esa-sceva/satcom-chunk-collection huggingface dataset:
# HPS-Net: Multi-Task Network for Medical Image Segmentation with Predictable Performance
## 3 The Proposed HPS-Net
### Training Procedure  
This section describes our cooperative training mode. The training process mainly includes two parts--forward propagation and backward propagation. In forward propagation, each batch of the medical images is firstly input into the posterior network and the prior network. The outputs of the two sub-networks represent multiple multidimensional normal distributions, so as to fit their learned latent spaces respectively. Then, the Kullback-Leibler divergence of each level is calculated as the loss value based on the outputs of the two sub-networks to describe the difference between the learned latent spaces. With the learned latent space of the prior network, samples can be generated and then be input into the likelihood network to get the segmentation hypotheses. By comparing the output segmentation hypotheses and the ground-truth segmentations, the cross entropy losses are computed. After that, the segmentation hypotheses are input into the measure network to generate the predicted measurement value, where the measure loss is applied for calculating the difference between the predicted measurement value and the ground-truth measurement value. The ground-truth measurement value is initially unknown, but it can be calculated with the output segmentation hypotheses and the ground-truth segmentations.  
In backward propagation, the gradients of the loss function with respect to the weights of the network are calculated, and this calculation process proceeds backward through the network from the final layer to the first layer. Specifically, the network parameters in the posterior network and the prior network are updated by the calculated Kullback-Leibler divergence of each level. The network parameters in the likelihood network are updated by the cross entropy losses, and the measure loss corresponds to the parameter update in the measure network. It should be noted that although there are connections between the likelihood network and the measure network, we limited the backpropagation starting from the measure loss to the range of the measurement network, so as to avoid interference with the prediction of the segmentation hypothesis. The model was trained using the Adam optimizer with a learning rate of 10\({}^{-3}\) and a batch-size of 12. All the input images and the ground-truth segmentations were resized to 128 \(\times\) 128. Batch-normalization was applied on all non-output layers. All models were trained for 36 h on an NVIDIA TESLA V100 GPU. After a model is trained, the segmentation hypotheses and the corresponding measurement values for an input image can be generated by first inputting the image to the prior network, then obtaining the samples using the prior network and finally generating the segmentation hypotheses and the measurement values using the likelihood network and the measure network, respectively.
# A Review on the Use of Microalgae for Sustainable Aquaculture
## 3 Microalgae-Assisted Aquaculture
### Technologies for Biomass Harvesting  
#### 3.4.1 Criteria for Harvesting Technology Selection  
To use microalgae biomass in various industries, previous studies have developed many harvesting technologies, such as centrifugation, filtration, gravity-driven sedimentation, flocculation by positively charged ions, harvesting by edible fungi, and flotation [65; 66]. In a real-world application, however,not all of these technologies are suitable to the biomass harvesting for aquaculture practice. To our knowledge, in the aquaculture industry, the biomass harvesting technologies should meet three specific requirements. First, the harvesting process should be performed at a low cost to improve the competitiveness of microalgae feed over traditional feed. Second, no toxic or unhealthy chemicals could be used in the harvesting process. Otherwise, contaminated biomass may negatively impact the safety of aquaculture products or even cause the failure of the aquaculture. For example, the accumulation of aluminum ions and polyacrylamide, which have been widely used for microalgae biomass harvesting at full-scale, in food chain may cause serious safety problems. Third, as the harvesting step could directly impact the hydraulic retention time (HRT) of the wastewater treatment system and water circulation frequency of aquaculture system, the harvesting process should be efficient and time-saving. According to the criteria mentioned above, two advanced technologies, fungi-assisted harvesting and flotation, may be applicable in microalgae-assisted aquaculture (Table 2).
