esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background Research  
A thorough literature on power quality monitoring and electrical network disturbances reveals a growing adoption of HOS in recent years. However, limited attention is given to fifth- and sixth-order statistics. While these indicators are explored in [9; 10; 11], they lack nominal value characterization for power signal analysis and their boundaries under events. For instance, ref. [10] focus on HOS for signal characterization, encompassing both normal operation and a series of disturbances, both simple and complex, underscoring the effectiveness of HOS in power quality monitoring but without defining indicators' nominal values. In contrast, ref. [11] estimates nominal values on even-order HOS, including kurtosis at \(-1.5\) and the sixth-order statistic at \(-6.2\) for a 50 Hz signal. Similarly, ref. [9] suggests nominal values of zero, \(-1.5\), zero and \(2.48\) for the third-, fourth-, fifth-, and sixth-order statistics, respectively. However, these values differ from those in [12; 13], where kurtosis is reported around \(1.5\). Furthermore, no consensus exists regarding the nominal value of the sixth-order moment. These references highlight one of the main disadvantages of using HOS: the difficulty in interpreting results [14]. Nevertheless, integrating additional indicators into the radar chart helps mitigate this issue.  
In recent studies, researchers are increasingly moving away from using a single indicator for PQ assessment, opting instead for a combination of tools. For instance, [9; 11] use various statistics, while [15] mentions the use of multiple indicators, including the crest factor, total harmonic distortion, and K-factor, for transient disturbance detection. Feature extraction from both time and frequency domains is discussed in [16], while [17] combines HOS and RMS for hybrid event detection. Similarly, ref. [18] uses multiple indicators like skewness, kurtosis, entropy, crest factor, and form factor to detect simple and complex events, a methodology also employed by [PERSON] et al. [19] and [PERSON] et al. [20], who classify PQ events using six key features.  
Unique visualization methods have also emerged to represent electrical disturbances. For example, ref. [12] introduces 2D diagrams showing signal state evolution during disturbances, leading to the development of the HOS Fingerprint tool for steady-state analysis [21]. In [22], a novel approach is proposed to detect abnormal operations, though these methods can be difficult for average users to interpret. Thus, the focus has shifted toward more accessible visualization tools, such as those outlined in [23; 24], which use color-based indicators and localized event depiction. However, these tools often rely on standards that may not fully capture the complex behavior of modern electrical loads.
# Elastic CRFs for Open-Ontology Slot Filling
## 5 Experiments
### Cross-Domain Task Results  
For the cross-domain tasks, we train models on one domain and test on the other. The common slots such as time, date are treated as known slots while the rest as unknown slots, such as theatre_name, restaurant_name. The evaluation metrics are the average exact-matching accuracies for values from known slots, unknown slots and total slots on the target domain. As shown in Table 2, eCRFs outperform other models in all conditions. In the cross-domain tasks, although there are some overlapping between the known slots on the two domains, the user utterances are different in expressing those slots and values. These results demonstrate that our eCRFs have greater generalization ability.  
Figures 4-6 show the prediction results for the same utterance on the movie domain with the eCRF and CT models. Figure 4 illustrates the predicted scores with only node potentials for eCRFs, while Figure 5 gives the predicted scores with both node and edge potentials. It can be seen that the boundaries of slot labels for some slots are mistakenly placed in Figure 4, e.g., the value _\"lincoln square cinemas\"_ for the unknown slot theatre_name is falsely predicted as two values _\"lincoln\"_ and _\"square cinemas\"_. When taking both node and edge potentials into account, correct predictions are obtained for all the three slots, as shown in Figure 5. The output probabilities of slot labels for the CT model are shown in Figure 6. Although the CT model gives the right prediction for the known slot date and unknown slot #ticks, it mistakenly predicts the value for the unknown slot theatre_name as _\"lincoln square\"_, as it fails to learn the semantic relations between slot labels.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Train** & **Test** & \multicolumn{3}{c}{**Average Accuracy for Known Slots**} & \multicolumn{3}{c}{**Average Accuracy for Unknown Slots**} & \multicolumn{3}{c}{**Average Accuracy for Total Slots**} \\ \cline{3-10}
**Domain** & **Domain** & **BT** & **CT** & **eCRF** & **BT** & **CT** & **eCRF** & **BT** & **CT** & **eCRF** \\ \hline sim-M & sim-R & 0.980 \(\pm\) 0.025 & 0.974 \(\pm\) 0.009 & **0.988 \(\pm\) 0.004** & 0.136 \(\pm\) 0.045 & 0.121 \(\pm\) 0.077 & **0.243 \(\pm\) 0.009** & 0.502 \(\pm\) 0.036 & 0.491 \(\pm\) 0.044 & **0.566 \(\pm\) 0.007** \\ \hline sim-R & sim-M & 0.814 \(\pm\) 0.064 & 0.915 \(\pm\) 0.013 & **0.926 \(\pm\) 0.024** & 0.165 \(\pm\) 0.040 & 0.246 \(\pm\) 0.017 & **0.377 \(\pm\) 0.031** & 0.508 \(\pm\) 0.035 & 0.599 \(\pm\) 0.006 & **0.667 \(\pm\) 0.020** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results for the cross-domain tasks: average exact matching accuracies for values from known slots, unknown slots and total slots on test domain for three models. Bold numbers mean the best results among three compared models.  
