esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 SITHON
### Imaging payload
#### 2.3.2 Inertial measuring unit for positioning and orientation  
As mentioned above, the direct geo-referencing of the acquired images is based on the combination of GPS measurements with inertial measurements for the camera orientation angles _(pitch, roll, and yaw)_. The IMU device operated by the SITHON system is the MIDG II of Microbotics (Figure 3 and Table 3).  
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline
**Flying velocity V** & **Flying height H** & **Blur** & **\% FPA** & **Area detected on the** \\
**(km/h)** & **(m)** & **(\(\upmu\)m)** & **pixel size** & **ground LxW (mxm)** \\ \hline
160 & 1,000 & 14.8 & 57\% & 416 x 312 \\
200 & 1,000 & 18.5 & 71\% & 416 x 312 \\
160 & 1,500 & 9.90 & 38\% & 624 x 468 \\
200 & 1,500 & 12.3 & 47\% & 624 x 468 \\
160 & 2,000 & 7.40 & 28\% & 832 x 624 \\
200 & 2,000 & 9.30 & 36\% & 832 x 624 \\ \hline \end{tabular}
\end{table}
Table 2: SITHON forward motion blur for different combinations of flying velocity and height (f=19,86 mm, FPA detector size=26 \(\upmu\)m, \(\upmu\)=1/60 sec).  
Figure 2: (a) 3-D calibration tests deployed in the Photogrammetry Laboratory (NTUA) and (b) a sample image taken during the calibration test. The black and white box indicates the effective area of measurements.  
It is a low-cost, light weight, small size and low power package which integrates an internal GPS receiver that collects GPS positions and velocity information and passes it to the data fusion processor to be combined with the inertial data to generate the state vector. During operations the device provides in real time differential measurements of linear accelerations and rotational rate increments. The actual position, velocity and platform attitude in relevance to an inertial coordinate system is obtained by integration of the dynamic differential recordings. Gyro and accelerometer data from the IMU device allow the accurate determination of both attitude _(pitch, roll, and yaw)_ and position _(X\({}_{o}\), Y\({}_{o}\), Z\({}_{o}\))_ for the camera projection center at the time of exposure. To account for inaccuracies derived by device drifts in long time operations, the MIDG II inertial system is using a GPS receiver, which is ideally coupled with the inertial data using a Kalman Filter. This allows accurate upgrading of the estimated navigation and positioning data [9].  
\begin{table}
\begin{tabular}{|l|l|} \hline IMU Physical dimensions & 3.8 x 2.2 x 4.25 cm \\ \hline IMU Weight & 55 gr \\ \hline IMU Input Voltage & 10 VCD-32 VDC \\ \hline IMU Power & 1,2 W max \\ \hline GPS antenna connector & 50 Ohm SMA \\ type & \\ \hline GPS antenna power & +5 Volt at centre conductor, \\  & 25 ma max \\ \hline GPS RF power input & -134 dBm min, -61 dBm max \\ \hline Measurements \\ \hline Attitude accuracy (roll, pitch, and yaw) & 0.4 \({}^{\circ}\) \\ \hline Position accuracy & 3 m CEP, WAAS/ \\  & EGNOS available \\ \hline Data output rates & Position 10 Hz, \\  & Velocity, attitude, acceleration \\  & 50 Hz \\ \hline \end{tabular}
\end{table}
Table 3: MIDG II Inertial Navigation Device characteristics  
Figure 3: The MIDG II GPS/INS device of Microbotics.
