esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This article starts from the premise that one of the global control strategies of the Permanent Magnet Synchronous Motor (PMSM), namely the Direct Torque Control (DTC) control strategy, is characterized by the fact that the internal flux and torque control loop usually uses ON-OFF controllers with hysteresis, which offer easy implementation and very short response times, but the oscillations introduced by them must be cancelled by the external speed loop controller. Typically, this is a PI speed controller, whose performance is good around global operating points and for relatively small variations in external parameters and disturbances, caused in particular by load torque variation. Exploiting the advantages of the DTC strategy, this article presents a way to improve the performance of the sensorless control system (SCS) of the PMSM using the Proportional Integrator (PI), PI Equilibrium Optimizer Algorithm (EOA), Fractional Order (FO) PI, Tilt Integral Derivative (TID) and FO Lead-Lag under constant flux conditions. Sliding Mode Control (SMC) and FOSMC are proposed under conditions where the flux is variable. The performance indicators of the control system are the usual ones: response time, settling time, overshoot, steady-state error and speed ripple, plus another one given by the fractal dimension (FD) of the PMSM rotor speed signal, and the hypothesis that the FD of the controlled signal is higher when the control system performs better is verified. The article also presents the basic equations of the PMSM, based on which the synthesis of integer and fractional controllers, the synthesis of an observer for estimating the PMSM rotor speed, electromagnetic torque and stator flux are presented. The comparison of the performance for the proposed control systems and the demonstration of the parametric robustness are performed by numerical simulations in Matlab/Simulink using Simscape Electrical and Fractional-Order Modelling and Control (FOMCON). Real-time control based on an embedded system using a TMS320F28379D controller demonstrates the good performance of the PMSM-SCS based on the DTC strategy in a complete Hardware-In-the-Loop (HIL) implementation.  
direct torque control; fractional-order calculus; sliding mode control; equilibrium optimizer algorithm
# Electronic Sensing Technologies in Food Quality Assessment: A Comprehensive Literature Review
## 1 Introduction
The human nose has about 400 olfactory receptors and can detect at least one trillion odors [14]. The human nose can assess odor, but individual evaluation can be biased. The human nose cannot be used to detect toxic gases. Additionally, the human nose has limitations regarding gas mixtures. Thus, the human nose is not a universal tool for identifying and classifying odors [15]. Volatile organic compounds (VOCs) are chemicals that evaporate at room temperature (vapor pressure \(\geq\)10 Pa at 20 \({}^{\circ}\)C) and are important indicators contributing to the odor signature specific to each food product [16; 17]. Volatile compounds, which are indicators of food freshness, undergo significant changes during processing and storage, so gas sensors are essential for their detection and the evaluation of food [18]. Excessive food oxidation indicates degradation of fats and oils, resulting in the overproduction of hexanal. Excess hexanal is generally not preferred by consumers and has been identified as one of the major off-flavors in foods such as meat, soy, and dairy products [19; 20]. It can also be considered a critical oxidation indicator; therefore, rapid, accurate, and real-time detection of hexanal is essential for food quality assessment [21]. The condition for reducing food waste and protecting consumer well-being is the accurate assessment of food freshness. For this purpose, e-noses are becoming essential tools, providing a multidimensional approach to monitoring the complex trajectory of odor changes inherent to food spoilage [22]. Electrochemical sensors are therefore of fundamental importance for the development of such devices and remain an active area of research. This is partly due to the way they interact and partly because advances in electronics have enabled their use to add important functions. In this sense, both devices have found important applications as monitoring systems. From our point of view, automatic, intelligent e-tongues and e-noses will soon appear as intelligent devices with on-line functionality [23]. Therefore, the aim of this paper was to comprehensively review the current scientific literature presenting the discussed issues, to systematize them, and to present the perspectives, risks, and applications of electronic sensors in food testing.
