esa-sceva/satcom-chunk-collection huggingface dataset:
# Single Trace Side Channel Analysis on NTRU Implementation
## 2 Background
### Side Channel Analysis and Related Work  
Although an algorithm is mathematically secure, naive implementation can make cryptosystem vulnerable to various attacks. The most important considerations in implementation are random number generator and leakage of the secret information. In most cryptosystems, the quality of the random numbers used directly determines the security of the system. Therefore, a predictable random value (i.e., low entropy source) may weaken the system. The studies for the randomness have done in the respect of entropy [14; 15; 16; 17; 18; 19]. However, the analysis herein discusses the implementation in the side of SCA.  
The SCA is first introduced by [PERSON] et al. in 1996 [6]. Subsequently, power analysis attacks such as the simple power analysis (SPA), differential power analysis (DPA), and correlation power analysis (CPA) [20; 21] have been proposed. Nowadays, any attack that exploits information gained from the implementation is considered as SCA. This includes cache attack, EM analysis, and attacks that exploit hardware vulnerabilities [22; 23; 24; 25]. However, we mainly focus on the power analysis attack. The power analysis attacks rely on the dependency between the power consumption of the device and the operated data during the execution of an algorithm. The SCA is an actual threat since it can recover the private key of the cryptosystem in practical time. To prevent these type of attack, masking and hiding are studied [26]. Masking refers to a method of computing secret information with random values, so that the actual value is unused during the encryption and decryption. Hiding removes the relationship between power consumption and the data. Hiding is one of the hardware level countermeasure which is focused on the security during the operation.  
Additionally, the Internet of Things (IoT) devices are advanced nowadays, the security against low-power design is essential [27]. However, the conventional PKC is difficult to implement on the resource-constrained environment. Therefore, there is a research on the physical unclonable function (PUF) as a light-weight authentication security primitive. For example, side-channel resistant PUF was intensively studied in [28].
## 2 Background
### ARM TrustZone  
A trusted execution environment (TEE) is part of the main processor, which provides an isolated environment to protect both the confidentiality and the integrity of code/data loaded inside. TrustZone is a TEE implementation of Arm processors, available in ARM Cortex-A processors (or processors built on the Armv7-A and Armv8-A architecture), as well as ARM Cortex-M processors (built on the Armv8-M architecture). The idea of TrustZone is to create two execution environments that run simultaneously on a single processor: a secure execution environment (i.e., a _secure world_), which runs sensitive applications, and  
Figure 1: The out-of-place update in flash memory. When data A is overwritten by data B in the file system, data B will be placed on another flash page, and data A will be invalidated without actually being deleted from its holding flash page.  
a non-secure execution environment (i.e., a _normal world_), which runs non-sensitive applications. Each world operates independently when using the same processor, and switching between them is orthogonal to all other capabilities of the processor. Memory/peripherals will be aware of the corresponding world of the core, and applications running in the normal world cannot access the memory space of the secure world. Therefore, critical applications can be run in a secure world, and their confidentiality and integrity cannot be compromised even if the adversary can compromise the OS. Leveraging TrustZone, our design will run a user-level manager of the malware detector in the TrustZone secure world to avoid being compromised by the OS-level malware.
# Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
## 3 Results
### Wireless Data Relay by Introducing Additional (Relayer) Drone
#### 3.2.1 Evaluation of the Efficiency of Hovering Relay by Networking Metrics  
The locations of wireless communication devices are shown in Figure 14. The average throughput in this test was 0.28 \(\pm\) 0.07 kbps for the on-ground relayer drone and 0.77 \(\pm\) 0.05 kbps for the hovering relayer drone kept around 3 m from the ground (Figure 15). The range test by the XCTU showed an over +15 dBm increase in the local RSSI by the hovering relay (Figure 16). The average PDR was 97 \(\pm\) 2.7% for the on-ground relayer and 100 \(\pm\) 0.3% for the hovering relayer (Figure 16).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Event** & **Time** & **pH** & **EC (mS m\({}^{-1}\))** & **DO (mg L\({}^{-1}\))** & **Water Temperature (\({}^{\circ}\)C)** \\ \hline
1 st landing & 17:43–17:48 & 7.53 & 9.81 & 8.61 & 24.31 \\ \hline
2 nd landing & 17:50–17:55 & 7.96 & 10.01 & 8.49 & 24.12 \\ \hline
3 rd landing & 17:57–18:02 & 8.43 & 10.16 & 8.15 & 24.25 \\ \hline
4 th landing & 18:03–18:08 & 8.55 & 10.18 & 7.88 & 24.25 \\ \hline \end{tabular}
\end{table}
Table 2: Water quality readings from time series data obtained using the amphibious drone system (Ogata Pond, 19 July 2023). EC, electrical conductivity; DO, dissolved oxygen. Landing locations are described in the extending relay test.  