# A High Compressibility Pressure--Sensitive Structure Based on CB@PU Yarn Network
###### Abstract  
In this work, a piezoresistive sensor structure based on carbon black (CB)@polyurethane (PU) yarn material was developed. Specifically, CB@PU yarn was constructed by the polymer-mediated water-based electrostatic deposition method. The distribution of the yarn was artificially controlled to fabricate conductive networks. The CB conductive layer was efficiently supported by the net-like structure of PU yarn, thus generating collaborative advantage. The as-fabricated pressure sensor not only displayed compressibility of over 97%, but also detected a wide pressure change from 25 Pa to 20 kPa. Furthermore, this sensor exhibited response time of less than 70 ms and reproducibility of over 10,000 cycles. The advantages of the CB@PU network ensured this pressure-sensitive structure enormous potential application in pressure sensitive equipment.  
pressure-sensitive; net-like structure; complete compression; responsive behaviors; piezoresistive sensors +
Footnote †: journal: Sensors
# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 3 IberSpeech-RTVE 2022 Evaluation Tasks
### Speaker Diarization and Identity Assignment Challenge
#### 3.2.2 Identity Assignment Scoring  
For the Identity Assignment Task, the assignment error rate (AER) is used, which is a slightly modified version of the previously described DER. This metric is defined as the amount of time incorrectly attributed to the speakers of interest divided by the total amount of time that those specific speakers are active. Mathematically, it can be expressed as:  
\[AER=\frac{FA+MISS+SPEAKEROR}{REFERENCE\;LENGTH} \tag{4}\]  
where:  
* _FA_ represents the False Alarm Time, which contains the length of the silence segments or speech segments that belong to unknown speakers incorrectly attributed to a certain speaker.
* _MISS_ represents the Missed Speech Time, which takes into account the length of the speech segments that belong to speakers of interest not attributed to any speaker.
* _SPEAKEREROR_ (Speaker Error Time) considers the length of the speech segments that belong to speakers of interest attributed to an incorrect speaker.
* _REFERENCE LENGTH_ is the sum of the lengths of all the speech segments uttered by the people of interest (i.e., those identities for which the participants will have audio to train their models).
## 5 Feasibility and Evaluation
### Architectural Comparison
#### 5.5.12. Testability  
While it is easy to create a test criterion against an individual component in a modular design, such as in a CBA, it is relatively complex to validate the entire system [29]. The increased number of components in EDAS, agent-based, centralized and distributed architectures makes it also hard to test and validate them. Interactions may be required in components distributed across network locations, which may make testing more complex [31]. On the other hand, end-user systems can easily be tested, as they are considerably less complex.
# Ultra-Low-Power FinFETs-Based TPCA-PUF Circuit for Secure IoT Devices
## 3 Simulation and Results
### PUF Evaluation Metrics  
We utilized three evaluation metrics to verify the proposed adiabatic PUF behavior.  
1. The Uniqueness: Used to determine the ability of a PUF to uniquely distinguish a chip among the other chips [35]. The ideal value of the uniqueness metric is 50%. The uniqueness is expressed as: \[\text{Uniquness}(\%)=\frac{2}{k(k-1)}\sum_{i=1}^{k-1}\sum_{j=i+1}^{k}\frac{ HD\big{(}R_{i},\ R_{j}\big{)}}{n}\times 100\] (4) where \(HD(R_{i},R_{j})\) represents the Hamming Distance of two different PUF instances' responses, with \(k\) = number of chips and \(n\) = bit-length.
2. The Reliability: Measures the reproducibility of the challenge-response pairs of a PUF instance with the varying environmental conditions, such as temperature and process variations. The reliability is mathematically expressed as: \[\text{Reliability}(\%)=100-\frac{1}{k}\sum_{i=1}^{k}\frac{HD\big{(}R_{i},\ R_{i,j}\big{)}}{n}\] (5) where \(k\) represents the total number of chips (\(k\) = 100 in this work), \(n\) is the number of PUF bits, and \(HD(R_{i},R_{i,j})\) is the Hamming Distance of \(j\)-th sampling of \(R_{i}\). The ideal value of the reliability is 100%.
