esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For isolated air-gapped computer networks, typically used by security-sensitive institutions such as government bodies, research and development organizations, critical infrastructure like electricity and nuclear facilities, banking institutions, and the military, the challenge of securing IoT becomes even more critical. While there are numerous published surveys covering research trends on security in the IoT domain, there are fewer surveys specifically addressing the security of IoT. [PERSON] in [14] identified some of the topics addressed by researchers in the IoT area, including ARP poisoning, MITM attacks and access authentication weaknesses. Another study by [PERSON] et al. [15] explored attacks on video surveillance systems discussed in the literature, and proposed security measures to mitigate such attacks, but did not pay attention to totally isolated networks. The authors in study [16] classified research into various categories, such as network infrastructure, communication, application, and user interaction, but the security aspect was out of the scope of that review. There is currently no comprehensive survey that thoroughly examines IP camera and IoT exploitation to penetrate air-gapped networks. Only a few researchers, such as [PERSON] and [PERSON] [13], have touched upon techniques like infrared LED, but as a small part of their primary research interests. This paper systematically reviews the existing research studies and use cases related to IP camera vulnerabilities and exploitation techniques. It primarily focuses on methods used to target organizations through their IoT systems installed behind a logically air-gapped network, the technique used, and the impact, followed by mitigation measures proposed in the literature. The paper maps each technique to a real-world use case and presents it with an attack diagram for enhanced clarity. The remainder of the review is organized as follows. Section 2 presents the literature research methodology, the data source research questions, and the screening process. The analysis of the top exploitation techniques currently used and potential security measures is presented in Section 3. A summary of the findings with a discussion is provided in Section 4.
## 2 Proposed SH-WPBS
### Battery Storage (BS)  
The BS controller consists of a lead-acid battery connected to a bi-directional buck-boost converter. It maintains DC-Link voltage through a PI controller as given in Figure 4. The state-of-charge (SOC) is given by Equation (8).  
\[SOC=100(1+\int i_{BS}~{}dtQ) \tag{8}\]  
Figure 3: Wind System with controller.  
Depending on the amount of power generated by the wind system, the battery operates in charging and discharging modes, satisfying the minimum and maximum constraints, which are determined by Equation (9).  
\[SOC_{\text{min}}\leq SOC\leq SOC_{\text{max}} \tag{9}\]  
The reference current \(i^{ref}_{dc}\) is approximated by reducing the DC-Link voltage error \(V_{dc,err}\) using a PI controller in mathematical terms. The approximation can be explained as given in Equations (10) and (11). The battery error current reference \(i_{BS,err}\)* is obtained by the PI of battery error current \(i_{BS,err}\). Where \(i_{BS,err}\) is the difference between reference DC-Link current \(i^{ref}_{dc}\) and battery current \(i^{ref}_{BS}\) given in Equation (12) obtained from LPF given in Equations (13) and (14).  
\[V_{dc,err}(t)=V^{ref}_{dc}-V_{dc} \tag{10}\]  
\[i^{ref}_{dc}=K_{p,3}\;V_{dc,err}(t)+K_{i,3}\int\limits_{0}^{t}V_{dc,err}(t)\;dt \tag{11}\]  
\[i_{BS,err}(t)=i^{ref}_{dc}-i^{ref}_{BS} \tag{12}\]  
\[i_{BS,err}\textsuperscript{*}=K_{p,4}\;i_{BS,err}(t)+K_{i,4}\int\limits_{0}^{t}i_ {BS,err}(t)\;dt \tag{13}\]  
where  
\[i^{ref}_{BS}=(\frac{1}{1+T.S})\ast i_{BS} \tag{14}\]  
\(K_{p3}=1.05\), \(K_{i3}=0.01\), \(K_{p4}=1.27\), and \(K_{i4}=3.07\) are chosen heuristically. Table 3 gives the power dispersion at DC-link under various working conditions of the SH-WPBS.  
Figure 4: BS controller.  
\begin{table}
\begin{tabular}{c c} \hline
**Level of WPG** & **Action** \\ \hline \(\text{WPG}\;>\;P_{dc}\) & Extra power produced by WPG is used to charge BS till it attains \(SOC_{\text{max}}\) \\ \(\text{WPG}=P_{dc}\) & WPG alone will supply \(P_{dc}\). \\ \(\text{WPG}\;<\;P_{dc}\) & The power difference is supplied by BS until it reaches \(SOC_{\text{min}}\) \\ No WPG & BS alone supplies \(P_{dc}\). \\ \hline \end{tabular}
\end{table}
Table 3: Power-sharing at DC-link Bus.
