esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Slope Diffuse Reflectance Instrument for Calibration-Free Broadband Spectroscopy
## 3 Results  
The phantom experiment described in Section 2.2 consisted of measuring four phantoms, each with both Multi-Distance Frequency-Domain Near-Infrared Spectroscopy (MD FD-NIRS; Figure 1a) and Dual-Slope Continuous Wave broadband Diffuse Reflectance Spectroscopy (DS CW-bDRS; Figure 2a). The phantoms were Milk and Water (MW), that with India Ink added (MW+II), then that again with NIR746A added (MW+II+N7), and then that yet again with NIR869A added (MW+II+N7+N8).  
Figure 4 shows the results of this experiment. The absolute absorption (\(\mu_{a}\)) spectrum is shown across wavelengths (\(\lambda\)) for all four phantoms and two measurement methods (Figure 4a). Additionally, Figure 4a shows a dashed line representing the modeled (as 99.1% water [40,41] and 0.9% lipid [42] volume fractions) absorption of the MW phantom. The coefficient of determination (\(R^{2}\)) was calculated for the MW data and model, yielding a value of 0.98, implying that 98% of the variance in the MW data is explained by the modeled MW absorption. In Figure 4b-d, the change in absorption (\(\Delta\mu_{a}\)) as a result of  
adding the three different dyes (II, N7, and N8) is shown. The error regions in all plots are dominated by the systematic uncertainty in the distances on the DS-CW-bDRS probe (estimated at \(\pm\)0.1 mm for chained dimensions) and within the MD-FD-NIRS measurement (estimated at \(\pm\)0.5 mm for initial position and \(\pm\)0.01 mm for scan pitch). Note the flat absorption contribution from II and the spectral features from N7 and N8. Particularly, a peak between 700 nm and 800 nm for N7 and a peak between 800 nm and 900 nm for N8. How these results serve to validate the instrument will be discussed in Section 4. Briefly, notice the agreement between the MD FD-NIRS and DS CW-bDRS measurements and the agreement between the expected MW spectrum and the DS CW-bDRS measured MW spectrum.  
Figure 4: Results from phantom experiment. (**a**) Absolute absorption (\(\mu_{a}\)) spectra as a function of wavelength (\(\lambda\)). Showing results from Dual-Slope Continuous-Wave broadband Diffuse Reflectance Spectroscopy (DS CW-bDRS) and Multi-Distance Frequency-Domain Near-Infrared Spectroscopy (MD FD-NIRS) measurements. Spectra shown for the following phantoms: Milk and Water (MW), MW plus India Ink (II), MW plus II plus NIR746A (N7), and, finally, MW plus II plus N7 plus NIR869A (N8). DS CW-bDRS points show individual wavelength measurements and lines show smoothed (moving average) spectra for visualization. Dashed line shows the expected spectrum for MW modeled as water and lipid. (**b**) Change in absorption (\(\Delta\mu_{a}\)) from adding N8 (i.e., \(\mu_{a}^{MW+II+N7+N8}-\mu_{a}^{MW+II+N7}\)), (**c**) \(\Delta\mu_{a}\) from adding N7 (i.e., \(\mu_{a}^{MW+N7}-\mu_{a}^{MW+II}\)), and (**d**) \(\Delta\mu_{a}\) from adding II (i.e., \(\mu_{a}^{MW+II}-\mu_{a}^{MW}\)).
# Investigation of 2 DOF PID Controller for Physio-Therapeutic Application for Elbow Rehabilitation
## 2 Mechanical Design of Exoskeleton
### Mathematical Model  
Since the motor is considered as the system, the mathematical design for the motor should first be considered. In the prototype, the brushless DC motor is internally geared down by a ratio of 20 that is sufficient for rehabilitation tasks, and, hence, no external gear reduction along the link is required. The actuation motor is a back-drivable brushless DC motor (Maxon RE50, 370955, 200 W, 36 V) with a gear ratio of 1:113 inside. This motor is chosen primarily because it reduces system friction, noise, and backlash, while still being compact [35].  