Figure 4: Potential scores with only node potentials in eCRFs for the cross-domain task. The darker the color, the higher the potential score.
## 7 Design and Assembly of Other Power Stage Circuits
Three 600 A current sensors from the LEM company were connected to the converter outputs to measure the current. In this study, the power stage was designed in three different structures. The dSpace DS1401 Digital Signal Processing unit was used for rapid control prototyping of the BLDC motor. The DS1401 DSP, which is compatible with MATLAB/Simulink blocks, embeds the Simulink blocks as hex code into the processor and generates the control signals. A real-time control system was created by transferring the PID control system and the fuzzy control system to the dSpace board via MATLAB/Simulink blocks for testing the HIL system and the power stage. By changing the control parameters in the HIL system, the optimum control parameters were determined and the system was operated in real-time. The tested power stage and control software were transferred to a dSP-based embedded structure. The BLDC motor, PID, and fuzzy control were designed using MATLAB R2018b Fuzzy Logic Toolbox. This HIL system provides a very good solution for rapid prototyping. The block diagram of this system is shown in Figure 24 [7]. The developed control algorithms were tested with this rapid prototyping. This experimental platform is shown in Figure 25. Cooling platforms in the electric vehicle HIL system were also tested as a practical application. For this purpose, the DC motor, DC generator, DS1401 dSpace Micro--AutoBox in the HIL structure, CAN-BUS Communication, PC connections, DC power supply, DC motor driver, and liquid cooling system were used. Figure 23: (**a**) Original three-phase 75 kW liquid cooled inverter circuit with six IGBT modules. (**b**) Figure in (**a**) viewed from different angle. Figure 17:0 SKiM459 GD12E4 IGBT module, adapter circuit, and IGBT driver assembly. A simulation model for the vehicle was obtained in this study by determining the basic parameters of a passenger electric vehicle. A brushless DC motor was selected according to the simulation result, a unique 75 kW DC-AC converter of three different types in compliance with automotive standards was designed to drive this motor, and practical test results demonstrated the success. The BLDC motor phase currents and voltages were monitored with an oscilloscope. This is shown in Figure 17:1 The simulation and the application results have shown compatibility, and the system performance has been successfully tested. The performance of the control algorithms, software, and the three types of power stages designed were tested. The BLDC motor phase currents (I\({}_{\text{f1}}\) Maroon; I\({}_{\text{2}}\) Gray; I\({}_{\text{B}}\) Orange) and, BLDC motor interphase voltages (V\({}_{\text{23}}\) Blue; V\({}_{\text{12}}\) Green; V\({}_{\text{13}}\) Red) were monitored with an oscilloscope as shown in Figure 17:2  
Figure 17:3 The BLDC motor’s rapid control prototyping block diagram with dSpace Micro—Auto Box. Figure 17:4 System engine-generator test platform with DS1401 dSpace Micro—Auto Box.
