esa-sceva/satcom-chunk-collection huggingface dataset:
# Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems
## 5 Mathematical Analysis
### Improvement in Channel Capacity: LR-Aided Receiver  
In this section, we present the achievable channel capacity gain due to LR aided MMSE receiver in comparison to the conventional MMSE receiver. The capacity for a MIMO system with MMSE [45; 46; 47] can be expressed as,  
\[C_{mmse}=log_{2}[\frac{1}{(I_{N_{r}}+SNRH^{H}H)^{-1}}] \tag{20}\]  
Similarly, for a LR-aided MMSE receiver, the achievable capacity can be represented as  
\[C_{MMSE}^{LR}=log_{2}[\frac{1}{(I_{N_{r}}+SNRH^{H}H)^{-1}}] \tag{21}\]  
At high SNR condition, the factor \((I_{N_{r}}+SNRH^{H}H)^{-1}\) can be simplified as  
\[(I_{N_{r}}+SNRH^{H}H)^{-1}=(SNRH^{H}H)^{-1}-I_{N_{r}}(SNRH^{H}H)^{-2}+o(SNR^{- 3}) \tag{22}\]  
\[=(SNRH^{H}H)^{-1}[1-I_{N_{r}}(SNRH^{H}H)^{-1}+o(SNR^{-2})] \tag{23}\]Therefore, one can calculate the capacity difference between the LR-MMSE and MMSE as,  
\[C_{MMSE}^{LR}-C_{mmse}=log_{2}(SNR.H^{H}H)-log_{2}[1-I_{N_{r}}(SNRH^{H}H)^{-1}+o( SNR^{-2})]\\ -log_{2}(SNR.\bar{H}^{H}\bar{H})+log_{2}[1-I_{N_{r}}(SNR\bar{H}^{H }\bar{H})^{-1}+o(SNR^{-2})] \tag{24}\]  
\[C_{MMSE}^{LR}-C_{mmse}=log_{2}(\frac{\bar{H}^{H}\bar{H}}{H^{H}H})+log_{2}(\frac {1-I_{N_{r}}(SNRH^{H}H)^{-1}+o(SNR^{-2})}{1-I_{N_{r}}(SNR\bar{H}^{H}\bar{H})^{- 1}+o(SNR^{-2})}) \tag{25}\]  
Equation (25) represents the capacity improvement due to LR-MMSE system.
# Backdoor Attacks on Deep Neural Networks via Transfer Learning from Natural Images
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model/Task**} & \multicolumn{2}{c}{**ChestX**} & \multicolumn{2}{c}{**Emergency**} & \multicolumn{2}{c}{**Face**} & \multicolumn{2}{c}{**Age**} \\  & **ER** & **ASR *1** & **ER** & **ASR *2** & **ER** & **ASR *3** & **ER** & **ASR *4** \\ \hline Xception & 4.1 (3.5) & 100 (48.9) & 2.8 (2.5) & 98.4 (71.8) & 0.9 (1.8) & 100 (50.0) & 13.3 (14.1) & 99.7 (51.3) \\ VGG-16 & 6.7 (5.0) & 56.9 (54.8) & 3.7 (3.1) & 69.6 (71.2) & 1.4 (0.9) & 50.0 (50.0) & 9.4 (8.6) & 52.7 (51.3) \\ VGG-19 & 2.2 (2.4) & 51.9 (48.3) & 4.2 (2.7) & 70.3 (71.7) & 1.4 (0.9) & 50.5 (50.0) & 9.3 (8.6) & 55.6 (51.3) \\ InceptionV3 & 2.4 (2.2) & 100 (50.9) & 2.6 (2.5) & 99.3 (71.9) & 2.7 (1.4) & 99.5 (49.1) & 13.3 (15.4) & 93.6 (52.7) \\ InceptionResNetV2 & 2.8 (3.0) & 100 (51.3) & 2.8 (2.4) & 88.8 (71.2) & 0.9 (0.9) & 100 (50.0) & 10.7 (12.0) & 100 (51.9) \\ ResNet50 & 2.8 (2.2) & 87.4 (50.2) & 3.1 (2.6) & 85.8 (71.5) & 1.4 (1.4) & 85.5 (50.5) & 12.6 (11.3) & 89.6 (53.0) \\ MobileNet & 2.4 (3.0) & 50.2 (48.5) & 2.8 (2.6) & 72.8 (70.9) & 0.9 (0.9) & 51.4 (50.0) & 12.4 (16.9) & 59.1 (36.6) \\ DenseNet121 & 2.6 (3.9) & 96.3 (53.5) & 2.8 (2.7) & 99.2 (70.4) & 1.8 (0.9) & 99.5 (50.0) & 14.6 (10.3) & 88.4 (51.3) \\ DenseNet169 & 2.4 (2.4) & 100 (49.4) & 3.1 (2.7) & 77.2 (70.8) & 0.9 (1.8) & 98.2 (48.2) & 9.7 (9.3) & 81.6 (51.1) \\ DenseNet201 & 1.3 (2.0) & 97.9 (51.3) & 2.6 (2.0) & 75.6 (71.3) & 0.5 (0.5) & 99.5 (50.5) & 9.7 (8.9) & 96.6 (51.4) \\ \hline \hline \end{tabular}  