# An Overview of Biopolymers for Drug Delivery Applications
## 2 Types of Biopolymers Used in Drug Delivery Systems
### Polysaccharides
#### 2.1.1 Chitosan
Due to their excellent functional properties in DDSs and decisive, non-invasive, and focused tissue locations, injectable hydrogels have attracted attention lately. Thus, designing DDSs to be responsive to hydrogel stimuli and release a drug to an external stimulus while having different advantages is an ambitious task. In this regard, chitosan-based hydrogels offer high potential for tissue engineering and drug delivery due to their biocompatibility, mucosal adhesion, and hemostatic activity [49].
## 3 Results
### Color and \(\beta\)-Carotene Content
At the same time, 35 kHz can be detected by the casual observer. This description is based on the results by [PERSON] and [PERSON] [54], in which the human perception of colors classifies the asymptotic color differences \(\Delta\)E. It is postulated that absolute color differences between 0 and 2 are unrecognizable, those between 2 and 3.5 are discernible by an uninformed observer, and above 3.5, a distinct color deviation is observed. These small changes confirm the lack of effect of ultrasound on carotenoid pigments, which has also been confirmed by tests in purees with carotenoid content. Regarding \(\Delta\)E and color coefficients, comparable outcomes have been documented in carrot juice subjected to ultrasound and high-voltage cold plasma treatments [53]. Researchers conducted tests involving non-thermal treatment of carrot juices, including using ultrasounds. Their findings indicated increased a* and b* values and minor changes in \(\Delta\)E. An exception was carrot puree treated with US at 35 kHz, for which the value was significantly different from the original juice (20.66 \(\pm\) 0.45). This is associated with significantly increased redness and yellowness values (Figure 3a,b). The high temperatures during sonication can lead to color degradation due to the isomerization of carotenoids. However, in this study, only a slight increase in temperature (1-5 \({}^{\circ}\)C) was observed, regardless of the carrot puree and extract type. Additionally, the total carotenoid content change was insignificant (Figure 4), suggesting that it could not have caused such a considerable difference in \(\Delta\)E. This indicates that the color change may result from the cavitation process, where rapid particle transfer within the entire volume may cause the disintegration of larger structures [55; 56] or may be due to the release of intercellular contents [26]. The concentration of carrot puree extract positively affected the carotenoid content (Figure 4). Following the sonication process, a trend in carotenoid content was noted, although the increase was not statistically significant. Previous research by [PERSON] et al. [57] indicated that the degradation rate of \(\beta\)-carotene decreases with higher ultrasonic power intensity and temperature. In previous studies, an increase in carotenoid content in sonicated carrot juice was observed [53; 58]. However, in some research, these changes were not statistically significant [53]. Our study also found similar results, as statistical analysis revealed no significant relationship between changes in carotenoid content and the use of ultrasound treatment.
# Chaotic Synchronizing Systems with Zero Time Delay and Free Couple via Iterative Learning Control
## 4 Conclusions  
This research exhibited the design of an iterative learning controller and the results of the simulation with an example to prove the mathematical theory of the chaotic system synchronization via the iterative learning control law. The demonstrations verified the mathematical theory as able to approximate the synchronization between systems. The ILC method is a convenient method to trace the trajectory of systems, but is not a perfect tracking for all situations. In addition, the iterative learning control law should be conditionally dependent on the system and would not be unique to the specific system. It was a significant challenge to find coefficient matrices that were the combination of the previous ILC law and the trajectory error in this research, respectively. Finally, the example in this article has not only verified the theory and concept in this paper, but also demonstrated the behavior of parameters in non-linear system control fields, which has not yet been completely discussed in other papers. The ILC method could be used for the non-linear system with time-delay and couple to adjust the learning control law, and the process should apply to adaptive control, sliding mode control, and fuzzy control. The primary research is essential for tracking systems such as robotic systems, secure communication systems, image identification systems, and many others, which are part of future developments and applications.  
