esa-sceva/satcom-chunk-collection huggingface dataset:
# Kinematic Analysis of Dynamic Coactivation During Arm Swing at the Shoulder and Elbow Joints
## 1 Introduction
Taken together, these studies highlight the importance of integrating biomechanical fidelity and neuromotor adaptability into the design of mechanical interfaces, ensuring not only alignment with human movement patterns but also real-time responsiveness to dynamic coactivation demands. Furthermore, most clinical analyses of arm swing have been conducted within the context of rehabilitation or pathological conditions, which may limit their direct applicability to the ergonomic design of exoskeletal systems. For instance, [PERSON] et al. [20] investigated the natural rhythmic coordination between the shoulder and elbow joints at varying walking speeds. However, the generalizability of their findings is constrained by a small sample size, reducing the robustness of their conclusions for broader ergonomic applications. Also, the analysis of dynamic coactivation during arm swing was conducted in a limited scope, focusing primarily on joint-specific kinematic variables rather than inter-joint coordination patterns. A deeper understanding of the intrinsic rhythm and coordination patterns of upper extremity motion during gait is essential for defining functional boundaries such as joint torque profiles and ranges of motion. These parameters are critical to optimizing the mechanical and control design of wearable exoskeletons. Such insight enables the development of systems that better align with human biomechanics, thereby improving limb dynamics and enhancing user comfort--particularly in long-duration or high-repetition applications aimed at augmenting locomotor function [21]. Moreover, integrating interlimb coordination principles into exoskeletal design can facilitate more naturalistic movement patterns. This not only promotes biomechanical efficiency but also allows users to engage in tasks with improved intuitiveness and reduced cognitive and physical load [7; 9]. This study aimed to investigate the influence of different walking speeds on shoulder and elbow joint kinematics, specifically focusing on range of motion, angular velocity, and angular acceleration during arm swing. It was hypothesized that joint kinematic patterns would exhibit significant variations depending on walking speed. The findings of this study are anticipated to inform the ergonomic development of upper-limb exoskeletons by enhancing alignment with the natural pattern of arm swing.
# Minimum Variance Distortionless Response--Hanbury Brown and Twiss Sound Source Localization
## 4 The Sound Source Detection Experiment
### Frequency  
In practical environments, most sound signals are multi-frequency. To investigate the effect of frequency on the localization performance, experiments were conducted on sound signals of different frequencies. The experimental results of MVDR-HBT for sound source coordinates (5, 5) on single-frequency and multi-frequency sound signals are shown in Figure 10 and were compared with the localization results of HBT, summarized in Table 6. It can be observed from Figure 10 and Table 6 that as the spectral width of the sound signal increases, the signal contains more information, resulting in more concentrated bright stripes and more accurate localization performance for MVDR-HBT. Although HBT exhibited improved localization accuracy for multi-frequency signals compared to single-frequency signals, there was still a gap in angle deviation compared to MVDR-HBT.
## 5 Results
### Redaction Effectiveness  
The core evaluation of our system focuses on its ability to accurately redact PII while minimizing over-redaction. We used four key metrics: redaction precision, redaction recall, F1-score for redaction effectiveness, and over-redaction rate. These metrics were calculated by comparing the system's automated redactions against human-annotated ground truth data, as detailed in Section 4. Table 1 presents the results.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Metric** & **Value** \\ \hline Redaction precision & 92.5\% \\ Redaction recall & 83.2\% \\ F1-score for redaction effectiveness & 87.6\% \\ Over-redaction rate & 3.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Redaction effectiveness metrics on label dataset (100 images).  
The system achieved a high redaction precision of 92.5%. This indicates that when the system did redact a text segment, it was highly likely to be actual PII, minimizing false positives and preserving the informational content of the labels. The redaction recall of 83.2% shows that the system successfully identified and redacted a substantial majority of the PII present in the dataset. While commendable, this also indicates room for improvement in capturing all instances of PII, reducing false negatives.  
The F1-score, which balances precision and recall, was 87.6%, demonstrating a robust and practically useful level of redaction effectiveness. The over-redaction rate was a low 3.1%, meaning that the system very rarely redacted non-PII text. This is crucial for maintaining the usability of the processed labels.  