The Equation (1) has been used to calculate the transfer function based on previous work on brushless DC motors [39].  
\[G(s)=\frac{\omega_{m}}{V}=\frac{K_{m}}{(J_{m}L)s^{2}+(B_{m}L+J_{m})s+(K_{b}K_{ m}+B_{m}R)} \tag{1}\]  
This equation can also be represented as follows as presented Equation (2) in many other literature [35].  
\[G(s)=\frac{\frac{1}{K_{b}}}{(\tau_{m}\tau_{e})s^{2}+(\tau_{m})s+1} \tag{2}\]  
Substituting for \(\tau_{e}\), \(\tau_{m}\) and \(K_{b}\) the open loop transfer function for the BLDC motor system can be written as:  
\[G(s)=\frac{862.06}{0.00544s^{2}+0.666s+1} \tag{3}\]  
where, \(\tau_{e}=0.666\) H\(\Omega^{-1}\), \(\tau_{m}=0.00818\) W\(\Omega^{-1}\), \(K_{b}=0.0035\) NmA\({}^{-1}\).
### Motivation and Contribution  
In view of the inspiration mentioned earlier and to the best of our knowledge, the evaluation of secrecy outage performance in CR-NOMA networks employing MCC and STschemes is an outstanding issue. Consequently, we propose a novel multiple-antenna CR-NOMA network architecture, wherein the secondary base station (BS) transmits signals to three secondary users while accounting for the presence of an eavesdropper over Rayleigh fading channels. The transmission strategy considers interference caused by the secondary network to the primary user (PD) and adopts a power constraint to ensure QoS of PD. This approach not only enhances the spectrum utilization of the network, but also ensures the security of the transmitted data. The article's primary contributions can be condensed as follows:  
* We investigate the secrecy outage performance for MISO-CR-NOMA networks with MCC and ST schemes. The exact closed-form expressions for the SOP of secondary users are derived. To obtain further insights, we conduct the asymptotic analysis of SOP for MCC and ST schemes when the transmitted power of BS reaches the QoS constraint;
* Based on the secrecy outage performance with MCC and ST schemes, we study the problem of optimal power allocation and minimum SOP, and design a simulated-annealing (SA) algorithm based on the QoS constraint to solve the problem;
* Through theoretical analysis and Monte Carlo simulation verification, we compare the secrecy outage performance of the overall secondary network and every secondary user with the ST scheme and the MCC scheme, verifying the accuracy of the optimal power allocation algorithm. It is shown that: (1) The MCC scheme outperforms the ST scheme in secrecy outage performance; (2) the proposed power allocation algorithm optimizes the secrecy outage performance of the secondary user network; and (3) the optimal power allocation factor changes when the BS transmission power changes.
# IoT-Chain and Monitoring-Chain Using Multilevel Blockchain for IoT Security
## 6 Experiment
### Consensus Algorithm  
In \(Network_{ic}\), the delay time is reduced by randomly selecting \(Node_{leator}\) for consensus when creating \(Block_{ic}\). As a result, only the delay time corresponding to 0.004 s is displayed. For a performance comparison, the PBFT consensus algorithm implemented in the Fusion-Chain of the IoT-Chain node was applied, and the PoW consensus algorithm with \(Nonce=2\) was applied to compare the delay time. As a result of generating \(Block_{ic}\) from \(Network_{ic}\) by applying the proposed consensus algorithm, there was an average delay of 0.0044 s, as depicted in Figure 11. However, when blocks were generated through the PBFT consensus algorithm, the average delay time was 0.1266 s. The average delay time of 5.528 s was measured when using PoW. Through the proposed consensus algorithm,  
Figure 10: Size comparison according to IoT-Chain export consensus.  
