esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design of Dynamic Torque-Coordinated Control That Considers Engine Starting Conditions
### Active Damping Feedback Compensation Control Considering System Resonance
#### 4.3.3 Determination of Feedback Gain Damping Coefficient  
Based on the simplified spring-mass model between point A and point B in Figure 8, the system transfer function \(G_{R}(s)\) from point A to point B can be obtained as follows:  
\[G_{R}(s)=\frac{\frac{1}{I_{R}}s^{2}+\frac{c_{T1}}{I_{R}L}s+\frac{k_{T1}}{I_{R}L}}{ s\Big{(}s^{2}+c_{T1}(\frac{1}{I_{R}}+\frac{1}{I_{L}})s+k_{T1}(\frac{1}{I_{R}}+ \frac{1}{I_{L}})\Big{)}} \tag{30}\]  
where \(I_{R}\) is the equivalent ring inertia; \(I_{L}\) is the equivalent moment of inertia at point B in Figure 8; and \(s\) is the Laplace operator.  
Figure 16 is the Bode diagram of the system before and after the damping feedback control. It shows the amplitude-frequency and phase-frequency characteristics of the transfer function \(G_{R}(s)\). From the solid red line before the damping feedback control, it could be seen that the transmission system resonates at 9.86 Hz. To reduce the resonance of the powertrain system further, the speed error of the output shaft was controlled by the damping feedback method, which is defined as  
\[T=f_{d}\Big{(}\hat{W}_{ob}i_{0}-\hat{\theta}_{R}\Big{)} \tag{31}\]  
By introducing the feedback gain damping coefficient \(f_{d}\) into Equation (32), the transmission system transfer function from point A to point B under closed-loop control can be obtained again as follows:  
\[G_{R}(s)=\frac{\frac{1}{I_{R}}s^{2}+\frac{c_{T1}}{I_{R}L}s+\frac{k_{T1}}{I_{R }L}}{s\Big{(}s^{2}+c_{T1}(\frac{1}{I_{R}}+\frac{1}{I_{L}}+\frac{f_{d}}{I_{R}}) s+k_{T1}(\frac{1}{I_{R}}+\frac{1}{I_{L}})\Big{)}} \tag{32}\]  
Figure 17 is a zero-pole distribution diagram based on the root locus, which is used to allocate the poles of the system. The transfer function \(G_{R}(s)\) has three poles before the damping feedback control, one of which is at the origin of Figure 17; the other two are a pair of conjugate complex poles with \(f_{d}\) = 0. Under the damping feedback closed-loop control, with the increase of \(f_{d}\), the two poles without origin will gradually approach the real axis and become two double roots at point A in Figure 17. When \(f_{d}\) continues to increase, one pole of the system moves to the left and the other pole moves to the right, and the response time of the system will increase. Based on this, in this paper, the two poles are located at the same point A in Figure 17, and the feedback gain damping coefficient \(f_{d}\) is 5.6228. In addition, the dotted line after the damping feedback control in Figure 16 is the Bode diagram result when \(f_{d}\) = 5.6228. The feedback gain-damping control optimized by the  
Figure 16: Bode diagram of system before and after damping feedback control.  
zero-pole distribution method can reduce the resonance peak of the transmission system better.
# Energy-Efficient Amplifiers Based on Quasi-Floating Gate Techniques
## 2 Quasi-Floating Gate Transistors
### Application of QFG Transistors to Energy-Efficient Amplifier Design
#### 2.2.3 Class AB Output Stage  
A basic scheme of a class AB output stage is shown at the left side of Figure 4c. A floating battery \(V_{AB}\) allows biasing the NMOS transistor and transfers input signal variations from node A to node B, providing output currents larger than the quiescent current. A QFG implementation is shown at the right side of Figure 4c, where the circuit of Figure 4b is used as active load of the input transistor, with \(V_{sig}\) = \(V_{in}\). Note that the QFG technique allows an optimal implementation of the floating battery, since it does not influence the biasing of the NMOS transistor, which is set by a matched diode-connected transistor, as in a class A topology. The value of \(V_{AB}\) can be either positive or negative; it is adapted to the supply voltage employed, preserving the quiescent gate voltages. This is not the case in conventional implementations of \(V_{AB}\) using, e.g., diode-connected transistors or resistors biased by DC current sources [65]. Moreover, the QFG implementation of \(V_{AB}\) does not require additional supply voltage or quiescent power and does not add extra nodes.
