esa-sceva/satcom-chunk-collection huggingface dataset:
# THz Data Analysis and Self-Organizing Map (SOM) for the Quality Assessment of Hazelnuts
## 4 Results  
Data are analyzed using a simple algorithm and the neural network to sort out the empty or rotten hazelnut, as explained in detail in the following paragraphs. To carry out the measurements, each hazelnut sample was placed inside a petri dish and surrounded by a layer of eccosorb in order to focus the radiation only on the hazelnut. The frequencies used are between 36 and 40 GHz: nine frequencies corresponding to the points shown in Figure 3 were used (36 GHz, 36.64 GHz, 37.21 GHz, 37.77 GHz, 38.34 GHz, 38.90 GHz, 39.47 GHz, 40.03 GHz, 40.60 GHz).  
Figure 3 shows the trend over frequency of the transmission values measured for ten hazelnut samples. A possible sorting based on the transmission values between healthy and rotten hazelnut seems possible for a small dataset and for single measurements. This  
Figure 3: Transmission values for 10 hazelnut samples. Healthy and rotten hazelnuts as a function of frequency. The \(x\)-axis shows the frequency range, while the \(y\)-axis shows the transmitted signal (mV). The healthy hazelnuts show a lower transmittance than rotten ones.  
Figure 2: Hazelnut samples.  
will not be any more possible on bigger datasets. This difference can be explained by the fact that at these frequencies, the water and fatty acids present inside the hazelnut absorb and reflect the radiation, showing lower transmission values.
## 3 Results and Discussion
### Visibility in Relation to PM\({}_{10}\)  
The association between visibility and PM\({}_{10}\) is generally acknowledged. Here, visibility was also found to be negatively correlated with PM\({}_{10}\), with correlation coefficients of \(-0.29\) (on the daily data) and \(-0.22\) (on the monthly data) at CB, \(-0.32\) (daily) and \(-0.23\) (monthly) at LC, and \(-0.51\) (daily) and \(-0.64\) (monthly) at RY. The correlation coefficient values using \(RH\)-corrected visibility do not differ much (only by 1-2% in magnitude for all cases). Such negative association between visibility and PM\({}_{10}\) is also manifested in mean levels of PM\({}_{10}\) under different visibility classes (Figure 4a). That is, Class III has an average PM\({}_{10}\) concentration of 42-60 ug m\({}^{-3}\), which is about 1.6-1.7 times higher than that in Class I. It was found that average visibility is significantly higher over the weekend (9.1 km, 7.1 km and 8.2 km for CB, LC and RY, respectively) than on weekdays (8.6 km, 6.8 km, 7.6 km) which can be explained with significant lower mean level of PM\({}_{10}\) during weekend at the three stations (Figure 4b-d). A non-parametric Wilcoxon rank sum test (at a significance level of 0.05) was employed here to inspect the statistical significance of the mean (i.e., average) difference between two samples. It is noticed that the patterns of weekday-weekend difference are similar for both visibility and \(RH\)-corrected visibility due to the lack of significant difference in relative humidity (on average) between the two periods (not shown). The above results provide evidence of anthropogenic influence (here, in view of PM\({}_{10}\)) on visibility.  
Figure 3: Monthly variation of (**a**) visibility (before met-screening); (**b**) visibility; (**c**) PM\({}_{10}\); (**d**) rain; (**e**) relative humidity; (**f**) temperature; (**g**) wind speed; (**h**) mixing height; (**i**) global radiation; and (**j**) recirculation factor.
# Improving RF Fingerprinting Methods by Means of D2D Communication Protocol
## 4 Experiment and Results
### Successful Floor Detection  
Finally, we checked the successful floor detection rate. In Figure 14 it can be seen compare to the traditional method, our proposed method shows slightly better correct floor detection rate with 94.46%. Although the absolute increase of performance (as successful floor detection rate) has been just 1.45% with respect to the traditional method, the relative floor detection error has been reduced in a 20% (from 6.99% to 5.45%). While all the benchmark results for correct floor detection rate were in between 85% to 92% in the previous study [13].
