esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Symptomatic and Asymptomatic Carotid Plaques: From Pathophysiology to Diagnostic Issues
Monocytes and macrophages in the atherosclerotic plaques are responsible for core necrosis and local thrombosis due to the production of matrix-degrading proteases and the release of tissue factors and lipids [46]. The adaptative immune system is also involved in the development of atherosclerosis, with a proatherogenic role of T-helper and B cells as opposed to the anti-atherogenic role of regulatory T cells [35; 47]. Given the role of immune cells in the progression of atherosclerosis, new models based on AI have been proposed in order to predict the risk of acute ischemic stroke on the basis of the recognition of the cellular composition of the atherosclerotic lesion using histopathological samples obtained from patients who underwent endarterectomy. In this concern, [PERSON] et al. [48] proposed a machine learning method (called CIBERSORT) combined with LASSO regression for differentiating cellular infiltration in stable and unstable plaques. They found a significantly higher expression of M0, M1, and M2 macrophages in the unstable lesions than in the stable lesions where T cells were predominant. Atherosclerotic plaques are the main cause of acute cardiovascular and cerebrovascular events [49]. They have different characteristics reflecting the affected district and composition that influence plaque's susceptibility to rupture or erosion. Apart from coronary lesions, peripheral artery disease plaques present a prevalent composition of collagen, smooth cells, and calcifications, which provide the plaque with higher stability [49; 50]. The modified American Heart Association (AHA) classification [49] categorizes, in a practical way, atherosclerotic plaques with the aim of highlighting their image appearance and recognizes (i) adaptive intimal thickening (AIT), (ii) pathological intimal thickening (PIT), (iii) fibroatheroma (FA), and (iv) fibrocalcific plaque (FC). Although for many years, the size of the plaque and the degree of luminal vessel narrowing have been considered pivotal in ischemic cardiovascular disease pathogenesis [51], nowadays, the importance of plaque qualitative properties, together with its thickness and dimensions, is recognized. Several determinants can be used to describe plaque characteristics: (i) symptomatic or asymptomatic; (ii) the degree of the stenosis; and (iii) vulnerability. According to the European Stroke Organisation guideline on endarterectomy and stenting for carotid artery stenosis [52], carotid stenosis is defined as _symptomatic_ if it has caused ischemic cerebrovascular events in the ipsilateral eye (transient monocular blindness or retinal infarction) or cerebral hemisphere (transient ischemic attack (TIA) or stroke) in the preceding six months and _asymptomatic_ if it is not associated with any ocular or cerebral ischemic events in the ipsilateral carotid territory within the preceding six months. The term \"symptomatic\" is then related to the presence of clinically overt manifestations of vessel alterations.
## 2 Materials and Methods
### Sensor Calibration  
Capacitive soil moisture sensors were evaluated and calibrated using another soil moisture sensor with high accuracy. Soil samples were collected and placed inside several small cups with different moisture percentages varying from dry to saturated conditions. The readings of each sensor were recorded separately, and finally, a comparison was made between the capacitive soil moisture sensors and other soil moisture content sensors. Using the regression, the correlation and coefficient were found for each capacitive soil moisture sensor. Consequently, these coefficients were used in programming to read the sensor data and adjust them accordingly.
# Boundary-Aware Concurrent Queue: A Fast and Scalable Concurrent FIFO Queue on GPU Environments
## 5 Experimental Results and Analysis
### Performance Analysis on Balanced Scenario (Figure 15)  
In our investigation of the balanced scenario, where each thread performs a pair of enqueue and dequeue operations, BACQ achieved a peak performance of 37.6 billion operations per second. The BACQ performance curve exhibits exponential growth until the queue reaches its maximum size (65,536 elements). At this point, the algorithm begins to experience some spinning as threads wait to match their tickets, but this has minimal impact on overall performance, resulting in only a 6.5% drop. This demonstrates BACQ's stability even under high contention.  