## 3 Evaluation of the Measured Values  
When the surface roughness was evaluated, the parameters of _Ra_--mean arithmetic deviation of the profile and those of _Rz_--the greatest height of the unevenness of the profile, were observed. The roughness of the surface was measured in 180 steps with a step size of 0.11 mm. The Gain (video signal strength gain) mode was set to 1. The shutter time (the exposure time that determines the time over which the surface is scanned) was 19.520 ms. After previous test measurements of this type of surface, these LPM device parameter setting values showed the clearest picture preview and the least noise in the image.
## Results  
Validation against simulated data demonstrates that the gradient-based approach is a fast and viable method for inference of BCM parameters. The estimation process requires approximately 300 evaluations of the forward model, with the averaged error in glottal area on the order of \(10^{-3}\) cm\({}^{2}\). Estimated subglottal pressures from the silicone VFs show reasonable agreement with known subglottal pressures, however estimated material properties have a large bias in comparison with the known material properties, a potential artefact of the high-dimensionality of the parameters space. Uncertainty in estimated parameters is also high, with standard deviations exceeding 40% of some parameter values, largely due to the high-dimensionality.
## 4 Experiment Results
### Computational Complexity Comparison  
In this section, we compare the computational complexity of AlexNet, ResNet-50, Inception v3, DenseNet-201, NasNet, and IMNets by counting the number of multiplications and additions required to process a single image. Furthermore, we compare between all mentioned models the total number of learnable parameters within each CNN model. We  
Figure 11: Diabetic retinopathy dataset ROC curve for the five best performing networks.  
Figure 12: Tuberculosis dataset ROC curve for the five best performing networks.  
calculate the number of learnable parameters for each layer, and then sum up the learnable parameters in each layer to obtain the total amount of learnable parameters in the entire network. Figures 13 and 14 and Table 6 show the results of our computational complexity study. In Figure 13, we show balanced accuracy on the malaria dataset versus the number of learnable parameters. On the other hand, in Figure 13 we show balanced accuracy versus the number of floating-point multiply-add (MAdd) operations for the same dataset. Note that the composite MAdd operations are determined for the input images size of \(112\times 112\) reported in Section 2.1. The diameter of each circle is proportional to the the total number of learnable parameters for Figure 13, and the circle size is the MAdd for Figure 14. Note that the IMNets have fewer learnable parameters, and fewer MAdd operations, as shown in Table 6.  
The numerical values for the total number of learnable parameters and MAdd counts are listed in Table 2 for the malaria dataset networks. Note that IMNet _A-E_ (the largest IMNet tested here) has fewer parameters than AlexNet by a factor of approximately 64, and by a factor of approximately 6 compared with NasNet. In terms of the MAdd count, IMNet _A-E_ has fewer than AlexNet by a factor of approximately 9, and fewer than NasNet by a factor of approximately 61.
# An Accurate Platform for Investigating TCP Performance in Wi-Fi Networks
## 3 Emulator-Based Approach for Investigating TCP Performance in a Wi-Fi Network
### Congestion Control Algorithms
#### 3.2.2 Delay-Based Algorithms  
_Bottleneck Bandwidth and Round-trip propagation time (BBR)_[41] is not a purely delay-based algorithm. However, we still classify BRR in this category. BBR adjusts the congestion window following two indicators: the bottleneck bandwidth (BtlBw) and the round-trip propagation time (RTprop).  
_New Vegas (NV)_[42] was designed to improve the performance of Vegas as the RTT increases.  
_Vegas_[43] detects congestion following an increase in RTT. It relies on accurate calculation of the base RTT value.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Tool/Script** & **Function** \\ \hline ping & Measuring RTT values \\ \hline iperf & Measuring throughput values \\ \hline Cal\_fairness script & Calculating fairness index \\ \hline Setting\_script & Setting CCAs and network parameters \\ \hline Experiment\_script & Creating Wi-Fi networks and running iperf, ping \\ \hline Other utilities & Making figures, monitoring other parameters, etc. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Utility list.