###### Abstract  
This study presents a generalized and systematic approach to modeling complex dynamic systems using Frequency-Based Substructuring (FBS). The aim is to develop an efficient method for system identification and subsystem decomposition, enabling the creation of reduced-order models for non-linear dynamic systems that are modular and reconfigurable. The methodology combines acceptance (Frequency Response Function, FRF) properties from individual subsystems to predict the overall system's response. This technique extends existing methods by [PERSON] and [PERSON] and adapts them to subsystems with full degrees of freedom (DoFs), making it suitable for flexible and distributed structures. To demonstrate its effectiveness, the method is applied to vehicle noise and vibration analysis, where subsystems are initially treated as rigid bodies, but are later adapted to flexible characteristics. The results show that this hybrid approach accurately predicts system responses, offering significant advantages for NVH target setting when subsystem FRF matrices are sourced either from testing or numerical simulations. This methodology enhances the capability to model complex dynamic systems with improved precision and reduced computational cost. A comparison with traditional modeling techniques confirms the validity of the approach.  
Keywords:frequency-based substructuring; receptance coupling; non-linear dynamic systems; vehicle NVH; reduced-order modeling; system identification; subsystem decomposition; reconfigurable dynamic systems +
Footnote †: journal: Applied sciences
# A High-Resolution, Wide-Swath SAR Imaging System Based on Tandem SAR Satellites
## 3 Signal Model and Imaging Algorithm
### Minimum-Energy-Based Imaging Algorithm
#### 3.2.2 Observation Vector Extraction  
In \(S_{1}(\eta,\tau)\), the target echoes appear as curves with range cell migration (RCM). And the RCM must be corrected to extract the observation vector \(\mathbf{S}_{i}\), which can be expressed as  
\[\mathbf{S}_{i}=\left[\sum_{k=1}^{K}S_{1}\left(\eta_{1},\tau_{\eta_{1},k}\right) \quad\sum_{k=1}^{K}S_{1}\left(\eta_{2},\tau_{\eta_{2},k}\right)\quad\cdots\quad \sum_{k=1}^{K}S_{1}\left(\eta_{N_{a}},\tau_{\eta_{N_{a}},k}\right)\right]^{ \mathrm{T}} \tag{10}\]  
where \(\tau_{\eta_{n},k}=2R(\eta_{n},k)/c\) is the main lobe's peak position of the \(k\)th pixel at \(\eta_{n}\). The illustration of the extraction of \(\mathbf{S}_{i}\) is shown in Figure 7.
# Reliability and Validity of the Orthotic Deformation Test
## 1 Introduction
Only when the orthoses resist or alter specific movements of the foot during weight-bearing activities are they able to improve foot and lower-limb functioning [28]. However, if the foot applies a larger load than the custom foot orthosis can resist, deformation results. Excessive deformation is one of the most common reasons why a plantar orthosis fails at ameliorating patients' symptoms [18]. This situation arises from inadequate initial stiffness of the orthosis design or from the wearing of orthoses over time [29]. Unfortunately, excessive deformation is difficult to detect directly during clinical evaluation of gait because the orthosis is hidden inside footwear; furthermore, the lateral sides inhibit observations of medial arch deformation. For this reason, it is necessary to conduct tests to identify the amount of deformation of plantar orthoses. [PERSON] describes the orthotic deformation test (ODT) to identify the amount of deformation in the lateral and medial sides of plantar orthoses [29]. It has been considered that 5 mm of deformation is excessive; however, this finding has not yet been validated. The ODT is sometimes performed by having a clinician apply a vertical force with their thumb to the dorsal surface of the medial longitudinal arch of the orthoses. The test is considered to be positive when the medial longitudinal arch deforms with the applied force. Due to the subjective nature of the test, results can vary between clinicians; there are general inconsistencies in the force applied on the orthoses, the practitioner's clinical experience, and the stiffness of the orthoses being evaluated. Furthermore, it has not been possible to determine the amount of deformation of orthoses that have not relieved symptoms. Additionally, despite the extensive use of this test, there is no set amount of deformation that corresponds to a positive or negative test. Due to such a lack of standards, the results of the ODT should be compared with results of an electromechanical test machine commonly used to accurately evaluate the stiffness of different orthoses [22]. It is crucial to determine the validity and reproducibility of the ODT because this test has important consequences for patient treatments. A positive test is commonly interpreted by clinicians as a sign of failure, necessitating that the orthoses be modified or changed. Clinicians and researchers should have access to information about reference thresholds when it comes to the deformation indicative of a positive or negative test. Despite the fact that mechanical tests have widely been used to check ankle-foot orthoses, no other studies have validated this subjective test according to the literature found. Therefore, the objective and the novelty of this investigation was to assess the validity of the ODT in comparison with a mechanical test. We also sought to evaluate the impact of researcher expertise on the reproducibility of the test.
