esa-sceva/satcom-chunk-collection huggingface dataset:
# Total Ionizing Dose Effects on a Delay-Based Physical Unclonable Function Implemented in FPGAs
## 4 Experimental Results and Discussion
### RO-PUF Quality Factors  
We have calculated the main metrics related to PUF quality in order to evaluate the suitability of the RO-PUF for space applications. To that end, a 511-bit response has been generated for each accumulated doses. This 511-bit response is extracted by comparing the average frequencies of adjacent pairs of ROs in the array.
## 5 Performance Evaluation
Different application can make a choice according to the requirement of network lifetime and stability. Figure 10: Rounds of first node die. Figure 9: Network lifetime. VD-PSO: Variable Dimension based Particle Swarm Optimization; EC-PSO: Energy Centers Searching using Particle Swarm Optimization. Additionally, we explore the total traffic and end-to-end delay of the network. We firstly defined the average number of hops as the average number of the packages which are forwarded. Bigger the average number of hops is, busier the traffic of the network is. Meanwhile, there is a positive correlation between the average number of hops and end-to-end delay. In Figure 12, We can clearly see that VD-PSO owns the best performance in terms of average number of hops because it sets rendezvous points for mobile data collector and most of the sensors using single hop to send data to the sink when the sink accesses the rendezvous point. Our proposed EC-PSO adopts multi-hop communication to transmit data to the sink and the CHs need to forward their members' packages. Therefore, average number of hops in EC-PSO is bigger than that in VD-PSO. The algorithm [PERSON] et al. proposed also uses multi-hop communication, however their communication distance is not scheduled. Therefore, its average number of hops is biggest in the three algorithms.
## 4 Proposed System Overview and Use of Open Link Data Integration for Polystore Databases  
Open Data Integration is proposed as a novel way of thinking about data integration across enormous, increasing data sources, with an emphasis on data exploration and discovery [43]. Integration is motivated by data analysis requirements, hence, data discovery is performed to aid in this analysis. Data analysis necessitates the discovery of data that precisely joins, unites, or aggregates with existing data--a paradigm termed \"query-driven data discover\". Some various databases and datasets are stored in multiple scientific data repositories.  
Let us consider the following example to set up an integration application with multiple sources.  
Two sources, \(DS_{1}\) and \(DS_{2}\), contain the data information. \(G_{12}\) is the match operator to create a schema mapping between source \(DS_{1}\) and \(DS_{2}\). M is the mediated schema by a schema merge operator to source \(DS_{1}\) and \(DS_{2}\) and the mapping \(G_{12}\). The merge operator will create a schema that includes all the information in the source \(DS_{1}\) and \(DS_{2}\). The merge operator will also create mapping from source \(DS_{1}\) and \(DS_{2}\) to M, \(G_{1M}\), \(G_{2M}\), as seen in Figure 4.  
Now, if we add another source, \(DS_{3}\) a system and \(DS_{3}\) is similar to \(DS_{1}\), as seen in Figure 5.  
Figure 4: Common task in data integration with two sources.  
Figure 5: Composing mapping by adding new source.  
From source \(DS_{1}\) and \(DS_{3}\), matching helps to create a mapping \(G_{31}\). Additionally, by composing mappings \(G_{31}\) and \(G_{1M}\) helps to create a mapping \(G_{3M}\).  
To unify querying over different databases spanning a range of data models is a tedious task. Therefore, we propose a web-based Polystore system in this study to manage the complicated problem of heterogeneous data and to provide domain-specific search results across several storage systems. We save the header information, as well as the keys and values, on our local server using open-source PostgreSQL. All data and images pertaining to the ZTF are saved in the IRSA Cloud, which is built, managed, and hosted by IPAC. In the near future, this mapping can be implemented to connect different databases by employing top-down approaches.
## 5 Simulation Results and Discussions
### Tracking Control with Acoustic Localization  
#### 5.1.1 Localization Results  
For the considered scenario, the three buoys are localized at the surface as shown in Figure 2(a). The TOAs of the signals from the three buoys calculated by Equations (8)-(16) considering the isogradient SSP Equation (7) are plotted in Figure 2(b). Since \(T_{\rm{s}}^{\rm{t}}=1\) s, there are a total 600 TOAs being calculated for each buoy. As the AUV moves from the initial position to the destination, the distance between the AUV and buoy 1 is increasing while the distances between the AUV and buoys 2 and 3 are decreasing. Therefore, it is observed in Figure 2(b) that the TOA trajectory of buoy 1 ascends while the other two trajectories descend. Note that since the AUV approaches towards buoy 2 faster than buoy 3, the TOA trajectory of buoy 2 declines also more rapidly than that of buoy 3.  