3. Bit-Error-Rate (BER): Indicates the error rate of the reliability of a PUF circuit. The ideal performance of the reliability is 100%. However, it is difficult to achieve in practice. BER is expressed as: \[\text{BER}(\%)=100-\text{Reliability}(\%)\] (6)To enable the calculation of uniqueness, reliability, and the BER (Equations (2)-(4)), we conducted Monte-Carlo simulations with bit size of \(n=4\), and the chip number of \(k=100\). The calculation result of the uniqueness is graphically depicted in Figure 8, and the results of the reliability and the BER are shown in Figures 9 and 10, respectively, all under the condition of \(\pm 10\%\)\(V_{TH}\) variations. Output nodes' capacitors also vary from 10 to 200 fF to emulate the practical chip connection. In Figures 8-10, the solid lines depict the proposed FinFET-based TPCA-PUF and the dotted lines show the FinFET-based QUALPUF.  
Figure 7: Energy consumption: (**a**) 10% of TOX variation and (**b**) 10% of V\({}_{\text{TH}}\) variation, with load capacitance varying from 10 to 200 fF.
# A Reward Population-Based Differential Genetic Harmony Search Algorithm
## 3 Improved Harmony Search Algorithm
### Reward Population-Based Differential Genetic Harmony Search Algorithm (RDEGA-HS)  
We hope the combination of the multi-population reward mechanism with a differential genetic harmony algorithm will increase the diversity of harmony and improve the convergence rate.  
The specific flow of the algorithm is as follows.  
Step 1. Initialization of algorithm parameters.  
Step 2. Random initialization of harmony memory.  
Step 3. The total harmony memory is divided into \(Q+1\) (\(Q\) is an integer greater than 0) sub-harmony memories, including \(Q\) common sub-harmony memories and one reward sub-memory.  
Step 4. As to all sub-harmony memories including the reward sub-memory, new harmonies are generated on the basis of differential genetic operations.  
Step 5. Update of each sub-harmony memory.  
Step 6. Judge whether the maximum number \(T^{1}_{max}\) of iterations has been reached. If no, Steps 4 and 5 are repeated. Otherwise, move on to Step 7.  
Step 7. Find the common sub-harmony memory with the best evolution effect at present, and combine it with the original reward harmony memory. Then, sort them from good to bad according to the fitness. On the premise of keeping the number of harmonies in the newly generated harmony memory unchanged, eliminate bad harmonies, and form a new reward harmony memory to replace the original reward harmony memory.  
Step 8. Judge whether the upper limit \(T^{2}_{max}\) of iteration has been reached. If yes, then exit the loop and output the optimal solution. Otherwise, return to Step 4.  
The pseudo-codes of the algorithm are shown in Algorithm 2.  
```
1: Initialize the algorithm parameters
2: Initialize the total harmony memory
3: For \(Pop=\cup Pop_{i}\)
4: Repeat
5: While time(2) \(<T^{2}_{max}\), do
6: Improve each new harmony as follows:
7: While time(1)\(<T_{max}\)
8: For all \(j\), do
9:\(x^{new}_{j}=\left\{\begin{aligned} x^{best}_{j}+F(x^{k}_{j}-x^{l}_{ j})&\text{with probability HMCR}\\ \text{random selection}&\text{with probability 1-HMCR}\\ \end{aligned}\right.\)
10: If with probability HMCR, then
11:\(x^{new}_{j}=\left\{\begin{aligned} x^{rand}_{j}+F(x^{k}_{j}-x^{l}_{ j})&\text{with probability PAR}\\ x^{new}_{j}&\text{with probability 1-PAR}\\ \end{aligned}\right.\)
12: Else, do
13:\(x^{new}_{j}=\left\{\begin{aligned} x^{new}_{j}& \text{with probability PAR}\\ x^{new}_{j}&\text{with probability 1-PAR}\\ \end{aligned}\right.\)
14: End If
15: End For
16: If the new harmony vector is better than the worst one in the
17: original HM, then
18: update the harmony memory
19: End If
20: End while
21: Update the reward harmony memory
22: End While
23: End For
24: Return best harmony
```  
**Algorithm 2**RDEGA-HS Algorithm
# Enhanced Conjunction Assessment in LEO: A Hybrid Monte Carlo and Spline-Based Method Using TLE Data
## 2 Materials and Methods
### Collision Probability Estimation Model  
Using the refined TCA, the collision probability is re-evaluated. The multi-stage refinement procedure described in Section 2.3 is applied to the nominal trajectory derived from the unperturbed TLE. This refined TCA serves as a reference time point for re-evaluating the collision probability across the Monte Carlo ensemble. Individual samples are not subjected to separate multi-stage refinements, as the computational cost would be prohibitive for large sample sizes. Instead, each sample's position is assessed at the refined TCA to determine whether it falls within the collision threshold, consistent with Equation (17). A sample is classified as a collision if the relative distance is less than a defined threshold \(D_{\text{thresh}}\). The probability and standard deviation are estimated as  
\[P_{c,MC}=\frac{N_{\text{col}}}{N_{\text{samples}}},\quad\sigma_{MC}=\sqrt{\frac {P_{c,MC}(1-P_{c,MC})}{N_{\text{samples}}}} \tag{16}\]  
where \(N_{\text{col}}\) is the number of collision samples, \(N_{\text{samples}}\) is the total number of generated samples, and \(\sigma_{MC}\) is the standard deviation.  
