esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Inoculum Size on Ethanol Production  
Three different inoculum sizes (1%, 3%, and 5% (_v/v_)) were investigated to determine the effect of inoculum size on kinetic parameters of ethanol fermentation from waste potato mash. Figure 2 shows the ethanol production (g/L), glucose consumption (g/L), and the cell population (log CFU/mL) over 48 h fermentation periods for all cases. The maximum ethanol productivity (6.48 g/L/h) and maximum growth rate (0.3 log CFU/mL/h) were obtained with 3% inoculation, which produced 30.99 g/L ethanol.  
Figure 2: Ethanol, glucose, and cell population in the fermentation broth with different inoculum size; 1% (**a**), 3% (**b**), and 5% (**c**).  
Although 5% inoculum sizes demonstrated a higher ethanol yield; growth rate, production rate and consumption rate were lower than parameters of 3% inoculum size. Among 1, 3, and 5% inoculum sizes, 3% was chosen to be the optimum inoculum by comparing production rate, maximum growth rate and produced ethanol. The highest production rate, growth rate, and produced ethanol were obtained for 3% inoculum size as 6.48 g/L/h, 0.3 log CFU/mL/h, and 30.99 g/L, respectively. There was no statistically significant difference in mean production rate among the inoculum sizes (\(p>0.05\)), however, growth rates were statistically different for inoculum sizes (\(p<0.05\)).  
The results demonstrated that there is an increase of ethanol yield up to 3%, however 5% inoculum causes a decrease of kinetic parameters for ethanol fermentation by _S. cerevisiae._ [PERSON] [7] reported that ethanol production increases by inoculum up to 4%. Furthermore, it was reported that 3% inoculum size was the optimum for ethanol production from carob [20]. Therefore, 3% inoculum size was also suggested to be the optimum level for ethanol by this study.
# Accuracy Characterization of a MEMS Accelerometer for Vibration Monitoring in a Rotating Framework
## 4 Conclusions  
An inexpensive wireless vibration monitoring system based on Raspberry PI and a MEMS accelerometer has been proposed, and its accuracy in acceleration measurements was evaluated by means of comparison with a reference measurement system. An actual fan blade from a gas turbine engine was used as a rotating framework, and its eigenmodes were excited by means of an _in-house_ prototype for PZT actuator control. The blade was  
Figure 6: Comparison between wireless and reference acceleration measurement systems within the considered rotor angular speeds. Mean acceleration amplitudes are reported alongside their corresponding error bars representing the standard deviation for: (**a**) the excitation of the first bending mode, (**c**) the excitation of the second mode, (**e**) the excitation of the third mode, and (**g**) excitation of the fourth mode. (**b**,**d**,**f**,**h**) show, respectively, the values of the mean absolute percentage errors (MAPE) for the first four bending modes excitation within the considered rotor speed range.  
rigidly coupled to a rotating shaft and the first four bending modes of the blade were excited by means of the PZT actuators to experimentally replicate its resonance conditions. The accelerations detected by both vibration monitoring systems were processed and compared to estimate the accuracy of the proposed wireless system under several rotor angular speeds (0, 120, 180, 240 and 300 rpm). The MEMS-based system showed fair accuracy for both frequency and acceleration amplitude evaluation with respect to the reference system. The mean absolute percentage error (MAPE) was always less than 5%, indicating the fair accuracy of the proposed wireless setup. Furthermore, the wireless system power consumption was always lower than 6 W during the whole measurement campaign. Such power demand can easily be provided from small-size solar panels. This suggests that the proposed wireless system may be exploited for wind turbine and turbomachinery fan compressor blade vibration monitoring.  
