esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Deep Mutual Learning via Knowledge Distillation
## 3 Results and Discussion
### Experiment on CIFAR-100
Next, in the last scenario, we take the same test steps as [22], namely with a larger student cohort, to observe the training process of multiple student networks by increasing the number of networks to demonstrate the advantages of our approach, as shown in Figure 5.  
By simultaneously training multiple students, the accuracy results increase when the number of student networks is added to the cohort. This test scenario uses WRN-40-2 as the teacher network and Resnet32 as the student network for our two proposed methods, while DML is the baseline as in the original form without using the soft probabilities of the teacher network. In all experiments, the variation in the number of additions to the student network shows the superiority of our method, especially when the total number of student networks being three, where the highest accuracy was achieved by FDDML with 73.75%, followed  
Figure 5: Accuracy performance (top-1) on CIFAR100 with teacher WRN-40-2 to different numbers of Resnet32 student networks in a cohort. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Network Architecture**} \\ \hline Teacher & WRN-40-2 & WRN-40-2 & WRN-40-2 & WRN-40-2 & Resnet110 & Resnet56 & Vgg13 \\ Student 1 & ShuffleV2 & WRN-28-2 & MobileNetv2 & Resnet32x4 & Resnet56 & Vgg8 & ShuffleV2 \\ Student 2 & Resnet32 & Resnet32 & Resnet32 & MobileNetv2 & WRN-16-2 & Resnet32 & MobileNetv2 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{} & & & & & & \\ \hline Teacher & 75.3 & 75.3 & 75.3 & 75.3 & 73.79 & 72.04 & 74.94 \\ Student 1 & 71.88 & 73.89 & 64.55 & 71.14 & 72.04 & 69.83 & 71.88 \\ Student 2 & 70.12 & 70.12 & 70.12 & 64.55 & 73.15 & 70.12 & 64.55 \\ \hline DML: [11] & & & & & & & \\ \hline Student 1 & 76.71 & 75.58 & 69.13 & 77.8 & 74.03 & 72.73 & 73.81 \\ Student 2 & 72.34 & 72.72 & 71.84 & 68.2 & 74.14 & 72.37 & 67.72 \\ \hline FDDML: & & & & & & & \\ \hline Student 1 & **77.31** & **77.04** & 69.93 & **78.68** & 74.55 & **73.61** & **75.71** \\ Student 2 & 73.23 & 73.52 & 73 & 70.5 & 74.91 & 73.04 & 69.82 \\ \hline HDDML: & & & & & & & \\ \hline Student 1 & 76.29 & 75.83 & 69.55 & 77.6 & 74.03 & 72.3 & 74.24 \\ Student 2 & 74.23 & 73.32 & **73.85** & 70 & **74.95** & 73.58 & 69.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparing resultstop-1 accuracy on the CIFAR-100 datasets with different student network. by HDDML with 73.65%, but this trend of accuracy gradually decreases as the number of students increased in a cohort. On the other hand, for DML itself, the trend of accuracy is increasing but is not better than our proposed method. This shows that the minimization of mimicry loss for the transfer of teacher knowledge to a pool of student networks trained simultaneously can work better.
# Polarization Sensitive Optical Coherence Tomography: A Review of Technology and Applications
## 3 PS-OCT Applications
### PS-OCT in Skin and Oropharyngeal Tissue  
Since the imaging regime of OCT is usually restricted to superficial layers of scattering structures (unless special probes such as catheters are used), skin is a preferred candidate for OCT imaging. Using PS-OCT, dermal layers with different scattering and polarization properties can be observed, including stratum corneum, dermis, and epidermis (Figure 7) [50; 64; 172; 173]. Oral and laryngeal tissue have also been imaged by PS-OCT. In the oropharyngeal tract, PS-OCT was demonstrated for investigating the mucosa of the vocal fold and for detecting lesions in the buccal mucosa based on increased birefringence [174; 175].  
