esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligibility and Listening Effort of Spanish Oesophageal Speech +
## 2 Literature Review  
### Intelligibility  
Speech intelligibility is a widely researched field, and several intelligibility measurement metrics (subjective and objective) have been explored and analysed. Subjective measures are based on the responses or opinions of the listeners. Some metrics in subjective intelligibility tests are Mean Opinion Scores (MOS) and Speech Reception Threshold (SRT). Objective measurements, on the other hand, include measures like Speech Transmission Index (STI), Short Term Objective Intelligibility (STOI) [9], and also ASR. Newer approaches of intelligibility measurement can be found in [10; 11; 12].  
The main advantage of objective measurements is that they are easy to replicate and to implement. Measures such as STOI are useful in cases where there is a degraded speech signal and a clean reference signal. This is not the case of our dataset because our tasks do not involve introducing any external degradation. Therefore, ASR was most suitable for our purposes. A detailed description of the ASR system we have used is presented in Section 3.1.4.  
Sentence transcription tasks have been widely used for subjective intelligibility measurements. Authors in [13] reported the agreement of sentence transcription tasks with a wide range of intelligibility quantification techniques and, in [6], the method is described as \"human speech recognition\". Moreover, the strengths of sentence repetition tasks are that they are \"fairly simple cognitive tasks\" and that they are \"consistent throughout the age span\" in the area of neurophysiological tests [14]. Therefore, we chose this approach to calculate HSR intelligibility. A more recent review of HSR and ASR methods can be found in [15].  
Intelligibility measurements for the analysis of pathological speech have been explored in [16; 17; 18]. Some of them are ASR-based [16; 18], while others are not [17].  
Some studies have been conducted in measuring the intelligibility of Spanish OS. Authors of [19] studied the voice intelligibility characteristics for Spanish OS and TOS. This HSR study was conducted for two-syllable words, and it reported that nasal sounds resulted in most transcription confusions for OS. Another study [20] showed how the formant frequencies were higher and the duration of vowels was longer for laryngectomees as compared to HS. The work in [21] describes a real time recognition system for vowel segments of Spanish OS.  
A lot of these studies focus on the micro-level of words and vowels. Sentence level intelligibility and LE studies on the intelligibility of Spanish oesophageal voice are a less traversed area of investigation. In addition, their focus is usually on one aspect, either ASR or HSR. Therefore, in this study, we used sentences as our stimuli and a combined approach of ASR and HSR intelligibility measurements. Moreover, in case of LE measurements, it is beneficial to have longer stimuli as the listener can make a better judgment of effort.
## 2 The Challenges of Image Classification
### Classifier Numerical Performance  
The numerical performance of classifiers expressed in terms of fast processing time and low RAM (random-access memory) usage is becoming a problem for the classification of the multi/hyperspectral remote sensing imagery featuring petabytes of data volume per year. In this respect, an accurate but slow classifier may not be applicable or its application may require significant financial resources and may not be easy to reprocess. Therefore, the aspect of numerical efficiency should be considered while selecting a classifier for a large remote sensing dataset.  
The numerical performance may differ significantly between classifier training and retrieval processes and as such should be evaluated separately. Classifier (re)training is performed rarely with an updated/corrected training dataset or after the improvement of a classifier (i.e., a new version). This implies that algorithm training may be computationally expensive in order to generate the best classification model. On the contrary, the classifier retrieval time should be as limited as possible in order to classify a large set of images. Ultimately, it has to be emphasized that the numerical performance of a classifier depends not only on the algorithm itself, but also on its implementation within a particular programming language (which may feature its own numerical limitations) and the hardware employed. Consequently, it might be difficult to objectively assess the numerical performance of several classifiers implemented in different programming languages and compiled with different (more/less aggressive) compiler flags used within the process of building computer software.
###### Abstract  
In wireless rechargeable sensor networks (WRSNs), a novel approach to energy replenishment is offered by the utilization of mobile chargers (MCs), which charge nodes via wireless energy transfer technology. However, previous research on mobile charging schemes has commonly prioritized charging efficiency as a performance index, neglecting the importance of quality of sensing coverage (QSC). As the network scale increases, the MC's charging power becomes unable to meet the energy needs of all nodes, leading to a decline in network QSC when nodes' energy is depleted. To solve this problem, we study the problem of mobile charging sequence scheduling for optimal network QSC (MSSQ) and propose an improved quantum-behaved particle swarm optimization (IQPSO) algorithm. With the attraction of potential energy in quantum space, this algorithm will adaptively adjust the contraction expansion coefficient iteratively, leading to a global optimal solution for the mobile charging sequence. Extensive simulation results demonstrate the superiority of IQPSO over the widely used QPSO and Greedy algorithms in terms of network QSC, especially in large-scale networks.  