Figure 3: Time of arrivals considering the sound speed profile.  
Figure 4 further shows the distance errors between the real horizontal distances \(d_{i}(t)\) and the estimated horizontal distances \(\hat{d}_{i}(t)\) as calculated with a binary search algorithm [27]. Small distance errors are observed. The micrometer magnitude in Figure 4 is because the threshold specified in the binary search algorithm for the estimated horizontal distance is set to \(1\times 10^{-5}\). The binary search algorithm makes trade-offs in terms of speed and accuracy.
# Software Design and Experimental Evaluation of a Reduced AES for IoT Applications
## 2 Related Work
### Development and Evaluation
AES also has gained a lot of popularity in the era of lightweight encryption for the purpose of secure transmission between IoT devices, due to its friendly software implementation [43]; however, [32] indicated that AES is not suitable for extremely constrained environments, such as RFID tags and wireless sensor networks, and they stressed the need for a new optimized block cipher. More recently, it was reported that AES may not act as an ultimate choice for the IoT environment, due to the fact that it requires extensive hardware resources [43]. The  
software evaluation of [44] compared Present and Celfia in terms of security strength, performance, and resource utilization, demonstrating that Present requires fewer memory footprints than Celfia, due to the fact that the latter utilizes two S-boxes; however, Present requires more encryption time and average CPU cycles than Celfia.
## 4 Experiment and Discussion
### Speech Recognition Experiment
#### 4.3.2 Results  
Figure 8 shows the recognition rate for speech. From the figure, the recognition rate of the proposed algorithm is best, with an average value of 77%. The recognition rate of patient S7 is the highest, reaching 86.4%, and that of S1 is the lowest, with a value 67.0%. Compared with the baseline algorithm, the recognition rates of three algorithms increase 16.2%, 3.4% and 4.5%, respectively. In addition, the performances of the IEC algorithm for patient S1 and the GP algorithm for patient S4 are lower than that of the baseline algorithm. As seen in the figure, the performance of the GP algorithm for patient S2, S3 and S4 is lower than that of the IEC algorithm.  
To show the effect of the self-fitting algorithm comparison, two-factor variance analysis is performed, and the results are shown in Table 2. From the table, compared with the IEC algorithm, the proposed algorithm shows statistical significance, i.e., the improvement effect of the algorithm is obvious. But patient S4 shows no statistical significance. Relatively speaking, compared with the IEC algorithm, the improvement effect of the GP algorithm is not obvious. Only the effects of patient S4 and S6 are obvious, but the effect of S4 with the GP algorithm is lower than that with the IEC algorithm.  
Figure 8: Comparisons of the recognition rate for speech.  
In addition, the statistics on test time shows that the P method needs 10-20 min, which is lower than the IEC method which needs 40-60 min, and lower than the GP method which needs 20-30 min.
## 4 Dataset Generation and Simulation Environment
### Rationale for a Simulation-Based Approach  
While real-world field data are the ultimate benchmark, a simulation-based approach offers several indispensable advantages for the initial, rigorous validation of a complex framework such as the MHMM:  
* Establishing an Unambiguous Ground Truth: The core task of the MHMM is to infer latent (hidden) proficiency states (Novice, Intermediate, Expert). In real-world settings, an operator's true, moment-to-moment proficiency level is unobservable and cannot be labeled with perfect accuracy. Our simulation provides an unambiguous ground truth where the underlying state sequence is known by design, allowing for precise, quantitative evaluation of model performance that is impossible with field data alone.
* Enabling Controlled, Systematic Stress Testing: Real-world data are often noisy and incomplete. A simulation allows for controlled experimentation where specific challenges can be isolated and systematically tested. As detailed in later, we can create scenarios to evaluate a model's robustness to sensor noise, its resilience to missing data, and its performance on imbalanced class distributions. This targeted stress testing is important for understanding a model's limitations before real-world deployment.
* Feasibility and Scalability: Collecting large-scale, longitudinal multimodal data in high-stakes environments (e.g., operating rooms, industrial settings) is logistically complex, prohibitively expensive, and fraught with ethical and privacy challenges. Simulation provides a practical method for generating the rich datasets required to train and validate sophisticated temporal models.
