esa-sceva/satcom-chunk-collection huggingface dataset:
# Smartphone Accelerometer for Gait Assessment: Validity and Reliability in Healthy Adults
## 2 Materials and Methods
### Measurements  
This study collected acceleration data using an acceleration sensor embedded in a smartphone (Galaxy S20, Samsung, Yongin, Republic of Korea). The dimensions of the smartphone were 151.7 mm \(\times\) 69.1 mm \(\times\) 7.9 mm (length \(\times\) width \(\times\) height), and it weighed 163 g. Data acquisition was facilitated through a custom-developed gait analysis application (ver. 1.0, Multi-Modality Medical Imaging Lab, Incheon, Republic of Korea). The sampling rate for data collection was set at 250 Hz. This sampling rate was chosen based on its demonstrated accuracy in detecting spatiotemporal gait parameters, such as step length and cadence, in prior validation studies [16]. Upon the initiation of walking, the administrator launched the application and pressed the start/stop button to record the data, which were subsequently stored on the smartphone's SD card.  
The recorded acceleration data were processed into gait variables using GaitAnalyzer_params (ver. 1.0, Multi Modality Medical Imaging Lab, Incheon, Republic of Korea), a software tool developed based on engineering algorithms within the Visual Studio 2017 (vc15) environment. The gait variables extracted at this stage included: walking distance (Distance, cm), walking time (Ambulation Time, s), walking velocity (Velocity, cm/s), mean normalized velocity (Leg Length/s), and cadence (Steps/Min).  
To validate these findings, the GAITRite walkway system (CIR Systems Inc., Franklin, NJ, USA), a floor-mounted, mobile gait analysis device, was employed to measure official spatiotemporal gait variables. The mat used in this study measured 180 \(\times\) 35.5 \(\times\) 0.25 inches, with an active area of 144 \(\times\) 24 inches containing pressure sensors. The sampling rate for the GAITRite system was set at 240 Hz. Data collected during walking were transmitted via a serial port to a connected PC, where they were stored and processed using GAITRite Plus software (ver. 4.7, CIR Systems Inc., Franklin, NJ, USA). The gait variables obtained from this system included walking distance (Distance, cm), walking time (Ambulation Time, s), walking speed (Velocity, cm/s), mean normalized velocity (Mean Normalized Velocity, Leg Length/s), and cadence (Steps/Min).
# Evading Logits-Based Detections to Audio Adversarial Examples by Logits-Traction Attack
## 4 Experimental Evaluation
### Effectiveness of Logits Traction Attack against Our Three Detections  
In this experiment, the values of the parameter \(p\) and threshold \(k\) obtained from each feature in the above experiments are preserved. These features are used to detect LT adversarial examples. This section only focuses on the FNR decline caused by LT adversarial examples, which means that the proportion of adversarial examples is not detected by the above features, Table 3:  
The experimental results show that the Logits-Traction adversarial examples can evade the logit dispersion-based detection. A false negative rate of more than 50% at the statistical level makes these three features no longer practical. The quantified logits dispersion of Logits-Traction adversarial examples has a more similar distribution to that of the original speech. As shown in Figure 8, there is no reasonable threshold to distinguish adversarial examples from the original speech.  