Further analysis of the results revealed that the primary cause of missed redactions (false negatives) was OCR errors, especially when dealing with stylized fonts or degraded image quality. Over-redactions (false positives), on the other hand, were typically caused by the LLM misinterpreting non-PII text that had superficial similarities to PII, such as a product code that resembled a phone number. These findings highlight the interconnectedness of the OCR and LLM components and point to areas for future refinement.
# Topology Optimization and Testing of Connecting Rod Based on Static and Dynamic Analyses
## 3 Computational Analysis
### Dynamic Analysis  
The objective of the dynamic analysis was to determine the unknown reaction forces and moments acting on the connecting rod while the engine was running at the speed of 1000 rpm. During the dynamic analysis, all the bodies were defined as rigid bodies and assembled using transitional, fixed, and revolute joints. The rotational velocity (\(\omega\)) was determined based on the defined maximum rpm. For the speed of 1000 rpm, the rotational velocity is defined as  
\[\omega=\frac{RPM\times 2\pi}{60}=104.72\ \text{rad/s} \tag{11}\]  
Figure 6: The sequence of the connecting rod TO from initial facets to the final subD hybrid conversion.  
Figure 7: (**a**) Original shape of the connecting rod. (**b**) 5.66% reduced mass from the original shape of the connecting rod.  
Once the connections were defined according to Table 3, the maximum unknown reaction forces acting on the connecting rod were probed using a joint probe in the solution section. Since all bodies were set as rigid, there was no meshing involved while determining the dynamic motion loads.  
While constructing the assembly, it was important to calculate the total degree of freedom (DOF), by assuming the required kinematic motions for each part individually [29]. This assembly had 13 degrees of freedom, and it is explained below as shown in Table 3.  
A rotational velocity \((\omega)\) of 104.72 rad/s was applied on the crankshaft as shown in Figure 8a. Once the motion study was completed, the maximum reaction loads as shown in Figure 8b,c were retrieved from the solution and exported as a text file to evaluate the reaction forces acting on the connecting rod in a separated static structural analysis.  
Figure 8: (**a**) Boundary conditions—A rotational velocity of 104.72 rad/s was applied to the crankshaft. (**b**,**c**) Maximum unknown reaction forces extracted using a joint probe while the piston was moving toward the top dead center and the bottom dead center.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Connections Between the Parts** & **Joints Used** & **Degrees of Freedom** \\ \hline Cylinder liners to ground & Fixed joint & 0 DOF \\ Crankshaft to ground & Fixed revolute joint & 1 DOF \\ Cylinder to piston & Transitional joint & 4 (x-axis) \\ Piston to connecting rod & Revolute joint & \((2)\times 4\) DOFs \\ Connecting rod to crankshaft & Revolute joint & \((2)\times 4\) DOFs \\ \hline \hline \end{tabular}
\end{table}
Table 3: Connections used in the engine assembly.  
From this study, the engine assembly had a good balance between the counterweight of the crankshaft and the newly obtained topology of the connecting rod, which is due to a minimum weight reduction and optimized topology. When the weight reduction was targeted at more than 7%, there was a noticeable imbalance and increase in vibration due to the connecting rod weight ratio and the counterweight of the crankshaft.
# Mouth Sounds: A Review of Acoustic Applications and Methodologies
## 2 Mouth Sounds and Their Acoustic Features
### Acoustic Features of Mouth Sounds
#### 2.2.1 Time Domain Features  
Time domain audio signal features are extracted directly from the audio signal samples [32] and help to analyze acoustic parameters, such as the signal-to-noise ratio of background noise in recordings [33]. Moreover, the most recurrent parameters in this review can be classified into three categories: (1) Zero-Crossing Rate (ZCR), (2) Power/Energy-Based Features (PEBF), including Short-Time Energy (STE) and Volume or Loudness; and (3) Amplitude-Based Features (ABF), including includes Shimmer, Amplitude Descriptor, Linear Predictive Coding, Maximum Phonation Time, and Speech Intensity Prominence. Features in these categories are described in this section, and the rest can be found in Appendix A.  
