esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Through the integration of multi-sensor fusion, IoT-enabled monitoring technologies, and appropriate data transmission frameworks, the Monit4 Healthy system enables connected health ecosystems. The system tackles important issues in IoT-driven healthcare by enhancing sensor interoperability, real-time health monitoring, and secure data exchange. This allows for effective, scalable, and flexible health data processing.  
The Monit4 Healthy system improves biomedical signal integrity, network performance, and energy efficiency using edge computing and the structured transmission of information approaches. Thorough health evaluations are supported by its modular sensor fusion architecture, which enables easy integration into clinical, telemedicine, and smart city infrastructures. By means of the flexible prioritization of real-time health data and the decrease in needless network loads, the system promises high-reliability, low-latency medical monitoring. By enabling interoperability with digital health solutions, and the potential integration into smart city infrastructures, the Monit4 Healthy system brings an opportunity to extend connected health ecosystems beyond conventional healthcare environments. To further consolidate its standing as a next-generation IoT-based health monitoring solution, future work and research will be centered around scalability, AI-driven analytics, and expanded implementations in current healthcare environments.  
Conceptualization, M.I. and V.-S.C.; methodology V.-S.C. and M.I.; software, V.-S.C., A.-G.M., M.-C.P. and O.B.; validation, V.-S.C., M.I. and A.A.; formal analysis, A.A.; investigation, A.-M.G.; writing--original draft preparation, M.I., A.-G.M., M.-C.P., A.A., O.B. and A.-M.G.; writing--review and editing, M.I., A.-G.M., M.-C.P., A.A., O.B. and A.-M.G.; visualization, V.-S.C.; supervision, A.A. and V.-S.C.; project administration, M.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AFH & Adaptive Frequency Hopping; \\ AI & Artificial Intelligence; \\ BLE & Bluetooth Low Energy; \\ CAD & Computer-Aided Design; \\ CLI & Command Line Interface; \\ ECG & Electrocardiography; \\ EMG & Electromyography; \\ FHIR & Fast Healthcare Interoperability Resources; \\ GDPR & General Data Protection Regulation; \\ GPS & Global Positioning System; \\ GSR & Galvanic Skin Response; \\ HIPAA & Health Insurance Portability and Accountability Act; \\ I\({}^{2}\)C & Inter-Integrated Circuit; \\ IaaS & Infrastructure as a Service; \\ IoT & Internet of Things; \\ LoRaWAN & Long-Range Wide-Area Network; \\ ORM & Object Relational Mapper; \\ MCU & Microcontroller Unit; \\ PPG & Photoplethysmogram; \\ RPM & Remote Patient Monitoring; \\ SpO\({}_{2}\) & Oxygen Saturation; \\ \end{tabular}
## 3 Results
### Microsurfacing Test Results
#### 3.3.4 Shaking Abrasion Test  
The shaking abrasion test was carried out to determine the loss of adhesiveness of mixtures studied due to their water sensitivity. This test was performed for the compositions with higher cohesion values previously obtained for both emulsions.  
Figure 17 presents the appearance of a sample before and after the test; it can be seen that the edges of the specimens are polished after the test.  
Table 4 presents the results of the tests carried out with the microsurfacing mixtures produced with both emulsions. The abrasion results are expressed as the mass loss percentage and were obtained by averaging the results of the four test specimens. The mixture with the best performance (lowest mass loss) was prepared with Emulsion 2. Table 4 also presents the water absorption of the mixtures and the volume of the specimens (Vv) after vacuum application, showing that the samples produced with Emulsion 1 had slightly higher volumes than those produced with Emulsion 2. Even though there were no significant differences between the two mixtures, the mixture with higher mass loss also presented a higher volume and water absorption.  
The abrasion test rated the microsurfacing mixtures produced with Emulsions 1 and 2 differently from the cohesion test. This demonstrates that the properties of the emulsions used in microsurfacing mixtures significantly influence the final performance of the material. The lower abrasion of the mixture with Emulsion 2 may be related to the potential higher polymer modification of this emulsion, as inferred from its residual binder's higher elastic recovery and softening point temperature. The higher amount of cement in that mixture may have also contributed to the lower abrasion and air void content values measured, which indirectly reduced water absorption.  
Figure 16: Cohesion values for all curing times for better mix designs for both mixtures.  
Figure 17: Samples before (**left**) and after (**right**) carrying out the shaking abrasion test.
## 2 Reactive Routing Protocols and Application of Jitter  
This section introduces the basic operations of reactive routing protocols, as used in some wireless _ad hoc_ and sensor networks. Then, the use and impact of jitter on flooding performance are briefly introduced and discussed.
