esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Conventional Channel Estimation Methods
### Semi-Blind Channel Estimation Method
#### 2.3.2 EM-Based Semi-Blind Channel Estimation  
As an iterative optimization algorithm, the EM algorithm [42] works to estimate parameters in probabilistic models with hidden variables. In semi-blind channel estimation, this algorithm is executed in the expectation step (E-step) and the maximization step (M-step). In the E-step, the algorithm fixes the current channel parameters and calculates the expectation of the correlation between the training sequence and the received signal. In the subsequent M-step, the algorithm updates the parameters to maximize the probability of consistency between predicted and actual data. Then, iterate these two steps until the parameters converge and achieve the channel estimation. In [43], [PERSON] et al. proposed an EM-based temporal semi-blind method for MIMO-OFDM time-varying channels. Their method solves the problem of inadequate pilot sequence arrangement in standard pilot-aided channel estimation, which hinders the direct application of interpolation approaches. Furthermore, the method can handle systems that do not satisfy the sampling theorem. Specifically, in cases where the sampling theorem is not met, the method can aptly initialize the EM algorithm by performing local Wiener interpolation on the frequency axis. In [44], [PERSON] et al. proposed two EM-based semi-blind algorithms (one introduces prior information while the other does not) for time division duplexing (TDD) massive MIMO systems. Experimentally, it is demonstrated that the EM algorithm, with the introduction of prior information, can perform better under low SNR conditions.  
The semi-blind channel estimation adds a few pilots, building on the foundation of the blind channel estimation. Compared to blind channel estimation, it can achieve faster convergence, lower computational complexity, and address phase ambiguity. In addition, it boasts a higher bandwidth utilization than the pilot-based method. However, the semi-blind channel estimation still requires substantial computation, making it relatively difficult to realize.  
Based on the introduction to conventional channel estimation methods, Table 2 summarizes their advantages and disadvantages.  
From Table 2, we can conclude that conventional channel estimation methods have limitations that will restrict their application in increasingly complex future communication systems. Therefore, researchers in related fields have set out to figure out how to improve the performance of channel estimation while simultaneously reducing its computational complexity. Deep learning has made a big splash in physical layer wireless communications in recent years, opening up a new horizon for scholars. Numerous studies have introduced deep learning as a novel approach to channel estimation and have obtained outstanding results. Section 3 highlights the application of deep learning in channel estimation.
# Analysis of Subharmonic Oscillation and Slope Compensation for a Differential Boost Inverter
## 2 Differential Boost Inverter under Two-Loop Control
In the low frequency averaged sense, the AC component of each converter is out of phase regarding the other converter. The DC component is the same for both converters. The voltage controller is conventionally a PI regulator aiming to make the load voltage \(v_{o}\) to accurately track the sinewave voltage reference \(v_{\text{ref}}\). Its transfer function can be expressed as \(H_{\text{pi}}(s)=k_{p}(\tau s+1)/(s\tau)\), where \(k_{p}\) is its proportional gain and \(\tau\) is its time constant. Figure 1: The differential boost inverter under two-loop control.
## 4 Experimental Environment for Truck Platooning  
### Selection of Truck-Platooning Experimental Methodology  
The first step in constructing an experimental environment for truck platooning is to select an appropriate experimental methodology. This study considered the follow ing criteria for methodology selection: (1) the experimental environment must enable the implementation of truck platooning technology and its see-through functionality; (2) the implemented technology must possess sufficient realism; (3) it should be able to accommodate various truck platooning scenarios to assess drivers' psychological stability; and (4) it must allow the measurement of physiological signals reflecting drivers' psychological stability.  
