esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Experimental Results
#### 4.2.1 System Evaluation  
To evaluate the performance of our counting system, we use four metrics: precision, recall, F-measure, and accuracy. These metrics were calculated using the following parameters: true positive (TP), which represents the successfully counted vehicles; false negative (FN), which represents the vehicles that our system is not able to count; and false positive (FP), which represents the vehicles that are incorrectly counted.  
The mathematical relationships between these parameters and the evaluation metrics are shown below:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{1}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{2}\]  
\[\text{F-measure}=\frac{2*\text{Precision}*\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{3}\]  
\[\text{Accuracy}=1-\frac{\text{counted number}+\text{real number}}{\text{ real number}} \tag{4}\]  
Our system has shown promising results in most of the tested videos. The table (Figure 13) displays the counting results of a previously tested video, with each of the six lanes at the intersection represented in the table. However, we did observe instances of false negatives in lanes 4, 5, and 6, which may be due to the detection not being entirely reliable, possibly due to the significant distance between the camera and these lanes.
# A Finite State Machine-Based Improved Cryptographic Technique
## 4 Results and Analysis of Proposed Work
### Effect of Signal-to-Noise Ratio on Image Quality  
As a result of channel interference, the sent data is garbled. If there was severe bit corruption, the quality of the restored picture may be severely reduced. Moreover, if many bits are corrupted, it is possible that the correct secret information may be obtained incorrectly. Hence, we can examine the impact of channel noise on the picture quality  
Figure 10: Effect of frame length on the BER performance of CTC.  
Figure 9: Complexity of iterative decoding grows with the number of iterations.  
Table 10 provides a detailed breakdown of image quality characteristics for legitimate and illegitimate photo recovery. According to the findings, a real user can correctly recover the image with a correlation coefficient of nearly 1 and a Peak Signal-to-Noise Ratio (PSNR) of almost infinity, whereas a false user can only do so with a correlation coefficient of nearly 0 and a PSNR of less than 8 dB. The results ensure that an ordinary user may successfully recover the image, but a malevolent user is left with a severely altered version of the original, with a correlation coefficient of approximately 1 and a PSNR of 6.5326 roughly.
## 4 Energy Management Systems
### Optimization-Based Energy Management Systems  
The previous section highlighted the effectiveness of rule-based energy management systems (EMS) in achieving power distribution objectives. Nonetheless, when the goal is to minimize both FC consumption and FC degradation, which ultimately reduces the overall operational cost, an optimization-based EMS becomes essential. The optimization-based EMS operates by utilizing an objective function that incorporates system operating parameters, imposes state variables as constraints, and seeks to minimize the objective function through optimization algorithms [37]. These optimization-based EMS solutions can be categorized into two types: global optimization and local optimization.
## 1 Introduction  
The Internet of Things (IoT) has emerged as a transformative paradigm in modern computing, enabling billions of interconnected devices to sense, communicate, and act upon their environments in real time [1, 2, 3]. From smart homes and wearable healthcaredevices to industrial automation and smart cities, IoT systems are deeply integrated into both consumer and mission-critical infrastructures [4; 5; 6]. According to recent forecasts, the global number of IoT devices is expected to surpass 30 billion by 2025, generating nearly 80 zettabytes of data annually. These systems operate across heterogeneous platforms and network protocols, producing complex, high-dimensional traffic that reflects intricate temporal and contextual patterns [7; 8; 9]. While this vast interconnectivity enhances operational efficiency and real-time decision-making, it also significantly expands the potential attack surface, especially given the often resource-constrained nature of IoT hardware and the absence of consistent security standards [10; 11; 12; 13].  
As IoT networks scale in size and complexity, they become increasingly attractive targets for cyber adversaries. Studies report that over half of all IoT devices exhibit vulnerabilities to medium or high-severity threats, and attacks exploiting weak authentication, firmware flaws, or open ports are rising sharply [14; 15; 16]. Sophisticated intrusion vectors such as distributed denial-of-service (DDoS), spoofing, reconnaissance, and malware injection can compromise device integrity and lead to widespread disruption or data breaches. In this landscape, the role of Intrusion Detection Systems (IDS) is vital, not merely to detect known threats, but to identify evolving and previously unseen attacks in real time [17]. However, conventional IDS methods often fall short when faced with the high-dimensional, imbalanced, and dynamic nature of IoT traffic [18]. This highlights an urgent need for intelligent, adaptive, and robust intrusion detection frameworks capable of extracting meaningful features from vast traffic streams and accurately discriminating between benign and malicious behaviors, even under rapidly shifting network conditions [19].