###### Abstract  
Participation of a wind turbine (WT) in primary frequency control (PFC) requires reserving some active power. The reserved power can be used to support the grid frequency. To maintain the required amount of reserve power, the WT is de-loaded to operate under its maximum power. The objective of this article is to design a control method for a WT system to maintain the reserved power of the WT, by controlling both pitch angle and rotor speed simultaneously in order to optimize the operation of the WT system. The pitch angle is obtained such that the stator current of the permanent magnet synchronous generator (PMSG) is reduced. Therefore, the resistive losses in the machine and the conduction losses of the converter are minimized. To avoid an excessive number of pitch motor operations, the wind forecast is implemented in order to predict consistent pitch angle valid for longer timeframe. Then, the selected pitch angle and the known curtailed power are used to find the optimal rotor speed by applying a nonlinear equation solver. To validate the proposed de-loading approach and control method, a detailed WT system is modeled in Matlab/Simulink (The Mathworks, Natick, MA, USA, 2017). Then, the proposed control scheme is validated using hardware-in-the-loop and real time simulation built in Opal-RT (10.4.14, Opal-RT Inc., Montreal, PQ, Canada).  
de-loading; droop curve; hardware-in-the-loop; reserve power; primary frequency control; optimal control; wind forecast
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 5 Scheme Implementation
### Data Storage Phase
#### 5.3.2 Processing Medical Record Numbers  
The medical record number \(N_{EMR}\) generated in the above process undergoes encryption and hashing. On one hand, the hash value of \(N_{EMR}\) is computed as shown in Equation (4):  
\[N_{EMR_{hash}}=Hash(N_{EMR}), \tag{4}\]  
where \(N_{EMR_{hash}}\) serves as the unique identifier stored on the data channel for medical data indexes \(TreatmentData\) and data usage records \(DataUsageRecord\). On the other hand, the ciphertext of the EMR's number \(C_{N_{EMR}}\) is generated as shown in Equation (5):  
\[C_{N_{EMR}}=En_{pk_{i}}(N_{EMR}), \tag{5}\]  
which is used as the unique identifier for the attribution information \(AttributionInfo\) stored on the attribution channel.
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 6 Evaluation
### Results of the Simulation
#### 6.2.2 Rank Deviation  
A good way to analyze the parameters qualitatively is by introducing _ranks_. Given a set of fixed parameters, if the list of variable parameter combinations is sorted by average energy consumption, the position of a parameter combination in that sorted list is called the _rank_. Combinations with the same consumed energy receive the same rank, skipping the ranks in between. An example of a ranking is shown in Table 2.  
The _rank deviation_ describes how far a parameter value is away from the mean across all variable combinations for a given fixed parameter. More precisely, if \(\mu\) denotes the mean across all combinations and \(\alpha\) refers to the average rank for a given parameter value \(q\), then \(\mu-\alpha\) is the _rank deviation_ and \(\mid\mu-\alpha\mid\) is the _absolute rank deviation_. Thus, if the rank deviation is positive for a parameter value, that value is better than the average.  
When considering the maximum absolute rank deviation for the variable parameters, the same pattern can be observed as for the relative energy deviation: The packet size has the largest effect, followed by the caching scheme, the caching strategy, and finally, the forwarding strategy. This is also highlighted by the shown plots: Each plot compares the different values of a given variable parameter and shows how its rank is compared to the average across all combinations for the fixed parameter combination. The rank has been averaged for all eight simulation runs. Only a small subset of the created plots are shown here, emphasizing the key points. It should be noted that for the random topology, the variance of the data is too big to extract useful information from it. For none of the variable parameters, a clear pattern for the parameter ranks can be deciphered. Therefore, the further analysis will concentrate on the other topologies only.