Figure 6: PS-OCT of the anterior eye. (**a**–**e**) PS-OCT of the cornea of a keratoconus patient (adapted with permission from [165], ARVO, 2007). (**a**) PS-OCT en-face image showing retardation at the posterior surface of the cornea. The red lines indicate the locations of the B-scans shown in (**b**–**e**); (**b**,**c**) Horizontal and vertical reflectivity B-scan images. Decreased corneal thickness can be observed in the center; (**d**,**e**) Corresponding retardation B-scans exhibiting irregular pattern. (**f**) PS-OCT of blebs in the anterior eye after glaucoma surgery (adapted with permission from [167], ARVO, 2014). Photographs (red lines indicate locations of OCT scans), phase retardation and intensity images with PS-OCT at one day, one week, and one month after surgery. Cases of partial increase of phase retardation after surgery. Arrows indicate irregular and abnormal phase retardation.  
Via dermal birefringence, PS-OCT provides access to tissue alterations caused by deformation, scarring, and burns [106; 177; 178; 179]. Figure 7j shows an example of scarred skin exhibiting significantly higher birefringence than normal skin (Figure 7j). Additionally, wound healing processes including collagen restoration can be followed with PS-OCT [180; 181]. Moreover, Stokes vector based depolarization imaging can reveal multiple scattering as well as pathological conditions in skin such as cancer [19; 24; 58], as will be discussed in the next section.
## 3 Experimental Verification
### Verification for the Case of Simply Supported EPCB  
[PERSON] and [PERSON] [10] tested the flexural behaviors of externally prestressed steel-concrete composite beams with different tendon profiles. Two specimens, named B3 and B4, are employed to verify the proposed model. B3 and B4 had similar structural dimensions (as shown in Figure 13) except for the tendon profiles, which were draped-style for B3 and straight-style for B4. The average compression strength of concrete \(f_{c}\) = 31.7 MPa and the elastic modulus \(E_{c}\) = 28.6 GPa. The yield strength of the steel beam \(f_{y}\) = 293 MPa and the yield stress of the cover-plate was 358 MPa. According to the push-out tests of the shear connector, the maximum interfacial shear capacity of a single connector was 75 kN and the authors recommended \(\alpha\) = 0.3, \(\beta\) = 0.55. The reinforcements of 8 mm diameter were embedded into the concrete slab, and the yield stress of the rebar was 428 MPa. The external prestressing was implemented using two nominal diameters of 15.7 mm seven-wire strands, whose \(A_{\text{p}}\) = 150 mm\({}^{2}\), \(E_{p}\) = 197.8 GPa and initial effective prestress \(\sigma_{\text{pe}}\) = 950 MPa.  
The behaviors of specimens are modeled with the proposed model and traditional model, respectively. In the traditional model, the composite beam is modeled by the traditional fiber beam element without shear-slip effects and the external tendons are modeled by multiple truss elements. In the proposed model, the nonlinear interfacial behaviors are considered in the developed fiber beam element and the external tendons are modeled by only one proposed slipping-tendon element. According to the test procedure, the prestressing is applied in the analysis model firstly and then two equal concentrated forces are loaded at mid-span until structural failure.  
Figure 11: Analysis of failure modes by the calculated results.  
Figure 12: Failure modes observed in the tests.  
Figure 13(a) shows the analysis results of specimen B3. The proposed model shows good agreement with the tests for both load-deflection curves and tendon stress increments. However, for the traditional model, the initial structural stiffness is overestimated due to the ignored interfacial slip and the structural capacity is also slightly overestimated. Meanwhile, the stress increments of the external tendon in the traditional model are different in the side and middle branches due to the strain-incompatible condition in the separated truss element. The stress increment of the side branch truss element (seg-1) is small and that of the middle truss element (seg-2) is large, as shown in Figure 13(b). Specim B4 is prestressed with straight tendons, in which case, the proposed slipping tendon element used is the same as the traditional truss element. The main differences between the two models are the interfacial slip effects. The traditional model overestimates the initial structural stiffness and the proposed model shows a better prediction. Compared with the traditional model, the proposed element significantly improves the prediction accuracy.
