esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Equipment Control and Programmable Logic Controllers  
Equipment control in industry is commonly achieved using PLCs, due to their high reliability and ubiquity [39]. Originally developed to replace hard-wired relay logic circuits, PLCs have been developed for many applications, including motion control, batch process control, distributed control systems and sequencing applications [40]. PLCs operate in a cyclical fashion, also known as a scan cycle, which consists of four main steps: input scan, programme scan, output scan and housekeeping (see Figure 2) [41]. They can be programmed using a variety of programming languages outlined in the IEC 61131-3 standard, which has been adopted by most major PLC manufacturers [42]. This standard consists of three programme organisation units (programmes, function blocks and functions) which can be constructed from five programming languages: three graphical (ladder diagram, function block diagram and sequential function chart) and two textual (instruction lists and structured text) [43].  
Figure 2: PLC Scan Cycle.
## 2 HGV Tracking with Modified Aerodynamic Model  
From above consideration, we know CKF is a good choice for high-degree nonlinear system, just like HGV tracking system. Based on CKF theory, the tracking problem of a nonlinear dynamic system can be defined by the following state-space model with additive noise in discrete time [15].  
Process equation:  
\[X_{k}=f(X_{k-1})+w_{k-1} \tag{1}\]  
Measurement equation:  
\[Y_{k}=h(X_{k})+\
u_{R,k} \tag{2}\]  
where \(X_{k}\) is the state variable at discrete time \(k\); and \(\{w_{k}\}\) and \(\{\
u_{R,k}\}\) are independent process and measurement Gaussian noise sequence with zero means and covariance matrixes \(Q_{k}\) and \(R_{k}\), respectively. Furthermore, the process equation will be established based on HGV aerodynamic model and the measurement equation will be obtained based on radar principle.
# Performance of Time Reversal Based Underwater Target Detection in Shallow Water
## 5 Conclusions  
When seabed clutter exists in the received signal, the scattering signal by the target is buried in the clutter signals, which makes accurate detection difficult. In this paper, a method using the time reversal technique is proposed to improve detection performance when the clutter environment is in shallow water. Using the wave focusing effect of the time reversal technique, the peak of the target signal can be enhanced. It is confirmed that the peak of the target buried in the clutter becomes higher when applying the time reversal technique by exploiting the time-varying characteristics of the seabed clutter. The numerical computation of the probability of detection and the false alarm probability demonstrates the improvement of the proposed method over the conventional approach. Furthermore, Monte Carlo simulation results also indicated that the proposed approach is better than the conventional one. In addition, the performance of the proposed approach was investigated in terms of the parameter of the K-distribution and the temporal coherence of the clutter channel. For a strong K-distributed channel and for a highly time-varying channel, the proposed approach shows a better probability of detection. In this study, it was observed that the time-reversal technique can improve the observability of the target peak in shallow water. Note that the kinds of time-varying clutter considered in this study were gravel, sand, and seaweed in the high-speed current. For further study, the strategy for reducing the computational complexity should be investigated for successful real-time applications.  
Figure 9: The probability of detection of the proposed approach for several coherence values when \(P_{FA}=10^{-4}\) and \(\alpha=1\).  
**Acknowledgments:** The authors gratefully acknowledge the financial support provided by Defense Acquisition Program Administration and Agency for Defense Development under the contract (UD140004 DD).  
**Author Contributions:** [PERSON] conceived and designed the experiments; [PERSON] performed the signal modeling and the simulation; [PERSON] provided the discussion on the target detection problem in the shallow water. [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Experimental Research  
### Calculation of Loader Steering Efficiency  
The pressure sensor and displacement sensor are used to collect the cylinder pressure, pump pressure, and cylinder displacement to calculate the output power of the pump, the consumed power of the cylinder, and the steering efficiency of the system.  
