esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Exploratory Study  
### SqliMicroBenchmark  
A robust and broadly representative benchmark is required to fairly assess the capabilities of LLMs in SQLI black-box detection. However, existing benchmarks in this field [2; 3; 9; 30; 36] have several limitations. Firstly, they often lack sufficient visibility and wide recognition, affecting the acknowledgment and fairness of comparative studies. Secondly, most existing benchmarks focus on simple SQLI vulnerability detection and typically do not include more complex SQLI vulnerabilities with defense mechanisms. Consequently, these benchmarks often fall short in terms of realism.  
We propose the SqliMicroBenchmark, based on Sqli-labs, a widely recognized SQLI testing range designed with real-world scenarios in mind, providing both representativity and practicality. As an open-source project, Sqli-labs offers standardized test environments and datasets, ensuring the comparability and reproducibility of experimental results. Furthermore, Sqli-labs encompasses a variety of SQLI vulnerability types, allowing for a comprehensive evaluation of LLMs' SQLI detection effectiveness across different injection scenarios. It is worth noting that we excluded some targets in Sqli-labs for testing purposes. Some targets were excluded because their detection requires an extremely limited number of requests (e.g., only five), a requirement beyond the capability of current SQLI black-box detection scanners. Additionally, we did not consider targets related to stacked injections in Sqli-labs, as stacked query injections are typically used for more in-depth attacks or exploits after an injection vulnerability has been identified, such as performing database management operations. In addition, to make our benchmarks more representative of real-world scenarios, we included five actual CVEs in SqliMicroBenchmark (CVE-2020-8637, CVE-2020-8638, CVE-2020-8841, CVE-2023-30605, CVE-2023-24812). We extracted relevant SQL statements from these CVEs and integrated them into our SqliMicroBenchmark. Our micro benchmark aims to evaluate SQLI black-box detection capabilities without encouraging any form of offensive behavior.  
The SqliMicroBenchmark encompasses 45 targets that exhibit SQLI vulnerabilities. These targets are categorized into two levels of difficulty--basic and advanced--depending on the presence of protective mechanisms at the injection points. Insufficient defense mechanisms at these points characterize the advanced difficulty targets. With the exception of CVE-2023-24812, the rest of the CVEs are basic difficulty targets. With the experimental evaluation on SqliMicroBenchmark, we can effectively assess the capability of LLMs in SQLI black-box detection and we provide a solid foundation for future research and practical applications.
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 3 Channel Estimation
### Channel Estimation Using mpeDMD  
As remarked in the previous section, the Toeplitz matrix obtained does not sufficiently match the theoretical eigenvalues from just using DMD. To address this, we first employ DMD to decrease the order of the system and then apply mpeDMD to the modified system. Figure 12 shows the results of sequentially using DMD and mpeDMD. mpeDMD is able to reuse the Toeplitz matrix shape of the empirical matrix R.  
At this point, it is crucial to emphasize the importance and innovation of the proposed method: Figure 8 illustrates the empirical correlation matrix commonly suggested in the MIMO literature. The matrix **R** is fundamental for Channel Estimation; thus, starting from a vague approximation of **R** will inevitably lead to poor results. As demonstrated in this article, the proposed methods allow for the derivation of a much more accurate **R**, leading to improved Channel Estimation and prediction. This results in a substantial enhancement in system performance through a highly efficient methodology capable of dealing with channels that closely resemble real-world conditions. Notably, this approach includes the treatment of correlation, which has been addressed in very few previous studies, and most importantly, it does so in a scalable and computationally feasible manner.
# An Image Processing Method for Measuring the Surface Area of Rapeseed Pods
## 2 Materials and Methods  
### Experimental Materials and Equipment  
#### 2.1.1 Materials  
The single rape plant samples used in this experiment were from the rapeseed experimental base of Hunan Agricultural University, Changsha, China (conducted from October 2017 to May 2018). The cultivars included the early maturing cultivar 420 (winter _Brassica napus_ L.A1, compact variety), medium maturing cultivar 1035 (winter _B. napus_ L.A2, standard variety), and hybrid cultivar 991 (winter _B. napus_ L.A3, tall variety). Three fertility treatments (compound fertilizer with total nutrient \(\geq\) 45%, each containing 15% of N, P\({}_{2}\)O\({}_{5}\), and K\({}_{2}\)O) were applied, specifically 0.053 kg/m\({}^{2}\) (B1), 0.068 kg/m\({}^{2}\) (B2), and 0.083 kg/m\({}^{2}\) (B3). Each treatment was repeated three times. A total area of 10.8 m\({}^{2}\) for each plot was planted with a density of 15 plants/m\({}^{2}\). The rape plants were sown in October 2017, and inter-seedling and transplantation were conducted at the seedling stage. Different cultivars and fertilization levels were used to create single plant samples of different sizes and shapes. On 29 April 2018 (the pod maturity period), the pod surface area of 3-4 representative plants in each plot was measured in the dimension of a single plant, yielding a total of 83 plants that were measured. In this study, the pod surface area of a single plant refers to the total surface area of all pods of the plant, including the areas of pod beaks.