# Free Vibration of a Taut Cable with Two Discrete Inertial Mass Dampers
## 4 Two IMDs at the Same End
### Comparison of Asymptotic and Numerical Solutions  
Figure 6 shows the comparison of asymptotic and numerical complex wavenumbers of a cable with two IMDs at the same end for various inertial masses, where two IMDs are installed at distances \(x_{1}\) of \(1\%L\) and \(x_{2}\) of \(2\%L\) from the left end of the cable, i.e., \(x_{1}=1\%L,x_{2}=2\%L\). Seeing that two IMDs are usually identical, some simplifications in the notation are introduced, i.e., \(b_{1}=b_{2}=b\), \(\chi_{1}=\chi_{2}=\chi\), \(c_{1}=c_{2}=c\), and \(\eta_{1}=\eta_{2}=\eta\). Similar to the case of two opposite IMDs, the loci start from the undamped wavenumber along a semicircular contour and finally attach to the real axis when damping coefficients of the IMDs increase from zero to infinity, and the effect of two IMDs installed at the same end of the cable on the cable frequency is also not significant. For a cable with two IMDs at the same end, although the asymptotic complex wavenumber agrees well with the numerical solution when the small inertial mass (\(\chi\leq 0.3/(n\pi x_{2}/L)\)) is used, it will lose accuracy when moderate or large inertial mass (\(0.6/(n\pi x_{2}/L)\leq\chi\leq 0.9/(n\pi x_{2}/L)\)) is adopted. Compared to the case of two opposite IMDs, prediction accuracies of the asymptotic solution are found to be relatively poor when two IMDs are installed at the same end of the cable. Hence, numerical results are used for the following parametric study.
# DPMF: Decentralized Probabilistic Matrix Factorization for Privacy-Preserving Recommendation
## 4 Evaluation
### Item Recommendation  
In this section, we compare our method with two famous models for the item recommendation task. One of them is the WRMF model, that assigns a weight and confidence to all user-item pairs, which helps to put the implicit data into the loss function for calculation. The other one is PMF, which we decribed in Section 2.2. Different from the traditional PMF model, it uses a logistic function to map the results to the range of \([0,1]\), and then establishes probability distribution function through Bernoulli distribution. These two models are tailored to solve the item recommendation problems using implicit feedback.  
As in Section 4.3, first we set \(\lambda=0.02\), \(W_{ij}=0.5\), \(d=3\) for conducting experiment on _FilmTrust_ and \(\lambda=0.06\), \(W_{ij}=1\), \(d=3\) for conducting an experiment on _Epinions_. Then we change the value of \(K\) to see how precision and recall changes. In Table 4, we can see that (1) as the increase of \(K\), the \(P@5\) and \(R@5\) of the three models all increase. (2) when \(K\) is small (\(K=3\)), our model and the other two models have similar performance. (3) when \(K\) is large (\(K>=6\)), our model has clear advantages over the other two models. This is mainly because DPMF uses more data to construct the model, so as the size of the latent factors \(K\) becomes larger, we are more able to explore the potential information hiding in the data.  
Figure 5: The contour map of RMSE as \(\lambda\) and \(W\) changes when training DPMF model on two datasets. (a) _FilmTrust_, (b) _Epinions_.  
Figure 6: The histogram of RMSE as \(K\) changes when training different RS model on two datasets. (a) _FilmTrust_, (b) _Epinions_.
## 5 Discussion
### Effects and Limitations of Incorporating Multi-Turn Dialogical Context  
Incorporating multi-turn dialogue context into annotation and model training is motivated by the need to capture forms of toxicity, such as irony, sarcasm, veiled hostility, or cumulative microaggressions, which are often invisible or ambiguous in isolated utterances. Unlike single-turn approaches, which risk missing the discursive and pragmatic cues surrounding a toxic comment, multi-turn context enables both annotators and models to evaluate intent, provocation, and evolving interpersonal dynamics.  
