esa-sceva/satcom-chunk-collection huggingface dataset:
Unraveling the Muscle Activation Equation: 3D Scoliosis-Specific Exercises and Muscle Response in Adolescent Idiopathic Scoliosis  
[PERSON]  
1 Fit Level Wellness Center, 06510 Ankara, Turkey; [EMAIL_ADDRESS]  
[PERSON]  
2 Gulhane Faculty of Physicotherapy and Rehabilitation, University of Health Sciences, 06010 Ankara, Turkey; [EMAIL_ADDRESS]  
[PERSON]  
3 Bios  
Scoliosis-specific exercises are designed to bring patients' asymmetrical posture into alignment and restore a correct upright position. It also includes sensorimotor and breathing exercises aimed at static/dynamic postural control, spinal stability, and recalibration of breathing patterns [13]. In a study examining the EMG activations of the paraspinal muscles during scoliosis exercises, it was observed that the EMG activity in the paraspinal muscle during exercise was higher than in the relaxed standing position. At the same time, EMG signals were found to be higher on the convex side of the curve during asymmetric exercise and on the concave side during symmetric exercise [9; 14; 15; 16]. However, the effect of different forms of stimulation on these muscle activations during exercise is not available in the literature.  
The aim of this study was to compare thoracic and lumbar erector spinae muscle activations during 3D elongation exercises given in three different ways in individuals with adolescent idiopathic scoliosis. The hypotheses of this study were: (1) the three different variations of the 3D elongation exercise will lead to significantly different activation levels in both thoracic and lumbar erector spinae muscles in individuals with AIS, (2) there will be a significant interaction effect between the type of 3D elongation exercise and the activation of thoracic and lumbar erector spinae muscles, and (3) there will be a significant difference in activation between thoracic and lumbar erector spinae muscles in all three exercise variations in individuals with AIS.
## 3 SPS: Grid-Based Single Phantom Node Source Location Privacy Protection Scheme
### The Routing Phase
#### 3.3.2 Step 2: The Phantom Node Sends Data Packets to The Sink Node  
The PN adopts the single-path routing method [27] to forward data packets to the SN. More specifically, after receiving the data packet, the PN randomly selects one node from the near-hop neighbor nodes as the next hop node, and forwards the packet to it. The next hop node repeats this process until the data packet reaches the SN.  
Figure 6 shows the flowchart of our proposed SPS scheme.  
Figure 5: Simplified grid.  
Figure 6: The flowchart of our proposed SPS scheme.  
In References [20; 21], the PN distribution diversity and phantom routing path randomness are used as important indicators for security performance analysis. In this paper, it is the sink node that helps the source node choose the PNs randomly, and the phantom routing path is determined only after the packet transmission direction is determined through the PN. Moreover, the next hop node in the target grid is also randomly selected, so the PN distribution and the phantom routing path are maximally randomized and diversified.
# Assessment of fNIRS Signal Processing Pipelines: Towards Clinical Applications
## 2 Materials and Methods
### The Proposed Order of CW-fNIRS Processing Steps  
The unprocessed \(\Delta OD\) signals at the two wavelengths were separately processed prior to the application of MBLL. This choice was motivated by the unpredictable effect of biases on both \(\Delta OD\) signals while solving the MBLL (Equation (3)), which conversely represents a mandatory linear operation of each single processing pipeline.  
After a quality check of the raw signals (described at Section 2.4), the first actual signal processing step performs MA reduction since it considers non-linear and time-variantoperations over the \(\Delta OD\) time course. In fact, all typologies of MA algorithms share the detection of outlying features localized in time and their correction. Accordingly, our suggestion is that no other processing algorithm should be applied prior to this step since any previous cleaning or filtering might hinder the detection phase. Hence, the second step was linear bandpass filtering (BPF), next followed by common-mode reduction of physiological interference by principal component analysis (PCA). In principle, the linearity of these steps should permit their full interchangeability. However, we preferred to filter data before PCA since the latter method works on the global components of the signal (see Section 2.7).  
