esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
## 5 Performance Analysis
### Transmit Power Impact on Delay Performance  
We assume that there are four SSs, each positioned at a distance of \(5\times 10^{5}\) m from the MS. We alter the maximum transmit power of the MS by 500 W, 1000 W, 1500 W, 3000 W, 6000 W, 10,000 W] and the bandwidth by [200 MHz, 400 MHz, 600 MHz], observing their impact on the maximum time delay, as shown in Figure 5. The horizontal axis represents the number of iterations, and the vertical axis represents the delay in seconds.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Default Value** \\ \hline B & 200 MHz \\ M & 4 \\ \(C_{0}\) & 3 \(\times\)\(10^{8}\) cycles/s \\ \(P_{m}\) & 5000 W \\ \(E_{1}\) & 3,000,000 J \\ \(\sigma^{2}\) & \(1\times 10^{-8}\) W \\ s & 0.1 \\ D & 1 GB \\ \(\gamma\) & 0.01 \\ \(IterationNumber\) & 200 \\ \(\mu_{0}\) & \(1\) \\ \(\lambda,\rho\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 4: The delay of different schemes with a varying number of iterations.  
As can be observed from Figure 5, it is easy to see that the curves show a downward trend with increasing power. However, with the increase in power, the decrease in the delay value is not obvious. This is because the relationship between power and delay is not linear but follows a diminishing reward. This conclusion is important from the engineering perspective because we need a moderate power setting that is cost-effective to achieve the trade-off between power and delay.
## 6 Discussion
### Performance Analysis
#### 6.2.2 Reconfiguration Performance  
Since the common sparse recovery algorithms can be classified into three main categories, Figure 11 illustrates the relationship between the reconstruction error and the compression ratio for the same sparse gas signal using different reconstruction algorithms, which are Convex and Relaxation (BP, TVS-MH), Greedy (OMP, gOMP), and Bayesian category (BCS). During the tests, a random signal with a sparseness of 50 and a length of 512 (\(K=50\) and \(N=512\)) was used as the gas signal, and the program ran 100 times to take the average values.  
It can be observed that among these five algorithms, the TVS-MH algorithm has more favorable reconstruction performances; in particular, TVS-MH algorithm exhibits steady performances in the test on CPU operation time, thereby suggesting its superiority.
## 3 Emergency Braking Control Strategy Design
### Rule-Based Braking Torque Allocating Method
Thus, the demanded torque for the mechanical brake and PMSM converted to the drive wheel can be written as:  
\[\left\{ \begin{array}{l}T_{\textit{bndem}}=r\hat{F}_{x}+\frac{1-\lambda_{d}}{r}J_ {\textit{eri}}\dot{v}_{x}-\hat{T}_{\textit{btr}}+\chi(T_{\textit{bndem}}-T_{ \textit{mio}}\dot{\iota}_{tr})\\ T_{\textit{bndem}}=\frac{\omega\textit{Leri}}{1-\lambda}(\varepsilon+F_{d}) \textit{sign}(s_{\textsc{s}})+\left(-\frac{1}{r}J_{\textit{eri}}\dot{v}_{x}+ \frac{\omega\textit{Leri}}{1-\lambda}k\right)s_{\textsc{s}}\end{array} \right. \tag{23}\]  
where \(\hat{T}_{\textit{btr}}\) is the estimated hydraulic retarder torque converted to the drive wheel and \(\chi\) is the compensating coefficient to compensate the difference of demanded dynamic braking torque and PMSM torque. The value of \(\chi\) can vary from 0-1 according to the response characteristics of the mechanical brake, the hydraulic retarder, the PMSM, and the drive system to achieve a better braking performance. It is usually set with a relatively small value to prevent large overshoot of \(\lambda\) due to the poor dynamic response of the mechanical brake. To reduce chattering, the symbolic function \(\textit{sign}(s_{\textsc{s}})\) can be replaced by a saturation function \(\textit{sat}\left(\frac{s_{\textsc{s}}}{\Phi}\right)\); thus, \(T_{\textit{bndem}}\) can be rewritten as:  
\[T_{\textit{bndem}}=\frac{\omega J_{\textit{eri}}}{1-\lambda}(\varepsilon+F_{d} )\textit{sat}\left(\frac{s_{\textsc{s}}}{\Phi}\right)+\left(-\frac{1}{r}J_{ \textit{eri}}\dot{v}_{x}+\frac{\omega J_{\textit{eri}}}{1-\lambda}k\right)s_{ \textsc{s}} \tag{24}\]  
where the saturation function \(\textit{sat}\left(\frac{s_{\textsc{s}}}{\Phi}\right)\) is defined as:  
\[\textit{sat}\left(\frac{s_{\textsc{s}}}{\Phi}\right)=\left\{ \begin{array}{ l}1,\ \ s_{\textsc{s}}>\Phi\\ \frac{s_{\textsc{s}}}{\Phi},\ \ |s_{\textsc{s}}|<\Phi\\ -1,\ \ s_{\textsc{s}}<-\Phi\end{array} \right. \tag{25}\]  
where \(\Phi\) is a positive real number.