Figure 9: IoT device for IoT-Chain network configuration.  
the block generation time was reduced by 96% compared to PBFT and 99% compared to PoW. Moreover, when a leader node is selected, a DDoS attack can occur because all nodes can know the leader node when using the method of sending a transaction to the leader node recorded in the last block, but the problem could be solved with a delay of only 0.0044 s. The experimental results confirmed that the latency was reduced compared to the representative consensus algorithm with high CPU computation and network communication.  
As depicted in Figure 12, which is the experimental result of comparing the average CPU usage of the leader node and the consensus participating node during consensus for block generation, it was confirmed that the proposed consensus algorithm had the lowest average CPU usage.  
The experiments were conducted by changing the consensus algorithm of IoT-Chain to PoW, PBFT, and the proposed consensus algorithm. In the case of CPU usage, average CPU usage was sampled for 1 s, and the average CPU usage values of the leader node (PBFT, proposed consensus algorithm) and mining node (PoW) were measured from block creation to consensus. The number of nodes of all IoT-Chains was the same, five, and the difficulty nonce value of PoW was set to two.
## 3 Results
### Out-of-Plane Deflection Validation of Interfacial Debonding  
The out-of-plane displacement was tracked by DIC and proved that the movement in the Z direction was greater for the IID panel at early load level as interfacial detachment occurred. As illustrated in Figure 5a, the out-of-plane displacement was not homogenous throughout the panel bottom side and there were zones with greater displacement as the load increased. The greatest displacement values were detected in both cases in the middle of the AOI. The extensively debonded zones are colored in red and their shape defines the potentially debonded zones under the TRC skin. Indicatively, at 3.5 kN/m\({}^{2}\) extended red zones of great displacement were detected for the IID case compared to the limited red zones for the REF panel. A point was fixed for both series at the zone of the maximum displacement, and the respective displacement is tracked in Figure 5b. A jump in displacement values was detected at 1.5 kN/m\({}^{2}\) for the IID sample, which is direct evidence of out-of-plane movement that can only be associated with interfacial debonding. Quantitatively, the out-of-plane deformation was limited to 95 um due to the fact that the TRC layer was restricted and under load, therefore the skin cannot freely move. In any case, the inspection guided by AE wave features trends and validated by DIC  
Figure 3: Rise time (RT) and Initiation frequency (IF) scatter plots (data in transparent font) for a representative time period (marked in Figure 2a,b) of a jump in AE energy. Averaging (each 250 points) trend line is plotted for (**a**) REF and (**b**) IID panels.  
displacement mapping defined the moment at which debonding was physically there, and it is remarkable that it occurred so prematurely.  
Figure 4: Principal E1 (%) strain maps at each step (in kN/m\({}^{2}\)) of incremental load for REF and IID panels.  
To validate the presence of local debonding, an extra line was plotted diagonally along the AOI and the displacement distribution along the line is plotted in Figure 5c. In the prior work of [PERSON] et al., a similar analysis protocol was adopted to demonstrate how the respective panels tilt under compressive loads [31], thus this work aims to validate the previous work and verify the tool efficacy. Compared to the random and limited fluctuations of the REF line, a clear jump in the displacement values along the IID series line was spotted at the middle zone (across the red colored area in Figure 5a). It is shown that the interfacial debonding is very local and limited to the size of 150 mm. Regarding the pre-debonding AE high-energy/RT activity, it is now evident that the damage was accumulated in the micro-scale level before the instant of detachment. From this perspective, AE feature analysis predicts the premature debonding onset and, in this way, contributes as an alarm method for avoiding this detrimental effect.
# Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models
## 3 Methodology
### Modeling: ARIMA and MLR
#### 3.3.1 ARIMA Model  
The ARIMA model is widely recognized for its effectiveness in analyzing and forecasting time-series data, particularly where seasonality and trends are present [9]. For this study, ARIMA was applied to historical sales data to identify recurring patterns and support inventory alignment with anticipated demand cycles. The mathematical foundation of ARIMA is as follows:  
\[\text{X}\_\text{t}=\varphi_{1}\text{X}\_\text{[t}-1]+\varphi_{2}\text{X}\_ \text{[t}-2]+\cdots+\varphi_{\text{p}}\text{X}\_\text{[t}-\text{p]}+\varepsilon \_\text{t}-\theta_{1}\varepsilon\_\text{[t}-1]-\theta_{2}\varepsilon\_\text{[t }-2]-\cdots-\theta\_\text{q}\varepsilon\_\text{[t}-\text{q]}\]  
where:  
* \(\text{X}_{\text{t}}\): Represents the sales volume at time t, also known as the dependent variable.
* \(\varphi\): Denotes the coefficients for autoregressive terms.
* \(\varepsilon_{\text{t}}\): Represents the error term at time t, capturing the part of the sales volume that is not explained by the model's predictive factors.
* \(\theta\): Represents the coefficients for moving average terms.
* p, q: Represent the number of autoregressive and moving average terms, respectively. The dependent variable (_Xt_) represents the sales volume at time \(t\). The ARIMA model decomposed seasonal and trend components of demand, allowing for precise forecasting of high-demand periods, which is critical for perishable goods in cold chains.
# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 3 Linguistic Analysis of Uyghur, Kazakh, and Kyrgyz
### Commonalities of the Three Languages  
From the writing aspect, the three languages are all written from right to left and the words are separated by blanks. In history, the three languages all underwent abrupt changes in their writing systems. For example, Kyrgyz was written in ancient Altaic letters and then gradually changed to Persian-Arabic letters. After the establishment of the Soviet Union (The USSR), Kyrgyz in The USSR began to use Latin letters from 1927 and Cyrillic letters from 1940. After the collapse of The USSR, people in Kyrgyzstan discussed the possibility of changing back to Latin letters, but to today, Cyrillic letters still dominate in Kyrgyzstan. In China, Kyrgyz is relatively stable and is always written in Arabic letters [106]. The other two languages have also experienced similar changes. In China, all three languages use Arabic letters, though a slight difference exists. The following discussion will focus on the three languages in China.  
From the linguistic aspect, the syntax rules of the three languages are generally the same and a large proportion of words look similar. In morphology, they share the same feature of suffix agglutination, a prominent characteristic of Altaic languages. Specifically, each word is composed of a stem as a skeleton plus multiple suffixes (additional components), and some words also contain a prefix. The agglutination is rather flexible, and the number of suffices could be as large as 10, making the vocabulary very large. Taking Uyghur as an example, there are about 40,000 common stems and 289 affixes, constructing more than 1.2 million words [107]. Despite the large vocabulary, the letter-to-phone conversion is rather simple, and in most cases, it is a one-to-one mapping.  
From the phonetic aspect, each word consists of several syllables and each syllable should contain a vowel. All three languages follow strict rules of phonetic harmony when syllables are combined to form words. There are two types of phonetic harmony: vowel harmony and consonant harmony. Vowel harmony means that the vowels appearing in different syllables of the same word are consistent in the articulation position and lip shape, that is either all are front vowels (or back vowels) or all are rounded vowels (or unrounded vowels). Consonant harmony means that consonants in successive syllables are either all voiced or all voiceless [108].  
From the acoustic aspect, the three languages are also similar. Experiments have shown that the patterns of the fundamental frequency (F0) of the three languages are quite similar for females and males. Besides, the averaged values of the first formant (F1) of the corresponding vowels in the three languages are substantially the same, while the difference is mainly in the second formant (F2): the F2 of Kazakh and Kyrgyz is clearly higher than that of Uyghur [109].