## 4 Proposed System
### Trajectory-based Message Delivery  
Suppose that \(sRSU_{j}\) collects and provides location-aware service information, such as traffic condition, gas station, and so on. Once \(trj_{d}=\{(pid_{di},loc_{i})\}\) of \(V_{d}\) is loaded, \(sRSU_{j}\) can disseminate location-aware service messages for \(V_{d}\) through an \(RSU_{i}\) deployed on around \(loc_{i}\). Message delivery from \(sRSU_{j}\) to \(V_{d}\) can be subdivided into three phases: (1) message distribution of \(sRSU_{j}\) to \(RSU_{i}\); (2) immediate message forwarding by \(RSU_{i}\) to \(V_{d}\) if \(V_{d}\) is within \(RSU_{i}\)'s transmission coverage; and (3) message carry-and-forwarding by other vehicles to \(V_{d}\) if \(V_{d}\) is out of \(RSU_{i}\)'s transmission coverage, as shown in the Figure 3.
# A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm
## 4 Simulation Results and Discussion  
To validate the performance of the IACO, we compare IACO with the ACO, GA, and PSO [15] from four aspects: convergence speed, MSRM, MMR and MPF in this section. We use the topology structure of the cognitive radio system as suggested in [6]. The user nodes are randomly generated in the rectangle region of \(10\times 10\). The number of primary users \(M\) is equal to orthogonal available spectrum number; the number of cognitive users is \(N\).  
Initialize IACO and ACO with \(\alpha=1,\beta=3,\rho=0.1,\tau_{0}=5,\tau_{1}=1\); In GA, the crossover rate \(pc=0.8\), mutation rate \(pm=0.8\); In PSO, two acceleration coefficients \(c_{1}=c_{2}=2\), inertia weight \(\omega=0.5\), maximum speed \(v_{x}=4\). The population sizes are all \(N\) in IACO, ACO, PSO and GA, the maximum evolution generation is 200. The four algorithms use the same topology, the iteration times in the convergence experiment are 500 while others are 50, finally taking the mean of the result.  
**Experiment 1**.: _Figure 4 shows the MSRM trend with the increasing times of iteration under \(M=15,N=10\) for different algorithms. In the first 50 iterations, GA is the slowest, ACO speeds up all the time but still inferior to PSO, and IACO is the best due to DE accelerating its convergence speed. At the later iteration, although PSO reaches the optimum firstly, its MSRM is lower than IACO; IACO has the highest MSRM in the end, because it adopts the VNS process to jump out of the local optimum._  
**Experiment 2**.: _In this experiment, we show the MSRM, MPF and MMR trends with the increasing number of available licensed spectrum for different algorithms in Figures 5-7, where the number of cognitive users is fixed on \(N=15\). In Figures 5-7, all curves increase all the time. It can be seen that, if \(M<N\), MSRM grows quickly whereas MPF and MMR increase slowly because of the competition for access to spectrum in cognitive  
Finally, we fix the cognitive user on \(N=5\), available spectrum \(M=5\) and use exhaustive method to obtain the optimal solution. The relative difference between each algorithm and the highest ideal value is obtained from \(rd=1-\frac{b}{B}\), where \(b\) is the efficiency value of algorithms, B is the highest ideal value. This experiment is based on MSRM, MMR, MPF. As shown in Table 2, IACO is already close to the optimal value at the 100 iterations, only MPF has a slight relative difference. IACO has the advantage of convergence and can reach the optimal value quickly.
# Determination of Hydrogen's Thermophysical Properties Using a Statistical Thermodynamic Method
## 2 Theoretical Model and Implementation
### Statistical Thermodynamic Model for Hydrogen
#### 2.1.1 Equilibrium Ortho-H\({}_{2}\) Fraction  
The nuclear rotation partition function reflects the distribution characteristics of particles in possible states, and represents the sum of all possible states or the occurrence probability of a single particle. Therefore, the ratio of the number of para-H\({}_{2}\) molecules to that of ortho-H\({}_{2}\) molecules in equilibrium H\({}_{2}\) is equal to the ratio of the nuclear rotational partition function of para-H\({}_{2}\) to that of ortho-H\({}_{2}\). The nuclear rotational partition functions of ortho-H\({}_{2}\) and para-H\({}_{2}\) can be expressed as [4; 20; 21]  
\[z_{0}^{r,n}=(i+1)(2i+1)\sum_{j=1,3,5...}^{\infty}(2j+1)\exp\biggl{(}-\frac{j(j +1)\theta_{r}}{T}\biggr{)} \tag{1}\]  
\[z_{\mathrm{P}}^{r,n}=i(2i+1)\sum_{j=0,2,4...}^{\infty}(2j+1)\exp\biggl{(}-\frac {j(j+1)\theta_{r}}{T}\biggr{)} \tag{2}\]  
where \(z^{r,n}\) is the nuclear rotational partition function, a unary function of temperature \(T\). \(i\) is the spin quantum number of a hydrogen molecule, \(i=1/2\). \(j\) is the angular momentum quantum number of a hydrogen molecule. \(j\) is restricted to even numbers for para-H\({}_{2}\), because the rotational wave function of para-H\({}_{2}\) molecules is symmetric; \(j\) is restricted to odd numbers for ortho-H\({}_{2}\), because the rotational wave function of ortho-H\({}_{2}\) molecules is antisymmetric. \(\theta_{r}\) is 84.837 K for hydrogen molecules.  
