esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of Approximate QAM Demodulation Circuits
### Approximate ML Architecture  
Typical ML methods use non-linear functions to calculate the demodulation symbol. To decrease the conventional complexity, the approximate ML (AML) method of [29] uses a closed function of the estimated channel and received signal to estimate the QAM symbol:  
\[\hat{s}=\sum_{p=1}^{\log_{2}\sqrt{M}}c_{p},\quad\text{where}\quad c_{p}=\sqrt {\frac{3M}{M-1}}2^{-p}e^{j\hat{s}(r-\sum_{m=1}^{p-1}c_{m})} \tag{4}\]  
The proposed AML architecture is presented in Figure 4. Our design pre-calculates and stores in ROMs the \(\log_{2}\sqrt{M}\) centers \(c_{p}\). Initially, the first center is identified by the quarter  
Figure 3: \(M\)-QAM demodulation architecture of the piecewise log likelihood ratio (PLLR) algorithm.  
Figure 2: \(M\)-QAM demodulation architecture of the approximate log likelihood ratio (ALLR) algorithm.  
in which the input is located. Subsequently, each center is identified by the quarter that is derived when all the previous centers are subtracted from the input. With this approach, we avoid storing all the intermediate centers to accumulate them as indicated by Equation (4). In contrast, the accumulation is performed as \(\hat{s}=r-(r-c_{1}-c_{2}-\cdots-c_{\log_{2}\sqrt{M}})=c_{1}+c_{2}+\cdots+c_{\log_ {2}\sqrt{M}}\). The AML architecture has a \(\log_{2}\sqrt{M}\)+1 CCs latency, i.e., 4 CCs and 5 CCs for 64-QAM and 256-QAM, respectively.
# An Adaptive Moving Window Kriging Based on K-Means Clustering for Spatial Interpolation
## 3 Methodology
### Moving Window Kriging  
Moving window kriging is an extension of the traditional kriging methods, which allows for a localized estimation of values at target points. Its concept is to first define a window or neighborhood around each target point. The available data points inside this specified window are thereafter used to estimate the unknown quantities within the framework of kriging algorithms. In particular, OK is employed in this study. Algorithm 3 for the moving window kriging involves the following steps:  
```
0: observed data \(Z(x_{1}),Z(x_{2}),\ldots,Z(x_{n})\) at positions \(x_{1},x_{2},\ldots,x_{n}\); target points \(x_{0i}\) for \(i=1,2,\ldots,p\)
0: estimated values \(Z(x_{0i})\) of \(Z(x_{0i})\) at the target points \(x_{0i}\) for \(i=1,2,\ldots,p\)
1:for\(i=1\) to \(p\)do
2: define a window of \(x_{0i}\), \(W_{0i}\)
3: identify the positions of observed data situated inside the window \(W_{0i}\)
4: apply the OK algorithm within the window \(W_{0i}\) to obtain estimated value \(\hat{Z}(x_{0i})\)
5:endfor
```  
**Algorithm 3** Moving window kriging algorithm
## 2 Materials and Methods  
### Some Definitions  
Given two strings \(T=t_{1}t_{2}\ldots t_{n}\) and \(P=p_{1}p_{2}\ldots p_{m}\) (with \(m\leq n\)), the convolution of \(T\) and \(P\) is a sequence \(C=c_{1},c_{2},\ldots,c_{n-m+1}\) where \(c_{i}=\sum_{j=1}^{m}t_{i+j-1}p_{j}\), for \(1\leq i\leq(n-m+1)\). This convolution can be computed in \(O(n\log m)\) time using the fast Fourier transform. If the convolutions are applied on binary inputs, as is often the case in pattern matching applications, some speedup techniques are presented in [23].  
In the context of randomized algorithms, by high probability, we mean a probability greater or equal to \((1-n^{-\epsilon})\) where \(n\) is the input size and \(\epsilon\) is a probability parameter usually assumed to be a constant greater than \(0\). The run time of a Las Vegas algorithm is said to be \(\widetilde{O}(f(n))\) if the run time is no more than \(ccf(n)\) with probability greater or equal to \((1-n^{-\epsilon})\) for all \(n\geq n_{0}\), where \(c\) and \(n_{0}\) are some constants and for any constant \(\epsilon\geq 1\).  