Finally, after MBLL application, the HRF can be estimated either by block averaging of \(\Delta[HbO_{2}]\) and \(\Delta[HbR]\) across trials or through linear estimation models such as the General Linear Model (GLM) [29]. While both approaches allow us to infer cortical activation and test its statistical significance, block averaging does not impose any temporal constraints over the shape of HRF. Conversely, the GLM approach allows us to model the measured response as a linear combination of effects and confounding factors. In the results section, we provide graphical and numerical evaluation of activation according to both approaches.  
Pre-processing and analysis were conducted in Matlab R2018b (The MathWorks, Inc., Natick, MA, USA) through user-defined scripts integrating available functions of Hommer2 developer's version [33] and NIRS Brain AnalyzIR Toolbox [34]. In conclusion, the proposed pipelines follow these consecutive stages: (1) MA reduction (herein eight variants); (2) bandpass filtering; (3) reduction of physiological interference by PCA; (4) MBLL for \(\Delta[HbO]\) and \(\Delta[HbR]\) computation; (5) block averaging and significance tests of single channel responses.
# A Wideband Digital Pre-Distortion Algorithm Based on Edge Signal Correction
## 4 Experimental Validation and Analysis
### Experimental Results Analysis  
Following the hardware experimental setup depicted in Figure 4, the experimental parameters were configured, and comparative experiments were conducted using the traditional Memory Polynomial DPD algorithm as a reference. The experimental results are recorded in Table 3, and the spectrum analyzer displays the power spectra of the band-limited signals, as shown in Figure 5.  
Table 3 presents a comparison of the ACPR values of the power amplifier's output signals at different frequency offsets for the two schemes:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline Source Signal Type & OFDM \\ Source Signal Bandwidth & 8 MHz \\ Source Signal Sampling Rate & 16 MSPS \\ Power Amplifier Center Frequency & 300 MHz \\ Model Non-linear Order and Memory Depth & K = 5, M = 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental parameter settings.  
Figure 5: Frequency spectrum at 300 MHz, MPDPD scheme and proposed DPD scheme.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Frequency Offset (MHz)** & **ACPR (dBm)** \\ \hline MPDPD Algorithm & 2 & \(-\)39.4\(--\)38.9 \\  & 4 & \(-\)40.2\(--\)38.3 \\ Proposed DPD Algorithm & 2 & \(-\)51.4\(--\)50.4 \\  & 4 & \(-\)52.9\(--\)51.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results.  
The experimental results demonstrate that in the channel with a 2 MHz frequency offset from the center frequency, the proposed DPD algorithm achieved an average ACPR value of \(-50.9\) dBm, an improvement of approximately 11.75 dBm compared to the traditional MPDPD algorithm. In the channel with a 4 MHz frequency offset from the center frequency, the proposed DPD algorithm achieved an average ACPR value of \(-52.1\) dBm, an improvement of approximately 12.85 dBm over the traditional MPDPD algorithm.  
Based on the experimental results, it can be concluded that the proposed algorithm can be implemented using low-cost, low-computational-power hardware. By precisely processing signals near the center frequency and performing intelligent calibration, the proposed wideband DPD algorithm based on edge signal correction effectively addresses excessive computational data and poor accuracy in traditional DPD algorithms. This algorithm not only enhances signal processing efficiency but also offers new insights and methodologies for the future development of wideband communication technologies.
## 3 Current Measurement Techniques
### Soil Moisture Measurement Techniques
#### 3.1.2 Inference from Thermal Properties  
Soil moisture has a large influence on the thermal conductivity and heat capacity of soils. Measurements of temperatures at different depths in the soil can therefore be used in combination with information on the heat fluxes to infer soil moisture. This can be achieved using the surface energy balance or heat flux plates. Another option is active heating, such as heat pulse probes [32]. Individual temperature sensors can, however, only achieve point measurements.  
Recently, the use of distributed temperature sensing (DTS), using fiber optic cables measuring several kilometers in length, has enabled high spatiotemporal resolution measurements over scales of several hundreds of meters, although the methods remain sensitive to the cable depths [33, 34]. Methods of DTS measurements using active heating of the cable are also available. These make use of the asymptotic behavior of dissipated heat [35].
