esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Nitrate Reductase Activity  
The nitrate content in plants usually depends on the activity of two reducing enzymes: nitrate reductase (NR) and nitrite reductase (NiR). It was found that both enzymes are induced by the presence of nitrate ions in the environment [70; 71; 72; 73], and light is necessary to obtain maximum activity [44; 74]. Plants grown in low-light conditions tend to have low levels of nitrate reductase. Notably, leaf nitrate reductase activity in the daily cycle often decreases as the day progresses, which may reflect its degradation and/or synthesis blockade [75]. Thanks to NR, inorganic forms of nitrogen are effectively incorporated into organic compounds [76].  
In the case of dill, celery, parsley, and beet, the enzyme activity in conventionally grown vegetables was significantly higher than in organic vegetables [Figure 1B], as shown: dill (local market--71.5 nmoles NO\({}_{2}\)\(\times\) h\({}^{-1}\)\(\times\) g\({}^{-1}\) FW; organic food store--13.38 nmoles NO\({}_{2}\)\({}^{-}\)\(\times\) h\({}^{-1}\)\(\times\) g\({}^{-1}\) FW; organic food store--12.83 nmoles NO\({}_{2}\)\({}^{-}\)\(\times\) h\({}^{-1}\)\(\times\) g\({}^{-1}\) FW), parsley (local market--138.15 nmoles NO\({}_{2}\)\({}^{-}\)\(\times\) h\({}^{-1}\)\(\times\) g\({}^{-1}\) FW; organic food store--9.88 nmoles NO\({}_{2}\)\({}^{-}\)\(\times\) h\({}^{-1}\)\(\times\) g\({}^{-1}\) FW), beet (local market--37.56 nmoles NO\({}_{2}\)\({}^{-}\)\(\times\) h\({}^{-1}\)\(\times\) g\({}^{-1}\) FW; organic food store--11.58 nmoles NO\({}_{2}\)\({}^{-}\)\(\times\) h\({}^{-1}\)\(\times\) g\({}^{-1}\) FW). This enzyme is considered an adaptive enzyme, so its lower activity in organic vegetables, where there should be fewer nitrates, is understandable. Only carrots and onions from an organic farm were characterised by higher enzyme activity than conventional farming vegetables. What is extremely surprising is the very high NR activity concerning the amount of determined nitrates in the case of onions. The literature data indicate that NR gene expression may also occur after depleting nitrogen sources [72]. The presence of nitrates is not an absolute condition for increased expression of the NR gene. Some available data indicate that tiny amounts of nitrate are sufficient to induce the enzyme [71; 72]. Moreover, it is known that in the daily cycle, nitrate reductase activity in leaves often decreases as the day progresses, which may reflect its degradation and/or blocking of its synthesis [75].  
The negative correlation between nitrate reductase activity and nitrate levels is presented in Figure 2. A linear correlation coefficient value close to 0 (\(r=-0.0182\)) does not always mean the absence of any correlation, only the lack of linear correlation [Table 2]. The most important thing is the significance of the correlation, which in this case is not significant (\(p=0.0892\)).  
Figure 2: The relationship between nitrate accumulation and nitrate reductase activity.
## 4 Robust Geometries against Fabrication Errors
### General Observations
#### 4.2.2 For Thicknesses Larger than 320 nm  
Figure 4a shows that when \(e>320\) nm, the slope for the fundamental mode wavelength is larger. Thus, if the thickness increases, \(\lambda_{SH}\) becomes smaller than \(\lambda_{f}\), and to fulfill the energy conservation again, the radius must be decreased. This is what is shown in Figure 2b: after \(e=320\) nm, \(R_{in}\) becomes smaller. This time, the increase in the thickness and the decrease in the radius have opposite effects on the wavelength, which fulfill the SHG conditions. Till \(e=390\) nm, the increase in the thickness is large enough to increase the wavelength as well. For a larger thickness, the variation of the wavelength with the thickness is too small to compensate for the radius decrease, and thus, the wavelength decreases when \(e\) increases.  
