esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT Firmware Emulation and Its Security Application in Fuzzing: A Critical Revisit
## 2 Feature of MCU Firmware
### Embedded Firmware Classification and Microcontroller-Based Firmware  
Firmware refers to all software on an embedded device, including drivers, systems, and applications. It can be categorized into three types based on the system types used [7].  
* **General-purpose OS-based firmware.** This firmware uses adapted versions of traditional operating systems like Linux and Windows for embedded environments, maintaining most desktop functionalities. They rely on high-end CPU architectures like x86 and ARM Cortex-A, allowing general OS functions such as memory isolation of processes. These systems can only run on high-end CPU-based embedded devices like routers, with system and application components compiled separately.
* **Specialized Embedded OS-based firmware.** This category of systems employs specialized embedded operating systems, predominantly real-time operating systems  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Hardware Dependence** & **OS Type** & **Layer Boundaries** & **Driver Diversity** & **Driver Interfaces** \\ \hline General Software & Low & Few & Clear & Low & Standard \\ General-purpose OS & Less (Application) & Few & Clear & Moderate & Standard \\ Based Firmware & High (Driver) & Many & Blur & No & Custom \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison among MCU Firmware, General Purpose OS-based Firmware and General Software.  
Figure 1: Number of papers related to MCU firmware emulators published in international conferences within 10 years.  
(RTOS), which have lower computing power but higher real-time requirements. Examples of such RTOS include Zephyr, FreeRTOS, and RIoT. They are typically found in commercial IoT products like small drones. In these systems, the OS and applications are compiled into a unified binary.
* **Bare-metal firmware.** This category includes firmware without an OS abstraction (also called Bare-metal Firmware). The application code directly accesses hardware and is also statically linked into a single binary file, as seen in small sensors.  
Due to limited computing and storage resources, only specialized embedded operating systems or bare-metal firmware can run on MCUs. In this paper, \"MCU firmware or firmware\" refers to this specialized embedded OS or bare-metal firmware.
# Check-QZP: A Lightweight Checkpoint Mechanism for Deep Learning Frameworks
## 4 Evaluation
### The Effectiveness of Checkpoint Operations  
We first present the effect of Check-QZP on checkpoint operations and describe how it works in a multi-tenant environment. Figure 3 shows the normalized elapsed times of the checkpoint operations. Check-QZP achieved a performance improvement of 87% over the baseline. Check-QZP incorporating parallelism operations exhibited lower delay times compared to compression methods. This is because the delays caused by checkpoint operations can be hidden by overlapping some operations in the training process. In contrast, other methods incorporating compression added delays while waiting for the completion of each checkpoint operation (see Figure 3). In particular, the delay was significantly affected by the I/O intensity. Meanwhile, the compression methods involve a trade-off between the compression rate (i.e., data reduction) and the time required for compression. This trade-off may affect overall performance in multi-tenant environments because multiple applications are performed concurrently and may compete for resources (e.g., CPU and memory). Figure 3 depicts the delays for Check-Z and Check-QZ caused by compression operations.  
\begin{table}
\begin{tabular}{l l} \hline  & **Description** \\ \hline CPU & Intel®XeonTM Gold 5215 \\ GPU & NVIDIA TeslaTM T4 \\ Memory & 64 GB \\ Storage & Samsung® SSD 980 PRO (1 TB) \\ OS & Ubuntu 20.04.6 LTS (64-bit) \\ PyTorch & version 2.0.0 \\ CUDA & version 11.7 \\ \hline \end{tabular}
\end{table}
Table 1: Hardware and software environment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Number of Images** & **Resolution** & **Volume** \\ \hline VisDrone Subset1 & 4561 & 1904 \(\times\) 1071 & 1.14 GB \\ VisDrone Subset2 & 1858 & 1344 \(\times\) 756 & 315 MB \\ VisDrone Subset3 & 377 & 2688 \(\times\) 1512 & 237 MB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**FIO** & **Options** \\ \hline Ioengine & Libaio \\ File size & 1 GiB \\ Block size & 4 KiB \\ Direct & False \\ Numjobs & 64 \\ Operation & Random read/write \\ \hline \end{tabular}
\end{table}
Table 3: FIO options.
## 5 Results and Use Case
### SOC Use Case Lifecycle--Implementation and Challenges  
Defining and setting up SIEM use cases is one of the main success indicators of the SOC as it is considered the backbone that supports analysts and threat modeling goals and converts business threats into technical detection rules that match and detect the possible threats and send an alert to the SOC. As shown in Figure 4, the three major components of the use case are as follows:  
* Rules, which detect and trigger alerts based on targeted events;
* Logic, which defines how events or rules will be considered;
* Action, which determines what action is required if logic or conditions are made.  
