esa-sceva/satcom-chunk-collection huggingface dataset:
# On Channel Estimation in LTE-Based Downlink Narrowband Internet of Things Systems
###### Abstract  
Narrowband Internet of Things (NB-IoT) systems were specified by 3 GPP in release 13 as a low power wide area network (LPWAN) technology to operate with a very narrow bandwidth of 180 kHz only. Due to fragile radio signal operating conditions (where a signal is weaker than noise), NB-IoT channel status becomes highly complex. Therefore, an effective and low complexity channel estimation will perform a significant role in the receiver operation. The linear minimum mean square error (LMMSE) scheme is very effective in estimating the channel but introduces massive complexity because of having complex matrix inversion. In this paper, we first derive the analytical model of the signal for long-term evolution (LTE)-based NB-IoT downlink systems and propose a reduced complexity LMMSE channel estimation for the downlink NB-IoT systems by applying singular value decomposition (SVD) technique along with partitioning the whole channel matrix into small submatrices. Furthermore, we apply the overlap band technique to optimize the performance of the proposed channel estimator. As a result of exploiting several submatrices instead of a larger channel matrix, the operational complexity is significantly optimized. Lastly, we propose a polyphase filter structure for implementing the interpolation procedure instead of the conventional interpolation method to further optimize the performance and complexity of the proposed channel estimator further. The performance of the proposed technique has been justified by the mean square error (MSE), bit error rate (BER), and instantaneous throughput for the related signal-to-noise ratio (SNR). The system complexity is verified by the number of complex multiplications used. Simulation evaluations indicate that with the sacrifice of negligible performance, the proposed modified LMMSE technique along with the proposed interpolation possesses a good balance between the performance and the system complexity that could help the proposed techniques to be applied successfully in the low complexity NB-IoT systems.  
NB-IoT; channel estimation; linear minimum mean square error; least square; singular value decomposition; polyphase decomposition +
Footnote †: journal:  
_Acticle_  
On Channel Estimation in LTE-Based Downlink Narrowband Internet of Things Systems
## 3 Inferring an Agent's Desires
### Reward Function Discrimination
#### 3.1.5. Linearly-Solvable MDPs  
A severe limitation of the approaches we have reviewed thus far is their requirement to solve the forward MDP on each iteration, which comes at a high computational cost. [PERSON] and [PERSON] [76] present a method based on linearly solvable MDPs (LMDP) [77], which was the first to not require solving the forward problem. LMDP provide an approximation to MDPs that enables finding solutions faster at a small cost in accuracy. This is achieved through a decomposition of the dynamics into the environment's passive dynamics \(\Pr(s^{\prime}|s)\) (not to be confused with transition dynamics \(\Pr(s^{\prime}|s,a)\)), assumed to be given, and the control dynamics \(\pi(s^{\prime}|s)\) (not to be confused with the policy \(\pi(a|s)\), though they are closely related).  
The reward function (here, cost to be minimised) \(R\) comprises a state term \(r(s)\geq 0\) (to be inferred) and a control term that is the KL-divergence between the control dynamics and the passive dynamics (in order for the KL divergence to be defined, it is required that \(\pi(s^{\prime}|s)=0\) when \(\Pr(s^{\prime}|s)=0\), a condition that is imposed),  
\[R(s,\pi(\cdot|s))=r(s)+D_{KL}(\pi(\cdot|s)||\Pr(\cdot|s)). \tag{41}\]  
A desirability function \(z(s)=\exp(-V(s))\) is used to define the optimal control dynamics  
\[\pi^{*}(s^{\prime}|s)=\frac{\Pr(s^{\prime}|s)z(s^{\prime})}{\sum_{\zeta}\Pr( \zeta|s)z(\zeta)}. \tag{42}\]  
When the demonstration sample size is larger than the number of states, the method can recover the value function analytically, as the MLE of the unconstrained, convex function  
\[\Pr(\tau|V)=\sum_{s^{\prime}}\bar{\mu}_{E}(s^{\prime})V(s^{\prime})+\sum_{s} \bar{\mu}_{E}(s)\log\sum_{s^{\prime}}\Pr(s^{\prime}|s)\exp\bigl{(}-V(s^{\prime })\bigr{)}, \tag{43}\]  
which is uniquely defined. The policy and reward function are subsequently recovered from the value function estimate through \(z(s)\). When the size of the demonstration sample is smaller than the number of states, the (negative) likelihood can be optimised with respect to \(z(s)\) instead, although the resulting function is nonconvex and its optimisation is slower and susceptible to local minima.  
