esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Blind Modulation Classification Techniques for OFDM Signals
## 4 Artificial Intelligence-Based Approach to MC
### ML-Based MC

Table 5: Summary of ML-based classifiers for OFDM signals. The MC problem for MIMO systems employing OFDM under imperfect timing synchronization scenarios is studied in [75]. The proposed algorithm first uses the HOC of the received signal to extract the unique features, which show the robustness to STO. After that, a random forest classifier is used as the decision criterion to perform the classification problem. The main benefits of random forests are their better classification performance and low exposure to noise. The number of subcarrier \(N=128\), CP length \(N_{cp}=N/4\), channel tap \(L=5\), number of transmitting antennas 2, number of receiving antennas 8 and frequency-selective channel are considered the simulation parameters. The simulation results show that the proposed classifier can work well in the presence of STO with satisfactory classification accuracy. In a realistic scenario, where perfect STO estimation is difficult to achieve, these algorithms can provide conceptual help. In the paper [76], a modulation classifier without knowing noise variance is studied for the OFDM system. In order to estimate the amount of phase rotation caused by flat fading, the authors investigate adopting the iterative closest point, which is a kind of template matching technique. Combining the least squares-based phase estimation, the classification performance of the proposed method can be improved significantly. The PCC at several SNRs when each correction is performed in flat fading, where four types of modulation schemes, i.e., BPSK, QPSK, 16-QAM, and 64-QAM, are used. From these results, it is found that the proposed method can be improved significantly.
## 3 Discussion
The reason for the inconsistent results may be attributed to the lower DSF concentration (0.4-0.8 uM) in above studies. We further explored the effects of DSF/Cu on other immortalized cells, and found that DSF/Cu (1 uM/1 uM) exhibited strong cytotoxic effects on the human colon epithelial cell line NCM460 as well as the human embryonic kidney 293T cell line (Figure S8). We think that although these nonmalignant cell lines have often been used as normal cells in some studies, similar to tumor cells, NP69-SV40T cells essentially acquired an immortalized nature, and the rapidly growing cells are sensitive to ROS. In addition, it has been reported that a significant depression in copper concentration could be found in tumor tissue and in the serum after treatment with anticancer drugs in tumor-bearing mice, while there was no significant change in the liver [38]. However, we checked the copper ion concentration, and no difference was found between tumors and other normal tissues, except for liver tissue (Figure S9). We suspect that this may be due to the addition of additional copper. These results suggest that DSF/Cu could target solid tumor tissues that are rapidly growing and have regenerative capacity. Taken together, our study revealed that DSF/Cu can strongly promote NPC cell and CAF apoptosis as well as necrosis in vitro and in vivo. ROS-induced ferroptosis played an important role in DSF/Cu-induced cytotoxicity. It is of certain significance to consider DSF/Cu as an adjuvant drug for the treatment of NPC in clinical practice.
## 3 System Performance
### Sampling Regularity and Time Synchronization
For each of the signals, the intersections with the abscissa, i.e., the time instances of zero acceleration, were determined. The offset \(t_{offset}\) was then determined from the difference of the intersection times from the signals (RPi B, C, and D) to the reference. Figure 9 shows the time-varying offset for RPi B, C, and D for a sample 10 s window relative to the reference measurement. It can be observed that the measured offset shows a noisy behavior and varies mostly within the range of \(\pm\)1 ms with maximum absolute offset of 1.2 ms. In Figure 7, it can be seen that time variations of the order of the time step occur, leading to randomized time shifts between the signals of the same order as shown in Figure 9. In other words, an uncertainty of relative timing between signals occurs but does not significantly exceed the length of one sampling time step. The reader is reminded that the MPU-6050 accelerometer is low-cost and that sensor communication leads to randomized delays. The determined time offsets were further processed to obtain a mean offset over the full measurement duration. Thereby, a moving average with a window size of 60 s was computed,  
Figure 8: Normalized histograms of measured time increments \(\Delta t\) from MPU-6050 accelerometers with a sampling frequency of 1 kHz, i.e., nominal \(\Delta t\) = 1 ms. (Dash lines indicate the corresponding 99 th percentile.) Figure 9: Determined time history of the offset \(t_{offset}\) over a sample time window of 10 s.  