To evaluate psychological stability during truck platooning, three experimental approaches were considered: real-vehicle experiments on a proving ground (PG), real-road experiments, and virtual environment experiments using a driving simulator. Although PGs allow for real-vehicle testing with high safety, they require long straight sections, making them impractical for truck platooning experiments. Real-road experiments offer the highest realism, but they are difficult to control, pose safety challenges, and increase accident risk. Although a driving simulator is slightly less realistic than real-road experiments, it is highly safe, easily controllable, and advantageous for applying and repeating various scenarios. After comparing the advantages and disadvantages of the three methods, this study determined that a driving simulator best met the objectives of the experiment. It not only prevents safety risks associated with testing the relatively novel truck platooning technology but also facilitates the repeated execution of diverse scenarios and enables the measurement and collection of drivers' physiological indicators necessary for psychological stability assessment.
## 3 Selected Research Results
### Machine Learning Methods
Studies in the literature have not determined the threshold of quality that can be considered suitable for classification; therefore, in this study, the strength of the relationship between variables in terms of the correlation coefficient values was considered the indicator of quality. Analyses were also performed to assess which artificial intelligence model was the most suitable for crack prediction. Figure 14: Minimum classification errors for 30 iterations of model 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network ID** & **Network Name** & **Quality Learning** & **Quality of Testing** & **Quality of Validation** \\ \hline
1 & MLP 6-10-2 & 88.2353 & 80.00000 & 76.66667 \\
2 & MLP 6-5-2 & 88.2353 & 80.00000 & 76.66667 \\
3 & MLP 6-6-2 & 88.2353 & 80.00000 & 76.66667 \\
4 & MLP 6-6-2 & 94.1176 & 80.00000 & 76.66667 \\
5 & MLP 6-7-2 & 100.0000 & 80.00000 & 76.66667 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of active networks in the computer program Statistica.
## 3 Results and Discussion  
### Chemical Composition of Essential Oil  
The hydrodistillation of the aerial parts of _T. luteum_ subsp. _flavovirens_, including the infloresances, produced an EO with a yield of 2 \(\times\) 10\({}^{-2}\) % (w/w), equivalent to 0.02 g. The chemical composition of the EO was analyzed via GC-MS, and the components were identified based on their LRI values compared to those reported in the literature. Thus, 53 components representing 83.9% of the whole chromatographic area were identified. The composition and percentages of those components are summarized in Table 1. The major compounds constituting the EO from _T. luteum_ subsp. _flavovirens_ were \(\beta\)-elemol (7.2%), (+)-\(\alpha\)-pinene (6%), \(\beta\)-eudesmol (5.5%), guaiol (4.2%), \(\alpha\)-bisabolol (4.2%), and \(\beta\)-caryophyllene (4.1%) (Figure 2). The identified volatiles were mainly terpenic compounds (82.4%), which were represented by high percentages of oxygenated sesquiterpenes (43.8%) and sesquiterpene hydrocarbons (16.7%) and lower proportions of monoterpene hydrocarbons (10.5%) and oxygenated monoterenes (8.9%). The non-terpenic components accounted for 1.5%. Taking previous works into account and comparing the present results to those reported in the literature for _T. luteum_ subsp. _flavovirens_ from Morocco [21; 22; 23; 24], we noticed that the two species share several volatile markers. Specifically, \(\beta\)-elemol, \(\beta\)-eudesmol, \(\alpha\)-pinene, and \(\beta\)-caryophyllene are present in similar amounts in the EOs obtained from the plants from both regions. Conversely, guaiol and \(\alpha\)-bisabolol can be considered more specific markers of the Tunisian species. Comparing our results with the data available for the EO of Algerian _T. luteum_ subsp. _flavovirens_, more differences were observed since \(\tau\)-cadinol (16.33%), caryophyllene oxide (13.23%), eudesma-4(15), 7-dien-1.\(\beta\).-ol (7.64%), and \(\beta\)-pinene (6.77%) have been reported as the most abundant constituents [25].