Meanwhile, compared with DeepSpeech, the effect of the Logit-Traction attack on the Logit-\(c^{+}\) feature in Lingvo is not obvious. This result indicates that the \(c^{+}\) feature of LT adversarial is still inferior to that of the original speech. Due to the expression in the  
Figure 8: Detecting LT adversarial examples by \(\sigma^{+2}\) feature in Lingvo.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Attack**} & \multirow{2}{*}{**Performance**} & \multicolumn{2}{c}{**DeepSpeech**} & \multirow{2}{*}{**Lingvo**} & \multirow{2}{*}{**MCV**} \\  & & & **Libri** & & & **MCV** \\ \hline \multirow{2}{*}{\(\text{Logit}-\Delta_{12}\)} & Threshold & \multirow{2}{*}{57.8} & \multirow{2}{*}{\(k=6\)} & \multirow{2}{*}{65.0} & \multirow{2}{*}{39.2} & \multirow{2}{*}{21.4} \\  & FNR & & & & & & \\ \hline \multirow{2}{*}{\(\text{Logit}-\sigma^{+2}\)} & Threshold & \multirow{2}{*}{56.1} & \multirow{2}{*}{\(k=12\)} & \multirow{2}{*}{59.6} & \multirow{2}{*}{41.4} & \multirow{2}{*}{31.5} \\  & FNR & & & & & & \\ \hline \multirow{2}{*}{\(\text{Logit}-c^{+}\)} & Threshold & \multirow{2}{*}{61.2} & \multirow{2}{*}{\(k=11\)} & \multirow{2}{*}{55.4} & \multirow{2}{*}{18.0} & \multirow{2}{*}{15.1} \\  & FNR & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Three features detect LT adversarial examples experiment.  
original speech, the logits value of the adversarial example has difficulty in reaching the same level. That is the native advantage of the Lingvo system. Customizing the adversarial example detection system for Lingvo can affect the number of larger values in logits.  
The experiments in Sections 4.3 and 4.4 mean that our Logit-traction attack will bring the logit distribution of the antagonistic example closer to the ideal Logit distribution of normal speech. However, the attack success rate is also affected by the characteristics of interest in the detection method.
## 3 Simulation
### Simulation Results
#### 3.1.1 Resolution Test USAF 1951  
A reconstructed focused image of the simulated resolution test chart is shown in Figure 6a. Note that, if compared with Figure 5a, the object is reasonably reconstructed, but some artifacts can be observed in the reconstructed image. These artifacts are related to the sampling of the original object because they are binary objects and so they are not band-limited. As will be seen in the following subsections, this same situation is repeated for the two objects studied in this work. However, we have chosen these examples because they are useful to relate magnification results in terms of the comparisons between simulations and experiments, which is one of the main goals of this work.  
Figure 6: (**a**) The resolution test image after reconstruction and focusing and (**b**) profile graph of the horizontal yellow line that crosses the reconstructed image. The red line is the average value of the light intensity that is transmitted through the transparent regions of the resolution test chart. The blue line is the half value of the average intensity. The blue line is used to determine the width of the bright and dark lines.  
In each displacement, Equation (8) is used to calculate \(\mathrm{M_{T}}\); here, \(x_{r}\) is the width of five vertical bars (three dark bars and two bright bars) corresponding to element 2 of group 4 in the reconstructed image, as is shown in Figure 6b.  
Figure 7a shows that the calculated value of \(\mathrm{M_{L}}\) remains practically unchanged and close to the theoretical value regardless of the displacement distance \(\Delta z_{o}\) of the resolution test chart. Similarly, the values of \(\mathrm{M_{T}}\) calculated at the same displacement distance \(\Delta z_{o}\) remain close to the theoretical value, as is shown in Figure 7b. The transverse magnification \(\mathrm{M_{T}}\) was calculated after determining the reconstruction distance \(\Delta z_{r}\) of the best focused image.
# Defacement Detection with Passive Adversaries
## 3 Keyed Learning of a Defacement Detector
### Methodology Motivations and Limitations
As a possible mitigation of this issue, we may observe that this situation can be detected at learning time, e.g., via cross-validation with a testing set. If this happens, a new session key could be generated, and the process would be repeated. Computational effort and convergence issues should however be addressed. As a second limitation, we cannot exclude the possibility that our adversary obtains a close enough classifier even without knowing the hidden information, for example, by relying on a large set of available data. In fact, keyed learning will hide the hypothesis space and other training parameters, but our adversary could use a different hypothesis space to learn, possibly with different methods, an equivalent or similar classifier. This issue could be studied in a PAC-learning context: for example, knowledge of the key would allow us to use a learnable hypothesis space \(H_{2}\), while our adversary, who does not know the key, is  
accurate classifiers. In our system we have used decision tree classifiers, as they are considered to be computationally hard to learn [44]. This is illustrated informally in Figure 3.  