[PERSON] conceived and designed the simulation; [PERSON] performed the simulation; [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Incorporation of Torsion Springs in a Knee Exoskeleton for Stance Phase Correction of Crouch Gait
###### Abstract  
Crouch gait is a motor complication that is commonly associated with cerebral palsy, spastic diplegia, stroke, and motor-neurological pathologies, broadly defined as knee flexion in excess of 20\({}^{\circ}\) in the gait cycle. Uncorrected crouch gait results in fatigue, joint degradation, and loss of ambulation. Torsion springs have been used in cycling to store energy in the knee flexion to reduce fatigue in the quadriceps during knee extension. SolidWorks was used to design a passive exoskeleton for the knee, incorporating torsion springs of stiffnesses 20,000 N/mm and 30,000 N/mm at the knee joint, to correct four different crouch gaits. OpenSim was used to gather data from the moments produced, and knee angles from each crouch gait and the normal gait. Motion analysis of the exoskeleton was simulated using knee angles for each crouch gait and compared with the moments produced with the normal gait moments in the stance phase of the gait cycle. All crouch gait moments were significantly reduced, and the correction of peak crouch moments was achieved, corresponding to the normal gait cycle during the stance phase. These results offer significant potential for nonsurgical and less invasive options for wearable exoskeletons in crouch gait correction.  
crouch gait; torsion springs; knee exoskeleton; stance phase; gait rehabilitation Article
## 3 Empirical Analysis and Problem Statement
### Empirical Analysis  
We conducted an empirical analysis to gain a precise understanding of the usage details of the Lua language in firmware. First, 552 firmware programs from 7 vendors were selected as the initial dataset. We wrote a program to automatically extract the system(rootfs) file from these firmware programs. Then, we traversed their rootfs to identify and record the language type of each script program (Lua, asp, php, etc.). The results regarding the Lua language are recorded in Table 2. The first row represents the brand of the images, and the second row represents the total number of images involved in the empirical analysis. If the number of Lua programs in the file system exceeds 50, we classify an image as special, while the third row records the number of special images. The fourth row (%) records the percentage of special images among all surveyed images, while the last row records the average number of Lua programs in the special images. The results presented in Table 1 reveal three facts.  
Fact-1: IoT vendors widely use Lua programs in their product development, accounting for up to 56.70% of all surveyed images.  
Fact-2: When vendors use Lua for development, it is usually in the form of multiple files.  
Fact-3: The use of Lua programs is not limited to a single vendor but multiple vendors.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Brand** & **Xiaomi** & **Tplink** & **Trendent** & **Linksys** & **Netgear** & **Totolink** & **Zyvel** \\ \hline Images & 192 & 133 & 81 & 53 & 39 & 23 & 31 \\ \hline Lua \textgreater{} 50 & 184 & 69 & 25 & 16 & 5 & 5 & 9 \\ \hline \% & 95.83\% & 51.88\% & 30.86\% & 30.19\% & 12.82\% & 21.74\% & 29.03\% \\ \hline Avg. & 209.39 & 168.39 & 173.68 & 149.63 & 125 & 117.34 & 95 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information about representative experimental platforms in empirical analysis.
## 3 Workflow Description
### Simulation Set-Up  
Once the definition of the control surfaces for monitoring the volumetric flow rate in each radiant channel has been completed, T-WorkFlow executes the CFD solver. In this regard, the _simpleFoam_ solver from OpenFOAM based on the SIMPLE algorithm [28] was chosen, as it is suitable for steady-state incompressible flow simulations, also considering the Reynolds-Averaged Navier-Stokes (RANS) simulations closed by different turbulence models. In this regard, in the T-WorkFlow graphical interface, we limited the user selection among the (i) \(k-\epsilon\)[22]; (ii) \(k-\omega\)[23]; (iii) \(k-\omega\) SST [24] models (cf. Section 3.1). The boundary conditions automatically imposed at the different surfaces of the radiator are tabulated in Table 1, where the subscript \"WF\" stands for \"Wall Function\", while the subscript \"ifd\" stands for \"internal field\" and \(U_{x};U_{y};U_{z}\) are the velocity vector components imposed as uniform at the inlet. These are derived from the volumetric flow rate at the inlet specified by the user (cf. Section 3.1), assuming the velocity vector is parallel to the normal of the inlet. The direction of the normal and the area of the inlet are automatically captured due to the numpy-stl library.  