Figure 4: Resonant wavelengths for modes (\(m=400\), \(Hz\) polarization, \(p=1\)) around \(2.3\)\(\upmu\)m (labeled as F) and (\(m=802\), \(Ez\) polarization, \(p=3\)) around \(1.15\)\(\upmu\)m (labeled as SH) in a Si microring of width \(w=800\) nm cladded by \(Si_{3}N_{4}\) with respect to (**a**) the thickness \(e\) and (**b**) the internal radius \(R_{in}\). Please note that the notations F and SH are chosen for the sake of simplicity but really correspond to fundamental and SH only at the crossing points. The colors are the same as in Figure 2b to make the correspondence easy, and an example of correspondence is given with points A and B.
###### Abstract  
A smart campus is an intelligent infrastructure where smart sensors and actuators collaborate to collect information and interact with the machines, tools, and users of a university campus. As in a smart city, a smart campus represents a challenging scenario for Internet of Things (IoT) networks, especially in terms of cost, coverage, availability, latency, power consumption, and scalability. The technologies employed so far to cope with such a scenario are not yet able to manage simultaneously all the previously mentioned demanding requirements. Nevertheless, recent paradigms such as fog computing, which extends cloud computing to the edge of a network, make possible low-latency and location-aware IoT applications. Moreover, technologies such as Low-Power Wide-Area Networks (LPWANs) have emerged as a promising solution to provide low-cost and low-power consumption connectivity to nodes spread throughout a wide area. Specifically, the Long-Range Wide-Area Network (LoRaWAN) standard is one of the most recent developments, receiving attention both from industry and academia. In this article, the use of a LoRaWAN fog computing-based architecture is proposed for providing connectivity to IoT nodes deployed in a campus of the University of A Coruna (UDC), Spain. To validate the proposed system, the smart campus has been recreated realistically through an in-house developed 3D Ray-Launching radio-planning simulator that is able to take into consideration even small details, such as traffic lights, vehicles, people, buildings, urban furniture, or vegetation. The developed tool can provide accurate radio propagation estimations within the smart campus scenario in terms of coverage, capacity, and energy efficiency of the network. The results obtained with the planning simulator can then be compared with empirical measurements to assess the operating conditions and the system accuracy. Specifically, this article presents experiments that show the accurate results obtained by the planning simulator in the largest scenario ever built for it (a campus that covers an area of 26,000 m\({}^{2}\)), which are corroborated with empirical measurements. Then, how the tool can be used to design the deployment of LoRaWAN infrastructure for three smart campus outdoor applications is explained: a mobility pattern detection system, a smart irrigation solution, and a smart traffic-monitoring deployment.  
Consequently, the presented results provide guidelines to smart campus designers and developers, and for easing LoRaWAN network deployment and research in other smart campuses and large environments such as smart cities.  
IoT; smart campus; sustainability; fog computing; outdoor applications; LPWAN; LoRaWAN; 3D Ray-Launching; smart cities; Wireless Sensor Networks (WSN)
## 3 Results
### Results of the Biofilm Production  
#### 3.2.1 Biofilm Production in M9 Minimal Medium  
All clinical isolates successfully formed biofilms in minimal M9, at 8 \({}^{\circ}\)C and 16 \({}^{\circ}\)C, but the strength of those biofilms differed significantly both in the number of viable cells (log CFU) and in the production of biomass (ODC in crystal violet staining). The strongest-detected biofilm produced by clinical strains in M9 at 8 \({}^{\circ}\)C was made by strain F187, with a 5.41 log CFU (ODc value: 0.0053), and the weakest-detected biofilm was made by F710, with a 3958 detected log CFU (ODc value: 0.0055). Also, the strains reacted differently to the increase in temperature from 8 to 16 degrees. Most of the isolates decreased biofilm production between 1.99% and 19.75% at 16 \({}^{\circ}\)C in comparison to 8 \({}^{\circ}\)C (\(p<0.05\)) (Table S1, Supplemental Materials). Only two strains (F410 and F710) increased biofilms production by 5.85% and 7.37%, respectively, at 16 \({}^{\circ}\)C in comparison to 8 \({}^{\circ}\)C (\(p<0.05\)). The largest number of strains, a total of 9, could not produce any biofilm at 25 degrees in the M9 minimal medium, that is, no viable cells were detected in the biomass (Table S1, 8 \({}^{\circ}\)C vs. 25 \({}^{\circ}\)C and 16 \({}^{\circ}\)C vs. 25 \({}^{\circ}\)C, \(p=0.0001\)). Only ATCC 7965 and F410 were capable of forming biofilms at 25 \({}^{\circ}\)C in M9. The complete results of this section are presented in Figure 3 and Supplemental Table S1.  
