esa-sceva/satcom-chunk-collection huggingface dataset:
# Failure Assessment of Embankment Dam Elements: Case Study of the Pirot Reservoir System
## 2 Theoretical Basis
### Remaining Load-Bearing Capacity  
In order to analyze the load-bearing capacity of the structure and obtain the remaining load-bearing capacity at each integration point of the model, a new vector is formed. This new vector represents a measure of the distance of the stress point from the failure surface for the same value of the first stress invariant (Figure 1). The remaining load-bearing capacity of the material is expressed in relation to limit value of the stress at which fracture occurs. In other words, this quantity shows the distribution of the unused strength of the material.  
The residual load-bearing capacity (in percentage) can be calculated at each finite element integration point, independent of the constitutive model, according to the following equation:  
\[RP=\left(1-\frac{q}{q_{\text{max}}}\right)\cdot 100\%. \tag{9}\]  
In Equation (9), the quantity \(q_{\text{max}}\) represents the distance of the failure surface for a specific stress state (for a known value of the first stress invariant \(I_{1}\)). The quantity \(q\) represents the distance of the stress point for the same stress state (same value of \(I_{1}\)).  
The distance to the failure surface \(q_{\text{max}}\), for a specific stress state, can be calculated by using the second invariant of deviatoric stress:  
\[q_{\text{max}}=\sqrt{3J_{2D}}. \tag{10}\]  
The value of the second invariant of deviatoric stress \(J_{2D}\) is calculated using the failure surface equation of the corresponding constitutive model. In the case of the Mohr-Coulomb constitutive model, which was used in the analysis of stability in this paper, the value of  
Figure 1: Remaining load-bearing capacity definition.  
the second invariant of deviatoric stress, for the specific stress state can be calculated using (7), as:  
\[\sqrt{J_{2D}}=\frac{c\cos\phi-\frac{1}{3}I_{1}\sin\phi}{\cos\theta-\frac{1}{ \sqrt{3}}\sin\theta\sin\phi}. \tag{11}\]  
The distance to the stress point \(q\) can also be calculated by applying the second invariant of deviatoric stress [34] according to:  
\[q=\sqrt{3J_{2D}{}^{*}}, \tag{12}\]  
where the second invariant of deviatoric stress is calculated as:  
\[J_{2D}{}^{*}=I_{2}+\frac{1}{3}I_{1}^{2}. \tag{13}\]  
In Equation (13) the quantities \(I_{1}\) and \(I_{2}\) represent the first and second stress invariants, respectively. These stress invariants [29,35] were calculated for the current stress state according to the expressions:  
\[I_{1}=\sigma_{x}+\sigma_{y}+\sigma_{z}, \tag{14}\]  
\[I_{2}=-\big{(}\sigma_{x}\sigma_{y}+\sigma_{y}\sigma_{z}+\sigma_{z}\sigma_{x} \big{)}+\sigma_{xy}^{2}+\sigma_{yz}^{2}+\sigma_{zx}^{2}. \tag{15}\]  
By substituting relations (10)-(15) into expression (9), the residual load-bearing capacity at each integration point of the finite element is calculated and the field of the remaining load-bearing capacity in the entire structure is formed.
