esa-sceva/satcom-chunk-collection huggingface dataset:
# Saliency Guided DNL-Yolo for Optical Remote Sensing Images for Off-Shore Ship Detection
## 1 Introduction
### Ship Detection Using Convolutional Neural Networks
Moreover, we added the disentangled non-local (DNL) module [41] into the different layers of the YOLOv4 network. The DNL module is an improved self-attention mechanism module, which can dig out the attention area and boundary information through pairwise and unary operators and enhance a network's ability to detect salient objects in an image. Finally, the extracted features are sent to the detection head to complete the detection of an offshore ship. The main contributions of this paper are as follows: First, a DPC algorithm is introduced for scene classification of image blocks. Second, because ships can be regarded as anomaly objects, different saliency detection methods (e.g., SR, HC, and CASD) are used to extract prominent objects from different marine backgrounds. The above-mentioned first step preprocessing operation can reduce the intra-class difference of each sea surface scene category, and the second step can enhance the characteristics of the target ship. Finally, the DNL-added YOLONet is proposed to integrate the salient features of the target effectively; therefore, better detection results can be obtained. Compared with YOLO, DNL-added YOLONet enhances the ability of network context modeling by introducing an attention mechanism. We have also produced a batch of ship data sets based on the panchromatic image data from the South China Sea. For the problem of an insufficient number of ship targets in thick cloud and scattered cloud images, an experiment creating simulation samples was carried out to obtain sufficient training samples. The remainder of this paper is organized as follows. The proposed ship detection framework is described in Section 2. The experiments and analysis are discussed in Section 3. The conclusion is drawn in Section 4.
# Text Alignment in the Service of Text Reuse Detection
## 4 Evaluation  
### Alignment Measurement  
Our goal is to evaluate the alignment results using precision, recall, and the \(F_{1}\) score by comparing them to a ground truth (GT). The GT and the algorithm results are represented as lists of tuples \((x,y)\), where \(x\) and \(y\) indicate the positions of aligned tokens in the first and second texts. For example, in the alignment shown in Figure 1, the tuple \((3,6)\) appears in the GT because the third token in the first text ('lesorek') should align with the sixth token in the second text.  
In cases where two tokens in one text align with a single token in another, this is represented by two tuples. For instance, in Figure 1, the second token in the first text ('yod'het') aligns with both the third and fourth tokens of the second text, creating two GT tuples: \((2,3)\) and \((2,4)\). This structure is effective in handling transpositions and multi-token alignments.  
To assess the computer's alignment, we calculate a confusion matrix:  
* True Positive (TP): A tuple exists in both the computer's results and the GT.
* False Positive (FP): A tuple exists in the computer's results but not in the GT.
* False Negative (FN): A tuple exists in the GT but not in the computer's results.
* True Negative (TN): A tuple is absent in both the computer's results and the GT.  
Using the confusion matrix, we calculate precision, recall, and \(F_{1}\) score as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
\[recall=\frac{TP}{TP+FN} \tag{9}\]  
\[f_{1}=2*\frac{Precision*Recall}{Precision+Recall} \tag{10}\]  
To measure these, we need a ground truth dataset with expert-aligned text pairs. In our study on rabbinic literature from the Middle Ages, synoptic editions are common. These editions visually display correspondences between different versions but are limited in handling word transpositions or multi-word alignments.  
For example, Figure 1 from [PERSON]'s Vayikra Rabbah synoptic edition [26] shows that misalignments can occur due to the linear structure, as seen with the cyan and yellow tokens.  
We built our ground truth dataset using two sources: Vayikra Rabbah [26] and a subset from the Friedberg Project for Talmud Bavli Variants [27], consisting of 234 texts with 69,700 words.
