esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Architecture of the Decentralized Framework
### Overview of the Main Components
```
1:\(ordinary\_data\leftarrow[]\)
2:\(critical\_data\leftarrow[]\)
3:whileilslisteningdo
4:\(data\_payload\gets receiveFromDataGateway()\)
5:switch(data_payload.type)
6:caseopen:
7:\(incident\_id\gets create\_incident(data\_payload)\)
8:sendToDataGateway(incident\_id)
9:\(store(incident\_id,data\_payload)\)
10:break
11:caseclose:
12:\(incident\_id\gets data\_payload.incident\_id\)
13:\(incident\_data\gets getInjectData(incident\_id)\)
14:\(agg\_data\leftarrow aggregateData(incident\_data,data\_payload)\)
15:\(ordinary\_data.append(agg\_data)\)
16:break
17:caseordinary:
18:\(incident\_id\gets data\_payload.incident\_id\)
19:\(store(incident\_id,data\_payload)\)
20:break
21:casecritical:
22:\(incident\_id\gets data\_payload.incident\_id\)
23:\(store(incident\_id,data\_payload)\)
24:\(agg\_data\leftarrow aggregateData(incident\_data,data\_payload)\)
25:\(critical\_data\_append(agg\_data)\)
26:break
27:endwhile
```  
**Algorithm 2** Healthcare Emergency Aggregator (HEA) workflow when receiving data  
Algorithm 2 shows a timeout mechanism allowing us to periodically aggregate and secure both ordinary and critical data. Hashes of aggregated data are sent to a blockchain to ensure integrity and traceability, with transaction receipts stored for verification purposes. This workflow ensures efficient data management, secure storage, and streamlined incident handling in dynamic and high-stakes healthcare environments. ```
1:whileisUpdo
2:switch(timeout)
3:casecritical:
4:\(agg\_data\leftarrow aggregateData(critical\_data)\)
5:\(trans\_receipt\gets sendToBlockchain(agg\_data)\)
6:\(store(critical\_data,trans\_receipt)\)
7:\(critical\_data\leftarrow[]\)
8:break
9:caseordinary:
10:\(agg\_data\leftarrow aggregateData(ordinary\_data)\)
11:\(trans\_receipt\gets sendToBlockchain(agg\_data)\)
12:\(store(ordinary\_data,trans\_receipt)\)
13:\(ordinary\_data\leftarrow[]\)
14:break
15:endwhile
```  
**Algorithm 2** Healthcare Emergency Aggregator (HEA) workflow when timeouts occur
## 2 Lifetime Reliability Sensing in FPGAs
### Lifetime Reliability at the Transistor Level
HCI occurs when carriers inside the channel are subjected to the adjacent electric field, producing sufficient energy and momentum to break the barriers surrounding the dielectric [46]. The carriers with adequate energy for the HCI stress will result in interface-state generation at the Si-SiO\({}_{2}\) interface [49]. Furthermore, [PERSON] and [PERSON] produced an equation of the threshold voltage degradation due to the HCI [46], as given in Equation (3):  
\[\Delta V_{Hx}(t)=C_{H}\exp\biggl{(}\frac{E_{ox}}{E_{0}}-\frac{\phi_{it}}{q \lambda E_{m}}\biggr{)}(g(t_{st})-g(t_{st},0)) \tag{3}\]  
where \(C_{H}\) and \(E_{0}\) are the process dependent parameters, \(E_{ox}\) is a vertical field, \(q\) is the electronic charge, \(\phi_{it}\) is the trap generation energy, \(\lambda\) is the hot electron mean free path, \(E_{m}\) is the lateral electrical field, and \(g\) is effective stressing time. The adequate stressing time caused by HCI aging depends on the number of switching events of the transistor. It can be calculated using \((AF\cdot F_{clk}\cdot t)\), where \(AF\) is the activity factor of the transistor, \(F_{clk}\) is the clock frequency, and \(t\) is the elapsed time. Therefore, [PERSON] and [PERSON] combined the degradation effect of the NBTI and HCI onto a shift in delay \(\Delta D(t)\) for logic gates in detail [46], as stated in Equation (4):  
\[\Delta D(t)=K_{B}\Delta f(t)+K_{H}\Delta g(t) \tag{4}\]  
where \(K_{B}=\sum_{i\in NMOS}S_{n,i}\psi B_{n,i}+\sum_{i\in PMOS}S_{p,i}\psi B_{p,i}\) and \(K_{H}=\sum_{i\in NMOS}S_{n,i}\psi H_{n,i}\). By using the CurveExpert(Chattaooga, TN, USA) [47] software, the frequency degradation due to the HCI at 27 \({}^{\circ}\)C could be calculated using Equation (5) for a 45 nm technology node:  