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 3 Dummy Packets Techniques
### General Fake Source (Gfs)  
The _GFS_[42] technique attempts to simulate the movement of a real asset at different locations in the network to mislead the adversary about the actual location of the source node, as shown in Figure 7. This mechanism can be implemented easily if the _RFID_ type is active. However, the goal of _GFS_ is to simulate the movement of the asset using a passive _RFID_. _GFS_ generates dummy traffic of a fake source. A shared token is used to determine which node should act as a fake source. Then, the fake source generates a fake event just after detecting the real asset. Next, the token is passed between nodes to simulate the movement of the real asset. To simulate a real asset, the number of intervals, which is represented by _simulateRound_, that a fake source will transmit should vary from node to node, to mislead the adversary. Another variable is the _realCount_; it is increased by one whenever a node sends a real message. Once a node stops sending real messages, the _realCount_ is reset to one, and _simulateRound_ is updated.  
The fake source is selected randomly and generates fake messages until _simulateRound_ becomes zero; then, the token is passed. However, to avoid passing the token between only two nodes, the last fake source is recorded in the _preNode_ variable. When the fake source has a real message, the sink should be informed and the token is passed to the next node. Further, if there are real or fake messages without a token, the message can be passed normally to the sink. However, if the fake message has a token, the receiving node will get the _preNode_ and _tokenID_. Then, the receiving node becomes the new fake source.  
Since the token creates extra traffic that might be noticed by the adversary, each fake source needs to send a fake report to make the token message look like an ordinary real report. Eventually, real and fake messages have to be transmitted together in every interval to distract the adversary. However, _GFS_ has some drawbacks: First, it assumes that the sink is placed in the center of the network. Second, if the token is passed among three nodes or more rather than between two nodes, there is no mechanism to handle this kind of situations. Third, _GFS_ could fail to provide anonymity because only fake messages are created when a real event takes place. Therefore, the adversary might detect that a real event is being sent resulting in exposing the location and time of the event in case the adversary has enough capabilities and resources to examine all nodes at once.
## 2 Authenticated Join Protocols
### Join Protocols (DJS and IJS)  
In Figure 1, we present two key establishment protocols given in [11]. The first protocol, called direct join to the sink (DJS), allows a node to join directly through the sink and is described in Figure 1a. In DJS, a new node \(I\) sends a direct request to \(S\) in order to establish a session key with it. Node \(I\) begins the join process by computing the symmetric key \(K_{DH}(I,S)\) with the sink \(S\). Then, node \(I\) generates a nonce \(n_{I}\) and adds its identity then encrypts it with \(K_{DH}(I,S)\) and sends it to \(S\). Upon reception, \(S\) computes \(K_{DH}(I,S)\) to decrypt the request. Then, \(S\) verifies the identity of \(I\) and generates a new session key \(K(I,S)\). The join response contains \(n_{I}\), the identity of \(S\) and the new symmetric session key \(K(I,S)\). The response is encrypted using \(pk(I)\) and is sent to \(I\). Only \(I\) is able to decrypt the response with its secret key \(sk(I)\). We note that \(n_{I}\) helps \(I\) to authenticate \(S\).  
The second protocol, called indirect join to the sink (IJS), allows a new node \(I\) to join the network through a neighbor node \(R\) that is already authenticated in the network. Node \(I\) sends an indirect request to \(S\) in order to establish a session key with \(R\). Node \(R\) forwards without any modification the request to \(S\) through intermediate nodes that are trusted to route the request towards \(S\). Only nodes \(I\) and \(S\) are able to decrypt the messages encrypted with \(K_{DH}(I,S)\), and only \(R\) and \(S\) are able to decrypt the messages encrypted with \(K_{DH}(S,R)\).
# A Gentle Introduction to Applications of Algorithmic Metatheorems for Space and Circuit Classes
## 2 The Concepts and Ideas Behind Courcelle's Theorem
### A Look at the Hidden Constants  
Before we proceed, a word of caution might be in order: Courcelle's Theorem is a beautiful theorem, and in the course of this paper we will see that it also has beautiful applications, _but_ there is a catch (as they say, there is no such thing as a free lunch): the \"hidden constants\" in Courcelle's Theorem are _huge_, and naively implementing the algorithms implied in the theorem leads to more or less useless implementations.  
