esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing the SRAM PUF with an XOR Gate
## 2 Background
### SRAM PUFs  
[PERSON] et al. (2007) [3] and [PERSON] et al. (2007) [7] were the first to introduce SRAM PUFs. These initial works used the inherent randomness present in the initial state of SRAM cells to generate random bits, which can be used in applications such as true random number generation and key generation [21]. The authors of these works used stable cells for authentication protocols and flaky cells for true random number generation protocols.  
Further analysis of SRAM PUFs was carried out in subsequent studies, such as those conducted by [PERSON] et al. and [PERSON] et al. [22; 23]. Their research involved characterizing SRAM PUFs on microcontrollers and FPGAs, where the intra-distance, inter-distance, and entropy of the SRAM PUFs were quantified.  
The aging effect can be used against an SRAM PUF [24]. However, [PERSON] et al. introduced a technique that leverages the aging effect to enhance uniformity and reduce its impact [25]. This approach allows for the modification of responses generated by the SRAM. Similarly, [PERSON] et al. proposed a design for an SRAM PUF that allows the modification of an SRAM cell to produce customizable CRPs [26]. The summarized findings from these studies are presented in Table 1.  
Moreover, a review article recently published by [PERSON] et al. examined the feasibility of an SRAM PUF, showing that it is still an exceptional memory PUF with enough randomness, reliability, and unpredictability to authenticate devices and generate random numbers on low-power devices [27].
# An Overview of the Process Mechanisms in the Laser Powder Directed Energy Deposition
## 3. Melt Pool Generation Mechanisms
### Thermal Behaviour  
The incident laser beam locally heats the building platform. The laser heat flux superheats the material, and the temperature obtained in the melt pool is largely above the melting temperature of the material [68]. For instance, [PERSON], et al. [93] showed that during the deposition of a 316L thin wall, the temperature of the melt pool reached the value of about 2000 K, which is 25% higher than the melting temperature of 316L. The shape and size of the melt pool depend on the intensity distribution of the laser beam [94], the resulting temperature distribution, and the maximum temperature reached in the material. Thus, it is evident that the temperature value and its distribution are key parameters that significantly affect the melt pool. [PERSON], et al. [95], during the deposition of a 316L thin wall, showed that the maximum temperature was obtained at the centre of the laser beam and then decreased linearly to the solidification temperature (1650 K) at a distance of around 2 mm from the centre, as represented in Figure 18.  
Among other process parameters, it is currently well-established that the process parameters that most influence the temperature distribution and, consequently, the melt pool dimensions [23] are the laser power (\(P\)), the powder flow rate (\(Q\)), and the travel speed (\(v\)) [20; 23; 96]. The following paragraphs describe the effect of these parameters on the width, height, and depth of the melt pool.
## 2 Materials and Methods
### Special Technique of Microstructural Analysis (STMA)
#### 2.4.3 Determination of Porosity and Permeability  
Porosity and permeability of rocks were determined via the quantitative method proposed in [27; 49]. The mapped microfractures were considered as a network of fractures. Cracks were approximated by disks. It was assumed that the maximum crack length corresponds to the disk diameter. In this study, the assumption was made that all the open microcracks that formed at a certain stage of deformation are interconnected. Thus, an equivalent crack with a total length of the disk diameter could be introduced. The effective porosity can be determined as follows:  
\[\Phi=(L\cdot\pi\cdot e)/S \tag{4}\]  
Figure 6: The result of detecting and marking microfractures in a part of the oriented thin section of the sample 58-1 using STMA.  
where L is the total length of a certain crack population, S is the area of the studied surface and e is the mean aperture of the crack ensemble [27; 49].  
The definition of fracture permeability is also based on the assumption that all fractures are connected and can be approximated by disks with an aperture equivalent to the fracture aperture. An assumption was introduced that cracks propagated perpendicularly to the plane of the oriented section and formed a non-uniform spatial distribution. This assumption allows to determine the microcrack permeability (K\({}_{\mathrm{f}}\)) [27]:  
\[\mathrm{K_{f}}=(\epsilon\cdot\pi\cdot\mathrm{N_{f}}\cdot\mathrm{c^{2}}\cdot \mathrm{e^{3}})/12 \tag{5}\]  
where \(\epsilon\) is the crack wall roughness coefficient, N\({}_{\mathrm{f}}\) is the volume density of crack population f, c is the mean value of half-length of the cracks in the ensemble and e is the mean aperture of cracks.  
