esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed System and Architecture
### RTPM Monitoring Architecture  
The system is designed to be able to securely monitor health-related data, e.g., temperature and the sweating level of a patient, as well as the ambient space for well-being monitoring (movement, light, pressure, magnetic flux, air quality, etc.). The system also monitors and alerts surrounding people with messages via LCDs, buzzers, and LEDs to make them aware of the monitoring events. If the system is disturbed, then alert functions are activated; otherwise, the system continues to measure and update the server and relevant stakeholders (doctors, nurses, care, friends, and family) depending on the condition of the patients and the environment in which the patient is monitored, as shown in Figure 4. The user needs to hold the temperature and moisture/humidity sensor for data collection, and the rest of the data of the environment and the patient's well-being are continuously monitored at the same time. If the data do not change, then the sensory information is not transmitted to the server to reduce system overhead; however, if the data remain constant for over 5 min, then the data are pushed to the server to ensure the system's liveness. The system can detect if there is movement, if the device is disturbed, if the brightness of the room is changed, or if the air quality and pressure of the room change. Such a system is perfect for monitoring population health in general and is perfect for a situation like the COVID-19 pandemic. The system can be deployed locally in a hospital, remote care, or home environment. The architecture ensures that the IoMT device is securely registered and authorised, the data are transmitted securely (confidentiality and integrity maintained), the data source is identified, only authorised users access or receive the data, security keys are safely exchanged or delivered, fresh session keys generated for every connection request, and the storage is securely locked.
# Channel Measurement and Modeling for 5G Urban Microcellular Scenarios
## 2 Urban Microcellular Measurement Campaigns
### Open Square Measurements  
The OS-CC measurements at 60 GHz were performed on \"Leipziger Platz\", which is an open square of octagonal shape and a diameter of approx. 150 m in the city center of Berlin. It is bordered by modern multi-story buildings, as can be seen in Figure 1a.  
The TX of the channel sounder was placed at four different positions on the square. The antenna was mounted on a pole of 3.5 m height to simulate street furniture deployment. In accordance with the SC-CC and SC-RA measurements, the RX was mounted on a trolley at a height of 1.5 m and moved along the sidewalk at constant speed of 0.5 m/s. Figure 1b shows a map of the square with the TX positions and the RX tracks. For the majority of the RX positions, there was line-of-sight (LOS) between the TX and RX antenna. The measurement bandwidth was 250 MHz and a transmit power of 15 dBm could be achieved. Vertically polarized antennas with omnidirectional pattern in azimuth and 2 dBi gain were used at the TX and RX. A CIR snapshot took 65.5 us and was recorded every 800 us, corresponding to 0.4 mm spacing between adjacent RX positions. The overall data set acquired for the OS-CC scenario comprises 2.4 million CIRs.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Scenario** & **OS-CC** & **SC-CC**[24] & SC-RA [25] \\ \hline \multirow{2}{*}{Site} & Berlin city center, & Berlin city center, & Berlin residential area \\  & Leipziger Platz & Potsdamer Str. & Kreuzberg \\ \hline Dimensions & Square diameter: \(\approx\)150 m & Street canyon width: \(\approx\)52 m & Street canyon width: 15–20 m \\ \hline Frequency & 60.0 GHz & 60.0 GHz & 60.4 GHz and 10.0 GHz \\ \hline Bandwidth & 250 MHz & 250 MHz & 250 MHz \\ \hline Antenna type (TX and RX) & omni, vertical pol. & omni, vertical pol. & omni, vertical pol. \\ \hline \multirow{2}{*}{HPBW in elevation} & \(\approx\)80 deg & \(\approx\)80 deg & \(\approx\)80 deg at 60.4 GHz, \\  & & & \(\approx\)60 deg at 10.0 GHz \\ \hline Antenna height (TX/RX) & 3.5 m/1.5 m & 3.5 m/1.5 m & 5.0/1.5 m \\ \hline Conditions & mainly LOS & mainly LOS & LOS/NLOS \\ \hline Distance & 2–50 m & 2–50 m & 4–210 m \\ \hline Spacing between meas. & \multirow{2}{*}{0.4 mm} & \multirow{2}{*}{0.4 mm} & \multirow{2}{*}{0.4 mm} \\ points for mobile RX & & & \\ \hline Number of collected CIRs & 2.4 million & 6.4 million & 6.3 million \\ \hline \end{tabular}
\end{table}
Table 1: Summary of measured urban microcellular (UMi) scenarios and main parameters.  