In comparison, BACQ shows a 33% performance gap when measured against the current state-of-the-art the Broker Queue Work Distributor (BWD). Meanwhile, the broker queue's performance degrades significantly after 16 million requests, with BACQ outperforming it by 3\(\times\) and surpassing the Gottlieb queue by a factor of 9\(\times\). The performance and scalability of these algorithms are degraded by the frequent use of expensive atomic operations, which arise due to the lack of space guarantees and the frequent encounters with boundary conditions under high contention.
## 3 Results and Discussion
### XRD Phase Analysis  
It can be seen from XRD (Figure 1) that the main component of sludge before and after calcination is quartz. And there is also a small amount of kaolin and mica, which is closely related to the economic development of Chaozhou, which is the largest production location for daily ceramic. Chaozhou No.1 Sewage Treatment Plant is also located in the same place. Therefore, the components of the wastewater are related to ceramic raw materials, which may be the reason why the sludge can be fired with well compressive strength without adding other clay components. When the temperature is 950 \({}^{\circ}\)C, hematite appears in the mineral phase, which indicates that there is a large amount of iron in the sludge,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Fe** & **Zn** & **Cu** & **Ni** & **Pb** & **Cr** & **Cd** \\  & **(\(\pm\)0.005)** * & **(\(\pm\)0.005)** & **(\(\pm\)0.002)** & **(\(\pm\)0.002)** & **(\(\pm\)0.002)** & **(\(\pm\)0.002)** & **(\(\pm\)0.002)** \\ \hline
105 \({}^{\circ}\)C & 0.081 & 0.065 & 0.009 & 0.023 & 0.009 & 0.012 & 0.005 \\
350 \({}^{\circ}\)C & 0.098 & 0.089 & 0.010 & 0.029 & 0.009 & 0.008 & 0.003 \\
650 \({}^{\circ}\)C & 0.026 & 0.021 & 0.005 & 0.007 & 0.001 & 0.001 & 0.001 \\
950 \({}^{\circ}\)C & 0.015 & 0.007 & 0.003 & 0.00 & 0.003 & 0.001 & 0.001 \\ \hline \end{tabular}  
* Individual errors are lower than the displayed value, and the maximum value is set here.  
\end{table}
Table 4: Toxic leaching of ceramics fired at different temperatures mg/g dried basis.  
but it belongs to the iron minerals with weak crystallinity, such as ferrihydrite, hydroxyl ferric oxide, and so on, which are difficult to be observed in XRD. With the increase of temperature, these iron-bearing minerals change into hematite after removing structural water, which can be observed in phase retrieval. These iron and aluminum components in sludge have strong capacity of adsorption and catalytic oxidation, which play an important role in wetland padding [25; 26; 27; 28].
## 3 Heat Transport Pipeline Damage Probability Analysis
### Pipe Diameter  
The pipe diameter of heat transport pipelines in the target area varied from 20 to 1200 mm. In general, steel pipes can be classified as small-diameter pipes for diameters of 200 mm or less, medium-diameter pipes for 200 to 400 mm, and large-diameter pipes for 400 mm or more. Therefore, damage probability was analyzed according to these pipe diameter categories. There were a total of 2215 data points that included attribute information on pipe diameter and could be used for analysis. Figure 4 shows the number of damage cases and damage probability by pipe diameter. In addition, Table 3 shows  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pipe Function** & \begin{tabular}{c} **Average Period of** \\ **Use (Year)** \\ \end{tabular} & **Pipe Length (km)** & \
\begin{tabular}{c} **Number of Damage** \\ **Cases (\#)** \\ \end{tabular} &
\begin{tabular}{c} **D Damage Probability** \\ **(\#/km/Year)** \\ \end{tabular} \\ \hline Supply pipe & 16.00 & 2454.00 & 1557 & 0.043 \\ Return pipe & 16.02 & 2452.26 & 683 & 0.019 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of damage cases and damage probability for each pipe function.  
Figure 3: Number of damage cases and damage probability according to the operator.  
the number of damage cases and probability according to the pipe diameter category. There was no significant correlation between pipe diameter and damage to heat transport pipelines, but the damage probability of medium-diameter pipes was the lowest among small-, medium-, and large-diameter pipes.