* Values were computed with \(K=\) “normal” for Xception, InceptionV3, and MobileNet, and with \(K=\) “pneumonia” for the other models. *2 Values were computed with \(K=\) “normal” for all models. *3 Values were computed with \(K=\) “Bush” for Inception-ResNetV2, and with \(K=\) “others” for the other models. *4 Values were computed with \(K=\) “minor” for InceptionV3, Inception-ResNetV2, ResNet-50, and DenseNet-169, and with \(K=\) “adult” for the other models.  
\end{table} 
Table 1: Error rate (ER; %) and attack success rate (ASR; %) values for the models from the backdoored ImageNet models for pneumonia classification from chest X-ray images (ChestX), emergency response monitoring from aerial images (Emergency), facial recognition (Face), and age classification from images of faces (Age). Values in brackets are ER and ASR for the models fine-tuned from the clean ImageNet models. neural cleanse failed to detect backdoors in the models fine-tuned from the backdoored ImageNet models.
Sensors_**2013**, _13_, 7454-7471; doi:10.3390/s130607454  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Proposal for Modeling Real Hardware, Weather and Marine Conditions for Underwater Sensor Networks**  
**[PERSON] *, [PERSON], [PERSON], [PERSON] and [PERSON]**  
Institut ITACA, Universitat Politecnica de Valencia, Edifici 8G, 46022, Valencia, Spain;  
E-Mails: [PERSON]DDRESS] (J.V.C.); [EMAIL_ADDRESS] (S.B.); [EMAIL_ADDRESS] (A.P.); [EMAIL_ADDRESS] (J.J.S.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-96-387-7000 (ext. 88251).  
_Received: 2 May 2013; in revised form: 3 June 2013 / Accepted: 3 June 2013 / Published: 7 June 2013_  
**Abstract:** Network simulators are useful for researching protocol performance, appraising new hardware capabilities and evaluating real application scenarios. However, these tasks can only be achieved when using accurate models and real parameters that enable the extraction of trustworthy results and conclusions. This paper presents an underwater wireless sensor network ecosystem for the ns-3 simulator. This ecosystem is composed of a new energy-harvesting model and a low-cost, low-power underwater wake-up modem model that, alongside existing models, enables the performance of accurate simulations by providing real weather and marine conditions from the location where the real application is to be deployed.  
**Keywords:** underwater wireless sensor networks, simulation platform, ns-3, modeling real hardware
# Data-Aided Frequency Offset Estimation for CE-OFDM Broadband Satellite Systems
## 6 Simulation Results and Analysis
Figure 11 shows the MSE performance of PSCE under different modulation indices when \(N_{p}\) was 31. It can be seen that the estimation performance of PSCE was not affected by the value of the modulation index. Figure 12 shows the BER performance of PSCE under different modulation indices when \(N_{p}\) was 31. The BER performance of PSCE with different CFOs and modulation techniques is shown in Figure 12. The PSCE can achieve good BER performance under different modulation indices and techniques. Figure 10: The MSE performance of PSCE under different modulation indices (\(2\pi h=0.8\), \(2\pi h=0.2\)). Figure 9: The bit error rate (BER) performance of PSCE (\(2\pi h=0.8\)).