## 2 Materials and Methods
### Anti-Inflammatory Activity Assays  
#### 2.4.1 Cell Culture and Treatment  
Human THP-1 monocytes (American Type Culture Collection, ATCC, Barcelona, Spain) were cultured in RPMI 1640 culture medium (Gibco, Madrid, Spain) supplemented with 10% FBS, 100 U/mL penicillin, 100 mg/mL streptomycin, 2 mM L-glutamine and 0.05 mM \(\beta\)-mercaptoethanol at 37 \({}^{\circ}\)C in 95% humidified air containing 5% CO\({}_{2}\). Cells were collected and plated at a density of \(5\times 105\) cells/mL in 24 wells plates. Differentiation to macrophages (THP-1/M cells) was induced by maintaining the THP-1 cells for 48 h in presence of 100 ng/mL phorbol 12-myristate 13-acetate (PMA) (Sigma, Madrid, Spain). After differentiation, cells were washed with PBS and incubated with 0.05 ug/mL lipopolysaccharideacharide (LPS) (Sigma, Madrid, Spain), in the presence of different concentrations of supercritical extracts for 24 h in a fetal bovine serum (FBS) free medium. Then, the supernatant was frozen at -80 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Run** & **P** & **T (\({}^{\circ}\)C)** & **t** & **F** & **P S1** & **P S2** & **T S1** & **T S2** & **EtOH** \\
**(MPa)** & **T (\({}^{\circ}\)C)** & **(h)** & **(g/min)** & **(MPa)** & **(MPa)** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **(\%)** \\ \hline E1 & 50 & 50 & 4 & 50 & 30 & 6 & 35 & 35 & 0 \\ E2 \({}^{\mathrm{a}}\) & 30 & 50 & 4 & 50 & 2 & 0 & 10 & 10 & 10 \\ E3-1 & 25 & 50 & 1.5 & 50 & 6 & 6 & 35 & 35 & 0 \\ E3-2 & 30 & 50 & 2.5 & 50 & 2 & 0 & 10 & 10 & 10 \\ \hline \end{tabular} \({}^{\mathrm{a}}\) Notes: Extraction carried out in duplicates at this experimental condition.  
\end{table}
Table 1: Experimental conditions for the supercritical fluid extraction of heather, marigold and combined heather + marigold (50:50) extraction. (P) pressure, (T) temperature, (t) time, (F) flow rate, (P S1) pressure in separator 1, (P S2) pressure in separator 2, (T S1) temperature in separator 1, (T S2) temperature in separator 2, (EtOH) mass of ethanol (w/w) used as a co-solvent.
## 3 Numerical Example
### Details of the Long-Span Suspension Bridge  
The Egongyan special track bridge is a self-anchored suspension bridge with a main span of 600 m, and it is the world's largest bridge of its kind, as shown in Figure 5. The main bridge span combination is 50 + 210 + 600 + 210 + 50 = 1120 m: a total of five spans. The total width of the main bridge deck is 22 m. The main beam is a mixed structure of steel box and concrete box. The steel box girder section is 926.4 m long, and the concrete box girder section is 170.16 m long. A steel-concrete joint section with a length of 11.72 m is present at the junction of the steel box and the concrete box on both sides. The steel box adopts six web sections made of Q345 qD and Q420 qE steel. The concrete box adopts a single-box three-chamber box girder, and it is made of C55 concrete.  
Figure 4: Flowchart of CNN-LSTM model-based damage identification method.  
The suspension bridge has 122 hangers in total, with 61 hangers on the upstream and downstream sides. A three-dimensional finite element model was built using Midas/civil 2019, which is professional software for bridge structure analysis. The model is divided into 937 nodes and 924 elements (674 beam elements and 250 tension-only elements). The main beams and bridge towers are simulated by beam elements, and the main cables and hangers are simulated by tension-only truss elements (cable elements).
## 3 Related Work
### Underwater Monitoring
#### Infrastructure Based Monitoring  
Several infrastructure-based underwater observation projects have been recently performed for long term oceanography studies [26, 27]. As a representative example, the Neptune project [26] established an undersea observatory network connected by fiber-optic and power cables, which provide high communication bandwidth and ample power to the observatories for real-time and long-term four dimensional remote oceanographic monitoring. Thirty Neptune nodes are spaced in the sea bed where each node is approximately 100 km apart, which leads to thousands of kilometers of cable length.  