These rules can be static, based on specific conditions, or dynamic, adapting to changing behaviors and patterns over time. However, implementing and managing a SOC use case comes with several challenges, as explained in the literature and revealed in our survey-based study alongside the SOC expert's notes. These challenges can be categorized into various aspects, including technology, processes, human factors, and metrics for evaluation.  
The use case lifecycle is managed inside the SOC by the use case factory, ensuring the engineering and the follow-up of the use cases from the threat and risk identification to the technical implementation and maintenance. Figure 5 shows a SOC use case implementation.  
Figure 4: SOC use case general structure.  
Use case lifecycle has several stages and can be summarized in the following steps:  
* Focus on business threats and risks that have financial, reputational, and data impact on the company and prove that the use case will add value and solve a problem related to the identified threats and attack scenarios;
* Once you know what you want, the next step is the design and the engineering of the use case, which consists of the identification of the data source and the use case logic;
* The defined logic and baseline in the use case need testing. Based on the testing results, tuning and continuous optimization will be required to ensure you reduce noise and make sure it matches the right patterns and attacks.  
Performance monitoring and continuous optimization.
# Reliable Visual Exploration System with Fault Tolerance Structure
## 4 Visual Localization and Mapping
### Parallel Constraint Front-End  
In the VLM front-end, a framework is designed for the place recognition of each new keyframe by considering multiple constraints in parallel so that an efficient solution is obtained. In addition, a multi-layer keyframe selection is introduced to limit the number of the keyframes. Fault prevention is in this case a by-product of a higher frame rate in this front-end.  
A parallel framework is designed to speed up the building of loop-closure constraints. We separate the building of the constraints into different threads, and they are run in parallel, as is shown in Figure 5. Using such a strategy, the frame rate can be improved, which is linearly proportional to the number of built constraints before. With a higher frame rate, map maintainability will improve.  
For keyframes selection, a multi-layer method is designed. The first step is calculating the distance of optical-flow; the second step is the difference of global descriptor over all of the keyframes. Since descriptor extraction is time-consuming, before descriptor extraction, we use an optical-flow method to calculate the appearance distance between the current frame and the last keyframe. Only when the image satisfies a similarity threshold will the global descriptor of the current image be extracted for keyframe selection and referenced frame recognition. Such a selection method can reduce on the time consumption by decreasing the time of the descriptor extraction.
## 3 Methods  
### Overview  
Research vineyards are located at NE Hansen research farm at South Dakota State University (SDSU, Brookings, SD, USA) and Iowa State University Horticulture Farm (ISU, Ames, IA, USA). Cultivars were grown in a randomized complete block design in both test vineyards in 2008 as part of the NE-1020 Cold Hardy Wine Grape Cultivar Trial. Grape clusters were randomly selected and tagged, so the same clusters were sampled from veraison to harvest for VOCs emitted in vivo from ripening grapes. Two in vivo sampling methods utilizing SPME were used, namely, extractions from whole air chambers enveloping ripening clusters in the 2012 season and vacuum-assisted extractions from single berries using modified glass vials in the 2013 season, respectively. Volatiles present in the headspace of crushed berries (i.e., skin, pulp, and seeds) were also investigated for the 2012 and 2013 growing seasons for comparisons with in vivo sampling. Berries from each cultivar were collected on the same day as in vivo sampling, collected from untagged clusters, crushed, and analyzed in comparison. These berries were kept in the dark and frozen at \(-20\)\({}^{\circ}\)C for batch analysis after harvest.  
A 65 \(\upmu\)m polydimethylsiloxane/divinylbenzene (PDMS/DVB) fused silica SPME fiber (P/N 57310-U, Sigma-Aldrich, St. Louis, MO, USA) was conditioned according to the manufacturer's specifications. Cleaned fibers were stored and transported in aluminum foil packets and sealed in mason jars. Foil and mason jars had been thermally cleaned overnight in an oven (at 110 \({}^{\circ}\)C) to minimize interferences from the environment. After sampling, described in the following sections, SPME fibers are wrapped in clean aluminum foil, placed and sealed in clean glass mason jars, and transported on ice packs to the lab for analysis on the same day. Samples of the volatiles collected on SPME fibers in Brookings, SD were prepared and used in the same manner and sent to Ames, IA via an overnight carrier for analyses. Volatiles emitted from whole clusters of Frontenac and Marquette cultivars were investigated for the 2012 growing season in both Iowa and South Dakota. Volatiles emitted from single berries of St. Croix and La Crescent cultivars were investigated for the 2013 growing season.
