esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Detailed Analysis of Developments in Selected Specialties
#### 3.7.15. Telemedicine in Pulmonology  
In Poland, telemedicine research and practice in pulmonology have concentrated on chronic respiratory diseases, particularly chronic obstructive pulmonary disease (COPD) and sleep-related breathing disorders, such as obstructive sleep apnea (OSA). This field encompasses telehealth projects, telemonitoring for exacerbations, and evaluation of innovative technologies, including virtual reality (VR). A pilot study assessed daily telemonitoring to predict acute exacerbations (ECOPD) and demonstrated the feasibility of providing early warning signs to prevent hospitalization [192]. A recent pilot project (2022-2023) implemented telemedical solutions for managing COPD in senior populations in Wroclaw and the Lower Silesian Voivodeship, offering insights into telehealth strategies for older adults with COPD [193]. A narrative review explored telehealth and VR applications in COPD management during COVID-19, highlighting VR's role of VR in pulmonary rehabilitation alongside telemedicine approaches [194]. A questionnaire-based study assessed OSA management across Europe, revealing an increased adoption of telemedicine approaches including remote diagnostics and therapy monitoring [195]. COPD management remains central to pulmonology telemedicine research in Poland, with telemonitoring showing promise for early detection of exacerbations. The integration of telehealth with innovative technologies has enhanced pulmonary rehabilitation. Regional pilot projects provide evidence for scaling telemedical care models for older adults with respiratory disease. European studies on OSA management have demonstrated that the increasing adoption of telemedicine is applicable in Polish practice. The field combines practical pilots and reviews, indicating growth while highlighting opportunities for larger implementations.
## 3 Experimental Results and Analysis  
### Stress-Strain Curves  
Figure 4 presents the stress-strain curves of granite specimens under varying strain rates and confining pressures. The results reveal three fundamental characteristics: (1) Distinct from the concave-upward shape of uniaxial compression curves [12], triaxial specimens demonstrated pronounced linear elasticity in the initial loading phase. This mechanical response stems from the confining pressure preloading stage in triaxial testing, where hydrostatic compression induces the closure and compaction of pre-existing  
Figure 3: Lab testing equipment used for triaxial quasi-static loading with AE monitoring. ((**a**) AE sensors on the specimens; (**b**) diametral displacement LVDT; (**c**) AE signal amplifiers; (**d**) AE monitoring system; (**e**) servo-control system; (**f**) MTS815 rock testing system and triaxial cell).  
fissures and structural discontinuities. The resultant densification elevates the material's initial integrity, thereby establishing a quasi-homogeneous state conducive to linear elastic behavior at the onset of axial loading. (2) While both the strain rate (10\({}^{-6}\) to 10\({}^{-2}\) s\({}^{-1}\)) and confining pressure (0-40 MPa) enhanced triaxial compressive strength (TCS), their efficiencies differed fundamentally. Taking the 10 MPa confining pressure dataset as an example, when the strain rate escalates from 10\({}^{-6}\) to 10\({}^{-2}\) s\({}^{-1}\) (four orders of magnitude increase), TCS rises from 228 MPa to 257 MPa, representing a 12.72% enhancement. Contrastingly, under a constant strain rate of 10\({}^{-6}\) s\({}^{-1}\), elevating the confining pressure from 10 MPa to 40 MPa (fourfold increase) elevates TCS from 172 MPa to 313 MPa--an 81.98% improvement. This 6.4-fold disparity in strengthening efficiency (81.98%/12.72%) quantitatively confirms the dominant role of the confining pressure over strain rate in TCS enhancement. (3) An analysis of stress-strain curves under combined confining pressure (0-40 MPa) and strain rate (10\({}^{-6}\) to 10\({}^{-2}\) s\({}^{-1}\)) conditions revealed pronounced brittle failure characteristics in granite specimens: an average post-peak stress drop gradient of \(-\)5.97 \(\times\) 10\({}^{5}\) MPa, residual strength ratios consistently below 10%, and the absence of strain hardening, even at maximum loading parameters (strain rate: 10\({}^{-6}\) s\({}^{-1}\); \(\sigma_{3}\): 40 MPa). Experimental evidence confirms that within the tested \(\sigma_{3}\) range (\(\leq\)40 MPa), specimens predominantly maintained brittle failure modes regardless of strain rate reduction to quasi-static levels (10\({}^{-6}\) s\({}^{-1}\)), indicating that confinement thresholds for brittle-ductile transition were not attained under these experimental constraints.
