esa-sceva/satcom-chunk-collection huggingface dataset:
# Reconstruction of Preclinical PET Images via Chebyshev Polynomial Approximation of the Sinogram
## 3 Materials and Methods
### Image Metrics
#### 3.4.2 Recovery Coefficient  
The recovery coefficient, denoted by \(RC\), is defined as the measured activity concentration in each hot fillable rod of the IQ phantom, divided by the mean activity concentration in the uniform cylindrical chamber, namely:  
\[RC_{i}=\frac{m_{i,h}}{m_{u}}, \tag{17}\]  
where \(m_{i,h}\) and \(m_{u}\) denote the average measured counts in the region-of-interest (ROI) corresponding to each hot rod, \(i=1,\ldots,5\), and in the uniform area, respectively, and \(a_{i,h}\) and \(a_{u}\) denote the actual counts in the ROI corresponding to each hot rod, \(i=1,\ldots,5\), and in the uniform area, respectively. The measurements corresponding to Equation (17) are averaged over all realizations, \(R\), namely:  
\[m_{i,h}=\frac{1}{R}\sum_{r=1}^{R}m_{i,h}^{(r)},\quad\text{and}\quad m_{u}=\frac{1 }{R}\sum_{r=1}^{R}m_{u}^{(r)}, \tag{18}\]  
where \(m_{i,h}^{(r)}\) and \(m_{u}^{(r)}\) denote the measured counts in the ROI corresponding to the hot rod \(i\) and the uniform region, respectively, in the realization \(r\). It is worth noting that the recovery coefficient (\(RC\)) quantifies the \"activity recovery\" in environments with zero activity background [30], therefore is indicative of the spatial resolution of the imaging system [31; 32; 33; 34]. The values of \(RC\) vary between 0 and 1. Furthermore, we note that, in the literature, the recovery coefficient is also referred to as \"hot recovery coefficient\" [35], similar to the so-called _contrast recovery coefficient_[36].  
It is worth noting that for the \(RC\) calculations related to the simulated IQ phantom, we have followed the methodology of the NEMA protocol [23]. In particular, according to the NEMA protocol, the image slices covering the central 10 mm in length of the five fillable rods were averaged, in order to obtain a single image slice of lower noise. Around each fillable rod, circular ROIs with diameters twice the physical diameter of the rods were drawn in the averaged image. The maximum values in each of these ROIs were measured. The transverse image pixel coordinates of the locations with the maximum ROI values were recorded and used to create line profiles along the five fillable rods in the axial direction. The pixel values measured along each axial line profile, divided by the mean activity concentration found in the uniform cylindrical chamber of the phantom, were used to determine the mean of the \(RC\) for each rod size.  
Furthermore, we included the contrast-to-noise-ratio (\(CNR\)) [37; 38], given by  
\[CNR=100\times\frac{RC}{\%STD}, \tag{19}\]  
where \(RC\) and \(\%STD\) are defined in Equations (16) and (17), respectively. The \(CNR\) metric combines both metrics presented above.
# Research on a Random Mask Infection Countermeasure against Double Fault Attacks
## 4 Experimental Analysis  
### Verification of Against Double-Fault Attacks  
The double-fault attacks assumed in this paper refer to a class of double-fault attacks that force single-bit data at the same location in two encryption paths to change to 0 or 1. To prove the effectiveness of our proposed random mask infection countermeasure to defend against double-fault attacks, the AES cryptographic circuit incorporating this countermeasure is simulated and attacked. In this paper, the AES is utilized with count mode. Assuming the location of the fault injection occurs after the \(R-1\) (\(R\) represents the number of rounds of the AES, and for AES-128, \(R=10\)) round of key-add operation andbefore the last round (that is, the \(R-\)th round) of subbytes (SB) operation, the schematic diagram of the double-fault attack is described in Figure 4.  
In Figure 4, the random mask infection countermeasure unit implements the mask, unmask, and random diffusion of fault data, and infects the diffused fault data back to the original encryption path (see Algorithm 1 for specific description). Here, we take AES\(-\)128 as an example to discuss, then Round R\(-\)1 is the 9 th round of the AES\(-\)128 cryptographic circuit. A and A' represent the state matrix after the completion of the 9 th round of encryption, N\({}_{1}\) and N\({}_{2}\) are random numbers used for the mask, and N\({}_{1}\)\(\
eq\) N\({}_{2}\). Ciphertext C is the final output ciphertext of the circuit.  