Figure 13: Example of a time series dataset from the water quality monitoring system on an amphibious drone (Ogata Pond, 19 July 2023): EC, electrical conductivity; DO, dissolved oxygen; F1, first flight; L1, first landing on water; F2, second flight; L2, second landing on water; F3, third flight; L3, third landing on water; H, hovering; L4, fourth landing on water. Numbers on the data represent readings obtained at equilibrium (pH, EC, and water temperature) and immediately after each landing on water (DO). Exceptional treatment of the data on DO was conducted for oversaturated water by algal bloom, as detected by manual sampling measurements of nearshore water.
# Mountain Muon Tomography Using a Liquid Scintillator Detector
## 2 Materials and Methods
### Direction Reconstruction
#### 2.3.1 Reconstruction Templates' Creation  
We use simulations to create templates for reconstructing cosmic-ray muon events. The parameters of a template include the true values of the direction and the entry point on the spherical detector shell, as well as the time information on the PMTs, which is recorded as it generates a response in the detector. The number of templates is about 500 k, which we expect to fully cover all of the possibilities for the muon passing through the detector. For the \(n\)-th template, we randomly sample the direction \(\textbf{P}_{n}=(\text{cos}\theta_{n},\phi_{n})\) and the entry point \((\text{cos}\alpha_{n},\beta_{n})\). The \(\theta_{n}\) and \(\alpha_{n}\) are zenith angles, while the \(\phi_{n}\) and \(\beta_{n}\) are azimuth angles.  
\(\textbf{P}_{n}\) and \((\text{cos}\alpha_{n},\beta_{n})\) serve as inputs to a detector response simulation (using a Geant4-based software package). From the output, we can obtain the time \(\tau_{n}^{i}\) at which the scintillation light, emitted by interactions between the incident muon and the LS, arrives at the \(i\)-th PMT. Based on \(\tau_{n}^{i}\) (\(i=\)1,2,...,\(N_{\text{PMT}}\)), we have an \(N_{\text{PMT}}\)-dimensional zero-centered arrival time vector \(\textbf{T}_{n}\), where \(N_{\text{PMT}}\) is the number of the PMTs. \(\textbf{P}_{n}\), \((\text{cos}\alpha_{n},\beta_{n})\), and \(\textbf{T}_{n}\) are calculated as follows:  
* \(\textbf{P}_{n}=(\text{cos}\theta_{n},\phi_{n})\): The cos\(\theta_{n}\) and \(\phi_{n}\) values are sampled from uniform distributions between \(-1\) and \(1\) and between \(0\) and \(2\pi\), respectively;
* \((\text{cos}\alpha_{n},\beta_{n})\): The entry point is sampled uniformly from the detector hemisphere facing the muon direction \(\textbf{P}_{n}\);
* \(\textbf{T}_{n}\): \(\tau_{n}^{i}\) (\(i=\)1,2,...,\(N_{\text{PMT}}\)) contains a global time that records when the muon arrives at the detector. The global time does not contain the direction information, and we remove it via centralization to zero: \[t_{n}^{j}\equiv\tau_{n}^{j}-\frac{1}{N_{\text{PMT}}}\sum_{i=1}^{N_{\text{ PMT}}}\tau_{n}^{i},\] (6) and we can define: \[\textbf{T}_{n}\equiv\Big{(}t_{n}^{1},\cdots,t_{n}^{N_{\text{PMT}}}\Big{)}.\] (7)
## 1 Introduction
The work obtained was slightly improved using different precisions for all observations. Currently, the Galileo has 28 satellites in space, including 22 operational (19 Full Operational Capability (FOC) and 3 In-Orbit Validation (IOV)), two on-height elliptical orbits with 'not usable' status, one 'unavailable status', one 'not usable' status and two recently launched with 'under commissioning' status [39]. Nowadays, the Galileo system allows precise positioning anywhere on Earth with accuracy similar to GPS [40; 41; 42; 43; 44]. However, Galileo has still not reached its FOC and new research is still required. The latest PPP advanced models, such as PPP-AR [45; 46; 47; 48; 49; 50; 51; 52], are not yet generally available--most are still in the research phase and require advanced algorithms and products. The conventional PPP model is still the most widely used. Also, the use of multi-GNSS observations significantly improves PPP performance. The greatest