wireless rechargeable sensor networks; quality of sensing coverage; mobile charging sequence scheduling; contraction expansion coefficient; improved quantum-behaved particle swarm optimization
# Pulmonary Fissure Detection in 3D CT Images Using a Multiple Section Model
###### Abstract  
As a typical landmark in human lungs, the detection of pulmonary fissures is of significance to computer aided diagnosis and surgery. However, the automatic detection of pulmonary fissures in CT images is a difficult task due to complex factors like their 3D membrane shape, intensity variation and adjacent interferences. Based on the observation that the fissure object often appears as thin curvilinear structures across 2D section images, we present an efficient scheme to solve this problem by merging the fissure line detection from multiple cross-sections in different directions. First, an existing oriented derivative of stick (ODoS) filter was modified for pulmonary fissure line enhancement. Then, an orientation partition scheme was applied to suppress the adhering clutters. Finally, a multiple section model was proposed for pulmonary fissure integration and segmentation. The proposed method is expected to improve fissure detection by extracting more weak objects while suppressing unrelated interferences. The performance of our scheme was validated in experiments using the publicly available open Lobe and Lung Analysis 2011 (LOLA11) dataset. Compared with manual references, the proposed scheme achieved a high segmentation accuracy, with a median \(F_{1}\)-score of 0.8916, which was much better than conventional methods.  
CT image; pulmonary fissure detection; image segmentation; fissure enhancement +
Footnote †: journal:  
0000-0001-5000-5000-0000
# XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications
## 6 Industry 4.0 Applications
### Motion Planning of Industrial Robots Inside a Digital Twin Created by V-REP Software
#### 6.1.3 Communication between Matlab and V-REP  
Communication between Matlab and V-REP is done using API developed by Coppelia robotics. Figure 10 demonstrates the overall communication requirement between Matlab and V-REP. As can be seen from this figure, proposed XOR BGSA with repository generates the path in terms of a binary sequence. This binary sequence is then translated to real movement with required resolution. Such movements are transmitted to V-REP in terms of positions and orientations of robot which is translated to joint angles of robot using built in inverse kinematic software. When a collision occurs, the target position of robot needs to be modified to the last possible point before collision in the system. After performing simulation, the results including total simulation time and distances are reported back as the real-world perception of the binary code in terms of a cost function. After all particles are evaluated, the proposed XOR BGSA iterates for another iteration to generate the next solutions.  
The parameters \(x_{i},y_{i},z_{i},a_{i},b_{i}\) and \(c_{i}\) are the position and orientation of robot after every movement. Since obstacles are static objects in V-REP, in case of a collision, the robot stops, and a collision flag is returned. Furthermore, the desired position given to the robot is not met because of collision. Hence,  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \(i\) & \(d_{i}\)(mm) & \(\theta\) & \(a_{i}\)(mm) & \(\alpha_{i}\)(rad) \\ \hline \(0\) & – & – & \(0\) & \(0\) \\ \hline \(1\) & \(89.16\) & \(q_{1}\) & \(0\) & \(\frac{\pi}{2}\) \\ \hline \(2\) & \(0\) & \(q_{2}\) & \(-425\) & \(0\) \\ \hline \(3\) & \(0\) & \(q_{3}\) & \(-392.25\) & \(0\) \\ \hline \(4\) & \(109.15\) & \(q_{4}\) & \(0\) & \(\frac{\pi}{2}\) \\ \hline \(5\) & \(94.65\) & \(q_{5}\) & \(0\) & \(-\frac{\pi}{2}\) \\ \hline \(6\) & \(82.3\) & \(q_{6}\) & \(0\) & \(0\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Denavit–Hartenberg (DH) parameters representing the forward kinematics.  
the difference between desired and obtained position of robot is a measure of collision. Such position difference can be used to penalize the cost function. The increment in the position suggested by the proposed XOR BGSA would be the actual position of robot plus the decoded increment from Table 5.
# Velocity-Aware Handover Self-Optimization Management for Next Generation Networks
## 2 System Model  
We consider a HetNet architecture that comprises several 4G macro cells and a 5G small cell BS where three small cells are uniformly distributed in each macro BS. Each macro BS has a three-sectored hexagonal layout, where each of the sectors operates as an individual cell and the small BSs are omni-directional single-sector cells. Figure 1 displays an example of the HetNets deployment scenario with three small cells placed on one macro cell. \(R\) and \(r\) represent the radius of macro and small cells, respectively.  