The pump output power can be expressed as  
\[P_{p}=p_{p}(A_{Lh}v_{L}+A_{Rh}v_{R})-p_{i}(A_{Ll}v_{L}+A_{Rl}v_{R}) \tag{21}\]  
where \(P_{p}\) is the pump output power. \(p_{p}\) is the pump outlet pressure. \(p_{i}\) is the pump inlet pressure. When the loader turns right, \(A_{Lh}\) is the effective working area of the rodless cavity of the left steering cylinder, \(A_{Rh}\) is the effective working area of the rod cavity of the right steering cylinder, \(A_{Ll}\) is the effective working area of the rod cavity of the left steering cylinder, \(A_{Rl}\) is the effective working area of the rodless cavity of the right steering cylinder; when the loader turns left, \(A_{Ll}\) is the effective working area of the rod cavity of the left steering cylinder, \(A_{Rh}\) is the effective working area of the rodless cavity of the right steering cylinder, \(A_{Ll}\) is the effective working area of the rodless cavity of the left steering cylinder, \(A_{Ll}\) is the effective working area of the rodless cavity of the left steering cylinder, and \(A_{Rl}\) is the effective working area of the rod cavity of the right steering cylinder. \(v_{L}\) and \(v_{R}\) are the speeds of the left and right steering cylinder pistons, respectively, which can be obtained through the differential displacement signal of the steering cylinder.  
The power consumed by the steering cylinder during steering can be described as  
\[P_{c}=p_{h}(A_{Lh}v_{L}+A_{Rh}v_{R})-p_{l}(A_{Ll}v_{L}+A_{Rl}v_{R}) \tag{22}\]  
where \(P_{c}\) is the power consumed by the steering cylinder during the steering process. \(p_{h}\) is the pressure in the high-pressure chamber of the cylinder. \(p_{l}\) is the pressure in the low-pressure chamber of the cylinder.  
The steering efficiency of the system can be obtained  
\[\eta_{z}=\frac{\int P_{cl}dt+\int P_{cr}dt}{\int P_{p}dt}\times 100\% \tag{23}\]  
where \(\eta_{z}\) is the steering efficiency of the system. \(P_{cl}\) and \(P_{cr}\) are the power consumption of the left and right steering cylinders, respectively.  
Figure 6: Simulation curves of steering system angle closed-loop control. (**a**) Handle input signal; (**b**) The steering stroke of the loader.
# Ferry Scheduling Optimization Considering Arrival Time Uncertainty and In-Place Time Differences
## 5 Conclusions  
Due to the uncertain arrival and departure times of flights, the utilization efficiency of security resources is low. The scheduling plan for ferry vehicles based on flight plans cannot effectively cope with fluctuations in inbound and outbound time. This article analyzed the scheduling problem of ferry vehicles. On the one hand, it considered the uncertainty of the arrival time of ferry services; on the other hand, it considered the difference in the in-place time of the ferry. We proposed a dynamic programming stochastic model with the optimization objective of minimizing the number of ferries. This enriches the content of airport ground support resource scheduling by making the scheduling of ferries closer to the actual situation. To better solve the dynamic programming stochastic model proposed, a GA was introduced based on Monte Carlo random sampling. The effectiveness of the proposed model was validated using actual data from an international airport in South China as an analysis case. Compared with traditional ferry service splitting, DPS can better improve resource utilization; compared with static scheduling schemes, the stochastic model scheme has better robustness. With the deepening of airport digitization, the credibility of flight on-site data will be further improved. This has laid a more solid foundation for the scheduling of airport operation support resources.  
In this paper, a stochastic dynamic programming model for a complex ferry scheduling problem was constructed. In this problem, in addition to considering the stochastic nature of service arrival times, the in-place time requirement was also emphasized. There are other similar application scenarios where this model can be effectively applied. For example, when loading and unloading cargo and mail at docks, the arrival time of the ship is uncertain. To effectively improve loading and unloading efficiency, trucks need to be continuously in place. Similarly, logistics includes scheduling of loading vehicles, scheduling of automated guided vehicles (AGVs) used for production transfers and so on. Therefore, the model can be generalized to practical applications.  