# On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning
## 4 System Model of Proposed TCP-Switching Mechanism
### Model Interpretation
#### 4.1.1 Proposed DRL-Based CCA Switching
_Preparation for Switching:_ The parameters of the predicted optimal CCA were retrieved, potential disruptions during the transition were analyzed, and the current state of network traffic was saved to facilitate a smooth migration to the new CCA. _Execution of Switching:_ New CCA parameters were communicated to the transport layer, variables were migrated smoothly to minimize disruption, and the new CCA was applied while monitoring initial performance. ```
Input: Historical network data, Real-time network metrics Output: CCA selection and smooth switching Step 1: Data Collection Collect historical network traffic data using tools (e.g., jperf3, Wireshark) Gather metrics: throughput, packet loss, RTT, congestion levels Step 2: Offline Training Train DRL model with collected historical data Model learns to identify patterns and select suitable CCA DRL model analyzes historical network metrics (e.g., throughput, packet loss, RTT, congestion levels) the model identifies patterns and correlations within these metrics that impact network performance The model learns to predict the best-performing CCA under a different network Select the optimal perfroming CCA Step 3: Validation Validate the DRL model with separate validation datasets Fine-tune model parameters to improve generalization Step 4: Real-time Monitoring while network is operational do  Continuously monitor real-time network metrics  Update network condition variables  Input real-time metrics to the trained DRL model  Predict the optimal CCA for current network conditions endwhile Step 5: Algorithm Selection Policy Develop policy based on the DRL model's decision-making Determine appropriate CCA based on observed conditions Step 6: Prepare for Switching Retrieve parameters of the predicted optimal CCA Analyze potential disruptions during the transition Save the current state of network traffic Step 7: Execute Switching  Communicate new CCA parameters to the transport layer  Migrate CCA variables smoothly, ensuring minimal disruption  Apply the new CCA and monitor initial performance Step 8: Post-Switch Evaluation Continue monitoring network metrics post-switch Compare performance with pre-switch metrics if performance improves then  Confirm new CCA selection else  Revert to the previous CCA or select an alternative endif
```  
**Algorithm 1** DRL-Based TCP Switching Algorithm
# Enhanced Local Fisher Discriminant Analysis for Indoor Positioning in Wireless Local Area Network
## 3 Evaluation
### Effect of Signal Perturbation on Positioning Accuracy  
Figure 7 shows the accuracy within 2 m _versus_ the number of training samples added per reference point by signal perturbation. As in many works [11; 32], we collect 100 original RSS training samples per reference point. For simplicity, we only evaluate the effect of signal perturbation on accuracy of LFDA, LDA, WKNN and ANN method. As seen in Figure 7, the proposed signal perturbation method improves all the compared positioning methods. Through signal perturbation, RSS samples, which are very likely to appear at each reference point, are added into training samples set. The ratio of training size to feature dimensions increases and enhances the generalization ability of these machine learning or pattern matching methods. For LFDA, it is more prone to data sampling density than LDA for its locality preserving property. Thus, through signal perturbation, the performance improvement is higher than that of LDA. In comparison, ANN also gets higher accuracy improvement than WKNN. That is because the overfitting problem of ANN is greatly alleviated by adding much more useful training samples. As seen in Figure 7, when the number of training samples added is bigger than 900, the accuracy improvement is little. Therefore, during offline phase, we just increase the number of training samples per reference point by signal perturbation from 100 to 1000.  
Figure 5: Accuracy within 2 m _versus_ feature dimensions.  
Figure 6: Cumulative percentage of eigenvalues _versus_ feature dimensions.
## 4 Discussion
### Effect of Body Location on External Load  
Contrary to our hypothesis, the knees registered the highest impacts (11,800 per limb over the hour), with 98% \(\leq\)4 g. The ankles suffered the second highest overall workload, accumulating over 8800 impacts on average, with 90% \(\leq\)4 g. Far lower total impacts were observed in the lumbar spine (\(\approx\)6000) and scapulae (\(\approx\)2800), nearly all \(\leq\)2 g (98-99%). This distal-to-proximal gradient in the dance load is likely owed to the foot-floor contacts underlying the steps and movements, which transmit attenuated forces up the athletes' kinetic chain [35]. The multi-joint coordination fundamental to dance techniques may also explain the greater impacts measured at the ankles and knees compared to more proximal sites [36]. Indeed, dance utilizes ankle plantar flexion/dorsiflexion, knee flexion/extension, and hip abduction/adduction to perform steps, transfers, and bounces [37]. As dancers dynamically shift their center of mass over the base of support, distal joints likely bear and dissipate these forces before they reach the lumbar spine [38].  