## 2 Materials and Methods
### A Method for Inferring Chemical Graphs
Theorem 3.1: _Let \(\mathcal{N}\) be an ANN with a piecewise-linear activation function for an input vector \(x\in\mathbb{R}^{k}\), \(n_{A}\) denote the number of nodes in the architecture and \(n_{B}\) denote the total number of break-points over all activation functions. Then there is an MILP \(\mathcal{M}(x,y;\mathcal{C}_{1})\) that consists of variable vectors \(x\in\mathcal{D}\)\((\subseteq\mathbb{R}^{k})\), \(y\in\mathbb{R}\), and an auxiliary variable vector \(z\in\mathbb{R}^{p}\) for some integer \(p=O(n_{A}+n_{B})\) and a set \(\mathcal{C}_{1}\) of \(O(n_{A}+n_{B})\) constraints on these variables such that: \(\psi_{\mathcal{N}}(x^{*})=y^{*}\) if and only if there is a vector \((x^{*},y^{*})\) feasible to \(\mathcal{M}(x,y;\mathcal{C}_{1})\)._  
See Appendix A for the set of constraints to define our AD \(\mathcal{D}\) in the MILP \(\mathcal{M}(x,y;\mathcal{C}_{1})\) in Theorem 3.1. To attain the admissibility of inferred vector \(x^{*}\), we also introduce a variable vector \(g\in\mathbb{R}^{q}\) for some integer \(q\) and a set \(\mathcal{C}_{2}\) of constraints on \(x\) and \(g\) such that \(x^{*}\in\mathcal{A}\) holds in the following sense: \((x^{*},g^{*})\) is feasible to the MILP \(\mathcal{M}(x,g;\mathcal{C}_{2})\) if and only if \(g^{*}\) forms a chemical graph \(G^{*}\in\mathcal{G}\) with \(f(G^{*})=x^{*}\). The Phase 2 consists of the next two steps. **Phase 2. **  
**Step 4:**: Formulate Problem (II-a) as the above MILP \(\mathcal{M}(x,y,g;\mathcal{C}_{1},\mathcal{C}_{2})\) based on \(\mathcal{G}\) and \(\mathcal{N}\). Find a set \(F^{*}\) of vectors \(x^{*}\in\mathcal{A}\cap\mathcal{D}\) such that \((1-\varepsilon)y^{*}\leq\psi_{\mathcal{N}}(x^{*})\leq(1+\varepsilon)y^{*}\) for a tolerance \(\varepsilon\) set to be a small positive real. See Figure 6 for an illustration of Step 4. **Step 5:**: To solve Problem (II-b), enumerate all graphs \(G^{*}\in\mathcal{G}\) such that \(f(G^{*})=x^{*}\) for each vector \(x^{*}\in F^{*}\). See Figure 7 for an illustration of Step 5. In this paper, we set a graph class \(\mathcal{G}\) to be the set of rank-2 graphs. In Step 4, we solve an MILP \(\mathcal{M}(x,g;\mathcal{C}_{2})\) that is formulated on a novel idea of representing rank-2 chemical graphs, as will be discussed in Section 2.3.2. In Step 5, we use branch-and-bound algorithms for enumerating rank-2 chemical compounds [25; 26].