benefits are from the use of GPS and Galileo together, which are the most compatible systems. Both systems have different characteristics which may require a different approach to stochastic modeling. The aim of this study was to analyze the effect of using different elevation angle-dependent weighting functions on the accuracy, convergence time and estimate troposphere (Zenith Path Delay (ZPD)) and ISB by the conventional PPP model. The result of this analysis is the proposal of the best weighing functions for GPS-only, Galileo-only and GPS+Galileo for the PPP method. The calculations were made for 13 Multi-GNSS Experiment and Pilot Project (MGEX) stations located globally for one week in 2021, for GPS, Galileo and GPS+Galileo constellations. This paper is organized as follows. Section 2 describes the model, solutions and data used with detailed descriptions of the weighting functions used. Also, this section shows the analysis of using different reference coordinates. Section 3 presents the results obtained from the conducted analysis. First, the results for a selected station of one day are presented. This was done in order to present more detailed results, which were then integrated for all stations and days analyzed. In Section 4, an approach to weighting observations for GPS+Galileo solution was proposed with the analysis results. The last Section 5 describes the discussion and conclusion of the research.
# Accuracy of Code GNSS Receivers under Various Conditions
###### Abstract  
The main objective of this research work was to study the accuracy of GNSS code receivers under poor sky visibility conditions based on measurements on three different objects (point, line, and surface) and additionally to test results on point positioning with good sky visibility conditions. The measurement was based on 3 smartphones (in the same mode to check repeatability) and 2 handheld receivers (working in GPS+GLONASS modes). The methodology was based on the RTK technique, whose coordinates were assumed as a reference. Based on the results, the significant influence of measuring in the vicinity of high trees on the obtained accuracy was observed for both the precise geodetic equipment and the tested code receivers. More favorable results of point positioning were observed when using mobile phones. On the other hand, in the case of measurement in motion, the handheld receivers guaranteed higher accuracy. Moreover, the study showed that handheld receivers might achieve a better accuracy than smartphones, and that position might be determined with a greater accuracy and reliability. Furthermore, handheld receivers were characterized by a smaller number of outliers.  
GNSS; GPS; smartphone; positioning; navigation Article
## 3 Theoretical Background and Problem Formulation  
### STPA Principals  
STPA is one of the new hazard analysis techniques based on the STAMP causality model. In order to better understand the principles of STPA, an outline of the STAMP model basics is essential. In STAMP-related techniques, the systems are examined as a dynamic balanced system orchestrated by feedback and control loops [28]. The interaction between the subordinate components of the system and the human operator is modeled as a closed control schema obeying the classic control rule, where the controlled process is formulated through: (i) the received/sent control actions by the controller and (ii) the feedback received/observed by the controller upon the executed action. STPA is the hazard analysis technique of STAMP, where, based on the designed control structure, potentially hazardous control actions are identified. Upon the definition of purpose analysis of examined system, the basic steps of STPA are as follows [16]:  
* Identify the losses considering all the stakeholders of the system;
* Identify system level hazards and link them to losses;
* Define system level constraints ;
* Model the general control structure;
* Identify unsafe control actions;
* Identify causes of unsafe control.  