The value function can be represented as a look-up table or approximated as a linear function of features. Additionally, they suggest a method to automatically initialise and adapt the features in continuous space, employing Gaussian radial basis function kernels. A further potential advantage of this method is that it does not require trajectories \((s,a)\), operating over state transitions \((s,s^{\prime})\) instead.  
Under passive dynamics, \(\Pr(\tau|s_{0})=\prod_{t=1}^{H}\Pr(s_{t}|s_{t-1})\) is the probability of a trajectory. For the same trajectory to occur when the control dynamics are applied, the probability is  
\[\Pr(\tau|s_{0},\pi)=\frac{\Pr(\tau|s_{0})\exp\Bigl{(}-\sum_{t=0}^{H}r(s_{t}) \Bigr{)}}{z(s_{0})}. \tag{44}\]  
Note the similarity with MaxEnt IRL. Under uniform passive dynamics, MaxEnt IRL is an equivalent approach for LMDP.
# Advanced Technology in ATSC3.0: Boosting Data Signal Rates Using Polarization Properties and FSS +
## 1 Introduction
In adaptive modulation, the modulation scheme is dynamically adjusted based on the received signal level, while transmit power control (TPC) is used to maintain the desired signal quality. However, in OFDM and OFDMA systems, applying adaptive modulation to all subcarriers requires substantial control information, leading to increased overhead and reduced system efficiency [18,19]. FSS offers an effective solution to this challenge. The FSS process equalizes the received signal-to-noise ratio (SNR), allowing for the implementation of adaptive modulation and TPC using a single feedback value [20]. As a result, the amount of required control information is significantly reduced, while effective modulation adaptation and power control are maintained. This paper demonstrates the effectiveness of utilizing XPD in ATSC 3.0 through simulation results, and some of the content is an extended version of our previous work [21]. This paper is organized as follows. The system model is described in Section 2. In Section 3, we show the proposed system. In Section 4, we show the computer simulation results. Finally, the conclusion is given in Section 5.
## 5 IETF ROLL Working Group
### Group Description and Key Protocols
#### 5.1.2 IPv6 Routing Protocol for Low Power and Lossy Networks
In a DODAG Information Solicitation message the base field will consist of a reserved 1 byte flag field, a 1 byte reserved field. Together, with the flag and reserved field, unassigned bits must be set to zero on transmission and must be ignored on reception. The DODAG Information Object (Figure 11) contains information that allows receiving nodes to learn and configure for joining a DODAG. The message can indicate if the DODAG is grounded (1 bit G-field), which _mode of operation_ (MOP) is followed and how preferable the root of this DODAG is compared to other DODAG roots (Prf-field). The message also contains the version number, the RPLInstanceID, DODAGID (128-bit IPv6 address that uniquely identifies a DODAG), a number of flags and the rank of the sending node. The _Destination Advertisement Object_ (DAO) message is illustrated in Figure 12. The RPLInstanceID, learned from the DIO, is copied into the DAO message. When the sender expect the receiver to send a DAO-ACK the K-flag is set. To indicate the presence of the DODAGID field, the \begin{table}
\begin{tabular}{c c} \hline \hline
**Code Field** & **RPL Message Type** \\ \hline
**0x00** & DODAG Information Solicitation (DIS) \\
**0x01** & DODAG Information Object (DIO) \\
**0x02** & Destination Advertisement Object (DAO) \\
**0x03** & Destination Advertisement Object Acknowledgment \\
**0x80** & Secure DODAG Information Solicitation \\
**0x81** & Secure DODAG Information Object (DIO) \\
**0x82** & Secure Destination Advertisement Object (DAO) \\
**0x83** & Secure Destination Advertisement Object Acknowledgment \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Code fields in RPL ICMPv6 messages. Figure 10: ICMPv6 header for RPL control messages. D-flag is used. The DAOSequence indicates the incremented sequence number incremented every time a node sends out a unique DAO message.
# IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities
## 3 Results
### Data Preparation  
Data preparation is critical to building accurate and reliable anomaly detection models. Initially, raw data was collected from the smart grid, resulting in a data set containing multiple dimensions and characteristics. However, this data set presented outliers, missing data, and noise. To address these problems, Python was used with libraries such as Pandas and NumPy. For example, data was cleaned by identifying and eliminating missing values in electricity consumption measurements, representing 1.5% of the total records.  
Another fundamental aspect is the role of engineering in improving data representation. Various techniques were applied, such as normalization and data scaling. In this case, voltage measurements were normalized to 0 to 1 to ensure uniform scaling across characteristics. In addition, relevant statistics, such as the moving average of the last 24 h of electricity consumption at each measurement point, were calculated, allowing long-term trends and daily patterns to be captured. Statistics included average, minimum, and maximum energy consumption at each location. Table 3 presents the statistical sample for 10 specific measurement points in more detail. These values provide a detailed view of the behavior of electrical consumption at different measurement points, which was essential for the analysis and development of our anomaly detection models.  
A selection of relevant features was performed to reduce the dimensionality and improve the efficiency of the model. The principal component analysis (PCA) method was used to identify the most informative dimensions in the data set. The PCA allowed us to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Measurement Point**} & **Average** & **Minimum** & **Maximum** \\  & **Consumption (kWh)** & **Consumption (kWh)** & **Consumption (kWh)** \\ \hline Point 1 & 25.4 & 18.2 & 30.1 \\ Point 2 & 28.6 & 22.5 & 34.2 \\ Point 3 & 23.8 & 20.1 & 27.5 \\ Point 4 & 27.2 & 21.9 & 32.6 \\ Point 5 & 26.1 & 19.8 & 31.4 \\ Point 6 & 24.9 & 18.6 & 29.8 \\ Point 7 & 29.3 & 23.7 & 35.2 \\ Point 8 & 22.7 & 17.5 & 28.4 \\ Point 9 & 25.8 & 19.4 & 30.7 \\ Point 10 & 26.7 & 20.9 & 32.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average electricity consumption and ranges at measurement points.  
identify the most informative dimensions in the data set. Initially, the data set contained 50 original features, each representing different aspects of electricity consumption. A new set of 20 features was obtained by applying PCA, shown in Table 4, maintaining 95% of the total variance. These 20 resulting features were selected based on their ability to explain the variability present in the data, allowing the most relevant information to be retained while less informative parts were discarded. This feature selection approach significantly improved the model's efficiency without sacrificing the results' quality.
# A Comparative Study of Weighting Methods for Local Reference Frame
## 3 Evaluation Methodology
### Evaluation Criterion  
In theory, if two LRFs (\(\mathbf{L}(\mathbf{p}^{m})\), \(\mathbf{L}(\mathbf{p}^{s})\)) of corresponding keypoints (\(\mathbf{p}^{m}\), \(\mathbf{p}^{s}\)) in the model and scene are error-free, the two LRFs should conform to the equation \(\mathbf{L}(\mathbf{p}^{s})=\mathbf{R}_{GT}\mathbf{L}(\mathbf{p}^{m})\), where \(\mathbf{R}_{GT}\) is the ground-truth rotation matrix. Therefore, we can use the criteria suggested in reference [1] to measure the error between two LRFs:  
\[error_{\mathbf{p}^{m},\mathbf{p}^{s}}=a\cos\!\left(\frac{trace(\mathbf{L}(\mathbf{p}^{s}) \overline{\mathbf{L}}(\mathbf{p}^{m})^{-1})-1}{2}\right)\!\frac{180}{\pi}, \tag{7}\]  
where \(\overline{\mathbf{L}}(\mathbf{p}^{m})=\mathbf{R}_{GT}\mathbf{L}(\mathbf{p}^{m})\). For each dataset, the ground-truth transformation is known in advance. The transformation of the Retrieval dataset is artificially set and then used to simulate the point clouds of the corresponding scenes. The transformations of the Laser Scanner, Kinect, and Space Time datasets are provided by the publishers. The transformations of the LiDAR Registration and Kinect Registration datasets are obtained by manual alignment.  