# Simulation and Design of Three 5G Antennas
## 2 Materials and Methods
### The Unique Performance Parameters of MIMO Terminal Antennas
#### 2.2.1 Correlation  
Diversity characteristics are one of the crucial attributes of MIMO antenna systems, and the evaluation of diversity performance can be achieved by analyzing the correlation coefficients \(\rho_{\mathrm{ij}}\). The envelope correlation coefficient (ECC) is expressed as the square of the correlation coefficient \(\rho_{\mathrm{ij}}\). When designing MIMO antennas, the specific calculation formula is given in Equation (12).  
\[\rho_{\mathrm{eij}}=\left|\rho_{\mathrm{ij}}\right|^{2}=\frac{\left|S^{*}_{ \mathrm{ii}}S_{\mathrm{ij}}+S^{*}_{\mathrm{ij}}S_{\mathrm{ij}}\right|^{2}}{ \left(1-\left|S_{\mathrm{ii}}\right|^{2}-\left|S_{\mathrm{ij}}\right|^{2} \right)\left(1-\left|S_{\mathrm{ij}}\right|^{2}-\left|S_{\mathrm{ij}}\right|^{ 2}\right)}, \tag{12}\]  
In this equation, \(\rho_{\mathrm{eij}}\) represents the envelope correlation coefficient ECC between antenna units i and j. \(S_{\mathrm{ii}}\) is the reflection coefficient for antenna unit i itself, while \(S_{\mathrm{ij}}\) represents the transmission coefficient between antenna unit i and antenna unit j. The specific formula for calculating the correlation coefficient ECC between antenna units i and j in a MIMO antenna system with N antenna units can be derived as shown in Equation (13)  
\[\rho_{\mathrm{eij}}=\left|\rho_{\mathrm{ij}}\right|^{2}=\frac{\left|S^{*}_{ \mathrm{i1}}S_{\mathrm{ij}}+S^{*}_{\mathrm{i2}}S_{\mathrm{2}}+S^{*}_{\mathrm{ i3}}S_{\mathrm{3}}+\cdots+S^{*}_{\mathrm{iN}}S_{\mathrm{N}}\right|^{2}}{ \left(1-\left|S_{\mathrm{ii}}\right|^{2}-\left|S_{\mathrm{2i}}\right|^{2}- \cdots-\left|S_{\mathrm{N}}\right|^{2}\right)\left(1-\left|S_{\mathrm{ij}} \right|^{2}-\left|S_{\mathrm{2}}\right|^{2}-\cdots-\left|S_{\mathrm{N}}\right| ^{2}\right)}, \tag{13}\]  
Low coupling between antennas generally achieves high isolation, but, theoretically, isolation and correlation coefficient are two completely different parameters. The correlation coefficient calculated using Equation (12) only considers the factor of mutual coupling between different input ports of antennas, while it disregards the coupling between the radiation fields of the antennas. Considering the presence of spatial radiation and related issues, the antenna pattern can be used for calculation, and the formula for this calculation is given in Equation (14)  
\[\left|\rho_{\mathrm{ij}}\right|^{2}=\frac{\left|\oint A_{\mathrm{ij}}(\Omega) \mathrm{d}\Omega\right|^{2}}{\left|\oint A_{\mathrm{ij}}(\Omega)\mathrm{d} \Omega\oint A_{\mathrm{ij}}(\Omega)\mathrm{d}\Omega\right|}, \tag{14}\]  
Therefore, in MIMO antenna systems, the level of mutual coupling or isolation cannot be the sole parameter by which to measure ECC. While achieving low mutual coupling and high isolation, it is also essential to ensure low correlation coefficients. This is necessary to obtain good diversity characteristics, and, typically, the value of ECC should be less than 0.5 to meet requirements.
