esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
A statistically significant difference was observed between the control and STZ groups (\({}^{\bullet}\)\(p\) = 0.005). The TUNEL assay and the IHC staining for c-caspase-3 had the same purpose, which was the determination and comparison of the frequency of apoptotic cells in taste buds. The horizontal sections of CVP from each group were immune-stained with an antibody against c-casp3, which is accountable for alterations in shape and biochemical composition during apoptosis. The IHC staining results showed relatively more c-casp3-positive taste cells in the STZ subgroup, as well as in the STZ/4 HR subgroup (Figure 5). The results of calculating the percentage of taste buds with stained (positive) cells in relation to the total number of taste buds showed that there was a statistical difference among the groups (Figure 6; \(p<0.001\)). In the post hoc test, the STZ subgroup had a significantly higher number of c-casp3-positive taste cells compared to the control, 4 HR, and STZ/4 HR subgroups (\(p=0.010\), \(<\)0.001, and 0.007, respectively). Figure 4: Circumvallate papillae TUNEL staining. TUNEL-positive nuclei (indicated by arrows) are observable both within the taste bud profile and the epithelium. The diabetic experimental subgroup (STZ/4 HR), healthy control subgroup (control), and healthy experimental subgroup (4 HR) all showed fewer TUNEL-positive taste bud cells compared to the diabetic control subgroup (STZ) (Original magnification, \(\times\)200 without counterstaining). Quantitative analysis highlighted a significantly greater number of TUNEL+ cells in the STZ group in contrast to the control, 4 HR, and STZ/4 HR groups (\({}^{*}p<0.05\)). Figure 5: Immunohistochemistryistochemical staining depicting the presence of cleaved caspase-3 (c-casp3) in different subgroups. The brown coloration, marked by arrows, indicates c-casp3 positive cells. (**a**) Diabetic control subgroup (STZ); (**b**) diabetic experimental subgroup (STZ/4 HR); (**c**) healthy control subgroup (Control); and (**d**) healthy experimental subgroup (4 HR). A notably higher quantity of c-casp3 positive cells was observed in the STZ group compared to the other subgroups (Original magnification, \(\times\)400 without counterstaining, bar = 20 \(\upmu\)m). Figure 6: The percentage of cleaved caspase-3 positive cells in the taste bud. There was a statistically significant difference between the STZ subgroup and the other subgroups (* \(p\) = 0.005). The results of the IP-HPLC analysis using blood serum are presented in Figure 7. In terms of 4 HR's effect on DM-induced rats, it up-regulated HKII by 27.8%, sirtuin 1 by 13.4%, and FOXOO3 by 15.4%.
## 2 Related Works
### Pub/Sub Protocols
The work in [19] analyzes the end-to-end delay and delivery rate of MQTT-SN considering several system parameters, such as the publication rate, the QoS level, etc., in order to derive admission and service control policies. The closest work to ours is [20], where authors present a QoS dynamic control aimed at maintaining a good tradeoff between end-to-end latency and packet delivery rate by automatically changing the QoS level. The method continuously monitors only the network latency and estimates the network conditions based solely on that. The QoS controller then maintains the QoS requested by a user if the estimated latency is within specific bounds, considered as normal conditions. If the network latency is above or below such specific bounds, the controller forces the minimum level of QoS. The rationale is that in case the network conditions are good, QoS 0 allows minimizing the end-to-end latency, allowing a good delivery rate; in case the network conditions are bad, higher levels of QoS (1 or 2) tend to increase the amount of traffic produced in the network (due to acks) without giving guarantees on the delivery rate (QoS1) or end-to-end latency (QoS2). Our work differs from the work in [20] in the following aspects: first, it is focused on the subscribers' QoS, rather than on the publisher one; second, it accepts specific delivery performance constraints from the clients, both in terms of latency and success delivery rate: indeed, the proposed QoS controller is able to work in different operation modes optimizing either the end-to-end latency or the message hitrate; finally, we perform extensive experiments in a simulated environment allowing us to modify at will the underlying network characteristics, whereas in [20], tests were performed with a small scale testbed.
