esa-sceva/satcom-chunk-collection huggingface dataset:
# Saliency Guided DNL-Yolo for Optical Remote Sensing Images for Off-Shore Ship Detection
###### Abstract  
The complexity of changeable marine backgrounds makes ship detection from satellite remote sensing images a challenging task. The ubiquitous interference of cloud and fog led to missed detection and false-alarms when using imagery-based optical satellite remote sensing. An off-shore ship detection method with scene classification and a saliency-tuned YOLONet is proposed to solve this problem. First, the image blocks are classified into four categories by a density peak clustering algorithm (DPC) according to their grayscale histograms, i.e., cloudless areas, thin cloud areas, scattered clouds areas, and thick cloud areas. Secondly, since the ships can be regarded as salient objects in a marine background, the spectral residue saliency detection method is used to extract prominent targets from different image blocks. Finally, the saliency tuned YOLONet network is designed to quickly and accurately detect ships from different marine backgrounds. We validated the proposed method using more than 2000 optical remote sensing images from the GF-1 satellite. The experimental results demonstrated that the proposed method obtained a better detection performance than other state-of-the-art methods.  
optical satellite image; ship detection; convolutional neural networks; deep learning Article  
Saliency Guided DNL-Yolo for Optical Remote Sensing Images for Off-Shore Ship Detection. _Appl. Sci._**2022**, _12_, 2629. [[https://doi.org/10.3390/app12052629](https://doi.org/10.3390/app12052629)]([https://doi.org/10.3390/app12052629](https://doi.org/10.3390/app12052629))
# SIMECK-T: An Ultra-Lightweight Encryption Scheme for Resource-Constrained Devices
### Related Work
It is a lightweight cipher, but the block size is not very efficient on resource-constrained devices, where a 64-bit block size is preferable. THREFISH [56] is part of the Skein hash function and is a large, flexible cipher. Its ARX operations work on 64-bit words, and it can have a block size of 256, 512, or 1024 bits. The block size is not very advantageous, and the 64-bit instruction set makes it less than ideal for resource-constrained devices. [PERSON] et al. [57] propose a stream cipher for IoT whose output has been validated to be random using the NIST Statistical Test Suite (STS) [24] and other randomness testing tools. SIMECK-T has been validated to produce random output, using the NIST SP800-22 [24] and AIS.31 [26] standards, and the law of the iterated logarithm (LIL) [27; 28]. [PERSON] et al. [31] proposed Hash XOR Permutation (HXP), a lightweight cryptosystem for the encryption of images with cloud storage and the Internet of Things in mind. HXP is hash-based, using a Secure Hash Algorithm (SHA) [58] with a truncated output, which is not optimal. The algorithm was evaluated using various security metrics, showing that security can be preserved without substitution boxes. The same metrics were applied by [PERSON] et al. for the evaluation of IRC6 [12]. The SIMECK-T cryptographic scheme will be verified against the security evaluation metrics applied to HXP [31]. As explained in Section 1.1, high-speed software-oriented ARX block ciphers are selected from the related work and modified to produce the same level of security using a reduced number of rounds. We opt for 64-bit block ciphers with a key size of 128 bits, in order to maintain a good balance between speed, lightness, and security. NSA-based SPECK and SIMON [7], and SIMECK [29], on one hand, and TEA [30], XTEA [53] and XXTEA [54] primitives, on the other, will be combined with RC4D [22] substitution boxes as demonstrated by the original SPECK-R [19] which uses RC4 [20]. SIMON is more appropriate for hardware implementations but is included for completeness.
# A Blockchain Copyright Protection Model Based on Vector Map Unique Identification
## 5 Experiment Results and Analysis
### Evaluation Index
#### 5.2.2 Evaluation Index for Watermark Correlation  
When authenticating map data, it is imperative to compare the extracted watermark with the original watermark stored in the blockchain to validate its authenticity. The evaluation of watermark similarity primarily encompasses metrics such as the Normalized Correlation Coefficient (NC), Structural Similarity Index (SSIM), Signal-to-Noise Ratio (SNR), among others. Notably, the NC index holds significant relevance in the domain of digital watermarking [41]. Hence, this paper adopts NC as the metric for assessing watermark correlation, with its calculation formula presented as Equation (23).  
\[NC=\frac{\sum\limits_{i=1}^{M}{\sum\limits_{j=1}^{N}{XNOR(W(i,j),W^{\prime}(i, j))}}}{M\times N} \tag{23}\]  
In the equation, \(W(i,j)\) and \(W^{\prime}(i,j)\) respectively, represent the original watermark information stored in the blockchain and the extracted watermark information from the infringing data, while \(M\times N\) denotes the size of the copyright information. The XNOR operation involves performing a logical XOR operation on two pixels and then inverting the result.  