Figure 1: Measurement environment “open square, city center” (OS-CC): (**a**) photo with transmitter (TX) pole at position Tx3 and (**b**) map with TX positions and receiver (RX) tracks.
## 3 Results and Discussion
### Design of Remediation Strategy and Implementation of Pilot Test
A visual comparison showed good distribution of the product, a homogeneous coating of soil particles, and evidence perceptible up to 3 meters away from the injection point. The most permeable aquifer portions (9-9.5 m from ground level) had a more evident change in color, probably due to a greater quantity of products able to permeate this zone. The passive groundwater sampling system demonstrated good distribution of the amendment in the intermediate aquifer. The geophysical surveys performed in near real time provided undoubtedly critical information, especially in terms of where and how in situ remediation actions affected different portions of the subsurface as an effect of subsoil hydraulic heterogeneity [58]. In fact, the physical variable of interest, electrical resistivity, is strongly related to state variables of key environmental interest [59]. In the case considered here, the injected solutes (PlumeStop(tm) and HRC(tm)) raised the electrical conductivity of natural groundwater, as shown also by laboratory tests (not shown here). Thus, it was relatively easy to track the injected plumes by using time-lapse ERT. Figure 9 shows the results of a preliminary PlumeStop(tm) injection conducted in November 2015 and monitored using an ERT line corresponding to the direction of line L1 (see Figure 4a) but slightly shifted to the north. Note how the low electrical resistivity manifests itself because of PlumeStop(tm) injection, corresponding with the sandy bodies also identified in Figure 5. The resistivity changes are not symmetrical with respect to the injection point (blue arrow in Figure 9) because of the strong heterogeneity of the shallow aquifer. The red zones in Figure 9 indicate no resistivity changes. These zones mainly correspond to aquiclude 1, where the product could not penetrate because of the lower permeability of this formation. Figure 9: (**a**) Trace of the ERT line and (**b**) section illustrating resistivity changes (in % with regard to (w.r.t.) background) caused by PlumeStop(tm) injection. The injection point corresponds to vertical blue arrow. The pilot test had to be representative of the process at full scale. Its implementation provided useful information about process efficiency and the actual extent of treatment, which varied depending on the site's subsurface characteristics. Analytical monitoring and integration of geophysical data during pilot test implementation facilitated the assessment of the remediation technology performance and the evaluation of possible modifications and integrations of the intervention strategy (configuration of injection points and quantity of product to be injected).
## 2 Overall Communication Solution of DSSS Transceiver
### Carrier Synchronization  
The Costas loop synchronization method is used in the carrier synchronization module [14]. Coatas loop synchronization belongs to the direct methods [15]. Compared to the pilot carrier method, direct method can extract the carrier from the signals without a pilot signal. Direct methods can achieve carrier synchronization using the modulated signal as an input. Direct methods not only save resources but are also very useful in cases of limited spectrum resources [16]. The block diagram of the Costas loop is shown in Figure 4. The process of the Costas loop can be expressed mathematically as follows:  
Let us define the input signal of the Costas loop as \(m\left(t\right)\cos\phi t\), where \(m\left(t\right)\) denotes the baseband signal, and \(\varphi\) denotes the angular frequency of the carrier of the transmitter. Hence, the signals of the orthorhombic channel I and channel Q can be expressed as Equations (3) and (4), respectively:  
\[I(t)=\cos(\phi t+\theta) \tag{3}\]  
\[Q(t)=\sin(\phi t+\theta) \tag{4}\]  
The phase difference between the local carrier signal and the input modulated signal is \(\varphi\). Hence, we obtain Equations (5) and (6).  
\[L_{t}\left(t\right)=m(t)\cos\phi t\times\cos(\phi t+\varphi)=\frac{1}{2}m(t) \left[\cos\varphi+\cos(2\phi t+\varphi)\right] \tag{5}\]  
\[L_{Q}(t)=m(t)\cos\phi t\times\sin(\phi t+\varphi)=\frac{1}{2}m(t)\left[\sin \varphi+\sin(2\phi t+\varphi)\right] \tag{6}\]  
Equations (7) and (8) are obtained after \(L_{t}\left(t\right)\) and \(L_{Q}(t)\) go through the low-pass filter:  
Figure 4: Block diagram of the Costas loop.  