* Ensuring Reproducibility: By generating data from a defined parameterized model, this study is fully reproducible. Other researchers can use the same generative process to benchmark their models against this study's results, fostering transparency and advancing the field.  
Therefore, this illustrative study uses simulation not as a substitute for real-world validation, but as a necessary first step to rigorously demonstrate the MHMM's methodological soundness and comparative advantages under a range of controlled conditions.
# An Optimized Terpene Profile for a New Medical Cannabis Oil
## 2 Materials and Methods
### GC/MS Analysis of Terpenes  
Analyses were performed with a Trace GC Ultra (Thermo-Fisher Scientific, Waltham, MA, USA) equipped with an STABILWAX-MS column (30 m \(\times\) 0.25 mm i.d. 0.25 \(\upmu\)m film thickness; Restek, Bellefonte, PA, USA), a quadrupole mass spectrometer Trace DSQII (ThermoFisher Scientific, Waltham, MA, USA). The injector temperature, in selected split mode 10:1, was 250 \({}^{\circ}\)C and those of the detector and transfer line were 250 and 210 \({}^{\circ}\)C, respectively. The oven temperature program was: 35\({}^{\circ}\)C 5 min, from 35 to 60 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min, then from 60 to 160 \({}^{\circ}\)C at 6 \({}^{\circ}\)C/min, from 160 to 200 \({}^{\circ}\)C at 20 \({}^{\circ}\)C/min and finally 200 \({}^{\circ}\)C at 20 min [32]. The quantification of identified terpenes was performed with a calibration curve (y = bx + a) of each relative standard (i.e., \(\beta\)-Caryophyllene, Limonene and \(\alpha\)-Pinene). Calibrations curves were reported in Figures S1-S3. MS spectra of eluting peaks were compared with those of terpene standard mixtures to characterize and divide the terpen according to their isoprene structure. Quantification of total mono/di/triterpenes, total sesquiterpenes and total oxidized terpen was calculated summarizing a single quantity of each terpene of the same chemical class.  
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{2}{c}{**Electrospray Ionization Source**} & **ESI** \\ \hline Ion Transfer Tube temperature & 350 \({}^{\circ}\)C \\ Vaporization temperature & 300 \({}^{\circ}\)C \\ Electrospray tension & 3.50 kV \\ Scanning acquisition & SIM \\ Isolation range & 2 \(m/z\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Instrumental Conditions for HPLC–MS-MS Analysis.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & **Retention** & \multirow{2}{*}{**MRM Transitions (_m/z_)**} & **Collision** & \multirow{2}{*}{**Ion Spray**} & **Dwell Time** \\  & **Time** & & **Energy** & **Voltage (V)** & **(ms)** \\ \hline CBDA & 3.84 & 357.4 & 313.2 & 22 & 3500 & 19.96 \\  & & 339.2 & 19 & & \\  & & 245.2 & 28 & & \\ \(\upmu\)9-THCA & 4.39 & 357.4 & 313.2 & 22 & 3500 & 19.96 \\  & & 339.2 & 19 & & \\  & & 135.1 & 20 & & \\ CBD & 3.77 & 315.4 & 193.1 & 22 & 3500 & 19.96 \\  & & & 259.2 & 19 & & \\ \(\upmu\)9-THC & 4.04 & 315.4 & 193.1 & 22 & 3500 & 19.96 \\  & & & 259.2 & 19 & & \\  & & & 223.00 & 20 & & \\ CBN & 3.96 & 311.4 & 240.92 & 18 & 3500 & 19.96 \\  & & & 293.17 & 17 & & \\  & & & 154.0 & & & \\ ISTD & 3.2 & 285.1 & 193.0 & & & \\  & & & 222.2 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: MRM transitions of target cannabinoids.
# Robust Background Subtraction via the Local Similarity Statistical Descriptor
## 3 Background Modeling
### Background Model Representation  
Most of the background subtraction methods rely on probability density functions [4; 6] or statistical parameters [7; 29]. However, these assumptions inevitably introduce a bias in the real-world scenarios. In ViBe [20], the authors proposed the idea that the observed pixel samples in history would have a higher probability of appearing again. Relying on the collection and maintenance of background model samples with a random approach, a sample consensus background modeling method was proposed and has shown excellent performance in background subtraction.  
