esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Quantitative Results
### Mobility Divergences  
Our results indicate that mobility divergences, for both pedestrian and vehicular modes, are not highly recurrent among participants; only 37.9% (102/269) of the data had the same geode, 167 geodesics occurred once, 50 geodesics repeated twice, and 22 geodesics happened three times (Figure 5). This percentage means that the majority of divergences occurred only once. However, some divergences did occur multiple times, in particular, one repeated twenty-two times. This divergence occurred between a frequent street crossing and a local university, when people walked between these two points. Our inference from the data that were collected indicates that this divergence occurred so much due to the bus taking a more transited street instead of a shorter route, with the latter being the official route that it should have always followed. On average, each participant incurred in 41 divergences representing 8% of mobility traces, five out of 12 participants decided to take a route other than the route suggested by Google Maps most of the time with divergences above the average. In the _a-posteriori_ analysis (see Section 6), the participants explained that, when walking, they would frequently take routes different than those suggested by Google maps, as the suggestions would not consider certain factors, such as accessibility and security; they also indicated that the route in Google maps was not the most efficient, as there were often alternative informal routes that they knew and that did not appear in maps.
# Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction
## 2 Method and Model Establishment
### Frequency Band Feature Extraction Method of UAV Sound
#### 2.2.4 Cepstral Domain Analysis  
After processing through the filter bank, the output signals exhibit high inter-band correlations due to the filter features, necessitating conversion from the frequency domain to the cepstral domain. First, the filter bank outputs are transformed into logarithmic energy spectra using Equation (14).  
\[S(n)=\ln\left(\sum_{k=0}^{N-1}|x_{sft}(k,t)|^{2}H_{n}(k)\right),0\leq n\leq M \tag{14}\]  
where \(x_{sft}(k,t)\) is the STFT output signal, \(H_{n}(k)\) denotes the filter bank, \(M\) is the number of filters, and \(n\) is the index of the filters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index of** & **Frequency Band (Hz)** & **Weight** \\
**Band** & \(x_{i}\sim x_{i+1}\) & \(w_{i}\) \\ \hline
0 & \([0\,,100]\) & 0.1 \\
1 & \((100\,,3000]\) & 4 \\
2 & \((3000\,,6000]\) & 2 \\
3 & \((6000\,,12,000]\) & 1 \\
4 & \((12,000\,,24,000]\) & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Frequency band distribution and weight.  
The logarithmic energy spectrum \(S(n)\) is then transformed by a discrete cosine transform to convert the signal from the frequency domain to the cepstral domain. The expression of the discrete cosine transform is Equation (15).  
\[C(k)=\sqrt{\frac{2}{N}}\cdot\sum_{n=0}^{N-1}S(n)\cos\left[\frac{(n+\frac{1}{2})(k +\frac{1}{2})\pi}{N}\right],0<k<L+1 \tag{15}\]  
Here, \(k\) indexes the cepstral coefficients, \(L\) specifies the feature dimension, and \(C(k)\) forms a feature vector for each short-term frame.  
The sample size of each frame of the original signal after frame splitting is the same, and the feature vector with the feature number of 40 is obtained after the feature extraction algorithm. The feature vector corresponding to 301 consecutive frames is obtained by framing and feature extraction of the UAV sound for 3 s. All frames within 3 s are combined into a 2-D feature matrix of 301 \(\times\) 40. The UAV sound features with time dimension are formed.
# Multimodal Power Management Based on Decision Tree for Internet of Wearable Things Systems
## 4 Experimental Results
### Power Consumption and Energy Harvester Analysis  
Figure 7 shows the supercapacitor's voltage recovery after one transmission cycle. After BLE pairing, the system enters an active sensing state performing the 512 measurements in 20.48 s. It is observed that the BLE transmission causes a 100 mV voltage drop; however, the supercapacitor recovery is almost instantaneous when the system achieves sleep mode. The processing state corresponds to the ML-decision tree algorithm implemented on the MCU, which estimates the next sleep period \(\hat{T}_{sleep}\).  
The supercapacitor charging test shows a quick charge recovery in a few seconds (\(\approx\)2 s). This suggests what could be considered the minimum sleep period to allow for the supercapacitor charging. However, other factors, such as temperature and solar radiation, must also be considered. In this study, the power management analysis based on the ML-decision tree considers a dynamic sleep period range of 5 to 15 min to guarantee the operation.  
Figure 6: Experimental results: (**a**) Wearable electronic prototype with decision tree power management; and (**b**) Decision tree split result example.  
