esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, a local channel attention module LSEB is proposed, and combined with the residual mechanism, a local channel attention residual network LSERNet is constructed. Extensive experiments are carried out with other models on the blast furnace tuyere image dataset collected by us. The results show that:  
1. Compared with the original SE module, the LSEB module strengthens the ability of the feature extraction, does not ignore some important feature information, and makes full use of the regional information;
2. Compared with other models, the LSERNet model constructed by us has achieved the highest accuracy in the case of the same model complexity as other models, which can effectively identify the abnormal working state of the blast furnace tuyere.  
The current limitation of the proposed method is that we only applied our own collected dataset with a high accuracy. When migrating to a new dataset, the model may need to be retrained, due to the different resolution of the sensors and different camera positions. All we need to do is retrain the model with a new dataset. Although retraining is required, the training time is acceptable and does not necessarily affect the applicability of the method.  
The model has been built, and the effect is very good, but it has not been actually installed in the factory. The next step is that, before the model is applied to the factory, a high-intensity test will be carried out to minimize the error rate, and a complete early warning system will be developed.  
Conceptualization, C.S. and R.W.; methodology, Z.L. and Y.L.; software, Y.L.; investigation, H.Z.; resources, C.S.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L.; supervision, M.J. and K.H.; funding acquisition, C.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (NSFC), grant number 61902205. Project manager [PERSON].
# A Comprehensive Review of Milk Components: Recent Developments on Extraction and Analysis Methods
## 4 Analytical Methods for Milk Macro-Constituents
### Analysis of Polysaccharides  
The analysis of polysaccharides in milk, such as complex carbohydrates and oligosaccharides, is essential for understanding its nutritional value and health benefits. Official methods are based on Fourier transform infrared spectroscopy (FTIR). FTIR provides a fingerprint of the functional groups in polysaccharides and can be used to confirm the presence of carbohydrates in milk [86]. High-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD) [87] and LC coupled to refractive index detection (HPLC-RID) [88] have been extensively used for oligosaccharides analysis in milk. HPLC-RID is particularly suitable for detecting and quantifying carbohydrates and polysaccharides, as no UV-active functional groups are requested. On the other hand, LC-MS can be used to detect oligosaccharides and polysaccharides, providing both qualitative and quantitative data, moreover, allowing trace analysis [87; 88; 89; 90; 91; 92].
# A Research on Manipulator-Path Tracking Based on Deep Reinforcement Learning
###### Abstract  
The continuous path of a manipulator is often discretized into a series of independent action poses during path tracking, and the inverse kinematic solution of the manipulator's poses is computationally challenging and yields inconsistent results. This research suggests a manipulator-route-tracking method employing deep-reinforcement-learning techniques to deal with this problem. The method of this paper takes an end-to-end-learning approach for closed-loop control and eliminates the process of obtaining the inverse answer by converting the path-tracking task into a sequence-decision issue. This paper first explores the feasibility of deep reinforcement learning in tracking the path of the manipulator. After verifying the feasibility, the path tracking of the multi-degree-of-freedom (multi-DOF) manipulator was performed by combining the maximum-entropy deep-reinforcement-learning algorithm. The experimental findings demonstrate that the approach performs well in manipulator-path tracking, avoids the need for an inverse kinematic solution and a dynamics model, and is capable of performing manipulator-tracking control in continuous space. As a result, this paper proposes that the method presented is of great significance for research on manipulator-path tracking.  
Keywords:path tracking; deep reinforcement learning; maximum entropy; inverse kinematics +
Footnote †: journal: Applied
## 5 Experimental Results
### Simulation Setup  
The experiment was conducted in MATLAB on a quad-core Dell machine with an Intel Core i5-8250U CPU and 16 GB of RAM. The simulation parameters are presented in Table 1. The following sections provide a thorough explanation of the results of the various simulation scenarios.
# VIIRS Day/Night Band--Correcting Striping and Nonuniformity over a Very Large Dynamic Range
## 1 Introduction
### On-Orbit DNB Calibration Process
#### 1.2.2 DNB RSB Automatic Calibration Process
An improvement that is included in the Land SIPS version is filtering out lunar stray light contamination from the offset data, after they found that it created a small bias when the moon is more than half full [17]. However, because of concerns regarding stray light, the Land SIPS version of automatic calibration no longer computes the gain ratios using the SV or BB signals [10].
