esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis
#### 2.3.2 Behavioral Movement and Recursive Analyses  
To analyze the movement behavior and habitat use of the satellite-tracked reef manta rays, we applied a state-space model (SSM) using the \"aniMotum\" R package [33]. SSM is widely used to assess the movement patterns of marine megafauna, including harbor seals, e.g., [34]; pygmy blue whales, e.g., [35]; silky sharks, e.g., [36]; whale sharks, e.g., [37]; and manta rays, e.g., [23]. For this analysis, we utilized both GPS and Argos position data, setting 1 m/s as the maximum speed for the tagged reef manta rays and using a 12 h time step and a time-varying move persistence model (mp) to regularize the positions recorded by the satellite tags. These regularized tracks enabled a detailed investigation of the movement behaviors of the reef manta rays. Using the movement persistence index (ranging from 0 to 1), we classified their behaviors into distinct categories: a low persistence index (\(\leq\)0.75) indicated area-restricted search (ARS) behavior, while a high persistence index (\(>\)0.75) signified traveling behavior [38].  
Reef manta rays are known to demonstrate high site fidelity and residency to certain sites of ecological significance, such as cleaning stations and feeding grounds, e.g., [11; 27]. To examine the revisit rates (revisitations) to these sites, we employed a recursive analysis using the'recurse' R package [39] to calculate two metrics: the total number of revisitations (N revisitations) and total visitation time (Table 2) at the regional and site levels. This analysis has commonly been used for terrestrial animals that have been tracked using GPS, including the eastern wild turkey (_Meleagris gallopavo silvestris_) [40] and coyotes (_Canis lattrans_) [41]. We used a circle with a 1.5 km radius to approximate the area occupied by reef manta rays when aggregating (e.g., feeding). From a technical perspective, this circle moved along the trajectory of the reef manta ray trajectories and the total number of trajectory segments entered into and exited from the circle was then calculated to determine the total number of revisitations. The total visitation time (in hours) denotes the total time spent within the radius across all the visitations from an animal's trajectory. In the case of multiple trajectories from different animals, the visitation time represents the total time spent across all the individuals within the radius [39]. At the regional level, we calculated the mean number of revisitations and the mean revisitation time across all individuals to obtain an overview of revisitations in each region. At the site level, we also calculated the mean number of revisitations and the mean total revisitation time specifically at primary reef manta ray aggregation sites.
# Interferometric Wavefront Sensing System Based on Deep Learning
## 1 Introduction  
When light is transmitted over long distances in space, it is often interfered by numerous factors (atmospheric turbulence, humidity, etc.) to distort the wavefront. The wavefront aberration of light has traditionally been an important factor affecting the imaging quality of optical systems. Adaptive optics technology (AO) is the most effective measure to overcome and compensate wavefront aberration [1]; it has a wide range of applications in astronomy, microscopy, radar, and other research fields. The wavefront detector is an important part of the adaptive optics systems. It can analyze the degree of the aberration and finally convert it into the control signal to the corrector to automatically compensate for the aberration, thereby improving the imaging quality of the optic systems. With the continuous development of technology, many fields have higher requirements for measurement technology. They not only need to have a faster measurement speed but also need to have higher accuracy. However, traditional wavefront detection methods such as the Hartmann-Shack method [2], Fourier spectroscopy method [3], etc. have been unable to meet these requirements. Therefore, new methods are needed to make up for the shortcomings of traditional technologies.  
Machine learning, including deep learning, has become an increasingly hot topic now. It is based on the biological neuron model of the human brain, which can learn from examples to solve the problem of function approximation or pattern classification [4]. In the early days, some people applied machine learning methods in the field of optics. They used a multilayer perceptron to measure the optical phase distortion caused by air turbulence [5]. Later, this method was used in the wavefront reconstruction system of the Hubble Telescope [6]. With the advent of neural networks, methods of applying neural networks to adaptive optics for wavefront detection and reconstruction were also proposed [7]. Recently, evidence reveals that the use of convolutional neural networks can realize the wavefront reconstruction of the point source [8]. Furthermore, Zernike coefficients were computedwith a deep neural network framework based on the wavefront of the point source and extended source received by the wavefront detector [9].  