As a third limitation, an adversary could observe system behavior to guess the key value, possibly combined with a brute-force approach (_key recovery_). Previous work has addressed this issue in the context of Intrusion Detection [22; 35], but further analysis is needed, applied to the present generalized methodology. Finally, one could argue that the key has limited impact, because, as stated in [18], <<_in real tasks the adversary hardly ever has complete knowledge about the classifier decision function_>>. However, in security applications, one should not rely on general lack of knowledge by an adversary, and hide information in a systematic and sound way, normally with a key--again an instance of [PERSON] principle.
# Deep Learning Framework for Advanced De-Identification of Protected Health Information
## 4 Results and Discussion
### Optimized Parameter Settings and Model Configuration for Bi-LSTM-CRF  
As outlined in Section 3.4, this section presents the optimized parameter settings for the Bi-LSTM-CRF model, tailored for effective PHI tag detections. The optimization of the Bi-LSTM-CRF model is crucial, as it enhances its ability to accurately capture sequential dependencies and improve tagging performance, especially in complex datasets. Table 4 provides a detailed summary of the optimal configuration achieved for the model's architecture, hyperparameter tuning, and optimization strategies. As the experimental results show, the architecture leverages an embedding layer with a dimensionality of 60 to convert input words into dense vector representations, which enhances the model's ability to learn meaningful relationships between words. A single-layer Bi-LSTM network with a hidden dimension of 80 is employed to capture the bidirectional context for each token, ensuring that both past and future contexts contribute to the model's understanding of sequential dependencies. A CRF layer, initialized with a transition matrix, scores transitions between output tags and enforces constraints to prevent invalid tag transitions, contributing to improved tagging accuracy. Hyperparameter tuning is performed to achieve an optimal balance between model performance and computational efficiency. The learning rate is set to 0.01, which provides a stable convergence. A weight decay parameter of \(1\times 10^{-4}\) is used to regularize the model, helping control overfitting by penalizing large weights. Additionally, a batch size of 32 is selected, which allows the model to process data efficiently while benefiting from gradient updates. The model is trained over 300 epochs, with the best performance observed at epoch 31, indicating the need for early stopping to prevent overfitting. Finally, the optimization is conducted using the SGD algorithm, chosen for its straightforward implementation and effectiveness in balancing learning speed and stability. The table summarizes these configurations, highlighting the model's robust framework for sequence labeling tasks.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Component** & **Parameter** & **Setting** \\ \hline \multirow{3}{*}{Model Architecture} & Embedding Dimension & 60 \\ \cline{2-3}  & Bi-LSTM Hidden Dimension & 80 \\ \cline{1-1} \cline{2-3}  & CRF Transition Matrix & Randomly initialized \\ \hline \hline \end{tabular}
\end{table}
Table 4: Best parameter settings for Bi-LSTM-CRF model architecture, hyperparameter tuning, and optimization.
# Effects of Aft-Body Extension Design on the Fundamental Characteristics of HWB Civil Aircraft
## 4 Characteristics Evaluation
### Basic Takeoff and Landing Performance Analysis
#### 4.2.1 Takeoff Performance Analysis
First, it can be concluded from the figure that the aft-body extension design can improve the takeoff performance of HWB aircraft, but there is still a gap between the takeoff performance of HWB civil aircraft and conventional TAW aircraft, and even the OPT1 aircraft with the largest degree of aft-body extension design cannot achieve the takeoff and gliding performance of the same class of TAW. Secondly, although the overall trend shows that the takeoff performance can be improved with the aft-body extension, the comparison of the takeoff data of aircraft OPT2 and OPT3 show that the effect of the aft-body extension design on the improvement of the takeoff performance is disturbed because the takeoff performance depends not only on the control arm of the aircraft but also on the ground-support reaction moment, which is mainly affected by the spacing between the center of mass and the main landing gear of the aircraft. In other words, the takeoff performance depends not only on the relative position of the center of mass to the aerodynamic center but also on the absolute position of the center of mass.
