esa-sceva/satcom-chunk-collection huggingface dataset:
# Factors Determining Ticagrelor-Induced Dyspnea in Patients with Acute Coronary Syndrome
## 3 Results  
### Characteristics of the Study Population  
The majority of the study population were male (n = 208; 75.1%). STEMI was most common among the enrolled patients (53.1%). On coronary angiography, 67.8% of the patients were found to have advanced coronary lesions (two- or three-vessel coronary artery disease, CAD). Diabetes mellitus was present in 12.3% of the patients who were treated most frequently with a combination of metformin and gliclazide. Hypothyroidism was documented in 2.5% of the patients. Patients with atrial fibrillation (14.4%) received dual therapy with ticagrelor plus low-dose (110 mg) dabigatran etexilate for the prevention of thromboembolism complications, and aspirin was administered only during hospitalization (6 days on average) (Table 3) [2].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Characteristic**} & \multicolumn{2}{c}{**Dyspnea**} & \multirow{2}{*}{_p_} \\ \cline{2-2} \cline{4-4}  & **No (n = 182)** & & **Yes (n = 95)** \\ \hline Gender, n (\%) & & & \\ Male & 138 (75.8) & 70 (73.7) & 0.696 \\ Female & 44 (24.2) & 25 (26.3) & \\ \hline Age, years & 64 (42–88) & 66 (32–90) & 0.066 \\ BMI, kg/m\({}^{2}\) & 29 (21–50) & 28 (21–45) & 0.859 \\ \hline MI, n (\%) & & & \\ NETEMI & 84 (46.2) & 46 (48.4) & 0.720 \\ STEMI & 98 (53.8) & 49 (51.6) & \\ \hline CAD, n (\%) & & & \\ One-vessel & 62 (31.1) & 27 (28.4) & 0.162 \\ Two-vessel & 57 (31.3) & 42 (44.2) & \\ Three-vessel & 63 (34.6) & 26 (27.4) & **0.034** \\ \hline DM, n (\%) & 23 (12.6) & 11 (11.6) & 0.799 \\ \hline Smoking, n (\%) & 50 (27.5) & 29 (30.5) & 0.068 \\ \hline Hypothyroidism, n (\%) & 2 (1.1) & 5 (5.3) & **0.049** \\ \hline AF, n (\%) & 24 (13.2) & 16 (16.8) & 0.411 \\ \hline Troponin I, ng/mL & 495 (20–89977) & 496 (20–85725) & 0.697 \\ Troponin I after 6 h, ng/mL & 7816 (20–635352) & 4324 (20–147527) & 0.146 \\ BNP, pmol/L & 131 (11–1858) & 140 (6–2000) & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of the study population.  
After initiation of ticagrelor use, 95 patients experienced dyspnea; 182 patients had no dyspnea.  
Within the 3-month period after ACS, 15 patients experienced bleeding from the digestive tract that was confirmed by esophagagostroudonoscopy; however, treatment with antiplatelet drugs was not discontinued. A bleeding event was considered as insignificant, and an additional therapy with drugs of the proton pump inhibitor class was administered.
###### Abstract
Second, the divergence between the optimal CH location and actual CH node location may be large, and a node belonging to another cluster may be mistakenly used as CH. Finally, a node may be selected as CH for multiple clusters given its closeness to the optimal location in different clusters. Consequently, the number of CH nodes may be smaller than the number of clusters, leading to suboptimal operation. Therefore, clustering should be adapted to consider the characteristics of WSNs, including the actual node locations. In this study, we modified SMO by using a sampling method for CH selection in WSNs. When sampling a population of nodes, their actual locations are always retrieved, thus preventing the abovementioned problems arising from the divergence between the optimal CH location and the actual node location. Moreover, multiple selections of nodes as CH among different clusters are prevented during sampling while avoiding complex computations. In fact, the modified SMO only provides optimal results from the best samples (i.e., actual node locations), as it only differs from the conventional SMO because its searching is constrained to samples. We first introduce the sampling-based SMO approach, and then we detail its application to WSNs by proposing the sampling-based SMO and energy-efficient CH selection (SSMOECHS). We also provide experimental results comparing SSMOECHS with existing protocols to illustrate CH selection and node energy efficiency over time. These results confirm that SSMOECHS improves the lifetime and stability of the WSN compared to similar protocols, namely LEACH-C [7], PSO-C [8], and SMOTECP [18]. The main contributions of this work are as follows:  
* To the best of our knowledge, this is the first work that applies the sampling method to SMO to improve the lifetime and stability of wireless sensor nodes. * We propose the sampling-based SMO and energy-efficient CH selection (SSMOECHS). * We increase the lifetime and stability of the network through the proposed SSMOECHS. * To evaluate the performance of our protocol, we compare it with similar protocols, namely LEACH-C [7], PSO-C [8], and SMOTECP [18]. The remainder of this manuscript is organized as follows. Section 2 summarizes related work. Section 3 introduces the sampling-based SMO, followed by the proposed SSMOECHS protocol in Section 4. Section 5 reports the experimental results and compares SSMOECHS with similar protocols and provides a discussion. We finally draw conclusions in Section 6.