# Efficient Wireless Monitoring and Control of a Grid-Connected Photovoltaic System
## 6 Conclusions  
A great number of monitoring applications are based on WSNs, given their advantages in terms of costs by drastically reducing the required cabling, their scalability, flexibility, and variety of network topologies, in addition to the ease of deployment and reduced maintenance. WSNs have successfully provided monitoring solutions in many fields, including PV power plants. Nowadays, many efforts are devoted to increasing the energy production of PV plants, which can be monitored to control the proper operation of the system and assess its production. This paper presents a wireless solution for a grid-connected PV system with two approaches for the control of the DC/DC converters and the inverters. The communication module has been developed to achieve the required low latency for the controllers to operate stably and in real-time. Communications are built using the IEEE 802.15.4 protocol because the aim is to construct a WSN with low power consumption, low complexity, and low data transmission rate. A low-cost wireless multi-sensor node has been deployed with a versatile design, which is able to perform as both a sensor node and a coordinator node. Long-term operation is allowed using solar cells and rechargeable batteries solution to power the nodes, requiring minimum human intervention.  
Experimental results show an MPPT efficiency higher than 99.05% by applying an adaptive Lyapunov control for the boost converter, which transfers energy with an efficiency between 93% and 98%, and for the DC/AC converter, the power transferred to the power grid ranges from 92% to 94.5%, injecting nearly sinusoidal grid currents at near unity power factor since the THD is only 2%. The robustness of the wireless grid-connected PV system has also been verified even under external perturbations in the environmental conditions, ensuring the stability of the system even when there are sudden changes in the irradiance.  
Figure 19: PV system signals: boost converter input voltage and current and DC/AC converter input voltage and output current.  
**Author Contributions:** Conceptualization, A.D.M. and J.A.G.-G.; methodology, A.D.M. and J.A.G.-G.; software, J.M.-G. and J.M.C.; validation, J.M.-G. and J.M.C.; formal analysis, A.D.M. and J.A.G.-G.; investigation, J.M.-G., A.D.M., J.M.C., J.A.G.-G. and A.H.; resources, J.M.-G. and J.M.C.; data curation, J.M.-G., J.M.C. and A.H.; writing--original draft preparation, A.D.M. and J.A.G.-G.; writing--review and editing, A.H.; visualization, J.M.-G.; supervision, A.D.M.; project administration, J.M.C. and J.A.G.-G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# Multi-Gateway-Based Energy Holes Avoidance Routing Protocol for WSN
## 4 Energy Depletion Model  
For a first-order radio model, energy consumed in transmission depends on the distance (\(d\)) between the transmitter and receiver [27]. When the distance is comparatively far away, the multiple-path fading channel model (\(\epsilon_{\text{two-ray-amp}}\); \(d^{4}\) power loss) is used, otherwise the free space model (\(\epsilon_{\text{friss-amp}}\); \(d^{2}\) power loss loss) is used. For a \(k\) bit message and distance \(d\), transmission energy (\(E_{\text{TX}}\)) consumption is as follows:  
Figure 2: MATLAB view of the MGBEHA protocol in (**a**) 500 \(\times\) 500 m\({}^{2}\) area; (**b**) 1000 \(\times\) 1000 m\({}^{2}\) area; and (**c**) 2000 \(\times\) 2000 m\({}^{2}\) area.  
\[E_{\rm TX}\left(k,d\right)=\left\{\begin{array}{c}k\times E_{\rm elec}+k\times \varepsilon_{\rm friss-amp}\times d^{2},d<d_{0}\\ k\times E_{\rm elec}+k\times\varepsilon_{\rm two-ray-amp}\times d^{4},d\geq d_{0} \end{array}\right\} \tag{1}\]  
Energy consumed in receiving \(k\) bits over distance \(d\) is as follows:  
\[E_{\rm RX}\left(k\right)=E_{\rm elec}\times k \tag{2}\]  
where \(E_{\rm RX}\) represent the energy consumed in receiving of k data bits and \(E_{\rm elec}\) is the energy used to activate the transmitter and receiver circuit board. \(\varepsilon_{\rm friss-amp}\) and \(\varepsilon_{\rm two-ray-amp}\) are amplifier energies used for free space and multipath fading model, respectively. The distance \(d_{0}\) is a threshold value known as a crossover point [27]. The energy consumed in the aggregation (\(E_{\rm DA}\)) of m messages is given by:  
\[E_{\rm m}\left(m,k\right)=m\times k\times E_{\rm DA} \tag{3}\]  
where the size of each message is \(k\) bits.