## 3 Results
### Identification of Bacteria
[35] highlighted the contribution of _Paenibacillus_ species towards the elimination or decomposition of the environmental pollutants, either by biofoculation or the enzymatic activities. They explain that _Paenibacillus_ can generate a variety of enzymes that metabolise aromatic and aliphatic organic contaminants, including the dehydrogenases, oxygenases, and ligninolytic enzymes to degrade these contaminants. Another study has demonstrated the effectiveness of _Paenibacillus_ strains in degrading textile dyes such as polyvinyl alcohol (PVA), a covering for textile and paper materials [37]. This confirms the high performance of _Paenibacillus_ in the decomposition of organic matter throughout this study, which is even more impressive than that previously achieved with the other endogenous strains [16]. Regarding _Enterobacter aerogenes_, [PERSON] and [PERSON] [38] revealed that these species could use lignocellulosic by-products, particularly glucose, for their metabolism, and were unable to use the recalcitrant molecules; this is due to their inability to release the enzyme required to decompose these molecules [39], which therefore explains the low degradation rate results obtained using that strain. Ultimately, the use of a consortium of isolates achieved a significantly more pronounced, precise and quicker degradation of organic matter than when the isolates were used separately, potentially due to the capacity of the endogenous bacteria involved in the consortium to co-exist with each other. The consortium of these three bacterial strains could apparently promote and boost the degradation of organic matter using different pathways and mechanisms [16; 39]. Several studies have shown that synergistic cooperation within a bacterial consortium can significantly accelerate the transformation of organic matter, particularly recalcitrant substances, which explains the results achieved in this study [8; 10; 39]. The consortium employed proved significantly (\(p<0.05\)) effective in improving composite process parameters, including temperature, TOC, ash, TKN and C/N, rapidly converting organic matter into compost in 10 weeks, instead of 12 weeks using other sets of strains (_Streptomyces cellulose_, _Achromobacter xylos oxidans_, and _Serratia liquefaciens_), and 44 weeks in the case of conventional composting without bioaugmentation. In this respect, the high rate of recalcitrant molecule degradation within the consortium could be attributed to the fact that these strains work together to degrade organic matter. For this reason, _Bacillus_ and _Paenibacillus_, with their enzymatic systems, could act as degraders of lignocellulosiccompounds, while _Enterobacter aerogenes_ could use the by-products as a source of carbon and energy.
## 2 Theoretical Background and Mathematical Models
### LEMP Coupling Effect on ETLs  
Electromagnetic fields are formed due to LEMP currents, and these fields can damage power grids [23]. Coupling between pulses and lines is achieved by utilizing different models of field line connections. Considering the geometry presented in Figure 4, these models are examined where the conductors are on perfectly conductive ground. Figure 5a,b illustrate how the coupling effect impacts the ETLs as a pair of electric and magnetic fields [23].  
These models were utilized to predict LEMP-induced overvoltages in power lines, utilizing the Rusck closed formulation and the Rusck coupling model.  
Figure 4: Geometric structure of coupling models (\(\mathrm{E}^{\mathrm{i}}_{\mathrm{v}}\), \(\mathrm{E}^{\mathrm{i}}_{\mathrm{y}}\), \(\mathrm{E}^{\mathrm{i}}_{\mathrm{z}}\): outward scattered electric field, dx: distance variation).  
Figure 3: (**a**). The shape of the current form at the basis of the LEMP channel according to the Heidler model. (**b**). The flow formed at the base of the LEMP channel according to the Heidler flow model (detailed model).
## 3 A-ECMS Based on Driving Pattern Recognition
### The Adaptation of the Equivalent Factor
#### 3.3.3 Selection of the Nominal Equivalent Factor  
In previous research, a fixed equivalent factor was used to represent each driving pattern [17; 18], which, however, ignored the influence of the SOC on the estimation of the equivalent factor. Because of the complexity of driving conditions, when the type of driving pattern changes, the SOC does not necessarily equal the corresponding initial SOC of the optimal equivalent factor, as shown in Figure 6. At this point, the fixed equivalent factor will lead to inaccurate estimation of the equivalent factor during the driving cycle.  
Figure 4: Velocity profile of the test cycle.  
Figure 5: Real-time driving pattern recognition (DPR) verification.  
Figure 7 presents the optimal equivalent factors for initial SOCs ranging from 0.55 to 0.8 for different driving patterns when the target final SOC was set to 0.7. It can be seen that for a certain initial SOC, the optimal equivalent factors corresponding to different standard cycles were very concentrated, except for SOCs ranging from 0.72 to 0.8 in urban driving. Further, the relationship between the optimal equivalent factor and the initial SOC was almost linear for each driving pattern. From a statistical sense, the average curve can well reflect the general tendrcy of a class of driving cycles. In this study, the average curve (shown by the red line) was used to represent the relationship between the initial SOC and the optimal equivalent factor for each driving pattern. Thus, the nominal equivalent factor in Equation (16) was periodically updated as a function of the driving pattern and the SOC.  
\[s_{nom}=f(CYC(t),SOC(t)) \tag{23}\]  
Figure 6: SOC state during driving pattern transition.  
Figure 7: Initial SOC versus optimal equivalent factor under different driving patterns. (**a**) CYC1, (**b**) CYC2, (**c**) CYC3, (**d**) CYC4.