Notably, most impacts remained below 6 g across all sites. Light (2-4 g) walking gait impacts fall in the range of 2-5 g, while runners' \(>\) 8 g impacts increase injury likelihood [39]. No definitive loading thresholds exist for safe dance [36]. However, based on running research, the observed loads likely pose minimal harm, though perhaps still enough stimulus to strengthen the dancers' lower extremity bones and muscles. Since aging reduces lower extremity muscle mass and strength critical for movement [5], dance could offer an osteogenic, muscle-building activity for older adults [14]. Indeed, interventions report dance helps older people to improve gait speed, balance, leg strength, and fall risk versus education or social programs [15, 16, 17]. The mild-to-moderate impacts dance elicits could mediate such functional benefits by loading musculoskeletal and neuromuscular tissues.
## 3 Results and Discussion
### Validation of the Cohesive Parameters  
To validate the finite element analysis, the simulation data were meticulously reproduced using parameters from a published article [28]. The single lap joint specimens were modeled with precise geometric details to match the reference study, with mechanical properties from Tables 2 and 3, and the geometrical properties are listed as follows based on Figure 1: the adherend thicknesses, \(t_{\text{P}}\) = 4.7 mm, the adhesive thickness, \(t_{\text{A}}\) = 0.2 mm, the grip length with shims, \(L_{\text{G}}\) = 40 mm, the adherend length, \(L_{\text{A}}\) = 100 mm, and the overlap distance, \(L_{\text{o}}\) = 30 mm. In the approach of this reference study, the interfacial and middle cohesive elements were used for the analysis to simulate the damage within the adhesive layer. These dimensions were chosen to ensure consistency with the reference study, allowing for a direct comparison of results with our model.  
While the reference study utilized a 2D plane stress model, our approach involved a 3D finite element model. This transition from 2D to 3D is significant as 3D models capture out-of-plane stresses and deformations that 2D models, constrained by plane stress or plane strain assumptions, may not fully represent. These out-of-plane effects in the 3D often result in a stiffer response in the force-displacement behavior of the joint, especially in regions where stress concentrations occur due to geometry transitions. The finite element mesh in our study was carefully constructed, adhering to standard criteria such as aspect ratio and element distortion, to ensure numerical accuracy and stability across the simulations.  
Upon reproducing the simulation results, as seen in Figure 5, a close examination of the force-displacement curves revealed slight variations in the slopes compared to the published data. These variations can be attributed to the differences between the 2D and 3D analyses. While the 2D plane stress model from the reference study inherently provides a softer response due to its inability to capture the full three-dimensional stress state, our 3D model incorporates these effects, providing a more realistic representation of the joint behavior under load. The boundary conditions, including the application of load and constraints, were carefully replicated to match the reference study, ensuring that any differences observed are due to the dimensional transition rather than discrepancies in setup. Thus, our model is validated against the real scenario presented in the reference study.
applied sciences  
A Novel Constant Power Factor Loop for Stable _V/f_ Control of PMSM in Comparison against Sensorless FOC with Luenberger-Type Back-EMF Observer Verified by Experiments [PERSON],*(c), [PERSON] and [PERSON]  
1 Department of Power Systems and Electric Drives, Faculty of Electrical Engineering and Information Technology, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia  
2 Vehicle Control & Networking Solutions, NXP Semiconductors, 1. Maje 1009, 756 61 Roznov pod Radhosten, Czech Republic  
*Correspondence: [EMAIL_ADDRESS]  
This paper proposes a novel constant power factor loop in the _V/f_ control strategy with stabilization for a permanent magnet synchronous motor (PMSM). The advantage of such an algorithm is the independence of the machine parameters, which vary under different operational conditions, e.g., with temperature, magnetic core saturation, and skin-effect. Furthermore, it is a low-cost and simple-to-implement sensorless solution. The proposed strategy is compared against traditional sensorless FOC with a Luenberger-type back-electromotive force (EMF) observer, which can be designed based on the machine model. The output of this kind of observer is typically an error signal, which can be specified for position deviation, requiring phase-locked loop (PLL) algorithm implementation. Employing PLL, a rotor speed and position can be estimated from such an error. Therefore, it is a complex sensorless technique with high-performance microcontroller unit (MCU) requirements. Both strategies are deeply analyzed, mathematically described, and compared within the paper. At the end of the paper, these sensorless strategies are supported by experimental verification with a traction PMSM designed for golf cart applications, and the pros and cons of both techniques are discussed.  
