esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### MDMIP Characterization  
Figure 3 shows TEM images of Fe\({}_{3}\)O\({}_{4}\), Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@MPS, and the MDMIPs. The figure shows that Fe\({}_{3}\)O\({}_{4}\) was monodispersed, with a mean diameter of 20 nm and a uniform size distribution. Figure 3b shows that the diameter of Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@MPS was approximately 300 nm; this confirms that SiO\({}_{2}\) and MPS layers were uniformly coated on Fe\({}_{3}\)O\({}_{4}\). After imprinting, the MDMIP diameter was about 400 nm, which suggests successful synthesis of core-shell MDMIPs.  
Figure 4A shows the FT-IR spectra of Fe\({}_{3}\)O\({}_{4}\), Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@MPS, and the MDMIPs. A peak at 579.8 cm\({}^{-1}\), attributed to the Fe-O stretching vibration, appears in all the spectra. This shows that the Fe\({}_{3}\)O\({}_{4}\) nanoparticles were successfully coated with the polymer. The spectrum of Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@MPS shows peaks from Si-O-Si, at 1087.1 cm\({}^{-1}\), Si-O-H, at 955 cm\({}^{-1}\), and S-O, at 802 cm\({}^{-1}\). These results indicate the formation of SiO\({}_{2}\) shells on the Fe\({}_{3}\)O\({}_{4}\) surfaces. The peak at 1622.9 cm\({}^{-1}\) in the Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@MPS spectrum is attributed to the C=C stretching vibration, indicating successful  
Figure 2: MS/MS spectra of FITC-Mel in negative-ion mode.  
modification of the Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) nanoparticles with vinyl groups. The C=O and C-O peaks, at 1733 and 1255 cm\({}^{-1}\), respectively, in the MDMIP spectrum indicate that EGDMA and MAA were successfully imprinted on the surfaces of the Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@MPS nanoparticles.  
The magnetic properties of Fe\({}_{3}\)O\({}_{4}\), Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@MPS, and the MDMIPs were studied using VSM. Figure 4B shows that the saturation magnetization values of Fe\({}_{3}\)O\({}_{4}\), Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@MPS, and the MDMIPs were 55.078, 35.247, and 22.149 emu/g, respectively. The magnetic hysteresis loops show that the remanence and coercivity values were zero, indicating that Fe\({}_{3}\)O\({}_{4}\), Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@MPS, and the MDMIPs were all superparamagnetic.
# Role of Irisin in Endocrine and Metabolic Disorders--Possible New Therapeutic Agent?
## 7 Prade-Willi Syndrome (PW)  
Irisin has both peripheral and central functions, and is suspected to be a molecular link between muscles and the brain. A neural pathway participating in the signal transduction of irisin from the cerebellum to adipocytes through intermediary synapses in the medulla and spinal cord might be involved. Irisin also influences hippocampal neurogenesis (i.e., high concentrations of irisin 50-100 nmol/L increase neuronal proliferation) [2].  
In the hypothalamus, irisin appears to be localized together with neuropeptide Y (NPY) in the neuronal cells of the paraventricular nucleus, while FNDC5 mRNA is expressed in the neuroendocrine reproductive axis. Hypothalamic FNDC5 expression is not dependent on nutrition but increases after leptin and insulin infusion [39].  
Patients with PW have a specific metabolic profile, compared with BMI-matched controls: insulin sensitivity is increased, and fasting insulin levels are lower. Furthermore, resting energy expenditure is markedly decreased as a consequence of decreased physical activity and decreased lean body mass. In individuals with PW, the visceral adipose tissue is reduced, compared with obese controls [39]. These metabolic particularities of PW support the study of irisin in such patients.  
The first study on irisin in PW adults showed increased salivary insulin levels, although plasma irisin was similar to control volunteers. Furthermore, plasma irisin was correlated with salivary insulin in normal controls, but not in PW patients. Salivary irisin significantly correlated with plasma lipids, negatively with HDL cholesterol, and positively with LDL cholesterol and triglycerides. These data suggest that irisin might play a compensatory role in this hypothalamic syndrome, in order to improve total cholesterol, LDL cholesterol, and HDL cholesterol serum concentrations in obese individuals with PW.  