Knowing the PPG sensing current consumption and their corresponding periods for each state, the prototype power consumption (mAh) is the following  
\[Q=\frac{\Gamma}{\rho}\cdot(I_{PPG}T_{PPG}+I_{BLE}T_{BLE}+I_{Slep}T_{Slepep}+I_{ Proc}T_{Proc}), \tag{4}\]  
where \(I_{PPG}\) = 1.97 mA is the sensor average current consumption with a data acquisition period of 20.4 s, \(I_{BLE}\) uses an average of 0.45 mA to send data packets in \(T_{BLE}\) = 7.5 s, \(I_{Proc}\) = 0.42 mA is the consumption during ML-decision tree processing in \(T_{Proc}\) = 0.1 s, and \(I_{Sleep}\) = 34.3 \(\mu\)A is the average current consumption of the MCU in sleep mode for the sleep period \(T_{Sleep}\). Moreover, \(T_{PPG}\) is configured at 25 Hz for the total of 512 measurements at 20.48 s. Here, \(\Gamma\) represents the number of sensing and transmission sequences during a day. If \(T_{Sleep}\) is fixed (\(\Gamma\) = 93.096, \(\rho\) = 3600), the supercapacitor daily discharge is computed in \(Q\) = 1.933 mAh. This means that the entire IoWT system requires a total energy of 25.74 J per day to operate continuously, assuming a supercapacitor voltage of \(\mathrm{V}_{scap}\) = 3.7 V.  
The selected solar PV module has a value of \(P_{MPPT}\) = 380.8 mW at typical STC (i.e., 1000 W/m\({}^{2}\) and a cell temperature of 25 \({}^{\circ}\)C). Considering only 35% efficiency because the solar PV is being used as a wearable and assuming only five hours of irradiance at standard conditions (STC) [29], the energy generation is 99.72 J per day, which is 3.87 times the required energy of the system. Therefore, under the described conditions, the IoWT operation is guaranteed.
## 3 Results and Discussion
### Spectral Blueshift in the Ultraviolet Sidebands  
A spectral blueshift was observed for the anti-Stokes emission in the ultraviolet region when a hollow fiber was filled at pressures higher than 0.4 atm. As can be seen in Figure 5b, the wavelengths of the emission are continuously blueshifted with increasing gas pressure. This blueshift was not observed for the gas cell at pressures up to 2 atm. The blueshift can be explained in terms of the pulse chirp of each emission. In Figure 8, we calculate the group delay of each emission with respect to P2 for (a) the gas cell and (b) the hollow fiber under the propagation mode of EH\({}_{11}\) after the propagation length of \(z=80\) and 600 mm, respectively. The results suggest that the group delay of the high-order anti-Stokes emission in the hollow fiber (AS4-6) significantly increases with an increase in gas pressure. The anti-Stokes emission delayed with respect to the pump pulses would temporally overlap at the trailing edges of the pump pulses and the phase could be modulated via XPM induced by the pump pulses, resulting in a spectral blueshift. On the other hand, the group delay is small in the gas cell for all orders of the anti-Stokes emission. Thus, the group delay could have a negligible effect on the frequency of the anti-Stokes emission in the case of the gas cell, as shown in the experimental results of Figure 2a.  
The group delays of AS4, AS5, and AS6 exceed the pulse durations of the pump pulses of 50 fs at pressures higher than 1.2 atm in the case of the hollow fiber (Figure 8b). These delays make the interaction between pump pulses and the high-order anti-Stokes pulses difficult for the generation of  
Figure 8: Group delay calculated for each emission against that of P2 for the gas cell (**a**) and the hollow fiber (**b**) at different gas pressures.  
higher-order anti-Stokes emission via FWM. For this reason, combinations of frequency components with small phase mismatches for FWM should be effective for the generation of high-order anti-Stokes emission at high pressures for the hollow fiber (Figure 5f).
# Analysis of All-Optical Generation of Graphene Surface Plasmons by a Frequency-Difference Process
## 3 Results and Discussion
### How to Detect the Optically Generated SPs?
The advantage of detecting the SP flux using either of these approaches, instead of relying on the plasmon-assisted down-conversion of probe photons is that the variation is a second-order process with respect to the optical fields, consequently, it should require lower intensities of the optical beams.