The macro and small BSs operate at low and high frequency bands, receptivity, with reuse frequency factor being equal to one. Table 1 lists the notations used in this paper. The set of macro and small cells are defined as \(N_{k}=1,...,N_{m}\), \(N_{l}=1,...,N_{n}\), respectively. The user set is denoted as \(N_{u}\) where \(U=1,...,U\), where U is randomly distributed in the network with a random mobility model. The UEs receive requested traffic through either the macro or small cells. At each small and macro cell, a distributed self-organizing network collects HO information and optimizes HCPs. The execution of the HO procedure initializes when a UE moves from a serving cell to a target cell in the same or different network. The serving cell makes an HO decision based on the measurement report (MR) from the UE to begin the HO process to a target cell. The path loss model for different bands in the urban area between a BS, and the user is expressed as [21]:  
\[PL_{u,k,l}=20\log_{10}(\frac{4\pi r_{0}}{\lambda_{l}})+20\log_{10}(\frac{d_{u, k}}{d_{0}})+\chi, \tag{1}\]  
where:  
\[BS=\begin{cases}small\ cell&if\qquad l=1\\ macro\ cell&\qquad otherwise\end{cases}\]  
Figure 1: System model for HetNets.  
where \(d_{0}\) and \(d_{u,k,l}\) represent the reference distance and the distance between the user \(U\) and BS \(k\), respectively (\(d_{u,k,l}\geq d_{0}\); \(d_{0}\) is assumed to be 50 m). \(\lambda_{l}\) is the wavelength at the carrier frequency \(f_{c,l}\). \(\chi\) is a Gaussian random variable with zero mean and variance \(\sigma^{2}\).  
The maximum quality of service (QoS) requirements limit the interference by reducing the RLF. The performance of each UE \(u\) should meet the minimum data rate requirement for QoS satisfaction. For channel modeling, the SINR experienced by UE \(u\) is modeled as [22; 23]:  
\[SINR_{u,k,l}=\frac{p_{u,k,l}g_{u,k,l}b_{ij}}{\sum_{i\in K\backslash\{k\}}\sum_ {j\in U\backslash\{u\}}p_{ij}g_{u,k,l}+P_{AWGN}}, \tag{2}\]  
where \(p_{u,k,l}\) is the received signal power at \(u\). \(g_{u,k,l}\) is the channel gain experienced by UE \(u\) at \(k\). \(b_{ij}\) is the binary association indicator of user \(u\) (\(b_{ij}=1\) indicates that user \(u\) associates with one BS. Otherwise, \(b_{ij}=0\).). \(p_{ij}\) represents the interference of the received signal power by UE \(u\) at \(k\). \(P_{AWGN}\) is the additive white Gaussian noise power.
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 5 Literature Review
### Encryption and Security Frameworks  
In this section, we delve into various papers that introduce a security framework for the healthcare industry leveraging encryption and blockchain technology.  
[PERSON] et al. [28] propose a blockchain-based framework for securing and improving the interoperability of electronic health records (EHRs). The framework integrates AES-256 encryption and Elliptic Curve Cryptography (ECC) to protect sensitive medical data while enabling seamless data sharing across healthcare institutions. Utilizing smart contracts, HealthChain ensures automated access control and data integrity. The proposed system enhances security, privacy, and resilience, outperforming traditional centralized EHR systems in terms of data protection and interoperability.
## 2 Materials and Methods
### Literature Review and Significance of Research
#### 2.1.3 Review of Pseudorange Multipath Estimation
Although carrier-phase multipath errors and noise exist in the equation, those values are comparatively much smaller than pseudorange multipath errors and noise, so they are thus neglected. Therefore, in this work, MP is adopted for the pseudorange multipath assessment.
## 6 Clinical Applications of POCUS  
Based on the studies reviewed, it is evident that the utilization of POCUS is crucial in addressing a broad range of health care issues and is an imperative tool in preventing negative health outcomes, especially in low and middle-income countries. The primary application of POCUS, as supported by most of the reviewed literature, focused on detecting abnormalities in obstetric/gymecologic, cardiac, and pulmonary care. Other examinations targeted the gallbladder, kidneys, gastrointestinal systems, genitourinary systems, and musculoskeletal systems. More specifically, several studies assessed the role and impact of ultrasound as an initial screening tool for individual patient management in various ailments such as tuberculosis, trauma, shock, malaria, dyspnea, among others. POCUS was also utilized to determine the presence of abscesses to support a presumptive diagnosis of melioidosis, pathological findings suggestive of HIV-associated tuberculosis, and the prevalence of early signs of acute respiratory distress syndrome in patients with malaria or sepsis [34,41,44].  