Zero-Crossing Rate (ZCR)The ZCR is defined as the rate of change in a signal sign during a time frame [34]. The ZCR can be interpreted as a measure of the noisiness of a signal, and it is useful to reflect the spectral characteristics of a signal without a domain transformation [32]. In speech, it is used to estimate the \(\mathrm{f}_{0}\) and to detect unvoiced and voiced segments [34]. This feature is presented in Figure 2.
# Field Measurements of Free Ascending Behavior of Occupants along Medium-Long Stairway
## 2 Field Measurements  
### Selected Stairs  
As the depth of most underground buildings is about 30 m, which is equivalent to the height of the ground building with 7-8 floors, the experiments were conducted in a 10-storey office building of height 42.0 m, and the ascending speed over 8 floors was studied. The stairway connecting two floors, as shown in Figure 2, consists of two flights, each with 14 treads, and the width of each flight is 1.8 m. The tread depth is 0.29 m, the riser is 0.15 m, and the stair gradient is 28\({}^{\circ}\). The landing size is 4.4 m \(\times\) 1.8 m.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Mean Ascending** & **General Information** & **Experiment Site** & **Source** \\
**Speed/m/s** & **of the Participants** & **Information** & **\\ \hline
0.52 & 73 persons, walking & Outer stairway & [PERSON] [21] \\  & upward freely & of 35.8 m high & \\ \hline
0.484 & 15 male students and 15 female & & \\  & students aged 5–9 years & & \\ \hline
0.513 & 15 male students and 15 female & & \\  & students aged 10–19 years & Residential building & \\ \hline
0.522 & 8 males and 22 females & with 40 floors * & [PERSON] et al. [22] \\  & aged 20–40 years & & \\ \hline
0.474 & 12 males and 18 females & & \\  & aged 41–55 years & & \\ \hline Male & & & \\
0.75 (1 to 25 levels) & & & \\
0.53 (26 to 50 levels) & 30 males and 30 females with an & Residential building & \\ \hline Female & average age of 23.4 years & with 50 floors & [PERSON] et al. [15] \\
0.55 (1 to 25 levels) & & & \\
0.42 (26 to 50 levels) & & & \\ \hline Male: & & & \\
1.71 (first level) & 0.68 (>14 levels) & 51 female students and 114 male & & \\  & students aged 18–25 years & experiments conducted & \\
1.11 (first level) & & on 1 st to 20 th level & \\
0.52 (>14 levels) & & & \\ \hline
0.67–0.75 & 27 men and 20 women with & Building with 13 floors, & \\  & average age of 33 years & 48 m high & \\ \hline
0.73–0.87 & 16 men and 13 women with & Building with 31 floors, & \\  & average age of 32 years & 109 m high & [PERSON] et al. [12] \\ \hline
0.66–0.79 & 21 men and 13 women with & A stopped escalator of & \\  & average age of 38 years & 33 m high and 66 m long & \\ \hline \end{tabular}  
* ascending speed depends on the maximum travel distance of each participant.  
\end{table}
Table 1: Individual ascending speed on long stairway from the literature.
## 3 Materials and Methods
### Protein Expression and Purification  
For the prokaryotic expression in _E. coli_, the mRNA sequence of _TaTS9_ was codon-optimized by using the ExpOptimizer and synthesized by [PERSON] (Shanghai, China). The recombinant plasmid pET28-_TaTS9_ containing four restriction sites (_Eco_RI, _Sat_, _Hind_III, and _Not_I), an N-terminal His\({}_{6}\) tag, and a TE cleavage site was transformed into _E. coli_ BL21 (DE3) and cultured at 37 \({}^{\circ}\)C and 220 rpm overnight in 200 mL LB medium supplemented with 100 \(\upmu\)g/mL ampicillin. When the OD\({}_{600}\) of the culture reached 0.6, the protein expression was induced by adding 0.5 mM isopropyl \(\upbeta\)-D-thiogalactopyranoside (IPTG), followed by further incubation at 160 rpm and 16 \({}^{\circ}\)C for 20 h. The cells were harvested, resuspended in 25 mL of buffer A (50 mM NaH\({}_{2}\)PO\({}_{4}\), 300 mM NaCl, and 20 mM imidazole; pH 8.0), and disrupted by ultrasonication on ice. The cell debris was removed by centrifugation, and the soluble protein in the supernatant was purified by an Ni-NTA affinity chromatography column containing 100-300 mM imidazole and concentrated by an ultrafiltration centrifugal tube (10 kDa; Millipore, Bedford, MA, USA). SDS-PAGE of 5% stacking gel and 12% separation gel was used to analyze the purity and apparent molecular masses of the purified protein. The protein concentration was measured using the Protein Assay Kit (Bio-Rad) in triplicate.