Our work is different from those systems in that AURP can be applied without significant underwater infrastructures such as underwater cables.
# Design of Shadow Filter Using Low-Voltage Multiple-Input Operational Transconductance Amplifiers
###### Abstract  
This paper introduces shadow filters that employ multiple-input operational transconductance amplifiers (MI-OTAs) as the active component. Two configurations of shadow filters are proposed. The first configuration, in contrast to previous designs, enables the adjustment of the quality factor without affecting the passband gains of the BPF, LPF, and HPF, thus achieving optimal frequency responses for these filters. The second configuration allows for the variation of the natural frequency without impacting the passband gains of the HPF, LPF, and BPF, maintaining constant passband gains. Moreover, the natural frequency can be electronically controlled by modifying parameters of the original biquad filters, providing advantages in compensating for process, voltage, and temperature variations. The MI-OTA is designed to provide multiple-input differential terminals using the multiple-input bulk-driven MOS transistor (MIBD-MOST) technique, allowing differential input signals to be converted into current output through its transconductance gain. The OTA operates at a supply voltage of 450 mV and consumes 81 nW of power, with the MOS transistors operating in weak inversion. The OTA and shadow filters were designed and simulated using a 0.18 \(\upmu\)m CMOS process to validate the functionality and performance of the proposed circuits.  
opereational transconductance amplifier; shadow filter; multiple-input bulk-driven MOS transistor; universal filter +
Footnote †: journal: Applied sciences  
1
# Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)
## 4 Experiments and Results
### The Performance of CNNs under TensorFlow  
The YOLOv4-Tiny performed better than the YOLOv3-Tiny within the TensorFlow framework at different growth stages of _Cirsium arrense_ and wheat crops (Table 7), and the models' real time detection accuracy values are shown in Figures 8 and 9. The YOLOv4-Tiny-based network achieved the highest accuracy in this study. The accuracy value for YOLOv4-Tiny (0.97) was comparatively higher than the YOLOv3-Tiny value (0.96). The precision value of YOLOv3-Tiny (0.91) was higher than the precision value of YOLOv4-Tiny (0.89); the F1-score and recall values of YOLOv4-Tiny (0.92 and 0.96) were higher than the YOLOv3-tiny values (0.90 and 0.90). However, the YOLOv4-Tiny performed better than the YOLOv3-Tiny, and showed a higher F1-score, recall, and overall accuracy as compared to YOLOv3 Tiny. That said, the YOLOv3-Tiny precision value (0.91) was better than the YOLOv4-Tiny precision value (0.89). Our study showed a higher precision, recall and F1-score, as compared to an earlier study [28].  
Figure 4: Confusion Matrix for Detection.  
Figure 5: Precision Results.  
Figure 6: _Cont._  
Figure 6: Recall Results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Model** & **Precision** & **Recall** & **Fiscore** \\ \hline YOLOv5s & 0.59 & 0.44 & 0.51 \\ YOLOv5m & 0.67 & 0.49 & 0.57 \\ YOLOv5l & 0.84 & 0.39 & 0.54 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Precision Recall and F1 score values for weeds dataset using these models.  
Figure 7: _Cont._
## 2 Autonomous Driving in Adverse Weather Conditions
### Impact of Adverse Weather on Sensor Performance  
#### 2.1.1 Optical Sensors (Cameras, LiDAR) in Rain, Snow, and Fog  
Optical sensors, particularly cameras, and Light Detection and Ranging (LiDAR) systems are fundamental components of autonomous vehicle perception systems. However, their performance can be significantly compromised in adverse weather conditions.  
Cameras: Visual cameras are susceptible to various weather-related challenges. [PERSON] et al. [31] developed a method to model and simulate rain for testing automotive sensor systems, highlighting the need for robust testing environments. [PERSON] et al. [32] found that snow on the car can significantly reduce visual performance, potentially impacting camera-based systems in autonomous vehicles.  
LiDAR: LiDAR systems, which use laser pulses to measure distances and create 3D maps of the environment, also face significant challenges in adverse weather. [PERSON] et al. [33] conducted a comprehensive study on the influence of weather on automotive LiDAR sensors, demonstrating that different weather conditions can significantly affect LiDAR performance.  