Regarding obstetric/gymecologic care, some articles focused on using POCUS for antenatal and maternal care, specifically screening for pregnancy-related abnormalities including vaginal bleeding, pre-eclampsia/eclampsia, prepartum hemorrhage, and causes of fetal/maternal mortality such as fetal malpresentation, placental location, multiple pregnancies, etc. [23,36,42]. Many articles also highlighted the applications of POCUS for cardiac applications, where the commonly sought findings were pericardial effusion, chamber size, left ventricular function, and intravascular volume, inferior vena cava diameter, and jugular venous pressure [52,55].  
In addition, POCUS was prevalent in emergency and trauma settings across various applications, utilizing FAST and Extended Focused Assessment with Sonography in Trauma (E-FAST) [46,48,57]. [PERSON] et al. studied the effectiveness of POCUS on patients in  
Figure 3: (**A**) Histogram of the brands of the utilized POCUS devices (**B**) a visualization of a generic POCUS device.  
Uganda presenting with trauma casualties, shock, cardiorespiratory symptoms, and those undergoing invasive procedures [38].
# The Effects of Methane Storage Capacity Using Upgraded Activated Carbon by KOH
## 3 Results
### Gas Delivery  
Previous research has only addressed the enhancement of storage efficiency without considering gas delivery, which is the amount of gas available under certain circumstances using porous adsorbents. The CH\({}_{4}\) storage capacity is not equal to useable gas capacity due to the strong interaction with the adsorbents [PERSON] et al. (2018). To provide intensive research, we confirmed that gas delivery after adsorption depended on the discharge rates (0.1 L/min, 0.5 L/min) at 0.4 MPa after adsorption at 10 MPa (Figure 6).  
Figure 5: Methane storage and residual capacity as a function of adsorption/desorption temperature for AC-WA. All adsorption experiments were performed at 3.5 MPa and with 12.0 g of AC.  
A CNG experiment was conducted at 0.5 L/min for comparison. The accessible CH\({}_{4}\) varied with different discharge rates. Useable masses of CH\({}_{4}\) after delivery of ANG were much higher than those in the CNG system. For instance, the mass of available CH\({}_{4}\) in the ANG system was 6.8% higher than in the CNG system at a constant flow rate (0.5 L/min, 0.4 MPa). In addition, the storage capacity in the presence of ACs was 25.4% higher than in the CNG system at the same pressure of 10 MPa (Figure 3b) and also the useable CH\({}_{4}\) in ANG was higher, as mentioned previously. Both the high storage capacity and the discharging rate of CH\({}_{4}\) in the ANG system lead to the high gas delivery.  
In the ANG system, deliverable CH\({}_{4}\) became greater when the discharge rates decreased. Over 87% (6.22 L) of total adsorbed CH\({}_{4}\) could be delivered at a discharge rate of 0.5 L/min, while over 98% of CH\({}_{4}\) (7.01 L) was available at 0.1 L/min. This was because of the changes in the heat of the carbon bed during CH\({}_{4}\) discharge. During the gas discharge, the temperature of the carbon bed might have cooled, thereby reducing the CH\({}_{4}\) delivery capacity. [PERSON] and [PERSON] also reported that the delivery capacity was highly influenced by the carbon bed temperature [38]. Thus, a heating system could be introduced to improve the CH\({}_{4}\) delivery capacity during discharge periods.
## 4 Field Test  
The thresholds of DR are different in different scenarios and conditions, and we can obtain them by testing in application scenarios. To obtain the thresholds we conducted a lot of field tests and tested the RSSI and packet loss ratio at different distances. To prove the effectiveness and reliability of LoRa 2.4 GHz and DCMDR, we tested the data transmitting time for one cluster head.  
All tests were conducted in Lingang area of Shanghai, as shown in Figure 6a. In the test scenario, there is a slight occlusion between the ground and the air. Figure 6b shows an aerial UAV platform with a dual-channel LoRa module and a cluster head node. The LoRa module uses SX1280, a rubber duck omnidirectional antenna, 2.4 GHz band, and 3 dBi gain. All tests were carried out within a fixed transmission power of 12.5 dBm, CR = 4/5, and pre-tone length = 8 symbols, and the transmission mode DR ranges from 0 to 6. The flying height of the UAV is 30 m, the low flight speed of 4 m/s is used to obtain the thresholds.