Irisin deregulation may be an answer for the nutritional phase in children with PW aged between 2 and 4 years. In this nutritional phase, they excessively gain weight without an increase in appetite or caloric intake. In contrast, food craving decreased along with irisin normalization in adults with PW [40].
## 1 Introduction
These discriminating kinematic parameters for HOA and after THA are critical in the selection for classification model inputs among several hundreds of kinematic trajectories produced by CGA. Still, there is a lack of consensus on which of these kinematic outcomes provide the best result in the diagnosis and follow up in patients. Gait modifications could already be subtly manifested before the onset of functional infirmities; thus, discriminating information for HOA is important. The results have potential implications for proper treatment and rehabilitation efforts after THA surgery from different severity levels. There are several methods to make accurate classifications and predictions utilizing gait patterns. Recently, the development of Deep Learning (DL), a subset of machine learning (ML) methods, has seen increasing advances in the field of computer science. Specifically, convolutional neural networks (CNNs) and long short-term memory (LSTM) have exhibited outstanding information extraction and modeling capabilities in different gait-related applications. The former is known for its excellent feature extraction capabilities, and the latter has strength in detecting time step dependencies from long sequential data. Quite recently, a CNN model was employed to extract STP in a single gait cycle [16]. A publicly available eGAIT dataset, based on inertial measurements, was used as input. Two DL models were considered, namely multi-classification and ensemble models. Decent results were achieved, but the authors emphasized the need for a larger dataset to improve DL performance. Additionally, a related study was conducted to predict different STP for knee osteoarthritis and total knee arthroplasty subjects through a CNN model [17] with inertial measurements as inputs. The architecture is not particularly deep with only two convolutional layers involved. Conversely, LSTM networks were designed to classify alterations or abnormalities of gait using wearable sensors [18,19]. Results were achieved with accuracy above 80% in both LSTM studies. It should be noted that hybrid LSTM-CNN (HLSTM-CNN) architectures have not been considered in the analysis of gait, but have shown promising results from other areas such as text classification [20], heart beat classification [21], and power flow prediction [22]. The rest of this paper is organized as follows: work related to ML-based HOA gait classification is given in Section 2, a brief description of the DL model is provided in Section 3, the proposed methodology and dataset description is presented in Section 4, and the results and discussion are presented in Section 5. Finally, conclusions of the research and future directions are summarized in Sections 6 and 7, respectively.
## 4 Experimental Evaluation
### Analysis of Visual Experiment Results  
In the domain of traffic classification, a confusion matrix is usually used to assess the classification outcomes of various traffic data, thereby evaluating the performance of the model. The performance of the CLSTM-MT model is described and illustrated by the confusion matrix in Figure 5. The prominent elements along the diagonal line signify that the CLSTM-MT model exhibits robust classification accuracy for each application, with minimal instances of misclassification.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**ISCXVPN2016**} & \multicolumn{5}{c}{**ISCXTOR2016**} \\ \cline{2-10}  & **1\%** & **5\%** & **10\%** & **15\%** & **20\%** & **1\%** & **5\%** & **10\%** & **15\%** & **20\%** \\ \hline Ours & 62.1\% & 80.3\% & 85.4\% & 93.4\% & 97.8\% & 41.0\% & 61.5\% & 73.4\% & 81.2\% & 86.3\% \\ Ours w/o 2-conv CNN & 51.1\% & 54.6\% & 69.4\% & 73.5\% & 85.6\% & 43.2\% & 51.8\% & 62.7\% & 71.5\% & 80.4\% \\ Ours w/o LSTM & 53.2\% & 62.3\% & 75.3\% & 81.1\% & 91.2\% & 41.4\% & 52.9\% & 65.3\% & 73.2\% & 82.2\% \\ Ours w/o MT & 32.4\% & 40.3\% & 52.9\% & 55.2\% & 62.7\% & 31.3\% & 37.6\% & 43.9\% & 51.0\% & 56.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the results of the ablation experiment.  