## 5 Materials and Method  
### Chemicals, Standards and Materials  
Microcystin standards (MC-LR, MC-RR and MC-YR) with purities \(\geq\)95% were purchased from Alexis Biochemicals (Lausen, Switzerland). The stock solution of three MC congeners was prepared in 1 mL of methanol solution at a concentration of 100 mg/L for each variant, which was kept at \(-20\)\({}^{\circ}\)C for later use. The C18 solid phase extraction (SPE) cartridges (500 mg, 6 mL) used in MCs analysis were obtained from Anpel Company (Shanghai, China). The standard compounds GEO, MIB and 2-isobutyl-3-methoxypyrazine (IBMP, as the internal standard) were purchased from Sigma-Aldrich Chemical Co. (St. Louis, MO, USA) with the concentration of 100 mg/L in methanol, while CYC and ION with purities \(\geq\)97% were purchased from Adamas Reagent Co., Ltd. (Basel, Switzerland). The stock solution of 1 mg/L with four target T&O compounds prepared in methanol was stored in the dark at 4 \({}^{\circ}\)C. Sodium chloride (NaCl, Sigma, St. Louis, MO, USA) was added to the samples to enhance the T&O compounds extraction from water.  
Methanol, trifluoracetic acid, acetic acid, acetonitrile and acetone were of HPLC grade from Tedia Company (Fairfield, OH, USA). Water used throughout the work was from a Milli-Q water purification system (Millipore, Billerica, MA, USA). All other reagents used were of analytical grade or better.
## 2 Data and Methods
### Flux Footprint Modeling  
The flux footprint prediction (FFP) model was used to compute the footprint of EC sites in this study. The FFP model is a dimensionalized parameterization of a backward Lagrangian stochastic dispersion model and was applied to a wide range of boundary layer stratifications and measurement heights. The input variables for the FFP model were: wind direction (WD), friction velocity (u\({}_{*}\)), wind speed (v), Obukhov length (L), instrument height (Z\({}_{\text{m}}\)) and canopy height (h\({}_{\text{c}}\)). The FFP model was able to provide results on size, position, and contour footprint climatology. More detailed information about footprint climatology calculation is available in [34]. The Obukhov length, canopy height, and instrument height were not directly provided in the FLUXNET2015 dataset. Obukhov length was therefore derived from a simple formula using the Monin-Obukhov similarity theory [35]. References for the canopy and instrument heights used here can be found in the references (Table S1).  
Figure 1: The spatial distribution map of the 78 FLUXNET2015 tower sites used in this study.  
The footprint calculations were conducted at half-hour time steps and aggregated to the 16-days resolution to generate two-dimensional contour lines of the footprint centered on the site. The footprints at the 80% contour of source weights were enough to truncate for analysis due to the extra 20% contour of footprints increasing with upwind distance from the receptor [20; 36]. We estimated the flux footprint climatology with a grid size of 30 m spatial resolution at 16-day intervals to keep consistence with time step and spatial resolution from Landsat NDVI.
## 3 Results
### Interference-Dependent Fluorescence  
To use AcTDA as a glucose probe, it needs to demonstrate high specificity for glucose over other interferences, such as six-carbon sugars and common drugs. Therefore, we measured the fluorescence changes of AcTDA with six different saccharides, including fructose, galactose, lactose, maltose, mannitol, and sucrose, and the most common drugs.  
For the drug ibuprofen, AcTDA did not show a meaningful fluorescence increase in the presence of maltose, sucrose, lactose, or ibuprofen (data not shown). Figure 7 shows  
Figure 5: Glucose-binding properties of TDA. (**a**) Emission spectra. (**b**) Normalized emission spectra. Fluorescence intensity at the maximum wavelength was used for the normalization. (**c**) Relative fluorescence intensity. (**d**) Benesi-Hildebrand plot.  
Figure 6: Gradient plot of integrated fluorescence intensity against absorbance for (**a**) AcTDA and fluorescein and (**b**) TDA and fluorescein.  
that the fluorescence increased when AcTDA was in the presence of fructose, galactose, and mannitol. The association constants for fructose and galactose were determined using the modified Benesi-Hildebrand plots (Figure S2). However, the modified Benesi-Hildebrand plot for AcTDA and mannitol did not exhibit a good fit (Figure S2). Therefore, we derived a plot to fit the fluorescence changes in response to mannitol (Figure S3) and obtained 213 \(\pm\) 4.5 M\({}^{-1}\) as the association constant. The association constants decreased in magnitude in the following order: mannitol (213 \(\pm\) 4.5 M\({}^{-1}\)) \(>\) fructose (164 \(\pm\) 3.1 M\({}^{-1}\)) \(>\) galactose (55 \(\pm\) 6.1 M\({}^{-1}\)). The association constant for glucose was at least 3.6-fold higher than those of the interfering saccharides. As the blood interference level (<0.1 mM) is much lower than that of glucose (3.6\(-\)5.8 mM), the quantity of glucose-bound AcTDA would be 123-fold higher than that of the interference-bound AcTDA [31]. Therefore, the interference could be considered negligible.  