# The RHO Family GTPases: Mechanisms of Regulation and Signaling
## 4 Downstream Effectors of RHO GTPases  
The ability of RHO GTPases to control a wide range of intracellular signaling pathways is attributed to their association with their cellular targets: effector proteins (Figure 5, Table 1). In contrast to regulators that interact with RHO GTPases to modulate their switch function, effectors require GTPases to be in a specific conformation to realize their own intrinsic function. To date, more than 70 potential effectors have been identified for RHOA, RAC1, and CDC42 [58].  
The effector proteins are either kinases or scaffolding proteins (Figure 5, Table 1). Kinases form an important class of RHO effectors and result in downstream phosphorylation cascades. Different RHO-associated serine/threonine kinases such as PAK (p21-activated kinase), ROCK (RHO-associated coiled-coil kinase), CRIK (citron kinase), and PKN (protein kinase novel) interact with and are regulated by their partner GTPases [138; 139; 140]. Another group of effectors comprise scaffolding proteins, which probably form a framework for signaling cascades, especially through filamentous actin dynamics. IQ motif-containing GTPase-activating protein 1 (IQGAP1) [141], mammalian homolog of Drosophila di-aphanous 1 (DIA1), Wiskott-Aldrich syndrome protein (WASP), and Rhotekin (RTKN) are the most extensively investigated effectors in this regard and facilitate complex formation in cells [142].
# Spectral Separation of Up-Conversion Luminescence Processes
## 2 Spectrum Separation Method
We introduce EN for the compound signal as  
\[n(\lambda,\mathrm{I})\equiv\frac{d}{d\ln\mathrm{I}}\ln(\mathrm{J}(\lambda, \mathrm{I})). \tag{5}\]  
Then, for some particular values of pump power \(\mathrm{I}\) we look for spectral regions of weakly changing \(n(\lambda,\mathrm{I})\), aiming to attribute it to some particular UC process (or, to be precise, the sum of processes with the same EN). Then, we check found values of EN against measurements for the set of pump power values close to the initially chosen \(\mathrm{I}\). If our suggestion of the processes with the same EN in the considered spectral intervals is asserted, we assign \(n(\lambda,\mathrm{I})\approx n_{j}\) for the \(j\)-th region, and proceed approximating \(n(\lambda,\mathrm{I})\) in all the wavelength range of our interest with the following expression obtained from Equations (1)-(3):  
\[n(\lambda,\mathrm{I})=\sum_{j=1}^{N}n_{j}\frac{\mathrm{J}_{j}(\lambda, \mathrm{I})}{\mathrm{J}(\lambda,\mathrm{I})}. \tag{6}\]  
Taking some \(M\geq N\) values of pump power, one can infer spectral functions \(f_{j}(\lambda)\) from Equation (6). For separation of just the two spectral components \(\mathrm{J}_{1,2}(\lambda,\mathrm{I})\), the approach in (6) is especially simple and illustrative. For this case, one can obtain the following equations from Equation (6):  
\[\mathrm{J}_{1}(\lambda,\mathrm{I})=\mathrm{J}(\lambda,\mathrm{I})\frac{n_{2}- n(\lambda,\mathrm{I})}{n_{2}-n_{1}},\quad\mathrm{J}_{2}(\lambda,\mathrm{I})= \mathrm{J}(\lambda,\mathrm{I})\frac{n_{1}-n(\lambda,\mathrm{I})}{n_{1}-n_{2}}, \tag{7}\]  
It is important to note that our method is actually self-testing in different ways. Firstly, and the most obviously, coincidence of the inferred \(f_{j}(\lambda)\) for different pump power values  
Figure 1: Energy level diagram of the sensitizer (\(\gamma\mathrm{b}^{3+}\)) and acceptor (\(\mathrm{Tm}^{3+}\)) and the considered UCL processes involving excitation with two, three, four and five photons (schematically shown by vertical upward going arrow lines); oblique dashed down-going arrow lines are shown paths of non-radiation relaxation. Pairs of vertical down-going arrow lines of the same color are showing component processes to be distinguished by our method. would justify the approach (5). Even if \(n_{j}\) actually depends on the pump power (which is often the case, as will be seen in Section 4), the factorization approximation (2) might still work, providing for satisfactory self-testing and validity of the method. Other self-testing features can be discerned from Equation (7). For \(n_{2}>n_{1}\), semi-positive definiteness of the inference results holds for  
\[n_{2}\geq n(\lambda,\text{I})\geq n_{1}. \tag{8}\]  
If assumptions (2, 3) are valid, Condition (8) is satisfied. Notice also that experimental imperfections and noise might lead to such point-to-point variations in the measured UC luminescence power as to make it necessary to apply smoothing procedures to the measured data to consistently estimate log-log derivatives [53].