For example, a reply like \"claro, porque los de siempre runca fallan\" (\"sure, because the usual suspects never fail\") may seem innocuous out of context, but when situated after a sequence of xenophobic exchanges, it reveals clear alignment with biased discourse. Similarly, indirect allusions, defensive humor, or quoted speech often only become legible as toxic when read within a chain of preceding comments, as shown by our dataset and examples of dialogues as shown in Tables 4 and 5.  
Empirically, our contextualized models demonstrated improved recall and F1 scores over their single-turn counterparts (see Table 8), confirming that multi-turn inputs do help capture some subtle and pragmatic bias phenomena. Annotators also reported that the three-turn window frequently enabled more confident and nuanced labeling decisions, especially for cases involving sarcasm or allusion.  
However, the observed improvements were modest. Several factors likely limit the full potential of context-aware modeling in our setting:  
1. Insufficient contextual richness: The Mediavida forum often features short, rapidly-shifting dialogues, where adjacent turns may not always provide enough semantic or pragmatic information to reveal hidden toxicity.
2. Fixed context window size: Our two-turn window (preceding the target) may be too narrow for some conversations and not necessary for overly sexist or racist turns, so dynamic window sizes may be worth exploring.
3. Model reliance on lexical features: Our error analysis confirmed that, despite contextual input, models tend to prioritize explicit lexical markers (e.g., slurs), with subtle cues from surrounding turns often underweighted.  
Thus, while multi-turn dialogical context offers clear qualitative advantages over traditional single-turn annotation, enabling both annotators and models to better detect pragmatic and implicit toxicity, the realized quantitative gains are currently bounded by the dataset's conversational depth and modeling limitations. Future work should explore variable-sized and adaptive context windows, as well as advanced attention-based architectures that can more effectively exploit nuanced dialogical structure.
## 5 Technical Challenges and Ethical Issues
### Key Challenges and Requirements
#### 5.1.6 Training and Workforce Readiness  
The successful implementation of DTs requires extensive training for healthcare professionals. Staff must understand the technology's complexities and be equipped to prioritize patient safety. Building this expertise is critical for integrating DTs into clinical practice effectively.
## 4 Experimental Results and Analysis
### Selection of Threshold in Filtering Operation  
The filtering operation of the proposed method uses an adjustable threshold set to a pixel value. Different values lead to different qualities in the final results. After many tests with different pixels, the optimal threshold value was found to be the range from 110 to 150. Figure 15 clarifies the relationships  
Figure 14: Enlarged recovery details of a large area attack on the Barbara image: (**a**) the method of [7]; (**b**) the proposed method.  
Figure 13: Tamper detection and recovery results of a large area attack on the Barbara image: (**a**) watermarked image; (**b**) tampered result of [7]; (**c**) tampered result of the proposed method; (**d**) tampered image; (**e**) recovery result of [7] (Peak signal-to-noise ratio (PSNR) = 27.34 dB); (**f**) recovery result of the proposed method (PSNR = 27.40 dB).  
of the pixel threshold value to the recovery quality in different tampering experiments, where each image name reflects its relative tampering type. In Figure 15, the horizontal ordinate represents the pixel threshold, which ranges from 110 to 150 at a step size of 5. On the vertical axis, we label the highest PSNR values both among the methods [7; 8; 9] and under different thresholds. As shown in Figure 15, the optimal pixel threshold varies among different images; thus, it should be selected according to different tests. In addition, the PSNR values after the threshold adjustment are close to or even better than those of the previous methods. This result demonstrates that the proposed method can achieve a great visual effect without losing the objective quality of an image.
# Smart Healthcare Network Management: A Comprehensive Review
## 5 Reference Architecture for Smart Healthcare System Network Management
### Smart Healthcare Element Layer  
Network devices such as wearables, implants, IoT devices, gateways, routers, switches, and backend devices are individual components of the network that run across one or more of the network protocol layers [89]. Network device performance can be evaluated using multiple categories: end-to-end, per-link, per-network, and per-element, each corresponding to a different scope. End-to-end measurements look at the collective behavior across multiple network devices or the entire network path for one or more traffic flows. They may include metrics such as availability, capacity, delay, jitter, throughput, error rates, and utilization. In contrast, per-link, per-network, and per-element assessments concentrate on particular types of hardware or individual segments of the network. Per-link metrics often highlight factors such as propagation delay and link utilization, while per-element metrics (for example, for an IP router) might include IP forwarding rates (like packets per second), buffer usage, and authentication failure logs. Furthermore, these lower-level characteristics can be combined to provide an aggregated end-to-end performance profile.  