## 2 Materials and Methods
### Data Analysis  
Data analyses were performed offline after completing all measurements. The hip and knee joint angles were calculated using a posture-reconstruction plug-in [25] running on DhaibaWorks. This plug-in reconstructed the lower limb motion through the combination of the IMU orientation with body segments and the individual body model with a link structure [24]. The kinematic data were sampled at 60 Hz. The validation of the hip and  
Figure 3: Control mechanism of the pneumatic artificial muscle (PAM) driver system. The signal from the pressure sensor triggers the PAM contraction. Parameter settings are randomized based on the delay from the trigger to open the solenoid valves and the contraction duration of the PAM through a slider controller with a 100 ms increment.  
knee flexion angles and stride length relative to those measured using optical MoCap were previously evaluated using the coefficient of determination and defined as very strong (R\({}^{2}\geq\) 0.64), with confirmed values of 0.73 (hip), 0.82 (knee), and 0.86 (stride length) [26].  
To ensure that the walking motion was stable, the first and last two-to-three steps were excluded from the analysis because these steps are necessary for the transition between the initiation and termination of gait and the steady-state walk of healthy people [27]. During steady-state walking, the data for 10 gait cycles were collected (Figure 4). The peak hip and knee flexion angles in the swing phase and the peak hip extension angle in the terminal stance were extracted. The hip excursion was calculated from the amplitude of the displacement between the extension and flexion angles. Moreover, the stride length was calculated as the distance of the heel feature points in the sagittal plane during the gait cycle. The sagittal plane of the body model during walking was defined as the plane that is the normal direction of the outer product of the vertical axis of the global coordinate system and the front face of the sternum. The walking speed was calculated from the stride length and time.  
We first specified the most effective parameter settings under which the hip flexion angle was maximized among all the contraction parameters. We then evaluated the relationship between walking performance and these settings. Finally, to identify the effect of the PAM assistance, the joint angles, stride length, stride time, and walking speed were compared with and without PAM assistance using a Wilcoxon signed-rank test. The statistical significance was set at \(p<0.05\). All data were analyzed using SPSS version 25.0 (SPSS Inc., Chicago, IL, USA).  
Figure 4: Example of time series data of the hip and knee flexion angles in one trial. The peak flexion angles of the hip and knee joints in the swing phase and the peak extension angle of the hip were extracted in 10 gait cycles. The hip excursion was calculated from the amplitude of the displacement between the extension and flexion angles.
# A Technical Survey on Delay Defects in Nanoscale Digital VLSI Circuits
## 2 Fault Models for Delay Defects  
Generally, the industries use two popular delay fault models for detecting delay defects. These include the transition delay fault (TDF) model [9] and path delay fault (PDF) model [10; 11]. A single vector pattern is generated for the stuck-at fault model, which is used for logical testing. But for the TDF and PDF models, two vector patterns are generated to test the delay faults. The first vector sets the initial transition value, and the second vector sets the final transition value and propagates the fault effect to the output. The TDF model assumes that the large delay defect is localized at a single node, such that any transition passing through this node will be detected regardless of the slack in the sensitized path. The TDF model considers two types of faults per node: slow to rise and slow to fall, for generating the test patterns. The defects that cause a small delay in signal propagation can escape the traditional TDF model-based testing. Figure 1a shows the pattern generation for the fault site at node A based on the TDF model. Here, the fault effect can be propagated from faulty node A to output C or D. Based on the assumption of the TDF model such that the delay is significant enough to fail the circuit when it is propagated through any path. The TDF pattern propagates the fault effect from faulty node A to output C, which is the shortest path, leaving the SDDs undetected. Scan based TDF 2 vector patterns is generated either through launch-off-capture (LOC) [12] or launch-off-shift (LOS) [13] mode. The advantage of LOC over LOS is discussed briefly in Section 2.1. Another well-known fault model is the PDF model. This model assumes that the delay defect is distributed across a path. The PDF model considers two types of faults per path: slow to rise and slow to fall for generating the test pattern. When an SDD occurs along a path, and if the cumulative delay of the path exceeds the clock period, the SDD gets detected. Figure 1b illustrates the PDF-based pattern generation. Here the slow to rise transition through path A to D is tested, detecting small delays that accumulate along the path. But for larger designs, the number of paths grows exponentially with the size of the design, and test pattern generation for all these paths will be time-consuming. But in the TDF model, the number of fault sites is proportional to the number of nodes in the design. Even though the TDF model fails to detect certain SDDs and HDDs, it is still widely used, with some changes to detect these defects. Since this model is widely adopted, it is essential to explore the various available pattern generation schemes and choose the appropriate one based on the requirement, which is discussed in the Section 2.1.