The Normalized Correlation Coefficient (NC) serves as a metric for assessing similarity, ranging from 0 to 1. A higher NC value, approaching 1, signifies greater similarity between the extracted watermark and the original watermark. Following a comprehensive analysis of extensive datasets and referencing established practices in digital watermarking, this paper sets 0.75 as the optimal NC threshold for watermark extraction. This threshold effectively balances robustness and imperceptibility, ensuring reliable detection of watermarks under various conditions while maintaining minimal visual distortion, consistent with benchmarks reported in related studies [42].
# Energy Optimisation of Industrial Limestone Grinding Using ANN
## 9 Optimisation Using Genetic Algorithms
* Mutation probability: 10%. * Gene space: Bounded ranges defined for each control variable. Two independent optimisation runs were conducted--one for minimizing indicator \(W\) and one for \(S\). Over 100 generations, the population evolved toward optimal solutions using crossover, mutation, and selection mechanisms. The final output of the algorithm includes the best-performing solution (chromosome) and the corresponding minimum value of the indicator:  
* \(\min f_{W}(\mathbf{x})\) at \(\mathbf{x}_{W}^{*}\);
* \(\min f_{S}(\mathbf{x})\) at \(\mathbf{x}_{S}^{*}\). This evolutionary strategy complements Bayesian optimisation by enabling a stochastic, global search of the parameter space. Its robustness to local minima and independence from gradient information make it particularly well-suited for optimising data-driven industrial models. The final optimisation results are summarized in Tables 10-13. It is worth noting that both optimisation methods identify very similar energy minima for the \(S\) and \(W\) indicators. Both genetic algorithms and Bayesian optimisation effectively find system operating parameters that result in significantly lower energy consumption compared to the previously used heuristic settings. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Unit** & **Value A** & **Value Ensemble** \\ \hline Mill screw conveyor motor speed & rpm & 29.9 & 49.9 \\ \hline Temperature after the mill & \({}^{\circ}\)C & 67.4 & 80.1 \\ \hline Airflow before the fan & m\({}^{3}\)/h & 160,000 & 176,722 \\ \hline Pressure difference across the mill & mbar & 22.7 & 36.9 \\ \hline \(S\) & kWh/Mg & 11.82 & 11.81 \\ \hline \(W\) & kWh/Mg & 1.00 & 1.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Genetic algorithm optimisation results for the A data. One striking observation is that despite the distinct control values selected from the assumed range of parameter variability, both methods achieve very similar indicator values. This suggests that the entire state space contains multiple local minima. Therefore, it is reasonable to use two different optimisation algorithms. However, genetic algorithms may discover alternative local minima, which could be more advantageous for the enterprise from an operational or production management perspective.
# Optimal Control of an Ultraviolet Water Disinfection System
## 4 Algorithm Using Hybrid Topology Particle Swarm Optimization
### Hybrid Topology Particle Swarm Optimization
#### 4.1.1 General Description of PSO  
A particle swarm system can optimize a given function with \(n\) decision variables, called the objective function, through a simulated movement of the swarm inside the space where the function is defined. The objective function can model the \"environment\" profile, i.e., the altitude of \"valleys\" and \"hills\", which is the basic topology of the optimization problem (OP). Each particle \"flies\" over new regions of the search space. In the PSO's initial version, the particles communicate among themselves through the intermediary of a global variable, memorizing the best position \"encountered\" by the swarm.  
As the search process proceeds, the exploration decreases, and the exploitation is intensified. Finally, the particles converge to the global best solution of the OP. Paper [18] presents the main aspects concerning the analysis of PSO algorithms. The convergence of PSO algorithms is treated in [11; 24], which guides the choice of algorithm parameters.  