The ortho-H\({}_{2}\) fraction in equilibrium H\({}_{2}\) is  
\[x_{\mathrm{o}}=\frac{N_{\mathrm{o}}}{N_{\mathrm{o}}+N_{\mathrm{P}}}=\frac{z_ {\mathrm{o}}^{r,n}}{z_{\mathrm{o}}^{r,n}+z_{\mathrm{P}}^{r,n}} \tag{3}\]
# Detecting Small Anatomical Structures in 3D Knee MRI Segmentation by Fully Convolutional Networks
## 4 Experiments and Results  
The knee MRI images provided by Sunnmore MR-Klinikk used in our experiments were manually annotated. The final dataset used to compare the architecture of U-net variants and DeepLabv3 plus variants contains 13 labels. Figure 4 shows the extraordinary imbalance of the labels on the final dataset. The background accounts for 60.43% on average in all samples. The largest label is AD (adipose tissue), accounting for 19.17%, while the smallest is ACL (anterior cruciate ligament), accounting for 0.03%. Table 1 shows the abbreviations and values of classes (structures) in performance matrices. The red classes in Table 1 are the small organs in terms of the statistics of Figure 4.  
The dataset contains annotated knee MRI images from 20 volunteers. For each knee image, three weighted volumes, including T1 (longitudinal relaxation time), PD (proton density), and FS (fat-saturation) are provided. Different weighted volumes provide different contrasts for different tissues. For example, T1-weighted images provide high contrast for fat, but low contrast for water. FS pulse sequences can improve the detection of musculoskeletal lesions. The sizes of these images is 400 \(\times\) 400 \(\times\) 400. We cut each image into patches to feed them into the networks and use a large selection of them for training and the remaining for validation. (An example MRI can be found in Section 4.3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classes** & **Abbreviations** & **Index** \\ \hline Background & BG & 0 \\ Bone & BO & 1 \\ Posterior cruciate ligament & PCL & 2 \\ Anterior cruciate ligament & ACL & 3 \\ Muscle & MU & 4 \\ Cortical bone & CB & 5 \\ Blood vessel (political artery/vein++) & BV & 6 \\ Artery & AR & 7 \\ Collateral ligament & CL & 8 \\ Tendons & TE & 9 \\ Menisci & ME & 10 \\ Adipose tissue (fat) & AD & 11 \\ Veins & VE & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Abbreviations and values of classes.  
Figure 4: Frequencies of voxels for each class.
# Characterization of Natural Gypsum Materials and Their Composites for Building Applications
## 2 Materials and Methods
### Ultrasonic Waves and Mechanical Properties  
The ultrasonic technique allows us to measure the acoustic impedance and the speed of sound into a materials and therefore the actual mechanical characteristics, which are strongly related to the textural characteristics and chemical composition of crystals [19; 20]. In order to measure the dispersion of the Lamb modes, a transmission technique was used by varying incidence angles for the ultrasonic waves. The ultrasound signals were created with a generator of short intensive pulses (Sofranel 5052 PR), which excited a broad band ultrasound emitter (Panametrics V 309), with a central frequency of 5 MHz and they were sent through the samples.  
Samples of AG and SG-natural rocks (\(59\times 30\times 8\) mm\({}^{3}\)) were fabricated in order to evaluate the Poisson coefficient \(\
u\) and Young modulus E. Waves transmitted at a constant pulse repetition frequency as a function of time were collected.  