## 4 Discussion
### Basis of the Process
[49] compared the automated headspace gas chromatographic method with a nitrogen-phosphorus detector with a standard spectrophotometric method. The results obtained by the two procedures were not different and the authors concluded that both methods are suitable for rapid diagnosis of cyanide in postmortem cases. The conversion of cyanide into HCN and its subsequent headspace solid-phase microextraction and detection by gas chromatography/mass spectrometry (GC/MS) in selected ion monitoring mode was proposed as well [24]. Cyanide in whole blood was also determined using ion chromatography with amperometric detection [50]. This method shows good linearity in the range of 0.002 to 0.05 \(\upmu\)g mL\({}^{-1}\) cyanide. Although all these methods are fast and sensitive enough for the rapid diagnosis of cyanide intoxication in clinical and forensic toxicology, they need relatively expensive equipment. Spectrophotometric methods like the proposed one require inexpensive reagents and instruments and can be applied to a limited number of samples as found in small hospitals. Nevertheless, the spectrophotometric methods are largely used in cyanide determination in blood [51]. However, a method using cobinamide showed reasonable agreement when analyzed by linear regression, but not when analyzed by a standard error of the estimate or paired \(t\)-test. It has a lower limit of quantification of 3.27 \(\upmu\)M cyanide. On the contrary, our proposed method is much more sensitive and specific. Several kinetic spectrophotometric methods have been proposed [27; 28; 41]. For example, a linear calibration plot in the range 0.15-0.60 \(\upmu\)g mL\({}^{-1}\) cyanide and the detection limit of 0.05 \(\upmu\)g mL\({}^{-1}\) cyanide were reported by [PERSON] et al. [27]. Instead, our proposed micro method was linear in the range 0.26-2.60 \(\upmu\)g mL\({}^{-1}\) KCN if the measurements are performed at 10 min, 15 min, and 20 min fixed time and in a larger range of concentrations when the slope values are plotted against cyanide concentrations. In addition, the proposed micro method can also be greatly improved by increasing the volume of absorbent and decreasing that of the reagent or by increasing the reaction temperature above 25 \({}^{\circ}\)C.
## 3 Methodology
### Preprocessing  
In the first step, the dataset is read, attributes are extracted, and items, such as product type, sales volume, and timestamps, are identified and collected as the relevant information. In addition to this, time series analysis is carried out by employing the ARIMA method in order to identify some temporal patterns in the data.  
Linear models, often referred to as Box-Jenkins models, are frequently utilized in the analysis of univariate time series data. These models depict the value of a sequence at a specific point in time by incorporating previous values and discrepancies in a straightforward approach. The ARIMA models can be expressed in the following equations:  
\[ARIMA(q,e,r)(Q,E,R)_{s} \tag{2}\]  
In the AR model, \(q\) stands for the number of parameters, e represents the degree of differencing, and r stands for the count of parameters in the MA model. Additionally, \(Q\) represents the number of parameters in the AR seasonal model, \(E\) indicates the level of seasonal differencing, and \(R\) signifies the count of parameters in the MA seasonal model. Lastly, \(s\) denotes the seasonality period.  
\[\phi_{q}(C)\varphi q_{s}(C^{s})\
abla^{e}\
abla^{Es}Y_{t}=\theta_{r}(C)\Theta_{ R_{s}}(C^{s})b_{t} \tag{3}\]  
In this context, \(Y_{t}\) represents the value observed at a specific time point (or a transformed value); \(\varphi_{q}(C)\) is the AR operator \(\big{[}\big{(}1-\varphi_{1}C-\varphi_{2}C^{2}\dots\dots\dots\cdot\varphi_{q}C ^{q}\big{)}\big{]}\); \(C\) is the back-shift operator, \(\big{[}CY_{t}=Y_{t-1}\big{]}\); \(\varphi_{q_{s}}(C^{s})\) refers to the seasonal AR operator \(\big{[}\big{(}1-\varphi_{1}C^{s}-\varphi_{2}C^{2s}\dots\dots\cdot\varphi_{q}C ^{q}\big{)}\big{]}\); \(C^{s}\) is the seasonal backshift operator \(C^{s}Y_{t}=Y_{t-s}\); \(\
abla^{e}\) denotes the differencing operator \(\big{[}\
abla^{e}Y_{t}=(1-C^{t})^{e}Y_{t}\big{]}\); \(\
abla^{Es}\) represents the seasonal differencing operator \(\big{[}\
abla^{Es}Y_{t}=\big{(}1-C^{t}\big{)}^{E}Y_{t}\big{]}\); \(b_{t}\) is the random error at time point \(t\); \(\big{[}b_{t}\sim N\big{(}0,\sigma_{b}^{2}\big{)}\big{]}\), \(\theta_{r}(C)\) is the MA operator; \(\big{[}\big{(}1-\theta_{1}C-\theta_{2}C^{2},\dots\dots\dots\theta_{1}C^{r} \big{)}\big{]}\); and \(\Theta_{Rs}(C^{s})\) signifies the seasonal MA operator \(\big{[}1-\Theta_{1}C^{s}-\Theta_{2}C^{2s}\dots\dots\dots\dots\Theta_{R}C^{Rs} \big{]}\).  