Figure 3: Biofilm production of clinical _A. salmonicida_ subsp. _salmonicida_ in minimal M9 after 7 days. Legend: ODc (columns)—measured optical density of formed biofilms; log CFU (lines)—number of viable bacteria in biofilms; F203, F262, F303, F352, F410, F458, F461, F710, FN1413, FN187—clinical isolates. The isolates are arranged in order from the strongest (left) to the weakest (right) biofilm producer (based on log CFU obtained at 8 \({}^{\circ}\)C).  
The ODc values (biomass) detected in biofilms at 8 \({}^{\circ}\)C and 16 \({}^{\circ}\)C were very low, ranging from 0.0017 to 0.0375, which is not in correspondence with a relatively high number of viable cells detected in biofilms. However, ODc values were also detected at 25 \({}^{\circ}\)C (ranging from 0.0059 to 0.0133), but given that at 25 \({}^{\circ}\)C, no viable cells were detected within biofilms in any repetition, these results obtained from the crystal violet method cannot be interpreted as an active (living) biofilm but as a deposit of dead cells or deposited products of planktonic cells (Table S1).
## 3 Flight Control System Health Assessment under Uncertainty
### Flight Control System Health Assessment Process under Uncertain Conditions  
All in all, as the input characteristic parameters have an unknown distribution, the uncertainty propagation analysis process of the health assessment is as follows in Figure 3:  
1. **Raw data preprocessing**: obtain the first four central moments' analytical form of characteristic parameters; meanwhile, obtain the mean, \(\mu\), variance, \(\sigma^{2}\), skewness, \(C_{s}\) and kurtosis, \(C_{k}\), of the raw flight data;
2. **Uncertainty quantification of input parameters:** according to the distribution of each feature parameter, choose the first or second \(\lambda-PDF\) expansion method to quantify their PDF;
3. **Uncertainty propagation based on MCS**: According to the PDF of each characteristic parameter, generate random numbers with the same distribution as the input of the flight control system health assessment model under uncertain conditions, and introduce them into the model mentioned in Section 2 to obtain multiple health indices;
4. Solve the PDF of the system health index via the maximum entropy.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques
### Time Difference of Arrival (TDoA)  
The TDoA measurements estimates the time lapse between the receiving node and the two signals received from two different transmitting nodes or the two different signals from the same transmitting node, as shown in Figure 2b. For instance, the \"Bat\" [31] and \"Cricket\" [32] utilize the RF signal for time synchronization between the transmitting and receiving nodes and the ultrasound signal for ranging. Furthermore, the \"BeepBeep\" [33] utilizes two devices equipped with microphones and speakers. Specifically, a radio signal is first sent from the transmitting device, followed by an acoustic pulse after a certain period of time. The receiving node records the radio signal timestamp and enables its microphone for detecting the acoustic pulse and recording its timestamp. The TDoA between the two pulses is used by the receiving node for estimating its distance from the transmitting node. In general, compared to ToA, the TDoA only requires time synchronization between the transmitting nodes and can provide higher positioning accuracy. However, this only applies to certain environments with dense transmitting nodes' deployments; therefore, the system's cost can be high [26]. The propagation distance difference \(\Delta d_{ij}\) at the receiving node side can be estimated as follows [26]:  
\[\Delta d_{ij}=d_{i}-d_{j}=v(t_{i}-t_{0})-v(t_{j}-t_{0})=v(t_{i}-t_{j})=v\Delta t _{i,j}, \tag{2}\]  
where \(i,j\) are the different transmitting nodes, or different signals transmitted from the same node, and \(v\) the velocity of the signal.