At present, most wavefront sensing methods use a detector to directly record the light intensity distribution of the wavefront [8; 9; 10] and then analyze the phase distribution and wavefront aberration. However, these methods only work well under dark conditions, because any interference from external light sources will be recorded by the detector, which directly affects the measurement result. Therefore, the application of these methods is very limited. In this study, neural network algorithms were applied to the interference system, which can realize the measurement of the distorted wavefront in the general environment. Finally, it is verified by experiments that this method not only has higher measurement accuracy and faster calculation speed but also has good performance in noisy environments.
# Time Series Anomaly Detection Using Signal Processing and Deep Learning
## 3 Methodology
### Model Training
#### 3.2.2 LSTM  
Long Short-Term Memory is a type of RNN, specifically designed to capture long-term dependencies in sequential data, making them ideal for catching normal patterns in time series data. Other traditional RNNs often struggle with learning patterns that are separated by many time steps, but LSTMs address this issue by incorporating memory cells and sophisticated gating mechanisms. LSTM units [52] are composed of hidden states at the current and previous time step (short-term memory), cell states at the current and previous time step (long-term memory), and the input vector at the current time step. They use a forget gate, an input gate, and an output gate to control what information is processed and saved in the short-term and long-term memory.  
The LSTM Autoencoder [53] extends the Autoencoder framework to handle sequential data by incorporating LSTM units for both the encoder and decoder. The architecture can be described as follows:  
* Encoder: Input data \(x=\{x_{1},x_{2},\ldots,x_{S}\}\), where \(S\) is the sequence length and \(x_{t}\in\mathbb{R}^{R}\) (t = 1, 2,..., S), are passed through the LSTM layers. The LSTM processes the sequence step by step, updating its hidden states \(h_{t}\) and cell states \(c_{t}\): \[o_{t},(h_{t},c_{t})=\text{LSTM}(x_{t},h_{t-1},c_{t-1}).\] The final hidden and cell states \((h_{S},c_{S})\) serve as the latent representation \(Z\) that summarizes the entire multivariate input sequence into two compact representations with dimensions (num_layers, hidden_size).
* Decoder: The hidden and cell states of the latent representation \(Z\) are used to initialize the decoder LSTM layers with \(h^{{}^{\prime}}_{0}=h_{S}\) and \(c^{{}^{\prime}}_{0}=c_{S}\) together with a zero input tensor \(\hat{x}_{0}\). The decoder then generates a reconstructed sequence of hidden states \(\delta=\{\delta_{1},\delta_{2},\ldots,\delta_{S}\}\) using \[\delta_{t},h^{{}^{\prime}}_{t},c^{{}^{\prime}}_{t}=\text{LSTM}(\hat{x}_{0},h^{ {}^{\prime}}_{t-1},c^{{}^{\prime}}_{t-1}).\] Finally, a fully connected layer is used to reconstruct \(\delta\) back to the multivariate time series input \(\hat{x}\): \[\hat{x}=\sigma(b+W\delta_{t}).\] LSTM Autoencoders efficiently capture temporal information in the data and the obtained latent representation captures patterns across the entire sequence in compressed form.