## 5 Decision Model Generation
### Proposed Classification Approaches  
Initial work with AutoML showed the promising use of two models in further, more advanced research: logistic regression and gradient boosting. Therefore, within the conducted research, two classifiers developed by two separate research groups were verified. The first model was logistic regression [54], representing statistical approaches valued by the medical community, while the second was the XGBoost method [55], implementing the gradient boosting model, which is a leading data-driven machine learning approach. The R implementation of generalised linear model fitting for logistic regression and the _xgboost_ package implementing the XGBoost method in the R [56] environment were used in the analysis.  
The initial data set was common to each of the approaches; however, the models were generated in separate processes. Therefore, different data preparation and feature selection steps could be applied and hence different splits into training and test data within the model generation process were possible. However, both generated models were evaluated on a common test data set and additional collection of data available online [24].  
To evaluate classifiers in the conducted research, sensitivity and specificity can be used, bearing in mind that, as stated earlier, false negative classification errors are more costly, and it is more important to achieve a high sensitivity value than high specificity. Moreover, when presenting the public health solutions and the results of medical analysis, the positive predictive values (PPV) and negative predictive values (NPV) are applied as they are commonly used to describe the performance of a screening and diagnostic test [57; 58]. Therefore, as part of the research, the Weighted Harmonic Mean (_WHM_) of NPV and PPV was used in the classifier optimisation process:  
\[WHM=\bigg{(}\frac{w}{NPV}+\frac{1-w}{PPV}\bigg{)}^{-1}. \tag{1}\]  
Figure 3: Heatmap of features and patients with hierarchical clustering due to missing information. Red color represents missing value, green color represents presence of information. Branches highlighted in red correspond to patients or features excluded from the further analysis due to high missing data levels.  
In a diagnostic test, the weight can be equally distributed between NPV and PPV. However, here, the screening test is applied; thus, the importance of NPV should be higher than that of PPV to maximally reduce the number of undetected COVID-19(+) patients (false negatives). This should be reflected in a value of the weight \(w\in[0,1]\).
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 4 Security Hardening of Kubernetes Components
### Identity and Access Management
#### 4.4.5 Node Authorization  
Node authorization is a specific access control mechanism intended to authenticate API requests initiated by the kubelet client component. It can permit or prohibit a kubelet from executing API operations such as the following:  
* Reading operations: pods, nodes, endpoints, secrets, config maps, and volumes.
* Execute operations: nodes, pods, and associated events.
* Authentication operations: read/write access to Certificate Signing Requests for TLS generation, and the creation of TokenReviews and SubjectAccessReviews for delegated authentication and authorization assessments.  
To configure the Node authorizer in Kubernetes, the kube-apiserver must be set up with the -authorization-config flag, as illustrated in Figure 11:  
Figure 10: Initializing the RBAC role (**a**) and Role binding the RBAC role (**b**).  
The auth-config.yaml file located in the /etc/kubernetes directory should contain the content depicted in Figure 12, where we can see the resources and actions permissible for kubelets:  
This configuration guarantees that kubelet is restricted to executing actions (such as get and list) on designated resources (pods, services, and nodes). By limiting the extent of their permissions, nodes can access only the information necessary for their operation. This method mitigates the risk of inadvertent or malevolent exploitation of resources. In conclusion, the node authorizer in Kubernetes is a specific access control mechanism intended to authenticate API requests initiated by kubelet components.  
Consequently, when a kubelet interacts with the Kubernetes API server, it must authenticate using the identity of its host node. The node authorizer ensures that the kubelet is permitted to execute only those operations for which it is authorized, contingent upon the resources allocated to its designated node. It limits the kubelet's ability to perform operations not directly associated with the pod operating on that node. Kubelets can access the Pod resources for which they are accountable, but they are prohibited from accessing pods on other nodes, secrets, or configuration data unless explicitly permitted.  
Despite robust access constraints, ensuring visibility into cluster activity is essential for identifying misconfigurations, unauthorized operations, and early signs of compromise. Security encompasses not just prevention but also detection and response. The subsequent section emphasizes observability and auditing within Kubernetes settings, encompassing audit logging, system monitoring, and integration with logging and visualization tools that help administrators maintain situational awareness and respond effectively to issues.  