# Study on Heat Transfer Performance and Anti-Fouling Mechanism of Ternary Ni-W-P Coating
###### Abstract  
Since the formation of fouling reduces heat transfer efficiency and causes energy loss, anti-fouling is desirable and may be achieved by coating. In this work, a nickel-tungsten-phosphorus (Ni-W-P) coating was prepared on the mild steel (1015) substrate using electroless plating by varying sodium tungstate concentration to improve its anti-fouling property. Surface morphology, microstructure, fouling behavior, and heat transfer performance of coatings were further reported. Also, the reaction path, transition state, and energy gradient change of calcite, aragonite, and vaterite were also calculated. During the deposition process, as the W and P elements were solids dissolved in the Ni crystal cell, the content of Ni element was obviously higher than that of the other two elements. Globular morphology was evenly covered on the surface. Consequently, the thermal conductivity of ternary Ni-W-P coating decreases from 8.48 W/m-K to 8.19 W/m-K with the increase of W content. Additionally, it goes up to 8.93 W/m-K with the increase of heat source temperature 343 K. Oxidation products are always accompanied by deposits of calcite-phase CaCO\({}_{3}\) fouling. Due to the low surface energy of Ni-W-P coating, Ca\({}^{2+}\) and [CO\({}_{3}\)]\({}^{2-}\) are prone to cross the transition state with a low energy barrier of 0.10 eV, resulting in the more formation of aragonite-phase CaCO\({}_{3}\) fouling on ternary Ni-W-P coating. Nevertheless, because of the interaction of high surface energy and oxidation products on the bare matrix or Ni-W-P coating with superior W content, free Ca\({}^{2+}\) and [CO\({}_{3}\)]\({}^{2-}\) can be easy to nucleate into calcite. As time goes on, the heat transfer efficiency of material with Ni-W-P coating is superior to the bare surface.  
Keywords:anti-fouling performance; first principle; heat-transfer characteristic; surface modification; thermal conductivity; transient state +
Footnote †: journal: Applied Sciences
# Surface-Modified Compounds Improve the Detection Sensitivity of Terahertz Metasurface Biosensors
###### Abstract  
Some trace elements in the human body, such as proteins and metabolites, are closely related to human health. Changes in the concentration of proteins can usually be used as indicators for the diagnosis of diseases. On the other side, some metabolites such as lactic acid and uric acid are closely related to human motor function. A large part of cancer markers are proteins, and their concentration changes in human serum and body fluid are usually closely related to cancer diagnosis, staging and prognosis. It has always been the goal of researchers to efficiently and sensitively detect the relevant trace elements in human body. Due to the characteristics of low energy, coherence and fingerprint, terahertz (THz) waves have been widely used in the detection of substances in recent years. Metasurface sensor is a new type of sensor with unique electromagnetic characteristics based on artificial design. The emergence of THz metasurface sensors provides a new technical means for the detection of trace elements proteins. Based on the above background, the research objects in this paper are three common protein cancer markers: carcinoembryonic antigen (CEA), neuron-specific enolase (NSE) and squamous epithelial cell carcinoma antigen (SCC), in order to study the detection performance of THz metasurface sensor and its improvement effect after modification. In the experiment, the cancer marker is dropped onto the surface of sensor, and the spectrum is detected by THz time-domain system (TDS), and the frequency shift is calculated by comparing with the resonance frequency of the blank sensor. Then the experiment was repeated by changing the concentration and class of the marker, and the sensitivity of sensors was calculated by fitting the relationship between the frequency shift and the concentration of markers. After that, two compounds (halloysite nanotube and Tungsten disulfide) were used to modify the metasurface sensor, and compared with the unmodified metasurface sensor. The experimental results show that for the detection of three markers, the metasurface sensor modified with HNT has significantly improved the detection sensitivity compared with the sensor modified with WS\({}_{2}\) and blank sensor. This provides a new means for THz metasurface sensors in the detection of biomarkers, which is suitable for biomedicine, human health monitoring and other fields.  