thus eliminating the randomized sampling discussed above and leading to a measurement of clock synchronization of the units. The time histories of the mean offset \(\tilde{t}_{offset}\) from RPi B to D with respect to RPi A are displayed in Figure 10. The variations over 60 min are less than \(\pm\)0.18 ms and therefore indicate very good time synchronization. In general, the study presented here confirms an effective time synchronization of the implementation that is within the range of what is required in typical SHM applications. Additionally, several test measurements were conducted with different sampling rates over a time span of an hour. The system was put under a heavy CPU load by spawning four processes generating random numbers. This scenario was chosen to verify that the real time prioritized polling process is not hampered by competing tasks running on the nodes. Table 3 summarizes the maximum and minimum values of time increments from the measurement for all sensors as well as the time increment and the 99% confidence interval for the recorded time series. It can be observed that the 99% confidence interval is less than 0.025 ms for all measurements. Overall, the results indicate a stability in the sensor sampling that is sufficient for most SHM applications and is comparable to the performance of dedicated microcontrollers.
## 2 Material Constitutive Model and Interface Cohesive Zone Model
### Static and Fatigue Damage Constitutive Model of the CA Mortar  
The CA mortar filling layer is a key component of the CRTS II high-speed railway ballastless slab track structure in China. It not only bears the vertical load but also restricts the horizontal movement of the slab and enhances the integrity of the track structure. However, the existing literature rarely reports the constitutive law of this layer material. In this paper, the static and fatigue viscoelastic-damage constitutive model of the CA mortar as determined in the final test report [26] is introduced in order to study the effect of fatigue degradation of CA mortar layer on the mechanical properties of the ballastless track-bridge structural system. Based on the BCM for concrete material [25], the static and fatigue viscoelastic-damage constitutive model (i.e., a fibre bundle-plastic chain model for CA mortar material) [26] was developed for characterising the mechanical behaviours of the material under external loading. The viscoelastic-damage model for CA mortar was verified to be able to capture the stochastic microstructure and the viscoelastic-damage behaviours of CA mortar material and describe the stress-strain curve under fatigue loading precisely and conveniently [26]. It considered the both the effects of microviscoelastic-damage behaviours due to micro asphalt gel and the fatigue failure due to external loading [26]. The predicted fatigue life of the CA mortar agrees with the experimental results [26]. The following Equation (12) presents the compression stress-strain law of the CA mortar material of the CRTS II slab track obtained by the uniaxial compression test [26]. \[\sigma=1.761\times 10^{-7}\varepsilon^{6.069\varepsilon^{-0.137}} \tag{12}\]  
Since the current literature [26] does not report a tensile test of the CA mortar material of the CRTS II slab track, for simplicity, the stress of the static and fatigue damage constitutive law for the CRTS II CA mortar material slab track under tension are taken as 1/10 of the press of the static and fatigue damage constitutive law under compression corresponding to certain strain [27]. The following is only a brief description of the fatigue damage constitutive model of CA mortar material under compression;  
\[\sigma=(1-D_{\text{M}})E_{\text{f0}}\varepsilon \tag{13}\]  
with the fatigue damage variable under the \(n\)th fatigue loading cycle presented below. \[D_{\text{M}}(n/N)=A_{\text{M},3}\cdot\left(-\frac{n/N}{n/N-A_{\text{M},4}} \right)^{\frac{1}{A_{\text{M},5}}} \tag{14}\]  
The irreversible deformation fatigue damage variable under the \(n\)th fatigue loading cycle can be calculated with the following Equation (15). \[D_{{}_{M}i}(n/N)=\frac{1}{7.694-3.342\times\left[1-e^{-\frac{D_{\text{M}}(n/N )}{0.343}}\right]-2.375\times\left[1-e^{-\frac{D_{\text{M}}(n/N)}{0.3113}}\right]} \tag{15}\]  
where \(A_{\text{M},3}\), \(A_{\text{M},4}\) and \(A_{\text{M},5}\) are parameters related to the fatigue damage characteristics of the CA mortar. Their values can be determined by the test results data fitting method.