Figure 11: API-server node authorization settings.  
Figure 12: Node authorization configuration.
## 5 Experimental Results and Discussion
### Case Study: Drones
The MyController class is responsible for the management \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Simulators** & **Real** & **Customized** & **Cluster** \\
**Dataset** & **Mobility** & **Formation** & \\ \hline FogNetSim++ [44] & ✗ & ✓ & ✗ \\ \hline MobFogSim [45] & ✓ & ✓ & ✗ \\ \hline YAFS [46] & ✗ & ✗ & ✓ \\ \hline IoTNetSim [47] & ✓ & ✓ & ✗ \\ \hline STEP-ONE [48] & ✓ & ✓ & ✗ \\ \hline iFogSim2 [45] & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between iFogSim2 and other simulation tools. of resources, enabling the calculation of Quality of Service (QoS) metrics such as network usage, service latency, run time, and energy consumption of devices, during the simulation. Application Model:  
As illustrated in Figure 12, the application of drones consists of the following modules:  
* Functional modules: The drone is equipped with functional modules, including sensor and actuator modules. These modules enable the drone to collect sensor data from its environment and perform actions based on the processed data. * Client module: likely can be located on the IoT device itself, preprocesses the data and sends it to the processing module. * Processing/main module: this module includes Fog devices and Cloud data centers. * Storage module: after processing, making the decision, etc., the results will be stored in this module which can be in Fog or Cloud.
# Current Trends in Fully Automated On-Line Analytical Techniques for Beverage Analysis
## 3 Solid-Phase Microextraction (SPME)
### SPME Extraction Materials  
The sorbent materials used in the SPME devices are the key to have a highly efficient extraction. [PERSON] et al. [46] presented a variety of extraction phases that can make SPME appropriate for different applications. Therefore, to obtain a better performance employing SPME, the extraction phases should be selective, low cost, chemically and thermally stable and mechanically robust. These  
Figure 2: Main solid-phase microextraction configurations: (**a**) fiber; (**b**) in-tube; (**c**) thin film; (**d**) magnetic; (**e**) pointer; and (**f**) stir bar. Adapted from Reference [45]. Reproduced by permission from Elsevier.  
sorbents are usually divided into liquid polymers and solid sorbents; the former absorbs and the latter adsorbs the target compounds. The most common coating materials utilized in SPME are polydimethylsiloxane (PDMS), divinylbenzene (DVB) and carboxen (CAR). They present different thicknesses and combinations of absorbent materials are being marketed [47]. However, commercial SPME devices are not easily accessible, some are very expensive and not as selective when the matrix effects are significant. As a consequence, home-made devices have gained wide acceptance by presenting a wide range of relatively economic and more specific sorbents for each application [47], from carbon materials to versatile polymers. These materials can be immobilized onto fused-silica fibers, stainless steel wires or other supports, employing electrochemical processes [48], physical deposition [28], sol-gel technology [49], or even simply with thermally stable adhesives [50].  
Of the new materials that are being worked on, stand outs are the ionic liquids functionalized with polymeric ionic liquids (IL/PIL), carbonaceous materials as graphene and carbon nanotubes (CNT), molecularly imprinted polymers (MIP), metallic and organic frames (MOF) [47].
## 4 Results  
### PEC Scatterer  
To test the efficiency of the method, a PEC scatterer is initially defined by radius \(\rho=3\lambda\), arc displacement \(d=0.5\rho=1.5\lambda\), incidence angle \(\varphi_{inc}=0\), auxiliary surface radius \(\rho_{aux}=\rho-0.075\lambda\), and originally 136 CPs and therefore 136 inner ASs. The solution to the problem without any deformation yields the results of Figure 5a, which depicts the quantified error in the generic boundary condition (BC) of the \(E\) field along the boundary stretch, i.e.,:  
\[\Delta E_{bc}=\frac{\left|\hat{\mathbf{n}}\times(\mathbf{E}_{in}-\mathbf{E}_{out})\right|} {|\mathbf{E}_{inc}|_{max}}, \tag{9}\]  
where in (9) \(\hat{\mathbf{n}}\) is the normal unit vector on the boundary, pointing outwards, and \(\mathbf{E}_{in},\ \mathbf{E}_{out}\) are the electric fields just inside and just outside the scatterer, respectively, the former obviously vanishing for the PEC case.  