# Blending Sea Surface Winds from the HY-2 Satellite Scatterometers Based on a 2D-Var Method
## 2 Data and Methods
### 2D-Var Method
On the other hand, two different background error correlation functions (as shown in Figure 2), namely the Gaussian structure function (GSF) and the empirical background error correlation (EBEC), are evaluated in order to figure out an appropriate 2D-Var implementation. The exact formulas of GSF and EBEC can be found in refs. [21; 22], such that they are not reproduced here in order to keep the presentation concise. Note that the GSF is with different length scales for the Tropics (solid thin curve, \(L_{\chi}=L_{\psi}=212\) km) and the extratropics (solid bold curve, \(L_{\chi}=L_{\psi}=424\) km), while the EBEC is with the same length scales (\(L_{\chi}=494\) km, \(L_{\psi}=552\) km) over the global region.
# A Vision of 6 th Generation of Fixed Networks (F6G): Challenges and Proposed Directions
###### Abstract
These requirements will aid in defining the research objectives and the outstanding challenges and proposing stratagems to aid in successfully fulfilling them in a cost- and energy-efficient way. Next, based on these requirements, we elaborate our vision in three directions: (a) Discuss state-of-the-art transceivers along with novel techniques to scale the bit rate flexibly up to 2 Tb/s (Section 3); (b) propose and elaborate on different options to enhance the transportation capacity and node connectivity as well as analyze the interplay between transported capacity and transparent reach (Section 4); and (c) analyze optical switching in the F6G era by proposing alternatives to satisfy ultra-high capacity and ultra-low latency demands, while enabling scalability from operating down to a fraction of the spectrum of a channel up to the full spectral capacity of the optical fiber, whereby channels fully occupy the available bands (Section 5). The presentation of our vision in these three directions will highlight the key challenges of F6G networks and explore solutions to address them at various levels of the network, as well as use prior knowledge to make informed predictions up to 2030 about the foreseeable evolution of key metrics such as attainable per-fiber capacity, data rate per-channel, etc. For each of the three directions, a thorough literature review is performed while the pros and cons of important published research works are highlighted and elaborated in order to designate what it is needed in order to seamlessly support the F6G services. This vision of the article is organized as follows. In Section 2, the key services of the F6G ecosystem along with their requirements are analyzed. Next, in Section 3, a thorough examination of the literature on optical transceivers is performed. Next, all-optical processing is addressed, proposing concrete steps for how 'to get there' in light of the pros and cons of ADCs and DACs. In Section 4, the barriers to optical transmission are examined, emphasizing how to push F6G capacity even higher, considering methodologies to attain spectrally efficient modulation formats, increased channel counts, and opening up additional transmission degrees-of-freedom by means of space division multiplexing (SDM) via the use of extra fibers, modes, or cores. Based on the relevant literature, we also pro vide predictions about the transported capacity per channel and per single mode fiber up to 2030. In Section 5, a thorough examination of the literature on optical switching is performed. Subsequently, the issue of optical switching is investigated regarding the needs of the F6G ecosystem for both cases of switching, namely ultra-fast vs. ultra-high capacity switching, each one tailored to different performance criteria. Finally, a summary of the key findings is provided in Section 6.