## 4 Minimum Requirements for Logical Errors
The width of each level-\(2\) cluster will then be \(w_{2}=(2m-1)w_{1}=(2m-1)^{2}\). Continuing in this way will lead to each level-\(n\) cluster having a width of \(w_{n}=(2m-1)^{n}\). Note also that each level-\(n\) cluster will contain \(m^{n}\) errors. The minimum means to form a spanning cluster is to have a level-\(n\) cluster where the left-most endpoint is closer to the left edge than to the right-most endpoint, and the right-most endpoint is closer to the right edge than to the left (or the same with left and right interchanged). The former ensures that the cluster is not isolated from the left edge, and the latter ensures that the both combined are not isolated from the right edge. The minimum width of a level-\(n\) cluster required to satisfy this is \(L/4\). This requires a cluster of level \(n=\ln(L/4)/\ln(2m-1)\). The required number of errors is then \(m^{n}=(L/4)^{\ln m/\ln(2m-1)}\). The exponent reaches its minimum value of \(\beta=\log_{3}2\approx 0.63\) for \(m=2\). It is clear that the non-uniform case where different level-\(n+1\) clusters can contain different numbers of level-\(n\) clusters, will not allow a cluster to become spanning with fewer errors. As such \(L^{\beta}\) is the minimum number of errors required for a spanning cluster, and hence forms a lower bound on \(\epsilon^{\prime}\). For an upper bound on \(\epsilon^{\prime}\) we can find a set of errors that causes the algorithm to fail. One such example can be constructed similarly to the above. However, instead of the two level-\(n\) clusters within a level-\(n+1\) one being a distance \(w_{n}\) apart, we instead put them \(w_{n}-1\) apart. This will cause the algorithm to repeatedly pair the wrong anyons, and so cause a logical error. Given this procedure it is easy to see that \(w_{n}=3w_{n-1}-1=(3^{n}+1)/2\). This requires a cluster of level \(n=\log_{3}(L/2-1)\) for a logical error, and so \((L/2-1)^{\beta}\) errors. Combining this with the lower bound, we find that \(\epsilon^{\prime}=(cL)^{\beta}\) for \(1/4\leq c\leq 1/2\). Note that this is not linear with \(L\), as we would ideally like, but its power law divergence would still be expected to provide good error suppression, as in the examples discussed in[15]. Note that the set of errors found here are related to the Cantor set [6, 16].
# Calibration for Sample-And-Hold Mismatches in \(M\)-Channel TIADCs Based on Statistics
## 1 Introduction
### Contribution of the Paper  
This paper proposes a statistics-based calibration method for S/H mismatches in \(M\)-channel TIADCs. First of all, a cost function is established assuming the WSS and modulo \(M\) quasi-stationary properties of the input signal, and the mismatch coefficients are identified by eliminating the cost function in a least-mean-square (LMS) sense. Next, the error is approximated with the aid of multipliers and differentiators, and Richardson iteration is employed to achieve higher precision. At last, the signal is calibrated by suppressing the error. The resource consumption of the proposed method is higher compared with the IFB-based method and the I/Q-based method, but the proposed method can apply to extensive types of signals and numbers of channels whereas the other two methods cannot.
## 2 Background Theory and Methods
### Algorithm for Determining Thermal Conductivity  
The thermal conductivity in the rear-side photothermal deflection method can be examined by comparing the phase delay values from the numerical analysis (measured by inputting a random thermal conductivity) and those from the experiment at various relative positions. When the difference between the numerical results and experimental results are minimal, the thermal conductivity value from the numerical analysis is designated as the thermal conductivity of the measurement target material. Representative methods include a phase gradient method, a zero-crossing method, and a phase curve method. This study used a thermal conductivity determination algorithm based on the phase curve method, as shown in Figure 7.  
Figure 6: Theoretical [27] and numerical analysis of temperature distribution on a specimen surface.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Copper**[34] & **Air**[35] \\ \hline Density(kg/m\({}^{3}\)) & 8930 & 1.161 \\ Specific Heat(J/kg-K) & 385 & 1007 \\ Thermal Conductivity(W/m-K) & 385 & 0.0263 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Input material properties used for analysis model verification (copper and air).  
Figure 7: Schematic diagram for determining thermal conductivity algorithm for rear-side photothermal deflection method.
## 3 Results  
### Characteristics of Included Studies  
The initial literature search yielded a total of 263 articles. After removing duplicate records, 128 articles were included for title and abstract screening. Subsequently, 68 studies were identified for further full-text evaluation. Finally, six qualified studies were deemed suitable for this meta-analysis (Figure 1).  
Figure 1: Study flow chart.  