## 2 System Model  
### Network Model  
The network model of this paper is shown in Figure 1, which is composed of a vehicle-mounted computing end, edge computing end, and cloud computing end. The workflow of task offloading: first, the vehicle generates task data that needs to be offloaded; second, the offloading decision collects the task execution requirements and the resource usage of the offloading platform, and gets the task execution plan through the offloading decision algorithm; finally, all the tasks are sent to the local RSU, configured with MEC server or the cloud, respectively. After offloading calculation, tasks that do not need to be offloaded are calculated locally, and tasks that need to be offloaded send task data to MEC or the cloud for task calculation through the offloading scheduling module, and the results are returned to the vehicle terminal after the calculation is completed.  
In the process of task execution, the task must depend on other tasks, so the priority of task offloading should be considered. Therefore, the task offloading priority matrix \(O_{i,j}\) is introduced to represent the priority relationship between task \(i\) and task \(j\).  
\[O_{i,j}=\left\{\begin{array}{ll}1,\text{ task }i\text{ is executed before task }j\\ 0,\text{ no constraint between task }i\text{ and task }j\\ -1,\text{ task }i\text{ is executed after task }j\end{array}\right.\]Since the number of tasks generated by the vehicle is not fixed and the relationship between tasks is not determined, it is possible to customize the matrix according to the specific number and relationship. The priority matrix of the four tasks is shown below. For example, if \(O_{1,2}=1\), it means that task 1 has priority over task 2; if \(O_{2,3}=0\), it means that there is no binding relationship between task 2 and task 3; if \(O_{3,4}=-1\), it means that task 4 has priority over task 3.  
\[\begin{pmatrix}0&1&1&1\\ -1&0&0&-1\\ -1&0&0&-1\\ -1&1&1&0\end{pmatrix}\]
# The Eye-Opening Arbiter-PUF FPGA Implementation with Auto Error Detection
## 6 Discussion and Comparison
### Comparison to State of the Art  
Table 1 summarizes the results obtained with this EoA-PUF and compares them with other state-of-the-art FPGA implementations. ASIC implementations are not included in the comparison because they are expected to outperform FPGA-based PUFs due to their design flexibility. However, they require a dedicated chip and cannot be included in a present FPGA design.  
Although the authors of [10] did not provide a readout time, it can be expected to be the fastest due to the principles of arbiter-PUFs. Nevertheless, it has a much higher error rate than the proposed design and, as it was also implemented with SDEs, might be biased, which was not addressed by the authors. As for all FPGA-PUFs, temperature variations also have a high impact on the error rate of the presented EoA-PUF implementation comparedto other implementations. Using the features of the EoA-PUF, the temperature dependency can be partly addressed by the proposed compensating techniques. These offer the reader a trade-off of complexity vs. accuracy, e.g., when product identification can be conducted in a controlled environment and therefore does not require temperature stability and thus compensation.  
The evaluated uniqueness and uniformity of the proposed FPGA EoA-PUF are close to the ideal value of 50% and comparable to other SotA implementations. Comparing the amount of slices utilized for the final design, our work can also compete with the SotA. Concerning readout speed, among the SotA implementations, only the TERO-PUF [35] and the DD-PUF feature a 1.37/2 times faster readout than the proposed EoA-PUF but also have higher error rates. Compared with an arbiter-PUF, which is closely related to the presented architecture, a speed comparison is difficult as other works did not provide numbers. As the EoA-PUF performs several oscillations (\(<\)20) before stopping, it has to be assumed to be a bit slower, but it results in a much better error rate. The other related architecture is the RO-PUF in [17], which is 11 times slower than our design. It must be emphasized that this PUF has a 30 times faster mean frequency as it is designed as a short weak PUF. Therefore, it oscillates more often, which increases the reliability, at the same evaluation time. The loop-PUF, also based on an RO architecture with a challenge input, from [19], achieves a comparably low error rate but at the cost of an approximately 20,000\(\times\) longer readout time than the proposed EoA-PUF. Overall, the EoA-PUF outperforms the other implementations as it combines a fast and stable readout with a mean readout time of 0.85 \(\upmu\)s and a BER \(\leq 10^{-4}\%\). Additionally, none of the other works feature the distinct advantage of the auto-detection of unstable bits.
