esa-sceva/satcom-chunk-collection huggingface dataset:
# An Asymmetric Encryption-Based Key Distribution Method for Wireless Sensor Networks
## 4 Analysis and Comparison
### Key Storage of Sensor Nodes  
In our scheme, the public key is pre-loaded into sensor's memory during the network initialization. Since the strength of encryption with the 256-bit ECC key is equal to that of the 3072-bit RSA key, a public key of 256 bits in length is used in our simulation. Moreover, two 16-byte session keys are used in the key distribution process. When a sensor receives the refreshed session key, the original key will be deleted to save the memory. Therefore, the memory overhead of each sensor is only 64 bytes, while that of the CH is 48 bytes.  
The key distribution in [30] is that \(k\) keys are pre-loaded into each sensor, while \(m\) keys (\(m\gg k\)) are pre-loaded into each CH. If any two nodes share a pairing key, they can establish a secure link. Thus, the greater the number of keys stored, the higher probability of sharing common keys. In [40], the memory is divided into two parts. One part is used to store \(\alpha\) pre-distributed keys, and the other part is used to store \(\beta\) post-deployment keys.  
Table 1 presents the key storage overheads in different schemes. For large- and medium-sized wireless sensor networks, sensors in our scheme require less storage space than those of other schemes. However, our cluster heads require slightly more memory space than those of [PERSON]'s scheme. Since the number of sensors is much larger than that of CHs, our scheme is valuable for resource-limited WSNs.  
Figure 6: Flowchart of authentication and key agreement in the mobile subscheme.
# Steel Surface Defect Detection Based on YOLOv8-TLC
## 3 YOLOv8-TLC Algorithm  
To address the issue of low detection accuracy in YOLOv8n for steel surface defect detection, this paper proposes the YOLOv8-TLC algorithm. To reduce the missed detection rate of steel surface defects, the YOLOv8-TLC algorithm adds a new scale detection head. It also incorporates the Large Selective Kernel (LSK) attention mechanism to enhance the focus on spatial regions most relevant to steel surface defects. Additionally, a Triple Space Pyramid (TSP) module is introduced, along with the design of a C2f-DS module, which improves the feature extraction capabilities of the backbone network and enhances the model's detection efficiency. The structure of the improved algorithm is shown in Figure 2.
# Investigating Models for the Transcription of Mathematical Formulas in Images
## 4 Experimental Setup
### Vision-Language Models  
To investigate the complete pipeline from formula images to the transcriptions, we analyzed the VL models _BLIP_[33], _GIT_[35], and _SWIN-CPT-2_[23; 33], as they achieve high performances in image captioning and cover different state-of-the-art vision encoders and text decoders. _TrOCR_[36], which we described as the MER model in Section 3.1, can also be categorized as a VL model. Consequently, for _TrOCR_, we analyzed the conversion of formula images to the transcriptions in addition to the conversion of formula images to LaTeX code.
## 3 Test Bench Layout
### DC-DC Converter  
The DC-DC converter is a key element of the architecture. Its main roles are to adjust the FC voltage level according to the DC bus voltage level and to be the power conditioning unit. The adopted DC-DC converter is controlled by two analogic signals: one that determines the voltage reference downstream from the converter and one that determines the current reference at the DC-DC output. The two controls always operate together, and the dominant one is the more-stringent between the two. Given a certain voltage reference and a certain current reference, if the load downstream from the converter is such that at the voltage reference the current is less than the current reference, the dominant parameter is the voltage reference and the current is determined as a consequence. On the contrary, if the load is such that the voltage reference causes a current that is greater than the current reference, the dominant signal is the current reference and the voltage is determined as a consequence of that limiting current. The DC-DC adopted on the setup has two ports for the voltage reference signal and the current reference signal, which require two 0-10 V analogue signals. The Arduino Mega generates two 0-5 V PWM signals that are firstly filtered by two RC filters (cut-off frequency fixed at 19.40 Hz) and are then  
\begin{table}
\begin{tabular}{l l} \hline \hline Battery technology & Lead-acid \\ Nominal Voltage & 12 V \\ Max discharge current & 340 A \\ Capacity & 40 Ah \\ \hline \hline \end{tabular}
\end{table}
Table 2: Single battery main properties.  
