esa-sceva/satcom-chunk-collection huggingface dataset:
# Clone Node Detection Attacks and Mitigation Mechanisms in Static Wireless Sensor Networks
## 7 Contribution and Future Proposal  
The primary goal of this paper is to make structured, methodical research on clone node detection in the static wireless sensor network. In this paper, we have researched clone node detection attacks in static wireless sensor networks, commonly named replication attacks. We also examine some security issues, some mitigation mechanisms (solutionsand countermeasures) to reduce the clone attacks. In conclusion, we propose an ontology-based approach Ontology for Replication Attacks in Static Wireless Sensor Networks \"ORASWSN\" to thwart this particular attack.  
We have shown that for the replication attacks to be executed, the attacker must have physical access to the sensor nodes so that he can capture, extract and modify them. As it is required in an ICS network, there can be a good physical safeguard as well for static WSNs systems because there is important and sensitive information that is stored in the nodes. Additionally, in every WSN system, we strongly recommend that cryptographic methods must be utilized to ensure the security of the communication between nodes is at a high level. We proposed an ontology-based approach that can be used as a good help for the detection and mitigation of replicas in the static WSN. Therefore, one of our future goals will be focused on the in-depth mitigation of the clone node attack in the static wireless sensor network.  
We consider that the mission of the attacker can never be related to the clone node attack. Yes, it can be a goal, an objective, or a strategy but not the mission. The reason is that the attacker has to perform some other additional attacks if he wants to gain more information about the system, such as DoS, eavesdropping, buffer overflow, etc.
## 5 Discussion
### Cohesive Soils  
Assume a shallow foundation located in a semi-infinite space. The failure mechanism of cohesive soil directly below the foundation consists of a triangular active zone (I), a fan-shaped zone (II), and a triangular passive zone (III) on its right side, as shown in Figure 6a. In this study, the fan-shaped zone is subdivided into \(n\) blocks (including \(n=1\), \(2\), \(3\), \(5\), \(6\), \(9\), \(10\), \(15\), \(18\), and \(30\)). Figure 14 especially shows the case study with block number \(n=1\). The Mohr-Coulomb interface parameters at each stage are \(c=c_{u}\) and \(\phi=0^{\circ}\), and the numerical analysis results are shown in Table 2. This table demonstrates that increasing the number of subdivisions, \(n\), in the transition zone significantly reduces the error between the numerical results and the analytical solution. When the transition zone is divided into \(18\) blocks, the error margin decreases to approximately \(0.19\%\), indicating that the finite element analysis achieves efficient convergence at this level of discretization. These findings suggest that appropriately refining the transition zone enhances the compatibility of displacement between rotational and sliding motions, as shown in Figure 15. Based on the analysis, it is recommended to subdivide the transition zone using a segmentation angle of \(5^{\circ}\), which corresponds to \(18\) blocks within the \(90^{\circ}\) fan-shaped sector, to achieve optimal accuracy and computational efficiency.  
Figure 14: Relationship between bearing capacity factor \(N_{c}\) and block number \(n\) of the transition zone of cohesive soils.
## 2 Methodology and Data Selection
Usually, KPIs are defined by mathematical expressions that involve sums, divisions, or percentages [26]. There can be correlation between KPIs or even to elements in the network topology. The correlation to a part of the network topology is referred to as aggregation level. KPIs definitions and requirements can be found in [3; 27; 28] for Release 16, for example. KPIs are arranged in six categories, namely, accessibility, retainability, integrity, availability, mobility, and energy efficiency. The main difficulty found when building KPIs is the source of information, which is, the way that the data are filtered and collected. Focusing on the radio access part of the network, three main sources of information can be defined. The drive test methods collect KPIs at sampling points in the region of interest while using a motor vehicle that is equipped with wireless signal receivers. Several sampling methods have been defined and, although the data obtained are reliable and real, KPIs cannot change dynamically with time, sampling points can only be defined in accessible regions and the cost is elevated. Network event counters do not provide a user vision, such as drive tests, but they do offer a global vision of the network. Finally, registering signaling traces between the user and the network assumes that the network elements can gather and forward to upper communication layers a massive amount of data. Note that network event counters and trace analysis are able to extract real-time information in order to assess the performance of the network from the operator point of view. In the following section, the above steps will be tailored while assuming a UMTS-based mobile communication network and appropriate KPIs will be selected according to the methodology requirements. Our system model comprises a MNO with cells that are clustered according to area coverage. Five clusters are defined, namely, urban macro, suburban, urban micro, rural macro, and rural micro, as shown in Figure 1.