Figure 5: Recall rate confusion matrix of the classification performance of ISCXVPN2016 traffic dataset by the CLSTM-Mean-Teacher model.  
Specifically, the horizontal axis represents the predicted category and the vertical axis represents the actual category. For each element of the matrix, the number in a cell represents the proportion of the number of samples that are one type of traffic but that are predicted to be another type of traffic to the number of samples that are actually that type of traffic. The darkened entries on the main diagonal indicate high precision and recall for individual classes, underscoring the model's efficacy in distinguishing between different types of traffic data. This suggests that the CLSTM-MT model achieves substantial accuracy across multiple categories, with only minor discrepancies attributable to confusion errors. Consequently, the model demonstrates strong generalization capabilities and reliable performance in real-world traffic classification tasks.  
Meanwhile, the training process of the accuracy and loss of the model on the ISCXVPN2016 dataset is revealed in Figure 6. It is observed that the accuracy rate of the model is 65.2%, and the loss is 1.87 at epoch 1. The accuracy of the model gradually converges to 96%, and the loss converges to less than 0.1. The above results indicate that our method has higher experimental accuracy and lower model loss and can perform well in encryption application traffic identification tasks with fewer data in the case of high cost of data annotation.
# Improvement of Network Flow Using Multi-Commodity Flow Problem
## 8 Conclusions  
Network traffic has continued to increase in recent years, and existing routing protocols risk congestion due to single paths. In this study, to achieve load balancing by multiple paths, we implemented routing using the multi-commodity flow problem on OpenFlow. Compared to OSPF, it is possible to maintain a high packet arrival rate even at high transmission bit rates, demonstrating the superiority of the proposed method. Compared to OSPF-ECMP, the proposed method, which considers paths in the entire network, was more load-balancing. Although the average end-to-end delay was inferior to that of OSPF, which chooses the optimal path, the packet arrival rate is more important from the viewpoint of service continuity. Problems include the computational complexity of the simplex method used for linear programming and reactive path control, so we aim to develop a mechanism that is more suitable for the actual environment in the future.  
Conceptualization, T.F. and T.M.; methodology, T.F.; software, T.F.; validation, T.F.; formal analysis, T.F.; investigation, T.F.; data curation, T.F.; writing--original draft preparation, T.F.; writing--review and editing, T.F.; visualization, T.F.; supervision, T.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement**: Not applicable.  
**Conflicts of Interest**: The authors declare no conflict of interest.
###### Abstract  
Exercise has been demonstrated to improve health in people with diabetes. However, exercise may increase risk for foot ulcers because of increased plantar pressure during most weight-bearing physical activities. To date, there is no study investigating the effect of various walking speeds and durations (i.e., the most common form of exercise in daily living) on the plantar foot. The objective of this study was to investigate the effect of various walking intensities on plantar tissue stiffness. A \(3\times 2\) factorial design, including three walking speeds (1.8, 3.6 and 5.4 mph) and two durations (10 and 20 min), was tested in 12 healthy participants. B-mode and elastographic ultrasound images were measured from the first metatarsal head to quantify plantar tissue stiffness after walking. Two-way ANOVA was used to examine the results. Our results showed that the walking speed factor caused a significant main effect of planar stiffness of the superficial layers (\(p\) = 0.007 and 0.003, respectively). However, the walking duration factor did not significantly affect the plantar stiffness. There was no interaction between the speed and duration factors on plantar tissue stiffness. Regarding the walking speed effect, there was a significant difference in the plantar stiffness between 1.8 and 3.6 mph (\(56.8\pm 0.8\%\) vs. \(53.6\pm 0.9\%\), \(p\) = 0.017) under 20 min walking duration. This finding is significant because moderate-to-fast walking speed (3.6 mph) can decrease plantar stiffness compared to slow walking speed (1.8 mph). This study suggests people at risk for foot ulcers walk at a preferred or fast speed (3.6 mph) rather than walk slowly (1.8 mph).  