###### Abstract  
The permeability characteristics of tailings directly affect the position of the infiltration line of the tailings dam, which is the most critical factor affecting tailings dam failures. In order to fully analyze the essence of its permeability characteristics, computed tomography (CT) technology is used to analyze the structure of different types of tailings from a microscopic perspective and carry out microscopic seepage simulation. The results showed the following findings: (1) The porosity of viscous tailings ranges from 25 to 35%, the distribution of surface porosity along the height is relatively uniform, and the distribution is shown as having a certain discrete nature with the increase in particle size. (2) Compared with silty and sandy tailings, the surface of viscous tailings is smoother and more round, and the shape factor can reach 0.95; (3) The data gap between the simulation and the measurements by CT scanning technology is less than 10%, and the estimation of the permeability characteristics is feasible, with good applicability in the simulation of tailings seepage. (4) In the microscopic pore throat structure, the permeability characteristics of the tailings are more affected by the radius of the throat than the pore radius, and the exponential function relationship between the permeability coefficient and the porosity satisfies a high correlation. In this paper, the relationship between the microstructure and permeability characteristics of tailings is analyzed by CT technology; the permeability is simulated and calculated, and a permeability coefficient prediction model for tailings is proposed in combination with the experiment, which can provide a new idea and method for the study of the permeability characteristics of tailings.  
Keywords:tailing; CT scan; microscopic characterization; pore throat model; permeability coefficient +
Footnote †: journal: Applied sciences
# Experimental Investigation into the Number of Phases in Debris Flows
## 4 Laboratory Experiments
### Rheometrical Test of Bulk Behavior of Experimental Debris Flow  
Rheometric tests (flow experiments) [4; 10; 26; 30; 73; 86; 87; 88] were conducted to check the coherency of the moving debris-flow slurry, that is, the possibility that water and solids flow as a whole.  
The rheology of eight slurries consisting of \(\leq\)2 and \(\leq\)10 mm sediments (Figures 1 and 2, and Table 1) was measured using an Andon Paar MCR 52 rotational rheometer. The container of the rheometer was a thin-walled stainless-steel bucket with a diameter of 115 mm and height of 48 mm, filled with a slurry volume of \(\sim\)500 mL. An eccentrically rotating sphere with a diameter of 15 mm fixed onto a thin holder was dragged through the slurries at variable rotational speeds [10] when the system functioned. The test was conducted by measuring the shear stress under an applied strain rate that was increased from 0 s\({}^{-1}\) to 30 s\({}^{-1}\) in 120 s, considering the typical shear rates for debris flows [7; 28; 89].  
Four experimental debris flows with \(d_{\text{max}}\) = 30 mm were tested using a large-scale two-point type rheometer [30; 74; 75; 90]. Its container (lined with a 13 mm grid wire mesh to avoid wall slip) had an inner diameter of 39.4 cm and a useful depth of 36.5 cm, which corresponded to a storage volume of \(\sim\)45 L. The H impeller was 17 cm wide and 15 cm high. The distance between the impeller and the wire mesh was 11.2 cm, and that between the impeller and the container bottom was 10 cm.
# Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information
## 4 Ground Subsidence Risk Prediction Model Based on Machine Learning
### LightGBM  
The LGBM algorithm employs a tree-based boosting technique alongside XGB. LGBM utilizes only a subset of the data, simplifying its characteristics and resulting in rapid processing speeds. LGBM utilizes cross-entropy to compute the loss function, is specialized in big data processing, and is generally recognized for its high performance [31].  