If the surface density and half-length of all cracks are known, it is possible to determine their volumetric density [27; 94]:  
\[\mathrm{N_{f}}=2/(\mathrm{S}\cdot\pi\cdot\mathrm{c}\cdot\mathrm{sin}\theta) \tag{6}\]  
where \(\theta\) is the angle of crack incidence. If we assume that the cracks are perpendicular to the plane of the thin section, then sin\(\theta\) = 1.  
Thus, Formula (5) can be simplified as follows [27]:  
\[\mathrm{K_{f}}=(\epsilon\cdot\mathrm{c}\cdot\mathrm{e^{3}})/6\mathrm{S} \tag{7}\]  
where K\({}_{\mathrm{f}}\) is the total or directive permeability (for a certain population of cracks), expressed in square meters (m\({}^{2}\)) or in darcy (1 darcy = 0.987\(\cdot\)10\({}^{-12}\) m\({}^{2}\)).  
It should be noted that the results of laboratory tests are in good correlation with the values of porosity and permeability calculated with this method [96; 97; 98].
# Hybrid Encoding Scheme for AMBTC Compressed Images Using Ternary Representation Technique
## 3 Proposed Method
### Decoding Procedures
To decode \(CS_{1}=11\mid 00010011\mid 1\ 1000010\mid 0\ 110000\mid 10101010 1110110\ 00002\), because the first two extracted bits are \(11_{2}\), the block to be decompressed is a complex  
Figure 3: Illustration of the decoding procedures of image blocks. block. Extract 8 bits and \(q_{1}^{0}=19\) is the decimal value of these 8 bits. The next bit is \(1_{2}\); therefore, \(d_{1}^{1}=66\) is the decimal value of the next \(\lceil\log_{2}(\sigma)\rceil=7\) bits and \(q_{1}^{1}=19+66=85\) can be obtained. Similarly, the next extracted bit is \(0_{2}\); therefore, \(d_{1}^{2}=48\) is obtained by converting the next \(\log_{2}(\gamma)=6\) bits to their decimal value, and \(q_{1}^{2}=85+48=133\) can be obtained. Finally, we have to reconstruct \(\left\{T_{1,j}\right\}_{j=0}^{15}\) from the remaining bits. Because the next extracted bit is \(1_{2}\), we extract one more bit, which is \(0_{2}\). Therefore, \(T_{1,0}=1\) is obtained. The remaining 15 ternary digits \(\left\{T_{1,j}\right\}_{j=1}^{15}\) can be decoded in the similar manner. Once we have \(\left\{q_{1}^{0},q_{1}^{1},q_{1}^{2}\right\}\) and \(\left\{T_{1,j}\right\}_{j=0^{\prime}}^{15}\) block \(I_{1}^{\prime}\) can be reconstructed. Figure 3b illustrates the decoding process of \(CS_{1}\).
# IoT-Enabled Smart Cities: Evolution and Outlook
## 3 Key Technologies Developed
### Networking Technologies
On the downside, the data rate supported by these technologies is significantly lower than their WPAN counterparts. The most widely spread technologies in this category are LoRa [62], SigFox [63], NB-IoT [64] and LTE-M [65]. Each has their respective advantages in terms of different IoT factors [66]  
Sigfox was developed and is operated by the same company, which builds the communication modules and operates its own IoT solution in 31 countries and is still under rollout worldwide [67]. LoRa was also first developed by a microelectronics company but has been already standardized by LoRa-Alliance and is deployed in 42 countries and is still under rollout in other countries owing to the investment of various mobile operators. Finally, NB-IoT is based on narrow band radio technology and is standardized by the 3 rd generation partnership project (3 GPP). Its specifications were published in Release 13 of the 3 GPP on June 2016. Like Sigfox, LoRa uses unlicensed ISM bands while NB-IoT works on LTE licensed frequency bands. As it has been said, the three of them supports coverage ranges of kilometres even in urban environments and can allow several years of battery-powered operation thanks to extremely reduced consumption. However, they are limited in data rate (from the maximum 100 bps of Sigfox to the 50 kbps of LoRa or 200 kbpsof NB-IoT) and payload size (12 bytes in SigFox or 243 bytes in LoRa). Another important difference among them is their deployment model. While SigFox and NB-IoT networks are rolled-out by licensed network operators only, LoRa can support privately owned networks since both the wireless technology and the network infrastructure specifications are openly available. In summary, there is not one single solution that fits all cases. Instead, IoT-enabled smart city deployments have made use of the wireless communication technologies available at the moment the deployment took place which best covered their requirements. In general, WPAN technologies were mainly used by the first IoT deployments as they were the only solution available, while nowadays LPWAN solutions are the most common choice. Future 5G networks are called to pick up the baton as they will not only support the low-power, wide coverage and scalability requirements but provide enhanced communication characteristics, such as reliability, quality of service and/or security, which are necessary to fulfil the communication requirements of some smart city services for which current communication technologies do not guarantee the required grade of service. Table 2 shows the key features of the above mentioned IoT network technologies.