The Differential Algebra Monte Carlo (DAMC) method provides an efficient alternative to standard Monte Carlo simulation for estimating collision probability. Unlike traditional Monte Carlo approaches that rely on propagating a large number of individual samples, DAMC uses high-order polynomial expansions to approximate the propagation of uncertainties through nonlinear dynamics. DAMC constructs a Taylor expansion of the propagated state vector \(\mathbf{x}(t)\) with respect to \(\mathbf{x}_{0}\) as follows [20]:  
\[\mathbf{x}(t)\approx\tau_{d}\left(\mathbf{x}_{0}\right)=\sum_{|a|\leq d}c_{ \alpha}\left(\mathbf{x}_{0}-\mathbf{x}\right) \tag{17}\]  
where \(\mathbf{x}_{0}\in\mathbb{R}^{n}\), \(\tau_{d}\) is the differential algebra-based expansion of order d, \(\mathbf{x}\) is the nominal state (expansion point), \(\alpha\) is a multi-index, and \(c_{\alpha}\) are the polynomial coefficients computed via automatic differentiation.  
After constructing this expansion, DAMC evaluates the collision probability by sampling the uncertainty space and applying the polynomial model directly, avoiding numerical integration. For each sample \(i\), the minimum distance \(d^{(i)}\) is computed efficiently.  
The probability of collision is then estimated using  
\[P_{c}^{DAMC}=\frac{1}{N}\sum_{i=1}^{N}I(d^{(i)}<d_{c}) \tag{18}\]  
This expression reflects the standard Monte Carlo estimate, a method particularly effective for large-scale or time-sensitive conjunction assessments that demand rapid and accurate probability estimation.
## 2 Materials and Methods
### Evaluation of Implant Primary Stability  
Depending on the manufacturer, the actual diameter, length, and taper of the burs are different. Since these conditions affect the IPS after implant insertion, the actual diameter, length, and taper of the burs used were adjusted within the 0.2 mm error range. Ossstem tapered burs (TPD3C, OSSTEM IMPLANT Co. Ltd., Seoul, Korea), with a final 4.3 mm (standard drilling) or 3.8 mm (under-drilling) major diameter and 10 mm length, were used for CD, and Versah universal tapered burs (VT, Versah LLC, Jackson, MI, USA), with a final 4.3 mm (standard drilling) or 4.0 mm (under-drilling) major diameter and 10 mm length, were used for OD. IPS was evaluated using two methods: resonance frequency analysis (RFA) and Periotest. Immediately after the implant insertion, the Periotest value (PTV) was evaluated, using the Periotest device (Periotest(r) M, Medizintechnik Gulden, E.K., Modautal, Germany), and the implant stability quotient (ISQ) was tested using the Ossetl device (Ossetl(r) ISQ, OSSTELL, Goteborg, Sweden), in each group of IPS test. PTV and ISQ were randomly measured twice per implant.