The cross entropy in LGBM is calculated using Equation (6). In this equation, N denotes the number of samples, K denotes the number of classes, \(y_{i,j}\) denotes a binary variable indicating whether the i-th sample belongs to the j-th class, and \(p_{i,j}\) denotes the probability that the i-th sample belongs to the j-th class. The training involves updating themodel to minimize the cumulative error obtained from the previous models and the result is then obtained [32].  
\[CE=\frac{1}{N}{\sum_{i=1}^{N}{\sum_{j=1}^{N}{y_{i,j}log(p_{i,j})}}} \tag{6}\]
## 3 Results and Discussion
### Industrial Virtual Water Consumption in Gansu Province between 2007 and 2012
#### 3.1.2 Virtual Water Flow and Water Resource Use among Primary, Secondary, and Tertiary Industries  
Between 2007 and 2012, the majority of virtual water in Gansu province flowed towards tertiary industry and the smallest portion flowed towards primary industry (Figure 2). Under the 'develop-the-west' strategy of the 11 th Five-Year Plan, Gansu focused on the development of infrastructure, basic industries and the tourism belt along the Silk Road. The \"One Belt and One Road\" has been one of the most important parts of China's strategy of domestic economic and social development, as well as an important part of China's foreign strategy. The \"One Belt\" refers to the Silk Road Economic Belt, and the \"One Road\" refers to the 21 st Century Maritime Silk Road [55,56]. During this project, the transportation component of the tourism industry has consumed a large amount of water resources to this day [57].  
The total volume of water resources demanded by the region is expressed in terms of gross, or total virtual water; this includes locally produced virtual water and that imported from other regions (Table 2) [58]. Compared to 2007, Gansu's primary industry virtual water use decreased by 16.63% in 2010. This reduction is attributed primarily to the emphasis on \"grain for green\" (conversion of farmland to forests) in the 'develop-the-west' strategy [59], which not only supports reduced virtual water consumption by primary industry, but also affects consumption in the other industry classes that consume raw materials from primary industry. In 2012, the total amount of virtual water increased significantly; this can be ascribed to the continuous development of the national economy which has brought about significant changes in the income level and consumption structure of Gansu residents, and thus contributes to an increase in water resource consumption [48,60]. In addition, the emergence and advancement of the production and service industries has contributed to increased virtual water use across all three industry categories themselves [61].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Water Use Coefficient in 2007** & **Water Use Coefficient in 2010** & **Water Use Coefficient in 2012** \\ \cline{2-6}  & **Direct** & **Complete** & **Direct** & **Complete** & **Direct** & **Complete** \\ \hline Primary Industry & 1545.58 & 1692.22 & 882.28 & 1005.38 & 762.16 & 873.44 \\ Secondary Industry & 32.69 & 450.32 & 20.21 & 371.90 & 17.08 & 309.87 \\ Tertiary Industry & 16.65 & 65.46 & 7.74 & 66.89 & 5.09 & 72.81 \\ \hline \hline \end{tabular}  
Unit: m\({}^{3}\)/million yuan.  
\end{table}
Table 1: Direct and complete water consumption coefficients of primary, secondary, and tertiary industries in Gansu province from 2007 to 2012.
## 4 Test and Results
### Multistage TNEP Problem  
The tests for the multistage TNEP problem were simulated with an annual discount rate I = 10% and considering three stages for all systems under study. In this case, the circuits that are added in Stage 1, are presented in the objective function with their nominal costs, and the costs of the circuits added in Stage 2 and 3 are multiplied by 0.729 and 0.478, respectively as indicated in [1].
