esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Structural Power Content Analysis Robustness for Satellite Navigation Applications +
## 1 Introduction  
With the growing dependency of many critical applications on Global Navigation Satellite Systems (GNSS), the importance of the integrity of GNSS signals is higher than ever. As the knowledge about and availability of commercial off-the-shelf (COTS) jammers and spoofers rises [1], GNSS jamming and spoofing is becoming an increasing threat to these applications [2, 3]. Dedicated jamming and spoofing attacks are designed and specifically aimed at disturbing GNSS receivers, after which they are fed with sophisticated misinformation [4]. Such an attack could lead to the receiver computing erroneous position, velocity, and time estimates, or satellites' statuses being set to unhealthy. Due to this threat, there is a growing need for real-time interference detection to effectively apply mitigation techniques. Over the years, many interference detection techniques have been developed, verified, and validated. Pre-despreading detection techniques have increased the effectiveness of monitoring systems with limited computational capabilities [5].  
In terms of spoofing detection, the Structural Power Content Analysis (SPCA) method, which exploits the nature of the transmitted Pseudo-Random Noise (PRN) codes from GNSS satellites, has been widely found to be effective in monitoring systems. This method, however, was also found to trigger when subjected to certain jamming waveforms and scenarios [6], thereby leading to an increased false (spoofing) alarm rate.  
In this paper, an analysis of the sensitivity of the SPCA algorithm on certain jamming signals is evaluated and, based on the results, alternative filtering approaches are examined in order to research false alarm mitigation possibilities and their effect on the spoofing detection performance of the method. Finally, the effectiveness of the proposed filtering techniques using simulated and publicly available spoofing datasets is demonstrated.
## 4 Performance Tests of the Proposed PRNG
### Speed Analysis  
In order to show the performance of the proposed PRNG, we implemented it in electronic devices with 8, 16 and 32-bits architectures. Table 5 shows the clock cycles for the different configurations of the proposed PRNG according to the following criteria: when the proposed PRNG is set to 8-bit, the number obtained in each iteration is concatenated until to form a 32-bit number. Similarly, when the proposed PRNG is set to 16-bit, it must concatenate two 16-bit numbers to form a 32-bit number. Therefore, the results reported in Table 5 correspond to the clock cycles consumed by the proposed PRNG when it is configured for 8, 16 or 32 bits to generate 32-bit numbers.  
Figure 13: Linear complexity profile for 1000 sequences of pseudorandom numbers for the proposed PRNG. (**a**) considering sequences from 1 to 200,000 bits. (**b**) zoom of (**a**) considering sequences from 100,000 to 100,050 bits.  
Additionally, we implemented the proposed PRNG for the 8, 16 and 32-bit configurations by using a C language compiler (MinGW) on an Intel Core i7-4800 MQ CPU-2.70 GHz with 24G RAM. For each case, we obtained the time required to generate 1,000,000 pseudorandom numbers performing this process 2000 times, and then the average time was calculated. These results are reported in Table 6 and they allow consider that the proposed PRNG can be implemented in an electronic system with limited hardware.
## 4 System Setup  
The proposed FESS has been modeled in MATLAB-Simulink in order to verify its performance by means of an extensive simulation study, which is preparatory to the prototyping stage. In particular, simulations have been carried out by means of a real-time simulator, i.e., OP-4500 by OPAL-RT [36], which enables fast simulations over relatively long time horizons (several hours). The simulation setup has been defined in accordance with Figure 1, particularly a domestic user has been considered, whose daily load profile is shown in Figure 9a. In addition, a PV power plant is also foreseen (5 kW), and two different production profiles have been considered in order to emulate sunny and cloudy days alternatively [37], as shown in Figure 9b. The main specification of the proposed FESS system is instead resumed in Table 2.  
Figure 8: Grid-tie converter control system overview.  
