(r(t-\delta t)\). Additionally, the radial velocity \(v_{s}(t)\) can be denoted as:  
\[v_First, the appropriate range of search values are set for \(v_{0}\), \(t_{0}\) and \(r_{0}\). Then, a set of values are substituted into Equation (18) to calculate \(v_{s}\). This value is compared with the radial velocity obtained from Equation (16), and the mean square error (MSE) is calculated. \(v_{0}\), \(t_{0}\) and \(r_{0}\) corresponding to the minimum MSE are the estimated moving velocity, time and range of the CPA point. Last, depending on the values of the CPA point, the moving range over time is obtained by Equation (17).
# A Hierarchical Mapping System for Flat Identifier to Locator Resolution Based on Active Degree
## 3 Mapping Items Flow Mechanism  
### Mapping Item in HMAA  
In the abovementioned framework, HMAA, AMS is responsible for dynamically allocating an RID to each legal endpoint of local ARs, and storing the local mapping items of communicating peers used most commonly. So there are two tables in AMS: LRMT (Locate Register Mapping Table) and ARMT (Active Remote Mapping Table). The LRMT item at least is the three-tuple (AID, RID, T), where AID and RID is a mapping correspondence (hereinafter similarly), and T is the established time of the mapping item. The ARMT item at least is the three-tuple (AID, RID, F), where F is the activity of the mapping item.  
There are two tables in NMS: NLMT (Neutral Locate Mapping Table) and NRMT (Neutral Remote Mapping Table). The NLMT stores the mapping item generated by the endpoint registration in the GROUP. The NLMT item at least is the six-tuple (AID, RID, RID\({}_{\text{a}}\), T, M, Flag), where RID\({}_{\text{a}}\) is the corresponding mobile routing identifier (mRID). The mRID is different from a general routing identifier (RID) in that a mobile routing identifier indicates its corresponding endpoint as a mobile endpoint. A mobile routing identifier is not assigned by default, but is assigned only if the endpoint is frequently mobile. T is the establishing time of the mapping item, and M is a measure of the change  
Figure 2: Hierarchical mapping architecture on active-degree (HMAA).  
rate of the mapping item with an initial value of 0. Flag is used for PR, which indicates that the routing identifier NMS reports to IMS is RID or RIDa. We set Flag = true if the reported routing identifier is RID, otherwise false. The NRMT stores less frequently used communication peers' mapping item called by local endpoints, which at least is the four-tuple (AID, RID, F, RIDm), where F is the activity of the mapping item.  
The format of the mapping item in IMS is at least the three-tuple (AID, RID, (RIDm)). The number of elements in set (RIDm) is limited, recording all cache location of the mapping item. As the aggregation of RID is available, we can easily merge these elements when the number of elements exceeds a const. Because the number of mapping items stored in IMS is large, we use DHT. Here we use the CHORD.  
In this framework, we need to be concerned with the following points: (1) mapping item initialization and update; (2) mapping item query; and (3) the mechanism by which mapping item flow in HMAA.
# Reactor Temperature Prediction Method Based on CPSO-RBF-BP Neural Network
## 2 RBF-BP Combined Neural Network Model  
### RBF Neural Network  
Figure 1 depicts the layout of the RBF neural network, which consists of three layers: m inputs, q hidden layers, and 1 output. The first input layer is used to transfer input variables to the hidden layer, which is composed of basis functions. As the overall output, the output layer is a linear mapping of the implicit layer's output. RBF neural networks offer a robust nonlinear approximation capability and a robust self-learning capability, allowing them to demonstrate good self-adaptive capabilities in the face of varied environmental changes. Industrial process control increasingly employs RBF neural networks [17].
# A Next-Generation Core Network Architecture for Mobile Networks
## 3 A Novel Architecture
### Virtual Core Network  
A VCN is dedicated to a single UE and handles: real-time media, data traffic and SIP signaling as well as all other functionalities that are typical of an EPC.  
Figure 6b summarizes the different behaviors of a VCN. In particular, when using a legacy eNodeB, a VCN implements a dedicated IMS core for VoLTE and a dedicated EPC for Internet access and all non-voice data traffic. When using an eeNodeB, a VCN implements: (i) a dedicated SIP Proxy server and an SIP Back-to-Back User Agent (B2 BUA) for VoLTE and (ii) a dedicated Internet gateway for Internet access and all non-voice data traffic. The SIP B2 BUA functionality is required as we want the VCN to remain in the call data-path. We will discuss call establishment in Section 5.
# Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy
## 2 Modeling of Integrated Car-Following System
### Vehicle Dynamics  
As illustrated in Figure 2, the longitudinal (\(x\)-direction), lateral (\(y\)-direction) and yaw dynamics during the car-following can be described as the equations below.  
\[\left\{\begin{array}{l}m(\dot{v}_{x}-v_{y}\omega_{r})+\frac{I_{f}+I_{r}}{r^{2}} \dot{v}_{x}=F_{xf}-F_{yf}\sin\delta+F_{xr}\ -mgf\cos\theta-mg\sin\theta-\frac{1}{2}C_{D}A\rho v _{x}^{2}\\ m(\dot{v}_{y}+v_{x}\omega_{r})=F_{yf}\cos\delta+F_{yr}\\ I_{z}\dot{\omega}_{r}=\pi F_{yf}\cos\delta-bF_{yr}+M_{z,des}\end{array}\right. \tag{1}\]  
where \(F_{xf}\) and \(F_{xr}\) are longitudinal tire contact patch forces, while \(F_{yf}\) and \(F_{yr}\) are lateral ones. \(J_{f}\) and \(J_{r}\) are the moment of inertia of the front and rear tires, whose rolling radius is \(r\). \(v_{x}\) is the longitudinal speed, \(v_{y}\) is the lateral speed, and \(\omega_{r}\) is the yaw rate. \(m\) is the mass of the vehicle, \(g\) is the gravitational acceleration, \(f\) is the rolling resistance coefficient, \(\delta\) is the road wheel angle of the front and \(\theta\) is the road slope. \(C_{D}\) is the drag coefficient, \(\rho\) is the air density, and \(A\) is the frontal area of the vehicle. \(a\) and \(b\) are the distances from the center of the mass (CM) of the vehicle to the front and rear axles respectively, \(I_{z}\) is the yaw inertia, and \(M_{z,des}\) is the desired yaw moment.  
Now the assumptions below are made to simplify Equation (1):  
* road wheel angle \(\delta\) is tiny so that \(\sin\delta\approx\delta\), \(\cos\delta\approx 1\).
* road slope \(\theta\) is assumed to be neglected.  
Then we obtain  
\[\dot{v}_{x}=\frac{1}{M}(F_{xf}+F_{xr}+F_{d}) \tag{2}\]  
\[\left\{\begin{array}{l}\dot{v}_{y}=\frac{1}{m}(F_{yf}+F_{yr}-mv_{x}\omega_{r} )\\ \dot{\omega}_{r}=\frac{1}{I_{z}}(aF_{yf}-bF_{yr}+M_{z,des})\end{array}\right. \tag{3}\]  
where \(M=m+\frac{I_{f}+I_{r}}{r^{2}}\), \(F_{d}=-F_{yf}\delta-mgf-\frac{1}{2}C_{D}A\rho v_{x}^{2}+mv_{y}\omega_{r}\).
# Spot Weld Inspections Using Active Thermography
## 5 Conclusions  
The aim of this research was to determine the best method to perform infrared thermography during car manufacturing. Several heating techniques are not suitable to use in a manufacturing process without extensive changes, such as safety precautions. Since a car body is a complex shape, it is not always possible to inspect the welds using back heating; therefore, a solution had to be found using only front heating. An additional requirement was found in the use of robotic inspection. In order to minimize the necessity of a human inspector, it should be possible to perform the inspections automatically using a robot. Keeping these requirements in mind, the best inspection method can be found in inductive thermography. These measurements can be performed using only front heating, and the camera and heating device can be mounted on the same robot. Using induction thermography for the inspection of spot welds, it is possible to determine whether the different characteristics that determine a good weld are present or not. A proper spot weld should show a distinguishable circular shape with a closed boundary and a different thermal behaviour inside and outside the welded regions. Boundaries that are not fully closed or have an irregular shape indicate that parts of the weld are not fully melted together or the weld puddle has blown out. It is not sufficient to evaluate a spot weld only on the shape of the weld itself. In order to deliver the needed strength, the welded materials have to melt into each other inside the welding region. Therefore, it is necessary to determine whether the internal structure of the weld has changed in comparison to the surrounding area. Further investigations can be made to the induction heating in order to minimize the needed heating time. Improvements could be found in optimizing the distance between the induction spools and the sample and by increasing the amount of loops in the induction coil. In future work, the use of artificial intelligence will also be investigated in order to automatically classify spot welds.  
Figure 13: Results of the principle component analysis of the B-series samples heated from the back.  
**Author Contributions:** Conceptualization, S.V. and B.R.; methodology, S.V. and B.R.; validation, S.V. and B.R.; resources, G.S.; data curation, S.V., B.R.; writing--original draft preparation, S.V.; writing--review and editing, S.V., B.R., X.M., G.S.; supervision, X.M. and G.S.; project administration, G.S.; funding acquisition, G.S. All authors have read and agreed to the published version of the manuscript.  
This research has been funded by Research Foundation-Flanders under grant Doctoral (PhD) grant strategic basic research (SB) 1 SC0819N ([PERSON]).  
Not applicable.  
We would like to thank Volvo Ghent for providing the samples used in this manuscript.  
The authors declare no conflict of interest.