# Alpha-Mangostin Suppresses LPS-Induced Inflammation in Human Dental Pulp Cells
## 3 Results  
### Effects of LPS and \(\alpha\)-MG on Cell Viability  
The WST-1 assay was performed to evaluate the cytotoxic effects of various concentrations of LPS and \(\alpha\)-MG on hDPCs (Figure 1). We observed that treatment with 1 \(\mu\)g/mL LPS did not affect cell viability. Additionally, no cytotoxicity was observed for 0 to 3 \(\mu\)g/mL \(\alpha\)-MG. However, \(\alpha\)-MG concentrations of 6 \(\mu\)g/mL or more led to significant cell damage (\(p<0.05\)). Consequently, \(\alpha\)-MG concentrations of 1.5 and 3 \(\mu\)g/mL were applied for all subsequent experiments.  
Effects of \(\alpha\)-MG on LPS-Induced mRNA and Protein Expression of Inflammatory Mediators in hDPCs  
The mRNA expression of inflammatory mediators was measured by qPCR to evaluate the effects of \(\alpha\)-MG on the inflammatory response. As shown in Figure 2, the mRNA expression of all inflammatory mediators increased significantly upon stimulation with 1 \(\mu\)g/mL LPS. However, pretreatment with 3 \(\mu\)g/mL \(\alpha\)-MG resulted in significant suppression of the increase in IL-6 and ICAM-1 (\(p<0.05\)) (Figure 2B,C). \(\alpha\)-MG pretreatment also attenuated IL-1\(\beta\) and VCAM-1 in a concentration-dependent manner, but there were no significant differences (\(p>0.05\)) (Figure 2A,D).  
Consistent with the results of mRNA expression, the protein expression levels of inflammatory mediators significantly increased by LPS; however, \(\alpha\)-MG pretreatment attenuated this increase in a dose-dependent manner (\(p<0.05\)) (Figure 3).  
Figure 1: The effect of \(\alpha\)-MG on the viability of hDPCs. Results are expressed as relative cell viabilities depending on the different concentrations of \(\alpha\)-MG. Cell viability significantly decreased in the 6 \(\mu\)g/mL \(\alpha\)-MG treated group (* \(p<0.05\) compared to the control group).
# A 12-Bit 1-GS/s Pipelined ADC with a Novel Timing Strategy in 40-nm CMOS Process
## 1 Introduction
The comparator is a key module of the pipelined ADC, and its speed and precision directly determine the performance that the ADC can achieve. Secondly, a four-input comparator combined with threshold offset calibration is designed for high-speed and high-precision performance. For the offset of the comparator, this paper corrects the threshold offset of the comparator by adjusting the bias current of the preamplifier [18]. In theory, adding adjustable current sources at the input can directly adjust the offset voltage, but it is difficult to turn off the switches. Therefore, it is more appropriate to add an adjustable current source at the output ports. This paper calculates the equivalent input offset voltage after introducing the bias current and displays it in the form of a picture. Thirdly, the sample-and-hold amplifier (SHA)-less architecture is known for its low power consumption and low ADC noise, but the performance of IF-sampling ADCs degrades due to the aperture error at high-input frequencies [19; 20; 21]. Overall, SHA is worth choosing for better sampling linearity and robustness. In addition, sampling with a bootstrap switch exhibits higher linearity and faster speed than MOS or CMOS switches. Fourth, operational amplifiers for MDAC circuits are required to increase swing, gain, and bandwidth. Both foreground and background calibrations have been used to correct errors and mismatches in this ADC. Firstly, this ADC adopts the method of automatic calibration by plotting a residual curve to calibrate the residual curve. [22]. This ADC investigates automatic calibration for residual curve calibration, which avoids the time-consuming and difficult manual adjustment. Second, the finite inter-stage gain of the MDAC is calibrated by injecting dither in the first three stages. Because the code-domain equalization calibration technology greatly increases the power consumption and complexity of analog circuits, this design uses pseudo-random noise injection calibration by directly adding a small amount of capacitance and voltage [23; 24]. The injection position is more flexible and can effectively improve the spectrum. This paper is organized as follows: Section 2 introduces the architecture of the whole ADC, especially the proposed pre-quantization timing. In Section 3, the circuit implementation and calibration of the 1 GS/s 12-bit pipelined ADC is described in detail. Section 4 offers a detailed discussion along with measured results for future work. Finally, conclusions are provided in Section 5.