Conceptualization, G.X.; Methodology, G.X. and Q.C.; Software, W.W.; Investigation, W.W.; Resources, Z.W.; Data curation, W.W.; Writing--original draft, G.X.; Writing--review & editing, G.X., N.M. and Z.W.; Supervision, Q.C.; Funding acquisition, N.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data is unavailable due to privacy or ethical restrictions.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Preliminaries  
In Figure 1, the diagram represents two configurations of the FDM based TCREs: CIRD and LIIRD ones. Both configurations are subdivided into 9 intervals equal to \(r\), both central discs have a radius of \(r\), and all the concentric rings have a width of \(r\). The CIRD configuration has each ring evenly spaced with inter-ring distances of \(3r\). For the LIIRD configuration, the distance between the central disc and the middle ring is \(2r\) and the distance between the middle ring and the outer ring is two times larger at \(4r\).  
Table 1 displays the average potentials \(v_{kr}\) on each concentric circle with radius \(kr\) with \(k\) ranging from 1 to 9 (Figure 1) based on [PERSON]'s calculation of the Laplacian potential using Taylor series expansion from [9] (see [25] for more details on its derivation):  
\[v_{kr}\cong v_{0}+\frac{2\cdot k^{2}}{4\cdot 2!}r^{2}\Delta v_{0}+\frac{2\cdot k ^{4}}{4\cdot 4!}r^{4}T_{4}+\frac{2\cdot k^{6}}{4\cdot 6!}r^{6}T_{6} \tag{1}\]  
where  
\[\Delta v_{0}=\frac{\partial^{2}v}{\partial x^{2}}+\frac{\partial^{2}v}{ \partial y^{2}} \tag{2}\]  
\[T_{4}=\frac{\partial^{4}v}{\partial x^{4}}+\frac{\partial^{4}v}{\partial y^{ 4}} \tag{3}\]  
\[T_{6}=\frac{\partial^{6}v}{\partial x^{6}}+\frac{\partial^{6}v}{\partial y^{ 6}} \tag{4}\]  
The following notations are used in Equation (1) and in Table 1: \(\Delta v_{0}\) is the surface Laplacian potential at the point with potential \(v_{0}\) (Equation (2)) and \(T_{4}\) and \(T_{6}\) are the fourth (Equation (3)) and the sixth (Equation (4)) order truncation terms respectively.  
Figure 1: Two finite dimensions model (FDM) based tripolar concentric ring electrodes (TCREs) configurations: constant inter-ring distances (CIRD) (**A**) and linearly increasing inter-ring distances (LIIRD) (**B**).  
Deriving Laplacian Estimates for Real Life Linearly Increasing Inter-ring Distances (LIIRD) Tripolar Concentric Ring Electrode (TCRE) Based on Negligible Dimensions Model (NDM) and Finite Dimensions Model (FDM)  
For the case of NDM, in [24] the method of optimal surface Laplacian estimation was proposed for a TCRE with concentric ring radii \(\alpha r\) and \(r\) where coefficient \(\alpha\) satisfies \(0<\alpha<1\). The method is based on using the matrix of truncation term coefficients (\(\alpha^{4}\) 1) to solve for its null space vector (\(-1/\alpha^{4}\), 1) followed by linearly combining two bipolar signals corresponding to differences between concentric ring and central disc potentials into a Laplacian estimate with the null space vector used as linear combination coefficients [24]. For the physical LIIRD TCRE we applied this method to actual TCRE measurements including middle ring radii (9.6 mm inner and 10.8 mm outer, Table 2) and outer ring radii (21.6 mm inner and 22.8 mm outer, Table 2) as well as to radii corresponding to the center of each ring (10.2 mm for the middle ring and 22.2 mm for the outer one).  
For the case of FDM, steps similar to the ones in Section 3.1 below were used to find the coefficients of the Laplacian estimate.