sensorless control; Luenberger-type back-EMF observer; _V/f_ control; power factor control; permanent magnet synchronous motor
# Bone Chemical Composition Assessment with Multi-Wavelength Photoacoustic Analysis
###### Abstract  
In this study, the feasibility of assessing the chemical composition in bone using the multi-wavelength photoacoustic analysis (MWPA) method was investigated. By illuminating a bone specimen using laser light with a wavelength tunable over an optical spectrum from 680 nm to 950 nm, the optical absorption spectrum of the bone was acquired. Then, with the optical absorption spectra of all the optically absorbing chemical components in the bone known, a spectral unmixing procedure was performed to quantitatively assess the relative content of each chemical component. The experimental results from porcine rib bones demonstrated that the contents of the chemical components, including not only non-organic materials such as minerals and water but also organic materials including oxygenated hemoglobin, deoxygenated hemoglobin, lipid, and collagen, can all be assessed by MWPA. As the chemical composition in the bone is directly associated with functional and metabolic activities, the finding from this study suggests that the MWPA method could offer a new diagnostic tool for the non-invasive evaluation of bone health.  
photoacoustic; bone; multi-wavelength photoacoustic analysis; osteoporosis; chemical composition +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods
### Dataset Collection and Preprocessing  
#### 3.2.1 PSD Dataset  
PSD offers insights into the frequency components of a signal and their power distribution across various frequencies. Due to the varying power distribution characteristics among different modulation formats, and its ease of acquisition, we can utilize PSD as a feature for identifying modulation formats. In this study, we subject the extracted I/Q signal \(x[n]=I[n]+jQ[n]\) to the Fourier transform, compute the power spectrum at each frequency point, and ultimately normalize it to obtain PSD. The formula is as follows:  
\[PSD[n]=\frac{1}{N}|DFT(x[n])|^{2},\;n=0,\;1,\;2\cdots N-1 \tag{1}\]  
In our system, we select OSNR ranging from 15 to 26 dB in 1 dB increments and chromatic dispersion (CD) values ranging from \(-100\) to \(100\) ps/nm in \(10\) ps/nm increments for each modulation format. This yields a total of 252 (12 \(\times\) 21) combinations of OSNR and dispersion values. To ensure diversity within the dataset, we generate data using \(10\) distinct random seeds for each combination, resulting in 2520 samples per modulation format. Overall, we collect 7560 data samples across the three modulation formats. From this dataset, we allocate 180 samples to the test set, while dividing the remaining samples into a validation set (30%) and a training set (70%). The PSD dataset is considered a one-dimensional input for the deep learning network. Figure 2 presents the results from visualizing a subset of the data.  
Figure 2: PSD for three different modulation formats: (**a**) QPSK, (**b**) 8 PSK, and (**c**) 16 QAM.
# Predicting Dog Emotions Based on Posture Analysis Using DeepLabCut
## 3 Materials and Methods
### Dog Emotion Detector
#### 3.3.2 Decision Tree  
To develop appropriate metrics for predicting a dog's emotion, various base postures are first broken down into the condition of individual body parts.  
For this purpose, seven body postures of a dog [26; 27] are considered:  
1. A **Neutral** position expresses that a dog is relaxed and approachable since he feels unconcerned about his environment.
2. An **alarmed** dog detected something in his surroundings and is in an aroused or attentive condition. This mood is usually followed by an inspection of the regarding area to identify a potential threat or an object of interest.
3. **Dominant aggression** is shown by a dog to communicate its social dominance and that it will answer a challenge with an aggressive attack.
4. A **defensive aggressive** dog may also attack, but is motivated by fear.
5. An **active submissive** canine is fearful and worried, and shows weak signals of submission.
6. Total surrender and submission is shown by **passive submissive** behavior and indicates extreme fear in the animal.  
Figure 2: Available keypoints for landmark detection datasets. The colors indicate which data set contains the respective point.  
Based on these postures, pose metrics are defined to represent the four target emotion classes. These are introduced in detail in the next chapter. After calculating the pose metrics for each input image, a grid search with cross-validation is performed in the same way as for a neural network.