The Poisson coefficient \(\
u\) and the Young's modulus E are related to the propagation of ultrasonic waves in contact with the gypsum sample, in particular to the longitudinal and transverse velocities  
Figure 2: Hot disk thermal measurement method.  
of the ultrasonic waves \(C_{L}\) and \(C_{T}\), respectively [19]. The Poisson coefficient is linked to the ratio of longitudinal and transverse velocities as [21]:  
\[\
u=\frac{\left[\
icefrac{{[}{C_{L}/C_{T}]}^{2}-2}{2\left[\
icefrac{{[}}{C_{L}/C_{ T}}\right]^{2}-1\right]}} \tag{4}\]  
From the [PERSON]'s coefficient of each gypsum sample, its Young's modulus is expressed by [21]:  
\[E=\frac{\left((1+\
u)(1-2\
u)\right)}{(1-\
u)}\ \rho.C_{L}^{2} \tag{5}\]  
where \(\rho\) is the density of the sample. The shear coefficient is determined as a function of the density and the transverse velocity according to:  
\[G=\ \rho.C_{T}^{2} \tag{6}\]  
Some pieces of Agadir and Safi natural rocks were characterized by Vickers hardness tests. The Vickers indenter is a 136\({}^{\circ}\) square-based diamond pyramid and the Vickers hardness (HV) is calculated as the relation between the applied load F and the impression surface and it is defined by the following equation [20; 22; 23]:  
\[HV=1.8544\frac{F}{d^{2}} \tag{7}\]  
where \(d\) is the average length of the impression diagonal (mm) measured by means of an integral microscope of the Vickers tester.
# Phase Calibrated Ring Oscillator PUF Design and Application
## 4 Phase Calibrated PUF
### Estimation of Intra-PUF Hamming Distance  
The bit error rate is one of the most critical characteristic of PUFs. To estimate the RBER of PCPUFs, we need to know the intra-PUF Hamming distance first, which can be calculated through the distributions of RO frequencies and the Bit Flipping Rate (BFR). As we mentioned, when the frequencies of two ROs are closer, temperature, voltage and aging effects during operation lead to higher BFR. We begin with measuring the rising edges of ROs in the unit time and show the probability distribution in Figure 12. The unit time is set to 80 ns (16 clock cycles). Since we add the measured data from Tap 0 to Tap 31 and compute the average value, the results may not be integral. A fitting Gaussian curve is generated according to the discrete data, which are used to provide an ideal discrete distribution \(D(f)\) for our computation.  
Figure 11: PUF phase calibration timing.  
Figure 12: Distribution of RO rising edges on average.  
We define a specific bit flipping rate \(\lambda\) according to the difference of the discrete data. Marked as \(f_{1}\) and \(f_{2}\), the frequencies of two ROs determine the probability of bit flipping. A measured \(\lambda(|f_{1}-f_{2}|)\) in Figure 13 shows the statistical BFR results, which follows part of a normal distribution \(3.61~{}\times~{}10^{8}N(-12.99,1.88^{2})\). When two ROs have a \(|f_{1}-f_{2}|\) larger than one, their output is very stable. However, the BFR increases sharply when the difference range is less than 0.5. To further improve the reliability of PUFs, these RO pairs can be blocked from the RO pair selection sets.  
For any single bit, the probability of error occurrence can be computed as:  
\[\sum_{f_{2}=0}^{\infty}\sum_{f_{1}=0}^{\infty}D(f_{1})\times D(f_{2})\times \lambda(|f_{1}-f_{2}|)=0.0029 \tag{3}\]  
The probability that 128-bit responses contain exactly \(n\) bit errors is:  
\[\binom{128}{n}0.0029^{n}(1-0.0029)^{128-n} \tag{4}\]  
According to Equation (4), the ideal intra-PUF Hamming distance is shown in Figure 14. Therefore, we can calculate the average number of errors, which is 0.3688 in a 128-bit response.  
Figure 14: Expected intra-PUF Hamming distance.  
Figure 13: Distribution of one-bit RO output flipping rate.
## 3 Results
### Determining Optimal No. of Clusters  
Firstly, verification with a synthetic dataset with known clusters was performed to verify the algorithm's reliability applied to study the metastable substructures by clustering. A synthetic dataset of random values having four clusters was generated, their gram matrices were computed, and optimal clustering of the eigenvectors was determined using the sum of squared error (SSE) score and silhouette scores. Figure 2 below shows the accuracy of the clusters generated.