Figure 3: FCS-C300 characteristic curves.  
multiplied by two through an operational amplifier (opamp). After that, since the adopted DC-DC converter has the negative pin of the signals in common with the negative output of the power stage, the signals are transmitted to the converter using two optocouplers. These optocouplers act as analogic isolators; therefore, the ground of the Arduino is not in common with the ground of the power stage. The adopted optocouplers are the HCNR201 manufactured by Broadcom (Broadcom Inc., San Jose, CA, USA). The main operational parameters of the DC-DC converter are shown in Table 3. Since the FC voltage level is in the range 60-30 V while the nominal voltage of the bus DC is 24 V, the adopted converter is a Buck converter. Downstream from the converter, a diode is placed to avoid current flow to the emulated FC, making the converter de facto unidirectional.
# Dynamics Test Research of Bounce of Carrier-Based Aircraft Arresting Hook
## 4 Test Verification
### Verification of Buffer Performance  
Take the sinking velocity of 3.6 m/s, 5 m/s, and 6.4 m/s as typical conditions for landing dynamics simulation, and get the buffer stroke curve of the main landing gear under three conditions, as shown in Figure 12. It can be seen from the figure that the first positive stroke compression is 399.5 mm, 423.5 mm, and 443.5 mm, respectively, and the experienced time is 0.25 s, 0.21 s, and 0.18 s, respectively. With the increase of sinking velocity, the first positive stroke decreases obviously and the time of the process of positive compression increases with good following.  
The buffer is filled with the final filling parameters determined in Section 3.1, and the buffer is used for the test, as shown in Figure 13. The measured buffer compression and compression time under different sinking velocities are compared with buffer performance results of the main landing gear in the simulation of the whole aircraft, as shown in Table 1. Different sinking velocity is obtained by different sinking height.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{\(\boldsymbol{V_{V}\) (m\(\cdot\)s\({}^{-1}\))} & \multicolumn{3}{c}{**Stroke Length**} & \multicolumn{3}{c}{**Time**} \\ \cline{2-7}  & \multirow{2}{*}{
\begin{tabular}{c} **Simulation** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{**Test (mm)**} & \multirow{2}{*}{**Error (\%)**} & \multirow{2}{*}{**Simulation** \\  & & & & & **(s)** & **Test (s)** \\ \hline
1 & 6.4 & 443.5 & 433 & \(-\)2.4 & 0.18 & 0.19 & 5.5 \\
2 & 5.0 & 423.5 & 410 & \(-\)3.2 & 0.21 & 0.2 & \(-\)4.8 \\
3 & 3.6 & 399.5 & 395 & \(-\)1.1 & 0.25 & 0.24 & \(-\)4 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of simulation and test results.  
Figure 12: Changes of stroke length of main landing gear at different sinking velocity.  
From Table 1 it can be seen that the test results of buffer are consistent with the simulation results of landing dynamics of the whole aircraft. The error between the first positive stroke and the first positive stroke of main landing gear is within 4%, and the error of the experienced time is within 6%, which meets the test requirements. Furthermore, the stroke length of the simulation is larger than that in the test generally, which may be due to a slight difference in the acceleration of gravity.