In the analysis of our algorithms, we will employ the following Chernoff bounds.  
Chernoff bounds [24]: These bounds can be used to closely approximate the tail ends of a binomial distribution.  
A Bernoulli trial has two outcomes, namely success and failure, the probability of success being \(p\). A binomial distribution with parameters \(n\) and \(p\), denoted as \(B(n,p)\), is the number of successes in \(n\) independent Bernoulli trials.  
Let \(X\) be a binomial random variable whose distribution is \(B(n,p)\). If \(m\) is any integer \(>np\), then the following are true:  
\[Prob.[X>m]\ \leq\ \left(\frac{np}{m}\right)^{m}\ e^{m-np} \tag{1}\]  
\[Prob.[X>(1+\delta)np]\ \leq\ e^{-\delta^{2}np/3};\ \text{and} \tag{2}\]\[Prob.[X<(1-\delta)np]\;\leq\;e^{-\delta^{2}np/2} \tag{3}\]  
for any \(0<\delta<1\).
###### Abstract  
The Maillard reaction (MR) is responsible for the development of color, taste and aroma in bakery products though the formation of numerous aroma compounds such as pyrazines, pyrroles and aldehydes, nonvolatile taste active compounds and melanoidins. In this article, we investigate the effect of quercetin 3\(\beta\)-D-rutinoside (rutin) supplementation, at the level of 5-50 mg per 100 g, of rye-buckwheat biscuits on the formation of phenolics and volatile Maillard reaction products (MRPs) such as pyrazines, furfuryl alcohol and furfural, determined by headspace solid phase microextraction followed by gas chromatography-mass spectrometry (HS-SPME/GC-MS), in addition to the effect on the antioxidant capacity. The study confirmed that rutin was stable under baking conditions as showed by its content in rye-buckwheat biscuits. Supplementation of biscuits with increasing amounts of rutin resulted in the progressive increase of total phenolics and antioxidant capacity measured by DPPH and OxHLIA assays, but it had no effect on their sensory quality. From the eighteen compounds identified by HS-SPME/GC-MS in the volatile fraction of biscuits were quantitated as a compounds-of-interest: methylpyrazine, ethylpyrazine, 2,3-; 2,5- and 2,6-dimethylpyrazines, as well as furfural, furfuryl alcohol and hexanal. The rutin supplementation of biscuits might be one of the factors to influence the formation of both desirable volatile compounds and undesirable toxic compounds. In conclusion, this study indicates for the significant role of polyphenols on the formation of volatile compounds in biscuits with possible future application in the development of healthy bakery products with high antioxidant capacity.  
volatiles; rutin; antioxidant capacity; pyrazines; furrans; sensory evaluation; HS-SPME/GC-MS +
Footnote †: journal: molecules
# Classifying X-Ray Tube Malfunctions: AI-Powered CT Predictive Maintenance System
## 4 Data
### Data Description  
The obtained dataset featured 137 tables, each representing one CT device, with an average length of 320,000 rows. Of these, 66 corresponded to CTs, where X-ray tubes were subsequently replaced (the logs ended with the date of replacement), and 71 of those were still functional at the time of the data collection. However, the data do not present the precise reason or moment of the breakdown. Hence, it is only possible to say whether such behaviour led to the breakdown. Metrics were collected during each scan performed (intervals of a few seconds), giving an extensive overview of the machine's behaviour during scanning. The measurements are not isochronous but rather depend on the scanning type, length, etc.  
The original collected dataset contained 91 features. Ten parameters with only NULL values, such as \"tank expansion\" and \"arcs:\", were removed from the dataset due to the lack of available measurements for interpolation. Parameters without NULL values were retained without further interpolation.  