## 2 Background and Motivation
### Typical SFC Environments  
As more network operators continue to adopt network slicing, which serves as the enabler for next-generation networks, the chaining of virtual network functions for efficient service delivery has become commonplace. Some common use cases can be found in today's service provider networks. Some available common environments are the Gi-LAN network used by mobile network operators, residential/consumer services, and inter/intra-data-centre networks. Mobile network operators deploy functions such as traffic optimizers, firewalls, carrier-grade network address translation (NAT), load balancers, and DPI, at the core of the network, which is designed for subscribers that access Internet-based services [38]. Here, we briefly explore these environments.
## 4 Energy Efficiency
### ADC Power Consumption  
The receiver power consumption also includes the power consumption of the ADCs. The power consumed by a single ADC is estimated by [12]  
\[\text{P}_{\text{ADC}}=f_{\text{s}}\times\text{FOM}\times 2^{b} \tag{48}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Detection** & \(\mathbf{C}_{\text{detection}}\) & \(\mathbf{C}_{\text{QR}}\) & \(\mathbf{C}_{\text{T}^{-1}}\) & \(\mathbf{C}_{\text{filter}}\) \\ \hline MMB-LR-SIC & \(8 KN+40K^{2}L+8K^{2}\) & \(8K^{2}(N-\frac{K}{3})\) & \(8(K^{3}+K^{2})\) & \(L[8K^{3}+8K+\sum_{l=1}^{K}(4t^{3}+8t^{2}+12 Kt)]\) \\ LR-SIC & \(8 KN+40K^{2}\) & \(8K^{2}(N-\frac{K}{3})\) & \(8(K^{3}+K^{2})\) & \(8K^{3}+8K+\sum_{l=1}^{K}(4t^{3}+8t^{2}+12 Kt)\) \\ MMSE & \(8 KN\) & 0 & 0 & \(4K^{3}+8K^{2}+12K^{2}N+4 KN\) \\ \hline \end{tabular}
\end{table}
Table 3: Computational complexity of signal detection and detector construction for MMB-LR-SIC, LR-SIC and MMSE detection.  
where \(b\) is the number of bits of the ADC, the figure of merit (FOM) means energy consumed per conversion step, and \(f_{\rm s}\) the sampling rate. Generally, an ADC with high-resolution leads to smaller quantization errors compared with a low-resolution ADC but also greater power consumption (48). In this paper, we assume a moderate value of \(\text{FOM}=1432.1\) fJ per conversion step [12; 47] to model ADCs. Note that the power consumption due to ADC is independent of the detection algorithm applied.
## 2 Materials and Methods
### Training and Validation of the ML-Based Algorithms  
In our study, several machine learning models were trained to evaluate and compare their ability to accurately predict the target variable. The models implemented included Linear Regression (LR), Polynomial Regression (PR, second-degree), Boosting-based ensemble regressors (BR), Support Vector Regression (SVR), and Neural Network-based regression models (NNs). Given the significant impact of hyperparameter tuning on the performance of models such as SVR and NN, as well as the associated costs, Bayesian optimization algorithm was employed. This approach constructs a probabilistic model of the objective function, allowing for the selection of the most promising parameters. Each iteration selects a hyperparameter that optimizes a selection criterion based on the trade-off between exploration (testing new areas of the hyperparameter space) and exploitation (refining hyperparameters that already show promise). The ultimate goal is to minimize the objective function or cost function, enhancing model accuracy and efficiency.  
The optimization and training of our machine learning models were conducted under a 10-fold cross-validation scheme to ensure robustness and generalizability. In this approach, the dataset was divided into ten equal folds, where in each iteration, nine folds were used for training, and the remaining fold served as the test set for internal validation. This process was repeated ten times, ensuring that each fold was used exactly once as the validation set, allowing the models to be trained and evaluated across all available data.  
To assess overall model performance, the Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) were computed for each fold, and their average across all iterations was used as the final performance metric. Additionally, the model fit was evaluated using the Coefficient of Determination (\(R^{2}\)), which quantifies the proportion of variance in the dependent variable explained by the independent variables. The final \(R^{2}\) value was obtained as the average across all folds, ensuring a reliable estimate of the model's explanatory power. An \(R^{2}\) closer to 1 indicates a stronger predictive capacity and a better model fit.  