\[\%\Delta f_{HCI(45 nm)}=\frac{1.417\times 10^{-4}+1.262 year^{0.236}}{2.185+year ^{0.236}} \tag{5}\]  
Figure 2: Frequency degradation versus aging for 45 nm and 32 nm technology ring oscillators at 27 \({}^{\circ}\)C and 100 \({}^{\circ}\)C caused by HCI and NBTI, respectively. As was deduced from Equation (5), the frequency degradation caused by the HCI for 8 years and 3 months was equal to 0.542% frequency degradation compared to new devices. In summary, the results indicate that the HCI and NBTI mainly caused the degradation effect, which is supported by [PERSON] et al. 's study, which reveals that the delay degradation effect due to the HCI and NBTI led to device aging [50]. However, most studies in the open literature have not explicitly examined the 45 nm technology node. Therefore, this simulation work using the Eldo simulator mainly focused on reliability concerns due to the HCI and NBTI degradation effect of a 23-stage ring oscillator for a 45 nm technology node.
## 3 Results
### Characterization of Permeates and Retentates  
#### 3.2.1 Physicochemical Properties  
Physicochemical properties of apple-cranberry cloudy juice before and after membrane processing by UF and DF are summarized in Table 1. As TSS, Glu, Fru, TS determined in ACJ were significantly higher (\(p<0.05\)) than in the rest of the samples (including UF and DF retentate considered as final products--low sugar juices), it could be stated that the main goal of the work, which was the reduction of sugars content in cloudy juice, was achieved. TS of cloudy juice was reduced from 9.8 \(\pm\) 0.2 to 7.5 \(\pm\) 0.2 and 5.7 \(\pm\) 0.2%, after UF and DF, respectively. There was also a significant difference in every parameter, except pH, between RUF and RDF, so the type of the processing (UF or DF) affected the properties of the product. It resulted mainly from the dilution by DF water during diafiltration, but also from the selective transport of juice components though the membrane (details about the selectivity--see Section 3.3) and \"washout\" effect. The same effects occurred in the work on grape and apple juice ultrafiltration presented by [PERSON] [30], pH value did not change significantly after membrane processing. TPC and color parameters were also affected by membrane processing, but the difference between values in ACJ and RUF were statistically not significant (\(p>0.05\)). The amounts of scavenged DPPH differed significantly for tested samples. The most effective radical scavenger was non-treated sample ACJ. RUF had also high radical-scavenging capacity, but significantly lower (\(p<0.05\)) than ACJ (the reduction by 13% compared with initial value). In both cases of the membrane process, permeates were significantly less effective radical scavengers than retentates. Moreover, RUF had significantly higher activity than RDF. It suggests that active compounds showing strong antioxidant capacity were selectively transmitted by the membrane, which was also confirmed by higher TPC in retentates. For processed juice, color is an important quality indicator [31]. The effect of membrane processing on cloudy juice color parameters and resulting \(\Delta\)E value is evident due to the clarification process taking place during UF and DF. Thus, \(\Delta\)E was at an extremely high level for permeates: PUF and PDF (the difference between these two samples was statistically not significant). Color parameters of RUF were not significantly different (\(p>0.05\)) than before UF, but the retentate after DF (RDF) had significantly (\(p<0.05\)) different color parameters than ACJ. Also, \(\Delta\)E was higher than for RUF, what could be caused by the addition of water during DF.