Figure 3: Block diagram of BPSK demodulation.  
\[L_{1}(t)=\frac{1}{2}\,m(t)\cos\varphi \tag{7}\]  
\[L_{2}(t)=\frac{1}{2}\,m(t)\sin\varphi \tag{8}\]  
We then multiply \(L_{1}(t)\) by \(L_{2}(t)\) to find the error signal, which is expressed by Equation (9):  
\[\varphi_{out}(t)=L_{1}L_{2}\approx\frac{1}{4}\,m^{2}(t)\varphi \tag{9}\]  
From the above equations, we see that the local NCO control signal will control the frequency and phase of NCO until the carrier loop is in synchronization [17]. The carrier signal can then be extracted by tracking the frequency and phase of the transmitter.
## 2 Mathematical Modeling and Methods
### The Composition of the Control System
#### 2.4.2 Pump-Motor Hydraulic System Control System
\begin{array}{l}\mathbf{J}=[\mathbf{Y_{r}}-\mathbf{F_{u}}\mathbf{x_{u}}(k)] ^{\mathrm{T}}\mathbf{R_{s}}[\mathbf{Y_{r}}-\mathbf{F_{u}}\mathbf{x_{u}}(k)] -2\Delta\mathbf{U}^{\mathrm{T}}\mathbf{G}^{\mathrm{T}}\mathbf{R_{s}}\Delta \mathbf{U}[\mathbf{Y_{r}}-\mathbf{F_{u}}\mathbf{x}(k)]\\ +\Delta\mathbf{U}^{\mathrm{T}}(\mathbf{G}^{\mathrm{T}}\mathbf{R_{s}}\mathbf{G} +\mathbf{R})\Delta\mathbf{U}\\ \frac{\partial\mathbf{J}}{\partial\Delta\mathbf{U}}=0\Rightarrow\Delta\mathbf{ U}=(\mathbf{G}^{\mathrm{T}}\mathbf{G}+\mathbf{R})^{-1}\mathbf{G}^{\mathrm{T}}(\mathbf{R_{s}}y_{r}(k)- \mathbf{F_{u}}\mathbf{x}(k))\end{array}  \tag{27}\]The state prediction value of the controlled system at time \(K+1\) is:  
\[x_{u}(k+1)=(\mathbf{A}_{u}-\mathbf{B}_{u}K_{E})x_{u}(k)+\mathbf{B}_{u}K_{r}y_{r}( k)=\mathbf{A}_{u}x_{u}(k)+\mathbf{B}_{u}\Delta u(k) \tag{28}\]  
\(K_{E}\) is the first row element of matrix \((\mathbf{GTG+R})-\mathbf{1 GTFu}\)); \(K_{r}\) is the first row element of matrix \((\mathbf{GTG+R})-\mathbf{1 GTRes}\)). \(K_{E}\) can be described as \(K_{E}=[K_{x}\,K_{r}]\). \(K_{x}\) can be expressed as the feedback gain related to the state variable, and \(K_{r}\) is expressed as the feedback gain which relates to the output of the control system. The system control block diagram of the state extension MPC is shown in Figure 7.
## 3 Results and Discussion
### Carcinogenic Health Risk Assessment  
The results (Table 4 and Figure 8) highlight that children exhibit higher exposure levels across ingestion pathways compared to adults. The lower exposure was reported through inhalation and dermal pathways for children. The total ILCR was notably higher for the children than the adults, suggesting heightened vulnerability. On the other hand, the industrial areas consistently demonstrate the highest exposure values with the following sequence: industrial areas > coastal area local market area > research areas > rural areas. The research area presents the lowest exposure levels, indicating a potentially lower risk of contamination. USEPA has categorized the degree of health risk based on the ILCR value as minimal danger (10 \(\times\) 10\({}^{-6}\)), possible danger (10 \(\times\) 10\({}^{-4}\)), and high danger (10 \(\times\) 10\({}^{-6}\)) [30]. The ILCR\({}_{\text{total}}\) value for children ranged from 2.03 \(\times\) 10\({}^{-8}\) to 9.07 \(\times\) 10\({}^{-7}\), whereas the value for adults was 1.96 \(\times\) 10\({}^{-8}\) to 8.04 \(\times\) 10\({}^{-7}\). The results revealed that no carcinogenic risk was found for both children and adults as the ILCR value was below the standard value  
Figure 7: Scatterplot of isomeric ratio of (**a**) Ant/(Ant + Phe), Flu/(Flu + Pyr); (**b**) BaA/(BaA + Chry), Ind/(Ind + BghiP).  
set by USEPA. The result suggests that a higher risk was reported from the industrial area because of ample sources of PAHs of those areas. A similar type of finding was reported by [PERSON] et al. [27] who mentioned the highest carcinogenic risk for both categories of people in the commercial areas. Though previous studies revealed that dermal and ingestion were the main pathways [18, 31, 48, 49], we found that ingestion for children and dermal and inhalation for adults were the main exposure pathways of PAHs in the human body.