## 2 Related Work and Foundational Concepts
### Software and System Development Life Cycles
#### 2.1.2 Agile Method  
In contrast, the Agile methodology offers a flexible and iterative approach, breaking development into small units called sprints that allow for continuous integration and deployment [5,11]. This adaptability is effective in environments where requirements evolve, such as in consumer IoT development [4]. However, the rapid pace of Agile development necessitates strong management and integrated security practices to prevent oversights and ensure that compliance is maintained throughout the iterative cycles [2].
## 2 Materials and Methods
### Time-of-Flight Secondary Ion Mass Spectrometry  
In order to reliably confirm the thickness and composition of Pd coating, Time-of-Flight Secondary Ion Mass Spectrometry (TOF-SIMS) was applied in combination with the above-described FIB-SEM. The principle of this method is based on stepwise secondary ion extraction from the specimen surface by etching it with FIB. This allows us to obtain the 3D ions' distributions considering the homogeneous layer-by-layer etching that requires some FIB calibration procedures. In this study, TOF-SIMS measurements were performed under the following conditions: ion beam voltage of 30 kV, ion beam current of 250 pA (to obtain gentle etching with a small penetration depth), positive ion polarity, dwell time of 20 \(\upmu\)s, resolution of 1024 \(\times\) 1024 pixels and view field of 20 \(\upmu\)m.
# A Multi-Resolution Approach to GAN-Based Speech Enhancement
## 5 Experiments and Results
### Performance of Multi-Scale Discriminator
#### 5.2.3 Real-Time Feasibility  
SERGAN and the proposed method were evaluated in terms of the real-time factor(RTF) to verify the real-time feasibility, which is defined as the ratio of the time taken to enhance the speech to the duration of the speech (small factors indicate faster processing). CPU and graphic card used for the experiment were Intel Xeon Silver 4214 CPU 2.20 GHz and single Nvidia Titan RTX 24 GB. Since the generator of AECNN and SERGAN is the same, their RTF has the same value. Therefore, we only compared the RTF of SERGAN and the proposed method in Table 3. As the input window length was about 1 s of speech (16,384 samples), and the overlap was 0.5 s of speech (8192 samples), the total processing delay of all models can be computed by the sum of the 0.5 s and the actual processing time of the algorithm. In Table 3, we observed that the RTF of SERGAN and the proposed model was small enough for the semi-real-time applications. The similar value of the RTF for SEGAN and the proposed model also verified that adding the up-sampling network did not significantly increase the computational complexity.
## 8 Conclusions  
In this paper, polar-coded DCSK and polar-coded QCSK in underwater noisy channels are analyzed and simulated. The BER formulas for DCSK and QCSK systems in a UWA noise channel are derived and compared with the simulation results. The experimental results prove that the nearest model that is applicable to the UWA noise channel is the t-distribution with five and six degrees of freedom. In addition, combining polar code with the DCSK system has the ability to achieve performance improvements of about 8, 11 and 14 dB in the SNR at BER = \(10^{-3}\) for coding rates of 0.435, 0.239 and 0.065, respectively, which can be a challenge in UWA noise channels.  