After removing features deemed useless (such as the machine's serial number, the tube's serial number, scan ID, region, etc.), we have tables that include 73 features. Most of the retained features are related to the X-ray tube. Some of them are only indirectly related to the X-ray tube:  
* **Rotation time**--Deviations in the rotation time could indicate a mechanical problem in the gantry rotation system, such as worn bearings or motor malfunction. Such problems might result in excessive vibrations during scanning, which could lead to lower quality of the images or even damage to the X-ray tube and other components
* **Scan duration**--Deviation from the expected scanning time may indicate a problem with the patient table or a mechanical issue with the gantry, which can cause reduced image quality and may increase radiation dose. Prolonged scanning times may lead to overheating of components, mechanical wear, and increased radiation exposure, potentially resulting in decreased performance and shorter device lifespan.
* **Frequency and current of the cooling pump**--could signalise problems with the cooling system in general
## 3 Results and Discussion
### Differential Scanning Calorimetry  
To check the possible reactivity of the red mud, differential scanning calorimetry measurements were made. This is a commonly used technique to study the pozzolanic activity of some new materials [49]. For that purpose, the mortars with the lowest and the highest content of red mud (RM5 and RM20 respectively) were tested, as well as the CEM I and CEM II mortars, which were taken as a reference for comparison. In this work, the heat per gram of sample released in the decomposition of the portlandite was studied, whose results for the abovementioned mortars are shown in Figure 7. As it can be observed, the heat for the cements with red mud is the lowest at every age, and it decreases with time.  
This evolution of the portlandite peak and the decreasing amount of heat released for samples prepared with red mud would indicate the presence of less portlandite. This could be produced by the possible pozzolanic reaction of the red mud with the portlandite formed in the clinker hydration, according to other studies [22; 27]. The possibility of carbonation was rejected, because samples did not present carbonation, which was checked using the phenolphthalein test. Following this, the results of the differential scanning calorimetry corroborated the pozzolanic activity of the red mud. In addition to this, they would be in line with the mercury intrusion porosimetry tests discussed previously.  
Figure 6: Evolution of Hg retained once porosimetry testing was completed for the studied mortars.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments
### Technological Considerations
#### 5.1.1 Primary Radar  
Primary radar is a technology that was widely used within manned aviation to separate aircraft from one another, and it is still sometimes used today as a back-up to the SSR systems, which are dependent upon aircraft transponders, or as a ground radar while aircraft are on the ground [39]. However, primary radar can also be used on board unmanned aircraft as a means of achieving the DAA requirements associated with flight in non-segregated airspace [40]. Unlike SSR and the use of ADS-B In and Out, primary radar does not depend on aircraft having transponders on board in order for them to be detected, and it also detects objects other than aircraft, such as buildings and trees, that would otherwise not be picked up [41]. This is important, as many countries allow the flight of aircraft with no radio on board (known as NORDO aircraft). These NORDO aircraft can only fly in uncontrolled airspace, but unlike other aircraft, they do not make radio calls with position reports, and they are also not listening to position reports of other aircraft. They are entirely dependent on visual detection for avoiding other aircraft. Equally, in many countries, ADS-B transponders are not required to be installed on aircraft for flight in uncontrolled airspace, so they would not be picked up on SSR. These aircraft would be picked up by primary radar, allowing for NORDO aircraft and aircraft without transponders to be picked up. Primary radar is also less effected by sunlight and adverse meteorological conditions, such as fog, dust, rain, snow, or smoke, all of which are a major hindrance to an optical system and which may also affect lidar [41; 42]. However, primary radar is not without its drawbacks as a DAA tool; namely, because it has a lower level of locational accuracy (due to longer wavelengths), it is often too large and has too much required power output to be fitted on unmanned aircraft, so it poses challenges for signal processing, and it is very costly to purchase and maintain [41; 42; 43]. However, it is also possible to use a more powerful primary radar as part of the ground control station instead of on the aircraft. This does not avoid the high costs of purchasing radar, however, but it may be a good alternative if the aircraft is not large enough to have onboard radar. If properly deployed, it has been shown to be able to detect cooperative and non-cooperative aircraft, but it is often still combined with other DAA technologies for maximum effect [44; 45]. There are also considerations in terms of where this is positioned, given the topography of different forest environments, with the potential requirement to use visual observers outside of the radar's coverage [45].
