esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### The Effect of Sintering Temperature on the Crystal Phase and the Magnetic Properties
Figure 7b shows the hysteresis curve (sintering in a N\({}_{2}\) environment flow of 5 L/min, an increase in temperature of 5 \({}^{\circ}\)C/min and then maintaining a constant temperature for 2 h, followed by cooling in the furnace) that demonstrates the effect of temperature on the magnetic properties of [Mn\({}_{0.52}\)Zn\({}_{0.46}\)]Fe\({}_{2}\)O\({}_{4}\) that is sintered in N\({}_{2}\) at temperatures from 500 \({}^{\circ}\)C to 950 \({}^{\circ}\)C. It is seen that when the sintering temperature exceeds 600 \({}^{\circ}\)C in an air atmosphere, the saturated magnetization (Ms) begins to decrease, so a N\({}_{2}\) environment was used for sintering. Figure 8b shows that for a sintering temperature of 700 \({}^{\circ}\)C the values are 11 M (emu/g, in an air atmosphere) and 17 M (emu/g, in a N\({}_{2}\) environment). At 800 \({}^{\circ}\)C, values are 2.57 and 42.36 M (emu/g). A sintering temperature of greater than 800 \({}^{\circ}\)C in a N\({}_{2}\) environment produces a decrease in the magnetic properties [26]. For a sintering temperature of 800 \({}^{\circ}\)C, the optimal duration for sintering is five hours. Iron-ore slurry is converted into \begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**pH**} & **Composition/Method** & **Mn\({}_{0.54}\)Zn\({}_{0.46}\)Fe\({}_{2}\)O\({}_{4}\)** \\ \cline{2-3}  & **XRD** & **SEM** \\ \hline
6 & 31.10 & 30 \\
7 & 32.48 & 35 \\
8 & 53.56 & 50 \\
9 & 56.45 & 50 \\
10 & 59.61 & 58 \\
11 & 40.10 & 60 \\
12 & 35.03 & 48 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The effect of pH on the mean diameter. Figure 7: The effect of sintering temperature in a N\({}_{2}\) environment on (**a**) the crystal phase and (**b**) the magnetic properties of Mn\({}_{0.54}\)Zn\({}_{0.46}\)Fe\({}_{2}\)O\({}_{4}\). ferrite. There is a decrease in the magnetic properties because trivalent Fe in ferrite is deoxygenized to produce bivalent Fe. The results for coercive force (Hc), saturated magnetization (Ms) and remanent magnetization (Mr) for [Mn\({}_{\mathrm{x}}\)Zn\({}_{1-x}\)]Fe\({}_{2}\)O\({}_{4}\) of different compositions show that the Hc value does not exceed 200 Oe and the Mr value approaches zero (soft ferrite has a lower remanent magnetization), which demonstrates that this series is soft ferrite. Table 1 shows the magnetic flux for [Mn\({}_{\mathrm{x}}\)Zn\({}_{1-\mathrm{x}}\)]Fe\({}_{2}\)O\({}_{4}\) that is sintered in N\({}_{2}\) at temperatures from 700 \({}^{\circ}\)C to 950 \({}^{\circ}\)C. Using the formula B = 4\(\pi\)\(\times\) Ms + Hc, the unit of magnetic flux (B) is calculated in gauss. The table shows that the magnetic flux is 222.34 gauss/g at 700 \({}^{\circ}\)C. The magnetic flux is 548.88 gauss/g at 800 \({}^{\circ}\)C. This value then begins to decrease when the temperature is increased because sintering for too long in a N\({}_{2}\) gas results in oxygen atoms in [Mn\({}_{0.54}\)Zn\({}_{0.46}\)]Fe\({}_{2}\)O\({}_{4}\) escaping.