# Global Regulatory Challenges for Medical Devices: Impact on Innovation and Market Access
## 4 Regulatory Frameworks of Major Markets for Medical Devices
### European Union
#### 4.1.1 Definition and Classification  
According to Article 2 of Regulation (EU) 2017/745, the term \"medical device\" refers to \"any instrument, apparatus, appliance, software, implant, reagent, material or other article intended by the manufacturer to be used, alone or in combination, for human beings for one or more of the following specific medical purposes: --diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of disease, --diagnosis, monitoring,treatment, alleviation of, or compensation for, an injury or disability, --investigation, replacement or modification of the anatomy or of a physiological or pathological process or state, --providing information by means of in vitro examination of specimens derived from the human body, including organ, blood and tissue donations and which does not achieve its principal intended action by pharmacological, immunological or metabolic means, in or on the human body, but which may be assisted in its function by such means. The following products shall also be deemed to be medical devices: --devices for the control or support of conception; --products specifically intended for the cleaning, disinfection or sterilization of devices as referred to in Article 1(4) and of those referred to in the first paragraph of this point.\" [11].  
In the EU, medical devices are classified into several classes, such as class I (Isp, Imf and Iris), class II (IIa, IIb) and class III, according to their intended purpose and intrinsic risks [11; 22]. Class I devices are considered low risk and non-invasive, and are admitted as everyday devices or appliances. In this category, we can also consider the Class Isp that includes sterile medical devices, the Class Imf that includes devices with a measuring function and the Class Irs that includes reusable surgical instruments. Class II devices are classified as Class IIa when they are considered low to medium risk and refer mainly to devices installed within the body for a short term, or as Class IIb when they are slightly more complex than class IIa devices. Class IIb devices are generally medium to high risk and often include devices installed within the body for longer periods. Lastly, Class III devices are strictly high-risk devices and for this reason, they follow a more rigorous process for market introduction [38].
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 8 Underwater SLAM Systems Evaluation and Comparison
### Comparison of Underwater SLAM Methods
#### 8.3.3 Analysis on Why DL Outperforms  
DL's superiority stems from three key capabilities:  
* Feature Learning: DL automatically learns robust features, such as Wang 2024's CNN-attention hybrid, which adapts to turbidity variations, unlike handcrafted features in traditional methods that fail in low visibility.
* Non-linear Modeling: Recurrent architectures (LSTM/GRU) in DL methods like Sudevan 2023 better model complex underwater dynamics.
* Multi-modal Fusion: DL's learned fusion, such as Jang 2021's optic-acoustic network, outperforms traditional sensor weighting by discovering complementary sensor relationships.  
However, traditional methods remain preferable when training data are scarce, computational resources are limited, or environments match the method's assumptions, for example, structured pipelines where geometry-based SLAM suffices.
## 3 Materials and Methods
### Normalization
#### 3.2.2 Freezing Layers  
Freezing layers in DL models is a technique where the weights of specific layers in a pre-trained model are locked, meaning they do not change during further training. This method is important in transfer learning, where a model that has been pre-trained on ImageNet is adapted to a new, usually smaller, dataset. The significance of freezing layers is that they help preserve the general features the model has already learned, such as detecting edges in images, which are often useful across different tasks. By freezing these layers, the model keeps its foundational knowledge, which reduces the risk of overfitting while allowing the other layers to focus on the new task. This technique not only speeds up the training process but also enhances the model's ability to generalize new data [26].  
During the transfer learning process, we applied the technique of freezing layers in the four DL models. This approach enabled the remaining layers to focus their training efforts on the Kvasir-Capsule and Kvasir v2 datasets. Additionally, the partial layer freezing method decreased the number of trainable parameters needed by disabling certain layers from re-training. As a result, we maintained the initial features obtained from transfer learning in the upper layers.