# Protecting Data at Risk of Unintentional Electromagnetic Emanation: TEMPEST Profiling
## 3 Sources of Electromagnetic Leakage
### Conductors
The cable assembly manufactured by Cinch Connectivity Solution (Lombard, IL 60148/USA) has, at its end, a robust, compact, and lightweight high-performance connector with gold-plated copper alloy screw-in contacts that provide multiple contacts for maximum reliability. It also has EMI ground spring gaskets on the plug shells and a braided EMI sleeve connected to the integrated back shells, providing 360\({}^{\circ}\) EMI shielding. In [74], the author outlines the use of cable shielding as one of the hardware solutions implemented to ensure compliance with rigorous information security standards during processing and transmission over a computer network. Shielded copper cables offer an additional layer of physical security by significantly limiting emanations. Implementing shielded cable can reduce cable separations, remove or reduce the need for signal isolation and filtering, and potentially reduce or eliminate the need for additional cable shielding. The paper suggests that foil-shielded/unshielded-twisted-pair (UTP) cable consists of a complete foil shield surrounding four unshielded twisted pairs and is typically used in situations where shielded cable is required; however, it may not be adequate for all TEMPEST installations. Additional signal isolation in some cases can be achieved through braided shields, tighter braids, braided foil, or individual pair shields with an overall foil shield. The author mentions independent testing showing that a combination of cabling and connectivity solutions effectively minimizes, if not eliminates, emanations when used within a secure network system.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 4 Challenges That Influence the Design of In-Body Antennas
### Impact of Effective Wavelength on In-Body Antenna  
In Ref. [79], the effective wavelength \(\lambda\) in any medium is defined as  
\[\lambda=\frac{2\pi}{\beta} \tag{12}\]  
The phase constant \(\beta\) is dependent on conductivity \(\sigma\) proportionally. The rise in medium conductivity reduces \(\lambda\), which therefore leads to miniaturization of the in-body antenna. In an ideal case, in order to facilitate a surgical procedure, the IBBDs have to be in the range of 1 to 10 mm in diameter for a length of 5 to 35 mm, while in the MedRadio and ISM bands, the free-space wavelength is approximately 74 and 12 cm, respectively [78]. This indicates that in-body antennas must be profoundly miniaturized, leading to antenna dimensions of some fractions of the free-space wavelength (typically \(\lambda/30\) and \(\lambda/5\) for the MedRadio and ISM bands, respectively).
## 6 Results and Discussion
### Fnn Performance: Scenario1  
For _Scenario1_, the FNN model outperformed the RNN models. Through our FNN experimental setup, we achieved a minimum validation loss of 170.41 MWatt, which is the lowest MAE against 195.31 MWatt, and 200.75 MWatt. This outcome was obtained by selecting a double-layered network with 32 nodes, a lookback period of 10 days, and training the model for 351 epochs.  
Using the optimized parameters mentioned above with a dataset size of 44,256 and a test size of 11,520, we conducted predictions for the electricity demand in 2013. The resulting MAPE was found to be 2.47%. It is important to note that this data setup specifically excluded holidays and weekends, focusing solely on weekdays. The results are presented in Figure 15, where the actual demand is denoted as _load_ and the forecasting results are denoted as _load_pred_.
# Assessment of Offshore Wind Resources, Based on Improved Particle Swarm Optimization
###### Abstract  
It is crucial to understand the characteristics of wind resources and optimize wind resources in the area that is being considered for offshore wind farm development. Based on the improved particle swarm optimization (IPSO) and the back propagation neural network (BPNN), the IPSO-BP hybrid intelligent algorithm model was established. The assessment of wind resource characteristics in the eastern waters of China, including average wind speed, extreme wind speed, wind power density, effective wind energy hours and wind direction distribution were all calculated. Additionally, the wind speed throughout the different years in Luchao Port, a famous seaport in China, was predicted. The results revealed that the wind power density is approximately 300 W/m\({}^{2}\) all year round and that the effective wind energy hours take up about 92% per hour. It was also identified that the wind direction distribution is stable in Luchao Port, implying that there are better wind energy resource reserves in this region. The IPSO-BP model has a strong tracking performance for wind speed changes, and can accurately predict the wind speed change in a short period. In addition, the prediction error of the IPSO-BP model is smaller when the time of training data is closer to the target one, and it can be controlled within a 5% range.  
wind resource assessment; offshore wind farm; improved particle swarm optimization; wind power density; wind speed prediction 2022  
[PERSON], [PERSON], D.