## 2 Materials and Methods  
### Data Sources and Processing  
The net exchange of CO\({}_{2}\) and water vapor between forest ecosystems and the atmosphere was measured every 30 min. Using the eddy covariance technique from FLUXNET, the IWUE can be calculated at the ecosystem level ([PERSON] et al., 2009). For this study, data from 67 research sites were collected from FLUXNET ([[http://fluxnet.fluxdata.org](http://fluxnet.fluxdata.org)]([http://fluxnet.fluxdata.org](http://fluxnet.fluxdata.org)), accessed on 1 May 2021) and published studies. These sites were divided into four forest ecosystem types, namely 18 deciduous broadleaf forests (DBF), 10 evergreen broadleaf forests (EBF), 34 evergreen needle-leaf forests (ENF), and 6 mixed forests (MF). The study sites were distributed in Asia, Europe, North America, and Oceania (Figure 1 and Supplementary Materials). All measurements were taken from 9:00 a.m. to 05:00 p.m. Annual-scale temperature, solar radiation, photosynthetic photon flux density, and vapor pressure deficit were calculated based on the daily average values, whereas precipitation was calculated based on the daily sum. The GPP and ET on the annual scale were calculated by accumulating the values for each day. The annual IWUE was calculated by dividing the annual GPP by the annual ET and multiplying it by the annual VPD. The LAI data were collected from the website [[http://www.glass.umd.edu/LAI/AVHRR/](http://www.glass.umd.edu/LAI/AVHRR/)]([http://www.glass.umd.edu/LAI/AVHRR/](http://www.glass.umd.edu/LAI/AVHRR/)), accessed on 1 May 2021.  
Missing data can be supplemented by the gap-filling method. Here, gap-filling of the eddy covariance and meteorological data was performed under three conditions: (1) when data of direct interest were missing, but all meteorological data were available, the missing value was replaced by the mean value under similar meteorological conditions with a look-up table (LUT), within a certain time window [11]; (2) When the temperature or VPD was missing but radiation was available, the missing value was replaced using the same LUT approach, though similar meteorological data can only be defined via Rg within a time window of 7 days; (3) In the case of a loss of Rg data, the missing value was replaced by the mean value at the same time of the day (1 h), using the mean diurnal course (MDC). If the value could not be filled after these steps, the procedure was repeated with an increased window size until the value could be filled [24].
# Pentaband Dual-Polarized Antenna for Multiservice Wireless Applications
## 2 Proposed Antenna Configuration and Description
### Quad-Port Antenna Design  
The single-port antenna design shown in Figure 6a has undergone a significant expansion to achieve a quad-port configuration, with overall dimensions of 62 \(\times\) 62 mm\({}^{2}\). To ensure that the antenna elements remain well separated without the presence of a large gap between them, the orientation of the elements has been adjusted. In this extended MIMO configuration, all bands are effectively isolated, except for the 5 GHz band, which presented a challenge in terms of achieving the necessary isolation. To overcome this challenge, a ground slit has been incorporated on the back side of the radiating element, as depicted in Figure 6b. This modification has proven effective in enhancing isolation for all bands, resulting in a high-performing quad-element antenna design.  
To assess the performance of the quad-port antenna, simulations were conducted by exciting one port and applying matched loads to the other ports. The scattering parameters were nearly identical across all antenna elements and similar to those of the single-element response. The S-parameter characteristics with and without a ground slit are depicted in Figure 7. In Figure 7, the S\({}_{12}\) parameter measures the power transmitted from one port of the antenna to another, and it should be typically less than \(-\)20 dB for effective isolation between the ports. Case #1 represents the MIMO antenna with a full ground plane, while Case #2 features a slotted ground plane. The introduction of a ground slit did not affect the reflection behavior, as observed from S\({}_{11}\). However, the mutual coupling between ports 1 and 4 in Case #1 at 5.15 GHz was less than 15 dB. By adding a half-wavelength ground slit, the coupling was reduced by over 8 dB from Case #1. Based on analyzing the specific resonance characteristics of the quad-port pentaband MIMO antenna, the 5 distinct frequency bands, as listed at 1.55 GHz (1.5-1.65 GHz), 2.5 GHz (2.4-2.7 GHz), 5.2 GHz (5-5.85 GHz), 7.3 GHz (7.1-7.4 GHz), and 8.15 GHz (7.9-8.4 GHz), achieve their respective mutual coupling reductions at around 27 dB, 37 dB, 21 dB, 29 dB, and 21 dB, respectively. The isolation between the antenna elements was also greater than 20 dB in all resonance bands, indicating good performance. The antenna's overall dimensions for the  
Figure 6: Quad-port pentaband antenna, (**a**) top and (**b**) bottom.  