Simulations have regarded different case studies in order to evaluate FESS energy performance at different operating conditions, as highlighted in Table 3. In particular, functionality tests have been performed at first, namely two full charging-discharging cycles have been carried out in order to evaluate FESS energy efficiency over different speed operating regions and DC-link voltage ratings (cases A1 and A2). A self-discharge test has been also carried out in order to estimate the self-discharge rate (case B1). Subsequently, the proposed FESS has been tested into a real scenario, namely during a sunny and a cloudy day, in order to evaluate its overall performance and efficiency over a wider time horizon (cases C1 and C2). All the results presented in the paper are sampled at 1 Hz, while the sampling time of EMS and FESS control system has been set at 100 \(\upmu\)s.
# A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
## 6 PULSAR Applications in Forest and Wildfire Management
### Detailed Forest Mapping  
Detailed forest mapping using UAVs is a crucial aspect of wildfire and prescribed fire sciences. This paper focuses on adapting a LiDAR sensor to the proposed UAV, an advanced tool capable of capturing high-resolution imagery that enables the precise mapping of vegetation types, fuel loads, and topography. As highlighted in [42], wildfire prevention efforts should concentrate on modifying forest fuels. At the landscape level, wildfires are ignited and spread through a complex interaction between weather, topography, and fuels. Fuel characteristics, such as the vertical and horizontal structure of vegetation, stand composition, and the properties of forest species, significantly influence the rate and intensity of fire spread. Alterations in fuel patterns lead to changes in fire behaviour. For prescribed burns, the information collected assists in planning controlled fires to ensure they align with ecosystem management goals. In practical scenarios, the proposed UAV equipped with a LiDAR sensor can be deployed to scan densely forested areas prone to fire during dry seasons. Analyzing these data aids in identifying strategic sites for fuel reduction treatments, such as thinning or controlled burns, substantially mitigating the risk of catastrophic forest fires. This enhances landscape understanding, enabling more accurate wildfire risk assessments and contributing to the development of strategic fire management plans [43].
## 2 Materials and Methods
### Other Techniques for Resonant Frequencies Identification
#### 2.4.1 Cross-Spectrum Method  
The cross-power spectral density (CPSD) is used to determine the similarity or relationship of the linear dynamic properties, between two different time series, as a function of frequency. This technique is especially useful when a high level of noise is present in the signals and it is not possible to find significant peaks in the power spectra that allow indicating the related periodicities among the signals. Because ambient noise can be considered as random Gaussian noise, there should not be correlation between the noise and the harmonics corresponding to the movement of the building. This is the case with the measured radar signals and only the shared harmonics between the signals will be present in the CPSD.  
To estimate the CPSD, the averaged Welch (1956) method can be used. To do so, we first divide the signals \(x\) and \(y\) in \(K\) overlapped and windowed segments of length (\(L\)). Then, we compute the Fourier Transform of the respective pair of segments, \(x^{i}\) and \(y^{i}\), via Discrete Fourier Transform (DFT) as follows:  
\[P^{i}_{xy}(f)=\frac{1}{L}\sum_{k=0}^{L-1}X^{i}(f)Y^{i\;*}(f),\qquad n=0,1\ldots,L-1 \tag{2}\]  
where \(X^{i}(f)\) and \(Y^{i\;*}(f)\) are the DFTs of the respective windowed segments and the superscript asterisk denotes the complex conjugate. Finally, we compute the CPSD, as in the Welch method, as follows  
\[\overline{P_{x}}(f)=\frac{1}{K}\sum_{i=1}^{K}P^{i}_{xy}(f) \tag{3}\]  
Figure 14 shows the CPSDs of the combinations between P3 and the other four points, in the same direction, for both L (a) and T (b) directions, i.e., between P3-L and P1-L, P2-L, P4-L and P5-L. The mean CPSD is highlighted in black. It can be seen that peaks in the frequencies of 2.487 and 2.786 Hz, indicated with dashed red and blue lines, respectively, are emphasized while the other peaks tend to attenuate.  