## 2 Materials and Methods
### Flood Return-Period Maps  
_(a) Estimation of return period at Each Point in the Floodplain_  
A map _rp(x,y)_ giving the flood return period at each point _(x,y)_ in the floodplain is generated using the flood inundation extents contained in the set of 30 binary FRP maps. The computation loops over the 30 maps in order of increasing return period. For a given FRP map, if a pixel _(x,y)_ is non-zero and _rp(x,y)_ has yet not been set, _rp(x.y)_ is set to the return period of the current map. An example return-period map is shown in Figure 3c for the Wraysbury area.  
_(b) Estimation of Effective Heights in Urban Area_  
An effective height map _eff_h(x,y)_ is determined in the urban area, given _rp(x,y)_ and the set of 30 binary FRP maps. In cases wherein the urban area is undefended, the effective height map is simply the DSM. In cases wherein some portion of the urban area is defended, this will be encapsulated in the FRP maps as high return periods at the defended pixels, which will result in these pixels having effective heights higher than the DSM heights at these pixels.  
To take into account the fall-off of water level down the reach, and that different parts of a domain may be flooded to different depths, the domain is subdivided into nonoverlapping rectangular \(m\times n\)-pixel subdomains of \(\sim\)1 km side. For each FRP map in each subdomain, edges are detected in the binary image using the Sobel edge detector. The edges are overlain on the DSM to obtain water level observations (WLOs) at the edges. The mean WLO is calculated for those edges that are not close to high slopes in the DSM, as the WLO of an edge is likely to be determined more accurately on a low slope [42]. For each subdomain, this results in a look-up table associating a mean WLO with each return period. For each return period, the mean WLOs in the subdomains are interpolated over the whole domain using bilinear interpolation.  
The interpolated maps of mean WLOs for each return period are then converted to effective heights in the urban areas. Given the return period of a pixel _rp(x,y)_, if its mean WLO is less than the DSM and the DSM is more than 1 m higher than the DTM, _eff_h(x,y)_ is set to the DSM height at the pixel, as the pixel is likely to coincide with a building or taller vegetation. If the mean WLO is \(\leq\)1 m higher than the DSM height at the pixel, _eff_h(x,y)_ is set to the DSM height. Otherwise, _eff_h(x,y)_ is set to the mean WLO for the pixel's return period, ensuring that defended urban areas have higher return periods.  
Note that, if only SAR data exists and there are no model results, the flow diagram of Figure 4 is still valid if the effective height map simply becomes the DSM.
## 4 Analysis of the Influence of Structural Parameters on the Erosion and Wear of Pipe Elbows
### Optimization of the Erosion Resistance of a Spherical Bend  
(1) Geometric model  
We examined the influence of pipe diameter on the erosion and wear of the bend. With other conditions remaining unchanged, we gradually expanded the pipe diameter at the inlet end of the bend. Four types of bend structures were formed: small ball type, medium ball type, hemispherical type and spherical type, as shown in Figure 23. All the model pipe diameters are \(D=50\) mm, the lengths of the inlet and outlet ends are \(L1=L3=400\) mm, the bending radii at the bends are \(R1=R2=2D=100\) mm and the intermediate straight sections are \(L2=300\) mm.  
(2) Numerical simulation calculation results and analysis  
As shown in Figure 24, the particle beams of the four tube types disperse in all directions after hitting the spherical wall. The particles eventually converge along the wall of the bent tube at the concave wall of the bent tube and then flow with the liquid phase again. After entering the second section of the straight pipe, the particles flow uniformly along the wall of the straight pipe. In contrast, the particle bundles of a typical pipeline structure are concentrated at the convex wall of the first bend to enter the straight tube and flow along the side of the straight pipe. The bent pipe, after changing the bent pipe structure, improves the utilization of the whole pipe.  
The small ball pipe bend and the medium ball pipe bend have a curved surface connection with the straight pipe. The maximum erosion location is in the transition area between the two sides of the bend and the straight pipe connection, instead of the concave wall surface and the straight pipe connection. In the hemispherical bends and spherical bends, the maximum erosion location is in the spherical bend and the straight pipe connection of the mutation. The erosion area is in the form of a band [31]. The erosion wear area at the second section of the second bend of the four pipe structures is widely and uniformly distributed, and the erosion rate is small. The location of the erosion peak is similar to the erosion location of a typical pipeline structure.  