After computing the errors of all the LRF pairs, we calculate the ratio of the LRF pairs in a dataset whose errors being smaller than 10 degrees by  
\[repeatability=\frac{N_{10}}{N_{t}}, \tag{8}\]  
where \(N_{10}\) denotes the number of the LRF pairs whose errors smaller than 10 degrees, \(N_{t}\) denotes the total number of the LRF pairs.  
Eight kinds of nuisances (i.e., Gaussian noise, shot noise (i.e., outliers), point density variation, keypoint localization error, clutter, occlusion, partial overlap, and varying support radii) are tested in this paper. The detailed definition of each nuisance can be found in reference [20].
###### Abstract  
We investigated the role of metal artifact reduction by taking 3D print impressions using 3D data of Computed Tomography (CT) images based on the algorithm applied. We manufactured a phantom of a human mandible tooth made of gypsum and nickel alloy to measure the metal artifacts. CT images were obtained by changing the phantom tube voltage and tube current. The signal intensity of the image generated by the metal artifacts before and after the iterative metal artifact reduction algorithm (iMAR) was measured. A 3D printing process was performed after converting the images, before and after iMAR application, into STL files using InVesalius version 3.1.1 by selecting the conditions that minimized the effect of the artifact. Regarding metal artifacts, the Hounsfield unit (HU) value showed low as the tube voltage increased. The iMAR-applied images acquired under the same conditions showed a significantly lower HU. The artifacts, in the form of flashes, persisted in the 3D-printed product of the image not subjected to iMAR, but were largely removed in the 3D-printed product following iMAR application. In this study, the application of iMAR and data acquired using high tube voltage eliminated a significant portion of the metal artifacts, resulting in an impression shape that was consistent with the human body.  
3D printing; metal artifact reduction; radiography Article
# Detecting Image Forgery over Social Media Using U-NET with Grasshopper Optimization
## 1 Introduction
### Related Work
#### 1.1.3 Issues and Motivation for Current Research  
In current articles, most researchers have addressed an important issue: minimum accuracy. These techniques can detect splicing or copy-move fraud, but not both. Another essential issue with these techniques is that they detect forgery with low accuracy. The issue of minimum accuracy has been a significant concern for researchers. While several techniques have been developed to detect splicing or copy-move fraud, they are often limited in their accuracy and cannot detect both types of forgeries.  
Therefore, we propose a novel approach that combines a deep learning-based method with an optimization algorithm. Specifically, we use a U-NET network with the grasshopper optimization algorithm to detect image forgeries. The U-NET network is a popular architecture for image segmentation tasks that has shown impressive results in various applications. In our approach, the U-NET network is used to segment the images into regions of interest, while the grasshopper optimization algorithm is used to optimize the segmentation process and improve the accuracy of the results.  
One of the key advantages of our approach is its ability to detect various types of image forgeries, including splicing and copy-move forgeries, with high accuracy. This is made possible by the use of the U-NET network, which is capable of capturing complex features and patterns in the image. Additionally, our approach can be applied in real-time systems, making it more efficient than some existing methods that have high computational complexity and lack usability in real-time systems.  
In comparison to existing methods, our approach offers several significant advantages. Firstly, it is capable of detecting a wide range of image forgeries with high accuracy, making it a versatile tool for image forensics. Secondly, our approach is efficient and can be applied in real-time systems, making it a practical solution for various applications, such as in forensic investigations or in the detection of fake images on social media. Finally, our approach combines deep learning with an optimization algorithm, which is a novel and promising approach in the field of image forensics. We believe that our approach has the potential to become a valuable tool in the field of image forensics, and we look forward to future research in this area.
## 4 Result  
### GNSS-Based Monitoring Data  
The monitoring results are depicted in Figure 13, with Figure 13a illustrating the monitoring outcomes for horizontal surface displacement and Figure 13b showing the monitoring results for vertical surface displacement.  