Finally, in adherence to the guidelines set by the Transparent Reporting of a Multivariate Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) initiative [21], the VascuNET and ExIC-FEp datasets were utilized for the external validation of the developed models. These datasets are completely separate from the data used in model training, ensuring that the validation process tests the models on entirely unseen data. To prevent data leakage and maintain the integrity of the evaluation, all variables in the validation datasets were normalized using the Z-score transformation parameters (mean and standard deviation) computed from the training dataset, thus ensuring that the external validation remains unbiased and reflects the true generalization ability of the models.
## 7 The SQS Instrument  
### Scientific Scope and X-ray Techniques  
The SQS (Small Quantum Systems) scientific instrument is dedicated to investigations of fundamental processes of light-matter interaction in the soft X-ray wavelength regime. In particular, studies of non-linear phenomena, such as multiple ionization and multi-photon processes, time-resolved experiments following dynamical processes on the femtosecond timescale, and investigations using coherent scattering techniques are targeted [79]. Principal research targets are isolated species in the gas phase, such as atoms, molecules, ions, clusters, nanoparticles and large bio-molecules. The use of soft X-ray photons enables controlled excitations of specific electronic subshells in atomic and site- or element specific excitation in molecular targets. One of the main goals of the SQS instrument is the complete characterization of the ionization and fragmentation process, at least for smaller systems, by analyzing all products created in the interaction of the target with the FEL pulses.  
Experiments at SQS typically use X-ray pulses of highest intensity to drive the probed system to highly excited states or initiate non-linear X-ray processes. The additional use of synchronized optical laser pulses will be applied to controlled manipulations of the electronic states and nuclear movement. Probing of the X-ray FEL interaction with the sample system will be performed either by direct coherent X-ray scattering to obtain structural information or by spectroscopic techniques. A focus is put on a variety of particle spectroscopy techniques, such as energy- and angle-resolved electron and ion spectroscopy allowing the determination of kinetic energies and momenta of the charged particles, and additional options for XUV and soft X-ray spectroscopy. In particular, the very open and flexible arrangement of the spectrometers will enable the application of various coincidence techniques, such as electron-electron, electron-ion and photon-electron/ion coincidences, which all require and therefore take full advantage of the high repetition rate available at the European XFEL.
# One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion
## 1 Introduction
Even under the same light intensity, the shooting angle and height may cause the color depth of the diseased area to be different, making the disease characteristics not significant, and thus affecting the detection accuracy. 2. The complex background makes it difficult to detect the target accurately. The image background of plant leaf disease is complex and may include leaves, trunks, weeds, fallen leaves, shadows, etc. The color and shape of the plant disease may be similar to other objects in the background, resulting in an increased difficulty of target detection. 3. Occlusion leads to missing target features and overlapping noise. Occlusion problems include blade occlusion caused by blade attitude changes, branch occlusion, light occlusion caused by external illumination, and mixed occlusion caused by different occlusion types. Due to occlusion, feature deletion and noise overlap lead to false detection or even missed detection. 4. The sparse target distribution affects the detection accuracy. Due to the limitation of the convolution receptive field, the connection between target pixels with sparse distribution is not strong, and the context extraction is not sufficient, which leads to the failure of modeling, thus affecting the detection accuracy. This paper proposes a multi-scale feature fusion convolutional neural network (MFFCNN) for the disease detection of maize leaves based on the anchor-free frame one-stage plant disease detection method, with the addition of the CA attention module and improvement of the SSP module based on YoLov5s. The main contributions of this paper are as follows:  
1. We add a coordinate attention (CA) module to the backbone network and increase the weight of key features to strengthen the effective information of the feature map. We improve the spatial pyramid pooling (SPP) module to reduce the loss of feature information caused by traditional pooling. We solve the problem of the insufficient dataset through data enhancement, enrich the training data, improve the generalization performance and robustness of the model, and prevent overfitting.