# An Automated Behaviour-Based Clustering of IoT Botnets
## 5 Findings and Discussion
### Comparison with Hierarchical Clustering  
In addition to the Mean-shift clustering, we also evaluated the effectiveness of the Agglomerative Hierarchical clustering for identifying IoT botnet variants with new capabilities from our dataset. We decided to evaluate the Agglomerative Hierarchical clustering since it is typically better at identifying small clusters than the Divisive Hierarchical clustering. We used the Python implementation of the Agglomerative clustering algorithm provided by scikit-learn which requires the linkage criterion and the number of clusters to be specified [38]. Using the cosine similarity matrix as input, we investigated the clusters identified with each of the following linkage criterions:'single', 'average', and 'complete'. The number of clusters was set to 59, which is the number of clusters and outliers identified by DBSCAN, and is close to the number of clusters and outliers identified by Mean-shift, 65.  
Using the 'complete' and 'average' linkages, the Agglomerative algorithm successfully recognised most of the IoT botnet variants equipped with new capabilities. However, it also identified multiple small clusters comprised of samples which exhibited generic IoT botnet behaviour. The differences between the behaviours of these small clusters are minor, and do not indicate that the clusters represent IoT botnet variants equipped with new capabilities. This limitation makes the Agglomerative clustering using the 'complete' and 'average' linkages less effective compared to the DBSCAN clustering.  
Using the'single' linkage, the Agglomerative algorithm identified the same clusters as the DBSCAN algorithm. The identified clusters, shown in Table 6, include one large cluster comprised of samples with generic IoT botnet behaviour, along with multiple small clusters and outliers representing IoT botnet variants equipped with new capabilities. Therefore, using the'single' linkage, the Agglomerative clustering is equally effective for identifying IoT botnet variants equipped with new capabilities as the DBSCAN clustering.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c} \hline \hline
**Cluster** & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 & 16 & 17 & \(-1\) \\ \hline
**No. of Samples** & 1193 & 9 & 7 & 11 & 3 & 3 & 4 & 3 & 2 & 2 & 2 & 2 & 12 & 2 & 2 & 2 & 2 & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Clusters identified by the Mean-shift algorithm.
## 3 Survey of Techniques for Updating
### The Execution Environment
#### 3.3.5 Component Oriented Middleware for Sensor Networks (COMiS)  
COMiS [61] is a component-based, generic middleware layer developed as part of the TinyMaCLaS project. It allows components to be loaded onto particular nodes, and supports network-wide software updating. Applications are written in the DCL (Distributed Compositional Language) scripting language [61]. Software components are registered locally, and then available for discovery by other components, using a broadcast algorithm, and subsequent connections. Both middle-ware and application components can be updated. Compiled component binaries are deployed into the network and installed (registered) at deployment nodes. Connection is established with (a subset of) nodes in the network using the discovery component. Then the update method is used to deploy the code to these nodes. On receiving an update, the COMiS listener component checks that the version number is greater before installing and relinking the new component, which is then restarted. A TTL field provides limited flooding control.  
COMiS contains no specific features relevant to autonomous updates, but has flexibility in terms of what can be updated and might prove a useful enabler.
## 5 Experimental Results and Discussion  
### Experimental Setup  
In this study, the AI models of interest were programmed and coded using Python, Keras [30], and the TensorFlow platform [31]. To obtain statistical results, AI models were trained multiple times, each with distinct random parameter initialization. To expedite both the training and evaluation phases, I performed experiments on a CUDA-supported GPU machine equipped with TITAN XP cores, supported by NVIDIA's Compute Unified Device Architecture (CUDA) technology [32]. All AI models were trained using the Adam optimizer [33], since Adam has fewer hyper-parameters to tune compared to other optimizers, making it easier to set up and less sensitive to hyper-parameter choices. Yet, due to the adaptive learning rate mechanism of the Adam optimizer, the Adam optimizer sometimes causes the learning rates to fluctuate more during training. To mitigate this effect, weight decay was incorporated into the Adam optimizer by adding a regularization term to the loss function. This regularization term encourages the Adam optimizer to favor smaller weights, helping prevent over-fitting and improving generalization. The training process spanned 150 epochs, a value determined empirically to achieve satisfactory convergence. To account for computational resource constraints, I adopted a uniform mini-batch size of 32 for all AI models during training. The steps per epoch determines how many times trainable parameters are updated during one epoch of training, and this parameter is determined by the size of the training dataset and the batch size used during training. For example, if I had 15,855 training samples and was using a batch size of 32, the steps per epoch would be rounded to 495.