From Figure 13a, it is evident that significant horizontal convergence displacements occur at monitoring points CYP-GTP-02 and CYP-GTP-03. As of 20 September 2020, the horizontal convergence displacements were 1135.97 mm and 1054.68 mm, respectively. The daily displacement rates at CYP-GTP-02 ranged from \(-\)0.5 to 28.7 mm/day, with the maximum deformation rate occurring on 15 June 2019. Similarly, at CYP-GTP-03, the daily displacement rates ranged from \(-\)2.9 to 30.8 mm/day, with the maximum deformation rate also occurring on 15 June 2019.  
From Figure 13b, it is evident that the monitoring points CYP-GTP-02 and CYP-GTP-03 also exhibit significant vertical displacement changes. As of 20 September 2020, the vertical displacements were 654.10 mm and 493.60 mm, respectively. The daily displacement rates at CYP-GTP-02 ranged from \(-\)8 to 23.8 mm/day, with the maximum deformation rate occurring on 13 June 2019. Similarly, at CYP-GTP-03, the daily displacement rates ranged from \(-\)12 to 16.4 mm/day, with the maximum deformation rate also occurring on 13 June 2019.  
The monitoring data underscore that, during the continuous decrease in reservoir water level, points 2 and 3 exhibited significant horizontal displacements and deformation rates, clearly influenced by the declining water levels. As the water level gradually rose, the horizontal convergence displacements gradually stabilized. Consequently, it is imperative to closely monitor the deformation of this landslide section during the flood season and periods of substantial reservoir water level changes.  
As of 20 September 2020, the cumulative horizontal displacement at CYP-GTP-01 was 350.41 mm, with a vertical displacement of 195.9 mm. The daily horizontal displacement rate ranged from \(-\)5.1 to 6.9 mm/day, and the daily vertical displacement rate ranged from \(-\)12 to 15 mm/day. The maximum deformation rates occurred on 19 June 2019, and 20 June 2019, respectively. Considering the soft lithology and the thick superficial deposits in this area, there is a certain degree of apparent deformation under natural conditions. Overall, the deformation is relatively smooth, with minimal changes in displacement rates during the intense reservoir level fluctuations, remaining within normal fluctuation ranges. Therefore, there is little correlation between the natural creep of the landslide deposit and reservoir level changes.  
Figure 12: Soil-Water Characteristic Curve: (**a**) volumetric moisture content function; (**b**) permeability coefficient function.
## 6 Conclusions  
This study investigated the shear performance of the RC beams strengthened by high-strength and high-toughness epoxy mortar. The influence of reinforcement layer thickness on the shear performance was analyzed, and the feasibility and effectiveness of this reinforcement material were verified. The truss arch model was applied to calculate the shear capacity of the high-strength and high-toughness epoxy mortar-strengthened RC beams. The following conclusions can be drawn:  
1. All the beams in the experiment suffered shear failure. The shear capacity and ductility of the RC beams strengthened by high-strength and high-toughness epoxy mortar were significantly improved. The ultimate shear capacity increased by up to 60.6%, and the corresponding displacement increased by 43.2%.
2. The high-strength and high-toughness epoxy mortar demonstrates superior adhesion to concrete. In all the strengthened beams, no debonding failure occurred between the reinforcement layer and the original concrete, and there was no slippage. Cracks were not identified in the reinforcement layer prior to the failure of the beams. The maximum strain of the epoxy mortar is conspicuously greater than that of common reinforcing bars, facilitating its ability to withstand forces throughout the entire process.
3. The \"thickness reduction coefficient\" was introduced into the shear capacity formula of the reinforced beam based on the truss-arch model. The maximum shear capacity was finally calculated and has a closely matched test result.  
W.L.: Conceptualization, Methodology, Supervision, Validation; T.W.: Data curation, Writing--Original draft preparation, Software; L.L.: Visualization; C.J.: Methodology. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Guangxi Science and Technology Planning Project (GuikeAB23075093, GuikeAD21159010), the Doctoral Foundation of Guangxi University of Science and Technology (No. 21Z26).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors would like to acknowledge the Guangxi Science and Technology Planning Project (GuikeAB23075093, GuikeAD21159010), the Doctoral Foundation of Guangxi University of Science and Technology (No: 21Z26).  
The authors declare no conflicts of interest.