# Semi-Automated Approach for Building Event Logs for Process Mining from Relational Database
###### Abstract  
Process mining is a novel alternative that uses event logs to discover, monitor, and improve real business processes through knowledge extraction. Event logs are a prerequisite for any process mining technique. The extraction of event data and event log building is a complex and time-intensive process, with human participation at several stages of the procedure. In this paper, we propose a framework to semi-automatically build an event log based on the XES standard from relational databases. The framework comprises the stages of requirements identification, event log construction, and event log evaluation. In the first stage, the data is interpreted to identify the relationship between the columns and business process activities, then the business process entities are defined. In the second stage, the hierarchical structure of the event log is specified. Likewise, a formal rule set is defined to allow mapping the database columns with the attributes specified in the event log structure, enabling the extraction of attributes. This task is implemented through a correlation method at the case, event, and activity levels, to automatic event log generation. We validate the event log through quality metrics, statistical analysis, and business process discovery. The former allows for determining the quality of the event log built using the metrics of accuracy, completeness, consistency, and uniqueness. The latter evaluates the business process models discovered through precision, coverage, and generalization metrics. The proposed approach was evaluated using the autonomous Internet of Things (IoT) air quality monitoring system's database and the patient admission and healthcare service delivery database, reaching acceptable values both in the event log quality and in the quality of the business process models discovered.  
event log; process mining; relational database; event data; business process; attribute extraction +
Footnote †: journal: Applied  
1
# Adaptive Semi-Supervised Algorithm for Intrusion Detection and Unknown Attack Identification
## 3 Methodology
### Algorithm Design  
#### 3.2.1 Network Model Structure  
We designed a robust GAN model suitable for network traffic data detection scenarios. The model comprises a generator, a discriminator, and a noise encoder, all working together to perform anomaly detection on traffic data.  
**Generator:** The structure of the generator is shown in Figure 3. It utilizes multiple layers of Transformer encoders, with each layer employing multi-head attention mechanisms to capture long-range dependencies within the network traffic data.  
The original network traffic data are first mapped to a high-dimensional feature space through an input embedding layer. Position encoding is used to incorporate positional information, aiding the model in understanding the order and temporal dependencies of the data. The multi-head attention mechanism extracts important temporal information from the network traffic and handles various complex traffic patterns. A feed-forward network further performs linear transformations and nonlinear activations on the traffic features, enhancing the expressiveness of the generated synthetic traffic data. The output of the generator is synthetic data that closely resembles real network traffic. The training objective of the generator is to produce highly realistic network traffic to dece  
Figure 3: Generator network structure.  
**Discriminator:** The structure of the discriminator is shown in Figure 4. The discriminator is responsible for distinguishing whether the input network traffic is real or generated by the generator. Its structure is similar to that of the generator and also utilizes multiple layers of Transformers.  
Similarly, the input is processed through an embedding layer and positional encoding, maintaining consistency with the generator structure. The discriminator employs a six-layer Transformer encoder structure, using LayerNorm and a feed-forward network to extract features from the input traffic [23]. Finally, the discriminator's output is passed through a Softmax activation function to generate classification probabilities for distinguishing between real and synthetic traffic.  
**Noise encoder:** The structure of the noise encoder is shown in Figure 5. The noise encoder is responsible for generating the initial noise data input to the generator.  
Figure 4: Discriminator network structure.  
Figure 5: Noise encoder network structure.  
The structure of the noise encoder is a simple fully connected neural network that processes the input noise using a LeakyReLU activation function and maps it to a feature space suitable for the generator. This design enhances the diversity of the generator's inputs, increasing the model's generalization capability. The Tanh output layer is used to map the generated noise to a fixed range, providing input to the generator.
# Highly Compact 4 \(\times\) 4 Flower-Shaped MIMO Antenna for Wideband Communications
## 5 Conclusions  
A very compact four-element MIMO antenna with enhanced isolation has been introduced in this paper for wideband communications. The MIMO antenna achieved return loss results of less than \(-10\) dB from \(5.8\) GHz up to \(11\) GHz, and all the results of the proposed MIMO antenna are within their acceptable limits. Moreover, the insertion loss results are lower than \(-20\) dB between various ports, and this is due to the existence of decoupling stubs, which are printed on both faces of cheaper FR4 dielectric material. Due to the high isolation between elements, the antenna succeeded in achieving an outstanding diversity performance in terms of _ECC_ \(<0.004\), _DG_ \(>9.97\) dB, and MEG \(\leq-3\) dB. The suggested antenna has nearly dipole-like radiation patterns in both planes with an acceptable average realized gain of \(4\) dBi. Finally, it can be deducted from all these merits that the proposed MIMO antenna is convenient to be embedded into portable electronic devices that can be operated for various wideband applications.  