## 6 Conclusions  
The research presented in this study has successfully developed a joint model architecture for Speech Emotion Recognition (SER) that integrates both discrete and dimensional emotion representations. The use of advanced feature encoders, such as Wav2 Vec 2.0 and HuBERT, along with adaptive weighting schemes, has resulted in a model that demonstrates superior performance compared to traditional methods. The joint model's ability to provide a comprehensive understanding of emotional states makes it versatile and applicable across various real-world scenarios, including customer service, mental health monitoring, the human-computer interaction, and educational tools.  
Despite the promising results, several areas for future work and improvement have been identified. Striking a balance between classification and regression tasks remains a challenge, and further exploration of parallel model architectures, weighting schemes, and feature encoders can help achieve better performance. Additionally, incorporating additional modalities, such as visual and textual information, can enhance the model's performance and provide a more comprehensive understanding of emotions.
## 1 Introduction
### Motivation  
A direct way to provide adequate security solutions to secure the RFID system is to encrypt all the communications among the reader, the tag, and the server. However, the tags are resource-constrained devices with limited computation power, storage capacity, and energy, which makes implementing the standard cryptographic algorithms on these tags a challenging task. Therefore, over the last several years, researchers have developed numerous lightweight authentication schemes that satisfy the security requirements of the RFID systems. These schemes are based on elliptic curve cryptography (ECC) [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22], hash functions [23; 24; 25], and rotation functions [26; 27; 28; 29] to secure communications in IoT environments.  
Most of the cryptographic primitives (such as ECC and hash functions) used in the proposed lightweight authentication protocols are mainly based on finite-field arithmetic operations of addition, multiplication, squaring, exponentiation, and division/inversion. Most of these operations are at the heart of the cryptographic primitives; hence, their performance has a significant effect on the overall performance of the whole authentication protocol. Since the finite-field multiplication operation is the basic building block of the other field operations of division/inversion and exponentiation, it is considered the fundamental building block of most cryptographic processors. Therefore, any slight improvement in its implementation leads to a significant increase in the cryptographic processor's whole performance.
# Toward Sustainable Mars Exploration: A Perspective on Collaborative Intelligent Systems
## 4 Communication, Navigation, and Observation
### Satellite Navigation  
Apart from telecommunication purposes, Mars orbiters can also feature navigation capabilities, creating a global navigation satellite system (GNSS) such as GPS or Galileo on Mars. Similar to the developments of Low Earth Orbit Positioning, Navigation and Timing (LEO-PNT) services on Earth [49; 50; 51], either a pseudoranging or Doppler positioning algorithm can be adopted to deploy PNT services on Mars. However, challenges exist in precise orbit determination, hardware radiation hardening, and dealing with different environmental conditions.  
As part of the Artemis program, NASA presented the Cislunar Autonomous Positioning System Technology Operations and Navigation Experiment (CAPSTONE) mission, in which a peer-to-peer navigation technology incorporates relative tracking between two or more vehicles on the Moon, i.e., orbiters, landers, and rovers. This reduces the need for ground-based tracking stations, which is of particular interest for deep space exploration. Moreover, the navigation performance increases with the number of participating vehicles, which motivates the use of a CAPS-like system for a vehicle swarm on Mars.  
Finally, when visual references are unavailable and cameras fail due to a lack of light, dust, or other observities, a Light detection, altimetry and velocimetry (LiDAV) sensor can serve as a primary navigation input [52]. All these technologies will contribute to guiding rovers, drones, and satellites via the most optimal route, avoiding collisions and obstacles.
# English Speech Emotion Classification Based on Multi-Objective Differential Evolution
## 3 Materials and Method
### Classifiers  
1. K-nearest neighbor: KNN is a simple classification algorithm that is easy to comprehend and implement [26]. It is non-parametric, and it does not make strong assumptions about the distribution of data. This can be useful when dealing with diverse and complex emotional speech data;
2. Random forest: Random forest is an ensemble method that consists of multiple decision trees [27]. The ensemble approach tends to produce results that are robust and accurate, which reduces the risk of overfitting and improves generalization. RF is a suitable option for speech data that contain various types of noise in real-world scenarios. These classifiers offer a balance between simplicity and effectiveness, which is important in the context of speech emotion recognition. Both KNN and RF have demonstrated their utility in emotion recognition, making them reasonable choices for this study;
3. K-fold cross validation: K-fold cross validation prevents overfitting and provides a more precise depiction of a model's true performance [28]. By dividing a dataset into multiple subsets, it continuously trains and evaluates a model on different combinations of these subsets.  