The huge constants come from two sources: first, in the proof, I wrote that computing tree decompositions \"in linear time is not so easy\", but by Bodlaender's Theorem \"this is, indeed, possible\". It would be more honest to replace this by \"in linear time is extremely difficult\" and \"this is, barely, possible\". Using Bodlaender's Theorem, we can compute a tree decomposition of an \(n\)-vertex graph of tree width \(k\) in time \(O(2^{p(k)}n)\) for some polynomial \(p\), but \(p\) is a high-degree polynomial that yields ridiculously large values already for \(k=2\).  
Things get worse, however, for a second reason. Recall that once we have computed the tree decomposition \(T\), we run a tree automaton on it that arises from the formula \(\phi^{\prime}\) as follows. Starting from automata for checking atomic formulas, we build more complicated automata recursively: logical conjunctions correspond to taking products; negations correspond to complements; and monadic existential quantifications correspond to nondeterminism. It is well-known in automata theory that complements and nondeterminism do not get along very well or, phrased more scientifically, in order to complement a nondeterministic automaton, we first have to turn it into a deterministic automaton. This means that when \(\phi^{\prime}\) starts with something like \(\exists M_{1}\forall M_{2}\exists M_{3}\forall M_{4}\cdots\), each additional quantifier will cause an _additional exponential blow-up_ of the size of the automaton. All told, the automaton will have a size that can only be described by a \"tower of exponentials\" whose height is given by the length of \(\phi^{\prime}\).  
Against the background of these sobering observations, there is some good news and some bad news. First, the bad news--the just-mentioned tower-of-exponentials arising in the algorithm _cannot be avoided_: It is shown in [14] that there is a family of problems on _trees_ (so the difficulty of computing the tree decompositions does not even arise) that can be described by monadic second-order formulas \(\phi\), but that cannot be decided faster than within a tower-of-exponentials in the length of each \(\phi\).  
The good news is that in many practical situations, once it has been established using [PERSON]'s Theorem that some problem can be solved in linear time _in principle_, a closer look at the problem often yields simpler, and much faster, direct solutions for the problem. We will see an instance of this effect later on when we have a look at the unary subset sum problem.
## 4 Results
### Spatiotemporal Evolution of LULC  
In order to explore the impact of the \"Kokyar Greening Project\" on the regional ecosystem pattern, this study used the random forest algorithm and 2432 Landsat images to invert the LULC data of the Kokyar Project Area from 2000 to 2021. Table 4 shows that the LULC data have good accuracy.  
As can be seen in Figure 10a, over the past 22 years the types of land cover in the study area have mainly been cropland, grassland, and bare land. The bare land was mainly distributed in the southern and northern areas, which belong to the Phase D area of the project area. The grassland and cropland were mainly in the central region. Over the past 22 years, the area of bare land has shown an annually downward trend, while the other four types have shown an overall upward trend. Among them, the area of bare land  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **OA (\%)** & **Kappa (Unitless)** \\ \hline
2000 & 85.16\% & 0.828 \\
2010 & 87.53\% & 0.841 \\
2020 & 84.74\% & 0.816 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy verification of LULC data.  
Figure 10: LULC distribution map of the Kokyar Project Area (**a**) and long-term series of areas of various land types (**b**).  
decreased by 348.83 km\({}^{2}\) from 2000 (435.64 km\({}^{2}\)) to 2021 (86.81 km\({}^{2}\)). The area of cropland increased the most, from 408.85 km\({}^{2}\) in 2000 to 700.26 km\({}^{2}\) in 2021, with an increase of 291.41 km\({}^{2}\); the area of grassland type increased by 56.71 km\({}^{2}\). The water and built-up areas in the Kokyar area slightly increased. This shows that over the past 22 years, the Kokyar Project Area has brought great changes to the ecosystem pattern of the region, and the vegetation parameters (FVC, NPP, and VEQI) and the RSEI have mainly increased in the form of cropland and grassland expansion.