# Application of a Real-Time Visualization Method of AUVs in Underwater Visual Localization
## 3 Results
### Experiment in an Artificial Pool  
An AUV was controlled to walk along the wall of a pool to build an underwater three-dimensional map composed of spatial points and lines (as shown in Figure 4). The AUV performed well with the PL-SLAM method. However, the other visual SLAM algorithms were prone to collapse. The phenomenon of system collapse due to the lack of features was greatly reduced, and the actual frame rate remained at around 20 fps. In order to display the trajectory of the AUV in real time, the pose information was translated into the path topic of the ROS (Robot Operating System). In Figure 5, the color axis represents the AUV's pose, and the yellow line represents the real time trajectory.
# A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification
## 2 Novel Hybrid Data-Fusion System
### Decision-Level Data Fusion Model for Damage Detection  
On the right side of Figure 1 is the decision-level data-fusion damage detection model. After data pre-processing, feature extraction and single PNN decision, the decision fusion was carried out. It is noted that the decision fusion based on single PNN classifier model is shown in Figure 2. Thus, this section focuses on the process of fusion computation for the decision level. Based on the contribution of single PNN model, we can obtain an initial decision for every given feature (sensor), then the weighted-average fusion algorithm is employed for fusion computation. Based on the conditional PDF\(f_{q}(X)\) corresponding to each class from PNN classifier of the test vector \(X\), weighted average method was used to fuse computation; assuming the class-conditional PDF\(f_{q,j}(X)\) for class \(\theta_{q}\) and \(j\)th sensor is known, a fused class-conditional PDF \(\overline{f}_{q}(X)\) for class \(\theta_{q}\) can be written as:  
\[\overline{f}_{q}(X)=\sum_{j=1}^{m}w_{q,j}*f_{q,j}(X) \tag{6}\]  
where \(w_{q,j}\) stands for the normalized coefficient for class \(\theta_{q}\) and \(j\)th sensor, which depends on the recognition accuracy of PNN classifiers in the PNN network training stage, and \(\sum\limits_{j}w_{q,j}=1\).  
Figure 3: Eigen-level data fusion model for damage detection.  
Decision results of \(X\) assigned to class \(\theta_{q}\) according to maximal fused probability density functions were defined as Equation (7). This majority voting technique adopts the winner-take-all principle.  
\[\theta_{q}(X)=\left\{\begin{array}{l}1,\max(\overline{f}_{q}(X)),(q=1,2,\cdots,s)\\ 0,\textit{others}\end{array}\right. \tag{7}\]  
For different damage, different classifiers may have different recognition results, then the maximal fused probability density function is employed to make the final decision results.
## 5 Conclusions  
This review highlights the synthesis methods for magnetite-silica core/shell nanostructures through different preparation routes and which have interesting applications in medical applications. Based on all the presented research, it has been shown that, for the preparation of magnetite nanoparticles coated with silica, the most used was the modified Stober method. Although, there are still some difficulties to be achieved in the synthesis method of magnetic composites with tunable features. However, in addition to the Stober method, other synthesis methods such as water-oil, micro-emulsion, gas deposition, coprecipitation, sol-gel, thermal deposition, and hydrothermal methods are presented.  
In addition to the synthesis methods of magnetite-silica core/shell nanostructures, this review illustrates their characteristics and their superficial effects through functionalization, but also focuses on their applications, especially in the biomedical field such as hyperthermia, MRI, and drug delivery systems. From the presented research, it can be stated that magnetite-silica core/shell nanostructures have beneficial applications in many therapies and, through surface functionalization, they can load various drugs and further assist their delivery to obtain personalized therapy for the treatment and diagnosis of specific diseases, combining the benefits provided by the two components, the magnetic core and the silica shell. It is important to mention that core@shell@shell nanostructures are of growing interest and, most probable, their applications will be expanded and partially replace pure silica materials. Besides, great attention was devoted to the development of mesoporous shells that can host and release biologically active agents under triggered conditions that can be initiated by hyperthermia or other factors such as photothermy and pH. It is worth mentioning that all the experimental research done in the biomedical field could provide promising outcomes and also show the potential of magnetite-silica core/shell nanostructures, and it is believed that their usage in clinical treatment is far from over.  