## 2 The Models and Analyses
### Finding a Nontrivial Equilibrium Point  
For Complete Survival Equilibrium: \(p_{i}^{*}\
eq 0,\forall i\) with \(f(t)=0\), the abundance equilibrium in a recursive form shown in [PERSON] et al., (1994) [2] is  
\[p_{i}^{*}=h-\frac{m_{i}}{c_{i}}-\sum_{j=1}^{i-1}p_{j}^{*}\cdot\left(1+\frac{c _{j}}{c_{i}}\right),i=2,\ldots,n,\]  
where \(p_{1}^{*}=h-\frac{m_{1}}{c_{1}}\),  
In general, if \(f(t)\
eq 0\), the equilibrium, \(p_{fi}^{*}\), can be written as  
\[p_{fi}^{*}=p_{i}^{*}+(-1)^{i}f(t)\prod_{j=1}^{\left\lceil\frac{i}{2}\right\rceil }c_{2j-2}/\prod_{j=1}^{\left\lceil\frac{i}{2}\right\rceil}c_{2j-1}\,,\quad i=1,\ldots,n \tag{2}\]  
and  
\[p_{2i+1}^{*}=h\frac{\prod\limits_{j=1}^{2i-1}c_{j}}{\prod\limits_{j=2i}^{2i}c_ {j}}+\sum\limits_{k=1}^{i}{(-m_{2k-1}+m_{2k})\frac{\prod\limits_{j=2k+1}^{2i-1} c_{j}}{\prod\limits_{j=2k}^{2i}c_{j}}}+\sum\limits_{k=1}^{i}{(-m_{2k-1}+m_{2k}) \frac{\prod\limits_{j=2k}^{2i}c_{j}}{2i+1}}, \tag{4}\]  
where \(p_{1}^{*}=h-\frac{m_{1}}{c_{3}}\).  
This equilibrium point is useful in determining habitat abundance. It has been known that the equilibrium is locally stable if the eigenvalues of the Jacobian matrix \(\lambda_{i}=c_{i}h-c_{j}\sum\limits_{j=1}^{i-1}{p_{fj}}^{*}-\sum\limits_{j=1}^ {i-1}{c_{j}{p_{fj}}^{*}}-2c_{i}{p_{fi}}^{*}-m_{i}+f<0,\forall i\). All species will converge to the equilibrium \(p_{fi}^{*}\
eq 0\), if the initial conditions are close enough to \(p_{fi}^{*}\). Unfortunately, finding the region of attraction of an equilibrium point for a nonlinear system is very difficult and usually impossible. As a result, we generally cannot conclude where the solution will converge analytically. However, due to the special form of the model, an analytic solution can be written explicitly, as in the next section.
## 6 Methodology
### System Overview
#### 6.1.4 Secure Boot  
The secure boot process is carried out in two distinct stages. The first stage is managed by an immutable root of trust provided by ST, which is natively embedded in the system flash. This RoT is fixed and never erased, providing a strong foundation for secure boot operations. TrustZone is enabled during the secure provisioning phase through a specific flash option byte (TZEN), and the system's clock speed is configured to 250 MHz to ensure stable operation. As part of the immutable artifacts, two key components are included: the RSS and the STiRoT. The STiRoT is responsible for authenticating, loading, and decrypting the STiRoT, which serves as the second-stage bootloader. This multi-layered approach ensures a robust and secure initialization process for the system. All cryptographic operations during this phase are hardware-accelerated, significantly enhancing performance and security.  
The RoT implements several cryptographic schemes to safeguard the boot process. These include ECDSA-P256 for image authentication, ECIES-P256 for confidentiality, and SHA-256 for integrity checks. These cryptographic operations provide a strong defense against tampering and unauthorized modifications, ensuring that only verified and trusted code is executed on the device. The STiRoT verifies both the integrity and authenticity of the user application firmware images, including the non-secure application and the SM binary.  
Both firmware binaries, the SM and the non-secure application, are encrypted using the ECIES-P256 algorithm for confidentiality and are authenticated with separate ECC secp256r1 keys. The Secure Monitor is encrypted and authenticated using proprietary keys, while the non-secure application is encrypted and authenticated using keys installed during the secure provisioning process. These keys are securely stored in the ITS flash area, which is protected by a temporal isolation mechanism. This mechanism ensures that the keys remain secure and isolated, preventing unauthorized access. Additionally, the firmware executing in a specific layer N is prevented from accessing the secure storage of any lower layers N-x. This separation enforces strict security boundaries, ensuring that the secure storage of lower layers remains inaccessible and maintaining proper isolation between the secure and non-secure components of the system.