## 4 Evaluation and Results
### Approximation of the Acceleration Vector Term  
The expected value of the Doppler rate by the acceleration vector term \(\mathbb{E}\left[\Delta f_{r,a}\right]\) shows different characteristics from those of the velocity vector terms. Figure 4a no longer has an expected value of zero compared with Figure 2a. This non-zero \(\mathbb{E}\left[\Delta f_{r,a}\right]\) value is due to the calculation of true anomaly from onboard state vector measurements, which also result in a non-zero \(\mathbb{E}\left[\text{QPE}_{a}\right]\). In addition, the analytical approximation model fits the numerical simulation result for all \(\
u\in\left[0,2\pi\right]\).  
For the comparison between \(\sigma\left[\Delta f_{r,a}\right]\) and \(\sigma\left[\text{QPE}_{a}\right]\), at most positions, the analytical approximation model fits the numerical simulation point by point. Apart from some simulation points, it maintains accuracy. From the comparison of Figure 4c,d, the influence of the integration time may not seem significant in contrast to the comparison of Figure 2c,d. In fact, both \(\sigma\left[\text{QPE}_{a}\right]\) and \(\sigma\left[\text{QPE}_{\text{p}}\right]\) are calculated with the same integration time series. This major difference in the curve appearance is caused by the relatively narrow fluctuation interval of \(\sigma\left[\text{QPE}_{p}\right]\).  
Figure 4: Acceleration vector term: numerical simulation results (blue) and analytical approximation results (red). The analytical approximation results are consistent with the numerical simulation results. The expected values in (**a,b**) no longer have an expected value of zero, indicating different characteristics from those of the velocity vector terms. The differences around \(90^{\circ}\) and \(270^{\circ}\) of true anomaly between the two sets of results in (**c,d**) are due to the ignored terms in the calculated true anomaly approximation derivations.  
Figure 3: The true anomaly result from measurements between numerical simulation and the analytical approximation model. (**a**) The analytical approximation model (red) fits the numerical simulation (blue) in most areas of true anomaly. However, at certain true anomalies, which are the unique positions of the satellite in its orbit, the difference between the two sets of results increases. (**b**) At around \(90^{\circ}\) and \(270^{\circ}\), significant peaks arise for approximately \(0.023^{\circ}\) in the standard deviation of \(\Delta\
u\).
# Joint SAR Image Time Series and PSInSAR Data Analytics: An LDA Based Approach
## 4 Experimental Setup
### Preprocessing
Variable offsets will then be estimated in both range and azimuth directions, by maximization of the amplitude's correlation index--Equation (8) this algorithm is identical to the one applied in the case of ERS images. A supplementary refinement step is considered to avoid the possible aforementioned problems: double interferograms are generated in the bursts' overlapping regions, offsets corrections are estimated by equaling those interferograms with zero. Therefore, it is essential, that in the case of the Sentinel-1 dataset, that the co-registration operation is implemented using at least two bursts of the images. The test region presented in the Figure 4B is situated in all the images at the division of two bursts, therefore those adjacent bursts were chosen for the alignment process. To validate the co-registration process of Sentinel-1 dataset, the interferogram of an image pair was generated. If the image alignment is precise, the burst delimitation should not be visible in the interferometric phase. Interferometric phase generated from the images pair acquired on 20 December 2015 (reference) and 20 August 2015 (slave), from which the Earth's curvature contribution has been subtracted, is presented in Figure 6. The perpendicular baseline of this interferometric pair equals 160.91 m, which is a relatively high value in order to maximize the interferogram's sensitivity to the terrain's topography, while its temporal baseline is under three months, in order to minimize the effects of temporal decorrelation. It can be noticed that burst delimitation is not visible in the generated interferometric phase, therefore the co-registration of the dataset's images is precise. Figure 5: Mean value of the amplitude’s cross correlation index, computed across the co-registered images dataset—Buzáu area.
# Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
###### Abstract
* The **incorporation of HCV-specific parameters**, such as acceleration constraints, spatial requirements, and cargo priority, into adaptive traffic signal logic. * **Empirical validation** Demonstrating a significant reduction in HCV delay and stop frequency, with up to a 50% decrease in idling time and improved traffic flow efficiency for platooned vehicles. Figure 1: Connected vehicles platooning using CACC [11]. This paper is structured as follows: Section 2 presents a review of existing literature on traffic signal optimization and heavy vehicle management, highlighting the need for HCV-focused strategies. Section 3 details the methodology, including the development of the adaptive signal-control algorithm, data-collection protocols, simulation modeling, and calibration processes. Section 4 presents the results, demonstrating the algorithm's effectiveness in reducing HCV delays and stops under varying traffic scenarios. Finally, Section 5 discusses real-world applicability, challenges, and future research directions, including the integration of AI-based optimizations and vehicle-to-infrastructure (V2I) advancements. By addressing the unique challenges of HCVs at signalized intersections, this study advances a platoon-centric paradigm for urban logistics management, offering practical insights for optimizing freight mobility within connected transportation systems.
# Dual-Band Wearable Antenna Integrated with Glasses for 5G and Wi-Fi Systems
## 2 Materials and Methods  
This research work elucidates the antenna design and the methodology employed in its research. It begins with a thorough exposition of the design specifications and the presentation of the preliminary design that did not meet the requirements. Then, the final design is presented. Given the reliance on computer simulations, the corresponding results are also detailed within this section.
## 2 Related Works
### RL
The AESMOTE framework combined RL with class-imbalance techniques to enhance the behavior of the environment agent and achieve improved performance in anomaly detection. AESMOTE introduced a dynamic environment fordetecting anomalies in the NSL-KDD dataset by employing various oversampling and undersampling methods. In the AESMOTE framework, the environment agent can self-learn without requiring supervision. The primary objective of AESMOTE was to provide a more effective sampling algorithm for the training dataset, aiming to achieve a balanced distribution for the classifier agent. The classifier agent learns the prediction performance and selects the most challenging samples for training, thus ensuring a balanced dataset. To evaluate the performance of AESMOTE, [PERSON] et al. conducted experiments on the NSL-KDD IDS dataset and employed metrics such as accuracy, recall, precision, F1 score, and time. Unfortunately, the authors did not explicitly mention the programming language used for implementation. Comparing AESMOTE with the AE-RL framework, [PERSON] et al. demonstrated superior performance, achieving an F1 score of 0.8243, accuracy of 0.8209, precision of 0.8411, and recall of 0.8209. These results highlighted the effectiveness of AESMOTE in enhancing the detection performance for anomaly detection in IDS datasets. In Section 5, AESMOTE is further compared with RLFOUA, which is regarded as the most recent and best-performing method for detecting attacks in IDS datasets.
# Road Infrastructure Investment Limits Based on Minimal Accidents Using Artificial Neural Network
## 2 Data and Methodology  
The data utilized in this study were obtained from the Organization for Economic Co-operation and Development (OECD) database. The OECD has about 36 member countries and several partner countries. Some of these countries include the United States of America (USA), Germany, France, Canada, Spain, Australia, Japan, Italy, etc. Annual data from member and partner countries for 22 years (1994-2016) were employed in this study. Historical data that were analyzed included annual road accidents, freight and passenger transport indicators, changes in the number of new vehicles registered, investments in new road infrastructure and existing road maintenance, and demographic and economic indicators (see Table 1). Some previous studies that have employed the OECD database include the statistical modeling of road mortality (35; 36), comparisons of death from road accidents with international terror (37), modeling traffic fatalities using autoregressive nonlinear time series (38), the risk of severe fatal accidents using Bayesian data analysis (39), etc. In this study, annual road accidents (ARAs) were modeled as a function of all the other listed variables using multiple linear regression (MLR), Poisson regression (PR) analysis, and artificial neural network (ANN). Because the ANN model is more accurate, it was utilized to closely examine how these variables collectively affect annual road accidents. Safe investment limits for new road infrastructure and for the maintenance of existing roads based on the minimum ARAs were estimated for a wide range of demographic, freight transport, passenger transport, and economic systems.