## 3 Method
### Data Acquisition  
Our ICG device was described in a previous study [5] in which analog ICG and PPG signals were all digitalized with a sampling frequency of 500 Hz. The PPG sensors were placed on the neck of the subject. The ICG and PPG signals were filtered to remove the baseline drift and the high-frequency noise using a second-order Butterworth bandpass filter in which the lower and upper cutoff frequencies were 0.2 and 10 Hz, respectively. Then, the DPPG and DICG signals were gotten from the PPG and ICG signals by the first-order discrete derivative, which passed a zero-phase forward and reverse second-order Butterworth lowpass filter. Its cutoff frequency was 10 Hz. The first zero crossing point for the DPPG signal during one heart cycle was used to segment the pulse. As the heart rates of the included subjects were not lower than 60 beats/minute, an image consisted of two pulses, PPG and DPPG, whose length (size) was set to 500 points. If the length of a pulse was less than 500 points, it was padded to become 500-points long with zero points. Figure 5 shows different 150 x 150 images obtained from the segmented PPG (blue line) and DPPG (orange line) signals with three different SQI levels. In Figure 5a, because the morphologies of the two PPG pulses within the systolic phase are perfect (i.e., the morphologies have a clear, distinct dicrotic notch and starting ejection point), their SQIs are high. The two PPG pulses in Figure 5b belong to the middle SQI ones, due to good morphology at the starting ejection point. However, their dicrotic notches are not distinct in the PPG signals. Therefore, the values of their differential signals at the dicrotic notch zone may not be larger  
Figure 4: Overall procedure for signal quality index (SQI) classification of PPG pulses.  
than zero. As shown in Figure 5c, the two PPG pulses own low SQIs since their amplitudes or baselines have been greatly distorted due to severe motion artifacts.  
Figure 5: PPG (blue line) and DPPG (orange line) pulses with different quality levels. (**a**) High quality, (**b**) middle quality, (**c**) low quality.
## 4 Discussion
### Implications for Scientists and Clinicians  
From the scientific point of view, it seems important to answer the question: Which of these substances are the most effective against \(H\). _pylori_? The heterogeneity of the results of the researchpresented in this review makes it difficult to clearly judge this issue. Based on a comparison of the level of bactericidal activity, speed of action, and an ability to act on both morphological forms of _H. pylori_ (spiral and coccoid), one can be tempted to select representatives from each group of compounds: essential oils [46] and propolis components [54] (plant compounds); SQ109 [62], 3-bromopyruvate [40] and serntraline [41] (synthetic compounds); linolenic acid and its derivatives [43; 65] (fatty acids), and (PGA)\({}_{\text{m}}\)-\(r\)-(PHLG-MHH)\({}_{\text{n}}\)[68] (peptides). It is suggested that promising results obtained in vitro for planktonic _H. pylori_ forms for these substances should be verified in vivo and for biofilm forms of these bacteria.  
From the clinical point of view, it seems important to answer the following questions: How can the presence of coccoid _H. pylori_ forms affect the therapeutic effect and choice of drugs? How can these morphological forms affect the diagnosis of the bacterium? Considering the first issue, based on a review of the literature on antibiotics, it appears that AMX is highly effective against spiral _H. pylori_ forms and the morphological transformation of AMX-exposed bacteria has no protective effect [37; 38; 39; 42]. A different case is observed when treating the pre-formed spherical forms of _H. pylori_, for which very low sensitivity to this antibiotic was demonstrated [42; 43]. The pre-formed coccoid forms of _H. pylori_ may be present in patients who have undergone multiple antibiotic therapies, hence the use of other antibiotics, e.g., CLR or MTZ, which have higher activity relative to coccoid _H. pylori_ forms, seems reasonable [42]. On the other hand, the diagnostic aspect of spherical _H. pylori_ forms should also be considered. Because these forms are nonculturable and have a low degree of metabolic activity, their presence may be associated with an increased risk of false-negative results [94; 95; 96]. This is especially important in patients after _H. pylori_ eradication or those receiving PPIs for a long term. At this point, the importance of biopsy analysis for the coccoid _H. pylori_ forms presence and modification of therapy based on patient's treatment history are indicated.