## 3 Methodology for Human Navigation Path Simulation via Inverse Reinforcement Learning
### Apprenticeship Learning
#### 3.2.2 Apprenticeship Learning Algorithm  
The AL algorithm proceeds as in Algorithm 1. The algorithm terminates when the learner's feature expectations \(\mu(\pi_{i})\) align with the expert's \(\mu_{E}\) within tolerance \(\epsilon\) or after iterations. Convergence is guaranteed under the geometric margin condition, where the iterative maximization of \(\omega^{T}(\mu_{E}-\mu(\pi))\) ensures progressive policy improvement.  
```
Input: Expert policy \(\pi_{E}\), Feature mapping \(\phi:\mathcal{S}\rightarrow\mathbb{R}^{d}\), Discount factor \(\gamma\), Margin \(t\), Max iterations \(K\), Convergence threshold \(\epsilon\) Output: Optimized policy \(\pi^{*}\)
1 Initialization:
2 Initialize weights \(\omega^{(0)}\sim\mathcal{N}(0,1)\)
3 Initialize policy \(\pi_{0}\) with random exploration
4 Extract expert features: \(\{\phi(s_{n}^{(E)})\}_{n=1}^{N}\leftarrow\pi_{E}\)
5 Compute expert feature expectations: \(\mu_{E}\leftarrow\frac{1}{N}\sum_{n=1}^{N}\sum_{t=0}^{\infty}\gamma^{t}\phi(s _{t}^{(E)})\)
6 Iterative Optimization:
7 for\(i\gets 1\) to \(K\)do
8 Sample trajectories \(\tau_{i}\sim\pi_{i-1}\)
9 Extract policy features: \(\{\phi(s_{t}^{(i)})\}_{t=0}^{T}\)
10 Compute learner expectations: \(\mu(\pi_{i-1})\leftarrow\frac{1}{|\tau_{i}|}\sum_{t=0}^{\infty}\gamma^{t}\phi( s_{t}^{(i)})\)
11 Reward Optimization:
12 Solve constrained quadratic program: \(\omega^{*}\leftarrow\arg\max_{\omega}[\omega^{\top}(\mu_{E}-\mu(\pi_{i-1}))]\)
13 subject to: \(\|\omega\|_{2}\leq 1\)// L2-norm constraint
14
15\(\omega^{\top}(\mu_{E}-\mu(\pi_{g^{\prime}}))\geq t\), \(\forall g^{\prime}<i\)// Margin constraints
16 Policy Update: \(\pi_{i}\leftarrow\textsc{PolicyUpdate}(\pi_{i-1},\omega^{*})\)// Using RL algorithm
17 Convergence Check: if\(\|\mu(\pi_{i})-\mu_{E}\|_{2}<\epsilon\)then
18 break // Terminate if feature expectations match
19
20 end if
21
22 end for return\(\pi^{*}\leftarrow\pi_{i}\)
```  
**Algorithm 1**Apprenticeship Learning  
The reward weights \(\omega\) are updated by solving a constrained quadratic program. The objective \(\omega^{T}(\mu_{E}-\mu(\pi-1))\) maximizes the margin between expert and learner feature expectations, while the constraints enforce consistency over all prior iterations. This ensures incremental policy improvement akin to the max-margin framework in apprenticeship learning.