# Applicability Study on Modified Argillaceous Slate as Subgrade Filling for High-Speed Railway
## 4 Dynamic Simulation Analysis of Soft-Rock-Modified Soil as Subgrade
### Dynamic Model Establishment  
#### 4.1.1 Dynamic Fundamentals  
The ballastless HSR track-subgrade three-dimensional space coupling system consists of several subsystems, including the rail, fastener, the pad under the rail, track plate, CA mortar, integral concrete track bed, bed layer and subgrade layer. The total energy is a sum function as follows:  
\[W(t)\ =\ W(t)_{\text{int}}+W(t)_{ext}+W(t)_{\text{int}}+W(t)_{kin}+W(t)_{dam}+W(t )_{cup} \tag{2}\]  
where \(W(t)_{\text{int}}\) is the strain energy of all the subsystem; \(W(t)_{ext}\) is the work of various external forces such as wheel forces; \(W(t)_{kin}\) is the kinetic energy of all the sub-structure; \(W(t)_{dam}\) is the work of damping force including structural damping and material damping; \(W(t)_{cup}\) stands for the work of the binding which is the increasing deformation energy created by the system of each substructural unit's displacement coupling constraints.  
When the structure moves from one equilibrium position to an adjacent position that is infinitely close to that equilibrium position, the total energy \(W(t)\) keeps constant. That is to say, the energy variation of the system has a stationary value:  
\[\delta W(t)\ =\ 0 \tag{3}\]  
Then, taking variants of Formula (2):  
\[\delta W(t)\ =\ \delta W(t)_{\text{int}}+\delta W(t)_{ext}+\delta Wt)_{\text{ int}}+\delta W(t)_{kin}+\delta W(t)_{dam}+\delta W(t)_{coup}\ =\ 0 \tag{4}\]  
Therein, \(W(t)_{coup}\) was established by the Lagrange augmentation method, which is a combination of the Lagrange multiplier and penalty function method. The system equation stiffness matrix turns to be non-definite after introducing the excess unknown variables. At the same time, for the given penalty \(\alpha\) and Lagrange multiplier \(\lambda\), the modified energy functional \(W^{*}\) is obtained as follows:  
\[W^{*}(u,\alpha,\lambda)\ =\ W(u)+\lambda^{T}g(u)+\frac{1}{2}\alpha g^{T}(u)g(u) \tag{5}\]  
where \(g(u)\ =\ 0\) is the constraint equation; \(W(u)\) is the energy \(W_{coup}\) divided by formula (1); and \(u\) is the displacement matrix.  
The Lagrange multiplier was used to determine coupling constraint equations. The energy of each subsystem in the model was determined and the coupling energy of each different element was integrated into the total energy matrix. The whole coupling system energy of a subgrade rail system is worked out at time \(t\). Finally, the dynamic matrix equation of each substructure of a ballastless subgrade-rail system can be obtained using the stationary energy principle. Thus, the total energy of the system is a sum of the energy of the above subsystems and using different elements of various characteristics will discretize it spaciously. The equation was resolved using the Newton-Raphson method [27, 31].