# WRTU-16T: Write-Enhanced Low-Power Radiation-Tolerant SRAM for Space Applications
## 2 Proposed WRTU-16T SRAM Architecture
### SEU Recovery Analysis
#### 2.5.1 SEU at Node Q  
An SEU at node Q causes a transient flip from logic '1' to logic '0'; transistor N4 turned OFF, and, in contrast, P2 and P6 turned ON. Consequently, the QB value becomes high impedance, but the values stored at other nodes remain unaffected. The Q node is restored from logic '0' to logic '1' by the activation of P3 and N2.  
Figure 3: Basic operation results of WRTU-16T.
# Automated Deep Learning Model for Sperm Head Segmentation, Pose Correction, and Classification
## 6 Discussion
### Pose Correction Result  
Table 4 presents the performance of the sperm head pose correction network on two datasets, Chenwy and HuSHem, evaluated on both validation (5-fold cross-validation) and test tasks. The IoU scores of 0.950 and 0.916 in validation for Chenwy and HuSHem, respectively, and 0.946 and 0.887 in the test phase, suggest that DNet is performing well in localizing the sperm heads. The standard deviation values in the table and the boxplot in Figure 8 both provide insights into the stability of the model's performance across datasets. PNet achieves perfect polarity prediction accuracy (100%) during validation on both datasets and maintains 100% accuracy on Chenwy, with a slight drop to 97.5% on HuSHem, and the confusion matrix shows only one mistake out of 40 predictions. These results demonstrate that the network effectively performs polarity prediction with accurate RoI extraction and reliable generalization across both datasets.  
Figure 9 illustrates the results of the sperm head pose correction network across two datasets. Although the pose correction network primarily extracts features from the original network, the original images are used here to demonstrate its function better. In both datasets, the network effectively normalizes the orientation of sperm heads, aligning them consistently despite variations in the original images. Furthermore, this correction is  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**IoU**} & \multicolumn{2}{c}{**Accuracy**} \\ \cline{3-6}  & & **Mean** & **Std** & **Mean** & **Std** \\ \hline \multirow{2}{*}{Validation (5 fold)} & [PERSON] & 0.950 & 0.001 & 100 & 0 \\  & [PERSON] & 0.916 & 0.009 & 100 & 0 \\ \multirow{2}{*}{Test} & [PERSON] & 0.946 & - & 100 & - \\  & [PERSON] & 0.887 & - & 97.5 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mean values and standard deviations for the metrics of sperm head detection and polarity classification.  
Figure 7: A sample of typical results for the original and retrained EdgeSAM is shown, with yellow contours for ground truth and red for segmentation, where the left side shows results from the retrained EdgeSAM, and the right side shows results from the original EdgeSAM.  
especially useful for removing the effects of background noise and the presence of other sperm, which may otherwise interfere with accurate classification.
# Broadband Plasmonic Metamaterial Optical Absorber for the Visible to Near-Infrared Region
## 3 Results Analysis
### Comparison  
A comparison of the proposed MA with some of the recent works has been presented in Table 3. However, only a few of them are applicable for solar applications. Some of them have a complex multilayer structure and makes difficulties during fabrication. Besides, these works are suffering from relatively low angular stability reduced the solar absorber efficiency. On the other hand, the proposed MA achieved wide angular stability and high photon conversion efficiency for solar applications with a comparatively simple structure. The operating wavelength, average absorption, number of layers, angular stability, material choice, dimension, use of anti-reflective layer, and A\({}_{\text{AM1,5}}\) have been compared in Table 3. The proposed compact broadband MA offers an average absorption of 92% and a near-unity peak absorption of 99.6%. at 600 nm with polarization insensitivity. The anti-reflective layer on the unit cell plays a vital role for maintaining broadband absorption. The proposed MA obtained 90.76% A\({}_{\text{AM1,5}}\) photon conversion efficiency, making the proposed pentagonal multilayered MA attractive for energy-harvesting applications.  
Figure 17: H-field distribution at different layers in (a) 400 nm, (b) 1600 nm, and (c) 2400 nm wavelength.