Figure 4: Deformation of the auxiliary surface, combined with densification in the vicinity of the tips: all inner ASs are allowed to approach the CPs, whereas only 1/4 of the outer ASs are allowed to do so.  
Figure 3: Polar radius decrease from 1.5 to 1 according to the proposed schemes.  
To improve the satisfaction of the BC, the deformation scheme proposed above was implemented. After several trials, the following parameters were finally invoked: All ASs were displaced as well as densified and the proximity factor was set equal to \(s=0.88\), whereas \(D_{start}=0.9\). No extra ASs were added since their presence proved to achieve only incremental improvement. The results are displayed in Figure 5b, which shows that the overall \(E\) field error at MPs was substantially reduced.  
It is worth noting that the proper placement of the auxiliary surface is of great importance in order to avoid caustics exclusion by the auxiliary surface or any potential resonance effects, as presented in [23]. To illustrate the possibly detrimental influence on the BC error, specifically, in this case, keeping the multitude of CPs consistent (and thus the multitude of ASs consistent) but moving the auxiliary surface further away from the scattering boundary (\(\rho_{aux}=\rho-0.15\lambda\)) produced the results in Figure 6a,b, before and after applying the deformation scheme, respectively. Severe complications are revealed in Figure 6a, while it may be deduced from Figure 6b that utilizing an improved auxiliary surface layout, compared to a conformal one, may be much less prone to accuracy degradation, presumably because caustics are still retained within the auxiliary surface.
## 4 Simulations and Discussions
### Simulation of the Sensing Area for RI Measurement
Figure 8: The spectrum of loss spectra of the LP\({}_{\mathrm{nm}}\) mode and the corresponding optical field (\(n=1.33\)). **(a)** Spectra and electricity of the LP\({}_{01}\) mode; **(b)** spectra and electricity of the LP\({}_{11}\) mode; **(c)** spectra and electricity of the LP\({}_{21}\) mode. Figure 9: The spectrum of loss of the LP\({}_{\mathrm{nm}}\) mode at different RI. **(a)** The spectrum of loss of the LP\({}_{01}\) mode. **(b)** The spectrum of loss of the LP\({}_{11}\) mode. **(c)** The spectrum of loss of the LP\({}_{21}\) mode. The loss data are not provided when \(n=1.38\), since there are no stable electric fields in our simulation. Besides the sensitivity of the sensor, another important indicator is the resolution of the RI. Referring to the parameters of a photodetector (0.1-0.35 A/W, 430-600 nm, DET10A2, THORLABS), we set the minimum detected photocurrent as 1.0 nA and the dark current as 0.3 nA, with a photoelectric conversion rate of 0.2 A/W. Then, the minimum detected loss \(I_{min}\) can be calculated by  
\[-I_{min}=10 log(\Delta P/P_{in}) \tag{7}\]  
\[\Delta P=P_{in}-P_{out} \tag{8}\]  
Assuming that the incident power (\(P_{in}\)) of the sensing area of this sensor is 10 mW and the output power (\(P_{out}\)) is \(5\times 10^{-6}\) mW corresponding to the minimum detected photocurrent, \(I_{min}\) is \(2.1715\times 10^{-6}\)dB. According to the sensitivity shown in Table 2, the RI resolution of our sensor, using the LP\({}_{11}\) mode (LP\({}_{21}\) mode), increases to \(2.6\times 10^{-4}\) RIU (\(2.4\times 10^{-4}\) RIU), which is 7.6 times higher than that of a similar sensing area (\(1.83\times 10^{-3}\)RIU), determined by [PERSON] et al. [16]. Therefore, these results show that because of the higher-order modes, the sensitivity and the RI resolution of the SPR sensor can be improved significantly.