terahertz time domain system; metasurface sensor; cancer markers; detection of protein
## 2 Materials and Methods  
### Data Source  
This research used emission data from the PRIMAP-hist dataset [53]. The PRIMAP-hist dataset combines several existing data sources for every country and Kyoto gas from 1750 to 2019, including all UNFCCC (United Nations Framework Convention on Climate Change) members and the vast majority of non-UNFCCC regions. The primary IPCC (Intergovernmental Panel on Climate Change) 2006 categories are resolved by the data. For CO\({}_{2}\), CH\({}_{4}\), and N\({}_{2}\)O, there are subsector statistics for energy, industrial processes, and agriculture. The PRIMAP-hist dataset compiles both country-reported and third-party data. However, in this paper, only country-reported data were used for Saudi Arabia. The PRIMAP-hist GHG emission dataset can be obtained from [53]. The population data were taken from the World Bank repository, where the country-wise population data are available from the year 1970 to 2021 [54]. However, since the GHG data are only available up to the year 2019, the population data up to the same year were used to calculate country-wise yearly per capita GHG emissions. In addition to population data, energy consumption, GDP, urbanization, per capita income (PCI), and foreign direct investment (FDI) were also collected and processed from the world bank repository [54]. To understand the trends in different data on Saudi Arabia, a scatter plot is provided in Figure 1. Most of the data show an upward trend from the year 1970 to 2021. In order to observe the correlation of different data, in Figure 2, a correlation matrix plot is provided. It is evident from the figure that GHG emissions are highly correlated with population, energy, and GDP and moderately correlated with other parameters.
## 5 Results and Discussion
### Experimental Results and Analysis of Laser Scribing Depth with Laser Incidence Angle
#### 5.3.2 Result of Side Slope Angle  
Under the given laser power, speed and frequency parameters, the depth of laser scribing at vertical incidence angle is smaller than the depth of laser scribing at \(\pm 7^{\circ}\)side slope angle, as shown in the first and second points in Figure 17a. With the increase of the side slope angle increases in the positive and negative directions, the scribing depth decreases linearly, as shown in Figure 17b.
## 5 Implementation and Caveats
### Incorrect Carrier Phase Reference Epoch  
The correlator integrates correlation results from \(t\) to \(t+T\) and dumps it at \(t+T\), then the phase detector (discriminator) estimates the error between the received true phase and the estimated phase in the NCO at \(t+T\). The loop filter predicts the next phase and phase rate for \(t+2T\) and controls the NCO accordingly. However, the phase error obtained at \(t+T\) actually indicates the phase error at \(t+T/2\). This is because the integrated correlator outputs are the mean values between \(t\) and \(t+T\) and the resulting phase error is also the mean phase error during the integration period because it is calculated using the mean correlation results. Since the NCO rate is constant over that period and the incoming phase rate is likewise typically assumed to be constant for \(T\) in the digital loop, the true and estimated phase and the phase error change linearly in that duration. Consequently, the mean phase error reasonably represents the phase error for the midpoint in the integration process (which is at \(t+T/2\) in this case).  
The reference epoch of the estimated phase can be defined differently by each receiver designer. Defining the phase to represent the start point of the integration has an effect such as the insertion of a delay for \(T/2\) (half-sample delay). In this work, for the simplicity of the analysis to easily assess the upper and lower limits of \(BT\), it is assumed that the carrier phase is defined at the midpoint of the integration interval without any delay, which is an ideal case. This definition is reasonable because the possible Doppler error does not affect the carrier phase discriminator value, and the delay effect on carrier measurement can be compensated later at the measurement extraction module in the baseband process of receivers.  
On the other hand, a real-world implementation of a DPLL might use a carrier phase value defined at the beginning of the integration interval for convenience. In this case, a Doppler error affects the carrier phase discriminator output (i.e., the Doppler-based phase error is the Doppler error multiplied by \(T/2\)). If this assumption is used (without accounting for the Doppler error) in combination with the digitization scheme presented here, the tracking stability is degraded. This phenomenon is shown in Figure 11. In fact, the stability drops for a second-order PLL with the SI/SI scheme from \(BT=0.75\) to \(BT-0.4\) (determined on an empirical basis) when \(t_{\rm D}=0\).  
Figure 10: Carrier phase tracking error for \(T=20\) ms, a second-order DPLL with SI/SI numerical integration, \(t_{\rm D}=0\), \(C/N_{0}=47.7\) dB-Hz and various bandwidths.