Conceptualization, A.J.A.-A. and F.S.H.; methodology, A.J.A.-A. and F.S.H.; software, A.J.A.-A. and F.S.H.; validation, Y.A.Y., A.J.A.-A. and F.S.H.; formal analysis, A.J.A.-A. and F.S.H.; investigation, Y.A.Y.; resources, Y.A.Y.; data curation, A.J.A.-A. and F.S.H.; writing--original draft preparation, Y.A.Y., A.J.A.-A. and F.S.H.; writing--review and editing, A.J.A.-A. and F.S.H.; visualization, A.J.A.-A. and F.S.H.; supervision, F.S.H.; project administration, Y.A.Y.; funding acquisition, Y.A.Y., A.J.A.-A. and F.S.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData will be made available on request.  
The authors declare no conflict of interest.  
Figure 8: Polar-coded QCSK performance with a t\(-\)distributed underwater channel and for \(\beta\) = 128 and 256.  
Figure 7: Polar-coded DCSK performance with a t\(-\)distributed underwater channel and for \(\beta\) = 128 and 256.
# Fine-Tuned Visual Transformer Masked Autoencoder Applied for Anomaly Detection in Satellite Images
###### Abstract  
Anomaly detection is a process in which outlier samples can be detected in a given dataset. The purpose of this study is to implement, test, and evaluate the possibility of using deep learning methods for outlier detection with the use of a fine-tuning approach. A Transformer Masked Autoencoder was fine-tuned for a custom satellite image dataset after being pre-trained on the ImageNet subset. The first process of training included building an internal representation of images from a normal class. After adjusting the model weights for this task, a custom dataset with normal and abnormal samples was used for the reconstruction error calculation. The results obtained in this study show that it is possible to distinguish between normal class representatives and outliers using the proposed approach. However, this is not sufficient for the model to be employed in real-life applications. With a given level of precision, the model requires additional knowledge about the subject to correctly classify the sample. To the best of our knowledge, this study is the first to apply ViTMAE for a custom satellite image database. An analysis of the misclassified samples shows that the model tends to generalize the image content and is not sufficiently robust for image noise. As a result of the analysis, a new anomaly indicator is proposed for further study.  
Keywords:transformer models; autoencoders; anomaly detection; deep learning; satellite images +
Footnote †: journal: Applied sciences
# Towards Machine Learning for Error Compensation in Additive Manufacturing
## 2 Advancements in Additive Manufacturing  
Additive manufacturing is a process of creating a physical object from digital design and is typically used for rapid prototyping. The process starts with the creation of a design using CAD software, the design is then digitally sliced into thin layers and uploaded to an additive manufacturing machine. The manufacturing process begins once an extremely thin layer of metal powder is spread across the platform. A heat source such as laser or extruder melts the first layer of the 3D design. Subsequently, another layer of material is spread across the platform. The layering or melting process is repeated until the part is completed. The support materials are then removed, and the physical object that has taken shape is revealed. Additive manufacturing allows us to produce parts that are lighter, stronger, and more durable than traditionally made parts. Overall build times are reasonably faster as the designers can add precise features and complex geometry with minimal work is spent on tooling thus drastically reduced the cost overhead associated with it. In DMLS, there has been substantial leap on resurourcing new materials in practice as new studies have been conducted to study on the inclusion of materials and their characteristics such as composite alloys in AM for different types of applications [61; 62; 63].  
In fact, additive manufacturing has allowed industry players to revolutionise the way they work. The level of design customization that was previously unthinkable of with conventional manufacturing, is no longer an issue in additive manufacturing which also offers reduction in both material resource and the turnaround time, which are significantly reduced with the introduction of machine learning based manufacturing frameworks. Generally, a checklist that has been compiled need to be referred to for structural design works and design reviews. These checklists are usually prepared from an accumulation of previous experiences and know-how of experienced engineers or designers working on similar products. The accumulated experience and know-how increase day by day leading to amplified work time in verification tasks, which also substantiates manual labour required on a design. For example, in structural design, custom shapes being modelled will reflect the intentions of a designer to fulfil the standard requirement and functionality of a model or design and would reflect on how suited the design is with the available machining capabilities in conventional manufacturing.  