# Assessment of Cardiovascular Disease in Autosomal Dominant Polycystic Kidney Disease
## 6 Flow-Mediated Dilation (FMD), Carotid Intima-Media Thickness (IMT), and Pulse Wave Velocity (PWV)
Carotid IMT is evaluated via the ultrasonographic technique as the distance between the lumen-intima and media-adveruitia is 1-2 cm proximal to the carotid bulb. The link between high IMT (intima-media thickness) and cardiovascular risk is well known. IMT represents the medial hypertrophy and thickening of smooth muscles and increases when the endothelium is damaged. An increased IMT contributes to the creation of a turbulent blood flow, which is a risk factor for atherosclerosis [3; 5; 36]. For these reasons, periodically valuating IMT, as a measure of endothelial dysfunction, is important to assess CV risk in ADPKD hypertensive and non-hypertensive patients. Carotid IMT and FMD have also been used to assess endothelial dysfunction in a study conducted on 54 ADPKD patients, 20 of them being smokers, and 45 healthy controls, 19 of them being non-smokers. Healthy smokers and ADPKD non-smoker patients had similar values of FMD and CIMT. Smoker ADPKD patients had a higher IMT and lower FMD compared to ADPKD non-smoker patients [46]. The increased stiffening of large elastic arteries in ADPKD patients can be detected through a higher Carotid-Femoral PWV and Carotid-Radial PWV. Pulse Wave Velocity is non-invasively measured through a transcutaneous tonometer positioned at the carotid, brachial, radial, and femoral arteries [49]. At each site, two distinct waves are recorded: the first one reflects the left ventricular ejection and the second one is the reflected wave from peripheral segments. Pulse wave velocity is calculated as distance divided by time between the lowest part of the obtained waveforms. From these values, the Stiffness Index can be calculated [49]. One study on 55 ADPKD patients has shown that increased arterial stiffness is related to the occurrence of ESKD and cardiovascular complications (myocardial infarction, stroke, and cardiovascular interventions) in ADPKD, particularly in patients with multiple cardiovascular risk factors (obesity, hypertension, diabetes mellitus, smoking, and lipid abnormalities) [49]. The evaluation of arterial stiffness and endothelial disfunction is one of the main predictive factors of cardiovascular complications and renal disease progression in ADPKD patients. It can already be identified in the early stage of renal disease and even in not-yet hypertensive patients [44]. Increased arterial stiffness is found in people with CKD, and its etiology has an influence on the degree of arterial stiffness: in ADPKD patients, arterial stiffness develops earlier and the progression is more rapid than in patients at comparable stages of kidney disease [49]. Further studies and trials are necessary to evaluate the therapeutic implications of these parameters.
# Influence of Incident Shock on Fuel Mixing in Scramjet
## 4 Results and Discussion
### Cold Flow Structure
In contrast, the internal cavity pressure remained comparatively elevated, prompting the formation of a compression shock wave at the leading edge of the cavity. During the transit of the shock wave, the airflow undergone a significant compression, causing the shear layer above the cavity to be uplifted and merged with the main flow. Concurrently, the shock wave that was generated at the leading edge of the cavity interacted with the reflected shock wave above the cavity. This interaction intensified the compression effect in the adjacent area, thereby forming a pronounced local high-pressure zone in this region. This phenomenon was notably different from what had been observed in cases 1 and 3; as the airflow passed the leading edge of the cavity, it experienced a sharp increase in average pressure, as demonstrated in Figure 16. When the high-speed airflow collided with the trailing edge of the cavity, its velocity decreased sharply, and the pressure rapidly climbed to a peak. Subsequently, with the expansion of the flow channel located behind the cavity, the airflow entered a phase of expansion and acceleration, during which the pressure gradually diminished until the air was eventually discharged. It is imperative to note that the incoming flow exhibited pronounced non-uniformity characteristics, with significant variations in pressure distribution across the entire flow cross-section. The injection was confined to the lower wall surface, and the location of shock wave reflection notably influenced the pressure distribution near the injection region. As illustrated in Figure 17, the wall surface pressure before the injection in case 2 was substantially higher than that in cases 1 and 3. Moreover, the reduced frequency of shock wave reflections in case 3 enhanced the compression effect, which subsequently led to a more intense expansion following the shock wave, resulting in lower wall surface pressure. Figure 14: Streamline distribution for different cases. Figure 15: Pressure distribution for different cases.
## 2 Overview of DME and JTIDS
### Frequency Distribution of DME and Titds  
The portion of the frequency spectrum between 950 MHz and 1150 MHz is called the Lx band. DME and JTIDS are all operating within the Lx band; the distribution of frequency is shown in Figure 3. DME operates in (962-1213) MHz every 1 MHz, and works in two modes named as X and Y, respectively; there are 126 channels in each mode. The carrier frequency of interrogation in all modes and reply in mode Y is (1025-1150) MHz. The carrier frequency of reply are (962-1024) MHz and (1151-1213) MHz when DME operate in mode X and Y, respectively. So the difference of carrier frequency between interrogation and reply is always 30 MHz.  