# Synchronization of Acoustic Signals for Steganographic Transmission
## 5 Results
### Hidden Transmission Effectiveness Assessment
#### 5.2.1 Steganographic Transmission on the VHF Radio Link  
Figure 19 shows a laboratory stand for steganographic transmission. The stand was built on the basis of two computers and two RRC 9211 radio stations.  
The research was carried out using three different radio modes:  
* Analogue Fixed Frequency (AFF) with F3E;
* Digital Fixed Frequency (DFF) with F1D, CVSD 16 kbit/s and encryption;
* Fast Frequency Hopping (FFH) with F1D and CVSD 16 kbit/s and encryption.  
The signals recorded on the receiving side were subjected to the synchronization procedure in accordance with the adopted synchronization method. Then, after obtaining the synchronization, the steganographic information was extracted. The bit error rate was adopted as a measure of the hidden transmission efficiency. The mean BER value and the  
Figure 17: Listening test results for various synchronization methods, \(K_{min}=500\).  
Figure 18: Listening test results for various synchronization methods, \(K_{min}=1250\).  
95% confidence interval (T-Student distribution) were determined. For the MES method, the BER value was determined based on the value of the BCH decoder syndrome. The study included one of the variants of the MES method for \(k=50\) and \(t=13\).  
Additive noises occurring in the considered communication channel caused the distortion of the received signals. These distortions were so significant that they prevented the correct operation of the PID synchronization procedure. The obtained results are shown in Figures 20 and 21.  
Increasing the \(K_{min}\) factor value from 500 to 1250 reduces the average BER value from 7% to 3% for AFF mode and from 12% to 6% for DFF and FFH modes. The higher BER values for digital modes may be due to the fact that in these operating modes there is lossy compression of the speech signal related to CVSD encoding.  
Comparing the synchronization methods, we can see that the obtained bit error rate values in a given operating mode and for a fixed value of \(K_{min}\) are similar to each other. Generally, the differences in values do not exceed 3.5% when analyzing both mean values and maximum values for the 95% confidence interval.  
Figure 19: Diagram of a laboratory stand for testing steganographic transmission in the VHF radio link.  
Figure 20: BER on the radio link for different synchronization methods, \(K_{min}=500\).
## 4 Pattern Recognition
### Supported Vector Machine  
Supported Vector Machines (SVM) are supervised models that search how to separate two classes using a discriminative hyperplane. SVM searches for a hyperplane that maximizes the separation margins of the system, i.e., the distances between the classes of the training points. In Figure 6a, these distances are shown as \(d_{1}\) and \(d_{2}\).  
As it can be seen in the previous Figure, the hyperplanes divide the input data in two different regions, one considered to be positive (\(y_{i}=1\) for \(H_{1}\)) and the other to be negative (\(y_{i}=-1\) for \(H_{2}\)). Thus, the hyperplanes, as shown in Figure 6a, are defined as \(H_{1}:y_{i}(w^{T}x_{i}+b)\geq 1\) and \(H_{2}:y_{i}(w^{T}x_{i}+b)\leq-1\). Those conditions can be combined into \(y_{i}(w^{T}x_{i}+b)\geq 1\). The main objective is that the classifier has a margin as big as possible,  
Figure 6: Support Vector Machine. (**a**) SVM to get the optimal hyperplane for generalization. (**b**) The expansion of SVM capabilities using the kernel trick.  
i.e., maximize the distance between both hyperplanes, defined as \(d=\frac{2}{||w||}\). This is the same as minimizing the function \(\frac{1}{2}w^{T}w\) constrained to the condition of \(y_{i}(w^{T}x_{i}+b)\geq 1\).  
Although SVM is a linear classifier, it can be extended to non-linear using the 'kernel trick', which maps the data into a different space (Figure 6b) where the data can be linearly separated. Furthermore, SVM is a binary classification but can be easily converted into a multi-class classifier using the technique of one vs. the rest, where a classifier is made for each class and discriminated against the rest of the classes. The winning class is the one with the higher final confidence value. The steps for SVM can be seen in Algorithm 3.  