## 4 Discussion
### Performance with Synthetic Interferograms  
In our investigation, we employed synthetic interferograms that incorporated strategically positioned Gaussian functions. The normalized interferogram \(I_{n}(x,y)\) was defined, allowing us to control the overall amplitude of the phase \(\rho_{1}\) and introduce several Gaussian components (A to E) into the phase \(\varphi(x,y)\). These Gaussian components were strategically placed in the northeast, northwest, southeast, southwest, and central positions within the interferograms. The parameters \(F_{xx}\) and \(F_{yy}\) were employed to regulate the width of these Gaussians, while \(\rho_{1}\) determined their overall scale. Additionally, the positions of these Gaussians were manipulated using \(N_{xx}\) and \(N_{yy}\).  
Evaluation of Demodulation Methods: In Figure 2, we presented the results obtained from different synthetic interferograms, categorizing them into various scenarios:  
* Figure 2a,b showcase two examples with five positive Gaussians;
* Figure 2c features an example with four positive Gaussians and one negative Gaussian;
* Figure 2d depicts an interferogram with two Gaussian components.  
For a more comprehensive analysis, 3D representations of the phases of these interferograms were provided in Figure 3, allowing us to compare the original phase \(\varphi(x,y)\) with the unwrapped phases computed using the FSD, FSD-HSO, and FSD-PSO algorithms.  
Fringe Frequency Variation Testing: To assess the methods' performance concerning fringe frequency, we generated interferograms with single Gaussian functions that varied in width and amplitude. These experiments are depicted in Figure 4, where we presented two cases: \(F_{xx}=F_{yy}=1\) and \(F_{xx}=F_{yy}=10\). Subsequently, we examined 3D views of the recovered phases in Figure 5.
## 3 System Overview  
The proposed testbed architecture is presented in Figure 2 and, as can be observed, it is aimed at recording the energy consumption and throughput of an IoT end device. The testbed coordinator is the device in charge of starting, stopping and recording the measurements during the tests. Such a device can be a PC, a laptop or a virtual machine. To power up the IoT end device, a dedicated power supply is used to obtain stable and accurate current measurements. A current sensor is placed between the power supply and the IoT end device to register the current consumed by the end device. The sensor is then connected to an SBC, which is in charge of starting the test procedure when requested by the testbed coordinator. Such a test procedure consists on downloading multiple files from the IoT end device while measuring the energy consumption with the help of the current sensor. To enable the communications among all the computational devices of the testbed, a dedicated communications gateway is used. The gateway is connected to the SBC and the testbed coordinator through a wired connection, while it communicates wirelessly with the IoT end device.
# A Distributed Indoor Mapping Method Based on Control-Network-Aided SLAM: Scheme and Analysis
## 3 Field Tests and Results  
### LiDAR/IMU Integrated System Overview  
As shown in Figure 3, a LiDAR/IMU integrated hardware system was designed and built; it contained an Xsens MTi-G IMU and Hokuyo UTM-30 LX-EW LiDAR. The data from the LiDAR and IMU were transmitted to the laptop through the network port and USB port, respectively. The Xsens MTi-G IMU is a MEMS-level device and its sampling frequency is 200 HZ; other major performance indicators are as follows: gyroscope bias is 200 degrees/h, accelerometer bias is 2000 mGal. UTM-30 LX-EW LiDAR operates at 40 Hz, its field of view is 270 degrees with an angular resolution of 0.25 degrees, and its maximum effective range is 30 m with a range accuracy of \(\pm\)30 mm at 0.1-10 m and \(\pm\)50 mm at 10-30 m.  
Figure 2: Distributed indoor mapping based on control-network-aided Simultaneous Localization and Mapping (SLAM) (LiDAR (Light Detection and Ranging) and IMU (International Mathematical Union)).
## 4 Discussion
### Relationship with the State-of-the-Art on General Research Productivity  
Our analysis of the societal factors that shape research output on topics such as blockchain, privacy, and cybersecurity largely converges with the broader state-of-the-art in research on country-level scientific productivity, while also offering some nuanced insights.  
Consistent with the literature, the models confirm the strong positive influence of economic factors on scientific output. The finding that GNI per capita is the strongest predictor across all the examined fields (total publications, blockchain, privacy, and cybersecurity) aligns with numerous studies highlighting the crucial role of a country's economic resources in fostering scientific productivity.  
The positive relationship between research spending as a percentage of GDP and publication rates in the models also corroborates the established understanding that targeted investment in R&D significantly impacts scientific output. This reinforces the widely accepted view that both overall economic health and specific research funding are key drivers of scientific productivity.  
However, the models reveal some interesting variations across different research fields, which adds nuance to the existing literature. For instance, the stronger effect of GNI per capita on blockchain publications compared to other fields suggests that emerging, technology-intensive areas may be particularly dependent on economic resources. This aligns with the studies indicating that different scientific disciplines may have varying sensitivities to economic factors.  