Fog presents a particular challenge for LiDAR systems. [PERSON] et al. [34] performed benchmark tests for LiDAR sensors in fog, revealing that detection capabilities can break down under dense fog conditions. [PERSON] et al. [35] further investigated the influences of various weather phenomena on automotive laser radar systems, providing insights into the limitations of these sensors in adverse conditions.  
Rain also poses significant challenges for LiDAR systems. Figure 1 compares the perception capabilities of autonomous driving systems across various adverse weather conditions. It shows real-world scenes (top row), visual perception results (middle row), and LiDAR point cloud data (bottom row) for five scenarios: (a) dense fog, (b) light fog, (c) moderate rain (30 mm/h), (d) heavy rain (80 mm/h), and (e) intense light. The visual representations demonstrate how different weather conditions affect the system's ability to detect and interpret its environment, highlighting the challenges in developing robust autonomous driving technologies for diverse weather conditions [36]. [PERSON] et al. [37] developed a method to predict the influence of rain on LiDAR in Advanced Driver Assistance Systems (ADAS), further emphasizing the need for adaptive algorithms in autonomous driving systems.
## 5 Performance Test Results and Discussion
### Torque Tests (Max Load Tests)  
The maximum transmissible torque was measured by loading the external brake and transmitting the load through the magnetic couplings inside the chamber. The output torque measurements are shown in Figure 13.  
Figure 12: Input and output speed measurement.  
The maximum transmissible torque was rated in 3 Nm, which gives a torque density of around 25 kNm/m\({}^{3}\). Although the torque and corresponding torque density are low for practical applications, they correspond properly with the Finite Element Method (FEM) model results validating them. Torque capability can be easily improved by reducing air gaps (too conservative), using NdFeB magnets (caring about the spin reorientation transition), and optimizing shapes and sizes of the teeth. In fact, recent designs have shown torque densities close to 100 kNm/m\({}^{3}\), in the same order of magnitude as conventional HD.
# Model-Based Deep Reinforcement Learning with Traffic Inference for Traffic Signal Control
## 5 Reinforcement Learning Based on Traffic Inference Model  
### Method Overview  
As current overall computer performance continues to rise and computing power continues to increase, a broad trend in deep reinforcement learning is that combining more computation with more powerful models and larger datasets produces more impressive results [17]. A growing number of examples prove this opinion: the key factor that allows algorithms such as Gorila [40], A3C [13], GPU A2C [41], distributed PPO [42], Alpha Zero [43], and Ape-X [17] to succeed is the ability to efficiently use more computational resources.  
Ape-X is a high-throughput reinforcement learning algorithm that utilizes a heavily parallelized environment. It distributes the generation and selection of experience data and separates it from the training of agents, and it has been demonstrated that this improvement is effective in improving the DRL algorithm. Ape-X divides the standard DRL algorithm into two parts. The first part explores the environment and evaluates the optimality of the current agent, and the collected data are then stored in a shared experience replay, which is called acting. The exploration policies for each environment are individually set, and all data are aggregated into a pool of prioritized experience. The data diversity obtained based on different exploration policies is richer and provides support for improving the performance of the agent. The second part is to continuously sample the batch data from the experience replay in a prioritized manner and continuously update the policy, which is called learning. Moreover, the two parts run asynchronously without using any synchronization strategy, which has the advantage that the two parts, action and learning, will each proceed as fast as possible without having to wait for each other. Inspired by this, our algorithm also adopts an asynchronous approach, including following its diverse exploration strategy.  
However, under the traffic signal control task, it is very costly for agents to perform trial and error. The high throughput model-free reinforcement learning algorithm, which is less focused on data efficiency and needs to constantly and frequently interact with the environment to obtain diverse data to improve performance, is less applicable under this task. We therefore propose a reinforcement learning algorithm based on a traffic inference model in which data is used more efficiently, thus improving data efficiency. This stems from the fact that the traffic inference model can continuously infer new data based on existing data instead of the data generated by the large amount of interaction with the environment that is needed to train the agent. Moreover, the traffic inference model continuously learns the patterns of traffic state evolution from the data generated at intersections.