Figure 7: Binding properties of AcTDA with interferences. (**a**,**c**,**e**) Emission spectra for AcTDA with fructose, galactose, and mannitol, respectively. (**b**,**d**,**f**) Relative fluorescence intensity with fructose, galactose, and mannitol, respectively.
# Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems
## 2 System Model
### 5G Pilot Structure  
In 5G wireless communication systems, the demodulation reference signals (DM-RS) are used as pilots to facilitate channel estimation. DM-RS signals are generated based on a sequence defined in the 3 GPP standard [36] as  
\[r(n)=\frac{1}{\sqrt{2}}[1-2c(2n)]+j\frac{1}{\sqrt{2}}[1-2c(2n+1)], \tag{11}\]where \(c(i)\) is the pseudo-random sequence and is defined by a length-31 Gold sequence as [36]  
\[c(n) = [x_{1}(n+1600)+x_{2}(n+1600)]mod2 \tag{12}\] \[x_{1}(n+31) = [x_{1}(n+3)+x_{1}(n)]mod2\] (13) \[x_{2}(n+31) = [x_{2}(n+3)+x_{2}(n+2)+x_{2}(n+1)+x_{2}(n)]mod2, \tag{14}\]  
where _mod_ is the modulo operator, and the 31-first sequence \(x_{1}(n)\) and \(x_{2}(n)\) are initialized as  
\[x_{1}(n) = \begin{cases}1,&n=0\\ 0,&n=1,2,\cdots,30\end{cases} \tag{15}\] \[c_{init} = \sum_{n=0}^{30}x_{2}(n)2^{n}. \tag{16}\]  
In the initialization of sequence \(x_{2}(n)\), the value of \(c_{init}\) depends on the application of the sequence \(c(n)\). In the channel estimation application, the value of \(c_{init}\) is calculated as [36]  
\[c_{init}=[2^{17}(N_{symb}^{slot}n_{s,f}+l+1)(2N_{IDSCID}^{n}+1)+2N_{ID}^{n_{SCID }}+n_{SCID}]mod2^{31}, \tag{17}\]  
where \(N_{symb}^{slot}=14\) is the number of OFDM symbols in slot 1, \(n_{s,f}=10\) is the number of slots in frame 1, and \(l\) is the OFDM symbol index. The parameters \(N_{ID}^{0},N_{ID}^{1}\in\{0,1,\cdots,65535\}\) and \(n_{SCID}\in\{0,1\}\) are the parameters of the 5G system. In our paper, for simplicity, we set these parameters equal to zero.  
The pilot signals are then mapped according to the pilot structure defined in [36]. In 5G systems, the pilots are arranged in a comb type across transmission antennas, as illustrated in Figure 4. The pilot symbols are uniformly spaced in the time domain, denoted by \(D_{t}\), and in the frequency domain, denoted by \(D_{f}\). The values of \(D_{t}\) and \(D_{f}\) depend on the different use cases of a 5G system, which are defined explicitly in, for example, [37]. Among transmission antennas, pilot signals are arranged in an alternating way. By applying this design of a pilot pattern into our paper, the pilot signal in each OFDM symbol is calculated as  
\[x_{p}(k) = r(n) \tag{18}\] \[k = D_{f}n+\Delta\] (19) \[n = 0,1,\cdots N_{P}, \tag{20}\]  
where \(k\) denotes the subcarrier index, \(N_{P}=N_{FFT}/D_{f}\) is the number of pilot signals in an OFDM symbol, and \(\Delta\) defines the pilot position in the frequency domain for each transmission antenna, the value of which can be found in Table 7.4.1.1.2-1 in [36].
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Genetic Approaches in Identification of mTOR Signaling Components
#### 4.2.1 Genetic Association of TSC with TOR Was Discovered in Drosophila  
Drosophila with homozygous _Tsc1_ deletion died after hatching into first-instr larva, which could be partially rescued by loss of one copy of the _dTOR_ gene [63]. Observations that _Tsc1_, _dTOR_, and double mutant cells are 1.9, 0.26, and 0.25 times the size of wild-type cells established an epistatic relationship that TOR functions downstream or in parallel to TSC2/TSC1 signaling in controlling cell size [63]. Further, a specific interaction between ectopically expressed TSC2 and dTOR was observed in Drosophila S2 cells [63].