The turbulence model variables \(k_{ifd}\), \(\epsilon_{ifd}\), and \(\omega_{ifd}\) are imposed as uniform on the different surfaces and they are evaluated following the indications in [29], as follows:  
\[k_{ifd}=1.5\left\|\mathbf{U}_{in}\right\|^{2}Tu^{2},\quad\epsilon_{ifd}=C_{\mu} \frac{k_{ifd}^{1.5}}{l},\quad\omega_{ifd}=\frac{\epsilon_{ifd}}{C_{\mu}k_{ifd}}, \tag{9}\]  
where \(\left\|\mathbf{U}_{in}\right\|\) is the magnitude of the inlet velocity vector, \(Tu\) is the turbulence intensity as imposed by the user (cf. Section 3.1), \(C_{\mu}=0.09\) is a model coefficient, and \(l=0.1D_{in}\), with \(D_{in}\) being the inlet diameter, is a reference length.
# Estimation of Stature from Tibia Length for Romanian Adult Population
###### Abstract  
The estimation of stature from bones plays an important role in identifying unknown bodies, body parts, or skeletal remains, as it is an important component of the biological profile in forensic identification. The objective of this study is to enhance the development of forensic anthropology in Romania by creating a new regression formula for stature estimation. This can be accomplished from the length of the tibia, taking into account sex and age, for the Romanian adult population. A total of 137 cases from a delimited territory of Romania were included in the study. The length of the tibia and the stature of the individuals were measured before autopsy. Statistical analysis was carried using (Statistical Package for the Social Sciences) SPSS 23. Tibia length showed a high correlation with stature for males, females, and the total sample. Four regression formulae were created for stature estimation of Romanian males, females, and unknown sex. The results of the analysis are impressive and have a good applicability in a forensic anthropological context. The data used in this paper provide reliable results with a large applicability in the future for estimating stature from the tibia length for the Romanian adult population.  
Keywords: stature estimation; tibia length; forensic anthropology +
Footnote †: journal:  
and on methods that help create accurate individual biological profiles from human remains [6; 7; 8; 9]. Thus, continuous work is being carried out to develop new methods that provide information with the highest degree of accuracy and up-to-date standards for contemporary population groups [2; 8].  
The main purpose of current research on stature estimation is based on mathematical formulas that help the forensic field to improve stature estimation methods. The literature shows an interest in specific population research (Bosnian, Turkish, and Mexican populations, etc.) [10; 11; 12; 13].  
Despite the recent developments in the forensic anthropology discipline, there are no anthropometric studies for the adult population in Romania. In this vein, the present study aims to provide standards for sex and stature estimation for contemporary Romanians using tibial length, and to set the basis for acquisition of biological data for this population that can lead to new methods for estimating other aspects of biological profile, such as age, sex, and ethnicity. The novelties of the current research compared to the existing ones, first of all and most importantly, are represented by the fact that it was performed only on the Romanian population, so it is a specific population study, and secondly, regression formulas were created that can be applied in the practice of forensic anthropology on the Romanian territory.