The swarm is compounded by \(N\) particles, with each particle coded through a three-component vector [20; 21], usually denoted by \(\big{(}X_{i},V_{i},P_{best_{i}}\big{)}\). Each component is an \(n\)-dimensional vector representing, respectively, the position, the speed, and the best personal position reached in the search process (see [20]). It holds as:  
\[X_{i}\ =\ \Big{(}x_{i}^{1},x_{i}^{2},\ldots,x_{i}^{d},\ldots,x_{i}^{n}\Big{)},V_{i }\ =\ \Big{(}v_{i}^{1},\ v_{i}^{2},\ldots,\ v_{i}^{d},\ldots,v_{i}^{n}\Big{)}\]  
\[P_{best_{i}}=\Big{(}p_{best_{i}}^{1},\ldots,p_{best_{i}}^{d},\ldots,p_{best_{ i}}^{n}\Big{)},\ i\ =\ 1,\ 2,\ \ldots,\ N\]  
The evolution of the particle swarm is implemented through an iterative process. Let \(P_{gbest}\) be the particles' best position until the current step, called the \"global best\" position. At the end of the algorithm, i.e., when convergence is reached, this is the solution of the OP.  
The essence of the PSO algorithm is updating the particle speed and position at each step of the search process. This update is performed using the following equations:  
\[\left\{\begin{array}{l}V_{i}(t\ +\ 1)\ =\ w\ \times\ V_{i}(t)\ +\ C_{1}\ \times\ rand_{1}\ \times\ (P_{best}(t)\ -\ X_{i}(t))\ +\ C_{2}\ \times\ rand_{2}\ \times\ \Big{(}P_{gbest}(t)\ -\ X_{i}(t)\Big{)}\\ X_{i}(t\ +\ 1)\ =\ X_{i}(t)\ +\ V_{i}(t\ +\ 1)\end{array}\right. \tag{18}\]  
where \(w\) is inertia weight; \(C_{1}\) and \(C_{2}\) are acceleration coefficients; \(rand_{1}\), \(rand_{2}\) are random numbers in the interval \([0,1]\); and \(t\) is the step number. This initial version often leads to premature convergence.
## 4 Design
### Combining Scheduled Traffic Support with EDF Scheduling  
A limitation of the D-TSN approach in [29] is that EDF scheduling may introduce jitter on the frame transmission, thus making it unsuitable for time-driven flows.  
D-ST overcomes this problem by combining D-TSN with the Enhancements for Scheduled Traffic defined in [27]. In particular, in the proposed combination, the highest priority queue is reserved to the ST, which is transmitted according to the gate mechanism provided by [27], while the remaining queues implement EDF scheduling for the other RT traffic classes. As a result, when ST transmissions are scheduled, the transmission gate of the ST queue will be open and the gates of the remaining queues will be closed. The non-ST frames may, therefore, experience a delay due to both the ST frame transmission and the gate temporal isolation mechanism, which prevents starting a non-ST frame transmission that could not complete before the next ST queue gate opening. However, as the ST frames are scheduled and their transmission times are a priori known, the additional delay they introduce on non-ST frames can be easily calculated. Conversely, when no ST traffic transmissions are planned, the ST queue gate will be closed, while the other gates will stay open.  
Figure 2 shows the forwarding process of an Ethernet switch port with a snapshot of each stream gate status at a generic time \(t\). Note that for each Stream Identifier (SID) the stream gate IPV changes over time.  
When a frame arrives to the Ethernet switch port, it is associated with a stream gate according to the frame VID value. For instance, in Figure 2 the VID no. 2 is associated with the stream gate with index 2, i.e., \(SG=2\). The stream gate 2 at time \(t\) has the IPV equal to 3, therefore the frames traversing the stream gate 2 are enqueued to the transmission queue 3. In this example, \(N\) is equal to 7 and the VID 9 is out of the range \([VID_{0},VID_{0}+(N-1)]\), so the frames with VID 9 are not scheduled according to the D-ST policy previously described. In fact, VID 9 is associated with the ST traffic at stream gate 7. Such a stream gate never changes its IPV, therefore the ST frames traversing the stream gate 7 are always enqueued to the highest priority queue, i.e., Q7.
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 4 MASU Evaluation Using Simulations
### Simulations Setup  
In this process we used the ns-3 simulator [30], since it allows us to represent diverse scenarios and collect a number of metrics with the purpose of assessing the impact of the underlying network infrastructure, as well as the mobility patterns of the MASU units, on the framework performance. The ns-3 enables the configuration of the nodes that run the MASU framework, the communication network that supports them and the physical space where they are placed. Consequently, the hardware capabilities of the nodes, their wireless network interfaces, physical position and mobility patterns were configured using this simulation tool. We performed 20 simulations for each particular scenario, and each simulations lasted 20 min.