Wearables, implants, and IoT devices are the main elements of the smart healthcare system as they connect directly to the patient [90]. Once installed, the broker should configure, manage, and keep them operational and updated. The network management system can poll individual components for status updates, and these components can either respond with the requested information or send automatic alerts in the event of abnormal behavior. Monitoring needs depend on the type of physiological data being measured. Some devices, such as spirometers and glucometers, only require attention when a reading is captured (discrete monitoring). Others require continuous data sampling and transmission, as is the case with electrocardiogram (ECG) devices, which constantly track cardiac signals [91].
## 2 Materials and Methods
### Evaluation of the Antioxidant Capacity
#### 2.7.4 FRAP Assay  
The reducing potential of the samples was determined using the FRAP assay as described elsewhere [32]. The method is based on the reduction of the Fe\({}^{3+}\)-tripyridtriazine complex to its ferrous-colored form at low pH in the presence of antioxidants. The FRAP reagent was freshly prepared daily and contained a ratio of 1:1:1 (\(v/v/v\)) of TPTZ (2,4,6-tripyridly-s-triazine) 10 mM solution in HCl 40 mM, FeCl\({}_{3}\cdot\)6H\({}_{2}\)O 20 mM and acetate buffer 3.0 mM, pH = 3.6. The aliquots of the extracts were transferred in glass test tubes and dissolved in distilled water to a final volume of 0.9 mL, followed by an addition of 0.3 mL of FRAP solution. The samples were stirred and incubated for 10 min in a 40 \({}^{\circ}\)C water bath, and the absorbance was measured at 593 nm using a photometer ([PERSON], VIS-7220G, Shenzhen, China). A standard curve was prepared using different concentrations of Trolox. The antioxidant activity was expressed as umol Trolox/g final product. Every sample was tested in triplicate.
## 2 Materials and Methods
### Analysis of Pretreatment Hydrolysates  
Carbohydrates, organic acids (acetic and formic acids) and carbohydrate byproducts (furfurfural and hydroxymethylfurfural, (HMF)) in the pretreatment hydrolysates were analyzed by a HPLC system coupled with an Aminex(r) column (HPX-87H, 300 \(\times\) 7.8 mm) (Bio-Rad), a refractive index detector, and a UV-visible detector, as described elsewhere [22]. Samples were filtered with a 0.45 um nitrocellulose syringe filter membrane, prior to the HPLC quantification. Carbohydrates and by-product yields, concerning the initial wood weight, were determined considering the compounds concentrations (arithmetic average of two measurements, at least) in the pretreatment hydrolysates and the final liquid-to-wood ratio (as described in the following for the soluble lignin yield (SLY)). Carbohydrates are expressed as anhydrous units. pH was measured at 20 \({}^{\circ}\)C, before and after pretreatment.  
The soluble lignin and related compounds' quantification in the pretreatment hydrolysates was performed through UV-visible spectrophotometry using a Thermo Scientific Helios Omega UV-Vis spectrophotometer (Thermo Scientific, Waltham, MA, USA), according to the aforementioned NREL's-laboratory analytical procedure [23]. Readings were performed in duplicate at a wavelength of 205 nm. Prior to the readings, appropriate dilutions were performed so that the read absorbances were in the range of 0.2 to 0.7. A sulfuric acid solution 3% (_w/w_) was used to dilute the samples and as a blank. An absorptivity coefficient of 110 Lg\({}^{-1}\)cm\({}^{-1}\) was used for the lignin [24]. Therefore, the Beer-Lambert law was used to determine the soluble lignin and related compounds concentration (SL) in the hydrolysates, taking into account the dilution factor [24]. The SLY in hydrolysates, regarding the initial wood weight, was calculated through Equation (1):  
\[\text{SLY}=(\text{SL}\times\text{Vt}/\text{W})\times 100 \tag{1}\]  
where SLY is the soluble lignin yield, (%); SL is the soluble lignin concentration, (gL\({}^{-1}\)); Vt is the total volume of the hydrolysate (L) and W is the initial weight of the oven-dried (o.d.) wood (g).