## 2 Materials and Methods
### Force Generation Mechanism  
In the designed mechanism, a specific spring with a predetermined spring constant is used. Before the activation phase, the FCC is closed, the liquid's flow rate is set to zero, the spring is compressed, and the required energy for the distraction process is stored in the system. When the device is activated and the FCC is unlocked, the spring starts to expand smoothly and continuously based on the flow rate of the liquid under the FCC component's control. The spring generates a continuous pushing force while expanding. There is a maximum and minimum amount of pushing force that the spring generates when expanding, which is based on the spring constant. The maximum pushing force is generated at the start position of the distractor when the spring is fully stretched, and the minimum generated force is at the end of the expansion when the spring is at the final position. It is important to use a spring with a predetermined spring constant that can generate sufficient pushing force during the activation of the device. The restoring force pushes the cylinder and the moving part of the distractor, and the BS eventually reaches the final position. Equation (1) presents [PERSON]'s law, which describes the elastic deformation of a solid in a linear special case of the law of elasticity. [PERSON]'s law shows that the spring deflection depends linearly on the acting force:  
\[F_{\text{s}}=-K\cdot x \tag{1}\]  
where \(K\) is the spring constant (N/mm), \(x\) is the spring travel (mm), and \(F_{s}\) is the acting force (N). The minus sign shows that, relative to the resting position, the spring force direction is opposite to the deflection direction. The spring constant can be specified using Equation (2), as presented below:  
\[K=\frac{G\cdot d^{4}}{8\cdot n\cdot D^{3}} \tag{2}\]  
where \(G\) is the shear modulus of the spring material (N/mm\({}^{2}\)), \(d\) is the wire size (mm), \(D\) is the mean coil diameter (mm), and \(n\) is the number of turns. Therefore, by using a suitable spring with a predetermined spring constant, generating sufficient DF during the activation phase can be achieved.
## 2 Materials and Methods
### Feature Extraction Techniques
#### 2.1.3 Discrete Wavelet Transform  
As its name implies, the DWT is any WT where its wavelet function is discretized and, as with the CWT, captures the information in a time domain and scale.  
DWT uses a family of orthonormal wavelets with unit energy, described by  
\[\psi_{a,b}(t)=2^{-a/2}\psi(2^{-j}t-b) \tag{8}\]  
where, as in the CWT, \(a\) is the scaling parameter and \(b\) the translation parameter of the mother wavelet \(psi\), being Daubechies 4 the mother wavelet used in this algorithm. To analyze the spectral resolution of the data, it is necessary to enter the scaling function defined as [30]:  
\[\phi(t)=\sum_{k=-1}^{N-2}(-1)^{k}C_{k+1}\psi(2t+k) \tag{9}\]  
The \(C_{k}\) are known as wavelet coefficients. These coefficients are arranged as a transformation matrix that is applied to a vector of data. In this way, the coefficients are stated in two different patterns, one that works as a fading filter (low-pass filter) and the other as a pattern that shows only the details of the information (high-pass filter).  
This concept of analyzing a signal using filter banks is known as Mallat tree decomposition. Inside the green box in Figure 6, we can see how a signal \(x(n)\) is decomposed into approximations \(a_{j}(n)\) and details \(d_{j}(n)\) by the effect of the filters \(h_{j}(n)\) and \(g_{j}(n)\).  
Once the multiresolution analysis has been applied, the energy of each detail and the last approximation \(x[n]\) is calculated, defined as:  
\[E_{x}=\sum_{-\infty}^{\infty}|x[n]|^{2} \tag{10}\]  
As in MFCCs, the signal was segmented into 12 windows of 512 data points, where the energy was calculated by the level of decomposition. In Figure 5, the average energy and its standard deviation of each segment per class are shown.  
Figure 5: Energy calculation: The red line and the blue shadows are the averages and their standard deviation for each record per class, respectively. The vertical lines separate the decomposition levels in the following order. Approximation 7 (A7), Detail 7 (D7), D6, D5, D4, D3, D2, D1.
## 5 Experimental Verification and Analysis
The measured average frequency resolutions were 101.17 kHz and 102.2 kHz, individually, calculated from the high resolution sampling data at 10 kHz. It can be seen that the two values were particularly close to the maximum achievable frequency resolution (\(\frac{1}{T_{c}}=100\) kHz) of the classical CTS system with the SAW filter as the compressor.