## 3 Imaging Attitude Control
### Coordinate Frames
#### 3.1.4 Aircraft Body (AC) Frame  
This frame also has its origin \(O_{AC}\) at the POS location. The \(X_{AC}\) direction points out the nose of the aircraft and is aligned with the aircraft body roll axis. \(Z_{AC}\) points out the bottom of the aircraft and is aligned with the aircraft body's heading axis. \(Y_{AC}\) forms an RH orthogonal set and nominally points out the right wing.
## 3 QoS Aware MAC Protocols for WMSNs
### [PERSON] et al. presented an intelligent hybrid MAC protocol IH-MAC [43]. Unlike EQ-MAC [39] that has CSMA/CA and TDMA operating individually under different network conditions, IH-MAC fuses the key concepts of CSMA/CA and TDMA together to create a new MAC mechanism. First of all, instead of global synchronization, IH-MAC adopts local neighborhood synchronization from S-MAC [118]. Another, although IH-MAC operates in slotted mode like TDMA, not all slots are contention-free and the contention-free slots are scheduled in a decentralized fashion. In fact, nodes claims ownership of slots through clock arithmetic:  
\[s\equiv i\pmod{n} \tag{4}\]  
where \(s\) is the slot number, \(i\) is the node ID, and \(n\) is the number of neighbors within a two-hop range. However, ownership of a slot does not guarantee absolute transmission opportunity. By using CSMA/CA with non-overlapped contention windows, the protocol gives unquestionable priority to the node possessing data of high QoS demand. If there is no such critical data then owners that have their IDs mapped to the same slot can contend for that slot. Only when previous cases do not apply will non-owner nodes have the chance to contend for the slot. A transmission power adjustment technique is also used in the contention-based period to conserve energy. On the other hand, contention-free slots are only claimed when traffic load is high. If the buffer size of a particular node exceeds a certain threshold, the node makes some of its owned slots into rendezvous slots using another clock arithmetic:  
\[s_{r}\equiv i\pmod{m} \tag{5}\]  
where \(s_{r}\) is the reserved slot number, \(m\) is multiple of \(n\). Information about the rendezvous slot is broadcast in the neighborhood and during the rendezvous slot neighbors of both sender and receiver go to sleep. As a result, no contention is required and only DATA and ACK are exchanged in a rendezvous slot just like a nominal TDMA time slot.  
Pros and Cons. By fusing CSMA/CA and TDMA together, IH-MAC avoids the lack of scalability from traditional TDMA while also reducing collision and improving channel utilization and access delay that are main drawbacks of legit CSMA/CA. Nevertheless, the need for managing a two-hop neighbor list adds extra cost to the protocol, and the loosely synchronized nodes can end up with early sleep problem.
## 4 Technological Issues
### The Sensor Node  
The sensor nodes used for our WSN are produced by MeshNetics. The board of each node (see Figure 1) contains the ZigBit module, which operates as the ZigBee/802.15.4 transceiver. It also includes sensors, buttons, DIP-switches, and a set of interfaces including USB 2.0 port.  
The main features of this component can be summarized as follows:  
* ZigBit module: it is an ultra-compact, low-power, high sensitivity 2.4 GHz 802.15.4/ZigBee OEM module from MeshNetics. The ZigBit module is based on Atmel's Z-Link 2.4 GHz platform and includes ATmega1281V Microcontroller and AT86 RF230 RF Transceiver.
* Sensors: the board incorporates light sensor TSL2550T from TAOS and temperature sensor LM73 CIMK from National Semiconductors. Both sensors are connected in parallel to the I2C BUS.
* USB to UART Bridge: CP2102, the USB to UART Bridge controller from Silicon Labs is installed on the board. It provides seamless USB interface to any RS-232 legacy device. The USB port is visible on the PC as generic COM port with a particular number.
* Silicon Serial for UID storage: UID (Unique Identifier) is HEX value, 8 bytes long. UID is used for setting unique MAC address of the node and is an hardware-defined value which is programmed into a chip (Silicon Serial Number DS2411R+ by Maxim/Dallas). UID is unique, and cannot be overwritten.