###### Abstract  
Landing with the knee in a valgus position may alter energy absorption strategies in the lower extremities and increase mechanical stress on the knee joint. We compared the energy absorption strategies in the lower extremities during valgus and varus landings. Seventeen females were divided into valgus and varus groups. Lower extremity kinetic data were obtained during drop jumps, using a three-dimensional motion analysis system. Negative mechanical work in the lower extremities were calculated during landing. The valgus group exhibited significantly more negative mechanical work at the knee, and less negative mechanical work at the hip, compared with the varus group. However, there was no difference in the negative mechanical work at the ankle between the two groups. Findings suggest that an increased valgus landing reduces the contribution of the hip to energy absorption and is associated with a reciprocal increased contribution by the knee. Hence a knee valgus landing position may be a key biomechanical factor that increases energy absorption in the knee, thereby increasing the risk of injury. Results further indicate that this can be prevented by adopting a knee varus position on landing, which facilitates absorption of the mechanical load at the hip, rather than at the knee.  
n  
injury prevention; angular impulse; biomechanics; shock attenuation; injury prevention Article
## 3 Results
### Structural Elucidation of Major Compounds  
Using various chromatographic methods, 15 compounds were isolated from the fermentation culture of F. foetens AQF6. By applying spectroscopic analyses in comparison with the literature data, a new compound, FUS (**1**), and 14 known compounds, phenylacetic acid (**2**) [15], p-hydroxybenzaldehyde (**3**) [16], p-hydroxybenzeneacetic acid (**4**) [17], 2-(4-hydroxyphenyl)ethanol (**5**) [18], salicylic acid (**6**) [19], 2-hydroxybenzeneacetic acid (**7**) [20], indole-3-acetic acid (**8**) [21], 6,8-dihydroxy-3-methylisocoumarin (**9**) [22], 2-acetyl-1,4,5,7-tetrahydrooxy-9,10-anthraquinone (**10**) [23], daidzezin (**11**) [24], isoformonetin (**12**) [25], \(\beta\)-adenosine (**13**) [26], \(\beta\)-2\({}^{\prime}\)-deoxyadenosine (**14**) [26], and cyclo-Pro-Val (**15**) [27] were identified (Figure 2). Compound **1** was identified as a white and amorphous powder. Its molecular formula C\({}_{15}\)H\({}_{28}\)O\({}_{3}\) was deduced by a sodium adduct ion at \(m/\)z 279.1933 (calcd. for C\({}_{15}\)H\({}_{28}\)O\({}_{3}\)Na\({}^{+}\), 279.1931) in high-resolution electrospray ionization mass spectrometry (HRESIMS), corresponding to two degrees of unsaturation. Three methyl singlets at \(\delta_{\rm H}\) 1.69 (H-11), 1.25 (H-12), and 1.17 (H-14), one methyl doublet at \(\delta_{\rm H}\) 1.05 (\(J\) = 7.2 Hz, H-13), and one oxymethylene broad singlet at \(\delta_{\rm H}\) 3.94 (H-15) were also observed in the \({}^{1}\)H NMR spectrum (Figures S1 and S2). Analysis of the \({}^{13}\)C NMR and HSQC spectra indicated 15 carbon signals attributable to one trisubstituted double bond [\(\delta_{\rm C}\) 127.0 (C-9) and 135.8 (C-10)], four methyls, five methylenes [including one oxymethylene at \(\delta_{\rm C}\) 69.0 (C-15)], two methines, and two oxygen-bearing nonprotonated carbons at \(\delta_{\rm C}\) 82.0 (C-1) and 75.5 (C-3) (Figures S3 and S4). This, in combination with HMBC correlations from H-11 to C-9, C-10,
# UAV Path Optimization for Precision Agriculture Wireless Sensor Networks
## 3 Proposed Strategy
### UAV Flight Strategy
Figure 11: (**a**) strategy adopted by UAV for SNs reconfiguration phase; (**b**) strategy adopted by UAV after the SN reconfiguration phase. During the entire UAV journey between the SNs, the autonomy of its battery is continuously checked (step 6). If it gets below a critical level (as for instance 10% [34]), the UAV returns to the BS (step 20) and transfers its Prohibition List and pending SN list to a new UAV (step 21) that will take the route (step 22), returning to step 2 in the flowchart. After the reconfiguration phase (step 23), we present the flowchart in Figure 11b, where the UAV starts flying according to the SNs scheduled states for each timeslot (step 24). The UAV initially checks the current TS, searches for the next available SN (step 25), and consults its battery charge (step 26). If there is enough autonomy, the UAV then heads for the SN (step 27). If the SN is already in active mode (step 28), the UAV collects its sensory data, and shuts it down until next round (step 29), flying to the next SN (step 25). When the UAV arrives, if the SN is still hibernating (step 28), then the UAV waits on standby mode (step 33) until the SN becomes active again. Even so, after the SN reconfiguration phase, the UAV autonomy control is still checked (step 26), always taking into account that it can be replaced by another UAV, which will assume the data collection through steps 30-32 from the flowchart. For the sake of simplicity, and without loss of generality, we assume that (i) when each SN wakes up its data are ready to be gathered by the UAV (few bytes); (ii) the transmission time of this information is negligible compared to the defined timeslot duration; and (iii) the cumulative impact of these times can also be neglected when considering the average battery life, since the total collection would not exceed the unit of kilobytes of information.