# Tensor-Based Approaches for Nonlinear and Multilinear Systems Modeling and Identification
## 6 Tensor-Based Approaches for Multilinear Systems
### Multilinear TISO Systems  
In the case of a memoryless tensor-input single-output (TISO) system, let us assume that the system transfer is a rank-one tensor \(\mathcal{A}\in\mathbb{R}^{I_{N}}\) written as  
\[\mathcal{A}=\overset{N}{\underset{n=1}{\mathrm{o}}}\mathbf{h}^{(n)}\iff a_{ \hat{l}_{N}}=\prod_{n=1}^{N}h_{j_{n}}^{(n)}, \tag{89}\]  
with \(\mathbf{h}^{(n)}\in\mathbb{R}^{I_{n}}\), for \(n\in\langle N\rangle\). The model output (82) is then given by  
\[\hat{y}(t)= \big{(}\overset{N}{\underset{n=1}{\mathrm{o}}}\mathbf{h}^{(n)} \big{)}\star_{N}\mathcal{X}(t)\] \[= \sum_{j_{1}=1}^{I_{1}}\cdots\sum_{j_{N}=1}^{J_{N}}\Big{(}\prod_{n= 1}^{N}h_{j_{n}}^{(n)}\Big{)}x_{\hat{l}_{N}}(t)\] \[= \big{(}\sum_{j_{1}=1}^{I_{1}}h_{j_{1}}^{(1)}x_{j_{1},\cdots,j_{N} }(t)\big{)}\cdots\big{(}\sum_{j_{N}=1}^{J_{N}}h_{j_{N}}^{(N)}x_{j_{1},\cdots,j_ {N}}(t)\big{)} \tag{90}\]  
or using the index convention  
\[\hat{y}(t)=\prod_{n=1}^{N}\Big{(}h_{j_{n}}^{(n)}x_{\hat{l}_{N}}(t)\Big{)} \tag{91}\]  
or equivalently  
\[\hat{y}(t)= \Big{(}\mathcal{X}(t)\times_{1}(\mathbf{h}^{(1)})^{T}\Big{)} \cdots\Big{(}\mathcal{X}(t)\times_{N}(\mathbf{h}^{(N)})^{T}\Big{)}\] \[= \mathcal{X}(t)\overset{N}{\underset{n=1}{\mathrm{o}}}(\mathbf{h}^ {(n)})^{T}. \tag{92}\]  
The resulting system output is multilinear (\(N\)-linear) with respect to the vector factors \(\mathbf{h}^{(n)}\). Each vector can be interpreted as the impulse response (IR) of length \(J_{n}\) of the subsystem associated to the \(n\)th mode of the input tensor \(\mathcal{X}(t)\). The output signal \(\hat{y}(t)\) is therefore a multilinear form in the \(N\) individual IR vectors (see Table 7).
## 3 Results
### Mid-Term Outcomes  
At 18 months after surgery, harvest site pain was present in three patients in each group. On the scale of 0 to 3, the mean pain intensity was 1.3 in the PRP group vs. 2.3 in the no-PRP group. Results of dynamic testing with maximum score of 2 points were 1.98 points in the PRP group vs. 1.8 points in the no-PRP group. For the KOOS scale, the results were as following: for the PRP group vs. the no-PRP group, respectively: in the Symptoms section 92 vs. 90; in the Pain section 93 vs. 89; in the Function, daily living section 94 vs. 92; in the Function, sport activities section 90 vs. 88; in the Quality of life section 83 vs. 85. As for the IKDC scale, 53 patients in the PRP group graded A or B and 1 patient graded C. In the no-PRP group, 48 patients graded A or B and 4 patients graded C or D. This difference was not significant (\(p\) = 0.201). The mean side-to-side difference in anterior tibial translation during the stability test performed with the use of the arthrometer (Rolimeter, Aircast Europa) was 1.3 mm in PRP group vs. 2.7 mm in no-PRP group. Tibial tunnel widening at 18 months after the surgery was present in 23 out of 54 patients in the PRP group (42.6%), compared with 28 out of 52 patients in the no-PRP group (53.8%), \(p\) = 0.246. Mean widening in the PRP group was 1.4 mm vs. 2.1 mm in the no-PRP group (Table 2).