The outcome of this procedure is the definition of several system states that condition to the appearance of any tracked unsafe control action can lead to hazardous situations. This renders STPA an appropriate tool for the design phase of any system by contributing in the architecture definition, the creation of safety requirements, the identification of hazardous conditions and the planning of mitigation actions and other critical preparatory steps during the development of the system. Ergo, STPA contributes mainly in the design phase of a safe system. There are yet no reports in the literature describing how the safety specifications of STPA have been used to support decision-making during the operational phase of systems, since the aim of the method is to model unsafe scenarios to prevent hazardous situations. This may be related to the fact that STPA emphasizes on the states of the system that are linked to one or more unsafe control actions and does not take into consideration normal execution of control actions related to nominal system operation states. Control actions in STPA are typically analyzed in terms four different aspects in order to extract the respective UCAs, namely (i) _not providing causes hazard_, (ii) _providing causes hazard_, (iii) _providing too early, too late or out of order_ and (iv) _stopped too soon or applied too long_.
# Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model
## 2 Methodology
### Data Clustering
#### 2.3.2 Clustering Algorithm  
In this paper, the weighted k-means clustering was implemented for clustering the data. The weights calculated during the feature vector extraction as given in Section 2.3.1, which improves robustness to outliers and the fast convergence of the k-means algorithm were reasons this method of clustering was chosen. Generally speaking, any kind of clustering algorithm could be used.
# Detection of Volatile Constituents from Food Lures by Tephritid Fruit Flies
## Materials and Methods
### Electrophysiology  
Flies were immobilized in a 200 \(\upmu\)L micropipette tip with the antennae protruding from its narrow aperture. Capillaries filled with Beadle-Ephrussi finger solutions (7.5 g NaCl, 0.35 g KCl, 0.29 g CaCl\({}_{2}\) dissolved in 1 L of distilled water) were used to create conduction between the electrodes and one insect antenna. The glass capillary attached to the reference electrode was inserted into the head of the fly, and the glass capillary on the recording electrode was connected to the tip of the antennae. The recording electrode was connected to a pre-amplifier probe and then to a high impedance GC amplifier interface box (IDAC-2; Syntech, Kirchzarten, Germany). Aliquots of 2 \(\upmu\)L of the headspace sample were injected into a GC. The temperature program of the GC oven was set to start at 40 \({}^{\circ}\)C and held for 3 min, increasing by 10 \({}^{\circ}\)C min\({}^{-1}\) to 280 \({}^{\circ}\)C and held for 8 min. The inlet was in splitless mode with temperature of 250 \({}^{\circ}\)C, pressure of 12.96 psi and total flow of 38.2 mL min\({}^{-1}\). The sample was carried by H\({}_{2}\) through an HP-5 MS, 5% Phenyl Methyl Siloxane capillary column (Agilent, 30.0 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m nominal). The effluent from the GC was split 1:1 between the flame ionization detector (FID) and the antenna. The capillary column for the EAD passed from a Gerstel (Muhlheim, Germany) olfactory detection port-2 transfer line tracking the GC oven temperature into a glass tube (30 cm \(\times\) 8 mm), where it was mixed with charcoal filtered and humidified air at a flow rate of 1.5 L min\({}^{-1}\) and passed over the fly's antenna. EAD peaks were analyzed using Syntech data acquisition system software (GC-EAD 2012 v1.2.4, Syntech, Kirchzarten, Germany).
## 4 Discussion
### Sleep Monitoring  
The application of machine learning to sleep analysis in preschool children represents a particularly promising development in early childhood research. The achievement of 96.2% accuracy using Random Forest algorithms for sleep duration prediction [24] establishes a significant benchmark in automated sleep assessment. This high accuracy rate is particularly noteworthy given the challenges of traditional sleep monitoring approaches in young children [26].  
The implementation of Hidden Markov Models has enabled more sophisticated analysis of sleep patterns and their relationships with behavioural outcomes [27; 28]. These probabilistic approaches have shown particular promise in analysing complex sleep patterns [11]. The ability to detect subtle patterns in sleep behaviour holds significant implications for understanding early childhood development and health outcomes [10].  
Despite these promising advances, the relatively small number of studies (\(n=4\)) in sleep analysis indicates an emerging field with considerable room for growth. The high accuracy rates achieved thus far suggest that machine learning approaches could provide valuable alternatives to traditional sleep assessment methods, particularly in naturalistic settings where conventional sleep studies are impractical [26; 28].