## 2 Materials and Methods  
### Dataset  
This retrospective study uses images from CT exams after anonymizing patient personal data. The study was approved by the Ethics Committee of \"Federico II\" University, Naples, Italy (approval number 81/20). The CT scans were obtained from the internal database of the U.O.C. of Maxillofacial Surgery of the University Hospital \"Federico II\", which collects examinations conducted from 2000 to 2020. We performed CT investigations of the facial mass on different devices (TC 16-64 slice) with thickness volumetric acquisition (0.5-2 mm) and variable in-plane resolution (0.5 \(\times\) 0.5-1 \(\times\) 1 mm). For the analysis, we selected only the images we obtained with the bone reconstruction algorithm. Two radiologists (R.C., L.U.) consensually examined, interpreted, and classified each CT image according to fracture rhymes' presence/absence. We also included control CT investigations from patients with the non-traumatic facial mass disorder.  
The number of CT scans corresponds to the number of patients (a CT scan for each patient). The total dataset consisted of 208 patients: 170 patients (11,260 slices of CT scans) labeled as with \"fracture\" and 38 patients (49,762 slices of CT scans) labeled as \"noFracture\". The total dataset was divided into training, validation, and test datasets. In particular, the training dataset consisted of 148 CT images (120 patients labeled as with \"fracture\" and 28 patients labeled as with \"noFracture\"). The validation dataset, used for statistical analysis, was characterized by 30 patients (5 with \"noFracture\" and 25 with \"fracture\"), and an additional 30 CT scans, comprising 25 \"fracture\" and 5 \"noFracture\" images, were used as a test dataset for final testing. It is worth noting that the total dataset was imbalanced on a patient level with the majority being fractured patients; while on a slice level, the dataset is imbalanced in favor of the slices labeled as \"noFracture\". Therefore, the dataset overall is not as imbalanced in favor of \"fracture\" images as can be assumed by only evaluating the patient-level data.
## 4 Microstructural Analysis  
### 3D Reconstruction and Analysis  
Images of the cross sections of specimen, as shown in Figure 6, are obtained when CT scanning is performed along different directions. In each cross sectional image, pixels with different gray levels indicate pores and solids. To reconstruct the 3D model of pores and solids in the specimen out of the cross sectional images, Dragon fly (Object Research Systems, Canada) was applied.  
Figure 7a shows the 3D reconstructed model of the cylindrical sample A, from which the 3D models of the pore network (see Figure 7b) and the solid phase (see Figure 7c) can be extracted. As a result, the volumetric fractions of the pore and the solid can be determined and they are listed in Table 2 for all the samples.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Rock Sample**} & \multicolumn{4}{c}{**Carbonate**} & \multicolumn{2}{c}{**Sandstone**} \\ \cline{2-7}  & **A** & **B** & **C** & **D** & **E** & **S** \\ \hline Pore network (\%) & 23.41 & 7.71 & 9.32 & 8.51 & 7.35 & 1.06 \\ Solid (\%) & 76.59 & 92.29 & 90.68 & 91.49 & 92.65 & 98.94 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Volumetric fractions of pore and solid in the rock samples.  
Figure 5: Distribution of cross sectional porosity along the direction of \(Z\) axis. (**a**) Carbonate. (**b**) Sandstone.  
Figure 6: Cross sectional images of sample A when scanning along Z, Y, X axes.
## 3 Results
Specifically, one paper monitored sleep routine and stress levels during exercising for e-sports [29] and another documented sleep patterns as a part of training program [46]. Figure 4: Number of publications per author. Differentiating between research focusing on sports training monitoring and that which emphasized physical activity observation, we uncovered three primary categories: sport training (six studies), physical activity (twenty-six studies), and activities of daily living (nine studies). Some studies bridged these categories by examining both activities of daily living and physical activities (Figure 7). One paper observed sleeping routine and stress levels while training, which was included in sport training [29], while another paper examined activity of workload [44], which was included in activities of daily living. Also, several papers examined ADL in addition with physical activities [28; 30; 34; 39; 43]. Within these studies, the objectives varied, with 7 studies concentrating on assessing the reliability and accuracy of devices, while the remaining 29 utilized smart technology as a means of data collection. Various studies have performed interventions in their research using or monitoring with wearable devices [14; 16; 17; 23; 26; 37; 42; 45]. Two papers examined the correlation between physical activity or sport and the COVID-19 pandemic using wearables [26; 46]. Figure 5: Number of authors per paper. Figure 6: Most used measuring devices. In summary, our investigation encompassed multiple dimensions of wearable device research within the last three years. We examined publication trends, temporal shifts, authorship dynamics, institutional affiliations, device preferences, monitored activities, and the research aims associated with these investigations. This comprehensive analysis offers a rich understanding of the landscape of wearable device research and its evolution over this period.