## 2 Design and Experimental Setup
### Wireless ECG Module
The MATLAB code for transmission and reception is developed and loaded into the microcontroller using the Arduino Integrated Development Environment (IDE) interface. The module's operating voltage is 3.3 V. However, since SCA does not have any symptoms, or early signs, the mere transmission of ECG signals is insufficient; learning SCA features is useful. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sl. No** & **nRF24L01 Pins** & **Arduino Pins** \\ \hline
1. & GND & GND \\
2. & V\({}_{CC}\) & 3.3 V \\
3. & C\({}_{E}\) & D7 \\
4. & CSN & D8 \\
5. & MOSI & D11 \\
6. & MISO & D12 \\
7. & SCK & D13 \\
8. & IRQ & NC \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Pin Configurations for nRF24L01 and Arduino.
# Key Recovery for Content Protection Using Ternary PUFs Designed with Pre-Formed ReRAM
## 2 One Way Unclonable Functions with Ternary States
### One Way Unclonable Functions with PUFs  
The one-way unclonable functions can be implemented with PUFs, exploiting nanocomponents that are unique and unclonable due to small variations occurring in their fabrication. PUFs are currently used for both authentication and key generation. PUFs are described by the one-way function of \(f\) converting \(n\)-bit challenges **C** = {c\({}_{1}\);... ; c\({}_{i}\);... ; c\({}_{n}\)} in \(m\)-bit responses **K**= {k\({}_{1}\);... ; k\({}_{j}\);... ; k\({}_{m}\)}; c\({}_{i}\) and k\({}_{j}\) \(\in\) {0, 1}:  
\[\textbf{K}=\text{f}(\textbf{C})\leftarrow\textbf{C} \tag{4}\]  
**C** = (**T**, _IDAccess_) of Section 2.1 is the stream of challenges, while **K** is the stream of responses. During key generation cycles, the responses of **K** should match the ones generated upfront during enrollment. The protocols based on PUFs are effective with error-correcting schemes that are able to handle moderate aging, and environmental effects [5; 6; 7; 8]. Examples of implementation with various PUFs are summarized in Table 1.
## 2 Covalent Interactions
### CNT-Based Nanocomposites
#### 2.1.2 Grafting-From through Inverse Emulsion Polymerization
Tailoring the CNT content also had an impact on the composite's morphology; according to the HRSEM images, the nanocomposites with low CNT content exhibited a more open network with larger-sized elastomeric islands. A four-point probe technique showed that the composite displayed high conductivity (\(\sim\)4 S cm\({}^{-1}\)) by forming continuous three-dimensional CNT/PANI networks [31]. They also tested the effectiveness of a three-dimensional segregated conductive SIS/CNT/PANI nanocomposite network as an electro-mechanical strain sensor for two different elastomers: SIS-14% and SIS-22% [32]. The mechanical properties (Young's modulus and stress at break) were much higher with SIS-22% than with SIS-14%. Both sensors displayed a uniform homogeneous relative resistance amplitude, but there  
Figure 9: Fabrication via in situ inverse emulsion polymerization of the styrene=isoprene-styrene (SIS)/CNT/PANI elastomeric nanocomposite film by precipitation-filtration or drop-casting and (**a**) schematic structure of the resulting composites and (**b**) HRSEM images (Reprinted from [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] The Structure and Electro-Mechanical Properties of Novel Hybrid CNT/PANI Nanocomposites. _Polym. Compos._**2014**, 35, 788–794 [29]; and from [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Electrically Conductive Hybrid Elastomeric Nanocomposites. _Composites_**2013**, 161, 1–3 [30], with permission (2020) from WILEY). was better relative resistance with the tougher matrix. However, the sensor containing the softer SIS exhibited greater sensitivity because of the weaker \(\pi\)-\(\pi\) interactions between the CNT and the polymer matrix. The sensors were also more sensitive closer to the percolation threshold. [PERSON] et al. [33] reported the synthesis of a nanotube/poly(styrene-co-acrylamide) composite fabricated by dynamic interfacial emulsion polymerization without using a surfactant, under sonication in which the acrylamide fraction attached to the CNT hydrophilic surfaces. The result was a stable, uniform dispersion, and a clear transparent coating prepared from the previous dispersion.