```
Input network:  Training set: \(S=\{(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{s},y_{s})\}\)  Regularization Parameter: C Tolerance and maximum number of iterations Init network:  Initialize \(\alpha_{i}=0,\forall_{i}\) and \(b=0\) Train network:  Quadratic Programing such as SMO Output network:  Lagrange Multipliers: \(\alpha\in\Re^{s}\)  Threshold: \(b\in\Re\)
```  
**Algorithm 3** SVM Pseudo-code.
## 5 Performance Analysis  
### Power Consumption of the End User Device  
Mathematically, the power consumption within the cooperative network in defined use cases can be given as follows: In case 1 the end user devices are only receiving the data using the LTE interface. Thus, the power consumption in this reference case is  
\[P_{\text{tot}}=N\times P_{\text{rx, LTE}}(R) \tag{7}\]  
where \(P_{\text{rx, LTE}}\) is the received signal power for a signal coming from the base station. In case 2, one node is receiving the data over the LTE link and transmits the data over WiFi to \(N\)-1 users, i.e.,  
\[P_{\text{tot}}=P_{\text{rx, LTE}}(R)+P_{\text{tx, WiFi}}(R)+(N-1)\times P_{ \text{rx, WiFi}}(R) \tag{8}\]  
In case 3, the same transmissions are conducted over the LTE interface. Thus, the total power consumption is  
\[P_{\text{tot}}=P_{\text{rx, LTE}}(R)+P_{\text{tx, LTE}\_\text{D2D}}(R)+(N-1) \times P_{\text{rx, LTE}\_\text{D2D}}(R) \tag{9}\]  
where \(P_{\text{tx, LTE}\_\text{D2D}}\) is the transmission power consumption of a UE and \(P_{\text{rx, LTE}\_\text{D2D}}\) is the received power consumption for a D2D signal. \(R\) is the required data rate over the link. In cases 4 and 5, the data rate is divided into multiple \(R/N\) rate streams that are then combined at the requesting node(s). In case 4, the total power consumption is  
\[P_{\text{tot}}=N\times P_{\text{rx, LTE}}(R/N)+N\times P_{\text{tx, WiFi}}(R/N)+N\times P_{\text{rx, WiFi}}(R-R/N) \tag{10}\]  
and in case 5 it is  
\[P_{\text{tot}}=N\times P_{\text{rx, LTE}}(R/N)+N\times P_{\text{tx, LTE}\_\text{D2D}}(R/N)+N\times P_{\text{rx, LTE}\_\text{D2D}}(R-R/N) \tag{11}\]  
The power consumption of the cluster head is given in Equations (8) and (9) by excluding the last term in the equation. In cases 4 and 5, the power consumption is equally shared between the nodes.
## 4 Research Methodology
### Application-Based Approaches  
Systems based on application-based approaches typically dispense one or more of the following services. A medical IoT-based system also encompasses resource management,communication infrastructure, and sensors to dispense patient, caregiver, and user services. This segment of the application-based category includes two subcategories: monitoring systems and recommender systems.
## 3 Roman Urdu Language Structure and Challenges  
In the recent era, social networks and weblogs publish a massive variety of content and constitute an indispensable hub of information mined for the research community. The unstructured big data (aka eData) produced by these platforms highly deviate in the context of standard dialect, grammatical rules, and lexicons [35]. These idiolects produce non-standard words in the language lexicon, phonological variations, and syntactic and grammatical variations, thus distinguishing the textual social media form of the language from its corresponding standard counterpart.  
Recently, on different social media platforms, the Roman Urdu language has been adopted as a contemporary trend and feasible medium for communication. It originated from the Urdu language, a morphologically rich language with a complex inflectional system. The Urdu language is considered to be the 21 st most widely spoken language worldwide and is also called the \"Lashkari language\" (July 5,July 3,July 36)[36]. It is the national and official language of Pakistan, i.e., \"Qaumi Zaban,\" and is primarily spoken across different regions and communities [37]. While Urdu is written in Nastaliq script, Roman Urdu language is written via LTR (Left-to-Right) writing and is based on Roman scripting. A survey statistic presented in [38] states that there are 300 million people across the globe who speak Urdu language and nearly 11 million Urdu users are in Pakistan, from which the maximum users on social media has recently shifted to informal, colloquial Roman Urdu language for the textual messaging. Roman Urdu is a linguistically rich language and highly differs in the aspect of word structures, irregularities arising from natural writing patterns, and grammatical compositions adopted by users as compared to formal Urdu. There are no diacritics marks ('zer', 'zabar', 'pesh', etc.) in roman Urdu. It is a deficit of standard lexicon and available resources and hence becomes extremely problematic when performing NLP tasks.  
A clear elaboration of Roman Urdu LTR pattern script and Urdu RTL scripting is depicted in Table 1.  