# A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems
## 3 Modeling of UWA Channel Problems and Challenges
### Doppler Shift
For a channel with time-invariant property, \(\left(Vt\right)_{c}=\infty\) results in no Doppler spread. In addition to the Doppler effect, waves cause some other channel effects. For certain geometrical constraints, the waves take on the qualities of a concave mirror and concentrate the acoustic energy, causing severe and significant changes in the channel magnitude and phase behavior [173; 174]. These focusing effects on the paths that interact with the surface have a large magnitude compared to the straight path causing instantaneous \(\pi/4\) phase shifts. The motion of waves can also introduce bubbles into the water column [14] creating a highly inconstant channel for the transmission of sound. It can also increase the coefficient of absorption or decrease the operative height of the water volume. There is a considerable reduction in transmission due to the Doppler shift in the channel for the UWAC. Literature reveals that two different sources contribute to the Doppler shift and spread [148]. The frequencies of the signal, variable acoustic velocity, and relative velocity of different objects, such as the transducers and hydrophones, contribute to the Doppler shift. Conversely, internal currents and the speed of wind on the surface of waves cause the Doppler spread. Doppler effect is the combination of Doppler spread and shift. The average Doppler shift in comparison to the overall Doppler spread is considerably larger [45]. Doppler causes forfeiture of orthogonality in the OFDM system. Therefore, the receiver performance has been degraded severely. In the systems for the UWC, there exists an analogy between the bandpass (Hz or kHz) and baseband subcarrier frequencies (kHz or Hz). There is a pronounced variation between bandpass and each subcarrier frequency. Consequently, the result of the Doppler shift will be different for the signal subcarriers. Due to the \(mth\) scatterer in baseband the \(\kappa th\) subcarrier shift is given as,  
\[F_{D_{\kappa m}}=\frac{v}{v_{\kappa}}f_{\kappa}\cos\theta_{m} \tag{39}\]  
where, \(\theta_{m}\) corresponds to the scattering angle of the \(mth\) scatterer and \(f_{\kappa}\) represents the frequency of \(\kappa th\) the subcarrier. The Doppler shift effects over OFDM subcarriers in UWAC are much more severe than the narrowband terrestrial communication. This is mostly because (a) the very low velocity of the acoustic signal as compared to the velocity of RF in terrestrial communication causes a large shift in the Doppler effect (b) the analogous amount of the Doppler to the small value frequencies of the subcarrier influences each subcarrier, and (c) because of the low frequencies of the bandpass, the variable effect of Doppler shift over the subcarriers. The variable effect of the Doppler shift over OFDM subcarriers is illustrated in Figure 18.
## 2 Materials and Methods
### Methods
#### 2.3.1 Gravity Center Model  
In physics, the gravity center refers to a point in a certain space where forces in all directions reach phase equilibrium [31]. Two common barycenter models are the quality gravity center model and the ecological gravity center model. The gravity mass center model is mainly used to describe the mass distribution, while the ecological gravity center model is suitable for describing the spatial distribution of biological communities or ecosystems. In addition, the gravity center model has also been widely applied in life, land use, and other fields. The migration trajectory and direction of the gravity center can directly indicate the change unevenness and the bias of certain phenomena from the perspective of spatial distribution [15; 18; 26]. Moreover, as typical geophysical and ecological indicators, there were obvious differences in change patterns for vegetation NPP across different periods and sub-regions. However, traditional statistical methods cannot reveal the laws governing changes in temporal and spatial distribution on a large scale [16]. Meanwhile, the migration distance and direction of vegetation NPP gravity center can indicate the increments and growth rate of vegetation NPP in different parts within the study region. The gravity center model can be applied to investigate the variation characteristics of vegetation NPP. The center of gravity model [32] is a mature tool in ArcGIS software with the following formulae:  
\[\overline{x}=\frac{\sum\limits_{i=1}^{n}z_{i}x_{i}}{\sum\limits_{i=1}^{n}z_{ i}} \tag{1}\]  
\[\overline{y}=\frac{\sum\limits_{i=1}^{n}z_{i}y_{i}}{\sum\limits_{i=1}^{n}z_{i}} \tag{2}\]  
Figure 4: Flow chart of the research method.  
where \((\overline{x},\overline{y})\) is the barycenter coordinates, \(z_{i}\) is the attribute value of the \(i\)-th plane space element, and \((x_{i},y_{i})\) is the attribute value of the i-th plane space element.