# Demand-Response Control in Smart Grids
## 4 Comparison between Centralized and Distributed Control for Demand Response  
In this section, we present centralized and distributed control for demand response in smart grids. Then, we compare the two approaches based on advantages and disadvantages of each method.
# Mitigating the Impact of Noise on Nonlinear Frequency Division Multiplexing +
## 4 Improved Detections  
Aiming at mitigating the noise problem and improving the performance of NFDM, some improved detection strategies have been proposed. For example, in the context of a discrete-spectrum modulation, the authors of [22] have proposed a time-domain strategy to detect a signal with seven eigenvalues, whose spectral amplitudes are modulated by QPSK symbols. The strategy consists of considering all the \(2^{14}\) possible combinations of symbols and selecting the one that corresponds to the time-domain waveform that is closest to the received one in Euclidean sense. Unfortunately, the complexity of this optimal strategy increases exponentially with the number of eigenvalues and the constellation order. In a similar context, a machine learning technique has been also proposed for detection, providing superior performance with respect to the standard FNFT detection [25].  
In principle, the optimal MAP strategy proposed in [22] for the detection of the discrete spectrum could be extended also to the scenario considered in this work for the detection of the continuous spectrum: compute the Euclidean distance between the received waveform and the \(16^{N_{b}}\) waveforms corresponding to all the possible transmitted sequences of \(N_{b}\) 16-QAM symbols, and select the sequence that minimizes this distance. However, this strategy is not feasible in practice, since it would require the computation (or storage) of \(16^{N_{b}}\) signals and the computation of all the corresponding  
Figure 2: Nonlinear inverse synthesis (NIS) modulation (**a**) Normalized (upper bound to the) squared minimum distance between transmitted NFDM waveforms. (**b**) Normalized squared minimum distance obtained after several realizations changing a different set of symbols.  
distances. A possible way to circumvent this difficulty is to resort to the causality property of the NFT demonstrated in [13]. Indeed, the combination of this property with the sequential structure of some NFT algorithms allows us to develop some improved detection techniques which, though suboptimal, yield an improved performance compared to the standard FNFT detection without an exponential increase of complexity [13, 23, 24]. In the remainder of this section, we illustrate the NFT causality property and its implications in terms of detection, we describe three improved detection strategies, and we discuss their complexity. The notations are as in [13], and \(\bar{\mathbf{r}}_{k}\) (and \(\mathbf{r}_{k}\)) indicates the vector containing the samples of the noisy (and noiseless) optical signal in the time window \([t_{k-1},t_{k})\).
## 3 Site-Specific Soil Behavior and TEC 2018  
The seismic performance of floors must be evaluated for buildings constructed in earthquake-prone areas. Local soil conditions should be identified, and the geotechnical parameters required for structural design must be determined and documented.  
The new Turkey Earthquake Hazard Map was developed based on knowledge gained from past earthquakes and earthquake regulation studies in Turkey (Figure 6) [33]. A new regulation was ultimately published in 2018, further emphasizing the impact of local ground conditions. The most recent Turkey Earthquake Code (TEC, 2018), which has been in effect since 2019, introduced an expanded section for determining site-specific local soil classifications, recommending that earthquake ground motion spectra be calculated based on the structure's location, distance to the fault, and soil properties, in contrast to previous regulations.  
Figure 5: Shear modulus curves of the BH-1 drilling point at different depths.  
As part of the regulation, local soil classes were revised, and design acceleration spectrum values were determined based on regional maps. These parameters, which are essential for building design, are derived from the new regulation. Since Turkey has been continuously affected by earthquakes throughout its history, earthquake regulations have been periodically updated in response to advancements in research and technology.  
According to TEC 2018, which came into effect in January 2019, soil is classified into six categories (Table 2). The classification is based on parameters such as V\({}_{\text{s(30)}}\), N\({}_{\text{60(30)}}\), and cu(\({}_{\text{(30)}}\)). The ZF category, which represents the weakest soil conditions according to the regulation, consists of soils that require site-specific investigation and evaluation.