Keywords:diabetic foot ulcers; elastography; ultrasound; walking durations; walking speeds +
Footnote †: journal: Applied Sciences
## 3 Methodology and Experimentation
### Performance Metrics  
Four different QoS parameters with two standard mobility models for three different routing protocols named DSDV, AODV, and AOMDV have been evaluated. The calculations of all QoS parameters are based on the below-given concepts.  
1. PDR: packet delivery ratio is the number of packets delivered out of the total number of packets sent. \[\mathrm{PDR}=\Sigma\left(\mathrm{TS}\right)/\Sigma\left(\mathrm{TR}\right)\] (3) where \(\mathrm{TS}=\mathrm{total}\) number of packet received by destination node, \(\mathrm{TR}=\mathrm{total}\) number of packet sent by source node.
2. AE2 ED: It is the sum of the delays experienced by a packet before reaching its destination. \[\mathrm{AE2 ED}=1/n\ \sum_{i=1}^{n}(\mathrm{PT}_{\mathrm{r}}-\ \mathrm{PT}_{ \mathrm{s}})\times 1000\ [\mathrm{ms}]\] (4) where i is the packet identifier and n are the total numbers of packets, \(\mathrm{PT}_{\mathrm{r}}\) is the Packet's reception time, \(\mathrm{PT}_{\mathrm{s}}\) is the Packet's sent time.
3. Average throughput: throughput is the number of packets delivered in a specific period. \[\mathrm{Average\ Throughput}=\left(\mathrm{R}_{\mathrm{s}}/\left(\mathrm{S}_{ \mathrm{t}}-\mathrm{S}_{\mathrm{tp}}\right)\right)\times\left(8/1000\right)\] (5) where \(\mathrm{R}_{\mathrm{s}}\) is the packet size for received data, \(\mathrm{S}_{\mathrm{t}}\) is the stop time for simulation, and \(\mathrm{S}_{\mathrm{tp}}=\mathrm{start}\) time for simulation. Table 3 represents the abbreviations used in different equations above.
## 3 Results
### Results at the Component Level
#### 3.1.1 Water Content in the Wall Section  
Figure 6e,f shows the moisture content, \(mc\) (kg/m\({}^{3}\)), of the insulating layer, the mineral wool and the thermal plaster, respectively, for counter wall configurations and thermal plaster. The \(mc\) in the whole element is reported in Figure 6a,b, while the boxplot graph in Figure 6c,d represents the \(mc\) in the stone layer. The results refer to the trend recorded in the last year of the simulation, since this is a year for which the data can be considered stabilized. All hourly water values as well as the maximum and minimum values are indicated in each box-plot for all design alternatives. The bottom and top of the box represent the first and the third quartiles, and the band inside the box is the median of the data. It appears, from the analysis, that the moisture content is higher for the wall assemblies calculated in Monfero; this evidence confirms the importance of using accurate climatic data (in particular wind and rain) to appreciate the influence of weather factors on \(mc\). In fact, the Monfero results generally reach higher moisture content values than Turin. Furthermore, the numerical simulation clearly shows that by focusing on the insulating layer, the thermal plaster configurations, especially P2, P3, P4 and P5, show \(mc\) levels that are more than double those of the counter wall configuration, and this is due to the different moisture storage capabilities. The data concerning the thermo-hygrometric behavior of the various solutions allow a further comment on the use of vapor retarders to be made. The data show, for both configuration C1 and C4, that the addition of a vapor retarder (C1R or C4R) is not effective and should be avoided; the use of membranes with very low vapor permeability were found to increase the wall moisture content, especially in the Monfero location where a larger amount of driving rain reaches the outdoor surface. In this case, the membrane did not allow moisture evaporation from the inside.  