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
## 5 Simulation Results  
In this section, we benchmark several common position and orientation estimators against the bounds derived in the previous sections on a MATLAB simulation platform. We also include a very brief discussion of position and orientation estimation for reference.
## 2 Literature Review
### STPA-Sec (STPA for Security)  
Most of the conventional security assessment approaches have adopted tactics models [13; 14]. In such models, the emphasis is placed on identifying the chains of events in which the particular threats of interest precipitate in order to reach their (malicious) goals, and then devising the tactics to break those chains. Such a threat-based approach works best when the security analysts possess a good understanding of the system assets as well as attackers/adversaries' capabilities, motives, etc. It enables the security analysts to pin-point the entry points (vulnerabilities of logical/physical system components) that are likely to be exploited by the attackers/adversaries, and thereafter suggest the most cost-effective ways to prevent attacks, hence losses. Such approach however, as argued in [13], seems lacking in securing today's diverse, interconnected, software-intensive systems. Security analysts adopting it might conduct the assessment without having the right set of priorities that aligns well with the higher-level goals or intended functions of the system; for instance, while disabling the access to particular data might be deemed imperative by the security team, it bothers those (e.g., operation team) who need the access to perform certain higher-level system functions.  
In view of the aforesaid limitations of threat-based (bottom-up) approaches, [PERSON] and [PERSON] introduced a top-down security analysis method, namely STPA-Sec [13; 14]. The process of STPA-Sec is largely identical to that of STPA. The major difference between STPA-Sec and STPA is that the former aims to prevent the system from getting into vulnerable states while the latter--hazardous states. However, STPA-Sec utilizes the same control loop diagram (depicted in Figure 1) of STPA, which does not explicitly show which causal factors are relevant to security; this may not be an issue to the experienced analysts though. Nevertheless, a control loop diagram containing more explicit and security-related information (including terminologies) is more likely to facilitate identification of hazardous/vulnerable scenarios, as advocated in [15] where the said diagram enhanced with some relevant security guide-words was applied in analysing a real-world complex and connected automotive system.
## 2 Related Work
### Search Results and Analysis
[19; 35; 37] focus on using deep learning techniques to help monitor and classify noise, detect noise level anomalies and even predict noise pollution for various periods, from 1 to 60 min. In ref. [38], [PERSON] et al. use deep learning to predict traffic noise. The authors achieved the best performance with a multivariate bi-directional GRU (Gated Recurrent Unit) model with many-to-many architecture, featuring both high accuracy and computation efficiency. The works described in refs. [29; 31; 36; 39] are less related to the scope of this paper. In ref. [31], [PERSON] et al., discuss the utilization of ML techniques to create frameworks for urban soundscape planning and, consequently, to create healthy urban soundscapes; papers [29; 39] focus on smart traffic control as a way to reduce noise pollution in cities. Overall, these works understand the importance of controlling urban noise, which is one of the most serious and underestimated environmental problems. According to [PERSON] et al., noise pollution from traffic and other human activities has a significant negative impact on the health and quality of life of the populations [33]. Compared with the literature, this work employs machine learning approaches to comprehend noise patterns during the nightlife period and provide decision makers with the tools they need to make better and more informed decisions. Our work uses real data and can be replicated in other cities.