## 4 Proposed Approach
### Explainability of Model Behavior  
In this work, we leveraged SHAP and LIME to offer detailed explanations for the model's predictions. SHAP is a well-known strategy for interpreting individual predictions from predictive machine learning models [55]. It computes the influence of each characteristic on given predictions using Shapley values, which are derived via cooperative game theory. The SHAP value for feature \(i\) in the context of prediction \(x\) can be defined as follows:  
\[\varnothing_{i}(x)=\sum S\mathbbm{C}\{1,2\ldots p\}\{i\}\frac{|S|!(p-|S|-1)!}{ p!}\left[f(S\cup\{i\})-f(S)\right] \tag{7}\]  
Here, the variable \(p\) is the total number of features, the SHAP value for feature \(i\) at instance \(x\) is denoted by \(\varnothing_{i}(x)\), the model's prediction function is given by \(f,S\) is the subset of the features excluding \(i\), and \(|S|\) indicates the number of features in subset \(S\). The term \(|S|!(p-|S|-1)!\) represents the number of possible ways to select the subset \(S\).  
In SHAP, the feature subsets \(S\) used in the value calculation are derived from all possible feature combinations. While the selection of these subsets introduces some level of random ness, which can cause slight errors in the final interpretation, the overall result remains stable and robust. The aggregation of contributions from all subsets reduces the impact of any single subset, ensuring that variations in feature importance estimates are minimal and do not significantly affect the overall interpretation of the model's predictions [55].  
LIME offers an alternative approach for interpreting individual predictions made by machine learning models. It functions by replicating the model's behavior locally within a given instance, using a simpler, easier-to-comprehend model. This local approximation is obtained by minimizing a loss function \(L(f,g,\ \pi_{x})\), where the proximity measure \(\pi_{x}\) defines how much weight is given to different data points based on their closeness to \(x\)[56]. The loss function is defined as follows:  
\[\hat{g}=argmin_{g}\ L(f,g,\ \pi_{x})+\Omega(g) \tag{8}\]  
Here, the interpretable model is denoted by \(\hat{g}\) and the complexity measure of \(g\) is represented by \(\Omega(g)\).  
In LIME, the weight function determines the importance of nearby points when approximating the model locally. In high-noise scenarios, the noise can distort the weighted data points, but LIME mitigates this by focusing on local areas, where the model's behavior is most consistent. Even with noisy data, the surrogate model is designed to reflect the true model's decision boundary, making explanations meaningful [57].  
The insights provided by both SHAP and LIME enable stakeholders to better understand and trust the results of machine learning applications. These techniques significantly increase our ability to verify the model's decision-making procedures and guarantee the accuracy of its predictions.
## 6 Hardware Experiments  
After sufficient numerical simulation, hardware experiments were implemented to examine the performance of the presented adaptive levitation controller on a single magnetic-suspension system, which is illustrated in Figure 10. The basic working principle of a single magnetic-suspension system is described in Figure 2. The test bed was composed of dSPACE(r), a levitation controller, a power-distribution cabinet, an eddy-current sensor, and a magnetic-suspension system. In particular, the MATLAB/Simulink(r) Real-Time Windows Target (RTWT) was utilized to set up the proposed control strategy in the loop-system hardware.  
Experiment results of the PID levitation controller with an uncertain time delay are provided in Figures 11-13. We can learn from the Figure 10 that the system becomes unstable after 14 s, at which point the instability causing the rail and electromagnetic is hitting repeatedly.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Experiment (Time Delay)**} & **PID** & **SMC** & **Proposed Method** \\ \hline \multirow{4}{*}{Case 1: normal} & Time & 2.10 s & 1.05 s & 0.82 s \\  & Error & 0.248 mm & 0.07 mm & \textless{}0.01 mm \\  & overshoot & 29.4\% & 0 & 0 \\  & Chattering & None & yes & None \\ \hline \multirow{4}{*}{Case 2: disturbance} & Time & 2.18 s & 1.12 s & 0.91 s \\  & Max Error & Unstable & 0.3 mm & \textless{}0.1 mm \\ \cline{1-1}  & Air-gap fluctuation & Unstable & \textgreater{}3.5\% & \textless{}1\% \\ \cline{1-1}  & Chattering & / & yes & none \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of numerical values.  
Figure 10: The experimental platform of the magnetic suspension system with dSPACE.  
In summary, Figures 14-16 show that the air gap of the magnetic suspension system was stable, and the time-delay influence could be significantly reduced with the proposed robust levitation controller.