# Agnostic Energy Consumption Models for Heterogeneous GPUs in Cloud Computing
###### Abstract  
The adoption of cloud computing has grown significantly among individuals and in organizations. According to this growth, Cloud Service Providers have continuously expanded and updated cloud-computing infrastructures, which have become more heterogeneous. Managing these heterogeneous resources in cloud infrastructures while ensuring Quality of Service (QoS) and minimizing energy consumption is a prominent challenge. Therefore, unifying energy consumption models to deal with heterogeneous cloud environments is essential in order to efficiently manage these resources. This paper deeply analyzes factors affecting power consumption and employs these factors to develop power models. Because of the strong correlation between power consumption and energy consumption, the influencing factors on power consumption, with the addition of other factors, are considered when developing energy consumption models to enhance the treatment in heterogeneous infrastructures in cloud computing. These models have been developed for two Virtual Machines (VMs) containing heterogeneous Graphics Processing Units (GPUs) architectures with different features and capabilities. Experiments evaluate the models through a cloud testbed between the actual and predicted values produced by the models. Deep Neural Network (DNN) power models are validated with shallow neural networks using performance counters as inputs. Then, the results are significantly enhanced by 8% when using hybrid inputs (performance counters, GPU and memory utilization). Moreover, a DNN energy-agnostic model to abstract the complexity of heterogeneous GPU architectures is presented for the two VMs. A comparison between the standard and agnostic energy models containing common inputs is conducted in each VM. Agnostic energy models with common inputs for both VMs show a slight enhancement in accuracy with input reduction.  
cloud computing; GPU; power modeling; energy modeling; machine learning Article
## 2 Materials and Methods  
### Ampcvd  
Figure 1 shows the AMPCVD, as well as the optical detection for emission spectroscopy measurements and exhaust gas detection devices. The 200 W atmospheric pressure 2.45 GHz microwave plasma generator includes a solid-state microwave source, a coaxial plasma generator, and a gas control system. The coaxial microwave plasma generator consists of two copper tubes. The diameters of the central and outer tubes are 6 and 20 mm, respectively, and their length is 90 mm. Through the center tunnel, a mixture of Ar gas and ethanol vapor is pumped into the plasma. H\({}_{2}\) gas fills the space between the center and outer tubes for shaping the plasma. The solid-state microwave source provides a stable 200 W microwave power for the generator to produce atmospheric pressure plasma. The gas control system includes three mass flowmeters to give an axial gas and a swirling gas to sustain the plasma flame.  
Figure 1: Schematic diagram of the atmospheric pressure microwave plasma chemical vapor deposition (AMPCVD) used for carbon nanotube (CNT) synthesis.  
In the experiment, ethanol was used as the carbon source because it is cheap, easy to access, and safe--the concentration of ethanol vapor is controlled by the temperature. The 304 stainless steel sheet was used as both the substrate and the catalyst because it has a high content of iron and nickel, which are well-known catalysts for the synthesis of CNTs [16]. Briefly, 200 sccm (standard cubic centimeter per minute) argon (Ar) for the bubbling of ethanol was maintained at a temperature of 20 \({}^{\circ}\)C as the carbon source and 800 sccm Ar was pumped into the plasma generator through the center gas path. Via a tangential injection hole, 200 sccm hydrogen (H\({}_{2}\)) was swirled into the plasma generator. Central two-way airflow was used to control the precursor concentration and the side hydrogen was used to stabilize the plasma and work as the deoxidizer. The exhaust gas was emitted into the air through a gas-washing bottle, and the gas-washing bottle prevented air return to the tube furnace chamber. The 304 stainless steel substrate (solid sheet, \(10\times 10\times 0.2\) mm) was placed at the center of the high-temperature zone as the substrate for CNTs.