###### Abstract  
This study aimed to compare the effectiveness of dextrose prolotherapy, ozone therapy, and home exercise programs in patients with knee osteoarthritis. Seventy-five patients with knee osteoarthritis were divided into three groups, with 25 in each group. At week 0 (baseline), week 3, and week 6, 12.5% dextrose (intraarticular and periarticular) was administered to the dextrose prolotherapy group. At week 0 (baseline), week 1, and week 2 15 ug/mL ozone (intraarticular and periarticular) was administered to the ozone therapy group. Both groups were also given a home exercise program. The third group was given a home-based exercise therapy program for 12 weeks. All groups were evaluated for VAS, WOMAC, TUG, ROM-active, and ROM-passive values at weeks 0 (baseline), 6, and 12. Ozone therapy more effectively improved VAS scores than dextrose prolotherapy and VAS and WOMAC scores than home-based exercise therapy in the 6 th week. Ozone therapy also more effectively improved VAS and WOMAC-stiffness scores than dextrose prolotherapy and VAS, WOMAC, and ROM-active scores than home-based exercise therapy in the 12 th week. Both dextrose prolotherapy and ozone therapy are effective in knee osteoarthritis treatment. Ozone therapy should be used in suitable cases rather than dextrose prolotherapy.  
dextrose prolotherapy; exercise; knee; osteoarthritis; ozone therapy +
Footnote †: journal: Medicalgrowth factors [15]. DPT application in the treatment of KOA provides positive benefits in functional gains [16; 17; 18]. PT is conditionally recommended for the treatment of KOA in the 2019 American College of Rheumatology (ACR) guidelines for the treatment of KOA [19].  
The use of ozone therapy (OT), another treatment method, in the outpatient treatment of KOA is increasing [20]. Ozone therapy has the advantages of being safe to use in intra-ticular (IA) approaches and ease of application [21]. Ozone has analgesic, anti-inflammatory effects via stimulation of antioxidant mechanisms, vasodilitation, and angiogenesis [20; 22]. OT provides significant improvement in pain and function in the short and medium term treatment of KOA [23; 24]. The sources of pain in KOA are the joint capsule, ligaments, synovium, bone, lateral part of the meniscus, tendons and extra-articular ligaments [23; 24]. The standard \"whole joint\" injection method includes IA injections (IA) and multiple peri-articular (PA) injections into soft tissues [25]. The whole joint injection method may more effectively reduce pain and improve functional status due to its effects on many points that are the source of pain. There are studies in which DPT has been applied together as IA and PA injections in KOA [26; 27; 28]. On the other hand, there is no study in which OT is applied using both IA and PA injections, like DPT. The current study applied DPT and ozone treatments to KOA patients with IA and PA methods. The pain relief and improvement in joint function of these treatments were compared with each other and with the home-based exercise treatment program.
###### Abstract  
This paper presents a thermal performance evaluation of a novel particle-to-air heat exchanger. The heat exchanger has a patented design with a shell-and-tube configuration. Solid particles move as a dense packed-bed inside the vertical tubes of the heat exchanger whereas air flows on the shell-side. This design avoids a number of limitations associated with the state-of-the-art heat exchangers in the same category, such as the stagnant/void zones and the prolonged residence time. The heat exchanger has a 50-kW thermal duty; it has been integrated into the particle-based concentrating solar power facility located at the campus of King Saud University in Riyadh, Saudi Arabia. The detailed description of the heat exchanger and the integration process is introduced. The recuperated air of the facility's power cycle is used to heat the solid particles being circulated inside the facility. The solid particles used in this study are engineered particles called Carbobead CP with 0.3 mm mean diameter. The effect of particle flow rate on the thermal performance of the heat exchanger is investigated. The results show that as the particle flow rate increases, the overall heat transfer coefficient (U) increases; a maximum value was measured to be 150 W/m2-oC based on LMTD calculations. The measurement accuracy was verified by repeating several tests; a slight variation was observed in the measured U. The results also show that only a small air pressure drop (~5 kPa) was measured across the heat exchanger. Furthermore, it was found that a significant part of the heat exchange occurred at the bottom section of the heat exchanger.  