Figure 14: The cross power spectral densities (CPSD) (grey lines) of the combination between the longitudinal (**a**) and transversal (**b**) signals in point P3 and the rest of the signals in the same directions. The black lines correspond to the mean CPSD and the frequencies 2.49 and 2.79 Hz are indicated with red and blue dashed lines, respectively.  
Similar results can be seen with the RAR signals. In Figure 15, the CPSD estimates for the RAR measurements are presented. The CPSDs for all combinations between the selected Rbin and the rest of the Rbins are shown in grey, i.e., for Rbin38, the CPSDs between Rbin38 and Rbin39, Rbin38 and Rbin40, Rbin38 and Rbin41, Rbin38 and Rbin42 and Rbin38 and Rbin43 are plotted together in grey. Finally, the mean value of the CPSD combinations is highlighted in black. The peaks at the resonant frequencies of 2.491 and 2.708 Hz clearly stand out. The peaks of different frequencies in the range from 1 to 2 Hz are also visible in most of the CPSDs. However, these peaks are outside the frequency range of interest for this building and do not appear to be stable in all the CPSDs.
# Photon Counting Statistics of a Microwave Cavity Coupled with Double Quantum Dots
## 2 Model and Theoretical Formalism
### Photon Emission Counting Statistics  
To keep track of the number \(m\) of photons emitted into the thermal reservoir from the microwave cavity, the \(m\)-resolved state density matrices \(\mu(m,\ t)\) can be written as [30]  
\[\begin{split}\frac{d}{dt}\mu(m,t)=\frac{1}{2}\gamma[1+\overline{n} ]\Big{[}2a\mu(m-1,t)a^{\dagger}-\Big{\{}a^{\dagger}a,\mu(m,t)\Big{\}}\Big{]}+ \frac{1}{2}\gamma\overline{n}\Big{[}2a^{\dagger}\mu(m,t)a-\Big{\{}a^{\dagger} a,\mu(m,t)\Big{\}}\Big{]}+\\ i\delta_{m}\Big{[}a^{\dagger}a,\mu(m,t)\Big{]}+\frac{1}{2}A_{+} \Big{[}2a^{\dagger}\mu(m,t)a-\Big{\{}a^{\dagger}a,\mu(m,t)\Big{\}}\Big{]}+\frac {1}{2}A_{-}\Big{[}2a\mu(m,t)a^{\dagger}-\Big{\{}a^{\dagger}a,\mu(m,t)\Big{\}} \Big{]}\end{split} \tag{17}\]  
The probability of \(m\) photons being emitted from the cavity to the thermal reservoir can be obtained through \(P(m,t)=\mathrm{Tr}\mu(m,t)\). If we perform the following Laplace transformation \(\mu(\mathrm{s},\mathrm{t})\equiv\sum_{m=0}^{\infty}\mu(m,t)e^{ms}\), Equation (17) can be transformed into  
\[\begin{split}\frac{d}{dt}\mu(s,t)=\frac{1}{2}\gamma[1+\overline{n }]\Big{[}2e^{s}a\mu(s,t)a^{\dagger}-\Big{\{}a^{\dagger}a,\mu(s,t)\Big{\}}\Big{]} +\frac{1}{2}\gamma\overline{n}\Big{[}2a^{\dagger}\mu(s,t)a-\Big{\{}a^{\dagger }a,\mu(s,t)\Big{\}}\Big{]}+\\ i\delta_{m}\Big{[}a^{\dagger}a,\mu(s,t)\Big{]}+\frac{1}{2}A_{+} \Big{[}2a^{\dagger}\mu(s,t)a-\Big{\{}a^{\dagger}a,\mu(s,t)\Big{\}}\Big{]}+ \frac{1}{2}A_{-}\Big{[}2a\mu(s,t)a^{\dagger}-\Big{\{}a^{\dagger}a,\mu(s,t) \Big{\}}\Big{]}\end{split} \tag{18}\]  
where \(s\) is called a counting field. In order to obtain the matrix elements \(\Big{\langle}n\big{|}\mu(s,t)\big{|}n\Big{\rangle}\), we introduce another Laplace transformation as follows:  
\[\mathcal{G}(\mathrm{s},\mathrm{q},\mathrm{t})\equiv\sum\
olimits_{n=0}^{ \infty}\Big{\langle}n\big{|}\mu(s,t)\big{|}n\Big{\rangle}e^{n\eta}. \tag{19}\]
# An Ultra-Low Temperature Transcritical R744 Refrigeration System for Future Detectors at CERN LHC
###### Abstract  
The cooling systems of the future tracking detectors of the ATLAS and CMS experiments at the Large Hadron Collider (LHC) at CERN will be entirely based on CO\({}_{2}\) refrigeration technology. The system is a booster refrigeration system, composed of a two stage primary part with transcritical R744 equipment and a low temperature secondary CO\({}_{2}\) pumped loop. The primary refrigeration sub-system installed on surface provides cold R744 at \(-53\,^{\circ}\)C to the CO\({}_{2}\) pumped loops installed 100 m underground and rejects the heat exchanged. The process must be reliable and remain stable regardless of the amount of heat exchanged, which will amount to hundreds of kilowatts and is expected to vary throughout the lifetime of the detectors. The paper discusses the concept adopted for the innovative transcritical R744 cycle and describes the technical details of the first prototype built.  