## 2 Materials and Methods  
### Study Area  
This study conducted research in the Manorom District, Chai Nat, Thailand, specifically focusing on a designated planting area. The study area, depicted in Figure 1, is located at Lat: 15.270978 and Long: 100.174656, encompassing a total area of 88,000 m\({}^{2}\). The planting area is divided into three plots, namely Plot 1, Plot 2, and Plot 3, with respective areas of 24,000 m\({}^{2}\), 33,600 m\({}^{2}\), and 30,400 m\({}^{2}\), as illustrated in Figure 1a. The Region of Interest (ROI), referring to a designated area of analysis within each plot measuring 4.5 \(\times\) 4.5 square meters, was determined. A total of 270 ROIs were selected for analysis, with each plot containing 90 ROIs, as depicted in Figure 1b. The plant began owing on 11 January 2023 and was harvested on 5 May 2023, totaling 115 days for this crop. Each plot was sown with the CP303 maize variety [31] with the distinctive features of a robust root and stem system, harvest age of 110-120 days, and good for planting in flat and sloped areas. A drip-tape irrigation system is employed in Plot 1, Plot 2, and Plot 3 as 364.02 m\({}^{3}\)/dunam, 330.21 m\({}^{3}\)/dunam, and 308.54 m\({}^{3}\)/dunam, respectively. Weather data for maize growth were collected with the Vantage Pro2 weather station [32], which provided the following data over the experimental period: rainfall, average temperature (TempAvg), max temperature (TempMax), min temperature (TempMin), relative humidity, and wind speed, as shown in Table 1.  
The primary objective of this study is to rigorously examine the productivity of crop yield within the three designated plots. By analyzing and assessing the agricultural output achieved in each plot, this investigation aims to provide valuable insights into the crop yield performance.
# Pulse Peak Delay-Total Focusing Method for Ultrasonic Tomography on Concrete Structure
###### Abstract  
An ultrasonic array device like the A1040 MIRA is used to non-destructively visualize the inside of concrete structures. A data set acquired by the ultrasonic array device is so unfocused that an image reconstruction algorithm is required to transform the data set into an understandable image. The image reconstruction algorithm like total focusing method exploits the time-of-flight of an ultrasonic pulse when focusing the image. While a high frequency ultrasonic pulse barely affects the accuracy of results, a low frequency ultrasonic pulse with a long wavelength causes an overall sagging of the resulting image around half wavelength of the pulse, which results in a poor quality of results. In this research, a modified total focusing method called pulse peak delay-total focusing method is proposed to calibrate the sagging in the resulting images due to the long wavelength of the pulse. The simulation of an ultrasonic array signal is implemented to validate the proposed method. The experimental results are compared with the simulation results to validate the proposed method. The simulation using the proposed method shows good agreement with experimental results. Analysis of results using potential damage curve and array performance indicator shows that the proposed method allows the higher accuracy, as well as the increased resolution of resulting images.  
ultrasonic array; concrete structure; pulse peak delay-total focusing method +
Footnote †: journal: Applied Sciences  
11
# Answer Set Programming for Regular Inference
## 2 Preliminaries
### Answer Set Programming
It has been fruitfully used in a range of applications. Early ASP solvers used backtracking to find solutions. With the evolution of Boolean SAT solvers, several ASP solvers were built on top them. The approach taken by these solvers was to convert the ASP formula into SAT propositions, apply the SAT solver, and then convert the solutions back to ASP form. Newer systems, such as Clasp (which is a part of the Clingo solver, [[https://potassco.org/clasp/](https://potassco.org/clasp/)]([https://potassco.org/clasp/](https://potassco.org/clasp/))), take advantage of the conflict-driven algorithms inspired by SAT, without the complete conversion into a Boolean-logic form. These approaches improve the performance significantly, often by an order of magnitude, over earlier backtracking algorithms [21].