## 2 System Description
Since this paper focuses on the post-FFT synchronization processing, we consider the situation where coarse CFO estimation has been done before FFT is performed. In addition, we assume the perfect knowledge of the STO at the receiver. After discarding the GI and performing \(N\)-point FFT for the \(l\)-th OFDM symbol, the received OFDM signal in receiver antenna \(r\) at subcarrier \(k\) appears as  
\[Y_{l}^{\prime}(k)=\frac{1}{N}\sum_{n=0}^{N-1}y^{\prime}((n+N_{g}+lN_{s})T^{ \prime})e^{-2\pi\epsilon{\rm ln}/N},\ r=1,2,\cdots,N_{R}, \tag{7}\]  
which is further derived as [33; 34]  
\[Y_{l}^{\prime}(k)=\frac{1}{\sqrt{N_{T}}}\sum_{m=1}^{N_{T}}\beta(k)H_{l}^{m,r}( k)X_{l}^{m}(k)e^{j2\pi\varphi(k)(lN_{k}+N_{g})/N}+C_{l}^{\prime}(k)+Z_{l}^{ \prime}(k),\ r=1,2,\cdots,N_{R} \tag{8}\]  
where \(\varphi(k)=\epsilon+k\xi\), \(\beta(k)=\sin(\pi(\varphi(k))/N\sin(\pi\varphi(k)/N)\), \(H_{l}^{m,r}(k)\) is the equivalent channel transfer function (CTF) from transmit antenna \(m\) to receive antenna \(r\) with variance \(\sigma_{H}^{2}\), \(C_{l}^{\prime}(k)\) is a zero-mean complex ICI with variance \(\sigma_{C}^{2}\) at receive antenna \(r\), and \(Z_{l}^{\prime}(k)\) is a zero-mean complex AWGN with variance \(\sigma_{Z}^{2}\) at receive antenna \(r\). For typical values of \(\epsilon\) and \(\xi\), \(\beta(k)\approx 1\) and \(\sigma_{C}^{2}\approx E_{X}\sigma_{H}^{2}\epsilon^{2}\pi^{2}/3\)[17]. Since the same common CPs are transmitted from the transmit antennas in the MIMO-OFDM ATSC  
Figure 1: System model of the multiple-input multiple-output orthogonal frequency division multiplexing-based advanced television systems committee (ATSC) receiver. system [10], an equivalent CTF at the receive antenna \(r\) can be treated as a sum of CTFs from \(N_{T}\) transmit antennas  
\[H_{l}^{r}(k)=\frac{1}{\sqrt{N_{T}}}\sum_{m=1}^{N_{T}}H_{l}^{m,r}(k), \tag{9}\]  
which is modeled as a complex zero-mean Gaussian process with variance \(\sigma_{H}^{2}\).
## 1 Introduction
Their research demonstrated that the XGBoost model exhibits notable advantages in terms of accuracy and interpretability, surpassing other models when the prediction results were compared. This highlighted the effectiveness of such ensemble methods, specifically the XGBoost model, in the context of underground engineering applications. In summary, traditional research methods in the field of geotechnical engineering have often relied on specific assumptions to simplify complex site conditions. However, such a simplified approach has its limitations and can adversely affect the accuracy of research outcomes. In contrast, while machine learning technologies have found widespread applications in predicting foundation deformations and surface settlements, there is a noticeable scarcity of studies focusing on tunnel uplift displacement. This study introduces an integrated learning algorithm, the BO-XGBoost model, for predicting the uplift displacements of tunnel vaults caused by excavations. The model combines the XGBoost algorithm with a Bayesian optimization (BO) algorithm, optimizing its hyperparameters. A comprehensive database of 170 cases was prepared through extensive engineering research. The data quality and features played a crucial role in determining the upper bound of the model's accuracy. Therefore, this paper provides a detailed description of the dataset processing. Next, the BO-XGBoost prediction model for tunnel uplift is presented, and its performance is evaluated in comparison to SVM, CART, and unoptimized XGBoost models. The evaluation criteria included root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R\({}^{2}\)) values, which were used to assess the model's learning and prediction abilities. Furthermore, the interpretability of the BO-XGBoost model is analyzed. The results demonstrate that the BO-XGBoost model exhibited the highest prediction performance for maximum tunnel -vault uplift displacements. Moreover, the interpretability of the model will offer valuable guidance to civil engineers during their decision-making processes.