## 4 The Deformation Detection Experiment and Results  
### Deformation Detection Experiment Conditions  
The primary experimental instruments are enumerated as follows: (a) transmitting CBS, (b) receiving CBS, (c) high-precision shift spiral micrometer, and (d) corner reflector. The geometric configuration of the CBS is illustrated in Figure 9a, where CBS-A (letter A in Figure 9) denotes the transmission station and CBS-B (letter B in Figure 9) denotes the reception station, with an altitude difference of 2 m. As depicted in Figure 9a, the SAR imaging configuration clearly exhibits a bistatic geometry. Figure 9b illustrates the LOS from the CBS to the observation target, with the red circle indicating the scene where the observation target of this experiment is located, and the green arrow line representing the history of the observation signal.  
Figure 8: Observation geometry diagram.  
Figure 9: (a) Communication Base Station placement status. (b) Observation scenario.  
Meanwhile, Figure 10 depicts the deformation target within the observation scene. A corner reflector is affixed to a high-precision shift spiral micrometer, which makes the corner movement with utmost precision. To achieve the optimal SNR, we orient the corner reflector towards the CBS. With EPC principle, the communication signal data can be converted into the monostatic mode of a ULA. Therefore, in this experiment, data processing was conducted with the Equivalent Phase Center position as the reference.  
The CBSs used in the experiment were a commercially available 5G CBS operating at a frequency of the carrier frequency of 4.9 GHz. Each unit had a power output of 8 W and a gain of 20 dBi. The horizontal Half-Power Beam Width (HPBW) was 80 degrees, and the vertical HPBW was 18 degrees. The data acquisition process involved placing corner reflectors at designated positions and controlling the transmitting CBS to emit detection signals. After reflection from the target, the echo signals were received by the receiving CBS. Continuous observations of the scene were conducted at 2.5 ms intervals, resulting in 256 echo data sets for the current scene. Subsequently, the corner reflectors were moved to simulate ground deformation, and another set of 256 echo data was acquired. By repeating the aforementioned steps, the necessary measured data for the experiment were obtained.
## 2 Materials and Methods
### Design of the Experimental Activities
#### 2.2.2 Industrial-Scale Phase  
The main aim of this phase was to validate, on a larger scale, the efficacy of the chemical pre-treatment and chemical dosage, and to estimate sludge production. Moreover, this phase was designed to evaluate the system's steady operation over an extended period (1 month), while also observing and addressing any variations in BWW quality. Figure 1 provides details about the layout of the plant used for BWW treatment.  
The BWW was stored in the accumulation tank, which had a capacity of 350 m\({}^{3}\). The tank was operated manually for the purpose of the test. The untreated BWW was initially  
Figure 1: Scheme of the industrial scale plant used for the BWW treatment. COA: coagulant; FLOC: flocculant. Pink and orange arrows indicate the dosage of chemicals and the untreated BWW, respectively. Blue and brown arrows refer to treated water and chemical sludge produced in the process, respectively.  
pumped into a tubular mixer, a device designed to accurately dispense chemicals and ensure the necessary amount of time for appropriate mixing of the wastewater with the reagents.  