The lack of rules and colloquial adoption of this language by social media users also results in a much higher word surface. For example, the word 'good' in English is written as 'acha' in Urdu in masculine form, feminine form, and singular and plural forms. However, the number of words increases many-fold when written in the Roman Urdu language such as 'acha' (masculine form), 'achi' (feminine form), achay (plural form), and so on. Moreover, people use their own spellings, writing structures, and elongated characters.  
Although the Urdu language has a formal structure and the availability of few resources, colloquial Roman Urdu has no standard and formal structure, and no resources are available to standardize it. The scarcity and lack of language resources make it challenging to analyze and apply data analytics techniques for deducing useful knowledge from text. To cope with this, rigorous language resources, language-specific preprocessing steps, techniques, and well-devised methodologies are needed.
## 3 Results  
Table 1 shows the descriptive characteristics of the sample, with 33% being left-dominant. Intrasession test-retest reliability of the average and peak isometric strength of the three positions of the starting blocks is shown in Table 2. A non-significance difference was found between the test and retest of average and peak force, except for the first session in the dominant leg position (\(p\) = 0.03). Very high and extremely high reliability of the average and peak isometric force of the three positions of the starting blocks were obtained with the ICC ranging from 0.63 to 0.91 and a CV close to 10%. On the other hand, the intersession test-retest reliability of the average and peak isometric strength of the three positions of the starting blocks is shown in Table 3. A non-significant difference was found between the test and retest of average and peak force (\(p>0.05\)). Very high and extremely high reliability of the average and peak isometric strength of the three positions of the starting blocks were obtained between the third and fourth sessions, with the ICC ranging between 0.84 and 0.92 and a CV close to 10%. Figure 3 shows the Bland-Altman plots of the reliability between the third and fourth session of the average and peak isometric force of the three positions of the blocks. The random error was close to eight kilograms and narrow LoA. Heteroscedasticity of errors was observed (\(\tau^{2}>0.10\)) between test and retest in bilateral peak force (\(\tau^{2}=0.142\)) and non-dominant peak force (\(\tau^{2}=0.101\)). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Participants.** & **Age** & **Dominant Foot** & **Experience** & **Weight** & **Height** & **BMI** \\ \hline  & **(year)** & & **(years)** & **(kg)** & **(m)** & **(kg/m\({}^{2}\))** \\ \hline S1 & 25.0 & Left & 9 & 75.0 & 1.69 & 26.3 \\ S2 & 24.0 & Left & 7 & 74.8 & 1.85 & 21.8 \\ S3 & 21.0 & Right & 6 & 69.2 & 1.68 & 24.7 \\ S4 & 20.0 & Right & 7 & 60.2 & 1.74 & 20.0 \\ S5 & 20.0 & Right & 8 & 68.9 & 1.74 & 22.9 \\ S6 & 22.0 & Right & 9 & 72.8 & 1.77 & 23.4 \\ S7 & 25.0 & Left & 7 & 87.0 & 1.75 & 28.4 \\ S8 & 24.0 & Right & 10 & 84.5 & 1.86 & 24.4 \\ S9 & 21.0 & Left & 9 & 57.4 & 1.71 & 19.6 \\ S10 & 27.0 & Right & 12 & 83.0 & 1.71 & 28.4 \\ S11 & 21.0 & Right & 6 & 77.8 & 1.74 & 25.7 \\ S12 & 21.0 & Right & 6 & 72.5 & 1.74 & 23.9 \\ S13 & 20.0 & Right & 8 & 61.2 & 1.84 & 18.1 \\ S14 & 20.0 & Right & 4 & 62.6 & 1.76 & 20.2 \\ S15 & 20.0 & Right & 6 & 70.7 & 1.83 & 21.2 \\ S16 & 21.0 & Left & 8 & 69.2 & 1.81 & 21.1 \\ S17 & 20.0 & Left & 7 & 64.5 & 1.75 & 21.1 \\ S18 & 20.0 & Right & 8 & 72.9 & 1.85 & 21.3 \\ \hline mean & 21.7 & & 7.40 & 70.88 & 1.77 & 22.74 \\ SD & 2.1 & & 1.77 & 8.12 & 0.06 & 2.88 \\ \hline \hline \end{tabular} Note: kg = kilograms; m = meters; BMI = body mass index; SD = standard deviation.  
\end{table} 
Table 1: Descriptive characteristics of the sample. Figure 3: Bland-Altman plots.