Figure 23: Space pipeline structure with different bend structures.  
Figure 25 displays that the maximum erosion rate of the small ball type is the smallest. The bend at the change in pipe shape has the least erosion wear. Meanwhile, the hemispherical-type bend has the most severe erosion wear at the second section of the bend, and the erosion rate is larger.
# Machine Learning Method for Prediction of Hearing Improvement After Stapedotomy
###### Abstract  
Otosclerosis is a localized disease of the bone derived from the otic capsule. Surgery is considered for patients with conductive hearing loss of at least 15 dB in frequencies 250 to 1000 Hz or higher. In some cases, the decision as to whether surgery (stapedotomy) should be performed is challenging. We developed a machine learning method that predicts a patient's postoperative hearing quality following stapedotomy, based on their preoperative hearing quality and other features. A separate set of regressors was trained to predict each postoperative hearing intensity on selected feature sets. For feature selection, the least absolute shrinkage and selection operator (Lasso) technique was used. Four models were constructed and evaluated: Lasso, Ridge, k-nearest neighbors, and random forest. The most successful predictions were made at air conduction frequencies between 1000 and 3000 Hz, with mean absolute errors of approximately 6 dB. Utilizing the nested CV method, the Lasso predictor achieved the highest overall prediction accuracy. This study presents the first stapedotomy result prediction method for operating surgeons using machine learning. The potential of audiogram estimation in predicting hearing recovery is demonstrated, offering an alternative to existing classification based models.  
stapedotomy; machine learning; hearing recovery; audiogram Article
## 5 Experiments and Evaluation
### Model Performance Evaluation
#### 5.2.2 Parameter Study  
We study the model performances with different values of two important hyperparameters: the embedding dimension and the layers of our model under 30% data sparsity. For each experiment, only the target hyperparameter is changed to observe the optimal one under different evaluation metrics. As shown in Figure 6, the best embedding dimension is 384 on both datasets. The best number of graph convolutional layers is two, as shown in Figure 7. The deeper layer may cause the over-smoothing problem in GCN [54], which leads to worse results.  
Figure 5: Comparisons of the Recommendation Performances on TalkingData Dataset under Different Sparsity Levels. **(a)**_Top2 Hitrate_. **(b)**_Top2 Accuracy_. **(c)**_nDCG_2.  
Figure 6: Performances of Different Embedding Dimensions on Two Datasets. **(a)**_Telecom Dataset_. **(b)**_Talking Dataset_.
# A Stair-Climbing Wheelchair with Novel Spoke Wheels for Smooth Motion
## 5 Experimental Validation
### Stair-Climbing Experiment  
To evaluate the stair-climbing performance of the Flexwheel-equipped wheelchair in real-world conditions, physical experiments were conducted on stairs with dimensions of 2 cm \(\times\) 4 cm. Wheelchair prototypes equipped with traditional spoke wheels and Flexwheels were tested under identical conditions. As shown in Figure 15, the Flexwheel configuration significantly improved stability and reduced vibration during ascent, aligning closely with the results observed in simulation.  
Figure 14: Experiment results of flat terrain motion.  
Figure 15: Experiment results of stair climbing.  
Additionally, comparative experiments were conducted on stairs measuring 4 cm \(\times\) 8 cm to further evaluate the system's robustness across a broader range of stair configurations. A summary of the results from the three stair-climbing experiments is presented in Table 5. The Flexwheel configuration consistently exhibited lower RMS values of Z-axis acceleration compared to the traditional spoke wheel configuration. These outcomes confirm the Flexwheel's superior performance in reducing oscillation and enhancing overall climbing stability, further validating the effectiveness of the design under practical conditions.