In this study, we used 5-Nearest Neighbor and RF (with 20 decision trees) classifiers to create models, and then assessed the performance of these models using 10-fold cross-validation.
## 1 Introduction
[16] acquired high-precision terrain information, which allowed for the collection of accurate elevation data and the generation of the DEM for landslide areas by UAVs. The DEM primarily focuses on terrain elevation, while the digital orthophoto map (DOM) concentrates on georeferenced imagery. The DEM represents the elevation of the Earth's surface as a grid of elevation points, with the resolution determined by the spacing between these points. It provides information on the variations in terrain height. Meanwhile, the DOM represents georeferenced aerial or satellite imagery. The resolution is determined by the pixel size, which reflects the ground area each pixel represents. It provides information on the Earth's surface as observed in the imagery. Then, the DEM focuses on accurately depicting terrain elevation, while the DOM aims for the accurate representation of Earth's surface features in the imagery. Furthermore, the DEM is used for terrain analysis and is valuable for geological studies and landform classification. The DOM is applied in cartography and map production for the accurate representation of ground features. High-resolution DEM and DOM data provide an accurate description of topography and geomorphology, which serve as important source data for landslide information extraction. However, in recent years, less research has applied the DEM and DOM for the extraction and analysis of information related to landslide identification. This study extracts landslide information in Jiangdingya by utilizing a combination of DEM topographic features and DOM image features. The study area, which is the Jiangdingya landslide in Nanyu Township, is characterized by diverse land cover types, including residential areas, vegetation, roads, and rivers. This diversity holds scientific significance and practical relevance for the research on landslide identification in complex terrains. The initial identification of landslides is conducted through supervised classification. In this process, slope information is utilized as a constraint for the supervised classification analysis. A threshold-based denoising filter and a morphological algorithm are introduced to further enhance the accuracy of landslide identification and extraction. These data can accurately depict geomorphic features of complex terrain and aid in identifying landslide-prone areas when combined with mathematical morphology processing. This contribution is important for identifying landslides in complex terrain and emergency disaster management. This approach provides value in addressing landslides in complex terrains.
## 2 Inversion Analysis
Subscript 'S' is added to the abbreviation of RMSE to distinguish the misfit in dispersion curves from RMSE in ML training. Lower RMSE\({}_{5}\) are considered the best match between the theoretical and experimental dispersion curves [30]. Since iteration is incorporated into the inversion analysis, there must be a measure with which to quantify the goodness of the estimated model parameters and the predicted shear wave velocity. The prediction error can be defined in several ways, but the maximum likelihood approach for the inversion analysis justifies the use of the \(L_{2}\) norm, which is a loss function that reflects least-squares errors, as defined in Equation (1). The maximum likelihood approach is one of the procedures used to find the optimum shear wave velocity for an experimental dispersion curve:  
\[L_{2}norm:\ \|e\|_{2}=\ \sqrt{\sum_{i}\lvert e_{i}\rvert^{2}} \tag{1}\]  
where \(e_{i}\) is the difference between the observed data and the shear wave velocity. The RMSE\({}_{5}\) in Equation (2) is a representation of the \(L_{2}\) norm. The RMSE\({}_{5}\) is independent of the number of datasets and provides the absolute sense of the average misfit:  
\[RMSE_{5}=\ \sqrt{\frac{1}{n}\sum_{i}\lvert e_{i}\rvert^{2}} \tag{2}\]  
where \(n\) is the number of datasets and \(i\) represents the variables. Evaluation of a stiffness profile requires an experimentally determined phase velocity dispersion curve which is produced via iterative forward modeling analysis or inversion analysis [15; 31]. This inherent problem of inversion analysis requires several tries of an initial guess or a reasonable initial guess. To simplify this inversion process, the experimental dispersion curve and the final shear wave velocity were used to train the ML algorithm, as indicated in Figure 2.
# Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns
## 7 Experiments and Results
### Finding the Best Number of Clusters and Their Corresponding Models
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{4}{c}{**KMB**} & \multicolumn{4}{c}{**Best \# of Clusters**} & \multicolumn{4}{c}{**Distribution of Classes Defects : Non-Defects**} \\ \cline{2-9}  & **1C** & **3C** & **SC** & **7C** & **9C** & **10** & **10** & **10** & **10** \\ \hline cm1 & 0.417 & 0.653 & 0.758 & 0.748 & **0.845** & 9 & 21.478.6, 5.994.1, 50.50, 0.100, 0.100, 41.75-83, 0.100, 13.87, 40.60 \\ \(\mu\)m1 & 0.505 & 0.585 & 0.596 & **0.612** & 0.606 & 7 & 19.780.3, 18.68.14, 50.64-9.4, 13.88, 7.100, 34.76-53, 68.23.18 \\ kcl 1 & 0.562 & 0.597 & **0.746** & 0.633 & 6.623 & 5 & 13.268.8, 42.95.7, 1.58, 34.17, 26.2-273, 8.2, 11.78.9 \\ kc3 & **0.759** & 0.569 & 0.481 & 0.447 & 0.491 & 1 & 28.874.2 & 28.874.2 \\ mcl 1 & 0.373 & 0.504 & 0.315 & 0.696 & **0.718** & 9 & 1.298.8, 1.6,98.4, 8.291.8, 0.9, 99.1, 0.100, 0.100, 1.29.88, 13.68.64, 0.100 \\ mw1 & **0.661** & 0.526 & 0.539 & 0.522 & 0.524 & 1 & 8.793.1 & 8.793.1 \\ pcl 0.565 & **0.642** & 0.497 & 0.429 & 0.484 & 3 & 21.678.4, 11.2, 21.88, 7.9.92.1 \\ pcl 0.402 & 0.402 & 0.484 & **0.913** & 0.672 & 0.840 & 5 & 0.100.1, 17.98.3, 2.397.7, 4.59.55, 16.78.33 \\ pcl 0.53 & **0.671** & 0.492 & 0.665 & 0.573 & 0.589 & 1 & 13.68.64 & 13.68.4 \\ pcl 0.695 & 0.664 & **0.697** & 0.503 & 0.229 & 5 & & 10.189.9, 25.97.4, 1.6, 2.697.4, 1.99, 16.68.04 \\ pcl 0.688 & 0.690 & 0.564 & **0.701** & 0.577 & 7 & 38.56.1, 21.87.82, 29.17.0, 36.76-53, 24.475.6, 72.72.73, 16.88.82 \\ \hline ant-17 & 0.603 & 0.554 & 0.461 & 0.536 & **0.726** & 9 & 26.373.7, 35.65, 8.39.17, 11.98.1, 4.895.2, 10.389.7, 92.37.7, 57.14-29, 21.978.1 \\ camel-16 & 0.500 & 0.496 & **0.607** & 0.524 & 0.594 & 5 & 11.588.2, 20.17-9.9, 3.896.2, 2.767.2, 42.16.78.4 \\ ivy-2.0 & 0.657 & **0.861** & 0.719 & 0.681 & 0.636 & 3 & 15.484.6, 6.100, 187.81.3 \\ jedi-43 & **0.716** & 0.323 & 0.475 & 0.608 & 0.627 & 1 & 1 & 1.698.4 \\ logg4-12 & 0.667 & 0.472 & 0.890 & 0.977 & **0.987** & 9 & 91.78.3, 1000, 1000, 1000, 1000, 1000, 85.714.3, 90.59.5 \\ lucene-2.4 & 0.809 & **0.869** & 0.781 & 0.633 & 0.591 & 3 & 42.55.5, 663.4, 51.148.9 \\ poi-30 & **0.838** & 0.741 & 0.625 & 0.635 & 0.639 & 1 & 61.13.89 \\ tontact-6.0 & 0.651 & 0.703 & **0.706** & 0.367 & 0.549 & 5 & 12.38.87, 5.194.9, 11.68.84, 0.100, 4.395.7 \\ xalan-2.6 & **0.793** & 0.736 & 0.774 & 0.767 & 0.727 & 1 & 46.253.8 \\ xerces-1.4 & 0.915 & **0.969** & 0.950 & 0.945 & 0.775 & 3 & 54.145.9, 93.26.8, 95.94.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: G-mean results of two-folds cross-validation for the proposed approach based on the ensemble classifiers.