## 4 Proposed Work
### Driving Vehicle Motion and Lane Change Detection  
In its range, RSU is responsible for vehicle monitoring and vehicle motion. Lane change behaviors are predicted. Vehicle motion is described by two sets of attributes as _Nearby Vehicle Report \((V_{R})\) and Past Mean Velocity \((M_{V})\)_. This information is collected and forwarded to the Hidden Markov Model (HMM) that predicts the vehicles motion accurately and also Lane Change Deviation is measured by _Vehicle Direction \((V_{D})\)_, _Distance to neighboring vehicles \((N_{D})\) and past sequences of lane change \((L_{\mathbb{C}})\)_, The proposed HMM is defined as follows,  
\[H=(n,m,\pi,a,b) \tag{1}\]  
where \(n\) represents the number of states, and the value of \(n\) is 3. In our work, we have three states (frequently state, no change and mild change). \(m\) represents the observation symbol and \(\pi\) represents the initial state distribution. \(a\) represents the transition probability of the state and \(b\) represents the probability of observation.  
In our work, HMM consider three states with the feature parameters such as \(V_{R}\) and \(M_{V}\) for motion prediction and \(V_{D},~{}N_{D},L_{\mathbb{C}}\) for lane detection. Then, the HMM is defined as follows,  
\[H=(\pi,a,b) \tag{2}\]  
Figure 2: Overall architecture of MODAL-IoCV.  
If the condition is expressed as Q (\(i\)=1, 2... \(n\)), \(n\) represents the condition limit of the Markov chain. The observed values are represented as \(R\) (\(j=1,2\ldots,m\)) where m represents the number of observed values as \(y_{t}\) and the Markov sequence is represented as \(x_{t}\). The probability matrix of the HMM for vehicle motion detection and lane detection is defined as follows,  
\[B=\left[A_{ij}\right]_{n\times n} \tag{3}\]  
where,  
\[A_{ij}=P(x_{t+1}=Q|x_{t}=Q),1\leq i,\;j\geq n \tag{4}\]  
The observation probability matrix is defined as follows,  
\[D=\left[C_{ij}\right]_{n\times m} \tag{5}\]  
where,  
\[C_{ij}=P(y_{t}=R|x_{t}=Q),1\leq i\leq n,\;j\leq m \tag{6}\]  
The initial condition probability vector is defined as follows  
\[\pi=P(x_{t}=Q),\;1\leq i\leq n \tag{7}\]  
Therefore, the probability of motion and lane change detection is defined using HMM, based on the probability, and the HMM provides three classes (frequently change, no change and mild change). Figure 2(a) illustrates the conceptual diagram of motion and lane change detection, Figure 2(b) represents the probability matrix which shows the current position and previous position, and Figure 2(c) represents the process of HMM, where I represents the initial state and E represents the end state.  
Figure 3: (**a**) conceptual diagram, (**b**) state positions, (**c**) hidden Markov model.
# Calculation of the Rearfoot Angle Representing Flatfoot from Comparison to the Cavanagh Arch Index
## 3 Results
### Correlation Results of Arc Index (AI) and Rear Foot Angle (RFA)  
Prior to the correlation analysis between AI and RFA determined, this study analyses a certain gender that has a higher RFA value. The present study has a similar result to [13] that reported the mean RFA of males is lower than females. According to the measurements, the person is assumed to have a flatfoot category if the RFA \(\geq 5^{\circ}\); and according to the results, the RFA \(\geq 5^{\circ}\) is mostly addressed to female subjects [13].  
There is a significant correlation between AI and RFA, with a correlation coefficient of R\({}^{2}\) for the left and the right foot being 0.63 and 0.73, respectively, as presented in Figure 5. The correlation equation between AI and RFA was obtained as RFA = 46.04 AI - 6.41 and RFA = 45.32 AI \(-\) 6.26 for the left and right foot, respectively, as expressed in Equation (2). The RFA value at AI = 0.26 (the minimum value of [PERSON]'s AI, which represents flatfoot) is 6.3\({}^{\circ}\). Meanwhile, the average RFA value of all subjects (left and right foot) is 16.0 \(\pm\) 4.0\({}^{\circ}\), which is calculated using Equation (2), and yield an AI range between 0.25-0.45.  