Conceptualization, A.R., G.B. and F.B.; methodology, A.R., G.B., F.B. and A.S.; validation, A.R., G.B. and F.B.; investigation, A.R. and G.B.; data curation, A.R. and G.B.; writing--original draft preparation, A.R., G.B., F.B. and A.S.; writing--review and editing, A.R., G.B., F.B. and A.S.; visualization, A.R. and G.B.; supervision, F.B. and A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 UFMC RadCom Signal Model  
The UFMC discrete-time baseband signal is the superposition of the sub-bandwise filtered subcarriers [11]; therefore, it can be expressed as follows:  
\[x_{u}[n]=\sum_{s=0}^{S-1}g_{s}[n]\otimes x_{s}[n] \tag{20}\]  
where \(\otimes\) denotes linear convolution, and \(g_{s}[n]\) is the filter used in the \(s\)-th sub-band. It is defined in (21) as:  
\[g_{s}[n]=g[n]e^{j\frac{2\pi Q/2n}{N_{c}}}e^{j\frac{2\pi(S_{0}+Q)n}{N_{c}}} \tag{21}\]  
with \(g[n]\) being the prototype filter of length \(L\), and \(S_{0}\) denoting the starting frequency of the lowest sub-band. \(x_{s}[n]\) is the \(s\)-th group of subcarriers, which is an OFDM symbol shifted to the appropriate sub-band. It is given by (22):  
\[x_{s}[n]=\sum_{r=-\infty}^{\infty}\sum_{q=0}^{Q-1}s_{s,q,r}e^{j\frac{2\pi q(n -N_{q,rm})}{N_{c}}}e^{j\frac{2\pi(S_{0}+Q)(n-N_{q,rm})}{N_{c}}}, \tag{22}\]where \(s_{s,d,r}\) are complex symbols transmitted on the \(q\)-th subcarrier in the \(s\)-th sub-band during the \(r\)-th period. They are spread over the overall signal and transformed into the time domain, with inverse discrete Fourier transform (IDFT) of length \(N_{c}\). Term \(e^{j\frac{2\pi(S_{0}+S)(m+1)}{N_{c}}}\) performs frequency shifting of both data and filter coefficients to the appropriate subband. Because of convolution, the resulting UFMC signal is of length \(N_{ufmc}=N_{c}+L-1\). The filtering operation makes it possible to suppress the OOB leakages, with the Dolph-Chebyshev filter being the most common in the literature [11].  
Replacing (21) and (22) in (20), and applying some simplifications, the transmitted UFMC signal can be written as in (23).  
\[x_{u}[n]=\sum_{s=0}^{S-1}\sum_{r=-\infty}^{+\infty}\sum_{q=0}^{ Q-1}\sum_{l=0}^{L-1}s_{s,d,r}S_{Q}\Big{[}n-l-rN_{ufmc}\Big{]}\] \[e^{j\frac{2\pi l}{N_{c}}}e^{j\frac{2\pi(S_{0}+S)(m+N_{ufmc})}{N_ {c}}}\]  
with \(g_{Q}[n]=g[n]e^{j\frac{2\pi l}{N_{c}}}\) being the prototype filter shifted to the sub-band center frequency [11]. Considering the same channel model as for OFDM, the received UFMC signal can be expressed as follows:  
\[y_{u}[n]=e^{j\frac{2\pi nl}{N_{c}}}\sum_{s=0}^{S-1}\sum_{r=-\infty }^{+\infty}\sum_{q=0}^{Q-1}\sum_{j=0}^{L_{u}-1}\sum_{l=0}^{L-1}s_{s,d,r}h[\eta_ {j}]e^{-j2\pi f_{d_{j}}(n+k)}g_{Q}\Big{[}n-l-\eta_{j}-rN_{ufmc}+\delta\Big{]}\] \[e^{j\frac{2\pi nl}{N_{c}}}e^{j\frac{2\pi(S_{0}+S)(m-\eta_{j}-rN_{ ufmc}+\delta)}{N_{c}}} \tag{23}\]  
Figure 4 depicts the synthesis of a UFMC signal.
# Fatigue Crack Growth from Notches:
## 4 Results  
### Typical CTOD-F Curves  
The present study of FCG is based on the CTOD parameter. Figure 3a presents a classical CTOD versus applied load, F, curve, measured at the first node behind the crack tip. The load cycle starts at minimum load (A) and increases progressively until point B with CTOD remaining constant and equal to zero, i.e., the crack is closed. The crack opens at point B and deforms elastically until point C, which is evidenced by the linear stretch B-C. At point C starts plastic deformation, increasing until reaching the maximum applied load (D). After point D begins the discharge of the specimen. Analogously, first the material exhibits elastic deformation between points D and G. The elastic-plastic transition occurs at point E, and the plastic deformation decreases until reaching (H), where the crack closes. Figure 3b shows plastic CTOD, CTOD\({}_{\mathrm{P}}\), versus F curve. CTOD\({}_{\mathrm{P}}\) is obtained from CTOD by subtracting the elastic CTOD, CTOD\({}_{\mathrm{e}}\). As said before, at stretches B-C and D-G, the material experiences elastic deformation and therefore CTOD\({}_{\mathrm{P}}\) remains constant. In the elastic-plastic regimes, C-D and G-H, there is a quick increase and decrease, respectively, of the plastic deformation, as shown in Figure 3b. Figure 3a,b show the elastic and plastic CTOD ranges, respectively, \(\delta_{e}\) and \(\delta_{p}\). A linear elastic relation was found between the plastic CTOD range and da/dN [25; 28]; therefore, the trends observed of \(\delta_{p}\) are those expected for da/dN.