The models' findings on the role of education, measured by average years of schooling, present an intriguing divergence from some previous research. While the literature generally suggests a positive relationship between educational attainment and scientific productivity, the models show a negligible or even negative effect for some fields. This unexpected result may indicate that in rapidly evolving technological domains, factors other than general educational levels, such as specialized expertise or entrepreneurial activity, play a more significant role.  
The positive association between the liberal democracy index and publication rates, particularly strong for privacy research, aligns with the studies suggesting that political openness and democratic governance can enhance scientific output. This adds to the growing body of evidence on the importance of political and institutional factors in shaping research productivity.
## 3 Results and Discussion
### The Lockdown Only  
Figure 6 represents the dynamic evolution of the different states of the System (1) and a comparison with the states by introducing the optimal controls \(u\) and \(v\) during the period of 6 April to 25 April 2020.  
From the evolution of the variables of the model, between the 6 th and 26 th April, with and without controls in Figure 6, we observe, clearly, the possibility of the impact of enhancing the efficacy of the lockdown in the epidemic situation of COVID-19 in Morocco. In fact, with an initial state \(E_{0}=2000\) estimated on 6 April, we note an increasing evolution of the number of people exposed, which reaches a peak of 4800 people on 15 April, then decreases to 3200 cases on 26 April. On the other hand, by introducing optimal controls, i.e., the optimal containment strategy which reduces contact between people with the disease and susceptible people, we observe that the number of people exposed decreased to reach a minimum value on 15 April, then rebounded and grew to reach 1900 cases on 26 April.  
The number of contacts \(E_{q}\) decreases from \(E_{0}^{q}=2000\) to reach 1800 cases on 8 April, then increases exponentially to reach its maximum value of 7650 at the end of the period. By introducing the optimal controls \(u\) and \(v\), the number of contacts \(E^{q}\) decreases at the start from 2000 to 1600 after 5 days and then increases to reach 3200 in 25 April.  
For (\(I^{q}\)) the number of infected increased from 1000 cases in the beginning to 3400. On the other hand, by applying optimal containment, the number of infected remained constant in the beginning of 6 April to 13 April, then increased slightly to reach 1400 by the end of the year.  
The estimated number of recovered, \(R\), with and without control, initially increases in the same, However, with control it reaches 220 cases on 25 April and the number for the non-control group exponentially to reach 540 cases.  
The estimated number of cases recovered, \(R\), with and without control, initially increases in the same way, however, with control it reaches 220 cases in 25 April while this number, without control, grows exponentially up to 540.  
Similarly, the number of deaths increases for both cases; we observe that they are the same between the period from 6 April to 13 April. In fact, the control strategy does not much affect the number of deaths as, with the optimal control, it reached 150 cases of death towards the end of the period and 168 for the cases without control.  
The optimal control strategy reduces the susceptibility of the population by 11,500 by 25 April. However, without control, the number would increase by 447,100 persons.  
Note that the optimal control pair \((u,v)\), as shown in Figure 7, should be initiated for the exposed population (on the day 8) before the infected population (on the day 12).
## 4 Results  
First, we wanted to prove that NIPP has no negative effects on cells and tissues. At room temperature the PS30 probe runs with a certain surface temperature of 24.7 \({}^{\circ}\)C (Figure 4a). The measurement of the surface temperature and the core temperature of the cell culture medium showed no change due to the application of NIPP (Figure 4b-d). Neither was there any change in the pH value (Figure 4e). For this reason, damage to the treated cells and tissues could be ruled out.  
To quantify the in vivo effect of NIPP described in the case report, human gingival keratinocytes were used for the following experiments. First, we investigated the influence of NIPP on HPGK's metabolic activity. NIPP treatment of 60 s caused a significant increase in cell viability of 8% after 24 h (\(p=0.0022\); Figure 5a). Next, we focused on crucial markers of proliferation: NIPP treatment resulted in a significant 1.6-fold increase in Ki67 mRNA levels (\(p=0.0476\)) and a 1.2-fold increase in PCNA mRNA levels (\(p=0.0476\); Figure 5b). Finally, we investigated effects of NIPP on in vitro wound healing using an established scratch-assay. As shown in Figure 5c, NIPP lead to a significant faster closure of an artificial scratch, as compared to untreated cells (12 h: 0.0159; 24 h: \(p=0.0317\); Figure 5c,d).  
Figure 4: Basic characterisation of NIPP. (**a**) Superficial temperature of NIPP probe PS30 at DBD mode; (**b,c**) superficial temperature of cell culture medium prior to NIPP treatment (-) up to 120 s of treatment, respectively; (**d**) submerged temperature of cell culture medium during 120 s of NIPP treatment; (**e**) pH of cell culture medium during 120 s of NIPP treatment.