## 3 Seismic Behavior of Micropiles
### Factors of Influence
#### 3.2.3 Factors Related to the MP-to-Structure Connection  
The usual approach when problems involving MPs are analyzed through numerical methods is to model the connection between the MP top and the pile cap as perfectly fixed or pinned; this approach is the same followed with other type of piles, such as bored piles (type-A MPs is the closest type of MPs family to bored piles). However, MP connection-to-structure is more complex due to the installation process and MP structural components. Neglecting this complexity can be beneficial in terms of seismic behavior, since it undergoes high potential of plasticization and nonlinearity during seismic events, which in turn can be a source for increasing the damping value.  
As an example, the influence of MP-to-structure connection was studied numerically by [PERSON] et al. [12], more precisely the influence of fixity condition: fully fixed or fully pinned. The authors concluded that the dynamic amplification factors for both the pile-cap and the superstructure are strongly dependent on these fixity conditions. Again, in real practice, MP-to-structure connection is neither fully fixed nor fully pinned, but commonly assumed to simplify both the design and analysis [12].  
More advanced numerical and analytical methods might be necessary in estimating the stiffness of the connection to give a precise estimation in the numerical model and to account the nonlinearity of the behavior, which is a topic that has not been thoroughly approached for MPs. This zone (superstructure base until the first few meters of the MP) is extremely critical for the overall behavior (MPs, soil, superstructure) due to the complexity of the interaction between the components, which influences wave propagation. Additionally, influential phenomena on the inertial interaction, such as grouting/soil debonding, cracking, and plasticization of the structural elements, are more likely to occur in this zone due to the concentration of internal forces. The sensitivity of the seismic behavior to the upper soil profile was also observed by [PERSON] et al. [18], but the authors related their observations to the soil heterogeneity, which causes what they called \"filtration\". When considering more accurately the effects of all components in this zone, e.g., gap formation phenomenon in the soil-MP interface, together with the concept of filtration discussed above, the influence on the inertial interaction can be understood more precisely and deeper results can be concluded [13,14].
# Performance Analysis of Physical Layer-Based Multiple-Input Multiple-Output on WiMAX (MIMO-WiMAX) +
## 5 Conclusions  
BER and SNR are two of the measures that are used in the analysis of the performance of the WiMAX PHY layer, based on the OFDM protocol. On the transmitter side, forward error correction encoding is used so that the data sent is completely free of error. According to the results of the simulations, lesser modulation schemes have superior performance compared to higher modulation schemes. According to the findings that were produced by the simulation model, BER is improved whenever there is a greater value of SNR. On the MIMO system, space-time block coding, abbreviated as STBC, was been applied. Based on the findings of the simulation, we have reached the conclusion that Alamouti 2 \(\times\) 2 has a lower BER compared to Alamouti 2 \(\times\) 1 and 1 \(\times\) 1. MIMO and WiMAX working together exhibit lower bit error rates (BERs) and greater spectral efficiencies than WiMAX working alone.  
Conceptualization, A.N. and U.D.; methodology, A.N.; software, A.N.; validation, U.D.; formal analysis, A.N. and U.D.; investigation, U.D.; resources, U.D.; writing--original draft preparation, A.N.; writing--review and editing, U.D. All authors have read and agreed to the published version of the manuscript.  
This work does not receive any funding from any sources.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** The data can be obtained from the corresponding author upon request. Informed consent was obtained from all subjects involved in the study.  
**Acknowledgments:** We acknowledge the institutional management and family members for their immense support.  
**Conflicts of Interest:** The authors and coauthors declare no conflict of interest.  
Figure 8: BER performance of MIMO\(-\)WiMAX Alamouti 2 \(\times\) 1, 2 \(\times\) 2.