# Design of a Lightweight Cryptographic Scheme for Resource-Constrained Internet of Things Devices
## 2 Related Work
Despite the fact that bit-shifting operations are often used in the diffusion layer of many lightweight block ciphers (e.g., PRESENT and NOKEEON), they actually lose their efficiency in software implementations. Therefore, (PRESENT-like and NOKEEON-like) diffusion techniques [17,18] are not an option for TWINE. [PERSON] et al. [19] presented PRINCE, which provides a new dimension to lightweight cryptography by achieving low latency. It also focuses on hardware implementation. It utilized a 128 bits key and was comprised of 64 bits block with 12 rounds. The S-box of this cipher was non-linear i.e., Feistel structure. The main advantage of the Feistel structure is that the same program code can be used for the encryption and decryption process. It also helps in reducing memory usage. The cipher can however be susceptible to related-key attacks if the Feistel structure uses alternating keys. Some other noteworthy mentions from this generation are Humming-Bird, KASUMI, and Piccolo  
In [20], a symmetric cryptographic algorithm, KLEIN, which has the benefit of better performance of software on legacy sensor platforms, is proposed. The fact that it uses a 4-bit S-box permutation via the algebraic normal form (ANF), rather than an 8-bit S-box, whether implemented in hardware or software, results in a tiny hardware implementation. KLEIN's design increases the available options of lightweight block ciphers for low-resource applications. In [21], [PERSON] et al. proposed a family of new lightweight variants of DES (data encryption standard), which are called DESL/DESX/DESXL (the lightweight modified versions of the well-known DES). The main idea of the new variants of DES is to use just one S-box recursively, instead of eight different S-boxes, to minimize the hardware implementation. mCrypton [22] is designed by following the overall architecture of Crypton [23], but with redesign and simplifications of each component function to enable much more compact implementation in both hardware and software. Based on the state of current small cypher results, it is essential to provide not only a small cypher footprint to fit into small memories, but also to enhance speed and cryptographic strength by making it difficult for linear and differential cryptanalysis.
# Active Flow Control of a Supercritical Airfoil and Flap with Sweeping Jets
## 1 Introduction
In an experiment at the NASA Langley Research Center, the control effect of oscillating and steady jets for two flap deflection angles on the Fast-MAC model was tested [18]; the results proved that when the flap deflection angle was 30\({}^{\circ}\), the oscillating jet saved approximately 54% of the mass flow rate compared with the steady jet. In 2018, [PERSON] et al. [19] used force and moment data, particle-image velocimetry data, and both steady-state and unsteady surface pressure data on the NACA0015 two-dimensional straight-wing model to prove that oscillating jets can produce more benefits with less mass flow in active-flag flow control and lift. To further promote AFC technology of the oscillating jet in engineering practice, researchers designed a high-lift general research model (CRM-HL) and used it in a series of numerical simulations and experimental studies [20; 21; 22]. [PERSON] et al. [20; 23] carried out CFD calculations using the model data used in the CRM-HL experiments. Other researchers used CFD that led to a series of related wind-tunnel experiments [21; 24]. Finally, researchers at the NASA Langley Research Center conducted subsonic wind-tunnel tests using a 10% scale civilian aircraft wing model. A variety of AFC schemes were tested in the experiments, and a lift coefficient increment of 0.5 was ultimately achieved [25]. In the aforementioned results, researchers focused more on the measurement of lift coefficient for different flow-control systems, the modification of the fluidic oscillator [26; 27], or the fundamental physics of the oscillating jet [28; 29; 30], but less on the specific effect of sweeping jet AFC on wing surface pressure [31] and the reason for the lift improvement after applying the sweeping jet. To address this, the work described in this paper uses the NASA SC(2)-0410 airfoil in the design of a two-dimensional straight-wing model. The influence of sweeping jet flow control on wing surface pressure distribution is analyzed via aerodynamic force measurement and wing surface pressure measurement. The outcome of the present study might help engineers understand how to better improve the lift during the takeoff stage and provide validation data for the flow control technology design and CFD simulation on AFC technology.
## 2 Study Area and Data
### Ground Synchronous Measurement  
Ensuring that the RCS of CR is in proximity to the theoretical value is indispensable in the radiometric calibration process. As previously mentioned, in addition to the environment in which CR is deployed, the mechanical imperfections of CR may introduce RCS deviation [11]. Deviations in the azimuth and elevation angle pointing of CR during satellite transit can also cause the RCS to vary from that of the theoretical value [14]. Therefore, the guarantee of the manufacture precision of CR used in the calibration experiment and its pointing accuracy during the synchronous experiment are critical.