CERN; refrigeration; R744; particle trackers +
Footnote †: journal: Physics Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
(hence reducing the amount of material in the tracking region), while presenting more favourable thermo-physical properties compared to other refrigerants. This approach has been successfully used for the first time in the LHCb Velo detector since 2008 [5]. Since then, it has been adopted also for the ATLAS Inner b-Layer (IBL) detector since 2014 [6] and the CMS \"Phase-1\" Pixel detector since 2016 [7].  
The specific CO\({}_{2}\) pumped loop developed for the thermal management of silicon detectors at LHC, commonly referred to as 2 PACL [8], is an oil-free system pumping subcooled liquid CO\({}_{2}\) into the detectors via small pipes (around 2 mm ID pipes), bringing it in saturated condition at the inlet of the detector heat exchangers, to efficiently evacuate all the heat produced by the electronics mounted on the silicon detectors [9] while keeping the silicon sensors very stably at the desired temperature set point.  
The 2 PACL system will be installed 100 m underground in a cavern hosting several services for the operation of the detector. However, it requires an external system to cool down the liquid CO\({}_{2}\) before pumping it towards the detector and condensate the two-phase CO\({}_{2}\) coming back from the detector. It is actually the secondary loop in a more complex cascade of system: the heat evacuated from the detector is exchanged with a primary loop, responsible for transporting the heat to the surface and to maintain the secondary loop CO\({}_{2}\) subcooled to below \(-50\,\mathrm{\SIUnitSymbolCelsius}\). In particular, the more stringent thermal requirements imposed by the harsh HL-HLC environment push to lower the subcool target for the secondary loop CO\({}_{2}\) down to \(-53\,\mathrm{\SIUnitSymbolCelsius}\).  
The design of an innovative primary system capable of achieving this ambitious goal is the focus of a research programme launched in common by CERN and NTNU.
## 2 Related Work
### Edd-Darts  
EDD-DARTS [20] constitutes an algorithm that sought to address the discrepancy problem of DARTS using an approach closely aligned with ours. They pointed out the challenge of adjusting the gradient of the alpha value solely through temperature control. Consequently, they opted to modify \(e^{a/t}\) by integrating a segment of the softmax function instead of adjusting the temperature. With each epoch, they computed the beta gradient with respect to the \(e^{a/t}\) value and adjusted the beta value. In essence, this process involved a direct intervention that facilitated the transition from alpha to beta by means of the softmax operation. Furthermore, this adjustment process incorporated a scaling factor to enable effective scaling. Additionally, they introduced an adaptive temperature scheme called entropy-based dynamic decay (EDD). Unlike conventional temperature adjustments, their approach modifies the \(e^{a/t}\) value. The extent of adjustment was determined by calculating the entropy of beta for each epoch; a higher entropy value led to more significant adjustments in the \(e^{a/t}\) value.  