# Unequal Interval Dynamic Traffic Flow Prediction with Singular Point Detection
## 6 Performance Evaluations
### Simulation Setup  
#### 6.1.1 Simulation Scenario  
We conduct the evaluation using Matlab, which runs on a Windows 10 computer with Core i7 and 8 GB memory. Especially, as shown in Figure 4, the traffic data used in the evaluation are realistic data collected in Yan'an Elevated Road, Shanghai, China [37]. The raw data are recorded every second, and the simulation uses the average traffic flow every minute. The traffic data in 2 h, i.e., 15:10-17:10, are used as the real-time observation data. The original datasets contain five characteristics, i.e., the interdistance between vehicles, the recorded vehicular speed, the ID of the lane, the traffic flow data, and the traffic density. We mainly used the fourth characteristic to execute our evaluations. The traffic flow data in the same period and the same road segment but on different dates are chosen as the historical (training) data, which have five groups of data provided via a database. Because our proposed algorithm repredicts the traffic flow based on the singular points' amount and position, the prediction's time interval is not fixed. Comparing algorithms' time interval defaults as 12 min, which can attain 10 traffic flow reproductions during the whole process. The details of the traffic data are shown in Table 2. This table also contains a few other key parameters of the following simulation.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Simulation Parameter** & **Value** \\ \hline default vehicular lane in road segment & 1 \\ duration of the observation/prediction & 120 min \\ interval of average traffic flow calculation & 1 min \\ comparing methods’ default prediction time interval & 12 min \\ maximum volume of wavelet scales & 8 \\ number of training data & 5 \\ level of Fourier series & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the simulation parameters.  
Figure 4: Illustration of the map for simulation.
## 3 Model and Research
### Sliding PID for Navigation System
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Axis** & **Ox (Poshold)** & **Oy (Poshold)** & **Oz (Poshold)** & **Oz (Plane)** & **As (Plane)** & **Yaw (Plane)** \\ \hline \(\mathbf{K_{pos}}\) & 0.5 & 1 & 2 & 1 & - & - \\ \hline \(\mathrm{T_{z}}\) & 12.54 & 11.57 & 6.07 & 3.87 & 3.335 & 12.44 \\ \hline \(\mathrm{K_{Pmax}}\) & 23.92 & 19.44 & 812.97 & 11.34 & 93.33 & 9 \\ \hline \(\mathbf{K_{P}}\) & 10.765 & 8.75 & 365.84 & 5.103 & 41.99 & 4.5 \\ \hline \(\mathbf{K_{I}}\) & 1.0370 & 0.9074 & 72.27 & 1.582 & 15.11 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PID based control system settings. At the Figure 4a, As\({}_{\text{tgt}}\) is the airspeed command in m/s, defined by user in the simulation environment, As is airspeed data from sensor, Az\({}_{\text{cmd}}\) is desired heading in degrees, yaw variable defines current drone heading. Altitude contour is using Alt\({}_{\text{cmd}}\) variable defines desired altitude, Z input is current altitude and V\({}_{\text{z}}\) is current vertical speed of the simulated UAV. At the Figure 4b, Hdg\({}_{\text{cmd}}\) input is acting as desired heading command, yaw is current heading, Pos\({}_{\text{cmd}}\) input is target position, Xe is current position and V\({}_{\text{x}}\), V\({}_{\text{y}}\) and V\({}_{\text{z}}\) are current drone velocities. Cmd variable is acting as block output for both systems. Figure 4 shows the PID-controlled systems for high-speed flight (Figure 4a) position hold (Figure 4b). It has to be noted that this system requires an additional switch block to perform the mission, or it cannot be used for the navigation. Switching between these controllers is realized using the state machine described in the next section and result transition process for acceleration or deceleration. As a result, a coarse linear control system is obtained, capable of controlling the aircraft with the maximum allowed deviations. However, the system has a very small velocity range, within which the object is stable due to the limitations applied. Finally, changing the cruise speed would require a complete reconfiguration of the system, creating problems for operation with a real object.