# Research on Two-Dimensional Digital Map Modeling Method Based on UAV Aerial Images
## 4 Experimental Validation
### Image Preprocessing  
Denoising the collected sequential images to obtain high SNR images is fundamental for image stitching based on the overlapping regions of sequential images. The quality of the stitched image depends on the choice of image acquisition equipment and algorithms during the image processing phase. During image capture, factors such as the drone's flight altitude, orientation, and weather conditions inevitably lead to issues like blurriness, uneven lighting, and occlusions. Denoising is therefore necessary to enhance image quality, which in turn improves the accuracy of feature extraction and feature matching in sequential images.  
Figure 4: The original UAV aerial photo display. **(a–c)** are the aerial photographs of streets collected from different locations.  
An appropriate filtering algorithm not only can highly restore the information in an image but also maintain the clarity of the image's edge contours, thereby ensuring the accuracy of edge extraction. In this study, the H-B filtering algorithm, based on Gaussian bilateral filtering, was employed for image denoising. After applying the H-B filter, SNR improved to approximately 40.85 dB, exceeding 38 dB, which qualifies as a high SNR for sequential images. The denoised images achieved noise reduction while preserving edge information from the original images, and also enhanced the clarity of details within the images, as shown in Figure 5. Following the image denoising process, high-quality materials suitable for panoramic image stitching were obtained.
# K/Ka-Band Very High Data-Rate Receivers: A Viable Solution for Future Moon Exploration Missions
## 4 Receiver Architecture
### Linearity and Signal Levels
This means that, until the in-band interference level is below \(-54.3\) dBm (at the input), the third order intermodulation will remain below the noise floor, having no measurable effects. Figure 4 reports a comprehensive view of the levels involved in both the uplink and in the receiver front-end. In particular the left axis represents the EIRP of ground transmitters on Earth whereas, the right axis, is the power level at the receiver input, on the Moon. The two vertical axes are scaled by \(-191\) dB, a quantity equal to the receiver antenna gain (about \(+40\) dBi) summed with the path loss for zenith and clear sky conditions (about \(-231\) dB) in the K-band. The nomogram represents also the receiver noise floor and the linearity limits related to the 1 dB compression point and to the third-order intermodulation (i.e., to the \(SFDR\)). The nominal \(+115\) dBm EIRP of the ground station sets the received power to about \(-76\) dBm, \(18\) dB above the noise floor. As already discussed, however, such a margin is optimistic and serves to guarantee for non zenithal links and atmospheric attenuation.
## 9 Sensor Security Test Case Set  
ICVs heavily rely on sensors for achieving autonomous driving, thereby introducing a broader attack surface and potential cybersecurity risks to vehicles. Various types of sensors are embedded in ICVs, including cameras, Lidar, ultrasonic radar, millimeter-wave radar, GPS, etc. The GPS signal tends to weaken after long-distance transmission, making it susceptible to interference from simulated GPS signals that can cause location deception. Cameras can be rendered ineffective by arrays of powerful light sources. Similarly, millimeter-wave radar can be disrupted by signals with similar waveforms.
# Real-Time Prediction of Fuel Consumption Based on Digital Map API
## 3 Methodology  
### Division Speed Interval  
The fuel consumption prediction model is based on the traffic condition predictions of the digital map. Given that the digital map can only predict a vehicle's operating mode over a time span, not on a second-by-second basis, all the vehicle trajectory data and fuel consumption data will be divided into many segments, which contain 60 s of continuous data. Then the average speed of each segment can be calculated by Equation (1).  
\[\overline{V}=\frac{S}{T}=3.6\times\frac{\sum_{i=1}^{60}v_{i}}{60}, \tag{1}\]  
where \(\overline{V}\) is the average trip speed in the unit of km/h, \(S\) is the trip distance in the unit of km, \(T\) is the driving time in the unit of \(h\), and \(v_{i}\) is the speed of the \(i\)-th second in the unit of m/s.  