# On the Security of Offloading Post-Processing
## 2 Quantum Key Distribution  
Contrary to most other cryptographic primitives, QKD is a cryptographic key-agreement protocol which derives its security from the physical layer (i.e., it uses a quantum channel to exchange quantum information which cannot be perfectly copied or eavesdropped according to the laws of quantum mechanics). In preparing and measuring the QKD protocols, so-called quantum bits (qubits) are encoded and transmitted over a quantum channel. Typically, the qubits are encoded on photons, and the transmission channels are either fiber optics or free space. Finally, the qubits are measured at the receiver and decoded. From the measurement of quantum bits, classical information is derived, and all of the following steps are carried out in the classical domain. However, due to their interaction with the environment and eavesdroppers, photons are subject to perturbation and absorption. To detect and cope with these modifications in the transmission channel, post-processing steps have to be applied in order to obtain the full key agreement primitive with practical correctness and secrecy guarantees.  
The outstanding property of QKD is that it is an information-theoretic secure (ITS) and universally composable (UC) key agreement protocol [10], given that its classical communication is performed over an authentic channel (note that all key-agreement protocols are insecure over non-authentic channels). ITS message authentication codes are based on universal hashing [11], which in the first round uses pre-shared keys, and in later rounds, QKD keys from previous rounds are a means to generate an ITS authentic channel [12]. Thus, QKD is a very powerful cryptographic primitive which cannot be realized with non-quantum protocols.
## 6 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**ON\(-\) Patients**} & \multicolumn{2}{c}{**ON+ Patients**} & \multicolumn{2}{c}{**ON+ vs. ON\(-\)**} \\ \cline{2-5}  & **Annual Rate (SEM)** & \(p\)**-Value** & **Annual Rate (SEM)** & \(p\)**-Value** & \(p\)**-Value** \\ \hline
**TMV (mm\({}^{3}\))** & \(-0.05\) (0.04) & 0.231 & \(-0.1\) (0.01) & **<0.001** & 0.145 \\
**CRT (\(\mu\)m)** & \(-0.8\) (0.57) & 0.162 & \(-2.41\) (0.63) & **<0.001** & 0.06 \\
**pRNFL-T (\(\mu\)m)** & \(-0.54\) (1.39) & 0.696 & \(-3.5\) (0.55) & **<0.001** & **0.049** \\ _Superie quadrant (\(\mu\)m)_ & \(-0.66\) (0.69) & 0.339 & \(-3.21\) (0.28) & **<0.001** & **0.001** \\ _Nasal quadrant (\(\mu\)m)_ & \(-1.33\) (0.77) & 0.084 & \(-1.15\) (0.42) & **0.007** & 0.835 \\ _Interior quadrant (\(\mu\)m)_ & \(-0.54\) (0.68) & 0.431 & \(-3.01\) (0.47) & **<0.001** & **0.003** \\ _Temporal quadrant (\(\mu\)m)_ & 0.23 (0.72) & 0.753 & \(-0.9\) (0.34) & **0.008** & 0.158 \\
**NRR-T (\(\mu\)m)** & \(-3.26\) (8.44) & 0.699 & \(-36.59\) (2.74) & **<0.001** & **<0.001** \\ \hline \hline \end{tabular}  
* Values in bold indicate significant difference (\(p\) values \(<0.05\)). ON\(+\) = patients with previous history of optic neuritis; ON\(-\) = patients without previous history of optic neuritis.  
\end{table} 
Table 4: Annual change rates of the OCT parameters in optic neuritis (ON)\(-\) and ON+ patients. thinning rate of the pRNFL that was more pronounced in ON+ patients. The greatest difference was relative to the NRR-T. This difference was probably determined by the existence of a possible phenotype related more closely to eye damage, which was already evident before the start of therapy. Furthermore, when comparing our data on the RNFL annual progression rate with other studies on patients with RR-MS and undergoing therapy other than fingolimod, there are not many studies. Among these, one of the most significant, in terms of the number of patients and duration of the follow-up, highlighted a higher rate of RNFL thinning in ON+ patients, as reported in our results. However, unlike our data, ON- patients presented statistically significant annual change rates [36]. Therefore, for the first time in the literature, in a cohort of RR-MS patients treated with fingolimod, we observed that the previous history of ON was associated with a faster progression of RNFL parameters, suggesting a greater risk of axonal loss. However, more studies are needed, as this study has some limitations like a relatively small sample size and the lack of an age-matched group of normal-sighted subjects to compare the progression of the parameters.