# FPGA-Based Pulse Compressor for Ultra Low Latency Visible Light Communications
## 1 Introduction
In the experiments, 3 different chirps (1.7 MHz bandwidth and lengths of 4.48, 8.96, 17.92 \(\upmu\)s, respectively) are tested. The gain in communication distance attainable by chirps with higher temporal durations are measured and compared to a theoretical model. For example, a 1.56 Mb/s link over a 2.12 m distance was demonstrated by employing the 4.48 \(\upmu\)s chirp. The communication distance raised to 3.20 m for the 17.92 \(\upmu\)s chirp. The latency, i.e., the time needed to the data packet from the input of the transmitter to the output of the receiver, was always below 40 us. The paper proceeds with Section 2, where the basics principles of the pulse compression are summarized; Section 3 describes the employed communication protocol; Section 4 reports about the implementations in FPGA of the chirp coder, the compressor, and the packet reconstruction; Section 5 describes the experimental set-up; Section 6 reports the measurements; finally, Section 7 discusses the results, and Section 8 closes the work.
## 5 Discussion  
### Improvement Made by the Proposed Methods  
A total of 48 susceptibility maps have been produced for three provinces by applying four types of RF and four classification systems for a large area of 23,200 km\({}^{2}\). Only 4% of this total area is considered landslide-affected, according to the COPUT inventory (1998). This fact points to the difficulty of finding clearly differentiated and generalisable results.  
An analysis of Table 10 leads to the conclusion that the general method of calculating FR in constant intervals of the same amplitude (GFR) gives the worst results of all the classification types. These are predictable results, as they do not consider the singularity of the distribution of a continuous variable that explains the characteristics of the terrain--such as slope.  
For a better visualisation of the results, Table 11 shows the normalised values in the interval [0,1] that correspond to the means shown in Table 10.  
Table 11 shows that the GY and quantile classifications show the highest values on mean for the 42 cases analysed. It is true that quantile classification offers the highest value,  
In addition, it is worthwhile noting that:  
1. The standard method of grouping classes into equal intervals (GFR) produces the worst results. Increasing the number of intervals with the same criteria does not produce an improvement.
2. The ranges defined in the ELSUS methodology (EFR) produce good results. However, the criterion used is not explained in the reference article.
3. The neighbourhood clustering method (CFR) is simple and offers good results that are generally better than EFR.
4. The regression fit method (RFR) is somewhat complex to apply but produces the best results.  
It can be confirmed with the data shown in Figure 7 that a justified improvement has been obtained with the new methods applied: CFR and GYouden. Moreover, by treating each case in three different areas (the three provinces), it can be concluded that the results achieved are sufficiently robust and generalisable.
## 3 Concept and Architecture of DigiHoriRobot
### Physical Layer Components  
The Physical Layer comprises all tangible elements involved in horticultural production and autonomous robotic manipulation:  
* Biological components: Crops such as aubergines and tomatoes, modeled from seedling to harvest, incorporating phenotypic traits and growth stages.
* Greenhouse infrastructure: A hydroponic cultivation setup equipped with environmental sensors for CO\({}_{2}\) concentration, temperature, relative humidity, and pH. QR tags are embedded within the planners to facilitate spatial identification and traceability.
* Robotic system: A mobile dual-arm platform capable of executing horticultural operations such as seeding [24] and harvesting [6]. These tasks are both time-critical and labor-intensive, representing key phases in the crop production process.  
The robotic platform integrates an advanced multimodal perception system, combining RGB and Time-of-Flight (ToF) vision to enable a 3D reconstruction of the environment and semantic mapping. This enables detection of relevant plant structures and facilitates complex manipulation in confined and delicate environments. The robot also incorporates learning-from-demonstration capabilities to generalize manipulation strategies across tasks with varying plant geometries and operational contexts, enhancing adaptability and reducing the need for manual reprogramming.  
Figure 1: Overview of the DigiHortiRobot architecture, showing the Physical Layer, Virtual Layer, and Implementation Framework.