Autocorrelation and partial autocorrelation functions are essential components in building ARIMA models. Examining the coefficients at various time delays helps determine the degree of differencing \(e\), seasonal differencing \(E\), and the number of parameters within the AR, MA, and seasonal AR components.
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 3 Kubernetes Architecture
### Extensibility and Declarative Configuration  
Kubernetes's primary advantage is its extensibility and declarative resource management method. Administrators and developers specify the desired state using YAML or JSON manifests.  
* Custom Resource Definitions (CRDs) facilitate introducing novel resource types, permitting integration with domain-specific logic.
* Operators enhance controller functionality to oversee intricate, stateful applications by utilizing domain expertise embedded in automation.
* Admission Controllers are modular components that intercept API requests to enforce cluster policies or execute validation and mutation.  
These extensibility mechanisms enable teams to customize Kubernetes to their requirements while ensuring uniform management practices across environments.  
The architecture of Kubernetes offers a modular and extensible framework for orchestrating containerized applications; however, each component--the control plane, node agents, and networking layers--poses distinct security challenges. Misconfigurations, excessive privileges, insecure defaults, and insufficient runtime protections are prevalent risks that can jeopardize cluster integrity. A comprehensive comprehension of these architectural components is crucial for implementing effective, layered security measures, which will be analyzed in detail in the subsequent section, encompassing identity management, auditability, hardening techniques, and secure communication. By implementing security measures at every architectural layer, administrators can markedly diminish the attack surface and enhance the overall resilience of Kubernetes deployments. We will examine fundamental mechanisms and optimal practices to safeguard each architectural layer, from securing the control plane and worker nodes to enforcing robust access controls, monitoring, and hardening techniques for constructing resilient and reliable Kubernetes environments.
## 4 Discussion  
The demands for high-precision, high-resolution, and large-scale carbon storage estimation are increasing. And a signal remote sensing resource no longer satisfied the requirement of large-scale accurate estimation of carbon storage. Optical remote sensing was easily influenced by \"saturation effects\", and the climate condition increased the difficulties in obtaining microwave remote sensing. Although spaceborne LiDAR can provide vertical structure information of vegetations, the result was discontinuous in space. Integrating multi-source remote sensing data, such as GEDI, ICESat-2, Landsat, and Sentinel, provided a new research direction for researchers. The key challenge in estimating forest structural characteristics using GEDI spaceborne LiDAR data combined with medium-resolution satellite images is to solve the problem of discontinuous distribution of GEDI footprints and the mismatch between GEDI and other image resolutions. Therefore, this study aimed to address these challenges and estimate the precision and accuracy of carbon storage by integrating GEDI spaceborne LiDAR and Landsat 9 optical satellite data. By providing a potential solution to the issue of partial coverage of GEDI spaceborne LiDAR footprints, this method had great potential for estimating forest carbon storage at the county scale and demonstrated the possibility of wide-scale applications.
## 2 Materials and Methods
### Background of SC Techniques  
#### 2.2.1 Ann  
Artificial neural networks (ANNs) are advanced numerical models evolved from the idea of simulating the interaction of the synapsis of brain neurons. An artificial neural network (ANN) is an adaptive method that alters its composition according to its outer or inner knowledge that runs within the network through the learning stage [55; 56; 57; 58]. In an ANN, associations among neurons generally carry different weights that show how influential the association is. The neuron measures by creating a weighted aggregate of its input, that is, the activation of each input neuron is multiplied by the corresponding link weight. ANNs are categorized into two main types; Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) [59]. The popularity of ANN models is associated with their robustness compared to linear numerical modeling methods [60]. Both MLP and RBF comprise non-linear universal approximators with feed-forward structures [61; 62].  