# Capturing Features and Performing Human Detection from Human Gaits Using RFID
## Materials and Methods
### Related Work
#### 2.1.1 Person Detection Technology  
Recently, person detection technology has become an important research direction and is used in various fields [5; 6; 7]. Traditional person detection methods are based on wearable sensors [8; 9] or cameras [10; 11; 12]. The use of these techniques can demonstrate excellent detection capabilities, but these approaches still have privacy concerns and cost concerns. In recent years, RFID, as a very promising technology, has shown excellent performance in many fields [13; 14], and it is completely feasible to use RFID for person detection. On the one hand, it has the characteristics of convenience and low cost; on the other hand, it has a good privacy protection effect, and can more effectively protect user information in person detection. In [15], the author proposed a person detection method using RFID which can be better applied to privacy scenes. Specifically, [16] proposed a non-contact human activity detection method which greatly increased the detection area by using a cross-circular polarization configuration between reader antennas and tag antennas. In contrast, RF-Detection combines person detection with human height-step length relationship, it takes step length as the main indicator in data collection, and it divides the data into five groups according to height (see the experiment section for details), which greatly reduces the complexity of data collection and classification. In addition, it can more accurately estimate the height range of new users and detect the number of users in a specific area according to their step length signals.
## 4 Experiment Verification
### Availability Aspect
#### 4.2.3 Key Generation Time per Packet Loss  
In the proposed method, the key generation time varies depending on packet loss. If it takes a long time to generate a key when there is a packet loss, the method is not suitable because it causes a delay in the system. In this section, the key generation time depending on packet loss is measured. Each execution measures the average time for 1000 times of key generation depending on packet loss to examine the availability of the proposed method. The formula for measuring performance is as Equation (1).  
\[\mu\Big{(}T_{\text{keygen}}^{\text{l}}\Big{)}=\sum_{\text{i}=0}^{n}\frac{T_{ \text{key}_{\text{i}}}^{\text{end}}-T_{\text{key}_{\text{i}}}^{\text{start}}} {n} \tag{1}\]  
\[\mu\Big{(}T_{\text{keygen}}^{\text{l}}\Big{)}:\text{1 loss packet },\text{ key generation average time.}\]  
\[T_{\text{key}_{\text{i}}}^{\text{start}}:\text{ i'th key generation start point}\]  
\[T_{\text{key}_{\text{i}}}^{\text{end}}:\text{ i'th key generation end point}\]  
As shown Figure 11, proposed method allows key generation without system delay of up to nine packet losses in a DataPeriodic1. When \(\text{BlockCondition}_{\text{K}}\) was set to 64 (defend a malicious attacker'stransaction pool brute force attacks), encryption keys can be generated without delay even if 14.06% of packet loss. When considering packet data loss rate of 2% due to interference between devices in Bluetooth low energy communication. The method proposed in the paper are applicable.