# Dynamic Identification of Tensile Force in Tie-Rods by Interferometric Radar Measurements
## 2 Tensile Force Dynamic Identification in Tie-Rods: Theoretical Approaches
Clearly, some of the simplifying assumptions of the model under examination may introduce errors on the estimated value of \(N\): indeed, the geometrical and inertial parameters of the cross-section of the tie-rod, along with the density and the elastic moduli, are considered as constant, and the elastic rotational constraints at the ends are assumed to have the same stiffness. Finally, shear deformations are neglected. Nevertheless, such assumptions could usually be considered reasonable in the majority of practical applications. Moreover, the structural model is more representative than that employed in the natural frequency-based method and thus, in principle, capable of more accurate results.
## 2 LNA Design Methodology
### Resistive Feedback Network for Improved Input Matching and Broadband Gain Response  
Equations (2)-(4) offer some key insights into the analysis of the LNA input impedance [15]. When we neglect the gate-drain capacitance of \(M_{1}\) in Figure 3a, the degeneration inductor \(L_{2}\) can supply a real impedance at the input of \(M_{1}\)[16]. However, it is important to note that \(Z_{in2}\) in Equation (3) can remain real only at a single resonant frequency [16]. To address this limitation and enhance the input matching bandwidth, we introduced a resistive feedback network (\(R_{f}\) and \(C_{f}\)) [15,17]. The stability and bandwidth of the LNA are influenced by the specific values chosen for \(R_{f}\) and \(C_{f}\). A small \(R_{f}\) and large \(C_{f}\) give more amplifier feedback characteristics and make the input match more sensitive to the interstage matching network. The input impedances of the LNA at various nodes can be approximated as described in [15].  
\[Z_{in1}\approx j\omega L_{1}+(Z_{in2}\|Z_{in3}) \tag{2}\]  
\[Z_{in2}\approx j\omega L_{2}+\frac{1}{j\omega C_{gs1}}+\frac{g_{m1}L_{2}}{C_{ gs1}} \tag{3}\]  
\[Z_{in3}\approx\frac{R_{f}+(j\omega L_{4}\|Z_{in4})}{1+\frac{g_{m1}S_{m2}(j \omega L_{4}\|Z_{in4})}{(g_{m2}+j\omega C_{gs2})\big{[}-\omega^{2}C_{gs1}(L_{1 }+L_{2})+j\omega g_{m1}L_{2}+1\big{]}}} \tag{4}\]  
where \(g_{m1}/g_{m2}\) and \(C_{gs1}/C_{gs2}\) are the transconductances and gate-source capacitance of transistors \(M_{1}/M_{2}\). The other component values can be found in Figure 3a.  
In Equation (4), one can see the presence of \(L_{4}\) and \(Z_{in4}\) also impacts the LNA input matching as indicated by its \(S_{11}\) frequency response. As suggested in [16], the inclusion of the resistive feedback component \(R_{f}\) can lead to a reduction in gain while enhancing the LNA's bandwidth. This is because the gain reduction smoothens the two gain peaks, which, in effect, reduces the gain difference between the gain peaks and the gain trough. This improvement is reflected in our pre-PEX simulation results, as depicted in Figure 6, which illustrates an extended \(S_{21}\) 3-dB gain bandwidth and input matching \(S_{11}\) over frequency.  
Figure 5: Pre-PEX simulated power gain circles (brown: 24 dB, green: 25 dB, blue: 26 dB) at (**a**) 24 GHz, **(b**) 30 GHz, and (**c**) 40 GHz with the load impedance locus (red trace) from 20 GHz to 40 GHz (red dots are the load impedances at their specified frequencies above the Smith Charts (i.e., 24, 20 or 40 GHz)).  
While there are advantages in terms of increased BW and improved input matching, \(R_{f}\) can introduce more noise from the interstage matching network, and the negative feedback reduces the gain of the LNA. In pre-PEX noise circle simulations presented in Figure 7, the LNA's lowest \(N\)F at 24 GHz is approximately 2.4 dB before adding resistive feedback to the LNA's first stage and rises to 2.8 dB after including resistive feedback. Additionally, after the parasitics of the layout were extracted using PEX \(RCC\), there was \(\sim\)0.1 dB additional \(N\)F degradation, as shown in Figure 7c.