JTIDS operates mainly in the Lx band between 960 MHz and 1215 MHz. The 51 frequencies assigned to JTIDS for TDMA transmissions are those between 969 MHz and 1206 MHz, 3 MHz apart. The frequency is not held constant during the time slot but is changed rapidly (every 13 microsecond) according to a predetermined pseudorandom pattern; this technique is called frequency hopping.
### Theoretical Framework  
This paper introduces a novel User Propagation Influence Linear Threshold (UPI-LT) model, which evaluates the influence of neighboring nodes on the dissemination of information based on their positions in the network. In this context, the propagation influence of a user is defined as the capability of that user to spread information, which is heavily influenced by the structural and topological attributes of its neighboring nodes. This propagation influence plays a critical role in determining the effectiveness and reach of information dissemination within the network.  
To capture this propagation influence, the UPI-LT model integrates several key factors that contribute to the spread of information. First, the influence of each neighboring node is assessed based on its PageRank score, network constraint (related to structural holes), and clustering coefficient, which represent different dimensions of a node's role within the network. The PageRank metric captures a node's global importance, while the constraint coefficient measures its bridging power in structural holes, and the clustering coefficient reflects the cohesion of the node's local neighborhood. In addition to the propagation influence, the model introduces a homophily coefficient to account for the similarity between users and their neighbors, which can significantly affect the likelihood of information adoption. This homophily effect is incorporated into the activation threshold of each node, modifying the threshold based on the degree of similarity between connected nodes. The more similar the users, the lower the activation threshold, leading to a higher probability of information propagation.  
Furthermore, the UPI-LT model incorporates a time decay factor to address the dynamic nature of information dissemination. Over time, the influence of information weakens as it propagates through the network. The time decay factor ensures that the cumulative influence of previously activated nodes diminishes, reflecting the natural decrease in information relevance and user engagement over time. This factor mitigates the impact of multiple activations and models the realistic attenuation of influence as the information spreads. The combined effect of these factors--the PageRank propagation influence of neighboring nodes (local clustering coefficient), the structural hole constraint, the homophily coefficient, and the time decay factor--provides a comprehensive framework for modeling information dissemination in social networks. The framework is depicted in Figure 6, illustrating the iterative process of calculating node activation based on these interconnected influences. This approach allows for a more nuanced and realistic simulation of how information flows through networks, accounting for both user attributes and temporal effects.  
Figure 5: Propagation process of linear threshold model.
# Total Ionizing Dose Effects in Advanced 28 nm Charge Trapping 3D NAND Flash Memory
## 2 Devices and Experimental Details  
The schematic diagram of the 3D NAND Flash memory array is depicted in Figure 1a, where the word lines (WLs) are illustrated by green layers, the bit lines by red lines at the top, and the polysilicon channels by purple pillars. The circuit configuration of a NAND Flash memory block is presented in Figure 1b. Each memory comprises multiple flash blocks; within each block are numerous flash pages, each containing multiple flash cells.  
Figure 1: (**a**) A schematic of a 3D NAND Flash array. (**b**) A circuit diagram of a NAND Flash memory block.  
Commercial 28 nm 3D CT NAND Flash memory chips were evaluated in this study. These flash memory chips underwent irradiation with Co-60 sources to assess their TID response, with a dose rate of 20 rad (Si)/s. Doses were recorded as absorbed dose in silicon. The devices under test received the following dose increments: 0 krad (Si), 10 krad (Si), 20 krad (Si), 30 krad (Si), and 40 krad (Si). Various factors influencing the test outcomes, such as dose, operating mode, data patterns, bias states, and annealing time, were meticulously considered. All measurements and irradiation procedures were carried out at room temperature. To discern the potential effects of non-total ionizing doses, reference devices were simultaneously evaluated alongside the irradiated specimens.