## 2 Remarks on the Special Issue  
The current Special Issue includes eight valuable papers that demonstrate recent activities on energy-saving technology with regard to the propulsor, rudder, hull form, and cavity. The twisted rudder [3, 4] has been widely used to improve propulsion efficiency and prevent cavity risk. To improve performance and address the side effect of cavity formation, a new type of twisted rudder has been proposed. In recent times, post-devices have been widely studied because the performance improvement of pre-devices, such as the pre-swirl stator and duct, is almost saturated. Moreover, the combination of pre- and post-devices has been studied to maximize performance [5, 6]. The post-device of an efficient rudder has been emphasized in the current AS Special issue, where two papers deal with the unconventional twisted rudder [3, 4]. Furthermore, reliability based on increasing rudder force and attenuating cavity problems is important, although the energy-saving issue is one of the top priorities. [PERSON] et al. (2021) proposed a new type of wavy rudder that may ensure reliability and efficiency.  
The two papers [7, 8] that studied hull form optimization deal with a recent advanced hull form optimization technology. Although hull form optimization is almost saturated, there is scope for improvement, which seems to be fundamentally important and cost effective.  
Although the cavitation issue is not directly related to energy saving, it is closely related to the energy saving caused by decreasing skin friction or tip vortex. Two papers [9, 10] in the Special Issue relate to the aforementioned cases. Super-cavitation has been applied to torpedoes for decreasing hull friction. [PERSON] et al. [9] conducted a parametric study with experiments on the optimum super-cavitator, aiming for the efficient and stable performance of torpedoes. Another paper deals with the wing tip vortex and its cavitation, which is an important phenomenon for cavity inception and energy loss. Numerical investigation has been conducted to determine the scaling of cavity inception.  
As the number of issues regarding energy saving is increasing, the reliability of full-scale extrapolation from the model test becomes an important subject. Although numerical simulation for full-scale performance has improved, full-scale validation is still essential for accurate prediction. In addition, full-scale measurement [11] is considered important to address the EEXI issue. Information regarding the measurement technique seems practically useful as feedback for the actual design. In particular, information regarding thrust measurement is valuable because thrust is rarely measured during a sea trial.  
In the final study (the last paper), the Coanda effect was successfully applied to the propeller to significantly improve efficiency (+8.7%) [12]. Multi-design point ships, e.g., naval vessels, would be a proper target for application.
###### Abstract  
In post-disaster search and rescue scenarios, the accurate image segmentation of individuals is essential for efficient resource allocation and effective rescue operations. However, challenges such as image blur and limited resources complicate personnel segmentation. This paper introduces an enhanced, lightweight version of the Pyramid Scene Parsing Network (MHC-PSPNet). By substituting ResNet50 with the more efficient MobileNetV2 as the model backbone, the computational complexity is significantly reduced. Furthermore, replacing the ReLU6 activation function in MobileNetV2 with H-Swish enhances segmentation accuracy without increasing the parameter count. To further amplify high-level semantic features, global pooled features are fed into an attention mechanism network. The experimental results demonstrate that MHC-PSPNet performs exceptionally well on our custom dataset, achieving 97.15% accuracy, 89.21% precision, an F1 score of 94.53%, and an Intersection over Union (IoU) of 83.82%. Compared to the ResNet50 version, parameters are reduced by approximately 18.6 times, while detection accuracy improves, underscoring the efficiency and practicality of the proposed algorithm.  
MHC-PSPNet; post-disaster personnel; image segmentation; lightweight algorithm; attention mechanism +
Footnote †: journal: applied sciences  
0
Footnote †: journal: applied sciences  
0
Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Seafarer Fatigue, Sleep and Ship Noise
### Seafarers' Fatigue and Ship Noise
#### 2.3.4 Impaired Cognitive Function  
A paper investigates the relationship between noise-related sleep deprivation and impaired cognitive function among seafarers. The presence of noise disturbances during sleep significantly affects seafarers' cognitive abilities, including attention, memory, and decision-making skills. By examining relevant literature and empirical studies, this research aims to explore the specific effects of fatigue-related impairments on seafarers' safety, performance, and the increased risk of errors and accidents [49].  