## 3 CWT-SSSM Feature-Based Damage Index
### Second-Order Strain Statistical Moments (SSSM)
Under the premise that only the contribution of the \(i\)th-order strain mode shape is considered, Formulas (11) and (12) can be rewritten as:  
\[H_{sr}^{\varepsilon i}(\omega)=\frac{\psi_{si}\varphi_{ri}}{\overline{m}_{i} (\omega_{i}^{2}-\omega^{2}+2j\omega\omega_{i}\zeta_{i})} \tag{13}\]  
\[SSSM_{s}^{i}=\int_{-\infty}^{+\infty}H_{sr}^{\varepsilon i}(\omega)\overline{ H_{sr}^{\varepsilon i}(\omega)}S_{0}d\omega=\int_{-\infty}^{+\infty}\left|H_{sr}^{ \varepsilon i}(\omega)\right|^{2}\!S_{0}d\omega=\frac{\pi S_{0}\psi_{si}^{2} \varphi_{ri}^{2}}{2\zeta_{i}\sqrt{\overline{m}_{i}\overline{k}_{i}^{3}}} \tag{14}\]  
where \(SSSM_{s}^{i}\) is the SSSM considering only the \(i\)th-order principal vibration in the \(s\)th degree of freedom; furthermore, \(SSSM_{s}^{i}\) is only related to the \(i\)th-order strain modal parameters (\(\psi_{si}\), \(\overline{m}_{i}\), \(\zeta_{i}\), and \(\overline{k}_{i}\)) and the white noise excitation (\(S_{0}\) and \(\varphi_{ri}\)). If the linear structural system contains \(m\) degrees of freedom, then the second-order strain statistical moment \(SSSM^{i}\) for these degrees of freedom considering only the \(i\)th-order strain modal contribution can be expressed as:  
\[SSSM^{i}=\left[SSSM^{i}_{1},SSSM^{i}_{2},SSSM^{i}_{3},\ldots,SSSM^{i}_{m}\right]= \left[R_{i}\psi^{2}_{1i},R_{i}\psi^{2}_{2i},R_{i}\psi^{2}_{3i},\ldots,R_{i}\psi^ {2}_{mi}\right] \tag{15}\]  
\[R_{i}=\frac{\pi S_{0}\sigma^{2}_{m}}{2\zeta_{i}\sqrt{\overline{m_{i}}\overline{ k_{i}}^{3}}} \tag{16}\]  
Formula (15) shows that the second-order strain statistical moment \(SSSM^{i}\) considering only the \(i\)th-order principal vibration contains the strain mode shape, which is more sensitive to local damage. It is worth mentioning that in practical applications, \(SSSM^{i}\) can be calculated in the time domain with a very simple calculation procedure. Taking \(SSSM^{i}_{m}\) as an example, firstly, EWT is used to separate the strain response IMF\({}^{i}_{m}\) considering only the \(i\)th-order principal vibration, and then \(SSSM^{i}_{m}\) is directly calculated by substituting IMF\({}^{i}_{m}\) into Formula (6). It is expressed in the formula as:  
\[x(t)=\sum_{i=1}^{n}z_{i}(t)\varphi_{i} \tag{8}\]0  
where \(N\) denotes the number of sampling points of IMF\({}^{i}_{m}\), and \(\mu\) is the mean value of IMF\({}^{i}_{m}\).
###### Abstract  
This paper investigates the economic feasibility of utilising energy flexibility in aluminium production as a viable solution to leverage electricity surpluses arising from the increasing number of photovoltaic (PV) system installations. Future trends suggest that the generation capacity of PV systems will soon surpass consumption, leading to significant electricity surpluses, particularly during the summer. This surplus electricity, which is anticipated to be available at low prices, offers a unique opportunity to evaluate different investment and utilisation scenarios for aluminium production while simultaneously decreasing its environmental impact. The results demonstrate that, despite their high initial investment cost, large-scale PV power plants can potentially deliver maximum economic gains over a ten-year period. Conversely, the direct utilisation of surpluses without substantial investment can yield savings of up to EUR 17 million within the same time frame for Slovenia's case with an aluminium smelter, which has a maximum power usage of 60 MW. The findings of this study have significant implications in terms of shaping future energy strategies and policies, emphasizing the value of integrating renewable energy sources and industrial processes for enhanced economic and environmental outcomes.  
energy flexibility; aluminium smelting; renewable energy; virtual battery; solar production