# Reinforcement Learning Based Resource Management for Network Slicing
## 6 Performance Evaluation
### Impact of the Trading Interval on the Algorithm Performance  
Generally, setting a short trading interval results in an increase of the computational cost. The short trading interval may affect controller overhead in the case when tenants request a very dynamic resource demand. Because the computational resource is unlimited, the trading interval should be long enough. Otherwise, the result of the resource trading may fluctuate severely, making a negative impact on the resource allocation performance. For these reasons, we conducted a simulation to assess how the profit and QoS change for different values of the trading interval \(h\).  
Figure 5 illustrates the comparison of the tenant profit achieved by the five algorithms. The static algorithms, i.e., _half-of-demand_ and _fixed-resource_, are not affected by the changes in the trading interval. The tenant has to pay for the resources that end-users do not actually use since the requested resources are statically provisioned. Therefore, there is no way to make an extra income from the unused resources resulting in a generally low profit. The dynamic strategies do not need to exclusively reserve resources every time, and they can trade the unused or additional resources at the next trading interval. As the trading interval increased, the _previous-slot-demand_ algorithm demonstrated a slightly decreasing profit because it did not consider future information, whereas the proposed algorithm was scarcely affected by the changes in the trading interval and earned the highest profit compared to the other algorithms.  
Figure 6 demonstrates that the proposed algorithm kept a very high QoS satisfaction rate because it allowed purchasing the required bandwidth in advance. This is the result of taking optimal actions according to the states for every \(t\) on the basis of the state-action pairs, i.e., the Q-values. On the other hand, the QoS satisfaction rate achieved by _previous-slot-demand_ strategy decreased with the increasing trading interval. Similar to the previously stated result for this strategy, this indicates that the algorithm only focuses on the demand at the previous trading interval without considering the upcoming demand. The obtained results show that our algorithm works well even with a large trading interval to avoid the control overhead due to dynamic resource trading.  
Figure 5: Profit with respect to the trading interval.
# ICN-Oriented Mobility Support Method for Dynamic Allocation of Mobile Data Flows
## 3 How Can Dynamic Allocation Flows Be Realized in Mobility Support?
### Late-Binding Node Selection Problem Statement
#### 3.3.1 Model Description  
We built a simple late-binding node selection problem model. As shown in Figure 2, it consists of a producer who has published \(F\) content, a mobile consumer, and \(S\) routers. Suppose that consumers request \(F\) content from the same producer and that \(F\) data flows will be generated between producers and consumers. In our solution, as described in Section 3.2, we can select the LBN in the router in the original path between the producer and the consumer by propagating MPNs and MPUs, thus intercepting the packets destined for the consumer, modifying their destination addresses, and forwarding them to the latest location of the consumer. The problem of late-binding node selection can be simplified to assigning a group of LBNs to a group of routers, taking into account the available bandwidth resources of the switching path and the space resources of the router's MFT. A good late-binding node selection strategy can minimize packet transmission delay between producers and consumers.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(Z_{i}^{j}\) & Boolean variable; \(Z_{i}^{j}\)= 1 if router \(i\) is late-binding node for flow \(j\), \(Z_{i}^{j}\) = 0 otherwise \\ \(D_{i}^{j}\) & Transmission delay of a packet from flow \(j\) when router \(i\) is the late-binding node \\ \(D_{i}^{j}\) & Packet transmission delay from the source to the late-binding node in flow \(j\) using \\  & late-binding node \(i\) \\ \(D_{id}^{j}\) & Packet transmission delay from late-binding node \(i\) to the destination \\ \(\mathrm{B}_{i}^{j}\) & Available path bandwidth from late-binding node \(i\) to the destination \\ \(b_{i}^{j}\) & Path bandwidth required for transport flow \(j\) from late-binding node \(i\) to the destination \\ \(N_{tmax}^{j}\) & Maximum number of entries in the MFT allowed for router \(i\) \\ \(n_{i}^{j}\) & Number of MFT entries in late-binding node \(i\) occupied by flow \(j\), default is 1 \\ \(N_{i-pre}\) & Number of entries in the MFT of router \(i\) before selecting the late-binding node \\ \(N_{i-max}\) & Number of entries in the MFT of router \(i\) after selecting the late-binding node \\ \(N_{i}\) & Number of available entries in the MFT of router \(i\) \\ \(S\) & Number of nodes available in the network \\ \(F\) & Number of flows generated between the producer and consumer \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of notations.  
Figure 2: Late-binding node selection problem model.