Characteristics of eligible studies were summarized in Table 1a,b. Of those, three were prospective and three were retrospective. Studies were conducted in Germany (\(n=3\)), UK (\(n=1\)), Italy (\(n=1\)), or Austria (\(n=1\)) and published between 2011 and 2020.  
A total of 419 patients for a total of 428 testicular masses were analyzed in this meta-analysis. The mean patient age ranged from 33 to 42 years. Median tumor size was reported in 4/6 studies, ranging from 6 to 13 mm. The most common malignant, benign, and non-neoplastic histopathological specimens were represented by seminoma, Leydig cell tumor, and segmental testis infarction, respectively.  
All studies reported doses of contrast agent used, ranging from 2.4 mL to 4.8 mL. Moreover, three studies relied on four or more radiologist readers, one had a single reader and one study did not provide any information. Finally, inter-observer agreement ranged from 0.65 to 0.98, when reported.
# Focus on Point: Parallel Multiscale Feature Aggregation for Lane Key Points Detection
## 3 Methodology
### Parallel Multi-Scale Feature Aggregation Network  
The overall architecture of MFANet is illustrated in Figure 3. The framework consists of an encoder and a decoder, which are connected in parallel. By integrating the context spatial information of multiple-scale feature mappings in parallel, the network can effectively utilize prior information from other lanes to enrich spatial relations about lane lines, which is necessary for cases with conditions such as weak visible lane markings. In addition, MFANet incorporates DAFU, which combines the advantages of interpolation coarse up-sampling and transpose convolution fine up-sampling through the strategy of nonlinear feature fusion. It can cast the sampled feature mapping to the higher resolution representation precisely.
## 2 SFN DVB-T Description  
According to the DVB-T document [1], DVB-T systems adopt the OFDM technique. Four different types of subcarriers are used, among which the pilot subcarriers exist. They are used for channel estimation in DVB-T applications, but they can be also used for positioning by correlating the incoming signal with a local-generated replica, with a method similar to that used in GNSS receivers. In order to avoid inter-symbol interference, the Cyclic Prex (CP) is introduced in the OFDM system. This property is very helpful for our positioning purpose. In the coming part, we will give a brief introduction of the SFN DVB-T system, highlighting the points that impact more on the capabilities of these signals to behave as Signals of Opportunities (SoOs) for positioning.
## 1 Introduction
Several green technologies have been used in the food industry to decontaminate production lines and products from microorganisms that could degrade product quality, causing recalls and the emergence of food-borne illnesses. Over the past decade, the use of plasma has gained widespread application in the food industry as a relatively new and promising non-thermal decontamination technology [13]. Cold plasma (CP) is the application of plasma as ionized or partially ionized gases to inactivate bacterial cells. The plasma is created either by heating the gas in a closed chamber under deep vacuum conditions or by using radiofrequency or microwave energy to excite the gas molecules to produce free radicals and electrons, which are the main components of the plasma [14]. These components have a destructive effect on microorganisms, including bacterial spores, yeast, moulds, and viruses. The antimicrobial effect of plasma is multimodal and includes both physical and chemical effects. However, the predominant mode of action is unknown. In addition, the impact of plasma on a microbial cell varies depending on the type of microorganisms (bacteria, fungi), working gas, kind of plasma, process parameters, substrate, condition of the substrate, and surface roughness [14]. For example, argon with oxygen and nitrogen emits four times more UV photons than pure argon [15]. Hence the mode of action could be significantly different in both cases. The advantage of cold plasma sterilization is that its components involved in destroying microorganisms are perishable. Therefore, there is no danger of them being present in the finished food. Given recent developments in plasma technology, many distinct plasma systems have been designed. Among them can be distinguished low-temperature atmospheric-pressure plasma (APP) [16] or microwave-driven discharge (MWD). The dielectric barrier discharge (DBD) plasma system, which is widely considered the most commonly used APP system [17], generates plasma between two electrodes that are covered with dielectric layers [18]. Therefore, this study aims to evaluate the suitability of low-temperature plasma for inactivating microorganisms present in freeze-dried bovine colostrum while preserving the biological value of the raw material.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 6 Proposed Solutions
### Machine/Deep Learning Approaches  
In recent years, machine learning and deep learning-based methods have been at the forefront of research. Thanks to advancements in the field of artificial intelligence, these ideas can ensure progress in HO decision-making, simultaneously reducing computational costs and addressing security concerns. Since information designs do not require frequent overhauls, the precision and effectiveness of asset utilization can be improved.  