**Author Contributions:** This review was written with contribution from all authors, conceptualization, E.A. and A.F., methodology, A.S., C.-I.I., L.N.C. and A.F., validation, A.F. and D.F., writing--original draft preparation A.S., C.-I.I. and D.F.; writing--review and editing A.S., A.F. and E.A., visualization, A.F., L.N.C., D.F. and E.A., supervision A.F. and E.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by UEFISCDI, grant number 522 PED/2018 funded by UEFISCDI. The APC was funded by University POLITEHNICA of Bucharest.  
The authors declare no conflict of interest.
# Optimization Design Study of Pipe Curtain Freezing Scheme for Sanya Estuary Submarine Channel
## 2 Freeze Program Design
### Arrangement of Freezing Pipes and Pipe Curtain Steel Pipes  
The freezing method employs a combination of freezing pipes and steel pipes, where the latter serves as a support structure. This system, which includes freezing pipes to create a permafrost curtain, effectively prevents water ingress and provides support. Based on the pipe curtain structure, the freezing curtain can be categorized into four distinct areas: the bottom plate, the west side wall, the east side wall, and the top plate. These areas are further divided into strong and weak freezing zones, each containing both types of zones (Figure 1). The strong freezing zone encompasses the inner and outer areas around the steel pipes' cutting and connecting weld plates, with the freezing wall in this zone meeting the necessary load-bearing and water-sealing requirements at the roof pipe's cutting. Conversely, the weak freezing zone is located around the occlusal pipes, with the freezing wall designed to resist the adverse effects of intense heat sources during roof pipe cutting and welding, ensuring closure and water-stopping requirements are met. The pipe curtain's frozen section is equipped with steel pipes and freezing pipes in a ring direction along the tunnel, with a total of 28 steel pipes installed to minimize gaps. This setup includes eight sets of occlusal steel pipes and four independent steel pipes. The occlusal steel pipes, formed by cutting and connecting steel plates between three pipes, serve as support reinforcement through freezing. The design includes an effective permafrost curtain thickness of \(\geq\)3.5 m for both the strong and weak freezing zones, with an average temperature of \(\leq\)\(-\)10 \({}^{\circ}\)C in the strong zone and \(\leq\)\(-\)8 \({}^{\circ}\)C in the weak zone. A total of 170 freezing pipes are arranged, with 93 in the outer circle and 77 in the inner circle, maintaining a spacing of 800 mm in the strong freezing zone and 950 mm in the weak freezing zone.
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 4 Security Hardening of Kubernetes Components
### Identity and Access Management  
Authentication and permission are essential components of Kubernetes security. They regulate access to cluster resources and ensure that only authenticated users or services can execute particular activities. Kubernetes, by default, turns off multiple authentication methods, which, if improperly configured, may permit malicious entities to access the API server or internal data without adequate verification. That highlights the necessity for intentional and secure configuration of identity management systems.  
This section examines the authentication domain comprehensively, detailing how Kubernetes verifies the identity of users, services, and applications via mechanisms such as client certificates, static and bootstrap tokens, and authenticating proxies. These tactics are essential for implementing mutual TLS authentication, improving identity verification, and operational adaptability.  
In addition to authentication, we analyze authorization solutions, emphasizing Role-Based Access Control (RBAC), which delineates specific permissions for users, groups, and service accounts. RBAC guarantees that each identity functions within explicitly delineated parameters, restricting the potential consequences of compromised credentials or errant services. We further address node authorization as a specific mechanism for safeguarding interactions initiated by the Kubelet.  
Collectively, these measures reinforce the principle of least privilege, safeguard against unlawful access, and facilitate adherence to contemporary infrastructure governance mandates. Due to Kubernetes's declarative configuration style and flexibility, these access controls may be seamlessly integrated into the cluster's operational routines via YAML or JSON manifests.