## 3 Results and Discussion
### Determination of Hardly Grinding Phases (HGP) in Converter Steel Slag  
As the grindability of various mineral phases in steel slag have great differences, the easily grinding phases (denoted as EGP) are firstly being ground to fine powder, while the poor grindability mineral phases are difficult to grind down, which seriously affects the grinding efficiency. However, different mineral phases in steel slag are very difficult to be separated out, so it is impossible to determine what is an easily grinding phase and hardly grinding phase from the grindability index of various mineral phases. In this paper, a simple method for determination of the hardly grinding phase in steel slag by oversize substance (i.e., residue) is provided. After grinding and then screening of steel slag powder, the oversize substance obtained can be considered as the hardly grinding phases (denoted as HGP) in steel slag. In order to determine the HGP in the grinding process, the steel slag powder after 10, 20, 30, 40, 50, 60, and 70 min grinding were screened with a 0.9 mm square-hole screen, and then the proportion, morphology, and compositions of oversized substances (i.e., HGP) were analyzed.
## 2 Computational Methodology
### Governing Fluid Flow Equations: Navier-Stokes Equations  
The Navier-Stokes equations typically consist of the conservation mass, momentum, and energy. The current work was solely dedicated to capturing the complex physics of the incompressible turbulent flow separation with and without a plasma actuator; however, we neglected the temperature rise due to the plasma as it was considered to be very low and thus ignored [24; 25]. In our computations, only the continuity and the momentum equations were considered, where the DBD plasma actuator was modeled as the source term in the momentum equation. The modified set of non-dimensional governing equations for the unsteady, incompressible, plasma induced flow is given by:  
\[\frac{\partial u^{\prime}}{\partial x^{\prime}}+\frac{\partial v^{\prime}}{ \partial y^{\prime}}+\frac{\partial w^{\prime}}{\partial z^{\prime}}=0 \tag{2}\]\[\frac{\partial u^{\prime}}{\partial t^{\prime}}+u^{\prime}\frac{ \partial u^{\prime}}{\partial x^{\prime}}+v^{\prime}\frac{\partial u^{\prime}}{ \partial y^{\prime}}+w^{\prime}\frac{\partial u^{\prime}}{\partial z^{\prime}}+ \frac{\partial p^{\prime}}{\partial x^{\prime}}-\frac{1}{Re}(\frac{\partial^ {2}u^{\prime}}{\partial x^{\prime 2}}+\frac{\partial^{2}u^{\prime}}{\partial y^{ \prime 2}}+\frac{\partial^{2}u^{\prime}}{\partial z^{\prime 2}})=F_{x,plasma} \tag{3}\] \[\frac{\partial v^{\prime}}{\partial t^{\prime}}+u^{\prime}\frac{ \partial v^{\prime}}{\partial x^{\prime}}+v^{\prime}\frac{\partial v^{\prime} }{\partial y^{\prime}}+w^{\prime}\frac{\partial v^{\prime}}{\partial z^{ \prime}}+\frac{\partial p^{\prime}}{\partial y^{\prime}}-\frac{1}{Re}(\frac{ \partial^{2}v^{\prime}}{\partial x^{\prime 2}}+\frac{\partial^{2}v^{\prime}}{ \partial y^{\prime 2}}+\frac{\partial^{2}v^{\prime}}{\partial z^{\prime 2}})=F_{y,plasma}\] (4) \[\frac{\partial w^{\prime}}{\partial t^{\prime}}+u^{\prime}\frac{ \partial w^{\prime}}{\partial x^{\prime}}+v^{\prime}\frac{\partial w^{\prime} }{\partial y^{\prime}}+w^{\prime}\frac{\partial w^{\prime}}{\partial z^{ \prime}}+\frac{\partial p^{\prime}}{\partial z^{\prime}}-\frac{1}{Re}(\frac{ \partial^{2}w^{\prime}}{\partial x^{\prime 2}}+\frac{\partial^{2}w^{\prime}}{ \partial y^{\prime 2}}+\frac{\partial^{2}w^{\prime}}{\partial z^{\prime 2}})=F_{z,plasma} \tag{5}\]  
Equation (2) represents the continuity equation, and the momentum equations are shown in (3)-(5) in the \(x\), \(y\), and \(z\) directions, respectively. The density is \(\rho\); \(P\) is the pressure; \(Re\) is the Reynolds number; and \(u\), \(v\), \(w\) are the velocity components in the \(x\), \(y\), and \(z\) directions, respectively. The right-hand side of the momentum equation represents the plasma body force source term in three-dimensional space. Equations (2)-(5) represent the complete set of equations used in this modified form of CALC-LES.