Figure 5: Internal load profiles (**a**) Heat gains convective; (**b**) Heat gains radiant; (**c**) Moisture source; (**d**) CO\({}_{2}\) source.  
Additional considerations concern the choice of the finishing coat that should be applied to the plaster. The results, for almost all the settings, show that the configuration designed with a Silicon Resin Finishing (\(\mu\) = 74) results in a slightly higher moisture content than the ones with a higher vapor permeability (\(\mu\) = 4).  
Figure 6: Box plots: Moisture Content of the overall configurations (**a**) in Monfero and (**b**) in Turin; Moisture Content of the stone layer (**c**) in Monfero and in (**d**) Turin; Moisture content of the insulating layer in Monfero (**e**) and Turin (**f**).
# Low-Power Two-Phase Clocking Adiabatic PUF Circuit
## 3 Design of 4-Bits Adiabatic PUF  
To validate the proposed adiabatic PUF cell, we designed a 4-bit cascaded adiabatic PUF, as depicted in Figure 6. Each local PUF is supplied by four power clocks, where each adjacent clock differs by a phase difference of 90\({}^{\circ}\). For instance, if the first cell, as shown in Figure 6, operates in the hold phase, the next cell in the same local PUF operates in the recovery phase. Similarly, the other two PUFs operate in the wait and evaluation phases, respectively. When all four outputs are sampled at a time, it leads to a length of 4 bits. Moreover, each local PUF cell is controlled by challenge bits; each adjacent bit has \(\frac{1}{4}\) delay time of one power clock cycle. Therefore, if one cycle of Vpc signal has 10 ns, then the second Cb signal will have a delay time of 2.5 ns, compared to the first Cb signal. This delay time allows the challenge bits to flip the response signals at the middle point of the wait phase of the Vpc signals, as shown in Figure 7a,b. Thus, the challenge bits are flipped adiabatically, as depicted in Figure 7c, thereby significantly reducing the energy.
# Deep Learning-Enhanced Autoencoder for Multi-Carrier Wireless Systems
## 3 Proposed GRU-MC-AE Network Architecture
```
1:Initialize Parameters:\(N\), \(M\), \(m\), \(R\) (bit rate), SNR, batch size, norm type, epochs, learning rate, hidden layers, loss function, train/test sizes, activation functions
2:Define noise_std based on training SNR
3:Define Channel Layers
4:Function channel(Z):
5: Initialize channel estimate \(H_{\text{est}}\) with noise
6: Calculate real and imaginary parts with noise added
7:Return received signal \(y\)
8:Function channel_test(Z, noise_std, test_size, imperfect_channel=False):
9:if imperfect_channel then
10: Calculate imperfect CSI error
11:endif
12: Add noise to channel estimates
13: Calculate received signal \(y\)
14:Return \(y\)
15:Build MC-AE Model
16: Create input layer for encoder
17: Add dense encoding layer
18: Normalize encoder output based on norm type
19: Reshape encoder output
20: Define decoder GRU layers and Dense output layer
21: Construct complete Autoencoder model
22:Train or Load Model
23:if pre-trained then
24: Load encoder and decoder weights
25:else
26: Generate training data
27: Compile and train AE model
28: Save encoder and decoder weights
29:endif
30: Generate test data
31: Encode test data
32:BLER and BER Calculation
33:Function calculate_BER_BLER(EbNodB_range, test_data, test_label, imperfect_channel)
34:Initialize BER and BLER arrays
35:for each SNR value in EbNodB_range do
36: Calculate noise_std for given SNR
37: Encode test data