# Enhancement of Feed Source through Three Dimensional Printing
###### Abstract  
The three-dimensional printed wideband prototype (WBP) was proposed, which is able to enhance the horn feed source by generating a more uniform phase distribution that is obtained after correcting aperture phase values. The noted phase variation obtained without the WBP was 163.65\({}^{\circ}\) for the horn source only, which was decreased to 19.68\({}^{\circ}\), obtained after the placement of the WBP at a \(\lambda/2\) distance above the feed horn aperture. The corrected phase value was observed at 6.25 mm (0.25\(\lambda\)) above the top face of the WBP. The use of a five-layer cubic structure is able to generate the proposed WBP with dimensions of 105 mm \(\times\) 105 mm \(\times\) 37.5 mm (4.2\(\times\) 4.2\(\times\) 1.5\(\lambda\)), which can improve directivity and gain by 2.5 dB throughout the operating frequency range with a lower side lobe level. The overall dimension of the 3D printed horn was 98.5 mm \(\times\) 75.6 mm \(\times\) 192.6 mm (3.94\(\times\) 3.02\(\times\) 7.71\(\lambda\)), where the 100 % infill value was maintained. The horn was painted with a double layer of copper throughout its surface. In a design frequency of 12 GHz, the computed directivity, gain, side lobe level in H- and E- planes were 20.5 dB, 20.5 dB, \(-\)26.5 dB, and \(-\)12.4 dB with only a 3D printed horn case and, with the proposed prototype placed above this feed source, these values improved to 22.1 dB, 21.9 dB, \(-\)15.5 dB, and \(-\)17.5 dB, respectively. The realized WBP was 294 g and the overall system was 448 g in weight, which signifies a light weight condition. The measured return loss values were less than 2, which supports that the WBP has matching behavior over the operating frequency range.  
3D printed prototype; radiation patterns; microwave communication +
Footnote †: journal: mricomachines
## 3 Data and Methodology
### Research Methods  
#### 3.2.1 Object-Based Image Analysis (OBIA)  
OBIA is a common classification approach in remote sensing that uses image objects through segmentation instead of image pixels for feature extraction. The classification is performed based on the features extracted for each image object with any statistical or machine learning classifiers such as ANN. OBIA has two main components, which are segmentation and classification. The segmentation divides the given image data into a set of image objects that have homogeneous features (including the spectrum, texture, and shape). For each image object, a set of spectral, spatial, and textural features can be extracted and used for the next stage of data processing, i.e., classification. The classification in OBIA is often performed with any statistical or machine learning methods, including SVM, ANN, and decision trees (DT).  
* OBIA features Several spectral, spatial, geometric, and textural features can be calculated for image objects and used for the classification of the image data. Spectral features such as minimum, maximum, mean, standard deviation, and range spectral information are the most common spectral features used in OBIA.On the other hand, spatial features including object area, object perimeter, elongation index, shape index, density, and rectangular fit are the common spatial/geometric features used in OBIA. In addition, several textural features such as contrast, dissimilarity, homogeneity, energy, correlation, and angular second moment are the most used textural features in OBIA studies.  
This research uses the common features described above in the OBIA-related experiments. The feature extraction tool was implemented in Python based on libraries such as SciPy ([[https://www.scipy.org](https://www.scipy.org)]([https://www.scipy.org](https://www.scipy.org))) (15 September 2022) and scikit-image ([[https://scikit-image.org](https://scikit-image.org)]([https://scikit-image.org](https://scikit-image.org))) (15 September 2022).  
* Segmentation Algorithm The commonly used multiresolution segmentation algorithm (MRS) [25] ([PERSON] et al. 2006) was utilized in this research to assess the classification methods that use OBIA for feature extraction. MRS generates image objects with greater geographical significance and strong adaptability [26] ([PERSON] et al. 2011).  
MRS acquires image objects by calculating the heterogeneity of the spectral and shape features and weighting the image layers. For image layer \(i\), the heterogeneity is calculated as [27] ([PERSON] et al. 2021):  
\[f_{i}=w_{i}h_{shape}+(1-w_{i})h_{color} \tag{1}\]  
where \(w_{i}\) is the user-defined weight of the shape heterogeneity of band \(i\), \(0\leq w_{i}\leq 1\). The shape heterogeneity \(h_{shape}\) is calculated from the compactness \(h_{cp}\) and smoothness \(h_{sm}\) as:  
\[h_{shape}=w_{cp}h_{cp}+\big{(}1-w_{cp}\big{)}h_{sm} \tag{2}\]  
where \(w_{cp}\) refers to the weight of the shape's compactness. The segmentation scale \(S\), shape heterogeneity weight \(w_{i}\), and compactness weight \(w_{cp}\) are the main parameters of the MRS algorithm.