Tests were performed on a portable chemical pre-treatment device equipped with a pumping station capable of handling 1 m\({}^{3}\cdot\)h\({}^{-1}\); diaphragm pumps for dispensing alkali, acid, and coagulant; a tubular mixer; a flotation unit with a hydraulic capacity of 1 m\({}^{3}\cdot\)h\({}^{-1}\); and a storage tank for collecting separated sludge with a volume of 0.5 m\({}^{3}\) (Figures S1 and S2). NaOH (30%) and HCl (31%) were used for adjusting the pH levels to alkaline and acidic, respectively. The sludge separation process occurred in the flotation unit, which had a volume of 1.2 m\({}^{3}\), and involved the separation of sludge from the treated water. The processed BWW descended with gravity into the municipal sewer system. Temperature, pH, and conductivity were monitored continuously while chemical parameters were evaluated on a daily basis. The temperature in the flotation unit stayed consistently between 35 and 40 \({}^{\circ}\)C without the need for a heat exchanger, thanks to the elevated temperature of the BWW.
###### Abstract
In reality, the KPCA model has proved to be effective in obtaining realistic principal components of a collection of the data and dealing with variable nonlinearity. By reflecting the source data onto a space with much fewer dimensions, the KPCA model reduces the complexity of the data. The KPCA is employed to build the model and discover nonlinear combinations of parameters that characterize the significant patterns in a dataset, while the SSGLR is employed to find flaws. Both are used to enhance the flaw detection method. The rest of the article is designed as follows. In Section 2, an overview of the KPCA-KDE procedure is provided, accompanied by an explanation of the two primary identification metrics, T\({}^{2}\) and Q, which are typically utilized with the KPCA for fault identification. The Generalized Likelihood Ratio (GLR) statistics using a sequential sampling scheme developed to identify faults are then discussed in Section 3. Section 4 sets out the KPCA-KDE-based SSGLR method of analysis and highlights how it can be applied to monitor online procedures. Section 5 provides an illustration of synthetic data assessing the efficacy of KPCA-KDE-SSGLR statistics under specific fault situations. Section 6 is designed to analyze and compare the effectiveness of KPCA-KDE-SSGLR and KLPP. Implementation of tracking initiatives to the TE benchmarking method is described in Section 7. Lastly, the conclusions grasped from the research are stated in Section 8.
## 4 Introduction to the ML Models  
### ML Model Selection  
Although traditional theoretical models have their explanatory power, they appear to be insufficient in handling complex nonlinear relationships. Therefore, ML and deep learning methods capture potential complex patterns in the data. In model selection, the decision trees (DTs) model [42] was first employed as a baseline model, as it is intuitive, easy to understand, and can handle nonlinear relationships suitable for feature importance analysis. The K-Nearest Neighbors (KNNs) [42] model was chosen for its simplicity and suitability for small datasets, allowing for an understanding of local data characteristics. Subsequently, ensemble methods, such as Random Forest (RF) [43], Bagging [44], AdaBoost [45], and Gradient Boosting [46], were selected due to their ability to improve model stability and accuracy through the integration of multiple base classifiers, providing robustness against outliers and noise. These methods are particularly suitable for handling the multi-factor effects on shear walls. Lastly, Deep Neural Networks (DNNs) [46] and Residual Networks (ResNet) [47] were chosen for their ability to handle high-dimensional nonlinear data and automatic feature extraction capabilities, making them ideal for large-scale datasets where complex feature interactions need to be identified and prediction accuracy improved. In summary, these models are chosen to balance their respective advantages and achieve an efficient prediction of RCSW ultimate bearing capacity, with the goal of improving prediction accuracy and providing valuable references for practical engineering applications.
# Visualizing Small Objects Using Amplitude-Modulated Laser Light at Microwave Frequencies
## 5 Conclusions  
We applied LADAR and spotlight-mode SAIL, both of which employed an amplitude-modulated laser at microwave frequencies, to validate this methodology by visualizing small targets. For resolution improvement, we substituted the infrared laser for microwaves as a carrier of the modulation signal in order to utilize a wider bandwidth (4 GHz), which is difficult to permit a license in the microwave band. It was confirmed that the LADAR employing a new FM transmitter module could visualize the target shape with range and cross-range resolutions of under 1 mm and 7 mm, which are consistent with our previous LADAR system utilizing a VNA. The spotlight-mode SAIL system and image formations were demonstrated for the first time. The range and cross-range resolutions of this system were found to be about 24 mm and 9 mm, respectively. These spatial resolutions were in good agreement with those predicted by the measurement principle: 37.5 mm and 8 mm, respectively. On microwave SARs, it is difficult to improve the spatial resolution by collecting data on a wider wavenumber area since the scattering cross section of an object will vary both in the incident angle and frequency. Our method utilizes a fixed wavelength laser as a probing beam, with scattering response possibly becoming more simple compared to that of microwaves. This issue will be evaluated in future experiments. From the comparison between these two measurement results, we can conclude that LADAR is advantageous when making measurements in a range that is shorter than 1000 m due to the cross-range resolution as well as when taking stationary target measurements. On the other hand, SAIL with the spot-light mode inverse SAR technique was thought to be advantageous when making longer-range measurements and moving-target measurements. However, these difficulties such as low-amplitude signals and the requirement of knowing the precise target position must be solved by analysis techniques in the future.