# Analysis of the Energy Efficiency of a Pneumatic Booster Regulator with Energy Recovery
## 6 Conclusions  
A new pneumatic booster regulator with energy recovery was proposed and its energy efficiency characteristics were modelled by considering the tank. It was consistent with the application of a booster regulator system. With this model, the energy efficiency was confirmed by using the concept of air power. Then, the effects of various factors on energy efficiency were comprehensively analysed, and the performance improvement of the VBA-R was verified by comparing it with the VBA. The conclusions can be summarised as follows:  
1. The system outputs, especially the pressure, were less than those of the booster regulator. When the air consumption increased, the boost ability decreased, and the tank output became much smaller;
2. The energy efficiency was directly affected by the supply pressure, and it decreased with increasing supply pressure or decreasing boost ratio;
3. When the supply pressure was constant, the energy efficiency decreased with the flow rate consumed by an increasing load;
4. Both the boost ratio and energy efficiency increased first and then decreased with the increasing recovery chamber diameter. When the diameter ratio was close to 1.3, the VBA-R has the largest boost ratio and energy efficiency;
5. The recovery chamber helped improve the boost ratio by 15-25% and energy efficiency by 5-10% under different operating conditions. Thus, it will save energy over long running times.  
The research work presented in this paper is financially supported by SMC Corporation. And thanks for the support by Wuhan International Scientific and Technological Cooperation Project, No. 2016030409020224.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments and analyzed the data; [PERSON] wrote the paper and [PERSON] modified the manuscript.  
The authors declare no conflict of interest.  
Figure 20: Comparison of VBA and VBA-R.
# Framework for Video Steganography Using Integer Wavelet Transform and JPEG Compression
## 2 Literature Review
From the literature review, it has been concluded that video steganography is gaining popularity. The existing techniques were compared based on the parameters mentionedin Table 1. All the parameters are properly achieved by video steganography techniques. Some of the issues related to existing steganography techniques are given as follows [17; 18]:  
* A relationship exists between concealing capacity, imperceptibility, and robustness. * On changing one parameter, the other two are affected. Such as, increasing concealing capacity, the imperceptibility and the robustness of the stego file decreases. * Text, images, and audio files are not able to store much information. * Computational cost increases when combining multiple techniques. * A technique can work with a specified format of cover as well as secret information only. * Combining cryptography and steganography increase the complexity of the technique.
## 4 Discussion
### Hardware Selections  
In AIRS hardware, we decided to include a spectrometer for irradiance measurement, because we use the system with hyperspectral sensors. However, this would not be necessary in all use cases. In applications using, for example, RGB cameras or simple multispectral sensors, the RGB photodiodes could already provide sufficient spectral information on irradiance. In addition, even if the full irradiance spectrum is needed in mathematics, the RGB photodiodes might contain enough spectral information to reconstruct the full spectrum. The spectral shapes present in natural illumination are usually predictable; thus, it might be possible to convert the RGB irradiances to full spectra using look-up-tables. In future sensors based on the AIRS concept, the inclusion of a spectrometer should be debated, as it is the largest and one of the most expensive components in the build. Leaving it out would allow crafting of a significantly more lightweight, compact, and affordable system.  
The AIRS includes separately a kinematic GNSS receiver and a GNSS-INS unit both with their own antennas. This inclusion of two GNSS devices is not a desirable feature but was purely due to the low position accuracy of the previously available GNSS-INS device. New INS systems are becoming available with better accuracy and reasonable prices. Thus, for new designs, we recommend usage of only a single INS unit with kinematic GNSS, which can simplify the integration of the sensors. If the system should be used also for real time direct georeferencing processing (without photogrammetry), we would recommend upgrading the GNSS/INS to a higher grade unit and preferably also including a separate IMU installed directly on the camera mount.