Numerical Investigation of High-Purity Polarization-Entangled Photon-Pair Generation in Non-Polded KTP Isomorphs  
[PERSON], [PERSON] and [PERSON]  
1  
Footnote 1  
+++ ==WARNING: Truncated because of repetitions==
1: email: 4  
++  
for periodic poling exceeds the limit, the crystal burns or breaks during the fabrication. Although the PPKTP is still an attractive source of entangled quantum systems with the high spectral purity of photon pairs, Type II SPDC is also possible even without PP structures since non-poled KTPs are biaxial birefringent crystals. In addition, the effective nonlinear optic coefficients of KTP in the direction of crystallographic axes are larger than that of BBO or BiBO. Therefore, it is necessary to investigate whether the generation of high-purity photon pairs through Type II EPM is still possible and useful for practical quantum systems, even without the PP structure in KTP. To the best of our knowledge, no studies have been reported on SPDC-based photon-pair sources under Type II EPM in non-poled KTP isomorphs.  
In this paper, we investigate both theoretically and numerically the high-purity polarization-entangled photon-pair generation in five kinds of non-poled KTP isomorphs--KTP, KTiOAsO\({}_{4}\) (KTA), RbTiOPO\({}_{4}\) (RTP), RbTiOAsO\({}_{4}\) (RTA), and CsTiOAsO\({}_{4}\) (CTA). The technique relies on the SPDC under Type II EPM, where the phase matching and the group velocity (GV) matching are simultaneously achieved between the interacting photons in non-poled crystals rather than PP ones. First, we investigated Type II EPM characteristics of five non-poled KTP isomorphs, in terms of the range of the beam propagation direction (or the spectral range of photon pairs) and the corresponding effective nonlinearities and beam walk-offs. We showed that the SPDC efficiency can be increased in non-poled KTP isomorphs by 29% to 77% compared to PPKTP. The joint spectral analyses showed that photon pairs can be generated with high purities of 0.995-0.997 with proper pump filtering. In contrast to the PPKTP case, where the EPM is achieved only at one specific wavelength, the spectral position of a photon pair in the non-poled KTP isomorphs can be chosen over the wide range of 1883.8-2068.1 nm. This means that the photon-pair spectra can be flexibly chosen in the range of interest while maintaining the high spectral purity of the photon pairs generated via Type II EPM.
# Ultra-Low-Power FinFETs-Based TPCA-PUF Circuit for Secure IoT Devices
## 1 Introduction
The previous work on two-phase clocking adiabatic PUF (TPCA-PUF) [33] cells is investigated using the dual-gate FinFET 45 nm process with 1.0 V nominal supply voltage. The uniqueness, reliability, and the BER of the proposed TPCA-PUF are compared with the QUALPUF [35] using the same process technology. The remainder of this paper is structured as follows: Section 2 introduces the FinFET-based adiabatic PUF by first explaining the fundamental adiabatic logic current trace compared to the traditional logic style, FinFET physical structures, the proposed FinFET adiabatic PUF, and its implementation into 4-bits LSI PUF. The simulation conditions, security evaluation metrics, and simulation results are described in Section 3. The evaluation of the proposed work in comparison with conventional PUFs is discussed in Section 4. Finally, Section 5 concludes the research findings of this work. Figure 1: Two identical PUF circuits on two different chips generate different responses (modified from [15]).