## 4 Experiments and Results
### Further Validation  
Thus far, we have screened out several key influencing factors. To verify the effectiveness of the proposed method, we refer to the evaluation metrics in feature selection and feature extraction [46; 47] to evaluate the experimental results.  
Since our dataset was highly imbalanced, we did not use accuracy as our evaluation metric. Instead, we used the F1 score and the Matthews correlation coefficient (MCC), which are both suitable measures of models tested with imbalanced datasets [48]. The F1 score is a comprehensive evaluation index that integrates two evaluation parameters, the accuracy rate and recall rate, to evaluate the overall performance of the classifier [49]. The MCC is essentially a correlation coefficient value between \(-1\) and \(+1\). A coefficient of \(+1\) represents a perfect prediction, \(0\) is an average random prediction and \(-1\) is an inverse prediction [50].  
For the pre-processed experimental dataset, we set \(1/4\) of them as the test group and the rest as the training group. A common logistic regression classifier was adopted as the classifier used in the experiment. When we took all 448 feature variables as the input of the classifier, we obtained F1 and MCC values of 0.4216 and 0.0612, respectively. When we took the selected 15 key influencing factors as the input of the classifier, the values of F1 and MCC were 0.6431 and 0.2209, respectively. On this basis, we sorted key influencing factors according to their importance, deleted the first to 15 th key influencing factors, and took the remaining key influencing factors as the input of the classifier to obtain the corresponding F1 and MCC evaluation index values. The experimental results are shown in Figure 8.  
In Figure 8, N = 1 indicates that the first key influencing factor is deleted, and the remaining 14 key influencing factors are used as characteristic variables. In this case, the obtained F1 value and MCC value are 0.548 and 0.096, respectively. N = 2 means that the second key influencing factor is deleted and the other remaining 14 key influencing factors are used as characteristic variables.  
Figure 8: Prediction performance of the classifier with different key influencing factors deleted.
# LoRa Enabled Smart Inverters for Microgrid Scenarios with Widespread Elements
## 3 LoRaWAN Specifications and Applications
### Review of LoRaWAN and LoRa Applications in Smart Systems
IPSs tend to detect any intentional or accidental unscheduled islanding situations of distributed energy resources (DERs). Such unintended islandings, if they are not detected in time and safety measures are properly implemented, can be very hazardous to equipment and also are human-life-threatening [37]. In the same line, a LoRaWAN architecture was proposed for monitoring wind turbine performances [38]. In this study, the communication system was designed and implemented from the sensor level up to the final web-based user interface, so it can be considered useful for other developers and researchers. As another smart grid application, but from a different viewpoint, a LoRaWAN communication system was evaluated for monitoring residential consumption, billing, and scheduling purposes. In this case, an advanced metering infrastructure (AMI) was proposed based on using LoRaWAN protocol and performance aspects, such as packet deliver ratio (PDR), packet collisions, throughput, and transmission power consumption were evaluated by simulations [39]. It should be noted that the utilization of LoRaWAN or other LPWAN technologies for smart grid applications is justifiable if the applications require long-range communications. Otherwise, higher-throughput and more robust communication protocols can be utilized to improve the PDR and increase the allowable payload size [40]. The scope of the current study also falls into the application of LoRa and LoRaWAN in smart grid systems. However, it is distinguishable from the other works of the existing literature, since the problem has been investigated from both control systems and communication engineering viewpoints. Up to the knowledge of the authors, there are not many alike available studies that provide the details for both the communication system and the control structures. This point can be considered as the novelty of this research study. As final remarks of this section, it could be stated that LoRaWAN may find applications where neither the well-established and high-throughput short-range technologies, such as WiFi or ZigBee, can be utilized due to coverage considerations, nor the robust and reliable cellular solutions because of network availability and power consumption considerations. This rule of thumb is also valid for other LPWAN systems since:  
* LPWANs either use ultra-narrow bands (UNB) or spread spectrum (SS) techniques which contain large latencies. * Star topology is utilized to create ultra low power end-nodes. * Packet loss and duplication may frequently occur due to the use of very simple MAC protocols. * Real-time data transfer may not be possible due to the random access nature of LPWANs MAC protocols.