particle-based heat exchanger; CSP; concentrated solar power; particle-based power tower system +
Footnote †: journal: Applied Sciences
# Integrated Earthquake Catalog of the Eastern Sector of the Russian Arctic
## 1 Introduction
In [34], an algorithm for merging two earthquake catalogs was developed, the main task of which was to identify the resulting duplicates and separate them from the aftershocks. The algorithm is based on the author's modification of the nearest neighbor method [35; 36] for duplicate identification. It is based on the fact that, unlike aftershocks, duplicates do not have a causal relationship. The algorithm establishes a correspondence between the events from two catalogs, after which the classification of earthquakes into unique and duplicates is performed using the Euclidean metric. The sequential application of the algorithm automates the integration of any number of earthquake catalogs. The developed algorithm efficiency was demonstrated in [34] using the example of merging the ComCat Advanced National Seismic System and the Japan Meteorological Agency catalogs for the aftershock sequence of the 2011 Tohoku earthquake. In this paper, a unified earthquake catalog is created for the Eastern Sector of the Arctic zone of the Russian Federation. For this purpose, the following main issues are solved:  
* The sequential merging of three regional catalogs of the GS RAS and the ISC catalog, which implies the identification of duplicate events in the border areas of responsibility of the different networks; and
* The unification of magnitude estimates in the integrated catalog by constructing regression relationships for the different types of magnitude/energy class due to the exact association of data from the different catalogs related to the same event.
# FLGQM: Robust Federated Learning Based on Geometric and Qualitative Metrics
## 3 Results and Discussion
### Experimental Setup
#### 3.1.3 Baseline Aggregation Rules  
We consider the following aggregation rules as our baseline for comparison.  
**FedAvg**[1]: The new global model is obtained by calculating the average of all local models.  
**Median**[18]: The median directly takes the median of the coordinates of each dimension of all local models to obtain the new global model.  
**Krum**[15]: In Krum, the Euclidean distance is employed to score the local models, and subsequently, the highest-rated model is chosen as the global model. For the \(i\)th client, assuming the existence of \(f\) malicious clients, the local model's score can be determined as follows.  
\[s_{i}=\sum_{w_{j}\in\Gamma_{i,K-f-2}}\|w_{j}-w_{i}\|_{2}^{2} \tag{12}\]  
where \(\Gamma_{i,K-f-2}\) is the set of \(K-f-2\) local models, and this \(K-f-2\) local model is the \(K-f-2\) model with the smallest Euclidean distance from \(w_{i}\).  
**Trimmed Mean**[18]: The trimmed mean is an aggregation rule that takes into account each model parameter and operates based on coordinates. For every model parameter, the server gathers all values from local models and arranges them in ascending order. Subsequently, it excludes the largest and smallest \(\mathcal{K}\) values, computes their average, and utilizes this average as the corresponding parameter value in the global model.
## 4 Discussion
### Test Parameters
#### 4.2.4 Deflection Levels  
Successful orthodontic treatment depends on applying optimal forces that balance the rate of tooth movement with minimal risk of irreversible tissue damage [39]. For tipping movements, the optimal force range is typically 50 to 75 g [40]. To achieve this,early systems reported effective deflection values ranging from 0.5 mm to 1.0 mm for Raintree Essix appliances [41] and 0.25 mm to 0.33 mm for Invisalign [42]. The latter range corresponds with the findings of [PERSON] et al. [23], who measured force delivery during the recovery phase using a span length of 24 mm (combined width of the maxillary central and lateral incisors) and an initial deflection of 2 mm, reduced at a rate of 1 mm/min to 0 mm. These parameters align with other studies in this review that applied similar deflections [22; 24; 30].  
The reviewed studies exhibited a broad range of deflection values, which can be grouped into small displacements (0.2-0.5 mm) used to simulate stepwise tooth movement and larger deformations (5-7 mm) aimed at characterizing full-force profiles under controlled conditions. Typical clinical deformations of aligners are generally smaller than many of the deflection ranges used in laboratory-based three-point bending tests and are most often represented in the 0.2 mm range per aligner stage. Therefore, the deflection levels used in mechanical testing may not directly correspond to the clinical forces experienced by patients. Estimation of the actual force systems delivered by aligners requires integrated clinical-biomechanical approaches or dedicated in vitro setups simulating realistic intraoral conditions, as described by [PERSON] et al. [43].
# Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction
## 3 Results and Discussion
### Input Data
#### 3.1.3 Cuprite Dataset  
Cuprite is one of the most widely used hyperspectral data for hyperspectral unmixing, which contains 224 bands ranging from 370 nm to 2480 nm. The noised bands (1-2 and 221-224) and the water absorption bands (104-113 and 148-167) are removed, and 188 bands are retained. A sub-image of 250 \(\times\) 190 pixels is utilized in our experiments, including 14 minerals. To avoid the minor difference between the variants of the same mineral, 12 minerals are finally considered as endmembers, which are alunite, andradite, buddingtonite, dumortierite, kaolinite1, kaolinite2, muscovite, montmorillonite, nontronite, pyrope, sphere and chalcedony as shown in Figure 4.
# Research on Sparse Representation Method of Acoustic Microimaging Signals
## 2 Materials and Methods  
### General Formulation  
#### 2.1.1 Acoustic Microimaging Signals Model  
Generally, in a reflective ultrasonic detection system, the A-scan AMI signal \(y(t)\) can be expressed as a linear combination of reflected echoes \(x_{i}(t)\) from different interfaces in the sample, i.e.,  
\[y(t)=\sum_{i=0}^{m}x_{i}(t)+n(t), \tag{1}\]  
where \(n(t)\) is the noise from the detection system and material. In scanning acoustic microscopy, the shape of the ultrasonic wave will change during propagation due to focusing effects, frequency-dependent attenuation and microstructural scattering of the medium. At this time, the AMI signal \(y(t)\) can be written as:  
\[y(t)=\sum_{i=0}^{m}c_{i}\varphi_{i}(t)+n(t), \tag{2}\]  
where \(c_{i}\) is the reflection coefficient, \(\varphi_{i}(t)\) is the \"quasi\" incident pulse at the \(i\)th interface, which is not a real incident pulse, but a pulse that includes dual-way transmission and attenuation. In the matrix format, Equation (2) becomes [8; 10]:  
\[y=c\Phi+n. \tag{3}\]  
In fact, the AMI signal \(y(t)\) is composed of a finite number of echoes, that is, \(m\) is finite, so it can be assumed that the AMI signal \(y(t)\) has a sparse representation in the appropriate atom dictionary \(\Phi\)[34].
# Optimization of Parametric Equalizer Filters in In-Vehicle Audio Systems with a Genetic Algorithm
###### Abstract  
This study aims to automate the optimization of a full-range speaker in an SUV's audio system according to the equal-loudness contours principle. The input signal and frequency responses of the amplifier and speaker were transferred to Matlab. Using ideal filter parameters, ten parametric equalizer models were created, and the speaker output was obtained using the convolution technique. The same filter settings were applied to the vehicle multimedia system, and the experimental results were obtained. The simulation and experimental results were compared, showing high similarity, with a Pearson correlation of 0.9295 and a root mean square error (RMSE) of 2.29. The results were compared with the ideal contour. The Pearson correlation coefficients for the simulation and experimental results were 0.6341 and 0.6715, with RMSE values of 4.88 and 2.57, showing low similarity. Consequently, each parametric equalizer filter's parameters were optimized using a genetic algorithm. The genetic algorithm was executed thirteen times for robustness, and the best parameters were selected. The optimized parameters were applied to the multimedia system, and the results were compared with the ideal contour. The correlation coefficients for the simulation and experimental results were 0.9692 and 0.9675, with RMSE values of 1.17 and 1.34. These results indicate that optimization aligns the speaker output closer to the ideal contour, enhancing in-vehicle audio system performance and increasing users' satisfaction.  
vehicle multimedia system; equal-loudness contours; parametric equalizer filter; genetic algorithm +
Footnote †: journal: Applied Sciences  
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