The traffic condition prediction is captured through the Application Programming Interface (API) of a digital map, which divides the traffic conditions into four types. Hence, the data segments are  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Trip ID** & **Vehicle ID** & **Start Time** & **Off Time** & **Start Longitude** & **Start Latitude** \\ \hline
300040001026 & 1f0 ba1 db-1790 & 23 October & 23 October & 18:59:04 & 116.4190498 & 39.8804229 \\ \hline
300040162728 & 1f0 ba1 db-1790 & 24 October & 24 October & 17:30:41 & 116.347289 & 39.9708974 \\ \hline  & & & & & \\ \hline
**Off** & **Off** & **Trip Time** & **Trip Distance** & **Trip Fuel (L)** & **Fuel per** & **Trip Average Speed** & **Engine Displacement** \\
**Longitude** & **Latitude** & **(s)** & **(m)** & **(L)** & **(mL)** & **(km/h)** & **Displacement** \\ \hline
116.446979 & 39.8956057 & 650 & 6653 & 0.696 & 1046 & 36.85 & 3.0 L \\ \hline
116.3598674 & 39.9055488 & 1790 & 10,051 & 1.607 & 1600 & 20.21 & 3.0 L \\ \hline \hline \end{tabular}
\end{table}
Table 2: The sample of the trip data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Collection Time** & **Collection Location** & **Data Size** & **Number of Vehicles** \\ \hline October 2019 & Beijing China & 5,000,000 & 143 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The general information about the data.  
divided into four groups to match the traffic condition predictions according to the average speed of each segment. Table 4 shows the correspondences.
# Paleomagnetic-Geodynamic Mapping of the Transition Zone from Ocean to the Continent: A Review
## 3 Paleomagnetic Mapping and Profiling in Northern Israel
### Paleomagnetic Profile across the Carmel-Galilee Region  
The paleomagnetic profile crosses the area of the Mt. Carmel, Northern Galilee, and the Korazim Plateau of the DST region (north of the Lake Kinneret), from SW to NE from the Pleshet terrane through the Galilee-Lebanon terrane to the Antilebanon terrane (Figure 4; see location of this profile in Figure 1).  
Figure 4: Paleomagnetic profile **II**–**II**\({}^{\prime}\) of the Carmel-Galilee region along the top of the accumulative surface of the Lower Cretaceous traps (location of this profile is shown in Figures 1 and 2). (1) borehole, (2) radiometric age, (3) dykes and sills, (4) traps, (5) faults, (6) established stratigraphic boundaries, (7) supposed position of the stratigraphic boundaries, (8–12) paleomagnetic zones: (8) Jalal, (9) Gissar, (10) Omolon, (11) Illawarra, (12) Kaima. The following main works were used for this map construction: [32; 54; 55; 60; 61; 63; 78; 89].  
The Pleshet and Antilebanon terranes are somewhat uplifted tectonically than the Galilee-Lebanon terrane. This profile is constructed along the top of the accumulative leveling surface of the Lower Cretaceous traps belonging to the Jalal-1 paleomagnetic zone (131-105 Ma).  
The underlying Triassic-Jurassic sequences that make up the cover of the MTB carbonate platform contain dikes and sills of Tithonian-Neocomian basaltoids (132-153 Ma) and belong to the Gissar paleomagnetic zone. Concerning the terranes themselves, the carbonate platform represents an autochthonous complex; however, both carbonates and intrusive traps were formed in the pre-collision stage, when the terranes bordered the southern part of the Neotethys Ocean at the boundary with the Gondwana foreland. The profile testifies that an allochthonous complex of ophiolites, occurring above the carbonate platform, has developed in the Galilee-Lebanon terrane. It forms four sheets of different ages. The lowest of them, the youngest, is composed of keratophyte melange aged 162-164 Ma (Omolon paleomagnetic zone) and is covered with relatively deep-water Upper Jurassic carbonates penetrated by intrusive traps of the Gissar zone. Two middle ophiolite plates are composed of spilite and olivine-basalt melange (188-206 Ma) and deep-water Jurassic carbonates and generally belong to the Omolon paleomagnetic zone. Finally, the upper ophiolite plate, the oldest, 197.4-222.4 Ma, is composed of basalt melange and covered with Jurassic carbonates (174 Ma). This age generally corresponds to the Illawarra-Omolon paleomagnetic zones (Figure 4).  
The dynamics of sequential attachment of ophiolite sheets during collision and shear movements of oceanic plates and the Galilee-Lebanon terrane during the Levantine phase of tectogenesis at the boundary of the Early and Late Hauterivian was considered earlier [77]. These authors have shown that the amplitude of horizontal displacements of ophiolite plates could reach 120 km.