Conceptualization, W.A.E.A.; methodology, W.A.E.A. and R.A.I.; software, W.A.E.A. and R.A.I.; validation, W.A.E.A. and R.A.I.; formal analysis, W.A.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
The authors would like to acknowledge the support of the Editorial Board Member for 100% waiver of the Article Processing Charges (APC) of this publication in Section \"Electrical, Electronics and Communications Engineering\".  
The authors declare no conflict of interest.
# Damage Evaluation in Tempered Vacuum Glazing via Multivariate Statistical Methods
## 3 Results
### HCA Process and Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{8}{c}{**Euclidean Distance**} \\ \cline{2-13}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** \\ \hline
1 & 0 & 2.898 & 2.837 & 0.891 & 2 & 3.248 & 0.574 & 2.511 & 2.834 & 2.257 & 2.194 & 0.601 & 0.384 \\
2 & 2.898 & 0 & 1.072 & 2.308 & 2.86 & 3.417 & 2.485 & 2.378 & 1.237 & 3.167 & 0.71 & 2.352 & 2.603 \\
3 & 2.837 & 1.072 & 0 & 2.023 & 2.08 & 2.397 & 2.299 & 1.422 & 0.168 & 2.337 & 1.208 & 2.235 & 2.47 \\
4 & 0.891 & 2.308 & 2.023 & 0 & 1.274 & 2.476 & 0.317 & 1.641 & 1.996 & 1.588 & 1.668 & 0.381 & 0.509 \\
5 & 2 & 2.86 & 2.08 & 1.274 & 0 & 1.249 & 1.522 & 0.839 & 1.959 & 0.34 & 2.435 & 1.646 & 1.685 \\
6 & 3.248 & 3.417 & 2.397 & 2.476 & 1.249 & 0 & 2.751 & 1.074 & 2.232 & 1.05 & 3.219 & 2 the better group among the latter two kinds is a challenge that requires a further analysis of element 2. While the scores of the group including samples 3, 9, 2, and 11 are above zero, those of the kind with samples 5, 10, 8, and 6 are below zero. Therefore, the quality of the former kind of samples is better. The relevant grade classification of TVG can be found in Table 16.  
Besides, the average values on elements 1 and 2 of the corresponding samples were calculated with respect to the three grades of damage, and the results are shown in Table 17.  
In this respect, evaluating the performance of new TVGs can be implemented through the calculation of Euclidean distances of sample's two principal components and their average values. Each minimum Euclidean distance between the samples allows them to be automatically classified into a certain kind.
# Measurement and Analysis of Radar Signals Modulated by the Respiration Movement of Birds
## 2 Method and Materials
### Measurement Systems
[2] The numbers of the legends on the right bar are the sequence number of the sampling frame. foam holder without tapes in other cases because we found that the duck was more still than we thought. It remained still, as shown in the photos in Figure 2. Since many real bird detection radars work with a narrow bandwidth as well as a low range resolution profile (LRRP), we needed to measure the LRRP of birds in a lab. In addition to LRRP data, we also used the high range resolution profile (HRRP) to separate radar signals of respiration (or wings) from other parts of the bird. Given that the radar bandwidth is \(B\), the range resolution, \(R_{\text{e}}\), is calculated by [25]  
\[R_{\text{e}}=\frac{c}{2B} \tag{7}\]  
where \(B\) is the transmitted bandwidth, and \(c\) is the propagation speed of the radar electromagnetic wave. The range resolutions within three radar bands are listed in Table 1.  
We select three radar bands (S-, X-, or Ku-band) after referring to the actual bird radar applications. Currently, radar systems used to detect birds mainly work in these bands, such as the S-band (or L-band, or C-band) weather surveillance radar, X-band marine navigation radar system, and Ku-band fire-control radar. Based on the ratio of a bird's size to the wavelength [25], radar echoes within the S-band from flying birds fall into the resonance region and within the X-band and Ku bands, which are in the optic region. Rules in the scattering regions explain different signal fluctuations in the three radar bands.