38: Pass encoded data through channel_test
39: Decode received data and calculate BER and BLER
40:endfor
41:Return BER and BLER
42:Perfect CSI BLER Calculation
43:Print \"Perfect CSI BLER\"
44:Set EbNodB_range = range(0, 15, 4)
45:BER, BLER \(\leftarrow\) calculate_BER_BLER(EbNodB_range, test_data, test_label, False)
46:Imperfect CSI BLER Calculation
47:Print \"Imperfect CSI BLER\"
48:Set EbNodB_range = range(0, 15, 4)
49:BER, BLER \(\leftarrow\) calculate_BER_BLER(EbNodB_range, test_data, test_label, True)
```  
**Algorithm 1** MC-AE Model Setup, Training, and Evaluations
## 3 Problem Description and Goal  
The MOJSSP can be described as n jobs \(\{J_{0},J_{1},\ldots,J_{n}\}\) under multiple objectives processed on m machines \(\{M_{0},M_{1},\ldots,M_{m}\}\) with different routes [31]. The processing order of each part and the processing time of each operation \(o_{ij}\) on a machine is determined. To illustrate the problem, the encoding of a benchmark of \(10\times 10\) job shop (LA18) [32] here can take an example as \(\{9,8,5,4,2,1,3,6,7,0,\ldots.5,8,9\}\). Each element in the operation sequence code represents a job. The \(i\)th occurrence of the same value means the \(i\)th operation of this job. Here, the MOJSSP aims to find an order to optimize the above two objectives simultaneously. The constraints include each job should be processed on only one machine at a time, each machine can process only one job at a time, and the preemption is not allowed [33].  
Notations used are listed below:  
\(i\), \(h\): index of jobs, \(i,h\in\{0,1,2,\ldots,n\}\)  
\(j\), \(l\): index of operations in a given job, \(j\), \(l\in\{0,1,2,\ldots,o\}\)  
\(k\), \(g\): index of machines, \(k\), \(g\in\{0,1,2,\ldots,m\}\)  
\(m\): number of machines  
\(n\): number of jobs  
\(o\): number of operations in a given job  
\(o_{ij}\): the \(j\)th operation of the job \(i\)  
\(C_{i}\): the completion time of the job \(i\)  
\(T_{ijk}\): the processing time of the \(j\)th operation of the job \(i\) on machine \(M_{k}\)  
\(E_{ijk}\) the completion time of the \(j\)th operation of the job \(i\) on machine \(M_{k}\)  
\(D_{i}\): the due date of the \(i\)th job  
\(M_{ij}\): set of available machines for the \(j\)th operation of job \(i\)  
Index:  
\[X_{ijk}=\left\{\begin{array}{ll}1,&\text{if $o_{ij}$ is machined on $M_{k}$}\\ 0,&\text{otherwise}\end{array}\right\}\]  
Two objective functions are simultaneously minimized:  
\[\min\quad\text{F1}=\max\{C_{i}|i=0,1,\ldots,n\} \tag{1}\]\[\text{F2}=\sum_{i=0}^{n}\left(C_{i}-D_{i}\right) \tag{2}\]  
Subject to:  
\[E_{ijk}>0,\ i=0,1,\ldots,n;j=0,1,\ldots o;k=0,1,\ldots,m \tag{3}\]  
\[E_{i(j+1)g}-E_{ijk}\geq X_{i(j+1)g}\times T_{i(j+1)g},i=0,1,\ldots,n;j=0,1, \ldots,o-1;k,g=0,1,\ldots,m \tag{4}\]  
\[E_{ijk}-E_{ilk}\geq X_{ijk}\times T_{ijk}\text{ or }E_{ilk}-E_{ijk}\geq X_{ilk} \times T_{hk},i=0,1,\ldots,n;j=0,1,\ldots,o;k=0,1,\ldots,m \tag{5}\]  
\[\sum_{j=0}^{o}X_{ijk}=1,\ \ \ \ \ i=0,1,\ldots,n;k=0,1,\ldots,m \tag{6}\]  
Equations (1) and (2) are used to minimize the makespan (F1) and total tardiness (F2), respectively. Equation (3) is the variable restriction. Equation (4) ensures the sequence of operations. Equation (5) is used to constrain operations so that they do not overlap. Equation (6) indicates an operation of a job could only be processed on one machine, that is, an operation cannot be divided, and it can only be processed on one machine from the beginning to completion.