Select an arbitrary pair of plaintext P and key K as follows:  
P: f3 01 a6 8a 9e 9f fa 50 84 45 81 d9 e2 90 d8 18  
K: 09 36 3c fc c8 d3 9c a3 7f 42 7e da da b2 c2 82  
The random masks N\({}_{1}\) and N\({}_{2}\) are as follows:  
N\({}_{1}\): d0 71 6a 29 02 14 4e ge d5 43 71 63 ec 8f a07  
N\({}_{2}\): d8 bf 52 38 fs 54 80 65 f4 f6 e1 ec e8 72 49 a5  
When the 9 th round of encryption operation ends, the intermediate states A and A' at the end of the 9 th round of encryption are as follows:  
A: 79 71 3f 8b 57 e3 4f 9d 93 c8 66 bb 06 4e 2e 76  
A: 7b la 81 47 5f 45 51 d5 f9 e0 e8 23 a4 cc 50 01  
The random numbers N\({}_{1}\) and N\({}_{2}\)' (N\({}_{1}\)' and N\({}_{2}\)' correspond to N\({}_{1}\) and N\({}_{2}\), respectively) corresponding to A and A' demasking are as follows:  
N\({}_{1}\)': ba bc 39 9e 37 96 ac d2 60 65 f4 4b 75 71 b5  
N\({}_{2}\)': b8 d7 87 63 96 91 88 e4 b8 48 5b bc 15 d5 65 d2  
When there is a fault induced in the first byte of A and A' at the same time (this means that there is a double-fault attack), then, the values of A and A' become as follow:  
A: 70 71 3f 8b 57 e3 4f 9d 93 c8 66 bb 06 4e 2e 76  
A: 70 1a 81 47 5f 45 51 d5 f9 e0 e8 23 a4 cc 50 01  
The difference M = (A\({}_{0}\oplus\)N\({}_{1}\)')(A\({}_{0}\)'\(\oplus\)N\({}_{2}\)') at this time is:  
M: 02 00 00 00 00 00 00 00 00 00 00 00 00 00 000  
From the above experiments, it can be seen that when the same fault is injected, M \(\
eq\) 0, the adversary cannot bypass our countermeasure, which indicates that the random mask infection mechanism designed in this paper can defend against double-fault attacks.
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 4 System Model  
Figure 2 shows the 5G RAN architecture considered in this work. It is a type of Cloud RAN with three tiers, namely macrocells, microcells, and femtocells. The macrocell BS, i.e., gNB, can be functionally split into one centralized unit (CU) and one or more active antenna units (AAU) and distributed units (DU). The AAU is an antenna-integrated remote radio head (RRH), while both DU and CU constitute the base band unit of the Cloud RAN. The DU comprises the physical, medium access control, and radio link control sublayers, whereas the CU comprises the remaining higher sub-layers. Both microcells and femtocells operate the same 5G radio access technology as the macrocell, but with different operating parameters such as different frequency, bandwidth, and transmit power.  
This work also considers the deployment of two types of MEC platforms: aggregated edge at the CU of gNB and far edge at each microcell. By using the network function virtualization (NFV) technique, instances of two 5G core network functions, AMF and UPF, can be deployed and hosted in the aggregated edge. The virtualized AMF can reduce the handover latency, while the virtualized UPF, along with the far edge at microcells, can improve the performance of user application traffic.  
Finally, three types of users, namely motorists, cyclists, and pedestrians, are considered, representing users of high, medium, and low mobility, respectively. As the users move, their connectivity to the network can be switched between cells of the same or different tier, as determined by the handover mechanism.  