An MLP is a type of feed-forward ANN, which comprises at least three layers of links: (1) input layer, (2) hidden layer and (3) output layer. Every link is a neuron that utilizes a non-linear activation function except for the input links. It is worth noting that the MLP employs a supervised learning procedure named backpropagation training [63; 64]. The MLP can be differentiated from a linear perceptron through its multiple layers and non-linear activation. MLP can also detect data which are not linearly associated [65].  
The RBF network is an ANN that employs RBFs as activation functions. The product of the system is a linear compound of RBFs of the data inputs and neuron parameters. Similar to the MLP, the RBF network typically comprises three layers: (1) input layer, (2) hidden layer with a non-linear RBF activation function and (3) linear output layer. The input layer should be signified as a vector of real numbers. At that stage, the product of the system is a scalar function of the input vector.  
MLP and RBF have significant architectural differences [66]. An MLP-NN might comprise one or more hidden layers, while the RBF-NN displays only one hidden layer. The MLP-NN might have several non-linear hidden layers, compared to the RBF-NN, that contains one non-linear hidden layer which is accompanied by a linear output layer. Regarding the activation function of every neuron, a dot product is executed in an MLP-NN, while an RBF-NN calculates a vector norm. Due to the nature of the activation functions, MLP can create global input-output non-linear mappings, whereas RBF-NNs can only produce local estimates. Figure 4 shows the significant differences between the architecture of MLP and RBF.  
Figure 4: Structure of MLP-ANN (**a**) and RBF-ANN (**b**).
## Abbreviations  
* **Terms**
* **AP** Access Point
* **AWGN** Additive White Gaussian Noise
* **BC** Broadcast Channel
* **C-RAN** Cloud Radio Access Network
* **DPC** Dirty Paper Coding
* **EDF** Earliest Deadline First
* **GBC** Gaussian Broadcast Channel
* **LOS** Line Of Sight
* **MIMO** Multiple Input Multiple Output
* **MISO** Multiple Input Single Output
* **MS** Mobile Station
* **MCS** Modulation and Coding Scheme
* **MU** Multi User
* **MU-MIMO** Multi User MIMO
* **OFDM** Orthogonal Frequency-Division Multiplexing
* **PG** Packet Generator
* **QoS** Quality of Service
* **QSI** Queue State Information
* **PR** Policy Runner
* **RPS** Reward Per Second
* **SINR** Signal-to-Interference plus Noise Ratio
* **SDN** Software-Defined Network
* **SNR** Signal-to-Noise Ratio
* **WLAN** Wireless Local Area Network
* **ZFBF** Zero Forcing Beam Forming
* **Notations** Arrival random variable
* **\(A_{i}\)** Packet length random variable
* **\(C\)** Number of servers
* **\(D_{i}\)** Deadline random variable
* **\(I_{i}\)** A packet that arrived at time** \(t_{i}\)**.**
* **\(P_{i}\)** Reward or priority random variable**
* **\(t_{i}\)** The time of the** \(i^{\text{th}}\) **arrival**
* **\(K\)** **Defines the queue prefix size for eligible packets (window size)**
* **\(M\)** The number of antennas**
* **\(N\)** The number of mobile stations**
* **\(\mathbf{H}\)** The channel state information**
* **\(\mathbf{H^{*}}\)** The conjugate transpose of matrix** \(H\)****
* **L** A lower triangular matrix
* **\(\mathcal{P}\)** The transmission power**
* **Q** An orthogonal matrix
* **R** An upper triangular matrix
* **W** The beamforming matrix
* **x** A vector presenting the transmitted signal
* **y** A vector presenting the received signal at the MS
* **z** A vector presenting the AWGN
## 2 Methodology
### Durability Test Protocol  
A durability test protocol was developed based on the Korean-World Harmonized Vehicle Cycle (K-WHVC) to analyze the degradation characteristics of a PEMFC stack subjected to load cycles representative of commercial vehicle operation. The K-WHVC, a modification of the World Harmonized Vehicle Cycle (WHVC), provides a more accurate representation of real-world driving patterns for heavy-duty vehicles in Korea [19] (Figure 1).  