This is where machine learning techniques could benefit a design process by eliminating the need for designers to undergo a redundant process of verification which can be considered as legwork tasks.
# Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic
## 6 Experimental Results
### Three Lanes Three Arms  
The experimental results are shown in Table 13 for an environment where three arms have three outgoing lanes, and one arm has four outgoing lanes, which have four environments. In the proposed method, there is an average for all four environments, and the values of WTAVG, WTLAVG, and WTCAVG can be reduced by 84.34%, 6.26%, and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Lane**} & \multicolumn{4}{c}{**Method**} \\ \cline{2-4}  & \multirow{2}{*}{**Traditional**} & **Original DQN** & **Proposed** \\  & & **with Fuzzy Logic**[29] & **Method** \\ \hline E-3 S-3 & 75.85 & 19.53 & 16.75 \\ \hline E-3 W-3 & 104.60 & 19.93 & 14.97 \\ \hline N-3 S-3 & 72.83 & 19.81 & 15.85 \\ \hline N-3 W-3 & 100.23 & 21.57 & 16.17 \\ \hline N-3 E-3 & 83.61 & 19.39 & 16.03 \\ \hline S-3 W-3 & 94.58 & 19.57 & 15.68 \\ \hline \hline \multicolumn{4}{c}{**Average total waiting time for vehicles to make a left turn(s)**} \\ \hline \multirow{2}{*}{**Lane**} & \multicolumn{4}{c}{**Method**} \\ \cline{2-4}  & \multirow{2}{*}{**Traditional**} & **Original DQN** & **Proposed** \\  & & **with Fuzzy logic**[29] & **Method** \\ \hline E-3 S-3 & 5.10 & 8.29 & 4.88 \\ \hline E-3 W-3 & 5.11 & 8.38 & 4.42 \\ \hline N-3 S-3 & 5.14 & 8.30 & 4.54 \\ \hline N-3 W-3 & 5.06 & 9.21 & 4.76 \\ \hline N-3 E-3 & 5.03 & 8.22 & 4.43 \\ \hline S-3 W-3 & 5.02 & 8.07 & 4.62 \\ \hline \hline \multicolumn{4}{c}{**Average total waiting time per car(s)**} \\ \hline \multirow{2}{*}{**Lane**} & \multicolumn{4}{c}{**Method**} \\ \cline{2-4}  & \multirow{2}{*}{**Traditional**} & **Original DQN** & **Proposed** \\  & & **with Fuzzy logic**[29] & **Method** \\ \hline E-3 S-3 & 11.44 & 5.17 & 4.08 \\ \hline E-3 W-3 & 12.86 & 5.15 & 3.96 \\ \hline N-3 S-3 & 11.37 & 5.16 & 4.08 \\ \hline N-3 W-3 & 12.47 & 5.31 & 4.11 \\ \hline N-3 E-3 & 12.02 & 5.11 & 4.13 \\ \hline S-3 W-3 & 12.16 & 5.10 & 4.02 \\ \hline \hline \end{tabular}
\end{table}
Table 12: The experiment results in a three-lane and two-arm environment.  
67.54% when compared with the traditional method, and 19.98%, 48.46%, and 20.66% when compared with the original DQN with the fuzzy logic method, respectively.  
The results are consistent with those in Sections 6.4 and 6.5, in which the proposed method yielded better results than all the methods when comparing all the metrics.  
The experimental results are presented in Section 6.7. The following additional values were used to compare the results:  
1. Total ambulance waiting time (\(\text{WTA}_{\text{Total}}\)) is the accumulated waiting time for all ambulances. \[\text{WTA}_{\text{Total}}=\sum_{\text{i=1}}^{\text{A}}\text{WTA}[\text{i}]\] (14) where A is the number of ambulances, and WTA is the accumulated waiting time of ambulances.