## 4 Experimental Results
### SACT Computational Efficiency  
Figure 21 shows the SACT speedup for the YN benchmarks. The communication through cooperation between threads (access to the critical section) affects the speedup. An increased number of threads lead to greater cooperation because each running thread has the need to deposit the best  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Problem**} & \multirow{2}{*}{**Size**} & \multirow{2}{*}{**Op/UB**} & \multicolumn{8}{c}{**\%RE**} \\ \cline{3-14}  & & & **SACT** & **PPSO** & **cGA-PR** & **PaGA** & **HGAPSA** & **HIMGA** & **NIMGA** & **PGS** & **PABC** & **HG** \\ \hline FT06 & \(6\times 6\) & 55 & 0 & – & – & 0 & – & 0 & 0 & 0 & 0 & 0 & – \\ FT10 & \(10\times 10\) & 930 & 0 & – & 0 & 7.2 & – & 0 & 0 & 0 & 0.9 & 0 & 0 \\ LA16 & \(10\times 10\) & 945 & 0 & 29 & – & 5.2 & – & 0 & 0.11 & 0 & – & 0 & 0 \\ LA17 & \(10\times 10\) & 784 & 0 & 33 & – & 1.2 & – & 0 & 0 & 0 & – & 0 & 0 \\ LA18 & \(10\times 10\) & 848 & 0 & 33 & – & 1.4 & – & 0 & 0 & 0 & – & 0 & 0 \\ LA19 & \(10\times 10\) & 842 & 0 & 37 & – & 3.7 & – & 0 & 0 & 0 & – & 0 & 0 \\ LA20 & \(10\times 10\) & 902 & 0 & 32 & – & 1.1 & – & 0 & 0.55 & 0 & – & 0.55 & 0 \\ LA36 & \(15\times 15\) & 1268 & 0 & 65 & – & – & 0.87 & 0 & 1.97 & 0 & – & – & 0 \\ LA37 & \(15\times 15\) & 1397 & 0 & 58 & – & – & 0.79 & 0 & 3.01 & 0 & – & – & 0 \\ LA38 & \(15\times 15\) & 1196 & 0 & 68 & 1.0 & – & 1.92 & 0 & 2.17 & 0 & – & – & 0 \\ LA39 & \(15\times 15\) & 1233 & 0 & 67 & – & – & 1.05 & 0 & 2.11 & 0 & – & – & 0 \\ LA40 & \(15\times 15\) & 1222 & 0 & 68 & 1.31 & – & 1.56 & 0.16 & 1.96 & 0.16 & – & – & 0 \\ ORB01 & \(10\times 10\) & 1059 & 0 & – & 0 & 8.5 & – & 0 & 0 & 0 & 0 & – & 0 \\ ORB02 & \(10\times 10\) & **888** & 0 & – & – & 4.6 & – & 0 & 0.23 & 0 & 0.1 & – & 0 \\ ORB03 & \(10\times 10\) & **1005** & 0 & – & 0 & 12.3 & – & 0 & 2.09 & 0 & 0 & – & 0 \\ ORB04 & \(10\times 10\) & **1005** & 0 & – & 0 & 5.7 & – & 0 & 1.39 & 0 & 0 & – & 0 \\ ORB05 & \(10\times 10\) & **887** & 0 & – & – & 5.5 & – & 0 & 0.68 & 0 & – & 0 \\ ORB06 & \(10\times 10\) & **1010** & 0 & – & – & 4.0 & – & 0 & 0.20 & 0 & – & – & 0 \\ ORB07 & \(10\times 10\) & **397** & 0 & – & – & 4.8 & – & 0 & 0 & 0 & – & – & 0 \\ ORB08 & \(10\times 10\) & **899** & 0 & – & 0 & 12.4 & – &  
solution in a critical section shared by all threads (see Figure 3), generating a bottleneck where in an instant of time the threads will have to wait their turn to access the critical section. This implies that increasing the number of threads will have a higher bottleneck, which results that the speedup moves away from the ideal. The speedup for YN2 is farthest from ideal with respect to other YN benchmarks. This may be because YN2 is taken more time to generate feasible solutions with the neighborhood generation mechanism [31]. So, it is possible that YN1, YN3, and YN4 will find solutions feasible faster. The neighborhood mechanism used in SACT is the only place in the algorithm that can perform differently on each benchmark. The speedup is very good for all YN benchmarks since is close to the ideal.