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PEP** & **Bleeding** & \multicolumn{4}{c}{**Infection**} & **Total** \\ \cline{3-6}  & & **Absent** & **Present** & **Total** \\ \hline \multirow{3}{*}{Absent} & Absent & 327 & 81.14\% & 31 & 7.69\% & 358 \\  & Present & 9 & 2.23\% & 1 & 0.25\% & 10 \\ \multirow{3}{*}{Present} & Absent & 29 & 7.20\% & 4 & 0.99\% & 33 \\  & Present & 2 & 0.50\% & 0 & 0.00\% & 2 \\ \hline \multicolumn{6}{l}{Yates chi-square test: \(\chi^{2}=6.193345\), \(p=0.027394\). ERCP: endoscopic retrograde cholangiocarcinomaretography; PEP: post-ERCP pancreatitis.} \\ \end{tabular}
\end{table} 
Table 3: Evaluation of the association between PEP and bleeding and infection (a dependent variable: bleeding and infection). AUC analyses showed that in both PEP and non-PEP patients, a qSOFA score \(\geq 1\) was a very good predictor for organ dysfunction (AUC 0.993, \(p<0.0001\)). Moreover, despite a lower predictive precision when compared to qSOFA, SIRS could also be considered a significant predictor for organic dysfunctions in PEP patients (AUC 0.926, \(p<0.0001\)). However, only qSOFA was shown to significantly predict mortality in PEP patients (AUC 0.885, \(p=0.003\)), with SIRS criteria showing a much lower prediction power. Neither SIRS  
Figure 1: Kaplan–Meier survival curve. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**PEP Present**} \\ \cline{2-6}  & **AUC** & **Standard Error** & \(p\)**-Value** & \multicolumn{2}{c}{**AUC–95\% CI**} \\  & & & & **Lower Limit** & **Upper Limit** \\ \hline \multicolumn{6}{l}{Organ dysfunction} \\ \multicolumn{6}{l}{SIRS} & 0.926 & 0.044 & 0.000 & 0.839 & 1.000 \\ SOFA & 0.993 & 0.010 & 0.000 & 0.973 & 1.000 \\ \multicolumn{6}{l}{Survival} \\ SIRS & 0.796 & 0.101 & 0.024 & 0.598 & 0.994 \\ SOFA & 0.885 & 0.097 & 0.003 & 0.696 & 1.000 \\ \multicolumn{6}{l}{Length of stay} \\ SIRS & 0.506 & 0.113 & 0.955 & 0.285 & 0.728 \\ SOFA & 0.466 & 0.112 & 0.763 & 0.247 & 0.685 \\ \hline \multicolumn{6}{l}{PEP absent} \\ \multicolumn{6}{l}{Organ dysfunction} \\ \multicolumn{6}{l}{SIRS} & 0.916 & 0.031 & 0.000 & 0.855 & 0.977 \\ SOFA & 0.977 & 0.018 & 0.000 & 0.941 & 1.000 \\ \multicolumn{6}{l}{Survival} \\ SIRS & 0.808 & 0.054 & 0.000 & 0.702 & 0.913 \\ SOFA & 0.860 & 0.052 & 0.000 & 0.759 & 0.962 \\ \multicolumn{6}{l}{Length of stay} \\ SIRS & 0.558 & 0.030 & 0.053 & 0.500 & 0.617 \\ SOFA & 0.546 & 0.030 & 0.131 & 0.487 & 0.604 \\ \hline \multicolumn{6}{l}{AUC: area under the curve; PEP: post-endoscopic retrograde cholangiocarcinomaretography pancreatitis; qSOFA: quick Sequential Organ Failure Assessment; SIRS: systemic inflammatory response syndrome.} \\ \end{tabular}
\end{table} 
Table 5: Comparative evaluation of the predictive values of the quick Sequential Organ Failure Assessment and systemic inflammatory response syndrome criteria for organ dysfunction, survival, and length of stay in post-endoscopic retrograde cholangiocarcinomaretography pancreatitis patients. nor qSOFA showed any predictive value for the length of stay of PEP patients. The data of ROC curve analyses for all parameters can be found in Figure 2.