DG is defined as the ratio of the total received power from all the antennas to the received power from a single antenna. It provides insight into the effectiveness of the MIMO system in improving signal quality and reducing the effects of fading and interference. The optimal value of DG should be 10 dB. The antenna demonstrates satisfactory diversity performance, as seen in Figure 9, where Equation (3) has been employed to calculate DG [38].  
\[DG=10\sqrt{1-\abs{\zeta_{ij}}^{2}} \tag{3}\]
## 4 Experiments and Results
### Environment Set Up  
In order to collect information and extract the most important API functions, a virtual environment has been established. In our experiments, we have used Ubuntu 18.04 on the host machine where the Cuckoo Sandbox is also installed in addition to python scripts we have developed for the project. We set up two virtual machines using VirtualBox and installed Windows XP to analyze the malware. The IDA Pro and PEid have been installed on a 3 rd virtual machine under Windows 7 and additional tools have been installed on virtual machines for malware analysis. Cuckoo submits files for analysis to Windows XP virtual machines, collects activity traces of a file, and generates different reports. Reports consist of, a dump of network traffic and csv reports which includes API functions used by file. Figure 4 gives an overview of our experiment set up where three virtual machines have been installed on Ubuntu as the host OS.  
Malware detection was performed at runtime using the cuckoo sandbox [43] tool because it provides a safe environment to execute malware without infecting the whole system. A laptop with an Intel Core i7-6700 HQ processor (it is a quad-core processor based on the Skylake architecture) and 16 GB RAM with 1 TB of HDD was used to carry out the experiments. The Ubuntu 18.04 and the Cuckoo Sandbox were installed along with additional Python scripts developed to extract all the information we need to perform the experiments. Windows 7 and Windows XP have been installed as the guest OS using the VirtualBox program where Cuckoo Sandbox submits files for analysis and retrieves different reports in JSON format. The virtual machine with Windows 7 has been mostly used for static analysis by IDA Pro and PEid to study the malware and retrieve the API call list. The purpose of this environment is to identify a group of API from malware and benign files and make a feature selection to be used for similarity to classify the given program as malicious or benign. Table 2 gives a summary of the dataset used in our experiments where the class column indicates whether the sample is benign or malware, the Quantity column gives the total number of each class, and the remaining two columns detail the distribution in terms of percentage and size (kilobyte).
## 9 Active T Shape Slot Antennas for Ultra-Wideband IoT and 5G Communication Systems  
A wideband active wearable receiving T shape slot antenna is shown in Figure 7a. The radiating element is designed to provide the optimal load to the active elements. The antenna electrical parameters were calculated, and tuned, by using Computer Aided Design, CAD, tools. The size of the slot antenna shown in Figure 7 is 116 mm \(\times\) 70 mm. The radiating element is connected to the LNA via an input matching network. An output matching network connects the amplifier port to the receiver. A DC bias network supplies the required voltages to the amplifiers. The amplifier specifications are given in Table 1. The amplifier complex S parameters and noise parameters are listed in Table 2. Active slot antenna S11 parameters are shown in Figure 7b. The antenna bandwidth is around 40% for VSWR better than 2:1. S11 parameters of the T shape wearable slot, on human body, attached to a 1 mm shirt with 2.2 dielectric constant, are presented in Figure 7c. The active slot antenna S21 parameters, gain, are presented in Figure 7d. The active computed and measured antenna gain is 18 \(\pm\) 2.5 dB for frequencies from 200 to 580 MHz. The active antenna gain is 12.5 \(\pm\) 2.5 dB for frequencies from 1 to 3 GHz. The active slot antenna noise figure is 0.5 \(\pm\) 0.3 dB for frequencies from 300 MHz to 3.2 GHz, as presented in Figure 7e.  
Figure 7: _Cont._  
A comparison and summary of electrical parameters of the wearable antennas presented in this paper is given in Table 5.