This approach gradually led to the creation of alpha values that progressively approximated discrete values as the search process advanced. However, this approach was designed to maintain the temperature consistently below 1. In contrast, our approach can accommodate situations where the temperature surpasses 1. In scenarios with temperatures higher than 1, intense competition among operators ensued. In the absence of such competition, some connections could be established without undergoing sufficient exploration. In practical situations, it's not uncommon for a single dominant operator to persist and eventually be selected. This implies that other operators might be deprived of the opportunity to exhibit their efficacy. To ensure a comprehensive search, a strategy based on our approach can be employed where the initial search phase begins with a temperature higher than 1 and gradually transitions to a temperature below 1 as the search concludes, facilitating the derivation of a discrete architecture.
## 4 Experimental Results and Discussion
### Assessment Methods
#### 4.1.1 PSNR  
Peak signal-to-noise ratio can be used to assess the distortion degree of the original image after processing. In other words, a larger PSNR represents better image quality and a larger ratio of signal and noise shows that the signal can be more easily extracted. The computational formula of PSNR can be expressed as:  
\[PSNR=10\times\log_{10}\left(\frac{N\times MAX_{I}^{2}}{\sum\limits_{i=1}^{N} \big{(}I_{orig}(i)-I_{comp}(i)\big{)}^{2}}\right) \tag{12}\]  
where \(MAX_{I}^{2}\) represents the maximum pixel value, \(I_{orig}\) is the pixel value in the original image, \(I_{comp}\) is the pixel value in the handled image, and \(N\) is the pixel quantity.
## 5 Experiment Evaluations
### Results and Discussion
Once again, as shown in Figure 9, the LM is slightly increased with the proposed approach, particularly when the generalization level is overestimated (i.e., \(k=3,5\)). Finally, Figure 10 shows the way that the execution time (shown in Figure 9) is split among three phases: (1) estimating the generalization level (Phase I); (2) executing a query and anonymizing query results (Phase II); and (3) aggregating (and further anonymizing) locally anonymized query results (Phase III). As can be seen in the figure, Phase III, which corresponds to aggregating (and further anonymizing) locally anonymized query results, has been identified as a major contributor to the execution time for all the cases. Especially, a significant increase in the execution of the Phase III is observed, when the generalization level is underestimated (i.e., \(k=13\)). This is because, in the case of an underestimation, the master node should perform further anonymization on the aggregated results until the \(k\)-anonymity is satisfied, which causes a significant increase in the execution time of the Phase III. Note that Phases I and III are applied by the master node, whereas Phase II is executed by each slave node. Thus, the distributed nature of the presented algorithm affects the execution time of Phase II. That is, if more slave nodes are used, the execution time of Phase 2 will be reduced. The experimental results in this section verify that, with the proposed method, significant processing time gains can be achieved without a significant reduction in the information on the released microdata. Figure 9: (a) The execution times and (b) loss metric (LM) when varying \(k\).
## 2 Design and Simulation
### Simulation of Particle Position Distribution with a Different Impact Distance  
In order to obtain the relationship between the particle attachment position distribution on the surface of the chip and the distance from the nozzle to the surface, we used the computational fluid dynamics (CFD) module and particle tracking module of the commercial software COMSOL MULTIPHYSICS to conduct the simulation. A schematic diagram is shown in Figure 7. All particles were set to be spherical with the same density and were uniformly distributed at the entrance of the nozzle for the convenience of simulation. The particle distribution on the surface with different impact distances was calculated by adjusting the distance between the nozzle and the chip surface (\(h\)).  
With the change of distance (\(h_{1}\) = 0.9 mm, \(h_{2}\) = 2.5 mm, \(h_{3}\) = 5 mm), the area and distribution density of the same amount of particles falling from the nozzle to the surface were significantly different: when impacting the chip from a shorter distance, particles were more concentrated in the center of the chip surface, and the impaction range was small; as the distance increased, the particle attachment range on the surface was larger, while the number of particles fell on the center area were relatively lower. The above analyzed conclusions guided the design of a SAWR-appropriate cascade impactor to ensure its particle selecting performance and chip response characteristics were good.