ViBe is a pixel-based flexible and lightweight background subtraction method. Each pixel \(p(x,y)\) in the background is modeled by a set of \(N\) recent background samples:  
\[\mathcal{B}(x,y)=\{v_{1},v_{2},\ldots,v_{N}\} \tag{9}\]  
where \(v_{i}\) is the pixel color of the \(i\)th background sample. To classify an input pixel \(p^{t}(x,y)\) at time \(t\) into foreground or background, it will be compared with the corresponding background model \(\mathcal{B}(x,y)\). Denoting the distance between the input pixel \(p^{t}(x,y)\) and background sample \(v_{i}\) as \(dist_{i}\), a match is defined as:  
\[\{v_{i}|dist_{i}<R,i\in[1,N]\} \tag{10}\]  
where \(R\) is a fixed maximum distance threshold. If the number of matches is larger than or equal to a given threshold \(\#_{min}\), then the input pixel \(p^{t}(x,y)\) is classified as background, otherwise it will be considered as foreground.  
The framework of the method presented in this paper is based on ViBe. However, the original algorithm only takes the color feature into consideration; in our background model representation, we integrate the LSSD features and color features to characterize sample representations. That is to say, each background sample \(v_{i}\) in Equation (9) consists of color and texture features. For a pixel \(p(x,y)\), its background samples in our method is modeled as:  
\[\mathcal{B}(x,y)=\{F_{1}(x,y),F_{2}(x,y),\cdots,F_{N}(x,y)\} \tag{11}\]  
where each background sample \(F_{i}(x,y)\) contains the color feature and LSSD feature, \(F_{i}(x,y)=\{I_{i}(x,y),LSSD_{i}(x,y)\}\).
## 1 Introduction
The first one is needed to change the direction of motion of the end-effector since it is not guaranteed that it is moving towards the SP; the latter is a linear trajectory path used to reach and stop the end-effector at the SP. Furthermore, the authors exploited the Wrench Exertion Capability (WEC) [12] index which belongs to the family of performance indexes that take into account the direction of motion [13; 14]. In this work, we considered the computation method presented in [15], which allows for computing cable tensions along the straight-line trajectory on-line. The algorithm offers great flexibility since it can be applied to any cable robot topology (i.e., under-actuated [16], fully actuated [17] or redundant cable robots [18]), in the presence of any number of broken cables, provided that a not-null static equilibrium workspace is available after cable failure. However, this approach does not take into account the physical features and the dynamic limits of the actuators, i.e., limited exertable torque, moment of inertia and presence of friction, which could affect the feasibility of the recovery strategy. Therefore, the aim of this work is to take into account the actuator limits to improve the recovery strategy introduced in [11]. The formulation of the optimization problems is improved by adding the constraints related to the actuator dynamics. Moreover, this work provides an alternative real-time approach for each part of the recovery trajectory. These algorithms can be more suitable than time-consuming linear programming approaches, enabling the use of this strategy in real-time applications. The recovery strategy has been tested in a simulation environment to show its feasibility. Lastly, the trajectories and acceleration constraints obtained by using the new formulation and without considering the actuator dynamics are compared. The paper is organized as follows: Section 2 introduces the cable robot model adopted and Section 3 presents the recovery strategy proposed considering the physics and dynamics of the actuators. The simulated tests and results are discussed in Section 4. Lastly, Section 5 concludes the work.
# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 8 Performance Evaluation of the Proposed FL-IDS
### Experiment 2  
This experiment was composed of four different scenarios, with an increasing number of clients (1, 2, 3, and 4) participating in the FT with no attack samples in their datasets. The scenarios are shown in Figure 15. This experiment was needed to assess the performance of the FL-IDS in a likely scenario in which some participants have no attack data.  
In this experiment, the same results as in Experiment 1 were obtained with non-relevant variation (\(\pm\)0.5%) after 2500 and 1200 rounds for the network and sensor data, respectively. With these rounded values, the impact of having 1, 2, 3, or 4 clients participating without attack data remained consistent.