## 3 Results and Discussion
### The Impact of Various Emission Intensities on the Total Emission Intensity  
The SPSS software v23 was used to conduct a KW test, and a significance difference analysis of carbon emission intensity was performed on the N\({}_{2}\)O, CH\({}_{4}\), chemicals, electricity, and the total greenhouse gas (Table S7). The carbon emission intensity of WWTPs is mainly affected by the indirect carbon emission intensity (r = 0.9724), with a very high correlation with the electricity carbon emission intensity (r = 0.9118) and a relatively low correlation with the direct carbon emission intensity. Reducing electricity consumption is a key way to control carbon emissions.  
As shown in Figure 4, further statistics on the actual electricity consumption distribution of the 49 WWTPs in 2022 showed that the electricity consumption of the biological treatment process accounted for the highest proportion, 65.8%, followed by the influent lift pump system at 18.8%, the advanced treatment system (such as filter beds, high-efficiency sedimentation tanks, etc.) at 9.5%, and the sludge dewatering and disinfection system (such as UV, ozone) had the lowest electricity consumption. This indicates that the electricity consumption of WWTPs is concentrated in the biochemical treatment and lift pump system, which are the core sources of electricity-related carbon emission intensity.  
To reduce the electricity consumption of WWTPs and lower the indirect carbon emission intensity, the operation and management can be optimized in the following aspects: improving the operational efficiency of influent lift pumps, optimizing equipment selection and operating parameters; optimizing the aeration system of the biological treatment process, such as using intelligent aeration or intermittent aeration; replacing inefficient equipment, such as replacing low-efficiency blowers with high-efficiency magnetic levitation blowers; and, for wastewater treatment plants with a low load rate, installing frequency converters on mixers and pushers.
## 3 The Experiment Assumptions
### Placement of Error Microphones  
The active casing is placed in the corner with its back and right panels. These casing panels are equally distant from both corresponding corner walls with distance \(d_{i}\), as marked in the scheme presented in Figure 2. Error microphone locations corresponding to the left, top and front active casing panels are marked with symbols L, T and F, respectively. They are located at a distance equal to 0.5 m from the corresponding panels' centers. Error microphones corresponding to back and right casing panels are placed in two different ways.  
In the first approach, referred to as Setup 1, error microphones corresponding to back and right active casing panels are placed between the casing and the corner. Back error microphone location is noted by symbol B and is half the distance between the back casing panel's center and the corresponding corner wall (the distance is equal to \(d_{i}/2\)). Analogously, right error microphone location is noted by symbol R and is half the distance between the right casing panel's center and the corresponding corner wall (the distance is also equal to \(d_{i}/2\)).  
In the second approach, referred to below as Setup 2, error microphones corresponding to back and right active casing panels are moved from the gap between the casing and the corner to arbitrarily selected locations among error microphones corresponding to front, left and top active casing panels, based on results of preliminary control experiments. The new back error microphone location is noted by symbol B\({}^{\prime}\) and is placed at the same height as the top error microphone, above the active casing's corner (see Figure 2b). The new right error microphone location is noted by symbol R\({}^{\prime}\) and is placed at the same height as both left and front error microphones, and its distance from active casing's edge is equal to 0.5 m. In such arrangement, distances between error microphones become smaller, which affects the performance of active control. Both setups are examined to compare their influence.  
It is noteworthy that when the distance \(d_{i}\) between the casing panels and corner walls changes, all of the error microphones have to be moved, following the casing position. On the other hand, room microphones are always at the same locations.