## 3 Results
### ORF1 Encoding Polyprotein  
ORF1 (88-6735 bp) encodes a polyprotein (2215 AA) of the RTV1 genome. ORF1 was targeted by seven rubber tree miRNAs as indicated by the miRanda algorithm: hbr-miR159a (locus 2178); hbr-miR319 (locus 2992); hbr-miR396 (a, b) (locus 6678); hbr-miR398 (locus 1839); hbr-miR408b (locus 6498); and hbr-miR6171 (locus 1633) (Figure 4a). Six potential miRNAs were identified for silencing the ORF1 of the RTV1 genome by RNA22: hbr-miR319 (loci 569, 3204); hbr-miR396a locus (6675); hbr-miR398 (locus 1838); hbr-miR482a (locus 3951); hbr-miR6167 (locus 1058); and hbr-miR6168 (loci 145, 647) (Figure 4b). Twenty two miRNAs were detected by the RNAhybrid : hbr-miR156 (locus 6492); hbr-miR159a (locus 2177); hbr-miR166a (locus 1060), hbr-miR396a (locus 6675), hbr-miR396b (locus 823), hbr-miR398 (locus 1840); hbr-miR408a (locus 573); hbr-miR408b (locus 6497); hbr-miR482b (locus 6500); hbr-miR2118 (locus 6592); hbr-miR6166 (locus 1515); hbr-miR6168 (locus 1641); hbr-miR6169 (locus 3882); hbr-miR6170 at (locus 6660); hbr-miR6171 (locus 2043); hbr-miR6172 (locus 4500); hbr-miR6173 (locus 3749); hbr-miR6174 (locus 4303); hbr-miR6482 (locus 2163); hbr-miR6483 (locus 1267); hbr-miR6485 (locus 3042); and hbr-miR9386 (locus 370) (Figure 4c). Tapirhybrid predicted only four miRNAs: hbr-miR396a (locus 6674); hbr-miR6167 (locus 2716); hbr-miR6171 (locus 1633); and hbr-miR6483 at (locus 106) (Figure 4d).  
The psRNATarget identified twelve miRNAs: hbr-miR159a (loci 2177, 3505, 2549, 4786); hbr-miR319 (locus 2548); hbr-miR396b (locus 6677); hbr-miR398 (locus 1838); hbr-miR482a (locus 627); hbr-miR6167 (locus 1459, 980); hbr-miR6169 (locus 1693); hbr-miR6171 (locus 1633, 4540); hbr-miR6482 (locus 399); hbr-miR6483 (locus 106, 3183); hbr-miR6484 (locus 4295); and hbr-miR9386 locus (4422) (Figure 4e,f; Tables 1, S3 and S4; File S1).  
Figure 3: Venn diagram plot of RTV1 representing common rubber tree genome-encoded miRNAs calculated by all the algorithms used in this study. Furthermore, a single rubber tree miRNA (hbr-miR6167) is predicted by all the algorithms used in this study.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Rubber Tree miRNAs** & \begin{tabular}{c} **Locus** \\ **miRNARanda** \\ \end{tabular} & \begin{tabular}{c} **Locus** \\ **RNA22** \\ \end{tabular} & \begin{tabular}{c} **Locus** \\ **RNA3 Hybrid** \\ \end{tabular} & \begin{tabular}{c} **Locus** \\ **TAPIR** \\ \end{tabular} & \begin{tabular}{c} **Position** \\ **p8 RNATarget** \\ \end{tabular} & \begin{tabular}{c} **MFE * \\ **miRNARanda** \\ \end{tabular} & \begin{tabular}{c} **MFE * \\ **RNA22** \\ \end{tabular} & \begin{tabular}{c} **MFE * \\ **RNAhybrid** \\ \end{tabular} & \
\begin{tabular}{c} **MFE-Ratio** \\ **TAPIR** \\ \end{tabular} &
\begin{tabular}{c} **Expectation** \\ **p8 RNATarget** \\ \end{tabular} \\ \hline hbr-miR1356 & & & 6492 & & & & & \(-20.6\) & & \\ hbr-miR159a & 2178 & & 2177 & \(-20.2\) & \(-15.2\) & \(-24.8\) & & 6.5 \\ hbr-miR159a(1) & & & & 3505 & & & & 6.5 \\ hbr-miR9a(2) & & & & 2549 & & & & 6.5 \\ hbr-miR9a(3
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 5 Proposed Continuous Authentication Method  
In the aforementioned section, the certificateless authentication and group key distribution scheme is introduced with the purpose of offering validated D2D communication channel. However, the authentication event is only conducted prior to the entire communication process, while the entire communication process is still vulnerable to all kinds of attacks and security risks. Therefore, the continuous authentication with behavioral biometrics is adopted in our scheme, providing the new perspective to dynamically and periodically detect the anomalies during the entire user session. Intuitively, the regarding steps are conducted as follows.