###### Abstract  
To accurately predict the remaining useful life (RUL) of rolling bearings under limited data and fluctuating load conditions, we propose a new method for constructing health indicators (HI) and a transfer learning prediction framework, which integrates Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Multi-head attention (MHA). Firstly, we combined Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) to fully extract temporal and spatial features from vibration signals. Then, the Multi-head attention mechanism (MHA) was added for weighted processing to improve the expression ability of the model. Finally, a new method for constructing Health indicators (HIs) was proposed in which the noise reduction and normalized vibration signals were taken as a HI, the L1 regularization method was added to avoid overfitting, and the model-based transfer learning method was used to realize the RUL prediction of bearings under small samples and variable load conditions. Experiments were conducted using the PHM2012 dataset from the FEMTO-ST research institute and XJTU-SY dataset. Three sets of 12 migration experiments were conducted under three different operating conditions on the PHM2012 dataset. The results show that the average \(RMSE\) of the proposed method was 0.0443, indicating high prediction accuracy under variable loads and small sample conditions. Three different operating conditions and two sets of four migration experiments were conducted on the XJTU-SY dataset, and the results show that the average \(RMSE\) of the proposed method was 0.0693, verifying the good generalization of the model under variable load conditions. In summary, the proposed HI construction method and prediction framework can effectively reduce the differences between features, with high stability and good generalizability.  
RUL of rolling bearings; transfer learning; CNN-GRU-MHA; small samples and variable load; health indicators +
Footnote †: journal: Applied sciences  
Acticle  
Remaining Useful Life of the Rolling Bearings Prediction Method Based on Transfer Learning Integrated with CNN-GRU-MHA
###### Abstract  
Measurement-Device-Independent Quantum Key Distribution (MDI-QKD) is a two-photon protocol devised to eliminate eavesdropping attacks that interrogate or control the detector in realized quantum key distribution systems. In MDI-QKD, the measurements are carried out by an untrusted third party, and the measurement results are announced openly. Knowledge or control of the measurement results gives the third party no information about the secret key. Error-free implementation of the MDI-QKD protocol requires the crypto-communicating parties, [PERSON] and [PERSON], to independently prepare and transmit single photons that are physically indistinguishable, with the possible exception of their polarization states. In this paper, we apply the formalism of quantum optics and Monte Carlo simulations to quantify the impact of small errors in wavelength, bandwidth, polarization and timing between [PERSON]'s photons and [PERSON]'s photons on the MDI-QKD quantum bit error rate (QBER). Using published single-photon source characteristics from two-photon interference experiments as a test case, our simulations predict that the finite tolerances of these sources contribute \((4.04\pm 20/\sqrt{N_{\text{sifted}}})\%\) to the QBER in an MDI-QKD implementation generating an \(N_{\text{sifted}}\)-bit sifted key.  
Keywords:measurement-device-independent quantum key distribution; quantum optics; two-photon interference +
Footnote †: journal: Electronics
# Orbit Decomposition Method for Rotordynamic Coefficients Identification of Annular Seals
###### Abstract  
The elliptical orbit whirl model is widely used to identify the frequency-dependent rotordynamic coefficients of annular seals. The existing solution technique of an elliptical orbit whirl model is the transient computational fluid dynamics (CFD) method. Its computational time is very long. For rapid computation, this paper proposes the orbit decomposition method. The elliptical whirl orbit is decomposed into the forward and backward circular whirl orbits. Under small perturbation circumstances, the fluid-induced forces of the elliptical orbit model can be obtained by the linear superposition of the fluid-induced forces arising from the two decomposed circular orbit models. Due to that the fluid-induced forces of circular orbit, the model can be calculated with the steady CFD method, and the transient computations can be replaced with steady ones when calculating the elliptical orbit whirl model. The computational time is significantly reduced. To validate the present method, its rotordynamic results are compared with those of the transient CFD method and experimental data. Comparisons show that the present method can accurately calculate the rotordynamic coefficients. Elliptical orbit parameter analysis reveals that the present method is valid when the whirl amplitude is less than 20% of seal clearance. The effect of ellipticity on rotordynamic coefficients can be ignored.  
annular seals; rotordynamic coefficients; elliptical orbit whirl model; orbit decomposition method; computational time +
Footnote †: journal: Applied Sciences  
[PERSON], [PERSON], [PERSON] Orbit Decomposition Method for Rotordynamic Coefficients Identification of Annular Seals