## 5 Conclusions  
We have presented double barrier quantum well resonant tunneling diode based optical waveguide photo-detectors for the implementation of novel optical injection locked microwave oscillators. These novel devices operate at optical communication wavelengths. We have demonstrated efficient detection of GHz modulated optical carriers and optical control of RTD high-frequency oscillator when RTD-PD devices are operated in the non-oscillating and oscillating regimes. When operating in the oscillating regimes clock recovery from return-to-zero format data by using injection locking of an RTD-PD free-running oscillator circuit was successfully demonstrated. Finally, transmission of digital modulated signals was demonstrated using a wireless/photonics arrangement that takes advantage of the optical injection locking characteristics of RTD-PD oscillator circuits. The error vector magnitude performance of the RTD-PD for different phase and frequency modulation formats was evaluated.  
RTD-PDs provide simple and compact circuit configurations and can find extensive applications in high-speed optical communications such as synchronization in communication networks and radio-over-fiber links. RTDs are very high-speed devices and we anticipate that the systems and devices we have presented here could be implemented at much higher frequencies.
## 4 Experiment Evaluation
### Experimental Results
#### 4.3.4 Comparison of User-Based and Product-Based Prediction Methods  
The ADLRP method analyzes user reviews versus product reviews. To investigate the influence of user features and item features on the prediction of ratings, this experiment compared the prediction results of the user-based ADLRP method with the item-based  
Figure 8: Rating prediction RMSEs of different features in/without consideration of aspect features.  
Figure 7: Rating prediction MAEs of different features in/without consideration of aspect features.  
ADLRP method. The user-based ADLRP method analyzes user reviews and extracts user features, including aspect features, sentiment intensity, and semantic features, and inputs the features into the model proposed in this study for rating prediction. Similarly, the item-based ADLRP method extracts features from product reviews for rating prediction. Both methods are based on the ADLRP method; the main difference lies in the features used. The number of aspects was set as 15 in the two methods, and the comparison results are shown in Figure 9.  
According to Figure 9, we can see that user-based ADLRP has the lowest prediction accuracy, followed by item-based ADLRP; the ADLRP method has the best prediction accuracy. By considering the features extracted from both user and product reviews during predicting rating, we can obtain more accurate user preferences and product ratings, and significantly improve prediction accuracy.
# Experimental Validation of Peer-to-Peer Distributed Voltage Control System
## 6 Results of the Experiment
### Discussion  
The proposed P2P voltage control system managed to increase the voltage quality of the voltage profiles. Some over-voltage issues remain, because the control algorithm needs around 3 min to bring back the voltages within the limits. However, it is in line with the European standard EN50160 as all 10 min mean rms values of the voltages are within the range \([V^{\mathrm{n}}-10\%,V^{\mathrm{n}}+10\%]\), where \(V^{\mathrm{n}}=1\) p.u.  
The key question motivating this research, was whether fully distributed voltage control systems are a technically effective alternative to centralized ones. The results discussed in this paper show that fully distributed P2P voltage control systems can indeed provide satisfactory regulation of the voltage in distribution networks.  
Technically, the P2P approach has shown good characteristics to be considered by DSOs to deliver high quality power to customers. The proposed P2P system could help in delivering easier access to prosumers' flexible supply and demand by making their active participation in the grid possible. This can be used to alleviate grid stress and defer or avoid grid upgrades, and consequently will help the DSOs to host more RESs.
# Grammatical Evolution-Based Feature Extraction for Hemiplegia Type Detection
## 5 Experimental Results  
The FSC4 RBF method was tested with four classification approaches (RBFNN, BFGS, SVM, and GenClass) in a custom-created dataset containing entries taken from ten healthy and twenty hemiplegic patients (eight had left hemiplegia and twelve had right hemiplegia). The data from the seven accelerometers were sent to a PC which divided them into 2-s windows. The signals were pre-processed with the help of a low-pass filter before undergoing a feature extraction stage using four time-domain and two frequency-domain features. The dataset contains the values after the pre-processing and the initial manual feature extraction stage. All input feature entries are real-valued. The low-pass filter contains a 6 Hz pass-band frequency, and each captured signal was sampled at 1000 Hz. The above features formed the input vector to the FSC4 RBF method and constituted the initial population of the modified GE algorithm. The modified GE algorithm automatically evolved these initial features into more advanced ones, which were used to train an RBFNN.