## 5 Experimental Work and Results
### Datasets Description  
The Knowledge Discovery and Data Mining (KDD) competition in 1999 offered tKDD99 dataset [15], which was given by [PERSON] and [PERSON] [16]. [PERSON] [17] differentiated such data from others by employing a mixture of boosting and bagging. After winning the first place in a competition, this work has been considered a benchmark by researchers. Generally, these data concern the area of security, specifically intrusion detection, and are therefore considered crucial in machine learning. Output classes are segmented into five divisions: user 2 root (U2R), denial of service (DOS), root 2 local (R2L), probe, and normal. This dataset includes 14 attack types in testing and 24 attack types in training, generating a total of 38 attacks. Theoretically, the 14 new attacks examine the capability of intrusion detection system (IDS) for generalization to unknown attacks, and these new attacks are barely identified by machine learning-based IDS [18].  
From Wi-Fi-based localization, two additional datasets, TampereU and UJIndoorLoc, were employed. Three buildings of Universitat Jaume I were included in the UJIndoorLoc database, which consist of at least four levels covering an area of almost 110,000 m\({}^{2}\)[19]. For classification applications, this database was used in building identification, regression, actual floor, and actual determination of latitude and longitude. In 2013, UJIndoorLoc was built in almost 25 Android devices with 20 distinct users. The database includes 19,937 training or reference records and 1111 validation or test records. In addition, the 529 attributes possess Wi-Fi fingerprints, consisting of information source coordinates.  
An indoor localization database; the TampereU dataset, was employed for evaluating the IPSs that rely on WLAN/Wi-Fi fingerprint. [PERSON] and [PERSON] developed the database to test indoor localization techniques [20]. Two buildings of the Tampere University of Technology with three and four levels were accounted in TampereU. Moreover, the database includes 1478 reference or training records about the first building, 489 test attributes, and 312 attributes pertaining to the second building. This database also stored the Wi-Fi fingerprint (309 wireless access points WAPs) and coordinates (longitude, latitude, and height).
# Effect of Transducer Burn-In on Subjective and Objective Parameters of Loudspeakers
###### Abstract  
Speaker burn-in is a controversial practice in the audio world, based on the belief that new devices reach optimal performance only after a certain period of use. Supporters claim it improves component flexibility, reduces initial distortion, and enhances sound quality--especially in the low-frequency range. Critics, however, emphasize the lack of scientific evidence for audible changes and point to the placebo effect in subjective listening tests. They argue that modern manufacturing and strict quality control minimize differences between new and \"burned-in\" devices. This study cites a standard describing a preliminary burn-in procedure, specifying the exact conditions and duration required. Objective tests revealed slight changes in speaker impedance and amplitude response after burn-in, but these differences are inaudible to the average listener. Notably, significant variation was observed between speakers of the same series, attributed to production line tolerances rather than use-related changes. The study also explored aging processes in speaker materials to better understand potential long-term effects. However, subjective listening tests showed that listeners rated the sound consistently across all test cases, regardless of whether the speaker had undergone burn-in. Overall, while minor physical changes may occur, their audible impact is negligible, especially for non-expert users.  
speaker burn-in; subjective measurement; objective measurement; aging processes; production tolerances; placebo effect; frequency response Article
## 4 Results
### Test Results under Accelerated Environmental Conditions
The results of long-term high-temperature environment exposure are shown in Figure 12. As the thickness of the hybrid panel increased from 9.2 to 12.2 to 15.2 mm, the flexural strengths of the control specimens were 299.36, 284.78, and 251.29 MPa, respectively, while those of sand-blasted specimens were 370.21, 439.68, and 467.21 MPa. Fracturing of the GFRP composite materials by heat occurs due to  
Figure 11: The flexural strength of hybrid panels after repeated freezing and thawing cycles. differences in the thermal expansion coefficients of the different materials, especially the glass fiber and the polymer resin. At high temperatures, the glass transition temperature of the polymer resin becomes an important factor in determining the strength of the hybrid panel. In addition, the possibility of fracture between the steel and GFRP panels is also high. Flexural strength decreased as the thickness of the panel increased. The reason for this was the combined influence of differences in the thermal expansion coefficients of the steel and GFRP panel and the glass transition temperature of the polymer resin. The reason for decreasing flexural strength as a function of increasing panel thickness was the influence of the resin glass transition temperature on the mechanical integrity of the GFRP panel. In addition, unmodified steel control panels were significantly weaker than sand-blasted panels due to fracturing at the steel/GFRP interface and the greater interfacial bond strength of the sand-blasted specimens. Regarding the residual strength, all of the tested panels satisfied the goal of this study (\(\geq 65\%\); Figure 12b). As the thickness of the hybrid panel increased from 9.2 to 12.2 to 15.2 mm, the control specimens showed residual strengths of 87.70%, 88.64%, and 90.35%, respectively, while those of sand-blasted specimens were 83.22%, 93.51%, and 95.13%. Thus, sand-blasted specimens exhibited higher residual strength because of their higher interfacial bond strengths. Interfacial delamination was also suppressed despite the significant differences in the thermal expansion coefficient. Figure 12: _Cont._
## 2 Theoretical Approach Establishment
### Influence-Line Identification Methods  
#### 2.2.1 E-VMD Method Establishment  
We use EMD to adaptively decompose the measured deflection time-range response \(x(n)\) to derive the _IMF_ components of the intrinsic modal function, calculate the correlation coefficients between each _IMF_ component where \(x(n)\), \(l_{1}(m)\), (\(m=1,2\ldots M\)), and \(M\) is the number of _IMFs_, and define the correlation thresholds \(l_{limit}\).  