###### Abstract  
The balance of the body conditions the quality and efficiency of the movements in daily and sports activities, its impairment generating problems in the manifestation of motor skills for all age groups. The aim of this study is to analyze how the gender and BMI values of university students influence the results of the static and dynamic balance tests applied. The investigated group consists of 195 undergraduate students, from various specializations of the Dunarea de Jos University in Galati (99 males and 96 females, ages = 20.16 \(\pm\) 1.98, BMI = 24.15 \(\pm\) 5.68). The independent variables gender and BMI levels (underweight, normal weight and overweight/obese) were defined. The participants were evaluated in May 2019 using a series of 7 tests: one leg standing test with eyes closed, stork test, flamingo test, Bass test, functional reach test, walk and turn field sobriety test and Fukuda test. The results provided using multivariate analysis (MANOVA) indicate balanced performance for the gender variable, but with significantly better values for women in the one leg standing test, flamingo test and functional reach test (F values are associated with thresholds \(p<0.05\)). Men obtain slightly better, but statistically insignificant (\(p>0.05\)), performances on the stork test, Bass test and Fukuda test. The BMI step comparison confirms the difficulties of the overweight group in assessing balance, with the lowest scores in maintaining static positions and the most errors in dynamic balance tests, with significant differences from normal and underweight in most tests (\(p<0.05\)). An interesting aspect is the slightly superior performance of the underweight compared to the normal weight group, for the one leg standing test, flamingo test and walk and turn field sobriety test. The analysis of Spearman correlation coefficients indicates a number of significant associations between elevated BMI values and decreased performance on balance tests.  
balance; students; stability; gender; obesity Article
## 4 Materials and Methods
### Gene Expression Analysis  
Mature seeds of Korean rice \"Imi\" (_Oryza sativa_) were sterilized with 70% ethanol and 2% sodium hypochlorite and washed five times with distilled water. The sterilized seeds were grown on Murashige and Skoog (MS) agar medium under the conditions of 16 h light/8 h dark at 28 \({}^{\circ}\)C over 10 days and then the leaves and roots of seedling stage (SS) plants were harvested. For plant preparation of the other two stages, vegetative stage (VS) and reproductive stage (RS), 5 days old healthy seedling plants were transplanted to soil and grown in a greenhouse under conditions of 16 h light/8 h dark at 28 \({}^{\circ}\)C [50].  
Korean rice (_Oryza sativa_ L. cv. lmi) seeds were obtained from \"National Institute of Agricultural Sciences (South Korea)\". For various hormone treatments, the sterilized seeds were grown on MS agar medium for 3 days and healthy seedling plants were transplanted and grown on four layers of gauze (5 \(\times\) 5 cm) in the Incu tissue culture frames (model 310071; SPL Lifesciences, Seoul, Korea) containing 45 mL of liquid MS medium (Duchefa, Haarlem, Netherlands) under 16 h light/8 h dark at 28 \({}^{\circ}\)C for 7 days, which were prepared by filling the culture frames containing four layers of gauze (5 \(\times\) 5 cm) with the liquid MS medium (Duchefa) up to the 45 mL-marked line and sterilizing them by autoclave. Plant hormones methyl jasmonic acid (MeJA), gibberellic acid (GA) and abscisic acid (ABA) were prepared as 10 \(\times\) stock solution of 1 mM and after checking whether 45 mL-liquid MS media were filled in the culture frames, 5 mL of 1 mM hormone solution (10\(\times\)) were added to each 45 mL of liquid MS medium with gentle stirring. For the 10-day old seedling plants that had been grown on gauze, whole plants containing leaves and roots were sampled at 0, 30 min, 1, 2, 4 and 6 h post treatment into liquid nitrogen for the expression pattern analysis of rice PTs genes.  
Total RNA was purified from the frozen powder (100 mg) of samples using an RNeasy Plant Mini Kit (Qiagen, Hilden, Germany) and DNase I (Qiagen) for removal of remnant genomic DNA contamination. The 1 st cDNA strand was synthesized using AccuPower(r) RT Premix (Bioneer, Daejeon, Korea) and cDNA was mixed with SYBR Green Real-time PCR master mix (Bio-Rad, Richmond, CA, USA) according to the manufacturer's instruction. The quantitative real-time PCR (qRT-PCR) assays were performed with a CFX Connect(tm) Real-Time System (Bio-Rad) under the following conditions: 1 cycle of 3 min at 95 \({}^{\circ}\)C, 40 cycles of 15 s at 95 \({}^{\circ}\)C and 1 cycle of 30 s at 60 \({}^{\circ}\)C [50]. All primers for qRT-PCR were designed as intron-spanning primers and are listed in Supplementary Table S1. Rice ubiquitin 5 (Os01g22490) and elongation factor 1a (Os03g08050) were used as reference genes for qRT-PCR for the spatial-and developmental expression patterns and the hormone treatment-responsive expression patterns, respectively.