# A pH Monitoring Algorithm for Orifice Plate Culture Medium
## 4 Discussion  
In the process of cell culture, in order to meet the environmental conditions of cell growth, it is necessary to reduce the effect of low pH on cell growth. Along with the growth of cells, the waste of cell metabolism increases gradually. This process changes from bright red to orange in the cell culture medium as shown by color indicators. Therefore, in the process of cell culture in vitro, it is necessary to monitor the change of pH--that is, the change of the color indicator. In image processing, this process can be shown by its tone.  
To solve the problem of excessive cell metabolites affecting cell growth, we need to monitor the change of pH during culture. This paper establishes the relationship between color distance characteristic values and pH and shows that there is an inverse relationship between pH value and the average hue. The cubic fit curve between the average of hue and its pH meets the requirements of the F-test and feasibility analysis. The root means square error between the predicted pH value and the real pH value of the fitting curve was 0.01588. Based on the experimental data, the accuracy of the predicted pH values is high. The results showed that pH monitoring of cell culture medium based on HSV color space did not require operator observation and midway sampling. This method reduces the possibility of cell contamination during sampling and is a low-cost and high-efficiency non-destructive pH monitoring method for cell culture media.
## 4 Experimental Results
### Baselines  
To validate the efficacy of our proposal, we selected supervised state-of-the-art models in the road-extraction domain. The selected models were U-Net [21], Attention U-Net [25], DeepLabV3+ with ASPP [13], SegNet [34], RFE-Link Net [28], DCSFEP [29], and D-LinkNet [27].
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 2 Materials and Methods
### Datasets
#### 2.2.5 Climate Data  
The climatic variables used in this study were obtained from the Parameter elevation Relationships on Independent Slope Model (PRISM) [66]. The PRISM climate group collected data from many stations across the CONUS and used sophisticated methods to derive climate datasets. The PRISM data are provided at two spatial resolutions: 4 km and 800 m [57]. The 4 km PRISM data are available to the public. The climatic variables used in this study include precipitation, maximum temperature, minimum temperature, mean temperature, minimum vapor pressure deficit, maximum vapor pressure deficit, and mean dew point temperature. The climatic variables used in this study are based on previous studies [20; 24; 27; 28; 33]. The PRISM datasets have different temporal resolutions: daily, monthly, and yearly [67]. In this study, we utilized the daily PRISM data and aggregated them to derive 16-day composites since time-series NDVI and EVI data have a temporal resolution of 16 days [54].
## 3 Code Synchronization Algorithm Based on the Segment Correlation
### Overall Design of the Code Synchronization Module  
The overall block diagram of the code synchronization module in the digital transceiver is shown in Figure 5. The workflow of the code synchronization process can be summarized as follows:  
1. A correlation operation between the received signal and the carrier signal is applied by the correlator.
2. Then, the computed value is analyzed by the code acquisition module. The phase difference between the carrier signal generated by the local NCO and the carrier signal of the received signal will meet the design requirement after it passes through the code acquisition module.
3. Tracking operation and adjustment will be performed. The phase difference between the local carrier signal and the carrier of the received signal is further decreased during the tracking process by adjusting the clock of the local code generator step by step [18].  
Figure 5: Block diagram of the code synchronization module.  
The code acquisition module and the code tracking module are the two main components of the code synchronization module. The main work of our paper is to propose a novel code acquisition algorithm.