## 4 Security Threats
### Negotiation
#### 4.2.1 Negotiation Obstruction
Concluding, negotiation obstruction may introduce significant problems in the normal operation of a DSA network. Firstly, it could affect competition between its nodes since a malicious one may choose to realize this attack exactly after his spectrum requests are received by the sharing mechanism. His competitors' requests may never reach their destination, leaving the decision mechanism with incomplete input. Unavoidably, the resulting decision may be much different now than the \"fair\" one, offering extra profits to the attacker. Moving one step forward, resource starvation, spectrum under-utilization, as well as sub-optimal network operation would sooner or later emerge as side effects of such misbehavior.
## 4 Simulation Examples
### Very Fast Transient Overvoltage Calculation  
In this case, we simulate a situation when the disconnector of 550 kV gas insulated switchgeear (GIS) acts on the no-load bus. During this process, very fast transient overvoltage (VFTO) is caused by slow contact action and poor arc-extinguishing performance of switch [21; 22]. Figure 10 is a simplified equivalent circuit diagram for this process. In Figure 10, \(e(t)\) is the AC power supply; \(L_{t}\) and \(C_{t}\) are used to simulate the lumped inductance and capacitance parameters of the transformer and other equipment. DS is the isolation switch, and its mathematical model can be established by a nonlinear time-varying resistance. \(C_{r}\) is the lumped ground capacitance with initial voltage to simulate the residual charge; L\({}_{1}\) and L\({}_{2}\) are import and export short bus, respectively. All parameters of this example in Figure 10 were taken from Reference [23].  
The mathematical model of the VFTO simulation was established by using the telegraph equation for the short bus L\({}_{1}\) and L\({}_{2}\). The short bus, L\({}_{1}\), was uniformly divided into \(N_{1}\) = 20 sections, and \(N_{2}\) = 7 of the short bus L\({}_{2}\) was selected. The following ordinary differential equations were obtained by the interpolation formulae [20]:  
\[\dot{x}=Ax+\delta(t) \tag{25}\]  
As the simulation time-step of VFTO was nanosecond scaled, it could be considered that \(A\) was the constant matrix in a time-step and \(\delta(t)\) was the incentive source of VFTO.  
The four-step L\({}_{2}\)MF and the implicit trapezoidal integration method were used to solve Equation (16). The integration step of the implicit trapezoidal integration method was \(h=1\) ns and a smaller integration step of L\({}_{2}\)MF was \(h=0.1\) ns. The simulation results in this case are shown in Figure 11.  
In this simulation test, the simulation time-step of VFTO was nanosecond scaled, which accurately simulated the change rule of the open-circuit voltage \(u(t)\) at the end of the line. Neither of these methods produced numerical oscillations, and the waveforms of VFTO for both the methods were almost the same.  
Figure 11: VFTO calculated by the four-step L\({}_{2}\)MF method and the TR method.  
Figure 10: A simplified GIS (gas insulated switchgeear) for very fast transient overvoltage (VFTO) simulation.
## 2 Multi-Constellation PPP Model  
In this study, we developed a PPP software and conducted several tests of the precision and stability of the data processing achieved using this PPP software. The results showed that the accuracy of statistical positioning could reach the millimetre level in the horizontal direction and the centimetre level in the vertical direction. The root mean square (RMS) value of the tropospheric delay was between 0.01 m and 0.02 m. The convergence time was primarily distributed from 10 min to 40 min. These tests will be described in a future research paper. Compared with other open-source PPP software, this software could batch process large amounts of data, and it used precision products provided by the IGS, Multi-GNSS Experiment (MGEX) and International GNSS Monitoring & Assessment System (iGMAS). The structure design of our software was simpler and had good stability. In this section, the observation equation, Kalman filter model and stochastic model used in our software will be discussed in detail.