Figure 2: Three-tier 5G RAN architecture with MEC deployment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Ref.** & **Solution Summary** & **Benefits and Drawbacks** \\ \hline \multirow{6}{*}{[24]} & \(\bullet\) & Proposes a HO algorithm for users of MAR applications in & \(\bullet\) & Reduced UE experienced delay \\  & & 5G-MEC network & Drawbacks: \\  & & Jointly considers RSS of BSs & \(\bullet\) & Potential performance tradeoff between MEC and non-MEC applications \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
## 3 Effects of the Mortar Disengagement on Dynamic Performances of the Vehicle- Track System
### Impacts of Mortar Disengagement on the Track Performances  
#### 3.2.1 Displacements of Track Structures  
Figure 12 compares the rail displacements under different mortar disengagement conditions in the time domain. The rail deflection peaks present the passage of four wheelsets of a vehicle. In the figure, the dynamic behavior of rail depends on LMD and the amplitudes of rail displacements increase with the increase of LMD. Meanwhile, Figure 12 also implies that only the amplitude of rail displacement increases when LMD is less than 1 m. However, with LMDs of 1.5 m and 2 m, there is a small rail deflection due to the self-weight of track system. Moreover, a visible local oscillation appears in the zone in red circle when the wheelsets pass through the signal acquisition point. The local disturbance corresponds to the dynamic interaction during the unloading phase as the phenomenon explained in the literature [25, 26]. Furthermore, this phenomenon suggests that mortar disengagement with large LMD aggravates the rail vibration for a while. Since the mortar disengagement is set under the end of the third slab, after the attenuation effect of fasteners, there is no obvious oscillation in rail deflection when the vehicle initially enters the disengagement zone.  
Figure 11: Vertical car body accelerations under different mortar disengagement conditions.  
Figure 12: Vertical rail displacements in time domain with an HMD of 2 mm.  
The maximum vertical displacements of rail and slab and the corresponding change rates with different LMDs and HMDs are presented in Figure 13. Overall, the changes of rail displacements due to mortar disengagement are obviously smaller than that of slab. Under the same mortar disengagement condition, the change rate of slab displacement is approximately twice that of rail displacement. The main reason is that the mortar disengagement directly leads to insufficient support at the bottom of slab. However, fasteners connecting the rail and slab weaken the force transmitted from slab and the effect of mortar disengagement on the rail is reduced.  
As shown in Figure 13a, the maximum rail displacements are hardly affected by mortar disengagement when LMD is no more than 1 m. When LMD reaches 2 m, the maximum rail displacements increase sharply with the increase of HMD and exceed the critical value of 2 mm with HMD of 1 mm. With the deterioration of mortar disengagement, the maximum rail displacements are increased from 1.18 mm to 2.87 mm, and nearly achieve an increase of 145%. As seen in Figure 13b, the maximum slab displacements increase significantly as soon as the mortar layer is separated from the slab. The growth rate of the slab displacement amplitude is as high as 272% with an LMD of 2 m and HMD of 4 mm. When LMD reaches 1.5 m, the maximum slab displacement increases significantly and is about to exceed the safety limit of 0.5 mm.
###### Abstract
Even though the FEA method was extensively used in the evaluation of a 3D model in terms of the stress distribution [8], strain measurement and mechanical properties, the geometrical and volumetric changes in the simulation can also be analyzed. For instance, in Reference [9], an approach to modelling the contact interface between transtibial residuum and prosthetic socket has been developed. However, the presented model was used to investigate the stress distribution along the residuum and no indication of comparison with experimental comparison were observed. Volumetric changes constitute an important quantitative factor for the determination of the level of comfort during the period the prosthetic socket is worn. In Reference [10], they concluded that patient-specific analyses of the residuum were important for evaluation of potential deep tissue injury as a result from soft tissues changes inside the prosthetic socket. The outcome enables the expert designer to modify the prosthesis and thus improve the stability and durability of the prosthetic socket. Combination of the FEA method with image processing allows the development of a geometrical model system for pre- and post-fabrication evaluation of prosthetic devices. In this study, we propose an overall evaluation scheme of two 3D transfemoral prosthetic socket models constructed using magnetic resonance images (MRI) (based on the UCLA and manual casting compression technique (MCCT)) in conjunction with a finite element method (FEM). The evaluation is based on the geometrical and volumetric changes of a residuum model before and after the simulation of the donning procedure. The geometrical model is compared with the model developed with precise image processing (IP) software that was considered as most accurate model to the real residuum. The procedure adopted in this study includes the following steps. First, the finite element models of the residuum and prosthetic socket were developed using MRI with the aid of CAD software using a construction methodology created by our team. The donning simulation was then performed which was created based on real-fitting conditions. The simulation results are converted to cross-sectional data along the z axis and reconstructed to imitate the MRI image specifications. The soft tissue volume is calculated based on the reconstructed simulated images. Finally, the volume calculations are compared with the experimental outcomes. The study is motivated by the high demand for the prosthetic socket in recent years but the process of socket fabrication requires special treatment and craftsship from prosthesis and an alternative method of pre-fabricated socket evaluation is necessary to shortening the overall process.
# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
## 6 Discussion
3 By integrating remote sensing and meteorological data, a real-time grassland biomass monitoring and early warning system can be established to assess risks such as climate anomalies and grassland degradation, providing technical support for ecological protection and disaster reduction. Additionally, atmospheric effects, such as water vapor and aerosol scattering, may introduce spectral biases in the shortwave infrared (SWIR) bands used for vegetation water content retrieval. For example, undetected aerosol particles could attenuate incident radiation, leading to underestimated AGB in hazy conditions. Future studies could mitigate this by implementing advanced atmospheric correction algorithms (e.g., FLAASH or ACORN), which refine surface reflectance by removing atmospheric interference. The 30 m spatial resolution of Landsat 9 imagery may also obscure microscale heterogeneity within grassland ecosystems, such as small-scale variations in soil moisture or grazing intensity. To address this, fusing Landsat data with higher-resolution sensors (e.g.,Sentinel-2 at 10 m or UAV-based hyperspectral imagery at sub-meter scales) could capture fine-scale patterns. Alternatively, downscaling techniques like regression kriging could enhance spatial detail while retaining regional coverage.
## 2 Materials and Methods
### Suspension  
Researchers obtained the SE from the secondary settling tank at the Chengkai WWTP in Changchun City, China. To avoid the effects from microbes, the collected SE was disinfected with sodium hypochlorite, ensuring that the Cl\({}^{-1}\) concentration was around 0.500 mg/L during recharge [16]. The disinfected SE's turbidity was 1.84 NTU, and the SS was 16.5 mg/L. The median particle size was 32.4 \(\upmu\)m. The particle density was 1088 kg/m\({}^{3}\). A background solution (SE filtered through a 0.220 \(\upmu\)m membrane) was used to dilute the SE and obtain various turbidity and SS values. The background solution was maintained at an identical set of hydrochemical conditions (turbidity = 0.160 NTU and SS = 1.67 \(\pm\) 0.380 mg/L) across all experiments.  
The study tested two SE dilution conditions: first, turbidity = 0.540 \(\pm\) 0.050 NTU, SS = 5.02 \(\pm\) 0.460 mg/L, and mean particle size = 25.8 \(\upmu\)m; second, turbidity = 1.09 \(\pm\) 0.050 NTU, SS = 9.89 \(\pm\) 0.520 mg/L\({}^{1}\), and median particle size = 29.61 \(\upmu\)m (see Figure 1c,d for floc characteristics). Overall, particle-size distribution in the SE was similar to typical SS characteristics from a previous report [20]. Water-quality parameters of the SE are listed in Table S2.
# Development of Parameters towards Voice Bifurcations
## 2 Materials and Methods
### Tonal Frequency Selection Algorithm for Glottal Source Signals  
The computation of the HDF and BI hinges on a proper strategy for selecting the two peaks, \(f_{0}\) and \(f_{1}\). The proposed approach is based on a periodogram, which is a nonparametric power spectral density (PSD) estimate. The key assumption of the algorithm is that the strongest spectral peak corresponds to the first harmonic of the voice signal (only if its harmonic structure is apparent). This is a reasonable assumption for a glottal source signal such as glottal area waveform. Note that this algorithm is not suitable for acoustic signals because the vocal tract effect can cause acoustic signals to violate this assumption.