Figure 5: _Cont._  
The AI range of the measurement results differs slightly from the highest and the lowest. Thus, the value of RFA \(\geq\) 6.3\({}^{\circ}\) can be used as a new diagnosis reference for flatfoot in the measurement of the RFA.  
\[\text{RFA}=46.04\text{AI}-6.41\ \text{(Left foot)} \tag{2}\]  
\[\text{RFA}=45.37\text{AI}-6.26\ \text{(Right foot)} \tag{3}\]  
From the previous studies [27], the degree of flatfoot is in the minor category if AI = 0.26-0.29, moderate if AI = 0.30-0.33, severe if AI = 0.34-0.36, and worst if AI = 0.37-0.39. The range of RFA values for each degree of flatfoot is presented in Table 2.  
The statistical analysis was carried out using the ANOVA method to examine the correlation level of RFA to AI. The analysis results are presented in Tables 3 and 4 for the left foot and right foot, respectively. The \(p\)-value shows the significance of RFA variables on AI variables. According to Tables 3 and 4, the \(p\)-values of 3.83 \(\times\) 10\({}^{-25}\) and 1.61 \(\times\) 10\({}^{-25}\) are much smaller than 0.05. To confirm the relationship between RFA and AI, a statistical test that compares the means of two samples is used; \(t\)-  
This research was also motivated by cases where patients who suffer from flatfoot commonly have a different back angle than those who do not; therefore, research is needed to determine the correlation between the angles behind the foot arising from the presence of a flatfoot.  
The RFA measurement is evident as a potential method for flatfoot diagnosis; however, this method has not been presented obviously as an applicable method for flatfoot measurement. This study examined the RFA by calculating the correlation with the well-known method for flatfoot detection.
# Muscle Activity and Biomechanics of Sprinting: A Meta-Analysis Review
## 4 Discussion
### Fatigue-Induced Biomechanical Changes  
Fatigue has a pronounced effect on muscle activation timing and sprinting mechanics, altering neuromuscular coordination and movement efficiency. One of the most notable adaptations observed in this study was the earlier activation of the hamstrings during the swing phase, which likely serves as a protective mechanism against muscle strain. This supports previous findings that neuromuscular adaptations under fatigue aim to reduce eccentric loading on the hamstrings, thereby minimizing the risk of injury [18].  
However, fatigue also leads to reduced hip and knee flexion, resulting in prolonged ground contact time and impaired sprint efficiency [19]. These kinematic alterations compromise an athlete's ability to maintain high-speed sprinting and may contribute to an increased risk of overuse injuries due to altered force distribution upon foot strike [4]. Studies have shown that fatigue-induced reductions in stride efficiency can significantly affect an athlete's ability to generate explosive force, ultimately slowing sprint velocity [20].  
These results reinforce the importance of neuromuscular endurance training to delay fatigue-induced mechanical inefficiencies in sprinting athletes. Specifically, given that hamstring injuries are common in high-speed running, preventive training should prioritize exercises that enhance fatigue resistance in the hamstring complex. Research indicates that the biceps femoris long head is particularly susceptible to strain under fatigue, emphasizing the necessity for eccentric strengthening interventions such as Nordic hamstring curls and isometric holds to build resilience [20].
## 2 Materials and Methods
### Evaluation of TSNP Application in Biosensing and Diagnostics
#### 2.4.2 AuTSNP Sucrose Sensitivity Test  
Assessment of the AuTSNPs performance as nanob biosensors was analyzed through a sensitivity test (sucrose test) adapted from [54] by changing the refractive index of the nanoplates' local environment. A range of AuTSNP volumes were suspended in a 1:1 ratio to different concentrations of sucrose solutions (10%, 20%, 30%, 40%, and 50% (\(w/v\)) in ultrapure water), increasing the refractive index as the sucrose concentration rises. UV-Vis spectrum measurements of each sample were recorded to observe changes in the LSPR spectra.