\[l_{1}(m)=\frac{\sum\limits_{n=1}^{N}\left(x(n)-\overline{x}(n)\right)(c_{k}( n)-\overline{c}_{k}(n))}{\sqrt{\sum\limits_{n=1}^{N}\left(x(n)-\overline{x}(n) \right)^{2}\sum\limits_{n=1}^{N}\left(c_{k}(n)-\overline{c}_{k}(n)\right)^{2}}} \tag{6}\]  
\[l_{\lim}=-\ln[\xi c/\left(1+\xi c\right)] \tag{7}\]where \(\xi\) is the resolution factor and \(c\) is an empirical constant. When \(l_{1}(m)\geq l_{\text{limt}}\) is satisfied, the corresponding \(m\) pieces _IMF_ component is defined as the useful component; otherwise, it is the noise component. Summing all the useful components yields the preprocessed signal, denoted as \(x_{0}(n)\).  
The VMD decomposition of signal \(x_{0}(n)\) is performed at different values of \(K\). A set of narrowband _IMF_ components is obtained for each decomposition. The narrowband _IMFs_ are determined by calculating the correlation coefficient \(l_{2}(k)\) in relation to \(x_{0}(n)\). The reconstructed signal \(x_{1}(n)\) is obtained by accumulating all the narrowband _IMF_ components that satisfy \(l_{2}(k)\geq l_{\text{limt}}(k=1,2,\cdots K)\). The mutual information relationship between the preprocessed signal \(x_{0}(n)\) and the reconstructed signal \(x_{1}(n)\) is calculated by the mutual information method, and the mutual information (MI) index is proposed.  
\[MI[x_{1},x_{0}]=H(x_{1})+H(x_{0})-H(x_{1},x_{0}) \tag{8}\]  
where \(H(x_{1})\), \(H(x_{0})\) and \(H(x_{1},x_{0})\) are the information entropy and joint entropy of \(x_{0}(n)\) and \(x_{1}(n)\), denoted as:  
\[H(x_{1})=-\sum_{i}p_{x_{1}}(x_{1i})\log_{2}p_{x_{1}}(x_{1i}) \tag{9}\]  
\[H(x_{0})=-\sum_{i}p_{x_{0}}\big{(}x_{0}\big{)}\log_{2}p_{x_{0}}\big{(}x_{0j} \big{)} \tag{10}\]  
\[H(x_{1},x_{0})=-\sum_{i,j}p_{x_{1},x_{0}}\big{(}x_{1i},x_{0j}\big{)}\log_{2}p_ {x_{1}x_{0}}\big{(}x_{1i},x_{0j}\big{)} \tag{11}\]  
where \(p_{x_{1}}(x_{1i})\), \(p_{x_{0}}\big{(}x_{0j}\big{)}\) and \(p_{x_{1},x_{0}}\big{(}x_{1i},x_{0j}\big{)}\) are the probability mass function and the joint probability mass function of \(x_{0}(n)\) and \(x_{1}(n)\), respectively. When the mutual information index _MI_ reaches the maximum value, the corresponding \(K\) value is the optimal number of modes obtained from the VMD of the preprocessed signal \(x_{0}(n)\). The quasi-static component of the vehicular time-range response is obtained by reconstructing all the narrowband _IMFs_ in the optimal number of modes \(x_{0}(n)\) that satisfy \(l_{2}(k)\geq l_{\text{limt}}(k=1,2,\cdots K)\). The method flow is shown in Figure 2.