# Detection of Respiratory Phases in a Breath Sound and Their Subsequent Utilization in a Diagnosis
## 2 Materials and Methods
### Signal Processing
#### 2.2.1 Basic Filtering  
The recording always contains a certain level of noise, and therefore the raw sound signal is very chaotic (Figure 3). Such a signal is much disorganized and moreover the searched respiratory phases and adventitious sounds are suppressed in the sound due to higher amplitude levels from the ambient noise.  
To reduce the level of noise in the raw sound signal, the signal is filtered using high-pass and low-pass filter based on finite impulse response (FIR) filter of 5 th order. The best results were achieved when the high-pass filter was set to 500 Hz and the low-pass filter was set to 1600 Hz. The setting of the high-pass filter helped to eliminate the muscle, heart and other low-frequency sounds. The setting of the low-pass filter was set to 1600 Hz in order to eliminate aliasing and increase the quality of the lower airway sounds. As was mentioned previously, the recordings contained some noise created by tapping or rubbing the sensor against the skin or hair. This noise is aperiodic and sudden of a length of tens of millisecond, with high amplitudes and wide frequency ranges, and therefore needs to be reduced by a different approach.
## Material and Methods
### Flow Cytometry  
For cell surface protein detection immunostaining and subsequent fluorescence quantification was performed. In brief, adherent cells were detached with versene (15040066, Gibco, Gaithersburg, MD, USA), a nonenzymatic cell dissociation reagent. The cell suspensions were spun at 700 rpm for 5 min, and the cells were resuspended in 200 uL 2% FCS in PBS (FACS buffer) solution per staining. Fc receptors were blocked with 1 uL human FcR blocking reagent (Miltenyi Biotec Gmbh, Bergisch Gladbach, Germany) for 10 min. Subsequently, saturating concentrations of conjugated specific or isotype control Abs were added and incubated for at least 15 min. Stained cells were washed with FACS buffer and resuspended in 400 uL FACS buffer for fluorescence detection. Cells were kept at 4 \({}^{\circ}\)C and in the dark throughout the staining. Protein expression was evaluated by APC or FITC channel intensity in a BD FACSCanto cytometer (BD, Franklin Lakes, NJ, USA). Protein expression was calculated as the difference of geometric mean fluorescence intensity (\(\Delta\)MFI) between specific Ab and isotype control staining. Cell surface detection of bound GP1-Fc fusion protein was detected as described before [50]. Briefly, adherent cells were detached with versene. 5 \(\times\) 10\({}^{5}\) cells/well were washed with 1\(\times\) DPBS containing Ca\({}^{2+}\) and Mg\({}^{2+}\) (14040133, Gibco, Gaithersburg, MD, USA) and Fc receptors blocked with human FcR blocking reagent. Subsequently, cells were incubated with 100 nM GP1-Fc or Fc for 1.5 h, washed with FACS Buffer\({}^{++}\) (2% FCS in PBS with Ca\({}^{2+}\)/Mg\({}^{2+}\)) and incubated with FITC-conjugated mouse Abs directed against human Fc fragment ab (1:200) for 45 min. Cells were washed twice with FACS Buffer\({}^{++}\) and fixed in 1 part FACS Buffer\({}^{++}\)/3 parts 3% PFA for 10 min. Cells were maintained at 4 \({}^{\circ}\)C throughout the processing. Bound protein was quantified by FITC channel intensity in a BD FACSCanto cytometer (BD, Franklin Lakes, NJ, USA) and subtracted from MFI of secondary Ab control. To monitor GFP, cells were detached with trypsin, washed with 1\(\times\) PBS and fixed with 3% PFA for 10 min at 4 \({}^{\circ}\)C and analyzed with a BD FACSCanto cytometer. Cell-cell fusion/transduction fluorescence quantification analysis was performed at the Hannover Medical School Cell Sorting Core Facility with a BD FACSAria Fusion (BD, Franklin Lakes, NJ, USA). Cells were prepared as described for GFP quantification. The gating strategy for heterokaryon detection and transduction is depicted in Figure S2. 10,000 events were recorded for single cell line controls, 50,000 for the PBS treatment control, and 10,000 ZsGreen1 positive events (heterokaryon cells) for PEG treatment. Data was analyzed with FlowJo7 software (Tree Star, Ashland, OR, USA).