## 1 Introduction
CENTERA eliminates the power-consuming reputation inquiries and computations required by a distributed approach; nodes are required to send minimal additional information, namely their next hop and a counter (p_counter) for the downlink neighbor (DL) towards the BS and each uplink neighbor, showing the number of packets sent through and forwarded from this neighbor. For the proper functioning of our routing protocol and the necessary validation of the nodes to each other and to the base station, CENTERA uses a secure and efficient authentication scheme suitable for the extremely limited sensor nodes in WSNs providing acceptable security levels while requiring minimal processing power and data transmission overhead. Based on the target environment, the attacker model, and the sensitivity of the data being collected, CENTERA uses the most appropriate authentication scheme--namely the symmetric key cipher RC5 in case of a safe environment under close administration; or the asymmetric key cipher identity-based encryption--elliptic curve cryptography (IBE_ECC) or PBC in the case of a hostile environment with a strong attacker model. The decision as to which technique to choose and the sizes of the keys and Message Authentication Code (MAC) and their installation in the sensor nodes occurs in the initialization phase prior to launching. The rest of the paper is organized as follows: Section 2 surveys the previous work in the area of trust-based routing protocols and authentication techniques used in wireless sensor networks. Section 3 presents the system model with the corresponding building blocks, definitions, and parameters. Section 4 explains a list of attacks and misbehaviors and analyzes the methods of detection by CENTERA. TOSSIM simulation verifications and some energy calculations are presented in Section 5. Finally, Section 6 presents some conclusions and future work.
## 2 Materials and Methods
### Study Visits and Intervention
#### 2.4.2 Baseline Visit  
Clinical parameters were recorded on all teeth, except the third molars. After the clinical examination, subjects were randomly assigned to one of the two groups: the test toothpaste (Bexident(r) Encias Uso Diario, ISDIN, Barcelona, Spain), containing CPC and cynenol as active ingredients; and the control toothpaste (Colgate Protection Caries toothpaste, Colgate-Palmolive Espana S.A., Madrid, Spain), containing fluoride and sodium monofluophosphate as active ingredients. No further treatment was provided during the study period. Participants received standardized verbal and written instructions asking them to brush three times per day, for two minutes, after breakfast, lunch and dinner, with a provided manual toothbrush (UltraThin ProGumCare(r) OralB, Madrid, Spain) and the assigned toothpaste, and were asked to fill out the provided compliance forms, as well as any comments or adverse effects experienced during the study period. All subjects were blinded to their product assignment and received their product kits, which included the manual toothbrush and the appropriate amount of the randomly assigned toothpaste in a white tube (coded A or B for the study coordinator, and with their unique identification code for the patient). No further toothpushing or interdental cleaning instructions were provided.  
2.4.3. Week 3 and Week 6 Visits  
Clinical parameters were recorded during the visit after 3 and 6 weeks of product use. Subjects were asked about the occurrence of adverse effects and their perceptions (PROMs) on the use of the assigned toothpaste by filling a questionnaire. Finally, the study coordinator collected empty and unused toothpaste tubes. At the end of the 6-week visit, all subjects received professional mechanical plaque removal (PMPR).
###### Abstract  
(1) Background: deep venous thrombosis (DVT) has long been recognized as the most devastating complication after total knee replacement (TKR). To prevent DVT, intermittent pneumatic compression to improve venous return in the lower leg has been advocated by surgeons. Physical activities such as active ankle pumping and early mobilization have been recommended as auxiliary measures to increase venous return in the lower leg and help in ambulation after TKR. In this study, in order to remind patients to exercise their ankle actively and efficiently after TKR, a foot band with motion sensor and reminder alarm was used. (2) Methods: The patients were randomly allocated into three groups according to the therapeutic protocols. The patients in group 1 conducted active ankle pumping without any reminders, those in group 2 underwent intermittent pneumatic compression, and those in group 3 conducted active ankle pumping with ankle motion sensor/reminder. The parameters of blood flow, namely, peak flow velocity and flow volume, in the bilateral common femoral vein and popltical vein on the 1 st, 3 rd, and 14 th days after surgery were measured using the echo technique, an index to evaluate the effect on promotion of venous return, among the three groups. (3) Results: The peak flow velocity and flow volume of the operative limb in group 3 (with motion sensor/reminder) were significantly higher than those in other groups. The peak flow velocity and flow volume in the popltical vein in group 3 increased by 112% and 93.8%, respectively, compared to group 1 on the 14 th day. No significant difference in peak flow velocity or flow volume was found in the nonoperative limb between the groups. (4) Conclusions: According to the results, a motion sensor/reminder with vibration alarms can improve the performance of active ankle pumping exercises in improving lower leg circulation, and hence may reduce the risk of DVT.  
deep venous thrombosis; total knee replacement; motion sensor; alarm; active ankle pumping exercise +
Footnote †: journal: Applied Sciences