## 3 Results
### Physical Properties
#### 3.2.3 Density and Density Growth Rate  
As shown in Table 3, the apparent density decreased with the decrease of the mass, as the amount of PMMA removed during sintering increased from HA0 to HA75. The results of the bone mineral density (BMD) from the Micro-CT obtained using 0.25 and 0.75 g/cm\({}^{3}\) of the BMD standard phantom corresponded to the tendency of the apparent density. BMD is defined as the volumetric density of calcium HA (CaHA) in a biological tissue (in g/cm3). It is calibrated using phantoms with a known density of CaHA [37]. The BMD after the sintering process was higher than that before the sintering process. As the amount of PMMA added increased, the BMD of the scaffold both before and after sintering decreased. However, as the amount of PMMA increased, it was observed that the difference in BMDs before and after sintering increased. Specifically, after sintering, HA75 became approximately 2.5 times denser than before sintering, whereas HA0 became approximately doubly denser (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **PMMA Addition (vol \%)** & **0** & **15** & **30** & **45** & **60** & **75** \\ \hline Archimedes’ principle & Apparent density (g/cm\({}^{3}\)) & 2.92 \(\pm\) 0.05 \({}^{\circ}\) & 2.88 \(\pm\) 0.05 \({}^{\circ}\) & 2.54 \(\pm\) 0.03 \({}^{\circ}\) & 2.43 \(\pm\) 0.77 \({}^{\circ}\) & 2.22 \(\pm\) 0.07 & 2.11 \({}^{\circ}\) \(\pm\) 0.14 \({}^{\circ}\) \\ \hline Micro-CT & \begin{tabular}{c} BMD \\ (g/cm\({}^{3}\)) \\ \end{tabular} & \
\begin{tabular}{c} Before sintering \\ After sintering \\ \end{tabular} & 0.57 \(\pm\) 0.23 \({}^{\circ}\) & 0.51 \(\pm\) 0.26 \({}^{\circ}\) & 0.38 \(\pm\) 0.21 \({}^{\circ}\) & 0.28 \(\pm\) 0.22 \({}^{\circ}\) & 0.17 \(\pm\) 0.21 \({}^{\circ}\) & 0.10 \(\pm\) 0.20 \({}^{\circ}\) \\ \hline \multicolumn{8}{c}{After sintering} &
\begin{tabular}{c} 1.18 \(\pm\) 0.44 \({}^{\circ}\) \\ \end{tabular} & 1.00 \(\pm\) 0.37 \({}^{\circ}\) & 0.75 \(\pm\) 0.32 \({}^{\circ}\) & 0.58 \(\pm\) 0.33 \({}^{\circ}\) & 0.40 \(\pm\) 0.30 \({}^{\circ}\) & 0.25 \(\pm\) 0.29 \({}^{\circ}\) \\ \hline \hline \end{tabular}  
* a,b,c Values with different superscripts in the same parameter are significantly different at \(p<0.001\).
* Significant difference between BMD before sintering and after sintering at \(p<0.001\).  
\end{table}
Table 3: Apparent density and bulk specific gravity of the HA scaffolds with respect to the PMMA addition amount.  
Figure 3: Linear shrinkage for each diameter and height of the scaffolds; the paired \(t\)-test for difference between height shrinkage and diameter was carried out and \(p<0.001\).
## 4 Literature Review on Supervised Learning Based Weather Nowcasting
#### Limitations of Existing Approaches  
[PERSON] et al. [31] highlight that support vector machines require feature reductions in order to be more accurate, so they might not be able to make use of all the information that a data set could provide. They also argue for the importance of collecting more data in order to train the machine learning models better. Another concern is that of feature selection: while other informative features do exist in the literature, it is not always straightforward to get them to a form usable for machine learning.  
As most works [31; 32] show, each system is mostly tested with some form of local real world data. This does not allow to accurately extrapolate a system's ability to adapt to other locations and their specific meteorological events and trends.  
A general concern which later on can morph into a limitation is the possible data imbalance. The performance of the proposed methods can be diminished by the small number of risky weather conditions compared with normal rain [35; 36]. The results from Reference [33] might not reflect the overall competence of ConvLSTM due to the fact that [PERSON] et al. used a rather small data set for testing and and a low threshold for rain-rate. Another limitation mentioned [35] is the fact that predictions tend to lower values of precipitations in time due to previously less confident predictions being fed back into the recurrent network. The architecture proposed by Reference [35] is limited to the train data and does not learn from new data. A concern experienced by others when using the proposed TrajGRU cell [36] is the speed. The implementation of this operator turns out to be slower than a simple ConvGRU.  
A common limitation of the solutions proposed by Reference [34] and Reference [37] is compromising the interpretability of the prediction results. The boosting-based solution [38] alleviate the issue of interpretability, but [PERSON] has highlighted in one of his studies [43] the sensitivity to outliers as a particular disadvantage of boosting. Outliers can negatively affect the final predictions since they might be excessively weighted during the boosting steps. An additional limitation of most of the existing solutions is that they do not combine multiple data sources thus being deprived by an expected enhancement of the predictive capability [44].