The paper explores the effects of sleep deprivation caused by noise disturbances on seafarers' attentional abilities. It highlights that insufficient sleep due to noise disruptions compromises seafarers' ability to sustain attention and focus. This leads to decreased attention spans and reduced vigilance, potentially impacting their performance and safety [50]. Noise-related sleep deprivation negatively affects seafarers' memory function. Sleep plays a crucial role in memory consolidation, and disruptions caused by noise disturbances can impair the formation and retrieval of memories, leading to decreased memory performance [51]. Seafarers exposed to sleep deprivation due to noise disturbances experience impaired decision-making abilities. Sleep is essential for optimal cognitive functioning, and the lack of quality sleep hinders seafarers' ability to make sound judgments and decisions, potentially compromising safety, and performance [52]. The fatigue-related impairments resulting from noise-related sleep deprivation can compromise seafarers' safety and performance. Fatigue diminishes cognitive functioning, leading to slower reaction times, decreased situational awareness, and reduced problem-solving abilities [53].
# Channel Model for Estimating Received Power Variations at a Mobile Terminal in a Cellular Network
## 2 The Channel Model
Within this context, the transmitter gain in the horizontal plane is defined by Equation (4) [11]:  
\[G_{\mathrm{Tx},h}\left[\mathrm{dBi}\right]=G_{\mathrm{max}}\left[\mathrm{dBi} \right]-\min\left(12\cdot\left(\frac{\theta+\gamma_{h}}{\mathrm{HPBW}_{h}} \right)^{2},\mathrm{FBR}\right) \tag{4}\]  
where \(G_{max}\) is the maximum gain of the transmitting antenna, \(\theta\) is the angle measured from the maximum power direction indicated by the antenna, \(\gamma_{h}\) is the minimum angle of the antenna beam, \(HPBW_{h}\) is the half-power beamwidth of the horizontal plane, and \(FBR\) is the front-back ratio. By substituting Equation (4) into Equation (2), the following expression is obtained for a specific point:  
\[ \begin{split} P_{\mathrm{Rx},0}\left[\mathrm{dBm}\right]& =P_{\mathrm{Tx}}\left[\mathrm{dBm}\right]+G_{\mathrm{max}}\left[ \mathrm{dBi}\right]\\ &\quad-\min\left(12\cdot\left(\frac{\theta_{0}+\gamma_{h}}{ \mathrm{HPBW}_{h}}\right)^{2},\mathrm{FBR}\right)\\ &\quad+G_{\mathrm{Rx}}\left[\mathrm{dBi}\right]+20\log_{10}( \lambda)-10\log_{10}(4\pi)-20\log_{10}(R_{0})\end{split}  \tag{5}\]  
Similarly, Equation (6) is derived for another consecutive measurement point:  
\[ \begin{split} P_{\mathrm{Rx},1}\left[\mathrm{dBm}\right]& =P_{\mathrm{Tx}}\left[\mathrm{dBm}\right]+G_{\mathrm{max}}\left[ \mathrm{dBi}\right]\\ &\quad-\min\left(12\cdot\left(\frac{\theta_{1}+\gamma_{h}}{ \mathrm{HPBW}_{h}}\right)^{2},\mathrm{FBR}\right)\\ &\quad+G_{\mathrm{Rx}}\left[\mathrm{dBi}\right]+20\log_{10}( \lambda)-10\log_{10}(4\pi)-20\log_{10}(R_{1})\end{split}  \tag{6}\]  
To simplify the theoretical analysis, the receiving antenna at the mobile station is assumed to exhibit a constant gain and an omnidirectional radiation pattern. As shown, the differences between Equations (5) and (6) arise from the angular dependence and the specific distance at each point (\(R,\theta\)). Therefore, it is evident that the received power depends solely on the angle between the direction of maximum power--defined by the azimuth--and the vector connecting the base station to the point of analysis, as well as on the magnitude of that distance. Based on Equation (6), the variation in the received power (\(\Delta P_{\mathrm{Rx}}\)) between two consecutive points can then be determined, as shown in Equation (7):  
\[ \begin{split}\Delta P_{\mathrm{Rx}}&=P_{\mathrm{Rx},0}-P_{\mathrm{Rx},1}\\ &=-\min\left(12\cdot\left(\frac{\theta_{0}+\gamma_{h}}{\mathrm{HPBW }_{h}}\right)^{2},\mathrm{FBR}\right)-20\log_{10}(R_{0})\\ &\quad+\min\left(12\cdot\left(\frac{\theta_{1}+\gamma_{h}}{ \mathrm{HPBW}_{h}}\right)^{2},\mathrm{FBR}\right)+20\log_{10}(R_{1})\end{split}  \tag{7}\]