## 3 Wireless Access-Metro Networks
### Scheduling and Resource Management
If an HT STA, which gains access to the channel using EDCA, runs out of frames to transmit, a TXOP holding HT STA can transmit a contention free end (CF-End) frame to truncate the TXOP and thereby improve bandwidth-efficiency. The reverse direction (RD) protocol provides bidirectional TXOP connections. More precisely, during an RD exchange sequence, the RD initiator can transmit PHY protocol data units (PPDUs) and obtain response PPDUs from the RD responder in a single TXOP. In the HT control field of an IEEE 802.11n PPDU, the RD grant (RDG)/More PPDU field is used to indicate the RD permission and the last transferred PPDU. In EDCA, an RD responder must transmit the same type of AC data frames as received, while in HCCCA an RD responder is allowed to transmit data frames of any TID. Bandwidth-efficiency is further improved by means of a reduced inter-frame space (RIFS) in HT greenfield deployments that cuts down the dead time between frame transmissions. In HCCCA, power save multi-poll (PSMP) is one of the new MAC layer features of next-generation WLAN to improve bandwidth-efficiency. The HT AP sends a PSMP frame in order to schedule the upstream and downstream transmissions of HT STAs, also known as uplink transmission time (UTT) and downlink transmission time (DTT), respectively. In a PSMP frame, the PSMP-UTT and PSMP-DTT fields indicate the durations of uplink and downlink streams dedicated to each HT STA. The PPDUs transmitted between PSMP-UTT and PSMP-DTT are separated by SIFS, while the PPDUs transmitted within them may be separated by RIFS or SIFS [22].
# A Neural Network Approach for the Analysis of Reproducible Ribo-Seq Profiles
## 4 Results and Discussion
### Performance of the Neural Network Models  
#### 4.2.1 MLP Classification Based on Nucleotide Frequencies  
In this experiment, a Multi-Layer Perceptron is used to predict the translation speed of sequences based only on their nucleotide composition. The model performance is summarized across five runs and reported in Table 6. It can be noted that the network reaches on average 82.24% accuracy over the test set, while the standard deviation between different runs is 2.20%. In fact, these results are surprising, given that the only information used by the model is the nucleotide composition of the sub-sequences. Moreover, the results are compared with those obtained by training the MLP on random sub-sequences, in order to demonstrate that the reproducible sub-sequences are significantly more informative. Indeed, as reported in Table 6, the performance obtained on reproducible sub-sequences is significantly higher than that obtained on random sub-sequences, validating the significance of our approach.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**MLP**} \\ \hline
**Run** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline
1 & 70.83 & 89.47 & 79.07 & 81.63 \\ \hline
2 & 70.80 & 89.51 & 79.06 & 80.62 \\ \hline
3 & 72.00 & 94.74 & 81.82 & 83.67 \\ \hline
4 & 66.67 & 94.74 & 78.26 & 79.59 \\ \hline
5 & 75.00 & 94.74 & 83.72 & 85.71 \\ \hline Average & 71.06 & 92.64 & 80.39 & 82.24 \\ \hline Standard Dev. & 2.68 & 2.57 & 2.06 & 2.20 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of the results obtained with the MLP model. We collect the obtained test set metrics computed over five different runs. The last two rows report the average over the runs and the corresponding standard deviation.  
Figure 7: Representation of the sub-sequences on the plane based on the relative frequencies of two nucleotides. Each sub-sequence is represented by a dot on the plane. More specifically, the relative frequency of thymine and cytosine (**a**), adenine and thymine (**b**), cytosine and guanine (**c**), adenine and cytosine (**d**), thymine and guanine (**e**), and adenine and guanine (**f**), respectively, is shown. The colors represent the speed of translation: orange and blue dots indicate slow and fast sub-sequences, respectively.
## 3 Results and Discussion
### Flow Characteristics Under Different Storage Pressures  
#### 3.2.1 Flow Analysis  
This section comprehensively analyzes the heat transfer and flow characteristics within the storage tank under varying storage pressures. Figure 6 presents the velocity vector diagrams at 0.6 MPa, 0.7 MPa, 1 MPa, 2 MPa, and 4 MPa. The results reveal that as the pressure increases, the maximum fluid flow velocity gradually decreases, the extent of natural convection-induced vortices diminishes, and the rate of fluid velocity increment over time slows down. From the velocity contour maps, it is evident that fluid motion initially generates velocity in the outer regions, which subsequently drives the acceleration of the inner fluid.  