## 3 Experiment Details
### Wall Panel Set Loading Protocols  
On the LEG, the load system is used on the experiment to apply the load perpendicularly to the longest plane of one of the walls. The pressure is applied through a hydraulic jack system, fixed to frames that would distribute the loads through neoprene pads which would be located on one of the jack extremes (Figure 14). The hydraulic jacks are able to apply a force of nearly 5000 kg, distributed on neoprene pads with a contact surface of 30.0 cm \(\times\) 30.0 cm. The hydraulic jacks were connected to the hydraulic hose, and it is through them that the pressure would be applied, because a manual activation pump, and the pressure applied would be measured thought the pressure cells (as shown on Figures 14 and 15). The load system was designed to adjust eight hydraulic jacks, on a way that the distribution of the applied loads on wall 1 would obey to the pattern schematized on Figure 8.  
Figure 14: Load system acting on the specimen: (**a**) General view of the load frame; (**b**) Detail view of the load system.  
Figure 13: Strain Gauges identification on Wall 3: (**a**) North face; (**b**) South face.  
It was decided to take the experiment to the most gradual manner, taking into account that the critical demand, the wind load, was 570 kg, and the corresponding displacement to that demand obtained with the analytical model was 8.97 mm.  
Every load cycle was identified as the step of the experiment composed by two phases: the first one in which the load would be applied as much as possible to get as close as possible to the nominal displacement expected, identified as load phase. Additionally, a second one, in which the model would be totally unloaded, was identified as unload phase. Before the elaboration of the experiment, it was planned to prepare 12 load cycles, two for every nominal displacement of 10 mm, 20 mm, 40 mm, 60 mm, 80 mm and 100 mm, and to decide about the following cycles taking into account the behavior shown on them.
## 3 Analysis of Test Results
### Deformation Characteristic Analysis
To further investigate the effect of \(\sigma_{2}\) on the deformation characteristics of sandstone specimens, the comprehensive stress-strain diagram under a consistent stress ratio (\(\sigma_{2}/\sigma_{3}\)) is illustrated in Figure 8. Notable differences are observable in the principal stress-strain curves throughout the entire precise triaxial deformation process of sandstone at various burial depths. This is primarily due to the deformation memory effect of sandstone when subjected to external forces [21], where simulated depths align with the profound impact of high in situ stress in deep strata on the rock mass. Deeper strata facilitate the exhibition of complex rock-like properties under high initial in situ stress, enhancing resistance to deformation. Simultaneously, substantial amounts of elastic energy are stored within the rock. When deep roadways are excavated and pressure is relieved, the released energy causes the rock fractures to connect with groundwater, potentially leading to mine water inrush disasters. This suggests that this strong effect may be a primary cause of frequent accidents in underground engineering. Figure 7: Relationship between principal stress and volume strain under different intermediate stresses: (**a**) 600 m buried depth; (**b**) 1000 m buried depth; (**c**) volume deformation at each stage.
## 3 Testing Framework
### Testing Environments
#### 3.2.1 Testing Machines  
The tests simulate up to 5000 concurrent users over a 30 second period. Such testing, if run on a single machine, induces the Java VM garbage collector (GC) mid-test, an artifact of the use of JMeter being used as the measuring instrument, thus polluting test results. The minimum and maximum heap of the Java VM is set to be 256 MB and 14 GB, respectively. In computer memory management, the young generation, specifically the Eden space, refers to a region in the heap memory used by garbage collectors in generational garbage collection algorithms. The Eden space is therefore maximised to ensure that GC induced by JMeter does not pollute the results, GC will not trigger until Eden starts to get close to full. The verbose GC setting is used throughout all tests to provide all GC information. For this reason, all tests are distributed over five slave test machines, see Section 4 for an overview. All test machines are hosted in Microsoft Azure within the same subnet, located in Western Europe, and the master machine is controlled via an Ubuntu console. All slave machines (and their master) are Azure D4s_v3 with four virtual cores (vCPU) based on 2.3 GHz Intel XEON E5-2673 v4 (Broadwell) processor, 16 GB RAM and  
Figure 4: Tendermint throughput over time in both KB per second (**top graph**) and **tps (bottom graph**). The 2 MB and 4 MB block sizes are compared using both 2 KB and 10 KB sized transactions. Each marker represents a new block.  
Figure 5: Hyperledger Fabric and Kafka throughput over time, in both KB per second (**top**) and tps (**bottom**). Comparing 2 KB and 10 KB transactions.  
32 GB SSD harddisk [32] (namely commodity hardware when the tests where conducted in 2021). Each of the slave machines runs a Docker image of the slave environment to ensure consistency across the tests [33]. The RAM and CPU of the testing machines are monitored throughout the tests.