# Energy Efficient Power Allocation for the Uplink of Distributed Massive MIMO Systems
## 1 Introduction
### Motivation and Contribution  
For massive MIMO systemS with circularly distributed antennas, higher achievable rates can be achieved with the increase of the transmit power and the number of antennas [26]. Meanwhile, the total power consumption increases as the increasement of power consumption of RF circuits and the transmit power. Inspired by this fact, we jointly optimized the transmit power, the power allocation coefficients, and the number of antennas to maximize the EE. The main contributions of this work are summarized as:  
* A tight EE expression is derived in closed-form for the distributed massive MIMO system with circular antenna array.
* To maximize the EE, an optimization problem was formulated to jointly determine the transmit power, power allocation, and the number of antennas. Further, the NP-hard problem is solved by one-dimension search.
* For a given number of antennas and the total transmit power, the optimal power allocation coefficients are derived in closed-form expressions.
* For a given number of antennas, the optimal total transmit power is derived in closed-form.
* To further maximize the EE, the number of transmit antennas are optimized by one-dimension search.
* Simulation results validate the accuracy and the low-complexity of our proposed scheme.  
The paper is organized as follows. Section 2 describes the system model. The EE are analyzed in Section 3. Section 4 formulates the joint optimization problem and proposes an optimal method with low complexity. Simulation results are provided in Section 5, and conclusions are drawn in Section 4.
# Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data
## 3 Methods
### Support Vector Machine Classifier  
SVM based on statistical learning theory (SLT) is one of the most popular learning algorithms for classification and has been intensively studied by [PERSON] et al. [59; 60; 61; 62]. Unlike traditional SLT, which is based on structural risk minimization (SRM), SVM is a new universal learning theory based on Empirical Risk Minimization (ERM). Given its excellent learning and generalization ability, SVM has been widely employed in many fields, such as financial forecasting, image processing, face detection, and handwritten digit recognition [63; 64; 65; 66; 67].  
The basic method of SVM classification can be generalized by the following two steps [64]:  
1. Map all input vectors to a high-dimensional feature space based on the kernel function, which is either linear or nonlinear.
2. Construct an optimal hyperplane based on support vectors to separate the data into two classes according to the theory of the least errors and maximal margin between the hyperplane and the closest training data. The training samples that are closest to the hyperplane are support vectors, whereas the others are uncorrelated for establishing the class divisions.  
We define \(\left(X_{1},\ y_{1}\right)\), \(\left(X_{2},\ y_{2}\right)\),..., \(\left(X_{n},y_{n}\right)\) as the input training samples, \(X_{i}\ \epsilon\ R^{n}\) as input vectors, and \(y_{i}\ \epsilon\ \left(-1,1\right)\) as the decision value of the training sample for each class. In terms of support vectors, the decision rule defined by an optimal hyperplane classifying the input data is given by the following equation:  
\[f\left(x\right)=sign\left(\sum_{i=1}^{n}y_{i}a_{i}K\left(X,X_{i}\right)+b\right) \tag{5}\]  
where \(X=\left(x_{1},x_{2},\ldots\ldots x_{n}\right)\) represents the input, the vectors \(X_{i}\ (i=1,2,\ldots\ldots\ N)\) are support vectors, \(a_{i}\) and \(b\) are specific parameters of the hyperplane, and \(K\left(X,X_{i}\right)\) is the kernel function used to construct machines with different types of decision surfaces in the feature space.  
The SVM has the ability to deal with multiple effective image features for optimal classification with a small training sample set [48; 68], which is an appropriate method to integrate textural and spectral features for lithological classification in this study.  
Figure 3: (**a**) Vegetation pixels in green with 0.4 < normalized difference vegetation index (\(NDVI\)) < 1.0; and (**b**) cloud pixels in white with 0.35 < reflectance of \(NIR<1.0\) in the study area.