To provide a quantitative basis for the observed flow behavior, the peak fluid flow velocity within the storage tank at a representative time point was extracted for each pressure condition. As shown in Table 3, the peak velocity demonstrates a clear decreasing trend with increasing storage pressure. Specifically, the peak velocity is 0.203 m/s at 0.6 MPa, decreasing to 0.170 m/s at 0.7 MPa, 0.157 m/s at 1.0 MPa, 0.035 m/s at 2.0 MPa, and further to 0.039 m/s at 4.0 MPa. This quantitative data indicates a substantial suppression of the maximum fluid speed as the storage pressure increases.  
Figure 5: The temperature corresponding to 0.6 MPa.  
Although the kinematic viscosity of both the saturated liquid and vapor CO\({}_{2}\) decreases with increasing pressure (as shown in Figure 7), this trend does not lead to increased fluid velocity. This is because natural convection in the tank is primarily governed by thermal gradients and buoyancy forces rather than viscosity alone. At higher pressures, the thermal driving force weakens due to reduced temperature gradients and decreased thermal expansion coefficients, which leads to less vigorous convection, even though the fluid becomes more mobile in terms of viscosity. Thus, the reduction in fluid motion is more directly attributable to diminished thermal driving forces rather than changes in viscosity alone.  
Figure 6: The velocity vector diagrams at 0.6 MPa, 0.7 MPa, 1 MPa, 2 MPa, and 4 MPa.  
\begin{table}
\begin{tabular}{c c} \hline
**Pressure (MPa)** & **Peak Velocity (m/s)** \\ \hline
0.6 & 0.203 \\
0.7 & 0.170 \\
1.0 & 0.157 \\
2.0 & 0.035 \\
4.0 & 0.039 \\ \hline \end{tabular}
\end{table}
Table 3: Peak fluid velocity at different pressures.  
Figure 7: The curve of CO\({}_{2}\) kinematic viscosity variation with pressure.
## 5 Results and Discussion  
The population sampled in this research consisted of 240 individuals with varied ages, education, and employment backgrounds; 228 complete replies were obtained. Their smartphone experiences were presented in a descriptive analysis, and by multiple regressions using SPSS version 20.  
To check the internal reliability or consistency, the composite reliability was examined, where the value should be higher than 0.70 for satisfaction. For this purpose, SmartPLS 3 was used to accomplish numerous tests, and the outcomes are presented in Table 1. The findings in Table 1 demonstrate that the overall values of composite reliability are higher than 0.7, which indicates that the data fulfilled the internal consistency reliability test. Convergent validity was the second test applied to this proposed model, and average variance extracted (AVE) was used for this. As shown in Table 1, the data satisfied convergent validity because the smallest AVE value was 0.981, which is acceptable.  
Indicator reliability was the third test used; it reflected the factor loading from each indicator. In this research, the R-squared (R2) of 0.736 was significant for the attitude towards using smartphones. On the other hand, for smartphone actual use, the R-squared was 0.991, and was considered significant. Discriminant validity was the last test in the reliability analysis. Primarily, all of the cross-loadings of an indicator have to be smaller than that indicator's loadings; as can be seen in Table 1, all indicator loadings were greater than all of their cross-loadings. Moreover, the highest squared correlation of any construct with some other latent construct must be smaller than the AVE of each latent construct. In other words, the latent construct correlations and the AVE square roots must be associated. The R2s of average variance extracted are shown diagonally in Table 1, where every square root value is greater compared to any other latent cross-correlations. Thus, the reflective measurement test satisfied this proposed model.
# Energy Performance of LR-FHSS: Analysis and Evaluation
###### Abstract  
Long-range frequency hopping spread spectrum (LR-FHSS) is a pivotal advancement in the LoRaWAN protocol that is designed to enhance the network's capacity and robustness, particularly in densely populated environments. Although energy consumption is paramount in LoRaWAN-based end devices, this is the first study in the literature, to our knowledge, that models the impact of this novel mechanism on energy consumption. In this article, we provide a comprehensive energy consumption analytical model of LR-FHSS, focusing on three critical metrics: average current consumption, battery lifetime, and energy efficiency of data transmission. The model is based on measurements performed on real hardware in a fully operational LR-FHSS network. While in our evaluation, LR-FHSS can show worse consumption figures than LoRa, we find that with optimal configuration, the battery lifetime of LR-FHSS end devices can reach 2.5 years for a 50 min notification period. For the most energy-efficient payload size, this lifespan can be extended to a theoretical maximum of up to 16 years with a one-day notification interval using a cell-coin battery.  