Using the speed data from K-WHVC driving environments (urban, rural, and motorway), dynamic load cycles matched to the road driving conditions of a 25-ton hydrogen-powered commercial vehicle equipped with a 200 kW fuel cell were extracted through MATLAB/Simulink simulations (2024a). Referencing these cycles, Figure 2 illustrates the dynamic load cycles employed in the durability tests. Each cycle spans 30 min, corresponding to 20 km of driving, and incorporates various load stages, including idle-, medium-, and high-load conditions.  
A zero-velocity condition in a vehicle translates to an idle mode for the power propulsion system. Consequently, the modified electric load demand schedule included an idle mode with a minimum current density of 0.02 A/cm\({}^{2}\)[21]. This minimum current density was selected based on the findings of [PERSON] et al. [8], which indicate that the degradation slope is optimized at voltages below 0.9 V. Under these conditions, the maximum current density was determined to be 1.19 A/cm\({}^{2}\).  
This durability test protocol was implemented on two PEMFC stacks, each comprising 10 cells. One stack was tested at 65 \({}^{\circ}\)C, whereas the other was subjected to a higher temperature of 80 \({}^{\circ}\)C. This temperature range aligns with typical PEMFC stack operating temperatures, which generally fall between 60 and 80 \({}^{\circ}\)C [22], with an upper limit of 80 \({}^{\circ}\)C [23]. Additional operating conditions maintained throughout the 1000 h evaluation period included 50% humidification for both the anode and cathode, and stoichiometric ratios of 1.5 for the anode and 2.0 for the cathode. To minimize the variability of operating conditions other than temperature and maintain consistency, humidification and stoichiometric ratios were set based on reference settings [24].  
Figure 1: Comparison of the World Harmonized Vehicle Cycle (WHVC) and Korean-World Harmonized Vehicle Cycle (K-WHVC) test protocols [20].  
Figure 2: Dynamic load cycle for a 25-ton heavy-duty truck extracted from the K-WHVC.
# Multi-Layer Attack Graph Analysis in the 5G Edge Network Using a Dynamic Hexagonal Fuzzy Method
## 6 Performance and Accuracy Evaluation: Case Study  
To evaluate VAA, we provide a 5G edge case based on the 3 GPP architecture [12] in Figure 9 that is deployed in our testbed using the open-source components described in Section 4. This architecture is based on the concepts of control and user planes split, service base architecture, and network slicing. Their main network functionalities are the Network Slice Selection Function (NSSF), the Authentication Server Function (AUSF), the Unified Data Management (UDM), the Access and Mobility Management Function (AMF), the Session Management Function (SMF), the Policy Control Function (PCF), the Application Function (AF), the User Equipment (UE), the Radio Access Network (RAN), the User Plane Function (UPF), and the Data Network (DN). A two-level SDN controllers hierarchy bridges between the functions of the control and user planes, specifically, between the SMFs and the UPFs. The 5G core NFs are implemented as VNFs in an NFVI in which the SDN Controllers are virtualized and implemented. Figure 9 shows the exploited assets in this case study in red color.  
Using the Metasploit framework [67], we ran some exploits based on the 5G Edge attack vector described in Section 3. These exploits target six vulnerabilities in the testbed namely, the _CVE-2019-15083_ (allows for an XSS injection that leads to control what software is installed on the admin workstation), _CVE-2013-0375_ (allows for remote injection of SQL code that leads to bypassing the AUSF), _CVE-2019-16026_ (leads to a denial of service (DoS) condition on the AMF), _CVE-2004-0415_ (allows for illegitimate access to portions of kernel memory that leads to illegitimate access to the SDN), _CVE-2002-0392_ (allows for remote execution of DoS attack that leads to disruption for the NFVI functionalities), _CVE-2004-0417_ (allows for an integer overflow in the CVS Apps that leads to illegitimate access to the RAN). Figure 10 shows the attack graph that was created us  
Figure 9: The 5G Edge-based 3 GPP planes in our testbed.  
approach described in Section 5.1. The main target of the attacker is to access and control the RAN module using the aforementioned vulnerabilities that belong to the three attack categories described in Section 3 (i.e., \(I\), \(S\), _P_).