In [84; 112], the UE's movement properties are recorded using a hidden layer. Social pooling is also used to capture the interaction between UEs. The four essential activities applied to complete HO are estimation, detailing, judgment, and execution. Unlike the standard HO, machine learning is employed to predict future trends. A confirmation method determines whether the customer should be transferred to another ABS. The optimization issues (HO and H-RRM) are defined by machine learning arrangement strategies that aim to capture relationships at worldly and spatial levels to create an appropriate HO choice. The buffer line is used to characterize the information entry rate, the apportioned range, and the impedances from BSs. Communication of the demonstrated framework is through the air-to-ground channel where the LoS path prevails. The optimization problem is then created, and the results are used to finish the decision-making process and remodel the HOs.  
Figure 9: To provide a complete picture, the main classification of proposed solutions can be illustrated as two main categories.
# A Review: Using Multiple Templates for Molecular Imprinted Polymer: Is It Good?
## 2 Multi-Template Strategy
### Influencing Factors  
#### 2.3.1 Template  
Templates are a crucial component in the MIP production process. This is because templates affect the selection of functional monomers, crosslinkers, initiators, and porogens [28]. The templates themselves are usually the target compounds that will be used for analysis [68]. The templates used in MIP synthesis must be able to form stable complexes with functional monomers. To form stable complexes, templates must have inert functional groups. In addition, templates must have good temperature and chemical stability [69] so that they do not degrade during polymerisation. Some polymerisation processes require heat, radical compounds, and porogens, which can affect the stability of the template [70; 71]. In making MT-MIP, these conditions also apply.  
[PERSON] et al. [72] stated that the molecular weight that produces the best MIP is in the range of 200-1200 Da. In general, templates can be divided into four groups, namely ions (Cu\({}^{2+}\), Cd\({}^{2+}\), and Pb\({}^{2+}\)), organic molecules (drugs and chemicals), biomacromolecules (albumin and adenosine), and cells (viruses) [27]. So far, the templates used in MT-MIP have mostly been compounds in the same group [32; 33; 73; 74; 75]. The selection of compounds in the same group aims to facilitate the easy selection of functional monomers, crosslinkers, initiators, and porogens because the templates have similar characteristics [73]. Examples of templates that have been used for MT-MIPs are cephalospororti antibiotics (cefoperazone, cefazolin, and cephalexin), non-steroidal anti-inflammatory drugs (NSAIDs; naproxen, ibuprofen, and diclofenac), and sulphonamide antibacterial drugs (sulphanilamide, sulphacetamide, sulphadiazine, sulphathiaze, sulphhamerazine, sulphamethiazole) [32; 73; 75; 76; 77].
## 3 LEO-SMC-ICN-IoT Caching Framework
### Content Popularity Prediction  
In the LEO-SMC-ICN-IoT, the higher the popularity ranking, the higher the request probability. The accurate prediction of content popularity can improve cache performance [46]. Referring to the study of request popularity for terrestrial networks, the probability of a content being requested generally follows the Zipf distribution [47], i.e.,  
\[Z_{u}=\frac{(u+q)^{-\gamma}}{\sum\limits_{u=1}^{U}(u+q)^{-\gamma}}, \tag{8}\]  
where \(U\) represents the total number of contents; \(u\) indicates the content popularity ranking, \(u=1\), \(2\), \(3\), \(\ldots\), \(U\); \(Z_{u}\) is the probability of requesting content with a content popularity rank of m; \(q\) is used to adjust the size of the content popularity rank; and \(\gamma\) is a parameter with values in [0.7, 1.3] [48]. The larger \(\gamma\) means that the set of very popular content in the content collection is relatively small.