LoRaWAN; LoRa; LR-FHSS; energy consumption; energy modeling; performance evaluation; Internet of Things; IoT; LPWAN +
Footnote †: journal: Sensors
# Power Control Techniques for Interference Management-
### Current Review Limitations
The authors could not identify related reviews on the current technologies, such as 5G wireless networks. Because of this limitation, the authors could not identify current research publications to address interference in 5G wireless networks. None mapped out opportunities or directions for future research on power control techniques in 5G wireless networks.
## 2 Geographic Hash Table (GHT), the first DCS Proposal
(iii) If the same refresh packet reaches twice a node that was not the home node, then it becomes the home node. Thus it consumes the packet and establishes its own timer to start generating refresh packets. Therefore, PRP provides fault-tolerance and is able to deal with mobile  
Figure 1: Geographic Hash Table (GHT) example. scenarios. It must be noted that PRP only provides local replication because all the replicas are placed nearby. In addition, the authors compare three different storage mechanisms: local storage (the producer store the measured data), DCS and external (sink) storage. A simple mathematical model, as well as a deep performance evaluation, are presented indicating when DCS outperforms the other two storage models. GHT focuses on the standard WSN scenario (see Section 1.1. ), therefore it assumes that consumption queries are always generated from the sink.
## 2 Preliminaries and Network Model
### Compressed Sensing  
In WSNs, assume that \(N\) sensor readings are denoted by \(\mathbf{X}=(x_{1},\cdots,x_{N})^{T}\), where \(x_{i},i\in[1,N]\) denotes the reading of node \(i\) with \(K\)-sparse representation at a basis \(\boldsymbol{\
abla}\in\mathbb{R}^{N\times N}\):  
\[\mathbf{X}=\boldsymbol{\
abla}\boldsymbol{\theta}, \tag{1}\]  
where \(\boldsymbol{\theta}\in\mathbb{R}^{N}\) is a coefficient vector corresponding to the sparse basis \(\boldsymbol{\
abla}\). \(\mathbf{X}\) is \(K\)-sparse and compressive if the vector \(\boldsymbol{\theta}\) has at most \(K(K\leq N)\) nonzero coefficients or \((N-K)\) smallest coefficients can be ignored.  
We assume the measurement matrix is \(\boldsymbol{\Phi}\in\mathbb{R}^{M\times N}\) and is uncorrelated with the basis \(\boldsymbol{\
abla}\), then the CS measurements of \(\mathbf{X}\) can be expressed as follows:  
\[\boldsymbol{Y}=\boldsymbol{\Phi}\mathbf{X}=\boldsymbol{\Phi}\boldsymbol{\
abla }\boldsymbol{\theta}=\boldsymbol{\Theta}\boldsymbol{\theta}, \tag{2}\]  
where \(M\ll\)\(N\) and \(\boldsymbol{\Theta}=\boldsymbol{\Phi}\boldsymbol{\
abla}\) is a sensing matrix. The original signal \(\boldsymbol{X}\) can be reconstructed with an overwhelming probability from \(M\) measurements by \(I_{1}\)-norm minimization as follows:  
\[\begin{array}{l}\min:\boldsymbol{\hat{X}}=\min\|\boldsymbol{X}\|_{1}\\ \text{s.t.:}\boldsymbol{Y}=\boldsymbol{\Phi}\boldsymbol{X},\end{array} \tag{3}\]  
where \(\boldsymbol{\hat{X}}\) denotes the reconstructed sparse signal of \(\boldsymbol{X}\).  
To reconstruct \(\mathbf{X}\), two factors must be considered: (1) \(\mathbf{X}\) is compressive at \(\boldsymbol{\
abla}\); and (2) \(\boldsymbol{\Phi}\) must satisfy the RIP with \(M\geq\epsilon\text{klg}(\boldsymbol{N}/\boldsymbol{k})\). Therefore, \(\boldsymbol{K}\)-sparse \(\mathbf{X}\) satisfies the following condition:  
\[\left(1-\varepsilon\right)\|\boldsymbol{\theta}\|_{2}^{2}\leq\|\boldsymbol{ \Phi}\boldsymbol{\theta}\|_{2}^{2}\leq(1+\varepsilon)\|\boldsymbol{\theta}\|_ {2}^{2}, \tag{4}\]  
where \(c,\varepsilon\in(0,1)\), while \(\boldsymbol{\Phi}\) satisfies RIP with the parameter \(\varepsilon\).