# Personalized Bone Reconstruction and Regeneration in the Treatment of Craniosomiasis
### Craniosynostosis: A Heterogeneous Condition

Table 1: _Cont._  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Syndrome(s) or Phenotype**} & **OMIM ID and/or PubMed Reference** & **Involved Suture(s)** & **Gene Symbol** \\ \hline
**IMAGE SYNDROME** & 614732 & Sagittal, metopic & _CDKN1C_ \\ \hline
**IMMUNOSKELETAL** & & & \\
**DYSPLASIA WITH** & 617425 & Multisource & _EXTL3_ \\
**NEURODEVELOPMENTAL** & & & \\
**ABNORMALITIES** & & & \\
**JACKSON-WEISS SYNDROME** & 123150 & Multisource & _FGFR1_ \\ \hline
**JACKSON-WEISS SYNDROME** & 123150 & Multisource & _FGFR2_ \\ \hline
**JOUBERT SYNDROME 2** & 608091 & Sagittal & _TMEM216_ \\ \hline
**KABUKI SYNDROME** & 147920 & Multisource & _KMT2D_ \\ \hline
**KAKUBI** & & & \\
**SYNDROME/AU-KLINE** & 600712 & Metopic & _HNRNPK_ \\
**SYNDROME** & & & \\ \hline
**KLEEBLATTSCHAEDEL** & 148800 & - & - \\ \hline
**LACRIMOAURIUOLDENTODIGITAL** & & & \\
**LEVY-HOLLISTER** & 149730 & Sagittal & _FGF10_ \\
**SYNDROME** & & & \\
**LYNDROME-LIKE** & & & \\
**CSO/GENITOPATELLAR** & & & \\
**SYNDROME/SAY BARBER** & 605880 & Sagittal & _KAT6B_ \\
**BIESECKY YOUNG SIMPSON** & & & \\
**SYNDROME** & & & \\ \hline
**LOEYS-DIETZ SYNDROME 1** & 609192 & Multisource & _TGFBR1_ \\ \hline
**LOEYS-DIETZ SYNDROME 2** & 610168 & Multisource & _TGFBR2_ \\ \hline
**LOEYS-DIETZ SYNDROME 3** & 613795 & Variable & _SMAD3_ \\ \hline
**LOEYS-DIETZ SYNDROME 4** & 614816 & \begin{tabular}{c} Sagittal, metopic, \\ multisource \\ \end{tabular}  & _TGFB2_ \\ \hline
**MEIER-GORLIN SYNDROME** & 224690 & Coronal & _CDC45_ \\ \hline
**MEIER-GORLIN SYNDROME 1** & 224690 & Variable & _ORC1_ \\ \hline
**MEIER-GORLIN SYNDROME 7** & 617063 & Coronal, multisource & _CDC45L_ \\ \hline
**MENTAL RETARDATION, AR 41** & 615637 & Sagittal & _KPTN_ \\ \hline
**MENTAL RETARDATION, AUTosomal DOMINANT 32** & 616268 & Coronal & _KAT6A_ \\ \hline
**MENTAL RETARDATION, AUTosomal DOMINANT 57** & 618050 & Coronal, sagittal, metopic, multisource & _TLK2_ \\ \hline
**MENTAL RETARDATION, X-LINKED, SYNDROMIC, TURNER TYPE** & & \\ \hline
**METAPHYSEAL** & \% 250215 & - & - \\
**ACROSCYPHODYSPLASIA** & & & \\ \hline
**METOPIC/PANSYNOSTOSIS** & [5] & Metopic, multisource & _RUNX2_ \\ \hline
**MICROCEPHAJY 1, PRIMARY, AUTosomal REcessIVE** & 251200 & Variable & _MCPH1_ \\ \hline
**M